def get_om10_cc(xi1, xi2, om10_id):
db = om10.DB(catalog="./qso_mock.fits")
lens = db.get_lens(om10_id)
zl = lens.ZLENS[0]
zs = lens.ZSRC[0]
qe = 1.0 / (1.0 - lens.ELLIP[0])
phi = lens.PHIE[0]
vd0 = lens.VELDISP[0] # needed from OM10
re = aa.re_sv(vd0, zl, zs)
lpars = np.zeros((6))
lpars[
0] = 0.0 # x coordinate of the center of lens (in units of Einstein radius).
lpars[
1] = 0.0 # y coordinate of the center of lens (in units of Einstein radius).
lpars[2] = 1.0 / qe # Axis ratio of lens.
lpars[3] = 0.0 # Core size of lens (in units of Einstein radius).
lpars[4] = re # Einstein radius of lens (in units of arcsec).
lpars[5] = phi # Orientation of lens (in units of degree).
alpha1, alpha2, kappa, shears1, shear2, mu = lensing_model_SIE(
xi1, xi2, lpars)
return alpha1, alpha2, mu, kappa
def main():
zl = 0.17
zs = 3.0
sigmav = 600 #km/s
re0 = aa.re_sv(sigmav,zl,zs)
me0 = aa.m200_sv(sigmav,zl)
#zs_arr = np.linspace(zl,10.0,100)
#re_arr = zs_arr*0.0
#for i in xrange(len(zs_arr)):
#re_arr[i] = aa.re_sv(sigmav,zl,zs_arr[i])
#pl.figure()
#pl.plot(zs_arr,re_arr,'-')
nrbins = 5
nnn = 128*nrbins
#dsx = boxsize/nnn
dsx = 0.396/nrbins # arcsec
bsz = dsx*nnn # in the units of Einstein Radius
print re0,me0,bsz
#nnn = 512
#bsz = 40 # in the units of Einstein Radius
#dsx = bsz/nnn # arcsec
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)
#----------------------------------------------------------------------
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 = re0 #Einstein radius of lens.
pha = 5.0 #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
#pl.figure()
#pl.contour(xi2,xi1,mua)
#pl.colorbar()
#pl.figure()
#pl.contour(yi2,yi1,mua)
#pl.colorbar()
mags_of_sources = 100.0
#ys1 = 0.85
#ys2 = -0.22
#ys11 = ys1
#ys12 = ys2
#xroot1,xroot2,nroots = trf.roots_zeros(xi1,xi2,ai1,ai2,ys1,ys2)
#idr1 = ((np.array(xroot1)+bsz/2.0-dsx/2.0)/dsx).astype('int')
#idr2 = ((np.array(xroot2)+bsz/2.0-dsx/2.0)/dsx).astype('int')
g_limage = xi1*0.0
#g_limage[idr1,idr2] = mags_of_sources*np.abs(mua[idr1,idr2])
ys1 = 0.67
ys2 = 0.75
ys21 = ys1
ys22 = ys2
xroot1,xroot2,nroots = trf.roots_zeros(xi1,xi2,ai1,ai2,ys1,ys2)
#pl.figure()
#pl.xlim(-bsz/2,bsz/2)
#pl.ylim(-bsz/2,bsz/2)
#pl.plot(ys11,ys12,'wo')
#pl.plot(ys21,ys22,'yo')
idr1 = ((np.array(xroot1)+bsz/2.0-dsx/2.0)/dsx).astype('int')
idr2 = ((np.array(xroot2)+bsz/2.0-dsx/2.0)/dsx).astype('int')
g_limage[idr1,idr2] = g_limage[idr1,idr2] + mags_of_sources*np.abs(mua[idr1,idr2])
#pl.figure()
#pl.contourf(g_limage)
#pl.colorbar()
#pl.figure()
#pl.contour(yi1,yi2,mua)
#pl.colorbar()
#print xroot1,xroot2
#pl.figure(figsize=(10,10))
#pl.xlim(-2.0,2.0)
#pl.ylim(-2.0,2.0)
#pl.plot(xroot1,xroot2,'go')
#pl.plot(ys1,ys2,'ko')
#----------------------------------------------------------------------
g_amp = 0.1*re # peak brightness value
g_sig = 300.*re # Gaussian "sigma" (i.e., size)
g_xcen = xc1 # x position of center (also try (0.0,0.14)
g_ycen = xc2 # y position of center
g_axrat = q # minor-to-major axis ratio
g_pa = pha+90 # major-axis position angle (degrees) c.c.w. from x axis
gpar = np.asarray([g_amp,g_sig,g_xcen,g_ycen,g_axrat,g_pa])
#----------------------------------------------------------------------
#g_simage = gauss_2d(xi1,xi2,gpar) # modeling source as 2d Gaussian with input parameters.
g_lensimage = gauss_2d(xi1,xi2,gpar)
g_limage = g_limage + g_lensimage/3.0/nrbins**2.0
#pl.figure()
#pl.contourf(g_lensimage)
#pl.colorbar()
#file_psf = "./sdsspsf.fits"
#g_psf = pyfits.getdata(file_psf)-1000.0
#g_psf = g_psf/np.sum(g_psf)
#g_psf = congrid.congrid(g_psf,[np.shape(g_psf)[0]*nrbins,np.shape(g_psf)[1]*nrbins])
#g_psf = sil.RectBivariateSpline(g_psf,
#bbox=[np.shape(g_psf)[0]*nrbins,np.shape(g_psf)[1]*nrbins,np.shape(g_psf)[0]*nrbins,np.shape(g_psf)[1]*nrbins], kx=3, ky=3, s=0)
#print np.shape(g_psf),np.max(g_lensimage)
#pl.figure()
##pl.contourf(g_limage)
##pl.colorbar()
#pl.plot(ys11,ys12,'wo')
#pl.plot(ys21,ys22,'yo')
#file_noise = "./sdssgal.fits"
#g_noise = pyfits.getdata(file_noise)-1000.0
#g_limage = ss.fftconvolve(g_limage,g_psf,mode="same")
#file_noise = "./sdssgal.fits"
#g_noise = pyfits.getdata(file_noise)-1000.0
nstd = 0.5001
g_noise=nstd*np.random.normal(0.0,1.0,(nnn,nnn))
g_limage = gaussian_filter(g_limage,5)
##pl.figure()
##pl.contourf(g_limage)
#pl.colorbar()
g_limage = g_limage+g_noise
output_filename = "./test.fits"
pyfits.writeto(output_filename,g_limage,clobber=True)
return 0
idr2 = ((np.array(xroot2)+bsz/2.0-dsx/2.0)/dsx).astype('int')
g_limage[idr1,idr2] = g_limage[idr1,idr2] + spar[2]*np.abs(mua[idr1,idr2])
return g_limage
#------------------------------------------------------------------------------
if __name__ == '__main__':
nnn = 512
bsz = 50.0 # in the units of Einstein Radius
dsx = bsz/nnn # arcsec
xi1,xi2 = make_r_coor(nnn,dsx)
#----------------------------------------------------------------------
zl = 0.17
zs = 3.0
sigmav = 600 #km/s
re0 = aa.re_sv(sigmav,zl,zs)
#----------------------------------------------------------------------
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 = re0 #Einstein radius of lens.
pha = 5.0 #Orintation of lens.
lpar = np.asarray([xc1,xc2,q,rc,re,pha])
ai1,ai2,mua = lens_equation_sie(xi1,xi2,lpar)
#----------------------------------------------------------------------
ys1 = 0.0
ys2 = 0.0
mags_of_sources = 100.0
spar = np.asarray([ys1,ys2,mags_of_sources])
#----------------------------------------------------------------------
def calculate_lc_of_imgs():
zl = 0.5
zs = 2.0
ql0 = 0.699999999999
rc0 = 0.000000000000
svt = 255.0
re0 = aa.re_sv(svt,zl,zs)
print re0
nnn = 512
bsz = 4.0
dsx = bsz/nnn
xb1 = 0.0
xb2 = 0.0
xl1 = 0.0
xl2 = 0.0
#ys1 = np.random.random(1)*(0.2-0.1)+0.05
#ys2 = np.random.random(1)*(0.2-0.1)+0.05
#print ys1,ys2,dsx
ys1 = 0.09892207 #Fold images
ys2 = 0.09783113 #Fold images
#ys1 = 0.14250078
#ys2 = 0.12287053
xroot1,xroot2,xroot3,nroots = roots_zeros(xb1,xb2,xl1,xl2,bsz,nnn,ys1,ys2)
xi1,xi2 = standard_grids(xb1,xb2,bsz,nnn)
alpha1,alpha2 = nie_alphas(xi1,xi2,xl1,xl2,re0,rc0,ql0)
yi1 = xi1-alpha1
yi2 = xi2-alpha2
xrv1,xrv2,xrv3,nroots = mapping_triangles(ys1,ys2,xi1,xi2,yi1,yi2)
xroot1 = np.array(xrv1)
xroot2 = np.array(xrv2)
xroot3 = np.array(xrv3)
xr1 = (xroot1[0,:]+xroot2[0,:]+xroot3[0,:])/3.0
xr2 = (xroot1[1,:]+xroot2[1,:]+xroot3[1,:])/3.0
td = td_nie_single(svt,xr1,xr2,ql0,rc0,0.5,2.0)
mu = nie_mu(xr1,xr2,re0,rc0,ql0)
time_days,mags = np.loadtxt("./SN_opsim.csv",dtype='string', delimiter=',', usecols=(1, 4), unpack=True)
time_days = time_days.astype("double")
days1 = time_days+td[0]
days2 = time_days+td[1]
days3 = time_days+td[2]
days4 = time_days+td[3]
days5 = time_days+td[4]
mags = mags.astype("double")
mags1 = mags-2.5*np.log10(np.abs(mu[0]))
mags2 = mags-2.5*np.log10(np.abs(mu[1]))
mags3 = mags-2.5*np.log10(np.abs(mu[2]))
mags4 = mags-2.5*np.log10(np.abs(mu[3]))
mags5 = mags-2.5*np.log10(np.abs(mu[4]))
#mui = lens.MAG[0]
#nim = lens.NIMG[0]
#ms = lens.MAGI_IN[0]
#mi = ms - 2.5*np.log10(np.abs(mui[:nim]))
#print "om10.plot_lens: source magnitudes, magnification, image magnitudes:",ms,mui[:nim],mi
#print time_days
#print mags
#from astropy.time import Time
#t = Time(time_days, format='mjd')
#print t.isot
pl.figure()
pl.ylim(32,22)
pl.plot(days1-49500,mags1,'r-')
pl.plot(days2-49500,mags2,'g-')
pl.plot(days3-49500,mags3,'y-')
pl.plot(days4-49500,mags4,'b-')
pl.plot(days5-49500,mags5,'m-')
mua = nie_mu(xi1,xi2,re0,rc0,ql0)
pl.figure(figsize=(5,5))
pl.xlim(-2.0,2.0)
pl.ylim(-2.0,2.0)
pl.plot(xr1[0],xr2[0],'ro')
pl.plot(xr1[1],xr2[1],'go')
pl.plot(xr1[2],xr2[2],'yo')
pl.plot(xr1[3],xr2[3],'bo')
pl.plot(xr1[4],xr2[4],'mo')
pl.contour(xi1,xi2,mua,colors=('r',))
pl.contour(yi1,yi2,mua,colors=('g',))
pl.plot(ys1,ys2,'ko')
return [xr1,xr2],[days1,days2,days3,days4,days5], [mags1,mags2,mags3,mags4,mags5]
