def findSample(self, meanN, nMax):
auxRandNumbers = cl_array.Array(self.queue, [meanN.shape[0], nMax],
numpy.float32)
self.generator.fill_uniform(auxRandNumbers)
sample = cl_array.Array(self.queue, [
meanN.shape[0],
],
dtype=numpy.int32)
self.program.countEmissions(self.queue, (meanN.size, ), None,
meanN.data, auxRandNumbers.data,
numpy.int32(nMax), sample.data,
numpy.int32(meanN.shape[0]))
return sample
def propagate(samples, shape, energies, distance, pixel_size, region=None,
apply_phase_factor=False, mollified=True, detector=None, offset=None,
queue=None, out=None, t=None, check=True, block=False):
"""Propagate *samples* with *shape* as (y, x) which are
:class:`syris.opticalelements.OpticalElement` instances at *energies* to *distance*. Use
*pixel_size*, limit coherence to *region*, *apply_phase_factor* is as by the Fresnel
approximation phase factor, *offset* is the sample offset. *queue* an OpenCL command queue,
*out* a PyOpenCL Array. If *block* is True, wait for the kernels to finish. If *check* is True,
check the transmission function sampling.
"""
if queue is None:
queue = cfg.OPENCL.queue
u = cl_array.Array(queue, shape, dtype=cfg.PRECISION.np_cplx)
intensity = cl_array.zeros(queue, shape, cfg.PRECISION.np_float)
for energy in energies:
u.fill(0)
u = transfer_many(samples, shape, pixel_size, energy, offset=offset,
queue=queue, out=u, t=t, check=check, block=block)
if distance != 0 * q.m:
lam = energy_to_wavelength(energy)
propagator = compute_propagator(u.shape[0], distance, lam, pixel_size, region=region,
apply_phase_factor=apply_phase_factor,
mollified=mollified, queue=queue, block=block)
fft_2(u, queue=queue, block=block)
u *= propagator
ifft_2(u, queue=queue, block=block)
if detector:
intensity += detector.convert(abs(u) ** 2, energy)
else:
intensity += abs(u) ** 2
return intensity
def get_array(bh_ary, queue):
_import_pyopencl_module()
from pyopencl import array as clarray
return clarray.Array(queue,
bh_ary.shape,
bh_ary.dtype,
data=get_buffer(bh_ary))
def transfer_many(objects, shape, pixel_size, energy, exponent=False, offset=None, queue=None,
out=None, t=None, check=True, block=False):
"""Compute transmission from more *objects*. If *exponent* is True, compute only the exponent,
if it is False, evaluate the exponent. Use *shape* (y, x), *pixel_size*, *energy*, *offset* as
(y, x), OpenCL command *queue*, *out* array, time *t*, check the sampling if *check* is True and
wait for OpenCL kernels if *block* is True. Returned *out* array is different from the input one
because of the pyopencl.clmath behavior.
"""
if queue is None:
queue = cfg.OPENCL.queue
if out is None:
out = cl_array.zeros(queue, shape, cfg.PRECISION.np_cplx)
u_sample = cl_array.Array(queue, shape, cfg.PRECISION.np_cplx)
lam = energy_to_wavelength(energy)
for i, sample in enumerate(objects):
out += sample.transfer(shape, pixel_size, energy, exponent=True, offset=offset, t=t,
queue=queue, out=u_sample, check=False, block=block)
if check and not is_wavefield_sampling_ok(out, queue=queue):
LOG.error('Insufficient transmission function sampling')
# Apply the exponent
if not exponent:
out = clmath.exp(out, queue=queue)
return out
def test_negative_dim_rejection(ctx_factory):
context = ctx_factory()
queue = cl.CommandQueue(context)
with pytest.raises(ValueError):
cl_array.Array(queue, shape=-10, dtype=np.float64)
with pytest.raises(ValueError):
cl_array.Array(queue, shape=(-10, ), dtype=np.float64)
for left_dim in (-1, 0, 1):
with pytest.raises(ValueError):
cl_array.Array(queue, shape=(left_dim, -1), dtype=np.float64)
for right_dim in (-1, 0, 1):
with pytest.raises(ValueError):
cl_array.Array(queue, shape=(-1, right_dim), dtype=np.float64)
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 test_vector_fill(ctx_factory):
context = ctx_factory()
queue = cl.CommandQueue(context)
a_gpu = cl_array.Array(queue, 100, dtype=cltypes.float4)
a_gpu.fill(cltypes.make_float4(0.0, 0.0, 1.0, 0.0))
a = a_gpu.get()
assert a.dtype == cltypes.float4
a_gpu = cl_array.zeros(queue, 100, dtype=cltypes.float4)
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
# Check the sampling
r_cutoff = compute_aliasing_limit(size, lam, pixel_size, 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, pixel_size, distance, fov=region, fourier=True)
if f_cutoff < min_n:
LOG.error('Propagator too wide, propagation distance too large or pixel size too small')
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),
cfg.PRECISION.np_float(pixel_size.simplified),
g_util.make_vcomplex(phase_factor),
np.int32(fresnel))
if block:
ev.wait()
if mollified:
fwtm = compute_aliasing_limit(size, lam, pixel_size, distance,
fov=size * pixel_size, fourier=True)
if region is not None:
fwtm_region = compute_aliasing_limit(size, lam, pixel_size, distance, region,
fourier=True)
fwtm = min(fwtm_region, fwtm)
sigma = fwnm_to_sigma(fwtm, n=10)
mollifier = get_gauss_2d(size, sigma, fourier=False, queue=queue, block=block)
out = out * mollifier
return out
def project_metaballs(metaballs,
shape,
pixel_size,
offset=None,
queue=None,
out=None,
block=False):
"""Project a list of :class:`.MetaBall` on an image plane with *shape*, *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.
"""
string = b"".join([body.pack() for body in metaballs])
n, m = shape
ps = pixel_size.simplified.magnitude
if offset is None:
offset = (0, 0) * q.m
if not queue:
queue = cfg.OPENCL.queue
bodies_mem = cl.Buffer(cfg.OPENCL.ctx,
cl.mem_flags.READ_ONLY | cl.mem_flags.COPY_HOST_PTR,
hostbuf=string)
pbodies_mem = cl.Buffer(cfg.OPENCL.ctx,
cl.mem_flags.READ_WRITE,
size=m * n * cfg.MAX_META_BODIES * 4 * 7)
left_mem = cl.Buffer(cfg.OPENCL.ctx,
cl.mem_flags.READ_WRITE,
size=m * n * 2 * cfg.MAX_META_BODIES)
right_mem = cl.Buffer(cfg.OPENCL.ctx,
cl.mem_flags.READ_WRITE,
size=m * n * 2 * cfg.MAX_META_BODIES)
offset = g_util.make_vfloat2(*offset.simplified.magnitude[::-1])
if out is None:
out = cl_array.Array(queue, shape, cfg.PRECISION.np_float)
ev = cfg.OPENCL.programs["geometry"].metaballs(
cfg.OPENCL.queue,
(m, n),
None,
out.data,
bodies_mem,
pbodies_mem,
left_mem,
right_mem,
np.int32(len(metaballs)),
offset,
cl_array.vec.make_int2(0, 0),
cl_array.vec.make_int4(0, 0, m, n),
g_util.make_vfloat2(ps[1], ps[0]),
np.int32(True),
)
if block:
ev.wait()
return out
def compute_RHS(self, line_da, f, dx):
f_local = line_da.create_local_vector()
line_da.global_to_local(f, f_local)
f_d = cl_array.to_device(self.queue, f_local)
x_d = cl_array.Array(self.queue, (line_da.nz, line_da.ny, line_da.nx),
dtype=np.float64)
self.compute_RHS_kernel(self.queue,
(line_da.nx, line_da.ny, line_da.nz), None,
f_d.data, x_d.data, np.float64(dx),
np.int32(line_da.rank), np.int32(line_da.size))
return x_d
def make_sequence(
self,
t_start,
t_end,
shape=None,
shot_noise=True,
amplifier_noise=True,
source_blur=True,
queue=None,
):
"""Make images between times *t_start* and *t_end*."""
if queue is None:
queue = cfg.OPENCL.queue
shape_0 = self.detector.camera.shape
if shape is None:
shape = shape_0
ps_0 = self.detector.pixel_size
ps = shape_0[0] / float(shape[0]) * ps_0
fps = self.detector.camera.fps
frame_time = 1 / fps
times = (np.arange(
t_start.simplified.magnitude,
t_end.simplified.magnitude,
frame_time.simplified.magnitude,
) * q.s)
image = cl_array.Array(queue, shape, dtype=cfg.PRECISION.np_float)
source_blur_kernel = None
if source_blur:
source_blur_kernel = self.make_source_blur(shape,
ps,
queue=queue,
block=False)
fmt = "Making sequence with shape {} and pixel size {} from {} to {}"
LOG.debug(fmt.format(shape, ps, t_start, t_end))
for t_0 in times:
image.fill(0)
t = t_0
t_next = self.get_next_time(t, ps)
while t_next < t_0 + frame_time:
LOG.debug("Motion blur: {} -> {}".format(t, t_next))
image += self.compute_intensity(t, t_next, shape, ps)
t = t_next
t_next = self.get_next_time(t, ps)
image += self.compute_intensity(t, t_0 + frame_time, shape, ps)
if source_blur:
image = ip.ifft_2(ip.fft_2(image) * source_blur_kernel).real
camera_image = self.detector.camera.get_image(
image, shot_noise=shot_noise, amplifier_noise=amplifier_noise)
LOG.debug("Image: {} -> {}".format(t_0, t_0 + frame_time))
yield camera_image
def transfer(thickness, refractive_index, wavelength, exponent=False, queue=None, out=None,
check=True, block=False):
"""Transfer *thickness* (can be either a numpy or pyopencl array) with *refractive_index* and
given *wavelength*. If *exponent* is True, compute the exponent of the function without applying
the wavenumber. Use command *queue* for computation 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. Returned *out* array is different from the input one because of the pyopencl.clmath
behavior.
"""
if queue is None:
queue = cfg.OPENCL.queue
if isinstance(thickness, cl_array.Array):
thickness_mem = thickness
else:
prep = thickness.simplified.magnitude.astype(cfg.PRECISION.np_float)
thickness_mem = cl_array.to_device(queue, prep)
if out is None:
out = cl_array.Array(queue, thickness_mem.shape, cfg.PRECISION.np_cplx)
if exponent or check:
wavenumber = cfg.PRECISION.np_float(2 * np.pi / wavelength.simplified.magnitude)
ev = cfg.OPENCL.programs['physics'].transmission_add(queue,
thickness_mem.shape[::-1],
None,
out.data,
thickness_mem.data,
cfg.PRECISION.np_cplx(
refractive_index),
wavenumber,
np.int32(1))
if check and not is_wavefield_sampling_ok(out, queue=queue):
LOG.error('Insufficient transmission function sampling')
if not exponent:
# Apply the exponent
out = clmath.exp(out, queue=queue)
else:
ev = cfg.OPENCL.programs['physics'].transfer(queue,
thickness_mem.shape[::-1],
None,
out.data,
thickness_mem.data,
cfg.PRECISION.np_cplx(refractive_index),
cfg.PRECISION.np_float(
wavelength.simplified.magnitude))
if block:
ev.wait()
return out
def empty(shape,
dtype,
cq: CommandQueue = None,
order="C",
allocator=None,
data=None,
offset=0,
strides=None,
events=None,
_flags=None):
cq = get_current_queue() if cq is None else cq
res = Array.from_array(cl_array.Array(cq, shape, dtype, order, allocator))
res.add_latest_event('empty')
return res
def __init__(self,
size,
dim,
mask=(OCLC_X | OCLC_V | OCLC_A | OCLC_M),
dtype=np.float32):
self.__dtype = dtype
self.__size = size
self.__dim = dim
self.__opt_arrays = dict()
self.__cl_context = cl.create_some_context()
self.__cl_queue = cl.CommandQueue(
self.__cl_context,
properties=cl.command_queue_properties.PROFILING_ENABLE)
if mask & OCLC_V:
self.__V_cla = cla.Array(self.__cl_queue, (size, dim), dtype)
else:
self.__V_cla = None
if mask & OCLC_X:
self.__X_cla = cla.Array(self.__cl_queue, (size, dim), dtype)
else:
self.__X_cla = None
if mask & OCLC_A:
self.__A_cla = cla.Array(self.__cl_queue, (size, dim), dtype)
else:
self.__A_cla = None
if mask & OCLC_M:
self.__M_cla = cla.Array(self.__cl_queue, (size, 1), dtype)
else:
self.__M_cla = None
def forward(ctx, input):
ctx.save_for_backward(input)
krnl = cl_reduct_krnl_build(ctx.cl_ctx,
np.float32,
neutral="0",
reduce_expr="a+b",
map_expr="x[i]",
arguments="__global float *x")
ret = krnl(
pycl_array.Array(ctx.cl_queue,
input.size,
dtype=np.float32,
data=input)).data
ret.shape = (1, )
ret.dtype = np.float32
return ret
def computeAcc(self, xd, yd, zd, vxd, vyd, vzd, qd, md, axd, ayd, azd, t,
dt):
# Compute average numbers of scattered photons
nbars = cl_array.zeros_like(xd)
if self.sigma == None:
self.program.compute_mean_scattered_photons_homogeneous_beam(
self.queue, (xd.size, ), None, xd.data,
yd.data, zd.data, vxd.data, vyd.data, vzd.data,
numpy.float32(self.k0[0]), numpy.float32(self.k0[1]),
numpy.float32(self.k0[2]), numpy.float32(self.gamma),
numpy.float32(self.delta0), numpy.float32(self.S),
numpy.float32(dt), numpy.int32(xd.size), nbars.data)
else:
self.program.compute_mean_scattered_photons_gaussian_beam(
self.queue, (xd.size, ), None, xd.data,
yd.data, zd.data, vxd.data, vyd.data, vzd.data,
numpy.float32(self.k0[0]), numpy.float32(self.k0[1]),
numpy.float32(self.k0[2]), numpy.float32(self.x0[0]),
numpy.float32(self.x0[1]), numpy.float32(self.x0[2]),
numpy.float32(self.sigma), numpy.float32(self.gamma),
numpy.float32(self.delta0), numpy.float32(self.S),
numpy.float32(dt), numpy.int32(xd.size), nbars.data)
# Compute scattered photons and associated recoil kicks
nMax = int(
math.ceil(10.0 * self.S * (self.gamma / 2.0 / numpy.pi) * dt))
actualNs = self.findSample(nbars, nMax)
recoilDirectionsD = cl_array.Array(self.queue, [nbars.size, nMax, 3],
dtype=numpy.float32)
self.generator.fill_normal(recoilDirectionsD)
# apply recoil kicks to particles
recoilMomentum = numpy.linalg.norm(
self.k0) * self._PlanckConstantReduced
self.program.computeKicks(self.queue, (xd.size, ),
None, md.data, actualNs.data,
numpy.int32(nMax), recoilDirectionsD.data,
numpy.float32(self.k0[0]),
numpy.float32(self.k0[1]),
numpy.float32(self.k0[2]),
numpy.float32(recoilMomentum),
numpy.float32(dt), axd.data, ayd.data,
azd.data, numpy.int32(xd.shape[0]))
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 try_offset(ctx):
queue = cl.CommandQueue(ctx)
a = cl_array.Array(queue, shape=(2048,), dtype="uint8")
b = a[3:]
try:
b.data
except Exception as ex:
pass
# print("Always fails due to ArrayHasOffsetError")
# print(repr(ex))
for offset in 3, 4, 8, 16, 32, 64, 128:
try:
b = a[offset:]
b.base_data[b.offset : b.offset + b.nbytes]
print(f"offset={b.offset} OK")
except Exception as ex:
# Fails in some platform due to clCreateSubBuffer MISALIGNED_SUB_BUFFER_OFFSET"
print(f"offset={b.offset} not OK")
def add_array_by_name(self, key, size=None, dim=None, dtype=None):
"""
Add a new array by name.
:param key: The key (or name) of the new array
:param size: (Default: current) The size of the new array, if not specified by default it uses the current size
:param dim: (Default: current) The dim of the new array, if not specified by default it uses the current dim
:param dtype: (Dafault: current) The dtype of the new array, if not specified by default it uses the context dtype
"""
if dim is None:
dim = self.__dim
if size is None:
size = self.__size
if dtype is None:
dtype = self.dtype
self.__opt_arrays[key] = cla.Array(self.__cl_queue, (size, dim),
dtype=dtype)
def alloc_buf(self, size=None, like=None, wrap_in_array=True):
mf = cl.mem_flags
if like is not None:
if like.base is not None:
hbuf = like.base
else:
hbuf = like
buf = cl.Buffer(self.ctx,
mf.READ_WRITE | mf.COPY_HOST_PTR,
hostbuf=hbuf)
self.buffers[buf] = hbuf
self.to_buf(buf)
if wrap_in_array:
self.arrays[buf] = clarray.Array(self.ctx,
like.shape,
like.dtype,
data=buf)
else:
buf = cl.Buffer(self.ctx, mf.READ_WRITE, size)
return buf
def setup_device(self, imshape):
print('Setting up with imshape = %s' % (str(imshape)))
self.imshape = imshape
self.clIm = cla.Array(self.q, imshape, numpy.float32)
self.clm = cla.empty_like(self.clIm)
self.clx = cla.empty_like(self.clIm)
self.cly = cla.empty_like(self.clIm)
self.clO = cla.zeros_like(self.clIm)
self.clM = cla.zeros_like(self.clIm)
self.clF = cla.empty_like(self.clIm)
self.clS = cla.empty_like(self.clIm)
self.clThisS = cla.empty_like(self.clIm)
self.clScratch = cla.empty_like(self.clIm)
self.radial_prg = pyopencl.Program(self.ctx, PROGRAM).build()
self.sobel = Sobel(self.ctx, self.q)
#self.sepcorr2d = NaiveSeparableCorrelation(self.ctx, self.q)
self.sepcorr2d = LocalMemorySeparableCorrelation(self.ctx, self.q)
self.accum = ElementwiseKernel(self.ctx, 'float *a, float *b',
'a[i] += b[i]')
self.norm_s = ElementwiseKernel(self.ctx,
'float *s, const float nRadii',
's[i] = -1 * s[i] / nRadii', 'norm_s')
self.accum_s = ElementwiseKernel(self.ctx,
'float *a, float *b, const float nr',
'a[i] -= b[i] / nr')
self.gaussians = {}
self.gaussian_prgs = {}
self.minmax = MinMaxKernel(self.ctx, self.q)
def setup_device(self, imshape):
print('Setting up with imshape = %s' % (str(imshape)))
self.cached_shape = imshape
self.clIm = cla.Array(self.q, imshape, np.float32)
self.clm = cla.empty_like(self.clIm)
self.clx = cla.empty_like(self.clIm)
self.cly = cla.empty_like(self.clIm)
self.clO = cla.zeros_like(self.clIm)
self.clM = cla.zeros_like(self.clIm)
self.clF = cla.empty_like(self.clIm)
self.clS = cla.empty_like(self.clIm)
self.clThisS = cla.empty_like(self.clIm)
self.clScratch = cla.empty_like(self.clIm)
self.radial_prg = pyopencl.Program(self.ctx, RADIAL_PROGRAM).build()
self.sobel = Sobel(self.ctx, self.q)
#self.sepcorr2d = NaiveSeparableCorrelation(self.ctx, self.q)
self.sepcorr2d = LocalMemorySeparableCorrelation(self.ctx, self.q)
self.accum = ElementwiseKernel(self.ctx,
'float *a, float *b',
'a[i] += b[i]')
self.norm_s = ElementwiseKernel(self.ctx,
'float *s, const float nRadii',
's[i] = -1 * s[i] / nRadii',
'norm_s')
self.accum_s = ElementwiseKernel(self.ctx,
'float *a, float *b, const float nr',
'a[i] -= b[i] / nr')
self.gaussians = {}
self.gaussian_prgs = {}
self.minmax = MinMaxKernel(self.ctx, self.q)
# starburst storage
clImageFormat = cl.ImageFormat(cl.channel_order.R,
cl.channel_type.FLOAT)
self.clIm2D = cl.Image(self.ctx,
mf.READ_ONLY,
clImageFormat,
imshape)
# Create sampler for sampling image object
self.imSampler = cl.Sampler(self.ctx,
False, # Non-normalized coordinates
cl.addressing_mode.CLAMP_TO_EDGE,
cl.filter_mode.LINEAR)
self.cl_find_ray_boundaries = FindRayBoundaries(self.ctx, self.q)
self.calcF = self.radial_prg.calcF
self.calcOM = self.radial_prg.calcOM
def arr_from_np(queue, nparr):
if nparr.dtype == np.object:
nparr = np.concatenate(nparr)
buf = cl.Buffer(ctx, mf.READ_WRITE | mf.COPY_HOST_PTR, hostbuf=nparr)
return clarray.Array(queue, nparr.shape, nparr.dtype, data=buf)
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 solve_reduced_system(self, line_da, x_UH, x_LH, x_R_d, reduced_solver):
nz, ny, nx = line_da.nz, line_da.ny, line_da.nx
line_rank = line_da.rank
line_size = line_da.size
x_UH_line = np.zeros(2 * line_size, dtype=np.float64)
x_LH_line = np.zeros(2 * line_size, dtype=np.float64)
line_da.gather([np.array([x_UH[0], x_UH[-1]]), 2, MPI.DOUBLE],
[x_UH_line, 2, MPI.DOUBLE])
line_da.gather([np.array([x_LH[0], x_LH[-1]]), 2, MPI.DOUBLE],
[x_LH_line, 2, MPI.DOUBLE])
lengths = np.ones(line_size)
displacements = np.arange(0, 2 * line_size, 2)
start_z, start_y, start_x = 0, 0, displacements[line_rank]
subarray_aux = MPI.DOUBLE.Create_subarray([nz, ny, 2 * line_size],
[nz, ny, 2],
[start_z, start_y, start_x])
subarray = subarray_aux.Create_resized(0, 8)
subarray.Commit()
x_R_faces_d = cl_array.Array(self.queue, (nz, ny, 2), np.float64)
self.copy_faces_kernel(self.queue, [1, ny, nz], None, x_R_d.data,
x_R_faces_d.data, np.int32(nx), np.int32(ny),
np.int32(nz), np.int32(line_da.mx),
np.int32(line_da.npx))
x_R_faces = x_R_faces_d.get()
x_R_faces_line = np.zeros([nz, ny, 2 * line_size], dtype=np.float64)
line_da.gatherv([x_R_faces, MPI.DOUBLE],
[x_R_faces_line, lengths, displacements, subarray])
if line_rank == 0:
a_reduced = np.zeros(2 * line_size, dtype=np.float64)
b_reduced = np.zeros(2 * line_size, dtype=np.float64)
c_reduced = np.zeros(2 * line_size, dtype=np.float64)
a_reduced[0::2] = -1.
a_reduced[1::2] = x_UH_line[1::2]
b_reduced[0::2] = x_UH_line[0::2]
b_reduced[1::2] = x_LH_line[1::2]
c_reduced[0::2] = x_LH_line[0::2]
c_reduced[1::2] = -1.
a_reduced[0], c_reduced[0] = 0.0, 0.0
b_reduced[0] = 1.0
a_reduced[-1], c_reduced[-1] = 0.0, 0.0
b_reduced[-1] = 1.0
a_reduced[1] = 0.
c_reduced[-2] = 0.
a_reduced_d = cl_array.to_device(self.queue, a_reduced)
b_reduced_d = cl_array.to_device(self.queue, b_reduced)
c_reduced_d = cl_array.to_device(self.queue, c_reduced)
c2_reduced_d = cl_array.to_device(self.queue, c_reduced)
d_reduced_d = cl_array.to_device(self.queue, x_R_faces_line)
reduced_solver.solve(a_reduced_d, b_reduced_d, c_reduced_d,
c2_reduced_d, d_reduced_d)
params = d_reduced_d.get()
else:
params = None
params_local = np.zeros([nz, ny, 2], dtype=np.float64)
line_da.scatterv([params, lengths, displacements, subarray],
[params_local, MPI.DOUBLE])
alpha = params_local[:, :, 0].copy()
beta = params_local[:, :, 1].copy()
return alpha, beta
def volume_empty(self):
return cl_array.Array(self.ctx,
queue=self.queue,
shape=(self.K, self.block_size),
dtype=np.float32)
def im2col_old(img, rec_field, n_filters, stride=1, zero_pad=0, wait_for=None):
"""
:type stride: int
:type zero_pad: int
"""
dtype = 'float' if img.dtype == np.float32 else 'double'
q = clplatf.qs[0]
d1, h1, w1 = img.shape
kh, kw = rec_field
out_h = kh * kw * d1
w2 = (w1 - kw + 2 * zero_pad) // stride + 1
h2 = (h1 - kh + 2 * zero_pad) // stride + 1
# TODO check if w2 or h2 is not int and raise something or zeropad...
out_w = w2 * h2
# alloc output
col = clarray.Array(q, (out_h, out_w), img.dtype)
prg = cl.Program(
clplatf.ctx, """
__kernel void im2col_k(__global %(dtype)s *img,
int h,
int w,
int o_h,
int o_w,
int kh,
int kw,
int stride,
int padding,
__global %(dtype)s *out) {
int gid = get_global_id(0);
int out_w = o_w * o_h;
int out_h = kw * kh;
int out_x = gid %% out_w;
int out_y = gid / out_w %% out_h;
int kx = out_y %% kw;
int ky = (out_y / kh) %% kh;
int ch = gid / (kh * kw * o_h * o_w);
int in_x = kx + (out_x %% o_w)*stride - padding;
int in_y = ky + (out_x / o_w)*stride - padding;
if (in_x >= 0 && in_x < w && in_y >= 0 && in_y < h) {
out[gid] = img[(h * ch + in_y) * w + in_x];
} else {
out[gid] = 0;
}
}
""" % locals()).build()
evt = prg.im2col_k(q, (out_h * out_w, ),
None,
img.data,
np.int32(h1),
np.int32(w1),
np.int32(h2),
np.int32(w2),
np.int32(kh),
np.int32(kw),
np.int32(stride),
np.int32(zero_pad),
col.data,
wait_for=wait_for)
return col, evt
def array(self, shape, dtype, strides=None, allocator=None):
return clarray.Array(self._queue,
shape,
dtype,
strides=strides,
allocator=allocator)
def make_tomography(self,
projections,
rotation,
pause,
num_ref_per_block=1,
num_proj_per_block=1,
num_dark_img=0,
start_frame=0,
shape=None,
shot_noise=True,
amplifier_noise=True,
source_blur=True,
queue=None):
"""Make sequence of *projections* projection images over 0 to *rotation* degrees. *pause*
after each image. Proceed in image blocks, with *num_ref_per_block* flatfields and
*num_proj_per_block* projections per block. Make *num_dark_img* dark images at the beginning.
Start with *start_frame* (must be less or equal total number of images). """
if queue is None:
queue = cfg.OPENCL.queue
shape_0 = self.detector.camera.shape
if shape is None:
shape = shape_0
ps_0 = self.detector.pixel_size
ps = shape_0[0] / float(shape[0]) * ps_0
image = cl_array.Array(queue, shape, dtype=cfg.PRECISION.np_float)
source_blur_kernel = None
#if source_blur:
#source_blur_kernel = self.make_source_blur(shape, ps, queue=queue, block=False)
angles = np.linspace(0, rotation, num=projections) * q.deg
angle_step_size = abs(angles[1] - angles[0])
overall_no_images = num_dark_img + projections + \
projections / num_proj_per_block * num_ref_per_block
DARK_IMAGE = 0
PROJECTION = 1
FLATFIELD = 2
darks = np.repeat(DARK_IMAGE, num_dark_img)
blocks = np.repeat(PROJECTION, num_proj_per_block)
blocks = np.append(blocks, np.repeat(FLATFIELD, num_ref_per_block))
blocks = np.tile(blocks, projections / num_proj_per_block)
image_type = np.append(darks, blocks)
self.clock = 0 * q.s
exptime = self.detector.camera._exp_time
counter_darkimages = 0
counter_projections = 0
counter_flatfields = 0
for i in np.arange(0, overall_no_images):
if start_frame > i:
self.clock += exptime + pause
if image_type[i] == DARK_IMAGE:
counter_darkimages += 1
elif image_type[i] == PROJECTION:
counter_projections += 1
elif image_type[i] == FLATFIELD:
counter_flatfields += 1
yield None, None
else:
image.fill(0)
t_0 = self.clock
t_next = self.get_next_time(self.clock, ps)
image_name = None
# Dark images:
if image_type[i] == DARK_IMAGE:
image_name = 'dark_{:>05}.tif'.format(counter_darkimages)
counter_darkimages += 1
self.clock += exptime + pause
# Projections:
elif image_type[i] == PROJECTION:
# Turn sample
self.tomo_rotate(angles[counter_projections])
while t_next < t_0 + exptime:
LOG.debug('Motion blur: {} -> {}'.format(t_0, t_next))
image += self.compute_intensity(
self.clock, t_next, shape, ps)
self.clock = t_next
t_next = self.get_next_time(self.clock, ps)
image += self.compute_intensity(self.clock, t_0 + exptime,
shape, ps)
self.clock = t_0 + exptime + pause
#if source_blur:
#image = ip.ifft_2(ip.fft_2(image) * source_blur_kernel).real
image_name = 'proj_{:>05}.tif'.format(counter_projections)
counter_projections += 1
LOG.debug('Projection: {} -> {}'.format(
t_0, t_0 + exptime))
# Flatfields:
elif image_type[i] == FLATFIELD:
while t_next < t_0 + exptime:
LOG.debug('Motion blur: {} -> {}'.format(t_0, t_next))
image += self.compute_intensity(self.clock,
t_next,
shape,
ps,
flat=True)
self.clock = t_next
t_next = self.get_next_time(self.clock, ps)
image += self.compute_intensity(self.clock,
t_0 + exptime,
shape,
ps,
flat=True)
self.clock = t_0 + exptime + pause
image_name = 'ref_{:>05}.tif'.format(counter_flatfields)
counter_flatfields += 1
else:
raise ValueError("Unknow image type requested. "\
"Options are: Dark image, projection, flatfield.")
camera_image = self.detector.camera.get_image(
image,
shot_noise=shot_noise,
amplifier_noise=amplifier_noise)
yield camera_image, image_name
def compute_intensity(self,
t_0,
t_1,
shape,
pixel_size,
queue=None,
block=False,
flat=False):
"""Compute intensity between times *t_0* and *t_1*."""
exp_time = (t_1 - t_0).simplified.magnitude
if queue is None:
queue = cfg.OPENCL.queue
u = cl_array.Array(queue, shape, dtype=cfg.PRECISION.np_cplx)
u_sample = cl_array.zeros(queue, shape, cfg.PRECISION.np_cplx)
intensity = cl_array.zeros(queue, shape, cfg.PRECISION.np_float)
for energy in self.energies:
u.fill(1)
for oeid, oe in enumerate(self.oe):
if flat and oe == self.sample:
continue
u *= oe.transfer(shape,
pixel_size,
energy,
t=t_0,
queue=queue,
out=u_sample,
check=False,
block=block)
# Propagate and blur optical element when not source
if self.distances[oeid] != 0 * q.m and oe != self.source:
lam = energy_to_wavelength(energy)
propagator = compute_propagator(u.shape[0],
self.distances[oeid],
lam,
pixel_size,
queue=queue,
block=block,
mollified=True)
ip.fft_2(u, queue=queue, block=block)
sdistance = np.sum(self.distances[:oeid + 1])
fwhm = (self.distances[oeid] * self.source.size /
sdistance).simplified
sigma = smath.fwnm_to_sigma(fwhm, n=2)
psf = ip.get_gauss_2d(shape,
sigma,
pixel_size=pixel_size,
fourier=True,
queue=queue,
block=block)
u *= psf
u *= propagator
ip.ifft_2(u, queue=queue, block=block)
intensity += self.detector.convert(abs(u)**2, energy)
return intensity * exp_time
