# License: BSD (3-clause)
import os.path as op
import numpy as np
import matplotlib.pyplot as plt
import mne
from mne import fit_dipole
from mne.datasets.brainstorm import bst_phantom_ctf
from mne.io import read_raw_ctf
print(__doc__)
###############################################################################
# The data were collected with a CTF system at 2400 Hz.
data_path = bst_phantom_ctf.data_path()
# Switch to these to use the higher-SNR data:
# raw_path = op.join(data_path, 'phantom_200uA_20150709_01.ds')
# dip_freq = 7.
raw_path = op.join(data_path, 'phantom_20uA_20150603_03.ds')
dip_freq = 23.
erm_path = op.join(data_path, 'emptyroom_20150709_01.ds')
raw = read_raw_ctf(raw_path, preload=True)
###############################################################################
# The sinusoidal signal is generated on channel HDAC006, so we can use
# that to obtain precise timing.
sinusoid, times = raw[raw.ch_names.index('HDAC006-4408')]
plt.figure()
fname.add('target_path', target_path) # Where to put everything
fname.add('fwd_discrete_true', '{data_path}/sample_audvis-meg-vol-7-discrete-fwd.fif') # noqa
fname.add('fwd_discrete_man', '{data_path}/sample_coregerror-meg-vol-7-discrete-fwd.fif') # noqa
fname.add('simulated_raw', '{target_path}/volume_simulated-raw-vertex{vertex:04d}-raw.fif') # noqa
fname.add('stc_signal', '{target_path}/volume_stc_signal-vertex{vertex:04d}-vl.stc') # noqa
fname.add('simulated_events', '{target_path}/volume_simulated-eve.fif')
fname.add('simulated_epochs', '{target_path}/volume_simulated-epochs-vertex{vertex:04d}-epo.fif') # noqa
fname.add('report', '{target_path}/volume_report-vertex{vertex:04d}.h5')
fname.add('report_html', '{target_path}/volume_report-vertex{vertex:04d}.html')
# no backwards compatability in naming:
if user == 'we' and args.noise == 0.0:
fname.add('lcmv_results', '{target_path}/lcmv_results/lcmv_results-vertex{vertex:04d}.csv') # noqa
fname.add('lcmv_results_2s', '{target_path}/lcmv_results/lcmv_results-2sources-vertex{vertex:04d}.csv') # noqa
fname.add('dics_results', '{target_path}/dics_results/dics_results-vertex{vertex:04d}.csv') # noqa
fname.add('dics_results_2s', '{target_path}/dics_results/dics_results-2sources-vertex{vertex:04d}.csv') # noqa
else:
fname.add('lcmv_results', '{target_path}/lcmv_results/lcmv_results-vertex{vertex:04d}-noise{noise:.1f}.csv') # noqa
fname.add('lcmv_results_2s', '{target_path}/lcmv_results/lcmv_results-2sources-vertex{vertex:04d}-noise{noise:.1f}.csv') # noqa
fname.add('dics_results', '{target_path}/dics_results/dics_results-vertex{vertex:04d}-noise{noise:.1f}.csv') # noqa
fname.add('dics_results_2s', '{target_path}/dics_results/dics_results-2sources-vertex{vertex:04d}-noise{noise:.1f}.csv') # noqa
# Brainstorm phantom data
phantom_fname = FileNames()
phantom_fname.add('data_path', bst_phantom_ctf.data_path())
phantom_fname.add('raw', '{data_path}/phantom_20uA_20150603_03.ds')
phantom_fname.add('ernoise', '{data_path}/emptyroom_20150709_01.ds')
# Set subjects_dir
os.environ['SUBJECTS_DIR'] = fname.subjects_dir
# License: BSD (3-clause)
import os.path as op
import numpy as np
import matplotlib.pyplot as plt
import mne
from mne import fit_dipole
from mne.datasets.brainstorm import bst_phantom_ctf
from mne.io import read_raw_ctf
print(__doc__)
###############################################################################
# The data were collected with a CTF system at 2400 Hz.
data_path = bst_phantom_ctf.data_path()
# Switch to these to use the higher-SNR data:
# raw_path = op.join(data_path, 'phantom_200uA_20150709_01.ds')
# dip_freq = 7.
raw_path = op.join(data_path, 'phantom_20uA_20150603_03.ds')
dip_freq = 23.
erm_path = op.join(data_path, 'emptyroom_20150709_01.ds')
raw = read_raw_ctf(raw_path, preload=True)
###############################################################################
# The sinusoidal signal is generated on channel HDAC006, so we can use
# that to obtain precise timing.
sinusoid, times = raw[raw.ch_names.index('HDAC006-4408')]
plt.figure()
# License: BSD (3-clause)
import os.path as op
import numpy as np
import matplotlib.pyplot as plt
import mne
from mne import fit_dipole
from mne.datasets.brainstorm import bst_phantom_ctf
from mne.io import read_raw_ctf
print(__doc__)
###############################################################################
# The data were collected with a CTF system at 2400 Hz.
data_path = bst_phantom_ctf.data_path(verbose=True)
# Switch to these to use the higher-SNR data:
# raw_path = op.join(data_path, 'phantom_200uA_20150709_01.ds')
# dip_freq = 7.
raw_path = op.join(data_path, 'phantom_20uA_20150603_03.ds')
dip_freq = 23.
erm_path = op.join(data_path, 'emptyroom_20150709_01.ds')
raw = read_raw_ctf(raw_path, preload=True)
###############################################################################
# The sinusoidal signal is generated on channel HDAC006, so we can use
# that to obtain precise timing.
sinusoid, times = raw[raw.ch_names.index('HDAC006-4408')]
plt.figure()
# License: BSD (3-clause)
import os.path as op
import numpy as np
import matplotlib.pyplot as plt
import mne
from mne import fit_dipole
from mne.datasets.brainstorm import bst_phantom_ctf
from mne.io import read_raw_ctf
print(__doc__)
###############################################################################
# The data were collected with a CTF system at 2400 Hz.
data_path = bst_phantom_ctf.data_path(verbose=True)
# Switch to these to use the higher-SNR data:
# raw_path = op.join(data_path, 'phantom_200uA_20150709_01.ds')
# dip_freq = 7.
raw_path = op.join(data_path, 'phantom_20uA_20150603_03.ds')
dip_freq = 23.
erm_path = op.join(data_path, 'emptyroom_20150709_01.ds')
raw = read_raw_ctf(raw_path, preload=True)
###############################################################################
# The sinusoidal signal is generated on channel HDAC006, so we can use
# that to obtain precise timing.
sinusoid, times = raw[raw.ch_names.index('HDAC006-4408')]
plt.figure()