def test_brain_add_label(renderer):
"""Test adding data in _Brain instance."""
from mne.label import read_label
brain = _Brain(subject_id, hemi='lh', size=500,
surf=surf, subjects_dir=subjects_dir)
label = read_label(fname_label)
brain.add_label(label, scalar_thresh=0.)
brain.close()
brain = _Brain(subject_id, hemi='split', size=500,
surf=surf, subjects_dir=subjects_dir)
brain.add_label(fname_label)
brain.close()
def test_brain_init(renderer):
"""Test initialization of the _Brain instance."""
hemi = 'both'
with pytest.raises(ValueError, match='size'):
_Brain(subject_id=subject_id, hemi=hemi, surf=surf, size=0.5)
with pytest.raises(TypeError, match='figure'):
_Brain(subject_id=subject_id, hemi=hemi, surf=surf, figure='foo')
with pytest.raises(ValueError, match='interaction'):
_Brain(subject_id=subject_id, hemi=hemi, surf=surf, interaction=0)
with pytest.raises(KeyError):
_Brain(subject_id=subject_id, hemi='foo', surf=surf)
_Brain(subject_id, hemi, surf, size=(300, 300), subjects_dir=subjects_dir)
def _create_testing_brain(hemi, surf='inflated'):
sample_src = read_source_spaces(src_fname)
# dense version
vertices = [s['vertno'] for s in sample_src]
n_time = 5
n_verts = sum(len(v) for v in vertices)
stc_data = np.zeros((n_verts * n_time))
stc_size = stc_data.size
stc_data[(np.random.rand(stc_size // 20) * stc_size).astype(int)] = \
np.random.RandomState(0).rand(stc_data.size // 20)
stc_data.shape = (n_verts, n_time)
stc = SourceEstimate(stc_data, vertices, 1, 1)
fmin = stc.data.min()
fmax = stc.data.max()
brain_data = _Brain(subject_id,
hemi,
surf,
size=300,
subjects_dir=subjects_dir)
hemi_list = ['lh', 'rh'] if hemi in ['both', 'split'] else [hemi]
for hemi_str in hemi_list:
hemi_idx = 0 if hemi_str == 'lh' else 1
data = getattr(stc, hemi_str + '_data')
vertices = stc.vertices[hemi_idx]
brain_data.add_data(data,
fmin=fmin,
hemi=hemi_str,
fmax=fmax,
colormap='hot',
vertices=vertices,
colorbar=True)
return brain_data
def test_brain_screenshot(renderer):
"""Test screenshot of a _Brain instance."""
brain = _Brain(subject_id, hemi='both', size=600,
surf=surf, subjects_dir=subjects_dir)
img = brain.screenshot(mode='rgb')
assert(img.shape == (600, 600, 3))
brain.close()
def test_brain_add_foci(renderer):
"""Test adding foci in _Brain instance."""
brain = _Brain(subject_id, hemi='lh', size=500,
surf=surf, subjects_dir=subjects_dir)
brain.add_foci([0], coords_as_verts=True,
hemi='lh', color='blue')
brain.close()
def test_brain_timeviewer(renderer):
"""Test _TimeViewer primitives."""
if renderer.get_3d_backend() == "mayavi":
pytest.skip() # Skip PySurfer.TimeViewer
else:
# Disable testing to allow interactive window
renderer.MNE_3D_BACKEND_TESTING = False
sample_src = read_source_spaces(src_fname)
# dense version
vertices = [s['vertno'] for s in sample_src]
n_time = 5
n_verts = sum(len(v) for v in vertices)
stc_data = np.zeros((n_verts * n_time))
stc_size = stc_data.size
stc_data[(np.random.rand(stc_size // 20) * stc_size).astype(int)] = \
np.random.RandomState(0).rand(stc_data.size // 20)
stc_data.shape = (n_verts, n_time)
stc = SourceEstimate(stc_data, vertices, 1, 1)
fmin = stc.data.min()
fmax = stc.data.max()
brain_data = _Brain(subject_id,
'split',
surf,
size=300,
subjects_dir=subjects_dir)
for hemi in ['lh', 'rh']:
hemi_idx = 0 if hemi == 'lh' else 1
data = getattr(stc, hemi + '_data')
vertices = stc.vertices[hemi_idx]
brain_data.add_data(data,
fmin=fmin,
hemi=hemi,
fmax=fmax,
colormap='hot',
vertices=vertices,
colorbar=True)
time_viewer = _TimeViewer(brain_data)
time_viewer.time_call(value=0)
time_viewer.orientation_call(value='lat', update_widget=True)
time_viewer.orientation_call(value='medial', update_widget=True)
time_viewer.smoothing_call(value=1)
time_viewer.fmin_call(value=12.0)
time_viewer.fmax_call(value=4.0)
time_viewer.fmid_call(value=6.0)
time_viewer.fmid_call(value=4.0)
time_viewer.fscale_call(value=1.1)
time_viewer.toggle_interface()
time_viewer.playback_speed_call(value=0.1)
time_viewer.toggle_playback()
time_viewer.apply_auto_scaling()
time_viewer.restore_user_scaling()
link_viewer = _LinkViewer([brain_data])
link_viewer.set_time_point(value=0)
link_viewer.set_playback_speed(value=0.1)
link_viewer.toggle_playback()
def test_brain_add_data(renderer):
"""Test adding data in _Brain instance."""
stc = read_source_estimate(fname_stc)
hemi = 'lh'
hemi_data = stc.data[:len(stc.vertices[0]), 10]
hemi_vertices = stc.vertices[0]
fmin = stc.data.min()
fmax = stc.data.max()
brain_data = _Brain(subject_id, hemi, surf, size=300,
subjects_dir=subjects_dir)
with pytest.raises(ValueError, match='thresh'):
brain_data.add_data(hemi_data, thresh=-1)
with pytest.raises(ValueError, match='remove_existing'):
brain_data.add_data(hemi_data, remove_existing=-1)
with pytest.raises(ValueError, match='time_label_size'):
brain_data.add_data(hemi_data, time_label_size=-1)
with pytest.raises(ValueError, match='scale_factor'):
brain_data.add_data(hemi_data, scale_factor=-1)
with pytest.raises(ValueError, match='vector_alpha'):
brain_data.add_data(hemi_data, vector_alpha=-1)
with pytest.raises(ValueError):
brain_data.add_data(array=np.array([0, 1, 2]))
with pytest.raises(ValueError):
brain_data.add_data(hemi_data, fmin=fmin, hemi=hemi,
fmax=fmax, vertices=None)
brain_data.add_data(hemi_data, fmin=fmin, hemi=hemi, fmax=fmax,
colormap='hot', vertices=hemi_vertices,
colorbar=False, time=None)
brain_data.add_data(hemi_data, fmin=fmin, hemi=hemi, fmax=fmax,
colormap='hot', vertices=hemi_vertices,
initial_time=0., colorbar=True, time=None)
def test_brain_add_text(renderer):
"""Test adding text in _Brain instance."""
brain = _Brain(subject_id,
hemi='lh',
size=250,
surf=surf,
subjects_dir=subjects_dir)
brain.add_text(x=0, y=0, text='foo')
def test_brain_init(renderer):
"""Test initialization of the _Brain instance."""
hemi = 'both'
with pytest.raises(ValueError, match='size'):
_Brain(subject_id=subject_id, hemi=hemi, surf=surf, size=0.5)
with pytest.raises(TypeError, match='figure'):
_Brain(subject_id=subject_id, hemi=hemi, surf=surf, figure='foo')
with pytest.raises(ValueError, match='interaction'):
_Brain(subject_id=subject_id, hemi=hemi, surf=surf, interaction=0)
with pytest.raises(KeyError):
_Brain(subject_id=subject_id, hemi='foo', surf=surf)
brain = _Brain(subject_id, hemi, surf, size=(300, 300),
subjects_dir=subjects_dir)
brain.show_view(view=dict(azimuth=180., elevation=90.))
brain.close()
def test_brain_init(renderer):
"""Test initialization of the _Brain instance."""
backend_name = renderer.get_3d_backend()
hemi = 'both'
with pytest.raises(ValueError, match='hemi'):
_Brain(subject_id=subject_id, hemi="split", surf=surf)
with pytest.raises(ValueError, match='figure'):
_Brain(subject_id=subject_id, hemi=hemi, surf=surf, figure=0)
with pytest.raises(ValueError, match='interaction'):
_Brain(subject_id=subject_id, hemi=hemi, surf=surf, interaction=0)
with pytest.raises(KeyError):
_Brain(subject_id=subject_id, hemi="foo", surf=surf)
brain = _Brain(subject_id, hemi, surf, subjects_dir=subjects_dir)
if backend_name != 'mayavi':
brain.show()
def test_brain_add_annotation(renderer):
"""Test adding data in _Brain instance."""
annots = ['aparc', 'PALS_B12_Lobes']
borders = [True, 2]
alphas = [1, 0.5]
brain = _Brain(subject_id='fsaverage',
hemi='lh',
size=500,
surf='inflated',
subjects_dir=subjects_dir)
for a, b, p in zip(annots, borders, alphas):
brain.add_annotation(a, b, p)
brain.close()
def test_brain_init(renderer):
"""Test initialization of the _Brain instance."""
hemi = 'both'
with pytest.raises(ValueError, match='"size" parameter must be'):
_Brain(subject_id=subject_id, hemi=hemi, surf=surf, size=[1, 2, 3])
with pytest.raises(TypeError, match='figure'):
_Brain(subject_id=subject_id, hemi=hemi, surf=surf, figure='foo')
with pytest.raises(ValueError, match='interaction'):
_Brain(subject_id=subject_id, hemi=hemi, surf=surf, interaction=0)
with pytest.raises(KeyError):
_Brain(subject_id=subject_id, hemi='foo', surf=surf)
for cortex in ['classic', 'low_contrast', 'high_contrast', 'bone']:
brain = _Brain(subject_id,
hemi,
surf,
size=(300, 300),
subjects_dir=subjects_dir,
title='test',
cortex=cortex)
brain.show_view(view=dict(azimuth=180., elevation=90.))
brain.close()
def test_brain_add_data(renderer):
"""Test adding data in _Brain instance."""
backend_name = renderer.get_3d_backend()
act_data = path.join(data_path, 'MEG/sample/sample_audvis-meg-eeg')
stc = read_source_estimate(act_data)
hemi = 'lh'
hemi_data = stc.data[:len(stc.vertices[0]), 10]
hemi_vertices = stc.vertices[0]
fmin = stc.data.min()
fmax = stc.data.max()
brain_data = _Brain(subject_id,
hemi,
surf,
size=300,
subjects_dir=subjects_dir)
with pytest.raises(ValueError):
brain_data.add_data(array=np.array([0, 1, 2]))
with pytest.raises(ValueError):
brain_data.add_data(hemi_data,
fmin=fmin,
hemi=hemi,
fmax=fmax,
vertices=None)
brain_data.add_data(hemi_data,
fmin=fmin,
hemi=hemi,
fmax=fmax,
colormap='hot',
vertices=hemi_vertices,
colorbar=False)
if backend_name != 'mayavi':
brain_data.show()
def test_brain(renderer):
"""Test initialization of the _Brain instance."""
from mne.label import read_label
hemi = 'lh'
surf = 'inflated'
cortex = 'low_contrast'
title = 'test'
size = (300, 300)
with pytest.raises(ValueError, match='"size" parameter must be'):
_Brain(subject_id=subject_id, hemi=hemi, surf=surf, size=[1, 2, 3])
with pytest.raises(TypeError, match='figure'):
_Brain(subject_id=subject_id, hemi=hemi, surf=surf, figure='foo')
with pytest.raises(ValueError, match='interaction'):
_Brain(subject_id=subject_id, hemi=hemi, surf=surf, interaction=0)
with pytest.raises(KeyError):
_Brain(subject_id=subject_id, hemi='foo', surf=surf)
brain = _Brain(subject_id,
hemi=hemi,
surf=surf,
size=size,
subjects_dir=subjects_dir,
title=title,
cortex=cortex)
# add_data
stc = read_source_estimate(fname_stc)
fmin = stc.data.min()
fmax = stc.data.max()
for h in brain._hemis:
if h == 'lh':
hi = 0
else:
hi = 1
hemi_data = stc.data[:len(stc.vertices[hi]), 10]
hemi_vertices = stc.vertices[hi]
with pytest.raises(TypeError, match='scale_factor'):
brain.add_data(hemi_data, hemi=h, scale_factor='foo')
with pytest.raises(TypeError, match='vector_alpha'):
brain.add_data(hemi_data, hemi=h, vector_alpha='foo')
with pytest.raises(ValueError, match='thresh'):
brain.add_data(hemi_data, hemi=h, thresh=-1)
with pytest.raises(ValueError, match='remove_existing'):
brain.add_data(hemi_data, hemi=h, remove_existing=-1)
with pytest.raises(ValueError, match='time_label_size'):
brain.add_data(hemi_data, hemi=h, time_label_size=-1)
with pytest.raises(ValueError, match='is positive'):
brain.add_data(hemi_data, hemi=h, smoothing_steps=-1)
with pytest.raises(TypeError, match='int or NoneType'):
brain.add_data(hemi_data, hemi=h, smoothing_steps='foo')
with pytest.raises(ValueError):
brain.add_data(array=np.array([0, 1, 2]), hemi=h)
with pytest.raises(ValueError):
brain.add_data(hemi_data,
fmin=fmin,
hemi=hemi,
fmax=fmax,
vertices=None)
brain.add_data(hemi_data,
fmin=fmin,
hemi=h,
fmax=fmax,
colormap='hot',
vertices=hemi_vertices,
smoothing_steps='nearest',
colorbar=False,
time=None)
brain.add_data(hemi_data,
fmin=fmin,
hemi=h,
fmax=fmax,
colormap='hot',
vertices=hemi_vertices,
smoothing_steps=1,
initial_time=0.,
colorbar=False,
time=None)
# add label
label = read_label(fname_label)
brain.add_label(label, scalar_thresh=0.)
brain.remove_labels()
# add foci
brain.add_foci([0], coords_as_verts=True, hemi=hemi, color='blue')
# add text
brain.add_text(x=0, y=0, text='foo')
# screenshot
brain.show_view(view=dict(azimuth=180., elevation=90.))
img = brain.screenshot(mode='rgb')
assert_allclose(img.shape, (size[0], size[1], 3),
atol=70) # XXX undo once size is fixed
# add annotation
annots = ['aparc', 'PALS_B12_Lobes']
borders = [True, 2]
alphas = [1, 0.5]
brain = _Brain(subject_id='fsaverage',
hemi=hemi,
size=size,
surf='inflated',
subjects_dir=subjects_dir)
for a, b, p in zip(annots, borders, alphas):
brain.add_annotation(a, b, p)
brain.close()
def test_brain_init(renderer, tmpdir, pixel_ratio):
"""Test initialization of the _Brain instance."""
from mne.label import read_label
hemi = 'lh'
surf = 'inflated'
cortex = 'low_contrast'
title = 'test'
size = (300, 300)
kwargs = dict(subject_id=subject_id, subjects_dir=subjects_dir)
with pytest.raises(ValueError, match='"size" parameter must be'):
_Brain(hemi=hemi, surf=surf, size=[1, 2, 3], **kwargs)
with pytest.raises(TypeError, match='figure'):
_Brain(hemi=hemi, surf=surf, figure='foo', **kwargs)
with pytest.raises(TypeError, match='interaction'):
_Brain(hemi=hemi, surf=surf, interaction=0, **kwargs)
with pytest.raises(ValueError, match='interaction'):
_Brain(hemi=hemi, surf=surf, interaction='foo', **kwargs)
with pytest.raises(KeyError):
_Brain(hemi='foo', surf=surf, **kwargs)
brain = _Brain(hemi=hemi,
surf=surf,
size=size,
title=title,
cortex=cortex,
units='m',
**kwargs)
assert brain.interaction == 'trackball'
# add_data
stc = read_source_estimate(fname_stc)
fmin = stc.data.min()
fmax = stc.data.max()
for h in brain._hemis:
if h == 'lh':
hi = 0
else:
hi = 1
hemi_data = stc.data[:len(stc.vertices[hi]), 10]
hemi_vertices = stc.vertices[hi]
with pytest.raises(TypeError, match='scale_factor'):
brain.add_data(hemi_data, hemi=h, scale_factor='foo')
with pytest.raises(TypeError, match='vector_alpha'):
brain.add_data(hemi_data, hemi=h, vector_alpha='foo')
with pytest.raises(ValueError, match='thresh'):
brain.add_data(hemi_data, hemi=h, thresh=-1)
with pytest.raises(ValueError, match='remove_existing'):
brain.add_data(hemi_data, hemi=h, remove_existing=-1)
with pytest.raises(ValueError, match='time_label_size'):
brain.add_data(hemi_data,
hemi=h,
time_label_size=-1,
vertices=hemi_vertices)
with pytest.raises(ValueError, match='is positive'):
brain.add_data(hemi_data,
hemi=h,
smoothing_steps=-1,
vertices=hemi_vertices)
with pytest.raises(TypeError, match='int or NoneType'):
brain.add_data(hemi_data, hemi=h, smoothing_steps='foo')
with pytest.raises(ValueError, match='dimension mismatch'):
brain.add_data(array=np.array([0, 1, 2]),
hemi=h,
vertices=hemi_vertices)
with pytest.raises(ValueError, match='vertices parameter must not be'):
brain.add_data(hemi_data,
fmin=fmin,
hemi=hemi,
fmax=fmax,
vertices=None)
with pytest.raises(ValueError, match='has shape'):
brain.add_data(hemi_data[:, np.newaxis],
fmin=fmin,
hemi=hemi,
fmax=fmax,
vertices=None,
time=[0, 1])
brain.add_data(hemi_data,
fmin=fmin,
hemi=h,
fmax=fmax,
colormap='hot',
vertices=hemi_vertices,
smoothing_steps='nearest',
colorbar=(0, 0),
time=None)
assert brain.data['lh']['array'] is hemi_data
assert brain.views == ['lateral']
assert brain.hemis == ('lh', )
brain.add_data(hemi_data[:, np.newaxis],
fmin=fmin,
hemi=h,
fmax=fmax,
colormap='hot',
vertices=hemi_vertices,
smoothing_steps=1,
initial_time=0.,
colorbar=False,
time=[0])
brain.set_time_point(0) # should hit _safe_interp1d
with pytest.raises(ValueError, match='consistent with'):
brain.add_data(hemi_data[:, np.newaxis],
fmin=fmin,
hemi=h,
fmax=fmax,
colormap='hot',
vertices=hemi_vertices,
smoothing_steps='nearest',
colorbar=False,
time=[1])
with pytest.raises(ValueError, match='different from'):
brain.add_data(hemi_data[:, np.newaxis][:, [0, 0]],
fmin=fmin,
hemi=h,
fmax=fmax,
colormap='hot',
vertices=hemi_vertices)
with pytest.raises(ValueError, match='need shape'):
brain.add_data(hemi_data[:, np.newaxis],
time=[0, 1],
fmin=fmin,
hemi=h,
fmax=fmax,
colormap='hot',
vertices=hemi_vertices)
with pytest.raises(ValueError, match='If array has 3'):
brain.add_data(hemi_data[:, np.newaxis, np.newaxis],
fmin=fmin,
hemi=h,
fmax=fmax,
colormap='hot',
vertices=hemi_vertices)
# add label
label = read_label(fname_label)
brain.add_label(label, scalar_thresh=0.)
brain.remove_labels()
brain.add_label(fname_label)
brain.add_label('V1', borders=True)
brain.remove_labels()
brain.remove_labels()
# add foci
brain.add_foci([0], coords_as_verts=True, hemi=hemi, color='blue')
# add text
brain.add_text(x=0, y=0, text='foo')
# add annotation
annots = [
'aparc',
path.join(subjects_dir, 'fsaverage', 'label',
'lh.PALS_B12_Lobes.annot')
]
borders = [True, 2]
alphas = [1, 0.5]
colors = [None, 'r']
brain = _Brain(subject_id='fsaverage',
hemi=hemi,
size=size,
surf='inflated',
subjects_dir=subjects_dir)
for a, b, p, color in zip(annots, borders, alphas, colors):
brain.add_annotation(a, b, p, color=color)
brain.show_view(dict(focalpoint=(1e-5, 1e-5, 1e-5)), roll=1, distance=500)
# image and screenshot
fname = path.join(str(tmpdir), 'test.png')
assert not path.isfile(fname)
brain.save_image(fname)
assert path.isfile(fname)
brain.show_view(view=dict(azimuth=180., elevation=90.))
img = brain.screenshot(mode='rgb')
if renderer._get_3d_backend() == 'mayavi':
pixel_ratio = 1. # no HiDPI when using the testing backend
want_size = np.array([size[0] * pixel_ratio, size[1] * pixel_ratio, 3])
assert_allclose(img.shape, want_size)
brain.close()
