)
citation = "Crain et al. (2015) (EAGLE NoAGN 25 Mpc)"
bibcode = "2015MNRAS.450.1937C"
name = "Galaxy Stellar Mass-Galaxy Size EAGLE NoAGN (25 Mpc)"
plot_as = "points"
redshift = 0.100_639
h_obs = 0.7
h = cosmology.h
M = raw.T[0] * unyt.Solar_Mass
R = raw.T[1] * unyt.kpc
bins = unyt.unyt_array(np.logspace(8.5, 12, 20), units=unyt.Solar_Mass)
# Now bin the line
centers, median, deviation = binned_median_line(x=M, y=R, x_bins=bins)
processed.associate_x(
centers, scatter=None, comoving=False, description="Galaxy Stellar Mass (30kpc, 3D)"
)
processed.associate_y(
median,
scatter=deviation,
comoving=False,
description="Galaxy Half-Mass Radius (whole subhalo, 3D)",
)
processed.associate_citation(citation, bibcode)
processed.associate_name(name)
processed.associate_comment(comment)
processed.associate_redshift(redshift)
processed.associate_plot_as(plot_as)
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)
subsig = lambda a: np.percentile(a, 16)
x_bins = np.percentile(x_all, np.linspace(1, 99, 10)) * unyt.dimensionless
x_mids = x_bins[:-1] + np.diff(x_bins) * 0.5
_, y_med, y_sigs = binned_median_line(x_all, y_all, x_bins=x_bins)
y_scatter = unyt.unyt_array((y_sigs[0], y_sigs[1]))
x_scatter = unyt.unyt_array([np.diff(x_bins) * 0.5] * 2)
output_filename = "DeLooze20_composite_median.hdf5"
# Meta-data
comment = f"Median and scatter compiled from multiple surveys"
citation = f"Compiled by De Looze et al. (2020)"
bibcode = "2020MNRAS.496.3668D"
name = "MHI/Mstar as a function of 12 + log10(O/H)"
plot_as = "points"
redshift = 0.0
redshift_lower = 0.0
redshift_upper = 3.0
def create_line(
self,
x: unyt_array,
y: unyt_array,
box_volume: Union[None, unyt_quantity] = None,
reverse_cumsum: bool = False,
minimum_additional_points: int = 0,
):
"""
Creates the line!
Parameters
----------
x: unyt_array
Horizontal axis data
y: unyt_array
Vertical axis data
box_volume: Union[None, unyt_quantity]
Box volume for the simulation, required for mass functions. Should
have associated volume units. Generally this is given as a comoving
quantity.
reverse_cumsum: bool
A boolean deciding whether to reverse the cumulative sum. If false,
the sum is computed from low to high values (along the X-axis). Relevant
only for cumulative histogram lines. Default is false.
minimum_additional_points: int, optional
Minimum number of additional data points with the highest values of x to
show in the median-line or mean-line plots.
Returns
-------
output: Tuple[unyt_array]
A five-length (mean, median lines) or three-length (mass_function,
histogram, cumulative_histogram) tuple of unyt arrays that takes the
following form: (bin centers, vertical values, vertical scatter,
additional_x [optional], additional_y [optional]).
"""
if self.bins is None:
self.generate_bins(values=x)
else:
self.bins.convert_to_units(x.units)
self.output = None
masked_x = x
masked_y = y
if self.lower is not None:
self.lower.convert_to_units(y.units)
mask = masked_y > self.lower
masked_x = masked_x[mask]
masked_y = masked_y[mask]
if self.upper is not None:
self.upper.convert_to_units(y.units)
mask = masked_y < self.upper
masked_x = masked_x[mask]
masked_y = masked_y[mask]
if self.median:
self.output = lines.binned_median_line(
x=masked_x,
y=masked_y,
x_bins=self.bins,
return_additional=True,
minimum_additional_points=minimum_additional_points,
)
elif self.mean:
self.output = lines.binned_mean_line(
x=masked_x,
y=masked_y,
x_bins=self.bins,
return_additional=True,
minimum_additional_points=minimum_additional_points,
)
elif self.mass_function:
mass_function_output = create_mass_function_given_bins(
masked_x, self.bins, box_volume=box_volume)
self.output = (
*mass_function_output,
unyt_array([], units=mass_function_output[0].units),
unyt_array([], units=mass_function_output[1].units),
)
elif self.luminosity_function:
luminosity_function_output = create_luminosity_function_given_bins(
masked_x, self.bins, box_volume=box_volume)
self.output = (
*luminosity_function_output,
unyt_array([], units=luminosity_function_output[0].units),
unyt_array([], units=luminosity_function_output[1].units),
)
elif self.histogram:
histogram_output = create_histogram_given_bins(
masked_x, self.bins, box_volume=box_volume)
self.output = (
*histogram_output,
unyt_array([], units=histogram_output[0].units),
unyt_array([], units=histogram_output[1].units),
)
elif self.cumulative_histogram:
histogram_output = create_histogram_given_bins(
masked_x,
self.bins,
box_volume=box_volume,
cumulative=True,
reverse=reverse_cumsum,
)
self.output = (
*histogram_output,
unyt_array([], units=histogram_output[0].units),
unyt_array([], units=histogram_output[1].units),
)
elif self.adaptive_mass_function:
*mass_function_output, self.bins = create_adaptive_mass_function(
masked_x,
lowest_mass=self.start,
highest_mass=self.end,
box_volume=box_volume,
return_bin_edges=True,
)
self.output = (
*mass_function_output,
unyt_array([], units=mass_function_output[0].units),
unyt_array([], units=mass_function_output[1].units),
)
else:
self.output = None
return self.output
"H=70 km/s/Mpc and O_lambda=0.7. Provided h-free so no h-correction made. "
f"This data is for the {band}-band only.")
citation = f"Lange et al. (2015, {band}-band)"
bibcode = "2015MNRAS.447.2603L"
name = "Galaxy Stellar Mass-Galaxy Size"
plot_as = "points"
redshift = 0.05
h_obs = 0.7
h = cosmology.h
half_mass = 10**(raw.T[1]) * unyt.kpc
M = 10**(raw.T[0]) * unyt.Solar_Mass
# We now need to bin this data
mass_bins = unyt.Solar_Mass * np.logspace(7, 12, 26)
mass_bin_centers, galaxy_sizes, galaxy_size_scatter = binned_median_line(
x=M, y=half_mass, x_bins=mass_bins)
processed.associate_x(
mass_bin_centers,
scatter=None,
comoving=False,
description="Galaxy Stellar Mass",
)
processed.associate_y(
galaxy_sizes,
scatter=galaxy_size_scatter,
comoving=False,
description="Galaxy Half-Mass Radius",
)
processed.associate_citation(citation, bibcode)
processed.associate_name(name)
base_n_bins=number_of_bins,
logarithmic=True,
stretch_final_bin=True,
)
_, bins_nsf = create_adaptive_bins(
stellar_mass_nsf,
lowest_value=stellar_mass_bin_range[0],
highest_value=stellar_mass_bin_range[1],
base_n_bins=number_of_bins,
logarithmic=True,
stretch_final_bin=True,
)
centers_sf, medians_sf, deviations_sf = binned_median_line(
x=stellar_mass_sf,
y=R[mask_sf],
x_bins=bins_sf,
return_additional=False)
centers_nsf, medians_nsf, deviations_nsf = binned_median_line(
x=stellar_mass_nsf,
y=R[mask_nsf],
x_bins=bins_nsf,
return_additional=False)
processed_sf.associate_x(centers_sf,
scatter=None,
comoving=False,
description="Galaxy Stellar Mass")
processed_sf.associate_y(
medians_sf,