validation and KW Hammer Lastfälle
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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": 9,
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"execution_count": 11,
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"metadata": {},
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"outputs": [],
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"source": [
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@@ -14,7 +14,7 @@
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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": 12,
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"metadata": {},
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"outputs": [],
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"source": [
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@@ -37,7 +37,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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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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@@ -46,7 +46,7 @@
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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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"execution_count": 14,
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"metadata": {},
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"outputs": [],
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"source": [
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@@ -56,7 +56,7 @@
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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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"execution_count": 15,
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"metadata": {},
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"outputs": [],
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"source": [
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@@ -71,12 +71,13 @@
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"UT_M1_p = UT_df['UL_T1_p'].to_numpy()\n",
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"UT_M2_p = UT_df['UL_T2_p'].to_numpy()\n",
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"UT_M1_LA = UT_df['UL_T1_LA'].to_numpy()\n",
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"UT_M2_LA = UT_df['UL_T2_LA'].to_numpy()"
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"UT_M2_LA = UT_df['UL_T2_LA'].to_numpy()\n",
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"UT_Ausl = UT_df['Ausl'].to_numpy()\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": 21,
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"execution_count": 16,
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"metadata": {},
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"outputs": [
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{
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@@ -85,7 +86,7 @@
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"(1657542740.9878564, 1657553169.3173888)"
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]
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},
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"execution_count": 21,
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"execution_count": 16,
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"metadata": {},
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"output_type": "execute_result"
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}
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@@ -116,30 +117,21 @@
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},
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{
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"cell_type": "code",
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"execution_count": 48,
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"execution_count": 21,
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"metadata": {},
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"outputs": [
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{
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"ename": "TypeError",
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"evalue": "Cannot construct a dtype from an array",
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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;31mTypeError\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\\consolidated pandas dataframes\\consolidate_validation_data.ipynb Cell 7\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/consolidated%20pandas%20dataframes/consolidate_validation_data.ipynb#X10sZmlsZQ%3D%3D?line=4'>5</a>\u001b[0m fig \u001b[39m=\u001b[39m plt\u001b[39m.\u001b[39mfigure()\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/consolidated%20pandas%20dataframes/consolidate_validation_data.ipynb#X10sZmlsZQ%3D%3D?line=5'>6</a>\u001b[0m plt\u001b[39m.\u001b[39mplot(UT_t_vec[mask_UT],TB_level[mask_UT])\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/consolidated%20pandas%20dataframes/consolidate_validation_data.ipynb#X10sZmlsZQ%3D%3D?line=6'>7</a>\u001b[0m validation_data_UT \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39;49marray([UT_t_vec[mask_UT],UT_M1_LA[mask_UT],UT_M2_LA[mask_UT],UT_M1_p[mask_UT],UT_M2_p[mask_UT]],TB_level[mask_UT])\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/consolidated%20pandas%20dataframes/consolidate_validation_data.ipynb#X10sZmlsZQ%3D%3D?line=7'>8</a>\u001b[0m validation_data_TB \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39marray([TB_t_vec[mask_TB],TB_M1_LA[mask_TB],TB_M2_LA[mask_TB],TB_M1_p[mask_TB],TB_M2_p[mask_TB]])\n",
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"\u001b[1;31mTypeError\u001b[0m: Cannot construct a dtype from an array"
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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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"mask_UT = np.logical_and(t1<UT_t_vec,UT_t_vec <t2)\n",
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"mask_TB = np.logical_and(t1<TB_t_vec,TB_t_vec <t2)\n",
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"\n",
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"\n",
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"fig = plt.figure()\n",
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"plt.plot(UT_t_vec[mask_UT],TB_level[mask_UT])\n",
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"validation_data_UT = np.array([UT_t_vec[mask_UT];UT_M1_LA[mask_UT];UT_M2_LA[mask_UT];UT_M1_p[mask_UT];UT_M2_p[mask_UT]];TB_level[mask_UT])\n",
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"validation_data_TB = np.array([TB_t_vec[mask_TB];TB_M1_LA[mask_TB];TB_M2_LA[mask_TB];TB_M1_p[mask_TB];TB_M2_p[mask_TB]])"
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"\n",
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"validation_data_UT = UT_df[mask_UT]\n",
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"validation_data_TB = TB_df[mask_TB]\n",
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"\n",
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"validation_data_UT.to_csv('Validation_data_UT.csv')\n",
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"validation_data_TB.to_csv('Validation_data_TB.csv')\n",
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"\n"
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]
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}
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],
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61313
Validation Data/raw data Untertweng/AL_SeitAuslPos.txt
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61313
Validation Data/raw data Untertweng/AL_SeitAuslPos.txt
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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_29732\\1290488230.py:2: 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\\georg\\AppData\\Local\\Temp\\ipykernel_24336\\3411177260.py:2: 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_25 15.20 M12 SS100%.csv\",sep=\";\",header=7,index_col=False)\n"
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]
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}
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@@ -53,7 +53,7 @@
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"df['M1-LA'] = pd.to_numeric(raw_data['M1-LA'])/100\n",
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"df['M2-LA'] = pd.to_numeric(raw_data['M2-LA'])/100\n",
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"df['Druck'] = pd.to_numeric(raw_data['P-DRL'])\n",
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"df['Pegel'] = pd.to_numeric(raw_data['Pegel-UW'])\n",
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"df['Pegel'] = pd.to_numeric(raw_data['PEGEL-UW'])\n",
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"\n",
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"val_t_vec_raw = np.array(df['timestamp']-df['timestamp'][0])\n",
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"val_LA1_vec_raw = np.array(df['M1-LA']) \n",
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@@ -501,7 +501,7 @@
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{
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"data": {
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"text/plain": [
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"[<matplotlib.lines.Line2D at 0x1e74e242940>]"
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"[<matplotlib.lines.Line2D at 0x18a94bcf820>]"
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]
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},
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"execution_count": 10,
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@@ -21,7 +21,8 @@
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"M1_p_df = pd.read_csv('M1_Druck.txt',delimiter=';')\n",
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"M2_p_df = pd.read_csv('M2_Druck.txt',delimiter=';')\n",
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"M1_LA_df = pd.read_csv('M1_LA.txt',delimiter=';')\n",
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"M2_LA_df = pd.read_csv('M2_LA.txt',delimiter=';')"
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"M2_LA_df = pd.read_csv('M2_LA.txt',delimiter=';')\n",
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"Ausl_df = pd.read_csv('AL_SeitAuslPos.txt',delimiter=';')"
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]
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},
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{
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@@ -34,7 +35,8 @@
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"M1_p_df['Timestamp'] = M1_p_df['TIMESTAMP UNIX']+M1_p_df['TIMESTAMP MS']/1000.\n",
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"M2_p_df['Timestamp'] = M2_p_df['TIMESTAMP UNIX']+M2_p_df['TIMESTAMP MS']/1000.\n",
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"M1_LA_df['Timestamp'] = M1_LA_df['TIMESTAMP UNIX']+M1_LA_df['TIMESTAMP MS']/1000.\n",
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"M2_LA_df['Timestamp'] = M2_LA_df['TIMESTAMP UNIX']+M2_LA_df['TIMESTAMP MS']/1000."
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"M2_LA_df['Timestamp'] = M2_LA_df['TIMESTAMP UNIX']+M2_LA_df['TIMESTAMP MS']/1000.\n",
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"Ausl_df['Timestamp'] = Ausl_df['TIMESTAMP UNIX']+Ausl_df['TIMESTAMP MS']/1000."
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]
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},
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{
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@@ -47,7 +49,8 @@
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"M1_p_df.set_index('Timestamp',inplace=True)\n",
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"M2_p_df.set_index('Timestamp',inplace=True)\n",
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"M1_LA_df.set_index('Timestamp',inplace=True)\n",
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"M2_LA_df.set_index('Timestamp',inplace=True)"
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"M2_LA_df.set_index('Timestamp',inplace=True)\n",
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"Ausl_df.set_index('Timestamp',inplace=True)"
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]
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},
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{
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@@ -61,12 +64,14 @@
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"M2_p_df.drop(columns=['VARIABLE','TIMESTAMP UNIX', 'TIMESTAMP MS'],inplace=True)\n",
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"M1_LA_df.drop(columns=['VARIABLE','TIMESTAMP UNIX', 'TIMESTAMP MS'],inplace=True)\n",
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"M2_LA_df.drop(columns=['VARIABLE','TIMESTAMP UNIX', 'TIMESTAMP MS'],inplace=True)\n",
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"Ausl_df.drop(columns=['VARIABLE','TIMESTAMP UNIX', 'TIMESTAMP MS'],inplace=True)\n",
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"\n",
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"pegel_df.rename(columns={'VALUE': 'TB-Pegel'},inplace=True)\n",
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"M1_p_df.rename(columns={'VALUE': 'M1-Druck'},inplace=True)\n",
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"M2_p_df.rename(columns={'VALUE': 'M2-Druck'},inplace=True)\n",
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"M1_LA_df.rename(columns={'VALUE': 'M1-LA'},inplace=True)\n",
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"M2_LA_df.rename(columns={'VALUE': 'M2-LA'},inplace=True)\n"
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"M2_LA_df.rename(columns={'VALUE': 'M2-LA'},inplace=True)\n",
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"Ausl_df.rename(columns={'VALUE': 'Ausl'},inplace=True)\n"
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]
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},
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{
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@@ -75,7 +80,7 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"UT_df = pegel_df.join([M1_LA_df,M1_p_df,M2_LA_df,M2_p_df],how='outer').sort_index()"
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"UT_df = pegel_df.join([M1_LA_df,M1_p_df,M2_LA_df,M2_p_df,Ausl_df],how='outer').sort_index()"
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]
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},
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{
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