old_contents: # -*- coding: utf-8 -*-
  """
  =====================================================
  Convert a 3-color image (JPG) to separate FITS images
  =====================================================
  
  This example opens an RGB JPEG image and writes out each channel as a separate
  FITS (image) file.
  
  This example uses `pillow <http://python-pillow.org>`_ to read the image,
  `matplotlib.pyplot` to display the image, and `astropy.io.fits` to save FITS files.
  
  
  *By: Erik Bray, Adrian Price-Whelan*
  
  *License: BSD*
  
  
  """
  
  import numpy as np
  from PIL import Image
  from astropy.io import fits
  
  ##############################################################################
  # Set up matplotlib and use a nicer set of plot parameters
  
  import matplotlib.pyplot as plt
  from astropy.visualization import astropy_mpl_style
  plt.style.use(astropy_mpl_style)
  
  ##############################################################################
  # Load and display the original 3-color jpeg image:
  
  image = Image.open('Hs-2009-14-a-web.jpg')
  xsize, ysize = image.size
  print("Image size: {} x {}".format(xsize, ysize))
  plt.imshow(image)
  
  ##############################################################################
  # Split the three channels (RGB) and get the data as Numpy arrays. The arrays
  # are flattened, so they are 1-dimensional:
  
  r, g, b = image.split()
  r_data = np.array(r.getdata()) # data is now an array of length ysize*xsize
  g_data = np.array(g.getdata())
  b_data = np.array(b.getdata())
  print(r_data.shape)
  
  ##############################################################################
  # Reshape the image arrays to be 2-dimensional:
  
  r_data = r_data.reshape(ysize, xsize)
  g_data = g_data.reshape(ysize, xsize)
  b_data = b_data.reshape(ysize, xsize)
  
  ##############################################################################
  # Write out the channels as separate FITS images
  
  red = fits.PrimaryHDU(data=r_data)
  red.header['LATOBS'] = "32:11:56" # add spurious header info
  red.header['LONGOBS'] = "110:56"
  red.writeto('red.fits')
  
  green = fits.PrimaryHDU(data=g_data)
  green.header['LATOBS'] = "32:11:56"
  green.header['LONGOBS'] = "110:56"
  green.writeto('green.fits')
  
  blue = fits.PrimaryHDU(data=b_data)
  blue.header['LATOBS'] = "32:11:56"
  blue.header['LONGOBS'] = "110:56"
  blue.writeto('blue.fits')
  
  ##############################################################################
  # Delete the files created
  import os
  os.remove('red.fits')
  os.remove('green.fits')
  os.remove('blue.fits')
  
new_contents: # -*- coding: utf-8 -*-
  """
  =====================================================
  Convert a 3-color image (JPG) to separate FITS images
  =====================================================
  
  This example opens an RGB JPEG image and writes out each channel as a separate
  FITS (image) file.
  
  This example uses `pillow <http://python-pillow.org>`_ to read the image,
  `matplotlib.pyplot` to display the image, and `astropy.io.fits` to save FITS files.
  
  
  *By: Erik Bray, Adrian Price-Whelan*
  
  *License: BSD*
  
  
  """
  
  import numpy as np
  from PIL import Image
  from astropy.io import fits
  
  ##############################################################################
  # Set up matplotlib and use a nicer set of plot parameters
  
  import matplotlib.pyplot as plt
  from astropy.visualization import astropy_mpl_style
  plt.style.use(astropy_mpl_style)
  
  ##############################################################################
  # Load and display the original 3-color jpeg image:
  
  image = Image.open('Hs-2009-14-a-web.jpg')
  xsize, ysize = image.size
  print("Image size: {} x {}".format(ysize, xsize))
  print("Image bands: {}".format(image.getbands()))
  ax = plt.imshow(image)
  
  ##############################################################################
  # Split the three channels (RGB) and get the data as Numpy arrays. The arrays
  # are flattened, so they are 1-dimensional:
  
  r, g, b = image.split()
  r_data = np.array(r.getdata()) # data is now an array of length ysize*xsize
  g_data = np.array(g.getdata())
  b_data = np.array(b.getdata())
  print(r_data.shape)
  
  ##############################################################################
  # Reshape the image arrays to be 2-dimensional:
  
  r_data = r_data.reshape(ysize, xsize) # data is now a matrix (ysize, xsize)
  g_data = g_data.reshape(ysize, xsize)
  b_data = b_data.reshape(ysize, xsize)
  print(r_data.shape)
  
  ##############################################################################
  # Write out the channels as separate FITS images.
  # Add and visualize header info
  
  red = fits.PrimaryHDU(data=r_data)
  red.header['LATOBS'] = "32:11:56" # add spurious header info
  red.header['LONGOBS'] = "110:56"
  red.writeto('red.fits')
  
  green = fits.PrimaryHDU(data=g_data)
  green.header['LATOBS'] = "32:11:56"
  green.header['LONGOBS'] = "110:56"
  green.writeto('green.fits')
  
  blue = fits.PrimaryHDU(data=b_data)
  blue.header['LATOBS'] = "32:11:56"
  blue.header['LONGOBS'] = "110:56"
  blue.writeto('blue.fits')
  
  for _ in red.header.items():
    print(_)
  
  ##############################################################################
  # Delete the files created
  import os
  os.remove('red.fits')
  os.remove('green.fits')
  os.remove('blue.fits')
  
subject: Fix minor cosmetic issues