iui-group-l-name-zensiert/1-first-project/jw/j_Data_Norm_wth_SW.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b5fd075a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Needed Imports\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "import os\n",
    "import pickle\n",
    "import matplotlib.pyplot as plt\n",
    "from math import isqrt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "805e21e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "os.environ['TF_FORCE_GPU_ALLOW_GROWTH'] = 'true'  # this is required\n",
    "os.environ['CUDA_VISIBLE_DEVICES'] = '2'          # set to '0' for GPU0, '1' for GPU1 or '2' for GPU2. Check \"gpustat\" in a terminal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "52b164a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "delim = ';'\n",
    "user_count = 100\n",
    "base_path = '/opt/iui-datarelease1-sose2021/'\n",
    "Xpickle_file = './X.pickle'\n",
    "ypickle_file = './y.pickle'\n",
    "\n",
    "# Function that opens and reads pickle Data from FS and returns the read data as NumpyArray\n",
    "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": 6,
   "id": "2b75bbc1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function used to save data as a pickle file\n",
    "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": 7,
   "id": "03037493",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function that loads data from the picklefiles and prints them into NumpyArrays (one for Data and one for Lables)\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))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b91b4622",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13102, 13102)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load Data\n",
    "X, y = load_data()\n",
    "len(X), len(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "817f4cef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(13102,)\n",
      "(13102,)\n"
     ]
    }
   ],
   "source": [
    "# Show Data Shape\n",
    "print(X.shape)\n",
    "print(y.shape)        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3c11cf82",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Show how many datasets are make how many percent  \n",
    "X_len = np.asarray(list(map(len, X)))\n",
    "l = []\n",
    "sq_xlen = pd.Series(X_len)\n",
    "ptiles = [x*0.01 for x in range(100)]\n",
    "for i in ptiles:\n",
    "    l.append(sq_xlen.quantile(i))\n",
    "plt.plot(l, ptiles)\n",
    "sq_xlen.describe(percentiles=[x*0.01 for x in range(90,100)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c34dd9d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove outliner data from the dataset\n",
    "threshold_p = 0.99\n",
    "threshold = int(sq_xlen.quantile(threshold_p))\n",
    "len_mask = np.where(X_len <= threshold)\n",
    "\n",
    "X_filter = X[len_mask]\n",
    "y_filter = y[len_mask]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "eb03d293",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Sliding Window Function\n",
    "def sliding_window(data):\n",
    "    input_data = data\n",
    "    _window_sz = 10\n",
    "    sum_windows_passed = 0\n",
    "    \n",
    "    \n",
    "    data_above_thresh = []\n",
    "    thresh = 70\n",
    "    \n",
    "    values_sum = 0\n",
    "    \n",
    "    for i in range(0, len(input_data), _window_sz):\n",
    "        for j in range(i, min(i + _window_sz, len(input_data))):\n",
    "            values_sum += input_data[j]\n",
    "        data_above_thresh.append(values_sum / _window_sz)\n",
    "        \n",
    "    return data_above_thresh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "1581a370",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f08c64055e0>]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "input_data = X[5]['Force']\n",
    "window_sz = 10\n",
    "sum_windows_passed = 0\n",
    "    \n",
    "    \n",
    "win_above_thresh = []\n",
    "thresh = 70\n",
    "    \n",
    "    \n",
    "for i in range(0, len(input_data), window_sz):\n",
    "    values_sum = 0\n",
    "    for j in range(i, min(i + window_sz, len(input_data))): \n",
    "        values_sum += input_data[j]\n",
    "\n",
    "    win_above_thresh.append(values_sum / window_sz)\n",
    "    \n",
    "plt.plot(win_above_thresh)\n",
    "plt.plot(X[5]['Force'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "f26eca93",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(array([140, 150, 160, 170, 190, 200, 210]),)\n"
     ]
    }
   ],
   "source": [
    "_blep = np.where(np.asarray(win_above_thresh) > thresh)\n",
    "\n",
    "for i in range(len(_blep[0])):\n",
    "    _blep[0][i] = _blep[0][i] * window_sz\n",
    "    \n",
    "print(_blep) # s.u. Range der Daten über threshold ist von 140 bis 180 und von 190 bis 220; \n",
    "             # Alles vor 140 und nach 220 ist 0 und kann gecutted werden"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "407f8efe",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_new = X[_blep]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "1c886109",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(13102,)\n",
      "(124,)\n"
     ]
    }
   ],
   "source": [
    "print(X.shape)\n",
    "print(X_new[5]['Force'].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "cfa4732e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((13102,), (257, 15), (257, 15))"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape, X[140].shape, X_new[0].shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "4a15e2ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f08c2e739a0>]"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(X[140]['Force'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "4128a3cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f08c2dcca90>]"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(X_new[0]['Force'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "9af3f711",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f08c2c16f40>]"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(X_new[1]['Force'])\n",
    "plt.plot(X[150]['Force'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "775983d4",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}