def sensors_to_surface(self, sensors_gid, surface_to_map_gid):
"""
Map EEG sensors onto the head surface (skin-air).
EEG sensor locations are typically only given on a unit sphere, that is,
they are effectively only identified by their orientation with respect
to a coordinate system. This method is used to map these unit vector
sensor "locations" to a specific location on the surface of the skin.
Assumes coordinate systems are aligned, i.e. common x,y,z and origin.
"""
# Normalize sensor and vertex locations to unit vectors
sensors_h5_class, sensors_h5_path = self._load_h5_of_gid(sensors_gid)
sensors_dt = Sensors()
with sensors_h5_class(sensors_h5_path) as sensors_h5:
sensors_h5.load_into(sensors_dt)
surface_h5_class, surface_h5_path = self._load_h5_of_gid(
surface_to_map_gid)
surface_dt = Surface()
with surface_h5_class(surface_h5_path) as surface_h5:
surface_h5.load_into(surface_dt)
return sensors_dt.sensors_to_surface(surface_dt).tolist()
def test_store_load_partial_sensors(tmph5factory):
sensors = Sensors(sensors_type="SEEG",
labels=numpy.array(["s1", "s2", "s3"]),
locations=numpy.zeros((3, 3)),
number_of_sensors=3)
tmp_file = tmph5factory("Sensors_{}.h5".format(sensors.gid))
with SensorsH5(tmp_file) as f:
f.store(sensors)
sensors_stored = Sensors()
with pytest.raises(TraitAttributeError):
sensors_stored.labels
with SensorsH5(tmp_file) as f:
f.load_into(sensors_stored)
assert sensors_stored.labels is not None
def from_tvb_file(self, filepath, remove_leading_zeros_from_labels=False):
self._tvb = TVBSensors.from_file(filepath, self._tvb)
if len(self._tvb.labels) > 0:
if remove_leading_zeros_from_labels:
self.remove_leading_zeros_from_labels()
self.file_path = filepath
return self
def build(type="EEG", nr_sensors=3):
return Sensors(sensors_type=type,
labels=numpy.array(["s"] * nr_sensors),
locations=numpy.ones((nr_sensors, 3)),
number_of_sensors=nr_sensors,
has_orientation=True,
orientations=numpy.zeros((nr_sensors, 3)),
usable=numpy.array([True] * nr_sensors))
def test_store_load_sensors(tmph5factory, sensors_factory):
sensors = sensors_factory("SEEG", 3)
tmp_file = tmph5factory("Sensors_{}.h5".format(sensors.gid))
with SensorsH5(tmp_file) as f:
f.store(sensors)
sensors_stored = Sensors()
with pytest.raises(TraitAttributeError):
sensors_stored.labels
with SensorsH5(tmp_file) as f:
f.load_into(sensors_stored)
assert sensors_stored.labels is not None
number_of_triangles=3,
edge_mean_length=1.0,
edge_min_length=0.0,
edge_max_length=2.0,
zero_based_triangles=False,
split_triangles=numpy.arange(0),
number_of_split_slices=1,
split_slices=dict(),
bi_hemispheric=False,
# surface_type="surface",
valid_for_simulations=True)
sensors = Sensors(sensors_type="SEEG",
labels=numpy.array(["s1", "s2", "s3"]),
locations=numpy.zeros((3, 3)),
number_of_sensors=3,
has_orientation=True,
orientations=numpy.zeros((3, 3)),
usable=numpy.array([True, False, True]))
volume = Volume(origin=numpy.zeros((3, 3)), voxel_size=numpy.zeros((3, 3)))
projection_matrix = ProjectionMatrix(projection_type="projSEEG",
sources=cortical_surface,
sensors=sensors,
projection_data=numpy.zeros((5, 3)))
local_connectivity = LocalConnectivity(
surface=cortical_surface,
matrix=scipy.sparse.csc_matrix(numpy.eye(8) + numpy.eye(8)[:, ::-1]),
cutoff=12,