def me(theta_Es, e_theta_Es, zlenses, zsources):
ntarg = theta_Es.size
M_E = numpy.zeros(ntarg)
e_M_E = numpy.zeros(ntarg)
vdisp = numpy.zeros(ntarg)
e_vdisp = numpy.zeros(ntarg)
for targ in numpy.arange(ntarg):
zsource = zsources[targ]
zlens = zlenses[targ]
theta_E = theta_Es[targ]
e_theta_E = e_theta_Es[targ]
# luminosity distances
d_LS = cosmo.luminosity_distance(zsource).value * 3.08e24
d_LL = cosmo.luminosity_distance(zlens).value * 3.08e24
# comoving distances
d_MS = d_LS / (1 + zsource)
d_ML = d_LL / (1 + zlens)
# angular diameter distances
d_ALS = 1 / (1 + zsource) * ( d_MS - d_ML )
d_AL = d_LL / (1 + zlens)**2
d_AS = d_LS / (1 + zsource)**2
# einstein radius in cm (7.1 kpc/" at z=0.7)
theta_E_cm = theta_E / 206265. * d_AL
e_theta_E_cm = e_theta_E / 206265. * d_AL
# get a distribution of Einstein radii
niters = 1e3
theta_E_iters = numpy.random.normal(loc = theta_E_cm, \
scale = e_theta_E_cm, size = niters)
# compute the mass enclosed within the Einstein radius
c = 3e10
G = 6.67e-8
sigma_crit = c**2 / 4 / pi / G * d_AS / d_AL / d_ALS
M_E_iters = pi * sigma_crit * theta_E_iters**2 / 2e33
M_E[targ] = numpy.mean(M_E_iters)
e_M_E[targ] = numpy.std(M_E_iters)
vdisp2 = theta_E_iters / d_AL / 4 / pi * c**2 * d_AS / d_ALS
vdisp[targ] = numpy.mean(numpy.sqrt(vdisp2) / 1e5)
e_vdisp[targ] = numpy.std(numpy.sqrt(vdisp2) / 1e5)
return M_E, e_M_E, vdisp, e_vdisp
def cosmoDL(redshift,
WMAP9=False,
H0=70.0,
Om0=0.30,
Planck15=False,
kpc=False):
"""
Get the Luminosity Distance at redshift=z.
This is simply a wrapper of astropy.cosmology
The input redsfhit can be an array
"""
if WMAP9:
from astropy.cosmology import WMAP9 as cosmo
elif Planck15:
from astropy.cosmology import Planck15 as cosmo
else:
from astropy.cosmology import FlatLambdaCDM
cosmo = FlatLambdaCDM(H0=H0, Om0=Om0)
dl = cosmo.luminosity_distance(redshift)
if not kpc:
return dl.value
else:
return dl.to(u.kpc).value
def get_spectrum(self, outwave=None, filters=None, component=-1, **params):
"""
"""
# Spectrum in Lsun/Hz per solar mass formed, restframe
wave, spectrum, mfrac = self.get_galaxy_spectrum(**params)
# Redshifting + Wavelength solution
# We do it ourselves.
a = 1 + self.params.get('zred', 0)
af = a
b = 0.0
if 'wavecal_coeffs' in self.params:
x = wave - wave.min()
x = 2.0 * (x / x.max()) - 1.0
c = np.insert(self.params['wavecal_coeffs'], 0, 0)
# assume coeeficients give shifts in km/s
b = chebval(x, c) / (lightspeed*1e-13)
wa, sa = wave * (a + b), spectrum * af # Observed Frame
if outwave is None:
outwave = wa
# Observed frame photometry, as absolute maggies
if filters is not None:
# Magic to only do filter projections for unique filters, and get a
# mapping back into this list of unique filters
# note that this may scramble order of unique_filters
fnames = [f.name for f in filters]
unique_names, uinds, filter_ind = np.unique(fnames, return_index=True, return_inverse=True)
unique_filters = np.array(filters)[uinds]
mags = getSED(wa, lightspeed/wa**2 * sa * to_cgs, unique_filters)
phot = np.atleast_1d(10**(-0.4 * mags))
else:
phot = 0.0
# Distance dimming and unit conversion
zred = self.params.get('zred', 0.0)
if (zred == 0) or ('lumdist' in self.params):
# Use 10pc for the luminosity distance (or a number
# provided in the dist key in units of Mpc)
dfactor = (self.params.get('lumdist', 1e-5) * 1e5)**2
else:
lumdist = cosmo.luminosity_distance(zred).value
dfactor = (lumdist * 1e5)**2
# Spectrum will be in maggies
sa *= to_cgs / dfactor / (3631*jansky_cgs)
# Convert from absolute maggies to apparent maggies
phot /= dfactor
# Mass normalization
mass = np.atleast_1d(self.params['mass'])
mass = np.squeeze(mass.tolist() + [mass.sum()])
sa = (sa * mass[:, None])
phot = (phot * mass[:, None])[component, filter_ind]
return sa, phot, mfrac
def vel_kms_to_masyr(vel=1000.0, z=1.0):
vel = vel * u.kilometer / u.second
vel_mas_per_year = (vel.to(u.kpc / u.yr) *
cosmo.arcsec_per_kpc_proper(z).to(u.mas / u.kpc))
if z < 0.1:
d = cosmo.angular_diameter_distance(z).to(u.kpc)
loc_vel_mas_per_year = ((vel.to(u.kpc / u.yr) / d) * (u.rad)).to(
u.mas / u.yr)
#table 6 says 0.7 mas is max resolution, for 93GHz band
print("D_A (z=%g) = " % z, cosmo.angular_diameter_distance(z))
print("D_L (z=%g) = " % z, cosmo.luminosity_distance(z))
print("arcsec/kpc (z=%g) =" % z, cosmo.arcsec_per_kpc_proper(z))
print("mas/kpc (z=%g) =" % z,
cosmo.arcsec_per_kpc_proper(z).to(u.mas / u.kpc))
print("physical res for 10 mas (z=%g) = " % z,
10 * u.mas / (cosmo.arcsec_per_kpc_proper(z).to(u.mas / u.pc)))
print("vel [km/s] = %g" % vel.to_value())
print("vel [km/s] =", vel)
print("vel [pc/Myr] =", vel.to(u.pc / u.Myr))
print(vel)
print("vel [kpc/yr] =", vel.to(u.kpc / u.yr))
print("vel [mas/yr] = ", vel_mas_per_year)
if z < 0.1: print("loc vel [mas/yr] = ", loc_vel_mas_per_year)
print("motion in 10 yr lifetime = ", vel_mas_per_year * 10 * u.yr)
return vel_mas_per_year
def Strain_Conv(source, natural_f, natural_h):
"""Converts frequency and strain in natural units (G=c=1) to Hertz and dimensionless, respectively.
Parameters
----------
source
Instance of gravitational wave source class
natural_f : array [Mf]
the frequency of the source in natural units (G=c=1)
natural_h : array [Mf]
the strain of the source in natural units (G=c=1)
"""
DL = cosmo.luminosity_distance(source.z)
DL = DL.to('m')
m_conv = const.G / const.c**3 #Converts M = [M] to M = [sec]
M_redshifted_time = source.M.to('kg') * (1 + source.z) * m_conv
#frequency and strain of source in detector frame
freq_conv = 1 / M_redshifted_time
#Normalized factor to match Stationary phase approx at low frequencies?
#Changed from sqrt(5/16/pi)
strain_conv = np.sqrt(
1 / 4 / np.pi) * (const.c / DL) * M_redshifted_time**2
f = natural_f * freq_conv
h_f = natural_h * strain_conv
return [f, h_f]
def findDist(self, **kwargs: {'calc': False}):
try:
if kwargs.get('calc') == True:
#c=299792.458* u.km / u.second
dh = const.c / cosmo.H(0)
om = 0.27
ok = 1 - om
oa = 0
#E=lambda x: sqrt(om*(1+x)**3+ok*(1+x)**2+oa)
dc = dh * integrate.quad(
lambda x: 1 / (sqrt(om * (1 + x)**3 + ok *
(1 + x)**2 + oa)), 0, self.z)
dm = dh * (1 / sqrt(ok)) * (
(np.exp(sqrt(ok) * (dc / dh)) - np.exp(-sqrt(ok) *
(dc / dh))) / 2)
dl = dm * (1 + self.z)
return dl
else:
a = cosmo.luminosity_distance(self.z) #.value * u.Mpc
return a
except:
s3 = 'The findDist() function failed for plate-ifu: ' + self.s
log.error(s3)
print(s3)
return 0.0
def __init__(self, name, z, x, y):
self.name = name
self.z = z
self.x = x
self.y = y
self.lDcm = cosmo.luminosity_distance(self.z)*u.Mpc.to(u.cm) / u.Mpc
self.radToKpc = conv.arcsec_per_kpc_proper(self.z)*0.05/u.arcsec*u.kpc
def calculate_Ke_marscher(nu_ssa_ghz, theta_obs_mas,
flux_extrapol_nu_ssa_obs_jy, delta, z, p):
"""
Calculate Ke using (3) from Marscher 1983 (1983ApJ...264..296M).
:param nu_ssa_ghz:
Frequency of observation (it is SSA frequency for radio core).
:param theta_obs_mas:
Width of the VLBI core (see paper for discussion).
:param flux_extrapol_nu_ssa_obs_jy:
Core flux density extrapolated from optical thin spectrum [Jy].
:param delta:
Doppler factor.
:param z:
Redshift.
:param p:
Exponent of particles energy distribution. Must be 1.5, 2.0, 2.5 or 3.
:return:
Value of B [G].
"""
if p not in (2.5, 3.0, 3.5):
raise Exception("p must be 1.5, 2.0, 2.5 or 3")
alpha = (p - 1.0) / 2
n = {0.25: 7.9, 0.5: 0.27, 0.75: 0.012, 1.0: 0.00059}
D_L_Gpc = WMAP9.luminosity_distance(z).to(u.Gpc).value
return n[alpha] * D_L_Gpc**(-1) ** theta_obs_mas**(-4*alpha-7.0) *\
nu_ssa_ghz**(-4*alpha-5.0) * flux_extrapol_nu_ssa_obs_jy**(2*alpha+3.0) *\
(1+z)**(2*alpha+6.0) * delta*(-2*alpha-4.0)
def __init__(self, lcspath):
#self.jmass=fits.getdata(lcspath+'tables/LCS_Spirals_all_fsps_v2.4_miles_chab_charlot_sfhgrid01.fits')
# use jmass.mstar_50 and jmass.mstar_err
#self.agc=fits.getdata(lcspath+'tables/LCS_Spirals_AGC.fits')
self.s = fits.getdata(lcspath + 'tables/LCS_all_size.fits')
self.gim2d = fits.getdata(lcspath + 'tables/LCS_all.gim2d.tab1.fits')
# dictionary to look up galaxies by NSAID
self.nsadict = dict(
(a, b) for a, b in zip(self.s.NSAID, np.arange(len(self.s.NSAID))))
self.NUVr = self.s.ABSMAG[:, 1] - self.s.ABSMAG[:, 4]
self.upperlimit = self.s[
'RE_UPPERLIMIT'] # converts this to proper boolean array
self.MAG24 = 2.5 * np.log10(3631. / (self.s.FLUX24 * 1.e-6))
self.dL = cosmo.luminosity_distance(self.s.ZDIST)
self.distmod_ZDIST = 5 * np.log10(self.dL.value * 1.e6) - 5
self.ABSMAG24 = self.MAG24 - self.distmod_ZDIST
self.NUV24 = self.s.ABSMAG[:, 1] - self.ABSMAG24
if __name__ != '__main__':
self.setup()
self.logstellarmass = self.s.MSTAR_50
self.clusterflag = (self.s.CLUSTER == b'Coma') | (
self.s.CLUSTER == b'A2063'
) # | (self.s.CLUSTER == b'Hercules') | (self.s.CLUSTER == b'A1367') # | (self.s.CLUSTER == b'A2052')
def get_catalog(params, map_struct):
cat, = Vizier.get_catalogs('J/ApJS/199/26/table3')
completeness = 0.5
alpha = -1.0
MK_star = -23.55
MK_max = MK_star + 2.5 * np.log10(gammaincinv(alpha + 2, completeness))
z = (u.Quantity(cat['cz']) / c.c).to(u.dimensionless_unscaled)
MK = cat['Ktmag'] - cosmo.distmod(z)
keep = (z > 0) & (MK < MK_max)
cat = cat[keep]
z = z[keep]
r = cosmo.luminosity_distance(z).to('Mpc').value
theta = 0.5 * np.pi - cat['DEJ2000'].to('rad').value
phi = cat['RAJ2000'].to('rad').value
ipix = hp.ang2pix(map_struct["nside"], theta, phi)
if "distnorm" in map_struct:
dp_dV = map_struct["prob"][ipix] * map_struct["distnorm"][ipix] * norm(
map_struct["distmu"][ipix],
map_struct["distsigma"][ipix]).pdf(r) / map_struct["pixarea"]
top50 = cat[np.flipud(np.argsort(dp_dV))][:50]
else:
top50 = cat[np.flipud(np.argsort(map_struct["prob"][ipix]))][:50]
catalogfile = os.path.join(params["outputDir"], 'catalog.dat')
top50['RAJ2000', 'DEJ2000', 'Ktmag'].write(catalogfile, format='ascii')
return top50
def generate_mass_estimates(galaxy_names, info_table, mini_tot_values,
mcor_tot_values):
"""Return a table containing the galaxy names, redshifts, present masses,
and processed solar masses over a galaxy's lifetime
Arguments:
galaxy_names (list) : A list of all galaxy names' whose mass must be
calculated
info_table (table): The data table with PISCO galaxy information
mini_tot_values (list) : A list of mini_tot values needed to calculate the processed solar
masses over a galaxy's lifetime
mcor_tot_values (list) : A list of mcor_tot values needed to calculate the present solar
masses in a galaxy
Returns:
A table containing the galaxy names, redshifts, present masses, and
processed solar masses over a galaxy's lifetime
"""
# solar_luminosity units : erg / sec
solar_luminosity = 3.826e33
redshifts = []
all_present_galaxy_mass = []
all_processed_solar_masses = []
for i, name in enumerate(galaxy_names):
redshift = np.asarray(info_table['z'])[np.where(
np.asarray(info_table['GALname']) == name)[0]]
luminosity_distance = cosmo.luminosity_distance(
redshift) * 3.0857e24 # Unit: cm
# mini_tot_values units : cm^2 * sec * solar mass / erg
# mcor_tot_values units : cm^2 * sec * solar mass / erg
# present_galaxy_mass units : solar mass
# processed_solar_masses units : solar mass
present_galaxy_mass = mcor_tot_values[i] * 4 * np.pi * (
luminosity_distance**2) / solar_luminosity
processed_solar_masses = mini_tot_values[i] * 4 * np.pi * (
luminosity_distance**2) / solar_luminosity
redshifts.append(redshift[0])
all_present_galaxy_mass.append('%.3E' %
Decimal(present_galaxy_mass[0].value))
all_processed_solar_masses.append(
'%.3E' % Decimal(processed_solar_masses[0].value))
table = Table(
{
'Galaxy Name': galaxy_names,
'Redshift': redshifts,
'STARLIGHT Present Galaxy Mass': all_present_galaxy_mass,
'STARLIGHT Processed Solar Masses': all_processed_solar_masses
},
names=[
'Galaxy Name', 'Redshift', 'Present Galaxy Mass',
'Processed Solar Masses'
])
return table
def cosmoDL(redshift,
WMAP9=False,
H0=70.0,
Om0=0.30,
Planck15=False,
kpc=False):
"""
Get the Luminosity Distance at redshift=z.
This is simply a wrapper of astropy.cosmology
The input redsfhit can be an array
"""
if WMAP9:
from astropy.cosmology import WMAP9 as cosmo
elif Planck15:
from astropy.cosmology import Planck15 as cosmo
else:
from astropy.cosmology import FlatLambdaCDM
cosmo = FlatLambdaCDM(H0=H0, Om0=Om0)
dl = cosmo.luminosity_distance(redshift)
if not kpc:
return dl.value
else:
return dl.to(u.kpc).value
def Templates(i):
print("galaxy", i + 1, "/", len(comb), comb[i])
###GALAXY GENERATION
t0 = time.time()
ind = comb[i][0] #Sersic Index n
b = get_bn(ind) #Normalisation Based on n
dx = comb[i][1]
dy = comb[i][2]
ell = comb[i][3]
a = comb[i][4]
Re = comb[i][5] * u.pc #size of galaxy half light
Re = Re.to(u.Mpc)
ReAng = (
(Re * (1 + Z)**2) / cosmo.luminosity_distance(Z)
) * 57.2958 * 3600 #size of galaxy in arcsec (converting from rad) via angular distance
galhalflight = ReAng / res
headers = '%s' % comb[i]
stamps = makeSersic(ind, b, galhalflight, ell, a, stampsize, -dy, -dx)
Galaxy = stamps
np.savetxt('GalaxyTemplates/%s_%s.txt' % (name, i + 1),
Galaxy,
header=headers)
#tot+=1
t1 = time.time()
t01 = time.time()
print("time taken", t1 - t0)
tottime = t01 - t00
print(i, "galaxies in", tottime, "total time")
def Mlim(z, M2L):
"""
Given some mass-to-light ratio, M2L, and redshift, z, use the flux_limit=10**-16 erg/cm2/s defined above to determine the corresponding stellar mass limit
"""
Mlimit = (4. * np.pi * (cosmo.luminosity_distance(z).value * Mpc)**2 *
flux_limit * M2L) / Msol
return Mlimit
def int_gb(x,T,N_y,N_gb,alpha,z):
pi=scipy.constants.pi
d_l = cosmo.luminosity_distance(z)
d_l = d_l.to(u.m)
L_ir = 4*pi*(d_l**2)*integrate.quad(gb_model,8e-6,1000e-6,args=(T,N_y,N_gb,alpha))
# Unit conversion: from Jy Hz m^2, to W, to erg/s:
L_ir = L_ir*1e26*1e-7
return L_ir
def findLum(self, **kwargs: {'use_mRatio': False, 'nod': False}):
#Might have to convert distance to something else
try:
if kwargs.get('nod') == True:
f = cosmo.luminosity_distance(self.z).value * u.Mpc
x = f.to(u.cm)
if self.efail:
return 0.0
fl = self.fluxFind()
if fl == 0.0:
return 0.0
if fl == 'Fail':
return 'Fail' # * u.erg / (u.cm ** 2 * u.second)
L = fl * 4 * pi * x**2
return L.value
if kwargs.get('use_mRatio') == True:
f = cosmo.luminosity_distance(self.z) #.value * u.Mpc
x = f.to(u.cm)
if self.efail:
return 0.0
fl = self.fluxFind(use_mRatio=True) * u.erg / (u.cm**2 *
u.second)
if fl == 0.0:
return 0.0
if fl == 'Fail':
return 'Fail'
L = fl * 4 * pi * x**2
return L
else:
f = cosmo.luminosity_distance(self.z) #.value * u.Mpc
x = f.to(u.cm)
if self.efail:
return 0.0
fl = self.fluxFind() #* u.erg / (u.cm ** 2 * u.second)
if fl == 0.0:
return 0.0
if fl == 'Fail':
return 'Fail'
L = fl * 4 * pi * x**2
return L
except:
es = 'The findLum() function failed for plate-ifu: ' + self.s
print(es)
log.error(es)
return 0.0
def mag_in_z(spec, z, f):
d_L = cosmo.luminosity_distance(z).to('cm')
k_cosmo = units.Lsun.to('erg/s') / (4 * np.pi * np.power(d_L, 2))
model_spec = zcor(spec, z)
model_spec['flux'] *= k_cosmo
x = spec2filterset(f.filterset, model_spec)
return x
def cal_distance_p_from_powerlaw(distance,
peakredshift=0.1,
powerlawindex=-0.3):
from astropy.cosmology import WMAP9 as cosmo
peakd = cosmo.luminosity_distance(
peakredshift).value ##returned value is in Mpc
pdis = np.abs(peakd - distance)**powerlawindex
return pdis
def Strain_Conv(
source, freqs, strain, inverse=False, in_frame="source", out_frame="observer"
):
"""Converts frequency and strain in natural units (G=c=1) to Hertz and raw Fourier strain amplitude (1/Hertz), respectively.
If inverse is true, it does the reverse and assumes the strain and frequency are given in the detector frame.
Parameters
----------
source
Instance of gravitational wave source class
freqs: array
the frequency of the source in either natural units (G=c=1) or Hertz
strain: array
the strain of the source in natural units (G=c=1) or raw Fourier strain amplitude (1/Hertz)
inverse: bool, optional
Converts non-naturalized (Hertz and dimensionless) frequency and strain to ``Mf`` and strain in G=c=1 units
in_frame: str, {'source','observer'}
If inverse is true, determines whether the source frequency ``f_gw`` is in the source or observer frame.
out_frame: str, {'observer','source'}
Determines whether the returned frequency is in the source or observer frame.
"""
DL = cosmo.luminosity_distance(source.z)
DL = DL.to("m")
m_conv = const.G / const.c ** 3 # Converts M = [M] to M = [sec]
M_time = source.M.to("kg") * m_conv
# M_redshifted_time = source.M.to("kg") * (1 + source.z) * m_conv
# frequency and strain of source in source frame
freq_conv = 1 / M_time
# Normalized factor to match Stationary phase approx at low frequencies
strain_conv = np.sqrt(5 / 24 / np.pi) * (const.c / DL) * M_time ** 2
if inverse:
if in_frame == "source":
# converted to source frame natural units
conv_freqs = freqs / freq_conv
conv_strain = strain / strain_conv
elif in_frame == "observer":
# converted to source frame natural units
conv_freqs = (freqs / freq_conv) * (1 + source.z)
conv_strain = strain / strain_conv / (1 + source.z) ** 2
else:
raise ValueError("The reference frame can only be observer or source.")
else:
if out_frame == "source":
# converted to source frame physical units
conv_freqs = freqs * freq_conv
conv_strain = strain * strain_conv
elif out_frame == "observer":
# converted to detector frame physical units
conv_freqs = (freqs * freq_conv) / (1 + source.z)
conv_strain = strain * strain_conv * (1 + source.z) ** 2
else:
raise ValueError("The reference frame can only be observer or source.")
return [conv_freqs, conv_strain]
def lum_dist_error(self, val, err):
'''
Use a black box error method to find the uncertanty in
astropy.cosmology.WMAP9.luminosity_distance().
Args:
val (float): A redshift value
err (float): Error in the redshift
Returns:
error (float): Error in the luminosity distance
'''
diff = (cosmo.luminosity_distance(val + err).cgs
- cosmo.luminosity_distance(val - err).cgs)
error = abs(.5 * diff.value)
return(error)
def __init__(self, ra, dec, z):
#Set the position and redshift of the source
self.pos = [ra, dec]
self.z = z
self.d = (cosmo.luminosity_distance(z)).value*3.086e24
self.d2 = 4.*np.pi*(self.d*self.d)
#Give the source a rest-frame SED:
self.sed = sed()
def main(args):
"""
:param args: command line arguments from argparse
"""
# Read in the CSV file of parameters
if args.type == "S":
kcorr_df = pd.read_csv("data/kcorr_sgrbs.csv", index_col="GRB")
elif args.type == "L":
kcorr_df = pd.read_csv("data/kcorr_lgrbs.csv", index_col="GRB")
else:
print("Please provide a valid type argument.\n" \
"The type of GRB: L - long, S - short")
sys.exit(2)
print(f"--> Loading the {args.type}GRB properties...")
grbs = kcorr_df.index.tolist() # Get list of GRB names
# Calculate the luminosity distance in cm and add to data frame
kcorr_df["dl_cm"] = (cosmo.luminosity_distance(kcorr_df["z"]).value
* 3.08568e24)
# Loop over GRBs
for grb in grbs:
# Create filepaths
if args.type == "S":
infile = os.path.join("data", "SGRBS", grb, "".join([grb, ".csv"]))
outfile = os.path.join("data", "SGRBS", grb, "".join([grb,
"_k.csv"]))
elif args.type == "L":
infile = os.path.join("data", "LGRBS", grb, "".join([grb, ".csv"]))
outfile = os.path.join("data", "LGRBS", grb, "".join([grb,
"_k.csv"]))
else:
print("Please provide a valid type argument.\n" \
"The type of GRB: L - long, S - short")
sys.exit(2)
# Read in GRB data file
print(f"--> Loading data for: {grb}...")
data = pd.read_csv(infile, index_col=False)
# Perform k-correction
print("--> Performing k-correction...")
k_data = k_correction(data, kcorr_df["Gamma"][grb],
kcorr_df["sigma"][grb], kcorr_df["z"][grb],
kcorr_df["dl_cm"][grb])
# Calculate geometric mean of asymmetric errors and add to data frame
k_data["Lum50err"] = gmean([k_data["Lum50pos"].values,
np.abs(k_data["Lum50neg"].values)])
# Output corrected to data
print("--> Writing to output file...")
k_data.to_csv(outfile, index=False)
def get_spectrum(self, outwave=None, filters=None, peraa=False, **params):
"""Given a theta vector, generate spectroscopy, photometry and any
extras (e.g. stellar mass).
:param theta:
ndarray of parameter values.
:param sps:
A python-fsps StellarPopulation object to be used for
generating the SED.
:returns spec:
The restframe spectrum in units of maggies.
:returns phot:
The apparent (redshifted) observed frame maggies in each of the
filters.
:returns extras:
A list of the ratio of existing stellar mass to total mass formed
for each component, length ncomp.
"""
self.params.update(**params)
# Pass the model parameters through to the sps object
ncomp = len(self.params['mass'])
for ic in range(ncomp):
s, p, x = self.one_sed(component_index=ic, filterlist=filters)
try:
spec += s
maggies += p
extra += [x]
except(NameError):
spec, maggies, extra = s, p, [x]
# `spec` is now in Lsun/Hz
if outwave is not None:
w = self.csp.wavelengths
spec = np.interp(outwave, w, spec)
# Distance dimming and unit conversion
if self.params['zred'] == 0:
# Use 10pc for the luminosity distance (or a number
# provided in the lumdist key in units of Mpc)
dfactor = (self.params.get('lumdist', 1e-5) * 1e5)**2
else:
lumdist = cosmo.luminosity_distance(self.params['zred']).value
dfactor = (lumdist * 1e5)**2 / (1 + self.params['zred'])
if peraa:
# spectrum will be in erg/s/cm^2/AA
spec *= to_cgs / dfactor * lightspeed / outwave**2
else:
# Spectrum will be in maggies
spec *= to_cgs / dfactor / 1e3 / (3631*jansky_mks)
return spec, maggies / dfactor, extra
def luminosityDistance(self, objectID):
self.getID()
self.getRedshift()
lumDist = 0
if (objectID in self.objID):
for i in range(0, len(self.objID)):
if (objectID == self.objID[i]):
lumDist = cosmo.luminosity_distance(self.redshift[i])
return lumDist
else:
raise ValueError(objectID, 'is not a valid object identifier.')
def dist2z(dist):
zs = np.arange(0.01, 10, 0.01)
dists = cosmo.luminosity_distance(zs).value
f = interpolate.interp1d(dists,
zs,
bounds_error=False,
fill_value="extrapolate",
kind='linear')
z = f(dist)
return z
def omega_rnu(shift_mas, nu1_ghz, nu2_ghz, z, k_r):
"""
Core-position offset measure (see Lobanov 1998).
:param shift_mas:
Core shift [mas] between frequencies ``nu1_ghz`` and ``nu2_ghz`` [GHz].
"""
D_L_pc = WMAP9.luminosity_distance(z).to(u.pc).value
nu2 = max([nu1_ghz, nu2_ghz])
nu1 = min([nu1_ghz, nu2_ghz])
return 4.85e-09 * shift_mas * D_L_pc * (nu1**(1 / k_r) * nu2**(1 / k_r) /
(nu2**(1 / k_r) - nu1**
(1 / k_r))) / (1 + z)**2
def findDist(self):
dh = 299792.458 / cosmo.H(0)
om = 0.27
ok = 1 - om
oa = 0
#E=lambda x: sqrt(om*(1+x)**3+ok*(1+x)**2+oa)
dc = dh * integrate.quad(
lambda x: 1 / (sqrt(om * (1 + x)**3 + ok *
(1 + x)**2 + oa)), 0, self.z)
dm = dh * (1 / sqrt(ok) * sinh(sqrt(ok) * (dc / dh)))
dl = dm * (1 + self.z)
a = cosmo.luminosity_distance(self.z).value
return a
def __init__(self, name, z, x, y):
self.z = z
self.y = y
self.x = x
self.name = name
#lDcm gives the luminosity distance in centimeters
#self.lDcm = cosmo.luminosity_distance(0.603)*u.Mpc.to(u.cm)/u.Mpc
#self.lDcm = cosmo.luminosity_distance(self.z)*u.Mpc.to(u.cm) / u.Mpc
#pixtoKpc is the conversion betweem pixels to rad to kpc based on z value
#self.pixtoKpc = conv.arcsec_per_kpc_proper(self.z)*0.05/u.arcsec*u.kpc
self.lDcm = cosmo.luminosity_distance(self.z) * u.Mpc.to(u.cm) / u.Mpc
self.radToKpc = conv.arcsec_per_kpc_proper(
self.z) * 0.05 / u.arcsec * u.kpc
def get_column(zin, cosmo, Mpc_cm=3.08568025e+24, z_r=6.0, delz=0.1):
'''
Returns
-------
HI column density of IGM at zin, in cm^-3.
'''
z = np.arange(z_r, zin, delz)
try:
nH = np.zeros(len(z), dtype='float')
except:
nH = 0
# From Cen & Haiman 2000
nH = 8.5e-5 * ((1. + z) / 8)**3 # in cm^-3
NH = 0
for zz in range(len(z)):
d1 = cosmo.luminosity_distance(z[zz] - delz).value #, **cosmo)
d2 = cosmo.luminosity_distance(z[zz] + delz).value #, **cosmo)
dx = (d2 - d1) * Mpc_cm
NH += nH[zz] * dx / (1. + z[zz])
return NH
def mips_to_lir(mips_flux,z):
'''
input flux must be in mJy
output is in Lsun
L_IR [Lsun] = fac_<band>(redshift) * flux [milliJy]
'''
# if we're at higher redshift, interpolate
# it decrease error due to redshift relative to rest-frame template (good)
# but adds nonlinear error due to distances (bad)
# else, scale the nearest conversion factor by the
# ratio of luminosity distances, since nonlinear error due to distances will dominate
if z > 0.1:
intfnc = interp1d(conversion['Redshift'],conversion['fac_MIPS24um'], bounds_error = False, fill_value = 0)
fac = intfnc(z)
else:
near_idx = np.abs(conversion['Redshift']-z).argmin()
lumdist_ratio = (WMAP9.luminosity_distance(z).value / WMAP9.luminosity_distance(conversion['Redshift'][near_idx]).value)**2
zfac_ratio = (1.+conversion['Redshift'][near_idx]) / (1.+z)
fac = conversion['fac_MIPS24um'][near_idx]*lumdist_ratio*zfac_ratio
return fac*mips_flux
def get_fnu(
self,
cosmo,
z,
include_IGM=True): # flux nJy, depends on redshift and cosmology
self.lamz = self.lam * (1. + z)
self.fnu = 1E23 * 1E9 * self.lnu * (1. + z) / (
4 * np.pi * cosmo.luminosity_distance(z).to('cm').value**2) # nJy
if include_IGM:
self.fnu *= IGM.madau(self.lamz, z)
def get_intrinsic_mag(sn_name, Z, B, AB):
MB = pandas.DataFrame(index=sn_name, columns=['MB'])
MB_arr = numpy.zeros(len(sn_name))
for i, n in enumerate(sn_name):
try:
mb = B.loc[n]
d = cosmo.luminosity_distance(Z.loc[n]) #
ab = AB.loc[n]
mu = 5 * numpy.log10(d / (10 * u.pc))
MB_arr[i] = mb - mu - ab
except:
MB_arr[i] = numpy.nan
MB['MB'] = MB_arr
return MB #Mpandas.DataFrame(index=sn_name, data=MB)
def calculate_B_zdzr2015(h_u, nu1_u, nu2_u, z, delta, dr_core_ang_u,
hoangle_rad, theta_los_rad, flux_u, p):
"""
Calculate B using (8) from paper Zdziarski et al. 2015 (10.1093/mnras/stv986).
Arguments with ``u`` postfix are values with units:
>>> import astropy.units as u
>>> flux_u = 1.0*u.Jy
>>> nu1_u = 15.4*u.GHz
:param h_u:
Distance from BH.
:param nu1_u:
Lower frequency.
:param nu2_u:
Higher frequency.
:param z:
redshift.
:param delta:
Doppler factor.
:param dr_core_ang_u:
Core shift.
:param hoangle_rad:
Half-opening angle [rad].
:param theta_los_rad:
LOS angle [rad]
:param flux_u:
Flux.
:param p:
Exponent of particles energy distribution. Must be 2 or 3 here.
:return:
Value of B [G].
"""
if p not in (2.0, 3.0):
raise Exception("p must be 2.0 or 3.0")
c3 = {2: 3.61, 3: 2.10, 4: 1.61}
c1 = {2: 1.14, 3: 1}
c2 = {2: 2. / 3, 3: 1}
D_L = WMAP9.luminosity_distance(z)
# B_cr = 2*np.pi*(const.m_e**2*const.c**3/(const.e.gauss*const.h)).cgs.value
result = (D_L * delta * (const.h / const.m_e)**7 /
(h_u * ((1 + z) * np.sin(theta_los_rad))**3 * const.c**12) *
(2 * np.pi * (const.m_e**2 * const.c**3 /
(const.e.gauss * const.h)) * dr_core_ang_u /
(nu1_u**(-1) - nu2_u**(-1)))**5 *
(const.alpha * c1[p] * c3[p] * np.tan(hoangle_rad) /
(flux_u * 24 * np.pi**3 * c2[p]))**2).cgs
return result.value
def add_6df(simplifiedmastercat, sixdf, tol=1*u.arcmin):
"""
Adds entries in the catalog for the 6dF survey, or updates v when missing
"""
from astropy import table
from astropy.coordinates import SkyCoord
from astropy.constants import c
ckps = c.to(u.km/u.s).value
catcoo = SkyCoord(simplifiedmastercat['RA'].view(np.ndarray)*u.deg, simplifiedmastercat['Dec'].view(np.ndarray)*u.deg)
sixdfcoo = SkyCoord(sixdf['obsra'].view(np.ndarray)*u.deg, sixdf['obsdec'].view(np.ndarray)*u.deg)
idx, dd, d3d = sixdfcoo.match_to_catalog_sky(catcoo)
msk = dd < tol
sixdfnomatch = sixdf[~msk]
t = table.Table()
t.add_column(table.MaskedColumn(name='RA', data=sixdfnomatch['obsra']))
t.add_column(table.MaskedColumn(name='Dec', data=sixdfnomatch['obsdec']))
t.add_column(table.MaskedColumn(name='PGC#', data=-np.ones(len(sixdfnomatch), dtype=int), mask=np.ones(len(sixdfnomatch), dtype=bool)))
t.add_column(table.MaskedColumn(name='NSAID', data=-np.ones(len(sixdfnomatch), dtype=int), mask=np.ones(len(sixdfnomatch), dtype=bool)))
t.add_column(table.MaskedColumn(name='othername', data=sixdfnomatch['targetname']))
t.add_column(table.MaskedColumn(name='vhelio', data=sixdfnomatch['z_helio']*ckps))
#t.add_column(table.MaskedColumn(name='vhelio_err', data=sixdfnomatch['zfinalerr']*ckps))
t.add_column(table.MaskedColumn(name='distance', data=WMAP9.luminosity_distance(sixdfnomatch['z_helio']).value))
#fill in anything else needed with -999 and masked
for nm in simplifiedmastercat.colnames:
if nm not in t.colnames:
t.add_column(table.MaskedColumn(name=nm, data=-999*np.ones(len(sixdfnomatch), dtype=int), mask=np.ones(len(sixdfnomatch), dtype=bool)))
t = table.vstack([simplifiedmastercat, t], join_type='exact')
#now update anything that *did* match but doesn't have another velocity
tcoo = SkyCoord(t['RA'].view(np.ndarray)*u.deg, t['Dec'].view(np.ndarray)*u.deg)
idx, dd, d3d = sixdfcoo.match_to_catalog_sky(tcoo)
msk = dd < tol
catmatch = t[idx[msk]]
sixdfmatch = sixdf[msk]
msk2 = t['vhelio'][idx[msk]].mask
t['vhelio'][idx[msk&msk2]] = sixdf['z_helio'][msk2]*ckps
return t
def OP(deproj, nmc=1000):
# Standard onion peeling
prof = deproj.profile
nbin = prof.nbin
rinam = prof.bins - prof.ebins
routam = prof.bins + prof.ebins
area = np.pi * (routam**2 - rinam**2) # full area in arcmin^2
# Projection volumes
if deproj.z is not None and deproj.cf is not None:
amin2kpc = cosmo.kpc_proper_per_arcmin(deproj.z).value
rin_cm = (prof.bins - prof.ebins) * amin2kpc * Mpc / 1e3
rout_cm = (prof.bins + prof.ebins) * amin2kpc * Mpc / 1e3
x = MyDeprojVol(rin_cm, rout_cm)
vol = np.transpose(x.deproj_vol())
dlum = cosmo.luminosity_distance(deproj.z).value * Mpc
K2em = 4. * np.pi * 1e14 * dlum**2 / (1 +
deproj.z)**2 / nhc / deproj.cf
# Projected emission measure profiles
em0 = prof.profile * K2em * area
e_em0 = prof.eprof * K2em * area
corr = EdgeCorr(nbin, rin_cm, rout_cm, em0)
else:
x = MyDeprojVol(rinam, routam)
vol = np.transpose(x.deproj_vol()).T
em0 = prof.profile * area
e_em0 = prof.profile * area
corr = EdgeCorr(nbin, rinam, routam)
# Deproject and propagate error using Monte Carlo
emres = np.repeat(e_em0, nmc).reshape(nbin, nmc) * np.random.randn(
nbin, nmc) + np.repeat(em0, nmc).reshape(nbin, nmc)
ct = np.repeat(corr, nmc).reshape(nbin, nmc)
allres = np.linalg.solve(vol, emres * (1. - ct))
ev0 = np.std(allres, axis=1)
v0 = np.median(allres, axis=1)
bsm = medsmooth(v0)
deproj.sb = bsm
deproj.sb_lo = bsm - ev0
deproj.sb_hi = bsm + ev0
deproj.dens = medsmooth(np.sign(bsm) * np.sqrt(np.abs(bsm)))
edens = 0.5 / np.sqrt(np.abs(bsm)) * ev0
deproj.dens_lo = deproj.dens - edens
deproj.dens_hi = deproj.dens + edens
def Get_Mono_Strain(source, f_gw, strain_const='Averaged'):
"""Calculates the strain from a binary black hole.
Parameters
----------
f_gw : float
The source frequency of the gravitational wave.
strain_const : {'Averaged','Optimal'}
'Averaged' gives the sky and inclination averaged strain from Robson et al. 2019 (eqn 27) <https://arxiv.org/pdf/1803.01944.pdf>
'Optimal' gives the optimally oriented, face-on, inclination (ie. inc=0) value
Returns
-------
float
the strain of a monochromatic source in the dector frame
"""
f_gw = utils.make_quant(f_gw, 'Hz')
if isinstance(strain_const, str):
DL = cosmo.luminosity_distance(source.z)
DL = DL.to('m')
#Converts M = [M] to M = [sec]
m_conv = const.G / const.c**3
eta = source.q / (1 + source.q)**2
M_redshifted_time = source.M.to('kg') * (1 + source.z) * m_conv
M_chirp = eta**(3 / 5) * M_redshifted_time
if strain_const == 'Optimal':
inc = 0.0
a = 1 + np.cos(inc)**2
b = -2 * np.cos(inc)
const_val = 2 * np.sqrt(.5 * (a**2 + b**2))
elif strain_const == 'Averaged':
const_val = 8 / np.sqrt(5)
else:
raise ValueError(
'Can only use "Averaged", or "Optimal" monochromatic strain calculation.'
)
return const_val * (const.c / DL) * (np.pi * f_gw)**(
2. / 3.) * M_chirp**(5. / 3.)
else:
raise ValueError(
'Can only use "Averaged", or "Optimal" monochromatic strain calculation.'
)
def __init__(self, p,z,e_erg,epsilon_e,epsilon_B,n_0,a_s,t_sec):
self.p = p
self.z = z
self.e_erg = e_erg
self.e_52 = self.e_erg * 1E-52
self.epsilon_e = epsilon_e
self.epsilon_B = epsilon_B
self.n_0 = n_0
self.a_s = a_s
self.t_sec = t_sec
self.t_min = self.t_sec / 60.
self.t_hr = self.t_sec / 3600.
self.t_day = self.t_sec / 86400.
self.epsilon_e_bar = self.epsilon_e*(self.p-2.)/(self.p-1.)
self.d_L = cosmo.luminosity_distance(self.z) #Weinberg, 1972, pp 420-424; Weedman, 1986, pp 60-62.
self.d_L_Mpc = self.d_L.value
self.d_L_cm = self.d_L.to(u.cm).value
self.d_L28 = self.d_L_cm * 1E-28
def manually_tweak_simplified_catalog(simplifiedcat):
"""
This just updates a few entries in the catalog that seem to be missing
velocities for unclear reasons.
No longer needed with `add_6df`: they are present.
"""
from astropy.coordinates import SkyCoord
from astropy.constants import c
infolines = """
46.480833 54.266111 2051.8 No velocity in NED PGC 011632
46.704583 54.588333 2859.3 No velocity in NED
50.288333 66.921667 2637.1 Velocity in NED, but no LEDA entry
99.794583 -1.5075000 2887.9 No velocity in NED
102.57375 -2.8605556 2699.9 No velocity in NED
102.93875 -3.6077778 2867.4 No velocity in NED
114.40708 -26.746667 2964.5 Velocity in NED, LEDA source, but not HyperLeda
116.32750 -32.516667 2162.0 Velocity in NED, LEDA source, but not HyperLeda
123.74833 -30.866667 1761.0 Velocity in NED, PGC 023091 (note in NED re; position error?)
""".split('\n')[1:-1]
ras = []
decs = []
vels = []
for l in infolines:
ls = l.split()
ras.append(float(ls[0]))
decs.append(float(ls[1]))
vels.append(float(ls[2]))
updatecoos = SkyCoord(ras*u.deg, decs*u.deg)
catcoos = SkyCoord(simplifiedcat['RA'].view(np.ndarray)*u.deg,
simplifiedcat['Dec'].view(np.ndarray)*u.deg)
idx, dd, d3d = updatecoos.match_to_catalog_sky(catcoos)
simplifiedcat = simplifiedcat.copy()
simplifiedcat['vhelio'][idx] = vels = np.array(vels)
simplifiedcat['distance'][idx] = WMAP9.luminosity_distance(
vels / c.to(u.km / u.s).value).to(u.Mpc).value
return simplifiedcat
def __init__(self, e52, n0, p, epsilonB, epsilonE, dL28=None, z=None, gamma0=100., iceffect=False, eblmodel=None):
self.energy = e52 * 1E52 * u.erg
self.ndensity = n0 * u.cm**-3
self.p = p
self.epsilonB = epsilonB
self.epsilonE = epsilonE
self.epsilonE_bar = epsilonE * (self.p-2.) / (self.p-1.)
if dL28==None and z==None:
logger.error('Distance and redshift are neither provided!!!')
dL28 = 5.
if dL28 is not None and z==None:
self.dL = dL28 * 1E28 * u.cm
distance = Distance(value=self.dL)
self.z = distance.z
elif dL28 is not None and z is not None:
self.dL = dL28 * 1E28 * u.cm
self.z = z
else:
self.z = z
self.dL = cosmo.luminosity_distance(self.z)
logger.debug('EBL model: {0}'.format(eblmodel))
self.ebl = InterpolateEBLmodels.read_model(eblmodel)[0] if eblmodel is not None else None
self.gamma0 = gamma0
self.iceffect = iceffect
print "new age: " + str(newages[newageind[arg]]) + ", EBV: " + str(EBV)
for i in range(arg, arg+1):
for j in range(len(zarr)):
newzspectrum = np.zeros(len(newwavspec)*2, dtype="float")
newzspectrum.shape = (len(newwavspec), 2)
newzspectrum[:,0] = newwavspec
newzspectrum[:,1] = newspecdata[newageind[i]+1,:]*3.826*10**33 #luminosity in erg/s/A
newzspectrum[:,1] = newzspectrum[:,1]*10**(-EBV*newcoef/2.5)
for k in range(len(newzspectrum)):
if newzspectrum[k,0] < 912.:
newzspectrum[k,1] = 0.
elif newzspectrum[k,0] > 912. and newzspectrum[k,0] < 1026.:
newzspectrum[k,1] = newzspectrum[k,1]*(1-D[j,1])
elif newzspectrum[k,0] > 1026. and newzspectrum[k,0] < 1216.:
newzspectrum[k,1] = newzspectrum[k,1]*(1-D[j,0])
newzspectrum[:,1] = newzspectrum[:,1]/(4*np.pi*(cosmo.luminosity_distance(zarr[j])*3.086*10**24)**2) #convert to observed flux at given redshift in erg/s/A/cm^2
newzspectrum[:,1] = newzspectrum[:,1]/(1+zarr[j]) #reduce flux by a factor of 1/(1+z) to account for redshifting
newagenorms[i, j] = np.mean(rebin_to_filter(newzspectrum, normfilt))
newzspectrum[:,1] = newzspectrum[:,1]/newagenorms[i, j] #reduce flux by a factor of 1/(1+z) to account for redshifting
newzspectrum[:,0] = newzspectrum[:,0]*(1+zarr[j]) #change wavelength values to account for redshifting
newoutput[:,j] = rebin_to_filter(newzspectrum, objfilter)
np.savetxt("../../models/spec/const/age_" + str(newages[newageind[i]]) + "_EBV_" + str(EBV) + ".txt", newoutput)
np.savetxt("../../models/spec/const/agenorms_" + str(newages[newageind[i]]) + "_EBV_" + str(EBV) + ".txt", newagenorms[i,:])
else:
for e in range(41):
EBV = 0.025*e
print "old age: " + str(oldages[oldageind[arg-8]]) + ", EBV: " + str(EBV)
for i in range(arg-8, arg-7):
for j in range(len(zarr)):
def get_spectrum(self, outwave=None, filters=None, peraa=False, **params):
"""Get a spectrum and SED for the given params.
:param outwave:
Desired *vacuum* wavelengths. Defaults to the values in
sps.ssp.wavelength.
:param peraa: (default: False)
If `True`, return the spectrum in erg/s/cm^2/AA instead of AB
maggies.
:returns spec:
Observed frame spectrum in AB maggies, unless `peraa=True` in which
case the units are erg/s/cm^2/AA.
:returns phot:
Observed frame photometry in AB maggies.
:returns mass_frac:
The ratio of the surviving stellar mass to the total mass formed.
"""
# Spectrum in Lsun/Hz per solar mass formed, restframe
wave, spectrum, mfrac = self.get_galaxy_spectrum(**params)
# Redshifting + Wavelength solution
if 'zred' in self.reserved_params:
# We do it ourselves.
a = 1 + self.params.get('zred', 0)
af = a
b = 0.0
else:
a, b = 1.0, 0.0
# FSPS shifts the wavelength vector but we need to decrease the
# flux by (1+z) here.
af = 1 + self.params.get('zred', 0)
if 'wavecal_coeffs' in self.params:
x = wave - wave.min()
x = 2.0 * (x / x.max()) - 1.0
c = np.insert(self.params['wavecal_coeffs'], 0, 0)
# assume coeeficients give shifts in km/s
b = chebval(x, c) / (lightspeed*1e-13)
wa, sa = wave * (a + b), spectrum * af # Observed Frame
if outwave is None:
outwave = wa
# Observed frame photometry, as absolute maggies
if filters is not None:
mags = getSED(wa, lightspeed/wa**2 * sa * to_cgs, filters)
phot = np.atleast_1d(10**(-0.4 * mags))
else:
phot = 0.0
# Spectral smoothing.
do_smooth = (('sigma_smooth' in self.params) and
('sigma_smooth' in self.reserved_params))
if do_smooth:
# We do it ourselves.
smspec = self.smoothspec(wa, sa, self.params['sigma_smooth'],
outwave=outwave, **self.params)
elif outwave is not wa:
# Just interpolate
smspec = np.interp(outwave, wa, sa, left=0, right=0)
else:
# no interpolation necessary
smspec = sa
# Distance dimming and unit conversion
if (self.params['zred'] == 0) or ('lumdist' in self.params):
# Use 10pc for the luminosity distance (or a number
# provided in the dist key in units of Mpc)
dfactor = (self.params.get('lumdist', 1e-5) * 1e5)**2
else:
lumdist = cosmo.luminosity_distance(self.params['zred']).value
dfactor = (lumdist * 1e5)**2
if peraa:
# spectrum will be in erg/s/cm^2/AA
smspec *= to_cgs / dfactor * lightspeed / outwave**2
else:
# Spectrum will be in maggies
smspec *= to_cgs / dfactor / 1e3 / (3631*jansky_mks)
# Convert from absolute maggies to apparent maggies
phot /= dfactor
# Mass normalization
mass = np.sum(self.params.get('mass', 1.0))
if np.all(self.params.get('mass_units', 'mstar') == 'mstar'):
# Convert from current stellar mass to mass formed
mass /= mfrac
return smspec * mass, phot * mass, mfrac
def plot_fit(self, i_obj, ax=None, is_simulation=False, starlight_input=None, percentile=16):
if ax is None:
fig = plt.figure(1)
fig.clf()
ax = fig.add_axes((0.1,0.1,.8,.8))
if self.l is None:
raise MAGALException('You must init the object w/ a Library!')
bmx_z, bmx_T = self.m.i_BMX[i_obj]
if bmx_z == 0: #FIXME:
i_z = np.argmin((self.l.z - self.i.z[0])**2)
else:
i_z = bmx_z
aux_model = self.l.library[i_z, bmx_T]['m_ab'] + self.m.s[i_obj, bmx_z, bmx_T]
if self.filters_exclude:
filterset_mask = ~np.sum([k == self.l.filterset['ID_filter'] for k in self.filters_exclude], axis=0, dtype=np.bool)
else:
filterset_mask = np.ones(len(self.l.filterset), dtype=np.bool)
if is_simulation: #FIXME
aux_inp = self.i.library[0, i_obj]['m_ab'] - 2.5 * self.i.properties[i_obj]['Mini_fib'] #FIXME
aux_err = self.i.library[0, i_obj]['e_ab'] #FIXME
else:
aux_inp = self.i.data.value[i_obj]['m_ab']
aux_err = self.i.data.value[i_obj]['e_ab']
filterset_mask = np.bitwise_and(filterset_mask, np.isfinite(aux_model))
filterset_mask = np.bitwise_and(filterset_mask, np.isfinite(aux_inp))
aux_lib = np.ma.masked_invalid(self.l.library['m_ab'])
aux_model_tot = [
((aux_lib[i_z,:,i_mag] + self.m.s[i_obj, i_z]) * self.m.likelihood[i_obj, i_z]).sum() / self.m.likelihood[i_obj, i_z][np.argwhere(~aux_lib[i_z,:,i_mag].mask)].sum()
for i_mag in np.argwhere(filterset_mask)]
aux_l = self.l.filterset['wl_central'][filterset_mask]
z_lib = self.l.z[i_z]
if z_lib == 0:
d_L_lib = 3.08567758e19 # 10 parsec in cm
else:
d_L_lib = cosmo.luminosity_distance(z_lib).to('cm')
# k_cosmo = 1/(4 * np.pi * np.power(d_L, 2)) #FIXME:
# aux_l2 = aux_l**-2
# ax.plot(aux_l, aux_l2 * 10**(-.4 * (aux_inp + 2.41)) * k_cosmo.value, '.', color='blue')
# ax.plot(aux_l, aux_l2 * 10**(-.4 * (aux_model[filterset_mask] + 2.41)) * k_cosmo.value, '.', color='red')
# ax.plot(aux_l, aux_inp+aux_err, color='black')
# ax.plot(aux_l, aux_inp-aux_err, color='black')
plt.plot(aux_l, aux_model_tot)
n_percentile = self.m.get('/Alhambra_24/1/statistics/model/n_percentiles')[i_obj][str(percentile)]
# print np.argsort(self.m.likelihood[0])[0][::-n_percentile]
print n_percentile
# for i_T in np.argsort(self.m.likelihood[0])[0][::-1][1:n_percentile]:
# aux_model = self.l.library[i_z, i_T]['m_ab'] + self.m.s[i_obj, bmx_z, i_T]
# plt.plot(aux_l, aux_model[filterset_mask], '.', color='black', linewidth=1, alpha=.1)
# # print 'i', i
if starlight_input:
z_sl = self.i.properties['z'][i_obj]
if z_sl == 0:
d_L = 3.08567758e19 # 10 parsec in cm
else:
d_L = cosmo.luminosity_distance(z_sl).to('cm')
k_cosmo = (1+z_sl)**3 / (4 * np.pi * np.power(d_L.value, 2)) #FIXME:
k_cosmo *= (4 * np.pi * np.power(d_L_lib.value, 2))
try:
print 'Trying to load', starlight_input
aux_slight = np.loadtxt(starlight_input, dtype=np.dtype([('wl', '<f8'), ('flux', '<f8'), ('error', '<f8'), ('flag', '<i8')])) #, )
wl_ = aux_slight['wl'] * (1+z_lib)
fl_ = aux_slight['flux'] * np.power(z_sl + 1., 2) / (1+z_lib)
# plt.plot(wl_, -2.5 * np.log10(aux_slight['flux'] * 1e-17 * wl_**2 / k_cosmo ) - 2.41, alpha=.3)
except IOError:
pass
zp_error = self.m.input_config.getfloat('FitGeneral', 'zp_error') #FIXME
plt.errorbar(aux_l, aux_inp[filterset_mask], fmt='o', color='green', linewidth=2, yerr=np.sqrt(aux_err[filterset_mask]**2+zp_error**2))
plt.plot(aux_l, aux_model[filterset_mask], 'o', color='red', linewidth=2)
def create_tables(self, uv_type, directory, radius, print_progress = False):
'''
Perform photometry on a directory of .fits files and create a list
of two tables. The first table contains the redshift, exposure time,
luminosity, and surface brightness for various supernova, along with
the associated error values. The second table is a log outlining any
files that do not contain a supernova or are missing checkfiles. If
print_progress is set equal to true, the path each fits file will be
printed before performing photometry on along with the number of
remaining files.
Args:
uv_type (str) : Specifies which type of uv to create a table
for. Use either "NUV" or "FUV".
directory (str) : A directory containing .fits files
radius (float): Radius of desired photometry aperture in kpc
print_progress (bool) : Whether or not to print the file path of each
fits file
Returns:
results (list): [Data table (Table), Log table (Table)]
'''
#Make sure we have redshift and coordinates of each supernova
if self.cord_dict.keys() != self.red_dict.keys():
raise ValueError('''Keys in coordinate and redshift dictionaries
(self.cord_dict, self.red_dict) do not match''')
label = uv_type + " " + str(radius) + "kpc "
#Define the tables that will be returned by the function
log = Table(names = ["File Path", "Issue"], dtype = [object, object])
out = Table(names = ["sn",
"Redshift",
"Redshift Error",
label + "Exposure Time",
"Flux",
"Flux Error",
label + "Luminosity",
label + "Luminosity Error",
label + "Surface Brightness",
label + "Surface Brightness Error"],
dtype = ("S70", "float64", "float64", "float64", "float64",
"float64", "float64", "float64", "float64", "float64"))
out["Redshift"].unit = u.dimensionless_unscaled
out["Redshift Error"].unit = u.dimensionless_unscaled
out[label + "Exposure Time"].unit = u.s
out["Flux"].unit = u.erg / u.s / u.Angstrom / u.kpc / u.kpc / u.cm / u.cm / np.pi
out["Flux Error"].unit = u.erg / u.s / u.Angstrom / u.kpc / u.kpc / u.cm / u.cm / np.pi
out[label + "Luminosity"].unit = u.erg / u.s / u.Angstrom / u.kpc / u.kpc
out[label + "Luminosity Error"].unit = u.erg / u.s / u.Angstrom / u.kpc / u.kpc
out[label + "Surface Brightness"].unit = u.erg / u.s / u.Angstrom / u.arcsec / u.arcsec
out[label + "Surface Brightness Error"].unit = u.erg / u.s / u.Angstrom / u.arcsec / u.arcsec
#Set parameters that are specific to NUV or FUV observations
if "N" in uv_type.upper():
file_key = "nd-int" #A string distinguing galex file types
flux_conv = 2.06 * 1e-16 #A conversion factor from counts per second to flux
elif "F" in uv_type.upper():
file_key = "fd-int"
flux_conv = 1.40 * 1e-15
#Create a list of files to perform photometry on
file_list = []
for path, subdirs, files in os.walk(directory):
for name in files:
if file_key in name and len(name.split(".")) < 3:
file_list.append(os.path.join(path, name))
count = len(file_list)
#Perform photometry on each .fits file
for fits_file in file_list:
if print_progress == True:
print(count, ":", fits_file, flush = True)
count -= 1
p = self.photometry(fits_file, radius)
for elt in p:
if elt[0] == "error":
log.add_row([elt[1], elt[2]])
if print_progress == True:
print("error", elt[2], "\n", flush = True)
else:
#We calculate the values to be entered in the table
redshift = float(self.red_dict[elt[0]])
peculiar_redshift = np.sqrt((1 + (300 / 299792.458)) / (1 - (300 / 299792.458))) - 1
redshift_err = np.sqrt((redshift / 1000)**2 + (peculiar_redshift)**2)
arcmin = cosmo.kpc_comoving_per_arcmin(redshift).value**2 #kpc^2 per arcmin^2
arcmin_err = self.conv_error(redshift, redshift_err)
photom = elt[2] #The photometry value
photom_err = elt[3]
flux = flux_conv * photom #convert cps to flux using the conversion factor
flux_err = self.flux_error(photom, photom_err, flux_conv)
ldist = cosmo.luminosity_distance(redshift).cgs.value #Luminosity Distance (cm)
ldist_err = self.lum_dist_error(redshift, redshift_err)
lum = flux * 4 * np.pi * (ldist**2) #luminosity = flux*4*pi*r^2
lum_err = self.luminosity_error(flux, flux_err, ldist, ldist_err)
sbrightness = lum * arcmin / 3600
sbrightness_err = self.surf_brightness_error(lum, lum_err, arcmin, arcmin_err)
out.add_row([elt[0], redshift, redshift_err, elt[1], flux, flux_err,
lum, lum_err, sbrightness, sbrightness_err])
out.sort(label + "Surface Brightness Error")
out_unique = unique(out, keys = "sn")
out_unique.sort("sn")
return([out_unique, log])
def get_spectrum(self, outwave=None, filters=None, peraa=False, **params):
"""Given a theta vector, generate spectroscopy, photometry and any
extras (e.g. stellar mass).
:param theta:
ndarray of parameter values.
:param sps:
A python-fsps StellarPopulation object to be used for
generating the SED.
:returns spec:
The restframe spectrum in units of maggies.
:returns phot:
The apparent (redshifted) observed frame maggies in each of the
filters.
:returns extras:
A list of the ratio of existing stellar mass to total mass formed
for each component, length ncomp.
"""
self.params.update(**params)
# Pass the model parameters through to the sps object
ncomp = len(self.params['mass'])
for ic in range(ncomp):
s, p, x = self.one_sed(component_index=ic, filterlist=filters)
try:
spec += s
maggies += p
extra += [x]
except(NameError):
spec, maggies, extra = s, p, [x]
# `spec` is now in Lsun/Hz, with the wavelength array being the
# observed frame wavelengths. Flux array (and maggies) have not been
# increased by (1+z) due to cosmological redshift
if outwave is not None:
w = self.csp.wavelengths
spec = np.interp(outwave, w, spec)
# Distance dimming and unit conversion
if (self.params['zred'] == 0) or ('lumdist' in self.params):
# Use 10pc for the luminosity distance (or a number provided in the
# lumdist key in units of Mpc). Do not apply cosmological (1+z)
# factor to the flux.
dfactor = (self.params.get('lumdist', 1e-5) * 1e5)**2
a = 1.0
else:
# Use the comsological luminosity distance implied by this
# redshift. Incorporate cosmological (1+z) factor on the flux.
lumdist = cosmo.luminosity_distance(self.params['zred']).value
dfactor = (lumdist * 1e5)**2
a = (1 + self.params['zred'])
if peraa:
# spectrum will be in erg/s/cm^2/AA
spec *= to_cgs * a / dfactor * lightspeed / outwave**2
else:
# Spectrum will be in maggies
spec *= to_cgs * a / dfactor / 1e3 / (3631*jansky_mks)
# Convert from absolute maggies to apparent maggies
maggies *= a / dfactor
return spec, maggies, extra
def test_z(x,c):
# c = true lumdist
# x = redshift
return WMAP9.luminosity_distance(x).value-c
def get_spectrum(self, outwave=None, filters=None, peraa=False, **params):
"""Get a spectrum and SED for the given params.
ripped from SSPBasis
addition: check for flag nebeminspec. if not true,
add emission lines directly to photometry
"""
# Spectrum in Lsun/Hz per solar mass formed, restframe
wave, spectrum, mfrac = self.get_galaxy_spectrum(**params)
# Redshifting + Wavelength solution
# We do it ourselves.
a = 1 + self.params.get('zred', 0)
af = a
b = 0.0
if 'wavecal_coeffs' in self.params:
x = wave - wave.min()
x = 2.0 * (x / x.max()) - 1.0
c = np.insert(self.params['wavecal_coeffs'], 0, 0)
# assume coeeficients give shifts in km/s
b = chebval(x, c) / (lightspeed*1e-13)
wa, sa = wave * (a + b), spectrum * af # Observed Frame
if outwave is None:
outwave = wa
spec_aa = lightspeed/wa**2 * sa # convert to perAA
# Observed frame photometry, as absolute maggies
if filters is not None:
mags = observate.getSED(wa, spec_aa * to_cgs, filters)
phot = np.atleast_1d(10**(-0.4 * mags))
else:
phot = 0.0
### if we don't have emission lines, add them
if (not self.params['nebemlineinspec']) and self.params['add_neb_emission']:
phot += self.nebline_photometry(filters,a-1)*to_cgs
# Spectral smoothing.
do_smooth = (('sigma_smooth' in self.params) and
('sigma_smooth' in self.reserved_params))
if do_smooth:
# We do it ourselves.
smspec = self.smoothspec(wa, sa, self.params['sigma_smooth'],
outwave=outwave, **self.params)
elif outwave is not wa:
# Just interpolate
smspec = np.interp(outwave, wa, sa, left=0, right=0)
else:
# no interpolation necessary
smspec = sa
# Distance dimming and unit conversion
zred = self.params.get('zred', 0.0)
if (zred == 0) or ('lumdist' in self.params):
# Use 10pc for the luminosity distance (or a number
# provided in the dist key in units of Mpc)
dfactor = (self.params.get('lumdist', 1e-5) * 1e5)**2
else:
lumdist = WMAP9.luminosity_distance(zred).value
dfactor = (lumdist * 1e5)**2
if peraa:
# spectrum will be in erg/s/cm^2/AA
smspec *= to_cgs / dfactor * lightspeed / outwave**2
else:
# Spectrum will be in maggies
smspec *= to_cgs / dfactor / 1e3 / (3631*jansky_mks)
# Convert from absolute maggies to apparent maggies
phot /= dfactor
# Mass normalization
mass = np.sum(self.params.get('mass', 1.0))
if np.all(self.params.get('mass_units', 'mstar') == 'mstar'):
# Convert from current stellar mass to mass formed
mass /= mfrac
return smspec * mass, phot * mass, mfrac
def read_magphys_output(objname=None):
'''
two output files
GALAXY_ID.fit has observed fluxes+uncertainties,
index of best-fit IR + optical models, chi^2, values of
best-fit parameters, fluxes in each filter, and likelihood
distribution of all parameters
GALAXY_ID.sed has main parameters of the best-fit model,
and the SED of the best-fit model.
'''
from astropy.cosmology import WMAP9
project_info = {
'idfile': os.getenv('APPS')+"/threedhst_bsfh/data/brownseds_data/photometry/namelist.txt",
'datname':os.getenv('APPS')+'/threedhst_bsfh/data/brownseds_data/photometry/table1.fits',
'photname':os.getenv('APPS')+'/threedhst_bsfh/data/brownseds_data/photometry/table3.fits',
'extinctname':os.getenv('APPS')+'/threedhst_bsfh/data/brownseds_data/photometry/table4.fits',
'herschname':os.getenv('APPS')+'/threedhst_bsfh/data/brownseds_data/photometry/kingfish.brownapertures.flux.fits',
'output': '/Users/joel/code/magphys/output'
}
if objname is None:
objname = np.loadtxt(project_info['idfile'],delimiter='#',dtype = str)[0]
#### first file ####
file1 = slice_name(objname.replace(' ',''))[:8]+'.fit'
with open(project_info['output']+'/'+file1[:10], 'r') as f:
# skip first line
f.readline()
##### observations #####
# flux in Lsun/Hz
obs = {}
obs['filters'] = np.array(f.readline().split()[1:],dtype=str)
obs['flux'] = np.array(f.readline().split(),dtype=float)
obs['flux_unc'] = np.array(f.readline().split(),dtype=float)
obs['phot_mask'] = (obs['flux'] != 0) | (obs['flux_unc'] != 0)
# skip four lines
for kk in xrange(4): f.readline()
##### metadata #####
metadata = {}
line = np.array(f.readline().split(),dtype=float)
metadata['bestfit_opt_ind'] = line[0]
metadata['bestfit_ir_ind'] = line[1]
metadata['chisq'] = line[2]
metadata['redshift'] = line[3]
##### best-fit model #####
# in Lsun/Hz
model = {}
model['parnames'] = np.array(f.readline().replace('.',' ').split()[1:],dtype=str)
model['parameters'] = np.array(f.readline().split(),dtype=float)
f.readline()
model['flux'] = np.array(f.readline().split(),dtype=float)
# skip three lines
for kk in xrange(3): f.readline()
##### parameter PDFs #####
pdfs = {'x_interp_perc': np.array([0.025,0.16,0.5,0.84,0.975]),
'percentiles': {},
'likelihood_distr': {},
'likelihood_x': {}
}
for par in model['parnames']:
for line in f:
# ignore comments
# if it's the last comment, switch to new parname
if line.find('#') != -1 and line.find('....') != -1:
continue
elif line.find('#') != -1:
break
_ = np.array(line.split(),dtype=float)
if _.shape[0] == 2:
pdfs['likelihood_x'][par] = np.append(pdfs['likelihood_x'].get(par,[]),_[0])
pdfs['likelihood_distr'][par] = np.append(pdfs['likelihood_distr'].get(par,[]),_[1])
else:
pdfs['percentiles'][par] = _
##### second file #####
file2 = slice_name(objname.replace(' ',''))[:8]+'.sed'
with open(project_info['output']+'/'+file2[:10], 'r') as f:
# skip two lines
for i in xrange(2): f.readline()
##### read model parameters
model['full_parnames'] = np.array(f.readline().replace('.',' ').split(),dtype=str)[1:]
model['full_parameters'] = np.array(f.readline().split(),dtype=float)
f.readline()
model['full_parnames'] = np.append(model['full_parnames'],np.array(f.readline().replace('.',' ').split(),dtype=str)[1:])
model['full_parameters'] = np.append(model['full_parameters'],np.array(f.readline().split(),dtype=float))
# skip three lines
for i in xrange(3): f.readline()
##### read spectrum #####
# starts in obslam
for line in f:
_ = np.array(line.split(),dtype=float)
model['lam'] = np.append(model.get('lam',[]),10**_[0])#*(1+metadata['redshift']))
model['spec'] = np.append(model.get('spec',[]),_[1])
model['spec_nodust'] = np.append(model.get('spec_nodust',[]),_[2])
##### units of [log(Lsun/angstrom)], change to maggies
# first save output as speclum
model['speclum'] = 10**model['spec']
model['speclum_nodust'] = 10**model['spec_nodust']
# now convert to Lsun/Hz
c = 3e18 # Angstroms / s
model['spec'] = (10**model['spec']) * model['lam']**2 / c
model['spec_nodust'] = (10**model['spec_nodust']) * model['lam']**2 / c
# from Lsun/Hz to maggies
lsun = 3.846e33 # ergs/s
jansky_cgs = 1e-23
to_maggies = lsun / (3631*jansky_cgs)
# cm^2
pc = 3.085677581467192e18 # cm
dfactor = 4*np.pi*(pc*WMAP9.luminosity_distance(metadata['redshift']).value *
1e6)**2 / (1+metadata['redshift'])
model['spec'] *= to_maggies / dfactor
model['spec_nodust'] *= to_maggies / dfactor
obs['flux'] *= to_maggies / dfactor
obs['flux_unc'] *= to_maggies / dfactor
model['flux'] *= to_maggies / dfactor
##### load SFH
metind = model['full_parnames'] == 'Z/Zo'
age,sfr = read_magphys_sfh(int(metadata['bestfit_opt_ind']),
float(model['full_parameters'][metind]))
sfh = {'age':age,'sfr':sfr}
##### add SFR_10 from best-fitting SFH
# in Msun/yr
magmass = model['full_parameters'][[model['full_parnames'] == 'M*/Msun']]
magsfr = sfh['sfr']*magmass
magtime = np.abs(np.max(sfh['age']) - sfh['age'])
sfr_10 = prosp_dutils.integral_average(magtime,magsfr,0,1e7)
if np.isfinite(sfr_10) == False:
print 1/0
model['full_parameters'] = np.append(model['full_parameters'],sfr_10)
model['full_parnames'] = np.append(model['full_parnames'],'SFR_10')
magphys = {'model': model,
'obs': obs,
'metadata': metadata,
'pdfs': pdfs,
'sfh': sfh}
return magphys
def cosmo_distance_extra(zred): return cosmo.luminosity_distance(zred).value**(-2) * (1+zred)**2
#width MUST be odd.
width = 15
pixr = int((width-1)/2)
# specify the position of the science target and the size of the region around the science target to consider
filters = np.array([475, 814, 1600]) #*u.nm
galaxies = ['J0826', 'J0901', 'J0905', 'J0944', 'J1107', 'J1219', 'J1341', 'J1506', 'J1558', 'J1613', 'J2116', 'J2140']
xcen = [3628, 3933, 3386.5, 3477.5, 3573, 3802, 3886, 4149, 3787, 4174, 3565, 4067]
ycen = [4153, 4136, 3503.2, 3404.3, 3339, 4169, 4164, 3921, 4187, 3826, 3434, 4054]
fluxvalues = [[0 for x in range(len(wavelengths))] for y in range(len(galaxies))]
zs = [0.603, 0.459, 0.712, 0.514, 0.467, 0.451, 0.658, 0.608, 0.402, 0.449, 0.728, 0.752]
#Ldcm is the luminosity distance in cm, even though astropy thinks it is in Mpc.
Ldcm = cosmo.luminosity_distance(zs)*u.Mpc.to(u.cm) / u.Mpc
totalphotons = 0
# define the radii to be used for aperture photometry
radii = np.arange(40)+1
area = [0 for x in range(len(radii))]
# percunc specifies the percent of variation we expect from systematic error...
# For now, we have chosen 0.05, or 5%.
percUnc = 0.05
#calculate area of each bagel
for i in range(0, len(area)):
if i == 0:
area[i] = math.pi*math.pow(radii[0],2)
else:
for j in range(69, 197):
print "Calculating colours for age: " + str(ages[j]) + " and Tau: " + tauvals[t]
spectrum = np.array([specdata[0, :], specdata[j+1, :]]).T
synmags = np.zeros(13*500, dtype="float")
synmags.shape = (500, 13)
synmags[:,0] = np.arange(0.01, 5.01, 0.01)
for i in range(1, 501):
z = 0.01*i
if ages[j]*(10**-9) < 14.00 - cosmo.lookback_time(z).value:
zspectrum = np.copy(spectrum)
zspectrum[:,1] = zspectrum[:,1]*3.826*10**33 #luminosity in erg/s/A
zspectrum[:,1] = zspectrum[:,1]/(4*np.pi*(cosmo.luminosity_distance(z).value*3.086*10**24)**2) #convert to observed flux at given redshift in erg/s/A/cm^2
zspectrum[:,1] = zspectrum[:,1]/(1+z) #reduce flux by a factor of 1/(1+z) to account for redshifting
agenorms[j, i-1] = 25.*np.sum(rebin_to_filter(zspectrum, normfilt))
zspectrum[:,1] = zspectrum[:,1]/agenorms[j,i-1]
zspectrum[:,0] = zspectrum[:,0]*(1+z) #change wavelength values to account for redshifting
output[:,j] = rebin_to_filter(zspectrum, objfilter)
else:
output[:,j] = np.zeros(len(objfilter))
np.savetxt("models/spec/" + tauvals[t] + "/age_" + str(ages[j]) + ".txt", output)
np.savetxt("models/spec/" + tauvals[t] + "/agenorms_" + str(ages[j]) + ".txt", agenorms[j,:])
def collate_data(runname, runname_sample, filename=None, regenerate=False, **opts):
### if it's already made, load it and give it back
# else, start with the making!
if os.path.isfile(filename) and (regenerate == False):
with open(filename, "r") as f:
outdict=hickle.load(f)
return outdict
### define output containers
out = {'objname':[]}
entries = ['ha_ew_obs', 'ha_ew_mod', 'ha_flux_mod', 'ha_flux_obs']
qvals = ['q50', 'q16', 'q84']
for e in entries:
out[e] = {}
if 'obs' in e:
out[e]['val'] = []
out[e]['err'] = []
else:
for q in qvals: out[e][q] = []
### load up ancillary dataset
basenames, _, _ = prosp_dutils.generate_basenames(runname)
field = [name.split('/')[-1].split('_')[0] for name in basenames]
nii_ha_fnc = nii_ha_ratio()
ancil = []
allfields = np.unique(field).tolist()
for f in allfields:
ancil.append(td_io.load_ancil_data(runname_sample,f))
for i, name in enumerate(basenames):
### if H-alpha is not detected, dump and continue
objfield = name.split('/')[-2]
objnumber = int(name.split('_')[-1])
fidx = allfields.index(objfield)
oidx = ancil[fidx]['phot_id'] == objnumber
if (ancil[fidx]['Ha_FLUX'][oidx][0] == -99):
continue
#### load input
# make sure all files exist
try:
prosp = load_prospector_extra(name)
print name.split('/')[-1]+' loaded.'
except:
continue
if prosp is None:
continue
out['objname'].append(name.split('/')[-1])
objnumber = int(out['objname'][-1].split('_')[1])
# fill in model data
# comes out in rest-frame EW and Lsun
for q in qvals: out['ha_ew_mod'][q].append(prosp['obs']['elines']['H alpha 6563']['ew'][q])
for q in qvals: out['ha_flux_mod'][q].append(prosp['obs']['elines']['H alpha 6563']['flux'][q])
# fill in observed data
# comes out in observed-frame EW and (10**-17 ergs / s / cm**2)
# account for NII / Halpha ratio, distance
zred = ancil[fidx]['z_best'][oidx][0]
mass = np.log10(prosp['extras']['stellar_mass']['q50'])
nii_correction = float(1-nii_ha_fnc(mass,zred))
lumdist = WMAP9.luminosity_distance(zred).value
dfactor = 4*np.pi*(u.Mpc.to(u.cm) * lumdist)**2
# fill in and march on
out['ha_flux_obs']['val'].append(ancil[fidx]['Ha_FLUX'][oidx][0] * 1e-17 * dfactor / 3.828e33 * nii_correction)
out['ha_flux_obs']['err'].append(ancil[fidx]['Ha_FLUX_ERR'][oidx][0] * 1e-17 * dfactor / 3.828e33 * nii_correction)
out['ha_ew_obs']['val'].append(ancil[fidx]['Ha_EQW'][oidx][0]/(1+zred) * nii_correction)
out['ha_ew_obs']['err'].append(ancil[fidx]['Ha_EQW_ERR'][oidx][0]/(1+zred) * nii_correction)
for key in out.keys():
if type(out[key]) == dict:
for key2 in out[key].keys(): out[key][key2] = np.array(out[key][key2])
else:
out[key] = np.array(out[key])
### dump files and return
hickle.dump(out,open(filename, "w"))
return out