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CMB4cast_main_test_JE.py
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CMB4cast_main_test_JE.py
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#!/usr/bin/env python
'''
Estimate performances of several CMB projects
wrt. their delensing power, their ability to separate components
as well as constrain cosmological parameters such as neutrino mass, w0/wa, r and nT
'''
import numpy as np
import pylab as pl
import healpy as hp
import forecasting_cosmo_self_consistent_forecast_smf as fc
import fnmatch
import operator
import residuals_computation_loc_calibration_errors as residuals_comp
from scipy import polyval, polyfit, optimize
import sys
import os
import python_camb_self_consistent
import argparse
import time
import matplotlib.cm as cm
from matplotlib import rc
import copy
import glob
import pickle
from scipy.ndimage import gaussian_filter1d
import random
import string
import subprocess as sp
from CMB4cast_utilities import *
import CMB4cast_noise
import CMB4cast_compsep
import CMB4cast_delens
import CMB4cast_Fisher
def pissoffpython():
import ctypes as ct
dl = np.ctypeslib.load_library('libdelens', '.')
dl.delensing_performance.argtypes = [ct.c_int, ct.c_int, \
ct.POINTER(ct.c_double), \
ct.POINTER(ct.c_double), \
ct.POINTER(ct.c_double), \
ct.POINTER(ct.c_double), \
ct.POINTER(ct.c_double), \
ct.POINTER(ct.c_double), \
ct.POINTER(ct.c_double), \
ct.c_double, ct.c_bool, \
ct.POINTER(ct.c_double), \
ct.POINTER(ct.c_double)]
def delens_est(l_min, l_max, c_l_ee_u, c_l_ee_l, c_l_bb_u, \
c_l_bb_l, c_l_pp, f_l_cor, n_l_ee_bb, thresh, \
no_iteration, n_l_pp, c_l_bb_res):
return dl.delensing_performance(ct.c_int(l_min), ct.c_int(l_max), \
c_l_ee_u.ctypes.data_as(ct.POINTER(ct.c_double)), \
c_l_ee_l.ctypes.data_as(ct.POINTER(ct.c_double)), \
c_l_bb_u.ctypes.data_as(ct.POINTER(ct.c_double)), \
c_l_bb_l.ctypes.data_as(ct.POINTER(ct.c_double)), \
c_l_pp.ctypes.data_as(ct.POINTER(ct.c_double)), \
f_l_cor.ctypes.data_as(ct.POINTER(ct.c_double)),\
n_l_ee_bb.ctypes.data_as(ct.POINTER(ct.c_double)), \
ct.c_double(thresh), ct.c_bool(no_iteration), \
n_l_pp.ctypes.data_as(ct.POINTER(ct.c_double)), \
c_l_bb_res.ctypes.data_as(ct.POINTER(ct.c_double)))
# common parameters
l_min = 2
l_max = 4000
thresh = 0.01
no_iteration = False
f_cor = 0.0
cib_delens = False
if (cib_delens):
f_l_cor_raw = np.genfromtxt('f_l_cor_planck_545.dat')
f_l_cor = f_l_cor_raw[0: l_max - l_min + 1, 1].flatten()
else:
f_l_cor = (np.ones(l_max - l_min + 1) * f_cor).flatten()
c_l_u=np.genfromtxt('fiducial_lenspotentialCls.dat')
c_l_l=np.genfromtxt('fiducial_lensedtotCls.dat')
ell_loc = c_l_u[0: l_max - l_min + 1, 0].flatten()
d_l_conv = 2.0 * np.pi / ell_loc / (ell_loc + 1.0)
c_l_ee_u = (c_l_u[0: l_max - l_min + 1, 2].flatten() * d_l_conv).flatten()
c_l_ee_l = (c_l_l[0: l_max - l_min + 1, 2].flatten() * d_l_conv).flatten()
c_l_bb_u = (c_l_u[0: l_max - l_min + 1, 3].flatten() * d_l_conv).flatten()
c_l_bb_l = (c_l_l[0: l_max - l_min + 1, 3].flatten() * d_l_conv).flatten()
c_l_pp = (c_l_u[0: l_max - l_min + 1, 5].flatten() / \
(ell_loc * (ell_loc + 1.0)) ** 2 * 2.0 * np.pi).flatten()
f_sky_new = 0.75
l_min_exp_new = int(np.ceil(2.0 * np.sqrt(np.pi / f_sky_new)))
spp = np.sqrt(2.0) * 0.58
beam = 1.0
beam = beam / 60.0 / 180.0 * np.pi
beam_area = beam * beam
beam_theta = beam / np.sqrt(8.0 * np.log(2.0))
n_l_ee_bb = np.zeros(l_max - l_min + 1)
for i in range(0, l_max - l_min + 1):
bl=np.exp(beam_theta * beam_theta * (l_min + i) * (l_min + i + 1))
n_l_ee_bb[i] = (beam_area * spp * spp * bl)
n_l_pp = np.zeros(l_max - l_min + 1)
c_l_bb_res = np.zeros(l_max - l_min + 1)
delens_est(l_min_exp_new, l_max, c_l_ee_u[l_min_exp_new - l_min:], \
c_l_ee_l[l_min_exp_new - l_min:], \
c_l_bb_u[l_min_exp_new - l_min:], \
c_l_bb_l[l_min_exp_new - l_min:], \
c_l_pp[l_min_exp_new - l_min:], \
f_l_cor[l_min_exp_new - l_min:], \
n_l_ee_bb[l_min_exp_new - l_min:], \
thresh, no_iteration, n_l_pp[l_min_exp_new - l_min:], \
c_l_bb_res[l_min_exp_new - l_min:])
############################################################################################################################################################################################################
############################################################################################################################################################################################################
# plotting stuff
pl.rc('font', family = 'serif')
pl.rcParams['text.latex.preamble'] = [r'\boldmath']
pl.rcParams['axes.linewidth'] = 1.5
pl.rcParams['lines.linewidth'] = 1.5
#t20cb = [(0, 107, 164), (255, 128, 14), (171, 171, 171), (89, 89, 89), \
t20cb = [(0, 107, 164), (255, 128, 14), (0, 0, 0), (89, 89, 89), \
(95, 158, 209), (200, 82, 0), (137, 137, 137), (163, 200, 236), \
(255, 188, 121), (207, 207, 207)]
cb_colors = [ ]
for col in range(len(t20cb)):
r, g, b = t20cb[col]
cb_colors.append((r / 255., g / 255., b / 255.))
# @TODO - could tidy up these so that the same keys are used, but whatever...
locs = {}
locs['ground'] = 1
locs['balloon'] = 2
locs['space'] = 3
locs['cross'] = 4
loc_markers = {}
loc_markers[locs['ground']] = 'v'
loc_markers[locs['balloon']] = 'o'
loc_markers[locs['space']] = '^'
loc_markers[locs['cross']] = 'x'
############################################################################################################################################################################################################
############################################################################################################################################################################################################
##############
## SETUP COSMOLOGIES. fiducial cosmology matches Planck 2015 TT, TE, EE + lowP +
## lensing + BAO + JLA + H0 constraints, c.f. arxiv:1502.01589v2.
## note that regarding neutrinos Planck gives "constraints assuming three
## species of degenerate massive neutrinos, neglecting the small differences in
## mass expected from the observed mass splittings. At the level of sensitivity
## of Planck this is an accurate approximation, but note that it does not quite
## match continuously on to the base \LambdaCDM model (which assumes two
## massless and one massive neutrino with \Sigma m_\nu = 0.06 eV)"
params_fid = {}
params_fid['h'] = 67.74
params_fid['ombh2'] = 0.02230
params_fid['omch2'] = 0.1188
params_fid['omnuh2'] = 0.0006451439 # 1 massive neutrino, \Sigma M_\nu = 0.06 eV #100.0/93000 #85.0/93000 #
params_fid['omk'] = 0.0
params_fid['YHe'] = 0.2453 # "in all cases Y_p is predicted by BBN, with posterior mean 0.2453"
params_fid['Neff'] = 3.046
params_fid['w'] = -1.0
params_fid['wa'] = 0.0
params_fid['tau'] = 0.066
params_fid['As'] = 2.142e-9
params_fid['ns'] = 0.9667
params_fid['alphas'] = 0.0
params_fid['r'] = 0.001
params_fid['nT'] = -params_fid['r']/8.0
params_fid['A'] = 0.1 ## residuals amplitude @ ell= 1
params_fid['b'] = -0.8 ## ell dependence of the residuals
params_fid['k_scalar'] = 0.05#05
params_fid['k_tensor'] = 0.002#05
params_fid['A_fgs_res'] = 1.0 ## residuals global amplitude
params_fid['b_fgs_res'] = -2.0 ## residuals global ell dependence
# cosmology with massless neutrinos
params_fid_mnu_0 = copy.copy(params_fid)
params_fid_mnu_0['omnuh2'] = 0.0
# cosmologies with varying r. assume consistency relation throughout
params_fid_r_0p1 = copy.copy(params_fid)
params_fid_r_0p03 = copy.copy(params_fid)
params_fid_r_0p01 = copy.copy(params_fid)
params_fid_r_0 = copy.copy(params_fid)
params_fid_r_0p1['r'] = 0.100
params_fid_r_0p03['r'] = 0.030
params_fid_r_0p01['r'] = 0.010
params_fid_r_0['r'] = 0.000
for params_fid_loc in [params_fid_r_0p1, params_fid_r_0p03, params_fid_r_0p01, params_fid_r_0]:
params_fid_loc['nT'] = -params_fid_loc['r']/8.0
################
## define priors, from Table IV and V of 1502.01589v2 Planck 2015 XIII: Cosmological parameters
params_fid_prior = {}
params_fid_prior['h'] = 0.46
params_fid_prior['ombh2'] = 0.00014
params_fid_prior['omch2'] = 0.0010
params_fid_prior['omnuh2'] = 0.194/93000
params_fid_prior['omk'] = 0.0040
params_fid_prior['YHe'] = 0.026
params_fid_prior['Neff'] = 0.33
params_fid_prior['w'] = 0.080
params_fid_prior['wa'] = 1.0
params_fid_prior['tau'] = 0.012
params_fid_prior['As'] = 4.9e-11
params_fid_prior['ns'] = 0.0040
params_fid_prior['alphas'] = 0.013
params_fid_prior['r'] = 0.113
params_fid_prior['nT'] = 1e4 ## ? not published
params_fid_prior['A_fgs_res'] = 1e8 ## residuals global amplitude
params_fid_prior['b_fgs_res'] = 1e8 ## residuals global ell dependence
# priors from external data sets
params_fid_prior_ext = {}
params_fid_prior_ext['h'] = 2.5e-2
params_fid_prior_ext['ombh2'] = 1e8
params_fid_prior_ext['omch2'] = 1e8
params_fid_prior_ext['omnuh2'] = 1e8
params_fid_prior_ext['omk'] = 1e8
params_fid_prior_ext['YHe'] = 1e8
params_fid_prior_ext['Neff'] = 1e8
params_fid_prior_ext['w'] = 1e8
params_fid_prior_ext['wa'] = 1e8
params_fid_prior_ext['tau'] = 1e8
params_fid_prior_ext['As'] = 1e8
params_fid_prior_ext['ns'] = 1e8
params_fid_prior_ext['alphas'] =1e8
params_fid_prior_ext['r'] = 1e8
params_fid_prior_ext['nT'] = 1e8 ## ? not published
params_fid_prior_ext['A_fgs_res'] = 1e8 ## residuals global amplitude
params_fid_prior_ext['b_fgs_res'] = 1e8 ## residuals global ell dependence
############################################################################################################################################################################################################
############################################################################################################################################################################################################
##############
## SETUP MODELS
## i.e., which parameters we vary together
## may want to vary Y_He along with N_eff as they're degenerate
## may also want to consider adding in a prior on H_0 on occasion?
## params_dev = ['alphas', 'r', 'nT', 'ns', 'As', 'tau', 'h', 'ombh2', \
## 'omch2', 'omnuh2', 'omk', 'YHe', 'Neff', 'w', 'wa']
params_dev_full = ['ns', 'As', 'tau', 'h', 'ombh2', 'omch2', \
'alphas', 'r', 'nT', 'omk', 'omnuh2', 'Neff', \
'YHe', 'w', 'wa' ]
params_dev_LCDM_r = ['ns', 'As', 'tau', 'h', 'ombh2', 'omch2', 'r']
params_dev_LCDM_r_nt = ['ns', 'As', 'tau', 'h', 'ombh2', 'omch2', 'r', 'nT']
params_dev_LCDM_full_inf = ['ns', 'As', 'tau', 'h', 'ombh2', 'omch2', \
'alphas', 'r', 'nT', 'omk']
params_dev_LCDM_Neff = ['ns', 'As', 'tau', 'h', 'ombh2', 'omch2', 'Neff']
params_dev_LCDM_mnu = ['ns', 'As', 'tau', 'h', 'ombh2', 'omch2', 'omnuh2']
params_dev_wwaCDM = ['ns', 'As', 'tau', 'h', 'ombh2', 'omch2', 'w', 'wa']
params_dev_wCDM = ['ns', 'As', 'tau', 'h', 'ombh2', 'omch2', 'w']
params_dev_LCDM_snu = ['ns', 'As', 'tau', 'h', 'ombh2', 'omch2', \
'omnuh2', 'Neff']
params_dev_LCDM_k = ['ns', 'As', 'tau', 'h', 'ombh2', 'omch2', 'omk']
params_dev_test_r_only = ['ns', 'As', 'tau', 'h', 'ombh2', 'omch2', 'r']
#params_dev_LCDM_k_alone = ['omk']
# priors should be specified in same order as model parameters
params_priors_full = [params_fid_prior[p] for p in params_dev_full]
params_priors_LCDM_r = [params_fid_prior[p] for p in params_dev_LCDM_r]
params_priors_LCDM_r_nt = [params_fid_prior[p] for p in params_dev_LCDM_r_nt]
params_priors_LCDM_full_inf = [params_fid_prior[p] for p in params_dev_LCDM_full_inf]
params_priors_LCDM_Neff = [params_fid_prior[p] for p in params_dev_LCDM_Neff]
params_priors_LCDM_mnu = [params_fid_prior[p] for p in params_dev_LCDM_mnu]
params_priors_wwaCDM = [params_fid_prior[p] for p in params_dev_wwaCDM]
params_priors_LCDM_snu = [params_fid_prior[p] for p in params_dev_LCDM_snu]
params_priors_LCDM_k = [params_fid_prior[p] for p in params_dev_LCDM_k]
# priors from external data sets
params_priors_full_ext = [params_fid_prior_ext[p] for p in params_dev_full]
params_priors_LCDM_r_ext = [params_fid_prior_ext[p] for p in params_dev_LCDM_r]
params_priors_LCDM_r_nt_ext = [params_fid_prior_ext[p] for p in params_dev_LCDM_r_nt]
params_priors_LCDM_full_inf_ext = [params_fid_prior_ext[p] for p in params_dev_LCDM_full_inf]
params_priors_LCDM_Neff_ext = [params_fid_prior_ext[p] for p in params_dev_LCDM_Neff]
params_priors_LCDM_mnu_ext = [params_fid_prior_ext[p] for p in params_dev_LCDM_mnu]
params_priors_wwaCDM_ext = [params_fid_prior_ext[p] for p in params_dev_wwaCDM]
params_priors_LCDM_snu_ext = [params_fid_prior_ext[p] for p in params_dev_LCDM_snu]
params_priors_LCDM_snu_k = [params_fid_prior_ext[p] for p in params_dev_LCDM_k]
############################################################################################################################################################################################################
############################################################################################################################################################################################################
##############
## SETUP EXPERIMENTAL CONFIGURATIONS
## e.g.
expts = {}
expts['LiteBIRD_baseline'] = {}
expts['LiteBIRD_baseline']['freqs'] = [60.0, 78.0, 100.0, 140.0, 195.0, 280.0]
expts['LiteBIRD_baseline']['uKCMBarcmin'] = [15.7, 9.9, 7.1, 5.6, 4.7, 5.7] #[ 10.3, 6.5, 4.7, 3.7, 3.1, 3.8 ]
expts['LiteBIRD_baseline']['FWHM'] = [54.1, 55.5, 56.8, 40.5, 38.4, 37.7] #[75.0, 58.0, 45.0, 32.0, 24.0, 16.0]
expts['LiteBIRD_baseline']['fsky'] = 0.7
expts['LiteBIRD_baseline']['bandpass'] = 0.3*np.ones(len(expts['LiteBIRD_baseline']['freqs']))
expts['LiteBIRD_baseline']['ell_min'] = lmin_computation(expts['LiteBIRD_baseline']['fsky'], 'space')
expts['LiteBIRD_baseline']['ell_max'] = 1500
expts['LiteBIRD_baseline']['prior_dust'] = 2*0.02
expts['LiteBIRD_baseline']['prior_sync'] = 2*0.2
expts['LiteBIRD_baseline']['loc'] = locs['space']
expts['LiteBIRD_baseline']['alpha_knee'] = 0.0*np.ones(len(expts['LiteBIRD_baseline']['freqs']))
expts['LiteBIRD_baseline']['ell_knee'] = 0.0*np.ones(len(expts['LiteBIRD_baseline']['freqs']))
expts['LiteBIRD_extended'] = {}
expts['LiteBIRD_extended']['freqs'] = [40.0, 50.0, 60.0, 68.4, 78.0, 88.5, 100.0, 118.9, 140.0, 166.0, 195.0, 234.9, 280.0, 337.4, 402.1] #[60.0, 78.0, 100.0, 140.0, 195.0, 280.0]
expts['LiteBIRD_extended']['uKCMBarcmin'] = [42.4586227 , 25.80081497, 20.16471461, 15.65583433, 12.52466746, 10.14498064, 11.89843409, 9.51874727, 7.64004715, 6.76332043, 5.13511366, 6.38758041, 10.14498064, 10.14498064, 19.16274122] #[ 10.3, 6.5, 4.7, 3.7, 3.1, 3.8 ]
expts['LiteBIRD_extended']['FWHM'] = [108, 86, 72, 63, 55, 49, 43, 36, 31, 26, 22, 18, 37, 31, 26] #[75.0, 58.0, 45.0, 32.0, 24.0, 16.0]
expts['LiteBIRD_extended']['fsky'] = 0.7
expts['LiteBIRD_extended']['bandpass'] = 0.3*np.ones(len(expts['LiteBIRD_extended']['freqs']))
expts['LiteBIRD_extended']['ell_min'] = lmin_computation(expts['LiteBIRD_extended']['fsky'], 'space')
expts['LiteBIRD_extended']['ell_max'] = 3000
expts['LiteBIRD_extended']['prior_dust'] = 2*0.02
expts['LiteBIRD_extended']['prior_sync'] = 2*0.2
expts['LiteBIRD_extended']['loc'] = locs['space']
expts['LiteBIRD_extended']['alpha_knee'] = 0.0*np.ones(len(expts['LiteBIRD_extended']['freqs']))
expts['LiteBIRD_extended']['ell_knee'] = 0.0*np.ones(len(expts['LiteBIRD_extended']['freqs']))
expts['LiteBIRD_update'] = {}
expts['LiteBIRD_update']['freqs'] = [40.0, 50.0, 60.0, 68.4, 78.0, 88.5, 100.0, 118.9, 140.0, 166.0, 195.0, 234.9, 280.0, 337.4, 402.1] #[60.0, 78.0, 100.0, 140.0, 195.0, 280.0]
expts['LiteBIRD_update']['uKCMBarcmin'] = [56.715, 34.019, 26.148, 20.263, 15.658, 12.569, 15.546, 12.389, 8.061, 8.353, 6.344, 8.054, 21.352, 25.462, 61.172]
expts['LiteBIRD_update']['FWHM'] = [108, 86, 72, 63, 55, 49, 43, 36, 31, 26, 22, 18, 37, 31, 26] #[75.0, 58.0, 45.0, 32.0, 24.0, 16.0]
expts['LiteBIRD_update']['fsky'] = 0.7
expts['LiteBIRD_update']['bandpass'] = 0.3*np.ones(len(expts['LiteBIRD_update']['freqs']))
expts['LiteBIRD_update']['ell_min'] = lmin_computation(expts['LiteBIRD_update']['fsky'], 'space')
expts['LiteBIRD_update']['ell_max'] = 3000
expts['LiteBIRD_update']['prior_dust'] = 2*0.02
expts['LiteBIRD_update']['prior_sync'] = 2*0.2
expts['LiteBIRD_update']['loc'] = locs['space']
expts['LiteBIRD_update']['alpha_knee'] = 0.0*np.ones(len(expts['LiteBIRD_update']['freqs']))
expts['LiteBIRD_update']['ell_knee'] = 0.0*np.ones(len(expts['LiteBIRD_update']['freqs']))
expts['Planck'] = {}
expts['Planck']['freqs'] = [ 30.0, 44.0, 70.0, 100.0, 143.0, 217.0, 353.0 ]
expts['Planck']['uKCMBarcmin'] = np.array([7.5, 7.5, 4.8, 1.3, 1.1, 1.6, 6.9])*40.0
expts['Planck']['FWHM'] = [ 33.16, 28.09, 13.08, 9.66, 7.27, 5.01, 4.86 ]
expts['Planck']['fsky'] = 0.5
expts['Planck']['bandpass'] = 0.3*np.ones(len(expts['Planck']['freqs']))
expts['Planck']['ell_min'] = lmin_computation(expts['Planck']['fsky'], 'space')
expts['Planck']['ell_max'] = 3999
expts['Planck']['information_channels'] = ['Tu', 'Eu', 'd']
expts['Planck']['prior_dust'] = 0.0
expts['Planck']['prior_sync'] = 0.0
expts['Planck']['loc'] = locs['space']
expts['Planck']['alpha_knee'] = 0.0*np.ones(len(expts['Planck']['freqs']))
expts['Planck']['ell_knee'] = 0.0*np.ones(len(expts['Planck']['freqs']))
# derived from SA NETs so that the noise after component separation was about 1uK.arcmin in polarization
expts['Stage-IV'] = {}
expts['Stage-IV']['freqs'] = [40.0, 90.0, 150.0, 220.0, 280.0]
expts['Stage-IV']['uKCMBarcmin'] = [ 3.0, 1.5, 1.5, 5.0, 9.0 ]
expts['Stage-IV']['FWHM'] = [ 11.0, 5.0, 3.0, 2.0, 1.5 ]
expts['Stage-IV']['fsky'] = 0.5
expts['Stage-IV']['bandpass'] = 0.3*np.ones(len(expts['Stage-IV']['freqs']))
expts['Stage-IV']['ell_min'] = lmin_computation(expts['Stage-IV']['fsky'], 'ground')
expts['Stage-IV']['ell_max'] = 3999
expts['Stage-IV']['prior_dust'] = 2*0.02
expts['Stage-IV']['prior_sync'] = 2*0.2
expts['Stage-IV']['loc'] = locs['ground']
expts['Stage-IV']['alpha_knee'] = 0.0*np.ones(len(expts['Stage-IV']['freqs']))
expts['Stage-IV']['ell_knee'] = 0.0*np.ones(len(expts['Stage-IV']['freqs']))
# C-BASS: informations about C-BASS
# http://www.astro.caltech.edu/cbass/posters/Dickinson_CBASS_Okinawa_June2013.pdf
expts['C-BASS'] = {}
expts['C-BASS']['freqs'] = np.array([ 5.0 ] )
expts['C-BASS']['uKCMBarcmin'] = np.array([ 100*45.0 ]) #[ 100*(45.0/pix_size_1024)*residuals_computation.BB_factor_computation( 5.0 ) ]
expts['C-BASS']['FWHM'] = [45.0]
expts['C-BASS']['fsky'] = 0.8
expts['C-BASS']['bandpass'] = 0.2*np.ones(len(expts['C-BASS']['freqs']))
expts['C-BASS']['ell_min'] = lmin_computation(expts['C-BASS']['fsky'], 'ground')
expts['C-BASS']['ell_max'] = 3999
expts['C-BASS']['prior_dust'] = 2*0.02
expts['C-BASS']['prior_sync'] = 2*0.2
expts['C-BASS']['loc'] = locs['ground']
expts['C-BASS']['uKRJ/pix'] = expts['C-BASS']['uKCMBarcmin'] * residuals_comp.BB_factor_computation( expts['C-BASS']['freqs'] ) / pix_size_map_arcmin
expts['C-BASS']['alpha_knee'] = 0.0*np.ones(len(expts['C-BASS']['freqs']))
expts['C-BASS']['ell_knee'] = 0.0*np.ones(len(expts['C-BASS']['freqs']))
# Quijote from http://max.ifca.unican.es/EWASS2015/TalksOnTheWeb/Number04_GenovaSantos.pdf
expts['Quijote'] = {}
expts['Quijote']['freqs'] = np.array([ 11.0, 13.0, 17.0, 19.0, 30.0, 42.0 ] )
#expts['Quijote']['uKCMBarcmin'] = np.array([ 4.7, 4.7, 4.7, 4.7, 0.84, 0.84 ])*60.0
# from http://arxiv.org/pdf/1401.4690.pdf, section 4.1 -- wide survey = 18,000deg2 ~ 44%
# 14 uK per 1 sq deg beam for 11-19 GHz, and ~3uK per beam for 30-40 GHz.
expts['Quijote']['uKCMBarcmin'] = np.array([ 14.0, 14.0, 14.0, 14.0, 3*0.37, 3*0.28 ])*60.0
expts['Quijote']['FWHM'] = np.array([ 0.92, 0.92, 0.6, 0.6, 0.37, 0.28 ])*60.0
expts['Quijote']['fsky'] = 18000.0 * (np.pi/180.0)**2 / (4.0*np.pi)
expts['Quijote']['bandpass'] = np.array([2,2,2,2,8,10]) / expts['Quijote']['freqs']
expts['Quijote']['ell_min'] = lmin_computation(expts['Quijote']['fsky'], 'ground')
expts['Quijote']['ell_max'] = 3999
expts['Quijote']['prior_dust'] = 2*0.02
expts['Quijote']['prior_sync'] = 2*0.2
expts['Quijote']['loc'] = locs['ground']
expts['Quijote']['uKRJ/pix'] = expts['Quijote']['uKCMBarcmin'] * residuals_comp.BB_factor_computation( expts['Quijote']['freqs'] ) / pix_size_map_arcmin
expts['Quijote']['alpha_knee'] = 0.0*np.ones(len(expts['Quijote']['freqs']))
expts['Quijote']['ell_knee'] = 0.0*np.ones(len(expts['Quijote']['freqs']))
# selection to consider
configurations = dict((expt, expts[expt]) for expt in ('LiteBIRD_extended', \
'LiteBIRD_update'))
############################################################################################################################################################################################################
########################################################################
# compute noise, delta_beta and Clres from noise per channel in RJ
#components_v = ['cmb-only', 'dust', 'sync', 'sync+dust' ]
components_v = ['cmb-only', 'sync+dust']#, 'sync+dust+dust']
params_fid_v = [params_fid, params_fid_r_0p1, params_fid_r_0p03, params_fid_r_0p01, params_fid_r_0]
params_fid_names_v = ['fid', 'fid_r_0p1', 'fid_r_0p03', \
'fid_r_0p01', 'fid_r_0']
params_dev_v = [params_dev_LCDM_r, params_dev_LCDM_r_nt, \
params_dev_LCDM_full_inf, params_dev_LCDM_mnu, \
params_dev_LCDM_Neff, params_dev_full, \
params_dev_wCDM, params_dev_LCDM_k]
params_dev_names_v = ['L_r', 'L_r_nt', \
'L_inf', 'L_mnu', \
'L_Neff', 'full', \
'L_w', 'L_k']
information_channels= ['Tu', 'Eu', 'Bu', 'd']
delensing_option_v = ['','CMBxCMB','CMBxCIB', 'CMBxLSS']
delensing_z_max = 3.5
############################################################################################################################################################################################################
#####################################################
## wrapper to call core_function from command line ##
#####################################################
def initialize():
#######################################################################
# parse the command-line options and set up others
args = grabargs()
params_fid_sel = [params_fid, params_fid_r_0p1]
params_dev_sel = [params_dev_LCDM_r, params_dev_LCDM_r_nt, \
params_dev_LCDM_full_inf, params_dev_LCDM_mnu, \
params_dev_LCDM_Neff, params_dev_wCDM, params_dev_LCDM_k]
params_prior_sel = [ params_priors_full_ext ]
#######################################################################
# build up the filename for results: just one file per run, for now
res_file = '_Ab_'.join(configurations.keys())
if args.cbass: res_file += '_xCBASS'
if args.quijote: res_file += '_xQUIJOTE'
res_file += '_' + '_'.join(information_channels)
res_file += '_' + '_'.join(components_v)
res_file += '_Bd_1.59_Td_19.6_Bs_-3.1'
if args.stolyarov:
res_file += '_stol_d'
elif args.stolyarov_sync:
res_file += '_stol_ds'
else:
res_file += '_np'
if args.calibration_error!=0.0:
res_file += '_calib_error_'+str(args.calibration_error)
res_file += '_' + '_'.join(['none' if x=='' else x for x in delensing_option_v])
if 'CMBxLSS' in delensing_option_v:
res_file += '_z_max_' + str(delensing_z_max)
for i in range(0, len(params_fid_sel)):
for j in range(0, len(params_fid_v)):
if params_fid_sel[i] == params_fid_v[j]:
res_file += '_' + params_fid_names_v[j]
for i in range(0, len(params_dev_sel)):
for j in range(0, len(params_dev_v)):
if params_dev_sel[i] == params_dev_v[j]:
res_file += '_' + params_dev_names_v[j]
res_file = 'fc_' + res_file
#######################################################################
# perform forecast and save results, or load pre-calculated results
print '#################### forecast ####################'
print 'looking for', res_file + '.pkl'
res_path = glob.glob(res_file + '.pkl')
if 1 or (not res_path) or args.fgs_vs_freq_vs_ell or args.power_spectrum_figure or args.fgs_power_spectrum or args.delens_power_spectrum or args.combo_power_spectrum:
print '#################### no existing results: forecasting! ####################'
foregrounds, sigmas, Nl, Cls_fid = core_function( configurations=configurations, \
components_v=components_v, camb=args.camb, params_fid_v=params_fid_sel, \
params_dev_v=params_dev_sel, information_channels=information_channels, \
delensing_option_v=delensing_option_v, delensing_z_max=delensing_z_max, \
param_priors_v=[], cross_only=args.cross_only, Bd=1.59, Td=19.6, Bs=-3.1, \
stolyarov=args.stolyarov, stolyarov_sync=args.stolyarov_sync,\
cbass=args.cbass, quijote=args.quijote, \
delens_command_line=args.delens_command_line, calibration_error=args.calibration_error,\
path2maps=args.path2maps, path2Cls=args.path2Cls )
save_obj('./', res_file, (foregrounds, sigmas, Nl))
else:
print '#################### loading existing results! ####################'
foregrounds, sigmas, Nl = load_obj('./', res_path[0])
#######################################################################
## output LaTeX table for each instrument
# which fiducial cosmologies, which params_dev for which sigma(parameter)...
ind_pfid_r, ind_pfid_nT, ind_pfid_ns, ind_pfid_as, ind_pfid_mnu, ind_pfid_neff, ind_pfid_w, ind_pfid_omk = 0,1,0,0,0,0,0,0
# from Stephen's email:
# -sigma(r) comes from LCDM+r
# -sigma(n_T) comes from LCDM+r+n_T [WITH r = 0.1!]
# -sigma(n_s) and sigma(alpha_s) come from LCDM+r+n_T+alpha_s (or did you want sigma(r, n_T) -- which presumably change a lot -- from this too?)
# -sigma(M_nu) comes from LCDM+M_nu
# -sigma(N_eff) comes from LCDM+N_eff
# -sigma(w_0) comes from wCDM
# -sigma(Omega_k) comes from LCDM+k
ind_pdev_r, ind_pdev_nT, ind_pdev_ns, ind_pdev_as, ind_pdev_mnu, ind_pdev_neff, ind_pdev_w, ind_pdev_omk = 0,1,2,2,3,4,5,6
exps_table = foregrounds.keys()
ind = -1
for exp in exps_table:
filename = 'latex_table_sigmas_'+str(exp)
filename += '_Bd_1.59_Td_19.6_Bs_-3.1'
print ' CREATING AND SAVING ', filename
if args.stolyarov_sync:
filename += '_A_expansion'
if args.cbass:
filename += '_CBASS'
if args.quijote:
filename += '_QUIJOTE'
if args.calibration_error!=0.0:
filename += '_calib_error_'+str(args.calibration_error)
output = r"""\begin{tabular}{|l|l||l|l||l|l||l|l||l|l|}
\hline
\multicolumn{10}{|c|}{""" + str(exp) + r"""} \\
\hline
\hline
\multicolumn{2}{|l||}{Delensing option $\rightarrow$} & \multicolumn{2}{c||}{no} & \multicolumn{2}{c||}{CMB x} & \multicolumn{2}{c||}{CMB x} & \multicolumn{2}{c|}{CMB x} \\
\multicolumn{2}{|l||}{$\downarrow$ comp. sep. option} & \multicolumn{2}{c||}{delensing} & \multicolumn{2}{c||}{CMB} & \multicolumn{2}{c||}{CIB} & \multicolumn{2}{c|}{LSS} \\
\hline
"""
for components in components_v:
label1 = ' iterative delensing CMBxCMB '
label2 = ' iterative delensing CMBxCIB '
label3 = ' iterative delensing CMBxLSS '
label0 = ' no delensing '
if components=='cmb-only' :
label0 += ' no comp-sep '
label1 += ' no comp-sep '
label2 += ' no comp-sep '
label3 += ' no comp-sep '
label_alpha1 = 'alpha_CMBxCMB'
label_alpha2 = 'alpha_CMBxCIB'
label_alpha3 = 'alpha_CMBxLSS'
else:
label0 += ' + post-comp-sep '
label1 += ' + post-comp-sep '
label2 += ' + post-comp-sep '
label3 += ' + post-comp-sep '
label_alpha1 = 'alpha_CMBxCMB_post_comp_sep'
label_alpha2 = 'alpha_CMBxCIB_post_comp_sep'
label_alpha3 = 'alpha_CMBxLSS_post_comp_sep'
delta_loc = ltx_round(foregrounds[exp][components]['delta'])
reff_loc = ltx_round(foregrounds[exp][components]['r_eff'])
## no delensing
sigma_r_loc_none = ltx_round(sigmas[exp][components][label0]['marginalized']['r'][ind_pfid_r,ind_pdev_r])
sigma_nT_loc_none = ltx_round(sigmas[exp][components][label0]['marginalized']['nT'][ind_pfid_nT,ind_pdev_nT])
sigma_ns_loc_none = ltx_round(sigmas[exp][components][label0]['marginalized']['ns'][ind_pfid_ns,ind_pdev_ns])
sigma_as_loc_none = ltx_round(sigmas[exp][components][label0]['marginalized']['alphas'][ind_pfid_as,ind_pdev_as])
sigma_Mnu_loc_none = ltx_round(sigmas[exp][components][label0]['marginalized']['omnuh2'][ind_pfid_mnu,ind_pdev_mnu]*93.0)
sigma_w_loc_none = ltx_round(sigmas[exp][components][label0]['marginalized']['w'][ind_pfid_w,ind_pdev_w])
sigma_Neff_loc_none = ltx_round(sigmas[exp][components][label0]['marginalized']['Neff'][ind_pfid_neff,ind_pdev_neff])
sigma_OmK_loc_none = ltx_round(sigmas[exp][components][label0]['marginalized']['omk'][ind_pfid_omk,ind_pdev_omk])
## CMBCMB delensing
sigma_r_loc_CMBxCMB = ltx_round(sigmas[exp][components][label0]['marginalized']['r'][ind_pfid_r,ind_pdev_r])
sigma_nT_loc_CMBxCMB = ltx_round(sigmas[exp][components][label0]['marginalized']['nT'][ind_pfid_nT,ind_pdev_nT])
sigma_ns_loc_CMBxCMB = ltx_round(sigmas[exp][components][label0]['marginalized']['ns'][ind_pfid_ns,ind_pdev_ns])
sigma_as_loc_CMBxCMB = ltx_round(sigmas[exp][components][label0]['marginalized']['alphas'][ind_pfid_as,ind_pdev_as])
sigma_Mnu_loc_CMBxCMB = ltx_round(sigmas[exp][components][label0]['marginalized']['omnuh2'][ind_pfid_mnu,ind_pdev_mnu]*93.0)
sigma_w_loc_CMBxCMB = ltx_round(sigmas[exp][components][label0]['marginalized']['w'][ind_pfid_w,ind_pdev_w])
sigma_Neff_loc_CMBxCMB = ltx_round(sigmas[exp][components][label0]['marginalized']['Neff'][ind_pfid_neff,ind_pdev_neff])
sigma_OmK_loc_CMBxCMB = ltx_round(sigmas[exp][components][label0]['marginalized']['omk'][ind_pfid_omk,ind_pdev_omk])
## CMBxCIB delensing
sigma_r_loc_CMBxCIB = ltx_round(sigmas[exp][components][label0]['marginalized']['r'][ind_pfid_r,ind_pdev_r])
sigma_nT_loc_CMBxCIB = ltx_round(sigmas[exp][components][label0]['marginalized']['nT'][ind_pfid_nT,ind_pdev_nT])
sigma_ns_loc_CMBxCIB = ltx_round(sigmas[exp][components][label0]['marginalized']['ns'][ind_pfid_ns,ind_pdev_ns])
sigma_as_loc_CMBxCIB = ltx_round(sigmas[exp][components][label0]['marginalized']['alphas'][ind_pfid_as,ind_pdev_as])
sigma_Mnu_loc_CMBxCIB = ltx_round(sigmas[exp][components][label0]['marginalized']['omnuh2'][ind_pfid_mnu,ind_pdev_mnu]*93.0)
sigma_w_loc_CMBxCIB = ltx_round(sigmas[exp][components][label0]['marginalized']['w'][ind_pfid_w,ind_pdev_w])
sigma_Neff_loc_CMBxCIB = ltx_round(sigmas[exp][components][label0]['marginalized']['Neff'][ind_pfid_neff,ind_pdev_neff])
sigma_OmK_loc_CMBxCIB = ltx_round(sigmas[exp][components][label0]['marginalized']['omk'][ind_pfid_omk,ind_pdev_omk])
## CMBxLSS delensing
sigma_r_loc_CMBxLSS = ltx_round(sigmas[exp][components][label0]['marginalized']['r'][ind_pfid_r,ind_pdev_r])
sigma_nT_loc_CMBxLSS = ltx_round(sigmas[exp][components][label0]['marginalized']['nT'][ind_pfid_nT,ind_pdev_nT])
sigma_ns_loc_CMBxLSS = ltx_round(sigmas[exp][components][label0]['marginalized']['ns'][ind_pfid_ns,ind_pdev_ns])
sigma_as_loc_CMBxLSS = ltx_round(sigmas[exp][components][label0]['marginalized']['alphas'][ind_pfid_as,ind_pdev_as])
sigma_Mnu_loc_CMBxLSS = ltx_round(sigmas[exp][components][label0]['marginalized']['omnuh2'][ind_pfid_mnu,ind_pdev_mnu]*93.0)
sigma_w_loc_CMBxLSS = ltx_round(sigmas[exp][components][label0]['marginalized']['w'][ind_pfid_w,ind_pdev_w])
sigma_Neff_loc_CMBxLSS = ltx_round(sigmas[exp][components][label0]['marginalized']['Neff'][ind_pfid_neff,ind_pdev_neff])
sigma_OmK_loc_CMBxLSS = ltx_round(sigmas[exp][components][label0]['marginalized']['omk'][ind_pfid_omk,ind_pdev_omk])
#############
output += r' \multirow{4}{*}{'+components+r'} & \multirow{3}{*}{$\Delta='+delta_loc+r'$} & \multicolumn{2}{c||}{$\alpha=1.0$} & \multicolumn{2}{c||}{$\alpha='+ltx_round(Nl[exp][components][label_alpha1])+r'$} & \multicolumn{2}{c||}{$\alpha='+ltx_round(Nl[exp][components][label_alpha2])+r'$} & \multicolumn{2}{c|}{$\alpha='+ltx_round(Nl[exp][components][label_alpha3])+r'$}\\'
output += r' & & $\sigma(r)='+ltx_round(sigmas[exp][components][label0]['marginalized']['r'][ind_pfid_r,ind_pdev_r])+r'$ & $\sigma(\mnu)='+ltx_round(sigmas[exp][components][label0]['marginalized']['omnuh2'][ind_pfid_mnu,ind_pdev_mnu]*93000)+r'$ & $\sigma(r)='+ltx_round(sigmas[exp][components][label1]['marginalized']['r'][ind_pfid_r,ind_pdev_r])+r'$ & $\sigma(\mnu)='+ltx_round(sigmas[exp][components][label1]['marginalized']['omnuh2'][ind_pfid_mnu,ind_pdev_mnu]*93000)+r'$ & $\sigma(r)='+ltx_round(sigmas[exp][components][label2]['marginalized']['r'][ind_pfid_r,ind_pdev_r])+r'$ & $\sigma(M_\nu)='+ltx_round(sigmas[exp][components][label2]['marginalized']['omnuh2'][ind_pfid_mnu,ind_pdev_mnu]*93000)+r'$ & $\sigma(r)='+ltx_round(sigmas[exp][components][label3]['marginalized']['r'][ind_pfid_r,ind_pdev_r])+r'$ & $\sigma(\mnu)='+ltx_round(sigmas[exp][components][label3]['marginalized']['omnuh2'][ind_pfid_mnu,ind_pdev_mnu]*93000)+r'$ \\'
#output += r' \multirow{4}{*}{'+components+r'} & \multirow{2}{*}{$\Delta='+ltx_round(foregrounds[exp][components]['delta'])+r'$} & $\sigma(r)='+ltx_round(sigmas[exp][components][label0]['marginalized']['r'][ind_pfid_r,ind_pdev_r])+r'$ & $\sigma(M_\nu)='+ltx_round(sigmas[exp][components][label0]['marginalized']['omnuh2'][ind_pfid_mnu,ind_pdev_mnu]*93.0)+r'$ & $\sigma(r)='+ltx_round(sigmas[exp][components][label1]['marginalized']['r'][ind_pfid_r,ind_pdev_r])+r'$ & $\sigma(M_\nu)='+ltx_round(sigmas[exp][components][label1]['marginalized']['omnuh2'][ind_pfid_mnu,ind_pdev_mnu]*93.0)+r'$ & $\sigma(r)='+ltx_round(sigmas[exp][components][label2]['marginalized']['r'][ind_pfid_r,ind_pdev_r])+r'$ & $\sigma(M_\nu)='+ltx_round(sigmas[exp][components][label2]['marginalized']['omnuh2'][ind_pfid_mnu,ind_pdev_mnu]*93.0)+r'$ & $\sigma(r)='+ltx_round(sigmas[exp][components][label3]['marginalized']['r'][ind_pfid_r,ind_pdev_r])+r'$ & $\sigma(M_\nu)='+ltx_round(sigmas[exp][components][label3]['marginalized']['omnuh2'][ind_pfid_mnu,ind_pdev_mnu]*93.0)+r'$ \\'
output += r' & & $\sigma(\nt)='+ltx_round(sigmas[exp][components][label0]['marginalized']['nT'][ind_pfid_nT,ind_pdev_nT])+r'$ & $\sigma(\w)='+ltx_round(sigmas[exp][components][label0]['marginalized']['w'][ind_pfid_w,ind_pdev_w])+r'$ & $\sigma(\nt)='+ltx_round(sigmas[exp][components][label1]['marginalized']['nT'][ind_pfid_nT,ind_pdev_nT])+r'$ & $\sigma(\w)='+ltx_round(sigmas[exp][components][label1]['marginalized']['w'][ind_pfid_w,ind_pdev_w])+r'$ & $\sigma(\nt)='+ltx_round(sigmas[exp][components][label2]['marginalized']['nT'][ind_pfid_nT,ind_pdev_nT])+r'$ & $\sigma(\w)='+ltx_round(sigmas[exp][components][label2]['marginalized']['w'][ind_pfid_w,ind_pdev_w])+r'$ & $\sigma(\nt)='+ltx_round(sigmas[exp][components][label3]['marginalized']['nT'][ind_pfid_nT,ind_pdev_nT])+r'$ & $\sigma(\w)='+ltx_round(sigmas[exp][components][label3]['marginalized']['w'][ind_pfid_w,ind_pdev_w])+r'$ \\'
output += r' & \multirow{2}{*}{$\reff='+ltx_round(foregrounds[exp][components]['r_eff'])+r'$} & $\sigma(\ns)='+ltx_round(sigmas[exp][components][label0]['marginalized']['ns'][ind_pfid_ns,ind_pdev_ns])+r'$ & $\sigma(\neff)='+ltx_round(sigmas[exp][components][label0]['marginalized']['Neff'][ind_pfid_neff,ind_pdev_neff])+r'$ & $\sigma(\ns)='+ltx_round(sigmas[exp][components][label1]['marginalized']['ns'][ind_pfid_ns,ind_pdev_ns])+r'$ & $\sigma(\neff)='+ltx_round(sigmas[exp][components][label1]['marginalized']['Neff'][ind_pfid_neff,ind_pdev_neff])+r'$ & $\sigma(\ns)='+ltx_round(sigmas[exp][components][label2]['marginalized']['ns'][ind_pfid_ns,ind_pdev_ns])+r'$ & $\sigma(\neff)='+ltx_round(sigmas[exp][components][label2]['marginalized']['Neff'][ind_pfid_neff,ind_pdev_neff])+r'$ & $\sigma(\ns)='+ltx_round(sigmas[exp][components][label3]['marginalized']['ns'][ind_pfid_ns,ind_pdev_ns])+r'$ & $\sigma(\neff)='+ltx_round(sigmas[exp][components][label3]['marginalized']['Neff'][ind_pfid_neff,ind_pdev_neff])+r'$ \\'
#output += r' & & $\sigma(\alpha_s)='+ltx_round(sigmas[exp][components][label0]['marginalized']['alphas'][ind_pfid_as,ind_pdev_as])+r'$ & $\alpha= 1.0 $ & $\sigma(\alpha_s)='+ltx_round(sigmas[exp][components][label1]['marginalized']['alphas'][ind_pfid_as,ind_pdev_as])+r'$ & $\alpha='+ltx_round(Nl[exp][components][label_alpha1])+r'$ & $\sigma(\alpha_s)='+ltx_round(sigmas[exp][components][label2]['marginalized']['alphas'][ind_pfid_as,ind_pdev_as])+r'$ & $\alpha='+ltx_round(Nl[exp][components][label_alpha2])+r'$ & $\sigma(\alpha_s)='+ltx_round(sigmas[exp][components][label3]['marginalized']['alphas'][ind_pfid_as,ind_pdev_as])+r'$ & $\alpha='+ltx_round(Nl[exp][components][label_alpha3])+r'$ \\'
output += r' & & $\sigma(\alphas)='+ltx_round(sigmas[exp][components][label0]['marginalized']['alphas'][ind_pfid_as,ind_pdev_as])+r'$ & $\sigma(\omk)='+ltx_round(sigmas[exp][components][label0]['marginalized']['omk'][ind_pfid_omk,ind_pdev_omk])+r'$ & $\sigma(\alphas)='+ltx_round(sigmas[exp][components][label1]['marginalized']['alphas'][ind_pfid_as,ind_pdev_as])+r'$ & $\sigma(\omk)='+ltx_round(sigmas[exp][components][label1]['marginalized']['omk'][ind_pfid_omk,ind_pdev_omk])+r'$ & $\sigma(\alphas)='+ltx_round(sigmas[exp][components][label2]['marginalized']['alphas'][ind_pfid_as,ind_pdev_as])+r'$ & $\sigma(\omk)='+ltx_round(sigmas[exp][components][label2]['marginalized']['omk'][ind_pfid_omk,ind_pdev_omk])+r'$ & $\sigma(\alphas)='+ltx_round(sigmas[exp][components][label3]['marginalized']['alphas'][ind_pfid_as,ind_pdev_as])+r'$ & $\sigma(\omk)='+ltx_round(sigmas[exp][components][label3]['marginalized']['omk'][ind_pfid_omk,ind_pdev_omk])+r'$ \\'
output += r"""
\hline"""
output += """
\end{tabular}"""
path = './'
text_file = open(os.path.join( path, filename+".tex"), "w")
text_file.write("%s" % output )
text_file.close()
print ' code successfully finished ! '
exit()
############################################################################################################################################################################################################
def derivatives_computation(Cls_fid, params_dev, params_fid, information_channels, eta=1.0, exe='./camb', path2Cls=''):
information_channels_tot = ['Tu', 'Eu', 'Bu', 'T', 'E', 'B', 'd']
nell = len(Cls_fid[information_channels[0]+information_channels[0]])
nparam = len(params_dev)
nch = len(information_channels_tot)
dCldp = {}
for p in range( nparam ):
## look for already computed and save on disk derivatives
#subname_search ='drv_'
subname ='drv_'
subname_search = os.path.join(path2Cls, 'drv_' )
subname = os.path.join(path2Cls, 'drv_' )
subname_search += '*'+str(params_dev[p])
subname += str(params_dev[p])
for i in range(len(information_channels)):
subname_search += '*'+information_channels[i][0]
subname += '_' + information_channels[i][0]
for q in range(len(params_fid.keys())):
subname += '_'+str(params_fid.keys()[q])+str(params_fid[params_fid.keys()[q]])
subname_search += '_'+str(params_fid.keys()[q])+str(params_fid[params_fid.keys()[q]])
print 'looking for files which have ', subname_search +'*.pkl' , ' in their name '
fnames = glob.glob( subname_search+'*.pkl' )
if fnames:
print '################################ loading already existing file ####################################'
dCldp_loc = load_obj('./', fnames[0]) # loading the first file of fnames list
dCldp[params_dev[p]] = dCldp_loc[params_dev[p]]
del dCldp_loc
else:
print '################################ derivative of Cls wrt. '+params_dev[p]+' #################################### '
if params_dev[p] == 'A':
dCldp[params_dev[p]] = {}
for ch1 in range(nch):
for ch2 in range(nch):
key = information_channels_tot[ch1]+information_channels_tot[ch2]
if 'T' in key or 'Tu' in key or 'd' in key or 'E' in key or 'Eu' in key:
dCldp['A'][key] = Cls_fid['ell']*0.0
else:
dCldp['A'][key] = Cls_fid['ell']**params_fid['b']
save_obj('./', subname, dCldp)
continue
elif params_dev[p] == 'b':
dCldp[params_dev[p]] = {}
for ch1 in range(nch):
for ch2 in range(nch):
key = information_channels_tot[ch1]+information_channels_tot[ch2]
print key
if 'T' in key or 'Tu' in key or 'd' in key or 'E' in key or 'Eu' in key:
dCldp['b'][key] = Cls_fid['ell']*0.0
else:
dCldp['b'][key] = params_fid['A']*params_fid['b']*Cls_fid['ell']**( params_fid['b'] - 1 )
save_obj('./', subname, dCldp)
continue
elif params_dev[p] == 'omk':
#########################################################################
print 'special derivatives for OmK'
params_v = [ 0.0, 0.005 ]
Cls_tot_loc = np.zeros(( len(params_v), nch, nch, nell ))
params_loc = params_fid.copy()
params_loc[params_dev[p]] = 0.005
Cls_loc = python_camb.submit_camb( l_max_scalar=4100, l_max_tensor=4100, h=params_loc['h'], ombh2=params_loc['ombh2'], omch2=params_loc['omch2'], omnuh2=params_loc['omnuh2'], omk=params_loc['omk'], YHe=params_loc['YHe'], Neff=params_loc['Neff'], w=params_loc['w'], wa=params_loc['wa'], tau=params_loc['tau'],As=params_loc['As'], ns=params_loc['ns'], alphas=params_loc['alphas'], nT=params_loc['nT'], r=params_loc['r'], k_scalar=params_fid['k_scalar'] , k_tensor=params_fid['k_tensor'], eta=1.0, exe=exe)
dCldp[params_dev[p]] = {}
print np.shape(Cls_tot_loc), np.shape(Cls_loc['TT'])
for ch1 in range(nch):
for ch2 in range(nch):
key = information_channels_tot[ch1]+information_channels_tot[ch2]
if not key in Cls_fid.keys():
Cls_tot_loc[0, ch1, ch2,:] = 0.0
Cls_tot_loc[1, ch1, ch2,:] = 0.0
else:
# 0th point is the fiducial Cls
Cls_tot_loc[0, ch1, ch2, :] = Cls_fid[key][:]*1.0
# point #1 is the increment cosmology
Cls_tot_loc[1, ch1, ch2, :] = Cls_loc[key][:]*1.0
dCldp[params_dev[p]][key] = np.zeros(nell)
if not key in Cls_fid.keys():
dCldp[params_dev[p]][key] = np.zeros(nell)
else:
for l in range(nell):
x=[ params_fid[params_dev[p]]+params_v[1], params_fid[params_dev[p]]+params_v[0] ]
dCldp_loc = Cls_tot_loc[1,ch1,ch2,l] - Cls_tot_loc[0,ch1,ch2,l]
dCldp_loc /= ( x[1]-x[0] )
if dCldp_loc != dCldp_loc: dCldp_loc = 0.0
dCldp[params_dev[p]][key][l] = dCldp_loc
save_obj('./', subname, dCldp)
continue
#########################################################################
elif params_dev[p] == 'omnuh2':
print ' taking larger steps for neutrinos '
params_v =[ 0.8, 0.9, 1.0, 1.1, 1.2 ]
elif params_fid[params_dev[p]] < 0.0:
print 'fiducial parameter is negative'
params_v = [ 1.1, 1.05, 1.0, 0.95, 0.90 ]
elif params_fid[params_dev[p]] == 0.0:
print 'fiducial parameter is null'
params_v =[ -0.1, -0.05, 0.0, 0.05, 0.1 ]
elif params_fid[params_dev[p]] < 1e-10:
print 'fiducial parameter is super small, i am considering slightly larger steps'
params_v =[ 0.8, 0.9, 1.0, 1.1, 1.2 ]
else:
params_v = [ 0.90, 0.95, 1.0, 1.05, 1.1 ]
Cls_tot_loc = np.zeros(( len(params_v), nch, nch, nell ))
for i in range( len(params_v) ):
# central point
if i == 2:
for ch1 in range(nch):
for ch2 in range(nch):
key = information_channels_tot[ch1]+information_channels_tot[ch2]
if not key in Cls_fid.keys():
Cls_tot_loc[i, ch1, ch2,:] = 0.0
else:
Cls_tot_loc[i, ch1, ch2,:] = Cls_fid[key][:]*1.0
else:
## two points smaller, two point higher
# make the right change
params_loc = params_fid.copy()
if params_fid[params_dev[p]] != 0.0:
params_loc[params_dev[p]] *= params_v[i]
else:
params_loc[params_dev[p]] += params_v[i]
# compute the Cls
Cls_loc = python_camb.submit_camb( h=params_loc['h'], ombh2=params_loc['ombh2'], omch2=params_loc['omch2'], omnuh2=params_loc['omnuh2'], omk=params_loc['omk'], YHe=params_loc['YHe'], Neff=params_loc['Neff'], w=params_loc['w'], wa=params_loc['wa'], tau=params_loc['tau'],As=params_loc['As'], ns=params_loc['ns'], alphas=params_loc['alphas'], nT=params_loc['nT'], r=params_loc['r'], k_scalar=params_fid['k_scalar'] , k_tensor=params_fid['k_tensor'], eta=1.0, exe=exe)
for ch1 in range(nch):
for ch2 in range(nch):
key = information_channels_tot[ch1]+information_channels_tot[ch2]
if not key in Cls_fid.keys():
Cls_tot_loc[i, ch1, ch2,:] = 0.0
else:
Cls_tot_loc[i, ch1, ch2, :] = Cls_loc[key][:]*1.0
del params_loc, Cls_loc, ch1, ch2, key
########################################################
#if params_dev[p] == 'b' or params_dev[p] == 'A':
# continue
########################################################
print '################################ interpolation of the derivative ####################################'
dCldp[params_dev[p]] = {}
for ch1 in range(nch):
for ch2 in range(nch):
key = information_channels_tot[ch1]+information_channels_tot[ch2]
## tune steps for different keys?
delta_step = 0.001
print key
dCldp[params_dev[p]][key] = np.zeros(nell)
if not key in Cls_fid.keys():
dCldp[params_dev[p]][key] = np.zeros(nell)
else:
for l in range(nell):
# interpolate the points
params_loc_v = np.zeros(len(params_v))
for k in range(len(params_v)):
if params_fid[params_dev[p]] != 0.0:
params_loc_v[k] = params_fid[params_dev[p]]*params_v[k]
else:
params_loc_v[k] = params_fid[params_dev[p]] + params_v[k]
f = scipy.interpolate.interp1d(params_loc_v , np.squeeze(Cls_tot_loc[:,ch1,ch2,l]), kind='cubic')
if params_fid[params_dev[p]] != 0.0:
dCldp_loc = f( params_fid[params_dev[p]]*(1.0 + delta_step) ) - f( params_fid[params_dev[p]]*(1.0 - delta_step) )
dCldp_loc /= ( params_fid[params_dev[p]]*2*delta_step )
else:
dCldp_loc = f( delta_step ) - f( - delta_step )
dCldp_loc /= ( 2*delta_step )
if dCldp_loc != dCldp_loc: dCldp_loc = 0.0
dCldp[params_dev[p]][key][l] = dCldp_loc
#######################################
'''
if l == 100 or l==1000:
if l== 100:
pl.figure()
pl.subplot(211)
pl.title(key+' @ l=100 and der = '+str(dCldp_loc))
pl.plot(params_loc_v , np.squeeze(Cls_tot_loc[:,ch1,ch2,l]), 'ko')
x = np.arange(np.min(params_loc_v)*1.001, np.max(params_loc_v)*0.999, params_fid[params_dev[p]]*0.0001)
pl.plot(x, f(x), 'k')
pl.plot( params_fid[params_dev[p]]*(1.0 + delta_step), f( params_fid[params_dev[p]]*(1.0 + delta_step) ), 'rx')
pl.plot( params_fid[params_dev[p]]*(1.0 - delta_step), f( params_fid[params_dev[p]]*(1.0-delta_step) ), 'rx' )
der = dCldp_loc*x + ( f(params_fid[params_dev[p]])- dCldp_loc*params_fid[params_dev[p]])
pl.plot(x, der, 'k--')
if l==1000:
pl.subplot(212)
pl.title(key+' @ l=100 and der = '+str(dCldp_loc))
pl.plot(params_loc_v , np.squeeze(Cls_tot_loc[:,ch1,ch2,l]), 'ro')
pl.plot(x, f(x), 'r')
pl.plot( params_fid[params_dev[p]]*(1.0 + delta_step), f( params_fid[params_dev[p]]*(1.0 + delta_step) ), 'kx')
pl.plot( params_fid[params_dev[p]]*(1.0 - delta_step), f( params_fid[params_dev[p]]*(1.0-delta_step) ), 'kx')
der2 = dCldp_loc*x + ( f(params_fid[params_dev[p]])- dCldp_loc*params_fid[params_dev[p]])
pl.plot(x, der2, 'r--')
'''
###############################################
del f, params_loc_v, dCldp_loc
del l, key, delta_step
del ch1, ch2
########################################################
print '################################ saving derivatives wrt.', params_dev[p],' to disk ####################################'
save_obj('./', subname, dCldp)
########################################################
return dCldp
#####################################################
## example call ##
#####################################################
#@app.task
def forecast( fsky=0.1, freqs=[95, 150, 220], uKCMBarcmin=[10.0, 10.0, 10.0], FWHM=[5.0, 3.0, 2.0], \
ell_max=2000, ell_min=20, Bd=1.59, Td=19.6, Bs=-3.1, \
components_v=[0,1,0,0], delensing_option_v=[0,1,0,0], \
params_dev_v=[1,1,1,1,1,1,1,1,1,1,1,1,1,1,1], information_channels_v=[1,1,1,1]):
configurations_loc= {}
configurations['my_exp'] = {}
configurations['my_exp']['freqs'] = freqs
configurations['my_exp']['uKCMBarcmin'] = uKCMBarcmin
configurations['my_exp']['FWHM'] = FWHM
configurations['my_exp']['fsky'] = fsky
configurations['my_exp']['bandpass'] = 0.3*np.ones(len(configurations['my_exp']['freqs']))
configurations['my_exp']['ell_min'] = ell_min
configurations['my_exp']['ell_max'] = ell_max
############
camb_loc = '/home/josquin/softwares/camb/./camb'
information_channels_ini =['Tu', 'Eu', 'Bu', 'd']
delensing_option_v_ini = ['','CMBxCMB','CMBxCIB', 'CMBxLSS']
params_dev_full_ini = ['ns', 'As', 'tau', 'h', 'ombh2', 'omch2', \
'alphas', 'r', 'nT', 'omk', 'omnuh2', 'Neff', \
'YHe', 'w', 'wa' ]
components_v_ini = ['cmb-only', 'dust', 'sync', 'sync+dust' ]
############
params_dev_v_loc = []
for i in range( len(params_dev_v) ):
if params_dev_v[i] == 1:
params_dev_v_loc.append(params_dev_full_ini[i] )
delensing_option_v_loc = []
for i in range( len(delensing_option_v) ):
if delensing_option_v[i] == 1:
delensing_option_v_loc.append(delensing_option_v_ini[i] )
information_channels_loc = []
for i in range( len(information_channels_v) ):
if information_channels_v[i] == 1:
information_channels_loc.append(information_channels_ini[i] )
components_v_loc = []
for i in range( len(components_v) ):
if components_v[i] == 1:
components_v_loc.append(components_v_ini[i] )
############
# actual computation
foregrounds, sigmas, Nl, Cls_fid = core_function(configurations_loc, components_v=components_v_loc, \
camb=camb_loc, fgs_scatter=False, delens_scatter=False, params_fid_v=[params_fid_v[0]], \
params_dev_v=[params_dev_v_loc], information_channels=information_channels_loc, \
delensing_option_v=delensing_option_v_loc, param_priors_v=[], cross_only=False, Bd=Bd, Td=Td, Bs=Bs )
############
# printing output
output = ''
for exp in sigmas.keys():
for components in sigmas[exp].keys():
output += ' components = '+components
for label in sigmas[exp][components].keys():
output += 'label = '+label
for parameter in sigmas[exp][components][label]['marginalized'].keys():
output += 'sigma('+parameter+') = '+ str( sigmas[exp][components][label]['marginalized']['r'] )
output += 'effective level of foregrounds residuals, r_eff = '+str( foregrounds[exp][components]['r_eff'] )
output += 'degradation of the noise after comp. sep. = '+str( foregrounds[exp][components]['delta'] )
return sigmas
#####################################################
## core functionalities ##
#####################################################
def core_function(configurations, components_v, camb, \
params_fid_v, params_dev_v, information_channels, delensing_option_v, \
delensing_z_max=-1.0, param_priors_v=[], cross_only=False, Bd=1.59, \
Td=19.6, Bs=-3.1, correlation_2_dusts=0.0, stolyarov=False, stolyarov_sync=False, cbass=False, quijote=False, \
delens_command_line=False, comp_sep_only=False, calibration_error=0.0, np_nside=4, no_lensing=False, A_lens=1.0, \
resolution=False, DESI=False, mpi_safe=False, path2maps='/Users/josquin1/Documents/Dropbox/planck_maps',\
path2Cls='/Users/josquin1/Documents/Dropbox/self_consistent_forecast/codes/' ):
################ entry checker
if Bd == 0.0 or Td == 0.0:
print 'Wrong dust grey body parameters'
exit()
for exp in configurations.keys():
if configurations[exp]['ell_min'] >= configurations[exp]['ell_max']:
print 'Wrong ell_min or ell_max'
exit()
#####################################################
## in preparation of the delensing for each experiment
# compute fiducial Cls computed between ell = 2 and 6000
print '################################ computing fiducial Cls ... ####################################'
name_fid = 'fidCls'
fnames_fid = glob.glob( os.path.join(path2Cls, name_fid+'*.pkl' ))
output = {}
for file_ in fnames_fid:
output[file_] = 0
for p in range(len(params_fid_v[0].keys())):
name_fid = '_'+str(params_fid_v[0].keys()[p])+'_'+str(params_fid_v[0][params_fid_v[0].keys()[p]] )+'_'
for file_ in fnames_fid:
if name_fid in file_:
output[file_] += 1
fnames_fid = [max(output.iteritems(), key=operator.itemgetter(1))[0]]
if output[fnames_fid[0]] < len(params_fid_v[0].keys())-1 :
fnames_fid = []
if not fnames_fid:
print '################# computing Cls file because it does not seem to be on disk #######################'
Cls_fid=python_camb_self_consistent.submit_camb( h=params_fid_v[0]['h'], ombh2=params_fid_v[0]['ombh2'], omch2=params_fid_v[0]['omch2'], \
omnuh2=params_fid_v[0]['omnuh2'], omk=params_fid_v[0]['omk'], YHe=params_fid_v[0]['YHe'], Neff=params_fid_v[0]['Neff'], w=params_fid_v[0]['w'], \
wa=params_fid_v[0]['wa'], tau=params_fid_v[0]['tau'],As=params_fid_v[0]['As'], ns=params_fid_v[0]['ns'], alphas=params_fid_v[0]['alphas'], nT=params_fid_v[0]['nT'], \
r=params_fid_v[0]['r'], k_scalar=params_fid_v[0]['k_scalar'] , k_tensor=params_fid_v[0]['k_tensor'], eta=1.0, lensing_z_max=delensing_z_max, exe = camb)
save_obj('./', name_fid, Cls_fid)
else:
print '################################ loading already existing Cls file ####################################'
Cls_fid = load_obj('/global/homes/j/josquin/FORECAST/self_consistent_forecast/codes/', fnames_fid[0])
experiments = sorted(configurations.keys(),reverse=True)
## computing uKRJ/pix for each experiment
for exp in experiments:
nfreqs = len(configurations[exp]['freqs'])
configurations[exp]['uKRJ/pix'] = np.zeros(nfreqs)
for f in range( nfreqs ):
uKCMB_perpixel_f = configurations[exp]['uKCMBarcmin'][f] / pix_size_map_arcmin
uKRJ_perpixel_f = uKCMB_perpixel_f*residuals_comp.BB_factor_computation( configurations[exp]['freqs'][f] )
configurations[exp]['uKRJ/pix'][f] = uKRJ_perpixel_f
del uKCMB_perpixel_f, uKRJ_perpixel_f
foregrounds = {}
ells = {}
ind = -1
for exp1 in experiments:
ind += 1
for exp2 in experiments[ind:]:
if exp1 == exp2 :
# experiment alone, exp is the name of the instrument that we study