def test_psf_photometry_discrete():
"""
Test psf_photometry with discrete PRF model.
"""
prf = DiscretePRF(test_psf, subsampling=1)
f = psf_photometry(image, positions, prf)
assert_allclose(f, fluxes, rtol=1E-6)
def test_psf_photometry_gaussian():
"""
Test psf_photometry with Gaussian PSF model.
"""
prf = GaussianPSF(gaussian_width)
f = psf_photometry(image, positions, prf)
assert_allclose(f, fluxes, rtol=1E-3)
def calc_fwhm(image_file):
stddev = 1.0 # pixels
round_sigma = (stddev * stddev)**0.5
psf_guess = psf.IntegratedGaussianPRF(flux=1, sigma=round_sigma)
psf_guess.flux.fixed = psf_guess.x_0.fixed = psf_guess.y_0.fixed = False
psf_guess.x_0.sigma = True
img = load_image(image_file)
outtabi = psf.psf_photometry(img, intab, psf_guess, fitshape)
pdb.set_trace()
return
def compute_psf_flux(image_data, sources, \
scatter_output_path=None, bar_output_path=None, hist_output_path=None, \
residual_path=None):
logger.info('Computing flux...')
coords = zip(sources['xcentroid'], sources['ycentroid'])
psf_gaussian = GaussianPSF(1)
computed_fluxes = psf_photometry(image_data, coords, psf_gaussian)
if scatter_output_path:
logger.info('Saving scatter plot...')
plt.close('all')
plt.scatter(sorted(sources['flux']), sorted(computed_fluxes))
plt.xlabel('Fluxes catalog')
plt.ylabel('Fluxes photutils')
plt.savefig(scatter_output_path)
if bar_output_path:
logger.info('Saving bar chart...')
plt.close('all')
plt.bar(xrange(len(computed_fluxes)), computed_fluxes)
plt.ylabel('Flux')
plt.savefig(bar_output_path)
if hist_output_path:
logger.info('Saving histogram...')
plt.close('all')
plt.hist(computed_fluxes, bins=50)
plt.xlabel('Flux')
plt.ylabel('Frequency')
plt.savefig(hist_output_path)
if residual_path:
residuals = subtract_psf(np.float64(image_data.copy()), psf_gaussian, coords, computed_fluxes)
# Plot it.
plt.close('all')
plt.figure(figsize=(16, 12))
plt.imshow(residuals, cmap='hot', vmin=-1, vmax=10, interpolation='None', origin='lower')
plt.plot(coords[0], coords[1], marker='o', markerfacecolor='None', markeredgecolor='y', linestyle='None')
plt.xlim(0, 1024)
plt.ylim(0, 512)
plt.colorbar(orientation='horizontal')
plt.savefig(residual_path)
CORR_PATH = sys.argv[1]
IMAGE_PATH = sys.argv[2]
OUTPUT_PATH = sys.argv[3] if len(sys.argv) > 3 else 'flux_comparison.png'
corr = fits.open(CORR_PATH)
coords = zip(corr[1].data['field_x'], corr[1].data['field_y'])
catalog_fluxes = corr[1].data['FLUX']
# Convert
factor = (u.MJy / u.sr * (0.402 * u.arcsec) ** 2 / u.pixel).to(u.mJy / u.pixel)
catalog_fluxes *= factor.value
im = fits.open(IMAGE_PATH)
data = im[0].data[2]
psf_gaussian = GaussianPSF(1)
computed_fluxes = psf_photometry(data, coords, psf_gaussian)
print len(catalog_fluxes), 'sources present in metadata'
# Check
import matplotlib.pyplot as plt
plt.scatter(catalog_fluxes, computed_fluxes)
plt.xlabel('Fluxes catalog')
plt.ylabel('Fluxes photutils')
#plt.plot(numpy.sort(computed_fluxes)/numpy.sort(catalog_fluxes))
#plt.xlabel('star #')
#plt.ylabel('computed/catalog')
plt.savefig(OUTPUT_PATH)
srcs = sep.extract(sim.bkg_sub_img, thresh=12*sim.bkg.globalrms)
posflux = srcs[['x','y', 'flux']]
psf_guess = psf.IntegratedGaussianPRF(flux=1, sigma=8)
psf_guess.flux.fixed = False
psf_guess.x_0.fixed = False
psf_guess.y_0.fixed = False
psf_guess.x_0.sigma = True
fitshape = (64,64)
intab = Table(names=['x_0', 'y_0', 'flux_0'], data=posflux)
#subimi = psf.subtract_psf(sim.bkg_sub_img, psf_guess, posflux)
outtabi = psf.psf_photometry(sim.bkg_sub_img, intab, psf_guess, fitshape,
store_fit_info=True)
outtabi['flux_input'] = intab['flux_0']
# with daofind there are lots of garbage
found = daofind(sim.bkg_sub_img, threshold=5*sim.bkg.globalrms, fwhm=10,
exclude_border=True)
intab2 = Table(names=['x_0', 'y_0', 'flux_0'], data=[found['xcentroid'],
found['ycentroid'], found['flux']])
outtabi2 = psf.psf_photometry(sim.bkg_sub_img, intab2, psf_guess, fitshape,
store_fit_info=True)
outtabi2['flux_input'] = intab2['flux_0']
CORR_PATH = sys.argv[1]
IMAGE_PATH = sys.argv[2]
OUTPUT_PATH = sys.argv[3] if len(sys.argv) > 3 else 'flux_comparison.png'
corr = fits.open(CORR_PATH)
coords = zip(corr[1].data['field_x'], corr[1].data['field_y'])
catalog_fluxes = corr[1].data['FLUX']
# Convert
factor = (u.MJy / u.sr * (0.402 * u.arcsec)**2 / u.pixel).to(u.mJy / u.pixel)
catalog_fluxes *= factor.value
im = fits.open(IMAGE_PATH)
data = im[0].data[2]
psf_gaussian = GaussianPSF(1)
computed_fluxes = psf_photometry(data, coords, psf_gaussian)
print len(catalog_fluxes), 'sources present in metadata'
# Check
import matplotlib.pyplot as plt
plt.scatter(catalog_fluxes, computed_fluxes)
plt.xlabel('Fluxes catalog')
plt.ylabel('Fluxes photutils')
#plt.plot(numpy.sort(computed_fluxes)/numpy.sort(catalog_fluxes))
#plt.xlabel('star #')
#plt.ylabel('computed/catalog')
plt.savefig(OUTPUT_PATH)