{
 "metadata": {
  "name": "",
  "signature": "sha256:2d47cbb18ec0b346ffc2e059dd64fc743ed69884e3b4ef422dfd0c3cb29bb992"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%pylab inline\n",
      "from isochrones import dartmouth\n",
      "import isochrones.starmodel as sm\n",
      "import numpy as np"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dar = dartmouth.Dartmouth_Isochrone()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mass,age,feh = (1,9.7,0.0)\n",
      "dar(mass,age,feh)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "{'Teff': 5862.6257036523411,\n",
        " 'age': 9.7,\n",
        " 'logL': array(0.04991101427726369),\n",
        " 'logg': array(4.413765494604435),\n",
        " 'mag': {'B': array(5.322317039276663),\n",
        "  'D51': array(4.829896998296746),\n",
        "  'H': array(3.244559043105774),\n",
        "  'I': array(3.9863547951691483),\n",
        "  'J': array(3.552087702921549),\n",
        "  'K': array(3.2103230990258416),\n",
        "  'Kepler': array(4.437794929356799),\n",
        "  'R': array(4.3099370039207),\n",
        "  'U': array(5.505880437164362),\n",
        "  'V': array(4.666147691696509),\n",
        "  'g': array(4.955313816556526),\n",
        "  'i': array(4.391011199640937),\n",
        "  'r': array(4.4912704596487405),\n",
        "  'z': array(4.387494381381158)},\n",
        " 'mass': array(1.0),\n",
        " 'radius': 1.0288008796300101}"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ages = np.linspace(9,9.8,200)\n",
      "radii1 = dar.radius(1,ages,0.0)\n",
      "radii2 = dar.radius(1,ages,0.2)\n",
      "radii3 = dar.radius(1,ages,-0.2)\n",
      "plot(ages,radii1,label='feh=0.0')\n",
      "plot(ages,radii2,label='feh=0.2')\n",
      "plot(ages,radii3,label='feh=-0.2')\n",
      "xlabel('log(age)'); ylabel('radius')\n",
      "legend(loc='upper left')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 19,
       "text": [
        "<matplotlib.legend.Legend at 0x11e4fbf10>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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7cSI8/7xnuqDOXTzH8G+GM2+LtKaGtBnClN5TytTe25fLXWMYADWB9kCMw5tp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\nUkoppZRSSimllFJKKaWUL/l/GGgAg4QzdqkAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x11d718550>"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ev = dar.evtrack(1.0,dage=0.01)\n",
      "plot((ev['Teff']),ev['logL'],'.')\n",
      "xlabel('Teff')\n",
      "ylabel('logL');"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x108de8f50>"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mod = sm.StarModel(dar,Teff=(5770,50),logg=(4.44,0.10),feh=(0.0,0.1))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mod.maxlike()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "array([  9.68308690e-01,   9.74036882e+00,  -4.58811816e-03])"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mod.fit_mcmc()\n",
      "mod.plot_samples('radius')\n",
      "mod.plot_samples('mass')\n",
      "mod.plot_samples('age')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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8fYaKM6GY/Zk0GZhDcEFLI62gP5MyxpmQVX9GmghShpn4hbt/7e5vAclhJtK+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\nAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x11d41d510>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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3Py3xXis5iIjUk7tbfT8bh4fpFgMtzezQpLK+wPt5ikdERBLy3pIAMLPHAAf+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\nl7tlp4qfAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x11d41d290>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Mjw3zzWDB07rtsMNgzpzg8aJFmS/H+uCDmdWP72Tv3j2z94qIxAubQI4DOrn7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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x11d7b7850>"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Kepler-93 (KOI-69):  published values: $M = 0.91 \\pm 0.06$, $R = 0.92 \\pm 0.02$ "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "reload(dartmouth.iso); reload(dartmouth); reload(sm)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 70,
       "text": [
        "<module 'isochrones.starmodel' from 'isochrones/starmodel.py'>"
       ]
      }
     ],
     "prompt_number": 70
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mags = {'H': (8.4459999999999997,0.08),\n",
      " 'J': (8.7710000000000008,0.08),\n",
      " 'K': (8.3699999999999992,0.08),\n",
      " 'g':(10.33,0.08),\n",
      " 'r':(9.83,0.08)}\n",
      "specprops = dict(Teff=(5626,64),logg=(4.47,0.07),feh=(-.16,0.1))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 77
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#smod = sm.StarModel(dar,**mags)\n",
      "smod = sm.StarModel(dar,**specprops)\n",
      "#smod.add_props(**specprops)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 89
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pars = smod.maxlike()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 87
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "smod.fit_mcmc()\n",
      "smod.plot_samples('radius')\n",
      "smod.plot_samples('mass')\n",
      "#smod.plot_samples('feh')\n",
      "#smod.plot_samples('distance')\n",
      "#smod.plot_samples('AV')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x109886190>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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1UFoae47bb4/d/uY3YxNFZSV88gmccUb6eJYvh+OOSzw8+fr15lzf+lb689i1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\nRK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x109886510>"
       ]
      }
     ],
     "prompt_number": 92
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mod = StarModel(dar,Teff=(5626,64),logg=(4.47,0.07),feh=(-.16,0.1))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mod.fit_mcmc()\n",
      "mod.plot_samples('radius')\n",
      "mod.plot_samples('mass')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x11d5653d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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3Ra/76CMlDIlPbV9ERMQRPWHkoB074JNPIsurV6ctFBHJIH5JGO2Bp4DhwAHA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\nAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x11d572bd0>"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "reload(dartmouth.iso); reload(dartmouth); reload(sm)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 152,
       "text": [
        "<module 'isochrones.starmodel' from 'isochrones/starmodel.py'>"
       ]
      }
     ],
     "prompt_number": 152
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "smod.properties"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 47,
       "text": [
        "{'H': (8.446, 0.04),\n",
        " 'J': (8.771, 0.04),\n",
        " 'K': (8.37, 0.04),\n",
        " 'Teff': (5626, 64),\n",
        " 'feh': (-0.16, 0.1),\n",
        " 'logg': (4.47, 0.07)}"
       ]
      }
     ],
     "prompt_number": 47
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sm.salpeter_prior(1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 76,
       "text": [
        "0.06042284436470413"
       ]
      }
     ],
     "prompt_number": 76
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from keputils import koiutils as ku\n",
      "ku.KICmags(1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 163,
       "text": [
        "{'H': 9.9199999999999999,\n",
        " 'J': 10.232000000000001,\n",
        " 'K': 9.8460000000000001,\n",
        " 'Kepler': 11.337999999999999,\n",
        " 'Ks': 9.8460000000000001,\n",
        " 'g': 11.679958099999999,\n",
        " 'h': 9.9199999999999999,\n",
        " 'i': 11.1171472,\n",
        " 'j': 10.232000000000001,\n",
        " 'k': 9.8460000000000001,\n",
        " 'kep': 11.337999999999999,\n",
        " 'r': 11.2425636,\n",
        " 'z': 11.072965399999999}"
       ]
      }
     ],
     "prompt_number": 163
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "reload(sm)\n",
      "smod = sm.StarModel(dar,\n",
      "                    Teff=(5783,64),logg=(4.36,0.07),feh=(-0.06,0.10))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 185
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "smod.maxlike(100)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 165,
       "text": [
        "array([  7.63118202e-01,   9.85065110e+00,  -8.49205074e-01,\n",
        "         1.54827301e+02,   6.59172367e-02])"
       ]
      }
     ],
     "prompt_number": 165
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "smod.fit_mcmc()\n",
      "smod.plot_samples('radius')\n",
      "smod.plot_samples('mass')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "200/200 walkers have % accepted > 0.15\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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1yilmPOJQw4aZRokiAUoCIlnq00/NeAMBxx9vEodHY/9lFSUBkSz19NNmCjV5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\nREREREREREREREREREREREREJOv9f7D07Q673vboAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10e64ae10>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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+SRjxCoETgZVeByKx/vOf5MkiEKj761NavoICuP322LI33lDCaGn8mDCygFnA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\nIiIiIiIiIiIiIiIiIgn8fxfinPlxudYMAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10e5e2450>"
       ]
      }
     ],
     "prompt_number": 186
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from simpledist import distributions as dists"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 187
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mdist = dists.Hist_Distribution(smod.prop_samples('mass',return_values=False),\n",
      "                                bins=50,name='mass')\n",
      "rdist = dists.Hist_Distribution(smod.prop_samples('radius',return_values=False),\n",
      "                                bins=50,name='radius')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 195
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mdist.save_hdf??"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 201
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mdist.plot()\n",
      "rdist.plot()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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9nBwb/bJvXyvpdO6sv3GVbjLA+vUwfry15HfuhOrV4cknNXWbBEdRkf19T55s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\n104mLftvAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10e929c50>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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uXEpidEXnF26JOr9Nm6LRe++NRgsKdv8dqeyWmxuN9ugRjV55ZTT69NPR6Jw5\n0ejy5dFocXFsr79jRzRaVJS88wsaL2/Gm5hzfTznQGDTHh/bDKR8/sXatbBypd1fuNAuwuzYASUl\nNqIufywpsaXsmzfDhg02cvC0bw8jR8LZZ1uTexGxGVWXXGK35cutf8/UqbabfcuWthFE+/Y25TA/\n38oukQjMmwfvvGO3PdWrB02a2AXRJk3KbjVr2u/yV1+V3VautN/fWrV2/5rly+2dQIMG9nU1a9rj\nzZvh66/ttmaN/b6fdBIceaS9dvv2+34nnS78DNc7A29i5RPPWKAPMKj0cfDrJSIiwZS0ft6LsRF6\n+3IfOwpYWNUXFxHJcGOS/QKPAVOAfKA3sBE4PNkvKiIiVVMPmAZsAZYBQ51GIyIiIiIiIiISKPXZ\nvXQyrILn/hFYgdXGZwJHJDu4BPB7fnnA7cAqbPHSvfibcunSpcB7QDHwcCXPHQOsAb7HWiIckNzQ\nEsLv+XUAXgK+BXalIK5E8Xt+o0qf9z32+/c3IOhrhf2e21DgU+zc1gFPA82SHl3VxfK753kV+/ms\ncEJJLFsJ3FsaQCNgBHA/+07Kg4CLgWOxhDgbmBzD67ji9/yuBrpgK04PKb1/XYpijNcq4AbgoUqe\ndyJwFdAfaA0cDFyf3NASwu/5bQcex9o8hInf86sBXAY0ALoDx2HTeoPM77m9hU1ProP9bP4A/D25\noSWE3/PzjMAGgwmbfl0T2Mbu0wUnAn/Zx3OvAZ4o9/hIYGuiAkmSWM5vLvCLco+HAV8lL7SEuoGK\n//pPAW4s97gfNgoPi8rOz9OecI28PX7PzzOG3VdCB1ks53Yg9vt5e/LCSTg/51cH+Az7w5uwkfch\nQAllvU0APsIS855eBXoCPwGqYW/lXvT5Oq7Ecn6w+7z2bKAFDlacxqGy+fhHYOftmQ80xmYbhUG6\nrzeI9fz6svt6jCDzc27eNOVNQCvsXWJY+Dm/m4D7gLWVPRH8J+9Ylsi/i/1V/Ax7a3MG1gslyGI5\nv/9ib00bAk2A32JvcfKTGWCCVPZW7ECspujx/k3C8IcJ0n+lbyzn90uspHdrkmJJND/n9iZQFxss\n7QBuSWpEiVXZ+R2NDXrv9vsN/SbvLUDtPT5WB0twe7oUq7W1wC7u/Ql4DavHBVUs5/dn4APgQ+yH\naRo2avf119Kxyv767/nv4HWG2Ne/QxBp5G1OxUZxA7GL6mEQy//damxSxMgkxZIMFZ1fNjbi/h27\nl/Mq/De8G2bOAAADR0lEQVTxm7z9LJH3nIStyFxdGshE7G13kFdkxnJ+xcBo7I9Te+yX471kB5gg\nlf31/xjoVO7xUdgfpQ1JiyixNPK2379/AT/H/j/DItb/u2rYO/uwqOj8agNdsWuFa7DqBcBKoGB/\nX+Q3eRdhU3P+RNkS+VPY9yyS+cCZ2KyNbOAcLDF+sY/nBkUs59es9JYF9MBmmoxLTZhxywGqY/8P\nOdg7on1NIZuEzcQ4HPuD+0diu0Dmit/zo/R53vTHvNJb0Pk9v/7Ao8DphGdA4ffchgMtS++3xt4B\nP5WKAKvIz/ltBJpig6WjgJNLP96FskReJftbIt8Ke1vdovRxPvAg8DVWP30PGJCIAJLM7/kdCyzF\nEv4iKp7vHhTjsXdB5W//x97nBjZDwfu/+zc2wgm68fg7vzblPr+z9LgktaHGZTz+zu81bDrk5nK3\nGSmONVbj8XduN2Jz17dgv39/xZJi0I3H/++epw328xnLVG4RERERERERERERERERERERERERERER\nEZGkaMPubTVfwFb7iohIgLXBR09kkbDSD7aERdC3mhNJKSVvCbJlwJVYs7MtwLVYg7NNWMe8U8s9\nNxvrXf0t8CXwsz2+V4Sy7c/Gs3vTsTbsPko/t/R7bMJ6nwyv6omIJJpGMxJ0Q7G+1OuxNqe9scZZ\nZwKPAO2wtrUXYgm7E9Yq9Gl2b8MZLfe4ovacNYE7seb4n2M7CTVIzKmIJI5G3hJkUeAubBPXYuA/\nWOIGmIol126lj8/E9jRchfUfv4n9N7OvrPH/LqAjtoHIWuCT+MIXSR4lbwm6FeXuj8R2MdpQeuuA\nbUcH1g+5/HPj3RS6CDgLuBjbUOR54NA4v5dI0ih5S9B5JY7W2A4xvwHqY/3XF1I2il6D9Uj2lL+/\npy3svudokz0+/zLWg74J8CnwQDyBiySTkreERU0ska/Dfm7Pw0benqnYZtDNscR+dQXf60OgD7Yz\nSx3gmnKfawQMLn29HdhIfGdCzkAkgZS8JSw+AW4DZmN17w7YBtCeB4CXgI+w3ZueYv8XJl/B9guc\nD8wFniv33GxsN6FV2EXSY4FfJ/A8REREREREREREREREREREREREREREREREREREREQkLv8Pek9z\nB6mM8AcAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10e929990>"
       ]
      }
     ],
     "prompt_number": 196
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "smod.plot_samples('Teff')\n",
      "smod.plot_samples('AV')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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hfQhcRKBTgl83zGjfXTBJbxbwGTDIxXFOJpBcfcBXmHta/wacDQUWgKXZziqs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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10e7c5ad0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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OEzx2YYb7XkXlPhEREYmyQE8a7bxeXwpcjcl2+wPmRn8/8PcQzrULGOSw/wcq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\nrkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10e7c56d0>"
       ]
      }
     ],
     "prompt_number": 184
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from keputils import kicutils as kicu"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 102
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "smod.sampler.__dict__"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 120,
       "text": [
        "{'_blobs': [],\n",
        " '_chain': array([[[  4.19466611e-01,   1.01338247e+01,   4.75670464e-01,\n",
        "            3.62338222e+02,   6.94623885e-02],\n",
        "         [  4.19466611e-01,   1.01338247e+01,   4.75670464e-01,\n",
        "            3.62338222e+02,   6.94623885e-02],\n",
        "         [  6.57124500e-01,   9.96214786e+00,   4.76381538e-02,\n",
        "            5.42217613e+02,   3.17897135e-01],\n",
        "         ..., \n",
        "         [  1.10383373e+00,   9.71602072e+00,  -5.33234894e-01,\n",
        "            2.91784485e+02,   4.13422179e-01],\n",
        "         [  1.10383373e+00,   9.71602072e+00,  -5.33234894e-01,\n",
        "            2.91784485e+02,   4.13422179e-01],\n",
        "         [  1.10383373e+00,   9.71602072e+00,  -5.33234894e-01,\n",
        "            2.91784485e+02,   4.13422179e-01]],\n",
        " \n",
        "        [[  2.63856642e-01,   9.58446497e+00,   4.44474505e-01,\n",
        "            7.86633346e+02,   9.81949902e-01],\n",
        "         [  2.63856642e-01,   9.58446497e+00,   4.44474505e-01,\n",
        "            7.86633346e+02,   9.81949902e-01],\n",
        "         [  6.28531706e-01,   9.43253492e+00,   1.12778503e-01,\n",
        "            8.08147765e+02,   8.90839854e-01],\n",
        "         ..., \n",
        "         [  1.44673251e+00,   9.38761872e+00,  -4.17009769e-01,\n",
        "            6.72385878e+02,   7.10410112e-01],\n",
        "         [  1.44673251e+00,   9.38761872e+00,  -4.17009769e-01,\n",
        "            6.72385878e+02,   7.10410112e-01],\n",
        "         [  1.44673251e+00,   9.38761872e+00,  -4.17009769e-01,\n",
        "            6.72385878e+02,   7.10410112e-01]],\n",
        " \n",
        "        [[  9.94105123e-01,   9.16888439e+00,  -5.34143431e-01,\n",
        "            3.68513778e+02,   7.70139820e-01],\n",
        "         [  9.94105123e-01,   9.16888439e+00,  -5.34143431e-01,\n",
        "            3.68513778e+02,   7.70139820e-01],\n",
        "         [  1.13280079e+00,   9.00629187e+00,  -5.59564108e-01,\n",
        "            2.33503344e+02,   8.73362476e-01],\n",
        "         ..., \n",
        "         [  1.65439320e+00,   9.18607825e+00,   2.33577113e-01,\n",
        "            2.18608224e+02,   4.97718660e-01],\n",
        "         [  1.65439320e+00,   9.18607825e+00,   2.33577113e-01,\n",
        "            2.18608224e+02,   4.97718660e-01],\n",
        "         [  1.65439320e+00,   9.18607825e+00,   2.33577113e-01,\n",
        "            2.18608224e+02,   4.97718660e-01]],\n",
        " \n",
        "        ..., \n",
        "        [[  5.34381859e-01,   9.02295605e+00,  -8.27349318e-01,\n",
        "            7.94998265e+02,   5.14975519e-02],\n",
        "         [  5.34381859e-01,   9.02295605e+00,  -8.27349318e-01,\n",
        "            7.94998265e+02,   5.14975519e-02],\n",
        "         [  5.34381859e-01,   9.02295605e+00,  -8.27349318e-01,\n",
        "            7.94998265e+02,   5.14975519e-02],\n",
        "         ..., \n",
        "         [  1.03256876e+00,   9.49841367e+00,  -9.04458436e-01,\n",
        "            1.12369764e+02,   4.80563771e-01],\n",
        "         [  1.03256876e+00,   9.49841367e+00,  -9.04458436e-01,\n",
        "            1.12369764e+02,   4.80563771e-01],\n",
        "         [  1.03256876e+00,   9.49841367e+00,  -9.04458436e-01,\n",
        "            1.12369764e+02,   4.80563771e-01]],\n",
        " \n",
        "        [[  1.03276918e+00,   9.12713638e+00,   1.81783262e-02,\n",
        "            8.61709878e+02,   4.27352924e-02],\n",
        "         [  1.03276918e+00,   9.12713638e+00,   1.81783262e-02,\n",
        "            8.61709878e+02,   4.27352924e-02],\n",
        "         [  1.03276918e+00,   9.12713638e+00,   1.81783262e-02,\n",
        "            8.61709878e+02,   4.27352924e-02],\n",
        "         ..., \n",
        "         [  1.38120008e+00,   9.43434737e+00,  -4.81977947e-01,\n",
        "            6.95375990e+02,   6.20674742e-01],\n",
        "         [  1.38120008e+00,   9.43434737e+00,  -4.81977947e-01,\n",
        "            6.95375990e+02,   6.20674742e-01],\n",
        "         [  1.38120008e+00,   9.43434737e+00,  -4.81977947e-01,\n",
        "            6.95375990e+02,   6.20674742e-01]],\n",
        " \n",
        "        [[  1.71494244e+00,   9.16285710e+00,   1.28160190e-01,\n",
        "            2.53059091e+02,   2.09118519e-01],\n",
        "         [  1.71494244e+00,   9.16285710e+00,   1.28160190e-01,\n",
        "            2.53059091e+02,   2.09118519e-01],\n",
        "         [  1.71494244e+00,   9.16285710e+00,   1.28160190e-01,\n",
        "            2.53059091e+02,   2.09118519e-01],\n",
        "         ..., \n",
        "         [  1.71850445e+00,   9.13536029e+00,  -2.51601354e-01,\n",
        "            2.68782756e+02,   7.81117185e-01],\n",
        "         [  1.71850445e+00,   9.13536029e+00,  -2.51601354e-01,\n",
        "            2.68782756e+02,   7.81117185e-01],\n",
        "         [  1.71850445e+00,   9.13536029e+00,  -2.51601354e-01,\n",
        "            2.68782756e+02,   7.81117185e-01]]]),\n",
        " '_lnprob': array([[ -7.25032681e+04,  -7.25032681e+04,  -5.60091809e+04, ...,\n",
        "          -1.65642302e+01,  -1.65642302e+01,  -1.65642302e+01],\n",
        "        [ -1.56646947e+05,  -1.56646947e+05,  -8.55477771e+04, ...,\n",
        "          -6.34404809e+01,  -6.34404809e+01,  -6.34404809e+01],\n",
        "        [ -1.83096160e+04,  -1.83096160e+04,  -5.36671460e+03, ...,\n",
        "          -4.51439807e+00,  -4.51439807e+00,  -4.51439807e+00],\n",
        "        ..., \n",
        "        [ -8.70946385e+04,  -8.70946385e+04,  -8.70946385e+04, ...,\n",
        "          -2.89959292e+00,  -2.89959292e+00,  -2.89959292e+00],\n",
        "        [ -4.57237474e+04,  -4.57237474e+04,  -4.57237474e+04, ...,\n",
        "          -5.80571249e+01,  -5.80571249e+01,  -5.80571249e+01],\n",
        "        [ -5.72875607e+00,  -5.72875607e+00,  -5.72875607e+00, ...,\n",
        "          -4.63087667e+00,  -4.63087667e+00,  -4.63087667e+00]]),\n",
        " '_random': <mtrand.RandomState at 0x108c1b090>,\n",
        " 'a': 2.0,\n",
        " 'args': [],\n",
        " 'dim': 5,\n",
        " 'iterations': 300,\n",
        " 'k': 200,\n",
        " 'kwargs': {},\n",
        " 'lnprobfn': <emcee.ensemble._function_wrapper at 0x11c7ad910>,\n",
        " 'naccepted': array([ 21.,  18.,  44.,  17.,  17.,  63.,   8.,  29.,  19.,  69.,  19.,\n",
        "         12.,  33.,  15.,  58.,  69.,  15.,  22.,  58.,  54.,  52.,  18.,\n",
        "         54.,  58.,  34.,  44.,  73.,  12.,  10.,  14.,  25.,  20.,  69.,\n",
        "         16.,  28.,  45.,  26.,  70.,  17.,  29.,  78.,  54.,   8.,  68.,\n",
        "         60.,  53.,  53.,  15.,  41.,  47.,  43.,  71.,  48.,  52.,  15.,\n",
        "         27.,  41.,  20.,  51.,  66.,  25.,  36.,  13.,   6.,   8.,  46.,\n",
        "         65.,  58.,  11.,  49.,  18.,  42.,  13.,  15.,  24.,  30.,  10.,\n",
        "         11.,  23.,  31.,  60.,  19.,  54.,  16.,  72.,  56.,  52.,  41.,\n",
        "         38.,  68.,   8.,  39.,   9.,  19.,  31.,  46.,  25.,  71.,  12.,\n",
        "         38.,  13.,  48.,  18.,  18.,  11.,  41.,  51.,  24.,  48.,  57.,\n",
        "         38.,  17.,  52.,  59.,  58.,  36.,  61.,  13.,  21.,   7.,  45.,\n",
        "         47.,  33.,  34.,  25.,  26.,  37.,  18.,  51.,  57.,  49.,   8.,\n",
        "         44.,  55.,  40.,  12.,  19.,  58.,  11.,   6.,  44.,  50.,  60.,\n",
        "         55.,  53.,  48.,  60.,  52.,  45.,  14.,  20.,  23.,  47.,  13.,\n",
        "          8.,  25.,  56.,  67.,  53.,  42.,  42.,  27.,  16.,  14.,  63.,\n",
        "          9.,  18.,  26.,  53.,  65.,  22.,  21.,  10.,  13.,  54.,  42.,\n",
        "         42.,  75.,  37.,  14.,  50.,  10.,  45.,   9.,  47.,  32.,  59.,\n",
        "         43.,  36.,  42.,  24.,  39.,  54.,  45.,  41.,  41.,  38.,  47.,\n",
        "         18.,  24.]),\n",
        " 'pool': None,\n",
        " 'runtime_sortingfn': None,\n",
        " 'threads': 1}"
       ]
      }
     ],
     "prompt_number": 120
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "smod.sampler.naccepted/300."
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 140,
       "text": [
        "array([ 0.22333333,  0.25666667,  0.25      ,  0.26333333,  0.25333333,\n",
        "        0.22      ,  0.27      ,  0.23      ,  0.15      ,  0.21666667,\n",
        "        0.17      ,  0.27333333,  0.18      ,  0.19666667,  0.21666667,\n",
        "        0.22      ,  0.25      ,  0.17333333,  0.14333333,  0.25      ,\n",
        "        0.19666667,  0.22333333,  0.21      ,  0.01666667,  0.22333333,\n",
        "        0.00333333,  0.23      ,  0.21666667,  0.25666667,  0.22      ,\n",
        "        0.24333333,  0.18666667,  0.23      ,  0.16666667,  0.25      ,\n",
        "        0.07666667,  0.19666667,  0.21666667,  0.22666667,  0.23333333,\n",
        "        0.02666667,  0.15333333,  0.27333333,  0.23666667,  0.15666667,\n",
        "        0.25      ,  0.10666667,  0.12666667,  0.10666667,  0.20666667,\n",
        "        0.26      ,  0.21      ,  0.24666667,  0.17333333,  0.29333333,\n",
        "        0.2       ,  0.15666667,  0.00333333,  0.32      ,  0.18666667,\n",
        "        0.23666667,  0.21333333,  0.17333333,  0.10666667,  0.04333333,\n",
        "        0.25      ,  0.16      ,  0.15      ,  0.27666667,  0.21      ,\n",
        "        0.16666667,  0.17666667,  0.20666667,  0.26333333,  0.23      ,\n",
        "        0.10333333,  0.20333333,  0.24333333,  0.15333333,  0.21666667,\n",
        "        0.21666667,  0.19666667,  0.28333333,  0.13      ,  0.00666667,\n",
        "        0.12666667,  0.23666667,  0.20666667,  0.23666667,  0.2       ,\n",
        "        0.18333333,  0.07      ,  0.13666667,  0.22      ,  0.11      ,\n",
        "        0.25666667,  0.04      ,  0.18      ,  0.13666667,  0.01      ,\n",
        "        0.2       ,  0.27333333,  0.17666667,  0.25333333,  0.04      ,\n",
        "        0.15333333,  0.24      ,  0.1       ,  0.29666667,  0.29      ,\n",
        "        0.2       ,  0.29333333,  0.18666667,  0.23333333,  0.20666667,\n",
        "        0.12      ,  0.07666667,  0.23      ,  0.12333333,  0.3       ,\n",
        "        0.26333333,  0.33      ,  0.19333333,  0.20666667,  0.23333333,\n",
        "        0.18      ,  0.20333333,  0.24666667,  0.02333333,  0.08      ,\n",
        "        0.24333333,  0.03666667,  0.20333333,  0.24666667,  0.16      ,\n",
        "        0.22333333,  0.19333333,  0.00666667,  0.19666667,  0.21666667,\n",
        "        0.32333333,  0.29      ,  0.01      ,  0.21333333,  0.26666667,\n",
        "        0.23      ,  0.00666667,  0.19      ,  0.02      ,  0.19      ,\n",
        "        0.26666667,  0.19666667,  0.03333333,  0.18333333,  0.22333333,\n",
        "        0.23666667,  0.15666667,  0.22666667,  0.14333333,  0.04      ,\n",
        "        0.01333333,  0.2       ,  0.16333333,  0.04666667,  0.23666667,\n",
        "        0.27      ,  0.19666667,  0.24666667,  0.21333333,  0.26      ,\n",
        "        0.28      ,  0.14      ,  0.21666667,  0.15333333,  0.14      ,\n",
        "        0.19666667,  0.30666667,  0.11333333,  0.19333333,  0.31      ,\n",
        "        0.22666667,  0.06333333,  0.20666667,  0.13333333,  0.21666667,\n",
        "        0.27333333,  0.26666667,  0.18      ,  0.23      ,  0.25      ,\n",
        "        0.32666667,  0.01666667,  0.16      ,  0.19333333,  0.22333333,\n",
        "        0.24333333,  0.31666667,  0.25333333,  0.12333333,  0.21333333])"
       ]
      }
     ],
     "prompt_number": 140
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "smod.sampler.chain.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 122,
       "text": [
        "(200, 300, 5)"
       ]
      }
     ],
     "prompt_number": 122
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy.random as rand"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 125
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "wokinds = np.where(smod.sampler.naccepted/smod.sampler.iterations > 0.15)[0]\n",
      "print len(wokinds)\n",
      "inds = rand.randint(len(wokinds),size=200)\n",
      "hist(smod.sampler.chain[wokinds[inds],:,:].mean(axis=1)[:,0])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "121\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 175,
       "text": [
        "(array([ 16.,  24.,  21.,  58.,  31.,  16.,  12.,   4.,   4.,  14.]),\n",
        " array([ 0.77136683,  0.82471312,  0.87805941,  0.9314057 ,  0.98475199,\n",
        "         1.03809828,  1.09144458,  1.14479087,  1.19813716,  1.25148345,\n",
        "         1.30482974]),\n",
        " <a list of 10 Patch objects>)"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x10e7b7e10>"
       ]
      }
     ],
     "prompt_number": 175
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "smod.fit_mcmc??"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 137
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dists.Hist_Distribution?"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 203
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}