def _create_volume(self):
volume = Volume(storage_path=self.storage_path)
volume.origin = [[0.0, 0.0, 0.0]]
volume.voxel_size = [self.parser.zooms[0], self.parser.zooms[1], self.parser.zooms[2]]
if self.parser.units is not None and len(self.parser.units) > 0:
volume.voxel_unit = self.parser.units[0]
return volume
def _create_volume(self):
volume = Volume()
volume.origin = numpy.array([[0.0, 0.0, 0.0]])
volume.voxel_size = numpy.array([self.parser.zooms[0], self.parser.zooms[1], self.parser.zooms[2]])
if self.parser.units is not None and len(self.parser.units) > 0:
volume.voxel_unit = self.parser.units[0]
return h5.store_complete(volume, self.storage_path), volume
def test_volume(self):
dt = Volume()
summary_info = dt._find_summary_info()
assert summary_info['Origin'].shape == (0, )
assert summary_info['Voxel size'].shape == (0, )
assert summary_info['Volume type'] == 'Volume'
assert summary_info['Units'] == 'mm'
assert dt.origin.shape == (0, )
assert dt.voxel_size.shape == (0, )
assert dt.voxel_unit == 'mm'
def test_store_load_volume(tmph5factory):
volume = Volume(origin=numpy.zeros((3, 3)), voxel_size=numpy.zeros((3, 3)))
tmp_file = tmph5factory("Volume_{}.h5".format(volume.gid))
with VolumeH5(tmp_file) as f:
f.store(volume)
volume_stored = Volume()
with pytest.raises(TraitAttributeError):
volume_stored.origin
with VolumeH5(tmp_file) as f:
f.load_into(volume_stored)
assert volume_stored.origin is not None
def test_spatialpatternvolume(self):
dt = patterns.SpatialPatternVolume(spatial=FiniteSupportEquation(), volume=Volume(origin=numpy.array([]), voxel_size=numpy.array([])), focal_points_volume=numpy.array([1]))
assert dt.space is None
assert dt.spatial is not None
assert dt.spatial_pattern is None
assert dt.volume is not None
assert dt.focal_points_volume is not None
def test_store_load_structuralMRI(tmph5factory):
volume = Volume(
origin=numpy.zeros((3, 3)),
voxel_size=numpy.zeros((3, 3))
)
structural_mri = StructuralMRI(
array_data=numpy.zeros((3, 3)),
weighting="T1",
volume=volume
)
tmp_file = tmph5factory("StructuralMRI_{}.h5".format(volume.gid))
with StructuralMRIH5(tmp_file) as f:
f.store(structural_mri)
structural_mri_stored = StructuralMRI()
with pytest.raises(TraitAttributeError):
structural_mri_stored.array_data
with pytest.raises(TraitAttributeError):
structural_mri_stored.volume
with StructuralMRIH5(tmp_file) as f:
f.load_into(structural_mri_stored)
assert structural_mri_stored.array_data.shape == (3, 3)
# referenced datatype is not loaded
with pytest.raises(TraitAttributeError):
structural_mri_stored.volume
def launch(self, data_file, apply_corrections=False, connectivity=None):
"""
Execute import operations:
"""
try:
parser = NIFTIParser(data_file)
# Create volume DT
volume = Volume(storage_path=self.storage_path)
volume.set_operation_id(self.operation_id)
volume.origin = [[0.0, 0.0, 0.0]]
volume.voxel_size = [
parser.zooms[0], parser.zooms[1], parser.zooms[2]
]
if parser.units is not None and len(parser.units) > 0:
volume.voxel_unit = parser.units[0]
if parser.has_time_dimension or not connectivity:
# Now create TimeSeries and fill it with data from NIFTI image
time_series = TimeSeriesVolume(storage_path=self.storage_path)
time_series.set_operation_id(self.operation_id)
time_series.volume = volume
time_series.title = "NIFTI Import - " + os.path.split(
data_file)[1]
time_series.labels_ordering = ["Time", "X", "Y", "Z"]
time_series.start_time = 0.0
if len(parser.zooms) > 3:
time_series.sample_period = float(parser.zooms[3])
else:
# If no time dim, set sampling to 1 sec
time_series.sample_period = 1
if parser.units is not None and len(parser.units) > 1:
time_series.sample_period_unit = parser.units[1]
parser.parse(time_series, True)
return [volume, time_series]
else:
region2volume_mapping = RegionVolumeMapping(
storage_path=self.storage_path)
region2volume_mapping.set_operation_id(self.operation_id)
region2volume_mapping.volume = volume
region2volume_mapping.connectivity = connectivity
region2volume_mapping.title = "NIFTI Import - " + os.path.split(
data_file)[1]
region2volume_mapping.dimensions_labels = ["X", "Y", "Z"]
region2volume_mapping.apply_corrections = apply_corrections
parser.parse(region2volume_mapping, False)
return [volume, region2volume_mapping]
except ParseException, excep:
logger = get_logger(__name__)
logger.exception(excep)
raise LaunchException(excep)
def launch(self, data_file, apply_corrections=False, connectivity=None):
"""
Execute import operations:
"""
try:
parser = NIFTIParser(data_file)
# Create volume DT
volume = Volume(storage_path=self.storage_path)
volume.set_operation_id(self.operation_id)
volume.origin = [[0.0, 0.0, 0.0]]
volume.voxel_size = [parser.zooms[0], parser.zooms[1], parser.zooms[2]]
if parser.units is not None and len(parser.units) > 0:
volume.voxel_unit = parser.units[0]
if parser.has_time_dimension or not connectivity:
# Now create TimeSeries and fill it with data from NIFTI image
time_series = TimeSeriesVolume(storage_path=self.storage_path)
time_series.set_operation_id(self.operation_id)
time_series.volume = volume
time_series.title = "NIFTI Import - " + os.path.split(data_file)[1]
time_series.labels_ordering = ["Time", "X", "Y", "Z"]
time_series.start_time = 0.0
if len(parser.zooms) > 3:
time_series.sample_period = float(parser.zooms[3])
else:
# If no time dim, set sampling to 1 sec
time_series.sample_period = 1
if parser.units is not None and len(parser.units) > 1:
time_series.sample_period_unit = parser.units[1]
parser.parse(time_series, True)
return [volume, time_series]
else:
region2volume_mapping = RegionVolumeMapping(storage_path=self.storage_path)
region2volume_mapping.set_operation_id(self.operation_id)
region2volume_mapping.volume = volume
region2volume_mapping.connectivity = connectivity
region2volume_mapping.title = "NIFTI Import - " + os.path.split(data_file)[1]
region2volume_mapping.dimensions_labels = ["X", "Y", "Z"]
region2volume_mapping.apply_corrections = apply_corrections
parser.parse(region2volume_mapping, False)
return [volume, region2volume_mapping]
except ParseException, excep:
logger = get_logger(__name__)
logger.exception(excep)
raise LaunchException(excep)
def launch(self, view_model):
resolution = view_model.resolution
weighting = view_model.weighting
inj_f_thresh = view_model.inj_f_thresh / 100.
vol_thresh = view_model.vol_thresh
project = dao.get_project_by_id(self.current_project_id)
manifest_file = self.file_handler.get_allen_mouse_cache_folder(
project.name)
manifest_file = os.path.join(manifest_file,
'mouse_connectivity_manifest.json')
cache = MouseConnectivityCache(resolution=resolution,
manifest_file=manifest_file)
# the method creates a dictionary with information about which experiments need to be downloaded
ist2e = dictionary_builder(cache, False)
# the method downloads experiments necessary to build the connectivity
projmaps = download_an_construct_matrix(cache, weighting, ist2e, False)
# the method cleans the file projmaps in 4 steps
projmaps = pms_cleaner(projmaps)
# download from the AllenSDK the annotation volume, the template volume
vol, annot_info = cache.get_annotation_volume()
template, template_info = cache.get_template_volume()
# rotate template in the TVB 3D reference:
template = rotate_reference(template)
# grab the StructureTree instance
structure_tree = cache.get_structure_tree()
# the method includes in the parcellation only brain regions whose volume is greater than vol_thresh
projmaps = areas_volume_threshold(cache, projmaps, vol_thresh,
resolution)
# the method exclude from the experimental dataset
# those exps where the injected fraction of pixel in the injection site is lower than than the inj_f_thr
projmaps = infected_threshold(cache, projmaps, inj_f_thresh)
# the method creates file order and keyword that will be the link between the SC order and the
# id key in the Allen database
[order, key_ord] = create_file_order(projmaps, structure_tree)
# the method builds the Structural Connectivity (SC) matrix
structural_conn = construct_structural_conn(projmaps, order, key_ord)
# the method returns the coordinate of the centres and the name of the brain areas in the selected parcellation
[centres, names] = construct_centres(cache, order, key_ord)
# the method returns the tract lengths between the brain areas in the selected parcellation
tract_lengths = construct_tract_lengths(centres)
# the method associated the parent and the grandparents to the child in the selected parcellation with
# the biggest volume
[unique_parents, unique_grandparents
] = parents_and_grandparents_finder(cache, order, key_ord,
structure_tree)
# the method returns a volume indexed between 0 and N-1, with N=tot brain areas in the parcellation.
# -1=background and areas that are not in the parcellation
vol_parcel = mouse_brain_visualizer(vol, order, key_ord,
unique_parents,
unique_grandparents,
structure_tree, projmaps)
# results: Connectivity, Volume & RegionVolumeMapping
# Connectivity
result_connectivity = Connectivity()
result_connectivity.centres = centres
result_connectivity.region_labels = numpy.array(names)
result_connectivity.weights = structural_conn
result_connectivity.tract_lengths = tract_lengths
result_connectivity.configure()
# Volume
result_volume = Volume()
result_volume.origin = numpy.array([[0.0, 0.0, 0.0]])
result_volume.voxel_size = numpy.array(
[resolution, resolution, resolution])
# result_volume.voxel_unit= micron
# Region Volume Mapping
result_rvm = RegionVolumeMapping()
result_rvm.volume = result_volume
result_rvm.array_data = vol_parcel
result_rvm.connectivity = result_connectivity
result_rvm.title = "Volume mouse brain "
result_rvm.dimensions_labels = ["X", "Y", "Z"]
# Volume template
result_template = StructuralMRI()
result_template.array_data = template
result_template.weighting = 'T1'
result_template.volume = result_volume
connectivity_index = h5.store_complete(result_connectivity,
self.storage_path)
volume_index = h5.store_complete(result_volume, self.storage_path)
rvm_index = h5.store_complete(result_rvm, self.storage_path)
template_index = h5.store_complete(result_template, self.storage_path)
return [connectivity_index, volume_index, rvm_index, template_index]
def test_get_min_max(self):
dt = Volume(array_data=numpy.array(range(30)))
assert dt.get_min_max_values() == (0, 29)
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,
)
def launch(self, resolution, weighting, inf_vox_thresh, vol_thresh):
resolution = int(resolution)
weighting = int(weighting)
inf_vox_thresh = float(inf_vox_thresh)
vol_thresh = float(vol_thresh)
project = dao.get_project_by_id(self.current_project_id)
manifest_file = self.file_handler.get_allen_mouse_cache_folder(
project.name)
manifest_file = os.path.join(manifest_file,
'mouse_connectivity_manifest.json')
cache = MouseConnectivityCache(resolution=resolution,
manifest_file=manifest_file)
# the method creates a dictionary with information about which experiments need to be downloaded
ist2e = DictionaireBuilder(cache, False)
# the method downloads experiments necessary to build the connectivity
projmaps = DownloadAndConstructMatrix(cache, weighting, ist2e, False)
# the method cleans the file projmaps in 4 steps
projmaps = pmsCleaner(projmaps)
#download from the AllenSDK the annotation volume, the ontology, the template volume
Vol, annot_info = cache.get_annotation_volume()
ontology = cache.get_ontology()
template, template_info = cache.get_template_volume()
#rotate template in the TVB 3D reference:
template = RotateReference(template)
# the method includes in the parcellation only brain regions whose volume is greater than vol_thresh
projmaps = AreasVolumeTreshold(cache, projmaps, vol_thresh, resolution,
Vol, ontology)
# the method includes in the parcellation only brain regions where at least one injection experiment had infected more than N voxel (where N is inf_vox_thresh)
projmaps = AreasVoxelTreshold(cache, projmaps, inf_vox_thresh, Vol,
ontology)
# the method creates file order and keyord that will be the link between the SC order and the id key in the Allen database
[order, key_ord] = CreateFileOrder(projmaps, ontology)
# the method builds the Structural Connectivity (SC) matrix
SC = ConstructingSC(projmaps, order, key_ord)
# the method returns the coordinate of the centres and the name of the brain areas in the selected parcellation
[centres, names] = Construct_centres(cache, ontology, order, key_ord)
# the method returns the tract lengths between the brain areas in the selected parcellation
tract_lengths = ConstructTractLengths(centres)
# the method associated the parent and the grandparents to the child in the selected parcellation with the biggest volume
[unique_parents, unique_grandparents
] = ParentsAndGrandParentsFinder(cache, order, key_ord, ontology)
# the method returns a volume indexed between 0 and N-1, with N=tot brain areas in the parcellation. -1=background and areas that are not in the parcellation
Vol_parcel = MouseBrainVisualizer(Vol, order, key_ord, unique_parents,
unique_grandparents, ontology,
projmaps)
# results: Connectivity, Volume & RegionVolumeMapping
# Connectivity
result_connectivity = Connectivity(storage_path=self.storage_path)
result_connectivity.centres = centres
result_connectivity.region_labels = names
result_connectivity.weights = SC
result_connectivity.tract_lengths = tract_lengths
# Volume
result_volume = Volume(storage_path=self.storage_path)
result_volume.origin = [[0.0, 0.0, 0.0]]
result_volume.voxel_size = [resolution, resolution, resolution]
# result_volume.voxel_unit= micron
# Region Volume Mapping
result_rvm = RegionVolumeMapping(storage_path=self.storage_path)
result_rvm.volume = result_volume
result_rvm.array_data = Vol_parcel
result_rvm.connectivity = result_connectivity
result_rvm.title = "Volume mouse brain "
result_rvm.dimensions_labels = ["X", "Y", "Z"]
# Volume template
result_template = StructuralMRI(storage_path=self.storage_path)
result_template.array_data = template
result_template.weighting = 'T1'
result_template.volume = result_volume
return [
result_connectivity, result_volume, result_rvm, result_template
]
class NIFTIParser():
"""
This class reads content of a NIFTI file and builds / returns a TimeSeries instance
filled with details.
"""
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.
"""
if data_file is None:
raise ParseException(
"Please select NIFTI file which contains data to import")
if not os.path.exists(data_file):
raise ParseException("Provided file %s does not exists" %
data_file)
try:
nifti_image = nib.load(data_file)
except nib.spatialimages.ImageFileError, e:
self.logger.exception(e)
msg = "File: %s does not have a valid NIFTI-1 format." % data_file
raise ParseException(msg)
nifti_image_hdr = nifti_image.get_header()
# Create volume for time series
volume = Volume(storage_path=self.storage_path)
volume.set_operation_id(self.operation_id)
volume.origin = [[0.0, 0.0, 0.0]]
# Now create TimeSeries and fill it with data from NIFTI image
time_series = TimeSeriesVolume(storage_path=self.storage_path)
time_series.set_operation_id(self.operation_id)
time_series.volume = volume
time_series.title = "NIFTI Import - " + os.path.split(data_file)[1]
time_series.labels_ordering = ["Time", "X", "Y", "Z"]
time_series.start_time = 0.0
# Copy data from NIFTI file to our TVB storage
# In NIFTI format time si the 4th dimension, while our TimeSeries has
# it as first dimension, so we have to adapt imported data
# Check if there is a time dimensions (4th dimension).
nifti_data_shape = nifti_image_hdr.get_data_shape()
has_time_dimension = len(nifti_data_shape) > 3
time_dim_size = nifti_data_shape[3] if has_time_dimension else 1
nifti_data = nifti_image.get_data()
if has_time_dimension:
for i in range(time_dim_size):
time_series.write_data_slice([nifti_data[:, :, :, i, ...]])
else:
time_series.write_data_slice([nifti_data])
time_series.close_file() # Force closing HDF5 file
# Extract sample unit measure
units = nifti_image_hdr.get_xyzt_units()
if units is not None and len(units) == 2:
volume.voxel_unit = units[0]
time_series.sample_period_unit = units[1]
# Extract sample rate
# Usually zooms defines values for x, y, z, time and other dimensions
zooms = nifti_image_hdr.get_zooms()
if has_time_dimension:
time_series.sample_period = float(zooms[3])
else:
time_series.sample_period = 1.0 # If no time dim, set sampling to 1 sec
# Get voxtel dimensions for x,y, z
volume.voxel_size = [zooms[0], zooms[1], zooms[2]]
return time_series
def launch(self, resolution, weighting, inj_f_thresh, vol_thresh):
resolution = int(resolution)
weighting = int(weighting)
inj_f_thresh = float(inj_f_thresh)/100.
vol_thresh = float(vol_thresh)
project = dao.get_project_by_id(self.current_project_id)
manifest_file = self.file_handler.get_allen_mouse_cache_folder(project.name)
manifest_file = os.path.join(manifest_file, 'mouse_connectivity_manifest.json')
cache = MouseConnectivityCache(resolution=resolution, manifest_file=manifest_file)
# the method creates a dictionary with information about which experiments need to be downloaded
ist2e = dictionary_builder(cache, False)
# the method downloads experiments necessary to build the connectivity
projmaps = download_an_construct_matrix(cache, weighting, ist2e, False)
# the method cleans the file projmaps in 4 steps
projmaps = pms_cleaner(projmaps)
# download from the AllenSDK the annotation volume, the template volume
vol, annot_info = cache.get_annotation_volume()
template, template_info = cache.get_template_volume()
# rotate template in the TVB 3D reference:
template = rotate_reference(template)
# grab the StructureTree instance
structure_tree = cache.get_structure_tree()
# the method includes in the parcellation only brain regions whose volume is greater than vol_thresh
projmaps = areas_volume_threshold(cache, projmaps, vol_thresh, resolution)
# the method exclude from the experimental dataset
# those exps where the injected fraction of pixel in the injection site is lower than than the inj_f_thr
projmaps = infected_threshold(cache, projmaps, inj_f_thresh)
# the method creates file order and keyword that will be the link between the SC order and the
# id key in the Allen database
[order, key_ord] = create_file_order(projmaps, structure_tree)
# the method builds the Structural Connectivity (SC) matrix
structural_conn = construct_structural_conn(projmaps, order, key_ord)
# the method returns the coordinate of the centres and the name of the brain areas in the selected parcellation
[centres, names] = construct_centres(cache, order, key_ord)
# the method returns the tract lengths between the brain areas in the selected parcellation
tract_lengths = construct_tract_lengths(centres)
# the method associated the parent and the grandparents to the child in the selected parcellation with
# the biggest volume
[unique_parents, unique_grandparents] = parents_and_grandparents_finder(cache, order, key_ord, structure_tree)
# the method returns a volume indexed between 0 and N-1, with N=tot brain areas in the parcellation.
# -1=background and areas that are not in the parcellation
vol_parcel = mouse_brain_visualizer(vol, order, key_ord, unique_parents, unique_grandparents,
structure_tree, projmaps)
# results: Connectivity, Volume & RegionVolumeMapping
# Connectivity
result_connectivity = Connectivity(storage_path=self.storage_path)
result_connectivity.centres = centres
result_connectivity.region_labels = names
result_connectivity.weights = structural_conn
result_connectivity.tract_lengths = tract_lengths
# Volume
result_volume = Volume(storage_path=self.storage_path)
result_volume.origin = [[0.0, 0.0, 0.0]]
result_volume.voxel_size = [resolution, resolution, resolution]
# result_volume.voxel_unit= micron
# Region Volume Mapping
result_rvm = RegionVolumeMapping(storage_path=self.storage_path)
result_rvm.volume = result_volume
result_rvm.array_data = vol_parcel
result_rvm.connectivity = result_connectivity
result_rvm.title = "Volume mouse brain "
result_rvm.dimensions_labels = ["X", "Y", "Z"]
# Volume template
result_template = StructuralMRI(storage_path=self.storage_path)
result_template.array_data = template
result_template.weighting = 'T1'
result_template.volume = result_volume
return [result_connectivity, result_volume, result_rvm, result_template]
def launch(self, resolution, weighting, inf_vox_thresh, vol_thresh):
resolution = int(resolution)
weighting = int(weighting)
inf_vox_thresh = float(inf_vox_thresh)
vol_thresh = float(vol_thresh)
project = dao.get_project_by_id(self.current_project_id)
manifest_file = self.file_handler.get_allen_mouse_cache_folder(project.name)
manifest_file = os.path.join(manifest_file, "mouse_connectivity_manifest.json")
cache = MouseConnectivityCache(resolution=resolution, manifest_file=manifest_file)
# the method creates a dictionary with information about which experiments need to be downloaded
ist2e = DictionaireBuilder(cache, False)
# the method downloads experiments necessary to build the connectivity
projmaps = DownloadAndConstructMatrix(cache, weighting, ist2e, False)
# the method cleans the file projmaps in 4 steps
projmaps = pmsCleaner(projmaps)
Vol, annot_info = cache.get_annotation_volume()
ontology = cache.get_ontology()
# the method includes in the parcellation only brain regions whose volume is greater than vol_thresh
projmaps = AreasVolumeTreshold(projmaps, vol_thresh, resolution, Vol, ontology)
# the method includes in the parcellation only brain regions where at least one injection experiment had infected more than N voxel (where N is inf_vox_thresh)
projmaps = AreasVoxelTreshold(cache, projmaps, inf_vox_thresh, Vol, ontology)
# the method creates file order and keyord that will be the link between the SC order and the id key in the Allen database
[order, key_ord] = CreateFileOrder(projmaps, ontology)
# the method builds the Structural Connectivity (SC) matrix
SC = ConstructingSC(projmaps, order, key_ord)
# the method returns the coordinate of the centres and the name of the brain areas in the selected parcellation
[centres, names] = Construct_centres(ontology, order, key_ord, Vol)
# the method returns the tract lengths between the brain areas in the selected parcellation
tract_lengths = ConstructTractLengths(centres)
# the method associated the parent and the grandparents to the child in the selected parcellation with the biggest volume
[unique_parents, unique_grandparents] = ParentsAndGrandParentsFinder(order, key_ord, Vol, ontology)
# the method returns a volume indexed between 0 and N-1, with N=tot brain areas in the parcellation. -1=background and areas that are not in the parcellation
Vol_parcel = MouseBrainVisualizer(Vol, order, key_ord, unique_parents, unique_grandparents, ontology, projmaps)
# results: Connectivity, Volume & RegionVolumeMapping
# Connectivity
result_connectivity = Connectivity(storage_path=self.storage_path)
result_connectivity.centres = centres
result_connectivity.region_labels = names
result_connectivity.weights = SC
result_connectivity.tract_lengths = tract_lengths
# Volume
result_volume = Volume(storage_path=self.storage_path)
result_volume.origin = [[0.0, 0.0, 0.0]]
result_volume.voxel_size = [resolution, resolution, resolution]
# result_volume.voxel_unit= micron
# Region Volume Mapping
result_rvm = RegionVolumeMapping(storage_path=self.storage_path)
result_rvm.volume = result_volume
result_rvm.array_data = Vol_parcel
result_rvm.connectivity = result_connectivity
result_rvm.title = "Volume mouse brain "
result_rvm.dimensions_labels = ["X", "Y", "Z"]
return [result_connectivity, result_rvm, result_volume]
