def get_patch(coords, sim_idx, map_type, cmb_type, pixel_fac = 2):
shape,wcs = enmap.fullsky_geometry(res=1.0*np.pi/180./60.)
def _process_coords(coords):
coords = np.array(coords)
if coords[1,1] > 180. : coords[1,1] -= 360.
if coords[0,1] > 180. : coords[0,1] -= 360.
coords *= np.pi/180.
return coords
zpsim_idx = '%05d' %sim_idx
coords = _process_coords(coords)
file_name = sim_file_temp.format(zpsim_idx, map_type, cmb_type, zpsim_idx)
emap = enmap.read_fits(file_name, box=coords, wcs_override=wcs)
nshape = tuple(np.array([i for i in emap.shape])*2)
data = simTools.resample_fft_withbeam(emap, nshape, doBeam=False, beamData=None)
oshape, owcs = enmap.scale_geometry(emap.shape, emap.wcs, pixel_fac)
assert(oshape == nshape)
pmap = enmap.enmap(data, owcs)
return emap.to_flipper()
def __init__(self,full_sky_pix,wdeg,hdeg,yoffset,mpi_comm=None,nsims=1,lmax=7000,pix_intermediate=None,bin_lmax=3000):
self.dtype = np.float32
self.bin_edges = np.arange(80,bin_lmax,100)
self.fshape, self.fwcs = enmap.fullsky_geometry(res=full_sky_pix*np.pi/180./60., proj="car")
self.fshape = self.fshape[-2:]
self.shape = {}
self.wcs = {}
self.taper = {}
self.w2 = {}
self.w3 = {}
self.w4 = {}
self.pix_intermediate = full_sky_pix if (pix_intermediate is None) else pix_intermediate
self.wdeg = wdeg
self.hdeg = hdeg
degree = u.degree
vwidth = hdeg/2.
hwidth = wdeg/2.
self.pos_south=np.array([[-vwidth+yoffset,-hwidth],[vwidth+yoffset,hwidth]])*degree
self.pos_eq=np.array([[-vwidth,-hwidth],[vwidth,hwidth]])*degree
# Get MPI comm
self.comm = mpi_comm
try:
self.rank = mpi_comm.Get_rank()
self.numcores = mpi_comm.Get_size()
except:
self.rank = 0
self.numcores = 1
if self.rank==0:
self.logger = io.get_logger("rotrecon")
num_each,each_tasks = mpi_distribute(nsims,self.numcores)
self.mpibox = MPIStats(self.comm,num_each,tag_start=333)
if self.rank==0: self.logger.info( "At most "+ str(max(num_each)) + " tasks...")
self.tasks = each_tasks[self.rank]
theory_file_root = "data/Aug6_highAcc_CDM"
powspec_file = "data/Aug6_highAcc_CDM_lenspotentialCls.dat"
self.ps = powspec.read_camb_full_lens(powspec_file).astype(self.dtype)
self.theory = cmb.loadTheorySpectraFromCAMB(theory_file_root,unlensedEqualsLensed=False,
useTotal=False,TCMB = 2.7255e6,lpad=9000,get_dimensionless=False)
self.lmax = lmax
self.count = 0
# MPI
comm = mpi.MPI.COMM_WORLD
rank = comm.Get_rank()
numcores = comm.Get_size()
Nsims = args.nsims
Njobs = Nsims
num_each,each_tasks = mpi.mpi_distribute(Njobs,numcores)
if rank==0: print ("At most ", max(num_each) , " tasks...")
my_tasks = each_tasks[rank]
# Theory
theory = cosmology.loadTheorySpectraFromCAMB(args.TheoryRoot,
unlensedEqualsLensed=False,useTotal=False,TCMB = 2.7255e6,lpad=9000,get_dimensionless=False)
# Geometry
fshape,fwcs = enmap.fullsky_geometry(res=args.res*np.pi/180./60.)
box = np.array([[-args.wy/2.,-args.wx/2.],[args.wy/2.,args.wx/2.]])*np.pi/180.
# Stats
st = stats.Stats(comm)
def init_geometry(ishape,iwcs):
modlmap = enmap.modlmap(ishape,iwcs)
bin_edges = np.arange(args.kellmin,args.kellmax,args.dell)
binner = stats.bin2D(modlmap,bin_edges)
if args.beam<1e-5:
kbeam = None
else:
kbeam = maps.gauss_beam(modlmap,args.beam)
lmax = modlmap.max()
ells = np.arange(2,lmax,1)
TCMB=2.7255e6,
lpad=9000,
get_dimensionless=False)
ells = np.arange(0, 7000, 1)
#pfunc = lambda x: theory.lCl('TT',x)
#pfunc = lambda x: x*0.+1.
pfunc = lambda x: x * 0. + (10. * np.pi / 180. / 60.)**2. #theory.gCl('kk',x)
ps = pfunc(ells).reshape((1, 1, 7000))
wdeg = 40.
hdeg = 15.
yoffset = 60.
pix = 0.5
lmax = 7000
fshape, fwcs = enmap.fullsky_geometry(res=pix * np.pi / 180. / 60., proj="car")
shape, wcs = enmap.rect_geometry(width_arcmin=wdeg * 60.,
px_res_arcmin=pix,
height_arcmin=hdeg * 60.,
yoffset_degree=yoffset)
with bench.show("taper"):
#mg = enmap.MapGen(shape,wcs,ps)
taper, sw2 = fmaps.get_taper(shape,
taper_percent=18.0,
pad_percent=4.0,
weight=None)
pxover = 0.5
maplmax = args.maplmax if args.maplmax is not None else lmax
io.dout_dir = args.path
Nsims = args.nsim
comm = MPI.COMM_WORLD
rank = comm.Get_rank()
numcores = comm.Get_size()
Ntot = Nsims
num_each, each_tasks = mpi_distribute(Ntot, numcores)
mpibox = Stats(comm, tag_start=333)
if rank == 0: print("At most ", max(num_each), " tasks...")
my_tasks = each_tasks[rank]
ps = powspec.read_camb_full_lens(args.theory + "_lenspotentialCls.dat")
shape, wcs = enmap.fullsky_geometry(res=args.res * np.pi / 180. / 60.,
proj="car")
shape = (args.ncomp, ) + shape
def save(suffix, imap, index):
enmap.write_fits(
args.path + "_" + suffix + "_" +
str(index).zfill(int(np.log10(args.nsim)) + 1) + ".fits", imap)
def load(suffix, index):
return enmap.read_fits(args.path + "_" + suffix + "_" +
str(index).zfill(int(np.log10(args.nsim)) + 1) +
".fits")
# We assume that site and pointing offsets are the same for all tods,
# so get them based on the first one
entry = filedb.data[ids[0]]
site = actdata.read(entry, ["site"]).site
# Determine the bounding box of our selected data
bounds = db.data["bounds"].reshape(2,-1).copy()
bounds[0] = utils.rewind(bounds[0], bounds[0,0], 360)
box = utils.widen_box(utils.bounding_box(bounds.T), 4*args.rad, relative=False)
waz, wel = box[1]-box[0]
# Use fullsky horizontally if we wrap too far
if waz <= 180:
shape, wcs = enmap.geometry(pos=box[:,::-1]*utils.degree, res=args.res*utils.degree, proj="car", ref=(0,0))
else:
shape, wcs = enmap.fullsky_geometry(res=args.res*utils.degree)
y1, y2 = np.sort(enmap.sky2pix(shape, wcs, [box[:,1]*utils.degree,[0,0]])[0].astype(int))
shape, wcs = enmap.slice_geometry(shape, wcs, (slice(y1,y2),slice(None)))
sys.stderr.write("using %s workspace with resolution %.2f deg" % (str(shape), args.res) + "\n")
# Get the hitmap for each block
hits = enmap.zeros((nblock,narray)+shape, wcs)
ndig = calc_ndig(nblock)
sys.stderr.write("estimating hitmap for block %*d/%d" % (ndig,0,nblock))
for bi in range(nblock):
for ai in range(narray):
block_db = db.select(block_inds[bi,ai])
hits[bi,ai] = fastweight.fastweight(shape, wcs, block_db, array_rad=args.rad*utils.degree, site=site, weight=args.weight)
sys.stderr.write("%s%*d/%d" % ("\b"*(1+2*ndig),ndig,bi+1,nblock))
sys.stderr.write("\n")
# We assume that site and pointing offsets are the same for all tods,
# so get them based on the first one
entry = filedb.data[ids[0]]
site = actdata.read(entry, ["site"]).site
# Determine the bounding box of our selected data
bounds = db.data["bounds"].reshape(2,-1).copy()
bounds[0] = utils.rewind(bounds[0], bounds[0,0], 360)
box = utils.widen_box(utils.bounding_box(bounds.T), 4*args.rad, relative=False)
waz, wel = box[1]-box[0]
# Use fullsky horizontally if we wrap too far
if waz <= 180:
shape, wcs = enmap.geometry(pos=box[:,::-1]*utils.degree, res=args.res*utils.degree, proj="car", ref=(0,0))
else:
shape, wcs = enmap.fullsky_geometry(res=args.res*utils.degree)
y1, y2 = np.sort(enmap.sky2pix(shape, wcs, [box[:,1]*utils.degree,[0,0]])[0].astype(int))
shape, wcs = enmap.slice_geometry(shape, wcs, (slice(y1,y2),slice(None)))
sys.stderr.write("using %s workspace with resolution %.2f deg" % (str(shape), args.res) + "\n")
# Get the hitmap for each block
hits = enmap.zeros((nblock,narray)+shape, wcs)
ndig = calc_ndig(nblock)
sys.stderr.write("estimating hitmap for block %*d/%d" % (ndig,0,nblock))
for bi in range(nblock):
for ai in range(narray):
block_db = db.select(block_inds[bi,ai])
hits[bi,ai] = fastweight.fastweight(shape, wcs, block_db, array_rad=args.rad*utils.degree, site=site, weight=args.weight)
sys.stderr.write("%s%*d/%d" % ("\b"*(1+2*ndig),ndig,bi+1,nblock))
sys.stderr.write("\n")
from enlib import lensing, powspec, utils, enmap, bench
import numpy as np, sys
powspec_file = "data/Aug6_highAcc_CDM_lenspotentialCls.dat"
ps = powspec.read_camb_full_lens(powspec_file).astype(np.float64)
geom = lambda x: enmap.fullsky_geometry(res=x * np.pi / 180. / 60., proj="car")
shape, wcs = geom(4.0)
#obs = enmap.posmap(shape,wcs)
#sys.exit()
with bench.show("2arc"):
full, = lensing.rand_map(shape,
wcs,
ps,
lmax=4000,
maplmax=4000,
verbose=False,
delta_theta=2. * np.pi / 180.,
seed=1)
with bench.show("2arc"):
full2, = lensing.rand_map(shape,
wcs,
ps,
lmax=4000,
maplmax=4000,
verbose=True,
seed=1)
