examples/mb-solve-two-weak-pulse-some-atoms-decay.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Two-Level: Weak Pulse through Some Atoms with Decay\n",
    "\n",
    "Time taken to solve this problem on a 2013 MacBook Pro:\n",
    "**36.2 s**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define the Problem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "mb_solve_json = \"\"\"\n",
    "{\n",
    "  \"atom\": {\n",
    "    \"decays\": [\n",
    "      {\n",
    "        \"channels\": [[0, 1]],\n",
    "        \"rate\": 1.0\n",
    "      }\n",
    "    ],\n",
    "    \"energies\": [],\n",
    "    \"fields\": [\n",
    "      {\n",
    "        \"coupled_levels\": [[0, 1]],\n",
    "        \"detuning\": 0.0,\n",
    "        \"detuning_positive\": true,\n",
    "        \"label\": \"probe\",\n",
    "        \"rabi_freq\": 1.0e-3,\n",
    "        \"rabi_freq_t_args\": {\n",
    "          \"ampl\": 1.0,\n",
    "          \"centre\": 0.0,\n",
    "          \"fwhm\": 1.0\n",
    "        },\n",
    "        \"rabi_freq_t_func\": \"gaussian\"\n",
    "      }\n",
    "    ],\n",
    "    \"num_states\": 2\n",
    "  },\n",
    "  \"t_min\": -2.0,\n",
    "  \"t_max\": 10.0,\n",
    "  \"t_steps\": 100,\n",
    "  \"z_min\": -0.2,\n",
    "  \"z_max\": 1.2,\n",
    "  \"z_steps\": 100,\n",
    "  \"z_steps_inner\": 1,\n",
    "  \"num_density_z_func\": \"square\",\n",
    "  \"num_density_z_args\": {\n",
    "    \"on\": 0.0,\n",
    "    \"off\": 1.0,\n",
    "    \"ampl\": 1.0\n",
    "  },\n",
    "  \"interaction_strengths\": [\n",
    "    10.0\n",
    "  ],\n",
    "  \"velocity_classes\": null,\n",
    "  \"method\": \"mesolve\",\n",
    "  \"opts\": {},\n",
    "  \"savefile\": \"qu/mb-solve-two-weak-pulse-some-atoms-decay\" \n",
    "}\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from maxwellbloch import mb_solve\n",
    "mb_solve_00 = mb_solve.MBSolve().from_json_str(mb_solve_json)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Solve the Problem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded tuple object.\n",
      "CPU times: user 1.97 ms, sys: 2.69 ms, total: 4.65 ms\n",
      "Wall time: 4.3 ms\n"
     ]
    }
   ],
   "source": [
    "%time Omegas_zt, states_zt = mb_solve_00.mbsolve(recalc=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot Output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "sns.set_style('darkgrid')\n",
    "fig = plt.figure(1, figsize=(16, 6))\n",
    "ax = fig.add_subplot(111)\n",
    "cmap_range = np.linspace(0.0, 1.0e-3, 11)\n",
    "cf = ax.contourf(mb_solve_00.tlist, mb_solve_00.zlist, \n",
    "                 np.abs(mb_solve_00.Omegas_zt[0]/(2*np.pi)), \n",
    "                 cmap_range, cmap=plt.cm.Blues)\n",
    "ax.set_title('Rabi Frequency ($\\Gamma / 2\\pi $)')\n",
    "ax.set_xlabel('Time ($1/\\Gamma$)')\n",
    "ax.set_ylabel('Distance ($L$)')\n",
    "for y in [0.0, 1.0]:\n",
    "    ax.axhline(y, c='grey', lw=1.0, ls='dotted')\n",
    "plt.colorbar(cf)\n",
    "plt.savefig('images/mb-solve-two-weak-pulse-some-atoms-decay.png')"
   ]
  }
 ],
 "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": "3.5.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}