def wrapPlotsOverEdges(self):
"""Wrap all plots at the edges of the specified curve (should
be a stimulus or a spiketrain."""
if not self.__selectedCurves:
return
wrapcurve = self.__selectedCurves[-1]
path = self.curve_path_dict[wrapcurve]
times = []
xdata = numpy.array(wrapcurve.data().xData())
ydata = numpy.array(wrapcurve.data().yData())
# It is a spike train, x values are spike times, wrap around those
if 'spikes' in path:
times = xdata
# It is a stimulus: take the leadin edges
elif 'stim' in path:
times = xdata[numpy.r_[False, numpy.diff(ydata) < 0].nonzero()[0]]
else:
ydata = analyzer.smooth(ydata)
mid = numpy.mean(ydata)
ydata = ydata[ydata > mid] # Threshold at midpoint
times = xdata[numpy.r_[True, ydata[1:] > ydata[:-1]] & numpy.r_[ydata[:-1] > ydata[1:], True]]
# start from the first edge, ignoring everything before it
# and put end of simulation as the upper bound
for curve in self.itemList():
ydata = numpy.array(curve.data().yData())
xdata = numpy.array(curve.data().xData())
path = self.curve_path_dict[curve]
path_curve_list = self.path_curve_dict[path]
path_curve_list.pop(path_curve_list.index(curve))
self.curve_path_dict.pop(curve)
curve.detach()
start = 0
end = len(xdata)
for ii in range(-1, - len(times) - 1, -1):
points = numpy.nonzero(xdata >= times[ii])[0]
if len(points) == 0:
continue
start = points[0]
xx = numpy.array(xdata[start:end] - times[ii])
xdata[start:end] = -1.0
new_curve = Qwt.QwtPlotCurve('%s #%d' % (curve.title().text(), len(times) + ii, ))
new_curve.setData(xx, ydata[start:end])
new_curve.setStyle(curve.style())
new_curve.setPen(QtGui.QPen(curve.pen()))
new_curve.setSymbol(Qwt.QwtSymbol(curve.symbol()))
new_curve.attach(self)
self.curve_path_dict[new_curve] = path
self.path_curve_dict[path].append(new_curve)
end = start
self.replot()
def plotPSTH(self, stimpath,
stimdata,
spikesdict,
simtime,
offset=0,
binsize=10e-3,
legendSuffix='',
rate=False,
normcells=True
):
"""Plot the distribution of spike times in a time window.
Bug in waiting: This should not be called for selections from
multiple files because simtime may vary.
"""
if not spikesdict:
return 0
stimdata = stimdata[:]
times = []
# It is a spike train, x values are spike times, wrap around those
if 'spikes' in stimpath:
times = stimdata
# It is a stimulus: take the leadin edges
elif 'stim' in stimpath:
times = numpy.linspace(0, simtime, stimdata.shape[0])[numpy.r_[False, numpy.diff(stimdata) < 0].nonzero()[0]]
else:
stimdata = analyzer.smooth(stimdata)
mid = numpy.mean(stimdata)
stimdata = stimdata[stimdata > mid] # Threshold at midpoint
times = numpy.linspace(0, simtime, stimdata.shape[0])[numpy.r_[True, stimdata[1:] > stimdata[:-1]] & numpy.r_[stimdata[:-1] > stimdata[1:], True]]
if (times is None) or (len(times) == 0):
return 0
start = times + offset
end = numpy.zeros(times.shape)
end[:-1] = start[1:]
end[-1] = simtime + offset # We assume
accumulated_data = []
for spikedata in spikesdict.values():
tpoints = spikedata[:]
for ii in range(len(times)):
ix = numpy.nonzero((tpoints >= start[ii]) & (tpoints < end[ii]))[0]
accumulated_data = numpy.r_[accumulated_data, tpoints[ix] - times[ii]]
if len(accumulated_data) == 0:
return 0
# set the bins by splitting interstimulus interval
interval = numpy.mean(numpy.diff(times))
bins = numpy.arange(offset, interval+offset, binsize)
bins = numpy.r_[bins, bins[-1] + binsize]
hist = numpy.histogram(accumulated_data, bins=bins)
xx = (hist[1][:-1] + hist[1][1:])/2.0
if rate:
yy = hist[0] / binsize
else:
yy = hist[0]
if normcells:
yy /= len(spikesdict)
path = stimpath + '_psth' + legendSuffix
new_curve = Qwt.QwtPlotCurve(path)
new_curve.setData(xx, yy)
pen = Qt.QPen(Qt.Qt.blue, 1, Qt.Qt.DashDotLine)
new_curve.setStyle(Qwt.QwtPlotCurve.Lines)
new_curve.setPen(pen)
pen = Qt.QPen(Qt.Qt.red, 1)
new_curve.setSymbol(Qwt.QwtSymbol(Qwt.QwtSymbol.XCross,
Qt.QBrush(),
pen,
Qt.QSize(3,3)))
new_curve.attach(self)
self.curve_path_dict[new_curve] = path
self.path_curve_dict[path].append(new_curve)
path = stimpath + '_bins' + legendSuffix
histmarkers = Qwt.QwtPlotCurve(path)
height = int(max(yy) + 0.5)
yy = numpy.ones(hist[1].shape) * height
histmarkers.setData(hist[1], yy)
pen = Qt.QPen(Qt.Qt.black, 1, Qt.Qt.DotLine)
histmarkers.setPen(pen)
histmarkers.setStyle(Qwt.QwtPlotCurve.Sticks)
histmarkers.attach(self)
self.curve_path_dict[histmarkers] = path
self.path_curve_dict[path].append(new_curve)
self.clearZoomStack()
self.replot()
return 1
