Classes and Objects

Welcome to the Classes and Objects page! Here, we will delve into the different classes and objects and their relationship. Refer to the Functions page to learn more about the functions that create these objects.

Note

Currently, not all Objects that exist in PT3S are documented. These will be added in the future.

m Object

The m object or dxWithMx object is a wrapper for Dx with attached Mx and the center piece in working with PT3S. It is created by Read SIR 3S Model and Results

dxAndMxHelperFcts.dxWithMx.__init__(self, dx, mx, crs=None)

Parameters:

dx (Dx.Dx()) – a Dx object
mx (Mx.Mx()) – a Mx object
crs (str, optional, default=None) – (=coordinate reference system) Determines crs used in geopandas-Dfs (Possible value:’EPSG:25832’). If None, crs will be read from SIR 3S’ database file.

A dxWithMx (or m) object has the following attributes:

Model: Dx object:

dx.dataFrames[…]: pandas-Dfs 1:1 from SIR 3S’ tables in database file

dx.dataFrames[…]: several pandas-Dfs derived from the 1:1 Dfs

Results: Mx object:

mx.df: pandas-Df (‘time curve data’) from from SIR 3S’ MXS file(s)

mx.dfVecAggs: pandas-Df (‘vector data’) from SIR 3S’ MXS file(s)

Miscellaneous:

wDirMx: Mx-directory of the model

SirCalcXmlFile: SirCalc’s Xml-File of the model

SirCalcExeFile: SirCalc Executable used to (re-)calculate the model

pandas-Dfs with Model- AND Result-Data:

V3_ROHR: Pipes

V3_FWVB: Housestations District Heating

V3_KNOT: Nodes

V3_VBEL: Edges

V3_ROHRVEC: Pipes including interior points

V3_AGSN: Longitudinal Sections; AGSN is the German abbreviation for longitudinal sections / cuts (defined in the SIR 3S model)

V3_AGSNVEC: Longitudinal Sections including Pipe interior points

geopandas-Dfs based upon the Dfs above:

gdf_ROHR: Pipes

gdf_FWVB: Housestations District Heating

gdf_KNOT: Nodes

NetworkX-Graphs:

G

GSig

Dx contains data for all models in the SIR 3S database. Mx contains only the results for one model. SYSTEMKONFIG / VIEW_MODELLE are used to determine which one.

Dataframes

From this object m, you can now access a variety of dataframes created by PT3S based on the model and results

>>> print([attr for attr in dir(m) if isinstance(getattr(m, attr), pd.DataFrame)])
['V3_AGSN', 'V3_AGSNVEC', 'V3_FWVB', 'V3_KNOT', 'V3_ROHR', 'V3_ROHRVEC', 'V3_VBEL', 'V3_WBLZ', 'dfAGSN', 'dfWBLZ', 'gdf_FWVB', 'gdf_KNOT', 'gdf_ROHR']

The following section presents these dataframes by documenting the functions that create them.

V3_ROHR

dxAndMxHelperFcts.dxWithMx._V3_ROHR(self, df_V3_ROHR)

V3_ROHR is a m object Attribute.

Parameters:: V3_ROHR – V3sErg[‘V3_ROHR’] from dx.MxAdd(mx)
Return df_V3_ROHR:: df_V3_ROHR expanded

ROHR is the German word for pipe (defined in the SIR 3S model). In the returned V3_ROHR (one row per Pipe) the following columns are added:

Column Name	Description
QMAVAbs	Absolute value of STAT ROHR~~~*~QMAV (i.e. t/h, m3/h, l/s, …)
VAVAbs	Absolute value of STAT ROHR~~~*~VAV
PHRAbs	Absolute value of STAT ROHR~~~*~PHR
JVAbs	Absolute value of STAT ROHR~~~*~JV
MAV	value of STAT ROHR~~~*~MAV (kg/s)
LAMBDA	value of STAT ROHR~~~*~LAMBDA

>>> print(m.V3_ROHR)
                      pk                 fkDE  ...        PHRAbs         JVAbs
  5442010239090746007  5306805303452857793  ...  1.041356e-02  2.858564e-01
  4917786378639043296  5306805303452857793  ...  1.464281e-02  2.559136e-01
  4762482310382009633  5306805303452857793  ...  1.084467e-02  2.645375e-01
  4987229536643024523  5306805303452857793  ...  1.804474e-03  2.640792e-01
  5722206630503885118  5306805303452857793  ...  1.978778e-03  2.640800e-01
..                   ...                  ...  ...           ...           ...
4919408196640282331  5306805303452857793  ...  7.555330e-25  2.897193e-23
4957957828740199285  5306805303452857793  ...  0.000000e+00  0.000000e+00
5499776682108862751  5306805303452857793  ...  0.000000e+00  0.000000e+00
5285235772707481262  5306805303452857793  ...  7.560484e-03  7.560484e-01
5373158549695106826  5306805303452857793  ...  7.642428e-03  7.642428e-01

[524 rows x 216 columns]

V3_KNOT

dxAndMxHelperFcts.dxWithMx._V3_KNOT(self, df_V3_KNOT)

V3_KNOT is a m object Attribute.

Parameters:: df_V3_KNOT (df) – V3sErg[‘V3_KNOT’] from dx.MxAdd(mx)
Return df_V3_VKNOT:: df_V3_KNOT expanded

KNOT is the German abbreviation for Nodes (defined in the SIR 3S model). In the returned V3_KNOT (one row per Node) the following columns are added:

Column Name	Description
PH	STAT PH-result (i.e. bar)
dPH	STAT (PHSL-PHRL)-result if Node-Partner is defined (i.e. bar)
QM	STAT QM-result (i.e. t/h, m3/h, l/s, …)
srcvector	Source signature vector eg. [30, 0, 20, 50]
T	STAT T-result
M	STAT M-result (kg/s)

>>> print(m.V3_KNOT)
                      pk                 fkDE  ...   QM       dPH
  5669301360686511351  5306805303452857793  ...  0.0  1.488921
  5397948523091900401  5306805303452857793  ...  0.0  1.467910
  5239335112004772156  5306805303452857793  ...  0.0  1.444288
  5298886695042021307  5306805303452857793  ...  0.0  1.434558
  4993257270457791438  5306805303452857793  ...  0.0  1.432193
..                   ...                  ...  ...  ...       ...
5705481928638455076  5306805303452857793  ...  0.0       NaN
4910948593863484232  5306805303452857793  ...  0.0       NaN
5032870132214455820  5306805303452857793  ...  0.0 -0.038701
4808122385198820357  5306805303452857793  ...  0.0 -0.038701
4810663080415972317  5306805303452857793  ...  0.0       NaN

[517 rows x 214 columns]

V3_VBEL

dxAndMxHelperFcts.dxWithMx._V3_VBEL(self, df_V3_VBEL)

V3_VBEL is a m object Attribute.

Parameters:: df_V3_VBEL (df) – V3sErg[‘V3_VBEL’] from dx.MxAdd(mx)
Return df_V3_VBEL:: df_V3_VBEL expanded

VBEL is the German abbreviation for Edges (defined in the SIR 3S model). In the returned V3_VBEL (one row per Edge) the following columns are added:

Column Name	Description
PH_i,_k	STAT PH-result (i.e. bar)
H_i,_k	STAT H-result (i.e. barBzg)
mlc_i,_k	STAT H-result
RHO_i,_k	STAT RHO-result
T_i,_k	STAT T-result
QM	STAT QM-Result
M	STAT M-Result (kg/s)

>>> print(m.V3_VBEL)
                                              pk  ...       mlc_k
OBJTYPE OBJID                                     ...
FWES    5194722485643852853  5194722485643852853  ...  577.049723
        5393031835236689087  5393031835236689087  ...  593.013845
FWVB    4611752310942477664  4611752310942477664  ...  574.339884
        4612528660388965271  4612528660388965271  ...  551.357794
        4612562908060328263  4612562908060328263  ...  574.527331
...                                          ...  ...         ...
VENT    5397401198360947165  5397401198360947165  ...   577.04972
        5514879539114546017  5514879539114546017  ...  586.490818
        5586353371153335311  5586353371153335311  ...   577.15915
        5652541284139882253  5652541284139882253  ...  592.208663
        5684594766069449552  5684594766069449552  ...  577.085888

[883 rows x 619 columns]

V3_AGSN

dxAndMxHelperFcts.dxWithMx._V3_AGSN(self, dfAGSN)

V3_AGSN is a m object Attribute.

Parameters:: dfAGSN (df) – m.dfAGSN
Returns:: V3_AGSN: dfAGSN expanded to V3_AGSN
Return type:: df

AGSN is the German abbreviation for longitudinal sections / cuts (defined in the SIR 3S model). A section (a cut) can consist of several layers. In district heating systems, for example, the SL layer (supply line) and the RL (return line) layer. The returned V3_AGSN (one row per Edge in (Section, Layer)) has the following columns:

Column Name	Description
Pos	Position of Edge in (Section, Layer) starting with 0; Pos=-1: startNODE-row (same index as Pos=0 row)
pk	Section-pk
tk	Section-tk
LFDNR	Section-LFDNR (numeric)
NAME	Section-Name
XL	Layer: 0: everything; 1: SL (the stuff before BLn in SIR 3S BLOB); 2: RL (the stuff after BLn in SIR 3S BLOB)
compNr	Number of the connected Component in (Section, Layer) starting with 1
nextNODE	Name of the next Node in cut-direction reached by the Edge (startNODE-Name for Pos=-1)
OBJTYPE	Edge-Type
OBJID	Edge-ID
L	0 for Pos=-1
DN
Am2
Vm3
NAME_CONT
NAME_i
NAME_k
ZKOR_n	nextNODE’s ZKOR
BESCHREIBUNG_n	nextNODE’s BESCHREIBUNG
KVR_n	nextNODE’s KVR
LSum	cumulated L up to nextNODE
direction	XL=0,1: 1 if edge defined in cut-direction, otherwise -1; XL=2: 1 if edge defined in reverse cut-direction, otherwise -1
PH_n	nextNODE’s STAT PH-result (i.e. bar) (startNODE’s result for Pos=-1)
H_n	nextNODE’s STAT H-result (i.e. barBzg) (startNODE’s result for Pos=-1)
mlc_n	nextNODE’s STAT H-result (startNODE’s result for Pos=-1)
RHO_n	nextNODE’s STAT RHO-result (startNODE’s result for Pos=-1)
T_n	nextNODE’s STAT T-result (startNODE’s result for Pos=-1)
QM	Edge STAT QM-Result (startNODE’s result for Pos=-1)
(‘STAT\|TIME\|TMIN\|…’, ‘QM’, a Timestamp, a Timestamp)	Edge QM-Results
“(‘STAT\|TIME\|TMIN\|…’, ‘KNOT…PH\|H\|…’, a Timestamp, a Timestamp)”_n	nextNODE’s Results
“(‘STAT\|TIME\|TMIN\|…’, ‘mlc\|…’, a Timestamp, a Timestamp)”_n	nextNODE’s Results calculated by PT3S

>>> print(m.V3_AGSN)
    Pos  ... ('TMAX', 'mlc', Timestamp('2023-02-12 23:00:00'), Timestamp('2023-02-13 23:00:00'))_n
  -1  ...                                          602.48758
   0  ...                                          602.48758
   1  ...                                         602.419501
   2  ...                                         602.385399
   3  ...                                         602.306951
..   ..  ...                                                ...
39  ...                                         582.046941
40  ...                                         582.197052
41  ...                                         582.361158
42  ...                                         582.680754
43  ...                                         583.017569

[537 rows x 52 columns]

V3_ROHRVEC

dxAndMxHelperFcts.dxWithMx._V3_ROHRVEC(self, V3_ROHR)

V3_ROHRVEC: Expanding V3_ROHR to V3_ROHRVEC (includes interior points). V3_ROHRVEC is a dxWithMx object Attribute.

Parameters:: V3_ROHR (df) – dxWithMx Attribute
Returns:: V3_ROHRVEC
Return type:: df

The interior points are defined by the output grid definition for pipes in the SIR 3S model. The numerical grid with which SIR 3S calculates is different from the output grid. The returned V3_ROHRVEC (one row per pipe and interior point) has the following columns:

Column Name	Description
pk	Pipe-pk
tk	Pipe-tk
…	…
L
…	…
NAME_i, NAME_k
mx2NofPts
dL (=L/(mx2NofPts-1))
…	…
mx2Idx
(‘STAT\|TIME\|TMIN\|…’, ‘ROHR…QMAV\|PHR\|…’, a Timestamp, a Timestamp)	Pipe-Results
“(‘STAT\|TIME\|TMIN\|…’, ‘KNOT…PH\|H\|…’, a Timestamp, a Timestamp)”_i	Pipe i-NODE Results
“(‘STAT\|TIME\|TMIN\|…’, ‘KNOT…PH\|H\|…’, a Timestamp, a Timestamp)”_k	Pipe k-NODE Results
QMAVAbs	Pipes STAT QMAV-Result (absolute value)
VAVAbs	Pipes STAT VAV-Result (absolute value)
PHRAbs	Pipes STAT PHR-Result (absolute value)
JVAbs	Pipes STAT JV-Result (absolute value)
IptIdx	An Index of the points: S: Start (i-NODE), E: End (k-NODE), interior points: 0,1,2,…
IptIdxAsNo	An Index of the points starting with 0 at i-NODE: 0,1,2,…
IptIdxAsNoRev	An Index of the points starting with 0 at k-NODE: 0,1,2,…
SVEC	x in Edge-direction (IptIdx=S: 0.; IptIdx=E: L)
SVECRev	(IptIdx=S: L; IptIdx=E: 0)
ZVEC	z
(‘STAT\|TIME\|TMIN\|…’, ‘ROHR…MVEC\|PVEC\|RHOVEC\|…’, a Timestamp, a Timestamp)	Point Results
(‘STAT\|TIME\|TMIN\|…’, ‘manPVEC\|mlcPVEC\|barBzgPVEC\|’, QMVEC\|tMVEC\|…’, a Timestamp, a Timestamp)	Point Results calculated by PT3S
manPVEC	from PVEC …
mlcPVEC	from PVEC …
barBzgPVEC	from PVEC …
QMVEC	from MVEC … m3/h
tMVEC	from MVEC … t/h

>>> print(m.V3_ROHRVEC)
                       pk  ... (TMAX, tMVEC, 2023-02-12 23:00:00, 2023-02-13 23:00:00)
   5442010239090746007  ...                                          98.646645
   5442010239090746007  ...                                          98.646645
   4917786378639043296  ...                                          55.044947
   4917786378639043296  ...                                          55.044947
   4762482310382009633  ...                                          56.352818
...                   ...  ...                                                ...
5499776682108862751  ...                                                0.0
5285235772707481262  ...                                          56.845562
5285235772707481262  ...                                          56.845562
5373158549695106826  ...                                         -30.267313
5373158549695106826  ...                                         -30.267313

[1070 rows x 294 columns]

V3_AGSNVEC

dxAndMxHelperFcts.dxWithMx._V3_AGSNVEC(self, V3_AGSN)

V3_AGSNVEC is a m object Attribute.

Parameters:: V3_AGSN (df) – m.V3_AGSN
Returns:: V3_AGSNVEC: V3_AGSN expanded to V3_AGSNVEC
Return type:: df

The returned V3_AGSNVEC (expands V3_AGSN from one row for each PIPE in (Section, Layer) to one row for each interior point) has the following columns:

Column Name	Description
V3_AGSN-columns:
Pos	Pos=-1: eliminated if Start-Edge is a Pipe
nextNODE	Pos=0: startNODE @IptIdxAsNo=0 if Start-Edge is a Pipe
LSum	Pos=0: 0. @IptIdxAsNo=0 if Start-Edge is a Pipe
…	…
cols mapped with VEC-Results:
LSum
ZKOR_n
PH_n
H_n
mlc_n
T_n
QM	(see m’s flowMVEC Attribute to determine which VEC-Result is used; default: QMVEC)
PH_n_end
H_n_end
mlc_n_end
T_n_end
QM_end
PH_n_min
H_n_min
mlc_n_min
T_n_min
QM_min	(buggy)
PH_n_max
H_n_max
mlc_n_max
T_n_max
QM_max	(buggy)
cols _n_1,2,3,… derived from mxsVecsResults2MxDfVecAggs=[idxt1,idxt2,idxt3,…,-1]	_1,_2,_3,… corresponds to sorted([idxt1,idxt2,idxt3,…]):
	PH_n_1,2,3,…
	H_n_1,2,3,…
	mlc_n_1,2,3,…
	T_n_1,2,3,…
	QM_1,2,3,…
V3_ROHRVEC-columns:
pk_ROHRVEC	Pipe-pk
tk_ROHRVEC	Pipe-tk
…	…
L_ROHRVEC
…	…
NAME_i_ROHRVEC, NAME_k_ROHRVEC
mx2NofPts
dL (=L/(mx2NofPts-1))
…	…
mx2Idx
IptIdx	rows S or E are eliminated for all pipes except the 1st
…	…

>>> print(m.V3_AGSNVEC)
    Pos                   pk  ...      QM_min      QM_max
   0  5755933101669454049  ...  287.090085  538.206592
   0  5755933101669454049  ...  287.090085  538.206592
   1  5755933101669454049  ...  287.090085  538.206592
   2  5755933101669454049  ... -538.206592 -287.090085
   3  5755933101669454049  ... -538.206592 -287.090085
..   ..                  ...  ...         ...         ...
39  4868980900521118307  ...  -28.479333  -15.303667
40  4868980900521118307  ...  -28.479333  -15.303667
41  4868980900521118307  ...  -26.661928  -14.329748
42  4868980900521118307  ...  -18.238317   -9.812775
43  4868980900521118307  ...   -5.141132   -2.769349

[537 rows x 352 columns]

GeoDataframes

dxAndMxHelperFcts.dxWithMx._gdfs(self, crs=None)

gdf_FWVB, gdf_FWVB and gdf_KNOT are m object attributes.

Parameters:: crs (str, optional, default=None) – (=coordinate reference system) Determines crs used in the geopandas-Dfs (Possible value: ‘EPSG:25832’). If None, crs will be read from SIR 3S’ database file.
Returns:: (gdf_FWVB, gdf_ROHR, gdf_KNOT)
Return type:: tuple of gdfs

In the returned GeoDataFrames (gdf_FWVB, gdf_ROHR, gdf_KNOT) the following columns are added to V3_FWVB, V3_ROHR, V3_KNOT:

geometry (in each gdf_ gdf) based on GEOMWKB (in each V3_ df)

>>> print(m.gdf_ROHR)
                      pk  ...                        geometry
  5669301360686511351  ...  POINT (713620.268 5578828.419)
  5397948523091900401  ...  POINT (713602.295 5578860.106)
  5239335112004772156  ...  POINT (713574.062 5578909.873)
  5298886695042021307  ...  POINT (713553.840 5578945.533)
  4993257270457791438  ...  POINT (713553.394 5578952.352)
..                   ...  ...                             ...
5705481928638455076  ...         POINT (645.000 285.000)
4910948593863484232  ...         POINT (650.000 170.000)
5032870132214455820  ...         POINT (530.000 285.000)
4808122385198820357  ...         POINT (530.000 170.000)
4810663080415972317  ...         POINT (530.000 103.000)

[517 rows x 215 columns]

>>> print(m.gdf_KNOT)
                      pk  ...                        geometry
  4743997951091160959  ...  POINT (713181.847 5578489.925)
  5014209100699808035  ...  POINT (713369.327 5578395.123)
  4627580049017248376  ...  POINT (713251.536 5578455.252)
  5018070164989726059  ...  POINT (713037.635 5578289.847)
  5668240250035163192  ...  POINT (713066.258 5578315.171)
..                   ...  ...                             ...
5604960832731508460  ...  POINT (713185.109 5578370.183)
5365421822901958434  ...  POINT (713186.745 5578333.649)
5430995747331401210  ...  POINT (713116.958 5578379.965)
5066450637911116733  ...  POINT (713080.421 5578361.379)
5540117011473392085  ...                            None

[338 rows x 204 columns]

>>> print(m.gdf_FWVB)
                      pk  ...                        geometry
  4743997951091160959  ...  POINT (713181.847 5578489.925)
  5014209100699808035  ...  POINT (713369.327 5578395.123)
  4627580049017248376  ...  POINT (713251.536 5578455.252)
  5018070164989726059  ...  POINT (713037.635 5578289.847)
  5668240250035163192  ...  POINT (713066.258 5578315.171)
..                   ...  ...                             ...
5604960832731508460  ...  POINT (713185.109 5578370.183)
5365421822901958434  ...  POINT (713186.745 5578333.649)
5430995747331401210  ...  POINT (713116.958 5578379.965)
5066450637911116733  ...  POINT (713080.421 5578361.379)
5540117011473392085  ...                            None

[338 rows x 204 columns]

Graphs

Also accesible are NetworkX-Graphs and a corresponding dictionnary with node positions.

>>> print([attr for attr in dir(m) if not attr.startswith('_') and isinstance(getattr(m, attr), (nx.Graph, nx.DiGraph, dict))])
['G', 'GSig', 'nodeposDctNx']

G

dxAndMxHelperFcts.dxWithMx._G(self, V3_VBEL, V3_KNOT)

G is a m object Attribute: The NetworkX Graph of the Hydraulic Model.

Parameters:

V3_VBEL (df) – edges
V3_KNOT (df) – nodes

Returns:

G

Return type:

NetworkX Graph

Returns:

nodeposDctNx (corresponding nodeposDct)

Return type:

dct

Note

Builds NetworkX Graph from V3_VBEL (from_pandas_edgelist) with edge attributes. NAME_i and NAME_k are used as source and target. Corresponding node attributes from V3_KNOT. edge and node attributes: keys: keys are tuples (not strings) if the source dfs contain cols which are tuples nodeposDctNx: coordinates of nodes relative to the lower left network corner.

GSig

dxAndMxHelperFcts.dxWithMx._GSig(self, V3_RVBEL, V3_RKNOT)

GSig is a m object Attribute: The NetworkX Graph of the Signal Model.

Parameters:

V3_RVBEL (df) – edges
V3_RKNOT (df) – nodes

Returns:

GSig

Return type:

NetworkX DiGraph

Note

Builds NetworkX Graph from V3_RVBEL (from_pandas_edgelist) with edge attributes. Corresponding node attributes from V3_RKNOT. The Signal Model Elements (like RSLW or RSTN) are the nodes of GSig and the connections between these Signal Model Elements are the edges of GSig. V3_RKNOT: nodes: node IDs: Kn V3_RVBEL: edges: node IDs: Kn_i (source) and Kn_k (target) node ID: KA (Element’s attribute KA is the ID of the Element’s output signal)

Miscellaneous

Also accesible SirCalcExecution- and -Xml-File and the dir to mx files

>>> print([attr for attr in dir(m) if not attr.startswith('_') and isinstance(getattr(m, attr), str)])
['SirCalcExeFile','SirCalcXmlFile', 'flowMVEC', 'wDirMx']

SirCalcExeFile exists if maxRecords=-1. Additioal forceSir3sRead=True might be necessary.

Dx object

You can find data regarding the model at the m.dx object

>>> dx=m.dx

From this object, you can access the model’s database and its tables.

>>> dx.dataFrames.keys() 
>>> dfALLG=dx.dataFrames['ALLG']

Dataframes

Note

Currently, by far not all dfs that are attributes of a Dx object are documented. Maybe this will be added in the future. V3-VBEL, _KNOT, _ROHR and _FWVB are dx attributes. The corresponding m attributes (V3-VBEL, _KNOT, _ROHR and _FWVB) extend the dx dfs with result columns.

dfLAYR

Dx.Dx._dfLAYR(self)

dfLAYR is a dx object Attribute.

Groups (also called Layers) are used in SIR 3S as a feature for data access, data filtering, and grouping.

The returned dfLAYR (one row per LAYR and OBJ) has the following columns:

Column Name	Description
LAYR:
pk
tk
LFDNR (numeric)
NAME
LAYR-Info:
AnzDerObjekteInGruppe
AnzDerObjekteDesTypsInGruppe
OBJ:
TYPE
ID
OBJ-Info:
NrDesObjektesDesTypsInGruppe
NrDesObjektesInGruppe
GruppenDesObjektsAnz
GruppenDesObjektsNamen

Mx object

You can find additional regarding the results at the m.mx object

>>> mx=m.mx

From this object you can acces the raw time curve and the vector data

>>> mx.df.head() 
>>> mx.dfVecAggs.head() 

Known Issues

Column tuple names

Columns containing values that are not generated by PT3S but directly read from SIR 3S are tuples like (‘STAT’, ‘ROHR~*~*~*~A’, Timestamp(‘2023-02-12 23:00:00’)).

>>> print([col for col in m.V3_ROHR.columns if isinstance(col, tuple)][:10])
[('STAT', 'ROHR~*~*~*~A', Timestamp('2023-02-12 23:00:00'), Timestamp('2023-02-12 23:00:00')), ('TIME', 'ROHR~*~*~*~A', Timestamp('2023-02-12 23:00:00'), Timestamp('2023-02-12 23:00:00')), ('TMIN', 'ROHR~*~*~*~A', Timestamp('2023-02-12 23:00:00'), Timestamp('2023-02-13 23:00:00')), ('TMAX', 'ROHR~*~*~*~A', Timestamp('2023-02-12 23:00:00'), Timestamp('2023-02-13 23:00:00')), ('STAT', 'ROHR~*~*~*~DTTR', Timestamp('2023-02-12 23:00:00'), Timestamp('2023-02-12 23:00:00')), ('TIME', 'ROHR~*~*~*~DTTR', Timestamp('2023-02-12 23:00:00'), Timestamp('2023-02-12 23:00:00')), ('TMIN', 'ROHR~*~*~*~DTTR', Timestamp('2023-02-12 23:00:00'), Timestamp('2023-02-13 23:00:00')), ('TMAX', 'ROHR~*~*~*~DTTR', Timestamp('2023-02-12 23:00:00'), Timestamp('2023-02-13 23:00:00')), ('STAT', 'ROHR~*~*~*~DWVERL', Timestamp('2023-02-12 23:00:00'), Timestamp('2023-02-12 23:00:00')), ('TIME', 'ROHR~*~*~*~DWVERL', Timestamp('2023-02-12 23:00:00'), Timestamp('2023-02-12 23:00:00'))]

Often times when using these names with other libraries like pandas, several issues can arise.

You are not able to access a pandas DataFrame column with such a name. You might have tried like this.

>>> m.V3_ROHR["('TIME', 'ROHR~*~*~*~DTTR', Timestamp('2023-02-12 23:00:00'), Timestamp('2023-02-12 23:00:00'))"] 
KeyError: "('TIME', 'ROHR~*~*~*~DTTR', Timestamp('2023-02-12 23:00:00'), Timestamp('2023-02-12 23:00:00'))"

Try this instead:

>>> import pandas as pd
>>> from pandas import Timestamp
>>> column_name = ('TIME', 'ROHR~*~*~*~DTTR', Timestamp('2023-02-12 23:00:00'), Timestamp('2023-02-12 23:00:00'))
>>> column_data = m.V3_ROHR[column_name]
>>> print(column_data)
0       0.013439
1       0.025363
2       0.017844
3       0.002976
4       0.003263
        ...
519    26.078106
520    17.345263
521    20.641092
522     0.002895
523      0.00293
Name: (TIME, ROHR~*~*~*~DTTR, 2023-02-12 23:00:00, 2023-02-12 23:00:00), Length: 524, dtype: object

You could also transform all indices from tuple to string:

>>> m.V3_ROHR.columns = [str(col) for col in m.V3_ROHR.columns]
>>> print(m.V3_ROHR["('TIME', 'ROHR~*~*~*~DTTR', Timestamp('2023-02-12 23:00:00'), Timestamp('2023-02-12 23:00:00'))"])
     0.025363
     0.017844
     0.002976
     0.003263
        ...
  26.078106
  17.345263
  20.641092
   0.002895
    0.00293