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https://github.com/AstraZeneca/kallisto
Efficiently calculate 3D-features for quantitative structure-activity relationship approaches.
https://github.com/AstraZeneca/kallisto
chemistry computational-chemistry machinelearning quantum-chemistry
Last synced: 14 days ago
JSON representation
Efficiently calculate 3D-features for quantitative structure-activity relationship approaches.
- Host: GitHub
- URL: https://github.com/AstraZeneca/kallisto
- Owner: AstraZeneca
- License: apache-2.0
- Created: 2021-01-27T12:08:57.000Z (about 4 years ago)
- Default Branch: master
- Last Pushed: 2024-02-01T10:34:54.000Z (almost 1 year ago)
- Last Synced: 2025-01-09T06:22:10.197Z (23 days ago)
- Topics: chemistry, computational-chemistry, machinelearning, quantum-chemistry
- Language: Python
- Homepage: https://ehjc.gitbook.io/kallisto/
- Size: 445 KB
- Stars: 62
- Watchers: 4
- Forks: 21
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
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README
##
![PyPI - Python Version](https://img.shields.io/pypi/pyversions/kallisto)
[![Documentation](https://img.shields.io/badge/GitBook-Docu-lightgrey)](https://ehjc.gitbook.io/kallisto/)
[![Maturity Level](https://img.shields.io/badge/Maturity%20Level-Under%20Development-orange)](https://img.shields.io/badge/Maturity%20Level-Under%20Development-orange)
[![Tests](https://github.com/AstraZeneca/kallisto/workflows/Tests/badge.svg)](https://github.com/AstraZeneca/kallisto/actions?workflow=Tests)
[![codecov](https://codecov.io/gh/AstraZeneca/kallisto/branch/master/graph/badge.svg?token=HI0U0R96X8)](https://codecov.io/gh/AstraZeneca/kallisto)
[![status](https://joss.theoj.org/papers/16126cbcfb826bf4810d243a009a6b02/status.svg)](https://joss.theoj.org/papers/16126cbcfb826bf4810d243a009a6b02)# Table of Contents
- Full Author List
- Introduction
- Installation
- Testing suite
- Reference# Full Author List
- Developer [Eike Caldeweyher](https://scholar.google.com/citations?user=25n8C3wAAAAJ&hl)
- Developer [Rocco Meli](https://scholar.google.com/citations?hl=de&user=s8cVcvYAAAAJ)
- Developer [Philipp Pracht](https://scholar.google.com/citations?user=PJiGPk0AAAAJ&hl)# Introduction
We developed the `kallisto` program for the efficient and robust calculation of atomic features using molecular geometries either in a `xmol` or a `Turbomole` format.
Furthermore, several modelling tools are implemented, e.g., to calculate root-mean squared deviations via quaternions (including rotation matrices), sorting of molecular geometries and many more. All features of `kallisto` are described in detail within our [documentation](https://ehjc.gitbook.io/kallisto/) ([GitBook repository](https://github.com/f3rmion/gitbook-kallisto)).## Main dependencies
```bash
click 7.1.2 Composable command line interface toolkit
numpy 1.20.1 NumPy is the fundamental package for array computing with Python.
scipy 1.6.0 SciPy: Scientific Library for Python
└── numpy >=1.16.5
```For a list of all dependencies have a look at the pyproject.toml file.
## Installation from PyPI
To install `kallisto` via `pip` use our published PyPI package
```bash
pip install kallisto
```## Installation from Source
Requirements to install `kallisto`from sources:
- [poetry](https://python-poetry.org/docs/#installation)
- [pyenv](https://github.com/pyenv/pyenv#installation) or [conda](https://docs.conda.io/projects/conda/en/latest/user-guide/install/index.html)
- python >=3.7First check that `poetry` is running correctly (v1.0.10 at the time of writing)
```bash
> poetry --version
Poetry version 1.0.10
```Create a virtual environment (via `pyenv` or `conda`) and activate it. Afterwards, clone the `kallisto` project from GitHub and install it using `poetry`
```bash
> git clone [email protected]:AstraZeneca/kallisto.git
> cd kallisto
> poetry install
```## Testing suite
The `kallisto` project uses [nox](https://nox.thea.codes/en/stable/tutorial.html#installation) as an automated unit test suite, which is therefore an additional dependency.
### Default nox session
The default session includes: linting (lint), type checks (mypy, pytype), and unit tests (tests).
```bash
> nox
```When everything runs smoothly through, you are ready to go! After one successful nox run, we can reuse the created virtual environment via the `-r` flag.
```bash
> nox -r
```Different unit test sessions are implemented (check the noxfile.py). They can be called separately via the run session `-rs` flag.
### Tests
Run all unit tests that are defined in the /tests directory.
```bash
> nox -rs tests
```### Lint
`kallisto` uses the [flake8](https://flake8.pycqa.org/en/latest/) linter (check the .flake8 config file).
```bash
> nox -rs lint
```### Black
`kallisto` uses the [black](https://github.com/psf/black) code formatter.
```bash
> nox -rs black
```### Safety
`kallisto` checks the security of dependencies via [safety](https://pyup.io/safety/).
```bash
> nox -rs safety
```### Mypy
`kallisto` checks for static types via [mypy](https://github.com/python/mypy) (check the mypy.ini config file).
```bash
> nox -rs mypy
```### Pytype
`kallisto` furthermore uses [pytype](https://github.com/google/pytype) for type checks.
```bash
> nox -rs pytype
```### Coverage
Unit test [coverage](https://coverage.readthedocs.io/en/coverage-5.4/) can be checked as well.
```bash
> nox -rs coverage
```## Reference
Always cite:
Eike Caldeweyher, J. Open Source Softw., _2021_, 6, 3050. DOI: [10.21105/joss.03050](https://doi.org/10.21105/joss.03050)
```
@article{Caldeweyher2021,
doi = {10.21105/joss.03050},
url = {https://doi.org/10.21105/joss.03050},
year = {2021},
volume = {6},
number = {60},
pages = {3050},
author = {Eike Caldeweyher},
title = {kallisto: A command-line interface to simplify computational modelling and the generation of atomic features},
journal = {J. Open Source Softw.}
}
```