class TraceView(ScalingMixin, BaseColorView, ManualClusteringView):
"""This view shows the raw traces along with spike waveforms.
Constructor
-----------
traces : function
Maps a time interval `(t0, t1)` to a `Bunch(data, color, waveforms)` where
* `data` is an `(n_samples, n_channels)` array
* `waveforms` is a list of bunchs with the following attributes:
* `data`
* `color`
* `channel_ids`
* `start_time`
* `spike_id`
* `spike_cluster`
spike_times : function
Teturns the list of relevant spike times.
sample_rate : float
duration : float
n_channels : int
channel_positions : array-like
Positions of the channels, used for displaying the channels in the right y order
channel_labels : list
Labels of all shown channels. By default, this is just the channel ids.
"""
_default_position = 'left'
auto_update = True
auto_scale = True
interval_duration = .25 # default duration of the interval
shift_amount = .1
scaling_coeff_x = 1.25
trace_quantile = .01 # quantile for auto-scaling
default_trace_color = (.5, .5, .5, 1)
trace_color_0 = (.353, .161, .443)
trace_color_1 = (.133, .404, .396)
default_shortcuts = {
'change_trace_size': 'ctrl+wheel',
'switch_color_scheme': 'shift+wheel',
'navigate': 'alt+wheel',
'decrease': 'alt+down',
'increase': 'alt+up',
'go_left': 'alt+left',
'go_right': 'alt+right',
'jump_left': 'shift+alt+left',
'jump_right': 'shift+alt+right',
'go_to_start': 'alt+home',
'go_to_end': 'alt+end',
'go_to': 'alt+t',
'go_to_next_spike': 'alt+pgdown',
'go_to_previous_spike': 'alt+pgup',
'narrow': 'alt++',
'select_spike': 'ctrl+click',
'select_channel_pcA': 'shift+left click',
'select_channel_pcB': 'shift+right click',
'switch_origin': 'alt+o',
'toggle_highlighted_spikes': 'alt+s',
'toggle_show_labels': 'alt+l',
'widen': 'alt+-',
}
default_snippets = {
'go_to': 'tg',
'shift': 'ts',
}
def __init__(
self, traces=None, sample_rate=None, spike_times=None, duration=None,
n_channels=None, channel_positions=None, channel_labels=None, **kwargs):
self.do_show_labels = True
self.show_all_spikes = False
self.get_spike_times = spike_times
# Sample rate.
assert sample_rate > 0
self.sample_rate = float(sample_rate)
self.dt = 1. / self.sample_rate
# Traces and spikes.
assert hasattr(traces, '__call__')
self.traces = traces
# self.waveforms = None
assert duration >= 0
self.duration = duration
assert n_channels >= 0
self.n_channels = n_channels
# Channel y ranking.
self.channel_positions = (
channel_positions if channel_positions is not None else
np.c_[np.zeros(n_channels), np.arange(n_channels)])
# channel_y_ranks[i] is the position of channel #i in the trace view.
self.channel_y_ranks = np.argsort(np.argsort(self.channel_positions[:, 1]))
assert self.channel_y_ranks.shape == (n_channels,)
# Channel labels.
self.channel_labels = (
channel_labels if channel_labels is not None else
['%d' % ch for ch in range(n_channels)])
assert len(self.channel_labels) == n_channels
# Initialize the view.
super(TraceView, self).__init__(**kwargs)
self.state_attrs += ('origin', 'do_show_labels', 'show_all_spikes', 'auto_scale')
self.local_state_attrs += ('interval', 'scaling',)
# Visuals.
self._create_visuals()
# Initial interval.
self._interval = None
self.go_to(duration / 2.)
self._waveform_times = []
self.canvas.panzoom.set_constrain_bounds((-1, -2, +1, +2))
def _create_visuals(self):
self.canvas.set_layout('stacked', n_plots=self.n_channels)
self.canvas.enable_axes(show_y=False)
self.trace_visual = UniformPlotVisual()
# Gradient of color for the traces.
if self.trace_color_0 and self.trace_color_1:
self.trace_visual.inserter.insert_frag(
'gl_FragColor.rgb = mix(vec3%s, vec3%s, (v_signal_index / %d));' % (
self.trace_color_0, self.trace_color_1, self.n_channels), 'end')
self.canvas.add_visual(self.trace_visual)
self.waveform_visual = PlotVisual()
self.canvas.add_visual(self.waveform_visual)
self.text_visual = TextVisual()
_fix_coordinate_in_visual(self.text_visual, 'x')
self.text_visual.inserter.add_varying(
'float', 'v_discard',
'float((n_boxes >= 50 * u_zoom.y) && '
'(mod(int(a_box_index), int(n_boxes / (50 * u_zoom.y))) >= 1))')
self.text_visual.inserter.insert_frag('if (v_discard > 0) discard;', 'end')
self.canvas.add_visual(self.text_visual)
@property
def stacked(self):
return self.canvas.stacked
# Internal methods
# -------------------------------------------------------------------------
def _plot_traces(self, traces, color=None):
traces = traces.T
n_samples = traces.shape[1]
n_ch = self.n_channels
assert traces.shape == (n_ch, n_samples)
color = color or self.default_trace_color
t = self._interval[0] + np.arange(n_samples) * self.dt
t = np.tile(t, (n_ch, 1))
box_index = self.channel_y_ranks
box_index = np.repeat(box_index[:, np.newaxis], n_samples, axis=1)
assert t.shape == (n_ch, n_samples)
assert traces.shape == (n_ch, n_samples)
assert box_index.shape == (n_ch, n_samples)
self.trace_visual.color = color
self.canvas.update_visual(
self.trace_visual,
t, traces,
data_bounds=self.data_bounds,
box_index=box_index.ravel(),
)
def _plot_spike(self, bunch):
# The spike time corresponds to the first sample of the waveform.
n_samples, n_channels = bunch.data.shape
assert len(bunch.channel_ids) == n_channels
# Generate the x coordinates of the waveform.
t = bunch.start_time + self.dt * np.arange(n_samples)
t = np.tile(t, (n_channels, 1)) # (n_unmasked_channels, n_samples)
# Determine the spike color.
i = bunch.select_index
c = bunch.spike_cluster
cs = self.color_schemes.get()
color = selected_cluster_color(i, alpha=1) if i is not None else cs.get(c, alpha=1)
# We could tweak the color of each spike waveform depending on the template amplitude
# on each of its best channels.
# channel_amps = bunch.get('channel_amps', None)
# if channel_amps is not None:
# color = np.tile(color, (n_channels, 1))
# assert color.shape == (n_channels, 4)
# color[:, 3] = channel_amps
# The box index depends on the channel.
box_index = self.channel_y_ranks[bunch.channel_ids]
box_index = np.repeat(box_index[:, np.newaxis], n_samples, axis=0)
self.waveform_visual.add_batch_data(
box_index=box_index,
x=t, y=bunch.data.T, color=color,
data_bounds=self.data_bounds,
)
def _plot_waveforms(self, waveforms, **kwargs):
"""Plot the waveforms."""
# waveforms = self.waveforms
assert isinstance(waveforms, list)
if waveforms:
self.waveform_visual.show()
self.waveform_visual.reset_batch()
for w in waveforms:
self._plot_spike(w)
self._waveform_times.append(
(w.start_time, w.spike_id, w.spike_cluster, w.get('channel_ids', None)))
self.canvas.update_visual(self.waveform_visual)
else: # pragma: no cover
self.waveform_visual.hide()
def _plot_labels(self, traces):
self.text_visual.reset_batch()
for ch in range(self.n_channels):
bi = self.channel_y_ranks[ch]
ch_label = self.channel_labels[ch]
self.text_visual.add_batch_data(
pos=[self.data_bounds[0], 0],
text=ch_label,
anchor=[+1., 0],
data_bounds=self.data_bounds,
box_index=bi,
)
self.canvas.update_visual(self.text_visual)
# Public methods
# -------------------------------------------------------------------------
def _restrict_interval(self, interval):
start, end = interval
# Round the times to full samples to avoid subsampling shifts
# in the traces.
start = int(round(start * self.sample_rate)) / self.sample_rate
end = int(round(end * self.sample_rate)) / self.sample_rate
# Restrict the interval to the boundaries of the traces.
if start < 0:
end += (-start)
start = 0
elif end >= self.duration:
start -= (end - self.duration)
end = self.duration
start = np.clip(start, 0, end)
end = np.clip(end, start, self.duration)
assert 0 <= start < end <= self.duration
return start, end
def plot(self, update_traces=True, update_waveforms=True):
if update_waveforms:
# Load the traces in the interval.
traces = self.traces(self._interval)
if update_traces:
logger.log(5, "Redraw the entire trace view.")
start, end = self._interval
# Find the data bounds.
if self.auto_scale or getattr(self, 'data_bounds', NDC) == NDC:
ymin = np.quantile(traces.data, self.trace_quantile)
ymax = np.quantile(traces.data, 1. - self.trace_quantile)
else:
ymin, ymax = self.data_bounds[1], self.data_bounds[3]
self.data_bounds = (start, ymin, end, ymax)
# Used for spike click.
self._waveform_times = []
# Plot the traces.
self._plot_traces(
traces.data, color=traces.get('color', None))
# Plot the labels.
if self.do_show_labels:
self._plot_labels(traces.data)
if update_waveforms:
self._plot_waveforms(traces.get('waveforms', []))
self._update_axes()
self.canvas.update()
def set_interval(self, interval=None):
"""Display the traces and spikes in a given interval."""
if interval is None:
interval = self._interval
interval = self._restrict_interval(interval)
if interval != self._interval:
logger.log(5, "Redraw the entire trace view.")
self._interval = interval
emit('is_busy', self, True)
self.plot(update_traces=True, update_waveforms=True)
emit('is_busy', self, False)
emit('time_range_selected', self, interval)
self.update_status()
else:
self.plot(update_traces=False, update_waveforms=True)
def on_select(self, cluster_ids=None, **kwargs):
self.cluster_ids = cluster_ids
if not cluster_ids:
return
# Make sure we call again self.traces() when the cluster selection changes.
self.set_interval()
def attach(self, gui):
"""Attach the view to the GUI."""
super(TraceView, self).attach(gui)
self.actions.add(self.toggle_show_labels, checkable=True, checked=self.do_show_labels)
self.actions.add(
self.toggle_highlighted_spikes, checkable=True, checked=self.show_all_spikes)
self.actions.add(self.toggle_auto_scale, checkable=True, checked=self.auto_scale)
self.actions.add(self.switch_origin)
self.actions.separator()
self.actions.add(
self.go_to, prompt=True, prompt_default=lambda: str(self.time))
self.actions.separator()
self.actions.add(self.go_to_start)
self.actions.add(self.go_to_end)
self.actions.separator()
self.actions.add(self.shift, prompt=True)
self.actions.add(self.go_right)
self.actions.add(self.go_left)
self.actions.add(self.jump_right)
self.actions.add(self.jump_left)
self.actions.separator()
self.actions.add(self.widen)
self.actions.add(self.narrow)
self.actions.separator()
self.actions.add(self.go_to_next_spike)
self.actions.add(self.go_to_previous_spike)
self.actions.separator()
self.set_interval()
@property
def status(self):
a, b = self._interval
return '[{:.2f}s - {:.2f}s]. Color scheme: {}.'.format(a, b, self.color_scheme)
# Origin
# -------------------------------------------------------------------------
@property
def origin(self):
"""Whether to show the channels from top to bottom (`top` option, the default), or from
bottom to top (`bottom`)."""
return getattr(self.canvas.layout, 'origin', Stacked._origin)
@origin.setter
def origin(self, value):
if value is None:
return
if self.canvas.layout:
self.canvas.layout.origin = value
else: # pragma: no cover
logger.warning(
"Could not set origin to %s because the layout instance was not initialized yet.",
value)
def switch_origin(self):
"""Switch between top and bottom origin for the channels."""
self.origin = 'bottom' if self.origin == 'top' else 'top'
# Navigation
# -------------------------------------------------------------------------
@property
def time(self):
"""Time at the center of the window."""
return sum(self._interval) * .5
@property
def interval(self):
"""Interval as `(tmin, tmax)`."""
return self._interval
@interval.setter
def interval(self, value):
self.set_interval(value)
@property
def half_duration(self):
"""Half of the duration of the current interval."""
if self._interval is not None:
a, b = self._interval
return (b - a) * .5
else:
return self.interval_duration * .5
def go_to(self, time):
"""Go to a specific time (in seconds)."""
half_dur = self.half_duration
self.set_interval((time - half_dur, time + half_dur))
def shift(self, delay):
"""Shift the interval by a given delay (in seconds)."""
self.go_to(self.time + delay)
def go_to_start(self):
"""Go to the start of the recording."""
self.go_to(0)
def go_to_end(self):
"""Go to end of the recording."""
self.go_to(self.duration)
def go_right(self):
"""Go to right."""
start, end = self._interval
delay = (end - start) * .1
self.shift(delay)
def go_left(self):
"""Go to left."""
start, end = self._interval
delay = (end - start) * .1
self.shift(-delay)
def jump_right(self):
"""Jump to right."""
delay = self.duration * .1
self.shift(delay)
def jump_left(self):
"""Jump to left."""
delay = self.duration * .1
self.shift(-delay)
def _jump_to_spike(self, delta=+1):
"""Jump to next or previous spike from the selected clusters."""
spike_times = self.get_spike_times()
if spike_times is not None and len(spike_times):
ind = np.searchsorted(spike_times, self.time)
n = len(spike_times)
self.go_to(spike_times[(ind + delta) % n])
def go_to_next_spike(self, ):
"""Jump to the next spike from the first selected cluster."""
self._jump_to_spike(+1)
def go_to_previous_spike(self, ):
"""Jump to the previous spike from the first selected cluster."""
self._jump_to_spike(-1)
def toggle_highlighted_spikes(self, checked):
"""Toggle between showing all spikes or selected spikes."""
self.show_all_spikes = checked
self.set_interval()
def widen(self):
"""Increase the interval size."""
t, h = self.time, self.half_duration
h *= self.scaling_coeff_x
self.set_interval((t - h, t + h))
def narrow(self):
"""Decrease the interval size."""
t, h = self.time, self.half_duration
h /= self.scaling_coeff_x
self.set_interval((t - h, t + h))
# Misc
# -------------------------------------------------------------------------
def toggle_show_labels(self, checked):
"""Toggle the display of the channel ids."""
logger.debug("Set show labels to %s.", checked)
self.do_show_labels = checked
self.text_visual.toggle()
self.canvas.update()
def toggle_auto_scale(self, checked):
"""Toggle automatic scaling of the traces."""
logger.debug("Set auto scale to %s.", checked)
self.auto_scale = checked
def update_color(self):
"""Update the view when the color scheme changes."""
self.plot(update_traces=False, update_waveforms=True)
# Scaling
# -------------------------------------------------------------------------
@property
def scaling(self):
"""Scaling of the channel boxes."""
return self.stacked._box_scaling[1]
@scaling.setter
def scaling(self, value):
self.stacked._box_scaling = (self.stacked._box_scaling[0], value)
def _get_scaling_value(self):
return self.scaling
def _set_scaling_value(self, value):
self.scaling = value
self.stacked.update()
# Spike selection
# -------------------------------------------------------------------------
def on_mouse_click(self, e):
"""Select a cluster by clicking on a spike."""
if 'Control' in e.modifiers:
# Get mouse position in NDC.
box_id, _ = self.canvas.stacked.box_map(e.pos)
channel_id = np.nonzero(self.channel_y_ranks == box_id)[0]
# Find the spike and cluster closest to the mouse.
db = self.data_bounds
# Get the information about the displayed spikes.
wt = [(t, s, c, ch) for t, s, c, ch in self._waveform_times if channel_id in ch]
if not wt:
return
# Get the time coordinate of the mouse position.
mouse_pos = self.canvas.panzoom.window_to_ndc(e.pos)
mouse_time = Range(NDC, db).apply(mouse_pos)[0][0]
# Get the closest spike id.
times, spike_ids, spike_clusters, channel_ids = zip(*wt)
i = np.argmin(np.abs(np.array(times) - mouse_time))
# Raise the select_spike event.
spike_id = spike_ids[i]
cluster_id = spike_clusters[i]
emit('select_spike', self, channel_id=channel_id,
spike_id=spike_id, cluster_id=cluster_id)
if 'Shift' in e.modifiers:
# Get mouse position in NDC.
box_id, _ = self.canvas.stacked.box_map(e.pos)
channel_id = int(np.nonzero(self.channel_y_ranks == box_id)[0][0])
emit('select_channel', self, channel_id=channel_id, button=e.button)
def on_mouse_wheel(self, e): # pragma: no cover
"""Scroll through the data with alt+wheel."""
super(TraceView, self).on_mouse_wheel(e)
if e.modifiers == ('Alt',):
start, end = self._interval
delay = e.delta * (end - start) * .1
self.shift(-delay)
class WaveformView(ScalingMixin, ManualClusteringView):
"""This view shows the waveforms of the selected clusters, on relevant channels,
following the probe geometry.
Constructor
-----------
waveforms : dict of functions
Every function maps a cluster id to a Bunch with the following attributes:
* `data` : a 3D array `(n_spikes, n_samples, n_channels_loc)`
* `channel_ids` : the channel ids corresponding to the third dimension in `data`
* `channel_labels` : a list of channel labels for every channel in `channel_ids`
* `channel_positions` : a 2D array with the coordinates of the channels on the probe
* `masks` : a 2D array `(n_spikes, n_channels)` with the waveforms masks
* `alpha` : the alpha transparency channel
The keys of the dictionary are called **waveform types**. The `next_waveforms_type`
action cycles through all available waveform types. The key `waveforms` is mandatory.
waveforms_type : str
Default key of the waveforms dictionary to plot initially.
"""
# Do not show too many clusters.
max_n_clusters = 8
_default_position = 'right'
ax_color = (.75, .75, .75, 1.)
tick_size = 5.
cluster_ids = ()
default_shortcuts = {
'toggle_waveform_overlap': 'o',
'toggle_show_labels': 'ctrl+l',
'next_waveforms_type': 'w',
'previous_waveforms_type': 'shift+w',
'toggle_mean_waveforms': 'm',
# Box scaling.
'widen': 'ctrl+right',
'narrow': 'ctrl+left',
'increase': 'ctrl+up',
'decrease': 'ctrl+down',
'change_box_size': 'ctrl+wheel',
# Probe scaling.
'extend_horizontally': 'shift+right',
'shrink_horizontally': 'shift+left',
'extend_vertically': 'shift+up',
'shrink_vertically': 'shift+down',
}
default_snippets = {
'change_n_spikes_waveforms': 'wn',
}
def __init__(self,
waveforms=None,
waveforms_type=None,
sample_rate=None,
**kwargs):
self._overlap = False
self.do_show_labels = True
self.channel_ids = None
self.filtered_tags = ()
self.wave_duration = 0. # updated in the plotting method
self.data_bounds = None
self.sample_rate = sample_rate
self._status_suffix = ''
assert sample_rate > 0., "The sample rate must be provided to the waveform view."
# Initialize the view.
super(WaveformView, self).__init__(**kwargs)
self.state_attrs += ('waveforms_type', 'overlap', 'do_show_labels')
self.local_state_attrs += ('box_scaling', 'probe_scaling')
# Box and probe scaling.
self.canvas.set_layout('boxed', box_pos=np.zeros((1, 2)))
# Ensure waveforms is a dictionary, even if there is a single waveforms type.
waveforms = waveforms or {}
waveforms = waveforms if isinstance(waveforms, dict) else {
'waveforms': waveforms
}
self.waveforms = waveforms
# Rotating property waveforms types.
self.waveforms_types = RotatingProperty()
for name, value in self.waveforms.items():
self.waveforms_types.add(name, value)
# Current waveforms type.
self.waveforms_types.set(waveforms_type)
assert self.waveforms_type in self.waveforms
self.text_visual = TextVisual()
self.canvas.add_visual(self.text_visual)
self.line_visual = LineVisual()
self.canvas.add_visual(self.line_visual)
self.tick_visual = UniformScatterVisual(marker='vbar',
color=self.ax_color,
size=self.tick_size)
self.canvas.add_visual(self.tick_visual)
# Two types of visuals: thin raw line visual for normal waveforms, thick antialiased
# agg plot visual for mean and template waveforms.
self.waveform_agg_visual = PlotAggVisual()
self.waveform_visual = PlotVisual()
self.canvas.add_visual(self.waveform_agg_visual)
self.canvas.add_visual(self.waveform_visual)
# Internal methods
# -------------------------------------------------------------------------
@property
def _current_visual(self):
if self.waveforms_type == 'waveforms':
return self.waveform_visual
else:
return self.waveform_agg_visual
def _get_data_bounds(self, bunchs):
m = min(_min(b.data) for b in bunchs)
M = max(_max(b.data) for b in bunchs)
# Symmetrize on the y axis.
M = max(abs(m), abs(M))
return [-1, -M, +1, M]
def get_clusters_data(self):
if self.waveforms_type not in self.waveforms:
return
bunchs = [
self.waveforms_types.get()(cluster_id)
for cluster_id in self.cluster_ids
]
clu_offsets = _get_clu_offsets(bunchs)
n_clu = max(clu_offsets) + 1
# Offset depending on the overlap.
for i, (bunch, offset) in enumerate(zip(bunchs, clu_offsets)):
bunch.index = i
bunch.offset = offset
bunch.n_clu = n_clu
bunch.color = selected_cluster_color(i, bunch.get('alpha', .75))
return bunchs
def _plot_cluster(self, bunch):
wave = bunch.data
if wave is None or not wave.size:
return
channel_ids_loc = bunch.channel_ids
n_channels = len(channel_ids_loc)
masks = bunch.get('masks', np.ones((wave.shape[0], n_channels)))
# By default, this is 0, 1, 2 for the first 3 clusters.
# But it can be customized when displaying several sets
# of waveforms per cluster.
n_spikes_clu, n_samples = wave.shape[:2]
assert wave.shape[2] == n_channels
assert masks.shape == (n_spikes_clu, n_channels)
# Find the x coordinates.
t = get_linear_x(n_spikes_clu * n_channels, n_samples)
t = _overlap_transform(t,
offset=bunch.offset,
n=bunch.n_clu,
overlap=self.overlap)
# HACK: on the GPU, we get the actual masks with fract(masks)
# since we add the relative cluster index. We need to ensure
# that the masks is never 1.0, otherwise it is interpreted as
# 0.
eps = .001
masks = eps + (1 - 2 * eps) * masks
# NOTE: we add the cluster index which is used for the
# computation of the depth on the GPU.
masks += bunch.index
# Generate the box index (one number per channel).
box_index = _index_of(channel_ids_loc, self.channel_ids)
box_index = np.tile(box_index, n_spikes_clu)
# Find the correct number of vertices depending on the current waveform visual.
if self._current_visual == self.waveform_visual:
# PlotVisual
box_index = np.repeat(box_index, n_samples)
assert box_index.size == n_spikes_clu * n_channels * n_samples
else:
# PlotAggVisual
box_index = np.repeat(box_index, 2 * (n_samples + 2))
assert box_index.size == n_spikes_clu * n_channels * 2 * (
n_samples + 2)
# Generate the waveform array.
wave = np.transpose(wave, (0, 2, 1))
nw = n_spikes_clu * n_channels
wave = wave.reshape((nw, n_samples))
assert self.data_bounds is not None
self._current_visual.add_batch_data(x=t,
y=wave,
color=bunch.color,
masks=masks,
box_index=box_index,
data_bounds=self.data_bounds)
# Waveform axes.
# --------------
# Horizontal y=0 lines.
ax_db = self.data_bounds
a, b = _overlap_transform(np.array([-1, 1]),
offset=bunch.offset,
n=bunch.n_clu,
overlap=self.overlap)
box_index = _index_of(channel_ids_loc, self.channel_ids)
box_index = np.repeat(box_index, 2)
box_index = np.tile(box_index, n_spikes_clu)
hpos = np.tile([[a, 0, b, 0]], (nw, 1))
assert box_index.size == hpos.shape[0] * 2
self.line_visual.add_batch_data(
pos=hpos,
color=self.ax_color,
data_bounds=ax_db,
box_index=box_index,
)
# Vertical ticks every millisecond.
steps = np.arange(np.round(self.wave_duration * 1000))
# A vline every millisecond.
x = .001 * steps
# Scale to [-1, 1], same coordinates as the waveform points.
x = -1 + 2 * x / self.wave_duration
# Take overlap into account.
x = _overlap_transform(x,
offset=bunch.offset,
n=bunch.n_clu,
overlap=self.overlap)
x = np.tile(x, len(channel_ids_loc))
# Generate the box index.
box_index = _index_of(channel_ids_loc, self.channel_ids)
box_index = np.repeat(box_index, x.size // len(box_index))
assert x.size == box_index.size
self.tick_visual.add_batch_data(
x=x,
y=np.zeros_like(x),
data_bounds=ax_db,
box_index=box_index,
)
def _plot_labels(self, channel_ids, n_clusters, channel_labels):
# Add channel labels.
if not self.do_show_labels:
return
self.text_visual.reset_batch()
for i, ch in enumerate(channel_ids):
label = channel_labels[ch]
self.text_visual.add_batch_data(
pos=[-1, 0],
text=str(label),
anchor=[-1.25, 0],
box_index=i,
)
self.canvas.update_visual(self.text_visual)
def plot(self, **kwargs):
"""Update the view with the current cluster selection."""
if not self.cluster_ids:
return
bunchs = self.get_clusters_data()
if not bunchs:
return
# All channel ids appearing in all selected clusters.
channel_ids = sorted(set(_flatten([d.channel_ids for d in bunchs])))
self.channel_ids = channel_ids
if bunchs[0].data is not None:
self.wave_duration = bunchs[0].data.shape[1] / float(
self.sample_rate)
else: # pragma: no cover
self.wave_duration = 1.
# Channel labels.
channel_labels = {}
for d in bunchs:
chl = d.get('channel_labels', ['%d' % ch for ch in d.channel_ids])
channel_labels.update({
channel_id: chl[i]
for i, channel_id in enumerate(d.channel_ids)
})
# Update the Boxed box positions as a function of the selected channels.
if channel_ids:
self.canvas.boxed.update_boxes(_get_box_pos(bunchs, channel_ids))
self.data_bounds = self.data_bounds or self._get_data_bounds(bunchs)
self._current_visual.reset_batch()
self.line_visual.reset_batch()
self.tick_visual.reset_batch()
for bunch in bunchs:
self._plot_cluster(bunch)
self.canvas.update_visual(self.tick_visual)
self.canvas.update_visual(self.line_visual)
self.canvas.update_visual(self._current_visual)
self._plot_labels(channel_ids, len(self.cluster_ids), channel_labels)
# Only show the current waveform visual.
if self._current_visual == self.waveform_visual:
self.waveform_visual.show()
self.waveform_agg_visual.hide()
elif self._current_visual == self.waveform_agg_visual:
self.waveform_agg_visual.show()
self.waveform_visual.hide()
self.canvas.update()
self.update_status()
def attach(self, gui):
"""Attach the view to the GUI."""
super(WaveformView, self).attach(gui)
self.actions.add(self.toggle_waveform_overlap,
checkable=True,
checked=self.overlap)
self.actions.add(self.toggle_show_labels,
checkable=True,
checked=self.do_show_labels)
self.actions.add(self.next_waveforms_type)
self.actions.add(self.previous_waveforms_type)
self.actions.add(self.toggle_mean_waveforms, checkable=True)
self.actions.separator()
# Box scaling.
self.actions.add(self.widen)
self.actions.add(self.narrow)
self.actions.separator()
# Probe scaling.
self.actions.add(self.extend_horizontally)
self.actions.add(self.shrink_horizontally)
self.actions.separator()
self.actions.add(self.extend_vertically)
self.actions.add(self.shrink_vertically)
self.actions.separator()
@property
def boxed(self):
"""Layout instance."""
return self.canvas.boxed
@property
def status(self):
return self.waveforms_type
# Overlap
# -------------------------------------------------------------------------
@property
def overlap(self):
"""Whether to overlap the waveforms belonging to different clusters."""
return self._overlap
@overlap.setter
def overlap(self, value):
self._overlap = value
self.plot()
def toggle_waveform_overlap(self, checked):
"""Toggle the overlap of the waveforms."""
self.overlap = checked
# Box scaling
# -------------------------------------------------------------------------
def widen(self):
"""Increase the horizontal scaling of the waveforms."""
self.boxed.expand_box_width()
def narrow(self):
"""Decrease the horizontal scaling of the waveforms."""
self.boxed.shrink_box_width()
@property
def box_scaling(self):
return self.boxed._box_scaling
@box_scaling.setter
def box_scaling(self, value):
self.boxed._box_scaling = value
def _get_scaling_value(self):
return self.boxed._box_scaling[1]
def _set_scaling_value(self, value):
w, h = self.boxed._box_scaling
self.boxed._box_scaling = (w, value)
self.boxed.update()
# Probe scaling
# -------------------------------------------------------------------------
@property
def probe_scaling(self):
return self.boxed._layout_scaling
@probe_scaling.setter
def probe_scaling(self, value):
self.boxed._layout_scaling = value
def extend_horizontally(self):
"""Increase the horizontal scaling of the probe."""
self.boxed.expand_layout_width()
def shrink_horizontally(self):
"""Decrease the horizontal scaling of the waveforms."""
self.boxed.shrink_layout_width()
def extend_vertically(self):
"""Increase the vertical scaling of the waveforms."""
self.boxed.expand_layout_height()
def shrink_vertically(self):
"""Decrease the vertical scaling of the waveforms."""
self.boxed.shrink_layout_height()
# Navigation
# -------------------------------------------------------------------------
def toggle_show_labels(self, checked):
"""Whether to show the channel ids or not."""
self.do_show_labels = checked
self.text_visual.show() if checked else self.text_visual.hide()
self.canvas.update()
def on_mouse_click(self, e):
"""Select a channel by clicking on a box in the waveform view."""
b = e.button
nums = tuple('%d' % i for i in range(10))
if 'Control' in e.modifiers or e.key in nums:
key = int(e.key) if e.key in nums else None
# Get mouse position in NDC.
channel_idx, _ = self.canvas.boxed.box_map(e.pos)
channel_id = self.channel_ids[channel_idx]
logger.debug("Click on channel_id %d with key %s and button %s.",
channel_id, key, b)
emit('select_channel',
self,
channel_id=channel_id,
key=key,
button=b)
@property
def waveforms_type(self):
return self.waveforms_types.current
@waveforms_type.setter
def waveforms_type(self, value):
self.waveforms_types.set(value)
def next_waveforms_type(self):
"""Switch to the next waveforms type."""
self.waveforms_types.next()
logger.debug("Switch to waveforms type %s.", self.waveforms_type)
self.plot()
def previous_waveforms_type(self):
"""Switch to the previous waveforms type."""
self.waveforms_types.previous()
logger.debug("Switch to waveforms type %s.", self.waveforms_type)
self.plot()
def toggle_mean_waveforms(self, checked):
"""Switch to the `mean_waveforms` type, if it is available."""
if self.waveforms_type == 'mean_waveforms' and 'waveforms' in self.waveforms:
self.waveforms_types.set('waveforms')
logger.debug("Switch to raw waveforms.")
self.plot()
elif 'mean_waveforms' in self.waveforms:
self.waveforms_types.set('mean_waveforms')
logger.debug("Switch to mean waveforms.")
self.plot()
class TraceView(ScalingMixin, ManualClusteringView):
"""This view shows the raw traces along with spike waveforms.
Constructor
-----------
traces : function
Maps a time interval `(t0, t1)` to a `Bunch(data, color, waveforms)` where
* `data` is an `(n_samples, n_channels)` array
* `waveforms` is a list of bunchs with the following attributes:
* `data`
* `color`
* `channel_ids`
* `start_time`
* `spike_id`
* `spike_cluster`
spike_times : function
Teturns the list of relevant spike times.
sample_rate : float
duration : float
n_channels : int
channel_vertical_order : array-like
Permutation of the channels. This 1D array gives the channel id of all channels from
top to bottom (or conversely, depending on `origin=top|bottom`).
channel_labels : list
Labels of all shown channels. By default, this is just the channel ids.
"""
_default_position = 'left'
auto_update = True
auto_scale = True
interval_duration = .25 # default duration of the interval
shift_amount = .1
scaling_coeff_x = 1.25
trace_quantile = .01 # quantile for auto-scaling
default_trace_color = (.5, .5, .5, 1)
default_shortcuts = {
'change_trace_size': 'ctrl+wheel',
'decrease': 'alt+down',
'increase': 'alt+up',
'go_left': 'alt+left',
'go_right': 'alt+right',
'go_to_start': 'alt+home',
'go_to_end': 'alt+end',
'go_to': 'alt+t',
'go_to_next_spike': 'alt+pgdown',
'go_to_previous_spike': 'alt+pgup',
'narrow': 'alt++',
'select_spike': 'ctrl+click',
'switch_origin': 'alt+o',
'toggle_highlighted_spikes': 'alt+s',
'toggle_show_labels': 'alt+l',
'widen': 'alt+-',
}
default_snippets = {
'go_to': 'tg',
'shift': 'ts',
}
def __init__(
self, traces=None, sample_rate=None, spike_times=None, duration=None, n_channels=None,
channel_vertical_order=None, channel_labels=None, **kwargs):
self.do_show_labels = True
self.show_all_spikes = False
self._scaling = 1.
self.get_spike_times = spike_times
# Sample rate.
assert sample_rate > 0
self.sample_rate = float(sample_rate)
self.dt = 1. / self.sample_rate
# Traces and spikes.
assert hasattr(traces, '__call__')
self.traces = traces
self.waveforms = None
assert duration >= 0
self.duration = duration
assert n_channels >= 0
self.n_channels = n_channels
# Channel permutation.
self._channel_perm = (
np.arange(n_channels) if channel_vertical_order is None else channel_vertical_order)
assert self._channel_perm.shape == (n_channels,)
self._channel_perm = np.argsort(self._channel_perm)
# Channel labels.
self.channel_labels = (
channel_labels if channel_labels is not None else
['%d' % ch for ch in range(n_channels)])
assert len(self.channel_labels) == n_channels
# Box and probe scaling.
self._origin = None
# Initialize the view.
super(TraceView, self).__init__(**kwargs)
self.state_attrs += ('origin', 'do_show_labels', 'show_all_spikes', 'auto_scale')
self.local_state_attrs += ('interval', 'scaling',)
self.canvas.set_layout('stacked', origin=self.origin, n_plots=self.n_channels)
self.canvas.enable_axes(show_y=False)
# Visuals.
self.trace_visual = UniformPlotVisual()
self.canvas.add_visual(self.trace_visual)
self.waveform_visual = PlotVisual()
self.canvas.add_visual(self.waveform_visual)
self.text_visual = TextVisual()
_fix_coordinate_in_visual(self.text_visual, 'x')
self.canvas.add_visual(self.text_visual)
# Make a copy of the initial box pos and size. We'll apply the scaling
# to these quantities.
self.box_size = np.array(self.canvas.stacked.box_size)
# Initial interval.
self._interval = None
self.go_to(duration / 2.)
self._waveform_times = []
@property
def stacked(self):
return self.canvas.stacked
def _permute_channels(self, x, inv=False):
cp = self._channel_perm
cp = np.argsort(cp)
return cp[x]
# Internal methods
# -------------------------------------------------------------------------
def _plot_traces(self, traces, color=None):
traces = traces.T
n_samples = traces.shape[1]
n_ch = self.n_channels
assert traces.shape == (n_ch, n_samples)
color = color or self.default_trace_color
t = self._interval[0] + np.arange(n_samples) * self.dt
t = np.tile(t, (n_ch, 1))
box_index = self._permute_channels(np.arange(n_ch))
box_index = np.repeat(box_index[:, np.newaxis], n_samples, axis=1)
assert t.shape == (n_ch, n_samples)
assert traces.shape == (n_ch, n_samples)
assert box_index.shape == (n_ch, n_samples)
self.trace_visual.color = color
self.canvas.update_visual(
self.trace_visual,
t, traces,
data_bounds=self.data_bounds,
box_index=box_index.ravel(),
)
def _plot_spike(self, bunch):
# The spike time corresponds to the first sample of the waveform.
n_samples, n_channels = bunch.data.shape
assert len(bunch.channel_ids) == n_channels
# Generate the x coordinates of the waveform.
t = bunch.start_time + self.dt * np.arange(n_samples)
t = np.tile(t, (n_channels, 1)) # (n_unmasked_channels, n_samples)
# The box index depends on the channel.
box_index = self._permute_channels(bunch.channel_ids)
box_index = np.repeat(box_index[:, np.newaxis], n_samples, axis=0)
self.waveform_visual.add_batch_data(
box_index=box_index,
x=t, y=bunch.data.T, color=bunch.color,
data_bounds=self.data_bounds,
)
def _plot_labels(self, traces):
self.text_visual.reset_batch()
for ch in range(self.n_channels):
bi = self._permute_channels(ch)
ch_label = self.channel_labels[ch]
self.text_visual.add_batch_data(
pos=[self.data_bounds[0], 0],
text=ch_label,
anchor=[+1., 0],
data_bounds=self.data_bounds,
box_index=bi,
)
self.canvas.update_visual(self.text_visual)
# Public methods
# -------------------------------------------------------------------------
def _restrict_interval(self, interval):
start, end = interval
# Round the times to full samples to avoid subsampling shifts
# in the traces.
start = int(round(start * self.sample_rate)) / self.sample_rate
end = int(round(end * self.sample_rate)) / self.sample_rate
# Restrict the interval to the boundaries of the traces.
if start < 0:
end += (-start)
start = 0
elif end >= self.duration:
start -= (end - self.duration)
end = self.duration
start = np.clip(start, 0, end)
end = np.clip(end, start, self.duration)
assert 0 <= start < end <= self.duration
return start, end
def set_interval(self, interval=None, change_status=True):
"""Display the traces and spikes in a given interval."""
if interval is None:
interval = self._interval
interval = self._restrict_interval(interval)
# Load the traces.
traces = self.traces(interval)
self.waveforms = traces.get('waveforms', [])
if interval != self._interval:
logger.debug("Redraw the entire trace view.")
self._interval = interval
start, end = interval
# Set the status message.
if change_status:
self.set_status('Interval: {:.3f} s - {:.3f} s'.format(start, end))
# Find the data bounds.
if self.auto_scale or getattr(self, 'data_bounds', NDC) == NDC:
ymin = np.quantile(traces.data, self.trace_quantile)
ymax = np.quantile(traces.data, 1. - self.trace_quantile)
else:
ymin, ymax = self.data_bounds[1], self.data_bounds[3]
self.data_bounds = (start, ymin, end, ymax)
# Used for spike click.
self._waveform_times = []
# Plot the traces.
self._plot_traces(
traces.data, color=traces.get('color', None))
# Plot the labels.
if self.do_show_labels:
self._plot_labels(traces.data)
# Plot the waveforms.
self.plot()
def on_select(self, cluster_ids=None, **kwargs):
self.cluster_ids = cluster_ids
if not cluster_ids:
return
# Make sure we call again self.traces() when the cluster selection changes.
self.set_interval()
def plot(self, **kwargs):
"""Plot the waveforms."""
waveforms = self.waveforms
assert isinstance(waveforms, list)
if waveforms:
self.waveform_visual.show()
self.waveform_visual.reset_batch()
for w in waveforms:
self._plot_spike(w)
self._waveform_times.append(
(w.start_time, w.spike_id, w.spike_cluster, w.get('channel_ids', None)))
self.canvas.update_visual(self.waveform_visual)
else: # pragma: no cover
self.waveform_visual.hide()
self._update_axes()
self.canvas.update()
def attach(self, gui):
"""Attach the view to the GUI."""
super(TraceView, self).attach(gui)
self.actions.add(self.toggle_show_labels, checkable=True, checked=self.do_show_labels)
self.actions.add(
self.toggle_highlighted_spikes, checkable=True, checked=self.show_all_spikes)
self.actions.add(self.toggle_auto_scale, checkable=True, checked=self.auto_scale)
self.actions.add(self.switch_origin)
self.actions.separator()
self.actions.add(
self.go_to, prompt=True, prompt_default=lambda: str(self.time))
self.actions.separator()
self.actions.add(self.go_to_start)
self.actions.add(self.go_to_end)
self.actions.separator()
self.actions.add(self.shift, prompt=True)
self.actions.add(self.go_right)
self.actions.add(self.go_left)
self.actions.separator()
self.actions.add(self.widen)
self.actions.add(self.narrow)
self.actions.separator()
self.actions.add(self.go_to_next_spike)
self.actions.add(self.go_to_previous_spike)
self.actions.separator()
self.set_interval()
# Origin
# -------------------------------------------------------------------------
@property
def origin(self):
"""Whether to show the channels from top to bottom (`top` option, the default), or from
bottom to top (`bottom`)."""
return self._origin
@origin.setter
def origin(self, value):
self._origin = value
if self.canvas.layout:
self.canvas.layout.origin = value
def switch_origin(self):
"""Switch between top and bottom origin for the channels."""
self.origin = 'top' if self._origin in ('bottom', None) else 'bottom'
# Navigation
# -------------------------------------------------------------------------
@property
def time(self):
"""Time at the center of the window."""
return sum(self._interval) * .5
@property
def interval(self):
"""Interval as `(tmin, tmax)`."""
return self._interval
@interval.setter
def interval(self, value):
self.set_interval(value)
@property
def half_duration(self):
"""Half of the duration of the current interval."""
if self._interval is not None:
a, b = self._interval
return (b - a) * .5
else:
return self.interval_duration * .5
def go_to(self, time):
"""Go to a specific time (in seconds)."""
half_dur = self.half_duration
self.set_interval((time - half_dur, time + half_dur))
def shift(self, delay):
"""Shift the interval by a given delay (in seconds)."""
self.go_to(self.time + delay)
def go_to_start(self):
"""Go to the start of the recording."""
self.go_to(0)
def go_to_end(self):
"""Go to end of the recording."""
self.go_to(self.duration)
def go_right(self):
"""Go to right."""
start, end = self._interval
delay = (end - start) * .1
self.shift(delay)
def go_left(self):
"""Go to left."""
start, end = self._interval
delay = (end - start) * .1
self.shift(-delay)
def _jump_to_spike(self, delta=+1):
"""Jump to next or previous spike from the selected clusters."""
spike_times = self.get_spike_times()
if spike_times is not None and len(spike_times):
ind = np.searchsorted(spike_times, self.time)
n = len(spike_times)
self.go_to(spike_times[(ind + delta) % n])
def go_to_next_spike(self, ):
"""Jump to the next spike from the first selected cluster."""
self._jump_to_spike(+1)
def go_to_previous_spike(self, ):
"""Jump to the previous spike from the first selected cluster."""
self._jump_to_spike(-1)
def toggle_highlighted_spikes(self, checked):
"""Toggle between showing all spikes or selected spikes."""
self.show_all_spikes = checked
self.set_interval()
def widen(self):
"""Increase the interval size."""
t, h = self.time, self.half_duration
h *= self.scaling_coeff_x
self.set_interval((t - h, t + h))
def narrow(self):
"""Decrease the interval size."""
t, h = self.time, self.half_duration
h /= self.scaling_coeff_x
self.set_interval((t - h, t + h))
# Misc
# -------------------------------------------------------------------------
def toggle_show_labels(self, checked):
"""Toggle the display of the channel ids."""
logger.debug("Set show labels to %s.", checked)
self.do_show_labels = checked
self.set_interval()
def toggle_auto_scale(self, checked):
"""Toggle automatic scaling of the traces."""
logger.debug("Set auto scale to %s.", checked)
self.auto_scale = checked
# Scaling
# -------------------------------------------------------------------------
def _apply_scaling(self):
self.canvas.layout.scaling = (self.canvas.layout.scaling[0], self._scaling)
@property
def scaling(self):
"""Scaling of the channel boxes."""
return self._scaling
@scaling.setter
def scaling(self, value):
self._scaling = value
self._apply_scaling()
def _get_scaling_value(self):
return self.scaling
def _set_scaling_value(self, value):
self.scaling = value
# Spike selection
# -------------------------------------------------------------------------
def on_mouse_click(self, e):
"""Select a cluster by clicking on a spike."""
if 'Control' in e.modifiers:
# Get mouse position in NDC.
box_id, _ = self.canvas.stacked.box_map(e.pos)
channel_id = self._permute_channels(box_id, inv=True)
# Find the spike and cluster closest to the mouse.
db = self.data_bounds
# Get the information about the displayed spikes.
wt = [(t, s, c, ch) for t, s, c, ch in self._waveform_times if channel_id in ch]
if not wt:
return
# Get the time coordinate of the mouse position.
mouse_pos = self.canvas.panzoom.window_to_ndc(e.pos)
mouse_time = Range(NDC, db).apply(mouse_pos)[0][0]
# Get the closest spike id.
times, spike_ids, spike_clusters, channel_ids = zip(*wt)
i = np.argmin(np.abs(np.array(times) - mouse_time))
# Raise the spike_click event.
spike_id = spike_ids[i]
cluster_id = spike_clusters[i]
emit('spike_click', self, channel_id=channel_id,
spike_id=spike_id, cluster_id=cluster_id)