def single_run_test(ind,ysc1,ysc2,q,vd,pha,zl,zs):
dsx_sdss = 0.396 # pixel size of SDSS detector.
R = 3.0000 #
nnn = 300 #Image dimension
bsz = 9.0 # arcsecs
dsx = bsz/nnn # pixel size of SDSS detector.
nstd = 59 #^2
xx01 = np.linspace(-bsz/2.0,bsz/2.0,nnn)+0.5*dsx
xx02 = np.linspace(-bsz/2.0,bsz/2.0,nnn)+0.5*dsx
xi2,xi1 = np.meshgrid(xx01,xx02)
#----------------------------------------------------------------------
dsi = 0.03
g_source = pyfits.getdata("./439.0_149.482739_1.889989_processed.fits")
g_source = np.array(g_source,dtype="<d")*10.0
g_source[g_source<=0.0001] = 1e-6
#----------------------------------------------------------------------
xc1 = 0.0 #x coordinate of the center of lens (in units of Einstein radius).
xc2 = 0.0 #y coordinate of the center of lens (in units of Einstein radius).
#q = 0.7 #Ellipticity of lens.
rc = 0.0 #Core size of lens (in units of Einstein radius).
re = re_sv(vd,zl,zs) #Einstein radius of lens.
#pha = 45.0 #Orintation of lens.
lpar = np.asarray([xc1,xc2,q,rc,re,pha])
#----------------------------------------------------------------------
ai1,ai2,mua = lens_equation_sie(xi1,xi2,lpar)
yi1 = xi1-ai1
yi2 = xi2-ai2
g_limage = lv4.call_ray_tracing(g_source,xi1,xi2,ysc1,ysc2,dsi)
g_limage[g_limage<=0.0001] = 1e-6
g_limage = p2p.cosccd2mag(g_limage)
g_limage = p2p.mag2sdssccd(g_limage)
#-------------------------------------------------------------
# Need to be Caliborate the mags
dA = Planck13.comoving_distance(zl).value*1000./(1+zl)
Re = dA*np.sin(R*np.pi/180./3600.)
counts =Brightness(Re,vd)
vpar = np.asarray([counts,R,xc1,xc2,q,pha])
#g_lens = deVaucouleurs(xi1,xi2,xc1,xc2,counts,R,1.0-q,pha)
g_lens = de_vaucouleurs_2d(xi1,xi2,vpar)
#pl.figure()
#pl.contourf(g_lens)
#pl.colorbar()
g_clean_ccd1 = g_lens*0.0+g_limage
g_clean_ccd2 = g_lens*1.0+g_limage
from scipy.ndimage.filters import gaussian_filter
pl.figure()
pl.contourf(g_clean_ccd1)
pl.colorbar()
pl.savefig("/Users/uranus/GitHub/data_arc_finding_cnn/unlensed_output_pngs/"+str(i)+"_lensed_imgs_only.png")
#-------------------------------------------------------------
g_images_psf1 = gaussian_filter(g_clean_ccd1, 2.0)
g_images_psf2 = gaussian_filter(g_clean_ccd2, 2.0)
#g_images_psf = ss.convolve(g_clean_ccd,g_psf,mode="same")
#g_images_psf = g_clean_ccd
#-------------------------------------------------------------
# Need to be Caliborate the mags
g_noise = noise_map(nnn,nnn,np.sqrt(nstd),"Gaussian")
#output_filename = "./output_fits/noise_map.fits"
#pyfits.writeto(output_filename,g_noise,clobber=True)
#-------------------------------------------------------------
g_final1 = g_images_psf1+g_noise
output_filename = "/Users/uranus/GitHub/data_arc_finding_cnn/unlensed_output_fits/"+str(i)+"_lensed_imgs_only.fits"
pyfits.writeto(output_filename,g_final1,clobber=True)
#-------------------------------------------------------------
g_final2 = g_images_psf2+g_noise
output_filename = "/Users/uranus/GitHub/data_arc_finding_cnn/unlensed_output_fits/"+str(i)+"_all_imgs.fits"
pyfits.writeto(output_filename,g_final2,clobber=True)
return 0
def single_run_test(ind, ysc1, ysc2, q, vd, pha, zl, zs):
dsx_sdss = 0.396 # pixel size of SDSS detector.
R = 3.0000 #
nnn = 300 #Image dimension
bsz = 9.0 # arcsecs
dsx = bsz / nnn # pixel size of SDSS detector.
nstd = 59 #^2
xx01 = np.linspace(-bsz / 2.0, bsz / 2.0, nnn) + 0.5 * dsx
xx02 = np.linspace(-bsz / 2.0, bsz / 2.0, nnn) + 0.5 * dsx
xi2, xi1 = np.meshgrid(xx01, xx02)
#----------------------------------------------------------------------
dsi = 0.03
g_source = pyfits.getdata("./439.0_149.482739_1.889989_processed.fits")
g_source = np.array(g_source, dtype="<d") * 10.0
g_source[g_source <= 0.0001] = 1e-6
#----------------------------------------------------------------------
xc1 = 0.0 #x coordinate of the center of lens (in units of Einstein radius).
xc2 = 0.0 #y coordinate of the center of lens (in units of Einstein radius).
#q = 0.7 #Ellipticity of lens.
rc = 0.0 #Core size of lens (in units of Einstein radius).
re = re_sv(vd, zl, zs) #Einstein radius of lens.
#pha = 45.0 #Orintation of lens.
lpar = np.asarray([xc1, xc2, q, rc, re, pha])
#----------------------------------------------------------------------
ai1, ai2, mua = lens_equation_sie(xi1, xi2, lpar)
yi1 = xi1 - ai1
yi2 = xi2 - ai2
g_limage = lv4.call_ray_tracing(g_source, yi1, yi2, ysc1, ysc2, dsi)
g_limage[g_limage <= 0.0001] = 1e-6
g_limage = p2p.cosccd2mag(g_limage)
g_limage = p2p.mag2sdssccd(g_limage)
#-------------------------------------------------------------
# Need to be Caliborate the mags
dA = Planck13.comoving_distance(zl).value * 1000. / (1 + zl)
Re = dA * np.sin(R * np.pi / 180. / 3600.)
counts = Brightness(Re, vd)
vpar = np.asarray([counts, R, xc1, xc2, q, pha])
#g_lens = deVaucouleurs(xi1,xi2,xc1,xc2,counts,R,1.0-q,pha)
g_lens = de_vaucouleurs_2d(xi1, xi2, vpar)
#pl.figure()
#pl.contourf(g_lens)
#pl.colorbar()
g_clean_ccd1 = g_lens * 0.0 + g_limage
g_clean_ccd2 = g_lens * 1.0 + g_limage
from scipy.ndimage.filters import gaussian_filter
pl.figure()
pl.contourf(g_clean_ccd1)
pl.colorbar()
pl.savefig("./output_pngs/" + str(i) + "_lensed_imgs_only.png")
#-------------------------------------------------------------
g_images_psf1 = gaussian_filter(g_clean_ccd1, 2.0)
g_images_psf2 = gaussian_filter(g_clean_ccd2, 2.0)
#g_images_psf = ss.convolve(g_clean_ccd,g_psf,mode="same")
#g_images_psf = g_clean_ccd
#-------------------------------------------------------------
# Need to be Caliborate the mags
g_noise = noise_map(nnn, nnn, np.sqrt(nstd), "Gaussian")
#output_filename = "./output_fits/noise_map.fits"
#pyfits.writeto(output_filename,g_noise,clobber=True)
#-------------------------------------------------------------
g_final1 = g_images_psf1 + g_noise
output_filename = "./output_fits/" + str(i) + "_lensed_imgs_only.fits"
pyfits.writeto(output_filename, g_final1, clobber=True)
#-------------------------------------------------------------
g_final2 = g_images_psf2 + g_noise
output_filename = "./output_fits/" + str(i) + "_all_imgs.fits"
pyfits.writeto(output_filename, g_final2, clobber=True)
return 0
def single_run_test(ind,ysc1,ysc2,q,vd,pha,zl,zs):
dsx_sdss = 0.396 # pixel size of SDSS detector.
R = 3.0000 #
#zl = 0.2 #zl is the redshift of the lens galaxy.
#zs = 1.0
#vd = 520 #Velocity Dispersion.
nnn = 128 #Image dimension
bsz = dsx_sdss*nnn # arcsecs
dsx = bsz/nnn # pixel size of SDSS detector.
nstd = 59 #^2
xx01 = np.linspace(-bsz/2.0,bsz/2.0,nnn)+0.5*dsx
xx02 = np.linspace(-bsz/2.0,bsz/2.0,nnn)+0.5*dsx
xi2,xi1 = np.meshgrid(xx01,xx02)
#----------------------------------------------------------------------
#ysc1 = 0.2
#ysc2 = 0.5
dsi = 0.03
g_source = pyfits.getdata("./439.0_149.482739_1.889989_processed.fits")
g_source = np.array(g_source,dtype="<d")*10.0
g_source[g_source<=0.0001] = 1e-6
#print np.sum(g_source)
#print np.max(g_source)
#pl.figure()
#pl.contourf(g_source)
#pl.colorbar()
#g_source = p2p.cosccd2mag(g_source)
##g_source = p2p.mag2sdssccd(g_source)
##print np.max(g_source*13*13*52.0)
#pl.figure()
#pl.contourf(g_source)
#pl.colorbar()
#----------------------------------------------------------------------
xc1 = 0.0 #x coordinate of the center of lens (in units of Einstein radius).
xc2 = 0.0 #y coordinate of the center of lens (in units of Einstein radius).
#q = 0.7 #Ellipticity of lens.
rc = 0.0 #Core size of lens (in units of Einstein radius).
re = re_sv(vd,zl,zs) #Einstein radius of lens.
#pha = 45.0 #Orintation of lens.
lpar = np.asarray([xc1,xc2,q,rc,re,pha])
#----------------------------------------------------------------------
ai1,ai2,mua = lens_equation_sie(xi1,xi2,lpar)
yi1 = xi1-ai1
yi2 = xi2-ai2
g_limage = lv4.call_ray_tracing(g_source,yi1,yi2,ysc1,ysc2,dsi)
g_limage[g_limage<=0.0001] = 1e-6
g_limage = p2p.cosccd2mag(g_limage)
g_limage = p2p.mag2sdssccd(g_limage)
#pl.figure()
#pl.imshow((g_limage),interpolation='lanczos',cmap=cm.gray)
#pl.colorbar()
#-------------------------------------------------------------
# Need to be Caliborate the mags
dA = Planck13.comoving_distance(zl).value*1000./(1+zl)
Re = dA*np.sin(R*np.pi/180./3600.)
counts =Brightness(Re,vd)
vpar = np.asarray([counts,R,xc1,xc2,q,pha])
#g_lens = deVaucouleurs(xi1,xi2,xc1,xc2,counts,R,1.0-q,pha)
g_lens = de_vaucouleurs_2d(xi1,xi2,vpar)
#pl.figure()
#pl.imshow((g_lens),interpolation='nearest',cmap=cm.gray)
#pl.colorbar()
g_clean_ccd = g_lens+g_limage
#pl.figure()
#pl.imshow((g_clean_ccd),interpolation='nearest',cmap=cm.gray)
#pl.colorbar()
g_clean_ccd = congrid.congrid(g_clean_ccd,[128,128])
#-------------------------------------------------------------
file_psf = "../PSF_and_noise/sdsspsf.fits"
g_psf = pyfits.getdata(file_psf)-1000.0
g_psf = g_psf/np.sum(g_psf)
#new_shape=[0,0]
#new_shape[0]=np.shape(g_psf)[0]*dsx_sdss/dsx
#new_shape[1]=np.shape(g_psf)[1]*dsx_sdss/dsx
#g_psf = rebin_psf(g_psf,new_shape)
g_images_psf = ss.fftconvolve(g_clean_ccd,g_psf,mode="same")
#g_images_psf = ss.convolve(g_clean_ccd,g_psf,mode="same")
#g_images_psf = g_clean_ccd
#pl.figure()
#pl.imshow((g_psf),interpolation='nearest',cmap=cm.gray)
#pl.colorbar()
#-------------------------------------------------------------
# Need to be Caliborate the mags
#g_noise = noise_map(nnn,nnn,np.sqrt(nstd),"Gaussian")
g_noise = noise_map(128,128,np.sqrt(nstd),"Gaussian")
g_final = g_images_psf+g_noise
#pl.figure()
#pl.imshow((g_final.T),interpolation='nearest',cmap=cm.gray)
#pl.colorbar()
g_final_rebin = congrid.congrid(g_final,[128,128])
#pl.figure()
#pl.imshow((g_final_rebin.T),interpolation='nearest',cmap=cm.gray)
#pl.colorbar()
#-------------------------------------------------------------
output_filename = "../output_fits/"+str(ind)+".fits"
pyfits.writeto(output_filename,g_final_rebin,clobber=True)
pl.show()
return 0
def single_run_test(ind, ysc1, ysc2, q, vd, pha, zl, zs):
dsx_sdss = 0.396 # pixel size of SDSS detector.
R = 3.0000 #
#zl = 0.2 #zl is the redshift of the lens galaxy.
#zs = 1.0
#vd = 520 #Velocity Dispersion.
nnn = 128 #Image dimension
bsz = dsx_sdss * nnn # arcsecs
dsx = bsz / nnn # pixel size of SDSS detector.
nstd = 59 #^2
xx01 = np.linspace(-bsz / 2.0, bsz / 2.0, nnn) + 0.5 * dsx
xx02 = np.linspace(-bsz / 2.0, bsz / 2.0, nnn) + 0.5 * dsx
xi2, xi1 = np.meshgrid(xx01, xx02)
#----------------------------------------------------------------------
#ysc1 = 0.2
#ysc2 = 0.5
dsi = 0.03
g_source = pyfits.getdata("./439.0_149.482739_1.889989_processed.fits")
g_source = np.array(g_source, dtype="<d") * 10.0
g_source[g_source <= 0.0001] = 1e-6
#print np.sum(g_source)
#print np.max(g_source)
#pl.figure()
#pl.contourf(g_source)
#pl.colorbar()
#g_source = p2p.cosccd2mag(g_source)
##g_source = p2p.mag2sdssccd(g_source)
##print np.max(g_source*13*13*52.0)
#pl.figure()
#pl.contourf(g_source)
#pl.colorbar()
#----------------------------------------------------------------------
xc1 = 0.0 #x coordinate of the center of lens (in units of Einstein radius).
xc2 = 0.0 #y coordinate of the center of lens (in units of Einstein radius).
#q = 0.7 #Ellipticity of lens.
rc = 0.0 #Core size of lens (in units of Einstein radius).
re = re_sv(vd, zl, zs) #Einstein radius of lens.
#pha = 45.0 #Orintation of lens.
lpar = np.asarray([xc1, xc2, q, rc, re, pha])
#----------------------------------------------------------------------
ai1, ai2, mua = lens_equation_sie(xi1, xi2, lpar)
yi1 = xi1 - ai1
yi2 = xi2 - ai2
g_limage = lv4.call_ray_tracing(g_source, yi1, yi2, ysc1, ysc2, dsi)
g_limage[g_limage <= 0.0001] = 1e-6
g_limage = p2p.cosccd2mag(g_limage)
g_limage = p2p.mag2sdssccd(g_limage)
#pl.figure()
#pl.imshow((g_limage),interpolation='lanczos',cmap=cm.gray)
#pl.colorbar()
#-------------------------------------------------------------
# Need to be Caliborate the mags
dA = Planck13.comoving_distance(zl).value * 1000. / (1 + zl)
Re = dA * np.sin(R * np.pi / 180. / 3600.)
counts = Brightness(Re, vd)
vpar = np.asarray([counts, R, xc1, xc2, q, pha])
#g_lens = deVaucouleurs(xi1,xi2,xc1,xc2,counts,R,1.0-q,pha)
g_lens = de_vaucouleurs_2d(xi1, xi2, vpar)
#pl.figure()
#pl.imshow((g_lens),interpolation='nearest',cmap=cm.gray)
#pl.colorbar()
g_clean_ccd = g_lens + g_limage
#pl.figure()
#pl.imshow((g_clean_ccd),interpolation='nearest',cmap=cm.gray)
#pl.colorbar()
g_clean_ccd = congrid.congrid(g_clean_ccd, [128, 128])
#-------------------------------------------------------------
file_psf = "../PSF_and_noise/sdsspsf.fits"
g_psf = pyfits.getdata(file_psf) - 1000.0
g_psf = g_psf / np.sum(g_psf)
#new_shape=[0,0]
#new_shape[0]=np.shape(g_psf)[0]*dsx_sdss/dsx
#new_shape[1]=np.shape(g_psf)[1]*dsx_sdss/dsx
#g_psf = rebin_psf(g_psf,new_shape)
g_images_psf = ss.fftconvolve(g_clean_ccd, g_psf, mode="same")
#g_images_psf = ss.convolve(g_clean_ccd,g_psf,mode="same")
#g_images_psf = g_clean_ccd
#pl.figure()
#pl.imshow((g_psf),interpolation='nearest',cmap=cm.gray)
#pl.colorbar()
#-------------------------------------------------------------
# Need to be Caliborate the mags
#g_noise = noise_map(nnn,nnn,np.sqrt(nstd),"Gaussian")
g_noise = noise_map(128, 128, np.sqrt(nstd), "Gaussian")
g_final = g_images_psf + g_noise
#pl.figure()
#pl.imshow((g_final.T),interpolation='nearest',cmap=cm.gray)
#pl.colorbar()
g_final_rebin = congrid.congrid(g_final, [128, 128])
#pl.figure()
#pl.imshow((g_final_rebin.T),interpolation='nearest',cmap=cm.gray)
#pl.colorbar()
#-------------------------------------------------------------
output_filename = "../output_fits/" + str(ind) + ".fits"
pyfits.writeto(output_filename, g_final_rebin, clobber=True)
pl.show()
return 0
