Bug Reports & Contributions¶
Contributions and bug reports are welcome from anyone! Some of the best features in h5py, including thread support, dimension scales, and the scale-offset filter, came from user code contributions.
Since we use GitHub, the workflow will be familiar to many people. If you have questions about the process or about the details of implementing your feature, always feel free to ask on the Google Groups list, either by emailing:
or via the web interface at:
Anyone can post to this list. Your first message will be approved by a moderator, so don’t worry if there’s a brief delay.
This guide is divided into three sections. The first describes how to file a bug report.
The second describes the mechanics of how to submit a contribution to the h5py project; for example, how to create a pull request, which branch to base your work on, etc. We assume you’re are familiar with Git, the version control system used by h5py. If not, here’s a great place to start.
Finally, we describe the various subsystems inside h5py, and give technical guidance as to how to implement your changes.
How to File a Bug Report¶
Bug reports are always welcome! The issue tracker is at:
If you’re unsure whether you’ve found a bug¶
Always feel free to ask on the mailing list (h5py at Google Groups). Discussions there are seen by lots of people and are archived by Google. Even if the issue you’re having turns out not to be a bug in the end, other people can benefit from a record of the conversation.
By the way, nobody will get mad if you file a bug and it turns out to be something else. That’s just how software development goes.
What to include¶
When filing a bug, there are two things you should include. The first is
the output of
>>> import h5py >>> print(h5py.version.info)
The second is a detailed explanation of what went wrong. Unless the bug is really trivial, include code if you can, either via GitHub’s inline markup:
``` import h5py h5py.explode() # Destroyed my computer! ```
or by uploading a code sample to Github Gist.
How to Get Your Code into h5py¶
This section describes how to contribute changes to the h5py code base. Before you start, be sure to read the h5py license and contributor agreement in “license.txt”. You can find this in the source distribution, or view it online at the main h5py repository at GitHub.
The basic workflow is to clone h5py with git, make your changes in a topic branch, and then create a pull request at GitHub asking to merge the changes into the main h5py project.
Here are some tips to getting your pull requests accepted:
- Let people know you’re working on something. This could mean posting a comment in an open issue, or sending an email to the mailing list. There’s nothing wrong with just opening a pull request, but it might save you time if you ask for advice first.
- Keep your changes focused. If you’re fixing multiple issues, file multiple pull requests. Try to keep the amount of reformatting clutter small so the maintainers can easily see what you’ve changed in a diff.
- Unit tests are mandatory for new features. This doesn’t mean hundreds (or even dozens) of tests! Just enough to make sure the feature works as advertised. The maintainers will let you know if more are needed.
Clone the h5py repository¶
The best way to do this is by signing in to GitHub and cloning the
h5py project directly. You’ll end up with a new repository under your
account; for example, if your username is
yourname, the repository
would be at http://github.com/yourname/h5py.
Then, clone your new copy of h5py to your local machine:
$ git clone http://github.com/yourname/h5py
Create a topic branch for your feature¶
Check out a new branch for the bugfix or feature you’re writing:
$ git checkout -b newfeature master
The exact name of the branch can be anything you want. For bug fixes, one approach is to put the issue number in the branch name.
We develop all changes against the master branch. If we’re making a bugfix release, a bot will backport merged pull requests.
Implement the feature!¶
You can implement the feature as a number of small changes, or as one big
commit; there’s no project policy. Double-check to make sure you’ve
included all your files; run
git status and check the output.
Run the tests¶
The easiest way to run the tests is with tox:
pip install tox # Get tox tox -e py37-test-deps # Run tests in one environment tox # Run tests in all possible environments tox -a # List defined environments
Write a release note¶
news/ folder, make a copy of
TEMPLATE.rst named after your branch.
Edit the new file, adding a sentence or two about what you’ve added or fixed.
Commit this to git too. These files will be used to make the release notes.
You don’t need this if your change doesn’t affect people building and using h5py, e.g. fixing a typo.
Push your changes back and open a pull request¶
Push your topic branch back up to your GitHub clone:
$ git push origin newfeature
Then, create a pull request based on your topic branch.
Work with the maintainers¶
Your pull request might be accepted right away. More commonly, the maintainers will post comments asking you to fix minor things, like add a few tests, clean up the style to be PEP-8 compliant, etc.
The pull request page also shows the results of building and testing the modified code on Travis and Appveyor CI and Azure Pipelines. Check back after about 30 minutes to see if the build succeeded, and if not, try to modify your changes to make it work.
When making changes after creating your pull request, just add commits to your topic branch and push them to your GitHub repository. Don’t try to rebase or open a new pull request! We don’t mind having a few extra commits in the history, and it’s helpful to keep all the history together in one place.
How to Modify h5py¶
This section is a little more involved, and provides tips on how to modify h5py. The h5py package is built in layers. Starting from the bottom, they are:
- The HDF5 C API (provided by libhdf5)
- Auto-generated Cython wrappers for the C API (
- Low-level interface, written in Cython, using the wrappers from (2)
- High-level interface, written in Python, with things like
- Unit test code
Rather than talk about the layers in an abstract way, the parts below are guides to adding specific functionality to various parts of h5py. Most sections span at least two or three of these layers.
Adding a function from the HDF5 C API¶
This is one of the most common contributed changes. The example below shows
how one would add the function
which determines the space on disk used by an HDF5 dataset. This function
is already partially wrapped in h5py, so you can see how it works.
It’s recommended that you follow along, if not by actually adding the feature then by at least opening the various files as we work through the example.
First, get ahold of
the function signature; the easiest place for this is at the online
HDF5 Reference Manual.
Then, add the function’s C signature to the file
hsize_t H5Dget_storage_size(hid_t dset_id)
This particular signature uses types (
hid_t) which are already
defined elsewhere. But if
the function you’re adding needs a struct or enum definition, you can
add it using Cython code to the file
The next step is to add a Cython function or method which calls the function
you added. The h5py modules follow the naming convention
of the C API; functions starting with
H5D are wrapped in
h5d.pyx, we notice that since this function takes a dataset
identifier as the first argument, it belongs as a method on the DatasetID
object. We write a wrapper method:
def get_storage_size(self): """ () => LONG storage_size Determine the amount of file space required for a dataset. Note this only counts the space which has actually been allocated; it may even be zero. """ return H5Dget_storage_size(self.id)
The first line of the docstring gives the method signature.
This is necessary because Cython will use a “generic” signature like
method(*args, **kwds) when the file is compiled. The h5py documentation
system will extract the first line and use it as the signature.
Next, we decide whether we want to add access to this function to the
high-level interface. That means users of the top-level
object will be able to see how much space on disk their files use. The
high-level interface is implemented in the subpackage
the Dataset object is in module
dataset.py. Opening it up, we add
a property on the
@property def storagesize(self): """ Size (in bytes) of this dataset on disk. """ return self.id.get_storage_size()
You’ll see that the low-level
DatasetID object is available on the
Dataset object as
obj.id. This is true of all the
high-level objects, like
Group as well.
Finally (and don’t skip this step), we write unit tests for this feature.
Since the feature is ultimately exposed at the high-level interface, it’s OK
to write tests for the
Dataset.storagesize property only. Unit tests for
the high-level interface are located in the “tests” subfolder, right near
It looks like the right file is
test_dataset.py. Unit tests are
implemented as methods on custom
each new feature should be tested by its own new class. In the
test_dataset module, we see there’s already a subclass called
BaseDataset, which implements some simple set-up and cleanup methods and
h5py.File object as
obj.f. We’ll base our test class on
class TestStorageSize(BaseDataset): """ Feature: Dataset.storagesize indicates how much space is used. """ def test_empty(self): """ Empty datasets take no space on disk """ dset = self.f.create_dataset("x", (100,100)) self.assertEqual(dset.storagesize, 0) def test_data(self): """ Storage size is correct for non-empty datasets """ dset = self.f.create_dataset("x", (100,), dtype='uint8') dset[...] = 42 self.assertEqual(dset.storagesize, 100)
This set of tests would be adequate to get a pull request approved. We don’t test every combination under the sun (different ranks, datasets with more than 2**32 elements, datasets with the string “kumquat” in the name…), but the basic, commonly encountered set of conditions.
To build and test our changes, we have to do a few things. First of all,
run the file
api_gen.py to re-generate the Cython wrappers from
$ python api_gen.py
Then build the project, which recompiles
$ python setup.py build
Finally, run the test suite, which includes the two methods we just wrote:
$ python setup.py test
If the tests pass, the feature is ready for a pull request.
Adding a function only available in certain versions of HDF5¶
At the moment, h5py must be backwards-compatible all the way back to
HDF5 1.8.4. Starting with h5py 2.2.0, it’s possible to conditionally
include functions which only appear in newer versions of HDF5. It’s also
possible to mark functions which require Parallel HDF5. For example, the
H5Fset_mpi_atomicity was introduced in HDF5 1.8.9 and requires
Parallel HDF5. Specifiers before the signature in
MPI 1.8.9 herr_t H5Fset_mpi_atomicity(hid_t file_id, hbool_t flag)
You can specify either, both or none of “MPI” or a version number in “X.Y.Z” format.
In the Cython code, these show up as “preprocessor” defines
HDF5_VERSION. So the low-level implementation (as a method on
h5py.h5f.FileID) looks like this:
IF MPI and HDF5_VERSION >= (1, 8, 9): def set_mpi_atomicity(self, bint atomicity): """ (BOOL atomicity) For MPI-IO driver, set to atomic (True), which guarantees sequential I/O semantics, or non-atomic (False), which improves performance. Default is False. Feature requires: 1.8.9 and Parallel HDF5 """ H5Fset_mpi_atomicity(self.id, <hbool_t>atomicity)
High-level code can check the version of the HDF5 library, or check to see if
the method is present on
Testing MPI-only features/code¶
Typically to run code under MPI,
mpirun must be used to start the MPI
processes. Similarly, tests using MPI features (such as collective IO), must
also be run under
mpirun. h5py uses pytest markers (specifically
pytest.mark.mpi and other markers from
pytest-mpi) to specify which tests
require usage of
mpirun, and will handle skipping the tests as needed. A
simple example of how to do this is:
@pytest.mark.mpi def test_mpi_feature(): import mpi4py # test the MPI feature
To run these tests, you’ll need to:
toxinstalled (e.g. via
pip install tox)
- Have HDF5 built with MPI as per Building against Parallel HDF5
$ CC='mpicc' HDF5_MPI=ON tox -e py37-test-deps-mpi4py
should run the tests. You may need to pass
HDF5_DIR depending on the
location of the HDF5 with MPI support. You can choose which python version to
build against by changing py37 (e.g. py36 runs python 3.6, this is a tox
feature), and test with the minimum version requirements by using
If you get an error similar to:
There are not enough slots available in the system to satisfy the 4 slots that were requested by the application: python Either request fewer slots for your application, or make more slots available for use.
then you need to reduce the number of MPI processes you are asking MPI to use. If you have already reduced the number of processes requested (or are running the default number which is 2), you will need to look up the documentation for your MPI implementation for handling this error. On OpenMPI (which is usually the default MPI implementation on most systems), running:
$ export OMPI_MCA_rmaps_base_oversubscribe=1
will instruct OpenMPI to allow more MPI processes than available cores on your system.
If you need to pass additional environment variables to your MPI implementation,
add these variables to the
passenv setting in the
tox.ini, and send us a PR
with that change noting the MPI implementation.