def bkg_subtraction(time, flux, scope="tpf", sigma=3):
"""Subtracts background flux from target pixel file.
Parameters
----------
scope : string, "tpf" or "postcard"
If `tpf`, will use data from the target pixel file only to estimate and remove the background.
If `postcard`, will use data from the entire postcard region to estimate and remove the background.
sigma : float
The standard deviation cut used to determine which pixels are representative of the background in each cadence.
"""
tpf_flux_bkg = []
sigma_clip = SigmaClip(sigma=sigma)
# bkg = MMMBackground(sigma_clip=sigma_clip)
bkg = SExtractorBackground(sigma_clip=sigma_clip)
bkg_MMM = MMMBackground(sigma_clip=sigma_clip)
bkg_ModeEstimator = ModeEstimatorBackground(median_factor=3.,
mean_factor=2.,
sigma_clip=sigma_clip)
bkg_Mean = MeanBackground(sigma_clip)
bkg_Median = MedianBackground(sigma_clip)
bkg_SExtractor = SExtractorBackground(sigma_clip)
bkg_MMM_value = bkg_MMM.calc_background(flux[0])
bkg_ModeEstimator_value = bkg_ModeEstimator.calc_background(flux[0])
bkg_Mean_value = bkg_Mean.calc_background(flux[0])
bkg_Median_value = bkg_Median.calc_background(flux[0])
bkg_SExtractor_value = bkg_SExtractor.calc_background(flux[0])
print("MMM Background = {}".format(bkg_MMM_value))
print("ModeEstimator Background = {}".format(bkg_ModeEstimator_value))
print("Mean Background = {}".format(bkg_Mean_value))
print("Median Background = {}".format(bkg_Median_value))
print("SExtractor Background = {}".format(bkg_SExtractor_value))
for i in range(len(time)):
bkg_value = bkg.calc_background(flux[i])
tpf_flux_bkg.append(bkg_value)
tpf_flux_bkg = np.array(tpf_flux_bkg)
return tpf_flux_bkg
def test_recovered_flux_aperture(science_image):
with fits.open(science_image) as hdul:
header = hdul[0].header
wcs = WCS(header)
footprint = wcs.calc_footprint()
# realize fakes randomly throughout the footprint
rx = rng.uniform(size=N_FAKE)
ry = rng.uniform(size=N_FAKE)
# keep things bright
mag = rng.uniform(low=15, high=18, size=N_FAKE)
minra, mindec = footprint.min(axis=0)
maxra, maxdec = footprint.max(axis=0)
coord = SkyCoord(minra + (maxra - minra) * rx,
mindec + (maxdec - mindec) * ry,
unit='deg')
fakes.inject_psf(science_image, mag, coord)
with fits.open(science_image) as hdul:
data = hdul[-2].data
header = hdul[0].header
table = hdul[-1].data
# subtract off the background
sigma_clip = SigmaClip(sigma=3.0)
bkg = MedianBackground(sigma_clip)
bkg_val = bkg.calc_background(data)
bkgsub = data - bkg_val
APERTURE_RADIUS = 3 * u.pixel
for row in table:
ra, dec, mag = row['fake_ra'], row['fake_dec'], row['fake_mag']
coord = SkyCoord(ra, dec, unit='deg')
apertures = photutils.SkyCircularAperture(coord, r=APERTURE_RADIUS)
phot_table = photutils.aperture_photometry(bkgsub, apertures, wcs=wcs)
flux = phot_table['aperture_sum'][0]
assert abs(-2.5 * np.log10(flux) + header['MAGZP'] + header['APCOR4'] -
mag) < 0.05
