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
# Use TestImage to set up the image im = TestImage(shift=True, nx=750, ny=750) im.addLines(spacing=50, width=10, value=10, angle=0) #im.addGaussian(xwidth=30, ywidth=30) #im.addSin(scale=100) #im.addCircle(radius=20) #im.addEllipseRandom(nEllipse=100, value=5) im.addNoise(sigma=1.) #im.hanningFilter() #im.zeroPad() # Calculate FFT/PSD2d/ACovF/PSD1d in one go (can do these separately too). # Use automatic binsize or user-defined binsize. im.calcAll(min_dr=1.0, min_npix=2) im.plotMore() #im.showImage() #im.showAcovf2d(log=True, imag=False) #im.showPsd1d() #im.showAcovf1d() #im.showSf() # Start at various points in inverting to reconstruct image (comment out stages above where you want to start). # What I find is that not including the phases means a randomly lumpy reconstructed image - nothing like original # image. # However, if you start with the 1d ACovF (even with no phase info), then you can reconstruct something which then # will let you recalculate a pretty good copy of the original 1d ACovF. # Similarly, if you start with the 1D PSD, you can reconstruct a pretty good copy of the 1d PSD. # BUT, starting with the 1d PSD means that the 1d ACovF will not be so good at large scales, and starting with the # 1d ACovF means that the reconstructed 1d PSD will not be so good at small scales (although these scales seem to be so
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
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')
import numpy
import pylab
from testImage import TestImage
for i in range(1, 5):
im =TestImage(shift=True, nx=500, ny=500)
im.addSin(scale=100)
#im.addLines(angle=30, width=20, spacing=50)
#im.image += 10.0
width=500/float(i)
print width
im.reflectEdges(width=width)
im.hanningFilter()
#im.showImage()
im.calcAll()
im.plotMore()
pylab.suptitle('Reflect edges (width %f) and hanning filter' %(width))
width= 0
for i in (0, 1, 2, 3, 4):
im = TestImage(shift=True, nx=500, ny=500)
im.addSin(scale=100)
#im.addLines(angle=30, width=20, spacing=50)
#im.image += 10.0
if i == 0:
title = 'Plain image only'
if i == 1:
im.hanningFilter()
title = 'Hanning filter only'
if i == 2:
im.zeroPad()
