debiased-spatial-whittle 0.3.3

Spatial Debiased Whittle likelihood for fast inference of spatio-temporal covariance models from gridded data

Spatial Debiased Whittle Likelihood

Introduction

This package implements the Spatial Debiased Whittle Likelihood (SDW) as presented in the article of the same name, by the following authors:

• Arthur P. Guillaumin
• Adam M. Sykulski
• Sofia C. Olhede
• Frederik J. Simons

The SDW extends ideas from the Whittle likelihood and Debiased Whittle Likelihood to random fields and spatio-temporal data. In particular, it directly addresses the bias issue of the Whittle likelihood for observation domains with dimension greater than 2. It also allows us to work with rectangular domains (i.e., rather than square), missing observations, and complex shapes of data.

Installation instructions

The package can be installed via one of the following methods.

1. Via the use of Poetry (https://python-poetry.org/), by running the following command:

   poetry add debiased-spatial-whittle
   

2. Otherwise, you can directly install via pip:

   pip install debiased-spatial-whittle
   

Development

Firstly, you need to install poetry. Then, git clone this repository, ad run the following command from the directory corresponding to the package.

poetry install

If you run into some issue regarding the Python version, you can run

poetry env use <path_to_python>

where <path_to_python> is the path to a Python version compatible with the requirements in pyproject.toml.

Unit tests

Unit tests are run with pytest. On Pull-requests, the unit tests will be run.

Documentation

The documentation is hosted on readthedocs. It is based on docstrings. Currently, it points to the joss_paper branch and is updated on any push to that branch.

PyPi

The package is updated on PyPi automatically on creation of a new release in Github. Note that currently the version in pyproject.toml needs to be manually updated. This should be fixed by adding a step in the workflow used for publication to Pypi.