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 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, 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 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 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