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multi_aperture_photometry.py
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multi_aperture_photometry.py
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"""
Analyze images using aperture photometry within Python and not with Astro ImageJ (AIJ)
Author: Kyle Koeller
Created: 05/07/2023
Last Updated: 08/24/2023
"""
# Python imports
import numpy as np
import pandas as pd
from pathlib import Path
import matplotlib.pyplot as plt
# import time
import warnings
from tqdm import tqdm
# Astropy imports
import ccdproc as ccdp
from astropy.coordinates import SkyCoord
from astropy.io import fits
# from astropy.nddata import CCDData
# from astropy.stats import sigma_clipped_stats
from photutils.aperture import CircularAperture, CircularAnnulus, aperture_photometry, ApertureStats
from astropy.wcs import WCS
import astropy.units as u
from astropy import wcs
# from astropy.visualization import ZScaleInterval
# turn off this warning that just tells the user,
# "The warning raised when the contents of the FITS header have been modified to be standards compliant."
warnings.filterwarnings("ignore", category=wcs.FITSFixedWarning)
def main(path="", pipeline=False, radec_list=None, obj_name=""):
"""
Main function for aperture photometry
Parameters
----------
radec_list: list
RADEC files for each filter
obj_name: str
Name of the target
path : str
Path to the folder containing the images.
pipeline : bool
If True, then the program is being run from the pipeline and will not ask for user input.
Returns
-------
N/A
"""
filt_list = ["Empty/B", "Empty/V", "Empty/R"]
if not pipeline:
# path = "D:\Research\Data\\NSVS_254037\\2018.09.18-reduced" # For testing purposes
path = input(
"Please enter a file pathway (i.e. C:\\folder1\\folder2\\[raw]) to where the reduced images are or type "
"the word 'Close' to leave: ")
# allows the user to input where the raw images are and where the calibrated images go to
radec_file = ""
while True:
try:
images_path = Path(path)
break
except FileNotFoundError:
print("File not found. Please try again.")
path = input(
"Please enter a file pathway (i.e. C:\\folder1\\folder2\\[reduced]) to where the reduced images are or type "
"the word 'Close' to leave: ")
if path.lower() == "close":
exit()
science_imagetyp = 'LIGHT'
files = ccdp.ImageFileCollection(images_path)
for filt in filt_list:
if "/B" in filt:
radec_file = input("Enter the file location for the RADEC file for the B filter: ")
elif "/V" in filt:
radec_file = input("Enter the file location for the RADEC file for the V filter: ")
elif "/R" in filt:
radec_file = input("Enter the file location for the RADEC file for the R filter: ")
image_list = files.files_filtered(imagetyp=science_imagetyp, filter=filt)
multiple_AP(image_list, images_path, filt, pipeline=pipeline, radec_file=radec_file)
else:
images_path = Path(path)
science_imagetyp = 'LIGHT'
files = ccdp.ImageFileCollection(images_path)
for filt in filt_list:
if "/B" in filt:
radec_file = radec_list[0]
elif "/V" in filt:
radec_file = radec_list[1]
elif "/R" in filt:
radec_file = radec_list[2]
image_list = files.files_filtered(imagetyp=science_imagetyp, filter=filt)
substring_to_match = obj_name
filtered_image_list = [file for file in image_list if substring_to_match in file]
multiple_AP(filtered_image_list, images_path, filt, pipeline=pipeline, radec_file=radec_file)
def multiple_AP(image_list, path, filter, pipeline=False, radec_file=""):
"""
Perform multi-aperture photometry on a list of images for a single target
Parameters
----------
filter: String
Filter used for the images
pipeline: Boolean
If True, then the program is being run from the pipeline and will not ask for user input.
radec_file: string
Location of a radec file. If not given, the user will be prompted to enter one.
path : pathway
Path to the folder containing the images.
image_list : List
Images to perform multi-aperture photometry on.
Returns
-------
None
"""
# Define the aperture parameters
# Define the aperture and annulus radii
aperture_radius = 20
annulus_radii = (30, 50)
read_noise = 10.83 # * u.electron # gathered from fits headers manually
# gain = 1.43 # * u.electron / u.adu # gathered from fits headers manually
# F_dark = 0.01 # dark current in u.electron / u.pix / u.s
if not pipeline:
while True:
try:
# df = pd.read_csv('NSVS_254037-B.radec', skiprows=7, sep=",", header=None)
df = pd.read_csv(radec_file, skiprows=7, sep=",", header=None)
break
except FileNotFoundError:
print("File not found. Please try again.")
radec_file = input("Please enter the RADEC file (i.e. C://folder1//folder2//[file name]: ")
else:
df = pd.read_csv(radec_file, skiprows=7, sep=",", header=None)
print("RADEC file found.\n")
magnitudes_comp = df[4]
magnitudes_comp = magnitudes_comp.replace(99.999, pd.NA).dropna().reset_index(drop=True)
ra = df[0]
dec = df[1]
# ref_star = df[2]
# centroid = df[3] # Not used (I don't think at least)
magnitudes = []
mag_err = []
hjd = []
bjd = []
for icount, image_file in tqdm(enumerate(image_list), desc="Performing aperture photometry on {} images".format(len(image_list))):
image_data, header = fits.getdata(path / image_file, header=True)
# All the following up till the 'if' statement stays under the for loop due to needing the header information
wcs_ = WCS(header)
# ccd = CCDData(image_data, wcs=wcs, unit='adu')
# Convert RA and DEC to pixel positions
sky_coords = SkyCoord(ra, dec, unit=(u.h, u.deg), frame='icrs')
pixel_coords = wcs_.world_to_pixel(sky_coords)
x_coords, y_coords = pixel_coords
# target_position = np.array(pixel_coords[0])
# comparison_positions = np.array(pixel_coords[1:])
target_position = (x_coords[0], y_coords[0])
comparison_positions = list(zip(x_coords[1:], y_coords[1:]))
hjd.append(header['HJD-OBS'])
bjd.append(header['BJD-OBS'])
# Create the apertures and annuli
target_aperture = CircularAperture(target_position, r=aperture_radius)
target_annulus = CircularAnnulus(target_position, *annulus_radii)
comparison_aperture = [CircularAperture(pos1, r=aperture_radius) for pos1 in comparison_positions]
comparison_annulus = [CircularAnnulus(pos2, *annulus_radii) for pos2 in comparison_positions]
target_phot_table = aperture_photometry(image_data, target_aperture)
# comparison_phot_table = aperture_photometry(image_data, comparison_aperture)
if icount == 0:
im_plot(image_data, target_aperture, comparison_aperture, target_annulus, comparison_annulus)
# Create a figure and axis
_, ax = plt.subplots(figsize=(11, 8))
comparison_phot_table = []
for comp_aperture, comp_annulus in zip(comparison_aperture, comparison_annulus):
# Perform aperture photometry on the star
aperture_phot_table = aperture_photometry(image_data, comp_aperture)
# Perform aperture photometry on the annulus (background)
annulus_phot_table = aperture_photometry(image_data, comp_annulus)
# Store the result in the comparison_phot_table list
comparison_phot_table.append((aperture_phot_table, annulus_phot_table))
# Perform annulus photometry to estimate the background
target_bkg_mean = ApertureStats(image_data, target_annulus).mean
# comparison_bkg_mean = ApertureStats(image_data, comparison_annulus).mean
# Calculate the total background for the comparison stars
comparison_bkg_mean = []
for annulus in comparison_annulus:
stats = ApertureStats(image_data, annulus)
if np.isnan(stats.mean) or np.isinf(stats.mean):
comparison_bkg_mean.append(0)
else:
comparison_bkg_mean.append(stats.mean)
# Calculate the total background for the target star
if np.isnan(target_bkg_mean) or np.isinf(target_bkg_mean):
target_bkg_mean = 0
# Multiply the background mean by the aperture area to get the total background
target_bkg = target_bkg_mean * target_aperture.area
comparison_bkg = [bkg_mean * aperture.area for bkg_mean, aperture in
zip(comparison_bkg_mean, comparison_aperture)]
# target_bkg = ApertureStats(image_data, target_aperture, local_bkg=target_bkg_mean).sum
# # comparison_bkg = ApertureStats(image_data, comparison_aperture, local_bkg=comparison_bkg_mean).sum
#
# comparison_bkg = []
# for aperture, bkg_mean in zip(comparison_aperture, comparison_bkg_mean):
# stats = ApertureStats(image_data, aperture, local_bkg=bkg_mean)
# comparison_bkg.append(stats.sum)
# Calculate the background subtracted counts
target_flx = target_phot_table['aperture_sum'] - target_bkg
target_flux_err = np.sqrt(target_phot_table['aperture_sum'] + target_aperture.area * read_noise**2)
# comparison_flx = comparison_phot_table['aperture_sum'] - comparison_bkg
# comp_flux_err = np.sqrt(comparison_phot_table['aperture_sum'] + comparison_aperture.area * read_noise ** 2)
comparison_flx = [phot_table[0]['aperture_sum'] - bkg
for phot_table, bkg in zip(comparison_phot_table, comparison_bkg)]
comp_flux_err = [np.sqrt(phot_table[0]['aperture_sum'] + aperture.area * read_noise ** 2)
for phot_table, aperture in zip(comparison_phot_table, comparison_aperture)]
comp_flux_err = np.array(comp_flux_err)
# calculate the relative flux for each comparison star and the target star
# rel_flx_T1 = target_flx / sum(comparison_flx)
count = 0
rel_flux_comps = []
for i in comparison_flx:
if i == comparison_flx[count]:
rel_flux_c = i / (sum(comparison_flx) - i)
rel_flux_comps.append(rel_flux_c)
count += 1
# rel_flux_comps = np.array(rel_flux_comps)
# find the number of pixels used to estimate the sky background
# n_b = (np.pi * annulus_radii[1]**2) - (np.pi * annulus_radii[0] ** 2) # main equation
# n_b_mask_comp = comparison_annulus.to_mask(method="center")
# n_b_comp = np.sum(n_b_mask_comp)
# n_b_mask_tar = target_annulus.to_mask(method="center")
# n_b_tar = np.sum(n_b_mask_tar.data)
"""
# find the number of pixels used in the aperture if the radius of the apertures is in arcseconds not pixels
focal_length = 4114 # mm
pixel_size = 9 # microns
pixel_size = pixel_size * 10 ** -3 # mm
ap_area = np.pi * aperture_radius.area**2 # area of the aperture in mm^2
plate_scale = 1/focal_length # rad/mm
plate_scale = plate_scale * 206265 # arcsec/mm
n_pix = ap_area / (plate_scale * pixel_size)**2 # number of pixels in the aperture
"""
"""
n_pix = np.pi * aperture_radius**2 # number of pixels in the aperture
# Calculate the total noise
sigma_f = 0.289 # quoted from Collins 2017 https://iopscience.iop.org/article/10.3847/1538-3881/153/2/77/pdf
F_s = 0.01 # number of sky background counts per pixel in ADU
# N_comp = np.sqrt(gain * comparison_flx + n_pix * (1 + (n_pix / n_b)) *
# (gain * F_s + F_dark + read_noise ** 2 + gain ** 2 + sigma_f ** 2)) / gain
N_comp = [np.sqrt(gain * flx + n_pix * (1 + (n_pix / n_b)) *
(gain * F_s + F_dark + read_noise ** 2 + gain ** 2 + sigma_f ** 2)) / gain
for flx in comparison_flx]
N_tar = np.sqrt(gain * target_flx + n_pix * (1 + (n_pix / n_b)) *
(gain * F_s + F_dark + read_noise ** 2 + gain ** 2 + sigma_f ** 2)) / gain
# calculate the total comparison ensemble noise
N_e_comp = np.sqrt(np.sum(np.array(N_comp) ** 2))
rel_flux_err = (rel_flx_T1/rel_flux_comps)*np.sqrt((N_tar**2/target_flx**2) +
(N_e_comp**2/sum(comparison_flx)**2))
"""
# calculate the total target magnitude and error
target_magnitude = (-np.log(sum(2.512**-magnitudes_comp))/np.log(2.512)) - \
(2.5*np.log10(target_flx/sum(comparison_flx)))
target_magnitude_error = 2.5*np.log10(1 + np.sqrt(((target_flux_err**2)/(target_flx**2)) +
(sum(comp_flux_err**2)/sum(comparison_flx)**2)))
# comparison_magnitude = -(2.5*np.log10(target_flx/sum(comparison_flx)))
# Append the calculated magnitude and error to the lists
magnitudes.append(target_magnitude.value[0])
mag_err.append(target_magnitude_error.value[0])
# Plot the magnitudes with error bars
# noinspection PyUnboundLocalVariable
ax.errorbar(hjd, magnitudes, yerr=mag_err, fmt='o', label="Source_AMag_T1")
# ax.scatter(hjd, magnitudes, marker='o', color='black')
# Set the labels and parameters
fontsize = 14
ax.set_xlabel('HJD', fontsize=fontsize)
ax.set_ylabel('Source_AMag_T1', fontsize=fontsize)
ax.invert_yaxis()
ax.grid()
ax.legend(loc="upper right", fontsize=fontsize).set_draggable(True)
ax.tick_params(axis='both', which='major', labelsize=fontsize)
plt.show()
light_curve_data = pd.DataFrame({
'HJD': hjd,
'BJD': bjd,
'Source_AMag_T1': magnitudes,
'Source_AMag_T1_Error': mag_err
})
if not pipeline:
output_file = input("Enter an output file name and location for the final light curve data in the {} filter "
"(ex: C:\\folder1\\folder2\\APASS_254037_B.txt): ".format(filter))
else:
output_file = path + "//APASS_254037_" + filter + "_LC_dat.txt"
light_curve_data.to_csv(output_file, index=False)
def im_plot(image_data, target_aperture, comparison_apertures, target_annulus, comparison_annuli):
"""
Plot the image with the apertures and annuli overlaid
Parameters
----------
image_data: array
Pixel data from the image
target_aperture: CircularAperture object
Target aperture location
comparison_apertures: list of CircularAperture objects
Comparison aperture locations
target_annulus: CircularAnnulus object
Target annulus location
comparison_annuli: list of CircularAnnulus objects
Comparison annulus locations
Returns
-------
None
"""
# First, plot the image
plt.figure(figsize=(8, 8))
plt.imshow(image_data, cmap='gray', origin='lower', vmin=np.percentile(image_data, 5),
vmax=np.percentile(image_data, 95))
plt.colorbar(label='Counts')
# Now plot the apertures
lw = 1.5 # line width
alpha = 1 # line opacity
target_aperture.plot(color='darkgreen', lw=lw, alpha=alpha)
for comparison_aperture in comparison_apertures:
comparison_aperture.plot(color='red', lw=lw, alpha=alpha)
# Now plot the annuli
target_annulus.plot(color='darkgreen', lw=lw, alpha=alpha)
for comparison_annulus in comparison_annuli:
comparison_annulus.plot(color='red', lw=lw, alpha=alpha)
plt.pause(1)
plt.show()
if __name__ == '__main__':
main()