def make_image_1():
P = objs.PlatonicSpheresCollection(pos, rad)
H = psfs.AnisotropicGaussian()
I = ilms.LegendrePoly3D(order=(5, 3, 3), constval=1.0)
B = ilms.Polynomial3D(order=(3, 1, 1), category='bkg', constval=0.01)
C = GlobalScalar('offset', 0.0)
return states.ImageState(im, [B, I, H, P, C], pad=16, model_as_data=True)
def make_image_0():
P = objs.PlatonicSpheresCollection(pos, rad)
H = psfs.AnisotropicGaussian()
I = ilms.BarnesStreakLegPoly2P1D(npts=(20, 10), local_updates=True)
B = GlobalScalar('bkg', 0.0)
C = GlobalScalar('offset', 0.0)
I.randomize_parameters()
return states.ImageState(im, [B, I, H, P, C], pad=16, model_as_data=True)
def table_psfs():
np.random.seed(12)
s = create_image()
sh = s.psf.shape
lpsfs = [
psfs.IdentityPSF(shape=sh, params=np.array([0.0])),
psfs.AnisotropicGaussian(shape=sh, params=(2.0, 1.0, 3.0)),
psfs.Gaussian4DLegPoly(shape=sh, order=(3, 3, 3)),
exactpsf.FixedSSChebLinePSF(
shape=sh,
zrange=(0, sh[0]),
cheb_degree=3,
cheb_evals=6,
support_size=FIXEDSS,
zslab=10.,
cutoffval=1. / 255,
measurement_iterations=3,
),
exactpsf.FixedSSChebLinePSF(
shape=sh,
zrange=(0, sh[0]),
cheb_degree=6,
cheb_evals=8,
support_size=FIXEDSS,
zslab=10.,
cutoffval=1. / 255,
measurement_iterations=3,
),
]
names = [
r'Identity',
r'Gaussian$(x,y)$',
r'Gaussian$(x,y,z,z^{\prime})$',
r'Cheby linescan (3,6)',
r'Cheby linescan (6,8)',
]
def vary_func(s, data):
s.set_psf(data)
return table(s, lpsfs, names, vary_func)
def table():
s = create_image(identity=True)
lpoly = [0.0, 0.0, 0.01, 0.05, 0.10]
dnames = ['0.0', '0.0', '0.01', '0.05', '0.10']
lilms = [
ilms.LegendrePoly2P1D(shape=s.ilm.shape, order=(1, 1, 1)),
ilms.LegendrePoly2P1D(shape=s.ilm.shape, order=(3, 3, 3)),
ilms.BarnesStreakLegPoly2P1DX3(shape=s.ilm.shape,
order=(1, 1, 1),
npts=(10, 5)),
ilms.BarnesStreakLegPoly2P1DX3(shape=s.ilm.shape,
order=(1, 1, 2),
npts=(30, 10)),
ilms.BarnesStreakLegPoly2P1DX3(shape=s.ilm.shape,
order=s.ilm.order,
npts=(30, 10, 5)),
]
lnames = [
r'Legendre 2+1D (0,0,0)',
r'Legendre 2+1D (2,2,2)',
r'Barnes (10, 5), $N_z=1$',
r'Barnes (30, 10), $N_z=2$',
r'Barnes (30, 10, 5), $N_z=3$',
]
lpsfs = [
psfs.IdentityPSF(shape=s.psf.shape, params=np.array([0.0])),
psfs.AnisotropicGaussian(shape=s.psf.shape, params=(2.0, 1.0, 3.0)),
psfs.Gaussian4DLegPoly(shape=s.psf.shape, order=(3, 3, 3)),
exactpsf.FixedSSChebLinePSF(
shape=s.psf.shape,
zrange=(0, s.psf.shape[0]),
cheb_degree=3,
cheb_evals=6,
support_size=FIXEDSS,
zslab=10.,
cutoffval=1. / 255,
measurement_iterations=3,
),
exactpsf.FixedSSChebLinePSF(
shape=s.psf.shape,
zrange=(0, s.psf.shape[0]),
cheb_degree=6,
cheb_evals=8,
support_size=FIXEDSS,
zslab=10.,
cutoffval=1. / 255,
measurement_iterations=3,
),
]
pnames = [
r'Identity',
r'Gaussian$(x,y)$',
r'Gaussian$(x,y,z,z^{\prime})$',
r'Cheby linescan (3,6)',
r'Cheby linescan (6,8)',
]
results = OrderedDict()
for i in xrange(len(lpoly)):
print dnames[i], lnames[i], pnames[i]
poly = lpoly[i]
ilm = lilms[i]
psf = lpsfs[i]
s = create_image(polydispersity=poly)
s.set_ilm(ilm)
s.set_psf(psf)
if isinstance(s.ilm, ilms.BarnesStreakLegPoly2P1DX3):
s.ilm.randomize_parameters(ptp=0.4, vmax=1.0, fourier=False)
s.reset()
s.model_to_true_image()
pos1 = trackpy(s)
pos0 = s.obj.pos.copy()
s.obj.set_pos_rad(pos1, s.obj.rad.mean() * np.ones(pos1.shape[0]))
s.reset()
slicer = s.get_difference_image().shape[0] / 2
results[i] = (
dnames[i],
lnames[i],
pnames[i],
s.get_difference_image()[slicer].copy(),
pos0,
pos1,
)
return results
from peri import states, util, models
from peri.comp import psfs, objs, ilms, GlobalScalar, ComponentCollection
from peri.test import nbody
import peri.opt.optimize as opt
import peri.opt.addsubtract as addsub
im = util.NullImage(shape=(32, ) * 3)
pos, rad, tile = nbody.create_configuration(3, im.tile)
P = ComponentCollection(
[objs.PlatonicSpheresCollection(pos, rad),
objs.Slab(2)], category='obj')
H = psfs.AnisotropicGaussian()
I = ilms.BarnesStreakLegPoly2P1D(npts=(25, 13, 3),
zorder=2,
local_updates=False)
B = ilms.LegendrePoly2P1D(order=(3, 1, 1), category='bkg', constval=0.01)
C = GlobalScalar('offset', 0.0)
I.randomize_parameters()
s = states.ImageState(im, [B, I, H, P, C], pad=16, model_as_data=True)
