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Commit ba31e7d3 authored by Michael Pawelzik's avatar Michael Pawelzik
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removing code doubling with use of measurment plot 

function for determing PK2PK values of phase and magnitude
function for changing annotation test in first subplot
parent 3e0e769c
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1 merge request!3feat: introduce external sensors
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Python_script/PostPlot.py
+ 68
− 349
View file @ ba31e7d3
......@@ -6,12 +6,11 @@ Created on Tue Mar 7 10:15:55 2023
"""
import pandas as pd
import matplotlib.pyplot as plt
import sys
from pathlib import Path
import os
import numpy as np
from scipy.optimize import curve_fit
import MeasurementPlot
import time
# after measurement has finished from stored csv-data a post plot of all temperature steps is plotted and store in
......@@ -24,6 +23,13 @@ class PostPlot:
if 'ipykernel' in sys.modules:
from IPython import get_ipython
get_ipython().run_line_magic('matplotlib', 'qt')
self.postplot_obj = MeasurementPlot.MeasurementPlot(legend_loc = 'upper left', \
legend_bbox_to_anchor = (1.09, 1))
# set parameter figure of class to object parameter figure
self.fig = self.postplot_obj.fig
# read csv-file and import data to data frame
def import_csv(self, csv_file):
......@@ -33,258 +39,68 @@ class PostPlot:
return data_frame
# plot function for data frame data, Michael
# labels for legend of external sensor is given in Parameterlist , Michael
# optinal calculation of regression, a plot of the fitted function and the calculation of correlation
# between DUT sensor Temperature and phase can be activated by parameterlist of function, michael
def plot_frame_data(self, data_frame, title ='', legend_sensor0 = ['Temperature Sensor0', 'Humidity Sensor0'], \
legend_sensor1 = ['Temperature Sensor1','Humidity Sensor1', 'Air Pressure Sensor1'], \
time_unit ='min', regression_state = False, plot_regression = False, plot_corr_coeff = False):
# set label of plots with legend entries in parameter list of plot frame data method
label_temp_sensor0 = legend_sensor0[0]
label_hum_sensor0 = legend_sensor0[1]
label_temp_sensor1 = legend_sensor1[0]
label_hum_sensor1 = legend_sensor1[1]
label_air_press_sensor1 = legend_sensor1[2]
data_frame.reset_index(inplace=True, drop=True)
# fetch plot data from data frame given in parameter list of function
timestamps = data_frame.TIMESTAMP
phases = data_frame.S21_PHASE
eq_indicator = data_frame.EQUILIBRIUM_INDICATOR
magnitudes = data_frame.S21_MAGNITUDE
temperatures = data_frame.READBACK_TEMPERATURE
humidities = data_frame.READBACK_HUMIDITY
temp_sensor0 = data_frame.TEMP_SENSOR0
temp_sensor1 = data_frame.TEMP_SENSOR1
hum_sensor0 = data_frame.HUM_SENSOR0
hum_sensor1 = data_frame.HUM_SENSOR1
air_press = data_frame.AIR_PRESSURE
temp_heater = data_frame.TEMP_HEATER
hum_heater = data_frame.HUM_HEATER
def plot_frame_data(self, data_frame, title ='', time_unit ='min'):
# time stamp of list entry zero is the start point of time axis, Michael
# substract all other timestamps with list entry of zero to scale time axis, Michael
time_sec = timestamps - timestamps[0]
# set scaling of time axis depending on the time unit given in parameter list, Michael
# default unit are minutes, Michael
time = self.scaling_time_axes(time_sec, time_unit)
# calulate correlation betwenn trace of temp sensor 0 (DUT temeprature) and trace of the phase which is measured
# by VNA, Michael
corr_coeff_phase= np.corrcoef(phases, temp_sensor0)[0][1]
# plot window with 5 subplots
self.fig, self.ax1 = plt.subplots(5, figsize=(25, 20))
# set title of plot
self.fig.suptitle("Measurement "+title, color="red")
# scale subplots to fit into space of figure so that axes labels and legends easily can be read, Michael
self.fig.subplots_adjust(bottom= 0.1, right=0.8, hspace = 0.4)
minimum, maximum = self.get_extended_min_max(time)
self.ax1[0].set_xlim(minimum, maximum)
self.ax1[1].set_xlim(minimum, maximum)
# set x-axes limits for addinal plot equal to the plot for magnitude and phase of DUT, Michael
self.ax1[2].set_xlim(minimum, maximum)
self.ax1[3].set_xlim(minimum, maximum)
self.ax1[4].set_xlim(minimum, maximum)
# First plot: Phase and magnitude of DUT
self.path_collection_phase = self.ax1[0].scatter(time, phases, c='red', marker='<', label='DUT Phase')
minimum, maximum = self.get_extended_min_max(phases)
self.ax1[0].set_ylim(minimum, maximum)
# reset index of data frame
data_frame.reset_index(inplace=True, drop=True)
self.magnitude_axis = self.ax1[0].twinx()
self.path_collection_mag = self.magnitude_axis.scatter(time, magnitudes, c='#3120E0', marker='4', label='DUT Magnitude')
minimum, maximum = self.get_extended_min_max(magnitudes)
self.magnitude_axis.set_ylim(minimum, maximum)
# self.postplot_obj = MeasurementPlot.MeasurementPlot(legend_loc = 'upper left', \
# legend_bbox_to_anchor = (1.09, 1))
self.equi_axis0 = self.ax1[0].twinx()
# fix range to 0..31 with some extra margin for plotting
self.equi_axis0.set_ylim(-1, 32)
# increase outward position of axes for equilibrium indicator , Michael
self.equi_axis0.spines['right'].set_position(('outward', 75))
self.path_collection_equi0 = self.equi_axis0.scatter(time, eq_indicator, c='black', marker=".", label='Equilibrium_Indicator')
# set y-Achlabel in all subplots to Time an in square brackets the selected time unit
for element in self.postplot_obj.ax1:
element.set_xlabel("Time [%s]" %time_unit)
if regression_state == True:
self.phase_t0 = phases[0]
self.K_phases = np.median(phases)
func, popt = self.choose_fit( time, phases)
annotate_string_fit = self.plot_fitted_func_param(func, *popt, time_unit = time_unit)
self.ax1[0].annotate(annotate_string_fit,xy=(0,min(phases)), xycoords='data', xytext=(-0.16,0),textcoords='axes fraction',fontsize = 16, horizontalalignment='left',verticalalignment='bottom')
if plot_regression == True:
fit_phaeses = func(time, *popt)
min_phases, max_phases = self.get_extended_min_max(phases)
min_fit_phases, max_fit_phases = self.get_extended_min_max(fit_phaeses)
minimum = min(min_phases, min_fit_phases)
maximum = max(max_phases, max_fit_phases)
self.ax1[0].set_ylim(minimum, maximum)
self.path_collection_curvefit = self.ax1[0].scatter(time, func(time, *popt), c='green', s = 1, marker = "." , label = 'fitted with\n' + func.__name__)
all_path_collections = [self.path_collection_phase, self.path_collection_mag, self.path_collection_equi0, self.path_collection_curvefit]
else:
all_path_collections = [self.path_collection_phase, self.path_collection_mag, self.path_collection_equi0]
else:
all_path_collections = [self.path_collection_phase, self.path_collection_mag, self.path_collection_equi0]
delta_phase = max(phases) - min(phases)
delta_magnitude = max(magnitudes) - min(magnitudes)
# plot delta values for phase and magnitude at left position outside the plot, Michael
annotate_string = "$\Delta\phi$($S_{21PkPk}$): %.3f °\n$\Delta$|$S_{21PkPk}$|: %.3f dB" % (delta_phase, delta_magnitude)
if plot_corr_coeff == True:
annotate_string +="\n"
annotate_string += "$R_{\phi(S_{21}),Temp_{DUT}}$ = %.3f" % corr_coeff_phase
self.ax1[0].annotate(annotate_string ,xy=(0,min(phases)), xycoords='data', xytext=(-0.16,1),textcoords='axes fraction',fontsize = 16, horizontalalignment='left',verticalalignment='bottom')
# make a copy of data_frame in parameterlist without changing original during modification
postplot_data_frame = data_frame.copy()
# units added to the y-axes labels, Michael
self.ax1[0].set_xlabel("TIME [%s]" %time_unit)
self.ax1[0].set_ylabel("PHASE [°]", color='red')
self.magnitude_axis.set_ylabel("MAGNITUDE [dB]", color='#3120E0')
# label of y-axis for equilibrium indicator is changed to Indiccator Value because it's shorter, Michael
self.equi_axis0.set_ylabel("INDICATOR VALUE", color='black')
self.ax1[0].grid(True, linestyle=":")
labels = [pc.get_label() for pc in all_path_collections]
# time stamp of index = 0 is the start point of time axis, Michael
# substract all other timestamps with time stamp of index zero to get time scale in
# seconds, Michael
time_sec = postplot_data_frame.TIMESTAMP - postplot_data_frame.TIMESTAMP[0]
# set legend at rigth upper position outside subplot for phase and magnitude, Michael
self.ax1[0].legend(all_path_collections, labels, loc='upper left', bbox_to_anchor=(1.09, 1))
# Second plot: Humidity and temperature of climate chamber requested from internal sensors of climate chamber
self.path_collection_temp = self.ax1[1].scatter(time, temperatures, c='blue', marker='p', label="Chamber Temperature")
minimum, maximum = self.get_extended_min_max(temperatures)
self.ax1[1].set_ylim(minimum, maximum)
# set scaling of time axis depending on the time unit given in parameter list, Michael
# default unit is minutes, Michael
time = self.scaling_time_axes(time_sec, time_unit)
self.humidity_axis = self.ax1[1].twinx()
self.path_collection_hum = self.humidity_axis.scatter(time, humidities , c='green', marker="*", label="Chamber Humidity")
minimum, maximum = self.get_extended_min_max(humidities)
self.humidity_axis.set_ylim(minimum, maximum)
# update Timestamps with calculated time values
postplot_data_frame.update(time)
self.equi_axis1 = self.ax1[1].twinx()
# refresh subflots with data in data frame
self.postplot_obj.draw(postplot_data_frame, pdf_name = '')
# increase outward position of axes for equilibrium indicator , Michael
self.equi_axis1.spines['right'].set_position(('outward', 75))
self.path_collection_equi1 = self.equi_axis1.scatter(time, eq_indicator, c='black', marker=".", label="Equilibrium_Indicator")
self.equi_axis1.set_ylim(-1, 32)
# units added to the y-axes labels, Michael
self.ax1[1].set_xlabel("TIME [%s]" %time_unit)
self.ax1[1].set_ylabel("TEMPERATURE [°C] ", color='blue')
self.humidity_axis.set_ylabel("HUMIDITY [%RH]", color='green')
# label of y-axis for equilibrium indicator ist changed to Indiccator Value because it's shorter, Michael
self.equi_axis1.set_ylabel("INDICATOR VALUE", color='black')
# cal PK2PK values of magnitude and phase
PK2PK = self.calc_mag_phase_pkpk_values(data_frame)
self.ax1[1].grid(True, linestyle=":")
all_path_collections = [self.path_collection_temp, self.path_collection_hum, self.path_collection_equi1]
labels = [pc.get_label() for pc in all_path_collections]
# set legend at rigth upper position outside subplot for temperature and humidity from internal sensors of
# climate chamber, Michael
self.ax1[1].legend(all_path_collections, labels, loc='upper left', bbox_to_anchor=(1.09, 1))
self.edit_annotation_in_plot(annotate_string = PK2PK)
# Third plot: temperatur external sensor0, humidity external sensors0, Michael
# sensor 0 is the sensor at port 0 of ahlborn and used for the DUT temperature, DUT humidity, Michael
# label for temeprature and humidity of sensor 0 can be configure by ext_sens_data.json, Michael
def edit_annotation_in_plot(self, annotate_string ='', anno_fontsize = 16):
self.path_collection_temp_sensor0 = self.ax1[2].scatter(time,temp_sensor0, c='red', marker='p', label=label_temp_sensor0)
minimum, maximum = self.get_extended_min_max(temp_sensor0)
self.ax1[2].set_ylim(minimum, maximum)
# update text of annotation in first subplot
self.postplot_obj.annotation.set_text(annotate_string)
# second y-axis on the right for humidity of external sensor0
self.ext_sens_hum_axis = self.ax1[2].twinx()
minimum, maximum = self.get_extended_min_max(hum_sensor0)
self.ext_sens_hum_axis.set_ylim(minimum, maximum)
self.path_collection_hum_sensor0 = self.ext_sens_hum_axis.scatter(time, hum_sensor0, c='purple', marker='*', label=label_hum_sensor0)
# edit annotation fontsize in first subplot
self.postplot_obj.annotation.set_fontsize(anno_fontsize)
def calc_mag_phase_pkpk_values(self, data_frame):
# units added to the y-axes labels, Michael
self.ax1[2].set_xlabel("TIME [%s]" %time_unit)
self.ax1[2].set_ylabel("TEMPERATURE [°C]", color='red')
self.ext_sens_hum_axis.set_ylabel("HUMIDITY [%RH]", color = 'purple')
self.ax1[2].grid(True, linestyle=":")
all_path_collections = [self.path_collection_temp_sensor0, self.path_collection_hum_sensor0]
labels = [pc.get_label() for pc in all_path_collections]
# calc PK2PK values of magnitude and phase
delta_phase = max(data_frame.S21_PHASE) - min(data_frame.S21_PHASE)
delta_magnitude = max(data_frame.S21_MAGNITUDE) - min(data_frame.S21_MAGNITUDE)
# set legend at rigth upper position outside subplot for temperature and humidity from external sensor 0, Michael
self.ax1[2].legend(all_path_collections, labels, loc='upper left', bbox_to_anchor=(1.09, 1))
# generate text for annoation in first subplot
delta_vals_string = "$\Delta\phi$($S_{21PkPk}$): %.3f °\n$\Delta$|$S_{21PkPk}$|: %.3f dB" \
% (delta_phase, delta_magnitude)
# Forth plot: temperatur external sensor1, humidity external sensors1, air pressure external sensor1, Michael
# sensor 1 is the sensor at port 1 of ahlborn and used for the room temperature, the room humidity
# and the air pressure in the room, Michael
# label for temeprature and humidity of sensor0 can be configure by ext_sens_data.json, Michael
self.path_collection_temp_sensor1 = self.ax1[3].scatter(time,temp_sensor1, c='green', marker='*', label=label_temp_sensor1)
minimum, maximum = self.get_extended_min_max(temp_sensor1)
self.ax1[3].set_ylim(minimum, maximum)
# second y-axis on the right for humidity
self.sec_ext_hum_sens_axis = self.ax1[3].twinx()
minimum, maximum = self.get_extended_min_max(hum_sensor1)
self.sec_ext_hum_sens_axis.set_ylim(minimum, maximum)
self.path_collection_hum_sensor1 = self.sec_ext_hum_sens_axis.scatter(time,hum_sensor1, c='orange', marker='>', label=label_hum_sensor1)
# third y-axis on the right for air pressure
self.press_axis = self.ax1[3].twinx()
self.press_axis.spines['right'].set_position(('outward', 60))
minimum, maximum = self.get_extended_min_max(air_press)
self.press_axis.set_ylim(minimum, maximum)
self.path_collection_press = self.press_axis.scatter(time,air_press, c='grey', marker='4', label=label_air_press_sensor1)
# units added to the y-axes labels, Michael
self.ax1[3].set_xlabel("TIME [%s]" %time_unit)
self.ax1[3].set_ylabel("TEMPERATURE [°C]", color='green')
self.sec_ext_hum_sens_axis.set_ylabel("HUMIDITY [%RH]", color = 'orange')
self.press_axis.set_ylabel("AIR PRESSURE [mb]", color ='grey')
self.ax1[3].grid(True, linestyle=":")
all_path_collections = [self.path_collection_temp_sensor1, self.path_collection_hum_sensor1, \
self.path_collection_press]
labels = [pc.get_label() for pc in all_path_collections]
# set legend at rigth upper position outside subplot for temperature and humidity from external sensor 1, Michael
self.ax1[3].legend(all_path_collections, labels, loc='upper left', bbox_to_anchor=(1.09, 1))
# Fifth plot: heater activity of temeprature heater and humidity heater
# values for activity are requested from climate chamber, Michael
self.path_collection_temp_heater = self.ax1[4].scatter(time,temp_heater, c='black', marker='<', label='Temp Heater')
self.path_collection_hum_heater = self.ax1[4].scatter(time, hum_heater, c='brown', marker='o', label='Hum Heater')
# get min max values for temperature heater and humdity heater, Michael
# select the lowest minimum and the highest maximum for limit of the y-axis, Michael
min_temp_heater, max_temp_heater = self.get_extended_min_max(temp_heater)
min_hum_heater, max_hum_heater = self.get_extended_min_max(hum_heater)
minimum = min(min_temp_heater, min_hum_heater)
maximum = max(max_temp_heater, max_hum_heater)
distance = maximum - minimum
self.ax1[4].set_ylim(minimum-0.05*distance, maximum+0.05*(distance))
# units added to the y-axes labels, Michael
self.ax1[4].set_xlabel("TIME [%s]" %time_unit)
self.ax1[4].set_ylabel("HEATER PERCENTAGE [%]", color='black')
self.ax1[4].grid(True, linestyle=":")
all_path_collections = [self.path_collection_temp_heater, \
self.path_collection_hum_heater]
labels = [pc.get_label() for pc in all_path_collections]
# set legend at rigth upper position outside subplot for temperature heater and humidity heater, Michael
self.ax1[4].legend(all_path_collections, labels, loc='upper left', bbox_to_anchor=(1.08, 1))
# sclae fontsize of all plot window elements to size 14, Michael
plt.rcParams.update({'font.size':16})
# add 5 % of the distance between min and max to the range
@staticmethod
def get_extended_min_max(array):
distance = array.max() - array.min()
if distance == 0.:
distance = 1
return array.min()-0.05*distance, array.max()+0.05*distance
return delta_vals_string
# save figure under the given storepath in parameterlist with the given filename in parameter list, Michael
# file extension is part of filename, Michael
def save_fig(self, storepath, filename):
......@@ -309,102 +125,16 @@ class PostPlot:
return time
# step response of PT1 with initial behavior , Michael
def PT1(self, x, K, T1):
return K * (1 - np.exp(-x/T1)) + self.phase_t0 * np.exp(-x/T1)
# step response of PT2 (D > 1) with initial behavior
def aperiodicPT2(self, x, K, T1, T2,):
return K + self.phase_t0* np.exp(-x/T1)/(T2-T1) - K *T1* np.exp(-x/T1)/(T2-T1) \
- self.phase_t0 * np.exp(-x/T2)/(T2-T1) + K* T2* np.exp(-x/T2)/(T2-T1)
# step response of PT2 (D = 1) with initial behavior, Michael
def aperiodic_borderPT2(self, x, K, T):
return K + self.phase_t0*np.exp(-x/T) - K * np.exp(-x/T) + self.phase_t0*np.exp(-x/T)*x/T + \
- K * np.exp(-x/T) * x/T
# step response of PT2 (0 < D < 1) with initial behavior, Michael
def periodicPT2(self, x, D, T):
return self.K_phases + (self.phase_t0 -self.K_phases)*np.exp(-D*x/T)*np.cos(np.sqrt(1-np.square(D))*x/T) + \
(self.phase_t0 - self.K_phases) * D * np.exp(-D*x/T)*np.sin(np.sqrt(1-np.square(D))*x/T)/np.sqrt(1-np.square(D))
# step response series of PT2 ( D = 1) and PT1 with initial behavior, Michael
def aperiodic_borderPT2_PT1(self, x, a, b, c, d, T1, T2):
return a + b*np.exp(-x/T1)+c*x*np.exp(-x/T1) + d*np.exp(-x/T2)
# method for cuvefit, Michael
# two different algorithms for curvefit are use depending on used step response for fit, Michael
def curvefit(self, func, time, phases):
if func.__name__ == 'periodicPT2':
popt, pcov = curve_fit(func, time, phases, method = 'trf', bounds = ([0.001, 0.001], [0.99, np.inf]))
else:
popt, pcov = curve_fit(func, time, phases, method = 'lm')
return popt, pcov
# method adds the parameter of the fit with the lowest error to annotation string, Michael
# contens of annotation string is plotted on the left side of subplot 1 if regression state is activated, Michael
def plot_fitted_func_param(self, func, *popt, time_unit):
if func.__name__ == 'PT1':
K = popt[0]
T1 = popt[1]
annotate_string = "K: %.2f\n$T_1$: %.3f %s" %(K, T1, time_unit)
elif func.__name__ == 'aperiodicPT2':
K = popt[0]
T1 = popt[1]
T2 = popt[2]
annotate_string = "K: %.2f\n$T_1$: %.3f %s\n$T_2$: %.3f %s" % (K, T1, time_unit, T2, time_unit)
elif func.__name__ == 'aperiodic_borderPT2':
K = popt[0]
T = popt[1]
annotate_string = "K: %.2f\nT: %.3f %s" %(K, T, time_unit)
elif func.__name__ == 'periodicPT2':
K = self.K_phases
D = popt[0]
T = popt[1]
T_2perc = 4*T/D
annotate_string = "K: %.2f\nD: %.3f\nT: %.3f %s\n$T_E$(2%%): %.2f %s" %(K, D, T, time_unit, T_2perc, time_unit)
elif func.__name__ == 'aperiodic_borderPT2_PT1':
T1 = popt[4]
T2 = popt[5]
annotate_string = "T_1$: %.3f %s\n$T_2$: %.3f %s" % (T1, time_unit, T2, time_unit)
else:
# default value
annotate_string =''
return annotate_string
# calculates the fit for all step responses and calulates the lowest error, Michael
# parameter of the plot with the lowest error is returned by this function, Michael
# if curve fit fails for one stept response it takes the next one and plots in comman window the functions
# which failes, Michael
def choose_fit(self, time, phases):
popt = []
perr = []
used_functions = []
functions = [self.PT1, self.aperiodic_borderPT2, self.aperiodicPT2, self.periodicPT2, \
self.aperiodic_borderPT2_PT1]
for func in functions:
try:
# call curvefit method and put the fitted parameter for step response function to a list, Michael
popt.append(self.curvefit(func, time, phases)[0])
# calculation of fitting error
perr.append(np.sqrt(np.square(np.subtract(phases, func(time, *popt[-1]))).mean()))
used_functions.append(func)
except RuntimeError:
print('curvefit of %s fails' %func.__name__)
# get position of parameters in list which has the lowest calculated error, Michael
pos = perr.index(min(perr))
# return function name and parameter with the lowest calculated error during curvefit, Michael
return used_functions[pos], popt[pos]
# for manually redo post plot after measurement has finished, Michael
if __name__ == '__main__':
# set result path for post plot
Results_Path = r'C:\Users\pawelzik\Desktop\Results\THRU_27032023'
Results_Path = r'C:\Users\pawelzik\Desktop\Results\THRU_27032023Copy'
# activate/deactivate calculation of regression with PT1 - PT3 elements, Michael
regression_state = False
# set time unit for post post plot
# default is minutes if entry in parameterlist left empty
......@@ -417,13 +147,13 @@ if __name__ == '__main__':
# search all csv files in results folder
csv_file_list = list(Path(Results_Path).glob("**/*.csv"))
# set entires for the external sensors of ahlborn in legend of plot windows
legend_sensor0=['TEMP-Sensor DUT', 'HUM-Sensor DUT']
legend_sensor1 = ['Room Temperature','Room Humidity', 'Air Pressure Room']
# create postplot object, Michael
plot_obj = PostPlot()
# empty data frame for concat the data frames from csv import to plot full transistion
concat_data_frame = pd.DataFrame()
# plot results for each csv-file
for index, csv_file in enumerate(csv_file_list):
......@@ -433,38 +163,27 @@ if __name__ == '__main__':
# determine title of plot from csv-filename, Michael
title = csv_file.name
if index < 1:
# set concate data frame to first dataframe in list, Michael
concate_data_frame = data_frame
# call post plot method, Michael
# concate datesframe for plotting full transistion data
concat_data_frame = pd.concat([concat_data_frame,data_frame],ignore_index=True, sort = False)
plot_obj.plot_frame_data(data_frame, title,legend_sensor0, legend_sensor1, time_unit, \
regression_state = False, plot_regression = True, plot_corr_coeff = False)
else:
concate_data_frame = concate_data_frame.append(data_frame)
plot_obj.plot_frame_data(data_frame, title,legend_sensor0, legend_sensor1, time_unit, \
regression_state, plot_regression = True)
plot_obj.plot_frame_data(data_frame, title, time_unit)
# set filename of post plot, Michael
filename = str(csv_file.stem) + '.svg'
filename = str(csv_file.stem) + '.pdf'
# store post plot under the path taht is set in storepath with the earlier defined filename, Michael
# store post plot under the path taht is set in storepath with the earlier defined filename
plot_obj.save_fig(storepath, filename)
# plot of all steps of a sweep is plotted in one diagram, Michael
# title of this plot will be always full transistion, Michel
# legend for external sensors is steh to the pre defined values of lgend_snesor0 and legend_sensor1, Michael
plot_obj.plot_frame_data(concate_data_frame, 'Full Transistion', legend_sensor0, legend_sensor1, time_unit)
plot_obj.plot_frame_data(concat_data_frame, 'Full Transistion', time_unit)
# filename of plot that contains all steps of sweep is set to FullTransistion, Michael
filename = 'Full_Transistion' + '.svg'
filename = 'Full_Transistion' + '.pdf'
# plot with the results of all steps is store under the predefined storpath with the earlier defined filename, Michael
plot_obj.save_fig(storepath, filename)
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