def run_split(units_in, ptps, labels, CONFIG, ptp_cut=5):
spike_index_list = []
for unit in units_in:
idx_ = np.where(labels == unit)[0]
ptps_ = ptps[idx_]
new_assignment = np.zeros(len(idx_), 'int32')
idx_big = np.where(ptps_ > ptp_cut)[0]
if len(idx_big) > 10:
mask = np.ones((len(idx_big), 1))
group = np.arange(len(idx_big))
vbParam = mfm.spikesort(ptps_[idx_big, None, None], mask, group,
CONFIG)
cc_assignment, stability, cc = anneal_clusters(vbParam)
# get ptp per cc
mean_ptp_cc = np.zeros(len(cc))
for k in range(len(cc)):
mean_ptp_cc[k] = np.mean(ptps_[idx_big][cc_assignment == k])
# reorder cc label by mean ptp
cc_assignment_ordered = np.zeros_like(cc_assignment)
for ii, k in enumerate(np.argsort(mean_ptp_cc)):
cc_assignment_ordered[cc_assignment == k] = ii
# cc with the smallest mean ptp will have the same assignment as ptps < ptp cut
new_assignment[idx_big] = cc_assignment_ordered
spike_index_list.append(np.vstack((idx_, new_assignment)).T)
return spike_index_list
def run_mfm(self, gen, pca_wf):
mask = np.ones((pca_wf.shape[0], 1))
group = np.arange(pca_wf.shape[0])
vbParam = mfm.spikesort(pca_wf[:, :, np.newaxis], mask, group,
self.CONFIG)
if self.verbose:
print("chan "+ str(self.channel)+', gen '\
+str(gen)+", "+str(vbParam.rhat.shape[1])+" clusters from ",pca_wf.shape)
return vbParam
def run_cluster_location(scores, spike_index, min_spikes, CONFIG):
"""
run clustering algorithm using MFM and location features
Parameters
----------
scores: list (n_channels)
A list such that scores[c] contains all scores whose main
channel is c
spike_times: list (n_channels)
A list such that spike_index[c] cointains all spike times
whose channel is c
CONFIG: class
configuration class
Returns
-------
spike_train: np.array (n_data, 2)
spike_train such that spike_train[j, 0] and spike_train[j, 1]
are the spike time and spike id of spike j
"""
logger = logging.getLogger(__name__)
n_channels = np.max(spike_index[:, 1]) + 1
global_score = None
global_vbParam = None
global_spike_index = None
global_tmp_loc = None
# run clustering algorithm per main channel
for channel in range(n_channels):
logger.info('Processing channel {}'.format(channel))
idx_data = np.where(spike_index[:, 1] == channel)[0]
score_channel = scores[idx_data]
spike_index_channel = spike_index[idx_data]
n_data = score_channel.shape[0]
if n_data > 1:
# make a fake mask of ones to run clustering algorithm
mask = np.ones((n_data, 1))
group = np.arange(n_data)
vbParam = mfm.spikesort(np.copy(score_channel), mask, group,
CONFIG)
# make rhat more sparse
vbParam.rhat[vbParam.rhat < 0.1] = 0
vbParam.rhat = vbParam.rhat / np.sum(
vbParam.rhat, 1, keepdims=True)
# clean clusters with nearly no spikes
vbParam = clean_empty_cluster(vbParam, min_spikes)
if vbParam.rhat.shape[1] > 0:
# add changes to global parameters
(global_vbParam, global_tmp_loc, global_score,
global_spike_index) = global_cluster_info(
vbParam, channel, score_channel, spike_index_channel,
global_vbParam, global_tmp_loc, global_score,
global_spike_index)
return global_vbParam, global_tmp_loc, global_score, global_spike_index
def run_cluster(scores, masks, groups, spike_index, min_spikes, CONFIG):
"""
run clustering algorithm using MFM
Parameters
----------
scores: list (n_channels)
A list such that scores[c] contains all scores whose main
channel is c
masks: list (n_channels)
mask for each data in scores
masks[c] is the mask of spikes in scores[c]
groups: list (n_channels)
coreset represented as group id.
groups[c] is the group id of spikes in scores[c]
spike_index: list (n_channels)
A list such that spike_index[c] cointains all spike times
whose channel is c
CONFIG: class
configuration class
Returns
-------
spike_train: np.array (n_data, 2)
spike_train such that spike_train[j, 0] and spike_train[j, 1]
are the spike time and spike id of spike j
"""
# FIXME: mutating parameter
# this function is passing a config object and mutating it,
# this is not a good idea as having a mutable object lying around the code
# can break things and make it hard to debug
# (09/27/17) Eduardo
logger = logging.getLogger(__name__)
n_channels = np.max(spike_index[:, 1]) + 1
global_score = None
global_vbParam = None
global_spike_index = None
global_tmp_loc = None
# run clustering algorithm per main channel
for channel in range(n_channels):
logger.info('Processing channel {}'.format(channel))
idx_data = np.where(spike_index[:, 1] == channel)[0]
score_channel = scores[idx_data]
mask_channel = masks[channel]
group_channel = groups[channel]
spike_index_channel = spike_index[idx_data]
n_data = score_channel.shape[0]
if n_data > 1:
# run clustering
vbParam = mfm.spikesort(score_channel, mask_channel, group_channel,
CONFIG)
# make rhat more sparse
vbParam.rhat[vbParam.rhat < 0.1] = 0
vbParam.rhat = vbParam.rhat / np.sum(
vbParam.rhat, 1, keepdims=True)
# clean clusters with nearly no spikes
vbParam = clean_empty_cluster(vbParam, min_spikes)
# add changes to global parameters
(global_vbParam, global_tmp_loc,
global_score, global_spike_index) = global_cluster_info(
vbParam, channel, score_channel, spike_index_channel,
global_vbParam, global_tmp_loc, global_score,
global_spike_index)
return global_vbParam, global_tmp_loc, global_score, global_spike_index
def runSorter(score_all, mask_all, clr_idx_all, group_all, channel_groups,
neighbors, n_features, config):
"""Run sorting algorithm for every channel group
Parameters
----------
Returns
-------
spike_train:
?
"""
# FIXME: mutating parameter
# this function is passing a config object and mutating it,
# this is not a good idea as having a mutable object lying around the code
# can break things and make it hard to debug
# (09/27/17) Eduardo
nG = len(channel_groups)
nmax = 10000
K = 0
spike_train = 0
bar = progressbar.ProgressBar(maxval=nG)
# iterate over every channel group (this is computed in config.py)
for g in range(nG):
# get the channels that conform this group
ch_idx = channel_groups[g]
neigh_chan = np.sum(neighbors[ch_idx], axis=0) > 0
score = np.zeros(
(nmax * ch_idx.shape[0], n_features, np.sum(neigh_chan)))
index = np.zeros((nmax * ch_idx.shape[0], 2), 'int32')
mask = np.zeros((nmax * ch_idx.shape[0], np.sum(neigh_chan)))
group = np.zeros(nmax * ch_idx.shape[0], 'int16')
count = 0
Ngroup = 0
for j in range(ch_idx.shape[0]):
c = ch_idx[j]
if score_all[c].shape[0] > 0:
ndataTemp = score_all[c].shape[0]
score[count:(count + ndataTemp), :,
neighbors[c][neigh_chan]] = score_all[c]
clr_idx_temp = clr_idx_all[c]
index[count:(count + ndataTemp)] = np.concatenate((np.ones(
(ndataTemp, 1)) * c, clr_idx_temp[:, np.newaxis]),
axis=1)
mask[count:(count + ndataTemp),
neighbors[c][neigh_chan]] = mask_all[c]
group[count:(count + ndataTemp)] = group_all[c] + Ngroup + 1
Ngroup += np.amax(group_all[c]) + 1
count += ndataTemp
score = score[:count]
index = index[:count]
mask = mask[:count]
group = group[:count] - 1
if score.shape[0] > 0:
L = spikesort(score, mask, group, config)
idx_triage = L == -1
L = L[~idx_triage]
index = index[~idx_triage]
spikeTrain_temp = np.concatenate((L[:, np.newaxis] + K, index),
axis=1)
K += np.amax(L) + 1
if g == 0:
spike_train = spikeTrain_temp
else:
spike_train = np.concatenate((spike_train, spikeTrain_temp))
bar.update(g + 1)
bar.finish()
return spike_train
def run(score,
spike_index_clear,
spike_index_collision,
output_directory='tmp/',
recordings_filename='standarized.bin'):
"""Process spikes
Parameters
----------
score: numpy.ndarray (n_spikes, n_features, n_channels)
3D array with the scores for the clear spikes, first simension is
the number of spikes, second is the nymber of features and third the
number of channels
spike_index_clear: numpy.ndarray (n_clear_spikes, 2)
2D array with indexes for clear spikes, first column contains the
spike location in the recording and the second the main channel
(channel whose amplitude is maximum)
spike_index_collision: numpy.ndarray (n_collided_spikes, 2)
2D array with indexes for collided spikes, first column contains the
spike location in the recording and the second the main channel
(channel whose amplitude is maximum)
output_directory: str, optional
Output directory (relative to CONFIG.data.root_folder) used to load
the recordings to generate templates, defaults to tmp/
recordings_filename: str, optional
Recordings filename (relative to CONFIG.data.root_folder/
output_directory) used to generate the templates, defaults to
whitened.bin
Returns
-------
spike_train_clear: numpy.ndarray (n_clear_spikes, 2)
A 2D array for clear spikes whose first column indicates the spike
time and the second column the neuron id determined by the clustering
algorithm
templates: numpy.ndarray (n_channels, waveform_size, n_templates)
A 3D array with the templates
spike_index_collision: numpy.ndarray (n_collided_spikes, 2)
A 2D array for collided spikes whose first column indicates the spike
time and the second column the neuron id determined by the clustering
algorithm
Examples
--------
.. literalinclude:: ../examples/process.py
"""
CONFIG = read_config()
MAIN_CHANNEL = 1
startTime = datetime.datetime.now()
Time = {'t': 0, 'c': 0, 'm': 0, 's': 0, 'e': 0}
logger = logging.getLogger(__name__)
nG = len(CONFIG.channelGroups)
nneigh = np.max(np.sum(CONFIG.neighChannels, 0))
n_coreset = 0
K = 0
# first column: spike_time
# second column: cluster id
spike_train_clear = np.zeros((0, 2), 'int32')
if CONFIG.clustering.clustering_method == 'location':
spike_index_clear_proc = np.zeros((0, 2), 'int32')
main_channel_index = spike_index_clear[:, MAIN_CHANNEL]
for i, c in enumerate(np.unique(main_channel_index)):
logger.info('Processing channel {}'.format(i))
idx = main_channel_index == c
score_c = score[idx]
spike_index_clear_c = spike_index_clear[idx]
##########
# Triage #
##########
# TODO: refactor this as CONFIG.doTriage was removed
doTriage = True
_b = datetime.datetime.now()
logger.info('Triaging events with main channel {}'.format(c))
index_keep = triage(score_c, 0, CONFIG.triage.nearest_neighbors,
CONFIG.triage.percent, doTriage)
Time['t'] += (datetime.datetime.now() - _b).total_seconds()
# add untriaged spike index to spike_index_clear_group
# and triaged spike index to spike_index_collision
spike_index_clear_proc = np.concatenate(
(spike_index_clear_proc, spike_index_clear_c[index_keep]),
axis=0)
spike_index_collision = np.concatenate(
(spike_index_collision, spike_index_clear_c[~index_keep]),
axis=0)
# TODO: add documentation for all of this part, until the
# "cleaning" commend
# keep untriaged score only
score_c = score_c[index_keep]
group = np.arange(score_c.shape[0])
mask = np.ones([score_c.shape[0], 1])
_b = datetime.datetime.now()
logger.info('Clustering events with main channel {}'.format(c))
if i == 0:
global_vbParam, global_maskedData = spikesort(
score_c, mask, group, CONFIG)
score_proc = score_c
else:
local_vbParam, local_maskedData = spikesort(
score_c, mask, group, CONFIG)
global_vbParam.muhat = np.concatenate(
[global_vbParam.muhat, local_vbParam.muhat], axis=1)
global_vbParam.Vhat = np.concatenate(
[global_vbParam.Vhat, local_vbParam.Vhat], axis=2)
global_vbParam.invVhat = np.concatenate(
[global_vbParam.invVhat, local_vbParam.invVhat], axis=2)
global_vbParam.lambdahat = np.concatenate(
[global_vbParam.lambdahat, local_vbParam.lambdahat],
axis=0)
global_vbParam.nuhat = np.concatenate(
[global_vbParam.nuhat, local_vbParam.nuhat], axis=0)
global_vbParam.ahat = np.concatenate(
[global_vbParam.ahat, local_vbParam.ahat], axis=0)
global_maskedData.sumY = np.concatenate(
[global_maskedData.sumY, local_maskedData.sumY], axis=0)
global_maskedData.sumYSq = np.concatenate(
[global_maskedData.sumYSq, local_maskedData.sumYSq],
axis=0)
global_maskedData.sumEta = np.concatenate(
[global_maskedData.sumEta, local_maskedData.sumEta],
axis=0)
global_maskedData.weight = np.concatenate(
[global_maskedData.weight, local_maskedData.weight],
axis=0)
global_maskedData.groupMask = np.concatenate(
[global_maskedData.groupMask, local_maskedData.groupMask],
axis=0)
global_maskedData.meanY = np.concatenate(
[global_maskedData.meanY, local_maskedData.meanY], axis=0)
global_maskedData.meanYSq = np.concatenate(
[global_maskedData.meanYSq, local_maskedData.meanYSq],
axis=0)
global_maskedData.meanEta = np.concatenate(
[global_maskedData.meanEta, local_maskedData.meanEta],
axis=0)
score_proc = np.concatenate([score_proc, score_c], axis=0)
logger.info('merging all channels')
L = np.ones(global_vbParam.muhat.shape[1])
global_vbParam.update_local(global_maskedData)
suffStat = suffStatistics(global_maskedData, global_vbParam)
global_vbParam, suffStat, L = merge_move(global_maskedData,
global_vbParam, suffStat,
CONFIG, L, 0)
assignmentTemp = np.argmax(global_vbParam.rhat, axis=1)
assignment = np.zeros(score_proc.shape[0], 'int16')
for j in range(score_proc.shape[0]):
assignment[j] = assignmentTemp[j]
idx_triage = cluster_triage(global_vbParam, score_proc, 3)
assignment[idx_triage] = -1
Time['s'] += (datetime.datetime.now() - _b).total_seconds()
############
# Cleaning #
############
# TODO: describe this step
spike_train_clear = np.concatenate([
spike_index_clear_proc[~idx_triage, 0:1:], assignment[~idx_triage,
np.newaxis]
],
axis=1)
spike_index_collision = np.concatenate(
[spike_index_collision, spike_index_clear_proc[idx_triage]])
else:
# according to the docs if clustering method is not 2+3, you can set
# 3 x neighboring_channels, but I do not see where the
# neighboring_channels is being parsed on this else statemente
c_idx = np.ones((CONFIG.recordings.n_channels, nneigh),
'int32') * CONFIG.recordings.n_channels
for c in range(CONFIG.recordings.n_channels):
ch_idx, _ = order_channels_by_distance(
c,
np.where(CONFIG.neighChannels[c])[0], CONFIG.geom)
c_idx[c, :ch_idx.shape[0]] = ch_idx
# iterate over every channel group [missing documentation for this
# function]. why is this order needed?
for g in range(nG):
logger.info("Processing group {} in {} groups.".format(g + 1, nG))
logger.info("Processiing data (triage, coreset, masking) ...")
channels = CONFIG.channelGroups[g]
neigh_chans = np.where(
np.sum(CONFIG.neighChannels[channels], axis=0) > 0)[0]
score_group = np.zeros(
(0, CONFIG.spikes.temporal_features, neigh_chans.shape[0]))
coreset_id_group = np.zeros((0), 'int32')
mask_group = np.zeros((0, neigh_chans.shape[0]))
spike_index_clear_group = np.zeros((0, 2), 'int32')
# go through every channel in the group
for c in channels:
# index of data whose main channel is c
idx = spike_index_clear[:, MAIN_CHANNEL] == c
if np.sum(idx) > 0:
# score whose main channel is c
score_c = score[idx]
# spike_index_clear whose main channel is c
spike_index_clear_c = spike_index_clear[idx]
##########
# Triage #
##########
# TODO: refactor this as CONFIG.doTriage was removed
doTriage = True
_b = datetime.datetime.now()
index_keep = triage(score_c, 0,
CONFIG.triage.nearest_neighbors,
CONFIG.triage.percent, doTriage)
Time['t'] += (datetime.datetime.now() - _b).total_seconds()
# add untriaged spike index to spike_index_clear_group
# and triaged spike index to spike_index_collision
spike_index_clear_group = np.concatenate(
(spike_index_clear_group,
spike_index_clear_c[index_keep]),
axis=0)
spike_index_collision = np.concatenate(
(spike_index_collision,
spike_index_clear_c[~index_keep]),
axis=0)
# keep untriaged score only
score_c = score_c[index_keep]
###########
# Coreset #
###########
# TODO: refactor this as CONFIG.doCoreset was removed
doCoreset = True
_b = datetime.datetime.now()
coreset_id = coreset(score_c, CONFIG.coreset.clusters,
CONFIG.coreset.threshold, doCoreset)
Time['c'] += (datetime.datetime.now() - _b).total_seconds()
###########
# Masking #
###########
_b = datetime.datetime.now()
mask = getmask(score_c, coreset_id,
CONFIG.clustering.masking_threshold,
CONFIG.spikes.temporal_features)
Time['m'] += (datetime.datetime.now() - _b).total_seconds()
################
# collect data #
################
# restructure score_c and mask to have same number of
# channels as score_group
score_temp = np.zeros(
(score_c.shape[0], CONFIG.spikes.temporal_features,
neigh_chans.shape[0]))
mask_temp = np.zeros((mask.shape[0], neigh_chans.shape[0]))
nneigh_c = np.sum(c_idx[c] < CONFIG.recordings.n_channels)
for j in range(nneigh_c):
c_interest = np.where(neigh_chans == c_idx[c, j])[0][0]
score_temp[:, :, c_interest] = score_c[:, :, j]
mask_temp[:, c_interest] = mask[:, j]
# add score, coreset_id, mask to the groups
score_group = np.concatenate((score_group, score_temp),
axis=0)
mask_group = np.concatenate((mask_group, mask_temp),
axis=0)
coreset_id_group = np.concatenate(
(coreset_id_group, coreset_id + n_coreset + 1), axis=0)
n_coreset += np.max(coreset_id) + 1
if score_group.shape[0] > 0:
##############
# Clustering #
##############
_b = datetime.datetime.now()
logger.info("Clustering...")
coreset_id_group = coreset_id_group - 1
n_coreset = 0
cluster_id = spikesort(score_group, mask_group,
coreset_id_group, CONFIG)
Time['s'] += (datetime.datetime.now() - _b).total_seconds()
############
# Cleaning #
############
# model based triage
idx_triage = (cluster_id == -1)
# concatenate spike index with cluster id of untriaged ones
# to create spike_train_clear
si_clustered = spike_index_clear_group[~idx_triage]
spt = si_clustered[:, [0]]
cluster_id = cluster_id[~idx_triage][:, np.newaxis]
spike_train_temp = np.concatenate((spt, cluster_id + K),
axis=1)
spike_train_clear = np.concatenate(
(spike_train_clear, spike_train_temp), axis=0)
K += np.amax(cluster_id) + 1
# concatenate triaged spike_index_clear_group
# into spike_index_collision
spike_index_collision = np.concatenate(
(spike_index_collision,
spike_index_clear_group[idx_triage]),
axis=0)
#################
# Get templates #
#################
_b = datetime.datetime.now()
logger.info("Getting Templates...")
path_to_recordings = os.path.join(CONFIG.data.root_folder,
output_directory, recordings_filename)
merge_threshold = CONFIG.templates.merge_threshold
spike_train_clear, templates = gam_templates(
spike_train_clear, path_to_recordings, CONFIG.spikeSize,
CONFIG.templatesMaxShift, merge_threshold, CONFIG.neighChannels)
Time['e'] += (datetime.datetime.now() - _b).total_seconds()
currentTime = datetime.datetime.now()
if CONFIG.clustering.clustering_method == 'location':
logger.info("Mainprocess done in {0} seconds.".format(
(currentTime - startTime).seconds))
logger.info("\ttriage:\t{0} seconds".format(Time['t']))
logger.info("\tclustering:\t{0} seconds".format(Time['s']))
logger.info("\ttemplates:\t{0} seconds".format(Time['e']))
else:
logger.info("\ttriage:\t{0} seconds".format(Time['t']))
logger.info("\tcoreset:\t{0} seconds".format(Time['c']))
logger.info("\tmasking:\t{0} seconds".format(Time['m']))
logger.info("\tclustering:\t{0} seconds".format(Time['s']))
logger.info("\ttemplates:\t{0} seconds".format(Time['e']))
return spike_train_clear, templates, spike_index_collision
