def _spike_trains_plot(win, trains, units, trial_length, events, epochs):
pW = BaseCurveWidget(win)
plot = pW.plot
if events is None:
events = []
if epochs is None:
epochs = []
offset = len(trains)
legend_items = []
for u, t in sorted(trains.iteritems(), key=lambda (u,v):u.name):
color = helper.get_object_color(u)
train = helper.add_spikes(plot, t, color, 2, 21, offset,
u.name, units)
if u.name:
legend_items.append(train)
if trial_length:
plot.add_item(make.curve([0, trial_length], [offset, offset], color='k'))
offset -= 1
helper.add_epochs(plot, epochs, units)
helper.add_events(plot, events, units)
plot.set_axis_title(BasePlot.X_BOTTOM, 'Time')
plot.set_axis_unit(BasePlot.X_BOTTOM, units.dimensionality.string)
win.add_plot_widget(pW, 0)
legend = make.legend(restrict_items=legend_items)
plot.add_item(legend)
win.add_legend_option([legend], True)
if len(trains) > 1:
plot.set_axis_limits(BasePlot.Y_LEFT, 0.5, len(trains) + 0.5)
win.add_custom_curve_tools()
win.show()
def raster(trains, units=None, show_lines=True, events=None, epochs=None):
""" Create a new plotting window with a rasterplot of spiketrains.
:param dict trains: Dictionary of spike trains indexed by a
Neo object (Unit or Segment).
:param Quantity units: Unit of X-Axis. If None, milliseconds are
used.
:param bool show_lines: Determines if a horizontal line will be shown
for each spike train.
:param sequence events: A sequence of neo `Event` objects that will
be marked on the plot.
"""
if not trains:
raise SpykeException("No spike trains for rasterplot")
if not units:
units = pq.ms
win_title = "Spike Trains"
win = PlotDialog(toolbar=True, wintitle=win_title, major_grid=False)
pW = BaseCurveWidget(win)
plot = pW.plot
if events is None:
events = []
if epochs is None:
epochs = []
offset = len(trains)
legend_items = []
for u, t in sorted(trains.iteritems(), key=lambda (u, v): u.name):
color = helper.get_object_color(u)
train = helper.add_spikes(plot, t, color, 2, 21, offset, u.name, units)
if u.name:
legend_items.append(train)
if show_lines:
plot.add_item(make.curve([t.t_start.rescale(units), t.t_stop.rescale(units)], [offset, offset], color="k"))
offset -= 1
helper.add_epochs(plot, epochs, units)
helper.add_events(plot, events, units)
plot.set_axis_title(BasePlot.X_BOTTOM, "Time")
plot.set_axis_unit(BasePlot.X_BOTTOM, units.dimensionality.string)
win.add_plot_widget(pW, 0)
legend = make.legend(restrict_items=legend_items)
plot.add_item(legend)
win.add_legend_option([legend], True)
if len(trains) > 1:
plot.set_axis_limits(BasePlot.Y_LEFT, 0.5, len(trains) + 0.5)
win.add_custom_curve_tools()
win.show()
return win
def raster(trains, time_unit=pq.ms, show_lines=True, events=None, epochs=None):
""" Create a new plotting window with a rasterplot of spiketrains.
:param dict trains: Dictionary of spike trains indexed by a
Neo object (Unit or Segment).
:param Quantity time_unit: Unit of X-Axis.
:param bool show_lines: Determines if a horizontal line will be shown
for each spike train.
:param sequence events: A sequence of neo `Event` objects that will
be marked on the plot.
"""
if not trains:
raise SpykeException('No spike trains for rasterplot')
if not time_unit:
time_unit = pq.ms
win_title = 'Spike Trains'
win = PlotDialog(toolbar=True, wintitle=win_title, major_grid=False)
pW = BaseCurveWidget(win)
plot = pW.plot
if events is None:
events = []
if epochs is None:
epochs = []
offset = len(trains)
legend_items = []
for u, t in trains.iteritems():
color = helper.get_object_color(u)
train = helper.add_spikes(plot, t, color, 2, 21, offset, u.name,
time_unit)
if u.name:
legend_items.append(train)
if show_lines:
plot.add_item(
make.curve([
t.t_start.rescale(time_unit),
t.t_stop.rescale(time_unit)
], [offset, offset],
color='k'))
offset -= 1
helper.add_epochs(plot, epochs, time_unit)
helper.add_events(plot, events, time_unit)
plot.set_axis_title(BasePlot.X_BOTTOM, 'Time')
plot.set_axis_unit(BasePlot.X_BOTTOM, time_unit.dimensionality.string)
win.add_plot_widget(pW, 0)
legend = make.legend(restrict_items=legend_items)
plot.add_item(legend)
win.add_legend_option([legend], True)
if len(trains) > 1:
plot.set_axis_limits(BasePlot.Y_LEFT, 0.5, len(trains) + 0.5)
win.add_custom_curve_tools()
win.show()
return win
def _plot_signal_array_on_window(win, signalarray, events=None, epochs=None,
spike_trains=None, spikes=None,
plot_separate=True):
if signalarray is None:
raise SpykeException(
'Cannot create signal plot: No signal data provided!')
if events is None:
events = []
if epochs is None:
epochs = []
if spike_trains is None:
spike_trains = {}
if spikes is None:
spikes = {}
# X-Axis
sample = (1 / signalarray.sampling_rate).simplified
x = sp.arange(signalarray.shape[0]) * sample
offset = 0 * signalarray.units
channels = range(signalarray.shape[1])
if plot_separate:
plot = None
for c in channels:
pW = BaseCurveWidget(win)
plot = pW.plot
helper.add_epochs(plot, epochs, x.units)
plot.add_item(make.curve(x, signalarray[:, c]))
helper.add_events(plot, events, x.units)
_add_spike_waveforms(plot, spikes, x.units, c, offset)
for train in spike_trains:
color = helper.get_object_color(train.unit)
helper.add_spikes(plot, train, color, units=x.units)
win.add_plot_widget(pW, c)
plot.set_axis_unit(BasePlot.Y_LEFT,
signalarray.dimensionality.string)
plot.set_axis_title(BasePlot.X_BOTTOM, 'Time')
plot.set_axis_unit(BasePlot.X_BOTTOM, x.dimensionality.string)
win.add_x_synchronization_option(True, channels)
win.add_y_synchronization_option(False, channels)
else:
channels.reverse()
pW = BaseCurveWidget(win)
plot = pW.plot
helper.add_epochs(plot, epochs, x.units)
# Find plot y offset
max_offset = 0 * signalarray.units
for i, c in enumerate(channels[1:], 1):
cur_offset = signalarray[:, channels[i - 1]].max() -\
signalarray[:, c].min()
if cur_offset > max_offset:
max_offset = cur_offset
offset -= signalarray[:, channels[0]].min()
for c in channels:
plot.add_item(make.curve(x, signalarray[:, c] + offset))
_add_spike_waveforms(plot, spikes, x.units, c, offset)
offset += max_offset
helper.add_events(plot, events, x.units)
for train in spike_trains:
color = helper.get_object_color(train.unit)
helper.add_spikes(plot, train, color, units=x.units)
win.add_plot_widget(pW, 0)
plot.set_axis_title(BasePlot.X_BOTTOM, 'Time')
plot.set_axis_unit(BasePlot.X_BOTTOM, x.dimensionality.string)
plot.set_axis_unit(BasePlot.Y_LEFT, signalarray.dimensionality.string)
win.add_custom_curve_tools(False)
units = set([s.unit for s in spike_trains])
units = units.union([s.unit for s in spikes])
helper.make_unit_legend(win, units, False)
win.show()
def signals(signals, events=None, epochs=None, spike_trains=None,
spikes=None, show_waveforms=True, use_subplots=True,
time_unit=pq.s, y_unit=None, progress=None):
""" Create a plot from a list of analog signals.
:param list signals: The list of :class:`neo.core.AnalogSignal` objects
to plot.
:param sequence events: A list of Event objects to be included in the
plot.
:param sequence epochs: A list of Epoch objects to be included in the
plot.
:param list spike_trains: A list of :class:`neo.core.SpikeTrain` objects
to be included in the plot. The ``unit`` property (if it exists) is
used for color and legend entries.
:param list spikes: A list :class:`neo.core.Spike` objects to be included
in the plot. The ``unit`` property (if it exists) is used for color
and legend entries.
:param bool show_waveforms: Determines if spikes from
:class:`neo.core.Spike` and :class:`neo.core.SpikeTrain` objects are
shown as waveforms (if available) or vertical lines.
:param bool use_subplots: Determines if a separate subplot for is created
each signal.
:param Quantity time_unit: The unit of the x axis.
:param progress: Set this parameter to report progress.
:type progress: :class:`spykeutils.progress_indicator.ProgressIndicator`
"""
if not signals:
raise SpykeException(
'Cannot create signal plot: No signal data provided!')
if not progress:
progress = ProgressIndicator()
# Plot title
win_title = 'Analog Signal'
if len(set((s.recordingchannel for s in signals))) == 1:
if signals[0].recordingchannel and signals[0].recordingchannel.name:
win_title += ' | Recording Channel: %s' %\
signals[0].recordingchannel.name
if len(set((s.segment for s in signals))) == 1:
if signals[0].segment and signals[0].segment.name:
win_title += ' | Segment: %s' % signals[0].segment.name
win = PlotDialog(toolbar=True, wintitle=win_title)
if events is None:
events = []
if epochs is None:
epochs = []
if spike_trains is None:
spike_trains = []
if spikes is None:
spikes = []
if show_waveforms:
for st in spike_trains:
if st.waveforms is not None:
spikes.extend(conversions.spike_train_to_spikes(st))
spike_trains = []
else:
unit_spikes = {}
for s in spikes:
unit_spikes.setdefault(s.unit, []).append(s)
for sps in unit_spikes.itervalues():
spike_trains.append(conversions.spikes_to_spike_train(sps, False))
spikes = []
channels = range(len(signals))
progress.set_ticks((len(spike_trains) + len(spikes) + 1) * len(channels))
offset = 0 * signals[0].units
if use_subplots:
plot = None
for c in channels:
pW = BaseCurveWidget(win)
plot = pW.plot
if signals[c].name:
win.set_plot_title(plot, signals[c].name)
elif signals[c].recordingchannel:
if signals[c].recordingchannel.name:
win.set_plot_title(plot, signals[c].recordingchannel.name)
sample = (1 / signals[c].sampling_rate).simplified
x = (sp.arange(signals[c].shape[0])) * sample + signals[c].t_start
x.units = time_unit
helper.add_epochs(plot, epochs, x.units)
if y_unit is not None:
plot.add_item(make.curve(x, signals[c].rescale(y_unit)))
else:
plot.add_item(make.curve(x, signals[c]))
helper.add_events(plot, events, x.units)
_add_spike_waveforms(plot, spikes, x.units, c, offset, progress)
for train in spike_trains:
color = helper.get_object_color(train.unit)
helper.add_spikes(plot, train, color, units=x.units)
progress.step()
win.add_plot_widget(pW, c)
plot.set_axis_unit(BasePlot.Y_LEFT,
signals[c].dimensionality.string)
progress.step()
plot.set_axis_title(BasePlot.X_BOTTOM, 'Time')
plot.set_axis_unit(BasePlot.X_BOTTOM, x.dimensionality.string)
else:
channels.reverse()
pW = BaseCurveWidget(win)
plot = pW.plot
helper.add_epochs(plot, epochs, time_unit)
# Find plot y offset
max_offset = 0 * signals[0].units
for i, c in enumerate(channels[1:], 1):
cur_offset = signals[channels[i - 1]].max() - signals[c].min()
if cur_offset > max_offset:
max_offset = cur_offset
offset -= signals[channels[0]].min()
for c in channels:
sample = (1 / signals[c].sampling_rate).simplified
x = (sp.arange(signals[c].shape[0])) * sample + signals[c].t_start
x.units = time_unit
if y_unit is not None:
plot.add_item(make.curve(x,
(signals[c] + offset).rescale(y_unit)))
else:
plot.add_item(make.curve(x, signals[c] + offset))
_add_spike_waveforms(plot, spikes, x.units, c, offset, progress)
offset += max_offset
progress.step()
helper.add_events(plot, events, x.units)
for train in spike_trains:
color = helper.get_object_color(train.unit)
helper.add_spikes(plot, train, color, units=x.units)
progress.step()
win.add_plot_widget(pW, 0)
plot.set_axis_title(BasePlot.X_BOTTOM, 'Time')
plot.set_axis_unit(BasePlot.X_BOTTOM, x.dimensionality.string)
plot.set_axis_unit(BasePlot.Y_LEFT, signals[0].dimensionality.string)
win.add_custom_curve_tools(False)
units = set([s.unit for s in spike_trains])
units = units.union([s.unit for s in spikes])
progress.done()
helper.make_window_legend(win, units, False)
win.show()
if use_subplots:
win.add_x_synchronization_option(True, channels)
win.add_y_synchronization_option(False, channels)
return win
def signals(signals,
events=None,
epochs=None,
spike_trains=None,
spikes=None,
show_waveforms=True,
use_subplots=True,
subplot_names=True,
time_unit=pq.s,
y_unit=None,
progress=None):
""" Create a plot from a list of analog signals.
:param list signals: The list of :class:`neo.core.AnalogSignal` objects
to plot.
:param sequence events: A list of Event objects to be included in the
plot.
:param sequence epochs: A list of Epoch objects to be included in the
plot.
:param list spike_trains: A list of :class:`neo.core.SpikeTrain` objects
to be included in the plot. The ``unit`` property (if it exists) is
used for color and legend entries.
:param list spikes: A list :class:`neo.core.Spike` objects to be included
in the plot. The ``unit`` property (if it exists) is used for color
and legend entries.
:param bool show_waveforms: Determines if spikes from
:class:`neo.core.Spike` and :class:`neo.core.SpikeTrain` objects are
shown as waveforms (if available) or vertical lines.
:param bool use_subplots: Determines if a separate subplot for is created
each signal.
:param bool subplot_names: Only valid if ``use_subplots`` is True.
Determines if signal (or channel) names are shown for subplots.
:param Quantity time_unit: The unit of the x axis.
:param progress: Set this parameter to report progress.
:type progress: :class:`spykeutils.progress_indicator.ProgressIndicator`
"""
if not signals:
raise SpykeException(
'Cannot create signal plot: No signal data provided!')
if not progress:
progress = ProgressIndicator()
# Plot title
win_title = 'Analog Signal'
if len(set((s.recordingchannel for s in signals))) == 1:
if signals[0].recordingchannel and signals[0].recordingchannel.name:
win_title += ' | Recording Channel: %s' %\
signals[0].recordingchannel.name
if len(set((s.segment for s in signals))) == 1:
if signals[0].segment and signals[0].segment.name:
win_title += ' | Segment: %s' % signals[0].segment.name
win = PlotDialog(toolbar=True, wintitle=win_title)
if events is None:
events = []
if epochs is None:
epochs = []
if spike_trains is None:
spike_trains = []
if spikes is None:
spikes = []
if show_waveforms:
for st in spike_trains:
if st.waveforms is not None:
spikes.extend(conversions.spike_train_to_spikes(st))
spike_trains = []
else:
unit_spikes = {}
for s in spikes:
unit_spikes.setdefault(s.unit, []).append(s)
for sps in unit_spikes.itervalues():
spike_trains.append(conversions.spikes_to_spike_train(sps, False))
spikes = []
channels = range(len(signals))
channel_indices = []
for s in signals:
if not s.recordingchannel:
channel_indices.append(-1)
else:
channel_indices.append(s.recordingchannel.index)
# Heuristic: If multiple channels have the same index, use channel order
# as index for spike waveforms
nonindices = max(0, channel_indices.count(-1) - 1)
if len(set(channel_indices)) != len(channel_indices) - nonindices:
channel_indices = range(len(signals))
progress.set_ticks((len(spike_trains) + len(spikes) + 1) * len(channels))
offset = 0 * signals[0].units
if use_subplots:
plot = None
for c in channels:
pW = BaseCurveWidget(win)
plot = pW.plot
if subplot_names:
if signals[c].name:
win.set_plot_title(plot, signals[c].name)
elif signals[c].recordingchannel:
if signals[c].recordingchannel.name:
win.set_plot_title(plot,
signals[c].recordingchannel.name)
sample = (1 / signals[c].sampling_rate).simplified
x = (sp.arange(signals[c].shape[0])) * sample + signals[c].t_start
x.units = time_unit
helper.add_epochs(plot, epochs, x.units)
if y_unit is not None:
plot.add_item(make.curve(x, signals[c].rescale(y_unit)))
else:
plot.add_item(make.curve(x, signals[c]))
helper.add_events(plot, events, x.units)
_add_spike_waveforms(plot, spikes, x.units, channel_indices[c],
offset, progress)
for train in spike_trains:
color = helper.get_object_color(train.unit)
helper.add_spikes(plot, train, color, units=x.units)
progress.step()
win.add_plot_widget(pW, c)
plot.set_axis_unit(BasePlot.Y_LEFT,
signals[c].dimensionality.string)
progress.step()
plot.set_axis_title(BasePlot.X_BOTTOM, 'Time')
plot.set_axis_unit(BasePlot.X_BOTTOM, x.dimensionality.string)
else:
channels.reverse()
pW = BaseCurveWidget(win)
plot = pW.plot
helper.add_epochs(plot, epochs, time_unit)
# Find plot y offset
max_offset = 0 * signals[0].units
for i, c in enumerate(channels[1:], 1):
cur_offset = signals[channels[i - 1]].max() - signals[c].min()
if cur_offset > max_offset:
max_offset = cur_offset
offset -= signals[channels[0]].min()
for c in channels:
sample = (1 / signals[c].sampling_rate).simplified
x = (sp.arange(signals[c].shape[0])) * sample + signals[c].t_start
x.units = time_unit
if y_unit is not None:
plot.add_item(
make.curve(x, (signals[c] + offset).rescale(y_unit)))
else:
plot.add_item(make.curve(x, signals[c] + offset))
_add_spike_waveforms(plot, spikes, x.units, channel_indices[c],
offset, progress)
offset += max_offset
progress.step()
helper.add_events(plot, events, x.units)
for train in spike_trains:
color = helper.get_object_color(train.unit)
helper.add_spikes(plot, train, color, units=x.units)
progress.step()
win.add_plot_widget(pW, 0)
plot.set_axis_title(BasePlot.X_BOTTOM, 'Time')
plot.set_axis_unit(BasePlot.X_BOTTOM, x.dimensionality.string)
plot.set_axis_unit(BasePlot.Y_LEFT, signals[0].dimensionality.string)
win.add_custom_curve_tools()
units = set([s.unit for s in spike_trains])
units = units.union([s.unit for s in spikes])
progress.done()
helper.make_window_legend(win, units, False)
win.show()
if use_subplots:
win.add_x_synchronization_option(True, channels)
win.add_y_synchronization_option(False, channels)
return win