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  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "Example use of the grid wrapper and then interactive plot creation"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# requires pyradex: grab it from\n",
      "# https://github.com/keflavich/pyradex/\n",
      "# Both RADEX and pyradex require a fortran compiler!\n",
      "# However, if you're on a mac, you can try using pip install with the precompiled binary I provide:\n",
      "# (this is a source distribution, but it includes the radex.so file)\n",
      "!pip install https://github.com/keflavich/pyradex/releases/download/0.2/pyradex-0.2.macosx-10.9-intel.tar.gz"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.html.widgets import interact, interactive, fixed\n",
      "from matplotlib import pyplot as plt\n",
      "from IPython.display import clear_output, display, HTML"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%run ph2co_grid_computation.py"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "/Users/adam/repos/astroquery/astroquery/lamda/__init__.py:21: UserWarning: Experimental: LAMDA has not yet been refactored to have its API match the rest of astroquery.\n",
        "  warnings.warn(\"Experimental: LAMDA has not yet been refactored to have its API match the rest of astroquery.\")\n",
        "WARNING:astropy:AstropyDeprecationWarning: 'dtypes' has been renamed to the singular 'dtype'.\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "WARNING: AstropyDeprecationWarning: 'dtypes' has been renamed to the singular 'dtype'. [astropy.table.table]\n",
        "      Tex               tau        ...      brightness           T_B      "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "       K                           ... erg / (cm2 Hz s sr)        K       \n",
        "---------------- ----------------- ... ------------------- ---------------\n",
        "-0.0817529700928  -0.0342780871976 ...   1.56037207206e-22  0.100352601934\n",
        "   6.09190527741     41.9634499416 ...   1.73595925381e-14   2.66518509246\n",
        " -0.171716538322 -0.00182074261889 ...   2.06896749361e-22 0.0053264336307\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "/Users/adam/repos/astropy/astropy/units/quantity.py:608: RuntimeWarning: invalid value encountered in true_divide\n",
        "  return np.true_divide(self, other)\n"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "styleargs = {'linewidth': 2, 'alpha': 0.5, 'color':'#5A228B'}\n",
      "\n",
      "def setup(tem=5,dens=5):\n",
      "    fig, ((ax1,ax2),(ax3,ax4)) = plt.subplots(2,2, sharex='col', sharey='row', squeeze=True, figsize=(10,7))\n",
      "    plt.subplots_adjust(hspace=0,wspace=0)\n",
      "    lines1, = ax1.plot(temperatures, taugrid_71M[dens,:], **styleargs)\n",
      "    ax1.set_ylim(-0.2,0.2)\n",
      "    p1, = ax1.plot(temperatures[tem],taugrid_71M[dens,tem], 'o',alpha=0.5, markeredgecolor='none')\n",
      "    lines2, = ax3.plot(temperatures, texgrid_71M[dens,:], **styleargs)\n",
      "    ax3.set_ylim(0,20)\n",
      "    p3, =ax3.plot(temperatures[tem],texgrid_71M[dens,tem], 'o',alpha=0.5, markeredgecolor='none')\n",
      "\n",
      "    lines3, = ax2.semilogx(densities, taugrid_71M[:,tem], **styleargs)\n",
      "    ax2.set_ylim(-0.2,0.2)\n",
      "    p2, = ax2.plot(densities[dens],taugrid_71M[dens,tem], 'o',alpha=0.5, markeredgecolor='none')\n",
      "    lines4, = ax4.semilogx(densities, texgrid_71M[:,tem], **styleargs)\n",
      "    p4, = ax4.plot(densities[dens],texgrid_71M[dens,tem], 'o',alpha=0.5, markeredgecolor='none')\n",
      "    ax4.set_ylim(0,20)\n",
      "    plt.suptitle(\"$T=%i$ K, $n=10^{%0.1f}$ cm$^{-3}$\" % (temperatures[tem],np.log10(densities[dens])),\n",
      "                 fontsize=20)\n",
      "    ax4.set_xlabel('$n(H_2)$',fontsize=20)\n",
      "    ax3.set_xlabel(\"T\",fontsize=20)\n",
      "    ax1.set_ylabel(r\"$\\tau$\",fontsize=20)\n",
      "    ax3.set_ylabel(\"$T_{ex}$\",fontsize=20)\n",
      "    plt.show()\n",
      "    return fig,lines1,lines2,lines3,lines4,p1,p2,p3,p4\n",
      "\n",
      "def run_plot_temden():\n",
      "    fig,lines1,lines2,lines3,lines4,p1,p2,p3,p4 = setup()\n",
      "    \n",
      "    @interact(tem=(0,temperatures.size-1),dens=(0,densities.size-1))\n",
      "    def plot_temden(tem,dens):\n",
      "        lines1.set_data(temperatures, taugrid_71M[dens,:])\n",
      "        lines2.set_data(temperatures, texgrid_71M[dens,:])\n",
      "        lines3.set_data(densities, taugrid_71M[:,tem])\n",
      "        lines4.set_data(densities, texgrid_71M[:,tem])\n",
      "        p1.set_data(temperatures[tem],taugrid_71M[dens,tem])\n",
      "        p2.set_data(densities[dens],taugrid_71M[dens,tem])\n",
      "        p3.set_data(temperatures[tem],texgrid_71M[dens,tem])\n",
      "        p4.set_data(densities[dens],texgrid_71M[dens,tem])\n",
      "\n",
      "        display(fig)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "run_plot_temden()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
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hoG+Iwb5hBnqHGOgfYqB3iMH+Yfp7hxjsG2Kgb4iBvmEG+4cYGhgGoLq+nDUvW8yydfOI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\ny46/0/4AAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x107589a90>"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    }
   ],
   "metadata": {}
  }
 ]
}