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import ipyvolume.pylab as p3
nrois = len(plane_seg)
dims = np.array([max(max(plane_seg['voxel_mask'][i][dim]) for i in range(nrois))
for dim in ['x', 'y', 'z']]).astype('int') + 1
fig = p3.figure()
for icolor, color in enumerate(color_wheel):
vol = np.zeros(dims)
sel = np.arange(icolor, nrois, len(color_wheel))
for isel in sel:
dat = plane_seg['voxel_mask'][isel]
vol[tuple(dat['x'].astype('int')),
tuple(dat['y'].astype('int')),
tuple(dat['z'].astype('int'))] = 1
p3.volshow(vol, tf=linear_transfer_function(color, max_opacity=.3))
return fig
def show_image(index=0):
p3.figure()
p3.volshow(indexed_timeseries.data[index], tf=linear_transfer_function([0, 0, 0], max_opacity=.3))
output.clear_output(wait=True)
with output:
p3.show()
else:
def show_grayscale_volume(vol: GrayscaleVolume, neurodata_vis_spec: dict):
import ipyvolume.pylab as p3
fig = p3.figure()
p3.volshow(vol.data, tf=linear_transfer_function([0, 0, 0], max_opacity=.1))
return fig