{ "cells": [ { "cell_type": "markdown", "id": "5556432f", "metadata": {}, "source": [ "# Tutorial 52: General Creation of Element Dataframes" ] }, { "cell_type": "markdown", "id": "8899e69a", "metadata": {}, "source": [ "This Example demonstrates the capabilities of the class Dataframes_SIR3S_Model that extends SIR3S_Model be abilities to work directley with pandas dataframes. It is shown how to create dataframes containing information about elements such as Nodes, Pipes, etc. existing in a SIR 3S Model. The methods presented are not user-defined and neither efficient, but get you the most important information quickly. For more detailed methods of creating dataframes, see Tutorial 51." ] }, { "cell_type": "markdown", "id": "e2d40c36", "metadata": {}, "source": [ "# Toolkit Release" ] }, { "cell_type": "code", "execution_count": 1, "id": "7fb5a07b", "metadata": {}, "outputs": [], "source": [ "#pip install " ] }, { "cell_type": "markdown", "id": "d6773e12", "metadata": {}, "source": [ "# Imports" ] }, { "cell_type": "markdown", "id": "134689cf", "metadata": {}, "source": [ "## SIR 3S Toolkit" ] }, { "cell_type": "markdown", "id": "69665f68", "metadata": {}, "source": [ "### Regular Import/Init" ] }, { "cell_type": "code", "execution_count": 2, "id": "79d0fb2c", "metadata": {}, "outputs": [], "source": [ "SIR3S_SIRGRAF_DIR = r\"C:\\3S\\SIR 3S\\SirGraf-90-15-00-20x64_Quebec-Upd1\" #change to local path" ] }, { "cell_type": "code", "execution_count": 3, "id": "0f41d17a", "metadata": {}, "outputs": [], "source": [ "from sir3stoolkit.core import wrapper" ] }, { "cell_type": "code", "execution_count": 4, "id": "d539c981", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "wrapper" ] }, { "cell_type": "code", "execution_count": 5, "id": "37d59df0", "metadata": {}, "outputs": [], "source": [ "wrapper.Initialize_Toolkit(SIR3S_SIRGRAF_DIR)" ] }, { "cell_type": "markdown", "id": "ebf2c56e", "metadata": {}, "source": [ "### Additional Import/Init for Dataframes class" ] }, { "cell_type": "code", "execution_count": 6, "id": "893f889e", "metadata": {}, "outputs": [], "source": [ "from sir3stoolkit.mantle.dataframes import SIR3S_Model_Dataframes" ] }, { "cell_type": "code", "execution_count": 7, "id": "4b65297f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Initialization complete\n" ] } ], "source": [ "s3s = SIR3S_Model_Dataframes()" ] }, { "cell_type": "markdown", "id": "2007993a", "metadata": {}, "source": [ "## Additional" ] }, { "cell_type": "code", "execution_count": 8, "id": "3a443734", "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "from shapely.geometry import Point\n", "import re\n", "import folium\n", "from folium.plugins import HeatMap\n", "import numpy as np\n", "import geopandas as gpd\n", "from shapely import wkt\n", "import matplotlib.pyplot as plt\n", "import contextily as cx" ] }, { "cell_type": "markdown", "id": "d9a78e4d", "metadata": {}, "source": [ "# Open Model" ] }, { "cell_type": "code", "execution_count": 9, "id": "20ff3c21", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Model is open for further operation\n" ] } ], "source": [ "s3s.OpenModel(dbName=r\"C:\\Users\\aUsername\\3S\\PT3S\\PT3S\\Examples\\Example3.db3\",\n providerType=s3s.ProviderTypes.SQLite,\n Mid=\"M-1-0-1\",\n saveCurrentlyOpenModel=False,\n namedInstance=\"\",\n userID=\"\",\n password=\"\")" ] }, { "cell_type": "markdown", "id": "ccc8f89c", "metadata": {}, "source": [ "# Generate Element Dataframes" ] }, { "cell_type": "markdown", "id": "8d6189f7", "metadata": {}, "source": [ "We can use the [generate_element_dataframe()](https://3sconsult.github.io/sir3stoolkit/references/sir3stoolkit.mantle.html#sir3stoolkit.mantle.dataframes.SIR3S_Model_Dataframes.generate_element_dataframe) method to quickly generate basic dataframes containing all instances of hydraulic element types (Node, Pipe, etc.) in a SIR 3S model. " ] }, { "cell_type": "markdown", "id": "e94e2f37", "metadata": {}, "source": [ "All model_data and most result values (self.GetResultProperties_from_elementType(onlySelectedVectors=True)) for the static timestamp are included. Result values are given as floats, unless they are in vectorized form (relevant only for pipes), in that case they are strings." ] }, { "cell_type": "markdown", "id": "bfe6454d", "metadata": {}, "source": [ "The pd.Dataframe will automatically be transformed into a gpd.GeoDataFrame if a SRID is defined in the model, after a geometry column is created." ] }, { "cell_type": "code", "execution_count": 10, "id": "16e105ab", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['AGSN_HydraulicProfile', 'AirVessel', 'Arrow', 'Atmosphere', 'BlockConnectionNode', 'CalcPari', 'CharacteristicLossTable', 'CharacteristicLossTable_Row', 'Circle', 'Compressor', 'CompressorTable', 'CompressorTable_Row', 'ControlEngineeringNexus', 'ControlMode', 'ControlPointTable', 'ControlPointTable_Row', 'ControlValve', 'ControlVariableConverter', 'ControlVariableConverterRSTE', 'CrossSectionTable', 'CrossSectionTable_Row', 'DPGR_DPKT_DatapointDpgrConnection', 'DPGR_DataPointGroup', 'DPKT_Datapoint', 'DamageRatesTable', 'DamageRatesTable_Row', 'DeadTimeElement', 'Demand', 'DifferentialRegulator', 'DirectionalArrow', 'DistrictHeatingConsumer', 'DistrictHeatingFeeder', 'Divider', 'DriveEfficiencyTable', 'DriveEfficiencyTable_Row', 'DrivePowerTable', 'DrivePowerTable_Row', 'EBES_FeederGroups', 'EfficiencyConverterTable', 'EfficiencyConverterTable_Row', 'ElementQuery', 'EnergyRecoveryTable', 'EnergyRecoveryTable_Row', 'EnvironmentTemp', 'FWBZ_DistrictHeatingReferenceValues', 'FlapValve', 'FlowControlUnit', 'FluidQualityParamSet', 'FluidQualityParamSet_OS', 'FluidThermalPropertyGroup', 'FreeDuct', 'FunctionGenerator', 'FunctionTable', 'FunctionTable_Row', 'GasComponent', 'GasMixture', 'GeneralSection', 'Gravitation', 'HeatExchanger', 'HeatFeederConsumerStation', 'HeaterCooler', 'Histeresis', 'House', 'Hydrant', 'Integrator', 'LAYR_Layer', 'LoadFactorTable', 'LoadFactorTable_Row', 'LogicalComparison', 'LogicalStorage', 'MeasuredVariableTable', 'MeasuredVariableTable_Row', 'MinMaxSelection', 'Multiplier', 'NetValve', 'Node', 'NonReturnValvesTable', 'NonReturnValvesTable_Row', 'NumericalDisplay', 'ObjectContainerSymbol', 'OpenContainer', 'Oval', 'PARZ_TransientCalculationParameters', 'PhaseSeparation', 'PidController', 'Pipe', 'PipeGroup', 'PipeTable', 'PipeTable_Row', 'PipeVertex', 'Polygon', 'Polyline', 'PressureRegulator', 'PressureZone', 'Pt1Controller', 'Pump', 'PumpCharTable', 'PumpCharTable_Row', 'PumpGroup', 'PumpOfPumpGroup', 'PumpSpeedTable', 'PumpSpeedTable_Row', 'RART_ControlMode', 'REGP_ControlParameters', 'RMES_DPTS_RmesInternalDataPoint', 'Rectangle', 'RegulatorsTable', 'RegulatorsTable_Row', 'ReturnTemperaturTable', 'ReturnTemperaturTable_Row', 'RoundRectangle', 'SIRGRAF', 'SPLZ_TimeSeries', 'SafetyValve', 'SetpointDevice', 'SolarCollector', 'StandPipe', 'Street', 'SummingPoint', 'SwitchInBlock', 'TemperatureTable', 'TemperatureTable_Row', 'Text', 'ThermalOutputTable', 'ThermalOutputTable_Row', 'ThermophysPropTable', 'ThermophysPropTable_Row', 'TransitionSymbol', 'Transmitter', 'TransportVariable', 'USCH_UserDefinedProperties', 'Unknown', 'VARA_ColorScale', 'VARA_ROWS_WidthOrScale', 'VRCT_ViewRectangle', 'Valve', 'ValveLiftTable', 'ValveLiftTable_Row', 'VarFlowTable', 'VarFlowTable_Row', 'VarPressureTable', 'VarPressureTable_Row', 'VentOpenCloseTable', 'VentOpenCloseTable_Row', 'VentValve', 'VentilatedPressureAirVessel', 'WBLZ_ThermalBalance', 'WeatherDataTable', 'WeatherDataTable_Row']\n" ] } ], "source": [ "object_types = [item for item in dir(s3s.ObjectTypes) if not (item.startswith('__') and item.endswith('__'))]\n", "print(object_types) # Check for hydraulic elmement types" ] }, { "cell_type": "markdown", "id": "fb750618", "metadata": {}, "source": [ "This function allows for little user definition the only paramters are element_type and tks of that element type to exclusively use. For more user defined dataframe creation see Tutorial 52." ] }, { "cell_type": "markdown", "id": "f8446b05", "metadata": {}, "source": [ "## Node" ] }, { "cell_type": "code", "execution_count": 11, "id": "1929d1bb", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "[2026-01-10 17:43:02,097] INFO in sir3stoolkit.mantle.dataframes: [generate_element_dataframe] Generating df for element type: ObjectTypes.Node ...\n", "[2026-01-10 17:43:02,098] DEBUG in sir3stoolkit.mantle.dataframes: [generate_element_dataframe] Generating df_model_data for element type: ObjectTypes.Node ...\n", "[2026-01-10 17:43:02,100] INFO in sir3stoolkit.mantle.dataframes: [model_data] Generating model_data dataframe for element type: ObjectTypes.Node\n", "[2026-01-10 17:43:02,107] INFO in sir3stoolkit.mantle.dataframes: [model_data] Retrieved 517 element(s) of element type ObjectTypes.Node.\n", "[2026-01-10 17:43:02,123] INFO in sir3stoolkit.mantle.dataframes: [Resolving model_data Properties] No properties given → using ALL model_data properties for ObjectTypes.Node.\n", "[2026-01-10 17:43:02,124] INFO in sir3stoolkit.mantle.dataframes: [Resolving model_data Properties] Using 37 model_data properties.\n", "[2026-01-10 17:43:02,124] INFO in sir3stoolkit.mantle.dataframes: [model_data] Retrieving model_data properties ['Name', 'Ktyp', 'Zkor', 'QmEin', 'Lfakt', 'Fkpzon', 'Fkfstf', 'Fkutmp', 'Fkfqps', 'Fkcont', 'Fk2lknot', 'Beschreibung', 'Idreferenz', 'Iplanung', 'Kvr', 'Qakt', 'Xkor', 'Ykor', 'NodeNamePosition', 'ShowNodeName', 'KvrKlartext', 'NumberOfVERB', 'HasBlockConnection', 'Tk', 'Pk', 'InVariant', 'GeometriesDiffer', 'SymbolFactor', 'bz.Drakonz', 'bz.Fk', 'bz.Fkpvar', 'bz.Fkqvar', 'bz.Fklfkt', 'bz.PhEin', 'bz.Tm', 'bz.Te', 'bz.PhMin'], geometry, end nodes...\n", "[2026-01-10 17:43:02,208] WARNING in sir3stoolkit.mantle.dataframes: [model_data] End nodes are not defined for element type ObjectTypes.Node. Dataframe is created without end nodes.\n", "[2026-01-10 17:43:03,437] INFO in sir3stoolkit.mantle.dataframes: [model_data] Transforming DataFrame to GeoDataFrame successful with EPSG: 25832\n", "[2026-01-10 17:43:03,444] INFO in sir3stoolkit.mantle.dataframes: [model_data] Done. Shape: (517, 39)\n", "[2026-01-10 17:43:03,444] DEBUG in sir3stoolkit.mantle.dataframes: [generate_element_dataframe] Generating df_results for element type: ObjectTypes.Node ...\n", "[2026-01-10 17:43:03,517] INFO in sir3stoolkit.mantle.dataframes: [results] Generating results dataframe for element type: ObjectTypes.Node\n", "[2026-01-10 17:43:03,597] INFO in sir3stoolkit.mantle.dataframes: [Resolving Timestamps] Only static timestamp 2023-02-13 00:00:00.000 +01:00 is used\n", "[2026-01-10 17:43:03,598] INFO in sir3stoolkit.mantle.dataframes: [Resolving Timestamps] 1 valid timestamp(s) will be used.\n", "[2026-01-10 17:43:03,598] INFO in sir3stoolkit.mantle.dataframes: [Resolving tks] Retrieved 517 element(s) of element type ObjectTypes.Node.\n", "[2026-01-10 17:43:03,602] INFO in sir3stoolkit.mantle.dataframes: [results] Using 21 result properties.\n", "[2026-01-10 17:43:03,610] INFO in sir3stoolkit.mantle.dataframes: [results] Retrieving result values...\n", "[2026-01-10 17:43:05,089] INFO in sir3stoolkit.mantle.dataframes: [results] Done. Shape: (1, 10857)\n", "[2026-01-10 17:43:05,089] DEBUG in sir3stoolkit.mantle.dataframes: [generate_element_dataframe] Merging df_model_data with df_results for element type: ObjectTypes.Node ...\n" ] }, { "data": { "text/html": [ "
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tkNameKtypZkorQmEinLfaktFkpzonFkfstfFkutmpFkfqpsFkcontFk2lknotBeschreibungIdreferenzIplanungKvrQaktXkorYkorNodeNamePositionShowNodeNameKvrKlartextNumberOfVERBHasBlockConnectionTkPkInVariantGeometriesDifferSymbolFactorbz.Drakonzbz.Fkbz.Fkpvarbz.Fkqvarbz.Fklfktbz.PhEinbz.Tmbz.Tebz.PhMingeometryBCINDDPDPHHHMAX_INSTHMIN_INSTIAKTIVLFAKTAKTPPDAMPFPHPHMINMAXDIFPH_EINPH_MINPMAX_INSTPMIN_INSTQMRHOTTTRVOLD
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" ], "text/plain": [ " tk Name Ktyp Zkor QmEin Lfakt \\\n", "0 4612618373909997110 V-K2133S QKON 543.56 0 1 \n", "1 4619205996903908050 V-K983S QKON 548.26 0 1 \n", "2 4619682681341516951 R-K2803S QKON 554.99 0 1 \n", "\n", " Fkpzon Fkfstf Fkutmp Fkfqps \\\n", "0 5520728169779652386 4798673252636751115 5591325053703727727 -1 \n", "1 5520728169779652386 4798673252636751115 5591325053703727727 -1 \n", "2 5520728169779652386 4798673252636751115 5591325053703727727 -1 \n", "\n", " Fkcont Fk2lknot \\\n", "0 5029128874972463118 5611267768413515094 \n", "1 5029128874972463118 5130743098019975840 \n", "2 5029128874972463118 5367059433340055050 \n", "\n", " Beschreibung Idreferenz \\\n", "0 Anfangsknoten generiert von SirDB 3S96AE619D388EA6F4CC9F24456148E088 \n", "1 Anfangsknoten generiert von SirDB 3S56C0B9A1652EF8E9B7AB8C5DEACA2DC4 \n", "2 Anfangsknoten generiert von SirDB 3S7BD49C919428AD75D6EA1203195E860E \n", "\n", " Iplanung Kvr Qakt Xkor Ykor NodeNamePosition \\\n", "0 1 1 0 714332.858074 5.578924e+06 1 \n", "1 1 1 0 713611.070733 5.578598e+06 1 \n", "2 1 2 0 713542.639481 5.578805e+06 1 \n", "\n", " ShowNodeName KvrKlartext NumberOfVERB HasBlockConnection \\\n", "0 False Vorlauf 0 False \n", "1 False Vorlauf 0 False \n", "2 False Rücklauf 0 False \n", "\n", " Tk Pk InVariant GeometriesDiffer \\\n", "0 4612618373909997110 4612618373909997110 False False \n", "1 4619205996903908050 4619205996903908050 False False \n", "2 4619682681341516951 4619682681341516951 False False \n", "\n", " SymbolFactor bz.Drakonz bz.Fk bz.Fkpvar bz.Fkqvar \\\n", "0 0.2 0 4612618373909997110 -1 -1 \n", "1 0.2 0 4619205996903908050 -1 -1 \n", "2 0.2 0 4619682681341516951 -1 -1 \n", "\n", " bz.Fklfkt bz.PhEin bz.Tm bz.Te bz.PhMin \\\n", "0 -1 0 0 0 0 \n", "1 -1 0 0 0 0 \n", "2 -1 0 0 0 0 \n", "\n", " geometry BCIND DP DPH H \\\n", "0 POINT (714332.858 5578924.328) 17.0 0.007826 0.007826 1.121131 \n", "1 POINT (713611.071 5578598.067) 17.0 1.650540 1.650540 4.942585 \n", "2 POINT (713542.639 5578804.842) 17.0 1.542220 1.542220 3.360220 \n", "\n", " HMAX_INST HMIN_INST IAKTIV LFAKTAKT P PDAMPF PH \\\n", "0 1.121131 1.121131 1.0 1.0 1.772014 0.012300 0.772014 \n", "1 4.942585 4.942585 0.0 1.0 5.132555 0.695561 4.132555 \n", "2 3.360220 3.360220 0.0 1.0 2.890204 0.197127 1.890204 \n", "\n", " PHMINMAXDIF PH_EIN PH_MIN PMAX_INST PMIN_INST QM RHO \\\n", "0 0.0 0.772014 0.0 1.772014 1.772014 0.0 1000.3000 \n", "1 0.0 4.132555 0.0 5.132555 5.132555 0.0 965.8268 \n", "2 0.0 1.890204 0.0 2.890204 2.890204 0.0 983.8152 \n", "\n", " T TTR VOLD \n", "0 10.00000 0.000000 0.0 \n", "1 89.78860 0.213703 0.0 \n", "2 59.76965 0.168082 0.0 " ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "(s3s.generate_element_dataframe(element_type=s3s.ObjectTypes.Node, tks=None)).head(3)" ] }, { "cell_type": "markdown", "id": "42c338bc", "metadata": {}, "source": [ "## Pipe" ] }, { "cell_type": "code", "execution_count": 12, "id": "7ac84f6c", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "[2026-01-10 17:43:05,138] INFO in sir3stoolkit.mantle.dataframes: [generate_element_dataframe] Generating df for element type: ObjectTypes.Pipe ...\n", "[2026-01-10 17:43:05,144] DEBUG in sir3stoolkit.mantle.dataframes: [generate_element_dataframe] Generating df_model_data for element type: ObjectTypes.Pipe ...\n", "[2026-01-10 17:43:05,145] INFO in sir3stoolkit.mantle.dataframes: [model_data] Generating model_data dataframe for element type: ObjectTypes.Pipe\n", "[2026-01-10 17:43:05,147] INFO in sir3stoolkit.mantle.dataframes: [model_data] Retrieved 524 element(s) of element type ObjectTypes.Pipe.\n", "[2026-01-10 17:43:05,150] INFO in sir3stoolkit.mantle.dataframes: [Resolving model_data Properties] No properties given → using ALL model_data properties for ObjectTypes.Pipe.\n", "[2026-01-10 17:43:05,151] INFO in sir3stoolkit.mantle.dataframes: [Resolving model_data Properties] Using 46 model_data properties.\n", "[2026-01-10 17:43:05,152] INFO in sir3stoolkit.mantle.dataframes: [model_data] Retrieving model_data properties ['Name', 'FkdtroRowd', 'Fkltgr', 'Fkstrasse', 'L', 'Lzu', 'Rau', 'Jlambs', 'Lambda0', 'Zein', 'Zaus', 'Zuml', 'Asoll', 'Indschall', 'Baujahr', 'Hal', 'Fkcont', 'Fk2lrohr', 'Beschreibung', 'Idreferenz', 'Iplanung', 'Kvr', 'LineWidthMM', 'DottedLine', 'DN', 'Di', 'KvrKlartext', 'HasClosedNSCHs', 'Tk', 'Pk', 'InVariant', 'Xkor', 'Ykor', 'GeometriesDiffer', 'bz.Fk', 'bz.Qsvb', 'bz.Irtrenn', 'bz.Leckstatus', 'bz.Leckstart', 'bz.Leckend', 'bz.Leckort', 'bz.Leckmenge', 'bz.Imptnz', 'bz.Zvlimptnz', 'bz.Kantenzv', 'bz.ITrennWithNSCH'], geometry, end nodes...\n", "[2026-01-10 17:43:06,477] INFO in sir3stoolkit.mantle.dataframes: [model_data] 2 non-empty end node columns were created.\n", "[2026-01-10 17:43:06,612] INFO in sir3stoolkit.mantle.dataframes: [model_data] Transforming DataFrame to GeoDataFrame successful with EPSG: 25832\n", "[2026-01-10 17:43:06,614] INFO in sir3stoolkit.mantle.dataframes: [model_data] Done. Shape: (524, 50)\n", "[2026-01-10 17:43:06,617] DEBUG in sir3stoolkit.mantle.dataframes: [generate_element_dataframe] Generating df_results for element type: ObjectTypes.Pipe ...\n", "[2026-01-10 17:43:06,678] INFO in sir3stoolkit.mantle.dataframes: [results] Generating results dataframe for element type: ObjectTypes.Pipe\n", "[2026-01-10 17:43:06,736] INFO in sir3stoolkit.mantle.dataframes: [Resolving Timestamps] Only static timestamp 2023-02-13 00:00:00.000 +01:00 is used\n", "[2026-01-10 17:43:06,736] INFO in sir3stoolkit.mantle.dataframes: [Resolving Timestamps] 1 valid timestamp(s) will be used.\n", "[2026-01-10 17:43:06,742] INFO in sir3stoolkit.mantle.dataframes: [Resolving tks] Retrieved 524 element(s) of element type ObjectTypes.Pipe.\n", "[2026-01-10 17:43:06,744] INFO in sir3stoolkit.mantle.dataframes: [results] Using 31 result properties.\n", "[2026-01-10 17:43:06,766] INFO in sir3stoolkit.mantle.dataframes: [results] Retrieving result values...\n", "[2026-01-10 17:43:10,358] INFO in sir3stoolkit.mantle.dataframes: [results] Done. Shape: (1, 16244)\n", "[2026-01-10 17:43:10,361] DEBUG in sir3stoolkit.mantle.dataframes: [generate_element_dataframe] Merging df_model_data with df_results for element type: ObjectTypes.Pipe ...\n" ] }, { "data": { "text/html": [ "
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tkNameFkdtroRowdFkltgrFkstrasseLLzuRauJlambsLambda0ZeinZausZumlAsollIndschallBaujahrHalFkcontFk2lrohrBeschreibungIdreferenzIplanungKvrLineWidthMMDottedLineDNDiKvrKlartextHasClosedNSCHsTkPkInVariantXkorYkorGeometriesDifferbz.Fkbz.Qsvbbz.Irtrennbz.Leckstatusbz.Leckstartbz.Leckendbz.Leckortbz.Leckmengebz.Imptnzbz.Zvlimptnzbz.Kantenzvbz.ITrennWithNSCHgeometryfkKIfkKKADTTRDWVERLDWVERLABSIAKTIVIRTRENNJVMVECPDAMPFPHRPMINPVECPVECMAX_INSTPVECMIN_INSTQMAVQMIQMKRHOIRHOKRHOVECSVECTITKTTRVECTVECVAVVIVKVOLDAWVLZVEC
04614463970292122863Rohr R-K4383S R-K4183S4689226368751411179477975287665684418854318450289033820317.78067400.051000010000050291288749724631184713734746689397424OSM: Knoten 450994211 -> Knoten 476971188; Län...166815824520.0050999994.0Rücklauf46144639702921228634614463970292122863False714262.4829305.578857e+06False461446397029212286300000000000LINESTRING (714262.483 5578857.42, 714269.543 ...473006605908996185749171890809650351200.07.7806740.00.01.00.00.0-2.837623E-10\\t-2.837623E-100.01230.01.6150811.61508\\t1.6886551.61508\\t1.6886551.61508\\t1.688655-0.0-0.0-0.01000.31000.31000.3\\t1000.30\\t7.7806749.9999949.9999944.795364E+07\\t4.795363E+0710\\t10-0.0-0.0-0.00.00.0545.09\\t544.34
14615723899944629797Rohr V-K203S V-K213S46892263687514111794779752876656844188572872605962003672664.28724000.051000010000050291288749724631184938076287810941486OSM: Knoten 390310977 -> Knoten 1368674233; Lä...24633100510.0050999994.0Vorlauf46157238999446297974615723899944629797False713738.2965675.579220e+06False461572389994462979700000000000LINESTRING (713738.297 5579219.902, 713793.23 ...512958437245866215053328259196900900610.064.287240.00.01.00.00.02.983143E-10\\t2.983143E-10\\t2.983143E-10\\t2.98...0.01230.03.3048843.304885\\t3.345665\\t3.386445\\t3.427225\\t3.4680...3.304885\\t3.345665\\t3.386445\\t3.427225\\t3.4680...3.304885\\t3.345665\\t3.386445\\t3.427225\\t3.4680...0.00.00.01000.31000.31000.3\\t1000.3\\t1000.3\\t1000.3\\t1000.3\\t1000.3...0\\t9.183891\\t18.36778\\t27.55167\\t36.73557\\t45....9.9999949.999994203.6129\\t212.7968\\t221.9807\\t231.1646\\t240.34...10\\t10\\t10\\t10\\t10\\t10\\t10\\t100.00.00.00.00.0565.84\\t565.4243\\t565.0086\\t564.5929\\t564.1771...
24621030304810285220Rohr R-K2573S R-K2583S5516336706687055417477975287665684418856448814175126160953.95683800.051000010000050291288749724631185625716875961234775OSM: Knoten 476971211 -> Knoten 264607350; Län...24386111020.0050100107.1Rücklauf46210303048102852204621030304810285220False713650.6134005.578990e+06False462103030481028522000000000000LINESTRING (713650.613 5578990.488, 713649.498...507079558016828391257258485779421386060.00.00229116.26220.0643470.00.00.211137-4.251046\\t-4.2510460.1980230.0008352.198672.210375\\t2.198672.210375\\t2.198672.210375\\t2.19867-15.30376-15.30376-15.30376983.7663983.7645983.7663\\t983.76450\\t3.95683859.8673959.8710.05545082\\t0.0531593759.8674\\t59.871-0.479662-0.479662-0.4796620.00.0563.01\\t563.14
\n", "
" ], "text/plain": [ " tk Name FkdtroRowd \\\n", "0 4614463970292122863 Rohr R-K4383S R-K4183S 4689226368751411179 \n", "1 4615723899944629797 Rohr V-K203S V-K213S 4689226368751411179 \n", "2 4621030304810285220 Rohr R-K2573S R-K2583S 5516336706687055417 \n", "\n", " Fkltgr Fkstrasse L Lzu Rau Jlambs \\\n", "0 4779752876656844188 5431845028903382031 7.780674 0 0.05 1 \n", "1 4779752876656844188 5728726059620036726 64.287240 0 0.05 1 \n", "2 4779752876656844188 5644881417512616095 3.956838 0 0.05 1 \n", "\n", " Lambda0 Zein Zaus Zuml Asoll Indschall Baujahr Hal \\\n", "0 0 0 0 0 1000 0 0 \n", "1 0 0 0 0 1000 0 0 \n", "2 0 0 0 0 1000 0 0 \n", "\n", " Fkcont Fk2lrohr \\\n", "0 5029128874972463118 4713734746689397424 \n", "1 5029128874972463118 4938076287810941486 \n", "2 5029128874972463118 5625716875961234775 \n", "\n", " Beschreibung Idreferenz Iplanung \\\n", "0 OSM: Knoten 450994211 -> Knoten 476971188; Län... 166815824 5 \n", "1 OSM: Knoten 390310977 -> Knoten 1368674233; Lä... 24633100 5 \n", "2 OSM: Knoten 476971211 -> Knoten 264607350; Län... 24386111 0 \n", "\n", " Kvr LineWidthMM DottedLine DN Di KvrKlartext HasClosedNSCHs \\\n", "0 2 0.005 0 999 994.0 Rücklauf \n", "1 1 0.005 0 999 994.0 Vorlauf \n", "2 2 0.005 0 100 107.1 Rücklauf \n", "\n", " Tk Pk InVariant Xkor \\\n", "0 4614463970292122863 4614463970292122863 False 714262.482930 \n", "1 4615723899944629797 4615723899944629797 False 713738.296567 \n", "2 4621030304810285220 4621030304810285220 False 713650.613400 \n", "\n", " Ykor GeometriesDiffer bz.Fk bz.Qsvb bz.Irtrenn \\\n", "0 5.578857e+06 False 4614463970292122863 0 0 \n", "1 5.579220e+06 False 4615723899944629797 0 0 \n", "2 5.578990e+06 False 4621030304810285220 0 0 \n", "\n", " bz.Leckstatus bz.Leckstart bz.Leckend bz.Leckort bz.Leckmenge \\\n", "0 0 0 0 0 0 \n", "1 0 0 0 0 0 \n", "2 0 0 0 0 0 \n", "\n", " bz.Imptnz bz.Zvlimptnz bz.Kantenzv bz.ITrennWithNSCH \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "\n", " geometry fkKI \\\n", "0 LINESTRING (714262.483 5578857.42, 714269.543 ... 4730066059089961857 \n", "1 LINESTRING (713738.297 5579219.902, 713793.23 ... 5129584372458662150 \n", "2 LINESTRING (713650.613 5578990.488, 713649.498... 5070795580168283912 \n", "\n", " fkKK A DTTR DWVERL DWVERLABS IAKTIV IRTRENN \\\n", "0 4917189080965035120 0.0 7.780674 0.0 0.0 1.0 0.0 \n", "1 5332825919690090061 0.0 64.28724 0.0 0.0 1.0 0.0 \n", "2 5725848577942138606 0.0 0.002291 16.2622 0.064347 0.0 0.0 \n", "\n", " JV MVEC PDAMPF \\\n", "0 0.0 -2.837623E-10\\t-2.837623E-10 0.0123 \n", "1 0.0 2.983143E-10\\t2.983143E-10\\t2.983143E-10\\t2.98... 0.0123 \n", "2 0.211137 -4.251046\\t-4.251046 0.198023 \n", "\n", " PHR PMIN PVEC \\\n", "0 0.0 1.615081 1.61508\\t1.688655 \n", "1 0.0 3.304884 3.304885\\t3.345665\\t3.386445\\t3.427225\\t3.4680... \n", "2 0.000835 2.19867 2.210375\\t2.19867 \n", "\n", " PVECMAX_INST \\\n", "0 1.61508\\t1.688655 \n", "1 3.304885\\t3.345665\\t3.386445\\t3.427225\\t3.4680... \n", "2 2.210375\\t2.19867 \n", "\n", " PVECMIN_INST QMAV QMI \\\n", "0 1.61508\\t1.688655 -0.0 -0.0 \n", "1 3.304885\\t3.345665\\t3.386445\\t3.427225\\t3.4680... 0.0 0.0 \n", "2 2.210375\\t2.19867 -15.30376 -15.30376 \n", "\n", " QMK RHOI RHOK \\\n", "0 -0.0 1000.3 1000.3 \n", "1 0.0 1000.3 1000.3 \n", "2 -15.30376 983.7663 983.7645 \n", "\n", " RHOVEC \\\n", "0 1000.3\\t1000.3 \n", "1 1000.3\\t1000.3\\t1000.3\\t1000.3\\t1000.3\\t1000.3... \n", "2 983.7663\\t983.7645 \n", "\n", " SVEC TI TK \\\n", "0 0\\t7.780674 9.999994 9.999994 \n", "1 0\\t9.183891\\t18.36778\\t27.55167\\t36.73557\\t45.... 9.999994 9.999994 \n", "2 0\\t3.956838 59.86739 59.871 \n", "\n", " TTRVEC \\\n", "0 4.795364E+07\\t4.795363E+07 \n", "1 203.6129\\t212.7968\\t221.9807\\t231.1646\\t240.34... \n", "2 0.05545082\\t0.05315937 \n", "\n", " TVEC VAV VI VK VOLDA WVL \\\n", "0 10\\t10 -0.0 -0.0 -0.0 0.0 0.0 \n", "1 10\\t10\\t10\\t10\\t10\\t10\\t10\\t10 0.0 0.0 0.0 0.0 0.0 \n", "2 59.8674\\t59.871 -0.479662 -0.479662 -0.479662 0.0 0.0 \n", "\n", " ZVEC \n", "0 545.09\\t544.34 \n", "1 565.84\\t565.4243\\t565.0086\\t564.5929\\t564.1771... \n", "2 563.01\\t563.14 " ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "(s3s.generate_element_dataframe(element_type=s3s.ObjectTypes.Pipe, tks=None)).head(3)" ] }, { "cell_type": "markdown", "id": "219eb668", "metadata": {}, "source": [ "As can be seen some result values are in vectorized form, since pipes have result values that are calculated for interior points." ] }, { "cell_type": "markdown", "id": "3660607a", "metadata": {}, "source": [ "# Pipe vector" ] }, { "cell_type": "code", "execution_count": 13, "id": "123ca8f0", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "[2026-01-10 17:43:10,410] INFO in sir3stoolkit.mantle.dataframes: [generate_element_dataframe] Generating df for element type: ObjectTypes.Pipe ...\n[2026-01-10 17:43:10,411] DEBUG in sir3stoolkit.mantle.dataframes: [generate_element_dataframe] Generating df_model_data for element type: ObjectTypes.Pipe ...\n[2026-01-10 17:43:10,411] INFO in sir3stoolkit.mantle.dataframes: [model_data] Generating model_data dataframe for element type: ObjectTypes.Pipe\n[2026-01-10 17:43:10,414] INFO in sir3stoolkit.mantle.dataframes: [model_data] Retrieved 524 element(s) of element type ObjectTypes.Pipe.\n[2026-01-10 17:43:10,416] INFO in sir3stoolkit.mantle.dataframes: [Resolving model_data Properties] No properties given → using ALL model_data properties for ObjectTypes.Pipe.\n[2026-01-10 17:43:10,417] INFO in sir3stoolkit.mantle.dataframes: [Resolving model_data Properties] Using 46 model_data properties.\n[2026-01-10 17:43:10,417] INFO in sir3stoolkit.mantle.dataframes: [model_data] Retrieving model_data properties ['Name', 'FkdtroRowd', 'Fkltgr', 'Fkstrasse', 'L', 'Lzu', 'Rau', 'Jlambs', 'Lambda0', 'Zein', 'Zaus', 'Zuml', 'Asoll', 'Indschall', 'Baujahr', 'Hal', 'Fkcont', 'Fk2lrohr', 'Beschreibung', 'Idreferenz', 'Iplanung', 'Kvr', 'LineWidthMM', 'DottedLine', 'DN', 'Di', 'KvrKlartext', 'HasClosedNSCHs', 'Tk', 'Pk', 'InVariant', 'Xkor', 'Ykor', 'GeometriesDiffer', 'bz.Fk', 'bz.Qsvb', 'bz.Irtrenn', 'bz.Leckstatus', 'bz.Leckstart', 'bz.Leckend', 'bz.Leckort', 'bz.Leckmenge', 'bz.Imptnz', 'bz.Zvlimptnz', 'bz.Kantenzv', 'bz.ITrennWithNSCH'], geometry, end nodes...\n[2026-01-10 17:43:11,711] INFO in sir3stoolkit.mantle.dataframes: [model_data] 2 non-empty end node columns were created.\n[2026-01-10 17:43:11,861] INFO in sir3stoolkit.mantle.dataframes: [model_data] Transforming DataFrame to GeoDataFrame successful with EPSG: 25832\n[2026-01-10 17:43:11,861] INFO in sir3stoolkit.mantle.dataframes: [model_data] Done. Shape: (524, 50)\n[2026-01-10 17:43:11,861] DEBUG in sir3stoolkit.mantle.dataframes: [generate_element_dataframe] Generating df_results for element type: ObjectTypes.Pipe ...\n[2026-01-10 17:43:11,928] INFO in sir3stoolkit.mantle.dataframes: [results] Generating results dataframe for element type: ObjectTypes.Pipe\n[2026-01-10 17:43:11,996] INFO in sir3stoolkit.mantle.dataframes: [Resolving Timestamps] Only static timestamp 2023-02-13 00:00:00.000 +01:00 is used\n[2026-01-10 17:43:11,997] INFO in sir3stoolkit.mantle.dataframes: [Resolving Timestamps] 1 valid timestamp(s) will be used.\n[2026-01-10 17:43:11,997] INFO in sir3stoolkit.mantle.dataframes: [Resolving tks] Retrieved 524 element(s) of element type ObjectTypes.Pipe.\n[2026-01-10 17:43:12,001] INFO in sir3stoolkit.mantle.dataframes: [results] Using 31 result properties.\n[2026-01-10 17:43:12,028] INFO in sir3stoolkit.mantle.dataframes: [results] Retrieving result values...\n[2026-01-10 17:43:15,515] INFO in sir3stoolkit.mantle.dataframes: [results] Done. Shape: (1, 16244)\n[2026-01-10 17:43:15,515] DEBUG in sir3stoolkit.mantle.dataframes: [generate_element_dataframe] Merging df_model_data with df_results for element type: ObjectTypes.Pipe ...\nc:\\Users\\aUsername\\AppData\\Local\\anaconda3\\Lib\\site-packages\\geopandas\\geodataframe.py:1968: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n super().__setitem__(key, value)\nc:\\Users\\aUsername\\AppData\\Local\\anaconda3\\Lib\\site-packages\\geopandas\\geodataframe.py:1968: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n super().__setitem__(key, value)\nc:\\Users\\aUsername\\AppData\\Local\\anaconda3\\Lib\\site-packages\\geopandas\\geodataframe.py:1968: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n super().__setitem__(key, value)\nc:\\Users\\aUsername\\AppData\\Local\\anaconda3\\Lib\\site-packages\\geopandas\\geodataframe.py:1968: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n super().__setitem__(key, value)\nc:\\Users\\aUsername\\AppData\\Local\\anaconda3\\Lib\\site-packages\\geopandas\\geodataframe.py:1968: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n super().__setitem__(key, value)\nc:\\Users\\aUsername\\AppData\\Local\\anaconda3\\Lib\\site-packages\\geopandas\\geodataframe.py:1968: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n super().__setitem__(key, value)\nc:\\Users\\aUsername\\AppData\\Local\\anaconda3\\Lib\\site-packages\\geopandas\\geodataframe.py:1968: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n super().__setitem__(key, value)\nc:\\Users\\aUsername\\AppData\\Local\\anaconda3\\Lib\\site-packages\\geopandas\\geodataframe.py:1968: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n super().__setitem__(key, value)\nc:\\Users\\aUsername\\AppData\\Local\\anaconda3\\Lib\\site-packages\\geopandas\\geodataframe.py:1968: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n super().__setitem__(key, value)\nc:\\Users\\aUsername\\AppData\\Local\\anaconda3\\Lib\\site-packages\\geopandas\\geodataframe.py:1968: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n super().__setitem__(key, value)\nc:\\Users\\aUsername\\AppData\\Local\\anaconda3\\Lib\\site-packages\\geopandas\\geodataframe.py:1968: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n super().__setitem__(key, value)\nc:\\Users\\aUsername\\AppData\\Local\\anaconda3\\Lib\\site-packages\\geopandas\\geodataframe.py:1968: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n super().__setitem__(key, value)\nc:\\Users\\aUsername\\AppData\\Local\\anaconda3\\Lib\\site-packages\\geopandas\\geodataframe.py:1968: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n super().__setitem__(key, value)\nc:\\Users\\aUsername\\AppData\\Local\\anaconda3\\Lib\\site-packages\\geopandas\\geodataframe.py:1968: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n super().__setitem__(key, value)\nc:\\Users\\aUsername\\AppData\\Local\\anaconda3\\Lib\\site-packages\\geopandas\\geodataframe.py:1968: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n super().__setitem__(key, value)\nc:\\Users\\aUsername\\AppData\\Local\\anaconda3\\Lib\\site-packages\\geopandas\\geodataframe.py:1968: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n super().__setitem__(key, value)\nc:\\Users\\aUsername\\AppData\\Local\\anaconda3\\Lib\\site-packages\\geopandas\\geodataframe.py:1968: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n super().__setitem__(key, value)\nc:\\Users\\aUsername\\AppData\\Local\\anaconda3\\Lib\\site-packages\\geopandas\\geodataframe.py:1968: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n super().__setitem__(key, value)\nc:\\Users\\aUsername\\AppData\\Local\\anaconda3\\Lib\\site-packages\\geopandas\\geodataframe.py:1968: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n super().__setitem__(key, value)\nc:\\Users\\aUsername\\AppData\\Local\\anaconda3\\Lib\\site-packages\\geopandas\\geodataframe.py:1968: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n super().__setitem__(key, value)\nc:\\Users\\aUsername\\AppData\\Local\\anaconda3\\Lib\\site-packages\\geopandas\\geodataframe.py:1968: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n super().__setitem__(key, value)\nc:\\Users\\aUsername\\AppData\\Local\\anaconda3\\Lib\\site-packages\\geopandas\\geodataframe.py:1968: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n super().__setitem__(key, value)\nc:\\Users\\aUsername\\AppData\\Local\\anaconda3\\Lib\\site-packages\\geopandas\\geodataframe.py:1968: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n super().__setitem__(key, value)\nc:\\Users\\aUsername\\AppData\\Local\\anaconda3\\Lib\\site-packages\\geopandas\\geodataframe.py:1968: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n super().__setitem__(key, value)\nc:\\Users\\aUsername\\AppData\\Local\\anaconda3\\Lib\\site-packages\\geopandas\\geodataframe.py:1968: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n super().__setitem__(key, value)\nc:\\Users\\aUsername\\AppData\\Local\\anaconda3\\Lib\\site-packages\\geopandas\\geodataframe.py:1968: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n super().__setitem__(key, value)\nc:\\Users\\aUsername\\AppData\\Local\\anaconda3\\Lib\\site-packages\\geopandas\\geodataframe.py:1968: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n super().__setitem__(key, value)\nc:\\Users\\aUsername\\AppData\\Local\\anaconda3\\Lib\\site-packages\\geopandas\\geodataframe.py:1968: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n super().__setitem__(key, value)\nc:\\Users\\aUsername\\AppData\\Local\\anaconda3\\Lib\\site-packages\\geopandas\\geodataframe.py:1968: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n super().__setitem__(key, value)\nc:\\Users\\aUsername\\AppData\\Local\\anaconda3\\Lib\\site-packages\\geopandas\\geodataframe.py:1968: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n super().__setitem__(key, value)\nc:\\Users\\aUsername\\AppData\\Local\\anaconda3\\Lib\\site-packages\\geopandas\\geodataframe.py:1968: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n super().__setitem__(key, value)\nc:\\Users\\aUsername\\AppData\\Local\\anaconda3\\Lib\\site-packages\\geopandas\\geodataframe.py:1968: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n super().__setitem__(key, value)\nc:\\Users\\aUsername\\AppData\\Local\\anaconda3\\Lib\\site-packages\\geopandas\\geodataframe.py:1968: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n super().__setitem__(key, value)\nc:\\Users\\aUsername\\AppData\\Local\\anaconda3\\Lib\\site-packages\\geopandas\\geodataframe.py:1968: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n super().__setitem__(key, value)\nc:\\Users\\aUsername\\AppData\\Local\\anaconda3\\Lib\\site-packages\\geopandas\\geodataframe.py:1968: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n super().__setitem__(key, value)\nc:\\Users\\aUsername\\AppData\\Local\\anaconda3\\Lib\\site-packages\\geopandas\\geodataframe.py:1968: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n super().__setitem__(key, value)\nc:\\Users\\aUsername\\AppData\\Local\\anaconda3\\Lib\\site-packages\\geopandas\\geodataframe.py:1968: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n super().__setitem__(key, value)\nc:\\Users\\aUsername\\AppData\\Local\\anaconda3\\Lib\\site-packages\\geopandas\\geodataframe.py:1968: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n super().__setitem__(key, value)\nc:\\Users\\aUsername\\AppData\\Local\\anaconda3\\Lib\\site-packages\\geopandas\\geodataframe.py:1968: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n super().__setitem__(key, value)\nc:\\Users\\aUsername\\AppData\\Local\\anaconda3\\Lib\\site-packages\\geopandas\\geodataframe.py:1968: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n super().__setitem__(key, value)\nc:\\Users\\aUsername\\AppData\\Local\\anaconda3\\Lib\\site-packages\\geopandas\\geodataframe.py:1968: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n super().__setitem__(key, value)\nc:\\Users\\aUsername\\AppData\\Local\\anaconda3\\Lib\\site-packages\\geopandas\\geodataframe.py:1968: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n super().__setitem__(key, value)\nc:\\Users\\aUsername\\AppData\\Local\\anaconda3\\Lib\\site-packages\\geopandas\\geodataframe.py:1968: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n super().__setitem__(key, value)\nc:\\Users\\aUsername\\AppData\\Local\\anaconda3\\Lib\\site-packages\\geopandas\\geodataframe.py:1968: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n super().__setitem__(key, value)\nc:\\Users\\aUsername\\AppData\\Local\\anaconda3\\Lib\\site-packages\\geopandas\\geodataframe.py:1968: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n super().__setitem__(key, value)\nc:\\Users\\aUsername\\AppData\\Local\\anaconda3\\Lib\\site-packages\\geopandas\\geodataframe.py:1968: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n super().__setitem__(key, value)\nc:\\Users\\aUsername\\AppData\\Local\\anaconda3\\Lib\\site-packages\\geopandas\\geodataframe.py:1968: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n super().__setitem__(key, value)\nc:\\Users\\aUsername\\AppData\\Local\\anaconda3\\Lib\\site-packages\\geopandas\\geodataframe.py:1968: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n super().__setitem__(key, value)\nc:\\Users\\aUsername\\AppData\\Local\\anaconda3\\Lib\\site-packages\\geopandas\\geodataframe.py:1968: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n super().__setitem__(key, value)\nc:\\Users\\aUsername\\AppData\\Local\\anaconda3\\Lib\\site-packages\\geopandas\\geodataframe.py:1968: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n super().__setitem__(key, value)\nc:\\Users\\aUsername\\AppData\\Local\\anaconda3\\Lib\\site-packages\\geopandas\\geodataframe.py:1968: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n super().__setitem__(key, value)\nc:\\Users\\aUsername\\AppData\\Local\\anaconda3\\Lib\\site-packages\\geopandas\\geodataframe.py:1968: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n super().__setitem__(key, value)\nc:\\Users\\aUsername\\AppData\\Local\\anaconda3\\Lib\\site-packages\\geopandas\\geodataframe.py:1968: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n super().__setitem__(key, value)\nc:\\Users\\aUsername\\AppData\\Local\\anaconda3\\Lib\\site-packages\\geopandas\\geodataframe.py:1968: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n super().__setitem__(key, value)\nc:\\Users\\aUsername\\AppData\\Local\\anaconda3\\Lib\\site-packages\\geopandas\\geodataframe.py:1968: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n super().__setitem__(key, value)\nc:\\Users\\aUsername\\AppData\\Local\\anaconda3\\Lib\\site-packages\\geopandas\\geodataframe.py:1968: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n super().__setitem__(key, value)\nc:\\Users\\aUsername\\AppData\\Local\\anaconda3\\Lib\\site-packages\\geopandas\\geodataframe.py:1968: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n super().__setitem__(key, value)\nc:\\Users\\aUsername\\AppData\\Local\\anaconda3\\Lib\\site-packages\\geopandas\\geodataframe.py:1968: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n super().__setitem__(key, value)\nc:\\Users\\aUsername\\AppData\\Local\\anaconda3\\Lib\\site-packages\\geopandas\\geodataframe.py:1968: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n super().__setitem__(key, value)\nc:\\Users\\aUsername\\AppData\\Local\\anaconda3\\Lib\\site-packages\\geopandas\\geodataframe.py:1968: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n super().__setitem__(key, value)\nc:\\Users\\aUsername\\AppData\\Local\\anaconda3\\Lib\\site-packages\\geopandas\\geodataframe.py:1968: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n super().__setitem__(key, value)\nc:\\Users\\aUsername\\AppData\\Local\\anaconda3\\Lib\\site-packages\\geopandas\\geodataframe.py:1968: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n super().__setitem__(key, value)\nc:\\Users\\aUsername\\AppData\\Local\\anaconda3\\Lib\\site-packages\\geopandas\\geodataframe.py:1968: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n super().__setitem__(key, value)\nc:\\Users\\aUsername\\AppData\\Local\\anaconda3\\Lib\\site-packages\\geopandas\\geodataframe.py:1968: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n super().__setitem__(key, value)\nc:\\Users\\aUsername\\AppData\\Local\\anaconda3\\Lib\\site-packages\\geopandas\\geodataframe.py:1968: PerformanceWarning: DataFrame is highly fragmented. This is usually the result of calling `frame.insert` many times, which has poor performance. Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n super().__setitem__(key, value)\n" ] }, { "data": { "text/html": [ "
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tkNameFkdtroRowdFkltgrFkstrasseLLzuRauJlambsLambda0ZeinZausZumlAsollIndschallBaujahrHalFkcontFk2lrohrBeschreibungIdreferenzIplanungKvrLineWidthMMDottedLineDNDiKvrKlartextHasClosedNSCHsTkPkInVariantXkorYkorGeometriesDifferbz.Fkbz.Qsvbbz.Irtrennbz.Leckstatusbz.Leckstartbz.Leckendbz.Leckortbz.Leckmengebz.Imptnzbz.Zvlimptnzbz.Kantenzvbz.ITrennWithNSCHgeometryfkKIfkKKADTTRDWVERLDWVERLABSIAKTIVIRTRENNJVPDAMPFPHRPMINQMAVQMIQMKRHOIRHOKTITKVAVVIVKVOLDAWVLMVEC_0MVEC_1MVEC_2MVEC_3MVEC_4MVEC_5MVEC_6MVEC_7MVEC_8MVEC_9MVEC_10MVEC_11MVEC_12MVEC_13MVEC_14MVEC_15MVEC_16MVEC_17PVEC_0PVEC_1PVEC_2PVEC_3PVEC_4PVEC_5PVEC_6PVEC_7PVEC_8PVEC_9PVEC_10PVEC_11PVEC_12PVEC_13PVEC_14PVEC_15PVEC_16PVEC_17PVECMAX_INST_0PVECMAX_INST_1PVECMAX_INST_2PVECMAX_INST_3PVECMAX_INST_4PVECMAX_INST_5PVECMAX_INST_6PVECMAX_INST_7PVECMAX_INST_8PVECMAX_INST_9PVECMAX_INST_10PVECMAX_INST_11PVECMAX_INST_12PVECMAX_INST_13PVECMAX_INST_14PVECMAX_INST_15PVECMAX_INST_16PVECMAX_INST_17PVECMIN_INST_0PVECMIN_INST_1PVECMIN_INST_2PVECMIN_INST_3PVECMIN_INST_4PVECMIN_INST_5PVECMIN_INST_6PVECMIN_INST_7PVECMIN_INST_8PVECMIN_INST_9PVECMIN_INST_10PVECMIN_INST_11PVECMIN_INST_12PVECMIN_INST_13PVECMIN_INST_14PVECMIN_INST_15PVECMIN_INST_16PVECMIN_INST_17RHOVEC_0RHOVEC_1RHOVEC_2RHOVEC_3RHOVEC_4RHOVEC_5RHOVEC_6RHOVEC_7RHOVEC_8RHOVEC_9RHOVEC_10RHOVEC_11RHOVEC_12RHOVEC_13RHOVEC_14RHOVEC_15RHOVEC_16RHOVEC_17SVEC_0SVEC_1SVEC_2SVEC_3SVEC_4SVEC_5SVEC_6SVEC_7SVEC_8SVEC_9SVEC_10SVEC_11SVEC_12SVEC_13SVEC_14SVEC_15SVEC_16SVEC_17TTRVEC_0TTRVEC_1TTRVEC_2TTRVEC_3TTRVEC_4TTRVEC_5TTRVEC_6TTRVEC_7TTRVEC_8TTRVEC_9TTRVEC_10TTRVEC_11TTRVEC_12TTRVEC_13TTRVEC_14TTRVEC_15TTRVEC_16TTRVEC_17TVEC_0TVEC_1TVEC_2TVEC_3TVEC_4TVEC_5TVEC_6TVEC_7TVEC_8TVEC_9TVEC_10TVEC_11TVEC_12TVEC_13TVEC_14TVEC_15TVEC_16TVEC_17ZVEC_0ZVEC_1ZVEC_2ZVEC_3ZVEC_4ZVEC_5ZVEC_6ZVEC_7ZVEC_8ZVEC_9ZVEC_10ZVEC_11ZVEC_12ZVEC_13ZVEC_14ZVEC_15ZVEC_16ZVEC_17
04614463970292122863Rohr R-K4383S R-K4183S4689226368751411179477975287665684418854318450289033820317.78067400.051000010000050291288749724631184713734746689397424OSM: Knoten 450994211 -> Knoten 476971188; Län...166815824520.0050999994.0Rücklauf46144639702921228634614463970292122863False714262.4829305.578857e+06False461446397029212286300000000000LINESTRING (714262.483 5578857.42, 714269.543 ...473006605908996185749171890809650351200.07.7806740.00.01.00.00.00.01230.01.615081-0.0-0.0-0.01000.31000.39.9999949.999994-0.0-0.0-0.00.00.0-2.837623e-10-2.837623e-10NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1.6150801.688655NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1.6150801.688655NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1.6150801.688655NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1000.30001000.3000NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN07.780674NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN4.795364e+074.795363e+07NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN10.000010.000NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN545.09544.3400NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
14615723899944629797Rohr V-K203S V-K213S46892263687514111794779752876656844188572872605962003672664.28724000.051000010000050291288749724631184938076287810941486OSM: Knoten 390310977 -> Knoten 1368674233; Lä...24633100510.0050999994.0Vorlauf46157238999446297974615723899944629797False713738.2965675.579220e+06False461572389994462979700000000000LINESTRING (713738.297 5579219.902, 713793.23 ...512958437245866215053328259196900900610.064.287240.00.01.00.00.00.01230.03.3048840.00.00.01000.31000.39.9999949.9999940.00.00.00.00.02.983143e-102.983143e-102.983143e-102.983143e-102.983143e-102.983143e-102.983143e-102.983143e-10NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN3.3048853.3456653.3864453.4272253.4680053.5087853.5495653.590345NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN3.3048853.3456653.3864453.4272253.4680053.5087853.5495653.590345NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN3.3048853.3456653.3864453.4272253.4680053.5087853.5495653.590345NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1000.30001000.30001000.31000.31000.31000.31000.31000.3NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN09.18389118.3677827.5516736.7355745.9194655.1033564.28724NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2.036129e+022.127968e+02221.9807231.1646240.3485249.5324258.7163267.9001NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN10.000010.00010.010.010.010.010.010.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN565.84565.4243565.0086564.5929564.1771563.7614563.3457562.93NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
24621030304810285220Rohr R-K2573S R-K2583S5516336706687055417477975287665684418856448814175126160953.95683800.051000010000050291288749724631185625716875961234775OSM: Knoten 476971211 -> Knoten 264607350; Län...24386111020.0050100107.1Rücklauf46210303048102852204621030304810285220False713650.6134005.578990e+06False462103030481028522000000000000LINESTRING (713650.613 5578990.488, 713649.498...507079558016828391257258485779421386060.00.00229116.26220.0643470.00.00.2111370.1980230.0008352.19867-15.30376-15.30376-15.30376983.7663983.764559.8673959.871-0.479662-0.479662-0.4796620.00.0-4.251046e+00-4.251046e+00NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2.2103752.198670NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2.2103752.198670NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2.2103752.198670NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN983.7663983.7645NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN03.956838NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN5.545082e-025.315937e-02NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN59.867459.871NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN563.01563.1400NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
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" ], "text/plain": [ " tk Name FkdtroRowd \\\n", "0 4614463970292122863 Rohr R-K4383S R-K4183S 4689226368751411179 \n", "1 4615723899944629797 Rohr V-K203S V-K213S 4689226368751411179 \n", "2 4621030304810285220 Rohr R-K2573S R-K2583S 5516336706687055417 \n", "\n", " Fkltgr Fkstrasse L Lzu Rau Jlambs \\\n", "0 4779752876656844188 5431845028903382031 7.780674 0 0.05 1 \n", "1 4779752876656844188 5728726059620036726 64.287240 0 0.05 1 \n", "2 4779752876656844188 5644881417512616095 3.956838 0 0.05 1 \n", "\n", " Lambda0 Zein Zaus Zuml Asoll Indschall Baujahr Hal \\\n", "0 0 0 0 0 1000 0 0 \n", "1 0 0 0 0 1000 0 0 \n", "2 0 0 0 0 1000 0 0 \n", "\n", " Fkcont Fk2lrohr \\\n", "0 5029128874972463118 4713734746689397424 \n", "1 5029128874972463118 4938076287810941486 \n", "2 5029128874972463118 5625716875961234775 \n", "\n", " Beschreibung Idreferenz Iplanung \\\n", "0 OSM: Knoten 450994211 -> Knoten 476971188; Län... 166815824 5 \n", "1 OSM: Knoten 390310977 -> Knoten 1368674233; Lä... 24633100 5 \n", "2 OSM: Knoten 476971211 -> Knoten 264607350; Län... 24386111 0 \n", "\n", " Kvr LineWidthMM DottedLine DN Di KvrKlartext HasClosedNSCHs \\\n", "0 2 0.005 0 999 994.0 Rücklauf \n", "1 1 0.005 0 999 994.0 Vorlauf \n", "2 2 0.005 0 100 107.1 Rücklauf \n", "\n", " Tk Pk InVariant Xkor \\\n", "0 4614463970292122863 4614463970292122863 False 714262.482930 \n", "1 4615723899944629797 4615723899944629797 False 713738.296567 \n", "2 4621030304810285220 4621030304810285220 False 713650.613400 \n", "\n", " Ykor GeometriesDiffer bz.Fk bz.Qsvb bz.Irtrenn \\\n", "0 5.578857e+06 False 4614463970292122863 0 0 \n", "1 5.579220e+06 False 4615723899944629797 0 0 \n", "2 5.578990e+06 False 4621030304810285220 0 0 \n", "\n", " bz.Leckstatus bz.Leckstart bz.Leckend bz.Leckort bz.Leckmenge \\\n", "0 0 0 0 0 0 \n", "1 0 0 0 0 0 \n", "2 0 0 0 0 0 \n", "\n", " bz.Imptnz bz.Zvlimptnz bz.Kantenzv bz.ITrennWithNSCH \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "2 0 0 0 0 \n", "\n", " geometry fkKI \\\n", "0 LINESTRING (714262.483 5578857.42, 714269.543 ... 4730066059089961857 \n", "1 LINESTRING (713738.297 5579219.902, 713793.23 ... 5129584372458662150 \n", "2 LINESTRING (713650.613 5578990.488, 713649.498... 5070795580168283912 \n", "\n", " fkKK A DTTR DWVERL DWVERLABS IAKTIV IRTRENN \\\n", "0 4917189080965035120 0.0 7.780674 0.0 0.0 1.0 0.0 \n", "1 5332825919690090061 0.0 64.28724 0.0 0.0 1.0 0.0 \n", "2 5725848577942138606 0.0 0.002291 16.2622 0.064347 0.0 0.0 \n", "\n", " JV PDAMPF PHR PMIN QMAV QMI QMK \\\n", "0 0.0 0.0123 0.0 1.615081 -0.0 -0.0 -0.0 \n", "1 0.0 0.0123 0.0 3.304884 0.0 0.0 0.0 \n", "2 0.211137 0.198023 0.000835 2.19867 -15.30376 -15.30376 -15.30376 \n", "\n", " RHOI RHOK TI TK VAV VI VK VOLDA \\\n", "0 1000.3 1000.3 9.999994 9.999994 -0.0 -0.0 -0.0 0.0 \n", "1 1000.3 1000.3 9.999994 9.999994 0.0 0.0 0.0 0.0 \n", "2 983.7663 983.7645 59.86739 59.871 -0.479662 -0.479662 -0.479662 0.0 \n", "\n", " WVL MVEC_0 MVEC_1 MVEC_2 MVEC_3 MVEC_4 \\\n", "0 0.0 -2.837623e-10 -2.837623e-10 NaN NaN NaN \n", "1 0.0 2.983143e-10 2.983143e-10 2.983143e-10 2.983143e-10 2.983143e-10 \n", "2 0.0 -4.251046e+00 -4.251046e+00 NaN NaN NaN \n", "\n", " MVEC_5 MVEC_6 MVEC_7 MVEC_8 MVEC_9 MVEC_10 MVEC_11 \\\n", "0 NaN NaN NaN NaN NaN NaN NaN \n", "1 2.983143e-10 2.983143e-10 2.983143e-10 NaN NaN NaN NaN \n", "2 NaN NaN NaN NaN NaN NaN NaN \n", "\n", " MVEC_12 MVEC_13 MVEC_14 MVEC_15 MVEC_16 MVEC_17 PVEC_0 PVEC_1 \\\n", "0 NaN NaN NaN NaN NaN NaN 1.615080 1.688655 \n", "1 NaN NaN NaN NaN NaN NaN 3.304885 3.345665 \n", "2 NaN NaN NaN NaN NaN NaN 2.210375 2.198670 \n", "\n", " PVEC_2 PVEC_3 PVEC_4 PVEC_5 PVEC_6 PVEC_7 PVEC_8 PVEC_9 \\\n", "0 NaN NaN NaN NaN NaN NaN NaN NaN \n", "1 3.386445 3.427225 3.468005 3.508785 3.549565 3.590345 NaN NaN \n", "2 NaN NaN NaN NaN NaN NaN NaN NaN \n", "\n", " PVEC_10 PVEC_11 PVEC_12 PVEC_13 PVEC_14 PVEC_15 PVEC_16 PVEC_17 \\\n", "0 NaN NaN NaN NaN NaN NaN NaN NaN \n", "1 NaN NaN NaN NaN NaN NaN NaN NaN \n", "2 NaN NaN NaN NaN NaN NaN NaN NaN \n", "\n", " PVECMAX_INST_0 PVECMAX_INST_1 PVECMAX_INST_2 PVECMAX_INST_3 \\\n", "0 1.615080 1.688655 NaN NaN \n", "1 3.304885 3.345665 3.386445 3.427225 \n", "2 2.210375 2.198670 NaN NaN \n", "\n", " PVECMAX_INST_4 PVECMAX_INST_5 PVECMAX_INST_6 PVECMAX_INST_7 \\\n", "0 NaN NaN NaN NaN \n", "1 3.468005 3.508785 3.549565 3.590345 \n", "2 NaN NaN NaN NaN \n", "\n", " PVECMAX_INST_8 PVECMAX_INST_9 PVECMAX_INST_10 PVECMAX_INST_11 \\\n", "0 NaN NaN NaN NaN \n", "1 NaN NaN NaN NaN \n", "2 NaN NaN NaN NaN \n", "\n", " PVECMAX_INST_12 PVECMAX_INST_13 PVECMAX_INST_14 PVECMAX_INST_15 \\\n", "0 NaN NaN NaN NaN \n", "1 NaN NaN NaN NaN \n", "2 NaN NaN NaN NaN \n", "\n", " PVECMAX_INST_16 PVECMAX_INST_17 PVECMIN_INST_0 PVECMIN_INST_1 \\\n", "0 NaN NaN 1.615080 1.688655 \n", "1 NaN NaN 3.304885 3.345665 \n", "2 NaN NaN 2.210375 2.198670 \n", "\n", " PVECMIN_INST_2 PVECMIN_INST_3 PVECMIN_INST_4 PVECMIN_INST_5 \\\n", "0 NaN NaN NaN NaN \n", "1 3.386445 3.427225 3.468005 3.508785 \n", "2 NaN NaN NaN NaN \n", "\n", " PVECMIN_INST_6 PVECMIN_INST_7 PVECMIN_INST_8 PVECMIN_INST_9 \\\n", "0 NaN NaN NaN NaN \n", "1 3.549565 3.590345 NaN NaN \n", "2 NaN NaN NaN NaN \n", "\n", " PVECMIN_INST_10 PVECMIN_INST_11 PVECMIN_INST_12 PVECMIN_INST_13 \\\n", "0 NaN NaN NaN NaN \n", "1 NaN NaN NaN NaN \n", "2 NaN NaN NaN NaN \n", "\n", " PVECMIN_INST_14 PVECMIN_INST_15 PVECMIN_INST_16 PVECMIN_INST_17 \\\n", "0 NaN NaN NaN NaN \n", "1 NaN NaN NaN NaN \n", "2 NaN NaN NaN NaN \n", "\n", " RHOVEC_0 RHOVEC_1 RHOVEC_2 RHOVEC_3 RHOVEC_4 RHOVEC_5 RHOVEC_6 \\\n", "0 1000.3000 1000.3000 NaN NaN NaN NaN NaN \n", "1 1000.3000 1000.3000 1000.3 1000.3 1000.3 1000.3 1000.3 \n", "2 983.7663 983.7645 NaN NaN NaN NaN NaN \n", "\n", " RHOVEC_7 RHOVEC_8 RHOVEC_9 RHOVEC_10 RHOVEC_11 RHOVEC_12 RHOVEC_13 \\\n", "0 NaN NaN NaN NaN NaN NaN NaN \n", "1 1000.3 NaN NaN NaN NaN NaN NaN \n", "2 NaN NaN NaN NaN NaN NaN NaN \n", "\n", " RHOVEC_14 RHOVEC_15 RHOVEC_16 RHOVEC_17 SVEC_0 SVEC_1 SVEC_2 \\\n", "0 NaN NaN NaN NaN 0 7.780674 NaN \n", "1 NaN NaN NaN NaN 0 9.183891 18.36778 \n", "2 NaN NaN NaN NaN 0 3.956838 NaN \n", "\n", " SVEC_3 SVEC_4 SVEC_5 SVEC_6 SVEC_7 SVEC_8 SVEC_9 SVEC_10 \\\n", "0 NaN NaN NaN NaN NaN NaN NaN NaN \n", "1 27.55167 36.73557 45.91946 55.10335 64.28724 NaN NaN NaN \n", "2 NaN NaN NaN NaN NaN NaN NaN NaN \n", "\n", " SVEC_11 SVEC_12 SVEC_13 SVEC_14 SVEC_15 SVEC_16 SVEC_17 \\\n", "0 NaN NaN NaN NaN NaN NaN NaN \n", "1 NaN NaN NaN NaN NaN NaN NaN \n", "2 NaN NaN NaN NaN NaN NaN NaN \n", "\n", " TTRVEC_0 TTRVEC_1 TTRVEC_2 TTRVEC_3 TTRVEC_4 TTRVEC_5 \\\n", "0 4.795364e+07 4.795363e+07 NaN NaN NaN NaN \n", "1 2.036129e+02 2.127968e+02 221.9807 231.1646 240.3485 249.5324 \n", "2 5.545082e-02 5.315937e-02 NaN NaN NaN NaN \n", "\n", " TTRVEC_6 TTRVEC_7 TTRVEC_8 TTRVEC_9 TTRVEC_10 TTRVEC_11 TTRVEC_12 \\\n", "0 NaN NaN NaN NaN NaN NaN NaN \n", "1 258.7163 267.9001 NaN NaN NaN NaN NaN \n", "2 NaN NaN NaN NaN NaN NaN NaN \n", "\n", " TTRVEC_13 TTRVEC_14 TTRVEC_15 TTRVEC_16 TTRVEC_17 TVEC_0 TVEC_1 \\\n", "0 NaN NaN NaN NaN NaN 10.0000 10.000 \n", "1 NaN NaN NaN NaN NaN 10.0000 10.000 \n", "2 NaN NaN NaN NaN NaN 59.8674 59.871 \n", "\n", " TVEC_2 TVEC_3 TVEC_4 TVEC_5 TVEC_6 TVEC_7 TVEC_8 TVEC_9 TVEC_10 \\\n", "0 NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", "1 10.0 10.0 10.0 10.0 10.0 10.0 NaN NaN NaN \n", "2 NaN NaN NaN NaN NaN NaN NaN NaN NaN \n", "\n", " TVEC_11 TVEC_12 TVEC_13 TVEC_14 TVEC_15 TVEC_16 TVEC_17 ZVEC_0 \\\n", "0 NaN NaN NaN NaN NaN NaN NaN 545.09 \n", "1 NaN NaN NaN NaN NaN NaN NaN 565.84 \n", "2 NaN NaN NaN NaN NaN NaN NaN 563.01 \n", "\n", " ZVEC_1 ZVEC_2 ZVEC_3 ZVEC_4 ZVEC_5 ZVEC_6 ZVEC_7 ZVEC_8 \\\n", "0 544.3400 NaN NaN NaN NaN NaN NaN NaN \n", "1 565.4243 565.0086 564.5929 564.1771 563.7614 563.3457 562.93 NaN \n", "2 563.1400 NaN NaN NaN NaN NaN NaN NaN \n", "\n", " ZVEC_9 ZVEC_10 ZVEC_11 ZVEC_12 ZVEC_13 ZVEC_14 ZVEC_15 ZVEC_16 \\\n", "0 NaN NaN NaN NaN NaN NaN NaN NaN \n", "1 NaN NaN NaN NaN NaN NaN NaN NaN \n", "2 NaN NaN NaN NaN NaN NaN NaN NaN \n", "\n", " ZVEC_17 \n", "0 NaN \n", "1 NaN \n", "2 NaN " ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "(s3s.generate_pipe_vector_dataframe()).head(3)" ] }, { "cell_type": "markdown", "id": "808b2c79", "metadata": {}, "source": [ "## DistrictHeatingConsumer" ] }, { "cell_type": "code", "execution_count": 14, "id": "71e17335", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "[2026-01-10 17:43:15,760] INFO in sir3stoolkit.mantle.dataframes: [generate_element_dataframe] Generating df for element type: ObjectTypes.DistrictHeatingConsumer ...\n", "[2026-01-10 17:43:15,761] DEBUG in sir3stoolkit.mantle.dataframes: [generate_element_dataframe] Generating df_model_data for element type: ObjectTypes.DistrictHeatingConsumer ...\n", "[2026-01-10 17:43:15,763] INFO in sir3stoolkit.mantle.dataframes: [model_data] Generating model_data dataframe for element type: ObjectTypes.DistrictHeatingConsumer\n", "[2026-01-10 17:43:15,765] INFO in sir3stoolkit.mantle.dataframes: [model_data] Retrieved 337 element(s) of element type ObjectTypes.DistrictHeatingConsumer.\n", "[2026-01-10 17:43:15,768] INFO in sir3stoolkit.mantle.dataframes: [Resolving model_data Properties] No properties given → using ALL model_data properties for ObjectTypes.DistrictHeatingConsumer.\n", "[2026-01-10 17:43:15,768] INFO in sir3stoolkit.mantle.dataframes: [Resolving model_data Properties] Using 55 model_data properties.\n", "[2026-01-10 17:43:15,768] INFO in sir3stoolkit.mantle.dataframes: [model_data] Retrieving model_data properties ['Name', 'Beschreibung', 'Ind0', 'W0', 'Qm0', 'Tvl0', 'Trs0', 'Lfk', 'Rho0', 'Dtmin', 'Indtr', 'Trsk', 'Fktrft', 'A', 'B', 'C', 'Vtyp', 'V0', 'P1soll', 'Dpvlmin', 'Fkzep1vl', 'Tsvl', 'Zevk', 'Dphaus', 'Dprlmin', 'Fkzep1rl', 'Tsrl', 'Imbg', 'Irfv', 'Fkcont', 'Idreferenz', 'Iplanung', 'CPM', 'NumberOfVERB', 'IndtrKlartext', 'M0Estimated', 'W0Estimated', 'Tk', 'Pk', 'InVariant', 'Xkor', 'Ykor', 'ShowDescription', 'PositionOfDescription', 'Angle', 'SymbolFactor', 'GeometriesDiffer', 'bz.Fk', 'bz.Indlast', 'bz.Indlfkt2', 'bz.Fklfkt', 'bz.Fklfkt2', 'bz.Fkqvar', 'bz.Fktevt', 'bz.IndlastKlartext'], geometry, end nodes...\n", "[2026-01-10 17:43:16,850] INFO in sir3stoolkit.mantle.dataframes: [model_data] 2 non-empty end node columns were created.\n", "[2026-01-10 17:43:16,998] INFO in sir3stoolkit.mantle.dataframes: [model_data] Transforming DataFrame to GeoDataFrame successful with EPSG: 25832\n", "[2026-01-10 17:43:16,998] INFO in sir3stoolkit.mantle.dataframes: [model_data] Done. Shape: (337, 59)\n", "[2026-01-10 17:43:16,998] DEBUG in sir3stoolkit.mantle.dataframes: [generate_element_dataframe] Generating df_results for element type: ObjectTypes.DistrictHeatingConsumer ...\n", "[2026-01-10 17:43:17,061] INFO in sir3stoolkit.mantle.dataframes: [results] Generating results dataframe for element type: ObjectTypes.DistrictHeatingConsumer\n", "[2026-01-10 17:43:17,131] INFO in sir3stoolkit.mantle.dataframes: [Resolving Timestamps] Only static timestamp 2023-02-13 00:00:00.000 +01:00 is used\n", "[2026-01-10 17:43:17,131] INFO in sir3stoolkit.mantle.dataframes: [Resolving Timestamps] 1 valid timestamp(s) will be used.\n", "[2026-01-10 17:43:17,133] INFO in sir3stoolkit.mantle.dataframes: [Resolving tks] Retrieved 337 element(s) of element type ObjectTypes.DistrictHeatingConsumer.\n", "[2026-01-10 17:43:17,133] INFO in sir3stoolkit.mantle.dataframes: [results] Using 20 result properties.\n", "[2026-01-10 17:43:17,147] INFO in sir3stoolkit.mantle.dataframes: [results] Retrieving result values...\n", "[2026-01-10 17:43:18,231] INFO in sir3stoolkit.mantle.dataframes: [results] Done. Shape: (1, 6740)\n", "[2026-01-10 17:43:18,231] DEBUG in sir3stoolkit.mantle.dataframes: [generate_element_dataframe] Merging df_model_data with df_results for element type: ObjectTypes.DistrictHeatingConsumer ...\n" ] }, { "data": { "text/html": [ "
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tkNameBeschreibungInd0W0Qm0Tvl0Trs0LfkRho0DtminIndtrTrskFktrftABCVtypV0P1sollDpvlminFkzep1vlTsvlZevkDphausDprlminFkzep1rlTsrlImbgIrfvFkcontIdreferenzIplanungCPMNumberOfVERBIndtrKlartextM0EstimatedW0EstimatedTkPkInVariantXkorYkorShowDescriptionPositionOfDescriptionAngleSymbolFactorGeometriesDifferbz.Fkbz.Indlastbz.Indlfkt2bz.Fklfktbz.Fklfkt2bz.Fkqvarbz.Fktevtbz.IndlastKlartextgeometryfkKIfkKKDHDPDPHIAKTIVINDUVLFHLFTMMHYUVMTHUVPHIRLPHIVLQMRHOIRHOKTITKTVMINWWSOLL
04611752310942477664Fernwärmeverbraucher V-K1203S R-K3683SGattendorf;95185;Obere Au;28;None;yes0139.043319060110003360-12-0.25-21100-1000.20.3-1001502912887497246311883088723814.19030Tabelle Temperatur TEVT, TRS(t)3.98186234.9191746117523109424776644611752310942477664False713675.3002325.578705e+06False300.1False4611752310942477664004835417738045943522-15395645951786400348Lastfaktor thermischPOINT (713675.3 5578705.193)4673701597187411685499578894538771166917.1784501.5838061.5838060.0-1.00.2883430.2831310.318879-0.829086-0.829086-3.333333e+32-3.333333e+321.147966966.0245983.789.459260.068.5078139.3675039.36750
14612528660388965271Fernwärmeverbraucher V-K1783S R-K4263SNone;None;None;None;None;yes0386.413419060110003360-12-0.25-21000-1000.20.3-10015029128874972463118105628731714.19030Tabelle Temperatur TEVT, TRS(t)11.06594034.9191746125286603889652714612528660388965271False714251.3323955.578925e+06False300.1False4612528660388965271005554262436821166605-15395645951786400348Lastfaktor thermischPOINT (714251.332 5578925.001)501510189172519860357518088373480527640.0697690.0068440.0068441.0-1.00.0000000.0000000.0000000.0000000.000000-3.333333e+32-3.333333e+320.0000001000.3000983.710.000060.010.000000.000000.00000
24612562908060328263Fernwärmeverbraucher V-K1623S R-K4103SGattendorf;95185;Langenbachstraße;4;None;yes0106.640919060110003360-12-0.25-21100-1000.20.3-1001502912887497246311883081818214.19030Tabelle Temperatur TEVT, TRS(t)3.05393634.9191746125629080603282634612562908060328263False713271.2718175.578980e+06False300.1False4612562908060328263004835417738045943522-15395645951786400348Lastfaktor thermischPOINT (713271.272 5578979.743)4965299629814639205477953653068799370116.7571401.5626621.5626620.0-1.00.2874070.2831310.243774-0.633813-0.633813-3.333333e+32-3.333333e+320.877587965.9670983.789.554960.068.5078130.1933730.19337
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" ], "text/plain": [ " tk Name \\\n", "0 4611752310942477664 Fernwärmeverbraucher V-K1203S R-K3683S \n", "1 4612528660388965271 Fernwärmeverbraucher V-K1783S R-K4263S \n", "2 4612562908060328263 Fernwärmeverbraucher V-K1623S R-K4103S \n", "\n", " Beschreibung Ind0 W0 Qm0 Tvl0 \\\n", "0 Gattendorf;95185;Obere Au;28;None;yes 0 139.0433 1 90 \n", "1 None;None;None;None;None;yes 0 386.4134 1 90 \n", "2 Gattendorf;95185;Langenbachstraße;4;None;yes 0 106.6409 1 90 \n", "\n", " Trs0 Lfk Rho0 Dtmin Indtr Trsk Fktrft A B C Vtyp V0 P1soll \\\n", "0 60 1 1000 3 3 60 -1 2 -0.25 -2 1 1 0 \n", "1 60 1 1000 3 3 60 -1 2 -0.25 -2 1 0 0 \n", "2 60 1 1000 3 3 60 -1 2 -0.25 -2 1 1 0 \n", "\n", " Dpvlmin Fkzep1vl Tsvl Zevk Dphaus Dprlmin Fkzep1rl Tsrl Imbg Irfv \\\n", "0 0 -1 0 0 0.2 0.3 -1 0 0 1 \n", "1 0 -1 0 0 0.2 0.3 -1 0 0 1 \n", "2 0 -1 0 0 0.2 0.3 -1 0 0 1 \n", "\n", " Fkcont Idreferenz Iplanung CPM NumberOfVERB \\\n", "0 5029128874972463118 830887238 1 4.1903 0 \n", "1 5029128874972463118 1056287317 1 4.1903 0 \n", "2 5029128874972463118 830818182 1 4.1903 0 \n", "\n", " IndtrKlartext M0Estimated W0Estimated \\\n", "0 Tabelle Temperatur TEVT, TRS(t) 3.981862 34.91917 \n", "1 Tabelle Temperatur TEVT, TRS(t) 11.065940 34.91917 \n", "2 Tabelle Temperatur TEVT, TRS(t) 3.053936 34.91917 \n", "\n", " Tk Pk InVariant Xkor \\\n", "0 4611752310942477664 4611752310942477664 False 713675.300232 \n", "1 4612528660388965271 4612528660388965271 False 714251.332395 \n", "2 4612562908060328263 4612562908060328263 False 713271.271817 \n", "\n", " Ykor ShowDescription PositionOfDescription Angle SymbolFactor \\\n", "0 5.578705e+06 False 3 0 0.1 \n", "1 5.578925e+06 False 3 0 0.1 \n", "2 5.578980e+06 False 3 0 0.1 \n", "\n", " GeometriesDiffer bz.Fk bz.Indlast bz.Indlfkt2 \\\n", "0 False 4611752310942477664 0 0 \n", "1 False 4612528660388965271 0 0 \n", "2 False 4612562908060328263 0 0 \n", "\n", " bz.Fklfkt bz.Fklfkt2 bz.Fkqvar bz.Fktevt \\\n", "0 4835417738045943522 -1 5395645951786400348 \n", "1 5554262436821166605 -1 5395645951786400348 \n", "2 4835417738045943522 -1 5395645951786400348 \n", "\n", " bz.IndlastKlartext geometry fkKI \\\n", "0 Lastfaktor thermisch POINT (713675.3 5578705.193) 4673701597187411685 \n", "1 Lastfaktor thermisch POINT (714251.332 5578925.001) 5015101891725198603 \n", "2 Lastfaktor thermisch POINT (713271.272 5578979.743) 4965299629814639205 \n", "\n", " fkKK DH DP DPH IAKTIV INDUV \\\n", "0 4995788945387711669 17.178450 1.583806 1.583806 0.0 -1.0 \n", "1 5751808837348052764 0.069769 0.006844 0.006844 1.0 -1.0 \n", "2 4779536530687993701 16.757140 1.562662 1.562662 0.0 -1.0 \n", "\n", " LFH LFT M MHYUV MTHUV PHIRL \\\n", "0 0.288343 0.283131 0.318879 -0.829086 -0.829086 -3.333333e+32 \n", "1 0.000000 0.000000 0.000000 0.000000 0.000000 -3.333333e+32 \n", "2 0.287407 0.283131 0.243774 -0.633813 -0.633813 -3.333333e+32 \n", "\n", " PHIVL QM RHOI RHOK TI TK TVMIN \\\n", "0 -3.333333e+32 1.147966 966.0245 983.7 89.4592 60.0 68.50781 \n", "1 -3.333333e+32 0.000000 1000.3000 983.7 10.0000 60.0 10.00000 \n", "2 -3.333333e+32 0.877587 965.9670 983.7 89.5549 60.0 68.50781 \n", "\n", " W WSOLL \n", "0 39.36750 39.36750 \n", "1 0.00000 0.00000 \n", "2 30.19337 30.19337 " ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "(s3s.generate_element_dataframe(element_type=s3s.ObjectTypes.DistrictHeatingConsumer, tks=None)).head(3)" ] } ], "metadata": { "kernelspec": { "display_name": "base", "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": "3.11.8" } }, "nbformat": 4, "nbformat_minor": 5 }