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, feel free to ask on Github itself, or on the h5py section of the HDF5 forum:

https://forum.hdfgroup.org/c/hdf-tools/h5py

Posting on this forum requires registering for a free account with HDF group.

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:

https://github.com/h5py/h5py/issues

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 h5py.version.info:

>>> 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¶

Changes which could affect people building and using h5py after the next release should have a news entry. You don’t need to do this if your changes don’t affect usage, e.g. adding tests or correcting comments.

In the 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.

News entries are merged into the what’s new documents for each release. They should allow someone to quickly understand what a new feature is, or whether a bug they care about has been fixed. E.g.:

Bug fixes
---------

* Fix reading data for region references pointing to an empty selection.

The Building h5py section is for changes which affect how people build h5py from source. It’s not about how we make prebuilt wheels; changes to that which make a visible difference can go in New features or Bug fixes.

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 (api_gen.py)
Low-level interface, written in Cython, using the wrappers from (2)
High-level interface, written in Python, with things like h5py.File.
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 H5Dget_storage_size, 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 api_functions.txt:

hsize_t   H5Dget_storage_size(hid_t dset_id)

This particular signature uses types (hsize_t, 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 api_types_hdf5.pxd.

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.

Opening 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 h5py.Dataset object will be able to see how much space on disk their files use. The high-level interface is implemented in the subpackage h5py._hl, and the Dataset object is in module dataset.py. Opening it up, we add a property on the Dataset object:

@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 high-level Dataset object as obj.id. This is true of all the high-level objects, like File and 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 dataset.py.

It looks like the right file is test_dataset.py. Unit tests are implemented as methods on custom unittest.UnitTest subclasses; 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 provides a h5py.File object as obj.f. We’ll base our test class on that:

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 api_functions.txt:

$ python api_gen.py

Then build the project, which recompiles h5d.pyx:

$ 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 function H5Fset_mpi_atomicity was introduced in HDF5 1.8.9 and requires Parallel HDF5. Specifiers before the signature in api_functions.txt communicate this:

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 MPI and 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 FileID objects.

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:

Have tox installed (e.g. via pip install tox)
Have HDF5 built with MPI as per Building against Parallel HDF5

Then running:

$ 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 mindeps rather than deps.

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.