eteq/Yoda-OO-Session2.ipynb

## Yoda-OO-Session2.ipynb
{
 "metadata": {
  "kernelspec": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "display_name": "IPython (Python 2)",
   "language": "python",
   "name": "python2"
  },
  "name": "",
  "signature": "sha256:0cbcc6837d40914cc848632956dac852c275d6d8ff01a1eb7a5541ace37c85bf"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Intro to Object-Oriented Programming for Astronomers"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Author: Erik Tollerud (@eteq)"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "When should you use Object-Oriented Programming?"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Recap"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Built around the concept of \"Objects\":\n",
      "\n",
      "* Fundamentally, they are things with both data and functions that operate on that data\n",
      "* Like a C or IDL \"struct\", but with attached functions\n",
      "* Objects also have \"inheritance\" (more later)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "OOP Languages:\n",
      "\n",
      "* C++ (but *not* C)\n",
      "* Java (all OOP all the time)\n",
      "* Python (important, but not required)\n",
      "* Javascript (most \"dynamic\" web pages)\n",
      "* Most other languages since the 90s"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Some terminology:\n",
      "\n",
      "* **object** - a thing that has data and can do something.  An \"instance\" of a \"class\"\n",
      "* **class** - The \"description\" of an object - defines what it does and (sometimes) what data it holds\n",
      "* **method** - A function associated with (\"bound to\") an object - the object's possible actions\n",
      "* **instantiate** - To create a new object of a class\n"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "So when should you actually use this?"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Some important guidelines:\n",
      "\n",
      "* There is *not* a right answer.  Every code/project will have different pieces that might be simpler one way than the other.\n",
      "* Classes and object-oriented design are tools to help organize code better, but they often require extra mental \"bandwidth\" to do right.\n",
      "* Simpler approaches are often easier for simple tasks, or for one-off more complex ones.\n",
      "* But beware thinking that *anything* will be used just once."
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Example 1: Andrew's stuff"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "If you know in advance that you are developing something that:\n",
      "\n",
      "* Has a lot of hierarchical structure\n",
      "* Will need to be extended by other people who shouldn't be altering the base classes\n",
      "* Is just going to be a lot of similar stuff\n",
      "\n",
      "Then OO is usually going to be a lot easier."
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Example 2: when it's needlessly complicated"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# some preliminary imports and setup\n",
      "import numpy as np\n",
      "\n",
      "import matplotlib\n",
      "from matplotlib import pyplot as plt\n",
      "%matplotlib inline\n",
      "\n",
      "matplotlib.rcParams['figure.figsize'] = (10,8)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x = np.linspace(-1, 2, 100)\n",
      "y =  x*2.3 + 2 + np.random.randn(len(x))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "class PlotMaker(object):\n",
      "    def __init__(self, x, y):\n",
      "        self.x = x\n",
      "        self.y = y\n",
      "        \n",
      "    def plot_data(self):\n",
      "        return plt.scatter(self.x, self.y, lw=0, marker='s')\n",
      "        \n",
      "pm = PlotMaker(x, y)\n",
      "pm.plot_data()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 26,
       "text": [
        "<matplotlib.collections.PathCollection at 0x1112467d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x11108d190>"
       ]
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#better: \n",
      "def my_scatter(x, y):\n",
      "    return plt.scatter(x, y, lw=0, marker='s')\n",
      "\n",
      "my_scatter(x, y)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 27,
       "text": [
        "<matplotlib.collections.PathCollection at 0x1114198d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x11108d490>"
       ]
      }
     ],
     "prompt_number": 27
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#or just\n",
      "plt.scatter(x, y, lw=0, marker='s')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 28,
       "text": [
        "<matplotlib.collections.PathCollection at 0x110c8a7d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAlQAAAHfCAYAAACWB0avAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQtXdn62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       "text": [
        "<matplotlib.figure.Figure at 0x111432990>"
       ]
      }
     ],
     "prompt_number": 28
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Example 3: Visualization/basic analysis of HI data cube"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from astropy import units as u\n",
      "from astropy.io import fits"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def get_xyz_of_obj(datacube, ra, dec, vel):\n",
      "    from astropy.wcs import WCS\n",
      "    \n",
      "    wcs = WCS(datacube[0].header)\n",
      "    return wcs.wcs_world2pix([[ra.to(u.deg).value, dec.to(u.deg).value, vel.to('m/s').value]],0)[0]\n",
      "\n",
      "def show_image_around(datacube, x, y, side, z):\n",
      "    subcube = datacube[0].data[z, y-side//2:y+side//2, x-side//2:x+side//2]\n",
      "    plt.imshow(subcube, interpolation='nearest')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 128
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def spectrum_plot(datacube, x, y, vlower, vupper):\n",
      "    from astropy.wcs import WCS\n",
      "    \n",
      "    wcs = WCS(datacube[0].header)\n",
      "    data = datacube[0].data\n",
      "    \n",
      "    ra, dec = wcs.wcs_pix2world([[x, y, 0]],0)[0][:2]\n",
      "    zlower = wcs.wcs_world2pix([[ra, dec, vlower.to('m/s').value]],0)[0][-1]\n",
      "    zupper = wcs.wcs_world2pix([[ra, dec, vupper.to('m/s').value]],0)[0][-1]\n",
      "    \n",
      "    zs = np.arange(len(data))\n",
      "    zrange = (zlower<=zs)&(zs<=zupper)\n",
      "    vs = wcs.wcs_pix2world(np.array([[x]*len(zs), [y]*len(zs), zs]).T,0)[:, -1]\n",
      "    print(vs.shape)\n",
      "    \n",
      "    plt.step(vs[zrange]/1000, data[zrange, y, x], where='mid')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "datacube1 = fits.open('/Users/erik/astrodata/galfa-hi/GALFA_HI_RA+DEC_004.00+10.35_W.fits.gz')\n",
      "\n",
      "ra1 = 3.69179166667*u.deg\n",
      "dec1 = 10.8130583333*u.deg\n",
      "\n",
      "x, y, z = get_xyz_of_obj(datacube1, ra1, dec1, 240*u.km/u.s)\n",
      "\n",
      "plt.figure()\n",
      "show_image_around(datacube1, x, y, 40, z)\n",
      "\n",
      "plt.figure()\n",
      "spectrum_plot(datacube1, x, y, 200*u.km/u.s, 300*u.km/u.s)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(2048,)\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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dx7Zzrp1+3aXuBdpUKbChqT51qbnHiZNtGVmne12SRpy1jefxCfAKFwCAChi4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\nBQxcAAAq+P+rovJmWaQiNwAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x116054910>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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PVfV/k/xgktfNDlN3a6aqPqOq3p8L+eSd3f1rWVJuGRq+ZpOtvjXJd3T3H8zv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\nTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1163faa50>"
       ]
      }
     ],
     "prompt_number": 130
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "datacube2 = fits.open('/Users/erik/astrodata/galfa-hi/GALFA_HI_RA+DEC_020.00+10.35_W.fits.gz')\n",
      "\n",
      "ra2 = 19.7987916667*u.deg\n",
      "dec2 = 11.1217277778*u.deg\n",
      "\n",
      "x, y, z = get_xyz_of_obj(datacube2, ra2, dec2, 610*u.km/u.s)\n",
      "\n",
      "plt.figure()\n",
      "show_image_around(datacube2, x, y, 40, z)\n",
      "\n",
      "plt.figure()\n",
      "spectrum_plot(datacube2, x, y, 550*u.km/u.s, 680*u.km/u.s)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(2048,)\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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dXF3jsDtbbndWXEr+26xi6JC/KvHh7vf6F/5kYBOBOsoH/vtNgYWl7k/56xr7BvwVfiMP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\ngCt00lQAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10fc71a50>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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T9QMXF3ny2htwOvCJbkry60m+OsnvJHlPklfvL2ifOv/Hk/xsd79lxmPd3dvwywOpSk+/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\nQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10fc67b90>"
       ]
      }
     ],
     "prompt_number": 136
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "The danger..."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from astropy.coordinates import SkyCoord, Angle\n",
      "from IPython.html.widgets import interact"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "hi22coo = SkyCoord('00h14m46.03s +10d48m47.01s')\n",
      "hi23coo = SkyCoord('01h19m11.71s +11d07m18.22s')\n",
      "v22 = 237 * u.km/u.s\n",
      "v23 = 617 * u.km/u.s"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "galfacubes = {}\n",
      "galfawcses = {}\n",
      "def load_galfa_fn(objname, fn=None):\n",
      "    from astropy.wcs import WCS\n",
      "    \n",
      "    if fn is None:\n",
      "        f = galfacubes[objname]\n",
      "        wcs = galfawcses[objname]\n",
      "    else:\n",
      "        if fn in galfacubes:\n",
      "            if galfacubes.get(objname, None) is not galfacubes[fn]:\n",
      "                raise ValueError('objname \"{0}\" and file \"{1}\" do not match'.format(objname, fn))\n",
      "            f = galfacubes[fn]\n",
      "            wcs = galfawcses[fn]\n",
      "        else:\n",
      "            f = galfacubes[fn] = fits.open(fn)\n",
      "            galfacubes[objname] = f\n",
      "            \n",
      "            wcs = galfawcses[fn] = WCS(f[0].header)\n",
      "            galfawcses[objname] = wcs        \n",
      "        \n",
      "    return f, f[0].data.T, wcs"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def get_xyz_of_obj(objnameorfn, coo, vel):\n",
      "    wcs = galfawcses[objnameorfn]\n",
      "    return wcs.wcs_world2pix([[coo.ra.degree,coo.dec.degree, vel.to('m/s').value]],0)[0]\n",
      "\n",
      "def show_target(objnameorfn, coo, vel, npix=50, vmin=None, vmax=None, box=3, imsum=None, jyax=False, smooth=1, showall=False):\n",
      "    datcube = load_galfa_fn(objnameorfn)[1]\n",
      "    x, y, z = get_xyz_of_obj(objnameorfn, coo, vel)\n",
      "    \n",
      "    if showall:\n",
      "        xslice = yslice = slice(None)\n",
      "        edges = None\n",
      "    else:\n",
      "        xslice = slice(x - npix/2 + 1, x + npix/2 + 1)\n",
      "        yslice = slice(y - npix/2 + 1, y + npix/2 + 1)\n",
      "        edges = np.floor(x-npix/2)+.5, np.floor(x+npix/2)+.5, np.floor(y-npix/2)+.5, np.floor(y+npix/2)+.5\n",
      "    \n",
      "    if imsum is None or imsum==1:\n",
      "        data = datcube[xslice, yslice, z].T\n",
      "    else:\n",
      "        if not hasattr(imsum, 'unit'):\n",
      "            imsum = imsum*u.km/u.s\n",
      "        lz = get_xyz_of_obj(objnameorfn, coo, vel - imsum/2)[-1]\n",
      "        uz = get_xyz_of_obj(objnameorfn, coo, vel + imsum/2)[-1]\n",
      "        data = datcube[xslice, yslice, lz:uz]\n",
      "        data = np.sum(data,axis=-1).T\n",
      "    \n",
      "    \n",
      "    plt.subplot(1,2,1)\n",
      "    plt.imshow(data,interpolation='none',origin='lower', vmin=vmin, vmax=vmax, extent=edges)\n",
      "    plt.colorbar()\n",
      "    \n",
      "    plt.axvline(x-box/2, color='k')\n",
      "    plt.axvline(x+box/2, color='k')\n",
      "    plt.axhline(y-box/2, color='k')\n",
      "    plt.axhline(y+box/2, color='k')\n",
      "    \n",
      "    if showall:\n",
      "        plt.xlim(x - npix/2, x + npix/2)\n",
      "        plt.ylim(y - npix/2, y + npix/2)\n",
      "        \n",
      "    plt.title(objnameorfn)\n",
      "    \n",
      "    plt.subplot(1,2,2)\n",
      "    dspec = datcube[x-box/2+1:x+box/2+1, y-box/2+1:y+box/2+1]\n",
      "    print('Used',np.prod(dspec.shape)/dspec.shape[-1],'pix')\n",
      "    dspec = dspec.sum(axis=0).sum(axis=0)\n",
      "    vs = galfawcses[objnameorfn].wcs_pix2world(0, 0, np.arange(dspec.shape[-1]),0)[-1]/1000\n",
      "    \n",
      "    if smooth>1:\n",
      "        kernel = np.hanning(smooth+2)\n",
      "        kernel = kernel / np.sum(kernel)\n",
      "        dspec = np.convolve(dspec, kernel, mode='same')\n",
      "    \n",
      "    plt.step(vs, dspec, where='mid')\n",
      "    \n",
      "    lv, uv = (vel-100*u.km/u.s).value, (vel+100*u.km/u.s).value\n",
      "    plt.xlim(lv, uv)\n",
      "    plt.ylim(np.min(dspec[(lv<vs)&(vs<uv)])-.05,np.max(dspec[(lv<vs)&(vs<uv)]) +.1)\n",
      "    plt.xlabel('$v$ [km/s]')\n",
      "    plt.ylabel('$T_b$ [K]')\n",
      "    \n",
      "    plt.axvline(vel.to('km/s').value,color='k', ls='--')\n",
      "    \n",
      "    if jyax:\n",
      "        if jyax is True:\n",
      "            jyax = 1*u.arcmin**2\n",
      "        ktojy = (1*u.K).to(u.Jy, u.brightness_temperature(jyax, 21*u.cm)).value\n",
      "        realax = plt.gca()\n",
      "        yl = plt.ylim()\n",
      "        plt.twinx()\n",
      "        plt.ylim(yl[0]*ktojy, yl[1]*ktojy)\n",
      "        plt.ylabel('Jy')\n",
      "\n",
      "        plt.axes(realax)\n",
      "    \n",
      "    return vs,dspec\n",
      "\n",
      "def fit_flux(vs, dspec, lv, uv, baselinevs=None, plotlines=False):\n",
      "    vmsk = (lv<vs)&(vs<uv)\n",
      "    if baselinevs:\n",
      "        bldat = []\n",
      "        blvs = []\n",
      "        for lb, ub in baselinevs:\n",
      "            blvs.extend(vs[(lb<vs)&(vs<ub)])\n",
      "            bldat.extend(dspec[(lb<vs)&(vs<ub)])\n",
      "        \n",
      "        baseline = np.poly1d(np.polyfit(blvs, bldat, 1))\n",
      "    else:\n",
      "        baseline = lambda v: np.zeros_like(v)\n",
      "        \n",
      "    intspec = np.sum(dspec[vmsk]-baseline(vs[vmsk]))\n",
      "    dv = np.diff(vs[vmsk]).mean()\n",
      "    \n",
      "    if plotlines:\n",
      "        oxls = plt.xlim()\n",
      "        oyls = plt.ylim()\n",
      "        \n",
      "        if baselinevs:\n",
      "            for lb, ub in baselinevs:\n",
      "                plt.axvline(lb, color='k', ls=':')\n",
      "                plt.axvline(ub, color='k', ls=':')\n",
      "        \n",
      "        plt.axvline(lv, color='k')\n",
      "        plt.axvline(uv, color='k')\n",
      "        plt.plot(vs, baseline(vs), color='r')\n",
      "        \n",
      "        plt.xlim(*oxls)\n",
      "        plt.ylim(*oyls)\n",
      "    \n",
      "    return intspec * dv * u.K * u.km/u.s"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data = load_galfa_fn('HI22', '/Users/erik/astrodata/galfa-hi/GALFA_HI_RA+DEC_004.00+10.35_W.fits.gz')[1]\n",
      "\n",
      "def plot_and_line_flux(vplt=v22.to('km/s').value, box=8, smooth=1, imsum=40):\n",
      "    vs, dspec = show_target('HI22', hi22coo, vplt*u.km/u.s, 31, box=box,imsum=imsum, jyax=True, smooth=smooth)\n",
      "        \n",
      "    tbdv = fit_flux(vs, dspec, 215,255,[(190, 215), (260,290)],plotlines=True)\n",
      "    flxdv = (tbdv/(u.km/u.s)).to(u.Jy, u.brightness_temperature((1*u.arcmin)**2, 21*u.cm))*u.km/u.s\n",
      "    plt.title('{0:.4}, {1:.4}'.format(tbdv, flxdv))\n",
      "interact(plot_and_line_flux, vplt=(150,350), box=(1, 20), smooth=(1,9),imsum=(1,100))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "('Used', 121.0, 'pix')\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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nsP8ywGXH842Hio5lAO8BJpAMiqw0VwINSesCfwT2jYir6qw/AtgJ2CIiXuh2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\nYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10eec0110>"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "What I could have done if I'd planned ahead and foreseen this:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "class HIDataCube(object):\n",
      "    def __init__(self, filename):\n",
      "        ...\n",
      "        \n",
      "    def show_near(self, ra, dec, size):\n",
      "        ...\n",
      "        \n",
      "    def spectral_plot(self, ra, dec, vlower, vupper):\n",
      "        ...\n",
      "        \n",
      "    def line_flux(self, velocity, width):\n",
      "        ...\n",
      "        \n",
      "    def fancy_interact_thingie(self):\n",
      "        ...\n",
      "        use self.show_near(...), self.spectral_plot(...), self.line_flux(...), etc.\n",
      "        ...\n",
      "        \n",
      "        \n",
      "dc = HIDataCube('/Users/erik/astrodata/galfa-hi/GALFA_HI_RA+DEC_004.00+10.35_W.fits.gz')\n",
      "dc.plot_near(ra, dec, 50)\n",
      "dc.spectral_plot(ra, dec, vlower, vupper)\n",
      "dc.fancy_interact_thingie(ra, dec, firstvguess)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Example 4: Fitting a model to data"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from astropy.io import ascii\n",
      "from astropy.modeling import Fittable1DModel, Parameter\n",
      "from astropy.modeling import fitting"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 29
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def arctan_model(x, offset, lower_slope, upper_slope, transition_x, transition_width):\n",
      "    dx = x - transition_x\n",
      "    first_term = dx * (lower_slope + upper_slope) / 2\n",
      "    second_term =  -dx * (lower_slope - upper_slope) * np.arctan(dx / transition_width) /  np.pi\n",
      "    return first_term + second_term + offset\n",
      "    \n",
      "# ... but then you have to use your own fitter/keep track of every parameter, etc."
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 30
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "class ArcTanTransitionModel(Fittable1DModel):\n",
      "    \n",
      "    offset = Parameter(default=0)\n",
      "    lower_slope = Parameter(default=1)\n",
      "    upper_slope = Parameter(default=2)\n",
      "    transition_x = Parameter(default=0)\n",
      "    transition_width = Parameter(default=1)\n",
      "\n",
      "    @staticmethod\n",
      "    def eval(x, offset, lower_slope, upper_slope, transition_x, transition_width):\n",
      "        dx = x - transition_x\n",
      "        first_term = dx * (lower_slope + upper_slope) / 2\n",
      "        second_term =  -dx * (lower_slope - upper_slope) * np.arctan(dx / transition_width) /  np.pi\n",
      "        return first_term + second_term + offset"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 31
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "model = ArcTanTransitionModel()\n",
      "print model\n",
      "\n",
      "x = np.linspace(-3,3,1000)\n",
      "\n",
      "plt.plot(x, model(x))\n",
      "\n",
      "model.upper_slope = 5\n",
      "plt.plot(x, model(x))\n",
      "\n",
      "print model"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Model: ArcTanTransitionModel\n",
        "Inputs: 1\n",
        "Outputs: 1\n",
        "Model set size: 1\n",
        "Parameters:\n",
        "    offset lower_slope upper_slope transition_x transition_width\n",
        "    ------ ----------- ----------- ------------ ----------------\n",
        "       0.0         1.0         2.0          0.0              1.0\n",
        "Model: ArcTanTransitionModel\n",
        "Inputs: 1\n",
        "Outputs: 1\n",
        "Model set size: 1\n",
        "Parameters:\n",
        "    offset lower_slope upper_slope transition_x transition_width\n",
        "    ------ ----------- ----------- ------------ ----------------\n",
        "       0.0         1.0         5.0          0.0              1.0\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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diMjMjJ5eRJKkHok2lqDMcro2m3xD7Tzqran0FleeJElaiM5Igk8B9wBvrhxHA8IN45Ik\nLdybgE2BzbPJe2qH0WCwPEmStADRxu7AW4FNssm/186jwWF5kiRpPtHGBpQJ4jtmk7+vnUeDxT1P\nkiTNJdp4EnAS8Ips8rLaeTR4LE+SJHXMNZLgI9nkSbXzaDBZniRJ4v6RBN8GzgU+WTmOBpjlSZI0\n9jojCT4N3A28KZseD0HUUHPDuCRJ5am6DYEtHEmgh2J5kiSNtWhjL+ANlJEE/6idR4PP8iRJGlvR\nxkbAZ4Adssnra+fRcHDPkyRpLEUbqwMnAAdkk5fXzqPhYXmSJI2daGMFymG/788mT62dR8MlsscP\nFEzldGJJkvol2lgS+C7wk2zyTbXzaLBMpbe48iRJGhudkQSfBW4D3lI5joaUG8YlSePkYGA9YMts\n8t7aYTScLE+SpLEQbbwIeA1lJMHttfNoeFmeJEkjL9rYFPgUsG02+cfaeTTc3PMkSRpp0cZTgOOA\nl2STV9XOo+FneZIkjaxo41GUkQSHZpNn1M6j0eCoAknSSIo2lgLOAC7NJt9WO4+Gg6MKJEljqTOS\n4HDgFuAdleNoxLhhXJI0it4DrA3Mzibvqx1Go8XyJEkaKdHG/sDLKCMJ7qidR6PH8iRJGhnRxpbA\nx4Cts8k/186j0eSeJ0nSSIg21gS+BeybTV5dO49Gl+VJkjT0oo2VKCMJ3p1Nfq92Ho02y5MkaahF\nG0sDJwLHZpNfrJ1Ho8/yJEkaWp2RBEcAfwQOqRxHY8IN45KkYfY+YHVgG0cSqF8sT5KkoRRtvBLY\nG9g0m7yzdh6ND8uTJGnoRBs7Au8Ftsgmb6qdR+PF8iRJGirRxnrAV4Hds8lf1s6j8eOGcUnS0Ig2\nHg+cArw+m/xh7TwaT5YnSdJQiDYeQZnl9Mls8tu182j0RPCcqbzO8iRJGnjRxhLAscAPgI9WjqMR\nE8GWEZwOnDSV11ueJEkDrTPL6XPAXcAbs8msHEkjIIKIYKcIzgeOpJTzp0zlvW4YlyQNukOAZwFb\nZZP31A6j4RbBLGAP4F3AEsD7gW9nck/nzx+S5UmSNLCijf2BlwObZJO3186j4RXBEsB+wMHAX4EG\nOC2TaQ9XtTxJkgZStLE1ZX/T1tnkn2vn0XCKYBngAODtwC+B1wHnZLLIt38tT5KkgRNtPB04Gnhx\nNnlN7TwaPhE8AngNcBBwCfCiTC7qxmdbniRJAyXaWIUykuCt2eQ5tfNouESwIvBGygrTmcAOmVzV\nzWv4tJ0kaWBEG8sBpwJHZpNfq51HwyOCx0bwUcqtuVWAjTPZt9vFCSxPkqQBEW0sDnwTuBJ4X+U4\nGhIRPDmCzwE/pfSadTN5VSa/6tU1LU+SpOo6s5w+CSwFvMZZTnooETwjgq9R9jPdDKyVyUGZXN/r\na1ueJEmD4C3AFsBe2eTdtcNocEXw3AhOAM4Grgaeksm7M7mpXxlmvGE8Iq4D/g7cC9ydmRvO9DMl\nSeMj2nghZYPvJtnk32vn0eCJIICtKIMt1wY+AuyXyR018nTjabsEZmfm/3XhsyRJYyTa2Az4NLB9\nNtnz2y0aLp3StDOlND0a+CDw9Uz+VTNXt0YVTGGYuSRJk6KNNYDjgJdkk1fUzqPB0TlC5QWU0hSU\nI1SOzeTeqsE6urXy9L2IuBf4fGYe3oXPlCSNsGjj0cB3gXdnk2fUzqPBEMGSwP6UI1RuAd5NOUJl\noB4g6EZ52iwz/xQRjwbOiohrM/P8LnyuJGkERRvLACcBx2STX6ydR/V1jlB5BfA24FrgVcC5g1aa\nJsy4PGXmnzr/vCkiTgA2BOYpTxFx6FzfzsnMOTO9riRp+EQbs4CjgN9QVhU0xiJYHngt8CbgQmCv\nTC7pb4aYDcye1ntyBqM0ImJZYFZm/iMiHkYZg95m5plzvSYz0z1RkjTmOrOcPkV5WmrnbLLqpl/V\nE8FKlCcsXwucDnwwk5/WTVVMpbfMdOVpZeCEiJj4rG/MXZwkSZrL2ymznLa0OI2nCB5Pmen1UuDb\nwEaZ/LpuqumbUXnKzN8C63UpiyRpREUb+1EOat00m/xb7TzqrwjWoJTnFwBHAs/M5I91Uy26bo0q\nkCRpgaKNbYCPAc/LJm+onUf9E8G6wDuBbSnzvNbI5Ja6qWbO41kkST0TbaxLOex372zy6tp51B8R\nbBrBqZT9TJcBT87k0FEoTuDKkySpR6KNJwCnAQdmk+fWzqPe6kwD344y2PJJwIcoT8/9s2qwHrA8\nSZK6LtpYgTIE8xPZ5DG186h3IlgM2J1SmpYBPgAcnck9VYP1kOVJktRV0cZSwInA9yh7nTSCIlgC\n2IcyDfw24H3AyZncVzVYH8xoztOULuCcJ0kaG9HGYpQhmLOAF2WTI/8/pOOmMw38ZZSn535NWWk6\ne1CngU9XP+Y8SZI0tw8DjwO2sziNlggeweQ08EuAF2dyUd1UdVieJEldEW28EdgZ2DybHLlNwuNq\nvmngZwDbZ/KTuqnqclSBJGnGoo29KIe67pRN/l/tPJq5CB4fwceBXwCPoUwD32/cixNYniRJMxRt\nbAF8Btglm/xd7TyamQjWiOBw4CrgPmCdTF49jMeo9Iq37SRJiyzaeBpwLLB/NnlF7TxadPNNA/8M\nIzINvBdceZIkLZJo47GUWU5vz8ZD4YfVfNPAf0yZBt5YnBbOlSdJ0rRFG4+gTA8/PJv8Su08mp5x\nmgbeC5YnSdK0RBtLUm7VXQy8v3IcTcNCpoEfk8ndVYMNGYdkSpKmrDME8+vAssBe2eTIHsExSuab\nBn478N+MyTTw6XJIpiSpa6KNAP4HeAKwvcVp8M03Dfw3wBsYoWngtVieJElT9VZge2CLbPLO2mG0\ncPNNA78U2CeTC+umGh2WJ0nSQ4o2XgL8B7BZNnlr7TxasPmmgZ8J7JDJVXVTjR5HFUiSHlS0sRPw\nEWDHbPL62nn0QPNNA18Z2DiTfS1OvWF5kiQtVLSxEfBVYM9s8me182heETx1AdPAX5XJrypHG2mW\nJ0nSAkUbawEnAQdkkz+snUeTIlg3gm8CFwF/BNbM5C2Z3FA52liwPEmSHqAzPfx04F3Z5Cm186iY\naxr4Gcw7DfzmytHGihvGJUnziDaWpxy7cng2eWTtPONuAdPAP4zTwKuyPEmS7hdtLE25VXceZfq0\nKplvGviylH8/jnYaeH1OGJckARBtzAKOoWw83iebvLdypLHkNPC6nDAuSZqSzvTww4BHATtZnPrP\naeDDw/IkSQI4BNgM2CqbvKt2mHHiNPDhY3mSpDEXbbwCOIAyPfxvtfOMC6eBDy9HFUjSGIs2dgfe\nS5ke/qfaecaB08CHn+VJksZUtLE18AVgl2zyF7XzjLoI1ozgi5Rp4InTwIeWt+0kaQxFGxsA3wJe\nmE1eVjvPKItgfeCdwNbAZyjTwB1qOcRceZKkMRNtrA2cArwym5xTOc5IiiAi2CqC04FTgYtxGvjI\ncOVJksZItPEEytEe78omT6ydZ9R0Bls+n7LS9GjKNPDdMvEJxhFieZKkMRFtrER5quuwbPJLtfOM\nkggWB14MvAO4hzIN/LhMnJc1gixPkjQGoo2HU86rOyGb/GjtPKNirsGWbwN+3/nnGQ62HG2WJ0ka\ncZ3z6k4EfkwZhqkZimB5ynymN1IGW+6XyQ/rplK/WJ4kaYRFG4sDRwH/B7wumx4faDriIliZMgn8\nVZSVvO0z+UndVOo3n7aTpBHVOa/uc8BywP6eV7foIlgtgk8D1wLLAxtksr/FaTxZniRpdH0QWAfY\n0/PqFk0Ez4zga8BlwN+BtTN5XSa/rRxNFVmeJGkERRtvB3YBds4mb6udZ9hEsEkEJwPfA66hzGh6\nZyY3Vo6mAeCeJ0kaMdHGyymbmTfPJm+pnWdYRBDA9pQZTasBHwFelMmdNXNp8FieJGmERBt7Uw76\nnZ1N3lA7zzCIYBawJ3AwsBTlducxmdxdNZgGluVJkkZEtLEL8ClgOw/6fWgRLAm8BHg75WnEFjg1\nk/uqBtPAszxJ0giINp4HHAnskk1eVTvPIItgOeCVwJsp+5leDZzrYEtNleVJkoZctLEJcDTwwmzy\nktp5BlUEKwIHAq8D5lDOnPtx1VAaSj5tJ0lDLNpYjzI9/KXZ5Lm18wyiCB4fwceAXwKPBzbPZG+L\nkxaV5UmShlS0sTZlyvXrssnv1s4zaCJYM4IvAhO3MdfN5BWZuB9MM+JtO0kaQtHG6sBZwMHZ5HG1\n8wySCJ5NGTewFfBpYI1MHNmgrrE8SdKQiTYeSxne+IFs8iu18wyCzoymrSil6RnAR4GXZeKAUHWd\n5UmShki08WhKcfpCNvmZ2nlqi2AxYFfgHcCKwIeAr2ficTTqGcuTJA2JaGMF4Azg+GzyQ7Xz1NSZ\n0bQfZUbTbZTSdEImHn6snrM8SdIQiDYeBpwGnA+8p3KcaiJ4OPAq4CDKjKbXA+c4o0n9ZHmSpAEX\nbSwLnAJcCxyUTY5dUYjgMcAbKAMtzwZ2ddSAanFUgSQNsGhjacocp+uBV2WTY3V0SARPjuAzlOK4\nIrBxJi+2OKkmy5MkDahoYyngeOAW4GXZ5Njs54lgvQi+CVwC3Ao8LZPXZvLrytEky5MkDaJoY0ng\n28AdwEvGoThFEBE8L4IzgFOBy4AnZ3JIJjdWjifdzz1PkjRgoo0lKGfV3Qfsk03eUzlST0UwC9id\nMm7gEcCHgW84bkCDyvIkSQMk2lgc+DqwFLBnNnl35Ug9E8FSwP8D3ka5NfcB4KRMxmpfl4aP5UmS\nBkS0MQv4CrACsFs2OZIrLxEsT3lq7o2Uc+deBZzruAENC8uTJA2AaGMx4AhgFWCXbPKflSN1XQSr\nUgrTK4HTgZ0zubJuKmn63DAuSZV1itPngdWBXbPJOytH6qoI1ojg85ShlssBG2Syn8VJw8qVJ0mq\nqFOc/hd4OrBjNnl75UhdE8EGlE3gs4HPAmtmclPVUFIXWJ4kqZJOcfoM8Cxgh2zyH5UjzVgEAWxL\nKU1rAR8DXpbJbVWDSV1keZKkCua6Vfd0SnH6e+VIMxLB4sBelCfnlqEc1PvNTP5VNZjUA5YnSeqz\nTnE6HFiTcqtuaFecIlgOOIByUO/1QAuc6rgBjTLLkyT1UWccwRGUzeE7ZZNDeTsrgpWBAykjB84D\n9snkorqppP7waTtJ6pNOcfoS8ERg52EsThGsFcEXmDyod9NMXmBx0jhx5UmS+qAzOfwrwGMoc5zu\nqBxpWiLYjLKfaVPKk3NrZfKXuqmkOixPktRjcx258kiGaI5TBIsBu1FK08qUJ+f2y2RkxilIi8Ly\nJEk91Dnk9xvAwylHrgz85PAIlqacOfcW4O+Ug3qPz+TeqsGkAWF5kqQeiTaWBo4BZgF7DHpximBF\n4LXAfwCXUTaDe+acNB83jEtSD0QbDwNOBu4C9hzk4hTB6hEcBvwSeAqwTSbPz2SOxUl6IMuTJHVZ\ntLE85eDbPwL7ZpMDOSgygudEcDTwI+BOYJ1MXpbJ1ZWjSQPN8iRJXRRtrAicDVwJHJBN3lM50jwi\niAh2iuD7wInApcDqmbwjkxsqx5OGgnueJKlLoo1VgLOA04B3ZpMDc8urswl8f8ok8HuA/wGOzuTu\nqsGkIWR5kqQuiDaeCHyPMsvp/YNSnCJ4DPA6ykbwHwFvAL7vXiZp0VmeJGmGoo01KCtOn8gmP1E7\nD0AET6esMu0FfAuYncnP6qaSRoPlSZJmINpYF/gOcGg2+cWqWYIAtgXeDKwPfAZYM5ObauaSRo3l\nSZIWUbSxBXAs8IZs8phqOYKlgH0opWkxyiTwPTIZ2PEI0jCzPEnSIog2/g04AtgvmzyrSoZgJeA1\nwOuBqyjHqJzpfiaptxxVIEnTFG38O/AF4Pk1ilMEa0fwOcpQy9WB7TLZIZMzLE5S77nyJEnTEG28\njbLSs3U2eW3frlv2M82m3JrbCPgssHYmN/Yrg6TC8iRJUxBtBOWA3J2BzbPJ6/ty3TKfaV/KiIGl\ngI8De2dyZz+uL+mBLE+S9BCijcWBLwJrAVtmk7f0/JrB4yjzmV5Jmc/0DuCsTO7r9bUlPTjLkyQ9\niGjj4cDEk3TbZpO39/R6wcbAG4EdgG8AW2Ty815eU9L0uGFckhYi2ngscC5wA7Bbr4pTBEtGsF8E\nFwNHAZcavLzrAAAXdUlEQVRQzps70OIkDR5XniRpAaKNZ1CGX34e+EAvjlvpHJ3yasrRKT8D/hs4\nLZN7u30tSd1jeZKk+UQbzwOOBg7KJr/R9c8P1qdsAN8d+DawQyY/6fZ1JPWG5UmS5hJt7A98FHhR\nNnlO1z43WBzYjbKfaXXg08BTM+n55nNJ3WV5kiTuH0VwCPAK4HnZ5NVd+dxgFcoTc68CrgMOA07I\n5J5ufL6k/rM8SRp70caSlKGT6wObZJN/mtHnlYGWm1NGDewIfAvYJZMrZ5pVUn2WJ0ljLdp4NHAc\ncAtlhtNti/xZwXLAfpQJ5EsCnwFem8lfu5FV0mBwVIGksRVtPBO4GPgB8IJFLU6ds+YOA35HWWl6\nM/C0TA6zOEmjx5UnSWMp2ng+8CXgLdnk16b9/rIBfFfKKtMzKBPI18vkD10NKmngWJ4kjZXOxvA3\nA2+hDL68cFrvDx4LvJwyn+k6yq254zK5q8tRJQ0oy5OksTHXxvDnABtnk7+f0vuCWcD2lCfmtqLM\nZtolkyt6lVXS4LI8SRoLnaNWjgP+DGw+lf1NnVWmAyjjC24CvgC8JJNF3lQuafi5YVzSyIs2tgAu\nBU7lITaGRzArgp0iOBH4KfA4YM9MnpvJ4RYnSa48SRpZnf1N/wG8G3hpNnn6Ql8bPI7JVaYbKatM\n+1uWJM3P8iRpJEUbywKfA9alDL78zQNeU56Y24EyAXxL4Bhg90wu72dWScPF8iRp5EQbqwPHA9cA\nm2aTd8zz58GawMuA/wf8ATgcV5kkTZF7niSNlGhjF+Ai4MvA/hPFKYKHR/DyCC4AzgNmAdtlsnEm\nR1icJE1VZGZvLxCRmRk9vYiksRdtLAG8H3gRsE82eUHnjLktKXuZdgPOAY4ETs/k7mphJQ2sqfSW\nGa88RcSOEXFtRPwyIt4x08+TpOmKNp5EWU16GrA+h+bvI3g38Cvgf4ErgTUz2SOTUyxOkmZiRitP\nETEL+DmwLXAD5VHgfTLzZ3O9xpUnST0TbewKHM5dD/8kH7r5D9y35EsoQzCPoawyXZZJb5fYJY2M\nqfSWmW4Y3xD4VWZe17ng0ZSl8Z892JskaaaijSW5b9aHuHeZfTn2qB/z893fDlxAKUy7ZXJn5YiS\nRtRMy9PjYJ5DMK8HNprhZ0rSQkUQbPH+XXn2ap/j5rUfyYlfvprbV/4O8NJM/lI7n6TRN9PyNKWl\n8Ig4dK5v52TmnBleV9KYieCJxL37sfEnD2STj67MT190Bmd8/KC8Z6mf184maXhFxGxg9rTeM8M9\nTxsDh2bmjp3v3wncl5kfmus17nmStEgieBSwJ7A/D7txHfb9t7+z0rV3s/idu+d/3X1N7XySRk8/\nnrb7EbBGRKwWEUtSHhE+eYafKWmMRfCICF4SwWnAb4Ed2eGg83jrKnfxuEu/xVL/eKbFSVJNM7pt\nl5n3RMR/AGdQBs4dMfeTdpI0FRE8DNgFeDHwPGAO8A02/+ABbPvO9wAvBfbNxlv+kupzSKakKiJY\nGtiJsmK9I3AhZbzAiZn8NdqYDRxBeYLujdnkrbWyShofU+ktlidJfRPBUpS5cC8C/g24HDgaOD6T\nmwGijeWADwB7AK/NJk+pFFfSGOrHnCdJelARLEtZWXoBsDNwNWWF6R2Z/Gme106uNv0AWMfVJkmD\nyJUnSV0XwfKUPUx7UlaaLgGOp9yS+9MDXt/GIyjn0u0OvCabPLWPcSXpfq48SeqbCB4N7EpZYdoc\nOBc4DnhVJrcs8D1tBOX23CeBs3C1SdIQsDxJWmQRPI5SfvaknCd3JvBV4MWZ/P1B39vGEymH9q4B\n7J9NntvjuJLUFZYnSVMWQQDrUjZ77wo8FTgVOAw4YyrnyUUbiwNvAN4FfAJ4YTZ5V89CS1KXWZ4k\nPagIlgS2opSlXYF7KcNwDwbOz+TuKX9WGxsCnwduATbJJn/Z/cSS1FuWJ0kP0DkWZSdKWdoeuJZS\nmHYGrsmc2rmW939eGytTxg/sCLwd+EY2PX5aRZJ6xPIkCYAInsLk6tJzgHMohemNmfx5kT6zjSWB\nA4F3Al8C1s4mH3QvlCQNOsuTNKY6t+M2o6wmPR94JHAK8DHg7EzumNHnt7ET8HHgN8Bm2eTPZ5ZY\nkgaDc56kMRLBYym343YGtgF+DnwH+C7wo0zum/E12lgT+CiwFnBQNnnaTD9TkvrFOU/SmItgcWBj\nSlnaCXgiZZzAicBrM/lL165V9jU1wN7Ah4G9fIpO0iiyPEkjJoLHUDZm70zZ7H0dZXXp9cAlmdzT\n1euVs+jeDLyRMuNp7Wzy5m5eQ5IGieVJGnIRzAKey2RhWhP4HqUwvTmTP/bkumVe08spq01zgOdm\nk7/pxbUkaZBYnqQhFMGTKKtK21P2Ll0PnE4ZA3DBdGYvTfvabSwG7AW0wJ+AXbPJH/XqepI0aNww\nLg2BCJYDZjNZmB5JOQvuTOCsBR222/UMk+fQtcCdwH8CZzivSdIoccO4NKQiWAxYn8mytAFwCaUs\n7QNc2Y0n46aUpZSmXSilCcrMptMsTZLGlStP0oDoHLK7HaUsbQfcRClLZwLnZnJ7X/OU0rQj8F/A\nUpS9TSdamiSNsqn0FsuTVEkEywBbMrm69FjKRu+JW3G/r5Kr7GnajbLC9DDgUOC4bLIvK12SVJPl\nSRognZlLG1A2eG9DeULuCuAMSmG6LJN7q+VrYwlgX+AdwO2Us+hOtDRJGieWJ6miCAJ4OpNlaSvg\nd8DZna/zMvlHvYRFtLEscADwVspRKh8AvuftOUnjyPIk9VkET2SyLG1DeSptoix9v5sTvWcq2lgJ\neDXl4N6LgA9kkxfXTSVJdfm0ndRjEawIbM1kWVoB+D6lLP1nJgM3NDLaeBrwJsoxKicA22aTP62b\nSpKGh+VJmoYIlgW2YLIsPRX4AaUsfQ74Sb9GCExH58m5bYGDgOcAn6Uco3Jj1WCSNIS8bSc9iM4m\n7+dSisc2lOJxOZO34i7J5F/1Ej64aGNpYD/KShPAx4Gjssl/1kslSYPLPU/SNHXOiXsW5VbcbMoq\n02+ZLEvnZ3JbtYBTFG08BXgN8O/ApcDHgLPdBC5JD849T9JD6EzyfialLG1Nmbt0I3AO8FXggExu\nqpdw6qKNWcDzgddRVsi+DGySTf6qZi5JGjWuPGmsdMYHPI3JsrQVcCulLM0B5vTjnLhuijZWAV5O\neXLuBsp+pm9nk3dWDSZJQ8jbdhp7nbK0BpNlaTZwB6UsnUMpS9dXC7iIOlPAt6GUph2AbwOfzSYv\nrxpMkoac5Uljp1OWVmeyLG0N3MtkWTonk9/VSzgz0cZqwMsoe5luBr4EfD2b/Gu9VJI0OixPGgsR\nPIl5V5aWYK6yBPwmk6HdKB1tLAPsQZkCvh5wFHBkNnlF1WCSNIIsTxpJETyOeVeWHkbZrzRRln4x\nzGUJ7p/LtD7lttyLgR8BRwInOWZAknrHp+00EiJYhbKiNFGWHgWcSylKHwOuGfayNCHaeALlcN79\ngeUot+XWzyZ/XzWYJOl+rjxp4ETwGMpTcLMpZWlV4DwmV5YGcor3ooo2VgBeQClM6wLHAl8HLsgm\nR+ZfpyQNA2/baShEsCqlLE18PRY4n8nVpSsyubdewu6LNpYEdqIUpu2B71EK03eyybtqZpOkcWZ5\n0kCK4PHMW5YeTVlZOrfzdeWolSW4f4jlZsA+wAuBqymF6dhs8taa2SRJhXueNBAieCLlFtxEWVqB\nyaL0aUbsNtzcOvOYNgX2BvYC/gJ8C3hONjm0IxMkaZy58qSu6sxZWo3JPUtbAcsyWZbOpWzwHsmy\nBPcXpo0ohemFlAnm36JM/b62ZjZJ0oPztp16rlOWnsK8ZWkJyuiAibJ07ag8DbcwndECGzJZmG4D\njqEUpmtqZpMkTZ3lSV3XKUtrMm9ZSuYtS78c9bIE968wbQjsSSlN/6QUpm9lk1fXzCZJWjSWJ83Y\nXAfpTpSlLYF/MW9ZGuoJ3tMRbSxB+TnsAexOuSV3AuW23E+y6fF/oCRJPWV50rRFsBjwDOYtS7dR\nStIc4NxMrqsUr4po42GUw3f3AJ4P/IJSmE7IJn9RM5skqbssT3pInbK0LpNlaQvKasrcZekPtfLV\nEm08Cvg3SmHaGriEUphOyiZvqJlNktQ7lic9QASzKIfLTpSlzSmPz8+hFKbzMhnLctA5GmU3SmHa\nADibUphOdQ6TJI0Hy5OIYHHg2UyWpc2AG5i3LP25Vr6aOhu+nwvsQlllejzwHeB44Mxs8o6K8SRJ\nFViexlAES1BWTSYGUm4K/I55y9JN1QJWFm0sB2xHKUvPB24GTgFOBS7MJkdusrkkaeosT2MggmUo\nj8tv2fnaCPg1k3uWzs/klmoBB0C08SRKWdqFsvJ2EZ3ClE3+pmY2SdJgsTyNoAgeDmxCKUpbAesD\nP6WcDXce8INM/lovYX2dM+Q2YvJ23CrAaZTVpTOzyb9XjCdJGmCWpxEQwSMpT8BNrCw9HbiMyYN0\nL8rktnoJB0O0sSKwPbAjsBNwI5O34y72dpwkaSosT0MoglWYtyytDlzI5MrSpZn8s17CwdDZ7P0c\nSlHaiTKb6lzgu8B3ssnr6qWTJA0ry9MQiOCJTBalrYBHAz9gsixdnsnd9RIOjmhjJcqwyp0oq0w3\nU8rSd4Hzs8m7KsaTJI0Ay9OA6Rx18lQmi9KWwDJMFqVzgZ9mcl+1kAOks3dpAyZXl9ambIL/LnC6\nq0uSpG6zPFU211EnW871dQ+TZ8KdB/xiXM6Fm4po4zGUVaWJ1aUbmVxdusDVJUlSL1me+qwzkHI9\nJovSFsD/MVmUzgN+Z1ma1Dlod2MmN3uvCXyfydWl31eMJ0kaM5anHotgKcptpYmytCnweyaL0vmZ\n/LFewsETbQSwBqUsbU+5ffkr4MzO1wXZ5L/qJZQkjTPLU5dFsCxllWRiz9JzgWuZd8bSzfUSDqbO\nIbvbMFmYZjFZls7OJsd24rkkabBYnmYoguUpq0kTm7vXBa5kcnP3DzNx4OJ8oo0lmbwVtz1lo/f5\nTBama7Pp8V88SZIWgeVpmiJYiXlnLK0JXMrknqWLM/Gw2Pl0bsWtSSlK21HK5i+Asyhl6UI3ekuS\nhoHl6SFE8FjmfRLuCcAFTN6G+1Em7r9ZgM5E74lbcdsBizHvrThvX0qSho7laZ4cBLAa8w6kXIFy\nO2miLF2ZyT21Mg6yaGNpyi3MbShlaS3Kz2xidenn3oqTJA27sS5PnbK0FpP7lbakbFSe2K90HvAz\nB1IuWGdA5bMpZWlbykG7PwXOphSmC30qTpI0asaqPEUwC1iHeW/D3c68M5Z+7YylBevsW1qLybK0\nFfBHSlk6Gzg3m/xbvYSSJPXeSJenCJagrIxMFKXNKdOoJ8rS+Zk4YPFBRBuPp5Slia97KEXpe8D3\ns8k/V4wnSVLfjVR5imBpYEMm9yttBPyGeQdS3jjT64yyaOORwNZMlqWVgHMoZels4NfuW5IkjbOh\nLk8RLEfZoDyxsvRs4GrmHUh5azezjppoY1lgM8ptuG0o4wQuYPJW3JXZpHu+JEnqGKryFMEjKbfe\nJsrSM4AfM3kb7sJMbutl1mEXbSxOmXo+sbL0XOAKJsvSRW7yliRp4Qa6PEWwMvMOpHwycBGTK0uX\nZPLPnoYbcp1N3s9gsixtCfyOyX1L52eT/6iXUJKk4TJQ5SmCJzC5X2lLYGXgB0yWpR9ncndPwwy5\nuSZ5P4+yd2k28A/g+5SydE42+ZdqASVJGnIDU54grwMexmRROhf4aSb39vTiQ65Tlp5MKUoTX/dQ\nNnl/n1KWfKJQkqQuGaTy9DTg585YemjRxhOZtywtQSlLE1+/8Yk4SZJ6Y2DK0yAczzKooo1VKSVp\n4lbcw4E5TJYljz2RJKlPLE8DKNp4NGWv0kRZegzlNubErbhrHB8gSVIdlqcBEG08irJJfuI23BMo\nhxFPrCxdlU2690uSpAFgeaog2lieMoJhoiw9Ffghk2Xpx9nkPfUSSpKkhbE89UG08TDKcM+JfUtP\nAy5h8jbcpdmkIxgkSRoClqceiDaWoRwbM7Gy9CzKJPTvUwrTxdmkwz0lSRpClqcu6JSljSmbvLcC\nNgCuYvI23A+zyTuqBZQkSV1jeVoEnbK0CZNl6TnATyjjA84FLvDIE0mSRpPlaQqijWWZLEuzgfUp\nK0tzOl8/zCY9kFiSpDFgeVqAzgbvTSmrSrOB9YArmbcs3V4pniRJqsjyBEQby1HK0mxKYXoWcDnl\nFtwc4ELLkiRJgjEtT52ytBmTZWldytNwc5clN3hLkqQHGIvyFG08nMmyNBt4JnAZk2XpIsuSJEma\nipEsT9HGI5i3LD0D+BGTT8NdlE3e2a3rSZKk8TES5alz3MnmTG7wfjpwKZMbvB1KKUmSumIoy9Nc\nZ8NNlKWJ407mdL4usSxJkqReGIryFG2sQClLsymFaS3gYib3LF2STd7V05CSJEkMaHmKNh7JZFma\nDawBXMRkWbrUsiRJkmoYmPLEoezOZFl6KnAhkxu8L80m/9XTEJIkSVMwlfK0eJ+yvI5Sll4P/Miy\nJEmShlX1PU+SJEmDYiq9ZbF+hZEkSRoFlidJkqRpsDxJkiRNg+VJkiRpGixPkiRJ02B5kiRJmgbL\nkyRJ0jRYniRJkqbB8iRJkjQNlidJkqRpsDxJkiRNg+VJkiRpGha5PEXEoRFxfURc3vnasZvBtOgi\nYnbtDOPGn3n/+TPvP3/m/efPfDDNZOUpgY9l5vqdr9O7FUozNrt2gDE0u3aAMTS7doAxNLt2gDE0\nu3YAPdBMb9tFV1JIkiQNiZmWpwMj4sqIOCIiVuhKIkmSpAEWmbnwP4w4C1hlAX90CHARcFPn+/cC\nq2bmyxfwGQu/gCRJ0oDJzAe9s/ag5WmqImI14JTMXGfGHyZJkjTAZvK03apzfbsH8JOZx5EkSRps\ni8/gvR+KiPUoT939Fnh1dyJJkiQNrq7ctpMkSRoXfZkwHhHv7TyVd0VEnB0RT+jHdcdZRHwkIn7W\n+bkfHxHL18406iLihRFxdUTcGxHPrp1nVEXEjhFxbUT8MiLeUTvPOIiIIyPixohwe0afRMQTIuKc\nzn+n/DQi3lA706iLiKUj4uJOV7kmIj6w0Nf2Y+UpIh6emf/o/PpA4FmZ+YqeX3iMRcR2wNmZeV9E\nfBAgMw+uHGukRcTawH3A54G3ZOaPK0caORExC/g5sC1wA3ApsE9m/qxqsBEXEVsAtwFf9cGg/oiI\nVYBVMvOKiFgOuAzY3b/rvRURy2bmHRGxOPAD4K2Z+YP5X9eXlaeJ4tSxHHBzP647zjLzrMy8r/Pt\nxcDja+YZB5l5bWb+onaOEbch8KvMvC4z7waOBnarnGnkZeb5wK21c4yTzPxzZl7R+fVtwM+Ax9ZN\nNfoy847OL5cEZgH/t6DX9e1g4Ij474j4PfBS4IP9uq4AOAD4Tu0QUhc8DvjDXN9f3/k9aWR1xgGt\nT/k/wuqhiFgsIq4AbgTOycxrFvS6mTxtN/8FFzZQ812ZeUpmHgIcEhEHAx8HXtata4+rh/qZd15z\nCPCvzDyqr+FG1FR+5uopn3DRWOncsjsWeGNnBUo91Lljs15nn/AZETE7M+fM/7qulafM3G6KLz0K\nV0G64qF+5hHx78DOwDZ9CTQGpvH3XL1xAzD3AydPoKw+SSMnIpYAjgO+npkn1s4zTjLzbxFxGrAB\nMGf+P+/X03ZrzPXtbsDl/bjuOIuIHYG3Abtl5j9r5xlDHprdGz8C1oiI1SJiSeBFwMmVM0ldFxEB\nHAFck5mfqJ1nHETEShPn9EbEMsB2LKSv9Otpu2OBtYB7gV8Dr83Mv/T8wmMsIn5J2fA2sdntwsx8\nXcVIIy8i9gAOA1YC/gZcnpk71U01eiJiJ+ATlM2cR2TmQh8nVndExDeBrYAVgb8A/5mZX6qbarRF\nxObAecBVTN6ufmdmnl4v1WiLiHWAr1AWlhYDvpaZH1ngax2SKUmSNHV9e9pOkiRpFFieJEmSpsHy\nJEmSNA2WJ0mSpGmwPEmSJE2D5UmSJGkaLE+SJEnT8P8BD8VGywCvnG4AAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x111432fd0>"
       ]
      }
     ],
     "prompt_number": 32
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "rlm = ascii.read('yoda-oo-rlm.dat')\n",
      "\n",
      "plt.scatter(rlm['logreff'], rlm['loglum'], lw=0)\n",
      "\n",
      "fitter = fitting.LevMarLSQFitter()\n",
      "fitmodel = fitter(model, rlm['logreff'], rlm['loglum'])\n",
      "print fitmodel\n",
      "\n",
      "\n",
      "x = np.sort(rlm['logreff'])\n",
      "plt.plot(x, fitmodel(x), color='r', lw=2)\n",
      "None"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Model: ArcTanTransitionModel\n",
        "Inputs: 1\n",
        "Outputs: 1\n",
        "Model set size: 1\n",
        "Parameters:\n",
        "        offset     lower_slope   upper_slope    transition_x  transition_width\n",
        "    ------------- ------------- -------------- -------------- ----------------\n",
        "    9.81336897625 4.88214960912 0.597155270839 0.399607582875   0.173581599732\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x1102900d0>"
       ]
      }
     ],
     "prompt_number": 33
    }
   ],
   "metadata": {}
  }
 ]
}

## Yoda-OO.ipynb

      
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