# 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()