example of how to use python's numpy to read and write files.

2013-04-19-numpy-example.ipynb

{
 "metadata": {
  "name": "2013-04-19-proper-numpy"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "import pylab as pl"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def GaussFunction(wavelength_array, amplitude, centroid, sigma, *args, **kwargs):\n",
      "    return amplitude * np.exp(-0.5 * ((wavelength_array - centroid) / sigma)**2)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "begin_wavelength = 5300.0\n",
      "end_wavelength = 5340.0\n",
      "pixel_size = 0.02 # Angstroms\n",
      "wavelength = np.arange(begin_wavelength, end_wavelength, pixel_size)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "centroid_1 = 5324.3\n",
      "amplitude_1 = 2.4\n",
      "sigma_1 = 3.5\n",
      "flux = GaussFunction(wavelength, amplitude_1, centroid_1, sigma_1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "pl.plot(wavelength, flux)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "[<matplotlib.lines.Line2D at 0x107e12990>]"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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QoHlz1Um0hWWvLyx7Mrz0dCAhQXUK7eGiaPrCsifD27EDiI9XnUJ7uCiavrDsydDKy+W7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\nAElFTkSuQmCC\n"
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.savetxt('example1.ascii', (wavelength, flux))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.savetxt('example2.ascii', (wavelength, flux), fmt='%-7.2f')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.savetxt('example3.ascii', np.transpose((wavelength, flux)))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "wave1, flux1 = np.loadtxt('example1.ascii')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "wave3, flux3 = np.loadtxt('example3.ascii', unpack=True) # somewhat slower for huge files"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}