def list_or_tuple_to_h5_model(h5_model, obj, path, container_path, obj_type):
# empty list or tuple get into metadata
if len(obj) == 0:
h5_model.add_or_update_metadata_attribute(path + "/type_str", obj_type)
if isinstance(obj, list):
h5_model.add_or_update_datasets_attribute(path, "[]")
else:
h5_model.add_or_update_datasets_attribute(path, "()")
return h5_model, None
# Try to store it as a ndarray of numbers or strings but not objects...
temp = np.array(obj)
if not (isequal_string(str(temp.dtype)[0], "O")):
h5_model.add_or_update_metadata_attribute(path + "/type_str",
obj.__class__.__name__)
if isinstance(obj, tuple):
h5_model.add_or_update_metadata_attribute(path + "/transform_str",
"tuple(obj)")
else:
h5_model.add_or_update_metadata_attribute(path + "/transform_str",
"obj.tolist()")
h5_model.add_or_update_datasets_attribute(path, temp)
return h5_model, None
else:
h5_model.add_or_update_metadata_attribute(
os.path.join(container_path[1:], "create_str"), "list()")
if isinstance(obj, tuple):
h5_model.add_or_update_metadata_attribute(
os.path.join(container_path[1:], "transform_str"),
"tuple(obj))")
return h5_model, iterable_to_dict(obj)
def convert_from_h5_model(self, obj=None, output_shape=None):
output_type = obj.__class__.__name__
if isinstance(obj, dict):
obj = sort_dict(obj)
elif np.in1d(output_type, ["tuple", "list"]):
obj = iterable_to_dict(obj)
elif isequal_string(output_type, "numpy.ndarray"):
if isequal_string(obj.dtype, "numpy.ndarray"):
obj = iterable_to_dict(obj.tolist())
else:
obj, output_type = create_object("/", self.metadata_dict)[:2]
if obj is None:
obj = OrderedDict()
if output_type is None:
output_type = obj.__class__.__name__
for abs_path in self.datasets_dict.keys():
child_obj = self.datasets_dict.pop(abs_path)
rel_path = abs_path.split("/", 1)[1]
build_hierarchical_object_recursively(obj, rel_path, child_obj,
"/", abs_path,
self.metadata_dict)
if np.in1d(output_type, ["tuple", "list"]):
obj = dict_to_list_or_tuple(obj, output_type)
elif isequal_string(output_type, "numpy.ndarray"):
obj = np.array(dict.values())
if isinstance(output_shape, tuple):
try:
obj = np.reshape(obj, output_shape)
except:
logger.warning(
"Failed to reshape read object to target shape " +
str(output_shape) + "!" +
"\nReturning array of shape " + str(obj.shape) + "!")
else:
obj = update_object(obj, "/", self.metadata_dict,
getORpop="pop")[0]
if isinstance(obj, dict) and output_type.lower().find("dict") < 0:
return OrderedDictDot(obj)
else:
return obj
def array_to_h5_model(h5_model, obj, path, container_path, obj_type):
if isequal_string(str(obj.dtype)[0], "O"):
h5_model.add_or_update_metadata_attribute(
os.path.join(container_path, "create_str"), "list()")
h5_model.add_or_update_metadata_attribute(
os.path.join(container_path, "transform_str"),
"np.reshape(obj, " + str(obj.shape) + ")")
return h5_model, iterable_to_dict(obj)
else:
h5_model.add_or_update_metadata_attribute(path + "/type_str", obj_type)
h5_model.add_or_update_metadata_attribute(
path + "/transform_str", "np.reshape(obj, " + str(obj.shape) + ")")
h5_model.add_or_update_datasets_attribute(path, obj)
return h5_model, None