Ninapro_prep.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ninapro DB2 Dataset Preparation Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from scipy.io import loadmat\n",
    "from sklearn.preprocessing import OneHotEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = \"Ninapro_DB2\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Title</th>\n",
       "      <th>Hand</th>\n",
       "      <th>Laterality</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>zip files</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Intact</td>\n",
       "      <td>Right Handed</td>\n",
       "      <td>Male</td>\n",
       "      <td>29</td>\n",
       "      <td>187</td>\n",
       "      <td>75</td>\n",
       "      <td>DB2_s1.zip</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Intact</td>\n",
       "      <td>Right Handed</td>\n",
       "      <td>Male</td>\n",
       "      <td>29</td>\n",
       "      <td>183</td>\n",
       "      <td>75</td>\n",
       "      <td>DB2_s2.zip</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Intact</td>\n",
       "      <td>Right Handed</td>\n",
       "      <td>Male</td>\n",
       "      <td>31</td>\n",
       "      <td>174</td>\n",
       "      <td>69</td>\n",
       "      <td>DB2_s3.zip</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Intact</td>\n",
       "      <td>Left Handed</td>\n",
       "      <td>Female</td>\n",
       "      <td>30</td>\n",
       "      <td>154</td>\n",
       "      <td>50</td>\n",
       "      <td>DB2_s4.zip</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Intact</td>\n",
       "      <td>Right Handed</td>\n",
       "      <td>Male</td>\n",
       "      <td>25</td>\n",
       "      <td>175</td>\n",
       "      <td>70</td>\n",
       "      <td>DB2_s5.zip</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>Intact</td>\n",
       "      <td>Right Handed</td>\n",
       "      <td>Male</td>\n",
       "      <td>35</td>\n",
       "      <td>172</td>\n",
       "      <td>79</td>\n",
       "      <td>DB2_s6.zip</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>Intact</td>\n",
       "      <td>Right Handed</td>\n",
       "      <td>Male</td>\n",
       "      <td>27</td>\n",
       "      <td>187</td>\n",
       "      <td>92</td>\n",
       "      <td>DB2_s7.zip</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>Intact</td>\n",
       "      <td>Right Handed</td>\n",
       "      <td>Male</td>\n",
       "      <td>45</td>\n",
       "      <td>173</td>\n",
       "      <td>73</td>\n",
       "      <td>DB2_s8.zip</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>Intact</td>\n",
       "      <td>Right Handed</td>\n",
       "      <td>Male</td>\n",
       "      <td>23</td>\n",
       "      <td>172</td>\n",
       "      <td>63</td>\n",
       "      <td>DB2_s9.zip</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>Intact</td>\n",
       "      <td>Right Handed</td>\n",
       "      <td>Male</td>\n",
       "      <td>34</td>\n",
       "      <td>173</td>\n",
       "      <td>84</td>\n",
       "      <td>DB2_s10.zip</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>Intact</td>\n",
       "      <td>Right Handed</td>\n",
       "      <td>Female</td>\n",
       "      <td>32</td>\n",
       "      <td>150</td>\n",
       "      <td>54</td>\n",
       "      <td>DB2_s11.zip</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>Intact</td>\n",
       "      <td>Right Handed</td>\n",
       "      <td>Male</td>\n",
       "      <td>29</td>\n",
       "      <td>184</td>\n",
       "      <td>90</td>\n",
       "      <td>DB2_s12.zip</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>Intact</td>\n",
       "      <td>Left Handed</td>\n",
       "      <td>Male</td>\n",
       "      <td>30</td>\n",
       "      <td>182</td>\n",
       "      <td>70</td>\n",
       "      <td>DB2_s13.zip</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>Intact</td>\n",
       "      <td>Right Handed</td>\n",
       "      <td>Female</td>\n",
       "      <td>30</td>\n",
       "      <td>173</td>\n",
       "      <td>59</td>\n",
       "      <td>DB2_s14.zip</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>Intact</td>\n",
       "      <td>Right Handed</td>\n",
       "      <td>Male</td>\n",
       "      <td>30</td>\n",
       "      <td>169</td>\n",
       "      <td>58</td>\n",
       "      <td>DB2_s15.zip</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>Intact</td>\n",
       "      <td>Right Handed</td>\n",
       "      <td>Male</td>\n",
       "      <td>34</td>\n",
       "      <td>173</td>\n",
       "      <td>76</td>\n",
       "      <td>DB2_s16.zip</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>Intact</td>\n",
       "      <td>Right Handed</td>\n",
       "      <td>Male</td>\n",
       "      <td>29</td>\n",
       "      <td>175</td>\n",
       "      <td>70</td>\n",
       "      <td>DB2_s17.zip</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>Intact</td>\n",
       "      <td>Right Handed</td>\n",
       "      <td>Female</td>\n",
       "      <td>30</td>\n",
       "      <td>169</td>\n",
       "      <td>90</td>\n",
       "      <td>DB2_s18.zip</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>Intact</td>\n",
       "      <td>Right Handed</td>\n",
       "      <td>Female</td>\n",
       "      <td>31</td>\n",
       "      <td>158</td>\n",
       "      <td>52</td>\n",
       "      <td>DB2_s19.zip</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>Intact</td>\n",
       "      <td>Right Handed</td>\n",
       "      <td>Female</td>\n",
       "      <td>26</td>\n",
       "      <td>155</td>\n",
       "      <td>52</td>\n",
       "      <td>DB2_s20.zip</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>Intact</td>\n",
       "      <td>Right Handed</td>\n",
       "      <td>Male</td>\n",
       "      <td>32</td>\n",
       "      <td>170</td>\n",
       "      <td>75</td>\n",
       "      <td>DB2_s21.zip</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>Intact</td>\n",
       "      <td>Left Handed</td>\n",
       "      <td>Female</td>\n",
       "      <td>28</td>\n",
       "      <td>162</td>\n",
       "      <td>54</td>\n",
       "      <td>DB2_s22.zip</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23</td>\n",
       "      <td>Intact</td>\n",
       "      <td>Right Handed</td>\n",
       "      <td>Male</td>\n",
       "      <td>25</td>\n",
       "      <td>170</td>\n",
       "      <td>66</td>\n",
       "      <td>DB2_s23.zip</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>Intact</td>\n",
       "      <td>Right Handed</td>\n",
       "      <td>Male</td>\n",
       "      <td>28</td>\n",
       "      <td>170</td>\n",
       "      <td>73</td>\n",
       "      <td>DB2_s24.zip</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>Intact</td>\n",
       "      <td>Left Handed</td>\n",
       "      <td>Male</td>\n",
       "      <td>31</td>\n",
       "      <td>168</td>\n",
       "      <td>70</td>\n",
       "      <td>DB2_s25.zip</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26</td>\n",
       "      <td>Intact</td>\n",
       "      <td>Left Handed</td>\n",
       "      <td>Male</td>\n",
       "      <td>30</td>\n",
       "      <td>186</td>\n",
       "      <td>90</td>\n",
       "      <td>DB2_s26.zip</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>Intact</td>\n",
       "      <td>Right Handed</td>\n",
       "      <td>Male</td>\n",
       "      <td>29</td>\n",
       "      <td>170</td>\n",
       "      <td>65</td>\n",
       "      <td>DB2_s27.zip</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>Intact</td>\n",
       "      <td>Right Handed</td>\n",
       "      <td>Female</td>\n",
       "      <td>29</td>\n",
       "      <td>160</td>\n",
       "      <td>61</td>\n",
       "      <td>DB2_s28.zip</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29</td>\n",
       "      <td>Intact</td>\n",
       "      <td>Right Handed</td>\n",
       "      <td>Male</td>\n",
       "      <td>27</td>\n",
       "      <td>171</td>\n",
       "      <td>64</td>\n",
       "      <td>DB2_s29.zip</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>Intact</td>\n",
       "      <td>Right Handed</td>\n",
       "      <td>Male</td>\n",
       "      <td>30</td>\n",
       "      <td>173</td>\n",
       "      <td>68</td>\n",
       "      <td>DB2_s30.zip</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>31</td>\n",
       "      <td>Intact</td>\n",
       "      <td>Right Handed</td>\n",
       "      <td>Male</td>\n",
       "      <td>29</td>\n",
       "      <td>185</td>\n",
       "      <td>98</td>\n",
       "      <td>DB2_s31.zip</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>32</td>\n",
       "      <td>Intact</td>\n",
       "      <td>Right Handed</td>\n",
       "      <td>Male</td>\n",
       "      <td>28</td>\n",
       "      <td>173</td>\n",
       "      <td>72</td>\n",
       "      <td>DB2_s32.zip</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>33</td>\n",
       "      <td>Intact</td>\n",
       "      <td>Right Handed</td>\n",
       "      <td>Male</td>\n",
       "      <td>25</td>\n",
       "      <td>183</td>\n",
       "      <td>71</td>\n",
       "      <td>DB2_s33.zip</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>34</td>\n",
       "      <td>Intact</td>\n",
       "      <td>Right Handed</td>\n",
       "      <td>Male</td>\n",
       "      <td>31</td>\n",
       "      <td>192</td>\n",
       "      <td>78</td>\n",
       "      <td>DB2_s34.zip</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>35</td>\n",
       "      <td>Intact</td>\n",
       "      <td>Right Handed</td>\n",
       "      <td>Female</td>\n",
       "      <td>24</td>\n",
       "      <td>170</td>\n",
       "      <td>52</td>\n",
       "      <td>DB2_s35.zip</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>36</td>\n",
       "      <td>Intact</td>\n",
       "      <td>Right Handed</td>\n",
       "      <td>Female</td>\n",
       "      <td>27</td>\n",
       "      <td>155</td>\n",
       "      <td>44</td>\n",
       "      <td>DB2_s36.zip</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>37</td>\n",
       "      <td>Intact</td>\n",
       "      <td>Right Handed</td>\n",
       "      <td>Male</td>\n",
       "      <td>34</td>\n",
       "      <td>190</td>\n",
       "      <td>105</td>\n",
       "      <td>DB2_s37.zip</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>38</td>\n",
       "      <td>Intact</td>\n",
       "      <td>Right Handed</td>\n",
       "      <td>Female</td>\n",
       "      <td>30</td>\n",
       "      <td>163</td>\n",
       "      <td>62</td>\n",
       "      <td>DB2_s38.zip</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>39</td>\n",
       "      <td>Intact</td>\n",
       "      <td>Right Handed</td>\n",
       "      <td>Male</td>\n",
       "      <td>31</td>\n",
       "      <td>183</td>\n",
       "      <td>96</td>\n",
       "      <td>DB2_s39.zip</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>40</td>\n",
       "      <td>Intact</td>\n",
       "      <td>Right Handed</td>\n",
       "      <td>Male</td>\n",
       "      <td>31</td>\n",
       "      <td>173</td>\n",
       "      <td>65</td>\n",
       "      <td>DB2_s40.zip</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Title    Hand    Laterality  Gender  Age  Height  Weight    zip files\n",
       "0       1  Intact  Right Handed    Male   29     187      75   DB2_s1.zip\n",
       "1       2  Intact  Right Handed    Male   29     183      75   DB2_s2.zip\n",
       "2       3  Intact  Right Handed    Male   31     174      69   DB2_s3.zip\n",
       "3       4  Intact   Left Handed  Female   30     154      50   DB2_s4.zip\n",
       "4       5  Intact  Right Handed    Male   25     175      70   DB2_s5.zip\n",
       "5       6  Intact  Right Handed    Male   35     172      79   DB2_s6.zip\n",
       "6       7  Intact  Right Handed    Male   27     187      92   DB2_s7.zip\n",
       "7       8  Intact  Right Handed    Male   45     173      73   DB2_s8.zip\n",
       "8       9  Intact  Right Handed    Male   23     172      63   DB2_s9.zip\n",
       "9      10  Intact  Right Handed    Male   34     173      84  DB2_s10.zip\n",
       "10     11  Intact  Right Handed  Female   32     150      54  DB2_s11.zip\n",
       "11     12  Intact  Right Handed    Male   29     184      90  DB2_s12.zip\n",
       "12     13  Intact   Left Handed    Male   30     182      70  DB2_s13.zip\n",
       "13     14  Intact  Right Handed  Female   30     173      59  DB2_s14.zip\n",
       "14     15  Intact  Right Handed    Male   30     169      58  DB2_s15.zip\n",
       "15     16  Intact  Right Handed    Male   34     173      76  DB2_s16.zip\n",
       "16     17  Intact  Right Handed    Male   29     175      70  DB2_s17.zip\n",
       "17     18  Intact  Right Handed  Female   30     169      90  DB2_s18.zip\n",
       "18     19  Intact  Right Handed  Female   31     158      52  DB2_s19.zip\n",
       "19     20  Intact  Right Handed  Female   26     155      52  DB2_s20.zip\n",
       "20     21  Intact  Right Handed    Male   32     170      75  DB2_s21.zip\n",
       "21     22  Intact   Left Handed  Female   28     162      54  DB2_s22.zip\n",
       "22     23  Intact  Right Handed    Male   25     170      66  DB2_s23.zip\n",
       "23     24  Intact  Right Handed    Male   28     170      73  DB2_s24.zip\n",
       "24     25  Intact   Left Handed    Male   31     168      70  DB2_s25.zip\n",
       "25     26  Intact   Left Handed    Male   30     186      90  DB2_s26.zip\n",
       "26     27  Intact  Right Handed    Male   29     170      65  DB2_s27.zip\n",
       "27     28  Intact  Right Handed  Female   29     160      61  DB2_s28.zip\n",
       "28     29  Intact  Right Handed    Male   27     171      64  DB2_s29.zip\n",
       "29     30  Intact  Right Handed    Male   30     173      68  DB2_s30.zip\n",
       "30     31  Intact  Right Handed    Male   29     185      98  DB2_s31.zip\n",
       "31     32  Intact  Right Handed    Male   28     173      72  DB2_s32.zip\n",
       "32     33  Intact  Right Handed    Male   25     183      71  DB2_s33.zip\n",
       "33     34  Intact  Right Handed    Male   31     192      78  DB2_s34.zip\n",
       "34     35  Intact  Right Handed  Female   24     170      52  DB2_s35.zip\n",
       "35     36  Intact  Right Handed  Female   27     155      44  DB2_s36.zip\n",
       "36     37  Intact  Right Handed    Male   34     190     105  DB2_s37.zip\n",
       "37     38  Intact  Right Handed  Female   30     163      62  DB2_s38.zip\n",
       "38     39  Intact  Right Handed    Male   31     183      96  DB2_s39.zip\n",
       "39     40  Intact  Right Handed    Male   31     173      65  DB2_s40.zip"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subject_info = pd.read_csv(path+\"/Ninapro_DB2_SubjectsInformation.csv\")\n",
    "subject_info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "mat = loadmat(path+'/'+'S1_E1_A1.mat')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1808331, 12)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mat['emg'].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_mat(file):\n",
    "    mat = loadmat(file)\n",
    "    inclin = mat['inclin']\n",
    "    stimulus = mat['stimulus']\n",
    "    repetition = mat['repetition']\n",
    "    emg = mat['emg']\n",
    "    emg_col = []\n",
    "    for i in range(1,13):\n",
    "        emg_col.append('emg'+str(i))\n",
    "    df = pd.DataFrame(columns=['repetition','inclin']+emg_col+['stimulus'])\n",
    "    df['repetition'] = mat['repetition'].reshape((mat['repetition'].shape[0],))\n",
    "    df['stimulus'] = stimulus\n",
    "    df['inclin'] = inclin\n",
    "    df[emg_col] = mat['emg']\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "fdf = read_mat(path+'/'+'S1_E1_A1.mat')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option(\"display.max_rows\", None, \"display.max_columns\", None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save('prepared_data/s1_raw_emg',fdf[['emg1','emg2','emg3','emg4','emg5','emg6','emg7','emg8']])\n",
    "np.save('prepared_data/s1_raw_stimulus',fdf[['stimulus']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "h = fdf.head(1000)\n",
    "h[['emg1','emg2','emg3','emg4','emg5','emg6']].plot(figsize=(20,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fdf['emg1'].plot(figsize=(20,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>emg1</th>\n",
       "      <th>emg2</th>\n",
       "      <th>emg3</th>\n",
       "      <th>emg4</th>\n",
       "      <th>emg5</th>\n",
       "      <th>emg6</th>\n",
       "      <th>emg7</th>\n",
       "      <th>emg8</th>\n",
       "      <th>emg9</th>\n",
       "      <th>emg10</th>\n",
       "      <th>emg11</th>\n",
       "      <th>emg12</th>\n",
       "      <th>move</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-2.851210e-06</td>\n",
       "      <td>3.355261e-06</td>\n",
       "      <td>1.598866e-06</td>\n",
       "      <td>-1.394234e-06</td>\n",
       "      <td>-2.082012e-06</td>\n",
       "      <td>2.749975e-06</td>\n",
       "      <td>-2.347791e-06</td>\n",
       "      <td>1.537436e-07</td>\n",
       "      <td>-2.484787e-07</td>\n",
       "      <td>1.469924e-06</td>\n",
       "      <td>-5.609089e-06</td>\n",
       "      <td>0.000040</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-4.697738e-06</td>\n",
       "      <td>3.355212e-06</td>\n",
       "      <td>1.430441e-06</td>\n",
       "      <td>-3.072682e-06</td>\n",
       "      <td>-3.063038e-06</td>\n",
       "      <td>2.246543e-06</td>\n",
       "      <td>-1.657156e-07</td>\n",
       "      <td>2.503453e-06</td>\n",
       "      <td>-3.561109e-07</td>\n",
       "      <td>2.478162e-06</td>\n",
       "      <td>-3.259393e-06</td>\n",
       "      <td>0.000050</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-2.683734e-06</td>\n",
       "      <td>4.026560e-06</td>\n",
       "      <td>9.263165e-07</td>\n",
       "      <td>-5.422526e-06</td>\n",
       "      <td>-3.695910e-06</td>\n",
       "      <td>1.239568e-06</td>\n",
       "      <td>2.352061e-06</td>\n",
       "      <td>5.524560e-06</td>\n",
       "      <td>-2.445606e-07</td>\n",
       "      <td>2.479306e-06</td>\n",
       "      <td>-7.045269e-08</td>\n",
       "      <td>0.000047</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.183707e-06</td>\n",
       "      <td>2.851566e-06</td>\n",
       "      <td>7.578904e-07</td>\n",
       "      <td>-6.597427e-06</td>\n",
       "      <td>-3.937123e-06</td>\n",
       "      <td>1.575385e-06</td>\n",
       "      <td>2.016400e-06</td>\n",
       "      <td>5.188681e-06</td>\n",
       "      <td>1.094450e-06</td>\n",
       "      <td>2.816148e-06</td>\n",
       "      <td>1.439995e-06</td>\n",
       "      <td>0.000032</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.197711e-06</td>\n",
       "      <td>6.694763e-07</td>\n",
       "      <td>1.764409e-06</td>\n",
       "      <td>-5.422437e-06</td>\n",
       "      <td>-5.652943e-06</td>\n",
       "      <td>7.362596e-07</td>\n",
       "      <td>8.414949e-07</td>\n",
       "      <td>8.244234e-07</td>\n",
       "      <td>1.154750e-06</td>\n",
       "      <td>2.817293e-06</td>\n",
       "      <td>9.700835e-08</td>\n",
       "      <td>0.000014</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5238688</th>\n",
       "      <td>-6.230766e-07</td>\n",
       "      <td>5.911040e-07</td>\n",
       "      <td>-5.486231e-06</td>\n",
       "      <td>-1.236290e-06</td>\n",
       "      <td>-2.134746e-06</td>\n",
       "      <td>-2.562491e-06</td>\n",
       "      <td>1.047846e-06</td>\n",
       "      <td>-1.344230e-05</td>\n",
       "      <td>6.216484e-07</td>\n",
       "      <td>-3.767954e-06</td>\n",
       "      <td>1.012069e-06</td>\n",
       "      <td>-0.000022</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5238689</th>\n",
       "      <td>2.901756e-06</td>\n",
       "      <td>1.766055e-06</td>\n",
       "      <td>-6.493326e-06</td>\n",
       "      <td>-1.404029e-06</td>\n",
       "      <td>-2.187698e-06</td>\n",
       "      <td>-3.401850e-06</td>\n",
       "      <td>1.719439e-06</td>\n",
       "      <td>-2.049242e-05</td>\n",
       "      <td>1.796249e-06</td>\n",
       "      <td>-2.928994e-06</td>\n",
       "      <td>1.179876e-06</td>\n",
       "      <td>-0.000018</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5238690</th>\n",
       "      <td>4.412399e-06</td>\n",
       "      <td>2.101759e-06</td>\n",
       "      <td>-6.829025e-06</td>\n",
       "      <td>-3.968205e-07</td>\n",
       "      <td>-1.896385e-06</td>\n",
       "      <td>-2.227017e-06</td>\n",
       "      <td>7.125424e-07</td>\n",
       "      <td>-3.073167e-05</td>\n",
       "      <td>1.963753e-06</td>\n",
       "      <td>-1.250788e-06</td>\n",
       "      <td>5.084372e-07</td>\n",
       "      <td>-0.000014</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5238691</th>\n",
       "      <td>5.251643e-06</td>\n",
       "      <td>1.933917e-06</td>\n",
       "      <td>-5.654080e-06</td>\n",
       "      <td>2.746891e-07</td>\n",
       "      <td>-1.210498e-07</td>\n",
       "      <td>2.906097e-07</td>\n",
       "      <td>-6.300568e-07</td>\n",
       "      <td>-4.751704e-05</td>\n",
       "      <td>9.563120e-07</td>\n",
       "      <td>-7.475257e-07</td>\n",
       "      <td>-1.630021e-07</td>\n",
       "      <td>-0.000012</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5238692</th>\n",
       "      <td>5.083792e-06</td>\n",
       "      <td>2.605322e-06</td>\n",
       "      <td>-4.143437e-06</td>\n",
       "      <td>-7.322931e-07</td>\n",
       "      <td>1.618387e-07</td>\n",
       "      <td>1.129745e-06</td>\n",
       "      <td>-1.301256e-06</td>\n",
       "      <td>-5.288866e-05</td>\n",
       "      <td>-1.058225e-06</td>\n",
       "      <td>-2.594154e-06</td>\n",
       "      <td>3.405036e-07</td>\n",
       "      <td>-0.000014</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5238693 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 emg1          emg2          emg3          emg4          emg5  \\\n",
       "0       -2.851210e-06  3.355261e-06  1.598866e-06 -1.394234e-06 -2.082012e-06   \n",
       "1       -4.697738e-06  3.355212e-06  1.430441e-06 -3.072682e-06 -3.063038e-06   \n",
       "2       -2.683734e-06  4.026560e-06  9.263165e-07 -5.422526e-06 -3.695910e-06   \n",
       "3        2.183707e-06  2.851566e-06  7.578904e-07 -6.597427e-06 -3.937123e-06   \n",
       "4        4.197711e-06  6.694763e-07  1.764409e-06 -5.422437e-06 -5.652943e-06   \n",
       "...               ...           ...           ...           ...           ...   \n",
       "5238688 -6.230766e-07  5.911040e-07 -5.486231e-06 -1.236290e-06 -2.134746e-06   \n",
       "5238689  2.901756e-06  1.766055e-06 -6.493326e-06 -1.404029e-06 -2.187698e-06   \n",
       "5238690  4.412399e-06  2.101759e-06 -6.829025e-06 -3.968205e-07 -1.896385e-06   \n",
       "5238691  5.251643e-06  1.933917e-06 -5.654080e-06  2.746891e-07 -1.210498e-07   \n",
       "5238692  5.083792e-06  2.605322e-06 -4.143437e-06 -7.322931e-07  1.618387e-07   \n",
       "\n",
       "                 emg6          emg7          emg8          emg9         emg10  \\\n",
       "0        2.749975e-06 -2.347791e-06  1.537436e-07 -2.484787e-07  1.469924e-06   \n",
       "1        2.246543e-06 -1.657156e-07  2.503453e-06 -3.561109e-07  2.478162e-06   \n",
       "2        1.239568e-06  2.352061e-06  5.524560e-06 -2.445606e-07  2.479306e-06   \n",
       "3        1.575385e-06  2.016400e-06  5.188681e-06  1.094450e-06  2.816148e-06   \n",
       "4        7.362596e-07  8.414949e-07  8.244234e-07  1.154750e-06  2.817293e-06   \n",
       "...               ...           ...           ...           ...           ...   \n",
       "5238688 -2.562491e-06  1.047846e-06 -1.344230e-05  6.216484e-07 -3.767954e-06   \n",
       "5238689 -3.401850e-06  1.719439e-06 -2.049242e-05  1.796249e-06 -2.928994e-06   \n",
       "5238690 -2.227017e-06  7.125424e-07 -3.073167e-05  1.963753e-06 -1.250788e-06   \n",
       "5238691  2.906097e-07 -6.300568e-07 -4.751704e-05  9.563120e-07 -7.475257e-07   \n",
       "5238692  1.129745e-06 -1.301256e-06 -5.288866e-05 -1.058225e-06 -2.594154e-06   \n",
       "\n",
       "                emg11     emg12  move  \n",
       "0       -5.609089e-06  0.000040     0  \n",
       "1       -3.259393e-06  0.000050     0  \n",
       "2       -7.045269e-08  0.000047     0  \n",
       "3        1.439995e-06  0.000032     0  \n",
       "4        9.700835e-08  0.000014     0  \n",
       "...               ...       ...   ...  \n",
       "5238688  1.012069e-06 -0.000022    49  \n",
       "5238689  1.179876e-06 -0.000018    49  \n",
       "5238690  5.084372e-07 -0.000014    49  \n",
       "5238691 -1.630021e-07 -0.000012    49  \n",
       "5238692  3.405036e-07 -0.000014     0  \n",
       "\n",
       "[5238693 rows x 13 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from nina_helper import *\n",
    "\n",
    "db2_path = \"Ninapro_DB2/\"\n",
    "\n",
    "# Decide window length (150ms window, 10ms increment)\n",
    "window_len = 20\n",
    "window_inc = 1\n",
    "\n",
    "# Choose subject and get info\n",
    "subject = 1\n",
    "info_dict = db2_info()  # Get info\n",
    "\n",
    "data_dict = nina_helper.import_db2(db2_path, subject)\n",
    "\n",
    "data_dict.keys()\n",
    "\n",
    "emg_col = []\n",
    "for i in range(1,13):\n",
    "    emg_col.append('emg'+str(i))\n",
    "df = pd.DataFrame(columns=emg_col)\n",
    "df['move'] = data_dict['move']\n",
    "df[emg_col] = data_dict['emg']\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4, 5, 6])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "info_dict['rep_labels']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nina_helper import *\n",
    "import numpy as np\n",
    "from tqdm.notebook import trange, tqdm\n",
    "\n",
    "db2_path = \"Ninapro_DB2/\"\n",
    "\n",
    "def one_hot(cat_idx,y_all):\n",
    "    y_hot = []\n",
    "    for y in y_all:\n",
    "        if y in cat_idx:\n",
    "            idx = list(cat_idx).index(y)\n",
    "            hot = np.zeros(len(cat_idx))\n",
    "            hot[idx] = 1\n",
    "            y_hot.append(hot)\n",
    "    return np.array(y_hot)\n",
    "\n",
    "def return_singleSubj_prep_data(subject,moves,window_len,window_inc):\n",
    "    data_dict = nina_helper.import_db2(db2_path, subject, rest_length_cap=5)\n",
    "\n",
    "    # Create a random test - training split based on repetition number (specify a set to include)\n",
    "    reps = info_dict['rep_labels']\n",
    "    nb_test_reps = 2\n",
    "    nb_splits = 12\n",
    "#     train_reps, test_reps = gen_split_rand(reps, nb_test_reps, nb_splits, base=[2, 5])\n",
    "    train_reps, test_reps = gen_split_balanced(reps, nb_test_reps, base=None)\n",
    "\n",
    "    # Normalise EMG data based on training set\n",
    "    emg_data = normalise_emg(data_dict['emg'], data_dict['rep'], train_reps[0, :])\n",
    "\n",
    "    # Window data: x_all data is 4D tensor [observation, time_step, channel, 1] for use with Keras\n",
    "    # y_all: movement label, length: number of windows\n",
    "    # r_all: repetition label, length: number of windows\n",
    "    x_all, y_all, r_all = get_windows(reps, window_len, window_inc,\n",
    "                                      emg_data, data_dict['move'],\n",
    "                                      data_dict['rep'],which_moves=moves)\n",
    "\n",
    "    test_idx = get_idxs(r_all, test_reps[0, :])\n",
    "    test_data = x_all[test_idx, :, :, :]\n",
    "\n",
    "#     one_hot_categorical = to_categorical(y_all)\n",
    "    one_hot_categorical = one_hot(moves,y_all)   # Uncomment this for all except Rest Dataprep\n",
    "    \n",
    "    return x_all, y_all, r_all,one_hot_categorical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(69696, 150, 12, 1)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# moves = np.array([9,10,11,12,13,14,15,16,17])\n",
    "moves = None\n",
    "x_all, y_all, r_all,one_hot_categorical = return_singleSubj_prep_data(2,moves,150,75)\n",
    "x_all.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(69696, 50)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "one_hot_categorical.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def return_prep_data(subject_list,moves,window_len,window_inc):\n",
    "    X,Y,R,Y_hot = return_singleSubj_prep_data(subject_list[0],moves,window_len,window_inc)\n",
    "    for subject in tqdm(subject_list[1:]):\n",
    "        x_all, y_all, r_all,one_hot_categorical = return_singleSubj_prep_data(subject,moves,window_len,window_inc)\n",
    "        X = np.vstack([X,x_all])\n",
    "        Y = np.hstack([Y,y_all])\n",
    "        R = np.hstack([R,r_all])\n",
    "        Y_hot = np.vstack([Y_hot,one_hot_categorical])\n",
    "    return X,Y,R,Y_hot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Wrist Motion Dataprep {All 9 Class}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# subject_list = list(range(1,11))\n",
    "# subject_list = list(range(1,31))\n",
    "subject_list = list(range(1,41))\n",
    "moves = np.array([9,10,11,12,13,14,15,16,17])\n",
    "window_len = 250\n",
    "window_inc = 50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "wrist_moves = ['Suplination - CW (rot axis Mid finger)',\n",
    "               'Pronation - CCW (rot axis Mid finger)',\n",
    "               'Suplination - CW (rot axis lit finger)',\n",
    "               'Pronation - CCW (rot axis lit finger)',\n",
    "               'Flexion - Bend Towards',' Extension - Bend Backward',\n",
    "               'Radial - Left - CCW',\n",
    "               'Radial - Righ - CW',\n",
    "               'Extension with closed hand']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "80df9632009045758eb4349a86d77b44",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=39.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(370616, 250, 12, 1)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X,Y,R,Y_hot = return_prep_data(subject_list,moves,window_len,window_inc)\n",
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save('prepared_data/norest_X_all_wrist_'+str(window_len)+'_'+str(len(subject_list)),X)\n",
    "np.save('prepared_data/norest_Y_all_wrist_'+str(window_len)+'_'+str(len(subject_list)),Y)\n",
    "np.save('prepared_data/norest_R_all_wrist_'+str(window_len)+'_'+str(len(subject_list)),R)\n",
    "np.save('prepared_data/norest_Y_hot_all_wrist_'+str(window_len)+'_'+str(len(subject_list)),Y_hot)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "File Coding - X_all_motion_windowLen_no.subjects"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Grasping Motion Dataprep {Rest,All Grasp} Converted to Binary{Rest/Grasp}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# subject_list = list(range(1,31))\n",
    "subject_list = list(range(1,41))\n",
    "moves = np.array([0,27,28,29,33,35,37,38])\n",
    "window_len = 250\n",
    "window_inc = 50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c44820ec1a484927b6e108f7c531e8ed",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=39.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(2157484, 250, 12, 1)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_grasp,Y_grasp,R_grasp,Y_hot_grasp = return_prep_data(subject_list,moves,window_len,window_inc)\n",
    "X_grasp.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save('prepared_data/X_all_grasp_'+str(window_len)+'_'+str(len(subject_list)),X_grasp)\n",
    "np.save('prepared_data/Y_all_grasp_'+str(window_len)+'_'+str(len(subject_list)),Y_grasp)\n",
    "np.save('prepared_data/R_all_grasp_'+str(window_len)+'_'+str(len(subject_list)),R_grasp)\n",
    "np.save('prepared_data/Y_hot_all_grasp_'+str(window_len)+'_'+str(len(subject_list)),Y_hot_grasp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pronation/Supplination Dataprep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "250 1\n"
     ]
    }
   ],
   "source": [
    "# subject_list = list(range(1,31))\n",
    "subject_list = list(range(1,41))\n",
    "moves = np.array([0,9,10,11,12])\n",
    "window_len = 250\n",
    "window_inc = 1\n",
    "print(window_len,window_inc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = ['Suplination - CW (rot axis Mid finger)',\n",
    "           'Pronation - CCW (rot axis Mid finger)',\n",
    "           'Suplination - CW (rot axis lit finger)',\n",
    "           'Pronation - CCW (rot axis lit finger)']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ERROR:root:Internal Python error in the inspect module.\n",
      "Below is the traceback from this internal error.\n",
      "\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "    \u001b[1;31m[... skipping hidden 1 frame]\u001b[0m\n",
      "\u001b[1;32m<ipython-input-12-aaf83d2da36f>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mX_ProSup\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mY_ProSup\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mR_ProSup\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mY_hot_ProSup\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mreturn_prep_data\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msubject_list\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mmoves\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mwindow_len\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mwindow_inc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mX_ProSup\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-7-fde3cd0b9ce1>\u001b[0m in \u001b[0;36mreturn_prep_data\u001b[1;34m(subject_list, moves, window_len, window_inc)\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mreturn_prep_data\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msubject_list\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mmoves\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mwindow_len\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mwindow_inc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m     \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mY\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mR\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mY_hot\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mreturn_singleSubj_prep_data\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msubject_list\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mmoves\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mwindow_len\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mwindow_inc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m     \u001b[1;32mfor\u001b[0m \u001b[0msubject\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mtqdm\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msubject_list\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-6-f78fb20ebd46>\u001b[0m in \u001b[0;36mreturn_singleSubj_prep_data\u001b[1;34m(subject, moves, window_len, window_inc)\u001b[0m\n\u001b[0;32m     32\u001b[0m     \u001b[1;31m# r_all: repetition label, length: number of windows\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 33\u001b[1;33m     x_all, y_all, r_all = get_windows(reps, window_len, window_inc,\n\u001b[0m\u001b[0;32m     34\u001b[0m                                       \u001b[0memg_data\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata_dict\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'move'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\nina_helper\\nina_helper.py\u001b[0m in \u001b[0;36mget_windows\u001b[1;34m(which_reps, window_len, window_inc, emg, movements, repetitons, which_moves, dtype)\u001b[0m\n\u001b[0;32m    680\u001b[0m         \u001b[0mwin_start\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mwin_end\u001b[0m \u001b[1;33m-\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mwindow_len\u001b[0m \u001b[1;33m-\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 681\u001b[1;33m         \u001b[0mX_data\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0memg\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mwin_start\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0mwin_end\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m  \u001b[1;31m# Include end\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    682\u001b[0m         \u001b[0mY_data\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmovements\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mwin_end\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: ",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\IPython\\core\\interactiveshell.py\u001b[0m in \u001b[0;36mshowtraceback\u001b[1;34m(self, exc_tuple, filename, tb_offset, exception_only, running_compiled_code)\u001b[0m\n\u001b[0;32m   2043\u001b[0m                         \u001b[1;31m# in the engines. This should return a list of strings.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2044\u001b[1;33m                         \u001b[0mstb\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_render_traceback_\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2045\u001b[0m                     \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'KeyboardInterrupt' object has no attribute '_render_traceback_'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\IPython\\core\\ultratb.py\u001b[0m in \u001b[0;36mget_records\u001b[1;34m(self, etb, number_of_lines_of_context, tb_offset)\u001b[0m\n\u001b[0;32m   1168\u001b[0m             \u001b[1;31m# (5 blanks lines) where none should be returned.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1169\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0m_fixed_getinnerframes\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0metb\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnumber_of_lines_of_context\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtb_offset\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1170\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mUnicodeDecodeError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\IPython\\core\\ultratb.py\u001b[0m in \u001b[0;36mwrapped\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    315\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 316\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    317\u001b[0m         \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\IPython\\core\\ultratb.py\u001b[0m in \u001b[0;36m_fixed_getinnerframes\u001b[1;34m(etb, context, tb_offset)\u001b[0m\n\u001b[0;32m    349\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 350\u001b[1;33m     \u001b[0mrecords\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfix_frame_records_filenames\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minspect\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetinnerframes\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0metb\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    351\u001b[0m     \u001b[1;31m# If the error is at the console, don't build any context, since it would\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python38\\lib\\inspect.py\u001b[0m in \u001b[0;36mgetinnerframes\u001b[1;34m(tb, context)\u001b[0m\n\u001b[0;32m   1502\u001b[0m     \u001b[1;32mwhile\u001b[0m \u001b[0mtb\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1503\u001b[1;33m         \u001b[0mframeinfo\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mtb\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtb_frame\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mgetframeinfo\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtb\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1504\u001b[0m         \u001b[0mframelist\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mFrameInfo\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mframeinfo\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python38\\lib\\inspect.py\u001b[0m in \u001b[0;36mgetframeinfo\u001b[1;34m(frame, context)\u001b[0m\n\u001b[0;32m   1460\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1461\u001b[1;33m     \u001b[0mfilename\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetsourcefile\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mframe\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mgetfile\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mframe\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1462\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mcontext\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python38\\lib\\inspect.py\u001b[0m in \u001b[0;36mgetsourcefile\u001b[1;34m(object)\u001b[0m\n\u001b[0;32m    707\u001b[0m     \u001b[1;31m# only return a non-existent filename if the module has a PEP 302 loader\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 708\u001b[1;33m     \u001b[1;32mif\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgetmodule\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobject\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfilename\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'__loader__'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    709\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mfilename\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python38\\lib\\inspect.py\u001b[0m in \u001b[0;36mgetmodule\u001b[1;34m(object, _filename)\u001b[0m\n\u001b[0;32m    753\u001b[0m             modulesbyfile[f] = modulesbyfile[\n\u001b[1;32m--> 754\u001b[1;33m                 os.path.realpath(f)] = module.__name__\n\u001b[0m\u001b[0;32m    755\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mfile\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mmodulesbyfile\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python38\\lib\\ntpath.py\u001b[0m in \u001b[0;36mrealpath\u001b[1;34m(path)\u001b[0m\n\u001b[0;32m    646\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 647\u001b[1;33m             \u001b[0mpath\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_getfinalpathname\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    648\u001b[0m             \u001b[0minitial_winerror\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: ",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\IPython\\core\\interactiveshell.py\u001b[0m in \u001b[0;36mshowtraceback\u001b[1;34m(self, exc_tuple, filename, tb_offset, exception_only, running_compiled_code)\u001b[0m\n\u001b[0;32m   2045\u001b[0m                     \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2046\u001b[1;33m                         stb = self.InteractiveTB.structured_traceback(etype,\n\u001b[0m\u001b[0;32m   2047\u001b[0m                                             value, tb, tb_offset=tb_offset)\n",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\IPython\\core\\ultratb.py\u001b[0m in \u001b[0;36mstructured_traceback\u001b[1;34m(self, etype, value, tb, tb_offset, number_of_lines_of_context)\u001b[0m\n\u001b[0;32m   1434\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtb\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtb\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1435\u001b[1;33m         return FormattedTB.structured_traceback(\n\u001b[0m\u001b[0;32m   1436\u001b[0m             self, etype, value, tb, tb_offset, number_of_lines_of_context)\n",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\IPython\\core\\ultratb.py\u001b[0m in \u001b[0;36mstructured_traceback\u001b[1;34m(self, etype, value, tb, tb_offset, number_of_lines_of_context)\u001b[0m\n\u001b[0;32m   1334\u001b[0m             \u001b[1;31m# Verbose modes need a full traceback\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1335\u001b[1;33m             return VerboseTB.structured_traceback(\n\u001b[0m\u001b[0;32m   1336\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0metype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtb\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtb_offset\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnumber_of_lines_of_context\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\IPython\\core\\ultratb.py\u001b[0m in \u001b[0;36mstructured_traceback\u001b[1;34m(self, etype, evalue, etb, tb_offset, number_of_lines_of_context)\u001b[0m\n\u001b[0;32m   1191\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1192\u001b[1;33m         formatted_exception = self.format_exception_as_a_whole(etype, evalue, etb, number_of_lines_of_context,\n\u001b[0m\u001b[0;32m   1193\u001b[0m                                                                tb_offset)\n",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\IPython\\core\\ultratb.py\u001b[0m in \u001b[0;36mformat_exception_as_a_whole\u001b[1;34m(self, etype, evalue, etb, number_of_lines_of_context, tb_offset)\u001b[0m\n\u001b[0;32m   1146\u001b[0m         \u001b[0mhead\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprepare_header\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0metype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlong_header\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1147\u001b[1;33m         \u001b[0mrecords\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_records\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0metb\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnumber_of_lines_of_context\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtb_offset\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1148\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\IPython\\core\\ultratb.py\u001b[0m in \u001b[0;36mget_records\u001b[1;34m(self, etb, number_of_lines_of_context, tb_offset)\u001b[0m\n\u001b[0;32m   1183\u001b[0m             \u001b[0minspect_error\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1184\u001b[1;33m             \u001b[0mtraceback\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_exc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfile\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mostream\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1185\u001b[0m             \u001b[0minfo\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'\\nUnfortunately, your original traceback can not be constructed.\\n'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python38\\lib\\traceback.py\u001b[0m in \u001b[0;36mprint_exc\u001b[1;34m(limit, file, chain)\u001b[0m\n\u001b[0;32m    162\u001b[0m     \u001b[1;34m\"\"\"Shorthand for 'print_exception(*sys.exc_info(), limit, file)'.\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 163\u001b[1;33m     \u001b[0mprint_exception\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0msys\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexc_info\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlimit\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlimit\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfile\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfile\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mchain\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mchain\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    164\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python38\\lib\\traceback.py\u001b[0m in \u001b[0;36mprint_exception\u001b[1;34m(etype, value, tb, limit, file, chain)\u001b[0m\n\u001b[0;32m    102\u001b[0m         \u001b[0mfile\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msys\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstderr\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 103\u001b[1;33m     for line in TracebackException(\n\u001b[0m\u001b[0;32m    104\u001b[0m             type(value), value, tb, limit=limit).format(chain=chain):\n",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python38\\lib\\traceback.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, exc_type, exc_value, exc_traceback, limit, lookup_lines, capture_locals, _seen)\u001b[0m\n\u001b[0;32m    508\u001b[0m         \u001b[1;31m# TODO: locals.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 509\u001b[1;33m         self.stack = StackSummary.extract(\n\u001b[0m\u001b[0;32m    510\u001b[0m             \u001b[0mwalk_tb\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mexc_traceback\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlimit\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlimit\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlookup_lines\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlookup_lines\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python38\\lib\\traceback.py\u001b[0m in \u001b[0;36mextract\u001b[1;34m(klass, frame_gen, limit, lookup_lines, capture_locals)\u001b[0m\n\u001b[0;32m    365\u001b[0m             \u001b[1;32mfor\u001b[0m \u001b[0mf\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 366\u001b[1;33m                 \u001b[0mf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mline\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    367\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python38\\lib\\traceback.py\u001b[0m in \u001b[0;36mline\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    287\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_line\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 288\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_line\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlinecache\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetline\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfilename\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlineno\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstrip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    289\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_line\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python38\\lib\\linecache.py\u001b[0m in \u001b[0;36mgetline\u001b[1;34m(filename, lineno, module_globals)\u001b[0m\n\u001b[0;32m     15\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mgetline\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlineno\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmodule_globals\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 16\u001b[1;33m     \u001b[0mlines\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetlines\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmodule_globals\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     17\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;36m1\u001b[0m \u001b[1;33m<=\u001b[0m \u001b[0mlineno\u001b[0m \u001b[1;33m<=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlines\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python38\\lib\\linecache.py\u001b[0m in \u001b[0;36mgetlines\u001b[1;34m(filename, module_globals)\u001b[0m\n\u001b[0;32m     46\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 47\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mupdatecache\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmodule_globals\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     48\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[0mMemoryError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python38\\lib\\linecache.py\u001b[0m in \u001b[0;36mupdatecache\u001b[1;34m(filename, module_globals)\u001b[0m\n\u001b[0;32m    135\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 136\u001b[1;33m         \u001b[1;32mwith\u001b[0m \u001b[0mtokenize\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfullname\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mfp\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    137\u001b[0m             \u001b[0mlines\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreadlines\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python38\\lib\\tokenize.py\u001b[0m in \u001b[0;36mopen\u001b[1;34m(filename)\u001b[0m\n\u001b[0;32m    391\u001b[0m     \"\"\"\n\u001b[1;32m--> 392\u001b[1;33m     \u001b[0mbuffer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_builtin_open\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'rb'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    393\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: ",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "    \u001b[1;31m[... skipping hidden 1 frame]\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\IPython\\core\\interactiveshell.py\u001b[0m in \u001b[0;36mshowtraceback\u001b[1;34m(self, exc_tuple, filename, tb_offset, exception_only, running_compiled_code)\u001b[0m\n\u001b[0;32m   2057\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2058\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mKeyboardInterrupt\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2059\u001b[1;33m             \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'\\n'\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_exception_only\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfile\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msys\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstderr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2060\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2061\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_showtraceback\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0metype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mevalue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstb\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\IPython\\core\\interactiveshell.py\u001b[0m in \u001b[0;36mget_exception_only\u001b[1;34m(self, exc_tuple)\u001b[0m\n\u001b[0;32m   2002\u001b[0m         \"\"\"\n\u001b[0;32m   2003\u001b[0m         \u001b[0metype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtb\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_exc_info\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mexc_tuple\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2004\u001b[1;33m         \u001b[0mmsg\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtraceback\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat_exception_only\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0metype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2005\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[1;34m''\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2006\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python38\\lib\\traceback.py\u001b[0m in \u001b[0;36mformat_exception_only\u001b[1;34m(etype, value)\u001b[0m\n\u001b[0;32m    138\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    139\u001b[0m     \"\"\"\n\u001b[1;32m--> 140\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mTracebackException\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0metype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat_exception_only\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    141\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    142\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python38\\lib\\traceback.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, exc_type, exc_value, exc_traceback, limit, lookup_lines, capture_locals, _seen)\u001b[0m\n\u001b[0;32m    522\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmsg\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mexc_value\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmsg\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    523\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mlookup_lines\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 524\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_load_lines\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    525\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    526\u001b[0m     \u001b[1;33m@\u001b[0m\u001b[0mclassmethod\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python38\\lib\\traceback.py\u001b[0m in \u001b[0;36m_load_lines\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    534\u001b[0m             \u001b[0mframe\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mline\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    535\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__context__\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 536\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__context__\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_load_lines\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    537\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__cause__\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    538\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__cause__\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_load_lines\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python38\\lib\\traceback.py\u001b[0m in \u001b[0;36m_load_lines\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    532\u001b[0m         \u001b[1;34m\"\"\"Private API. force all lines in the stack to be loaded.\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    533\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mframe\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstack\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 534\u001b[1;33m             \u001b[0mframe\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mline\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    535\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__context__\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    536\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__context__\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_load_lines\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python38\\lib\\traceback.py\u001b[0m in \u001b[0;36mline\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    286\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mline\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    287\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_line\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 288\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_line\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlinecache\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetline\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfilename\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlineno\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstrip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    289\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_line\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    290\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python38\\lib\\linecache.py\u001b[0m in \u001b[0;36mgetline\u001b[1;34m(filename, lineno, module_globals)\u001b[0m\n\u001b[0;32m     14\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     15\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mgetline\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlineno\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmodule_globals\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 16\u001b[1;33m     \u001b[0mlines\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetlines\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmodule_globals\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     17\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;36m1\u001b[0m \u001b[1;33m<=\u001b[0m \u001b[0mlineno\u001b[0m \u001b[1;33m<=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlines\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     18\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mlines\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mlineno\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python38\\lib\\linecache.py\u001b[0m in \u001b[0;36mgetlines\u001b[1;34m(filename, module_globals)\u001b[0m\n\u001b[0;32m     45\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     46\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 47\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mupdatecache\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmodule_globals\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     48\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[0mMemoryError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     49\u001b[0m         \u001b[0mclearcache\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python38\\lib\\linecache.py\u001b[0m in \u001b[0;36mupdatecache\u001b[1;34m(filename, module_globals)\u001b[0m\n\u001b[0;32m    134\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    135\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 136\u001b[1;33m         \u001b[1;32mwith\u001b[0m \u001b[0mtokenize\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfullname\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mfp\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    137\u001b[0m             \u001b[0mlines\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreadlines\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    138\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[0mOSError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python38\\lib\\tokenize.py\u001b[0m in \u001b[0;36mopen\u001b[1;34m(filename)\u001b[0m\n\u001b[0;32m    390\u001b[0m     \u001b[0mdetect_encoding\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    391\u001b[0m     \"\"\"\n\u001b[1;32m--> 392\u001b[1;33m     \u001b[0mbuffer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_builtin_open\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'rb'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    393\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    394\u001b[0m         \u001b[0mencoding\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlines\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdetect_encoding\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbuffer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreadline\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "X_ProSup,Y_ProSup,R_ProSup,Y_hot_ProSup = return_prep_data(subject_list,moves,window_len,window_inc)\n",
    "X_ProSup.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save('prepared_data/X_all_ProSup_'+str(window_len)+'_'+str(len(subject_list)),X_ProSup)\n",
    "np.save('prepared_data/Y_all_ProSup_'+str(window_len)+'_'+str(len(subject_list)),Y_ProSup)\n",
    "np.save('prepared_data/R_all_ProSup_'+str(window_len)+'_'+str(len(subject_list)),R_ProSup)\n",
    "np.save('prepared_data/Y_hot_all_ProSup_'+str(window_len)+'_'+str(len(subject_list)),Y_hot_ProSup)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Radial/Ulnar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# subject_list = list(range(1,31))\n",
    "subject_list = list(range(1,41))\n",
    "moves = np.array([15,16])\n",
    "\n",
    "window_len = 250\n",
    "window_inc = 50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = ['Radial - CCW','Ulnar - CW',]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8f6d5198794a462db1331f5ab38c248f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=39.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(83114, 250, 12, 1)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_radial,Y_radial,R_radial,Y_hot_radial = return_prep_data(subject_list,moves,window_len,window_inc)\n",
    "X_radial.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save('prepared_data/X_all_Radial_'+str(window_len)+'_'+str(len(subject_list)),X_radial)\n",
    "np.save('prepared_data/Y_all_Radial_'+str(window_len)+'_'+str(len(subject_list)),Y_radial)\n",
    "np.save('prepared_data/R_all_Radial_'+str(window_len)+'_'+str(len(subject_list)),R_radial)\n",
    "np.save('prepared_data/Y_hot_all_Radial_'+str(window_len)+'_'+str(len(subject_list)),Y_hot_radial)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Flexion/Extension Dataprep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# subject_list = list(range(1,31))\n",
    "subject_list = list(range(1,41))\n",
    "moves = np.array([13,14])\n",
    "\n",
    "window_len = 250\n",
    "window_inc = 50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = ['Flexion - Bend Towards',\n",
    "          'Extension - Bend Backward']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e18a5ffa7aea4eaf8eee481145710e3b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=39.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(67284, 250, 12, 1)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_FlexEx,Y_FlexEx,R_FlexEx,Y_hot_FlexEx = return_prep_data(subject_list,moves,window_len,window_inc)\n",
    "X_FlexEx.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save('prepared_data/X_all_FlexEx_'+str(window_len)+'_'+str(len(subject_list)),X_FlexEx)\n",
    "np.save('prepared_data/Y_all_FlexEx_'+str(window_len)+'_'+str(len(subject_list)),Y_FlexEx)\n",
    "np.save('prepared_data/R_all_FlexEx_'+str(window_len)+'_'+str(len(subject_list)),R_FlexEx)\n",
    "np.save('prepared_data/Y_hot_all_FlexEx_'+str(window_len)+'_'+str(len(subject_list)),Y_hot_FlexEx)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Rest/Motion Dataprep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "subject_list = list(range(1,21))\n",
    "# subject_list = list(range(1,41))\n",
    "moves = None\n",
    "\n",
    "window_len = 250\n",
    "window_inc = 50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = ['Rest',\n",
    "          'Motion']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ec7ac51d31854603bb9cc65e2da52f24",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=19.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(2088042, 250, 12, 1)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_rest,Y_rest,R_rest,Y_hot_rest = return_prep_data(subject_list,moves,window_len,window_inc)\n",
    "X_rest.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save('prepared_data/X_all_rest_'+str(window_len)+'_'+str(len(subject_list)),X_rest)\n",
    "np.save('prepared_data/Y_all_rest_'+str(window_len)+'_'+str(len(subject_list)),Y_rest)\n",
    "np.save('prepared_data/R_all_rest_'+str(window_len)+'_'+str(len(subject_list)),R_rest)\n",
    "np.save('prepared_data/Y_hot_all_rest_'+str(window_len)+'_'+str(len(subject_list)),Y_hot_rest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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