im2 = PImagePlots(shift=True, nx= imsize, ny=imsize)
im2.invertSf(sfx=xrpix, sf=sfpix)
im2.invertAcovf1d()
im2.invertAcovf2d(useI=True)
im2.invertPsd2d(useI=True)
im2.invertFft(useI=True)
im2.showImageI()
pylab.savefig('clouds_2.png', format='png')
# Compare structure functions, but scaled (due to loss of amplitude info with random phases)
im2.image = im2.imageI.real
im2.calcAll(min_npix=2, min_dr=1)
im2.plotMore()
pylab.savefig('clouds_2_dat.png', format='png')
im2.sfx = im2.sfx * pixscale
im2.sf = im2.sf/im2.sf.max()
im.sf = im.sf/im.sf.max()
im.showSf(linear=True, comparison=im2, legendlabels=['Clouds 1 (scaled)', 'Clouds 2 (scaled)'])
rm = rad_fov
condition = (numpy.abs(xsf - rm) < 0.2)
SF2 = SF - SF.min()
SF2 = SF2 / SF[condition].mean()
pylab.plot(xsf, SF2, 'k:', label='Original/SDSS (scaled)')
pylab.xlim(0, rm)
pylab.ylim(0, 1.2)
pylab.legend(numpoints=1, fancybox=True, fontsize='smaller')
pylab.title('Structure Function')
pylab.xlabel('Degrees')
pylab.savefig('clouds_sf_new.png', format='png')
print 'Image After Trimming: Rad_fov', rad_fov, 'Imsize', imsize, 'Pixscale', pixscale, 'deg/pix', '(', pixscale*60.*60., 'arcsec/pix)'
im2.setImage(image.real)
imageStats(im2.image)
im2.showImage(copy=True)
im2.hanningFilter()
im2.calcAll()
im2.showPsd2d()
im2.showAcovf2d()
im2.showAcovf1d(comparison=im)
# Compare structure functions, but scaled (due to loss of amplitude info with random phases)
im.sfx = im.sfx * pixscale
im2.sfx = im2.sfx * pixscale
scaleval = im.sf[numpy.abs(im.sfx - goal_rad_fov)<0.05].mean()
print 'Cloud 1 scaleval', scaleval
im.sf = im.sf/scaleval
scaleval = im2.sf[numpy.abs(im2.sfx - goal_rad_fov)<0.05].mean()
print 'Cloud 2 scaleval', scaleval
im2.sf = im2.sf/scaleval
im.showSf(linear=True, comparison=im2, legendlabels=['Clouds 1', 'Clouds 2'])
scaleval = SF[numpy.where(numpy.abs(xsf - goal_rad_fov)<0.2)].mean()
print 'ARMA scaleval', scaleval
SF2 = SF / scaleval
pylab.plot(xsf, SF2, 'k:', label='Original/ARMA')
pylab.xlim(0, goal_rad_fov*1.2)
pylab.ylim(0, 1.2)
pylab.legend(numpoints=1, fancybox=True, fontsize='smaller')
pylab.title('Structure Function (mag)')
pylab.xlabel('Degrees')
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