GSoC2015_Model_Rasterization

.gitignore

.ipynb_checkpoints

compound_models_convolve.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Compound models and convolution\n",
    "\n",
    "This notebook sketches how to integrate the convolution operation into the compound model framework"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from astropy.modeling.models import Gaussian1D\n",
    "from astropy.modeling.core import _make_arithmetic_operator, BINARY_OPERATORS, _CompoundModelMeta\n",
    "from scipy.signal import fftconvolve\n",
    "from functools import partial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "source = Gaussian1D(1, 0, 1, name='Source')\n",
    "\n",
    "# PSF is not normalized, but OK for a quick test\n",
    "psf = Gaussian1D(1, 0, 1, name='PSF')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# adding convolution is straight forward\n",
    "# this should be done in the astropy.convolve function\n",
    "\n",
    "# wrap fftconvolve to pass arguments\n",
    "# any other mode than 'same' won't work for compound models (because the they expect the same shape for input and output)\n",
    "convolve_func = partial(fftconvolve, mode='same')\n",
    "\n",
    "# add convolution to the binary operators\n",
    "BINARY_OPERATORS['convolve'] = _make_arithmetic_operator(convolve_func)\n",
    "\n",
    "# create compound model from two model and convolution operator\n",
    "conv = _CompoundModelMeta._from_operator('convolve', source, psf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.00342127,  0.03246698,  0.18679594,  0.65211678,  1.38024565,\n",
       "        1.7726372 ,  1.38024565,  0.65211678,  0.18679594,  0.03246698,\n",
       "        0.00342127])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# evaluate\n",
    "x = np.arange(-5, 6)\n",
    "conv(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.00342127,  0.03246698,  0.18679594,  0.65211678,  1.38024565,\n",
       "        1.7726372 ,  1.38024565,  0.65211678,  0.18679594,  0.03246698,\n",
       "        0.00342127])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check he result\n",
    "fftconvolve(source(x), psf(x), 'same')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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render_model_example.ipynb