Tutorial 52: General Creation of Element Dataframes
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.
Toolkit Release
[1]:
#pip install
Imports
SIR 3S Toolkit
Regular Import/Init
[2]:
SIR3S_SIRGRAF_DIR = r"C:\3S\SIR 3S\SirGraf-90-15-00-20x64_Quebec-Upd1" #change to local path
[3]:
from sir3stoolkit.core import wrapper
[4]:
wrapper
[4]:
<module 'sir3stoolkit.core.wrapper' from 'C:\\Users\\aUsername\\3S\\sir3stoolkit\\src\\sir3stoolkit\\core\\wrapper.py'>
[5]:
wrapper.Initialize_Toolkit(SIR3S_SIRGRAF_DIR)
Additional Import/Init for Dataframes class
[6]:
from sir3stoolkit.mantle.dataframes import SIR3S_Model_Dataframes
[7]:
s3s = SIR3S_Model_Dataframes()
Initialization complete
Additional
[8]:
import pandas as pd
from shapely.geometry import Point
import re
import folium
from folium.plugins import HeatMap
import numpy as np
import geopandas as gpd
from shapely import wkt
import matplotlib.pyplot as plt
import contextily as cx
Open Model
[9]:
s3s.OpenModel(dbName=r"C:\Users\aUsername\3S\PT3S\PT3S\Examples\Example3.db3",
providerType=s3s.ProviderTypes.SQLite,
Mid="M-1-0-1",
saveCurrentlyOpenModel=False,
namedInstance="",
userID="",
password="")
Model is open for further operation
Generate Element Dataframes
We can use the generate_element_dataframe() method to quickly generate basic dataframes containing all instances of hydraulic element types (Node, Pipe, etc.) in a SIR 3S model.
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.
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.
[10]:
object_types = [item for item in dir(s3s.ObjectTypes) if not (item.startswith('__') and item.endswith('__'))]
print(object_types) # Check for hydraulic elmement types
['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']
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.
Node
[11]:
(s3s.generate_element_dataframe(element_type=s3s.ObjectTypes.Node, tks=None)).head(3)
[2026-01-10 17:43:02,097] INFO in sir3stoolkit.mantle.dataframes: [generate_element_dataframe] Generating df for element type: ObjectTypes.Node ...
[2026-01-10 17:43:02,098] DEBUG in sir3stoolkit.mantle.dataframes: [generate_element_dataframe] Generating df_model_data for element type: ObjectTypes.Node ...
[2026-01-10 17:43:02,100] INFO in sir3stoolkit.mantle.dataframes: [model_data] Generating model_data dataframe for element type: ObjectTypes.Node
[2026-01-10 17:43:02,107] INFO in sir3stoolkit.mantle.dataframes: [model_data] Retrieved 517 element(s) of element type ObjectTypes.Node.
[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.
[2026-01-10 17:43:02,124] INFO in sir3stoolkit.mantle.dataframes: [Resolving model_data Properties] Using 37 model_data properties.
[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...
[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.
[2026-01-10 17:43:03,437] INFO in sir3stoolkit.mantle.dataframes: [model_data] Transforming DataFrame to GeoDataFrame successful with EPSG: 25832
[2026-01-10 17:43:03,444] INFO in sir3stoolkit.mantle.dataframes: [model_data] Done. Shape: (517, 39)
[2026-01-10 17:43:03,444] DEBUG in sir3stoolkit.mantle.dataframes: [generate_element_dataframe] Generating df_results for element type: ObjectTypes.Node ...
[2026-01-10 17:43:03,517] INFO in sir3stoolkit.mantle.dataframes: [results] Generating results dataframe for element type: ObjectTypes.Node
[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
[2026-01-10 17:43:03,598] INFO in sir3stoolkit.mantle.dataframes: [Resolving Timestamps] 1 valid timestamp(s) will be used.
[2026-01-10 17:43:03,598] INFO in sir3stoolkit.mantle.dataframes: [Resolving tks] Retrieved 517 element(s) of element type ObjectTypes.Node.
[2026-01-10 17:43:03,602] INFO in sir3stoolkit.mantle.dataframes: [results] Using 21 result properties.
[2026-01-10 17:43:03,610] INFO in sir3stoolkit.mantle.dataframes: [results] Retrieving result values...
[2026-01-10 17:43:05,089] INFO in sir3stoolkit.mantle.dataframes: [results] Done. Shape: (1, 10857)
[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 ...
[11]:
| tk | 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 | BCIND | DP | DPH | H | HMAX_INST | HMIN_INST | IAKTIV | LFAKTAKT | P | PDAMPF | PH | PHMINMAXDIF | PH_EIN | PH_MIN | PMAX_INST | PMIN_INST | QM | RHO | T | TTR | VOLD | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 4612618373909997110 | V-K2133S | QKON | 543.56 | 0 | 1 | 5520728169779652386 | 4798673252636751115 | 5591325053703727727 | -1 | 5029128874972463118 | 5611267768413515094 | Anfangsknoten generiert von SirDB | 3S96AE619D388EA6F4CC9F24456148E088 | 1 | 1 | 0 | 714332.858074 | 5.578924e+06 | 1 | False | Vorlauf | 0 | False | 4612618373909997110 | 4612618373909997110 | False | False | 0.2 | 0 | 4612618373909997110 | -1 | -1 | -1 | 0 | 0 | 0 | 0 | POINT (714332.858 5578924.328) | 17.0 | 0.007826 | 0.007826 | 1.121131 | 1.121131 | 1.121131 | 1.0 | 1.0 | 1.772014 | 0.012300 | 0.772014 | 0.0 | 0.772014 | 0.0 | 1.772014 | 1.772014 | 0.0 | 1000.3000 | 10.00000 | 0.000000 | 0.0 |
| 1 | 4619205996903908050 | V-K983S | QKON | 548.26 | 0 | 1 | 5520728169779652386 | 4798673252636751115 | 5591325053703727727 | -1 | 5029128874972463118 | 5130743098019975840 | Anfangsknoten generiert von SirDB | 3S56C0B9A1652EF8E9B7AB8C5DEACA2DC4 | 1 | 1 | 0 | 713611.070733 | 5.578598e+06 | 1 | False | Vorlauf | 0 | False | 4619205996903908050 | 4619205996903908050 | False | False | 0.2 | 0 | 4619205996903908050 | -1 | -1 | -1 | 0 | 0 | 0 | 0 | POINT (713611.071 5578598.067) | 17.0 | 1.650540 | 1.650540 | 4.942585 | 4.942585 | 4.942585 | 0.0 | 1.0 | 5.132555 | 0.695561 | 4.132555 | 0.0 | 4.132555 | 0.0 | 5.132555 | 5.132555 | 0.0 | 965.8268 | 89.78860 | 0.213703 | 0.0 |
| 2 | 4619682681341516951 | R-K2803S | QKON | 554.99 | 0 | 1 | 5520728169779652386 | 4798673252636751115 | 5591325053703727727 | -1 | 5029128874972463118 | 5367059433340055050 | Anfangsknoten generiert von SirDB | 3S7BD49C919428AD75D6EA1203195E860E | 1 | 2 | 0 | 713542.639481 | 5.578805e+06 | 1 | False | Rücklauf | 0 | False | 4619682681341516951 | 4619682681341516951 | False | False | 0.2 | 0 | 4619682681341516951 | -1 | -1 | -1 | 0 | 0 | 0 | 0 | POINT (713542.639 5578804.842) | 17.0 | 1.542220 | 1.542220 | 3.360220 | 3.360220 | 3.360220 | 0.0 | 1.0 | 2.890204 | 0.197127 | 1.890204 | 0.0 | 1.890204 | 0.0 | 2.890204 | 2.890204 | 0.0 | 983.8152 | 59.76965 | 0.168082 | 0.0 |
Pipe
[12]:
(s3s.generate_element_dataframe(element_type=s3s.ObjectTypes.Pipe, tks=None)).head(3)
[2026-01-10 17:43:05,138] INFO in sir3stoolkit.mantle.dataframes: [generate_element_dataframe] Generating df for element type: ObjectTypes.Pipe ...
[2026-01-10 17:43:05,144] DEBUG in sir3stoolkit.mantle.dataframes: [generate_element_dataframe] Generating df_model_data for element type: ObjectTypes.Pipe ...
[2026-01-10 17:43:05,145] INFO in sir3stoolkit.mantle.dataframes: [model_data] Generating model_data dataframe for element type: ObjectTypes.Pipe
[2026-01-10 17:43:05,147] INFO in sir3stoolkit.mantle.dataframes: [model_data] Retrieved 524 element(s) of element type ObjectTypes.Pipe.
[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.
[2026-01-10 17:43:05,151] INFO in sir3stoolkit.mantle.dataframes: [Resolving model_data Properties] Using 46 model_data properties.
[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...
[2026-01-10 17:43:06,477] INFO in sir3stoolkit.mantle.dataframes: [model_data] 2 non-empty end node columns were created.
[2026-01-10 17:43:06,612] INFO in sir3stoolkit.mantle.dataframes: [model_data] Transforming DataFrame to GeoDataFrame successful with EPSG: 25832
[2026-01-10 17:43:06,614] INFO in sir3stoolkit.mantle.dataframes: [model_data] Done. Shape: (524, 50)
[2026-01-10 17:43:06,617] DEBUG in sir3stoolkit.mantle.dataframes: [generate_element_dataframe] Generating df_results for element type: ObjectTypes.Pipe ...
[2026-01-10 17:43:06,678] INFO in sir3stoolkit.mantle.dataframes: [results] Generating results dataframe for element type: ObjectTypes.Pipe
[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
[2026-01-10 17:43:06,736] INFO in sir3stoolkit.mantle.dataframes: [Resolving Timestamps] 1 valid timestamp(s) will be used.
[2026-01-10 17:43:06,742] INFO in sir3stoolkit.mantle.dataframes: [Resolving tks] Retrieved 524 element(s) of element type ObjectTypes.Pipe.
[2026-01-10 17:43:06,744] INFO in sir3stoolkit.mantle.dataframes: [results] Using 31 result properties.
[2026-01-10 17:43:06,766] INFO in sir3stoolkit.mantle.dataframes: [results] Retrieving result values...
[2026-01-10 17:43:10,358] INFO in sir3stoolkit.mantle.dataframes: [results] Done. Shape: (1, 16244)
[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 ...
[12]:
| tk | 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 | fkKI | fkKK | A | DTTR | DWVERL | DWVERLABS | IAKTIV | IRTRENN | JV | MVEC | PDAMPF | PHR | PMIN | PVEC | PVECMAX_INST | PVECMIN_INST | QMAV | QMI | QMK | RHOI | RHOK | RHOVEC | SVEC | TI | TK | TTRVEC | TVEC | VAV | VI | VK | VOLDA | WVL | ZVEC | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 4614463970292122863 | Rohr R-K4383S R-K4183S | 4689226368751411179 | 4779752876656844188 | 5431845028903382031 | 7.780674 | 0 | 0.05 | 1 | 0 | 0 | 0 | 0 | 1000 | 0 | 0 | 5029128874972463118 | 4713734746689397424 | OSM: Knoten 450994211 -> Knoten 476971188; Län... | 166815824 | 5 | 2 | 0.005 | 0 | 999 | 994.0 | Rücklauf | 4614463970292122863 | 4614463970292122863 | False | 714262.482930 | 5.578857e+06 | False | 4614463970292122863 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | LINESTRING (714262.483 5578857.42, 714269.543 ... | 4730066059089961857 | 4917189080965035120 | 0.0 | 7.780674 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | -2.837623E-10\t-2.837623E-10 | 0.0123 | 0.0 | 1.615081 | 1.61508\t1.688655 | 1.61508\t1.688655 | 1.61508\t1.688655 | -0.0 | -0.0 | -0.0 | 1000.3 | 1000.3 | 1000.3\t1000.3 | 0\t7.780674 | 9.999994 | 9.999994 | 4.795364E+07\t4.795363E+07 | 10\t10 | -0.0 | -0.0 | -0.0 | 0.0 | 0.0 | 545.09\t544.34 | ||
| 1 | 4615723899944629797 | Rohr V-K203S V-K213S | 4689226368751411179 | 4779752876656844188 | 5728726059620036726 | 64.287240 | 0 | 0.05 | 1 | 0 | 0 | 0 | 0 | 1000 | 0 | 0 | 5029128874972463118 | 4938076287810941486 | OSM: Knoten 390310977 -> Knoten 1368674233; Lä... | 24633100 | 5 | 1 | 0.005 | 0 | 999 | 994.0 | Vorlauf | 4615723899944629797 | 4615723899944629797 | False | 713738.296567 | 5.579220e+06 | False | 4615723899944629797 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | LINESTRING (713738.297 5579219.902, 713793.23 ... | 5129584372458662150 | 5332825919690090061 | 0.0 | 64.28724 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 2.983143E-10\t2.983143E-10\t2.983143E-10\t2.98... | 0.0123 | 0.0 | 3.304884 | 3.304885\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... | 0.0 | 0.0 | 0.0 | 1000.3 | 1000.3 | 1000.3\t1000.3\t1000.3\t1000.3\t1000.3\t1000.3... | 0\t9.183891\t18.36778\t27.55167\t36.73557\t45.... | 9.999994 | 9.999994 | 203.6129\t212.7968\t221.9807\t231.1646\t240.34... | 10\t10\t10\t10\t10\t10\t10\t10 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 565.84\t565.4243\t565.0086\t564.5929\t564.1771... | ||
| 2 | 4621030304810285220 | Rohr R-K2573S R-K2583S | 5516336706687055417 | 4779752876656844188 | 5644881417512616095 | 3.956838 | 0 | 0.05 | 1 | 0 | 0 | 0 | 0 | 1000 | 0 | 0 | 5029128874972463118 | 5625716875961234775 | OSM: Knoten 476971211 -> Knoten 264607350; Län... | 24386111 | 0 | 2 | 0.005 | 0 | 100 | 107.1 | Rücklauf | 4621030304810285220 | 4621030304810285220 | False | 713650.613400 | 5.578990e+06 | False | 4621030304810285220 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | LINESTRING (713650.613 5578990.488, 713649.498... | 5070795580168283912 | 5725848577942138606 | 0.0 | 0.002291 | 16.2622 | 0.064347 | 0.0 | 0.0 | 0.211137 | -4.251046\t-4.251046 | 0.198023 | 0.000835 | 2.19867 | 2.210375\t2.19867 | 2.210375\t2.19867 | 2.210375\t2.19867 | -15.30376 | -15.30376 | -15.30376 | 983.7663 | 983.7645 | 983.7663\t983.7645 | 0\t3.956838 | 59.86739 | 59.871 | 0.05545082\t0.05315937 | 59.8674\t59.871 | -0.479662 | -0.479662 | -0.479662 | 0.0 | 0.0 | 563.01\t563.14 |
As can be seen some result values are in vectorized form, since pipes have result values that are calculated for interior points.
Pipe vector
[13]:
(s3s.generate_pipe_vector_dataframe()).head(3)
[2026-01-10 17:43:10,410] INFO in sir3stoolkit.mantle.dataframes: [generate_element_dataframe] Generating df for element type: ObjectTypes.Pipe ...
[2026-01-10 17:43:10,411] DEBUG in sir3stoolkit.mantle.dataframes: [generate_element_dataframe] Generating df_model_data for element type: ObjectTypes.Pipe ...
[2026-01-10 17:43:10,411] INFO in sir3stoolkit.mantle.dataframes: [model_data] Generating model_data dataframe for element type: ObjectTypes.Pipe
[2026-01-10 17:43:10,414] INFO in sir3stoolkit.mantle.dataframes: [model_data] Retrieved 524 element(s) of element type ObjectTypes.Pipe.
[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.
[2026-01-10 17:43:10,417] INFO in sir3stoolkit.mantle.dataframes: [Resolving model_data Properties] Using 46 model_data properties.
[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...
[2026-01-10 17:43:11,711] INFO in sir3stoolkit.mantle.dataframes: [model_data] 2 non-empty end node columns were created.
[2026-01-10 17:43:11,861] INFO in sir3stoolkit.mantle.dataframes: [model_data] Transforming DataFrame to GeoDataFrame successful with EPSG: 25832
[2026-01-10 17:43:11,861] INFO in sir3stoolkit.mantle.dataframes: [model_data] Done. Shape: (524, 50)
[2026-01-10 17:43:11,861] DEBUG in sir3stoolkit.mantle.dataframes: [generate_element_dataframe] Generating df_results for element type: ObjectTypes.Pipe ...
[2026-01-10 17:43:11,928] INFO in sir3stoolkit.mantle.dataframes: [results] Generating results dataframe for element type: ObjectTypes.Pipe
[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
[2026-01-10 17:43:11,997] INFO in sir3stoolkit.mantle.dataframes: [Resolving Timestamps] 1 valid timestamp(s) will be used.
[2026-01-10 17:43:11,997] INFO in sir3stoolkit.mantle.dataframes: [Resolving tks] Retrieved 524 element(s) of element type ObjectTypes.Pipe.
[2026-01-10 17:43:12,001] INFO in sir3stoolkit.mantle.dataframes: [results] Using 31 result properties.
[2026-01-10 17:43:12,028] INFO in sir3stoolkit.mantle.dataframes: [results] Retrieving result values...
[2026-01-10 17:43:15,515] INFO in sir3stoolkit.mantle.dataframes: [results] Done. Shape: (1, 16244)
[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 ...
c:\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()`
super().__setitem__(key, value)
c:\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()`
super().__setitem__(key, value)
c:\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()`
super().__setitem__(key, value)
c:\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()`
super().__setitem__(key, value)
c:\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()`
super().__setitem__(key, value)
c:\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()`
super().__setitem__(key, value)
c:\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()`
super().__setitem__(key, value)
c:\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()`
super().__setitem__(key, value)
c:\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()`
super().__setitem__(key, value)
c:\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()`
super().__setitem__(key, value)
c:\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()`
super().__setitem__(key, value)
c:\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()`
super().__setitem__(key, value)
c:\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()`
super().__setitem__(key, value)
c:\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()`
super().__setitem__(key, value)
c:\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()`
super().__setitem__(key, value)
c:\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()`
super().__setitem__(key, value)
c:\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()`
super().__setitem__(key, value)
c:\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()`
super().__setitem__(key, value)
c:\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()`
super().__setitem__(key, value)
c:\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()`
super().__setitem__(key, value)
c:\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()`
super().__setitem__(key, value)
c:\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()`
super().__setitem__(key, value)
c:\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()`
super().__setitem__(key, value)
c:\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()`
super().__setitem__(key, value)
c:\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()`
super().__setitem__(key, value)
c:\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()`
super().__setitem__(key, value)
c:\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()`
super().__setitem__(key, value)
c:\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()`
super().__setitem__(key, value)
c:\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()`
super().__setitem__(key, value)
c:\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()`
super().__setitem__(key, value)
c:\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()`
super().__setitem__(key, value)
c:\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()`
super().__setitem__(key, value)
c:\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()`
super().__setitem__(key, value)
c:\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()`
super().__setitem__(key, value)
c:\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()`
super().__setitem__(key, value)
c:\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()`
super().__setitem__(key, value)
c:\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()`
super().__setitem__(key, value)
c:\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()`
super().__setitem__(key, value)
c:\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()`
super().__setitem__(key, value)
c:\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()`
super().__setitem__(key, value)
c:\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()`
super().__setitem__(key, value)
c:\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()`
super().__setitem__(key, value)
c:\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()`
super().__setitem__(key, value)
c:\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()`
super().__setitem__(key, value)
c:\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()`
super().__setitem__(key, value)
c:\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()`
super().__setitem__(key, value)
c:\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()`
super().__setitem__(key, value)
c:\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()`
super().__setitem__(key, value)
c:\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()`
super().__setitem__(key, value)
c:\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()`
super().__setitem__(key, value)
c:\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()`
super().__setitem__(key, value)
c:\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()`
super().__setitem__(key, value)
c:\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()`
super().__setitem__(key, value)
c:\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()`
super().__setitem__(key, value)
c:\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()`
super().__setitem__(key, value)
c:\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()`
super().__setitem__(key, value)
c:\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()`
super().__setitem__(key, value)
c:\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()`
super().__setitem__(key, value)
c:\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()`
super().__setitem__(key, value)
c:\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()`
super().__setitem__(key, value)
c:\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()`
super().__setitem__(key, value)
c:\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()`
super().__setitem__(key, value)
c:\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()`
super().__setitem__(key, value)
c:\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()`
super().__setitem__(key, value)
c:\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()`
super().__setitem__(key, value)
[13]:
| tk | 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 | fkKI | fkKK | A | DTTR | DWVERL | DWVERLABS | IAKTIV | IRTRENN | JV | PDAMPF | PHR | PMIN | QMAV | QMI | QMK | RHOI | RHOK | TI | TK | VAV | VI | VK | VOLDA | WVL | MVEC_0 | MVEC_1 | MVEC_2 | MVEC_3 | MVEC_4 | MVEC_5 | MVEC_6 | MVEC_7 | MVEC_8 | MVEC_9 | MVEC_10 | MVEC_11 | MVEC_12 | MVEC_13 | MVEC_14 | MVEC_15 | MVEC_16 | MVEC_17 | PVEC_0 | PVEC_1 | PVEC_2 | PVEC_3 | PVEC_4 | PVEC_5 | PVEC_6 | PVEC_7 | PVEC_8 | PVEC_9 | PVEC_10 | PVEC_11 | PVEC_12 | PVEC_13 | PVEC_14 | PVEC_15 | PVEC_16 | PVEC_17 | PVECMAX_INST_0 | PVECMAX_INST_1 | PVECMAX_INST_2 | PVECMAX_INST_3 | PVECMAX_INST_4 | PVECMAX_INST_5 | PVECMAX_INST_6 | PVECMAX_INST_7 | PVECMAX_INST_8 | PVECMAX_INST_9 | PVECMAX_INST_10 | PVECMAX_INST_11 | PVECMAX_INST_12 | PVECMAX_INST_13 | PVECMAX_INST_14 | PVECMAX_INST_15 | PVECMAX_INST_16 | PVECMAX_INST_17 | PVECMIN_INST_0 | PVECMIN_INST_1 | PVECMIN_INST_2 | PVECMIN_INST_3 | PVECMIN_INST_4 | PVECMIN_INST_5 | PVECMIN_INST_6 | PVECMIN_INST_7 | PVECMIN_INST_8 | PVECMIN_INST_9 | PVECMIN_INST_10 | PVECMIN_INST_11 | PVECMIN_INST_12 | PVECMIN_INST_13 | PVECMIN_INST_14 | PVECMIN_INST_15 | PVECMIN_INST_16 | PVECMIN_INST_17 | RHOVEC_0 | RHOVEC_1 | RHOVEC_2 | RHOVEC_3 | RHOVEC_4 | RHOVEC_5 | RHOVEC_6 | RHOVEC_7 | RHOVEC_8 | RHOVEC_9 | RHOVEC_10 | RHOVEC_11 | RHOVEC_12 | RHOVEC_13 | RHOVEC_14 | RHOVEC_15 | RHOVEC_16 | RHOVEC_17 | SVEC_0 | SVEC_1 | SVEC_2 | SVEC_3 | SVEC_4 | SVEC_5 | SVEC_6 | SVEC_7 | SVEC_8 | SVEC_9 | SVEC_10 | SVEC_11 | SVEC_12 | SVEC_13 | SVEC_14 | SVEC_15 | SVEC_16 | SVEC_17 | TTRVEC_0 | TTRVEC_1 | TTRVEC_2 | TTRVEC_3 | TTRVEC_4 | TTRVEC_5 | TTRVEC_6 | TTRVEC_7 | TTRVEC_8 | TTRVEC_9 | TTRVEC_10 | TTRVEC_11 | TTRVEC_12 | TTRVEC_13 | TTRVEC_14 | TTRVEC_15 | TTRVEC_16 | TTRVEC_17 | TVEC_0 | TVEC_1 | TVEC_2 | TVEC_3 | TVEC_4 | TVEC_5 | TVEC_6 | TVEC_7 | TVEC_8 | TVEC_9 | TVEC_10 | TVEC_11 | TVEC_12 | TVEC_13 | TVEC_14 | TVEC_15 | TVEC_16 | TVEC_17 | ZVEC_0 | ZVEC_1 | ZVEC_2 | ZVEC_3 | ZVEC_4 | ZVEC_5 | ZVEC_6 | ZVEC_7 | ZVEC_8 | ZVEC_9 | ZVEC_10 | ZVEC_11 | ZVEC_12 | ZVEC_13 | ZVEC_14 | ZVEC_15 | ZVEC_16 | ZVEC_17 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 4614463970292122863 | Rohr R-K4383S R-K4183S | 4689226368751411179 | 4779752876656844188 | 5431845028903382031 | 7.780674 | 0 | 0.05 | 1 | 0 | 0 | 0 | 0 | 1000 | 0 | 0 | 5029128874972463118 | 4713734746689397424 | OSM: Knoten 450994211 -> Knoten 476971188; Län... | 166815824 | 5 | 2 | 0.005 | 0 | 999 | 994.0 | Rücklauf | 4614463970292122863 | 4614463970292122863 | False | 714262.482930 | 5.578857e+06 | False | 4614463970292122863 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | LINESTRING (714262.483 5578857.42, 714269.543 ... | 4730066059089961857 | 4917189080965035120 | 0.0 | 7.780674 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0123 | 0.0 | 1.615081 | -0.0 | -0.0 | -0.0 | 1000.3 | 1000.3 | 9.999994 | 9.999994 | -0.0 | -0.0 | -0.0 | 0.0 | 0.0 | -2.837623e-10 | -2.837623e-10 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1.615080 | 1.688655 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1.615080 | 1.688655 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1.615080 | 1.688655 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1000.3000 | 1000.3000 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0 | 7.780674 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 4.795364e+07 | 4.795363e+07 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 10.0000 | 10.000 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 545.09 | 544.3400 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ||
| 1 | 4615723899944629797 | Rohr V-K203S V-K213S | 4689226368751411179 | 4779752876656844188 | 5728726059620036726 | 64.287240 | 0 | 0.05 | 1 | 0 | 0 | 0 | 0 | 1000 | 0 | 0 | 5029128874972463118 | 4938076287810941486 | OSM: Knoten 390310977 -> Knoten 1368674233; Lä... | 24633100 | 5 | 1 | 0.005 | 0 | 999 | 994.0 | Vorlauf | 4615723899944629797 | 4615723899944629797 | False | 713738.296567 | 5.579220e+06 | False | 4615723899944629797 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | LINESTRING (713738.297 5579219.902, 713793.23 ... | 5129584372458662150 | 5332825919690090061 | 0.0 | 64.28724 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0123 | 0.0 | 3.304884 | 0.0 | 0.0 | 0.0 | 1000.3 | 1000.3 | 9.999994 | 9.999994 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 2.983143e-10 | 2.983143e-10 | 2.983143e-10 | 2.983143e-10 | 2.983143e-10 | 2.983143e-10 | 2.983143e-10 | 2.983143e-10 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 3.304885 | 3.345665 | 3.386445 | 3.427225 | 3.468005 | 3.508785 | 3.549565 | 3.590345 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 3.304885 | 3.345665 | 3.386445 | 3.427225 | 3.468005 | 3.508785 | 3.549565 | 3.590345 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 3.304885 | 3.345665 | 3.386445 | 3.427225 | 3.468005 | 3.508785 | 3.549565 | 3.590345 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1000.3000 | 1000.3000 | 1000.3 | 1000.3 | 1000.3 | 1000.3 | 1000.3 | 1000.3 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0 | 9.183891 | 18.36778 | 27.55167 | 36.73557 | 45.91946 | 55.10335 | 64.28724 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 2.036129e+02 | 2.127968e+02 | 221.9807 | 231.1646 | 240.3485 | 249.5324 | 258.7163 | 267.9001 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 10.0000 | 10.000 | 10.0 | 10.0 | 10.0 | 10.0 | 10.0 | 10.0 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 565.84 | 565.4243 | 565.0086 | 564.5929 | 564.1771 | 563.7614 | 563.3457 | 562.93 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ||
| 2 | 4621030304810285220 | Rohr R-K2573S R-K2583S | 5516336706687055417 | 4779752876656844188 | 5644881417512616095 | 3.956838 | 0 | 0.05 | 1 | 0 | 0 | 0 | 0 | 1000 | 0 | 0 | 5029128874972463118 | 5625716875961234775 | OSM: Knoten 476971211 -> Knoten 264607350; Län... | 24386111 | 0 | 2 | 0.005 | 0 | 100 | 107.1 | Rücklauf | 4621030304810285220 | 4621030304810285220 | False | 713650.613400 | 5.578990e+06 | False | 4621030304810285220 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | LINESTRING (713650.613 5578990.488, 713649.498... | 5070795580168283912 | 5725848577942138606 | 0.0 | 0.002291 | 16.2622 | 0.064347 | 0.0 | 0.0 | 0.211137 | 0.198023 | 0.000835 | 2.19867 | -15.30376 | -15.30376 | -15.30376 | 983.7663 | 983.7645 | 59.86739 | 59.871 | -0.479662 | -0.479662 | -0.479662 | 0.0 | 0.0 | -4.251046e+00 | -4.251046e+00 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 2.210375 | 2.198670 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 2.210375 | 2.198670 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 2.210375 | 2.198670 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 983.7663 | 983.7645 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0 | 3.956838 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 5.545082e-02 | 5.315937e-02 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 59.8674 | 59.871 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 563.01 | 563.1400 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
DistrictHeatingConsumer
[14]:
(s3s.generate_element_dataframe(element_type=s3s.ObjectTypes.DistrictHeatingConsumer, tks=None)).head(3)
[2026-01-10 17:43:15,760] INFO in sir3stoolkit.mantle.dataframes: [generate_element_dataframe] Generating df for element type: ObjectTypes.DistrictHeatingConsumer ...
[2026-01-10 17:43:15,761] DEBUG in sir3stoolkit.mantle.dataframes: [generate_element_dataframe] Generating df_model_data for element type: ObjectTypes.DistrictHeatingConsumer ...
[2026-01-10 17:43:15,763] INFO in sir3stoolkit.mantle.dataframes: [model_data] Generating model_data dataframe for element type: ObjectTypes.DistrictHeatingConsumer
[2026-01-10 17:43:15,765] INFO in sir3stoolkit.mantle.dataframes: [model_data] Retrieved 337 element(s) of element type ObjectTypes.DistrictHeatingConsumer.
[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.
[2026-01-10 17:43:15,768] INFO in sir3stoolkit.mantle.dataframes: [Resolving model_data Properties] Using 55 model_data properties.
[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...
[2026-01-10 17:43:16,850] INFO in sir3stoolkit.mantle.dataframes: [model_data] 2 non-empty end node columns were created.
[2026-01-10 17:43:16,998] INFO in sir3stoolkit.mantle.dataframes: [model_data] Transforming DataFrame to GeoDataFrame successful with EPSG: 25832
[2026-01-10 17:43:16,998] INFO in sir3stoolkit.mantle.dataframes: [model_data] Done. Shape: (337, 59)
[2026-01-10 17:43:16,998] DEBUG in sir3stoolkit.mantle.dataframes: [generate_element_dataframe] Generating df_results for element type: ObjectTypes.DistrictHeatingConsumer ...
[2026-01-10 17:43:17,061] INFO in sir3stoolkit.mantle.dataframes: [results] Generating results dataframe for element type: ObjectTypes.DistrictHeatingConsumer
[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
[2026-01-10 17:43:17,131] INFO in sir3stoolkit.mantle.dataframes: [Resolving Timestamps] 1 valid timestamp(s) will be used.
[2026-01-10 17:43:17,133] INFO in sir3stoolkit.mantle.dataframes: [Resolving tks] Retrieved 337 element(s) of element type ObjectTypes.DistrictHeatingConsumer.
[2026-01-10 17:43:17,133] INFO in sir3stoolkit.mantle.dataframes: [results] Using 20 result properties.
[2026-01-10 17:43:17,147] INFO in sir3stoolkit.mantle.dataframes: [results] Retrieving result values...
[2026-01-10 17:43:18,231] INFO in sir3stoolkit.mantle.dataframes: [results] Done. Shape: (1, 6740)
[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 ...
[14]:
| tk | 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 | fkKI | fkKK | DH | DP | DPH | IAKTIV | INDUV | LFH | LFT | M | MHYUV | MTHUV | PHIRL | PHIVL | QM | RHOI | RHOK | TI | TK | TVMIN | W | WSOLL | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 4611752310942477664 | Fernwärmeverbraucher V-K1203S R-K3683S | Gattendorf;95185;Obere Au;28;None;yes | 0 | 139.0433 | 1 | 90 | 60 | 1 | 1000 | 3 | 3 | 60 | -1 | 2 | -0.25 | -2 | 1 | 1 | 0 | 0 | -1 | 0 | 0 | 0.2 | 0.3 | -1 | 0 | 0 | 1 | 5029128874972463118 | 830887238 | 1 | 4.1903 | 0 | Tabelle Temperatur TEVT, TRS(t) | 3.981862 | 34.91917 | 4611752310942477664 | 4611752310942477664 | False | 713675.300232 | 5.578705e+06 | False | 3 | 0 | 0.1 | False | 4611752310942477664 | 0 | 0 | 4835417738045943522 | -1 | 5395645951786400348 | Lastfaktor thermisch | POINT (713675.3 5578705.193) | 4673701597187411685 | 4995788945387711669 | 17.178450 | 1.583806 | 1.583806 | 0.0 | -1.0 | 0.288343 | 0.283131 | 0.318879 | -0.829086 | -0.829086 | -3.333333e+32 | -3.333333e+32 | 1.147966 | 966.0245 | 983.7 | 89.4592 | 60.0 | 68.50781 | 39.36750 | 39.36750 | |
| 1 | 4612528660388965271 | Fernwärmeverbraucher V-K1783S R-K4263S | None;None;None;None;None;yes | 0 | 386.4134 | 1 | 90 | 60 | 1 | 1000 | 3 | 3 | 60 | -1 | 2 | -0.25 | -2 | 1 | 0 | 0 | 0 | -1 | 0 | 0 | 0.2 | 0.3 | -1 | 0 | 0 | 1 | 5029128874972463118 | 1056287317 | 1 | 4.1903 | 0 | Tabelle Temperatur TEVT, TRS(t) | 11.065940 | 34.91917 | 4612528660388965271 | 4612528660388965271 | False | 714251.332395 | 5.578925e+06 | False | 3 | 0 | 0.1 | False | 4612528660388965271 | 0 | 0 | 5554262436821166605 | -1 | 5395645951786400348 | Lastfaktor thermisch | POINT (714251.332 5578925.001) | 5015101891725198603 | 5751808837348052764 | 0.069769 | 0.006844 | 0.006844 | 1.0 | -1.0 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | -3.333333e+32 | -3.333333e+32 | 0.000000 | 1000.3000 | 983.7 | 10.0000 | 60.0 | 10.00000 | 0.00000 | 0.00000 | |
| 2 | 4612562908060328263 | Fernwärmeverbraucher V-K1623S R-K4103S | Gattendorf;95185;Langenbachstraße;4;None;yes | 0 | 106.6409 | 1 | 90 | 60 | 1 | 1000 | 3 | 3 | 60 | -1 | 2 | -0.25 | -2 | 1 | 1 | 0 | 0 | -1 | 0 | 0 | 0.2 | 0.3 | -1 | 0 | 0 | 1 | 5029128874972463118 | 830818182 | 1 | 4.1903 | 0 | Tabelle Temperatur TEVT, TRS(t) | 3.053936 | 34.91917 | 4612562908060328263 | 4612562908060328263 | False | 713271.271817 | 5.578980e+06 | False | 3 | 0 | 0.1 | False | 4612562908060328263 | 0 | 0 | 4835417738045943522 | -1 | 5395645951786400348 | Lastfaktor thermisch | POINT (713271.272 5578979.743) | 4965299629814639205 | 4779536530687993701 | 16.757140 | 1.562662 | 1.562662 | 0.0 | -1.0 | 0.287407 | 0.283131 | 0.243774 | -0.633813 | -0.633813 | -3.333333e+32 | -3.333333e+32 | 0.877587 | 965.9670 | 983.7 | 89.5549 | 60.0 | 68.50781 | 30.19337 | 30.19337 |