{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "42f984d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import os\n",
    "# os.environ['TF_FORCE_GPU_ALLOW_GROWTH'] = 'true'\n",
    "# os.environ['CUDA_VISIBLE_DEVICES'] = '2'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ec63028f",
   "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",
    "delim = ';'\n",
    "\n",
    "user_count = 100\n",
    "base_path = '/opt/iui-datarelease1-sose2021/'\n",
    "Xpickle_file = './X.pickle'\n",
    "ypickle_file = './y.pickle'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "dc9ddde3",
   "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": 4,
   "id": "e99c1e58",
   "metadata": {},
   "outputs": [],
   "source": [
    "x,y = load_pickles()\n",
    "xshorted_pickle_file = './X_shorted.pickle'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7138aeeb",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# yshorted_pickle_file = \"./y_shorted.pickle\"  wird glaube ich nicht benötigt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "abba0b88",
   "metadata": {},
   "outputs": [],
   "source": [
    "def shorten(l):\n",
    "    temp        = l\n",
    "    thresh      = 100\n",
    "    temp_over_T = 0\n",
    "    isOver      = False\n",
    "    temp_short  = []\n",
    "    temp_final  = []\n",
    "    \n",
    "    for a in range (0, len(temp) ):\n",
    "        if(temp[a]>thresh):\n",
    "            temp_over_T = a\n",
    "            isOver      = True\n",
    "            break\n",
    "    \n",
    "    for x in range(temp_over_T-3, len(temp)):\n",
    "        temp_short.append(f[x])\n",
    "        \n",
    "    isOver = False\n",
    "        \n",
    "    for y in range ((len(temp_short)-1),0, -1):     \n",
    "        if(temp_short[y] > thresh):\n",
    "            temp_over_T  = y\n",
    "            isOver       = True\n",
    "            break\n",
    "        \n",
    "    for z in range(0, temp_over_T+1):\n",
    "        temp_final.append(temp_short[z])\n",
    "    \n",
    "    temp_final.append(0)\n",
    "    return temp_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d06fb083",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "557f0030",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def save_shorted(l):\n",
    "    _p = open(xshorted_pickle_file, 'wb')\n",
    "    pickle.dump(l, _p)\n",
    "    _p.close()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7dcb89ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "def shorten_pickle(l):\n",
    "    temp        = l\n",
    "    thresh      = 100\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",
    "        \n",
    "#         for b in range (0, len(temp_X)):    \n",
    "#             if(temp_X[b]>thresh):\n",
    "#                 temp_over_T = b\n",
    "#                 isOver      = True ##gucken ob löschen\n",
    "#                 break\n",
    "            \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",
    "#         isOver = False\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",
    "#                 isOver       = True\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",
    "#    temp_final =  shorten_v2()\n",
    "   \n",
    "    \n",
    "    \n",
    "    return temp_final\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "537d751b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# shorted_f = shorten_pickle(f_data)\n",
    "    \n",
    "# print(shorted_f[0])\n",
    "# print (\"Saving now...\")\n",
    "# save_shorted(shorted_f)\n",
    "\n",
    "# print (\"Saving complete\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a23e6a6a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_shorted():\n",
    "    _p = open(xshorted_pickle_file, 'rb')\n",
    "    x = pickle.load(_p)\n",
    "    _p.close()\n",
    "        \n",
    "   \n",
    "    print(\"done\")\n",
    "    return (np.asarray(x, dtype=pd.DataFrame))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8b41532e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# %%time\n",
    "# frame = load_shorted()\n",
    "# print (\"returned\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4db9e8f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "def shorten_v2(l):\n",
    "    \n",
    "    \n",
    "    temp_X      = l['Force']\n",
    "    thresh      = 100\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": 13,
   "id": "99c61605",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = list(map(shorten_v2, x))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "bd7aff75",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[list([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",
      "----------------------\n",
      "[list([656.0, 729.0, 1036.0, 1253.0, 1465.0, 1662.0, 1707.0, 1730.0, 1897.0, 1890.0, 1885.0, 1856.0, 1833.0, 1789.0, 1746.0, 1752.0, 1766.0, 1799.0, 1780.0, 1759.0, 1743.0, 1732.0, 1735.0, 1824.0, 1788.0, 1737.0, 1637.0, 1547.0, 1443.0, 1199.0, 862.0, 400.0, 127.0, 0])]\n"
     ]
    }
   ],
   "source": [
    "data = np.asarray(data)\n",
    "#data.shape\n",
    "print(data[0])\n",
    "print (\"----------------------\")\n",
    "print(data[len(data)-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f57350ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13102, 1)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "78157f79",
   "metadata": {},
   "outputs": [],
   "source": [
    "save_shorted(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ec8f2fea",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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