diff --git a/Python_script/PostPlot.py b/Python_script/PostPlot.py
index c47b26e42c429ea36256cbaeb136818549cf4cc3..e52205a35e3238d1523f7d3a1251bafb20c5815e 100644
--- a/Python_script/PostPlot.py
+++ b/Python_script/PostPlot.py
@@ -9,8 +9,9 @@ import pandas as pd
import sys
from pathlib import Path
import os
-import MeasurementPlot
-import curvefit_and_correlation
+from MeasurementPlot import MeasurementPlot
+import numpy as np
+
# after measurement has finished from stored csv-data a post plot of all temperature steps is plotted and store in
@@ -18,18 +19,22 @@ import curvefit_and_correlation
# oftional correclation and regression can be calculated and plotted in results, Michael
# option is configureable via ext_sens_data.json, Michael
class PostPlot:
- def __init__(self, env_cond_sensors = ''):
+ def __init__(self, env_cond_sensors = '', legend_loc = 'upper left', \
+ legend_bbox_to_anchor = (1.09, 1)):
# set python for opening an separate plot window
if 'ipykernel' in sys.modules:
from IPython import get_ipython
get_ipython().run_line_magic('matplotlib', 'qt')
- self.measplot_obj = MeasurementPlot.MeasurementPlot(env_cond_sensors = env_cond_sensors,\
- legend_loc = 'upper left', legend_bbox_to_anchor = (1.09, 1))
+ self.measplot = MeasurementPlot(env_cond_sensors = env_cond_sensors,\
+ legend_loc = legend_loc, legend_bbox_to_anchor = legend_bbox_to_anchor)
+
+ self.legend_loc = legend_loc
+ self.legend_bbox_to_anchor = legend_bbox_to_anchor
# set parameter figure of class to object parameter figure
- self.fig = self.measplot_obj.fig
+ self.fig = self.measplot.fig
# read csv-file and import data to data frame
def import_csv(self, csv_file):
@@ -48,7 +53,7 @@ class PostPlot:
data_frame.reset_index(inplace=True, drop=True)
# set y-Achlabel in all subplots to Time an in square brackets the selected time unit
- for element in self.measplot_obj.ax1:
+ for element in self.measplot.ax1:
element.set_xlabel("Time [%s]" %time_unit)
# make a copy of data_frame in parameterlist without changing original during modification
@@ -67,7 +72,7 @@ class PostPlot:
postplot_data_frame.update(time_vals)
# refresh subflots with data in data frame
- self.measplot_obj.draw(postplot_data_frame, pdf_name = '')
+ self.measplot.draw(postplot_data_frame, pdf_name = '')
# cal PK2PK values of magnitude and phase
PK2PK = self.calc_mag_phase_pkpk_values(data_frame)
@@ -78,13 +83,12 @@ class PostPlot:
def edit_annotation_in_plot(self, annotate_string ='', anno_fontsize = 16):
- self.fig
-
# update text of annotation in first subplot
- self.measplot_obj.annotation.set_text(annotate_string)
+ self.measplot.annotation.set_text(annotate_string)
# edit annotation fontsize in first subplot
- self.measplot_obj.annotation.set_fontsize(anno_fontsize)
+ self.measplot.annotation.set_fontsize(anno_fontsize)
+
def calc_mag_phase_pkpk_values(self, data_frame):
@@ -98,7 +102,38 @@ class PostPlot:
return delta_vals_string
-
+
+ def add_curvefit_to_plot(self, y_lim = None , xvals = [], yvals = [], \
+ trace_color = 'None', trace_label = ''):
+
+ if y_lim != None:
+ # set axis for phaseplot to min, max values
+ self.measplot.ax1[0].set_ylim(y_lim)
+
+ # set color of trace for curvefit result to green (trace is visible)
+ self.measplot.path_collection_fit.set_color(trace_color)
+
+ # set label of trace curvefit results to name of fit function name
+ self.measplot.path_collection_fit.set_label(trace_label)
+
+ # refrest data in meas plot
+ self.measplot.path_collection_fit.set_offsets(np.c_[xvals, yvals])
+
+ # get legend handles and labels y-axes from subplot magnitude, phase
+ handles_phase, labels_phase = self.measplot.ax1[0].get_legend_handles_labels()
+ handles_mag, labels_mag = self.measplot.magnitude_axis.get_legend_handles_labels()
+ handles_equi0, labels_eqi0 = self.measplot.equi_axis0.get_legend_handles_labels()
+
+ handles = handles_phase + handles_mag + handles_equi0
+ labels = labels_phase + labels_mag + labels_eqi0
+
+ # update legend subplot phase, magnitude
+ self.measplot.ax1[0].legend(handles, labels, loc = self.legend_loc, \
+ bbox_to_anchor = self.legend_bbox_to_anchor)
+
+ # refresh plot window
+ self.measplot.fig.canvas.flush_events()
+
# 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):
@@ -140,26 +175,17 @@ if __name__ == '__main__':
time_unit = 'min'
# set storepath for the post plots, Michael
- storepath = Results_Path + '\\Plots'
+ storepath = Results_Path + '\\PostPlots'
# search all csv files in results folder
csv_file_list = list(Path(Results_Path).glob("**/*.csv"))
- # choose annotation option
- plot_correlation_coeff = False
- plot_regression_coeff = False
-
- # activate plot curvefitting
-
- plot_trace_curvefit = False
trace_selection = ""
# create postplot object, Michael
plot_obj = PostPlot(env_cond_sensors= trace_selection)
-
- fit_corr_obj = curvefit_and_correlation.reg_and_corr(plot_obj)
# empty data frame for concat the data frames from csv import to plot full transistion
concat_data_frame = pd.DataFrame()
@@ -178,34 +204,9 @@ if __name__ == '__main__':
# plot frame data
- plot_obj.plot_frame_data(data_frame, title, time_unit)
+ plot_obj.plot_frame_data(data_frame, title, time_unit)
- if plot_correlation_coeff == True:
-
- # determine correlation coefficient between func 1 and func 2
- corr_coeff =fit_corr_obj.calc_correlation(func_1 = data_frame.S21_PHASE , \
- func_2 = data_frame.TEMP_DUT)
-
- # plot correlation coefficient
- plot_obj.edit_annotation_in_plot(annotate_string = corr_coeff)
-
- elif plot_regression_coeff == True:
-
- # start curvefit and determine coefficients best fit function
- reg_coeff = fit_corr_obj.calc_regression_coeff_phase_S21(data_frame, time_unit = 'min')
-
- # plot regression coefficients
- plot_obj.edit_annotation_in_plot(annotate_string = reg_coeff)
-
- plot_obj.edit_annotation_in_plot(annotate_string = reg_coeff)
-
- if plot_trace_curvefit == True:
-
- fit_corr_obj.plot_phase_curve_fit(data_frame = data_frame, \
- postplot_obj = plot_obj.measplot_obj, \
- time_unit = time_unit, state = True)
-
# set filename of post plot, Michael
filename = str(csv_file.stem) + '.pdf'
@@ -217,10 +218,6 @@ if __name__ == '__main__':
# title of this plot will be always full transistion, Michel
plot_obj.plot_frame_data(concat_data_frame, 'Full Transistion', time_unit)
- fit_corr_obj.plot_phase_curve_fit(data_frame = concat_data_frame, \
- postplot_obj = plot_obj.measplot_obj, \
- time_unit = time_unit, state = False)
-
# filename of plot that contains all steps of sweep is set to FullTransistion, Michael
filename = 'Full_Transistion' + '.pdf'
diff --git a/Python_script/TestData_JBY240/AnalysisPlots/2023_07_29-15_38_40_results_25.0deg_55.0rh.pdf b/Python_script/TestData_JBY240/AnalysisPlots/2023_07_29-15_38_40_results_25.0deg_55.0rh.pdf
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diff --git a/Python_script/curvefit_and_correlation.py b/Python_script/curvefit_and_correlation.py
index 9952f9221418c6a2c6928b8dade1b965165f0ccc..7a4d3c7d17f4e338a7d5a7bdd5e4d42bf0b096ee 100644
--- a/Python_script/curvefit_and_correlation.py
+++ b/Python_script/curvefit_and_correlation.py
@@ -6,56 +6,65 @@ Created on Thu Jul 27 08:53:36 2023
"""
import numpy as np
from scipy.optimize import curve_fit
-
-
+from PostPlot import PostPlot
+import pandas as pd
+from pathlib import Path
class reg_and_corr:
- def __init__(self, post_plot_obj, legend_loc = 'upper left', \
+ def __init__(self, env_cond_sensors = '', legend_loc = 'upper left', \
legend_bbox_to_anchor = (1.09, 1)):
- self.post_plot_obj = post_plot_obj
+ self.postplot = PostPlot(env_cond_sensors = env_cond_sensors)
self.K_phases = None
self.phase_t0 = None
self.legend_loc = legend_loc
self.legend_bbox_to_anchor = legend_bbox_to_anchor
+
def calc_correlation(self, func_1 , func_2 , state = 'False'):
- # calulate correlation between two parameter from data frame
- corr_coeff= np.corrcoef(func_1, func_2)[0][1]
-
- annotate_string = "$R_{\phi(S_{21}),Temp_{DUT}}$ = %.3f" % corr_coeff
- return annotate_string
+ if state == True:
+
+ # calulate correlation between two parameter from data frame
+ corr_coeff= np.corrcoef(func_1, func_2)[0][1]
+
+ annotate_string_corr_coeff = "$R_{\phi(S_{21}),Temp_{DUT}}$ = %.3f" % corr_coeff
+
+ self.postplot.edit_annotation_in_plot(annotate_string_corr_coeff)
def calc_regression_coeff_phase_S21(self, data_frame, time_unit = 'min', state = False):
- phases = data_frame.S21_PHASE.tolist()
- self.phase_t0 = phases[0]
-
- self.K_phases = np.median(data_frame.S21_PHASE)
-
- # 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 = data_frame.TIMESTAMP - data_frame.TIMESTAMP[0]
-
- # set scaling of time axis depending on the time unit given in parameter list, Michael
- # default unit is minutes, Michael
- time_vals = self.scaling_time_axes(time_sec, time_unit)
-
- func, popt = self.choose_fit(time_vals, data_frame.S21_PHASE)
-
- annotate_string_fit = self.plot_fitted_func_param(func, *popt, time_unit = time_unit)
+ if state == True:
+ phases = data_frame.S21_PHASE.tolist()
+ self.phase_t0 = phases[0]
+
+ self.K_phases = np.median(data_frame.S21_PHASE)
+
+ # 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 = data_frame.TIMESTAMP - data_frame.TIMESTAMP[0]
+
+ # set scaling of time axis depending on the time unit given in parameter list, Michael
+ # default unit is minutes, Michael
+ time_vals = self.scaling_time_axes(time_sec, time_unit)
+
+ func, popt = self.choose_fit(time_vals, data_frame.S21_PHASE)
+
+ annotate_string_reg_coeff = self.plot_fitted_func_param(func, *popt, time_unit = time_unit)
+
+ self.postplot.edit_annotation_in_plot(annotate_string_reg_coeff)
- return annotate_string_fit
- def plot_phase_curve_fit(self, data_frame, postplot_obj, time_unit = 'min', state = False):
+
+
+ def plot_phase_curve_fit(self, data_frame, time_unit = 'min', state = False):
if state == True:
-
+
phases = data_frame.S21_PHASE.tolist()
self.phase_t0 = phases[0]
@@ -90,41 +99,18 @@ class reg_and_corr:
# determine max of measured phases and max of curvefit
maximum = max(max_phases, max_fit_phases)
- # set axis for phaseplot to min, max values
- postplot_obj.ax1[0].set_ylim(minimum, maximum)
-
- # set color of trace for curvefit result to green (trace is visible)
- postplot_obj.path_collection_fit.set_color('green')
-
- # set label of trace curvefit results to name of fit function name
- postplot_obj.path_collection_fit.set_label('fitted with\n' + func.__name__)
-
-
-
- postplot_obj.path_collection_fit.set_offsets(np.c_[time_vals, func(time_vals, *popt)])
+ self.postplot.add_curvefit_to_plot(xvals =time_vals, \
+ yvals = func(time_vals, *popt), \
+ y_lim =[minimum, maximum], \
+ trace_color = 'green', \
+ trace_label = 'fitted by\n' + func.__name__)
else:
- # set color of trace for curvefit result to None (trace is invisible)
- postplot_obj.path_collection_fit.set_color('None')
- # set label of trace curvefit results to '' (no label)
- postplot_obj.path_collection_fit.set_label('')
-
- # get legend handles and labels y-axes from subplot magnitude, phase
- handles_phase, labels_phase = postplot_obj.ax1[0].get_legend_handles_labels()
- handles_mag, labels_mag = postplot_obj.magnitude_axis.get_legend_handles_labels()
- handles_equi0, labels_eqi0 = postplot_obj.equi_axis0.get_legend_handles_labels()
-
- handles = handles_phase + handles_mag + handles_equi0
- labels = labels_phase + labels_mag + labels_eqi0
-
- # update legend subplot phase, magnitude
- postplot_obj.ax1[0].legend(handles, labels, loc = postplot_obj.legend_loc, \
- bbox_to_anchor = postplot_obj.legend_bbox_to_anchor)
-
- # refresh plot window
- postplot_obj.fig.canvas.draw()
- postplot_obj.fig.canvas.flush_events()
-
+ self.postplot.add_curvefit_to_plot(xvals =[], \
+ yvals = [], \
+ y_lim = None, \
+ trace_color = 'None', \
+ trace_label = '')
@@ -250,5 +236,95 @@ class reg_and_corr:
if distance == 0.:
distance = 1
return array.min()-0.05*distance, array.max()+0.05*distance
+
+
+# for manually redo post plot after measurement has finished and insert analysis function, Michael
+if __name__ == '__main__':
+
+
+ # set result path for post plot
+ Results_Path = r'C:\git\climate-lab-test-stand\Python_script\TestData_JBY240'
+
+
+ # set time unit for post post plot
+ # default is minutes if entry in parameterlist left empty
+ # possible entries: 'min' for minutes, 'hours' for hours and 'sec' for seconds
+ time_unit = 'min'
+
+ # set storepath for the post plots, Michael
+ storepath = Results_Path + '\\AnalysisPlots'
+
+ # search all csv files in results folder
+ csv_file_list = list(Path(Results_Path).glob("**/*.csv"))
+
+ # choose annotation option
+ plot_correlation_coeff = False
+ plot_regression_coeff = False
+
+ # activate plot curvefitting
+
+ plot_trace_curvefit = True
+
+ trace_selection = ""
+
+
+ # create postplot object, Michael
+ analysisplot_obj = reg_and_corr(env_cond_sensors= trace_selection)
+
+
+ # 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):
+
+ # import csv-data from csv-files in list, Michael
+ data_frame = analysisplot_obj.postplot.import_csv(str(csv_file))
+
+ # determine title of plot from csv-filename, Michael
+ title = csv_file.name
+
+ # concate datesframe for plotting full transistion data
+ concat_data_frame = pd.concat([concat_data_frame,data_frame],ignore_index=True, sort = False)
+
+
+ # plot frame data
+ analysisplot_obj.postplot.plot_frame_data(data_frame, title, time_unit)
+
+
+ # determine correlation coefficient between func 1 and func 2 and plot coefficient
+ analysisplot_obj.calc_correlation(func_1 = data_frame.S21_PHASE , \
+ func_2 = data_frame.TEMP_DUT, state = plot_correlation_coeff )
+
+
+
+
+ # start curvefit and determine coefficients best fit function and plot coefficients
+ analysisplot_obj.calc_regression_coeff_phase_S21(data_frame, time_unit = 'min', \
+ state = plot_regression_coeff)
+
+
+ analysisplot_obj.plot_phase_curve_fit(data_frame = data_frame, time_unit = time_unit, \
+ state = plot_trace_curvefit)
+
+ # set filename of post plot, Michael
+ filename = str(csv_file.stem) + '.pdf'
+
+ # store post plot under the path taht is set in storepath with the earlier defined filename
+ analysisplot_obj.postplot.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
+ analysisplot_obj.postplot.plot_frame_data(concat_data_frame, 'Full Transistion', time_unit)
+
+ analysisplot_obj.plot_phase_curve_fit(data_frame = concat_data_frame, time_unit = time_unit, \
+ state = False)
+
+ # filename of plot that contains all steps of sweep is set to FullTransistion, Michael
+ filename = 'Full_Transistion' + '.pdf'
+ # plot with the results of all steps is store under the predefined storpath with the
+ # earlier defined filename, Michael
+ analysisplot_obj.postplot.save_fig(storepath, filename)
\ No newline at end of file
diff --git a/Python_script/prototype.py b/Python_script/prototype.py
index 453b878e46e906fd19287d1f310ece6b3cd17cdd..731a53ca253190a1d7d82ea16863dadbb4358e30 100755
--- a/Python_script/prototype.py
+++ b/Python_script/prototype.py
@@ -497,7 +497,7 @@ def plot_output(output_basename, measurements_appendices, show_blocking_plot, co
list_of_frames = []
# storepath is set to working directory with subfolder Plots, Michael
- storepath = os.path.join(os.getcwd(),'Plots')
+ storepath = os.path.join(os.getcwd(),'PostPlots')
# create objet for PostPlot class in PostPlot module, Michael
post_plot = PostPlot.PostPlot(env_cond_sensors = config_data['env_cond_sensors'])
for index, m in enumerate(measurements_appendices):
diff --git a/Python_script/the.pdf b/Python_script/the.pdf
index ac526011e9e8b3ca5fbdf2d09f162a6f08e05130..c18b780e37f189477bd2ee1d08699945553a8e2c 100644
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