Metadata-Version: 2.4
Name: contimod_graphene
Version: 0.1.0a1
Summary: Standalone multilayer graphene Hamiltonians and single-particle utilities.
Project-URL: Homepage, https://github.com/skilledwolf/contimod_graphene
Project-URL: Repository, https://github.com/skilledwolf/contimod_graphene
Project-URL: Issues, https://github.com/skilledwolf/contimod_graphene/issues
Author-email: Tobias Wolf <public@wolft.xyz>
License: BSD 2-Clause License
        
        Copyright (c) 2026, Tobias Wolf
        All rights reserved.
        
        Redistribution and use in source and binary forms, with or without
        modification, are permitted provided that the following conditions are met:
        
        1. Redistributions of source code must retain the above copyright notice, this
           list of conditions and the following disclaimer.
        
        2. Redistributions in binary form must reproduce the above copyright notice,
           this list of conditions and the following disclaimer in the documentation
           and/or other materials provided with the distribution.
        
        THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
        AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
        IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
        DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
        FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
        DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
        SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
        CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
        OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
        OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
License-File: LICENSE
Keywords: condensed matter,graphene,physics
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: BSD License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Requires-Python: >=3.10
Requires-Dist: jax
Requires-Dist: numpy
Requires-Dist: scipy
Provides-Extra: dev
Requires-Dist: black; extra == 'dev'
Requires-Dist: build; extra == 'dev'
Requires-Dist: matplotlib; extra == 'dev'
Requires-Dist: pytest; extra == 'dev'
Requires-Dist: twine; extra == 'dev'
Provides-Extra: docs
Requires-Dist: matplotlib; extra == 'docs'
Requires-Dist: myst-nb; extra == 'docs'
Requires-Dist: sphinx; extra == 'docs'
Requires-Dist: sphinx-book-theme; extra == 'docs'
Description-Content-Type: text/markdown

# contimod_graphene

`contimod_graphene` is a standalone Python package for multilayer graphene tight-binding Hamiltonians, parameter sets, basis metadata, and related single-particle utilities.

This is a public preview release series. The package is still pre-1.0, so APIs, parameter conventions, and preset details may still change between minor releases; pin an exact version if you need stable downstream behavior.

Current scope includes:
- **Bernal (ABA) stacking**
- **Rhombohedral (ABC) stacking**
- Immutable, JSON-backed parameter sets
- Standalone model objects with thin wrappers over the kernel layer
- Basis and symmetry helpers
- JAX-friendly batched Hamiltonian evaluation

It includes both zero-field Hamiltonians and Landau-level (LL) Hamiltonians.

The maintained starting points are:
- the top-level public API shown below
- [docs/usage.md](https://github.com/skilledwolf/contimod_graphene/blob/main/docs/usage.md)
- [examples/standalone_gallery.py](https://github.com/skilledwolf/contimod_graphene/blob/main/examples/standalone_gallery.py)
- [examples/standalone_quickstart.py](https://github.com/skilledwolf/contimod_graphene/blob/main/examples/standalone_quickstart.py)

For examples/tests on this machine, prefer `JAX_PLATFORMS=cpu`; the Apple Metal backend still hits known JAX failures in this repo.

## Quick Start

For an ABC-trilayer Hamiltonian, its low-energy 2-band reduction, and a bilayer LL matrix:

```python
import numpy as np
import contimod_graphene as cg

params = cg.GrapheneTBParameters.preset("tlg").replace(U=20.0, Delta=0.0)
abc = cg.RhombohedralMultilayer(n_layers=3, params=params)
ab = cg.BernalMultilayer(n_layers=2)

print(abc.hamiltonian(0.1, 0.0).shape)
print(np.round(np.linalg.eigvalsh(np.asarray(abc.two_band_hamiltonian(0.02, 0.0))), 3))
print(ab.landau_level_hamiltonian(10.0, n_cut=6, valley="K").shape)
```

```text
(6, 6)
[-10.178  11.608]
(22, 22)
```

If you want the maintained script rather than a pasted snippet:

```bash
JAX_PLATFORMS=cpu python examples/standalone_quickstart.py
```

The built-in ABC/TLG preset carries `U=30.0` meV and `Delta=-1.15` meV. The quickstart pins `Delta=0.0` so the example isolates the outer-layer bias `U`.

The main public entry points are:
- `GrapheneTBParameters`
- `load_parameter_set(name_or_path)`
- `list_parameter_sets()`
- `BernalMultilayer`
- `RhombohedralMultilayer`

Physicist-friendly aliases are also available:
- `ABAMultilayer`
- `ABCMultilayer`

For a slightly longer walkthrough with equations, conventions, and more outputs, see [docs/usage.md](https://github.com/skilledwolf/contimod_graphene/blob/main/docs/usage.md). For maintained example material, start with [examples/standalone_quickstart.py](https://github.com/skilledwolf/contimod_graphene/blob/main/examples/standalone_quickstart.py), [docs/examples.md](https://github.com/skilledwolf/contimod_graphene/blob/main/docs/examples.md), and [examples/README.md](https://github.com/skilledwolf/contimod_graphene/blob/main/examples/README.md).

## Physics At A Glance

The package exposes three common surfaces:

$$
H(\mathbf{k}) \psi_{n\mathbf{k}} = E_n(\mathbf{k}) \psi_{n\mathbf{k}}
$$

$$
H^{ABC_N}_{2\mathrm{band}} \propto
\begin{pmatrix}
0 & (\pi^\dagger)^N \\
\pi^N & 0
\end{pmatrix},
\qquad E \propto k^N
$$

$$
\dim H_{\mathrm{LL}} = n_{\mathrm{layers}} \left(2 n_{\mathrm{cut}} - 1\right)
$$

Useful parameter conventions:
- Bernal `Delta` is the package A/B sublattice mass term, while Bernal `delta` is the dimer/non-dimer onsite offset.
- Rhombohedral `Delta` matches the usual trilayer `Δ2` meaning for `n_layers=3`; for thicker stacks it is reused as a package-specific inversion-even layer-curvature parameter.
- LL builders return dense matrices, so the size formula above matters quickly when you increase `n_cut`.

## Low-Level Modules

The low-level kernel modules remain available for advanced use, JAX-focused workflows, and direct access to the functional core:
- `contimod_graphene.bernal`
- `contimod_graphene.rhombohedral`
- `contimod_graphene.params`
- `contimod_graphene.basis`
- `contimod_graphene.symmetry`
- `contimod_graphene.utils`

## Installation

### Use the package

Install directly from GitHub:

```bash
pip install git+https://github.com/skilledwolf/contimod_graphene.git
```

Quick smoke check:

```bash
python -c "import contimod_graphene as cg; print(cg.list_parameter_sets())"
```

### Develop locally

Clone the repository and install it in editable mode:
```bash
pip install -e ".[dev]"
```

If you prefer `hatch`, create and enter the managed development environment with:
```bash
hatch env create
hatch shell
```

### Containerized Jupyter

If you want a throwaway Jupyter environment, use `repo2docker`:

```bash
jupyter-repo2docker https://github.com/skilledwolf/contimod_graphene.git
```

Requirements: Docker plus `repo2docker`. This path is convenient, but not tested on every platform.

## Credit 
This package is developed and maintained by Dr. Tobias Wolf. Feel free to contact us, and please give us credit if you use this work. 

## Downstream Note

`contimod_graphene` is intended to stand on its own. A separate downstream package, `contimod`, builds on top of this model layer for discretization, mesh/state containers, and many-body workflows, but it is not required for normal use here.
