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)