Skip to main content

GfTool

master

Build status master

Coverage master

Documentation Status master

develop

Build status develop

Coverage develop

Documentation Status

PyPI release conda-forge release DOI

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. For example, a selection of lattice Green’s functions or rather the corresponding DOSs:

Selection of DOSs

Also have a look at the tutorial to get an idea.

The main use case of GfTool was DMFT and its real space generalization, in particular using CT-QMC algorithms.

Installation

The package is available on PyPI:

$ pip install gftool

For conda users, GfTool is also available on conda-forge

$ conda install -c conda-forge 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 package’s 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 important 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.

    • Calculates a rational polynomial as interpolation of the data points.

    • Note: the module is out-dated, so it’s not well documented at a bit awkward to use. Consider using polepade instead.

polepade
  • Analytic continuation via a pole-based variant of the Padé algorithm.

    • Based on explicit calculation of the pole in a least-squares sense.

    • Allows including uncertainties as weights.

siam
  • Basic Green’s functions for the non-interacting Single Impurity Anderson model.

Metadata

Release files for gftool 0.11.1

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for gftool 0.11.1
File Size Uploaded
gftool-0.11.1.tar.gz 132.1 kB Details

Built distribution (wheel)

Table of built distributions (wheels) for gftool 0.11.1
File Interpreter ABI Platform
gftool-0.11.1-py3-none-any.whl Python 3 none any Details

Total release size: 279.6 kB

Release files / gftool-0.11.1.tar.gz

Download URL gftool-0.11.1.tar.gz
Size 132.1 kB
Tags Source
SHA-256 checksum
How to use checksums
d10d1fb751a1cdd0e6385958f5528525d8cbfbc30acdae69d0f2d1b0ff2f9ba5
BLAKE2b-256 checksum
How to use checksums
84cca9f3b7fb4355af836a7b4ed0ae11c209a668ff1cbd13387381b9225b4af2
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/6.1.0 CPython/3.10.18

Release files / gftool-0.11.1-py3-none-any.whl

Download URL gftool-0.11.1-py3-none-any.whl
Size 147.5 kB
Tags Python 3
SHA-256 checksum
How to use checksums
8921262eff62df3e0a7b7ba725c4be2691b987e8c9b3cbec9d50617063721377
BLAKE2b-256 checksum
How to use checksums
21e3dd3876fc1215b0062b7961c70e8df2fce16fb5f0e3ddce7bca483a702578
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/6.1.0 CPython/3.10.18

Release history Release notifications | RSS feed

This release

0.11.1 This release

2 release files

0.11.0

2 release files

0.10.0

2 release files

0.9.1

1 release file

0.9.0

1 release file

0.8.1

1 release file

0.8.0

1 release file

0.7.0

1 release file

0.6.1

1 release file

0.6.0

1 release file

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page