def get_sfr_z_pdf(z_max, binsize):
z = np.arange(0.0, z_max, binsize)
z_dz = np.arange(0.0 + binsize, z_max + binsize, binsize)
v_z = cosmo.comoving_volume(z)
v_z_dz = cosmo.comoving_volume(z_dz)
v_dz = v_z_dz - v_z
norm_v_dz = v_dz / np.nanmax(v_dz)
sfr_z = lsstt.sims.calculate_SFR(z)
sfr_norm = sfr_z / np.nanmax(sfr_z)
volumetric_rate = norm_v_dz * sfr_norm
normed_volumetric_rate = volumetric_rate / np.nanmax(volumetric_rate)
return InterpolatedUnivariateSpline(z, normed_volumetric_rate)
def test_four_volume(self):
self.assertAlmostEqual(
Planck15.comoving_volume(2.3).value / 1e9,
redshift.total_four_volume(lamb=1,
analysis_time=1,
max_redshift=2.3),
4,
)
def measure_sfrd(stacked_object, area_deg=1.62, tsfrd=False, cosmo=cosmo):
if area_deg == 1.62:
print 'defaulting to uVista/COSMOS area of 1.62deg2'
area_sr = area_deg * (3.1415926535 / 180.)**2
sfrd = np.zeros(np.shape(stacked_object.simstack_nuInu_array))
for i in range(stacked_object.nz):
zn = stacked_object.z_nodes[i:i+2]
z_suf = '{:.2f}'.format(zn[0])+'-'+'{:.2f}'.format(zn[1])
vol = cosmo.comoving_volume(zn[1]) - cosmo.comoving_volume(zn[0])
for iwv in range(stacked_object.nw):
for j in range(stacked_object.nm):
mn = stacked_object.m_nodes[j:j+2]
m_suf = '{:.2f}'.format(mn[0])+'-'+'{:.2f}'.format(mn[1])
for p in range(stacked_object.npops):
arg = clean_args('z_'+z_suf+'__m_'+m_suf+'_'+stacked_object.pops[p])
ng = len(stacked_object.bin_ids[arg])
sfr = conv_lir_to_sfr * stacked_object.simstack_flux_array[iwv,i,j,p]
sfrd[iwv,i,j,p] += float(ng) / area_sr * sfr
if tsfrd == True:
return np.sum(np.sum(np.sum(sfrd,axis=1),axis=1),axis=1)
else:
return sfrd
def measure_sfrd(stacked_object, area_deg=1.62, tsfrd=False, cosmo=cosmo):
if area_deg == 1.62:
print 'defaulting to uVista/COSMOS area of 1.62deg2'
area_sr = area_deg * (3.1415926535 / 180.)**2
sfrd = np.zeros(np.shape(stacked_object.simstack_nuInu_array))
for i in range(stacked_object.nz):
zn = stacked_object.z_nodes[i:i + 2]
z_suf = '{:.2f}'.format(zn[0]) + '-' + '{:.2f}'.format(zn[1])
vol = cosmo.comoving_volume(zn[1]) - cosmo.comoving_volume(zn[0])
for iwv in range(stacked_object.nw):
for j in range(stacked_object.nm):
mn = stacked_object.m_nodes[j:j + 2]
m_suf = '{:.2f}'.format(mn[0]) + '-' + '{:.2f}'.format(mn[1])
for p in range(stacked_object.npops):
arg = clean_args('z_' + z_suf + '__m_' + m_suf + '_' +
stacked_object.pops[p])
ng = len(stacked_object.bin_ids[arg])
sfr = conv_lir_to_sfr * stacked_object.simstack_flux_array[
iwv, i, j, p]
sfrd[iwv, i, j, p] += float(ng) / area_sr * sfr
if tsfrd == True:
return np.sum(np.sum(np.sum(sfrd, axis=1), axis=1), axis=1)
else:
return sfrd
def zint(mass, zmin, zmax, nstep=1000):
z = np.linspace(zmin, zmax, nstep)
integral = 0
if mass > 100:
#Shitty check if mass was given as just the exponant
mass = np.log(mass)
for i in range(len(z) - 1):
ztemp = (z[i + 1] + z[i]) / 2
print(i)
integrand = lambda m: mass_function.massFunction(
m, ztemp, mdef='500m', model='tinker08', q_out='dndlnM')
integrated_density = log_int(integrand, mass, 20.5)
#Comoving volume computed over full sky i.e. 4pi steradians. Scale accordingly
comov_vol = Planck15.comoving_volume(ztemp).value
integral += integrated_density * comov_vol * (z[i + 1] - z[i])
return integral
def test_limiting_case_easy():
"""
This tests that the number of objects sampled by the `PowerLawRates` is
equal to the number expected for a simple case with a rate given by
3.0e-5 * (h/0.7)**3, when 3.0e-5 is passed as the argument.
"""
rng = np.random.RandomState(1)
pl = PowerLawRates(rng,
sky_area=None,
zlower=1.0e-8,
zhigher=1.2,
num_bins=200,
sky_fraction=1.,
zbin_edges=None,
alpha_rate=3.0e-5,
survey_duration=1.0,
cosmo=cosmo,
beta_rate=1.0)
vol = cosmo.comoving_volume(z=1.2).value
np.testing.assert_allclose(vol * 3.0e-5 * (cosmo.h / 0.7)**3,
pl.z_sample_sizes.sum(),
rtol=0.001)
def schechter_LF(z,
lambdaemitted,
alpha,
Lstar0,
betaL,
phistar0,
betaphi,
zpaper,
param,
fluxscale,
em,
filter,
style=""):
print("alpha = ", alpha)
print("z = ", z)
test = numpy.arange(30, 55, 0.01)
L = (10**test)
#I have two different parametrizations for Lstar and phistar; the first one is from Comparat et al 2016, and the second one is from Sobral et al 2015
if param == "first":
Lstar = fluxscale * Lstar0 * ((1 + z)**betaL)
print("Lstar = ", Lstar)
phistar = phistar0 * ((1 + z)**betaphi)
print("phistar = ", phistar)
if param == "second":
Lstar = 10**(0.45 * z + Lstar0)
print("Lstar = ", Lstar)
phistar = 10**(-0.38 * (z**2) + z + phistar0)
print("phistar = ", phistar)
if param == "third":
answersLya = lineqLya(z=z)
Lstar = answersLya[
0] #this is linearly parametrized from Ciardullo+ 2012
print("Lstar = ", Lstar)
phistar = answersLya[
1] #this is linearly parametrized from Ciardullo+ 2012
print("phistar = ", phistar)
phi = phistar * ((L / Lstar)**(alpha + 1)) * (numpy.e**(-L / Lstar))
print(
"Note: Each paper uses a slightly different convention; I decided to consolidate them with the following:"
)
print("using the LF equation with the alpha+1 exponent as follows: ")
print("phi = phistar*((L/Lstar)**(alpha+1))*(numpy.e**(-L/Lstar))")
#this deletes parts of the arrays that are so small python counts them as zero; otherwise, I would not be able to take the logarithm of the array
L = L[numpy.where(phi != 0)]
phi = phi[numpy.where(phi != 0)]
testarray = test[numpy.where(phi != 0)]
log10L = numpy.log10(L)
log10phi = numpy.log10(phi)
#the following calculates values I use in and plug into the lumlim function
if filter == "uband":
#ABmag is the coadded depth for a 5 sigma magnitude limit in this filter
ABmag = 26.1
lambdalow = 305.30 #in nm
lambdahigh = 408.60 #in nm
if filter == "gband":
#ABmag is the coadded depth for a 5 sigma magnitude limit in this filter
ABmag = 27.4
lambdalow = 386.30 #in nm
lambdahigh = 567.00 #in nm
if filter == "rband":
#ABmag is the coadded depth for a 5 sigma magnitude limit in this filter
ABmag = 27.5
lambdalow = 536.90 #in nm
lambdahigh = 706.00 #in nm
if filter == "iband":
#ABmag is the coadded depth for a 5 sigma magnitude limit in this filter
ABmag = 26.8
lambdalow = 675.90 #in nm
lambdahigh = 833.00 #in nm
if filter == "zband":
#ABmag is the coadded depth for a 5 sigma magnitude limit in this filter
ABmag = 26.1
lambdalow = 802.90 #in nm
lambdahigh = 938.60 #in nm
if filter == "yband":
#ABmag is the coadded depth for a 5 sigma magnitude limit in this filter
ABmag = 24.9
lambdalow = 908.30 #in nm
lambdahigh = 1099.60 #in nm
#uses previously defined function to get median transmission wavelenth
lambdaarray = filter_int(filter=filter)
lambdacenter = lambdaarray[0]
#lambdaemitted = #will be one of the three emission lines #uses nm #these are redshifts
filterendlow = (lambdalow / lambdaemitted) - 1
filterendhigh = (lambdahigh / lambdaemitted) - 1
filtercenter = (lambdacenter / lambdaemitted) - 1
#I don't think I used the following at all
#deltafilter = filterendhigh-filterendlow
#print("for",em,"deltafilter =",deltafilter)
#finds luminosity limit in center of z band
center = lumlim(z=filtercenter, em=em, filter=filter)
#sets up an array that I use to plot
lumarray = numpy.full(15, numpy.log10(center))
yarray = numpy.arange(-10, 5, 1)
figure(1)
plot(log10L, log10phi, style, label=zpaper)
plot(lumarray, yarray, style + "--")
xlim(40, 45)
ylim(-7, 0)
legend(loc="upper right")
#LaTeX is used with $ signs in the titles below
xlabel("$\log_{10}(L [ergs/s])$")
ylabel("$\log_{10}(\phi [\t{Mpc}^{-3}])$")
title("Schechter Luminosity Function, " + filter)
print("z = ", z, " and alpha = ", alpha, " are plotted, ")
#saves image
pyplot.savefig('/home/lanaeid/Desktop/fig1' + filter + '.png',
bbox_inches='tight')
print(
"now the number density is calculated by integrating the LF using cumtrapz:"
)
#cumptrapz integrates the opposite way than I need to integrate, so I flip it twice in the process
phiflip = phi[::-1]
phiflipint = scipy.integrate.cumtrapz(phiflip, x=testarray)
num_dens = phiflipint[::-1]
Lmin = numpy.delete(L, (len(L) - 1))
#shifted_Lmin = Lmin*fluxscale
log10Lmin = numpy.log10(Lmin)
#log10shifted_Lmin = numpy.log10(shifted_Lmin)
figure(2)
plot(log10Lmin, num_dens, style, label=zpaper)
plot(lumarray, yarray, style + "--")
legend(loc="upper right")
xlim(40, 44)
ylim(0, 0.03)
#LaTeX is used with $ signs in the titles below
xlabel("$\log_{10}(L_{min} [ergs/s])$")
ylabel("$\log_{10}(\phi [\t{Mpc}^{-3}])$")
title("Number Density, " + filter)
#saves image
pyplot.savefig('/home/lanaeid/Desktop/fig2' + filter + '.png',
bbox_inches='tight')
#the following finds the comoving number density above a certain detection limit (shot noise limit),
#which I calculated in the previous part of the code (also shown below), where I saved the variable named center
#runs through the LF code for chosen emission line
#then gets number density above the luminosity limit using the center value
#first, I have to shorten the phi array to contain only values above the luminosity limit
#EDIT HERE
#first define array of 100 evenly spaced wavelengths
#use these to get an array of redshifts with which to get the comoving volume, for each of them
#basically getting the thing I found below but for everything across the FWHM
#uses astropy.cosmology.Planck15 to find comoving volume in shell between redshifts at each end of the z filter
comovingvolmin = cosmo.comoving_volume(filterendlow) #units are in Mpc^3
comovingvolmax = cosmo.comoving_volume(filterendhigh) #units are in Mpc^3
comovingvol = comovingvolmax - comovingvolmin
comovingvol = comovingvol.value
print("comovingvol =", comovingvol, "Mpc^3")
#shortens the array to be above the luminosity limit, then integrates to get comoving number density
philim = phi[numpy.where(L > center)]
testarraydos = test[numpy.where(L > center)]
comovingphi = scipy.integrate.trapz(philim, x=testarraydos)
print("comovingphi =", comovingphi, "Mpc^-3") #units?
#finds total number of galaxies and areal number density
totalnumgalaxies = comovingphi * comovingvol
arealphi = totalnumgalaxies / (4 * numpy.pi)
print("arealphi =", arealphi, "steradian^-1")
show()
def schechter_LF(z,lambdaemitted,alpha,Lstar0,betaL,phistar0,betaphi,zpaper,param,fluxscale,em,style = ""):
print("alpha = ", alpha)
print("z = ", z)
test = numpy.arange(30,55,0.01)
L = (10**test)
#I have two different parametrizations for Lstar and phistar; the first one is from Comparat et al 2016, and the second one is from Sobral et al 2015
if param == "first":
Lstar = fluxscale*Lstar0*((1+z)**betaL)
print("Lstar = ", Lstar)
phistar = phistar0*((1+z)**betaphi)
print("phistar = ", phistar)
if param == "second":
Lstar = 10**(0.45*z + Lstar0)
print("Lstar = ", Lstar)
phistar = 10**(-0.38*(z**2) + z + phistar0)
print("phistar = ", phistar)
if param == "third": #THIS NEEDS TO BE FIXED
answersLya = lineqLya(z)
Lstar = answersLya[0] #this is linearly parametrized from Ciardullo+ 2012
print("Lstar = ",Lstar)
phistar = answersLya[1] #this is linearly parametrized from Ciardullo+ 2012
print("phistar = ",phistar)
phi = phistar*((L/Lstar)**(alpha+1))*(numpy.e**(-L/Lstar))
print("Note: Each paper uses a slightly different convention; I decided to consolidate them with the following:")
print("using the LF equation with the alpha+1 exponent as follows: ")
print("phi = phistar*((L/Lstar)**(alpha+1))*(numpy.e**(-L/Lstar))")
#this deletes parts of the arrays that are so small python counts them as zero; otherwise, I would not be able to take the logarithm of the array
L = L[numpy.where(phi!=0)]
phi = phi[numpy.where(phi!=0)]
testarray = test[numpy.where(phi!=0)]
log10L = numpy.log10(L)
log10phi = numpy.log10(phi)
#the following calculates values I use in and plug into the lumlim function
lambdalow = 818.95 #in nm
lambdahigh = 921.15 #in nm
lambdacenter = (lambdalow+lambdahigh)/2 #in nm #NO THIS IS NOT CORRECT, FIX THIS
#lambdaemitted = #will be one of the three emission lines #in nm
zendlow = (lambdalow/lambdaemitted)-1
zendhigh = (lambdahigh/lambdaemitted)-1
zcenter = (lambdacenter/lambdaemitted)-1
deltaz = zendhigh-zendlow
print("for",em,"deltaz =",deltaz)
#finds luminosity limit in center of z band
center = lumlim(z = zcenter,em = em)
#why did I have it reprint out: print("luminosity limit for the center of the z band with a 5 sigma detection limit is",center,"erg/s") ?
lumarray = numpy.full(15,numpy.log10(center))
yarray = numpy.arange(-10,5,1)
figure(1)
plot(log10L,log10phi,style,label = zpaper)
plot(lumarray,yarray,style+"--")
xlim(40,45)
ylim(-7,0)
legend(loc = "upper right")
#LaTeX is used with $ signs in the titles below
xlabel("$\log_{10}(L [ergs/s])$")
ylabel("$\log_{10}(\phi [\t{Mpc}^{-3}])$")
title("Schechter Luminosity Function") #emline+
print("z = ", z, " and alpha = ", alpha, " are plotted, ")
#saves image
#plot.savefig('/home/Desktop/fig1.png',bbox_inches = 'tight')
print("now the number density is calculated by integrating the LF using cumtrapz:")
#cumptrapz integrates the opposite way than I need to integrate, so I flip it twice in the process
phiflip = phi[::-1]
phiflipint = scipy.integrate.cumtrapz(phiflip,x=testarray)
num_dens = phiflipint[::-1]
Lmin = numpy.delete(L,(len(L)-1))
#shifted_Lmin = Lmin*fluxscale
log10Lmin = numpy.log10(Lmin)
#log10shifted_Lmin = numpy.log10(shifted_Lmin)
figure(2)
plot(log10Lmin,num_dens,style,label = zpaper)
#show()
plot(lumarray,yarray,style+"--")
#show()
legend(loc = "upper right")
xlim(40,44)
ylim(0,0.03)
#LaTeX is used with $ signs in the titles below
xlabel("$\log_{10}(L_{min} [ergs/s])$")
ylabel("$\log_{10}(\phi [\t{Mpc}^{-3}])$")
title("Number Density") #emline+
#saves image
#plot.savefig('/home/Desktop/fig2.png',bbox_inches = 'tight')
#the following finds the comoving number density above a certain detection limit (shot noise limit),
#which I calculated in the previous part of the code (also shown below), where I saved the variable named center
#runs through the LF code for chosen emission line
#then gets number density above the luminosity limit using the center value
#first, I have to shorten the phi array to contain only values above the luminosity limit
#uses astropy.cosmology.Planck15 to find comoving volume in shell between redshifts at each end of the z filter
comovingvolmin = cosmo.comoving_volume(zendlow) #units are in Mpc^3
comovingvolmax = cosmo.comoving_volume(zendhigh) #units are in Mpc^3
comovingvol = comovingvolmax-comovingvolmin
comovingvol = comovingvol.value
print("comovingvol =",comovingvol,"Mpc^3")
#shortens the array to be above the luminosity limit, then integrates to get comoving number density
philim = phi[numpy.where(L>center)]
testarraydos = test[numpy.where(L>center)]
comovingphi = scipy.integrate.trapz(philim,x=testarraydos)
print("comovingphi =",comovingphi,"Mpc^-3") #units?
#finds total number of galaxies and areal number density
totalnumgalaxies = comovingphi*comovingvol
arealphi = totalnumgalaxies/(4*numpy.pi)
print("arealphi =",arealphi,"steradian^-1")
import time
import matplotlib.pyplot as plt
names = ['z', 'm1', 'm2']
data = np.genfromtxt(
'../../../simulation/data_ready_june_snap_lim_1.txt', names=True, dtype=None)
z_coal = data['redshift']
dz = 0.001
zs_i = np.arange(0.0, 8.0+dz, dz)
zs_i_plus_1 = zs_i + dz
zs = zs_i + dz/2.
# TODO: check units
dVc_dz = (cosmo.comoving_volume(zs_i_plus_1).value - cosmo.comoving_volume(zs_i).value)/dz
dz_dt = dz/(cosmo.age(zs_i_plus_1).value*1e9 - cosmo.age(zs_i).value*1e9)
weights = np.abs(np.interp(z_coal, zs, dz_dt*dVc_dz/(1+zs)))
kde = KDEResample(
np.asarray([data['redshift'], data['mass_new_prev_in'], data['mass_new_prev_out']]).T,
weights, names)
kde.make_kernel()
st = time.time()
gc = GenerateCatalog(0.8, 100.0, kde, num_catalogs=100000)
cats = gc.make_catalogs()
print(time.time() - st)
plt.hist(
