def cg_other_est():
"""Test all HSM shear estimators give same value as REGAUSS (default)"""
in_p = cg_fn.LSST_Args()
filt = galsim.Bandpass('data/baseline/total_r.dat',
wave_type='nm').thin(rel_err=1e-4)
in_p.b_SED, in_p.d_SED, in_p.c_SED = cg_fn.get_template_seds(in_p)
gal = cg_fn.get_gal_cg(in_p)
psf_args = cg_fn.psf_params()
meas_args = cg_fn.meas_args(shear_est='KSB')
meas_args.bp = filt
gcg, gnocg = cg_fn.calc_cg_crg(gal, meas_args, psf_args)
# Previously measured value at default galaxy parameters
np.testing.assert_array_almost_equal((gcg / gnocg - 1).T[0],
[0.00106263, 0.00106594],
decimal=5)
meas_args.shear_est = 'BJ'
gcg, gnocg = cg_fn.calc_cg_crg(gal, meas_args, psf_args)
# Previously measured value at default galaxy parameters
np.testing.assert_array_almost_equal((gcg / gnocg - 1).T[0],
[0.00106263, 0.00106594],
decimal=5)
meas_args.shear_est = 'LINEAR'
gcg, gnocg = cg_fn.calc_cg_crg(gal, meas_args, psf_args)
# Previously measured value at default galaxy parameters
np.testing.assert_array_almost_equal((gcg / gnocg - 1).T[0],
[0.00106263, 0.00106594],
decimal=5)
def same_sed():
"""bulge and disk have same sed, cg bias must be zero"""
in_p = cg_fn.LSST_Args(disk_SED_name='E')
filt = galsim.Bandpass('data/baseline/total_r.dat',
wave_type='nm').thin(rel_err=1e-4)
in_p.b_SED, in_p.d_SED, in_p.c_SED = cg_fn.get_template_seds(in_p)
gal = cg_fn.get_gal_cg(in_p)
meas_args = cg_fn.meas_args()
meas_args.bp = filt
psf_args = cg_fn.psf_params()
gcg, gnocg = cg_fn.calc_cg_crg(gal, meas_args, psf_args)
np.testing.assert_array_almost_equal((gcg / gnocg - 1).T[0], [0, 0],
decimal=5)
def achr_psf():
"""LSST PSF is chromatic, CG bias must be 0"""
in_p = cg_fn.LSST_Args()
filt = galsim.Bandpass('data/baseline/total_r.dat',
wave_type='nm').thin(rel_err=1e-4)
in_p.b_SED, in_p.d_SED, in_p.c_SED = cg_fn.get_template_seds(in_p)
gal = cg_fn.get_gal_cg(in_p)
meas_args = cg_fn.meas_args()
meas_args.bp = filt
psf_args = cg_fn.psf_params(alpha=0)
gcg, gnocg = cg_fn.calc_cg_crg(gal, meas_args, psf_args)
np.testing.assert_array_almost_equal((gcg / gnocg - 1).T[0],
[0, 0],
decimal=5)
def with_cg():
"""Raise error if no cg bias in default setting"""
in_p = cg_fn.LSST_Args()
filt = galsim.Bandpass('data/baseline/total_r.dat',
wave_type='nm').thin(rel_err=1e-4)
in_p.b_SED, in_p.d_SED, in_p.c_SED = cg_fn.get_template_seds(in_p)
gal = cg_fn.get_gal_cg(in_p)
meas_args = cg_fn.meas_args()
meas_args.bp = filt
psf_args = cg_fn.psf_params()
gcg, gnocg = cg_fn.calc_cg_crg(gal, meas_args, psf_args)
np.testing.assert_raises(AssertionError,
np.testing.assert_array_almost_equal,
(gcg / gnocg - 1).T[0], [0, 0], decimal=5)
# Previously measured value at default galaxy parameters
np.testing.assert_array_almost_equal((gcg / gnocg - 1).T[0],
[0.00106263, 0.00106594],
decimal=5)
def get_lsst_para(cat):
"""Create parametric bulge+disk galaxy for input parametrs form catsim.
@cat: catsim row containig catsim galaxy parametrs
"""
# HST scale
scale = 0.2
b_r, b_g = a_b2re_e(cat['a_b'], cat['b_b'])
d_r, d_g = a_b2re_e(cat['a_d'], cat['b_d'])
b_s = galsim.Shear(g=b_g, beta=cat['pa_bulge'] * galsim.degrees)
d_s = galsim.Shear(g=d_g, beta=cat['pa_disk'] * galsim.degrees)
input_p = cg_fn.LSST_Args(scale=scale, redshift=cat['redshift'],
bulge_n=cat['disk_n'], disk_n=cat['bulge_n'],
disk_HLR=d_r, bulge_HLR=b_r,
bulge_e=[b_s.e1, b_s.e2],
disk_e=[d_s.e1, d_s.e2],
bulge_frac=0.5)
input_p.T_flux = 2
gal, PSF, con = get_gal(input_p, cat)
return gal
def with_CRG():
"""Raise error if no cg bias in default setting"""
in_p = cg_fn.LSST_Args()
filt = galsim.Bandpass('data/baseline/total_r.dat',
wave_type='nm').thin(rel_err=1e-4)
in_p.b_SED, in_p.d_SED, in_p.c_SED = cg_fn.get_template_seds(in_p)
gal = cg_fn.get_gal_cg(in_p)
meas_args = cg_fn.meas_args()
meas_args.bp = filt
psf_args = cg_fn.psf_params()
CRG1, CRG2 = cg_fn.get_CRG_basic(gal, in_p)
gcg1, gnocg1 = cg_fn.calc_cg_crg(CRG1, meas_args, psf_args)
gcg2, gnocg2 = cg_fn.calc_cg_crg(CRG2, meas_args, psf_args)
# Previously measured value at default galaxy parameters
np.testing.assert_array_almost_equal((gcg1 / gnocg1 - 1).T[0],
[0.00128506, 0.0012862],
decimal=5)
np.testing.assert_array_almost_equal((gcg2 / gnocg2 - 1).T[0],
[0.00106303, 0.00106446],
decimal=5)
def get_lsst_noise(cat, rng, row):
"""Create CRG for a given input parametrs form catsim.
Args:
cat: astropy row containig catsim galaxy parametrs
"""
# HST scale
exposure_time = 5520 # 6900
r_zero_point = 43.70 # 44.80
i_zero_point = 32.36 # 33.17
area = 32.4 * 10000 # np.pi * (667 / 2.)**2
scale = 0.2
r_sky_brightness = 21.2
i_sky_brightness = 20.5
# r0 = 6.67 * 100 / 2.
psf_sig = 0.297
npix = int(max(cat['BulgeHalfLightRadius'],
cat['DiskHalfLightRadius'], 2 * psf_sig) * 8 / scale)
print "Number of pixels is ", npix
b_r, b_g = a_b2re_e(cat['a_b'], cat['b_b'])
d_r, d_g = a_b2re_e(cat['a_d'], cat['b_d'])
b_s = galsim.Shear(g=b_g, beta=cat['pa_bulge'] * galsim.degrees)
d_s = galsim.Shear(g=d_g, beta=cat['pa_disk'] * galsim.degrees)
input_p = cg_fn.LSST_Args(scale=scale, npix=npix,
redshift=cat['redshift'], bulge_frac=0.5,
disk_n=cat['bulge_n'], bulge_n=cat['disk_n'],
disk_HLR=d_r, bulge_HLR=b_r,
bulge_e=[b_s.e1, b_s.e2],
disk_e=[d_s.e1, d_s.e2])
r = galsim.Bandpass('data/baseline/total_r.dat',
wave_type='nm').thin(rel_err=1e-4)
input_p.bp = r
input_p.T_flux = 2
gal, PSF, con = get_gal(input_p, cat)
r = galsim.Bandpass('data/baseline/total_r.dat', zeropoint='AB',
wave_type='nm').thin(rel_err=1e-4)
i = galsim.Bandpass('data/baseline/total_i.dat', zeropoint='AB',
wave_type='nm').thin(rel_err=1e-4)
# old throughputs did not have one of the mirrors
# get bandpass
# r = galsim.Bandpass('data/LSST_r.dat', zeropoint='AB',
# wave_type='nm').thin(rel_err=1e-4)
# i = galsim.Bandpass('data/LSST_i.dat', zeropoint='AB',
# wave_type='nm').thin(rel_err=1e-4)
# draw galaxy image
gal_im_r = con.drawImage(r, nx=npix, ny=npix, scale=scale, area=area,
exptime=exposure_time)
gal_im_i = con.drawImage(i, nx=npix, ny=npix, scale=scale, area=area,
exptime=exposure_time)
flux = np.array([gal_im_r.array.sum(),
gal_im_i.array.sum()])
snr_num = np.array([np.sum(gal_im_r.array**2), np.sum(gal_im_i.array**2)])
# correlated noise to add to image
r_mean_sky_level = get_flux(r_sky_brightness, exposure_time,
r_zero_point) * scale**2
i_mean_sky_level = get_flux(i_sky_brightness, exposure_time,
i_zero_point) * scale**2
noise_r = galsim.PoissonNoise(rng=rng, sky_level=r_mean_sky_level)
noise_i = galsim.PoissonNoise(rng=rng, sky_level=i_mean_sky_level)
# Add noise
gal_im_r.addNoise(noise_r)
gal_im_i.addNoise(noise_i)
var_r = noise_r.getVariance()
var_i = noise_i.getVariance()
var = np.array([var_r, var_i])
# Compute sn_ellip_gauss
try:
res_r = galsim.hsm.FindAdaptiveMom(gal_im_r)
res_i = galsim.hsm.FindAdaptiveMom(gal_im_i)
aperture_noise_r = np.sqrt(var_r * 2 * np.pi * (res_r.moments_sigma**2))
aperture_noise_i = np.sqrt(var_i * 2 * np.pi * (res_i.moments_sigma**2))
sn_ellip_gauss_r = res_r.moments_amp / aperture_noise_r
sn_ellip_gauss_i = res_i.moments_amp / aperture_noise_i
row['mom_sigma'] = [res_r.moments_sigma, res_i.moments_sigma]
except:
sn_ellip_gauss_r = -10
sn_ellip_gauss_i = -10
row['LSST_sn_ellip_gauss'] = np.array([sn_ellip_gauss_r, sn_ellip_gauss_i])
row['LSST_noise_var'] = var
row['LSST_flux'] = flux
print "LSST flux", flux
print "LSST noise var ", var
row['LSST_SNR'] = np.sqrt(snr_num / var)
t_flux_r = get_flux(cat['r_ab'], exposure_time, r_zero_point)
t_flux_i = get_flux(cat['i_ab'], exposure_time, i_zero_point)
t_fluxs = np.array([t_flux_r, t_flux_i])
row['LSST_target_flux'] = t_fluxs