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model_LR_1111.py
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import os
import subprocess
from glob import glob
from abc import ABCMeta, abstractmethod
from functools import lru_cache, wraps
import numpy as np
from pandas import DataFrame,Series,read_csv
from numpy import inf, nan, log, log10, exp, abs, min, max, ndarray
from scipy.stats import norm
from .from_taisuke.py_src.g_minus_2 import log_likelihood_g_minus_2
from .from_taisuke.py_src.vacuum_stability import stability
from .from_taisuke.py_src.par_to_phys_1111 import par_to_phys
from .pymicromegas import PyMicrOmegas, Project
from .polygon import Polygon
from .model import Model, Collider
############ config ############
__filedir__ = os.path.dirname(__file__) + "/"
#### for collider constraints ####
hepdata_fname = __filedir__ + "HEPData-ins1750597-v1-csv.tar.gz" # 13 TeV
hepdata_fname_8tev = __filedir__ + "HEPData-ins1286761-v1-csv.tar.gz" # 8 TeV
hepdata_dir = __filedir__ + os.path.basename(hepdata_fname).split(".")[0] + "/"
hepdata_dir_8tev = __filedir__ + os.path.basename(hepdata_fname_8tev).split(".")[0] + "/"
hepdata_degen_dir = __filedir__ + "coll_degenerated/"
constraint_se_l = hepdata_dir + "Exclusioncontour(obs)7.csv"
constraint_se_r = hepdata_dir + "Exclusioncontour(obs)8.csv"
constraint_smu_l = hepdata_dir + "Exclusioncontour(obs)10.csv"
constraint_smu_r = hepdata_dir + "Exclusioncontour(obs)11.csv"
constraint_se_l_8tev = hepdata_dir_8tev + "Table41.csv"
constraint_se_r_8tev = hepdata_dir_8tev + "Table39.csv"
constraint_smu_l_8tev = hepdata_dir_8tev + "Table47.csv"
constraint_smu_r_8tev = hepdata_dir_8tev + "Table45.csv"
constraint_se_l_degen = hepdata_degen_dir + "eL_limit_degen.dat"
constraint_se_r_degen = hepdata_degen_dir + "eR_limit_degen.dat"
constraint_smu_l_degen = hepdata_degen_dir + "muL_limit_degen.dat"
constraint_smu_r_degen = hepdata_degen_dir + "muR_limit_degen.dat"
#constraint_lep_se_r_degen = hepdata_degen_dir + "LEP_eR.dat"
#constraint_lep_smu_r_degen = hepdata_degen_dir + "LEP_muR.dat"
constraint_lep_se = hepdata_degen_dir + "LEP_data_eR.dat"
constraint_lep_smu = hepdata_degen_dir + "LEP_data_muR.dat"
hepdict = {
"slepton" : r"m($\tilde{l}$) [GeV]",
"neutralino" : r"m($\tilde{\chi}_{1}^{0}$) [GeV]",
}
#### for micromegas ####
mdl_file_paths = glob(__filedir__+"from_taisuke/models/*.mdl")
dof_fname = __filedir__ + "pymicromegas/eos2020.dat"
dof_fname_1 = __filedir__ + "pymicromegas/eos2020_err1.dat"
dof_fname_2 = __filedir__ + "pymicromegas/eos2020_err2.dat"
dof_fname_11 = __filedir__ + "pymicromegas/eos2020_err11.dat"
dof_fname_22 = __filedir__ + "pymicromegas/eos2020_err22.dat"
class LeptophilicDMOneHanded(Model):
'''
Implementation of Leptophilic DM model.
'''
def __init__(self,config_fname,
enable_vacuum_stability = True,
enable_collider_const = False,
enable_micromegas_likeli = False,
enable_micromegas_prior = False,
enable_gm2 = False,
project_name = "LeptophilicDM_easy_version2",
dir_models = "/from_taisuke/models_easy_version2",
which = None
):
"""
initialize Leptophilic DM model.
Note: stablility condition is always enabled. Other conditions (collider, micromegas, g-2) is optional.
"""
self.config = read_csv(config_fname,comment="#")
if which == "L":
self.fix = {"m_e_R":2000,"y_R":0}
elif which == "R":
self.fix = {"m_e_L":2000,"y_L":0,"m_nu":2000}
else:
raise RuntimeError("\"which\" is not specified. Select L or R.")
self.enable_vacuum_stability = enable_vacuum_stability
self.enable_collider_const = enable_collider_const
self.enable_micromegas_likeli = enable_micromegas_likeli
self.enable_micromegas_prior = enable_micromegas_prior
self.enable_gm2 = enable_gm2
#### initilalize project if called for the first time ####
mo = PyMicrOmegas()
if not mo.project_exists(project_name):
micromegas = mo.load_project(project_name)
mdl_file_paths = glob(os.path.dirname(__file__) + f"{dir_models}/*.mdl")
micromegas.load_mdl_files(mdl_file_paths)
micromegas.compile()
# initialize project
print(micromegas({},["OMEGA"]))
self.micromegas = Project(project_name)
#### collider constraints ####
self.coll_se_l = Collider(constraint_se_l)
self.coll_se_r = Collider(constraint_se_r)
self.coll_smu_l = Collider(constraint_smu_l)
self.coll_smu_r = Collider(constraint_smu_r)
self.coll_se_l_8tev = Collider(constraint_se_l_8tev)
self.coll_se_r_8tev = Collider(constraint_se_r_8tev)
self.coll_smu_l_8tev = Collider(constraint_smu_l_8tev)
self.coll_smu_r_8tev = Collider(constraint_smu_r_8tev)
self.coll_se_l_degen = Collider(constraint_se_l_degen,delim_whitespace=True)
self.coll_se_r_degen = Collider(constraint_se_r_degen,delim_whitespace=True)
self.coll_smu_l_degen = Collider(constraint_smu_l_degen,delim_whitespace=True)
self.coll_smu_r_degen = Collider(constraint_smu_r_degen,delim_whitespace=True)
self.coll_lep_se = Collider(constraint_lep_se,delim_whitespace=True)
self.coll_lep_smu = Collider(constraint_lep_smu,delim_whitespace=True)
# extend collider constraints to mx = 0 (y axis),
# otherwise some narrow regions are remained to be un-excluded
for coll in [self.coll_se_l,self.coll_se_r,self.coll_smu_l,self.coll_smu_r,
self.coll_se_l_8tev,self.coll_se_r_8tev,self.coll_smu_l_8tev,self.coll_smu_r_8tev
]:
new_points = np.array([
[coll.x[0],0],
*coll.points,
[coll.x[-1],0]
])
coll.reset_points(new_points)
for coll in [self.coll_lep_se,self.coll_lep_smu]:
new_points = np.array([
[0,0],
*coll.points,
[coll.x[-1],0]
])
coll.reset_points(new_points)
for coll_degen in [self.coll_se_l_degen,self.coll_se_r_degen,
self.coll_smu_l_degen,self.coll_smu_r_degen]:
new_points = np.array([
[0,coll_degen.y[0]],
*coll_degen.points,
[0,coll_degen.y[-1]]
])
coll_degen.reset_points(new_points)
@property
def param_names(self):
"""
parameter names.
Note: Paramters with mass-dimension should defined in GeV unit.
"""
return self.config.name
def to_par(self,array):
"""
make parameter dictionary (Series) from input array.
"""
sr = Series(array,index=self.param_names)
return sr
def to_par_physical(self,array):
"""
['Mx','MSLE','MSRE','MSNE','MSLM','MSRM','MSNM','MSLT','MSRT','MSNT',
'lamHSLE','lamHSRE','lamHSNE','lamHHSLE','lamHHSRE','lamHHSNE',
'lamHSLM','lamHSRM','lamHSNM','lamHHSLM','lamHHSRM','lamHHSNM',
'lamHSLT','lamHSLRT','lamHSRLT','lamHSRT','lamHSNT',
'lamHHSLT','lamHHSLRT','lamHHSRLT','lamHHSRT','lamHHSRT','lamHHSNT',
'yL','yR','yLT','yLRT','yRLT','yRT']
"""
ret_dict = par_to_phys(self.to_par(array))
if isinstance(ret_dict,str):
return ret_dict
else:
return Series(ret_dict)
def collider_excludes(self,par_physical):
m_sle = par_physical["MSLE"] # left-handed slepton mass
m_sre = par_physical["MSRE"] # right-handed slepton mass
m_slm = par_physical["MSLM"] # left-handed smuon mass
m_srm = par_physical["MSRM"] # right-handed smuon mass
m_x = par_physical["Mx"] # dark matter mass
if self.coll_se_l.excludes(m_sle,m_x): return True
if self.coll_se_r.excludes(m_sre,m_x): return True
if self.coll_smu_l.excludes(m_slm,m_x): return True
if self.coll_smu_r.excludes(m_srm,m_x): return True
if self.coll_se_l_8tev.excludes(m_sle,m_x): return True
if self.coll_se_r_8tev.excludes(m_sre,m_x): return True
if self.coll_smu_l_8tev.excludes(m_slm,m_x): return True
if self.coll_smu_r_8tev.excludes(m_srm,m_x): return True
#if self.coll_lep_se_r_degen.excludes(m_sre,m_sre-m_x): return True
#if self.coll_lep_smu_r_degen.excludes(m_srm,m_srm-m_x): return True
if self.coll_lep_se.excludes(m_sle,m_x): return True
if self.coll_lep_smu.excludes(m_slm,m_x): return True
if self.coll_lep_se.excludes(m_sre,m_x): return True
if self.coll_lep_smu.excludes(m_srm,m_x): return True
if self.coll_se_l_degen.excludes(m_sle,m_sle-m_x): return True
if self.coll_se_r_degen.excludes(m_sre,m_sre-m_x): return True
if self.coll_smu_l_degen.excludes(m_slm,m_slm-m_x): return True
if self.coll_smu_r_degen.excludes(m_srm,m_srm-m_x): return True
return False
def is_consistent_with_relic(self,par_physical):
omega_obs = 0.120 # PLANCK(2018) 0.120 += 0.001
par_physical = par_physical.to_dict()
omega_11 = self.micromegas(par_physical,flags=["OMEGA"],dof_fname=dof_fname_11)["Omega"]
omega_22 = self.micromegas(par_physical,flags=["OMEGA"],dof_fname=dof_fname_22)["Omega"]
omegas = [omega_11,omega_22]
if min(omegas) < 0 : return False
if min(omegas) <= omega_obs <= max(omegas):
return True
else:
return False
def lnl_relic_abundance(self,par_physical,return_omega=True):
omega_obs = 0.120 # PLANCK(2018) 0.120 += 0.001
ln_omega_obs = log(omega_obs)
par_physical = par_physical.to_dict()
omega = self.micromegas(par_physical,flags=["OMEGA"],dof_fname=dof_fname)["Omega"]
if omega < 0: return (-inf,omega if return_omega else -inf)
ln_omega = log(omega)
ln_omega_1 = log(self.micromegas(par_physical,flags=["OMEGA"],dof_fname=dof_fname_1)["Omega"])
ln_omega_2 = log(self.micromegas(par_physical,flags=["OMEGA"],dof_fname=dof_fname_2)["Omega"])
ln_omega_12 = [ln_omega_1,ln_omega_2]
ln_omega_lo = min(ln_omega_12)
ln_omega_hi = max(ln_omega_12)
#if not (ln_omega_lo < ln_omega < ln_omega_hi): return -inf
d_ln_omega = max(abs(ln_omega_12 - ln_omega))
if d_ln_omega == 0: return -inf
#print(dict(loc=ln_omega,scale=d_ln_omega))
lnl = norm.logpdf(ln_omega_obs,loc=ln_omega,scale=d_ln_omega)
if return_omega:
return lnl,omega
else:
return lnl
def lnl_gm2(self,array,par_physical=None):
if type(array) != ndarray:
raise RuntimeError(f'array is not numpy.ndarray but {type(array)}')
par = self.to_par(array)
if par_physical is None:
par_physical = self.to_par_physical(array)
kwargs = {
"mx": par_physical["Mx"],
"ml": par_physical["MSLM"],
"mr": par_physical["MSRM"],
"A" : par["A"],
"yl": par_physical["yL"],
"yr": par_physical["yR"]
}
return log_likelihood_g_minus_2(**kwargs)
def lnlikelihood(self,array):
"""
return log_likelihood value for given input array.
input:
array: numpy.ndarray, shape = (n_params,)
"""
#### example ####
#lower = self.config.lo.values
#upper = self.config.hi.values
#lnls = norm.logpdf(array,loc=(lower+upper)/2,scale=(upper-lower)/2)
#lnls = 0
################
#time.sleep(1e-1)
lnl = 0
blobs = {}
par_physical = self.to_par_physical(array)
if type(par_physical) == str:
raise RuntimeError(f"\"par_physical\" is string \"{par_physical}\"" + "\n" + f"array: {array}")
#return -inf,nan,nan,nan
# collider constraint
#if self.enable_collider_const:
collider_excludes = self.collider_excludes(par_physical)
# relic density
#if self.enable_micromegas_likeli:
lnl_relic_abundance,omega = self.lnl_relic_abundance(par_physical,return_omega=True)
#if self.enable_micromegas_prior:
is_consistent_with_relic = self.is_consistent_with_relic(par_physical)
# g-2 constraint
#if self.enable_gm2:
#lnl_gm2 = self.lnl_gm2(array,par_physical)
if self.enable_collider_const: lnl += (-inf if collider_excludes else 0)
if self.enable_micromegas_likeli: lnl += lnl_relic_abundance
if self.enable_micromegas_prior: lnl += (-inf if is_consistent_with_relic else 0)
#if self.enable_gm2: lnl += lnl_gm2
blobs["collider_excludes"] = collider_excludes
blobs["omega"] = omega
blobs["lnl_relic_abundance"] = lnl_relic_abundance
blobs["is_consistent_with_relic"] = is_consistent_with_relic
return lnl, blobs
def lnprior(self,array):
"""
return log_prior value for given input array.
input:
array: numpy.ndarray, shape = (n_params,)
"""
lower = self.config.lo.values
upper = self.config.hi.values
prior_type = self.config.prior.values
# Hard-cut prior
if not np.all((lower <= array) & (array <= upper)):
return -inf
par_physical = self.to_par_physical(array)
# vacuum stability
if self.enable_vacuum_stability and isinstance(par_physical,str): return -inf
# log-prior
lnps = -np.log(array[prior_type=="log"]) # d(log x) = x^-1 dx = exp(-log x) dx
#lnps += np.zeros(array[prior_type=="flat"].shape)
return np.sum(lnps)
def to_full_array(self,fixed_array):
dim = len(self.config)
idx_fixed = [list(self.config.name).index(key) for key in self.fix.keys()]
val_fixed = list(self.fix.values())
full_array = np.zeros(dim)
idx_free = [i for i in range(dim) if (i not in idx_fixed)]
full_array[idx_free] = fixed_array
full_array[idx_fixed] = val_fixed
return full_array
def _lnposterior(self,array):
lnp = self.lnprior(array)
if not lnp > -inf:
return (-inf, nan, False,nan,nan,False)
lnl,blobs = self.lnlikelihood(array)
return (lnl+lnp, lnl, *blobs.values())
@property
def lnposterior_return(self):
d = {
"lnpost":float,
"lnlike":float,
"collider_excludes":bool,
"omega":float,
"lnl_relic_abundance":float,
"is_consistent_with_relic":bool
}
return d
def lnposterior(self,array):
if self.fix is None:
raise RuntimeError("no fix sepcified!")
return self._lnposterior(self.to_full_array(array))