Thomas Fitoussi / simulation-analysis

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parameters_impact.py 5.46 KB
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4d2bede0   Thomas Fitoussi   add script for me... 
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#!/bin/python
from numpy import zeros, size, nditer, average, savetxt, set_printoptions
from src.read import select_events
from src.analytic import degre, yr
set_printoptions(precision=2)
c419cc5a   Thomas Fitoussi   Updated analysis ... 
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Redshifts=["0.04","0.14","0.4","1","2"]
4d2bede0   Thomas Fitoussi   add script for me... 
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powerlaw_index=1.2

###############################################################################
print "Compute mean obs vs EGMF strength ..."
EGMFs=["12","13","14","15","16","17","18","19","20","21","22"]

theta_mean  = zeros((size(Redshifts)+1,size(EGMFs)+1))
theta_mean1 = theta_mean.copy()
theta_mean2 = theta_mean.copy()
dt_mean     = theta_mean.copy()
dt_mean1    = theta_mean.copy()
dt_mean2    = theta_mean.copy()
ratio_gen   = theta_mean.copy()

def compute_mean_delay_and_theta(i,j):
   if i==0:
      theta_mean[i,0] = 0
      theta_mean1[i,0]= 0
      theta_mean2[i,0]= 0
      theta_mean[i,j] = 10**(-float(EGMFs[j-1]))
      theta_mean1[i,j]= 10**(-float(EGMFs[j-1]))
      theta_mean2[i,j]= 10**(-float(EGMFs[j-1]))
      
      dt_mean[i,0] = 0
      dt_mean1[i,0]= 0
      dt_mean2[i,0]= 0
      dt_mean[i,j] = 10**(-float(EGMFs[j-1]))
      dt_mean1[i,j]= 10**(-float(EGMFs[j-1]))
      dt_mean2[i,j]= 10**(-float(EGMFs[j-1]))
      
      ratio_gen[i,0]= 0
      ratio_gen[i,j]= 10**(-float(EGMFs[j-1]))
      
   else:
      theta_mean[i,0]  = Redshifts[i-1]
      theta_mean1[i,0] = Redshifts[i-1]
      theta_mean2[i,0] = Redshifts[i-1]
      dt_mean[i,0]     = Redshifts[i-1]
      dt_mean1[i,0]    = Redshifts[i-1]
      dt_mean2[i,0]    = Redshifts[i-1]
      ratio_gen[i,0]   = Redshifts[i-1]
      fileId="Simulations/z="+Redshifts[i-1]+"/EGMF=1e-"+EGMFs[j-1]+"G"
      weight, energy, time, theta, phi, Esource, gen = select_events(fileId,Erange=[1,1e3],powerlaw_index=1.2)
      dt_mean[i,j]=average(time,weights=weight)/yr
      theta_mean[i,j]=average(theta,weights=weight)/degre
      cond = (gen==2)
      nb_gen1 = sum(weight[cond])
      dt_mean1[i,j]=average(time[cond],weights=weight[cond])/yr
      theta_mean1[i,j]=average(theta[cond],weights=weight[cond])/degre
      cond = (gen==4)
      nb_gen2 = sum(weight[cond])
      dt_mean2[i,j]=average(time[cond],weights=weight[cond])/yr
      theta_mean2[i,j]=average(theta[cond],weights=weight[cond])/degre
      
      ratio_gen[i,j-1] = nb_gen2/nb_gen1  

it=nditer(theta_mean1, flags=['multi_index'], op_flags=['readwrite'])
while not it.finished:
   compute_mean_delay_and_theta(it.multi_index[0],it.multi_index[1])    
   it.iternext()

savetxt("Results/theta_mean_vs_EGMF.dat"      ,theta_mean ,fmt='%1.4e')
savetxt("Results/theta_mean_vs_EGMF-gen=1.dat",theta_mean1,fmt='%1.4e')
savetxt("Results/theta_mean_vs_EGMF-gen=2.dat",theta_mean2,fmt='%1.4e')
savetxt("Results/delay_mean_vs_EGMF.dat"      ,dt_mean    ,fmt='%1.4e')
savetxt("Results/delay_mean_vs_EGMF-gen=1.dat",dt_mean1   ,fmt='%1.4e')
savetxt("Results/delay_mean_vs_EGMF-gen=2.dat",dt_mean2   ,fmt='%1.4e')
savetxt("Results/ratio_gen_EGMF.dat"          ,ratio_gen  ,fmt='%1.4e')
   
print(ratio_gen)


###############################################################################
print "Compute mean obs vs EGMF coherence length ..."

L_Bs=["0.001","0.010","0.100","1","10","100","1000"]

theta_mean=zeros((size(Redshifts)+1,size(L_Bs)+1))
dt_mean=theta_mean.copy()

def compute_mean_delay_and_theta(i,j):
   if i==0:
      theta_mean[i,0]=0
      dt_mean[i,0]=0
      theta_mean[i,j]=L_Bs[j-1]
      dt_mean[i,j]=L_Bs[j-1]

   else:
      theta_mean[i,0]  = Redshifts[i-1]
      dt_mean[i,0]     = Redshifts[i-1]
      if (L_Bs[j-1]=="1"):
         fileId="Simulations/z="+Redshifts[i-1]+"/EGMF=1e-15G"
      else:
         fileId="Simulations/z="+Redshifts[i-1]+"/lambda_B="+L_Bs[j-1]+"Mpc"
      weight, energy, time, theta, phi, Esource, generation = select_events(fileId,Erange=[1,1e3],powerlaw_index=1.2)
      dt_mean[i,j]=average(time,weights=weight)/yr
      theta_mean[i,j]=average(theta,weights=weight)/degre
   
it=nditer(theta_mean, flags=['multi_index'], op_flags=['readwrite'])
while not it.finished:
   compute_mean_delay_and_theta(it.multi_index[0],it.multi_index[1])    
   it.iternext()
   
set_printoptions(precision=2)
print(theta_mean)
print "========================================"
print(dt_mean)

savetxt("Results/theta_mean_vs_LB.dat",theta_mean,fmt='%1.4e')
savetxt("Results/delay_mean_vs_LB.dat",dt_mean,fmt='%1.4e')

###############################################################################
print "Compute mean obs vs jet opening angle ..."
opening_angle = [90,75,60,45,30,15,10,5,2,1,0.7,0.5,0.1,1e-2] # degres 

def compute_theta_mean(i,j):
   if i==0:
      if j==0:
         theta_mean[i,j]=0
         dt_mean[i,0]=0
      else:
         theta_mean[i,j]=opening_angle[j-1]
         dt_mean[i,j]=opening_angle[j-1]
   else:
      if j==0:
         theta_mean[i,j]=float(Redshifts[i-1])
      else:
         fileId="Simulations/z="+Redshifts[i-1]+"/EGMF=1e-15G"
         weight, energy, time, theta, phi, Esource, gen = select_events(fileId,Erange=[1,1e3],powerlaw_index=1.2,tjet=opening_angle[j-1])
         theta_mean[i,j]=average(theta,weights=weight)/degre
         dt_mean[i,j]=average(time,weights=weight)/yr
   
theta_mean=zeros((size(Redshifts)+1,size(opening_angle)+1))
dt_mean=theta_mean.copy()
it=nditer(theta_mean, flags=['multi_index'], op_flags=['readwrite'])
while not it.finished:
   compute_theta_mean(it.multi_index[0],it.multi_index[1])
   it.iternext()
    
set_printoptions(precision=3)
print(theta_mean)

savetxt("Results/theta_mean_vs_jet_opening.dat",theta_mean,fmt='%1.4e')
savetxt("Results/delay_mean_vs_jet_opening.dat",dt_mean,fmt='%1.4e')