def test_aff_to_hdr():
hdr = {}
affine = np.diag([1,2,3,1])
affine[:3,3] = [10,11,12]
tv.aff_to_hdr(affine, hdr)
yield assert_array_almost_equal(tv.aff_from_hdr(hdr), affine)
# put flip into affine
aff2 = affine.copy()
aff2[:,2] *=-1
tv.aff_to_hdr(aff2, hdr)
yield assert_array_almost_equal(tv.aff_from_hdr(hdr), aff2)
def test_aff_to_hdr():
for version in (1, 2):
hdr = {'version': version}
affine = np.diag([1, 2, 3, 1])
affine[:3, 3] = [10, 11, 12]
tv.aff_to_hdr(affine, hdr)
yield assert_array_almost_equal(tv.aff_from_hdr(hdr), affine)
# put flip into affine
aff2 = affine.copy()
aff2[:, 2] *= -1
tv.aff_to_hdr(aff2, hdr)
yield assert_array_almost_equal(tv.aff_from_hdr(hdr), aff2)
if version == 2:
yield assert_array_almost_equal(hdr['vox_to_ras'], aff2)
def test_aff_to_hdr():
for version in (1, 2):
hdr = {'version': version}
affine = np.diag([1,2,3,1])
affine[:3,3] = [10,11,12]
tv.aff_to_hdr(affine, hdr)
yield assert_array_almost_equal(tv.aff_from_hdr(hdr), affine)
# put flip into affine
aff2 = affine.copy()
aff2[:,2] *=-1
tv.aff_to_hdr(aff2, hdr)
yield assert_array_almost_equal(tv.aff_from_hdr(hdr), aff2)
if version == 2:
yield assert_array_almost_equal(hdr['vox_to_ras'], aff2)
def test_get_affine():
hdr = tv.empty_header()
# default header gives useless affine
yield assert_array_equal(tv.aff_from_hdr(hdr), np.diag([0, 0, 0, 1]))
hdr['voxel_size'] = 1
yield assert_array_equal(tv.aff_from_hdr(hdr), np.diag([0, 0, 0, 1]))
# DICOM direction cosines
hdr['image_orientation_patient'] = [1, 0, 0, 0, 1, 0]
yield assert_array_equal(tv.aff_from_hdr(hdr), np.diag([-1, -1, 1, 1]))
# RAS direction cosines
hdr['image_orientation_patient'] = [-1, 0, 0, 0, -1, 0]
yield assert_array_equal(tv.aff_from_hdr(hdr), np.eye(4))
# translations
hdr['origin'] = [1, 2, 3]
exp_aff = np.eye(4)
exp_aff[:3, 3] = [-1, -2, 3]
yield assert_array_equal(tv.aff_from_hdr(hdr), exp_aff)
# now use the easier vox_to_ras field
hdr = tv.empty_header()
aff = np.eye(4)
aff[:3, :] = np.arange(12).reshape(3, 4)
hdr['vox_to_ras'] = aff
yield assert_array_equal(tv.aff_from_hdr(hdr), aff)
# mappings work too
d = {
'version': 1,
'voxel_size': np.array([1, 2, 3]),
'image_orientation_patient': np.array([1, 0, 0, 0, 1, 0]),
'origin': np.array([10, 11, 12])
}
aff = tv.aff_from_hdr(d)
def test_get_affine():
hdr = tv.empty_header()
# default header gives useless affine
yield assert_array_equal(tv.aff_from_hdr(hdr),
np.diag([0,0,0,1]))
hdr['voxel_size'] = 1
yield assert_array_equal(tv.aff_from_hdr(hdr),
np.diag([0,0,0,1]))
# DICOM direction cosines
hdr['image_orientation_patient'] = [1,0,0,0,1,0]
yield assert_array_equal(tv.aff_from_hdr(hdr),
np.diag([-1,-1,1,1]))
# RAS direction cosines
hdr['image_orientation_patient'] = [-1,0,0,0,-1,0]
yield assert_array_equal(tv.aff_from_hdr(hdr),
np.eye(4))
# translations
hdr['origin'] = [1,2,3]
exp_aff = np.eye(4)
exp_aff[:3,3] = [-1,-2,3]
yield assert_array_equal(tv.aff_from_hdr(hdr),
exp_aff)
# now use the easier vox_to_ras field
hdr = tv.empty_header()
aff = np.eye(4)
aff[:3,:] = np.arange(12).reshape(3,4)
hdr['vox_to_ras'] = aff
yield assert_array_equal(tv.aff_from_hdr(hdr), aff)
# mappings work too
d = {'version': 1,
'voxel_size': np.array([1,2,3]),
'image_orientation_patient': np.array([1,0,0,0,1,0]),
'origin': np.array([10,11,12])}
aff = tv.aff_from_hdr(d)
def load_reduce_translate(fname, reduction=1):
""" Loads a trackvis file using DIPY io, only keeping a ratio of
1/reduction tracks. Performs trajectory approximation to reduce the
track lengths.
Parameter
---------
fname : str
The Trackvis file
reduction : int, optional
The reduction factor (keep only 1 line per set of reduction lines)
Returns
-------
tracks : list
A list of tracks
"""
lines, hdr = tv.read(fname)
print 'loaded,',
sys.stdout.flush()
#ras = tv.get_affine(hdr)
ras = tv.aff_from_hdr(hdr)
if not ras[:3,-1].any():
# dot(ras[:3,:3],md_vox) + t = (0,0,0) --> t = -dot(ras[:3,:3],md_vox)
md_vox = hdr['dim']/2
t = -np.dot(ras[:3,:3], md_vox)
ras[:3,-1] = t
tracks = [l[0] for l in lines[::reduction]]
del lines
ras_cmap = ni_api.AffineTransform.from_params('ijk', 'xyz', ras)
flat_tracks, breaks = flatten_tracks(tracks)
del tracks
flat_tracks_xyz = ras_cmap(flat_tracks)
tracks = recombine_flattened_tracks(flat_tracks_xyz, breaks)
print 'translated,',
sys.stdout.flush()
tracks_reduced = [tp.approximate_ei_trajectory(line, alpha=np.pi/4)
for line in tracks ]
print 'reduced,'
sys.stdout.flush()
return tracks_reduced
def init(self):
import dipy.io.trackvis as tv
lines,hdr = tv.read(self.fname)
ras = tv.aff_from_hdr(hdr)
self.affine=ras
tracks = [l[0] for l in lines]
if self.yellow_indices != None :
tracks = [t for t in tracks if tm.length(t) > 20]
print 'tracks loaded'
#self.data = [100*np.array([[0,0,0],[1,0,0],[2,0,0]]).astype(np.float32) ,100*np.array([[0,1,0],[0,2,0],[0,3,0]]).astype(np.float32)]#tracks[:20000]
if self.dummy_data:
self.data = [100*np.array([[0,0,0],[1,0,0],[2,0,0]]).astype(np.float32) ,100*np.array([[0,1,0],[0,2,0],[0,3,0]]).astype(np.float32)]
if self.data_subset!=None:
self.data = tracks[self.data_subset[0]:self.data_subset[1]]
else:
self.data = tracks
data_stats = np.concatenate(tracks)
self.min=np.min(data_stats,axis=0)
self.max=np.max(data_stats,axis=0)
self.mean=np.mean(data_stats,axis=0)
del data_stats
del lines
self.multiple_colors()
def __init__(self,fname,ang_table=None,colormap=None, line_width=3., shrink=None,subset=None,tracks=None,text=None,text_color=np.array([1,0,0])):
self.position = (0,0,0)
self.fname = fname
self.manycolors = True
self.bbox = None
self.list_index = None
self.affine = None
self.data = None
self.list_index = None
self.rot_angle = 0
self.colormap = None
self.text = text
self.min = None
self.max = None
self.mean = None
self.material_color = False
self.fadeout = False
self.fadein = False
self.fadeout_speed = 0.
self.fadein_speed = 0.
self.min_length = 20.
self.angle = 0.
self.angular_speed = .5
self.line_width = line_width
self.opacity = 1.
self.near_pick = None
self.far_pick = None
self.near_pick_prev = None
self.far_pick_prev = None
self.picked_track = None
self.pick_color = [1,1,0]
self.brain_color = [1,1,1]
self.yellow_indices = None
self.dummy_data = False
self.tracks = tracks
if subset != None:
self.data_subset = subset #[0,20000]#None
else:
self.data_subset = None
self.orbit_demo = False
self.orbit_anglez = 0.
self.orbit_anglez_rate = 10.
self.orbit_anglex = 0.
self.orbit_anglex_rate = 2.
self.angle_table = ang_table
self.angle_table_index = 0
self.shrink = shrink
self.picking_example = False
self.partial_colors = False
import dipy.io.trackvis as tv
if self.tracks == None:
lines,hdr = tv.read(self.fname)
ras = tv.aff_from_hdr(hdr)
self.affine=ras
tracks = [l[0] for l in lines]
del lines
else:
tracks = self.tracks
if self.yellow_indices != None :
tracks = [t for t in tracks if tm.length(t) > 20]
print '%d tracks loaded' % len(tracks)
#self.data = [100*np.array([[0,0,0],[1,0,0],[2,0,0]]).astype(np.float32) ,100*np.array([[0,1,0],[0,2,0],[0,3,0]]).astype(np.float32)]#tracks[:20000]
if self.dummy_data:
self.data = [100*np.array([[0,0,0],[1,0,0],[2,0,0]]).astype(np.float32) ,100*np.array([[0,1,0],[0,2,0],[0,3,0]]).astype(np.float32)]
if self.data_subset!=None:
self.data = tracks[self.data_subset[0]:self.data_subset[1]]
else:
self.data = tracks
if self.shrink != None:
self.data = [ self.shrink*t for t in self.data]
data_stats = np.concatenate(tracks)
self.min=np.min(data_stats,axis=0)
self.max=np.max(data_stats,axis=0)
self.mean=np.mean(data_stats,axis=0)
del data_stats
def __init__(self,fname,colormap=None, line_width=1., shrink=None, thinning = 0,
angle_table = None, manycolors = False, brain_color=[1,1,1]):
self.position = (0,0,0)
self.fname = fname
self.manycolors = manycolors
#self.color = monocolor
self.bbox = None
self.list_index = None
self.affine = None
self.data = None
self.list_index = None
self.rot_angle = 0
self.colormap = None
self.min = None
self.max = None
self.mean = None
self.material_color = False
self.fadeout = False
self.fadein = False
self.fadeout_speed = 0.
self.fadein_speed = 0.
self.min_length = 20.
self.angle = 0.
self.angular_speed = .5
self.line_width = line_width
self.opacity = 1.
self.opacity_rate = 0.
self.near_pick = None
self.far_pick = None
self.near_pick_prev = None
self.far_pick_prev = None
self.picked_track = None
self.pick_color = [1,1,0]
#self.brain_color = [1,1,1] # white
#self.brain_color = [.941,.862,.510] # buff
self.brain_color = brain_color
self.yellow_indices = None
self.dummy_data = False
self.data_subset = [0,20000]#None
self.orbit_demo = False
self.orbit_anglez =0.
self.orbit_anglez_rate = 10.
self.orbit_anglex = 0.
self.orbit_anglex_rate = 2.
self.angle_table = angle_table
if angle_table != None:
print 'Tracks angle_table shape %s' % str(self.angle_table.shape)
self.angle_table_index = 0
#print 'angle_table_index %d' % self.angle_table_index
self.shrink = shrink
self.picking_example = False
import dipy.io.trackvis as tv
lines,hdr = tv.read(self.fname)
ras = tv.aff_from_hdr(hdr
)
self.affine=ras
tracks = [l[0] for l in lines]
print 'tracks %d loaded' % len(tracks)
self.thinning = thinning
if self.yellow_indices != None :
tracks = [t for t in tracks if tm.length(t) > 20]
if self.thinning != 0:
tracks = [tracks[k] for k in range(0,len(tracks),self.thinning)]
print '%d tracks active' % len(tracks)
#self.data = [100*np.array([[0,0,0],[1,0,0],[2,0,0]]).astype(np.float32) ,100*np.array([[0,1,0],[0,2,0],[0,3,0]]).astype(np.float32)]#tracks[:20000]
if self.dummy_data:
self.data = [100*np.array([[0,0,0],[1,0,0],[2,0,0]]).astype(np.float32) ,100*np.array([[0,1,0],[0,2,0],[0,3,0]]).astype(np.float32)]
if self.data_subset!=None:
self.data = tracks[self.data_subset[0]:self.data_subset[1]]
else:
self.data = tracks
if self.shrink != None:
self.data = [ self.shrink*t for t in self.data]
data_stats = np.concatenate(tracks)
self.min=np.min(data_stats,axis=0)
self.max=np.max(data_stats,axis=0)
self.mean=np.mean(data_stats,axis=0)
del data_stats
del lines
