# 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
imsize = int(2*rad_fov / pixscale)
if (imsize%2 != 0):
imsize = imsize + 1
xr = xsf / pixscale # xr = in pixels, over range that want to simulate
print 'Image: Rad_fov', rad_fov, 'Imsize', imsize, 'Pixscale', pixscale, 'deg/pix', '(', pixscale*60.*60., 'arcsec/pix)'
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()
print 'Rad_fov', rad_fov, 'Imsize', imsize, 'Pixscale', pixscale, 'deg/pix', '(', pixscale*60.*60., 'arcsec/pix)'
# set up 1d SF with desired scale and pixel scale
"""
condition = (xsf <= rad_fov)
xr = xsf[condition] / pixscale # xr = in pixels, over range that want to simulate
xrpix = numpy.arange(0, imsize, 1.0)
sfpix = numpy.interp(xrpix, xr, SF[condition])
"""
condition = (xsf <= rad_fov*numpy.sqrt(2))
xr = xsf[condition] / pixscale # xr = in pixels, over range that want to simulate
xrpix = numpy.arange(0, imsize/2.*numpy.sqrt(2), 1.0)
sfpix = numpy.interp(xrpix, xr, SF[condition])
# try making image
im = PImagePlots(shift=True, nx= imsize, ny=imsize)
im.makeImageFromSf(sfx=xrpix, sf=sfpix)
im.showImageI()
pylab.savefig('clouds_1.png', format='png')
im.image = im.imageI.real
#im.hanningFilter()
im.calcAll(min_npix=2, min_dr=1)
im.plotMore()
pylab.savefig('clouds_1_dat.png', format='png')
# Rescale sfx to be in physical (degrees)
im.sfx = im.sfx * pixscale
# make another image, as should see difference due to different random phases
im2 = PImagePlots(shift=True, nx= imsize, ny=imsize)
im2.invertSf(sfx=xrpix, sf=sfpix)
im2.invertAcovf1d()