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satComputeNCumvsLvWithErrors_forOliver.py
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Fri, Oct 18, 09:58

satComputeNCumvsLvWithErrors_forOliver.py
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#!/usr/bin/env python3
import sys,os,string
import argparse
import satlib
import numpy as np
import Ptools as pt
import pickle
from scipy.interpolate import splrep,splev
from tqdm import tqdm
####################################################################
# option parser
####################################################################
description=""
epilog =""""""
parser = argparse.ArgumentParser(description=description,epilog=epilog,formatter_class=argparse.RawDescriptionHelpFormatter)
parser.add_argument("--M0",
action="store",
dest="M0",
metavar='FLOAT',
type=float,
default=1e12,
help='host halo mass')
parser.add_argument("--Mmin",
action="store",
dest="Mmin",
metavar='FLOAT',
type=float,
default=5e7,
help='the min halo mass')
parser.add_argument("--Mmax",
action="store",
dest="Mmax",
metavar='FLOAT',
type=float,
default=1e11,
help='the max halo mass')
parser.add_argument("--DMmass",
action="store",
dest="DMmass",
metavar='FLOAT',
type=float,
default=2.0,
help='dark matter particle mass')
parser.add_argument("-N",
action="store",
dest="N",
metavar='INT',
type=int,
default=1000,
help='number of bins')
parser.add_argument("--Ngal",
action="store",
dest="Ngal",
metavar='INT',
type=int,
default=100,
help='number of galaxies')
parser.add_argument("--Nrealisations",
action="store",
dest="Nrealisations",
metavar='INT',
type=int,
default=100,
help='number of realizations')
parser.add_argument("--LvvsMh_model",
action="store",
type=str,
dest="LvvsMh_model",
default = 'rj2018',
help="Lv Mh model")
parser.add_argument("-p",
action="store",
dest="ps",
metavar='FILE NAME',
type=str,
default=None,
help='output file name')
parser.add_argument("--dpi",
action="store",
dest="dpi",
type=float,
default=300,
help="DPI of the saved file",
metavar=" FLOAT")
parser.add_argument("--data",
action="store_true",
dest="data",
default=False,
help='add data')
parser.add_argument("--filter",
action="store",
dest="filter",
metavar='STR',
type=str,
default=None,
help='filter name')
parser.add_argument("--distance",
action="store",
dest="distance",
type=float,
default=35,
help="distance in Mpc",
metavar=" FLOAT")
####################################################################
# main
####################################################################
def MakePlot(opt):
# get Mh vs Lv relation
Mhbins,Lvmins,Lvmaxs = satlib.GetStellarMassHaloMassRelation(model=opt.LvvsMh_model)
# halo bins
lnMh = np.linspace(np.log10(opt.Mmin),np.log10(opt.Mmax),opt.N)
Mh0 = 10**lnMh
# define the DM models
#CDM = satlib.CDM_Sawala_Model(opt.Mmin,opt.Mmax,opt.N)
#WDM = satlib.CDM_Forouhar_Model(opt.Mmin,opt.Mmax,opt.N)
CDM = satlib.GEN_Model(opt.Mmin,opt.Mmax,opt.N,MDM=None,fWDM=0,M0=opt.M0)
# number of haloes for the CDM
Nh_CDM = CDM.NumberOfHaloes()
Nbins = 100
Nsats_CDM = np.zeros((opt.Ngal,Nbins-1))
Ns_CDM_realisations = np.zeros((opt.Nrealisations,Nbins-1))
for realisation in tqdm(range(opt.Nrealisations)):
for j in range(opt.Ngal):
#print(realisation,j)
#################################################
# generate N haloes for the CDM
#################################################
Mhs = CDM.fctSample(np.random.random(Nh_CDM))
###################
# loop over haloes
logLv = np.zeros(len(Mhs))
for i,Mh in enumerate(Mhs):
if Mh < 1e8:
continue
# find the nearest values in Mbins
# and set a Luminosity
idx = np.argmin( np.fabs( Mh-Mhbins ) )
logLvmins = np.log10(Lvmins[idx])
logLvmaxs = np.log10(Lvmaxs[idx])
logLv[i] = np.random.uniform(logLvmins,logLvmaxs)
######################
# cumulative
bins = np.linspace(2,12,Nbins)
Ns,Lv = np.histogram(logLv,bins)
# now, need to cumulate
# invert vector first and reinvert after
Nsat = np.add.accumulate(Ns[-np.arange(1,len(Ns)+1)])
Nsat = Nsat[-np.arange(1,len(Nsat)+1)]
Lv = Lv[1:]
# add
Nsats_CDM[j] = Nsat
Ns_CDM_mean = Nsats_CDM.mean(axis=0)
Ns_CDM_realisations[realisation] = Ns_CDM_mean
# plot means
#pt.plot(10**Lv,Ns_CDM_mean,lw=1,alpha=0.5, c='blue')
#pt.plot(10**Lv,Ns_WDM_mean,lw=1,alpha=0.5, c='red')
Ns_CDM_realisations_mean = Ns_CDM_realisations.mean(axis=0)
Ns_CDM_realisations_std = Ns_CDM_realisations.std(axis=0)
Ns_CDM_meanp = Ns_CDM_realisations_mean + 3*Ns_CDM_realisations_std
Ns_CDM_meanm = Ns_CDM_realisations_mean - 3*Ns_CDM_realisations_std
if opt.filter is not None:
# convert Lv assumed to be Msol in Mag
Mag = np.zeros(len(Lv))
for i in range(len(Lv)):
Mag[i] = satlib.Msol2Mag(10**Lv[i],filter=opt.filter)
# to apparent magnitude
opt.distance = opt.distance * 1e6 # Mpc to pc
Mag = Mag + 5*np.log10(opt.distance) - 5
ax = pt.gca()
ax.fill_between(Mag,Ns_CDM_meanp,Ns_CDM_meanm,facecolor='blue',alpha=0.5,label=r"$\textrm{CDM}$")
xmin = 30
xmax = 15
ymin = 1
ymax = 500
pt.SetAxis(xmin,xmax,ymin,ymax,log="y")
xlabel = r"$\rm{%s\,\,mag}$"%(opt.filter)
# 1e5 Msol
pt.plot([23.2,23.2],[0.1,1000],c='k',ls=':')
else:
ax = pt.gca()
ax.fill_between(10**Lv,Ns_CDM_meanp,Ns_CDM_meanm,facecolor='blue',alpha=0.5,label=r"$\textrm{CDM}$")
xmin = 5e2
xmax = 5e9
ymin = 1
ymax = 500
pt.SetAxis(xmin,xmax,ymin,ymax,log="xy")
xlabel = r"$\rm{V-band\,\,Luminosity}$"
pt.plot([1e5,1e5],[0.1,1000],c='k',ls=':')
'''
if opt.data:
################################################################################
# add data from Nadler, Drlica etc.
ax = pt.gca()
s = 0.0
k = 1
def read_data(f):
data = np.loadtxt(f,delimiter=',')
Mv = data[:,0]
N = data[:,1]
Lv = pow(10, (4.74 - Mv) / 2.5)
return Lv, N
###################
# Nadler
Lvp, Np = read_data("data/Nadler_rawt1.txt")
Lvm, Nm = read_data("data/Nadler_rawb1.txt")
ap = splrep(Lvp,Np,s=s,k=k)
am = splrep(Lvm,Nm,s=s,k=k)
Np= splev(Lvp,ap)
Nm= splev(Lvp,am)
#pt.plot(Lvp,Np,'k-')
#pt.plot(Lvm,Nm,'k-')
ax.fill_between(Lvp,Np,Nm,facecolor='green',alpha=0.05,label=r"$\textrm{Nadler\,\,et\,\,al.\,\,2018a}$")
Lvp, Np = read_data("data/Nadler_rawt2.txt")
Lvm, Nm = read_data("data/Nadler_rawb2.txt")
ap = splrep(Lvp,Np,s=s,k=k)
am = splrep(Lvm,Nm,s=s,k=k)
Np= splev(Lvp,ap)
Nm= splev(Lvp,am)
#pt.plot(Lvp,Np,'k-')
#pt.plot(Lvm,Nm,'k-')
ax.fill_between(Lvp,Np,Nm,facecolor='green',alpha=0.05)
###################
# Drilca
Lv, N = read_data("data/Drlica_raw_weighted.txt")
pt.plot(Lv,N,'k-',label=r"$\textrm{DES+PS1,\,\,weighted\,\,(Drlica-Wagner\,\,et\,\,al.\,\,2020)}$",alpha=0.5)
Lv, N = read_data("data/Drlica_raw_detected.txt")
pt.plot(Lv,N,'k--',label=r"$\textrm{DES+PS1,\,\,detected\,\,(Drlica-Wagner\,\,et\,\,al.\,\,2020)}$",alpha=0.5)
Lv, N = read_data("data/Drlica_raw_all.txt")
pt.plot(Lv,N,'r-',label=r"$\textrm{All\,\,known\,\,satellites\,\,(Drlica-Wagner\,\,et\,\,al.\,\,2020)}$",alpha=0.5)
'''
# Oliver data
Lv = np.array([5.91561634e+06, 2.14783047e+07, 8.55066713e+07, 1.12719746e+06,
3.73250158e+07, 1.95884467e+08, 2.83139200e+06, 3.73250158e+07,
1.78648757e+06, 1.02801630e+08, 2.83139200e+05, 4.09260660e+08,
5.91561634e+06])
logLv = np.log10(Lv)
bins = np.array([5,5.5,6,6.5,7,7.5,8])
bins = np.linspace(4,9,20)
h,bins = np.histogram(logLv, bins=bins)
h = np.flip(h)
h = np.add.accumulate(h)
h = np.flip(h)
#pt.scatter(10**bins[1:],h,marker='s',color="k",s=50,label=r'$\rm{M83}$')
pt.plot(10**bins[1:],h,color="k",label=r'$\rm{M83}$')
###########################
# finalize
###########################
ylabel = r"$\rm{Cumulative\,\,number\,\,of\,\,galaxies}\,\,(D<300\,\rm{kpc})$"
pt.xlabel(xlabel,fontsize=pt.labelfont)
pt.ylabel(ylabel,fontsize=pt.labelfont)
pt.grid(False)
pt.legend()
pt.title(r"$\rm{%d\,\,galaxies\,\,observed\,\,+\,\,model=%s}$"%(opt.Ngal,opt.LvvsMh_model),fontsize=pt.labelfont)
#pt.plot([1e5,1e5],[0.1,1000],c='k',ls=':')
if __name__ == '__main__':
opt = parser.parse_args()
files = []
pt.InitPlot(files, opt)
pt.labelfont=20
# pt.figure(figsize=(8*2,6*2))
# pt.figure(dpi=10)
#fig = pt.gcf()
# fig.subplots_adjust(left=0.1)
# fig.subplots_adjust(right=1)
# fig.subplots_adjust(bottom=0.12)
# fig.subplots_adjust(top=0.95)
# fig.subplots_adjust(wspace=0.25)
# fig.subplots_adjust(hspace=0.02)
MakePlot(opt)
pt.EndPlot(files, opt)

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