ibllib.pipes.ephys_alignment

Classes

EphysAlignment

class EphysAlignment(xyz_picks, chn_depths=None, track_prev=None, feature_prev=None, brain_atlas=None, speedy=False)

Bases: object

get_insertion_track(xyz_picks, speedy=False)

Extends probe trajectory from bottom of brain to upper bound of allen atlas

Parameters:

xyz_picks (np.array((n, 3)) - n: no. of unique points) – points defining probe trajectory in 3D space (xyz)

Return xyz_track:

points defining extended trajectory in 3D space (xyz)

Return track_extent:

cumulative distance between two extremes of xyz_track (bottom of

brain and top of atlas) offset by distance to probe tip :type track_extent: np.array((2))

get_track_and_feature()

Return track, feature and xyz_track variables

static feature2track(trk, feature, track)

Estimate new values of trk according to interpolated fit between feature and track space

Parameters:

• trk (np.array) – points in track space to convert feature space

• feature (np.array((n_lines + 2)) n_lines: no. of user reference lines) – reference coordinates in feature space (ephys plots)

• track (np.array((n_lines + 2))) – reference coordinates in track space (histology track)

Return fcn(trk):

interpolated values of trk

static track2feature(ft, feature, track)

Estimate new values of ft according to interpolated fit between track and feature space

Parameters:

• ft (np.array) – points in feature space to convert track space

• feature (np.array((n_lines + 2)) n_lines: no. of user reference lines) – reference coordinates in feature space (ephys plots)

• track (np.array((n_lines + 2))) – reference coordinates in track space (histology track)

Return fcn(ft):

interpolated values of ft

static feature2track_lin(trk, feature, track)

Estimate new values of trk according to linear fit between feature and track space, only implemented if no. of reference points >= 3

Parameters:

• trk (np.array) – points in track space to convert feature space

• feature (np.array((n_lines + 2)) n_lines: no. of user reference lines) – reference coordinates in feature space (ephys plots)

• track (np.array((n_lines + 2))) – reference coordinates in track space (histology track)

Return fcn(trk):

linear fit values of trk

static adjust_extremes_uniform(feature, track)

Change the value of the first and last reference points (non user chosen points) such that coordinates outside user picked regions are left unchanged

Parameters:

• feature (np.array((n_lines + 2)) n_lines: no. of user reference lines) – reference coordinates in feature space (ephys plots)

• track (np.array((n_lines + 2))) – reference coordinates in track space (histology track)

Return track:

reference coordinates in track space with first and last value adjusted

adjust_extremes_linear(feature, track, extend_feature=1)

Change the value of the first and last reference points (non user chosen points) such that coordinates outside user picked regions have a linear fit applied

Parameters:

• feature (np.array((n_lines + 2))) – reference coordinates in feature space (ephys plots)

• track (np.array((n_lines + 2))) – reference coordinates in track space (histology track)

• extend_feature (float) – amount to extend extreme coordinates before applying linear fit

Return feature:

reference coordinates in feature space with first and last value adjusted

Return track:

reference coordinates in track space with first and last value adjusted

scale_histology_regions(feature, track, region=None, region_label=None)

Recompute locations of brain region boundaries using interpolated fit based on reference lines

Parameters:

• feature (np.array((n_lines + 2)) n_lines: no. of user reference lines) – reference coordinates in feature space (ephys plots)

• track (np.array((n_lines + 2))) – reference coordinates in track space (histology track)

Return region:

new coordinates of histology boundaries after applying interpolation

Return region_label:

new coordinates of histology labels positions after applying interpolation

static get_histology_regions(xyz_coords, depth_coords, brain_atlas=None, mapping=None)

Find all brain regions and their boundaries along the depth of probe or track

Parameters:

• xyz_coords (np.array((n_points, 3)) n_points: no. of points) – 3D coordinates of points along probe or track

• depth_coords (np.array((n_points))) – depth along probe or track where each xyz_coord is located

Return region:

coordinates bounding each brain region

Return region_label:

label for each brain region and coordinate of where to place label

Return region_colour:

allen atlas rgb colour for each brain region along track

Return region_id:

allen atlas id for each brain region along track

static get_nearest_boundary(xyz_coords, allen, extent=100, steps=8, parent=True, brain_atlas=None)

Finds distance to closest neighbouring brain region along trajectory. For each point in xyz_coords computes the plane passing through point and perpendicular to trajectory and finds all brain regions that lie in that plane up to a given distance extent from specified point. Additionally, if requested, computes distance between the parents of regions.

Parameters:

• xyz_coords (np.array((n_points, 3)) n_points: no. of points) – 3D coordinates of points along probe or track

• allen – dataframe containing allen info. Loaded from allen_structure_tree in

ibllib/atlas :type allen: pandas Dataframe :param extent: extent of plane in each direction from origin in (um) :type extent: float :param steps: no. of steps to discretise plane into :type steps: int :param parent: Whether to also compute nearest distance between parents of regions :type parent: bool :return nearest_bound: dict containing results :type nearest_bound: dict

static arrange_into_regions(depth_coords, region_ids, distance, region_colours)

Arrange output from get_nearest_boundary into a form that can be plot using pyqtgraph or matplotlib

Parameters:

• depth_coords (np.array((n_points))) – depth along probe or track where each point is located

• region_ids – brain region id at each depth along probe

• distance (np.array((n_points))) – distance to nearest boundary in plane at each point

• region_colours (list of strings len(n_points)) – allen atlas hex colour for each region id

Return all_x:

dist values for each region along probe track

Return all_y:

depth values for each region along probe track

Return all_colour:

colour assigned to each region along probe track

get_scale_factor(region, region_orig=None)

Find how much each brain region has been scaled following interpolation

Parameters:

region (np.array((n_bound, 2)) n_bound: no. of histology boundaries) – scaled histology boundaries

Return scaled_region:

regions that have unique scaling applied

Return scale_factor:

scale factor applied to each scaled region

get_channel_locations(feature, track, depths=None)

Gets 3d coordinates from a depth along the electrophysiology feature. 2 steps 1) interpolate from the electrophys features depths space to the probe depth space 2) interpolate from the probe depth space to the true 3D coordinates if depths is not provided, defaults to channels local coordinates depths

get_brain_locations(xyz_channels)

Finds the brain regions from 3D coordinates of electrode locations

Parameters:

xyz_channels (np.array((n_elec, 3)) n_elec: no. of electrodes (384)) – 3D coordinates of electrodes on probe

Return brain_regions:

brain region object for each electrode

:type dict

get_perp_vector(feature, track)

Finds the perpendicular vector along the trajectory at the depth of reference lines

Parameters:

• feature (np.array((n_lines + 2)) n_lines: no. of user reference lines) – reference coordinates in feature space (ephys plots)

• track (np.array((n_line+2))) – reference coordinates in track space (histology track)

Return slice_lines:

coordinates of perpendicular lines