def test_sensors(self):
dt = sensors.Sensors(load_default=True)
dt.configure()
summary_info = dt.summary_info
self.assertEqual(summary_info['Sensor type'], '')
self.assertEqual(summary_info['Number of Sensors'], 62)
self.assertFalse(dt.has_orientation)
self.assertEqual(dt.labels.shape, (62, ))
self.assertEqual(dt.locations.shape, (62, 3))
self.assertEqual(dt.number_of_sensors, 62)
self.assertEqual(dt.orientations.shape, (0, ))
self.assertEqual(dt.sensors_type, '')
## Mapping 62 sensors on a Skin surface should work
surf = SkinAir(load_default=True)
surf.configure()
mapping = dt.sensors_to_surface(surf)
self.assertEqual(mapping.shape, (62, 3))
## Mapping on a surface with holes should fail
dummy_surf = SkinAir()
dummy_surf.vertices = numpy.array(range(30)).reshape(10, 3)
dummy_surf.triangles = numpy.array(range(9)).reshape(3, 3)
dummy_surf.configure()
try:
dt.sensors_to_surface(dummy_surf)
self.fail("Should have failed for this simple surface!")
except Exception:
pass
def test_sensors(self):
dt = sensors.Sensors(load_file="eeg_brainstorm_65.txt")
dt.configure()
summary_info = dt._find_summary_info()
assert summary_info['Sensor type'] == ''
assert summary_info['Number of Sensors'] == 65
assert not dt.has_orientation
assert dt.labels.shape == (65,)
assert dt.locations.shape == (65, 3)
assert dt.number_of_sensors == 65
assert dt.orientations.shape == (0,)
assert dt.sensors_type == ''
## Mapping 62 sensors on a Skin surface should work
surf = SkinAir(load_file="outer_skin_4096.zip")
surf.configure()
mapping = dt.sensors_to_surface(surf)
assert mapping.shape == (65, 3)
## Mapping on a surface with holes should fail
dummy_surf = SkinAir()
dummy_surf.vertices = numpy.array(range(30)).reshape(10, 3).astype('f')
dummy_surf.triangles = numpy.array(range(9)).reshape(3, 3)
dummy_surf.configure()
try:
dt.sensors_to_surface(dummy_surf)
pytest.fail("Should have failed for this simple surface!")
except Exception:
pass
def test_sensors(self):
dt = sensors.Sensors.from_file()
dt.configure()
summary_info = dt.summary_info()
assert summary_info['Sensor type'] is None
assert summary_info['Number of Sensors'] == 65
assert not dt.has_orientation
assert dt.labels.shape == (65,)
assert dt.locations.shape == (65, 3)
assert dt.number_of_sensors == 65
assert dt.orientations is None
assert dt.sensors_type is None
## Mapping 62 sensors on a Skin surface should work
surf = SkinAir.from_file()
surf.configure()
mapping = dt.sensors_to_surface(surf)
assert mapping.shape == (65, 3)
## Mapping on a surface with holes should fail
dummy_surf = SkinAir()
dummy_surf.vertices = numpy.array(list(range(30))).reshape(10, 3).astype('f')
dummy_surf.triangles = numpy.array(list(range(9))).reshape(3, 3)
dummy_surf.configure()
try:
dt.sensors_to_surface(dummy_surf)
self.fail("Should have failed for this simple surface!")
except Exception:
pass
def launch(self, uploaded, surface_type, zero_based_triangles = False):
"""
Execute import operations: unpack ZIP and build Surface object as result.
"""
if uploaded is None:
raise LaunchException ("Please select ZIP file which contains data to import")
self.logger.debug("Start to import surface: '%s' from file: %s"%(surface_type, uploaded))
try:
files = FilesHelper().unpack_zip(uploaded, self.storage_path)
except IOError:
exception_str = "Did not find the specified ZIP at %s" % uploaded
raise LaunchException(exception_str)
vertices = []
normals = []
triangles = []
for file_name in files:
if file_name.lower().find(self.VERTICES_TOKEN) >= 0:
vertices.append(file_name)
continue
if file_name.lower().find(self.NORMALS_TOKEN) >= 0:
normals.append(file_name)
continue
if file_name.lower().find(self.TRIANGLES_TOKEN) >= 0:
triangles.append(file_name)
# Now detect and instantiate correct surface type
self.logger.debug("Create surface instance")
if surface_type == CORTICAL:
surface = CorticalSurface()
elif surface_type == INNER_SKULL:
surface = BrainSkull()
elif surface_type == OUTER_SKULL:
surface = SkullSkin()
elif surface_type == OUTER_SKIN:
surface = SkinAir()
elif surface_type == EEG_CAP:
surface = EEGCap()
elif surface_type == FACE:
surface = FaceSurface()
else:
exception_str = "Could not determine surface type (selected option %s)" % surface_type
raise LaunchException(exception_str)
surface.storage_path = self.storage_path
all_vertices, all_normals, all_triangles = self._process_files(vertices, normals, triangles)
FilesHelper.remove_files(files, True)
surface.zero_based_triangles = zero_based_triangles
surface.vertices = all_vertices
surface.vertex_normals = all_normals
if zero_based_triangles:
surface.triangles = all_triangles
else :
surface.triangles = all_triangles - 1
surface.triangle_normals = None
# Now check if the triangles of the surface are valid
triangles_min_vertex = numpy.amin(surface.triangles)
if triangles_min_vertex < 0:
if triangles_min_vertex == -1 and not zero_based_triangles:
raise RuntimeError("Triangles contain a negative vertex index. Maybe you have a ZERO based surface.")
else:
raise RuntimeError("Your triangles contain a negative vertex index: %d" %triangles_min_vertex)
no_of_vertices = len(surface.vertices)
triangles_max_vertex = numpy.amax(surface.triangles)
if triangles_max_vertex >= no_of_vertices:
if triangles_max_vertex == no_of_vertices and zero_based_triangles:
raise RuntimeError("Your triangles contain an invalid vertex index: %d. \
Maybe your surface is NOT ZERO Based."%triangles_max_vertex)
else:
raise RuntimeError("Your triangles contain an invalid vertex index: %d." %triangles_max_vertex)
self.logger.debug("Surface ready to be stored")
return surface
class GIFTIParser():
"""
This class reads content of a GIFTI file and builds / returns a Surface instance
filled with details.
"""
UNIQUE_ID_ATTR = "UniqueID"
SUBJECT_ATTR = "SubjectID"
ASP_ATTR = "AnatomicalStructurePrimary"
DATE_ATTR = "Date"
DESCRIPTION_ATTR = "Description"
NAME_ATTR = "Name"
TIME_STEP_ATTR = "TimeStep"
def __init__(self, storage_path, operation_id):
self.logger = get_logger(__name__)
self.storage_path = storage_path
self.operation_id = operation_id
def parse(self, data_file):
"""
Parse NIFTI file and returns TimeSeries for it.
"""
self.logger.debug("Start to parse GIFTI file: %s"%data_file)
if data_file is None:
raise ParseException ("Please select GIFTI file which contains data to import")
if not os.path.exists(data_file):
raise ParseException ("Provided file %s does not exists"%data_file)
try:
gifti_image = giftiio.read(data_file)
self.logger.debug("File parsed successfully")
except Exception, excep:
self.logger.exception(excep)
msg = "File: %s does not have a valid GIFTI format." % data_file
raise ParseException(msg)
# Now try to determine what data is stored in GIFTI file
data_arrays = gifti_image.darrays
self.logger.debug("Determine data type stored in GIFTI file")
# First check if it's a surface
if (len(data_arrays) == 2
and intent_codes.code["NIFTI_INTENT_POINTSET"] == data_arrays[0].intent
and data_type_codes.code["NIFTI_TYPE_FLOAT32"] == data_arrays[0].datatype
and intent_codes.code["NIFTI_INTENT_TRIANGLE"] == data_arrays[1].intent
and data_type_codes.code["NIFTI_TYPE_INT32"] == data_arrays[1].datatype):
# Now try to determine what type of surface we have
data_array_meta = data_arrays[0].meta
surface = None
gid = None
subject = None
title = None
if (data_array_meta is not None and data_array_meta.data is not None
and len(data_array_meta.data) > 0):
anatomical_structure_primary = None
for meta_pair in data_array_meta.data:
if meta_pair.name == self.ASP_ATTR:
anatomical_structure_primary = meta_pair.value
elif meta_pair.name == self.UNIQUE_ID_ATTR:
gid = meta_pair.value.replace("{", "").replace("}", "")
elif meta_pair.name == self.SUBJECT_ATTR:
subject = meta_pair.value
elif meta_pair.name == self.NAME_ATTR:
title = meta_pair.value
# Based on info found in meta create correct surface type
if anatomical_structure_primary == "Head":
surface = SkinAir()
elif anatomical_structure_primary.startswith("Cortex"):
surface = CorticalSurface()
if surface is None:
raise ParseException("Could not determine type of the surface")
# Now fill TVB data type with info
if gid is not None:
surface.gid = gid
surface.storage_path = self.storage_path
surface.set_operation_id(self.operation_id)
surface.zero_based_triangles = True
surface.vertices = data_arrays[0].data
surface.triangles = data_arrays[1].data
if subject is not None:
surface.subject = subject
if title is not None:
surface.title = title
return surface
elif(len(data_arrays) > 1
and intent_codes.code["NIFTI_INTENT_TIME_SERIES"] == data_arrays[0].intent
and data_type_codes.code["NIFTI_TYPE_FLOAT32"] == data_arrays[0].datatype):
# Create TVB time series to be filled
time_series = TimeSeriesSurface()
time_series.storage_path = self.storage_path
time_series.set_operation_id(self.operation_id)
time_series.start_time = 0.0
time_series.sample_period = 1.0
# First process first data_array and extract important data from it's metadata
data_array_meta = data_arrays[0].meta
if (data_array_meta is not None and data_array_meta.data is not None
and len(data_array_meta.data) > 0):
for meta_pair in data_array_meta.data:
if meta_pair.name == self.UNIQUE_ID_ATTR:
gid = meta_pair.value.replace("{", "").replace("}", "")
if gid is not None and len(gid) > 0:
time_series.gid = gid
elif meta_pair.name == self.SUBJECT_ATTR:
time_series.subject = meta_pair.value
elif meta_pair.name == self.NAME_ATTR:
time_series.title = meta_pair.value
elif meta_pair.name == self.TIME_STEP_ATTR:
time_series.sample_period = float(meta_pair.value)
# Now read time series data
for data_array in data_arrays:
time_series.write_data_slice([data_array.data])
# Close file after writing data
time_series.close_file()
return time_series
else:
raise ParseException("Could not map data from GIFTI file to a TVB data type")