如何将 a.fits 二进制表转换为图像？

Question

I am working on a Data Analysis project for the Emirates Mars Mission(EMM).

I am having difficulty viewing images from the UV Emission Data (from the EMUS instrument). The.fits data is only present in BinaryTables and not Images when I use Astropy or FitsViewer to read them.

UI of fits viewer showing rows of Binary Table Data

Also, I am aware that Images can be produced since the same data was used to produce an image of the Martian Corona like the one I have attached below.

False-color images from the EMUS instrument

I would appreciate any type of help - either Astropy code or software that might help me.

PS: I'm using l2a data from the science data center if anyone would like to download it.

PS2: SAO DS9 is unable to even open the.fits file

Answer 1

SA0 DS9 has trouble with the Binary HDU data types. If you want to view using this viewer you would want to convert to ImageHDU first.

Let's say that the SC_GEOM HDU is all of the same type and you would like to view this data as an image. In heasarc fits viewer you can explore the data types and confirm whether or not the table data is all of the same desired type. Then grabbing the data and viewing it in Matpotlib can be done in a few simple lines of code.

from astropy.io import fits
from astropy import table
import matplotlib.pyplot as plt

# grab the data with 8 being the SC_GEOM HDU index in the FITS file
table_data = table.Table(fits.getdata('data.fits', 8))

# this is the most elegant way I can think of to grab a 27x153 data
# as a numpy array
array_data = table_data.to_pandas().values

# use Matplotlib to show the image
plt.imshow(array_data)

If you want to convert the data to an ImageHDU so that DS9 can open it. The following can be done.

from astropy.io import fits
from astropy import table
new_hdulist = fits.HDUList()
# convert to ImageHDU
with fits.open('data.fits') as ff:
    # transferring the primary HDU from the original file
    # all FITS files should start with a PrimaryHDU
    new_hdulist.append(ff[0])
    
    for hdu in ff[1:]:
        # grab each table HDU
        t = table.Table(hdu.data)
        # grab the header
        header = hdu.header
        # transfer header and data to an ImageHDU
        img_hdu = fits.ImageHDU(data=t.to_pandas().values, header=header)
        # append new ImageHDU to the list of HDU to write out
        new_hdulist.append(img_hdu)
    # write the HDUList to a new file with all ImageHDU instead of Tables
    new_hdulist.writeto('newfile.fits')