{ "cells": [ { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import time\n", "from datetime import datetime\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "def unpack_line(str):\n", " index_1 = str.find(';')\n", " index_2 = str.find(';',index_1)\n", " index_3 = str.find(';',index_2)\n", " index_4 = str.find(';',index_3)\n", " index_5 = str.find(';',index_4)\n", " parameter = str[0:index_1]\n", " value = str[index_2:index_3]\n", " timestamp = str[index_5:] \n", " return parameter,value,timestamp\n", "\n" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "ename": "KeyboardInterrupt", "evalue": "", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", "\u001b[1;32mv:\\georg\\Documents\\Persönliche Dokumente\\Arbeit\\Kelag\\Coding\\Python\\DT_Slot_3\\Kelag_DT_Slot_3\\Validation Data\\read_validation_data_long.ipynb Cell 3\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 6\u001b[0m timestamp_old \u001b[39m=\u001b[39m \u001b[39m0.\u001b[39m\n\u001b[0;32m 7\u001b[0m \u001b[39mwith\u001b[39;00m \u001b[39mopen\u001b[39m(\u001b[39m'\u001b[39m\u001b[39mAugust_1_22.txt\u001b[39m\u001b[39m'\u001b[39m) \u001b[39mas\u001b[39;00m txt_file:\n\u001b[1;32m----> 8\u001b[0m \u001b[39mfor\u001b[39;00m line \u001b[39min\u001b[39;00m txt_file:\n\u001b[0;32m 9\u001b[0m parameter_new, value_new, timestamp_new \u001b[39m=\u001b[39m unpack_line(line)\n", "File \u001b[1;32mc:\\Users\\georg\\anaconda3\\envs\\DT_Slot_3\\lib\\encodings\\cp1252.py:22\u001b[0m, in \u001b[0;36mIncrementalDecoder.decode\u001b[1;34m(self, input, final)\u001b[0m\n\u001b[0;32m 21\u001b[0m \u001b[39mclass\u001b[39;00m \u001b[39mIncrementalDecoder\u001b[39;00m(codecs\u001b[39m.\u001b[39mIncrementalDecoder):\n\u001b[1;32m---> 22\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mdecode\u001b[39m(\u001b[39mself\u001b[39m, \u001b[39minput\u001b[39m, final\u001b[39m=\u001b[39m\u001b[39mFalse\u001b[39;00m):\n\u001b[0;32m 23\u001b[0m \u001b[39mreturn\u001b[39;00m codecs\u001b[39m.\u001b[39mcharmap_decode(\u001b[39minput\u001b[39m,\u001b[39mself\u001b[39m\u001b[39m.\u001b[39merrors,decoding_table)[\u001b[39m0\u001b[39m]\n", "\u001b[1;31mKeyboardInterrupt\u001b[0m: " ] } ], "source": [ "df = pd.DataFrame(columns=['Timestamp','M1-LA','M1-Druck','M2-LA','M2-Druck'])\n", "\n", "\n", "parameter_old = ''\n", "value_old = 0.\n", "timestamp_old = 0.\n", "value_list = []\n", "timestamp_list = []\n", "with open('August_1_22.txt') as txt_file:\n", " for line in txt_file:\n", " parameter_new, value_new, timestamp_new = unpack_line(line)\n", " if parameter_new != parameter_old:\n", " if parameter_old islike \"\"\n", " \n", " value_list = []\n", " timestamp_list = []\n", "\n", "\n" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3.8.13 ('DT_Slot_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": "3.8.13" }, "orig_nbformat": 4, "vscode": { "interpreter": { "hash": "4a28055eb8a3160fa4c7e4fca69770c4e0a1add985300856aa3fcf4ce32a2c48" } } }, "nbformat": 4, "nbformat_minor": 2 }