Example 7: Source Spectrum and Fluid Age

This example demonstrates how GeoDataFrames (gdfs) and V3_dataframe created by PT3S can be used with matplotlib to create an interactive depiction of a source spectrum.

Example 7.1: Source Spectrum

PT3S Release

[1]:

#pip install PT3S -U --no-deps

Necessary packages for this Example

When running this example for the first time on your machine, please execute the cell below. Afterward, you may need to restart the kernel (using the ‘fast-forward’ button).

[2]:

#!pip install -q shapely ipywidgets

Imports

[3]:

import os
import logging
import pandas as pd
from pandas import Timestamp
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import geopandas as gpd
from shapely.geometry import LineString, Point
from matplotlib.patches import Circle
import ipywidgets as widgets
from matplotlib.collections import LineCollection
from matplotlib.cm import get_cmap
import networkx
import re
from collections import deque
from shapely.geometry import LineString
import networkx
from scipy.sparse import csc_matrix
from matplotlib.colors import Normalize



#...

try:
    from PT3S import dxAndMxHelperFcts
except:
    import dxAndMxHelperFcts

try:
    from PT3S import Rm
except:
    import Rm

try:
    from PT3S import ncd
except:
    import ncd
#...

[4]:

import importlib
from importlib import resources

[5]:

#importlib.reload(ncd)

Logging

[7]:

logger = logging.getLogger()

if not logger.handlers:
    logFileName = r"Example7.log"
    logledirection = logging.DEBUG

    logging.basicConfig(
        filename=logFileName,
        filemode='w',
        ledirection=logledirection,
        format="%(asctime)s ; %(name)-60s ; %(ledirectionname)-7s ; %(message)s"
    )

    fileHandler = logging.FileHandler(logFileName)
    logger.addHandler(fileHandler)

    consoleHandler = logging.StreamHandler()
    consoleHandler.setFormatter(logging.Formatter("%(ledirectionname)-7s ; %(message)s"))
    consoleHandler.setLedirection(logging.INFO)
    logger.addHandler(consoleHandler)

Read Model and Results

[8]:

dbFilename="Example7"
dbFile = resources.files("PT3S").joinpath("Examples", f"{dbFilename}.db3")

[9]:

m=dxAndMxHelperFcts.readDxAndMx(dbFile=dbFile
                                ,preventPklDump=True
                                ,maxRecords=-1
                                #,SirCalcExePath=r"C:\3S\SIR 3S\SirCalc-90-14-02-12_Potsdam.fix1_x64\SirCalc.exe"
                                )

Preparing Data

[10]:

dfKNOT=m.gdf_KNOT

[11]:

dfROHR=m.gdf_ROHR

[12]:

# Get soure signatures for start and end knot
dfROHR['srcvector_fkKI'] = dfROHR['fkKI'].map(dfKNOT.set_index('tk')['srcvector'])
dfROHR['srcvector_fkKK'] = dfROHR['fkKK'].map(dfKNOT.set_index('tk')['srcvector'])

[13]:

QM=('STAT',
  'ROHR~*~*~*~QMAV',
  Timestamp('2025-09-23 22:00:00'),
  Timestamp('2025-09-23 22:00:00'))

[14]:

dfROHR['srcvector_plot'] = np.where(dfROHR[QM] > 0, dfROHR['srcvector_fkKI'], dfROHR['srcvector_fkKK'])

[15]:

dfROHR = dfROHR[dfROHR['KVR'] != 2.0]

Plotting

[16]:

colors = [np.array([255, 0, 0]), np.array([0, 0, 255])]

DH Feeder

[17]:

pA=dfKNOT[dfKNOT['pk']=="5130118215602471518"]['geometry']

[18]:

pB=dfKNOT[dfKNOT['pk']=="5428490852894646653"]['geometry']

[19]:

src_A_coords = (pA.x, pA.y)
src_B_coords = (pB.x, pB.y)

[20]:

edge_colors = [c / 255.0 for c in colors]

Plot

[21]:

fig, ax = plt.subplots(figsize=Rm.DINA3q)
ncd.plot_src_spectrum(ax, dfROHR,'srcvector_plot', colors, dn_col='DN', lw_min=0.5, lw_max=6.0)

# Points
pts = np.array([src_A_coords, src_B_coords])
ax.scatter(pts[:, 0], pts[:, 1],
           s=300, marker='*',
           c=edge_colors,
           edgecolors='white', linewidths=1.0,
           zorder=10, label='Sources')

plt.show()

xlim = ax.get_xlim()
ylim = ax.get_ylim()
canvas_center = ((xlim[0] + xlim[1]) / 2, (ylim[0] + ylim[1]) / 2)

fig.savefig('Example7_Output_1.pdf')

For each pipe a color (between A: red and B: blue) is mixed based on the srcvector present in its start and end node.

[22]:

ax = ncd.plot_src_spectrum(
    gdf=dfROHR,
    attribute="srcvector_plot",
    colors=[np.array([255,0,0]), np.array([0,0,255])],
    dn_col='DN', lw_min=0.5, lw_max=6.0,
    plot_pies=True,
    n_pies=7,
    ratio_decimals=2,
    pie_radius_rel=0.04,
    connector_kwargs=dict(color='black', lw=1.2, ls='--', alpha=0.7),
    debug_pie_centers=True,
    force_equal_aspect=True,
    draw_mixture_scale=False
)

Example 7.2 Fluid age

Preparing Data

[23]:

dfVBEL=m.V3_VBEL

[24]:

dfVBEL=dfVBEL.reset_index()

[25]:

lookup_TTR = dfKNOT.set_index('tk')['TTR']

[26]:

dfVBEL['TTR_KI'] = dfVBEL['fkKI'].map(lookup_TTR)
dfVBEL['TTR_KK'] = dfVBEL['fkKK'].map(lookup_TTR)

Geometry

[27]:

lookup_geometry = dfROHR.set_index('tk')['geometry']
lookup_XKOR = dfKNOT.set_index('tk')['XKOR']
lookup_YKOR = dfKNOT.set_index('tk')['YKOR']

[28]:

dfVBEL['geometry']=dfVBEL['OBJID'].map(lookup_geometry)
dfVBEL['XKOR_KI']=dfVBEL['fkKI'].map(lookup_XKOR)
dfVBEL['XKOR_KK']=dfVBEL['fkKK'].map(lookup_XKOR)
dfVBEL['YKOR_KI']=dfVBEL['fkKI'].map(lookup_YKOR)
dfVBEL['YKOR_KK']=dfVBEL['fkKK'].map(lookup_YKOR)

[29]:

dfVBEL['geometry'] = dfVBEL.apply(
    lambda row: row['geometry'] if pd.notnull(row['geometry']) else LineString([(row['XKOR_KI'], row['YKOR_KI']), (row['XKOR_KK'], row['YKOR_KK'])]),
    axis=1
)

KVR

[30]:

lookup_kvr = dfKNOT.set_index('tk')['KVR']

[31]:

dfVBEL['KVR_KI']=dfVBEL['fkKI'].map(lookup_kvr)

[32]:

dfVBEL['KVR_KK']=dfVBEL['fkKK'].map(lookup_kvr)

DN

[33]:

lookup_DN = dfROHR.set_index('tk')['DN']

[34]:

dfVBEL['DN']=dfVBEL['OBJID'].map(lookup_DN)

[35]:

dfVBEL['DN']=dfVBEL['DN'].fillna(100) # assume DN=100mm for all non-pipe edges

[36]:

dfVBEL = dfVBEL[(dfVBEL['XKOR_KI'].isna()) | (dfVBEL['XKOR_KI'] > 40000)]
dfVBEL = dfVBEL[(dfVBEL['XKOR_KK'].isna()) | (dfVBEL['XKOR_KK'] > 40000)]

[37]:

dfVBEL.head(3)

[37]:

	OBJTYPE	OBJID	pk	fkDE	rk	fkKI	BESCHREIBUNG	DGR	DKL	ALPHA	...	mlc_k	TTR_KI	TTR_KK	geometry	XKOR_KI	XKOR_KK	YKOR_KI	YKOR_KK	KVR_KI	KVR_KK
2	FWVB	4615167946623235098	4727459786481949539	5613149064237404433	4727459786481949539	5748663417295233893	groesster FWVB von MEHRFACH-FWVB am selben Kno...	NaN	NaN	NaN	...	88.827044	0.388912	0.0	LINESTRING (48964.7865681636 97889.1867773421,...	48964.786568	48964.786568	97889.186777	97889.186777	1.0	2.0
3	FWVB	4615393182465694100	5282002010269240713	5613149064237404433	5282002010269240713	5096987444312174224	groesster FWVB von MEHRFACH-FWVB am selben Kno...	NaN	NaN	NaN	...	95.780795	1.873637	0.0	LINESTRING (46651.6069416646 97713.81722454, 4...	46651.606942	46651.606942	97713.817225	97713.817225	1.0	2.0
4	FWVB	4616022158288753538	4762579314203629624	5613149064237404433	4762579314203629624	5724334807077180319	groesster FWVB von MEHRFACH-FWVB am selben Kno...	NaN	NaN	NaN	...	82.566185	1.233922	0.353222	LINESTRING (54845.3976874947 97480.8326112069,...	54845.397687	54845.397687	97480.832611	97480.832611	1.0	2.0

3 rows × 618 columns

Plotting

[38]:

ax, nodes = ncd.plot_ttr_network(
    dfVBEL,
    dn_col="DN",
    geometry_col="geometry",
    ttr_norm="percentile", ttr_percentiles=(5, 95),
    linewidth_range=(0.5, 8),
    highlight_keys=[5011622445515665493, 5152316559510921719],
    highlight_match="both",
    highlight_marker_size=180
)
plt.show()