examples/mb-solve-vee-sech-1.41pi-1.41pi.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Wall time: ~5m\n",
    "\n",
    "- [] Check the Hamiltonian"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "NOTEBOOK_NAME = \"mb-solve-vee-sech-1.41pi-1.41pi\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t_width 0.3796628587572578\n",
      "ampl 0.592839341136\n",
      "ampl_2 0.592839341136\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "sech_fwhm_conv = 1./2.6339157938\n",
    "t_width = 1.0*sech_fwhm_conv # [τ]\n",
    "\n",
    "print('t_width', t_width)\n",
    "\n",
    "n = np.sqrt(2) # For a pulse area of nπ    \n",
    "ampl = n/t_width/(2*np.pi) # Pulse amplitude [2π Γ]\n",
    "\n",
    "print('ampl', ampl)\n",
    "\n",
    "n = np.sqrt(2) # For a pulse area of nπ    \n",
    "ampl_2 = n/t_width/(2*np.pi) # Pulse amplitude [2π Γ]\n",
    "\n",
    "print('ampl_2', ampl_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.sqrt(2)\n",
    "b = np.sqrt(2)\n",
    "\n",
    "np.sqrt(a**2 + b**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "mb_solve_json = \"\"\"\n",
    "{\n",
    "  \"atom\": {\n",
    "    \"decays\": [\n",
    "      { \"channels\": [[0,1], [0,2]], \n",
    "        \"rate\": 0.0\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\": 0.592839341136,\n",
    "        \"rabi_freq_t_args\": \n",
    "          {\n",
    "             \"ampl\": 1.0,\n",
    "             \"centre\": 0.0,\n",
    "             \"width\": 0.3796628587572578\n",
    "          },\n",
    "        \"rabi_freq_t_func\": \"sech\"\n",
    "      },\n",
    "      {\n",
    "        \"coupled_levels\": [[0, 2]],\n",
    "        \"detuning\": 0.0,\n",
    "        \"detuning_positive\": true,\n",
    "        \"label\": \"coupling\",\n",
    "        \"rabi_freq\": 0.592839341136,\n",
    "        \"rabi_freq_t_args\": \n",
    "          {\n",
    "             \"ampl\": 1.0,\n",
    "             \"centre\": 0.0,\n",
    "             \"width\": 0.3796628587572578\n",
    "          },\n",
    "        \"rabi_freq_t_func\": \"sech\"\n",
    "      }\n",
    "    ],\n",
    "    \"num_states\": 3\n",
    "  },\n",
    "  \"t_min\": -2.0,\n",
    "  \"t_max\": 10.0,\n",
    "  \"t_steps\": 120,\n",
    "  \"z_min\": -0.2,\n",
    "  \"z_max\": 1.2,\n",
    "  \"z_steps\": 140,\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\": [10.0, 10.0],\n",
    "  \"velocity_classes\": {},\n",
    "  \"method\": \"mesolve\",\n",
    "  \"opts\": {},\n",
    "  \"savefile\": \"qu/%s\"\n",
    "}\n",
    "\"\"\"%(NOTEBOOK_NAME)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded tuple object.\n",
      "CPU times: user 4.7 ms, sys: 6.64 ms, total: 11.3 ms\n",
      "Wall time: 15.2 ms\n"
     ]
    }
   ],
   "source": [
    "from maxwellbloch import mb_solve\n",
    "mb_solve_00 = mb_solve.MBSolve().from_json_str(mb_solve_json)\n",
    "\n",
    "%time Omegas_zt, states_zt = mb_solve_00.mbsolve(recalc=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 1152x864 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "\n",
    "sns.set_style('darkgrid')\n",
    "fig = plt.figure(1, figsize=(16, 12))\n",
    "\n",
    "# Probe \n",
    "ax = fig.add_subplot(211)\n",
    "cmap_range = np.linspace(0.0, 0.8, 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 $)')\n",
    "ax.set_ylabel('Distance ($L$)')\n",
    "ax.text(0.02, 0.95, 'Probe',\n",
    "        verticalalignment='top', horizontalalignment='left',\n",
    "        transform=ax.transAxes, color='grey', fontsize=16)\n",
    "plt.colorbar(cf)\n",
    "\n",
    "# Coupling\n",
    "ax = fig.add_subplot(212)\n",
    "cmap_range = np.linspace(0.0, 0.8, 11)\n",
    "cf = ax.contourf(mb_solve_00.tlist, mb_solve_00.zlist, \n",
    "                 np.abs(mb_solve_00.Omegas_zt[1]/(2*np.pi)), \n",
    "                 cmap_range, cmap=plt.cm.Greens)\n",
    "ax.set_xlabel('Time ($1/\\Gamma$)')\n",
    "ax.set_ylabel('Distance ($L$)')\n",
    "ax.text(0.02, 0.95, 'Coupling',\n",
    "        verticalalignment='top', horizontalalignment='left',\n",
    "        transform=ax.transAxes, color='grey', fontsize=16)\n",
    "plt.colorbar(cf)\n",
    "\n",
    "# Both\n",
    "for ax in fig.axes:\n",
    "    for y in [0.0, 1.0]:\n",
    "        ax.axhline(y, c='grey', lw=1.0, ls='dotted')\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.savefig('images/{0}_1.png'.format(NOTEBOOK_NAME))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1152x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "total_area = np.sqrt(mb_solve_00.fields_area()[0]**2 + mb_solve_00.fields_area()[1]**2)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(16, 4))\n",
    "ax.plot(mb_solve_00.zlist, mb_solve_00.fields_area()[0]/np.pi, label='Probe', clip_on=False)\n",
    "ax.plot(mb_solve_00.zlist, mb_solve_00.fields_area()[1]/np.pi, label='Coupling', clip_on=False)\n",
    "ax.plot(mb_solve_00.zlist, total_area/np.pi, label='Total', ls='dashed', clip_on=False)\n",
    "ax.legend()\n",
    "ax.set_ylim([0.0, 2.0])\n",
    "ax.set_xlabel('Distance ($L$)')\n",
    "ax.set_ylabel('Pulse Area ($\\pi$)');"
   ]
  }
 ],
 "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
}