def test_coreg_model():
"""Test CoregModel"""
from mne.gui._coreg_gui import CoregModel
tempdir = _TempDir()
trans_dst = os.path.join(tempdir, 'test-trans.fif')
model = CoregModel()
assert_raises(RuntimeError, model.save_trans, 'blah.fif')
model.mri.subjects_dir = subjects_dir
model.mri.subject = 'sample'
assert_false(model.mri.fid_ok)
model.mri.lpa = [[-0.06, 0, 0]]
model.mri.nasion = [[0, 0.05, 0]]
model.mri.rpa = [[0.08, 0, 0]]
assert_true(model.mri.fid_ok)
model.hsp.file = raw_path
assert_allclose(model.hsp.lpa, [[-7.137e-2, 0, 5.122e-9]], 1e-4)
assert_allclose(model.hsp.rpa, [[+7.527e-2, 0, 5.588e-9]], 1e-4)
assert_allclose(model.hsp.nasion, [[+3.725e-9, 1.026e-1, 4.191e-9]], 1e-4)
assert_true(model.has_fid_data)
lpa_distance = model.lpa_distance
nasion_distance = model.nasion_distance
rpa_distance = model.rpa_distance
avg_point_distance = np.mean(model.point_distance)
model.fit_auricular_points()
old_x = lpa_distance**2 + rpa_distance**2
new_x = model.lpa_distance**2 + model.rpa_distance**2
assert_true(new_x < old_x)
model.fit_fiducials()
old_x = lpa_distance**2 + rpa_distance**2 + nasion_distance**2
new_x = (model.lpa_distance**2 + model.rpa_distance**2 +
model.nasion_distance**2)
assert_true(new_x < old_x)
model.fit_hsp_points()
assert_true(np.mean(model.point_distance) < avg_point_distance)
model.save_trans(trans_dst)
trans = mne.read_trans(trans_dst)
assert_allclose(trans['trans'], model.head_mri_trans)
# test restoring trans
x, y, z, rot_x, rot_y, rot_z = .1, .2, .05, 1.5, 0.1, -1.2
model.trans_x = x
model.trans_y = y
model.trans_z = z
model.rot_x = rot_x
model.rot_y = rot_y
model.rot_z = rot_z
trans = model.head_mri_trans
model.reset_traits(
["trans_x", "trans_y", "trans_z", "rot_x", "rot_y", "rot_z"])
assert_equal(model.trans_x, 0)
model.set_trans(trans)
assert_almost_equal(model.trans_x, x)
assert_almost_equal(model.trans_y, y)
assert_almost_equal(model.trans_z, z)
assert_almost_equal(model.rot_x, rot_x)
assert_almost_equal(model.rot_y, rot_y)
assert_almost_equal(model.rot_z, rot_z)
# info
assert_true(isinstance(model.fid_eval_str, string_types))
assert_true(isinstance(model.points_eval_str, string_types))
model.get_prepare_bem_model_job('sample')
model.load_trans(fname_trans)
from mne.gui._coreg_gui import CoregFrame
x = CoregFrame(raw_path, 'sample', subjects_dir)
os.environ['_MNE_GUI_TESTING_MODE'] = 'true'
try:
with warnings.catch_warnings(record=True): # traits spews warnings
warnings.simplefilter('always')
x._init_plot()
finally:
del os.environ['_MNE_GUI_TESTING_MODE']
def test_coreg_model():
"""Test CoregModel"""
from mne.gui._coreg_gui import CoregModel
tempdir = _TempDir()
trans_dst = os.path.join(tempdir, 'test-trans.fif')
model = CoregModel()
assert_raises(RuntimeError, model.save_trans, 'blah.fif')
model.mri.use_high_res_head = False
model.mri.subjects_dir = subjects_dir
model.mri.subject = 'sample'
assert_false(model.mri.fid_ok)
model.mri.lpa = [[-0.06, 0, 0]]
model.mri.nasion = [[0, 0.05, 0]]
model.mri.rpa = [[0.08, 0, 0]]
assert_true(model.mri.fid_ok)
model.hsp.file = raw_path
assert_allclose(model.hsp.lpa, [[-7.137e-2, 0, 5.122e-9]], 1e-4)
assert_allclose(model.hsp.rpa, [[+7.527e-2, 0, 5.588e-9]], 1e-4)
assert_allclose(model.hsp.nasion, [[+3.725e-9, 1.026e-1, 4.191e-9]], 1e-4)
assert_true(model.has_fid_data)
lpa_distance = model.lpa_distance
nasion_distance = model.nasion_distance
rpa_distance = model.rpa_distance
avg_point_distance = np.mean(model.point_distance)
model.fit_auricular_points()
old_x = lpa_distance ** 2 + rpa_distance ** 2
new_x = model.lpa_distance ** 2 + model.rpa_distance ** 2
assert_true(new_x < old_x)
model.fit_fiducials()
old_x = lpa_distance ** 2 + rpa_distance ** 2 + nasion_distance ** 2
new_x = (model.lpa_distance ** 2 + model.rpa_distance ** 2 +
model.nasion_distance ** 2)
assert_true(new_x < old_x)
model.fit_hsp_points()
assert_true(np.mean(model.point_distance) < avg_point_distance)
model.save_trans(trans_dst)
trans = mne.read_trans(trans_dst)
assert_allclose(trans['trans'], model.head_mri_trans)
# test restoring trans
x, y, z, rot_x, rot_y, rot_z = .1, .2, .05, 1.5, 0.1, -1.2
model.trans_x = x
model.trans_y = y
model.trans_z = z
model.rot_x = rot_x
model.rot_y = rot_y
model.rot_z = rot_z
trans = model.head_mri_trans
model.reset_traits(["trans_x", "trans_y", "trans_z", "rot_x", "rot_y",
"rot_z"])
assert_equal(model.trans_x, 0)
model.set_trans(trans)
assert_almost_equal(model.trans_x, x)
assert_almost_equal(model.trans_y, y)
assert_almost_equal(model.trans_z, z)
assert_almost_equal(model.rot_x, rot_x)
assert_almost_equal(model.rot_y, rot_y)
assert_almost_equal(model.rot_z, rot_z)
# info
assert_true(isinstance(model.fid_eval_str, string_types))
assert_true(isinstance(model.points_eval_str, string_types))
# scaling job
sdir, sfrom, sto, scale, skip_fiducials, bemsol = \
model.get_scaling_job('sample2', False, True)
assert_equal(sdir, subjects_dir)
assert_equal(sfrom, 'sample')
assert_equal(sto, 'sample2')
assert_equal(scale, model.scale)
assert_equal(skip_fiducials, False)
# find BEM files
bems = set()
for fname in os.listdir(os.path.join(subjects_dir, 'sample', 'bem')):
match = re.match('sample-(.+-bem)\.fif', fname)
if match:
bems.add(match.group(1))
assert_equal(set(bemsol), bems)
sdir, sfrom, sto, scale, skip_fiducials, bemsol = \
model.get_scaling_job('sample2', True, False)
assert_equal(bemsol, [])
assert_true(skip_fiducials)
model.load_trans(fname_trans)
from mne.gui._coreg_gui import CoregFrame
x = CoregFrame(raw_path, 'sample', subjects_dir)
os.environ['_MNE_GUI_TESTING_MODE'] = 'true'
try:
with warnings.catch_warnings(record=True): # traits spews warnings
warnings.simplefilter('always')
x._init_plot()
finally:
del os.environ['_MNE_GUI_TESTING_MODE']
def test_coreg_model():
"""Test CoregModel."""
from mne.gui._coreg_gui import CoregModel
tempdir = _TempDir()
trans_dst = op.join(tempdir, 'test-trans.fif')
model = CoregModel()
pytest.raises(RuntimeError, model.save_trans, 'blah.fif')
model.mri.subjects_dir = subjects_dir
model.mri.subject = 'sample'
assert not model.mri.fid_ok
model.mri.lpa = [[-0.06, 0, 0]]
model.mri.nasion = [[0, 0.05, 0]]
model.mri.rpa = [[0.08, 0, 0]]
assert (model.mri.fid_ok)
model.hsp.file = raw_path
assert_allclose(model.hsp.lpa, [[-7.137e-2, 0, 5.122e-9]], 1e-4)
assert_allclose(model.hsp.rpa, [[+7.527e-2, 0, 5.588e-9]], 1e-4)
assert_allclose(model.hsp.nasion, [[+3.725e-9, 1.026e-1, 4.191e-9]], 1e-4)
assert model.has_lpa_data
assert model.has_nasion_data
assert model.has_rpa_data
assert len(model.hsp.eeg_points) > 1
assert len(model.mri.bem_low_res.surf.rr) == 2562
assert len(model.mri.bem_high_res.surf.rr) == 267122
lpa_distance = model.lpa_distance
nasion_distance = model.nasion_distance
rpa_distance = model.rpa_distance
avg_point_distance = np.mean(model.point_distance)
model.nasion_weight = 1.
model.fit_fiducials(0)
old_x = lpa_distance ** 2 + rpa_distance ** 2 + nasion_distance ** 2
new_x = (model.lpa_distance ** 2 + model.rpa_distance ** 2 +
model.nasion_distance ** 2)
assert new_x < old_x
model.fit_icp(0)
new_dist = np.mean(model.point_distance)
assert new_dist < avg_point_distance
model.save_trans(trans_dst)
trans = mne.read_trans(trans_dst)
assert_allclose(trans['trans'], model.head_mri_t)
# test restoring trans
x, y, z = 100, 200, 50
rot_x, rot_y, rot_z = np.rad2deg([1.5, 0.1, -1.2])
model.trans_x = x
model.trans_y = y
model.trans_z = z
model.rot_x = rot_x
model.rot_y = rot_y
model.rot_z = rot_z
trans = model.mri_head_t
model.reset_traits(["trans_x", "trans_y", "trans_z", "rot_x", "rot_y",
"rot_z"])
assert_equal(model.trans_x, 0)
model.set_trans(trans)
assert_array_almost_equal(model.trans_x, x)
assert_array_almost_equal(model.trans_y, y)
assert_array_almost_equal(model.trans_z, z)
assert_array_almost_equal(model.rot_x, rot_x)
assert_array_almost_equal(model.rot_y, rot_y)
assert_array_almost_equal(model.rot_z, rot_z)
# info
assert (isinstance(model.fid_eval_str, str))
assert (isinstance(model.points_eval_str, str))
# scaling job
assert not model.can_prepare_bem_model
model.n_scale_params = 1
assert (model.can_prepare_bem_model)
model.prepare_bem_model = True
sdir, sfrom, sto, scale, skip_fiducials, labels, annot, bemsol = \
model.get_scaling_job('sample2', False)
assert_equal(sdir, subjects_dir)
assert_equal(sfrom, 'sample')
assert_equal(sto, 'sample2')
assert_allclose(scale, model.parameters[6:9])
assert_equal(skip_fiducials, False)
# find BEM files
bems = set()
for fname in os.listdir(op.join(subjects_dir, 'sample', 'bem')):
match = re.match(r'sample-(.+-bem)\.fif', fname)
if match:
bems.add(match.group(1))
assert_equal(set(bemsol), bems)
model.prepare_bem_model = False
sdir, sfrom, sto, scale, skip_fiducials, labels, annot, bemsol = \
model.get_scaling_job('sample2', True)
assert_equal(bemsol, [])
assert (skip_fiducials)
model.load_trans(fname_trans)
model.save_trans(trans_dst)
trans = mne.read_trans(trans_dst)
assert_allclose(trans['trans'], model.head_mri_t)
assert_allclose(invert_transform(trans)['trans'][:3, 3] * 1000.,
[model.trans_x, model.trans_y, model.trans_z])
def test_coreg_model():
"""Test CoregModel."""
from mne.gui._coreg_gui import CoregModel
tempdir = _TempDir()
trans_dst = op.join(tempdir, 'test-trans.fif')
model = CoregModel()
pytest.raises(RuntimeError, model.save_trans, 'blah.fif')
model.mri.subjects_dir = subjects_dir
model.mri.subject = 'sample'
assert not model.mri.fid_ok
model.mri.lpa = [[-0.06, 0, 0]]
model.mri.nasion = [[0, 0.05, 0]]
model.mri.rpa = [[0.08, 0, 0]]
assert (model.mri.fid_ok)
model.hsp.file = raw_path
assert_allclose(model.hsp.lpa, [[-7.137e-2, 0, 5.122e-9]], 1e-4)
assert_allclose(model.hsp.rpa, [[+7.527e-2, 0, 5.588e-9]], 1e-4)
assert_allclose(model.hsp.nasion, [[+3.725e-9, 1.026e-1, 4.191e-9]], 1e-4)
assert model.has_lpa_data
assert model.has_nasion_data
assert model.has_rpa_data
assert len(model.hsp.eeg_points) > 1
assert len(model.mri.bem_low_res.surf.rr) == 2562
assert len(model.mri.bem_high_res.surf.rr) == 267122
lpa_distance = model.lpa_distance
nasion_distance = model.nasion_distance
rpa_distance = model.rpa_distance
avg_point_distance = np.mean(model.point_distance)
model.nasion_weight = 1.
model.fit_fiducials(0)
old_x = lpa_distance**2 + rpa_distance**2 + nasion_distance**2
new_x = (model.lpa_distance**2 + model.rpa_distance**2 +
model.nasion_distance**2)
assert new_x < old_x
model.fit_icp(0)
new_dist = np.mean(model.point_distance)
assert new_dist < avg_point_distance
model.save_trans(trans_dst)
trans = mne.read_trans(trans_dst)
assert_allclose(trans['trans'], model.head_mri_t)
# test restoring trans
x, y, z = 100, 200, 50
rot_x, rot_y, rot_z = np.rad2deg([1.5, 0.1, -1.2])
model.trans_x = x
model.trans_y = y
model.trans_z = z
model.rot_x = rot_x
model.rot_y = rot_y
model.rot_z = rot_z
trans = model.mri_head_t
model.reset_traits(
["trans_x", "trans_y", "trans_z", "rot_x", "rot_y", "rot_z"])
assert_equal(model.trans_x, 0)
model.set_trans(trans)
assert_array_almost_equal(model.trans_x, x)
assert_array_almost_equal(model.trans_y, y)
assert_array_almost_equal(model.trans_z, z)
assert_array_almost_equal(model.rot_x, rot_x)
assert_array_almost_equal(model.rot_y, rot_y)
assert_array_almost_equal(model.rot_z, rot_z)
# info
assert (isinstance(model.fid_eval_str, str))
assert (isinstance(model.points_eval_str, str))
# scaling job
assert not model.can_prepare_bem_model
model.n_scale_params = 1
assert (model.can_prepare_bem_model)
model.prepare_bem_model = True
sdir, sfrom, sto, scale, skip_fiducials, labels, annot, bemsol = \
model.get_scaling_job('sample2', False)
assert_equal(sdir, subjects_dir)
assert_equal(sfrom, 'sample')
assert_equal(sto, 'sample2')
assert_allclose(scale, model.parameters[6:9])
assert_equal(skip_fiducials, False)
# find BEM files
bems = set()
for fname in os.listdir(op.join(subjects_dir, 'sample', 'bem')):
match = re.match(r'sample-(.+-bem)\.fif', fname)
if match:
bems.add(match.group(1))
assert_equal(set(bemsol), bems)
model.prepare_bem_model = False
sdir, sfrom, sto, scale, skip_fiducials, labels, annot, bemsol = \
model.get_scaling_job('sample2', True)
assert_equal(bemsol, [])
assert (skip_fiducials)
model.load_trans(fname_trans)
model.save_trans(trans_dst)
trans = mne.read_trans(trans_dst)
assert_allclose(trans['trans'], model.head_mri_t)
assert_allclose(
invert_transform(trans)['trans'][:3, 3] * 1000.,
[model.trans_x, model.trans_y, model.trans_z])
def test_coreg_model():
"""Test CoregModel"""
from mne.gui._coreg_gui import CoregModel
tempdir = _TempDir()
trans_dst = os.path.join(tempdir, 'test-trans.fif')
model = CoregModel()
assert_raises(RuntimeError, model.save_trans, 'blah.fif')
model.mri.subjects_dir = subjects_dir
model.mri.subject = 'sample'
assert_false(model.mri.fid_ok)
model.mri.lpa = [[-0.06, 0, 0]]
model.mri.nasion = [[0, 0.05, 0]]
model.mri.rpa = [[0.08, 0, 0]]
assert_true(model.mri.fid_ok)
model.hsp.file = raw_path
assert_allclose(model.hsp.lpa, [[-7.137e-2, 0, 5.122e-9]], 1e-4)
assert_allclose(model.hsp.rpa, [[+7.527e-2, 0, 5.588e-9]], 1e-4)
assert_allclose(model.hsp.nasion, [[+3.725e-9, 1.026e-1, 4.191e-9]], 1e-4)
assert_true(model.has_fid_data)
lpa_distance = model.lpa_distance
nasion_distance = model.nasion_distance
rpa_distance = model.rpa_distance
avg_point_distance = np.mean(model.point_distance)
model.fit_auricular_points()
old_x = lpa_distance ** 2 + rpa_distance ** 2
new_x = model.lpa_distance ** 2 + model.rpa_distance ** 2
assert_true(new_x < old_x)
model.fit_fiducials()
old_x = lpa_distance ** 2 + rpa_distance ** 2 + nasion_distance ** 2
new_x = (model.lpa_distance ** 2 + model.rpa_distance ** 2
+ model.nasion_distance ** 2)
assert_true(new_x < old_x)
model.fit_hsp_points()
assert_true(np.mean(model.point_distance) < avg_point_distance)
model.save_trans(trans_dst)
trans = mne.read_trans(trans_dst)
assert_allclose(trans['trans'], model.head_mri_trans)
# test restoring trans
x, y, z, rot_x, rot_y, rot_z = .1, .2, .05, 1.5, 0.1, -1.2
model.trans_x = x
model.trans_y = y
model.trans_z = z
model.rot_x = rot_x
model.rot_y = rot_y
model.rot_z = rot_z
trans = model.head_mri_trans
model.reset_traits(["trans_x", "trans_y", "trans_z", "rot_x", "rot_y",
"rot_z"])
assert_equal(model.trans_x, 0)
model.set_trans(trans)
assert_almost_equal(model.trans_x, x)
assert_almost_equal(model.trans_y, y)
assert_almost_equal(model.trans_z, z)
assert_almost_equal(model.rot_x, rot_x)
assert_almost_equal(model.rot_y, rot_y)
assert_almost_equal(model.rot_z, rot_z)
# info
assert_true(isinstance(model.fid_eval_str, string_types))
assert_true(isinstance(model.points_eval_str, string_types))
model.get_prepare_bem_model_job('sample')
model.load_trans(fname_trans)
from mne.gui._coreg_gui import CoregFrame
x = CoregFrame(raw_path, 'sample', subjects_dir)
os.environ['_MNE_GUI_TESTING_MODE'] = 'true'
try:
with warnings.catch_warnings(record=True): # traits spews warnings
warnings.simplefilter('always')
x._init_plot()
finally:
del os.environ['_MNE_GUI_TESTING_MODE']
def test_coreg_model():
"""Test CoregModel"""
from mne.gui._coreg_gui import CoregModel
model = CoregModel()
assert_raises(RuntimeError, model.save_trans, 'blah.fif')
model.mri.subjects_dir = subjects_dir
model.mri.subject = 'sample'
assert_false(model.mri.fid_ok)
model.mri.lpa = [[-0.06, 0, 0]]
model.mri.nasion = [[0, 0.05, 0]]
model.mri.rpa = [[0.08, 0, 0]]
assert_true(model.mri.fid_ok)
model.hsp.file = raw_path
assert_allclose(model.hsp.lpa, [[-7.137e-2, 0, 5.122e-9]], 1e-4)
assert_allclose(model.hsp.rpa, [[+7.527e-2, 0, 5.588e-9]], 1e-4)
assert_allclose(model.hsp.nasion, [[+3.725e-9, 1.026e-1, 4.191e-9]], 1e-4)
assert_true(model.has_fid_data)
lpa_distance = model.lpa_distance
nasion_distance = model.nasion_distance
rpa_distance = model.rpa_distance
avg_point_distance = np.mean(model.point_distance)
model.fit_auricular_points()
old_x = lpa_distance ** 2 + rpa_distance ** 2
new_x = model.lpa_distance ** 2 + model.rpa_distance ** 2
assert_true(new_x < old_x)
model.fit_fiducials()
old_x = lpa_distance ** 2 + rpa_distance ** 2 + nasion_distance ** 2
new_x = (model.lpa_distance ** 2 + model.rpa_distance ** 2
+ model.nasion_distance ** 2)
assert_true(new_x < old_x)
model.fit_hsp_points()
assert_true(np.mean(model.point_distance) < avg_point_distance)
model.save_trans(trans_dst)
trans = mne.read_trans(trans_dst)
assert_allclose(trans['trans'], model.head_mri_trans)
# test restoring trans
x, y, z, rot_x, rot_y, rot_z = .1, .2, .05, 1.5, 0.1, -1.2
model.trans_x = x
model.trans_y = y
model.trans_z = z
model.rot_x = rot_x
model.rot_y = rot_y
model.rot_z = rot_z
trans = model.head_mri_trans
model.reset_traits(["trans_x", "trans_y", "trans_z", "rot_x", "rot_y",
"rot_z"])
assert_equal(model.trans_x, 0)
model.set_trans(trans)
assert_almost_equal(model.trans_x, x)
assert_almost_equal(model.trans_y, y)
assert_almost_equal(model.trans_z, z)
assert_almost_equal(model.rot_x, rot_x)
assert_almost_equal(model.rot_y, rot_y)
assert_almost_equal(model.rot_z, rot_z)
# info
assert_true(isinstance(model.fid_eval_str, string_types))
assert_true(isinstance(model.points_eval_str, string_types))
def test_coreg_model():
"""Test CoregModel"""
from mne.gui._coreg_gui import CoregModel
model = CoregModel()
assert_raises(RuntimeError, model.save_trans, 'blah.fif')
model.mri.subjects_dir = subjects_dir
model.mri.subject = 'sample'
assert_false(model.mri.fid_ok)
model.mri.lpa = [[-0.06, 0, 0]]
model.mri.nasion = [[0, 0.05, 0]]
model.mri.rpa = [[0.08, 0, 0]]
assert_true(model.mri.fid_ok)
model.hsp.file = raw_path
assert_allclose(model.hsp.lpa, [[-7.137e-2, 0, 5.122e-9]], 1e-4)
assert_allclose(model.hsp.rpa, [[+7.527e-2, 0, 5.588e-9]], 1e-4)
assert_allclose(model.hsp.nasion, [[+3.725e-9, 1.026e-1, 4.191e-9]], 1e-4)
assert_true(model.has_fid_data)
lpa_distance = model.lpa_distance
nasion_distance = model.nasion_distance
rpa_distance = model.rpa_distance
avg_point_distance = np.mean(model.point_distance)
model.fit_auricular_points()
old_x = lpa_distance**2 + rpa_distance**2
new_x = model.lpa_distance**2 + model.rpa_distance**2
assert_true(new_x < old_x)
model.fit_fiducials()
old_x = lpa_distance**2 + rpa_distance**2 + nasion_distance**2
new_x = (model.lpa_distance**2 + model.rpa_distance**2 +
model.nasion_distance**2)
assert_true(new_x < old_x)
model.fit_hsp_points()
assert_true(np.mean(model.point_distance) < avg_point_distance)
model.save_trans(trans_dst)
trans = mne.read_trans(trans_dst)
assert_allclose(trans['trans'], model.head_mri_trans)
# test restoring trans
x, y, z, rot_x, rot_y, rot_z = .1, .2, .05, 1.5, 0.1, -1.2
model.trans_x = x
model.trans_y = y
model.trans_z = z
model.rot_x = rot_x
model.rot_y = rot_y
model.rot_z = rot_z
trans = model.head_mri_trans
model.reset_traits(
["trans_x", "trans_y", "trans_z", "rot_x", "rot_y", "rot_z"])
assert_equal(model.trans_x, 0)
model.set_trans(trans)
assert_almost_equal(model.trans_x, x)
assert_almost_equal(model.trans_y, y)
assert_almost_equal(model.trans_z, z)
assert_almost_equal(model.rot_x, rot_x)
assert_almost_equal(model.rot_y, rot_y)
assert_almost_equal(model.rot_z, rot_z)
# info
assert_true(isinstance(model.fid_eval_str, string_types))
assert_true(isinstance(model.points_eval_str, string_types))
def test_coreg_model():
"""Test CoregModel."""
from mne.gui._coreg_gui import CoregModel
tempdir = _TempDir()
trans_dst = op.join(tempdir, 'test-trans.fif')
model = CoregModel()
assert_raises(RuntimeError, model.save_trans, 'blah.fif')
model.mri.use_high_res_head = False
model.mri.subjects_dir = subjects_dir
model.mri.subject = 'sample'
assert_false(model.mri.fid_ok)
model.mri.lpa = [[-0.06, 0, 0]]
model.mri.nasion = [[0, 0.05, 0]]
model.mri.rpa = [[0.08, 0, 0]]
assert_true(model.mri.fid_ok)
model.hsp.file = raw_path
assert_allclose(model.hsp.lpa, [[-7.137e-2, 0, 5.122e-9]], 1e-4)
assert_allclose(model.hsp.rpa, [[+7.527e-2, 0, 5.588e-9]], 1e-4)
assert_allclose(model.hsp.nasion, [[+3.725e-9, 1.026e-1, 4.191e-9]], 1e-4)
assert_true(model.has_fid_data)
assert len(model.hsp.eeg_points) > 1
lpa_distance = model.lpa_distance
nasion_distance = model.nasion_distance
rpa_distance = model.rpa_distance
avg_point_distance = np.mean(model.point_distance)
model.fit_auricular_points()
old_x = lpa_distance**2 + rpa_distance**2
new_x = model.lpa_distance**2 + model.rpa_distance**2
assert_true(new_x < old_x)
model.fit_fiducials()
old_x = lpa_distance**2 + rpa_distance**2 + nasion_distance**2
new_x = (model.lpa_distance**2 + model.rpa_distance**2 +
model.nasion_distance**2)
assert_true(new_x < old_x)
model.fit_hsp_points()
assert_true(np.mean(model.point_distance) < avg_point_distance)
model.save_trans(trans_dst)
trans = mne.read_trans(trans_dst)
assert_allclose(trans['trans'], model.head_mri_trans)
# test restoring trans
x, y, z, rot_x, rot_y, rot_z = .1, .2, .05, 1.5, 0.1, -1.2
model.trans_x = x
model.trans_y = y
model.trans_z = z
model.rot_x = rot_x
model.rot_y = rot_y
model.rot_z = rot_z
trans = model.head_mri_trans
model.reset_traits(
["trans_x", "trans_y", "trans_z", "rot_x", "rot_y", "rot_z"])
assert_equal(model.trans_x, 0)
model.set_trans(trans)
assert_almost_equal(model.trans_x, x)
assert_almost_equal(model.trans_y, y)
assert_almost_equal(model.trans_z, z)
assert_almost_equal(model.rot_x, rot_x)
assert_almost_equal(model.rot_y, rot_y)
assert_almost_equal(model.rot_z, rot_z)
# info
assert_true(isinstance(model.fid_eval_str, string_types))
assert_true(isinstance(model.points_eval_str, string_types))
# scaling job
assert_false(model.can_prepare_bem_model)
model.n_scale_params = 1
assert_true(model.can_prepare_bem_model)
model.prepare_bem_model = True
sdir, sfrom, sto, scale, skip_fiducials, labels, annot, bemsol = \
model.get_scaling_job('sample2', False)
assert_equal(sdir, subjects_dir)
assert_equal(sfrom, 'sample')
assert_equal(sto, 'sample2')
assert_equal(scale, model.scale)
assert_equal(skip_fiducials, False)
# find BEM files
bems = set()
for fname in os.listdir(op.join(subjects_dir, 'sample', 'bem')):
match = re.match(r'sample-(.+-bem)\.fif', fname)
if match:
bems.add(match.group(1))
assert_equal(set(bemsol), bems)
model.prepare_bem_model = False
sdir, sfrom, sto, scale, skip_fiducials, labels, annot, bemsol = \
model.get_scaling_job('sample2', True)
assert_equal(bemsol, [])
assert_true(skip_fiducials)
model.load_trans(fname_trans)