fig3.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PhysRevA.96.022128 - Figure 3\n",
    "\n",
    "Critical phase\n",
    "\n",
    "Data in markers taken with WebPlotDigitizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import API\n",
    "import pwpp\n",
    "from pwpp import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fntsz = 16\n",
    "markersize = 5\n",
    "\n",
    "f = open('data/fig3/wpd.json')\n",
    "data = json.load(f)\n",
    "#\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",
    "plt.plot(datatmp2[:,0][indxs],datatmp2[:,1][indxs],'.',markersize=markersize,color='#EDB120')\n",
    "#\n",
    "datatmp = data['datasetColl'][1]['data']\n",
    "datatmp2 = np.array([datatmp[i]['value'] for i in range(len(datatmp))])\n",
    "indxs = np.argsort(datatmp2[:,0])\n",
    "plt.plot(datatmp2[:,0][indxs],datatmp2[:,1][indxs],'.',markersize=markersize,color='#EDB120')\n",
    "#\n",
    "datatmp = data['datasetColl'][2]['data']\n",
    "datatmp2 = np.array([datatmp[i]['value'] for i in range(len(datatmp))])\n",
    "indxs = np.argsort(datatmp2[:,0])\n",
    "plt.plot(datatmp2[:,0][indxs],datatmp2[:,1][indxs],'.',markersize=markersize,color='b')\n",
    "#\n",
    "datatmp = data['datasetColl'][3]['data']\n",
    "datatmp2 = np.array([datatmp[i]['value'] for i in range(len(datatmp))])\n",
    "indxs = np.argsort(datatmp2[:,0])\n",
    "plt.plot(datatmp2[:,0][indxs],datatmp2[:,1][indxs],'.',markersize=markersize,color='b')\n",
    "\n",
    "# a dashed\n",
    "lbd=0.8;\n",
    "a0=0.855*sqrt(1e23/1e18)*lbd;\n",
    "w0=1.55e-9;\n",
    "n=11.2;\n",
    "glst = np.logspace(3,log10(2e4));\n",
    "def phic2mod(glst):\n",
    "    return [phicmod(g0, a0, w0, n) for g0 in glst];\n",
    "def phic2(glst):\n",
    "    return [phic(g0, a0, w0, n) for g0 in glst];\n",
    "phiclst = phic2mod(glst);\n",
    "plt.plot(glst,phiclst,'--',color='#EDB120')\n",
    "plt.xlim([1000,2e4])\n",
    "plt.ylim([0,100])\n",
    "plt.xlabel('$\\gamma$',size=fntsz)\n",
    "plt.ylabel('$\\phi_c$',size=fntsz)\n",
    "\n",
    "# a solid\n",
    "lbd=0.8;\n",
    "a0=0.855*sqrt(1e23/1e18)*lbd;\n",
    "w0=1.55e-9;\n",
    "n=11.2;\n",
    "glst = np.logspace(3,log10(2e4));\n",
    "def phic2mod(glst):\n",
    "    return [phicmod(g0, a0, w0, n) for g0 in glst];\n",
    "def phic2(glst):\n",
    "    return [phic(g0, a0, w0, n) for g0 in glst];\n",
    "phiclst = phic2(glst);\n",
    "plt.plot(glst,phiclst,'-',color='#EDB120')\n",
    "\n",
    "# b dashed\n",
    "lbd=0.8;\n",
    "a0=0.855*sqrt(1e21/1e18)*lbd;\n",
    "w0=1.55e-9;\n",
    "n=11.2;\n",
    "glst = np.logspace(3,log10(2e4));\n",
    "def phic2mod(glst):\n",
    "    return [phicmod(g0, a0, w0, n) for g0 in glst];\n",
    "def phic2(glst):\n",
    "    return [phic(g0, a0, w0, n) for g0 in glst];\n",
    "phiclst = phic2mod(glst);\n",
    "plt.plot(glst,phiclst,'b--')\n",
    "\n",
    "# b solid\n",
    "lbd=0.8;\n",
    "a0=0.855*sqrt(1e21/1e18)*lbd;\n",
    "w0=1.55e-9;\n",
    "n=11.2;\n",
    "glst = np.logspace(3,log10(2e4));\n",
    "def phic2mod(glst):\n",
    "    return [phicmod(g0, a0, w0, n) for g0 in glst];\n",
    "def phic2(glst):\n",
    "    return [phic(g0, a0, w0, n) for g0 in glst];\n",
    "phiclst = phic2(glst);\n",
    "plt.plot(glst,phiclst,'b-')\n",
    "\n",
    "plt.xticks([1000,2000,5000,10000,20000])\n",
    "plt.yticks([0,20,40,60,80,100])\n",
    "plt.xscale('log')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}