new-models-demo.ipynb

{
 "metadata": {
  "name": "new-models-demo"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "New Model Classes Demo"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This is a demo of how the refactored model classes work in `sncosmo`."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%pylab inline\n",
      "import numpy as np\n",
      "import sncosmo"
     ],
     "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": [
      "First, modeling of transient sources has been separated from any information about where or when the source is. For example, redshift (`z`) and observed time of phase 0 (`t0`) are *not* part of the transient source model. All of these \"source models\" are subclasses of `SourceModel`. The current subclasses are:\n",
      "\n",
      "* `TimeSeriesModel` (parameters: `amplitude`)\n",
      "* `StretchModel` (parameters: `amplitude`, `s`)\n",
      "* `SALT2Model` (parameters: `x0`, `x1`, `c`)\n",
      "\n",
      "As an example, we will create a `TimeSeriesModel` from scratch. The flux of such a model is defined as\n",
      "\n",
      "$F(t, \\lambda) = A M(t, \\lambda)$ \n",
      "\n",
      "where $A$ is the amplitude parameter and $M(t, \\lambda)$ is a fixed spectral time series grid.\n",
      "\n",
      "\n",
      "First, we will define some dummy model phases, wavelengths and flux values, in order to define $M(t, \\lambda)$ :"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "phases = np.linspace(-20., 50., 8)  # Phases in days\n",
      "wavelengths = np.linspace(3000., 9000., 7) # Wavelengths in angstroms\n",
      "\n",
      "# 2-d array of spectral flux density\n",
      "flux = np.repeat(np.array([[0.], [1.], [2.], [3.], [3.], [2.], [1.], [0.]]), 7, axis=1)\n",
      "\n",
      "print \"phases =\", phases\n",
      "print \"wavelengths =\", wavelengths\n",
      "print \"flux =\"\n",
      "print flux"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "phases = [-20. -10.   0.  10.  20.  30.  40.  50.]\n",
        "wavelengths = [ 3000.  4000.  5000.  6000.  7000.  8000.  9000.]\n",
        "flux =\n",
        "[[ 0.  0.  0.  0.  0.  0.  0.]\n",
        " [ 1.  1.  1.  1.  1.  1.  1.]\n",
        " [ 2.  2.  2.  2.  2.  2.  2.]\n",
        " [ 3.  3.  3.  3.  3.  3.  3.]\n",
        " [ 3.  3.  3.  3.  3.  3.  3.]\n",
        " [ 2.  2.  2.  2.  2.  2.  2.]\n",
        " [ 1.  1.  1.  1.  1.  1.  1.]\n",
        " [ 0.  0.  0.  0.  0.  0.  0.]]\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Next, initialize the model and print it. You can see that there is only one parameter, `amplitude`."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "source = sncosmo.TimeSeriesModel(phases, wavelengths, flux, name=\"flatspectrum\")\n",
      "print source"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "class      : TimeSeriesModel\n",
        "name       : flatspectrum\n",
        "version    : None\n",
        "phases     : [-20, .., 50] days (8 points)\n",
        "wavelengths: [3000, .., 9000] Angstroms (7 points)\n",
        "parameters:\n",
        "  amplitude = 1.0\n"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Setting and retrieving parameters:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "source.param_names"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "['amplitude']"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "source.parameters"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "array([ 1.])"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "source.set(amplitude=2.)\n",
      "source.parameters"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "array([ 2.])"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Get spectrum on a given day:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "source.flux(phase=-10., wave=[3500., 4500., 5500.])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "array([ 2.,  2.,  2.])"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Get the integrated flux through some bandpass at some phase."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "source.bandflux('desg', phase=-10.)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "261434392882715.03"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Or, equivalently, get the magnitude through that bandpass."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "source.bandmag('desg', 'ab', phase=-10.)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "-21.558345976990982"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "There's a shortcut to get the phase of peak integrated flux through a bandpass:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "source.peakphase('desg')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "15.0"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "And also a shortcut to get the magnitude at that peak phase:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "source.peakmag('desg', 'ab')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "-22.802890669665558"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Finally, there is a shortcut to adjust the model's parameters to achieve a given peak magnitude:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "source.set_peakmag(-19., 'bessellb', 'ab')  # changes source.parameters\n",
      "source.parameters"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "array([ 0.07120762])"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "ObsModel"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "An \"Observer-frame Model\" or `ObsModel` represents the observables of a source at some redshift and explosion time, seen through a series of effects, such as dust. It combines:\n",
      "\n",
      "* a redshift (`z`) and explosion time (`t0`)\n",
      "* a `SourceModel` instance\n",
      "* zero or more `PropagationEffect`s"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.display import Image\n",
      "Image(filename='obsmodel.png')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAlgAAALUCAYAAADXFEx/AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
       "prompt_number": 13,
       "text": [
        "<IPython.core.display.Image at 0x3a87fd0>"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We already have a `SourceModel` instance from above. We can create a new `ObsModel` that contains this `SourceModel` and nothing else:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "model = sncosmo.ObsModel(source_model=source)\n",
      "print model"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "<ObsModel at 0x3af41d0>\n",
        "source:\n",
        "  class      : TimeSeriesModel\n",
        "  name       : flatspectrum\n",
        "  version    : None\n",
        "  phases     : [-20, .., 50] days (8 points)\n",
        "  wavelengths: [3000, .., 9000] Angstroms (7 points)\n",
        "parameters:\n",
        "  z         = 0.0\n",
        "  t0        = 0.0\n",
        "  amplitude = 0.071207621649608394\n"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Get and set parameter values:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "model.param_names"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 15,
       "text": [
        "['z', 't0', 'amplitude']"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "model.parameters"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 16,
       "text": [
        "array([ 0.        ,  0.        ,  0.07120762])"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "model.set(z=0.5, t0=55000., amplitude=10.)\n",
      "model.parameters"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 17,
       "text": [
        "array([  5.00000000e-01,   5.50000000e+04,   1.00000000e+01])"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The source model contained within the obsmodel is accessible via the `source` parameter. You can see that its parameters are kept in sync:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "model.source.parameters"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 18,
       "text": [
        "array([ 10.])"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Adding propagation effects"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "One can add `PropagationEffect`s to the observer-frame model. Currently, there is only one such built-in class, which is for dust. Basically, `PropagationEffect`s have a set of parameters, and implement a method `propagate()`:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dust = sncosmo.InterpolatedRvDust(model='f99')\n",
      "print dust"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "class      : InterpolatedRvDust\n",
        "model      : 'f99'\n",
        "r_v        : 3.1\n",
        "wavelengths: [1000, .., 30000] Angstroms (2000 points)\n",
        "parameters:\n",
        "  ebv = 0.0\n"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dust.param_names"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 20,
       "text": [
        "['ebv']"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dust.parameters"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 21,
       "text": [
        "array([ 0.])"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dust.set(ebv=0.1)\n",
      "dust.propagate(np.array([4000.]), np.array([1.])) # accepts wavelengths, fluxes"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 22,
       "text": [
        "array([ 0.66603976])"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "When adding the dust to the model, we give it a name, and a frame. The frame indicates if it is in the source rest-frame ('rest') or the observer frame ('obs'). In the future, frame could accept a redshift to indicate that the effect is at an intermediate redshift."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "model.add_effect(dust, name='host', frame='rest')\n",
      "print model"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "<ObsModel at 0x3af41d0>\n",
        "source:\n",
        "  class      : TimeSeriesModel\n",
        "  name       : flatspectrum\n",
        "  version    : None\n",
        "  phases     : [-20, .., 50] days (8 points)\n",
        "  wavelengths: [3000, .., 9000] Angstroms (7 points)\n",
        "effect (name='host' frame='rest'):\n",
        "  class      : InterpolatedRvDust\n",
        "  model      : 'f99'\n",
        "  r_v        : 3.1\n",
        "  wavelengths: [1000, .., 30000] Angstroms (2000 points)\n",
        "parameters:\n",
        "  z         = 0.5\n",
        "  t0        = 55000.0\n",
        "  amplitude = 10.0\n",
        "  hostebv   = 0.10000000000000001\n"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "model.set(hostebv=0.2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 24
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "model.parameters"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 25,
       "text": [
        "array([  5.00000000e-01,   5.50000000e+04,   1.00000000e+01,\n",
        "         2.00000000e-01])"
       ]
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Using ObsModel"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now that we've created our full model (which is a very simple source with some host-galaxy dust in front of it), we can do various things with it.\n",
      "\n",
      "What are the native observer-frame wavelengths of the model?"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "model.wavelengths"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 26,
       "text": [
        "array([  4500.,   6000.,   7500.,   9000.,  10500.,  12000.,  13500.])"
       ]
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "What are the native observer-frame times?"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "model.times"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 27,
       "text": [
        "array([ 54970.,  54985.,  55000.,  55015.,  55030.,  55045.,  55060.,\n",
        "        55075.])"
       ]
      }
     ],
     "prompt_number": 27
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "(The supported range in time and wavelength can also be accessed in the following way:)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "model.mintime, model.maxtime"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 28,
       "text": [
        "(54970.0, 55075.0)"
       ]
      }
     ],
     "prompt_number": 28
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "model.minwave, model.maxwave"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 29,
       "text": [
        "(4500.0, 13500.0)"
       ]
      }
     ],
     "prompt_number": 29
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "What is the spectrum at a give time?"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "model.flux(time=55007., wave=[5000., 6000., 7000.])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 30,
       "text": [
        "array([ 6.58238786,  7.4578882 ,  8.33515911])"
       ]
      }
     ],
     "prompt_number": 30
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "What is the flux through some bandpass (in ph/s/cm^2)?"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "model.bandflux('desr', time=55007.)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 31,
       "text": [
        "1831639920994439.2"
       ]
      }
     ],
     "prompt_number": 31
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "... scaled to equivalent counts for some zeropoint?"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "model.bandflux('desr', time=55007., zp=15., zpsys='ab')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 32,
       "text": [
        "2982134225990273.5"
       ]
      }
     ],
     "prompt_number": 32
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "If we want magnitude instead of counts?"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "model.bandmag('desr', 'ab', time=55007.)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 33,
       "text": [
        "-23.686317967925724"
       ]
      }
     ],
     "prompt_number": 33
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Distinction between source flux and observed flux"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Both the `SourceModel` and the `ObsModel` have methods `flux()`, `bandflux()` and `bandmag()`. You can do any of the following:\n",
      "```\n",
      "model.flux(...)\n",
      "model.bandflux(...)\n",
      "model.bandmag(...)\n",
      "model.source.flux(...)\n",
      "model.source.bandflux(...)\n",
      "model.source.bandmag(...)\n",
      "```\n",
      "There are two differences between the first three and last three of the above methods:\n",
      "\n",
      "1. The inputs to the `ObsModel` are observer-frame times and observer-frame wavelengths or bandpasses, whereas the inputs to the `SourceModel` methods are rest-frame *phases* and rest-frame wavelengths.\n",
      "2. The `SourceModel` methods do *not* include the propagation effects.\n",
      "\n",
      "You can set the peak magnitude of the source in some rest-frame bandpass:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "model.source.set_peakmag(-19., 'desg', 'ab')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 34
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "You can also set the *absolute magnitude*, via"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from astropy.cosmology import FlatLambdaCDM\n",
      "model.set_source_peakabsmag(-19., 'desg', 'ab', cosmo=FlatLambdaCDM(H0=70., Om0=0.3))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 35
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Note again that this is the absolute magnitude of the source, *not including* and propagation effects."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 35
    }
   ],
   "metadata": {}
  }
 ]
}