def cumulative_mass_compare_plot(
run_name: str,
snap_filepath_parent: str = None,
snap_filepath_zoom: List[str] = None,
velociraptor_properties_zoom: List[str] = None,
output_directory: str = None) -> None:
"""
This function compares the cumulative mass of DMO zooms to their
corresponding parent halo. It also allows to assess numerical
convergence by overlapping multiple profiles from zooms with different
resolutions. The cumulative mass profiles are then listed in the legend,
where the DM particle mass is quoted.
The zoom inputs are in the form of arrays of strings, to allow
for multiple entries. Each entry is a zoom snap/VR output resolution.
The function allows not to plot either the parent mass profile
or the zooms. At least one of them must be entered.
:param run_name: str
A custom and identifiable name for the run. Currently the standard
name follows the scheme {AUTHOR_INITIALS}{HALO_ID}, e.g. SK0 or EA1.
This argument must be defined.
:param snap_filepath_parent: str
The complete path to the snapshot of the parent box.
If parameter is None, the mass profile of the parent box is not
displayed.
:param snap_filepath_zoom: list(str)
The list of complete paths to the snapshots of the zooms
at different resolution. Note: the order must match that of
the `velociraptor_properties_zoom` parameter. If parameter
is None, the mass profile of the zoom is not displayed and the
`velociraptor_properties_zoom` parameter is ignored.
:param velociraptor_properties_zoom: list(str)
The list of complete paths to the VR outputs (properties) of the
zooms at different resolution. Note: the order must match that of
the `snap_filepath_zoom` parameter. If `snap_filepath_zoom` is None,
then this parameter is ignored. If this parameter is None and
`snap_filepath_zoom` is defined, raises an error.
:param output_directory: str
The output directory where to save the plot. This code assumes
that the output directory exists. If it does not exist, matplotlib
will return an error. This argument must be defined.
:return: None
"""
# ARGS CHECK #
assert run_name
assert snap_filepath_parent or snap_filepath_zoom
if snap_filepath_zoom and velociraptor_properties_zoom:
assert len(snap_filepath_zoom) == len(velociraptor_properties_zoom)
elif not snap_filepath_zoom:
velociraptor_properties_zoom = None
elif snap_filepath_zoom and not velociraptor_properties_zoom:
raise ValueError
assert output_directory
# TEMPORARY #
# Split the run_name into author and halo_id to make everything work fine for now
import re
match = re.match(r"([a-z]+)([0-9]+)", run_name, re.I)
author = None
halo_id = None
if match:
author, halo_id = match.groups()
halo_id = int(halo_id)
fig, (ax,
ax_residual) = plt.subplots(nrows=2,
ncols=1,
figsize=(3.5, 4.1),
sharex=True,
gridspec_kw={'height_ratios': [3, 1]})
# PARENT #
if snap_filepath_parent:
# Load VR output gathered from the halo selection process
lines = np.loadtxt(f"{output_directory}/halo_selected_{author}.txt",
comments="#",
delimiter=",",
unpack=False).T
M200c = lines[1] * 1e13
R200c = lines[2]
Xcminpot = lines[3]
Ycminpot = lines[4]
Zcminpot = lines[5]
M200c = unyt.unyt_quantity(M200c[halo_id], unyt.Solar_Mass)
R200c = unyt.unyt_quantity(R200c[halo_id], unyt.Mpc)
xCen = unyt.unyt_quantity(Xcminpot[halo_id], unyt.Mpc)
yCen = unyt.unyt_quantity(Ycminpot[halo_id], unyt.Mpc)
zCen = unyt.unyt_quantity(Zcminpot[halo_id], unyt.Mpc)
# Construct spatial mask to feed into swiftsimio
size = radius_bounds[1] * R200c
mask = sw.mask(snap_filepath_parent)
region = [[xCen - size, xCen + size], [yCen - size, yCen + size],
[zCen - size, zCen + size]]
mask.constrain_spatial(region)
data = sw.load(snap_filepath_parent, mask=mask)
# Get DM particle coordinates and compute radial distance from CoP in R200 units
posDM = data.dark_matter.coordinates / data.metadata.a
r = np.sqrt((posDM[:, 0] - xCen)**2 + (posDM[:, 1] - yCen)**2 +
(posDM[:, 2] - zCen)**2)
# Calculate particle mass and rho_crit
unitLength = data.metadata.units.length
unitMass = data.metadata.units.mass
rho_crit = unyt.unyt_quantity(
data.metadata.cosmology['Critical density [internal units]'],
unitMass / unitLength**3)[0].to('Msun/Mpc**3')
rhoMean = rho_crit * data.metadata.cosmology['Omega_m']
vol = data.metadata.boxsize[0]**3
numPart = data.metadata.n_dark_matter
particleMass = rhoMean * vol / numPart
parent_mass_resolution = particleMass
particleMasses = np.ones_like(r.value) * particleMass
# Construct bins and compute density profile
lbins = np.logspace(np.log10(radius_bounds[0]),
np.log10(radius_bounds[1]), bins)
r_scaled = r / R200c
hist, bin_edges = np.histogram(r_scaled,
bins=lbins,
weights=particleMasses)
bin_centre = np.sqrt(bin_edges[1:] * bin_edges[:-1])
cumulative_mass_parent = np.cumsum(hist)
# Plot density profile for each selected halo in volume
cumulative_mass_parent[cumulative_mass_parent == 0] = np.nan
parent_label = f'Parent: $m_\\mathrm{{DM}} = {latex_float(parent_mass_resolution.value[0])}\\ {parent_mass_resolution.units.latex_repr}$'
ax.plot(bin_centre,
cumulative_mass_parent,
c="grey",
linestyle="-",
label=parent_label)
# Compute convergence radius
conv_radius = convergence_radius(r, particleMasses, rho_crit) / R200c
ax.axvline(conv_radius, color='grey', linestyle='--')
ax_residual.axvline(conv_radius, color='grey', linestyle='--')
t = ax.text(conv_radius,
ax.get_ylim()[1],
'Convergence radius',
ha='center',
va='top',
rotation='vertical',
alpha=0.6)
t.set_bbox(dict(facecolor='white', alpha=0.6, edgecolor='none'))
# ZOOMS #
if snap_filepath_zoom:
# Set-up colors
cmap_discrete = plt.cm.get_cmap(cmap_name,
len(velociraptor_properties_zoom) + 3)
cmaplist = [cmap_discrete(i) for i in range(cmap_discrete.N)]
for snap_path, vrprop_path, color in zip(snap_filepath_zoom,
velociraptor_properties_zoom,
cmaplist):
# Load velociraptor data
with h5py.File(vrprop_path, 'r') as vr_file:
M200c = vr_file['/Mass_200crit'][0] * 1e10
R200c = vr_file['/R_200crit'][0]
Xcminpot = vr_file['/Xcminpot'][0]
Ycminpot = vr_file['/Ycminpot'][0]
Zcminpot = vr_file['/Zcminpot'][0]
M200c = unyt.unyt_quantity(M200c, unyt.Solar_Mass)
R200c = unyt.unyt_quantity(R200c, unyt.Mpc)
xCen = unyt.unyt_quantity(Xcminpot, unyt.Mpc)
yCen = unyt.unyt_quantity(Ycminpot, unyt.Mpc)
zCen = unyt.unyt_quantity(Zcminpot, unyt.Mpc)
# Construct spatial mask to feed into swiftsimio
size = radius_bounds[1] * R200c
mask = sw.mask(snap_path)
region = [[xCen - size, xCen + size], [yCen - size, yCen + size],
[zCen - size, zCen + size]]
mask.constrain_spatial(region)
data = sw.load(snap_path, mask=mask)
# Get DM particle coordinates and compute radial distance from CoP in R200 units
posDM = data.dark_matter.coordinates / data.metadata.a
r = np.sqrt((posDM[:, 0] - xCen)**2 + (posDM[:, 1] - yCen)**2 +
(posDM[:, 2] - zCen)**2)
# Calculate particle mass and rho_crit
unitLength = data.metadata.units.length
unitMass = data.metadata.units.mass
rho_crit = unyt.unyt_quantity(
data.metadata.cosmology['Critical density [internal units]'],
unitMass / unitLength**3)[0].to('Msun/Mpc**3')
particleMasses = data.dark_matter.masses.to('Msun')
zoom_mass_resolution = particleMasses
# Construct bins and compute density profile
lbins = np.logspace(np.log10(radius_bounds[0]),
np.log10(radius_bounds[1]), bins)
r_scaled = r / R200c
hist, bin_edges = np.histogram(r_scaled,
bins=lbins,
weights=particleMasses)
bin_centre = np.sqrt(bin_edges[1:] * bin_edges[:-1])
cumulative_mass_zoom = np.cumsum(hist)
# Plot density profile for each selected halo in volume
cumulative_mass_zoom[cumulative_mass_zoom == 0] = np.nan
zoom_label = f'Zoom: $m_\\mathrm{{DM}} = {latex_float(zoom_mass_resolution.value[0])}\\ {zoom_mass_resolution.units.latex_repr}$'
ax.plot(bin_centre,
cumulative_mass_zoom,
c=color,
linestyle="-",
label=zoom_label)
# Compute convergence radius
conv_radius = convergence_radius(r, particleMasses,
rho_crit) / R200c
ax.axvline(conv_radius, color=color, linestyle='--')
t = ax.text(conv_radius,
ax.get_ylim()[1],
'Convergence radius',
ha='center',
va='top',
rotation='vertical',
alpha=0.6)
t.set_bbox(dict(facecolor='white', alpha=0.6, edgecolor='none'))
# RESIDUALS #
if snap_filepath_parent and snap_filepath_zoom:
residual = (cumulative_mass_zoom -
cumulative_mass_parent) / cumulative_mass_parent
ax_residual.axhline(0, color='grey', linestyle='-')
ax_residual.plot(bin_centre, residual, c=color, linestyle="-")
ax_residual.axvline(conv_radius, color=color, linestyle='--')
ax.text(
0.975,
0.025,
(f"Halo {run_name:s} DMO\n"
f"$z={data.metadata.z:3.3f}$\n"
"Zoom VR output:\n"
f"$M_{{200c}}={latex_float(M200c.value)}\\ {M200c.units.latex_repr}$\n"
f"$R_{{200c}}={latex_float(R200c.value)}\\ {R200c.units.latex_repr}$"
),
color="black",
ha="right",
va="bottom",
backgroundcolor='white',
transform=ax.transAxes,
)
ax.axvline(1, color="grey", linestyle='--')
ax_residual.axvline(1, color="grey", linestyle='--')
ax_residual.set_xlim(radius_bounds[0], radius_bounds[1])
ax_residual.set_ylim(residual_bounds[0], residual_bounds[1])
ax.set_xscale('log')
ax.set_yscale('log')
ax.set_ylabel(f"$M_{{\\rm DM}} (< R)\\ [{M200c.units.latex_repr}]$")
ax_residual.set_ylabel(f"$\\Delta M\\ /\\ M_{{\\rm parent}}\\ (< R)$")
ax_residual.set_xlabel(r"$R\ /\ R_{200c}$")
ax.legend(loc="upper right")
fig.tight_layout()
fig.savefig(f"{output_directory}/{run_name}_cumulative_mass_compare.png")
plt.close(fig)
return
def density_profile_compare_plot(
run_name: str,
snap_filepath_parent: str = None,
velociraptor_properties_parent: str = None,
snap_filepath_zoom: List[str] = None,
velociraptor_properties_zoom: List[str] = None,
output_directory: str = None) -> None:
"""
This function compares the density profiles of DMO zooms to their
corresponding parent halo. It also allows to assess numerical
convergence by overlapping multiple profiles from zooms with different
resolutions. The density profiles are then listed in the legend,
where the DM particle mass is quoted.
The zoom inputs are in the form of arrays of strings, to allow
for multiple entries. Each entry is a zoom snap/VR output resolution.
The function allows not to plot either the parent density profile
or the zooms. At least one of them must be entered.
:param run_name: str
A custom and identifiable name for the run. Currently the standard
name follows the scheme {AUTHOR_INITIALS}{HALO_ID}, e.g. SK0 or EA1.
This argument must be defined.
:param snap_filepath_parent: str
The complete path to the snapshot of the parent box.
If parameter is None, the density ptofile of the parent box is not
displayed.
:param snap_filepath_zoom: list(str)
The list of complete paths to the snapshots of the zooms
at different resolution. Note: the order must match that of
the `velociraptor_properties_zoom` parameter. If parameter
is None, the density ptofile of the zoom is not displayed and the
`velociraptor_properties_zoom` parameter is ignored.
:param velociraptor_properties_zoom: list(str)
The list of complete paths to the VR outputs (properties) of the
zooms at different resolution. Note: the order must match that of
the `snap_filepath_zoom` parameter. If `snap_filepath_zoom` is None,
then this parameter is ignored. If this parameter is None and
`snap_filepath_zoom` is defined, raises an error.
:param output_directory: str
The output directory where to save the plot. This code assumes
that the output directory exists. If it does not exist, matplotlib
will return an error. This argument must be defined.
:return: None
"""
# ARGS CHECK #
assert snap_filepath_parent or snap_filepath_zoom
if snap_filepath_zoom and velociraptor_properties_zoom:
assert len(snap_filepath_zoom) == len(velociraptor_properties_zoom)
elif not snap_filepath_zoom:
velociraptor_properties_zoom = None
elif snap_filepath_zoom and not velociraptor_properties_zoom:
raise ValueError
assert output_directory
fig, (ax,
ax_residual) = plt.subplots(nrows=2,
ncols=1,
figsize=(7, 8),
dpi=resolution // 8,
sharex=True,
gridspec_kw={'height_ratios': [3, 1]})
# PARENT #
if snap_filepath_parent:
# Rendezvous over parent VR catalogue using zoom information
with h5py.File(velociraptor_properties_zoom[0], 'r') as vr_file:
idx, M200c, R200c, Xcminpot, Ycminpot, Zcminpot = find_object(
vr_properties_catalog=velociraptor_properties_parent,
sample_structType=10,
sample_mass_lower_lim=vr_file['/Mass_200crit'][0] * 1e10 * 0.8,
sample_x=vr_file['/Xcminpot'][0],
sample_y=vr_file['/Ycminpot'][0],
sample_z=vr_file['/Zcminpot'][0],
)
M200c = unyt.unyt_quantity(M200c, unyt.Solar_Mass)
R200c = unyt.unyt_quantity(R200c, unyt.Mpc)
xCen = unyt.unyt_quantity(Xcminpot, unyt.Mpc)
yCen = unyt.unyt_quantity(Ycminpot, unyt.Mpc)
zCen = unyt.unyt_quantity(Zcminpot, unyt.Mpc)
# Construct spatial mask to feed into swiftsimio
size = radius_bounds[1] * R200c
mask = sw.mask(snap_filepath_parent)
region = [[xCen - size, xCen + size], [yCen - size, yCen + size],
[zCen - size, zCen + size]]
mask.constrain_spatial(region)
data = sw.load(snap_filepath_parent, mask=mask)
# Get DM particle coordinates and compute radial distance from CoP in R200 units
posDM = data.dark_matter.coordinates / data.metadata.a
r = np.sqrt(
wrap(posDM[:, 0] - xCen, data.metadata.boxsize[0])**2 +
wrap(posDM[:, 1] - yCen, data.metadata.boxsize[1])**2 +
wrap(posDM[:, 2] - zCen, data.metadata.boxsize[2])**2)
# Calculate particle mass and rho_crit
unitLength = data.metadata.units.length
unitMass = data.metadata.units.mass
rho_crit = unyt.unyt_quantity(
data.metadata.cosmology_raw['Critical density [internal units]'],
unitMass / unitLength**3).to('Msun/Mpc**3')
rhoMean = rho_crit * data.metadata.cosmology.Om0
vol = data.metadata.boxsize[0]**3
numPart = data.metadata.n_dark_matter
particleMass = rhoMean * vol / numPart
parent_mass_resolution = particleMass
particleMasses = np.ones_like(r.value) * particleMass
# Construct bins and compute density profile
# NOTE: numpy.histogram does not preserve units, so restore them after.
lbins = np.logspace(np.log10(radius_bounds[0]),
np.log10(radius_bounds[1]), bins)
r_scaled = r / R200c
hist, bin_edges = np.histogram(r_scaled,
bins=lbins,
weights=particleMasses)
hist *= unyt.Solar_Mass
bin_centre = np.sqrt(bin_edges[1:] * bin_edges[:-1])
volume_shell = (4. * np.pi / 3.) * (R200c**3) * ((bin_edges[1:])**3 -
(bin_edges[:-1])**3)
densities_parent = hist / volume_shell / rho_crit
# Plot density profile for each selected halo in volume
densities_parent[densities_parent == 0] = np.nan
parent_label = (
f'Parent: $m_\\mathrm{{DM}} = {latex_float(parent_mass_resolution.value[0])}\\ '
f'{parent_mass_resolution.units.latex_repr}$')
ax.plot(bin_centre,
densities_parent,
c="grey",
linestyle="-",
label=parent_label)
# Compute convergence radius
conv_radius = convergence_radius(r, particleMasses, rho_crit) / R200c
ax.axvline(conv_radius, color='grey', linestyle='--')
ax_residual.axvline(conv_radius, color='grey', linestyle='--')
t = ax.text(conv_radius,
ax.get_ylim()[1],
'Convergence radius',
ha='center',
va='top',
rotation='vertical',
alpha=0.6)
t.set_bbox(dict(facecolor='white', alpha=0.6, edgecolor='none'))
# ZOOMS #
if snap_filepath_zoom:
# Set-up colors
cmap_discrete = plt.cm.get_cmap(cmap_name,
len(velociraptor_properties_zoom) + 3)
cmaplist = [cmap_discrete(i) for i in range(cmap_discrete.N)]
for snap_path, vrprop_path, color in zip(snap_filepath_zoom,
velociraptor_properties_zoom,
cmaplist):
# Load velociraptor data
with h5py.File(vrprop_path, 'r') as vr_file:
M200c = vr_file['/Mass_200crit'][0] * 1e10
R200c = vr_file['/R_200crit'][0]
Xcminpot = vr_file['/Xcminpot'][0]
Ycminpot = vr_file['/Ycminpot'][0]
Zcminpot = vr_file['/Zcminpot'][0]
M200c = unyt.unyt_quantity(M200c, unyt.Solar_Mass)
R200c = unyt.unyt_quantity(R200c, unyt.Mpc)
xCen = unyt.unyt_quantity(Xcminpot, unyt.Mpc)
yCen = unyt.unyt_quantity(Ycminpot, unyt.Mpc)
zCen = unyt.unyt_quantity(Zcminpot, unyt.Mpc)
# Construct spatial mask to feed into swiftsimio
size = radius_bounds[1] * R200c
mask = sw.mask(snap_path)
region = [[xCen - size, xCen + size], [yCen - size, yCen + size],
[zCen - size, zCen + size]]
mask.constrain_spatial(region)
data = sw.load(snap_path, mask=mask)
# Get DM particle coordinates and compute radial distance from CoP in R200 units
posDM = data.dark_matter.coordinates / data.metadata.a
r = np.sqrt(
wrap(posDM[:, 0] - xCen, data.metadata.boxsize[0])**2 +
wrap(posDM[:, 1] - yCen, data.metadata.boxsize[1])**2 +
wrap(posDM[:, 2] - zCen, data.metadata.boxsize[2])**2)
# Calculate particle mass and rho_crit
unitLength = data.metadata.units.length
unitMass = data.metadata.units.mass
rho_crit = unyt.unyt_quantity(
data.metadata.
cosmology_raw['Critical density [internal units]'],
unitMass / unitLength**3).to('Msun/Mpc**3')
particleMasses = data.dark_matter.masses.to('Msun')
zoom_mass_resolution = particleMasses
# Construct bins and compute density profile
# NOTE: numpy.histogram does not preserve units, so restore them after.
lbins = np.logspace(np.log10(radius_bounds[0]),
np.log10(radius_bounds[1]), bins)
r_scaled = r / R200c
hist, bin_edges = np.histogram(r_scaled,
bins=lbins,
weights=particleMasses)
hist *= unyt.Solar_Mass
bin_centre = np.sqrt(bin_edges[1:] * bin_edges[:-1])
volume_shell = (4. * np.pi / 3.) * (R200c**3) * (
(bin_edges[1:])**3 - (bin_edges[:-1])**3)
densities_zoom = hist / volume_shell / rho_crit
# Plot density profile for each selected halo in volume
densities_zoom[densities_zoom == 0] = np.nan
zoom_label = (
f'Zoom: $m_\\mathrm{{DM}} = {latex_float(zoom_mass_resolution.value[0])}\\ '
f'{zoom_mass_resolution.units.latex_repr}$')
ax.plot(bin_centre,
densities_zoom,
c=color,
linestyle="-",
label=zoom_label)
# Compute convergence radius
conv_radius = convergence_radius(r, particleMasses,
rho_crit) / R200c
ax.axvline(conv_radius, color=color, linestyle='--')
t = ax.text(conv_radius,
ax.get_ylim()[1],
'Convergence radius',
ha='center',
va='top',
rotation='vertical',
alpha=0.5)
t.set_bbox(dict(facecolor='white', alpha=0.6, edgecolor='none'))
# RESIDUALS #
if snap_filepath_parent and snap_filepath_zoom:
residual = (densities_zoom -
densities_parent) / densities_parent
ax_residual.axhline(0, color='grey', linestyle='-')
ax_residual.plot(bin_centre, residual, c=color, linestyle="-")
ax_residual.axvline(conv_radius, color=color, linestyle='--')
ax.text(
0.025,
0.025,
(f"Halo {run_name:s} DMO\n"
f"$z={data.metadata.z:3.3f}$\n"
"Zoom VR output:\n"
f"$M_{{200c}}={latex_float(M200c.value)}\\ {M200c.units.latex_repr}$\n"
f"$R_{{200c}}={latex_float(R200c.value)}\\ {R200c.units.latex_repr}$"
),
color="black",
ha="left",
va="bottom",
backgroundcolor='white',
alpha=0.5,
transform=ax.transAxes,
)
ax.axvline(1, color="grey", linestyle='--')
ax_residual.axvline(1, color="grey", linestyle='--')
ax_residual.set_xlim(radius_bounds[0], radius_bounds[1])
ax_residual.set_ylim(residual_bounds[0], residual_bounds[1])
ax.set_xscale('log')
ax.set_yscale('log')
ax.set_ylabel(r"$\rho_{DM}\ /\ \rho_c$")
ax_residual.set_ylabel(f"$\\Delta \\rho\\ /\\ \\rho_{{\\rm parent}}$")
ax_residual.set_xlabel(r"$R\ /\ R_{200c}$")
ax.legend(loc="upper right")
fig.tight_layout()
fig.savefig(f"{output_directory}/{run_name}_density_profile_compare.png")
plt.close(fig)
print(f"Saved: {output_directory}/{run_name}_density_profile_compare.png")
plt.close('all')
return
def profile_3d_single_halo(
path_to_snap: str,
path_to_catalogue: str,
weights: str,
hse_dataset: pd.Series = None,
) -> tuple:
# Read in halo properties
with h5.File(path_to_catalogue, 'r') as h5file:
scale_factor = float(h5file['/SimulationInfo'].attrs['ScaleFactor'])
M200c = unyt.unyt_quantity(h5file['/Mass_200crit'][0] * 1.e10,
unyt.Solar_Mass)
M500c = unyt.unyt_quantity(h5file['/SO_Mass_500_rhocrit'][0] * 1.e10,
unyt.Solar_Mass)
R200c = unyt.unyt_quantity(h5file['/R_200crit'][0],
unyt.Mpc) / scale_factor
R500c = unyt.unyt_quantity(h5file['/SO_R_500_rhocrit'][0],
unyt.Mpc) / scale_factor
XPotMin = unyt.unyt_quantity(h5file['/Xcminpot'][0],
unyt.Mpc) / scale_factor
YPotMin = unyt.unyt_quantity(h5file['/Ycminpot'][0],
unyt.Mpc) / scale_factor
ZPotMin = unyt.unyt_quantity(h5file['/Zcminpot'][0],
unyt.Mpc) / scale_factor
# If no custom aperture, select r500c as default
if hse_dataset is not None:
assert R500c.units == hse_dataset["R500hse"].units
assert M500c.units == hse_dataset["M500hse"].units
R500c = hse_dataset["R500hse"]
M500c = hse_dataset["M500hse"]
# Read in gas particles
mask = sw.mask(path_to_snap, spatial_only=False)
region = [[
XPotMin - radius_bounds[1] * R500c, XPotMin + radius_bounds[1] * R500c
], [
YPotMin - radius_bounds[1] * R500c, YPotMin + radius_bounds[1] * R500c
], [
ZPotMin - radius_bounds[1] * R500c, ZPotMin + radius_bounds[1] * R500c
]]
mask.constrain_spatial(region)
mask.constrain_mask("gas", "temperatures",
Tcut_halogas * mask.units.temperature,
1.e12 * mask.units.temperature)
data = sw.load(path_to_snap, mask=mask)
# Convert datasets to physical quantities
R500c *= scale_factor
XPotMin *= scale_factor
YPotMin *= scale_factor
ZPotMin *= scale_factor
data.gas.coordinates.convert_to_physical()
data.gas.masses.convert_to_physical()
data.gas.temperatures.convert_to_physical()
data.gas.densities.convert_to_physical()
data.gas.pressures.convert_to_physical()
data.gas.entropies.convert_to_physical()
data.dark_matter.masses.convert_to_physical()
# Select hot gas within sphere
posGas = data.gas.coordinates
deltaX = posGas[:, 0] - XPotMin
deltaY = posGas[:, 1] - YPotMin
deltaZ = posGas[:, 2] - ZPotMin
deltaR = np.sqrt(deltaX**2 + deltaY**2 + deltaZ**2)
# Calculate particle mass and rho_crit
unitLength = data.metadata.units.length
unitMass = data.metadata.units.mass
rho_crit = unyt.unyt_quantity(
data.metadata.cosmology_raw['Critical density [internal units]'] /
scale_factor**3, unitMass / unitLength**3)
dm_masses = data.dark_matter.masses.to('Msun')
zoom_mass_resolution = dm_masses[0]
# Since useful for different applications, attach datasets
data.gas.mass_weighted_temperatures = data.gas.masses * data.gas.temperatures
# Rescale profiles to r500c
radial_distance = deltaR / R500c
assert radial_distance.units == unyt.dimensionless
# Compute convergence radius
conv_radius = convergence_radius(deltaR, data.gas.masses.to('Msun'),
rho_crit.to('Msun/Mpc**3')) / R500c
# Construct bins for mass-weighted quantities and retrieve bin_edges
lbins = np.logspace(np.log10(radius_bounds[0]), np.log10(radius_bounds[1]),
bins) * radial_distance.units
mass_weights, bin_edges = histogram_unyt(radial_distance,
bins=lbins,
weights=data.gas.masses)
# Replace zeros with Nans
mass_weights[mass_weights == 0] = np.nan
bin_centre = np.sqrt(bin_edges[1:] * bin_edges[:-1])
# Allocate weights
if weights.lower() == 'gas_mass':
hist = mass_weights / M500c.to(mass_weights.units)
ylabel = r'$M(dR) / M_{500{\rm crit}}$'
elif weights.lower() == 'gas_mass_cumulative':
hist = cumsum_unyt(mass_weights) / M500c.to(mass_weights.units)
ylabel = r'$M(<R) / M_{500{\rm crit}}$'
elif weights.lower() == 'gas_density':
hist, _ = histogram_unyt(radial_distance,
bins=lbins,
weights=data.gas.densities)
hist /= rho_crit.to(hist.units)
hist *= bin_centre**2
ylabel = r'$(\rho_{\rm gas}/\rho_{\rm crit})\ (R/R_{500{\rm crit}})^3 $'
elif weights.lower() == 'mass_weighted_temps':
weights_field = data.gas.mass_weighted_temperatures
hist, _ = histogram_unyt(radial_distance,
bins=lbins,
weights=weights_field)
hist /= mass_weights
if sampling_method.lower() == 'no_binning':
bin_centre = radial_distance
hist = data.gas.temperatures
ylabel = r'$T$ [K]'
elif weights.lower() == 'mass_weighted_temps_kev':
weights_field = data.gas.mass_weighted_temperatures
hist, _ = histogram_unyt(radial_distance,
bins=lbins,
weights=weights_field)
hist /= mass_weights
hist = (hist * unyt.boltzmann_constant).to('keV')
# Make dimensionless, divide by (k_B T_500crit)
# unyt.G.in_units('Mpc*(km/s)**2/(1e10*Msun)')
norm = unyt.G * mean_molecular_weight * M500c * unyt.mass_proton / 2 / R500c
norm = norm.to('keV')
hist /= norm
ylabel = r'$(k_B T/k_B T_{500{\rm crit}})$'
elif weights.lower() == 'entropy':
if sampling_method.lower() == 'shell_density':
volume_shell = (4. * np.pi / 3.) * (R500c**3) * (
(bin_edges[1:])**3 - (bin_edges[:-1])**3)
density_gas = mass_weights / volume_shell
mean_density_R500c = (3 * M500c * obs.cosmic_fbary /
(4 * np.pi * R500c**3)).to(density_gas.units)
kBT, _ = histogram_unyt(
radial_distance,
bins=lbins,
weights=data.gas.mass_weighted_temperatures)
kBT *= unyt.boltzmann_constant
kBT /= mass_weights
kBT = kBT.to('keV')
kBT_500crit = unyt.G * mean_molecular_weight * M500c * unyt.mass_proton / 2 / R500c
kBT_500crit = kBT_500crit.to(kBT.units)
# Note: the ratio of densities is the same as ratio of electron number densities
hist = kBT / kBT_500crit * (mean_density_R500c / density_gas)**(2 /
3)
elif sampling_method.lower() == 'particle_density':
n_e = data.gas.densities
ne_500crit = 3 * M500c * obs.cosmic_fbary / (4 * np.pi * R500c**3)
kBT = unyt.boltzmann_constant * data.gas.mass_weighted_temperatures
kBT_500crit = unyt.G * mean_molecular_weight * M500c * unyt.mass_proton / 2 / R500c
weights_field = kBT / kBT_500crit * (ne_500crit / n_e)**(2 / 3)
hist, _ = histogram_unyt(radial_distance,
bins=lbins,
weights=weights_field)
hist /= mass_weights
elif sampling_method.lower() == 'no_binning':
n_e = data.gas.densities
ne_500crit = 3 * M500c * obs.cosmic_fbary / (4 * np.pi * R500c**3)
kBT = unyt.boltzmann_constant * data.gas.temperatures
kBT_500crit = unyt.G * mean_molecular_weight * M500c * unyt.mass_proton / 2 / R500c
weights_field = kBT / kBT_500crit * (ne_500crit / n_e)**(2 / 3)
bin_centre = radial_distance
hist = weights_field
ylabel = r'$K/K_{500{\rm crit}}$'
elif weights.lower() == 'entropy_physical':
if sampling_method.lower() == 'shell_density':
volume_shell = (4. * np.pi / 3.) * (R500c**3) * (
(bin_edges[1:])**3 - (bin_edges[:-1])**3)
density_gas = mass_weights / volume_shell
number_density_gas = density_gas / (mean_molecular_weight *
unyt.mass_proton)
number_density_gas = number_density_gas.to('1/cm**3')
kBT, _ = histogram_unyt(
radial_distance,
bins=lbins,
weights=data.gas.mass_weighted_temperatures)
kBT *= unyt.boltzmann_constant
kBT /= mass_weights
kBT = kBT.to('keV')
# Note: the ratio of densities is the same as ratio of electron number densities
hist = kBT / number_density_gas**(2 / 3)
hist = hist.to('keV*cm**2')
elif sampling_method.lower() == 'particle_density':
number_density_gas = data.gas.densities / (mean_molecular_weight *
unyt.mass_proton)
number_density_gas = number_density_gas.to('1/cm**3')
kBT = unyt.boltzmann_constant * data.gas.mass_weighted_temperatures
weights_field = kBT / number_density_gas**(2 / 3)
hist, _ = histogram_unyt(radial_distance,
bins=lbins,
weights=weights_field)
hist /= mass_weights
hist = hist.to('keV*cm**2')
elif sampling_method.lower() == 'no_binning':
number_density_gas = data.gas.densities / (mean_molecular_weight *
unyt.mass_proton)
number_density_gas = number_density_gas.to('1/cm**3')
kBT = unyt.boltzmann_constant * data.gas.temperatures
weights_field = kBT / number_density_gas**(2 / 3)
bin_centre = radial_distance
hist = weights_field
ylabel = r'$K$ [keV cm$^2$]'
elif weights.lower() == 'pressure':
if sampling_method.lower() == 'shell_density':
volume_shell = (4. * np.pi / 3.) * (R500c**3) * (
(bin_edges[1:])**3 - (bin_edges[:-1])**3)
density_gas = mass_weights / volume_shell
number_density_gas = density_gas / (mean_molecular_weight *
unyt.mass_proton)
number_density_gas = number_density_gas.to('1/cm**3')
kBT, _ = histogram_unyt(
radial_distance,
bins=lbins,
weights=data.gas.mass_weighted_temperatures)
kBT *= unyt.boltzmann_constant
kBT /= mass_weights
kBT = kBT.to('keV')
# Note: the ratio of densities is the same as ratio of electron number densities
hist = kBT * number_density_gas
hist = hist.to('keV/cm**3')
elif sampling_method.lower() == 'particle_density':
weights_field = data.gas.pressures * data.gas.masses
hist, _ = histogram_unyt(radial_distance,
bins=lbins,
weights=weights_field)
hist /= mass_weights
# Make dimensionless, divide by P_500crit
norm = 500 * obs.cosmic_fbary * rho_crit * unyt.G * M500c / 2 / R500c
hist /= norm.to(hist.units)
hist *= bin_centre**3
ylabel = r'$(P/P_{500{\rm crit}})\ (R/R_{500{\rm crit}})^3 $'
else:
raise ValueError(f"Unrecognized weighting field: {weights}.")
return bin_centre, hist, ylabel, conv_radius, M500c, R500c