ibllib.pipes.histology

Functions

coverage	Computes a coverage volume from
coverage_grid
create_channel_dict	Create channel dictionary in form to upload to alyx
create_trajectory_dict	Create trajectory dictionary in form to upload to alyx
detect_missing_histology_tracks	Compares the number of probe insertions to the number of registered histology tracks to see if there is a discrepancy so that missing tracks can be properly logged in the database
get_brain_regions	param xyz numpy array of 3D coordinates corresponding to a picked track or a trajectory
get_micro_manipulator_data	Looks for all ephys sessions for a given subject and get the probe micro-manipulator trajectories.
get_picked_tracks	This outputs reads in the Lasagna output and converts the picked tracks in the IBL coordinates
interpolate_along_track	Get the coordinates of points along a track according to their distances from the first point.
load_track_csv	Loads a lasagna track and convert to IBL-ALlen coordinate framework
plot2d_all	Plot all tracks on a single 2d slice
plot3d_all	Plot all tracks on a single 2d slice
register_aligned_track	Register ephys aligned trajectory and channel locations to Alyx Here we update Alyx models on the database in 2 steps 1) The trajectory computed from the final electrode channel locations 2) Channel locations are set to the trajectory
register_track	Register the user picks to a probe in Alyx Here we update Alyx models on the database in 3 steps 1) The user picks converted to IBL coordinates will be stored in the json field of the corresponding probe insertion models 2) The trajectory computed from the histology track is created or patched 3) Channel locations are set in the table
register_track_files	param path_tracks path to directory containing tracks; also works with a single file name

load_track_csv(file_track, brain_atlas=None)

Loads a lasagna track and convert to IBL-ALlen coordinate framework

Parameters

file_track –

Returns

xyz

get_picked_tracks(histology_path, glob_pattern='*_pts_transformed.csv', brain_atlas=None)

This outputs reads in the Lasagna output and converts the picked tracks in the IBL coordinates

Parameters

histology_path – Path object: folder path containing tracks

Returns

xyz coordinates in

get_micro_manipulator_data(subject, one=None, force_extract=False)

Looks for all ephys sessions for a given subject and get the probe micro-manipulator trajectories. If probes ALF object not on flat-iron, attempts to perform the extraction from meta-data and task settings file.

plot2d_all(trajectories, tracks, brain_atlas=None)

Plot all tracks on a single 2d slice

Parameters

• trajectories – dictionary output of the Alyx REST query on trajectories

• tracks –

Returns

plot3d_all(trajectories, tracks, brain_atlas=None)

Plot all tracks on a single 2d slice

Parameters

• trajectories – dictionary output of the Alyx REST query on trajectories

• tracks –

Returns

interpolate_along_track(xyz_track, depths)

Get the coordinates of points along a track according to their distances from the first point.

Parameters

• xyz_track – np.array [npoints, 3]. Usually the first point is the deepest

• depths – distance from the first point of the track, usually the convention is the

deepest point is 0 and going up :return: xyz_channels

get_brain_regions(xyz, channels_positions=None, brain_atlas=None)

Parameters

xyz – numpy array of 3D coordinates corresponding to a picked track or a trajectory

the deepest point is assumed to be the tip. :param channels_positions: :param brain_atlas: :return: brain_regions (associated to each channel),

insertion (object atlas.Insertion, defining 2 points of entries (tip and end of probe))

register_track(probe_id, picks=None, one=None, overwrite=False, channels=True, brain_atlas=None)

Register the user picks to a probe in Alyx Here we update Alyx models on the database in 3 steps 1) The user picks converted to IBL coordinates will be stored in the json field of the corresponding probe insertion models 2) The trajectory computed from the histology track is created or patched 3) Channel locations are set in the table

register_aligned_track(probe_id, xyz_channels, chn_coords=None, one=None, overwrite=False, channels=True, brain_atlas=None)

Register ephys aligned trajectory and channel locations to Alyx Here we update Alyx models on the database in 2 steps 1) The trajectory computed from the final electrode channel locations 2) Channel locations are set to the trajectory

create_trajectory_dict(probe_id, insertion, provenance)

Create trajectory dictionary in form to upload to alyx

Parameters

• id (probe) – unique id of probe insertion

• insertion (object atlas.Insertion) – Insertion object describing entry and tip of trajectory

• provenance (string) – ‘Histology track’ or ‘Ephys aligned histology track’

Return tdict

create_channel_dict(traj, brain_locations)

Create channel dictionary in form to upload to alyx

Parameters

• traj (dict) – alyx trajectory object to attach channel information to

• brain_locations – information about location of electrode channels in brain atlas

Return tdict

register_track_files(path_tracks, one=None, overwrite=False, brain_atlas=None)

Parameters

• path_tracks – path to directory containing tracks; also works with a single file name

• one –

Returns

detect_missing_histology_tracks(path_tracks=None, one=None, subject=None, brain_atlas=None)

Compares the number of probe insertions to the number of registered histology tracks to see if there is a discrepancy so that missing tracks can be properly logged in the database

Parameters

• path_tracks – path to track files to be registered

• subject – subject nickname for which to detect missing tracks

coverage(trajs, ba=None, dist_fcn=[100, 150])

Computes a coverage volume from

Parameters

• trajs – dictionary of trajectories from Alyx rest endpoint (one.alyx.rest…)

• ba – ibllib.atlas.BrainAtlas instance

Returns

3D np.array the same size as the volume provided in the brain atlas

coverage_grid(xyz_channels, spacing=500, ba=None)