def raster_from_epoch(signals, epoch, occurrences=0, channels=0,
xlabel='Time', ylabel='Value',
linestyle='-', linewidth=1,
ax=None, title=None):
"""TODO: doc"""
raise NotImplementedError
if occurrences is None:
return
occurrences = pad_to_signals(signals, occurrences)
channels = pad_to_signals(signals, channels)
legend = [s.name for s in signals]
times = []
values = []
for s, o, c in zip(signals, occurrences, channels):
# Get occurrences x chans x time
extracted = s.extract_epoch(epoch)
# Get values from specified occurrence and channel
value_vector = extracted[o][c]
# Convert bins to time (relative to start of epoch)
# TODO: want this to be absolute time relative to start of data?
time_vector = np.arange(0, len(value_vector)) / s.fs
times.append(time_vector)
values.append(value_vector)
plot_timeseries(times, values, xlabel, ylabel, legend=legend,
linestyle=linestyle, linewidth=linewidth,
ax=ax, title=title)
def timeseries_from_epoch(signals,
epoch,
occurrences=0,
channels=0,
xlabel='Time',
ylabel='Value',
linestyle='-',
linewidth=1,
ax=None,
title=None,
pre_dur=None,
dur=None,
PreStimSilence=None):
"""TODO: doc"""
if occurrences is None:
return
occurrences = pad_to_signals(signals, occurrences)
channels = pad_to_signals(signals, channels)
if PreStimSilence is None:
d = signals[0].get_epoch_bounds('PreStimSilence')
if len(d):
PreStimSilence = np.mean(np.diff(d))
else:
PreStimSilence = 0
if pre_dur is None:
pre_dur = PreStimSilence
legend = [s.name for s in signals]
times = []
values = []
for s, o, c in zip(signals, occurrences, channels):
# Get occurrences x chans x time
extracted = s.extract_epoch(epoch)
# Get values from specified occurrence and channel
value_vector = extracted[o][c]
# Convert bins to time (relative to start of epoch)
# TODO: want this to be absolute time relative to start of data?
time_vector = np.arange(0, len(value_vector)) / s.fs - PreStimSilence
# limit time range if specified
good_bins = (time_vector >= -PreStimSilence)
if dur is not None:
good_bins[time_vector > dur] = False
times.append(time_vector[good_bins])
values.append(value_vector[good_bins])
plot_timeseries(times,
values,
xlabel,
ylabel,
legend=legend,
linestyle=linestyle,
linewidth=linewidth,
ax=ax,
title=title)
def timeseries_from_signals(signals,
channels=0,
xlabel='Time',
ylabel='Value',
linestyle='-',
linewidth=1,
ax=None,
title=None):
"""TODO: doc"""
channels = pad_to_signals(signals, channels)
times = []
values = []
legend = []
for s, c in zip(signals, channels):
# Get values from specified channel
value_vector = s.as_continuous()[c]
# Convert indices to absolute time based on sampling frequency
time_vector = np.arange(0, len(value_vector)) / s.fs
times.append(time_vector)
values.append(value_vector)
if s.chans is not None:
legend.append(s.name + ' ' + s.chans[c])
plot_timeseries(times,
values,
xlabel,
ylabel,
legend=legend,
linestyle=linestyle,
linewidth=linewidth,
ax=ax,
title=title)
def timeseries_from_signals(signals=None,
channels=0,
no_legend=False,
time_range=None,
rec=None,
sig_name=None,
**options):
"""
Plot one or more timeseries extracted from a list of signals
:param signals: List of signals to plot
:param channels: List of channels, one per signal(??)
:param no_legend: True/False guess what this means?
:param time_range: if not None, plot time_range[0]:time_range[1] (seconds) of the signal
:return: Matplotlib axes containing the plot
"""
if channels is None:
channels = 0
if signals is None:
signals = [rec[sig_name]]
channels = pad_to_signals(signals, channels)
times = []
values = []
legend = []
for i, s in enumerate(signals):
if len(signals) > 1:
chanset = [channels[i]]
else:
chanset = channels
for c in chanset:
if type(c) is str:
c = 0
# Get values from specified channel
value_vector = s.as_continuous()[c]
# Convert indices to absolute time based on sampling frequency
time_vector = np.arange(0, len(value_vector)) / s.fs
times.append(time_vector)
values.append(value_vector)
if s.chans is not None:
legend.append(s.name + ' ' + s.chans[c])
if no_legend:
legend = None
if time_range is not None:
time_range = np.round(np.array(time_range) * s.fs).astype(int)
times = [t[np.arange(time_range[0], time_range[1])] for t in times]
values = [v[np.arange(time_range[0], time_range[1])] for v in values]
ax = plot_timeseries(times, values, legend=legend, **options)
return ax
def timeseries_from_signals(signals,
channels=0,
xlabel='Time',
ylabel='Value',
ax=None,
title=None):
"""TODO: doc"""
channels = pad_to_signals(signals, channels)
legend = [s.name for s in signals]
times = []
values = []
for s, c in zip(signals, channels):
# Get values from specified channel
value_vector = s.as_continuous()[c]
# Convert indices to absolute time based on sampling frequency
time_vector = np.arange(0, len(value_vector)) / s.fs
times.append(time_vector)
values.append(value_vector)
plot_timeseries(times, values, xlabel, ylabel, legend, ax=ax, title=title)
