def new_model(self):
if self.seed is not None:
numpy.random.seed(self.seed)
vx_field=random_field(self.nf,self.power)
vy_field=random_field(self.nf,self.power)
vz_field=random_field(self.nf,self.power)
vx_field,vy_field,vz_field=make_div_free(self.nf,vx_field,vy_field,vz_field)
base_sphere=uniform_unit_sphere(self.targetN,base_grid=self.base_grid)
x,y,z=base_sphere.make_xyz()
self.actualN=len(x)
vx=interpolate_trilinear(x,y,z,vx_field)
vy=interpolate_trilinear(x,y,z,vy_field)
vz=interpolate_trilinear(x,y,z,vz_field)
mass=numpy.ones_like(x)/self.actualN
vx=vx-vx.mean()
vy=vy-vy.mean()
vz=vz-vz.mean()
Ep=3./5
self.internalE=Ep*self.ethep_ratio
Ek=0.5*mass[0]*(vx**2+vy**2+vz**2).sum()
vfac=sqrt(self.ekep_ratio*Ep/Ek)
vx=vx*vfac
vy=vy*vfac
vz=vz*vfac
Ek=0.5*mass[0]*(vx**2+vy**2+vz**2).sum()
internal_energy=numpy.ones_like(x)*self.internalE
return (mass,x,y,z,vx,vy,vz,internal_energy)
def glass(N, target_rms=0.05):
"""
make glass for initial condition generation
"""
if target_rms < 0.001:
print "warning: target_rms highly unlikely to succeed"
L = 1 | nbody_system.length
dt = 0.01 | nbody_system.time
x, y, z = uniform_random_unit_cube(N).make_xyz()
vx, vy, vz = uniform_unit_sphere(N).make_xyz()
p = datamodel.Particles(N)
p.x = L * x
p.y = L * y
p.z = L * z
p.h_smooth = 0. * L
p.vx = 0.1 * vx | (nbody_system.speed)
p.vy = 0.1 * vy | (nbody_system.speed)
p.vz = 0.1 * vz | (nbody_system.speed)
p.u = (0.1 * 0.1) | nbody_system.speed**2
p.mass = (8. / N) | nbody_system.mass
sph = Fi(use_gl=False, mode='periodic', redirection='none')
sph.initialize_code()
sph.parameters.use_hydro_flag = True
sph.parameters.radiation_flag = False
sph.parameters.self_gravity_flag = False
sph.parameters.gamma = 1
sph.parameters.isothermal_flag = True
sph.parameters.integrate_entropy_flag = False
sph.parameters.timestep = dt
sph.parameters.verbosity = 0
sph.parameters.pboxsize = 2 * L
sph.parameters.artificial_viscosity_alpha = 1.
sph.parameters.beta = 2.
sph.commit_parameters()
sph.gas_particles.add_particles(p)
sph.commit_particles()
# sph.start_viewer()
t = 0. | nbody_system.time
rms = 1.
# i = 0
while rms > target_rms:
t = t + (0.25 | nbody_system.time)
sph.evolve_model(t)
h = sph.particles.h_smooth.value_in(nbody_system.length)
rho = h**(-3.)
rms = rho.std() / rho.mean()
print rms, target_rms
x = sph.particles.x.value_in(nbody_system.length)
y = sph.particles.y.value_in(nbody_system.length)
z = sph.particles.z.value_in(nbody_system.length)
sph.stop()
return x, y, z
def new_model(self):
base_sphere=uniform_unit_sphere(self.targetN,base_grid=self.base_grid)
x_uni,y_uni,z=base_sphere.make_xyz()
self.actualN=len(x_uni)
rad=numpy.sqrt(x_uni**2 + y_uni**2)
phi=numpy.arctan2(y_uni,x_uni)
n_vec=2000
phi_new_vec=numpy.linspace(-numpy.pi, numpy.pi, n_vec)
phi_old_vec=phi_new_vec + self.rho_peturb*(numpy.sin(2.*phi_new_vec)/2.)
phi_new=numpy.interp(phi,phi_old_vec,phi_new_vec)
x=rad*numpy.cos(phi_new)
y=rad*numpy.sin(phi_new)
rad=numpy.sqrt(x**2 + y**2)
phi=numpy.arctan2(y,x)
vel=self.omega*rad
vx=-vel*numpy.sin(phi)
vy= vel*numpy.cos(phi)
vz=0.
mass=numpy.ones_like(x)/self.actualN
Ep=3./5
self.internalE=Ep*self.ethep_ratio
internal_energy=numpy.ones_like(x)*self.internalE
return (mass,x,y,z,vx,vy,vz,internal_energy)
def new_sph_particles_from_stellar_wind(stars, mgas):
new_sph = datamodel.Particles(0)
for si in stars:
Ngas = int(-si.Mwind / mgas)
if Ngas == 0:
continue
Mgas = mgas * Ngas
si.Mwind += Mgas
add = datamodel.Particles(Ngas)
add.mass = mgas
add.h_smooth = 0.0 | units.parsec
dx, dy, dz = uniform_unit_sphere(Ngas).make_xyz()
add.x = si.x + (dx * si.radius)
add.y = si.y + (dy * si.radius)
add.z = si.z + (dz * si.radius)
for ri in range(len(add)):
r = add[ri].position - si.position
r = r / r.length()
v_wind = (constants.G * si.mass / (add[ri].position - si.position).length()).sqrt()
add.u = 0.5 * (v_wind) ** 2
add.vx = si.vx + r[0] * si.terminal_wind_velocity
add.vy = si.vy + r[1] * si.terminal_wind_velocity
add.vz = si.vz + r[2] * si.terminal_wind_velocity
new_sph.add_particles(add)
return new_sph
def new_sph_particles_from_stellar_wind(stars, mgas):
new_sph = datamodel.Particles(0)
for si in stars:
Ngas = int(-si.Mwind / mgas)
if Ngas == 0:
continue
Mgas = mgas * Ngas
si.Mwind += Mgas
si.mass -= Mgas
add = datamodel.Particles(Ngas)
add.mass = mgas
add.h_smooth = 0. | units.parsec
dx, dy, dz = uniform_unit_sphere(Ngas).make_xyz()
add.x = si.x + (dx * si.wind_radius)
add.y = si.y + (dy * si.wind_radius)
add.z = si.z + (dz * si.wind_radius)
for ri in range(len(add)):
r = add[ri].position - si.position
r = r / r.length()
v_wind = (constants.G * si.mass /
(add[ri].position - si.position).length()).sqrt()
add[ri].u = 0.5 * (v_wind)**2
add[ri].vx = si.vx + r[0] * si.terminal_wind_velocity
add[ri].vy = si.vy + r[1] * si.terminal_wind_velocity
add[ri].vz = si.vz + r[2] * si.terminal_wind_velocity
new_sph.add_particles(add)
return new_sph
def new_model(self):
if self.seed is not None:
numpy.random.seed(self.seed)
vx_field = random_field(self.nf, self.power)
vy_field = random_field(self.nf, self.power)
vz_field = random_field(self.nf, self.power)
vx_field, vy_field, vz_field = make_div_free(self.nf, vx_field,
vy_field, vz_field)
base_sphere = uniform_unit_sphere(self.targetN,
base_grid=self.base_grid)
x, y, z = base_sphere.make_xyz()
self.actualN = len(x)
vx = interpolate_trilinear(x, y, z, vx_field)
vy = interpolate_trilinear(x, y, z, vy_field)
vz = interpolate_trilinear(x, y, z, vz_field)
mass = numpy.ones_like(x) / self.actualN
vx = vx - vx.mean()
vy = vy - vy.mean()
vz = vz - vz.mean()
Ep = 3. / 5
self.internalE = Ep * self.ethep_ratio
Ek = 0.5 * mass[0] * (vx**2 + vy**2 + vz**2).sum()
vfac = sqrt(self.ekep_ratio * Ep / Ek)
vx = vx * vfac
vy = vy * vfac
vz = vz * vfac
Ek = 0.5 * mass[0] * (vx**2 + vy**2 + vz**2).sum()
internal_energy = numpy.ones_like(x) * self.internalE
return (mass, x, y, z, vx, vy, vz, internal_energy)
def new_model(self):
base_sphere = uniform_unit_sphere(self.targetN,
base_grid=self.base_grid)
x_uni, y_uni, z = base_sphere.make_xyz()
self.actualN = len(x_uni)
rad = numpy.sqrt(x_uni**2 + y_uni**2)
phi = numpy.arctan2(y_uni, x_uni)
n_vec = 2000
phi_new_vec = numpy.linspace(-numpy.pi, numpy.pi, n_vec)
phi_old_vec = phi_new_vec + self.rho_peturb * (
numpy.sin(2. * phi_new_vec) / 2.)
phi_new = numpy.interp(phi, phi_old_vec, phi_new_vec)
x = rad * numpy.cos(phi_new)
y = rad * numpy.sin(phi_new)
rad = numpy.sqrt(x**2 + y**2)
phi = numpy.arctan2(y, x)
vel = self.omega * rad
vx = -vel * numpy.sin(phi)
vy = vel * numpy.cos(phi)
vz = 0.
mass = numpy.ones_like(x) / self.actualN
Ep = 3. / 5
self.internalE = Ep * self.ethep_ratio
internal_energy = numpy.ones_like(x) * self.internalE
return (mass, x, y, z, vx, vy, vz, internal_energy)
def glass(N, target_rms=0.05):
"""
make glass for initial condition generation
"""
if target_rms < 0.001:
print "warning: target_rms highly unlikely to succeed"
L=1| nbody_system.length
dt=0.01 | nbody_system.time
x,y,z=uniform_random_unit_cube(N).make_xyz()
vx,vy,vz=uniform_unit_sphere(N).make_xyz()
p=datamodel.Particles(N)
p.x=L*x
p.y=L*y
p.z=L*z
p.h_smooth=0. * L
p.vx= 0.1*vx | (nbody_system.speed)
p.vy= 0.1*vy | (nbody_system.speed)
p.vz= 0.1*vz | (nbody_system.speed)
p.u= (0.1*0.1) | nbody_system.speed**2
p.mass=(8./N) | nbody_system.mass
sph=Fi(use_gl=False,mode='periodic',redirection='none')
sph.initialize_code()
sph.parameters.use_hydro_flag=True
sph.parameters.radiation_flag=False
sph.parameters.self_gravity_flag=False
sph.parameters.gamma=1
sph.parameters.isothermal_flag=True
sph.parameters.integrate_entropy_flag=False
sph.parameters.timestep=dt
sph.parameters.verbosity=0
sph.parameters.pboxsize=2*L
sph.parameters.artificial_viscosity_alpha = 1.
sph.parameters.beta = 2.
sph.commit_parameters()
sph.gas_particles.add_particles(p)
sph.commit_particles()
# sph.start_viewer()
t=0. | nbody_system.time
rms=1.
i=0
while rms > target_rms:
t=t+(0.25 | nbody_system.time)
sph.evolve_model(t)
h=sph.particles.h_smooth.value_in(nbody_system.length)
rho=h**(-3.)
rms=rho.std()/rho.mean()
print rms, target_rms
x=sph.particles.x.value_in(nbody_system.length)
y=sph.particles.y.value_in(nbody_system.length)
z=sph.particles.z.value_in(nbody_system.length)
sph.stop()
return x,y,z
def new_sph_particles_from_stellar_wind(stars, mgas):
new_sph = datamodel.Particles(0)
for si in stars:
p = si.position
v = si.velocity
Ngas = int(-si.Mwind / mgas)
print("new Ngas=", si.mass, Ngas, end=' ')
if Ngas == 0:
continue
Mgas = mgas * Ngas
si.Mwind += Mgas
# Ngas = 10
# mgas = Mgas/10.
print("new Ngas=", Ngas, mgas)
add = datamodel.Particles(Ngas)
add.mass = mgas
add.h_smooth = 0. | units.parsec
dx, dy, dz = uniform_unit_sphere(Ngas).make_xyz()
add.x = si.x + (dx * si.radius)
add.y = si.y + (dy * si.radius)
add.z = si.z + (dz * si.radius)
for ri in range(len(add)):
r = add[ri].position - p
r = r / r.length()
v_wind = (constants.G * si.mass /
(add[ri].position - p).length()).sqrt()
add.u = 0.5 * (v_wind)**2
v_wind = si.terminal_wind_velocity
add.vx = v.x + r[0] * v_wind
add.vy = v.y + r[1] * v_wind
add.vz = v.z + r[2] * v_wind
new_sph.add_particles(add)
return new_sph
def __init__(self, targetN, convert_nbody = None, base_grid=None, rscale=1/1.695,
mass_cutoff = 0.999, do_scale = False):
self.targetN = targetN
self.convert_nbody = convert_nbody
self.rscale=rscale
self.mass_frac=mass_cutoff
self.do_scale = do_scale
self.internal_energy = 0.25 / self.rscale
self.base_sphere=uniform_unit_sphere(targetN,base_grid)
def __init__(self, targetN, convert_nbody=None, base_grid=None, rscale=1/1.695,
mass_cutoff=0.999, do_scale=False):
self.targetN = targetN
self.convert_nbody = convert_nbody
self.rscale = rscale
self.mass_frac = mass_cutoff
# self.do_scale = do_scale
# self.internal_energy = 0.25 / self.rscale
self.base_sphere = uniform_unit_sphere(targetN, base_grid)
def __init__(self, targetN, convert_nbody = None, base_grid=None, rscale=1/1.695,
mass=1.,seed=345672,mass_frac=.999):
numpy.random.seed(seed)
self.targetN = targetN
self.convert_nbody = convert_nbody
self.rscale=rscale
self.mass=mass
self.mass_frac=mass_frac
self.internal_energy=0.25*self.mass/self.rscale
self.base_sphere=uniform_unit_sphere(targetN,base_grid)
def __init__(self,
targetN,
convert_nbody=None,
base_grid=None,
rscale=1 / 1.695,
mass=1.,
seed=345672,
mass_frac=.999):
numpy.random.seed(seed)
self.targetN = targetN
self.convert_nbody = convert_nbody
self.rscale = rscale
self.mass = mass
self.mass_frac = mass_frac
self.internal_energy = 0.25 * self.mass / self.rscale
self.base_sphere = uniform_unit_sphere(targetN, base_grid)
def iliev_test_5_ic(N=10000,
Ns=10,
L=15. | units.kpc):
"""
iliev test 5 particle distributions
N= number of gas part
Ns= number of sources (recommended to be order 10 for smoothrad
distribution
L=half boxsize
"""
mp = rhoinit*(2*L)**3/N
# x,y,z=uniform_random_unit_cube(N).make_xyz()
x, y, z = glass(N, target_rms=0.05)
p = datamodel.Particles(N)
p.x = L*x
p.y = L*y
p.z = L*z
p.h_smooth = 0. | units.parsec
p.vx = 0. | (units.km/units.s)
p.vy = 0. | (units.km/units.s)
p.vz = 0. | (units.km/units.s)
p.u = uinit
p.rho = rhoinit
p.mass = mp
p.flux = 0. | (units.s**-1)
p.xion = 0.
sources = datamodel.Particles(Ns)
x, y, z = uniform_unit_sphere(Ns).make_xyz()
sources.x = L*x*(1./N)**(1./3)/10
sources.y = L*y*(1./N)**(1./3)/10
sources.z = L*z*(1./N)**(1./3)/10
sources.rho = rhoinit/100.
sources.flux = (5.e48/Ns) | (units.s**-1)
sources.xion = 1.
return p, sources
def random_positions(N, rmin, rmax):
"""
The particles start out in a random position between
the surface of the star and the distance that the
previously released particles have reached.
This assumes that the current wind velocity is
comparable to the previous wind velocity.
Note that the stellar position is not added yet here.
TODO: consider optimizing this by making a large pool
once, and draw positions from that.
"""
random_positions = numpy.transpose(uniform_unit_sphere(N).make_xyz())
vector_lengths = numpy.sqrt((random_positions**2).sum(1))
unit_vectors = random_positions/as_three_vector(vector_lengths)
distance = rmin + (vector_lengths * rmax)
position = unit_vectors * as_three_vector(distance)
return position, unit_vectors
def iliev_test_5_ic(N=10000, Ns=10, L=15. | units.kpc):
"""
iliev test 5 particle distributions
N= number of gas part
Ns= number of sources (recommended to be order 10 for smoothrad
distribution
L=half boxsize
"""
mp = rhoinit * (2 * L)**3 / N
# x,y,z=uniform_random_unit_cube(N).make_xyz()
x, y, z = glass(N, target_rms=0.05)
p = datamodel.Particles(N)
p.x = L * x
p.y = L * y
p.z = L * z
p.h_smooth = 0. | units.parsec
p.vx = 0. | (units.km / units.s)
p.vy = 0. | (units.km / units.s)
p.vz = 0. | (units.km / units.s)
p.u = uinit
p.rho = rhoinit
p.mass = mp
p.flux = 0. | (units.s**-1)
p.xion = 0.
sources = datamodel.Particles(Ns)
x, y, z = uniform_unit_sphere(Ns).make_xyz()
sources.x = L * x * (1. / N)**(1. / 3) / 10
sources.y = L * y * (1. / N)**(1. / 3) / 10
sources.z = L * z * (1. / N)**(1. / 3) / 10
sources.rho = rhoinit / 100.
sources.flux = (5.e48 / Ns) | (units.s**-1)
sources.xion = 1.
return p, sources
def iliev_test_7_ic(N=10000, Ns=10, L=6.6 | units.kpc):
print "Initializing iliev_test_7"
mp = rhoinit * (2 * L)**3 / N
Nc = ((rhoclump * 4 * constants.pi / 3 * (0.8 | units.kpc)**3) / mp)
Nc = int(Nc)
print Nc
try:
f = open("glass%9.9i.pkl" % N, "rb")
x, y, z = cPickle.load(f)
f.close()
except:
x, y, z = glass_unit_cube(N, target_rms=0.05).make_xyz()
f = open("glass%9.9i.pkl" % N, "wb")
cPickle.dump((x, y, z), f)
f.close()
sel = numpy.where(((x -
(5. / 6.6))**2 + y**2 + z**2)**0.5 > (0.8 / 6.6))[0]
x = x[sel]
y = y[sel]
z = z[sel]
p = Particles(len(x))
print len(x)
# set particles homogeneously in space
p.x = L * x
p.y = L * y
p.z = L * z
# set other properties
p.h_smooth = 0. | units.parsec
p.vx = 0. | (units.km / units.s)
p.vy = 0. | (units.km / units.s)
p.vz = 0. | (units.km / units.s)
p.u = uinit
p.rho = rhoinit
p.mass = mp
p.flux = 0. | (units.s**-1)
p.xion = 0. | units.none
sources = Particles(Ns)
x, y, z = uniform_unit_sphere(Ns).make_xyz()
if Ns == 1:
x, y, z = 0., 0., 0.
sources.x = L * x * (1. / N)**(1. / 3) / 10
sources.y = L * y * (1. / N)**(1. / 3) / 10
sources.z = L * z * (1. / N)**(1. / 3) / 10
sources.luminosity = (1.2e52 / Ns) | (units.s**-1)
sources.SpcType = 1.
clump = core.Particles(Nc)
x, y, z = uniform_unit_sphere(Nc).make_xyz()
clump.x = (5. | units.kpc) + (0.8 | units.kpc) * x
clump.y = (0.8 | units.kpc) * y
clump.z = (0.8 | units.kpc) * z
clump.h_smooth = 0. | units.parsec
clump.vx = 0. | (units.km / units.s)
clump.vy = 0. | (units.km / units.s)
clump.vz = 0. | (units.km / units.s)
clump.u = uclump
clump.rho = rhoclump
clump.mass = mp
clump.flux = 0. | (units.s**-1)
clump.xion = 0. | units.none
p.add_particles(clump)
return p, sources
def mechanical_feedback(self):
star_particles = self.star_particles.copy_to_memory()
channel = self.particles.new_channel_to(star_particles)
channel.copy_attributes(["x", "y", "z", "vx", "vy", "vz"])
channel.copy_attribute("mass", "grav_mass")
del channel
channel = self.star_particles_addition.new_channel_to(star_particles)
channel.copy_attribute("Emech_last_feedback")
del channel
new_sph = datamodel.Particles(0)
star_particles.dmass = star_particles.grav_mass - star_particles.mass
star_particles.u = (
star_particles.Emech -
star_particles.Emech_last_feedback) / star_particles.dmass
if numpy.any((star_particles.Emech -
star_particles.Emech_last_feedback) < zero):
print "feedback error"
raise Exception
losers = star_particles.select_array(lambda x: x > self.mgas,
["dmass"])
while len(losers) > 0:
add = datamodel.Particles(len(losers))
add.mass = self.mgas
add.h_smooth = 0. | units.parsec
dx, dy, dz = uniform_unit_sphere(len(losers)).make_xyz()
add.x = losers.x + self.feedback_radius * dx
add.y = losers.y + self.feedback_radius * dy
add.z = losers.z + self.feedback_radius * dz
add.vx = losers.vx
add.vy = losers.vy
add.vz = losers.vz
add.u = self.feedback_efficiency * losers.u
losers.grav_mass -= self.mgas
losers.Emech_last_feedback += self.mgas * losers.u
new_sph.add_particles(add)
losers = star_particles.select_array(
lambda x, y: x - y > self.mgas, ["grav_mass", "mass"])
print "gas particles added:", len(new_sph)
if len(new_sph) == 0:
return zero
self.sph.gas_particles.add_particles(new_sph)
feedback_energy_added = (new_sph.mass * new_sph.u).sum()
self.total_feedback_energy = self.total_feedback_energy + feedback_energy_added
channel = star_particles.new_channel_to(self.particles)
channel.copy_attribute("grav_mass", "mass")
del channel
channel = star_particles.new_channel_to(self.star_particles_addition)
channel.copy_attribute("Emech_last_feedback")
del channel
return feedback_energy_added
