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 __init__(self, catsim_cat):
self.catalog = catsim_cat
# ****** THIS ASSUMES THAT THE ENVIRONMENT VARIABLE OM10_DIR IS SET *******
lensdb = om10.DB(catalog=os.environ['OM10_DIR'] +
"/data/qso_mock.fits")
self.lenscat = lensdb.lenses.copy()
return
def __init__(self,
catsim_cat,
om10_cat='twinkles_tdc_rung4.fits',
density_param=1.):
"""
Input:
catsim_cat:
The results array from an instance catalog.
density_param:
A float between 0. and 1.0 that determines the fraction of eligible agn objects that become lensed.
Output:
updated_catalog:
A new results array with lens systems added.
"""
self.catalog = catsim_cat
# ****** THIS ASSUMES THAT THE ENVIRONMENT VARIABLE OM10_DIR IS SET *******
lensdb = om10.DB(catalog=om10_cat)
self.lenscat = lensdb.lenses.copy()
self.density_param = density_param
self.bandpassDict = BandpassDict.loadTotalBandpassesFromFiles(
bandpassNames=['i'])
specFileStart = 'Burst'
for key, val in sorted(iteritems(SpecMap.subdir_map)):
if re.match(key, specFileStart):
galSpecDir = str(val)
galDir = str(
getPackageDir('sims_sed_library') + '/' + galSpecDir + '/')
self.LRG_name = 'Burst.25E09.1Z.spec'
self.LRG = Sed()
self.LRG.readSED_flambda(str(galDir + self.LRG_name))
def get_halo_IDs(om10_id):
db = om10.DB(catalog="./qso_mock.fits")
lens = db.get_lens(om10_id)
#nim= lens.NIMG[0]
#md = lens.APMAG_I[0]
#ms = lens.MAGI_IN[0]
ys1 = lens.XSRC[0]
ys2 = lens.YSRC[0]
#xl = 0.0
#yl = 0.0
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
mtotal0 = aa.m200_sv(vd0, zl) # needed from OM10
print "Input: ", phi, qe, mtotal0, vd0
#----------------------------------------------------------------------
# index, snapid,haloid,subhid,ag_stellar,ag_total,eq_stellar,eq_total,ms_stellar,ms_total,vd_stellar
#
snapid, haloid, subhid, ag_total, eq_stellar, ms_total, vd_stellar = np.loadtxt(
"../produce_e_catalogs/results.dat",
usecols=(1, 2, 3, 5, 6, 9, 10),
unpack=True)
nhalos = len(subhid)
ksi_om10 = (ag_total/phi-1.0)**2.0/nhalos*0.0+(eq_stellar/qe-1.0)**2.0/nhalos*0.4 \
+ (ms_total/mtotal0-1.0)**2.0/nhalos*0.01+(vd_stellar*(1.0+zl)/vd0-1.0)**2.0/nhalos*100.4
idx = ksi_om10 == np.min(ksi_om10)
print "Matched: ", ag_total[idx], eq_stellar[idx], ms_total[
idx], vd_stellar[idx]
snapid_n = int(snapid[idx])
haloid_n = int(haloid[idx])
subhid_n = int(subhid[idx])
lpar = [snapid_n, haloid_n, subhid_n, zl]
spar = [ys1, ys2, lens.MAGI_IN[0], zs]
#----------------------------------------------------------------------
mui = lens.MAG[0]
nim = lens.NIMG[0]
ms = lens.MAGI_IN[0]
muis = ms - 2.5 * np.log10(np.abs(mui[:nim]))
ximg1 = lens.XIMG[0][:nim]
ximg2 = lens.YIMG[0][:nim]
#print "om10.plot_lens: source magnitudes, magnification, image magnitudes:",ms,mui[:nim],mi
ipar = [ximg1, ximg2, muis]
return lpar, spar, ipar, lens
# hdus.header.set('CRVAL1',0.0,'Right ascension (J2000 degrees)')
# Write them out
hdus.writeto(scifile,clobber=True)
hduv.writeto(varfile,clobber=True)
return
# ======================================================================
if __name__ == '__main__':
# Some examples!
db = om10.DB(catalog=os.path.expandvars("$OM10_DIR/data/qso_mock.fits"))
# Get one lens:
id = 7176527
lens = db.get_lens(id)
# Set up imager:
imager = om10.Imager(survey='PS1')
# imager.set('fov',6.0) # Needs to change imsize too!
# Make a single image of the lens:
def setUp(self):
self.db = om10.DB()
self.db.paint(synthetic=True)
def __init__(self,
catsim_cat,
visit_mjd,
specFileMap,
sed_path,
om10_cat='twinkles_lenses_v2.fits',
sne_cat='dc2_sne_cat.csv',
density_param=1.,
cached_sprinkling=False,
agn_cache_file=None,
sne_cache_file=None,
defs_file=None):
"""
Parameters
----------
catsim_cat: catsim catalog
The results array from an instance catalog.
visit_mjd: float
The mjd of the visit
specFileMap:
This will tell the instance catalog where to write the files
om10_cat: optional, defaults to 'twinkles_lenses_v2.fits
fits file with OM10 catalog
sne_cat: optional, defaults to 'dc2_sne_cat.csv'
density_param: `np.float`, optioanl, defaults to 1.0
the fraction of eligible agn objects that become lensed and should
be between 0.0 and 1.0.
cached_sprinkling: boolean
If true then pick from a preselected list of galtileids
agn_cache_file: str
sne_cache_file: str
defs_file: str
Returns
-------
updated_catalog:
A new results array with lens systems added.
"""
twinklesDir = getPackageDir('Twinkles')
om10_cat = os.path.join(twinklesDir, 'data', om10_cat)
self.catalog = catsim_cat
# ****** THIS ASSUMES THAT THE ENVIRONMENT VARIABLE OM10_DIR IS SET *******
lensdb = om10.DB(catalog=om10_cat, vb=False)
self.lenscat = lensdb.lenses.copy()
self.density_param = density_param
self.bandpassDict = BandpassDict.loadTotalBandpassesFromFiles(
bandpassNames=['i'])
self.sne_catalog = pd.read_csv(
os.path.join(twinklesDir, 'data', sne_cat))
#self.sne_catalog = self.sne_catalog.iloc[:101] ### Remove this after testing
self.used_systems = []
self.visit_mjd = visit_mjd
self.sn_obj = SNObject(0., 0.)
self.write_dir = specFileMap.subdir_map['(^specFileGLSN)']
self.sed_path = sed_path
self.cached_sprinkling = cached_sprinkling
if self.cached_sprinkling is True:
if ((agn_cache_file is None) | (sne_cache_file is None)):
raise AttributeError(
'Must specify cache files if using cached_sprinkling.')
#agn_cache_file = os.path.join(twinklesDir, 'data', 'test_agn_galtile_cache.csv')
self.agn_cache = pd.read_csv(agn_cache_file)
#sne_cache_file = os.path.join(twinklesDir, 'data', 'test_sne_galtile_cache.csv')
self.sne_cache = pd.read_csv(sne_cache_file)
else:
self.agn_cache = None
self.sne_cache = None
if defs_file is None:
self.defs_file = os.path.join(twinklesDir, 'data',
'catsim_defs.csv')
else:
self.defs_file = defs_file
specFileStart = 'Burst'
for key, val in sorted(iteritems(SpecMap.subdir_map)):
if re.match(key, specFileStart):
galSpecDir = str(val)
self.galDir = str(
getPackageDir('sims_sed_library') + '/' + galSpecDir + '/')
self.imSimBand = Bandpass()
self.imSimBand.imsimBandpass()
#self.LRG_name = 'Burst.25E09.1Z.spec'
#self.LRG = Sed()
#self.LRG.readSED_flambda(str(galDir + self.LRG_name))
#return
#Calculate imsimband magnitudes of source galaxies for matching
agn_fname = str(
getPackageDir('sims_sed_library') + '/agnSED/agn.spec.gz')
src_iband = self.lenscat['MAGI_IN']
src_z = self.lenscat['ZSRC']
self.src_mag_norm = []
for src, s_z in zip(src_iband, src_z):
agn_sed = Sed()
agn_sed.readSED_flambda(agn_fname)
agn_sed.redshiftSED(s_z, dimming=True)
self.src_mag_norm.append(matchBase().calcMagNorm(
[src], agn_sed, self.bandpassDict))
#self.src_mag_norm = matchBase().calcMagNorm(src_iband,
# [agn_sed]*len(src_iband),
#
# self.bandpassDict)
self.defs_dict = {}
with open(self.defs_file, 'r') as f:
for line in f:
line_defs = line.split(',')
if len(line_defs) > 1:
self.defs_dict[line_defs[0]] = line_defs[1].split('\n')[0]
phs = source_cat[5]
ndex = source_cat[6]
zs = source_cat[7]
#g_limage = sersic_SB_2D_tot(yi1,yi2,ysc1,ysc2,mag_tot,Reff_arc,qs,phs,zs)
Da_s = p13.angular_diameter_distance(2.0).value
Reff = Reff_arc * Da_s / apr * 1e6 # pc
Ieff = sersic_mag_tot_to_Ieff(mag_tot, Reff, ndex, zs)
g_limage = sersic_2d(yi1, yi2, ysc1, ysc2, Ieff, Reff_arc, qs, phs, ndex)
return g_limage
if __name__ == '__main__':
db = om10.DB(catalog="../qso_mock.fits")
#lid = 7176527
lid = 630751
lens = db.get_lens(lid)
lid = lens.LENSID[0]
xl1 = 0.0
xl2 = 0.0
vd = lens.VELDISP[0] # needed from OM10
zd = lens.ZLENS[0]
zs = lens.ZSRC[0]
ql = 1.0 - lens.ELLIP[0]
phi = lens.PHIE[0]
print ql
#how to calculate I_eff_mag, R_eff_arc, qs, phs
pylab.axis([-xmax, xmax, -xmax, xmax])
# Add a grid:
pylab.grid(color='grey', linestyle='--', linewidth=0.5)
# Plot graph to file:
if saveImg:
pngfile = "om10_qso_ID=" + str(id) + ".png"
pylab.savefig(pngfile)
print "om10.plot_lens: figure saved to file:", pngfile
# ======================================================================
if __name__ == '__main__':
db = om10.DB(catalog="data/qso_mock.fits")
# Pull out a specific lens and plot it:
id = 7176527
lens = db.get_lens(id)
om10.plot_lens(lens)
# Plot 3 random lenses and plot them:
lenses = db.select_random(maglim=21.4, area=30000.0, IQ=1.0, Nlens=3)
if lenses is not None:
for id in lenses.LENSID:
lens = db.get_lens(id)
om10.plot_lens(lens)
# ======================================================================
def setUp(self):
print "\nStarting a DB instance from scratch:"
self.db = om10.DB(catalog=None)
return
"""
Script that runs to generate toy catalogs
"""
import lenspop
import om10
import desc.slrealizer
import format_sdss_to_om10
import time
# initialize om10
db = om10.DB(vb=False, catalog='../../../data/qso_mock.fits')
# we select LSST-like lenses by quering with these values
db.select_random(maglim=23.3, area=5000.0, IQ=0.75)
# calculate the synthetic magnitude for the OM10 catalog -- need this
db.paint(synthetic=True)
# calculate the sizes for the OM10 catalog -- if you want to assume the point-source-like-galaxies, get rid of this
#db.calculate_size()
# initialize realizer
realizer = desc.slrealizer.SLRealizer(
catalog=db, observation='../../../data/twinkles_observation_history.csv')
t0 = time.time()
realizer.make_source_catalog(
save_dir='../../../data/source_catalog_galsim_noise_perfect.csv')
t1 = time.time()
print("Making the source catalog took %f seconds." % (t1 - t0))
realizer.make_object_catalog(
source_table_dir='../../../data/source_catalog_galsim_noise_perfect.csv',
save_dir='../../../data/object_catalog_galsim_noise_perfect.csv')
t2 = time.time()
print("Making the object catalog took %f seconds." % (t2 - t1))
def gauss_2d(x, y, xc, yc, sig):
res0 = ((x - xc)**2 + (y - yc)**2) / sig**2
res = np.exp(-0.5 * res0)
return res
if __name__ == '__main__':
print lef.lambda_e_tot(0.09182339)
bsz = 4.0
nnn = 512
dsx = bsz / nnn
x1, x2 = make_r_coor(nnn, dsx)
#---------------------------------------------
db = om10.DB(catalog="/Users/uranus/GitHub/OM10/data/qso_mock.fits")
# lid = 7176527
# lid = 8519202
# lid = 30184793
# lid = 14864406
# lid = 347938
lid = 21393434
lens = db.get_lens(lid)
om10.plot_lens(lens)
xl1 = 0.0
xl2 = 0.0
vd = lens.VELDISP[0] # needed from OM10
ql = 1.0 - lens.ELLIP[0]
le = lef.lambda_e_tot(lens.ELLIP[0])
def get_halo_cc(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
mtotal0 = aa.m200_sv(vd0, zl) # needed from OM10
#----------------------------------------------------------------------
# index, snapid,haloid,subhid,ag_stellar,ag_total,eq_stellar,eq_total,ms_stellar,ms_total,vd_stellar
#
snapid, haloid, subhid, ag_total, eq_stellar, ms_total, vd_stellar = np.loadtxt(
"../produce_e_catalogs/results.dat",
usecols=(1, 2, 3, 5, 6, 9, 10),
unpack=True)
print "Input: ", qe, phi, vd0, mtotal0
nhalos = len(subhid)
ksi_om10 = (ag_total/phi-1.0)**2.0/nhalos*0.0+(eq_stellar/qe-1.0)**2.0/nhalos*0.4 \
+ (ms_total/mtotal0-1.0)**2.0/nhalos*0.01+(vd_stellar*(1.0+zl)/vd0-1.0)**2.0/nhalos*100.4
idx = ksi_om10 == np.min(ksi_om10)
print "Matched: ", eq_stellar[idx], ag_total[idx], vd_stellar[
idx], ms_total[idx]
snapid_n = int(snapid[idx])
#haloid_n = int(haloid[idx])
subhid_n = int(subhid[idx])
bsz = 30.0 # ckpc
nnn = 512
stars = il.snapshot.loadSubhalo(basePath, snapid_n, subhid_n, 'stars')
x1 = stars['Coordinates'][:, 0] #/1e3/(1.0+zl) # Mpc/h, comoving
x2 = stars['Coordinates'][:, 1] #/1e3/(1.0+zl) # Mpc/h, comoving
x3 = stars['Coordinates'][:, 2] #/1e3/(1.0+zl) # Mpc/h, comoving
ptnl_p = stars['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]
(x1new, x2new) = trf.xy_rotate(x1, x2, xcp1, xcp2, -14.0)
sdens = read_sdens_from_illustris(stars, x1new, x2new, x3 - xcp3,
bsz / 512 * 10.0, nnn) * (1.0 + zl)**2.0
kappa = sdens / mm.sigma_crit(zl, zs)
bsz = bsz / 1e3 / mm.Dc(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)
mua = pcs.call_alphas_to_mu(ai1, ai2, nnn, dsx)
return xi1, xi2, ai1, ai2, mua, kappa
def setUp(self):
# self.db = om10.DB(catalog=os.path.expandvars("$OM10_DIR/data/qso_mock.fits"))
self.db = om10.DB(catalog=os.path.expandvars("data/qso_mock.fits"))
return
