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 __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)
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 CorrelogramView(ScalingMixin, ManualClusteringView):
"""A view showing the autocorrelogram of the selected clusters, and all cross-correlograms
of cluster pairs.
Constructor
-----------
correlograms : function
Maps `(cluster_ids, bin_size, window_size)` to an `(n_clusters, n_clusters, n_bins) array`.
firing_rate : function
Maps `(cluster_ids, bin_size)` to an `(n_clusters, n_clusters) array`
"""
# Do not show too many clusters.
max_n_clusters = 20
_default_position = 'left'
cluster_ids = ()
# Bin size, in seconds.
bin_size = 1e-3
# Window size, in seconds.
window_size = 50e-3
# Refactory period, in seconds
refractory_period = 2e-3
# Whether the normalization is uniform across entire rows or not.
uniform_normalization = False
default_shortcuts = {
'change_window_size': 'ctrl+wheel',
'change_bin_size': 'alt+wheel',
}
default_snippets = {
'set_bin': 'cb',
'set_window': 'cw',
'set_refractory_period': 'cr',
}
def __init__(self,
correlograms=None,
firing_rate=None,
sample_rate=None,
**kwargs):
super(CorrelogramView, self).__init__(**kwargs)
self.state_attrs += ('bin_size', 'window_size', 'refractory_period',
'uniform_normalization')
self.local_state_attrs += ()
self.canvas.set_layout(layout='grid')
# Outside margin to show labels.
self.canvas.gpu_transforms.add(Scale(.9))
assert sample_rate > 0
self.sample_rate = float(sample_rate)
# Function clusters => CCGs.
self.correlograms = correlograms
# Function clusters => firing rates (same unit as CCG).
self.firing_rate = firing_rate
# Set the default bin and window size.
self._set_bin_window(bin_size=self.bin_size,
window_size=self.window_size)
self.correlogram_visual = HistogramVisual()
self.canvas.add_visual(self.correlogram_visual)
self.line_visual = LineVisual()
self.canvas.add_visual(self.line_visual)
self.text_visual = TextVisual(color=(1., 1., 1., 1.))
self.canvas.add_visual(self.text_visual)
# -------------------------------------------------------------------------
# Internal methods
# -------------------------------------------------------------------------
def _iter_subplots(self, n_clusters):
for i in range(n_clusters):
for j in range(n_clusters):
yield i, j
def get_clusters_data(self, load_all=None):
ccg = self.correlograms(self.cluster_ids, self.bin_size,
self.window_size)
fr = self.firing_rate(self.cluster_ids,
self.bin_size) if self.firing_rate else None
assert ccg.ndim == 3
n_bins = ccg.shape[2]
bunchs = []
m = ccg.max()
for i, j in self._iter_subplots(len(self.cluster_ids)):
b = Bunch()
b.correlogram = ccg[i, j, :]
if not self.uniform_normalization:
# Normalization row per row.
m = ccg[i, j, :].max()
b.firing_rate = fr[i, j] if fr is not None else None
b.data_bounds = (0, 0, n_bins, m)
b.pair_index = i, j
b.color = selected_cluster_color(i, 1)
if i != j:
b.color = add_alpha(_override_hsv(b.color[:3], s=.1, v=1))
bunchs.append(b)
return bunchs
def _plot_pair(self, bunch):
# Plot the histogram.
self.correlogram_visual.add_batch_data(hist=bunch.correlogram,
color=bunch.color,
ylim=bunch.data_bounds[3],
box_index=bunch.pair_index)
# Plot the firing rate.
gray = (.25, .25, .25, 1.)
if bunch.firing_rate is not None:
# Line.
pos = np.array([[
0, bunch.firing_rate, bunch.data_bounds[2], bunch.firing_rate
]])
self.line_visual.add_batch_data(pos=pos,
color=gray,
data_bounds=bunch.data_bounds,
box_index=bunch.pair_index)
# # Text.
# self.text_visual.add_batch_data(
# pos=[bunch.data_bounds[2], bunch.firing_rate],
# text='%.2f' % bunch.firing_rate,
# anchor=(-1, 0),
# box_index=bunch.pair_index,
# data_bounds=bunch.data_bounds,
# )
# Refractory period.
xrp0 = round(
(self.window_size * .5 - self.refractory_period) / self.bin_size)
xrp1 = round((self.window_size * .5 + self.refractory_period) /
self.bin_size) + 1
ylim = bunch.data_bounds[3]
pos = np.array([[xrp0, 0, xrp0, ylim], [xrp1, 0, xrp1, ylim]])
self.line_visual.add_batch_data(pos=pos,
color=gray,
data_bounds=bunch.data_bounds,
box_index=bunch.pair_index)
def _plot_labels(self):
n = len(self.cluster_ids)
# Display the cluster ids in the subplots.
for k in range(n):
self.text_visual.add_batch_data(
pos=[-1, 0],
text=str(self.cluster_ids[k]),
anchor=[-1.25, 0],
data_bounds=None,
box_index=(k, 0),
)
self.text_visual.add_batch_data(
pos=[0, -1],
text=str(self.cluster_ids[k]),
anchor=[0, -1.25],
data_bounds=None,
box_index=(n - 1, k),
)
# # Display the window size in the bottom right subplot.
# self.text_visual.add_batch_data(
# pos=[1, -1],
# anchor=[1.25, 1],
# text='%.1f ms' % (1000 * .5 * self.window_size),
# box_index=(n - 1, n - 1),
# )
def plot(self, **kwargs):
"""Update the view with the current cluster selection."""
self.canvas.grid.shape = (len(self.cluster_ids), len(self.cluster_ids))
bunchs = self.get_clusters_data()
self.correlogram_visual.reset_batch()
self.line_visual.reset_batch()
self.text_visual.reset_batch()
for bunch in bunchs:
self._plot_pair(bunch)
self._plot_labels()
self.canvas.update_visual(self.correlogram_visual)
self.canvas.update_visual(self.line_visual)
self.canvas.update_visual(self.text_visual)
self.canvas.update()
# -------------------------------------------------------------------------
# Public methods
# -------------------------------------------------------------------------
def toggle_normalization(self, checked):
"""Change the normalization of the correlograms."""
self.uniform_normalization = checked
self.plot()
def toggle_labels(self, checked):
"""Show or hide all labels."""
if checked:
self.text_visual.show()
else:
self.text_visual.hide()
self.canvas.update()
def attach(self, gui):
"""Attach the view to the GUI."""
super(CorrelogramView, self).attach(gui)
self.actions.add(self.toggle_normalization,
shortcut='n',
checkable=True)
self.actions.add(self.toggle_labels, checkable=True, checked=True)
self.actions.separator()
self.actions.add(self.set_bin,
prompt=True,
prompt_default=lambda: self.bin_size * 1000)
self.actions.add(self.set_window,
prompt=True,
prompt_default=lambda: self.window_size * 1000)
self.actions.add(self.set_refractory_period,
prompt=True,
prompt_default=lambda: self.refractory_period * 1000)
self.actions.separator()
# -------------------------------------------------------------------------
# Methods for changing the parameters
# -------------------------------------------------------------------------
def _set_bin_window(self, bin_size=None, window_size=None):
"""Set the bin and window sizes (in seconds)."""
bin_size = bin_size or self.bin_size
window_size = window_size or self.window_size
bin_size = _clip(bin_size, 1e-6, 1e3)
window_size = _clip(window_size, 1e-6, 1e3)
assert 1e-6 <= bin_size <= 1e3
assert 1e-6 <= window_size <= 1e3
assert bin_size < window_size
self.bin_size = bin_size
self.window_size = window_size
self.update_status()
@property
def status(self):
b, w = self.bin_size * 1000, self.window_size * 1000
return '{:.1f} ms ({:.1f} ms)'.format(w, b)
def set_refractory_period(self, value):
"""Set the refractory period (in milliseconds)."""
self.refractory_period = _clip(value, .1, 100) * 1e-3
self.plot()
def set_bin(self, bin_size):
"""Set the correlogram bin size (in milliseconds).
Example: `1`
"""
self._set_bin_window(bin_size=bin_size * 1e-3)
self.plot()
def set_window(self, window_size):
"""Set the correlogram window size (in milliseconds).
Example: `100`
"""
self._set_bin_window(window_size=window_size * 1e-3)
self.plot()
def increase(self):
"""Increase the window size."""
self.set_window(1000 * self.window_size * 1.1)
def decrease(self):
"""Decrease the window size."""
self.set_window(1000 * self.window_size / 1.1)
def on_mouse_wheel(self, e): # pragma: no cover
"""Change the scaling with the wheel."""
super(CorrelogramView, self).on_mouse_wheel(e)
if e.modifiers == ('Alt', ):
self._set_bin_window(bin_size=self.bin_size * 1.1**e.delta)
self.plot()
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])
def on_view_attached(view, gui):
if isinstance(view, AmplitudeView):
# Create batch of vertical lines (full height)
self.line_visual = LineVisual()
_fix_coordinate_in_visual(self.line_visual, 'y')
view.canvas.add_visual(self.line_visual)
# Create batch of annotative text
self.text_visual = TextVisual(self.line_color)
_fix_coordinate_in_visual(self.text_visual, 'y')
self.text_visual.inserter.insert_vert(
'gl_Position.x += 0.001;', 'after_transforms')
view.canvas.add_visual(self.text_visual)
@view.actions.add(shortcut='alt+b',
checkable=True,
name='Toggle event markers')
def toggle(on):
"""Toggle event markers"""
# Use `show` and `hide` instead of `toggle` here in
# case synchronization issues
if on:
logger.debug('Toggle on markers.')
self.line_visual.show()
self.text_visual.show()
view.show_events = True
else:
logger.debug('Toggle off markers.')
self.line_visual.hide()
self.text_visual.hide()
view.show_events = False
view.canvas.update()
@view.actions.add(shortcut='shift+alt+e',
prompt=True,
name='Go to event',
alias='ge')
def Go_to_event(event_num):
trace_view = gui.get_view(TraceView)
if 0 < event_num <= events.size:
trace_view.go_to(events[event_num - 1])
# Disable the menu until events are successfully added
view.actions.disable('Go to event')
view.actions.disable('Toggle event markers')
if not hasattr(view, 'show_events'):
view.show_events = True
view.state_attrs += ('show_events', )
# Read event markers from file
filename = controller.dir_path / 'eventmarkers.txt'
try:
events = np.genfromtxt(filename, usecols=0, dtype=None)
except (FileNotFoundError, OSError):
logger.warn('Event marker file not found: `%s`.', filename)
view.show_events = False
return
# Create list of event names
labels = list(map(str, range(1, events.size + 1)))
# Read event names from file (if present)
filename = controller.dir_path / 'eventmarkernames.txt'
try:
eventnames = np.loadtxt(filename,
usecols=0,
dtype=str,
max_rows=events.size)
labels[:eventnames.size] = np.atleast_1d(eventnames)
except (FileNotFoundError, OSError):
logger.info(
'Event marker names file not found (optional):'
' `%s`. Fall back to numbering.', filename)
# Obtain seconds from samples
if events.dtype == int:
logger.debug('Converting input from samples to seconds.')
events = events / controller.model.sample_rate
logger.debug('Add event markers to amplitude view.')
# Obtain horizontal positions
x = -1 + 2 * events / view.duration
x = x.repeat(4, 0).reshape(-1, 4)
x[:, 1::2] = 1, -1
# Add lines and update view
self.line_visual.reset_batch()
self.line_visual.add_batch_data(pos=x, color=self.line_color)
view.canvas.update_visual(self.line_visual)
# Add text and update view
self.text_visual.reset_batch()
self.text_visual.add_batch_data(pos=x[:, :2],
anchor=(1, -1),
text=labels)
view.canvas.update_visual(self.text_visual)
# Finally enable the menu
logger.debug('Enable menu items.')
view.actions.enable('Go to event')
view.actions.enable('Toggle event markers')
if view.show_events:
view.actions.get('Toggle event markers').toggle()
else:
self.line_visual.hide()
self.text_visual.hide()
def __init__(self, amplitudes=None, amplitudes_type=None, duration=None, path=None,sample_rate=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.show_background_clusters=True
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)
self.line_visual = LineVisual()
self.canvas.add_visual(self.line_visual)
blocksizes_path=op.join(path,'blocksizes.npy')
if op.exists(blocksizes_path):
self.blocksizes=np.load(blocksizes_path)[0]
self.blockstarts=np.load(op.join(path,'blockstarts.npy'))[0]
self.blocksizes_time=self.blocksizes/sample_rate
self.blockstarts_time=self.blockstarts/sample_rate
self.gap=np.diff(self.blockstarts)-self.blocksizes[:-1]
self.gap_time=np.diff(self.blockstarts_time)-self.blocksizes_time[:-1]
self.show_block_gap=False
self.show_block_lines=True
else:
self.show_block_gap=False
self.show_block_lines=False
# Scatter plot.
self.visual = ScatterVisual()
self.canvas.add_visual(self.visual)
self.canvas.panzoom.set_constrain_bounds((-2, -2, +2, +2))
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',
'toggle_other_clusters': '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, path=None,sample_rate=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.show_background_clusters=True
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)
self.line_visual = LineVisual()
self.canvas.add_visual(self.line_visual)
blocksizes_path=op.join(path,'blocksizes.npy')
if op.exists(blocksizes_path):
self.blocksizes=np.load(blocksizes_path)[0]
self.blockstarts=np.load(op.join(path,'blockstarts.npy'))[0]
self.blocksizes_time=self.blocksizes/sample_rate
self.blockstarts_time=self.blockstarts/sample_rate
self.gap=np.diff(self.blockstarts)-self.blocksizes[:-1]
self.gap_time=np.diff(self.blockstarts_time)-self.blocksizes_time[:-1]
self.show_block_gap=False
self.show_block_lines=True
else:
self.show_block_gap=False
self.show_block_lines=False
# 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 and self.show_background_clusters:
# Add None cluster which means background spikes.
cluster_ids = [None] + cluster_ids
color_ind_offset = -1
else:
color_ind_offset=0
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 + color_ind_offset, 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)
if self.show_block_lines:
line_times = self.blocksizes_time[:-1].cumsum()
line_times=np.append(line_times,[0,self.data_bounds[2]])
line_points=[]
for time in line_times:
line_points.append(np.array([time, self.data_bounds[1], time, self.data_bounds[3]]))
pos = np.array(line_points)
self.line_visual.add_batch_data(
pos=pos,
color=(1, .5, 0, 1.),
data_bounds=self.data_bounds)
self.canvas.update_visual(self.line_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)
self.actions.add(self.toggle_other_clusters, 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 toggle_other_clusters(self):
self.show_background_clusters = not self.show_background_clusters
print(self.show_background_clusters)
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."""
# from pdb import set_trace
# set_trace()
if 'Shift' 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)
