fig2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PhysRevA.96.022128 - Figure 2\n",
    "Positron number produced in a collision between a beam of $N_\\gamma$ photons with energy $\\omega$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import API\n",
    "import pwpp\n",
    "from pwpp import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "epsmaq = 1e-6;\n",
    "xdim = 10\n",
    "\n",
    "def ppm_fun(tau,lbd,I22,w):\n",
    "    return ppm(0.855*sqrt(I22*1e22/1e18)*lbd,2*pi*c/(lbd*1e-6)*hbar/e*1e-9,tau*c/(lbd*1e-6)*1e-15,w);\n",
    "ppm_fun = np.vectorize(ppm_fun)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████| 10/10 [00:13<00:00,  1.39s/it]\n"
     ]
    }
   ],
   "source": [
    "# tau\n",
    "I22 = 2; #[]\n",
    "lbd = 0.8; #[\\mu m]\n",
    "w = 0.1; #[GeV]\n",
    "w0 = 2*pi*c/(lbd*1e-6)*hbar/e*1e-9; # [GeV]\n",
    "a0 = 0.855*sqrt(I22*1e22/1e18)*lbd; # []\n",
    "taulst1 = np.logspace(1,2,xdim)\n",
    "taulst2 = np.logspace(1,2,xdim);\n",
    "Np_tau1 = ppm_fun(taulst1,lbd,I22,w)\n",
    "Np_tau2 = np.zeros(len(taulst2));\n",
    "for k in trange(len(taulst2)):\n",
    "    tau = taulst2[k];\n",
    "    n = tau*c/(lbd*1e-6)*1e-15; # []\n",
    "    Ppmk = 0;\n",
    "    phimin = -3*(2*pi**2*n**2)/log(2);\n",
    "    phimax = -phimin;\n",
    "    phidim = int(1e5);\n",
    "    dphi = (phimax-phimin)/phidim;\n",
    "    for i in range(phidim):\n",
    "        phi = phimin + (i-1)*dphi;\n",
    "        chi0 = 2*w*w0*a0/m**2;\n",
    "        chi = max(epsmaq,chi0 * abs(sin(phi)) * exp(-log(2)*phi**2/(2*pi**2*n**2)));\n",
    "        dPpmdphi = alpha * m**2/(2*w0*w) * chi * TErber(chi);\n",
    "        Ppmk = Ppmk + dphi*dPpmdphi;\n",
    "    Np_tau2[k] = Ppmk;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████| 10/10 [00:13<00:00,  1.32s/it]\n"
     ]
    }
   ],
   "source": [
    "# lbd\n",
    "I22 = 2; #[]\n",
    "w = 0.1; #[GeV]\n",
    "tau = 30;#[fs]\n",
    "lbdlst1 = np.logspace(log10(0.1),log10(10),xdim);\n",
    "lbdlst2 = np.logspace(log10(0.1),log10(10),xdim);\n",
    "Np_lbd1 = ppm_fun(tau,lbdlst1,I22,w)\n",
    "Np_lbd2 = np.zeros(len(lbdlst2));\n",
    "for k in trange(len(lbdlst2)):\n",
    "    lbd = lbdlst2[k];\n",
    "    w0 = 2*pi*c/(lbd*1e-6)*hbar/e*1e-9; # [GeV]\n",
    "    a0 = 0.855*sqrt(I22*1e22/1e18)*lbd; # []\n",
    "    n = tau*c/(lbd*1e-6)*1e-15; # []\n",
    "    Ppmk = 0;\n",
    "    phimin = -3*(2*pi**2*n**2)/log(2);\n",
    "    phimax = -phimin;\n",
    "    phidim = int(1e5);\n",
    "    dphi = (phimax-phimin)/phidim;\n",
    "    for i in range(phidim):\n",
    "        phi = phimin + (i-1)*dphi;\n",
    "        chi0 = 2*w*w0*a0/m**2;\n",
    "        chi = max(epsmaq,chi0 * abs(sin(phi)) * exp(-log(2)*phi**2/(2*pi**2*n**2)));\n",
    "        dPpmdphi = alpha * m**2/(2*w0*w) * chi * TErber(chi);\n",
    "        Ppmk = Ppmk + dphi*dPpmdphi;\n",
    "    Np_lbd2[k] = Ppmk;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████| 10/10 [00:11<00:00,  1.11s/it]\n"
     ]
    }
   ],
   "source": [
    "# I22\n",
    "lbd = 0.8; #[\\mu m]\n",
    "tau = 30; #[fs]\n",
    "w = 0.1; #[GeV]\n",
    "I22lst1 = np.logspace(log10(0.1),log10(10),xdim);\n",
    "I22lst2 = np.logspace(log10(0.1),log10(10),xdim);\n",
    "Np_I221 = ppm_fun(tau,lbd,I22lst1,w)\n",
    "Np_I222 = np.zeros(len(I22lst2));\n",
    "for k in trange(len(I22lst2)):\n",
    "    I22 = I22lst2[k];\n",
    "    w0 = 2*pi*c/(lbd*1e-6)*hbar/e*1e-9; # [GeV]\n",
    "    a0 = 0.855*sqrt(I22*1e22/1e18)*lbd; # []\n",
    "    n = tau*c/(lbd*1e-6)*1e-15; # []\n",
    "    dphi = (phimax-phimin)/phidim;\n",
    "    Ppmk = 0;\n",
    "    phimin = -3*(2*pi**2*n**2)/log(2);\n",
    "    phimax = -phimin;\n",
    "    phidim = int(1e5);\n",
    "    dphi = (phimax-phimin)/phidim;\n",
    "    for i in range(phidim):\n",
    "        phi = phimin + (i-1)*dphi;\n",
    "        chi0 = 2*w*w0*a0/m**2;\n",
    "        chi = max(epsmaq,chi0 * abs(sin(phi)) * exp(-log(2)*phi**2/(2*pi**2*n**2)));\n",
    "        dPpmdphi = alpha * m**2/(2*w0*w) * chi * TErber(chi);\n",
    "        Ppmk = Ppmk + dphi*dPpmdphi;\n",
    "    Np_I222[k] = Ppmk;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████| 10/10 [00:12<00:00,  1.24s/it]\n"
     ]
    }
   ],
   "source": [
    "# w\n",
    "I22 = 1; #[]\n",
    "tau = 30; #[fs]\n",
    "lbd = 0.8; #[\\mu m]\n",
    "w0 = 2*pi*c/(lbd*1e-6)*hbar/e*1e-9; # [GeV]\n",
    "a0 = 0.855*sqrt(I22*1e22/1e18)*lbd; # []\n",
    "n = tau*c/(lbd*1e-6)*1e-15; # []\n",
    "wlst1 = np.logspace(log10(0.1),log10(1),xdim);\n",
    "wlst2 = np.logspace(log10(0.1),log10(1),xdim);\n",
    "Np_w1 = ppm_fun(tau,lbd,I22,wlst1)\n",
    "Np_w2 = np.zeros(len(wlst2));\n",
    "for k in trange(len(wlst2)):\n",
    "    w = wlst2[k];\n",
    "    Ppmk = 0;\n",
    "    phimin = -3*(2*pi**2*n**2)/log(2);\n",
    "    phimax = -phimin;\n",
    "    phidim = int(1e5);\n",
    "    dphi = (phimax-phimin)/phidim;\n",
    "    for i in range(phidim):\n",
    "        phi = phimin + (i-1)*dphi;\n",
    "        chi0 = 2*w*w0*a0/m**2;\n",
    "        chi = max(epsmaq,chi0 * abs(sin(phi)) * exp(-log(2)*phi**2/(2*pi**2*n**2)));\n",
    "        dPpmdphi = alpha * m**2/(2*w0*w) * chi * TErber(chi);\n",
    "        Ppmk = Ppmk + dphi*dPpmdphi;\n",
    "    Np_w2[k] = Ppmk;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 576x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot all\n",
    "fig, axes = plt.subplots(nrows=2, ncols=2,figsize=(8,8))\n",
    "fntsz = 16\n",
    "\n",
    "axes[0,0].plot(taulst1,1e10*Np_tau1,'k-','LineWidth',3)\n",
    "axes[0,0].plot(taulst2,1e10*Np_tau2,'y--','LineWidth',1)\n",
    "#\n",
    "f = open('data/fig2a/wpd.json')\n",
    "data = json.load(f)\n",
    "datatmp = data['datasetColl'][0]['data']\n",
    "datatmp2 = np.array([datatmp[i]['value'] for i in range(len(datatmp))])\n",
    "indxs = np.argsort(datatmp2[:,0])\n",
    "axes[0,0].plot(datatmp2[:,0][indxs],datatmp2[:,1][indxs],'k.')\n",
    "#\n",
    "axes[0,0].set_xlabel(r'$\\tau[\\mathrm{fs}]$', size=fntsz)\n",
    "axes[0,0].set_ylabel(r'$10^{10} N_\\pm /N_\\gamma$',size=fntsz)\n",
    "axes[0,0].set_xticks([10,20,50,100]);\n",
    "axes[0,0].set_yticks([0.2,0.5,1]);\n",
    "axes[0,0].set_ylim([0.15,1.5])\n",
    "axes[0,0].set_xscale('log')\n",
    "axes[0,0].set_yscale('log')\n",
    "\n",
    "axes[0,1].plot(lbdlst1,1e10*Np_lbd1,'k-','LineWidth',3)\n",
    "axes[0,1].plot(lbdlst2,1e10*Np_lbd2,'y--','LineWidth',1)\n",
    "#\n",
    "f = open('data/fig2b/wpd.json')\n",
    "data = json.load(f)\n",
    "datatmp = data['datasetColl'][0]['data']\n",
    "datatmp2 = np.array([datatmp[i]['value'] for i in range(len(datatmp))])\n",
    "indxs = np.argsort(datatmp2[:,0])\n",
    "axes[0,1].plot(datatmp2[:,0][indxs],datatmp2[:,1][indxs],'k.')\n",
    "#\n",
    "axes[0,1].set_xlabel(r'$\\lambda[\\mu \\mathrm{m}]$', size=fntsz)\n",
    "axes[0,1].set_ylabel(r'$10^{10} N_\\pm /N_\\gamma$', size=fntsz)\n",
    "axes[0,1].set_xticks([0.1,0.5,1,5,10])\n",
    "axes[0,1].set_yticks([0,0.2,0.4,0.6,0.8])\n",
    "axes[0,1].set_ylim([0,0.8])\n",
    "axes[0,1].set_xscale('log')\n",
    "\n",
    "axes[1,0].plot(I22lst1,log10(Np_I221),'k-','LineWidth',3)\n",
    "axes[1,0].plot(I22lst2,log10(Np_I222),'y--','LineWidth',1)\n",
    "#\n",
    "f = open('data/fig2c/wpd.json')\n",
    "data = json.load(f)\n",
    "datatmp = data['datasetColl'][0]['data']\n",
    "datatmp2 = np.array([datatmp[i]['value'] for i in range(len(datatmp))])\n",
    "indxs = np.argsort(datatmp2[:,0])\n",
    "axes[1,0].plot(datatmp2[:,0][indxs],datatmp2[:,1][indxs],'k.')\n",
    "#\n",
    "axes[1,0].set_xlabel(r'$I_{22}$', size=fntsz)\n",
    "axes[1,0].set_ylabel(r'$\\log_{10}(N_\\pm /N_\\gamma)$', size=fntsz);\n",
    "axes[1,0].set_xticks([0.1,0.5,1,5,10]);\n",
    "axes[1,0].set_yticks([-40,-30,-20,-10,0])\n",
    "axes[1,0].set_ylim([-50,0])\n",
    "axes[1,0].set_xscale('log')\n",
    "\n",
    "axes[1,1].plot(wlst1*1000,log10(Np_w1),'k-','LineWidth',3)\n",
    "axes[1,1].plot(wlst2*1000,log10(Np_w2),'y--','LineWidth',1)\n",
    "#\n",
    "f = open('data/fig2d/wpd.json')\n",
    "data = json.load(f)\n",
    "datatmp = data['datasetColl'][0]['data']\n",
    "datatmp2 = np.array([datatmp[i]['value'] for i in range(len(datatmp))])\n",
    "indxs = np.argsort(datatmp2[:,0])\n",
    "axes[1,1].plot(datatmp2[:,0][indxs],datatmp2[:,1][indxs],'k.')\n",
    "#\n",
    "axes[1,1].set_xlabel(r'$\\omega[MeV]$', size=fntsz)\n",
    "axes[1,1].set_ylabel(r'$\\log_{10}(N_\\pm /N_\\gamma)$', size=fntsz)\n",
    "axes[1,1].set_xticks([100,200,500,1000]);\n",
    "axes[1,1].set_yticks([-15,-10,-5,0]);\n",
    "axes[1,1].set_ylim([-15,0])\n",
    "axes[1,1].set_xscale('log')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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