coronalyze

coronalyze is a JAX-based Python library for post-processing coronagraphic direct imaging data. It is designed as a companion to coronagraphoto, providing analysis tools for simulated and (maybe) real coronagraphic observations.

Key Features

• SNR Estimation: Mawet et al. (2014) aperture photometry SNR with small-sample corrections
• PSF Subtraction: KLIP/PCA and reference differential imaging
• Planet Injection: Forward modeling for yield simulations
• JAX-Native: Fully JIT-compilable, differentiable, and GPU-accelerated

Installation

pip install coronalyze

(Note: You may need to install JAX separately to match your specific hardware acceleration requirements.)

Quick Start

import coronalyze as cz
import jax.numpy as jnp

# Calculate SNR at planet positions using Mawet et al. (2014)
snr_values = cz.snr(residual_image, planet_positions, fwhm=4.5)

# Or use the estimator pattern for efficient batch processing
estimator = cz.snr_estimator(fwhm=4.5)
snr_values = estimator(residual_image, planet_positions)

Pairing with coronagraphoto

import coronagraphoto as cg
import coronalyze as cz

# Generate image with coronagraphoto
obs = cg.Observation(scene, coronagraph, settings)
obs.create_images()
science_image = obs.get_total_image()

# Get the star model for subtraction
star_model = obs.get_star_image()

# Subtract and calculate SNR
residual = cz.subtract_star(science_image, star_model)
snr_values = cz.snr(residual, planet_positions, fwhm=4.5)

Design Philosophy

Like its companion coronagraphoto, coronalyze provides primitives rather than black-box functions. You compose the analysis pipeline that fits your science case.

Documentation

Full documentation is available at coronalyze.readthedocs.io.