daofind_positions.ipynb

{
 "metadata": {
  "name": "",
  "signature": "sha256:f13a666bd99ca9996ca5ede669fc00490c6794852c443726a4348f9031423240"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline\n",
      "import numpy as np\n",
      "import matplotlib.pyplot as plt\n",
      "import photutils\n",
      "image = np.zeros((21, 21))\n",
      "image[10, 10] = 100\n",
      "sources = photutils.daofind(image, 1.0, 0.0)\n",
      "plt.imshow(image, interpolation='nearest', origin='bottom', cmap='gray')\n",
      "x, y = sources['xcen'], sources['ycen']\n",
      "plt.scatter(x, y)\n",
      "positions = zip(x, y)\n",
      "for r in [0.5, 0.5*np.sqrt(2), 1.0]:\n",
      "    aperture = photutils.CircularAperture(positions, r)\n",
      "    aperture.plot(color='red')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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2bANAF1xYb/z48Zs81tTUlPPOO2+Dx77zne9k1113XffnCy+8MAsWLMjs2bNz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\nAIUS8wAAUCgxDwAAhRLzAAB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4oKgo1ooi0USvq/LycmvLTG5urr5+/SpJio6Olsvlkt/v\nV39/v6Sh/c85OTnKz8+fmqAxbb1+/VqFhYWShv6+uVyuMed6PB7duHFjxHeRkq94aVSILV68WLdv\n31Z5ebmePXumjo4Oud1uLV26VLt379bKlSvDHSJsJD4+XgUFBaqvr1dzc7N+/vwpn88np9OppKQk\nZWdna+fOnVqwYEG4Q8U0ZBjGpM9xuVwqLS3VgwcP9OTJE33+/FkxMTHKyMjQ1q1btW3btimIFHYy\nketq+JwjR46otrZW79+/1/fv3+X1ejVr1iylpKRo2bJl2r59+4iuXIgcwTs4hmFoYGBg3PbKw9tR\nBkVKvmJlHgAAALApe99XAAAAACIYxTwAAABgUxTzAAAAgE1RzAMAAAA2RTEPAAAA2BTFPAAAAGBT\nFPMAAACATVHMAwAAADZFMQ8AAADYFMU8AAAAYFMU8wAAAIBNUcwDAAAANkUxDwAAANgUxTwAAABg\nUxTzAAAAgE1RzAMAAAA2RTEPAAAA2NR/D46Fb/RcAusAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x117041c50>"
       ]
      }
     ],
     "prompt_number": 23
    }
   ],
   "metadata": {}
  }
 ]
}