Adding Poisson noise with a Gaussian distribution

Question

I managed to add poisson noise to my .fits image, but I need to add noise that is distributed like a gaussian with mean/median (mu_0) of 0 and an increasingly wider distribution (sigma). I couldn't find the syntax governing the adding of noise in this way, so can somebody please walk me through it? At the moment, the poisson noise I added is evenly distributed, which isn't what I want; I need the gaussian randomness.

Here's the relevant bit of code:

    im = pf.open(name)
    isinstance(im,list)
    im0 = im[0]
    print im0.data.shape
    print np.var(im0.data)
    poissonNoise = np.random.poisson(poisson, im0.data.shape).astype(float)
    test = im0.data + poissonNoise
    print np.var(test)
    im0.data = test
    stringee = 'POISSON'
    pf.writeto(stringee+poisson+name, data=test, clobber=True, header=im0.header)
    check = pf.open(stringee+poisson+name)
    np.var(check[0].data)

Answer 1

For gaussian distributions (normal distributions) use np.random.normal .

normalNoise = np.random.normal(center, scale, shape).astype(float)

See more here : NumPy Normal and more generally for all the types: NumPy Random