def test_meg_inverse():
"""Test plotting of MEG inverse solution."""
_set_backend()
brain = Brain(*std_args)
stc_fname = os.path.join(data_dir, 'meg_source_estimate-lh.stc')
stc = io.read_stc(stc_fname)
vertices = stc['vertices']
colormap = 'hot'
data = stc['data']
data_full = (brain.geo['lh'].nn[vertices][..., np.newaxis] *
data[:, np.newaxis])
time = np.linspace(stc['tmin'], stc['tmin'] + data.shape[1] * stc['tstep'],
data.shape[1], endpoint=False)
def time_label(t):
return 'time=%0.2f ms' % (1e3 * t)
for use_data in (data, data_full):
brain.add_data(use_data, colormap=colormap, vertices=vertices,
smoothing_steps=1, time=time, time_label=time_label)
brain.scale_data_colormap(fmin=13, fmid=18, fmax=22, transparent=True)
assert brain.data_dict['lh']['time_idx'] == 0
brain.set_time(.1)
assert brain.data_dict['lh']['time_idx'] == 2
# viewer = TimeViewer(brain)
# multiple data layers
pytest.raises(ValueError, brain.add_data, data, vertices=vertices,
time=time[:-1])
brain.add_data(data, colormap=colormap, vertices=vertices,
smoothing_steps=1, time=time, time_label=time_label,
initial_time=.09)
assert brain.data_dict['lh']['time_idx'] == 1
data_dicts = brain._data_dicts['lh']
assert len(data_dicts) == 3
assert data_dicts[0]['time_idx'] == 1
assert data_dicts[1]['time_idx'] == 1
# shift time in both layers
brain.set_data_time_index(0)
assert data_dicts[0]['time_idx'] == 0
assert data_dicts[1]['time_idx'] == 0
brain.set_data_smoothing_steps(2)
# add second data-layer without time axis
brain.add_data(data[:, 1], colormap=colormap, vertices=vertices,
smoothing_steps=2)
brain.set_data_time_index(2)
assert len(data_dicts) == 4
# change surface
brain.set_surf('white')
# remove all layers
brain.remove_data()
assert brain._data_dicts['lh'] == []
brain.close()
def test_meg_inverse():
"""Test plotting of MEG inverse solution."""
_set_backend()
brain = Brain(*std_args)
stc_fname = os.path.join(data_dir, 'meg_source_estimate-lh.stc')
stc = io.read_stc(stc_fname)
vertices = stc['vertices']
colormap = 'hot'
data = stc['data']
data_full = (brain.geo['lh'].nn[vertices][..., np.newaxis] *
data[:, np.newaxis])
time = np.linspace(stc['tmin'], stc['tmin'] + data.shape[1] * stc['tstep'],
data.shape[1], endpoint=False)
def time_label(t):
return 'time=%0.2f ms' % (1e3 * t)
for use_data in (data, data_full):
brain.add_data(use_data, colormap=colormap, vertices=vertices,
smoothing_steps=1, time=time, time_label=time_label)
brain.scale_data_colormap(fmin=13, fmid=18, fmax=22, transparent=True)
assert_equal(brain.data_dict['lh']['time_idx'], 0)
brain.set_time(.1)
assert_equal(brain.data_dict['lh']['time_idx'], 2)
# viewer = TimeViewer(brain)
# multiple data layers
assert_raises(ValueError, brain.add_data, data, vertices=vertices,
time=time[:-1])
brain.add_data(data, colormap=colormap, vertices=vertices,
smoothing_steps=1, time=time, time_label=time_label,
initial_time=.09)
assert_equal(brain.data_dict['lh']['time_idx'], 1)
data_dicts = brain._data_dicts['lh']
assert_equal(len(data_dicts), 3)
assert_equal(data_dicts[0]['time_idx'], 1)
assert_equal(data_dicts[1]['time_idx'], 1)
# shift time in both layers
brain.set_data_time_index(0)
assert_equal(data_dicts[0]['time_idx'], 0)
assert_equal(data_dicts[1]['time_idx'], 0)
brain.set_data_smoothing_steps(2)
# add second data-layer without time axis
brain.add_data(data[:, 1], colormap=colormap, vertices=vertices,
smoothing_steps=2)
brain.set_data_time_index(2)
assert_equal(len(data_dicts), 4)
# change surface
brain.set_surf('white')
# remove all layers
brain.remove_data()
assert_equal(brain._data_dicts['lh'], [])
brain.close()
def test_meg_inverse():
"""Test plotting of MEG inverse solution
"""
mlab.options.backend = 'test'
brain = Brain(*std_args)
stc_fname = os.path.join(data_dir, 'meg_source_estimate-lh.stc')
stc = io.read_stc(stc_fname)
data = stc['data']
vertices = stc['vertices']
time = np.linspace(stc['tmin'], stc['tmin'] + data.shape[1] * stc['tstep'],
data.shape[1], endpoint=False)
colormap = 'hot'
def time_label(t):
return 'time=%0.2f ms' % (1e3 * t)
brain.add_data(data, colormap=colormap, vertices=vertices,
smoothing_steps=10, time=time, time_label=time_label)
brain.scale_data_colormap(fmin=13, fmid=18, fmax=22, transparent=True)
assert_equal(brain.data_dict['lh']['time_idx'], 0)
brain.set_time(.1)
assert_equal(brain.data_dict['lh']['time_idx'], 2)
# viewer = TimeViewer(brain)
brain.add_data(data, colormap=colormap, vertices=vertices,
smoothing_steps=10, time=time, time_label=time_label,
initial_time=.09, remove_existing=True)
assert_equal(brain.data_dict['lh']['time_idx'], 1)
brain.close()
def test_meg_inverse():
"""Test plotting of MEG inverse solution."""
mlab.options.backend = 'test'
brain = Brain(*std_args)
stc_fname = os.path.join(data_dir, 'meg_source_estimate-lh.stc')
stc = io.read_stc(stc_fname)
data = stc['data']
vertices = stc['vertices']
time = np.linspace(stc['tmin'], stc['tmin'] + data.shape[1] * stc['tstep'],
data.shape[1], endpoint=False)
colormap = 'hot'
def time_label(t):
return 'time=%0.2f ms' % (1e3 * t)
brain.add_data(data, colormap=colormap, vertices=vertices,
smoothing_steps=10, time=time, time_label=time_label)
brain.scale_data_colormap(fmin=13, fmid=18, fmax=22, transparent=True)
assert_equal(brain.data_dict['lh']['time_idx'], 0)
brain.set_time(.1)
assert_equal(brain.data_dict['lh']['time_idx'], 2)
# viewer = TimeViewer(brain)
# multiple data layers
assert_raises(ValueError, brain.add_data, data, vertices=vertices,
time=time[:-1])
brain.add_data(data, colormap=colormap, vertices=vertices,
smoothing_steps=10, time=time, time_label=time_label,
initial_time=.09)
assert_equal(brain.data_dict['lh']['time_idx'], 1)
data_dicts = brain._data_dicts['lh']
assert_equal(len(data_dicts), 2)
assert_equal(data_dicts[0]['time_idx'], 1)
assert_equal(data_dicts[1]['time_idx'], 1)
# shift time in both layers
brain.set_data_time_index(0)
assert_equal(data_dicts[0]['time_idx'], 0)
assert_equal(data_dicts[1]['time_idx'], 0)
# remove all layers
brain.remove_data()
assert_equal(brain._data_dicts['lh'], [])
brain.close()
def test_meg_inverse():
"""Test plotting of MEG inverse solution."""
mlab.options.backend = 'test'
brain = Brain(*std_args)
stc_fname = os.path.join(data_dir, 'meg_source_estimate-lh.stc')
stc = io.read_stc(stc_fname)
data = stc['data']
vertices = stc['vertices']
time = np.linspace(stc['tmin'],
stc['tmin'] + data.shape[1] * stc['tstep'],
data.shape[1],
endpoint=False)
colormap = 'hot'
def time_label(t):
return 'time=%0.2f ms' % (1e3 * t)
brain.add_data(data,
colormap=colormap,
vertices=vertices,
smoothing_steps=10,
time=time,
time_label=time_label)
brain.scale_data_colormap(fmin=13, fmid=18, fmax=22, transparent=True)
assert_equal(brain.data_dict['lh']['time_idx'], 0)
brain.set_time(.1)
assert_equal(brain.data_dict['lh']['time_idx'], 2)
# viewer = TimeViewer(brain)
brain.add_data(data,
colormap=colormap,
vertices=vertices,
smoothing_steps=10,
time=time,
time_label=time_label,
initial_time=.09,
remove_existing=True)
assert_equal(brain.data_dict['lh']['time_idx'], 1)
brain.close()
def plot_source_estimates(stc,
subject=None,
surface='inflated',
hemi='lh',
colormap='auto',
time_label='auto',
smoothing_steps=10,
transparent=None,
alpha=1.0,
time_viewer=False,
config_opts=None,
subjects_dir=None,
figure=None,
views='lat',
colorbar=True,
clim='auto',
cortex="classic",
size=800,
background="black",
foreground="white",
initial_time=None,
time_unit=None):
"""Plot SourceEstimates with PySurfer
Note: PySurfer currently needs the SUBJECTS_DIR environment variable,
which will automatically be set by this function. Plotting multiple
SourceEstimates with different values for subjects_dir will cause
PySurfer to use the wrong FreeSurfer surfaces when using methods of
the returned Brain object. It is therefore recommended to set the
SUBJECTS_DIR environment variable or always use the same value for
subjects_dir (within the same Python session).
Parameters
----------
stc : SourceEstimates
The source estimates to plot.
subject : str | None
The subject name corresponding to FreeSurfer environment
variable SUBJECT. If None stc.subject will be used. If that
is None, the environment will be used.
surface : str
The type of surface (inflated, white etc.).
hemi : str, 'lh' | 'rh' | 'split' | 'both'
The hemisphere to display.
colormap : str | np.ndarray of float, shape(n_colors, 3 | 4)
Name of colormap to use or a custom look up table. If array, must
be (n x 3) or (n x 4) array for with RGB or RGBA values between
0 and 255. If 'auto', either 'hot' or 'mne' will be chosen
based on whether 'lims' or 'pos_lims' are specified in `clim`.
time_label : str | callable | None
Format of the time label (a format string, a function that maps
floating point time values to strings, or None for no label). The
default is ``time=%0.2f ms``.
smoothing_steps : int
The amount of smoothing
transparent : bool | None
If True, use a linear transparency between fmin and fmid.
None will choose automatically based on colormap type.
alpha : float
Alpha value to apply globally to the overlay.
time_viewer : bool
Display time viewer GUI.
config_opts : dict
Deprecated parameter.
subjects_dir : str
The path to the freesurfer subjects reconstructions.
It corresponds to Freesurfer environment variable SUBJECTS_DIR.
figure : instance of mayavi.core.scene.Scene | list | int | None
If None, a new figure will be created. If multiple views or a
split view is requested, this must be a list of the appropriate
length. If int is provided it will be used to identify the Mayavi
figure by it's id or create a new figure with the given id.
views : str | list
View to use. See surfer.Brain().
colorbar : bool
If True, display colorbar on scene.
clim : str | dict
Colorbar properties specification. If 'auto', set clim automatically
based on data percentiles. If dict, should contain:
``kind`` : str
Flag to specify type of limits. 'value' or 'percent'.
``lims`` : list | np.ndarray | tuple of float, 3 elements
Note: Only use this if 'colormap' is not 'mne'.
Left, middle, and right bound for colormap.
``pos_lims`` : list | np.ndarray | tuple of float, 3 elements
Note: Only use this if 'colormap' is 'mne'.
Left, middle, and right bound for colormap. Positive values
will be mirrored directly across zero during colormap
construction to obtain negative control points.
cortex : str or tuple
specifies how binarized curvature values are rendered.
either the name of a preset PySurfer cortex colorscheme (one of
'classic', 'bone', 'low_contrast', or 'high_contrast'), or the
name of mayavi colormap, or a tuple with values (colormap, min,
max, reverse) to fully specify the curvature colors.
size : float or pair of floats
The size of the window, in pixels. can be one number to specify
a square window, or the (width, height) of a rectangular window.
background : matplotlib color
Color of the background of the display window.
foreground : matplotlib color
Color of the foreground of the display window.
initial_time : float | None
The time to display on the plot initially. ``None`` to display the
first time sample (default).
time_unit : 's' | 'ms'
Whether time is represented in seconds (expected by PySurfer) or
milliseconds. The current default is 'ms', but will change to 's'
in MNE 0.14. To avoid a deprecation warning specify ``time_unit``
explicitly.
Returns
-------
brain : Brain
A instance of surfer.viz.Brain from PySurfer.
"""
import surfer
from surfer import Brain, TimeViewer
import mayavi
# import here to avoid circular import problem
from ..source_estimate import SourceEstimate
surfer_version = LooseVersion(surfer.__version__)
v06 = LooseVersion('0.6')
if surfer_version < v06:
raise ImportError("This function requires PySurfer 0.6 (you are "
"running version %s). You can update PySurfer "
"using:\n\n $ pip install -U pysurfer" %
surfer.__version__)
if time_unit is None:
if initial_time is not None:
warn(
"The time_unit parameter default will change from 'ms' to "
"'s' in MNE 0.14 and be removed in 0.15. To avoid this "
"warning specify the parameter explicitly.",
DeprecationWarning)
time_unit = 'ms'
elif time_unit not in ('s', 'ms'):
raise ValueError("time_unit needs to be 's' or 'ms', got %r" %
(time_unit, ))
if initial_time is not None and surfer_version > v06:
kwargs = {'initial_time': initial_time}
initial_time = None # don't set it twice
else:
kwargs = {}
if time_label == 'auto':
if time_unit == 'ms':
time_label = 'time=%0.2f ms'
else:
def time_label(t):
return 'time=%0.2f ms' % (t * 1e3)
if not isinstance(stc, SourceEstimate):
raise ValueError('stc has to be a surface source estimate')
if hemi not in ['lh', 'rh', 'split', 'both']:
raise ValueError('hemi has to be either "lh", "rh", "split", '
'or "both"')
# check `figure` parameter (This will be performed by PySurfer > 0.6)
if figure is not None:
if isinstance(figure, int):
# use figure with specified id
size_ = size if isinstance(size, (tuple, list)) else (size, size)
figure = [mayavi.mlab.figure(figure, size=size_)]
elif not isinstance(figure, (list, tuple)):
figure = [figure]
if not all(isinstance(f, mayavi.core.scene.Scene) for f in figure):
raise TypeError('figure must be a mayavi scene or list of scenes')
# convert control points to locations in colormap
ctrl_pts, colormap = _limits_to_control_points(clim, stc.data, colormap)
# Construct cmap manually if 'mne' and get cmap bounds
# and triage transparent argument
if colormap in ('mne', 'mne_analyze'):
colormap = mne_analyze_colormap(ctrl_pts)
scale_pts = [-1 * ctrl_pts[-1], 0, ctrl_pts[-1]]
transparent = False if transparent is None else transparent
else:
scale_pts = ctrl_pts
transparent = True if transparent is None else transparent
subjects_dir = get_subjects_dir(subjects_dir=subjects_dir,
raise_error=True)
subject = _check_subject(stc.subject, subject, True)
if hemi in ['both', 'split']:
hemis = ['lh', 'rh']
else:
hemis = [hemi]
title = subject if len(hemis) > 1 else '%s - %s' % (subject, hemis[0])
with warnings.catch_warnings(record=True): # traits warnings
brain = Brain(subject,
hemi=hemi,
surf=surface,
curv=True,
title=title,
cortex=cortex,
size=size,
background=background,
foreground=foreground,
figure=figure,
subjects_dir=subjects_dir,
views=views,
config_opts=config_opts)
if time_unit == 's':
times = stc.times
else: # time_unit == 'ms'
times = 1e3 * stc.times
for hemi in hemis:
hemi_idx = 0 if hemi == 'lh' else 1
if hemi_idx == 0:
data = stc.data[:len(stc.vertices[0])]
else:
data = stc.data[len(stc.vertices[0]):]
vertices = stc.vertices[hemi_idx]
with warnings.catch_warnings(record=True): # traits warnings
brain.add_data(data,
colormap=colormap,
vertices=vertices,
smoothing_steps=smoothing_steps,
time=times,
time_label=time_label,
alpha=alpha,
hemi=hemi,
colorbar=colorbar,
**kwargs)
# scale colormap and set time (index) to display
brain.scale_data_colormap(fmin=scale_pts[0],
fmid=scale_pts[1],
fmax=scale_pts[2],
transparent=transparent)
if initial_time is not None:
brain.set_time(initial_time)
if time_viewer:
TimeViewer(brain)
return brain
brain = Brain(nip, hemi, 'inflated', size=(800, 400),
subjects_dir=data_mri_directory)
# Add source time course
brain.add_data(stc_data[hemi], colormap=colormap,
vertices=stc_vertices[hemi], smoothing_steps=10,
time=time_array, time_label=time_label,
hemi=hemi, initial_time=0.)
# Scale the F-map
brain.scale_data_colormap(fmin=fmin, fmid=fmid, fmax=fmax,
transparent=True)
# Add the image array to all images
for time in times:
brain.set_time(time)
fig_temp[hemi].append(brain.save_montage(filename=None,
orientation='h'))
fig_temp[hemi] = np.concatenate(fig_temp[hemi], axis=0)
brain.close()
# Correct for different lengths
min_len = min(len(fig_temp['lh']), len(fig_temp['rh']))
fig_temp['lh'] = fig_temp['lh'][0:min_len, :, :]
fig_temp['rh'] = fig_temp['rh'][0:min_len, :, :]
# Collapse hemispheric images into one
fig_array = np.concatenate([fig_temp['lh'], fig_temp['rh']], axis=1)
def plot_source_estimates(stc, subject=None, surface='inflated', hemi='lh',
colormap='auto', time_label='auto',
smoothing_steps=10, transparent=None, alpha=1.0,
time_viewer=False, config_opts=None,
subjects_dir=None, figure=None, views='lat',
colorbar=True, clim='auto', cortex="classic",
size=800, background="black", foreground="white",
initial_time=None, time_unit=None):
"""Plot SourceEstimates with PySurfer
Note: PySurfer currently needs the SUBJECTS_DIR environment variable,
which will automatically be set by this function. Plotting multiple
SourceEstimates with different values for subjects_dir will cause
PySurfer to use the wrong FreeSurfer surfaces when using methods of
the returned Brain object. It is therefore recommended to set the
SUBJECTS_DIR environment variable or always use the same value for
subjects_dir (within the same Python session).
Parameters
----------
stc : SourceEstimates
The source estimates to plot.
subject : str | None
The subject name corresponding to FreeSurfer environment
variable SUBJECT. If None stc.subject will be used. If that
is None, the environment will be used.
surface : str
The type of surface (inflated, white etc.).
hemi : str, 'lh' | 'rh' | 'split' | 'both'
The hemisphere to display.
colormap : str | np.ndarray of float, shape(n_colors, 3 | 4)
Name of colormap to use or a custom look up table. If array, must
be (n x 3) or (n x 4) array for with RGB or RGBA values between
0 and 255. If 'auto', either 'hot' or 'mne' will be chosen
based on whether 'lims' or 'pos_lims' are specified in `clim`.
time_label : str | callable | None
Format of the time label (a format string, a function that maps
floating point time values to strings, or None for no label). The
default is ``time=%0.2f ms``.
smoothing_steps : int
The amount of smoothing
transparent : bool | None
If True, use a linear transparency between fmin and fmid.
None will choose automatically based on colormap type.
alpha : float
Alpha value to apply globally to the overlay.
time_viewer : bool
Display time viewer GUI.
config_opts : dict
Deprecated parameter.
subjects_dir : str
The path to the freesurfer subjects reconstructions.
It corresponds to Freesurfer environment variable SUBJECTS_DIR.
figure : instance of mayavi.core.scene.Scene | list | int | None
If None, a new figure will be created. If multiple views or a
split view is requested, this must be a list of the appropriate
length. If int is provided it will be used to identify the Mayavi
figure by it's id or create a new figure with the given id.
views : str | list
View to use. See surfer.Brain().
colorbar : bool
If True, display colorbar on scene.
clim : str | dict
Colorbar properties specification. If 'auto', set clim automatically
based on data percentiles. If dict, should contain:
``kind`` : str
Flag to specify type of limits. 'value' or 'percent'.
``lims`` : list | np.ndarray | tuple of float, 3 elements
Note: Only use this if 'colormap' is not 'mne'.
Left, middle, and right bound for colormap.
``pos_lims`` : list | np.ndarray | tuple of float, 3 elements
Note: Only use this if 'colormap' is 'mne'.
Left, middle, and right bound for colormap. Positive values
will be mirrored directly across zero during colormap
construction to obtain negative control points.
cortex : str or tuple
specifies how binarized curvature values are rendered.
either the name of a preset PySurfer cortex colorscheme (one of
'classic', 'bone', 'low_contrast', or 'high_contrast'), or the
name of mayavi colormap, or a tuple with values (colormap, min,
max, reverse) to fully specify the curvature colors.
size : float or pair of floats
The size of the window, in pixels. can be one number to specify
a square window, or the (width, height) of a rectangular window.
background : matplotlib color
Color of the background of the display window.
foreground : matplotlib color
Color of the foreground of the display window.
initial_time : float | None
The time to display on the plot initially. ``None`` to display the
first time sample (default).
time_unit : 's' | 'ms'
Whether time is represented in seconds (expected by PySurfer) or
milliseconds. The current default is 'ms', but will change to 's'
in MNE 0.14. To avoid a deprecation warning specify ``time_unit``
explicitly.
Returns
-------
brain : Brain
A instance of surfer.viz.Brain from PySurfer.
"""
import surfer
from surfer import Brain, TimeViewer
import mayavi
# import here to avoid circular import problem
from ..source_estimate import SourceEstimate
surfer_version = LooseVersion(surfer.__version__)
v06 = LooseVersion('0.6')
if surfer_version < v06:
raise ImportError("This function requires PySurfer 0.6 (you are "
"running version %s). You can update PySurfer "
"using:\n\n $ pip install -U pysurfer" %
surfer.__version__)
if initial_time is not None and surfer_version > v06:
kwargs = {'initial_time': initial_time}
initial_time = None # don't set it twice
else:
kwargs = {}
if time_unit is None:
warn("The time_unit parameter default will change from 'ms' to 's' "
"in MNE 0.14. To avoid this warning specify the parameter "
"explicitly.", DeprecationWarning)
time_unit = 'ms'
elif time_unit not in ('s', 'ms'):
raise ValueError("time_unit needs to be 's' or 'ms', got %r" %
(time_unit,))
if time_label == 'auto':
if time_unit == 'ms':
time_label = 'time=%0.2f ms'
else:
def time_label(t):
return 'time=%0.2f ms' % (t * 1e3)
if not isinstance(stc, SourceEstimate):
raise ValueError('stc has to be a surface source estimate')
if hemi not in ['lh', 'rh', 'split', 'both']:
raise ValueError('hemi has to be either "lh", "rh", "split", '
'or "both"')
# check `figure` parameter (This will be performed by PySurfer > 0.6)
if figure is not None:
if isinstance(figure, int):
# use figure with specified id
size_ = size if isinstance(size, (tuple, list)) else (size, size)
figure = [mayavi.mlab.figure(figure, size=size_)]
elif not isinstance(figure, (list, tuple)):
figure = [figure]
if not all(isinstance(f, mayavi.core.scene.Scene) for f in figure):
raise TypeError('figure must be a mayavi scene or list of scenes')
# convert control points to locations in colormap
ctrl_pts, colormap = _limits_to_control_points(clim, stc.data, colormap)
# Construct cmap manually if 'mne' and get cmap bounds
# and triage transparent argument
if colormap in ('mne', 'mne_analyze'):
colormap = mne_analyze_colormap(ctrl_pts)
scale_pts = [-1 * ctrl_pts[-1], 0, ctrl_pts[-1]]
transparent = False if transparent is None else transparent
else:
scale_pts = ctrl_pts
transparent = True if transparent is None else transparent
subjects_dir = get_subjects_dir(subjects_dir=subjects_dir,
raise_error=True)
subject = _check_subject(stc.subject, subject, True)
if hemi in ['both', 'split']:
hemis = ['lh', 'rh']
else:
hemis = [hemi]
title = subject if len(hemis) > 1 else '%s - %s' % (subject, hemis[0])
with warnings.catch_warnings(record=True): # traits warnings
brain = Brain(subject, hemi, surface, True, title, cortex, size,
background, foreground, figure, subjects_dir, views,
config_opts=config_opts)
if time_unit == 's':
times = stc.times
else: # time_unit == 'ms'
times = 1e3 * stc.times
for hemi in hemis:
hemi_idx = 0 if hemi == 'lh' else 1
if hemi_idx == 0:
data = stc.data[:len(stc.vertices[0])]
else:
data = stc.data[len(stc.vertices[0]):]
vertices = stc.vertices[hemi_idx]
with warnings.catch_warnings(record=True): # traits warnings
brain.add_data(data, colormap=colormap, vertices=vertices,
smoothing_steps=smoothing_steps, time=times,
time_label=time_label, alpha=alpha, hemi=hemi,
colorbar=colorbar, **kwargs)
# scale colormap and set time (index) to display
brain.scale_data_colormap(fmin=scale_pts[0], fmid=scale_pts[1],
fmax=scale_pts[2], transparent=transparent)
if initial_time is not None:
brain.set_time(initial_time)
if time_viewer:
TimeViewer(brain)
return brain
