def _compute_correlograms(self, clusters_selected, wizard=None):
# Get the correlograms parameters.
spiketimes = get_array(self.loader.get_spiketimes("all"))
sample_rate = self.loader.freq
# print spiketimes.dtype
# Make a copy of the array so that it does not change before the
# computation of the correlograms begins.
clusters = np.array(get_array(self.loader.get_clusters("all")))
# Get excerpts
nexcerpts = USERPREF.get("correlograms_nexcerpts", 50)
excerpt_size = USERPREF.get("correlograms_excerpt_size", 10000)
spiketimes_excerpts = get_excerpts(spiketimes, nexcerpts=nexcerpts, excerpt_size=excerpt_size)
clusters_excerpts = get_excerpts(clusters, nexcerpts=nexcerpts, excerpt_size=excerpt_size)
# corrbin = self.loader.corrbin
# ncorrbins = self.loader.ncorrbins
corrbin = SETTINGS.get("correlograms.corrbin", 0.001)
ncorrbins = SETTINGS.get("correlograms.ncorrbins", 101)
# Ensure ncorrbins is odd.
if ncorrbins % 2 == 0:
ncorrbins += 1
# Get cluster indices that need to be updated.
# clusters_to_update = (self.statscache.correlograms.
# not_in_key_indices(clusters_selected))
clusters_to_update = clusters_selected
# If there are pairs that need to be updated, launch the task.
if len(clusters_to_update) > 0:
# Set wait cursor.
self.mainwindow.set_busy(computing_correlograms=True)
# Launch the task.
self.tasks.correlograms_task.compute(
spiketimes_excerpts,
clusters_excerpts,
clusters_to_update=clusters_to_update,
clusters_selected=clusters_selected,
ncorrbins=ncorrbins,
corrbin=corrbin,
sample_rate=sample_rate,
wizard=wizard,
)
# Otherwise, update directly the correlograms view without launching
# the task in the external process.
else:
# self.update_correlograms_view()
return ("_update_correlograms_view", (wizard,), {})
def _compute_correlograms(self, clusters_selected, wizard=None):
# Get the correlograms parameters.
spiketimes = get_array(self.loader.get_spiketimes('all'))
# print spiketimes.dtype
# Make a copy of the array so that it does not change before the
# computation of the correlograms begins.
clusters = np.array(get_array(self.loader.get_clusters('all')))
# Get excerpts
nexcerpts = USERPREF.get('correlograms_nexcerpts', 100)
excerpt_size = USERPREF.get('correlograms_excerpt_size', 20000)
spiketimes_excerpts = get_excerpts(spiketimes,
nexcerpts=nexcerpts,
excerpt_size=excerpt_size)
clusters_excerpts = get_excerpts(clusters,
nexcerpts=nexcerpts,
excerpt_size=excerpt_size)
# corrbin = self.loader.corrbin
# ncorrbins = self.loader.ncorrbins
corrbin = SETTINGS.get('correlograms.corrbin', .001)
ncorrbins = SETTINGS.get('correlograms.ncorrbins', 100)
# Get cluster indices that need to be updated.
clusters_to_update = (
self.statscache.correlograms.not_in_key_indices(clusters_selected))
# If there are pairs that need to be updated, launch the task.
if len(clusters_to_update) > 0:
# Set wait cursor.
self.mainwindow.set_busy(computing_correlograms=True)
# Launch the task.
self.tasks.correlograms_task.compute(
spiketimes_excerpts,
clusters_excerpts,
clusters_to_update=clusters_to_update,
clusters_selected=clusters_selected,
ncorrbins=ncorrbins,
corrbin=corrbin,
wizard=wizard)
# Otherwise, update directly the correlograms view without launching
# the task in the external process.
else:
# self.update_correlograms_view()
return ('_update_correlograms_view', (wizard, ), {})
def get_featureview_data(loader, autozoom):
data = dict(
features=loader.get_features(),
features_background=loader.get_features_background(),
spiketimes=loader.get_spiketimes(),
masks=loader.get_masks(),
clusters=loader.get_clusters(),
clusters_selected=loader.get_clusters_selected(),
cluster_colors=loader.get_cluster_colors(),
nchannels=loader.nchannels,
fetdim=loader.fetdim,
nextrafet=loader.nextrafet,
freq=loader.freq,
autozoom=autozoom,
duration=loader.get_duration(),
alpha_selected=USERPREF.get('feature_selected_alpha', .75),
alpha_background=USERPREF.get('feature_background_alpha', .1),
time_unit=USERPREF['features_info_time_unit'] or 'second',
)
return data
def get_featureview_data(loader, autozoom):
data = dict(
features=loader.get_features(),
features_background=loader.get_features_background(),
spiketimes=loader.get_spiketimes(),
masks=loader.get_masks(),
clusters=loader.get_clusters(),
clusters_selected=loader.get_clusters_selected(),
cluster_colors=loader.get_cluster_colors(),
nchannels=loader.nchannels,
fetdim=loader.fetdim,
nextrafet=loader.nextrafet,
freq=loader.freq,
autozoom=autozoom,
duration=loader.get_duration(),
alpha_selected=USERPREF.get('feature_selected_alpha', .75),
alpha_background=USERPREF.get('feature_background_alpha', .1),
time_unit=USERPREF['features_info_time_unit'] or 'second',
)
return data
def refresh_preferences_callback(self, checked=None):
log.debug("Refreshing user preferences.")
USERPREF.refresh()
def refresh_preferences_callback(self, checked=None):
log.debug("Refreshing user preferences.")
USERPREF.refresh()
def set_data(self,
features=None,
features_background=None,
spiketimes=None, # a subset of all spikes, disregarding cluster
masks=None, # masks for all spikes in selected clusters
clusters=None, # clusters for all spikes in selected clusters
clusters_selected=None,
cluster_colors=None,
fetdim=None,
nchannels=None,
channels=None,
nextrafet=None,
autozoom=None, # None, or the target cluster
duration=None,
freq=None,
alpha_selected=.75,
alpha_background=.25,
time_unit=None,
):
if features is None:
features = np.zeros((0, 2))
features_background = np.zeros((0, 2))
masks = np.zeros((0, 1))
clusters = np.zeros(0, dtype=np.int32)
clusters_selected = []
cluster_colors = np.zeros(0, dtype=np.int32)
fetdim = 2
nchannels = 1
nextrafet = 0
if features.shape[1] == 1:
features = np.tile(features, (1, 4))
if features_background.shape[1] == 1:
features_background = np.tile(features_background, (1, 4))
assert fetdim is not None
self.duration = duration
self.spiketimes = spiketimes
self.freq = freq
self.interaction_manager.get_processor('grid').update_viewbox()
# Feature background alpha value.
self.alpha_selected = alpha_selected
self.alpha_background = alpha_background
# can be 'second' or 'samples'
self.time_unit = time_unit
# Extract the relevant spikes, but keep the other ones in features_full
self.clusters = clusters
# Contains all spikes, needed for splitting.
self.features_full = features
self.features_full_array = get_array(features)
# Keep a subset of all spikes in the view.
self.nspikes_full = len(features)
# > features_nspikes_per_cluster_max spikes ==> take a selection
nspikes_max = USERPREF.get('features_nspikes_per_cluster_max', 1000)
k = self.nspikes_full // nspikes_max + 1
# self.features = features[::k]
subsel = slice(None, None, k)
self.features = select(features, subsel)
self.features_array = get_array(self.features)
# self.features_background contains all non-selected spikes
self.features_background = features_background
self.features_background_array = get_array(self.features_background)
# Background spikes are those which do not belong to the selected clusters
self.npoints_background = self.features_background_array.shape[0]
self.nspikes_background = self.npoints_background
if channels is None:
channels = range(nchannels)
self.nspikes, self.ndim = self.features.shape
self.fetdim = fetdim
self.nchannels = nchannels
self.channels = channels
self.nextrafet = nextrafet
self.npoints = self.features.shape[0]
if masks is None:
masks = np.ones_like(self.features, dtype=np.float32)
self.masks = masks
# Subselection
self.masks = select(self.masks, subsel)
self.masks_array = get_array(self.masks)
if self.spiketimes is not None:
self.spiketimes = select(self.spiketimes, subsel)
self.clusters = select(self.clusters, subsel)
self.clusters_array = get_array(self.clusters)
self.feature_indices = get_indices(self.features)
self.feature_full_indices = get_indices(self.features_full)
self.feature_indices_array = get_array(self.feature_indices)
self.cluster_colors = get_array(cluster_colors, dosort=True)
# Relative indexing.
if self.npoints > 0:
self.clusters_rel = np.digitize(self.clusters_array, sorted(clusters_selected)) - 1
self.clusters_rel_ordered = np.argsort(clusters_selected)[self.clusters_rel]
else:
self.clusters_rel = np.zeros(0, dtype=np.int32)
self.clusters_rel_ordered = np.zeros(0, dtype=np.int32)
self.clusters_unique = sorted(clusters_selected)
self.nclusters = len(clusters_selected)
self.masks_full = self.masks_array.T.ravel()
self.clusters_full_depth = self.clusters_rel_ordered
self.clusters_full = self.clusters_rel
# prepare GPU data
self.data = np.empty((self.nspikes, 2), dtype=np.float32)
self.data_full = np.empty((self.nspikes_full, 2), dtype=np.float32)
self.data_background = np.empty((self.nspikes_background, 2),
dtype=np.float32)
# set initial projection
self.projection_manager.set_data()
self.autozoom = autozoom
if autozoom is None:
self.projection_manager.reset_projection()
else:
self.projection_manager.auto_projection(autozoom)
# update the highlight manager
self.highlight_manager.initialize()
self.selection_manager.initialize()
self.selection_manager.cancel_selection()
