def import_error(self,):
message = MessageBox()
message.about(self, 'Warning!', "Data were not loaded. \n Please, be sure that:\n "
"1. Data have 1 or 2 columns.\n"
"2. Data are longer than 100 points.\n"
"3. Delimiter is correctly specified.\n"
"4. Rows in data contain only numeric values\n")
def import_error(self,):
message = MessageBox()
message.about(self, 'Warning!', "Data were not loaded. \n Please, be sure that:\n "
"1. Data have 1 or 2 columns.\n"
"2. Data are longer than 100 points.\n"
"3. Delimiter is correctly specified.\n"
"4. Rows in data contain only numeric values\n")
def dot_pick_enable(self, ): # if checked, user can choose peaks
try: # if figure and canvas is initiated
if self.chbxDotPickEnable.isChecked():
self.cid_click = self.canvas.mpl_connect('button_press_event', self.on_click)
self.cid_motion = self.canvas.mpl_connect('motion_notify_event', self.on_motion)
else:
self.canvas.mpl_disconnect(self.cid_click)
self.canvas.mpl_disconnect(self.cid_motion)
except:
message = MessageBox()
message.about(self, 'Warning!', "File was not loaded! \n Please be sure that your file has \
\n 1) 1 or 2 columns; \n 2) check headers, footers and delimeter \n and try again.")
def dot_pick_enable(self, ): # if checked, user can choose peaks
try: # if figure and canvas is initiated
if self.chbxDotPickEnable.isChecked():
self.cid_click = self.canvas.mpl_connect('button_press_event', self.on_click)
self.cid_motion = self.canvas.mpl_connect('motion_notify_event', self.on_motion)
else:
self.canvas.mpl_disconnect(self.cid_click)
self.canvas.mpl_disconnect(self.cid_motion)
except:
message = MessageBox()
message.about(self, 'Warning!', "File was not loaded! \n Please be sure that your file has \
\n 1) 1 or 2 columns; \n 2) check headers, footers and delimeter \n and try again.")
def data_preprocessing(self, data_to_preprocess):
try:
# Detrend dataset
if self.chbxDetrendData.isChecked():
self.data_detrended = sig.detrend(data_to_preprocess)
else:
self.data_detrended = data_to_preprocess
# Application of Savitzkyi-Golay filter for data smoothing
sg_window_frame = self.BoxSGwindowFrame.value()
sg_polynom_degree = self.BoxSGpolynomDegree.value()
self.data_after_filter = sig.savgol_filter(self.data_detrended, sg_window_frame, sg_polynom_degree)
except:
message = MessageBox()
message.about(self, 'Warning!',
"Not possible to detrend and/or smooth data! \n Please check your dataset and try again.")
def data_preprocessing(self, data_to_preprocess):
try:
# Detrend dataset
if self.chbxDetrendData.isChecked():
self.data_detrended = sig.detrend(data_to_preprocess)
else:
self.data_detrended = data_to_preprocess
# Application of Savitzkyi-Golay filter for data smoothing
sg_window_frame = self.BoxSGwindowFrame.value()
sg_polynom_degree = self.BoxSGpolynomDegree.value()
self.data_after_filter = sig.savgol_filter(self.data_detrended, sg_window_frame, sg_polynom_degree)
except:
message = MessageBox()
message.about(self, 'Warning!',
"Not possible to detrend and/or smooth data! \n Please check your dataset and try again.")
def save_data(self, ):
try:
file_name = QtGui.QFileDialog.getSaveFileName(self, 'Save file')
writer = pd.ExcelWriter('{}.xlsx'.format(file_name))
self.multiple_data_sets.to_excel(writer, index=True, sheet_name='Results')
writer.sheets['Results'].set_zoom(80)
writer.sheets['Results'].set_column('A:A', 5)
writer.sheets['Results'].set_column('B:X', 23)
writer.save()
message = MessageBox()
message.about(self, 'Data saved', "Data were saved!")
self.multiple_data_sets = pd.DataFrame()
self.BtnSaveFullDataset.setStyleSheet("background-color: #FAF6F2")
self.BtnLoadFile.setStyleSheet("background-color: #7CF2BD")
except:
message = MessageBox()
message.about(self, 'Warning!', "Data were not exported to Excel! \n Please try again.")
def save_data(self, ):
try:
file_name = QFileDialog.getSaveFileName(self, 'Save file')[0]
writer = pd.ExcelWriter('{}.xlsx'.format(file_name))
self.multiple_data_sets.to_excel(writer, index=True, sheet_name='Results')
writer.sheets['Results'].set_zoom(80)
writer.sheets['Results'].set_column('A:A', 5)
writer.sheets['Results'].set_column('B:X', 23)
writer.save()
message = MessageBox()
message.about(self, 'Data saved', "Data were saved!")
self.multiple_data_sets = pd.DataFrame()
self.BtnSaveFullDataset.setStyleSheet("background-color: #FAF6F2")
self.BtnLoadFile.setStyleSheet("background-color: #7CF2BD")
except Exception as e:
message = MessageBox()
message.about(self, 'Warning!', "Data were not exported to Excel! \n Please try again.")
print(e)
def coordinates_analysis(self, ):
"""
Main function
"""
coord_x, coord_y = zip(*self.coordinates)
leftpb_x, leftpb_y = zip(*self.left_peak_border)
rightpb_x, rightpb_y= zip(*self.right_peak_border)
# absolute amplitude % and MAX
relative_amplitude = []
ampl_max = max(self.amplitudes)
relative_amplitude[:] = [(i / ampl_max) for i in self.amplitudes]
# create temporal Pandas DataFrame for sorting and calculation:
temp_dataset = list(
zip(coord_x, self.amplitudes, relative_amplitude, leftpb_x, leftpb_y, rightpb_x, rightpb_y, self.area))
df = pd.DataFrame(data=temp_dataset,
columns=['Peak Time',
'Amplitude',
'Relative Amplitude \n (F/Fmax)',
'Peak Start Time',
'Peak Start Ordinate',
'Peak Stop Time',
'Peak Stop Ordinate',
'Area'])
# Sort data in DataFrame according to the time of peak appearance
df_sorted = df.sort_values(['Peak Time'], ascending=True)
df_sorted.index = range(0, len(df_sorted)) # reset indexing
# calculate periods
periods = []
for i in range(1, len(df_sorted['Peak Time'])):
periods.append(df_sorted.at[i, 'Peak Time'] - df_sorted.at[i - 1, 'Peak Time'])
periods.insert(0, np.nan) # add placeholder because len(periods)=len(peaks)-1
# calculate frequencies based on calculated periods
frequencies = []
frequencies[:] = [(1 / i) for i in periods]
# Analise peak start - stop time (left and right peak borders)
peak_full_time = []
for i in range(0, len(df_sorted['Peak Time']), 1):
peak_full_time.append(df_sorted.at[i, 'Peak Stop Time'] - df_sorted.at[i, 'Peak Start Time'])
peak_up_time = []
for i in range(0, len(df_sorted['Peak Time']), 1):
peak_up_time.append(df_sorted.at[i, 'Peak Time'] - df_sorted.at[i, 'Peak Start Time'])
peak_down_time = []
for i in range(0, len(df_sorted['Peak Time']), 1):
peak_down_time.append(df_sorted.at[i, 'Peak Stop Time'] - df_sorted.at[i, 'Peak Time'])
# Compute area under the peak using the composite trapezoidal rule.
peak_area = []
for i in range(0, len(df_sorted['Peak Time']), 1):
peak_area.append(np.trapz(df_sorted.at[i, 'Area']))
# Analise the peak decay area
half_decay_time = []
half_decay_amplitude = []
for i in range(0, len(df_sorted['Peak Time']), 1):
half_decay_ampl = df_sorted.at[i, 'Amplitude'] / 2 # calculate the half of the amplitude
peak_index = self.x.index(df_sorted.at[i, 'Peak Time']) # find index of the peak time
stop_idx = self.x.index(df_sorted.at[i, 'Peak Stop Time']) # find index of the right peak border
data_decay_region = self.data_after_filter[peak_index:stop_idx] # determine the amplitude region where to search for halftime decay index
time_decay_region = self.x[peak_index:stop_idx]
half_decay_idx = (np.abs(data_decay_region - half_decay_ampl)).argmin() # find the closet value in data_decay_region that corresponds to the half amplitude
half_decay_amplitude.append(half_decay_ampl)
half_decay_time.append(time_decay_region[half_decay_idx] - df_sorted.at[i, 'Peak Time'])
# Compute amplitude normalised to the baseline
normalised_amplitude = []
sg_window_frame = self.BoxSGwindowFrame.value()
sg_polynom_degree = self.BoxSGpolynomDegree.value()
orig_data_filtered = sig.savgol_filter(self.y, sg_window_frame, sg_polynom_degree)
for i in range(0, len(df_sorted['Peak Time']), 1):
start_idx = self.x.index(df_sorted.at[i, 'Peak Start Time'])
F0 = orig_data_filtered[start_idx]
amplitude_normed_computation = df_sorted.at[i, 'Amplitude'] / F0
normalised_amplitude.append(amplitude_normed_computation)
# normalised amplitude %
relative_normalised_amplitude = []
maxATB = max(normalised_amplitude)
relative_normalised_amplitude[:] = [(i / maxATB) for i in normalised_amplitude]
# normalised amplitude MAX
normalised_amplitude_max = list(range(0, len(df_sorted['Peak Time']) - 1))
normalised_amplitude_max[:] = [np.nan for _ in normalised_amplitude_max]
normalised_amplitude_max.insert(0, maxATB)
# add file name as first column
file_name = list(range(0, len(df_sorted['Peak Time']) - 1))
file_name[:] = [np.nan for _ in file_name]
file_name.insert(0, self.graph_name)
# add maximum amplitude
absolute_amplitude_max = list(range(0, len(df_sorted['Peak Time']) - 1))
absolute_amplitude_max[:] = [np.nan for _ in absolute_amplitude_max]
absolute_amplitude_max.insert(0, max(df_sorted['Amplitude']))
# peak sorting
big_peaks_number = [p for p in self.amplitudes if (p > ampl_max * 0.66)]
medium_peaks_number = [p for p in self.amplitudes if (p > ampl_max * 0.33 and p <= ampl_max * 0.66)]
small_peaks_number = [p for p in self.amplitudes if (p > 0 and p <= ampl_max * 0.33)]
big_peaks_frequency = list(range(0, len(df_sorted['Peak Time']) - 1))
big_peaks_frequency[:] = [np.nan for _ in big_peaks_frequency]
big_peaks_frequency.insert(0, len(big_peaks_number) / (self.x[-1] - self.x[0]))
medium_peaks_frequency = list(range(0, len(df_sorted['Peak Time']) - 1))
medium_peaks_frequency[:] = [np.nan for _ in medium_peaks_frequency]
medium_peaks_frequency.insert(0, len(medium_peaks_number) / (self.x[-1] - self.x[0]))
small_peaks_frequency = list(range(0, len(df_sorted['Peak Time']) - 1))
small_peaks_frequency[:] = [np.nan for _ in small_peaks_frequency]
small_peaks_frequency.insert(0, len(small_peaks_number) / (self.x[-1] - self.x[0]))
final_dataset = list(zip(file_name,
df_sorted['Peak Time'],
df_sorted['Amplitude'],
df_sorted['Relative Amplitude \n (F/Fmax)'],
absolute_amplitude_max,
normalised_amplitude,
relative_normalised_amplitude,
normalised_amplitude_max,
periods,
frequencies,
half_decay_time,
half_decay_amplitude,
df_sorted['Peak Start Time'],
df_sorted['Peak Start Ordinate'],
df_sorted['Peak Stop Time'],
df_sorted['Peak Stop Ordinate'],
peak_up_time,
peak_down_time,
peak_full_time,
peak_area,
big_peaks_frequency,
medium_peaks_frequency,
small_peaks_frequency))
final_dataframe = pd.DataFrame(data=final_dataset,
columns=['File name',
'Peak time',
'Absolute amplitude',
'Absolute amplitude (%)',
'Absolute amplitude MAX',
'Normalised amplitude',
'Normalised amplitude (%)',
'Normalised amplitude MAX',
'Period',
'Frequency',
'Half-decay time',
'Half-decay amplitude',
'Start time',
'Start ordinate',
'Stop time',
'Stop ordinate',
'Ascending time',
'Decay time',
'Full peak time',
'AUC',
'Big peaks, Hz',
'Mid peaks, Hz',
'Small peaks, Hz'])
# specify data for export acording to the settings tab in GUI
# and append current analysed dataset to existing ones
try:
columns_to_delete_for_export = []
if not self.chbxFileName.isChecked(): columns_to_delete_for_export.append('File name')
if not self.chbxPeakTime.isChecked(): columns_to_delete_for_export.append('Peak time')
if not self.chbxAmplAbs.isChecked(): columns_to_delete_for_export.append('Absolute amplitude')
if not self.chbxAmplAbsRel.isChecked(): columns_to_delete_for_export.append('Absolute amplitude (%)')
if not self.chbxAmplAbsMax.isChecked(): columns_to_delete_for_export.append('Absolute amplitude MAX')
if not self.chbxAmplNorm.isChecked(): columns_to_delete_for_export.append('Normalised amplitude')
if not self.chbxAmplNormRel.isChecked(): columns_to_delete_for_export.append('Normalised amplitude (%)')
if not self.chbxAmplNormMax.isChecked(): columns_to_delete_for_export.append('Normalised amplitude MAX')
if not self.chbxPeriod.isChecked(): columns_to_delete_for_export.append('Period')
if not self.chbxFreq.isChecked(): columns_to_delete_for_export.append('Frequency')
if not self.chbxHalfDecayTime.isChecked(): columns_to_delete_for_export.append('Half-decay time')
if not self.chbxHalfDecayAmpl.isChecked(): columns_to_delete_for_export.append('Half-decay amplitude')
if not self.chbxLeftBorderTime.isChecked(): columns_to_delete_for_export.append('Start time')
if not self.chbxLeftBorder.isChecked(): columns_to_delete_for_export.append('Start ordinate')
if not self.chbxRightBorderTime.isChecked(): columns_to_delete_for_export.append('Stop time')
if not self.chbxRightBorder.isChecked(): columns_to_delete_for_export.append('Stop ordinate')
if not self.chbxTimeToPeak.isChecked(): columns_to_delete_for_export.append('Ascending time')
if not self.chbxDecayTime.isChecked(): columns_to_delete_for_export.append('Decay time')
if not self.chbxFullPeakTime.isChecked(): columns_to_delete_for_export.append('Full peak time')
if not self.chbxAUC.isChecked(): columns_to_delete_for_export.append('AUC')
if not self.chbxSmallPeaks.isChecked(): columns_to_delete_for_export.append('Big peaks, Hz')
if not self.chbxMidPeaks.isChecked(): columns_to_delete_for_export.append('Mid peaks, Hz')
if not self.chbxBigPeaks.isChecked(): columns_to_delete_for_export.append('Small peaks, Hz')
final_dataframe.drop(columns_to_delete_for_export, axis=1, inplace=True)
self.multiple_data_sets = self.multiple_data_sets.append(final_dataframe)
if self.chbxSaveFig.isChecked():
os.makedirs('_Figures', exist_ok=True)
dpi = self.BoxDPI.value()
plt.savefig(os.path.join('_Figures', 'Fig_{figName}.png'.format(figName=self.graph_name)), dpi=dpi)
del df
del df_sorted
del final_dataframe
dialog = MessageBox.question(self, '', "Current dataset was analysed \n and added to previous ones (if exist). \n Would you like to load next file? ",
QtGui.QMessageBox.Yes, QtGui.QMessageBox.No)
if dialog == QtGui.QMessageBox.Yes:
self.load_file()
else:
self.rmmpl()
self.BtnSaveFullDataset.setStyleSheet("background-color: #7CF2BD")
self.BtnLoadFile.setStyleSheet("background-color: #7CF2BD")
except:
message = MessageBox()
message.about(self, 'Warning!', "Data were not added to existing dataset. \n Plese be sure that you did not change the output settings.")
def coordinates_analysis(self, ):
"""
Main function
"""
coord_x, coord_y = zip(*self.coordinates)
leftpb_x, leftpb_y = zip(*self.left_peak_border)
rightpb_x, rightpb_y= zip(*self.right_peak_border)
# absolute amplitude % and MAX
relative_amplitude = []
ampl_max = max(self.amplitudes)
relative_amplitude[:] = [(i / ampl_max) for i in self.amplitudes]
# create temporal Pandas DataFrame for sorting and calculation:
temp_dataset = list(
zip(coord_x, self.amplitudes, relative_amplitude, leftpb_x, leftpb_y, rightpb_x, rightpb_y, self.area))
df = pd.DataFrame(data=temp_dataset,
columns=['Peak Time',
'Amplitude',
'Relative Amplitude \n (F/Fmax)',
'Peak Start Time',
'Peak Start Ordinate',
'Peak Stop Time',
'Peak Stop Ordinate',
'Area'])
# Sort data in DataFrame according to the time of peak appearance
df_sorted = df.sort_values(['Peak Time'], ascending=True)
df_sorted.index = range(0, len(df_sorted)) # reset indexing
# calculate periods
periods = []
for i in range(1, len(df_sorted['Peak Time'])):
periods.append(df_sorted.at[i, 'Peak Time'] - df_sorted.at[i - 1, 'Peak Time'])
periods.insert(0, np.nan) # add placeholder because len(periods)=len(peaks)-1
# calculate frequencies based on calculated periods
frequencies = []
frequencies[:] = [(1 / i) for i in periods]
# Analise peak start - stop time (left and right peak borders)
peak_full_time = []
for i in range(0, len(df_sorted['Peak Time']), 1):
peak_full_time.append(df_sorted.at[i, 'Peak Stop Time'] - df_sorted.at[i, 'Peak Start Time'])
peak_up_time = []
for i in range(0, len(df_sorted['Peak Time']), 1):
peak_up_time.append(df_sorted.at[i, 'Peak Time'] - df_sorted.at[i, 'Peak Start Time'])
peak_down_time = []
for i in range(0, len(df_sorted['Peak Time']), 1):
peak_down_time.append(df_sorted.at[i, 'Peak Stop Time'] - df_sorted.at[i, 'Peak Time'])
# Compute area under the peak using the composite trapezoidal rule.
peak_area = []
for i in range(0, len(df_sorted['Peak Time']), 1):
peak_area.append(np.trapz(df_sorted.at[i, 'Area']))
# Analise the peak decay area
half_decay_time = []
half_decay_amplitude = []
for i in range(0, len(df_sorted['Peak Time']), 1):
half_decay_ampl = df_sorted.at[i, 'Amplitude'] / 2 # calculate the half of the amplitude
peak_index = self.x.index(df_sorted.at[i, 'Peak Time']) # find index of the peak time
stop_idx = self.x.index(df_sorted.at[i, 'Peak Stop Time']) # find index of the right peak border
data_decay_region = self.data_after_filter[peak_index:stop_idx] # determine the amplitude region where to search for halftime decay index
time_decay_region = self.x[peak_index:stop_idx]
half_decay_idx = (np.abs(data_decay_region - half_decay_ampl)).argmin() # find the closet value in data_decay_region that corresponds to the half amplitude
half_decay_amplitude.append(half_decay_ampl)
half_decay_time.append(time_decay_region[half_decay_idx] - df_sorted.at[i, 'Peak Time'])
# Compute amplitude normalised to the baseline
normalised_amplitude = []
sg_window_frame = self.BoxSGwindowFrame.value()
sg_polynom_degree = self.BoxSGpolynomDegree.value()
orig_data_filtered = sig.savgol_filter(self.y, sg_window_frame, sg_polynom_degree)
for i in range(0, len(df_sorted['Peak Time']), 1):
start_idx = self.x.index(df_sorted.at[i, 'Peak Start Time'])
F0 = orig_data_filtered[start_idx]
amplitude_normed_computation = df_sorted.at[i, 'Amplitude'] / F0
normalised_amplitude.append(amplitude_normed_computation)
# normalised amplitude %
relative_normalised_amplitude = []
maxATB = max(normalised_amplitude)
relative_normalised_amplitude[:] = [(i / maxATB) for i in normalised_amplitude]
# normalised amplitude MAX
normalised_amplitude_max = list(range(0, len(df_sorted['Peak Time']) - 1))
normalised_amplitude_max[:] = [np.nan for _ in normalised_amplitude_max]
normalised_amplitude_max.insert(0, maxATB)
# add file name as first column
file_name = list(range(0, len(df_sorted['Peak Time']) - 1))
file_name[:] = [np.nan for _ in file_name]
file_name.insert(0, self.graph_name)
# add maximum amplitude
absolute_amplitude_max = list(range(0, len(df_sorted['Peak Time']) - 1))
absolute_amplitude_max[:] = [np.nan for _ in absolute_amplitude_max]
absolute_amplitude_max.insert(0, max(df_sorted['Amplitude']))
# peak sorting
big_peaks_number = [p for p in self.amplitudes if (p > ampl_max * 0.66)]
medium_peaks_number = [p for p in self.amplitudes if (p > ampl_max * 0.33 and p <= ampl_max * 0.66)]
small_peaks_number = [p for p in self.amplitudes if (p > 0 and p <= ampl_max * 0.33)]
big_peaks_frequency = list(range(0, len(df_sorted['Peak Time']) - 1))
big_peaks_frequency[:] = [np.nan for _ in big_peaks_frequency]
big_peaks_frequency.insert(0, len(big_peaks_number) / (self.x[-1] - self.x[0]))
medium_peaks_frequency = list(range(0, len(df_sorted['Peak Time']) - 1))
medium_peaks_frequency[:] = [np.nan for _ in medium_peaks_frequency]
medium_peaks_frequency.insert(0, len(medium_peaks_number) / (self.x[-1] - self.x[0]))
small_peaks_frequency = list(range(0, len(df_sorted['Peak Time']) - 1))
small_peaks_frequency[:] = [np.nan for _ in small_peaks_frequency]
small_peaks_frequency.insert(0, len(small_peaks_number) / (self.x[-1] - self.x[0]))
final_dataset = list(zip(file_name,
df_sorted['Peak Time'],
df_sorted['Amplitude'],
df_sorted['Relative Amplitude \n (F/Fmax)'],
absolute_amplitude_max,
normalised_amplitude,
relative_normalised_amplitude,
normalised_amplitude_max,
periods,
frequencies,
half_decay_time,
half_decay_amplitude,
df_sorted['Peak Start Time'],
df_sorted['Peak Start Ordinate'],
df_sorted['Peak Stop Time'],
df_sorted['Peak Stop Ordinate'],
peak_up_time,
peak_down_time,
peak_full_time,
peak_area,
big_peaks_frequency,
medium_peaks_frequency,
small_peaks_frequency))
final_dataframe = pd.DataFrame(data=final_dataset,
columns=['File name',
'Peak time',
'Absolute amplitude',
'Absolute amplitude (%)',
'Absolute amplitude MAX',
'Normalised amplitude',
'Normalised amplitude (%)',
'Normalised amplitude MAX',
'Period',
'Frequency',
'Half-decay time',
'Half-decay amplitude',
'Start time',
'Start ordinate',
'Stop time',
'Stop ordinate',
'Ascending time',
'Decay time',
'Full peak time',
'AUC',
'Big peaks, Hz',
'Mid peaks, Hz',
'Small peaks, Hz'])
# specify data for export acording to the settings tab in GUI
# and append current analysed dataset to existing ones
try:
columns_to_delete_for_export = []
if not self.chbxFileName.isChecked(): columns_to_delete_for_export.append('File name')
if not self.chbxPeakTime.isChecked(): columns_to_delete_for_export.append('Peak time')
if not self.chbxAmplAbs.isChecked(): columns_to_delete_for_export.append('Absolute amplitude')
if not self.chbxAmplAbsRel.isChecked(): columns_to_delete_for_export.append('Absolute amplitude (%)')
if not self.chbxAmplAbsMax.isChecked(): columns_to_delete_for_export.append('Absolute amplitude MAX')
if not self.chbxAmplNorm.isChecked(): columns_to_delete_for_export.append('Normalised amplitude')
if not self.chbxAmplNormRel.isChecked(): columns_to_delete_for_export.append('Normalised amplitude (%)')
if not self.chbxAmplNormMax.isChecked(): columns_to_delete_for_export.append('Normalised amplitude MAX')
if not self.chbxPeriod.isChecked(): columns_to_delete_for_export.append('Period')
if not self.chbxFreq.isChecked(): columns_to_delete_for_export.append('Frequency')
if not self.chbxHalfDecayTime.isChecked(): columns_to_delete_for_export.append('Half-decay time')
if not self.chbxHalfDecayAmpl.isChecked(): columns_to_delete_for_export.append('Half-decay amplitude')
if not self.chbxLeftBorderTime.isChecked(): columns_to_delete_for_export.append('Start time')
if not self.chbxLeftBorder.isChecked(): columns_to_delete_for_export.append('Start ordinate')
if not self.chbxRightBorderTime.isChecked(): columns_to_delete_for_export.append('Stop time')
if not self.chbxRightBorder.isChecked(): columns_to_delete_for_export.append('Stop ordinate')
if not self.chbxTimeToPeak.isChecked(): columns_to_delete_for_export.append('Ascending time')
if not self.chbxDecayTime.isChecked(): columns_to_delete_for_export.append('Decay time')
if not self.chbxFullPeakTime.isChecked(): columns_to_delete_for_export.append('Full peak time')
if not self.chbxAUC.isChecked(): columns_to_delete_for_export.append('AUC')
if not self.chbxSmallPeaks.isChecked(): columns_to_delete_for_export.append('Big peaks, Hz')
if not self.chbxMidPeaks.isChecked(): columns_to_delete_for_export.append('Mid peaks, Hz')
if not self.chbxBigPeaks.isChecked(): columns_to_delete_for_export.append('Small peaks, Hz')
final_dataframe.drop(columns_to_delete_for_export, axis=1, inplace=True)
self.multiple_data_sets = self.multiple_data_sets.append(final_dataframe)
if self.chbxSaveFig.isChecked():
os.makedirs('_Figures', exist_ok=True)
dpi = self.BoxDPI.value()
plt.savefig(os.path.join('_Figures', 'Fig_{figName}.png'.format(figName=self.graph_name)), dpi=dpi)
del df
del df_sorted
del final_dataframe
dialog = MessageBox.question(self, '', "Current dataset was analysed \n and added to previous ones (if exist). \n Would you like to load next file? ",
QMessageBox.Yes, QMessageBox.No)
if dialog == QMessageBox.Yes:
self.load_file()
else:
self.rmmpl()
self.BtnSaveFullDataset.setStyleSheet("background-color: #7CF2BD")
self.BtnLoadFile.setStyleSheet("background-color: #7CF2BD")
except:
message = MessageBox()
message.about(self, 'Warning!', "Data were not added to existing dataset. \n Plese be sure that you did not change the output settings.")
