3D rendering single subject¶

Generates 3D rendering of all probe trajectories for a single subject.

The trajectory plotted are (pair): - Channel locations based on the user picks (from histology) - Linear Interpolation based on the picks One color per pair.

[1]:

# Author: Olivier
# environment installation guide https://github.com/int-brain-lab/iblenv
# run "%qui qt" magic command from Ipython prompt for interactive mode
import numpy as np
from mayavi import mlab
mlab.init_notebook()

import ibllib.plots
from atlaselectrophysiology import rendering
import ibllib.atlas as atlas
from oneibl.one import ONE
from brainbox.core import Bunch

one = ONE(base_url="https://alyx.internationalbrainlab.org")
subject = 'KS003'


ba = atlas.AllenAtlas(25)
channels_rest = one.alyx.rest('channels', 'list', subject=subject)
channels = Bunch({
    'atlas_id': np.array([ch['brain_region'] for ch in channels_rest]),
    'xyz': np.c_[np.array([ch['x'] for ch in channels_rest]),
                 np.array([ch['y'] for ch in channels_rest]),
                 np.array([ch['z'] for ch in channels_rest])] / 1e6,
    'axial_um': np.array([ch['axial'] for ch in channels_rest]),
    'lateral_um': np.array([ch['lateral'] for ch in channels_rest]),
    'trajectory_id': np.array([ch['trajectory_estimate'] for ch in channels_rest])
})

fig = rendering.figure()
for m, probe_id in enumerate(np.unique(channels['trajectory_id'])):
    traj_dict = one.alyx.rest('trajectories', 'read', id=probe_id)
    ses = traj_dict['session']
    label = (f"{ses['subject']}/{ses['start_time'][:10]}/"
             f"{str(ses['number']).zfill(3)}/{traj_dict['probe_name']}")
    print(label)

    color = ibllib.plots.color_cycle(m)
    it = np.where(channels['trajectory_id'] == probe_id)[0]
    xyz = channels['xyz'][it]
    ins = atlas.Insertion.from_track(xyz, brain_atlas=ba)

    mlapdv = ba.xyz2ccf(ins.xyz)
    # display the interpolated tracks
    mlab.plot3d(mlapdv[:, 1], mlapdv[:, 2], mlapdv[:, 0],
                line_width=3, color=color, tube_radius=20)
    # display the channels locations
    mlapdv_channels = ba.xyz2ccf(xyz)
    mlab.points3d(mlapdv_channels[:, 1], mlapdv_channels[:, 2], mlapdv_channels[:, 0],
                  color=color, scale_factor=50)
    # setup the labels at the top of the trajectories
    mlab.text3d(mlapdv[0, 1], mlapdv[0, 2], mlapdv[0, 0] - 500, label,
                line_width=4, color=tuple(color), figure=fig, scale=150)

Notebook initialized with ipy backend.
Connected to https://alyx.internationalbrainlab.org as mayo
KS003/2019-11-20/001/probe01
KS003/2019-11-25/001/probe00
KS003/2019-11-21/001/probe00
KS003/2019-11-19/001/probe01
KS003/2019-11-21/001/probe01
KS003/2019-11-20/001/probe00
KS003/2019-11-19/001/probe00