[ENH] Add emc helper functions - vectors #76
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dPys
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dPys
changed the title
See travis errors in nipreps#76,nipreps#77, nipreps#78, etc.
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. | ||
| 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. | ||
| 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)
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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❌ Patch coverage is
Additional details and impacted files@@ Coverage Diff @@
## master #76 +/- ##
==========================================
- Coverage 52.37% 50.42% -1.95%
==========================================
Files 16 22 +6
Lines 907 1285 +378
Branches 114 168 +54
==========================================
+ Hits 475 648 +173
- Misses 431 622 +191
- Partials 1 15 +14 ☔ View full report in Codecov by Sentry. 🚀 New features to boost your workflow:
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Co-Authored-By: Ariel Rokem <[email protected]>
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I'm going to clean up the repo. However, I still believe this function would perfectly be part of DIPY or a smaller nipreps package for diffusion MRI (I'm thinking of calling it When that happens, I'll make sure to annotate @dPys as the co-author in all commits. |
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*All of these support the signal prediction routines.
*Each function will need a unit test.