Bo Zhang (NAOC, mailto:bozhang@nao.cas.cn) will have a few lessons on python.
python to process astronomical data.These lectures are organized as below:
APLpy : Astronomical Plotting Library in Python.
For convenient fits image or cubes plotting with astronomical coordinates
APLpy is based on matplotlib, so any matplotlib originated functions/classes can be easily adopted.
For image reprojection or mosaicing purpose, APLpy relies on Montage, which is developed by IPAC team. To enable functions like making RGB cube, one should first install Montage package.
For plotting plenty of similar images, one could define a function to set up a set of standard properties (i.e. def standard_setup( )), such as axis and tick labels and use this user-defined function instead of the calling the APLpy functions time to time.
Of course, a different setting by directly calling APLpy functions will overide the settings after calling the user-defined standard setup function.
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# import matplotlib.pyplot as plt
# import numpy as np
%pylab inline
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import aplpy
from astropy.io import fits
import numpy as np
import matplotlib.pyplot as plt
def standard_setup(fig):
fig.show_colorscale(cmap='gist_gray')
fig.tick_labels.set_font(size='large')
fig.frame.set_linewidth(1)
fig.frame.set_color('black')
fig.add_colorbar()
fig.colorbar.show()
fig.colorbar.set_location('top')
fig.colorbar.set_font(size='medium', weight='medium', \
stretch='normal', family='sans-serif', \
style='normal', variant='normal')
fig.list_layers()
#fig.axis_labels.hide_y()
#fig.tick_labels.hide_y()
#fig.axis_labels.hide_x()
#fig.tick_labels.hide_x()
dirpath = r'./data/wise_image/'
outpath = r'./data/wise_image/'
filepath1= dirpath+r'w4_cut.fits'
filepath2= dirpath+r'w3_cut.fits'
filepath3= dirpath+r'w2_cut.fits'
aplpy.make_rgb_cube([filepath3,filepath2,filepath1], outpath+r'wise234_RGB.fits')
aplpy.make_rgb_image(outpath+r'wise234_RGB.fits',
outpath+r'rgb_image_wise234.png',
stretch_r='linear',
vmid_r=None,
pmin_r=10,
pmax_r=98,
exponent_r=2,
vmin_g=None,
vmax_g=None,
pmin_g=10,
pmax_g=98,
stretch_g='linear',
vmid_g=None,
exponent_g=2,
vmin_b=None,
vmax_b=None,
pmin_b=10,
pmax_b=98,
stretch_b='linear',
vmid_b=None)
fits.setval(outpath+r'wise234_RGB.fits','CTYPE3',value='RGB')
# Launch APLpy figure of 2D cube
img = aplpy.FITSFigure(outpath+r'wise234_RGB.fits',dimensions=[0,1],slices=[2],figsize=(10,9))
standard_setup(img)
img.recenter(084.78475,+30.09747,width=0.12,height=0.12)
img.show_rgb(outpath+r'rgb_image_wise234.png')
img.colorbar.hide()
img.add_label(0.9,0.9, 'WISE 2/3/4\nRGB', color='black', relative=True, size='large',layer='source')
img.add_scalebar(0.05,"1 pc",color='white', corner='bottom left')
#add 850 um continuum contour
img.show_contour(r'./data/G178_final.850.fits',levels=np.linspace(0.08,0.23,5),colors='white')
# img.save(outpath+r'rgb_W3_contour.pdf')
plt.show()
To take care of fits files of Healpix pixelization schemes (http://healpix.sourceforge.net/downloads.php), including fitsfile io and transformation to standard coordinate systems.
This is particularly useful for all-sky survey such as Planck data, or SCUBA2 data release 1.
In [3]:
import os
import healpy as hp
from astropy.io import fits
import montage_wrapper as mt
#read the fitsfile
plankCO = hp.read_map('HFI_CompMap_CO-Type3_2048_R1.10.fits')#standard planck survey data from online archive
pixNum = 8192
#view the fitsfile in Cartesian projection
imageData = hp.cartview(plankCO, title='Histogram equalized Ecliptic',
unit='K km/s', min=1,max=500, cmap = 'gist_heat_r',
norm = 'log', cbar= False, return_projected_map = True,
xsize = pixNum)
#fits header editing
hdu = fits.PrimaryHDU(imageData.data)
hdu.header['CTYPE1'] = 'GLON-CAR'
hdu.header['CRVAL1'] = 0.0
hdu.header['CDELT1'] = -360.0/pixNum
hdu.header['CRPIX1'] = pixNum/2.0+0.5
hdu.header['CTYPE2'] = 'GLAT-CAR'
hdu.header['CRVAL2'] = 0.0
hdu.header['CDELT2'] = 360.0/pixNum
hdu.header['CRPIX2'] = pixNum/4.0+0.5
hdu.header['BUNIT'] = 'K*km/s'
hdu.header['TELESCOP'] = 'Planck'
hdu.writeto('PlanckCO_Type3_car.fits', clobber= True)
#target fitsheader editing
hdrTAN = hdu.header.copy()
hdrTAN['CTYPE1'] = 'GLON-TAN'
hdrTAN['CTYPE2'] = 'GLAT-TAN'
#fitsheader and file output
#use mongtage as the reprojection engine, one could also implement other reprojection packages
if os.path.isfile('TAN.hdr'):
os.remove('TAN.hdr')
hdrTAN.totextfile('TAN.hdr')
if os.path.isfile('PlanckCO_Type3_tan.fits'):
os.remove('PlanckCO_Type3_tan.fits')
mt.reproject('PlanckCO_Type3_car.fits',
'PlanckCO_Type3_tan.fits',
header = 'TAN.hdr')
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