def _plt_src(name, kw_brain_obj, active_data, active_vert, sources, kw_source_obj, kw_activation, show): # Define a brain object and a source object : logger.info('Define a Brain and Source objects') from visbrain.objects import BrainObj, SourceObj, SceneObj brain_obj, source_obj = name + '_brain', name + '_sources' b_obj = BrainObj(brain_obj, **kw_brain_obj) s_obj = SourceObj(source_obj, sources, **kw_source_obj) s_obj.visible_obj = False # Add data to the BrainObj if needed : if isinstance(active_data, np.ndarray): logger.info("Add active data between " "[%2f, %2f]" % (active_data.min(), active_data.max())) b_obj.add_activation(data=active_data, vertices=active_vert, **kw_activation) # Return either a scene or a BrainObj and SourceObj : if show is True: # Display inside the Brain GUI # Define a Brain instance : from visbrain import Brain brain = Brain(brain_obj=b_obj, source_obj=s_obj) # By default, display colorbar if activation : if isinstance(active_data, np.ndarray): brain.menuDispCbar.setChecked(True) brain._fcn_menu_disp_cbar() brain.show() elif show is 'scene': # return a SceneObj logger.info('Define a unique scene for the Brain and Source objects') sc = SceneObj() sc.add_to_subplot(s_obj) sc.add_to_subplot(b_obj, use_this_cam=True) return sc else: # return the BrainObj and SourceObj s_obj.visible_obj = True return b_obj, s_obj
def mne_plot_source_estimation(sbj, sbj_dir, fwd_file, stc_file=None, hemisphere='both', parc='aparc', active_data=0, kw_brain_obj={}, kw_source_obj={}, kw_activation={}, show=True): """Plot source estimation. Parameters ---------- sbj : string The subject name. sbj_dir : string Path to the subject directory. fwd_file : string The file name of the forward solution, which should end with -fwd.fif or -fwd.fif.gz. stc_file : string | None Path to the *.stc inverse solution file. hemisphere : {'left', 'both', 'right'} The hemisphere to plot. parc : string | 'aparc' The parcellation to use, e.g., ‘aparc’ or ‘aparc.a2009s’. active_data : array_like, int | 0 The data to set to vertices. If an stc file is provided and if `active_data` is an integer, it describes the time instant in which you want to see the activation. Otherwise, `active_data` must be an array with the same same shape as the number of active vertices. kw_brain_obj : dict | {} Additional inputs to pass to the `BrainObj` class. kw_source_obj : dict | {} Additional inputs to pass to the `SourceObj` class. kw_activation : dict | {} Additional inputs to pass to the `BrainObj.add_activation` method. show : bool | False If True, the window of the `Brain` module is automatically displayed. If False, a BrainObj and a SourceObj are returned. Finally, if 'scene' a SceneObj is returned. Returns ------- b_obj : BrainObj A predefined `BrainObj` (if `show=False`) s_obj : SourceObj A predefined `SourceObj`, hide by default (if `show=False`) """ # Test that mne is installed and import : is_mne_installed(raise_error=True) import mne from mne.source_space import head_to_mni hemi_idx = {'left': [0], 'right': [1], 'both': [0, 1]}[hemisphere] # Read the forward solution : fwd = mne.read_forward_solution(fwd_file) logger.debug('Read the forward solution') # Get source space : fwd_src = fwd['src'] # Get the MRI (surface RAS)-> head matrix mri_head_t = fwd['mri_head_t'] # Head to MNI conversion logger.info("Head to MNI conversion") mesh, sources = [], [] for hemi in hemi_idx: vert_ = fwd_src[hemi]['rr'] sources_ = fwd_src[hemi]['rr'][fwd_src[hemi]['vertno']] m_ = head_to_mni(vert_, sbj, mri_head_t, subjects_dir=sbj_dir) s_ = head_to_mni(sources_, sbj, mri_head_t, subjects_dir=sbj_dir) mesh.append(m_) sources.append(s_) # Get active vertices : # fwd_src contains the source spaces, the first 2 are the cortex # (left and right hemi, the others are related to the substructures) if len(hemi_idx) == 1: active_vert = fwd_src[hemi_idx[0]]['vertno'] else: active_left = fwd_src[0]['vertno'] active_right = fwd_src[1]['vertno'] + mesh[0].shape[0] active_vert = np.r_[active_left, active_right] logger.info('%i active vertices detected ' % len(active_vert)) # Add data to the mesh : if isinstance(active_data, np.ndarray): if len(active_data) != len(active_vert): logger.error("The length of `active data` (%i) must be the same " "the length of the number of active vertices " "(%i)" % (len(active_data), len(active_vert))) active_data = active_vert = None else: logger.info("Array of active data used.") elif isinstance(stc_file, str) and isinstance(active_data, int): # Get active data : assert os.path.isfile(stc_file) n_tp = active_data data = mne.read_source_estimate(stc_file).data active_data = np.abs(data[:, n_tp] / data[:, n_tp].max()) logger.info("Time instant %i used for activation" % n_tp) else: logger.info("No active data detected.") active_data = active_vert = None # Concatenate vertices, faces and sources : vertices = np.concatenate(mesh) lr_index = np.r_[np.ones((len(mesh[0]), )), np.zeros((len(mesh[1]), ))] sources = np.concatenate(sources) # Get faces : if len(hemi_idx) == 1: faces = fwd_src[hemi_idx[0]]['tris'] else: _faces_l = fwd_src[0]['tris'] _faces_r = fwd_src[1]['tris'] + _faces_l.max() + 1 faces = np.r_[_faces_l, _faces_r].astype(int) # Define a brain object and a source object : logger.info('Define a Brain and Source objects') from visbrain.objects import BrainObj, SourceObj, SceneObj b_obj = BrainObj(sbj + '_brain', vertices=vertices, faces=faces, lr_index=lr_index.astype(bool), **kw_brain_obj) s_obj = SourceObj(sbj + '_src', sources, visible=False, **kw_source_obj) # Add data to the BrainObj if needed : if isinstance(active_data, np.ndarray): logger.info("Add active data between " "[%2f, %2f]" % (active_data.min(), active_data.max())) b_obj.add_activation(data=active_data, vertices=active_vert, **kw_activation) # Return either a scene or a BrainObj and SourceObj : if show: # Display inside the Brain GUI # Define a Brain instance : from visbrain import Brain brain = Brain(brain_obj=b_obj, source_obj=s_obj) # Remove all brain templates except the one of the subject : brain._brain_template.setEnabled(False) # By default, display colorbar if activation : if isinstance(active_data, np.ndarray): brain.menuDispCbar.setChecked(True) brain._fcn_menu_disp_cbar() brain.show() elif show is 'scene': # return a SceneObj logger.info('Define a unique scene for the Brain and Source objects') sc = SceneObj() sc.add_to_subplot(s_obj) sc.add_to_subplot(b_obj, use_this_cam=True) return sc else: # return the BrainObj and SourceObj return b_obj, s_obj