def _counts(self):
cts = self.counts.copy()
cts[self.mask < 0.9] = np.nan
self.ngals = np.nansum(cts)
self.nmean = np.nanmean(cts)
area_sqdeg = enmap.area(self.shape, self.wcs) * (180. / np.pi)**2.
self.frac = self.mask.sum() * 1. / self.mask.size
self.area_sqdeg = self.frac * area_sqdeg
self.ngal_per_arcminsq = self.ngals / (self.area_sqdeg * 60. * 60.)
def fcov_to_rcorr(shape, wcs, p2d, N):
"""Convert a 2D PS into a pix-pix covariance
"""
ncomp = p2d.shape[0]
p2d *= np.prod(shape[-2:]) / enmap.area(shape, wcs)
ocorr = enmap.zeros((ncomp, ncomp, N * N, N * N), wcs)
for i in range(ncomp):
for j in range(i, ncomp):
dcorr = ps2d_to_mat(p2d[i, j].copy(), N).reshape((N * N, N * N))
ocorr[i, j] = dcorr.copy()
if i != j: ocorr[j, i] = dcorr.copy()
return ocorr
def sim_points(shape, wcs, info): # Simulate the point amplitudes N = enmap.area(shape, wcs) * info.density*(180/np.pi)**2 n = N*(info.minamp/info.amp)**(info.alpha+1) amps = info.minamp*np.random.uniform(0,1,n)**(1/(info.alpha+1)) amps = np.maximum(-100*np.abs(info.amp),np.minimum(100*np.abs(info.amp), amps)) # Simulate the polarization psi = np.random.uniform(0,np.pi,n) amps = amps[None,:] * np.array([psi*0+1,np.cos(2*psi)*info.pol,np.sin(2*psi)*info.pol]) # Simulate positions uniformly in pixels ipos = np.array([np.random.uniform(0,shape[-2],n),np.random.uniform(0,shape[-1],n)]) pos = enmap.pix2sky(wcs, ipos) # Draw the points on a canvas using convolution. This requires all points # to have integer pixel positions and the same radius. rawmap = np.zeros(shape) for i in range(shape[0]): rawmap[i][tuple(ipos.astype(int))] = amps[i] l = np.sum(enmap.lmap(shape,wcs)**2,0)**0.5 kernel = np.exp(-0.5*l**2*info.rad**2) # actually perform the convolution pmap = enmap.ifft(enmap.fft(rawmap)*kernel[None]).real print np.max(pmap), np.min(pmap) return pmap
Ly, Lx = enmap.extent(shape, wcs)
Ny, Nx = shape[-2:]
if not (do_mask):
mask = mask * 0. + 1.
else:
if do_apod:
mask = nmt.mask_apodization_flat(mask,
Lx,
Ly,
aposize=apod_width,
apotype=apotype)
# Mask fraction
frac = np.sum(mask) * 1. / mask.size
area = enmap.area(shape, wcs) * frac * (180. / np.pi)**2.
print("Masked area sq.deg.: ", area)
w2 = np.mean(mask**2.) # Naive power scaling factor
N = args.Nsims
s = stats.Stats() # Stats collector
for i in range(N):
if (i + 1) % 10 == 0: print(i + 1)
# Get sim maps (npol,ny,nx) array
imaps = mg.get_map(scalar=False, iau=iau) #*mask
if i == 0:
iniFile = "input/recon.ini"
Config = SafeConfigParser()
Config.optionxform = str
Config.read(iniFile)
if rank == 0: print("Params...")
pol = False
shape_dat, wcs_dat = aio.enmap_from_config_section(Config,
analysis_section,
pol=pol)
analysis_resolution = np.min(
enmap.extent(shape_dat, wcs_dat) / shape_dat[-2:]) * 60. * 180. / np.pi
if rank == 0:
print(("px: ", analysis_resolution, "shape : ", shape_dat))
area = enmap.area(shape_dat, wcs_dat) * (180. / np.pi)**2.
print(("area: ", area, " sq.deg."))
fsky = area / 41250.
print(("fsky: ", fsky))
min_ell = fmaps.minimum_ell(shape_dat, wcs_dat)
if rank == 0: print("Ell bounds...")
lb = aio.ellbounds_from_config(Config, "reconstruction_alex", min_ell)
tellmin = lb['tellminY']
tellmax = lb['tellmaxY']
pellmin = lb['pellminY']
pellmax = lb['pellmaxY']
kellmin = lb['kellmin']
kellmax = lb['kellmax']
if rank == 0: print("Patches data...")