def generate_spike_windows(signal, sampling_freq, event_times,
pre_padding=None,
post_padding=None,
min_num_channels=None,
peak_drift=None,
exclude_overlappers=None):
# event times are in sec, peak_drift must be converted to sec (from ms).
collapsed_event_times = collapse_event_times(event_times,
min_num_channels, peak_drift/1000.0)
pre_padding_percent = pre_padding/float(pre_padding+post_padding)
# from sampling frequency (in Hz) and pre/post_padding (in ms) determine
# window_size (in samples)
window_duration = (pre_padding + post_padding) / 1000.0 # now in secs
# +1 to account for the sample itself.
window_size = int(window_duration * sampling_freq) + 1
if len(collapsed_event_times) == 0:
# need to return something compatible in shape with what would have
# been returned if there were event_times.
return [numpy.empty((0, window_size)),numpy.empty(0),
numpy.empty((0, window_size)),numpy.empty(0)]
spike_index_list = numpy.array(collapsed_event_times,
dtype=numpy.float64)*sampling_freq
spike_index_list = numpy.array(spike_index_list, dtype=numpy.int32)
(spike_windows, spike_indexes, excluded_windows, excluded_indexes) =\
window_spikes(signal, spike_index_list,
window_size=window_size,
pre_padding=pre_padding_percent,
exclude_overlappers=exclude_overlappers)
return [numpy.vstack(spike_windows),
numpy.array(spike_indexes)/float(sampling_freq),
excluded_windows,
numpy.array(excluded_indexes)/float(sampling_freq)]
def _plot(self, trial, figure, invert_colors=False, large_bin_size=50,
small_bin_size=1, min_num_channels=3, peak_drift=0.3):
bounds1 = (0.0, 3000.0) # 0 to 3 seconds
bounds2 = (0.0, 20.0) # first 20 ms
event_times = trial.event_times.data
# event_times is in sec, so make peak_drift also in sec.
collapsed_event_times = collapse_event_times(event_times,
min_num_channels, peak_drift/1000.0)
isis = collapsed_event_times[1:] - collapsed_event_times[:-1]
isis *= 1000.0 # to get it to ms
def as_frac(x=None, y=None):
f = figure
canvas_size_in_pixels = (f.get_figwidth()*f.get_dpi(),
f.get_figheight()*f.get_dpi())
return as_fraction(x=x, y=y,
canvas_size_in_pixels=canvas_size_in_pixels)
figure.set_facecolor(background[invert_colors])
figure.set_edgecolor(foreground[invert_colors])
figure.subplots_adjust(left=as_frac(x=80),
right=1.0-as_frac(x=35),
bottom=as_frac(y=40),
top=1.0-as_frac(y=25),
wspace=as_frac(x=80))
a1 = figure.add_subplot(121)
a2 = figure.add_subplot(122)
a1.hist(isis, bins=int((bounds1[1]-bounds1[0])/large_bin_size),
range=bounds1, fc=face_color[invert_colors],
ec=background[invert_colors])
a2.hist(isis, bins=int((bounds2[1]-bounds2[0])/small_bin_size),
range=bounds2, fc=face_color[invert_colors],
ec=background[invert_colors])
a1.text(0.98, 0.95, '%d Spike Events Found'
% len(collapsed_event_times),
color=foreground[invert_colors],
horizontalalignment='right',
verticalalignment='center',
transform=a1.transAxes)
a1.set_ylabel('Count', color=foreground[invert_colors])
a1.set_xlabel('Interspike Interval (ms)',
color=foreground[invert_colors])
a2.set_xlabel('Interspike Interval (ms)',
color=foreground[invert_colors])
# axes color fixing
for axes in [a1, a2]:
frame = axes.patch
frame.set_facecolor(background[invert_colors])
frame.set_edgecolor(foreground[invert_colors])
for spine in axes.spines.values():
spine.set_color(foreground[invert_colors])
lines = axes.get_xticklines()
lines.extend(axes.get_yticklines())
for line in lines:
line.set_color(foreground[invert_colors])
labels = axes.get_xticklabels()
labels.extend(axes.get_yticklabels())
for label in labels:
label.set_color(foreground[invert_colors])