def test_fit_1gauss_noisy(ellip=0.2, s2n=10000):
import images
import fimage
numpy.random.seed(35)
sigma = 1.4
T=2*sigma**2
nsig=5
dim = int(nsig*T)
if (dim % 2) == 0:
dim += 1
dims=array([dim,dim])
cen=(dims-1.)/2.
theta=23.7*numpy.pi/180.
#eta=-0.7
#ellip=(1+tanh(eta))/2
eta = ellip2eta(ellip+1.e-8)
print >>stderr,'ellip:',ellip
pars=array([cen[0],cen[1],eta,theta,1.,T])
print >>stderr,'pars'
gmix = gmix_fit.pars2gmix_coellip_pick(pars,ptype='eta')
nsub=1
im0=gmix2image_em(gmix,dims,nsub=nsub)
im,skysig = fimage.add_noise(im0, s2n,check=True)
images.multiview(im,title='nsub: %d' % nsub)
p0=pars.copy()
p0[0] += 1*(randu()-0.5) # cen0
p0[1] += 1*(randu()-0.5) # cen1
p0[2] += 0.2*(randu()-0.5) # eta
p0[3] += 0.5*(randu()-0.5) # theta radians
p0[4] += 0.1*(randu()-0.5) # p
p0[5] += 1*(randu()-0.5) # T
print_pars(pars,front='pars: ')
print_pars(p0, front='guess: ')
gf=gmix_fit.GMixFitCoellip(im, p0, ptype='eta',verbose=True)
print 'numiter:',gf.numiter
print_pars(gf.popt, front='popt: ')
print_pars(gf.perr, front='perr: ')
images.imprint(gf.pcov)
def _get_Rnoise(self, data, weights=None, show=False):
import images
noise0 = self['target_noise']
print("noise0:",noise0)
if 'detrend_noises' in self:
target_noises = array( self['detrend_noises'] )
else:
target_noises = noise0*array( self['detrend_factors'] )
ndiff = target_noises - noise0
xvals = 2*noise0*ndiff
if weights is not None:
Rdt, Rdt_psf = self._get_Rnoise_means_weighted(data, weights)
else:
Rdt, Rdt_psf = self._get_Rnoise_means(data)
plot_psf=True
if Rdt_psf is None:
plot_psf=False
Rdt_psf = 0.0*Rdt[:,:,0]
A = zeros( (2,2) )
Apsf = zeros(2)
p='%s (%.4g +/- %.4g) + (%.4g +/ %.4g) deltan'
for i in xrange(2):
res = fitline(xvals, Rdt_psf[:,i])
Apsf[i] = res['slope']
if plot_psf:
plot_line_fit(
self['run'],
'Rnoise-detrend-Rpsf%d' % (i+1),
xvals, Rdt_psf[:,i],res,
r'$2 n \Delta n$',
r'$\Delta R^{PSF}_%d$' % (i+1),
show=show,
label_error=False,
)
for j in xrange(2):
res = fitline(xvals, Rdt[:,i,j])
A[i,j] = res['slope']
n='A[%d,%d]' % (i+1,j+1)
s=res['slope']
serr=res['slope_err']
o=res['offset']
oerr=res['offset_err']
print(p % (n,o,oerr,s,serr))
plt=plot_line_fit(
self['run'],
'Rnoise-detrend-R%d%d' % (i+1,j+1),
xvals,
Rdt[:,i,j],
res,
r'$2 n \Delta n$',
r'$\Delta R_{%d,%d}$' % (i+1,j+1),
show=show,
label_error=False,
)
Rnoise = A*noise0**2
Rnoise_psf = Apsf*noise0**2
print("Rnoise")
images.imprint(Rnoise, fmt='%.4g')
print("Rnoise_psf")
print('%.4g %.4g' % tuple(Rnoise_psf))
return Rnoise, Rnoise_psf
def calc_gmean(self, data, deep_data=None):
"""
get gtrue, gmeas, gcov
"""
import ngmix
gtrue = data['shear_true'].mean(axis=0)
print("getting weights")
wts = self.get_weights(data)
if wts is None:
print(" weights are None")
if deep_data is not None:
print("getting deep data weights")
deep_wts = self.get_weights(deep_data)
if deep_wts is None:
print(" weights are None")
#print("using sens:",self.sens_field)
chunksize = 1000
if chunksize > data.size / 10.0:
chunksize = data.size / 10
#print("chunksize:",chunksize)
if self.sens_style == 'lensfit':
raise RuntimeError("lensfit not working yet")
if self.sens_field is None:
sens = numpy.ones((data.size, 2))
else:
sens = data[self.sens_field].copy()
gmeas, gcov = ngmix.lensfit.lensfit_jackknife(data[self.g_field],
sens,
weights=wts,
chunksize=chunksize)
elif self.sens_style == 'metacal':
if self.sens_field is None:
sens = numpy.ones((data.size, 2, 2))
else:
import images
if deep_data is not None:
print(" using mean sensitivity from deep data")
wsum = deep_wts.sum()
wa = deep_wts[:, newaxis, newaxis]
s = deep_data[self.sens_field]
sens_mean = (s * wa).sum(axis=0) / wsum
sens = numpy.zeros((data.size, 2, 2))
sens[:, 0, 0] = sens_mean[0, 0]
sens[:, 0, 1] = sens_mean[0, 1]
sens[:, 1, 0] = sens_mean[1, 0]
sens[:, 1, 1] = sens_mean[1, 1]
else:
print(" using sensitivity from this data")
sens = data[self.sens_field].copy()
print("mean sens:")
images.imprint(sens.mean(axis=0), fmt='%g')
res = ngmix.metacal.jackknife_shear(data[self.g_field],
sens,
weights=wts,
chunksize=chunksize)
gmeas = res['shear']
gcov = res['shear_cov']
elif self.sens_style is None:
gmeas = data[self.g_field].mean(axis=0)
gcov = numpy.zeros((2, 2))
gcov[0, 0] = data[self.g_field][:, 0].std() / numpy.sqrt(data.size)
gcov[1, 1] = data[self.g_field][:, 1].std() / numpy.sqrt(data.size)
else:
raise ValueError("bad sens_style: '%s'" % self.sens_style)
return gtrue, gmeas, gcov
def test_fit_1gauss_galsim(ellip=0.2, s2n=10000):
import images
import galsim
import admom
numpy.random.seed(35)
#sigma = 1.4
sigma = 1
T=2*sigma**2
e = 0.2
theta=23.7
e1,e2 = etheta2e1e2(e,theta)
fimage_cov = ellip2mom(T, e=e, theta=theta)
print 'e: ',e
print 'e1:',e1
print 'e2:',e2
pixel_scale = 1.
nsig=5
dim = int(nsig*T)
if (dim % 2) == 0:
dim += 1
dims=array([dim,dim])
cen=(dims-1)/2
pix = galsim.Pixel(xw=pixel_scale, yw=pixel_scale)
gobj = galsim.Gaussian(sigma=sigma)
gobj.applyDistortion(galsim.Ellipse(e1=e1,e2=e2))
gcobj = galsim.Convolve([gobj,pix])
im0 = galsim.ImageD(int(dims[1]),int(dims[0]))
gcobj.draw(image=im0, dx=pixel_scale)
images.multiview(im0.array)
galsim_nsub=16
ares = admom.admom(im0.array,cen[0],cen[1],guess=T/2,nsub=galsim_nsub)
print 'galsim sigma:',sqrt( (ares['Irr']+ares['Icc'])/2 )
print 'galsim admom e1:',ares['e1']
print 'galsim admom e2:',ares['e2']
print 'galsim center:',ares['row'],ares['col']
fnsub=16
fim0 = model_image('gauss',dims,cen,fimage_cov,nsub=fnsub)
fares = admom.admom(fim0,cen[0],cen[1],guess=T/2,nsub=fnsub)
print 'fimage sigma:',sqrt( (fares['Irr']+fares['Icc'])/2 )
print 'fimage admom e1:',fares['e1']
print 'fimage admom e2:',fares['e2']
print 'fimage center:',fares['row'],fares['col']
return
theta=23.7*numpy.pi/180.
print >>stderr,'ellip:',ellip
pars=array([cen[0],cen[1],eta,theta,1.,T])
print >>stderr,'pars'
gmix = gmix_fit.pars2gmix_coellip_pick(pars,ptype='eta')
nsub=1
im0=gmix2image_em(gmix,dims,nsub=nsub)
im,skysig = fimage.add_noise(im0, s2n,check=True)
images.multiview(im,title='nsub: %d' % nsub)
p0=pars.copy()
p0[0] += 1*(randu()-0.5) # cen0
p0[1] += 1*(randu()-0.5) # cen1
p0[2] += 0.2*(randu()-0.5) # eta
p0[3] += 0.5*(randu()-0.5) # theta radians
p0[4] += 0.1*(randu()-0.5) # p
p0[5] += 1*(randu()-0.5) # T
print_pars(pars,front='pars: ')
print_pars(p0, front='guess: ')
gf=gmix_fit.GMixFitCoellip(im, p0, ptype='eta',verbose=True)
print 'numiter:',gf.numiter
print_pars(gf.popt, front='popt: ')
print_pars(gf.perr, front='perr: ')
images.imprint(gf.pcov)
def calc_gmean(self, data, deep_data=None):
"""
get gtrue, gmeas, gcov
"""
import ngmix
gtrue = data['shear_true'].mean(axis=0)
print("getting weights")
wts=self.get_weights(data)
if wts is None:
print(" weights are None")
if deep_data is not None:
print("getting deep data weights")
deep_wts = self.get_weights(deep_data)
if deep_wts is None:
print(" weights are None")
#print("using sens:",self.sens_field)
chunksize=1000
if chunksize > data.size/10.0:
chunksize = data.size/10
#print("chunksize:",chunksize)
if self.sens_style=='lensfit':
raise RuntimeError("lensfit not working yet")
if self.sens_field is None:
sens = numpy.ones( (data.size, 2) )
else:
sens = data[self.sens_field].copy()
gmeas, gcov = ngmix.lensfit.lensfit_jackknife(data[self.g_field],
sens,
weights=wts,
chunksize=chunksize)
elif self.sens_style=='metacal':
if self.sens_field is None:
sens = numpy.ones( (data.size,2,2) )
else:
import images
if deep_data is not None:
print(" using mean sensitivity from deep data")
wsum = deep_wts.sum()
wa = deep_wts[:,newaxis,newaxis]
s=deep_data[self.sens_field]
sens_mean = (s*wa).sum(axis=0)/wsum
sens = numpy.zeros( (data.size,2,2) )
sens[:,0,0] = sens_mean[0,0]
sens[:,0,1] = sens_mean[0,1]
sens[:,1,0] = sens_mean[1,0]
sens[:,1,1] = sens_mean[1,1]
else:
print(" using sensitivity from this data")
sens = data[self.sens_field].copy()
print("mean sens:")
images.imprint(sens.mean(axis=0), fmt='%g')
res = ngmix.metacal.jackknife_shear(data[self.g_field],
sens,
weights=wts,
chunksize=chunksize)
gmeas=res['shear']
gcov=res['shear_cov']
elif self.sens_style is None:
gmeas = data[self.g_field].mean(axis=0)
gcov=numpy.zeros( (2,2) )
gcov[0,0] = data[self.g_field][:,0].std()/numpy.sqrt(data.size)
gcov[1,1] = data[self.g_field][:,1].std()/numpy.sqrt(data.size)
else:
raise ValueError("bad sens_style: '%s'" % self.sens_style)
return gtrue, gmeas, gcov
def _print_pcov(self, res):
if res['flags']==0:
import images
import esutil as eu
images.imprint(eu.stat.cov2cor( res['pcov']) , fmt='%10f')
