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# Author: Christian Brodbeck <christianbrodbeck@nyu.edu>
#
# License: BSD (3-clause)
import os
import re
import numpy as np
from numpy.testing import assert_allclose
from nose.tools import (assert_equal, assert_almost_equal, assert_false,
assert_raises, assert_true)
import warnings
import mne
from mne.datasets import testing
from mne.io.kit.tests import data_dir as kit_data_dir
from mne.utils import (_TempDir, requires_traits, requires_mne,
requires_freesurfer, run_tests_if_main, requires_mayavi)
from mne.externals.six import string_types
# backend needs to be set early
try:
from traits.etsconfig.api import ETSConfig
except ImportError:
pass
else:
ETSConfig.toolkit = 'qt4'
data_path = testing.data_path(download=False)
raw_path = os.path.join(data_path, 'MEG', 'sample',
'sample_audvis_trunc_raw.fif')
fname_trans = os.path.join(data_path, 'MEG', 'sample',
'sample_audvis_trunc-trans.fif')
kit_raw_path = os.path.join(kit_data_dir, 'test_bin_raw.fif')
subjects_dir = os.path.join(data_path, 'subjects')
warnings.simplefilter('always')
@testing.requires_testing_data
@requires_traits
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']
@testing.requires_testing_data
@requires_traits
@requires_mne
@requires_freesurfer
def test_coreg_model_with_fsaverage():
"""Test CoregModel with the fsaverage brain data"""
tempdir = _TempDir()
from mne.gui._coreg_gui import CoregModel
mne.create_default_subject(subjects_dir=tempdir)
model = CoregModel()
model.mri.use_high_res_head = False
model.mri.subjects_dir = tempdir
model.mri.subject = 'fsaverage'
assert_true(model.mri.fid_ok)
model.hsp.file = raw_path
lpa_distance = model.lpa_distance
nasion_distance = model.nasion_distance
rpa_distance = model.rpa_distance
avg_point_distance = np.mean(model.point_distance)
# test hsp point omission
model.trans_y = -0.008
model.fit_auricular_points()
model.omit_hsp_points(0.02)
assert_equal(model.hsp.n_omitted, 1)
model.omit_hsp_points(reset=True)
assert_equal(model.hsp.n_omitted, 0)
model.omit_hsp_points(0.02, reset=True)
assert_equal(model.hsp.n_omitted, 1)
# scale with 1 parameter
model.n_scale_params = 1
model.fit_scale_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_scale_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_scale_hsp_points()
avg_point_distance_1param = np.mean(model.point_distance)
assert_true(avg_point_distance_1param < avg_point_distance)
# scaling job
sdir, sfrom, sto, scale, skip_fiducials, bemsol = \
model.get_scaling_job('scaled', False, True)
assert_equal(sdir, tempdir)
assert_equal(sfrom, 'fsaverage')
assert_equal(sto, 'scaled')
assert_equal(scale, model.scale)
assert_equal(set(bemsol), set(('inner_skull-bem',)))
sdir, sfrom, sto, scale, skip_fiducials, bemsol = \
model.get_scaling_job('scaled', False, False)
assert_equal(bemsol, [])
# scale with 3 parameters
model.n_scale_params = 3
model.fit_scale_hsp_points()
assert_true(np.mean(model.point_distance) < avg_point_distance_1param)
# test switching raw disables point omission
assert_equal(model.hsp.n_omitted, 1)
with warnings.catch_warnings(record=True):
model.hsp.file = kit_raw_path
assert_equal(model.hsp.n_omitted, 0)
@testing.requires_testing_data
@requires_mayavi
def test_coreg_gui():
"""Test Coregistration GUI"""
from mne.gui._coreg_gui import CoregFrame
frame = CoregFrame()
frame.edit_traits()
frame.model.mri.subjects_dir = subjects_dir
frame.model.mri.subject = 'sample'
assert_false(frame.model.mri.fid_ok)
frame.model.mri.lpa = [[-0.06, 0, 0]]
frame.model.mri.nasion = [[0, 0.05, 0]]
frame.model.mri.rpa = [[0.08, 0, 0]]
assert_true(frame.model.mri.fid_ok)
run_tests_if_main()
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