def generate_spinning_brain_frames(trajectories, nframes_per_cycle=30, figsize=[800, 700]):
'''
Plots spike amps versus the time
Parameters
-------------
trajectories: list of dicts
list of dictionaries of trajectories fetched from ONE
nframes_per_cycle: int, optional
number of frames per cycle of spinning, default 30.
figsize: list, [width, height], optional
size of the images, default [800, 700]
Return
------------
frames: list of numpy.darray
image in rgb for each frame
'''
fig = rendering.figure()
for index, trj in enumerate(trajectories):
if trj['coordinate_system'] == 'IBL-Allen':
brain_atlas = ba_allen
elif trj['coordinate_system'] == 'Needles-Allen':
brain_atlas = ba_needles
else:
brain_atlas = ba_allen
ins = atlas.Insertion.from_dict(trj, brain_atlas=brain_atlas)
ins = atlas.Insertion.from_dict(trj, brain_atlas=ba_allen)
mlapdv = brain_atlas.xyz2ccf(ins.xyz)
if trj['provenance'] == 'Ephys aligned histology track':
color = (0, 0, 1.) # Blue
mlab.plot3d(mlapdv[:, 1], mlapdv[:, 2], mlapdv[:, 0],
line_width=3, color=color, tube_radius=20)
frames = []
for i in range(nframes_per_cycle):
mlab.view(azimuth=0, elevation=0 - i * (360 / nframes_per_cycle))
mlab.roll(180)
mlab.test_plot3d()
f = mlab.gcf()
f.scene._lift()
frames.append(mlab.screenshot(mode='rgb', antialiased=True))
return frames
#%gui qt
import numpy as np
from mayavi import mlab
from one.api import ONE
from iblutil.util import Bunch
import ibllib.plots
from atlaselectrophysiology import rendering
import ibllib.atlas as atlas
one = ONE(base_url='https://alyx.internationalbrainlab.org')
subjects = [
'dop_24', 'dop_14', 'dop_13', 'dop_16', 'dop_21', 'dop_22', 'dop_36'
]
fig = rendering.figure()
for subject in subjects:
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]),
'list',
session=eid,
provenance='Histology track',
probe=probe_label)[0]
ses = one.alyx.rest('sessions', 'read', id=eid)
channels = bbone.load_channel_locations(eid=ses,
one=one,
probe=probe_label)[probe_label]
insertion = atlas.Insertion.from_dict(traj)
ins.insertion.append(insertion)
ins.channels.append(channels)
ins.session.append(ses)
pickle.dump(ins, open(file_pickle, 'wb'))
# Display part
fig = rendering.figure(grid=False) # set grid=True for ugly axes
for m in np.arange(len(ins.eid)):
print(ins.session[m]['subject'], ins.session[m]['start_time'][:10],
ins.session[m]['number'], ins.probe_label[m])
color = ibllib.plots.color_cycle(m)
mlapdv = brain_atlas.xyz2ccf(ins.insertion[m].xyz)
# display the trajectories
mlab.plot3d(mlapdv[:, 1],
mlapdv[:, 2],
mlapdv[:, 0],
line_width=1,
tube_radius=10,
color=color)
xyz_channels = np.c_[ins.channels[m].x, ins.channels[m].y,
ins.channels[m].z]
mlapdv_channels = brain_atlas.xyz2ccf(xyz_channels)
traj_coords = np.empty((len(traj_ids), len(depths), 3))
# For each trajectory compute the xyz coords at positions depths along trajectory
for iT, traj in enumerate(trajectories):
traj_coords[iT, :] = histology.interpolate_along_track(np.vstack([traj.tip, traj.entry]),
depths)
# Find the average distance between all positions compared to trjaectory of interest
avg_dist = np.mean(np.sqrt(np.sum((traj_coords - traj_coords[chosen_traj]) ** 2, axis=2)), axis=1)
# Sort according to those that are closest
closest_traj = np.argsort(avg_dist)
close_sessions = []
# Make a 3D plot showing trajectory of interest (in black) and the 10 nearest trajectories (blue)
fig = rendering.figure(grid=False)
for iSess, sess_idx in enumerate(closest_traj[0:10]):
mlapdv = brain_atlas.xyz2ccf(traj_coords[sess_idx])
if iSess == 0:
mlab.plot3d(mlapdv[:, 1], mlapdv[:, 2], mlapdv[:, 0],
line_width=1, tube_radius=10, color=(0, 0, 0))
else:
mlab.plot3d(mlapdv[:, 1], mlapdv[:, 2], mlapdv[:, 0],
line_width=1, tube_radius=10, color=(0.0, 0.4, 0.5))
mlab.text3d(mlapdv[0, 1], mlapdv[0, 2], mlapdv[0, 0], str(iSess),
line_width=4, color=(0, 0, 0), figure=fig, scale=150)
close_sessions.append((sess_with_hist[sess_idx]['session']['subject'] + ' ' +
sess_with_hist[sess_idx]['session']['start_time'][:10] +
mlapdv[0, 2],
mlapdv[0, 0] - 500,
label,
line_width=4,
color=color,
figure=fig_handle,
scale=500)
return mlapdv, ins
ba = atlas.AllenAtlas(25)
prob_des = one.load_dataset(eid, 'probes.description.json')
n_probe = len(prob_des[0])
# Plot empty atlas template
fig3D = rendering.figure()
# Loop over probes
for i_probe in range(0, n_probe):
# Get single probe trajectory
probe_label = prob_des[0][i_probe].get('label')
# Histology (red)
traj_h = one.alyx.rest('trajectories',
'list',
session=eid,
provenance='Histology track',
probe=probe_label)[0]
color_h = (1., 0., 0.)
# Planned (blue)