# What’s new in h5py 3.8

## New features

- h5py now has pre-built packages for Python 3.11.

- h5py is compatible with HDF5 1.14 (PR 2187). Pre-built packages on PyPI still include HDF5 1.12 for now.

- Fancy indexing now accepts tuples, or any other sequence type, rather than only lists and NumPy arrays. This also includes `range` objects, but this will normally be less efficient than the equivalent slice.

- New property `Dataset.is_scale` for checking if the dataset is a dimension scale (PR 2168).

- `Group.require_dataset()` now validates `maxshape` for resizable datasets (PR 2116).

- `File` now has a `meta_block_size` argument and property. This influences how the space for metadata, including the initial header, is allocated.

- Chunk cache can be configured per individual HDF5 dataset (PR 2127). Use `Group.create_dataset()` for new datasets or `Group.require_dataset()` for already existing datasets. Any combination of the `rdcc_nbytes`, `rdcc_w0`, and `rdcc_nslots` arguments is allowed. The file defaults apply to those omitted.

- HDF5 file names for ros3 driver can now also be `s3://` resource locations (PR 2140). h5py will translate them into AWS path-style URLs for use by the driver.

- When using the ros3 driver, AWS authentication will be activated only if all three driver arguments are provided. Previously AWS authentication was active if any one of the arguments was set causing an error from the HDF5 library.

- `Dataset.fields()` now implements the `__array__()` method (PR 2151). This speeds up accessing fields with functions that expect this, like `np.asarray()`.

- Low-level `h5py.h5d.DatasetID.chunk_iter()` method that invokes a user-supplied callable object on every written chunk of one dataset (PR 2202). It provides much better performance when iterating over a large number of chunks.

## Exposing HDF5 functions

- `H5Dchunk_iter` as `h5py.h5d.DatasetID.chunk_iter()`.

- H5Pset_meta_block_size and H5Pget_meta_block_size (PR 2106).

## Bug fixes

- Fixed getting the default fill value (an empty string) for variable-length string data (PR 2132).

- Complex float16 data could cause a `TypeError` when trying to coerce to the currently unavailable numpy.dtype(‘c4’). Now a compound type is used instead (PR 2157).

- h5py 3.7 contained a performance regression when using a boolean mask array to index a 1D dataset, which is now fixed (PR 2193).

## Building h5py

- Parallel HDF5 can be built with Microsoft MS-MPI (PR 2147). See Building against Parallel HDF5 for details.

- Some ‘incompatible function pointer type’ compile time warnings were fixed (PR 2142).

- Fix for finding HDF5 DLL in mingw (PR 2105).