{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "\n=====================================================\nConvert a 3-color image (JPG) to separate FITS images\n=====================================================\n\nThis example opens an RGB JPEG image and writes out each channel as a separate\nFITS (image) file.\n\nThis example uses `pillow <http://python-pillow.org>`_ to read the image,\n`matplotlib.pyplot` to display the image, and `astropy.io.fits` to save FITS files.\n\n-------------------\n\n*By: Erik Bray, Adrian Price-Whelan*\n\n*License: BSD*\n\n-------------------\n\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "import numpy as np\nfrom PIL import Image\nfrom astropy.io import fits"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Set up matplotlib and use a nicer set of plot parameters\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\nfrom astropy.visualization import astropy_mpl_style\nplt.style.use(astropy_mpl_style)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Load and display the original 3-color jpeg image:\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "image = Image.open('Hs-2009-14-a-web.jpg')\nxsize, ysize = image.size\nprint(\"Image size: {} x {}\".format(xsize, ysize))\nplt.imshow(image)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Split the three channels (RGB) and get the data as Numpy arrays. The arrays\nare flattened, so they are 1-dimensional:\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "r, g, b = image.split()\nr_data = np.array(r.getdata()) # data is now an array of length ysize*xsize\ng_data = np.array(g.getdata())\nb_data = np.array(b.getdata())\nprint(r_data.shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Reshape the image arrays to be 2-dimensional:\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "r_data = r_data.reshape(ysize, xsize)\ng_data = g_data.reshape(ysize, xsize)\nb_data = b_data.reshape(ysize, xsize)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Write out the channels as separate FITS images\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "red = fits.PrimaryHDU(data=r_data)\nred.header['LATOBS'] = \"32:11:56\" # add spurious header info\nred.header['LONGOBS'] = \"110:56\"\nred.writeto('red.fits')\n\ngreen = fits.PrimaryHDU(data=g_data)\ngreen.header['LATOBS'] = \"32:11:56\"\ngreen.header['LONGOBS'] = \"110:56\"\ngreen.writeto('green.fits')\n\nblue = fits.PrimaryHDU(data=b_data)\nblue.header['LATOBS'] = \"32:11:56\"\nblue.header['LONGOBS'] = \"110:56\"\nblue.writeto('blue.fits')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Delete the files created\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "import os\nos.remove('red.fits')\nos.remove('green.fits')\nos.remove('blue.fits')"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.7"
    }
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  "nbformat": 4,
  "nbformat_minor": 0
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