Lia Corrales, Kris Stern, Stephanie T. Douglas, Kelle Cruz
FITS, file input/output, image manipulation, numpy, matplotlib, histogram, colorbar
This tutorial demonstrates the use of astropy.utils.data
to download a data file, then uses astropy.io.fits
to open the file, and lastly uses matplotlib
to view the image with different color scales and stretches and to make histograms. In this tutorial we've also included a demonstration of simple image stacking.
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import numpy as np
# Set up matplotlib
import matplotlib.pyplot as plt
%matplotlib inline
from astropy.io import fits
Download the example FITS files for this tutorial.
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from astropy.utils.data import download_file
image_file = download_file('http://data.astropy.org/tutorials/FITS-images/HorseHead.fits', cache=True )
Let's open the FITS file to find out what it contains.
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hdu_list = fits.open(image_file)
hdu_list.info()
Generally, the image information is located in the PRIMARY
block. The blocks are numbered and can be accessed by indexing hdu_list
.
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image_data = hdu_list[0].data
Our data is now stored as a 2D numpy array. But how do we know the dimensions of the image? We can look at the shape
of the array.
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print(type(image_data))
print(image_data.shape)
Great! At this point, we can close the FITS file because we've stored everything we wanted to a variable.
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hdu_list.close()
If you don't need to examine the FITS header, you can call fits.getdata
to bypass the previous steps.
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image_data = fits.getdata(image_file)
print(type(image_data))
print(image_data.shape)
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plt.imshow(image_data, cmap='gray')
plt.colorbar()
# To see more color maps
# http://wiki.scipy.org/Cookbook/Matplotlib/Show_colormaps
Let's get some basic statistics about our image:
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print('Min:', np.min(image_data))
print('Max:', np.max(image_data))
print('Mean:', np.mean(image_data))
print('Stdev:', np.std(image_data))
To make a histogram with matplotlib.pyplot.hist()
, we'll need to cast the data from a 2D array to something one dimensional.
In this case, let's use the ndarray.flatten()
to return a 1D numpy array.
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print(type(image_data.flatten()))
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histogram = plt.hist(image_data.flatten(), bins='auto')
What if we want to use a logarithmic color scale? To do so, we'll need to load the LogNorm
object from matplotlib
.
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from matplotlib.colors import LogNorm
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plt.imshow(image_data, cmap='gray', norm=LogNorm())
# I chose the tick marks based on the histogram above
cbar = plt.colorbar(ticks=[5.e3,1.e4,2.e4])
cbar.ax.set_yticklabels(['5,000','10,000','20,000'])
You can also perform math with the image data like any other numpy array. In this particular example, we'll stack several images of M13 taken with a ~10'' telescope.
Let's start by opening a series of FITS files and store the data in a list, which we've named image_concat
.
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base_url = 'http://data.astropy.org/tutorials/FITS-images/M13_blue_{0:04d}.fits'
image_list = [download_file(base_url.format(n), cache=True)
for n in range(1, 5+1)]
image_concat = [fits.getdata(image) for image in image_list]
Now we'll stack the images by summing the concatenated list.
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# The long way
final_image = np.zeros(shape=image_concat[0].shape)
for image in image_concat:
final_image += image
# The short way
# final_image = np.sum(image_concat, axis=0)
We're going to show the image, but need to decide on the best stretch. To do so let's plot a histogram of the data.
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image_hist = plt.hist(final_image.flatten(), bins='auto')
We'll use the keywords vmin
and vmax
to set limits on the color scaling for imshow
.
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plt.imshow(final_image, cmap='gray', vmin=2E3, vmax=3E3)
plt.colorbar()
We can do this with the writeto()
method.
Warning: you'll receive an error if the file you are trying to write already exists. That's why we've set overwrite=True
.
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outfile = 'stacked_M13_blue.fits'
hdu = fits.PrimaryHDU(final_image)
hdu.writeto(outfile, overwrite=True)
Determine the mean, median, and standard deviation of a part of the stacked M13 image where there is not light from M13. Use those statistics with a sum over the part of the image that includes M13 to estimate the total light in this image from M13.
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Show the image of the Horsehead Nebula, but in units of surface brightness (magnitudes per square arcsecond). (Hint: the physical size of the image is 15x15 arcminutes.)
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Now write out the image you just created, preserving the header the original image had, but add a keyword 'UNITS' with the value 'mag per sq arcsec'. (Hint: it may be helpful to read the astropy.io.fits documentation if you're not sure how to include both the header and the data.)
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