def inversion(path, obj_params, data_params, opt_params, mask_params):
"""
Perform an inversion using given parameters.
"""
import numpy as np
import lo, siddon
import fitsarray as fa
import solar, models
# data
data = solar.read_data(path, **data_params)
if data is None:
return
data = solar.sort_data_array(data)
# create object
obj = make_object(obj_params)
# configuration persistency
out_header = persistency_header(obj.header, data_params,
mask_params, opt_params)
# pop optimization parameters
model = opt_params.pop("model")
optimizer = opt_params.pop("optimizer")
hypers = opt_params.pop("hypers")
if opt_params.has_key("input"):
opt_params["x0"] = fa.FitsArray(file=opt_params["input"])
# model
P, D, obj_mask, data_mask = model(data, obj, **mask_params)
# apply masking to data
data *= (1 - data_mask)
data[np.isnan(data)] = 0.
# inversion
b = data.ravel()
exec("sol = lo." + optimizer + "(P, b, D, hypers, **opt_params)")
# reshape result
sol = fa.asfitsarray(sol.reshape(obj_mask.shape), header=out_header)
return sol
def inversion(path, obj_params, data_params, opt_params, mask_params):
"""
Perform an inversion using given parameters.
"""
import numpy as np
import lo, siddon
import fitsarray as fa
import solar, models
# data
data = solar.read_data(path, **data_params)
if data is None:
return
data = solar.sort_data_array(data)
# create object
obj = make_object(obj_params)
# configuration persistency
out_header = persistency_header(obj.header, data_params, mask_params,
opt_params)
# pop optimization parameters
model = opt_params.pop("model")
optimizer = opt_params.pop("optimizer")
hypers = opt_params.pop("hypers")
if opt_params.has_key("input"):
opt_params["x0"] = fa.FitsArray(file=opt_params["input"])
# model
P, D, obj_mask, data_mask = model(data, obj, **mask_params)
# apply masking to data
data *= (1 - data_mask)
data[np.isnan(data)] = 0.
# inversion
b = data.ravel()
exec("sol = lo." + optimizer + "(P, b, D, hypers, **opt_params)")
# reshape result
sol = fa.asfitsarray(sol.reshape(obj_mask.shape), header=out_header)
return sol
def _radius_map(my_map):
from fitsarray import asfitsarray
x, y, z = asfitsarray(my_map).axes()
x2 = x ** 2
y2 = y ** 2
z2 = z ** 2
X2, Y2 = np.meshgrid(x2, y2)
R = np.dstack([X2 + Y2 + z2i for z2i in z2])
return R
def define_map_mask(cube, obj_rmin=None, obj_rmax=None, obj_cylinder=None,
remove_nan=False, **kwargs):
"""
Output a mask of shape cube.shape.
"""
if remove_nan:
obj_mask = np.isnan(cube)
else:
obj_mask = np.zeros(cube.shape, dtype=bool)
if obj_rmin is not None or obj_rmax is not None or obj_cylinder is not None:
R = map_radius(fa.asfitsarray(cube))
if obj_rmin is not None:
obj_mask[R < obj_rmin] = 1
if obj_rmax is not None:
obj_mask[R > obj_rmax] = 1
if obj_cylinder is not None:
map_ = cylinder(fa.asfitsarray(cube, obj_cylinder))
obj_mask[map_ == 1] = 1
return obj_mask
def _pb_data_coef(data):
"""Returns pb coefficients for a data array."""
from fitsarray import asfitsarray
coefs = np.zeros(data.shape)
# loop on images assuming images are on last axis
for i in xrange(data.shape[-1]):
# get phyiscal coordinates of pixels
im = asfitsarray(solar.slice_data(data, i))
alpha, beta = im.axes()
Alpha, Beta = np.meshgrid(alpha, beta)
# define coefficients as square of impact parameter
coefs[..., i] = _impact_parameter(Alpha, Beta, im.header['D']) ** 2
return coefs
def define_map_mask(cube,
obj_rmin=None,
obj_rmax=None,
obj_cylinder=None,
remove_nan=False,
**kwargs):
"""
Output a mask of shape cube.shape.
"""
if remove_nan:
obj_mask = np.isnan(cube)
else:
obj_mask = np.zeros(cube.shape, dtype=bool)
if obj_rmin is not None or obj_rmax is not None or obj_cylinder is not None:
R = map_radius(fa.asfitsarray(cube))
if obj_rmin is not None:
obj_mask[R < obj_rmin] = 1
if obj_rmax is not None:
obj_mask[R > obj_rmax] = 1
if obj_cylinder is not None:
map_ = cylinder(fa.asfitsarray(cube, obj_cylinder))
obj_mask[map_ == 1] = 1
return obj_mask
def _pb_map_coef(my_map, u):
"""Returns pb map coefficients corresponding to a map array."""
from fitsarray import asfitsarray
coefs = np.zeros(my_map.shape)
x, y, z = asfitsarray(my_map).axes()
x2 = x ** 2
y2 = y ** 2
z2 = z ** 2
X2, Y2 = np.meshgrid(x2, y2)
# loop on z to avoid memory explosion !
for i, z2i in enumerate(z2):
R2 = X2 + Y2 + z2i
R = np.sqrt(R2)
O = _r2omega(R)
C1, C2 = _pb_thomson_coef(O)
coefs[..., i] = ((1 - u) * C1 + u * C2) / (R2 ** 2)
# set infinite values due to divide by zero to 0.
coefs[1 - np.isfinite(coefs)] = 0.
return coefs * np.pi * sigma / 2.
def check_model(model, im_h, obj_h):
obj = siddon.simu.object_from_header(obj_h)
obj = siddon.fa.InfoArray(data=obj, header=dict(obj.header))
obj[:] = 1.
im_h['n_images'] = 100
im_h['max_lon'] = 3 * np.pi
data = siddon.simu.circular_trajectory_data(**im_h)
if obj.dtype == data.dtype:
P, D, obj_mask, data_mask = model(data,
obj,
obj_rmin=1.,
decimate=True)
Mo = lo.decimate(obj_mask)
w = (Mo.T * (P.T * np.ones(data.size))).reshape(obj_mask.shape)
is_seen = (w != 0)
new_obj = fa.FitsArray(obj_mask.shape)
new_obj = 1.
new_obj *= (1 - obj_mask)
data[:] = (P * Mo * new_obj.ravel()).reshape(data.shape)
hypers = new_obj.ndim * (1e-10, )
sol = lo.acg(P, data.ravel(), D, hypers=hypers, tol=1e-20)
sol = fa.asfitsarray((Mo.T * sol).reshape(obj_mask.shape),
header=obj.header)
assert_almost_equal(sol[is_seen], new_obj[is_seen], decimal=1)
'CDELT1':cdelt[0], 'CDELT2':cdelt[1], 'CDELT3':cdelt[2],
'CRVAL1':0., 'CRVAL2':0., 'CRVAL3':0.,}
cube = fa.zeros(shape, header=header)
t = time.time()
cube = tomograpy.backprojector(data, cube, obstacle="sun")
print("backprojection time : " + str(time.time() - t))
# inversion
t = time.time()
u = .5
kwargs={
"obj_rmin":1.5,
"obj_rmax":3.,
"data_rmin":1.5,
"data_rmax":2.5,
"mask_negative":True
}
P, D, obj_mask, data_mask = tomograpy.models.thomson(data, cube, u, **kwargs)
# bpj
b = data.flatten()
bpj = (P.T * b).reshape(cube.shape)
hypers = 1e3 * np.ones(3)
sol = lo.acg(P, b, D, hypers, maxiter=100, tol=1e-6)
print("inversion time : %f" % (time.time() - t))
# reshape solution
sol.resize(cube.shape)
sol = fa.asfitsarray(sol, header=cube.header)
# reproject solution
reproj = P * sol.ravel()
reproj.resize(data.shape)
