def main():
fclusters = sys.argv[1]
fsatellites = sys.argv[2]
#print fclusters,fsatellites
ra, dec, z, rich, Ms, Izspec = np.loadtxt(fclusters,
unpack=True,
skiprows=1)
xx1, xx2, xx3, xx4, ras, decs, mss, Pmem = np.loadtxt(fsatellites,
unpack=True,
skiprows=1)
for i in range(len(ra)):
if rich[i] >= 10.0 and rich[i] < 30000.0 and z[i] >= 0.55 and z[
i] < 1.10:
rax = ra[i]
decx = dec[i]
zx = z[i]
dl = cosmos.Da(zx)
ix = xx1 == rax
iy = Pmem >= 0.8
idx = ix & iy
rr1 = ras[idx]
rr2 = decs[idx]
for j in range(len(xx1[idx])):
sep = dl * separation(rax, decx, rr1[j], rr2[j]) * (1.0 + zx)
if sep >= 0.1 and sep <= 0.2:
print rr1[j], rr2[j], zx, Pmem[j]
def readsource(filename, iredshift):
ra,dec,e1,e2,res,wt,m,c1,c2,erms,z1,\
z2,z3,z4,z5,z6=np.loadtxt(filename,unpack=True)
if iredshift == 1:
zs = z1
if iredshift == 2:
zs = z2
if iredshift == 3:
zs = z3
if iredshift == 4:
zs = z4
if iredshift == 5:
zs = z5
if iredshift == 6:
zs = z6
dis = np.zeros(len(ra))
for i in range(len(ra)):
dis[i] = cosmos.Da(zs[i])
ix = res > 1.0 / 3.0
shapes = {"ra":ra[ix],"dec":dec[ix],"z":zs[ix],\
"dis":dis[ix],"e1":e1,"e2":e2,\
"erms":erms,"res":res,"m":m,"c1":c1,"c2":c2}
return shapes
def run_main():
#----------------------------------------------------------------------
SnapID = 135
HaloID = 50
zl = 0.5
zs = 2.0
nnn = 512
dm = il.snapshot.loadHalo(basePath, SnapID, HaloID, 'dm')
#npp = dm['count']
x1 = dm['Coordinates'][:, 0] #/1e3/(1.0+zl) # Mpc/h, comoving
x2 = dm['Coordinates'][:, 1] #/1e3/(1.0+zl) # Mpc/h, comoving
x3 = dm['Coordinates'][:, 2] #/1e3/(1.0+zl) # Mpc/h, comoving
ptnl_p = dm['Potential']
idx_ptnl_p_min = ptnl_p == ptnl_p.min()
xcp1 = x1[idx_ptnl_p_min]
xcp2 = x2[idx_ptnl_p_min]
xcp3 = x3[idx_ptnl_p_min]
bsz = 50.0 # kpc/h
sdens = read_sdens_from_illustris(dm, x1 - xcp1, x2 - xcp2, x3 - xcp3, bsz,
nnn)
#kappa = sdens/mm.sigma_crit(zl,zs)
#----------------------------------------------------------------------
bsz = bsz / 1e3 / mm.Da(zl) * mm.apr #arcsec
dsx = bsz / nnn #arcsec
xi1, xi2 = make_r_coor(nnn, dsx)
ai1, ai2 = pcs.call_cal_alphas0(sdens, nnn, bsz, zl, zs)
yi1 = xi1 - ai1
yi2 = xi2 - ai2
phi = pcs.call_cal_phi(sdens, nnn, bsz, zl, zs)
mua = pcs.call_alphas_to_mu(ai1, ai2, nnn, dsx)
#----------------------------------------------------------------------
ys1 = 0.0
ys2 = 0.0
xroot1, xroot2, nroots = trf.roots_zeros(xi1, xi2, ai1, ai2, ys1, ys2)
#pl.figure()
#pl.plot(ys1,ys2,'ko')
#pl.plot(xroot1,xroot2,'rs')
#pl.contour(xi1,xi2,mua)
#pl.contour(yi1,yi2,mua)
#pl.colorbar()
muii = pcs.call_inverse_cic_single(mua, xroot1, xroot2, dsx)
phii = pcs.call_inverse_cic_single(phi, xroot1, xroot2, dsx)
Kc = (1.0 + zl) / mm.vc * (mm.Da(zl) * mm.Da(zs) / mm.Da2(
zl, zs)) * mm.Mpc / (1e3 * mm.dy) / mm.apr / mm.apr
td = Kc * (0.5 * ((xroot1 - ys1)**2.0 + (xroot2 - ys2)**2.0) - phii)
time_days, mags = np.loadtxt("./SN_opsim.csv",
dtype='string',
delimiter=',',
usecols=(1, 4),
unpack=True)
time_days = time_days.astype("double")
ldays = np.zeros((nroots, len(time_days)))
#for i in xrange(nroots):
#ldays[i,:] = time_days+td[i]
ldays[0, :] = time_days + td[0]
ldays[1, :] = time_days + td[1]
ldays[2, :] = time_days + td[2]
mags = mags.astype("double")
lmags = np.zeros((nroots, len(time_days)))
#for i in xrange(nroots):
#lmags[i,:] = mags-2.5*np.log10(np.abs(muii[i]))
lmags[0, :] = mags - 2.5 * np.log10(np.abs(muii[0]))
lmags[1, :] = mags - 2.5 * np.log10(np.abs(muii[1]))
lmags[2, :] = mags - 2.5 * np.log10(np.abs(muii[2]))
cmap = mpl.cm.jet_r
#------------------------------------------------------------------------------
#linestyle=linestyle, color=color
pl.figure(figsize=(8, 8))
pl.xlabel("Days")
pl.ylabel("Mags")
pl.ylim(27, 21)
#for i in xrange(nroots):
#pl.plot(ldays[i,:]-49500+820,lmags[i,:],linestyle='-',color=cmap(i/float(nroots)))
pl.plot(ldays[0, :] - 49500 + 820, lmags[0, :], linestyle='-', color="r")
pl.plot(ldays[1, :] - 49500 + 820, lmags[1, :], linestyle='-', color="g")
pl.plot(ldays[2, :] - 49500 + 820, lmags[2, :], linestyle='-', color="b")
pl.figure(figsize=(8, 8))
pl.xlim(-5.0, 5.0)
pl.ylim(-5.0, 5.0)
pl.xlabel("arcsec")
pl.ylabel("arcsec")
#for i in xrange(nroots):
#pl.plot(xroot1[i],xroot2[i],marker='o',color=cmap(i/float(nroots)))
#markersize=10
pl.plot(xroot1[0], xroot2[0], marker='o', color="r", markersize=10)
pl.plot(xroot1[1], xroot2[1], marker='o', color="g", markersize=10)
pl.plot(xroot1[2], xroot2[2], marker='o', color="b", markersize=10)
pl.contour(xi1, xi2, mua, colors=('r', ))
pl.contour(yi1, yi2, mua, colors=('g', ))
pl.plot(ys1, ys2, 'ks')
print td
def re_sv(sv, z1, z2):
res = 4.0 * np.pi * (sv**2.0 / mm.vc**2.0) * mm.Da2(
z1, z2) / mm.Da(z2) * mm.apr
return res
def run_main():
#----------------------------------------------------------------------
SnapID = 135
HaloID = 50
bsz = 200.0 # kpc/h
zl = 0.5
zs = 2.0
#zs0= 10.0
nnn = 512
dsi = 0.03
dm = il.snapshot.loadHalo(basePath, SnapID, HaloID, 'dm')
#npp = dm['count']
x1 = dm['Coordinates'][:, 0] #/1e3/(1.0+zl) # Mpc/h, comoving
x2 = dm['Coordinates'][:, 1] #/1e3/(1.0+zl) # Mpc/h, comoving
x3 = dm['Coordinates'][:, 2] #/1e3/(1.0+zl) # Mpc/h, comoving
ptnl_p = dm['Potential']
idx_ptnl_p_min = ptnl_p == ptnl_p.min()
xcp1 = x1[idx_ptnl_p_min]
xcp2 = x2[idx_ptnl_p_min]
xcp3 = x3[idx_ptnl_p_min]
sdens = read_sdens_from_illustris(dm, x1 - xcp1, x2 - xcp2, x3 - xcp3, bsz,
nnn)
#kappa = sdens/mm.sigma_crit(zl,zs)
#----------------------------------------------------------------------
bsz = bsz / 1e3 / mm.Da(zl) * mm.apr #arcsec
dsx = bsz / nnn #arcsec
print bsz
print dsx
xi1, xi2 = make_r_coor(nnn, dsx)
ai1, ai2 = pcs.call_cal_alphas0(sdens, nnn, bsz, zl, zs)
yi1 = xi1 - ai1
yi2 = xi2 - ai2
phi = pcs.call_cal_phi(sdens, nnn, bsz, zl, zs)
mua = pcs.call_alphas_to_mu(ai1, ai2, nnn, dsx)
#----------------------------------------------------------------------
ys1 = 0.0
ys2 = 0.0
xroot1, xroot2, nroots = trf.roots_zeros(xi1, xi2, ai1, ai2, ys1, ys2)
#pl.figure()
#pl.plot(ys1,ys2,'ko')
#pl.plot(xroot1,xroot2,'rs')
#pl.contour(xi1,xi2,mua)
#pl.contour(yi1,yi2,mua)
#pl.colorbar()
muii = pcs.call_inverse_cic_single(mua, xroot1, xroot2, dsx)
phii = pcs.call_inverse_cic_single(phi, xroot1, xroot2, dsx)
Kc = (1.0 + zl) / mm.vc * (mm.Da(zl) * mm.Da(zs) / mm.Da2(
zl, zs)) * mm.Mpc / (1e3 * mm.dy) / mm.apr / mm.apr
td = Kc * (0.5 * ((xroot1 - ys1)**2.0 + (xroot2 - ys2)**2.0) - phii)
time_days, mags = np.loadtxt("./SN_opsim.csv",
dtype='string',
delimiter=',',
usecols=(1, 4),
unpack=True)
time_days = time_days.astype("double")
#------------------------------------------------------------------------------
ldays = np.zeros((nroots, len(time_days)))
for i in xrange(nroots):
ldays[i, :] = time_days + td[i]
mags = mags.astype("double")
lmags = np.zeros((nroots, len(time_days)))
for i in xrange(nroots):
lmags[i, :] = mags - 2.5 * np.log10(np.abs(muii[i]))
#cmap = mpl.cm.jet_r
cmap = mpl.cm.gist_earth
pl.figure(figsize=(8, 8))
pl.xlabel("Days")
pl.ylabel("Mags")
pl.ylim(30, 22)
for i in xrange(nroots):
pl.plot(ldays[i, :] - 49500 + 31010 - 50,
lmags[i, :],
linestyle='-',
color=cmap(i / float(nroots)),
markersize=10)
#pl.plot(ldays[0,:]-49500+31010-50,lmags[0,:],linestyle='-',color="k")
#pl.plot(ldays[1,:]-49500+31010-50,lmags[1,:],linestyle='-',color="b")
#pl.plot(ldays[2,:]-49500+31010-50,lmags[2,:],linestyle='-',color="g")
#pl.plot(ldays[3,:]-49500+31010-50,lmags[3,:],linestyle='-',color="m")
#pl.plot(ldays[4,:]-49500+31010-50,lmags[4,:],linestyle='-',color="r")
pl.figure(figsize=(8, 8))
#pl.xlim(-bsz/2.0,bsz/2.0)
#pl.ylim(-bsz/2.0,bsz/2.0)
pl.xlim(-10.0, 10.0)
pl.ylim(-10.0, 10.0)
pl.xlabel("arcsec")
pl.ylabel("arcsec")
for i in xrange(nroots):
pl.plot(xroot1[i],
xroot2[i],
marker='o',
color=cmap(i / float(nroots)),
markersize=10)
#pl.plot(xroot1[0],xroot2[0],marker='o',color="k",markersize=15) #
#pl.plot(xroot1[1],xroot2[1],marker='o',color="b",markersize=15) #
#pl.plot(xroot1[2],xroot2[2],marker='o',color="g",markersize=15) #
#pl.plot(xroot1[3],xroot2[3],marker='o',color="m",markersize=15) #
#pl.plot(xroot1[4],xroot2[4],marker='o',color="r",markersize=15) #
pl.contour(xi1, xi2, mua, colors=('r', ))
pl.contour(yi1, yi2, mua, colors=('g', ))
pl.plot(ys1, ys2, 'cs')
print td
#------------------------------------------------------------------------------
ysc1 = 0.0
ysc2 = 0.0
g_source = pyfits.getdata(
"./dry_run_pipeline/439.0_149.482739_1.889989_processed.fits")
g_source = np.array(g_source, dtype="<d")
pl.figure()
pl.contourf(g_source)
pl.colorbar()
std_srcs = np.std(g_source)
print std_srcs, std_srcs * 4.0
gthhd = 6.0 * std_srcs
g_source = g_source - gthhd
g_source[g_source <= 0.0] = 0.0
lensed_img = lv4.call_ray_tracing(g_source, yi1, yi2, ysc1, ysc2, dsi)
#lensed_img = pcs.call_ray_tracing_single(ai1,ai2,nnn,bsz,zl)
lenses_img = slf.loadin_fits(HaloID, zl, bsz, nnn)
lroots = 10.0**((lmags - 25.523) / (-2.5))
final_img = lensed_img + lenses_img / 100.0
res = stacking_points_and_galaxies(xroot1, xroot2, lroots[:, 70],
final_img, bsz, nnn)
pl.figure()
pl.contourf(xi1, xi2, res)
pl.colorbar()
pyfits.writeto("all.fits", res, clobber=True)
return res
def run_main():
HaloID = 0
SnapID = 135
bsz = 500.0 # ckpc
zl = 0.5
zs0 = 10.0
nnn = 512
dm = il.snapshot.loadHalo(basePath, SnapID, HaloID, 'dm')
#npp = dm['count']
x1 = dm['Coordinates'][:, 0] #/1e3/(1.0+zl) # Mpc/h, comoving
x2 = dm['Coordinates'][:, 1] #/1e3/(1.0+zl) # Mpc/h, comoving
x3 = dm['Coordinates'][:, 2] #/1e3/(1.0+zl) # Mpc/h, comoving
ptnl_p = dm['Potential']
idx_ptnl_p_min = ptnl_p == ptnl_p.min()
xcp1 = x1[idx_ptnl_p_min]
xcp2 = x2[idx_ptnl_p_min]
xcp3 = x3[idx_ptnl_p_min]
#sdens = pcs.call_sph_sdens_arrays(x1,x2,x3,xc1,xc2,xc3,bsz,nnn,npp,pmass)*1e6
sdens = read_sdens_from_illustris(dm, x1 - xcp1, x2 - xcp2, x3 - xcp3, bsz,
nnn) * (1.0 + zl)**2.0
#kappa = sdens/mm.sigma_crit(zl,zs0)
#pl.figure()
#pl.contourf(sdens)
#pl.colorbar()
##print np.max(kappa)
#----------------------------------------------------------------------
bsz = bsz / 1e3 / mm.Da(zl) * mm.apr #arcsec
dsx = bsz / nnn #arcsec
print bsz
xi1, xi2 = make_r_coor(nnn, dsx)
ai1, ai2 = pcs.call_cal_alphas0(sdens, nnn, bsz, zl, zs0)
lensed_img = pcs.call_ray_tracing_single(ai1, ai2, nnn, bsz, zl)
lenses_img = slf.loadin_fits(HaloID, zl, bsz, nnn)
#----------------------------------------------------------------------
#zs = 2.0
#dsi = 0.03
#ysc1 = 0.0
#ysc2 = 0.0
#ai1,ai2 = pcs.call_cal_alphas0(sdens,nnn,bsz,zl,zs)
#yi1 = xi1-ai1
#yi2 = xi2-ai2
#g_source = pyfits.getdata("./dry_run_pipeline/439.0_149.482739_1.889989_processed.fits")
#g_source = np.array(g_source,dtype="<d")
#g_source[g_source<=0.0001] = 1e-6
#lensed_img = lv4.call_ray_tracing(g_source,yi1,yi2,ysc1,ysc2,dsi)
#----------------------------------------------------------------------
pl.figure()
pl.contourf(lensed_img)
pl.colorbar()
final_img = lensed_img + lenses_img / 100.0
pyfits.writeto("test.fits", final_img, clobber=True)
pl.figure()
pl.contourf(final_img)
pl.colorbar()
return 0
import numpy import mycosmology as cosmos z1 = 0.01 z2 = 0.2 omega_north = 2.14 Da1 = cosmos.Da(z1) * (1.0 + z1) Da2 = cosmos.Da(z2) * (1.0 + z2) volume = omega_north * (1.0 / 3.0) * (Da2**3 - Da1**3) z1 = 0.01 z2 = 1.0 Da1 = cosmos.Da(z1) * (1.0 + z1) Da2 = cosmos.Da(z2) * (1.0 + z2) volume2 = omega_north * (1.0 / 3.0) * (Da2**3 - Da1**3) print(volume2 / volume) #print 3660.0*400**3/volume