import numpy as np
import tomograpy
import lo
# object
obj = tomograpy.centered_cubic_map(3, 32)
obj[:] = tomograpy.phantom.shepp_logan(obj.shape)
# data
radius = 200.
a = tomograpy.fov(obj.header, radius)
data = tomograpy.centered_stack(a,
128,
n_images=60,
radius=radius,
max_lon=np.pi)
# projector
P = tomograpy.lo(data.header, obj.header)
# projection
t = time.time()
data = tomograpy.projector(data, obj)
print("projection time : " + str(time.time() - t))
# data
y = data.flatten()
# backprojection
t = time.time()
x0 = P.T * y
bpj = x0.reshape(obj.shape)
print("projection time : " + str(time.time() - t))
# priors
Ds = [lo.diff(obj.shape, axis=i) for i in xrange(3)]
# inversion using scipy.sparse.linalg
t = time.time()
# data
path = os.path.join(os.getenv('HOME'), 'data', '171dec08')
obsrvtry = 'STEREO_A'
time_window = ['2008-12-01T00:00:00.000', '2008-12-03T00:00:00.000']
# one image every time_step seconds
time_step = 4 * 3600.
data = tomograpy.secchi.read_data(path, bin_factor=4,
obsrvtry=obsrvtry,
time_window=time_window,
time_step=time_step)
# cube
shape = 3 * (128,)
header = {'CRPIX1':64., 'CRPIX2':64., 'CRPIX3':64.,
'CDELT1':0.0234375, 'CDELT2':0.0234375, 'CDELT3':0.0234375,
'CRVAL1':0., 'CRVAL2':0., 'CRVAL3':0.,}
cube = fa.zeros(shape, header=header)
# model
P = tomograpy.lo(data.header, cube.header)
D = [lo.diff(cube.shape, axis=i) for i in xrange(cube.ndim)]
hypers = cube.ndim * (1e0, )
# inversion
t = time.time()
A = P.T * P + np.sum([h * d.T * d for h, d in zip(hypers, D)])
b = P.T * data.flatten()
#callback = lo.iterative.CallbackFactory(verbose=True)
#x, info = spl.bicgstab(A, b, maxiter=100, callback=callback)
x, info = lo.acg(P, data.flatten(), D, hypers, maxiter=100,)
sol = cube.copy()
sol[:] = x.reshape(cube.shape)
print(time.time() - t)
#!/usr/bin/env python """ Small projection test to compare with IDL tomograpy. """ import numpy as np import tomograpy im = tomograpy.centered_stack(0.0016, 32, n_images=1, radius=200., fill=0.) cube = tomograpy.centered_cubic_map(3, 256, fill=1.) P = tomograpy.lo(im.header, cube.header, obstacle="sun") im[:] = (P * cube.ravel()).reshape(im.shape)
#!/usr/bin/env python
import time
import numpy as np
import tomograpy
import lo
# object
obj = tomograpy.centered_cubic_map(3, 32)
obj[:] = tomograpy.phantom.shepp_logan(obj.shape)
# data
radius = 200.
a = tomograpy.fov(obj.header, radius)
data = tomograpy.centered_stack(a, 128, n_images=60, radius=radius,
max_lon=np.pi)
# projector
P = tomograpy.lo(data.header, obj.header)
# projection
t = time.time()
data = tomograpy.projector(data, obj)
print("projection time : " + str(time.time() - t))
# data
y = data.flatten()
# backprojection
t = time.time()
x0 = P.T * y
bpj = x0.reshape(obj.shape)
print("projection time : " + str(time.time() - t))
# priors
Ds = [lo.diff(obj.shape, axis=i) for i in xrange(3)]
# inversion using scipy.sparse.linalg
t = time.time()
sol = lo.acg(P, y, Ds, 1e-2 * np.ones(3), maxiter=100, tol=1e-20)
