thomasvrussell / phrosty

Basic package for photometry on the RomanDESC sims.

phrosty

NOTE: If you use this, do a git pull every day. I change this almost daily.

"phrosty": PHotometry for ROman Simulations. Help me figure out what the "ty" is from, or help me rename this package.

Basic package for working with the Roman-DESC simulations, associated with Aldoroty et al. 2024 in prep.

Install by cloning this directory, navigating to that directory in your local terminal, and then using

pip install -e .

Using the image subtraction module:

Make sure you update the output_files_rootdir variable in imagesubtraction.py, as well as rootdir (points to original RomanDESC images) in utils.py. Otherwise, you will get path errors.

DEPENDENCIES: You need to clone and install this directory: https://github.com/laldoroty/sfft/tree/no_keys_in_header

Note that this fork of the original SFFT package may be merged via pull request soon. You also need all dependencies listed in the installation instructions for SFFT.

You also need GalSim, but you can't pip install it at this time because the features you need are not yet pushed to pip. So, clone and install. https://github.com/GalSim-developers/GalSim

Lastly, you need to clone and install the v2.0 branch of roman_imsim. https://github.com/matroxel/roman_imsim

from phrosty.imagesubtraction import *
from phrosty.plotting import showimage

# Which images are we using? 
band = 'H158'
refvisit = 19138
refsca = 14
scivisit = 36445
scisca = 1
ra = 8.037774
dec = -42.752337

# Display a raw image. 
showimage(band='H158',pointing=scivisit,sca=scisca)

# Do sky subtraction on both the science and reference images, then display one.
sci_skysub_path = sky_subtract(band='H158',pointing=scivisit,sca=scisca)
ref_skysub_path = sky_subtract(band='H158',pointing=refvisit,sca=refsca)
showimage(path=sci_skysub_path,data_ext=0)

# Align the science image to the reference image and display. 
sci_imalign_path = imalign(ref_skysub_path,sci_skysub_path)
showimage(path=sci_imalign_path,data_ext=0)

# Retrieve PSFs at the chosen RA, dec location for each image, and rotate to the alignment. 
sci_psf_path = get_psf(ra,dec,sci_imalign_path,sci_skysub_path,'H158',
                        scivisit,scisca,'H158',refvisit,refsca)
showimage(path=sci_psf_path,data_ext=0)
ref_psf_path = get_psf(ra,dec,ref_skysub_path,ref_skysub_path,'H158',
                        refvisit,refsca,'H158',refvisit,refsca)
showimage(path=ref_psf_path,data_ext=0)

# Cross-convolve the PSFs and images.
convolvedpaths = crossconvolve(sci_imalign_path, sci_psf_path, ref_skysub_path, ref_psf_path)
for path in convolvedpaths:
    showimage(path,data_ext=0)

# Cut out 1000x1000 stamps centered at the chosen RA, dec. 
spaths = []
for path in convolvedpaths:
    spath = stampmaker(ra, dec, path)
    spaths.append(spath)
    showimage(spath,data_ext=0)

# Do SFFT subtraction. 
scipath, refpath = spaths
diff, soln = difference(scipath, refpath, sci_psf_path, ref_psf_path)
showimage(diff, data_ext=0)

# Make your decorrelation kernel.
dcker_path = decorr_kernel(scipath, refpath, sci_psf_path, ref_psf_path, diff, soln)

# Decorrelate the difference image. 
decorr_diff_path = decorr_img(diff, dcker_path, imgtype='difference')
showimage(decorr_path, data_ext=0)

# Decorrelate the cross-convolved science image so you can get the zeropoint from this correctly.
decorr_zpt_path = decorr_img(spaths[0], dcker_path, imgtype='science')

# Calculate your final PSF to be used on your difference image and decorrelated cross-convolved science image. 
psfpath = calc_psf(spaths[0], spaths[1], sci_psf_path, ref_psf_path, dcker_path)

Old readme contents:

Currently, you will also need GalSim (clone from git repo), roman_imsim (also clone from git repo), and SFFT (clone from my forked repo **FOR NOW. A pull request on the original repo will be merged soon.)

You will likely need to change the "rootdir" variable in utils.py, and output_files_rootdir in imagesubtraction.py.

This package is modular. Modules are compatible with each other, and they interface easily, but you do not need to use all the modules. It contains:

sourcedetection.py
objectidentification.py
photometry.py
utils.py
plotaesthetics.py
plotting.py
imagesubtraction.py

You'll need:

• Your science image
• The truth file corresponding to that science image
• WCS information

Photometry usage example: (THIS IS OLD! PROBABLY DOESN'T WORK!)

# Standard imports
import numpy as np
import pandas as pd

# Astro imports
from astropy.io import fits
from astropy.table import Table
from astropy.wcs import WCS

# This package
from phrosty.sourcedetection import detect_sources, plot_sources, catalog_matching
from phrosty.photometry import ap_phot

imgpath = '/path/to/science/image.fits.gz'
truthpath = '/path/to/truth/file.txt'

hdu = fits.open(imgpath)
wcs = WCS(hdu[1].header)
img = hdu[1].data

objects = detect_sources(img)
# plot_sources(img, objects)

# Note: column names in your truth object table must be distinct from the names of the output columns,
# and the suffix must be '_truth'. e.g. don't put 'x' in your truth object table, put 'x_truth'.
truth_colnames = ['object_id', 'ra_truth', 'dec_truth', 'x_truth', 'y_truth', 'realized_flux', 'flux_truth', 'mag_truth']
truthread = pd.read_csv(truthpath, skipinitialspace=True, names=truth_colnames, sep=' ', comment='#')
cat = Table(Table.from_pandas(truthread), masked=True)

matched_catalog = catalog_matching(objects, cat, wcs)
detected_coords = matched_catalog[matched_catalog['detection'] == 1]

apphot = ap_phot(img, detected_coords)