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FIESTA: Field Interpolation and Estimation using Spatial Techniques and Algorithms

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Introduction

FIESTA (Field Interpolation and Estimation using Spatial Techniques and Algorithms) is a Python library for interpolating and estimating fields from regularly and irregularly distributed data in two and three dimensions. It provides a range of numerical methods, including particle-to-grid assignment, Delaunay Tessellation Field Estimation (DTFE), Smoothed Particle Hydrodynamics (SPH) interpolation, and grid-based interpolation. Computationally intensive operations are accelerated using numba for improved performance.

The library also provides optional MPI parallelisation for processing large datasets by distributing computations across multiple processes. This functionality relies on mpi4py and the MPI object provided by the shift-fft package (see documentation), which is passed as an additional argument to MPI-enabled functions.

Dependencies

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.

Documentation

In depth documentation and tutorials are provided here.

Showcase

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

exampledens

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

examplevz

License

FIESTA is released under the MIT License. See LICENSE for details.

Citation

If you use FIESTA in your work, please cite:

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    @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 krishna.naidoo.11@ucl.ac.uk.

Metadata

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