GreeDS

I-PCA (Iterative Principal Component Analysis) tools developed to process ADI cube.

Refactored implementation of the original code from Pairet et al. Can be used independently of the optimization Inverse Problem part (MAYONNAISE) + few fixes such as deprecated packages

Updates :

• More options such as: choosing starting rank, having an incremental number of iterations per rank, and also output options.
• NEW: Can be used with references thus leveraging Angular and Reference Differential Imaging Strategy (ARDI).

Install package

Clone the project:

git clone https://github.com/Sand-jrd/mustard

Go to the project directory:

cd GreeDS

Install dependencies:

py setup.py install

Usage/Example

This package contains only one function. All necessary information is in the function comments. Follow instructions in the demo to test the algorithm with your own datasets.

Import the function:

from GreeDS import GreeDS

Load your dataset and call the function:

from vip_hci.fits import open_fits
cube = open_fits("your_cube.fits")
angles = open_fits("your_PA_angles.fits")

Set parameters:

r = 10  # Iteration over PCA-rank
l = "incr"  # Iteration per rank
r_start = 1  # PCA-rank to start iteration (good for faint signals)
pup_size = 6  # Radius of numerical mask to hide coro

That's it. Call GreeDS and get your results:

res = GreeDS(cube, angles, r=r, l=l, r_start=r_start, pup=pup_size)

Related

Also check out other packages for Exoplanet/disk direct imaging:

• MUSTARD: Inverse problem approach to process ADI cube.
• VIP - Vortex Image Processing package: Tools for high-contrast imaging of exoplanets and circumstellar disks.

Also see docs about the maths behind the algorithms (GreeDS/MUSTARD) and their comparison:

• Inverse-problem versus principal component analysis methods for angular differential imaging of circumstellar disks. The mustard algorithm
• Slides

Feedback/Support

You can contact me by email: sjuillard@uliege.be