def __init__(self,files,axes,filters,oldModels=None):
"""
All arguments (files,axes,filters,redshifts) are required.
`files' is a list of filenames. Each filename points to a pickle'd
set of SPS spectra.
`axes' is a dictionary; the keys are each of the parameters of the
SPS model. Each key points to a dictionary with two keys: `points'
and `eval.' The `points' value is an array describing the point
the axis is defined on, and `eval' is a spline model for the axis.
`filters' is a string (or list of strings) containing the name(s) of
the filter(s) to be evaulated.
`redshifts' is the redshift (or list of redshifts) at which the SEDs
should be evaluated.
"""
from stellarpop import tools
self.filters = {}
if type(filters)==type([]):
for filter in filters:
self.filters[filter] = tools.filterfromfile(filter)
else:
self.filters[filter] = tools.filterfromfile(filter)
self.filter_names = self.filters.keys()
self.axes_names = axes.keys()
self.naxes = len(axes.keys())
self.axes = axes
self.files = files
self.models = {}
self.models = self.create_models(oldModels)
def __init__(self, spex, axes, filters, redshift=None):
"""
All arguments (files,axes,filters,redshifts) are required.
`files' is a list of filenames. Each filename points to a cPickle'd
set of SPS spectra.
`axes' is a dictionary; the keys are each of the parameters of the
SPS model. Each key points to a dictionary with two keys: `points'
and `eval.' The `points' value is an array describing the point
the axis is defined on, and `eval' is a spline model for the axis.
`filters' is a string (or list of strings) containing the name(s) of
the filter(s) to be evaulated.
`redshifts' is the redshift (or list of redshifts) at which the SEDs
should be evaluated.
"""
from stellarpop import tools
self.filters = {}
if type(filters) == type([]):
for filter in filters:
self.filters[filter] = tools.filterfromfile(filter)
else:
self.filters[filter] = tools.filterfromfile(filter)
self.filter_names = self.filters.keys()
self.axes = axes
self.redshift = redshift
self.spex = spex
self.models = {}
self.models = self.create_models()
del self.spex
def MassB2toB(B,z,age='4.000'):
sed = tools.getSED('BC_Z=1.0_age='+age+'gyr')
bfilt = tools.filterfromfile('F555W_ACS')
restfilt = tools.filterfromfile('F435W_ACS')
B_modrest = tools.ABFM(restfilt,sed,0.0)
B_modobs = tools.ABFM(bfilt,sed,z)
B_rest = B + B_modrest - B_modobs
mass_B = 0.4*(B_rest - B_modrest)
B_sun = 5.372
DL = dist.luminosity_distance(z)
DM = 5.*np.log10(DL*1e6) - 5.
B_restabs = B_rest - DM
lum = -0.4*(B_restabs - B_sun)
return lum, B_rest
def Colourspline(self):
from stellarpop import tools
sed = tools.getSED('BC_Z=1.0_age=10.00gyr')
#different SEDs don't change things much
rband=tools.filterfromfile('r_SDSS')
z=self.zlbins
self.colourspline={}
for band in self.bands:
if band!="VIS":
c=z*0
Cband=tools.filterfromfile(band)
for i in range(len(z)):
c[i] = - (tools.ABFM(Cband,sed,z[i]) - tools.ABFM(rband,sed,0))
self.colourspline[band]=interpolate.splrep(z,c)
def Colourspline(self):
from stellarpop import tools
sed = tools.getSED('BC_Z=1.0_age=10.00gyr')
#different SEDs don't change things much
rband = tools.filterfromfile('r_SDSS')
z = self.zlbins
self.colourspline = {}
for band in self.bands:
if band != "VIS":
c = z * 0
Cband = tools.filterfromfile(band)
for i in range(len(z)):
c[i] = -(tools.ABFM(Cband, sed, z[i]) -
tools.ABFM(rband, sed, 0))
self.colourspline[band] = interpolate.splrep(z, c)
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 MassK(K,z):
sed = tools.getSED('BC_Z=1.0_age=7.000gyr')
kfilt = tools.filterfromfile('Kp_NIRC2')
K_modrest = tools.ABFM(kfilt,sed,0.0)
K_modobs = tools.ABFM(kfilt,sed,z)
K_rest = K + K_modrest - K_modobs
mass_K = 0.4*(K_rest - K_modrest)
K_sun = 5.19
DL = dist.luminosity_distance(z)
DM = 5.*np.log10(DL*1e6) - 5.
K_restabs = K_rest - DM
lum = -0.4*(K_restabs - K_sun)
return lum, K_rest
def MassB1(B,z):
sed = tools.getSED('BC_Z=1.0_age=7.000gyr')
bfilt = tools.filterfromfile('F606W_ACS')
B_modrest = tools.ABFM(bfilt,sed,0.0)
B_modobs = tools.ABFM(bfilt,sed,z)
B_rest = B + B_modrest - B_modobs
mass_B = 0.4*(B_rest - B_modrest)
B_sun = 4.74
DL = dist.luminosity_distance(z)
DM = 5.*np.log10(DL*1e6) - 5.
B_restabs = B_rest - DM
lum = -0.4*(B_restabs - B_sun)
return lum, B_rest
def MassR(R,z):
sed = tools.getSED('BC_Z=1.0_age=7.000gyr')
rfilt = tools.filterfromfile('F814W_ACS')
R_modrest = tools.ABFM(rfilt,sed,0.0)
R_modobs = tools.ABFM(rfilt,sed,z)
R_rest = R + R_modrest - R_modobs
mass_R = 0.4*(R_rest - R_modrest)
R_sun = 4.57
DL = dist.luminosity_distance(z)
DM = 5.*np.log10(DL*1e6) - 5.
R_restabs = R_rest - DM
lum = -0.4*(R_restabs - R_sun)
return lum, R_rest
def get_sdss_filters():
"""Retrieves the SDSS filter throughputs from files in stellarpop/filters
Returns
-------
dict
Each key is one of string characters 'u', 'g', 'r', 'i', 'z'
Each value is a tuple of (Numpy ndarray of wavelengths in Angstroms,
Numpy ndarray of throughputs)
"""
from stellarpop import tools
filters_dict = {}
for band in 'ugriz':
filters_dict[band] = tools.filterfromfile('%s_SDSS' %band)
return filters_dict
def get_sdss_filters():
"""Retrieves the SDSS filter throughputs from files in stellarpop/filters
Returns
-------
dict
Each key is one of string characters 'u', 'g', 'r', 'i', 'z'
Each value is a tuple of (Numpy ndarray of wavelengths in Angstroms,
Numpy ndarray of throughputs)
"""
from stellarpop import tools
filters_dict = {}
for band in 'ugriz':
filters_dict[band] = tools.filterfromfile('%s_SDSS' % band)
return filters_dict
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
import numpy as np
import pylab as pl
import pyfits as py
from stellarpop import tools
from astLib import astCalc
from scipy.interpolate import splrep, splev, splint
''' Predict the offset as a function of age - currently from luminosity evolution alone. Assuming a single redshift of the source population of z=0.55, which I reckon is about the average (but haven't calculated...! How lazy.) '''
ages = ['0.001','0.010','0.125','0.250','0.375','0.500','0.625','0.750','0.875','1.000','1.250','1.500','1.700','1.750','2.000','2.200','2.250','2.500','3.500','4.500','5.500','6.000','7.000','8.000','9.000','10.00','12.00','15.00','20.00']
age_array = np.array([0.001,0.010,0.125,0.250,0.375,0.500,0.625,0.750,0.875,1.000,1.250,1.500,1.700,1.750,2.000,2.200,2.250,2.500,3.500,4.500,5.500,6.000,7.000,8.000,9.000,10.00,12.00,15.00,20.00])
z=0.55
tl = astCalc.tl(z)
bfilt1 = tools.filterfromfile('F555W_ACS')
bfilt = tools.filterfromfile('F606W_ACS')
rfilt = tools.filterfromfile('F814W_ACS')
bmags = np.zeros(len(ages))
rmags = bmags*0.
for i in range(len(ages)):
sed = tools.getSED('BC_Z=1.0_age='+str(ages[i])+'gyr')
bmags[i] = tools.ABFM(bfilt,sed,0)
rmags[i] = tools.ABFM(rfilt,sed,0)
'''pl.figure()
pl.plot(age_array-tl,bmags,'Navy',label='b, z=0')
pl.plot(age_array,bmags,'CornflowerBlue',label='b,z=0.55')
pl.plot(age_array-tl,rmags,'Crimson',label='r, z=0')
pl.plot(age_array,rmags,'LightCoral',label='r,z=0.55')
pl.legend(loc='lower right')
pl.xlabel('age / Gyr')
pl.ylabel('AB magnitude')'''
lum_R, R_rest = MassR(R,z)
print 'J0913, source galaxy: ', '%.2f'%B_rest, '%.2f'%R_rest, '%.2f'%lum_B, '%.2f'%lum_R
# J0837 - want to know its mass to compare with dust lane!
z = 0.528
B=20.75
lum_B, B_rest = MassB1(B,z)
print 'J0837: ', '%.2f'%B_rest,'%.2f'%lum_B
plot=False
if plot == True:
## visualise filters
sed = tools.getSED('BC_Z=1.0_age=7.000gyr')
bfilt = tools.filterfromfile('F606W_ACS')
rfilt = tools.filterfromfile('F814W_ACS')
kfilt = tools.filterfromfile('Kp_NIRC2')
pl.figure()
pl.plot(sed[0],sed[1],'k')
pl.plot(bfilt[0],bfilt[1]*1e-5,'CornflowerBlue',label='F606W')
pl.plot(rfilt[0],rfilt[1]*1e-5,'Crimson',label='F814W')
pl.plot(kfilt[0],kfilt[1]*5e-8,'Orange',label='K')
pl.xscale('log')
pl.legend(loc='upper right')
## plot redshifted filters - say for the source
z=0.69
pl.plot(bfilt[0]/(1.+z),bfilt[1]*1e-5,'Blue',label='F606W')
pl.plot(rfilt[0]/(1.+z),rfilt[1]*1e-5,'DarkRed',label='F814W')
pl.plot(kfilt[0]/(1.+z),kfilt[1]*5e-8,'DarkOrange',label='K')
import os
os.system(cmd)
sys.exit()
if vega is None:
vega = False # No vega flag = use AB mags
if convert is None:
convert = False
if m1 is None or f1 is None or z1 is None or SEDtype is None:
print "Error: Incomplete input information. Use -u for usage."
sys.exit()
from stellarpop import tools
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)
import os
os.system(cmd)
sys.exit()
if vega is None:
vega = False # No vega flag = use AB mags
if convert is None:
convert = False
if m1 is None or f1 is None or z1 is None or SEDtype is None:
print "Error: Incomplete input information. Use -u for usage."
sys.exit()
from stellarpop import tools
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)