def _fit_one(self, source_id, pars=None):
"""
Workhorse method to perfom home-brewed metacalibration on an object. Returns the
unsheared ellipticities of each galaxy, as well as a label indicating whether the
galaxy passed the selection cuts for each shear step (i.e. no_shear,1p,1m,2p,2m).
"""
index = source_id
jaclist = self._get_jacobians(source_id)
obslist = self._get_source_observations(source_id)
mcal_shear = 0.01
if pars is None:
lm_pars = {'maxfev': 2000, 'xtol': 5.0e-5, 'ftol': 5.0e-5}
max_pars = {'method': 'lm', 'lm_pars': lm_pars}
psf_model = 'gauss' # should come up with diagnostics for PSF quality
gal_model = 'gauss'
mcal_obs = ngmix.metacal.get_all_metacal(obslist)
# Make a dictionary to hold the results
result = {}
mcal_obs_keys = ['noshear', '1p', '1m', '2p', '2m']
# Run the actual metacalibration fits on the observed galaxies with Bootstrapper()
# and flag
for ikey in mcal_obs_keys:
boot = ngmix.Bootstrapper(mcal_obs[ikey])
boot.fit_psfs(psf_model, 1.)
boot.fit_max(gal_model, max_pars)
res = boot.get_fitter().get_result()
#result.update({ikey:res['g']})
try:
res['Tpsf'] = boot.psf_fitter._gm.get_T()
except:
res['Tpsf'] = boot.psf_fitter._result['T']
result.update({ikey: res})
R1 = (result['1p']['g'][0] - result['1m']['g'][0]) / (2 * mcal_shear)
R2 = (result['2p']['g'][1] - result['2m']['g'][1]) / (2 * mcal_shear)
print(f"R1: {R1:.3} \nR2:{R2:.3} ")
mcr = result
self.metacal = mcr
# # # Also get regular galaxy shape EM fit; if this fails you're in trouble
gal_obs = self._one_gal_obs(source_id=index)
boot = ngmix.Bootstrapper(gal_obs)
boot.fit_psfs(psf_model, 1.)
boot.fit_max(gal_model=gal_model, pars=max_pars)
gal_fit = boot.get_fitter()
self.gal_fit = gal_fit
return mcr, gal_fit
def _fit_one(self, source_id, pars=None):
"""
workhorse method to perfom home-brewed metacalibration on an object
"""
index = source_id
psf_cutout = self.medsObj.get_cutout(index, 0, type='psf')
image_cutout = self.medsObj.get_cutout(index, 0)
weight_cutout = self.medsObj.get_cutout(index, 0, type='weight')
# to help Bootstrapper distinguish between object and background
image_cutout[image_cutout <= 0] = 1E-2
psf_noise = 1e-6
this_psf = psf_cutout + 1e-6 * np.random.randn(psf_cutout.shape[0],
psf_cutout.shape[1])
psf_weight_image = np.zeros_like(this_psf) + 1. / psf_noise**2
weight_image = np.ones_like(image_cutout) * 1e9
jj_im = ngmix.jacobian.DiagonalJacobian(scale=0.206,
x=(image_cutout.shape[0]) / 2,
y=(image_cutout.shape[1]) / 2)
jj_psf = ngmix.jacobian.DiagonalJacobian(scale=0.206,
x=psf_cutout.shape[0] / 2,
y=psf_cutout.shape[1] / 2)
psf_obs = ngmix.Observation(psf_cutout,
weight=psf_weight_image,
jacobian=jj_psf)
gal_obs = ngmix.Observation(image_cutout,
weight=weight_image,
jacobian=jj_im,
psf=psf_obs)
mcal_shear = 0.01
if pars is None:
lm_pars = {'maxfev': 2000, 'xtol': 5.0e-5, 'ftol': 5.0e-5}
max_pars = {'method': 'lm', 'lm_pars': lm_pars}
psf_model = 'gauss'
gal_model = 'exp'
mcal_obs = ngmix.metacal.get_all_metacal(gal_obs)
# Make a dictionary to hold the results.
result = {}
# Run the actual metacalibration fits on the observed galaxies with Bootstrapper()
for ikey in mcal_obs.keys():
boot = ngmix.Bootstrapper(mcal_obs[ikey])
boot.fit_psfs('gauss', 1.)
boot.fit_max('exp', max_pars)
res = boot.get_fitter().get_result()
result.update({ikey: res['g']})
#fit,ax = plt.subplots(nrows=1,ncols=2,figsize=(14,7))
R1 = (result['1p'][0] - result['1m'][0]) / (2 * mcal_shear)
R2 = (result['2p'][1] - result['2m'][1]) / (2 * mcal_shear)
print(f"R1: {R1:.3} \nR2:{R2:.3} ")
mcr = result
#mcr = {'g1_noshear':result['noshear'][0], 'g2_noshear':result['noshear'][1], 'r11':R1, 'r22':R2}
self.metacal = mcr
# # # Also get regular galaxy shape EM fit, allowing for exception
boot = ngmix.Bootstrapper(gal_obs)
boot.fit_psfs(psf_model, 1.)
boot.fit_max(gal_model='exp', pars=max_pars)
gal_fit = boot.get_fitter()
self.gal_fit = gal_fit
return mcr, gal_fit
psf=psfObs)
image_obslist_realsigma.append(imageObs)
del (this_gal_stamp, imageObs)
# What does it look like to you?
plt.imshow(image_cutout)
####
#### do ngmix fitting
####
mcal_obs_realsigma = ngmix.metacal.get_all_metacal(image_obslist_realsigma)
# just do no_shear fit
boot_realsigma = ngmix.Bootstrapper(mcal_obs_realsigma['noshear'])
boot_realsigma.fit_psfs(psf_model, 1.)
# Alright, now let's see the actual galaxy fit
# wake up. This means... running the boot fit, and then plotting up that galaxy
boot_realsigma.fit_max(gal_model, max_pars, prior=prior)
res_realsigma = boot_realsigma.get_fitter().get_result()
# Wanna see the galaxy model get rendered?
junk_im = []
for i in range(n_obs):
junk_im.append(image_obslist_realsigma[i].image)
junk_avg = np.mean(junk_im, axis=0)
gal_gm2 = boot_realsigma.get_fitter().get_convolved_gmix()
gal_model_im2 = gal_gm2.make_image(image_cutout.shape, jacobian=jj_im)
weight=weight_image,
jacobian=jj_im,
psf=psf_obs)
# Make the metacal observations.
mcal_obs = ngmix.metacal.get_all_metacal(gal_obs)
# Make a dictionary to hold the results.
result = {}
# Do the fit.
lm_pars = {'maxfev': 2000, 'xtol': 5.0e-5, 'ftol': 5.0e-5}
max_pars = {'method': 'lm', 'lm_pars': lm_pars}
for ikey in mcal_obs.keys():
boot = ngmix.Bootstrapper(mcal_obs[ikey])
boot.fit_psfs('gauss', 1.)
boot.fit_max('exp', max_pars)
res = boot.get_fitter().get_result()
result.update({ikey: res['g']})
#fit,ax = plt.subplots(nrows=1,ncols=2,figsize=(14,7))
R1 = (result['1p'][0] - result['1m'][0]) / (2 * mcal_shear)
R2 = (result['2p'][1] - result['2m'][1]) / (2 * mcal_shear)
print(f"R1: {R1:.3} \nR2:{R2:.3} ")
gal_sheared = gal_ideal.shear(galsim.Shear(g1=true_shear, g2=0.0))
gal_sheared_observed = galsim.Convolve([gal_sheared, psf_metacal])
gal_sheared_image = gal_sheared_observed.drawImage(scale=0.206)
gal_sheared_weight = np.ones_like(gal_sheared_image.array) * 1e9
def _fit_one(self, source_id, pars=None):
"""
Workhorse method to perfom home-brewed metacalibration on an object. Returns the
unsheared ellipticities of each galaxy, as well as a label indicating whether the
galaxy passed the selection cuts for each shear step (i.e. no_shear,1p,1m,2p,2m).
"""
jaclist = _get_jacobians(source_id)
obslist = self._get_source_observations(source_id)
obslist = image_obslist
mcal_shear = 0.01
lm_pars = {'maxfev': 2000, 'xtol': 5.0e-5, 'ftol': 5.0e-5}
max_pars = {'method': 'lm', 'lm_pars': lm_pars, 'find_center': True}
prior = _get_priors()
psf_model = 'em5' # should come up with diagnostics for PSF quality
gal_model = 'gauss'
mcal_obs = ngmix.metacal.get_all_metacal(obslist)
# Make a dictionary to hold the results
result = {}
mcal_obs_keys = ['noshear', '1p', '1m', '2p', '2m']
# Run the actual metacalibration fits on the observed galaxies with Bootstrapper()
# and flag
#for ikey in mcal_obs_keys:
ikey = 'noshear'
boot = ngmix.Bootstrapper(mcal_obs[ikey])
boot.fit_psfs(psf_model, 1.)
boot.fit_max(gal_model, max_pars, prior=prior)
res = boot.get_fitter().get_result()
#result.update({ikey:res['g']})
try:
res['Tpsf'] = boot.psf_fitter._gm.get_T()
except:
res['Tpsf'] = boot.psf_fitter._result['T']
result.update({ikey: res})
t_arr = []
for p in boot.mb_obs_list[0]:
print(p.psf.get_gmix().get_e1e2T())
t_arr.append(p.psf.get_gmix().get_e1e2T()[2])
np.mean(t_arr)
""" Wanna see the PSF model get rendered?? """
#psf_cutout = psf_cutouts[3]
#psf_cutout = gpsf_cutout.array
psf_gm = boot.psf_fitter.get_gmix()
psf_model_im = psf_gm.make_image(psf_cutout.shape, jacobian=jj_psf)
fig, (ax1, ax2, ax3) = plt.subplots(nrows=1, ncols=3, figsize=(21, 7))
ax1.imshow(psf_cutout) #,vmin=0,vmax=(psf_cutout.max()*.90))
ax2.imshow(psf_model_im) #,vmin=0,vmax=(psf_cutout.max()*.90))
ax3.imshow(psf_cutout - psf_model_im)
""" Wanna see the galaxy model get rendered? """
image_cutout = image_cutouts[i]
gal_gm = boot.get_fitter().get_convolved_gmix()
gal_model_im = gal_gm.make_image(image_cutout.shape, jacobian=jj)
fig, (ax1, ax2, ax3) = plt.subplots(nrows=1, ncols=3, figsize=(21, 7))
ax1.imshow(junk_avg) #,vmin=0,vmax=(psf_cutout.max()*.90))
ax2.imshow(gal_model_im) #,vmin=0,vmax=(psf_cutout.max()*.90))
ax3.imshow(junk_avg - gal_model_im)
R1 = (result['1p']['g'][0] - result['1m']['g'][0]) / (2 * mcal_shear)
R2 = (result['2p']['g'][1] - result['2m']['g'][1]) / (2 * mcal_shear)
print(f"R1: {R1:.3} \nR2:{R2:.3} ")
return boot.get_fitter()