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%%bash
pip install aplpy
pip install https://github.com/ericmandel/pyds9/archive/master.zip
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%%bash
curl -O https://astropy.stsci.edu/data/galactic_center/gc_bolocam_gps.fits
curl -O https://astropy.stsci.edu/data/galactic_center/gc_2mass_k.fits
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%matplotlib inline
import pylab as pl
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from astropy.io import fits
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# load the data (no headers, first extension by default)
# if there are extensions, fits.getdata('file.fits', ext=extension_number)
# if you want many extension, use fits.open('file.fits'), then access each independently
stellardata = fits.getdata('gc_2mass_k.fits')
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# show the image: vmax sets the brightest displayed pixel
# cmap can be any of the valid matplotlib colormaps (pl.cm....)
pl.imshow(stellardata, cmap='viridis', vmax=1000)
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dustdata = fits.getdata('gc_bolocam_gps.fits')
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pl.contour(dustdata)
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dustdata.shape, dustdata.flatten().shape
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np.any(np.isnan(dustdata))
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# subset of the data that is not nan
# implicitly flattens
non_nan_dustdata = dustdata[~np.isnan(dustdata)]
non_nan_dustdata = dustdata[np.isfinite(dustdata)]
non_nan_dustdata = np.compress(np.isfinite(dustdata.flatten()), dustdata.flatten())
len(non_nan_dustdata)
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pl.hist(dustdata[~np.isnan(dustdata)], bins=np.linspace(0,2,50))
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pl.contour(dustdata, levels=np.linspace(0.2, 10, 10))
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pl.figure(figsize=(12,12))
pl.imshow(stellardata, cmap='gray')
pl.contour(dustdata[100:-100, 100:-100], levels=np.linspace(0.2, 10, 10))
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import aplpy
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%matplotlib nbagg
FF = aplpy.FITSFigure('gc_2mass_k.fits')
FF.show_grayscale(vmax=1000)
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%matplotlib nbagg
FF = aplpy.FITSFigure('gc_2mass_k.fits')
FF.show_grayscale(vmax=1000)
# convention not generally needed, only for specific (CAR) FITS projections
FF.show_contour('gc_bolocam_gps.fits', convention='calabretta')
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%matplotlib nbagg
FF = aplpy.FITSFigure('gc_2mass_k.fits')
FF.show_grayscale(vmax=1000)
# convention not generally needed, only for specific (CAR) FITS projections
FF.show_contour('gc_bolocam_gps.fits', convention='calabretta')
scalebar = FF.add_scalebar(0.1, label='0.1$^\circ$', color='orange')
FF.scalebar.set_corner('top right')
FF.scalebar.set_font_size(40)
FF.scalebar.set_font_weight('bold')
FF.scalebar.set_linewidth(4)
FF.scalebar.set_label('0.1$^\circ$')
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import astroquery
from astropy import units as u
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from astroquery.irsa import Irsa
from astroquery.vizier import Vizier
from astroquery.eso import Eso
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Eso.ROW_LIMIT = 500
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Eso.query_instrument('naco', help=True)
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tbl = Eso.query_instrument('naco', target='Sgr A*')
tbl
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rslt = Irsa.query_region('Sgr A*', radius=10*u.arcmin, catalog='pt_src_cat')
#rslt
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bright = rslt[rslt['k_m'] < 9]
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%matplotlib nbagg
FF = aplpy.FITSFigure('gc_2mass_k.fits')
FF.show_grayscale(vmax=1000, invert=True)
# convention not generally needed, only for specific (CAR) FITS projections
FF.show_contour('gc_bolocam_gps.fits', convention='calabretta', colors=['r'])
scalebar = FF.add_scalebar(0.1, label='0.1$^\circ$', color='orange')
FF.scalebar.set_corner('top right')
FF.scalebar.set_font_size(40)
FF.scalebar.set_font_weight('bold')
FF.scalebar.set_linewidth(4)
FF.scalebar.set_label('0.1$^\circ$')
FF.show_markers(bright['ra'], bright['dec'])
Aside: running external code files
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%run file.py
%run -i file.py
execfile('file.py') # is equivalent to %run -i ...
Visualization Part 2: pyds9
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import pyds9
from astropy.io import fits
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DD = pyds9.DS9('mine')
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DD.set('frame 1')
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DD.set_pyfits(fits.open('gc_2mass_k.fits'))
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DD.set('frame lock wcs')
DD.set('frame 2')
DD.set_pyfits(fits.open('gc_bolocam_gps.fits'))
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DD.set('single')
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For more information about ds9 xpa access points, see: ds9.si.edu/doc/ref/xpa.html