Skip to main content

Calculate superconductivity in flat-band systems.

Project description

quant-met

Test Coverage Status PyPI - Python Version PyPI - Version

This is a python package to treat superconductivity in flat-band systems.

Installation

The package can be installed via

pip install quant-met

Usage

For usage examples see documentation.

Contributing

This is a personal project, very geared to the work I did in my master's thesis. If someone is using this and experiencing bugs or want the software extended, feel free to open an issue!

Developing

You can also help develop this software further. This should help you get set up to start this.

Prerequisites:

  • make
  • python
  • conda

Set up the development environment:

  • clone the repository
  • run make environment
  • now activate the conda environment conda activate quant-met-dev

You can manually run tests using for example tox -e py312 (for running against python 3.12). After pushing your branch, all tests will also be run via Github Actions.

Using pre-commit, automatic linting and formatting is done before every commit, which may cause the first commit to fail. A second try should then succeed.

After you are done working on an issue and all tests are running successful, you can add a new piece of changelog via scriv create and make a pull request.

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

quant_met-0.0.3.tar.gz (11.2 kB view details)

Uploaded Source

Built Distribution

quant_met-0.0.3-py3-none-any.whl (16.1 kB view details)

Uploaded Python 3

File details

Details for the file quant_met-0.0.3.tar.gz.

File metadata

  • Download URL: quant_met-0.0.3.tar.gz
  • Upload date:
  • Size: 11.2 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: poetry/1.8.3 CPython/3.12.1 Linux/6.5.0-1022-azure

File hashes

Hashes for quant_met-0.0.3.tar.gz
Algorithm Hash digest
SHA256 c342c76b2df3b03c42a21c96c0245ee1932c7766e42e7eb8e6161524647d1418
MD5 0057d833e0607466808add56b05ea62a
BLAKE2b-256 a10f0dd23aa554b4b496138ccb26b26462d77f73825e34a733470239165eb427

See more details on using hashes here.

File details

Details for the file quant_met-0.0.3-py3-none-any.whl.

File metadata

  • Download URL: quant_met-0.0.3-py3-none-any.whl
  • Upload date:
  • Size: 16.1 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: poetry/1.8.3 CPython/3.12.1 Linux/6.5.0-1022-azure

File hashes

Hashes for quant_met-0.0.3-py3-none-any.whl
Algorithm Hash digest
SHA256 e3b0b6dbeaf5f402343577b01a52c34b13ddabf6959d9fd0f5cf0aa7bd816f18
MD5 1de09025e1d69c351fa674ed8c302715
BLAKE2b-256 eb71a056c7aa2f3952adbed20300a8ff3195cf3e67323a8e0fe875b4b0ca4e61

See more details on using hashes here.

Supported by

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