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
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]))
sb_age_list_z_2 = []
sb_age_list_gyr_2 = []
zrange2 = np.arange(0,26)
for i in zrange2:
age = cosmo.age(cat_z[high_index[0]][i])
age_diff = age.value - (EpA['AGE'][high_index[0]][i] / 1000)
sb_age_list_gyr_2.append(age_diff * age.unit)
if age_diff >= 0:
sb_age_list_z_2.append(astropy.cosmology.z_at_value(cosmo.age, sb_age_list_gyr_2[i]))
snap_redshifts = np.empty(0)
redshift_start = 5.
redshift_end = 1.e-7
snip_time_delta = 5.0e-3 # Time difference between snipshots in Gyr (Yannick suggests 5 Myr)
all_snapshots = True
#######################################################################################
Planck18 = LambdaCDM(cosmology['h'] * 100,
cosmology['Omega_m'],
cosmology['Omega_lambda'],
Ob0=cosmology['Omega_b'])
time_start = Planck18.age(redshift_start)
time_end = Planck18.age(redshift_end)
snip_time_delta *= Gyr
number_of_snips = int((time_end - time_start) / snip_time_delta)
snip_times = np.linspace(time_start, time_end, number_of_snips, dtype=np.float)
snip_redshifts = np.zeros(len(snip_times), dtype=np.float64)
for i in range(len(snip_times)):
snip_redshifts[i] = z_at_value(Planck18.age, snip_times[i])
if len(snap_redshifts) > 0:
# Delete duplicate redshifts
for z_snap in snap_redshifts:
idx_close = np.where(
np.abs(snip_redshifts - z_snap) <= snip_time_delta.value / 2)[0]
18.08, 15.28, 13.06, 11.26, 9.79, 8.57, 7.54, 6.67, 5.92, 5.28, 4.72, 4.24,
3.81, 3.43, 3.09, 2.79, 2.52, 2.28, 2.06, 1.86, 1.68, 1.51, 1.36, 1.21,
1.08, 0.96, 0.85, 0.74, 0.64, 0.55, 0.46, 0.37, 0.29, 0.21, 0.14, 0.07,
1.e-7
])
snip_time_delta = 5.0e-3 # Time interval between snipshots in Gyr (Yannick suggests 5 Myr)
#######################################################################################
Planck18 = LambdaCDM(cosmology['h'] * 100,
cosmology['Omega_m'],
cosmology['Omega_lambda'],
Ob0=cosmology['Omega_b'])
time_start = Planck18.age(snap_redshifts[0])
time_end = Planck18.age(snap_redshifts[-1])
snip_time_delta *= Gyr
number_of_snips = int((time_end - time_start) / snip_time_delta)
snip_times = np.linspace(time_start, time_end, number_of_snips, dtype=np.float)
snip_redshifts = np.zeros(len(snip_times), dtype=np.float64)
for i in range(len(snip_times)):
snip_redshifts[i] = z_at_value(Planck18.age, snip_times[i])
# Delete duplicate redshifts
for z_snap in snap_redshifts:
idx_close = np.where(np.abs(snip_redshifts - z_snap) <= 5.0e-6)[0]
snip_redshifts = np.delete(snip_redshifts, idx_close)
# Collect all outputs
