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run.py
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run.py
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import os
import sys
import csv
import h5py
import operator
import numpy as np
import seaborn as sns
import scipy.stats as stats
import pandas as pd
import matplotlib.pyplot as plt
from PyQt4 import QtCore, QtGui
from ui.main_ui import Ui_MainWindow
from dialogs.SpikeRatesDialog import SpikeRatesDialog
from dialogs.MultiIODialog import MultiIODialog
from dialogs.ABRDialog import ABRDialog
from dialogs.TuningCurveDialog import TuningCurveDialog
from util.spikerates import ResponseStats
from util.spikerates import ResponseStatsSpikes
from util.spikestats import get_spike_times
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
# If channel didn't load, set default as 0
if not target_chan:
target_chan = 0
# 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 not test_data[target_trace, i, :].any():
return
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 not test_data[target_trace, i, target_chan, :].any():
return
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)
try:
comment = h_file[target_seg].attrs['comment']
self.ui.lineEdit_comments.setText(comment)
except:
print 'Failed to load 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()
self.ui.comboBox_channel.addItem('channel_1')
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 = []