def open_klusters_oneshank(filename):
filenames = find_filenames(filename)
fileindex = find_index(filename)
# Open small Klusters files.
data = {}
metadata = read_xml(filenames['xml'], fileindex)
data['clu'] = read_clusters(filenames['clu'])
# Read .aclu data.
if 'aclu' in filenames and os.path.exists(filenames['aclu']):
data['aclu'] = read_clusters(filenames['aclu'])
else:
data['aclu'] = data['clu']
# Read .acluinfo data.
if 'acluinfo' in filenames and os.path.exists(filenames['acluinfo']):
data['acluinfo'] = read_cluster_info(filenames['acluinfo'])
# If the ACLUINFO does not exist, try CLUINFO (older file extension)
elif 'cluinfo' in filenames and os.path.exists(filenames['cluinfo']):
data['acluinfo'] = read_cluster_info(filenames['cluinfo'])
else:
data['acluinfo'] = default_cluster_info(np.unique(data['aclu']))
# Read group info.
if 'groupinfo' in filenames and os.path.exists(filenames['groupinfo']):
data['groupinfo'] = read_group_info(filenames['groupinfo'])
else:
data['groupinfo'] = default_group_info()
# Find out the number of columns in the .fet file.
with open(filenames['fet'], 'r') as f:
f.readline()
# Get the number of non-empty columns in the .fet file.
data['fetcol'] = len(
[col for col in f.readline().split(' ') if col.strip() != ''])
metadata['nspikes'] = len(data['clu'])
data['fileindex'] = fileindex
# Open big Klusters files.
data['fet'] = MemMappedText(filenames['fet'], np.int64, skiprows=1)
if 'spk' in filenames and os.path.exists(filenames['spk'] or ''):
data['spk'] = MemMappedBinary(filenames['spk'],
np.int16,
rowsize=metadata['nchannels'] *
metadata['nsamples'])
if 'uspk' in filenames and os.path.exists(filenames['uspk'] or ''):
data['uspk'] = MemMappedBinary(filenames['uspk'],
np.int16,
rowsize=metadata['nchannels'] *
metadata['nsamples'])
if 'mask' in filenames and os.path.exists(filenames['mask'] or ''):
data['mask'] = MemMappedText(filenames['mask'], np.float32, skiprows=1)
# data['metadata'] = metadata
data.update(metadata)
return data
def set_filenames(self, filename):
filenames = find_filenames(filename)
self.filename_kwik = filenames['hdf5_kwik']
self.filename_log = filenames['kvwlg']
self.filename_clu = filenames['clu']
self.filename_kwa = filenames['hdf5_kwa']
self.filename_raw_kwd = filenames['hdf5_raw']
self.filename = self.filename_kwik
self.filenames = filenames
def open_klusters_oneshank(filename):
filenames = find_filenames(filename)
fileindex = find_index(filename)
# Open small Klusters files.
data = {}
metadata = read_xml(filenames['xml'], fileindex)
data['clu'] = read_clusters(filenames['clu'])
# Read .aclu data.
if 'aclu' in filenames and os.path.exists(filenames['aclu']):
data['aclu'] = read_clusters(filenames['aclu'])
else:
data['aclu'] = data['clu']
# Read .acluinfo data.
if 'acluinfo' in filenames and os.path.exists(filenames['acluinfo']):
data['acluinfo'] = read_cluster_info(filenames['acluinfo'])
# If the ACLUINFO does not exist, try CLUINFO (older file extension)
elif 'cluinfo' in filenames and os.path.exists(filenames['cluinfo']):
data['acluinfo'] = read_cluster_info(filenames['cluinfo'])
else:
data['acluinfo'] = default_cluster_info(np.unique(data['aclu']))
# Read group info.
if 'groupinfo' in filenames and os.path.exists(filenames['groupinfo']):
data['groupinfo'] = read_group_info(filenames['groupinfo'])
else:
data['groupinfo'] = default_group_info()
# Find out the number of columns in the .fet file.
with open(filenames['fet'], 'r') as f:
f.readline()
# Get the number of non-empty columns in the .fet file.
data['fetcol'] = len([col for col in f.readline().split(' ') if col.strip() != ''])
metadata['nspikes'] = len(data['clu'])
data['fileindex'] = fileindex
# Open big Klusters files.
data['fet'] = MemMappedText(filenames['fet'], np.int64, skiprows=1)
if 'spk' in filenames and os.path.exists(filenames['spk'] or ''):
data['spk'] = MemMappedBinary(filenames['spk'], np.int16,
rowsize=metadata['nchannels'] * metadata['nsamples'])
if 'uspk' in filenames and os.path.exists(filenames['uspk'] or ''):
data['uspk'] = MemMappedBinary(filenames['uspk'], np.int16,
rowsize=metadata['nchannels'] * metadata['nsamples'])
if 'mask' in filenames and os.path.exists(filenames['mask'] or ''):
data['mask'] = MemMappedText(filenames['mask'], np.float32, skiprows=1)
# data['metadata'] = metadata
data.update(metadata)
return data
def open(self, filename=None):
"""Open everything."""
if filename is None:
filename = self.filename
else:
self.filename = filename
dir, basename = os.path.split(filename)
# Converting to kwik if needed
# kwik = find_filename(basename, 'kwik', dir=dir)
# xml = find_filename(basename, 'xml', dir=dir)
# self.filename_clu = find_filename(basename, 'clu', dir=dir)
self._filenames = find_filenames(filename)
kwik = find_filename(basename, 'kwik', dir=dir)
xml = self._filenames['xml']
clu = self._filenames['clu']
self.log_filename = find_filename_or_new(filename, 'kvlog', dir=dir)
# Backup the .clu file.
clu_original = find_filename_or_new(filename, 'clu_original')
if os.path.exists(clu) and not os.path.exists(clu_original):
shutil.copyfile(clu, clu_original)
if not kwik:
assert xml, ValueError("I need the .xml file!")
klusters_to_kwik(filename=xml,
dir=dir,
progress_report=self._report_progress_open)
self.experiment = Experiment(basename, dir=dir, mode='a')
# CONSISTENCY CHECK
# add missing clusters
add_missing_clusters(self.experiment)
# TODO
# self.initialize_logfile()
# Load the similarity measure chosen by the user in the preferences
# file: 'gaussian' or 'kl'.
# Refresh the preferences file when a new file is opened.
# USERPREF.refresh()
self.similarity_measure = self.userpref[
'similarity_measure'] or 'gaussian'
debug("Similarity measure: {0:s}.".format(self.similarity_measure))
info("Opening {0:s}.".format(self.experiment.name))
self.shanks = sorted(self.experiment.channel_groups.keys())
self.freq = self.experiment.application_data.spikedetekt.sample_rate
self.fetdim = self.experiment.application_data.spikedetekt.nfeatures_per_channel
self.nsamples = self.experiment.application_data.spikedetekt.waveforms_nsamples
self.set_shank(self.shanks[0])
def open_klusters(filename):
indices = find_indices(filename)
triplet = filename_to_triplet(filename)
filenames_shanks = {}
for index in indices:
filenames_shanks[index] = triplet_to_filename(triplet[:2] + (index, ))
klusters_data = {
index: open_klusters_oneshank(filename)
for index, filename in filenames_shanks.iteritems()
}
shanks = filenames_shanks.keys()
# Find the dataset filenames and load the metadata.
filenames = find_filenames(filename)
# Metadata common to all shanks.
metadata = read_xml(filenames['xml'], 1)
# Metadata specific to each shank.
metadata.update(
{shank: read_xml(filenames['xml'], shank)
for shank in shanks})
metadata['shanks'] = sorted(shanks)
metadata['has_masks'] = (
('mask' in filenames and filenames['mask'] is not None)
or ('fmask' in filenames and filenames['fmask'] is not None))
klusters_data['name'] = triplet[0]
klusters_data['metadata'] = metadata
klusters_data['shanks'] = shanks
klusters_data['filenames'] = filenames
# Load probe file.
filename_probe = filenames['probe']
# It no probe file exists, create a default, linear probe with the right
# number of channels per shank.
if not filename_probe:
# Generate a probe filename.
filename_probe = find_filename_or_new(filename,
'default.probe',
have_file_index=False)
shanks = {
shank: klusters_data[shank]['nchannels']
for shank in filenames_shanks.keys()
}
probe_python = generate_probe(shanks, 'complete')
# with open(filename_probe, 'w') as f:
# f.write(probe_python)
# save_probe(filename_probe, probe_python)
klusters_data['prb'] = probe_python
else:
probe_ns = {}
execfile(filename_probe, {}, probe_ns)
klusters_data['probe'] = probe_ns
return klusters_data
def open(self, filename=None):
"""Open everything."""
if filename is None:
filename = self.filename
else:
self.filename = filename
dir, basename = os.path.split(filename)
# Converting to kwik if needed
# kwik = find_filename(basename, 'kwik', dir=dir)
# xml = find_filename(basename, 'xml', dir=dir)
# self.filename_clu = find_filename(basename, 'clu', dir=dir)
self._filenames = find_filenames(filename)
kwik = find_filename(basename, 'kwik', dir=dir)
xml = self._filenames['xml']
clu = self._filenames['clu']
self.log_filename = find_filename_or_new(filename, 'kvlog', dir=dir)
# Backup the .clu file.
clu_original = find_filename_or_new(filename, 'clu_original')
if os.path.exists(clu) and not os.path.exists(clu_original):
shutil.copyfile(clu, clu_original)
if not kwik:
assert xml, ValueError("I need the .xml file!")
klusters_to_kwik(filename=xml, dir=dir,
progress_report=self._report_progress_open)
self.experiment = Experiment(basename, dir=dir, mode='a')
# CONSISTENCY CHECK
# add missing clusters
add_missing_clusters(self.experiment)
# TODO
# self.initialize_logfile()
# Load the similarity measure chosen by the user in the preferences
# file: 'gaussian' or 'kl'.
# Refresh the preferences file when a new file is opened.
# USERPREF.refresh()
self.similarity_measure = self.userpref['similarity_measure'] or 'gaussian'
debug("Similarity measure: {0:s}.".format(self.similarity_measure))
info("Opening {0:s}.".format(self.experiment.name))
self.shanks = sorted(self.experiment.channel_groups.keys())
self.freq = self.experiment.application_data.spikedetekt.sample_rate
self.fetdim = self.experiment.application_data.spikedetekt.nfeatures_per_channel
self.nsamples = self.experiment.application_data.spikedetekt.waveforms_nsamples
self.set_shank(self.shanks[0])
def open_klusters(filename):
indices = find_indices(filename)
triplet = filename_to_triplet(filename)
filenames_shanks = {}
for index in indices:
filenames_shanks[index] = triplet_to_filename(triplet[:2] + (index,))
klusters_data = {index: open_klusters_oneshank(filename)
for index, filename in filenames_shanks.iteritems()}
shanks = filenames_shanks.keys()
# Find the dataset filenames and load the metadata.
filenames = find_filenames(filename)
# Metadata common to all shanks.
metadata = read_xml(filenames['xml'], 1)
# Metadata specific to each shank.
metadata.update({shank: read_xml(filenames['xml'], shank)
for shank in shanks})
metadata['shanks'] = sorted(shanks)
metadata['has_masks'] = (('mask' in filenames
and filenames['mask'] is not None) or (
'fmask' in filenames
and filenames['fmask'] is not None
))
klusters_data['name'] = triplet[0]
klusters_data['metadata'] = metadata
klusters_data['shanks'] = shanks
klusters_data['filenames'] = filenames
# Load probe file.
filename_probe = filenames['probe']
# It no probe file exists, create a default, linear probe with the right
# number of channels per shank.
if not filename_probe:
# Generate a probe filename.
filename_probe = find_filename_or_new(filename, 'default.probe',
have_file_index=False)
shanks = {shank: klusters_data[shank]['nchannels']
for shank in filenames_shanks.keys()}
probe_python = generate_probe(shanks, 'complete')
# with open(filename_probe, 'w') as f:
# f.write(probe_python)
# save_probe(filename_probe, probe_python)
klusters_data['prb'] = probe_python
else:
probe_ns = {}
execfile(filename_probe, {}, probe_ns)
klusters_data['probe'] = probe_ns
return klusters_data
