def create_mb(mb_mod, bg_rms, coord1, coord2, psf_bg_rms, psf_im, noise):
#creats multiband object
mb = MultiBandObsList()
for i in range(len(mb_mod[:])):
o = observation(mb_mod[i], bg_rms, coord1, coord2, psf_bg_rms, psf_im)
o.noise = noise[i]
olist = ObsList()
olist.append(o)
mb.append(olist)
return mb
def test_obslist_smoke():
rng = np.random.RandomState(seed=11)
meta = {'duh': 5}
obslist = ObsList(meta=meta)
for _ in range(3):
obslist.append(Observation(image=rng.normal(size=(13, 15))))
assert obslist.meta == meta
rng = np.random.RandomState(seed=11)
for obs in obslist:
assert np.all(obs.image == rng.normal(size=(13, 15)))
def test_obslist_s2n_zeroweight():
rng = np.random.RandomState(seed=11)
obslist = ObsList()
for _ in range(3):
obs = Observation(
image=rng.normal(size=(13, 15)),
weight=np.zeros((13, 15)),
store_pixels=False,
)
obslist.append(obs)
assert np.allclose(obslist.get_s2n(), -9999)
def test_obslist_s2n():
rng = np.random.RandomState(seed=11)
obslist = ObsList()
numer = 0
denom = 0
for _ in range(3):
obs = Observation(image=rng.normal(size=(13, 15)))
numer += np.sum(obs.image)
denom += np.sum(1.0 / obs.weight)
obslist.append(obs)
s2n = obslist.get_s2n()
assert s2n == numer / np.sqrt(denom)
def _get_mof_obs(self):
im, psf_im, coords, dims, dx1, dy1, noises = self.sim()
bg_rms = self.sim['Image']['Bgrms'] / np.sqrt(len(im))
bg_rms_psf = self.sim['Psf']['Bgrms_psf']
psf_ccen = (np.array(psf_im.shape) - 1.0) / 2.0
psf_jacob = ngmix.UnitJacobian(
row=psf_ccen[0],
col=psf_ccen[1],
)
psf_weight = psf_im * 0 + 1.0 / bg_rms_psf**2
psf_obs = ngmix.Observation(
psf_im,
weight=psf_weight,
jacobian=psf_jacob,
)
psf_gmix = self._fit_psf_admom(psf_obs)
psf_obs.set_gmix(psf_gmix)
mb = MultiBandObsList()
for i in range(len(im)):
if self.show:
import images
tim = im[i] / im[i].max()
tim = np.log10(tim - tim.min() + 1.0)
images.view(tim)
if 'q' == input('hit a key: (q to quit) '):
stop
weight = im[i] * 0 + 1.0 / bg_rms**2
jacobian = ngmix.UnitJacobian(
row=0,
col=0,
)
obs = ngmix.Observation(
im[i],
weight=weight,
jacobian=jacobian,
psf=psf_obs,
)
obs.noise = noises[i]
olist = ObsList()
olist.append(obs)
mb.append(olist)
return mb, coords
def test_multibandobslist_smoke():
rng = np.random.RandomState(seed=11)
meta = {'duh': 5}
mbobs = MultiBandObsList(meta=meta)
for _ in range(5):
obslist = ObsList()
for _ in range(3):
obslist.append(Observation(image=rng.normal(size=(13, 15))))
mbobs.append(obslist)
assert mbobs.meta == meta
rng = np.random.RandomState(seed=11)
for obslist in mbobs:
for obs in obslist:
assert np.all(obs.image == rng.normal(size=(13, 15)))
def get_exp_list(self, psf2=None):
if psf2 is None:
psf2 = self.psfs
obs_list = ObsList()
psf_list = ObsList()
w = []
for i in range(len(self.gals)):
im = self.gals[i].array
im_psf = self.psfs[i].array
im_psf2 = psf2[i].array
weight = 1 / self.skys[i].array
jacob = self.gals[i].wcs.jacobian()
dx = self.offsets[i].x
dy = self.offsets[i].y
gal_jacob = Jacobian(row=self.gals[i].true_center.y + dy,
col=self.gals[i].true_center.x + dx,
dvdrow=jacob.dvdy,
dvdcol=jacob.dvdx,
dudrow=jacob.dudy,
dudcol=jacob.dudx)
psf_jacob2 = gal_jacob
mask = np.where(weight != 0)
w.append(np.mean(weight[mask]))
noise = old_div(np.ones_like(weight), w[-1])
psf_obs = Observation(im_psf,
jacobian=gal_jacob,
meta={
'offset_pixels': None,
'file_id': None
})
psf_obs2 = Observation(im_psf2,
jacobian=psf_jacob2,
meta={
'offset_pixels': None,
'file_id': None
})
obs = Observation(im,
weight=weight,
jacobian=gal_jacob,
psf=psf_obs,
meta={
'offset_pixels': None,
'file_id': None
})
obs.set_noise(noise)
obs_list.append(obs)
psf_list.append(psf_obs2)
return obs_list, psf_list, np.array(w)
def test_multibandobslist_s2n_zeroweight():
rng = np.random.RandomState(seed=11)
mbobs = MultiBandObsList()
for _ in range(5):
obslist = ObsList()
for _ in range(3):
img = rng.normal(size=(13, 15))
obslist.append(
Observation(
image=img,
weight=np.zeros((13, 15)),
store_pixels=False,
))
mbobs.append(obslist)
assert np.allclose(mbobs.get_s2n(), -9999)
def test_multibandobslist_s2n():
rng = np.random.RandomState(seed=11)
mbobs = MultiBandObsList()
numer = 0
denom = 0
for _ in range(5):
obslist = ObsList()
for _ in range(3):
img = rng.normal(size=(13, 15))
obslist.append(Observation(image=img))
numer += np.sum(img)
denom += np.sum(1.0 / obslist[-1].weight)
mbobs.append(obslist)
s2n = mbobs.get_s2n()
assert s2n == numer / np.sqrt(denom)
def obslist_data():
dims = (12, 12)
rng = np.random.RandomState(seed=10)
jac = DiagonalJacobian(x=5.5, y=5.5, scale=0.25)
obslist = ObsList()
obslist.append(
Observation(
image=rng.normal(size=dims),
jacobian=jac.copy(),
weight=np.exp(rng.normal(size=dims)),
meta={"blue": 10},
)
)
obslist.append(
Observation(
image=rng.normal(size=dims),
jacobian=jac.copy(),
weight=np.exp(rng.normal(size=dims)),
meta={"blue": 8},
)
)
psf = Observation(
image=rng.normal(size=dims),
weight=np.exp(rng.normal(size=dims)),
jacobian=jac.copy(),
meta={"blue": 5},
)
obslist[1].psf = psf
return obslist
def test_multibandobslist_set():
rng = np.random.RandomState(seed=11)
meta = {'duh': 5}
mbobs = MultiBandObsList(meta=meta)
for _ in range(5):
obslist = ObsList()
for _ in range(3):
obslist.append(Observation(image=rng.normal(size=(13, 15))))
mbobs.append(obslist)
assert mbobs.meta == meta
new_meta = {'blah': 6}
mbobs.meta = new_meta
assert mbobs.meta == new_meta
mbobs.meta = None
assert len(mbobs.meta) == 0
with pytest.raises(TypeError):
mbobs.meta = [10]
with pytest.raises(TypeError):
mbobs.set_meta([10])
new_meta = {'bla': 6}
new_meta.update(mbobs.meta)
mbobs.update_meta_data({'bla': 6})
assert mbobs.meta == new_meta
with pytest.raises(TypeError):
mbobs.update_meta_data([10])
rng = np.random.RandomState(seed=12)
new_obs = Observation(image=rng.normal(size=(13, 15)))
rng = np.random.RandomState(seed=11)
for obslist in mbobs:
for obs in obslist:
assert np.all(obs.image == rng.normal(size=(13, 15)))
mbobs[1][2] = new_obs
assert np.all(mbobs[1][2].image == new_obs.image)
rng = np.random.RandomState(seed=13)
obslist = ObsList()
for _ in range(4):
obslist.append(Observation(image=rng.normal(size=(13, 15))))
mbobs[2] = obslist
rng = np.random.RandomState(seed=13)
for obs in mbobs[2]:
assert np.all(obs.image == rng.normal(size=(13, 15)))
def get_exp_list(gals_array, psfs_array, offsets, skys_array, gal_true, gal_jacobs, psf2=None):
#def get_exp_list(gal, psf, sky_stamp, psf2=None):
if psf2 is None:
psf2 = psfs_array
obs_list=ObsList()
psf_list=ObsList()
w = []
for i in range(len(gals_array)):
im = gals_array[i]
im_psf = psfs_array[i]
im_psf2 = psf2[i]
weight = 1/skys_array[i]
jacob = gal_jacobs[i]
dx = offsets[i].x
dy = offsets[i].y
gal_jacob = Jacobian(
row=gal_true[i].y+dy,
col=gal_true[i].x+dx,
dvdrow=jacob.dvdy,
dvdcol=jacob.dvdx,
dudrow=jacob.dudy,
dudcol=jacob.dudx)
psf_jacob2 = gal_jacob
print(gal_jacob)
mask = np.where(weight!=0)
w.append(np.mean(weight[mask]))
noise = old_div(np.ones_like(weight),w[-1])
psf_obs = Observation(im_psf, jacobian=gal_jacob, meta={'offset_pixels':None,'file_id':None})
psf_obs2 = Observation(im_psf2, jacobian=psf_jacob2, meta={'offset_pixels':None,'file_id':None})
obs = Observation(im, weight=weight, jacobian=gal_jacob, psf=psf_obs, meta={'offset_pixels':None,'file_id':None})
obs.set_noise(noise)
obs_list.append(obs)
psf_list.append(psf_obs2)
return obs_list,psf_list,np.array(w)
def test_get_mbobs():
rng = np.random.RandomState(seed=11)
obs = Observation(image=rng.normal(size=(11, 13)))
mbobs = get_mb_obs(obs)
rng = np.random.RandomState(seed=11)
assert np.all(mbobs[0][0].image == rng.normal(size=(11, 13)))
assert len(mbobs) == 1
assert len(mbobs[0]) == 1
rng = np.random.RandomState(seed=12)
obslist = ObsList()
for _ in range(3):
obslist.append(Observation(image=rng.normal(size=(11, 13))))
mbobs = get_mb_obs(obslist)
rng = np.random.RandomState(seed=12)
for obs in mbobs[0]:
assert np.all(obs.image == rng.normal(size=(11, 13)))
assert len(mbobs) == 1
assert len(mbobs[0]) == 3
rng = np.random.RandomState(seed=13)
mbobs = MultiBandObsList()
for _ in range(5):
obslist = ObsList()
for _ in range(3):
obslist.append(Observation(image=rng.normal(size=(13, 15))))
mbobs.append(obslist)
new_mbobs = get_mb_obs(mbobs)
rng = np.random.RandomState(seed=13)
for obslist in new_mbobs:
for obs in obslist:
assert np.all(obs.image == rng.normal(size=(13, 15)))
with pytest.raises(ValueError):
get_mb_obs(None)
def test_obslist_set():
rng = np.random.RandomState(seed=11)
meta = {'duh': 5}
obslist = ObsList(meta=meta)
for _ in range(3):
obslist.append(Observation(image=rng.normal(size=(13, 15))))
assert obslist.meta == meta
new_meta = {'blah': 6}
obslist.meta = new_meta
assert obslist.meta == new_meta
new_meta = {'bla': 6}
new_meta.update(obslist.meta)
obslist.update_meta_data({'bla': 6})
assert obslist.meta == new_meta
rng = np.random.RandomState(seed=12)
new_obs = Observation(image=rng.normal(size=(13, 15)))
rng = np.random.RandomState(seed=11)
for obs in obslist:
assert np.all(obs.image == rng.normal(size=(13, 15)))
obslist[1] = new_obs
assert np.all(obslist[1].image == new_obs.image)
def get_obslist(self, iobj, weight_type='weight'):
"""Get an ngmix ObsList for all observations.
Parameters
----------
iobj : int
Index of the object.
weight_type: string, optional
Weight type. can be one of
'weight': the actual weight map
'uberseg': uberseg modified weight map
'cweight': weight map zeroed outside the object's seg map
'cseg': weight map zeroed outside of circular aperture that
doesn't touch any other object.
'cseg-canonical': same as 'cseg' but uses the postage stamp
center instead of the object's position.
Default is 'weight'
Returns
-------
obslist : ngmix.ObsList
An `ObsList` of all observations.
"""
obslist = ObsList()
for icut in range(self._cat['ncutout'][iobj]):
try:
obs = self.get_obs(iobj, icut, weight_type=weight_type)
obslist.append(obs)
except GMixFatalError:
print('zero weight observation found, skipping')
if len(obslist) > 0:
obs = obslist[0]
if 'flux' in obs.meta:
obslist.meta['flux'] = obs.meta['flux']
if 'T' in obs.meta:
obslist.meta['T'] = obs.meta['T']
return obslist
def make_multiband_coadd_stamp():
oversample = 4
H = './fiducial_H158_2285117.fits'
J = './fiducial_J129_2285117.fits'
F = './fiducial_F184_2285117.fits'
truth = fio.FITS('/hpc/group/cosmology/phy-lsst/my137/roman_H158/g1002/truth/fiducial_lensing_galaxia_g1002_truth_gal.fits')[-1]
m_H158 = meds.MEDS(H)
m_J129 = meds.MEDS(J)
m_F184 = meds.MEDS(F)
indices_H = np.arange(len(m_H158['number'][:]))
indices_J = np.arange(len(m_J129['number'][:]))
indices_F = np.arange(len(m_F184['number'][:]))
roman_H158_psfs = get_psf_SCA('H158')
roman_J129_psfs = get_psf_SCA('J129')
roman_F184_psfs = get_psf_SCA('F184')
for i,ii in enumerate(indices_H):
if i%100==0:
print('made it to object',i)
try_save = False
ind = m_H158['number'][ii]
t = truth[ind]
if (ind not in m_J129['number']) or (ind not in m_F184['number']):
continue
print(i)
# if i==13:
sca_Hlist = m_H158[ii]['sca'] # List of SCAs for the same object in multiple observations.
ii_J = m_J129[m_J129['number']==ind]['id'][0]
sca_Jlist = m_J129[ii_J]['sca']
m2_H158_coadd = [roman_H158_psfs[j-1] for j in sca_Hlist[:m_H158['ncutout'][i]]]
m2_J129_coadd = [roman_J129_psfs[j-1] for j in sca_Jlist[:m_J129['ncutout'][ii_J]]]
ii_F = m_F184[m_F184['number']==ind]['id'][0]
sca_Flist = m_F184[ii_F]['sca']
m2_F184_coadd = [roman_F184_psfs[j-1] for j in sca_Flist[:m_F184['ncutout'][ii_F]]]
obs_Hlist,psf_Hlist,included_H,w_H = get_exp_list_coadd(m_H158,ii,oversample,m2=m2_H158_coadd)
obs_Jlist,psf_Jlist,included_J,w_J = get_exp_list_coadd(m_J129,ii_J,oversample,m2=m2_J129_coadd)
obs_Flist,psf_Flist,included_F,w_F = get_exp_list_coadd(m_F184,ii_F,oversample,m2=m2_F184_coadd)
# check if masking is less than 20%
if len(obs_Hlist)==0 or len(obs_Jlist)==0 or len(obs_Flist)==0:
continue
coadd_H = psc.Coadder(obs_Hlist,flat_wcs=True).coadd_obs
coadd_H.psf.image[coadd_H.psf.image<0] = 0 # set negative pixels to zero.
coadd_H.psf.set_meta({'offset_pixels':None,'file_id':None})
coadd_H.set_meta({'offset_pixels':None,'file_id':None})
coadd_J = psc.Coadder(obs_Jlist,flat_wcs=True).coadd_obs
coadd_J.psf.image[coadd_J.psf.image<0] = 0 # set negative pixels to zero.
coadd_J.psf.set_meta({'offset_pixels':None,'file_id':None})
coadd_J.set_meta({'offset_pixels':None,'file_id':None})
coadd_F = psc.Coadder(obs_Flist,flat_wcs=True).coadd_obs
coadd_F.psf.image[coadd_F.psf.image<0] = 0 # set negative pixels to zero.
coadd_F.psf.set_meta({'offset_pixels':None,'file_id':None})
coadd_F.set_meta({'offset_pixels':None,'file_id':None})
obs_list = ObsList()
multiband = [coadd_H, coadd_J, coadd_F]
for f in range(3):
obs_list.append(multiband[f])
multiband_coadd = psc.Coadder(obs_list,flat_wcs=True).coadd_obs
print('single snr', get_snr(obs_Jlist), get_snr(obs_Hlist), get_snr(obs_Flist))
print('coadd snr', get_snr([coadd_J]), get_snr([coadd_H]), get_snr([coadd_F]))
print('final', get_snr(obs_list))
def single_vs_coadd_images():
local_Hmeds = './fiducial_F184_2285117.fits'
truth = fio.FITS('/hpc/group/cosmology/phy-lsst/my137/roman_F184/g1002/truth/fiducial_lensing_galaxia_g1002_truth_gal.fits')[-1]
m_H158 = meds.MEDS(local_Hmeds)
indices_H = np.arange(len(m_H158['number'][:]))
roman_H158_psfs = get_psf_SCA('F184')
oversample = 4
metacal_keys=['noshear', '1p', '1m', '2p', '2m']
res_noshear=np.zeros(len(m_H158['number'][:]),dtype=[('ind',int), ('ra',float), ('dec',float), ('flags',int),('int_e1',float), ('int_e2',float),('coadd_px',float), ('coadd_py',float), ('coadd_flux',float), ('coadd_snr',float), ('coadd_e1',float), ('coadd_e2',float), ('coadd_hlr',float),('coadd_psf_e1',float), ('coadd_psf_e2',float), ('coadd_psf_T',float)])
res_1p=np.zeros(len(m_H158['number'][:]),dtype=[('ind',int), ('ra',float), ('dec',float), ('flags',int),('int_e1',float), ('int_e2',float),('coadd_px',float), ('coadd_py',float), ('coadd_flux',float), ('coadd_snr',float), ('coadd_e1',float), ('coadd_e2',float), ('coadd_hlr',float),('coadd_psf_e1',float), ('coadd_psf_e2',float), ('coadd_psf_T',float)])
res_1m=np.zeros(len(m_H158['number'][:]),dtype=[('ind',int), ('ra',float), ('dec',float), ('flags',int),('int_e1',float), ('int_e2',float),('coadd_px',float), ('coadd_py',float), ('coadd_flux',float), ('coadd_snr',float), ('coadd_e1',float), ('coadd_e2',float), ('coadd_hlr',float),('coadd_psf_e1',float), ('coadd_psf_e2',float), ('coadd_psf_T',float)])
res_2p=np.zeros(len(m_H158['number'][:]),dtype=[('ind',int), ('ra',float), ('dec',float), ('flags',int),('int_e1',float), ('int_e2',float),('coadd_px',float), ('coadd_py',float), ('coadd_flux',float), ('coadd_snr',float), ('coadd_e1',float), ('coadd_e2',float), ('coadd_hlr',float),('coadd_psf_e1',float), ('coadd_psf_e2',float), ('coadd_psf_T',float)])
res_2m=np.zeros(len(m_H158['number'][:]),dtype=[('ind',int), ('ra',float), ('dec',float), ('flags',int),('int_e1',float), ('int_e2',float),('coadd_px',float), ('coadd_py',float), ('coadd_flux',float), ('coadd_snr',float), ('coadd_e1',float), ('coadd_e2',float), ('coadd_hlr',float),('coadd_psf_e1',float), ('coadd_psf_e2',float), ('coadd_psf_T',float)])
res_tot=[res_noshear, res_1p, res_1m, res_2p, res_2m]
for i,ii in enumerate(indices_H): # looping through all the objects in meds file.
if i%100==0:
print('object number ',i)
# if i not in [1,600]:
# continue
ind = m_H158['number'][ii]
t = truth[ind]
sca_Hlist = m_H158[ii]['sca'] # List of SCAs for the same object in multiple observations.
m2_H158_coadd = [roman_H158_psfs[j-1] for j in sca_Hlist[:m_H158['ncutout'][i]]]
obs_Hlist,psf_Hlist,included_H,w_H = get_exp_list_coadd(m_H158,ii,oversample,m2=m2_H158_coadd)
s2n_test = get_snr(obs_Hlist)
# if i in [1,600]: #in [ 309, 444, 622, 644, 854, 1070, 1282, 1529]:
# for l in range(len(obs_Hlist)):
# #print(i, obs_Hlist[l].jacobian, obs_Hlist[l].psf.jacobian)
# print(i, obs_Hlist[l].weight)
# np.savetxt('/hpc/group/cosmology/masaya/wfirst_simulation/paper/single_image_oversample4_08scaling_'+str(i)+'_'+str(l)+'.txt', obs_Hlist[l].image)
# np.savetxt('/hpc/group/cosmology/masaya/wfirst_simulation/paper/single_psf_oversample4_08scaling_'+str(i)+'.txt', obs_Hlist[0].psf.image)
coadd_H = psc.Coadder(obs_Hlist,flat_wcs=True).coadd_obs
coadd_H.psf.image[coadd_H.psf.image<0] = 0 # set negative pixels to zero.
coadd_H.set_meta({'offset_pixels':None,'file_id':None})
obs_list = ObsList()
if oversample == 4:
new_coadd_psf_block = block_reduce(coadd_H.psf.image, block_size=(4,4), func=np.sum)
new_coadd_psf_jacob = Jacobian( row=15.5,
col=15.5,
dvdrow=(coadd_H.psf.jacobian.dvdrow*oversample),
dvdcol=(coadd_H.psf.jacobian.dvdcol*oversample),
dudrow=(coadd_H.psf.jacobian.dudrow*oversample),
dudcol=(coadd_H.psf.jacobian.dudcol*oversample))
coadd_psf_obs = Observation(new_coadd_psf_block, jacobian=new_coadd_psf_jacob, meta={'offset_pixels':None,'file_id':None})
coadd_H.psf = coadd_psf_obs
obs_list.append(coadd_H)
s2n_coadd = get_snr(obs_list)
# if i in [1,600]:
# print(i, coadd_H.weight)
#np.savetxt('/hpc/group/cosmology/masaya/wfirst_simulation/paper/coadd_image_oversample4_08scaling_'+str(i)+'.txt', coadd_H.image)
# np.savetxt('/hpc/group/cosmology/masaya/wfirst_simulation/paper/coadd_weight_oversample4_08scaling_'+str(i)+'.txt', coadd_H.weight)
#np.savetxt('/hpc/group/cosmology/masaya/wfirst_simulation/paper/coadd_psf_over-downsample4_08scaling_'+str(i)+'.txt', coadd_H.psf.image)
iteration=0
for key in metacal_keys:
res_tot[iteration]['ind'][i] = ind
res_tot[iteration]['ra'][i] = t['ra']
res_tot[iteration]['dec'][i] = t['dec']
res_tot[iteration]['int_e1'][i] = t['int_e1']
res_tot[iteration]['int_e2'][i] = t['int_e2']
iteration+=1
res_ = measure_shape_metacal(obs_list, t['size'], method='bootstrap', fracdev=t['bflux'],use_e=[t['int_e1'],t['int_e2']])
print('signal to noise test', i, s2n_test, s2n_coadd, res_['noshear']['s2n_r'])
# if res_[key]['s2n'] > 1e7:
# print('coadd snr', res_[key]['s2n'])
# np.savetxt('large_coadd_snr_image.txt', coadd_H.image)
iteration=0
for key in metacal_keys:
if res_==0:
res_tot[iteration]['ind'][i] = 0
elif res_[key]['flags']==0:
res_tot[iteration]['coadd_px'][i] = res_[key]['pars'][0]
res_tot[iteration]['coadd_py'][i] = res_[key]['pars'][1]
res_tot[iteration]['coadd_snr'][i] = res_[key]['s2n']
res_tot[iteration]['coadd_e1'][i] = res_[key]['pars'][2]
res_tot[iteration]['coadd_e2'][i] = res_[key]['pars'][3]
res_tot[iteration]['coadd_hlr'][i] = res_[key]['pars'][4]
iteration+=1
mask=res_tot[0]['ind']!=0
print(len(res_tot[0]), len(res_tot[0][mask]))
#print(res_['noshear'].dtype.names)
print('done')
def test_obslist_append_err():
obslist = ObsList()
with pytest.raises(AssertionError):
obslist.append(None)
def get_exp_list_coadd(m, i, m2=None):
def make_jacobian(dudx, dudy, dvdx, dvdy, x, y):
j = galsim.JacobianWCS(dudx, dudy, dvdx, dvdy)
return j.withOrigin(galsim.PositionD(x, y))
oversample = 1
#def psf_offset(i,j,star_):
m3 = [0]
#relative_offset=[0]
for jj, psf_ in enumerate(m2): # m2 has psfs for each observation.
if jj == 0:
continue
gal_stamp_center_row = m['orig_start_row'][i][jj] + m['box_size'][
i] / 2 - 0.5 # m['box_size'] is the galaxy stamp size.
gal_stamp_center_col = m['orig_start_col'][i][jj] + m['box_size'][
i] / 2 - 0.5 # m['orig_start_row/col'] is in SCA coordinates.
psf_stamp_size = 32
# Make the bounds for the psf stamp.
b = galsim.BoundsI(
xmin=(m['orig_start_col'][i][jj] +
(m['box_size'][i] - 32) / 2. - 1) * oversample + 1,
xmax=(m['orig_start_col'][i][jj] +
(m['box_size'][i] - 32) / 2. + psf_stamp_size - 1) *
oversample,
ymin=(m['orig_start_row'][i][jj] +
(m['box_size'][i] - 32) / 2. - 1) * oversample + 1,
ymax=(m['orig_start_row'][i][jj] +
(m['box_size'][i] - 32) / 2. + psf_stamp_size - 1) *
oversample)
# Make wcs for oversampled psf.
wcs_ = make_jacobian(
m.get_jacobian(i, jj)['dudcol'] / oversample,
m.get_jacobian(i, jj)['dudrow'] / oversample,
m.get_jacobian(i, jj)['dvdcol'] / oversample,
m.get_jacobian(i, jj)['dvdrow'] / oversample,
m['orig_col'][i][jj] * oversample,
m['orig_row'][i][jj] * oversample)
# Taken from galsim/roman_psfs.py line 266. Update each psf to an object-specific psf using the wcs.
scale = galsim.PixelScale(wfirst.pixel_scale / oversample)
psf_ = wcs_.toWorld(scale.toImage(psf_),
image_pos=galsim.PositionD(wfirst.n_pix / 2,
wfirst.n_pix / 2))
# Convolve with the star model and get the psf stamp.
#st_model = galsim.DeltaFunction(flux=1.)
#st_model = st_model.evaluateAtWavelength(wfirst.getBandpasses(AB_zeropoint=True)['H158'].effective_wavelength)
#st_model = st_model.withFlux(1.)
#st_model = galsim.Convolve(st_model, psf_)
psf_ = galsim.Convolve(psf_, galsim.Pixel(wfirst.pixel_scale))
psf_stamp = galsim.Image(b, wcs=wcs_)
# Galaxy is being drawn with some subpixel offsets, so we apply the offsets when drawing the psf too.
offset_x = m['orig_col'][i][jj] - gal_stamp_center_col
offset_y = m['orig_row'][i][jj] - gal_stamp_center_row
offset = galsim.PositionD(offset_x, offset_y)
if (offset_x <= -1.0 or offset_y <= -1.0):
print(offset)
elif (offset_x >= 1.0 or offset_y >= 1.0):
print(offset)
psf_.drawImage(image=psf_stamp, offset=offset, method='no_pixel')
m3.append(psf_stamp.array)
obs_list = ObsList()
psf_list = ObsList()
included = []
w = []
# For each of these objects create an observation
for j in range(m['ncutout'][i]):
if j == 0:
continue
# if j>1:
# continue
im = m.get_cutout(i, j, type='image')
weight = m.get_cutout(i, j, type='weight')
im_psf = m3[j]
im_psf2 = im_psf
if np.sum(im) == 0.:
#print(local_meds, i, j, np.sum(im))
print('no flux in image ', i, j)
continue
jacob = m.get_jacobian(i, j)
# Get a galaxy jacobian.
gal_jacob = Jacobian(
row=(m['orig_row'][i][j] - m['orig_start_row'][i][j]),
col=(m['orig_col'][i][j] - m['orig_start_col'][i][j]),
dvdrow=jacob['dvdrow'],
dvdcol=jacob['dvdcol'],
dudrow=jacob['dudrow'],
dudcol=jacob['dudcol'])
psf_center = (32 / 2.) + 0.5
# Get a oversampled psf jacobian.
if oversample == 1:
psf_jacob2 = Jacobian(
row=15.5 +
(m['orig_row'][i][j] - m['orig_start_row'][i][j] + 1 -
(m['box_size'][i] / 2. + 0.5)) * oversample,
col=15.5 +
(m['orig_col'][i][j] - m['orig_start_col'][i][j] + 1 -
(m['box_size'][i] / 2. + 0.5)) * oversample,
dvdrow=jacob['dvdrow'] / oversample,
dvdcol=jacob['dvdcol'] / oversample,
dudrow=jacob['dudrow'] / oversample,
dudcol=jacob['dudcol'] / oversample)
elif oversample == 4:
psf_jacob2 = Jacobian(
row=63.5 +
(m['orig_row'][i][j] - m['orig_start_row'][i][j] + 1 -
(m['box_size'][i] / 2. + 0.5)) * oversample,
col=63.5 +
(m['orig_col'][i][j] - m['orig_start_col'][i][j] + 1 -
(m['box_size'][i] / 2. + 0.5)) * oversample,
dvdrow=jacob['dvdrow'] / oversample,
dvdcol=jacob['dvdcol'] / oversample,
dudrow=jacob['dudrow'] / oversample,
dudcol=jacob['dudcol'] / oversample)
# Create an obs for each cutout
mask = np.where(weight != 0)
if 1. * len(weight[mask]) / np.product(np.shape(weight)) < 0.8:
continue
w.append(np.mean(weight[mask]))
noise = np.ones_like(weight) / w[-1]
psf_obs = Observation(im_psf,
jacobian=gal_jacob,
meta={
'offset_pixels': None,
'file_id': None
})
psf_obs2 = Observation(im_psf2,
jacobian=psf_jacob2,
meta={
'offset_pixels': None,
'file_id': None
})
#obs = Observation(im, weight=weight, jacobian=gal_jacob, psf=psf_obs, meta={'offset_pixels':None,'file_id':None})
# oversampled PSF
obs = Observation(im,
weight=weight,
jacobian=gal_jacob,
psf=psf_obs2,
meta={
'offset_pixels': None,
'file_id': None
})
obs.set_noise(noise)
obs_list.append(obs)
psf_list.append(psf_obs2)
included.append(j)
return obs_list, psf_list, np.array(included) - 1, np.array(w)
def multiband_coadd():
local_Hmeds = './fiducial_H158_2285117.fits'
local_Jmeds = './fiducial_J129_2285117.fits'
local_Fmeds = './fiducial_F184_2285117.fits'
truth = fio.FITS(
'/hpc/group/cosmology/phy-lsst/my137/roman_H158/g1002/truth/fiducial_lensing_galaxia_g1002_truth_gal.fits'
)[-1]
m_H158 = meds.MEDS(local_Hmeds)
m_J129 = meds.MEDS(local_Jmeds)
m_F184 = meds.MEDS(local_Fmeds)
indices_H = np.arange(len(m_H158['number'][:]))
indices_J = np.arange(len(m_J129['number'][:]))
indices_F = np.arange(len(m_F184['number'][:]))
roman_H158_psfs = get_psf_SCA('H158')
roman_J129_psfs = get_psf_SCA('J129')
roman_F184_psfs = get_psf_SCA('F184')
oversample = 1
metacal_keys = ['noshear', '1p', '1m', '2p', '2m']
res_noshear = np.zeros(len(m_H158['number'][:]),
dtype=[('ind', int), ('ra', float), ('dec', float),
('flags', int), ('coadd_px', float),
('coadd_py', float), ('coadd_flux', float),
('coadd_snr', float), ('coadd_e1', float),
('coadd_e2', float), ('coadd_hlr', float),
('coadd_psf_e1', float),
('coadd_psf_e2', float),
('coadd_psf_T', float)])
res_1p = np.zeros(len(m_H158['number'][:]),
dtype=[('ind', int), ('ra', float), ('dec', float),
('flags', int), ('coadd_px', float),
('coadd_py', float), ('coadd_flux', float),
('coadd_snr', float), ('coadd_e1', float),
('coadd_e2', float), ('coadd_hlr', float),
('coadd_psf_e1', float), ('coadd_psf_e2', float),
('coadd_psf_T', float)])
res_1m = np.zeros(len(m_H158['number'][:]),
dtype=[('ind', int), ('ra', float), ('dec', float),
('flags', int), ('coadd_px', float),
('coadd_py', float), ('coadd_flux', float),
('coadd_snr', float), ('coadd_e1', float),
('coadd_e2', float), ('coadd_hlr', float),
('coadd_psf_e1', float), ('coadd_psf_e2', float),
('coadd_psf_T', float)])
res_2p = np.zeros(len(m_H158['number'][:]),
dtype=[('ind', int), ('ra', float), ('dec', float),
('flags', int), ('coadd_px', float),
('coadd_py', float), ('coadd_flux', float),
('coadd_snr', float), ('coadd_e1', float),
('coadd_e2', float), ('coadd_hlr', float),
('coadd_psf_e1', float), ('coadd_psf_e2', float),
('coadd_psf_T', float)])
res_2m = np.zeros(len(m_H158['number'][:]),
dtype=[('ind', int), ('ra', float), ('dec', float),
('flags', int), ('coadd_px', float),
('coadd_py', float), ('coadd_flux', float),
('coadd_snr', float), ('coadd_e1', float),
('coadd_e2', float), ('coadd_hlr', float),
('coadd_psf_e1', float), ('coadd_psf_e2', float),
('coadd_psf_T', float)])
res_tot = [res_noshear, res_1p, res_1m, res_2p, res_2m]
for i, ii in enumerate(
indices_H): # looping through all the objects in meds file.
if i % 100 == 0:
print('object number ', i)
ind = m_H158['number'][ii]
t = truth[ind]
sca_Hlist = m_H158[ii][
'sca'] # List of SCAs for the same object in multiple observations.
sca_Jlist = m_J129[ii]['sca']
sca_Flist = m_F184[ii]['sca']
m2_H158_coadd = [
roman_H158_psfs[j - 1] for j in sca_Hlist[:m_H158['ncutout'][i]]
]
m2_J129_coadd = [
roman_J129_psfs[j - 1] for j in sca_Jlist[:m_J129['ncutout'][i]]
]
m2_F184_coadd = [
roman_F184_psfs[j - 1] for j in sca_Flist[:m_F184['ncutout'][i]]
]
obs_Hlist, psf_Hlist, included_H, w_H = get_exp_list_coadd(
m_H158, ii, m2=m2_H158_coadd)
coadd_H = psc.Coadder(obs_Hlist, flat_wcs=True).coadd_obs
coadd_H.psf.image[
coadd_H.psf.image < 0] = 0 # set negative pixels to zero.
coadd_H.set_meta({'offset_pixels': None, 'file_id': None})
obs_Jlist, psf_Jlist, included_J, w_J = get_exp_list_coadd(
m_J129, ii, m2=m2_J129_coadd)
coadd_J = psc.Coadder(obs_Jlist, flat_wcs=True).coadd_obs
coadd_J.psf.image[
coadd_J.psf.image < 0] = 0 # set negative pixels to zero.
coadd_J.set_meta({'offset_pixels': None, 'file_id': None})
obs_Flist, psf_Flist, included_F, w_F = get_exp_list_coadd(
m_F184, ii, m2=m2_F184_coadd)
coadd_F = psc.Coadder(obs_Flist, flat_wcs=True).coadd_obs
coadd_F.psf.image[
coadd_F.psf.image < 0] = 0 # set negative pixels to zero.
coadd_F.set_meta({'offset_pixels': None, 'file_id': None})
coadd = [coadd_H, coadd_J, coadd_F]
mb_obs_list = MultiBandObsList()
#coadd = [coadd_H]
for band in range(3):
obs_list = ObsList()
new_coadd_psf_block = block_reduce(coadd[band].psf.image,
block_size=(4, 4),
func=np.sum)
new_coadd_psf_jacob = Jacobian(
row=15.5,
col=15.5,
dvdrow=(coadd[band].psf.jacobian.dvdrow * oversample),
dvdcol=(coadd[band].psf.jacobian.dvdcol * oversample),
dudrow=(coadd[band].psf.jacobian.dudrow * oversample),
dudcol=(coadd[band].psf.jacobian.dudcol * oversample))
coadd_psf_obs = Observation(new_coadd_psf_block,
jacobian=new_coadd_psf_jacob,
meta={
'offset_pixels': None,
'file_id': None
})
coadd[band].psf = coadd_psf_obs
obs_list.append(coadd[band])
mb_obs_list.append(obs_list)
iteration = 0
for key in metacal_keys:
res_tot[iteration]['ind'][i] = ind
res_tot[iteration]['ra'][i] = t['ra']
res_tot[iteration]['dec'][i] = t['dec']
iteration += 1
#print(i, t['size'], mb_obs_list[0][0].image.sum(), mb_obs_list[1][0].image.sum(), mb_obs_list[2][0].image.sum())
res_ = measure_shape_metacal_multiband(
mb_obs_list,
t['size'],
method='bootstrap',
fracdev=t['bflux'],
use_e=[t['int_e1'], t['int_e2']])
iteration = 0
for key in metacal_keys:
if res_ == 0:
res_tot[iteration]['ind'][i] = 0
elif res_[key]['flags'] == 0:
res_tot[iteration]['coadd_px'][i] = res_[key]['pars'][0]
res_tot[iteration]['coadd_py'][i] = res_[key]['pars'][1]
res_tot[iteration]['coadd_snr'][i] = res_[key]['s2n']
res_tot[iteration]['coadd_e1'][i] = res_[key]['pars'][2]
res_tot[iteration]['coadd_e2'][i] = res_[key]['pars'][3]
res_tot[iteration]['coadd_hlr'][i] = res_[key]['pars'][4]
iteration += 1
mask = res_tot[0]['ind'] != 0
print(len(res_tot[0]), len(res_tot[0][mask]))
#print(res_['noshear'].dtype.names)
print('done')
def get_exp_list(gal, psf, offsets, sky_stamp, psf2=None):
#def get_exp_list(gal, psf, sky_stamp, psf2=None):
if psf2 is None:
psf2 = psf
obs_list = ObsList()
psf_list = ObsList()
w = []
for i in range(len(gal)):
im = gal[i].array
im_psf = psf[i].array
im_psf2 = psf2[i].array
weight = 1 / sky_stamp[i].array
jacob = gal[i].wcs.jacobian()
dx = offsets[i].x
dy = offsets[i].y
gal_jacob = Jacobian(row=gal[i].true_center.y + dy,
col=gal[i].true_center.x + dx,
dvdrow=jacob.dvdy,
dvdcol=jacob.dvdx,
dudrow=jacob.dudy,
dudcol=jacob.dudx)
#gal_jacob = Jacobian(
# row=gal[i].true_center.x+dx,
# col=gal[i].true_center.y+dy,
# dvdrow=jacob.dudx,
# dvdcol=jacob.dudy,
# dudrow=jacob.dvdx,
# dudcol=jacob.dvdy)
psf_jacob2 = gal_jacob
mask = np.where(weight != 0)
w.append(np.mean(weight[mask]))
noise = old_div(np.ones_like(weight), w[-1])
psf_obs = Observation(im_psf,
jacobian=gal_jacob,
meta={
'offset_pixels': None,
'file_id': None
})
psf_obs2 = Observation(im_psf2,
jacobian=psf_jacob2,
meta={
'offset_pixels': None,
'file_id': None
})
obs = Observation(im,
weight=weight,
jacobian=gal_jacob,
psf=psf_obs,
meta={
'offset_pixels': None,
'file_id': None
})
obs.set_noise(noise)
obs_list.append(obs)
psf_list.append(psf_obs2)
#print(obs_list)
return obs_list, psf_list, np.array(w)
