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[ENH] Add emc helper functions - vectors #76
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[ENH] Add emc helper functions - vectors #76
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Best reviewed: commit by commit
Optimal code review plan (1 warning)
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See travis errors in nipreps#76,nipreps#77, nipreps#78, etc.
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Unit tests for _nonoverlapping_qspace_samples
would be great to have and it is a very low hanging fruit - we want this function to be super-robust.
dmriprep/utils/vectors.py
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def _rasb_to_bvec_list(in_rasb): | ||
""" | ||
Create a list of b-vectors from a rasb gradient table. | ||
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Parameters | ||
---------- | ||
in_rasb : str or os.pathlike | ||
File path to a RAS-B gradient table. | ||
""" | ||
import numpy as np | ||
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ras_b_mat = np.genfromtxt(in_rasb, delimiter="\t") | ||
bvec = [vec for vec in ras_b_mat[:, 0:3] if not np.isclose(all(vec), 0)] | ||
return list(bvec) | ||
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def _rasb_to_bval_floats(in_rasb): | ||
""" | ||
Create a list of b-values from a rasb gradient table. | ||
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Parameters | ||
---------- | ||
in_rasb : str or os.pathlike | ||
File path to a RAS-B gradient table. | ||
""" | ||
import numpy as np | ||
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ras_b_mat = np.genfromtxt(in_rasb, delimiter="\t") | ||
return [float(bval) for bval in ras_b_mat[:, 3] if bval > 0] |
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Can you point me at the location where these are needed? I believe our Gradients class has already this covered.
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@oesteban -- These are all used in signal prediction
See: https://github.com/nipreps/dmriprep/pull/62/files#diff-4fa7776741ad932fa7371fd581572c12R176-R322
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(and not covered in Gradients class)
Hello @dPys, Thank you for updating! Cheers! There are no style issues detected in this Pull Request. 🍻 To test for issues locally, Comment last updated at 2020-03-24 20:09:36 UTC |
Codecov Report
@@ Coverage Diff @@
## master #76 +/- ##
=========================================
- Coverage 52.37% 51.77% -0.6%
=========================================
Files 16 21 +5
Lines 907 1240 +333
Branches 114 164 +50
=========================================
+ Hits 475 642 +167
- Misses 431 586 +155
- Partials 1 12 +11
Continue to review full report at Codecov.
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Co-Authored-By: Ariel Rokem <arokem@gmail.com>
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*All of these support the signal prediction routines.
*Each function will need a unit test.