Refactor of NoisyChannels tests and split into separate methods

tests/conftest.py

@@ -14,15 +14,60 @@ def montage():
 
 @pytest.fixture(scope="session")
 def raw():
-    """Fixture for physionet EEG subject 4, dataset 1."""
+    """Return an `mne.io.Raw` object for use with unit tests.
+
+    This fixture downloads and reads in subject 4, run 1 from the Physionet
+    BCI2000 (eegbci) open dataset. This recording is quite noisy and is thus a
+    good candidate for testing the PREP pipeline.
+
+    File attributes:
+    - Channels: 64 EEG
+    - Sample rate: 160 Hz
+    - Duration: 61 seconds
+
+    This is only run once per session to save time downloading.
+
+    """
     mne.set_log_level("WARNING")
-    # load in subject 1, run 1 dataset
+
+    # Download and read S004R01.edf from the BCI2000 dataset
     edf_fpath = eegbci.load_data(4, 1, update_path=True)[0]
+    raw = mne.io.read_raw_edf(edf_fpath, preload=True)
+    eegbci.standardize(raw)  # Fix non-standard channel names
+
+    return raw
+
 
-    # using sample EEG data (https://physionet.org/content/eegmmidb/1.0.0/)
+@pytest.fixture(scope="session")
+def raw_clean(montage):
+    """Return an `mne.io.Raw` object with no bad channels for use with tests.
+
+    This fixture downloads and reads in subject 1, run 1 from the Physionet
+    BCI2000 (eegbci) open dataset, interpolates its two bad channels (T10 & F8),
+    and performs average referencing on the data. Intended for use with tests
+    where channels are made artifically bad.
+
+    File attributes:
+    - Channels: 64 EEG
+    - Sample rate: 160 Hz
+    - Duration: 61 seconds
+
+    This is only run once per session to save time downloading.
+
+    """
+    mne.set_log_level("WARNING")
+
+    # Download and read S001R01.edf from the BCI2000 dataset
+    edf_fpath = eegbci.load_data(1, 1, update_path=True)[0]
     raw = mne.io.read_raw_edf(edf_fpath, preload=True)
+    eegbci.standardize(raw)  # Fix non-standard channel names
+
+    # Interpolate the file's few bad channels to produce a clean dataset
+    raw.set_montage(montage)
+    raw.info["bads"] = ["T10", "F8"]
+    raw.interpolate_bads()
 
-    # The eegbci data has non-standard channel names. We need to rename them:
-    eegbci.standardize(raw)
+    # Re-reference the data after interpolating bad channels
+    mne.set_eeg_reference(raw, 'average', copy=False, ch_type='eeg')
 
     return raw