[REL] 0.3.0

Author: sappelhoff

.codecov.yml

@@ -136,9 +136,9 @@ Follow this workflow:
 1. go to your python environment for `pyprep`
 1. make sure all tests pass and the docs are built cleanly
 1. update `docs/whats_new.rst`, renaming the "current" headline to the new
-   version and adding all "Authors" for the release. "Authors" are all people
-   who committed code or in other ways contributed to `pyprep` for this release
-   (e.g., by reviewing PRs).
+   version and updating the "Authors" section. "Authors" are all people
+   who committed code or in other ways contributed to `pyprep` (e.g., by
+   reviewing PRs, moderating discussions).
 1. commit the change and `git push` to master (or make a pull request).
    Start your commit message with `[REL]`.
 1. make an annotated tag `git tag -a -m "1.2.3" 1.2.3 upstream/master` (This

.github/CONTRIBUTING.md

@@ -37,7 +37,7 @@ jobs:
 
     - name: Test with pytest
       run: |
-        pytest tests/ --cov=pyprep/ --cov-report=xml --verbose
+        pytest tests/ --cov=pyprep/ --cov-report=xml --cov-config=setup.cfg --verbose
 
     - name: Build docs
       run: |

.github/workflows/python_tests.yml

@@ -13,10 +13,20 @@ Here we list a changelog of pyprep.
 
 .. currentmodule:: pyprep
 
-.. _current:
-
-Current
+Authors
 -------
+People who contributed to this software across releases (in alphabetical order)
+
+* `Aamna Lawrence`_
+* `Adam Li`_
+* `Stefan Appelhoff`_
+* `Victor Xiang`_
+* `Yorguin Mantilla`_
+
+.. _changes_0_3_0:
+
+Version 0.3.0
+-------------
 
 Changelog
 ~~~~~~~~~
@@ -71,17 +81,6 @@ Changelog
 
 - Add :func:`find_bad_epochs` based on the FASTER algorithm, by `Stefan Appelhoff`_ (`0fa9c06 <https://github.com/sappelhoff/pyprep/commit/0fa9c065481c4cbaaf83b275f92b16b8807810b5>`_)
 
-
-Authors
--------
-People who contributed to this software (in alphabetical order)
-
-* Stefan Appelhoff
-* Aamna Lawrence
-* Adam Li
-* Yorguin Mantilla
-* Victor Xiang
-
 .. _Stefan Appelhoff: http://stefanappelhoff.com/
 .. _Aamna Lawrence: https://github.com/AamnaLawrence
 .. _Adam Li: https://github.com/adam2392/

docs/whats_new.rst

@@ -12,10 +12,10 @@ license = MIT
 license_files = LICENSE
 platforms = any
 classifiers =
-  Topic :: Scientific/Engineering,
-  Programming Language :: Python :: 3,
-  Intended Audience :: Science/Research,
-  Development Status :: 3 - Alpha,
+  Topic :: Scientific/Engineering
+  Programming Language :: Python :: 3
+  Intended Audience :: Science/Research
+  Development Status :: 3 - Alpha
   License :: OSI Approved :: MIT License
   Operating System :: POSIX :: Linux
   Operating System :: Unix
@@ -59,11 +59,28 @@ docstring-convention = numpy
 max-line-length = 88
 extend-ignore =
   # See https://github.com/PyCQA/pycodestyle/issues/373
-  E203,
+  E203
 exclude =
-  versioneer.py,
-  _version.py,
+  versioneer.py
+  _version.py
   *docs/_build/*
   *docs/auto_examples/*
   *build/lib*
   examples/
+
+[coverage:run]
+omit =
+  # Do not include test script in coverage report
+  *tests*
+  setup.py
+  pyprep/_version.py
+
+[coverage:report]
+# Regexes for lines to exclude from consideration
+exclude_lines =
+  # Have to re-enable the standard pragma
+  pragma: no cover
+
+  # Don't complain if non-runnable code isn't run:
+  if 0:
+  if __name__ == .__main__.:

setup.cfg

@@ -21,13 +21,8 @@ def raw():
 
     # using sample EEG data (https://physionet.org/content/eegmmidb/1.0.0/)
     raw = mne.io.read_raw_edf(edf_fpath, preload=True)
-    raw.rename_channels(lambda s: s.strip("."))
-    raw.rename_channels(
-        lambda s: s.replace("c", "C")
-        .replace("o", "O")
-        .replace("f", "F")
-        .replace("t", "T")
-        .replace("Tp", "TP")
-        .replace("Cp", "CP")
-    )
+
+    # The eegbci data has non-standard channel names. We need to rename them:
+    eegbci.standardize(raw)
+
     return raw

tests/conftest.py

@@ -6,6 +6,8 @@
 
 from pyprep.noisy import Noisydata, find_bad_epochs
 
+RNG = np.random.RandomState(1337)
+
 
 def make_random_mne_object(
     sfreq=1000.0, t_secs=600, n_freq_comps=5, freq_range=[10, 60]
@@ -65,7 +67,7 @@ def make_random_mne_object(
     for chan in range(n_chans):
         # Each channel signal is a sum of random freq sine waves
         for freq_i in range(n_freq_comps):
-            freq = np.random.randint(low, high, signal_len)
+            freq = RNG.randint(low, high, signal_len)
             signal[chan, :] += np.sin(2 * np.pi * t * freq)
 
     signal *= 1e-6  # scale to Volts
@@ -89,7 +91,7 @@ def make_random_mne_object(
 # Make some arbitrary events sampled from the mid-section of raw.times
 n_events = 3
 ival_secs = [0.2, 0.8]
-marker_samples = np.random.choice(raw.times[5:-5], size=n_events, replace=False)
+marker_samples = RNG.choice(raw.times[5:-5], size=n_events, replace=False)
 events = np.asarray(
     [marker_samples, np.zeros(n_events), np.ones(n_events)], dtype="int64"
 )
@@ -106,9 +108,7 @@ def test_find_bad_epochs(epochs=epochs):
     assert bads == []
 
 
-@pytest.mark.parametrize(
-    "input", [raw, {"key": 1}, [1, 2, 3], np.random.random((3, 3))]
-)
+@pytest.mark.parametrize("input", [raw, {"key": 1}, [1, 2, 3], RNG.random((3, 3))])
 def test_init(input):
     """Test the class initialization."""
     # Initialize with an mne object should work
@@ -138,7 +138,7 @@ def test_find_bad_by_nan(raw=raw):
     m, n = raw_tmp._data.shape
 
     # Insert a nan value for a random channel
-    rand_chn_idx = int(np.random.randint(0, m, 1))
+    rand_chn_idx = int(RNG.randint(0, m, 1))
     rand_chn_lab = raw_tmp.ch_names[rand_chn_idx]
     raw_tmp._data[rand_chn_idx, n - 1] = np.nan
 
@@ -157,7 +157,7 @@ def test_find_bad_by_flat(raw=raw):
     raw_tmp._data *= 1e100
 
     # Now insert one random flat channel
-    rand_chn_idx = int(np.random.randint(0, m, 1))
+    rand_chn_idx = int(RNG.randint(0, m, 1))
     rand_chn_lab = raw_tmp.ch_names[rand_chn_idx]
     raw_tmp._data[rand_chn_idx, :] = np.ones_like(raw_tmp._data[1, :])
 
@@ -169,13 +169,13 @@ def test_find_bad_by_flat(raw=raw):
 
 def test_find_bad_by_deviation(raw=raw):
     """Test find_bad_by_deviation."""
-    np.random.seed(12345)
+    RNG.seed(12345)
 
     raw_tmp = raw.copy()
     m, n = raw_tmp._data.shape
 
     # Now insert one random channel with very low deviations
-    rand_chn_idx = int(np.random.randint(0, m, 1))
+    rand_chn_idx = int(RNG.randint(0, m, 1))
     rand_chn_lab = raw_tmp.ch_names[rand_chn_idx]
     raw_tmp._data[rand_chn_idx, :] = np.ones_like(raw_tmp._data[1, :])
 
@@ -186,7 +186,7 @@ def test_find_bad_by_deviation(raw=raw):
 
     # Insert a channel with very high deviation
     raw_tmp = raw.copy()
-    rand_chn_idx = int(np.random.randint(0, m, 1))
+    rand_chn_idx = int(RNG.randint(0, m, 1))
     rand_chn_lab = raw_tmp.ch_names[rand_chn_idx]
     arbitrary_scaling = 5
     raw_tmp._data[rand_chn_idx, :] *= arbitrary_scaling
@@ -206,15 +206,15 @@ def test_find_bad_by_correlation(
 
     # The test data is correlated well
     # We insert a badly correlated channel and see if it is detected.
-    rand_chn_idx = int(np.random.randint(0, m, 1))
+    rand_chn_idx = int(RNG.randint(0, m, 1))
     rand_chn_lab = raw_tmp.ch_names[rand_chn_idx]
 
     # Use cosine instead of sine to create a signal
     low = freq_range[0]
     high = freq_range[1]
     signal = np.zeros((1, n))
     for freq_i in range(n_freq_comps):
-        freq = np.random.randint(low, high, n)
+        freq = RNG.randint(low, high, n)
         signal[0, :] += np.cos(2 * np.pi * raw.times * freq)
 
     raw_tmp._data[rand_chn_idx, :] = signal * 1e-6
@@ -232,13 +232,13 @@ def test_find_bad_by_hf_noise(raw=raw, n_freq_comps=n_freq_comps):
 
     # The test data has low hf noise
     # We insert a a chan with a lot hf noise
-    rand_chn_idx = int(np.random.randint(0, m, 1))
+    rand_chn_idx = int(RNG.randint(0, m, 1))
     rand_chn_lab = raw_tmp.ch_names[rand_chn_idx]
 
     # Use freqs between 90 and 100 Hz to insert hf noise
     signal = np.zeros((1, n))
     for freq_i in range(n_freq_comps):
-        freq = np.random.randint(90, 100, n)
+        freq = RNG.randint(90, 100, n)
         signal[0, :] += np.sin(2 * np.pi * raw.times * freq)
 
     raw_tmp._data[rand_chn_idx, :] = signal * 1e-6
@@ -261,7 +261,7 @@ def test_find_bad_by_ransac(raw=raw):
 
 def test_ransac_too_few_preds(raw=raw):
     """Test that ransac throws an error for few predictors."""
-    chns = np.random.choice(raw.ch_names, size=3, replace=False)
+    chns = RNG.choice(raw.ch_names, size=3, replace=False)
     raw_tmp = raw.copy()
     raw_tmp.pick_channels(chns)
     nd = Noisydata(raw_tmp)

tests/test_noisy.py

@@ -21,7 +21,7 @@ def test_prep_pipeline(raw, montage):
         "reref_chs": ch_names_eeg,
         "line_freqs": np.arange(60, sample_rate / 2, 60),
     }
-    prep = PrepPipeline(raw_copy, prep_params, montage)
+    prep = PrepPipeline(raw_copy, prep_params, montage, random_state=42)
     prep.fit()
 
     EEG_raw = raw_copy.get_data(picks="eeg") * 1e6