def test_proj():
"""Test SSP proj operations
"""
for proj in [True, False]:
raw = Raw(fif_fname, preload=False, proj=proj)
assert_true(all(p['active'] == proj for p in raw.info['projs']))
data, times = raw[0:2, :]
data1, times1 = raw[0:2]
assert_array_equal(data, data1)
assert_array_equal(times, times1)
# test adding / deleting proj
if proj:
assert_raises(ValueError, raw.add_proj, [],
{'remove_existing': True})
assert_raises(ValueError, raw.del_proj, 0)
else:
projs = deepcopy(raw.info['projs'])
n_proj = len(raw.info['projs'])
raw.del_proj(0)
assert_true(len(raw.info['projs']) == n_proj - 1)
raw.add_proj(projs, remove_existing=False)
assert_true(len(raw.info['projs']) == 2 * n_proj - 1)
raw.add_proj(projs, remove_existing=True)
assert_true(len(raw.info['projs']) == n_proj)
# test apply_proj() with and without preload
for preload in [True, False]:
raw = Raw(fif_fname, preload=preload, proj=False)
data, times = raw[:, 0:2]
raw.apply_proj()
data_proj_1 = np.dot(raw._projector, data)
# load the file again without proj
raw = Raw(fif_fname, preload=preload, proj=False)
# write the file with proj. activated, make sure proj has been applied
raw.save(op.join(tempdir, 'raw.fif'), proj=True, overwrite=True)
raw2 = Raw(op.join(tempdir, 'raw.fif'), proj=False)
data_proj_2, _ = raw2[:, 0:2]
assert_allclose(data_proj_1, data_proj_2)
assert_true(all(p['active'] for p in raw2.info['projs']))
# read orig file with proj. active
raw2 = Raw(fif_fname, preload=preload, proj=True)
data_proj_2, _ = raw2[:, 0:2]
assert_allclose(data_proj_1, data_proj_2)
assert_true(all(p['active'] for p in raw2.info['projs']))
# test that apply_proj works
raw.apply_proj()
data_proj_2, _ = raw[:, 0:2]
assert_allclose(data_proj_1, data_proj_2)
assert_allclose(data_proj_2, np.dot(raw._projector, data_proj_2))
def test_proj():
"""Test SSP proj operations
"""
for proj in [True, False]:
raw = Raw(fif_fname, preload=False, proj=proj)
assert_true(all(p['active'] == proj for p in raw.info['projs']))
data, times = raw[0:2, :]
data1, times1 = raw[0:2]
assert_array_equal(data, data1)
assert_array_equal(times, times1)
# test adding / deleting proj
if proj:
assert_raises(ValueError, raw.add_proj, [],
{'remove_existing': True})
assert_raises(ValueError, raw.del_proj, 0)
else:
projs = deepcopy(raw.info['projs'])
n_proj = len(raw.info['projs'])
raw.del_proj(0)
assert_true(len(raw.info['projs']) == n_proj - 1)
raw.add_proj(projs, remove_existing=False)
assert_true(len(raw.info['projs']) == 2 * n_proj - 1)
raw.add_proj(projs, remove_existing=True)
assert_true(len(raw.info['projs']) == n_proj)
# test apply_proj() with and without preload
for preload in [True, False]:
raw = Raw(fif_fname, preload=preload, proj=False)
data, times = raw[:, 0:2]
raw.apply_proj()
data_proj_1 = np.dot(raw._projector, data)
# load the file again without proj
raw = Raw(fif_fname, preload=preload, proj=False)
# write the file with proj. activated, make sure proj has been applied
raw.save(op.join(tempdir, 'raw.fif'), proj=True, overwrite=True)
raw2 = Raw(op.join(tempdir, 'raw.fif'), proj=False)
data_proj_2, _ = raw2[:, 0:2]
assert_allclose(data_proj_1, data_proj_2)
assert_true(all(p['active'] for p in raw2.info['projs']))
# read orig file with proj. active
raw2 = Raw(fif_fname, preload=preload, proj=True)
data_proj_2, _ = raw2[:, 0:2]
assert_allclose(data_proj_1, data_proj_2)
assert_true(all(p['active'] for p in raw2.info['projs']))
# test that apply_proj works
raw.apply_proj()
data_proj_2, _ = raw[:, 0:2]
assert_allclose(data_proj_1, data_proj_2)
assert_allclose(data_proj_2, np.dot(raw._projector, data_proj_2))
def test_rank_estimation():
"""Test raw rank estimation
"""
raw = Raw(fif_fname)
n_meg = len(pick_types(raw.info, meg=True, eeg=False, exclude="bads"))
n_eeg = len(pick_types(raw.info, meg=False, eeg=True, exclude="bads"))
raw = Raw(fif_fname, preload=True)
assert_array_equal(raw.estimate_rank(), n_meg + n_eeg)
raw = Raw(fif_fname, preload=False)
raw.apply_proj()
n_proj = len(raw.info["projs"])
assert_array_equal(raw.estimate_rank(tstart=10, tstop=20), n_meg + n_eeg - n_proj)
def test_rank_estimation():
"""Test raw rank estimation
"""
raw = Raw(fif_fname)
n_meg = len(pick_types(raw.info, meg=True, eeg=False, exclude='bads'))
n_eeg = len(pick_types(raw.info, meg=False, eeg=True, exclude='bads'))
raw = Raw(fif_fname, preload=True)
assert_array_equal(raw.estimate_rank(), n_meg + n_eeg)
raw = Raw(fif_fname, preload=False)
raw.apply_proj()
n_proj = len(raw.info['projs'])
assert_array_equal(raw.estimate_rank(tstart=10, tstop=20),
n_meg + n_eeg - n_proj)
import matplotlib.pyplot as plt
import numpy as np
import mne
from mne.fiff import Raw
from mne.preprocessing.ica import ICA
from mne.datasets import sample
###############################################################################
# Setup paths and prepare epochs data
data_path = sample.data_path()
raw_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw.fif'
raw = Raw(raw_fname, preload=True)
raw.apply_proj()
picks = mne.fiff.pick_types(raw.info, meg=True, eeg=False, eog=True,
ecg=True, stim=False, exclude='bads')
tmin, tmax, event_id = -0.2, 0.5, 1
baseline = (None, 0)
reject = None
events = mne.find_events(raw, stim_channel='STI 014')
epochs = mne.Epochs(raw, events, event_id, tmin, tmax, proj=False, picks=picks,
baseline=baseline, preload=True, reject=reject)
random_state = np.random.RandomState(42)
###############################################################################
import matplotlib.pyplot as plt
import numpy as np
import mne
from mne.fiff import Raw
from mne.preprocessing.ica import ICA
from mne.datasets import sample
###############################################################################
# Setup paths and prepare epochs data
data_path = sample.data_path()
raw_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw.fif'
raw = Raw(raw_fname, preload=True)
raw.apply_proj()
picks = mne.fiff.pick_types(raw.info,
meg=True,
eeg=False,
eog=True,
ecg=True,
stim=False,
exclude='bads')
tmin, tmax, event_id = -0.2, 0.5, 1
baseline = (None, 0)
reject = None
events = mne.find_events(raw, stim_channel='STI 014')
epochs = mne.Epochs(raw,
