Jupyter Notebook Tutorial¶
%matplotlib inline
import pylab as pl
import healpy as hp
import numpy as np
import h5py as h5
from PS4Cast import *
import astropy
from astropy import units as u, constants as C
dir_ps='path/to/PS4C/'
lens=np.array(hp.read_cl(dir_ps+'data/lensedCls.fits'))
l=(np.arange(len(lens[-1])))
tens=np.array(hp.read_cl(dir_ps+'data/r_0.05_tensCls.fits'))
cltot=[i+k for i,k in zip(lens,tens)]
cbb_80=cltot[2][80]
cbb_1000=cltot[2][1000]
Current Ground Based¶
nu=[95,150]
sens=[0.1,0.1]
fwhm=[4,3.5]
fsky=.05
s2= Experiment(ID='CMB-S2', sensitivity= sens, frequency=nu , fwhm=fwhm , fsky=fsky,nchannels=2,
units_sensitivity='Jy',units_beam='arcmin')
forecasts2=Forecaster(pb, ps4c_dir=dir_ps, sigmadetection=3. )
forecasts2.forecast_pi2scaling(verbose=False)
forecasts2()
forecast2.plot_powerspectra(spectra_to_plot='Bonly',FG='total', xlim=[50,2000], ylim=[1e-3,1e1], savefig='../pspaper/cmbs2_Bmodes.pdf')
LiteBIRD¶
freqs=[40,50,60, 68, 78,89, 100,119, 140,166]
netarr=[53.4,32.3,25.1,19.6,15.3,12.4,15.6,12.6,8.3,8.7]
fwhms= [(C.c.cgs/(f*1e9/ u.s ) /(50.*u.cm)* u.rad ) .to(u.arcmin).value for f in freqs]
fsky=.73
litebird= Experiment(ID='LiteBIRD', sensitivity=netarr, frequency=freqs ,nchannels=len(freqs), fwhm=fwhms , fsky=fsky,
units_sensitivity='uKarcmin',units_beam='arcmin')
forecastlitebird=Forecaster(litebird,sigmadetection=3., ps4c_dir=dir_ps)
forecastlitebird.forecast_pi2scaling(verbose=False)
forecastlitebird(model='c2ex')
forecastlitebird.print_info()
=========================================================================================
LiteBIRD Specifics
Frequency ...... [ 40. 50. 60. 68. 78. 89. 100. 119. 140. 166.] GHz
Flux limit ...... [ 0.14589884 0.08824967 0.06857792 0.10710177 0.08360495 0.06775826
0.08524426 0.06885114 0.09070864 0.09508014] Jy
Resolution ...... [ 51.53052772 41.22442218 34.35368515 30.31207513 26.42591165
23.15978774 20.61221109 17.32118579 14.72300792 12.41699463] arcmin
# channels ...... 10
Fraction of sky ...... 0.73
Beam angle ...... [ 2.54592634e-04 1.62939286e-04 1.13152282e-04 8.80943371e-05
6.69540130e-05 5.14263622e-05 4.07348215e-05 2.87654978e-05
2.07830722e-05 1.47825597e-05] sr
//////////////////////////////////////////////////////////////////////////////////////////
Forecasted quantities
Frequency #sources[S,P] Confusion <Pi> <Pi^2>x1e3 D^TT(lensing) D^BB(lensing)
40.0 GHz 496 3 171.958mJy 4.26 2.17 15208.7 uK2 16.4795 uK2
50.0 GHz 913 9 98.5265mJy 4.28 2.25 3988.99 uK2 4.48189 uK2
60.0 GHz 957 6 46.2629mJy 4.30 2.33 1249.09 uK2 1.45431 uK2
68.0 GHz 571 3 33.8506mJy 4.31 2.39 1233.54 uK2 1.47706 uK2
78.0 GHz 763 4 24.0354mJy 4.33 2.48 602.159 uK2 0.746345 uK2
89.0 GHz 882 8 15.3532mJy 4.35 2.57 287.989 uK2 0.370506 uK2
100.0 GHz 679 7 11.4785mJy 4.37 2.67 250.716 uK2 0.334577 uK2
119.0 GHz 866 10 7.43595mJy 4.41 2.84 124.197 uK2 0.176284 uK2
140.0 GHz 488 4 2.05056mJy 4.45 3.03 84.9906 uK2 0.128869 uK2
166.0 GHz 462 4 1.47748mJy 4.50 3.28 65.0669 uK2 0.106753 uK2
==========================================================================================
forecastlitebird.plot_powerspectra(spectra_to_plot='Bonly', FG='total',
savefig='litebird_Bmodes.pdf', xlim=[2,1000],ylim=[1e-5,1e2])
