def _divergence_clean(self, kx, ky, kz):
mylog.info("Perform divergence cleaning.")
self.gx = np.fft.fftn(self.gx)
self.gy = np.fft.fftn(self.gy)
self.gz = np.fft.fftn(self.gz)
# These k's are different because we are
# using the finite difference form of the
# divergence operator.
kxd = np.sin(kx * self.dx) / self.dx
kyd = np.sin(ky * self.dy) / self.dy
kzd = np.sin(kz * self.dz) / self.dz
kkd = np.sqrt(kxd * kxd + kyd * kyd + kzd * kzd)
with np.errstate(invalid='ignore', divide='ignore'):
kxd /= kkd
kyd /= kkd
kzd /= kkd
kxd[np.isnan(kxd)] = 0.0
kyd[np.isnan(kyd)] = 0.0
kzd[np.isnan(kzd)] = 0.0
self.gx, self.gy, self.gz = \
[(1.0-kxd*kxd)*self.gx-kxd*kyd*self.gy-kxd*kzd*self.gz,
-kyd*kxd*self.gx+(1.0-kyd*kyd)*self.gy-kyd*kzd*self.gz,
-kzd*kxd*self.gx-kzd*kyd*self.gy+(1.0-kzd*kzd)*self.gz]
self.gx = np.fft.ifftn(self.gx).real
self.gy = np.fft.ifftn(self.gy).real
self.gz = np.fft.ifftn(self.gz).real
del kxd, kyd, kzd, kkd
def check_virial(self):
r"""
Computes the radial density profile for the collisionless
particles computed from integrating over the distribution
function, and the relative difference between this and the
input density profile.
Returns
-------
rho : NumPy array
The density profile computed from integrating the
distribution function.
chk : NumPy array
The relative difference between the input density
profile and the one calculated using this method.
"""
n = self.num_elements
rho = np.zeros(n)
pden = self.model[f"{self.ptype}_density"].d
rho_int = lambda e, psi: self.f(e)*np.sqrt(2*(psi-e))
for i, e in enumerate(self.ee):
rho[i] = 4.*np.pi*quad(rho_int, 0., e, args=(e,))[0]
chk = (rho[::-1]-pden)/pden
mylog.info("The maximum relative deviation of this profile from "
"virial equilibrium is %g", np.abs(chk).max())
return rho[::-1], chk
def setup_athena_ics(ics):
r"""
Parameters
----------
ics : ClusterICs object
The ClusterICs object to generate the Athena ICs from.
"""
mylog.info("Add the following lines to athinput.cluster3d: ")
def from_dens_and_temp(cls,
rmin,
rmax,
density,
temperature,
stellar_density=None,
num_points=1000):
"""
Construct a hydrostatic equilibrium model using gas density
and temperature profiles.
Parameters
----------
rmin : float
Minimum radius of profiles in kpc.
rmax : float
Maximum radius of profiles in kpc.
density : :class:`~cluster_generator.radial_profiles.RadialProfile`
A radial profile describing the gas mass density.
temperature : :class:`~cluster_generator.radial_profiles.RadialProfile`
A radial profile describing the gas temperature.
stellar_density : :class:`~cluster_generator.radial_profiles.RadialProfile`, optional
A radial profile describing the stellar mass density, if desired.
num_points : integer, optional
The number of points the profiles are evaluated at.
"""
mylog.info("Computing the profiles from density and temperature.")
rr = np.logspace(np.log10(rmin),
np.log10(rmax),
num_points,
endpoint=True)
fields = OrderedDict()
fields["radius"] = unyt_array(rr, "kpc")
fields["density"] = unyt_array(density(rr), "Msun/kpc**3")
fields["temperature"] = unyt_array(temperature(rr), "keV")
fields["pressure"] = fields["density"] * fields["temperature"]
fields["pressure"] /= mu * mp
fields["pressure"].convert_to_units("Msun/(Myr**2*kpc)")
pressure_spline = InterpolatedUnivariateSpline(rr,
fields["pressure"].d)
dPdr = unyt_array(pressure_spline(rr, 1), "Msun/(Myr**2*kpc**2)")
fields["gravitational_field"] = dPdr / fields["density"]
fields["gravitational_field"].convert_to_units("kpc/Myr**2")
fields["gas_mass"] = unyt_array(integrate_mass(density, rr), "Msun")
fields["total_mass"] = -fields["radius"]**2 * fields[
"gravitational_field"] / G
total_mass_spline = InterpolatedUnivariateSpline(
rr, fields["total_mass"].v)
dMdr = unyt_array(total_mass_spline(rr, nu=1), "Msun/kpc")
fields["total_density"] = dMdr / (4. * np.pi * fields["radius"]**2)
return cls._from_scratch(fields, stellar_density=stellar_density)
def _compute_vector_potential(self, kx, ky, kz):
kk = np.sqrt(kx**2 + ky**2 + kz**2)
mylog.info("Compute vector potential.")
# Rotate vector potential
self.gx = np.fft.fftn(self.gx)
self.gy = np.fft.fftn(self.gy)
self.gz = np.fft.fftn(self.gz)
with np.errstate(invalid='ignore', divide='ignore'):
alpha = np.arccos(kx / np.sqrt(kx * kx + ky * ky))
alpha[ky < 0.0] -= 2.0 * np.pi
alpha[ky < 0.0] *= -1.
with np.errstate(invalid='ignore', divide='ignore'):
beta = np.arccos(kz / kk)
alpha[np.isinf(alpha)] = 0.0
alpha[np.isnan(alpha)] = 0.0
beta[np.isnan(beta)] = 0.0
beta[np.isinf(beta)] = 0.0
self._rot_3d(3, alpha)
self._rot_3d(2, beta)
with np.errstate(invalid='ignore', divide='ignore'):
self.gx, self.gy = (
0.0 + 1.0j) * self.gy / kk, -(0.0 + 1.0j) * self.gx / kk
self.gz = np.zeros(self.gx.shape, dtype="complex")
del kk
self.gx[np.isinf(self.gx)] = 0.0
self.gx[np.isnan(self.gx)] = 0.0
self.gy[np.isinf(self.gy)] = 0.0
self.gy[np.isnan(self.gy)] = 0.0
self._rot_3d(2, -beta)
self._rot_3d(3, -alpha)
self.gx = np.fft.ifftn(self.gx).real
self.gy = np.fft.ifftn(self.gy).real
self.gz = np.fft.ifftn(self.gz).real
def setup_flash_ics(ics, use_particles=True, regenerate_particles=False):
r"""
Generate the "flash.par" lines needed for use
with the GalaxyClusterMerger setup in FLASH. If the particles
(dark matter and potentially star) have not been
created yet, they will be created at this step.
Parameters
----------
ics : ClusterICs object
The ClusterICs object to generate the GAMER ICs from.
use_particles : boolean, optional
If True, set up particle distributions. Default: True
regenerate_particles : boolean, optional
If particle files have already been created, particles
are being used, and this flag is set to True, the particles
will be re-created. Default: False
"""
if use_particles:
ics._generate_particles(
regenerate_particles=regenerate_particles)
outlines = [
f"testSingleCluster {ics.num_halos} # number of halos"
]
for i in range(ics.num_halos):
vel = ics.velocity[i].to("km/s")
outlines += [
f"profile{i+1}\t=\t{ics.profiles[i]}\t# profile table of cluster {i+1}",
f"xInit{i+1}\t=\t{ics.center[i][0]}\t# X-center of cluster {i+1} in kpc",
f"yInit{i+1}\t=\t{ics.center[i][1]}\t# Y-center of cluster {i+1} in kpc",
f"vxInit{i+1}\t=\t{vel[0]}\t# X-velocity of cluster {i+1} in km/s",
f"vyInit{i+1}\t=\t{vel[1]}\t# Y-velocity of cluster {i+1} in km/s",
]
if use_particles:
outlines.append(
f"Merger_File_Par{i+1}\t=\t{ics.particle_files[i]}\t# particle file of cluster {i+1}",
)
mylog.info("Add the following lines to flash.par: ")
for line in outlines:
print(line)
def no_gas(cls,
rmin,
rmax,
total_density,
stellar_density=None,
num_points=1000):
rr = np.logspace(np.log10(rmin),
np.log10(rmax),
num_points,
endpoint=True)
fields = OrderedDict()
fields["radius"] = unyt_array(rr, "kpc")
fields["total_density"] = unyt_array(total_density(rr), "Msun/kpc**3")
mylog.info("Integrating total mass profile.")
fields["total_mass"] = unyt_array(integrate_mass(total_density, rr),
"Msun")
fields["gravitational_field"] = -G * fields["total_mass"] / (
fields["radius"]**2)
fields["gravitational_field"].convert_to_units("kpc/Myr**2")
return cls._from_scratch(fields, stellar_density=stellar_density)
def check_hse(self):
r"""
Determine the deviation of the model from hydrostatic equilibrium.
Returns
-------
chk : NumPy array
An array containing the relative deviation from hydrostatic
equilibrium as a function of radius.
"""
if "pressure" not in self.fields:
raise RuntimeError("This ClusterModel contains no gas!")
rr = self.fields["radius"].v
pressure_spline = InterpolatedUnivariateSpline(
rr, self.fields["pressure"].v)
dPdx = unyt_array(pressure_spline(rr, 1), "Msun/(Myr**2*kpc**2)")
rhog = self.fields["density"] * self.fields["gravitational_field"]
chk = dPdx - rhog
chk /= rhog
mylog.info(
"The maximum relative deviation of this profile from "
"hydrostatic equilibrium is %g",
np.abs(chk).max())
return chk
def from_dens_and_tden(cls,
rmin,
rmax,
density,
total_density,
stellar_density=None,
num_points=1000):
"""
Construct a hydrostatic equilibrium model using gas density
and total density profiles
Parameters
----------
rmin : float
Minimum radius of profiles in kpc.
rmax : float
Maximum radius of profiles in kpc.
density : :class:`~cluster_generator.radial_profiles.RadialProfile`
A radial profile describing the gas mass density.
total_density : :class:`~cluster_generator.radial_profiles.RadialProfile`
A radial profile describing the total mass density.
stellar_density : :class:`~cluster_generator.radial_profiles.RadialProfile`, optional
A radial profile describing the stellar mass density, if desired.
num_points : integer, optional
The number of points the profiles are evaluated at.
"""
mylog.info("Computing the profiles from density and total density.")
rr = np.logspace(np.log10(rmin),
np.log10(rmax),
num_points,
endpoint=True)
fields = OrderedDict()
fields["radius"] = unyt_array(rr, "kpc")
fields["density"] = unyt_array(density(rr), "Msun/kpc**3")
fields["total_density"] = unyt_array(total_density(rr), "Msun/kpc**3")
mylog.info("Integrating total mass profile.")
fields["total_mass"] = unyt_array(integrate_mass(total_density, rr),
"Msun")
fields["gas_mass"] = unyt_array(integrate_mass(density, rr), "Msun")
fields["gravitational_field"] = -G * fields["total_mass"] / (
fields["radius"]**2)
fields["gravitational_field"].convert_to_units("kpc/Myr**2")
g = fields["gravitational_field"].in_units("kpc/Myr**2").v
g_r = InterpolatedUnivariateSpline(rr, g)
dPdr_int = lambda r: density(r) * g_r(r)
mylog.info("Integrating pressure profile.")
P = -integrate(dPdr_int, rr)
dPdr_int2 = lambda r: density(r) * g[-1] * (rr[-1] / r)**2
P -= quad(dPdr_int2, rr[-1], np.inf, limit=100)[0]
fields["pressure"] = unyt_array(P, "Msun/kpc/Myr**2")
fields[
"temperature"] = fields["pressure"] * mu * mp / fields["density"]
fields["temperature"].convert_to_units("keV")
return cls._from_scratch(fields, stellar_density=stellar_density)
def _from_scratch(cls, fields, stellar_density=None):
rr = fields["radius"].d
mylog.info("Integrating gravitational potential profile.")
tdens_func = InterpolatedUnivariateSpline(rr,
fields["total_density"].d)
gpot_profile = lambda r: tdens_func(r) * r
gpot1 = fields["total_mass"] / fields["radius"]
gpot2 = unyt_array(4. * np.pi * integrate(gpot_profile, rr),
"Msun/kpc")
fields["gravitational_potential"] = -G * (gpot1 + gpot2)
fields["gravitational_potential"].convert_to_units("kpc**2/Myr**2")
if "density" in fields and "gas_mass" not in fields:
mylog.info("Integrating gas mass profile.")
m0 = fields["density"].d[0] * rr[0]**3 / 3.
fields["gas_mass"] = unyt_array(
4.0 * np.pi *
cumtrapz(fields["density"] * rr * rr, x=rr, initial=0.0) + m0,
"Msun")
if stellar_density is not None:
fields["stellar_density"] = unyt_array(stellar_density(rr),
"Msun/kpc**3")
mylog.info("Integrating stellar mass profile.")
fields["stellar_mass"] = unyt_array(
integrate_mass(stellar_density, rr), "Msun")
mdm = fields["total_mass"].copy()
ddm = fields["total_density"].copy()
if "density" in fields:
mdm -= fields["gas_mass"]
ddm -= fields["density"]
if "stellar_mass" in fields:
mdm -= fields["stellar_mass"]
ddm -= fields["stellar_density"]
mdm[ddm.v < 0.0] = mdm.max()
ddm[ddm.v < 0.0] = 0.0
if ddm.sum() < 0.0 or mdm.sum() < 0.0:
mylog.warning(
"The total dark matter mass is either zero or negative!!")
fields["dark_matter_density"] = ddm
fields["dark_matter_mass"] = mdm
if "density" in fields:
fields["gas_fraction"] = fields["gas_mass"] / fields["total_mass"]
fields["electron_number_density"] = \
fields["density"].to("cm**-3", "number_density", mu=mue)
fields["entropy"] = \
fields["temperature"]*fields["electron_number_density"]**mtt
return cls(rr.size, fields)
def generate_gas_particles(self,
num_particles,
r_max=None,
sub_sample=1,
compute_potential=False,
prng=None):
"""
Generate a set of gas particles in hydrostatic equilibrium.
Parameters
----------
num_particles : integer
The number of particles to generate.
r_max : float, optional
The maximum radius in kpc within which to generate
particle positions. If not supplied, it will generate
positions out to the maximum radius available. Default: None
sub_sample : integer, optional
This option allows one to generate a sub-sample of unique
particle radii, densities, and energies which will then be
repeated to fill the required number of particles. Default: 1,
which means no sub-sampling.
compute_potential : boolean, optional
If True, the gravitational potential for each particle will
be computed. Default: False
prng : :class:`~numpy.random.RandomState` object, integer, or None
A pseudo-random number generator. Typically will only
be specified if you have a reason to generate the same
set of random numbers, such as for a test. Default is None,
which sets the seed based on the system time.
"""
from cluster_generator.utils import parse_prng
prng = parse_prng(prng)
mylog.info("We will be assigning %d particles." % num_particles)
mylog.info("Compute particle positions.")
num_particles_sub = num_particles // sub_sample
radius_sub, mtot = generate_particle_radii(self["radius"].d,
self["gas_mass"].d,
num_particles_sub,
r_max=r_max,
prng=prng)
if sub_sample > 1:
radius = np.tile(radius_sub, sub_sample)[:num_particles]
else:
radius = radius_sub
theta = np.arccos(prng.uniform(low=-1., high=1., size=num_particles))
phi = 2. * np.pi * prng.uniform(size=num_particles)
fields = OrderedDict()
fields["gas", "particle_position"] = unyt_array([
radius * np.sin(theta) * np.cos(phi),
radius * np.sin(theta) * np.sin(phi), radius * np.cos(theta)
], "kpc").T
mylog.info("Compute particle thermal energies, densities, and masses.")
e_arr = 1.5 * self.fields["pressure"] / self.fields["density"]
get_energy = InterpolatedUnivariateSpline(self.fields["radius"], e_arr)
if sub_sample > 1:
energy = np.tile(get_energy(radius_sub),
sub_sample)[:num_particles]
else:
energy = get_energy(radius)
fields["gas", "thermal_energy"] = unyt_array(energy, "kpc**2/Myr**2")
fields["gas", "particle_mass"] = unyt_array([mtot / num_particles] *
num_particles, "Msun")
get_density = InterpolatedUnivariateSpline(self.fields["radius"],
self.fields["density"])
if sub_sample > 1:
density = np.tile(get_density(radius_sub),
sub_sample)[:num_particles]
else:
density = get_density(radius)
fields["gas", "density"] = unyt_array(density, "Msun/kpc**3")
mylog.info("Set particle velocities to zero.")
fields["gas",
"particle_velocity"] = unyt_array(np.zeros((num_particles, 3)),
"kpc/Myr")
if compute_potential:
energy_spline = InterpolatedUnivariateSpline(
self["radius"].d, -self["gravitational_potential"])
phi = -energy_spline(radius_sub)
if sub_sample > 1:
phi = np.tile(phi, sub_sample)
fields["gas",
"particle_potential"] = unyt_array(phi, "kpc**2/Myr**2")
return ClusterParticles("gas", fields)
def setup_gamer_ics(ics, regenerate_particles=False, use_tracers=False):
r"""
Generate the "Input_TestProb" lines needed for use
with the ClusterMerger setup in GAMER. If the particles
(dark matter and potentially star) have not been
created yet, they will be created at this step. New profile
files will also be created which have all fields in CGS units
for reading into GAMER. If a magnetic field file is present
in the ICs, a note will be given about how it should be named
for GAMER to use it.
NOTE: Gas particles in the initial conditions will be interpreted
as tracer particles.
Parameters
----------
ics : ClusterICs object
The ClusterICs object to generate the GAMER ICs from.
regenerate_particles : boolean, optional
If particle files have already been created and this
flag is set to True, the particles will be
re-created. Default: False
use_tracers : boolean
Set to True to add tracer particles. Default: False
"""
gamer_ptypes = ["dm", "star"]
if use_tracers:
gamer_ptypes.insert(0, "gas")
gamer_ptype_num = {"gas": 0, "dm": 2, "star": 3}
hses = [ClusterModel.from_h5_file(hf) for hf in ics.profiles]
parts = ics._generate_particles(
regenerate_particles=regenerate_particles)
outlines = [
f"Merger_Coll_NumHalos\t\t{ics.num_halos}\t# number of halos"
]
for i in range(ics.num_halos):
particle_file = f"{ics.basename}_gamerp_{i+1}.h5"
parts[i].write_sim_input(particle_file, gamer_ptypes, gamer_ptype_num)
hse_file_gamer = ics.profiles[i].replace(".h5", "_gamer.h5")
hses[i].write_model_to_h5(hse_file_gamer, overwrite=True,
in_cgs=True, r_max=ics.r_max)
vel = ics.velocity[i].to_value("km/s")
outlines += [
f"Merger_File_Prof{i+1}\t\t{hse_file_gamer}\t# profile table of cluster {i+1}",
f"Merger_File_Par{i+1}\t\t{particle_file}\t# particle file of cluster {i+1}",
f"Merger_Coll_PosX{i+1}\t\t{ics.center[i][0].v}\t# X-center of cluster {i+1} in kpc",
f"Merger_Coll_PosY{i+1}\t\t{ics.center[i][1].v}\t# Y-center of cluster {i+1} in kpc",
f"Merger_Coll_VelX{i+1}\t\t{vel[0]}\t# X-velocity of cluster {i+1} in km/s",
f"Merger_Coll_VelY{i+1}\t\t{vel[1]}\t# Y-velocity of cluster {i+1} in km/s"
]
mylog.info("Write the following lines to Input__TestProblem: ")
for line in outlines:
print(line)
num_particles = sum([ics.tot_np[key] for key in ics.tot_np])
mylog.info(f"In the Input__Parameter file, "
f"set PAR__NPAR = {num_particles}.")
if ics.mag_file is not None:
mylog.info(f"Rename the file '{ics.mag_file}' to 'B_IC' "
f"and place it in the same directory as the "
f"Input__* files, and set OPT__INIT_BFIELD_BYFILE "
f"to 1 in Input__Parameter")
def __init__(self,
left_edge,
right_edge,
ddims,
l_min,
l_max,
padding=0.1,
alpha=-11. / 3.,
g_rms=1.0,
ctr1=None,
ctr2=None,
ctr3=None,
r1=None,
r2=None,
r3=None,
g1=None,
g2=None,
g3=None,
vector_potential=False,
divergence_clean=False,
prng=None,
r_max=None):
prng = parse_prng(prng)
super(GaussianRandomField,
self).__init__(left_edge,
right_edge,
ddims,
padding=padding,
vector_potential=vector_potential,
divergence_clean=divergence_clean)
nx, ny, nz = self.ddims
num_halos = 0
if r1 is not None:
num_halos += 1
if r2 is not None:
num_halos += 1
if r3 is not None:
num_halos += 1
if num_halos >= 1:
if ctr1 is None:
ctr1 = 0.5 * (self.left_edge + self.right_edge)
else:
ctr1 = parse_value(ctr1, "kpc").v
r1 = parse_value(r1, "kpc").v
g1 = parse_value(g1, self._units)
if num_halos >= 2:
if ctr2 is None:
raise RuntimeError(
"Need to specify 'ctr2' for the second halo!")
ctr2 = parse_value(ctr2, "kpc").v
r2 = parse_value(r2, "kpc").v
g2 = parse_value(g2, self._units)
if num_halos == 3:
if ctr3 is None:
raise RuntimeError(
"Need to specify 'ctr3' for the second halo!")
ctr3 = parse_value(ctr3, "kpc").v
r3 = parse_value(r3, "kpc").v
g3 = parse_value(g3, self._units)
# Derived stuff
l_min = parse_value(l_min, "kpc").v
l_max = parse_value(l_max, "kpc").v
k0 = 2. * np.pi / l_min
k1 = 2. * np.pi / l_max
mylog.info("Setting up the Gaussian random fields.")
v = np.exp(2. * np.pi * 1j * prng.random((3, nx, ny, nz)))
v[:, 0, 0, 0] = 2. * np.sign(
(v[:, 0, 0, 0].imag < 0.0).astype("int")) - 1. + 0j
v[:, nx // 2, ny // 2, nz // 2] = 2. * np.sign(
(v[:, nx // 2, ny // 2, nz // 2].imag <
0.0).astype("int")) - 1. + 0j
v[:, 0, ny // 2, nz // 2] = 2. * np.sign(
(v[:, 0, ny // 2, nz // 2].imag < 0.0).astype("int")) - 1. + 0j
v[:, nx // 2, 0, nz // 2] = 2. * np.sign(
(v[:, nx // 2, 0, nz // 2].imag < 0.0).astype("int")) - 1. + 0j
v[:, nx // 2, ny // 2, 0] = 2. * np.sign(
(v[:, nx // 2, ny // 2, 0].imag < np.pi).astype("int")) - 1. + 0j
v[:, 0, 0, nz // 2] = 2. * np.sign(
(v[:, 0, 0, nz // 2].imag < np.pi).astype("int")) - 1. + 0j
v[:, 0, ny // 2, 0] = 2. * np.sign(
(v[:, 0, ny // 2, 0].imag < np.pi).astype("int")) - 1. + 0j
v[:, nx // 2, 0, 0] = 2. * np.sign(
(v[:, nx // 2, 0, 0].imag < np.pi).astype("int")) - 1. + 0j
np.multiply(v, np.sqrt(-2. * np.log(prng.random((3, nx, ny, nz)))), v)
kx, ky, kz = self._compute_waves()
kk = np.sqrt(kx**2 + ky**2 + kz**2)
with np.errstate(invalid='ignore', divide='ignore'):
sigma = (1.0 + (kk / k1)**2)**(0.25 * alpha) * np.exp(-0.5 *
(kk / k0)**2)
sigma[np.isinf(sigma)] = 0.0
sigma[np.isnan(sigma)] = 0.0
del kk
v[:, nx - 1:0:-1, ny - 1:0:-1,
nz - 1:nz // 2:-1] = np.conjugate(v[:, 1:nx, 1:ny, 1:nz // 2])
v[:, nx - 1:0:-1, ny - 1:ny // 2:-1,
nz // 2] = np.conjugate(v[:, 1:nx, 1:ny // 2, nz // 2])
v[:, nx - 1:0:-1, ny - 1:ny // 2:-1, 0] = np.conjugate(v[:, 1:nx,
1:ny // 2, 0])
v[:, nx - 1:0:-1, 0, nz - 1:nz // 2:-1] = np.conjugate(v[:, 1:nx, 0,
1:nz // 2])
v[:, 0, ny - 1:0:-1, nz - 1:nz // 2:-1] = np.conjugate(v[:, 0, 1:ny,
1:nz // 2])
v[:, nx - 1:nx // 2:-1, ny // 2,
nz // 2] = np.conjugate(v[:, 1:nx // 2, ny // 2, nz // 2])
v[:, nx - 1:nx // 2:-1, ny // 2, 0] = np.conjugate(v[:, 1:nx // 2,
ny // 2, 0])
v[:, nx - 1:nx // 2:-1, 0, nz // 2] = np.conjugate(v[:, 1:nx // 2, 0,
nz // 2])
v[:, 0, ny - 1:ny // 2:-1, nz // 2] = np.conjugate(v[:, 0, 1:ny // 2,
nz // 2])
v[:, nx - 1:nx // 2:-1, 0, 0] = np.conjugate(v[:, 1:nx // 2, 0, 0])
v[:, 0, ny - 1:ny // 2:-1, 0] = np.conjugate(v[:, 0, 1:ny // 2, 0])
v[:, 0, 0, nz - 1:nz // 2:-1] = np.conjugate(v[:, 0, 0, 1:nz // 2])
gx = np.fft.ifftn(sigma * v[0, :, :, :]).real
gy = np.fft.ifftn(sigma * v[1, :, :, :]).real
gz = np.fft.ifftn(sigma * v[2, :, :, :]).real
del sigma, v
g_avg = np.sqrt(np.mean(gx * gx + gy * gy + gz * gz))
gx /= g_avg
gy /= g_avg
gz /= g_avg
x, y, z = self._compute_coords()
if num_halos == 0:
g_rms = parse_value(g_rms, self._units)
mylog.info(f"Scaling the fields by the constant value {g_rms}.")
else:
if num_halos >= 1:
mylog.info("Scaling the fields by cluster 1.")
rr1 = np.sqrt((x - ctr1[0])**2 + (y - ctr1[1])**2 +
(z - ctr1[2])**2)
if r_max is not None:
rr1[rr1 > r_max] = r_max
idxs1 = np.searchsorted(r1, rr1) - 1
dr1 = (rr1 - r1[idxs1]) / (r1[idxs1 + 1] - r1[idxs1])
g_rms = ((1. - dr1) * g1[idxs1] + dr1 * g1[idxs1 + 1])**2
if num_halos >= 2:
mylog.info("Scaling the fields by cluster 2.")
rr2 = np.sqrt((x - ctr2[0])**2 + (y - ctr2[1])**2 +
(z - ctr2[2])**2)
if r_max is not None:
rr2[rr2 > r_max] = r_max
idxs2 = np.searchsorted(r2, rr2) - 1
dr2 = (rr2 - r2[idxs2]) / (r2[idxs2 + 1] - r2[idxs2])
g_rms += ((1. - dr2) * g2[idxs2] + dr2 * g2[idxs2 + 1])**2
if num_halos == 3:
mylog.info("Scaling the fields by cluster 3.")
rr3 = np.sqrt((x - ctr3[0])**2 + (y - ctr3[1])**2 +
(z - ctr3[2])**2)
if r_max is not None:
rr3[rr3 > r_max] = r_max
idxs3 = np.searchsorted(r3, rr3) - 1
dr3 = (rr3 - r3[idxs3]) / (r3[idxs3 + 1] - r3[idxs3])
g_rms += ((1. - dr3) * g3[idxs3] + dr3 * g3[idxs3 + 1])**2
g_rms = np.sqrt(g_rms).in_units(self._units).d
gx *= g_rms
gy *= g_rms
gz *= g_rms
self.x = x[:, 0, 0]
self.y = y[0, :, 0]
self.z = z[0, 0, :]
self.gx = gx
self.gy = gy
self.gz = gz
rescale = (self.gx**2 + self.gy**2 + self.gz**2).sum()
del x, y, z, g_rms
if self.divergence_clean:
self._divergence_clean(kx, ky, kz)
rescale /= (self.gx**2 + self.gy**2 + self.gz**2).sum()
self.gx *= rescale
self.gy *= rescale
self.gz *= rescale
if self.vector_potential:
self._compute_vector_potential(kx, ky, kz)
def generate_particles(self, num_particles, r_max=None, sub_sample=1,
compute_potential=False, prng=None):
"""
Generate a set of dark matter or star particles in virial equilibrium.
Parameters
----------
num_particles : integer
The number of particles to generate.
r_max : float, optional
The maximum radius in kpc within which to generate
particle positions. If not supplied, it will generate
positions out to the maximum radius available. Default: None
sub_sample : integer, optional
This option allows one to generate a sub-sample of unique
particle radii and velocities which will then be repeated
to fill the required number of particles. Default: 1, which
means no sub-sampling.
compute_potential : boolean, optional
If True, the gravitational potential for each particle will
be computed. Default: False
prng : :class:`~numpy.random.RandomState` object, integer, or None
A pseudo-random number generator. Typically will only
be specified if you have a reason to generate the same
set of random numbers, such as for a test. Default is None,
which sets the seed based on the system time.
Returns
-------
particles : :class:`~cluster_generator.particles.ClusterParticles`
A set of dark matter or star particles.
"""
from cluster_generator.utils import parse_prng
num_particles_sub = num_particles // sub_sample
key = {"dark_matter": "dm", "stellar": "star"}[self.ptype]
density = f"{self.ptype}_density"
mass = f"{self.ptype}_mass"
energy_spline = InterpolatedUnivariateSpline(self.model["radius"].d,
self.ee[::-1])
prng = parse_prng(prng)
mylog.info("We will be assigning %s %s particles.",
num_particles, self.ptype)
mylog.info("Compute %s particle positions.", num_particles)
nonzero = self.model[density] > 0.0
radius_sub, mtot = generate_particle_radii(self.model["radius"].d[nonzero],
self.model[mass].d[nonzero],
num_particles_sub, r_max=r_max,
prng=prng)
if sub_sample > 1:
radius = np.tile(radius_sub, sub_sample)[:num_particles]
else:
radius = radius_sub
theta = np.arccos(prng.uniform(low=-1., high=1., size=num_particles))
phi = 2.*np.pi*prng.uniform(size=num_particles)
fields = OrderedDict()
fields[key, "particle_position"] = unyt_array(
[radius*np.sin(theta)*np.cos(phi), radius*np.sin(theta)*np.sin(phi),
radius*np.cos(theta)], "kpc").T
mylog.info("Compute %s particle velocities.", self.ptype)
psi = energy_spline(radius_sub)
vesc = 2.*psi
fv2esc = vesc*self.f(psi)
vesc = np.sqrt(vesc)
velocity_sub = generate_velocities(
psi, vesc, fv2esc, self.f._eval_args[0], self.f._eval_args[1],
self.f._eval_args[2])
if sub_sample > 1:
velocity = np.tile(velocity_sub, sub_sample)[:num_particles]
else:
velocity = velocity_sub
theta = np.arccos(prng.uniform(low=-1., high=1., size=num_particles))
phi = 2.*np.pi*prng.uniform(size=num_particles)
fields[key, "particle_velocity"] = unyt_array(
[velocity*np.sin(theta)*np.cos(phi),
velocity*np.sin(theta)*np.sin(phi),
velocity*np.cos(theta)], "kpc/Myr").T
fields[key, "particle_mass"] = unyt_array(
[mtot/num_particles]*num_particles, "Msun")
if compute_potential:
if sub_sample > 1:
phi = -np.tile(psi, sub_sample)
else:
phi = -psi
fields[key, "particle_potential"] = unyt_array(
phi, "kpc**2/Myr**2")
return ClusterParticles(key, fields)