def my_raster_plot(self, id_list=None, t_start=None, t_stop=None, display=True, kwargs={}, reduce=1, id_start=0):
"""
Generate a raster plot for the SpikeList in a subwindow of interest,
defined by id_list, t_start and t_stop.
Inputs:
id_list - can be a integer (and then N cells are randomly selected) or a list of ids. If None,
we use all the ids of the SpikeList
t_start - in ms. If not defined, the one of the SpikeList object is used
t_stop - in ms. If not defined, the one of the SpikeList object is used
kwargs - dictionary contening extra parameters that will be sent to the plot
function
Examples:
>> z = subplot(221)
>> spikelist.raster_plot(display=z, kwargs={'color':'r'})
See also
SpikeTrain.raster_plot
"""
subplot = get_display(display)
if id_list == None:
id_list = self.id_list
spk = self
else:
spk = self.id_slice(id_list)
if t_start is None: t_start = spk.t_start
if t_stop is None: t_stop = spk.t_stop
if t_start != spk.t_start or t_stop != spk.t_stop:
spk = spk.time_slice(t_start, t_stop)
ids, spike_times = spk.convert(format="[ids, times]")
idx = numpy.where((spike_times >= t_start) & (spike_times <= t_stop))[0]
new_id_list=numpy.arange(len(id_list))+id_start
ids_map = dict(zip(id_list, new_id_list))
new_ids=[ids_map[id] for id in ids]
if len(spike_times) > 0:
subplot.plot(spike_times[::reduce], new_ids[::reduce], ',', **kwargs)
xlabel = "Time (ms)"
ylabel = "Neuron #"
set_labels(subplot, xlabel, ylabel)
min_id = numpy.min(new_id_list)
max_id = numpy.max(new_id_list)
length = t_stop - t_start
set_axis_limits(subplot, t_start-0.05*length, t_stop+0.05*length, min_id-2, max_id+2)
pylab.draw()
def runTest(self):
f = plotting.get_display(True)
x = range(10)
pylab.plot(x)
plotting.set_axis_limits(pylab, 0., 123., -123., 456.)
# set up a SimpleMultiplot with arbitrary values
self.nrows = 1
self.ncolumns = 1
title = 'testMultiplot'
xlabel = 'testXlabel'
ylabel = 'testYlabel'
scaling = ('linear','log')
self.smt = plotting.SimpleMultiplot(nrows=self.nrows, ncolumns=self.ncolumns, title=title, xlabel=xlabel, ylabel=ylabel, scaling=scaling)
plotting.set_axis_limits(self.smt.panel(0), 0., 123., -123., 456.)
def runTest(self):
f = plotting.get_display(True)
x = range(10)
pylab.plot(x)
plotting.set_axis_limits(pylab, 0., 123., -123., 456.)
# set up a SimpleMultiplot with arbitrary values
self.nrows = 1
self.ncolumns = 1
title = 'testMultiplot'
xlabel = 'testXlabel'
ylabel = 'testYlabel'
scaling = ('linear', 'log')
self.smt = plotting.SimpleMultiplot(nrows=self.nrows,
ncolumns=self.ncolumns,
title=title,
xlabel=xlabel,
ylabel=ylabel,
scaling=scaling)
plotting.set_axis_limits(self.smt.panel(0), 0., 123., -123., 456.)
def event_triggered_average(self, events, average = True, t_min = 0, t_max = 100, display = False, with_time = False, kwargs={}):
"""
Return the spike triggered averaged of an analog signal according to selected events,
on a time window t_spikes - tmin, t_spikes + tmax
Can return either the averaged waveform (average = True), or an array of all the
waveforms triggered by all the spikes.
Inputs:
events - Can be a SpikeTrain object (and events will be the spikes) or just a list
of times
average - If True, return a single vector of the averaged waveform. If False,
return an array of all the waveforms.
t_min - Time (>0) to average the signal before an event, in ms (default 0)
t_max - Time (>0) to average the signal after an event, in ms (default 100)
display - if True, a new figure is created. Could also be a subplot.
kwargs - dictionary contening extra parameters that will be sent to the plot
function
Examples:
>> vm.event_triggered_average(spktrain, average=False, t_min = 50, t_max = 150)
>> vm.event_triggered_average(spktrain, average=True)
>> vm.event_triggered_average(range(0,1000,10), average=False, display=True)
"""
if isinstance(events, SpikeTrain):
events = events.spike_times
ylabel = "Spike Triggered Average"
else:
assert numpy.iterable(events), "events should be a SpikeTrain object or an iterable object"
ylabel = "Event Triggered Average"
assert (t_min >= 0) and (t_max >= 0), "t_min and t_max should be greater than 0"
assert len(events) > 0, "events should not be empty and should contained at least one element"
time_axis = numpy.linspace(-t_min, t_max, (t_min+t_max)/self.dt)
N = len(time_axis)
Nspikes = 0.
subplot = get_display(display)
if average:
result = numpy.zeros(N, float)
else:
result = []
# recalculate everything into timesteps, is more stable against rounding errors
# and subsequent cutouts with different sizes
events = numpy.floor(numpy.array(events)/self.dt)
t_min_l = numpy.floor(t_min/self.dt)
t_max_l = numpy.floor(t_max/self.dt)
t_start = numpy.floor(self.t_start/self.dt)
t_stop = numpy.floor(self.t_stop/self.dt)
for spike in events:
if ((spike-t_min_l) >= t_start) and ((spike+t_max_l) < t_stop):
spike = spike - t_start
if average:
result += self.signal[(spike-t_min_l):(spike+t_max_l)]
else:
result.append(self.signal[(spike-t_min_l):(spike+t_max_l)])
Nspikes += 1
if average:
result = result/Nspikes
else:
result = numpy.array(result)
if not subplot or not HAVE_PYLAB:
if with_time:
return result, time_axis
else:
return result
else:
xlabel = "Time (ms)"
set_labels(subplot, xlabel, ylabel)
if average:
subplot.plot(time_axis, result, **kwargs)
else:
for idx in xrange(len(result)):
subplot.plot(time_axis, result[idx,:], c='0.5', **kwargs)
subplot.hold(1)
result = numpy.sum(result, axis=0)/Nspikes
subplot.plot(time_axis, result, c='k', **kwargs)
xmin, xmax, ymin, ymax = subplot.axis()
subplot.plot([0,0],[ymin, ymax], c='r')
set_axis_limits(subplot, -t_min, t_max, ymin, ymax)
pylab.draw()
def event_triggered_average(self,
events,
average=True,
t_min=0,
t_max=100,
display=False,
with_time=False,
kwargs={}):
"""
Return the spike triggered averaged of an analog signal according to selected events,
on a time window t_spikes - tmin, t_spikes + tmax
Can return either the averaged waveform (average = True), or an array of all the
waveforms triggered by all the spikes.
Inputs:
events - Can be a SpikeTrain object (and events will be the spikes) or just a list
of times
average - If True, return a single vector of the averaged waveform. If False,
return an array of all the waveforms.
t_min - Time (>0) to average the signal before an event, in ms (default 0)
t_max - Time (>0) to average the signal after an event, in ms (default 100)
display - if True, a new figure is created. Could also be a subplot.
kwargs - dictionary contening extra parameters that will be sent to the plot
function
Examples:
>> vm.event_triggered_average(spktrain, average=False, t_min = 50, t_max = 150)
>> vm.event_triggered_average(spktrain, average=True)
>> vm.event_triggered_average(range(0,1000,10), average=False, display=True)
"""
if isinstance(events, SpikeTrain):
events = events.spike_times
ylabel = "Spike Triggered Average"
else:
assert numpy.iterable(
events
), "events should be a SpikeTrain object or an iterable object"
ylabel = "Event Triggered Average"
assert (t_min >= 0) and (t_max >=
0), "t_min and t_max should be greater than 0"
assert len(
events
) > 0, "events should not be empty and should contained at least one element"
time_axis = numpy.linspace(-t_min, t_max, (t_min + t_max) / self.dt)
N = len(time_axis)
Nspikes = 0.
subplot = get_display(display)
if average:
result = numpy.zeros(N, float)
else:
result = []
# recalculate everything into timesteps, is more stable against rounding errors
# and subsequent cutouts with different sizes
events = numpy.floor(numpy.array(events) / self.dt)
t_min_l = numpy.floor(t_min / self.dt)
t_max_l = numpy.floor(t_max / self.dt)
t_start = numpy.floor(self.t_start / self.dt)
t_stop = numpy.floor(self.t_stop / self.dt)
for spike in events:
if ((spike - t_min_l) >= t_start) and ((spike + t_max_l) < t_stop):
spike = spike - t_start
if average:
result += self.signal[(spike - t_min_l):(spike + t_max_l)]
else:
result.append(self.signal[(spike - t_min_l):(spike +
t_max_l)])
Nspikes += 1
if average:
result = result / Nspikes
else:
result = numpy.array(result)
if not subplot or not HAVE_PYLAB:
if with_time:
return result, time_axis
else:
return result
else:
xlabel = "Time (ms)"
set_labels(subplot, xlabel, ylabel)
if average:
subplot.plot(time_axis, result, **kwargs)
else:
for idx in xrange(len(result)):
subplot.plot(time_axis, result[idx, :], c='0.5', **kwargs)
subplot.hold(1)
result = numpy.sum(result, axis=0) / Nspikes
subplot.plot(time_axis, result, c='k', **kwargs)
xmin, xmax, ymin, ymax = subplot.axis()
subplot.plot([0, 0], [ymin, ymax], c='r')
set_axis_limits(subplot, -t_min, t_max, ymin, ymax)
pylab.draw()