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
def example_lines():
"""Plot for example_lines, illustrating entire forward process both with and without hanning filter."""
im = TestImage(shift=True, nx=1000, ny=1000)
im.addLines(width=10, spacing=75, value=5, angle=45)
im.zeroPad()
im.calcAll()
im.plotMore()
pylab.savefig('example_lines_a.%s' %(figformat), format='%s' %(figformat))
pylab.close()
im = TestImage(shift=True, nx=1000, ny=1000)
im.addLines(width=10, spacing=75, value=5, angle=45)
im.hanningFilter()
im.zeroPad()
im.calcAll()
im.plotMore()
pylab.savefig('example_lines_b.%s' %(figformat), format='%s' %(figformat))
pylab.close()
return
def elliptical():
"""Generate a test image with random ellipses and background noise."""
im = TestImage(shift=True, nx=1000, ny=1000)
im.addEllipseRandom(nEllipse=100, value=5)
im.addNoise(sigma=1)
im.hanningFilter()
#im.zeroPad()
im.calcAll(min_npix=2, min_dr=1)
im.plotMore()
pylab.savefig('elliptical.%s' %(figformat), format='%s' %(figformat))
# Invert from ACovF 1d without phases
im.invertAcovf1d()
im.invertAcovf2d(useI=True)
im.invertPsd2d(useI=True)
im.invertFft(useI=True)
im.showImageAndImageI()
pylab.savefig('elliptical_invert.%s' %(figformat), format='%s' %(figformat))
pylab.close()
return
def inversion():
"""Generate some example images & invert them to reconstruct the original image."""
im = TestImage(shift=True, nx=1000, ny=1000)
#im.addEllipseGrid(gridX=200, gridY=100, semiX=50, semiY=25, value=1)
im.addLines(width=20, spacing=200, value=1, angle=45)
im.addSin(scale=300)
im.hanningFilter()
im.zeroPad()
#cmap = pylab.cm.gray_r
cmap = None
clims = im.showImage(cmap=cmap)
pylab.savefig('invert_image.%s' %(figformat), format='%s' %(figformat))
im.calcAll(min_npix=1, min_dr=1)
# Invert from ACovF and show perfect reconstruction.
im.invertAcovf2d()
im.invertPsd2d(useI=True)
im.invertFft(useI=True)
im.showImageI(clims=clims, cmap=cmap)
pylab.savefig('invert_acovf2d_good.%s' %(figformat), format='%s' %(figformat))
# Invert from ACovF 2d without phases
im.invertAcovf2d(usePhasespec=False, seed=42)
im.invertPsd2d(useI=True)
im.invertFft(useI=True)
im.showImageI(clims=clims, cmap=cmap)
pylab.savefig('invert_acovf2d_nophases.%s' %(figformat), format='%s' %(figformat))
# Invert from ACovF 1d with phases
im.invertAcovf1d(phasespec=im.phasespec)
im.invertAcovf2d(useI=True)
im.invertPsd2d(useI=True)
im.invertFft(useI=True)
im.showImageI(clims=clims, cmap=cmap)
pylab.savefig('invert_acovf1d_phases.%s' %(figformat), format='%s' %(figformat))
# Invert from ACovF 1d without phases
im.invertAcovf1d(seed=42)
im.invertAcovf2d(useI=True)
im.invertPsd2d(useI=True)
im.invertFft(useI=True)
im.showImageI(clims=clims, cmap=cmap)
pylab.savefig('invert_acovf1d_nophases.%s' %(figformat), format='%s' %(figformat))
# Recalculate 1-d PSD and ACovF from this last reconstructed image (ACovF1d no phases)
im2 = PImagePlots()
im2.setImage(im.imageI)
im2.calcAll(min_npix=1, min_dr=1)
legendlabels=['Reconstructed', 'Original']
im2.showPsd1d(comparison=im, legendlabels=legendlabels)
pylab.savefig('invert_recalc_ACovF_Psd1d.%s' %(figformat), format='%s' %(figformat))
im2.showAcovf1d(comparison=im, legendlabels=legendlabels)
pylab.savefig('invert_recalc_ACovF_Acovf1d.%s' %(figformat), format='%s' %(figformat))
# Invert from PSD and show perfect reconstruction.
im.invertPsd2d()
im.invertFft(useI=True)
im.showImageI(clims=clims, cmap=cmap)
pylab.savefig('invert_psd2d_good.%s' %(figformat), format='%s' %(figformat))
# Invert from PSD 2d without phases
im.invertPsd2d(usePhasespec=False, seed=42)
im.invertFft(useI=True)
im.showImageI(clims=clims, cmap=cmap)
pylab.savefig('invert_psd2d_nophases.%s' %(figformat), format='%s' %(figformat))
# Invert from PSD 1d with phases
im.invertPsd1d(phasespec=im.phasespec)
im.invertPsd2d(useI=True)
im.invertFft(useI=True)
im.showImageI(clims=clims, cmap=cmap)
pylab.savefig('invert_psd1d_phases.%s' %(figformat), format='%s' %(figformat))
# Invert from PSD 1d without phases
im.invertPsd1d(seed=42)
im.invertPsd2d(useI=True)
im.invertFft(useI=True)
im.showImageI(clims=clims, cmap=cmap)
pylab.savefig('invert_psd1d_nophases.%s' %(figformat), format='%s' %(figformat))
# Recalculate 1-d PSD and ACovF from this last reconstructed image (PSD1d no phases)
im2 = PImagePlots()
im2.setImage(im.imageI)
im2.calcAll(min_npix=1, min_dr=1)
im2.showPsd1d(comparison=im, legendlabels=legendlabels)
pylab.savefig('invert_recalc_PSD_Psd1d.%s' %(figformat), format='%s' %(figformat))
im2.showAcovf1d(comparison=im, legendlabels=legendlabels)
pylab.savefig('invert_recalc_PSD_Acovf1d.%s' %(figformat), format='%s' %(figformat))
pylab.close()
return
import pylab from testImage import TestImage # 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')
