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 check_model(self):
n = len(self.ee)
rho = np.zeros(n)
rho_int = lambda e, psi: self.f(e)*np.sqrt(2*(psi-e))
for i, e in enumerate(self.ee[::-1]):
rho[i] = 4.*np.pi*quad(rho_int, 0., e, args=(e))[0]
chk = np.abs(rho-self.pden)/self.pden
mylog.info("The maximum relative deviation of this profile from "
"virial equilibrium is %g" % np.abs(chk).max())
return rho, chk
def _generate_df(self):
pden = self.model[f"{self.ptype}_density"][::-1]
density_spline = InterpolatedUnivariateSpline(self.ee, pden)
g = np.zeros(self.num_elements)
dgdp = lambda t, e: 2*density_spline(e-t*t, 1)
pbar = tqdm(leave=True, total=self.num_elements,
desc="Computing particle DF ")
for i in range(self.num_elements):
g[i] = quad(dgdp, 0., np.sqrt(self.ee[i]), epsabs=1.49e-05,
epsrel=1.49e-05, args=(self.ee[i]))[0]
pbar.update()
pbar.close()
g_spline = InterpolatedUnivariateSpline(self.ee, g)
ff = g_spline(self.ee, 1)/(np.sqrt(8.)*np.pi**2)
self.f = InterpolatedUnivariateSpline(self.ee, ff)
self.df = unyt_array(ff[::-1], "Msun*Myr**3/kpc**6")
def __init__(self, num_particles, rr, gpot, pden, mdm):
fields = OrderedDict()
ee = gpot[::-1]
density_spline = InterpolatedUnivariateSpline(ee, pden[::-1])
energy_spline = InterpolatedUnivariateSpline(rr, gpot)
num_points = gpot.shape[0]
g = np.zeros(num_points)
dgdp = lambda t, e: 2*density_spline(e-t*t, 1)
pbar = get_pbar("Computing particle DF.", num_points)
for i in range(num_points):
g[i] = quad(dgdp, 0., np.sqrt(ee[i]), args=(ee[i]))[0]
pbar.update(i)
pbar.finish()
g_spline = InterpolatedUnivariateSpline(ee, g)
f = lambda e: g_spline(e, 1)/(np.sqrt(8.)*np.pi**2)
self.ee = ee
self.f = f
self.rr = rr
self.pden = pden
mylog.info("We will be assigning %d particles." % num_particles)
mylog.info("Compute particle positions.")
u = np.random.uniform(size=num_particles)
P_r = np.insert(mdm, 0, 0.0)
P_r /= P_r[-1]
radius = np.interp(u, P_r, np.insert(rr, 0, 0.0), left=0.0, right=1.0)
theta = np.arccos(np.random.uniform(low=-1.,high=1.,size=num_particles))
phi = 2.*np.pi*np.random.uniform(size=num_particles)
fields["particle_radius"] = YTArray(radius, "kpc")
fields["particle_position_x"] = YTArray(radius*np.sin(theta)*np.cos(phi), "kpc")
fields["particle_position_y"] = YTArray(radius*np.sin(theta)*np.sin(phi), "kpc")
fields["particle_position_z"] = YTArray(radius*np.cos(theta), "kpc")
mylog.info("Compute particle velocities.")
psi = energy_spline(radius)
vesc = 2.*psi
fv2esc = vesc*f(psi)
vesc = np.sqrt(vesc)
velocity = generate_velocities(psi, vesc, fv2esc, f)
theta = np.arccos(np.random.uniform(low=-1.,high=1.,size=num_particles))
phi = 2.*np.pi*np.random.uniform(size=num_particles)
fields["particle_velocity"] = YTArray(velocity, "kpc/Myr")
fields["particle_velocity_x"] = YTArray(velocity*np.sin(theta)*np.cos(phi), "kpc/Myr")
fields["particle_velocity_y"] = YTArray(velocity*np.sin(theta)*np.sin(phi), "kpc/Myr")
fields["particle_velocity_z"] = YTArray(velocity*np.cos(theta), "kpc/Myr")
fields["particle_mass"] = YTArray([mdm.max()/num_particles], "Msun")
fields["particle_potential"] = YTArray(psi, "kpc**2/Myr**2")
fields["particle_energy"] = fields["particle_potential"]-0.5*fields["particle_velocity"]**2
super(VirialEquilibrium, self).__init__(num_particles, fields, "spherical")
