def graph_multi_io_test(self):
self.dialog = MultiIODialog()
filename = self.filename = self.ui.lineEdit_file_name.text()
# Validate filename
if not self.valid_filename(filename):
return
self.dialog.populate_checkboxes(filename, self.ui.doubleSpinBox_threshold.value())
self.dialog.setWindowIcon(QtGui.QIcon('images/horsey.ico'))
self.dialog.setWindowTitle('Sparkle Analysis - I/O Test')
self.dialog.show()
def graph_abr(self):
self.dialog = ABRDialog()
filename = self.filename = self.ui.lineEdit_file_name.text()
# Validate filename
if not self.valid_filename(filename):
return
self.dialog.populate_boxes(filename)
self.dialog.setWindowIcon(QtGui.QIcon('images/horsey.ico'))
self.dialog.setWindowTitle('Sparkle Analysis - ABRs')
self.dialog.show()
def graph_tuning_curve(self):
self.dialog = TuningCurveDialog()
filename = self.filename = self.ui.lineEdit_file_name.text()
# Validate filename
if not self.valid_filename(filename):
return
self.dialog.populate_boxes(filename)
self.dialog.set_threshold(self.ui.doubleSpinBox_threshold.value())
self.dialog.set_test_num(self.ui.comboBox_test_num.currentIndex())
self.dialog.set_channel_num(self.ui.comboBox_channel.currentIndex())
self.dialog.set_trace_num(self.ui.comboBox_trace.currentIndex())
self.dialog.setWindowIcon(QtGui.QIcon('images/horsey.ico'))
self.dialog.setWindowTitle('Sparkle Analysis - Tuning Curve Generator')
self.dialog.show()
class MyForm(QtGui.QMainWindow):
def __init__(self, parent=None):
QtGui.QWidget.__init__(self, parent)
self.ui = Ui_MainWindow()
self.ui.setupUi(self)
self.dialog = QtGui.QMainWindow
self.filename = ''
self.threshold = 0
self.message_num = 0
self.ui.textEdit.setReadOnly(True)
# TODO Complete Spike Rates
# self.ui.pushButton_spike_rates.setEnabled(False)
QtCore.QObject.connect(self.ui.pushButton_raster, QtCore.SIGNAL("clicked()"), self.graph_raster)
QtCore.QObject.connect(self.ui.pushButton_historgram, QtCore.SIGNAL("clicked()"), self.graph_historgram)
QtCore.QObject.connect(self.ui.pushButton_tuning_curve, QtCore.SIGNAL("clicked()"), self.graph_tuning_curve)
# QtCore.QObject.connect(self.ui.pushButton_io_test, QtCore.SIGNAL("clicked()"), self.graph_io_test)
QtCore.QObject.connect(self.ui.pushButton_io_test, QtCore.SIGNAL("clicked()"), self.graph_multi_io_test)
QtCore.QObject.connect(self.ui.pushButton_spike_rates, QtCore.SIGNAL("clicked()"), self.graph_spike_rates)
QtCore.QObject.connect(self.ui.pushButton_abr, QtCore.SIGNAL("clicked()"), self.graph_abr)
QtCore.QObject.connect(self.ui.pushButton_browse, QtCore.SIGNAL("clicked()"), self.browse)
QtCore.QObject.connect(self.ui.pushButton_auto_threshold, QtCore.SIGNAL("clicked()"), self.auto_threshold)
QtCore.QObject.connect(self.ui.radioButton_normal, QtCore.SIGNAL("toggled(bool)"), self.generate_view)
QtCore.QObject.connect(self.ui.doubleSpinBox_threshold, QtCore.SIGNAL("valueChanged(const QString&)"), self.update_thresh)
QtCore.QObject.connect(self.ui.comboBox_test_num, QtCore.SIGNAL("currentIndexChanged(const QString&)"), self.load_traces)
QtCore.QObject.connect(self.ui.comboBox_trace, QtCore.SIGNAL("currentIndexChanged(const QString&)"), self.load_reps)
QtCore.QObject.connect(self.ui.comboBox_rep, QtCore.SIGNAL("currentIndexChanged(const QString&)"), self.load_channels)
QtCore.QObject.connect(self.ui.comboBox_channel, QtCore.SIGNAL("currentIndexChanged(const QString&)"), self.generate_view)
QtCore.QObject.connect(self.ui.checkBox_custom_window, QtCore.SIGNAL("toggled(bool)"), self.update_window)
QtCore.QObject.connect(self.ui.doubleSpinBox_xmax, QtCore.SIGNAL("valueChanged(const QString&)"), self.update_window)
QtCore.QObject.connect(self.ui.doubleSpinBox_xmin, QtCore.SIGNAL("valueChanged(const QString&)"), self.update_window)
QtCore.QObject.connect(self.ui.doubleSpinBox_ymax, QtCore.SIGNAL("valueChanged(const QString&)"), self.update_window)
QtCore.QObject.connect(self.ui.doubleSpinBox_ymin, QtCore.SIGNAL("valueChanged(const QString&)"), self.update_window)
self.ui.view.sigRangeChanged.connect(self.update_view_range)
self.ui.view.threshLine.sigPositionChangeFinished.connect(self.update_thresh2)
def browse(self):
self.ui.comboBox_test_num.clear()
QtGui.QFileDialog(self)
self.filename = QtGui.QFileDialog.getOpenFileName()
self.ui.lineEdit_file_name.setText(self.filename)
# If the filename is not blank, attempt to extract test numbers and place them into the combobox
if self.filename != '':
if '.hdf5' in self.filename:
try:
h_file = h5py.File(unicode(self.filename), 'r')
except IOError:
self.add_message('Error: I/O Error')
self.ui.label_test_num.setEnabled(False)
self.ui.label_comments.setEnabled(False)
self.ui.lineEdit_comments.setEnabled(False)
self.ui.lineEdit_comments.setText('')
self.ui.comboBox_test_num.setEnabled(False)
return
tests = {}
for key in h_file.keys():
if 'segment' in key:
for test in h_file[key].keys():
tests[test] = int(test.replace('test_', ''))
sorted_tests = sorted(tests.items(), key=operator.itemgetter(1))
for test in sorted_tests:
self.ui.comboBox_test_num.addItem(test[0])
self.ui.label_test_num.setEnabled(True)
self.ui.label_comments.setEnabled(True)
self.ui.lineEdit_comments.setEnabled(True)
self.ui.comboBox_test_num.setEnabled(True)
h_file.close()
else:
self.add_message('Error: Must select a .hdf5 file.')
self.ui.label_test_num.setEnabled(False)
self.ui.label_comments.setEnabled(False)
self.ui.lineEdit_comments.setEnabled(False)
self.ui.lineEdit_comments.setText('')
self.ui.comboBox_test_num.setEnabled(False)
return
else:
self.add_message('Error: Must select a file to open.')
self.ui.label_test_num.setEnabled(False)
self.ui.label_comments.setEnabled(False)
self.ui.lineEdit_comments.setEnabled(False)
self.ui.lineEdit_comments.setText('')
self.ui.comboBox_test_num.setEnabled(False)
return
def auto_threshold(self):
thresh_fraction = 0.7
filename = self.filename = self.ui.lineEdit_file_name.text()
# Validate filename
if self.valid_filename(filename):
h_file = h5py.File(unicode(self.filename), 'r')
target_test = self.ui.comboBox_test_num.currentText()
else:
return
# Find target segment
for segment in h_file.keys():
for test in h_file[segment].keys():
if target_test == test:
target_seg = segment
target_test = test
trace_data = h_file[target_seg][target_test].value
if len(trace_data.shape) == 4:
trace_data = trace_data.squeeze()
# Still shape of 4
if len(trace_data.shape) == 4:
target_chan = int(self.ui.comboBox_channel.currentText().replace('channel_', '')) - 1
trace_data = trace_data[:, :, target_chan, :]
trace_data = trace_data.squeeze()
# Compute threshold from average maximum of traces
max_trace = []
for n in range(len(trace_data[1, :, 0])):
max_trace.append(np.max(np.abs(trace_data[1, n, :])))
average_max = np.array(max_trace).mean()
thresh = thresh_fraction * average_max
self.ui.doubleSpinBox_threshold.setValue(thresh)
self.update_thresh()
h_file.close()
def generate_view(self):
filename = self.filename = self.ui.lineEdit_file_name.text()
# Validate filename
if self.valid_filename(filename):
h_file = h5py.File(unicode(self.filename), 'r')
target_test = self.ui.comboBox_test_num.currentText()
else:
return
# clear view
self.clear_view()
if self.ui.radioButton_normal.isChecked():
self.ui.view.invertPolarity(False)
if self.ui.radioButton_inverse.isChecked():
self.ui.view.invertPolarity(True)
for key in h_file.keys():
if 'segment' in key:
for test in h_file[key].keys():
if target_test == test:
target_seg = key
target_test = test
# Makes the assumption that all of the traces are of the same type
stim_info = eval(h_file[target_seg][target_test].attrs['stim'])
self.ui.label_stim_type.setText(stim_info[1]['components'][0]['stim_type'])
fs = h_file[target_seg].attrs['samplerate_ad']
target_trace = []
target_rep = []
target_chan = []
# Get the values from the combo boxes
self.ui.comboBox_test_num.currentText()
if self.ui.comboBox_trace.currentText() != '':
target_trace = int(self.ui.comboBox_trace.currentText().replace('trace_', '')) - 1
if self.ui.comboBox_rep.currentText() != 'All' and self.ui.comboBox_rep.currentText() != '':
target_rep = int(self.ui.comboBox_rep.currentText().replace('rep_', '')) - 1
if self.ui.comboBox_channel.currentText() != '':
target_chan = int(self.ui.comboBox_channel.currentText().replace('channel_', '')) - 1
test_data = h_file[target_seg][target_test].value
# Get the presentation data depending on if there is a channel field or not
if len(test_data.shape) == 4:
presentation = test_data[target_trace, target_rep, target_chan, :]
elif len(test_data.shape) == 3:
presentation = test_data[target_trace, target_rep, :]
len_presentation = len(presentation)
# Get the length of the window and length of presentation depending on if all is selected or not
if len_presentation != 0:
window = len(presentation) / float(fs)
else:
if len(test_data.shape) == 4:
window = len(test_data[0, 0, 0, :]) / float(fs)
len_presentation = len(test_data[0, 0, 0, :])
elif len(test_data.shape) == 3:
window = len(test_data[0, 0, :]) / float(fs)
len_presentation = len(test_data[0, 0, :])
xlist = np.linspace(0, float(window), len_presentation)
ylist = presentation
# Set window size
if not self.ui.checkBox_custom_window.checkState():
if len(presentation) > 0:
ymin = min(presentation)
ymax = max(presentation)
else:
ymin = 0
ymax = 0
if len(test_data.shape) == 3:
rep_len = test_data.shape[1]
for i in range(rep_len):
if min(test_data[target_trace, i, :]) < ymin:
ymin = min(test_data[target_trace, i, :])
if max(test_data[target_trace, i, :]) > ymax:
ymax = max(test_data[target_trace, i, :])
else:
rep_len = test_data.shape[1]
for i in range(rep_len):
if min(test_data[target_trace, i, target_chan, :]) < ymin:
ymin = min(test_data[target_trace, i, target_chan, :])
if max(test_data[target_trace, i, target_chan, :]) > ymax:
ymax = max(test_data[target_trace, i, target_chan, :])
self.ui.view.setXRange(0, window, 0)
self.ui.view.setYRange(ymin, ymax, 0.1)
if self.ui.comboBox_rep.currentText() == 'All':
self.ui.view.tracePlot.clear()
# Fix xlist to be the length of presentation
if len(test_data.shape) == 3:
self.ui.view.addTraces(xlist, test_data[target_trace, :, :])
else:
self.ui.view.addTraces(xlist, test_data[target_trace, :, target_chan, :])
self.ui.radioButton_normal.setChecked(True)
self.ui.radioButton_normal.setEnabled(False)
self.ui.radioButton_inverse.setEnabled(False)
else:
self.ui.view.updateData(axeskey='response', x=xlist, y=ylist)
self.ui.radioButton_normal.setEnabled(True)
self.ui.radioButton_inverse.setEnabled(True)
h_file.close()
def valid_filename(self, filename):
# Validate filename
if filename != '':
if '.hdf5' in filename:
try:
temp_file = h5py.File(unicode(self.filename), 'r')
temp_file.close()
except IOError:
self.add_message('Error: I/O Error')
return False
else:
self.add_message('Error: Must select a .hdf5 file.')
return False
else:
self.add_message('Error: Must select a file to open.')
return False
return True
def load_traces(self):
self.ui.comboBox_trace.clear()
# Validate filename
if self.valid_filename(self.filename):
h_file = h5py.File(unicode(self.filename), 'r')
target_test = self.ui.comboBox_test_num.currentText()
else:
self.ui.label_trace.setEnabled(False)
self.ui.comboBox_trace.setEnabled(False)
return
if self.ui.comboBox_test_num.currentText() == 'All' or self.ui.comboBox_test_num.currentText() == '':
self.ui.label_trace.setEnabled(False)
self.ui.comboBox_trace.setEnabled(False)
self.ui.comboBox_trace.clear()
h_file.close()
return
else:
self.ui.label_trace.setEnabled(True)
self.ui.comboBox_trace.setEnabled(True)
for key in h_file.keys():
if 'segment' in key:
for test in h_file[key].keys():
if target_test == test:
target_seg = key
target_test = test
traces = h_file[target_seg][target_test].value.shape[0]
for i in range(traces):
self.ui.comboBox_trace.addItem('trace_' + str(i+1))
self.ui.label_trace.setEnabled(True)
self.ui.comboBox_trace.setEnabled(True)
comment = h_file[target_seg].attrs['comment']
self.ui.lineEdit_comments.setText(comment)
h_file.close()
def load_reps(self):
self.ui.comboBox_rep.clear()
# Validate filename
if self.valid_filename(self.filename):
h_file = h5py.File(unicode(self.filename), 'r')
target_test = self.ui.comboBox_test_num.currentText()
else:
self.ui.label_rep.setEnabled(False)
self.ui.comboBox_rep.setEnabled(False)
return
if self.ui.comboBox_trace.currentText() == 'All' or self.ui.comboBox_trace.currentText() == '':
self.ui.label_rep.setEnabled(False)
self.ui.comboBox_rep.setEnabled(False)
self.ui.comboBox_rep.clear()
h_file.close()
return
else:
self.ui.label_rep.setEnabled(True)
self.ui.comboBox_rep.setEnabled(True)
self.ui.comboBox_rep.addItem('All')
for key in h_file.keys():
if 'segment' in key:
for test in h_file[key].keys():
if target_test == test:
target_seg = key
target_test = test
reps = h_file[target_seg][target_test].value.shape[1]
for i in range(reps):
self.ui.comboBox_rep.addItem('rep_' + str(i+1))
h_file.close()
def load_channels(self):
self.ui.comboBox_channel.clear()
# Validate filename
if self.valid_filename(self.filename):
h_file = h5py.File(unicode(self.filename), 'r')
target_test = self.ui.comboBox_test_num.currentText()
else:
self.ui.label_rep.setEnabled(False)
self.ui.comboBox_rep.setEnabled(False)
return
if self.ui.comboBox_test_num.count() == 0:
h_file.close()
self.clear_view()
return
for key in h_file.keys():
if 'segment' in key:
for test in h_file[key].keys():
if target_test == test:
target_seg = key
target_test = test
if len(h_file[target_seg][target_test].value.shape) > 3:
channels = h_file[target_seg][target_test].value.shape[2]
else:
channels = 1
if channels == 1:
self.ui.comboBox_channel.addItem('channel_1')
else:
for i in range(channels):
self.ui.comboBox_channel.addItem('channel_' + str(i+1))
if self.ui.comboBox_rep.currentText() == '' or self.ui.comboBox_channel.count() < 2:
self.ui.label_channel.setEnabled(False)
self.ui.comboBox_channel.setEnabled(False)
self.ui.comboBox_channel.clear()
else:
self.ui.label_channel.setEnabled(True)
self.ui.comboBox_channel.setEnabled(True)
if self.ui.comboBox_trace.currentText() != '' and self.ui.comboBox_rep.currentText() != '' and self.ui.comboBox_channel != '':
self.generate_view()
h_file.close()
def update_thresh(self):
self.ui.view.setThreshold(self.ui.doubleSpinBox_threshold.value())
self.ui.view.update_thresh()
def update_thresh2(self):
self.ui.doubleSpinBox_threshold.setValue(self.ui.view.getThreshold())
def update_window(self):
if self.ui.checkBox_custom_window.checkState():
self.ui.view.setXRange(self.ui.doubleSpinBox_xmin.value(), self.ui.doubleSpinBox_xmax.value(), 0)
self.ui.view.setYRange(self.ui.doubleSpinBox_ymin.value(), self.ui.doubleSpinBox_ymax.value(), 0)
def update_view_range(self):
view_range = self.ui.view.visibleRange()
self.ui.doubleSpinBox_xmin.setValue(view_range.left())
self.ui.doubleSpinBox_xmax.setValue(view_range.right())
self.ui.doubleSpinBox_ymin.setValue(view_range.top())
self.ui.doubleSpinBox_ymax.setValue(view_range.bottom())
def count_spikes(self):
pass
def generate_rasters(self):
filename = self.filename = self.ui.lineEdit_file_name.text()
# Validate filename
if not self.valid_filename(filename):
return
target_test = self.ui.comboBox_test_num.currentText()
thresh = self.ui.doubleSpinBox_threshold.value()
h_file = h5py.File(unicode(filename), 'r')
# Find target segment
for segment in h_file.keys():
for test in h_file[segment].keys():
if target_test == test:
target_seg = segment
target_test = test
fs = h_file[target_seg].attrs['samplerate_ad']
reps = h_file[target_seg][target_test].attrs['reps']
start_time = h_file[target_seg][target_test].attrs['start']
trace_data = h_file[target_seg][target_test].value
if len(trace_data.shape) == 4:
pass
stim_info = eval(h_file[target_seg][target_test].attrs['stim'])
duration = trace_data.shape[-1] / fs * 1000
if stim_info[1]['components'][0]['stim_type'] != 'Pure Tone':
self.add_message('Cannot generate raster with stim type "' + str(stim_info[1]['components'][0]['stim_type']) + '".')
h_file.close()
return
autoRasters = {}
for tStim in range(1, len(stim_info)):
freq = int(stim_info[tStim]['components'][0]['frequency'])
spl = int(stim_info[tStim]['components'][0]['intensity'])
trace_key = str(freq) + '_' + str(spl)
num_reps = trace_data.shape[1] # ???: Same as reps = h_file[target_seg][target_test].attrs['reps']
spikeTrains = pd.DataFrame([])
nspk = 0
for tRep in range(reps):
if len(trace_data.shape) == 3:
trace = trace_data[tStim][tRep]
pass
elif len(trace_data.shape) == 4:
tchan = int(self.ui.comboBox_channel.currentText().replace('channel_', '')) - 1
trace = trace_data[tStim][tRep][tchan]
pass
else:
self.add_message('Cannot handle trace_data of shape: ' + str(trace_data.shape))
return
spike_times = 1000 * np.array(get_spike_times(trace, thresh, fs))
spike_times_s = pd.Series(spike_times)
if spike_times_s.size > nspk:
spikeTrains = spikeTrains.reindex(spike_times_s.index)
nspk = spike_times_s.size
spikeTrains[str(tRep)] = spike_times_s
autoRasters[trace_key] = spikeTrains
h_file.close()
return autoRasters
def graph_spike_rates(self):
self.dialog = SpikeRatesDialog()
filename = self.filename = self.ui.lineEdit_file_name.text()
# Validate filename
if not self.valid_filename(filename):
return
self.dialog.populate_checkboxes(filename)
self.dialog.setWindowIcon(QtGui.QIcon('images/horsey.ico'))
self.dialog.setWindowTitle('Sparkle Analysis - Spike Rates')
self.dialog.show()
def graph_multi_io_test(self):
self.dialog = MultiIODialog()
filename = self.filename = self.ui.lineEdit_file_name.text()
# Validate filename
if not self.valid_filename(filename):
return
self.dialog.populate_checkboxes(filename, self.ui.doubleSpinBox_threshold.value())
self.dialog.setWindowIcon(QtGui.QIcon('images/horsey.ico'))
self.dialog.setWindowTitle('Sparkle Analysis - I/O Test')
self.dialog.show()
def graph_abr(self):
self.dialog = ABRDialog()
filename = self.filename = self.ui.lineEdit_file_name.text()
# Validate filename
if not self.valid_filename(filename):
return
self.dialog.populate_boxes(filename)
self.dialog.setWindowIcon(QtGui.QIcon('images/horsey.ico'))
self.dialog.setWindowTitle('Sparkle Analysis - ABRs')
self.dialog.show()
def graph_tuning_curve(self):
self.dialog = TuningCurveDialog()
filename = self.filename = self.ui.lineEdit_file_name.text()
# Validate filename
if not self.valid_filename(filename):
return
self.dialog.populate_boxes(filename)
self.dialog.set_threshold(self.ui.doubleSpinBox_threshold.value())
self.dialog.set_test_num(self.ui.comboBox_test_num.currentIndex())
self.dialog.set_channel_num(self.ui.comboBox_channel.currentIndex())
self.dialog.set_trace_num(self.ui.comboBox_trace.currentIndex())
self.dialog.setWindowIcon(QtGui.QIcon('images/horsey.ico'))
self.dialog.setWindowTitle('Sparkle Analysis - Tuning Curve Generator')
self.dialog.show()
def graph_io_test(self):
filename = self.filename = self.ui.lineEdit_file_name.text()
# Validate filename
if not self.valid_filename(filename):
return
target_test = self.ui.comboBox_test_num.currentText()
thresh = self.ui.doubleSpinBox_threshold.value()
h_file = h5py.File(unicode(filename), 'r')
# Find target segment
for segment in h_file.keys():
for test in h_file[segment].keys():
if target_test == test:
target_seg = segment
target_test = test
fs = h_file[target_seg].attrs['samplerate_ad']
reps = h_file[target_seg][target_test].attrs['reps']
start_time = h_file[target_seg][target_test].attrs['start']
trace_data = h_file[target_seg][target_test].value
if len(trace_data.shape) == 4:
pass
stim_info = eval(h_file[target_seg][target_test].attrs['stim'])
duration = trace_data.shape[-1] / fs * 1000
autoRasters = {}
for tStim in range(1, len(stim_info)):
spl = int(stim_info[tStim]['components'][0]['intensity'])
traceKey = 'None_' + str(spl).zfill(2)
spikeTrains = pd.DataFrame([])
nspk = 0
for tRep in range(reps):
if len(trace_data.shape) == 3:
trace = trace_data[tStim][tRep]
pass
elif len(trace_data.shape) == 4:
tchan = int(self.ui.comboBox_channel.currentText().replace('channel_', '')) - 1
trace = trace_data[tStim][tRep][tchan]
pass
else:
self.add_message('Cannot handle trace_data of shape: ' + str(trace_data.shape))
return
spike_times = 1000 * np.array(get_spike_times(trace, thresh, fs))
spike_times_s = pd.Series(spike_times)
if spike_times_s.size >= nspk:
spikeTrains = spikeTrains.reindex(spike_times_s.index)
nspk = spike_times_s.size
spikeTrains[str(tRep)] = spike_times_s
autoRasters[traceKey] = spikeTrains
rasters = autoRasters
h_file.close()
tuning = []
sortedKeys = sorted(rasters.keys())
for traceKey in sortedKeys:
spl = int(traceKey.split('_')[-1])
raster = rasters[traceKey]
res = ResponseStats(raster)
tuning.append({'intensity': spl, 'response': res[0], 'responseSTD': res[1]})
tuningCurves = pd.DataFrame(tuning)
tuningCurves.plot(x='intensity', y='response', yerr='responseSTD', capthick=1, label=str(target_test))
plt.legend(loc='upper left', fontsize=12, frameon=True)
sns.despine()
plt.grid(False)
plt.xlabel('Intensity (dB)', size=14)
plt.ylabel('Response Rate (Hz)', size=14)
plt.tick_params(axis='both', which='major', labelsize=14)
plt.title(str.split(str(filename), '/')[-1].replace('.hdf5', '') + ' '
+ str(self.ui.comboBox_test_num.currentText()).replace('test_', 'Test '))
plt.show()
def graph_raster(self):
filename = self.filename = self.ui.lineEdit_file_name.text()
# Validate filename
if not self.valid_filename(filename):
return
target_test = self.ui.comboBox_test_num.currentText()
thresh = self.ui.doubleSpinBox_threshold.value()
h_file = h5py.File(unicode(filename), 'r')
# Find target segment
for segment in h_file.keys():
for test in h_file[segment].keys():
if target_test == test:
target_seg = segment
target_test = test
target_trace = int(self.ui.comboBox_trace.currentText().replace('trace_', '')) - 1
fs = h_file[target_seg].attrs['samplerate_ad']
reps = h_file[target_seg][target_test].attrs['reps']
start_time = h_file[target_seg][target_test].attrs['start']
trace_data = h_file[target_seg][target_test].value
if len(trace_data.shape) == 4:
pass
stim_info = eval(h_file[target_seg][target_test].attrs['stim'])
duration = trace_data.shape[-1] / fs * 1000
autoRasters = {}
tStim = target_trace
spikeTrains = pd.DataFrame([])
nspk = 0
for tRep in range(reps):
if len(trace_data.shape) == 3:
trace = trace_data[tStim][tRep]
pass
elif len(trace_data.shape) == 4:
tchan = int(self.ui.comboBox_channel.currentText().replace('channel_', '')) - 1
trace = trace_data[tStim][tRep][tchan]
pass
else:
self.add_message('Cannot handle trace_data of shape: ' + str(trace_data.shape))
h_file.close()
return
spike_times = 1000 * np.array(get_spike_times(trace, thresh, fs))
spike_times_s = pd.Series(spike_times)
if spike_times_s.size > nspk:
spikeTrains = spikeTrains.reindex(spike_times_s.index)
nspk = spike_times_s.size
spikeTrains[str(tRep)] = spike_times_s
rasters = spikeTrains
h_file.close()
if len(rasters.shape) > 1:
spks = np.array([])
trns = np.array([])
for trnNum in range(len(rasters.columns)):
spkTrn = np.array(rasters.iloc[:, trnNum].dropna())
trns = np.hstack([trns, (trnNum + 1) * np.ones(len(spkTrn))])
spks = np.hstack([spks, spkTrn])
# --- Raster plot of spikes ---
sns.set_style("white")
sns.set_style("ticks")
raster_f = plt.figure(figsize=(8, 2))
sns.despine()
plt.grid(False)
ax = plt.scatter(spks, trns, marker='s', s=5, color='k')
plt.ylim(len(rasters.columns) + 0.5, 0.5)
plt.xlim(0, duration)
plt.xlabel('Time (ms)')
plt.ylabel('Presentation cycle')
plt.title(str.split(str(filename), '/')[-1].replace('.hdf5', '') + ' '
+ str(self.ui.comboBox_test_num.currentText()).replace('test_', 'Test '))
plt.tick_params(axis='both', which='major', labelsize=14)
raster_f.show()
else:
self.add_message('Only spike timing information provided, requires presentation numbers for raster.')
return ax
def graph_historgram(self):
filename = self.filename = self.ui.lineEdit_file_name.text()
# Validate filename
if not self.valid_filename(filename):
return
target_test = self.ui.comboBox_test_num.currentText()
thresh = self.ui.doubleSpinBox_threshold.value()
h_file = h5py.File(unicode(filename), 'r')
# Find target segment
for segment in h_file.keys():
for test in h_file[segment].keys():
if target_test == test:
target_seg = segment
target_test = test
target_trace = int(self.ui.comboBox_trace.currentText().replace('trace_', '')) - 1
fs = h_file[target_seg].attrs['samplerate_ad']
reps = h_file[target_seg][target_test].attrs['reps']
start_time = h_file[target_seg][target_test].attrs['start']
trace_data = h_file[target_seg][target_test].value
if len(trace_data.shape) == 4:
pass
stim_info = eval(h_file[target_seg][target_test].attrs['stim'])
duration = trace_data.shape[-1] / fs * 1000
autoRasters = {}
tStim = target_trace
spikeTrains = pd.DataFrame([])
nspk = 0
for tRep in range(reps):
if len(trace_data.shape) == 3:
trace = trace_data[tStim][tRep]
pass
elif len(trace_data.shape) == 4:
tchan = int(self.ui.comboBox_channel.currentText().replace('channel_', '')) - 1
trace = trace_data[tStim][tRep][tchan]
pass
else:
self.add_message('Cannot handle trace_data of shape: ' + str(trace_data.shape))
return
spike_times = 1000 * np.array(get_spike_times(trace, thresh, fs))
spike_times_s = pd.Series(spike_times)
if spike_times_s.size > nspk:
spikeTrains = spikeTrains.reindex(spike_times_s.index)
nspk = spike_times_s.size
spikeTrains[str(tRep)] = spike_times_s
rasters = spikeTrains
h_file.close()
if len(rasters.shape) > 1:
spks = np.array([])
trns = np.array([])
for trnNum in range(len(rasters.columns)):
spkTrn = np.array(rasters.iloc[:, trnNum].dropna())
trns = np.hstack([trns, (trnNum + 1) * np.ones(len(spkTrn))])
spks = np.hstack([spks, spkTrn])
spikeTimes = rasters.stack()
else:
spikeTimes = rasters.dropna()
# --- Histogram of spike times (1 ms bins)---
sns.set_style("white")
sns.set_style("ticks")
histogram_f = plt.figure(figsize=(8, 3))
axHist = spikeTimes.hist(bins=int(duration / 1), range=(0, duration)) # , figsize=(8,3))
sns.despine()
plt.xlim(0, duration)
plt.xlabel('Time (ms)', size=14)
plt.ylabel('Number of spikes', size=14)
plt.title(str.split(str(filename), '/')[-1].replace('.hdf5', '') + ' '
+ str(self.ui.comboBox_test_num.currentText()).replace('test_', 'Test '))
plt.tick_params(axis='both', which='major', labelsize=14)
plt.grid(False)
histogram_f.show()
return axHist
def graph_abr_old(self):
# Assumes there will only be one channel
print 'Graphing ABR'
filename = self.filename = self.ui.lineEdit_file_name.text()
# Validate filename
if not self.valid_filename(filename):
return
# clear view
self.clear_view()
target_test = self.ui.comboBox_test_num.currentText()
h_file = h5py.File(unicode(filename), 'r')
# Find target segment
for segment in h_file.keys():
for test in h_file[segment].keys():
if target_test == test:
target_seg = segment
target_test = test
# Makes the assumption that all of the traces are of the same type
stim_info = eval(h_file[target_seg][target_test].attrs['stim'])
self.ui.label_stim_type.setText(stim_info[1]['components'][0]['stim_type'])
fs = h_file[target_seg].attrs['samplerate_ad']
trace_data = h_file[target_seg][target_test].value
if len(trace_data.shape) == 4:
pass
samples = trace_data.shape[-1]
traces = trace_data.shape[0]
reps = trace_data.shape[1]
average = np.empty(samples)
abr = np.empty(shape=(traces, 1, 1, samples))
intensity = []
# Average the rep data into one trace
if len(trace_data.shape) == 4:
for t in range(traces):
for s in range(samples):
# print s
for r in range(reps):
# print r
average[s] += trace_data[0, r, 0, s]
average[s] /= reps + 1
abr[t, 0, 0, s] = average[s]
intensity.append(stim_info[t]['components'][0]['intensity'])
elif len(trace_data.shape) == 3:
for t in range(traces):
for s in range(samples):
# print s
for r in range(reps):
# print r
average[s] += trace_data[0, r, s]
average[s] /= reps + 1
abr[t, 0, 0, s] = average[s]
intensity.append(stim_info[t]['components'][0]['intensity'])
else:
self.add_message('Cannot handle trace_data of shape: ' + str(trace_data.shape))
return
self.ui.view.tracePlot.clear()
# Get the presentation data depending on if there is a channel field or not
if len(abr.shape) == 4:
presentation = abr[[], [], [], :]
elif len(abr.shape) == 3:
presentation = abr[[], [], :]
len_presentation = len(presentation)
# Get the length of the window and length of presentation depending on if all is selected or not
if len_presentation != 0:
window = len(presentation) / float(fs)
else:
if len(abr.shape) == 4:
window = len(abr[0, 0, 0, :]) / float(fs)
len_presentation = len(abr[0, 0, 0, :])
elif len(abr.shape) == 3:
window = len(abr[0, 0, :]) / float(fs)
len_presentation = len(abr[0, 0, :])
xlist = np.linspace(0, float(window), len_presentation)
ylist = presentation
# Fix xlist to be the length of presentation
if len(abr.shape) == 3:
self.ui.view.addTracesABR(xlist, abr[:, 0, :], intensity)
else:
self.ui.view.addTracesABR(xlist, abr[:, 0, 0, :], intensity)
self.ui.radioButton_normal.setChecked(True)
self.ui.radioButton_normal.setEnabled(False)
self.ui.radioButton_inverse.setEnabled(False)
def GetFreqsAttns(self, freqTuningHisto): # Frequency Tuning Curve method
""" Helper method for ShowSTH() to organize the frequencies in ascending order separated for each intensity.
:param freqTuningHisto: dict of pandas.DataFrames with spike data
:type freqTuningHisto: str
:returns: ordered list of frequencies (DataFrame keys())
numpy array of frequencies
numpy array of intensities
"""
freqs = np.array([])
attns = np.array([])
for histoKey in list(freqTuningHisto):
if histoKey != 'None_None':
freq = histoKey.split('_')[0]
freqs = np.hstack([freqs, float(freq) / 1000])
attn = histoKey.split('_')[1]
attns = np.hstack([attns, float(attn)])
attnCount = stats.itemfreq(attns)
freqs = np.unique(freqs)
attns = np.unique(attns)
if np.max(attnCount[:, 1]) != np.min(attnCount[:, 1]):
abortedAttnIdx = np.where(attnCount[:, 1] != np.max(attnCount[:, 1]))
attns = np.delete(attns, abortedAttnIdx)
orderedKeys = []
for attn in attns:
freqList = []
for freq in freqs:
key = str(int(freq * 1000)) + '_' + str(int(attn))
freqList.append(key)
orderedKeys.append(freqList)
return orderedKeys, freqs, attns
def add_message(self, message):
self.message_num += 1
self.ui.textEdit.append('[' + str(self.message_num) + ']: ' + message + '\n')
def clear_view(self):
self.ui.view.clearTraces()
self.ui.view.clearMouse()
self.ui.view.clearFocus()
self.ui.view.clearMask()
self.ui.view.clearData(axeskey='response')
self.ui.view.tracePlot.clear()
self.ui.view.rasterPlot.clear()
self.ui.view.stimPlot.clear()
self.ui.view.trace_stash = []
