[MRG] add ieeg tests

mne_bids/io.py

@@ -17,7 +17,16 @@
                           '.cnt',  # Neuroscan
                           ]
 
-ALLOWED_EXTENSIONS = allowed_extensions_meg + allowed_extensions_eeg
+allowed_extensions_ieeg = ['.vhdr',  # BrainVision, accompanied by .vmrk, .eeg
+                           '.edf',  # European Data Format
+                           '.set',  # EEGLAB, potentially accompanied by .fdt
+                           '.mef',  # MEF: Multiscale Electrophysiology File
+                           '.nwb',  # Neurodata without borders
+                           ]
+
+ALLOWED_EXTENSIONS = (allowed_extensions_meg +
+                      allowed_extensions_eeg +
+                      allowed_extensions_ieeg)
 
 reader = {'.con': io.read_raw_kit, '.sqd': io.read_raw_kit,
           '.fif': io.read_raw_fif, '.pdf': io.read_raw_bti,
@@ -43,8 +52,6 @@ def _read_raw(raw_fname, electrode=None, hsp=None, hpi=None, config=None,
     """Read a raw file into MNE, making inferences based on extension."""
     fname, ext = _parse_ext(raw_fname)
 
-    # MEG File Types
-    # --------------
     # KIT systems
     if ext in ['.con', '.sqd']:
         raw = io.read_raw_kit(raw_fname, elp=electrode, hsp=hsp,
@@ -65,9 +72,15 @@ def _read_raw(raw_fname, electrode=None, hsp=None, hpi=None, config=None,
     elif ext in ['.edf', '.bdf']:
         raw = reader[ext](raw_fname, preload=True)
 
+    # MEF and NWB are allowed, but not yet implemented
+    elif ext in ['.mef', '.nwb']:
+        raise ValueError('Got "{}" as extension. This is an allowed extension '
+                         'but there is no IO support for this file format yet.'
+                         .format(ext))
+
     # No supported data found ...
     # ---------------------------
     else:
-        raise ValueError("Raw file name extension must be one of %\n"
-                         "Got %" % (ALLOWED_EXTENSIONS, ext))
+        raise ValueError('Raw file name extension must be one of {}\n'
+                         'Got {}'.format(ALLOWED_EXTENSIONS, ext))
     return raw

mne_bids/mne_bids.py

@@ -52,19 +52,24 @@
                      '.meg4': 'CTF'}
 
 eeg_manufacturers = {'.vhdr': 'BrainProducts', '.eeg': 'BrainProducts',
-                     '.edf': 'Mixed', '.bdf': 'Biosemi', '.set': 'Mixed',
-                     '.fdt': 'Mixed', '.cnt': 'Neuroscan'}
+                     '.edf': 'n/a', '.bdf': 'Biosemi', '.set': 'n/a',
+                     '.fdt': 'n/a', '.cnt': 'Neuroscan'}
+
+ieeg_manufacturers = {'.vhdr': 'BrainProducts', '.eeg': 'BrainProducts',
+                      '.edf': 'n/a', '.set': 'n/a', '.fdt': 'n/a',
+                      '.mef': 'n/a', '.nwb': 'n/a'}
 
 # Merge the manufacturer dictionaries in a python2 / python3 compatible way
 MANUFACTURERS = dict()
 MANUFACTURERS.update(meg_manufacturers)
 MANUFACTURERS.update(eeg_manufacturers)
+MANUFACTURERS.update(ieeg_manufacturers)
 
 # List of synthetic channels by manufacturer that are to be excluded from the
 # channel list. Currently this is only for stimulus channels.
 IGNORED_CHANNELS = {'KIT/Yokogawa': ['STI 014'],
                     'BrainProducts': ['STI 014'],
-                    'Mixed': ['STI 014'],
+                    'n/a': ['STI 014'],  # for unknown manufacturers, ignore it
                     'Biosemi': ['STI 014'],
                     'Neuroscan': ['STI 014']}
 
@@ -135,10 +140,10 @@ def _channels_tsv(raw, fname, overwrite=False, verbose=True):
                       ('name', raw.info['ch_names']),
                       ('type', ch_type),
                       ('units', units),
-                      ('description', description),
-                      ('sampling_frequency', np.full((n_channels), sfreq)),
                       ('low_cutoff', np.full((n_channels), low_cutoff)),
                       ('high_cutoff', np.full((n_channels), high_cutoff)),
+                      ('description', description),
+                      ('sampling_frequency', np.full((n_channels), sfreq)),
                       ('status', status)]))
     df.drop(ignored_indexes, inplace=True)
 
@@ -486,6 +491,7 @@ def _sidecar_json(raw, task, manufacturer, fname, kind, overwrite=False,
         ('EEGPlacementScheme', _infer_eeg_placement_scheme(raw)),
         ('Manufacturer', manufacturer)]
     ch_info_json_ieeg = [
+        ('iEEGReference', 'n/a'),
         ('ECOGChannelCount', n_ecogchan),
         ('SEEGChannelCount', n_seegchan)]
     ch_info_ch_counts = [
@@ -646,8 +652,6 @@ def write_raw_bids(raw, bids_basename, output_path, events_data=None,
     orient = ORIENTATION.get(ext, 'n/a')
     unit = UNITS.get(ext, 'n/a')
     manufacturer = MANUFACTURERS.get(ext, 'n/a')
-    if manufacturer == 'Mixed':
-        manufacturer = 'n/a'
 
     # save all meta data
     _participants_tsv(raw, subject_id, "n/a", participants_fname, overwrite,
@@ -700,9 +704,7 @@ def write_raw_bids(raw, bids_basename, output_path, events_data=None,
                     'Please contact MNE developers if you have '
                     'any questions.')
         else:
-            # TODO insert arg `split_naming=split_naming`
-            #      when MNE releases 0.17
-            raw.save(bids_fname, overwrite=True)
+            raw.save(bids_fname, overwrite=True, split_naming='bids')
 
     # CTF data is saved in a directory
     elif ext == '.ds':

mne_bids/tests/test_io.py

@@ -2,8 +2,11 @@
 # Authors: Stefan Appelhoff <stefan.appelhoff@mailbox.org>
 #
 # License: BSD (3-clause)
+import os.path as op
 import pytest
 
+from mne.utils import _TempDir
+
 from mne_bids.io import _parse_ext, _read_raw
 
 
@@ -25,5 +28,17 @@ def test_read_raw():
     """Test the raw reading."""
     # Use a file ending that does not exist
     f = 'file.bogus'
-    with pytest.raises(ValueError):
+    with pytest.raises(ValueError, match='file name extension must be one of'):
         _read_raw(f)
+
+
+def test_not_implemented():
+    """Test the not yet implemented data formats raise an adequate error."""
+    for not_implemented_ext in ['.mef', '.nwb']:
+        data_path = _TempDir()
+        raw_fname = op.join(data_path, 'test' + not_implemented_ext)
+        with open(raw_fname, 'w'):
+            pass
+        with pytest.raises(ValueError, match=('there is no IO support for '
+                                              'this file format yet')):
+            _read_raw(raw_fname)

mne_bids/tests/test_mne_bids.py

@@ -45,8 +45,6 @@
     shell = True
 
 
-# MEG Tests
-# ---------
 def test_fif():
     """Test functionality of the write_raw_bids conversion for fif."""
     output_path = _TempDir()
@@ -184,8 +182,6 @@ def test_bti():
     run_subprocess(cmd, shell=shell)
 
 
-# EEG Tests
-# ---------
 def test_vhdr():
     """Test write_raw_bids conversion for BrainVision data."""
     output_path = _TempDir()
@@ -205,6 +201,16 @@ def test_vhdr():
                                   overwrite=True)
     assert len([f for f in os.listdir(data_path) if op.isfile(f)]) == 0
 
+    # Also cover iEEG
+    # We use the same data and pretend that eeg channels are ecog
+    raw.set_channel_types({raw.ch_names[i]: 'ecog'
+                           for i in mne.pick_types(raw.info, eeg=True)})
+    output_path = _TempDir()
+    write_raw_bids(raw, bids_basename, output_path, overwrite=False)
+
+    cmd = ['bids-validator', '--bep010', output_path]
+    run_subprocess(cmd, shell=shell)
+
 
 def test_edf():
     """Test write_raw_bids conversion for European Data Format data."""
@@ -244,6 +250,16 @@ def test_edf():
     df = pd.read_csv(scans_tsv, sep='\t')
     assert df.shape[0] == 2
 
+    # Also cover iEEG
+    # We use the same data and pretend that eeg channels are ecog
+    raw.set_channel_types({raw.ch_names[i]: 'ecog'
+                           for i in mne.pick_types(raw.info, eeg=True)})
+    output_path = _TempDir()
+    write_raw_bids(raw, bids_basename, output_path)
+
+    cmd = ['bids-validator', '--bep010', output_path]
+    run_subprocess(cmd, shell=shell)
+
 
 def test_bdf():
     """Test write_raw_bids conversion for Biosemi data."""
@@ -283,6 +299,16 @@ def test_set():
     cmd = ['bids-validator', '--bep006', output_path]
     run_subprocess(cmd, shell=shell)
 
+    # Also cover iEEG
+    # We use the same data and pretend that eeg channels are ecog
+    raw.set_channel_types({raw.ch_names[i]: 'ecog'
+                           for i in mne.pick_types(raw.info, eeg=True)})
+    output_path = _TempDir()
+    write_raw_bids(raw, bids_basename, output_path)
+
+    cmd = ['bids-validator', '--bep010', output_path]
+    run_subprocess(cmd, shell=shell)
+
 
 def test_cnt():
     """Test write_raw_bids conversion for Neuroscan data."""

mne_bids/tests/test_utils.py

@@ -9,18 +9,48 @@
 from datetime import datetime
 
 from scipy.io import savemat
+from numpy.random import random
 import mne
 from mne.datasets import testing
 from mne.utils import _TempDir
 
 from mne_bids.utils import (make_bids_folders, make_bids_basename,
                             _check_types, print_dir_tree, _age_on_date,
                             _get_brainvision_paths, copyfile_brainvision,
-                            copyfile_eeglab, _infer_eeg_placement_scheme)
+                            copyfile_eeglab, _infer_eeg_placement_scheme,
+                            _handle_kind)
 
 base_path = op.join(op.dirname(mne.__file__), 'io')
 
 
+def test_handle_kind():
+    """Test the automatic extraction of kind from the data."""
+    # Create a dummy raw
+    n_channels = 1
+    sampling_rate = 100
+    data = random((n_channels, sampling_rate))
+    channel_types = ['grad', 'eeg', 'ecog']
+    expected_kinds = ['meg', 'eeg', 'ieeg']
+    # do it once for each type ... and once for "no type"
+    for chtype, kind in zip(channel_types, expected_kinds):
+        info = mne.create_info(n_channels, sampling_rate, ch_types=[chtype])
+        raw = mne.io.RawArray(data, info)
+        assert _handle_kind(raw) == kind
+
+    # if the situation is ambiguous (EEG and iEEG channels both), raise error
+    with pytest.raises(ValueError, match='Both EEG and iEEG channels found'):
+        info = mne.create_info(2, sampling_rate,
+                               ch_types=['eeg', 'ecog'])
+        raw = mne.io.RawArray(random((2, sampling_rate)), info)
+        _handle_kind(raw)
+
+    # if we cannot find a proper channel type, we raise an error
+    with pytest.raises(ValueError, match='Neither MEG/EEG/iEEG channels'):
+        info = mne.create_info(n_channels, sampling_rate, ch_types=['misc'])
+        raw = mne.io.RawArray(data, info)
+        _handle_kind(raw)
+
+
 def test_print_dir_tree():
     """Test printing a dir tree."""
     with pytest.raises(ValueError):

mne_bids/utils.py

@@ -88,10 +88,14 @@ def _handle_kind(raw):
     """Get kind."""
     if 'meg' in raw:
         kind = 'meg'
-    elif 'eeg' in raw:
-        kind = 'eeg'
-    elif 'ecog' in raw:
+    elif 'ecog' in raw and 'eeg' not in raw:
         kind = 'ieeg'
+    elif 'eeg' in raw and 'ecog' not in raw:
+        kind = 'eeg'
+    elif 'eeg' in raw and 'ecog' in raw:
+        raise ValueError('Both EEG and iEEG channels found in data.'
+                         'There is currently no specification on how'
+                         'to handle this data. Please proceed manually.')
     else:
         raise ValueError('Neither MEG/EEG/iEEG channels found in data.'
                          'Please use raw.set_channel_types to set the '