def from_file(source_file="connectivity_76.zip", instance=None):
if instance is None:
result = Connectivity()
else:
result = instance
source_full_path = try_get_absolute_path("tvb_data.connectivity", source_file)
if source_file.endswith(".h5"):
reader = H5Reader(source_full_path)
result.weights = reader.read_field("weights")
result.centres = reader.read_field("centres")
result.region_labels = reader.read_field("region_labels")
result.orientations = reader.read_optional_field("orientations")
result.cortical = reader.read_optional_field("cortical")
result.hemispheres = reader.read_field("hemispheres")
result.areas = reader.read_optional_field("areas")
result.tract_lengths = reader.read_field("tract_lengths")
else:
reader = ZipReader(source_full_path)
result.weights = reader.read_array_from_file("weights")
result.centres = reader.read_array_from_file("centres", use_cols=(1, 2, 3))
result.region_labels = reader.read_array_from_file("centres", dtype=numpy.str, use_cols=(0,))
result.orientations = reader.read_optional_array_from_file("average_orientations")
result.cortical = reader.read_optional_array_from_file("cortical", dtype=numpy.bool)
result.hemispheres = reader.read_optional_array_from_file("hemispheres", dtype=numpy.bool)
result.areas = reader.read_optional_array_from_file("areas")
result.tract_lengths = reader.read_array_from_file("tract_lengths")
return result
def from_h5_file(cls, source_file, **kwargs):
result = cls()
reader = H5Reader(source_file)
for attr in dir(result):
try:
val = reader.read_field(attr)
except:
continue
if isinstance(val, np.ndarray) and val.dtype == "O":
setattr(result, attr, np.array([np.str(v) for v in val]))
else:
setattr(result, attr, val)
for attr, value in kwargs.items():
setattr(result, attr, value)
return result
def from_file(source_file="connectivity_76.zip", instance=None):
source_full_path = try_get_absolute_path("tvb_data.connectivity",
source_file)
if source_file.endswith(".h5"):
reader = H5Reader(source_full_path)
weights = reader.read_field("weights")
centres = reader.read_field("centres")
region_labels = reader.read_field("region_labels")
orientations = reader.read_optional_field("orientations")
cortical = reader.read_optional_field("cortical")
hemispheres = reader.read_field("hemispheres")
areas = reader.read_optional_field("areas")
tract_lengths = reader.read_field("tract_lengths")
else:
reader = ZipReader(source_full_path)
weights = reader.read_array_from_file("weights")
if reader.has_file_like("centres"):
centres = reader.read_array_from_file("centres",
use_cols=(1, 2, 3))
region_labels = reader.read_array_from_file("centres",
dtype=numpy.str,
use_cols=(0, ))
else:
centres = reader.read_array_from_file("centers",
use_cols=(1, 2, 3))
region_labels = reader.read_array_from_file("centers",
dtype=numpy.str,
use_cols=(0, ))
orientations = reader.read_optional_array_from_file(
"average_orientations")
cortical = reader.read_optional_array_from_file("cortical",
dtype=numpy.bool)
hemispheres = reader.read_optional_array_from_file(
"hemispheres", dtype=numpy.bool)
areas = reader.read_optional_array_from_file("areas")
tract_lengths = reader.read_array_from_file("tract_lengths")
return weights, centres, region_labels, orientations, cortical, hemispheres, areas, tract_lengths
