def plotCloudImage(self):
"""Generate some additional information and plots about the cloud image, if desired."""
from pImagePlots import PImagePlots
import pylab
im = PImagePlots()
im.setImage(self.cloudimage)
im.showImage(copy=True)
im.hanningFilter()
im.calcAll()
im.showPsd2d()
im.showAcovf2d()
im.showAcovf1d()
im.showSf(linear=True)
#pylab.show()
return
im = PImagePlots(shift=True, nx=imsize, ny=imsize)
im.makeImageFromSf(sfx=xr, sf=SF)
im.showAcovf1d()
im.showPsd2dI()
# Trim image to desired final size
trim = round((imsize - final_imsize)/2.0)
print 'Trimming about %d pixels from each side' %(trim)
image = im.imageI[trim:trim+final_imsize, trim:trim+final_imsize]
image = rescaleImage(image.real, sigma_goal, kappa)
imsize = len(image)
pixscale = pixscale
rad_fov = imsize/2.0*pixscale
print 'Image After Trimming: Rad_fov', rad_fov, 'Imsize', imsize, 'Pixscale', pixscale, 'deg/pix', '(', pixscale*60.*60., 'arcsec/pix)'
im.setImage(image.real)
imageStats(im.image)
im.showImage(copy=True)
im.hanningFilter()
im.calcAll()
im.showPsd2d()
im.showAcovf2d()
im.showAcovf1d(comparison=im)
# And also try to make an image with the same (final) pixel scale, but that will be created (first)
# larger and then scaled down, to avoid introducing artifacts from the ACovF1d being truncated.
final_imsize = 1500 # pixels desired in final image
final_rad_fov = 1.75*numpy.sqrt(2) # degrees
final_pixscale = 2*final_rad_fov / float(final_imsize)
# Start with larger image
pixscale = final_pixscale
rad_fov = final_rad_fov*2.
imsize = int(2*rad_fov / pixscale)
im.invertPsd2d(usePhasespec=False) #im.invertPsd2d(useI=True) # 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()
def clouds():
"""Read an example of the french group's cloud generation."""
# oldCloud.npy and newCloud.npy are images of size 240x240 that cover a fov of 4.0 deg
# (if cloud generation code is understood correctly).
# old clouds
oldClouds = numpy.load('oldCloud.npy')
fov = 4.0 #rad_fov = 2.0
nx = len(oldClouds)
pixscale = fov / float(nx)
im = PImagePlots(shift=True)
im.setImage(oldClouds)
im.showImage()
pylab.savefig('clouds_oldimage.%s' %(figformat), format='%s' %(figformat))
#im.hanningFilter()
im.calcAll(min_npix=2, min_dr=1)
im.plotMore()
pylab.savefig('clouds_old.%s' %(figformat), format='%s' %(figformat))
# new clouds
newClouds = numpy.load('newCloud.npy')
im2 = PImagePlots(shift=True)
im2.setImage(newClouds)
im2.showImage()
pylab.savefig('clouds_newimage.%s' %(figformat), format='%s' %(figformat))
#im2.hanningFilter()
im2.calcAll(min_npix=2, min_dr=1)
im2.plotMore()
pylab.savefig('clouds_new.%s' %(figformat), format='%s' %(figformat))
# compare structure functions
# translate x axis from pixels to degrees .. 240 pix = 4.0 deg (?)
im.sfx = im.sfx *pixscale
im2.sfx = im2.sfx *pixscale
# and scale SF's to just run between 0 and 1 (because of loss of amplitude info with random phases)
im.sf = im.sf / im.sf.max()
im2.sf = im2.sf / im2.sf.max()
legendlabels = ['Old clouds (scaled SF)', 'New clouds (scaled SF)']
im.showSf(comparison=im2, legendlabels=legendlabels, linear=True)
pylab.xlim(0, fov/2.0)
pylab.ylim(0, 1.2)
pylab.title('Structure Function')
pylab.xlabel('Degrees')
pylab.savefig('clouds_sf.%s' %(figformat), format='%s' %(figformat))
# look at phase spectrum
pylab.figure()
n, b, p = pylab.hist(im.phasespec.flatten(), bins=75, range=[-numpy.pi, numpy.pi],
alpha=0.2, label='Old clouds phases')
n, b, p = pylab.hist(im2.phasespec.flatten(), bins=b, range=[-numpy.pi, numpy.pi],
alpha=0.2, label='New clouds phases')
pylab.legend(fancybox=True, fontsize='smaller')
pylab.savefig('clouds_phasehist.%s' %(figformat), format='%s' %(figformat))
# the phase spectrum seems to be flatly distributed between -pi and pi
pylab.figure()
pylab.subplot(121)
pylab.title('Old clouds')
pylab.imshow(im.phasespec, origin='lower')
pylab.colorbar(shrink=0.6)
pylab.subplot(122)
pylab.title('New clouds')
pylab.imshow(im2.phasespec, origin='lower')
pylab.colorbar(shrink=0.6)
pylab.suptitle('Phase spectrum')
pylab.savefig('clouds_phasespec.%s' %(figformat), format='%s' %(figformat))
pylab.close()
return