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 AmplitudeView(MarkerSizeMixin, LassoMixin, ManualClusteringView):
"""This view displays an amplitude plot for all selected clusters.
Constructor
-----------
amplitudes : dict
Dictionary `{amplitudes_type: function}`, for different types of amplitudes.
Each function maps `cluster_ids` to a list
`[Bunch(amplitudes, spike_ids, spike_times), ...]` for each cluster.
Use `cluster_id=None` for background amplitudes.
"""
# Do not show too many clusters.
max_n_clusters = 8
_default_position = 'right'
# Alpha channel of the markers in the scatter plot.
marker_alpha = 1.
time_range_color = (1., 1., 0., .25)
# Number of bins in the histogram.
n_bins = 100
# Alpha channel of the histogram in the background.
histogram_alpha = .5
# Quantile used for scaling of the amplitudes (less than 1 to avoid outliers).
quantile = .99
# Size of the histogram, between 0 and 1.
histogram_scale = .25
default_shortcuts = {
'change_marker_size': 'alt+wheel',
'next_amplitudes_type': 'a',
'previous_amplitudes_type': 'shift+a',
'select_x_dim': 'shift+left click',
'select_y_dim': 'shift+right click',
'select_time': 'alt+click',
}
def __init__(self, amplitudes=None, amplitudes_type=None, duration=None):
super(AmplitudeView, self).__init__()
self.state_attrs += ('amplitudes_type', )
self.canvas.enable_axes()
self.canvas.enable_lasso()
# Ensure amplitudes is a dictionary, even if there is a single amplitude.
if not isinstance(amplitudes, dict):
amplitudes = {'amplitude': amplitudes}
assert amplitudes
self.amplitudes = amplitudes
# Rotating property amplitudes types.
self.amplitudes_types = RotatingProperty()
for name, value in self.amplitudes.items():
self.amplitudes_types.add(name, value)
# Current amplitudes type.
self.amplitudes_types.set(amplitudes_type)
assert self.amplitudes_type in self.amplitudes
self.cluster_ids = ()
self.duration = duration or 1.
# Histogram visual.
self.hist_visual = HistogramVisual()
self.hist_visual.transforms.add([
Range(NDC, (-1, -1, 1, -1 + 2 * self.histogram_scale)),
Rotate('cw'),
Scale((1, -1)),
Translate((2.05, 0)),
])
self.canvas.add_visual(self.hist_visual)
self.canvas.panzoom.zoom = self.canvas.panzoom._default_zoom = (.75, 1)
self.canvas.panzoom.pan = self.canvas.panzoom._default_pan = (-.25, 0)
# Yellow vertical bar showing the selected time interval.
self.patch_visual = PatchVisual(primitive_type='triangle_fan')
self.patch_visual.inserter.insert_vert(
'''
const float MIN_INTERVAL_SIZE = 0.01;
uniform float u_interval_size;
''', 'header')
self.patch_visual.inserter.insert_vert(
'''
gl_Position.y = pos_orig.y;
// The following is used to ensure that (1) the bar width increases with the zoom level
// but also (2) there is a minimum absolute width so that the bar remains visible
// at low zoom levels.
float w = max(MIN_INTERVAL_SIZE, u_interval_size * u_zoom.x);
// HACK: the z coordinate is used to store 0 or 1, depending on whether the current
// vertex is on the left or right edge of the bar.
gl_Position.x += w * (-1 + 2 * int(a_position.z == 0));
''', 'after_transforms')
self.canvas.add_visual(self.patch_visual)
# Scatter plot.
self.visual = ScatterVisual()
self.canvas.add_visual(self.visual)
self.canvas.panzoom.set_constrain_bounds((-2, -2, +2, +2))
def _get_data_bounds(self, bunchs):
"""Compute the data bounds."""
if not bunchs: # pragma: no cover
return (0, 0, self.duration, 1)
m = min(
np.quantile(bunch.amplitudes, 1 - self.quantile)
for bunch in bunchs if len(bunch.amplitudes))
m = min(0, m) # ensure ymin <= 0
M = max(
np.quantile(bunch.amplitudes, self.quantile) for bunch in bunchs
if len(bunch.amplitudes))
return (0, m, self.duration, M)
def _add_histograms(self, bunchs):
# We do this after get_clusters_data because we need x_max.
for bunch in bunchs:
bunch.histogram = _compute_histogram(
bunch.amplitudes,
x_min=self.data_bounds[1],
x_max=self.data_bounds[3],
n_bins=self.n_bins,
normalize=True,
ignore_zeros=True,
)
return bunchs
def show_time_range(self, interval=(0, 0)):
start, end = interval
x0 = -1 + 2 * (start / self.duration)
x1 = -1 + 2 * (end / self.duration)
xm = .5 * (x0 + x1)
pos = np.array([
[xm, -1],
[xm, +1],
[xm, +1],
[xm, -1],
])
self.patch_visual.program['u_interval_size'] = .5 * (x1 - x0)
self.patch_visual.set_data(pos=pos,
color=self.time_range_color,
depth=[0, 0, 1, 1])
self.canvas.update()
def _plot_cluster(self, bunch):
"""Make the scatter plot."""
ms = self._marker_size
if not len(bunch.histogram):
return
# Histogram in the background.
self.hist_visual.add_batch_data(hist=bunch.histogram,
ylim=self._ylim,
color=add_alpha(
bunch.color, self.histogram_alpha))
# Scatter plot.
self.visual.add_batch_data(pos=bunch.pos,
color=bunch.color,
size=ms,
data_bounds=self.data_bounds)
def get_clusters_data(self, load_all=None):
"""Return a list of Bunch instances, with attributes pos and spike_ids."""
if not len(self.cluster_ids):
return
cluster_ids = list(self.cluster_ids)
# Don't need the background when splitting.
if not load_all:
# Add None cluster which means background spikes.
cluster_ids = [None] + cluster_ids
bunchs = self.amplitudes[self.amplitudes_type](cluster_ids,
load_all=load_all) or ()
# Add a pos attribute in bunchs in addition to x and y.
for i, (cluster_id, bunch) in enumerate(zip(cluster_ids, bunchs)):
spike_ids = _as_array(bunch.spike_ids)
spike_times = _as_array(bunch.spike_times)
amplitudes = _as_array(bunch.amplitudes)
assert spike_ids.shape == spike_times.shape == amplitudes.shape
# Ensure that bunch.pos exists, as it used by the LassoMixin.
bunch.pos = np.c_[spike_times, amplitudes]
assert bunch.pos.ndim == 2
bunch.cluster_id = cluster_id
bunch.color = (
selected_cluster_color(i - 1, self.marker_alpha)
# Background amplitude color.
if cluster_id is not None else (.5, .5, .5, .5))
return bunchs
def plot(self, **kwargs):
"""Update the view with the current cluster selection."""
bunchs = self.get_clusters_data(**kwargs)
if not bunchs:
return
self.data_bounds = self._get_data_bounds(bunchs)
bunchs = self._add_histograms(bunchs)
# Use the same scale for all histograms.
self._ylim = max(bunch.histogram.max()
for bunch in bunchs) if bunchs else 1.
self.visual.reset_batch()
self.hist_visual.reset_batch()
for bunch in bunchs:
self._plot_cluster(bunch)
self.canvas.update_visual(self.visual)
self.canvas.update_visual(self.hist_visual)
self._update_axes()
self.canvas.update()
self.update_status()
def attach(self, gui):
"""Attach the view to the GUI."""
super(AmplitudeView, self).attach(gui)
# Amplitude type actions.
def _make_amplitude_action(a):
def callback():
self.amplitudes_type = a
self.plot()
return callback
for a in self.amplitudes_types.keys():
name = 'Change amplitudes type to %s' % a
self.actions.add(_make_amplitude_action(a),
show_shortcut=False,
name=name,
view_submenu='Change amplitudes type')
self.actions.add(self.next_amplitudes_type, set_busy=True)
self.actions.add(self.previous_amplitudes_type, set_busy=True)
@property
def status(self):
return self.amplitudes_type
@property
def amplitudes_type(self):
return self.amplitudes_types.current
@amplitudes_type.setter
def amplitudes_type(self, value):
self.amplitudes_types.set(value)
def next_amplitudes_type(self):
"""Switch to the next amplitudes type."""
self.amplitudes_types.next()
logger.debug("Switch to amplitudes type: %s.",
self.amplitudes_types.current)
self.plot()
def previous_amplitudes_type(self):
"""Switch to the previous amplitudes type."""
self.amplitudes_types.previous()
logger.debug("Switch to amplitudes type: %s.",
self.amplitudes_types.current)
self.plot()
def on_mouse_click(self, e):
"""Select a time from the amplitude view to display in the trace view."""
if 'Alt' in e.modifiers:
mouse_pos = self.canvas.panzoom.window_to_ndc(e.pos)
time = Range(NDC, self.data_bounds).apply(mouse_pos)[0][0]
emit('select_time', self, time)