def test_edf_stim_ch_pick_up(test_input, EXPECTED):
"""Test stim_channel."""
# This is fragile for EEG/EEG-CSD, so just omit csd
KIND_DICT = get_channel_type_constants()
TYPE_LUT = {v['kind']: k for k, v in KIND_DICT.items() if k not in
('csd', 'chpi')} # chpi not needed, and unhashable (a list)
fname = op.join(data_dir, 'test_stim_channel.edf')
raw = read_raw_edf(fname, stim_channel=test_input)
ch_types = {ch['ch_name']: TYPE_LUT[ch['kind']] for ch in raw.info['chs']}
assert ch_types == EXPECTED
def _channel_type_old(info, idx):
"""Get channel type using old, slower scheme."""
ch = info['chs'][idx]
# iterate through all defined channel types until we find a match with ch
# go in order from most specific (most rules entries) to least specific
channel_types = sorted(
get_channel_type_constants().items(), key=lambda x: len(x[1]))[::-1]
for t, rules in channel_types:
for key, vals in rules.items(): # all keys must match the values
if ch.get(key, None) not in np.array(vals):
break # not channel type t, go to next iteration
else:
return t
raise ValueError('Unknown channel type for {}'.format(ch["ch_name"]))
def test_array_raw():
"""Test creating raw from array."""
# creating
raw = read_raw_fif(fif_fname).crop(2, 5)
data, times = raw[:, :]
sfreq = raw.info['sfreq']
ch_names = [(ch[4:] if 'STI' not in ch else ch)
for ch in raw.info['ch_names']] # change them, why not
types = list()
for ci in range(101):
types.extend(('grad', 'grad', 'mag'))
types.extend(['ecog', 'seeg', 'hbo']) # really 4 meg channels
types.extend(['stim'] * 9)
types.extend(['dbs']) # really eeg channel
types.extend(['eeg'] * 60)
picks = np.concatenate([
pick_types(raw.info, meg=True)[::20],
pick_types(raw.info, meg=False, stim=True),
pick_types(raw.info, meg=False, eeg=True)[::20]
])
del raw
data = data[picks]
ch_names = np.array(ch_names)[picks].tolist()
types = np.array(types)[picks].tolist()
types.pop(-1)
# wrong length
pytest.raises(ValueError, create_info, ch_names, sfreq, types)
# bad entry
types.append('foo')
pytest.raises(KeyError, create_info, ch_names, sfreq, types)
types[-1] = 'eog'
# default type
info = create_info(ch_names, sfreq)
assert_equal(info['chs'][0]['kind'],
get_channel_type_constants()['misc']['kind'])
# use real types
info = create_info(ch_names, sfreq, types)
raw2 = _test_raw_reader(RawArray,
test_preloading=False,
data=data,
info=info,
first_samp=2 * data.shape[1])
data2, times2 = raw2[:, :]
assert_allclose(data, data2)
assert_allclose(times, times2)
assert ('RawArray' in repr(raw2))
pytest.raises(TypeError, RawArray, info, data)
# filtering
picks = pick_types(raw2.info, meg=True, misc=True, exclude='bads')[:4]
assert_equal(len(picks), 4)
raw_lp = raw2.copy()
kwargs = dict(fir_design='firwin', picks=picks)
raw_lp.filter(None, 4.0, h_trans_bandwidth=4., **kwargs)
raw_hp = raw2.copy()
raw_hp.filter(16.0, None, l_trans_bandwidth=4., **kwargs)
raw_bp = raw2.copy()
raw_bp.filter(8.0,
12.0,
l_trans_bandwidth=4.,
h_trans_bandwidth=4.,
**kwargs)
raw_bs = raw2.copy()
raw_bs.filter(16.0,
4.0,
l_trans_bandwidth=4.,
h_trans_bandwidth=4.,
**kwargs)
data, _ = raw2[picks, :]
lp_data, _ = raw_lp[picks, :]
hp_data, _ = raw_hp[picks, :]
bp_data, _ = raw_bp[picks, :]
bs_data, _ = raw_bs[picks, :]
sig_dec = 15
assert_array_almost_equal(data, lp_data + bp_data + hp_data, sig_dec)
assert_array_almost_equal(data, bp_data + bs_data, sig_dec)
# plotting
raw2.plot()
raw2.plot_psd(tmax=2., average=True, n_fft=1024, spatial_colors=False)
plt.close('all')
# epoching
events = find_events(raw2, stim_channel='STI 014')
events[:, 2] = 1
assert len(events) > 2
epochs = Epochs(raw2, events, 1, -0.2, 0.4, preload=True)
evoked = epochs.average()
assert_equal(evoked.nave, len(events) - 1)
# complex data
rng = np.random.RandomState(0)
data = rng.randn(1, 100) + 1j * rng.randn(1, 100)
raw = RawArray(data, create_info(1, 1000., 'eeg'))
assert_allclose(raw._data, data)
# Using digital montage to give MNI electrode coordinates
n_elec = 10
ts_size = 10000
Fs = 512.
ch_names = [str(i) for i in range(n_elec)]
ch_pos_loc = np.random.randint(60, size=(n_elec, 3)).tolist()
data = np.random.rand(n_elec, ts_size)
montage = make_dig_montage(ch_pos=dict(zip(ch_names, ch_pos_loc)),
coord_frame='head')
info = create_info(ch_names, Fs, 'ecog')
raw = RawArray(data, info)
raw.set_montage(montage)
raw.plot_psd(average=False) # looking for nonexistent layout
raw.plot_psd_topo()
# Authors: Clemens Brunner <*****@*****.**>
#
# License: BSD (3-clause)
from qtpy.QtWidgets import (QDialog, QVBoxLayout, QDialogButtonBox, QComboBox,
QAbstractItemView, QTableView, QStyledItemDelegate)
from qtpy.QtGui import QStandardItemModel, QStandardItem
from qtpy.QtCore import Qt, QSortFilterProxyModel, Slot
from mne.io.pick import channel_type, get_channel_type_constants
channel_types = [k.upper() for k in get_channel_type_constants().keys()]
class ChannelPropertiesDialog(QDialog):
def __init__(self, parent, info, title="Channel Properties"):
super().__init__(parent)
self.setWindowTitle(title)
self.model = QStandardItemModel(info["nchan"], 4)
self.model.setHorizontalHeaderLabels(["#", "Label", "Type", "Bad"])
for index, ch in enumerate(info["chs"]):
item = QStandardItem()
item.setData(index, Qt.DisplayRole)
item.setFlags(item.flags() & ~Qt.ItemIsEditable)
self.model.setItem(index, 0, item)
self.model.setItem(index, 1, QStandardItem(ch["ch_name"]))
kind = channel_type(info, index).upper()
self.model.setItem(index, 2, QStandardItem(str(kind)))
bad = QStandardItem()
bad.setData(ch["ch_name"] in info["bads"], Qt.UserRole)
