def getKernel(screenfile='gaussian_screen.bin'):
''' isotropic case '''
hdr = slimscat.fetch_hdr(screenfile=screenfile)
theta = (hdr['wavelength'] * np.log(4)**0.5) / \
(hdr['r0']*np.pi*(1+hdr['m']))
sigma = (theta / (2 * np.sqrt(2 * np.log(2))))
return sigma * 206265 * 1e6 / hdr['dx']
def getKernel(screenfile='gaussian_screen.bin'):
''' isotropic case '''
hdr = slimscat.fetch_hdr(screenfile=screenfile)
theta = (hdr['wavelength'] * np.log(4)**0.5) / \
(hdr['r0']*np.pi*(1+hdr['m']))
sigma = (theta/(2*np.sqrt(2*np.log(2))))
return sigma*206265*1e6/hdr['dx']
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
sigma = getKernel(__screenfile__)
# sample positions
t1 = np.tile(np.arange(m.nx), m.nx)
t0 = np.repeat(np.arange(m.nx), m.nx, axis=0)
t = np.hstack([t0[:, np.newaxis], t1[:, np.newaxis]])
# fit
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
sigma = getKernel(__screenfile__)
# sample positions
t1 = np.tile(np.arange(m.nx), m.nx)
t0 = np.repeat(np.arange(m.nx), m.nx, axis=0)
t = np.hstack([t0[:, np.newaxis], t1[:, np.newaxis]])
# fit
