示例#1
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    #massOverh = 2.e14
    overdensity = 180.
    critical = False
    atClusterZ = False
    comS = cc.results.comoving_radial_distance(cc.cmbZ)*cc.h
    comL = cc.results.comoving_radial_distance(zL)*cc.h
    winAtLens = (comS-comL)/comS
    kappa,r500 = NFWkappa(cc,massOverh,concentration,zL,modrmap* 180.*60./np.pi,winAtLens,
                              overdensity=overdensity,critical=critical,atClusterZ=atClusterZ)

    return kappa

cc = ClusterCosmology(lmax=8500,pickling=True)
widtharc = 50.
px = 0.5
shape,wcs = enmap.get_enmap_patch(widtharc,px)
modrmap = enmap.modrmap(shape,wcs)
modlmap = enmap.modlmap(shape,wcs)
rbins = np.arange(0.,10.,1.0)
rbinner = stats.bin2D(modrmap*60.*180./np.pi,rbins)
true_mass = 2.e14
true2d = get_nfw(true_mass)
kellmin = 200
kellmax = 8500
true2d = enmap.ndmap(fmaps.filter_map(true2d,true2d*0.+1.,modlmap,lowPass=kellmax,highPass=kellmin),wcs)


Nsims = 1000
cov = np.ones((1,1,8500))*0.00000001
ngen = enmap.MapGen(shape,wcs,cov)
示例#2
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Nsims = 10

beam_arcmin = 1.0
noise_T_uK_arcmin = 0.01
noise_P_uK_arcmin = 0.01

# beam_arcmin = 0.
# noise_T_uK_arcmin = 0.
# noise_P_uK_arcmin = 0.

pol_list = ['TT', 'EB']  #,'EE','ET','TE','TB']

pol = False if pol_list == ['TT'] else True

shape_sim, wcs_sim = enmap.get_enmap_patch(patch_width_arcmin,
                                           sim_pixel_scale,
                                           proj="car",
                                           pol=pol)
modr_sim = enmap.modrmap(shape_sim, wcs_sim) * 180. * 60. / np.pi

# === COSMOLOGY ===
cc = ClusterCosmology(lmax=lmax, pickling=True)  #,fill_zero=False)
TCMB = 2.7255e6
theory = cc.theory

# === EXPERIMENT ===
lxmap_sim, lymap_sim, modlmap_sim, angmap_sim, lx_sim, ly_sim = fmaps.get_ft_attributes_enmap(
    shape_sim, wcs_sim)
pix_ells = np.arange(0, modlmap_sim.max(), 1)

ntfunc = lambda x: modlmap_dat * 0. + (np.pi / (180. * 60)
                                       )**2. * noise_T_uK_arcmin**2. / TCMB**2.
示例#3
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from orphics.theory.cosmology import Cosmology
import orphics.tools.stats as stats
import numpy as np
import orphics.tools.io as io
from enlib import enmap

# let's define the bin edges for this test
ellmin = 2
ellmax = 4000
bin_width = 200
bin_edges = np.arange(ellmin, ellmax, bin_width)

# a typical map might be 400sq.deg. with 0.5 arcmin pixels
shape, wcs = enmap.get_enmap_patch(width_arcmin=20 * 60., px_res_arcmin=0.5)

# let's get the "mod ell" or |ell| map, which tells us the magnitude of
# the angular wavenumbers in each fourier pixel
modlmap = enmap.modlmap(shape, wcs)

# this let's us create a 2D fourier space binner
binner2d = stats.bin2D(modlmap, bin_edges)

# the 1d binner just needs to know about the bin edges
binner1d = stats.bin1D(bin_edges)

# initialize a cosmology; make sure you have an "output" directory
# for pickling to work
cc = Cosmology(lmax=6000, pickling=True)
theory = cc.theory

# the fine ells we will use
示例#4
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# noiseless
beam = 0.
noiseT = 0.

# cosmology
cc = Cosmology(lmax=lmax_global, pickling=True)  #,dimensionless=False)
theory = cc.theory
ellrange = np.arange(0, lmax_global, 1)
clkk = theory.gCl('kk', ellrange)
lcltt = theory.lCl('TT', ellrange)
ucltt = theory.uCl('TT', ellrange)

# quadratic estimator
deg = 5.
px = 0.5
shape, wcs = enmap.get_enmap_patch(deg * 60., px, proj="car", pol=False)
template = fmaps.simple_flipper_template_from_enmap(shape, wcs)
lxmap_dat, lymap_dat, modlmap_dat, angmap_dat, lx_dat, ly_dat = fmaps.get_ft_attributes_enmap(
    shape, wcs)
lbin_edges = np.arange(kellmin, kellmax, 200)
lbinner_dat = stats.bin2D(modlmap_dat, lbin_edges)
fmask = fmaps.fourierMask(lx_dat,
                          ly_dat,
                          modlmap_dat,
                          lmin=kellmin,
                          lmax=kellmax)
nlgen = qe.NlGenerator(template, theory)
nTX, nPX, nTY, nPY = nlgen.updateNoise(beamX=beam,
                                       noiseTX=noiseT,
                                       noisePX=0.,
                                       tellminX=tellmin,
示例#5
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    return sgn*kappa

from alhazen.maxlike import lnlike

out_dir = os.environ['WWW']+"plots/maxlike_hdv_nodim_"

lmax = 8000
cc = ClusterCosmology(lmax=lmax,pickling=True)
theory = cc.theory

arc = 10.0
px = 0.5
lens_order = 5


shape,wcs = enmap.get_enmap_patch(arc,px,proj="car")


noise_T = 1.0
    
pa = fmaps.PatchArray(shape,wcs,dimensionless=False,skip_real=False)
pa.add_theory(cc,theory,lmax)
pa.add_gaussian_beam(1.0)
pa.add_white_noise_with_atm(noise_T,0.,0,1,0,1)

Npoints = 60
mrange = np.linspace(1,3,Npoints)*1.e14


trueM = 2e14
kappa = nfwkappa(trueM)
示例#6
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import numpy as np

out_dir = ""

lmax = 2000

cc = ClusterCosmology(lmax=lmax, pickling=True)
TCMB = 2.7255e6
theory = cc.theory

ps = cmb.enmap_power_from_orphics_theory(theory, lmax, lensed=False)
cov = ps

width_deg = 20.
pix = 2.0
shape, wcs = enmap.get_enmap_patch(width_deg * 60., pix, proj="CAR", pol=True)

m1 = enmap.rand_map(shape,
                    wcs,
                    cov,
                    scalar=True,
                    seed=None,
                    power2d=False,
                    pixel_units=False)
modlmap = m1.modlmap()

io.quickPlot2d(m1[0], out_dir + "m1.png")
cltt = ps[0, 0]
ells = np.arange(0, cltt.size)
pl = io.Plotter(scaleY='log')
pl.add(ells, cltt * ells**2.)