def select_files(self):
self._ui.teFilesList.clear()
file_dialog = QFileDialog()
file_dialog.setFileMode(QFileDialog.ExistingFiles)
names = file_dialog.getOpenFileNames()[0]
folder_path = None
min_dt = None
max_dt = None
max_frequency = None
for path in names:
if folder_path is None:
folder_path = os.path.dirname(path)
self._ui.leInputFolder.setText(folder_path)
file_name = os.path.basename(path)
t = file_name.split('.')
if len(t) != 2:
continue
name, extension = t
if extension not in ('00', 'xx'):
continue
bin_data = BinaryFile()
bin_data.path = path
bin_data.record_type = 'XYZ'
is_correct, _ = bin_data.check_correct()
if not is_correct:
continue
if min_dt is None:
min_dt = bin_data.datetime_start
max_dt = bin_data.datetime_stop
max_frequency = bin_data.signal_frequency
else:
min_dt = max(min_dt, bin_data.datetime_start)
max_dt = min(max_dt, bin_data.datetime_stop)
max_frequency = max(max_frequency, bin_data.signal_frequency)
self._ui.teFilesList.append(file_name)
if min_dt is None:
return None
if min_dt >= max_dt:
self._ui.teFilesList.clear()
msg = QMessageBox()
msg.setIcon(QMessageBox.Information)
msg.setText("Отсутствует перекрытие по времени")
msg.exec_()
else:
self._ui.dtMinDt.setMinimumDateTime(min_dt)
self._ui.dtMinDt.setMaximumDateTime(max_dt)
self._ui.dtMaxDt.setMinimumDateTime(min_dt)
self._ui.dtMaxDt.setMaximumDateTime(max_dt)
self._ui.dtMinDt.setDateTime(min_dt)
self._ui.dtMaxDt.setDateTime(min_dt + datetime.timedelta(hours=1))
self._ui.sbResampleFrequency.setMinimum(0)
self._ui.sbResampleFrequency.setMaximum(max_frequency)
self._ui.sbResampleFrequency.setValue(max_frequency)
from SeisCore.BinaryFile.BinaryFile import BinaryFile
bin_data = BinaryFile()
bin_data.path=r'/media/michael/Data/Projects/GRP/DemkinskoeDeposit' \
r'/Demkinskoye_data/4772-4773/Mail/DM04_063_122_2019-07-30_07-14-11/DM04_063_122_2019-07-30_07-14-11.xx'
# bin_data.signal_frequency=1000
bin_data.record_type = 'ZXY'
# bin_data.read_date_time_start=datetime.datetime(year=2019, month=7, day=28,
# hour=13, minute=0, second=0)
# bin_data.read_date_time_stop=bin_data.read_date_time_start+datetime\
# .timedelta(hours=1)
# signal=bin_data.signals
print(bin_data.datetime_start, bin_data.datetime_stop)
print(bin_data.signals.shape)
def calculation(self):
folder_path = self._ui.leInputFolder.text()
export_folder_path = self._ui.leOutputFolder.text()
file_list = self._ui.teFilesList.toPlainText()
dt_start = self._ui.dtMinDt.dateTime()
dt_stop = self._ui.dtMaxDt.dateTime()
if folder_path is None or file_list is None or export_folder_path is None:
return None
files = file_list.split('\n')
extract_data = None
for index, file in enumerate(files):
full_path = os.path.join(folder_path, file)
bin_data = BinaryFile()
bin_data.path = full_path
bin_data.record_type = 'XYZ'
bin_data.use_avg_values = False
bin_data.read_date_time_start = dt_start.toPyDateTime()
bin_data.read_date_time_stop = dt_stop.toPyDateTime()
bin_data.resample_frequency = self._ui.sbResampleFrequency.value()
if bin_data.signals is None:
continue
if extract_data is None:
discrete_count = bin_data.signals.shape[0]
extract_data = np.empty(shape=(discrete_count, 3 * len(files)),
dtype=np.int)
extract_data[:, 3 * index:3 * (index + 1)] = bin_data.signals
if extract_data is None:
return None
sum_trace = np.empty(shape=(extract_data.shape[0], 3),
dtype=np.int)
channel_plots = [self.channel_a_plot, self.channel_b_plot, self.channel_c_plot]
spectrum_plots = [self.channel_a_spectrum, self.channel_b_spectrum,
self.channel_c_spectrum]
window_size = self._ui.sbWindowSize.value()
overlap_size = self._ui.sbOverlapSize.value()
median_filter = self._ui.sbMedianFilter.value()
marmett_filter = self._ui.sbMarmettFilter.value()
resample_frequency = self._ui.sbResampleFrequency.value()
if marmett_filter == 0:
marmett_filter = None
if median_filter == 0:
median_filter = None
spectrums = None
for i in range(3):
channel_signals = extract_data[:, i:3 * len(files):3]
sum_trace[:, i] = np.sum(channel_signals, axis=1)
channel_plot = channel_plots[i]
spectrum_plot = spectrum_plots[i]
channel_plot.clear()
channel_plot.plot(sum_trace[:, i], pen=(196, 255, 18))
spectrum_plot.clear()
spectrum_data = average_spectrum(signal=sum_trace[:, i],
frequency=resample_frequency,
window=window_size,
overlap=overlap_size,
med_filter=median_filter,
marmett_filter=marmett_filter)
if spectrums is None:
spectrums = np.empty(shape=(spectrum_data.shape[0], 4),
dtype=np.float)
spectrums[:, :2] = spectrum_data
else:
spectrums[:, 1 + i] = spectrum_data[:, 1]
spectrum_plot.plot(spectrum_data, pen=(255, 0, 0))
tmp_file_a = os.path.join(export_folder_path, 'sum_trace.npy')
tmp_file_b = os.path.join(export_folder_path, 'spectrums.npy')
np.save(tmp_file_a, sum_trace)
np.save(tmp_file_b, spectrums)
output_file_sum_trace = os.path.join(export_folder_path, 'SumTraces.dat')
output_file_spectrums = os.path.join(export_folder_path, 'Spectrums.dat')
np.savetxt(output_file_sum_trace, sum_trace, fmt='%i', delimiter='\t',
header='Channel_1\tChannel_2\tChannel_3', comments='')
np.savetxt(output_file_spectrums, spectrums, fmt='%f', delimiter='\t',
header='Frequency\tChannel_1\tChannel_2\tChannel_3',
comments='')