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Increase affine consistency in dipy.tracking.utils #1939

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merged 17 commits into from Aug 1, 2019
Merged

Increase affine consistency in dipy.tracking.utils #1939

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a700df9
First pass of deleting/fixing affine usage
frheault Jul 29, 2019
c4e523d
Fixing all broken function for density, connectivity and seeding
frheault Jul 29, 2019
aec04cc
Fix second half of dependacy from utils
frheault Jul 29, 2019
1046084
Added back move_streamlines under a new name for precision
frheault Jul 29, 2019
7761d5c
First testing phase (test/examples)
frheault Jul 29, 2019
f8b6257
Merge branch 'update_file_format_example' of https://github.com/frhea…
frheault Jul 29, 2019
4c807b3
Merge with updated examples
frheault Jul 29, 2019
6fd641a
Flake8 on everything changed
frheault Jul 29, 2019
2b8dc6d
Fixed invalid reference in tracking workflow
frheault Jul 29, 2019
b23f155
Merge branch 'master' of https://github.com/nipy/dipy into increase_a…
frheault Jul 30, 2019
2706051
Updated the api_changes
frheault Jul 30, 2019
1ebc15f
Correct typo in docstring, fix example in seeds_from_masks
frheault Jul 30, 2019
639d922
Reduce line length
frheault Jul 30, 2019
80c00ec
Added back unsused variable in test, apparently necessary
frheault Jul 30, 2019
6f846fb
Fixed doctest error
frheault Jul 30, 2019
7ca62f5
Remove cast for transform_streamlines
frheault Jul 31, 2019
bc38dd1
Rebased after PR #1925
frheault Jul 31, 2019
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9 changes: 3 additions & 6 deletions dipy/tracking/local/localtracking.py
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Expand Up @@ -111,12 +111,9 @@ def _tracker(self, seed, first_step, streamline):
def __iter__(self):
# Make tracks, move them to point space and return
track = self._generate_streamlines()
seeds = None
if self.save_seeds:
track, seeds = zip(*track)
return utils.move_streamlines(track,
self.affine,
seeds=seeds)

return utils.transform_tracking_output(track, self.affine,
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What is this function? Why not transform_streamlines from dipy.tracking.streamlines?

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My reasoning was that transform_streamlines should do one thing and one thing only, to stay as simple as possible.

This function takes a generator as a input that can be a tuple or not, and in 99% of the case where people want to transform_streamlines it is just that.
This function with the generator, the tuple and the saving_seeds option is used only once in all dipy and is specifically for the tracking.

I think merging the two function would add to the confusion. And complexify a function that should remain as simple as simple (transform_streamlines)

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ok, make sense!

save_seeds=self.save_seeds)

def _generate_streamlines(self):
"""A streamline generator"""
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6 changes: 3 additions & 3 deletions dipy/tracking/local/tests/test_tracking.py
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Expand Up @@ -297,7 +297,7 @@ def allclose(x, y):
npt.assert_(len(streamlines[1]) == 1) # OUTSIDEIMAGE

# Test that all points are within the image volume
seeds = seeds_from_mask(np.ones(mask.shape), density=2)
seeds = seeds_from_mask(np.ones(mask.shape), np.eye(4), density=2)
streamline_generator = LocalTracking(dg, tc, seeds, np.eye(4), 0.5,
return_all=True)
streamlines = Streamlines(streamline_generator)
Expand Down Expand Up @@ -349,7 +349,7 @@ def test_particle_filtering_tractography():
np.zeros(simple_gm.shape)])
simple_csf = np.ones(simple_wm.shape) - simple_wm - simple_gm
tc = ActTissueClassifier.from_pve(simple_wm, simple_gm, simple_csf)
seeds = seeds_from_mask(simple_wm, density=2)
seeds = seeds_from_mask(simple_wm, np.eye(4), density=2)

# Random pmf in every voxel
shape_img = list(simple_wm.shape)
Expand Down Expand Up @@ -382,7 +382,7 @@ def test_particle_filtering_tractography():
npt.assert_almost_equal(np.linalg.norm(s[i] - s[i + 1]),
step_size)
# Test that all points are within the image volume
seeds = seeds_from_mask(np.ones(simple_wm.shape), density=1)
seeds = seeds_from_mask(np.ones(simple_wm.shape), np.eye(4), density=1)
pft_streamlines_generator = ParticleFilteringTracking(
dg, tc, seeds, np.eye(4), step_size, max_cross=1, return_all=True)
pft_streamlines = Streamlines(pft_streamlines_generator)
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8 changes: 4 additions & 4 deletions dipy/tracking/streamline.py
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Expand Up @@ -7,8 +7,8 @@
import numpy as np
from nibabel.affines import apply_affine
from nibabel.streamlines import ArraySequence as Streamlines
from dipy.tracking.streamlinespeed import (set_number_of_points, length,
compress_streamlines)
from dipy.tracking.streamlinespeed import (compress_streamlines, length,
set_number_of_points)
from dipy.tracking.distances import bundles_distances_mdf
import dipy.tracking.utils as ut
from dipy.core.geometry import dist_to_corner
Expand Down Expand Up @@ -633,8 +633,8 @@ def _extract_vals(data, streamlines, affine=None, threedvec=False):
isinstance(streamlines, types.GeneratorType) or
isinstance(streamlines, Streamlines)):
if affine is not None:
streamlines = ut.move_streamlines(streamlines,
np.linalg.inv(affine))
streamlines = transform_streamlines(streamlines,
np.linalg.inv(affine))

vals = []
for sl in streamlines:
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1 change: 1 addition & 0 deletions dipy/tracking/tests/test_life.py
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Expand Up @@ -159,6 +159,7 @@ def test_fit_data():
gtab = grad.gradient_table(fbval, fbvec)
ni_data = nib.load(fdata)
data = ni_data.get_data()

tensor_streamlines = nib.streamlines.load(fstreamlines).streamlines
sft = StatefulTractogram(tensor_streamlines, ni_data, Space.RASMM)
sft.to_vox()
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9 changes: 4 additions & 5 deletions dipy/tracking/tests/test_streamline.py
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Expand Up @@ -11,11 +11,10 @@

from dipy.testing import assert_true
from numpy.testing import (assert_array_equal, assert_array_almost_equal,
assert_raises, run_module_suite, assert_allclose,
assert_raises, assert_allclose,
assert_almost_equal, assert_equal)

from dipy.tracking.streamline import Streamlines
import dipy.tracking.utils as ut
from dipy.tracking.streamline import (set_number_of_points,
length,
relist_streamlines,
Expand Down Expand Up @@ -1110,15 +1109,15 @@ def test_values_from_volume():

affine = np.eye(4)
affine[:, 3] = [-100, 10, 1, 1]
x_sl1 = ut.move_streamlines(sl1, affine)
x_sl2 = ut.move_streamlines(sl1, affine)
x_sl1 = transform_streamlines(sl1, affine)
x_sl2 = transform_streamlines(sl1, affine)

vv = values_from_volume(data, x_sl1, affine=affine)
npt.assert_almost_equal(vv, ans1, decimal=decimal)

# The generator has already been consumed so needs to be
# regenerated:
x_sl1 = list(ut.move_streamlines(sl1, affine))
x_sl1 = list(transform_streamlines(sl1, affine))
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this is already returning a list

vv = values_from_volume(data, x_sl1, affine=affine)
npt.assert_almost_equal(vv, ans1, decimal=decimal)

Expand Down