def get_correlogramsview_data(exp, correlograms, clusters=[],
channel_group=0, clustering='main', wizard=None,
nclusters_max=None, ncorrbins=50, corrbin=.001):
clusters = np.array(clusters, dtype=np.int32)
clusters_data = getattr(exp.channel_groups[channel_group].clusters, clustering)
cluster_groups_data = getattr(exp.channel_groups[channel_group].cluster_groups, clustering)
freq = exp.application_data.spikedetekt.sample_rate
# cluster_colors = clusters_data.color[clusters]
# cluster_colors = pandaize(cluster_colors, clusters)
# get colors from application data:
cluster_colors = pd.Series([_get_color(clusters_data, cl)
for cl in clusters], index=clusters)
# cluster_colors = pd.Series([
# next_color(cl)
# if cl in clusters_data else 1
# for cl in clusters], index=clusters)
# TODO: cache and optimize this
spike_clusters = getattr(exp.channel_groups[channel_group].spikes.clusters,
clustering)[:]
sizes = np.bincount(spike_clusters)
cluster_sizes = sizes[clusters]
clusters_selected0 = clusters
nclusters_max = nclusters_max or USERPREF['correlograms_max_nclusters']
# Subset of selected clusters if there are too many clusters.
if len(clusters_selected0) < nclusters_max:
clusters_selected = clusters_selected0
else:
clusters_selected = clusters_selected0[:nclusters_max]
correlograms = correlograms.submatrix(clusters_selected)
cluster_colors = select(cluster_colors, clusters_selected)
# Compute the baselines.
# corrbin = SETTINGS.get('correlograms.corrbin', CORRBIN_DEFAULT)
# ncorrbins = SETTINGS.get('correlograms.ncorrbins', NCORRBINS_DEFAULT)
duration = exp.channel_groups[channel_group].spikes.concatenated_time_samples[:][-1] - exp.channel_groups[channel_group].spikes.concatenated_time_samples[:][0]
duration /= freq
if duration == 0:
duration = 1.
baselines = get_baselines(cluster_sizes, duration, corrbin)
baselines = baselines[:nclusters_max,:nclusters_max]
data = dict(
correlograms=correlograms,
baselines=baselines,
clusters_selected=clusters_selected,
cluster_colors=cluster_colors,
ncorrbins=ncorrbins,
corrbin=corrbin,
keep_order=wizard,
)
return data
def get_correlogramsview_data(loader, statscache):
clusters_selected0 = loader.get_clusters_selected()
# Subset of selected clusters if there are too many clusters.
max_nclusters = USERPREF['correlograms_max_nclusters']
if len(clusters_selected0) < max_nclusters:
clusters_selected = clusters_selected0
else:
clusters_selected = clusters_selected0[:max_nclusters]
correlograms = statscache.correlograms.submatrix(clusters_selected)
# Compute the baselines.
sizes = get_array(select(loader.get_cluster_sizes(), clusters_selected))
colors = select(loader.get_cluster_colors(), clusters_selected)
corrbin = SETTINGS.get('correlograms.corrbin', CORRBIN_DEFAULT)
ncorrbins = SETTINGS.get('correlograms.ncorrbins', NCORRBINS_DEFAULT)
duration = corrbin * ncorrbins
baselines = get_baselines(sizes, duration, corrbin)
data = dict(
correlograms=correlograms,
baselines=baselines,
clusters_selected=clusters_selected,
cluster_colors=colors,
ncorrbins=ncorrbins,
corrbin=corrbin,
)
return data
def get_correlogramsview_data(loader, statscache):
clusters_selected0 = loader.get_clusters_selected()
# Subset of selected clusters if there are too many clusters.
max_nclusters = USERPREF['correlograms_max_nclusters']
if len(clusters_selected0) < max_nclusters:
clusters_selected = clusters_selected0
else:
clusters_selected = clusters_selected0[:max_nclusters]
correlograms = statscache.correlograms.submatrix(
clusters_selected)
# Compute the baselines.
sizes = get_array(select(loader.get_cluster_sizes(), clusters_selected))
colors = select(loader.get_cluster_colors(), clusters_selected)
corrbin = SETTINGS.get('correlograms.corrbin', CORRBIN_DEFAULT)
ncorrbins = SETTINGS.get('correlograms.ncorrbins', NCORRBINS_DEFAULT)
duration = corrbin * ncorrbins
baselines = get_baselines(sizes, duration, corrbin)
data = dict(
correlograms=correlograms,
baselines=baselines,
clusters_selected=clusters_selected,
cluster_colors=colors,
ncorrbins=ncorrbins,
corrbin=corrbin,
)
return data
def get_correlogramsview_data(exp, correlograms, clusters=[],
channel_group=0, clustering='main', wizard=None,
nclusters_max=None, ncorrbins=50, corrbin=.001):
clusters = np.array(clusters, dtype=np.int32)
clusters_data = getattr(exp.channel_groups[channel_group].clusters, clustering)
cluster_groups_data = getattr(exp.channel_groups[channel_group].cluster_groups, clustering)
freq = exp.application_data.spikedetekt.sample_rate
cluster_colors = clusters_data.color[clusters]
cluster_colors = pandaize(cluster_colors, clusters)
# TODO: cache and optimize this
spike_clusters = getattr(exp.channel_groups[channel_group].spikes.clusters,
clustering)[:]
sizes = np.bincount(spike_clusters)
cluster_sizes = sizes[clusters]
clusters_selected0 = clusters
nclusters_max = nclusters_max or USERPREF['correlograms_max_nclusters']
# Subset of selected clusters if there are too many clusters.
if len(clusters_selected0) < nclusters_max:
clusters_selected = clusters_selected0
else:
clusters_selected = clusters_selected0[:nclusters_max]
correlograms = correlograms.submatrix(clusters_selected)
cluster_colors = select(cluster_colors, clusters_selected)
# Compute the baselines.
# corrbin = SETTINGS.get('correlograms.corrbin', CORRBIN_DEFAULT)
# ncorrbins = SETTINGS.get('correlograms.ncorrbins', NCORRBINS_DEFAULT)
duration = exp.channel_groups[channel_group].spikes.concatenated_time_samples[:][-1] - exp.channel_groups[channel_group].spikes.concatenated_time_samples[:][0]
duration /= freq
if duration == 0:
duration = 1.
baselines = get_baselines(cluster_sizes, duration, corrbin)
baselines = baselines[:nclusters_max,:nclusters_max]
data = dict(
correlograms=correlograms,
baselines=baselines,
clusters_selected=clusters_selected,
cluster_colors=cluster_colors,
ncorrbins=ncorrbins,
corrbin=corrbin,
keep_order=wizard,
)
return data