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Contributing

RosettaSciIO is meant to be a community maintained project. We welcome contributions in the form of bug reports, documentation, code (in particular :ref:`new io plugins <defining-plugins>`), feature requests, and more. In the following we refer to some resources to help you make useful contributions.

Issues

The issue tracker can be used to report bugs or propose new features. When reporting a bug, the following is useful:

  • give a minimal example demonstrating the bug,
  • copy and paste the error traceback.

Making test data files

Test data files are typically generated using third party software, for example using a proprietary software on a scientific instrument. These files are added to the test suite of RosettaSciIO to make sure that future code development will not introduce bugs or feature regressions. It is important that the test data files area as small as possible to avoid working with a repository that contains GBs of test data. Indeed, the test suite is made of severals hundreds of test data files and this number of files will keep growing as new features and formats are added to RosettaSciIO.

Users can contribute by generating these files on softwares they have access to and by making these files available openly; then a RosettaSciIO developer will help with adding these data to the test suite.

What characterizes good test data files:

  • Relevant features: the test data files do not need to contain any meaningful data, but they need to cover as much as possible of the format functionalities.
  • Small size:
    • Acquire minimum number of pixels or channels. In case of maps or spectrum images acquire a non-square grid (e.g. "x" and "y" have different lengths).
    • If possible, generate data that contains no signal (e.g. zeros) as files containing only very few values will compress very well.

Pull Requests

If you want to contribute to the RosettaSciIO source code, you can send us a pull request against the main branch. Small bug fixes are corrections to the user guide are typically a good starting point. But don't hesitate also for significant code contributions, such as support for a new file format - if needed, we'll help you to get the code ready to common standards.

Please refer to the HyperSpy developer guide in order to get started and for detailed contributing guidelines.

Lint

To keep the code style consistent (and more readable), black is used to check the code formatting. When the code doesn't comply with the expected formatting, the pre-commit.ci build will fail. In practise, the code formatting can be fixed by installing black and running it on the source code or by using :ref:`pre-commit hooks <pre-commit-hooks>`. Alternatively, adding the message pre-commit.ci autofix in a pull request will push a commit with the fixes using pre-commit.ci.

Adding and Updating Test Data

The test data are located in the corresponding subfolder of the rsciio/tests/data folder. The test data are not packaged in the distribution files (wheel, sdist) to keep the packages as small as possible in size. When running the test suite, the test data will be downloaded from GitHub using pooch. When adding or updating test data, it is necessary to update the test data registry.

To add or update test data:

  1. use git as usual to add files to the repository.

  2. Update rsciio.tests.registry.txt by running :py:func:`~.tests.registry_utils.update_registry` (Unix only):

    from rsciio.tests.registry_utils import update_registry

update_registry()

    On windows, you can use :ref:`pre-commit.ci <pre-commit-hooks>` by adding a message to the pull request to update the registry.

Note

The url used by pooch to download the test data can be set by the environment variable POOCH_BASE_URL, otherwise, the default is to download the data from the hyperspy/rosettasciio GitHub repository.

Review

As quality assurance, to improve the code, and to ensure a generalized functionality, pull requests need to be thoroughly reviewed by at least one other member of the development team before being merged.

Pre-commit Hooks

Two pre-commit hooks are set up:

  • Linting: run black
  • Update test data registry (Unix only)

These can be run locally by using pre-commit. Alternatively, the comment pre-commit.ci autofix can be added to a PR to fix the formatting using pre-commit.ci.

Defining new RosettaSciIO plugins

Each read/write plugin resides in a separate directory, e.g. spamandeggs the name of which should be descriptive of the file type/manufacturer/software. This directory should contain the following files:

  • __init__.py -- Defines the exposed API functions, file_reader and optionally file_writer

    from ._api import file_reader, file_writer


__all__ = [
    "file_reader",
    "file_writer",
]


def __dir__():
    return sorted(__all__)

  • specifications.yaml -- The characteristics of the IO plugin in yaml format:

    name: <String> # unique, concise, no whitespace; corresponding to directory name (e.g. ``spamandeggs``)
name_aliases: [<String>]  # List of strings, may contain whitespaces (empty if no alias defined)
description: <String>
full_support: <Bool>      # Whether all the Hyperspy features are supported
file_extensions: <Tuple of string>  # Recognised file extension
default_extension: <Int>  # Index of the extension that will be used by default
writes: <Bool>/[Nested list]  # Writing capabilities
# if only limited dimensions are supported, the supported combinations of signal
# dimensions (sd) and navigation dimensions (nd) are given as list [[sd, nd], ...]
non_uniform_axis: <Bool>  # Support for non-uniform axis

  • _api.py -- Python file that implements the actual reader. The IO functionality should be interfaced with the following functions:

Tests covering the functionality of the plugin should be added to the tests directory with the naming test_spamandeggs.py corresponsing to the plugin residing in the directory spamandeggs. Data files for the tests should be placed in a corresponding subdirectory - see the :ref:`Adding and Updating Test Data <adding-and-updating-test-data>` section for more information.

Documentation should be added both as docstring, as well as to the user guide, for which a corresponding spamandeggs.rst file should be created in the directory doc/user_guide/supported_formats/ and the format added to the lists in doc/user_guide/supported_formats/index.rst and doc/user_guide/supported_formats/supported_formats.rst.

A few standard docstring components are provided by rsciio._docstrings.py and should be used (see existing plugins).

The docstrings are automatically added in the user guide using the following lines

API functions
^^^^^^^^^^^^^

.. automodule:: rsciio.spamandeggs
   :members:

The docstrings follow Numpy docstring style. The links to RosettaSciIO API and other Sphinx documented API are checked when building the documentation and broken links will raise warnings. In order to identify potentially broken links during pull request review, the Documentation GitHub CI workflow is set to fail when the doc build raises warnings.

Note

It is advisable to clone the files of an existing plugin when initiating a new plugin.

RosettaSciIO version

The version of RosettaSciIO is defined by setuptools_scm and retrieve by importlib.metadata at runtime in case of user installation.

  • Version at build time: the version is defined from the tag or the "distance from the tag".

  • Version at runtime: use the version of the package (sdist or wheel), which would have been defined at build time. At runtime, the version is obtained using importlib.metadata as follow:

    from importlib.metadata import version
__version__ = version("rosettasciio")

  • Version at runtime for editable installation: the version is defined from the tag or "the distance from the tag".

Note

To define the version in development installation or at build time, setuptools_scm uses the git history with all commits, and shallow checkout will provide incorrect version. For user installation in site-package, setuptools_scm is not used.

Dependencies

RosettaSciIO strive to be easy to install with a minimum of dependencies and depends solely on standard library modules, numpy and dask. Non-pure python (binaries) dependencies are optional for the following reasons:

  • provide maximum flexibility in usability and avoid forcing user to install library that they don't need: for user-cases, where only a file reader are necessary, it should be possible to install RosettaSciIO without installing large or non-pure python dependencies, which are not always easy to install.
  • Some binaries dependencies are not supported for all python implementation (pypy or pyodide) or for all platforms.

Maintenance

Please refer to the HyperSpy developer guide for maintenance guidelines.