Commit 4d96d628b5845d34c589e9d83453fc258598d623

Authored by Thomas Fitoussi
1 parent 30f3bf5d
Exists in master

orrection in "analysis.py" due to renaming

Characteristic quantities.ipynb
1 1 {
2 2 "cells": [
3 3 {
  4 + "cell_type": "markdown",
  5 + "metadata": {},
  6 + "source": [
  7 + "### This Code aims to draw characteristic quantities versus EGMF and redshift\n",
  8 + "\n",
  9 + "Because for redshift upper than 1 there is no TeV photons, we will use the GeV band\n",
  10 + "\n",
  11 + "Characteristic quantities are: mean delay and mean time delay\n"
  12 + ]
  13 + },
  14 + {
4 15 "cell_type": "code",
5   - "execution_count": 20,
  16 + "execution_count": 4,
6 17 "metadata": {
7 18 "collapsed": false,
8 19 "scrolled": true
... ... @@ -12,124 +23,155 @@
12 23 "name": "stdout",
13 24 "output_type": "stream",
14 25 "text": [
15   - "EGMF15\n",
16   - " Delay: [ 2.85193729e+15 2.87403559e+15 8.71410978e+13 2.27258066e+08]\n",
17   - " theta: [ 2.71638609e+00 2.75065935e+00 8.57760367e-01 5.04526487e-04]\n",
18   - "EGMF14\n",
19   - " Delay: [ 2.83120782e+15 2.83996390e+15 9.27616197e+14 2.94303071e+10]\n",
20   - " theta: [ 2.67981039 2.69771759 2.28075445 0.00527583]\n",
21   - "EGMF16\n",
22   - " Delay: [ 2.34497614e+15 2.34041139e+15 8.50424793e+12 1.28371736e+06]\n",
23   - " theta: [ 2.96537084e+00 3.00652572e+00 1.26365075e-01 5.01672934e-05]\n",
24   - "z=0.0308\n",
25   - " Delay: [ 2.85193729e+15 2.87403559e+15 8.71410978e+13 2.27258066e+08]\n",
26   - " theta: [ 2.71638609e+00 2.75065935e+00 8.57760367e-01 5.04526487e-04]\n",
27   - "z=0.1\n",
28   - " Delay: [ 7.73212228e+15 7.67116451e+15 3.04985256e+14 2.48820202e+08]\n",
29   - " theta: [ 3.22734894e+00 3.20961641e+00 1.12923060e+00 1.47618370e-04]\n",
30   - "z=1\n",
31   - " Delay: [ 3.04611878e+16 2.71642665e+16 1.91346441e+13 0.00000000e+00]\n",
32   - " theta: [ 1.65744175 1.63955398 0.03787762 0. ]\n"
  26 + "[[ 0.00000000e+00 1.00000000e-08 1.00000000e-09 1.00000000e-10\n",
  27 + " 1.00000000e-11 1.00000000e-12 1.00000000e-13 1.00000000e-14\n",
  28 + " 1.00000000e-15 1.00000000e-16 1.00000000e-17 1.00000000e-18]\n",
  29 + " [ 3.08000000e-02 2.31669167e+00 2.46272189e+00 2.31761730e+00\n",
  30 + " 2.35409581e+00 2.19476260e+00 2.19649884e+00 2.30721120e+00\n",
  31 + " 2.42420603e+00 2.75069472e+00 2.78753184e+00 4.18989200e-01]\n",
  32 + " [ 1.00000000e-01 5.03021487e+00 5.17248664e+00 5.17318998e+00\n",
  33 + " 5.02288445e+00 5.06413069e+00 5.17880568e+00 5.20734079e+00\n",
  34 + " 5.04978855e+00 4.95592646e+00 2.65673680e+00 3.95433192e-01]\n",
  35 + " [ 5.00000000e-01 5.42277169e+00 5.47854199e+00 5.52659330e+00\n",
  36 + " 5.67044186e+00 5.53964475e+00 5.61695768e+00 5.55298921e+00\n",
  37 + " 5.37630821e+00 4.77957703e+00 2.00147238e+00 2.29919715e-01]\n",
  38 + " [ 1.00000000e+00 4.29019100e+00 4.29747752e+00 4.43425212e+00\n",
  39 + " 4.34672581e+00 4.36502152e+00 4.39432230e+00 4.30122813e+00\n",
  40 + " 4.07557647e+00 3.40072648e+00 9.92819407e-01 9.84245819e-02]\n",
  41 + " [ 2.00000000e+00 4.36694310e+00 4.32006031e+00 4.29913362e+00\n",
  42 + " 4.38270454e+00 4.28456317e+00 4.35261766e+00 4.23559394e+00\n",
  43 + " 3.81561053e+00 2.54938196e+00 4.67573547e-01 5.24824684e-02]]\n",
  44 + "========================================\n",
  45 + "[[ 0.00000000e+00 1.00000000e-08 1.00000000e-09 1.00000000e-10\n",
  46 + " 1.00000000e-11 1.00000000e-12 1.00000000e-13 1.00000000e-14\n",
  47 + " 1.00000000e-15 1.00000000e-16 1.00000000e-17 1.00000000e-18]\n",
  48 + " [ 3.08000000e-02 1.60470842e+15 1.57648280e+15 1.58144197e+15\n",
  49 + " 1.63046762e+15 1.56922509e+15 1.60240329e+15 1.54999595e+15\n",
  50 + " 1.55111818e+15 1.36769539e+15 3.97493300e+14 5.75147193e+12]\n",
  51 + " [ 1.00000000e-01 5.41158326e+15 5.68576605e+15 5.70251767e+15\n",
  52 + " 5.52322164e+15 5.44810792e+15 5.64840914e+15 5.62853346e+15\n",
  53 + " 5.25726175e+15 4.48577135e+15 9.91017717e+14 1.36177755e+13]\n",
  54 + " [ 5.00000000e-01 2.41804767e+16 2.37623266e+16 2.44916015e+16\n",
  55 + " 2.49931585e+16 2.48178986e+16 2.50055740e+16 2.43188573e+16\n",
  56 + " 2.33421908e+16 1.80598946e+16 2.16234523e+15 2.41590545e+13]\n",
  57 + " [ 1.00000000e+00 3.42065092e+16 3.48746289e+16 3.55725057e+16\n",
  58 + " 3.56122244e+16 3.43933196e+16 3.52897430e+16 3.31823713e+16\n",
  59 + " 3.21234051e+16 2.09827453e+16 1.19849654e+15 9.39172158e+12]\n",
  60 + " [ 2.00000000e+00 6.02478400e+16 5.73622982e+16 6.04580297e+16\n",
  61 + " 5.94871078e+16 5.89450726e+16 6.02765357e+16 5.42390170e+16\n",
  62 + " 4.79504034e+16 2.43448368e+16 3.96613703e+14 5.74073019e+12]]\n"
33 63 ]
34 64 }
35 65 ],
36 66 "source": [
37   - "from xml.dom import minidom\n",
  67 + "from matplotlib.pyplot import figure, show\n",
  68 + "from numpy import zeros, size, nditer, average\n",
38 69 "from modules.read import ReadResults\n",
39 70 "from modules.constants import degre\n",
40   - "from numpy import average, zeros,arange\n",
41 71 "\n",
42   - "xmlfile = minidom.parse(\"simulations.xml\")\n",
43   - "simulations = xmlfile.getElementsByTagName(\"simu\")\n",
  72 + "Redshifts=[\"0.0308\",\"0.1\",\"0.5\",\"1\",\"2\"]\n",
  73 + "EGMFs=[\"08\",\"09\",\"10\",\"11\",\"12\",\"13\",\"14\",\"15\",\"16\",\"17\",\"18\"]\n",
  74 + "powerlaw_index=2\n",
44 75 "\n",
45   - "for simu in simulations:\n",
46   - " fileId = simu.getAttribute(\"simulation_dir\")\n",
47   - " simuId = simu.getAttribute(\"id\")\n",
48   - " weight,energy,time,theta=ReadResults(fileId,[1,2,3,5]) \n",
49   - " \n",
50   - " Emin = [1e-3,1e0,1e3] #GeV\n",
51   - " Emax = [1e0,1e3,1e5] #GeV\n",
52   - " \n",
53   - " mean_delays=zeros(4)\n",
54   - " mean_theta=zeros(4)\n",
55   - " mean_delays[0]=average(time,weights=weight) \n",
56   - " mean_theta[0]=average(abs(theta),weights=weight)*degre \n",
57   - " for n in arange(0,3,1):\n",
58   - " cond= (energy>Emin[n]) & (energy<Emax[n])\n",
59   - " if len(time[cond])!=0: \n",
60   - " mean_delays[n+1]=average(time[cond],weights=weight[cond]) \n",
61   - " mean_theta[n+1]=average(abs(theta[cond]),weights=weight[cond])*degre \n",
  76 + "theta_mean=zeros((size(Redshifts)+1,size(EGMFs)+1))\n",
  77 + "dt_mean=theta_mean.copy()\n",
  78 + "\n",
  79 + "it=nditer(theta_mean, flags=['multi_index'], op_flags=['readwrite'])\n",
  80 + "while not it.finished:\n",
  81 + " i=it.multi_index[0]\n",
  82 + " j=it.multi_index[1]\n",
  83 + " if i==0:\n",
  84 + " if j==0:\n",
  85 + " theta_mean[i,j]=0\n",
  86 + " dt_mean[i,j]=0\n",
  87 + " else:\n",
  88 + " theta_mean[i,j]=10**(-float(EGMFs[j-1]))\n",
  89 + " dt_mean[i,j]=10**(-float(EGMFs[j-1]))\n",
  90 + " else:\n",
  91 + " if j==0:\n",
  92 + " theta_mean[i,j]=float(Redshifts[i-1])\n",
  93 + " dt_mean[i,j]=float(Redshifts[i-1])\n",
  94 + " else:\n",
  95 + " fileId=\"z=\"+Redshifts[i-1]+\"-EGMF\"+EGMFs[j-1]+\"-lambda_B=1Mpc-Dominguez\"\n",
  96 + " weightini,time_delay,theta,Esource = ReadResults(\"Simulations/\"+fileId,cols=[1,3,6,7])\n",
  97 + " weight_source = (Esource/min(Esource))**(1-powerlaw_index)\n",
  98 + " weight = weightini* weight_source\n",
  99 + " cond= (Esource<1e3) & (Esource>1e0) # GeV band\n",
  100 + " theta_mean[i,j]=average(theta[cond],weights=weight[cond])*degre\n",
  101 + " dt_mean[i,j]=average(time_delay[cond],weights=weight[cond])\n",
62 102 " \n",
63   - " print simuId\n",
64   - " print\" Delay:\",mean_delays[:]\n",
65   - " print\" theta:\",mean_theta[:]"
  103 + " it.iternext()\n",
  104 + "\n",
  105 + "print theta_mean\n",
  106 + "print \"========================================\"\n",
  107 + "print dt_mean"
66 108 ]
67 109 },
68 110 {
69   - "cell_type": "markdown",
70   - "metadata": {},
  111 + "cell_type": "code",
  112 + "execution_count": 5,
  113 + "metadata": {
  114 + "collapsed": false,
  115 + "scrolled": true
  116 + },
  117 + "outputs": [],
71 118 "source": [
72   - "###Case \"z=1\" has no TeV photons!"
  119 + "#%matplotlib inline\n",
  120 + "from matplotlib.pyplot import figure, show\n",
  121 + "from mpl_toolkits.mplot3d import Axes3D\n",
  122 + "from numpy import log10\n",
  123 + "\n",
  124 + "fig = figure()\n",
  125 + "ax = fig.add_subplot(111,projection='3d') \n",
  126 + "\n",
  127 + "B=theta_mean[0,1:]\n",
  128 + "i=1\n",
  129 + "for z in Redshifts:\n",
  130 + " ax.plot(theta_mean[i,1:],log10(dt_mean[i,1:]),log10(B),\"--*\",label=\"z=\"+z)\n",
  131 + " i+=1\n",
  132 + " \n",
  133 + "ax.legend(loc=\"best\")\n",
  134 + "ax.set_xlabel(\"$\\\\theta$ [deg]\")\n",
  135 + "ax.set_ylabel(\"Time delay [s]\")\n",
  136 + "ax.set_zlabel(\" log B [Gauss]\")\n",
  137 + "show()"
73 138 ]
74 139 },
75 140 {
76 141 "cell_type": "code",
77   - "execution_count": 23,
  142 + "execution_count": 6,
78 143 "metadata": {
79 144 "collapsed": false
80 145 },
81   - "outputs": [
82   - {
83   - "data": {
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JLiJHArcBLYEPVPW5AO+zPhBjYqh0XpGG7Ruyb90+hk0eRv9B/V3NtG//Po54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\n8vzbUb3IK/mrOkn1Sv6qWH7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85   - "text/plain": [
86   - "<matplotlib.figure.Figure at 0x7f690e15ab10>"
87   - ]
88   - },
89   - "metadata": {},
90   - "output_type": "display_data"
91   - }
92   - ],
  146 + "outputs": [],
93 147 "source": [
94 148 "#%matplotlib inline\n",
95 149 "from matplotlib.pyplot import figure, show\n",
96   - "from numpy import array\n",
97   - "from modules.constants import degre\n",
  150 + "from numpy import log10\n",
98 151 "\n",
99 152 "fig = figure() \n",
100   - "ax1 = fig.add_subplot(111)\n",
101   - "\n",
102   - "EGMF = [1e-16,1e-15,1e-14]\n",
103   - "EGMF_delay_TeV = array([1.28371736e+06,2.27258066e+08,2.94303071e+10])\n",
104   - "EGMF_delay_GeV = array([8.50424793e+12,8.71410978e+13,9.27616197e+14])\n",
105   - "EGMF_delay_MeV = array([2.34041139e+15,2.87403559e+15,2.83996390e+15])\n",
106   - "EGMF_theta_TeV = array([8.75584447e-07,8.80564837e-06,9.20805607e-05])*degre\n",
107   - "EGMF_theta_GeV = array([2.20548661e-03,1.49707426e-02,3.98066746e-02])*degre\n",
108   - "EGMF_theta_MeV = array([5.24737729e-02,4.80080623e-02,4.70840543e-02])*degre\n",
109   - "\n",
110   - "ax1.plot(EGMF_theta_TeV,EGMF_delay_TeV,'--<',label=\"TeV\")\n",
111   - "ax1.plot(EGMF_theta_GeV,EGMF_delay_GeV,'--<',label=\"GeV\")\n",
112   - "ax1.plot(EGMF_theta_MeV,EGMF_delay_MeV,'--<',label=\"MeV\")\n",
113   - "\n",
114   - "\n",
115   - "z = [0.0308,0.1,1]\n",
116   - "EGMF_delay_TeV = array([2.27258066e+08,2.48820202e+08])\n",
117   - "EGMF_delay_GeV = array([8.71410978e+13,3.04985256e+14,1.91346441e+13])\n",
118   - "EGMF_delay_MeV = array([2.87403559e+15,7.67116451e+15,2.71642665e+16])\n",
119   - "EGMF_theta_TeV = array([8.80564837e-06,1.47618370e-04])\n",
120   - "EGMF_theta_GeV = array([1.49707426e-02,1.12923060e+00,0.03787762])\n",
121   - "EGMF_theta_MeV = array([4.80080623e-02,3.20961641e+00,1.63955398])\n",
  153 + "ax1 = fig.add_subplot(121) \n",
  154 + "ax2 = fig.add_subplot(122)\n",
122 155 "\n",
123   - "ax1.plot(EGMF_theta_TeV,EGMF_delay_TeV,'--*')\n",
124   - "ax1.plot(EGMF_theta_GeV,EGMF_delay_GeV,'--*')\n",
125   - "ax1.plot(EGMF_theta_MeV,EGMF_delay_MeV,'--*')\n",
  156 + "B=theta_mean[0,1:]\n",
  157 + "i=1\n",
  158 + "for z in Redshifts:\n",
  159 + " ax1.plot(B,theta_mean[i,1:],'--*',label=\"z=\"+z)\n",
  160 + " ax2.plot(B,dt_mean[i,1:],'--*',label=\"z=\"+z)\n",
  161 + " i+=1\n",
126 162 "\n",
127 163 "ax1.set_xscale('log') \n",
128 164 "ax1.set_yscale('log')\n",
129 165 "ax1.grid(b=True,which='major')\n",
130 166 "ax1.legend(loc=\"best\")\n",
131   - "ax1.set_xlabel(\"average arrival angle [degre]\")\n",
132   - "ax1.set_ylabel(\"average time delay [s]\")\n",
  167 + "ax1.set_xlabel(\"B [Gauss]\")\n",
  168 + "ax1.set_ylabel(\"average arrival angle [degre]\")\n",
  169 + "ax2.set_xscale('log') \n",
  170 + "ax2.set_yscale('log')\n",
  171 + "ax2.grid(b=True,which='major')\n",
  172 + "ax2.legend(loc=\"best\")\n",
  173 + "ax2.set_xlabel(\"B [Gauss]\")\n",
  174 + "ax2.set_ylabel(\"average time delay [s]\")\n",
133 175 "show()\n"
134 176 ]
135 177 },
... ...
analysis.py
... ... @@ -6,7 +6,7 @@ from numpy import append, savetxt, shape, array, newaxis, zeros, arange
6 6 from modules.read import ReadResults, ReadProfile, resultdir
7 7 from modules.spectrum import spectrum
8 8 from modules.maps import computeMap
9   -from modules.angle import radial
  9 +from modules.arrival_angle import arrivalAngle
10 10 from modules.timing import timing
11 11 from modules.observables import param_vs_energy, delay_vs_theta
12 12 from modules.constants import degre
... ... @@ -75,12 +75,12 @@ for simu in simulations:
75 75 Angle_Energy = ener[:,newaxis]
76 76 Angle_Energy = append(Angle_Energy,angle[:,newaxis],axis=1)
77 77  
78   - # RADIAL ====================================================================#
79   - print " ... Computing radial distribution"
  78 + # Arrival angle =============================================================#
  79 + print " ... Computing arrival angle distribution"
80 80 nbBins = 100
81   - theta2,dndtheta = radial(theta,weight,nbBins,theta_range=[theta_min,theta_max])
82   - Radial = theta2[:,newaxis]
83   - Radial = append(Radial,dndtheta[:,newaxis],axis=1)
  81 + theta2,dndtheta = arrivalAngle(theta_arrival,weight,nbBins,theta_range=[theta_min,theta_max])
  82 + arrival_Angle = theta2[:,newaxis]
  83 + arrival_Angle = append(arrival_Angle,dndtheta[:,newaxis],axis=1)
84 84  
85 85 # TIME DISTRIBUTION AND TIME DELAY VERSUS ANGLE =============================#
86 86 print " ... Computing time distribution"
... ... @@ -102,10 +102,10 @@ for simu in simulations:
102 102 for n in arange(0,3,1):
103 103 cond= (energy>Emin[n]) & (energy<Emax[n])
104 104  
105   - # RADIAL =================================================================#
  105 + # Arrival angle ===========================================================#
106 106 nbBins = 100
107   - theta2,dndtheta = radial(theta[cond],weight[cond],nbBins,theta_range=[theta_min,theta_max])
108   - Radial = append(Radial,dndtheta[:,newaxis],axis=1)
  107 + theta2,dndtheta = arrivalAngle(theta_arrival[cond],weight[cond],nbBins,theta_range=[theta_min,theta_max])
  108 + arrival_Angle = append(arrival_Angle,dndtheta[:,newaxis],axis=1)
109 109  
110 110 # TIME DISTRIBUTION AND TIME DELAY VERSUS ANGLE ==========================#
111 111 nbBins = 200
... ... @@ -130,10 +130,10 @@ for simu in simulations:
130 130 ener,flux = spectrum(energy[cond],weight[cond],nbBins)
131 131 Spectrum = append(Spectrum,flux[:,newaxis],axis=1)
132 132  
133   - # RADIAL =================================================================#
  133 + # Arrival angle ==========================================================#
134 134 nbBins = 100
135   - theta2,dndtheta = radial(theta[cond],weight[cond],nbBins,theta_range=[theta_min,theta_max])
136   - Radial = append(Radial,dndtheta[:,newaxis],axis=1)
  135 + theta2,dndtheta = arrivalAngle(theta_arrival[cond],weight[cond],nbBins,theta_range=[theta_min,theta_max])
  136 + arrival_Angle = append(arrival_Angle,dndtheta[:,newaxis],axis=1)
137 137  
138 138 print " ... ", ((n+1)*100)/3,"% done"
139 139  
... ... @@ -163,10 +163,10 @@ for simu in simulations:
163 163 ener,angle = param_vs_energy(theta_arrival[cond],energy[cond],nbBins)
164 164 Angle_Energy = append(Angle_Energy,angle[:,newaxis],axis=1)
165 165  
166   - # RADIAL =================================================================#
  166 + # arrivalAngle =================================================================#
167 167 nbBins = 100
168   - theta2,dndtheta = radial(theta[cond],weight[cond],nbBins,theta_range=[theta_min,theta_max])
169   - Radial = append(Radial,dndtheta[:,newaxis],axis=1)
  168 + theta2,dndtheta = arrivalAngle(theta_arrival[cond],weight[cond],nbBins,theta_range=[theta_min,theta_max])
  169 + arrival_Angle = append(arrival_Angle,dndtheta[:,newaxis],axis=1)
170 170  
171 171 # TIME DISTRIBUTION AND TIME DELAY VERSUS ANGLE ==========================#
172 172 nbBins = 200
... ... @@ -180,7 +180,7 @@ for simu in simulations:
180 180 savetxt(output_dir+"/Spectrum.txt",Spectrum)
181 181 savetxt(output_dir+"/Source_spectrum.txt",Source)
182 182 savetxt(output_dir+"/Angle_vs_Energy.txt",Angle_Energy)
183   - savetxt(output_dir+"/Radial_distribution.txt",Radial)
  183 + savetxt(output_dir+"/arrival_Angle_distribution.txt",arrival_Angle)
184 184 savetxt(output_dir+"/Timing.txt",Timing)
185 185 savetxt(output_dir+"/Delay_versus_angle.txt",Delay_vs_angle)
186 186 savetxt(output_dir+"/Generation.txt",Gen_cont)
... ...
modules/analytic.py
1 1 from constants import *
2   -from numpy import sqrt, abs, cos, sin, arcsin, searchsorted, loadtxt
  2 +from numpy import sqrt, abs, cos, sin, arcsin, searchsorted, loadtxt, log
3 3 from scipy.integrate import quad
4 4 from read import ReadProfile,resultdir
5 5  
... ... @@ -24,11 +24,11 @@ def Analytic_theta_vs_energy(E_ic,fileId):
24 24  
25 25 def Analytic_delay_vs_theta(theta,fileId):
26 26 Esource,distSource=ReadProfile(fileId,[1,2]) #GeV #Mpc
  27 + lgg = lambda_gg(Esource)[0]
27 28 E0_source = Esource*1e-3 #TeV
28   - lgg = 1.94 #lambda_gg(E0_source)
29 29 delta_ic = arcsin(distSource/lgg*sin(theta))
30 30 Dic0=Dic(E0_source/2)
31   - c_delta_t = lgg*(1-cos(delta_ic)) - distSource*(1-cos(theta))
  31 + c_delta_t = (lgg*(1-cos(delta_ic)) - distSource*(1-cos(theta)))*log(lgg/theta)
32 32 return c_delta_t *Mpc/c # sec.
33 33  
34 34 def Analytic_delay_vs_energy(Egamma, fileId):
... ... @@ -36,11 +36,11 @@ def Analytic_delay_vs_energy(Egamma, fileId):
36 36 RL0=RL(Esource/2,B)
37 37 Dic0=Dic(Esource/2)
38 38 E_e = Ee(Egamma)
39   - #lgg = lambda_gg(Esource)
40   - lgg=1.9440974051313842
  39 + lgg = lambda_gg(Esource)[0]
  40 + #lgg=1.9440974051313842
41 41 delta_ic = Dic0/(2*RL0)*((Esource/2/E_e)**2 -1)
42 42 theta = arcsin(lgg/Dsource*sin(delta_ic))
43   - c_delta_t = lgg*(1-cos(delta_ic)) - Dsource*(1-cos(theta))
  43 + c_delta_t = (lgg*(1-cos(delta_ic)) - Dsource*(1-cos(theta)))*log(lgg/theta)
44 44 return c_delta_t *Mpc/c # sec.
45 45  
46 46 # Compton accumulation
... ...
simple_case.ipynb
... ... @@ -2,31 +2,25 @@
2 2 "cells": [
3 3 {
4 4 "cell_type": "code",
5   - "execution_count": 1,
  5 + "execution_count": 5,
6 6 "metadata": {
7 7 "collapsed": false,
8   - "scrolled": true
  8 + "scrolled": false
9 9 },
10 10 "outputs": [
11 11 {
12 12 "name": "stderr",
13 13 "output_type": "stream",
14 14 "text": [
15   - "modules/observables.py:26: RuntimeWarning: invalid value encountered in divide\n",
16   - " ampl /= nb\n",
17   - "modules/observables.py:17: RuntimeWarning: invalid value encountered in divide\n",
18   - " return thetacenter, dt/dN\n",
19   - "modules/analytic.py:29: RuntimeWarning: invalid value encountered in arcsin\n",
20   - " delta_ic = arcsin(distSource/lgg*sin(theta))\n",
21   - "/usr/lib64/python2.7/site-packages/matplotlib/scale.py:100: RuntimeWarning: invalid value encountered in less_equal\n",
22   - " a[a <= 0.0] = 1e-300\n"
  15 + "modules/analytic.py:42: RuntimeWarning: invalid value encountered in arcsin\n",
  16 + " theta = arcsin(lgg/Dsource*sin(delta_ic))\n"
23 17 ]
24 18 },
25 19 {
26 20 "data": {
27   - "image/png": 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Hu2yZdv19bdlrqTXUTMWW62/pJ8TnifXbuPy6ruu/JeuanNpIHSkm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\nM+3ateOuu+4KWkZOCf0iuohcIiJPiMizIlIWtJ5sUQghbefOncuxY8c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  21 + "image/png": 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Ur/jc5z6HD3zgA7j55psxMRH+ng0Co4pEvxvAKiJaCeABAGcAOGtEtAwUoehq67CXkdWF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\nAK4DcEU/3K2G9cX64Q9/OFSafH1KU/U+2TYomsrG95Omsn3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28 22 "text/plain": [
29   - "<matplotlib.figure.Figure at 0x7f888c5c1510>"
  23 + "<matplotlib.figure.Figure at 0x7fc34dbc22d0>"
30 24 ]
31 25 },
32 26 "metadata": {},
... ... @@ -34,9 +28,9 @@
34 28 },
35 29 {
36 30 "data": {
37   - "image/png": 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Dk1eeX89Jv7Klmb8qEk2eZLKahFcsh/u4j9nM7nCSu2p3QKcZyaVM8jrNs+hI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\nFdgEuBj4OnDEsOTrQc6RvI89yPnxbr6XZUpl0i3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7iHwdrgyfAj5VNlgTKhImbXWRp+h406ZNtbx/i/1+tra0Mnb+sv6tFqjOpRX/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\nj5tiMYzxYj6QBlHVLlr1IR5KeOi2FSm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38 32 "text/plain": [
39   - "<matplotlib.figure.Figure at 0x7f886cdc2890>"
  33 + "<matplotlib.figure.Figure at 0x7fc3552bf750>"
40 34 ]
41 35 },
42 36 "metadata": {},
... ... @@ -44,9 +38,9 @@
44 38 },
45 39 {
46 40 "data": {
47   - "image/png": 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lNuU+12CfG3KIsUgEchG8fQywZXytP4e9gd8787rj56m5QmVuvQ2Jwu7CZzMn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\nDx06VBStS5cu5bbbbmP58uWcdtppRamjvRB03tvLZ4kfqifSJPaKJMJ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VGSLyOZzXfSeY\noaorPfIuBH6H4wHwVVX9KEB9hlEwNrVltBdeAMaLSC9w/MCLSJ8seX4HJJ9piMgAn3RrcKbNEJGL\ngZ4pcb8GRgGDgBXZRKrqi8BnRSTVde6pKZ9XADckHvKLSF8R6ezm3Qn8L/BTbFrLKCFmSIx2gar+\nCbgD+J2I/AHHSHw+EZ2aNOX8JmCQ+1D7j8D0tHQJZgEXu8trx+NMmTW79bYALwK/0uA+G64Eviki\nO0VkPdAA3OLGPQJsAza79f2C1jMLTwFfBpYErMswCsb8kRhGgbie5Y6p6jERGQr8PGU5bwec5y7j\nVXWHR95q4Leq+rUiayxJPUb7xEYkhlE4fYCNIrIFZ6VXLYCIfAV4A2dFVxsj4nIU6J66ITFsRGQk\njvtUe2ZiFAUbkRiGYRgFYSMSwzAMoyDMkBiGYRgFYYbEMAzDKAgzJIZhGEZBmCExDMMwCsIMiWEY\nhlEQ/w9nPWqJ75y+NAAAAABJRU5ErkJggg==\n",
  41 + "image/png": 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xoKSRXH311XzrW9+yzgHxYfPNo912zZwNzdOaNWt49tlnY9PVzjvv\nzOabb87+++/P8uXL480bdZSXuc3L73//e+655x4GDhzIJz7xCQBmf2U2F1/8BK+++irnnz8qlSet\nRZiagOYpCw8c/ABwYLTdi2djRNOhrpE2EVlajREC7E5uhUmFWEPQzn0zQdAcR6ydBI75xzSlBZ9E\n/iVs/pPNLf7M/stOsMtKl5nO7MAWBDpUuEmavEKlnGPcFwJsCa9l/ryTLBppfaW1rxaN6I9oRJ5q\nRaP4SLYBnjfuXyiWxRCRVSLyNREZI9HSzouWlhaCICAIAq666irrSwvDsEfvp02bZvG6dOlS6/my\nZaUZQilFGIb85S9/4aSTTmLrrbcmDEPrIKwrrrgiNleJCNdee23suNf9a5r9+/dP8DNnzhxWrFjB\nnnvuCUQZ5GEYsuGGG7L99tvH0WVmf6ZQHDx4MI888kisJS1YsCD2+3R0dJR9H4VCdK8ni/b2djup\nTdn19Vb3m2++eXGCLj1XSicPluiFYRgJAqN/K8GwJdpoUGsc+rnZvr29vTS5T1UwVZWE0LKig1lK\n41EqLAm5ZRAqw3wVFOL+CIpOc1MoHKsoFAolweQ81/dxvk3K8xhTlTX+wqRC/Pz848+HAjRPLTnj\nrwyu5MrgSuY/Pj+mV5hkBBu0KIt+GIa28HgZ6161KNsB36Ls739SIf4+9PsPw9BaWPjuzf7L3be3\nt1dVf325D8OQlpaWeL6sGXp7ju78EOX/LjTupwA3GvcnAD/pBF1pdOy5555ClNogf/zjH0VE5Jxz\nzpF+/frJpZdeGj/baqutvO3vvfdeOfXUU+N6X/7yl0VEZO3atXHZkiVL4vrTpk0TQC677LJUnm6+\n+WYBZPz48Vb5bbfdFtMEZNddd42fLVmyRJ566il56623ZMKECQLIvHnz5Ic//KEAcuaZZ8Z1n3vu\nOfnHP/4hH3zwgZhf0auvvirTpk2T73znO7Jq1aoEX7qu/jtjxgwB5KSTThKC5Hdtlvmeu7QJsNs4\nTQiw6sX8GO30c4tfsy5GXw6t+DpAuABhK4SPIZyFTdeo595X8tHMeJ+JyPQx06VAwfpk1U+79t37\nviNfnXLfV46uRXHu7PSc3igayQpgO+N+OyKtpGo0ama7hj7DHOzciXXr1sW76AJ861v2iYAaBx98\ncGxyMmHmhYhhtzH32kpDoRCtWtvb27nzzjs57bTTKBQKHH/88axbty6OzNL+FoCTTz6Z3XbbjSee\neIInD3uSt956i/Hjx8e+E62lQHSo1dixY3nhhRcsk9IWU7fgiiuu4Pvf/77lf4nGUzI/iUTa2803\n3wxEjvuEyUMpy5RTziQiRR+I6fPQ/SWc5drkhCciyswLKUaQxdFk2pluONFTcdFCWA0sBa6FW2/9\nRSS+Hfic8BHtdHOSmdBoR4NFZiZfdnyoQq/vxOzfZworl2OS9b3k/pDuR1inzPZGESTzgDFKqVFK\nqUHAl4A/doZQEAQNFxVhCjbTOa0TEmfOnAnALbfcwhFHHMGYMWPiw6kqRVqC4cknn8ydd97J5z73\nuVSevv/977PXXnsxc+ZMHnroIa655hoOPPBAvv3tb6OU4vbbb2fOnDl885vfjNuYe23JRcLGG29M\n//79vYJEbz9vCkqAG468Ib4ePHiwxZM48839998f70nmjf6SUsSWN+TXY5c370X7VxzHemGScayt\nlHwYblJjyT+iLN59E6cvuVBkN/gGsCPwPpx00knwa1DnJn0e2nzkDD8xJrN/M0IskS9jlA0/1E4M\n9flONL2E03yZv39XoGQ57OuNRlxUNhJPTU1NvVOQKKVmAn8FdlRKPa+UOllEPgROA+4FngZ+JSLP\ndDdv3QFT09CC5IUXIuVr6dKl/OEPf+DZZ5+Ns9Zrxbhx45gyZQo77rhjap1tttmGefPmceyxx3L1\n1VfH5b/61a+AKP9j3333tYSgKUhM6ATFhQsXxpO53ufrd7/7XWlCV2Run+LCFB5mZn1W8mHamelp\nk5orvFRbtH+UmTHvJuPpMpGksLIEWlASNqkT8w8Enl0HRwKDgGeAfxh8BnZostu+kvKsJEOAPfbZ\nI3bIu8iKtqokqdAVvlk5MTl6F3oiaus4EdlaRDYQke1E5OZi+T0iMlZEdhCRygLcPWhE05apIU2c\nODG+14LkuuuuA+DCCy/kwgsvjO+zoHcE1lFZtfCUBjdc2cTcuXMBWLFihVW+3XaRhfK9995LTMzD\nhg0rTUaSzHrP4kkLks9//vN8/etft4/P9UAnwllQntwMlXhs0xhdJGMeiOWssn0TrLvqj2hk7Iqr\nhVXQDyYQnf54AKz7f+vs44EDhczIGHMFZb5oqUQSZ8YknyqcRvvrWALX4Sfr3pcYmcZX2nttNOsE\nNBZPfc20VTc0omnLhGpT8cpeC5Kvfe1rrFmzhsMPP5yLLrrIu+Ouizlz5jBr1izvMbqVZrPXAyNG\njLDuvZrKZ6MNK0866SSrrlknKyESSoJEn3PiyghIn1BiASH2JKWLSlqDsuuDncRo0gsME5cq0TTL\nvD6BombiXmufTYyhwIGO78vRHkrCSmxho8t03RQtxhpjRiiuzjWJo7lS6pnlPl6zQn/dsGffu0vz\nsVQUMp3Di15r2lofYWpIM0bN4MEHHwTg5ZdfjsuHDBmSEDBZGDt2LIcccohl5lm1ahWvvvqq5dCv\nhKc0DBs2LL52V+tz5szh+uuvT5wf7xMk8mdh9uzZCd/GZpttFl8PHDgwkye96/E777zjfa59G74V\nrU/omAJAxBhgYlPF0DJb6fo+M5gWOqY/wiwvmaecyT2wk/gSdN3rx4EPjHvToR/T9AcMuO8gWbdU\nx801WTVrlTcr3vWRaF59TveEbwlS61m0Ka9d+dBo1gloTJ5qRS5Iuhlmroh7/no1gsSHYcOGMWLE\niMwIrXLQO/J+9atfjXNCIDlx7rvvvt5Dr3yCJO2fffLkyWbodia23nprjjnmGA444ADvcy8JJzEw\n5sc0Z7UVI61MR7phyioUvK4Wm44blWVGRwWS+kybu9Ic5aVxlMqWTV0GszaEG2DOZM9uvynRW5Wa\nicy+zGiuOSSd8C4KLYVE1FY5c1u55MRKfTwucl9LN6OW2OFG+0Djx6JfcMEFQhTYKbfeeqv17LHH\nHouf9RSmTJkiAwYMkN/97nedav/ggw8KIE1NTZ1qX4+hV5uT4Ms/0XqHr25a7kRJV3HoGDkXvjZW\nv047829U/pSwRTG3RyHf/e53hf9JyU1xeHWf+/pLG6PJt5tzMn3MdKt92vvyvbdy30davSz+clSP\n4pzT6blXSQWrwd4CpZS0trbGh7U0IoYNGxZva3Lbbbdx/PHHx88efvhhJk2aBFDRKr0RsWLFCu6+\n+2623HJLjjjiiMTzSnI80uo+88wzrFy5krFjxzJy5Ei7ruPHqLZP1/RStn6xP8sHYZxJEpvOAuVd\noVt1NAI/rYRW0wEUiGIfAbYEjgZG+h3lac5z7/NAEqY8F9N/Od2rkUAUPrzHPnvEdH3jNp9ppJXn\n6FrobPe2tjZEpPNqXC1SqNE+NKhGUigU4msoZYrPnDnTqvfUU09VrJFcccUVMm3aNFmxYkXNPDUK\nyvF07LHHCiC33367VV7p155Y6eK/TuPJpyHotmnt43I8bRyNxLcS92aVT42uH374YRk9erTQD5k/\nf75f00jRUFI1qpR2abTMrPgruVIKFMry4bs3ecnqv1oUCoW6aLn1RCP+71GjRjIgVcLk6HK40VU6\nlFcf4pQFvW/X0KFD+Z//+Z/6M9dDyNIGtO9IKWVpIOWUN1034Wz2tIs1haImUEz6j6BX68qx8QdY\n0V3m3/gYXsTuz9oyvshPq/g1HYwys1/gjflvMOTUIeyxx7iID3S/pb8V52xknativANLy6J03a6i\nfa30Ub8LHl+A3C1ejSSNl6xclYQ25+HTbdc8o7nXave9CWWd7cVdd8t9hnYHs70VppntoIMOAmDj\njTdm/PjxVj09UVYTvpuV61EpT40ApSKeskwbekLo169fWeFht6u8jjWRBcr7nqToJHfNQVBKTrQQ\nlBIWSw76Uj++aDPdzmwbC5bRJRpDhmwKY/BGabmRY5XCl7Rphku70I7898a8Z5WvmrUqbm/ykSZM\n3PycrHrmX7fcKkvJuelJNNr/Xj1QiUbyEvBiBXS2K1MnB7DDDjvwwAMPcPnllyeyzTsjSHorXJ9G\nucl+9erV/OY30SnLae9HT1S1LkDjlW0g0Orn0dIeAvN5cdKMNQujTRA91lqM6yPx8h1IvOpXKEsj\niO+d+qB5+iFwDATbJngxxxGPW/PSWnoPWUgIG0M7CVVo0bW1x6TvxBfmW4u/pBpfXI7aUcmM9YyI\njM76AK91NaO9Gb69tnwhvkOGDOHiiy+2TjLsDp56Ar5JM4snvY0M2El6Fs3W2oWIpgMRj5qnRBiq\nx7TmOs4tgQQoTG1Hm88cc5W7/5Wb4xGo5LkfVpJjkcaXFHA2DN4WnvJkmpsJi7rc1bTS6js5MLrc\n2pfMGFPWd2JpIKrzwsMNb44FWgPmbDQiT7WiEkGSfZh2hP1qZWR9wVe+8hV++9vfejdlHDJkCN/9\n7nc544wzeoCzxoaphbjZ9J1FWn6Ir14yQz3Z2DRfmeYqXW76OaJJW2zh527DYvSrhYy5Wk/ksRh4\n8coXYQfgPeDO4qeYy+kKLD1GnwZS8heVBIg7Ds2v3pfM+w4z/CRWXg3lc168wirDB5PnlHQ9Kg7/\nVUpNI9JJ9bciwBrgCRFpT23YjVBKyfriWNtggw344IMPaGtr48ILL+xpdrocixYtYuedd2bs2LHW\n4V4aWeG/iboZZo+0Zz76rlPd8pU4Jiu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q+DorR3VBEAQNt99/o/EDvZMnvZV89LvvHtTj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\nhmEkhRkSwzAMIynMkBiGYRhJ8f8BUkzrCgJli+QAAAAASUVORK5CYII=\n",
48 42 "text/plain": [
49   - "<matplotlib.figure.Figure at 0x7f886cd41050>"
  43 + "<matplotlib.figure.Figure at 0x7fc3552d1cd0>"
50 44 ]
51 45 },
52 46 "metadata": {},
... ... @@ -56,7 +50,7 @@
56 50 "source": [
57 51 "%matplotlib inline\n",
58 52 "import modules.observables\n",
59   - "modules.observables.drawObservables(\"simple case\")"
  53 + "modules.observables.drawObservables(\"simple case 10kpc\")"
60 54 ]
61 55 },
62 56 {
... ...