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EXAMPLE_PROFILE1.py
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EXAMPLE_PROFILE1.py
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import sys, platform, os
sys.path.insert(0, os.environ['ROOTDIR'] +
'/external_modules/code/CAMB/build/lib.linux-x86_64-'
+os.environ['PYTHON_VERSION'])
import functools
import numpy as np
import ipyparallel
import sys, platform, os
import math
import euclidemu2
import scipy
from getdist import IniFile
import itertools
import iminuit
import camb
import cosmolike_lsst_y1_interface as ci
import copy
import argparse
import random
import emcee
# ------------------------------------------------------------------------------
# ------------------------------------------------------------------------------
# ------------------------------------------------------------------------------
CLprobe="xi"
path= os.environ['ROOTDIR'] + "/external_modules/data/lsst_y1"
data_file="LSST_Y1_M1_GGL0.05.dataset"
IA_model = 0
IA_redshift_evolution = 3
# Init Cosmolike & Read LSST-Y1 data file
ini = IniFile(os.path.normpath(os.path.join(path, data_file)))
data_vector_file = ini.relativeFileName('data_file')
cov_file = ini.relativeFileName('cov_file')
mask_file = ini.relativeFileName('mask_file')
ntheta = ini.int("n_theta")
theta_min_arcmin = ini.float("theta_min_arcmin")
theta_max_arcmin = ini.float("theta_max_arcmin")
lens_file = ini.relativeFileName('nz_lens_file')
source_file = ini.relativeFileName('nz_source_file')
lens_ntomo = ini.int("lens_ntomo")
source_ntomo = ini.int("source_ntomo")
ci.initial_setup()
ci.init_cosmo_runmode(is_linear=False)
ci.init_source_sample(filename=source_file, ntomo_bins=int(source_ntomo))
ci.init_lens_sample(filename=lens_file, ntomo_bins=int(lens_ntomo))
ci.init_IA(ia_model=int(IA_model), ia_redshift_evolution=int(IA_redshift_evolution))
# Init Cosmolike
ci.init_probes(possible_probes=CLprobe)
ci.init_binning(int(ntheta), theta_min_arcmin, theta_max_arcmin)
ci.init_data_real(cov_file, mask_file, data_vector_file)
# ------------------------------------------------------------------------------
# ------------------------------------------------------------------------------
# ------------------------------------------------------------------------------
def get_camb_cosmology(omegam, omegab, H0, ns, As_1e9 , w, w0pwa, mnu,
AccuracyBoost=1.0, kmax=10, k_per_logint=20,
CAMBAccuracyBoost=1.1, non_linear_emul=2):
As = lambda As_1e9: 1e-9 * As_1e9
wa = lambda w0pwa, w: w0pwa - w
omegabh2 = lambda omegab, H0: omegab*(H0/100)**2
omegach2 = lambda omegam, omegab, mnu, H0: (omegam-omegab)*(H0/100)**2-(mnu*(3.046/3)**0.75)/94.0708
omegamh2 = lambda omegam, H0: omegam*(H0/100)**2
CAMBAccuracyBoost = CAMBAccuracyBoost*AccuracyBoost
kmax = max(kmax/2.0, kmax*(1.0 + 3*(AccuracyBoost-1)))
k_per_logint = max(k_per_logint/2.0, int(k_per_logint) + int(3*(AccuracyBoost-1)))
extrap_kmax = max(max(2.5e2, 3*kmax), max(2.5e2, 3*kmax) * AccuracyBoost)
z_interp_1D = np.concatenate( (np.concatenate((np.linspace(0,2.0,1000),
np.linspace(2.0,10.1,200)),
axis=0),
np.linspace(1080,2000,20)),
axis=0)
z_interp_2D = np.concatenate(( np.linspace(0, 2.0, 95),
np.linspace(2.25, 10, 5)),
axis=0)
log10k_interp_2D = np.linspace(-4.2, 2.0, 1200)
pars = camb.set_params(H0=H0,
ombh2=omegabh2(omegab, H0),
omch2=omegach2(omegam, omegab, mnu, H0),
mnu=mnu,
omk=0,
tau=0.06,
As=As(As_1e9),
ns=ns,
halofit_version='takahashi',
lmax=10,
AccuracyBoost=CAMBAccuracyBoost,
lens_potential_accuracy=1.0,
num_massive_neutrinos=1,
nnu=3.046,
accurate_massive_neutrino_transfers=False,
k_per_logint=k_per_logint,
kmax=kmax);
pars.set_dark_energy(w=w, wa=wa(w0pwa, w), dark_energy_model='ppf');
pars.NonLinear = camb.model.NonLinear_both
pars.set_matter_power(redshifts = z_interp_2D, kmax = kmax, silent = True);
results = camb.get_results(pars)
PKL = results.get_matter_power_interpolator(var1="delta_tot",
var2="delta_tot",
nonlinear=False,
extrap_kmax=extrap_kmax,
hubble_units=False,
k_hunit=False);
PKNL = results.get_matter_power_interpolator(var1="delta_tot",
var2="delta_tot",
nonlinear=True,
extrap_kmax=extrap_kmax,
hubble_units=False,
k_hunit=False);
lnPL = np.empty(len(log10k_interp_2D)*len(z_interp_2D))
for i in range(len(z_interp_2D)):
lnPL[i::len(z_interp_2D)] = np.log(PKL.P(z_interp_2D[i],
np.power(10.0,log10k_interp_2D)))
lnPL += np.log(((H0/100.)**3))
lnPNL = np.empty(len(log10k_interp_2D)*len(z_interp_2D))
if non_linear_emul == 1:
params = { 'Omm' : omegam,
'As' : As(As_1e9),
'Omb' : omegab,
'ns' : ns,
'h' : H0/100.,
'mnu' : mnu,
'w' : w,
'wa' : wa(w0pwa, w)
}
kbt, bt = euclidemu2.get_boost(params,
z_interp_2D,
np.power(10.0,
np.linspace(-2.0589,
0.973,
len(log10k_interp_2D)))
)
log10k_interp_2D = log10k_interp_2D - np.log10(H0/100.)
for i in range(len(z_interp_2D)):
lnbt = scipy.interpolate.interp1d(np.log10(kbt),
np.log(bt[i]),
kind = 'linear',
fill_value = 'extrapolate',
assume_sorted = True)(log10k_interp_2D)
lnbt[np.power(10,log10k_interp_2D) < 8.73e-3] = 0.0
lnPNL[i::len(z_interp_2D)] = lnPL[i::len(z_interp_2D)] + lnbt
elif non_linear_emul == 2:
for i in range(len(z_interp_2D)):
lnPNL[i::len(z_interp_2D)] = np.log(PKNL.P(z_interp_2D[i],
np.power(10.0,log10k_interp_2D)))
log10k_interp_2D = log10k_interp_2D - np.log10(H0/100.)
lnPNL += np.log(((H0/100.)**3))
G_growth = np.sqrt(PKL.P(z_interp_2D,0.0005)/PKL.P(0,0.0005))
G_growth = G_growth*(1 + z_interp_2D)/G_growth[len(G_growth)-1]
chi = results.comoving_radial_distance(z_interp_1D, tol=1e-4) * (H0/100.)
return (log10k_interp_2D, z_interp_2D, lnPL, lnPNL, G_growth, z_interp_1D, chi)
# ------------------------------------------------------------------------------
# ------------------------------------------------------------------------------
# ------------------------------------------------------------------------------
def chi2(params, AccuracyBoost=1.0, non_linear_emul=2):
As_1e9 = params[0]
ns = params[1]
H0 = params[2]
omegab = params[3]
omegam = params[4]
DZ_S1 = params[5]
DZ_S2 = params[6]
DZ_S3 = params[7]
DZ_S4 = params[8]
DZ_S5 = params[9]
A1_1 = params[10]
A1_2 = params[11]
M1 = params[12]
M2 = params[13]
M3 = params[14]
M4 = params[15]
M5 = params[16]
w = -0.9
w0pwa = -0.9
mnu = 0.06
(log10k_interp_2D, z_interp_2D, lnPL, lnPNL,
G_growth, z_interp_1D, chi) = get_camb_cosmology(omegam=omegam,
omegab=omegab,
H0=H0,
ns=ns,
As_1e9=As_1e9,
w=w,
w0pwa=w0pwa,
mnu=mnu,
AccuracyBoost=AccuracyBoost,
non_linear_emul=non_linear_emul)
ci.init_accuracy_boost(AccuracyBoost,
AccuracyBoost,
int(1+5*(AccuracyBoost-1)))
ci.set_cosmology(omegam=omegam,
H0=H0,
log10k_2D=log10k_interp_2D,
z_2D=z_interp_2D,
lnP_linear=lnPL,
lnP_nonlinear=lnPNL,
G=G_growth,
z_1D=z_interp_1D,
chi=chi)
ci.set_nuisance_shear_calib(M=[M1, M2, M3, M4, M5])
ci.set_nuisance_shear_photoz(bias=[DZ_S1, DZ_S2, DZ_S3, DZ_S4, DZ_S5])
ci.set_nuisance_ia(A1 = [A1_1, A1_2, 0, 0, 0],
A2 = [0, 0, 0, 0, 0],
B_TA = [0, 0, 0, 0, 0])
datavector = np.array(ci.compute_data_vector_masked())
return ci.compute_chi2(datavector)
# ------------------------------------------------------------------------------
# ------------------------------------------------------------------------------
# ------------------------------------------------------------------------------
x = np.array([
2.1, # As
0.96, # ns
67.0, # H0
0.04, # omegab
0.30, # omegam
0.04, # S1
0.0016, # S2
0.03, # S3
-0.08, # S4
-8.67127e-05, # S5
0.7, # A11
-1.5, # A12
0.001, # M1
0.002, # M2
0.003, # M3
0.004, # M4
0.001 # M5
])
bounds = np.array([
[1.9, 2.5], # As
[0.89, 1.05], # ns
[50.0, 90.0], # H0
[0.027, 0.07], # omegab
[0.2,0.4], # omegam
[-0.12, 0.12], # S1
[-0.12, 0.12], # S2
[-0.12, 0.12], # S3
[-0.12, 0.12], # S4
[-0.12, 0.12], # S5
[-5.0, 5.0], # A11
[-5.0, 5.0], # A12
[-0.12, 0.12], # M1
[-0.12, 0.12], # M2
[-0.12, 0.12], # M3
[-0.12, 0.12], # M4
[-0.12, 0.12] # M5
])
start = np.array([
1.90, # As
0.90, # ns
60.0, # H0
0.028, # omegab
0.20, # omegam
-0.20, # S1
-0.20, # S2
-0.20, # S3
-0.20, # S4
-0.20, # S5
-3.00, # A11
-3.00, # A12
-0.20, # M1
-0.20, # M2
-0.20, # M3
-0.20, # M4
-0.20 # M5
])
stop = np.array([
2.50, # As
1.04, # ns
80.0, # H0
0.05, # omegab
0.40, # omegam
0.20, # S1
0.20, # S2
0.20, # S3
0.20, # S4
0.20, # S5
3.00, # A11
3.00, # A12
0.20, # M1
0.20, # M2
0.20, # M3
0.20, # M4
0.20 # M5
])
name = [
"As", # As
"ns", # ns
"H0", # H0
"omegab", # omegab
"omm", # omegam
"S1", # S1
"S2", # S2
"S3", # S3
"S4", # S4
"S5", # S5
"A11", # A11
"A12", # A12
"M1", # M1
"M2", # M2
"M3", # M3
"M4", # M4
"M5" # M5
]
# ------------------------------------------------------------------------------
# ------------------------------------------------------------------------------
# ------------------------------------------------------------------------------
parser = argparse.ArgumentParser(prog='EXAMPLE_PROFILE1')
parser.add_argument("--AB",
dest="AccuracyBoost",
help="Accuracy Boost of CAMB/Cosmolike calculation",
type=float,
nargs='?',
const=1,
default=1.1)
parser.add_argument("--tol",
dest="tolerance",
help="Minimizer Tolerance",
type=float,
nargs='?',
const=1,
default=0.02)
parser.add_argument("--maxfeval",
dest="maxfeval",
help="Minimizer: maximum number of likelihood evaluations",
type=int,
nargs='?',
const=1,
default=50000)
parser.add_argument("--maxiter",
dest="maxiter",
help="Minimizer: maximum number of minimizer iterations",
type=int,
nargs='?',
const=1,
default=5)
parser.add_argument("--minmethod",
dest="minmethod",
help="Minimizer: minimizer method",
type=int,
nargs='?',
const=1,
default=1)
parser.add_argument("--outroot",
dest="outroot",
help="Name of the Output File",
nargs='?',
const=1)
parser.add_argument("--profile",
dest="profile",
help="Which Parameter to Profile",
type=int,
nargs='?',
const=1)
parser.add_argument("--mpi",
dest="mpi",
help="Number of MPI cores",
type=int,
nargs='?',
const=1)
# need to use parse_known_args because of mpifuture
args, unknown = parser.parse_known_args()
non_linear_emul = 2
AccuracyBoost = args.AccuracyBoost
tol = args.tolerance
maxfeval = args.maxfeval
maxiter = args.maxiter
min_method = args.minmethod
outroot = args.outroot
index = args.profile
nummpi = args.mpi
cov_file = 'EXAMPLE_MCMC1.covmat'
cov = np.loadtxt(cov_file)
# need to delete the w line
cov = np.delete(cov, (5), axis=0)
cov = np.delete(cov, (5), axis=1)
# ------------------------------------------------------------------------------
# ------------------------------------------------------------------------------
# ------------------------------------------------------------------------------
def min_chi2(x0, bounds, min_method, fixed=-1, AccuracyBoost=1.0,
tol=0.01, maxfev=300000, non_linear_emul=2, maxiter=10, cov=cov):
def mychi2(params, *args):
z, fixed, T = args
params = np.array(params)
if fixed > -1:
params = np.insert(params, fixed, z)
return chi2(params=params,
AccuracyBoost=AccuracyBoost,
non_linear_emul=non_linear_emul)/T
if fixed > -1:
z = x0[fixed]
x0 = np.delete(x0, (fixed))
bounds = np.delete(bounds, (fixed), axis=0)
args = (z, fixed, 1.0)
else:
args = (0.0, -2.0, 1.0)
def log_prior(params):
params = np.array(copy.deepcopy(params))
prior = 1.0
for i in range(len(params)):
if (params[i] < bounds[i][0]) or (params[i] > bounds[i][1]):
return -np.inf
else:
prior *= np.log(1.0/(bounds[i][1]-bounds[i][0]))
return prior
def logprob(params, *args):
lp = log_prior(params)
if not np.isfinite(lp):
return -np.inf
else:
return -0.5*mychi2(params, *args) + lp
cov = np.delete(cov, (fixed), axis=0)
cov = np.delete(cov, (fixed), axis=1)
class GaussianStep:
def __init__(self, stepsize=0.2):
self.cov = stepsize*cov
def __call__(self, x):
return np.random.multivariate_normal(x, self.cov, size=1)
if min_method == 1:
x = copy.deepcopy(x0)
tmp = iminuit.minimize(fun=mychi2,
x0=x,
args=args,
bounds=bounds,
method="migrad",
tol=tol,
options = {'stra' : 2,
'maxfun': int(maxfev)})
x = copy.deepcopy(tmp.x)
GS = GaussianStep()
tmp = scipy.optimize.basinhopping(func=mychi2,
x0=x,
T=0.1,
target_accept_rate=0.25,
niter=maxiter,
stepsize=0.02,
interval=100,
niter_success=3,
take_step=GS,
minimizer_kwargs={"method": 'Nelder-Mead',
"args": args,
"bounds": bounds,
"options": {'adaptive' : True,
'xatol' : tol,
'fatol' : tol,
'maxfev' : maxfev,
'maxiter' : maxiter}})
x = copy.deepcopy(tmp.x)
tmp = iminuit.minimize(fun=mychi2,
x0=x,
args=args,
bounds=bounds,
method="migrad",
tol=tol,
options = {'stra' : 2,
'maxfun': int(maxfev)})
result = tmp.fun
elif min_method == 2:
x = copy.deepcopy(x0)
tmp = iminuit.minimize(fun=mychi2,
x0=x,
args=args,
bounds=bounds,
method="migrad",
tol=tol,
options = {'stra' : 2,
'maxfun': int(maxfev)})
x = copy.deepcopy(tmp.x)
# https://stats.stackexchange.com/a/456073
tmp = scipy.optimize.dual_annealing(func=mychi2,
x0=x,
args=args,
bounds=bounds,
maxfun=maxfev,
no_local_search=True,
maxiter=maxiter,
visit=1.01,
accept=1,
initial_temp=5230.0,
restart_temp_ratio=0.0002)
x = copy.deepcopy(tmp.x)
tmp = iminuit.minimize(fun=mychi2,
x0=x,
args=args,
bounds=bounds,
method="migrad",
tol=tol,
options = {'stra' : 2,
'maxfun': int(maxfev)})
result = tmp.fun
elif min_method == 3:
tmp = scipy.optimize.shgo(func=mychi2,
args=args,
bounds=bounds,
n=50,
iters=maxiter,
options={'f_tol' : tol,
'maxfev' : maxfev,
'maxiter' : maxiter},
minimizer_kwargs={"method": 'Nelder-Mead',
"args": args,
"bounds": bounds,
"options": {'adaptive' : True,
'xatol' : tol,
'fatol' : tol,
'maxfev' : maxfev,
'maxiter' : maxiter}})
result = tmp.fun
elif min_method == 4:
x = copy.deepcopy(x0)
partial = []
for i in range(maxiter):
tmp = iminuit.minimize(fun=mychi2,
x0=x,
args=args,
bounds=bounds,
method="migrad",
tol=tol,
options = {'stra' : 2,
'maxfun': maxfev})
partial.append(tmp.fun)
print(f"MN, i = {i}, chi2 = {tmp.fun}, ns = {args[0]}")
x = GaussianStep(stepsize=0.0025)(tmp.x)[0,:]
result = min(partial)
elif min_method == 5: # adapted from PROCOLI
nwalkers = int(3)
ndim = int(x0.shape[0])
nsteps = maxfev
temperature = np.array([1.0, 0.33, 0.25, 0.2, 0.1, 0.005, 0.001])
stepsz = np.array([1.0, 1.00, 0.80, 0.5, 0.2, 0.100, 0.050])
partial = []
for i in range(7):
x = [] # Initial point
for j in range(nwalkers):
x.append(GaussianStep(stepsize=temperature[i]*stepsz[i])(x0)[0,:])
x = np.array(x)
GScov = copy.deepcopy(cov)
GScov *= temperature[i]*stepsz[i]
sampler = emcee.EnsembleSampler(nwalkers,
ndim,
logprob,
args=(args[0], args[1], temperature[i]),
moves=[(emcee.moves.GaussianMove(cov=GScov),1.)])
sampler.run_mcmc(x, nsteps, skip_initial_state_check=True)
samples = sampler.get_chain(flat=True, thin=1, discard=0)
j = np.argmin(-1.0*np.array(sampler.get_log_prob(flat=True)))
x0 = copy.deepcopy(samples[j])
partial.append(mychi2(samples[j], *args))
print(f"emcee: i = {i}, chi2 = {min(partial)}, ns = {args[0]}")
tmp = iminuit.minimize(fun=mychi2,
x0=x0,
args=args,
bounds=bounds,
method="migrad",
tol=tol,
options = {'stra' : 2,
'maxfun': maxfev})
x0 = copy.deepcopy(tmp.x)
partial.append(tmp.fun)
print(f"MN: i = {i}, chi2 = {result}, ns = {args[0]}")
result = min(partial)
return result
# ------------------------------------------------------------------------------
# ------------------------------------------------------------------------------
# ------------------------------------------------------------------------------
def prf(val, index):
x0 = copy.deepcopy(x)
x0[index] = val
res = min_chi2(x0=x0,
bounds=bounds,
min_method=min_method,
fixed=index,
AccuracyBoost=AccuracyBoost,
tol=tol,
maxfev=maxfeval,
non_linear_emul=non_linear_emul,
maxiter=maxiter)
return res
# ------------------------------------------------------------------------------
# ------------------------------------------------------------------------------
# ------------------------------------------------------------------------------
from mpi4py.futures import MPIPoolExecutor
if __name__ == '__main__':
executor = MPIPoolExecutor()
param = np.linspace(start=start[index], stop=stop[index], num=nummpi)
print(param)
res = np.array(list(executor.map(functools.partial(prf,index=index),param)))
print("Profile = ", res)
out = outroot + "_" + str(random.randint(0,1000)) + "_minmethod_" + str(min_method) + "_" + name[index] + ".txt"
print("Output file = ", out)
np.savetxt(out, np.c_[param, res])
executor.shutdown()
# TO RUN THIS SCRIPT
#mpirun -n 13 --oversubscribe --mca btl vader,tcp,self --bind-to core:overload-allowed --rank-by core --map-by numa:pe=${OMP_NUM_THREADS} python -m mpi4py.futures EXAMPLE_PROFILE1.py --AB 1.0 --tol 0.02 --maxiter 5 --maxfeval 10000 --profile 1 --mpi 12 --outroot "monday" --minmethod 1
#mpirun -n 13 --oversubscribe --mca btl vader,tcp,self --bind-to core:overload-allowed --rank-by core --map-by numa:pe=${OMP_NUM_THREADS} python -m mpi4py.futures EXAMPLE_PROFILE1.py --AB 1.0 --tol 0.02 --maxiter 3 --maxfeval 12000 --profile 1 --mpi 12 --outroot "monday" --minmethod 2
#mpirun -n 13 --oversubscribe --mca btl vader,tcp,self --bind-to core:overload-allowed --rank-by core --map-by numa:pe=${OMP_NUM_THREADS} python -m mpi4py.futures EXAMPLE_PROFILE1.py --AB 1.0 --tol 0.02 --maxiter 2 --maxfeval 500 --profile 1 --mpi 12 --outroot "monday" --minmethod 3
#mpirun -n 13 --oversubscribe --mca btl vader,tcp,self --bind-to core:overload-allowed --rank-by core --map-by numa:pe=${OMP_NUM_THREADS} python -m mpi4py.futures EXAMPLE_PROFILE1.py --AB 1.0 --tol 0.02 --maxiter 3 --maxfeval 4000 --profile 1 --mpi 12 --outroot "monday" --minmethod 4
#mpirun -n 13 --oversubscribe --mca btl vader,tcp,self --bind-to core:overload-allowed --rank-by core --map-by numa:pe=${OMP_NUM_THREADS} python -m mpi4py.futures EXAMPLE_PROFILE1.py --AB 1.0 --tol 0.02 --maxfeval 3000 --profile 1 --mpi 12 --outroot "monday" --minmethod 5