def __init__(self, data_header, cube_header, ng=1, **kwargs):
self.data_header = data_header
self.map_header = cube_header
xout = dataarray_from_header(data_header)
xout[:] = 0
xin = fa.fitsarray_from_header(cube_header)
xin.header = dict(xin.header)
xin[:] = 0
self.xin = xin
self.xout = xout
def matvec(x):
y = dataarray_from_header(data_header)
y[:] = 0
for i in xrange(ng):
yi = y[..., i::ng]
yi.header = y.header[i::ng]
projector4d(yi, x, **kwargs)
del yi
return y
def rmatvec(x):
y = fa.fitsarray_from_header(cube_header)
y[:] = 0
for i in xrange(ng):
xi = x[..., i::ng]
xi.header = xi.header[i::ng]
backprojector4d(xi, y, **kwargs)
del xi
return y
lo.NDSOperator.__init__(self, xin=xin, xout=xout, matvec=matvec, rmatvec=rmatvec, dtype=xout.dtype)
def __init__(self, data_header, cube_header, **kwargs):
self.data_header = data_header
self.map_header = dict(cube_header)
xin = fa.fitsarray_from_header(cube_header)
xin.header = dict(xin.header)
xin[:] = 0
xout = dataarray_from_header(data_header)
xout[:] = 0
self.xin = xin
self.xout = xout
shapein = xin.shape
shapeout = xout.shape
def matvec(x):
x = fa.InfoArray(data=x, header=dict(cube_header))
y = xout
y[:] = 0.
projector(y, x, **kwargs)
return y
def rmatvec(x):
x = fa.InfoArray(data=x, header=data_header)
y = xin
y[:] = 0.
backprojector(x, y, **kwargs)
return y
lo.NDSOperator.__init__(self, shapein, shapeout, xin=xin, xout=xout,
matvec=matvec, rmatvec=rmatvec, dtype=xout.dtype)
def rmatvec(x):
y = fa.fitsarray_from_header(cube_header)
y[:] = 0
for i in xrange(ng):
xi = x[..., i::ng]
xi.header = xi.header[i::ng]
backprojector4d(xi, y, **kwargs)
del xi
return y
def centered_image(pshape, shape, fill=0., dtype=np.float64):
"""
Generate a centered cubic map.
Arguments
---------
pshape : physical shape
shape : shape in pixels
Returns
-------
cube: 3d FitsArray
"""
header = centered_image_header(pshape, shape, dtype=dtype)
im = fa.fitsarray_from_header(header)
im[:] = fill
return im
def centered_image(pshape, shape, fill=0., dtype=np.float64):
"""
Generate a centered cubic map.
Arguments
---------
pshape : physical shape
shape : shape in pixels
Returns
-------
cube: 3d FitsArray
"""
header = centered_image_header(pshape, shape, dtype=dtype)
im = fa.fitsarray_from_header(header)
im[:] = fill
return im
def centered_cubic_map(pshape, shape, fill=0., dtype=np.float64):
"""
Generate a centered cubic map header
Arguments
---------
pshape : physical shape
shape : shape in pixels
Output
------
cube: 3d FitsArray
"""
header = centered_cubic_map_header(pshape, shape, dtype=dtype)
# generate cube and exit
map = fa.fitsarray_from_header(header)
map = fa.InfoArray(data=map, header=dict(header))
map[:] = fill
return map
def centered_cubic_map(pshape, shape, fill=0., dtype=np.float64):
"""
Generate a centered cubic map header
Arguments
---------
pshape : physical shape
shape : shape in pixels
Output
------
cube: 3d FitsArray
"""
header = centered_cubic_map_header(pshape, shape, dtype=dtype)
# generate cube and exit
map = fa.fitsarray_from_header(header)
map = fa.InfoArray(data=map, header=dict(header))
map[:] = fill
return map
def siddon_lo(data_header, cube_header, obstacle=None):
"""
A linear operator performing projection and backprojection using
Siddon.
"""
data = dataarray_from_header(data_header)
data[:] = 0
cube = fa.fitsarray_from_header(cube_header)
cube[:] = 0
def matvec(x):
y = dataarray_from_header(data_header)
y[:] = 0
projector(y, x, obstacle=obstacle)
return y
def rmatvec(x):
y = fa.fitsarray_from_header(cube_header)
y[:] = 0
backprojector(x, y, obstacle=obstacle)
return y
return lo.ndsubclass(cube, data, matvec=matvec, rmatvec=rmatvec, dtype=data.dtype)
"""
Regroup functions to easily perform simulations.
"""
import copy
import numpy as np
import siddon
import fitsarray as fa
default_image_dict = {'NAXIS':2, 'NAXIS1':1, 'NAXIS2':1,
'BITPIX':-64, 'SIMPLE':True,
'CRPIX1':0.5, 'CRPIX2':0.5, 'CDELT1':1., 'CDELT2':1.,
'LON':0., 'LAT':0., 'ROL':0.,
'D':1., 'M1':1., 'M2':0., 'M3':0.}
image_keys = default_image_dict.keys()
default_image = fa.fitsarray_from_header(default_image_dict)
default_object_dict = {'NAXIS':3, 'NAXIS1':1, 'NAXIS2':1, 'NAXIS3':1,
'BITPIX':-64, 'SIMPLE':True,
'CRPIX1':0.5, 'CRPIX2':0.5, 'CRPIX3':0.5,
'CRVAL1':0., 'CRVAL2':0., 'CRVAL3':0.,
'CDELT1':1., 'CDELT2':1., 'CDELT3':1., }
object_keys = default_object_dict.keys()
default_object = fa.fitsarray_from_header(default_object_dict)
class Image(fa.InfoArray):
"""
A subclass of FitsArray with mandatory keywords defining an image
"""
def __new__(subtype, shape, data=None, dtype=float, buffer=None, offset=0,
strides=None, order=None, header=None):
if header is None:
def make_object(*kargs):
import fitsarray as fa
header = make_object_header(*kargs)
return fa.fitsarray_from_header(header)
def make_object(*kargs):
import fitsarray as fa
header = make_object_header(*kargs)
return fa.fitsarray_from_header(header)
'BITPIX': -64,
'SIMPLE': True,
'CRPIX1': 0.5,
'CRPIX2': 0.5,
'CDELT1': 1.,
'CDELT2': 1.,
'LON': 0.,
'LAT': 0.,
'ROL': 0.,
'D': 1.,
'M1': 1.,
'M2': 0.,
'M3': 0.
}
image_keys = default_image_dict.keys()
default_image = fa.fitsarray_from_header(default_image_dict)
default_object_dict = {
'NAXIS': 3,
'NAXIS1': 1,
'NAXIS2': 1,
'NAXIS3': 1,
'BITPIX': -64,
'SIMPLE': True,
'CRPIX1': 0.5,
'CRPIX2': 0.5,
'CRPIX3': 0.5,
'CRVAL1': 0.,
'CRVAL2': 0.,
'CRVAL3': 0.,
'CDELT1': 1.,
def rmatvec(x):
y = fa.fitsarray_from_header(cube_header)
y[:] = 0
backprojector4d(x, y, obstacle=obstacle)
return y