def upload_data(self, xyz_channels, channels=True):
if not self.resolved:
channel_upload = True
# Create new trajectory and overwrite previous one
histology.register_aligned_track(self.probe_id, xyz_channels,
chn_coords=self.chn_coords, one=self.one,
overwrite=True, channels=channels)
else:
channel_upload = False
return channel_upload
def upload_channels(self, alignment_key, upload_alyx, upload_flatiron):
"""
Upload channels to alyx and flatiron based on the alignment specified by the alignment key
"""
feature = np.array(self.alignments[alignment_key][0])
track = np.array(self.alignments[alignment_key][1])
ephysalign = EphysAlignment(self.xyz_picks,
self.depths,
track_prev=track,
feature_prev=feature,
brain_atlas=self.brain_atlas)
# Find the channels
channels_mlapdv = np.int32(
ephysalign.get_channel_locations(feature, track) * 1e6)
channels_brainID = ephysalign.get_brain_locations(channels_mlapdv /
1e6)['id']
# Find the clusters
r = regions_from_allen_csv()
clusters_mlapdv = channels_mlapdv[self.cluster_chns]
clusters_brainID = channels_brainID[self.cluster_chns]
clusters_brainAcro = r.get(ids=clusters_brainID).acronym
# upload datasets to flatiron
files_to_register = []
if upload_flatiron:
ftp_patcher = FTPPatcher(one=self.one)
insertion = self.one.alyx.rest('insertions', 'read', id=self.eid)
alf_path = self.one.path_from_eid(insertion['session']).joinpath(
'alf', insertion['name'])
alf_path.mkdir(exist_ok=True, parents=True)
# Make the channels.mlapdv dataset
f_name = alf_path.joinpath('channels.mlapdv.npy')
np.save(f_name, channels_mlapdv)
files_to_register.append(f_name)
# Make the channels.brainLocationIds dataset
f_name = alf_path.joinpath(
'channels.brainLocationIds_ccf_2017.npy')
np.save(f_name, channels_brainID)
files_to_register.append(f_name)
# Make the clusters.mlapdv dataset
f_name = alf_path.joinpath('clusters.mlapdv.npy')
np.save(f_name, clusters_mlapdv)
files_to_register.append(f_name)
# Make the clusters.brainLocationIds dataset
f_name = alf_path.joinpath(
'clusters.brainLocationIds_ccf_2017.npy')
np.save(f_name, clusters_brainID)
files_to_register.append(f_name)
# Make the clusters.brainLocationAcronym dataset
f_name = alf_path.joinpath(
'clusters.brainLocationAcronyms_ccf_2017.npy')
np.save(f_name, clusters_brainAcro)
files_to_register.append(f_name)
self.log.info("Writing datasets to FlatIron")
ftp_patcher.create_dataset(path=files_to_register,
created_by=self.one._par.ALYX_LOGIN)
# Need to change channels stored on alyx as well as the stored key is not the same as the
# latest key
if upload_alyx:
if alignment_key != self.align_keys_sorted[0]:
histology.register_aligned_track(self.eid,
channels_mlapdv / 1e6,
chn_coords=SITES_COORDINATES,
one=self.one,
overwrite=True,
channels=self.channels)
ephys_traj = self.one.alyx.rest(
'trajectories',
'list',
probe_insertion=self.eid,
provenance='Ephys aligned histology track')
patch_dict = {'json': self.alignments}
self.one.alyx.rest('trajectories',
'partial_update',
id=ephys_traj[0]['id'],
data=patch_dict)
return files_to_register
def upload_channels(self, alignment_key, upload_alyx, upload_flatiron):
"""
Upload channels to alyx and flatiron based on the alignment specified by the alignment key
"""
feature = np.array(self.alignments[alignment_key][0])
track = np.array(self.alignments[alignment_key][1])
try:
meta_dset = self.one.list_datasets(self.insertion['session'], '*ap.meta',
collection=f'raw_ephys_data/{self.insertion["name"]}')
meta_file = self.one.load_dataset(self.insertion['session'], meta_dset[0].split('/')[-1],
collection=f'raw_ephys_data/{self.insertion["name"]}',
download_only=True)
geometry = spikeglx.read_geometry(meta_file)
chns = np.c_[geometry['x'], geometry['y']]
except Exception as err:
self.log.warning(f"Could not compute channel locations from meta file, errored with message: {err}. "
f"Will use default Neuropixel 1 channels")
geometry = trace_header(version=1)
chns = np.c_[geometry['x'], geometry['y']]
ephysalign = EphysAlignment(self.xyz_picks, chns[:, 1],
track_prev=track,
feature_prev=feature,
brain_atlas=self.brain_atlas)
channels_mlapdv = np.int32(ephysalign.get_channel_locations(feature, track) * 1e6)
channels_atlas_id = ephysalign.get_brain_locations(channels_mlapdv / 1e6)['id']
# Need to change channels stored on alyx as well as the stored key is not the same as the latest key
if upload_alyx:
if alignment_key != self.align_keys_sorted[0]:
histology.register_aligned_track(self.eid, channels_mlapdv / 1e6,
chn_coords=chns, one=self.one,
overwrite=True, channels=self.channels_flag,
brain_atlas=self.brain_atlas)
ephys_traj = self.one.alyx.get(f'/trajectories?&probe_insertion={self.eid}'
'&provenance=Ephys aligned histology track',
clobber=True)
patch_dict = {'json': self.alignments}
self.one.alyx.rest('trajectories', 'partial_update', id=ephys_traj[0]['id'],
data=patch_dict)
files_to_register = []
if upload_flatiron:
ftp_patcher = FTPPatcher(one=self.one)
alf_path = self.one.eid2path(self.insertion['session']).joinpath('alf', self.insertion["name"])
alf_path.mkdir(exist_ok=True, parents=True)
f_name = alf_path.joinpath('electrodeSites.mlapdv.npy')
np.save(f_name, channels_mlapdv)
files_to_register.append(f_name)
f_name = alf_path.joinpath('electrodeSites.brainLocationIds_ccf_2017.npy')
np.save(f_name, channels_atlas_id)
files_to_register.append(f_name)
f_name = alf_path.joinpath('electrodeSites.localCoordinates.npy')
np.save(f_name, chns)
files_to_register.append(f_name)
probe_collections = self.one.list_collections(self.insertion['session'], filename='channels*',
collection=f'alf/{self.insertion["name"]}*')
for collection in probe_collections:
chns = self.one.load_dataset(self.insertion['session'], 'channels.localCoordinates', collection=collection)
ephysalign = EphysAlignment(self.xyz_picks, chns[:, 1],
track_prev=track,
feature_prev=feature,
brain_atlas=self.brain_atlas)
channels_mlapdv = np.int32(ephysalign.get_channel_locations(feature, track) * 1e6)
channels_atlas_id = ephysalign.get_brain_locations(channels_mlapdv / 1e6)['id']
alf_path = self.one.eid2path(self.insertion['session']).joinpath(collection)
alf_path.mkdir(exist_ok=True, parents=True)
f_name = alf_path.joinpath('channels.mlapdv.npy')
np.save(f_name, channels_mlapdv)
files_to_register.append(f_name)
f_name = alf_path.joinpath('channels.brainLocationIds_ccf_2017.npy')
np.save(f_name, channels_atlas_id)
files_to_register.append(f_name)
self.log.info("Writing datasets to FlatIron")
ftp_patcher.create_dataset(path=files_to_register,
created_by=self.one.alyx.user)
return files_to_register