def process_for_redshift(z, mstar_bins, gsmf_at_z):
"""
Output an HDF5 file containing the GSMF at a given redshift.
z: the redshift to produce the GSMF for. The given value corresponds to the lower
edge of a range in redshift, which has width 0.25 for z < 1.5 and 0.5 for z >= 1.5
mstar_bins: the list of stellar mass bins for which the GSMF is tabulated
gsmf_at_z: the slice of the GSMF array at the chosen redshift
"""
processed = ObservationalData()
plot_as = "points"
h = cosmology.h
M = 10 ** mstar_bins * (h / ORIGINAL_H) ** (-2) * unyt.Solar_Mass
Phi = 10 ** gsmf_at_z[:, 0] * (h / ORIGINAL_H) ** 3 * unyt.Mpc ** (-3)
# y_scatter should be a 1xN or 2xN array describing offsets from
# the median point 'y'
# Errors are log error dz = 1/ln(10) dy/y
# We want dy = y ln(10) dz
Phi_err = (
(10 ** gsmf_at_z[:, [0]] * np.log(10) * gsmf_at_z[:, [2, 1]]).T
* (h / ORIGINAL_H) ** 3
* unyt.Mpc ** (-3)
)
processed.associate_x(
M, scatter=None, comoving=True, description="Galaxy Stellar Mass"
)
processed.associate_y(Phi, scatter=Phi_err, comoving=True, description="Phi (GSMF)")
processed.associate_redshift(sum(z) * 0.5, *z)
processed.associate_plot_as(plot_as)
return processed
def process_for_redshift(z, Mstar_bins, gsmf_at_z):
"""
Output an `ObservationalData` instance containing the GSMF at a given redshift.
z: the redshift to produce the GSMF for
Mstar_bins: the list of stellar mass bins for which the GSMF is tabulated
gsmf_at_z: the values of the GSMF at the chosen redshift
"""
processed = ObservationalData()
plot_as = "line"
h = cosmology.h
M = Mstar_bins * (h / ORIGINAL_H)**(-2) * unyt.Solar_Mass
M_err = None
Phi = gsmf_at_z * (h / ORIGINAL_H)**3 * unyt.Mpc**(-3)
Phi_err = None
processed.associate_x(M,
scatter=M_err,
comoving=True,
description="Galaxy Stellar Mass")
processed.associate_y(Phi,
scatter=Phi_err,
comoving=True,
description="Phi (GSMF)")
processed.associate_redshift(z)
processed.associate_plot_as(plot_as)
return processed
def process_for_redshift(z, gdmf_and_Mstar_at_z):
"""
Output an HDF5 file containing the GDMF at a given redshift.
z: the redshift to produce the GDMF for. The given value corresponds to the lower
edge of a range in redshift of width 0.5, except for the first bin 0.2 < z < 0.5,
and the last bin 3.0 < z < 4.0
gdmf_and_mstar_at_z: the array containing stellar mass bins and the GDMF at the
chosen redshift
"""
processed = ObservationalData()
comment = (
"Beeston et al. (2018). Obtained using H-ATLAS+GAMA"
f"data, h-corrected for SWIFT using Cosmology: {cosmology.name}.")
citation = "Beeston et al. (2018)"
bibcode = "2018MNRAS.479.1077B"
name = "GDMF from H-ATLAS+GAMA"
plot_as = "points"
redshift = z
h = cosmology.h
Mstar_bins = gdmf_and_Mstar_at_z[:, 0]
M = 10**Mstar_bins * (h / ORIGINAL_H)**(-2) * unyt.Solar_Mass
Phi = 10**gdmf_and_Mstar_at_z[:, 1] * (h / ORIGINAL_H)**3 * unyt.Mpc**(-3)
# y_scatter should be a 1xN or 2xN array describing offsets from
# the median point 'y'
# Errors are log error dz = 1/ln(10) dy/y
# We want dy = y ln(10) dz
M_err = ((10**gdmf_and_Mstar_at_z[:, 2][:, None] * np.log(10) *
gdmf_and_Mstar_at_z[:, [2, 3]]).T * (h / ORIGINAL_H)**3 *
unyt.Solar_Mass)
Phi_err = ((10**gdmf_and_Mstar_at_z[:, 2][:, None] * np.log(10) *
gdmf_and_Mstar_at_z[:, [4, 5]]).T * (h / ORIGINAL_H)**3 *
unyt.Mpc**(-3))
processed.associate_x(M,
scatter=M_err,
comoving=True,
description="Galaxy Dust Mass")
processed.associate_y(Phi,
scatter=Phi_err,
comoving=True,
description="Phi (GDMF)")
processed.associate_citation(citation, bibcode)
processed.associate_name(name)
processed.associate_comment(comment)
processed.associate_redshift(redshift)
processed.associate_plot_as(plot_as)
processed.associate_cosmology(cosmology)
return processed
def process_for_redshift(z, gsmf_and_Mstar_at_z):
"""
Output an HDF5 file containing the GSMF at a given redshift.
z: the redshift to produce the GSMF for. The given value corresponds to the lower
edge of a range in redshift of width 0.5, except for the first bin 0.2 < z < 0.5,
and the last bin 3.0 < z < 4.0
gsmf_and_mstar_at_z: the array containing stellar mass bins and the GSMF at the
chosen redshift
"""
processed = ObservationalData()
comment = (
"Assuming Chabrier IMF and Vmax selection, quoted redshift is lower bound of range. "
f"h-corrected for SWIFT using Cosmology: {cosmology.name}.")
citation = "Ilbert et al. (2013)"
bibcode = "2013A&A...556A..55I"
name = "GSMF from UltraVISTA"
plot_as = "points"
redshift = z
h = cosmology.h
Mstar_bins = gsmf_and_Mstar_at_z[:, 0]
M = 10**Mstar_bins * (h / ORIGINAL_H)**(-2) * unyt.Solar_Mass
Phi = 10**gsmf_and_Mstar_at_z[:, 1] * (h / ORIGINAL_H)**3 * unyt.Mpc**(-3)
# y_scatter should be a 1xN or 2xN array describing offsets from
# the median point 'y'
# Errors are log error dz = 1/ln(10) dy/y
# We want dy = y ln(10) dz
Phi_err = ((10**gsmf_and_Mstar_at_z[:, 1][:, None] * np.log(10) *
gsmf_and_Mstar_at_z[:, 2:]).T * (h / ORIGINAL_H)**3 *
unyt.Mpc**(-3))
processed.associate_x(M,
scatter=None,
comoving=True,
description="Galaxy Stellar Mass")
processed.associate_y(Phi,
scatter=Phi_err,
comoving=True,
description="Phi (GSMF)")
processed.associate_citation(citation, bibcode)
processed.associate_name(name)
processed.associate_comment(comment)
processed.associate_redshift(redshift)
processed.associate_plot_as(plot_as)
processed.associate_cosmology(cosmology)
return processed
def process_for_redshift(z, gsmf_and_Mstar_at_z):
"""
Output an HDF5 file containing the GSMF at a given redshift.
z: the redshift to produce the GSMF for. The given value corresponds to the lower
edge of a range in redshift of width 0.5, except for the first bin 0.2 < z < 0.5,
and the last bin 3.0 < z < 4.0
gsmf_and_mstar_at_z: the array containing stellar mass bins and the GSMF at the
chosen redshift
"""
processed = ObservationalData()
plot_as = "points"
h = cosmology.h
Mstar_bins = gsmf_and_Mstar_at_z[:, 0]
Mstar_Chab = Mstar_bins - np.log10(
kroupa_to_chabrier_mass
) # convert from Kroupa IMF
M = 10 ** Mstar_Chab * (h / ORIGINAL_H) ** (-2) * unyt.Solar_Mass
M_err = (
(10 ** Mstar_Chab * np.log(10) * gsmf_and_Mstar_at_z[:, 1])
* (h / ORIGINAL_H) ** (-2)
* unyt.Solar_Mass
)
Phi = 10 ** gsmf_and_Mstar_at_z[:, 2] * (h / ORIGINAL_H) ** 3 * unyt.Mpc ** (-3)
# y_scatter should be a 1xN or 2xN array describing offsets from
# the median point 'y'
# Errors are log error dz = 1/ln(10) dy/y
# We want dy = y ln(10) dz
Phi_err = (
(
10 ** gsmf_and_Mstar_at_z[:, 2][:, None]
* np.log(10)
* gsmf_and_Mstar_at_z[:, [4, 3]]
).T
* (h / ORIGINAL_H) ** 3
* unyt.Mpc ** (-3)
)
processed.associate_x(
M, scatter=M_err, comoving=True, description="Galaxy Stellar Mass"
)
processed.associate_y(Phi, scatter=Phi_err, comoving=True, description="Phi (GSMF)")
processed.associate_redshift(sum(z) * 0.5, *z)
processed.associate_plot_as(plot_as)
return processed
def process_for_redshift(z, gsmf_and_Mstar_at_z):
"""
Output an HDF5 file containing the GSMF at a given redshift.
z: the redshift to produce the GSMF for. The given value corresponds to the lower
edge of a range in redshift of width 0.5 for z < 3.0, and width 1.0 for z > 3.0
gsmf_and_mstar_at_z: the array containing stellar mass bins and the GSMF at the
chosen redshift
"""
processed = ObservationalData()
comment = (
"Assuming Chabrier IMF and Vmax selection, quoted redshift is lower bound of range. "
f"h-corrected for SWIFT using Cosmology: {cosmology.name}.")
citation = "Deshmukh et al. (2018)"
bibcode = "2018ApJ...864..166D"
name = "GSMF from SMUVS"
plot_as = "points"
redshift = z
h = cosmology.h
Mstar_bins = gsmf_and_Mstar_at_z[:, 0]
M = 10**Mstar_bins * (h / ORIGINAL_H)**(-2) * unyt.Solar_Mass
# GSMF and errors are stored in datafile with units 10^-4 Mpc^-3 dex^-1
Phi = 1e-4 * gsmf_and_Mstar_at_z[:,
1] * (h / ORIGINAL_H)**3 * unyt.Mpc**(-3)
# y_scatter should be a 1xN or 2xN array describing offsets from
# the median point 'y'
Phi_err = (1e-4 * gsmf_and_Mstar_at_z[:, 2:].T * (h / ORIGINAL_H)**3 *
unyt.Mpc**(-3))
processed.associate_x(M,
scatter=None,
comoving=True,
description="Galaxy Stellar Mass")
processed.associate_y(Phi,
scatter=Phi_err,
comoving=True,
description="Phi (GSMF)")
processed.associate_citation(citation, bibcode)
processed.associate_name(name)
processed.associate_comment(comment)
processed.associate_redshift(redshift)
processed.associate_plot_as(plot_as)
processed.associate_cosmology(cosmology)
return processed
def process_for_redshift(z, sfrf_at_z):
"""
Output an HDF5 file containing the SFRF at a given redshift.
z: the redshift to produce the SFRF for.
sfrf_at_z: the array containing SFR and Phi_SFR bins at the chosen redshift
"""
processed = ObservationalData()
comment = (
"Assuming Chabrier IMF and Vmax selection. Includes dust corrections as "
"described in Katsianis et. al. 2017, section 2. "
f"h-corrected for SWIFT using Cosmology: {cosmology.name}.")
citation = "van der Burg et. al. (2010)"
bibcode = "2010A&A...523A..74V"
name = "SFRF from CFHT Legacy Survey"
plot_as = "points"
redshift = z
h = cosmology.h
SFR_bins = sfrf_at_z[:, 0]
SFR = SFR_bins * unyt.Solar_Mass / unyt.year
# SFRF and errors are stored in datafile with units 10^-2 Mpc^-3 dex^-1
Phi = sfrf_at_z[:, 1] * unyt.Mpc**(-3)
# y_scatter should be a 1xN or 2xN array describing offsets from
# the median point 'y'
Phi_err = sfrf_at_z[:, 2:].T * unyt.Mpc**(-3)
processed.associate_x(SFR,
scatter=None,
comoving=True,
description="Star Formation Rate")
processed.associate_y(Phi,
scatter=Phi_err,
comoving=True,
description="Phi (SFRF)")
processed.associate_citation(citation, bibcode)
processed.associate_name(name)
processed.associate_comment(comment)
processed.associate_redshift(redshift)
processed.associate_plot_as(plot_as)
processed.associate_cosmology(cosmology)
return processed
def process_for_redshift(z, gsmf_and_Mstar_at_z):
"""
Output an HDF5 file containing the GSMF at a given redshift.
z: the redshift to produce the GSMF for. The given value corresponds to the lower
edge of a range in redshift of width 0.5 for z < 3.0, and width 1.0 for z > 3.0
gsmf_and_mstar_at_z: the array containing stellar mass bins and the GSMF at the
chosen redshift
"""
processed = ObservationalData()
plot_as = "points"
redshift = z
h = cosmology.h
Mstar_bins = 10**gsmf_and_Mstar_at_z[:, 0]
M = Mstar_bins * (h / ORIGINAL_H)**(-2) * unyt.Solar_Mass
# Mass errors are log error dz = 1/ln(10) dy/y
# We want dy = y ln(10) dz
M_err = (Mstar_bins * np.log(10) * gsmf_and_Mstar_at_z[:, 1] *
(h / ORIGINAL_H)**(-2) * unyt.Solar_Mass)
Phi = gsmf_and_Mstar_at_z[:, 2] * (h / ORIGINAL_H)**3 * unyt.Mpc**(-3)
# y_scatter should be a 1xN or 2xN array describing offsets from
# the median point 'y'
Phi_err = gsmf_and_Mstar_at_z[:,
3:].T * (h / ORIGINAL_H)**3 * unyt.Mpc**(-3)
processed.associate_x(M,
scatter=M_err,
comoving=True,
description="Galaxy Stellar Mass")
processed.associate_y(Phi,
scatter=Phi_err,
comoving=True,
description="Phi (GSMF)")
processed.associate_citation(citation, bibcode)
processed.associate_name(name)
processed.associate_comment(comment)
processed.associate_redshift(redshift)
processed.associate_plot_as(plot_as)
processed.associate_cosmology(cosmology)
return processed
def process_for_redshift(z, sfrf_at_z):
"""
Output an HDF5 file containing the SFRF at a given redshift.
z: the redshift to produce the SFRF for.
sfrf_at_z: the array containing SFR and Phi_SFR bins at the chosen redshift
"""
processed = ObservationalData()
comment = (
"Assuming Chabrier IMF and Vmax selection. Includes dust corrections as "
"described in Katsianis et. al. 2017, section 2. "
f"h-corrected for SWIFT using Cosmology: {cosmology.name}."
)
citation = "Smit et. al. (2012)"
bibcode = "2013MNRAS.428.1128S"
name = "SFRF from Bouwens+2007;2011"
plot_as = "points"
redshift = z
h = cosmology.h
SFR_bins = sfrf_at_z[:, 0]
SFR = 10 ** SFR_bins * unyt.Solar_Mass / unyt.year
Phi = sfrf_at_z[:, 1] * unyt.Mpc ** (-3)
# y_scatter should be a 1xN or 2xN array describing offsets from
# the median point 'y'
Phi_err = sfrf_at_z[:, 2] * unyt.Mpc ** (-3)
processed.associate_x(
SFR, scatter=None, comoving=True, description="Star Formation Rate"
)
processed.associate_y(Phi, scatter=Phi_err, comoving=True, description="Phi (SFRF)")
processed.associate_citation(citation, bibcode)
processed.associate_name(name)
processed.associate_comment(comment)
processed.associate_redshift(redshift)
processed.associate_plot_as(plot_as)
processed.associate_cosmology(cosmology)
return processed
def process_for_redshift(z, gsmf_and_Mstar_at_z):
"""
Output an HDF5 file containing the GSMF at a given redshift.
z: the redshift to produce the GSMF for.
gsmf_and_mstar_at_z: the array containing stellar mass bins and the GSMF at the
chosen redshift
"""
processed = ObservationalData()
plot_as = "points"
h = cosmology.h
Mstar_bins = gsmf_and_Mstar_at_z[:, 0]
M = 10**Mstar_bins * (h / ORIGINAL_H)**(-2) * unyt.Solar_Mass
M_err = ((10**Mstar_bins * np.log(10) * gsmf_and_Mstar_at_z[:, 1]) *
(h / ORIGINAL_H)**(-2) * unyt.Solar_Mass)
Phi = 10**gsmf_and_Mstar_at_z[:, 2] * (h / ORIGINAL_H)**3 * unyt.Mpc**(-3)
# y_scatter should be a 1xN or 2xN array describing offsets from
# the median point 'y'
# Errors are log error dz = 1/ln(10) dy/y
# We want dy = y ln(10) dz
Phi_err = ((10**gsmf_and_Mstar_at_z[:, 2][:, None] *
np.abs(gsmf_and_Mstar_at_z[:, [4, 3]]) * np.log(10)).T *
(h / ORIGINAL_H)**3 * unyt.Mpc**(-3))
processed.associate_x(M,
scatter=M_err,
comoving=True,
description="Galaxy Stellar Mass")
processed.associate_y(Phi,
scatter=Phi_err,
comoving=True,
description="Phi (GSMF)")
processed.associate_redshift(z)
processed.associate_plot_as(plot_as)
return processed
def process_for_redshift(z, gsmf_and_Mstar_at_z):
"""
Output an HDF5 file containing the GSMF at a given redshift.
z: the redshift range to produce the GSMF for.
gsmf_and_mstar_at_z: the array containing stellar mass bins and the GSMF at the
chosen redshift
"""
processed = ObservationalData()
plot_as = "points"
h = cosmology.h
Mstar_bins = (gsmf_and_Mstar_at_z[:, 0] * salpeter_to_chabrier_mass
) # convert from Salpeter IMF
M = Mstar_bins * h**-2 * unyt.Solar_Mass
Phi = 10**gsmf_and_Mstar_at_z[:, 1] * h**3 * unyt.Mpc**(-3)
# y_scatter should be a 1xN or 2xN array describing offsets from
# the median point 'y'
# Errors are log error dz = 1/ln(10) dy/y
# We want dy = y ln(10) dz
Phi_err = ((10**gsmf_and_Mstar_at_z[:, [1]] * np.log(10) *
gsmf_and_Mstar_at_z[:, [3, 2]]).T * h**3 * unyt.Mpc**(-3))
processed.associate_x(M,
scatter=None,
comoving=True,
description="Galaxy Stellar Mass")
processed.associate_y(Phi,
scatter=Phi_err,
comoving=True,
description="Phi (GSMF)")
processed.associate_redshift(sum(z) * 0.5, *z)
processed.associate_plot_as(plot_as)
return processed
def cosmic_star_formation_history_true():
# Meta-data
name = f"Fit to true star formation rate history from Behroozi et al. (2019)"
comment = (
"The data is taken from https://www.peterbehroozi.com/data.html. "
"The cosmic star formation rate is the true one. This means that no"
"observational systematics (e.g., due to dust) are accounted for. "
"Uses the Chabrier initial mass function. "
"The scatter shows the 16th-84th percentile range from the posterior. "
"Cosmology: Omega_m=0.307, Omega_lambda=0.693, h=0.678, sigma_8=0.823, "
"n_s=0.96. "
"Shows total true cosmic star formation rate (Msun/yr/Mpc^3) for "
"the best-fitting model from Behroozi et al. (2019)")
citation = "Behroozi et al. (2019) [True]"
bibcode = "2019MNRAS.488.3143B"
plot_as = "line"
output_filename = "Behroozi2019_true.hdf5"
output_directory = "../"
# Create observational data instance
processed = ObservationalData()
processed.associate_citation(citation, bibcode)
processed.associate_name(name)
processed.associate_comment(comment)
processed.associate_cosmology(cosmology)
if not os.path.exists(output_directory):
os.mkdir(output_directory)
# Load raw Behroozi2019 data
data = np.loadtxt(f"../raw/Behroozi2019_csfrs.dat")
# Fetch the fields we need
scale_factor = unyt.unyt_array(data[:, 0], units="dimensionless")
SFR, SFR_plus, SFR_minus = data[:, 7], data[:, 9], data[:, 8]
# Define scatter with respect to the best-fit value (16 and 84 percentiles)
SFR_scatter = unyt.unyt_array((SFR_minus, SFR_plus),
units="Msun/yr/Mpc**3")
redshift, redshift_lower, redshift_upper = 5.0, 0.0, 10.0
processed.associate_x(
scale_factor,
scatter=None,
comoving=False,
description="Cosmic scale factor",
)
processed.associate_y(
SFR * SFR_scatter.units,
scatter=SFR_scatter,
comoving=False,
description="Cosmic average star formation rate density",
)
processed.associate_redshift(redshift, redshift_lower, redshift_upper)
processed.associate_plot_as(plot_as)
output_path = f"{output_directory}/{output_filename}"
if os.path.exists(output_path):
os.remove(output_path)
processed.write(filename=output_path)
Z_asm = 6.29
alpha = 0.21
else:
Z_asm = 4.84
alpha = 0.35
log10_M_star_min = 9.5
log10_M_star_max = 11.5
M_star = np.arange(log10_M_star_min, log10_M_star_max, 0.2)
Z_gas = (Z_asm + alpha * M_star) * unyt.dimensionless # 12 + log(O/H)
M_star = 10**M_star * unyt.Solar_Mass
processed.associate_x(M_star,
scatter=None,
comoving=True,
description="Galaxy Stellar Mass")
processed.associate_y(Z_gas,
scatter=None,
comoving=True,
description="Gas phase metallicity")
processed.associate_redshift(z, redshift_lower, redshift_upper)
processed.associate_plot_as(plot_as)
multi_z.associate_dataset(processed)
output_path = f"{output_directory}/{output_filename}"
if os.path.exists(output_path):
os.remove(output_path)
multi_z.write(filename=output_path)
outobj.associate_x(
oabundance_med,
scatter=x_scatter,
comoving=True,
description="Gas phase 12 + log10(O/H)",
)
outobj.associate_y(
logMHIMstar_med,
scatter=y_scatter,
comoving=True,
description="HI mass to stellar mass ratio",
)
outobj.associate_citation(citation, bibcode)
outobj.associate_name(name)
outobj.associate_comment(comment)
outobj.associate_redshift(redshift, redshift_lower, redshift_upper)
outobj.associate_plot_as(plot_as)
outobj.associate_cosmology(cosmology)
output_path = f"{output_directory}/{output_filename}"
if os.path.exists(output_path):
os.remove(output_path)
outobj.write(filename=output_path)
# Also output median and scatter points for composite sample
x_all = np.hstack(x_all) * unyt.dimensionless
y_all = np.hstack(y_all) * unyt.dimensionless
plussig = lambda a: np.percentile(a, 84)
name = "Stellar mass - H2 Gas to Stellar Mass ratio"
plot_as = "points"
redshift = 0.0
h = h_sim
# Write everything
processed = ObservationalData()
processed.associate_x(M_star,
scatter=None,
comoving=True,
description="Galaxy Stellar Mass")
processed.associate_y(
MH2_per_Mstar,
scatter=y_scatter,
comoving=True,
description="Stellar mass - H2 Gas to Stellar Mass ratio",
)
processed.associate_citation(citation, bibcode)
processed.associate_name(name)
processed.associate_comment(comment)
processed.associate_redshift(redshift, 0, 2)
processed.associate_plot_as(plot_as)
processed.associate_cosmology(cosmology)
output_path = f"{output_directory}/{output_filename}"
if os.path.exists(output_path):
os.remove(output_path)
processed.write(filename=output_path)
smf = unyt.unyt_array(N, units=1 / unyt.Mpc**3)
smf_scatter = unyt.unyt_array(sigma, units=1 / unyt.Mpc**3)
processed.associate_x(
mass,
scatter=None,
comoving=False,
description=f"Galaxy Stellar Mass ({aperture} kpc)",
)
processed.associate_y(
smf,
scatter=smf_scatter,
comoving=True,
description="Galaxy Stellar Mass Function",
)
processed.associate_redshift(redshift,
redshift_lower=redshift - 0.25,
redshift_upper=redshift + 0.25)
processed.associate_plot_as(plot_as)
multi_z.associate_dataset(processed)
output_path = f"{output_directory}/Schaye2015_{box_size}_{aperture}kpc.hdf5"
if os.path.exists(output_path):
os.remove(output_path)
multi_z.write(filename=output_path)
def cosmic_star_formation_history_novak():
# Meta-data
name = f"Star formation rate density from Novak et al. (2017)"
comment = (
"based on JVLA COSMOS radio observations at 3 GHz "
"cosmology is H0=70, OmegaM=0.3, OmegaL=0.7 "
"Uses a Chabrier IMF"
)
citation = "Novak et al. (2017)"
bibcode = "2017A&A...602A...5N"
plot_as = "points"
output_filename = "Novak2017.hdf5"
output_directory = "../"
# Create observational data instance
processed = ObservationalData()
processed.associate_citation(citation, bibcode)
processed.associate_name(name)
processed.associate_comment(comment)
processed.associate_cosmology(cosmology)
if not os.path.exists(output_directory):
os.mkdir(output_directory)
# Load raw Novak2017 data
data = np.loadtxt(f"../raw/sfr_novak2017.dat")
# Fetch the fields we need
z, delta_z_minus, delta_z_plus = data[:, 0], data[:, 1], data[:, 2]
SFR, delta_SFR_minus, delta_SFR_plus = data[:, 3], data[:, 4], data[:, 5]
a = 1.0 / (1.0 + z)
# division turns large into small, so minus <-> plus
a_minus = 1.0 / (1.0 + z + delta_z_plus)
a_plus = 1.0 / (1.0 + z + delta_z_minus)
delta_a_minus = a - a_minus
delta_a_plus = a_plus - a
a_bin = unyt.unyt_array(a, units="dimensionless")
a_scatter = unyt.unyt_array((delta_a_minus, delta_a_plus), units="dimensionless")
# convert from log10(SFRD) to SFRD and carry the uncertainties
SFR_minus = 10.0 ** (SFR + delta_SFR_minus)
SFR_plus = 10.0 ** (SFR + delta_SFR_plus)
SFR = 10.0 ** SFR
SFR_scatter = unyt.unyt_array(
(SFR - SFR_minus, SFR_plus - SFR), units="Msun/yr/Mpc**3"
)
SFR = unyt.unyt_array(SFR, units="Msun/yr/Mpc**3")
processed.associate_x(
a_bin, scatter=a_scatter, comoving=False, description="Cosmic scale factor"
)
processed.associate_y(
SFR,
scatter=SFR_scatter,
comoving=False,
description="Cosmic average star formation rate density",
)
z_minus = (z + delta_z_minus).min()
z_plus = (z + delta_z_plus).max()
processed.associate_redshift(0.5 * (z_minus + z_plus), z_minus, z_plus)
processed.associate_plot_as(plot_as)
output_path = f"{output_directory}/{output_filename}"
if os.path.exists(output_path):
os.remove(output_path)
processed.write(filename=output_path)
zhis = np.sort(list(set(z_hi)))
for i in range(zs.size):
bdx = z_lo == z_lo[i]
output_path = f"{output_directory}/{output_filename.format(stringify_z(zs[i]))}"
print(output_path)
processed.associate_x(
oabundance[bdx],
scatter=None,
comoving=False,
description="[O/H]",
)
processed.associate_y(
d2m[bdx],
scatter=None,
comoving=False,
description="D2M",
)
processed.associate_citation(citation, bibcode)
processed.associate_name(name)
processed.associate_comment(comment)
processed.associate_redshift(zs[i], zlos[i], zhis[i])
processed.associate_plot_as(plot_as)
processed.associate_cosmology(cosmology)
if os.path.exists(output_path):
os.remove(output_path)
processed.write(filename=output_path)
smf_scatter = unyt.unyt_array(sigma, units=1 / unyt.Mpc ** 3)
processed.associate_x(
mass,
scatter=None,
comoving=False,
description=f"Galaxy Stellar Mass ({aperture} kpc)",
)
processed.associate_y(
smf,
scatter=smf_scatter,
comoving=True,
description="Galaxy Stellar Mass Function",
)
processed.associate_citation(citation, bibcode)
processed.associate_name(name)
processed.associate_comment(comment)
processed.associate_redshift(
redshift, redshift_lower=redshift, redshift_upper=0.1
)
processed.associate_plot_as(plot_as)
processed.associate_cosmology(cosmology)
output_path = f"{output_directory}/Schaye2015_{box_size}_{aperture}kpc.hdf5"
if os.path.exists(output_path):
os.remove(output_path)
processed.write(filename=output_path)
def Phi_passive_galaxies():
# Meta-data
name = f"Fit to the quenched galaxy stellar mass function at z=[{redshift_header_info:s}]"
comment = (
"The data is taken from https://www.peterbehroozi.com/data.html. "
"The stellar mass is the observed stellar mass as defined in Behroozi et al. "
"(2019) eq. 25. "
"The quenched fractions are defined using the standard criterion where specific"
" star formation rate < 1e-11 yr^-1. "
"Uses the Chabrier initial mass function. "
"GSMF is incomplete below 10**7.0 Msun at z=0 and 10**8.5 Msum at z=8. "
"Cosmology: Omega_m=0.307, Omega_lambda=0.693, h=0.678, sigma_8=0.823, "
"n_s=0.96. "
"Shows the quenched galaxy stellar mass function (number densities in comoving"
" Mpc^-3 dex^-1 vs. stellar mass).")
# Store metadata at the top level
multi_z = MultiRedshiftObservationalData()
multi_z.associate_citation(citation, bibcode)
multi_z.associate_name(name)
multi_z.associate_comment(comment)
multi_z.associate_cosmology(cosmology)
multi_z.associate_maximum_number_of_returns(1)
output_filename = "Behroozi2019_passive.hdf5"
output_directory = "../"
if not os.path.exists(output_directory):
os.mkdir(output_directory)
for z, dz_lower, dz_upper, a_str in zip(redshifts, redshifts_lower,
redshifts_upper,
scale_factors_str):
# Create a single observational-data instance at redshift z
processed = ObservationalData()
# Load raw Behroozi2019 data
data = np.loadtxt(f"../raw/Behroozi2019_smf_a{a_str}.dat")
# Fetch the fields we need
log_M_star, Phi, Phi_plus, Phi_minus = (
data[:, 0],
data[:, 7],
data[:, 8],
data[:, 9],
)
# We don't want to plot zeros
mask = np.where(Phi > 0.0)
# Transform stellar mass
M_star = (10.0**log_M_star) * unyt.Solar_Mass
# Define scatter with respect to the best-fit value (16 and 84 percentiles)
Phi_scatter = unyt.unyt_array((Phi_minus[mask], Phi_plus[mask]),
units=unyt.Mpc**(-3))
# Compute \Delta z
redshift_lower, redshift_upper = [z - dz_lower, z + dz_upper]
processed.associate_x(
M_star[mask],
scatter=None,
comoving=False,
description="Galaxy Stellar Mass",
)
processed.associate_y(
Phi[mask] * (h_sim / ORIGINAL_H)**3 * unyt.Mpc**(-3),
scatter=Phi_scatter * (h_sim / ORIGINAL_H)**3,
comoving=True,
description="Phi (GSMF)",
)
processed.associate_redshift(z, redshift_lower, redshift_upper)
processed.associate_plot_as(plot_as)
multi_z.associate_dataset(processed)
output_path = f"{output_directory}/{output_filename}"
if os.path.exists(output_path):
os.remove(output_path)
multi_z.write(filename=output_path)
def cosmic_star_formation_history_enia():
# Meta-data
name = f"Star formation rate density from Enia et al. (2022)"
comment = ("Uses the Chabrier initial mass function. "
"Cosmology: Planck 2016: H0=67.8, OmegaM=0.308.")
citation = "Enia et al. (2022)"
bibcode = "2022arXiv220200019E"
plot_as = "points"
output_filename = "Enia2022.hdf5"
output_directory = "../"
# Create observational data instance
processed = ObservationalData()
processed.associate_citation(citation, bibcode)
processed.associate_name(name)
processed.associate_comment(comment)
processed.associate_cosmology(cosmology)
if not os.path.exists(output_directory):
os.mkdir(output_directory)
# Load raw Enia2022 data
data = np.loadtxt(f"../raw/sfr_enia2022.dat")
# Fetch the fields we need
z_minus, z_plus = data[:, 0], data[:, 1]
SFR, SFR_stderr = data[:, 2], data[:, 3]
# Enia (2022) fig. 10 uses specific values for z in each bin, but does not
# explicitly list those in their table 4. We simply use the middle of each
# bin.
z = 0.5 * (z_minus + z_plus)
a = 1.0 / (1.0 + z)
a_minus = 1.0 / (1.0 + z_plus)
a_plus = 1.0 / (1.0 + z_minus)
a_bin = unyt.unyt_array(a, units="dimensionless")
a_scatter = unyt.unyt_array((a - a_minus, a_plus - a),
units="dimensionless")
# convert from log10(SFRD) to SFRD and carry the uncertainties
SFR_minus = 10.0**(SFR - SFR_stderr)
SFR_plus = 10.0**(SFR + SFR_stderr)
SFR = 10.0**SFR
SFR_scatter = unyt.unyt_array((SFR - SFR_minus, SFR_plus - SFR),
units="Msun/yr/Mpc**3")
SFR = unyt.unyt_array(SFR, units="Msun/yr/Mpc**3")
processed.associate_x(a_bin,
scatter=a_scatter,
comoving=False,
description="Cosmic scale factor")
processed.associate_y(
SFR,
scatter=SFR_scatter,
comoving=False,
description="Cosmic average star formation rate density",
)
z_minus = z_minus.min()
z_plus = z_plus.max()
processed.associate_redshift(0.5 * (z_minus + z_plus), z_minus, z_plus)
processed.associate_plot_as(plot_as)
output_path = f"{output_directory}/{output_filename}"
if os.path.exists(output_path):
os.remove(output_path)
processed.write(filename=output_path)
a = unyt.unyt_array([a], units=unyt.dimensionless)
SNIa_rate = unyt.unyt_array([SNIa_rate], units=1.0 / (unyt.yr * unyt.Mpc**3))
SNIa_scatter = unyt.unyt_array(([SNIa_err_m], [SNIa_err_p]),
units=1.0 / (unyt.yr * unyt.Mpc**3))
processed.associate_x(a,
scatter=None,
comoving=False,
description="Cosmic scale factor")
processed.associate_y(
SNIa_rate,
scatter=SNIa_scatter,
comoving=False,
description="Cosmic SNIa rate",
)
processed.associate_citation(citation, bibcode)
processed.associate_name(name)
processed.associate_comment(comment)
zmin = 0.0
zmax = 1000.0
processed.associate_redshift(z, zmin, zmax)
processed.associate_plot_as(plot_as)
processed.associate_cosmology(cosmology)
output_path = f"../Frohmaier2019.hdf5"
if os.path.exists(output_path):
os.remove(output_path)
processed.write(filename=output_path)
zindex = np.digitize(z, zbins[1:], right=True)
for i in range(zcens.size):
bdx = zindex == i
output_path = f"{output_directory}/{output_filename.format(stringify_z(zcens[i]))}"
print(output_path)
processed.associate_x(
M[bdx],
scatter=unyt.unyt_array((M_lo[bdx], M_hi[bdx])),
comoving=False,
description="Galaxy Stellar Mass",
)
processed.associate_y(
Mdust[bdx],
scatter=unyt.unyt_array((Mdust_lo[bdx], Mdust_hi[bdx])),
comoving=False,
description="Galaxy Dust Mass",
)
processed.associate_citation(citation, bibcode)
processed.associate_name(name)
processed.associate_comment(comment)
processed.associate_redshift(zcens[i])
processed.associate_plot_as(plot_as)
processed.associate_cosmology(cosmology)
if os.path.exists(output_path):
os.remove(output_path)
processed.write(filename=output_path)
def cddf_zwaan():
# Meta-data
name = f"CDDF fit from Zwaan et al. (2005)"
comment = ""
citation = "Zwaan et al. (2005)"
bibcode = "2005MNRAS.364.1467Z"
plot_as = "line"
output_filename = "Zwaan2005.hdf5"
output_directory = "../"
# Create observational data instance
processed = ObservationalData()
processed.associate_citation(citation, bibcode)
processed.associate_name(name)
processed.associate_comment(comment)
processed.associate_cosmology(cosmology)
if not os.path.exists(output_directory):
os.mkdir(output_directory)
# Fit parameters (equation 3 in the paper)
NHI_star = 10.0**21.2
f_star = 0.0193
beta = 1.24
logNHI = np.linspace(19.8, 22.1, 100)
dlogNHI = logNHI[1] - logNHI[0]
logNHI_minus = logNHI - 0.5 * dlogNHI
logNHI_plus = logNHI + 0.5 * dlogNHI
dlogNHI = logNHI_plus - logNHI_minus
dNHI = 10.0**logNHI_plus - 10.0**logNHI_minus
NHI = 10.0**logNHI
f_NHI = (f_star / NHI_star) * (NHI_star / NHI)**beta * np.exp(
-NHI / NHI_star)
# convert from d/dN to d/dlogN
f_NHI *= dNHI / dlogNHI
NHI_bin = unyt.unyt_array(NHI, units="cm**(-2)")
NHI_scatter = unyt.unyt_array(
(NHI - 10.0**logNHI_minus, 10.0**logNHI_plus - NHI),
units="cm**(-2)",
)
f_NHI_bin = unyt.unyt_array(f_NHI, units="dimensionless")
processed.associate_x(NHI_bin,
scatter=NHI_scatter,
comoving=False,
description="Column density")
processed.associate_y(
f_NHI_bin,
scatter=None,
comoving=False,
description="Column density distribution function",
)
z_minus = 0.0
z_plus = 0.0
processed.associate_redshift(0.5 * (z_minus + z_plus), z_minus, z_plus)
processed.associate_plot_as(plot_as)
output_path = f"{output_directory}/{output_filename}"
if os.path.exists(output_path):
os.remove(output_path)
processed.write(filename=output_path)
citation = "Schaye et al. (2015) (EAGLE)"
bibcode = "2015MNRAS..446..521S"
name = "Gas phase metal mass density obtained from the Ref EAGLE model"
redshift = np.mean(z)
plot_as = "points"
# Write everything
processed = ObservationalData()
processed.associate_x(a,
scatter=None,
comoving=True,
description="Scale-factor")
processed.associate_y(rho_Z_gas,
scatter=None,
comoving=True,
description="Gas Phase Metal Mass Density")
processed.associate_citation(citation, bibcode)
processed.associate_name(name)
processed.associate_comment(comment)
processed.associate_redshift(redshift)
processed.associate_plot_as(plot_as)
processed.associate_cosmology(cosmology)
processed.associate_redshift(redshift, np.min(redshift), np.max(redshift))
output_path = f"{output_directory}/{output_filename}"
if os.path.exists(output_path):
os.remove(output_path)
processed.write(filename=output_path)
def cddf_ho():
# Meta-data
name = f"CDDF from Ho et al. (2021)"
comment = ""
citation = "Ho et al. (2021)"
bibcode = "2021MNRAS.507..704H"
plot_as = "points"
output_filename = "Ho2021.hdf5"
output_directory = "../"
# Create observational data instance
processed = ObservationalData()
processed.associate_citation(citation, bibcode)
processed.associate_name(name)
processed.associate_comment(comment)
processed.associate_cosmology(cosmology)
if not os.path.exists(output_directory):
os.mkdir(output_directory)
# Load raw data
data = np.loadtxt(f"../raw/cddf_Ho2021.dat")
# Fetch the fields we need
logNHI_minus = data[:, 0]
logNHI_plus = data[:, 1]
# add pre-factor 10^{-21}
f_NHI = data[:, 2] * 1.0e-21
f_NHI_minus = data[:, 3] * 1.0e-21
f_NHI_plus = data[:, 4] * 1.0e-21
logNHI = 0.5 * (logNHI_minus + logNHI_plus)
dlogNHI = logNHI_plus - logNHI_minus
dNHI = 10.0**logNHI_plus - 10.0**logNHI_minus
# convert from d/dN to d/dlogN
f_NHI *= dNHI / dlogNHI
f_NHI_minus *= dNHI / dlogNHI
f_NHI_plus *= dNHI / dlogNHI
NHI_bin = unyt.unyt_array(10.0**logNHI, units="cm**(-2)")
NHI_scatter = unyt.unyt_array(
(10.0**logNHI - 10.0**logNHI_minus, 10.0**logNHI_plus - 10.0**logNHI),
units="cm**(-2)",
)
f_NHI_bin = unyt.unyt_array(f_NHI, units="dimensionless")
f_NHI_scatter = unyt.unyt_array((f_NHI - f_NHI_minus, f_NHI_plus - f_NHI),
units="dimensionless")
processed.associate_x(NHI_bin,
scatter=NHI_scatter,
comoving=False,
description="Column density")
processed.associate_y(
f_NHI_bin,
scatter=f_NHI_scatter,
comoving=False,
description="Column density distribution function",
)
z_minus = 2.0
z_plus = 5.0
processed.associate_redshift(0.5 * (z_minus + z_plus), z_minus, z_plus)
processed.associate_plot_as(plot_as)
output_path = f"{output_directory}/{output_filename}"
if os.path.exists(output_path):
os.remove(output_path)
processed.write(filename=output_path)
[
10**(binned_data[0]) - 10**(binned_data[0] - array_x_bin_std_down),
10**(binned_data[0] + array_x_bin_std_up) - 10**(binned_data[0]),
],
units="Msun/kpc**2",
)
processed.associate_x(SigmaH2,
scatter=SigmaH2_err,
comoving=False,
description="$\\Sigma_{\\rm H_2}$")
processed.associate_y(SigmaSFR,
scatter=SigmaSFR_err,
comoving=False,
description="$\\Sigma_{\\rm SFR}$")
processed.associate_citation(citation, bibcode)
processed.associate_name(name)
processed.associate_comment(comment)
processed.associate_redshift(0.0, 0.0, 0.0)
processed.associate_plot_as(plot_as)
processed.associate_cosmology(cosmology)
output_path = f"../Abdurrouf2022.hdf5"
if os.path.exists(output_path):
os.remove(output_path)
processed.write(filename=output_path)
def passive_fractions_centrals():
# Meta-data
name = ("Fit to the passive fraction - stellar mass (centrals) "
f"at z=[{redshift_header_info:s}]")
comment = (
"The data is taken from https://www.peterbehroozi.com/data.html. "
"The quenched fractions are defined using the standard criterion where "
"specific star formation rate < 1e-11 yr^-1. "
"The stellar mass is the observed stellar mass as defined in Behroozi et al. "
"(2019) eq. 25. "
"Uses the Chabrier initial mass function. "
"The passive fractions are given by the 50th percentile of the posterior "
"distribution of the fitting model. "
"Cosmology: Omega_m=0.307, Omega_lambda=0.693, h=0.678, sigma_8=0.823, "
"n_s=0.96. "
"Shows the passive fraction of centrals versus galaxy stellar mass.")
# Store metadata at the top level
multi_z = MultiRedshiftObservationalData()
multi_z.associate_citation(citation, bibcode)
multi_z.associate_name(name)
multi_z.associate_comment(comment)
multi_z.associate_cosmology(cosmology)
multi_z.associate_maximum_number_of_returns(1)
output_filename = "Behroozi2019_centrals.hdf5"
output_directory = "../"
if not os.path.exists(output_directory):
os.mkdir(output_directory)
for z, dz_lower, dz_upper, a_str in zip(redshifts, redshifts_lower,
redshifts_upper,
scale_factors_str):
# Create a single observational-data instance at redshift z
processed = ObservationalData()
# Load raw Behroozi2019 data
data = np.loadtxt(f"../raw/Behroozi2019_qf_groupstats_a{a_str}.dat")
# Fetch the fields we need
log_M_star, QF, QF_plus, QF_minus = (
data[:, 0],
data[:, 1],
data[:, 2],
data[:, 3],
)
# We don't want to plot zeros
mask = np.where(QF > 0.0)
# Transform stellar mass
M_star = (10.0**log_M_star) * unyt.Solar_Mass
# Define scatter with respect to the best-fit value (16 and 84 percentiles)
QF_scatter = unyt.unyt_array((QF_minus[mask], QF_plus[mask]),
units="dimensionless")
# Compute \Delta z
redshift_lower, redshift_upper = [z - dz_lower, z + dz_upper]
processed.associate_x(
M_star[mask],
scatter=None,
comoving=False,
description="Galaxy Stellar Mass",
)
processed.associate_y(
QF[mask] * unyt.dimensionless,
scatter=QF_scatter,
comoving=False,
description="Passive Fraction (centrals)",
)
processed.associate_redshift(z, redshift_lower, redshift_upper)
processed.associate_plot_as(plot_as)
multi_z.associate_dataset(processed)
output_path = f"{output_directory}/{output_filename}"
if os.path.exists(output_path):
os.remove(output_path)
multi_z.write(filename=output_path)
else:
x_vals = tables[i][:, 4] * units[i]
fh2 = 10**tables[i][:, 6] * unitless
fh2_plus_err = (10**(tables[i][:, 6] + tables[i][:, 7]) * unitless) - fh2
fh2_minus_err = fh2 - (10**(tables[i][:, 6] - tables[i][:, 7]) * unitless)
fh2_err = np.row_stack([fh2_minus_err, fh2_plus_err])
processed.associate_x(x_vals,
scatter=None,
comoving=False,
description=labels[i])
processed.associate_y(fh2,
scatter=fh2_err,
comoving=False,
description="Average Galaxy H2 fraction")
processed.associate_citation(citation, bibcode)
processed.associate_name(name)
processed.associate_comment(comment)
processed.associate_redshift(redshift)
processed.associate_plot_as(plot_as)
processed.associate_cosmology(cosmology)
output_path = f"{output_directory}/{output_filename.format(filetag[i])}"
if os.path.exists(output_path):
os.remove(output_path)
processed.write(filename=output_path)
a = unyt.unyt_array(a, units=unyt.dimensionless)
SNIa_rate = unyt.unyt_array(SNIa_rate, units=1.0 / (unyt.yr * unyt.Mpc**3))
SNIa_scatter = unyt.unyt_array((SNIa_err_m, SNIa_err_p),
units=1.0 / (unyt.yr * unyt.Mpc**3))
processed.associate_x(a,
scatter=None,
comoving=False,
description="Cosmic scale factor")
processed.associate_y(
SNIa_rate,
scatter=SNIa_scatter,
comoving=False,
description="Cosmic SNIa rate",
)
processed.associate_citation(citation, bibcode)
processed.associate_name(name)
processed.associate_comment(comment)
zmin = z.min()
zmax = z.max()
processed.associate_redshift(0.5 * (zmin + zmax), zmin, zmax)
processed.associate_plot_as(plot_as)
processed.associate_cosmology(cosmology)
output_path = f"../Dilday2010.hdf5"
if os.path.exists(output_path):
os.remove(output_path)
processed.write(filename=output_path)
