def ln_likelihood_2d(mu_obs, sig_obs, z_vector, theta):
#print (h)
cosmo = LambdaCDM(Om0=theta[0], Ode0=theta[1], H0=100 * h)
mu_pred = cosmo.distmod(z_vector).value
#print ('pred', )
chi_2 = np.sum(np.power(((mu_obs - mu_pred) / sig_obs), 2.0))
return (-chi_2 / 2.0)
def lumdis(z, key):
Ho = cosmology[key].H #hubble constant
mo = cosmology[key].Om #matter
l = cosmology[key].Ol #dark energyH
k = cosmology[key].Ok #curvature
cos = LambdaCDM(H0=Ho, Om0=mo, Ode0=l) #Ok0 = k should work but it doesn't
return cos.luminosity_distance(z)
def test_redshifts_from_comoving_density():
from skypy.galaxy.redshift import redshifts_from_comoving_density
from astropy.cosmology import LambdaCDM
# random cosmology
H0 = np.random.uniform(50, 100)
Om = np.random.uniform(0.1, 0.9)
Ol = np.random.uniform(0.1, 0.9)
cosmo = LambdaCDM(H0=H0, Om0=Om, Ode0=Ol)
# fixed comoving density of Ngal galaxies total
Ngal = 1000
redshift = np.arange(0.0, 2.001, 0.1)
density = Ngal/cosmo.comoving_volume(redshift[-1]).to_value('Mpc3')
fsky = 1.0
# sample galaxies over full sky without Poisson noise
z_gal = redshifts_from_comoving_density(redshift, density, fsky, cosmo, noise=False)
# make sure number of galaxies is correct (no noise)
assert np.isclose(len(z_gal), Ngal, atol=1, rtol=0)
# test the distribution of the sample against the analytical CDF
V_max = cosmo.comoving_volume(redshift[-1]).to_value('Mpc3')
D, p = kstest(z_gal, lambda z: cosmo.comoving_volume(z).to_value('Mpc3')/V_max)
assert p > 0.01, 'D = {}, p = {}'.format(D, p)
def NFW_fit(e, theta, Lambda, z, h=0.7):
a = np.linspace(-10, 10, 100)
x, y = np.meshgrid(a, a)
q = (1. - e) / (1. + e)
r = np.sqrt(q * (x**2) + (y**2) / q)
# SIMET RELATION FOR M200
M0 = (2.21e14) / h
alpha = 1.33
M200 = M0 * ((Lambda / 40.)**alpha)
# Compute cosmological parameters
cosmo = LambdaCDM(H0=h * 100, Om0=0.3, Ode0=0.7)
H = cosmo.H(z).value / (1.0e3 * pc) #H at z_pair s-1
roc = (3.0 *
(H**2.0)) / (8.0 * np.pi * G) #critical density at z_pair (kg.m-3)
roc_mpc = roc * ((pc * 1.0e6)**3.0)
# Compute R_200
R200 = r200_nfw(M200, roc_mpc)
# S_nfw R_200
S_nfw = SIGMA_nfw((r, sigma_c, z, h), R200)
x_rot = (x * np.cos(t) + y * np.sin(t))
y_rot = (-1. * x * np.sin(t) + y * np.cos(t))
w = np.ones(len(x_rot[mask]))
e, ang = momentos(x_rot[mask], y_rot[mask], w)
return e, np.rad2deg(ang)
def lumdis(z, key):
Ho = cosmology[key].H
mo = cosmology[key].Om
deo = cosmology[key].Ode
l = cosmology[key].Ok
cos = LambdaCDM(H0=Ho, Om0=mo, Ode0=l)
return cos.luminosity_distance(z)
def get_circle_rad(reffile, inp):
z = reffile['z']
re_UV = reffile['hlr_f814w_kpc']
re_opt = reffile['hlr_f160w_kpc']
scalar_list = []
for i in z:
cosmo = LambdaCDM(H0=70 * u.km / u.s / u.Mpc,
Tcmb0=2.725 * u.K,
Om0=0.3,
Ode0=.7)
scale = cosmo.kpc_proper_per_arcmin(i).to('kpc/arcsec')
scalar = float(scale / (1.0 * (u.kpc / u.arcsec)))
scalar_list.append(scalar)
new_scale = []
for i in scalar_list:
a = (1 / 3600) * (1 / i)
new_scale.append(a)
UV_rad = []
opt_rad = []
if inp == 'UV':
for i, j in zip(new_scale, re_UV):
a = i * j
UV_rad.append(a)
return UV_rad
if inp == 'opt':
for i, j in zip(new_scale, re_opt):
b = i * j
opt_rad.append(b)
return opt_rad
class LambdaCDMBenchmarks:
H0 = 65 * u.km / u.s / u.Mpc
TCMB0 = 2.7 * u.K
params = [
LambdaCDM(H0, 0.6, 0.7, 0),
LambdaCDM(H0, 0.25, 0.65, TCMB0, 3.04),
LambdaCDM(H0, 0.6, 0.7, TCMB0, 4),
LambdaCDM(H0, 0.4, 0.2, TCMB0, 3.04),
FlatLambdaCDM(H0, 0.25, 0),
FlatLambdaCDM(H0, 0.25, TCMB0, 3.04),
FlatLambdaCDM(H0, 0.25, TCMB0, 3.04, [0.05, 0.1, 0.15] * u.eV)
]
def setup(self, cosmo):
self.cosmology = cosmo
self.test_zs = np.linspace(0.1, 5.0, 200)
def teardown(self, cosmo):
del self.cosmology
del self.test_zs
def time_lumdist(self, cosmo):
self.cosmology.luminosity_distance(self.test_zs)
def time_age(self, cosmo):
self.cosmology.age(self.test_zs)
def _init_from_params(self, H0, Omega_b0, Omega_dm0, Omega_k0):
Om0 = Omega_b0 + Omega_dm0
Ob0 = Omega_b0
Ode0 = 1.0 - Om0 - Omega_k0
self.be_cosmo = LambdaCDM(H0=H0, Om0=Om0, Ob0=Ob0, Ode0=Ode0)
def calc_absmag(rmag, galZ, gmag, imag, h, O_matter, O_lambda):
# Calculating the distance modulus
cosmo = LambdaCDM(H0=h * 100, Om0=O_matter, Ode0=O_lambda)
galDl = (cosmo.luminosity_distance(galZ).to('pc')).value
DM = 5. * np.log10(galDl / 10.)
# Calculating the K-corrections
kcorfile = np.loadtxt('kcorrection_list.txt').T
zbins = kcorfile[5]
kparams = kcorfile[6:9]
# Calculating the K-correction per redshift bin
galKcor = np.zeros(len(galZ))
for k in range(len(zbins)):
zmask = (zbins[k] - 0.005 <= galZ) & (galZ < zbins[k] + 0.005)
a, b, c = kparams[:, k]
Kcor = a * (gmag - imag)**2 + b * (gmag - imag) + c
galKcor[zmask] = Kcor[zmask]
# Calculating the absolute magnitude
rmag_abs = rmag - DM + galKcor
return rmag_abs
def z_angular_mpc(zmean):
'''returns the minimum angular cut to be applied
in order to discard nonlinearities
'''
cosmo = LambdaCDM(H0=67, Om0=0.319, Ode0=0.681, Ob0=0.049)
angle = cosmo.arcsec_per_kpc_comoving(zmean).value * 12000. / 60
return angle
def scale_einstein_radius(z_lens, z_src, H0=70, Om0=0.3, Ode0=0.7):
cosmo = LambdaCDM(H0=H0, Om0=Om0, Ode0=Ode0)
d_ls = cosmo.angular_diameter_distance_z1z2(z_lens, z_src)
d_s = cosmo.angular_diameter_distance_z1z2(0, z_src)
d = d_ls / d_s
return d.value
def partial_profile(RA0, DEC0, Z, RIN, ROUT, ndots, h):
ndots = int(ndots)
cosmo = LambdaCDM(H0=100 * h, Om0=0.25, Ode0=0.75)
dl = cosmo.angular_diameter_distance(Z).value
KPCSCALE = dl * (((1.0 / 3600.0) * np.pi) / 180.0) * 1000.0
delta = ROUT / (3600 * KPCSCALE)
t0 = time.time()
mask = (S.ra < (RA0 + delta)) & (S.ra > (RA0 - delta)) & (
S.dec > (DEC0 - delta)) & (S.dec < (DEC0 + delta)) & (S.z_v >
(Z + 0.1))
catdata = S[mask]
ds = cosmo.angular_diameter_distance(catdata.z_v).value
dls = cosmo.angular_diameter_distance_z1z2(Z, catdata.z_v).value
BETA_array = dls / ds
Dl = dl * 1.e6 * pc
sigma_c = (((cvel**2.0) / (4.0 * np.pi * G * Dl)) *
(1. / BETA_array)) * (pc**2 / Msun)
rads, theta, test1, test2 = eq2p2(np.deg2rad(catdata.ra),
np.deg2rad(catdata.dec), np.deg2rad(RA0),
np.deg2rad(DEC0))
r = np.rad2deg(rads) * 3600 * KPCSCALE
e1 = catdata.gamma1
e2 = -1. * catdata.gamma2
#get tangential ellipticities
et = (-e1 * np.cos(2 * theta) - e2 * np.sin(2 * theta)) * sigma_c
k = catdata.kappa * sigma_c
r = np.rad2deg(rads) * 3600 * KPCSCALE
bines = np.logspace(np.log10(RIN), np.log10(ROUT), num=ndots + 1)
dig = np.digitize(r, bines)
SIGMA = []
DSIGMA = []
for nbin in range(ndots):
mbin = dig == nbin + 1
SIGMA = np.append(SIGMA, np.average(k[mbin]))
DSIGMA = np.append(DSIGMA, np.average(k[mbin]))
return [SIGMA, DSIGMA]
def setup(self):
self.H0_true = 70
self.omega_m_true = 0.3
self._ok_true = 0.1
self.cosmo = FlatLambdaCDM(H0=self.H0_true, Om0=self.omega_m_true, Ob0=0.05)
self.cosmo_interp = CosmoInterp(cosmo=self.cosmo, z_stop=3, num_interp=100)
self.cosmo_ok = LambdaCDM(H0=self.H0_true, Om0=self.omega_m_true, Ode0=1.0 - self.omega_m_true - self._ok_true)
self.cosmo_interp_ok = CosmoInterp(cosmo=self.cosmo_ok, z_stop=3, num_interp=100)
self.cosmo_ok_neg = LambdaCDM(H0=self.H0_true, Om0=self.omega_m_true, Ode0=1.0 - self.omega_m_true + self._ok_true)
self.cosmo_interp_ok_neg = CosmoInterp(cosmo=self.cosmo_ok_neg, z_stop=3, num_interp=100)
def generate_model_data_vector(self, times, parameters):
# parameters = get_parameter_set()
cosmo = LambdaCDM(H0=parameters[0],
Om0=parameters[1],
Ode0=parameters[2],
Ob0=parameters[3],
Tcmb0=2.725)
model_data_vector = cosmo.age(times).value
#not keeping units here
return model_data_vector
def test_redshifts_from_comoving_density():
from skypy.galaxy.redshift import redshifts_from_comoving_density
from astropy.cosmology import LambdaCDM
from astropy import units
# random cosmology
H0 = np.random.uniform(50, 100)
Om = np.random.uniform(0.1, 0.9)
Ol = np.random.uniform(0.1, 0.9)
cosmo = LambdaCDM(H0=H0, Om0=Om, Ode0=Ol)
# fixed comoving density of Ngal galaxies total
Ngal = 1000
redshift = np.arange(0.0, 2.001, 0.1)
density = Ngal / cosmo.comoving_volume(redshift[-1]).to_value('Mpc3')
sky_area = 4 * np.pi * units.steradian
# sample galaxies over full sky without Poisson noise
z_gal = redshifts_from_comoving_density(redshift,
density,
sky_area,
cosmo,
noise=False)
# make sure number of galaxies is correct (no noise)
assert np.isclose(len(z_gal), Ngal, atol=1, rtol=0)
# test the distribution of the sample against the analytical CDF
V_max = cosmo.comoving_volume(redshift[-1]).to_value('Mpc3')
D, p = kstest(z_gal,
lambda z: cosmo.comoving_volume(z).to_value('Mpc3') / V_max)
assert p > 0.01, 'D = {}, p = {}'.format(D, p)
# Test that an error is raised if sky_area has the wrong units
sky_area = 1 * units.degree
with pytest.raises(units.UnitsError):
redshifts_from_comoving_density(redshift,
density,
sky_area,
cosmo,
noise=False)
# Test that an error is raised if sky_area has no units
sky_area = 1
with pytest.raises(TypeError):
redshifts_from_comoving_density(redshift,
density,
sky_area,
cosmo,
noise=False)
def ln_likelihood_2d(mu_obs, inv_covmat, z_vector, theta, choice):
if choice=='FL2':
cosmo=FlatLambdaCDM(Om0=theta[0], H0=100*theta[1])
if choice=='OL2':
cosmo = LambdaCDM(H0=70, Om0=theta[0], Ode0=theta[1])
if choice=='FW2':
cosmo=FlatwCDM(H0=73.8, Om0=theta[0], w0=theta[1])
if choice=='FW3':
cosmo=FlatwCDM(Om0=theta[0], w0=theta[1], H0=100*theta[2])
if choice=='OL3':
cosmo=LambdaCDM(Om0=theta[0], Ode0=theta[1], H0=100*theta[2])
mu_th=cosmo.distmod(z_vector).value
r=(mu_obs-mu_th).reshape(-1, 1) #Check bracketing
chi_2=np.sum(np.matmul(np.matmul(r.T, inv_covmat), r))
return (-chi_2/2.0)
def astropyify_ccl_cosmo(cosmoin):
""" Given a CCL cosmology object, create an astropy cosmology object
Parameters
----------
cosmoin : astropy.cosmology.FlatLambdaCDM or pyccl.core.Cosmology
astropy or CCL cosmology object
Returns
-------
cosmodict : astropy.cosmology.LambdaCDM
Astropy cosmology object
Notes
-----
Need to replace:
`import pyccl as ccl
cosmo_ccl = ccl.Cosmology(Omega_c=0.27, Omega_b=0.045, h=0.67, A_s=2.1e-9, n_s=0.96)`
with
`from astropy.cosmology import FlatLambdaCDM
astropy_cosmology_object = FlatLambdaCDM(H0=70, Om0=0.27, Ob0=0.045)
cosmo_ccl = cclify_astropy_cosmo(astropy_cosmology_object)``
"""
if isinstance(cosmoin, LambdaCDM):
return cosmoin
if isinstance(cosmoin, dict):
omega_m = cosmoin['Omega_b'] + cosmoin['Omega_c']
return LambdaCDM(H0=cosmoin['H0'],
Om0=omega_m,
Ob0=cosmoin['Omega_b'],
Ode0=1.0 - omega_m)
raise TypeError(
"Only astropy LambdaCDM objects or dicts can be converted to astropy.")
def __init__(self, h5file, verbose=True):
"""
Tool to replicate functionality of `PhotoZ.show_fit` but with
data read from a stored HDF5 file rather than a "live" object
"""
from astropy.cosmology import LambdaCDM
self.h5file = h5file
self.param = params_from_hdf5(h5file)
photoz.PhotoZ.set_zgrid(self)
self.NZ = len(self.zgrid)
self.templates = templates_from_hdf5(h5file, verbose=verbose)
self.NTEMP = len(self.templates)
self.set_attrs_from_hdf5()
self.set_template_error()
self.cosmology = LambdaCDM(H0=self.param['H0'],
Om0=self.param['OMEGA_M'],
Ode0=self.param['OMEGA_L'],
Tcmb0=2.725,
Ob0=0.048)
self.set_tempfilt()
def time_est():
max_only = True
times = np.zeros(len(ndata))
K = 10
pimax = 40. #Mpc/h
rpmax = 40 #Mpc/h
basisfunc = estimator.tophat
rpbins = np.logspace(np.log10(0.1), np.log10(rpmax), K)
cosmo = LambdaCDM(H0=70, Om0=0.3, Ode0=0.7)
wp = True
for i in range(len(ndata)):
nd = ndata[i]
data1fn = '../../lss/mangler/samples/a0.6452_0001.v5_ngc_ifield_ndata{}.rdzw'.format(
nd)
rand1fn = '../../lss/mangler/samples/a0.6452_rand20x.dr12d_cmass_ngc_ifield_ndata{}.rdz'.format(
nd)
start1 = time.time()
rps, wprp = run.run(data1fn, rand1fn, data1fn, rand1fn, pimax, rpmax,
basisfunc, K, cosmo, wp, rpbins)
end1 = time.time()
times[i] = end1 - start1
time_arrs = [times]
plot_times(time_arrs, ndata)
def load_cosmology(H0 = 69.7, Ob0 = 0.0464, Om0 = 0.1138 / (69.7/100)**2, Ode0 = 0.721, \
Tcmb0 = 2.73, m_nu = 0):
new_model = LambdaCDM(H0 = H0, Om0 = Om0, Ob0 = Ob0, Ode0 = Ode0, Tcmb0 = Tcmb0, \
m_nu = m_nu*u.eV)
return new_model
def test_cclify_astropy_cosmo():
""" Unit tests for md.cllify_astropy_cosmo """
# Make some base objects
truth = {'H0': 70., 'Om0': 0.3, 'Ob0': 0.05}
apycosmo_flcdm = FlatLambdaCDM(**truth)
apycosmo_lcdm = LambdaCDM(Ode0=1.0 - truth['Om0'], **truth)
cclcosmo = {
'Omega_c': truth['Om0'] - truth['Ob0'],
'Omega_b': truth['Ob0'],
'h': truth['H0'] / 100.,
'H0': truth['H0']
}
# Test for exception if missing baryon density (everything else required)
missbaryons = FlatLambdaCDM(H0=truth['H0'], Om0=truth['Om0'])
assert_raises(KeyError, md.cclify_astropy_cosmo, missbaryons)
# Test output if we pass FlatLambdaCDM and LambdaCDM objects
assert_equal(md.cclify_astropy_cosmo(apycosmo_flcdm), cclcosmo)
assert_equal(md.cclify_astropy_cosmo(apycosmo_lcdm), cclcosmo)
# Test output if we pass a CCL object (a dict right now)
assert_equal(md.cclify_astropy_cosmo(cclcosmo), cclcosmo)
# Test for exception if anything else is passed in
assert_raises(TypeError, md.cclify_astropy_cosmo, 70.)
assert_raises(TypeError, md.cclify_astropy_cosmo, [70., 0.3, 0.25, 0.05])
def general_fit(H0, Om, Ol, Ok=0):
cozmol = LambdaCDM(H0=H0, Om0=Om,
Ode0=Ol) #again, Ok0 = k should work but it doent
def cosmol_x(row, z, err, cosmology):
z = row[z]
model = float(cosmology.luminosity_distance(z) / u.Mpc)
x = ((row['dis_mpc'] - model)**2) / row[err]**2
return x
#makes a column with a name based off of the cosmology you are testing and produces indivitual values for the sum
#format: 'x^2_H0_OM_OL_OK'
current_column = 'x^2_%(H0)f_%(OM)f_%(OL)f_%(OK)f' % {
'H0': H0,
'OM': Om,
'OL': Ol,
'OK': Ok
}
full[current_column] = full.apply(
lambda row: cosmol_x(row, 'z', 'dis_Ftab_err', cozmol), axis=1)
full1[current_column] = full1.apply(
lambda row: cosmol_x(row, 'redshift', 'dis_FTab_averr', cozmol),
axis=1)
full.loc['X_sum', current_column] = full[current_column].sum()
full1.loc['X_sum', current_column] = full1[current_column].sum()
return x_sum([full, full1], current_column)
def print_close_cosmo():
"""Print cosmological functions for a close cosmology."""
H0 = 70.
Om0 = 0.27
Ode0 = 0.7
cosmo = LambdaCDM(H0=H0, Om0=Om0, Ode0=Ode0)
print('================')
print('Close cosmology:')
print('================')
print('H0={} [km/s/Mpc], Om0={}, Ode0={}, Orel0={}, Ok0={}\n'.format(
H0, Om0, Ode0,
cosmo.Ogamma(0.) + cosmo.Onu(0.), cosmo.Ok(0.)))
redshifts = (0., 0.1, 10., 1000.)
print_curves(redshifts, cosmo)
def myloglike_H0(cube, ndim, nparams):
H0 = cube[0]
Om0 = cube[1]
Ode0 = cube[2]
cosmo = LambdaCDM(H0=H0, Om0=Om0, Ode0=Ode0)
c = 299792458.0*1e-3
vr_mean, vr_std = redshift*c, redshift_error*c
dc = cosmo.comoving_distance(redshift).value
prob = np.log(kde_eval_single(kdedir_dist,[dc])[0])
if np.isnan(prob):
prob = -np.inf
return prob
def test_astropyify_ccl_cosmo(modeling_data):
""" Unit tests for astropyify_ccl_cosmo """
# Make a bse object
truth = {'H0': 70., 'Om0': 0.3, 'Ob0': 0.05}
apycosmo_flcdm = FlatLambdaCDM(**truth)
apycosmo_lcdm = LambdaCDM(Ode0=1.0-truth['Om0'], **truth)
cclcosmo = {'Omega_c': truth['Om0']-truth['Ob0'], 'Omega_b': truth['Ob0'],
'h': truth['H0']/100., 'H0': truth['H0']}
def H0_to_theta(hubble, omega_nu, omnuh2, omega_m, ommh2, omega_c, omch2,
omega_b, ombh2, omega_lambda, omlamh2):
h = hubble / 100.
if np.isnan(omnuh2):
omnuh2 = omega_nu * h**2
if np.isnan(omega_m):
omega_m = ommh2 / h**2
if np.isnan(omega_b):
omega_b = ombh2 / h**2
if np.isnan(omega_lambda):
omega_lambda = omlamh2 / h**2
if np.isnan(omega_c):
omega_c = omch2 / h**2
if np.isnan(omega_m):
omega_m = omega_c + omega_b + omega_nu
if np.isnan(omega_m):
omega_m = (omch2 + ombh2 + omnuh2) / h**2
if np.isnan([omnuh2, hubble, omega_m, omega_lambda, omega_b]).any():
return np.nan
m_nu = 93.14 * omnuh2 * units.eV
cosmo = LambdaCDM(hubble,
omega_m,
omega_lambda,
m_nu=m_nu / 3,
Ob0=omega_b,
Tcmb0=2.7255,
Neff=3.046)
ombh2 = cosmo.Ob0 * cosmo.h**2
omdmh2 = cosmo.Om0 * cosmo.h**2
zstar = 1048 * (1 + 0.00124 * ombh2**(-0.738)) * (
1 + (0.0783 * ombh2**(-0.238) / (1 + 39.5 * ombh2**0.763)) *
(omdmh2 + ombh2)**(0.560 / (1 + 21.1 * ombh2**1.81)))
astar = 1 / (1 + zstar)
rs = scipy.integrate.romberg(dsound_da,
1e-8,
astar,
rtol=5e-5,
args=(cosmo, ),
divmax=10) # in Mpc
DA = cosmo.angular_diameter_distance(zstar).to("Mpc").value / astar
return rs / DA
def __init__(self,
Om_L=0.68440,
Om_b=0.04911,
Om_c=0.26442,
H0=67.27,
Om_M=None,
Om_k=None,
**kwargs):
"""
Default parameter values are Planck 2015 TT,TE,EE+lowP.
(Table 4 of https://doi.org/10.1051/0004-6361/201525830)
Parameters:
-----------
Om_L : float, Omega Lambda naught, default=0.68440
cosmological constant energy density fraction at z=0
Om_b : float, Omega baryon naught, default=0.04911
baryon energy density fraction at z=0
Om_c : float, Omega cold dark matter naught, default=0.26442
cdm energy density fraction at z=0
H0 : float, Hubble constant, default=67.31 km s-1 Mpc-1
Om_M : float, Omega matter naught, default=None [optional]
matter energy density faction at z=0
Om_k : float, Omega curvature naught, default=None [optional]
curvature energy density at z=0
"""
# Setup parameters
if Om_M is not None:
if np.isclose(Om_b + Om_c, Om_M, atol=1e-5) is False:
Om_b = Om_M * 0.156635
Om_c = Om_M * 0.843364
else:
Om_M = Om_b + Om_c
if Om_k is None:
Om_k = 1 - Om_L - Om_M
### TODO add radiation component to class and to distance functions
self.Om_L = Om_L
self.Om_b = Om_b
self.Om_c = Om_c
self.Om_M = Om_M
self.Om_k = Om_k
self.H0 = H0
self.h = self.H0 / 100.0
self.params = ["Om_L", "Om_b", "Om_c", "Om_M", "Om_k", "H0"]
if _astropy:
self.lcdm = LambdaCDM(H0=self.H0, Om0=self.Om_M, Ode0=self.Om_L)
def test_dxdz():
# Default cosmology (Vanilla)
Xz = cosm_xz(3.)
np.isclose(Xz, 7.68841320742732)
dxdz = cosm_xz(3., flg_return=1)
np.isclose(dxdz, 3.5847294011983)
# Open
copen = LambdaCDM(H0=70., Om0=0.3, Ode0=0.)
dxdz = cosm_xz(3., cosmo=copen, flg_return=1)
np.isclose(dxdz, 2.90092902756)
def getHeader(self): header = Gadget2Header(ext._gadget2.getHeader(self.fp)) header["files"] = [self.fp.name] header["w0"] = -1.0 header["wa"] = 0.0 header["comoving_distance"] = LambdaCDM(H0=header["h"]*100,Om0=header["Om0"],Ode0=header["Ode0"]).comoving_distance(header["redshift"]).to(u.kpc).value * header["h"] return header
def __init__(self,
r,
M200=1.e14,
ellip=0.25,
z=0.2,
h=0.7,
misscentred=False,
s_off=0.4,
components=['t0', 't', 'tcos', 'xsin'],
verbose=True,
Yanmiss=False):
"""
init method, it sets up all variables
"""
if not isinstance(r, (np.ndarray)):
r = np.array([r])
self.r = r
self.M200 = M200
self.ellip = ellip
self.z = z
self.h = h
self.misscentred = misscentred
self.s_off = s_off
self.components = components
self.verbose = verbose
self.Yanmiss = Yanmiss
# Compute cosmological parameters
self.cosmo = LambdaCDM(H0=self.h * 100, Om0=0.3, Ode0=0.7)
# H at z_pair s-1
self.H = self.cosmo.H(self.z).value / (1.0e3 * self.pc)
# critical density at z_pair (kg.m-3)
self.roc = (3.0 * (self.H**2.0)) / (8.0 * np.pi * self.G)
self.roc_mpc = self.roc * ((self.pc * 1.0e6)**3.0)
# Compute R_200
self.R200 = profiles_fit.r200_nfw(self.M200, self.roc_mpc)
# Scaling sigma_off
self.s_off = self.s_off / self.h
M_g = m_r - DM - delta_g + g_r - z_cal
return M_g
## Read in the fits file
hdulist = pyfits.open('/hd0/Research/Clustering/Boss/dr11/dr11v2/cmass-dr11v2-N-Anderson.dat.fits')
## Uncomment the following line to view the info about the table
#print hdulist.info()
## Read the tabular portion of the fits file into the variable 'table'. This assumes that the table of interest is located in extension 1
table = hdulist[1].data
cosmo = LambdaCDM(H0=100, Om0=0.274, Ode0=0.726)
ra=table.field('RA')
dec=table.field('DEC')
redshift=table.field('Z')
fibcol=table.field('WEIGHT_CP')
poly=table.field('IPOLY')
sector=table.field('ISECT')
extinction=table.field('EXTINCTION')
extinction_r=extinction[:,3]
flux=table.field('MODELFLUX')
flux_r=flux[:,3]
M_g=Calculate_Magnitude(flux_r,extinction_r,redshift)
array=np.column_stack((ra,dec,redshift,fibcol,poly,M_g))
from astropy.cosmology import LambdaCDM
import astropy.cosmology
from astropy.io import fits
import numpy as np
import astropy.units as u
"""
created arrays of the Spectra IDs for relations between data sets
EpA data set contained calculated ages of the galaxies
"""
cat_ID = cat['BESTOBJID']
EpA = fits.getdata('~/EpA_Results.fits')
cosmo = LambdaCDM(H0=70, Om0=0.3, Ode0=0.7)
"""
two sections below for converting the age data to Z values and GYrs so that they could be plotted
"""
sb_age_list_gyr=[]
sb_age_list_z = []
zrange = np.arange(0,1396)
for i in zrange:
age = cosmo.age(cat_z[i])
age_diff = age.value - (EpA['AGE'][i] / 1000)
sb_age_list_gyr.append(age_diff * age.unit)
if age_diff >= 0:
sb_age_list_z.append(astropy.cosmology.z_at_value(cosmo.age, sb_age_list_gyr[i]))
