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
示例#2
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    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)
示例#3
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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()
示例#4
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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