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