yippy 1.7.0

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

---

yippy

A wrapper to create a coronagraph object from a yield input package (a "YIP"). A core feature is its ability to use Fourier interpolation to generate off axis PSFs at arbitrary locations in the (x,y) plane efficiently. yippy uses JAX to speed up computation by default, with an optional Python backend.

Installation

pip install yippy

Use

Typical use will look like

from lod_unit import lod
from yippy import Coronagraph

# Create a coronagraph object by specifying the path to the yield input package
coro = Coronagraph("input/LUVOIR_VVC")

# Off-axis PSF at a given point source position in the (x,y) plane
x_pos = 2 * lod # 2 lambda/D
y_pos = 5 * lod # 5 lambda/D
offaxis_psf = coro.offax(x_pos, y_pos)

# On-axis intensity map with a stellar diameter
stellar_diameter = 1*lod
stellar_intensity = coro.stellar_intensity(stellar_diameter)

# Sky transmission map for extended sources
sky_trans = coro.sky_trans()

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 = aplc.offax(x_pos, y_pos)

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

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

JAX

The default backend is JAX, which is a high-performance numerical computing library that we use for JIT compilation and GPU/TPU support. By default, JAX uses 32-bit floating point precision, which leads to faster computation and lower memory overhead but results in lower precision (~1e-6 precision). If you need precision at the 1e-16 level, set use_x64=True.

Off-axis PSF options

• use_jax: Use JAX for computation. Default is True.
• use_x64: Use 64-bit floating point precision. Default is False.
• x_symmetric: Off-axis PSF is symmetric about the x-axis. Default is True.
• y_symmetric: Off-axis PSF is symmetric about the y-axis. Default is False.
• cpu_cores: Number of CPU cores to use. Default is 1.
• platform: Computing platform to use for JAX computation. Options are cpu, gpu, tpu. Default is cpu.

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.