volterra_prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# System Identification With Order 2 and 3 Volterra Series"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Volterra dynamic system identification model for prediction of all modalities, given all modelities exist as inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy.io import loadmat\n",
    "import glob\n",
    "from collections import deque\n",
    "import concurrent.futures\n",
    "import math, decimal\n",
    "from tqdm import tqdm_notebook\n",
    "from random import shuffle\n",
    "from sklearn import linear_model\n",
    "import matplotlib.pyplot as plt\n",
    "import pickle\n",
    "import h5py\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Show The Running Machine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GeForce RTX 2080 Ti\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "if torch.cuda.is_available():\n",
    "    print(torch.cuda.get_device_name(0))\n",
    "else:\n",
    "    print(\"Cuda is not available.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading The Dataset (Trial by Trial)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Dataset/WC1200Hz.mat',\n",
       " 'Dataset/P11200Hz.mat',\n",
       " 'Dataset/JT1200Hz.mat',\n",
       " 'Dataset/BT1200Hz.mat',\n",
       " 'Dataset/YA1200Hz.mat',\n",
       " 'Dataset/YZ1200Hz.mat',\n",
       " 'Dataset/HCH1200Hz.mat',\n",
       " 'Dataset/CC1200Hz.mat',\n",
       " 'Dataset/ED1200Hz.mat',\n",
       " 'Dataset/YK1200Hz.mat',\n",
       " 'Dataset/KH1200Hz.mat']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the participant data from Dataset directory\n",
    "global t0, fs, dt, num_eeg_ch, num_emg_ch, num_force_ch, num_ch, num_conds\n",
    "t0 = 0\n",
    "fs = 1200\n",
    "dt = 1.0/fs\n",
    "num_eeg_ch = 14\n",
    "num_emg_ch = 4\n",
    "num_force_ch = 3\n",
    "num_ch = num_eeg_ch + num_emg_ch + num_force_ch\n",
    "num_conds = 18\n",
    "\n",
    "# Get a list of files to process\n",
    "data_files = list(map(loadmat, glob.glob(\"Dataset/*.mat\")))\n",
    "\n",
    "columns=['EEG-1','EEG-2','EEG-3','EEG-4','EEG-5',\n",
    "         'EEG-6','EEG-7','EEG-8','EEG-9','EEG-10',\n",
    "         'EEG-11','EEG-12','EEG-13','EEG-14','EMG-1',\n",
    "         'EMG-2','EMG-3','EMG-4','Force-x', \n",
    "         'Force-y','Force-z']\n",
    "\n",
    "glob.glob(\"Dataset/*.mat\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading and processing in parallel.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e3a3644d523a46be828c3b1c6fd22686",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=11), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "Total number of trials of all the conditions in the file: 17648\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
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       "    }\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>EEG-1</th>\n",
       "      <th>EEG-2</th>\n",
       "      <th>EEG-3</th>\n",
       "      <th>EEG-4</th>\n",
       "      <th>EEG-5</th>\n",
       "      <th>EEG-6</th>\n",
       "      <th>EEG-7</th>\n",
       "      <th>EEG-8</th>\n",
       "      <th>EEG-9</th>\n",
       "      <th>EEG-10</th>\n",
       "      <th>...</th>\n",
       "      <th>EEG-12</th>\n",
       "      <th>EEG-13</th>\n",
       "      <th>EEG-14</th>\n",
       "      <th>EMG-1</th>\n",
       "      <th>EMG-2</th>\n",
       "      <th>EMG-3</th>\n",
       "      <th>EMG-4</th>\n",
       "      <th>Force-x</th>\n",
       "      <th>Force-y</th>\n",
       "      <th>Force-z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>23.167676</td>\n",
       "      <td>15.756166</td>\n",
       "      <td>16.982271</td>\n",
       "      <td>7.548239</td>\n",
       "      <td>12.165545</td>\n",
       "      <td>16.050811</td>\n",
       "      <td>17.137191</td>\n",
       "      <td>13.227083</td>\n",
       "      <td>9.266905</td>\n",
       "      <td>7.512412</td>\n",
       "      <td>...</td>\n",
       "      <td>11.071793</td>\n",
       "      <td>15.797413</td>\n",
       "      <td>-1.692378</td>\n",
       "      <td>330.187059</td>\n",
       "      <td>-27.502973</td>\n",
       "      <td>65.432741</td>\n",
       "      <td>23.912648</td>\n",
       "      <td>-109.325610</td>\n",
       "      <td>-108.847827</td>\n",
       "      <td>141.124114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>23.388043</td>\n",
       "      <td>15.663438</td>\n",
       "      <td>17.082774</td>\n",
       "      <td>7.631282</td>\n",
       "      <td>11.843375</td>\n",
       "      <td>15.766723</td>\n",
       "      <td>16.879430</td>\n",
       "      <td>13.008864</td>\n",
       "      <td>9.280284</td>\n",
       "      <td>7.512905</td>\n",
       "      <td>...</td>\n",
       "      <td>10.780715</td>\n",
       "      <td>15.378491</td>\n",
       "      <td>-1.952203</td>\n",
       "      <td>372.457695</td>\n",
       "      <td>26.856463</td>\n",
       "      <td>72.639315</td>\n",
       "      <td>22.333125</td>\n",
       "      <td>-109.355651</td>\n",
       "      <td>-108.871542</td>\n",
       "      <td>141.092792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>23.375689</td>\n",
       "      <td>16.079692</td>\n",
       "      <td>16.881640</td>\n",
       "      <td>8.375121</td>\n",
       "      <td>11.841599</td>\n",
       "      <td>15.110183</td>\n",
       "      <td>16.128607</td>\n",
       "      <td>13.034910</td>\n",
       "      <td>9.795444</td>\n",
       "      <td>8.026000</td>\n",
       "      <td>...</td>\n",
       "      <td>10.635129</td>\n",
       "      <td>15.264251</td>\n",
       "      <td>-1.816383</td>\n",
       "      <td>387.614039</td>\n",
       "      <td>41.563909</td>\n",
       "      <td>59.159702</td>\n",
       "      <td>53.029517</td>\n",
       "      <td>-109.079003</td>\n",
       "      <td>-108.597323</td>\n",
       "      <td>141.407490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23.117547</td>\n",
       "      <td>16.968430</td>\n",
       "      <td>16.375178</td>\n",
       "      <td>9.692186</td>\n",
       "      <td>12.141716</td>\n",
       "      <td>14.098265</td>\n",
       "      <td>14.904380</td>\n",
       "      <td>13.286291</td>\n",
       "      <td>10.747573</td>\n",
       "      <td>8.983246</td>\n",
       "      <td>...</td>\n",
       "      <td>10.625847</td>\n",
       "      <td>15.440065</td>\n",
       "      <td>-1.319323</td>\n",
       "      <td>411.775341</td>\n",
       "      <td>33.800404</td>\n",
       "      <td>11.972148</td>\n",
       "      <td>84.321955</td>\n",
       "      <td>-108.313240</td>\n",
       "      <td>-107.825555</td>\n",
       "      <td>142.278150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>22.607069</td>\n",
       "      <td>18.259958</td>\n",
       "      <td>15.572219</td>\n",
       "      <td>11.457832</td>\n",
       "      <td>12.698434</td>\n",
       "      <td>12.763784</td>\n",
       "      <td>13.251260</td>\n",
       "      <td>13.724284</td>\n",
       "      <td>12.041608</td>\n",
       "      <td>10.289564</td>\n",
       "      <td>...</td>\n",
       "      <td>10.726951</td>\n",
       "      <td>15.869341</td>\n",
       "      <td>-0.521487</td>\n",
       "      <td>425.944948</td>\n",
       "      <td>30.060213</td>\n",
       "      <td>-1.020151</td>\n",
       "      <td>113.543132</td>\n",
       "      <td>-107.062548</td>\n",
       "      <td>-106.577970</td>\n",
       "      <td>143.656656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>21.843903</td>\n",
       "      <td>19.856290</td>\n",
       "      <td>14.492529</td>\n",
       "      <td>13.520832</td>\n",
       "      <td>13.442912</td>\n",
       "      <td>11.152268</td>\n",
       "      <td>11.234785</td>\n",
       "      <td>14.293043</td>\n",
       "      <td>13.559942</td>\n",
       "      <td>11.830745</td>\n",
       "      <td>...</td>\n",
       "      <td>10.897771</td>\n",
       "      <td>16.496071</td>\n",
       "      <td>0.495415</td>\n",
       "      <td>437.147827</td>\n",
       "      <td>-21.468003</td>\n",
       "      <td>-16.771261</td>\n",
       "      <td>4.943470</td>\n",
       "      <td>-105.229937</td>\n",
       "      <td>-104.768403</td>\n",
       "      <td>145.656779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>20.833954</td>\n",
       "      <td>21.638484</td>\n",
       "      <td>13.165325</td>\n",
       "      <td>15.715646</td>\n",
       "      <td>14.288552</td>\n",
       "      <td>9.318883</td>\n",
       "      <td>8.936981</td>\n",
       "      <td>14.924068</td>\n",
       "      <td>15.172012</td>\n",
       "      <td>13.482282</td>\n",
       "      <td>...</td>\n",
       "      <td>11.086708</td>\n",
       "      <td>17.249268</td>\n",
       "      <td>1.635170</td>\n",
       "      <td>426.127487</td>\n",
       "      <td>72.915952</td>\n",
       "      <td>-84.849660</td>\n",
       "      <td>6.150464</td>\n",
       "      <td>-103.179686</td>\n",
       "      <td>-102.705583</td>\n",
       "      <td>147.981331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>19.589768</td>\n",
       "      <td>23.475570</td>\n",
       "      <td>11.627494</td>\n",
       "      <td>17.875457</td>\n",
       "      <td>15.138274</td>\n",
       "      <td>7.325171</td>\n",
       "      <td>6.450980</td>\n",
       "      <td>15.541790</td>\n",
       "      <td>16.744681</td>\n",
       "      <td>15.118633</td>\n",
       "      <td>...</td>\n",
       "      <td>11.236083</td>\n",
       "      <td>18.048420</td>\n",
       "      <td>2.794801</td>\n",
       "      <td>376.158771</td>\n",
       "      <td>208.203595</td>\n",
       "      <td>-73.512438</td>\n",
       "      <td>229.191044</td>\n",
       "      <td>-101.222873</td>\n",
       "      <td>-100.739405</td>\n",
       "      <td>150.205150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>18.131030</td>\n",
       "      <td>25.234476</td>\n",
       "      <td>9.921857</td>\n",
       "      <td>19.844882</td>\n",
       "      <td>15.892970</td>\n",
       "      <td>5.235784</td>\n",
       "      <td>3.875166</td>\n",
       "      <td>16.069961</td>\n",
       "      <td>18.152512</td>\n",
       "      <td>16.622045</td>\n",
       "      <td>...</td>\n",
       "      <td>11.287999</td>\n",
       "      <td>18.809779</td>\n",
       "      <td>3.872869</td>\n",
       "      <td>284.776003</td>\n",
       "      <td>312.750142</td>\n",
       "      <td>-100.656649</td>\n",
       "      <td>432.189563</td>\n",
       "      <td>-99.488109</td>\n",
       "      <td>-99.002041</td>\n",
       "      <td>152.156293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>16.485085</td>\n",
       "      <td>26.789877</td>\n",
       "      <td>8.095636</td>\n",
       "      <td>21.491034</td>\n",
       "      <td>16.460244</td>\n",
       "      <td>3.115665</td>\n",
       "      <td>1.307599</td>\n",
       "      <td>16.438218</td>\n",
       "      <td>19.287123</td>\n",
       "      <td>17.890394</td>\n",
       "      <td>...</td>\n",
       "      <td>11.190282</td>\n",
       "      <td>19.452967</td>\n",
       "      <td>4.777465</td>\n",
       "      <td>247.681891</td>\n",
       "      <td>155.468385</td>\n",
       "      <td>-144.824506</td>\n",
       "      <td>219.147662</td>\n",
       "      <td>-98.213434</td>\n",
       "      <td>-97.734556</td>\n",
       "      <td>153.576374</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       EEG-1      EEG-2      EEG-3      EEG-4      EEG-5      EEG-6  \\\n",
       "0  23.167676  15.756166  16.982271   7.548239  12.165545  16.050811   \n",
       "1  23.388043  15.663438  17.082774   7.631282  11.843375  15.766723   \n",
       "2  23.375689  16.079692  16.881640   8.375121  11.841599  15.110183   \n",
       "3  23.117547  16.968430  16.375178   9.692186  12.141716  14.098265   \n",
       "4  22.607069  18.259958  15.572219  11.457832  12.698434  12.763784   \n",
       "5  21.843903  19.856290  14.492529  13.520832  13.442912  11.152268   \n",
       "6  20.833954  21.638484  13.165325  15.715646  14.288552   9.318883   \n",
       "7  19.589768  23.475570  11.627494  17.875457  15.138274   7.325171   \n",
       "8  18.131030  25.234476   9.921857  19.844882  15.892970   5.235784   \n",
       "9  16.485085  26.789877   8.095636  21.491034  16.460244   3.115665   \n",
       "\n",
       "       EEG-7      EEG-8      EEG-9     EEG-10  ...     EEG-12     EEG-13  \\\n",
       "0  17.137191  13.227083   9.266905   7.512412  ...  11.071793  15.797413   \n",
       "1  16.879430  13.008864   9.280284   7.512905  ...  10.780715  15.378491   \n",
       "2  16.128607  13.034910   9.795444   8.026000  ...  10.635129  15.264251   \n",
       "3  14.904380  13.286291  10.747573   8.983246  ...  10.625847  15.440065   \n",
       "4  13.251260  13.724284  12.041608  10.289564  ...  10.726951  15.869341   \n",
       "5  11.234785  14.293043  13.559942  11.830745  ...  10.897771  16.496071   \n",
       "6   8.936981  14.924068  15.172012  13.482282  ...  11.086708  17.249268   \n",
       "7   6.450980  15.541790  16.744681  15.118633  ...  11.236083  18.048420   \n",
       "8   3.875166  16.069961  18.152512  16.622045  ...  11.287999  18.809779   \n",
       "9   1.307599  16.438218  19.287123  17.890394  ...  11.190282  19.452967   \n",
       "\n",
       "     EEG-14       EMG-1       EMG-2       EMG-3       EMG-4     Force-x  \\\n",
       "0 -1.692378  330.187059  -27.502973   65.432741   23.912648 -109.325610   \n",
       "1 -1.952203  372.457695   26.856463   72.639315   22.333125 -109.355651   \n",
       "2 -1.816383  387.614039   41.563909   59.159702   53.029517 -109.079003   \n",
       "3 -1.319323  411.775341   33.800404   11.972148   84.321955 -108.313240   \n",
       "4 -0.521487  425.944948   30.060213   -1.020151  113.543132 -107.062548   \n",
       "5  0.495415  437.147827  -21.468003  -16.771261    4.943470 -105.229937   \n",
       "6  1.635170  426.127487   72.915952  -84.849660    6.150464 -103.179686   \n",
       "7  2.794801  376.158771  208.203595  -73.512438  229.191044 -101.222873   \n",
       "8  3.872869  284.776003  312.750142 -100.656649  432.189563  -99.488109   \n",
       "9  4.777465  247.681891  155.468385 -144.824506  219.147662  -98.213434   \n",
       "\n",
       "      Force-y     Force-z  \n",
       "0 -108.847827  141.124114  \n",
       "1 -108.871542  141.092792  \n",
       "2 -108.597323  141.407490  \n",
       "3 -107.825555  142.278150  \n",
       "4 -106.577970  143.656656  \n",
       "5 -104.768403  145.656779  \n",
       "6 -102.705583  147.981331  \n",
       "7 -100.739405  150.205150  \n",
       "8  -99.002041  152.156293  \n",
       "9  -97.734556  153.576374  \n",
       "\n",
       "[10 rows x 21 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def extract_data(file):\n",
    "    data_splitBy_trials = [] # list of data frames, each df corresponding to a trial\n",
    "\n",
    "    for cond in tqdm_notebook(range(num_conds), ascii=True):\n",
    "        num_trials = file['EEGSeg_Ch'][0,0][0,cond].shape[0]\n",
    "        # if a trial has long enough samples\n",
    "        if file['EEGSeg_Ch'][0,0][0,cond].shape[1] >= 400:\n",
    "            for trial in range(num_trials): \n",
    "                data = pd.DataFrame(columns=columns)\n",
    "                for ch in range(num_eeg_ch):\n",
    "                    data.iloc[:,ch] = file['EEGSeg_Ch'][0,ch][0,cond][trial,:]\n",
    "                for ch in range(num_emg_ch):\n",
    "                    data.iloc[:,ch+num_eeg_ch] = file['EMGSeg_Ch'][0,ch][0,cond][trial,:]\n",
    "                for ch in range(num_force_ch):\n",
    "                    data.iloc[:,ch+num_eeg_ch+num_emg_ch] = file['ForceSeg_Ch'][0,ch][0,cond][trial,:]\n",
    "\n",
    "                # mean subtraction in each trial from the eeg and emg columns for removing dc drift\n",
    "                data.iloc[:,:18] -= data.iloc[:,:18].mean()\n",
    "                # convert volts to microvolts for EEG and EMG channels\n",
    "                data.iloc[:,:18] *= 1e6\n",
    "                # convert volts to milivolts for force channels\n",
    "                data.iloc[:,18:] *= 1e3  \n",
    "                data_splitBy_trials.append(data)\n",
    "                \n",
    "    train_data, test_data = train_test_split(data_splitBy_trials)\n",
    "    return train_data, test_data\n",
    "\n",
    "\n",
    "def train_test_split(data, train_ratio=0.5):\n",
    "    shuffle(data) # shuffle list, in-place operator\n",
    "    num_train = int(train_ratio*len(data))\n",
    "    train_data, test_data = data[:num_train], data[num_train:]\n",
    "    return train_data, test_data\n",
    "\n",
    "\n",
    "train_data = [] # train data is a list (all participants each trial) of dataframes\n",
    "test_data = [] # test data is a list (each participant) of list (each trial) of dataframes \n",
    "\n",
    "print('Reading and processing in parallel.')\n",
    "\n",
    "# Create a pool of processes. By default, one is created for each CPU in your machine.\n",
    "with concurrent.futures.ProcessPoolExecutor() as executor:\n",
    "    # Process the list of files, but split the work across the process pool to use all CPUs.\n",
    "    # executor.map() function takes in the helper function to call and the list of data to process with it.\n",
    "    # executor.map() function returns results in  the same order as the list of data given to the process.\n",
    "    for train, test in tqdm_notebook(executor.map(extract_data, data_files), total=len(data_files)):\n",
    "        train_data.extend(train)\n",
    "        test_data.append(test)\n",
    "        \n",
    "total_trials = len(train_data)\n",
    "for test in test_data:\n",
    "    total_trials += len(test)\n",
    "print('Total number of trials of all the conditions in the file: %i' %total_trials) \n",
    "train_data[0].head(10) # first 10 samples of the first trial"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save Train and Test Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"train_data.txt\", \"wb\") as fp:   #Pickling\n",
    "    pickle.dump(train_data, fp)\n",
    "    \n",
    "with open(\"test_data.txt\", \"wb\") as fp:   \n",
    "    pickle.dump(test_data, fp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load Train and Test Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"train_data.txt\", \"rb\") as fp:   # Unpickling\n",
    "    train_data = pickle.load(fp)\n",
    "    \n",
    "with open(\"test_data.txt\", \"rb\") as fp:   \n",
    "    test_data = pickle.load(fp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualization of The Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "t0 = 0\n",
    "fs = 1200\n",
    "dt = 1.0/fs\n",
    "trial_id = 40 # choose one trial\n",
    "num_samples = train_data[trial_id].shape[0]\n",
    "time = (np.arange(0, num_samples) * dt + t0) * 1e3\n",
    "# pick one channel from eeg, emg and force\n",
    "eeg_signal_1 = train_data[trial_id].iloc[:,0] # trial id, eeg_ch1\n",
    "emg_signal_1 = train_data[trial_id].iloc[:,14] # trial id, emg_ch1\n",
    "force_signal_3 = train_data[trial_id].iloc[:,20] # trial id, force_ch3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def get_ave_values(xvalues, yvalues, n):\n",
    "    signal_length = len(xvalues)\n",
    "    if signal_length % n == 0:\n",
    "        padding_length = 0\n",
    "    else:\n",
    "        padding_length = n - signal_length//n % n\n",
    "    xarr = np.array(xvalues)\n",
    "    yarr = np.array(yvalues)\n",
    "    xarr.resize(signal_length//n, n)\n",
    "    yarr.resize(signal_length//n, n)\n",
    "    xarr_reshaped = xarr.reshape((-1,n))\n",
    "    yarr_reshaped = yarr.reshape((-1,n))\n",
    "    x_ave = xarr_reshaped[:,0]\n",
    "    y_ave = np.nanmean(yarr_reshaped, axis=1)\n",
    "    return x_ave, y_ave\n",
    "\n",
    "def plot_signal_plus_average(ax, time, signal, average_over):\n",
    "    time_ave, signal_ave = get_ave_values(time, signal, average_over)\n",
    "    ax.plot(time, signal, label='signal')\n",
    "    ax.plot(time_ave, signal_ave, label = 'time average (n={})'.format(average_over))\n",
    "    ax.set_xlim([time[0], time[-1]])\n",
    "    ax.legend(loc='upper right')\n",
    "\n",
    "fig, axes = plt.subplots(3, figsize=(12,12))\n",
    "fig.subplots_adjust(hspace=0.3)\n",
    "plot_signal_plus_average(axes[0], time, eeg_signal_1, average_over=3)\n",
    "\n",
    "axes[0].set_ylabel('Amplitude (\\u03bcV)', fontsize=16)\n",
    "axes[0].set_title('EEG Channel-1', fontsize=16)\n",
    "plot_signal_plus_average(axes[1], time, emg_signal_1, average_over=3)\n",
    "\n",
    "axes[1].set_ylabel('Amplitude (\\u03bcV)', fontsize=16)\n",
    "axes[1].set_title('EMG Channel-1', fontsize=16)\n",
    "plot_signal_plus_average(axes[2], time, force_signal_3, average_over=3)\n",
    "\n",
    "axes[2].set_ylabel('Amplitude (mV)', fontsize=16)\n",
    "axes[2].set_xlabel('Time (sec)', fontsize=16)\n",
    "axes[2].set_title('Force Channel-3', fontsize=16)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Volterra Series (Test Case)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Volterra Series Order One"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Column1  Column2  Column3  Column4  Column5\n",
      "0         0        1        2        3        4\n",
      "1         5        6        7        8        9\n",
      "2        10       11       12       13       14\n",
      "3        15       16       17       18       19\n",
      "4        20       21       22       23       24\n",
      "5        25       26       27       28       29\n",
      "6        30       31       32       33       34\n",
      "7        35       36       37       38       39\n",
      "8        40       41       42       43       44\n",
      "9        45       46       47       48       49\n",
      "10       50       51       52       53       54\n",
      "11       55       56       57       58       59\n",
      "12       60       61       62       63       64\n",
      "13       65       66       67       68       69\n",
      "14       70       71       72       73       74\n",
      "15       75       76       77       78       79\n",
      "16       80       81       82       83       84\n",
      "17       85       86       87       88       89\n",
      "18       90       91       92       93       94\n",
      "19       95       96       97       98       99\n",
      "20      100      101      102      103      104\n",
      "21      105      106      107      108      109\n",
      "22      110      111      112      113      114\n",
      "23      115      116      117      118      119\n",
      "24      120      121      122      123      124\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>var1(t)</th>\n",
       "      <th>var2(t)</th>\n",
       "      <th>var3(t)</th>\n",
       "      <th>var4(t)</th>\n",
       "      <th>var5(t)</th>\n",
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       "      <th>var4(t-5)</th>\n",
       "      <th>var5(t-5)</th>\n",
       "      <th>...</th>\n",
       "      <th>var1(t-2)</th>\n",
       "      <th>var2(t-2)</th>\n",
       "      <th>var3(t-2)</th>\n",
       "      <th>var4(t-2)</th>\n",
       "      <th>var5(t-2)</th>\n",
       "      <th>var1(t-1)</th>\n",
       "      <th>var2(t-1)</th>\n",
       "      <th>var3(t-1)</th>\n",
       "      <th>var4(t-1)</th>\n",
       "      <th>var5(t-1)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>25</td>\n",
       "      <td>26</td>\n",
       "      <td>27</td>\n",
       "      <td>28</td>\n",
       "      <td>29</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>15.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>30</td>\n",
       "      <td>31</td>\n",
       "      <td>32</td>\n",
       "      <td>33</td>\n",
       "      <td>34</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>...</td>\n",
       "      <td>20.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>29.0</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>35</td>\n",
       "      <td>36</td>\n",
       "      <td>37</td>\n",
       "      <td>38</td>\n",
       "      <td>39</td>\n",
       "      <td>10.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>...</td>\n",
       "      <td>25.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>40</td>\n",
       "      <td>41</td>\n",
       "      <td>42</td>\n",
       "      <td>43</td>\n",
       "      <td>44</td>\n",
       "      <td>15.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>...</td>\n",
       "      <td>30.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>39.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>45</td>\n",
       "      <td>46</td>\n",
       "      <td>47</td>\n",
       "      <td>48</td>\n",
       "      <td>49</td>\n",
       "      <td>20.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>...</td>\n",
       "      <td>35.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>44.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>50</td>\n",
       "      <td>51</td>\n",
       "      <td>52</td>\n",
       "      <td>53</td>\n",
       "      <td>54</td>\n",
       "      <td>25.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>...</td>\n",
       "      <td>40.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>49.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>55</td>\n",
       "      <td>56</td>\n",
       "      <td>57</td>\n",
       "      <td>58</td>\n",
       "      <td>59</td>\n",
       "      <td>30.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>...</td>\n",
       "      <td>45.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>54.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>60</td>\n",
       "      <td>61</td>\n",
       "      <td>62</td>\n",
       "      <td>63</td>\n",
       "      <td>64</td>\n",
       "      <td>35.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>...</td>\n",
       "      <td>50.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>59.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>65</td>\n",
       "      <td>66</td>\n",
       "      <td>67</td>\n",
       "      <td>68</td>\n",
       "      <td>69</td>\n",
       "      <td>40.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>...</td>\n",
       "      <td>55.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>64.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>70</td>\n",
       "      <td>71</td>\n",
       "      <td>72</td>\n",
       "      <td>73</td>\n",
       "      <td>74</td>\n",
       "      <td>45.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>...</td>\n",
       "      <td>60.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>69.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>75</td>\n",
       "      <td>76</td>\n",
       "      <td>77</td>\n",
       "      <td>78</td>\n",
       "      <td>79</td>\n",
       "      <td>50.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>...</td>\n",
       "      <td>65.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>74.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>80</td>\n",
       "      <td>81</td>\n",
       "      <td>82</td>\n",
       "      <td>83</td>\n",
       "      <td>84</td>\n",
       "      <td>55.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>...</td>\n",
       "      <td>70.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>79.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>85</td>\n",
       "      <td>86</td>\n",
       "      <td>87</td>\n",
       "      <td>88</td>\n",
       "      <td>89</td>\n",
       "      <td>60.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>...</td>\n",
       "      <td>75.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>84.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>90</td>\n",
       "      <td>91</td>\n",
       "      <td>92</td>\n",
       "      <td>93</td>\n",
       "      <td>94</td>\n",
       "      <td>65.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>...</td>\n",
       "      <td>80.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>89.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>95</td>\n",
       "      <td>96</td>\n",
       "      <td>97</td>\n",
       "      <td>98</td>\n",
       "      <td>99</td>\n",
       "      <td>70.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>...</td>\n",
       "      <td>85.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>94.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    var1(t)  var2(t)  var3(t)  var4(t)  var5(t)  var1(t-5)  var2(t-5)  \\\n",
       "5        25       26       27       28       29        0.0        1.0   \n",
       "6        30       31       32       33       34        5.0        6.0   \n",
       "7        35       36       37       38       39       10.0       11.0   \n",
       "8        40       41       42       43       44       15.0       16.0   \n",
       "9        45       46       47       48       49       20.0       21.0   \n",
       "10       50       51       52       53       54       25.0       26.0   \n",
       "11       55       56       57       58       59       30.0       31.0   \n",
       "12       60       61       62       63       64       35.0       36.0   \n",
       "13       65       66       67       68       69       40.0       41.0   \n",
       "14       70       71       72       73       74       45.0       46.0   \n",
       "15       75       76       77       78       79       50.0       51.0   \n",
       "16       80       81       82       83       84       55.0       56.0   \n",
       "17       85       86       87       88       89       60.0       61.0   \n",
       "18       90       91       92       93       94       65.0       66.0   \n",
       "19       95       96       97       98       99       70.0       71.0   \n",
       "\n",
       "    var3(t-5)  var4(t-5)  var5(t-5)  ...  var1(t-2)  var2(t-2)  var3(t-2)  \\\n",
       "5         2.0        3.0        4.0  ...       15.0       16.0       17.0   \n",
       "6         7.0        8.0        9.0  ...       20.0       21.0       22.0   \n",
       "7        12.0       13.0       14.0  ...       25.0       26.0       27.0   \n",
       "8        17.0       18.0       19.0  ...       30.0       31.0       32.0   \n",
       "9        22.0       23.0       24.0  ...       35.0       36.0       37.0   \n",
       "10       27.0       28.0       29.0  ...       40.0       41.0       42.0   \n",
       "11       32.0       33.0       34.0  ...       45.0       46.0       47.0   \n",
       "12       37.0       38.0       39.0  ...       50.0       51.0       52.0   \n",
       "13       42.0       43.0       44.0  ...       55.0       56.0       57.0   \n",
       "14       47.0       48.0       49.0  ...       60.0       61.0       62.0   \n",
       "15       52.0       53.0       54.0  ...       65.0       66.0       67.0   \n",
       "16       57.0       58.0       59.0  ...       70.0       71.0       72.0   \n",
       "17       62.0       63.0       64.0  ...       75.0       76.0       77.0   \n",
       "18       67.0       68.0       69.0  ...       80.0       81.0       82.0   \n",
       "19       72.0       73.0       74.0  ...       85.0       86.0       87.0   \n",
       "\n",
       "    var4(t-2)  var5(t-2)  var1(t-1)  var2(t-1)  var3(t-1)  var4(t-1)  \\\n",
       "5        18.0       19.0       20.0       21.0       22.0       23.0   \n",
       "6        23.0       24.0       25.0       26.0       27.0       28.0   \n",
       "7        28.0       29.0       30.0       31.0       32.0       33.0   \n",
       "8        33.0       34.0       35.0       36.0       37.0       38.0   \n",
       "9        38.0       39.0       40.0       41.0       42.0       43.0   \n",
       "10       43.0       44.0       45.0       46.0       47.0       48.0   \n",
       "11       48.0       49.0       50.0       51.0       52.0       53.0   \n",
       "12       53.0       54.0       55.0       56.0       57.0       58.0   \n",
       "13       58.0       59.0       60.0       61.0       62.0       63.0   \n",
       "14       63.0       64.0       65.0       66.0       67.0       68.0   \n",
       "15       68.0       69.0       70.0       71.0       72.0       73.0   \n",
       "16       73.0       74.0       75.0       76.0       77.0       78.0   \n",
       "17       78.0       79.0       80.0       81.0       82.0       83.0   \n",
       "18       83.0       84.0       85.0       86.0       87.0       88.0   \n",
       "19       88.0       89.0       90.0       91.0       92.0       93.0   \n",
       "\n",
       "    var5(t-1)  \n",
       "5        24.0  \n",
       "6        29.0  \n",
       "7        34.0  \n",
       "8        39.0  \n",
       "9        44.0  \n",
       "10       49.0  \n",
       "11       54.0  \n",
       "12       59.0  \n",
       "13       64.0  \n",
       "14       69.0  \n",
       "15       74.0  \n",
       "16       79.0  \n",
       "17       84.0  \n",
       "18       89.0  \n",
       "19       94.0  \n",
       "\n",
       "[15 rows x 30 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.arange(125)\n",
    "a = a.reshape((25,5))\n",
    "input_df = pd.DataFrame({'Column1': a[:, 0], 'Column2': a[:, 1], 'Column3': a[:, 2],\n",
    "                         'Column4': a[:, 3], 'Column5': a[:, 4]})\n",
    "\n",
    "print(input_df)\n",
    "\n",
    "def order_one_Volterra(input_df, M=5, dropnan=True):\n",
    "    '''\n",
    "    Assume a finite memory of M. \n",
    "    Given the input dataframe, show at each row variables at t-M, t-M+1, ... t variable values\n",
    "    Also can be used for order 1 Volterra series input.\n",
    "    '''\n",
    "    n_vars = len(input_df.columns)\n",
    "    cols, names = list(), list()\n",
    "    cols.append(input_df.shift(0))\n",
    "    names += [('var%d(t)' % (j+1)) for j in range(n_vars)]\n",
    "    for i in range(M, 0, -1):\n",
    "        cols.append(input_df.shift(i))\n",
    "        names += [('var%d(t-%d)' % (j+1, i)) for j in range(n_vars)]\n",
    "    # put it all together\n",
    "    agg = pd.concat(cols, axis=1)\n",
    "    agg.columns = names\n",
    "    # drop rows with NaN values\n",
    "    if dropnan:\n",
    "        agg.dropna(inplace=True)\n",
    "    return agg\n",
    "\n",
    "order_one_Volterra(input_df).head(15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Volterra Series Order Two"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        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>var1(t-5)(t-5)</th>\n",
       "      <th>var2(t-5)(t-5)</th>\n",
       "      <th>var3(t-5)(t-5)</th>\n",
       "      <th>var4(t-5)(t-5)</th>\n",
       "      <th>var5(t-5)(t-5)</th>\n",
       "      <th>var1(t-5)(t-4)</th>\n",
       "      <th>var2(t-5)(t-4)</th>\n",
       "      <th>var3(t-5)(t-4)</th>\n",
       "      <th>var4(t-5)(t-4)</th>\n",
       "      <th>var5(t-5)(t-4)</th>\n",
       "      <th>...</th>\n",
       "      <th>var1(t-1)(t-2)</th>\n",
       "      <th>var2(t-1)(t-2)</th>\n",
       "      <th>var3(t-1)(t-2)</th>\n",
       "      <th>var4(t-1)(t-2)</th>\n",
       "      <th>var5(t-1)(t-2)</th>\n",
       "      <th>var1(t-1)(t-1)</th>\n",
       "      <th>var2(t-1)(t-1)</th>\n",
       "      <th>var3(t-1)(t-1)</th>\n",
       "      <th>var4(t-1)(t-1)</th>\n",
       "      <th>var5(t-1)(t-1)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>...</td>\n",
       "      <td>300.0</td>\n",
       "      <td>336.0</td>\n",
       "      <td>374.0</td>\n",
       "      <td>414.0</td>\n",
       "      <td>456.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>441.0</td>\n",
       "      <td>484.0</td>\n",
       "      <td>529.0</td>\n",
       "      <td>576.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>25.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>...</td>\n",
       "      <td>500.0</td>\n",
       "      <td>546.0</td>\n",
       "      <td>594.0</td>\n",
       "      <td>644.0</td>\n",
       "      <td>696.0</td>\n",
       "      <td>625.0</td>\n",
       "      <td>676.0</td>\n",
       "      <td>729.0</td>\n",
       "      <td>784.0</td>\n",
       "      <td>841.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>100.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>196.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>176.0</td>\n",
       "      <td>204.0</td>\n",
       "      <td>234.0</td>\n",
       "      <td>266.0</td>\n",
       "      <td>...</td>\n",
       "      <td>750.0</td>\n",
       "      <td>806.0</td>\n",
       "      <td>864.0</td>\n",
       "      <td>924.0</td>\n",
       "      <td>986.0</td>\n",
       "      <td>900.0</td>\n",
       "      <td>961.0</td>\n",
       "      <td>1024.0</td>\n",
       "      <td>1089.0</td>\n",
       "      <td>1156.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>225.0</td>\n",
       "      <td>256.0</td>\n",
       "      <td>289.0</td>\n",
       "      <td>324.0</td>\n",
       "      <td>361.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>336.0</td>\n",
       "      <td>374.0</td>\n",
       "      <td>414.0</td>\n",
       "      <td>456.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1050.0</td>\n",
       "      <td>1116.0</td>\n",
       "      <td>1184.0</td>\n",
       "      <td>1254.0</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>1225.0</td>\n",
       "      <td>1296.0</td>\n",
       "      <td>1369.0</td>\n",
       "      <td>1444.0</td>\n",
       "      <td>1521.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>400.0</td>\n",
       "      <td>441.0</td>\n",
       "      <td>484.0</td>\n",
       "      <td>529.0</td>\n",
       "      <td>576.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>546.0</td>\n",
       "      <td>594.0</td>\n",
       "      <td>644.0</td>\n",
       "      <td>696.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>1476.0</td>\n",
       "      <td>1554.0</td>\n",
       "      <td>1634.0</td>\n",
       "      <td>1716.0</td>\n",
       "      <td>1600.0</td>\n",
       "      <td>1681.0</td>\n",
       "      <td>1764.0</td>\n",
       "      <td>1849.0</td>\n",
       "      <td>1936.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>625.0</td>\n",
       "      <td>676.0</td>\n",
       "      <td>729.0</td>\n",
       "      <td>784.0</td>\n",
       "      <td>841.0</td>\n",
       "      <td>750.0</td>\n",
       "      <td>806.0</td>\n",
       "      <td>864.0</td>\n",
       "      <td>924.0</td>\n",
       "      <td>986.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1800.0</td>\n",
       "      <td>1886.0</td>\n",
       "      <td>1974.0</td>\n",
       "      <td>2064.0</td>\n",
       "      <td>2156.0</td>\n",
       "      <td>2025.0</td>\n",
       "      <td>2116.0</td>\n",
       "      <td>2209.0</td>\n",
       "      <td>2304.0</td>\n",
       "      <td>2401.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>900.0</td>\n",
       "      <td>961.0</td>\n",
       "      <td>1024.0</td>\n",
       "      <td>1089.0</td>\n",
       "      <td>1156.0</td>\n",
       "      <td>1050.0</td>\n",
       "      <td>1116.0</td>\n",
       "      <td>1184.0</td>\n",
       "      <td>1254.0</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2250.0</td>\n",
       "      <td>2346.0</td>\n",
       "      <td>2444.0</td>\n",
       "      <td>2544.0</td>\n",
       "      <td>2646.0</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>2601.0</td>\n",
       "      <td>2704.0</td>\n",
       "      <td>2809.0</td>\n",
       "      <td>2916.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1225.0</td>\n",
       "      <td>1296.0</td>\n",
       "      <td>1369.0</td>\n",
       "      <td>1444.0</td>\n",
       "      <td>1521.0</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>1476.0</td>\n",
       "      <td>1554.0</td>\n",
       "      <td>1634.0</td>\n",
       "      <td>1716.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2750.0</td>\n",
       "      <td>2856.0</td>\n",
       "      <td>2964.0</td>\n",
       "      <td>3074.0</td>\n",
       "      <td>3186.0</td>\n",
       "      <td>3025.0</td>\n",
       "      <td>3136.0</td>\n",
       "      <td>3249.0</td>\n",
       "      <td>3364.0</td>\n",
       "      <td>3481.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1600.0</td>\n",
       "      <td>1681.0</td>\n",
       "      <td>1764.0</td>\n",
       "      <td>1849.0</td>\n",
       "      <td>1936.0</td>\n",
       "      <td>1800.0</td>\n",
       "      <td>1886.0</td>\n",
       "      <td>1974.0</td>\n",
       "      <td>2064.0</td>\n",
       "      <td>2156.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3300.0</td>\n",
       "      <td>3416.0</td>\n",
       "      <td>3534.0</td>\n",
       "      <td>3654.0</td>\n",
       "      <td>3776.0</td>\n",
       "      <td>3600.0</td>\n",
       "      <td>3721.0</td>\n",
       "      <td>3844.0</td>\n",
       "      <td>3969.0</td>\n",
       "      <td>4096.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2025.0</td>\n",
       "      <td>2116.0</td>\n",
       "      <td>2209.0</td>\n",
       "      <td>2304.0</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>2250.0</td>\n",
       "      <td>2346.0</td>\n",
       "      <td>2444.0</td>\n",
       "      <td>2544.0</td>\n",
       "      <td>2646.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3900.0</td>\n",
       "      <td>4026.0</td>\n",
       "      <td>4154.0</td>\n",
       "      <td>4284.0</td>\n",
       "      <td>4416.0</td>\n",
       "      <td>4225.0</td>\n",
       "      <td>4356.0</td>\n",
       "      <td>4489.0</td>\n",
       "      <td>4624.0</td>\n",
       "      <td>4761.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2500.0</td>\n",
       "      <td>2601.0</td>\n",
       "      <td>2704.0</td>\n",
       "      <td>2809.0</td>\n",
       "      <td>2916.0</td>\n",
       "      <td>2750.0</td>\n",
       "      <td>2856.0</td>\n",
       "      <td>2964.0</td>\n",
       "      <td>3074.0</td>\n",
       "      <td>3186.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4550.0</td>\n",
       "      <td>4686.0</td>\n",
       "      <td>4824.0</td>\n",
       "      <td>4964.0</td>\n",
       "      <td>5106.0</td>\n",
       "      <td>4900.0</td>\n",
       "      <td>5041.0</td>\n",
       "      <td>5184.0</td>\n",
       "      <td>5329.0</td>\n",
       "      <td>5476.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>3025.0</td>\n",
       "      <td>3136.0</td>\n",
       "      <td>3249.0</td>\n",
       "      <td>3364.0</td>\n",
       "      <td>3481.0</td>\n",
       "      <td>3300.0</td>\n",
       "      <td>3416.0</td>\n",
       "      <td>3534.0</td>\n",
       "      <td>3654.0</td>\n",
       "      <td>3776.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5250.0</td>\n",
       "      <td>5396.0</td>\n",
       "      <td>5544.0</td>\n",
       "      <td>5694.0</td>\n",
       "      <td>5846.0</td>\n",
       "      <td>5625.0</td>\n",
       "      <td>5776.0</td>\n",
       "      <td>5929.0</td>\n",
       "      <td>6084.0</td>\n",
       "      <td>6241.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>3600.0</td>\n",
       "      <td>3721.0</td>\n",
       "      <td>3844.0</td>\n",
       "      <td>3969.0</td>\n",
       "      <td>4096.0</td>\n",
       "      <td>3900.0</td>\n",
       "      <td>4026.0</td>\n",
       "      <td>4154.0</td>\n",
       "      <td>4284.0</td>\n",
       "      <td>4416.0</td>\n",
       "      <td>...</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>6156.0</td>\n",
       "      <td>6314.0</td>\n",
       "      <td>6474.0</td>\n",
       "      <td>6636.0</td>\n",
       "      <td>6400.0</td>\n",
       "      <td>6561.0</td>\n",
       "      <td>6724.0</td>\n",
       "      <td>6889.0</td>\n",
       "      <td>7056.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>4225.0</td>\n",
       "      <td>4356.0</td>\n",
       "      <td>4489.0</td>\n",
       "      <td>4624.0</td>\n",
       "      <td>4761.0</td>\n",
       "      <td>4550.0</td>\n",
       "      <td>4686.0</td>\n",
       "      <td>4824.0</td>\n",
       "      <td>4964.0</td>\n",
       "      <td>5106.0</td>\n",
       "      <td>...</td>\n",
       "      <td>6800.0</td>\n",
       "      <td>6966.0</td>\n",
       "      <td>7134.0</td>\n",
       "      <td>7304.0</td>\n",
       "      <td>7476.0</td>\n",
       "      <td>7225.0</td>\n",
       "      <td>7396.0</td>\n",
       "      <td>7569.0</td>\n",
       "      <td>7744.0</td>\n",
       "      <td>7921.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>4900.0</td>\n",
       "      <td>5041.0</td>\n",
       "      <td>5184.0</td>\n",
       "      <td>5329.0</td>\n",
       "      <td>5476.0</td>\n",
       "      <td>5250.0</td>\n",
       "      <td>5396.0</td>\n",
       "      <td>5544.0</td>\n",
       "      <td>5694.0</td>\n",
       "      <td>5846.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7650.0</td>\n",
       "      <td>7826.0</td>\n",
       "      <td>8004.0</td>\n",
       "      <td>8184.0</td>\n",
       "      <td>8366.0</td>\n",
       "      <td>8100.0</td>\n",
       "      <td>8281.0</td>\n",
       "      <td>8464.0</td>\n",
       "      <td>8649.0</td>\n",
       "      <td>8836.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15 rows × 125 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    var1(t-5)(t-5)  var2(t-5)(t-5)  var3(t-5)(t-5)  var4(t-5)(t-5)  \\\n",
       "5              0.0             1.0             4.0             9.0   \n",
       "6             25.0            36.0            49.0            64.0   \n",
       "7            100.0           121.0           144.0           169.0   \n",
       "8            225.0           256.0           289.0           324.0   \n",
       "9            400.0           441.0           484.0           529.0   \n",
       "10           625.0           676.0           729.0           784.0   \n",
       "11           900.0           961.0          1024.0          1089.0   \n",
       "12          1225.0          1296.0          1369.0          1444.0   \n",
       "13          1600.0          1681.0          1764.0          1849.0   \n",
       "14          2025.0          2116.0          2209.0          2304.0   \n",
       "15          2500.0          2601.0          2704.0          2809.0   \n",
       "16          3025.0          3136.0          3249.0          3364.0   \n",
       "17          3600.0          3721.0          3844.0          3969.0   \n",
       "18          4225.0          4356.0          4489.0          4624.0   \n",
       "19          4900.0          5041.0          5184.0          5329.0   \n",
       "\n",
       "    var5(t-5)(t-5)  var1(t-5)(t-4)  var2(t-5)(t-4)  var3(t-5)(t-4)  \\\n",
       "5             16.0             0.0             6.0            14.0   \n",
       "6             81.0            50.0            66.0            84.0   \n",
       "7            196.0           150.0           176.0           204.0   \n",
       "8            361.0           300.0           336.0           374.0   \n",
       "9            576.0           500.0           546.0           594.0   \n",
       "10           841.0           750.0           806.0           864.0   \n",
       "11          1156.0          1050.0          1116.0          1184.0   \n",
       "12          1521.0          1400.0          1476.0          1554.0   \n",
       "13          1936.0          1800.0          1886.0          1974.0   \n",
       "14          2401.0          2250.0          2346.0          2444.0   \n",
       "15          2916.0          2750.0          2856.0          2964.0   \n",
       "16          3481.0          3300.0          3416.0          3534.0   \n",
       "17          4096.0          3900.0          4026.0          4154.0   \n",
       "18          4761.0          4550.0          4686.0          4824.0   \n",
       "19          5476.0          5250.0          5396.0          5544.0   \n",
       "\n",
       "    var4(t-5)(t-4)  var5(t-5)(t-4)  ...  var1(t-1)(t-2)  var2(t-1)(t-2)  \\\n",
       "5             24.0            36.0  ...           300.0           336.0   \n",
       "6            104.0           126.0  ...           500.0           546.0   \n",
       "7            234.0           266.0  ...           750.0           806.0   \n",
       "8            414.0           456.0  ...          1050.0          1116.0   \n",
       "9            644.0           696.0  ...          1400.0          1476.0   \n",
       "10           924.0           986.0  ...          1800.0          1886.0   \n",
       "11          1254.0          1326.0  ...          2250.0          2346.0   \n",
       "12          1634.0          1716.0  ...          2750.0          2856.0   \n",
       "13          2064.0          2156.0  ...          3300.0          3416.0   \n",
       "14          2544.0          2646.0  ...          3900.0          4026.0   \n",
       "15          3074.0          3186.0  ...          4550.0          4686.0   \n",
       "16          3654.0          3776.0  ...          5250.0          5396.0   \n",
       "17          4284.0          4416.0  ...          6000.0          6156.0   \n",
       "18          4964.0          5106.0  ...          6800.0          6966.0   \n",
       "19          5694.0          5846.0  ...          7650.0          7826.0   \n",
       "\n",
       "    var3(t-1)(t-2)  var4(t-1)(t-2)  var5(t-1)(t-2)  var1(t-1)(t-1)  \\\n",
       "5            374.0           414.0           456.0           400.0   \n",
       "6            594.0           644.0           696.0           625.0   \n",
       "7            864.0           924.0           986.0           900.0   \n",
       "8           1184.0          1254.0          1326.0          1225.0   \n",
       "9           1554.0          1634.0          1716.0          1600.0   \n",
       "10          1974.0          2064.0          2156.0          2025.0   \n",
       "11          2444.0          2544.0          2646.0          2500.0   \n",
       "12          2964.0          3074.0          3186.0          3025.0   \n",
       "13          3534.0          3654.0          3776.0          3600.0   \n",
       "14          4154.0          4284.0          4416.0          4225.0   \n",
       "15          4824.0          4964.0          5106.0          4900.0   \n",
       "16          5544.0          5694.0          5846.0          5625.0   \n",
       "17          6314.0          6474.0          6636.0          6400.0   \n",
       "18          7134.0          7304.0          7476.0          7225.0   \n",
       "19          8004.0          8184.0          8366.0          8100.0   \n",
       "\n",
       "    var2(t-1)(t-1)  var3(t-1)(t-1)  var4(t-1)(t-1)  var5(t-1)(t-1)  \n",
       "5            441.0           484.0           529.0           576.0  \n",
       "6            676.0           729.0           784.0           841.0  \n",
       "7            961.0          1024.0          1089.0          1156.0  \n",
       "8           1296.0          1369.0          1444.0          1521.0  \n",
       "9           1681.0          1764.0          1849.0          1936.0  \n",
       "10          2116.0          2209.0          2304.0          2401.0  \n",
       "11          2601.0          2704.0          2809.0          2916.0  \n",
       "12          3136.0          3249.0          3364.0          3481.0  \n",
       "13          3721.0          3844.0          3969.0          4096.0  \n",
       "14          4356.0          4489.0          4624.0          4761.0  \n",
       "15          5041.0          5184.0          5329.0          5476.0  \n",
       "16          5776.0          5929.0          6084.0          6241.0  \n",
       "17          6561.0          6724.0          6889.0          7056.0  \n",
       "18          7396.0          7569.0          7744.0          7921.0  \n",
       "19          8281.0          8464.0          8649.0          8836.0  \n",
       "\n",
       "[15 rows x 125 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def order_two_Volterra(input_df, M=5, dropnan=True):\n",
    "    n_vars = len(input_df.columns)\n",
    "    cols, names = list(), list()\n",
    "    for i in range(M, 0, -1):\n",
    "        for k in range(M, 0, -1):\n",
    "            cols.append(input_df.shift(i)*input_df.shift(k))\n",
    "            names += [('var%d(t-%d)(t-%d)' % (j+1, i, k)) for j in range(n_vars)]\n",
    "    # put it all together\n",
    "    agg = pd.concat(cols, axis=1)\n",
    "    agg.columns = names\n",
    "    # drop rows with NaN values\n",
    "    if dropnan:\n",
    "        agg.dropna(inplace=True)\n",
    "    return agg\n",
    "\n",
    "order_two_Volterra(input_df).head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Volterra Series Order Three"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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>var1(t-5)(t-5)(t-5)</th>\n",
       "      <th>var2(t-5)(t-5)(t-5)</th>\n",
       "      <th>var3(t-5)(t-5)(t-5)</th>\n",
       "      <th>var4(t-5)(t-5)(t-5)</th>\n",
       "      <th>var5(t-5)(t-5)(t-5)</th>\n",
       "      <th>var1(t-5)(t-5)(t-4)</th>\n",
       "      <th>var2(t-5)(t-5)(t-4)</th>\n",
       "      <th>var3(t-5)(t-5)(t-4)</th>\n",
       "      <th>var4(t-5)(t-5)(t-4)</th>\n",
       "      <th>var5(t-5)(t-5)(t-4)</th>\n",
       "      <th>...</th>\n",
       "      <th>var1(t-1)(t-1)(t-2)</th>\n",
       "      <th>var2(t-1)(t-1)(t-2)</th>\n",
       "      <th>var3(t-1)(t-1)(t-2)</th>\n",
       "      <th>var4(t-1)(t-1)(t-2)</th>\n",
       "      <th>var5(t-1)(t-1)(t-2)</th>\n",
       "      <th>var1(t-1)(t-1)(t-1)</th>\n",
       "      <th>var2(t-1)(t-1)(t-1)</th>\n",
       "      <th>var3(t-1)(t-1)(t-1)</th>\n",
       "      <th>var4(t-1)(t-1)(t-1)</th>\n",
       "      <th>var5(t-1)(t-1)(t-1)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>...</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>7056.0</td>\n",
       "      <td>8228.0</td>\n",
       "      <td>9522.0</td>\n",
       "      <td>10944.0</td>\n",
       "      <td>8000.0</td>\n",
       "      <td>9261.0</td>\n",
       "      <td>10648.0</td>\n",
       "      <td>12167.0</td>\n",
       "      <td>13824.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>125.0</td>\n",
       "      <td>216.0</td>\n",
       "      <td>343.0</td>\n",
       "      <td>512.0</td>\n",
       "      <td>729.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>396.0</td>\n",
       "      <td>588.0</td>\n",
       "      <td>832.0</td>\n",
       "      <td>1134.0</td>\n",
       "      <td>...</td>\n",
       "      <td>12500.0</td>\n",
       "      <td>14196.0</td>\n",
       "      <td>16038.0</td>\n",
       "      <td>18032.0</td>\n",
       "      <td>20184.0</td>\n",
       "      <td>15625.0</td>\n",
       "      <td>17576.0</td>\n",
       "      <td>19683.0</td>\n",
       "      <td>21952.0</td>\n",
       "      <td>24389.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>1331.0</td>\n",
       "      <td>1728.0</td>\n",
       "      <td>2197.0</td>\n",
       "      <td>2744.0</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>1936.0</td>\n",
       "      <td>2448.0</td>\n",
       "      <td>3042.0</td>\n",
       "      <td>3724.0</td>\n",
       "      <td>...</td>\n",
       "      <td>22500.0</td>\n",
       "      <td>24986.0</td>\n",
       "      <td>27648.0</td>\n",
       "      <td>30492.0</td>\n",
       "      <td>33524.0</td>\n",
       "      <td>27000.0</td>\n",
       "      <td>29791.0</td>\n",
       "      <td>32768.0</td>\n",
       "      <td>35937.0</td>\n",
       "      <td>39304.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3375.0</td>\n",
       "      <td>4096.0</td>\n",
       "      <td>4913.0</td>\n",
       "      <td>5832.0</td>\n",
       "      <td>6859.0</td>\n",
       "      <td>4500.0</td>\n",
       "      <td>5376.0</td>\n",
       "      <td>6358.0</td>\n",
       "      <td>7452.0</td>\n",
       "      <td>8664.0</td>\n",
       "      <td>...</td>\n",
       "      <td>36750.0</td>\n",
       "      <td>40176.0</td>\n",
       "      <td>43808.0</td>\n",
       "      <td>47652.0</td>\n",
       "      <td>51714.0</td>\n",
       "      <td>42875.0</td>\n",
       "      <td>46656.0</td>\n",
       "      <td>50653.0</td>\n",
       "      <td>54872.0</td>\n",
       "      <td>59319.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>8000.0</td>\n",
       "      <td>9261.0</td>\n",
       "      <td>10648.0</td>\n",
       "      <td>12167.0</td>\n",
       "      <td>13824.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>11466.0</td>\n",
       "      <td>13068.0</td>\n",
       "      <td>14812.0</td>\n",
       "      <td>16704.0</td>\n",
       "      <td>...</td>\n",
       "      <td>56000.0</td>\n",
       "      <td>60516.0</td>\n",
       "      <td>65268.0</td>\n",
       "      <td>70262.0</td>\n",
       "      <td>75504.0</td>\n",
       "      <td>64000.0</td>\n",
       "      <td>68921.0</td>\n",
       "      <td>74088.0</td>\n",
       "      <td>79507.0</td>\n",
       "      <td>85184.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>15625.0</td>\n",
       "      <td>17576.0</td>\n",
       "      <td>19683.0</td>\n",
       "      <td>21952.0</td>\n",
       "      <td>24389.0</td>\n",
       "      <td>18750.0</td>\n",
       "      <td>20956.0</td>\n",
       "      <td>23328.0</td>\n",
       "      <td>25872.0</td>\n",
       "      <td>28594.0</td>\n",
       "      <td>...</td>\n",
       "      <td>81000.0</td>\n",
       "      <td>86756.0</td>\n",
       "      <td>92778.0</td>\n",
       "      <td>99072.0</td>\n",
       "      <td>105644.0</td>\n",
       "      <td>91125.0</td>\n",
       "      <td>97336.0</td>\n",
       "      <td>103823.0</td>\n",
       "      <td>110592.0</td>\n",
       "      <td>117649.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>27000.0</td>\n",
       "      <td>29791.0</td>\n",
       "      <td>32768.0</td>\n",
       "      <td>35937.0</td>\n",
       "      <td>39304.0</td>\n",
       "      <td>31500.0</td>\n",
       "      <td>34596.0</td>\n",
       "      <td>37888.0</td>\n",
       "      <td>41382.0</td>\n",
       "      <td>45084.0</td>\n",
       "      <td>...</td>\n",
       "      <td>112500.0</td>\n",
       "      <td>119646.0</td>\n",
       "      <td>127088.0</td>\n",
       "      <td>134832.0</td>\n",
       "      <td>142884.0</td>\n",
       "      <td>125000.0</td>\n",
       "      <td>132651.0</td>\n",
       "      <td>140608.0</td>\n",
       "      <td>148877.0</td>\n",
       "      <td>157464.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>42875.0</td>\n",
       "      <td>46656.0</td>\n",
       "      <td>50653.0</td>\n",
       "      <td>54872.0</td>\n",
       "      <td>59319.0</td>\n",
       "      <td>49000.0</td>\n",
       "      <td>53136.0</td>\n",
       "      <td>57498.0</td>\n",
       "      <td>62092.0</td>\n",
       "      <td>66924.0</td>\n",
       "      <td>...</td>\n",
       "      <td>151250.0</td>\n",
       "      <td>159936.0</td>\n",
       "      <td>168948.0</td>\n",
       "      <td>178292.0</td>\n",
       "      <td>187974.0</td>\n",
       "      <td>166375.0</td>\n",
       "      <td>175616.0</td>\n",
       "      <td>185193.0</td>\n",
       "      <td>195112.0</td>\n",
       "      <td>205379.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>64000.0</td>\n",
       "      <td>68921.0</td>\n",
       "      <td>74088.0</td>\n",
       "      <td>79507.0</td>\n",
       "      <td>85184.0</td>\n",
       "      <td>72000.0</td>\n",
       "      <td>77326.0</td>\n",
       "      <td>82908.0</td>\n",
       "      <td>88752.0</td>\n",
       "      <td>94864.0</td>\n",
       "      <td>...</td>\n",
       "      <td>198000.0</td>\n",
       "      <td>208376.0</td>\n",
       "      <td>219108.0</td>\n",
       "      <td>230202.0</td>\n",
       "      <td>241664.0</td>\n",
       "      <td>216000.0</td>\n",
       "      <td>226981.0</td>\n",
       "      <td>238328.0</td>\n",
       "      <td>250047.0</td>\n",
       "      <td>262144.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>91125.0</td>\n",
       "      <td>97336.0</td>\n",
       "      <td>103823.0</td>\n",
       "      <td>110592.0</td>\n",
       "      <td>117649.0</td>\n",
       "      <td>101250.0</td>\n",
       "      <td>107916.0</td>\n",
       "      <td>114868.0</td>\n",
       "      <td>122112.0</td>\n",
       "      <td>129654.0</td>\n",
       "      <td>...</td>\n",
       "      <td>253500.0</td>\n",
       "      <td>265716.0</td>\n",
       "      <td>278318.0</td>\n",
       "      <td>291312.0</td>\n",
       "      <td>304704.0</td>\n",
       "      <td>274625.0</td>\n",
       "      <td>287496.0</td>\n",
       "      <td>300763.0</td>\n",
       "      <td>314432.0</td>\n",
       "      <td>328509.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>125000.0</td>\n",
       "      <td>132651.0</td>\n",
       "      <td>140608.0</td>\n",
       "      <td>148877.0</td>\n",
       "      <td>157464.0</td>\n",
       "      <td>137500.0</td>\n",
       "      <td>145656.0</td>\n",
       "      <td>154128.0</td>\n",
       "      <td>162922.0</td>\n",
       "      <td>172044.0</td>\n",
       "      <td>...</td>\n",
       "      <td>318500.0</td>\n",
       "      <td>332706.0</td>\n",
       "      <td>347328.0</td>\n",
       "      <td>362372.0</td>\n",
       "      <td>377844.0</td>\n",
       "      <td>343000.0</td>\n",
       "      <td>357911.0</td>\n",
       "      <td>373248.0</td>\n",
       "      <td>389017.0</td>\n",
       "      <td>405224.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>166375.0</td>\n",
       "      <td>175616.0</td>\n",
       "      <td>185193.0</td>\n",
       "      <td>195112.0</td>\n",
       "      <td>205379.0</td>\n",
       "      <td>181500.0</td>\n",
       "      <td>191296.0</td>\n",
       "      <td>201438.0</td>\n",
       "      <td>211932.0</td>\n",
       "      <td>222784.0</td>\n",
       "      <td>...</td>\n",
       "      <td>393750.0</td>\n",
       "      <td>410096.0</td>\n",
       "      <td>426888.0</td>\n",
       "      <td>444132.0</td>\n",
       "      <td>461834.0</td>\n",
       "      <td>421875.0</td>\n",
       "      <td>438976.0</td>\n",
       "      <td>456533.0</td>\n",
       "      <td>474552.0</td>\n",
       "      <td>493039.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>216000.0</td>\n",
       "      <td>226981.0</td>\n",
       "      <td>238328.0</td>\n",
       "      <td>250047.0</td>\n",
       "      <td>262144.0</td>\n",
       "      <td>234000.0</td>\n",
       "      <td>245586.0</td>\n",
       "      <td>257548.0</td>\n",
       "      <td>269892.0</td>\n",
       "      <td>282624.0</td>\n",
       "      <td>...</td>\n",
       "      <td>480000.0</td>\n",
       "      <td>498636.0</td>\n",
       "      <td>517748.0</td>\n",
       "      <td>537342.0</td>\n",
       "      <td>557424.0</td>\n",
       "      <td>512000.0</td>\n",
       "      <td>531441.0</td>\n",
       "      <td>551368.0</td>\n",
       "      <td>571787.0</td>\n",
       "      <td>592704.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>274625.0</td>\n",
       "      <td>287496.0</td>\n",
       "      <td>300763.0</td>\n",
       "      <td>314432.0</td>\n",
       "      <td>328509.0</td>\n",
       "      <td>295750.0</td>\n",
       "      <td>309276.0</td>\n",
       "      <td>323208.0</td>\n",
       "      <td>337552.0</td>\n",
       "      <td>352314.0</td>\n",
       "      <td>...</td>\n",
       "      <td>578000.0</td>\n",
       "      <td>599076.0</td>\n",
       "      <td>620658.0</td>\n",
       "      <td>642752.0</td>\n",
       "      <td>665364.0</td>\n",
       "      <td>614125.0</td>\n",
       "      <td>636056.0</td>\n",
       "      <td>658503.0</td>\n",
       "      <td>681472.0</td>\n",
       "      <td>704969.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>343000.0</td>\n",
       "      <td>357911.0</td>\n",
       "      <td>373248.0</td>\n",
       "      <td>389017.0</td>\n",
       "      <td>405224.0</td>\n",
       "      <td>367500.0</td>\n",
       "      <td>383116.0</td>\n",
       "      <td>399168.0</td>\n",
       "      <td>415662.0</td>\n",
       "      <td>432604.0</td>\n",
       "      <td>...</td>\n",
       "      <td>688500.0</td>\n",
       "      <td>712166.0</td>\n",
       "      <td>736368.0</td>\n",
       "      <td>761112.0</td>\n",
       "      <td>786404.0</td>\n",
       "      <td>729000.0</td>\n",
       "      <td>753571.0</td>\n",
       "      <td>778688.0</td>\n",
       "      <td>804357.0</td>\n",
       "      <td>830584.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15 rows × 625 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    var1(t-5)(t-5)(t-5)  var2(t-5)(t-5)(t-5)  var3(t-5)(t-5)(t-5)  \\\n",
       "5                   0.0                  1.0                  8.0   \n",
       "6                 125.0                216.0                343.0   \n",
       "7                1000.0               1331.0               1728.0   \n",
       "8                3375.0               4096.0               4913.0   \n",
       "9                8000.0               9261.0              10648.0   \n",
       "10              15625.0              17576.0              19683.0   \n",
       "11              27000.0              29791.0              32768.0   \n",
       "12              42875.0              46656.0              50653.0   \n",
       "13              64000.0              68921.0              74088.0   \n",
       "14              91125.0              97336.0             103823.0   \n",
       "15             125000.0             132651.0             140608.0   \n",
       "16             166375.0             175616.0             185193.0   \n",
       "17             216000.0             226981.0             238328.0   \n",
       "18             274625.0             287496.0             300763.0   \n",
       "19             343000.0             357911.0             373248.0   \n",
       "\n",
       "    var4(t-5)(t-5)(t-5)  var5(t-5)(t-5)(t-5)  var1(t-5)(t-5)(t-4)  \\\n",
       "5                  27.0                 64.0                  0.0   \n",
       "6                 512.0                729.0                250.0   \n",
       "7                2197.0               2744.0               1500.0   \n",
       "8                5832.0               6859.0               4500.0   \n",
       "9               12167.0              13824.0              10000.0   \n",
       "10              21952.0              24389.0              18750.0   \n",
       "11              35937.0              39304.0              31500.0   \n",
       "12              54872.0              59319.0              49000.0   \n",
       "13              79507.0              85184.0              72000.0   \n",
       "14             110592.0             117649.0             101250.0   \n",
       "15             148877.0             157464.0             137500.0   \n",
       "16             195112.0             205379.0             181500.0   \n",
       "17             250047.0             262144.0             234000.0   \n",
       "18             314432.0             328509.0             295750.0   \n",
       "19             389017.0             405224.0             367500.0   \n",
       "\n",
       "    var2(t-5)(t-5)(t-4)  var3(t-5)(t-5)(t-4)  var4(t-5)(t-5)(t-4)  \\\n",
       "5                   6.0                 28.0                 72.0   \n",
       "6                 396.0                588.0                832.0   \n",
       "7                1936.0               2448.0               3042.0   \n",
       "8                5376.0               6358.0               7452.0   \n",
       "9               11466.0              13068.0              14812.0   \n",
       "10              20956.0              23328.0              25872.0   \n",
       "11              34596.0              37888.0              41382.0   \n",
       "12              53136.0              57498.0              62092.0   \n",
       "13              77326.0              82908.0              88752.0   \n",
       "14             107916.0             114868.0             122112.0   \n",
       "15             145656.0             154128.0             162922.0   \n",
       "16             191296.0             201438.0             211932.0   \n",
       "17             245586.0             257548.0             269892.0   \n",
       "18             309276.0             323208.0             337552.0   \n",
       "19             383116.0             399168.0             415662.0   \n",
       "\n",
       "    var5(t-5)(t-5)(t-4)  ...  var1(t-1)(t-1)(t-2)  var2(t-1)(t-1)(t-2)  \\\n",
       "5                 144.0  ...               6000.0               7056.0   \n",
       "6                1134.0  ...              12500.0              14196.0   \n",
       "7                3724.0  ...              22500.0              24986.0   \n",
       "8                8664.0  ...              36750.0              40176.0   \n",
       "9               16704.0  ...              56000.0              60516.0   \n",
       "10              28594.0  ...              81000.0              86756.0   \n",
       "11              45084.0  ...             112500.0             119646.0   \n",
       "12              66924.0  ...             151250.0             159936.0   \n",
       "13              94864.0  ...             198000.0             208376.0   \n",
       "14             129654.0  ...             253500.0             265716.0   \n",
       "15             172044.0  ...             318500.0             332706.0   \n",
       "16             222784.0  ...             393750.0             410096.0   \n",
       "17             282624.0  ...             480000.0             498636.0   \n",
       "18             352314.0  ...             578000.0             599076.0   \n",
       "19             432604.0  ...             688500.0             712166.0   \n",
       "\n",
       "    var3(t-1)(t-1)(t-2)  var4(t-1)(t-1)(t-2)  var5(t-1)(t-1)(t-2)  \\\n",
       "5                8228.0               9522.0              10944.0   \n",
       "6               16038.0              18032.0              20184.0   \n",
       "7               27648.0              30492.0              33524.0   \n",
       "8               43808.0              47652.0              51714.0   \n",
       "9               65268.0              70262.0              75504.0   \n",
       "10              92778.0              99072.0             105644.0   \n",
       "11             127088.0             134832.0             142884.0   \n",
       "12             168948.0             178292.0             187974.0   \n",
       "13             219108.0             230202.0             241664.0   \n",
       "14             278318.0             291312.0             304704.0   \n",
       "15             347328.0             362372.0             377844.0   \n",
       "16             426888.0             444132.0             461834.0   \n",
       "17             517748.0             537342.0             557424.0   \n",
       "18             620658.0             642752.0             665364.0   \n",
       "19             736368.0             761112.0             786404.0   \n",
       "\n",
       "    var1(t-1)(t-1)(t-1)  var2(t-1)(t-1)(t-1)  var3(t-1)(t-1)(t-1)  \\\n",
       "5                8000.0               9261.0              10648.0   \n",
       "6               15625.0              17576.0              19683.0   \n",
       "7               27000.0              29791.0              32768.0   \n",
       "8               42875.0              46656.0              50653.0   \n",
       "9               64000.0              68921.0              74088.0   \n",
       "10              91125.0              97336.0             103823.0   \n",
       "11             125000.0             132651.0             140608.0   \n",
       "12             166375.0             175616.0             185193.0   \n",
       "13             216000.0             226981.0             238328.0   \n",
       "14             274625.0             287496.0             300763.0   \n",
       "15             343000.0             357911.0             373248.0   \n",
       "16             421875.0             438976.0             456533.0   \n",
       "17             512000.0             531441.0             551368.0   \n",
       "18             614125.0             636056.0             658503.0   \n",
       "19             729000.0             753571.0             778688.0   \n",
       "\n",
       "    var4(t-1)(t-1)(t-1)  var5(t-1)(t-1)(t-1)  \n",
       "5               12167.0              13824.0  \n",
       "6               21952.0              24389.0  \n",
       "7               35937.0              39304.0  \n",
       "8               54872.0              59319.0  \n",
       "9               79507.0              85184.0  \n",
       "10             110592.0             117649.0  \n",
       "11             148877.0             157464.0  \n",
       "12             195112.0             205379.0  \n",
       "13             250047.0             262144.0  \n",
       "14             314432.0             328509.0  \n",
       "15             389017.0             405224.0  \n",
       "16             474552.0             493039.0  \n",
       "17             571787.0             592704.0  \n",
       "18             681472.0             704969.0  \n",
       "19             804357.0             830584.0  \n",
       "\n",
       "[15 rows x 625 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def order_three_Volterra(input_df, M=5, dropnan=True):\n",
    "    n_vars = len(input_df.columns)\n",
    "    cols, names = list(), list()\n",
    "    for i in range(M, 0, -1):\n",
    "        for k in range(M, 0, -1):\n",
    "            for l in range(M, 0, -1):\n",
    "                cols.append(input_df.shift(i)*input_df.shift(k)*input_df.shift(l))\n",
    "                names += [('var%d(t-%d)(t-%d)(t-%d)' % (j+1, i, k, l)) for j in range(n_vars)]\n",
    "    # put it all together\n",
    "    agg = pd.concat(cols, axis=1)\n",
    "    agg.columns = names\n",
    "    # drop rows with NaN values\n",
    "    if dropnan:\n",
    "        agg.dropna(inplace=True)\n",
    "    return agg\n",
    "\n",
    "order_three_Volterra(input_df).head(15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Aggregate Volterra Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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",
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       "    .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>var1(t)</th>\n",
       "      <th>var2(t)</th>\n",
       "      <th>var3(t)</th>\n",
       "      <th>var4(t)</th>\n",
       "      <th>var5(t)</th>\n",
       "      <th>var1(t-5)</th>\n",
       "      <th>var2(t-5)</th>\n",
       "      <th>var3(t-5)</th>\n",
       "      <th>var4(t-5)</th>\n",
       "      <th>var5(t-5)</th>\n",
       "      <th>...</th>\n",
       "      <th>var1(t-1)(t-1)(t-2)</th>\n",
       "      <th>var2(t-1)(t-1)(t-2)</th>\n",
       "      <th>var3(t-1)(t-1)(t-2)</th>\n",
       "      <th>var4(t-1)(t-1)(t-2)</th>\n",
       "      <th>var5(t-1)(t-1)(t-2)</th>\n",
       "      <th>var1(t-1)(t-1)(t-1)</th>\n",
       "      <th>var2(t-1)(t-1)(t-1)</th>\n",
       "      <th>var3(t-1)(t-1)(t-1)</th>\n",
       "      <th>var4(t-1)(t-1)(t-1)</th>\n",
       "      <th>var5(t-1)(t-1)(t-1)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>25</td>\n",
       "      <td>26</td>\n",
       "      <td>27</td>\n",
       "      <td>28</td>\n",
       "      <td>29</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>7056.0</td>\n",
       "      <td>8228.0</td>\n",
       "      <td>9522.0</td>\n",
       "      <td>10944.0</td>\n",
       "      <td>8000.0</td>\n",
       "      <td>9261.0</td>\n",
       "      <td>10648.0</td>\n",
       "      <td>12167.0</td>\n",
       "      <td>13824.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>30</td>\n",
       "      <td>31</td>\n",
       "      <td>32</td>\n",
       "      <td>33</td>\n",
       "      <td>34</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>...</td>\n",
       "      <td>12500.0</td>\n",
       "      <td>14196.0</td>\n",
       "      <td>16038.0</td>\n",
       "      <td>18032.0</td>\n",
       "      <td>20184.0</td>\n",
       "      <td>15625.0</td>\n",
       "      <td>17576.0</td>\n",
       "      <td>19683.0</td>\n",
       "      <td>21952.0</td>\n",
       "      <td>24389.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>35</td>\n",
       "      <td>36</td>\n",
       "      <td>37</td>\n",
       "      <td>38</td>\n",
       "      <td>39</td>\n",
       "      <td>10.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>...</td>\n",
       "      <td>22500.0</td>\n",
       "      <td>24986.0</td>\n",
       "      <td>27648.0</td>\n",
       "      <td>30492.0</td>\n",
       "      <td>33524.0</td>\n",
       "      <td>27000.0</td>\n",
       "      <td>29791.0</td>\n",
       "      <td>32768.0</td>\n",
       "      <td>35937.0</td>\n",
       "      <td>39304.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>40</td>\n",
       "      <td>41</td>\n",
       "      <td>42</td>\n",
       "      <td>43</td>\n",
       "      <td>44</td>\n",
       "      <td>15.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>...</td>\n",
       "      <td>36750.0</td>\n",
       "      <td>40176.0</td>\n",
       "      <td>43808.0</td>\n",
       "      <td>47652.0</td>\n",
       "      <td>51714.0</td>\n",
       "      <td>42875.0</td>\n",
       "      <td>46656.0</td>\n",
       "      <td>50653.0</td>\n",
       "      <td>54872.0</td>\n",
       "      <td>59319.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>45</td>\n",
       "      <td>46</td>\n",
       "      <td>47</td>\n",
       "      <td>48</td>\n",
       "      <td>49</td>\n",
       "      <td>20.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>...</td>\n",
       "      <td>56000.0</td>\n",
       "      <td>60516.0</td>\n",
       "      <td>65268.0</td>\n",
       "      <td>70262.0</td>\n",
       "      <td>75504.0</td>\n",
       "      <td>64000.0</td>\n",
       "      <td>68921.0</td>\n",
       "      <td>74088.0</td>\n",
       "      <td>79507.0</td>\n",
       "      <td>85184.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>50</td>\n",
       "      <td>51</td>\n",
       "      <td>52</td>\n",
       "      <td>53</td>\n",
       "      <td>54</td>\n",
       "      <td>25.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>...</td>\n",
       "      <td>81000.0</td>\n",
       "      <td>86756.0</td>\n",
       "      <td>92778.0</td>\n",
       "      <td>99072.0</td>\n",
       "      <td>105644.0</td>\n",
       "      <td>91125.0</td>\n",
       "      <td>97336.0</td>\n",
       "      <td>103823.0</td>\n",
       "      <td>110592.0</td>\n",
       "      <td>117649.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>55</td>\n",
       "      <td>56</td>\n",
       "      <td>57</td>\n",
       "      <td>58</td>\n",
       "      <td>59</td>\n",
       "      <td>30.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>...</td>\n",
       "      <td>112500.0</td>\n",
       "      <td>119646.0</td>\n",
       "      <td>127088.0</td>\n",
       "      <td>134832.0</td>\n",
       "      <td>142884.0</td>\n",
       "      <td>125000.0</td>\n",
       "      <td>132651.0</td>\n",
       "      <td>140608.0</td>\n",
       "      <td>148877.0</td>\n",
       "      <td>157464.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>60</td>\n",
       "      <td>61</td>\n",
       "      <td>62</td>\n",
       "      <td>63</td>\n",
       "      <td>64</td>\n",
       "      <td>35.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>...</td>\n",
       "      <td>151250.0</td>\n",
       "      <td>159936.0</td>\n",
       "      <td>168948.0</td>\n",
       "      <td>178292.0</td>\n",
       "      <td>187974.0</td>\n",
       "      <td>166375.0</td>\n",
       "      <td>175616.0</td>\n",
       "      <td>185193.0</td>\n",
       "      <td>195112.0</td>\n",
       "      <td>205379.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>65</td>\n",
       "      <td>66</td>\n",
       "      <td>67</td>\n",
       "      <td>68</td>\n",
       "      <td>69</td>\n",
       "      <td>40.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>...</td>\n",
       "      <td>198000.0</td>\n",
       "      <td>208376.0</td>\n",
       "      <td>219108.0</td>\n",
       "      <td>230202.0</td>\n",
       "      <td>241664.0</td>\n",
       "      <td>216000.0</td>\n",
       "      <td>226981.0</td>\n",
       "      <td>238328.0</td>\n",
       "      <td>250047.0</td>\n",
       "      <td>262144.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>70</td>\n",
       "      <td>71</td>\n",
       "      <td>72</td>\n",
       "      <td>73</td>\n",
       "      <td>74</td>\n",
       "      <td>45.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>...</td>\n",
       "      <td>253500.0</td>\n",
       "      <td>265716.0</td>\n",
       "      <td>278318.0</td>\n",
       "      <td>291312.0</td>\n",
       "      <td>304704.0</td>\n",
       "      <td>274625.0</td>\n",
       "      <td>287496.0</td>\n",
       "      <td>300763.0</td>\n",
       "      <td>314432.0</td>\n",
       "      <td>328509.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>75</td>\n",
       "      <td>76</td>\n",
       "      <td>77</td>\n",
       "      <td>78</td>\n",
       "      <td>79</td>\n",
       "      <td>50.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>...</td>\n",
       "      <td>318500.0</td>\n",
       "      <td>332706.0</td>\n",
       "      <td>347328.0</td>\n",
       "      <td>362372.0</td>\n",
       "      <td>377844.0</td>\n",
       "      <td>343000.0</td>\n",
       "      <td>357911.0</td>\n",
       "      <td>373248.0</td>\n",
       "      <td>389017.0</td>\n",
       "      <td>405224.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>80</td>\n",
       "      <td>81</td>\n",
       "      <td>82</td>\n",
       "      <td>83</td>\n",
       "      <td>84</td>\n",
       "      <td>55.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>...</td>\n",
       "      <td>393750.0</td>\n",
       "      <td>410096.0</td>\n",
       "      <td>426888.0</td>\n",
       "      <td>444132.0</td>\n",
       "      <td>461834.0</td>\n",
       "      <td>421875.0</td>\n",
       "      <td>438976.0</td>\n",
       "      <td>456533.0</td>\n",
       "      <td>474552.0</td>\n",
       "      <td>493039.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>85</td>\n",
       "      <td>86</td>\n",
       "      <td>87</td>\n",
       "      <td>88</td>\n",
       "      <td>89</td>\n",
       "      <td>60.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>...</td>\n",
       "      <td>480000.0</td>\n",
       "      <td>498636.0</td>\n",
       "      <td>517748.0</td>\n",
       "      <td>537342.0</td>\n",
       "      <td>557424.0</td>\n",
       "      <td>512000.0</td>\n",
       "      <td>531441.0</td>\n",
       "      <td>551368.0</td>\n",
       "      <td>571787.0</td>\n",
       "      <td>592704.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>90</td>\n",
       "      <td>91</td>\n",
       "      <td>92</td>\n",
       "      <td>93</td>\n",
       "      <td>94</td>\n",
       "      <td>65.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>...</td>\n",
       "      <td>578000.0</td>\n",
       "      <td>599076.0</td>\n",
       "      <td>620658.0</td>\n",
       "      <td>642752.0</td>\n",
       "      <td>665364.0</td>\n",
       "      <td>614125.0</td>\n",
       "      <td>636056.0</td>\n",
       "      <td>658503.0</td>\n",
       "      <td>681472.0</td>\n",
       "      <td>704969.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>95</td>\n",
       "      <td>96</td>\n",
       "      <td>97</td>\n",
       "      <td>98</td>\n",
       "      <td>99</td>\n",
       "      <td>70.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>...</td>\n",
       "      <td>688500.0</td>\n",
       "      <td>712166.0</td>\n",
       "      <td>736368.0</td>\n",
       "      <td>761112.0</td>\n",
       "      <td>786404.0</td>\n",
       "      <td>729000.0</td>\n",
       "      <td>753571.0</td>\n",
       "      <td>778688.0</td>\n",
       "      <td>804357.0</td>\n",
       "      <td>830584.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15 rows × 780 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    var1(t)  var2(t)  var3(t)  var4(t)  var5(t)  var1(t-5)  var2(t-5)  \\\n",
       "5        25       26       27       28       29        0.0        1.0   \n",
       "6        30       31       32       33       34        5.0        6.0   \n",
       "7        35       36       37       38       39       10.0       11.0   \n",
       "8        40       41       42       43       44       15.0       16.0   \n",
       "9        45       46       47       48       49       20.0       21.0   \n",
       "10       50       51       52       53       54       25.0       26.0   \n",
       "11       55       56       57       58       59       30.0       31.0   \n",
       "12       60       61       62       63       64       35.0       36.0   \n",
       "13       65       66       67       68       69       40.0       41.0   \n",
       "14       70       71       72       73       74       45.0       46.0   \n",
       "15       75       76       77       78       79       50.0       51.0   \n",
       "16       80       81       82       83       84       55.0       56.0   \n",
       "17       85       86       87       88       89       60.0       61.0   \n",
       "18       90       91       92       93       94       65.0       66.0   \n",
       "19       95       96       97       98       99       70.0       71.0   \n",
       "\n",
       "    var3(t-5)  var4(t-5)  var5(t-5)  ...  var1(t-1)(t-1)(t-2)  \\\n",
       "5         2.0        3.0        4.0  ...               6000.0   \n",
       "6         7.0        8.0        9.0  ...              12500.0   \n",
       "7        12.0       13.0       14.0  ...              22500.0   \n",
       "8        17.0       18.0       19.0  ...              36750.0   \n",
       "9        22.0       23.0       24.0  ...              56000.0   \n",
       "10       27.0       28.0       29.0  ...              81000.0   \n",
       "11       32.0       33.0       34.0  ...             112500.0   \n",
       "12       37.0       38.0       39.0  ...             151250.0   \n",
       "13       42.0       43.0       44.0  ...             198000.0   \n",
       "14       47.0       48.0       49.0  ...             253500.0   \n",
       "15       52.0       53.0       54.0  ...             318500.0   \n",
       "16       57.0       58.0       59.0  ...             393750.0   \n",
       "17       62.0       63.0       64.0  ...             480000.0   \n",
       "18       67.0       68.0       69.0  ...             578000.0   \n",
       "19       72.0       73.0       74.0  ...             688500.0   \n",
       "\n",
       "    var2(t-1)(t-1)(t-2)  var3(t-1)(t-1)(t-2)  var4(t-1)(t-1)(t-2)  \\\n",
       "5                7056.0               8228.0               9522.0   \n",
       "6               14196.0              16038.0              18032.0   \n",
       "7               24986.0              27648.0              30492.0   \n",
       "8               40176.0              43808.0              47652.0   \n",
       "9               60516.0              65268.0              70262.0   \n",
       "10              86756.0              92778.0              99072.0   \n",
       "11             119646.0             127088.0             134832.0   \n",
       "12             159936.0             168948.0             178292.0   \n",
       "13             208376.0             219108.0             230202.0   \n",
       "14             265716.0             278318.0             291312.0   \n",
       "15             332706.0             347328.0             362372.0   \n",
       "16             410096.0             426888.0             444132.0   \n",
       "17             498636.0             517748.0             537342.0   \n",
       "18             599076.0             620658.0             642752.0   \n",
       "19             712166.0             736368.0             761112.0   \n",
       "\n",
       "    var5(t-1)(t-1)(t-2)  var1(t-1)(t-1)(t-1)  var2(t-1)(t-1)(t-1)  \\\n",
       "5               10944.0               8000.0               9261.0   \n",
       "6               20184.0              15625.0              17576.0   \n",
       "7               33524.0              27000.0              29791.0   \n",
       "8               51714.0              42875.0              46656.0   \n",
       "9               75504.0              64000.0              68921.0   \n",
       "10             105644.0              91125.0              97336.0   \n",
       "11             142884.0             125000.0             132651.0   \n",
       "12             187974.0             166375.0             175616.0   \n",
       "13             241664.0             216000.0             226981.0   \n",
       "14             304704.0             274625.0             287496.0   \n",
       "15             377844.0             343000.0             357911.0   \n",
       "16             461834.0             421875.0             438976.0   \n",
       "17             557424.0             512000.0             531441.0   \n",
       "18             665364.0             614125.0             636056.0   \n",
       "19             786404.0             729000.0             753571.0   \n",
       "\n",
       "    var3(t-1)(t-1)(t-1)  var4(t-1)(t-1)(t-1)  var5(t-1)(t-1)(t-1)  \n",
       "5               10648.0              12167.0              13824.0  \n",
       "6               19683.0              21952.0              24389.0  \n",
       "7               32768.0              35937.0              39304.0  \n",
       "8               50653.0              54872.0              59319.0  \n",
       "9               74088.0              79507.0              85184.0  \n",
       "10             103823.0             110592.0             117649.0  \n",
       "11             140608.0             148877.0             157464.0  \n",
       "12             185193.0             195112.0             205379.0  \n",
       "13             238328.0             250047.0             262144.0  \n",
       "14             300763.0             314432.0             328509.0  \n",
       "15             373248.0             389017.0             405224.0  \n",
       "16             456533.0             474552.0             493039.0  \n",
       "17             551368.0             571787.0             592704.0  \n",
       "18             658503.0             681472.0             704969.0  \n",
       "19             778688.0             804357.0             830584.0  \n",
       "\n",
       "[15 rows x 780 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Volterra 3 series input is then the column-wise concatenation of \n",
    "# order 1, order2 and order 3 volterra.\n",
    "\n",
    "agg1 = order_one_Volterra(input_df)\n",
    "agg2 = order_two_Volterra(input_df)\n",
    "agg3 = order_three_Volterra(input_df)\n",
    "volterra_ord3 = agg1.join(agg2).join(agg3)\n",
    "volterra_ord3.head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "var1(t)\n",
      "var2(t)\n",
      "var3(t)\n",
      "var4(t)\n",
      "var5(t)\n",
      "var1(t-5)\n",
      "var2(t-5)\n",
      "var3(t-5)\n",
      "var4(t-5)\n",
      "var5(t-5)\n",
      "var1(t-4)\n",
      "var2(t-4)\n",
      "var3(t-4)\n",
      "var4(t-4)\n",
      "var5(t-4)\n",
      "var1(t-3)\n",
      "var2(t-3)\n",
      "var3(t-3)\n",
      "var4(t-3)\n",
      "var5(t-3)\n",
      "var1(t-2)\n",
      "var2(t-2)\n",
      "var3(t-2)\n",
      "var4(t-2)\n",
      "var5(t-2)\n",
      "var1(t-1)\n",
      "var2(t-1)\n",
      "var3(t-1)\n",
      "var4(t-1)\n",
      "var5(t-1)\n",
      "var1(t-5)(t-5)\n",
      "var2(t-5)(t-5)\n",
      "var3(t-5)(t-5)\n",
      "var4(t-5)(t-5)\n",
      "var5(t-5)(t-5)\n",
      "var1(t-5)(t-4)\n",
      "var2(t-5)(t-4)\n",
      "var3(t-5)(t-4)\n",
      "var4(t-5)(t-4)\n",
      "var5(t-5)(t-4)\n",
      "var1(t-5)(t-3)\n",
      "var2(t-5)(t-3)\n",
      "var3(t-5)(t-3)\n",
      "var4(t-5)(t-3)\n",
      "var5(t-5)(t-3)\n",
      "var1(t-5)(t-2)\n",
      "var2(t-5)(t-2)\n",
      "var3(t-5)(t-2)\n",
      "var4(t-5)(t-2)\n",
      "var5(t-5)(t-2)\n",
      "var1(t-5)(t-1)\n",
      "var2(t-5)(t-1)\n",
      "var3(t-5)(t-1)\n",
      "var4(t-5)(t-1)\n",
      "var5(t-5)(t-1)\n",
      "var1(t-4)(t-5)\n",
      "var2(t-4)(t-5)\n",
      "var3(t-4)(t-5)\n",
      "var4(t-4)(t-5)\n",
      "var5(t-4)(t-5)\n",
      "var1(t-4)(t-4)\n",
      "var2(t-4)(t-4)\n",
      "var3(t-4)(t-4)\n",
      "var4(t-4)(t-4)\n",
      "var5(t-4)(t-4)\n",
      "var1(t-4)(t-3)\n",
      "var2(t-4)(t-3)\n",
      "var3(t-4)(t-3)\n",
      "var4(t-4)(t-3)\n",
      "var5(t-4)(t-3)\n",
      "var1(t-4)(t-2)\n",
      "var2(t-4)(t-2)\n",
      "var3(t-4)(t-2)\n",
      "var4(t-4)(t-2)\n",
      "var5(t-4)(t-2)\n",
      "var1(t-4)(t-1)\n",
      "var2(t-4)(t-1)\n",
      "var3(t-4)(t-1)\n",
      "var4(t-4)(t-1)\n",
      "var5(t-4)(t-1)\n",
      "var1(t-3)(t-5)\n",
      "var2(t-3)(t-5)\n",
      "var3(t-3)(t-5)\n",
      "var4(t-3)(t-5)\n",
      "var5(t-3)(t-5)\n",
      "var1(t-3)(t-4)\n",
      "var2(t-3)(t-4)\n",
      "var3(t-3)(t-4)\n",
      "var4(t-3)(t-4)\n",
      "var5(t-3)(t-4)\n",
      "var1(t-3)(t-3)\n",
      "var2(t-3)(t-3)\n",
      "var3(t-3)(t-3)\n",
      "var4(t-3)(t-3)\n",
      "var5(t-3)(t-3)\n",
      "var1(t-3)(t-2)\n",
      "var2(t-3)(t-2)\n",
      "var3(t-3)(t-2)\n",
      "var4(t-3)(t-2)\n",
      "var5(t-3)(t-2)\n",
      "var1(t-3)(t-1)\n",
      "var2(t-3)(t-1)\n",
      "var3(t-3)(t-1)\n",
      "var4(t-3)(t-1)\n",
      "var5(t-3)(t-1)\n",
      "var1(t-2)(t-5)\n",
      "var2(t-2)(t-5)\n",
      "var3(t-2)(t-5)\n",
      "var4(t-2)(t-5)\n",
      "var5(t-2)(t-5)\n",
      "var1(t-2)(t-4)\n",
      "var2(t-2)(t-4)\n",
      "var3(t-2)(t-4)\n",
      "var4(t-2)(t-4)\n",
      "var5(t-2)(t-4)\n",
      "var1(t-2)(t-3)\n",
      "var2(t-2)(t-3)\n",
      "var3(t-2)(t-3)\n",
      "var4(t-2)(t-3)\n",
      "var5(t-2)(t-3)\n",
      "var1(t-2)(t-2)\n",
      "var2(t-2)(t-2)\n",
      "var3(t-2)(t-2)\n",
      "var4(t-2)(t-2)\n",
      "var5(t-2)(t-2)\n",
      "var1(t-2)(t-1)\n",
      "var2(t-2)(t-1)\n",
      "var3(t-2)(t-1)\n",
      "var4(t-2)(t-1)\n",
      "var5(t-2)(t-1)\n",
      "var1(t-1)(t-5)\n",
      "var2(t-1)(t-5)\n",
      "var3(t-1)(t-5)\n",
      "var4(t-1)(t-5)\n",
      "var5(t-1)(t-5)\n",
      "var1(t-1)(t-4)\n",
      "var2(t-1)(t-4)\n",
      "var3(t-1)(t-4)\n",
      "var4(t-1)(t-4)\n",
      "var5(t-1)(t-4)\n",
      "var1(t-1)(t-3)\n",
      "var2(t-1)(t-3)\n",
      "var3(t-1)(t-3)\n",
      "var4(t-1)(t-3)\n",
      "var5(t-1)(t-3)\n",
      "var1(t-1)(t-2)\n",
      "var2(t-1)(t-2)\n",
      "var3(t-1)(t-2)\n",
      "var4(t-1)(t-2)\n",
      "var5(t-1)(t-2)\n",
      "var1(t-1)(t-1)\n",
      "var2(t-1)(t-1)\n",
      "var3(t-1)(t-1)\n",
      "var4(t-1)(t-1)\n",
      "var5(t-1)(t-1)\n",
      "var1(t-5)(t-5)(t-5)\n",
      "var2(t-5)(t-5)(t-5)\n",
      "var3(t-5)(t-5)(t-5)\n",
      "var4(t-5)(t-5)(t-5)\n",
      "var5(t-5)(t-5)(t-5)\n",
      "var1(t-5)(t-5)(t-4)\n",
      "var2(t-5)(t-5)(t-4)\n",
      "var3(t-5)(t-5)(t-4)\n",
      "var4(t-5)(t-5)(t-4)\n",
      "var5(t-5)(t-5)(t-4)\n",
      "var1(t-5)(t-5)(t-3)\n",
      "var2(t-5)(t-5)(t-3)\n",
      "var3(t-5)(t-5)(t-3)\n",
      "var4(t-5)(t-5)(t-3)\n",
      "var5(t-5)(t-5)(t-3)\n",
      "var1(t-5)(t-5)(t-2)\n",
      "var2(t-5)(t-5)(t-2)\n",
      "var3(t-5)(t-5)(t-2)\n",
      "var4(t-5)(t-5)(t-2)\n",
      "var5(t-5)(t-5)(t-2)\n",
      "var1(t-5)(t-5)(t-1)\n",
      "var2(t-5)(t-5)(t-1)\n",
      "var3(t-5)(t-5)(t-1)\n",
      "var4(t-5)(t-5)(t-1)\n",
      "var5(t-5)(t-5)(t-1)\n",
      "var1(t-5)(t-4)(t-5)\n",
      "var2(t-5)(t-4)(t-5)\n",
      "var3(t-5)(t-4)(t-5)\n",
      "var4(t-5)(t-4)(t-5)\n",
      "var5(t-5)(t-4)(t-5)\n",
      "var1(t-5)(t-4)(t-4)\n",
      "var2(t-5)(t-4)(t-4)\n",
      "var3(t-5)(t-4)(t-4)\n",
      "var4(t-5)(t-4)(t-4)\n",
      "var5(t-5)(t-4)(t-4)\n",
      "var1(t-5)(t-4)(t-3)\n",
      "var2(t-5)(t-4)(t-3)\n",
      "var3(t-5)(t-4)(t-3)\n",
      "var4(t-5)(t-4)(t-3)\n",
      "var5(t-5)(t-4)(t-3)\n",
      "var1(t-5)(t-4)(t-2)\n",
      "var2(t-5)(t-4)(t-2)\n",
      "var3(t-5)(t-4)(t-2)\n",
      "var4(t-5)(t-4)(t-2)\n",
      "var5(t-5)(t-4)(t-2)\n",
      "var1(t-5)(t-4)(t-1)\n",
      "var2(t-5)(t-4)(t-1)\n",
      "var3(t-5)(t-4)(t-1)\n",
      "var4(t-5)(t-4)(t-1)\n",
      "var5(t-5)(t-4)(t-1)\n",
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      "var2(t-5)(t-3)(t-5)\n",
      "var3(t-5)(t-3)(t-5)\n",
      "var4(t-5)(t-3)(t-5)\n",
      "var5(t-5)(t-3)(t-5)\n",
      "var1(t-5)(t-3)(t-4)\n",
      "var2(t-5)(t-3)(t-4)\n",
      "var3(t-5)(t-3)(t-4)\n",
      "var4(t-5)(t-3)(t-4)\n",
      "var5(t-5)(t-3)(t-4)\n",
      "var1(t-5)(t-3)(t-3)\n",
      "var2(t-5)(t-3)(t-3)\n",
      "var3(t-5)(t-3)(t-3)\n",
      "var4(t-5)(t-3)(t-3)\n",
      "var5(t-5)(t-3)(t-3)\n",
      "var1(t-5)(t-3)(t-2)\n",
      "var2(t-5)(t-3)(t-2)\n",
      "var3(t-5)(t-3)(t-2)\n",
      "var4(t-5)(t-3)(t-2)\n",
      "var5(t-5)(t-3)(t-2)\n",
      "var1(t-5)(t-3)(t-1)\n",
      "var2(t-5)(t-3)(t-1)\n",
      "var3(t-5)(t-3)(t-1)\n",
      "var4(t-5)(t-3)(t-1)\n",
      "var5(t-5)(t-3)(t-1)\n",
      "var1(t-5)(t-2)(t-5)\n",
      "var2(t-5)(t-2)(t-5)\n",
      "var3(t-5)(t-2)(t-5)\n",
      "var4(t-5)(t-2)(t-5)\n",
      "var5(t-5)(t-2)(t-5)\n",
      "var1(t-5)(t-2)(t-4)\n",
      "var2(t-5)(t-2)(t-4)\n",
      "var3(t-5)(t-2)(t-4)\n",
      "var4(t-5)(t-2)(t-4)\n",
      "var5(t-5)(t-2)(t-4)\n",
      "var1(t-5)(t-2)(t-3)\n",
      "var2(t-5)(t-2)(t-3)\n",
      "var3(t-5)(t-2)(t-3)\n",
      "var4(t-5)(t-2)(t-3)\n",
      "var5(t-5)(t-2)(t-3)\n",
      "var1(t-5)(t-2)(t-2)\n",
      "var2(t-5)(t-2)(t-2)\n",
      "var3(t-5)(t-2)(t-2)\n",
      "var4(t-5)(t-2)(t-2)\n",
      "var5(t-5)(t-2)(t-2)\n",
      "var1(t-5)(t-2)(t-1)\n",
      "var2(t-5)(t-2)(t-1)\n",
      "var3(t-5)(t-2)(t-1)\n",
      "var4(t-5)(t-2)(t-1)\n",
      "var5(t-5)(t-2)(t-1)\n",
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      "var2(t-5)(t-1)(t-5)\n",
      "var3(t-5)(t-1)(t-5)\n",
      "var4(t-5)(t-1)(t-5)\n",
      "var5(t-5)(t-1)(t-5)\n",
      "var1(t-5)(t-1)(t-4)\n",
      "var2(t-5)(t-1)(t-4)\n",
      "var3(t-5)(t-1)(t-4)\n",
      "var4(t-5)(t-1)(t-4)\n",
      "var5(t-5)(t-1)(t-4)\n",
      "var1(t-5)(t-1)(t-3)\n",
      "var2(t-5)(t-1)(t-3)\n",
      "var3(t-5)(t-1)(t-3)\n",
      "var4(t-5)(t-1)(t-3)\n",
      "var5(t-5)(t-1)(t-3)\n",
      "var1(t-5)(t-1)(t-2)\n",
      "var2(t-5)(t-1)(t-2)\n",
      "var3(t-5)(t-1)(t-2)\n",
      "var4(t-5)(t-1)(t-2)\n",
      "var5(t-5)(t-1)(t-2)\n",
      "var1(t-5)(t-1)(t-1)\n",
      "var2(t-5)(t-1)(t-1)\n",
      "var3(t-5)(t-1)(t-1)\n",
      "var4(t-5)(t-1)(t-1)\n",
      "var5(t-5)(t-1)(t-1)\n",
      "var1(t-4)(t-5)(t-5)\n",
      "var2(t-4)(t-5)(t-5)\n",
      "var3(t-4)(t-5)(t-5)\n",
      "var4(t-4)(t-5)(t-5)\n",
      "var5(t-4)(t-5)(t-5)\n",
      "var1(t-4)(t-5)(t-4)\n",
      "var2(t-4)(t-5)(t-4)\n",
      "var3(t-4)(t-5)(t-4)\n",
      "var4(t-4)(t-5)(t-4)\n",
      "var5(t-4)(t-5)(t-4)\n",
      "var1(t-4)(t-5)(t-3)\n",
      "var2(t-4)(t-5)(t-3)\n",
      "var3(t-4)(t-5)(t-3)\n",
      "var4(t-4)(t-5)(t-3)\n",
      "var5(t-4)(t-5)(t-3)\n",
      "var1(t-4)(t-5)(t-2)\n",
      "var2(t-4)(t-5)(t-2)\n",
      "var3(t-4)(t-5)(t-2)\n",
      "var4(t-4)(t-5)(t-2)\n",
      "var5(t-4)(t-5)(t-2)\n",
      "var1(t-4)(t-5)(t-1)\n",
      "var2(t-4)(t-5)(t-1)\n",
      "var3(t-4)(t-5)(t-1)\n",
      "var4(t-4)(t-5)(t-1)\n",
      "var5(t-4)(t-5)(t-1)\n",
      "var1(t-4)(t-4)(t-5)\n",
      "var2(t-4)(t-4)(t-5)\n",
      "var3(t-4)(t-4)(t-5)\n",
      "var4(t-4)(t-4)(t-5)\n",
      "var5(t-4)(t-4)(t-5)\n",
      "var1(t-4)(t-4)(t-4)\n",
      "var2(t-4)(t-4)(t-4)\n",
      "var3(t-4)(t-4)(t-4)\n",
      "var4(t-4)(t-4)(t-4)\n",
      "var5(t-4)(t-4)(t-4)\n",
      "var1(t-4)(t-4)(t-3)\n",
      "var2(t-4)(t-4)(t-3)\n",
      "var3(t-4)(t-4)(t-3)\n",
      "var4(t-4)(t-4)(t-3)\n",
      "var5(t-4)(t-4)(t-3)\n",
      "var1(t-4)(t-4)(t-2)\n",
      "var2(t-4)(t-4)(t-2)\n",
      "var3(t-4)(t-4)(t-2)\n",
      "var4(t-4)(t-4)(t-2)\n",
      "var5(t-4)(t-4)(t-2)\n",
      "var1(t-4)(t-4)(t-1)\n",
      "var2(t-4)(t-4)(t-1)\n",
      "var3(t-4)(t-4)(t-1)\n",
      "var4(t-4)(t-4)(t-1)\n",
      "var5(t-4)(t-4)(t-1)\n",
      "var1(t-4)(t-3)(t-5)\n",
      "var2(t-4)(t-3)(t-5)\n",
      "var3(t-4)(t-3)(t-5)\n",
      "var4(t-4)(t-3)(t-5)\n",
      "var5(t-4)(t-3)(t-5)\n",
      "var1(t-4)(t-3)(t-4)\n",
      "var2(t-4)(t-3)(t-4)\n",
      "var3(t-4)(t-3)(t-4)\n",
      "var4(t-4)(t-3)(t-4)\n",
      "var5(t-4)(t-3)(t-4)\n",
      "var1(t-4)(t-3)(t-3)\n",
      "var2(t-4)(t-3)(t-3)\n",
      "var3(t-4)(t-3)(t-3)\n",
      "var4(t-4)(t-3)(t-3)\n",
      "var5(t-4)(t-3)(t-3)\n",
      "var1(t-4)(t-3)(t-2)\n",
      "var2(t-4)(t-3)(t-2)\n",
      "var3(t-4)(t-3)(t-2)\n",
      "var4(t-4)(t-3)(t-2)\n",
      "var5(t-4)(t-3)(t-2)\n",
      "var1(t-4)(t-3)(t-1)\n",
      "var2(t-4)(t-3)(t-1)\n",
      "var3(t-4)(t-3)(t-1)\n",
      "var4(t-4)(t-3)(t-1)\n",
      "var5(t-4)(t-3)(t-1)\n",
      "var1(t-4)(t-2)(t-5)\n",
      "var2(t-4)(t-2)(t-5)\n",
      "var3(t-4)(t-2)(t-5)\n",
      "var4(t-4)(t-2)(t-5)\n",
      "var5(t-4)(t-2)(t-5)\n",
      "var1(t-4)(t-2)(t-4)\n",
      "var2(t-4)(t-2)(t-4)\n",
      "var3(t-4)(t-2)(t-4)\n",
      "var4(t-4)(t-2)(t-4)\n",
      "var5(t-4)(t-2)(t-4)\n",
      "var1(t-4)(t-2)(t-3)\n",
      "var2(t-4)(t-2)(t-3)\n",
      "var3(t-4)(t-2)(t-3)\n",
      "var4(t-4)(t-2)(t-3)\n",
      "var5(t-4)(t-2)(t-3)\n",
      "var1(t-4)(t-2)(t-2)\n",
      "var2(t-4)(t-2)(t-2)\n",
      "var3(t-4)(t-2)(t-2)\n",
      "var4(t-4)(t-2)(t-2)\n",
      "var5(t-4)(t-2)(t-2)\n",
      "var1(t-4)(t-2)(t-1)\n",
      "var2(t-4)(t-2)(t-1)\n",
      "var3(t-4)(t-2)(t-1)\n",
      "var4(t-4)(t-2)(t-1)\n",
      "var5(t-4)(t-2)(t-1)\n",
      "var1(t-4)(t-1)(t-5)\n",
      "var2(t-4)(t-1)(t-5)\n",
      "var3(t-4)(t-1)(t-5)\n",
      "var4(t-4)(t-1)(t-5)\n",
      "var5(t-4)(t-1)(t-5)\n",
      "var1(t-4)(t-1)(t-4)\n",
      "var2(t-4)(t-1)(t-4)\n",
      "var3(t-4)(t-1)(t-4)\n",
      "var4(t-4)(t-1)(t-4)\n",
      "var5(t-4)(t-1)(t-4)\n",
      "var1(t-4)(t-1)(t-3)\n",
      "var2(t-4)(t-1)(t-3)\n",
      "var3(t-4)(t-1)(t-3)\n",
      "var4(t-4)(t-1)(t-3)\n",
      "var5(t-4)(t-1)(t-3)\n",
      "var1(t-4)(t-1)(t-2)\n",
      "var2(t-4)(t-1)(t-2)\n",
      "var3(t-4)(t-1)(t-2)\n",
      "var4(t-4)(t-1)(t-2)\n",
      "var5(t-4)(t-1)(t-2)\n",
      "var1(t-4)(t-1)(t-1)\n",
      "var2(t-4)(t-1)(t-1)\n",
      "var3(t-4)(t-1)(t-1)\n",
      "var4(t-4)(t-1)(t-1)\n",
      "var5(t-4)(t-1)(t-1)\n",
      "var1(t-3)(t-5)(t-5)\n",
      "var2(t-3)(t-5)(t-5)\n",
      "var3(t-3)(t-5)(t-5)\n",
      "var4(t-3)(t-5)(t-5)\n",
      "var5(t-3)(t-5)(t-5)\n",
      "var1(t-3)(t-5)(t-4)\n",
      "var2(t-3)(t-5)(t-4)\n",
      "var3(t-3)(t-5)(t-4)\n",
      "var4(t-3)(t-5)(t-4)\n",
      "var5(t-3)(t-5)(t-4)\n",
      "var1(t-3)(t-5)(t-3)\n",
      "var2(t-3)(t-5)(t-3)\n",
      "var3(t-3)(t-5)(t-3)\n",
      "var4(t-3)(t-5)(t-3)\n",
      "var5(t-3)(t-5)(t-3)\n",
      "var1(t-3)(t-5)(t-2)\n",
      "var2(t-3)(t-5)(t-2)\n",
      "var3(t-3)(t-5)(t-2)\n",
      "var4(t-3)(t-5)(t-2)\n",
      "var5(t-3)(t-5)(t-2)\n",
      "var1(t-3)(t-5)(t-1)\n",
      "var2(t-3)(t-5)(t-1)\n",
      "var3(t-3)(t-5)(t-1)\n",
      "var4(t-3)(t-5)(t-1)\n",
      "var5(t-3)(t-5)(t-1)\n",
      "var1(t-3)(t-4)(t-5)\n",
      "var2(t-3)(t-4)(t-5)\n",
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      "var4(t-3)(t-4)(t-5)\n",
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      "var1(t-3)(t-4)(t-4)\n",
      "var2(t-3)(t-4)(t-4)\n",
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      "var4(t-3)(t-4)(t-4)\n",
      "var5(t-3)(t-4)(t-4)\n",
      "var1(t-3)(t-4)(t-3)\n",
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      "var4(t-3)(t-4)(t-3)\n",
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      "var1(t-3)(t-4)(t-2)\n",
      "var2(t-3)(t-4)(t-2)\n",
      "var3(t-3)(t-4)(t-2)\n",
      "var4(t-3)(t-4)(t-2)\n",
      "var5(t-3)(t-4)(t-2)\n",
      "var1(t-3)(t-4)(t-1)\n",
      "var2(t-3)(t-4)(t-1)\n",
      "var3(t-3)(t-4)(t-1)\n",
      "var4(t-3)(t-4)(t-1)\n",
      "var5(t-3)(t-4)(t-1)\n",
      "var1(t-3)(t-3)(t-5)\n",
      "var2(t-3)(t-3)(t-5)\n",
      "var3(t-3)(t-3)(t-5)\n",
      "var4(t-3)(t-3)(t-5)\n",
      "var5(t-3)(t-3)(t-5)\n",
      "var1(t-3)(t-3)(t-4)\n",
      "var2(t-3)(t-3)(t-4)\n",
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      "var4(t-3)(t-3)(t-4)\n",
      "var5(t-3)(t-3)(t-4)\n",
      "var1(t-3)(t-3)(t-3)\n",
      "var2(t-3)(t-3)(t-3)\n",
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      "var4(t-3)(t-3)(t-3)\n",
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      "var1(t-3)(t-3)(t-2)\n",
      "var2(t-3)(t-3)(t-2)\n",
      "var3(t-3)(t-3)(t-2)\n",
      "var4(t-3)(t-3)(t-2)\n",
      "var5(t-3)(t-3)(t-2)\n",
      "var1(t-3)(t-3)(t-1)\n",
      "var2(t-3)(t-3)(t-1)\n",
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      "var4(t-3)(t-3)(t-1)\n",
      "var5(t-3)(t-3)(t-1)\n",
      "var1(t-3)(t-2)(t-5)\n",
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      "var4(t-3)(t-2)(t-4)\n",
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      "var1(t-3)(t-2)(t-3)\n",
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      "var1(t-3)(t-1)(t-5)\n",
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      "var1(t-3)(t-1)(t-4)\n",
      "var2(t-3)(t-1)(t-4)\n",
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      "var4(t-3)(t-1)(t-4)\n",
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      "var1(t-3)(t-1)(t-3)\n",
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      "var1(t-3)(t-1)(t-2)\n",
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      "var4(t-3)(t-1)(t-2)\n",
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      "var1(t-3)(t-1)(t-1)\n",
      "var2(t-3)(t-1)(t-1)\n",
      "var3(t-3)(t-1)(t-1)\n",
      "var4(t-3)(t-1)(t-1)\n",
      "var5(t-3)(t-1)(t-1)\n",
      "var1(t-2)(t-5)(t-5)\n",
      "var2(t-2)(t-5)(t-5)\n",
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      "var1(t-2)(t-5)(t-3)\n",
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      "var1(t-2)(t-5)(t-2)\n",
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      "var3(t-2)(t-5)(t-1)\n",
      "var4(t-2)(t-5)(t-1)\n",
      "var5(t-2)(t-5)(t-1)\n",
      "var1(t-2)(t-4)(t-5)\n",
      "var2(t-2)(t-4)(t-5)\n",
      "var3(t-2)(t-4)(t-5)\n",
      "var4(t-2)(t-4)(t-5)\n",
      "var5(t-2)(t-4)(t-5)\n",
      "var1(t-2)(t-4)(t-4)\n",
      "var2(t-2)(t-4)(t-4)\n",
      "var3(t-2)(t-4)(t-4)\n",
      "var4(t-2)(t-4)(t-4)\n",
      "var5(t-2)(t-4)(t-4)\n",
      "var1(t-2)(t-4)(t-3)\n",
      "var2(t-2)(t-4)(t-3)\n",
      "var3(t-2)(t-4)(t-3)\n",
      "var4(t-2)(t-4)(t-3)\n",
      "var5(t-2)(t-4)(t-3)\n",
      "var1(t-2)(t-4)(t-2)\n",
      "var2(t-2)(t-4)(t-2)\n",
      "var3(t-2)(t-4)(t-2)\n",
      "var4(t-2)(t-4)(t-2)\n",
      "var5(t-2)(t-4)(t-2)\n",
      "var1(t-2)(t-4)(t-1)\n",
      "var2(t-2)(t-4)(t-1)\n",
      "var3(t-2)(t-4)(t-1)\n",
      "var4(t-2)(t-4)(t-1)\n",
      "var5(t-2)(t-4)(t-1)\n",
      "var1(t-2)(t-3)(t-5)\n",
      "var2(t-2)(t-3)(t-5)\n",
      "var3(t-2)(t-3)(t-5)\n",
      "var4(t-2)(t-3)(t-5)\n",
      "var5(t-2)(t-3)(t-5)\n",
      "var1(t-2)(t-3)(t-4)\n",
      "var2(t-2)(t-3)(t-4)\n",
      "var3(t-2)(t-3)(t-4)\n",
      "var4(t-2)(t-3)(t-4)\n",
      "var5(t-2)(t-3)(t-4)\n",
      "var1(t-2)(t-3)(t-3)\n",
      "var2(t-2)(t-3)(t-3)\n",
      "var3(t-2)(t-3)(t-3)\n",
      "var4(t-2)(t-3)(t-3)\n",
      "var5(t-2)(t-3)(t-3)\n",
      "var1(t-2)(t-3)(t-2)\n",
      "var2(t-2)(t-3)(t-2)\n",
      "var3(t-2)(t-3)(t-2)\n",
      "var4(t-2)(t-3)(t-2)\n",
      "var5(t-2)(t-3)(t-2)\n",
      "var1(t-2)(t-3)(t-1)\n",
      "var2(t-2)(t-3)(t-1)\n",
      "var3(t-2)(t-3)(t-1)\n",
      "var4(t-2)(t-3)(t-1)\n",
      "var5(t-2)(t-3)(t-1)\n",
      "var1(t-2)(t-2)(t-5)\n",
      "var2(t-2)(t-2)(t-5)\n",
      "var3(t-2)(t-2)(t-5)\n",
      "var4(t-2)(t-2)(t-5)\n",
      "var5(t-2)(t-2)(t-5)\n",
      "var1(t-2)(t-2)(t-4)\n",
      "var2(t-2)(t-2)(t-4)\n",
      "var3(t-2)(t-2)(t-4)\n",
      "var4(t-2)(t-2)(t-4)\n",
      "var5(t-2)(t-2)(t-4)\n",
      "var1(t-2)(t-2)(t-3)\n",
      "var2(t-2)(t-2)(t-3)\n",
      "var3(t-2)(t-2)(t-3)\n",
      "var4(t-2)(t-2)(t-3)\n",
      "var5(t-2)(t-2)(t-3)\n",
      "var1(t-2)(t-2)(t-2)\n",
      "var2(t-2)(t-2)(t-2)\n",
      "var3(t-2)(t-2)(t-2)\n",
      "var4(t-2)(t-2)(t-2)\n",
      "var5(t-2)(t-2)(t-2)\n",
      "var1(t-2)(t-2)(t-1)\n",
      "var2(t-2)(t-2)(t-1)\n",
      "var3(t-2)(t-2)(t-1)\n",
      "var4(t-2)(t-2)(t-1)\n",
      "var5(t-2)(t-2)(t-1)\n",
      "var1(t-2)(t-1)(t-5)\n",
      "var2(t-2)(t-1)(t-5)\n",
      "var3(t-2)(t-1)(t-5)\n",
      "var4(t-2)(t-1)(t-5)\n",
      "var5(t-2)(t-1)(t-5)\n",
      "var1(t-2)(t-1)(t-4)\n",
      "var2(t-2)(t-1)(t-4)\n",
      "var3(t-2)(t-1)(t-4)\n",
      "var4(t-2)(t-1)(t-4)\n",
      "var5(t-2)(t-1)(t-4)\n",
      "var1(t-2)(t-1)(t-3)\n",
      "var2(t-2)(t-1)(t-3)\n",
      "var3(t-2)(t-1)(t-3)\n",
      "var4(t-2)(t-1)(t-3)\n",
      "var5(t-2)(t-1)(t-3)\n",
      "var1(t-2)(t-1)(t-2)\n",
      "var2(t-2)(t-1)(t-2)\n",
      "var3(t-2)(t-1)(t-2)\n",
      "var4(t-2)(t-1)(t-2)\n",
      "var5(t-2)(t-1)(t-2)\n",
      "var1(t-2)(t-1)(t-1)\n",
      "var2(t-2)(t-1)(t-1)\n",
      "var3(t-2)(t-1)(t-1)\n",
      "var4(t-2)(t-1)(t-1)\n",
      "var5(t-2)(t-1)(t-1)\n",
      "var1(t-1)(t-5)(t-5)\n",
      "var2(t-1)(t-5)(t-5)\n",
      "var3(t-1)(t-5)(t-5)\n",
      "var4(t-1)(t-5)(t-5)\n",
      "var5(t-1)(t-5)(t-5)\n",
      "var1(t-1)(t-5)(t-4)\n",
      "var2(t-1)(t-5)(t-4)\n",
      "var3(t-1)(t-5)(t-4)\n",
      "var4(t-1)(t-5)(t-4)\n",
      "var5(t-1)(t-5)(t-4)\n",
      "var1(t-1)(t-5)(t-3)\n",
      "var2(t-1)(t-5)(t-3)\n",
      "var3(t-1)(t-5)(t-3)\n",
      "var4(t-1)(t-5)(t-3)\n",
      "var5(t-1)(t-5)(t-3)\n",
      "var1(t-1)(t-5)(t-2)\n",
      "var2(t-1)(t-5)(t-2)\n",
      "var3(t-1)(t-5)(t-2)\n",
      "var4(t-1)(t-5)(t-2)\n",
      "var5(t-1)(t-5)(t-2)\n",
      "var1(t-1)(t-5)(t-1)\n",
      "var2(t-1)(t-5)(t-1)\n",
      "var3(t-1)(t-5)(t-1)\n",
      "var4(t-1)(t-5)(t-1)\n",
      "var5(t-1)(t-5)(t-1)\n",
      "var1(t-1)(t-4)(t-5)\n",
      "var2(t-1)(t-4)(t-5)\n",
      "var3(t-1)(t-4)(t-5)\n",
      "var4(t-1)(t-4)(t-5)\n",
      "var5(t-1)(t-4)(t-5)\n",
      "var1(t-1)(t-4)(t-4)\n",
      "var2(t-1)(t-4)(t-4)\n",
      "var3(t-1)(t-4)(t-4)\n",
      "var4(t-1)(t-4)(t-4)\n",
      "var5(t-1)(t-4)(t-4)\n",
      "var1(t-1)(t-4)(t-3)\n",
      "var2(t-1)(t-4)(t-3)\n",
      "var3(t-1)(t-4)(t-3)\n",
      "var4(t-1)(t-4)(t-3)\n",
      "var5(t-1)(t-4)(t-3)\n",
      "var1(t-1)(t-4)(t-2)\n",
      "var2(t-1)(t-4)(t-2)\n",
      "var3(t-1)(t-4)(t-2)\n",
      "var4(t-1)(t-4)(t-2)\n",
      "var5(t-1)(t-4)(t-2)\n",
      "var1(t-1)(t-4)(t-1)\n",
      "var2(t-1)(t-4)(t-1)\n",
      "var3(t-1)(t-4)(t-1)\n",
      "var4(t-1)(t-4)(t-1)\n",
      "var5(t-1)(t-4)(t-1)\n",
      "var1(t-1)(t-3)(t-5)\n",
      "var2(t-1)(t-3)(t-5)\n",
      "var3(t-1)(t-3)(t-5)\n",
      "var4(t-1)(t-3)(t-5)\n",
      "var5(t-1)(t-3)(t-5)\n",
      "var1(t-1)(t-3)(t-4)\n",
      "var2(t-1)(t-3)(t-4)\n",
      "var3(t-1)(t-3)(t-4)\n",
      "var4(t-1)(t-3)(t-4)\n",
      "var5(t-1)(t-3)(t-4)\n",
      "var1(t-1)(t-3)(t-3)\n",
      "var2(t-1)(t-3)(t-3)\n",
      "var3(t-1)(t-3)(t-3)\n",
      "var4(t-1)(t-3)(t-3)\n",
      "var5(t-1)(t-3)(t-3)\n",
      "var1(t-1)(t-3)(t-2)\n",
      "var2(t-1)(t-3)(t-2)\n",
      "var3(t-1)(t-3)(t-2)\n",
      "var4(t-1)(t-3)(t-2)\n",
      "var5(t-1)(t-3)(t-2)\n",
      "var1(t-1)(t-3)(t-1)\n",
      "var2(t-1)(t-3)(t-1)\n",
      "var3(t-1)(t-3)(t-1)\n",
      "var4(t-1)(t-3)(t-1)\n",
      "var5(t-1)(t-3)(t-1)\n",
      "var1(t-1)(t-2)(t-5)\n",
      "var2(t-1)(t-2)(t-5)\n",
      "var3(t-1)(t-2)(t-5)\n",
      "var4(t-1)(t-2)(t-5)\n",
      "var5(t-1)(t-2)(t-5)\n",
      "var1(t-1)(t-2)(t-4)\n",
      "var2(t-1)(t-2)(t-4)\n",
      "var3(t-1)(t-2)(t-4)\n",
      "var4(t-1)(t-2)(t-4)\n",
      "var5(t-1)(t-2)(t-4)\n",
      "var1(t-1)(t-2)(t-3)\n",
      "var2(t-1)(t-2)(t-3)\n",
      "var3(t-1)(t-2)(t-3)\n",
      "var4(t-1)(t-2)(t-3)\n",
      "var5(t-1)(t-2)(t-3)\n",
      "var1(t-1)(t-2)(t-2)\n",
      "var2(t-1)(t-2)(t-2)\n",
      "var3(t-1)(t-2)(t-2)\n",
      "var4(t-1)(t-2)(t-2)\n",
      "var5(t-1)(t-2)(t-2)\n",
      "var1(t-1)(t-2)(t-1)\n",
      "var2(t-1)(t-2)(t-1)\n",
      "var3(t-1)(t-2)(t-1)\n",
      "var4(t-1)(t-2)(t-1)\n",
      "var5(t-1)(t-2)(t-1)\n",
      "var1(t-1)(t-1)(t-5)\n",
      "var2(t-1)(t-1)(t-5)\n",
      "var3(t-1)(t-1)(t-5)\n",
      "var4(t-1)(t-1)(t-5)\n",
      "var5(t-1)(t-1)(t-5)\n",
      "var1(t-1)(t-1)(t-4)\n",
      "var2(t-1)(t-1)(t-4)\n",
      "var3(t-1)(t-1)(t-4)\n",
      "var4(t-1)(t-1)(t-4)\n",
      "var5(t-1)(t-1)(t-4)\n",
      "var1(t-1)(t-1)(t-3)\n",
      "var2(t-1)(t-1)(t-3)\n",
      "var3(t-1)(t-1)(t-3)\n",
      "var4(t-1)(t-1)(t-3)\n",
      "var5(t-1)(t-1)(t-3)\n",
      "var1(t-1)(t-1)(t-2)\n",
      "var2(t-1)(t-1)(t-2)\n",
      "var3(t-1)(t-1)(t-2)\n",
      "var4(t-1)(t-1)(t-2)\n",
      "var5(t-1)(t-1)(t-2)\n",
      "var1(t-1)(t-1)(t-1)\n",
      "var2(t-1)(t-1)(t-1)\n",
      "var3(t-1)(t-1)(t-1)\n",
      "var4(t-1)(t-1)(t-1)\n",
      "var5(t-1)(t-1)(t-1)\n"
     ]
    }
   ],
   "source": [
    "for col in volterra_ord3.columns:\n",
    "    print(col)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Linear Regression Based On Volterra Series And One Step Ahead Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of samples: 4814986\n"
     ]
    }
   ],
   "source": [
    "# train data is a list of dataframes,\n",
    "# for each trial, before finding the corresponding volterra series\n",
    "# create an empty array with the correct size and than populating it \n",
    "num_train_trials = len(train_data)\n",
    "# total num samples from all of the trials\n",
    "num_all_samples = 0\n",
    "for trial in train_data:\n",
    "    num_all_samples += trial.shape[0]\n",
    "\n",
    "k = len(train_data[0].columns) # number of features in the dataset\n",
    "M = 5 # memory\n",
    "d = 2 # volterra order\n",
    "\n",
    "print(\"Total number of samples: %i\" %num_all_samples)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6e5f5111b0d74716903a32b75d2da484",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=8822), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# get a dataframe of volterra series for each trial in the train and test datasets\n",
    "\n",
    "num_rows = num_all_samples - M*num_train_trials\n",
    "test_volterra = [] # list is appended for each participant which is a list of numpy arrays(trials)\n",
    " \n",
    "'''\n",
    "return the aggregate volterra series\n",
    "'''\n",
    "def get_aggregate_volterra(input_df, order=2):\n",
    "    agg1 = order_one_Volterra(input_df)\n",
    "    agg2 = order_two_Volterra(input_df)\n",
    "    if order == 2:\n",
    "        return agg1.join(agg2)\n",
    "    if order == 3:\n",
    "        agg3 = order_three_Volterra(input_df)\n",
    "        return agg1.join(agg2).join(agg3)\n",
    "\n",
    "if d == 2:\n",
    "    train_volterra = np.zeros((num_rows, k*(1+M+M**2)))\n",
    "if d == 3:\n",
    "    train_volterra = np.zeros((num_rows, k*(1+M+M**2+M**3)))\n",
    "\n",
    "start = 0\n",
    "end = 0\n",
    "for i, trial in enumerate(tqdm_notebook(train_data, ascii=True)): \n",
    "    end = start + len(trial) - M\n",
    "    train_volterra[start:end,:] = get_aggregate_volterra(trial).values[:,:] \n",
    "    start = end"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save Volterra Series For Train Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "if d == 2:\n",
    "    path = 'train_volterra_d2.h5'\n",
    "if d == 3:\n",
    "    path ='train_volterra_d3.h5'\n",
    "    \n",
    "hf_train_volterra = h5py.File(path, 'w') # requires about 125 gb for order 3 model\n",
    "hf_train_volterra.create_dataset('dataset', data=train_volterra, chunks=True, compression='gzip')\n",
    "hf_train_volterra.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load Volterra Series For Train Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "if d == 2:\n",
    "    path = 'train_volterra_d2.h5'\n",
    "if d == 3:\n",
    "    path ='train_volterra_d3.h5'\n",
    "    \n",
    "hf = h5py.File(path, 'r')\n",
    "train_volterra = np.array(hf.get('dataset'))\n",
    "hf.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## MIMO Order 2 Volterra Series Kernel Learning "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 2.18439028e+01,  1.98562897e+01,  1.44925289e+01, ...,\n",
       "         1.14623893e+04,  1.13588637e+04,  2.06372349e+04],\n",
       "       [ 2.08339541e+01,  2.16384837e+01,  1.31653246e+01, ...,\n",
       "         1.10733395e+04,  1.09764182e+04,  2.12158974e+04],\n",
       "       [ 1.95897683e+01,  2.34755705e+01,  1.16274943e+01, ...,\n",
       "         1.06460476e+04,  1.05484368e+04,  2.18984743e+04],\n",
       "       ...,\n",
       "       [ 1.08329142e+01,  2.92924334e+01,  2.41702556e+00, ...,\n",
       "         9.35298914e+03,  9.26254468e+03,  2.40990456e+04],\n",
       "       [ 8.89523625e+00,  2.92425329e+01,  6.45354030e-01, ...,\n",
       "         9.27238679e+03,  9.18251232e+03,  2.42386985e+04],\n",
       "       [ 7.04482603e+00,  2.87566054e+01, -9.67559192e-01, ...,\n",
       "         9.21272029e+03,  9.12601854e+03,  2.43420918e+04]])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_volterra[:10,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4770876, 651)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_volterra.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ec96fefcca854789906e7c5098ab5e9b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=21), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# alpha is the complexity parameter that controls the amount of shrinkage: \n",
    "# the larger the value of , the greater the amount of shrinkage and \n",
    "# thus the coefficients become more robust to collinearity.\n",
    "# for alpha=0, ridge regression is same as OLS solution\n",
    "\n",
    "classifiers = []\n",
    "for k in tqdm_notebook(range(len(train_data[0].columns)), ascii=True):\n",
    "    clf = linear_model.Ridge(alpha=.1, copy_X=True, fit_intercept=True, max_iter=None,\n",
    "                             normalize=False, random_state=None, solver='auto', tol=0.001)\n",
    "    X = train_volterra[:,len(train_data[0].columns):]\n",
    "    Y = train_volterra[:,k]\n",
    "    clf.fit(X, Y)\n",
    "    classifiers.append(clf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save The Classifiers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "if d == 2:\n",
    "    path = 'models_d2.h5'\n",
    "if d == 3:\n",
    "    path ='models_d3.h5'\n",
    "    \n",
    "with open(path, \"wb\") as fp:   #Pickling\n",
    "    pickle.dump(classifiers, fp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load The Classifiers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "if d == 2:\n",
    "    path = 'models_d2.h5'\n",
    "if d == 3:\n",
    "    path ='models_d3.h5'\n",
    "    \n",
    "with open(path, \"rb\") as fp:   \n",
    "    classifiers = pickle.load(fp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## One-Step-Ahead Predictions On The Test Data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>EEG-1</th>\n",
       "      <th>EEG-2</th>\n",
       "      <th>EEG-3</th>\n",
       "      <th>EEG-4</th>\n",
       "      <th>EEG-5</th>\n",
       "      <th>EEG-6</th>\n",
       "      <th>EEG-7</th>\n",
       "      <th>EEG-8</th>\n",
       "      <th>EEG-9</th>\n",
       "      <th>EEG-10</th>\n",
       "      <th>...</th>\n",
       "      <th>EEG-12</th>\n",
       "      <th>EEG-13</th>\n",
       "      <th>EEG-14</th>\n",
       "      <th>EMG-1</th>\n",
       "      <th>EMG-2</th>\n",
       "      <th>EMG-3</th>\n",
       "      <th>EMG-4</th>\n",
       "      <th>Force-x</th>\n",
       "      <th>Force-y</th>\n",
       "      <th>Force-z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11.172495</td>\n",
       "      <td>9.906304</td>\n",
       "      <td>8.183386</td>\n",
       "      <td>5.469978</td>\n",
       "      <td>10.026749</td>\n",
       "      <td>5.411751</td>\n",
       "      <td>13.101759</td>\n",
       "      <td>5.271709</td>\n",
       "      <td>7.384106</td>\n",
       "      <td>13.638465</td>\n",
       "      <td>...</td>\n",
       "      <td>6.465120</td>\n",
       "      <td>9.529604</td>\n",
       "      <td>17.550970</td>\n",
       "      <td>-2076.578438</td>\n",
       "      <td>149.435679</td>\n",
       "      <td>-616.918304</td>\n",
       "      <td>213.825070</td>\n",
       "      <td>-460.023493</td>\n",
       "      <td>-461.359888</td>\n",
       "      <td>-198.734596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11.231420</td>\n",
       "      <td>10.179886</td>\n",
       "      <td>8.812767</td>\n",
       "      <td>7.074219</td>\n",
       "      <td>10.494218</td>\n",
       "      <td>5.612990</td>\n",
       "      <td>13.266267</td>\n",
       "      <td>5.341786</td>\n",
       "      <td>6.682698</td>\n",
       "      <td>12.692010</td>\n",
       "      <td>...</td>\n",
       "      <td>6.660906</td>\n",
       "      <td>9.319198</td>\n",
       "      <td>16.893660</td>\n",
       "      <td>-1888.780035</td>\n",
       "      <td>173.664967</td>\n",
       "      <td>-320.677166</td>\n",
       "      <td>126.727783</td>\n",
       "      <td>-459.953159</td>\n",
       "      <td>-461.337566</td>\n",
       "      <td>-198.768392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11.693834</td>\n",
       "      <td>10.847950</td>\n",
       "      <td>9.872570</td>\n",
       "      <td>9.206766</td>\n",
       "      <td>11.353098</td>\n",
       "      <td>6.220426</td>\n",
       "      <td>13.791612</td>\n",
       "      <td>5.850335</td>\n",
       "      <td>6.348856</td>\n",
       "      <td>11.913641</td>\n",
       "      <td>...</td>\n",
       "      <td>7.234725</td>\n",
       "      <td>9.442563</td>\n",
       "      <td>16.486787</td>\n",
       "      <td>-1877.298690</td>\n",
       "      <td>-44.458230</td>\n",
       "      <td>-174.576869</td>\n",
       "      <td>-49.955286</td>\n",
       "      <td>-459.840536</td>\n",
       "      <td>-461.422890</td>\n",
       "      <td>-198.823988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12.495120</td>\n",
       "      <td>11.837241</td>\n",
       "      <td>11.294004</td>\n",
       "      <td>11.705740</td>\n",
       "      <td>12.547150</td>\n",
       "      <td>7.201719</td>\n",
       "      <td>14.647703</td>\n",
       "      <td>6.779158</td>\n",
       "      <td>6.399590</td>\n",
       "      <td>11.366192</td>\n",
       "      <td>...</td>\n",
       "      <td>8.166683</td>\n",
       "      <td>9.882083</td>\n",
       "      <td>16.355065</td>\n",
       "      <td>-2085.256502</td>\n",
       "      <td>-147.395452</td>\n",
       "      <td>-749.336076</td>\n",
       "      <td>19.916259</td>\n",
       "      <td>-459.768772</td>\n",
       "      <td>-461.518794</td>\n",
       "      <td>-198.785454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13.541842</td>\n",
       "      <td>13.051120</td>\n",
       "      <td>12.982864</td>\n",
       "      <td>14.386290</td>\n",
       "      <td>13.995254</td>\n",
       "      <td>8.497069</td>\n",
       "      <td>15.777316</td>\n",
       "      <td>8.076647</td>\n",
       "      <td>6.824068</td>\n",
       "      <td>11.088842</td>\n",
       "      <td>...</td>\n",
       "      <td>9.409945</td>\n",
       "      <td>10.599587</td>\n",
       "      <td>16.497785</td>\n",
       "      <td>-2287.540697</td>\n",
       "      <td>77.671687</td>\n",
       "      <td>-1094.898894</td>\n",
       "      <td>121.020638</td>\n",
       "      <td>-459.776133</td>\n",
       "      <td>-461.604357</td>\n",
       "      <td>-194.633827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>14.720240</td>\n",
       "      <td>14.379105</td>\n",
       "      <td>14.827691</td>\n",
       "      <td>17.056141</td>\n",
       "      <td>15.598369</td>\n",
       "      <td>10.024059</td>\n",
       "      <td>17.101050</td>\n",
       "      <td>9.661725</td>\n",
       "      <td>7.585413</td>\n",
       "      <td>11.094450</td>\n",
       "      <td>...</td>\n",
       "      <td>10.894232</td>\n",
       "      <td>11.539596</td>\n",
       "      <td>16.889271</td>\n",
       "      <td>-2108.490206</td>\n",
       "      <td>166.691224</td>\n",
       "      <td>-132.943024</td>\n",
       "      <td>48.928820</td>\n",
       "      <td>-459.833235</td>\n",
       "      <td>-461.628228</td>\n",
       "      <td>-153.898731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>15.906680</td>\n",
       "      <td>15.707386</td>\n",
       "      <td>16.709317</td>\n",
       "      <td>19.533297</td>\n",
       "      <td>17.247922</td>\n",
       "      <td>11.684365</td>\n",
       "      <td>18.524329</td>\n",
       "      <td>11.430582</td>\n",
       "      <td>8.624735</td>\n",
       "      <td>11.369388</td>\n",
       "      <td>...</td>\n",
       "      <td>12.531429</td>\n",
       "      <td>12.633911</td>\n",
       "      <td>17.481614</td>\n",
       "      <td>-1561.707929</td>\n",
       "      <td>-60.581287</td>\n",
       "      <td>-221.023788</td>\n",
       "      <td>106.283390</td>\n",
       "      <td>-459.892243</td>\n",
       "      <td>-461.582899</td>\n",
       "      <td>-79.675987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>16.978949</td>\n",
       "      <td>16.929364</td>\n",
       "      <td>18.510911</td>\n",
       "      <td>21.661576</td>\n",
       "      <td>18.835149</td>\n",
       "      <td>13.371953</td>\n",
       "      <td>19.945939</td>\n",
       "      <td>13.265548</td>\n",
       "      <td>9.866924</td>\n",
       "      <td>11.875855</td>\n",
       "      <td>...</td>\n",
       "      <td>14.222758</td>\n",
       "      <td>13.807214</td>\n",
       "      <td>18.209603</td>\n",
       "      <td>-1354.081668</td>\n",
       "      <td>-113.152583</td>\n",
       "      <td>-1246.768036</td>\n",
       "      <td>311.695897</td>\n",
       "      <td>-459.979147</td>\n",
       "      <td>-461.460173</td>\n",
       "      <td>-29.180832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>17.827184</td>\n",
       "      <td>17.955103</td>\n",
       "      <td>20.127438</td>\n",
       "      <td>23.323722</td>\n",
       "      <td>20.260255</td>\n",
       "      <td>14.982016</td>\n",
       "      <td>21.267184</td>\n",
       "      <td>15.045268</td>\n",
       "      <td>11.227611</td>\n",
       "      <td>12.556449</td>\n",
       "      <td>...</td>\n",
       "      <td>15.867000</td>\n",
       "      <td>14.983122</td>\n",
       "      <td>18.997308</td>\n",
       "      <td>-1602.283791</td>\n",
       "      <td>-219.636281</td>\n",
       "      <td>-622.714856</td>\n",
       "      <td>-127.724446</td>\n",
       "      <td>-460.060626</td>\n",
       "      <td>-461.381853</td>\n",
       "      <td>-10.786779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>18.363597</td>\n",
       "      <td>18.718627</td>\n",
       "      <td>21.473715</td>\n",
       "      <td>24.450290</td>\n",
       "      <td>21.440476</td>\n",
       "      <td>16.419605</td>\n",
       "      <td>22.400612</td>\n",
       "      <td>16.655162</td>\n",
       "      <td>12.620659</td>\n",
       "      <td>13.340473</td>\n",
       "      <td>...</td>\n",
       "      <td>17.368851</td>\n",
       "      <td>16.090203</td>\n",
       "      <td>19.765449</td>\n",
       "      <td>-1512.735262</td>\n",
       "      <td>-189.833959</td>\n",
       "      <td>-16.948195</td>\n",
       "      <td>-280.446447</td>\n",
       "      <td>-460.041970</td>\n",
       "      <td>-461.386651</td>\n",
       "      <td>-35.095852</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       EEG-1      EEG-2      EEG-3      EEG-4      EEG-5      EEG-6  \\\n",
       "0  11.172495   9.906304   8.183386   5.469978  10.026749   5.411751   \n",
       "1  11.231420  10.179886   8.812767   7.074219  10.494218   5.612990   \n",
       "2  11.693834  10.847950   9.872570   9.206766  11.353098   6.220426   \n",
       "3  12.495120  11.837241  11.294004  11.705740  12.547150   7.201719   \n",
       "4  13.541842  13.051120  12.982864  14.386290  13.995254   8.497069   \n",
       "5  14.720240  14.379105  14.827691  17.056141  15.598369  10.024059   \n",
       "6  15.906680  15.707386  16.709317  19.533297  17.247922  11.684365   \n",
       "7  16.978949  16.929364  18.510911  21.661576  18.835149  13.371953   \n",
       "8  17.827184  17.955103  20.127438  23.323722  20.260255  14.982016   \n",
       "9  18.363597  18.718627  21.473715  24.450290  21.440476  16.419605   \n",
       "\n",
       "       EEG-7      EEG-8      EEG-9     EEG-10  ...     EEG-12     EEG-13  \\\n",
       "0  13.101759   5.271709   7.384106  13.638465  ...   6.465120   9.529604   \n",
       "1  13.266267   5.341786   6.682698  12.692010  ...   6.660906   9.319198   \n",
       "2  13.791612   5.850335   6.348856  11.913641  ...   7.234725   9.442563   \n",
       "3  14.647703   6.779158   6.399590  11.366192  ...   8.166683   9.882083   \n",
       "4  15.777316   8.076647   6.824068  11.088842  ...   9.409945  10.599587   \n",
       "5  17.101050   9.661725   7.585413  11.094450  ...  10.894232  11.539596   \n",
       "6  18.524329  11.430582   8.624735  11.369388  ...  12.531429  12.633911   \n",
       "7  19.945939  13.265548   9.866924  11.875855  ...  14.222758  13.807214   \n",
       "8  21.267184  15.045268  11.227611  12.556449  ...  15.867000  14.983122   \n",
       "9  22.400612  16.655162  12.620659  13.340473  ...  17.368851  16.090203   \n",
       "\n",
       "      EEG-14        EMG-1       EMG-2        EMG-3       EMG-4     Force-x  \\\n",
       "0  17.550970 -2076.578438  149.435679  -616.918304  213.825070 -460.023493   \n",
       "1  16.893660 -1888.780035  173.664967  -320.677166  126.727783 -459.953159   \n",
       "2  16.486787 -1877.298690  -44.458230  -174.576869  -49.955286 -459.840536   \n",
       "3  16.355065 -2085.256502 -147.395452  -749.336076   19.916259 -459.768772   \n",
       "4  16.497785 -2287.540697   77.671687 -1094.898894  121.020638 -459.776133   \n",
       "5  16.889271 -2108.490206  166.691224  -132.943024   48.928820 -459.833235   \n",
       "6  17.481614 -1561.707929  -60.581287  -221.023788  106.283390 -459.892243   \n",
       "7  18.209603 -1354.081668 -113.152583 -1246.768036  311.695897 -459.979147   \n",
       "8  18.997308 -1602.283791 -219.636281  -622.714856 -127.724446 -460.060626   \n",
       "9  19.765449 -1512.735262 -189.833959   -16.948195 -280.446447 -460.041970   \n",
       "\n",
       "      Force-y     Force-z  \n",
       "0 -461.359888 -198.734596  \n",
       "1 -461.337566 -198.768392  \n",
       "2 -461.422890 -198.823988  \n",
       "3 -461.518794 -198.785454  \n",
       "4 -461.604357 -194.633827  \n",
       "5 -461.628228 -153.898731  \n",
       "6 -461.582899  -79.675987  \n",
       "7 -461.460173  -29.180832  \n",
       "8 -461.381853  -10.786779  \n",
       "9 -461.386651  -35.095852  \n",
       "\n",
       "[10 rows x 21 columns]"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "participant_id = 8\n",
    "trial = 123\n",
    "test_data[participant_id][trial].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e9761a1fb3bc48909ea96c073b73ebc8",
       "version_major": 2,
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      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=21), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>EEG-1</th>\n",
       "      <th>EEG-2</th>\n",
       "      <th>EEG-3</th>\n",
       "      <th>EEG-4</th>\n",
       "      <th>EEG-5</th>\n",
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       "      <th>Force-z</th>\n",
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       "      <td>14.828505</td>\n",
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       "      <td>15.599810</td>\n",
       "      <td>10.025611</td>\n",
       "      <td>17.102479</td>\n",
       "      <td>9.664166</td>\n",
       "      <td>7.584305</td>\n",
       "      <td>11.095948</td>\n",
       "      <td>...</td>\n",
       "      <td>10.894943</td>\n",
       "      <td>11.541600</td>\n",
       "      <td>16.890433</td>\n",
       "      <td>-2257.730468</td>\n",
       "      <td>266.623431</td>\n",
       "      <td>-1009.505265</td>\n",
       "      <td>217.084531</td>\n",
       "      <td>-460.116883</td>\n",
       "      <td>-461.575627</td>\n",
       "      <td>-186.897352</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15.910167</td>\n",
       "      <td>15.709234</td>\n",
       "      <td>16.710823</td>\n",
       "      <td>19.534346</td>\n",
       "      <td>17.249945</td>\n",
       "      <td>11.686536</td>\n",
       "      <td>18.526503</td>\n",
       "      <td>11.433829</td>\n",
       "      <td>8.624016</td>\n",
       "      <td>11.371694</td>\n",
       "      <td>...</td>\n",
       "      <td>12.532816</td>\n",
       "      <td>12.636479</td>\n",
       "      <td>17.483410</td>\n",
       "      <td>-1877.500735</td>\n",
       "      <td>168.967165</td>\n",
       "      <td>237.457830</td>\n",
       "      <td>-6.811667</td>\n",
       "      <td>-455.560944</td>\n",
       "      <td>-461.449084</td>\n",
       "      <td>-88.271283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>16.982768</td>\n",
       "      <td>16.931357</td>\n",
       "      <td>18.512621</td>\n",
       "      <td>21.663471</td>\n",
       "      <td>18.837251</td>\n",
       "      <td>13.374044</td>\n",
       "      <td>19.948089</td>\n",
       "      <td>13.269039</td>\n",
       "      <td>9.866318</td>\n",
       "      <td>11.878574</td>\n",
       "      <td>...</td>\n",
       "      <td>14.224606</td>\n",
       "      <td>13.809776</td>\n",
       "      <td>18.211565</td>\n",
       "      <td>-1232.253940</td>\n",
       "      <td>-156.141041</td>\n",
       "      <td>-492.678395</td>\n",
       "      <td>139.427306</td>\n",
       "      <td>-452.493675</td>\n",
       "      <td>-461.411660</td>\n",
       "      <td>-25.532284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17.830708</td>\n",
       "      <td>17.956682</td>\n",
       "      <td>20.128791</td>\n",
       "      <td>23.324513</td>\n",
       "      <td>20.261629</td>\n",
       "      <td>14.983422</td>\n",
       "      <td>21.268816</td>\n",
       "      <td>15.048221</td>\n",
       "      <td>11.226360</td>\n",
       "      <td>12.558757</td>\n",
       "      <td>...</td>\n",
       "      <td>15.868409</td>\n",
       "      <td>14.985372</td>\n",
       "      <td>18.998662</td>\n",
       "      <td>-1374.828668</td>\n",
       "      <td>76.411333</td>\n",
       "      <td>-1745.167334</td>\n",
       "      <td>452.417507</td>\n",
       "      <td>-409.473206</td>\n",
       "      <td>-461.757854</td>\n",
       "      <td>-17.231902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>18.366589</td>\n",
       "      <td>18.720059</td>\n",
       "      <td>21.474962</td>\n",
       "      <td>24.450967</td>\n",
       "      <td>21.441682</td>\n",
       "      <td>16.420560</td>\n",
       "      <td>22.402002</td>\n",
       "      <td>16.657653</td>\n",
       "      <td>12.619163</td>\n",
       "      <td>13.342557</td>\n",
       "      <td>...</td>\n",
       "      <td>17.370115</td>\n",
       "      <td>16.091839</td>\n",
       "      <td>19.766569</td>\n",
       "      <td>-1695.944135</td>\n",
       "      <td>-268.611856</td>\n",
       "      <td>182.766478</td>\n",
       "      <td>-174.381464</td>\n",
       "      <td>-405.819172</td>\n",
       "      <td>-462.116270</td>\n",
       "      <td>-19.591325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>18.533256</td>\n",
       "      <td>19.184405</td>\n",
       "      <td>22.492114</td>\n",
       "      <td>25.024628</td>\n",
       "      <td>22.317956</td>\n",
       "      <td>17.608647</td>\n",
       "      <td>23.279126</td>\n",
       "      <td>17.999795</td>\n",
       "      <td>13.964762</td>\n",
       "      <td>14.153733</td>\n",
       "      <td>...</td>\n",
       "      <td>18.648501</td>\n",
       "      <td>17.069723</td>\n",
       "      <td>20.440447</td>\n",
       "      <td>-1343.203379</td>\n",
       "      <td>-49.494238</td>\n",
       "      <td>-298.653345</td>\n",
       "      <td>-0.941508</td>\n",
       "      <td>-477.850266</td>\n",
       "      <td>-459.515701</td>\n",
       "      <td>-64.915919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>18.304132</td>\n",
       "      <td>19.340664</td>\n",
       "      <td>23.147965</td>\n",
       "      <td>25.073371</td>\n",
       "      <td>22.856869</td>\n",
       "      <td>18.490893</td>\n",
       "      <td>23.854095</td>\n",
       "      <td>18.998893</td>\n",
       "      <td>15.191185</td>\n",
       "      <td>14.914996</td>\n",
       "      <td>...</td>\n",
       "      <td>19.639358</td>\n",
       "      <td>17.869041</td>\n",
       "      <td>20.954356</td>\n",
       "      <td>-1448.205375</td>\n",
       "      <td>-86.111148</td>\n",
       "      <td>-258.783807</td>\n",
       "      <td>160.261806</td>\n",
       "      <td>-482.234719</td>\n",
       "      <td>-461.632718</td>\n",
       "      <td>-116.148553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>17.690444</td>\n",
       "      <td>19.209445</td>\n",
       "      <td>23.441884</td>\n",
       "      <td>24.676742</td>\n",
       "      <td>23.055824</td>\n",
       "      <td>19.041795</td>\n",
       "      <td>24.111154</td>\n",
       "      <td>19.612987</td>\n",
       "      <td>16.248427</td>\n",
       "      <td>15.560895</td>\n",
       "      <td>...</td>\n",
       "      <td>20.306408</td>\n",
       "      <td>18.462663</td>\n",
       "      <td>21.262887</td>\n",
       "      <td>-1416.591886</td>\n",
       "      <td>34.815684</td>\n",
       "      <td>109.320286</td>\n",
       "      <td>111.192994</td>\n",
       "      <td>-460.606464</td>\n",
       "      <td>-462.026074</td>\n",
       "      <td>-76.231860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>16.734730</td>\n",
       "      <td>18.833281</td>\n",
       "      <td>23.398831</td>\n",
       "      <td>23.935632</td>\n",
       "      <td>22.937360</td>\n",
       "      <td>19.260667</td>\n",
       "      <td>24.060149</td>\n",
       "      <td>19.830386</td>\n",
       "      <td>17.101936</td>\n",
       "      <td>16.038435</td>\n",
       "      <td>...</td>\n",
       "      <td>20.637202</td>\n",
       "      <td>18.839538</td>\n",
       "      <td>21.338673</td>\n",
       "      <td>-758.007888</td>\n",
       "      <td>85.186479</td>\n",
       "      <td>-71.714647</td>\n",
       "      <td>314.047267</td>\n",
       "      <td>-461.168626</td>\n",
       "      <td>-459.257784</td>\n",
       "      <td>-83.743262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>15.506402</td>\n",
       "      <td>18.271072</td>\n",
       "      <td>23.064733</td>\n",
       "      <td>22.979215</td>\n",
       "      <td>22.546796</td>\n",
       "      <td>19.172705</td>\n",
       "      <td>23.735145</td>\n",
       "      <td>19.671423</td>\n",
       "      <td>17.737693</td>\n",
       "      <td>16.312520</td>\n",
       "      <td>...</td>\n",
       "      <td>20.644985</td>\n",
       "      <td>19.005180</td>\n",
       "      <td>21.175952</td>\n",
       "      <td>-1486.714238</td>\n",
       "      <td>122.757883</td>\n",
       "      <td>-303.339224</td>\n",
       "      <td>317.471739</td>\n",
       "      <td>-465.600760</td>\n",
       "      <td>-460.866839</td>\n",
       "      <td>-69.122530</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       EEG-1      EEG-2      EEG-3      EEG-4      EEG-5      EEG-6  \\\n",
       "0  14.723115  14.380287  14.828505  17.058188  15.599810  10.025611   \n",
       "1  15.910167  15.709234  16.710823  19.534346  17.249945  11.686536   \n",
       "2  16.982768  16.931357  18.512621  21.663471  18.837251  13.374044   \n",
       "3  17.830708  17.956682  20.128791  23.324513  20.261629  14.983422   \n",
       "4  18.366589  18.720059  21.474962  24.450967  21.441682  16.420560   \n",
       "5  18.533256  19.184405  22.492114  25.024628  22.317956  17.608647   \n",
       "6  18.304132  19.340664  23.147965  25.073371  22.856869  18.490893   \n",
       "7  17.690444  19.209445  23.441884  24.676742  23.055824  19.041795   \n",
       "8  16.734730  18.833281  23.398831  23.935632  22.937360  19.260667   \n",
       "9  15.506402  18.271072  23.064733  22.979215  22.546796  19.172705   \n",
       "\n",
       "       EEG-7      EEG-8      EEG-9     EEG-10  ...     EEG-12     EEG-13  \\\n",
       "0  17.102479   9.664166   7.584305  11.095948  ...  10.894943  11.541600   \n",
       "1  18.526503  11.433829   8.624016  11.371694  ...  12.532816  12.636479   \n",
       "2  19.948089  13.269039   9.866318  11.878574  ...  14.224606  13.809776   \n",
       "3  21.268816  15.048221  11.226360  12.558757  ...  15.868409  14.985372   \n",
       "4  22.402002  16.657653  12.619163  13.342557  ...  17.370115  16.091839   \n",
       "5  23.279126  17.999795  13.964762  14.153733  ...  18.648501  17.069723   \n",
       "6  23.854095  18.998893  15.191185  14.914996  ...  19.639358  17.869041   \n",
       "7  24.111154  19.612987  16.248427  15.560895  ...  20.306408  18.462663   \n",
       "8  24.060149  19.830386  17.101936  16.038435  ...  20.637202  18.839538   \n",
       "9  23.735145  19.671423  17.737693  16.312520  ...  20.644985  19.005180   \n",
       "\n",
       "      EEG-14        EMG-1       EMG-2        EMG-3       EMG-4     Force-x  \\\n",
       "0  16.890433 -2257.730468  266.623431 -1009.505265  217.084531 -460.116883   \n",
       "1  17.483410 -1877.500735  168.967165   237.457830   -6.811667 -455.560944   \n",
       "2  18.211565 -1232.253940 -156.141041  -492.678395  139.427306 -452.493675   \n",
       "3  18.998662 -1374.828668   76.411333 -1745.167334  452.417507 -409.473206   \n",
       "4  19.766569 -1695.944135 -268.611856   182.766478 -174.381464 -405.819172   \n",
       "5  20.440447 -1343.203379  -49.494238  -298.653345   -0.941508 -477.850266   \n",
       "6  20.954356 -1448.205375  -86.111148  -258.783807  160.261806 -482.234719   \n",
       "7  21.262887 -1416.591886   34.815684   109.320286  111.192994 -460.606464   \n",
       "8  21.338673  -758.007888   85.186479   -71.714647  314.047267 -461.168626   \n",
       "9  21.175952 -1486.714238  122.757883  -303.339224  317.471739 -465.600760   \n",
       "\n",
       "      Force-y     Force-z  \n",
       "0 -461.575627 -186.897352  \n",
       "1 -461.449084  -88.271283  \n",
       "2 -461.411660  -25.532284  \n",
       "3 -461.757854  -17.231902  \n",
       "4 -462.116270  -19.591325  \n",
       "5 -459.515701  -64.915919  \n",
       "6 -461.632718 -116.148553  \n",
       "7 -462.026074  -76.231860  \n",
       "8 -459.257784  -83.743262  \n",
       "9 -460.866839  -69.122530  \n",
       "\n",
       "[10 rows x 21 columns]"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "volterra_trial = get_aggregate_volterra(test_data[participant_id][trial]).values\n",
    "test_predictions = np.zeros((volterra_trial.shape[0], len(train_data[0].columns)))\n",
    "\n",
    "for k in tqdm_notebook(range(len(train_data[0].columns)), ascii=True):\n",
    "    test_predictions[:,k] = classifiers[k].predict(volterra_trial[:,len(train_data[0].columns):])\n",
    "\n",
    "columns=['EEG-1','EEG-2','EEG-3','EEG-4','EEG-5',\n",
    "         'EEG-6','EEG-7','EEG-8','EEG-9','EEG-10',\n",
    "         'EEG-11','EEG-12','EEG-13','EEG-14','EMG-1',\n",
    "         'EMG-2','EMG-3','EMG-4','Force-x', \n",
    "         'Force-y','Force-z']\n",
    "test_predictions = pd.DataFrame(data=test_predictions, columns=columns)\n",
    "test_predictions.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "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>EEG-1</th>\n",
       "      <th>EEG-2</th>\n",
       "      <th>EEG-3</th>\n",
       "      <th>EEG-4</th>\n",
       "      <th>EEG-5</th>\n",
       "      <th>EEG-6</th>\n",
       "      <th>EEG-7</th>\n",
       "      <th>EEG-8</th>\n",
       "      <th>EEG-9</th>\n",
       "      <th>EEG-10</th>\n",
       "      <th>...</th>\n",
       "      <th>EEG-12</th>\n",
       "      <th>EEG-13</th>\n",
       "      <th>EEG-14</th>\n",
       "      <th>EMG-1</th>\n",
       "      <th>EMG-2</th>\n",
       "      <th>EMG-3</th>\n",
       "      <th>EMG-4</th>\n",
       "      <th>Force-x</th>\n",
       "      <th>Force-y</th>\n",
       "      <th>Force-z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11.172495</td>\n",
       "      <td>9.906304</td>\n",
       "      <td>8.183386</td>\n",
       "      <td>5.469978</td>\n",
       "      <td>10.026749</td>\n",
       "      <td>5.411751</td>\n",
       "      <td>13.101759</td>\n",
       "      <td>5.271709</td>\n",
       "      <td>7.384106</td>\n",
       "      <td>13.638465</td>\n",
       "      <td>...</td>\n",
       "      <td>6.465120</td>\n",
       "      <td>9.529604</td>\n",
       "      <td>17.550970</td>\n",
       "      <td>-2076.578438</td>\n",
       "      <td>149.435679</td>\n",
       "      <td>-616.918304</td>\n",
       "      <td>213.825070</td>\n",
       "      <td>-460.023493</td>\n",
       "      <td>-461.359888</td>\n",
       "      <td>-198.734596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11.231420</td>\n",
       "      <td>10.179886</td>\n",
       "      <td>8.812767</td>\n",
       "      <td>7.074219</td>\n",
       "      <td>10.494218</td>\n",
       "      <td>5.612990</td>\n",
       "      <td>13.266267</td>\n",
       "      <td>5.341786</td>\n",
       "      <td>6.682698</td>\n",
       "      <td>12.692010</td>\n",
       "      <td>...</td>\n",
       "      <td>6.660906</td>\n",
       "      <td>9.319198</td>\n",
       "      <td>16.893660</td>\n",
       "      <td>-1888.780035</td>\n",
       "      <td>173.664967</td>\n",
       "      <td>-320.677166</td>\n",
       "      <td>126.727783</td>\n",
       "      <td>-459.953159</td>\n",
       "      <td>-461.337566</td>\n",
       "      <td>-198.768392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11.693834</td>\n",
       "      <td>10.847950</td>\n",
       "      <td>9.872570</td>\n",
       "      <td>9.206766</td>\n",
       "      <td>11.353098</td>\n",
       "      <td>6.220426</td>\n",
       "      <td>13.791612</td>\n",
       "      <td>5.850335</td>\n",
       "      <td>6.348856</td>\n",
       "      <td>11.913641</td>\n",
       "      <td>...</td>\n",
       "      <td>7.234725</td>\n",
       "      <td>9.442563</td>\n",
       "      <td>16.486787</td>\n",
       "      <td>-1877.298690</td>\n",
       "      <td>-44.458230</td>\n",
       "      <td>-174.576869</td>\n",
       "      <td>-49.955286</td>\n",
       "      <td>-459.840536</td>\n",
       "      <td>-461.422890</td>\n",
       "      <td>-198.823988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12.495120</td>\n",
       "      <td>11.837241</td>\n",
       "      <td>11.294004</td>\n",
       "      <td>11.705740</td>\n",
       "      <td>12.547150</td>\n",
       "      <td>7.201719</td>\n",
       "      <td>14.647703</td>\n",
       "      <td>6.779158</td>\n",
       "      <td>6.399590</td>\n",
       "      <td>11.366192</td>\n",
       "      <td>...</td>\n",
       "      <td>8.166683</td>\n",
       "      <td>9.882083</td>\n",
       "      <td>16.355065</td>\n",
       "      <td>-2085.256502</td>\n",
       "      <td>-147.395452</td>\n",
       "      <td>-749.336076</td>\n",
       "      <td>19.916259</td>\n",
       "      <td>-459.768772</td>\n",
       "      <td>-461.518794</td>\n",
       "      <td>-198.785454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13.541842</td>\n",
       "      <td>13.051120</td>\n",
       "      <td>12.982864</td>\n",
       "      <td>14.386290</td>\n",
       "      <td>13.995254</td>\n",
       "      <td>8.497069</td>\n",
       "      <td>15.777316</td>\n",
       "      <td>8.076647</td>\n",
       "      <td>6.824068</td>\n",
       "      <td>11.088842</td>\n",
       "      <td>...</td>\n",
       "      <td>9.409945</td>\n",
       "      <td>10.599587</td>\n",
       "      <td>16.497785</td>\n",
       "      <td>-2287.540697</td>\n",
       "      <td>77.671687</td>\n",
       "      <td>-1094.898894</td>\n",
       "      <td>121.020638</td>\n",
       "      <td>-459.776133</td>\n",
       "      <td>-461.604357</td>\n",
       "      <td>-194.633827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>14.720240</td>\n",
       "      <td>14.379105</td>\n",
       "      <td>14.827691</td>\n",
       "      <td>17.056141</td>\n",
       "      <td>15.598369</td>\n",
       "      <td>10.024059</td>\n",
       "      <td>17.101050</td>\n",
       "      <td>9.661725</td>\n",
       "      <td>7.585413</td>\n",
       "      <td>11.094450</td>\n",
       "      <td>...</td>\n",
       "      <td>10.894232</td>\n",
       "      <td>11.539596</td>\n",
       "      <td>16.889271</td>\n",
       "      <td>-2108.490206</td>\n",
       "      <td>166.691224</td>\n",
       "      <td>-132.943024</td>\n",
       "      <td>48.928820</td>\n",
       "      <td>-459.833235</td>\n",
       "      <td>-461.628228</td>\n",
       "      <td>-153.898731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>15.906680</td>\n",
       "      <td>15.707386</td>\n",
       "      <td>16.709317</td>\n",
       "      <td>19.533297</td>\n",
       "      <td>17.247922</td>\n",
       "      <td>11.684365</td>\n",
       "      <td>18.524329</td>\n",
       "      <td>11.430582</td>\n",
       "      <td>8.624735</td>\n",
       "      <td>11.369388</td>\n",
       "      <td>...</td>\n",
       "      <td>12.531429</td>\n",
       "      <td>12.633911</td>\n",
       "      <td>17.481614</td>\n",
       "      <td>-1561.707929</td>\n",
       "      <td>-60.581287</td>\n",
       "      <td>-221.023788</td>\n",
       "      <td>106.283390</td>\n",
       "      <td>-459.892243</td>\n",
       "      <td>-461.582899</td>\n",
       "      <td>-79.675987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>16.978949</td>\n",
       "      <td>16.929364</td>\n",
       "      <td>18.510911</td>\n",
       "      <td>21.661576</td>\n",
       "      <td>18.835149</td>\n",
       "      <td>13.371953</td>\n",
       "      <td>19.945939</td>\n",
       "      <td>13.265548</td>\n",
       "      <td>9.866924</td>\n",
       "      <td>11.875855</td>\n",
       "      <td>...</td>\n",
       "      <td>14.222758</td>\n",
       "      <td>13.807214</td>\n",
       "      <td>18.209603</td>\n",
       "      <td>-1354.081668</td>\n",
       "      <td>-113.152583</td>\n",
       "      <td>-1246.768036</td>\n",
       "      <td>311.695897</td>\n",
       "      <td>-459.979147</td>\n",
       "      <td>-461.460173</td>\n",
       "      <td>-29.180832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>17.827184</td>\n",
       "      <td>17.955103</td>\n",
       "      <td>20.127438</td>\n",
       "      <td>23.323722</td>\n",
       "      <td>20.260255</td>\n",
       "      <td>14.982016</td>\n",
       "      <td>21.267184</td>\n",
       "      <td>15.045268</td>\n",
       "      <td>11.227611</td>\n",
       "      <td>12.556449</td>\n",
       "      <td>...</td>\n",
       "      <td>15.867000</td>\n",
       "      <td>14.983122</td>\n",
       "      <td>18.997308</td>\n",
       "      <td>-1602.283791</td>\n",
       "      <td>-219.636281</td>\n",
       "      <td>-622.714856</td>\n",
       "      <td>-127.724446</td>\n",
       "      <td>-460.060626</td>\n",
       "      <td>-461.381853</td>\n",
       "      <td>-10.786779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>18.363597</td>\n",
       "      <td>18.718627</td>\n",
       "      <td>21.473715</td>\n",
       "      <td>24.450290</td>\n",
       "      <td>21.440476</td>\n",
       "      <td>16.419605</td>\n",
       "      <td>22.400612</td>\n",
       "      <td>16.655162</td>\n",
       "      <td>12.620659</td>\n",
       "      <td>13.340473</td>\n",
       "      <td>...</td>\n",
       "      <td>17.368851</td>\n",
       "      <td>16.090203</td>\n",
       "      <td>19.765449</td>\n",
       "      <td>-1512.735262</td>\n",
       "      <td>-189.833959</td>\n",
       "      <td>-16.948195</td>\n",
       "      <td>-280.446447</td>\n",
       "      <td>-460.041970</td>\n",
       "      <td>-461.386651</td>\n",
       "      <td>-35.095852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>18.529838</td>\n",
       "      <td>19.182464</td>\n",
       "      <td>22.490303</td>\n",
       "      <td>25.022896</td>\n",
       "      <td>22.316438</td>\n",
       "      <td>17.607170</td>\n",
       "      <td>23.277469</td>\n",
       "      <td>17.997046</td>\n",
       "      <td>13.965302</td>\n",
       "      <td>14.151147</td>\n",
       "      <td>...</td>\n",
       "      <td>18.646516</td>\n",
       "      <td>17.067263</td>\n",
       "      <td>20.438907</td>\n",
       "      <td>-1460.543945</td>\n",
       "      <td>-89.489539</td>\n",
       "      <td>-234.519584</td>\n",
       "      <td>-38.883723</td>\n",
       "      <td>-459.963381</td>\n",
       "      <td>-461.430013</td>\n",
       "      <td>-84.457703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>18.301323</td>\n",
       "      <td>19.338857</td>\n",
       "      <td>23.146547</td>\n",
       "      <td>25.073175</td>\n",
       "      <td>22.855954</td>\n",
       "      <td>18.490350</td>\n",
       "      <td>23.853281</td>\n",
       "      <td>18.997145</td>\n",
       "      <td>15.192429</td>\n",
       "      <td>14.913095</td>\n",
       "      <td>...</td>\n",
       "      <td>19.637956</td>\n",
       "      <td>17.867584</td>\n",
       "      <td>20.953683</td>\n",
       "      <td>-1509.167366</td>\n",
       "      <td>17.560403</td>\n",
       "      <td>-44.360278</td>\n",
       "      <td>96.642338</td>\n",
       "      <td>-459.888399</td>\n",
       "      <td>-461.513728</td>\n",
       "      <td>-100.652173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>17.688222</td>\n",
       "      <td>19.207735</td>\n",
       "      <td>23.440793</td>\n",
       "      <td>24.675242</td>\n",
       "      <td>23.055183</td>\n",
       "      <td>19.041477</td>\n",
       "      <td>24.110763</td>\n",
       "      <td>19.611683</td>\n",
       "      <td>16.249350</td>\n",
       "      <td>15.559355</td>\n",
       "      <td>...</td>\n",
       "      <td>20.305160</td>\n",
       "      <td>18.461745</td>\n",
       "      <td>21.262457</td>\n",
       "      <td>-1089.708060</td>\n",
       "      <td>81.128756</td>\n",
       "      <td>31.504328</td>\n",
       "      <td>295.051300</td>\n",
       "      <td>-459.836990</td>\n",
       "      <td>-461.627752</td>\n",
       "      <td>-94.805814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>16.733060</td>\n",
       "      <td>18.831892</td>\n",
       "      <td>23.397852</td>\n",
       "      <td>23.935548</td>\n",
       "      <td>22.937001</td>\n",
       "      <td>19.260429</td>\n",
       "      <td>24.059905</td>\n",
       "      <td>19.829406</td>\n",
       "      <td>17.102570</td>\n",
       "      <td>16.037086</td>\n",
       "      <td>...</td>\n",
       "      <td>20.636118</td>\n",
       "      <td>18.838624</td>\n",
       "      <td>21.338254</td>\n",
       "      <td>-1246.015653</td>\n",
       "      <td>113.732497</td>\n",
       "      <td>-207.179212</td>\n",
       "      <td>306.122862</td>\n",
       "      <td>-459.916174</td>\n",
       "      <td>-461.651385</td>\n",
       "      <td>-83.478987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15.505204</td>\n",
       "      <td>18.269710</td>\n",
       "      <td>23.063903</td>\n",
       "      <td>22.977772</td>\n",
       "      <td>22.546740</td>\n",
       "      <td>19.172933</td>\n",
       "      <td>23.735363</td>\n",
       "      <td>19.671001</td>\n",
       "      <td>17.738596</td>\n",
       "      <td>16.311596</td>\n",
       "      <td>...</td>\n",
       "      <td>20.644236</td>\n",
       "      <td>19.004750</td>\n",
       "      <td>21.175886</td>\n",
       "      <td>-2342.601418</td>\n",
       "      <td>110.871474</td>\n",
       "      <td>-312.234727</td>\n",
       "      <td>207.521879</td>\n",
       "      <td>-456.681341</td>\n",
       "      <td>-461.613357</td>\n",
       "      <td>-76.003075</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        EEG-1      EEG-2      EEG-3      EEG-4      EEG-5      EEG-6  \\\n",
       "0   11.172495   9.906304   8.183386   5.469978  10.026749   5.411751   \n",
       "1   11.231420  10.179886   8.812767   7.074219  10.494218   5.612990   \n",
       "2   11.693834  10.847950   9.872570   9.206766  11.353098   6.220426   \n",
       "3   12.495120  11.837241  11.294004  11.705740  12.547150   7.201719   \n",
       "4   13.541842  13.051120  12.982864  14.386290  13.995254   8.497069   \n",
       "5   14.720240  14.379105  14.827691  17.056141  15.598369  10.024059   \n",
       "6   15.906680  15.707386  16.709317  19.533297  17.247922  11.684365   \n",
       "7   16.978949  16.929364  18.510911  21.661576  18.835149  13.371953   \n",
       "8   17.827184  17.955103  20.127438  23.323722  20.260255  14.982016   \n",
       "9   18.363597  18.718627  21.473715  24.450290  21.440476  16.419605   \n",
       "10  18.529838  19.182464  22.490303  25.022896  22.316438  17.607170   \n",
       "11  18.301323  19.338857  23.146547  25.073175  22.855954  18.490350   \n",
       "12  17.688222  19.207735  23.440793  24.675242  23.055183  19.041477   \n",
       "13  16.733060  18.831892  23.397852  23.935548  22.937001  19.260429   \n",
       "14  15.505204  18.269710  23.063903  22.977772  22.546740  19.172933   \n",
       "\n",
       "        EEG-7      EEG-8      EEG-9     EEG-10  ...     EEG-12     EEG-13  \\\n",
       "0   13.101759   5.271709   7.384106  13.638465  ...   6.465120   9.529604   \n",
       "1   13.266267   5.341786   6.682698  12.692010  ...   6.660906   9.319198   \n",
       "2   13.791612   5.850335   6.348856  11.913641  ...   7.234725   9.442563   \n",
       "3   14.647703   6.779158   6.399590  11.366192  ...   8.166683   9.882083   \n",
       "4   15.777316   8.076647   6.824068  11.088842  ...   9.409945  10.599587   \n",
       "5   17.101050   9.661725   7.585413  11.094450  ...  10.894232  11.539596   \n",
       "6   18.524329  11.430582   8.624735  11.369388  ...  12.531429  12.633911   \n",
       "7   19.945939  13.265548   9.866924  11.875855  ...  14.222758  13.807214   \n",
       "8   21.267184  15.045268  11.227611  12.556449  ...  15.867000  14.983122   \n",
       "9   22.400612  16.655162  12.620659  13.340473  ...  17.368851  16.090203   \n",
       "10  23.277469  17.997046  13.965302  14.151147  ...  18.646516  17.067263   \n",
       "11  23.853281  18.997145  15.192429  14.913095  ...  19.637956  17.867584   \n",
       "12  24.110763  19.611683  16.249350  15.559355  ...  20.305160  18.461745   \n",
       "13  24.059905  19.829406  17.102570  16.037086  ...  20.636118  18.838624   \n",
       "14  23.735363  19.671001  17.738596  16.311596  ...  20.644236  19.004750   \n",
       "\n",
       "       EEG-14        EMG-1       EMG-2        EMG-3       EMG-4     Force-x  \\\n",
       "0   17.550970 -2076.578438  149.435679  -616.918304  213.825070 -460.023493   \n",
       "1   16.893660 -1888.780035  173.664967  -320.677166  126.727783 -459.953159   \n",
       "2   16.486787 -1877.298690  -44.458230  -174.576869  -49.955286 -459.840536   \n",
       "3   16.355065 -2085.256502 -147.395452  -749.336076   19.916259 -459.768772   \n",
       "4   16.497785 -2287.540697   77.671687 -1094.898894  121.020638 -459.776133   \n",
       "5   16.889271 -2108.490206  166.691224  -132.943024   48.928820 -459.833235   \n",
       "6   17.481614 -1561.707929  -60.581287  -221.023788  106.283390 -459.892243   \n",
       "7   18.209603 -1354.081668 -113.152583 -1246.768036  311.695897 -459.979147   \n",
       "8   18.997308 -1602.283791 -219.636281  -622.714856 -127.724446 -460.060626   \n",
       "9   19.765449 -1512.735262 -189.833959   -16.948195 -280.446447 -460.041970   \n",
       "10  20.438907 -1460.543945  -89.489539  -234.519584  -38.883723 -459.963381   \n",
       "11  20.953683 -1509.167366   17.560403   -44.360278   96.642338 -459.888399   \n",
       "12  21.262457 -1089.708060   81.128756    31.504328  295.051300 -459.836990   \n",
       "13  21.338254 -1246.015653  113.732497  -207.179212  306.122862 -459.916174   \n",
       "14  21.175886 -2342.601418  110.871474  -312.234727  207.521879 -456.681341   \n",
       "\n",
       "       Force-y     Force-z  \n",
       "0  -461.359888 -198.734596  \n",
       "1  -461.337566 -198.768392  \n",
       "2  -461.422890 -198.823988  \n",
       "3  -461.518794 -198.785454  \n",
       "4  -461.604357 -194.633827  \n",
       "5  -461.628228 -153.898731  \n",
       "6  -461.582899  -79.675987  \n",
       "7  -461.460173  -29.180832  \n",
       "8  -461.381853  -10.786779  \n",
       "9  -461.386651  -35.095852  \n",
       "10 -461.430013  -84.457703  \n",
       "11 -461.513728 -100.652173  \n",
       "12 -461.627752  -94.805814  \n",
       "13 -461.651385  -83.478987  \n",
       "14 -461.613357  -76.003075  \n",
       "\n",
       "[15 rows x 21 columns]"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data[participant_id][trial].head(15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualization Of Predictions Over Test Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x1728 with 15 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the predictions in time domain given thresholded DWT coeffs \n",
    "# for different combinations of eeg+emg+force:\n",
    "\n",
    "nPreds = 195\n",
    "P = 5\n",
    "x_gt=np.arange(0, (nPreds+P)/fs, 1/fs)\n",
    "x_pred=np.arange(P/fs, (nPreds+P)/fs, 1/fs)\n",
    "T = math.floor(10*(nPreds+P)/fs) + 1\n",
    "\n",
    "if T != 1:\n",
    "    z = [decimal.Decimal(i) / decimal.Decimal(10) for i in range(0, T)]\n",
    "else:\n",
    "    T = math.floor(100*(nPreds+P)/fs) + 1\n",
    "    z = [decimal.Decimal(i) / decimal.Decimal(100) for i in range(0, T, 2)]\n",
    "\n",
    "sns.set(style=\"white\", palette=\"muted\", color_codes=True, font_scale=1.5)\n",
    "# Set up the matplotlib figure (all forecasts subplot figure)\n",
    "f, axes = plt.subplots(5, 3, figsize=(14, 24), sharex=False, sharey=False)\n",
    "sns.despine()\n",
    "\n",
    "plt.setp(axes, xticks=z, xticklabels=z)\n",
    "\n",
    "for j in range(1,15,1):\n",
    "    sns.set()\n",
    "    axes[math.floor((j-1)/3),(j-1)%3].plot(x_gt, test_data[participant_id][trial].iloc[:nPreds+P,j-1].values, \n",
    "                                           color='b', label=\"True\")\n",
    "    axes[math.floor((j-1)/3), (j-1)%3].plot(x_pred, test_predictions.iloc[:nPreds,j-1].values, \n",
    "                                            color='r', linestyle='--', label=\"Prediction\")\n",
    "    axes[math.floor((j-1)/3), (j-1)%3].axvline(x=P/fs, linestyle='--', color='k')\n",
    "    axes[math.floor((j-1)/3), (j-1)%3].title.set_text(\"EEG-%i\" %j)\n",
    "    axes[math.floor((j-1)/3), (j-1)%3].set_yticks(axes[math.floor((j-1)/3), (j-1)%3].get_yticks()[1:-1:2])\n",
    "    axes[0, 0].legend(frameon=False)\n",
    "    plt.setp(axes[0, 0].get_legend().get_texts(), fontsize='14')\n",
    "\n",
    "    axes[4,2].plot(x_gt, test_data[participant_id][trial].iloc[:nPreds+P,-1].values, \n",
    "                   color='b', label=\"True\")\n",
    "    axes[4,2].plot(x_pred, test_predictions.iloc[:nPreds, -1].values, \n",
    "                   color='r', linestyle='--', label=\"Prediction\")\n",
    "    axes[4,2].axvline(x=P/fs, linestyle='--', color='k')\n",
    "    axes[4,2].ticklabel_format(axis='y', style='sci', scilimits=(4,-4))\n",
    "    axes[4,2].title.set_text(\"Force-z\")\n",
    "    axes[4,2].set_yticks(axes[4, 2].get_yticks()[1:-1:2])\n",
    "    #axes[4,2].legend() \n",
    "\n",
    "plt.savefig(\"VolterraResults/predictions.pdf\", bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}