def test_gmix_lists():
gml = ngmix.gmix.GMixList()
gml.append(ngmix.GMix(ngauss=3))
gml.append(ngmix.GMix(ngauss=3))
gml[1] = ngmix.GMix(ngauss=3)
mgml = ngmix.gmix.MultiBandGMixList()
mgml.append(gml)
mgml.append(gml)
mgml[1] = gml
def test_gmix_errors():
with pytest.raises(ValueError):
ngmix.GMix()
with pytest.raises(ValueError):
ngmix.GMixModel([1] * 6, "blah")
with pytest.raises(ValueError):
ngmix.GMix(pars=[1] * 10)
with pytest.raises(ValueError):
ngmix.GMix(ngauss=-1)
with pytest.raises(ValueError):
ngmix.GMixModel([1] * 10, "gauss")
pars = [0, 0, 0, 0, 10, 1]
gm = ngmix.GMixModel(pars, "gauss")
with pytest.raises(ValueError):
gm.get_sheared(0.1)
with pytest.raises(TypeError):
gm.convolve(3)
with pytest.raises(ValueError):
gm.make_image([1])
obs = ngmix.Observation(np.zeros((10, 10)))
with pytest.raises(ValueError):
gm.fill_fdiff(obs, np.zeros((10, 10)), start=100000)
with pytest.raises(TypeError):
gm.make_galsim_object(gsparams=3)
with pytest.raises(ValueError):
gm = ngmix.GMixCoellip([1] * 3)
with pytest.raises(ValueError):
ngmix.gmix.get_model_name(-1)
with pytest.raises(ValueError):
ngmix.gmix.get_model_num('asdfsa')
with pytest.raises(ValueError):
ngmix.gmix.get_model_ngauss('asdfsa')
with pytest.raises(ValueError):
ngmix.gmix.get_model_npars('asdfsa')
with pytest.raises(ValueError):
tgm = gm.copy()
tgm._model_name = 'sdfasd'
tgm._set_fill_func()
def make_data(
rng,
counts=100.0,
g1=0.0,
g2=0.0,
noise=0.0,
):
pixel_scale = 0.263
dims = [32] * 2
cen = (np.array(dims) - 1) / 2
dvdrow, dvdcol, dudrow, dudcol = pixel_scale, -0.02, 0.01, pixel_scale
jacobian = ngmix.Jacobian(
row=cen[0],
col=cen[1],
dvdrow=dvdrow,
dvdcol=dvdcol,
dudrow=dudrow,
dudcol=dudcol,
)
pars1 = [
-3.25 * pixel_scale,
-3.25 * pixel_scale,
0.05,
0.025,
0.55,
0.4 * counts,
]
pars2 = [
3.0 * pixel_scale,
0.5 * pixel_scale,
-0.1,
-0.05,
0.27,
0.6 * counts,
]
gm1 = ngmix.GMixModel(pars1, "gauss")
gm2 = ngmix.GMixModel(pars2, "gauss")
full_pars = np.hstack([gm1.get_full_pars(), gm2.get_full_pars()])
gm = ngmix.GMix(pars=full_pars)
im0 = gm.make_image(dims, jacobian=jacobian)
im = im0 + rng.normal(size=im0.shape, scale=noise)
obs = ngmix.Observation(image=im, jacobian=jacobian)
return obs, gm
def _make_output(*, cat, psf_ngauss, res, nband, ngauss_per, time):
"""
combine the input catalog with the rsult
we will add
flags
coadd_flags
coadd_psf_pars[6]
pars[ngauss_per*6]
band_flags[nband]
band_psf_pars[nband, 6]
band_pars[nband, ngauss_per*6]
"""
resfields = ('flags', 'numiter', 'sky')
nobj = cat.size
output = _make_output_struct(
nobj=nobj,
nband=nband,
ngauss_per=ngauss_per,
psf_ngauss=psf_ngauss,
)
output['number'] = cat['number']
output['time'] = time
output = _add_extra(output, cat)
if 'fof_id' in cat.dtype.names:
output['fof_id'] = cat['fof_id']
output['fof_size'] = nobj
output['flags'] = res['flags']
# if the user never tried the fit this will not be present
if 'coadd_result' in res:
cres = res['coadd_result']
for n in resfields:
if n in cres:
output['coadd_%s' % n] = cres[n]
pgmix = res['coadd_psf_gmix']
ppars = pgmix.get_full_pars()
output['coadd_psf_T'] = pgmix.get_T()
for i in range(nobj):
output['coadd_psf_pars'][i, :] = ppars
gmix = res['coadd_gmix']
if gmix is not None:
pars = gmix.get_full_pars()
output['coadd_pars'] = pars.reshape(output['coadd_pars'].shape)
for i in range(nobj):
ipars = output['coadd_pars'][i]
igm = ngmix.GMix(pars=ipars)
output['coadd_T'][i] = igm.get_T()
output['coadd_flux'][i] = igm.get_flux()
if 'band_results' in res:
for band in range(nband):
bres = res['band_results'][band]
for n in resfields:
if n in bres:
output['band_%s' % n][:, band] = bres[n]
pgmix = res['band_psf_gmix'][band]
bppars = pgmix.get_full_pars()
output['band_psf_T'][:, band] = pgmix.get_T()
for i in range(nobj):
output['band_psf_pars'][i, :, band] = bppars
bgmix = res['band_gmix'][band]
if bgmix is not None:
bpars = bgmix.get_full_pars()
bpars = bpars.reshape(nobj, 6 * ngauss_per)
output['band_pars'][:, :, band] = bpars
for i in range(nobj):
ipars = output['band_pars'][i, :, band]
igm = ngmix.GMix(pars=ipars)
output['band_flux'][i, band] = igm.get_flux()
return output
def make_data(
rng,
counts=100.0,
g1=0.0,
g2=0.0,
noise=0.0,
):
pixel_scale = 0.263
psf_fwhm = 0.9
dims = [32]*2
cen = (np.array(dims)-1)/2
dvdrow, dvdcol, dudrow, dudcol = pixel_scale, -0.02, 0.01, pixel_scale
jacobian = ngmix.Jacobian(
row=cen[0],
col=cen[1],
dvdrow=dvdrow,
dvdcol=dvdcol,
dudrow=dudrow,
dudcol=dudcol,
)
T1 = 0.2
T2 = 0.05
pars1 = [
-3.25*pixel_scale, -3.25*pixel_scale, 0.1, 0.05, T1, 0.4*counts,
]
pars2 = [
3.0*pixel_scale, 0.5*pixel_scale, -0.2, -0.1, T2, 0.6*counts,
]
gm1 = ngmix.GMixModel(pars1, "gauss")
gm2 = ngmix.GMixModel(pars2, "gauss")
full_pars = np.hstack([gm1.get_full_pars(), gm2.get_full_pars()])
gm0 = ngmix.GMix(pars=full_pars)
Tpsf = ngmix.moments.fwhm_to_T(psf_fwhm)
psf_pars = [0.0, 0.0, 0.01, -0.005, Tpsf, 1.0]
psf = ngmix.GMixModel(pars=psf_pars, model="turb")
psf_im = psf.make_image(dims, jacobian=jacobian)
gm = gm0.convolve(psf)
im0 = gm.make_image(dims, jacobian=jacobian)
im = im0 + rng.normal(size=im0.shape, scale=noise)
# to fit with psfs, the psf observation must be set and it
# must have a gaussian mixture set
psf_obs = ngmix.Observation(
image=psf_im, jacobian=jacobian, gmix=psf,
)
obs = ngmix.Observation(image=im, jacobian=jacobian, psf=psf_obs)
return obs, gm0