def test_admom_smoke(g1_true, g2_true, wcs_g1, wcs_g2):
rng = np.random.RandomState(seed=100)
fwhm = 0.9
image_size = 107
cen = (image_size - 1) / 2
gs_wcs = galsim.ShearWCS(0.125, galsim.Shear(g1=wcs_g1,
g2=wcs_g2)).jacobian()
obj = galsim.Gaussian(fwhm=fwhm).shear(g1=g1_true,
g2=g2_true).withFlux(400)
im = obj.drawImage(nx=image_size,
ny=image_size,
wcs=gs_wcs,
method='no_pixel').array
noise = np.sqrt(np.sum(im**2)) / 1e18
wgt = np.ones_like(im) / noise**2
scale = np.sqrt(gs_wcs.pixelArea())
g1arr = []
g2arr = []
Tarr = []
for _ in range(50):
shift = rng.uniform(low=-scale / 2, high=scale / 2, size=2)
xy = gs_wcs.toImage(galsim.PositionD(shift))
im = obj.shift(dx=shift[0],
dy=shift[1]).drawImage(nx=image_size,
ny=image_size,
wcs=gs_wcs,
method='no_pixel',
dtype=np.float64).array
jac = Jacobian(y=cen + xy.y,
x=cen + xy.x,
dudx=gs_wcs.dudx,
dudy=gs_wcs.dudy,
dvdx=gs_wcs.dvdx,
dvdy=gs_wcs.dvdy)
_im = im + (rng.normal(size=im.shape) * noise)
obs = Observation(image=_im, weight=wgt, jacobian=jac)
fitter = Admom(obs, rng=rng)
try:
fitter.go(fwhm_to_T(fwhm) + rng.normal() * 0.01)
res = fitter.get_result()
if res['flags'] == 0:
gm = fitter.get_gmix()
_g1, _g2, _T = gm.get_g1g2T()
g1arr.append(_g1)
g2arr.append(_g2)
Tarr.append(_T)
except GMixRangeError:
pass
g1 = np.mean(g1arr)
g2 = np.mean(g2arr)
gtol = 1e-6
assert np.abs(g1 - g1_true) < gtol, (g1,
np.std(g1arr) / np.sqrt(len(g1arr)))
assert np.abs(g2 - g2_true) < gtol, (g2,
np.std(g2arr) / np.sqrt(len(g2arr)))
if g1_true == 0 and g2_true == 0:
T = np.mean(Tarr)
assert np.abs(T - fwhm_to_T(fwhm)) < 1e-6
def test_admom_smoke(g1_true, g2_true, wcs_g1, wcs_g2):
rng = np.random.RandomState(seed=100)
fwhm = 0.9
image_size = 107
cen = (image_size - 1) / 2
gs_wcs = galsim.ShearWCS(0.125, galsim.Shear(g1=wcs_g1,
g2=wcs_g2)).jacobian()
obj = galsim.Gaussian(fwhm=fwhm).shear(g1=g1_true,
g2=g2_true).withFlux(400)
im = obj.drawImage(nx=image_size,
ny=image_size,
wcs=gs_wcs,
method='no_pixel').array
noise = np.sqrt(np.sum(im**2)) / 1e18
wgt = np.ones_like(im) / noise**2
scale = np.sqrt(gs_wcs.pixelArea())
g1arr = []
g2arr = []
Tarr = []
for _ in range(50):
shift = rng.uniform(low=-scale / 2, high=scale / 2, size=2)
xy = gs_wcs.toImage(galsim.PositionD(shift))
im = obj.shift(dx=shift[0], dy=shift[1]).drawImage(
nx=image_size,
ny=image_size,
wcs=gs_wcs,
method='no_pixel',
dtype=np.float64,
).array
jac = Jacobian(y=cen + xy.y,
x=cen + xy.x,
dudx=gs_wcs.dudx,
dudy=gs_wcs.dudy,
dvdx=gs_wcs.dvdx,
dvdy=gs_wcs.dvdy)
_im = im + (rng.normal(size=im.shape) * noise)
obs = Observation(image=_im, weight=wgt, jacobian=jac)
Tguess = fwhm_to_T(fwhm) + rng.normal() * 0.01
res = run_admom(obs=obs, guess=Tguess)
if res['flags'] == 0:
gm = res.get_gmix()
_g1, _g2, _T = gm.get_g1g2T()
g1arr.append(_g1)
g2arr.append(_g2)
Tarr.append(_T)
fim = res.make_image()
assert fim.shape == im.shape
res['flags'] = 5
with pytest.raises(RuntimeError):
res.make_image()
with pytest.raises(RuntimeError):
res.get_gmix()
g1 = np.mean(g1arr)
g2 = np.mean(g2arr)
gtol = 1.5e-6
assert np.abs(g1 - g1_true) < gtol, (g1,
np.std(g1arr) / np.sqrt(len(g1arr)))
assert np.abs(g2 - g2_true) < gtol, (g2,
np.std(g2arr) / np.sqrt(len(g2arr)))
if g1_true == 0 and g2_true == 0:
T = np.mean(Tarr)
assert np.abs(T - fwhm_to_T(fwhm)) < 1e-6
with pytest.raises(ValueError):
_ = run_admom(None, None)
# cover some branches
tres = copy.deepcopy(res)
tres['flags'] = 0
tres['sums_cov'][:, :] = np.nan
tres = ngmix.admom.admom.get_result(tres)
assert tres['e1err'] == 9999.0
tres = copy.deepcopy(res)
tres['flags'] = 0
tres['pars'][4] = -1
tres = ngmix.admom.admom.get_result(tres)
assert tres['flags'] == 0x8
def test_ml_fitting_exp_obj_gauss_psf_smoke(g1_true, g2_true, wcs_g1, wcs_g2):
rng = np.random.RandomState(seed=10)
image_size = 33
cen = (image_size - 1) / 2
gs_wcs = galsim.ShearWCS(0.25, galsim.Shear(g1=wcs_g1,
g2=wcs_g2)).jacobian()
scale = np.sqrt(gs_wcs.pixelArea())
g_prior = ngmix.priors.GPriorBA(0.1)
cen_prior = ngmix.priors.CenPrior(0, 0, scale, scale)
T_prior = ngmix.priors.FlatPrior(0.01, 2)
F_prior = ngmix.priors.FlatPrior(1e-4, 1e9)
prior = ngmix.joint_prior.PriorSimpleSep(cen_prior, g_prior, T_prior,
F_prior)
gal = galsim.Exponential(half_light_radius=0.5).shear(
g1=g1_true, g2=g2_true).withFlux(400)
obj = galsim.Convolve([gal, galsim.Gaussian(fwhm=0.5)])
psf_im = galsim.Gaussian(fwhm=0.5).drawImage(nx=33,
ny=33,
wcs=gs_wcs,
method='no_pixel').array
psf_gmix = ngmix.gmix.make_gmix_model(
[0, 0, 0, 0, fwhm_to_T(0.5), 1], "gauss")
psf_obs = Observation(image=psf_im, gmix=psf_gmix)
im = obj.drawImage(nx=image_size,
ny=image_size,
wcs=gs_wcs,
method='no_pixel').array
noise = np.sqrt(np.sum(im**2)) / 1e16
wgt = np.ones_like(im) / noise**2
guess = np.ones(6) * 0.1
guess[0] = 0
guess[1] = 0
guess[2] = g1_true
guess[3] = g2_true
guess[4] = fwhm_to_T(0.5)
guess[5] = 400
g1arr = []
g2arr = []
farr = []
xarr = []
yarr = []
for _ in range(50):
shift = rng.uniform(low=-scale / 2, high=scale / 2, size=2)
xy = gs_wcs.toImage(galsim.PositionD(shift))
im = obj.shift(dx=shift[0],
dy=shift[1]).drawImage(nx=image_size,
ny=image_size,
wcs=gs_wcs,
method='no_pixel',
dtype=np.float64).array
jac = Jacobian(y=cen + xy.y,
x=cen + xy.x,
dudx=gs_wcs.dudx,
dudy=gs_wcs.dudy,
dvdx=gs_wcs.dvdx,
dvdy=gs_wcs.dvdy)
_im = im + (rng.normal(size=im.shape) * noise)
obs = Observation(image=_im, weight=wgt, jacobian=jac, psf=psf_obs)
fitter = LMSimple(obs, 'exp', prior=prior)
fitter.go(guess + rng.normal(size=6) * 0.01)
res = fitter.get_result()
if res['flags'] == 0:
_g1, _g2, _ = res['g'][0], res['g'][1], res['pars'][4]
g1arr.append(_g1)
g2arr.append(_g2)
farr.append(res['pars'][5])
xarr.append(res['pars'][1])
yarr.append(res['pars'][0])
g1 = np.mean(g1arr)
g2 = np.mean(g2arr)
gtol = 1e-5
assert np.abs(g1 - g1_true) < gtol
assert np.abs(g2 - g2_true) < gtol
xerr = np.std(xarr) / np.sqrt(len(xarr))
assert np.abs(np.mean(xarr)) < xerr * 5
yerr = np.std(yarr) / np.sqrt(len(yarr))
assert np.abs(np.mean(yarr)) < yerr * 5
def test_admom_smoke(g1_true, g2_true, wcs_g1, wcs_g2, weight_fac):
rng = np.random.RandomState(seed=100)
fwhm = 0.9
image_size = 107
cen = (image_size - 1) / 2
gs_wcs = galsim.ShearWCS(0.125, galsim.Shear(g1=wcs_g1,
g2=wcs_g2)).jacobian()
obj = galsim.Gaussian(fwhm=fwhm).shear(g1=g1_true,
g2=g2_true).withFlux(400)
im = obj.drawImage(nx=image_size,
ny=image_size,
wcs=gs_wcs,
method='no_pixel').array
noise = np.sqrt(np.sum(im**2)) / 1e18
wgt = np.ones_like(im) / noise**2
scale = np.sqrt(gs_wcs.pixelArea())
g1arr = []
g2arr = []
Tarr = []
for _ in range(50):
shift = rng.uniform(low=-scale / 2, high=scale / 2, size=2)
xy = gs_wcs.toImage(galsim.PositionD(shift))
im = obj.shift(dx=shift[0],
dy=shift[1]).drawImage(nx=image_size,
ny=image_size,
wcs=gs_wcs,
method='no_pixel',
dtype=np.float64).array
jac = Jacobian(y=cen + xy.y,
x=cen + xy.x,
dudx=gs_wcs.dudx,
dudy=gs_wcs.dudy,
dvdx=gs_wcs.dvdx,
dvdy=gs_wcs.dvdy)
_im = im + (rng.normal(size=im.shape) * noise)
obs = Observation(image=_im, weight=wgt, jacobian=jac)
# use a huge weight so that we get the raw moments back out
fitter = GaussMom(obs, fwhm * weight_fac, rng=rng)
try:
fitter.go()
res = fitter.get_result()
if res['flags'] == 0:
if weight_fac > 1:
_g1, _g2 = e1e2_to_g1g2(res['e'][0], res['e'][1])
else:
_g1, _g2 = res['e'][0], res['e'][1]
g1arr.append(_g1)
g2arr.append(_g2)
Tarr.append(res['pars'][4])
except GMixRangeError:
pass
g1 = np.mean(g1arr)
g2 = np.mean(g2arr)
gtol = 1e-9
assert np.abs(g1 - g1_true) < gtol, (g1,
np.std(g1arr) / np.sqrt(len(g1arr)))
assert np.abs(g2 - g2_true) < gtol, (g2,
np.std(g2arr) / np.sqrt(len(g2arr)))
# T test should only pass when the weight function is constant so
# weight_fac needs to be rally big
if g1_true == 0 and g2_true == 0 and weight_fac > 1:
T = np.mean(Tarr)
assert np.abs(T - fwhm_to_T(fwhm)) < 1e-6
def test_ml_fitting_exp_obj_gauss_psf_smoke(
g1_true, g2_true, wcs_g1, wcs_g2, fit_model):
rng = np.random.RandomState(seed=10)
image_size = 33
cen = (image_size - 1)/2
gs_wcs = galsim.ShearWCS(
0.25, galsim.Shear(g1=wcs_g1, g2=wcs_g2)).jacobian()
scale = np.sqrt(gs_wcs.pixelArea())
gal = galsim.Exponential(
half_light_radius=0.5
).shear(
g1=g1_true, g2=g2_true
).withFlux(
400,
)
obj = galsim.Convolve([gal, galsim.Gaussian(fwhm=0.5)])
psf_im = galsim.Gaussian(fwhm=0.5).drawImage(
nx=33, ny=33, wcs=gs_wcs, method='no_pixel').array
psf_gmix = ngmix.gmix.make_gmix_model(
[0, 0, 0, 0, fwhm_to_T(0.5), 1], "gauss")
psf_obs = Observation(
image=psf_im,
gmix=psf_gmix
)
im = obj.drawImage(
nx=image_size,
ny=image_size,
wcs=gs_wcs,
method='no_pixel',
).array
noise = np.sqrt(np.sum(im**2)) / 1e16
wgt = np.ones_like(im) / noise**2
prior = get_prior(fit_model=fit_model, rng=rng, scale=scale)
guess = prior.sample()
guess[0] = 0
guess[1] = 0
guess[2] = g1_true
guess[3] = g2_true
guess[4] = fwhm_to_T(0.5)
if fit_model == 'bd':
guess[5] = 1.0
guess[6] = 0.5
guess[7] = 400
elif fit_model == 'bdf':
guess[6] = 400
else:
guess[5] = 400
g1arr = []
g2arr = []
farr = []
xarr = []
yarr = []
fitter = Fitter(model=fit_model, prior=prior)
for _ in range(50):
shift = rng.uniform(low=-scale/2, high=scale/2, size=2)
xy = gs_wcs.toImage(galsim.PositionD(shift))
im = obj.shift(
dx=shift[0], dy=shift[1]
).drawImage(
nx=image_size,
ny=image_size,
wcs=gs_wcs,
method='no_pixel',
dtype=np.float64).array
jac = Jacobian(
y=cen + xy.y, x=cen + xy.x,
dudx=gs_wcs.dudx, dudy=gs_wcs.dudy,
dvdx=gs_wcs.dvdx, dvdy=gs_wcs.dvdy)
_im = im + (rng.normal(size=im.shape) * noise)
obs = Observation(
image=_im,
weight=wgt,
jacobian=jac,
psf=psf_obs)
res = fitter.go(
obs=obs, guess=guess + rng.normal(size=guess.size) * 0.01,
)
if res['flags'] == 0:
_g1, _g2, _ = res['g'][0], res['g'][1], res['pars'][4]
g1arr.append(_g1)
g2arr.append(_g2)
farr.append(res['pars'][5])
xarr.append(res['pars'][1])
yarr.append(res['pars'][0])
g1 = np.mean(g1arr)
g2 = np.mean(g2arr)
if fit_model == 'bd':
# bd fitting is highly degenerate, we don't recover the
# ellipticity with as much accuracy
gtol = 0.002
else:
gtol = 1.0e-5
assert np.abs(g1 - g1_true) < gtol
assert np.abs(g2 - g2_true) < gtol
xerr = np.std(xarr) / np.sqrt(len(xarr))
assert np.abs(np.mean(xarr)) < xerr * 5
yerr = np.std(yarr) / np.sqrt(len(yarr))
assert np.abs(np.mean(yarr)) < yerr * 5
def test_ml_max_fitting_gauss_smoke(g1_true, g2_true, wcs_g1, wcs_g2):
rng = np.random.RandomState(seed=42)
# allow some small relative bias due to approximate exp function
tol = 1.0e-5
flux = 400 # in galsim units
image_size = 33
cen = (image_size - 1) / 2
gs_wcs = galsim.ShearWCS(0.25, galsim.Shear(g1=wcs_g1,
g2=wcs_g2)).jacobian()
scale = np.sqrt(gs_wcs.pixelArea())
g_prior = ngmix.priors.GPriorBA(sigma=0.2, rng=rng)
cen_prior = ngmix.priors.CenPrior(
cen1=0,
cen2=0,
sigma1=scale,
sigma2=scale,
rng=rng,
)
T_prior = ngmix.priors.FlatPrior(minval=0.1, maxval=2, rng=rng)
F_prior = ngmix.priors.FlatPrior(minval=1e-4, maxval=1e9, rng=rng)
prior = ngmix.joint_prior.PriorSimpleSep(
cen_prior=cen_prior,
g_prior=g_prior,
T_prior=T_prior,
F_prior=F_prior,
)
obj = galsim.Gaussian(fwhm=0.9).shear(g1=g1_true,
g2=g2_true).withFlux(flux, )
im = obj.drawImage(nx=image_size,
ny=image_size,
wcs=gs_wcs,
method='no_pixel').array
noise = np.sqrt(np.sum(im**2)) / 1.e6
wgt = np.ones_like(im) / noise**2
g1arr = []
g2arr = []
Tarr = []
farr = []
xarr = []
yarr = []
for _ in range(10):
shift = rng.uniform(low=-scale / 2, high=scale / 2, size=2)
xy = gs_wcs.toImage(galsim.PositionD(shift))
im = obj.shift(dx=shift[0],
dy=shift[1]).drawImage(nx=image_size,
ny=image_size,
wcs=gs_wcs,
method='no_pixel',
dtype=np.float64).array
jac = Jacobian(
y=cen + xy.y,
x=cen + xy.x,
dudx=gs_wcs.dudx,
dudy=gs_wcs.dudy,
dvdx=gs_wcs.dvdx,
dvdy=gs_wcs.dvdy,
)
_im = im + rng.normal(size=im.shape, scale=noise)
obs = Observation(
image=_im,
weight=wgt,
jacobian=jac,
)
prior = None
fitter = Fitter(model='gauss', prior=prior)
guess = np.zeros(6)
guess[0:2] = rng.uniform(low=-0.1 * scale, high=0.1 * scale, size=2)
guess[2] = g1_true + rng.uniform(low=-0.01, high=0.01)
guess[3] = g2_true + rng.uniform(low=-0.01, high=0.01)
guess[4] = fwhm_to_T(0.9) * rng.uniform(low=0.99, high=1.01)
guess[5] = flux * rng.uniform(low=0.99, high=1.01)
res = fitter.go(obs=obs, guess=guess)
if res['flags'] == 0:
_g1, _g2, _T = res['g'][0], res['g'][1], res['pars'][4]
g1arr.append(_g1)
g2arr.append(_g2)
Tarr.append(_T)
farr.append(res['pars'][5])
xarr.append(res['pars'][1])
yarr.append(res['pars'][0])
assert len(g1arr) > 0
g1 = np.mean(g1arr)
g2 = np.mean(g2arr)
assert np.abs(g1 - g1_true) < tol
assert np.abs(g2 - g2_true) < tol
if g1_true == 0 and g2_true == 0:
T = np.mean(Tarr)
Ttrue = fwhm_to_T(0.9)
assert T / Ttrue - 1 < tol
fmn = np.mean(farr)
assert np.abs(fmn / flux - 1) < tol
xerr = np.std(xarr) / np.sqrt(len(xarr))
assert np.abs(np.mean(xarr)) < xerr * 5
yerr = np.std(yarr) / np.sqrt(len(yarr))
assert np.abs(np.mean(yarr)) < yerr * 5
def test_ml_fitting_gauss_smoke(g1_true, g2_true, wcs_g1, wcs_g2):
rng = np.random.RandomState(seed=42)
# allow some small relative bias due to approximate exp function
tol = 1.0e-5
image_size = 33
cen = (image_size - 1) / 2
gs_wcs = galsim.ShearWCS(0.25, galsim.Shear(g1=wcs_g1,
g2=wcs_g2)).jacobian()
scale = np.sqrt(gs_wcs.pixelArea())
g_prior = ngmix.priors.GPriorBA(0.2)
cen_prior = ngmix.priors.CenPrior(0, 0, scale, scale)
T_prior = ngmix.priors.FlatPrior(0.1, 2)
F_prior = ngmix.priors.FlatPrior(1e-4, 1e9)
prior = ngmix.joint_prior.PriorSimpleSep(cen_prior, g_prior, T_prior,
F_prior)
obj = galsim.Gaussian(fwhm=0.9).shear(g1=g1_true, g2=g2_true).withFlux(400)
im = obj.drawImage(nx=image_size,
ny=image_size,
wcs=gs_wcs,
method='no_pixel').array
noise = np.sqrt(np.sum(im**2)) / 1e16
wgt = np.ones_like(im) / noise**2
g1arr = []
g2arr = []
Tarr = []
farr = []
xarr = []
yarr = []
for _ in range(100):
shift = rng.uniform(low=-scale / 2, high=scale / 2, size=2)
xy = gs_wcs.toImage(galsim.PositionD(shift))
im = obj.shift(dx=shift[0],
dy=shift[1]).drawImage(nx=image_size,
ny=image_size,
wcs=gs_wcs,
method='no_pixel',
dtype=np.float64).array
jac = Jacobian(y=cen + xy.y,
x=cen + xy.x,
dudx=gs_wcs.dudx,
dudy=gs_wcs.dudy,
dvdx=gs_wcs.dvdx,
dvdy=gs_wcs.dvdy)
_im = im + (rng.normal(size=im.shape) * noise)
obs = Observation(image=_im, weight=wgt, jacobian=jac)
fitter = LMSimple(obs, 'gauss', prior=prior)
guess = np.ones(6) * 0.1
guess[0] = 0
guess[1] = 0
guess[2] = g1_true
guess[3] = g2_true
guess[4] = fwhm_to_T(0.9)
guess[5] = 400 * scale * scale
fitter.go(guess + rng.normal(size=6) * 0.01)
res = fitter.get_result()
if res['flags'] == 0:
_g1, _g2, _T = res['g'][0], res['g'][1], res['pars'][4]
g1arr.append(_g1)
g2arr.append(_g2)
Tarr.append(_T)
farr.append(res['pars'][5])
xarr.append(res['pars'][1])
yarr.append(res['pars'][0])
g1 = np.mean(g1arr)
g2 = np.mean(g2arr)
assert np.abs(g1 - g1_true) < tol
assert np.abs(g2 - g2_true) < tol
if g1_true == 0 and g2_true == 0:
T = np.mean(Tarr)
Ttrue = fwhm_to_T(0.9)
assert T / Ttrue - 1 < tol
fmn = np.mean(farr) / scale / scale
assert np.abs(fmn / 400 - 1) < tol
xerr = np.std(xarr) / np.sqrt(len(xarr))
assert np.abs(np.mean(xarr)) < xerr * 5
yerr = np.std(yarr) / np.sqrt(len(yarr))
assert np.abs(np.mean(yarr)) < yerr * 5