# Authors: Alexandre Gramfort # Denis A. Engemann # Eric Larson # # License: BSD-3-Clause from copy import deepcopy DEFAULTS = dict( color=dict(mag='darkblue', grad='b', eeg='k', eog='k', ecg='m', emg='k', ref_meg='steelblue', misc='k', stim='k', resp='k', chpi='k', exci='k', ias='k', syst='k', seeg='saddlebrown', dbs='seagreen', dipole='k', gof='k', bio='k', ecog='k', hbo='#AA3377', hbr='b', fnirs_cw_amplitude='k', fnirs_fd_ac_amplitude='k', fnirs_fd_phase='k', fnirs_od='k', csd='k', whitened='k', gsr='#666633', temperature='#663333'), si_units=dict(mag='T', grad='T/m', eeg='V', eog='V', ecg='V', emg='V', misc='AU', seeg='V', dbs='V', dipole='Am', gof='GOF', bio='V', ecog='V', hbo='M', hbr='M', ref_meg='T', fnirs_cw_amplitude='V', fnirs_fd_ac_amplitude='V', fnirs_fd_phase='rad', fnirs_od='V', csd='V/m²', whitened='Z', gsr='S', temperature='C'), units=dict(mag='fT', grad='fT/cm', eeg='µV', eog='µV', ecg='µV', emg='µV', misc='AU', seeg='mV', dbs='µV', dipole='nAm', gof='GOF', bio='µV', ecog='µV', hbo='µM', hbr='µM', ref_meg='fT', fnirs_cw_amplitude='V', fnirs_fd_ac_amplitude='V', fnirs_fd_phase='rad', fnirs_od='V', csd='mV/m²', whitened='Z', gsr='S', temperature='C'), # scalings for the units scalings=dict(mag=1e15, grad=1e13, eeg=1e6, eog=1e6, emg=1e6, ecg=1e6, misc=1.0, seeg=1e3, dbs=1e6, ecog=1e6, dipole=1e9, gof=1.0, bio=1e6, hbo=1e6, hbr=1e6, ref_meg=1e15, fnirs_cw_amplitude=1.0, fnirs_fd_ac_amplitude=1.0, fnirs_fd_phase=1., fnirs_od=1.0, csd=1e3, whitened=1., gsr=1., temperature=1.), # rough guess for a good plot scalings_plot_raw=dict(mag=1e-12, grad=4e-11, eeg=20e-6, eog=150e-6, ecg=5e-4, emg=1e-3, ref_meg=1e-12, misc='auto', stim=1, resp=1, chpi=1e-4, exci=1, ias=1, syst=1, seeg=1e-4, dbs=1e-4, bio=1e-6, ecog=1e-4, hbo=10e-6, hbr=10e-6, whitened=10., fnirs_cw_amplitude=2e-2, fnirs_fd_ac_amplitude=2e-2, fnirs_fd_phase=2e-1, fnirs_od=2e-2, csd=200e-4, dipole=1e-7, gof=1e2, gsr=1., temperature=0.1), scalings_cov_rank=dict(mag=1e12, grad=1e11, eeg=1e5, # ~100x scalings seeg=1e1, dbs=1e4, ecog=1e4, hbo=1e4, hbr=1e4), ylim=dict(mag=(-600., 600.), grad=(-200., 200.), eeg=(-200., 200.), misc=(-5., 5.), seeg=(-20., 20.), dbs=(-200., 200.), dipole=(-100., 100.), gof=(0., 1.), bio=(-500., 500.), ecog=(-200., 200.), hbo=(0, 20), hbr=(0, 20), csd=(-50., 50.)), titles=dict(mag='Magnetometers', grad='Gradiometers', eeg='EEG', eog='EOG', ecg='ECG', emg='EMG', misc='misc', seeg='sEEG', dbs='DBS', bio='BIO', dipole='Dipole', ecog='ECoG', hbo='Oxyhemoglobin', ref_meg='Reference Magnetometers', fnirs_cw_amplitude='fNIRS (CW amplitude)', fnirs_fd_ac_amplitude='fNIRS (FD AC amplitude)', fnirs_fd_phase='fNIRS (FD phase)', fnirs_od='fNIRS (OD)', hbr='Deoxyhemoglobin', gof='Goodness of fit', csd='Current source density', stim='Stimulus', gsr='Galvanic skin response', temperature='Temperature', ), mask_params=dict(marker='o', markerfacecolor='w', markeredgecolor='k', linewidth=0, markeredgewidth=1, markersize=4), coreg=dict( mri_fid_opacity=1.0, dig_fid_opacity=1.0, mri_fid_scale=5e-3, dig_fid_scale=8e-3, extra_scale=4e-3, eeg_scale=4e-3, eegp_scale=20e-3, eegp_height=0.1, ecog_scale=5e-3, seeg_scale=5e-3, dbs_scale=5e-3, fnirs_scale=5e-3, source_scale=5e-3, detector_scale=5e-3, hpi_scale=4e-3, head_color=(0.988, 0.89, 0.74), hpi_color=(1., 0., 1.), extra_color=(1., 1., 1.), meg_color=(0., 0.25, 0.5), ref_meg_color=(0.5, 0.5, 0.5), helmet_color=(0.0, 0.0, 0.6), eeg_color=(1., 0.596, 0.588), eegp_color=(0.839, 0.15, 0.16), ecog_color=(1., 1., 1.), dbs_color=(0.82, 0.455, 0.659), seeg_color=(1., 1., .3), fnirs_color=(1., .647, 0.), source_color=(1., .05, 0.), detector_color=(.3, .15, .15), lpa_color=(1., 0., 0.), nasion_color=(0., 1., 0.), rpa_color=(0., 0., 1.), ), noise_std=dict(grad=5e-13, mag=20e-15, eeg=0.2e-6), eloreta_options=dict(eps=1e-6, max_iter=20, force_equal=False), depth_mne=dict(exp=0.8, limit=10., limit_depth_chs=True, combine_xyz='spectral', allow_fixed_depth=False), depth_sparse=dict(exp=0.8, limit=None, limit_depth_chs='whiten', combine_xyz='fro', allow_fixed_depth=True), interpolation_method=dict(eeg='spline', meg='MNE', fnirs='nearest'), volume_options=dict( alpha=None, resolution=1., surface_alpha=None, blending='mip', silhouette_alpha=None, silhouette_linewidth=2.), prefixes={'k': 1e-3, 'h': 1e-2, '': 1e0, 'd': 1e1, 'c': 1e2, 'm': 1e3, 'µ': 1e6, 'u': 1e6, 'n': 1e9, 'p': 1e12, 'f': 1e15}, transform_zooms=dict( translation=None, rigid=None, affine=None, sdr=None), transform_niter=dict( translation=(10000, 1000, 100), rigid=(10000, 1000, 100), affine=(10000, 1000, 100), sdr=(10, 10, 5)), volume_label_indices=( # Left and middle 4, # Left-Lateral-Ventricle 5, # Left-Inf-Lat-Vent 8, # Left-Cerebellum-Cortex 10, # Left-Thalamus-Proper 11, # Left-Caudate 12, # Left-Putamen 13, # Left-Pallidum 14, # 3rd-Ventricle 15, # 4th-Ventricle 16, # Brain-Stem 17, # Left-Hippocampus 18, # Left-Amygdala 26, # Left-Accumbens-area 28, # Left-VentralDC # Right 43, # Right-Lateral-Ventricle 44, # Right-Inf-Lat-Vent 47, # Right-Cerebellum-Cortex 49, # Right-Thalamus-Proper 50, # Right-Caudate 51, # Right-Putamen 52, # Right-Pallidum 53, # Right-Hippocampus 54, # Right-Amygdala 58, # Right-Accumbens-area 60, # Right-VentralDC ), report_stc_plot_kwargs=dict( views=('lateral', 'medial'), hemi='split', backend='pyvistaqt', time_viewer=False, show_traces=False, size=(450, 450), background='white', time_label=None, add_data_kwargs={ 'colorbar_kwargs': { 'label_font_size': 12, 'n_labels': 5 } } ) ) def _handle_default(k, v=None): """Avoid dicts as default keyword arguments. Use this function instead to resolve default dict values. Example usage:: scalings = _handle_default('scalings', scalings) """ this_mapping = deepcopy(DEFAULTS[k]) if v is not None: if isinstance(v, dict): this_mapping.update(v) else: for key in this_mapping: this_mapping[key] = v return this_mapping HEAD_SIZE_DEFAULT = 0.095 # in [m] _BORDER_DEFAULT = 'mean' _INTERPOLATION_DEFAULT = 'cubic' _EXTRAPOLATE_DEFAULT = 'auto'