Impact parameters - jet.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Impact of the parameters: Jet opening angle\n",
"\n",
"Here is studied a more realistic case:\n",
"* z=0.1\n",
"* source spectrum is generated assuming a powerlaw spectrum (between Emin=1MeV and Emax=100TeV)\n",
"* EGMF (turbulent magnetic field): 1E-15 Gauss, L_B= 1 Mpc\n",
"* EBL model: Dominguez\n",
"\n",
"This part shows the impact on the halo effect if we play with the jet opening angle."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "ValueError",
"evalue": "Data has no positive values, and therefore can not be log-scaled.",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-1-1c2b73d421fa>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mmodules\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marrival_angle\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mmodules\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marrival_angle\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrawArrivalAngle\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"Dominguez\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"jet opening\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;32m/home/tfitoussi/Programmes/simulation-analysis/modules/arrival_angle.py\u001b[0m in \u001b[0;36mdrawArrivalAngle\u001b[1;34m(files, plot)\u001b[0m\n\u001b[0;32m 65\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlegend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"best\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 66\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_xscale\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'log'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 67\u001b[1;33m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_yscale\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'log'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 68\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgrid\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mb\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mwhich\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'major'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 69\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_xlabel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"$\\\\theta$ [deg]\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/usr/lib64/python2.7/site-packages/matplotlib/axes/_base.pyc\u001b[0m in \u001b[0;36mset_yscale\u001b[1;34m(self, value, **kwargs)\u001b[0m\n\u001b[0;32m 2853\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'nonposy'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'clip'\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2854\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0myaxis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_set_scale\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2855\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mautoscale_view\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mscalex\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2856\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_update_transScale\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2857\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/usr/lib64/python2.7/site-packages/matplotlib/axes/_base.pyc\u001b[0m in \u001b[0;36mautoscale_view\u001b[1;34m(self, tight, scalex, scaley)\u001b[0m\n\u001b[0;32m 1982\u001b[0m \u001b[0my1\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[0mdelta\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1983\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0m_tight\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1984\u001b[1;33m \u001b[0my0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my1\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mylocator\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mview_limits\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1985\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_ybound\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1986\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/usr/lib64/python2.7/site-packages/matplotlib/ticker.pyc\u001b[0m in \u001b[0;36mview_limits\u001b[1;34m(self, vmin, vmax)\u001b[0m\n\u001b[0;32m 1523\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mminpos\u001b[0m \u001b[1;33m<=\u001b[0m \u001b[1;36m0\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0misfinite\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mminpos\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1524\u001b[0m raise ValueError(\n\u001b[1;32m-> 1525\u001b[1;33m \u001b[1;34m\"Data has no positive values, and therefore can not be \"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1526\u001b[0m \"log-scaled.\")\n\u001b[0;32m 1527\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mValueError\u001b[0m: Data has no positive values, and therefore can not be log-scaled."
]
}
],
"source": [
"import modules.arrival_angle\n",
"\n",
"modules.arrival_angle.drawArrivalAngle([\"Dominguez\"],plot=\"jet opening\")"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[ 0.00000000e+00 9.00000000e+01 7.50000000e+01 6.00000000e+01\n",
" 4.50000000e+01 3.00000000e+01 1.50000000e+01 1.00000000e+01\n",
" 5.00000000e+00 2.00000000e+00 1.00000000e+00 7.00000000e-01\n",
" 5.00000000e-01 1.00000000e-01]\n",
" [ 3.08000000e-02 3.57865465e-01 3.56765826e-01 3.50705300e-01\n",
" 3.41102475e-01 3.15290787e-01 2.57836367e-01 2.24899108e-01\n",
" 1.74379677e-01 1.32259137e-01 1.18005188e-01 1.12787674e-01\n",
" 1.09491235e-01 1.03609499e-01]\n",
" [ 1.00000000e-01 8.06054000e-01 7.99492862e-01 7.88724421e-01\n",
" 7.65492082e-01 7.06751178e-01 5.73907487e-01 4.92284655e-01\n",
" 3.71708933e-01 2.77904344e-01 2.46397150e-01 2.36637395e-01\n",
" 2.30677280e-01 2.18810503e-01]\n",
" [ 5.00000000e-01 2.71216020e-01 2.70231390e-01 2.66348902e-01\n",
" 2.59861203e-01 2.43571730e-01 1.97521581e-01 1.70237097e-01\n",
" 1.27157818e-01 9.43017198e-02 8.20916281e-02 7.94482274e-02\n",
" 7.78658134e-02 7.58695778e-02]\n",
" [ 1.00000000e+00 8.53579623e-02 8.53514996e-02 8.47879334e-02\n",
" 8.27085557e-02 7.89132755e-02 6.81511957e-02 5.98865530e-02\n",
" 4.59449140e-02 3.24704684e-02 2.76260519e-02 2.62968789e-02\n",
" 2.54137949e-02 2.43419140e-02]\n",
" [ 2.00000000e+00 3.38050901e-02 3.36881936e-02 3.32179938e-02\n",
" 3.32179938e-02 3.21545425e-02 3.10414583e-02 2.93814472e-02\n",
" 2.43627659e-02 1.72113213e-02 1.36588016e-02 1.24788817e-02\n",
" 1.16812493e-02 1.02321505e-02]]\n"
]
}
],
"source": [
"from matplotlib.pyplot import figure, show\n",
"from numpy import zeros, size, nditer, average, savetxt\n",
"from modules.read import ReadResults\n",
"from modules.constants import degre\n",
"\n",
"Redshifts=[\"0.0308\",\"0.1\",\"0.5\",\"1\",\"2\"]\n",
"opening_angle = [90,75,60,45,30,15,10,5,2,1,0.7,0.5,0.1] # degres \n",
"powerlaw_index=2\n",
"\n",
"theta_mean=zeros((size(Redshifts)+1,size(opening_angle)+1))\n",
"\n",
"it=nditer(theta_mean, flags=['multi_index'], op_flags=['readwrite'])\n",
"while not it.finished:\n",
" i=it.multi_index[0]\n",
" j=it.multi_index[1]\n",
" if i==0:\n",
" if j==0:\n",
" theta_mean[i,j]=0\n",
" else:\n",
" theta_mean[i,j]=opening_angle[j-1]\n",
"\n",
" else:\n",
" if j==0:\n",
" theta_mean[i,j]=float(Redshifts[i-1])\n",
" \n",
" else:\n",
" fileId=\"z=\"+Redshifts[i-1]+\"-EGMF15-lambda_B=1Mpc-Dominguez-Emax=100TeV\"\n",
" weightini,E,pos1,pos2,theta,Esource = ReadResults(\"Simulations/\"+fileId,cols=[1,2,4,6,8,9])\n",
" weight_source = (Esource/min(Esource))**(1-powerlaw_index)\n",
" weight = weightini* weight_source\n",
" cond= (E<1e3) & (E>1e0) & (pos1*degre<=opening_angle[j-1]) & (pos2*degre<=opening_angle[j-1])# GeV band\n",
" theta_mean[i,j]=average(theta[cond],weights=weight[cond])*degre\n",
" \n",
" it.iternext()\n",
" \n",
"\n",
"print theta_mean\n",
"\n",
"savetxt(\"Results/theta_mean_vs_jet_opening.dat\",theta_mean,fmt='%1.4e')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
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"outputs": [
{
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6X9+t0VvZNmUbq1nNetZzH/fxVou3GDBjAH0j+2JZxXsPPB4PN998My1btuTl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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f9fc4166310>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%matplotlib inline\n",
"from numpy import loadtxt\n",
"\n",
"fig1 = figure(figsize=(12,9)) \n",
"ax1 = fig1.add_subplot(111) \n",
"\n",
"theta_mean = loadtxt(\"Results/theta_mean_vs_jet_opening.dat\")\n",
"i=1\n",
"for z in Redshifts:\n",
" ax1.plot(theta_mean[0,1:],theta_mean[i,1:],'--*',label=\"z=\"+z)\n",
" i+=1\n",
"\n",
"ax1.set_xscale('log') \n",
"ax1.set_yscale('log')\n",
"ax1.grid(b=True,which='major')\n",
"ax1.legend(loc=\"best\")\n",
"ax1.set_xlabel(\"jet opening angle [degre]\")\n",
"ax1.set_ylabel(\"average arrival angle [degre]\")\n",
"\n",
"show()"
]
}
],
"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
}