name = "Cosmic Average Star Formation Rate Density"
plot_as = "line"
a = raw[:, 0]
z = 1.0 / a - 1.0
SFR = 10.0**raw[:, 1]
a = unyt.unyt_array(a, units=unyt.dimensionless)
SFR = unyt.unyt_array(SFR, units=unyt.Msun / unyt.yr / unyt.Mpc**3)
processed.associate_x(a,
scatter=None,
comoving=False,
description="Cosmic scale factor")
processed.associate_y(
SFR,
scatter=None,
comoving=False,
description="Cosmic average star formation rate density",
)
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"../FIREbox.hdf5"
if os.path.exists(output_path):
os.remove(output_path)
"The halo mass used is the 'virial' mass within a spherical region whose mean "
"density is 200 times the critical density of the Universe today. "
"Plotted is the stellar mass versus halo mass at z=0.")
output_filename = "Read2017.hdf5"
# Write everything
processed = ObservationalData()
processed.associate_x(
M_halo,
scatter=M_halo_scatter,
comoving=False,
description="Halo Mass ($M_{200, {\rm crit}}$)",
)
processed.associate_y(M_star,
scatter=M_star_scatter,
comoving=False,
description="Galaxy Stellar Mass")
processed.associate_citation(citation, bibcode)
processed.associate_name(name)
processed.associate_comment(comment)
processed.associate_redshift(redshift, redshift_lower, redshift_upper)
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)
SNIa_rate = ratenu * 1.0e-5 * (h / 0.7)**3
SNIa_err_m = -err_m * 1.0e-5 * (h / 0.7)**3
SNIa_err_p = err_p * 1.0e-5 * (h / 0.7)**3
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"../Graur2011.hdf5"
if os.path.exists(output_path):
os.remove(output_path)
"There is also a correction to convert the metallicities to the 12+log10(O/H) convention."
)
citation = "Lee et al. (2006)"
bibcode = "2006ApJ...647..970L"
name = "Stellar mass - Gas phase metallicity relation"
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(
Z_median, scatter=y_scatter, comoving=True, description="Gas phase metallicity"
)
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}"
if os.path.exists(output_path):
os.remove(output_path)
processed.write(filename=output_path)
Zmm = fitcurve(M_star, alpha - alpha_err, beta - beta_err)
Z_max = np.maximum(np.maximum(Zpp, Zpm), np.maximum(Zmp, Zmm))
Z_min = np.minimum(np.minimum(Zpp, Zpm), np.minimum(Zmp, Zmm))
Z_scatter = unyt.unyt_array([Z - Z_min, Z_max - Z], units="dimensionless")
Z = unyt.unyt_array(Z, units="dimensionless")
# Write everything
processed = ObservationalData()
processed.associate_x(M_star,
scatter=None,
comoving=False,
description="Galaxy Stellar Mass")
processed.associate_y(
Z,
scatter=Z_scatter,
comoving=False,
description=f"Stellar metallicity ({label})",
)
processed.associate_citation(citation, bibcode)
processed.associate_name(name)
processed.associate_comment(comment)
processed.associate_redshift(redshift.mean(), *redshift)
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)
name = "Fit to the stellar mass - stellar halo mass relation at z=0."
plot_as = "line"
redshift = 0.0
h = h_sim
# Write everything
processed = ObservationalData()
processed.associate_x(
halo_masses * unyt.Solar_Mass,
scatter=None,
comoving=False,
description="Halo Mass ($M_{200, {\rm crit}}$)",
)
processed.associate_y(
(stellar_masses / halo_masses) * unyt.dimensionless,
scatter=None,
comoving=True,
description="Galaxy Stellar Mass / Halo Mass ($M_* / M_{200, {\rm crit}}$)",
)
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}"
if os.path.exists(output_path):
os.remove(output_path)
processed.write(filename=output_path)
redshift = 0.0
Fe_over_H_Asplund09 = 7.50
Mstar = 10.0**raw[:, 0]
metal = 10.0**(raw[:, 1] - Fe_over_H_Asplund09)
Mstar = unyt.unyt_array(Mstar, units=unyt.Msun)
metal = unyt.unyt_array(metal, units=unyt.dimensionless)
processed.associate_x(Mstar,
scatter=None,
comoving=False,
description="Galaxy Stellar Mass")
processed.associate_y(
metal,
scatter=None,
comoving=False,
description="Galaxy Stellar Metallicity (3 kpc aperture)",
)
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"../FIREbox.hdf5"
if os.path.exists(output_path):
os.remove(output_path)
processed.write(filename=output_path)
y_scatter = unyt.unyt_array(
(M_star_ratio_50 - M_star_ratio_16, M_star_ratio_84 - M_star_ratio_50)
)
# A fitting function gives us the data at any z. Hence, no need to have \Delta z
redshift_lower, redshift_upper = [z, z]
processed.associate_x(
M_halo,
scatter=None,
comoving=True,
description=f"Halo Mass ({latex_name:s})",
)
processed.associate_y(
M_star_ratio_50,
scatter=y_scatter,
comoving=True,
description="Galaxy Stellar Mass (30kpc, 3D) / Halo Mass ({latex_name:s})",
)
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)
bibcode = "2018A&A...609A..37C"
name = "Neutral Gas Mass - Dust Mass data"
plot_as = "points"
redshift = 0.02
h = h_sim
x_scatter = unyt.unyt_array([M_nerr, M_nerr])
y_scatter = unyt.unyt_array([M_derr, M_derr])
# Write everything
processed = ObservationalData()
processed.associate_x(
M_neut, scatter=x_scatter, comoving=True, description="Galaxy Star Formation Rate"
)
processed.associate_y(
M_dust, scatter=y_scatter, comoving=True, description="Galaxy Neutral Gas Mass"
)
processed.associate_citation(citation, bibcode)
processed.associate_name(name)
processed.associate_comment(comment)
processed.associate_redshift(redshift, 0, 0.5)
processed.associate_plot_as(plot_as)
processed.associate_cosmology(cosmology)
output_path = f"{output_directory}/{output_filename_werr}"
if os.path.exists(output_path):
os.remove(output_path)
processed.write(filename=output_path)
redshift = 0.0
redshift_lower = 0.0
redshift_upper = 3.0
h = 0.7
# Write everything
outobj = ObservationalData()
outobj.associate_x(
logD2S_med,
scatter=x_scatter,
comoving=True,
description="Gas phase dust-to-stellar mass ratio",
)
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)
citation = "NGC6822 (Gratier et al. 2010)"
bibcode = "2010A&A...512A..68G"
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_p_Mstar,
scatter=None,
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)
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)
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 = (
"Pozzi et al. (2020), Figure 5. Obtained using Herschel+GAMA"
"data, and derived using the MAGPHYS SED fitting package."
f"h-corrected for SWIFT using Cosmology: {cosmology.name}."
)
citation = "Pozzi et al. (2020)"
bibcode = "2020MNRAS.491.5073P"
name = "GDMF from Herschel+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
SigmaH2_err = unyt.unyt_array(
[
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/pc**2",
)
processed.associate_x(SigmaH2,
scatter=SigmaH2_err,
comoving=False,
description="H2 Surface density")
processed.associate_y(
SigmaSFR,
scatter=SigmaSFR_err,
comoving=False,
description="Star Formation Rate Surface Density",
)
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"../Querejeta2021.hdf5"
if os.path.exists(output_path):
os.remove(output_path)
def cddf_noterdaeme():
# Meta-data
name = f"CDDF from Noterdaeme et al. (2012)"
comment = ""
citation = "Noterdaeme et al. (2012)"
bibcode = "2012A&A...547L...1N"
plot_as = "points"
output_filename = "Noterdaeme2012.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_Noterdaeme2012.dat")
# Fetch the fields we need
logNHI_minus = data[:, 0]
logNHI_plus = data[:, 1]
f_NHI = 10.0**data[:, 2]
f_NHI_minus = 10.0**(data[:, 2] - data[:, 4])
f_NHI_plus = 10.0**(data[:, 2] + data[:, 4])
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 = 3.0
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)
