def _do_assign(self, spike_ids, new_spike_clusters):
"""Make spike-cluster assignments after the spike selection has
been extended to full clusters."""
# Ensure spike_clusters has the right shape.
spike_ids = _as_array(spike_ids)
if len(new_spike_clusters) == 1 and len(spike_ids) > 1:
new_spike_clusters = (np.ones(len(spike_ids), dtype=np.int64) *
new_spike_clusters[0])
old_spike_clusters = self._spike_clusters[spike_ids]
assert len(spike_ids) == len(old_spike_clusters)
assert len(new_spike_clusters) == len(spike_ids)
# Update the spikes per cluster structure.
old_clusters = _unique(old_spike_clusters)
# NOTE: shortcut to a merge if this assignment is effectively a merge
# i.e. if all spikes are assigned to a single cluster.
# The fact that spike selection has been previously extended to
# whole clusters is critical here.
new_clusters = _unique(new_spike_clusters)
if len(new_clusters) == 1:
return self._do_merge(spike_ids, old_clusters, new_clusters[0])
# We return the UpdateInfo structure.
up = _assign_update_info(spike_ids, old_spike_clusters,
new_spike_clusters)
# We update the new cluster id (strictly increasing during a session).
self._new_cluster_id = max(self._new_cluster_id, max(up.added) + 1)
# We make the assignments.
self._spike_clusters[spike_ids] = new_spike_clusters
return up
def _assign_update_info(spike_ids, old_spike_clusters, new_spike_clusters):
old_clusters = _unique(old_spike_clusters)
new_clusters = _unique(new_spike_clusters)
descendants = list(set(zip(old_spike_clusters, new_spike_clusters)))
update_info = UpdateInfo(
description='assign',
spike_ids=spike_ids,
added=list(new_clusters),
deleted=list(old_clusters),
descendants=descendants,
)
return update_info
def _assign_update_info(spike_ids, old_spike_clusters, new_spike_clusters):
old_clusters = _unique(old_spike_clusters)
new_clusters = _unique(new_spike_clusters)
descendants = list(set(zip(old_spike_clusters,
new_spike_clusters)))
update_info = UpdateInfo(description='assign',
spike_ids=spike_ids,
added=list(new_clusters),
deleted=list(old_clusters),
descendants=descendants,
)
return update_info
def _do_assign(self, spike_ids, new_spike_clusters):
"""Make spike-cluster assignments after the spike selection has
been extended to full clusters."""
# Ensure spike_clusters has the right shape.
spike_ids = _as_array(spike_ids)
if len(new_spike_clusters) == 1 and len(spike_ids) > 1:
new_spike_clusters = (np.ones(len(spike_ids), dtype=np.int64) *
new_spike_clusters[0])
old_spike_clusters = self._spike_clusters[spike_ids]
assert len(spike_ids) == len(old_spike_clusters)
assert len(new_spike_clusters) == len(spike_ids)
# Update the spikes per cluster structure.
old_clusters = _unique(old_spike_clusters)
# NOTE: shortcut to a merge if this assignment is effectively a merge
# i.e. if all spikes are assigned to a single cluster.
# The fact that spike selection has been previously extended to
# whole clusters is critical here.
new_clusters = _unique(new_spike_clusters)
if len(new_clusters) == 1:
return self._do_merge(spike_ids, old_clusters, new_clusters[0])
# We return the UpdateInfo structure.
up = _assign_update_info(spike_ids,
old_spike_clusters,
new_spike_clusters)
# We update the new cluster id (strictly increasing during a session).
self._new_cluster_id = max(self._new_cluster_id, max(up.added) + 1)
# We make the assignments.
self._spike_clusters[spike_ids] = new_spike_clusters
# OPTIM: we update spikes_per_cluster manually.
new_spc = _spikes_per_cluster(new_spike_clusters, spike_ids)
self._update_cluster_ids(to_remove=old_clusters, to_add=new_spc)
return up
def _extend_spikes(spike_ids, spike_clusters):
"""Return all spikes belonging to the clusters containing the specified
spikes."""
# We find the spikes belonging to modified clusters.
# What are the old clusters that are modified by the assignment?
old_spike_clusters = spike_clusters[spike_ids]
unique_clusters = _unique(old_spike_clusters)
# Now we take all spikes from these clusters.
changed_spike_ids = _spikes_in_clusters(spike_clusters, unique_clusters)
# These are the new spikes that need to be reassigned.
extended_spike_ids = np.setdiff1d(changed_spike_ids, spike_ids,
assume_unique=True)
return extended_spike_ids
def _extend_spikes(spike_ids, spike_clusters):
"""Return all spikes belonging to the clusters containing the specified
spikes."""
# We find the spikes belonging to modified clusters.
# What are the old clusters that are modified by the assignment?
old_spike_clusters = spike_clusters[spike_ids]
unique_clusters = _unique(old_spike_clusters)
# Now we take all spikes from these clusters.
changed_spike_ids = _spikes_in_clusters(spike_clusters, unique_clusters)
# These are the new spikes that need to be reassigned.
extended_spike_ids = np.setdiff1d(changed_spike_ids,
spike_ids,
assume_unique=True)
return extended_spike_ids
def _update_cluster_ids(self, to_remove=None, to_add=None):
# Update the list of non-empty cluster ids.
self._cluster_ids = _unique(self._spike_clusters)
# Clusters to remove.
if to_remove is not None:
for clu in to_remove:
self._spikes_per_cluster.pop(clu, None)
# Clusters to add.
if to_add:
for clu, spk in to_add.items():
self._spikes_per_cluster[clu] = spk
# If spikes_per_cluster is invalid, recompute the entire
# spikes_per_cluster array.
coherent = np.all(np.in1d(self._cluster_ids,
sorted(self._spikes_per_cluster),
))
if not coherent:
logger.debug("Recompute spikes_per_cluster manually: "
"this is long.")
sc = self._spike_clusters
self._spikes_per_cluster = _spikes_per_cluster(sc)
def _update_cluster_ids(self, to_remove=None, to_add=None):
# Update the list of non-empty cluster ids.
self._cluster_ids = _unique(self._spike_clusters)
# Clusters to remove.
if to_remove is not None:
for clu in to_remove:
self._spikes_per_cluster.pop(clu, None)
# Clusters to add.
if to_add:
for clu, spk in to_add.items():
self._spikes_per_cluster[clu] = spk
# If spikes_per_cluster is invalid, recompute the entire
# spikes_per_cluster array.
coherent = np.all(
np.in1d(
self._cluster_ids,
sorted(self._spikes_per_cluster),
))
if not coherent:
logger.debug("Recompute spikes_per_cluster manually: "
"this is long.")
sc = self._spike_clusters
self._spikes_per_cluster = _spikes_per_cluster(sc)
def cluster_ids(self):
"""Ordered list of ids of all non-empty clusters."""
return _unique(self._spike_clusters)
def correlograms(spike_times,
spike_clusters,
cluster_ids=None,
sample_rate=1.,
bin_size=None,
window_size=None,
symmetrize=True,
):
"""Compute all pairwise cross-correlograms among the clusters appearing
in `spike_clusters`.
Parameters
----------
spike_times : array-like
Spike times in seconds.
spike_clusters : array-like
Spike-cluster mapping.
cluster_ids : array-like
The list of unique clusters, in any order. That order will be used
in the output array.
bin_size : float
Size of the bin, in seconds.
window_size : float
Size of the window, in seconds.
Returns
-------
correlograms : array
A `(n_clusters, n_clusters, winsize_samples)` array with all pairwise
CCGs.
"""
assert sample_rate > 0.
assert np.all(np.diff(spike_times) >= 0), ("The spike times must be "
"increasing.")
# Get the spike samples.
spike_times = np.asarray(spike_times, dtype=np.float64)
spike_samples = (spike_times * sample_rate).astype(np.int64)
spike_clusters = _as_array(spike_clusters)
assert spike_samples.ndim == 1
assert spike_samples.shape == spike_clusters.shape
# Find `binsize`.
bin_size = np.clip(bin_size, 1e-5, 1e5) # in seconds
binsize = int(sample_rate * bin_size) # in samples
assert binsize >= 1
# Find `winsize_bins`.
window_size = np.clip(window_size, 1e-5, 1e5) # in seconds
winsize_bins = 2 * int(.5 * window_size / bin_size) + 1
assert winsize_bins >= 1
assert winsize_bins % 2 == 1
# Take the cluster oder into account.
if cluster_ids is None:
clusters = _unique(spike_clusters)
else:
clusters = _as_array(cluster_ids)
n_clusters = len(clusters)
# Like spike_clusters, but with 0..n_clusters-1 indices.
spike_clusters_i = _index_of(spike_clusters, clusters)
# Shift between the two copies of the spike trains.
shift = 1
# At a given shift, the mask precises which spikes have matching spikes
# within the correlogram time window.
mask = np.ones_like(spike_samples, dtype=np.bool)
correlograms = _create_correlograms_array(n_clusters, winsize_bins)
# The loop continues as long as there is at least one spike with
# a matching spike.
while mask[:-shift].any():
# Number of time samples between spike i and spike i+shift.
spike_diff = _diff_shifted(spike_samples, shift)
# Binarize the delays between spike i and spike i+shift.
spike_diff_b = spike_diff // binsize
# Spikes with no matching spikes are masked.
mask[:-shift][spike_diff_b > (winsize_bins // 2)] = False
# Cache the masked spike delays.
m = mask[:-shift].copy()
d = spike_diff_b[m]
# # Update the masks given the clusters to update.
# m0 = np.in1d(spike_clusters[:-shift], clusters)
# m = m & m0
# d = spike_diff_b[m]
d = spike_diff_b[m]
# Find the indices in the raveled correlograms array that need
# to be incremented, taking into account the spike clusters.
indices = np.ravel_multi_index((spike_clusters_i[:-shift][m],
spike_clusters_i[+shift:][m],
d),
correlograms.shape)
# Increment the matching spikes in the correlograms array.
_increment(correlograms.ravel(), indices)
shift += 1
# Remove ACG peaks.
correlograms[np.arange(n_clusters),
np.arange(n_clusters),
0] = 0
if symmetrize:
return _symmetrize_correlograms(correlograms)
else:
return correlograms
def cluster_ids(self):
"""Ordered list of ids of all non-empty clusters."""
return _unique(self._spike_clusters)
def correlograms(
spike_times,
spike_clusters,
cluster_ids=None,
sample_rate=1.,
bin_size=None,
window_size=None,
symmetrize=True,
):
"""Compute all pairwise cross-correlograms among the clusters appearing
in `spike_clusters`.
Parameters
----------
spike_times : array-like
Spike times in seconds.
spike_clusters : array-like
Spike-cluster mapping.
cluster_ids : array-like
The list of unique clusters, in any order. That order will be used
in the output array.
bin_size : float
Size of the bin, in seconds.
window_size : float
Size of the window, in seconds.
Returns
-------
correlograms : array
A `(n_clusters, n_clusters, winsize_samples)` array with all pairwise
CCGs.
"""
assert sample_rate > 0.
assert np.all(np.diff(spike_times) >= 0), ("The spike times must be "
"increasing.")
# Get the spike samples.
spike_times = np.asarray(spike_times, dtype=np.float64)
spike_samples = (spike_times * sample_rate).astype(np.int64)
spike_clusters = _as_array(spike_clusters)
assert spike_samples.ndim == 1
assert spike_samples.shape == spike_clusters.shape
# Find `binsize`.
bin_size = np.clip(bin_size, 1e-5, 1e5) # in seconds
binsize = int(sample_rate * bin_size) # in samples
assert binsize >= 1
# Find `winsize_bins`.
window_size = np.clip(window_size, 1e-5, 1e5) # in seconds
winsize_bins = 2 * int(.5 * window_size / bin_size) + 1
assert winsize_bins >= 1
assert winsize_bins % 2 == 1
# Take the cluster oder into account.
if cluster_ids is None:
clusters = _unique(spike_clusters)
else:
clusters = _as_array(cluster_ids)
n_clusters = len(clusters)
# Like spike_clusters, but with 0..n_clusters-1 indices.
spike_clusters_i = _index_of(spike_clusters, clusters)
# Shift between the two copies of the spike trains.
shift = 1
# At a given shift, the mask precises which spikes have matching spikes
# within the correlogram time window.
mask = np.ones_like(spike_samples, dtype=np.bool)
correlograms = _create_correlograms_array(n_clusters, winsize_bins)
# The loop continues as long as there is at least one spike with
# a matching spike.
while mask[:-shift].any():
# Number of time samples between spike i and spike i+shift.
spike_diff = _diff_shifted(spike_samples, shift)
# Binarize the delays between spike i and spike i+shift.
spike_diff_b = spike_diff // binsize
# Spikes with no matching spikes are masked.
mask[:-shift][spike_diff_b > (winsize_bins // 2)] = False
# Cache the masked spike delays.
m = mask[:-shift].copy()
d = spike_diff_b[m]
# # Update the masks given the clusters to update.
# m0 = np.in1d(spike_clusters[:-shift], clusters)
# m = m & m0
# d = spike_diff_b[m]
d = spike_diff_b[m]
# Find the indices in the raveled correlograms array that need
# to be incremented, taking into account the spike clusters.
indices = np.ravel_multi_index(
(spike_clusters_i[:-shift][m], spike_clusters_i[+shift:][m], d),
correlograms.shape)
# Increment the matching spikes in the correlograms array.
_increment(correlograms.ravel(), indices)
shift += 1
# Remove ACG peaks.
correlograms[np.arange(n_clusters), np.arange(n_clusters), 0] = 0
if symmetrize:
return _symmetrize_correlograms(correlograms)
else:
return correlograms
def _init_data(self):
if op.exists(self.dat_path):
logger.debug("Loading traces at `%s`.", self.dat_path)
traces = _dat_to_traces(
self.dat_path,
n_channels=self.n_channels_dat,
dtype=self.dtype or np.int16,
offset=self.offset,
)
n_samples_t, _ = traces.shape
assert _ == self.n_channels_dat
else:
if self.dat_path is not None:
logger.warning("Error while loading data: File %s not found.",
self.dat_path)
traces = None
n_samples_t = 0
logger.debug("Loading amplitudes.")
amplitudes = read_array('amplitudes').squeeze()
n_spikes, = amplitudes.shape
self.n_spikes = n_spikes
# Create spike_clusters if the file doesn't exist.
if not op.exists(filenames['spike_clusters']):
shutil.copy(filenames['spike_templates'],
filenames['spike_clusters'])
logger.debug("Loading %d spike clusters.", self.n_spikes)
spike_clusters = read_array('spike_clusters').squeeze()
spike_clusters = spike_clusters.astype(np.int32)
assert spike_clusters.shape == (n_spikes, )
self.spike_clusters = spike_clusters
logger.debug("Loading spike templates.")
spike_templates = read_array('spike_templates').squeeze()
spike_templates = spike_templates.astype(np.int32)
assert spike_templates.shape == (n_spikes, )
self.spike_templates = spike_templates
logger.debug("Loading spike samples.")
spike_samples = read_array('spike_samples').squeeze()
assert spike_samples.shape == (n_spikes, )
logger.debug("Loading templates.")
templates = read_array('templates')
templates[np.isnan(templates)] = 0
# templates = np.transpose(templates, (2, 1, 0))
# Unwhiten the templates.
logger.debug("Loading the whitening matrix.")
self.whitening_matrix = read_array('whitening_matrix')
if op.exists(filenames['templates_unw']):
logger.debug("Loading unwhitened templates.")
templates_unw = read_array('templates_unw')
templates_unw[np.isnan(templates_unw)] = 0
else:
logger.debug("Couldn't find unwhitened templates, computing them.")
logger.debug("Inversing the whitening matrix %s.",
self.whitening_matrix.shape)
wmi = np.linalg.inv(self.whitening_matrix)
logger.debug("Unwhitening the templates %s.", templates.shape)
templates_unw = np.dot(np.ascontiguousarray(templates),
np.ascontiguousarray(wmi))
# Save the unwhitened templates.
write_array('templates_unw.npy', templates_unw)
n_templates, n_samples_templates, n_channels = templates.shape
self.n_templates = n_templates
logger.debug("Loading similar templates.")
self.similar_templates = read_array('similar_templates')
assert self.similar_templates.shape == (self.n_templates,
self.n_templates)
logger.debug("Loading channel mapping.")
channel_mapping = read_array('channel_mapping').squeeze()
channel_mapping = channel_mapping.astype(np.int32)
assert channel_mapping.shape == (n_channels, )
# Ensure that the mappings maps to valid columns in the dat file.
assert np.all(channel_mapping <= self.n_channels_dat - 1)
logger.debug("Loading channel positions.")
channel_positions = read_array('channel_positions')
assert channel_positions.shape == (n_channels, 2)
if op.exists(filenames['features']):
logger.debug("Loading features.")
all_features = np.load(filenames['features'], mmap_mode='r')
features_ind = read_array('features_ind').astype(np.int32)
# Feature subset.
if op.exists(filenames['features_spike_ids']):
features_spike_ids = read_array('features_spike_ids') \
.astype(np.int32)
assert len(features_spike_ids) == len(all_features)
self.features_spike_ids = features_spike_ids
ns = len(features_spike_ids)
else:
ns = self.n_spikes
self.features_spike_ids = None
assert all_features.ndim == 3
n_loc_chan = all_features.shape[2]
self.n_features_per_channel = all_features.shape[1]
assert all_features.shape == (
ns,
self.n_features_per_channel,
n_loc_chan,
)
# Check sparse features arrays shapes.
assert features_ind.shape == (self.n_templates, n_loc_chan)
else:
all_features = None
features_ind = None
self.all_features = all_features
self.features_ind = features_ind
if op.exists(filenames['template_features']):
logger.debug("Loading template features.")
template_features = np.load(filenames['template_features'],
mmap_mode='r')
template_features_ind = read_array('template_features_ind'). \
astype(np.int32)
template_features_ind = template_features_ind.copy()
n_sim_tem = template_features.shape[1]
assert template_features.shape == (n_spikes, n_sim_tem)
assert template_features_ind.shape == (n_templates, n_sim_tem)
else:
template_features = None
template_features_ind = None
self.template_features_ind = template_features_ind
self.template_features = template_features
self.n_channels = n_channels
# Take dead channels into account.
if traces is not None:
# Find the scaling factor for the traces.
scaling = 1. / self._data_lim(traces[:10000])
traces = _concatenate_virtual_arrays(
[traces],
channel_mapping,
scaling=scaling,
)
else:
scaling = 1.
# Amplitudes
self.all_amplitudes = amplitudes
self.amplitudes_lim = np.max(self.all_amplitudes)
# Templates
self.templates = templates
self.templates_unw = templates_unw
assert self.templates.shape == self.templates_unw.shape
self.n_samples_templates = n_samples_templates
self.n_samples_waveforms = n_samples_templates
self.template_lim = np.max(np.abs(self.templates))
self.duration = n_samples_t / float(self.sample_rate)
self.spike_times = spike_samples / float(self.sample_rate)
assert np.all(np.diff(self.spike_times) >= 0)
self.cluster_ids = _unique(self.spike_clusters)
# n_clusters = len(self.cluster_ids)
self.channel_positions = channel_positions
self.all_traces = traces
# Only filter the data for the waveforms if the traces
# are not already filtered.
if not getattr(self, 'hp_filtered', False):
logger.debug("HP filtering the data for waveforms")
filter_order = 3
else:
filter_order = None
n_closest_channels = getattr(self, 'max_n_unmasked_channels', 16)
mask_threshold = getattr(self, 'waveform_mask_threshold', None)
self.closest_channels = get_closest_channels(
self.channel_positions,
n_closest_channels,
)
self.template_masks = get_masks(self.templates, self.closest_channels)
self.all_masks = MaskLoader(self.template_masks, self.spike_templates)
# Fetch waveforms from traces.
nsw = self.n_samples_waveforms
if traces is not None:
waveforms = WaveformLoader(
traces=traces,
masks=self.all_masks,
spike_samples=spike_samples,
n_samples_waveforms=nsw,
filter_order=filter_order,
sample_rate=self.sample_rate,
mask_threshold=mask_threshold,
)
else:
waveforms = None
self.all_waveforms = waveforms
# Read the cluster groups.
logger.debug("Loading the cluster groups.")
self.cluster_groups = {}
if op.exists(filenames['cluster_groups']):
with open(filenames['cluster_groups'], 'r') as f:
reader = csv.reader(f, delimiter='\t')
# Skip the header.
for row in reader:
break
for row in reader:
cluster, group = row
cluster = int(cluster)
self.cluster_groups[cluster] = group
for cluster_id in self.cluster_ids:
if cluster_id not in self.cluster_groups:
self.cluster_groups[cluster_id] = None
def _init_data(self):
if op.exists(self.dat_path):
logger.debug("Loading traces at `%s`.", self.dat_path)
traces = _dat_to_traces(self.dat_path,
n_channels=self.n_channels_dat,
dtype=self.dtype or np.int16,
offset=self.offset,
)
n_samples_t, _ = traces.shape
assert _ == self.n_channels_dat
else:
if self.dat_path is not None:
logger.warning("Error while loading data: File %s not found.",
self.dat_path)
traces = None
n_samples_t = 0
logger.debug("Loading amplitudes.")
amplitudes = read_array('amplitudes').squeeze()
n_spikes, = amplitudes.shape
self.n_spikes = n_spikes
# Create spike_clusters if the file doesn't exist.
if not op.exists(filenames['spike_clusters']):
shutil.copy(filenames['spike_templates'],
filenames['spike_clusters'])
logger.debug("Loading %d spike clusters.", self.n_spikes)
spike_clusters = read_array('spike_clusters').squeeze()
spike_clusters = spike_clusters.astype(np.int32)
assert spike_clusters.shape == (n_spikes,)
self.spike_clusters = spike_clusters
logger.debug("Loading spike templates.")
spike_templates = read_array('spike_templates').squeeze()
spike_templates = spike_templates.astype(np.int32)
assert spike_templates.shape == (n_spikes,)
self.spike_templates = spike_templates
logger.debug("Loading spike samples.")
spike_samples = read_array('spike_samples').squeeze()
assert spike_samples.shape == (n_spikes,)
logger.debug("Loading templates.")
templates = read_array('templates')
templates[np.isnan(templates)] = 0
# templates = np.transpose(templates, (2, 1, 0))
# Unwhiten the templates.
logger.debug("Loading the whitening matrix.")
self.whitening_matrix = read_array('whitening_matrix')
if op.exists(filenames['templates_unw']):
logger.debug("Loading unwhitened templates.")
templates_unw = read_array('templates_unw')
templates_unw[np.isnan(templates_unw)] = 0
else:
logger.debug("Couldn't find unwhitened templates, computing them.")
logger.debug("Inversing the whitening matrix %s.",
self.whitening_matrix.shape)
wmi = np.linalg.inv(self.whitening_matrix)
logger.debug("Unwhitening the templates %s.",
templates.shape)
templates_unw = np.dot(np.ascontiguousarray(templates),
np.ascontiguousarray(wmi))
# Save the unwhitened templates.
write_array('templates_unw.npy', templates_unw)
n_templates, n_samples_templates, n_channels = templates.shape
self.n_templates = n_templates
logger.debug("Loading similar templates.")
self.similar_templates = read_array('similar_templates')
assert self.similar_templates.shape == (self.n_templates,
self.n_templates)
logger.debug("Loading channel mapping.")
channel_mapping = read_array('channel_mapping').squeeze()
channel_mapping = channel_mapping.astype(np.int32)
assert channel_mapping.shape == (n_channels,)
# Ensure that the mappings maps to valid columns in the dat file.
assert np.all(channel_mapping <= self.n_channels_dat - 1)
self.channel_order = channel_mapping
logger.debug("Loading channel positions.")
channel_positions = read_array('channel_positions')
assert channel_positions.shape == (n_channels, 2)
if op.exists(filenames['features']):
logger.debug("Loading features.")
all_features = np.load(filenames['features'], mmap_mode='r')
features_ind = read_array('features_ind').astype(np.int32)
# Feature subset.
if op.exists(filenames['features_spike_ids']):
features_spike_ids = read_array('features_spike_ids') \
.astype(np.int32)
assert len(features_spike_ids) == len(all_features)
self.features_spike_ids = features_spike_ids
ns = len(features_spike_ids)
else:
ns = self.n_spikes
self.features_spike_ids = None
assert all_features.ndim == 3
n_loc_chan = all_features.shape[2]
self.n_features_per_channel = all_features.shape[1]
assert all_features.shape == (ns,
self.n_features_per_channel,
n_loc_chan,
)
# Check sparse features arrays shapes.
assert features_ind.shape == (self.n_templates, n_loc_chan)
else:
all_features = None
features_ind = None
self.all_features = all_features
self.features_ind = features_ind
if op.exists(filenames['template_features']):
logger.debug("Loading template features.")
template_features = np.load(filenames['template_features'],
mmap_mode='r')
template_features_ind = read_array('template_features_ind'). \
astype(np.int32)
template_features_ind = template_features_ind.copy()
n_sim_tem = template_features.shape[1]
assert template_features.shape == (n_spikes, n_sim_tem)
assert template_features_ind.shape == (n_templates, n_sim_tem)
else:
template_features = None
template_features_ind = None
self.template_features_ind = template_features_ind
self.template_features = template_features
self.n_channels = n_channels
# Take dead channels into account.
if traces is not None:
# Find the scaling factor for the traces.
scaling = 1. / self._data_lim(traces[:10000])
traces = _concatenate_virtual_arrays([traces],
channel_mapping,
scaling=scaling,
)
else:
scaling = 1.
# Amplitudes
self.all_amplitudes = amplitudes
self.amplitudes_lim = np.max(self.all_amplitudes)
# Templates
self.templates = templates
self.templates_unw = templates_unw
assert self.templates.shape == self.templates_unw.shape
self.n_samples_templates = n_samples_templates
self.n_samples_waveforms = n_samples_templates
self.template_lim = np.max(np.abs(self.templates))
self.duration = n_samples_t / float(self.sample_rate)
self.spike_times = spike_samples / float(self.sample_rate)
assert np.all(np.diff(self.spike_times) >= 0)
self.cluster_ids = _unique(self.spike_clusters)
# n_clusters = len(self.cluster_ids)
self.channel_positions = channel_positions
self.all_traces = traces
# Only filter the data for the waveforms if the traces
# are not already filtered.
if not getattr(self, 'hp_filtered', False):
logger.debug("HP filtering the data for waveforms")
filter_order = 3
else:
filter_order = None
n_closest_channels = getattr(self, 'max_n_unmasked_channels', 16)
self.closest_channels = get_closest_channels(self.channel_positions,
n_closest_channels,
)
self.template_masks = get_masks(self.templates, self.closest_channels)
self.all_masks = MaskLoader(self.template_masks, self.spike_templates)
# Fetch waveforms from traces.
nsw = self.n_samples_waveforms
if traces is not None:
waveforms = WaveformLoader(traces=traces,
spike_samples=spike_samples,
n_samples_waveforms=nsw,
filter_order=filter_order,
sample_rate=self.sample_rate,
)
else:
waveforms = None
self.all_waveforms = waveforms
# Read the cluster groups.
logger.debug("Loading the cluster groups.")
self.cluster_groups = load_metadata(filenames['cluster_groups'],
self.cluster_ids)
def _update_cluster_ids(self):
# Update the list of non-empty cluster ids.
self._cluster_ids = _unique(self._spike_clusters)
