pybinding-hj 0.10.1
Modernized fork of pybinding - numerical tight-binding calculations with Python 3.14 and NumPy 2.x support
License: BSD
Requires Python: >=3.10
Keywords: pybinding, tight-binding, solid-state, physics, cmt, condensed-matter

<img src="/docs/pb.png?raw=true" alt="pybinding" width=220px/>

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Pybinding is a Python package for numerical tight-binding calculations in solid state physics.
The main features include:

* **Declarative model construction** - The user just needs to describe *what* the model should be,
  but not *how* to build it. Pybinding will take care of the numerical details of building the
  Hamiltonian matrix so users can concentrate on the physics, i.e. the quantum properties of the
  model.

* **Fast compute** - Pybinding's implementation of the kernel polynomial method allows for very
  fast calculation of various physical properties of tight-binding systems. Exact diagonalization
  is also available through the use of scipy's eigenvalue solvers. The framework is very flexible
  and allows the addition of user-defined computation routines.

* **Result analysis and visualization** - The package contains utility functions for post-processing
  the raw result data. The included plotting functions are tailored for tight-binding problems to
  help visualize the model structure and to make sense of the results.

The code interface is written in Python with the aim to be as user-friendly and flexible as
possible. Under the hood, C++11 is used to accelerate demanding tasks to deliver high performance
with low memory usage.

See the [documentation] for more details.

## Install

Pybinding can be installed on Windows, Linux or Mac, with the following prerequisites:

* [Python] 3.6 or newer (Python 2.x is not supported)
* The [SciPy] stack of scientific packages, with required versions:
  * numpy >= v1.12
  * scipy >= v0.19
  * matplotlib >= v2.0
* If you're using Linux, you'll also need GCC >= v5.0 (or clang >= v3.5) and CMake >= v3.1.

Detailed [install instructions] are part of the documentation, but if you already have all the
prerequisites, it's just a simple case of using `pip`, Python's usual package manager:

    pip install pybinding


## Features

The goal of the project is to develop a tight-binding code framework which is fast, flexible and
easy to use. This is just a quick overview of some of the features. See the [documentation] for
more details and a guide to get started.

* Construction of arbitrary tight-binding lattices and geometries: 1 to 3 dimensions
  (including multilayer 2D systems), periodic or finite size (with fine control of edges)
* Easy polygon shape definition for 2D systems and freeform shapes for n-dimensional systems
* Geometric deformations and defects: defined via displacement and state functions
* Fields and arbitrary effects: defined via hopping and onsite energy functions
* Kernel polynomial method: a fast way to compute Green's function, spectral densities of
  arbitrary operators, electrical conductivity, or various other user-defined KPM methods
* Exact diagonalization: standard dense and sparse eigenvalues solvers (always available)
  and the [FEAST] solver (only available when compiled with Intel's MKL)
* Transport: scattering systems with semi-infinite leads can be constructed in pybinding and then
  solved using the [Kwant compatibility] layer
* Model and result objects have builtin plotting functions for easy visualization


## Benchmarks

One of the main features of pybinding is an easy-to-use and fast model builder. This can be a
demanding task for large or complicated systems. Great care was taken to make this process fast.

The following figures compare the performance of pybinding with the [Kwant] package. They present
the time and memory required to build a Hamiltonian matrix which describes a tight-binding system.
Pybinding features good performance and a low memory footprint by using contiguous data structures
and vectorized operations.

<p align="center">
  <img src="/docs/benchmarks/system_build.png?raw=true" alt="Tight-binding model build benchmark"/>
</p>

See the [benchmarks] section of the documentation for details on the testbed hardware and software,
as well as the source code which can be used to reproduce the results.

## Citing

Pybinding is free to use under the simple conditions of the [BSD open source license](license.md).
If you wish to use results produced with this package in a scientific publication, please just
mention the package name in the text and cite the Zenodo DOI of this project:

[![DOI](https://zenodo.org/badge/20541/dean0x7d/pybinding.svg)](https://zenodo.org/badge/latestdoi/20541/dean0x7d/pybinding)

You'll find a *"Cite as"* section in the bottom right of the Zenodo page. You can select a citation
style from the dropdown menu or export the data in BibTeX and similar formats.


## Questions?

If you have any questions, feel free to join the [chat room on Gitter].
You can also open an issue at the [tracker].


[documentation]: http://docs.pybinding.site/
[install instructions]: http://docs.pybinding.site/page/install/index.html
[Python]: https://www.python.org/
[SciPy]: http://www.scipy.org/
[FEAST]: http://www.ecs.umass.edu/~polizzi/feast/index.htm
[Kwant compatibility]: http://docs.pybinding.site/page/advanced/kwant.html
[Kwant]: http://kwant-project.org/
[benchmarks]: http://docs.pybinding.site/page/benchmarks/index.html
[chat room on Gitter]: https://gitter.im/dean0x7d/pybinding
[tracker]: https://github.com/dean0x7d/pybinding/issues