Databases Reference
In-Depth Information
You're not limited to using paths for source and destination. If you already have an open
Dataset
object, for example, you can copy it to a
Group
or
File
object:
>>>
dset
=
f
[
'/mygroup/apples'
]
>>>
f
.
copy
(
dset
,
f
)
>>>
f
.
visit
(
printname
)
apples
oranges
mygroup
mygroup/dataset
mygroup/subgroup
mygroup2
mygroup2/dataset
mygroup2/subgroup
Since the destination is a group, the dataset is created with its “base name” of
apples
,
analagous to how files are moved with the UNIX
cp
command.
There's no requirement that the source and destination be the same file. This is one of
the advantages of using
File
or
Group
objects instead of paths; the corresponding objects
will be copied regardless of which file they reside in. If you're trying to write generic
code, it's good to keep this in mind.
Object Comparison and Hashing
Let's take a break from links and iteration to discuss a more subtle aspect of how HDF5
behaves. In lots of the preceding examples, we used Python's equality operator to see if
two groups are “the same thing”:
>>>
f
=
h5py
.
File
(
'objectdemo.hdf5'
,
'w'
)
>>>
grpx
=
f
.
create_group
(
'x'
)
>>>
grpy
=
f
.
create_group
(
'y'
)
>>>
grpx
==
f
[
'x'
]
True
>>>
grpx
==
grpy
False
If we investigate further, we discover that this kind of equality testing is independent of
whether the Python objects are one and the same:
>>>
id
(
grpx
)
# Uniquely identifies the Python object "grpx"
73399280
>>>
id
(
f
[
'x'
])
73966416
In h5py, equality testing uses the low-level HDF5 facilities to determine which refer‐
ences (
identifiers
, in the HDF5 lingo) point to the same groups or datasets on disk. This
information is also used to compute the hash of an object, which means you can safely
use
Group
,
File
, and
Dataset
objects as dictionary keys or as the members of sets:
