def __init__(self, parent=None):
QtGui.QWidget.__init__(self)
self.ui = Ui_Form_multi_io()
self.ui.setupUi(self)
self.filename = ''
self.checkboxes = []
self.comboboxes = []
self.spnboxes = []
self.threshold = 0
QtCore.QObject.connect(self.ui.pushButton_multi_io, QtCore.SIGNAL("clicked()"), self.graph_multi_io_test)
QtCore.QObject.connect(self.ui.pushButton_auto_threshold, QtCore.SIGNAL("clicked()"), self.estimate_thresholds)
class MultiIODialog(QtGui.QMainWindow):
def __init__(self, parent=None):
QtGui.QWidget.__init__(self)
self.ui = Ui_Form_multi_io()
self.ui.setupUi(self)
self.filename = ''
self.checkboxes = []
self.comboboxes = []
self.spnboxes = []
self.threshold = 0
QtCore.QObject.connect(self.ui.pushButton_multi_io, QtCore.SIGNAL("clicked()"), self.graph_multi_io_test)
QtCore.QObject.connect(self.ui.pushButton_auto_threshold, QtCore.SIGNAL("clicked()"), self.estimate_thresholds)
def populate_checkboxes(self, filename, thresh):
self.filename = filename
self.ui.label_title.setText(str.split(str(filename), '/')[-1])
h_file = h5py.File(unicode(filename), 'r')
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))
# Create the layout to populate
layout = QtGui.QGridLayout()
title_test = QtGui.QLabel('Test')
title_chan = QtGui.QLabel('Channel')
title_thresh = QtGui.QLabel('Threshold')
font = QtGui.QFont()
font.setPointSize(10)
font.setBold(True)
title_test.setFont(font)
title_chan.setFont(font)
title_thresh.setFont(font)
layout.addWidget(title_test, 0, 0)
layout.addWidget(title_chan, 0, 1)
layout.addWidget(title_thresh, 0, 2)
row_count = 1
for test in sorted_tests:
checkbox = QtGui.QCheckBox(test[0])
combobox = QtGui.QComboBox()
spnbox = QtGui.QDoubleSpinBox()
layout.addWidget(checkbox, row_count, 0)
self.checkboxes.append(checkbox)
# Find target segment
for segment in h_file.keys():
for s_test in h_file[segment].keys():
if test[0] == s_test:
target_seg = segment
target_test = s_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:
combobox.addItem('channel_1')
else:
for i in range(channels):
combobox.addItem('channel_' + str(i+1))
if combobox.count() < 2:
combobox.setEnabled(False)
layout.addWidget(combobox, row_count, 1)
self.comboboxes.append(combobox)
spnbox.setSuffix(' V')
spnbox.setDecimals(4)
spnbox.setSingleStep(0.0001)
spnbox.setMinimum(-100)
spnbox.setMaximum(100)
spnbox.setValue(thresh)
layout.addWidget(spnbox, row_count, 2)
self.spnboxes.append(spnbox)
row_count += 1
self.ui.scrollAreaWidgetContents.setLayout(layout)
h_file.close()
def estimate_thresholds(self):
thresh_fraction = 0.7
h_file = h5py.File(unicode(self.filename), 'r')
for row in range(len(self.checkboxes)):
for segment in h_file.keys():
for test in h_file[segment].keys():
if self.checkboxes[row].text() == 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:
tchan = int(self.comboboxes[row].currentText().replace('channel_', '')) - 1
trace_data = trace_data[:, :, tchan, :]
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.spnboxes[row].setValue(thresh)
def graph_multi_io_test(self):
h_file = h5py.File(unicode(self.filename), 'r')
target_rows = []
for i in range(len(self.checkboxes)):
if self.checkboxes[i].checkState():
target_rows.append(i)
axes = []
for row in target_rows:
for segment in h_file.keys():
for test in h_file[segment].keys():
if self.checkboxes[row].text() == 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
stim_info = eval(h_file[target_seg][target_test].attrs['stim'])
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.comboboxes[row].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, self.spnboxes[row].value(), 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
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)
if axes:
tuningCurves.plot(x='intensity', y='response', ax=axes, yerr='responseSTD', capthick=1, label=str(target_test) + ' : ' + str(self.spnboxes[row].value()) + ' V')
else:
axes = tuningCurves.plot(x='intensity', y='response', yerr='responseSTD', capthick=1, label=str(target_test) + ' : ' + str(self.spnboxes[row].value()) + ' V')
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(self.filename), '/')[-1].replace('.hdf5', '') + ' Multi I/O')
plt.show()
h_file.close()
