def render_probe_3d(rsites: pd.DataFrame,
save_path: Path = None,
targets: tuple = None):
targets = list(targets) or []
# create brainrender scene
scene = br.Scene(screenshots_folder=save_path)
# add probe track
track = np.vstack(rsites.registered_brain_coordinates.values)
colors = [
color if region in targets else
(blue_grey if region not in ('unknown', 'OUT') else 'k')
for color, region in zip(rsites.color.values,
rsites.brain_region.values)
]
pts = scene.add(br.actors.Points(track, colors=colors, radius=15))
scene.add_silhouette(pts, lw=2)
# add brain regions
for region in rsites.brain_region.unique():
if region in targets:
alpha = 0.4
targets.pop(targets.index(region))
else:
alpha = 0.01
scene.add_brain_region(region, alpha=alpha)
# add remainning targets
if targets:
scene.add_brain_region(*targets, alpha=0.4)
# slice
plane = scene.atlas.get_plane(norm=(0, 0, 1),
pos=scene.root._mesh.centerOfMass())
scene.slice(plane)
# render
cam = {
"pos": (7196, 247, -38602),
"viewup": (0, -1, 0),
"clippingRange": (29133, 44003),
"focalPoint": (7718, 4290, -3507),
"distance": 35331,
}
if save_path is None:
scene.render(camera=cam)
else:
scene.render(camera=cam, interactive=False)
scene.screenshot("activity_probe_rendering")
scene.close()
del scene
from brainrender.atlas_specific import get_streamlines_for_region
from brainrender.actors.streamlines import make_streamlines
SELECTED = "locomotion"
save_folder = Path(
"/Users/federicoclaudi/Dropbox (UCL)/Rotation_vte/Presentations/Presentations/Fiete lab"
)
br.settings.SHOW_AXES = False
br.settings.vsettings.screenshotTransparentBackground - True
scene = br.Scene(screenshots_folder=save_folder, inset=False)
scene.add_brain_region("RSP", alpha=1)
scene.add_brain_region("MOs", alpha=0.2)
streams = get_streamlines_for_region("RSP")[:2]
scene.add(
*make_streamlines(
*streams,
color=scene.atlas._get_from_structure("RSP", "rgb_triplet"),
alpha=0.5,
)
)
camera = {
"pos": (-6374, -5444, 26602),
br.settings.SHOW_AXES = False
br.settings.ROOT_ALPHA = 0.1
probes_folder = Path(
r"D:\Dropbox (UCL)\Rotation_vte\Locomotion\reconstructed_probe_location")
probes_files = files(probes_folder, pattern="*_0.npy")
save_folder = Path(
r"D:\Dropbox (UCL)\Rotation_vte\Presentations\Presentations\Fiete lab")
colors = make_palette(blue_grey_dark, blue_grey_light, len(probes_files))
# ----------------------------- create renderings ---------------------------- #
for repeat in range(3):
# scene and brain regions
scene = br.Scene(inset=False, screenshots_folder=save_folder)
scene.add_brain_region("CUN", "GRN", "PPN", alpha=0.9)
# add probes
for n, probe in enumerate(probes_files):
coords = np.load(probe)
probe_actor = scene.add(
br.actors.Points(coords[::4, :], colors=colors[n], radius=60))
# scene.add_silhouette(probe_actor, lw=.5)
# slice
if repeat == 2:
plane = scene.atlas.get_plane(norm=(0, 0, -1),
pos=scene.root._mesh.centerOfMass())
camera = "sagittal"
def render_probe_regions_slices(rsites: pd.DataFrame,
save_path: Path = None,
targets: tuple = None):
targets = list(targets) if targets is not None else []
for target_region in rsites.brain_region.unique():
for side in ("frontal", "sagittal2", "top"):
scene_targets = targets.copy()
scene = br.Scene(title=target_region, screenshots_folder=save_path)
# add probe track
track = np.vstack(rsites.registered_brain_coordinates.values)
track_actor = scene.add(
br.actors.Points(track, colors="k", radius=80))
# add brain regions
actors = [track_actor]
for region in rsites.brain_region.unique():
if region == target_region:
alpha = 0.9
elif region in scene_targets:
scene_targets.pop(scene_targets.index(region))
alpha = 0.5
else:
alpha = 0.05
act = scene.add_brain_region(region, alpha=alpha)
if act is not None:
actors.append(act)
# add remaining target regions
if scene_targets:
actors.extend(scene.add_brain_region(*scene_targets,
alpha=0.4))
# slice
coords = np.mean(
np.vstack(rsites.loc[rsites.brain_region == target_region].
registered_brain_coordinates.values),
axis=0,
)
if side == "frontal":
shift = np.array([250, 0, 0])
elif side == "top":
shift = np.array([0, 250, 0])
else:
coords[2] = -coords[2]
shift = np.array([0, 0, 500])
p1 = coords - shift
p2 = coords + shift
for p, norm in zip((p1, p2), (1, -1)):
if side == "frontal":
nrm = (norm, 0, 0)
elif side == "top":
nrm = (0, norm, 0)
else:
nrm = (0, 0, norm)
plane = scene.atlas.get_plane(pos=p, norm=nrm)
scene.slice(plane, actors=actors, close_actors=True)
scene.slice(plane, actors=scene.root, close_actors=False)
# show/save
if save_path is None:
scene.render(camera=side)
else:
scene.render(camera=side, interactive=False)
scene.screenshot(
f"activity_probe_slice_{target_region}_{side}")
scene.close()
del scene
probes_folder = Path(
r"D:\Dropbox (UCL)\Rotation_vte\Locomotion\reconstructed_probe_location")
colors = {
'1100279_0': grey,
'1100281_0': grey_dark,
'1100516_0': indigo,
'1110750_0': green,
'1110751_0': black,
}
["k", "b", "r", "m", green]
# scene and brain regions
scene = br.Scene()
scene.add_brain_region("CUN", "GRN", alpha=.88)
scene.add_brain_region("IC",
"PRNr",
"PRNc",
"SCm",
alpha=0.25,
silhouette=False)
scene.add_brain_region("PPN", alpha=0.8, silhouette=False)
# add probes
for n, probe in enumerate(files(probes_folder, pattern="*_0.npy")):
coords = np.load(probe)
probe_actor = scene.add(
br.actors.Points(coords, colors=colors[probe.stem], radius=40))
logger.info(
#Associating BrainR Regions with their values
for month in range(0, len(tp)):
brainR.insert(3 + month, "Val_MPI{}".format(tp[month]), "")
for idx, regions in enumerate(brainR["ROInames"]):
brainR['Val_MPI{}'.format(
tp[month])][idx] = data.loc[regions, "MPI{}".format(tp[month])]
###Still need ==> to implement this for each month
# Ipsilateral Data
ipsi_pred = brainR[brainR['Side'] == 'ipsi']
# Contralateral Data
contra_pred = brainR[brainR['Side'] == 'contra']
#BrainRenders
ipsi_pred_mpi1 = ipsi_pred[["BrainR", "Val_MPI1"]]
contra_pred_mpi1 = contra_pred[["BrainR", "Val_MPI1"]]
scene = brainrender.Scene(atlas_name="allen_mouse_10um")
#Adding the ipsilateral regions
for reg in contra_pred_mpi1.index:
region = contra_pred_mpi1.loc[reg, 'BrainR']
color = cmap(contra_pred_mpi1.loc[reg, 'Val_MPI1'])[:3]
scene.add_brain_region(reg, color=color, alpha=0.5)
# #scene.axes_indices(None)
scene.render()
#Adding the controlateral regions