- Analysis Plot and Post Plot separated

- new function ins post plot for plotting analysis parameter
- PlotData from Test of modules added

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'
     

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

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):