kbarbary/sncosmo-fitting-example.ipynb

## sncosmo-fitting-example.ipynb
{
 "metadata": {
  "name": "sncosmo-fitting-example"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import sncosmo\n",
      "from astropy.table import Table\n",
      "%pylab inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Welcome to pylab, a matplotlib-based Python environment [backend: module://IPython.zmq.pylab.backend_inline].\n",
        "For more information, type 'help(pylab)'.\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Load some example data:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "meta, data = sncosmo.load_example_data()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "`meta` is a dictionary and `data` is a structured numpy array:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print meta\n",
      "print len(data)\n",
      "print data.dtype.names"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "OrderedDict([('c', 0.2), ('x1', 0.5), ('z', 0.5), ('t0', 55100.0), ('mabs', -19.5), ('model', 'salt2')])\n",
        "40\n",
        "('time', 'band', 'flux', 'fluxerr', 'zp', 'zpsys')\n"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "See what the data look like:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print Table(data)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "     time      band        flux          fluxerr      zp  zpsys\n",
        "------------- ----- ----------------- -------------- ---- -----\n",
        "      55070.0 sdssg    0.813499900062 0.651728140824 25.0    ab\n",
        "55072.0512821 sdssr  -0.0852238865812 0.651728140824 25.0    ab\n",
        "55074.1025641 sdssi -0.00681659003089 0.651728140824 25.0    ab\n",
        "55076.1538462 sdssz     2.23929135407 0.651728140824 25.0    ab\n",
        "55078.2051282 sdssg  -0.0308977349373 0.651728140824 25.0    ab\n",
        "55080.2564103 sdssr     2.35450321853 0.651728140824 25.0    ab\n",
        "55082.3076923 sdssi     4.23328285744 0.651728140824 25.0    ab\n",
        "55084.3589744 sdssz     5.16995357932 0.651728140824 25.0    ab\n",
        "55086.4102564 sdssg     1.78013572224 0.651728140824 25.0    ab\n",
        "55088.4615385 sdssr     7.95116990982 0.651728140824 25.0    ab\n",
        "55090.5128205 sdssi     8.36265803263 0.651728140824 25.0    ab\n",
        "          ...   ...               ...            ...  ...   ...\n",
        "55129.4871795 sdssr     3.24977363743 0.651728140824 25.0    ab\n",
        "55131.5384615 sdssi       3.667604199 0.651728140824 25.0    ab\n",
        "55133.5897436 sdssz     6.95983276461 0.651728140824 25.0    ab\n",
        "55135.6410256 sdssg     -1.5628537367 0.651728140824 25.0    ab\n",
        "55137.6923077 sdssr     1.75545327677 0.651728140824 25.0    ab\n",
        "55139.7435897 sdssi      2.4540241384 0.651728140824 25.0    ab\n",
        "55141.7948718 sdssz     5.74302682345 0.651728140824 25.0    ab\n",
        "55143.8461538 sdssg    0.721629233787 0.651728140824 25.0    ab\n",
        "55145.8974359 sdssr   -0.278723356717 0.651728140824 25.0    ab\n",
        "55147.9487179 sdssi     1.77867957888 0.651728140824 25.0    ab\n",
        "      55150.0 sdssz     4.85353440991 0.651728140824 25.0    ab\n"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Plot the data:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sncosmo.plotlc(data)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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RERHxbf5FvWD8+PGcO3eORYsWsWXLFgDee+890tPTOXPmjMsDulNmJuw/YHUKERGRsqPI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\nM9HpNbnI6wAAAABJRU5ErkJggg==\n"
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Get a model, then fit the model to the data, using the `iminuit` minimizer. Here, we are varying all 5 parameters of the model, bounding the redshift to be between 0.3 and 0.7."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "model = sncosmo.get_model('salt2')\n",
      "res, m = sncosmo.fit_model(model, data, ['fscale', 'x1', 'c', 't0', 'z'],\n",
      "                           bounds={'z':(0.3, 0.7)}, method='iminuit',\n",
      "                           return_minuit=True, print_level=1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "starting point:\n",
        "    fscale 1.15619723916e-17 (None, None)\n",
        "    x1 0.0 (None, None)\n",
        "    c 0.0 (None, None)\n",
        "    t0 55098.9415911 (55078.941591100593, 55118.941591100593)\n",
        "    z 0.5 (0.3, 0.7)\n"
       ]
      },
      {
       "html": [
        "<hr>"
       ],
       "output_type": "display_data"
      },
      {
       "html": [
        "\n",
        "        <table>\n",
        "            <tr>\n",
        "                <td title=\"Minimum value of function\">FCN = 44.7949902906</td>\n",
        "                <td title=\"Total number of call to FCN so far\">TOTAL NCALL = 174</td>\n",
        "                <td title=\"Number of call in last migrad\">NCALLS = 174</td>\n",
        "            </tr>\n",
        "            <tr>\n",
        "                <td title=\"Estimated distance to minimum\">EDM = 1.93852950266e-08</td>\n",
        "                <td title=\"Maximum EDM definition of convergence\">GOAL EDM = 1e-05</td>\n",
        "                <td title=\"Error def. Amount of increase in FCN to be defined as 1 standard deviation\">\n",
        "                UP = 1.0</td>\n",
        "            </tr>\n",
        "        </table>\n",
        "        \n",
        "        <table>\n",
        "            <tr>\n",
        "                <td align=\"center\" title=\"Validity of the migrad call\">Valid</td>\n",
        "                <td align=\"center\" title=\"Validity of parameters\">Valid Param</td>\n",
        "                <td align=\"center\" title=\"Is Covariance matrix accurate?\">Accurate Covar</td>\n",
        "                <td align=\"center\" title=\"Positive definiteness of covariance matrix\">PosDef</td>\n",
        "                <td align=\"center\" title=\"Was covariance matrix made posdef by adding diagonal element\">Made PosDef</td>\n",
        "            </tr>\n",
        "            <tr>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">False</td>\n",
        "            </tr>\n",
        "            <tr>\n",
        "                <td align=\"center\" title=\"Was last hesse call fail?\">Hesse Fail</td>\n",
        "                <td align=\"center\" title=\"Validity of covariance\">HasCov</td>\n",
        "                <td align=\"center\" title=\"Is EDM above goal EDM?\">Above EDM</td>\n",
        "                <td align=\"center\"></td>\n",
        "                <td align=\"center\" title=\"Did last migrad call reach max call limit?\">Reach calllim</td>\n",
        "            </tr>\n",
        "            <tr>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">False</td>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">True</td>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">False</td>\n",
        "                <td align=\"center\"></td>\n",
        "                <td align=\"center\" style=\"background-color:#92CCA6\">False</td>\n",
        "            </tr>\n",
        "        </table>\n",
        "        "
       ],
       "output_type": "display_data"
      },
      {
       "html": [
        "\n",
        "        <table>\n",
        "            <tr>\n",
        "                <td><a href=\"#\" onclick=\"$('#wAWgyTGoUz').toggle()\">+</a></td>\n",
        "                <td title=\"Variable name\">Name</td>\n",
        "                <td title=\"Value of parameter\">Value</td>\n",
        "                <td title=\"Parabolic error\">Parab Error</td>\n",
        "                <td title=\"Minos lower error\">Minos Error-</td>\n",
        "                <td title=\"Minos upper error\">Minos Error+</td>\n",
        "                <td title=\"Lower limit of the parameter\">Limit-</td>\n",
        "                <td title=\"Upper limit of the parameter\">Limit+</td>\n",
        "                <td title=\"Is the parameter fixed in the fit\">FIXED</td>\n",
        "            </tr>\n",
        "        \n",
        "            <tr>\n",
        "                <td>1</td>\n",
        "                <td>fscale</td>\n",
        "                <td>1.246162e-17</td>\n",
        "                <td>9.361884e-20</td>\n",
        "                <td>0.000000e+00</td>\n",
        "                <td>0.000000e+00</td>\n",
        "                <td></td>\n",
        "                <td></td>\n",
        "                <td></td>\n",
        "            </tr>\n",
        "            \n",
        "            <tr>\n",
        "                <td>2</td>\n",
        "                <td>x1</td>\n",
        "                <td>6.052362e-01</td>\n",
        "                <td>6.243494e-02</td>\n",
        "                <td>0.000000e+00</td>\n",
        "                <td>0.000000e+00</td>\n",
        "                <td></td>\n",
        "                <td></td>\n",
        "                <td></td>\n",
        "            </tr>\n",
        "            \n",
        "            <tr>\n",
        "                <td>3</td>\n",
        "                <td>c</td>\n",
        "                <td>1.469979e-01</td>\n",
        "                <td>6.317050e-03</td>\n",
        "                <td>0.000000e+00</td>\n",
        "                <td>0.000000e+00</td>\n",
        "                <td></td>\n",
        "                <td></td>\n",
        "                <td></td>\n",
        "            </tr>\n",
        "            \n",
        "            <tr>\n",
        "                <td>4</td>\n",
        "                <td>t0</td>\n",
        "                <td>5.510034e+04</td>\n",
        "                <td>8.261884e-03</td>\n",
        "                <td>0.000000e+00</td>\n",
        "                <td>0.000000e+00</td>\n",
        "                <td>55078.9415911</td>\n",
        "                <td>55118.9415911</td>\n",
        "                <td></td>\n",
        "            </tr>\n",
        "            \n",
        "            <tr>\n",
        "                <td>5</td>\n",
        "                <td>z</td>\n",
        "                <td>5.397582e-01</td>\n",
        "                <td>2.298044e-03</td>\n",
        "                <td>0.000000e+00</td>\n",
        "                <td>0.000000e+00</td>\n",
        "                <td>0.3</td>\n",
        "                <td>0.7</td>\n",
        "                <td></td>\n",
        "            </tr>\n",
        "            \n",
        "            </table>\n",
        "        \n",
        "            <pre id=\"wAWgyTGoUz\" style=\"display:none;\">\n",
        "            <textarea rows=\"16\" cols=\"50\" onclick=\"this.select()\" readonly>\\begin{tabular}{|c|r|r|r|r|r|r|r|c|}\n",
        "\\hline\n",
        " & Name & Value & Para Error & Error+ & Error- & Limit+ & Limit- & FIXED\\\\\n",
        "\\hline\n",
        "1 & fscale & 1.246e-17 & 9.362e-20 &  &  &  &  & \\\\\n",
        "\\hline\n",
        "2 & x1 & 6.052e-01 & 6.243e-02 &  &  &  &  & \\\\\n",
        "\\hline\n",
        "3 & c & 1.470e-01 & 6.317e-03 &  &  &  &  & \\\\\n",
        "\\hline\n",
        "4 & t0 & 5.510e+04 & 8.262e-03 &  &  & 5.508e+04 & 5.512e+04 & \\\\\n",
        "\\hline\n",
        "5 & z & 5.398e-01 & 2.298e-03 &  &  & 3.000e-01 & 7.000e-01 & \\\\\n",
        "\\hline\n",
        "\\end{tabular}</textarea>\n",
        "            </pre>\n",
        "            "
       ],
       "output_type": "display_data"
      },
      {
       "html": [
        "<hr>"
       ],
       "output_type": "display_data"
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The result `res` is basically a dictionary, but it also has attribute access:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print res.keys()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "['errors', 'params', 'matrix', 'ncalls', 'covariance', 'fval']\n"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print res.ncalls  # number of function calls\n",
      "print res.params  # best fit values\n",
      "print res.errors  # 1-sigma uncertainty\n",
      "print res.covariance[('c', 'z')]  # covariance matrix"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "208\n",
        "{'z': 0.5469809728761886, 'c': 0.1503997631832851, 'x1': 0.6096052763496054, 't0': 55100.39455466397, 'fscale': 1.2368207670705453e-17}\n",
        "{'z': 0.003049518045485755, 'c': 0.0062617349664103195, 'x1': 0.058602161826494546, 't0': 0.008133257892041001, 'fscale': 9.082119703955519e-20}\n",
        "1.89640237593e-05\n"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Since we set `return_minuit=True` in the function call, the `Minuit` object used in the fit is returned along with `res`, and we can use it to do some interesting things:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "m.draw_contour('c', 'z', show_sigma=True, bound=16);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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ujPL0BFq2ZC1+tXFlJMgxIkahDnbtAg4cAPbs4a0kXyQnJ6Nz585o3rw55heCelX5Ja/z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\n"
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "To plot the data along with the best-fit model, set the model parameters to the best-fit values and plot."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "model.set(**res.params)\n",
      "sncosmo.plotlc(data, model=model)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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Hj6Zjx45ERkaya9cuoqKiWLFiBV27duXGjRts3Wratqbt2rUjNjYWtVrNnj17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\nW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      }
     ],
     "prompt_number": 10
    }
   ],
   "metadata": {}
  }
 ]
}