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Fix more warnings #48

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Merged
cbrnr merged 7 commits into from
Oct 14, 2019
Merged

Fix more warnings #48

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c9c6d13
Add tests for local XDF files
cbrnr Oct 8, 2019
4c7c967
Finding breaks is only possible if there are at least 2 time stamps
cbrnr Oct 8, 2019
a604c32
Prune test module
cbrnr Oct 8, 2019
6b57c34
Ignore divide by zero warning
cbrnr Oct 9, 2019
fb79d21
Add changelog entry
cbrnr Oct 9, 2019
d0134f6
Remove test for local files
cbrnr Oct 10, 2019
0a87a25
Avoid division by zero, no silencing necessary
cbrnr Oct 11, 2019
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3 changes: 2 additions & 1 deletion CHANGELOG.md
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Original file line number Diff line number Diff line change
@@ -1,9 +1,10 @@
## [1.16.2] - 2019-?
## [1.16.2] - 2019-??-??
### Fixed
- Compare nominal to effective sampling rates only for regularly sampled streams ([#47](https://github.com/xdf-modules/xdf-Python/pull/47) by [Clemens Brunner](https://github.com/cbrnr)).

### Changed
- Speed up loading of numerical data ([#46](https://github.com/xdf-modules/xdf-python/pull/46) by [Tristan Stenner](https://github.com/tstenner)).
- Avoid/suppress some NumPy warnings ([#48](https://github.com/xdf-modules/xdf-Python/pull/48) by [Clemens Brunner](https://github.com/cbrnr)).

## [1.16.1] - 2019-09-28
### Fixed
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5 changes: 3 additions & 2 deletions pyxdf/pyxdf.py
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Expand Up @@ -484,7 +484,7 @@ def _clock_sync(streams,
# if requested, this is only for cases where "everything goes
# wrong" during recording note that this is a fancy feature that
# is not needed for normal XDF compliance.
if handle_clock_resets:
if handle_clock_resets and len(clock_times) > 1:
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tstenner marked this conversation as resolved.
# First detect potential breaks in the synchronization data;
# this is only necessary when the importer should be able to
# deal with recordings where the computer that served a
Expand Down Expand Up @@ -630,7 +630,8 @@ def _robust_fit(A, y, rho=1, iters=1000):
for k in range(iters):
x = np.linalg.solve(U, (np.linalg.solve(L, Aty + np.dot(A.T, z - u))))
d = np.dot(A, x) - y + u
tmp = np.maximum(0, (1 - (1 + 1 / rho) / np.abs(d)))
d_inv = np.divide(1, d, where=d != 0)
tmp = np.maximum(0, (1 - (1 + 1 / rho) * np.abs(d_inv)))
z = rho / (1 + rho) * d + 1 / (1 + rho) * tmp * d
u = d - z
return x
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