def __init__(self, comm=MPI.COMM_WORLD, nnz=1, dtype=np.float64,
nside=16):
if libsharp is None:
raise RuntimeError('libsharp not available')
self.data = None
self._comm = comm
self._nnz = nnz
self._dtype = dtype
self._nest = False
self._nside = nside
self._cache = Cache()
self._libsharp_grid, self._local_ring_indices = distribute_rings(
self._nside, self._comm.rank, self._comm.size)
# returns start index of the ring and number of pixels
startpix, ringpix, _, _, _ = hp.ringinfo(
self._nside, self._local_ring_indices.astype(np.int64))
local_npix = self._libsharp_grid.local_size()
self._local_pixels = self._cache.create(
"local_pixels", shape=(local_npix,), type=np.int64)
expand_pix(startpix, ringpix, local_npix, self._local_pixels)
self.data = self._cache.create(
"data", shape=(local_npix, self._nnz), type=self._dtype)
def __init__(self, comm=MPI.COMM_WORLD, nnz=1, dtype=np.float64, nside=16):
if libsharp is None:
raise RuntimeError('libsharp not available')
self.data = None
self._comm = comm
self._nnz = nnz
self._dtype = dtype
self._nest = False
self._nside = nside
self._cache = Cache()
self._libsharp_grid, self._local_ring_indices = distribute_rings(
self._nside, self._comm.rank, self._comm.size)
# returns start index of the ring and number of pixels
startpix, ringpix, _, _, _ = hp.ringinfo(
self._nside, self._local_ring_indices.astype(np.int64))
local_npix = self._libsharp_grid.local_size()
self._local_pixels = self._cache.create("local_pixels",
shape=(local_npix, ),
type=np.int64)
expand_pix(startpix, ringpix, local_npix, self._local_pixels)
self.data = self._cache.create("data",
shape=(local_npix, self._nnz),
type=self._dtype)
def distribute_rings_libsharp(mpi_comm, nside, lmax):
"""Create a libsharp map distribution based on rings
Build a libsharp grid object to distribute a HEALPix map
balancing North and South distribution of rings to achieve
the best performance on Harmonic Transforms
Returns the grid object and the pixel indices array in RING ordering
Parameters
---------
mpi_comm : mpi4py.MPI.Comm
mpi4py communicator
nside : int
nside of the map
Returns
-------
grid : libsharp.healpix_grid
libsharp object that includes metadata about HEALPix distributed rings
local_pix : np.ndarray
integer array of local pixel indices in the current MPI process in RING
ordering
"""
import libsharp
nrings = 4 * nside - 1 # four missing pixels
# ring indices are 1-based
ring_indices_emisphere = np.arange(2 * nside, dtype=np.int32) + 1
local_ring_indices = ring_indices_emisphere[mpi_comm.rank::mpi_comm.size]
# to improve performance, symmetric rings north/south need to be in the same rank
# therefore we use symmetry to create the full ring indexing
if local_ring_indices[-1] == 2 * nside:
# has equator ring
local_ring_indices = np.concatenate(
[local_ring_indices[:-1], nrings - local_ring_indices[::-1] + 1])
else:
# does not have equator ring
local_ring_indices = np.concatenate(
[local_ring_indices, nrings - local_ring_indices[::-1] + 1])
libsharp_grid = libsharp.healpix_grid(nside, rings=local_ring_indices)
# returns start index of the ring and number of pixels
startpix, ringpix, _, _, _ = hp.ringinfo(nside,
local_ring_indices.astype(int))
local_npix = libsharp_grid.local_size()
local_pixels = expand_pix(startpix, ringpix, local_npix).astype(int)
local_m_indices = np.arange(mpi_comm.rank,
lmax + 1,
mpi_comm.size,
dtype=np.int32)
libsharp_order = libsharp.packed_real_order(lmax, ms=local_m_indices)
return local_pixels, libsharp_grid, libsharp_order
def polar_profile(m, nest=False):
"""Obtain the marginalized polar profile of sky map.
Parameters
----------
m : np.ndarray
The input HEALPix array.
nest : bool, default=False
Indicates whether the input sky map is in nested rather than
ring-indexed HEALPix coordinates (default: ring).
Returns
-------
theta : np.ndarray
The polar angles (i.e., the colatitudes) of the isolatitude rings.
m_int : np.ndarray
The normalized probability density, such that `np.trapz(m_int, theta)`
is approximately `np.sum(m)`.
"""
npix = len(m)
nside = hp.npix2nside(npix)
nrings, = hp.pix2ring(nside, np.asarray([npix]))
startpix, ringpix, costheta, sintheta, _ = hp.ringinfo(
nside, np.arange(1, nrings))
if nest:
m = hp.reorder(m, n2r=True)
theta = np.arccos(costheta)
m_int = np.asarray([
m[i:i + j].sum() * stheta * 0.5 * npix / j
for i, j, stheta in zip(startpix, ringpix, sintheta)
])
return theta, m_int
def polar_profile(m, nest=False):
"""Obtain the marginalized polar profile of sky map.
Parameters
----------
m : np.ndarray
The input HEALPix array.
nest : bool, default=False
Indicates whether the input sky map is in nested rather than
ring-indexed HEALPix coordinates (default: ring).
Returns
-------
theta : np.ndarray
The polar angles (i.e., the colatitudes) of the isolatitude rings.
m_int : np.ndarray
The normalized probability density, such that `np.trapz(m_int, theta)`
is approximately `np.sum(m)`.
"""
npix = len(m)
nside = hp.npix2nside(npix)
nrings, = hp.pix2ring(nside, np.asarray([npix]))
startpix, ringpix, costheta, sintheta, _ = hp.ringinfo(
nside, np.arange(1, nrings))
if nest:
m = hp.reorder(m, n2r=True)
theta = np.arccos(costheta)
m_int = np.asarray(
[m[i:i+j].sum() * stheta * 0.5 * npix / j
for i, j, stheta in zip(startpix, ringpix, sintheta)])
return theta, m_int
)
else:
# does not have equator ring
local_ring_indices = np.concatenate(
[local_ring_indices,
nrings - local_ring_indices[::-1] + 1]
)
print("rank", rank, "n_rings", len(local_ring_indices))
if not mpi:
local_ring_indices = None
grid = libsharp.healpix_grid(nside, rings=local_ring_indices)
# returns start index of the ring and number of pixels
startpix, ringpix, _, _, _ = hp.ringinfo(nside, local_ring_indices.astype(np.int64))
local_npix = grid.local_size()
def expand_pix(startpix, ringpix, local_npix):
"""Turn first pixel index and number of pixel in full array of pixels
to be optimized with cython or numba
"""
local_pix = np.empty(local_npix, dtype=np.int64)
i = 0
for start, num in zip(startpix, ringpix):
local_pix[i:i+num] = np.arange(start, start+num)
i += num
return local_pix
for ifov, fov in enumerate(fovs):
raAvgmap = truemap.copy()
raAvgFOVmap = truemap.copy()
decHi = lat + fov * .5
decLo = lat - fov * .5
if decHi >= 90.:
decHi = 90.
if decLo <= -90.:
decLo = -90.
fovPix = 0.
ringLo = int(4 * nside * (90. - decHi) / 180.)
ringHi = int(4 * nside * (90. - decLo) / 180.)
for ring in range(1, 4 * nside + 1):
ringInfo = h.ringinfo(nside, np.array([ring]))
startpix = ringInfo[0]
ringpix = ringInfo[1]
avg = sum(truemap[startpix:startpix + ringpix] / ringpix)
for pix in range(startpix, startpix + ringpix):
raAvgmap[pix] -= avg
# Limit FOV
if ring in range(ringLo, ringHi + 1):
raAvgFOVmap[pix] = raAvgmap[pix]
fovPix += 1
else:
raAvgFOVmap[pix] = 0.
cov = 1. * fovPix / len(raAvgmap)
covs[ilat][ifov][dipole] = cov
# has equator ring
local_ring_indices = np.concatenate(
[local_ring_indices[:-1], nrings - local_ring_indices[::-1] + 1])
else:
# does not have equator ring
local_ring_indices = np.concatenate(
[local_ring_indices, nrings - local_ring_indices[::-1] + 1])
print("rank", rank, "n_rings", len(local_ring_indices))
if not mpi:
local_ring_indices = None
grid = libsharp.healpix_grid(nside, rings=local_ring_indices)
# returns start index of the ring and number of pixels
startpix, ringpix, _, _, _ = hp.ringinfo(nside,
local_ring_indices.astype(np.int64))
local_npix = grid.local_size()
def expand_pix(startpix, ringpix, local_npix):
"""Turn first pixel index and number of pixel in full array of pixels
to be optimized with cython or numba
"""
local_pix = np.empty(local_npix, dtype=np.int64)
i = 0
for start, num in zip(startpix, ringpix):
local_pix[i:i + num] = np.arange(start, start + num)
i += num
return local_pix