added code to read validation data txts
This commit is contained in:
Brantegger Georg
@@ -2,7 +2,7 @@
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 8,
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"execution_count": 9,
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"metadata": {},
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"outputs": [],
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"source": [
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@@ -15,66 +15,187 @@
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"execution_count": 10,
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"metadata": {},
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"outputs": [],
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"source": [
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"%matplotlib qt5\n",
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"\n",
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"def unpack_line(str):\n",
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" time_format = \"%d.%m.%Y %H:%M:%S.%f\"\n",
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" index_1 = str.find(';')\n",
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" index_2 = str.find(';',index_1)\n",
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" index_3 = str.find(';',index_2)\n",
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" index_4 = str.find(';',index_3)\n",
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" index_5 = str.find(';',index_4)\n",
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" index_2 = str.find(';',index_1+1)\n",
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" index_3 = str.find(';',index_2+1)\n",
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" index_4 = str.find(';',index_3+1)\n",
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" index_5 = str.find(';',index_4+1)\n",
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" parameter = str[0:index_1]\n",
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" value = str[index_2:index_3]\n",
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" timestamp = str[index_5:] \n",
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" value = float(str[index_2+1:index_3])\n",
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" timestamp = time.mktime(datetime.strptime(str[index_5+1:-2],time_format).timetuple())\n",
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" return parameter,value,timestamp\n",
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"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"execution_count": 11,
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"metadata": {},
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"outputs": [
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{
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"ename": "KeyboardInterrupt",
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"evalue": "",
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"output_type": "error",
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"traceback": [
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"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
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"\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<cell line: 7>\u001b[1;34m()\u001b[0m\n\u001b[0;32m <a href='vscode-notebook-cell:/v%3A/georg/Documents/Pers%C3%B6nliche%20Dokumente/Arbeit/Kelag/Coding/Python/DT_Slot_3/Kelag_DT_Slot_3/Validation%20Data/read_validation_data_long.ipynb#W1sZmlsZQ%3D%3D?line=5'>6</a>\u001b[0m timestamp_old \u001b[39m=\u001b[39m \u001b[39m0.\u001b[39m\n\u001b[0;32m <a href='vscode-notebook-cell:/v%3A/georg/Documents/Pers%C3%B6nliche%20Dokumente/Arbeit/Kelag/Coding/Python/DT_Slot_3/Kelag_DT_Slot_3/Validation%20Data/read_validation_data_long.ipynb#W1sZmlsZQ%3D%3D?line=6'>7</a>\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----> <a href='vscode-notebook-cell:/v%3A/georg/Documents/Pers%C3%B6nliche%20Dokumente/Arbeit/Kelag/Coding/Python/DT_Slot_3/Kelag_DT_Slot_3/Validation%20Data/read_validation_data_long.ipynb#W1sZmlsZQ%3D%3D?line=7'>8</a>\u001b[0m \u001b[39mfor\u001b[39;00m line \u001b[39min\u001b[39;00m txt_file:\n\u001b[0;32m <a href='vscode-notebook-cell:/v%3A/georg/Documents/Pers%C3%B6nliche%20Dokumente/Arbeit/Kelag/Coding/Python/DT_Slot_3/Kelag_DT_Slot_3/Validation%20Data/read_validation_data_long.ipynb#W1sZmlsZQ%3D%3D?line=8'>9</a>\u001b[0m parameter_new, value_new, timestamp_new \u001b[39m=\u001b[39m unpack_line(line)\n",
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"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",
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"\u001b[1;31mKeyboardInterrupt\u001b[0m: "
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]
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}
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],
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"outputs": [],
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"source": [
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"df = pd.DataFrame(columns=['Timestamp','M1-LA','M1-Druck','M2-LA','M2-Druck'])\n",
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"M1_LA_df = pd.DataFrame(columns=['Timestamp','M1-LA'])\n",
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"M2_LA_df = pd.DataFrame(columns=['Timestamp','M2-LA'])\n",
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"M1_Druck_df = pd.DataFrame(columns=['Timestamp','M1-Druck'])\n",
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"M2_Druck_df = pd.DataFrame(columns=['Timestamp','M2-Druck'])\n",
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"\n",
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"\n",
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"parameter_old = ''\n",
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"value_old = 0.\n",
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"timestamp_old = 0.\n",
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"value_list = []\n",
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"timestamp_list = []\n",
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"with open('August_1_22.txt') as txt_file:\n",
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"with open('Juni_1_22.txt') as txt_file:\n",
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" for line in txt_file:\n",
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" parameter_new, value_new, timestamp_new = unpack_line(line)\n",
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" if parameter_new != parameter_old:\n",
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" if parameter_old islike \"\"\n",
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" if 'M1' in parameter_old and 'Stell_Leitapparat' in parameter_old:\n",
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" M1_LA_df['Timestamp'] = timestamp_list[:]\n",
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" M1_LA_df['M1-LA'] = value_list[:]\n",
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" if 'M1' in parameter_old and 'Spiraldruck' in parameter_old:\n",
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" M1_Druck_df['Timestamp'] = timestamp_list[:]\n",
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" M1_Druck_df['M1-Druck'] = value_list[:]\n",
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" if 'M2' in parameter_old and 'Stell_Leitapparat' in parameter_old:\n",
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" M2_LA_df['Timestamp'] = timestamp_list[:]\n",
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" M2_LA_df['M2-LA'] = value_list[:]\n",
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" if 'M2' in parameter_old and 'Spiraldruck' in parameter_old:\n",
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" M2_Druck_df['Timestamp'] = timestamp_list[:]\n",
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" M2_Druck_df['M2-Druck'] = value_list[:]\n",
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" \n",
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" value_list = []\n",
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" timestamp_list = []\n",
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" value_list.append(value_new)\n",
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" timestamp_list.append(timestamp_new)\n",
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"\n",
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"\n"
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" parameter_old = parameter_new\n",
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" else:\n",
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" if value_new != value_list[-1]:\n",
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" value_list.append(value_new)\n",
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" timestamp_list.append(timestamp_new) \n",
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"\n",
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" if 'M1' in parameter_old and 'Stell_Leitapparat' in parameter_old:\n",
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" M1_LA_df['Timestamp'] = timestamp_list[:]\n",
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" M1_LA_df['M1-LA'] = value_list[:]\n",
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" if 'M1' in parameter_old and 'Spiraldruck' in parameter_old:\n",
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" M1_Druck_df['Timestamp'] = timestamp_list[:]\n",
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" M1_Druck_df['M1-Druck'] = value_list[:]\n",
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" if 'M2' in parameter_old and 'Stell_Leitapparat' in parameter_old:\n",
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" M2_LA_df['Timestamp'] = timestamp_list[:]\n",
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" M2_LA_df['M2-LA'] = value_list[:]\n",
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" if 'M2' in parameter_old and 'Spiraldruck' in parameter_old:\n",
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" M2_Druck_df['Timestamp'] = timestamp_list[:]\n",
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" M2_Druck_df['M2-Druck'] = value_list[:]\n",
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"\n",
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"M1_LA_df.set_index(['Timestamp'],inplace=True)\n",
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"M1_Druck_df.set_index(['Timestamp'],inplace=True)\n",
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"M2_LA_df.set_index(['Timestamp'],inplace=True)\n",
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"M2_Druck_df.set_index(['Timestamp'],inplace=True)\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[<matplotlib.lines.Line2D at 0x2e6c96b7430>]"
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]
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},
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"execution_count": 12,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"fig1=plt.figure()\n",
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"plt.plot(M1_LA_df['M1-LA'])\n",
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"fig2=plt.figure()\n",
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"plt.plot(M1_Druck_df['M1-Druck'])\n",
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"fig3=plt.figure()\n",
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"plt.plot(M2_LA_df['M2-LA'])\n",
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"fig4=plt.figure()\n",
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"plt.plot(M2_Druck_df['M2-Druck'])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"metadata": {},
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"outputs": [],
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"source": [
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"df = M1_LA_df.join([M2_LA_df,M1_Druck_df,M2_Druck_df],how='outer')\n",
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"df.sort_index(axis=0,inplace=True)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[<matplotlib.lines.Line2D at 0x2e6c8733c10>]"
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]
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},
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"execution_count": 14,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"fig1=plt.figure()\n",
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"plt.plot(df['M1-LA'])\n",
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"fig2=plt.figure()\n",
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"plt.plot(df['M1-Druck'])\n",
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"fig3=plt.figure()\n",
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"plt.plot(df['M2-LA'])\n",
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"fig4=plt.figure()\n",
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"plt.plot(df['M2-Druck'])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 17,
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"metadata": {},
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"outputs": [],
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"source": [
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"t_vec = df.index.to_numpy()\n",
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"M1_LA = df['M1-LA'].to_numpy() "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 18,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[<matplotlib.lines.Line2D at 0x2e6c8563340>]"
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]
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},
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"execution_count": 18,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"fig5=plt.figure()\n",
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"plt.plot(t_vec,M1_LA)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3.8.13 ('DT_Slot_3')",
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"display_name": "Python 3.8.13 ('Georg_DT_Slot3')",
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"language": "python",
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"name": "python3"
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},
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@@ -93,7 +214,7 @@
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"orig_nbformat": 4,
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},
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@@ -2,7 +2,7 @@
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 13,
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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@@ -15,14 +15,14 @@
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"C:\\Users\\georg\\AppData\\Local\\Temp\\ipykernel_34540\\1340824978.py:1: ParserWarning: Length of header or names does not match length of data. This leads to a loss of data with index_col=False.\n",
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"C:\\Users\\BRANT\\AppData\\Local\\Temp\\7\\ipykernel_7624\\1340824978.py:1: ParserWarning: Length of header or names does not match length of data. This leads to a loss of data with index_col=False.\n",
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" raw_data = pd.read_csv(\"2015_08_24 18.00 M1 SS100%.csv\",sep=\";\",header=7,index_col=False)\n"
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]
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}
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@@ -43,7 +43,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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@@ -55,23 +55,23 @@
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},
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{
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"cell_type": "code",
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"execution_count": 16,
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"data": {
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]
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},
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"execution_count": 16,
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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},
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "9c2016ba1ceb4d07a17a483bbf50f9a6",
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"model_id": "7cd05b652d4a40ca973b5895a8777a64",
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"version_major": 2,
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"version_minor": 0
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},
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@@ -104,7 +104,7 @@
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3.8.13 ('DT_Slot_3')",
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"display_name": "Python 3.8.13 ('Georg_DT_Slot3')",
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"language": "python",
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"name": "python3"
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},
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@@ -123,7 +123,7 @@
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"orig_nbformat": 4,
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"vscode": {
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}
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}
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},
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