Skip to main content

A high-level tool for manipulating crystallographic files

Project description

hikari

PyPI version Python version codecov CodeFactor Documentation Status tests

hikari is a simple Python3.9+ package intended for manipulating and running scripts on basic crystallographic files: .hkl, .fcf, .cif, and to some extent .res and .lst.

The following section contains a brief explanation of how to install and use hikari. For a full description please see the documentation.

Getting started

Hikari is registered in PyPI under the name hikari-toolkit. In order to start working with the package, install it using:

$ pip install hikari-toolkit

Since it runs on Python 3.9+ and requires specific versions of some popular packages such as numpy, you might be interested in using hikari in a virtual environment. On Linux, it can be created using virtualenvwrapper:

$ mkvirtualenv -p /usr/bin/python3.9 hikari-venv

After running python from this virtual environment, the package should be available in the namespace via import hikari.

Usage

For the sake of usage, hikari is essentially divided into a few sub-modules, including dataframes, symmetry, utility, and scripts. Dataframes contain objects responsible for basic manipulation of files, for example, the hikari.dataframes.CifFrame is responsible for reading, modifying and writing the Crystal Information Files. Scripts, on the other hand, contain ready to use sets of dataframe instructions and aim to solve certain problems, like reformatting the file or evaluating data completeness in an experiment. In the majority of cases, you will be most likely more interested in the latter.

Contributing

Interested in contributing? Check out the contributing guidelines. Please note that this project is released with a Code of Conduct. By contributing to this project, you agree to abide by its terms.

License

hikari was created by Daniel Tchoń. It is licensed under the terms of the MIT license.

Credits

This software is made by Daniel Tchoń, and distributed under an MIT license. It is in constant development and all tips, suggestions, or contributions are welcome and can be sent here. If you have utilised hikari in academic work, please consider citing this article.

Dr hab. Anna Makal and prof. dr hab. Krzysztof Woźniak are acknowledged for helpful discussions about crystallography behind the subject. Dr Jarosław Kalinowski and mgr inż. Damian Tchoń are acknowledged for insightful tips on project structure and code optimisation.

picometer was created with the help of cookiecutter and the py-pkgs-cookiecutter template. It is published with the help of poetry, Python Semantic Versioning, and Gitmoji.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

hikari_toolkit-0.3.2.tar.gz (221.2 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

hikari_toolkit-0.3.2-py3-none-any.whl (233.0 kB view details)

Uploaded Python 3

File details

Details for the file hikari_toolkit-0.3.2.tar.gz.

File metadata

  • Download URL: hikari_toolkit-0.3.2.tar.gz
  • Upload date:
  • Size: 221.2 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.0.1 CPython/3.12.8

File hashes

Hashes for hikari_toolkit-0.3.2.tar.gz
Algorithm Hash digest
SHA256 ae70feadfad3fe1549e7879a531066b8588405ab42bd97564208d067b22b7131
MD5 bd98984f679b1fa96cb23a6df9763456
BLAKE2b-256 786d6df308b3608acf41fdaee16bebd7b27db57e0b3617a4cedc1457a2cbe797

See more details on using hashes here.

File details

Details for the file hikari_toolkit-0.3.2-py3-none-any.whl.

File metadata

  • Download URL: hikari_toolkit-0.3.2-py3-none-any.whl
  • Upload date:
  • Size: 233.0 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.0.1 CPython/3.12.8

File hashes

Hashes for hikari_toolkit-0.3.2-py3-none-any.whl
Algorithm Hash digest
SHA256 d7fee51b34287c1f212dab2c935da8bf0abc2f19c40ff8f361fb1afce5982a78
MD5 88d56c4dae543faaf3e84956cca8d74a
BLAKE2b-256 42ffca559a90f05bf3a9bb657222f8e76f5ecc5219b08c906e7ae05d2cea6b4d

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page