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 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 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)
# Write everything
outobj = ObservationalData()
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
h = 0.7
# Write everything
outobj_comw = ObservationalData()
outobj_comw.associate_x(oabundance,
scatter=None,
comoving=True,
description="Gas Phase 12 + log10(O/H)")
outobj_comw.associate_y(
d2g_comw_med,
scatter=y_scatter_comw,
comoving=True,
description="Dust-to-gas ratio (using X_CO,Z)",
)
outobj_comw.associate_citation(citation_comw, bibcode)
outobj_comw.associate_name(name_comw)
outobj_comw.associate_comment(comment)
outobj_comw.associate_redshift(redshift, redshift_lower, redshift_upper)
outobj_comw.associate_plot_as(plot_as)
outobj_comw.associate_cosmology(cosmology)
outobj_coz = copy.deepcopy(outobj_comw)
outobj_coz.associate_y(
d2g_coz_med,
scatter=y_scatter_coz,
comoving=True,
description="Dust-to-gas ratio (using X_CO,Z)",
)
outobj_comw.associate_citation(citation_coz, bibcode)
outobj_coz.associate_name(name_coz)
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)
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)
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)
redshift_distant = 2.0
redshift_local = 0.0
h = h_sim
# Write distant
processed_distant = ObservationalData()
processed_distant.associate_x(oabundance,
scatter=None,
comoving=False,
description="[O/H]")
processed_distant.associate_y(d2g_distant,
scatter=None,
comoving=False,
description="Dust-to-Gas Ratio")
processed_distant.associate_citation(citation_distant, bibcode)
processed_distant.associate_name(name_distant)
processed_distant.associate_comment(comment)
processed_distant.associate_redshift(redshift_distant, 0.5, 5.5)
processed_distant.associate_plot_as(plot_as)
processed_distant.associate_cosmology(cosmology)
output_path = f"{output_directory}/{output_filename_distant}"
if os.path.exists(output_path):
os.remove(output_path)
processed_distant.write(filename=output_path)
# Write local
processed_local = ObservationalData()
processed_local.associate_x(oabundance,
def cddf_berg():
# Meta-data
comment = ""
bibcode = "2019MNRAS.488.4356B"
plot_as = "points"
output_directory = "../"
for dataset in ["subDLA", "DLA"]:
output_filename = f"Berg2019_{dataset}.hdf5"
citation = f"Berg et al. (2019) - {dataset}s"
name = f"CDDF from Berg et al. (2019) - {dataset}s"
# 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_Berg2019_{dataset}.dat")
# Fetch the fields we need
logNHI = data[:, 0]
f_NHI = data[:, 3]
f_NHI_minus = data[:, 2]
f_NHI_plus = data[:, 4]
# create NHI bins from the given values
# for each interval in log space, we assign half to the lower bin and
# half to the upper bin. We further assume that the lowest and highest
# bin are symmetric (in log space) around the central value
logNHI_plus = np.zeros(logNHI.shape)
logNHI_minus = np.zeros(logNHI.shape)
logNHI_plus[:-1] = 0.5 * (logNHI[1:] + logNHI[:-1])
logNHI_minus[1:] = 0.5 * (logNHI[1:] + logNHI[:-1])
logNHI_plus[-1] = 2.0 * logNHI[-1] - logNHI_minus[-1]
logNHI_minus[0] = 2.0 * logNHI[0] - logNHI_plus[0]
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 = 2.3
z_plus = 3.2
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)
def cosmic_star_formation_history_gruppioni():
# Meta-data
name = f"Star formation rate density from Gruppioni et al. (2020)"
comment = ("Uses the Chabrier initial mass function. "
"Cosmology: H0=70, OmegaM=0.3, OmegaL=0.7")
citation = "Gruppioni et al. (2020)"
bibcode = "2020A&A...643A...8G"
plot_as = "points"
output_filename = "Gruppioni2020.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 Gruppioni2020 data
data = np.loadtxt(f"../raw/sfr_gruppioni2020.dat")
# Fetch the fields we need
z_minus, z_plus = data[:, 0], data[:, 1]
SFR, SFR_min, SFR_max = data[:, 2], data[:, 3], data[:, 4]
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")
SFR_scatter = unyt.unyt_array((SFR - SFR_min, SFR_max - 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)
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)
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)
def cddf_kim():
# Meta-data
name = f"CDDF from Kim et al. (2013)"
comment = ""
citation = "Kim et al. (2013)"
bibcode = "2013A&A...552A..77K"
plot_as = "points"
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_Kim2013.dat")
# Fetch the fields we need
logNHI = data[:, 0]
# create NHI bins from the given values
# for each interval in log space, we assign half to the lower bin and
# half to the upper bin. We further assume that the lowest and highest
# bin are symmetric (in log space) around the central value
logNHI_plus = np.zeros(logNHI.shape)
logNHI_minus = np.zeros(logNHI.shape)
logNHI_plus[:-1] = 0.5 * (logNHI[1:] + logNHI[:-1])
logNHI_minus[1:] = 0.5 * (logNHI[1:] + logNHI[:-1])
logNHI_plus[-1] = 2.0 * logNHI[-1] - logNHI_minus[-1]
logNHI_minus[0] = 2.0 * logNHI[0] - logNHI_plus[0]
dlogNHI = logNHI_plus - logNHI_minus
dNHI = 10.0**logNHI_plus - 10.0**logNHI_minus
for zm, zp, ofs in zip([1.9, 1.9, 2.4], [3.2, 2.4, 3.2], [1, 4, 7]):
output_filename = f"Kim2013_z{zm:.1f}_{zp:.1f}.hdf5"
f_NHI = data[:, ofs]
# mask out empty rows
mask = f_NHI < 0
f_NHI_plus = data[:, ofs + 1]
f_NHI_minus = data[:, ofs + 2]
# if Delta(f)_minus is not gives, assume the same as Delta(f)_plus
f_NHI_minus[f_NHI_minus == 0] = f_NHI_plus[f_NHI_minus == 0]
f_NHI_plus = 10.0**(f_NHI + f_NHI_plus)
f_NHI_minus = 10.0**(f_NHI - f_NHI_minus)
f_NHI = 10.0**f_NHI
# 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[mask], units="cm**(-2)")
NHI_scatter = unyt.unyt_array(
(
10.0**logNHI[mask] - 10.0**logNHI_minus[mask],
10.0**logNHI_plus[mask] - 10.0**logNHI[mask],
),
units="cm**(-2)",
)
f_NHI_bin = unyt.unyt_array(f_NHI[mask], units="dimensionless")
f_NHI_scatter = unyt.unyt_array(
(f_NHI[mask] - f_NHI_minus[mask], f_NHI_plus[mask] - f_NHI[mask]),
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 = zm
z_plus = zp
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)
