def lnprob(theta, *args):
lp = lnprior(theta)
if not np.isfinite(lp):
return -np.inf
return lnlike(theta, *args) + lp
if __name__ == '__main__':
# create model
ftot = 3.
fwhm = 37.
snr = 100
dx = 4
nx = 32
m = model(ftot, fwhm, nx=nx, dx=dx)
#mNoisy = initialize(m.source,dx)
mNoisy = addNoise(m.source, dx)
noise = mNoisy - m.source
plt.subplot(121)
plt.imshow(m.source)
plt.subplot(122)
plt.imshow(mNoisy)
plt.figure()
plt.imshow(noise)
#plt.show()
# fetch scattering kernel
sigma = getKernel()
def lnprob(theta,*args):
lp = lnprior(theta)
if not np.isfinite(lp):
return -np.inf
return -1 * (lnlike(theta,*args) + lp)
if __name__ == '__main__':
# create model
ftot = 3.
fwhm = 37.
snr = 100
#dx = 1; nx = 128;
dx = 4; nx = 32;
m = model(ftot,fwhm,nx=nx,dx=dx)
#mNoisy = initialize(m.source,dx)
mNoisy = addNoise(m.source,dx)
noise = mNoisy - m.source
plt.subplot(121); plt.imshow(m.source)
plt.subplot(122); plt.imshow(mNoisy)
plt.figure(); plt.imshow(noise)
#plt.show()
# fetch scattering kernel
sigma = getKernel(__screenfile__)
kernel = m.broaden(sigma)
# estimate noise
def lnprob(theta,m,d,w):
lp = lnprior(theta)
if not np.isfinite(lp):
return -np.inf
return lnlike(theta,m,d,w) + lp
if __name__ == '__main__':
# create our model
nx = 128
dx = 1.
ftot = 3.
fwhm = 37.
snr = 100
m = model(ftot,fwhm,nx,dx)
#w = m/m.sum()
# add noise
noise = m.source.max()/snr*np.random.randn(m.nx,m.nx)
mNoisy = m.source + noise
w = 1./noise.std()**2
# initialize sampler
initial = [ftot,fwhm]
ndim = len(initial)
nwalkers = 100
sampler = emcee.EnsembleSampler(nwalkers, ndim, lnprob, \
args=[m,mNoisy,w])
# burn
def lnprob(theta, *args):
""" minimize this """
lp = lnprior(theta)
ans = (lp + lnlike(theta, *args)) if np.isfinite(lp) else -np.inf
return -1 * ans
if __name__ == "__main__":
# create model
ftot = 3.0
fwhm = 37.0
dx = 4.0
nx = 32
m = model(ftot, fwhm, dx=dx, nx=nx)
# mNoisy = initialize(m,dx)
mNoisy = addNoise(m.source, dx)
mTrue = m.source
noise = mNoisy - m.source
mTrue = m.source.copy()
# check that our model and simulation have matching resolutions
hdr = slimscat.fetch_hdr(__screenfile__)
assert hdr["dx"] == dx
# plt.subplot(121); plt.imshow(m.source); plt.subplot(122); plt.imshow(mNoisy)
# plt.figure(); plt.imshow(noise)
# plt.show()
# fetch scattering kernel
lp = lnprior(theta)
if not np.isfinite(lp):
return -np.inf
return lnlike(theta, m, d, w) + lp
if __name__ == '__main__':
# create our model
nx = 128
dx = 1.
ftot = 3.
fwhm = 37.
snr = 100
m = model(ftot, fwhm, nx, dx)
#w = m/m.sum()
# add noise
noise = m.source.max() / snr * np.random.randn(m.nx, m.nx)
mNoisy = m.source + noise
w = 1. / noise.std()**2
# initialize sampler
initial = [ftot, fwhm]
ndim = len(initial)
nwalkers = 100
sampler = emcee.EnsembleSampler(nwalkers, ndim, lnprob, \
args=[m,mNoisy,w])
# burn
#return -0.5 * np.sum(w*(mBroad-d)**2)
def lnprob(theta,*args):
lp = lnprior(theta)
if not np.isfinite(lp):
return -np.inf
return lnlike(theta,*args) + lp
if __name__ == '__main__':
# create model
ftot = 3.
fwhm = 37.
snr = 100
dx = 4; nx = 32;
m = model(ftot,fwhm,nx=nx,dx=dx)
#mNoisy = initialize(m.source,dx)
mNoisy = addNoise(m.source,dx)
noise = mNoisy - m.source
plt.subplot(121); plt.imshow(m.source)
plt.subplot(122); plt.imshow(mNoisy)
plt.figure(); plt.imshow(noise)
#plt.show()
# fetch scattering kernel
sigma = getKernel()
kernel = m.broaden(sigma)
# estimate noise
def lnprob(theta, *args):
''' minimize this '''
lp = lnprior(theta)
ans = (lp + lnlike(theta, *args)) \
if np.isfinite(lp) else -np.inf
return -1 * ans
if __name__ == '__main__':
# create model
ftot = 3.
fwhm = 37.
dx = 4.
nx = 32
m = model(ftot, fwhm, dx=dx, nx=nx)
#mNoisy = initialize(m,dx)
mNoisy = addNoise(m.source, dx)
mTrue = m.source
noise = mNoisy - m.source
mTrue = m.source.copy()
# check that our model and simulation have matching resolutions
hdr = slimscat.fetch_hdr(__screenfile__)
assert hdr['dx'] == dx
#plt.subplot(121); plt.imshow(m.source); plt.subplot(122); plt.imshow(mNoisy)
#plt.figure(); plt.imshow(noise)
#plt.show()
# fetch scattering kernel
