Example 3: Longitudinal Sections

This example demonstrates how longitudinal sections data read by PT3S can be accessed and used to create plots with Matplotlib.

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 bokeh

Note: you may need to restart the kernel to use updated packages.

Imports

[3]:

import os
import logging
import pandas as pd
import datetime
import numpy as np
import subprocess

import matplotlib
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import matplotlib.gridspec as gridspec
import matplotlib.ticker as ticker
import matplotlib.colors as mcolors
from matplotlib.pyplot import Polygon
from matplotlib.ticker import FuncFormatter
from matplotlib.dates import DateFormatter, MinuteLocator
import matplotlib.ticker as ticker

from bokeh.plotting import figure, show
from bokeh.io import output_notebook
from bokeh.models import CustomJS, ColumnDataSource, CheckboxGroup, LinearAxis, Range1d
from bokeh.layouts import column


try:
    from PT3S import dxAndMxHelperFcts
except:
    import dxAndMxHelperFcts

try:
    from PT3S import Rm
except:
    import Rm

[4]:

import importlib
from importlib import resources

[5]:

#importlib.reload(dxAndMxHelperFcts)

Logging

[6]:

logger = logging.getLogger()

if not logger.handlers:
    logFileName = r"Example3.log"
    loglevel = logging.DEBUG

    logging.basicConfig(
        filename=logFileName,
        filemode='w',
        level=loglevel,
        format="%(asctime)s ; %(name)-60s ; %(levelname)-7s ; %(message)s"
    )

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

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

Read Model and Results

[7]:

dbFilename="Example3"
dbFile = resources.files("PT3S").joinpath("Examples", f"{dbFilename}.db3")

[8]:

m=dxAndMxHelperFcts.readDxAndMx(dbFile=dbFile
                                ,preventPklDump=True
                                ,maxRecords=-1
                                ,mxsVecsResults2MxDfVecAggs=[7,13,19,-1]
                                #,SirCalcExePath=r"C:\3S\SIR 3S\SirCalc-90-14-02-12_Potsdam.fix1_x64\SirCalc.exe"
)

INFO    ; Dx.__init__: dbFile (abspath): c:\users\aUserName\3s\pt3s\PT3S\Examples\Example3.db3 exists readable ...
INFO    ; PT3S.dxAndMxHelperFcts.readDxAndMx:
+..\PT3S\Examples\Example3.db3 is newer than
+..\PT3S\Examples\WDExample3\B1\V0\BZ1\M-1-0-1.1.MX1:
+SIR 3S' dbFile is newer than SIR 3S' mx1File
+in this case the results are maybe dated or (worse) incompatible to the model
INFO    ; PT3S.dxAndMxHelperFcts.readDxAndMx:
+..\PT3S\Examples\WDExample3\B1\V0\BZ1\M-1-0-1.XML is newer than
+..\PT3S\Examples\WDExample3\B1\V0\BZ1\M-1-0-1.1.MX1:
+SirCalc's xmlFile is newer than SIR 3S' mx1File
+in this case the results are maybe dated or (worse) incompatible to the model
INFO    ; PT3S.dxAndMxHelperFcts.readDxAndMx: Model is being recalculated using C:\\3S\SIR 3S Entwicklung\SirCalc-90-15-02-03_Quebec_x64\SirCalc.exe
INFO    ; Mx.setResultsToMxsFile: Mxs: ..\PT3S\Examples\WDExample3\B1\V0\BZ1\M-1-0-1.1.MXS reading ...
INFO    ; dxWithMx.__init__: Example3: processing dx and mx ...

Longitudinal Sections: V3_AGSN

[9]:

m.V3_AGSN.head()

[9]:

	Pos	pk	tk	LFDNR	NAME	compNr	nextNODE	OBJTYPE	OBJID	...	RHO_n	mlc_n	('STAT', 'mlc', Timestamp('2023-02-12 23:00:00'), Timestamp('2023-02-12 23:00:00'))_n	('TIME', 'mlc', Timestamp('2023-02-12 23:00:00'), Timestamp('2023-02-12 23:00:00'))_n	('TMIN', 'mlc', Timestamp('2023-02-12 23:00:00'), Timestamp('2023-02-13 23:00:00'))_n	('TMAX', 'mlc', Timestamp('2023-02-12 23:00:00'), Timestamp('2023-02-13 23:00:00'))_n	('TIME', 'mlc', Timestamp('2023-02-13 06:00:00'), Timestamp('2023-02-13 06:00:00'))_n	('TIME', 'mlc', Timestamp('2023-02-13 12:00:00'), Timestamp('2023-02-13 12:00:00'))_n	('TIME', 'mlc', Timestamp('2023-02-13 18:00:00'), Timestamp('2023-02-13 18:00:00'))_n	('TIME', 'mlc', Timestamp('2023-02-13 23:00:00'), Timestamp('2023-02-13 23:00:00'))_n
0	-1	5755933101669454049	5755933101669454049	1.0	Längsschnitt	1	V-E0	ROHR	5691533564979419761	...	965.700012	592.989116	592.964582	592.964582	592.96461	603.808833	602.036334	599.220665	602.192192	593.485427
0	0	5755933101669454049	5755933101669454049	1.0	Längsschnitt	1	V-K1683S	ROHR	5691533564979419761	...	965.701172	592.964582	592.964582	592.964582	592.96461	603.808833	602.036334	599.220665	602.192192	593.485427
1	1	5755933101669454049	5755933101669454049	1.0	Längsschnitt	1	V-K1693S	ROHR	5048873293262650113	...	965.702148	592.944601	592.944601	592.944601	592.944655	603.733968	601.97052	599.16915	602.125588	593.462842
2	2	5755933101669454049	5755933101669454049	1.0	Längsschnitt	1	V-K2163S	ROHR	5715081934973525403	...	965.702637	592.934593	592.934593	592.934593	592.934659	603.696467	601.93755	599.14334	602.09222	593.451528
3	3	5755933101669454049	5755933101669454049	1.0	Längsschnitt	1	V-K2043S	ROHR	5413647981880727734	...	965.703735	592.911548	592.911548	592.911548	592.911641	603.610202	601.861708	599.083971	602.015467	593.425478

5 rows × 85 columns

Plot Section No. 1

Define Axes

[10]:

def fyPH(ax,offset=0):
    ax.spines["left"].set_position(("outward", offset))
    ax.set_ylabel('PH Druck in bar')
    #ax.set_ylim(1,6)
    #ax.set_yticks(sorted(np.append(np.linspace(1,6,11),[])))
    ax.yaxis.set_ticks_position('left')
    ax.yaxis.set_label_position('left')

def fymlc(ax,offset=60):
    ax.spines["left"].set_position(("outward", offset))
    ax.set_ylabel('mlc Druckhöhe in mlc')
    #ax.set_ylim(1,6)
    #ax.set_yticks(sorted(np.append(np.linspace(1,6,11),[])))
    ax.yaxis.set_ticks_position('left')
    ax.yaxis.set_label_position('left')

def fybarBzg(ax,offset=120):
    ax.spines["left"].set_position(("outward", offset))
    ax.set_ylabel('H Druck in barBzg')
    #ax.set_ylim(1,6)
    #ax.set_yticks(sorted(np.append(np.linspace(1,6,11),[])))
    ax.yaxis.set_ticks_position('left')
    ax.yaxis.set_label_position('left')

def fyM(ax,offset=180):
    Rm.pltLDSHelperY(ax)
    ax.spines["left"].set_position(("outward",offset))
    ax.set_ylabel('QM Massenstrom in t/h')
    #ax.set_ylim(500,550)
    #ax.set_yticks(sorted(np.append(np.linspace(500,550,11),[])))
    ax.yaxis.set_ticks_position('left')
    ax.yaxis.set_label_position('left')

def fyT(ax,offset=240):
    Rm.pltLDSHelperY(ax)
    ax.spines["left"].set_position(("outward",offset))
    ax.set_ylabel('T Tempertatur in °C')
    ax.set_ylim(55,95)
    #ax.set_yticks(sorted(np.append(np.linspace(0,95,11),[])))
    ax.yaxis.set_ticks_position('left')
    ax.yaxis.set_label_position('left')

Plotfunction

[11]:

def plot(dfAGSN=pd.DataFrame()
        ,dfAGSNRL=pd.DataFrame()
        ,PHCol='PH_n'
        ,mlcCol='mlc_n'
        ,zKoorCol='ZKOR_n'
        ,barBzgCol='H_n'
        ,QMCol='QM'
        ,TCol='T_n'
        ,xCol='LSum'

):

    fig, ax0 = plt.subplots(figsize=Rm.DINA3q)

    ax0.set_yticks(np.linspace(0, 10, 21))
    ax0.yaxis.set_ticklabels([])
    ax0.grid()

    #PH
    ax1 = ax0.twinx()
    fyPH(ax1)
    PH_SL=ax1.plot(dfAGSN[xCol], dfAGSN[PHCol], color='red', label='PH SL',ls='dotted')
    PH_RL=ax1.plot(dfAGSNRL[xCol], dfAGSNRL[PHCol], color='blue', label='PH RL',ls='dotted')

    #mlc
    ax11 = ax0.twinx()
    fymlc(ax11)
    mlc_SL=ax11.plot(dfAGSN[xCol], dfAGSN[mlcCol], color='red', label='mlc SL')
    mlc_RL=ax11.plot(dfAGSNRL[xCol], dfAGSNRL[mlcCol], color='blue', label='mlc RL')

    z=ax11.plot(dfAGSN[xCol], dfAGSN[zKoorCol], color='black', label='z',ls='dashed',alpha=.5)

    #barBZG
    ax12 = ax0.twinx()
    fybarBzg(ax12)
    barB_SL=ax12.plot(dfAGSN[xCol], dfAGSN[barBzgCol], color='red', label='H SL',ls='dashdot')
    barB_RL=ax12.plot(dfAGSNRL[xCol], dfAGSNRL[barBzgCol], color='blue', label='H RL',ls='dashdot')

    #M
    ax2 = ax0.twinx()
    fyM(ax2)
    QM_SL=ax2.step(dfAGSN[xCol], dfAGSN[QMCol]*dfAGSN['direction'], color='orange', label='M SL')
    QM_RL=ax2.step(dfAGSNRL[xCol], dfAGSNRL[QMCol]*dfAGSNRL['direction'], color='cyan', label='M RL',ls='--')

    #T
    ax3 = ax0.twinx()
    fyT(ax3)
    T_SL=ax3.plot(dfAGSN[xCol], dfAGSN[TCol], color='pink', label='T SL')
    T_RL=ax3.plot(dfAGSNRL[xCol], dfAGSNRL[TCol], color='lavender', label='T RL')

    ax0.set_title('Longitudinal Section for '+dbFilename)

    # added these three lines
    lns =  PH_SL+ PH_RL + mlc_SL+ mlc_RL + barB_SL+ barB_RL+ QM_SL+ QM_RL + T_SL+ T_RL + z
    labs = [l.get_label() for l in lns]
    ax0.legend(lns, labs)#, loc=0)

    plt.show()

Plot without vector data

[12]:

dfAGSN=m.V3_AGSN[
    (m.V3_AGSN['LFDNR']==1)
    &
    (m.V3_AGSN['XL']==1)
]

[13]:

dfAGSNRL=m.V3_AGSN[
    (m.V3_AGSN['LFDNR']==1)
    &
    (m.V3_AGSN['XL']==2)
]

[14]:

plot(dfAGSN,dfAGSNRL)

Plot with vector data (with pipe interior points)

[15]:

dfAGSNVec=m.V3_AGSNVEC[
    (m.V3_AGSNVEC['LFDNR']==1)
    &
    (m.V3_AGSNVEC['XL']==1)
]

[16]:

dfAGSNVecRL=m.V3_AGSNVEC[
 (m.V3_AGSNVEC['LFDNR']==1)
    &
    (m.V3_AGSNVEC['XL']==2)
]

[17]:

t0rVec=pd.Timestamp(m.mx.df.index[0].strftime('%Y-%m-%d %X'))#.%f'))

[18]:

manPVEC=('STAT',
                      'manPVEC',
                      t0rVec,
                      t0rVec)

[19]:

manPVEC

[19]:

('STAT',
 'manPVEC',
 Timestamp('2023-02-12 23:00:00'),
 Timestamp('2023-02-12 23:00:00'))

[20]:

# for convenient colnames in celloutput
dfAGSNVec['manPVEC']=dfAGSNVec[manPVEC]
dfAGSNVecRL['manPVEC']=dfAGSNVecRL[manPVEC]

[21]:

mlcPVEC=('STAT',
                      'mlcPVEC',
                      t0rVec,
                      t0rVec)

[22]:

# for convenient colnames in celloutput
dfAGSNVec['mlcPVEC']=dfAGSNVec[mlcPVEC]
dfAGSNVecRL['mlcPVEC']=dfAGSNVecRL[mlcPVEC]

[23]:

QMVEC=('STAT',
                      'QMVEC',
                      t0rVec,
                      t0rVec)

[24]:

QMVEC=('STAT',
                      'QMVEC',
                      t0rVec,
                      t0rVec)

[25]:

# for convenient colnames in celloutput
dfAGSNVec['QMVEC']=dfAGSNVec[QMVEC]
dfAGSNVecRL['QMVEC']=dfAGSNVecRL[QMVEC]

[26]:

TVEC=('STAT',
'ROHR~*~*~*~TVEC',
t0rVec,
t0rVec)

[27]:

# for convenient colnames in celloutput
dfAGSNVec['TVEC']=dfAGSNVec[TVEC]
dfAGSNVecRL['TVEC']=dfAGSNVecRL[TVEC]

[28]:

plot(dfAGSNVec,dfAGSNVecRL
        ,PHCol=manPVEC
        ,mlcCol=mlcPVEC
        ,zKoorCol='ZVEC'
        ,barBzgCol='H_n'
        ,QMCol=QMVEC
        ,TCol=TVEC
        ,xCol='LSum'

    )

Plot different timesteps with Bokeh

[29]:

def bokeh_plot(df, x_col, y_cols, y2_cols, y3_cols):
    output_notebook()

    p = figure(width=1366, height=768, title="Longitudinal Section for "+dbFilename+" at different timestamps", x_axis_label=x_col, y_axis_label="PH Druck in bar", tools="pan,wheel_zoom,box_zoom,reset", y_range=Range1d(start=0, end=6))

    # Define line styles and colors
    line_styles = ['solid', 'dashed', 'dotted']
    colors = ['blue', 'red', 'green']
    lines = []

    # lines for y
    for i, y_col in enumerate(y_cols):
        line = p.line(df[x_col], df[y_col], legend_label=f"{y_col} (06:00)" if i == 0 else f"{y_col} (12:00)" if i == 1 else f"{y_col} (18:00)", line_width=2, line_dash=line_styles[i % len(line_styles)], color=colors[0])
        lines.append(line)

    #second y-axis
    p.extra_y_ranges = {"y2": Range1d(start=0, end=600)}
    p.add_layout(LinearAxis(y_range_name="y2", axis_label="QM Massenstrom in t/h"), 'left')

    # lines for y2
    for i, y2_col in enumerate(y2_cols):
        line = p.line(df[x_col], df[y2_col], legend_label=f"{y2_col} (06:00)" if i == 0 else f"{y2_col} (12:00)" if i == 1 else f"{y2_col} (18:00)", line_width=2, line_dash=line_styles[i % len(line_styles)], color=colors[1], y_range_name="y2")
        lines.append(line)

    #third y-axis
    p.extra_y_ranges["y3"] = Range1d(start=590, end=610)
    p.add_layout(LinearAxis(y_range_name="y3", axis_label="mlc in m"), 'left')

    # lines for y3
    for i, y3_col in enumerate(y3_cols):
        line = p.line(df[x_col], df[y3_col], legend_label=f"{y3_col} (06:00)" if i == 0 else f"{y3_col} (12:00)" if i == 1 else f"{y3_col} (18:00)", line_width=2, line_dash=line_styles[i % len(line_styles)], color=colors[2], y_range_name="y3")
        lines.append(line)

    # Create a CheckboxGroup
    labels = [f"{y_col}" for i, y_col in enumerate(y_cols + y2_cols + y3_cols)]
    checkbox = CheckboxGroup(labels=labels, active=list(range(len(labels))))

    # CustomJS to toggle visibility
    checkbox.js_on_change('active', CustomJS(args=dict(lines=lines), code="""
        for (let i = 0; i < lines.length; i++) {
            lines[i].visible = this.active.includes(i);
        }
    """))

    show(column(p, checkbox))

[30]:

bokeh_plot(dfAGSNVec, 'LSum',['PH_n_1', 'PH_n_2', 'PH_n_3'], ['QM_1', 'QM_2', 'QM_3'], ['mlc_n_1', 'mlc_n_2', 'mlc_n_3'])

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