def runTest(self):
a = plotting.get_display(True)
assert a != None
a = plotting.get_display(False)
assert a == None
a = plotting.get_display(1234)
assert a == 1234
def runTest(self):
a = plotting.get_display(True)
assert a != None
a = plotting.get_display(False)
assert a == None
a = plotting.get_display(1234)
assert a == 1234
def my_image_firing_rate_slide_window(self, bin=100, id_list=[], step=1, stop=None, display=True, kwargs={}):
'''
Function that create an image with bin size sliding window firing rate as
calculated at step intervals. kwargs - dictionary contening extra
parameters that will be sent to the plot function
Arguments:
bin Bin size of sliding window
display If True, a new figure is created. Could also be a subplot
id_list List of ids to calculate firing rate for
step Step size for moving sliding window (ms)
stop End of spike train
kwargs Additional plot arguments
'''
ax = get_display(display)
if not any(id_list): id_list = self.ids
t, r, spk = self.my_firing_rate_sliding_window(bin, display, id_list,
step, stop, kwargs)
kwargs.update( { 'origin' : 'lower', } )
image = ax.imshow(r, extent=[t[0],t[-1],self.ids[0],self.ids[-1]],
**kwargs)
ax.set_xlabel('Time (ms)')
ax.set_ylabel('Neuron #')
ax.set_aspect(aspect='auto')
return image
def show_results(result):
"""
visualizes the result of the parameter search.
Parameters:
result - list of result dictionaries.
"""
import numpy
t_smooth = 100. # ms. integration time to show fiber activity
snrs = numpy.sort([r['snr'] for r in result])
neuron_rates = numpy.zeros(len(snr))
for snr_i in range(len(snrs)):
neuron_rates[r_i] = [
r['neuron_rate'] for r in result
if (r['source_rate'] == snrs[snr_i])
][0]
import NeuroTools.plotting as plotting
pylab = plotting.get_display(True)
pylab.rcParams.update(plotting.pylab_params())
print rates, neuron_rates
subplot = pylab.imshow(neuron_rates,
interpolation='nearest',
origin='lower')
plotting.set_labels(subplot.get_axes(), xlabel='rate', ylabel='weight')
pylab.colorbar()
# could add fancy xticks and yticks here
import tempfile, os
(fd, figfilename) = tempfile.mkstemp(prefix='parameter_search_result',
suffix='.png',
dir=os.getcwd())
pylab.gcf().savefig(figfilename)
def plot_spike_histogram(self,
bin=10,
display=True,
id_list=[],
n_rep=1,
normalized=True,
kwargs={}):
'''
Plot an mean spike histogram for for all ids generated by
spike_histogram_n_rep
Arguments:
bin - the time bin used to gather the data
display - If True, a new figure is created. Could also be a subplot
id_list - list with ids to use for creation of histogram
n_rep - Number of experimental repetitions with same stimulation.
E.g. if n_runs=3 then it is assumed that three
experimental runs is recorded and the data will be
sliced up into three time intervals.
normalized - if True, the histogram are in Hz (spikes/second),
otherwise they are in spikes/bin
'''
ax = get_display(display)
timeAxis, histograms = self.spike_histogram_n_rep(
bin, id_list, n_rep, normalized)
ax.bar(left=t_vec,
height=data,
width=0.8,
bottom=None,
hold=None,
**kwargs)
def show_results(result):
"""
visualizes the result of the parameter search.
Parameters:
result - list of result dictionaries.
"""
import numpy
rates = numpy.sort([r['source_rate'] for r in result])
weights = numpy.sort([r['weight'] for r in result])
neuron_rates = numpy.zeros((len(rates), len(weights)))
for r_i in range(len(rates)):
for w_i in range(len(weights)):
neuron_rates[r_i, w_i] = [r['neuron_rate'] for r in result
if (r['source_rate'] == rates[r_i])
and (r['weight'] == weights[w_i])][0]
import NeuroTools.plotting as plotting
pylab = plotting.get_display(True)
pylab.rcParams.update(plotting.pylab_params())
subplot = pylab.imshow(neuron_rates,
interpolation = 'nearest',
origin = 'lower')
plotting.set_labels(subplot.get_axes(),
xlabel = 'rate',
ylabel = 'weight')
pylab.colorbar()
# could add fancy xticks and yticks here
import tempfile, os
(fd, figfilename) = tempfile.mkstemp(prefix = 'parameter_search_result',
suffix = '.png',
dir = os.getcwd())
pylab.gcf().savefig(figfilename)
def plot(self, id_list=None, v_thresh=None, display=True, kwargs={}):
"""
Plot all cells in the AnalogSignalList defined by id_list
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
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:
>> z = subplot(221)
>> aslist.plot(5, display=z, kwargs={'color':'r'})
"""
subplot = get_display(display)
id_list = self._AnalogSignalList__sub_id_list(id_list)
time_axis = self.time_axis()
if not subplot or not HAVE_PYLAB:
print(PYLAB_ERROR)
else:
xlabel = "Time (ms)"
ylabel = "Conductance (nS)"
set_labels(subplot, xlabel, ylabel)
for id in id_list:
subplot.plot(time_axis, self.analog_signals[id].signal, **kwargs)
subplot.hold(1)
pylab.draw()
def plot(self, id_list=None, v_thresh=None, display=True, kwargs={}):
"""
Plot all cells in the AnalogSignalList defined by id_list
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
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:
>> z = subplot(221)
>> aslist.plot(5, display=z, kwargs={'color':'r'})
"""
subplot = get_display(display)
id_list = self._AnalogSignalList__sub_id_list(id_list)
time_axis = self.time_axis()
if not subplot or not HAVE_PYLAB:
print PYLAB_ERROR
else:
xlabel = "Time (ms)"
ylabel = "Conductance (nS)"
set_labels(subplot, xlabel, ylabel)
for id in id_list:
subplot.plot(time_axis, self.analog_signals[id].signal,
**kwargs)
subplot.hold(1)
pylab.draw()
def firing_rate(self,
bin=200,
display=True,
id_list=[],
n_rep=1,
kwargs={}):
'''
Plot all the instantaneous firing rates along time (in Hz)
over all ids in id_list from histogram generated by spike_histogram_n_rept.
Inputs:
bin - the time bin used to gather the data
display - If True, a new figure is created. Could also be a subplot
id_list - list with ids to use for creation of histogram
n_rep - Number of experimental repetitions with same stimulation.
E.g. if n_runs=3 then it is assumed that three
experimental runs is recorded and the data will be
sliced up into three time intervals.
kwargs - dictionary contening extra parameters that will be sent to the plot
function
'''
ax = get_display(display)
timeAxis, histograms = self.spike_histogram_n_rep(
bin, id_list, n_rep, True)
firingRates = numpy.mean(histograms, axis=0)
ax.plot(timeAxis, firingRates, **kwargs)
ax.set_xlabel('Time (ms)')
ax.set_ylabel('Frequency (spike/s)')
def raster_plot_cluster(self,
id_list=[],
t_start=None,
t_stop=None,
display=True,
clusters=[],
kwargs={}):
"""
(functional?) 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
display - if True, a new figure is created. Could also be a subplot
clusters - vector containing code for cluster belonging of each spike
train opatain from clustering analysis. Plotted accordingly.
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
"""
ax = get_display(display)
spk = self.list
if t_start is None: t_start = spk.t_start
if t_stop is None: t_stop = spk.t_stop
ids, spike_times = spk.convert(format="[ids, times]")
idx = numpy.where((spike_times >= t_start)
& (spike_times <= t_stop))[0]
sorted_index = numpy.argsort(
clusters) # Sort spike trains accoringly to clusters
for i, id in enumerate(self.ids):
ids[ids == id] = -self.ids[sorted_index[i]]
ids = abs(ids)
if len(spike_times) > 0:
ax.plot(spike_times, ids, ',', **kwargs)
xlabel = "Time (ms)"
ylabel = "Neuron #"
set_labels(ax, xlabel, ylabel)
min_id = numpy.min(spk.id_list())
max_id = numpy.max(spk.id_list())
length = t_stop - t_start
set_axis_limits(ax, t_start - 0.05 * length, t_stop + 0.05 * length,
min_id - 2, max_id + 2)
pylab.draw()
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 image_spike_histogram(sself,
bin=10,
display=True,
id_list=[],
n_rep=1,
normalized=True,
kwargs={}):
'''
Plot an image of all the spike_histograms generated by
spike_histogram_n_rep
Arguments:
bin - the time bin used to gather the data
display - If True, a new figure is created. Could also be a subplot
id_list - list with ids to use for creation of histogram
n_rep - Number of experimental repetitions with same stimulation.
E.g. if n_runs=3 then it is assumed that three
experimental runs is recorded and the data will be
sliced up into three time intervals.
normalized - if True, the histogram are in Hz (spikes/second),
otherwise they are in spikes/bin
kwargs . Additional plot arguments
'''
ax = get_display(display)
timeAxis, histograms = self.spike_histogram_n_rep(
bin, id_list, n_rep, normalized)
kwargs.update({
'origin': 'lower',
})
image = ax.imshow(histograms, **kwargs)
ax.set_xlabel('Time (ms)')
ax.set_ylabel('Neuron #')
ax.set_aspect(aspect='auto')
n_points = len(timeAxis)
xticks = numpy.arange(0, n_points, n_points * 0.2)
xticklabels = numpy.arange(0, timeAxis[-1], timeAxis[-1] * 0.2)
ax.set_xticks(xticks)
ax.set_xticklabels([str(l) for l in xticklabels])
n_ids = len(id_list)
yticks = numpy.arange(0, n_ids, n_ids * 0.2)
ax.set_yticks(yticks)
ax.set_yticklabels(id_list[yticks])
return image
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 composite_plot_movie(SL, time_bin=10, t_start=None, t_stop=None, output="animation.mpg", bounds=(0, 5), fps=10, display=True, maxrate=None, ratebin=None, kwargs={}):
pylab.ioff()
from NeuroTools.plotting import get_display
subplot = get_display(display)
assert os.system('mencoder') != 32512, "mencoder not found!"
if t_start is None:
t_start = SL.t_start
if t_stop is None:
t_stop = SL.t_stop
if maxrate is None:
maxrate = 100
if ratebin is None:
ratebin = 100
files = []
im = pylab.figure(**kwargs)
count = 0
idx = 0
axS, axR = STCompositePlot(SL, t_start=0, t_stop=time_bin,
t_start_rate=0, t_stop_rate=ratebin, kwargs=kwargs)
axR.hold(False)
axS.hold(False)
if t_start != SL.t_start or t_stop != SL.t_stop:
spk = SL.time_slice(t_start, t_stop)
raise RuntimeError('Not implemented')
while (t_start < t_stop):
STCompositePlot(SL, t_start=0, t_stop=t_start + time_bin, t_start_rate=t_start,
t_stop_rate=t_start + ratebin, display=[axS, axR], kwargs=kwargs)
axS.set_xlim([0, t_stop])
axR.set_xlim([0, maxrate])
fname = "_tmp_spikes_%05d.png" % count
pylab.savefig(fname)
files.append(fname)
t_start += time_bin
count += 1
print('Generated frame {0}'.format(count))
command = "mencoder 'mf://_tmp_*.png' -mf type=png:fps=%d -ovc lavc -lavcopts vcodec=wmv2 -oac copy -o %s" % (fps, output)
os.system(command)
for fname in files:
os.remove(fname)
def runTest(self):
f = plotting.get_display(True)
x = range(10)
p = pylab.plot(x)
plotting.set_labels(pylab, 'the x axis', 'the y axis')
# 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_labels(self.smt.panel(0), 'the x axis', 'the y axis')
def plot(self, ylabel="Analog Signal", display=True, kwargs={}):
"""
Plot the AnalogSignal
Inputs:
ylabel - A string to sepcify the label on the yaxis.
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:
>> z = subplot(221)
>> signal.plot(ylabel="Vm", display=z, kwargs={'color':'r'})
"""
subplot = get_display(display)
time_axis = self.time_axis()
if not subplot or not HAVE_PYLAB:
print(PYLAB_ERROR)
else:
xlabel = "Time (ms)"
set_labels(subplot, xlabel, ylabel)
subplot.plot(time_axis, self.signal, **kwargs)
pylab.draw()
def plot(self, ylabel="Analog Signal", display=True, kwargs={}):
"""
Plot the AnalogSignal
Inputs:
ylabel - A string to sepcify the label on the yaxis.
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:
>> z = subplot(221)
>> signal.plot(ylabel="Vm", display=z, kwargs={'color':'r'})
"""
subplot = get_display(display)
time_axis = self.time_axis()
if not subplot or not HAVE_PYLAB:
print PYLAB_ERROR
else:
xlabel = "Time (ms)"
set_labels(subplot, xlabel, ylabel)
subplot.plot(time_axis, self.signal, **kwargs)
pylab.draw()
def plot(self, id_list=None, v_thresh=None, display=True, kwargs={}):
"""
Plot all cells in the AnalogSignalList defined by id_list
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
v_thresh- For graphical purpose, plot a spike when Vm > V_thresh. If None,
just plot the raw Vm
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:
>> z = subplot(221)
>> aslist.plot(5, v_thresh = -50, display=z, kwargs={'color':'r'})
"""
subplot = get_display(display)
id_list = self._AnalogSignalList__sub_id_list(id_list)
time_axis = self.time_axis()
if not subplot or not HAVE_MATPLOTLIB:
print MATPLOTLIB_ERROR
else:
xlabel = "Time (ms)"
ylabel = "Membrane Potential (mV)"
set_labels(subplot, xlabel, ylabel)
for id in id_list:
to_be_plot = self.analog_signals[id].signal
if v_thresh is not None:
to_be_plot = pylab.where(to_be_plot >= v_thresh - 0.02,
v_thresh + 0.5, to_be_plot)
if len(time_axis) > len(to_be_plot):
time_axis = time_axis[:-1]
if len(to_be_plot) > len(time_axis):
to_be_plot = to_be_plot[:-1]
subplot.plot(time_axis, to_be_plot, **kwargs)
subplot.hold(1)
def plot(self, id_list=None, v_thresh=None, display=True, kwargs={}):
"""
Plot all cells in the AnalogSignalList defined by id_list
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
v_thresh- For graphical purpose, plot a spike when Vm > V_thresh. If None,
just plot the raw Vm
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:
>> z = subplot(221)
>> aslist.plot(5, v_thresh = -50, display=z, kwargs={'color':'r'})
"""
subplot = get_display(display)
id_list = self._AnalogSignalList__sub_id_list(id_list)
time_axis = self.time_axis()
if not subplot or not HAVE_MATPLOTLIB:
print(MATPLOTLIB_ERROR)
else:
xlabel = "Time (ms)"
ylabel = "Membrane Potential (mV)"
set_labels(subplot, xlabel, ylabel)
for id in id_list:
to_be_plot = self.analog_signals[id].signal
if v_thresh is not None:
to_be_plot = pylab.where(to_be_plot>=v_thresh-0.02, v_thresh+0.5, to_be_plot)
if len(time_axis) > len(to_be_plot):
time_axis = time_axis[:-1]
if len(to_be_plot) > len(time_axis):
to_be_plot = to_be_plot[:-1]
subplot.plot(time_axis, to_be_plot, **kwargs)
subplot.hold(1)
def crosscorrelate(sua1,
sua2,
lag=None,
n_pred=1,
predictor=None,
display=False,
kwargs={}):
"""Cross-correlation between two series of discrete events (e.g. spikes).
Calculates the cross-correlation between
two vectors containing event times.
Returns ``(differeces, pred, norm)``. See below for details.
Adapted from original script written by Martin P. Nawrot for the
FIND MATLAB toolbox [1]_.
Parameters
----------
sua1, sua2 : 1D row or column `ndarray` or `SpikeTrain`
Event times. If sua2 == sua1, the result is the autocorrelogram.
lag : float
Lag for which relative event timing is considered
with a max difference of +/- lag. A default lag is computed
from the inter-event interval of the longer of the two sua
arrays.
n_pred : int
Number of surrogate compilations for the predictor. This
influences the total length of the predictor output array
predictor : {None, 'shuffle'}
Determines the type of bootstrap predictor to be used.
'shuffle' shuffles interevent intervals of the longer input array
and calculates relative differences with the shorter input array.
`n_pred` determines the number of repeated shufflings, resulting
differences are pooled from all repeated shufflings.
display : boolean
If True the corresponding plots will be displayed. If False,
int, int_ and norm will be returned.
kwargs : dict
Arguments to be passed to np.histogram.
Returns
-------
differences : np array
Accumulated differences of events in `sua1` minus the events in
`sua2`. Thus positive values relate to events of `sua2` that
lead events of `sua1`. Units are the same as the input arrays.
pred : np array
Accumulated differences based on the prediction method.
The length of `pred` is ``n_pred * length(differences)``. Units are
the same as the input arrays.
norm : float
Normalization factor used to scale the bin heights in `differences` and
`pred`. ``differences/norm`` and ``pred/norm`` correspond to the linear
correlation coefficient.
Examples
--------
>> crosscorrelate(np_array1, np_array2)
>> crosscorrelate(spike_train1, spike_train2)
>> crosscorrelate(spike_train1, spike_train2, lag = 150.0)
>> crosscorrelate(spike_train1, spike_train2, display=True,
kwargs={'bins':100})
See also
--------
ccf
.. [1] Meier R, Egert U, Aertsen A, Nawrot MP, "FIND - a unified framework
for neural data analysis"; Neural Netw. 2008 Oct; 21(8):1085-93.
"""
assert predictor is 'shuffle' or predictor is None, "predictor must be \
either None or 'shuffle'. Other predictors are not yet implemented."
#Check whether sua1 and sua2 are SpikeTrains or arrays
sua = []
for x in (sua1, sua2):
#if isinstance(x, SpikeTrain):
if hasattr(x, 'spike_times'):
sua.append(x.spike_times)
elif x.ndim == 1:
sua.append(x)
elif x.ndim == 2 and (x.shape[0] == 1 or x.shape[1] == 1):
sua.append(x.ravel())
else:
raise TypeError("sua1 and sua2 must be either instances of the" \
"SpikeTrain class or column/row vectors")
sua1 = sua[0]
sua2 = sua[1]
if sua1.size < sua2.size:
if lag is None:
lag = np.ceil(10 * np.mean(np.diff(sua1)))
reverse = False
else:
if lag is None:
lag = np.ceil(20 * np.mean(np.diff(sua2)))
sua1, sua2 = sua2, sua1
reverse = True
#construct predictor
if predictor is 'shuffle':
isi = np.diff(sua2)
sua2_ = np.array([])
for ni in xrange(1, n_pred + 1):
idx = np.random.permutation(isi.size - 1)
sua2_ = np.append(
sua2_,
np.add(np.insert((np.cumsum(isi[idx])), 0, 0),
sua2.min() + (np.random.exponential(isi.mean()))))
#calculate cross differences in spike times
differences = np.array([])
pred = np.array([])
for k in xrange(0, sua1.size):
differences = np.append(
differences, sua1[k] -
sua2[np.nonzero((sua2 > sua1[k] - lag) & (sua2 < sua1[k] + lag))])
if predictor == 'shuffle':
for k in xrange(0, sua1.size):
pred = np.append(
pred, sua1[k] - sua2_[np.nonzero((sua2_ > sua1[k] - lag)
& (sua2_ < sua1[k] + lag))])
if reverse is True:
differences = -differences
pred = -pred
norm = np.sqrt(sua1.size * sua2.size)
# Plot the results if display=True
if display:
subplot = get_display(display)
if not subplot or not HAVE_PYLAB:
return differences, pred, norm
else:
# Plot the cross-correlation
try:
counts, bin_edges = np.histogram(differences, **kwargs)
edge_distances = np.diff(bin_edges)
bin_centers = bin_edges[1:] - edge_distances / 2
counts = counts / norm
xlabel = "Time"
ylabel = "Cross-correlation coefficient"
#NOTE: the x axis corresponds to the upper edge of each bin
subplot.plot(bin_centers,
counts,
label='cross-correlation',
color='b')
if predictor is None:
set_labels(subplot, xlabel, ylabel)
pylab.draw()
elif predictor is 'shuffle':
# Plot the predictor
norm_ = norm * n_pred
counts_, bin_edges_ = np.histogram(pred, **kwargs)
counts_ = counts_ / norm_
subplot.plot(bin_edges_[1:], counts_, label='predictor')
subplot.legend()
pylab.draw()
except ValueError:
print("There are no correlated events within the selected lag"\
" window of %s" % lag)
else:
return differences, pred, norm
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 crosscorrelate(sua1, sua2, lag=None, n_pred=1, predictor=None,
display=False, kwargs={}):
"""Cross-correlation between two series of discrete events (e.g. spikes).
Calculates the cross-correlation between
two vectors containing event times.
Returns ``(differeces, pred, norm)``. See below for details.
Adapted from original script written by Martin P. Nawrot for the
FIND MATLAB toolbox [1]_.
Parameters
----------
sua1, sua2 : 1D row or column `ndarray` or `SpikeTrain`
Event times. If sua2 == sua1, the result is the autocorrelogram.
lag : float
Lag for which relative event timing is considered
with a max difference of +/- lag. A default lag is computed
from the inter-event interval of the longer of the two sua
arrays.
n_pred : int
Number of surrogate compilations for the predictor. This
influences the total length of the predictor output array
predictor : {None, 'shuffle'}
Determines the type of bootstrap predictor to be used.
'shuffle' shuffles interevent intervals of the longer input array
and calculates relative differences with the shorter input array.
`n_pred` determines the number of repeated shufflings, resulting
differences are pooled from all repeated shufflings.
display : boolean
If True the corresponding plots will be displayed. If False,
int, int_ and norm will be returned.
kwargs : dict
Arguments to be passed to np.histogram.
Returns
-------
differences : np array
Accumulated differences of events in `sua1` minus the events in
`sua2`. Thus positive values relate to events of `sua2` that
lead events of `sua1`. Units are the same as the input arrays.
pred : np array
Accumulated differences based on the prediction method.
The length of `pred` is ``n_pred * length(differences)``. Units are
the same as the input arrays.
norm : float
Normalization factor used to scale the bin heights in `differences` and
`pred`. ``differences/norm`` and ``pred/norm`` correspond to the linear
correlation coefficient.
Examples
--------
>> crosscorrelate(np_array1, np_array2)
>> crosscorrelate(spike_train1, spike_train2)
>> crosscorrelate(spike_train1, spike_train2, lag = 150.0)
>> crosscorrelate(spike_train1, spike_train2, display=True,
kwargs={'bins':100})
See also
--------
ccf
.. [1] Meier R, Egert U, Aertsen A, Nawrot MP, "FIND - a unified framework
for neural data analysis"; Neural Netw. 2008 Oct; 21(8):1085-93.
"""
assert predictor is 'shuffle' or predictor is None, "predictor must be \
either None or 'shuffle'. Other predictors are not yet implemented."
#Check whether sua1 and sua2 are SpikeTrains or arrays
sua = []
for x in (sua1, sua2):
#if isinstance(x, SpikeTrain):
if hasattr(x, 'spike_times'):
sua.append(x.spike_times)
elif x.ndim == 1:
sua.append(x)
elif x.ndim == 2 and (x.shape[0] == 1 or x.shape[1] == 1):
sua.append(x.ravel())
else:
raise TypeError("sua1 and sua2 must be either instances of the" \
"SpikeTrain class or column/row vectors")
sua1 = sua[0]
sua2 = sua[1]
if sua1.size < sua2.size:
if lag is None:
lag = np.ceil(10*np.mean(np.diff(sua1)))
reverse = False
else:
if lag is None:
lag = np.ceil(20*np.mean(np.diff(sua2)))
sua1, sua2 = sua2, sua1
reverse = True
#construct predictor
if predictor is 'shuffle':
isi = np.diff(sua2)
sua2_ = np.array([])
for ni in xrange(1,n_pred+1):
idx = np.random.permutation(isi.size-1)
sua2_ = np.append(sua2_, np.add(np.insert(
(np.cumsum(isi[idx])), 0, 0), sua2.min() + (
np.random.exponential(isi.mean()))))
#calculate cross differences in spike times
differences = np.array([])
pred = np.array([])
for k in xrange(0, sua1.size):
differences = np.append(differences, sua1[k] - sua2[np.nonzero(
(sua2 > sua1[k] - lag) & (sua2 < sua1[k] + lag))])
if predictor == 'shuffle':
for k in xrange(0, sua1.size):
pred = np.append(pred, sua1[k] - sua2_[np.nonzero(
(sua2_ > sua1[k] - lag) & (sua2_ < sua1[k] + lag))])
if reverse is True:
differences = -differences
pred = -pred
norm = np.sqrt(sua1.size * sua2.size)
# Plot the results if display=True
if display:
subplot = get_display(display)
if not subplot or not HAVE_PYLAB:
return differences, pred, norm
else:
# Plot the cross-correlation
try:
counts, bin_edges = np.histogram(differences, **kwargs)
edge_distances = np.diff(bin_edges)
bin_centers = bin_edges[1:] - edge_distances/2
counts = counts / norm
xlabel = "Time"
ylabel = "Cross-correlation coefficient"
#NOTE: the x axis corresponds to the upper edge of each bin
subplot.plot(bin_centers, counts, label='cross-correlation', color='b')
if predictor is None:
set_labels(subplot, xlabel, ylabel)
pylab.draw()
elif predictor is 'shuffle':
# Plot the predictor
norm_ = norm * n_pred
counts_, bin_edges_ = np.histogram(pred, **kwargs)
counts_ = counts_ / norm_
subplot.plot(bin_edges_[1:], counts_, label='predictor')
subplot.legend()
pylab.draw()
except ValueError:
print "There are no correlated events within the selected lag"\
" window of %s" % lag
else:
return differences, pred, norm
def event_triggered_average(self,
eventdict,
events_ids=None,
analogsignal_ids=None,
average=True,
t_min=0,
t_max=100,
ylim=None,
display=False,
mode='same',
kwargs={}):
"""
Returns the event triggered averages of the analog signals inside the list.
The events can be a SpikeList object or a dict containing times.
The average is performed 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 SpikeList object (and events will be the spikes) or just a dict
of times
average - If True, return a single vector of the averaged waveform. If False,
return an array of all the waveforms.
mode - 'same': the average is only done on same ids --> return {'eventids':average};
'all': for all ids in the eventdict the average from all ananlog signals is returned --> return {'eventids':{'analogsignal_ids':average}}
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)
events_ids - when given only perform average over these ids
analogsignal_ids = when given only perform average on these ids
display - if True, a new figure is created for each average. Could also be a subplot.
ylim - ylim of the plot
kwargs - dictionary contening extra parameters that will be sent to the plot
function
Examples
>> vmlist.event_triggered_average(spikelist, average=False, t_min = 50, t_max = 150, mode = 'same')
>> vmlist.event_triggered_average(spikelist, average=True, mode = 'all')
>> vmlist.event_triggered_average({'1':[200,300,'3':[234,788]]}, average=False, display=True)
"""
if isinstance(eventdict, SpikeList):
eventdict = eventdict.spiketrains
figure = get_display(display)
subplotcount = 1
if events_ids is None:
events_ids = eventdict.keys()
if analogsignal_ids is None:
analogsignal_ids = self.analog_signals.keys()
x = numpy.ceil(numpy.sqrt(len(analogsignal_ids)))
y = x
results = {}
first_done = False
for id in events_ids:
events = eventdict[id]
if len(events) <= 0:
continue
if mode is 'same':
if self.analog_signals.has_key(id) and id in analogsignal_ids:
sp = pylab.subplot(x, y, subplotcount)
results[id] = self.analog_signals[
id].event_triggered_average(events,
average=average,
t_min=t_min,
t_max=t_max,
display=sp,
kwargs=kwargs)
pylab.ylim(ylim)
pylab.title('Event: %g; Signal: %g' % (id, id))
subplotcount += 1
elif mode is 'all':
if first_done:
figure = get_display(display)
first_done = True
subplotcount_all = 1
results[id] = {}
for id_analog in analogsignal_ids:
analog_signal = self.analog_signals[id_analog]
sp = pylab.subplot(x, y, subplotcount_all)
results[id][
id_analog] = analog_signal.event_triggered_average(
events,
average=average,
t_min=t_min,
t_max=t_max,
display=sp,
kwargs=kwargs)
pylab.ylim(ylim)
pylab.title('Event: %g; Signal: %g' % (id, id_analog))
subplotcount_all += 1
if not figure or not HAVE_PYLAB:
return results
def event_triggered_average(self, eventdict, events_ids = None, analogsignal_ids = None, average = True, t_min = 0, t_max = 100, ylim = None, display = False, mode = 'same', kwargs={}):
"""
Returns the event triggered averages of the analog signals inside the list.
The events can be a SpikeList object or a dict containing times.
The average is performed 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 SpikeList object (and events will be the spikes) or just a dict
of times
average - If True, return a single vector of the averaged waveform. If False,
return an array of all the waveforms.
mode - 'same': the average is only done on same ids --> return {'eventids':average};
'all': for all ids in the eventdict the average from all ananlog signals is returned --> return {'eventids':{'analogsignal_ids':average}}
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)
events_ids - when given only perform average over these ids
analogsignal_ids = when given only perform average on these ids
display - if True, a new figure is created for each average. Could also be a subplot.
ylim - ylim of the plot
kwargs - dictionary contening extra parameters that will be sent to the plot
function
Examples
>> vmlist.event_triggered_average(spikelist, average=False, t_min = 50, t_max = 150, mode = 'same')
>> vmlist.event_triggered_average(spikelist, average=True, mode = 'all')
>> vmlist.event_triggered_average({'1':[200,300,'3':[234,788]]}, average=False, display=True)
"""
if isinstance(eventdict, SpikeList):
eventdict = eventdict.spiketrains
figure = get_display(display)
subplotcount = 1
if events_ids is None:
events_ids = eventdict.keys()
if analogsignal_ids is None:
analogsignal_ids = self.analog_signals.keys()
x = numpy.ceil(numpy.sqrt(len(analogsignal_ids)))
y = x
results = {}
first_done = False
for id in events_ids:
events = eventdict[id]
if len(events) <= 0:
continue
if mode is 'same':
if self.analog_signals.has_key(id) and id in analogsignal_ids:
sp = pylab.subplot(x,y,subplotcount)
results[id] = self.analog_signals[id].event_triggered_average(events,average=average,t_min=t_min,t_max=t_max,display=sp,kwargs=kwargs)
pylab.ylim(ylim)
pylab.title('Event: %g; Signal: %g'%(id,id))
subplotcount += 1
elif mode is 'all':
if first_done:
figure = get_display(display)
first_done = True
subplotcount_all = 1
results[id] = {}
for id_analog in analogsignal_ids:
analog_signal = self.analog_signals[id_analog]
sp = pylab.subplot(x,y,subplotcount_all)
results[id][id_analog] = analog_signal.event_triggered_average(events,average=average,t_min=t_min,t_max=t_max,display=sp,kwargs=kwargs)
pylab.ylim(ylim)
pylab.title('Event: %g; Signal: %g'%(id,id_analog))
subplotcount_all += 1
if not figure or not HAVE_PYLAB:
return results
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 firing_rate_sliding_window(self,
bin=100,
display=True,
id_list=[],
step=1,
stop=None,
kwargs={}):
'''
Calculate spike rates at ``sample_step`` using s sliding rectangular
window.
Arguments:
bin Bin size of sliding window
display If True, a new figure is created. Could also be a subplot
id_list List of ids to calculate firing rate for
step Step size for moving sliding window (ms)
stop End of spike train
kwargs Additional plot arguments
Here the window is centered over over each time point at sampe_step with
window size equalling bin_size. Takes spike times, number of
neurons no_neurons, bin_size and sample_step as input argument.
'''
ax = get_display(display)
if stop is None: stop = self.t_stop
if not any(id_list): id_list = self.ids
spikes = {} # dictionary with spike times
for id in id_list:
spikes[id] = self.spiketrains[id].spike_times.copy()
n = int((stop - bin) / step) # number of windows
f = bin / 2 # first window at bin/2
l = step * n + bin / 2 # last window at bin*n - bin/2
# Time axis for sliding windows, n_win + 1 due to end points
# [ 0, 1, 2] -> two windows and tre timings
timeAxis = numpy.linspace(f, l, n + 1)
firingRates = [] # sliding time window data
dataSpk = []
#! Calculate number of spikes in ms bins and then sliding window rates
for id in id_list:
spk = spikes[id]
dataSpk.append(spk)
i = 0
j = bin / 2
j_max = stop
rates = []
#! For each ms i in 0 to stop at step intervals
for tPoint in timeAxis:
sum = numpy.sum(
(tPoint - bin / 2 <= spk) * (spk < tPoint + bin / 2))
rates.append(1000.0 * sum / float(bin))
firingRates.append(rates)
firingRates = numpy.array(firingRates) # Convert to numpy array
meanFiringRates = numpy.mean(firingRates, axis=0)
ax.plot(timeAxis, meanFiringRates, **kwargs)
ax.set_xlabel('Time (ms)')
ax.set_ylabel('Frequency (spike/s)')
return timeAxis, firingRates, dataSpk
