def rotate_velocities(self,
particle_type: int,
tilt: str = 'z',
boost: unyt.unyt_array = None) -> unyt.array:
velocities = self.data.subfind_particles[f'PartType{particle_type}'][
'Velocity']
if boost is not None:
velocities[:, 0] -= boost[0]
velocities[:, 1] -= boost[1]
velocities[:, 2] -= boost[2]
vx, vy, vz = velocities.value.T
if tilt == 'y':
new_vel = np.vstack((vx, vz, vy)).T
elif tilt == 'z':
new_vel = np.vstack((vx, -vy, vz)).T
elif tilt == 'x':
new_vel = np.vstack((-vz, -vy, vx)).T
elif tilt == 'faceon':
face_on_rotation_matrix = rotation_matrix_from_vector(
self.angular_momentum_hot_gas.value)
new_vel = np.einsum('ijk,ik->ij', face_on_rotation_matrix,
velocities.value)
elif tilt == 'edgeon':
edge_on_rotation_matrix = rotation_matrix_from_vector(
self.angular_momentum_hot_gas.value, axis='y')
new_vel = np.einsum('ijk,ik->ij', edge_on_rotation_matrix,
velocities.value)
return new_vel * velocities.units
def rotate_coordinates(self,
particle_type: int,
tilt: str = 'z') -> unyt.array:
cop = self.data.subfind_tab.FOF.GroupCentreOfPotential
coord = self.data.subfind_particles[f'PartType{particle_type}'][
'Coordinates']
coord[:, 0] -= cop[0]
coord[:, 1] -= cop[1]
coord[:, 2] -= cop[2]
x, y, z = coord.value.T
if tilt == 'y':
new_coord = np.vstack((x, z, y)).T
elif tilt == 'z':
new_coord = np.vstack((x, y, z)).T
elif tilt == 'x':
new_coord = np.vstack((z, y, x)).T
elif tilt == 'faceon':
face_on_rotation_matrix = rotation_matrix_from_vector(
self.angular_momentum_hot_gas.value)
new_coord = np.einsum('ijk,ik->ij', face_on_rotation_matrix,
coord.value)
elif tilt == 'edgeon':
edge_on_rotation_matrix = rotation_matrix_from_vector(
self.angular_momentum_hot_gas.value, axis='y')
new_coord = np.einsum('ijk,ik->ij', edge_on_rotation_matrix,
coord.value)
new_coord *= coord.units
# new_coord[:, 0] += cop[0]
# new_coord[:, 1] += cop[1]
# new_coord[:, 2] += cop[2]
return new_coord
def calculate_integrated_quantities(data, ang_momentum, radius, mode):
face_on_rotation_matrix = rotation_matrix_from_vector(ang_momentum)
x, y, _ = np.matmul(face_on_rotation_matrix, data[:, :3].T)
r = np.sqrt(x**2 + y**2)
select = r <= radius
surface = np.pi * radius**2
if mode == 0:
m = data[select, 9]
if mode == 1:
m = data[select, 9] + data[select, 8]
# If we have gas within rhalfMs
if len(m) > 0:
Sigma_gas = np.log10(np.sum(m) / surface) - 6.0 # Msun / pc^2
sfr = data[select, 10]
sfr = sfr[sfr > 0]
Sigma_SFR = np.log10(np.sum(sfr) / surface) # Msun / yr / kpc^2
else:
Sigma_gas = -6
Sigma_SFR = -6
return Sigma_gas, Sigma_SFR
def get_rotation(
image_attributes: ImageAttributes, galaxy_attributes: GalaxyAttributes
):
"""
Gets the rotation matrix and center if required.
"""
if image_attributes.projection == "faceon":
return (
rotation_matrix_from_vector(galaxy_attributes.normal_vector),
galaxy_attributes.center,
)
elif image_attributes.projection == "edgeon":
return (
rotation_matrix_from_vector(galaxy_attributes.normal_vector, "y"),
galaxy_attributes.center,
)
else:
return None, None
def rotate(coord: np.ndarray,
angular_momentum_hot_gas: np.ndarray,
tilt: str = 'x'):
if tilt == 'x':
rotation_matrix = rotation_matrix_from_vector(
np.array([1, 0, 0], dtype=np.float))
elif tilt == 'y':
rotation_matrix = rotation_matrix_from_vector(
np.array([0, 1, 0], dtype=np.float))
elif tilt == 'z':
rotation_matrix = rotation_matrix_from_vector(
np.array([0, 0, 1], dtype=np.float))
elif tilt == 'faceon':
rotation_matrix = rotation_matrix_from_vector(angular_momentum_hot_gas)
elif tilt == 'edgeon':
rotation_matrix = rotation_matrix_from_vector(angular_momentum_hot_gas,
axis='y')
new_coord = np.matmul(rotation_matrix, coord.T).T
return new_coord
def rotate_coordinates(self, particle_type: int, tilt: str = 'z') -> unyt.array:
cop = self.data.read_catalogue_subfindtab('/FOF/GroupCentreOfPotential')
coord = self.data.read_snapshot(f'PartType{particle_type}/Coordinates')
coord[:, 0] -= cop[0]
coord[:, 1] -= cop[1]
coord[:, 2] -= cop[2]
x, y, z = coord.T
if tilt == 'y':
new_coord = np.vstack((x, z, y)).T
elif tilt == 'z':
new_coord = np.vstack((x, y, z)).T
elif tilt == 'x':
new_coord = np.vstack((z, y, x)).T
elif tilt == 'faceon':
face_on_rotation_matrix = rotation_matrix_from_vector(self.angular_momentum_hot_gas)
new_coord = np.einsum('ijk,ik->ij', face_on_rotation_matrix, coord)
elif tilt == 'edgeon':
edge_on_rotation_matrix = rotation_matrix_from_vector(self.angular_momentum_hot_gas, axis='y')
new_coord = np.einsum('ijk,ik->ij', edge_on_rotation_matrix, coord)
return new_coord
def KS_relation(data, ang_momentum, mode, method, size):
image_diameter = 60
extent = [-30, 30] # kpc
number_of_pixels = int(image_diameter / size + 1)
face_on_rotation_matrix = rotation_matrix_from_vector(ang_momentum)
if method == "grid":
# Calculate the surface density maps using grid of pixel size
partsDATA = data.copy()
map_mass = project_gas(partsDATA, mode, number_of_pixels, extent,
face_on_rotation_matrix)
map_metals = integrate_metallicity_using_grid(partsDATA,
number_of_pixels, extent,
face_on_rotation_matrix)
star_formation_rate_mask = partsDATA[:, 10] > 0.0
partsDATA = partsDATA[star_formation_rate_mask, :]
map_SFR = project_gas(partsDATA, 2, number_of_pixels, extent,
face_on_rotation_matrix)
else:
partsDATA = data.copy()
map_mass = project_gas_with_azimuthal_average(partsDATA, mode,
face_on_rotation_matrix,
size)
map_metals = project_metals_with_azimuthal_average(
partsDATA, face_on_rotation_matrix, size)
star_formation_rate_mask = np.where(partsDATA[:, 10] > 0.0)[0]
partsDATA = partsDATA[star_formation_rate_mask, :]
if len(star_formation_rate_mask) > 0:
map_SFR = project_gas_with_azimuthal_average(
partsDATA, 2, face_on_rotation_matrix, size)
else:
map_SFR = np.zeros(len(map_mass))
# Bounds
map_SFR[map_SFR <= 0] = 1e-6
map_mass[map_mass <= 0] = 1e-6
surface_density = np.log10(map_mass.flatten()) # Msun / kpc^2
surface_density -= 6 # Msun / pc^2
SFR_surface_density = np.log10(map_SFR.flatten()) # Msun / yr / kpc^2
tgas = surface_density - SFR_surface_density + 6.0
return surface_density, SFR_surface_density, tgas, map_metals
def rotate_velocities(self, particle_type: int, tilt: str = 'z', boost = None) -> unyt.array:
velocities = self.data.read_snapshot(f'PartType{particle_type}/Velocity')
if boost is not None:
velocities[:, 0] -= boost[0]
velocities[:, 1] -= boost[1]
velocities[:, 2] -= boost[2]
vx, vy, vz = velocities.T
if tilt == 'y':
new_vel = np.vstack((vx, vz, vy)).T
elif tilt == 'z':
new_vel = np.vstack((vx, -vy, vz)).T
elif tilt == 'x':
new_vel = np.vstack((-vz, -vy, vx)).T
elif tilt == 'faceon':
face_on_rotation_matrix = rotation_matrix_from_vector(self.angular_momentum_hot_gas)
new_vel = np.einsum('ijk,ik->ij', face_on_rotation_matrix, velocities)
elif tilt == 'edgeon':
edge_on_rotation_matrix = rotation_matrix_from_vector(self.angular_momentum_hot_gas, axis='y')
new_vel = np.einsum('ijk,ik->ij', edge_on_rotation_matrix, velocities)
return new_vel
def test_slice(filename):
"""
Checks that a slice of a single particle snapshot is invariant under
rotations around the particle
Parameters
----------
filename: str
name of file providing metadata to copy
"""
# Start from the beginning, open the file
output_filename = "single_particle.hdf5"
create_single_particle_dataset(filename, output_filename)
data = load(output_filename)
# Compute rotation matrix for rotating around particle
centre = data.gas.coordinates[0]
rotate_vec = [0.5, 0.5, 0.5]
matrix = rotation_matrix_from_vector(rotate_vec, axis="z")
boxsize = data.metadata.boxsize
z_range = boxsize[2]
slice_z = centre[2] / z_range
unrotated = slice_gas(data,
resolution=1024,
slice=slice_z,
project="masses",
parallel=True)
rotated = slice_gas(
data,
resolution=1024,
slice=slice_z,
project="masses",
rotation_center=centre,
rotation_matrix=matrix,
parallel=True,
)
# Check that we didn't miss the particle
assert unrotated.any()
assert rotated.any()
assert array_equal(rotated, unrotated)
remove(output_filename)
def surface_ratios(data, ang_momentum, method, size):
face_on_rotation_matrix = rotation_matrix_from_vector(ang_momentum)
if method == "grid":
image_diameter = 60
extent = [-30, 30] # kpc
number_of_pixels = int(image_diameter / size + 1)
# Calculate the maps using grid
map_H2 = project_gas(data, 0, number_of_pixels, extent,
face_on_rotation_matrix)
map_HI = project_gas(data, 3, number_of_pixels, extent,
face_on_rotation_matrix)
else:
# Calculate the maps using azimuthally-average shells
map_H2 = project_gas_with_azimuthal_average(data, 0,
face_on_rotation_matrix,
size)
map_HI = project_gas_with_azimuthal_average(data, 3,
face_on_rotation_matrix,
size)
map_gas = map_H2 + map_HI
# Bounds
map_H2[map_H2 <= 0] = 1e-6
map_gas[map_gas <= 0] = 1e-6
H2_to_neutral_ratio = map_H2 / map_gas
neutral_gas_surface_density = np.log10(
map_gas.flatten()) # HI+H2 Msun / kpc^2
molecular_gas_surface_density = np.log10(
map_H2.flatten()) # H2 Msun / kpc^2
neutral_gas_surface_density -= 6 # HI+H2 Msun / pc^2
molecular_gas_surface_density -= 6 # H2 Msun / pc^2
H2_to_neutral_ratio_density = np.log10(
H2_to_neutral_ratio.flatten()) # no units
return (
neutral_gas_surface_density,
molecular_gas_surface_density,
H2_to_neutral_ratio_density,
)
def test_render(filename):
"""
Checks that a volume render of a single particle snapshot is invariant under
rotations around the particle
Parameters
----------
filename: str
name of file providing metadata to copy
"""
# Start from the beginning, open the file
output_filename = "single_particle.hdf5"
create_single_particle_dataset(filename, output_filename)
data = load(output_filename)
# Compute rotation matrix for rotating around particle
centre = data.gas.coordinates[0]
rotate_vec = [0.5, 0.5, 0.5]
matrix = rotation_matrix_from_vector(rotate_vec, axis="z")
boxsize = data.metadata.boxsize
unrotated = render_gas(data,
resolution=256,
project="masses",
parallel=True)
rotated = render_gas(
data,
resolution=256,
project="masses",
rotation_center=centre,
rotation_matrix=matrix,
parallel=True,
)
assert array_equal(rotated, unrotated)
remove(output_filename)
def rotation_align_with_vector(coordinates: np.ndarray,
rotation_center: np.ndarray,
vector: np.ndarray) -> np.ndarray:
# Normalise vector for more reliable handling
vector /= np.linalg.norm(vector)
# Get the de-rotation matrix:
# axis='z' is the default and corresponds to face-on (looking down z-axis)
# axis='y' corresponds to edge-on (maximum rotational signal)
rotation_matrix = rotation_matrix_from_vector(vector, axis='y')
if rotation_center is not None:
# Rotate co-ordinates as required
x, y, z = np.matmul(rotation_matrix, (coordinates - rotation_center).T)
x += rotation_center[0]
y += rotation_center[1]
z += rotation_center[2]
else:
x, y, z = coordinates.T
return np.vstack((x, y, z)).T
def plot_galaxy_parts(partsDATA, parttype, ang_momentum, halo_data, index,
output_path, simulation_name):
# Plot ranges
r_limit = 5 * halo_data.half_mass_radius_star[index]
r_img = 30.0
if r_limit < r_img:
r_img = r_limit
xmin = -r_img
ymin = -r_img
xmax = r_img
ymax = r_img
pos_parts = partsDATA[:, 0:3].copy()
face_on_rotation_matrix = rotation_matrix_from_vector(ang_momentum,
axis="z")
edge_on_rotation_matrix = rotation_matrix_from_vector(ang_momentum,
axis="y")
pos_face_on = np.matmul(face_on_rotation_matrix, pos_parts.T)
pos_face_on = pos_face_on.T
pos_edge_on = np.matmul(edge_on_rotation_matrix, pos_parts.T)
pos_edge_on = pos_edge_on.T
if parttype == 0:
density = np.log10(partsDATA[:, 11])
if parttype == 4:
density = np.log10(partsDATA[:, 8])
# Sort particles for better viewing
arg_sort = np.argsort(density)
density = density[arg_sort]
pos_face_on = pos_face_on[arg_sort, :]
pos_edge_on = pos_edge_on[arg_sort, :]
denmin = np.min(density)
denmax = np.max(density)
rcParams.update(params)
fig = plt.figure()
ax = plt.subplot(1, 2, 1)
if parttype == 4:
title = "Stellar component"
if parttype == 0:
title = "Gas component"
ax.set_title(title)
ax.tick_params(labelleft=True, labelbottom=True, length=0)
plt.xlabel("x [kpc]")
plt.ylabel("y [kpc]")
ax.set_xlim(xmin, xmax)
ax.set_ylim(ymin, ymax)
plt.scatter(
pos_face_on[:, 0],
pos_face_on[:, 1],
c=density,
alpha=1,
s=10,
vmin=denmin,
vmax=denmax,
cmap="magma",
edgecolors="none",
)
ax.autoscale(False)
ax = plt.subplot(1, 2, 2)
if parttype == 4:
kappa = halo_data.kappa_co[index]
mass = halo_data.log10_stellar_mass[index]
ac = halo_data.axis_ca[index]
cb = halo_data.axis_cb[index]
ba = halo_data.axis_ba[index]
radius = halo_data.half_mass_radius_star[index]
title = r" $\kappa_{\mathrm{co}} = $%0.2f" % (kappa)
title += " - $\log_{10}$ $M_{*}/M_{\odot} = $%0.2f" % (mass)
title += " \n c/a = %0.2f," % (ac)
title += " c/b = %0.2f," % (cb)
title += " b/a = %0.2f" % (ba)
title += "\n Stellar half mass radius %0.2f kpc" % (radius)
if parttype == 0:
kappa = halo_data.gas_kappa_co[index]
mass = halo_data.log10_gas_mass[index]
ac = halo_data.gas_axis_ca[index]
cb = halo_data.gas_axis_cb[index]
ba = halo_data.gas_axis_ba[index]
radius = halo_data.half_mass_radius_star[index]
title = r" $\kappa_{\mathrm{co}} = $%0.2f" % (kappa)
title += " - $\log_{10}$ $M_{gas}/M_{\odot} = $%0.2f" % (mass)
title += " \n c/a = %0.2f," % (ac)
title += " c/b = %0.2f," % (cb)
title += " b/a = %0.2f" % (ba)
title += "\n Stellar half mass radius %0.2f kpc" % (radius)
ax.set_title(title)
ax.tick_params(labelleft=True, labelbottom=True, length=0)
plt.xlabel("x [kpc]")
plt.ylabel("z [kpc]")
ax.set_xlim(xmin, xmax)
ax.set_ylim(ymin, ymax)
plt.scatter(
pos_edge_on[:, 0],
pos_edge_on[:, 1],
c=density,
alpha=1,
s=10,
vmin=denmin,
vmax=denmax,
cmap="magma",
edgecolors="none",
)
ax.autoscale(False)
cbar_ax = fig.add_axes([0.86, 0.22, 0.018, 0.5])
cbar_ax.tick_params(labelsize=15)
cb = plt.colorbar(ticks=[4, 6, 8, 10], cax=cbar_ax)
cb.set_label(label=r"$\log_{10}$ $\rho$ [M$_{\odot}$/kpc$^{3}$]",
labelpad=0.5)
if parttype == 4:
outfile = (f"{output_path}/galaxy_sparts_%i_" % (index) +
simulation_name + ".png")
if parttype == 0:
outfile = f"{output_path}/galaxy_parts_%i_" % (
index) + simulation_name + ".png"
fig.savefig(outfile, dpi=150)
plt.close("all")
def plot_galaxy(parts_data, parttype, ang_momentum, halo_data, index,
output_path, simulation_name):
# partsDATA contains particles data and is structured as follow
# [ (:3)Position[Mpc]: (0)X | (1)Y | (2)Z ]
if parttype == 4:
cmap = plt.cm.magma
if parttype == 0:
cmap = plt.cm.viridis
pos_parts = parts_data[:, 0:3].copy()
face_on_rotation_matrix = rotation_matrix_from_vector(ang_momentum,
axis="z")
edge_on_rotation_matrix = rotation_matrix_from_vector(ang_momentum,
axis="y")
pos_face_on = np.matmul(face_on_rotation_matrix, pos_parts.T)
pos_face_on = pos_face_on.T
pos_edge_on = np.matmul(edge_on_rotation_matrix, pos_parts.T)
pos_edge_on = pos_edge_on.T
hsml_parts = parts_data[:, 7]
mass = parts_data[:, 3]
r_limit = 5 * halo_data.half_mass_radius_star[index]
r_img = 30.0
if r_limit < r_img:
r_img = r_limit
xmin = -r_img
ymin = -r_img
xmax = r_img
ymax = r_img
rcParams.update(params)
fig = plt.figure()
ax = plt.subplot(1, 2, 1)
if parttype == 4:
title = "Stellar component"
if parttype == 0:
title = "HI+H2 gas"
ax.set_title(title)
###### plot one side ########################
qv = QuickView(
pos_face_on,
mass=mass,
hsml=hsml_parts,
logscale=True,
plot=False,
r="infinity",
p=0,
t=0,
extent=[xmin, xmax, ymin, ymax],
x=0,
y=0,
z=0,
)
img = qv.get_image()
ext = qv.get_extent()
ax.tick_params(labelleft=True, labelbottom=True, length=0)
plt.xlabel("x [kpc]")
plt.ylabel("y [kpc]")
ax.set_xlim(xmin, xmax)
ax.set_ylim(ymin, ymax)
img = get_normalized_image(img)
ax.imshow(img, cmap=cmap, extent=ext)
ax.autoscale(False)
###### plot another side ########################
ax = plt.subplot(1, 2, 2)
if parttype == 4:
kappa = halo_data.kappa_co[index]
mass_galaxy = halo_data.log10_stellar_mass[index]
ac = halo_data.axis_ca[index]
cb = halo_data.axis_cb[index]
ba = halo_data.axis_ba[index]
title = r" $\kappa_{\mathrm{co}} = $%0.2f" % (kappa)
title += " - $\log_{10}$ $M_{*}/M_{\odot} = $%0.2f" % (mass_galaxy)
title += " \n c/a = %0.2f," % (ac)
title += " c/b = %0.2f," % (cb)
title += " b/a = %0.2f" % (ba)
if parttype == 0:
kappa = halo_data.gas_kappa_co[index]
mass_galaxy = halo_data.log10_gas_mass[index]
ac = halo_data.gas_axis_ca[index]
cb = halo_data.gas_axis_cb[index]
ba = halo_data.gas_axis_ba[index]
title = r" $\kappa_{\mathrm{co}} = $%0.2f" % (kappa)
title += " - $\log_{10}$ $M_{gas}/M_{\odot} = $%0.2f" % (mass_galaxy)
title += " \n c/a = %0.2f," % (ac)
title += " c/b = %0.2f," % (cb)
title += " b/a = %0.2f" % (ba)
ax.set_title(title)
qv = QuickView(
pos_edge_on,
mass=mass,
hsml=hsml_parts,
logscale=True,
plot=False,
r="infinity",
p=0,
t=0,
extent=[xmin, xmax, ymin, ymax],
x=0,
y=0,
z=0,
)
img = qv.get_image()
ext = qv.get_extent()
ax.tick_params(labelleft=True, labelbottom=True, length=0)
plt.xlabel("x [kpc]")
plt.ylabel("z [kpc]")
ax.set_xlim(xmin, xmax)
ax.set_ylim(ymin, ymax)
img = get_normalized_image(img)
ims = ax.imshow(img, cmap=cmap, extent=ext)
ax.autoscale(False)
cbar_ax = fig.add_axes([0.86, 0.22, 0.018, 0.5])
cbar_ax.tick_params(labelsize=15)
cb = plt.colorbar(ims, ticks=[4, 6, 8, 10, 12], cax=cbar_ax)
cb.set_label(label=r"$\log_{10}$ $\Sigma$ [M$_{\odot}$/kpc$^{2}$]",
labelpad=0.5)
if parttype == 0:
outfile = f"{output_path}/galaxy_gas_%i_" % (
index) + simulation_name + ".png"
if parttype == 4:
outfile = f"{output_path}/galaxy_stars_%i_" % (
index) + simulation_name + ".png"
fig.savefig(outfile, dpi=150)
plt.close("all")
return
def create_scatter(
snapshot: SWIFTDataset,
halo: Halo,
image: Image,
projection: Projection,
resolution: int,
) -> unyt_array:
"""
Creates a projected image for a given image class, and snapshot.
Parameters
----------
snapshot: SWIFTDataset,
The opened dataset.
halo: Halo
Halo with properties to visualise
image: Image
Image class to visualise this time around
projection: Projection
Which projection to make
resolution: int
Image size along each axis.
Returns
-------
grid: unyt.unyt_array
Output grid, in the requested units.
"""
region_given_r = lambda r: [
halo.position[0] - r,
halo.position[0] + r,
halo.position[1] - r,
halo.position[1] + r,
halo.position[2] - r,
halo.position[2] + r,
]
radius = image.get_radius(stellar_half_mass=halo.radius_100_kpc_star,
r_200_crit=halo.radius_200_crit)
particle_data = getattr(snapshot, image.particle_type, "gas")
region = region_given_r(radius)
rotation_center = None
rotation_matrix = None
# If the L vector is poorly constrained this will complain,
# but we don't really care.
with np.testing.suppress_warnings() as sup:
sup.filter(RuntimeWarning)
if projection == Projection.EDGE_ON:
rotation_center = halo.position.to(particle_data.coordinates.units)
rotation_matrix = rotation_matrix_from_vector(halo.L.v, "y")
elif projection == Projection.FACE_ON:
rotation_center = halo.position.to(particle_data.coordinates.units)
rotation_matrix = rotation_matrix_from_vector(halo.L.v, "z")
if hasattr(particle_data, "smoothing_lengths"):
backend = "fast"
else:
backend = "histogram"
common_attributes = dict(
data=particle_data,
boxsize=snapshot.metadata.boxsize,
resolution=resolution,
region=region,
mask=None,
rotation_matrix=rotation_matrix,
rotation_center=rotation_center,
parallel=False,
backend=backend,
)
mass_image = project_pixel_grid(project="masses", **common_attributes)
if image.visualise == "projected_densities":
# We're done!
x_range = region[1] - region[0]
y_range = region[3] - region[2]
units = 1.0 / (x_range * y_range)
units.convert_to_units(1.0 / (x_range.units * y_range.units))
units *= particle_data.masses.units
grid = unyt_array(mass_image, units=units)
else:
# Need to make the complementary image.
cache_name = f"_CACHE_MASSWEIGHTED_{image.visualise}"
cache = getattr(particle_data, cache_name, None)
particle_array = getattr(particle_data, image.visualise)
if cache is None:
cache = particle_array * particle_data.masses
setattr(
particle_data,
cache_name,
cache,
)
weighted_image = project_pixel_grid(project=cache_name,
**common_attributes)
# Deal with zeroes:
mass_image[mass_image == 0.0] = 1.0
# K * 1e10 Msun -> K * Msun for the 'units' internally. So we need
# to reconstruct the true ratio, although this should be ideally the
# same as particle_array.units.
units = cache.units / particle_data.masses.units
grid = unyt_array(weighted_image / mass_image, units=units)
# Fill if required
output_units = None
unit_steal = [image.output_units, image.vmin, image.vmax, grid]
while output_units is None:
potential_unit = unit_steal.pop(0)
if potential_unit is not None:
output_units = potential_unit.units
grid.convert_to_units(output_units)
if image.fill_below is not None:
mask = grid < image.fill_below.to(output_units)
grid[mask] = image.fill_below.to(output_units)
return grid
def render_luminosity_map(
parts_data,
luminosity,
filtname,
ang_momentum,
halo_data,
index,
output_path,
simulation_name,
):
pos_parts = parts_data[:, 0:3].copy()
face_on_rotation_matrix = rotation_matrix_from_vector(ang_momentum)
edge_on_rotation_matrix = rotation_matrix_from_vector(ang_momentum,
axis="y")
pos_face_on = np.matmul(face_on_rotation_matrix, pos_parts.T)
pos_face_on = pos_face_on.T
pos_edge_on = np.matmul(edge_on_rotation_matrix, pos_parts.T)
pos_edge_on = pos_edge_on.T
hsml_parts = parts_data[:, 7]
r_limit = 5 * halo_data.half_mass_radius_star[index]
r_img = 30.0
if r_limit < r_img:
r_img = r_limit
xmin = -r_img
ymin = -r_img
xmax = r_img
ymax = r_img
rcParams.update(params)
fig = plt.figure()
ax = plt.subplot(1, 2, 1)
###### plot one side ########################
qv = QuickView(
pos_face_on,
mass=luminosity,
hsml=hsml_parts,
logscale=True,
plot=False,
r="infinity",
p=0,
t=0,
extent=[xmin, xmax, ymin, ymax],
x=0,
y=0,
z=0,
)
img = qv.get_image()
ext = qv.get_extent()
ax.tick_params(labelleft=True, labelbottom=True, length=0)
plt.xlabel("x [kpc]")
plt.ylabel("y [kpc]")
ax.set_xlim(xmin, xmax)
ax.set_ylim(ymin, ymax)
img = get_normalized_image(img, vmin=img.max() - 2.3)
ax.imshow(img, cmap="magma", extent=ext, vmin=img.max() - 2.3)
ax.autoscale(False)
###### plot another side ########################
ax = plt.subplot(1, 2, 2)
qv = QuickView(
pos_edge_on,
mass=luminosity,
hsml=hsml_parts,
logscale=True,
plot=False,
r="infinity",
p=90,
t=0,
extent=[xmin, xmax, ymin, ymax],
x=0,
y=0,
z=0,
)
img = qv.get_image()
ext = qv.get_extent()
ax.tick_params(labelleft=True, labelbottom=True, length=0)
plt.xlabel("x [kpc]")
plt.ylabel("z [kpc]")
ax.set_xlim(xmin, xmax)
ax.set_ylim(ymin, ymax)
img = get_normalized_image(img, vmin=img.max() - 2.3)
ims = ax.imshow(img, cmap="magma", vmin=img.max() - 2.3, extent=ext)
ax.autoscale(False)
cbar_ax = fig.add_axes([0.86, 0.22, 0.018, 0.5])
cbar_ax.tick_params(labelsize=15)
cb = plt.colorbar(ims, ticks=[4, 6, 8, 10, 12], cax=cbar_ax)
cb.set_label(label=r"$\log_{10}$ $\Sigma$ [Jy/kpc$^{2}$]", labelpad=0.5)
outfile = (f"{output_path}/galaxy_%s_map_%i_" % (filtname, index) +
simulation_name + ".png")
fig.savefig(outfile, dpi=150)
plt.close("all")
return
def process_single_halo(
self,
zoom_obj: Zoom = None,
path_to_snap: str = None,
path_to_catalogue: str = None,
mask_radius: Tuple[float, str] = (6, 'r500'),
map_centre: Union[str, list, np.ndarray] = 'vr_centre_of_potential',
temperature_range: Optional[tuple] = None,
depth_offset: Optional[float] = None,
return_type: Union[type, str] = 'class',
inscribe_mask: bool = False,
):
sw_data, vr_data = self.get_handles_from_zoom(
zoom_obj,
path_to_snap,
path_to_catalogue,
mask_radius_r500=15,
)
map_centres_allowed = [
'vr_centre_of_potential'
]
if type(map_centre) is str and map_centre.lower() not in map_centres_allowed:
raise AttributeError((
f"String-commands for `map_centre` only support "
f"`vr_centre_of_potential`. Got {map_centre} instead."
))
elif (type(map_centre) is list or type(map_centre) is np.ndarray) and len(map_centre) != 3:
raise AttributeError((
f"List-commands for `map_centre` only support "
f"length-3 lists. Got {map_centre} "
f"(length {len(map_centre)}) instead."
))
self.map_centre = map_centre
centre_of_potential = [
vr_data.positions.xcminpot[0].to('Mpc') / vr_data.a,
vr_data.positions.ycminpot[0].to('Mpc') / vr_data.a,
vr_data.positions.zcminpot[0].to('Mpc') / vr_data.a
]
if self.map_centre == 'vr_centre_of_potential':
_xCen = centre_of_potential[0]
_yCen = centre_of_potential[1]
_zCen = centre_of_potential[2]
elif type(self.map_centre) is list or type(self.map_centre) is np.ndarray:
_xCen = self.map_centre[0] * Mpc / vr_data.a
_yCen = self.map_centre[1] * Mpc / vr_data.a
_zCen = self.map_centre[2] * Mpc / vr_data.a
if xlargs.debug:
print(f"Centre of potential: {[float(f'{i.v:.3f}') for i in centre_of_potential]} Mpc")
print(f"Map centre: {[float(f'{i.v:.3f}') for i in [_xCen, _yCen, _zCen]]} Mpc")
self.depth = _zCen / sw_data.metadata.boxsize[0]
if depth_offset is not None:
self.depth += depth_offset * Mpc / sw_data.metadata.boxsize[0]
if xlargs.debug:
percent = f"{depth_offset * Mpc / _zCen * 100:.1f}"
print((
f"Imposing offset in slicing depth: {depth_offset:.2f} Mpc.\n"
f"Percentage shift compared to centre: {percent} %"
))
_r500 = vr_data.spherical_overdensities.r_500_rhocrit[0].to('Mpc') / vr_data.a
if mask_radius[1] == 'r500':
mask_radius_r500 = mask_radius[0] * _r500
else:
mask_radius_r500 = unyt_quantity(mask_radius[0], units=mask_radius[1])
if inscribe_mask:
mask_radius_r500 /= np.sqrt(3)
region = [
_xCen - mask_radius_r500,
_xCen + mask_radius_r500,
_yCen - mask_radius_r500,
_yCen + mask_radius_r500
]
if temperature_range is not None:
temp_filter = np.where(
(sw_data.gas.temperatures > temperature_range[0]) &
(sw_data.gas.temperatures < temperature_range[1])
)[0]
if xlargs.debug:
percent = f"{len(temp_filter) / len(sw_data.gas.temperatures) * 100:.1f}"
print((
f"Filtering particles by temperature: {temperature_range} K.\n"
f"Total particles: {len(sw_data.gas.temperatures)}\n"
f"Particles within bounds: {len(temp_filter)} = {percent} %"
))
sw_data.gas.coordinates = sw_data.gas.coordinates[temp_filter]
sw_data.gas.smoothing_lengths = sw_data.gas.smoothing_lengths[temp_filter]
sw_data.gas.masses = sw_data.gas.masses[temp_filter]
sw_data.gas.densities = sw_data.gas.densities[temp_filter]
sw_data.gas.temperatures = sw_data.gas.temperatures[temp_filter]
# Rotate about CoP if required
center = [_xCen, _yCen, _zCen]
rotate_vec = [0, 0, 1]
matrix = rotation_matrix_from_vector(rotate_vec, axis='z')
common_kwargs = dict(
rotation_matrix=matrix,
rotation_center=center,
data=sw_data,
resolution=self.resolution,
parallel=self.parallel,
region=region,
slice=self.depth
)
if self._project_quantity == 'entropies':
number_density = (sw_data.gas.densities / mh).to('cm**-3') / mean_molecular_weight
entropy = kb * sw_data.gas.temperatures / number_density ** (2 / 3)
sw_data.gas.entropies_physical = entropy.to('keV*cm**2')
gas_map = slice_gas(project='entropies_physical', **common_kwargs).to('keV*cm**2/Mpc**3')
elif self._project_quantity == 'temperatures':
sw_data.gas.mwtemps = sw_data.gas.masses * sw_data.gas.temperatures
mass_weighted_temp_map = slice_gas(project='mwtemps', **common_kwargs)
mass_map = slice_gas(project='masses', **common_kwargs)
with np.errstate(divide='ignore', invalid='ignore'):
gas_map = mass_weighted_temp_map / mass_map
gas_map = gas_map.to('K')
else:
gas_map = slice_gas(project=self._project_quantity, **common_kwargs)
units = gas_map.units
gas_map = gas_map.value
gas_map = np.ma.array(
gas_map,
mask=(gas_map <= 0.),
fill_value=np.nan,
copy=True,
dtype=np.float64
)
output_values = [
gas_map,
region,
units,
[_xCen, _yCen, _zCen],
_r500,
sw_data.metadata.z
]
output_names = [
'map',
'region',
'units',
'centre',
'r500',
'z'
]
if return_type is tuple:
output = tuple(output_values)
elif return_type is dict:
output = dict(zip(output_names, output_values))
elif return_type == 'class':
OutputClass = namedtuple('OutputClass', output_names)
output = OutputClass(*output_values)
else:
raise TypeError(f"Return type {return_type} not recognised.")
return output
