Collection of commonly used Green's functions and utilities
Project description
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Collection of commonly used Green’s functions and utilities. The main purpose of this module is to have a tested and thus reliable basis to do numerics. It happened to me too often, that I just made a mistake copying the Green’s function and was then wondering what was wrong with my algorithm. The main use case of GfTools was DMFT and its real space generalization, in particular using CT-QMC algorithms.
Installation
The package is available on PyPI:
$ pip install gftool
Alternatively, it can be installed via GitHub. You can install it using
$ pip install https://github.com/DerWeh/gftools/archive/VERSION.zip
where VERSION can be a release (e.g. 0.5.1) or a branch (e.g. develop). (As always, it is not advised to install it into your system Python, consider using pipenv, venv, conda, pyenv, or similar tools.) Of course you can also clone or fork the project.
If you clone the project, you can locally build the documentation:
$ pip install -r requirements-doc.txt
$ python setup.py build_sphinx
Documentation
The documentation and API is on ReadTheDocs. The documentation of specific branches can also be accessed: master doc, develop doc. There is also a GitHub page: documentation.
Currently the packages main content is
- gftool
collection of non-interacting Green’s functions and spectral functions, see also the lattice submodule.
utility functions like Matsubara frequencies and Fermi functions.
reliable calculation of particle numbers via Matsubara sums
- cpa/beb
Single site approximation to disorder
diagonal disorder only (CPA) and diagonal and off-diagonal (BEB)
average local Green’s function and component Green’s functions
- fourier
Fourier transforms from Matsubara frequencies to imaginary time and back, including the handling of high-frequencies moments (especially import for transforms from Matsubara to imaginary time)
Laplace transform from real times to complex frequencies
- matrix
helper for Green’s functions in matrix form
- pade
analytic continuation via the Padé algorithm
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