Tutorial 51: Dataframes_SIR3S_Model

This Example demonstrates the capabilities of the class Dataframes_SIR3S_Model that extends SIR3S_Model be abilities to work directley with pandas dataframes.

Toolkit Release

#pip install

Imports

SIR 3S Toolkit

Regular Import/Init

SIR3S_SIRGRAF_DIR = r"C:\3S\SIR 3S Entwicklung\SirGraf-90-15-00-19_x64" #change to local path

from sir3stoolkit.core import wrapper

wrapper

<module 'sir3stoolkit.core.wrapper' from 'C:\\Users\\aUsername\\3S\\sir3stoolkit\\src\\sir3stoolkit\\core\\wrapper.py'>

wrapper.Initialize_Toolkit(SIR3S_SIRGRAF_DIR)

Additional Import/Init for Dataframes class

from sir3stoolkit.mantle.dataframes import SIR3S_Model_Dataframes

s3s = SIR3S_Model_Dataframes()

Initialization complete

Additional

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

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

Manual Dataframe creation

Metadata - Nodes

We can use the generate_element_metadata_dataframe() method to obtain a dataframe for all instances of a specifc component type (Nodes, Pipes, etc.) with user defined metadata properties (all non-result data related to a component) to be added. For this example we will use nodes.

In this case we are interested in Nodes, let’s check what metadata properties are available for this element.

s3s.GetPropertiesofElementType(s3s.ObjectTypes.Node)

['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',
 'IsMarked',
 'InVariant',
 'GeometriesDiffer',
 'SymbolFactor',
 'bz.Drakonz',
 'bz.Fk',
 'bz.Fkpvar',
 'bz.Fkqvar',
 'bz.Fklfkt',
 'bz.PhEin',
 'bz.Tm',
 'bz.Te',
 'bz.PhMin']

In the below subcases differnt choices of parameters for creating the metadata dataframe manually are shown. Note that not all possible combination of paramters are shown here.

Subcase 1: Minimal

Let’s say for now we only want a dataframe with all node tks without any related data.

(s3s.generate_element_metadata_dataframe(element_type=s3s.ObjectTypes.Node
                                        ,properties=[]
                                        )).head(3)

[2025-12-05 20:20:45,646] INFO in sir3stoolkit.mantle.dataframes: [metadata] Generating metadata dataframe for element type: ObjectTypes.Node
[2025-12-05 20:20:45,657] INFO in sir3stoolkit.mantle.dataframes: [metadata] Retrieved 517 element(s) of element type ObjectTypes.Node.
[2025-12-05 20:20:45,660] INFO in sir3stoolkit.mantle.dataframes: [Resolving tks] Retrieved 517 element(s) of element type ObjectTypes.Node.
[2025-12-05 20:20:45,682] INFO in sir3stoolkit.mantle.dataframes: [Resolving Metadata Properties] Using 0 metadata properties.
[2025-12-05 20:20:45,683] INFO in sir3stoolkit.mantle.dataframes: [metadata] Retrieving ...
[2025-12-05 20:20:45,687] INFO in sir3stoolkit.mantle.dataframes: [metadata] Done. Shape: (517, 1)

	tk
0	5669301360686511351
1	5397948523091900401
2	5239335112004772156

Subcase 2: Specific Properties

Let’s say we now want the minimal dataframe extended with supply/return info (kvr) and height (Zkor) of the nodes.

(s3s.generate_element_metadata_dataframe(element_type=s3s.ObjectTypes.Node
                                        ,properties=["Kvr", "Zkor"]
                                        )).head(3)

[2025-12-05 20:20:45,721] INFO in sir3stoolkit.mantle.dataframes: [metadata] Generating metadata dataframe for element type: ObjectTypes.Node
[2025-12-05 20:20:45,725] INFO in sir3stoolkit.mantle.dataframes: [metadata] Retrieved 517 element(s) of element type ObjectTypes.Node.
[2025-12-05 20:20:45,730] INFO in sir3stoolkit.mantle.dataframes: [Resolving tks] Retrieved 517 element(s) of element type ObjectTypes.Node.
[2025-12-05 20:20:45,733] INFO in sir3stoolkit.mantle.dataframes: [Resolving Metadata Properties] Using 2 metadata properties.
[2025-12-05 20:20:45,735] INFO in sir3stoolkit.mantle.dataframes: [metadata] Retrieving metadata properties ['Kvr', 'Zkor']...
[2025-12-05 20:20:45,823] INFO in sir3stoolkit.mantle.dataframes: [metadata] Done. Shape: (517, 3)

	tk	Kvr	Zkor
0	5669301360686511351	1	554,04
1	5397948523091900401	1	556,16
2	5239335112004772156	1	561,01

Subcase 3: Maximal

Let’s say now we want basically all available metadata (non-result data) related to nodes.

If geometry=True, the 2D Point geometry will be added for Nodes and it will be attempted to transform the DataFrame into a GeoDataFrame using the SRID (eg. EPSG: 25832) defined in the model. If no SRID is defined, the DataFrame will remain as a DataFrame, but the geometry column with be added nonetheless.

(s3s.generate_element_metadata_dataframe(element_type=s3s.ObjectTypes.Node
                                        ,properties=None # Instead of an empty or filled list, we pass None to indicate we want all available properties
                                        ,geometry=True # geometry data has to be explicitly requested
                                        )).head(3)

[2025-12-05 20:20:45,856] INFO in sir3stoolkit.mantle.dataframes: [metadata] Generating metadata dataframe for element type: ObjectTypes.Node
[2025-12-05 20:20:45,861] INFO in sir3stoolkit.mantle.dataframes: [metadata] Retrieved 517 element(s) of element type ObjectTypes.Node.
[2025-12-05 20:20:45,866] INFO in sir3stoolkit.mantle.dataframes: [Resolving tks] Retrieved 517 element(s) of element type ObjectTypes.Node.
[2025-12-05 20:20:45,869] INFO in sir3stoolkit.mantle.dataframes: [Resolving Metadata Properties] No properties given → using ALL metadata properties for ObjectTypes.Node.
[2025-12-05 20:20:45,871] INFO in sir3stoolkit.mantle.dataframes: [Resolving Metadata Properties] Using 38 metadata properties.
[2025-12-05 20:20:45,873] INFO in sir3stoolkit.mantle.dataframes: [metadata] Retrieving metadata 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', 'IsMarked', 'InVariant', 'GeometriesDiffer', 'SymbolFactor', 'bz.Drakonz', 'bz.Fk', 'bz.Fkpvar', 'bz.Fkqvar', 'bz.Fklfkt', 'bz.PhEin', 'bz.Tm', 'bz.Te', 'bz.PhMin'], geometry...
[2025-12-05 20:20:49,425] INFO in sir3stoolkit.mantle.dataframes: [metadata] Transforming DataFrame to GeoDataFrame successful with EPSG: 25832
[2025-12-05 20:20:49,431] INFO in sir3stoolkit.mantle.dataframes: [metadata] Done. Shape: (517, 40)

	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	IsMarked	InVariant	GeometriesDiffer	SymbolFactor	bz.Drakonz	bz.Fk	bz.Fkpvar	bz.Fkqvar	bz.Fklfkt	bz.PhEin	bz.Tm	bz.Te	bz.PhMin	geometry
0	5669301360686511351	V-K03S	QKON	554,04	0	1	5520728169779652386	4798673252636751115	5591325053703727727	-1	5029128874972463118	5761544923588724980	Abzw. Am Sonnenbühl	3S3768E2953006F125B34916F6A1681667	1	1	0	713620,267807079	5578828,41875394	1	False	Vorlauf	0	False	5669301360686511351	5669301360686511351	False	False	False	0,2	0	5669301360686511351	-1	-1	-1	0	0	0	0	POINT (713620.268 5578828.419)
1	5397948523091900401	V-K13S	QKON	556,16	0	1	5520728169779652386	4798673252636751115	5591325053703727727	-1	5029128874972463118	4749864932928780363	Anfangsknoten generiert von SirDB	3SC30ED954AA2F1D59B1F92FFEBCCB7B5B	1	1	0	713602,294599536	5578860,10567987	1	False	Vorlauf	0	False	5397948523091900401	5397948523091900401	False	False	False	0,2	0	5397948523091900401	-1	-1	-1	0	0	0	0	POINT (713602.295 5578860.106)
2	5239335112004772156	V-K23S	QKON	561,01	0	1	5520728169779652386	4798673252636751115	5591325053703727727	-1	5029128874972463118	4899862460906451371	Anfangsknoten generiert von SirDB	3SC30ED954AA2F1D59B1F92FFEBCCB7B5B	1	1	0	713574,061626605	5578909,87290685	1	False	Vorlauf	0	False	5239335112004772156	5239335112004772156	False	False	False	0,2	0	5239335112004772156	-1	-1	-1	0	0	0	0	POINT (713574.062 5578909.873)

Subcase 4: Container Filtering + Element Type Col

Let’s say we only want nodes inside a specific container and we also want to have an element type col adding “Node” to each row (useful when joining with other dataframes later).

for container_id in (s3s.GetTksofElementType(s3s.ObjectTypes.ObjectContainerSymbol)):
    print(f"{container_id}: {s3s.GetValue(container_id, 'Name')[0]}")

5029128874972463118: M-1-0-1
5027846505677995694: Erzeugung
5172832702839270493: TS
5152808213068069018: Laststeuerung
5467315850619588583: Sekundärwerte
5320319133990336755: Netztrennung

If we filter for a container with no nodes, we will run into an error.

s3s.generate_element_metadata_dataframe(element_type=s3s.ObjectTypes.Node
                                        ,properties=["Kvr", "Zkor"]
                                        ,geometry=True
                                        ,filter_container_tks=["5152808213068069018"]
                                        ,element_type_col=True
                                        )

[2025-12-05 20:20:49,525] INFO in sir3stoolkit.mantle.dataframes: [metadata] Generating metadata dataframe for element type: ObjectTypes.Node
[2025-12-05 20:20:49,535] INFO in sir3stoolkit.mantle.dataframes: [metadata] Retrieved 517 element(s) of element type ObjectTypes.Node.
[2025-12-05 20:20:49,540] INFO in sir3stoolkit.mantle.dataframes: [Resolving tks] Retrieved 517 element(s) of element type ObjectTypes.Node.
[2025-12-05 20:20:49,592] ERROR in sir3stoolkit.mantle.dataframes: [Resolving tks] No elements remain after container filtering.

pandas.core.frame.DataFrame

But if we add the main container id to the list, we will find nodes.

(s3s.generate_element_metadata_dataframe(element_type=s3s.ObjectTypes.Node
                                        ,properties=[]
                                        ,geometry=True
                                        ,filter_container_tks=["5029128874972463118", "5152808213068069018"]
                                        ,element_type_col=True
                                        )).head(3)

[2025-12-05 20:20:49,619] INFO in sir3stoolkit.mantle.dataframes: [metadata] Generating metadata dataframe for element type: ObjectTypes.Node
[2025-12-05 20:20:49,624] INFO in sir3stoolkit.mantle.dataframes: [metadata] Retrieved 517 element(s) of element type ObjectTypes.Node.
[2025-12-05 20:20:49,628] INFO in sir3stoolkit.mantle.dataframes: [Resolving tks] Retrieved 517 element(s) of element type ObjectTypes.Node.
[2025-12-05 20:20:49,671] INFO in sir3stoolkit.mantle.dataframes: [Resolving tks] 500 tks remain after container filtering.
[2025-12-05 20:20:49,675] INFO in sir3stoolkit.mantle.dataframes: [Resolving Metadata Properties] Using 0 metadata properties.
[2025-12-05 20:20:49,678] INFO in sir3stoolkit.mantle.dataframes: [metadata] Retrieving geometry...
[2025-12-05 20:20:49,818] INFO in sir3stoolkit.mantle.dataframes: [metadata] Transforming DataFrame to GeoDataFrame successful with EPSG: 25832
[2025-12-05 20:20:49,821] INFO in sir3stoolkit.mantle.dataframes: [metadata] Done. Shape: (500, 3)

	tk	geometry	element type
0	5669301360686511351	POINT (713620.268 5578828.419)	Node
1	5397948523091900401	POINT (713602.295 5578860.106)	Node
2	5239335112004772156	POINT (713574.062 5578909.873)	Node

Subcase 5: Non-Container Filtering

If we want to filter for more than just the container, we have to filter tks outside the function and then pass them.

all_tks = s3s.GetTksofElementType(s3s.ObjectTypes.Node)

len(all_tks)

517

good_tks = []

for tk in all_tks:
    if s3s.GetValue(tk, "Kvr")[0] == "1": # Supply/Vorlauf
        good_tks.append(tk)

len(good_tks)

256

(s3s.generate_element_metadata_dataframe(element_type=s3s.ObjectTypes.Node
                                        ,tks=good_tks
                                        ,properties=[]
                                        ,geometry=True
                                        )).head(3)

[2025-12-05 20:20:50,101] INFO in sir3stoolkit.mantle.dataframes: [metadata] Generating metadata dataframe for element type: ObjectTypes.Node
[2025-12-05 20:20:50,107] INFO in sir3stoolkit.mantle.dataframes: [metadata] Retrieved 517 element(s) of element type ObjectTypes.Node.
[2025-12-05 20:20:50,113] INFO in sir3stoolkit.mantle.dataframes: [Resolving tks] Retrieved 517 element(s) of element type ObjectTypes.Node.
[2025-12-05 20:20:50,119] INFO in sir3stoolkit.mantle.dataframes: [Resolving tks] 256 tks remain after filtering for given tks.
[2025-12-05 20:20:50,125] INFO in sir3stoolkit.mantle.dataframes: [Resolving Metadata Properties] Using 0 metadata properties.
[2025-12-05 20:20:50,127] INFO in sir3stoolkit.mantle.dataframes: [metadata] Retrieving geometry...
[2025-12-05 20:20:50,299] INFO in sir3stoolkit.mantle.dataframes: [metadata] Transforming DataFrame to GeoDataFrame successful with EPSG: 25832
[2025-12-05 20:20:50,301] INFO in sir3stoolkit.mantle.dataframes: [metadata] Done. Shape: (256, 2)

	tk	geometry
0	5669301360686511351	POINT (713620.268 5578828.419)
1	5397948523091900401	POINT (713602.295 5578860.106)
2	5239335112004772156	POINT (713574.062 5578909.873)

Result Data - Pipes

We can use the generate_element_metadata_dataframe() method to obtain a dataframe for all instances of a specifc component type (Nodes, Pipes, etc.) with user defined result values for certain timestamps to be added. For this example we will use pipes.

Let’s check what result properties are available for Pipes.

s3s.GetResultProperties_from_elementType(elementType=s3s.ObjectTypes.Pipe
                                         ,onlySelectedVectors=False
                                         )

['A',
 'ACALC',
 'CPI',
 'CPK',
 'DH',
 'DP',
 'DRAGRED',
 'DRAKONZ',
 'DSI',
 'DSK',
 'DTTR',
 'DWVERL',
 'DWVERLABS',
 'ETAAV',
 'FS',
 'HR',
 'HVEC',
 'IAKTIV',
 'IRTRENN',
 'JV',
 'JV2',
 'LAMBDA',
 'LECKEINAUS',
 'LECKMENGE',
 'LECKORT',
 'LINEPACK',
 'LINEPACKGEOM',
 'LINEPACKRATE',
 'MAINELEMENT',
 'MAV',
 'MI',
 'MK',
 'MKOND',
 'MMAX_INST',
 'MMIN_INST',
 'MVEC',
 'MVECMAX_INST',
 'MVECMIN_INST',
 'PAV',
 'PDAMPF',
 'PHR',
 'PHVEC',
 'PMAX',
 'PMIN',
 'PR',
 'PVEC',
 'PVECMAX_INST',
 'PVECMIN_INST',
 'QI2',
 'QK2',
 'QMAV',
 'QMI',
 'QMK',
 'QMMAX_INST',
 'QMMIN_INST',
 'QMVEC',
 'QSVB',
 'RHOAV',
 'RHOI',
 'RHOK',
 'RHOVEC',
 'SVEC',
 'TAV',
 'TI',
 'TK',
 'TTRVEC',
 'TVEC',
 'TVECMAX_INST',
 'TVECMIN_INST',
 'VAV',
 'VI',
 'VK',
 'VMAX_INST',
 'VMIN_INST',
 'VOLDA',
 'WALTERI',
 'WALTERK',
 'WVL',
 'ZAUS',
 'ZEIN',
 'ZHKNR',
 'ZVEC']

Let’s also check what simulation timestamps are available in the model.

s3s.GetTimeStamps()[0] # list of simulation timestamps

['2023-02-13 00:00:00.000 +01:00',
 '2023-02-13 01:00:00.000 +01:00',
 '2023-02-13 02:00:00.000 +01:00',
 '2023-02-13 03:00:00.000 +01:00',
 '2023-02-13 04:00:00.000 +01:00',
 '2023-02-13 05:00:00.000 +01:00',
 '2023-02-13 06:00:00.000 +01:00',
 '2023-02-13 07:00:00.000 +01:00',
 '2023-02-13 08:00:00.000 +01:00',
 '2023-02-13 09:00:00.000 +01:00',
 '2023-02-13 10:00:00.000 +01:00',
 '2023-02-13 11:00:00.000 +01:00',
 '2023-02-13 12:00:00.000 +01:00',
 '2023-02-13 13:00:00.000 +01:00',
 '2023-02-13 14:00:00.000 +01:00',
 '2023-02-13 15:00:00.000 +01:00',
 '2023-02-13 16:00:00.000 +01:00',
 '2023-02-13 17:00:00.000 +01:00',
 '2023-02-13 18:00:00.000 +01:00',
 '2023-02-13 19:00:00.000 +01:00',
 '2023-02-13 20:00:00.000 +01:00',
 '2023-02-13 21:00:00.000 +01:00',
 '2023-02-13 22:00:00.000 +01:00',
 '2023-02-13 23:00:00.000 +01:00',
 '2023-02-14 00:00:00.000 +01:00']

s3s.GetTimeStamps()[1] # Static

'2023-02-13 00:00:00.000 +01:00'

In the below subcases differnt choices of parameters for creating the result dataframe manually are shown. Note that not all possible combination of paramters are shown here.

Subcase 1: Static + All Result Values

(s3s.generate_element_results_dataframe(element_type=s3s.ObjectTypes.Pipe
                                        ,properties=None # None => All available properties
                                        ,timestamps=[s3s.GetTimeStamps()[1]] # static
                                        )).head(3)

[2025-12-05 20:20:51,296] INFO in sir3stoolkit.mantle.dataframes: [results] Generating results dataframe for element type: ObjectTypes.Pipe
[2025-12-05 20:20:51,895] INFO in sir3stoolkit.mantle.dataframes: [Resolving Timestamps] Only static timestamp 2023-02-13 00:00:00.000 +01:00 is used
[2025-12-05 20:20:51,895] INFO in sir3stoolkit.mantle.dataframes: [Resolving Timestamps] 1 valid timestamp(s) will be used.
[2025-12-05 20:20:51,906] INFO in sir3stoolkit.mantle.dataframes: [Resolving tks] Retrieved 524 element(s) of element type ObjectTypes.Pipe.
[2025-12-05 20:20:51,913] INFO in sir3stoolkit.mantle.dataframes: [results] No properties given → using ALL non-vectorized result properties for ObjectTypes.Pipe.
[2025-12-05 20:20:51,917] INFO in sir3stoolkit.mantle.dataframes: [results] Using 66 result properties.
[2025-12-05 20:20:52,416] INFO in sir3stoolkit.mantle.dataframes: [results] Retrieving result properties...
[2025-12-05 20:21:11,168] INFO in sir3stoolkit.mantle.dataframes: [results] Done. Shape: (1, 34584)

tk	5442010239090746007																																																																4917786378639043296																																																																4762482310382009633																																																																4987229536643024523																																																																5722206630503885118																																																																5089459212485379804																																																																5526871502288882655																																																						...	5263881428814102490																																																						5139154449387664728																																																																4919408196640282331																																																																4957957828740199285																																																																5499776682108862751																																																																5285235772707481262																																																																5373158549695106826
name	Rohr V-K03S V-K13S																																																																Rohr V-K13S V-K23S																																																																Rohr V-K23S V-K33S																																																																Rohr V-K33S V-K43S																																																																Rohr V-K43S V-K53S																																																																Rohr V-K53S V-K63S																																																																Rohr V-K63S V-K73S																																																						...	Rohr R-K4913S R-K4923S																																																						Rohr R-K4923S R-K4933S																																																																Rohr R-K4933S R-K3223S																																																																Rohr R-K4583S R-K4943S																																																																Rohr R-K4943S R-K4953S																																																																Rohr V-TS-S V-K1403S																																																																Rohr R-TS-S R-K3883S
end_nodes	('5669301360686511351', '5397948523091900401', '-1', '-1')																																																																('5397948523091900401', '5239335112004772156', '-1', '-1')																																																																('5239335112004772156', '5298886695042021307', '-1', '-1')																																																																('5298886695042021307', '4993257270457791438', '-1', '-1')																																																																('4993257270457791438', '5317865645994989592', '-1', '-1')																																																																('5317865645994989592', '5085491372308261877', '-1', '-1')																																																																('5085491372308261877', '4989694427717344427', '-1', '-1')																																																						...	('5169329782661335841', '5085846975953784416', '-1', '-1')																																																						('5085846975953784416', '5330057762618897371', '-1', '-1')																																																																('5330057762618897371', '5000360659046308972', '-1', '-1')																																																																('4863932769493405228', '5295548655235318963', '-1', '-1')																																																																('5295548655235318963', '5475286394948521111', '-1', '-1')																																																																('5172212715463480789', '5392476601138952594', '-1', '-1')																																																																('5093129494518007690', '5337935179219974656', '-1', '-1')
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timestamp
2023-02-13 00:00:00.000 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1 rows × 34584 columns

Note that a multi index on the columns is created, holding the most important metadata.

• Level 0: tk (device ID)

• Level 1: name (device name)

• Level 2: end_nodes (tuple of connected node IDs as string)

• Level 3: property (result name)

As can be seen a lot of 99999.0 placeholder values are inserted, because we attempt to access many result values that SIR Calc can calculate but are not requested for this model. Therefore using all available result properties(properties=None), is generally not recommended.

Subcase 2: All Timestamps + Specific Values

(s3s.generate_element_results_dataframe(element_type=s3s.ObjectTypes.Pipe
                                        ,properties=["DTTR", "PHR"] # Specify result properties of interest
                                        ,timestamps=None # None => All available simulation timestamps
                                        )).head(5)

[2025-12-05 20:21:12,097] INFO in sir3stoolkit.mantle.dataframes: [results] Generating results dataframe for element type: ObjectTypes.Pipe
[2025-12-05 20:21:12,098] INFO in sir3stoolkit.mantle.dataframes: [Resolving Timestamps] No timestamps were given. Checking available simulation timestamps (SIR3S_Model.GetTimeStamps()[0]).
[2025-12-05 20:21:12,224] INFO in sir3stoolkit.mantle.dataframes: [Resolving Timestamps] 25 simulation timestamp(s) are available.
[2025-12-05 20:21:12,370] INFO in sir3stoolkit.mantle.dataframes: [Resolving Timestamps] 25 valid timestamp(s) will be used.
[2025-12-05 20:21:12,373] INFO in sir3stoolkit.mantle.dataframes: [Resolving tks] Retrieved 524 element(s) of element type ObjectTypes.Pipe.
[2025-12-05 20:21:12,376] INFO in sir3stoolkit.mantle.dataframes: [results] Using 2 result properties.
[2025-12-05 20:21:12,398] INFO in sir3stoolkit.mantle.dataframes: [results] Retrieving result properties...
[2025-12-05 20:21:16,497] INFO in sir3stoolkit.mantle.dataframes: [results] Done. Shape: (25, 1048)

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timestamp
2023-02-13 00:00:00.000 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2023-02-13 01:00:00.000 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2023-02-13 02:00:00.000 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2023-02-13 03:00:00.000 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2023-02-13 04:00:00.000 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5 rows × 1048 columns

Subcase 3: Specific Timestamps + Specific Values

We can specify the list of timestamps to include either as a list of str timestamps …

(s3s.generate_element_results_dataframe(element_type=s3s.ObjectTypes.Pipe
                                        ,properties=["DTTR", "PHR"]
                                        ,timestamps=['2023-02-13 00:00:00.000 +01:00', '2023-02-13 05:00:00.000 +01:00', '2023-02-13 23:00:00.000 +01:00']
                                        )).head(3)

[2025-12-05 20:21:17,262] INFO in sir3stoolkit.mantle.dataframes: [results] Generating results dataframe for element type: ObjectTypes.Pipe
[2025-12-05 20:21:17,425] INFO in sir3stoolkit.mantle.dataframes: [Resolving Timestamps] 3 valid timestamp(s) will be used.
[2025-12-05 20:21:17,429] INFO in sir3stoolkit.mantle.dataframes: [Resolving tks] Retrieved 524 element(s) of element type ObjectTypes.Pipe.
[2025-12-05 20:21:17,431] INFO in sir3stoolkit.mantle.dataframes: [results] Using 2 result properties.
[2025-12-05 20:21:17,449] INFO in sir3stoolkit.mantle.dataframes: [results] Retrieving result properties...
[2025-12-05 20:21:18,142] INFO in sir3stoolkit.mantle.dataframes: [results] Done. Shape: (3, 1048)

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timestamp
2023-02-13 00:00:00.000 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2023-02-13 05:00:00.000 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.120093e-26	50.19061	8.256394e-26	47.19448	0.0	50.84296	0.000000e+00	50.56349	0.000000e+00	41.89281	3.425513e-25	9.602071	0.000000e+00	25.53992	8.743657e-26	0.007024	0.012662	0.007381	0.013305	0.013097	0.023608	0.010650	0.019198	0.010117	0.018237	35.89539	5.198812e-25	72.46398	3.332966e-25	71.66468	3.426732e-25	49.03616	0.000000e+00	54.56744	0.0	92.76953	0.0	0.014680	0.028270	0.012296	0.022888	0.011619	0.022988	0.018141	0.046047	0.003513	0.004915	0.002770	0.003875	0.003194	0.004469	0.004149	0.004302	0.013845	0.043946	0.004094	0.007662	0.001529	0.007767	0.000674	0.003426	0.000961	0.004879	0.005084	0.029736	0.026259	0.028185	0.015075	0.025199	0.003717	0.008547	0.002055	0.006528	0.002091	0.006640	0.001858	0.002108	0.002485	0.003726	0.027575	0.058676	0.026825	0.027331	0.008892	0.005536	0.021468	0.036464	0.053924	0.110877	0.019613	0.017299	0.002492	0.007259	0.001806	0.005260	0.002199	0.004600	0.018623	0.029626	76.11499	0.0	0.029568	0.050150	0.128456	0.012809	0.001700	0.000519	0.002814	0.000859	0.013999	0.004272	0.011938	0.005220	0.002867	0.001752	0.002700	0.000391	0.106233	0.008861	0.001217	0.002151	0.010063	0.036757	0.001140	0.001893	0.001179	0.001957	0.001796	0.001567	0.009383	0.008186	0.017686	0.005688	0.022716	0.017651	0.049580	0.054644	0.017395	0.060463	0.021960	0.013213	0.006123	0.033649	0.008222	0.025826	0.002600	0.032896	0.003251	0.027973	0.003369	0.015016	0.004035	0.016381	0.002829	0.017908	0.002682	0.016980	0.004158	0.012511	0.014857	0.036563	0.006540	0.016094	0.024614	0.013945	22.46283	8.703624e-26	11.13411	0.000000e+00	18.74616	0.000000e+00	27.98768	3.370467e-25	32.47894	8.216519e-26	63.81877	0.0	0.021233	0.034099	0.017405	0.031686	0.005091	0.005500	0.013665	0.020281	0.014228	0.045979	0.023096	0.030761	0.012696	0.020338	0.008797	0.006864	0.003140	0.005660	0.002559	0.004612	10.56986	1.601792e-13	14.57936	2.209406e-13	19.87988	3.012663e-13	24.45074	3.705344e-13	23.61157	3.578180e-13	50.96676	7.723678e-13	21.2934	7.592590e-25	16.31768	3.365692e-25	21.15114	1.355570e-24	6.620245	3.288472e-26	5.387928	2.104679e-26	11.07489	0.0	6.690278	3.419088e-25	21.84747	0.000000e+00	18.86606	7.686561e-26	26.74521	2.125622e-24	17.53896	2.096616e-24	15.2731	7.622794e-25	7.780674	3.348380e-25	35.01923	5.306928e-13	18.98862	2.877595e-13	14.42032	3.387078e-25	16.00222	1.189204e-24	40.99183	2.592197e-23	37.58163	2.039686e-23	3.547835	2.117795e-24	34.96974	2.003576e-26	105.8952	0.0	80.62691	3.415578e-22	36.44118	0.0	31.60874	0.0	0.030736	0.003433	0.024853	0.009923	0.010120	0.014618	0.003533	0.006369	0.002765	0.004983	18.81272	8.659940e-23	15.6488	0.0	0.017752	0.026051	45.47781	0.0	0.052653	0.078171	34.57665	0.0	8.426783	2.095524e-26	22.79576	8.529783e-26	33.26721	8.494776e-26	18.7625	0.000000e+00	14.43095	5.837866e-25	0.020773	0.012497	0.013443	0.043912	16.76996	0.0	0.002947	0.005312	0.001282	0.002310	0.001645	0.001884	0.006653	0.011992	0.002870	0.005173	0.002017	0.003637	0.002672	0.009729	0.017432	0.019575	64.70937	0.0	80.91752	3.388132e-25	102.0578	1.663247e-24	47.21349	3.419750e-25	0.009775	0.016620	0.010824	0.006462	0.018221	0.010878	42.44363	6.859804e-22	12.77701	5.185884e-23	27.49709	1.120308e-23	5.377964	1.355252e-24	2.779566	7.785946e-25	2.251996	1.390716e-25	9.412791	1.905699e-25	3.21121	1.458254e-25	2.603956	6.693012e-27	5.753166	1.095352e-26	17.9649	5.368832e-27	0.055606	0.036134	0.011015	0.005235	0.009503	0.004245	26.05582	2.110089e-24	0.005387	0.009818	0.011003	0.020997	25.21477	2.158086e-26	13.48946	8.444945e-26	8.799599	2.130270e-26	11.09779	0.000000e+00	26.07811	2.231977e-26	17.34526	9.618897e-26	20.64109	0.000000e+00	0.001839	0.018179	0.001861	0.018278
2023-02-13 23:00:00.000 +01:00	0.012623	0.011749	0.023859	0.016444	0.016756	0.012244	0.002793	0.002041	0.003063	0.002238	0.003401	0.002485	0.037095	0.020862	0.001529	0.002533	0.002773	0.001198	0.002115	0.000914	0.011310	0.004887	0.014989	0.005354	0.014022	0.010450	0.053944	0.004016	0.054312	0.037035	165.7806	0.0	3.842942	1.233485e-24	64.19534	3.433975e-25	51.01035	1.717848e-24	83.9612	3.078732e-24	64.28724	3.057601e-24	43.0797	1.950208e-24	0.086543	0.004188	0.007364	0.002898	0.016020	0.007661	0.030491	0.017585	0.012041	0.010019	0.001767	0.003435	0.005187	0.009506	0.005163	0.009462	0.009032	0.015425	0.003833	0.006084	0.022495	0.025841	52.3749	4.777743e-21	13.53016	8.613386e-26	12.6086	5.305003e-25	10.49419	3.407186e-25	8.190004	3.051250e-24	13.37522	1.939846e-25	0.030186	0.033411	0.007174	0.003777	0.010693	0.008414	0.008871	0.009509	0.014659	0.009091	0.012744	0.009837	0.021902	0.011939	14.29888	3.371437e-25	6.310922	0.0	59.923	0.0	99.99299	0.0	14.00829	0.000000e+00	5.560995	1.357677e-27	17.37406	5.393882e-27	126.116	1.875575e-25	21.51779	2.191737e-26	0.026467	0.028999	0.036583	0.005552	0.122498	0.003055	0.015923	0.000050	42.03592	6.468936e-26	23.02995	2.181941e-21	117.7475	1.679409e-23	25.77541	3.033984e-24	18.48974	8.841090e-26	20.08946	7.572553e-25	20.21883	4.763323e-26	71.85966	0.0	24.84862	1.329217e-27	25.31222	1.323418e-27	30.19752	2.135992e-26	36.38173	2.084475e-26	34.99198	8.935195e-26	6.78462	0.000000e+00	33.2108	2.120093e-26	50.19061	2.271663e-26	47.19448	0.0	50.84296	0.000000e+00	50.56349	1.374829e-24	41.89281	1.290790e-24	9.602071	7.631413e-25	25.53992	0.0	0.010174	0.005924	0.010691	0.006224	0.018969	0.011044	0.015426	0.008981	0.014654	0.008532	35.89539	3.527767e-25	72.46398	3.332966e-25	71.66468	0.000000e+00	49.03616	0.0	54.56744	0.0	92.76953	0.0	0.021297	0.013189	0.017839	0.010679	0.017419	0.010080	0.028351	0.018637	0.005589	0.001925	0.004407	0.001518	0.005082	0.001750	0.006672	0.001652	0.022964	0.015893	0.007827	0.002128	0.002177	0.003756	0.000960	0.001657	0.001368	0.002360	0.012564	0.003573	0.037473	0.013563	0.021563	0.012073	0.005365	0.004026	0.002968	0.003073	0.003019	0.003126	0.002684	0.000991	0.003590	0.001752	0.039849	0.027568	0.025921	0.035213	0.012877	0.002592	0.031109	0.017047	0.114211	0.018371	0.028309	0.008150	0.003289	0.004057	0.002383	0.002940	0.002871	0.002621	0.026858	0.013982	76.11499	0.0	0.042565	0.023762	0.188262	0.005870	0.002479	0.000240	0.004104	0.000397	0.020413	0.001975	0.017388	0.002418	0.004172	0.000813	0.002599	0.000506	0.154330	0.004122	0.001755	0.001015	0.014507	0.017366	0.001643	0.000895	0.001699	0.000926	0.002588	0.000742	0.013516	0.003873	0.025451	0.002697	0.032707	0.008361	0.071528	0.025767	0.025390	0.027938	0.031936	0.006277	0.008936	0.015760	0.011994	0.012120	0.003792	0.015417	0.004740	0.013128	0.004908	0.007065	0.005878	0.007710	0.004118	0.008439	0.003905	0.008002	0.006048	0.005914	0.021562	0.017382	0.009492	0.007652	0.035680	0.006673	22.46283	4.268130e-24	11.13411	1.344570e-24	18.74616	0.000000e+00	27.98768	3.370467e-25	32.47894	3.437697e-25	63.81877	0.0	0.030458	0.016265	0.025092	0.014963	0.007346	0.002593	0.019722	0.009555	0.026667	0.013247	0.033376	0.014458	0.018337	0.009571	0.012705	0.003231	0.004548	0.002648	0.003706	0.002158	10.56986	1.001101e-14	14.57936	1.380865e-14	19.87988	1.882899e-14	24.45074	2.315835e-14	23.61157	2.236347e-14	50.96676	4.827263e-14	21.2934	1.359316e-24	16.31768	2.134837e-24	21.15114	7.623296e-25	6.620245	3.288472e-26	5.387928	8.463556e-26	11.07489	2.132729e-26	6.690278	0.000000e+00	21.84747	2.991816e-25	18.86606	3.416249e-26	26.74521	1.318506e-23	17.53896	8.386463e-24	15.2731	6.860515e-24	7.780674	3.065275e-24	35.01923	3.316902e-14	18.98862	1.798540e-14	14.42032	3.648389e-24	16.00222	4.150936e-24	40.99183	8.379603e-26	37.58163	1.937900e-25	3.547835	5.276278e-27	34.96974	8.929517e-26	105.8952	0.000000e+00	80.62691	3.315349e-22	36.44118	0.0	31.60874	1.365174e-25	0.044353	0.001618	0.035899	0.004665	0.014670	0.006827	0.005118	0.002980	0.004004	0.002331	18.81272	8.689763e-23	15.6488	0.000000e+00	0.025736	0.012165	45.47781	0.0	0.075801	0.037022	34.57665	0.0	8.426783	5.379443e-27	22.79576	7.565950e-25	33.26721	1.359164e-24	18.7625	1.353823e-24	14.43095	5.837866e-25	0.030215	0.005939	0.019490	0.020506	16.76996	4.299126e-24	0.004268	0.002485	0.001856	0.001081	0.002381	0.000882	...	13.37522	1.873481e-25	0.030740	0.034340	0.007307	0.003895	0.010891	0.008650	0.009035	0.009752	0.014931	0.009341	0.012981	0.010091	0.022309	0.012243	14.29888	8.785053e-26	6.310922	0.0	59.923	2.004137e-26	99.99299	0.0	14.00829	5.208222e-27	5.560995	1.598605e-25	17.37406	3.386581e-25	126.116	2.230173e-26	21.51779	0.0	0.026962	0.029326	0.034437	0.006656	0.108074	0.004166	0.012268	0.000088	42.03592	1.905105e-25	23.02995	1.878140e-25	117.7475	0.000000e+00	25.77541	0.000000e+00	18.48974	3.401727e-25	20.08946	2.046252e-26	20.21883	5.586558e-27	71.85966	0.0	24.84862	1.187250e-26	25.31222	1.679232e-27	30.19752	8.331704e-26	36.38173	0.000000e+00	34.99198	8.935195e-26	6.78462	2.116374e-26	33.2108	1.023282e-24	50.19061	1.714827e-24	47.19448	0.0	50.84296	3.682639e-25	50.56349	0.000000e+00	41.89281	1.370205e-24	9.602071	3.328046e-25	25.53992	8.743657e-26	0.010364	0.006000	0.010891	0.006305	0.019324	0.011187	0.015714	0.009097	0.014928	0.008642	35.89539	3.313467e-25	72.46398	0.000000e+00	71.66468	3.168207e-25	49.03616	3.333252e-25	54.56744	0.0	92.76953	0.0	0.021693	0.013412	0.018170	0.010861	0.017191	0.010909	0.026716	0.022098	0.005169	0.002372	0.004076	0.001871	0.004700	0.002157	0.006104	0.002083	0.020343	0.021206	0.005994	0.003746	0.002248	0.003708	0.000991	0.001635	0.001412	0.002329	0.007475	0.014174	0.038627	0.013541	0.022191	0.012054	0.005464	0.004140	0.003023	0.003152	0.003074	0.003206	0.002734	0.001023	0.003656	0.001803	0.040590	0.028306	0.039571	0.013104	0.013114	0.002702	0.031686	0.017532	0.079507	0.053341	0.028830	0.008397	0.003662	0.003505	0.002654	0.002540	0.003230	0.002228	0.027351	0.014384	76.11499	0.0	0.043339	0.024556	0.190972	0.006193	0.002518	0.000250	0.004169	0.000414	0.020736	0.002062	0.017669	0.002516	0.004241	0.000843	0.003994	0.000190	0.156957	0.004315	0.001787	0.001044	0.014772	0.017789	0.001673	0.000922	0.001730	0.000954	0.002635	0.000768	0.013763	0.004013	0.025912	0.002826	0.033301	0.008670	0.072846	0.026736	0.025848	0.028463	0.032315	0.006479	0.009042	0.015973	0.012137	0.012319	0.003837	0.015589	0.004796	0.013300	0.004966	0.007182	0.005948	0.007841	0.004167	0.008576	0.003951	0.008131	0.006119	0.006034	0.021815	0.017791	0.009603	0.007830	0.036092	0.006885	22.46283	8.070851e-26	11.13411	0.000000e+00	18.74616	0.000000e+00	27.98768	3.370467e-25	32.47894	8.216519e-26	63.81877	0.0	0.031011	0.016977	0.025554	0.015415	0.007481	0.002676	0.020087	0.009837	0.020898	0.022310	0.033992	0.014897	0.018674	0.009869	0.012937	0.003351	0.004633	0.002682	0.003775	0.002185	10.56986	1.601792e-13	14.57936	2.209400e-13	19.87988	3.012656e-13	24.45074	3.705337e-13	23.61157	3.578166e-13	50.96676	7.723658e-13	21.2934	3.398290e-25	16.31768	8.414230e-26	21.15114	0.000000e+00	6.620245	1.198788e-26	5.387928	2.149698e-26	11.07489	0.0	6.690278	8.347781e-26	21.84747	2.991816e-25	18.86606	0.000000e+00	26.74521	1.018240e-23	17.53896	8.386463e-24	15.2731	6.860515e-24	7.780674	3.065275e-24	35.01923	5.306957e-13	18.98862	2.877617e-13	14.42032	1.354831e-24	16.00222	4.281133e-25	40.99183	5.361206e-25	37.58163	5.383056e-25	3.547835	2.110511e-26	34.96974	2.003576e-26	105.8952	0.0	80.62691	3.335332e-22	36.44118	0.0	31.60874	0.0	0.045167	0.001711	0.036566	0.004884	0.014943	0.007031	0.005213	0.003018	0.004079	0.002361	18.81272	8.659533e-23	15.6488	0.0	0.026215	0.012595	45.47781	0.0	0.077190	0.038310	34.57665	0.0	8.426783	2.095524e-26	22.79576	0.000000e+00	33.26721	8.494776e-26	18.7625	8.461391e-26	14.43095	2.230933e-25	0.030568	0.006128	0.019852	0.021088	16.76996	0.0	0.004348	0.002517	0.001891	0.001095	0.002426	0.000897	0.009816	0.005682	0.004234	0.002451	0.002977	0.001723	0.003939	0.004607	0.025701	0.009325	64.70937	0.0	80.91752	0.000000e+00	102.0578	7.392210e-25	47.21349	0.000000e+00	0.014357	0.008107	0.015883	0.003185	0.026738	0.005361	42.44363	7.165161e-22	12.77701	5.619025e-23	27.49709	2.144340e-26	5.377964	0.000000e+00	2.779566	7.959043e-26	2.251996	4.380058e-26	9.412791	1.905699e-25	3.21121	8.308066e-27	2.603956	6.693012e-27	5.753166	2.281439e-25	17.9649	5.368832e-27	0.082039	0.017324	0.016248	0.002517	0.014018	0.002041	26.05582	1.026496e-23	0.007949	0.004672	0.016239	0.009987	25.21477	2.158086e-26	13.48946	2.146868e-26	8.799599	0.000000e+00	11.09779	1.884764e-25	26.07811	5.378880e-25	17.34526	0.000000e+00	20.64109	2.102439e-26	0.002716	0.008551	0.002748	0.008642

3 rows × 1048 columns

… or as a list of int indices.

simulation_timestamps=s3s.GetTimeStamps()[0]

for idx, timestamp in enumerate(simulation_timestamps):
    print(idx, timestamp)

0 2023-02-13 00:00:00.000 +01:00
1 2023-02-13 01:00:00.000 +01:00
2 2023-02-13 02:00:00.000 +01:00
3 2023-02-13 03:00:00.000 +01:00
4 2023-02-13 04:00:00.000 +01:00
5 2023-02-13 05:00:00.000 +01:00
6 2023-02-13 06:00:00.000 +01:00
7 2023-02-13 07:00:00.000 +01:00
8 2023-02-13 08:00:00.000 +01:00
9 2023-02-13 09:00:00.000 +01:00
10 2023-02-13 10:00:00.000 +01:00
11 2023-02-13 11:00:00.000 +01:00
12 2023-02-13 12:00:00.000 +01:00
13 2023-02-13 13:00:00.000 +01:00
14 2023-02-13 14:00:00.000 +01:00
15 2023-02-13 15:00:00.000 +01:00
16 2023-02-13 16:00:00.000 +01:00
17 2023-02-13 17:00:00.000 +01:00
18 2023-02-13 18:00:00.000 +01:00
19 2023-02-13 19:00:00.000 +01:00
20 2023-02-13 20:00:00.000 +01:00
21 2023-02-13 21:00:00.000 +01:00
22 2023-02-13 22:00:00.000 +01:00
23 2023-02-13 23:00:00.000 +01:00
24 2023-02-14 00:00:00.000 +01:00

(s3s.generate_element_results_dataframe(element_type=s3s.ObjectTypes.Pipe
                                        ,properties=["DTTR", "PHR"]
                                        ,timestamps=[0, 5, -2]
                                        )).head(3)

[2025-12-05 20:21:19,132] INFO in sir3stoolkit.mantle.dataframes: [results] Generating results dataframe for element type: ObjectTypes.Pipe
[2025-12-05 20:21:19,288] INFO in sir3stoolkit.mantle.dataframes: [Resolving Timestamps] 3 valid timestamp(s) will be used.
[2025-12-05 20:21:19,291] INFO in sir3stoolkit.mantle.dataframes: [Resolving tks] Retrieved 524 element(s) of element type ObjectTypes.Pipe.
[2025-12-05 20:21:19,294] INFO in sir3stoolkit.mantle.dataframes: [results] Using 2 result properties.
[2025-12-05 20:21:19,330] INFO in sir3stoolkit.mantle.dataframes: [results] Retrieving result properties...
[2025-12-05 20:21:19,945] INFO in sir3stoolkit.mantle.dataframes: [results] Done. Shape: (3, 1048)

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V-K423S V-K483S		Rohr V-K483S V-K493S		Rohr V-K493S V-K503S		Rohr V-K503S V-K513S		Rohr V-K513S V-K523S		Rohr V-K523S V-K533S		Rohr V-K533S V-K543S		Rohr V-K543S V-K553S		Rohr V-K553S V-K563S		Rohr V-K573S V-K583S		Rohr V-K153S V-K593S		Rohr V-K593S V-K603S		Rohr V-K603S V-K283S		Rohr V-K353S V-K613S		Rohr V-K343S V-K623S		Rohr V-K623S V-K633S		Rohr V-K633S V-K643S		Rohr V-K643S V-K653S		Rohr V-K653S V-K663S		Rohr V-K663S V-K673S		Rohr V-K673S V-K683S		Rohr V-K683S V-K693S		Rohr V-K693S V-K703S		Rohr V-K703S V-K713S		Rohr V-K713S V-K723S		Rohr V-K723S V-K733S		Rohr V-K733S V-K743S		Rohr V-K743S V-K753S		Rohr V-K753S V-K763S		Rohr V-K223S V-K773S		Rohr V-K783S V-K793S		Rohr V-K793S V-K803S		Rohr V-K803S V-K813S		Rohr V-K813S V-K823S		Rohr V-K823S V-K833S		Rohr V-K833S V-K843S		Rohr V-K843S V-K853S		Rohr V-K853S V-K863S		Rohr V-K863S V-K873S		Rohr V-K873S V-K883S		Rohr V-K883S V-K893S		Rohr V-K893S V-K903S		Rohr V-K903S V-K913S		Rohr V-K913S V-K923S		Rohr V-K923S V-K503S		Rohr V-K503S 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timestamp
2023-02-13 00:00:00.000 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2023-02-13 05:00:00.000 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2023-02-13 23:00:00.000 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3 rows × 1048 columns

Subcase 4: Specific Timestamps + Specific Values + Container Filtering

As for metadatat dataframes we can give a list of container tks to filter for.

(s3s.generate_element_results_dataframe(element_type=s3s.ObjectTypes.Pipe
                                        ,properties=["DTTR", "PHR"]
                                        ,timestamps=[0, 5, -2]
                                        ,filter_container_tks=[s3s.GetMainContainer()[0]]
                                        )).head(3)

[2025-12-05 20:21:20,790] INFO in sir3stoolkit.mantle.dataframes: [results] Generating results dataframe for element type: ObjectTypes.Pipe
[2025-12-05 20:21:20,868] INFO in sir3stoolkit.mantle.dataframes: [Resolving Timestamps] 3 valid timestamp(s) will be used.
[2025-12-05 20:21:20,871] INFO in sir3stoolkit.mantle.dataframes: [Resolving tks] Retrieved 524 element(s) of element type ObjectTypes.Pipe.
[2025-12-05 20:21:20,910] INFO in sir3stoolkit.mantle.dataframes: [Resolving tks] 524 tks remain after container filtering.
[2025-12-05 20:21:20,913] INFO in sir3stoolkit.mantle.dataframes: [results] Using 2 result properties.
[2025-12-05 20:21:20,929] INFO in sir3stoolkit.mantle.dataframes: [results] Retrieving result properties...
[2025-12-05 20:21:21,616] INFO in sir3stoolkit.mantle.dataframes: [results] Done. Shape: (3, 1048)

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timestamp
2023-02-13 00:00:00.000 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2023-02-13 05:00:00.000 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2023-02-13 23:00:00.000 +01:00	0.012623	0.011749	0.023859	0.016444	0.016756	0.012244	0.002793	0.002041	0.003063	0.002238	0.003401	0.002485	0.037095	0.020862	0.001529	0.002533	0.002773	0.001198	0.002115	0.000914	0.011310	0.004887	0.014989	0.005354	0.014022	0.010450	0.053944	0.004016	0.054312	0.037035	165.7806	0.0	3.842942	1.233485e-24	64.19534	3.433975e-25	51.01035	1.717848e-24	83.9612	3.078732e-24	64.28724	3.057601e-24	43.0797	1.950208e-24	0.086543	0.004188	0.007364	0.002898	0.016020	0.007661	0.030491	0.017585	0.012041	0.010019	0.001767	0.003435	0.005187	0.009506	0.005163	0.009462	0.009032	0.015425	0.003833	0.006084	0.022495	0.025841	52.3749	4.777743e-21	13.53016	8.613386e-26	12.6086	5.305003e-25	10.49419	3.407186e-25	8.190004	3.051250e-24	13.37522	1.939846e-25	0.030186	0.033411	0.007174	0.003777	0.010693	0.008414	0.008871	0.009509	0.014659	0.009091	0.012744	0.009837	0.021902	0.011939	14.29888	3.371437e-25	6.310922	0.0	59.923	0.0	99.99299	0.0	14.00829	0.000000e+00	5.560995	1.357677e-27	17.37406	5.393882e-27	126.116	1.875575e-25	21.51779	2.191737e-26	0.026467	0.028999	0.036583	0.005552	0.122498	0.003055	0.015923	0.000050	42.03592	6.468936e-26	23.02995	2.181941e-21	117.7475	1.679409e-23	25.77541	3.033984e-24	18.48974	8.841090e-26	20.08946	7.572553e-25	20.21883	4.763323e-26	71.85966	0.0	24.84862	1.329217e-27	25.31222	1.323418e-27	30.19752	2.135992e-26	36.38173	2.084475e-26	34.99198	8.935195e-26	6.78462	0.000000e+00	33.2108	2.120093e-26	50.19061	2.271663e-26	47.19448	0.0	50.84296	0.000000e+00	50.56349	1.374829e-24	41.89281	1.290790e-24	9.602071	7.631413e-25	25.53992	0.0	0.010174	0.005924	0.010691	0.006224	0.018969	0.011044	0.015426	0.008981	0.014654	0.008532	35.89539	3.527767e-25	72.46398	3.332966e-25	71.66468	0.000000e+00	49.03616	0.0	54.56744	0.0	92.76953	0.0	0.021297	0.013189	0.017839	0.010679	0.017419	0.010080	0.028351	0.018637	0.005589	0.001925	0.004407	0.001518	0.005082	0.001750	0.006672	0.001652	0.022964	0.015893	0.007827	0.002128	0.002177	0.003756	0.000960	0.001657	0.001368	0.002360	0.012564	0.003573	0.037473	0.013563	0.021563	0.012073	0.005365	0.004026	0.002968	0.003073	0.003019	0.003126	0.002684	0.000991	0.003590	0.001752	0.039849	0.027568	0.025921	0.035213	0.012877	0.002592	0.031109	0.017047	0.114211	0.018371	0.028309	0.008150	0.003289	0.004057	0.002383	0.002940	0.002871	0.002621	0.026858	0.013982	76.11499	0.0	0.042565	0.023762	0.188262	0.005870	0.002479	0.000240	0.004104	0.000397	0.020413	0.001975	0.017388	0.002418	0.004172	0.000813	0.002599	0.000506	0.154330	0.004122	0.001755	0.001015	0.014507	0.017366	0.001643	0.000895	0.001699	0.000926	0.002588	0.000742	0.013516	0.003873	0.025451	0.002697	0.032707	0.008361	0.071528	0.025767	0.025390	0.027938	0.031936	0.006277	0.008936	0.015760	0.011994	0.012120	0.003792	0.015417	0.004740	0.013128	0.004908	0.007065	0.005878	0.007710	0.004118	0.008439	0.003905	0.008002	0.006048	0.005914	0.021562	0.017382	0.009492	0.007652	0.035680	0.006673	22.46283	4.268130e-24	11.13411	1.344570e-24	18.74616	0.000000e+00	27.98768	3.370467e-25	32.47894	3.437697e-25	63.81877	0.0	0.030458	0.016265	0.025092	0.014963	0.007346	0.002593	0.019722	0.009555	0.026667	0.013247	0.033376	0.014458	0.018337	0.009571	0.012705	0.003231	0.004548	0.002648	0.003706	0.002158	10.56986	1.001101e-14	14.57936	1.380865e-14	19.87988	1.882899e-14	24.45074	2.315835e-14	23.61157	2.236347e-14	50.96676	4.827263e-14	21.2934	1.359316e-24	16.31768	2.134837e-24	21.15114	7.623296e-25	6.620245	3.288472e-26	5.387928	8.463556e-26	11.07489	2.132729e-26	6.690278	0.000000e+00	21.84747	2.991816e-25	18.86606	3.416249e-26	26.74521	1.318506e-23	17.53896	8.386463e-24	15.2731	6.860515e-24	7.780674	3.065275e-24	35.01923	3.316902e-14	18.98862	1.798540e-14	14.42032	3.648389e-24	16.00222	4.150936e-24	40.99183	8.379603e-26	37.58163	1.937900e-25	3.547835	5.276278e-27	34.96974	8.929517e-26	105.8952	0.000000e+00	80.62691	3.315349e-22	36.44118	0.0	31.60874	1.365174e-25	0.044353	0.001618	0.035899	0.004665	0.014670	0.006827	0.005118	0.002980	0.004004	0.002331	18.81272	8.689763e-23	15.6488	0.000000e+00	0.025736	0.012165	45.47781	0.0	0.075801	0.037022	34.57665	0.0	8.426783	5.379443e-27	22.79576	7.565950e-25	33.26721	1.359164e-24	18.7625	1.353823e-24	14.43095	5.837866e-25	0.030215	0.005939	0.019490	0.020506	16.76996	4.299126e-24	0.004268	0.002485	0.001856	0.001081	0.002381	0.000882	...	13.37522	1.873481e-25	0.030740	0.034340	0.007307	0.003895	0.010891	0.008650	0.009035	0.009752	0.014931	0.009341	0.012981	0.010091	0.022309	0.012243	14.29888	8.785053e-26	6.310922	0.0	59.923	2.004137e-26	99.99299	0.0	14.00829	5.208222e-27	5.560995	1.598605e-25	17.37406	3.386581e-25	126.116	2.230173e-26	21.51779	0.0	0.026962	0.029326	0.034437	0.006656	0.108074	0.004166	0.012268	0.000088	42.03592	1.905105e-25	23.02995	1.878140e-25	117.7475	0.000000e+00	25.77541	0.000000e+00	18.48974	3.401727e-25	20.08946	2.046252e-26	20.21883	5.586558e-27	71.85966	0.0	24.84862	1.187250e-26	25.31222	1.679232e-27	30.19752	8.331704e-26	36.38173	0.000000e+00	34.99198	8.935195e-26	6.78462	2.116374e-26	33.2108	1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3 rows × 1048 columns

Subcase 5: Specific Timestamps + Specific Values + Filtering

If we want to filter for more than just the container, we have to filter tks outside the function and then pass them. Works same way as for metadata.

all_tks = s3s.GetTksofElementType(s3s.ObjectTypes.Pipe)

len(all_tks)

524

good_tks = []

s3s.GetTksofElementType(s3s.ObjectTypes.PipeGroup) # Leitungsgruppe

['5109995988251362606', '4779752876656844188']

for tk in all_tks:
    if s3s.GetValue(tk, "Fkltgr")[0] == '4779752876656844188': # Leitungsgruppe
        if s3s.GetValue(tk, "bz.Irtrenn")[0] == "0": # Rohr nicht getrennt
            good_tks.append(tk)

len(good_tks)

480

(s3s.generate_element_results_dataframe(element_type=s3s.ObjectTypes.Pipe
                                        ,tks=good_tks # here we specify that not all available tks should be used
                                        ,properties=["DTTR", "PHR"]
                                        ,timestamps=[0, 5, -2]
                                        ,filter_container_tks=[s3s.GetMainContainer()[0]]
                                        )).head(3)

[2025-12-05 20:21:22,644] INFO in sir3stoolkit.mantle.dataframes: [results] Generating results dataframe for element type: ObjectTypes.Pipe
[2025-12-05 20:21:22,732] INFO in sir3stoolkit.mantle.dataframes: [Resolving Timestamps] 3 valid timestamp(s) will be used.
[2025-12-05 20:21:22,734] INFO in sir3stoolkit.mantle.dataframes: [Resolving tks] Retrieved 524 element(s) of element type ObjectTypes.Pipe.
[2025-12-05 20:21:22,738] INFO in sir3stoolkit.mantle.dataframes: [Resolving tks] 480 tks remain after filtering for given tks.
[2025-12-05 20:21:22,778] INFO in sir3stoolkit.mantle.dataframes: [Resolving tks] 480 tks remain after container filtering.
[2025-12-05 20:21:22,778] INFO in sir3stoolkit.mantle.dataframes: [results] Using 2 result properties.
[2025-12-05 20:21:22,805] INFO in sir3stoolkit.mantle.dataframes: [results] Retrieving result properties...
[2025-12-05 20:21:23,410] INFO in sir3stoolkit.mantle.dataframes: [results] Done. Shape: (3, 960)

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timestamp
2023-02-13 00:00:00.000 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2023-02-13 05:00:00.000 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2023-02-13 23:00:00.000 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3 rows × 960 columns

Subcase 6: Specific Vectorized results + Specific Timestamps

For pipes we have interior point result values. These are usually found as vectors with the postfix VEC.

available_result_props=s3s.GetResultProperties_from_elementType(s3s.ObjectTypes.Pipe,False)

available_result_vector_props=[available_result_props for available_result_props in available_result_props if "VEC" in available_result_props]

available_result_vector_props

['HVEC',
 'MVEC',
 'MVECMAX_INST',
 'MVECMIN_INST',
 'PHVEC',
 'PVEC',
 'PVECMAX_INST',
 'PVECMIN_INST',
 'QMVEC',
 'RHOVEC',
 'SVEC',
 'TTRVEC',
 'TVEC',
 'TVECMAX_INST',
 'TVECMIN_INST',
 'ZVEC']

(s3s.generate_element_results_dataframe(element_type=s3s.ObjectTypes.Pipe
                                        ,properties=["TTRVEC", "MVEC"]
                                        ,timestamps=[0, 5, -2]
                                        ,use_vector_results=True
                                        )).head(3)

[2025-12-05 20:24:02,651] INFO in sir3stoolkit.mantle.dataframes: [results] Generating results dataframe for element type: ObjectTypes.Pipe
[2025-12-05 20:24:02,732] INFO in sir3stoolkit.mantle.dataframes: [Resolving Timestamps] 3 valid timestamp(s) will be used.
[2025-12-05 20:24:02,734] INFO in sir3stoolkit.mantle.dataframes: [Resolving tks] Retrieved 524 element(s) of element type ObjectTypes.Pipe.
[2025-12-05 20:24:02,736] INFO in sir3stoolkit.mantle.dataframes: [results] Using 2 result properties.
[2025-12-05 20:24:02,747] INFO in sir3stoolkit.mantle.dataframes: [results] Retrieving result properties...
[2025-12-05 20:24:03,775] INFO in sir3stoolkit.mantle.dataframes: [results] Done. Shape: (3, 5036)

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timestamp
2023-02-13 00:00:00.000 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2023-02-13 05:00:00.000 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2023-02-13 23:00:00.000 +01:00	0.303047	0.306203	0.309359	0.312514	0.315670	15.62682	15.62682	15.62682	15.62682	15.62682	0.315670	0.319647	0.323623	0.327600	0.331576	0.335553	0.339529	8.764987	8.764987	8.764987	8.764987	8.764987	8.764987	8.764987	0.402825	0.406176	0.409527	0.412879	0.416230	0.419581	8.942981	8.942981	8.942981	8.942981	8.942981	8.942981	0.419581	0.422374	8.942981	8.942981	0.422374	0.425437	8.942981	8.942981	0.425437	0.428838	8.942981	8.942981	0.428838	0.432960	0.437082	0.441203	0.445325	0.449447	0.453568	0.457690	0.461811	0.465933	8.173226	8.173226	8.173226	8.173226	8.173226	8.173226	8.173226	8.173226	8.173226	8.173226	0.465933	0.467462	7.180723	7.180723	0.467462	0.470235	4.523406	4.523406	0.470235	0.472350	4.523406	4.523406	0.472350	0.476120	0.479890	0.483660	4.523406	4.523406	4.523406	4.523406	0.483660	0.488656	0.493652	0.498648	4.235434	4.235434	4.235434	4.235434	0.498648	0.503322	0.507996	0.512671	2.899950	2.899950	2.899950	2.899950	0.512671	0.523459	0.534248	0.545037	0.555826	0.566615	0.473623	0.473623	0.473623	0.473623	0.473623	0.473623	0.314673	0.319610	0.324547	0.329485	0.334422	0.339360	0.344297	0.349235	0.354172	0.359109	0.364047	0.368984	2.812376	2.812376	2.812376	2.812376	2.812376	2.812376	2.812376	2.812376	2.812376	2.812376	2.812376	2.812376	166.2484	166.2484	166.2484	166.2484	166.2484	166.2484	166.2484	166.2484	166.2484	166.2484	166.2484	166.2484	166.2484	166.2484	166.2484	166.2484	166.2484	166.2484	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.566614	4.409556	7.617018e-10	7.617018e-10	4.409556	13.58032	22.75108	31.92184	41.09261	50.26336	59.43413	68.60489	1.000444e-10	1.000444e-10	1.000444e-10	1.000444e-10	1.000444e-10	1.000444e-10	1.000444e-10	1.000444e-10	68.60489	77.10661	85.60835	94.11007	102.6118	111.1135	119.6152	2.510205e-10	2.510205e-10	2.510205e-10	2.510205e-10	2.510205e-10	2.510205e-10	2.510205e-10	119.6152	128.9443	138.2733	147.6023	156.9313	166.2603	175.5894	184.9184	194.2474	203.5764	2.619345e-10	2.619345e-10	2.619345e-10	2.619345e-10	2.619345e-10	2.619345e-10	2.619345e-10	2.619345e-10	2.619345e-10	2.619345e-10	203.5764	212.76030	221.94420	231.12810	240.31200	249.49590	258.67980	267.8637	2.983143e-10	2.983143e-10	2.983143e-10	2.983143e-10	2.983143e-10	2.983143e-10	2.983143e-10	2.983143e-10	267.8637	276.47960	285.09560	293.71150	302.32740	310.9434	2.910383e-10	2.910383e-10	2.910383e-10	2.910383e-10	2.910383e-10	2.910383e-10	0.467131	0.452707	0.438284	0.423860	0.409436	0.395012	0.380589	-0.406804	-0.406804	-0.406804	-0.406804	-0.406804	-0.406804	-0.406804	0.380589	0.376907	0.373225	-1.459382	-1.459382	-1.459382	0.373225	0.369220	0.365215	0.361210	0.357205	-4.684504	-4.684504	-4.684504	-4.684504	-4.684504	0.357205	0.352849	0.348493	0.344137	0.339782	0.335426	0.331070	0.326714	-8.244256	-8.244256	-8.244256	-8.244256	-8.244256	-8.244256	-8.244256	-8.244256	0.326714	0.323704	0.320693	0.317683	0.314673	-9.350779	-9.350779	-9.350779	-9.350779	-9.350779	0.255571	0.257338	20.09868	20.09868	0.257338	0.259931	0.262524	19.69836	19.69836	19.69836	0.262524	0.265106	0.267688	19.69836	19.69836	19.69836	0.267688	0.269946	0.272204	0.274462	0.276720	19.22980	19.22980	19.22980	19.22980	19.22980	0.276720	0.278636	0.280552	18.75591	18.75591	18.75591	0.280552	0.283766	0.286979	0.290193	0.293407	0.296620	0.299834	0.303047	16.79336	16.79336	16.79336	16.79336	16.79336	16.79336	16.79336	16.79336	218.927700	210.198600	201.46940	192.74030	184.01110	175.28200	166.55280	-4.749410e-10	-4.749410e-10	-4.749410e-10	-4.749410e-10	-4.749410e-10	-4.749410e-10	-4.749410e-10	58.50719	51.742120	4.497703e+01	-1.091394e-10	-1.091394e-10	-1.091394e-10	44.97703	38.672730	32.36843	-2.805791e-10	-2.805791e-10	-2.805791e-10	32.36843	27.12133	21.87424	-2.464731e-10	-2.464731e-10	-2.464731e-10	21.87424	13.68423	-8.349161e-10	-8.349161e-10	13.68423	6.996624	0.309012	-1.618901e-10	-1.618901e-10	-1.618901e-10	0.309012	0.303981	0.298950	0.293919	0.288888	0.283857	0.278826	-1.214988	-1.214988	-1.214988	-1.214988	-1.214988	-1.214988	-1.214988	0.278826	0.275239	0.271652	-1.614302	-1.614302	-1.614302	0.271652	0.268087	0.264523	0.260959	-1.855206	-1.855206	-1.855206	-1.855206	0.260959	0.256523	0.252088	-2.064118	-2.064118	-2.064118	0.252088	0.247202	0.242315	0.237429	-2.721709	-2.721709	-2.721709	-2.721709	0.237429	0.233181	0.228933	0.224685	...	45.55675	45.55675	45.55675	45.55675	45.55675	45.55675	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.006432	0.012865	0.019297	0.025730	0.032162	0.038595	0.045027	0.051460	0.057892	0.064325	0.070757	0.077190	0.915013	0.915013	0.915013	0.915013	0.915013	0.915013	0.915013	0.915013	0.915013	0.915013	0.915013	0.915013	0.915013	34.67685	34.67685	34.67685	34.67685	34.67685	0.0	0.0	0.0	0.0	0.0	0.0	8.426783	6.821210e-11	6.821210e-11	43.64276	43.64276	43.64276	43.64276	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	33.26721	24.95041	16.63361	8.316803	0.00000	-6.912160e-11	-6.912160e-11	-6.912160e-11	-6.912160e-11	-6.912160e-11	0.000000e+00	9.381248e+00	1.876250e+01	9.185896e-11	9.185896e-11	9.185896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3 rows × 5036 columns

Note that an additional multi index is created that indexes the interior points.

Merge Metadata and Result Dataframes (for one timestamp)

Create Dataframes - For pipes

Metadata

df_pipes_metadata = s3s.generate_element_metadata_dataframe(element_type=s3s.ObjectTypes.Pipe
                                                            ,properties=["L", "DN"]
                                                            ,geometry=True
                                                            )

[2025-12-05 20:21:26,625] INFO in sir3stoolkit.mantle.dataframes: [metadata] Generating metadata dataframe for element type: ObjectTypes.Pipe
[2025-12-05 20:21:26,627] INFO in sir3stoolkit.mantle.dataframes: [metadata] Retrieved 524 element(s) of element type ObjectTypes.Pipe.
[2025-12-05 20:21:26,659] INFO in sir3stoolkit.mantle.dataframes: [Resolving tks] Retrieved 524 element(s) of element type ObjectTypes.Pipe.
[2025-12-05 20:21:26,662] INFO in sir3stoolkit.mantle.dataframes: [Resolving Metadata Properties] Using 2 metadata properties.
[2025-12-05 20:21:26,662] INFO in sir3stoolkit.mantle.dataframes: [metadata] Retrieving metadata properties ['L', 'DN'], geometry...
[2025-12-05 20:21:26,830] INFO in sir3stoolkit.mantle.dataframes: [metadata] Transforming DataFrame to GeoDataFrame successful with EPSG: 25832
[2025-12-05 20:21:26,830] INFO in sir3stoolkit.mantle.dataframes: [metadata] Done. Shape: (524, 4)

df_pipes_metadata.head(3)

	tk	L	DN	geometry
0	5442010239090746007	36,42935	150	LINESTRING (713620.268 5578828.419, 713602.295...
1	4917786378639043296	57,21781	125	LINESTRING (713602.295 5578860.106, 713574.062...
2	4762482310382009633	40,99482	125	LINESTRING (713574.062 5578909.873, 713553.84 ...

Results

df_pipes_results = s3s.generate_element_results_dataframe(element_type=s3s.ObjectTypes.Pipe
                                                        ,properties=["DTTR", "PHR"]
                                                        ,timestamps=[0]
                                                        ,filter_container_tks=[s3s.GetMainContainer()[0]]
                                                        )

[2025-12-05 20:21:26,870] INFO in sir3stoolkit.mantle.dataframes: [results] Generating results dataframe for element type: ObjectTypes.Pipe
[2025-12-05 20:21:26,947] INFO in sir3stoolkit.mantle.dataframes: [Resolving Timestamps] Only static timestamp 2023-02-13 00:00:00.000 +01:00 is used
[2025-12-05 20:21:26,947] INFO in sir3stoolkit.mantle.dataframes: [Resolving Timestamps] 1 valid timestamp(s) will be used.
[2025-12-05 20:21:26,957] INFO in sir3stoolkit.mantle.dataframes: [Resolving tks] Retrieved 524 element(s) of element type ObjectTypes.Pipe.
[2025-12-05 20:21:26,992] INFO in sir3stoolkit.mantle.dataframes: [Resolving tks] 524 tks remain after container filtering.
[2025-12-05 20:21:26,994] INFO in sir3stoolkit.mantle.dataframes: [results] Using 2 result properties.
[2025-12-05 20:21:27,002] INFO in sir3stoolkit.mantle.dataframes: [results] Retrieving result properties...
[2025-12-05 20:21:27,343] INFO in sir3stoolkit.mantle.dataframes: [results] Done. Shape: (1, 1048)

df_pipes_results.head(3)

tk	5442010239090746007		4917786378639043296		4762482310382009633		4987229536643024523		5722206630503885118		5089459212485379804		5526871502288882655		4809089329099085219		5170001087020228683		5625716875961234775		5501916898830363065		5192970129555982907		5027225217797463802		5107343838750080243		4840056338230495043		5700872284015490704		5290590848336792638		4781821593135991761		4770083637349644299		5140383994159421977		4615723899944629797		5485743457841402038		5061682782623660813		4868922965129592512		5253822763469470014		5468996363795343031		5339499657518903527		4811306899232458618		4928779983564758026		4965578769510488820		5600841315591171772		4995034655758156710		5209057104414292468		5726827761099671871		5080791944541542127		4996979568467922520		4893521402166887668		4943852467771545411		5294908835443772392		5407639481169980338		5494345815849234157		5559756735287401600		4688455252738739891		5120913591494019846		5189136483684313738		5027271755713739562		4843364651593046845		4763398616537422727		5211657964305638265		4899878437571936054		5017291090985710926		4968195666913259627		4872293615039533867		5073852406571450055		5460451359190976355		5680559127926603859		5701217076123185999		5343389149567015241		5004534437760811077		5638727652971481339		4933247170382646480		5144558095415012328		4676843093813396651		5238385499211955735		5431464167607748731		5021757307925561906		5735390708503613465		5602200558168325137		4976126317058169697		5611058499224570176		4951309552891536851		4734625425618016004		5003475594426980558		5199984776776235548		5146987453990281112		4664282009021425543		4730098385004060223		5143417057295500465		4870893641534242754		4751771958060245818		5431352512414392985		4756919352045195395		4950699445422043390		5443612009555325057		5130272059781459965		5030450908543536722		5425728209090108132		4897883139609927597		5385910423114382788		4815053638180850703		5520594566396266859		4822932207368532352		4943632133505495565		5136719248606301905		5222700144096421193		5209596079625295991		5215757535605464866		5089350764087843246		4922208128354560629		5176883269076091732		4882993749777364317		5374456943006507008		4843836890906700052		5535463940319820632		5012186927559751405		4986912592668362327		5119633900217288954		5293317689029296733		4766100927082653384		5638908776577558151		5244494914545547298		4818046255571175936		4826614335972511296		5223081641641736592		5248841557788033271		5612538195203776712		5446352019210100674		5198739705259994278		5120004980087359327		5081007708328729580		5332936729509410779		5193926613850153217		5669055683311185692		5142801769428353146		5570925755149935463		5089627882145801048		5749509498631743083		5313314888424094938		5672198676406030965		5492269585462839687		4878085949235357089		5009009696813138306		5417154223408487165		4888087184557203150		5482200169109927461		5506250915346749683		5249576152024204638		5377200445080630699		5293057810555927426		4869711570166799356		5116990929886803340		5735615340560878820		4881353465437199724		5621715799375882738		5012002718030878203		5398733809575429001		5028247962181469636		4900101542637177072		5277687384123072302		5494173646602854486		5052735000191334434		5539639795342428023		5278182790120832611		5199049455293895634		4921179458989199657		4916972176026821426		5022571509924021844		4763784987464972068		5277721701940071811		5134029809569092929		5078017899111203433		5254059088049802980		5371971925339056665		5431036277668526595		5483354135827032665		5285379841869150294		5293405653296791451		4819969964299896962		5351904744714142235		5646757598295302988		5691533564979419761		5048873293262650113		5643610361723733925		5199821632200720341		5532148500642435090		5595641383032896800		5473664242867087532		5537095863454943245		4971302338639629382		5013314486814762862		5242300102291724928		5546837522852121331		5019054790932573745		4728990773140988900		5367689158219221191		5218286473243188911		5539804019387586531		5617925833435212075		5306936070034225058		5358411267245496507		4713734746689397424		5149494653460530987		5084014870632527368		5657819787231956123		4869220947327072403		4715322105206840823		5187233458674428963		5629674368074184841		5435501069470547583		5330653354008278132		5046788308257498968		5533352733021876828		4768631712844719739		5005484350725146842		4872282937361972118		5016948138155572756		4703401536268630837		5134010500800575790		5406399909528044374		4885580927738649460		4963336754663987411		4884019193798941071		4933076724177745599		5159803586983620393		5516466299205310619		5650023590814923905		5148527180660772222		5733296470387480717		4843558237591196246		5574298204235077852		5540212992243853698		5204052626441544440		5413647981880727734		5715081934973525403		5049850420107484598		...	5171452993003233254		5504198339205496589		4719357763484034357		5225837880074756830		5510181626553100351		4760946687921724871		5614427492096377103		4927641140942847414		5729604610944816597		4882552315784164880		5257981584043456915		4938996375166925756		5421398405406913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name	Rohr V-K03S V-K13S		Rohr V-K13S V-K23S		Rohr V-K23S V-K33S		Rohr V-K33S V-K43S		Rohr V-K43S V-K53S		Rohr V-K53S V-K63S		Rohr V-K63S V-K73S		Rohr V-K73S V-K83S		Rohr V-K83S V-K93S		Rohr V-K93S V-K103S		Rohr V-K103S V-K113S		Rohr V-K113S V-K123S		Rohr V-K123S V-K133S		Rohr V-K133S V-K143S		Rohr V-K153S V-K163S		Rohr V-K163S V-K143S		Rohr V-K143S V-K173S		Rohr V-K173S V-K183S		Rohr V-K183S V-K193S		Rohr V-K193S V-K203S		Rohr V-K203S V-K213S		Rohr V-K213S V-K223S		Rohr V-K233S V-K243S		Rohr V-K243S V-K253S		Rohr V-K253S V-K263S		Rohr V-K263S V-K273S		Rohr V-K273S V-K153S		Rohr V-K283S V-K293S		Rohr V-K293S V-K303S		Rohr V-K303S V-K313S		Rohr V-K313S V-K323S		Rohr V-K323S V-K333S		Rohr V-K333S V-K03S		Rohr V-K03S V-K343S		Rohr V-K353S V-K363S		Rohr V-K363S V-K373S		Rohr V-K373S V-K383S		Rohr V-K383S V-K393S		Rohr V-K393S V-K403S		Rohr V-K403S V-K413S		Rohr V-K413S V-K423S		Rohr V-K423S V-K433S		Rohr V-K433S V-K443S		Rohr V-K443S V-K453S		Rohr V-K453S V-K463S		Rohr V-K463S V-K473S		Rohr V-K423S V-K483S		Rohr V-K483S V-K493S		Rohr V-K493S V-K503S		Rohr V-K503S V-K513S		Rohr V-K513S V-K523S		Rohr V-K523S V-K533S		Rohr V-K533S V-K543S		Rohr V-K543S V-K553S		Rohr V-K553S V-K563S		Rohr V-K573S V-K583S		Rohr V-K153S V-K593S		Rohr V-K593S V-K603S		Rohr V-K603S V-K283S		Rohr V-K353S V-K613S		Rohr V-K343S V-K623S		Rohr V-K623S V-K633S		Rohr V-K633S V-K643S		Rohr V-K643S V-K653S		Rohr V-K653S V-K663S		Rohr V-K663S V-K673S		Rohr V-K673S V-K683S		Rohr V-K683S V-K693S		Rohr V-K693S V-K703S		Rohr V-K703S V-K713S		Rohr V-K713S V-K723S		Rohr V-K723S V-K733S		Rohr V-K733S V-K743S		Rohr V-K743S V-K753S		Rohr V-K753S V-K763S		Rohr V-K223S V-K773S		Rohr V-K783S V-K793S		Rohr V-K793S V-K803S		Rohr V-K803S V-K813S		Rohr V-K813S V-K823S		Rohr V-K823S V-K833S		Rohr V-K833S V-K843S		Rohr V-K843S V-K853S		Rohr V-K853S V-K863S		Rohr V-K863S V-K873S		Rohr V-K873S V-K883S		Rohr V-K883S V-K893S		Rohr V-K893S V-K903S		Rohr V-K903S V-K913S		Rohr V-K913S V-K923S		Rohr V-K923S V-K503S		Rohr V-K503S V-K353S		Rohr V-K933S V-K943S		Rohr V-K943S V-K953S		Rohr V-K953S V-K963S		Rohr V-K963S V-K973S		Rohr V-K973S V-K983S		Rohr V-K983S V-K993S		Rohr V-K993S V-K1003S		Rohr V-K1003S V-K1013S		Rohr V-K1013S V-K1023S		Rohr V-K1023S V-K1033S		Rohr V-K283S V-K1043S		Rohr V-K1043S V-K1053S		Rohr V-K1053S V-K1063S		Rohr V-K1063S V-K1073S		Rohr V-K1073S V-K1083S		Rohr V-K1083S V-K573S		Rohr V-K1093S V-K1103S		Rohr V-K1103S V-K1113S		Rohr V-K1113S V-K1123S		Rohr V-K1123S V-K1133S		Rohr V-K1133S V-K1143S		Rohr V-K1143S V-K1153S		Rohr V-K1153S V-K953S		Rohr V-K1163S V-K1173S		Rohr V-K1173S V-K1153S		Rohr V-K1153S V-K1033S		Rohr V-K1033S V-K1183S		Rohr V-K1183S V-K1193S		Rohr V-K1193S V-K1203S		Rohr V-K1203S V-K1213S		Rohr V-K1213S V-K1223S		Rohr V-K1223S V-K03S		Rohr V-K1233S V-K1223S		Rohr V-K23S V-K1243S		Rohr V-K1243S V-K1253S		Rohr V-K1253S V-K1263S		Rohr V-K1263S V-K1273S		Rohr V-K1273S V-K1283S		Rohr V-K1283S V-K1293S		Rohr V-K1293S V-K1303S		Rohr V-K1303S V-K13S		Rohr V-K83S V-K1313S		Rohr V-K1313S V-K1323S		Rohr V-K1323S V-K1333S		Rohr V-K1333S V-K1343S		Rohr V-K1343S V-K1353S		Rohr V-K1353S V-K1363S		Rohr V-K1363S V-K1373S		Rohr V-K133S V-SP01		Rohr V-K333S V-K1393S		Rohr V-K963S V-TS-P		Rohr V-K1403S V-K1413S		Rohr V-K1403S V-K1423S		Rohr V-K1423S V-K1433S		Rohr V-K1433S V-K1443S		Rohr V-K1443S V-K1453S		Rohr V-K1453S V-K1463S		Rohr V-K1463S V-K1473S		Rohr V-K1473S V-K1483S		Rohr V-K1483S V-K1493S		Rohr V-K1493S V-K1503S		Rohr V-K1503S V-K1513S		Rohr V-K1513S V-K1523S		Rohr V-K1523S V-K1533S		Rohr V-K1533S V-K1543S		Rohr V-K1543S V-K1553S		Rohr V-K1553S V-K1563S		Rohr V-K1563S V-K1573S		Rohr V-K1573S V-K1583S		Rohr V-K1583S V-K623S		Rohr V-SP02 V-K1603S		Rohr V-K1603S V-K1613S		Rohr V-K1613S V-K1623S		Rohr V-K1623S V-K253S		Rohr V-K1603S V-K1633S		Rohr V-K263S V-K1643S		Rohr V-K1643S V-K1653S		Rohr V-K1653S V-K1663S		Rohr V-E0 V-K1683S		Rohr V-K1683S V-K1693S		Rohr V-K1703S V-K1713S		Rohr V-K1713S V-K1723S		Rohr V-K1723S V-K1733S		Rohr V-K1733S V-K1743S		Rohr V-K1743S V-K1753S		Rohr V-K1753S V-K1763S		Rohr V-K1763S V-K1773S		Rohr V-K1773S V-K1783S		Rohr V-K1783S V-K1793S		Rohr V-K1793S V-K1803S		Rohr V-K1803S V-K1813S		Rohr V-K1813S V-K1823S		Rohr V-K1833S V-K1843S		Rohr V-K1843S V-K1853S		Rohr V-K1853S V-K1863S		Rohr V-K1873S V-K1883S		Rohr V-K1883S V-K1893S		Rohr V-K1893S V-K1903S		Rohr V-K1903S V-K1703S		Rohr V-K1703S V-K1913S		Rohr V-K1913S V-K1763S		Rohr V-K1763S V-K1923S		Rohr V-K1923S V-K1833S		Rohr V-K523S V-K1933S		Rohr V-K1933S V-K1943S		Rohr V-K1943S V-K1953S		Rohr V-K1953S V-K1963S		Rohr V-K1963S V-K1973S		Rohr V-K243S V-K1983S		Rohr V-K483S V-K1993S		Rohr V-K1993S V-K2003S		Rohr V-K2003S V-K2013S		Rohr V-K2013S V-K2023S		Rohr V-K2023S V-K573S		Rohr V-K833S V-K2033S		Rohr V-K2033S V-K2043S		Rohr V-K1693S V-K2053S		Rohr V-K2053S V-K813S		Rohr V-K2063S V-K2073S		Rohr V-K323S V-K2083S		Rohr V-K2083S V-K1093S		Rohr V-K1093S V-K1073S		Rohr V-K2093S V-K2103S		Rohr V-K2103S V-K2113S		Rohr V-K2113S V-K2123S		Rohr V-K2123S V-K2133S		Rohr V-K2133S 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Rohr R-K2873S R-K2883S		Rohr R-K2883S R-K2893S		Rohr R-K2893S R-K2903S		Rohr R-K2903S R-K2913S		Rohr R-K2913S R-K2923S		Rohr R-K2923S R-K2933S		Rohr R-K2933S R-K2943S		Rohr R-K2943S R-K2953S		Rohr R-K2903S R-K2963S		Rohr R-K2963S R-K2973S		Rohr R-K2973S R-K2983S		Rohr R-K2983S R-K2993S		Rohr R-K2993S R-K3003S		Rohr R-K3003S R-K3013S		Rohr R-K3013S R-K3023S		Rohr R-K3023S R-K3033S		Rohr R-K3033S R-K3043S		Rohr R-K3053S R-K3063S		Rohr R-K2633S R-K3073S		Rohr R-K3073S R-K3083S		Rohr R-K3083S R-K2763S		Rohr R-K2833S R-K3093S		Rohr R-K2823S R-K3103S		Rohr R-K3103S R-K3113S		Rohr R-K3113S R-K3123S		Rohr R-K3123S R-K3133S		Rohr R-K3133S R-K3143S		Rohr R-K3143S R-K3153S		Rohr R-K3153S R-K3163S		Rohr R-K3163S R-K3173S		Rohr R-K3173S R-K3183S		Rohr R-K3183S R-K3193S		Rohr R-K3193S R-K3203S		Rohr R-K3203S R-K3213S		Rohr R-K3213S R-K3223S		Rohr R-K3223S R-K3233S		Rohr R-K3233S R-K3243S		Rohr R-K2703S R-K3253S		Rohr R-K3263S R-K3273S		Rohr R-K3273S R-K3283S		Rohr R-K3283S R-K3293S		Rohr R-K3293S 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timestamp
2023-02-13 00:00:00.000 +01:00	0.013437	0.010421	0.02538	0.014615	0.017832	0.010872	0.002972	0.001812	0.003259	0.001987	0.00362	0.002207	0.039475	0.018532	0.001627	0.002248	0.002951	0.001065	0.00225	0.000812	0.012035	0.004345	0.015949	0.004761	0.014919	0.009292	0.057364	0.003588	0.057824	0.03289	165.7806	0.0	3.842942	1.716550e-24	64.19534	1.348729e-24	51.01035	3.590149e-24	83.9612	2.910067e-24	64.28724	3.057601e-24	43.0797	2.808299e-24	0.092052	0.003743	0.00784	0.002577	0.017054	0.006804	0.032463	0.015606	0.012821	0.008887	0.001881	0.003044	0.005522	0.008423	0.005497	0.008385	0.009616	0.01367	0.00408	0.005393	0.023947	0.022913	52.3749	1.071175e-21	13.53016	8.613386e-26	12.6086	1.907738e-25	10.49419	3.382087e-25	8.190004	5.406732e-24	13.37522	1.080843e-23	0.032132	0.029689	0.007637	0.003357	0.011384	0.007474	0.009444	0.008443	0.015609	0.008072	0.01357	0.008731	0.023328	0.010592	14.29888	0.0	6.310922	0.0	59.923	0.0	99.99299	0.0	14.00829	1.040817e-24	5.560995	1.187491e-26	17.37406	1.318112e-25	126.116	1.806440e-26	21.51779	0.0	0.028187	0.025678	0.038993	0.004927	0.130641	0.002714	0.017016	0.000044	42.03592	6.468936e-26	23.02995	4.574885e-22	117.7475	1.332600e-24	25.77541	3.371093e-25	18.48974	7.554162e-25	20.08946	7.676642e-25	20.21883	4.763323e-26	71.85966	0.0	24.84862	3.323043e-26	25.31222	1.191076e-26	30.19752	1.922393e-25	36.38173	5.327634e-25	34.99198	7.761986e-25	6.78462	8.465496e-26	33.2108	3.392148e-25	50.19061	1.364690e-24	47.19448	0.0	50.84296	0.0	50.56349	1.374829e-24	41.89281	0.0	9.602071	8.320115e-26	25.53992	0.0	0.010837	0.005245	0.011387	0.005511	0.020205	0.009779	0.01643	0.007952	0.015608	0.007554	35.89539	3.267765e-25	72.46398	1.359455e-24	71.66468	1.318481e-24	49.03616	0.0	54.56744	0.0	92.76953	0.0	0.022689	0.01168	0.019005	0.009458	0.018562	0.008927	0.030196	0.016532	0.005952	0.001709	0.004693	0.001348	0.005412	0.001554	0.007105	0.001468	0.024451	0.014112	0.00833	0.001894	0.002318	0.00333	0.001022	0.001468	0.001456	0.002092	0.013375	0.003172	0.039895	0.012035	0.022959	0.010705	0.005711	0.003578	0.003159	0.00273	0.003213	0.002776	0.002857	0.000881	0.003821	0.001556	0.042426	0.024482	0.027605	0.031204	0.013715	0.002306	0.033139	0.015127	0.12158	0.01634	0.030131	0.007245	0.003501	0.003602	0.002537	0.002611	0.003056	0.002328	0.028583	0.012432	76.11499	0.0	0.045279	0.021159	0.200912	0.005209	0.002643	0.000213	0.004376	0.000352	0.021765	0.001752	0.018536	0.002145	0.004447	0.000721	0.00277	0.000449	0.164455	0.003665	0.001868	0.000903	0.015436	0.015433	0.001748	0.000796	0.001808	0.000823	0.002753	0.00066	0.014381	0.00345	0.027072	0.002408	0.034794	0.007449	0.076126	0.022937	0.027069	0.024719	0.033999	0.005591	0.00952	0.013961	0.012777	0.010746	0.00404	0.013655	0.005048	0.011634	0.005227	0.006267	0.00626	0.00684	0.004385	0.007488	0.004158	0.0071	0.006439	0.005254	0.022945	0.015467	0.010101	0.006809	0.037958	0.005952	22.46283	1.356824e-24	11.13411	0.0	18.74616	0.0	27.98768	0.0	32.47894	0.0	63.81877	0.0	0.032378	0.014526	0.026701	0.013308	0.007818	0.002306	0.020992	0.008493	0.028376	0.01179	0.035534	0.012847	0.01952	0.008508	0.013524	0.002875	0.004844	0.002345	0.003947	0.00191	10.56986	1.001124e-14	14.57936	1.380879e-14	19.87988	1.882914e-14	24.45074	2.315844e-14	23.61157	2.236373e-14	50.96676	4.827340e-14	21.2934	7.592590e-25	16.31768	7.572807e-25	21.15114	7.623296e-25	6.620245	3.288472e-26	5.387928	0.0	11.07489	2.132729e-26	6.690278	0.0	21.84747	8.901271e-26	18.86606	3.416249e-26	26.74521	5.516478e-25	17.53896	1.341834e-24	15.2731	1.355163e-24	7.780674	7.405621e-25	35.01923	3.316785e-14	18.98862	1.798485e-14	14.42032	1.625796e-26	16.00222	8.560002e-26	40.99183	1.024805e-23	37.58163	1.021212e-23	3.547835	1.192627e-24	34.96974	0.0	105.8952	7.490393e-26	80.62691	3.496503e-22	36.44118	0.0	31.60874	4.774695e-26	0.047206	0.001445	0.038217	0.004156	0.015629	0.006058	0.005451	0.002638	0.004265	0.002064	18.81272	8.667887e-23	15.6488	1.906363e-25	0.027419	0.010804	45.47781	0.0	0.080638	0.032971	34.57665	0.0	8.426783	4.756933e-26	22.79576	8.529783e-26	33.26721	8.494776e-26	18.7625	0.0	14.43095	1.602735e-25	0.032167	0.00529	0.020764	0.018192	16.76996	0.0	0.004546	0.0022	0.001977	0.000957	0.002536	0.000782	...	13.37522	6.133660e-24	0.03272	0.030559	0.007778	0.003467	0.011594	0.007695	0.009619	0.008671	0.015899	0.008306	0.013823	0.00897	0.023763	0.010876	14.29888	0.0	6.310922	0.0	59.923	2.004137e-26	99.99299	0.0	14.00829	5.208222e-27	5.560995	2.977277e-25	17.37406	1.354632e-24	126.116	2.230173e-26	21.51779	0.0	0.028714	0.025998	0.036713	0.00591	0.115293	0.003702	0.013112	0.000078	42.03592	2.116783e-26	23.02995	1.547077e-23	117.7475	0.0	25.77541	3.371093e-25	18.48974	0.0	20.08946	2.220325e-26	20.21883	5.586558e-27	71.85966	0.0	24.84862	1.196296e-26	25.31222	1.092795e-26	30.19752	3.375001e-25	36.38173	0.0	34.99198	8.019399e-26	6.78462	2.116374e-26	33.2108	7.632334e-25	50.19061	1.353707e-24	47.19448	0.0	50.84296	0.0	50.56349	3.662396e-25	41.89281	5.320806e-24	9.602071	3.423812e-25	25.53992	8.743657e-26	0.011039	0.005319	0.0116	0.005589	0.02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1 rows × 1048 columns

Merge

df_pipes_results.columns = df_pipes_results.columns.droplevel([1, 2])
df_pipes_results = df_pipes_results.T.unstack(level=0).T
df_pipes_results = df_pipes_results.droplevel(0, axis=0)
df_pipes = df_pipes_metadata.merge(on="tk",
                    how="outer",
                    right=df_pipes_results)

df_pipes.head(3)

	tk	L	DN	geometry	DTTR	PHR
0	4614463970292122863	7,780674	999	LINESTRING (714262.483 5578857.42, 714269.543 ...	7.780674	3.348380e-25
1	4615723899944629797	64,28724	999	LINESTRING (713738.297 5579219.902, 713793.23 ...	64.287240	3.057601e-24
2	4621030304810285220	3,956838	100	LINESTRING (713650.613 5578990.488, 713649.498...	0.002291	8.354341e-04