MIRI PSF Photometry with Photutils

MIRI PSF Photometry with Photutils#

Author: Ori Fox

Submitted: November, 2023
Updated: November, 2023

Use case: PSF Photometry using Photutils. The purpose here is to illustrate the workflow and runtime for using Photutils in a variety of use cases.

Generally, PSF photometry for data from a space telescope is most accurately performed on pre-mosaiced data. The mosaic process changes the inherent PSF, blurring it both due to resampling and mixing PSFs at different detector positions and rotations. Additionally, accurate theoretical PSF models (e.g., from STPSF) are not available for mosaiced data. While an empirical PSF could be constructed (e.g., using Photutils ePSFBuilder) for mosaiced data, the results will generally not be as accurate as performing PSF photometry on the pre-mosaiced data.

NOTE: A companion notebook exists that illustrates how to use perform PSF photometry on both Level 2 and Level 3 data using a new software program called space_phot.
Data: MIRI Data PID 1028 (Calibration Program; Single Star Visit 006 A5V dwarf 2MASSJ17430448+6655015) and MIRI Data PID 1171 (LMC; Multiple Stars).
Tools: photutils, stpsf, jwst
Cross-Instrument: MIRI
Documentation: This notebook is part of a STScI’s larger post-pipeline Data Analysis Tools Ecosystem and can be downloaded directly from the JDAT Notebook Github directory.

Table of contents#

Introduction
1.1 Python Imports
1.2 Set up STPSF and Synphot
Download JWST MIRI Data
Single Bright Object
3.1 Single Level 2 File
3.2 Generate empirical PSF grid for MIRI F770W using STPSF
3.3 PSF Photometry
Faint/Upper Limit, Single Object
4.1 Multiple, Level2 Files
Stellar Field (LMC)
5.1 Multiple Stars, Single Level 2 File
5.2 Generate empirical PSF grid for MIRI F560W using STPSF
5.3 PSF Photometry

1. Introduction #

GOALS:

Perform PSF photometry on JWST MIRI images with the Photutils PSF Photometry tools using a grid of empirical PSF models from STPSF.

The notebook shows how to:

generate a grid of empirical PSF models from STPSF
perform PSF photometry on the image using the PSFPhotometry class

Data:

MIRI Data PID 1028 (Calibration Program), F770W
MIRI Data PID 1171 (LMC), F560W/F770W

1.1 Python Imports #

import glob
import os
import shutil
import tarfile
from pandas import DataFrame

import matplotlib.pyplot as plt
import numpy as np
import stpsf
from urllib.parse import urlparse
import requests
import astropy.units as u
from astropy.coordinates import SkyCoord
from astropy.io import fits
from astropy.nddata import extract_array
from astropy.table import Table
from astropy.visualization import simple_norm
from astroquery.mast import Observations
from jwst.datamodels import ImageModel
from photutils.aperture import CircularAperture
from photutils.background import LocalBackground, MADStdBackgroundRMS, MMMBackground
from photutils.detection import DAOStarFinder
from photutils.psf import GriddedPSFModel, PSFPhotometry

1.2 Download and Set up Required Data for STPSF and Synphot #

# Set environmental variables
os.environ["STPSF_PATH"] = "./stpsf-data/stpsf-data"
os.environ["PYSYN_CDBS"] = "./grp/redcat/trds/"

# required stpsf data
boxlink = 'https://stsci.box.com/shared/static/kqfolg2bfzqc4mjkgmujo06d3iaymahv.gz'
boxfile = './stpsf-data-LATEST.tar.gz'
synphot_url = 'http://ssb.stsci.edu/trds/tarfiles/synphot5.tar.gz'
synphot_file = './synphot5.tar.gz'

stpsf_folder = './stpsf-data'
synphot_folder = './grp'


def download_file(url, dest_path, timeout=60):
    parsed_url = urlparse(url)
    if parsed_url.scheme not in ["http", "https"]:
        raise ValueError(f"Unsupported URL scheme: {parsed_url.scheme}")

    response = requests.get(url, stream=True, timeout=timeout)
    response.raise_for_status()
    with open(dest_path, "wb") as f:
        for chunk in response.iter_content(chunk_size=8192):
            f.write(chunk)


# Gather stpsf files
psfExist = os.path.exists(stpsf_folder)
if not psfExist:
    os.makedirs(stpsf_folder)
    download_file(boxlink, boxfile)
    gzf = tarfile.open(boxfile)
    gzf.extractall(stpsf_folder, filter='data')

# Gather synphot files
synExist = os.path.exists(synphot_folder)
if not synExist:
    os.makedirs(synphot_folder)
    download_file(synphot_url, synphot_file)
    gzf = tarfile.open(synphot_file)
    gzf.extractall('./', filter='data')

2. Download JWST MIRI Data #

# Download Proposal ID 1028 F770W data

# Define source and destination directories
source_dir = 'mastDownload/JWST/'
destination_dir = 'mast/01028/'

if os.path.isdir(destination_dir):
    print(f'Data already downloaded to {os.path.abspath(destination_dir)}')
else:
    # Query the MAST (Mikulski Archive for Space Telescopes) database for observations
    # with proposal ID 1028 and the F770W filter
    obs = Observations.query_criteria(proposal_id=1028, filters=['F770W'])
    
    # Get a list of products associated with the located observation
    plist = Observations.get_product_list(obs)
    
    # Filter the product list to include only specific product subgroups
    fplist = Observations.filter_products(plist, productSubGroupDescription=['CAL', 'I2D', 'ASN'])
    
    # Download the selected products from the MAST database
    Observations.download_products(fplist)
    
    # Create the destination directory
    os.makedirs(destination_dir)
    
    # Use glob to find all files matching the pattern
    files_to_copy = glob.glob(os.path.join(source_dir, 'j*/jw01028*'))

    # Copy the matching files to the destination directory
    for file_path in files_to_copy:
        shutil.copy(file_path, destination_dir)

Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01028-o006_20250617t142750_image2_00013_asn.json to ./mastDownload/JWST/jw01028006001_02101_00004_mirimage/jw01028-o006_20250617t142750_image2_00013_asn.json ...

 [Done]

Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01028-o006_20250617t142750_image2_00014_asn.json to ./mastDownload/JWST/jw01028006001_02101_00003_mirimage/jw01028-o006_20250617t142750_image2_00014_asn.json ...

 [Done]

Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01028-o006_20250617t142750_image2_00015_asn.json to ./mastDownload/JWST/jw01028006001_02101_00002_mirimage/jw01028-o006_20250617t142750_image2_00015_asn.json ...

 [Done]

Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01028-o006_20250617t142750_image2_00016_asn.json to ./mastDownload/JWST/jw01028006001_02101_00001_mirimage/jw01028-o006_20250617t142750_image2_00016_asn.json ...

 [Done]

Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01028-o006_20250617t142750_image3_00004_asn.json to ./mastDownload/JWST/jw01028-o006_t001_miri_f770w/jw01028-o006_20250617t142750_image3_00004_asn.json ...

 [Done]

Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01028-o006_t001_miri_f770w_i2d.fits to ./mastDownload/JWST/jw01028-o006_t001_miri_f770w/jw01028-o006_t001_miri_f770w_i2d.fits ...

 [Done]

Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01028006001_02101_00001_mirimage_cal.fits to ./mastDownload/JWST/jw01028006001_02101_00001_mirimage/jw01028006001_02101_00001_mirimage_cal.fits ...

 [Done]

Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01028006001_02101_00001_mirimage_i2d.fits to ./mastDownload/JWST/jw01028006001_02101_00001_mirimage/jw01028006001_02101_00001_mirimage_i2d.fits ...

 [Done]

Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01028006001_02101_00002_mirimage_cal.fits to ./mastDownload/JWST/jw01028006001_02101_00002_mirimage/jw01028006001_02101_00002_mirimage_cal.fits ...

 [Done]

Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01028006001_02101_00002_mirimage_i2d.fits to ./mastDownload/JWST/jw01028006001_02101_00002_mirimage/jw01028006001_02101_00002_mirimage_i2d.fits ...

 [Done]

Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01028006001_02101_00003_mirimage_cal.fits to ./mastDownload/JWST/jw01028006001_02101_00003_mirimage/jw01028006001_02101_00003_mirimage_cal.fits ...

 [Done]

Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01028006001_02101_00003_mirimage_i2d.fits to ./mastDownload/JWST/jw01028006001_02101_00003_mirimage/jw01028006001_02101_00003_mirimage_i2d.fits ...

 [Done]

Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01028006001_02101_00004_mirimage_cal.fits to ./mastDownload/JWST/jw01028006001_02101_00004_mirimage/jw01028006001_02101_00004_mirimage_cal.fits ...

 [Done]

Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01028006001_02101_00004_mirimage_i2d.fits to ./mastDownload/JWST/jw01028006001_02101_00004_mirimage/jw01028006001_02101_00004_mirimage_i2d.fits ...

 [Done]

# Download Proposal ID 1171 F550W and F770W data

# Define source and destination directories
source_dir = 'mastDownload/JWST/'
destination_dir = 'mast/01171/'

if os.path.isdir(destination_dir):
    print(f'Data already downloaded to {os.path.abspath(destination_dir)}')
else:
    # Query the MAST (Mikulski Archive for Space Telescopes) database for observations
    # with proposal ID 1171 and the F550W and F770W filters
    obs = Observations.query_criteria(proposal_id=1171, filters=['F560W', 'F770W'])
    
    # Get a list of products associated with the located observation
    plist = Observations.get_product_list(obs)
    
    # Filter the product list to include only specific product subgroups
    fplist = Observations.filter_products(plist, productSubGroupDescription=['CAL', 'I2D', 'ASN'])
    
    # Download the selected products from the MAST database
    Observations.download_products(fplist)
    
    # Create the destination directory
    os.makedirs(destination_dir)
    
    # Use glob to find all files matching the pattern
    files_to_copy = glob.glob(os.path.join(source_dir, 'j*/jw01171*'))
    
    # Copy the matching files to the destination directory
    for file_path in files_to_copy:
        shutil.copy(file_path, destination_dir)

Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01171-o001_20231102t191130_image2_00005_asn.json to ./mastDownload/JWST/jw01171001001_02101_00004_mirimage/jw01171-o001_20231102t191130_image2_00005_asn.json ...

 [Done]

Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01171-o001_20231102t191130_image2_00006_asn.json to ./mastDownload/JWST/jw01171001001_02101_00003_mirimage/jw01171-o001_20231102t191130_image2_00006_asn.json ...

 [Done]

Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01171-o001_20231102t191130_image2_00007_asn.json to ./mastDownload/JWST/jw01171001001_02101_00002_mirimage/jw01171-o001_20231102t191130_image2_00007_asn.json ...

 [Done]

Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01171-o001_20231102t191130_image2_00008_asn.json to ./mastDownload/JWST/jw01171001001_02101_00001_mirimage/jw01171-o001_20231102t191130_image2_00008_asn.json ...

 [Done]

Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01171-o001_20231102t191130_image3_00002_asn.json to ./mastDownload/JWST/jw01171-o001_t001_miri_f770w/jw01171-o001_20231102t191130_image3_00002_asn.json ...

 [Done]

Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01171-o001_t001_miri_f770w_i2d.fits to ./mastDownload/JWST/jw01171-o001_t001_miri_f770w/jw01171-o001_t001_miri_f770w_i2d.fits ...

 [Done]

Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01171-o002_20231102t191130_image2_00005_asn.json to ./mastDownload/JWST/jw01171002001_02101_00004_mirimage/jw01171-o002_20231102t191130_image2_00005_asn.json ...

 [Done]

Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01171-o002_20231102t191130_image2_00006_asn.json to ./mastDownload/JWST/jw01171002001_02101_00003_mirimage/jw01171-o002_20231102t191130_image2_00006_asn.json ...

 [Done]

Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01171-o002_20231102t191130_image2_00007_asn.json to ./mastDownload/JWST/jw01171002001_02101_00002_mirimage/jw01171-o002_20231102t191130_image2_00007_asn.json ...

 [Done]

Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01171-o002_20231102t191130_image2_00008_asn.json to ./mastDownload/JWST/jw01171002001_02101_00001_mirimage/jw01171-o002_20231102t191130_image2_00008_asn.json ...

 [Done]

Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01171-o002_20231102t191130_image3_00002_asn.json to ./mastDownload/JWST/jw01171-o002_t001_miri_f770w/jw01171-o002_20231102t191130_image3_00002_asn.json ...

 [Done]

Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01171-o002_t001_miri_f770w_i2d.fits to ./mastDownload/JWST/jw01171-o002_t001_miri_f770w/jw01171-o002_t001_miri_f770w_i2d.fits ...

 [Done]

Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01171-o003_20231102t191130_image2_00005_asn.json to ./mastDownload/JWST/jw01171003001_02101_00004_mirimage/jw01171-o003_20231102t191130_image2_00005_asn.json ...

 [Done]

Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01171-o003_20231102t191130_image2_00006_asn.json to ./mastDownload/JWST/jw01171003001_02101_00003_mirimage/jw01171-o003_20231102t191130_image2_00006_asn.json ...

 [Done]

Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01171-o003_20231102t191130_image2_00007_asn.json to ./mastDownload/JWST/jw01171003001_02101_00002_mirimage/jw01171-o003_20231102t191130_image2_00007_asn.json ...

 [Done]

Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01171-o003_20231102t191130_image2_00008_asn.json to ./mastDownload/JWST/jw01171003001_02101_00001_mirimage/jw01171-o003_20231102t191130_image2_00008_asn.json ...

 [Done]

Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01171-o003_20231102t191130_image3_00002_asn.json to ./mastDownload/JWST/jw01171-o003_t001_miri_f770w/jw01171-o003_20231102t191130_image3_00002_asn.json ...

 [Done]

Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01171-o003_t001_miri_f770w_i2d.fits to ./mastDownload/JWST/jw01171-o003_t001_miri_f770w/jw01171-o003_t001_miri_f770w_i2d.fits ...

 [Done]

Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01171-o004_20231102t191130_image2_00005_asn.json to ./mastDownload/JWST/jw01171004001_02101_00004_mirimage/jw01171-o004_20231102t191130_image2_00005_asn.json ...

 [Done]

Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01171-o004_20231102t191130_image2_00006_asn.json to ./mastDownload/JWST/jw01171004001_02101_00003_mirimage/jw01171-o004_20231102t191130_image2_00006_asn.json ...

 [Done]

Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01171-o004_20231102t191130_image2_00007_asn.json to ./mastDownload/JWST/jw01171004001_02101_00002_mirimage/jw01171-o004_20231102t191130_image2_00007_asn.json ...

 [Done]

Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01171-o004_20231102t191130_image2_00008_asn.json to ./mastDownload/JWST/jw01171004001_02101_00001_mirimage/jw01171-o004_20231102t191130_image2_00008_asn.json ...

 [Done]

Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01171-o004_20231102t191130_image3_00002_asn.json to ./mastDownload/JWST/jw01171-o004_t001_miri_f560w/jw01171-o004_20231102t191130_image3_00002_asn.json ...

 [Done]

Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01171-o004_t001_miri_f560w_i2d.fits to ./mastDownload/JWST/jw01171-o004_t001_miri_f560w/jw01171-o004_t001_miri_f560w_i2d.fits ...

 [Done]

Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01171-o005_20231102t191130_image2_00005_asn.json to ./mastDownload/JWST/jw01171005001_02101_00004_mirimage/jw01171-o005_20231102t191130_image2_00005_asn.json ...

 [Done]

Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01171-o005_20231102t191130_image2_00006_asn.json to ./mastDownload/JWST/jw01171005001_02101_00003_mirimage/jw01171-o005_20231102t191130_image2_00006_asn.json ...

 [Done]

Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01171-o005_20231102t191130_image2_00007_asn.json to ./mastDownload/JWST/jw01171005001_02101_00002_mirimage/jw01171-o005_20231102t191130_image2_00007_asn.json ...

 [Done]

Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01171-o005_20231102t191130_image2_00008_asn.json to ./mastDownload/JWST/jw01171005001_02101_00001_mirimage/jw01171-o005_20231102t191130_image2_00008_asn.json ...

 [Done]

Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01171-o005_20231102t191130_image3_00002_asn.json to ./mastDownload/JWST/jw01171-o005_t001_miri_f770w/jw01171-o005_20231102t191130_image3_00002_asn.json ...

 [Done]

Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01171-o005_t001_miri_f770w_i2d.fits to ./mastDownload/JWST/jw01171-o005_t001_miri_f770w/jw01171-o005_t001_miri_f770w_i2d.fits ...

 [Done]

Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01171001001_02101_00001_mirimage_cal.fits to ./mastDownload/JWST/jw01171001001_02101_00001_mirimage/jw01171001001_02101_00001_mirimage_cal.fits ...

 [Done]

Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01171001001_02101_00001_mirimage_i2d.fits to ./mastDownload/JWST/jw01171001001_02101_00001_mirimage/jw01171001001_02101_00001_mirimage_i2d.fits ...

 [Done]

Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01171001001_02101_00002_mirimage_cal.fits to ./mastDownload/JWST/jw01171001001_02101_00002_mirimage/jw01171001001_02101_00002_mirimage_cal.fits ...

 [Done]

Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01171001001_02101_00002_mirimage_i2d.fits to ./mastDownload/JWST/jw01171001001_02101_00002_mirimage/jw01171001001_02101_00002_mirimage_i2d.fits ...

 [Done]

Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01171001001_02101_00003_mirimage_cal.fits to ./mastDownload/JWST/jw01171001001_02101_00003_mirimage/jw01171001001_02101_00003_mirimage_cal.fits ...

 [Done]

Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01171001001_02101_00003_mirimage_i2d.fits to ./mastDownload/JWST/jw01171001001_02101_00003_mirimage/jw01171001001_02101_00003_mirimage_i2d.fits ...

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Downloading URL https://mast.stsci.edu/api/v0.1/Download/file?uri=mast:JWST/product/jw01171001001_02101_00004_mirimage_cal.fits to ./mastDownload/JWST/jw01171001001_02101_00004_mirimage/jw01171001001_02101_00004_mirimage_cal.fits ...

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3. Single Bright Star #

The purpose of this section is to illustrate how to perform PSF photometry on a single bright star. While aperture photometry is feasible in isolated cases, the user may find PSF photometry preferable in crowded fields or complicated backgrounds.

3.1 Single Level 2 File #

In this example, we fit a single, bright source in a single Level 2 images. For a collection of Level 2 images, we could fit each Level 2 image individually and then average the measured fluxes.

Useful references:
HST Documentation on PSF Photometry: https://www.stsci.edu/hst/instrumentation/wfc3/data-analysis/psf
WFPC2 Stellar Photometry with HSTPHOT: https://ui.adsabs.harvard.edu/abs/2000PASP..112.1383D/abstract
Photutils PSF Fitting Photometry: https://photutils.readthedocs.io/en/stable/user_guide/psf.html

# get the level 2 filenames
path = "./mast/01028/"
level2 = sorted(glob.glob(os.path.join(path, '*cal.fits')))
level2

['./mast/01028/jw01028006001_02101_00001_mirimage_cal.fits',
 './mast/01028/jw01028006001_02101_00002_mirimage_cal.fits',
 './mast/01028/jw01028006001_02101_00003_mirimage_cal.fits',
 './mast/01028/jw01028006001_02101_00004_mirimage_cal.fits']

# display the first level-2 image
data = fits.getdata(level2[0])
norm = simple_norm(data, 'sqrt', percent=99)

fig, ax = plt.subplots(figsize=(20, 12))
im = ax.imshow(data, origin='lower', norm=norm, cmap='gray')
clb = plt.colorbar(im, label='MJy/sr')
ax.set_xlabel('Pixels')
ax.set_ylabel('Pixels')
plt.show()

../../../_images/56f1b85051ea1d00693f320e05330e1d1acfb15835a42ec8f1b2def0704ef46b.png

# Change all DQ flagged pixels to NaN.
# Here, we'll overwrite the original CAL file.
# Reference for JWST DQ Flag Definitions: https://jwst-pipeline.readthedocs.io/en/latest/jwst/references_general/references_general.html
# In this case, we choose all DQ > 10, but users are encouraged to choose their own values accordingly.
filename = level2[0]
with ImageModel(filename) as model:
    model.data[model.dq >= 10] = np.nan
    model.save(filename)

# Re-display the image
data = fits.getdata(level2[0])
norm = simple_norm(data, 'sqrt', percent=99)

fig, ax = plt.subplots(figsize=(20, 12))
im = ax.imshow(data, origin='lower', norm=norm, cmap='gray')
clb = plt.colorbar(im, label='MJy/sr')
ax.set_xlabel('Pixels')
ax.set_ylabel('Pixels')
plt.show()

# Zoom in to see the source. In this case, our source is from MIRI Program ID #1028, a Calibration Program.
# We are using Visit 006, which targets the A5V dwarf 2MASSJ17430448+6655015
# Reference Link: http://simbad.cds.unistra.fr/simbad/sim-basic?Ident=2MASSJ17430448%2B6655015&submit=SIMBAD+search
source_location = SkyCoord('17:43:04.4879', '+66:55:01.837', unit=(u.hourangle, u.deg))
with ImageModel(filename) as model:
    x, y = model.meta.wcs.world_to_pixel(source_location)

cutout = extract_array(data, (21, 21), (y, x))

fig, ax = plt.subplots(figsize=(8, 8))
norm2 = simple_norm(cutout, 'log', percent=99)
im = ax.imshow(cutout, origin='lower', norm=norm2, cmap='gray')
clb = plt.colorbar(im, label='MJy/sr', shrink=0.8)
ax.set_title('PID1028, Obs006')

ax.set_xlabel('Pixels')
ax.set_ylabel('Pixels')
plt.show()

../../../_images/f67b936859aa9dfcba7d877bcb984410c0f7f41c1d36ddc1071efb5e14927fc0.png

3.2 Generate empirical PSF grid for MIRI F770W using STPSF #

Let’s now use STPSF to generate an empirical grid of ePSF models for MIRI F770W. The output will be a Photutils GriddedPSFModel containing a 2x2 grid of detector-position-dependent empirical PSFs, each oversampled by a factor of 4. Note that we save the PSF grid to a FITS file (via save=True) called miri_mirim_f770w_fovp101_samp4_npsf4.fits. To save time in future runs, we load this FITS file directly into a GriddedPSFModel object:

psfgrid_filename = 'miri_mirim_f770w_fovp101_samp4_npsf4.fits'

if not os.path.exists(psfgrid_filename):
    miri = stpsf.MIRI()
    miri.filter = 'F770W'
    miri.detector = 'MIRIM'
    psf_model = miri.psf_grid(num_psfs=4, all_detectors=False, verbose=True, save=True)
else:
    psf_model = GriddedPSFModel.read(psfgrid_filename)

psf_model

Running instrument: MIRI, filter: F770W
  Running detector: MIRIM
    Position 1/4: (0, 0) pixels

    Position 1/4 centroid: (np.float64(201.4023624749013), np.float64(201.47046390597333))
    Position 2/4: (0, 1023) pixels

    Position 2/4 centroid: (np.float64(201.30690755776234), np.float64(201.3328394993336))
    Position 3/4: (1023, 0) pixels

    Position 3/4 centroid: (np.float64(201.46001999611337), np.float64(201.57406698621335))
    Position 4/4: (1023, 1023) pixels

    Position 4/4 centroid: (np.float64(201.71435554877664), np.float64(201.48197003471248))
  Saving file: miri_mirim_f770w_fovp101_samp4_npsf4.fits

<GriddedPSFModel(flux=1., x_0=0., y_0=0.)>

# display the PSF grid
psf_model.plot_grid()

../../../_images/dfb069bcd239792acc29ae1a438fe7a7d38b623379164ac02e8351a5f1c44a03.png

# display the PSF grid deltas from the mean ePSF
psf_model.plot_grid(deltas=True)

../../../_images/7e41036467798516be1d984f31715b93831bfd2c840bd01139482f3d45835961.png

3.3 PSF Photometry #

Now let’s use our gridded PSF model to perform PSF photometry.

# load data and convert units from MJy/sr to uJy
with ImageModel(filename) as model:
    unit = u.Unit(model.meta.bunit_data)
    data = model.data << unit
    error = model.err << unit

    # use pixel area map because of geometric distortion in level-2 data 
    pixel_area = model.area * model.meta.photometry.pixelarea_steradians * u.sr
    data *= pixel_area
    error *= pixel_area
    
    data = data.to(u.uJy)
    error = error.to(u.uJy)

data.unit, error.unit

(Unit("uJy"), Unit("uJy"))

To perform photometry on a single source we can input a Table containing its (x, y) position.

init_params = Table()
init_params['x'] = [x]
init_params['y'] = [y]
init_params

Table length=1

x	y
float64	float64
691.0126389413533	512.5838903334229

# we turn off the finder because we input the source position
fit_shape = 5
localbkg_estimator = LocalBackground(5, 10, bkg_estimator=MMMBackground())
psfphot = PSFPhotometry(psf_model, fit_shape, finder=None, aperture_radius=fit_shape, 
                        localbkg_estimator=localbkg_estimator, progress_bar=True)
phot = psfphot(data, error=error, init_params=init_params)
phot

QTable length=1

id	group_id	group_size	local_bkg	x_init	y_init	flux_init	x_fit	y_fit	flux_fit	x_err	y_err	flux_err	npixfit	qfit	cfit	flags
			uJy			uJy			uJy			uJy
int64	int64	int64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	int64	float64	float64	int64
1	1	1	1.4397251162293783	691.0126389413533	512.5838903334229	475.7955370966203	691.1798603021881	512.9107570382915	465.11685232449025	0.0010116489713053985	0.001024766468371471	0.5138472957774761	25	0.7057654379338035	-0.017056128265791032	0

# convert fit flux from uJy to ABmag
flux = phot['flux_fit']
flux_err = phot['flux_err']
mag = phot['flux_fit'].to(u.ABmag)
magerr = 2.5 * np.log10(1.0 + (flux_err / flux))
magerr = magerr.value * u.ABmag
mag, magerr

(<Magnitude [17.23109481] mag(AB)>, <Magnitude [0.00119883] mag(AB)>)

fig, ax = plt.subplots(ncols=3, figsize=(12, 4))

shape = (21, 21)
cutout1 = extract_array(data.value, shape, (y, x))
norm = simple_norm(cutout1, 'log', percent=98)
im1 = ax[0].imshow(cutout1, origin='lower', norm=norm)
ax[0].set_title(r'Data ($\mu$Jy)')
plt.colorbar(im1, shrink=0.7)

model = psfphot.make_model_image(data.shape, psf_shape=shape)
cutout2 = extract_array(model, shape, (y, x))
im2 = ax[1].imshow(cutout2, origin='lower', norm=norm)
ax[1].set_title('Fit PSF Model')
plt.colorbar(im2, shrink=0.7)

resid = psfphot.make_residual_image(data, psf_shape=shape)
cutout3 = extract_array(resid, shape, (y, x))
norm3 = simple_norm(cutout3.value, 'sqrt', percent=99)
im3 = ax[2].imshow(cutout3.value, origin='lower', norm=norm3)
ax[2].set_title('Residual')
plt.colorbar(im3, shrink=0.7)
plt.show()

../../../_images/e806b737002fe41882b3fcfd18367b2a5a61834da0d499e6cbb518ade341e8ea.png

4. Faint/Upper Limit, Single Object #

The purpose of this section is to illustrate how to calculate an upper limit at a fixed (x, y) position using forced PSF photometry a blank part of the sky.

We’ll use the same data as Section 3.

# load data and convert units from MJy/sr to uJy
with ImageModel(filename) as model:
    unit = u.Unit(model.meta.bunit_data)
    data = model.data << unit
    error = model.err << unit
    
    pixel_area = pixel_area = model.meta.photometry.pixelarea_steradians * u.sr
    data *= pixel_area
    error *= pixel_area
    
    data = data.to(u.uJy)
    error = error.to(u.uJy)

source_location = SkyCoord('17:43:00.0332', '+66:54:42.677', unit=(u.hourangle, u.deg))
with ImageModel(filename) as model:
    x, y = model.meta.wcs.world_to_pixel(source_location)

cutout = extract_array(data.value, (21, 21), (y, x))

fig, ax = plt.subplots()
norm = simple_norm(cutout, 'sqrt', percent=95)
im = ax.imshow(cutout, origin='lower', norm=norm, cmap='gray')
clb = plt.colorbar(im, label=r'$\mu$Jy')
ax.set_title('PID1028, Obs006')

ax.set_xlabel('Pixels')
ax.set_ylabel('Pixels')
plt.show()

../../../_images/ec98cf79a1dd29444c256720d7733385ae386343e35c05d98a4ad49bf4a2de11.png

# to perform forced photometry, we set the (x, y) source position
# AND we fix the PSF model position so that it does not vary in the fit
# (only flux will be fit)
init_params = Table()
init_params['x'] = [x]
init_params['y'] = [y]

# This requires photutils 1.11.0
psf_model_forced = psf_model.copy()
psf_model_forced.x_0.fixed = True
psf_model_forced.y_0.fixed = True
psf_model_forced.fixed

{'flux': False, 'x_0': True, 'y_0': True}

fit_shape = 5
localbkg_estimator = LocalBackground(5, 10, bkg_estimator=MMMBackground())
psfphot = PSFPhotometry(psf_model_forced, fit_shape, finder=None, aperture_radius=fit_shape, 
                        localbkg_estimator=localbkg_estimator, progress_bar=True)

phot = psfphot(data, error=error, init_params=init_params)
phot

QTable length=1

id	group_id	group_size	local_bkg	x_init	y_init	flux_init	x_fit	y_fit	flux_fit	x_err	y_err	flux_err	npixfit	qfit	cfit	flags
			uJy			uJy			uJy			uJy
int64	int64	int64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	int64	float64	float64	int64
1	1	1	1.2926821657735057	559.0832487866767	772.8615809731978	99.69285815735192	559.0832487866767	772.8615809731978	-0.38089014331448917	nan	nan	0.06349041646685509	25	-93.32672605807173	0.4099578318266419	4

# To calculate upper limit, multiply the flux_err by your desired sigma
sigma = 3.0
limit = sigma * phot['flux_err']
limit.to(u.ABmag)

\[[25.700426] \; \mathrm{mag}$$\mathrm{\left( \mathrm{AB} \right)}\]

Note: you can go significantly deeper with the Level 3 combined data product#

5. Stellar Field (LMC) #

In this case, we are going to do the same steps as in Section 3, but for multiple stars. The purpose is to illustrate the workflow and runtime for using Photutils on a large number of stars.

5.1 Multiple Stars, Single Level 2 File #

# Find stars in Level 3 File
path = './mast/01171/'
level3 = os.path.join(path, 'jw01171-o004_t001_miri_f560w_i2d.fits')
level3

'./mast/01171/jw01171-o004_t001_miri_f560w_i2d.fits'

# Get rough estimate of background (there are better ways to do background subtraction)
bkgrms = MADStdBackgroundRMS()
mmm_bkg = MMMBackground()

with ImageModel(level3) as model:
    wcs_l3 = model.meta.wcs
    std = bkgrms(model.data)
    bkg = mmm_bkg(model.data)
    data_bkgsub = model.data.copy()
    data_bkgsub -= bkg 

# Find stars
# F560W FWHM = 1.882 pix
fwhm_psf = 1.882
threshold = 5.0
daofind = DAOStarFinder(threshold=threshold * std, fwhm=fwhm_psf, exclude_border=True, min_separation=10)
found_stars = daofind(data_bkgsub)

found_stars.pprint_all(max_lines=10)

 id     xcentroid          ycentroid          sharpness       roundness1      roundness2      npix     peak       flux    mag      daofind_mag     
--- ------------------ ------------------ ------------------ ----------- -------------------- ---- ----------- ---------- --- ---------------------
  1  546.2615248590856  5.400907067001771 0.7041937380599614  0.46773428  0.04273256330788263   25  -0.8653275 -50.092262 nan   -0.5413295965257243
  2  789.0983970730795  6.312157835017224 0.6691237944354219 -0.17942303  -0.8079555096758687   25 -0.57612133  -34.95916 nan   -0.1035249124958943
  3  845.9799634503643  3.675797141070524 0.6829976607805458 0.043498766 -0.42254093349077243   25  -1.1719772 -46.178497 nan    -0.179222451244508
...                ...                ...                ...         ...                  ...  ...         ...        ... ...                   ...
788 1005.1187213604594 1062.3368573824348 0.6862059219157299 -0.44935676  -0.8216906677208756   25 -0.69887817 -38.484135 nan -0.037972898002874234
789  399.3766175963356 1064.9409739772366 0.3060837128458461  -0.1559235 -0.21203561885367186   25 -0.74871683 -34.341244 nan -0.024118448038772944
790  566.9700693080139  1065.048267845045 0.5650444893283237 -0.37038124  -0.5505482861764636   25  0.51578784 -21.200844 nan   -0.5359357398718912
Length = 790 rows

# plot the found stars
norm = simple_norm(data_bkgsub, 'sqrt', percent=99)
fig, ax = plt.subplots(figsize=(10, 10))
ax.imshow(data_bkgsub, origin='lower', norm=norm)

xypos = zip(found_stars['xcentroid'], found_stars['ycentroid'])
aper = CircularAperture(xypos, r=10)
aper.plot(ax, color='red')
plt.show()

../../../_images/c59a6b92721970b1a9fdbf1922eccd86a86d99204c54fbc6a55d8e3499215b37.png

fig, ax = plt.subplots(nrows=2, figsize=(10, 8))

ax[0].scatter(found_stars['mag'], found_stars['sharpness'], s=10, color='k')
ax[0].set_xlabel('mag')
ax[0].set_ylabel('sharpness')

ax[1].scatter(found_stars['mag'], found_stars['roundness2'], s=10, color='k')
ax[1].set_xlabel('mag')
ax[1].set_ylabel('roundness')

mag0 = -3.0
mag1 = -5.0
for ax_ in ax:
    ax_.axvline(mag0, color='red', linestyle='dashed')
    ax_.axvline(mag1, color='red', linestyle='dashed')

sh0 = 0.40
sh1 = 0.82
ax[0].axhline(sh0, color='red', linestyle='dashed')
ax[0].axhline(sh1, color='red', linestyle='dashed')

rnd0 = -0.40
rnd1 = 0.40
ax[1].axhline(rnd0, color='red', linestyle='dashed')
ax[1].axhline(rnd1, color='red', linestyle='dashed')
plt.show()

../../../_images/6bcb66e8bd7af3de0a7d51294516291a741c8e22b50a1f2e462cdee3832d4f44.png

mask = ((found_stars['mag'] < mag0) & (found_stars['mag'] > mag1) & (found_stars['roundness2'] > rnd0)
        & (found_stars['roundness2'] < rnd1) & (found_stars['sharpness'] > sh0) 
        & (found_stars['sharpness'] < sh1) & (found_stars['xcentroid'] > 100) & (found_stars['xcentroid'] < 700)
        & (found_stars['ycentroid'] > 100) & (found_stars['ycentroid'] < 700))

found_stars_sel = found_stars[mask]

print('Number of stars found originally:', len(found_stars))
print('Number of stars in final selection:', len(found_stars_sel))

Number of stars found originally: 790
Number of stars in final selection: 63

# plot the selected stars
norm = simple_norm(data_bkgsub, 'sqrt', percent=99)
fig, ax = plt.subplots(figsize=(10, 10))
ax.imshow(data_bkgsub, origin='lower', norm=norm)

xypos = zip(found_stars_sel['xcentroid'], found_stars_sel['ycentroid'])
aper = CircularAperture(xypos, r=10)
aper.plot(ax, color='red')
plt.show()

../../../_images/38921486e4c60d5cbc54007253dbc8d6834ebfabaa488c100700dcf56f9f0464.png

5.2 Generate empirical PSF grid for MIRI F560W using STPSF #

psfgrid_filename = 'miri_mirim_f560w_fovp101_samp4_npsf4.fits'

if not os.path.exists(psfgrid_filename):
    miri = stpsf.MIRI()
    miri.filter = 'F560W'
    miri.detector = 'MIRIM'
    psf_model = miri.psf_grid(num_psfs=4, all_detectors=False, verbose=True, save=True)
else:
    psf_model = GriddedPSFModel.read(psfgrid_filename)

psf_model

Running instrument: MIRI, filter: F560W
  Running detector: MIRIM
    Position 1/4: (0, 0) pixels

    Position 1/4 centroid: (np.float64(201.39504365744267), np.float64(201.46837962104274))
    Position 2/4: (0, 1023) pixels

    Position 2/4 centroid: (np.float64(201.30209346068543), np.float64(201.31984343070144))
    Position 3/4: (1023, 0) pixels

    Position 3/4 centroid: (np.float64(201.42830769164874), np.float64(201.59926840411683))
    Position 4/4: (1023, 1023) pixels

    Position 4/4 centroid: (np.float64(201.75498594600967), np.float64(201.4970454286808))
  Saving file: miri_mirim_f560w_fovp101_samp4_npsf4.fits

<GriddedPSFModel(flux=1., x_0=0., y_0=0.)>

psf_model.plot_grid()

../../../_images/bc74157d6af7253113b9830b414ebbc3fc74f89be3492710e716663969403cb1.png

# get the level 2 image
# here, we'll use the PID 1171 files
path = "./mast/01171/"
level2 = sorted(glob.glob(os.path.join(path, 'jw01171004*cal.fits')))
filename = level2[0]
print(filename)

# load data and convert units from MJy/sr to uJy
with ImageModel(filename) as model:
    unit = u.Unit(model.meta.bunit_data)
    model.data[model.dq >= 10] = np.nan
    data = model.data << unit
    error = model.err << unit
    
    pixel_area = pixel_area = model.meta.photometry.pixelarea_steradians * u.sr
    data *= pixel_area
    error *= pixel_area
    
    data = data.to(u.uJy)
    error = error.to(u.uJy)

    wcs = model.meta.wcs

data.unit, error.unit

./mast/01171/jw01171004001_02101_00001_mirimage_cal.fits

(Unit("uJy"), Unit("uJy"))

5.3 PSF Photometry #

# translate (x, y) positions from the level 3 image to the level 2 image
xc = found_stars_sel['xcentroid']
yc = found_stars_sel['ycentroid']
sc = wcs_l3.pixel_to_world(xc, yc)

x, y = wcs.world_to_pixel(sc)
init_params = Table()
init_params['x'] = x
init_params['y'] = y

# we need to remove stars in the masked region of
# the level-2 data
mask = x > 404
init_params = init_params[mask]

mask

array([ True, False,  True,  True,  True,  True,  True,  True,  True,
       False,  True,  True,  True,  True,  True,  True,  True, False,
       False,  True,  True, False,  True,  True,  True, False, False,
       False,  True, False,  True, False, False,  True,  True,  True,
       False,  True,  True, False,  True,  True,  True,  True,  True,
       False,  True,  True, False,  True,  True,  True,  True, False,
        True, False,  True,  True,  True, False,  True,  True, False])

# plot the selected stars
norm = simple_norm(data.value, 'sqrt', percent=99)
fig, ax = plt.subplots(figsize=(10, 10))
ax.imshow(data.value, origin='lower', norm=norm)

xypos = zip(init_params['x'], init_params['y'])
aper = CircularAperture(xypos, r=10)
aper.plot(ax, color='red')
plt.show()

../../../_images/9b28ffe12ba3fc511b7d9fa5701510d92c7a6d5b8cae8ef03c780c892a81d250.png

fit_shape = 5
localbkg_estimator = LocalBackground(5, 10, bkg_estimator=MMMBackground())
psfphot = PSFPhotometry(psf_model, fit_shape, finder=None, aperture_radius=fit_shape, 
                        localbkg_estimator=localbkg_estimator, progress_bar=True)
phot = psfphot(data, error=error, init_params=init_params)
phot

QTable length=44

id	group_id	group_size	local_bkg	x_init	y_init	flux_init	x_fit	y_fit	flux_fit	x_err	y_err	flux_err	npixfit	qfit	cfit	flags
			uJy			uJy			uJy			uJy
int64	int64	int64	float64	float64	float64	float64	float64	float64	float64	float64	float64	float64	int64	float64	float64	int64
1	1	1	0.5917736126746966	583.6953414360433	57.24687010219543	64.37585446860986	583.6271426055622	57.25527770031939	22.086381960509662	0.009881550234131384	0.009997062015888385	0.22361477408075406	25	1.592554525858295	-0.008029093203134859	0
2	2	1	0.6100208181015989	630.1870920844856	77.50324935933168	66.95979612654335	630.1322550593235	77.35470921519108	26.07989809910072	0.009206323120281227	0.009803111243434009	0.26646535979914693	25	1.8008472979147259	0.05008110417948685	0
3	3	1	0.5691058405479137	545.155567265627	78.01775415078902	67.61121880086363	545.1085783092715	78.00943495517794	30.20290385941652	0.007770814531898795	0.00879892521245536	0.27799187555778915	25	1.3869661801118796	0.08939326254907773	0
4	4	1	0.593688857401901	514.4647302272357	79.17731661955338	63.13382471169785	514.3618419546613	79.23008476850025	24.294878276946108	0.009238670171341971	0.009431616434272603	0.23037622229648375	24	1.4271814366031326	0.00254400481920013	1
5	5	1	0.6032650537579973	526.0810431249562	80.86596145105023	67.36284381417863	525.9712113828396	80.89624822108314	26.617028281645563	0.00857942095488135	0.009139997122203222	0.2580218388975531	25	1.4770514988460586	0.06073624762697728	0
6	6	1	0.5452195879137307	453.1467736232926	84.12057244156529	61.86177209688229	453.0798262686204	84.02618794960281	24.453046575420387	0.008229781101782904	0.008808001067226913	0.2213714474830289	25	2.2921925794144675	0.17402357161377432	0
7	7	1	0.6332202497470021	636.5067980659654	93.27692547171921	66.69100788310536	636.4583861214122	93.0805291826597	23.58011500743861	0.009731880876593928	0.009559658300160204	0.2490521394977686	25	2.0419826414367943	0.1729030083154531	0
8	8	1	0.6027188777403019	656.1494582554864	99.97668270363584	88.83133597743618	656.2523643578978	99.95865845658875	54.48655531668756	0.00524416240204533	0.005599566381274772	0.32797720995728463	25	0.7425576584319004	0.07986712430142177	0
9	9	1	0.547520673274994	481.54703925766387	118.92392090277184	56.459257597781644	481.48800673642165	118.92764364438601	19.1782491888176	0.010521843238961201	0.010579545425558872	0.20213058094361303	25	2.412526246075053	0.08655417856521475	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
36	36	1	0.5620971093618923	412.2166106708058	492.0471019628243	68.8067629570188	412.2467288371631	492.0295971073806	32.60878691133045	0.006984811247766581	0.007054154009861644	0.2329917311819081	25	1.2044795601111933	0.07156229957054645	0
37	37	1	0.5360806073094238	603.7650329383015	516.3310355219018	57.06107361620856	603.8016419674484	516.4558972595488	21.67602670755186	0.00887441082565338	0.009241322983321627	0.19719255380655115	25	1.8726410278722834	0.08225042566395441	0
38	38	1	0.5449298500494144	438.43239918428213	517.6858561328448	63.63512050110673	438.36556392354873	517.8102994947366	28.61568130285468	0.007781994712886895	0.007872559012099491	0.23308950425313937	25	1.6292314338781626	0.0684033972255797	0
39	39	1	0.5434614263082806	557.926455966104	531.9502486117964	61.31849260874046	558.0156371212586	531.9575790458696	20.78786146792204	0.00911902301788379	0.009514214304968042	0.20148472945846096	25	1.4269703915870038	0.03015436493065666	0
40	40	1	0.5319761503161045	626.789868315454	541.4060866848747	55.3254156791539	626.815640367721	541.3608540990855	15.839598349366721	0.01138819331808432	0.012361404289308334	0.18182948752514208	25	2.475724919321479	0.1541262984442095	0
41	41	1	0.5237871754813839	628.1186930495217	550.9848648146874	53.318885866652614	628.111638030972	551.0077395394245	16.06438745117986	0.011450387359860667	0.011672388512044371	0.18770635400852514	25	2.2006463348774012	0.026898928342503625	0
42	42	1	0.561615267280857	493.2502114845369	581.8827941123477	59.53039625260884	493.2556683895723	581.9368573578724	21.736153878921936	0.009074998292551963	0.009287889534871405	0.1985728294800299	25	2.245885366275252	0.13960944851282267	0
43	43	1	0.5316955510606156	597.327038102174	587.6829662730439	61.735091743228274	597.4619912123752	587.7448120705658	25.343537625859778	0.00836804812627431	0.008521205045241686	0.21284260819942533	25	2.016375429048198	-0.005845087229945111	0
44	44	1	0.5456375295271254	433.97407354382995	606.001088871638	58.52389361531568	433.9640921192637	606.007780305856	19.233796064376076	0.009871826918899146	0.01019920748914034	0.191789929450615	25	1.6630678103751408	-0.016245994910779434	0

# convert fit flux from uJy to ABmag
flux = phot['flux_fit']
flux_err = phot['flux_err']
mag = phot['flux_fit'].to(u.ABmag)
magerr = 2.5 * np.log10(1.0 + (flux_err / flux))
magerr = magerr.value * u.ABmag
mag, magerr

(<Magnitude [20.53968855, 20.35923527, 20.19987825, 20.43621318,
             20.33710108, 20.42916756, 20.4686352 , 19.55927662,
             20.69297761, 20.65126457, 19.32377519, 20.04306398,
             20.40416966, 19.70731474, 20.11691711, 20.29396076,
             20.44007513, 20.47336615, 20.48695953, 20.12105398,
             20.2501405 , 21.16800096, 20.79285496, 20.02802598,
             20.33465086, 20.34476846, 19.47693267, 20.36948414,
             20.43489364, 20.47643891, 20.41885784, 20.39525343,
             20.31342508, 20.14212217, 20.36406716, 20.11666339,
             20.56005081, 20.25848977, 20.60547547, 20.90063959,
             20.88533957, 20.55704325, 20.39033191, 20.68983748] mag(AB)>,
 <Magnitude [0.01093732, 0.01103697, 0.00994756, 0.01024699, 0.01047429,
             0.00978486, 0.01140736, 0.00651591, 0.01138331, 0.01133476,
             0.0065102 , 0.00798444, 0.00939263, 0.00744215, 0.00802427,
             0.00869982, 0.01027089, 0.00948007, 0.00967182, 0.00842352,
             0.00898325, 0.01438394, 0.01198467, 0.00790335, 0.00930763,
             0.00886982, 0.00599879, 0.0093634 , 0.00927861, 0.00958065,
             0.00929012, 0.00903172, 0.00896606, 0.00792852, 0.00901809,
             0.00773007, 0.00983257, 0.00880806, 0.01047274, 0.01239263,
             0.01261288, 0.00987382, 0.00908026, 0.0107728 ] mag(AB)>)

# Write to File
df = DataFrame({"RA": sc.ra.deg[mask], "DEC": sc.dec.deg[mask], "Mag": mag.value, "Mag Err": magerr.value})
df.to_csv('miri_photometry_photutils.txt', index=False)  