def distribute_observation(detectors,
observations,
rank=None,
comm=MPI.COMM_WORLD):
size = comm.Get_size()
if size == 1:
return detectors.copy(), list(observations)
if rank is None:
rank = comm.Get_rank()
nthreads = openmp_num_threads()
ndetectors = np.sum(~detectors)
nobservations = len(observations)
# number of observations. They should approximatively be of the same length
nx = nobservations
# number of detectors, grouped by the number of cpu cores
ny = int(np.ceil(ndetectors / nthreads))
# we start with the minimum blocksize and increase it until we find a
# configuration that covers all the observations
blocksize = int(np.ceil(nx * ny / size))
while True:
# by looping over x first, we favor larger numbers of detectors and
# fewer numbers of observations per processor, to minimise inter-
# processor communication in case of correlations between
# detectors
for xblocksize in range(1, blocksize + 1):
if blocksize / xblocksize != blocksize // xblocksize:
continue
yblocksize = int(blocksize // xblocksize)
nx_block = int(np.ceil(nx / xblocksize))
ny_block = int(np.ceil(ny / yblocksize))
if nx_block * ny_block <= size:
break
if nx_block * ny_block <= size:
break
blocksize += 1
ix = rank // ny_block
iy = rank % ny_block
# check that the processor has something to do
if ix >= nx_block:
idetector = slice(0, 0)
iobservation = slice(0, 0)
else:
idetector = slice(iy * yblocksize * nthreads, (iy+1) * yblocksize * \
nthreads)
iobservation = slice(ix * xblocksize, (ix + 1) * xblocksize)
detectors_ = detectors.copy()
igood = np.where(~detectors_.ravel())[0]
detectors_.ravel()[igood[0:idetector.start]] = True
detectors_.ravel()[igood[idetector.stop:]] = True
observations_ = observations[iobservation]
return detectors_, observations_
def distribute_observation(detectors, observations, rank=None,
comm=MPI.COMM_WORLD):
size = comm.Get_size()
if size == 1:
return detectors.copy(), list(observations)
if rank is None:
rank = comm.Get_rank()
nthreads = openmp_num_threads()
ndetectors = np.sum(~detectors)
nobservations = len(observations)
# number of observations. They should approximatively be of the same length
nx = nobservations
# number of detectors, grouped by the number of cpu cores
ny = int(np.ceil(ndetectors / nthreads))
# we start with the minimum blocksize and increase it until we find a
# configuration that covers all the observations
blocksize = int(np.ceil(nx * ny / size))
while True:
# by looping over x first, we favor larger numbers of detectors and
# fewer numbers of observations per processor, to minimise inter-
# processor communication in case of correlations between
# detectors
for xblocksize in range(1, blocksize+1):
if blocksize / xblocksize != blocksize // xblocksize:
continue
yblocksize = int(blocksize // xblocksize)
nx_block = int(np.ceil(nx / xblocksize))
ny_block = int(np.ceil(ny / yblocksize))
if nx_block * ny_block <= size:
break
if nx_block * ny_block <= size:
break
blocksize += 1
ix = rank // ny_block
iy = rank % ny_block
# check that the processor has something to do
if ix >= nx_block:
idetector = slice(0,0)
iobservation = slice(0,0)
else:
idetector = slice(iy * yblocksize * nthreads, (iy+1) * yblocksize * \
nthreads)
iobservation = slice(ix * xblocksize, (ix+1) * xblocksize)
detectors_ = detectors.copy()
igood = np.where(~detectors_.ravel())[0]
detectors_.ravel()[igood[0:idetector.start]] = True
detectors_.ravel()[igood[idetector.stop:]] = True
observations_ = observations[iobservation]
return detectors_, observations_
def get_projection_operator(self, sampling, scene, i_band=None, verbose=True):
"""
Return the peak sampling operator.
Parameters
----------
sampling : QubicSampling
The pointing information.
scene : QubicScene
The observed scene.
verbose : bool, optional
If true, display information about the memory allocation.
"""
if not isinstance(scene.nu, float) and i_band == None:
mask = [self.detector.index < 2295//2,
self.detector.index > 2295//2]
dtype = self.synthetic_beam.dtype
if scene.nside > 8192:
dtype_index = np.dtype(np.int64)
else:
dtype_index = np.dtype(np.int32)
theta0, phi, vals = _peak_angles_fraction(self, scene, self.synthetic_beam.fraction, 0)
theta1, phi, vals = _peak_angles_fraction(self, scene, self.synthetic_beam.fraction, 1)
ncolmax = max(theta0.shape[-1], theta1.shape[-1])
cls = {'I' : FSRMatrix,
'QU' : FSRRotation2dMatrix,
'IQU': FSRRotation3dMatrix}[scene.kind]
ndims = len(scene.kind)
s = cls((len(self) * len(sampling) * ndims, 12 * scene.nside**2 * ndims),
ncolmax=ncolmax,
dtype=dtype,
dtype_index=dtype_index,
verbose=verbose)
for i in [0, 1]:
focal_plane = self[mask[i]]
p = focal_plane.get_projection_operator(sampling, scene, i_band=i)
smask = np.multiply.outer(mask[i], np.ones(len(sampling)), dtype=bool)
smask = np.asarray(smask).reshape(-1)
s.data[smask, :p.matrix.data.shape[-1]] = p.matrix.data
# set_trace()
# sh = s.data[mask[i], p.matrix.data.shape[-1]:].shape
# tp = {'I' : "int, float",
# 'IQ' : "int, float, float",
# 'IQU' : "int, float, float, float"}[scene.kind]
# s.data[mask[i], p.matrix.data.shape[-1]:] = np.ones(sh, dtype=tp)
shapeout = {'I' : (len(self), len(sampling)),
'IQ' : (len(self), len(sampling), 2),
'IQU' : (len(self), len(sampling), 3)}[scene.kind]
return ProjectionOperator(s, shapeout=shapeout)
else:
rotation = sampling.cartesian_galactic2instrument
dtype = self.synthetic_beam.dtype
fraction = self.synthetic_beam.fraction
ndetectors = len(self)
ntimes = len(sampling)
nside = scene.nside
theta, phi, vals = _peak_angles_fraction(self, scene, fraction, i_band)
ncolmax = theta.shape[-1]
thetaphi = _pack_vector(theta, phi) # (ndetectors, ncolmax, 2)
direction = Spherical2CartesianOperator('zenith,azimuth')(thetaphi)
e_nf = direction[:, None, :, :]
if nside > 8192:
dtype_index = np.dtype(np.int64)
else:
dtype_index = np.dtype(np.int32)
cls = {'I': FSRMatrix,
'QU': FSRRotation2dMatrix,
'IQU': FSRRotation3dMatrix}[scene.kind]
ndims = len(scene.kind)
s = cls((ndetectors * ntimes * ndims, 12 * nside**2 * ndims),
ncolmax=ncolmax, dtype=dtype, dtype_index=dtype_index,
verbose=verbose)
index = s.data.index.reshape((ndetectors, ntimes, ncolmax))
nthreads = openmp_num_threads()
try:
import mkl
mkl.set_num_threads(1)
except:
pass
def func_thread(i):
# e_nf[i] shape: (1, ncolmax, 3)
# e_ni shape: (ntimes, ncolmax, 3)
e_ni = rotation.T(e_nf[i].swapaxes(0, 1)).swapaxes(0, 1)
index[i] = Cartesian2HealpixOperator(nside)(e_ni)
pool = Pool(nthreads)
pool.map(func_thread, xrange(ndetectors))
pool.close()
pool.join()
try:
mkl.set_num_threads(nthreads)
except:
pass
if scene.kind == 'I':
value = s.data.value.reshape(ndetectors, ntimes, ncolmax)
value[...] = vals[:, None, :]
shapeout = (ndetectors, ntimes)
else:
func = 'pointing_matrix_rot{0}d_i{1}_m{2}'.format(
ndims, dtype_index.itemsize, dtype.itemsize)
try:
getattr(flib.polarization, func)(
rotation.data.T, direction.T, s.data.ravel().view(np.int8),
vals.T)
except AttributeError:
raise TypeError(
'The projection matrix cannot be created with types: {0} a'
'nd {1}.'.format(dtype, dtype_index))
if scene.kind == 'QU':
shapeout = (ndetectors, ntimes, 2)
else:
shapeout = (ndetectors, ntimes, 3)
return ProjectionOperator(s, shapeout=shapeout)
product)
from pyoperators.utils.mpi import MPI, distribute_shape, distribute_shapes
from pysimulators import Map
from pysimulators.acquisitionmodels import (block_diagonal, PointingMatrix,
ProjectionInMemoryOperator, ProjectionOnFlyOperator)
from pysimulators.wcsutils import fitsheader2shape, str2fitsheader
from . import tamasisfortran as tmf
from . import var
from .utils import diff, diffT, diffTdiff, shift
from .mpiutils import gather_fitsheader_if_needed, scatter_fitsheader
try:
import fftw3
MAX_FFTW_NUM_THREADS = 1 if fftw3.planning.lib_threads is None \
else openmp_num_threads()
except ImportError:
print('Warning: Library PyFFTW3 is not installed.')
except:
print('Warning: Problem loading PyFFTW3.')
__all__ = [
'CompressionAverage', # obsolete
'CompressionAverageOperator',
'MPIDistributionLocalOperator',
'DdTdd', # Obsolete
'DdTddOperator',
'DiscreteDifference', # obsolete
'DiscreteDifferenceOperator',
'DownSamplingOperator',
'FftHalfComplexOperator',
from pyoperators.utils.mpi import MPI, distribute_shape, distribute_shapes
from pysimulators import Map
from pysimulators.acquisitionmodels import (block_diagonal, PointingMatrix,
ProjectionInMemoryOperator,
ProjectionOnFlyOperator)
from pysimulators.wcsutils import fitsheader2shape, str2fitsheader
from . import tamasisfortran as tmf
from . import var
from .utils import diff, diffT, diffTdiff, shift
from .mpiutils import gather_fitsheader_if_needed, scatter_fitsheader
try:
import fftw3
MAX_FFTW_NUM_THREADS = 1 if fftw3.planning.lib_threads is None \
else openmp_num_threads()
except ImportError:
print('Warning: Library PyFFTW3 is not installed.')
except:
print('Warning: Problem loading PyFFTW3.')
__all__ = [
'CompressionAverage', # obsolete
'CompressionAverageOperator',
'MPIDistributionLocalOperator',
'DdTdd', # Obsolete
'DdTddOperator',
'DiscreteDifference', # obsolete
'DiscreteDifferenceOperator',
'DownSamplingOperator',
'FftHalfComplexOperator',
