ipta_mdc_makehdf5.ipynb

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    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "%load_ext Cython\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "from __future__ import division\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "matplotlib.rcParams['savefig.dpi'] = 1.5 * matplotlib.rcParams['savefig.dpi']\n",
    "\n",
    "import numpy as np\n",
    "import sys,os,glob,h5py\n",
    "import cPickle as pickle\n",
    "\n",
    "import libstempo as T2\n",
    "import libstempo.plot as LP\n",
    "\n",
    "import NX01_psr\n",
    "import NX01_datafile\n",
    "\n",
    "try:\n",
    "    from IPython.core.display import clear_output\n",
    "    have_ipython = True\n",
    "except ImportError:\n",
    "    have_ipython = False\n",
    "\n",
    "Msol = 1.98855*10.0**30.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "parpath = '/Users/staylor/Research/IPTA_MDC/IPTA_Challenge1_open/Challenge_Data/Dataset1/'\n",
    "timpath = '/Users/staylor/Research/IPTA_MDC/IPTA_Challenge1_open/Challenge_Data/Dataset1/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "parfiles = sorted(glob.glob(parpath+'*.par'))\n",
    "timfiles = sorted(glob.glob(timpath+'*.tim'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "parfiles = [par for par in parfiles if 'red' not in par]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "len(parfiles), len(timfiles)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Now moving on to processing these pulsars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#######################################\n",
    "# PASSING THROUGH TEMPO2 VIA libstempo\n",
    "#######################################\n",
    "\n",
    "t2psr = []\n",
    "for ii in range(len(parfiles)):\n",
    "    \n",
    "    t2psr.append( T2.tempopulsar(parfile = parfiles[ii], timfile = timfiles[ii], ) )\n",
    "    t2psr[ii].fit(iters=10)\n",
    "    \n",
    "    if np.any(np.isfinite(t2psr[ii].residuals())==False)==True:\n",
    "        t2psr[ii] = T2.tempopulsar(parfile = parfiles[ii], timfile = timfiles[ii])\n",
    "                 \n",
    "    clear_output()\n",
    "    print '\\r', '{0} of {1}'.format(ii+1,len(parfiles))\n",
    "    sys.stdout.flush()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Check out some plots if you want\n",
    "ind = 0\n",
    "print t2psr[ind].name\n",
    "plt.errorbar(t2psr[ind].toas(),t2psr[ind].residuals(),1e-6*t2psr[ind].toaerrs,fmt='.',alpha=0.2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "psr = [NX01_psr.PsrObj(p) for p in t2psr]\n",
    "[p.grab_all_vars(jitterbin=1.0,makeGmat=False,\n",
    "                 fastDesign=True,planetssb=True) for p in psr] # according to the 9 year paper,\n",
    "                                                               # the jitterbin used is 1s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "for ii in range(len(psr)):\n",
    "    psr[ii].parfile = parfiles[ii] \n",
    "    psr[ii].timfile = timfiles[ii]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "dirname = os.getcwd() + '/ipta_mdc_hdf5files/'\n",
    "if not os.path.exists(dirname):\n",
    "    os.makedirs(dirname)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "for ii,p in enumerate(psr):\n",
    "    df = NX01_datafile.DataFile(dirname + '/' + p.name + '.hdf5')\n",
    "    df.addTempoPulsar(p)\n",
    "    \n",
    "    clear_output()\n",
    "    print '\\r', '{0} of {1}'.format(ii+1,len(psr))\n",
    "    sys.stdout.flush()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "fil = open(dirname + '/psrList_iptamdc.txt','w')\n",
    "print >>fil, \"NAME\", \"HDF5-PATH\", \"PARFILE-PATH\", \"TIMFILE-PATH\"\n",
    "print >>fil, \"#############################################\"\n",
    "for p in psr:\n",
    "    print >>fil, p.name, dirname+'/'+p.name+'.hdf5'\n",
    "fil.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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