snowfake

Make Gravner-Griffeath "snowfakes"! This code implements:

Janko Gravner, David Griffeath (2008). Modeling snow crystal growth II: A mesoscopic lattice map with plausible dynamics. Physica D: Nonlinear Phenomena 237 (3), p 385-404. DOI: 10.1016/j.physd.2007.09.008.

Installation

You can install this package with pip (be careful not to type "snowflake"):

pip install snowfake

Installing scikit-image allows you to use a different affine transformation, but I haven't figured out yet if it's better or not.

pip install snowfake[skimage]

Documentation

Read the documentation

Example

You can produce a random snowfake with:

import snowfake
s = snowfake.random()

Alternatively, this code produces the crystal in Figure 5b of the Gravner & Griffeath (2008):

from snowfake import Snowfake

params =  {
    'ρ': 0.35,  # or 'rho': 0.35 if you prefer...
    'β': 1.4,
    'α': 0.001,
    'θ': 0.015,
    'κ': 0.05,
    'μ': 0.015,
    'γ': 0.01,
    'σ': 0.00005,
    'random': False,
}
s = Snowfake(size=801, **params)

Now you're ready to grow and plot the snowfake:

s.grow()
s.plot()

The various physical parameter arrays are available as s.a (attachment flag), s.b (boundary mass), s.c (the crystal itself) and s.d (the vapour). The arrays exist on hexgrids; you can rectify them with, for example, s.rectify('c').

The parameter σ (note that you can also spell out sigma if you prefer) can be a 1D array with one sample per epoch. This will vary the vapour density ρ through time. The parameter ρ can be a 2D array of shape (size, size); this will vary the initial vapour density through space.

Testing

You can run the tests (requires pytest and pytest-cov) with

pytest

Building

This repo uses PEP 517-style packaging. Read more about this and about Python packaging in general.

Building the project requires build, so first:

pip install build

Then to build snowfake locally:

python -m build

The builds both .tar.gz and .whl files, either of which you can install with pip.

Continuous integration

This repo has two GitHub 'workflows' or 'actions':

• Push to main: Run all tests on all version of Python. This is the Run tests workflow.
• Publish a new release: Build and upload to PyPI. This is the Publish to PyPI workflow. Publish using the GitHub interface, for example (read more