from dipy.viz import actor, window, widget

shape = data.shape

if not world_coords:

from dipy.tracking.streamline import transform_streamlines

streamlines = transform_streamlines(streamlines, np.linalg.inv(affine))

ren = window.Renderer()

if streamlines is not None:

stream_actor = actor.line(streamlines)

if not world_coords:

image_actor = actor.slicer(vol, affine=np.eye(4))

else:

image_actor = actor.slicer(vol, affine)

slicer_opacity = opacity #.6

image_actor.opacity(slicer_opacity)

if streamlines is not None:

ren.add(stream_actor)

ren.add(image_actor)

show_m = window.ShowManager(ren, size=(1200, 900))

show_m.initialize()

def change_slice(obj, event):

z = int(np.round(obj.get_value()))

image_actor.display_extent(0, shape[0] - 1,

0, shape[1] - 1, z, z)

slider = widget.slider(show_m.iren, show_m.ren,

callback=change_slice,

min_value=0,

max_value=shape[2] - 1,

value=shape[2] / 2,

label="Move slice",

right_normalized_pos=(.98, 0.6),

size=(120, 0), label_format="%0.lf",

color=(1., 1., 1.),

selected_color=(0.86, 0.33, 1.))

global size

size = ren.GetSize()

def win_callback(obj, event):

global size

if size != obj.GetSize():

slider.place(ren)

size = obj.GetSize()

show_m.initialize()

show_m.add_window_callback(win_callback)

show_m.render()

show_m.start()

# ren.zoom(1.5)

# ren.reset_clipping_range()

# window.record(ren, out_path='bundles_and_a_slice.png', size=(1200, 900),

# reset_camera=False)

renderer = window.Renderer()

renderer.add(actor.line(streamlines))

renderer.add(actor.slicer(vol, affine))

renderer = window.Renderer()

renderer.add(fvtk.point(gtab.gradients, (1,0,0), point_radius=100))

renderer.add(fvtk.point(-gtab.gradients, (1,0,0), point_radius=100))

data, affine = load_nifti(fdwi)

bvals, bvecs = read_bvals_bvecs(fbval, fbvec)

gtab = gradient_table(bvals, bvecs, b0_threshold=50)

if fmask is None:

from dipy.segment.mask import median_otsu

b0_mask, mask = median_otsu(data) # TODO: check parameters to improve the mask

else:

mask, mask_affine = load_nifti(fmask)

mask = np.squeeze(mask) #fix mask dimensions

# compute DTI model

from dipy.reconst.dti import TensorModel

tenmodel = TensorModel(gtab)#, fit_method='OLS') #, min_signal=5000)

# fit the dti model

tenfit = tenmodel.fit(data, mask=mask)

# save fa

ffa = dname + 'tensor_fa.nii.gz'

fa_img = nib.Nifti1Image(tenfit.fa.astype(np.float32), affine)

nib.save(fa_img, ffa)

sh_order = 8 #TODO: check what that does

if data.shape[-1] < 15:

raise ValueError('You need at least 15 unique DWI volumes to '

'compute fiber ODFs. You currently have: {0}'

' DWI volumes.'.format(data.shape[-1]))

elif data.shape[-1] < 30:

sh_order = 6

# compute the response equation ?

from dipy.reconst.csdeconv import auto_response

response, ratio = auto_response(gtab, data)

response = list(response)

peaks_sphere = get_sphere('symmetric362')

#TODO: check what that does

peaks_csd = peaks_from_model(model=tenmodel,

data=data,

sphere=peaks_sphere,

relative_peak_threshold=.5, #.5

min_separation_angle=25,

mask=mask,

return_sh=True,

sh_order=sh_order,

normalize_peaks=True,

parallel=False)

peaks_csd.affine = affine

fpeaks = dname + 'peaks.npz'

save_peaks(fpeaks, peaks_csd)

from dipy.io.trackvis import save_trk

from dipy.tracking import utils

from dipy.tracking.local import (ThresholdTissueClassifier,

LocalTracking)

stopping_thr = 0.25 #0.25

pam = load_peaks(fpeaks)

#ffa = dname + 'tensor_fa_nomask.nii.gz'

fa, fa_affine = load_nifti(ffa)

classifier = ThresholdTissueClassifier(fa,

stopping_thr)

# seeds

seed_mask = fa > 0.4 #0.4 #TODO: check this parameter

seeds = utils.seeds_from_mask(

seed_mask,

density=seed_density,

affine=affine)

# tractography, if affine then in world coordinates

streamlines = LocalTracking(pam, classifier,

seeds, affine=affine, step_size=.5)

# Compute streamlines and store as a list.

streamlines = list(streamlines)

ftractogram = dname + 'tractogram.trk'

#save .trk

save_trk_old_style(ftractogram, streamlines, affine, fa.shape)

if show:

#render

fa_img = nib.load(ffa)

fa = fa_img.get_data()

affine = fa_img.get_affine()

img = nib.load(fdwi)

data = img.get_data()

from nibabel import trackvis

streams, hdr = trackvis.read(ftrk)

streamlines = [s[0] for s in streams]

# threshold on streamline length

from dipy.tracking.utils import length

lengths = list(length(streamlines))

new_streamlines = [ s for s, l in zip(streamlines, lengths) if l > thr_length ] #3.5

# info length streamlines

print(len(streamlines))

print(len(new_streamlines))

print(max(length(streamlines)))

print(min(length(streamlines)))

print(max(length(new_streamlines)))

print(min(length(new_streamlines)))

# show new tracto

new_streamlines = list(new_streamlines)

new_lengths = list(length(new_streamlines))

fnew_tractogram = dname + 'filteredtractogram.trk'

save_trk_old_style(fnew_tractogram, new_streamlines, affine, fa.shape)

if show: