def gaussian_example():
"""Generate the plots for the example gaussian ... a more detailed version of this is walked through in the
testGaussian.py code. """
gauss = TestImage(shift=True, nx=1000, ny=1000)
sigma_x = 10.
gauss.addGaussian(xwidth=sigma_x, ywidth=sigma_x, value=1)
gauss.zeroPad()
gauss.calcAll(min_npix=2, min_dr=1)
gauss.plotMore()
pylab.savefig('gauss_all.%s' %(figformat), format='%s' %(figformat))
# pull slice of image
x = numpy.arange(0, gauss.nx, 1.0)
d = gauss.image[round(gauss.ny/2.0)][:]
mean = gauss.xcen
sigma = sigma_x
fitval, expval = dofit(x, d, mean, sigma)
doplot(x, d, fitval, expval, 'Image slice', xlabel='Pixels')
pylab.savefig('gauss_image.%s' %(figformat), format='%s' %(figformat))
# pull slice of FFT
x = gauss.xfreq
d = fftpack.ifftshift(gauss.fimage)[0][:].real
d = numpy.abs(d)
idx = numpy.argsort(x)
d = d[idx]
x = x[idx]
mean = 0
sigma_fft = 1/(2.0*numpy.pi*sigma_x)
fitval, expval = dofit(x, d, mean, sigma_fft)
doplot(x, d, fitval, expval, 'FFT slice', xlabel='Frequency')
pylab.xlim(-.2, .2)
pylab.savefig('gauss_fft.%s' %(figformat), format='%s' %(figformat))
# pull slice from PSD
d = fftpack.ifftshift(gauss.psd2d)[0][:].real
d = d[idx]
mean = 0
sigma_psd_freq = sigma_fft/numpy.sqrt(2)
fitval, expval = dofit(x, d, mean, sigma_psd_freq)
doplot(x, d, fitval, expval, 'PSD 2-d slice', xlabel='Frequency')
pylab.xlim(-.2, .2)
pylab.savefig('gauss_psd_freq.%s' %(figformat), format='%s' %(figformat))
# and look at slice from PSD in spatial scale
x = numpy.arange(-gauss.xcen, gauss.nx-gauss.xcen, 1.0)
d = gauss.psd2d[round(gauss.ny/2.0)][:].real
mean = 0
sigma_psd_pix = 1/(sigma_x*numpy.sqrt(2))*numpy.sqrt(gauss.nx*gauss.ny)/(2.0*numpy.pi)
fitval, expval = dofit(x, d, mean, sigma_psd_pix)
doplot(x, d, fitval, expval, 'PSD 2-d slice, spatial scale', xlabel='"Pixels"')
pylab.xlim(-200, 200)
pylab.savefig('gauss_psd_x.%s' %(figformat), format='%s' %(figformat))
# Show 1d PSD in both frequency and pixel space
gauss.showPsd1d(linear=True)
pylab.savefig('gauss_psd1d_all.%s' %(figformat), format='%s' %(figformat))
# and check 1d PSD in frequency space (spatial space doesn't work ...)
x = gauss.rfreq
d = gauss.psd1d.real
sigma = sigma_psd_freq
fitval, expval = dofit(x, d, 0., sigma)
doplot(x, d, fitval, expval, 'PSD 1-d', xlabel='Frequency')
pylab.savefig('gauss_psd1d.%s' %(figformat), format='%s' %(figformat))
# pull slice from ACovF
x = numpy.arange(-gauss.xcen, gauss.nx-gauss.xcen, 1.)
d = gauss.acovf.real[round(gauss.ny/2.0)][:]
mean = 0
sigma_acovf = sigma_x*numpy.sqrt(2)
fitval, expval = dofit(x, d, mean, sigma_acovf)
doplot(x, d, fitval, expval, 'ACovF 2-d slice', xlabel='Pixels')
pylab.xlim(-200, 200)
pylab.savefig('gauss_acovf.%s' %(figformat), format='%s' %(figformat))
# and check 1d ACovF
x = gauss.acovfx
d = gauss.acovf1d.real
sigma_acovf = sigma_x*numpy.sqrt(2)
fitval, expval = dofit(x, d, mean, sigma_acovf)
doplot(x, d, fitval, expval, 'ACovF 1-d', xlabel='Pixels')
pylab.savefig('gauss_acovf1d.%s' %(figformat), format='%s' %(figformat))
pylab.close()
return
# Plain image, with noise im = TestImage() im.addLines(width=10, spacing=50, value=5) #im.addNoise() im.hanningFilter() im.zeroPad() im.calcAll() #im.plotAll(title='Gaussian Noise') im.plotMore(title='Gaussian Noise') pylab.show() exit() # Gaussian image im = TestImage() im.addGaussian(xwidth=20, ywidth=20) im.hanningFilter() im.zeroPad() im.calcAll() im.plotAll(title='Gaussian') # Sin, s=100 im = TestImage() im.addSin(scale=100) im.hanningFilter() im.zeroPad() im.calcAll() im.plotAll(title='Sin, scale=100') # Sin, s=50 im = TestImage()
