def calculate_rf_lens_magnitudes(lens_redshift, velocity_dispersion,
filters_dict):
"""Calculates the reference-frame lens magnitudes in multiple filters
Parameters
----------
lens_redshift : float
Redshift of the lens
velocity_dispersion : float
Velocity dispersion of the lens
filters_dict : dict
(See output of `get_sdss_filters` for details)
Throughputs of various filters
Returns
-------
dict
Each key is one of string characters 'u', 'g', 'r', 'i', 'z'
representing the filter
Each value is the reference-frame apparent magnitude of the quasar
in the 'key' filter, of type float
"""
from stellarpop import tools
from lenspop import population_functions, distances
from astropy.cosmology import FlatLambdaCDM
# Instantiate Distance
distance = distances.Distance() #TODO: necessary?
# Instantiate LensPopulation
lenspop = population_functions.LensPopulation_()
# Instantiate FlatLambdaCDM cosmology with reasonable parameters
cosmology = FlatLambdaCDM(H0=70.0, Om0=0.3)
lens_sed = tools.getSED('BC_Z=1.0_age=9.000gyr')
velocity_dispersion = np.atleast_1d(velocity_dispersion)
# Absolute --> apparent magnitude conversion in the R-band
lens_abmag_r = tools.ABFilterMagnitude(filters_dict['r'], lens_sed,
lens_redshift)
distance_modulus = cosmology.distmod(lens_redshift).value
lens_appmag_r = lens_abmag_r + distance_modulus
# [Reference frame] Absolute --> apparent magnitude conversion in the R-band
rf_lens_abmag_r, _ = lenspop.EarlyTypeRelations(velocity_dispersion)
rf_lens_appmag = {}
rf_lens_appmag['r'] = rf_lens_abmag_r + distance_modulus
# Quantity which is added to ~magnitude to convert it into reference-frame ~magnitude
offset_rf = rf_lens_abmag_r - lens_abmag_r
# Converting absolute magnitude to reference-frame apparent magnitude
for band in 'ugiz':
rf_lens_appmag[band] = tools.ABFilterMagnitude(
filters_dict[band], lens_sed,
lens_redshift) + offset_rf + distance_modulus
return rf_lens_appmag
def create_models(self):
import scipy, cPickle
from stellarpop import tools
from stellarpop.ndinterp import ndInterp
index = {}
shape = []
axes = {}
axes_index = 0
for key in self.axes_names:
index[key] = {}
shape.append(self.axes[key]['points'].size)
axes[axes_index] = self.axes[key]['eval']
axes_index += 1
for i in range(self.axes[key]['points'].size):
index[key][self.axes[key]['points'][i]] = i
models = {}
model = scipy.empty(shape) * scipy.nan
for f in self.filter_names:
models[f] = {}
for z in self.redshifts:
models[f][z] = model.copy()
for file in self.files:
f = open(file, 'rb')
data = cPickle.load(f)
wave = cPickle.load(f)
f.close()
for key in data.keys():
obj = data[key]
jj = key
spec = obj['sed']
ind = []
for key in self.axes_names:
try:
ind.append([index[key][obj[key]]])
except:
print key, index[key]
print obj
df
for f in self.filter_names:
for i in range(len(self.redshifts)):
z = self.redshifts[i]
# correction is the units correction factor
correction = self.corrections[i]
sed = [wave, spec * correction]
mag = tools.ABFilterMagnitude(self.filters[f], sed, z)
if scipy.isnan(mag) == True:
df
models[f][z][ind] = mag
for f in self.filter_names:
for z in self.redshifts:
model = models[f][z].copy()
if scipy.isnan(model).any():
models[f][z] = None
else:
models[f][z] = ndInterp(axes, model)
return models
def getColor(f1, f2, z):
import numpy
from stellarpop import tools
import spasmoid
dir = spasmoid.__path__[0] + "/data"
qso = numpy.loadtxt('%s/vandenBerk.dat' % dir)
w = qso[:, 0].copy()
m = qso[:, 1].copy()
s = qso[:, 2].copy()
f1 = tools.filterfromfile(f1)
f2 = tools.filterfromfile(f2)
sed = [w, m]
color = tools.ABFilterMagnitude(f1, sed, z) - tools.ABFilterMagnitude(
f2, sed, z)
return color
def calculate_rf_quasar_magnitudes(quasar_redshift, apparent_magnitude_i,
filters_dict):
"""Calculates the reference-frame quasar magnitudes in multiple filters
Parameters
----------
quasar_redshift : float
Redshift of the source quasar
apparent_magnitude_i : float
Apparent magnitude of the source AGN in the I-band
filters_dict : dict
(See output of `get_sdss_filters` for details)
Throughputs of various filters
Returns
-------
dict
Each key is one of string characters 'u', 'g', 'r', 'i', 'z'
representing the filter
Each value is the reference-frame apparent magnitude of the quasar
in the 'key' filter, of type float
"""
from stellarpop import tools
quasar_sed = tools.getSED('agn')
q_offset = apparent_magnitude_i - tools.ABFilterMagnitude(
filters_dict['i'], quasar_sed, quasar_redshift)
rf_quasar_appmag = {}
if quasar_redshift < 3.9:
rf_quasar_appmag['u'] = tools.ABFilterMagnitude(
filters_dict['u'], quasar_sed, quasar_redshift) + q_offset
else:
rf_quasar_appmag['u'] = 99.0
for band in 'griz':
rf_quasar_appmag[band] = tools.ABFilterMagnitude(
filters_dict[band], quasar_sed, quasar_redshift) + q_offset
return rf_quasar_appmag
def calculate_rest_frame_r_magnitude(self, sed, veldisp, redshift, cosmo):
"""
Computes rest-frame r-band magnitude of a lens galaxy
Parameters
----------
sed : string
Name of SED to use
redshift : float
Redshift of object
d : float
Distance modulus to object
veldisp : float
For lens galaxies, the velocity dispersion can be passed in to
provide the absolute magnitude via the Fundamental Plane
Returns
-------
RF_Rmag_app : float
Reference r-band apparent magnitude
offset_RF_abs : float
Magnitude offset for converting absolute to apparent magnitude
Notes
-----
We don't need this function when painting quasars, because the OM10
catalog contains a reliable i-band apparent magnitude for each source.
"""
lenspop_constructor = population_functions.LensPopulation_()
# Reference Frame Absolute R magnitude
RF_RMag_abs, _ = lenspop_constructor.EarlyTypeRelations(veldisp)
RMag_abs = tools.ABFilterMagnitude(Rfilter, sed, redshift)
distModulus = cosmo.distmod(redshift).value
Rmag_app = RMag_abs + distModulus
offset_abs_app = RMag_abs - Rmag_app
offset_RF_abs = RF_RMag_abs - RMag_abs
RF_Rmag_app = RF_RMag_abs - offset_abs_app
return RF_Rmag_app, offset_RF_abs, distModulus
def paint(self, Nmax=None, verbose=False,
lrg_input_cat='$OM10_DIR/data/LRGo.txt',
qso_input_cat='$OM10_DIR/data/QSOo.txt',
synthetic=False):
"""
Add new columns to the table, for the magnitudes in various filters.
Parameters
----------
synthetic : boolean
Use `lenspop` to make synthetic magnitudes in various filters
target : string
Paint lenses ('lens') or sources ('source')
lrg_input_cat : string
Name of LRG catalog, if not using synthetic paint
qso_input_cat : string
Name of QSO catalog, if not using synthetic paint
verbose : boolean
print progress to stdout
Notes
-----
* Synthetic painting is very slow, as we loop over each object.
* The treatment of QSOs may be flawed: the offset calculation has not
been tested.
"""
if synthetic==False:
# read data from SDSS
f=open(os.path.expandvars(lrg_input_cat),'r')
lrg=loadtxt(f)
f.close()
g=open(os.path.expandvars(qso_input_cat),'r')
qso=loadtxt(g)
g.close()
###MY OWN REDSHIFT ONLY MATCHING HERE:
lens_props = ['MAGG_LENS','MAGR_LENS','MAGI_LENS','MAGZ_LENS', \
'MAGW1_LENS','MAGW2_LENS','MAGW3_LENS','MAGW4_LENS', 'SDSS_FLAG_LENS']
src_props = ['MAGG_SRC','MAGR_SRC','MAGI_SRC','MAGZ_SRC', \
'MAGW1_SRC','MAGW2_SRC','MAGW3_SRC','MAGW4_SRC', 'SDSS_FLAG_SRC']
tmp_lens = Table(np.zeros((len(self.sample),len(lens_props)),dtype='f8'),names=lens_props)
tmp_src = Table(np.zeros((len(self.sample),len(src_props)),dtype='f8'),names=src_props)
if verbose: print('setup done')
lrg_sort = lrg[np.argsort(lrg[:,0]),:]
qso_sort = qso[np.argsort(qso[:,0]),:]
lens_count = 0
for lens in self.sample:
#paint lens
ind = np.searchsorted(lrg_sort[:,0],lens['ZLENS'])
if ind >= len(lrg_sort): ind = len(lrg_sort) - 1
tmp_lens[lens_count] = lrg_sort[ind,6:] - lrg_sort[ind,8] + lens['APMAG_I'] #assign colors, not mags
#paint source
qso_ind = np.searchsorted(qso_sort[:,0],lens['ZSRC'])
if qso_ind >= len(qso_sort): qso_ind = len(qso_sort) - 1
tmp_src[lens_count] = qso_sort[qso_ind,1:] - qso_sort[qso_ind,3] + lens['MAGI']
lens_count += 1
self.sample = hstack([self.sample,tmp_lens,tmp_src])
if synthetic==True:
lens_count = 0
total = len(self.sample)
Rfilter = tools.filterfromfile('r_SDSS')
Ufilter = tools.filterfromfile('u_SDSS')
# sort the Ufilter array
Ufilterarg=np.sort(Ufilter[1])
Ufilter = (Ufilter[0], Ufilterarg, 1)
Gfilter = tools.filterfromfile('g_SDSS')
Ifilter = tools.filterfromfile('i_SDSS')
Zfilter = tools.filterfromfile('z_SDSS')
self.Nlenses=len(self.sample)
bands = ('r_SDSS_lens', 'g_SDSS_lens', 'i_SDSS_lens', 'z_SDSS_lens', 'u_SDSS_lens','r_SDSS_quasar', 'g_SDSS_quasar', 'i_SDSS_quasar', 'z_SDSS_quasar', 'u_SDSS_quasar')
if verbose: print('OM10: computing synthetic magnitudes in the following bands: ', bands)
# call a distance class constructor
d = distances.Distance()
# number of data in the table of calculated magnitude
totalEntrees = self.Nlenses*10.0
t = Table(np.arange(totalEntrees).reshape(self.Nlenses, 10),
names=bands)
Lsed = tools.getSED('BC_Z=1.0_age=9.000gyr')
Qsed = tools.getSED('agn')
from astropy.cosmology import FlatLambdaCDM
cosmo = FlatLambdaCDM(H0=70, Om0=0.3)
lenspop_constructor = population_functions.LensPopulation_()
for lens in self.sample:
# calculate the quasar magnitude
redshift = lens['ZSRC']
RF_Imag_app_q = lens['MAGI_IN']
Qoffset = RF_Imag_app_q - tools.ABFilterMagnitude(Ifilter, Qsed, redshift)
RF_Rmag_app_q = tools.ABFilterMagnitude(Rfilter, Qsed, redshift) + Qoffset
RF_Gmag_app_q = tools.ABFilterMagnitude(Gfilter, Qsed, redshift) + Qoffset
RF_Zmag_app_q = tools.ABFilterMagnitude(Zfilter, Qsed, redshift) + Qoffset
if(redshift<3.9):
RF_Umag_app_q = tools.ABFilterMagnitude(Ufilter, Qsed, redshift) + Qoffset
elif(redshift>=3.9):
RF_Umag_app_q = 99
# calculate the lens magnitude
veldisp = np.atleast_1d(lens['VELDISP'])
redshift = lens['ZLENS']
# Reference Frame Absolute R magnitude
RF_RMag_abs, _ = lenspop_constructor.EarlyTypeRelations(veldisp)
RMag_abs = tools.ABFilterMagnitude(Rfilter, Lsed, redshift)
distMod = cosmo.distmod(redshift).value
Rmag_app = RMag_abs + distMod
offset_abs_app = RMag_abs - Rmag_app
offset_RF_abs = RF_RMag_abs - RMag_abs
RF_Rmag_app = RF_RMag_abs - offset_abs_app
# Get filters and calculate magnitudes for each filter:
RF_Umag_app = tools.ABFilterMagnitude(Ufilter, Lsed, redshift) + offset_RF_abs + distMod
RF_Gmag_app = tools.ABFilterMagnitude(Gfilter, Lsed, redshift) + offset_RF_abs + distMod
RF_Imag_app = tools.ABFilterMagnitude(Ifilter, Lsed, redshift) + offset_RF_abs + distMod
RF_Zmag_app = tools.ABFilterMagnitude(Zfilter, Lsed, redshift) + offset_RF_abs + distMod
t['u_SDSS_lens'][lens_count] = RF_Umag_app
t['r_SDSS_lens'][lens_count] = RF_Rmag_app
t['g_SDSS_lens'][lens_count] = RF_Gmag_app
t['i_SDSS_lens'][lens_count] = RF_Imag_app
t['z_SDSS_lens'][lens_count] = RF_Zmag_app
t['u_SDSS_quasar'][lens_count] = RF_Umag_app_q
t['r_SDSS_quasar'][lens_count] = RF_Rmag_app_q
t['g_SDSS_quasar'][lens_count] = RF_Gmag_app_q
t['i_SDSS_quasar'][lens_count] = RF_Imag_app_q
t['z_SDSS_quasar'][lens_count] = RF_Zmag_app_q
lens_count = lens_count + 1
dot = np.mod(lens_count, total/np.min([79,total])) == 0
if verbose and dot:
print('.', end="")
# Update the sample by adding the table of calculated magnitude
self.sample.add_columns(t.columns.values())
self.lenses = self.sample.copy()
return
f1 = tools.filterfromfile(f1)
if f2 is None:
f2 = f1
else:
f2 = tools.filterfromfile(f2)
if z2 is None:
z2 = z1
SED = tools.getSED(SEDtype)
if vega:
filtermag = tools.VegaFilterMagnitude(f1, SED, z1)
# vegatmp = tools.getSED('Vega')
# vegaAB = tools.ABFilterMagnitude(f1,vegatmp,0.)
else:
filtermag = tools.ABFilterMagnitude(f1, SED, z1)
magoffset = m1 - filtermag
#print magoffset
if vega:
m2 = tools.VegaFilterMagnitude(f2, SED, z2) + magoffset
else:
m2 = tools.ABFilterMagnitude(f2, SED, z2) + magoffset
if z1 != z2:
from stellarpop import distances
from math import log10
dist = distances.Distance()
dist.OMEGA_M = Om
dist.OMEGA_L = OL
dist.h = H0 / 100.
if z2 != 0.:
def create_models(self):
import numpy, cPickle
from stellarpop import tools
from ndinterp import ndInterp
index = {}
shape = []
axes = {}
axes_index = 0
for key in self.axes_names:
index[key] = {}
shape.append(self.axes[key]['points'].size)
axes[axes_index] = self.axes[key]['eval']
axes_index += 1
for i in range(self.axes[key]['points'].size):
index[key][self.axes[key]['points'][i]] = i
self.redshifts = self.axes['redshift']['points']
self.corrections = self.luminosity_correction()
zindex = self.axes_names.index('redshift')
models = {}
model = numpy.empty(shape) * numpy.nan
for f in self.filter_names:
models[f] = model.copy()
for file in self.files:
f = open(file, 'rb')
data = cPickle.load(f)
wave = cPickle.load(f)
f.close()
for key in data.keys():
obj = data[key]
jj = key
spec = obj['sed']
ind = []
for key in self.axes_names:
if key == 'redshift':
continue
try:
ind.append([index[key][obj[key]]])
except:
print key, index[key]
print obj
df
ind.insert(zindex, None)
for f in self.filter_names:
for i in range(self.redshifts.size):
z = self.redshifts[i]
# correction is the units correction factor
correction = self.corrections[i]
sed = [wave, spec * correction]
mag = tools.ABFilterMagnitude(self.filters[f], sed, z)
if numpy.isnan(mag) == True:
df
ind[zindex] = i
models[f][ind] = mag
self.interpAxes = axes
return models
for f in self.filter_names:
model = models[f].copy()
if numpy.isnan(model).any():
models[f] = None
else:
models[f] = ndInterp(axes, model, order=1)
return models