Get started with plugins¶
The main way to customize a NOMAD installation is through the use of plugins. A NOMAD Plugin is a Git repository that contains a Python package that an administrator can install into a NOMAD deployment to add custom features. This page contains the basics of how to create, develop and publish a NOMAD Plugin.
Plugin anatomy¶
Tip
We provide a template repository which you can use to create the initial plugin layout for you.
Plugin Git repositories should roughly follow this layout:
├── nomad-example
│ ├── src
| │ ├── nomad_example
| | │ ├── apps
| | │ │ ├── __init__.py
| | │ ├── normalizers
| | │ │ ├── mynormalizer.py
| | │ │ ├── __init__.py
| | │ ├── schema_packages
| | │ │ ├── mypackage.py
| | │ │ ├── __init__.py
| | │ ├── parsers
| | │ │ ├── myparser.py
| | │ │ ├── __init__.py
│ ├── docs
│ ├── tests
│ ├── pyproject.toml
│ ├── LICENSE.txt
│ ├── README.md
We suggest using the following convention for naming the repository name and the plugin package:
- repository name:
nomad-<plugin name> - package name:
nomad_<plugin name>
In the folder structure you can see that a single plugin can contain multiple types of customizations: apps, parsers, schema packages and normalizers. These are called a plugin entry points and you will learn more about them next.
Plugin entry points¶
Plugin entry points represent different types of customizations that can be added to a NOMAD installation. The following plugin entry point types are currently supported:
Entry points contain configuration, but also a separate resource, which should live in a separate Python module. This split enables lazy-loading: the configuration can be loaded immediately, while the resource is loaded later when/if it is required. This can significantly improve startup times, as long as all time-consuming initializations are performed only when loading the resource. This split also helps to avoid cyclical imports between the plugin code and the nomad-lab package.
For example the entry point instance for a parser is contained in .../parsers/__init__.py and it contains e.g. the name, version and any additional entry point-specific parameters that control its behaviour. The entry point has a load method than can be called lazily to return the resource, which is a Parser instance defined in .../parsers/myparser.py.
In pyproject.toml you can expose plugin entry points for automatic discovery. E.g. to expose an app and a package, you would add the following to pyproject.toml:
[project.entry-points.'nomad.plugin']
myapp = "nomad_example.parsers:myapp"
mypackage = "nomad_example.schema_packages:mypackage"
Here it is important to use the nomad.plugin group name in the project.entry-points header. The plugin name used on the left side (mypackage) can be arbitrary, what matters is that the key ("nomad_example.schema_packages:mypackage") is a path pointing to a plugin entry point instance inside the python code. This unique key will be used to identify the plugin entry point when e.g. accessing it to read some of it's configuration values.
You can read more about how to write different types of entry points in their dedicated documentation pages or learn more about the Python entry point mechanism.
Controlling loading of plugin entry points¶
By default, plugin entry points are automatically loaded, and as an administrator you only need to install the Python package. You can, however, control which entry points to load by explicitly including/excluding them in your nomad.yaml. For example, if a plugin has the following pyproject.toml:
You could disable the parser entry point in your nomad.yaml with:
Extending and using the entry point¶
The plugin entry point is an instance of a pydantic model. This base model may already contain entry point-specific fields (such as the file extensions that a parser plugin will match) but it is also possible to extend this model to define additional fields that control your plugin behaviour.
To specify new configuration options, you can add new pydantic fields to the subclass. For example, if we wanted to add a new configuration option for a parser, we could do the following:
from pydantic import Field
from nomad.config.models.plugins import ParserEntryPoint
class MyParserEntryPoint(ParserEntryPoint):
parameter: int = Field(0, description='Config parameter for this parser.')
where we have defined a new subclass of ParserEntryPoint and added a new configuration field parameter. The plugin users can then control these settings in their nomad.yaml using plugins.entry_points.options:
Note that the model will also validate the values coming from nomad.yaml, and you should utilize the validation mechanisms of pydantic to provide users with helpful messages about invalid configuration.
In your code, you can then access the whole entry point by loading it with config.get_plugin_entry_point:
from nomad.config import config
configuration = config.get_plugin_entry_point('nomad_example.parsers:myparser')
print(f'The parser parameter is: {configuration.parameter}')
Plugin development guidelines¶
Linting and formatting¶
While developing NOMAD plugins, we highly recommend using a Python linter, such as Ruff, to analyze and enforce coding standards in your plugin projects. This also ensures smoother integration and collaboration. If you have used our template repository, you will automatically have ruff defined as a development dependency with suitable defaults set in pyproject.toml together with a GitHub actions that runs the linting and formatting checks on each push to the Git repository.
Testing¶
For testing, you should use pytest, and a folder structure that mimics the package layout with test modules named after the tested module. For example, if you are developing a parser in myparser.py, the test folder structure should look like this:
├── nomad-example-plugin
│ ├── src
| │ ├── nomad_example
| | │ ├── parsers
| | │ │ ├── myparser.py
| | │ │ ├── __init__.py
│ ├── tests
| │ ├── parsers
| | │ ├── test_myparser.py
| | │ ├── conftest.py
| │ ├── conftest.py
Any shared test utilities (such as pytest fixtures) should live in conftest.py modules placed at the appropriate level in the folder hierarchy, i.e. utilities dealing with parsers would live in tests/parsers/conftest.py, while root level utilities would live in tests/conftest.py. If you have used our template repository, you will automatically have an initial test folder structure, pytest defined as a development dependency in pyproject.toml and a GitHub action that runs the test suite on each push to the Git repository.
In the pytest framework, test cases are created by defining functions with the test_ prefix, which perform assertions. A typical test case could look like this:
def test_parse_file():
parser = MyParser()
archive = EntryArchive()
parser.parse('tests/data/example.out', archive, logging)
sim = archive.data
assert len(sim.model) == 2
assert len(sim.output) == 2
assert archive.workflow2.x_example_magic_value == 42
You can run all the tests in the tests/ directory with:
Documentation¶
As your plugin matures, you should also think about documenting its usage. We recommend using mkdocs to create your documentation as a set of markdown files. If you have used our template repository, you will automatically have an initial documentation folder structure, mkdocs defined as a development dependency in pyproject.toml and a GitHub action that builds the docs to a separate gh-pages branch each push to the Git repository. Note that if you wish to host the documentation using GitHub pages, you need to enable this in the repository settings.
Publishing a plugin¶
Attention
The standard processes for publishing plugins and using plugins from other developers are still being worked out. The "best" practices mentioned in the following are preliminary. We aim to set up a dedicated plugin registry that allows you to publish your plugin and find plugins from others.
GitHub repository¶
The simplest way to publish a plugin is to have it live in a publicly shared Git repository. The package can then be installed with:
Note
If you develop a plugin in the context of FAIRmat or the NOMAD CoE, put your plugin repositories in the corresponding GitHub organization.
PyPI/pip package¶
You may additionally publish the plugin package in PyPI. Learn from the PyPI documentation how to create a package for PyPI. We recommend to use the pyproject.toml-based approach.
The PyPI documentation provides further information about how to publish a package to PyPI. If you have access to the MPCDF GitLab and NOMAD's presence there, you can also
use the nomad-FAIR package registry: