def calc_streams_in_roi(dpy_file,roi_dat,stream_idxs): aff_eye = np.eye(4) D = Dpy(dpy_file, 'r') streams = D.read_tracksi(stream_idxs) D.close() streamsinroi = list(target_line_based(streams,roi_dat,aff_eye)) return streamsinroi
def calc_streams_in_roi(dpy_file,roi_dat,stream_idxs):
aff_eye = np.eye(4)
D = Dpy(dpy_file, 'r')
streams = D.read_tracksi(stream_idxs)
D.close()
# hack to deal with apparent nibabel-dipy conflict
if type(streams) == nib.streamlines.array_sequence.ArraySequence:
streams = list(streams)
streamsinroi = list(target_line_based(streams,roi_dat,aff_eye))
return streamsinroi
def test_target_line_based_out_of_bounds():
test_cases = [
(np.array([[-10, 0, 0], [0, 0, 0]]), 0),
(np.array([[-10, 0, 0], [1, -10, 0]]), 0),
(np.array([[0, 0, 0], [10, 10, 10]]), 0),
(np.array([[-2, -0.6, -0.6], [2, -0.6, -0.6]]), 0),
(np.array([[-10000, 0, 0], [0, 0, 0]]), 0),
(np.array([[-10, 0, 0], [10, 0, 0]]), 1),
(np.array([[1, -10, 0], [1, 10, 0]]), 1),
]
for streamline, expected_matched in test_cases:
mask = np.zeros((2, 1, 1), dtype=np.uint8)
mask[1, 0, 0] = 1
matched = list(target_line_based([streamline], np.eye(4), mask))
assert len(matched) == expected_matched
def warp_streamlines(
adjusted_affine,
ref_grid_aff,
mapping,
warped_fa_img,
streams_in_curr_grid,
brain_mask,
):
from dipy.tracking import utils
from dipy.tracking.streamline import (
values_from_volume,
transform_streamlines,
Streamlines,
)
# Deform streamlines, isocenter, and remove streamlines outside brain
streams_in_brain = [
sum(d, s) for d, s in zip(
values_from_volume(mapping.get_forward_field(),
streams_in_curr_grid, ref_grid_aff),
streams_in_curr_grid,
)
]
streams_final_filt = Streamlines(
utils.target_line_based(
transform_streamlines(
transform_streamlines(streams_in_brain,
np.linalg.inv(adjusted_affine)),
np.linalg.inv(warped_fa_img.affine),
),
np.eye(4),
brain_mask,
include=True,
))
return streams_final_filt
