def test_map_shape():
obj = tomograpy.centered_cubic_map(3, cube_shape0)
data = tomograpy.centered_stack(tomograpy.fov(obj, d),
data_shape0,
n_images=n_images)
# projection
pj_times = np.empty(len(cube_shapes) + 1)
pj_times[0] = time.time()
for i, s in enumerate(cube_shapes):
obj = tomograpy.centered_cubic_map(3, s)
tomograpy.projector(data, obj)
pj_times[i + 1] = time.time()
pj_times = pj_times[1:] - pj_times[:-1]
# backprojection
bpj_times = np.empty(len(cube_shapes) + 1)
bpj_times[0] = time.time()
for i, s in enumerate(cube_shapes):
obj = tomograpy.centered_cubic_map(3, s)
tomograpy.backprojector(data, obj)
bpj_times[i + 1] = time.time()
bpj_times = bpj_times[1:] - bpj_times[:-1]
# pretty print
text = ''
text += 'Cube shape'
text += ''.join([' & ' + str(s) + "$^3$" for s in cube_shapes])
text += ' \\\\ \n' + 'Projection (s)'
text += ''.join([' & %2.2f' % pjt for pjt in pj_times])
text += ' \\\\ \n' + 'Backprojection (s)'
text += ''.join([' & %2.2f' % bpjt for bpjt in bpj_times])
text += ' \\\\ \n'
print text
def test_map_shape():
obj = tomograpy.centered_cubic_map(3, cube_shape0)
data = tomograpy.centered_stack(tomograpy.fov(obj, d), data_shape0, n_images=n_images)
# projection
pj_times = np.empty(len(cube_shapes) + 1)
pj_times[0] = time.time()
for i, s in enumerate(cube_shapes):
obj = tomograpy.centered_cubic_map(3, s)
tomograpy.projector(data, obj)
pj_times[i + 1] = time.time()
pj_times = pj_times[1:] - pj_times[:-1]
# backprojection
bpj_times = np.empty(len(cube_shapes) + 1)
bpj_times[0] = time.time()
for i, s in enumerate(cube_shapes):
obj = tomograpy.centered_cubic_map(3, s)
tomograpy.backprojector(data, obj)
bpj_times[i + 1] = time.time()
bpj_times = bpj_times[1:] - bpj_times[:-1]
# pretty print
text = ''
text += 'Cube shape'
text += ''.join([' & ' + str(s) + "$^3$" for s in cube_shapes])
text += ' \\\\ \n' + 'Projection (s)'
text += ''.join([' & %2.2f' % pjt for pjt in pj_times])
text += ' \\\\ \n' + 'Backprojection (s)'
text += ''.join([' & %2.2f' % bpjt for bpjt in bpj_times])
text += ' \\\\ \n'
print text
def test_cores():
obj = tomograpy.centered_cubic_map(3, cube_shape0)
data = tomograpy.centered_stack(tomograpy.fov(obj, d),
data_shape0,
n_images=n_images)
# projection
pj_times = np.empty(nthread_max + 1)
pj_times[0] = time.time()
for nt in xrange(nthread_max):
tomograpy.projector(data, obj, nthread=nt + 1)
pj_times[nt + 1] = time.time()
pj_times = pj_times[1:] - pj_times[:-1]
# backprojection
bpj_times = np.empty(nthread_max + 1)
bpj_times[0] = time.time()
for nt in xrange(nthread_max):
tomograpy.backprojector(data, obj, nthread=nt + 1)
bpj_times[nt + 1] = time.time()
bpj_times = bpj_times[1:] - bpj_times[:-1]
# pretty print
text = ''
text += 'Cores'
text += ''.join([' & ' + str(i + 1) for i in xrange(nthread_max)])
text += ' \\\\ \n' + 'Projection (s)'
text += ''.join([' & %2.2f' % pjt for pjt in pj_times])
text += ' \\\\ \n' + 'Backprojection (s)'
text += ''.join([' & %2.2f' % bpjt for bpjt in bpj_times])
text += ' \\\\ \n'
print text
def test_cores():
obj = tomograpy.centered_cubic_map(3, cube_shape0)
data = tomograpy.centered_stack(tomograpy.fov(obj, d), data_shape0, n_images=n_images)
# projection
pj_times = np.empty(nthread_max + 1)
pj_times[0] = time.time()
for nt in xrange(nthread_max):
tomograpy.projector(data, obj, nthread=nt + 1)
pj_times[nt + 1] = time.time()
pj_times = pj_times[1:] - pj_times[:-1]
# backprojection
bpj_times = np.empty(nthread_max + 1)
bpj_times[0] = time.time()
for nt in xrange(nthread_max):
tomograpy.backprojector(data, obj, nthread=nt + 1)
bpj_times[nt + 1] = time.time()
bpj_times = bpj_times[1:] - bpj_times[:-1]
# pretty print
text = ''
text += 'Cores'
text += ''.join([' & ' + str(i + 1) for i in xrange(nthread_max)])
text += ' \\\\ \n' + 'Projection (s)'
text += ''.join([' & %2.2f' % pjt for pjt in pj_times])
text += ' \\\\ \n' + 'Backprojection (s)'
text += ''.join([' & %2.2f' % bpjt for bpjt in bpj_times])
text += ' \\\\ \n'
print text
'2008-12-15T00:00:00.000'],
time_step=8 * 3600.
)
data = tomograpy.solar.sort_data_array(data)
# scale A and B images
# the ratio of sensitivity between EUVI A and B
calibration_ba = {171:0.902023, 195:0.974536, 284:0.958269, 304:1.05954}
for i in xrange(data.shape[-1]):
if data.header[i]['OBSRVTRY'] == 'STEREO_B':
data[..., i] /= calibration_ba[data.header[i]['WAVELNTH']]
# make sure it is 64 bits data
data.header[i]['BITPIX'] = -64
# cube
cube = tomograpy.centered_cubic_map(3, 64, fill=0.)
# model
kwargs = {'obj_rmin':1., 'obj_rmax':1.5, 'data_rmin':1., 'data_rmax':1.3,
'mask_negative':False, 'mask_nan':True}
P, D, obj_mask, data_mask = tomograpy.models.srt(data, cube, **kwargs)
# apply mask to data
data *= (1 - data_mask)
data[np.isnan(data)] = 0.
# hyperparameters
hypers = cube.ndim * (1e0, )
# inversion
# expected time
b = data.ravel()
b[np.isnan(b)] = 0.
t = time.time()
bpj = P.T * b
#!/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)]
#!/usr/bin/env python
import time
import numpy as np
import tomograpy
# object
obj = tomograpy.centered_cubic_map(3, 128, fill=1.)
# number of images
n = 20
# reshape object for 4d model
obj4 = obj.reshape(obj.shape + (1, )).repeat(n, axis=-1)
obj4.header['NAXIS'] = 4
obj4.header['NAXIS4'] = obj4.shape[3]
obj4.header['CRVAL4'] = 0.
# data
radius = 200
a = tomograpy.fov(obj.header, radius)
data = tomograpy.centered_stack(a,
128,
n_images=n,
radius=radius,
max_lon=np.pi)
data[:] = np.zeros(data.shape)
# projection
t = time.time()
data = tomograpy.projector4d(data, obj4)
print("projection time : " + str(time.time() - t))
# backprojection
x0 = obj4.copy()
x0[:] = 0.
t = time.time()
#!/usr/bin/env python
import time
import numpy as np
import tomograpy
import lo
# object
obj = tomograpy.centered_cubic_map(10, 64)
obj[:] = tomograpy.phantom.shepp_logan(obj.shape)
# data
radius = 200
a = tomograpy.fov(obj, radius)
data = tomograpy.centered_stack(a, 128, n_images=60, radius=radius, max_lon=np.pi)
# model
kwargs = {"pb":"pb", "obj_rmin":1.5, "data_rmin":1.5}
P, D, obj_mask, data_mask = tomograpy.models.thomson(data, obj, u=.5, **kwargs)
# projection
t = time.time()
data[:] = (P * obj.ravel()).reshape(data.shape)
print("projection time : " + str(time.time() - t))
# data
# backprojection
t = time.time()
x0 = P.T * data.ravel()
bpj = x0.reshape(obj.shape)
print("backprojection time : " + str(time.time() - t))
# inversion using scipy.sparse.linalg
t = time.time()
sol = lo.acg(P, data.ravel(), D, 1e-3 * np.ones(3), maxiter=100, tol=1e-8)
sol = sol.reshape(obj.shape)
print("inversion time : " + str(time.time() - t))
#!/usr/bin/env python
import time
import numpy as np
import tomograpy
# object
obj = tomograpy.centered_cubic_map(3, 128, fill=1.)
# data
radius = 200.
a = tomograpy.fov(obj.header, radius)
data = tomograpy.centered_stack(a, 128, n_images=17, radius=200., max_lon=np.pi)
# projection
t = time.time()
data = tomograpy.projector(data, obj, obstacle="sun")
print("projection time : " + str(time.time() - t))
# backprojection
obj0 = tomograpy.centered_cubic_map(3, 128, fill=0.)
t = time.time()
obj0 = tomograpy.backprojector(data, obj0, obstacle="sun")
print("backprojection time : " + str(time.time() - t))
#!/usr/bin/env python
import time
import numpy as np
import tomograpy
import lo
# object
obj = tomograpy.centered_cubic_map(10, 64)
obj[:] = tomograpy.phantom.shepp_logan(obj.shape)
# data
radius = 200
a = tomograpy.fov(obj, radius)
data = tomograpy.centered_stack(a,
128,
n_images=60,
radius=radius,
max_lon=np.pi)
# model
kwargs = {"pb": "pb", "obj_rmin": 1.5, "data_rmin": 1.5}
P, D, obj_mask, data_mask = tomograpy.models.thomson(data, obj, u=.5, **kwargs)
# projection
t = time.time()
data[:] = (P * obj.ravel()).reshape(data.shape)
print("projection time : " + str(time.time() - t))
# data
# backprojection
t = time.time()
x0 = P.T * data.ravel()
bpj = x0.reshape(obj.shape)
print("backprojection time : " + str(time.time() - t))
# inversion using scipy.sparse.linalg
t = time.time()
#!/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, 64, fill=1.)
# number of images
n = 64
# reshape object for 4d model
obj4 = obj[..., np.newaxis].repeat(n, axis=-1)
obj4.header['NAXIS'] = 4
obj4.header['NAXIS4'] = obj4.shape[3]
obj4.header['CRVAL4'] = 0.
# data
radius = 200
a = tomograpy.fov(obj.header, radius)
data1 = tomograpy.centered_stack(a, 64, n_images=n/2, radius=radius,
max_lon=np.pi, fill=0.)
data2 = tomograpy.centered_stack(a, 64, n_images=n/2, radius=radius,
min_lon=np.pi / 2., max_lon=1.5 * np.pi, fill=0.)
data = tomograpy.solar.concatenate((data1, data2))
# times
DT = 1000.
dt_min = 100.
dates = np.arange(n / 2) * DT / 2.
dates = np.concatenate(2 * (dates, ))
dates = [time.strftime('%Y-%m-%dT%H:%M:%S', time.localtime((t))) for t in dates]
for i in xrange(len(data.header)):
#!/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)
