lambda_gg.ipynb
35.3 KB
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{
"cells": [
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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BAWRmyvo0O3fK7UbW1jBuHIweLVO7tcFtRQaFwYuJpmnLkNZGqhCi11XXJwAf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\n978M8MjeXAcAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f4b4d950350>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%matplotlib inline\n",
"\n",
"from sys import argv\n",
"import matplotlib.pyplot as plt\n",
"from matplotlib import gridspec\n",
"from numpy import *\n",
"from scipy.integrate import quad\n",
"from modules.analytic import Ethreshold_gg\n",
"\n",
"c=2.99792458e10 # cm.s-1\n",
"Mpc=(3.0856776e+16)*1e8 # Mpc to cm\n",
"H0=67.8*1e5/(Mpc) # s-1\n",
"omegaM = 0.3\n",
"omegaK = 0\n",
"omegaL = 0.7\n",
"zlim=-0.\n",
"def properIntegrand(z):\n",
" return -c/(H0*(1+z)*sqrt(omegaM*(1+z)**3+omegaK*(1+z)**2+omegaL))\n",
"def comobileIntegrand(z):\n",
" return -c/(H0*sqrt(omegaM*(1+z)**3+omegaK*(1+z)**2+omegaL))\n",
"\n",
"color=['b','r','g','m','c','y']\n",
"\n",
"# Proper distance figure\n",
"#========================\n",
"fig1 = plt.figure()\n",
"ax11 = fig1.add_subplot(111)\n",
"\n",
"labels=[\"0\",\"0.5\",\"1\",\"2\",\"3\"]\n",
"theory=[1,2,3,4,5]\n",
"ind=0\n",
"for lab in labels:\n",
" # theoritical curve ===================================================\n",
" # f: without cosmo\n",
" # g: with cosmo\n",
" e,f,g=loadtxt('lambda_e.dat',unpack=True,usecols=[0,int(theory[ind]),int(theory[ind])+5])\n",
" e = e*511.e3/1.e9 #GeV\n",
" cond= (e>100) & (e<1e5)\n",
" e=e[cond]\n",
" g=g[cond]\n",
" f=f[cond]\n",
" p = ax11.plot(e,g,\"-\"+color[ind],label=\"z = \"+lab) \n",
" ax11.plot(e,f,color=p[0].get_color(),linestyle='--')\n",
"\n",
" ind=ind+1\n",
"\n",
"ax11.axvline(x=Ethreshold_gg(), ymin=0., ymax = 1., color='r', linewidth=2)\n",
"\n",
"ax11.legend(loc=\"best\")\n",
"ax11.grid(b=True,which='major')\n",
"ax11.set_ylim([1e-4,1e4])\n",
"ax11.set_xscale('log')\n",
"ax11.set_yscale('log')\n",
"ax11.set_xlabel(\"energy [GeV]\")\n",
"ax11.set_ylabel(\"$\\lambda_{\\gamma\\gamma}$ [Mpc]\")\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.10"
}
},
"nbformat": 4,
"nbformat_minor": 0
}