def ks2_to_alf(ks_path, out_path, ampfactor=1, label=None, force=True):
"""
Convert Kilosort 2 output to ALF dataset for single probe data
:param ks_path:
:param out_path:
:return:
"""
m = ephysqc.phy_model_from_ks2_path(ks_path)
ac = alf.EphysAlfCreator(m)
ac.convert(out_path, label=label, force=force, ampfactor=ampfactor)
def ks2_to_alf(ks_path, out_path, sr=30000, nchannels=385, label=None, force=True):
"""
Convert Kilosort 2 output to ALF dataset for single probe data
:param ks_path:
:param out_path:
:return:
"""
m = model.TemplateModel(dir_path=ks_path,
dat_path=[],
sample_rate=sr,
n_channels_dat=nchannels)
ac = alf.EphysAlfCreator(m)
ac.convert(out_path, label=label, force=force)
def ks2_to_alf(ks_path, out_path, ampfactor=1, label=None, force=True):
"""
Convert Kilosort 2 output to ALF dataset for single probe data
:param ks_path:
:param out_path:
:return:
TODO change/remove `ampfactor` after seeing how amplitudes are calculated by ks2
"""
m = ephysqc.phy_model_from_ks2_path(ks_path)
ephysqc.unit_metrics_ks2(ks_path, m, save=True)
ac = alf.EphysAlfCreator(m)
ac.convert(out_path, label=label, force=force, ampfactor=ampfactor)
def ks2_to_alf(ks_path,
bin_path,
out_path,
bin_file=None,
ampfactor=1,
label=None,
force=True):
"""
Convert Kilosort 2 output to ALF dataset for single probe data
:param ks_path:
:param bin_path: path of raw data
:param out_path:
:return:
"""
m = ephysqc.phy_model_from_ks2_path(ks2_path=ks_path,
bin_path=bin_path,
bin_file=bin_file)
ephysqc.spike_sorting_metrics_ks2(ks_path, m, save=True)
ac = alf.EphysAlfCreator(m)
ac.convert(out_path, label=label, force=force, ampfactor=ampfactor)
def merge_probes(ses_path):
"""
Merge spike sorting output from 2 probes and output in the session ALF folder the combined
output in IBL format
:param ses_path: session containing probes to be merged
:return: None
"""
def _sr(ap_file):
md = spikeglx.read_meta_data(ap_file.with_suffix('.meta'))
return spikeglx._get_fs_from_meta(md)
ses_path = Path(ses_path)
out_dir = ses_path.joinpath('alf').joinpath('tmp_merge')
ephys_files = glob_ephys_files(ses_path)
subdirs, labels, efiles_sorted, srates = zip(
*sorted([(ep.ap.parent, ep.label, ep, _sr(ep.ap)) for ep in ephys_files if ep.get('ap')]))
# if there is only one file, just convert the output to IBL format et basta
if len(subdirs) == 1:
ks2_to_alf(subdirs[0], ses_path / 'alf')
return
else:
_logger.info('converting individual spike-sorting outputs to ALF')
for subdir, label, ef, sr in zip(subdirs, labels, efiles_sorted, srates):
ks2alf_path = subdir / 'ks2_alf'
if ks2alf_path.exists():
shutil.rmtree(ks2alf_path, ignore_errors=True)
ks2_to_alf(subdir, ks2alf_path, label=label, sr=sr, force=True)
probe_info = [{'label': lab} for lab in labels]
mt = merge.Merger(subdirs=subdirs, out_dir=out_dir, probe_info=probe_info).merge()
# Create the cluster channels file, this should go in the model template as 2 methods
tmp = mt.sparse_templates.data
n_templates, n_samples, n_channels = tmp.shape
template_peak_channels = np.argmax(tmp.max(axis=1) - tmp.min(axis=1), axis=1)
cluster_probes = mt.channel_probes[template_peak_channels]
spike_clusters_rel = _index_of(mt.spike_clusters, _unique(mt.spike_clusters))
spike_probes = cluster_probes[spike_clusters_rel]
# sync spikes according to the probes
# how do you make sure they match the files:
for ind, probe in enumerate(efiles_sorted):
assert(labels[ind] == probe.label) # paranoid, make sure they are sorted
if not probe.get('ap'):
continue
sync_file = probe.ap.parent.joinpath(probe.ap.name.replace('.ap.', '.sync.')
).with_suffix('.npy')
if not sync_file.exists():
error_msg = f'No synchronisation file for {sync_file}'
_logger.error(error_msg)
raise FileNotFoundError(error_msg)
sync_points = np.load(sync_file)
fcn = interp1d(sync_points[:, 0] * srates[ind],
sync_points[:, 1], fill_value='extrapolate')
mt.spike_times[spike_probes == ind] = fcn(mt.spike_samples[spike_probes == ind])
# And convert to ALF
ac = alf.EphysAlfCreator(mt)
ac.convert(ses_path / 'alf', force=True)
# remove the temporary directory
shutil.rmtree(out_dir)