diff --git a/1-first-project/ies/Tensor.ipynb b/1-first-project/ies/Tensor.ipynb new file mode 100644 index 0000000..196aa82 --- /dev/null +++ b/1-first-project/ies/Tensor.ipynb @@ -0,0 +1,385 @@ +{ + "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": [] + } + ], + "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 +} diff --git a/1-first-project/ies/X.pickle b/1-first-project/ies/X.pickle new file mode 100644 index 0000000..b0e4e64 Binary files /dev/null and b/1-first-project/ies/X.pickle differ diff --git a/1-first-project/ies/X_shorted.pickle b/1-first-project/ies/X_shorted.pickle new file mode 100644 index 0000000..7ba39c0 Binary files /dev/null and b/1-first-project/ies/X_shorted.pickle differ diff --git a/1-first-project/ies/single_first_five.png b/1-first-project/ies/single_first_five.png new file mode 100644 index 0000000..f2fccc4 Binary files /dev/null and b/1-first-project/ies/single_first_five.png differ diff --git a/1-first-project/ies/ten_force_entries_all_alphs.png b/1-first-project/ies/ten_force_entries_all_alphs.png new file mode 100644 index 0000000..d71e420 Binary files /dev/null and b/1-first-project/ies/ten_force_entries_all_alphs.png differ diff --git a/1-first-project/ies/y.pickle b/1-first-project/ies/y.pickle new file mode 100644 index 0000000..455f102 Binary files /dev/null and b/1-first-project/ies/y.pickle differ