Generic data Reduction for nulling Interferometry Package

Reaching extreme interferometric contrasts relies as much on the hardware as on the data processing technique, which is one of the main research pillars of SCIFY. Over the past decade, self-calibration data reduction techniques have been developed and proven to improve the final contrast after post-processing by a factor of at least 10 over classical reduction techniques (Hanot et al. 2011, Mennesson et al. 2011, Defrère et al. 2016, Mennesson et al. 2016, Norris et al. 2020, Martinod et al. 2021). Over the year, several nulling self-calibration pipelines have been written. Within SCIFY, the goals are:

1. to develop a generic nulling self-calibration pipeline with all state-of-the-art features of high-contrast nulling data reduction and validate it on existing nulling data obtained with the LBTI survey;
2. to primarly focus on the use case of NOTT
3. to improve the versatility and performance of the pipeline by adding dispersed modes and better ways to compute the error bars (e.g., MCMC);
4. to make this software open-source so that it can serve the whole community and serve as a basis for future developments

Documentation

Find the documentation here.

For the documentation of specific releases, see the ReadTheDocs.

Tutorials

1. How to get the histograms of the data and the models
2. How to scan the parameter space with a binomial likelihood estimator
3. How to perform a fit with a binomial likelihood estimator
4. How to use a MCMC approach
5. How to build your own model of the instrument
6. Use Neural Posterior Estimation for a fast and amortized inference

Installation

Dependencies

• numpy >= 1.26.2
• scipy >= 1.11.4
• matplotlib >= 3.6.3
• h5py >= 3.8.0
• emcee >= 3.1.4
• numdifftools >= 0.9.41
• astropy >= 5.2.1
• cupy >= 11.5.0 (optional and not downloaded during the installation)
• sbi >= 0.23.2 (optional and not downloaded during the installation)
• pytorch >= 2.1.2 (optional and not downloaded during the installation)

From PIP

1. Use the command pip install grip-nulling

To uninstall: pip uninstall grip-nulling

From the repo

1. Clone, download the repo or check one of the releases.
2. Open the directory then a terminal
3. Use the command pip install . or conda install .
4. Visit the documentation and its tutorial to discover more about the library

To uninstall:

1. Open a terminal and the environment
2. Do not locate yourself in the folder of the package or the parent
3. Type pip uninstall grip
4. Delete the directory grip

GPU powering

If you have a GPU, greatly boost the performance of GRIP by using Cupy <https://cupy.dev/>_.

Using Neural Posterior Estimation

To use the Neural Posterior Estimation technique, the libraries SBI and PyTorch must be installed separately.

GPU is not necessary to use the NPE feature of GRIP.

Publication and citation

The paper is published on JATIS and also on Arxiv.

The library is also referenced on ASCL.net.

Please use one of the following citations whenever you publish data reduced with GRIP and the relevant publication(s) for the algorithms you use within GRIP. They are usually mentioned in the documentation.

@article{10.1117/1.JATIS.11.2.028003,
author = {Marc-Antoine Martinod and Denis Defr{\`e}re and Romain Laugier and Steve Ertel and Olivier Absil and Barnaby R. M. Norris and Bertrand Mennesson},
title = {{GRIP: a generic data reduction package for nulling interferometry}},
volume = {11},
journal = {Journal of Astronomical Telescopes, Instruments, and Systems},
number = {2},
publisher = {SPIE},
pages = {028003},
keywords = {data methods, software, signal processing, high contrast imaging, high angular resolution, optimization, model fitting, universal, Nulling interferometry, Calibration, Histograms, Equipment, Data modeling, Device simulation, Monte Carlo methods, Instrument modeling, Stars, Telescopes},
year = {2025},
doi = {10.1117/1.JATIS.11.2.028003},
 URL = {https://doi.org/10.1117/1.JATIS.11.2.028003}
}

or the ADS reference on ASCL.

Acknowledgements

GRIP is a development carried out in the context of the SCIFY project. SCIFY has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (grant agreement No 8660).

The documentation of the software package is funded by the European Union's Horizon 2020 research and innovation program under grant agreement No. 101004719.