yippy 2.2.0

A Python and JAX wrapper to create a coronagraph object from a yield input package

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yippy

A Python and JAX library for loading coronagraph yield input packages (YIPs) and computing coronagraph performance metrics. yippy provides Fourier-based off-axis PSF interpolation, throughput/contrast/core-area curves, 2D performance maps, and export to EXOSIMS and AYO formats.

Installation

pip install yippy

Quick Start

from yippy import Coronagraph
from yippy.datasets import fetch_coronagraph

# Download an example YIP (cached after first call)
yip_path = fetch_coronagraph()

# Create a coronagraph object
coro = Coronagraph(yip_path)

# Off-axis PSF at a given (x, y) position
from lod_unit import lod
offaxis_psf = coro.offax(2 * lod, 5 * lod)

# Performance metrics at any separation
throughput = coro.throughput(5.0)        # scalar or array
contrast   = coro.raw_contrast(5.0)
occ_trans  = coro.occulter_transmission(5.0)

# 2D performance maps (pixel grids)
throughput_map = coro.throughput_map()
core_area_map  = coro.core_area_map()

Two-Class Design

yippy provides two coronagraph classes for different use cases:

	Coronagraph	EqxCoronagraph
Purpose	Full-featured analysis & export	JIT-compiled simulation
Backend	NumPy/SciPy + JAX	Pure JAX/Equinox
JIT-compatible	No	Yes (eqx.filter_jit)
GPU/TPU support	PSF generation only	Everything
I/O & export	EXOSIMS FITS, AYO CSV	None (simulation only)
Performance curves	Computed on init	Converted from Coronagraph

Coronagraph -- Analysis & Data Management

The primary class for loading YIPs, computing performance curves, and exporting to external formats:

from yippy import Coronagraph

coro = Coronagraph("path/to/yip")

# 1D performance curves (scalar or array separations in lam/D)
coro.throughput(5.0)
coro.raw_contrast(5.0)
coro.occulter_transmission(5.0)
coro.core_area(5.0)
coro.core_mean_intensity(5.0)

# Noise floors
coro.noise_floor_exosims(5.0)           # |raw_contrast| / ppf
coro.noise_floor_ayo(5.0, ppf=30.0)     # core_mean_intensity / ppf

# 2D maps (full pixel grids)
coro.separation_map()
coro.throughput_map()
coro.core_area_map()
coro.core_mean_intensity_map()
coro.noise_floor_ayo_map(ppf=30.0)

# Export
coro.to_exosims()
coro.dump_ayo_csv("output.csv")

EqxCoronagraph -- JIT-Compatible Simulation

A pure JAX/Equinox module for use inside jax.jit-compiled pipelines:

from yippy import EqxCoronagraph
import equinox as eqx

# Create from a YIP path directly
coro = EqxCoronagraph("path/to/yip")

# All methods are JIT-traceable
@eqx.filter_jit
def simulate(coro, x, y):
    psf = coro.create_psf(x, y)
    stellar = coro.stellar_intens(0.01)
    throughput = coro.throughput(5.0)
    return psf, stellar, throughput

Performance Metrics

Individual metric functions are available in yippy.performance for standalone analysis:

from yippy.performance import (
    compute_throughput_curve,
    compute_raw_contrast_curve,
    compute_core_area_curve,
    compute_occ_trans_curve,
    compute_core_mean_intensity_curve,
    compute_truncation_throughput_curve,     # PSF truncation-ratio aperture
    compute_truncation_core_area_curve,
)

# Compute individual curves
separations, throughputs = compute_throughput_curve(coro)
separations, contrasts  = compute_raw_contrast_curve(coro)

PSF Truncation Ratio

When psf_trunc_ratio is set (e.g. Coronagraph(path, psf_trunc_ratio=0.3)), throughput and core area are computed using an adaptive aperture that includes all oversampled pixels exceeding ratio * peak. This matches AYO's photap_frac / omega_lod calculation and is recommended for ETC integration.

Example Data

yippy ships with pooch-managed example data for testing and notebooks:

from yippy.datasets import fetch_coronagraph

# Downloads and caches an example apodized vortex coronagraph
yip_path = fetch_coronagraph()  # "eac1_aavc_512"

Units

Yield input packages use $\lambda / D$ units so yippy treats them as the default and uses the lod_unit package to define the lod unit. However, it can use three different astropy units: pixels (as defined by the yield input package), angular separation (angle units), or apparent separation (length units). If no units are provided it assumes the input is in $\lambda / D$.

import astropy.units as u
# pixels
x_pos = 2 * u.pix
y_pos = 5 * u.pix
offaxis_psf = coro.offax(x_pos, y_pos)

# angular separation
telescope_diameter = 10 * u.m
wavelength = 500 * u.nm
offaxis_psf = coro.offax(x_pos, y_pos, lam=wavelength, D=telescope_diameter)

# apparent separation
star_dist = 10 * u.pc
offaxis_psf = coro.offax(x_pos, y_pos, lam=wavelength, D=telescope_diameter, dist=star_dist)

JAX

yippy uses JAX for JIT compilation and GPU/TPU-accelerated PSF generation. JAX defaults to 32-bit precision; if you need 64-bit precision, configure it before importing yippy or any other JAX-based library:

# At the very top of your script
from hwoutils import enable_x64, set_platform

enable_x64()           # switch to float64
set_platform("cpu")    # or "gpu", "gpu,cpu" for fallback

# Now it's safe to import yippy
from yippy import Coronagraph

Or via environment variables (safest):

JAX_ENABLE_X64=True JAX_PLATFORMS=cpu python my_script.py

See the JAX Configuration Guide in hwoutils for details and common gotchas.

Constructor options

• use_jax: Use JAX for PSF computation. Default is True.
• x_symmetric: Off-axis PSFs are symmetric about the x-axis. Default is True.
• y_symmetric: Off-axis PSFs are symmetric about the y-axis. Default is True.
• cpu_cores: Number of CPU cores for parallel PSF generation via shard_map. Default is 4.

Parallel processing of off-axis PSFs

The base call of coronagraph.offax(x,y) is the most user-friendly, but is not the most efficient. When generating many PSFs it is recommended to convert all required (x,y) positions into arrays of floats (in $\lambda / D$) and use the coronagraph.offax.create_psfs_parallel(x_arr, y_arr) function. This function uses JAX's shard_map to distribute the computation across multiple devices or CPU cores.