def compare1d_psd_acovf():
"""Compare 1d ACovF in physical coordinates to 1d PSD in physical coordinates, for two similar but different images."""
im = TestImage(shift=True, nx=1000, ny=1000)
scale = 100
im.addSin(scale=scale)
im.hanningFilter()
im.zeroPad()
im.calcAll(min_npix=1, min_dr=1)
im.showImage()
pylab.grid()
pylab.savefig('compare1d_image1.%s' %(figformat), format='%s' %(figformat))
im.showPsd2d()
pylab.savefig('compare1d_psd2d1.%s' %(figformat), format='%s' %(figformat))
im.showPsd1d()
pylab.savefig('compare1d_psd1.%s' %(figformat), format='%s' %(figformat))
im.showAcovf1d()
pylab.savefig('compare1d_acovf1.%s' %(figformat), format='%s' %(figformat))
im = TestImage(shift=True, nx=1000, ny=1000)
im.addSin(scale=scale*2)
im.hanningFilter()
im.zeroPad()
im.calcAll(min_npix=1, min_dr=1)
im.showImage()
pylab.grid()
pylab.savefig('compare1d_image2.%s' %(figformat), format='%s' %(figformat))
im.showPsd2d()
pylab.savefig('compare1d_psd2d2.%s' %(figformat), format='%s' %(figformat))
im.showPsd1d()
pylab.savefig('compare1d_psd2.%s' %(figformat), format='%s' %(figformat))
im.showAcovf1d()
pylab.savefig('compare1d_acovf2.%s' %(figformat), format='%s' %(figformat))
pylab.close()
return
# Start here to invert from FFT (useI = False, and will get perfect reconstruction). im.invertFft(useI=True) # Use im2 to recalculate 1d PSD/ACovF starting from the reconstructed image, without altering the original. im2 = PImagePlots() im2.setImage(im.imageI.real, copy=True) im2.calcAll(min_dr=1.0, min_npix=2) im2.plotMore() # Now start plotting things, in comparison. clims = im.showImage() #print clims im2.showImage(clims=clims) im2.showImage() im.showFft(clims=clims) im2.showFft(clims=clims) im.showPsd2d() im2.showPsd2d() im.showPhases() im2.showPhases() im.showAcovf2d() im2.showAcovf2d(imag=False) im.showPsd1d(comparison=im2) im.showAcovf1d(comparison=im2) im.showSf(linear=True, comparison=im2) pylab.show() exit()
