Tutorial 2 exercise solutions

Exercise 1
 >>> import pyfits
 >>> tab = pyfits.getdata('fuse.fits')
 >>> tab.info()
 >>> flux =  10.**12*tab.field('flux')
 >>> error = 10.**12*tab.field('flux')
 >>> plot(flux, error,'.')

Exercise 2, 3
 >>> im = pyfits.getdata('pix.fits')
 >>> from numarray.convolve import boxcar
 >>> sim = boxcar(im, (31, 31))
 >>> sim = 100*sim/sim.max()
 >>> contour(sim, contours= [90, 70, 50, 30, 10])
 >>> imshow(im, vmax=500)
 >>> contour(sim, [90,70,50,30,10],hold=True)

Exercise 4
 >>> imshow(im, vmax=500)
 >>> gray()
 >>> text(380,72,'my hometown!',color='yellow'
 >>> savefig('fluxerror.ps')

Exercise 5
This example wasn't really doable with the level of knowledge given
but a solution is given anyway (turned out overplotting with a
transparent background on an image is not quite so simple).

 >>> ax = subplot(111, frameon=False) # leads to a clear plot background 
so images show through
 >>> imshow(im, vmax=500)
 >>> sim = 500.*im/8000. # scale image to prevent shrinking image when 
plot value is larger than 512
 >>>                     # no matter, image will still rescale due to 
auto ticking
 >>> def buttonclick(event):
...     # left button, note use of int() to make position acceptable 
for indexing
...     if event.button==1: plot(sim[int(event.ydata)], hold=True)
...     if event.button==2: plot(sim[:,int(event.xdata)], hold=True)
...
 >>> cid=connect('button_press_event',buttonclick)
 >>> # click away. Note that old overplot remain. More work is needed to 
make this more useful,
 >>> # particularly with regard to plot scaling and keeping the image 
size unchanged.
 >>> disconnect(cid)