pydantic_espresso 0.0.1

Pydantic models for Quantum ESPRESSO

pydantic-espresso

Pydantic models for Quantum ESPRESSO.

The models are auto-generated from the INPUT_*.def files shipped with Quantum ESPRESSO. Supported QE versions are 7.6 and newer (the schema for older releases lacks the structured <units>, <dimensionality>, and <default kind="..."> metadata that the generator depends on).

💪 Getting Started

from pydantic_espresso.models.pw.develop import PWInput

inp = PWInput(
    system={"ibrav": 0, "nat": 1, "ntyp": 1, "ecutwfc": 30.0},
    cell_parameters={"unit": "alat", "vectors": [[1, 0, 0], [0, 1, 0], [0, 0, 1]]},
    atomic_positions={
        "unit": "alat",
        "positions": [{"species": "H", "position": [0.0, 0.0, 0.0]}],
    },
    k_points={"kind": "gamma"},
)

# Pydantic enforces both the QE input schema and the field constraints:
inp.system.ibrav = -1          # ValidationError: invalid Bravais lattice index
inp.system.nbnd = "a"          # ValidationError: not an int
inp.control.calculation = "bands"   # OK

Each numeric field carries its physical units and dimensionality as Quantity metadata, threaded into both the pydantic field info and the JSON schema:

from pydantic_espresso.models.pw.develop import SystemNamelist
from pydantic_espresso.quantity import quantity_for

q = quantity_for(SystemNamelist.model_fields["ecutwfc"])
# Quantity(units='Ry', dimensionality='energy')

PWInput.model_json_schema()["$defs"]["SystemNamelist"]["properties"]["ecutwfc"]
# {'type': 'number', 'units': 'Ry', 'dimensionality': 'energy', ...}

QE namelist branches with mutually-exclusive layouts (e.g. PP's PLOT namelist discriminated on iflag) are exposed as pydantic discriminated unions, so the right variant is picked automatically from the input data.

Readable error reports

Construction raises a standard pydantic.ValidationError. Pass the caught error to pydantic_espresso.errors.explain for a grouped, recursive summary that names each missing input together with its type, units, and help text:

from pydantic import ValidationError
from pydantic_espresso.errors import explain
from pydantic_espresso.models.pw.develop import PWInput

try:
    inp = PWInput(
        system={"ibrav": 0, "ecutwfc": 30.0},                     # missing nat, ntyp
        cell_parameters={"vectors": [[1, 0, 0], [0, 1, 0], [0, 0, 1]]},  # missing 'unit'
        k_points={"kind": "automatic"},                           # missing grid
        # atomic_positions omitted entirely
    )
except ValidationError as exc:
    print(explain(exc, PWInput))

PWInput is missing required inputs:
  system:
      - nat (int): number of atoms in the unit cell (ALL atoms, except if space_group is set, in
        which case, INEQUIVALENT atoms)
      - ntyp (int): number of types of atoms in the unit cell
  atomic_positions: required — choose 'unit': ['alat', 'bohr', 'angstrom', 'crystal', 'crystal_sg']
  k_points [kind='automatic']:
      - grid (tuple[int, int, int]): Monkhorst-Pack mesh dimensions (nk1, nk2, nk3): number of
        k-points along each reciprocal-lattice direction of the uniform grid.
  cell_parameters: missing discriminator 'unit' (one of ['alat', 'bohr', 'angstrom'])

Command Line Interface

The pydantic_espresso command line tool ships with the package but requires the optional dev extra (which pulls in click, python-gitlab, and defusedxml). Install it with pip install pydantic_espresso[dev] or uv pip install "pydantic_espresso[dev]".

It can be used from the console with the --help flag to show all subcommands:

$ pydantic_espresso --help

The JSON schema for pw.x can be generated with the command

$ pydantic_espresso schema pw

🚀 Installation

The most recent code and data can be installed directly from GitHub with uv:

$ uv pip install git+https://github.com/elinscott/pydantic-espresso.git

or with pip:

$ python3 -m pip install git+https://github.com/elinscott/pydantic-espresso.git

👐 Contributing

Contributions, whether filing an issue, making a pull request, or forking, are appreciated. See CONTRIBUTING.md for more information on getting involved.

👋 Attribution

⚖️ License

The code in this package is licensed under the MIT License.

🍪 Cookiecutter

This package was created with @audreyfeldroy's cookiecutter package using @cthoyt's cookiecutter-snekpack template.

🛠️ For Developers

See developer instructions

The final section of the README is for if you want to get involved by making a code contribution.

Development Installation

To install in development mode, use the following:

$ git clone git+https://github.com/elinscott/pydantic-espresso.git
$ cd pydantic-espresso
$ uv pip install -e .

Alternatively, install using pip:

$ python3 -m pip install -e .

Updating Package Boilerplate

This project uses cruft to keep boilerplate (i.e., configuration, contribution guidelines, documentation configuration) up-to-date with the upstream cookiecutter package. Install cruft with either uv tool install cruft or python3 -m pip install cruft then run:

$ cruft update

More info on Cruft's update command is available here.

🥼 Testing

After cloning the repository and installing tox with uv tool install tox --with tox-uv or python3 -m pip install tox tox-uv, the unit tests in the tests/ folder can be run reproducibly with:

$ tox -e py

Additionally, these tests are automatically re-run with each commit in a GitHub Action.

📖 Building the Documentation

The documentation can be built locally using the following:

$ git clone git+https://github.com/elinscott/pydantic-espresso.git
$ cd pydantic-espresso
$ tox -e docs
$ open docs/build/html/index.html

The documentation automatically installs the package as well as the docs extra specified in the pyproject.toml. sphinx plugins like texext can be added there. Additionally, they need to be added to the extensions list in docs/source/conf.py.

The documentation can be deployed to ReadTheDocs using this guide. The .readthedocs.yml YAML file contains all the configuration you'll need. You can also set up continuous integration on GitHub to check not only that Sphinx can build the documentation in an isolated environment (i.e., with tox -e docs-test) but also that ReadTheDocs can build it too.

Configuring ReadTheDocs

1. Log in to ReadTheDocs with your GitHub account to install the integration at https://readthedocs.org/accounts/login/?next=/dashboard/
2. Import your project by navigating to https://readthedocs.org/dashboard/import then clicking the plus icon next to your repository
3. You can rename the repository on the next screen using a more stylized name (i.e., with spaces and capital letters)
4. Click next, and you're good to go!

📦 Making a Release

Configuring Zenodo

Zenodo is a long-term archival system that assigns a DOI to each release of your package.

1. Log in to Zenodo via GitHub with this link: https://zenodo.org/oauth/login/github/?next=%2F. This brings you to a page that lists all of your organizations and asks you to approve installing the Zenodo app on GitHub. Click "grant" next to any organizations you want to enable the integration for, then click the big green "approve" button. This step only needs to be done once.
2. Navigate to https://zenodo.org/account/settings/github/, which lists all of your GitHub repositories (both in your username and any organizations you enabled). Click the on/off toggle for any relevant repositories. When you make a new repository, you'll have to come back to this

After these steps, you're ready to go! After you make "release" on GitHub (steps for this are below), you can navigate to https://zenodo.org/account/settings/github/repository/elinscott/pydantic-espresso to see the DOI for the release and link to the Zenodo record for it.

Registering with the Python Package Index (PyPI)

You only have to do the following steps once.

1. Register for an account on the Python Package Index (PyPI)
2. Navigate to https://pypi.org/manage/account and make sure you have verified your email address. A verification email might not have been sent by default, so you might have to click the "options" dropdown next to your address to get to the "re-send verification email" button
3. 2-Factor authentication is required for PyPI since the end of 2023 (see this blog post from PyPI). This means you have to first issue account recovery codes, then set up 2-factor authentication
4. Issue an API token from https://pypi.org/manage/account/token

Configuring your machine's connection to PyPI

You have to do the following steps once per machine.

$ uv tool install keyring
$ keyring set https://upload.pypi.org/legacy/ __token__
$ keyring set https://test.pypi.org/legacy/ __token__

Note that this deprecates previous workflows using .pypirc.

Uploading to PyPI

After installing the package in development mode and installing tox with uv tool install tox --with tox-uv or python3 -m pip install tox tox-uv, run the following from the console:

$ tox -e finish

This script does the following:

1. Uses bump-my-version to switch the version number in the pyproject.toml, CITATION.cff, src/pydantic_espresso/version.py, and docs/source/conf.py to not have the -dev suffix
2. Packages the code in both a tar archive and a wheel using uv build
3. Uploads to PyPI using uv publish.
4. Push to GitHub. You'll need to make a release going with the commit where the version was bumped.
5. Bump the version to the next patch. If you made big changes and want to bump the version by minor, you can use tox -e bumpversion -- minor after.

Releasing on GitHub

1. Navigate to https://github.com/elinscott/pydantic-espresso/releases/new to draft a new release
2. Click the "Choose a Tag" dropdown and select the tag corresponding to the release you just made
3. Click the "Generate Release Notes" button to get a quick outline of recent changes. Modify the title and description as you see fit
4. Click the big green "Publish Release" button

This will trigger Zenodo to assign a DOI to your release as well.