fiesta-pkg 1.0.4

FIESTA is a python library for general interpolation of field values from uniform and non-uniform input points in 2D and 3D.

FIESTA: Field Interpolation and Estimation using Spatial Techniques and Algorithms

Introduction

FIESTA is a python library for general interpolation from uniform and non-uniform input points. The library is written in python with numba acceleration for speed. The library has the optional capability to be used on large data-sets by distributing jobs across multiple processes via MPI. This relies on the mpi4py library and the MPI object from the shift-fft package (see here) which is passed as an additional object into MPI related functions.

Dependencies

• numba
• numpy
• scipy
• shift-fft
• mpi4py [Optional: enables MPI distributed processes]

Installation

FIESTA can be installed via pip:

    pip install fiesta-pkg

or by cloning the repository:

    git clone https://github.com/knaidoo29/fiesta.git
    cd FIESTA
    pip install .

Once this is done you should be able to call fiesta from python:

    import fiesta

To use the MPI functionality please take a look at the documentation in FIESTA which instructs users how to use the shift-fft MPI object and how to run these distributed jobs successfully without errors or MPI related hanging.

Showcase

The 2D density field computed with FIESTA (from the outputs of a cosmological simulation):

The velocity-field along the z-axis computed with FIESTA (from the outputs of a cosmological simulation):

Documentation

In depth documentation and tutorials are provided here.

Citation

If you use FIESTA in your work, please cite:

    @software{naidoo_fiesta_2026,
      author       = {Krishna Naidoo},
      title        = {FIESTA: Field Interpolation and Estimation using Spatial Techniques and Algorithms},
      month        = may,
      year         = 2026,
      publisher    = {Zenodo},
      doi          = {10.5281/zenodo.20328944},
      url          = {https://doi.org/10.5281/zenodo.20328944},
    }

Support

If you have any issues with the code or want to suggest ways to improve it please open a new issue (here) or (if you don't have a github account) email [e-mail].