def get_gauss_2d(shape, sigma, pixel_size=1, fourier=False, queue=None, block=False):
"""Get 2D Gaussian of *shape* with standard deviation *sigma* and *pixel_size*. If *fourier* is
True the fourier transform of it is returned so it is faster for usage by convolution. Use
command *queue* if specified. If *block* is True, wait for the kernel to finish.
"""
shape = make_tuple(shape)
pixel_size = get_magnitude(make_tuple(pixel_size))
sigma = get_magnitude(make_tuple(sigma))
LOG.debug('get_gauss_2d, shape: %s, sigma: %s, pixel size: %s, fourier: %s',
shape, sigma, pixel_size, fourier)
if queue is None:
queue = cfg.OPENCL.queue
out = cl.array.Array(queue, shape, dtype=cfg.PRECISION.np_float)
if fourier:
ev = cfg.OPENCL.programs['improc'].gauss_2d_f(queue,
shape[::-1],
None,
out.data,
g_util.make_vfloat2(sigma[1], sigma[0]),
g_util.make_vfloat2(pixel_size[1],
pixel_size[0]))
else:
ev = cfg.OPENCL.programs['improc'].gauss_2d(queue,
shape[::-1],
None,
out.data,
g_util.make_vfloat2(sigma[1], sigma[0]),
g_util.make_vfloat2(pixel_size[1],
pixel_size[0]))
if block:
ev.wait()
return out
def transfer(self,
shape,
pixel_size,
energy,
exponent=False,
offset=None,
t=None,
queue=None,
out=None,
check=True,
block=False):
"""Transfer function of the element on an image plane of size *shape*, use *pixel_size*,
*energy*, *offset* is the physical spatial offset of the element as (y, x), transfer at time
*t*. If *exponent* is true, compute the exponent of the transfer function without applying
the wavenumber. Use *queue* for OpenCL computations and *out* pyopencl array. If *block* is
True, wait for the kernel to finish. If *check* is True, the function is checked for
aliasing artefacts.
"""
shape = make_tuple(shape, num_dims=2)
pixel_size = make_tuple(pixel_size, num_dims=2)
if offset is None:
offset = (0, 0) * q.m
if queue is None:
queue = cfg.OPENCL.queue
return self._transfer(shape,
pixel_size,
energy,
offset,
exponent=exponent,
t=t,
queue=queue,
out=out,
check=check,
block=block)
def project(self,
shape,
pixel_size,
offset=None,
t=None,
queue=None,
out=None,
block=False):
"""Project thickness at time *t* to the image plane of size *shape* which is either 1D and
is extended to (n, n) or is 2D as HxW. *pixel_size* is the point size, also either 1D or 2D.
*offset* is the physical spatial body offset as (y, x). *queue* is an OpenCL command queue,
*out* is the pyopencl array used for result. If *block* is True, wait for the kernel to
finish.
"""
shape = make_tuple(shape, num_dims=2)
pixel_size = make_tuple(pixel_size, num_dims=2)
if offset is None:
offset = (0, 0) * q.m
if queue is None:
queue = cfg.OPENCL.queue
return self._project(shape,
pixel_size,
offset,
t=t,
queue=queue,
out=None,
block=block)
def transfer_fourier(self, shape, pixel_size, energy, t=None, queue=None,
out=None, block=False):
"""Transfer function of the element in Fourier space of size *shape*, use *pixel_size*,
*energy* and comput the function at time *t*. Use *queue* for OpenCL computations and *out*
pyopencl array. If *block* is True, wait for the kernel to finish.
"""
shape = make_tuple(shape, num_dims=2)
pixel_size = make_tuple(pixel_size, num_dims=2)
if queue is None:
queue = cfg.OPENCL.queue
return self._transfer_fourier(shape, pixel_size, energy, t=t, queue=queue, out=out,
block=block)
def compute_slices(self, shape, pixel_size, queue=None, out=None, offset=None):
"""Compute slices with *shape* as (z, y, x), *pixel_size*. Use *queue* and *out* for
outuput. Offset is the starting point offset as (x, y, z).
"""
if queue is None:
queue = cfg.OPENCL.queue
if out is None:
out = cl_array.zeros(queue, shape, dtype=np.uint8)
pixel_size = make_tuple(pixel_size, num_dims=2)
v_1, v_2, v_3 = self._make_inputs(queue, pixel_size)
psm = pixel_size.simplified.magnitude
max_dx = self.max_triangle_x_diff.simplified.magnitude / psm[1]
if offset is None:
offset = gutil.make_vfloat3(0, 0, 0)
else:
offset = offset.simplified.magnitude
offset = gutil.make_vfloat3(offset[0] / psm[1], offset[1] / psm[0], offset[2] / psm[1])
cfg.OPENCL.programs['mesh'].compute_slices(queue,
(shape[2], shape[0]),
None,
v_1.data,
v_2.data,
v_3.data,
out.data,
np.int32(shape[1]),
np.int32(self.num_triangles),
offset,
cfg.PRECISION.np_float(max_dx))
return out
def rescale(image, shape, sampler=None, queue=None, out=None, block=False):
"""Rescale *image* to *shape* and use *sampler* which is a :class:`pyopencl.Sampler` instance.
Use OpenCL *queue* and *out* pyopencl Array. If *block* is True, wait for the copy to finish.
"""
if cfg.PRECISION.cl_float == 8:
raise TypeError('Double precision mode not supported')
shape = make_tuple(shape)
# OpenCL order
factor = float(shape[1]) / image.shape[1], float(shape[0]) / image.shape[0]
LOG.debug('rescale, shape: %s, final_shape: %s, factor: %s', image.shape, shape, factor)
if queue is None:
queue = cfg.OPENCL.queue
if out is None:
out = cl.array.Array(queue, shape, dtype=cfg.PRECISION.np_float)
if not sampler:
sampler = cl.Sampler(cfg.OPENCL.ctx, False, cl.addressing_mode.CLAMP_TO_EDGE, cl.filter_mode.LINEAR)
image = g_util.get_image(image)
ev = cfg.OPENCL.programs['improc'].rescale(queue,
shape[::-1],
None,
image,
out.data,
sampler,
g_util.make_vfloat2(*factor))
if block:
ev.wait()
return out
def decimate(image, shape, sigma=None, average=False, queue=None, block=False):
"""Decimate *image* so that its dimensions match the final *shape*, which has to be a divisor of
the original shape. Remove low frequencies by a Gaussian filter with *sigma* pixels. If *sigma*
is None, use the FWHM of one low resolution pixel. Use command *queue*, if *block* is True, wait
for the copy to finish.
"""
if queue is None:
queue = cfg.OPENCL.queue
image = g_util.get_array(image, queue=queue)
shape = make_tuple(shape)
pow_shape = tuple([next_power_of_two(n) for n in image.shape])
orig_shape = image.shape
if image.shape != pow_shape:
image = pad(image, region=(0, 0) + pow_shape, queue=queue)
if sigma is None:
sigma = tuple([fwnm_to_sigma(float(image.shape[i]) / shape[i], n=2) for i in range(2)])
LOG.debug(
"decimate, shape: %s, final_shape: %s, sigma: %s, average: %s",
image.shape,
shape,
sigma,
average,
)
fltr = get_gauss_2d(image.shape, sigma, fourier=True, queue=queue, block=block)
image = image.astype(cfg.PRECISION.np_cplx)
fft_2(image, queue=queue, block=block)
image *= fltr
ifft_2(image, queue=queue, block=block)
image = crop(image.real, (0, 0) + orig_shape, queue=queue, block=block)
return bin_image(image, shape, average=average, queue=queue, block=block)
def compute_slices(self,
shape,
pixel_size,
queue=None,
out=None,
offset=None):
"""Compute slices with *shape* as (z, y, x), *pixel_size*. Use *queue* and *out* for
outuput. Offset is the starting point offset as (x, y, z).
"""
if queue is None:
queue = cfg.OPENCL.queue
if out is None:
out = cl_array.zeros(queue, shape, dtype=np.uint8)
pixel_size = make_tuple(pixel_size, num_dims=2)
v_1, v_2, v_3 = self._make_inputs(queue, pixel_size)
psm = pixel_size.simplified.magnitude
max_dx = self.max_triangle_x_diff.simplified.magnitude / psm[1]
if offset is None:
offset = gutil.make_vfloat3(0, 0, 0)
else:
offset = offset.simplified.magnitude
offset = gutil.make_vfloat3(offset[0] / psm[1], offset[1] / psm[0],
offset[2] / psm[1])
cfg.OPENCL.programs['mesh'].compute_slices(
queue, (shape[2], shape[0]), None, v_1.data, v_2.data, v_3.data,
out.data, np.int32(shape[1]), np.int32(self.num_triangles), offset,
cfg.PRECISION.np_float(max_dx))
return out
def __init__(
self,
energies,
flux,
sample_distance,
size,
trajectory,
pixel_size=None,
phase_profile="plane",
):
"""Source with a pre-computed *flux* at given *energies*. *flux* can be a 1D array, in which
case there is no spatial distribution of intensities, or it can be a 3D array, in which case
every 2D image *flux[i]* represents spatial intensity distribution for *energies[i]*.
"""
super(FixedSpectrumSource, self).__init__(sample_distance,
size,
trajectory,
phase_profile=phase_profile)
if len(flux) != len(energies):
raise XRaySourceError("Flux must have the same length as energies")
if flux.ndim == 3 and pixel_size is None:
raise XRaySourceError(
"pixel_size must be specified for 3D flux input")
self._pixel_size = make_tuple(pixel_size, num_dims=2)
self._energies = energies
self._flux = flux
def rescale(image, shape, sampler=None, queue=None, out=None, block=False):
"""Rescale *image* to *shape* and use *sampler* which is a :class:`pyopencl.Sampler` instance.
Use OpenCL *queue* and *out* pyopencl Array. If *block* is True, wait for the copy to finish.
"""
if cfg.PRECISION.cl_float == 8:
raise TypeError("Double precision mode not supported")
shape = make_tuple(shape)
# OpenCL order
factor = float(shape[1]) / image.shape[1], float(shape[0]) / image.shape[0]
LOG.debug("rescale, shape: %s, final_shape: %s, factor: %s", image.shape, shape, factor)
if queue is None:
queue = cfg.OPENCL.queue
if out is None:
out = cl.array.Array(queue, shape, dtype=cfg.PRECISION.np_float)
if not sampler:
sampler = cl.Sampler(
cfg.OPENCL.ctx, False, cl.addressing_mode.CLAMP_TO_EDGE, cl.filter_mode.LINEAR
)
image = g_util.get_image(image)
ev = cfg.OPENCL.programs["improc"].rescale(
queue, shape[::-1], None, image, out.data, sampler, g_util.make_vfloat2(*factor)
)
if block:
ev.wait()
return out
def project(self, shape, pixel_size, offset=None, t=None, queue=None, out=None,
block=False):
"""Project thickness at time *t* to the image plane of size *shape* which is either 1D and
is extended to (n, n) or is 2D as HxW. *pixel_size* is the point size, also either 1D or 2D.
*offset* is the physical spatial body offset as (y, x). *queue* is an OpenCL command queue,
*out* is the pyopencl array used for result. If *block* is True, wait for the kernel to
finish.
"""
shape = make_tuple(shape, num_dims=2)
pixel_size = make_tuple(pixel_size, num_dims=2)
if offset is None:
offset = (0, 0) * q.m
if queue is None:
queue = cfg.OPENCL.queue
return self._project(shape, pixel_size, offset, t=t, queue=queue, out=None, block=block)
def decimate(image, shape, sigma=None, average=False, queue=None, block=False):
"""Decimate *image* so that its dimensions match the final *shape*, which has to be a divisor of
the original shape. Remove low frequencies by a Gaussian filter with *sigma* pixels. If *sigma*
is None, use the FWHM of one low resolution pixel. Use command *queue*, if *block* is True, wait
for the copy to finish.
"""
if queue is None:
queue = cfg.OPENCL.queue
image = g_util.get_array(image, queue=queue)
shape = make_tuple(shape)
pow_shape = tuple([next_power_of_two(n) for n in image.shape])
orig_shape = image.shape
if image.shape != pow_shape:
image = pad(image, region=(0, 0) + pow_shape, queue=queue)
if sigma is None:
sigma = tuple([fwnm_to_sigma(float(image.shape[i]) / shape[i], n=2) for i in range(2)])
LOG.debug('decimate, shape: %s, final_shape: %s, sigma: %s, average: %s', image.shape, shape,
sigma, average)
fltr = get_gauss_2d(image.shape, sigma, fourier=True, queue=queue, block=block)
image = image.astype(cfg.PRECISION.np_cplx)
fft_2(image, queue=queue, block=block)
image *= fltr
ifft_2(image, queue=queue, block=block)
image = crop(image.real, (0, 0) + orig_shape, queue=queue, block=block)
return bin_image(image, shape, average=average, queue=queue, block=block)
def project(self,
shape,
pixel_size,
offset=None,
t=None,
queue=None,
out=None,
block=False):
"""Project thickness at time *t* (if it is None no transformation is applied) to the image
plane of size *shape* which is either 1D and is extended to (n, n) or is 2D as HxW.
*pixel_size* is the point size, also either 1D or 2D. *offset* is the physical spatial body
offset as (y, x). *queue* is an OpenCL command queue, *out* is the pyopencl array used for
result. If *block* is True, wait for the kernel to finish.
"""
pixel_size = make_tuple(pixel_size, 2)
if offset is None:
offset = (0, 0) * q.m
if t is not None:
self.move(t)
if self.cache_projection:
if (self._p_cache['time'] is None
or np.any(self._p_cache['ps'] != pixel_size)
or self._p_cache['shape'] != shape
or np.any(self._p_cache['offset'] != offset)):
moved = True
self.update_projection_cache(t=t,
shape=shape,
pixel_size=pixel_size,
offset=offset)
else:
# 0.99 to make sure we recompute when next_time from cached time is current t
moved = self.moved(min(self._p_cache['time'], t),
max(self._p_cache['time'], t),
0.99 * min(pixel_size),
bind=False)
if moved:
LOG.debug('{} computing projection at {}'.format(self, t))
self._p_cache['time'] = t
self._p_cache['projection'] = super(MovableBody, self).project(
shape,
pixel_size,
offset=offset,
t=t,
queue=queue,
out=None,
block=block)
projection = self._p_cache['projection']
else:
projection = super(MovableBody, self).project(shape,
pixel_size,
offset=offset,
t=t,
queue=queue,
out=None,
block=block)
return projection
def get_extrema(sgn):
func = np.max if sgn > 0 else np.min
x_ps = util.make_tuple(pixel_size)[1]
res = [(ball.position[2] + sgn *
(2 * ball.radius + x_ps)).simplified.magnitude
for ball in metaballs]
return func(res)
def project_metaballs_naive(metaballs,
shape,
pixel_size,
offset=None,
z_step=None,
queue=None,
out=None,
block=False):
"""Project a list of :class:`.MetaBall` on an image plane with *shape*, *pixel_size*. *z_step*
is the physical step in the z-dimension, if not specified it is the same as *pixel_size*.
*offset* is the physical spatial body offset as (y, x). Use OpenCL *queue* and *out* pyopencl
Array instance for returning the result. If *block* is True, wait for the kernel to finish.
"""
def get_extrema(sgn):
func = np.max if sgn > 0 else np.min
x_ps = util.make_tuple(pixel_size)[1]
res = [(ball.position[2] + sgn *
(2 * ball.radius + x_ps)).simplified.magnitude
for ball in metaballs]
return func(res)
if offset is None:
offset = (0, 0) * q.m
if not queue:
queue = cfg.OPENCL.queue
if out is None:
out = cl_array.Array(queue, shape, cfg.PRECISION.np_float)
string = b"".join([body.pack() for body in metaballs])
data = np.fromstring(string, dtype=np.float32)
data = cl_array.to_device(queue, data)
n, m = shape
ps = util.make_tuple(pixel_size.simplified.magnitude)
z_step = ps[1] if z_step is None else z_step.simplified.magnitude
z_range = get_extrema(-1), get_extrema(1)
offset = g_util.make_vfloat2(*offset.simplified.magnitude[::-1])
ev = cfg.OPENCL.programs["geometry"].naive_metaballs(
cfg.OPENCL.queue,
(m, n),
None,
out.data,
data.data,
np.int32(len(metaballs)),
offset,
g_util.make_vfloat2(*z_range),
cfg.PRECISION.np_float(z_step),
g_util.make_vfloat2(*ps[::-1]),
np.int32(True),
)
if block:
ev.wait()
return out
def get_gauss_2d(shape, sigma, pixel_size=None, fourier=False):
shape = make_tuple(shape)
sigma = get_magnitude(make_tuple(sigma))
if pixel_size is None:
pixel_size = (1, 1)
else:
pixel_size = get_magnitude(make_tuple(pixel_size))
if fourier:
i = np.fft.fftfreq(shape[1]) / pixel_size[1]
j = np.fft.fftfreq(shape[0]) / pixel_size[0]
i, j = np.meshgrid(i, j)
return np.exp(-2 * np.pi ** 2 * ((i * sigma[1]) ** 2 + (j * sigma[0]) ** 2))
else:
x = (np.arange(shape[1]) - shape[1] / 2) * pixel_size[1]
y = (np.arange(shape[0]) - shape[0] / 2) * pixel_size[0]
x, y = np.meshgrid(x, y)
gauss = np.exp(- x ** 2 / (2. * sigma[1] ** 2) - y ** 2 / (2. * sigma[0] ** 2))
return np.fft.ifftshift(gauss)
def get_gauss_2d(shape, sigma, pixel_size=None, fourier=False):
shape = make_tuple(shape)
sigma = get_magnitude(make_tuple(sigma))
if pixel_size is None:
pixel_size = (1, 1)
else:
pixel_size = get_magnitude(make_tuple(pixel_size))
if fourier:
i = np.fft.fftfreq(shape[1]) / pixel_size[1]
j = np.fft.fftfreq(shape[0]) / pixel_size[0]
i, j = np.meshgrid(i, j)
return np.exp(-2 * np.pi ** 2 * ((i * sigma[1]) ** 2 + (j * sigma[0]) ** 2))
else:
x = (np.arange(shape[1]) - shape[1] // 2) * pixel_size[1]
y = (np.arange(shape[0]) - shape[0] // 2) * pixel_size[0]
x, y = np.meshgrid(x, y)
gauss = np.exp(-(x ** 2) / (2.0 * sigma[1] ** 2) - y ** 2 / (2.0 * sigma[0] ** 2))
return np.fft.ifftshift(gauss)
def main():
args = parse_args()
syris.init(device_index=0)
shape = (args.n, args.n)
pixel_size = 1 * q.um
if args.method == "random":
# Random metaballs creation
metaballs, objects_all = create_metaballs_random(
args.n,
pixel_size,
args.num,
args.min_radius,
args.max_radius,
distance_from_center=args.distance_from_center,
)
elif args.method == "file":
# 1e6 because packing converts to meters
values = np.fromfile(args.input, dtype=np.float32) * 1e6
metaballs, objects_all = create_metaballs(
values.reshape(len(values) // 4, 4), pixel_size)
else:
distance = args.distance or args.n / 4
positions = [
(args.n / 2 - distance, args.n / 2, 0, args.n / 6),
(args.n / 2 + distance, args.n / 2, 0, args.n / 6),
]
metaballs, objects_all = create_metaballs(positions, pixel_size)
if args.output:
with open(args.output, mode="wb") as out_file:
out_file.write(objects_all)
z_min, z_max = get_z_range(metaballs)
print("z min, max:", z_min.rescale(q.um), z_max.rescale(q.um),
args.n * pixel_size + z_min)
if args.algorithm == "fast":
traj = Trajectory([(0, 0, 0)] * q.m)
comp = MetaBalls(traj, metaballs)
thickness = comp.project(shape, pixel_size).get()
else:
print("Z steps:",
int(((z_max - z_min) / pixel_size).simplified.magnitude + 0.5))
thickness = project_metaballs_naive(metaballs, shape,
make_tuple(pixel_size)).get()
if args.output_thickness:
imageio.imwrite(args.output_thickness, thickness)
show(thickness)
plt.show()
def make_sphere(n, radius, pixel_size=1 * q.m, material=None, queue=None):
"""Make a sphere with image shape (*n*, *n*), *radius* and *pixel_size*. Sphere center is in n /
2 + 0.5, which means between two adjacent pixels. *pixel_size*, *material* and *queue*, which is
an OpenCL command queue, are used to create :class:`.StaticBody`.
"""
pixel_size = make_tuple(pixel_size, num_dims=2)
image = np.zeros((n, n), dtype=cfg.PRECISION.np_float)
y, x = np.mgrid[-n // 2 : n // 2, -n // 2 : n // 2]
x = (x + 0.5) * pixel_size[1].simplified.magnitude
y = (y + 0.5) * pixel_size[0].simplified.magnitude
radius = radius.simplified.magnitude
valid = np.where(x ** 2 + y ** 2 < radius ** 2)
image[valid] = 2 * np.sqrt(radius ** 2 - x[valid] ** 2 - y[valid] ** 2)
return StaticBody(image * q.m, pixel_size, material=material, queue=queue)
def test_make_tuple():
assert make_tuple(1) == (1, 1)
assert make_tuple(1, num_dims=3) == (1, 1, 1)
assert make_tuple((1, 2)) == (1, 2)
assert_raises(ValueError, make_tuple, (1, 1), num_dims=3)
assert tuple(make_tuple(1 * q.m).simplified.magnitude) == (1, 1)
assert tuple(make_tuple(1 * q.m,
num_dims=3).simplified.magnitude) == (1, 1, 1)
assert tuple(make_tuple((1, 2) * q.m).simplified.magnitude) == (1, 2)
assert_raises(ValueError, make_tuple, (1, 1) * q.mm, num_dims=3)
def _transfer(
self,
shape,
pixel_size,
energy,
offset,
exponent=False,
t=None,
queue=None,
out=None,
check=True,
block=False,
):
"""Compute the flat field wavefield. Returned *out* array is different from the input
one.
"""
if queue is None:
queue = cfg.OPENCL.queue
if out is None:
out = cl_array.Array(queue, shape, dtype=cfg.PRECISION.np_cplx)
ps = make_tuple(pixel_size)
if t is None:
x, y, z = self.trajectory.control_points.simplified.magnitude[0]
else:
x, y, z = self.trajectory.get_point(t).simplified.magnitude
x += offset[1].simplified.magnitude
y += offset[0].simplified.magnitude
center = (x, y, z)
phase = self.phase_profile != "plane"
parabola = self.phase_profile == "parabola"
compute_exponent = exponent or check and phase
self._transfer_real(shape, center, ps, energy, compute_exponent, phase,
parabola, out, queue, block)
if compute_exponent:
if check and phase and not is_wavefield_sampling_ok(out,
queue=queue):
LOG.error("Insufficient beam phase sampling")
if not exponent:
out = clmath.exp(out, queue=queue)
return out
def __init__(self,
filename,
sample_distance,
dE,
size,
pixel_size,
trajectory,
phase_profile='sphere',
fluctuation=None):
super(SpectraSource, self).__init__()
self.sample_distance = sample_distance.simplified
self.dE = dE
self.pixel_size = make_tuple(pixel_size, num_dims=2)
self.size = size.simplified
self.trajectory = trajectory
self._phase_profile = None
self.phase_profile = phase_profile
self.fluctuation = fluctuation
self._spline2d = self.import_spectra_file(filename)
def main():
args = parse_args()
syris.init(device_index=0)
shape = (args.n, args.n)
pixel_size = 1 * q.um
if args.method == 'random':
# Random metaballs creation
metaballs, objects_all = create_metaballs_random(args.n, pixel_size, args.num,
args.min_radius, args.max_radius)
elif args.method == 'file':
# 1e6 because packing converts to meters
values = np.fromfile(args.input, dtype=np.float32) * 1e6
metaballs, objects_all = create_metaballs(values.reshape(len(values) / 4, 4))
else:
distance = args.distance or args.n / 4
positions = [(args.n / 2 - distance, args.n / 2, 0, args.n / 6),
(args.n / 2 + distance, args.n / 2, 0, args.n / 6)]
metaballs, objects_all = create_metaballs(positions)
if args.output:
with open(args.output, mode='wb') as out_file:
out_file.write(objects_all)
z_min, z_max = get_z_range(metaballs)
print 'z min, max:', z_min.rescale(q.um), z_max.rescale(q.um), args.n * pixel_size + z_min
if args.algorithm == 'fast':
traj = Trajectory([(0, 0, 0)] * q.m)
comp = MetaBalls(traj, metaballs)
thickness = comp.project(shape, pixel_size).get()
else:
print 'Z steps:', int(((z_max - z_min) / pixel_size).simplified.magnitude + 0.5)
thickness = project_metaballs_naive(metaballs, shape, make_tuple(pixel_size)).get()
if args.output_thickness:
save_image(args.output_thickness, thickness)
show(thickness)
plt.show()
def __init__(self,
electron_energy,
el_current,
magnetic_field,
sample_distance,
dE,
size,
pixel_size,
trajectory,
profile_approx=True,
phase_profile='plane'):
"""The parameters are *electron_energy*, electric *el_current*, *magnetic_field*, place it
into *sample_distance* (distance between the source and a sample), take into account energy
spacing *dE* which sets the amount of photons obtained for an energy to be:
.. math::
\Phi = \int_{E - dE / 2}^{E + dE / 2} \Phi(E) dE
Set its *size* (y, x) specified as FWHM and approximate it by a Gaussian. *pixel_size* is
the effective pixel size. *trajectory* is the trajectory defining the source position in
space and time. If *profile_approx* is True, the profile at a given vertical observation
angle will not be integrated over the relevant energies but will be calculated for the mean
energy and multiplied by *dE*. *phase_profile* can be one of 'plane', 'parabola' and
'sphere', where plane denotes constant phase profile (plane wave approximation) and parabola
is the parabolic approximation of the real spherical profile.
"""
super(BendingMagnet, self).__init__()
self.electron_energy = electron_energy.simplified
self.el_current = el_current.simplified
self.magnetic_field = magnetic_field
self.sample_distance = sample_distance.simplified
self.dE = dE
self.size = size.simplified
self.pixel_size = make_tuple(pixel_size, num_dims=2)
self.trajectory = trajectory
self.profile_approx = profile_approx
self._phase_profile = None
self.phase_profile = phase_profile
def project(self, shape, pixel_size, offset=None, t=None, queue=None, out=None,
block=False):
"""Project thickness at time *t* (if it is None no transformation is applied) to the image
plane of size *shape* which is either 1D and is extended to (n, n) or is 2D as HxW.
*pixel_size* is the point size, also either 1D or 2D. *offset* is the physical spatial body
offset as (y, x). *queue* is an OpenCL command queue, *out* is the pyopencl array used for
result. If *block* is True, wait for the kernel to finish.
"""
pixel_size = make_tuple(pixel_size, 2)
if offset is None:
offset = (0, 0) * q.m
if t is not None:
self.move(t)
if self.cache_projection:
if (self._p_cache['time'] is None or np.any(self._p_cache['ps'] != pixel_size) or
self._p_cache['shape'] != shape or np.any(self._p_cache['offset'] != offset)):
moved = True
self.update_projection_cache(t=t, shape=shape, pixel_size=pixel_size, offset=offset)
else:
# 0.99 to make sure we recompute when next_time from cached time is current t
moved = self.moved(min(self._p_cache['time'], t),
max(self._p_cache['time'], t), 0.99 * min(pixel_size),
bind=False)
if moved:
LOG.debug('{} computing projection at {}'.format(self, t))
self._p_cache['time'] = t
self._p_cache['projection'] = super(MovableBody, self).project(shape, pixel_size,
offset=offset,
t=t, queue=queue,
out=None,
block=block)
projection = self._p_cache['projection']
else:
projection = super(MovableBody, self).project(shape, pixel_size, offset=offset,
t=t, queue=queue, out=None, block=block)
return projection
def compute_propagator(size, distance, lam, pixel_size, fresnel=True, region=None,
apply_phase_factor=False, mollified=True, queue=None, block=False):
"""Create a propagator with (*size*, *size*) dimensions for propagation *distance*, wavelength
*lam* and *pixel_size*. If *fresnel* is True, use the Fresnel approximation, if it is False, use
the full propagator (don't approximate the square root). *region* is the diameter of the the
wavefront area which is capable of interference. If *apply_phase_factor* is True, apply the
phase factor defined by Fresnel approximation. If *mollified* is True the aliased frequencies
are suppressed. If command *queue* is specified, execute the kernel on it. If *block* is True,
wait for the kernel to finish.
"""
if size % 2:
raise ValueError('Only even sizes are supported')
if queue is None:
queue = cfg.OPENCL.queue
pixel_size = make_tuple(pixel_size)
def check_cutoff(ps):
# Check the sampling
r_cutoff = compute_aliasing_limit(size, lam, ps, distance, fov=region, fourier=False)
min_n = 4
if r_cutoff < min_n:
LOG.error('Propagator too narrow, propagation distance too small or pixel size too large')
f_cutoff = compute_aliasing_limit(size, lam, ps, distance, fov=region, fourier=True)
if f_cutoff < min_n:
LOG.error('Propagator too wide, propagation distance too large or pixel size too small')
check_cutoff(pixel_size[1])
check_cutoff(pixel_size[0])
out = cl_array.Array(queue, (size, size), cfg.PRECISION.np_cplx)
if apply_phase_factor:
phase_factor = np.exp(2 * np.pi * distance.simplified / lam.simplified * 1j)
else:
phase_factor = 0 + 0j
ev = cfg.OPENCL.programs['physics'].propagator(queue,
(size / 2 + 1, size / 2 + 1),
None,
out.data,
cfg.PRECISION.np_float(distance.simplified),
cfg.PRECISION.np_float(lam.simplified),
g_util.make_vfloat2(*pixel_size[::-1].simplified),
g_util.make_vcomplex(phase_factor),
np.int32(fresnel))
if block:
ev.wait()
if mollified:
def compute_sigma_component(ps):
fwtm = compute_aliasing_limit(size, lam, ps, distance,
fov=size * ps, fourier=True)
if region is not None:
fwtm_region = compute_aliasing_limit(size, lam, ps, distance, region,
fourier=True)
fwtm = min(fwtm_region, fwtm)
sigma = fwnm_to_sigma(fwtm, n=10)
return sigma
sigma = (compute_sigma_component(pixel_size[0]), compute_sigma_component(pixel_size[1]))
mollifier = get_gauss_2d(size, sigma, fourier=False, queue=queue, block=block)
out = out * mollifier
return out
def compute_propagator(
size,
distance,
lam,
pixel_size,
fresnel=True,
region=None,
apply_phase_factor=False,
mollified=True,
queue=None,
block=False,
):
"""Create a propagator with (*size*, *size*) dimensions for propagation *distance*, wavelength
*lam* and *pixel_size*. If *fresnel* is True, use the Fresnel approximation, if it is False, use
the full propagator (don't approximate the square root). *region* is the diameter of the the
wavefront area which is capable of interference. If *apply_phase_factor* is True, apply the
phase factor defined by Fresnel approximation. If *mollified* is True the aliased frequencies
are suppressed. If command *queue* is specified, execute the kernel on it. If *block* is True,
wait for the kernel to finish.
"""
if size % 2:
raise ValueError("Only even sizes are supported")
if queue is None:
queue = cfg.OPENCL.queue
pixel_size = make_tuple(pixel_size)
def check_cutoff(ps):
# Check the sampling
r_cutoff = compute_aliasing_limit(size,
lam,
ps,
distance,
fov=region,
fourier=False)
min_n = 4
if r_cutoff < min_n:
LOG.warning(
"Propagator too narrow, propagation distance too small or pixel size too large"
)
f_cutoff = compute_aliasing_limit(size,
lam,
ps,
distance,
fov=region,
fourier=True)
if f_cutoff < min_n:
LOG.warning(
"Propagator too wide, propagation distance too large or pixel size too small"
)
check_cutoff(pixel_size[1])
check_cutoff(pixel_size[0])
out = cl_array.Array(queue, (size, size), cfg.PRECISION.np_cplx)
if apply_phase_factor:
phase_factor = np.exp(2 * np.pi * distance.simplified /
lam.simplified * 1j)
else:
phase_factor = 0 + 0j
ev = cfg.OPENCL.programs["physics"].propagator(
queue,
(size // 2 + 1, size // 2 + 1),
None,
out.data,
cfg.PRECISION.np_float(distance.simplified),
cfg.PRECISION.np_float(lam.simplified),
g_util.make_vfloat2(*pixel_size[::-1].simplified),
g_util.make_vcomplex(phase_factor),
np.int32(fresnel),
)
if block:
ev.wait()
if mollified:
def compute_sigma_component(ps):
fwtm = compute_aliasing_limit(size,
lam,
ps,
distance,
fov=size * ps,
fourier=True)
if region is not None:
fwtm_region = compute_aliasing_limit(size,
lam,
ps,
distance,
region,
fourier=True)
fwtm = min(fwtm_region, fwtm)
sigma = fwnm_to_sigma(fwtm, n=10)
return sigma
sigma = (compute_sigma_component(pixel_size[0]),
compute_sigma_component(pixel_size[1]))
mollifier = get_gauss_2d(size,
sigma,
fourier=False,
queue=queue,
block=block)
out = out * mollifier
return out
def _transfer(self,
shape,
pixel_size,
energy,
offset,
exponent=False,
t=None,
queue=None,
out=None,
check=True,
block=False):
"""Compute the flat field wavefield. Returned *out* array is different from the input one."""
if queue is None:
queue = cfg.OPENCL.queue
ps = make_tuple(pixel_size)
if t is None:
x, y, z = self.trajectory.control_points.simplified.magnitude[0]
else:
x, y, z = self.trajectory.get_point(t).simplified.magnitude
x += offset[1].simplified.magnitude
y += offset[0].simplified.magnitude
center = (x, y, z)
cl_center = gutil.make_vfloat3(*center)
cl_ps = gutil.make_vfloat2(*pixel_size.simplified.magnitude[::-1])
fov = np.arange(0, shape[0]) * ps[0] - y * q.m
angles = np.arctan((fov / self.sample_distance).simplified)
profile = self._create_vertical_profile(energy, angles, ps[0]).rescale(
1 / q.s).magnitude
profile = cl_array.to_device(queue,
profile.astype(cfg.PRECISION.np_float))
if out is None:
out = cl_array.Array(queue, shape, dtype=cfg.PRECISION.np_cplx)
z_sample = self.sample_distance.simplified.magnitude
lam = energy_to_wavelength(energy).simplified.magnitude
phase = self.phase_profile != 'plane'
parabola = self.phase_profile == 'parabola'
if exponent or check and phase:
ev = cfg.OPENCL.programs['physics'].make_flat(
queue, shape[::-1], None, out.data, profile.data, cl_center,
cl_ps, cfg.PRECISION.np_float(z_sample),
cfg.PRECISION.np_float(lam), np.int32(True), np.int32(phase),
np.int32(parabola))
if check and phase and not is_wavefield_sampling_ok(out,
queue=queue):
LOG.error('Insufficient beam phase sampling')
if not exponent:
out = clmath.exp(out, queue=queue)
else:
ev = cfg.OPENCL.programs['physics'].make_flat(
queue, shape[::-1], None,
out.data, profile.data, cl_center, cl_ps,
cfg.PRECISION.np_float(z_sample), cfg.PRECISION.np_float(lam),
np.int32(exponent), np.int32(phase), np.int32(parabola))
if block:
ev.wait()
return out
def __init__(self, thickness, pixel_size, material=None, queue=None):
super(StaticBody, self).__init__(material)
self.thickness = g_util.get_array(thickness.simplified.magnitude, queue=queue)
self.pixel_size = make_tuple(pixel_size, num_dims=2)
