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Trigger github actions on PR #5

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Merged
merged 4 commits into from
Jun 3, 2020
Merged

Trigger github actions on PR #5

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Trigger github actions on PR
nbara Jun 3, 2020
483f445
Update pythonpackage.yml
nbara Jun 3, 2020
54785c2
Update pythonpackage.yml
nbara Jun 3, 2020
8fb5fc9
pep
nbara Jun 3, 2020
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8 changes: 5 additions & 3 deletions .github/workflows/pythonpackage.yml
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Original file line number Diff line number Diff line change
Expand Up @@ -2,9 +2,11 @@ name: unit-tests

on:
push:
branches-ignore:
- 'gh-pages'

branches:
- master
pull_request:
branches:
- master
jobs:
build:

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8 changes: 4 additions & 4 deletions meegkit/star.py
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Expand Up @@ -74,7 +74,7 @@ def star(X, thresh=1, closest=[], depth=1, pca_thresh=1e-15, n_smooth=10,

# Phase 1
# -------------------------------------------------------------------------
# Find time intervals where at least one channel is excentric -> w == 0
# Find time intervals where at least one channel is eccentric -> w == 0
# Compute covariance on artifact-free data.

iter = n_iter
Expand All @@ -87,7 +87,7 @@ def star(X, thresh=1, closest=[], depth=1, pca_thresh=1e-15, n_smooth=10,
# Compute channel data estimated from its neighbours
z = _project_channel(X[:, neighbours], c0, ch, neighbours)

# Compute excentricity over time
# Compute eccentricity over time
d[:, ch] = _eccentricity(X[:, ch][:, None], z, w, n_smooth).T
d[:, ch] = d[:, ch] / thresh

Expand Down Expand Up @@ -122,7 +122,7 @@ def star(X, thresh=1, closest=[], depth=1, pca_thresh=1e-15, n_smooth=10,
d = _eccentricity(X, None, w, n_smooth)

rank = np.argsort(d, axis=1)[:, ::-1].astype(float)
rank[np.where(w)[0], :] = np.nan # exclude parts that are not excentric
rank[np.where(w)[0], :] = np.nan # exclude parts that are not eccentric

depth = np.min((depth, n_chans - 1))
ww = np.ones(X.shape)
Expand All @@ -134,7 +134,7 @@ def star(X, thresh=1, closest=[], depth=1, pca_thresh=1e-15, n_smooth=10,
for ch in np.arange(n_chans):
neighbours = _closest_neighbours(closest, ch, n_chans)

# find samples where channel `ch` is the most excentric
# find samples where channel `ch` is the most eccentric
bad_samples = np.where(ch == rank[:, i_depth])[0]
if i_depth != 0: # exclude if not very bad
bad_samples = np.delete(
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