def test_read_spm_ctf():
"""Test CTF reader with omitted samples."""
data_path = spm_face.data_path()
raw_fname = op.join(data_path, 'MEG', 'spm',
'SPM_CTF_MEG_example_faces1_3D.ds')
raw = read_raw_ctf(raw_fname)
extras = raw._raw_extras[0]
assert_equal(extras['n_samp'], raw.n_times)
assert_false(extras['n_samp'] == extras['n_samp_tot'])
def test_read_spm_ctf():
"""Test CTF reader with omitted samples."""
data_path = spm_face.data_path()
raw_fname = op.join(data_path, 'MEG', 'spm',
'SPM_CTF_MEG_example_faces1_3D.ds')
raw = read_raw_ctf(raw_fname)
extras = raw._raw_extras[0]
assert_equal(extras['n_samp'], raw.n_times)
assert_false(extras['n_samp'] == extras['n_samp_tot'])
def test_read_spm_ctf():
"""Test CTF reader with omitted samples."""
data_path = spm_face.data_path()
raw_fname = op.join(data_path, 'MEG', 'spm',
'SPM_CTF_MEG_example_faces1_3D.ds')
raw = read_raw_ctf(raw_fname)
extras = raw._raw_extras[0]
assert_equal(extras['n_samp'], raw.n_times)
assert extras['n_samp'] != extras['n_samp_tot']
# Test that LPA, nasion and RPA are correct.
coord_frames = np.array([d['coord_frame'] for d in raw.info['dig']])
assert np.all(coord_frames == FIFF.FIFFV_COORD_HEAD)
cardinals = {d['ident']: d['r'] for d in raw.info['dig']}
assert cardinals[1][0] < cardinals[2][0] < cardinals[3][0] # x coord
assert cardinals[1][1] < cardinals[2][1] # y coord
assert cardinals[3][1] < cardinals[2][1] # y coord
for key in cardinals.keys():
assert_allclose(cardinals[key][2], 0, atol=1e-6) # z coord
def test_read_spm_ctf():
"""Test CTF reader with omitted samples."""
data_path = spm_face.data_path()
raw_fname = op.join(data_path, 'MEG', 'spm',
'SPM_CTF_MEG_example_faces1_3D.ds')
raw = read_raw_ctf(raw_fname)
extras = raw._raw_extras[0]
assert_equal(extras['n_samp'], raw.n_times)
assert extras['n_samp'] != extras['n_samp_tot']
# Test that LPA, nasion and RPA are correct.
coord_frames = np.array([d['coord_frame'] for d in raw.info['dig']])
assert np.all(coord_frames == FIFF.FIFFV_COORD_HEAD)
cardinals = {d['ident']: d['r'] for d in raw.info['dig']}
assert cardinals[1][0] < cardinals[2][0] < cardinals[3][0] # x coord
assert cardinals[1][1] < cardinals[2][1] # y coord
assert cardinals[3][1] < cardinals[2][1] # y coord
for key in cardinals.keys():
assert_allclose(cardinals[key][2], 0, atol=1e-6) # z coord
import os.path as op
import numpy as np
from scipy.misc import imread
import matplotlib.pyplot as plt
import mne
from mne import io
from mne.datasets import spm_face
from mne.minimum_norm import apply_inverse, make_inverse_operator
from mne.cov import compute_covariance
##############################################################################
# Get data
data_path = spm_face.data_path()
subjects_dir = data_path + '/subjects'
raw_fname = data_path + '/MEG/spm/SPM_CTF_MEG_example_faces%d_3D_raw.fif'
raw = io.Raw(raw_fname % 1, preload=True) # Take first run
picks = mne.pick_types(raw.info, meg=True, exclude='bads')
raw.filter(1, 30, method='iir', n_jobs=1)
events = mne.find_events(raw, stim_channel='UPPT001')
event_ids = {"faces": 1, "scrambled": 2}
tmin, tmax = -0.2, 0.5
baseline = None # no baseline as high-pass is applied
reject = dict(mag=3e-12)
import mne
from mne.io import read_raw_fif, read_raw_ctf, read_raw_bti, read_raw_kit
from mne.io import read_raw_artemis123
from mne.datasets import sample, spm_face, testing
from mne.viz import plot_alignment, set_3d_title
print(__doc__)
bti_path = op.abspath(op.dirname(mne.__file__)) + '/io/bti/tests/data/'
kit_path = op.abspath(op.dirname(mne.__file__)) + '/io/kit/tests/data/'
raws = {
'Neuromag':
read_raw_fif(sample.data_path() + '/MEG/sample/sample_audvis_raw.fif'),
'CTF 275':
read_raw_ctf(spm_face.data_path() +
'/MEG/spm/SPM_CTF_MEG_example_faces1_3D.ds'),
'Magnes 3600wh':
read_raw_bti(op.join(bti_path, 'test_pdf_linux'),
op.join(bti_path, 'test_config_linux'),
op.join(bti_path, 'test_hs_linux')),
'KIT':
read_raw_kit(op.join(kit_path, 'test.sqd')),
'Artemis123':
read_raw_artemis123(
op.join(testing.data_path(), 'ARTEMIS123',
'Artemis_Data_2017-04-14-10h-38m-59s_Phantom_1k_HPI_1s.bin')),
}
for system, raw in sorted(raws.items()):
meg = ['helmet', 'sensors']
_record_warnings)
from mne.datasets import testing, spm_face, brainstorm
from mne.io.constants import FIFF
ctf_dir = testing.data_path(download=False) / 'CTF'
ctf_fname_continuous = 'testdata_ctf.ds'
ctf_fname_1_trial = 'testdata_ctf_short.ds'
ctf_fname_2_trials = 'testdata_ctf_pseudocontinuous.ds'
ctf_fname_discont = 'testdata_ctf_short_discontinuous.ds'
ctf_fname_somato = 'somMDYO-18av.ds'
ctf_fname_catch = 'catch-alp-good-f.ds'
somato_fname = op.join(
brainstorm.bst_raw.data_path(download=False), 'MEG', 'bst_raw',
'subj001_somatosensory_20111109_01_AUX-f.ds'
)
spm_path = spm_face.data_path(download=False)
block_sizes = {
ctf_fname_continuous: 12000,
ctf_fname_1_trial: 4801,
ctf_fname_2_trials: 12000,
ctf_fname_discont: 1201,
ctf_fname_somato: 313,
ctf_fname_catch: 2500,
}
single_trials = (
ctf_fname_continuous,
ctf_fname_1_trial,
)
ctf_fnames = tuple(sorted(block_sizes.keys()))
import os.path as op
import mne
from mne.io import read_raw_fif, read_raw_ctf, read_raw_bti, read_raw_kit
from mne.io import read_raw_artemis123
from mne.datasets import sample, spm_face, testing
from mne.viz import plot_alignment, set_3d_title
print(__doc__)
bti_path = op.abspath(op.dirname(mne.__file__)) + '/io/bti/tests/data/'
kit_path = op.abspath(op.dirname(mne.__file__)) + '/io/kit/tests/data/'
raws = {
'Neuromag': read_raw_fif(sample.data_path() +
'/MEG/sample/sample_audvis_raw.fif'),
'CTF 275': read_raw_ctf(spm_face.data_path() +
'/MEG/spm/SPM_CTF_MEG_example_faces1_3D.ds'),
'Magnes 3600wh': read_raw_bti(op.join(bti_path, 'test_pdf_linux'),
op.join(bti_path, 'test_config_linux'),
op.join(bti_path, 'test_hs_linux')),
'KIT': read_raw_kit(op.join(kit_path, 'test.sqd')),
'Artemis123': read_raw_artemis123(op.join(
testing.data_path(), 'ARTEMIS123',
'Artemis_Data_2017-04-14-10h-38m-59s_Phantom_1k_HPI_1s.bin')),
}
for system, raw in sorted(raws.items()):
meg = ['helmet', 'sensors']
# We don't have coil definitions for KIT refs, so exclude them
if system != 'KIT':
meg.append('ref')
# %%
# Neuromag
# --------
kwargs = dict(eeg=False, coord_frame='meg', show_axes=True, verbose=True)
raw = read_raw_fif(sample.data_path() / 'MEG' / 'sample' /
'sample_audvis_raw.fif')
fig = plot_alignment(raw.info, meg=('helmet', 'sensors'), **kwargs)
set_3d_title(figure=fig, title='Neuromag')
# %%
# CTF
# ---
raw = read_raw_ctf(spm_face.data_path() / 'MEG' / 'spm' /
'SPM_CTF_MEG_example_faces1_3D.ds')
fig = plot_alignment(raw.info, meg=('helmet', 'sensors', 'ref'), **kwargs)
set_3d_title(figure=fig, title='CTF 275')
# %%
# BTi
# ---
bti_path = op.abspath(op.dirname(mne.__file__)) + '/io/bti/tests/data/'
raw = read_raw_bti(op.join(bti_path, 'test_pdf_linux'),
op.join(bti_path, 'test_config_linux'),
op.join(bti_path, 'test_hs_linux'))
fig = plot_alignment(raw.info, meg=('helmet', 'sensors', 'ref'), **kwargs)
set_3d_title(figure=fig, title='Magnes 3600wh')
