Module H5A¶
Provides access to the low-level HDF5 “H5A” attribute interface.
Functional API¶
- h5py.h5a.create(ObjectID loc, STRING name, TypeID tid, SpaceID space, **kwds) AttrID ¶
Create a new attribute, attached to an existing object.
- STRING obj_name (“.”)
Attach attribute to this group member instead
- PropID lapl
Link access property list
for obj_name
- h5py.h5a.open(ObjectID loc, STRING name=, INT index=, **kwds) AttrID ¶
Open an attribute attached to an existing object. You must specify exactly one of either name or idx. Keywords are:
- STRING obj_name (“.”)
Attribute is attached to this group member
- PropID lapl (None)
Link access property list
for obj_name
INT index_type (
h5.INDEX_NAME
)INT order (
h5.ITER_INC
)
- h5py.h5a.exists(ObjectID loc, STRING name, **kwds) BOOL ¶
Determine if an attribute is attached to this object. Keywords:
- STRING obj_name (“.”)
Look for attributes attached to this group member
- PropID lapl (None):
Link access property list
for obj_name
- h5py.h5a.rename(ObjectID loc, STRING name, STRING new_name, **kwds)¶
Rename an attribute. Keywords:
- STRING obj_name (“.”)
Attribute is attached to this group member
- PropID lapl (None)
Link access property list
for obj_name
- h5py.h5a.delete(ObjectID loc, STRING name=, INT index=, **kwds)¶
Remove an attribute from an object. Specify exactly one of “name” or “index”. Keyword-only arguments:
- STRING obj_name (“.”)
Attribute is attached to this group member
- PropID lapl (None)
Link access property list
for obj_name
INT index_type (
h5.INDEX_NAME
)INT order (
h5.ITER_INC
)
- h5py.h5a.get_num_attrs(ObjectID loc) INT ¶
Determine the number of attributes attached to an HDF5 object.
- h5py.h5a.get_info(ObjectID loc, STRING name=, INT index=, **kwds) AttrInfo ¶
Get information about an attribute, in one of two ways:
If you have the attribute identifier, just pass it in
If you have the parent object, supply it and exactly one of either name or index.
- STRING obj_name (“.”)
Use this group member instead
- PropID lapl (None)
Link access property list
for obj_name- INT index_type (
h5.INDEX_NAME
) Which index to use
- INT order (
h5.ITER_INC
) What order the index is in
- h5py.h5a.iterate(ObjectID loc, CALLABLE func, INT index=0, **kwds) <Return value from func> ¶
Iterate a callable (function, method or callable object) over the attributes attached to this object. You callable should have the signature:
func(STRING name) => Result
or if the keyword argument “info” is True:
func(STRING name, AttrInfo info) => Result
Returning None continues iteration; returning anything else aborts iteration and returns that value. Keywords:
- BOOL info (False)
Callback is func(STRING name, AttrInfo info), not func(STRING name)
- INT index_type (
h5.INDEX_NAME
) Which index to use
- INT order (
h5.ITER_INC
) Index order to use
Info objects¶
Attribute objects¶
- class h5py.h5a.AttrID¶
Logical representation of an HDF5 attribute identifier.
Objects of this class can be used in any HDF5 function call which expects an attribute identifier. Additionally, all
H5A*
functions which always take an attribute instance as the first argument are presented as methods of this class.Hashable: No
Equality: Identifier comparison
- dtype¶
A Numpy-stype dtype object representing the attribute’s datatype
- get_name() STRING name ¶
Determine the name of this attribute.
- get_storage_size() INT ¶
Get the amount of storage required for this attribute.
- name¶
The attribute’s name
- read(NDARRAY arr, TypeID mtype=None)¶
Read the attribute data into the given Numpy array. Note that the Numpy array must have the same shape as the HDF5 attribute, and a conversion-compatible datatype.
The Numpy array must be writable and C-contiguous. If this is not the case, the read will fail with an exception.
If provided, the HDF5
TypeID
mtype will override the array’s dtype.
- shape¶
A Numpy-style shape tuple representing the attribute’s dataspace
- write(NDARRAY arr)¶
Write the contents of a Numpy array to the attribute. Note that the Numpy array must have the same shape as the HDF5 attribute, and a conversion-compatible datatype.
The Numpy array must be C-contiguous. If this is not the case, the write will fail with an exception.