iui-group-l-name-zensiert/1-first-project/ies/Tensor_v2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3168ef63",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "from math import isqrt\n",
    "import pickle\n",
    "from tqdm import tqdm\n",
    "import os\n",
    "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import LabelEncoder, LabelBinarizer\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, Flatten, BatchNormalization\n",
    "\n",
    "os.environ['TF_FORCE_GPU_ALLOW_GROWTH'] = 'true'\n",
    "os.environ['CUDA_VISIBLE_DEVICES'] = '2'\n",
    "\n",
    "delim = ';'\n",
    "user_count = 100\n",
    "base_path = '/opt/iui-datarelease2-sose2021/'\n",
    "Xpickle_file = './X2.pickle'\n",
    "ypickle_file = './y2.pickle'\n",
    "xshorted_pickle_file = './X2_shorted.pickle'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ce4d9517",
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_pickles():\n",
    "    _p = open(Xpickle_file, 'rb')\n",
    "    X = pickle.load(_p)\n",
    "    _p.close()\n",
    "        \n",
    "    _p = open(ypickle_file, 'rb')\n",
    "    y = pickle.load(_p)\n",
    "    _p.close()\n",
    "    \n",
    "    return (np.asarray(X, dtype=pd.DataFrame), np.asarray(y, dtype=str))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5ed60433",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def load_data():\n",
    "    if os.path.isfile(Xpickle_file) and os.path.isfile(ypickle_file):\n",
    "        return load_pickles()\n",
    "    data = []\n",
    "    label = []\n",
    "    for user in range(0, user_count):\n",
    "        user_path = base_path + str(user) + '/split_letters_csv/'\n",
    "        for file in os.listdir(user_path):\n",
    "            file_name = user_path + file\n",
    "            letter = ''.join(filter(lambda x: x.isalpha(), file))[0]\n",
    "            data.append(pd.read_csv(file_name, delim))\n",
    "            label.append(letter)\n",
    "    return (np.asarray(data, dtype=pd.DataFrame), np.asarray(label, dtype=str))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9fefd9fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "def shorten_pickle(l):\n",
    "    \n",
    "    temp        = l\n",
    "    thresh      = 80\n",
    "    temp_over_T = 0\n",
    "    isOver      = False\n",
    "    temp_short  = [] ##Daten nachdem vorne abgeschnitten wird\n",
    "    temp_final  = [] ##Daten nachdem auch hinten abgeschnitten wurde\n",
    "    \n",
    "    temp_X       = [] ## Zweite Dimension von Temp, beinhaltet force \n",
    "    temp_short_X = []\n",
    "    \n",
    "    for a in tqdm(range (0, len(temp))): \n",
    "         temp_X = temp[a] \n",
    "                \n",
    "    temp_X      = l['Force']\n",
    "    thresh      = 80\n",
    "    temp_over_T = 0\n",
    "  \n",
    "    temp_short  = [] ##Daten nachdem vorne abgeschnitten wird\n",
    "    temp_final  = [] ##Daten nachdem auch hinten abgeschnitten wurde\n",
    "    temp_short_X = []\n",
    "    \n",
    "   \n",
    "        \n",
    "    for b in range (0, len(temp_X)):    \n",
    "        if(temp_X[b]>thresh):\n",
    "            temp_over_T = b\n",
    "            break\n",
    "               \n",
    "    for x in range (temp_over_T,len(temp_X)):\n",
    "        temp_short_X.append(temp_X[x]) ##hier werden die Daten von y appended in eine liste ( Von form data[x][y])\n",
    "            \n",
    "      \n",
    "    for y in range ((len(temp_short_X)-1),0, -1):     \n",
    "         if(temp_short_X[y] > thresh):\n",
    "            temp_over_T  = y\n",
    "            break\n",
    "        \n",
    "    for z in range(0, temp_over_T+1):\n",
    "        temp_short.append(temp_short_X[z]) ##hier wird [y] als einzelne Datei gespeichert\n",
    "        \n",
    "    temp_short.append(0) # Damit beim Plot die linie bis nach unten geht\n",
    "        \n",
    "        \n",
    "    temp_final.append(temp_short)\n",
    "    \n",
    "    return temp_final\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b6c30ab7",
   "metadata": {},
   "outputs": [],
   "source": [
    "def shorten_pickle_v2(npList):\n",
    "    temp        = npList['Force']\n",
    "    \n",
    "    print (\"I was here\")\n",
    "    \n",
    "    thresh      = 80\n",
    "    leeway      = 1\n",
    "    \n",
    "    temps_over_T = np.where(temp > thresh)[0]\n",
    "    print(\"I Was here too\")\n",
    "    return npList[max(temps_over_T[0],0):temps_over_T[-1]]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "20cb0504",
   "metadata": {},
   "outputs": [],
   "source": [
    "def shorten_v2(l):\n",
    "    \n",
    "    \n",
    "    temp_X       = l['Force']\n",
    "    thresh       = 100\n",
    "    temp_over_T  = 0\n",
    "    maxVal       = 0\n",
    "    temp_short   = [] ##Daten nachdem vorne abgeschnitten wird\n",
    "    temp_final   = [] ##Daten nachdem auch hinten abgeschnitten wurde\n",
    "    temp_short_X = []\n",
    "    \n",
    "    \n",
    "    for a in range (0, len(temp_X) ):\n",
    "        if(temp_X[a]>maxVal):\n",
    "            maxVal = temp_X[a]\n",
    "            \n",
    "\n",
    "    threshVal = maxVal*0.05\n",
    "    \n",
    "    \n",
    "        \n",
    "    for b in range (0, len(temp_X)):    \n",
    "        if(temp_X[b]>threshVal):\n",
    "            temp_over_T = b\n",
    "            break\n",
    "               \n",
    "    for x in range (temp_over_T,len(temp_X)):\n",
    "        temp_short_X.append(temp_X[x]) ##hier werden die Daten von y appended in eine liste ( Von form data[x][y])\n",
    "            \n",
    "      \n",
    "    for y in range ((len(temp_short_X)-1),0, -1):     \n",
    "         if(temp_short_X[y] > threshVal):\n",
    "            temp_over_T  = y\n",
    "            break\n",
    "        \n",
    "    for z in range(0, temp_over_T+1):\n",
    "        temp_short.append(temp_short_X[z]) ##hier wird [y] als einzelne Datei gespeichert\n",
    "        \n",
    "    temp_short.append(0) # Damit beim Plot die linie bis nach unten geht\n",
    "        \n",
    "        \n",
    "    temp_final.append(temp_short)\n",
    "    \n",
    "    return temp_final\n",
    "    \n",
    "   \n",
    "   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ec39550d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def save_pickle():\n",
    "#     _p = open(np.asarray(data, dtype=pd.DataFrame), 'wb')\n",
    "    _p = open(Xpickle_file, 'wb')\n",
    "    pickle.dump(x, _p)\n",
    "    _p.close()\n",
    "\n",
    "#     _p = open(np.asarray(label, dtype=str), 'wb')\n",
    "    _p = open(ypickle_file, 'wb')\n",
    "    pickle.dump(y, _p)\n",
    "    _p.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "63509028",
   "metadata": {},
   "outputs": [],
   "source": [
    "## save_pickle() pickles sind erstellt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f8dbeac5",
   "metadata": {},
   "outputs": [],
   "source": [
    "x,y = load_pickles()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b479a77c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "26179\n"
     ]
    }
   ],
   "source": [
    "print (len(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3c7919a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = list(map(shorten_v2, x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "9592e2ca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[108.0, 362.0, 683.0, 994.0, 1306.0, 1380.0, 1526.0, 1724.0, 1895.0, 1985.0, 2077.0, 2040.0, 1997.0, 1917.0, 1623.0, 1357.0, 1087.0, 785.0, 450.0, 146.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 67.0, 615.0, 927.0, 1212.0, 1450.0, 1483.0, 1546.0, 1634.0, 1617.0, 1691.0, 1654.0, 1663.0, 1618.0, 1550.0, 1519.0, 1519.0, 1742.0, 1892.0, 1966.0, 1966.0, 2092.0, 2040.0, 2048.0, 1997.0, 1953.0, 1946.0, 1926.0, 1791.0, 1497.0, 1112.0, 678.0, 279.0, 0]]\n"
     ]
    }
   ],
   "source": [
    "print(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "7f294d3c",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "setting an array element with a sequence.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;31mTypeError\u001b[0m: float() argument must be a string or a number, not 'list'",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-19-b8705bdaeaac>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdataNP\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mdataNP\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: setting an array element with a sequence."
     ]
    }
   ],
   "source": [
    "dataNP = np.array(data, dtype=float)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "fda2e601",
   "metadata": {},
   "outputs": [
    {
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       "   0]],\n",
       " ...]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#from tensorflow.keras.layers import Conv2D, MaxPooling2D für cnn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d1125102",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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