def get_geometry_regions(ncomp,n,res,hole_radius):
tshape,twcs = enmap.geometry(pos=(0,0),shape=(n,n),res=res,proj='car')
modrmap = enmap.modrmap(tshape,twcs)
# Select the hole (m1) and context(m2) across all components
amodrmap = np.repeat(modrmap.reshape((1,n,n)),ncomp,0)
m1 = np.where(amodrmap.reshape(-1)<hole_radius)[0]
m2 = np.where(amodrmap.reshape(-1)>=hole_radius)[0]
return m1,m2
proj='plain')
# Select the fiducial cluster profile from Hu, DeDeo, Vale 2007
massOverh = 2e14
z = 0.7
c = 3.2
cc = cutils.get_hdv_cc()
# Set up lensing
def lens_map(imap):
return plensing.displace_map(imap, alpha, order=args.lens_order)
# Map of distances from center
modrmap = enmap.modrmap(shape, wcs)
# The convergence ~ projected mass density
kappa = lensing.nfw_kappa(massOverh,
modrmap,
cc,
zL=z,
concentration=c,
overdensity=180.,
critical=False,
atClusterZ=False)
if rank == 0: enmap.write_map(f'{savedir}kappa.fits', kappa)
# Deflection field
alpha = lensing.alpha_from_kappa(kappa)
# Fourier magnitude map
modlmap = enmap.modlmap(shape, wcs)
def test_thumbnails(self):
print("Testing thumbnails...")
# Make a geometry far away from the equator
dec_min = 70 * u.degree
dec_max = 80 * u.degree
res = 0.5 * u.arcmin
shape, wcs = enmap.band_geometry((dec_min, dec_max), res=res)
# Create a set of point source positions separated by
# 2 degrees but with 1 column wrapping around the RA
# direction
width = 120 * u.arcmin
Ny = int((dec_max - dec_min) / (width))
Nx = int((2 * np.pi / (width)))
pys = np.linspace(0, shape[0], Ny)[1:-1]
pxs = np.linspace(0, shape[1], Nx)[:-1]
Ny = len(pys)
Nx = len(pxs)
xx, yy = np.meshgrid(pxs, pys)
xx = xx.reshape(-1)
yy = yy.reshape(-1)
ps = np.vstack((yy, xx))
decs, ras = enmap.pix2sky(shape, wcs, ps)
# Simulate these sources with unit peak value and 2.5 arcmin FWHM
N = ps.shape[1]
srcs = np.zeros((N, 3))
srcs[:, 0] = decs
srcs[:, 1] = ras
srcs[:, 2] = ras * 0 + 1
sigma = 2.5 * u.fwhm * u.arcmin
omap = pointsrcs.sim_srcs(shape, wcs, srcs, beam=sigma)
# Reproject thumbnails centered on the sources
# with gnomonic/tangent projection
proj = "tan"
r = 10 * u.arcmin
ret = reproject.thumbnails(omap,
srcs[:, :2],
r=r,
res=res,
proj=proj,
apod=2 * u.arcmin,
order=3,
oversample=2,
pixwin=False)
# Create a reference source at the equator to compare this against
ishape, iwcs = enmap.geometry(shape=ret.shape,
res=res,
pos=(0, 0),
proj=proj)
imodrmap = enmap.modrmap(ishape, iwcs)
model = np.exp(-imodrmap**2. / 2. / sigma**2.)
# Make sure all thumbnails agree with the reference at the
# sub-percent level
for i in range(ret.shape[0]):
diff = ret[i] - model
assert np.all(np.isclose(diff, 0, atol=1e-3))
