def cdata2region_mapping(region_mapping_data, meta, storage_path):
"""
From a CData entry in CFF, create RegionMapping entity.
"""
tmpdir = os.path.join(
gettempdir(), region_mapping_data.parent_cfile.get_unique_cff_name())
LOG.debug("Using temporary folder for Region_Mapping import: " + tmpdir)
_zipfile = ZipFile(region_mapping_data.parent_cfile.src, 'r', ZIP_DEFLATED)
region_mapping_path = _zipfile.extract(region_mapping_data.src, tmpdir)
gid = dao.get_last_data_with_uid(meta[constants.KEY_SURFACE_UID],
surfaces.CorticalSurface)
surface_data = ABCAdapter.load_entity_by_gid(gid)
gid = dao.get_last_data_with_uid(meta[constants.KEY_CONNECTIVITY_UID],
Connectivity)
connectivity = ABCAdapter.load_entity_by_gid(gid)
region_mapping = surfaces.RegionMapping(storage_path=storage_path)
region_mapping.array_data = read_list_data(region_mapping_path,
dtype=numpy.int32)
region_mapping.connectivity = connectivity
region_mapping.surface = surface_data
uid = meta[constants.KEY_UID] if constants.KEY_UID in meta else None
if os.path.isdir(tmpdir):
shutil.rmtree(tmpdir)
return region_mapping, uid
def cdata2eeg_mapping(eeg_mapping_data, meta, storage_path, expected_shape=0):
"""
Currently not used
"""
tmpdir = os.path.join(gettempdir(), eeg_mapping_data.parent_cfile.get_unique_cff_name())
LOG.debug("Using temporary folder for EEG_Mapping import: " + tmpdir)
_zipfile = ZipFile(eeg_mapping_data.parent_cfile.src, 'r', ZIP_DEFLATED)
eeg_projection_path = _zipfile.extract(eeg_mapping_data.src, tmpdir)
eeg_projection_data = read_matlab_data(eeg_projection_path, constants.DATA_NAME_PROJECTION)
if eeg_projection_data.shape[1] < expected_shape:
padding = numpy.zeros((eeg_projection_data.shape[0], expected_shape - eeg_projection_data.shape[1]))
eeg_projection_data = numpy.hstack((eeg_projection_data, padding))
gid = dao.get_last_data_with_uid(meta[constants.KEY_SURFACE_UID], surfaces.CorticalSurface)
surface_data = ABCAdapter.load_entity_by_gid(gid)
projection_matrix = projections.ProjectionSurfaceEEG(storage_path = storage_path)
projection_matrix.projection_data = eeg_projection_data
projection_matrix.sources = surface_data
projection_matrix.sensors = None
### TODO if we decide to use this method, we will need to find a manner to fill the sensors.
return projection_matrix
def cdata2local_connectivity(local_connectivity_data, meta, storage_path, expected_length=0):
"""
From a CData entry in CFF, create LocalConnectivity entity.
"""
##### expected_length = cortex.region_mapping.shape[0]
tmpdir = os.path.join(gettempdir(), local_connectivity_data.parent_cfile.get_unique_cff_name())
LOG.debug("Using temporary folder for Local Connectivity import: " + tmpdir)
_zipfile = ZipFile(local_connectivity_data.parent_cfile.src, 'r', ZIP_DEFLATED)
local_connectivity_path = _zipfile.extract(local_connectivity_data.src, tmpdir)
gid = dao.get_last_data_with_uid(meta[constants.KEY_SURFACE_UID], surfaces.CorticalSurface)
surface_data = ABCAdapter.load_entity_by_gid(gid)
local_connectivity = surfaces.LocalConnectivity()
local_connectivity.storage_path = storage_path
local_connectivity_data = read_matlab_data(local_connectivity_path, constants.DATA_NAME_LOCAL_CONN)
if local_connectivity_data.shape[0] < expected_length:
padding = sparse.csc_matrix((local_connectivity_data.shape[0],
expected_length - local_connectivity_data.shape[0]))
local_connectivity_data = sparse.hstack([local_connectivity_data, padding])
padding = sparse.csc_matrix((expected_length - local_connectivity_data.shape[0],
local_connectivity_data.shape[1]))
local_connectivity_data = sparse.vstack([local_connectivity_data, padding])
local_connectivity.equation = None
local_connectivity.matrix = local_connectivity_data
local_connectivity.surface = surface_data
uid = meta[constants.KEY_UID] if constants.KEY_UID in meta else None
if os.path.isdir(tmpdir):
shutil.rmtree(tmpdir)
return local_connectivity, uid
def cdata2eeg_mapping(eeg_mapping_data, meta, storage_path, expected_shape=0):
"""
Currently not used
"""
tmpdir = os.path.join(gettempdir(),
eeg_mapping_data.parent_cfile.get_unique_cff_name())
LOG.debug("Using temporary folder for EEG_Mapping import: " + tmpdir)
_zipfile = ZipFile(eeg_mapping_data.parent_cfile.src, 'r', ZIP_DEFLATED)
eeg_projection_path = _zipfile.extract(eeg_mapping_data.src, tmpdir)
eeg_projection_data = read_matlab_data(eeg_projection_path,
constants.DATA_NAME_PROJECTION)
if eeg_projection_data.shape[1] < expected_shape:
padding = numpy.zeros((eeg_projection_data.shape[0],
expected_shape - eeg_projection_data.shape[1]))
eeg_projection_data = numpy.hstack((eeg_projection_data, padding))
gid = dao.get_last_data_with_uid(meta[constants.KEY_SURFACE_UID],
surfaces.CorticalSurface)
surface_data = ABCAdapter.load_entity_by_gid(gid)
projection_matrix = projections.ProjectionSurfaceEEG(
storage_path=storage_path)
projection_matrix.projection_data = eeg_projection_data
projection_matrix.sources = surface_data
projection_matrix.sensors = None
### TODO if we decide to use this method, we will need to find a manner to fill the sensors.
return projection_matrix
def cdata2region_mapping(region_mapping_data, meta, storage_path):
"""
From a CData entry in CFF, create RegionMapping entity.
"""
tmpdir = os.path.join(gettempdir(), region_mapping_data.parent_cfile.get_unique_cff_name())
LOG.debug("Using temporary folder for Region_Mapping import: " + tmpdir)
_zipfile = ZipFile(region_mapping_data.parent_cfile.src, 'r', ZIP_DEFLATED)
region_mapping_path = _zipfile.extract(region_mapping_data.src, tmpdir)
gid = dao.get_last_data_with_uid(meta[constants.KEY_SURFACE_UID], surfaces.CorticalSurface)
surface_data = ABCAdapter.load_entity_by_gid(gid)
gid = dao.get_last_data_with_uid(meta[constants.KEY_CONNECTIVITY_UID], Connectivity)
connectivity = ABCAdapter.load_entity_by_gid(gid)
region_mapping = surfaces.RegionMapping(storage_path = storage_path)
region_mapping.array_data = read_list_data(region_mapping_path, dtype=numpy.int32)
region_mapping.connectivity = connectivity
region_mapping.surface = surface_data
uid = meta[constants.KEY_UID] if constants.KEY_UID in meta else None
if os.path.isdir(tmpdir):
shutil.rmtree(tmpdir)
return region_mapping, uid
def cdata2local_connectivity(local_connectivity_data,
meta,
storage_path,
expected_length=0):
"""
From a CData entry in CFF, create LocalConnectivity entity.
"""
##### expected_length = cortex.region_mapping.shape[0]
tmpdir = os.path.join(
gettempdir(),
local_connectivity_data.parent_cfile.get_unique_cff_name())
LOG.debug("Using temporary folder for Local Connectivity import: " +
tmpdir)
_zipfile = ZipFile(local_connectivity_data.parent_cfile.src, 'r',
ZIP_DEFLATED)
local_connectivity_path = _zipfile.extract(local_connectivity_data.src,
tmpdir)
gid = dao.get_last_data_with_uid(meta[constants.KEY_SURFACE_UID],
surfaces.CorticalSurface)
surface_data = ABCAdapter.load_entity_by_gid(gid)
local_connectivity = surfaces.LocalConnectivity()
local_connectivity.storage_path = storage_path
local_connectivity_data = read_matlab_data(local_connectivity_path,
constants.DATA_NAME_LOCAL_CONN)
if local_connectivity_data.shape[0] < expected_length:
padding = sparse.csc_matrix(
(local_connectivity_data.shape[0],
expected_length - local_connectivity_data.shape[0]))
local_connectivity_data = sparse.hstack(
[local_connectivity_data, padding])
padding = sparse.csc_matrix(
(expected_length - local_connectivity_data.shape[0],
local_connectivity_data.shape[1]))
local_connectivity_data = sparse.vstack(
[local_connectivity_data, padding])
local_connectivity.equation = None
local_connectivity.matrix = local_connectivity_data
local_connectivity.surface = surface_data
uid = meta[constants.KEY_UID] if constants.KEY_UID in meta else None
if os.path.isdir(tmpdir):
shutil.rmtree(tmpdir)
return local_connectivity, uid
def _prepare_custom_parameter(self, arg):
""" Overwrite to prepare specific parameters. """
current_value = self.arguments[arg]
if isinstance(current_value, dict) and ATT_UID in current_value:
uid = current_value[ATT_UID]
current_type = model.DataType
## Search current entity's type in adapter.get_input_tree result
full_input_tree = self.callable_object.get_input_tree()
for att_def in full_input_tree:
if att_def[ATT_NAME] == arg:
current_type = att_def[ATT_TYPE]
break
### Retrieve entity of the correct Type from DB.
current_value = dao.get_last_data_with_uid(uid, current_type)
return current_value
def _prepare_custom_parameter(self, arg):
""" Overwrite to prepare specific parameters. """
current_value = self.arguments[arg]
if isinstance(current_value, dict) and ATT_UID in current_value:
uid = current_value[ATT_UID]
current_type = model.DataType
## Search current entity's type in adapter.get_input_tree result
full_input_tree = self.callable_object.get_input_tree()
for att_def in full_input_tree:
if att_def[ATT_NAME] == arg:
current_type = att_def[ATT_TYPE]
break
### Retrieve entity of the correct Type from DB.
current_value = dao.get_last_data_with_uid(uid, current_type)
return current_value
def test_handle_event(self):
"""
Test a defined handler for the store project method.
"""
path_to_events = os.path.dirname(__file__)
event_handlers.read_events([path_to_events])
data = dict(name="test_project", description="test_description", users=[])
initial_projects = dao.get_projects_for_user(self.test_user.id)
self.assertEqual(len(initial_projects), 0, "Database reset probably failed!")
test_project = self.project_service.store_project(self.test_user, True, None, **data)
# Operations will start asynchronously; Give them time.
time.sleep(1)
gid = dao.get_last_data_with_uid("test_uid")
self.assertTrue(gid is not None, "Nothing was stored in database!")
datatype = dao.get_datatype_by_gid(gid)
self.assertEqual(datatype.type, "Datatype1", "Wrong data stored!")
self.project_service._remove_project_node_files(test_project.id, gid)
def test_handle_event(self):
"""
Test a defined handler for the store project method.
"""
path_to_events = os.path.dirname(__file__)
eventhandler.read_events([path_to_events])
data = dict(name="test_project",
description="test_description",
users=[])
initial_projects = dao.get_projects_for_user(self.test_user.id)
self.assertEqual(len(initial_projects), 0,
"Database reset probably failed!")
test_project = self.project_service.store_project(
self.test_user, True, None, **data)
# Operations will start asynchronously; Give them time.
time.sleep(1)
gid = dao.get_last_data_with_uid("test_uid")
self.assertTrue(gid is not None, "Nothing was stored in database!")
datatype = dao.get_datatype_by_gid(gid)
self.assertEqual(datatype.type, "Datatype1", "Wrong data stored!")
self.project_service._remove_project_node_files(test_project.id, gid)
