class AmplitudeView(MarkerSizeMixin, LassoMixin, ManualClusteringView):
"""This view displays an amplitude plot for all selected clusters.
Constructor
-----------
amplitudes : function
Maps `cluster_ids` to a list `[Bunch(amplitudes, spike_ids), ...]` for each cluster.
Use `cluster_id=None` for background amplitudes.
"""
_default_position = 'right'
# Alpha channel of the markers in the scatter plot.
marker_alpha = 1.
# 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': 'ctrl+wheel',
'next_amplitude_type': 'a',
'previous_amplitude_type': 'shift+a',
'select_x_dim': 'alt+left click',
'select_y_dim': 'alt+right click',
}
def __init__(self, amplitudes=None, amplitude_name=None, duration=None):
super(AmplitudeView, self).__init__()
self.state_attrs += ('amplitude_name',)
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
self.amplitude_names = list(amplitudes.keys())
# Current amplitude type.
self.amplitude_name = amplitude_name or self.amplitude_names[0]
assert self.amplitude_name in amplitudes
self.cluster_ids = ()
self.duration = duration or 1
# Histogram visual.
self.hist_visual = HistogramVisual()
self.hist_visual.transforms.add_on_gpu([
Range(NDC, (-1, -1, 1, -1 + 2 * self.histogram_scale)), Rotate('ccw')])
self.canvas.add_visual(self.hist_visual)
# Scatter plot.
self.visual = ScatterVisual()
self.canvas.add_visual(self.visual)
# Amplitude name.
self.text_visual = TextVisual()
self.canvas.add_visual(self.text_visual, exclude_origins=(self.canvas.panzoom,))
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)
m = min(0, m) # ensure ymin <= 0
M = max(np.quantile(bunch.amplitudes, self.quantile) for bunch in bunchs)
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=False,
ignore_zeros=True,
)
return bunchs
def _plot_cluster(self, bunch):
"""Make the scatter plot."""
ms = self._marker_size
# 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 _plot_amplitude_name(self):
"""Show the amplitude name."""
self.text_visual.add_batch_data(pos=[0, 1], anchor=[0, -1], text=self.amplitude_name)
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.amplitude_name](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()
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()
self.text_visual.reset_batch()
for bunch in bunchs:
self._plot_cluster(bunch)
self._plot_amplitude_name()
self.canvas.update_visual(self.visual)
self.canvas.update_visual(self.hist_visual)
self.canvas.update_visual(self.text_visual)
self._update_axes()
self.canvas.update()
def attach(self, gui):
"""Attach the view to the GUI."""
super(AmplitudeView, self).attach(gui)
self.actions.add(self.next_amplitude_type, set_busy=True)
self.actions.add(self.previous_amplitude_type, set_busy=True)
def _change_amplitude_type(self, dir=+1):
i = self.amplitude_names.index(self.amplitude_name)
n = len(self.amplitude_names)
self.amplitude_name = self.amplitude_names[(i + dir) % n]
logger.debug("Switch to amplitude type: %s.", self.amplitude_name)
self.plot()
def next_amplitude_type(self):
"""Switch to the next amplitude type."""
self._change_amplitude_type(+1)
def previous_amplitude_type(self):
"""Switch to the previous amplitude type."""
self._change_amplitude_type(-1)
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)
class FeatureView(MarkerSizeMixin, ScalingMixin, ManualClusteringView):
"""This view displays a 4x4 subplot matrix with different projections of the principal
component features. This view keeps track of which channels are currently shown.
Constructor
-----------
features : function
Maps `(cluster_id, channel_ids=None, load_all=False)` to
`Bunch(data, channel_ids, channel_labels, spike_ids , masks)`.
* `data` is an `(n_spikes, n_channels, n_features)` array
* `channel_ids` contains the channel ids of every row in `data`
* `channel_labels` contains the channel labels of every row in `data`
* `spike_ids` is a `(n_spikes,)` array
* `masks` is an `(n_spikes, n_channels)` array
This allows for a sparse format.
attributes : dict
Maps an attribute name to a 1D array with `n_spikes` numbers (for example, spike times).
"""
# Do not show too many clusters.
max_n_clusters = 8
_default_position = 'right'
cluster_ids = ()
# Whether to disable automatic selection of channels.
fixed_channels = False
feature_scaling = 1.
default_shortcuts = {
'change_marker_size': 'alt+wheel',
'increase': 'ctrl++',
'decrease': 'ctrl+-',
'add_lasso_point': 'ctrl+click',
'stop_lasso': 'ctrl+right click',
'toggle_automatic_channel_selection': 'c',
}
def __init__(self, features=None, attributes=None, **kwargs):
super(FeatureView, self).__init__(**kwargs)
self.state_attrs += ('fixed_channels', 'feature_scaling')
assert features
self.features = features
self._lim = 1
self.grid_dim = _get_default_grid() # 2D array where every item a string like `0A,1B`
self.n_rows, self.n_cols = np.array(self.grid_dim).shape
self.canvas.set_layout('grid', shape=(self.n_rows, self.n_cols))
self.canvas.enable_lasso()
# Channels being shown.
self.channel_ids = None
# Attributes: extra features. This is a dictionary
# {name: array}
# where each array is a `(n_spikes,)` array.
self.attributes = attributes or {}
self.visual = ScatterVisual()
self.canvas.add_visual(self.visual)
self.text_visual = TextVisual()
self.canvas.add_visual(self.text_visual)
self.line_visual = LineVisual()
self.canvas.add_visual(self.line_visual)
def set_grid_dim(self, grid_dim):
"""Change the grid dim dynamically.
Parameters
----------
grid_dim : array-like (2D)
`grid_dim[row, col]` is a string with two values separated by a comma. Each value
is the relative channel id (0, 1, 2...) followed by the PC (A, B, C...). For example,
`grid_dim[row, col] = 0B,1A`. Each value can also be an attribute name, for example
`time`. For example, `grid_dim[row, col] = time,2C`.
"""
self.grid_dim = grid_dim
self.n_rows, self.n_cols = np.array(grid_dim).shape
self.canvas.grid.shape = (self.n_rows, self.n_cols)
# Internal methods
# -------------------------------------------------------------------------
def _iter_subplots(self):
"""Yield (i, j, dim)."""
for i in range(self.n_rows):
for j in range(self.n_cols):
dim = self.grid_dim[i][j]
dim_x, dim_y = dim.split(',')
yield i, j, dim_x, dim_y
def _get_axis_label(self, dim):
"""Return the channel label from a dimension, if applicable."""
if str(dim[:-1]).isdecimal():
n = len(self.channel_ids)
channel_id = self.channel_ids[int(dim[:-1]) % n]
return self.channel_labels[channel_id] + dim[-1]
else:
return dim
def _get_channel_and_pc(self, dim):
"""Return the channel_id and PC of a dim."""
if self.channel_ids is None:
return
assert dim not in self.attributes # This is called only on PC data.
s = 'ABCDEFGHIJ'
# Channel relative index, typically just 0 or 1.
c_rel = int(dim[:-1])
# Get the channel_id from the currently-selected channels.
channel_id = self.channel_ids[c_rel % len(self.channel_ids)]
pc = s.index(dim[-1])
return channel_id, pc
def _get_axis_data(self, bunch, dim, cluster_id=None, load_all=None):
"""Extract the points from the data on a given dimension.
bunch is returned by the features() function.
dim is the string specifying the dimensions to extract for the data.
"""
if dim in self.attributes:
return self.attributes[dim](cluster_id, load_all=load_all)
masks = bunch.get('masks', None)
channel_id, pc = self._get_channel_and_pc(dim)
# Skip the plot if the channel id is not displayed.
if channel_id not in bunch.channel_ids: # pragma: no cover
return Bunch(data=np.zeros((bunch.data.shape[0],)))
# Get the column index of the current channel in data.
c = list(bunch.channel_ids).index(channel_id)
if masks is not None:
masks = masks[:, c]
return Bunch(data=self.feature_scaling * bunch.data[:, c, pc], masks=masks)
def _get_axis_bounds(self, dim, bunch):
"""Return the min/max of an axis."""
if dim in self.attributes:
# Attribute: specified lim, or compute the min/max.
vmin, vmax = bunch.get('lim', (0, 0))
assert vmin is not None
assert vmax is not None
return vmin, vmax
return (-self._lim, +self._lim)
def _plot_points(self, bunch, clu_idx=None):
if not bunch:
return
cluster_id = self.cluster_ids[clu_idx] if clu_idx is not None else None
for i, j, dim_x, dim_y in self._iter_subplots():
px = self._get_axis_data(bunch, dim_x, cluster_id=cluster_id)
py = self._get_axis_data(bunch, dim_y, cluster_id=cluster_id)
# Skip empty data.
if px is None or py is None: # pragma: no cover
logger.warning("Skipping empty data for cluster %d.", cluster_id)
return
assert px.data.shape == py.data.shape
xmin, xmax = self._get_axis_bounds(dim_x, px)
ymin, ymax = self._get_axis_bounds(dim_y, py)
assert xmin <= xmax
assert ymin <= ymax
data_bounds = (xmin, ymin, xmax, ymax)
masks = _get_masks_max(px, py)
# Prepare the batch visual with all subplots
# for the selected cluster.
self.visual.add_batch_data(
x=px.data, y=py.data,
color=_get_point_color(clu_idx),
# Reduced marker size for background features
size=self._marker_size,
masks=_get_point_masks(clu_idx=clu_idx, masks=masks),
data_bounds=data_bounds,
box_index=(i, j),
)
# Get the channel ids corresponding to the relative channel indices
# specified in the dimensions. Channel 0 corresponds to the first
# best channel for the selected cluster, and so on.
label_x = self._get_axis_label(dim_x)
label_y = self._get_axis_label(dim_y)
# Add labels.
self.text_visual.add_batch_data(
pos=[1, 1],
anchor=[-1, -1],
text=label_y,
data_bounds=None,
box_index=(i, j),
)
self.text_visual.add_batch_data(
pos=[0, -1.],
anchor=[0, 1],
text=label_x,
data_bounds=None,
box_index=(i, j),
)
def _plot_axes(self):
self.line_visual.reset_batch()
for i, j, dim_x, dim_y in self._iter_subplots():
self.line_visual.add_batch_data(
pos=[[-1., 0., +1., 0.],
[0., -1., 0., +1.]],
color=(.5, .5, .5, .5),
box_index=(i, j),
data_bounds=None,
)
self.canvas.update_visual(self.line_visual)
def _get_lim(self, bunchs):
if not bunchs: # pragma: no cover
return 1
m, M = min(bunch.data.min() for bunch in bunchs), max(bunch.data.max() for bunch in bunchs)
M = max(abs(m), abs(M))
return M
def _get_scaling_value(self):
return self.feature_scaling
def _set_scaling_value(self, value):
self.feature_scaling = value
self.plot(fixed_channels=True)
# Public methods
# -------------------------------------------------------------------------
def clear_channels(self):
"""Reset the current channels."""
self.channel_ids = None
self.plot()
def get_clusters_data(self, fixed_channels=None, load_all=None):
# Get the feature data.
# Specify the channel ids if these are fixed, otherwise
# choose the first cluster's best channels.
c = self.channel_ids if fixed_channels else None
bunchs = [self.features(cluster_id, channel_ids=c) for cluster_id in self.cluster_ids]
bunchs = [b for b in bunchs if b]
if not bunchs: # pragma: no cover
return []
for cluster_id, bunch in zip(self.cluster_ids, bunchs):
bunch.cluster_id = cluster_id
# Choose the channels based on the first selected cluster.
channel_ids = list(bunchs[0].get('channel_ids', [])) if bunchs else []
common_channels = list(channel_ids)
# Intersection (with order kept) of channels belonging to all clusters.
for bunch in bunchs:
common_channels = [c for c in bunch.get('channel_ids', []) if c in common_channels]
# The selected channels will be (1) the channels common to all clusters, followed
# by (2) remaining channels from the first cluster (excluding those already selected
# in (1)).
n = len(channel_ids)
not_common_channels = [c for c in channel_ids if c not in common_channels]
channel_ids = common_channels + not_common_channels[:n - len(common_channels)]
assert len(channel_ids) > 0
# Choose the channels automatically unless fixed_channels is set.
if (not fixed_channels or self.channel_ids is None):
self.channel_ids = channel_ids
assert len(self.channel_ids)
# Channel labels.
self.channel_labels = {}
for d in bunchs:
chl = d.get('channel_labels', ['%d' % ch for ch in d.get('channel_ids', [])])
self.channel_labels.update({
channel_id: chl[i] for i, channel_id in enumerate(d.get('channel_ids', []))})
return bunchs
def plot(self, **kwargs):
"""Update the view with the selected clusters."""
# Determine whether the channels should be fixed or not.
added = kwargs.get('up', {}).get('added', None)
# Fix the channels if the view updates after a cluster event
# and there are new clusters.
fixed_channels = (
self.fixed_channels or kwargs.get('fixed_channels', None) or added is not None)
# Get the clusters data.
bunchs = self.get_clusters_data(fixed_channels=fixed_channels)
bunchs = [b for b in bunchs if b]
if not bunchs:
return
self._lim = self._get_lim(bunchs)
# Get the background data.
background = self.features(channel_ids=self.channel_ids)
# Plot all features.
self._plot_axes()
# NOTE: the columns in bunch.data are ordered by decreasing quality
# of the associated channels. The channels corresponding to each
# column are given in bunch.channel_ids in the same order.
# Plot points.
self.visual.reset_batch()
self.text_visual.reset_batch()
self._plot_points(background) # background spikes
# Plot each cluster.
for clu_idx, bunch in enumerate(bunchs):
self._plot_points(bunch, clu_idx=clu_idx)
# Upload the data on the GPU.
self.canvas.update_visual(self.visual)
self.canvas.update_visual(self.text_visual)
self.canvas.update()
def attach(self, gui):
"""Attach the view to the GUI."""
super(FeatureView, self).attach(gui)
self.actions.add(
self.toggle_automatic_channel_selection,
checked=not self.fixed_channels, checkable=True)
self.actions.add(self.clear_channels)
self.actions.separator()
def toggle_automatic_channel_selection(self, checked):
"""Toggle the automatic selection of channels when the cluster selection changes."""
self.fixed_channels = not checked
@property
def status(self):
if self.channel_ids is None: # pragma: no cover
return ''
channel_labels = [self.channel_labels[ch] for ch in self.channel_ids[:2]]
return 'channels: %s' % ', '.join(channel_labels)
# Dimension selection
# -------------------------------------------------------------------------
def on_select_channel(self, sender=None, channel_id=None, key=None, button=None):
"""Respond to the click on a channel from another view, and update the
relevant subplots."""
channels = self.channel_ids
if channels is None:
return
if len(channels) == 1:
self.plot()
return
assert len(channels) >= 2
# Get the axis from the pressed button (1, 2, etc.)
if key is not None:
d = np.clip(len(channels) - 1, 0, key - 1)
else:
d = 0 if button == 'Left' else 1
# Change the first or second best channel.
old = channels[d]
# Avoid updating the view if the channel doesn't change.
if channel_id == old:
return
channels[d] = channel_id
# Ensure that the first two channels are different.
if channels[1 - min(d, 1)] == channel_id:
channels[1 - min(d, 1)] = old
assert channels[0] != channels[1]
# Remove duplicate channels.
self.channel_ids = _uniq(channels)
logger.debug("Choose channels %d and %d in feature view.", *channels[:2])
# Fix the channels temporarily.
self.plot(fixed_channels=True)
self.update_status()
def on_mouse_click(self, e):
"""Select a feature dimension by clicking on a box in the feature view."""
b = e.button
if 'Alt' in e.modifiers:
# Get mouse position in NDC.
(i, j), _ = self.canvas.grid.box_map(e.pos)
dim = self.grid_dim[i][j]
dim_x, dim_y = dim.split(',')
dim = dim_x if b == 'Left' else dim_y
other_dim = dim_y if b == 'Left' else dim_x
if dim not in self.attributes:
# When a regular (channel, PC) dimension is selected.
channel_pc = self._get_channel_and_pc(dim)
if channel_pc is None:
return
channel_id, pc = channel_pc
logger.debug("Click on feature dim %s, channel id %s, PC %s.", dim, channel_id, pc)
else:
# When the selected dimension is an attribute, e.g. "time".
pc = None
# Take the channel id in the other dimension.
channel_pc = self._get_channel_and_pc(other_dim)
channel_id = channel_pc[0] if channel_pc is not None else None
logger.debug("Click on feature dim %s.", dim)
emit('select_feature', self, dim=dim, channel_id=channel_id, pc=pc)
def on_request_split(self, sender=None):
"""Return the spikes enclosed by the lasso."""
if (self.canvas.lasso.count < 3 or not len(self.cluster_ids)): # pragma: no cover
return np.array([], dtype=np.int64)
assert len(self.channel_ids)
# Get the dimensions of the lassoed subplot.
i, j = self.canvas.layout.active_box
dim = self.grid_dim[i][j]
dim_x, dim_y = dim.split(',')
# Get all points from all clusters.
pos = []
spike_ids = []
for cluster_id in self.cluster_ids:
# Load all spikes.
bunch = self.features(cluster_id, channel_ids=self.channel_ids, load_all=True)
if not bunch:
continue
px = self._get_axis_data(bunch, dim_x, cluster_id=cluster_id, load_all=True)
py = self._get_axis_data(bunch, dim_y, cluster_id=cluster_id, load_all=True)
points = np.c_[px.data, py.data]
# Normalize the points.
xmin, xmax = self._get_axis_bounds(dim_x, px)
ymin, ymax = self._get_axis_bounds(dim_y, py)
r = Range((xmin, ymin, xmax, ymax))
points = r.apply(points)
pos.append(points)
spike_ids.append(bunch.spike_ids)
pos = np.vstack(pos)
spike_ids = np.concatenate(spike_ids)
# Find lassoed spikes.
ind = self.canvas.lasso.in_polygon(pos)
self.canvas.lasso.clear()
return np.unique(spike_ids[ind])
class ScatterView(MarkerSizeMixin, LassoMixin, ManualClusteringView):
"""This view displays a scatter plot for all selected clusters.
Constructor
-----------
coords : function
Maps `cluster_ids` to a list `[Bunch(x, y, spike_ids, data_bounds), ...]` for each cluster.
"""
_default_position = 'right'
default_shortcuts = {
'change_marker_size': 'ctrl+wheel',
}
def __init__(self, coords=None, **kwargs):
super(ScatterView, self).__init__(**kwargs)
# Save the marker size in the global and local view's config.
self.canvas.enable_axes()
self.canvas.enable_lasso()
assert coords
self.coords = coords
self.visual = ScatterVisual()
self.canvas.add_visual(self.visual)
def _plot_cluster(self, bunch):
ms = self._marker_size
self.visual.add_batch_data(
pos=bunch.pos, color=bunch.color, size=ms, data_bounds=self.data_bounds)
def _get_split_cluster_data(self, bunchs):
"""Get the data when there is one Bunch per cluster."""
# Add a pos attribute in bunchs in addition to x and y.
for i, (cluster_id, bunch) in enumerate(zip(self.cluster_ids, bunchs)):
bunch.cluster_id = cluster_id
if 'pos' not in bunch:
assert bunch.x.ndim == 1
assert bunch.x.shape == bunch.y.shape
bunch.pos = np.c_[bunch.x, bunch.y]
assert bunch.pos.ndim == 2
assert 'spike_ids' in bunch
bunch.color = selected_cluster_color(i, .75)
return bunchs
def _get_collated_cluster_data(self, bunch):
"""Get the data when there is a single Bunch for all selected clusters."""
assert 'spike_ids' in bunch
if 'pos' not in bunch:
assert bunch.x.ndim == 1
assert bunch.x.shape == bunch.y.shape
bunch.pos = np.c_[bunch.x, bunch.y]
assert bunch.pos.ndim == 2
bunch.color = spike_colors(bunch.spike_clusters, self.cluster_ids)
return bunch
def get_clusters_data(self, load_all=None):
"""Return a list of Bunch instances, with attributes pos and spike_ids."""
if not load_all:
bunchs = self.coords(self.cluster_ids) or ()
elif 'load_all' in inspect.signature(self.coords).parameters:
bunchs = self.coords(self.cluster_ids, load_all=load_all) or ()
else:
logger.warning(
"The view `%s` may not load all spikes when using the lasso for splitting.",
self.__class__.__name__)
bunchs = self.coords(self.cluster_ids)
if isinstance(bunchs, dict):
return [self._get_collated_cluster_data(bunchs)]
elif isinstance(bunchs, (list, tuple)):
return self._get_split_cluster_data(bunchs)
raise ValueError("The output of `coords()` should be either a list of Bunch, or a Bunch.")
def plot(self, **kwargs):
"""Update the view with the current cluster selection."""
bunchs = self.get_clusters_data()
# Hide the visual if there is no data.
if not bunchs:
self.visual.hide()
self.canvas.update()
return
self.data_bounds = self._get_data_bounds(bunchs)
self.visual.reset_batch()
for bunch in bunchs:
self._plot_cluster(bunch)
self.canvas.update_visual(self.visual)
self.visual.show()
self._update_axes()
self.canvas.update()