def get_dummy_particles():
x, y = numpy.mgrid[-5 * dx : box_length + 5 * dx + 1e-10 : dx, -5 * dx : box_height + 5 * dx + 1e-10 : dx]
xd, yd = x.ravel(), y.ravel()
md = numpy.ones_like(xd) * m
hd = numpy.ones_like(xd) * h
rhod = numpy.ones_like(xd) * ro
cd = numpy.ones_like(xd) * co
pd = numpy.zeros_like(xd)
dummy_fluid = base.get_particle_array(name="dummy_fluid", type=Fluid, x=xd, y=yd, h=hd, rho=rhod, c=cd, p=pd)
# remove indices within the square
indices = []
np = dummy_fluid.get_number_of_particles()
x, y = dummy_fluid.get("x", "y")
for i in range(np):
if -dx / 2 <= x[i] <= box_length + dx / 2:
if -dx / 2 <= y[i] <= box_height + dx / 2:
indices.append(i)
to_remove = base.LongArray(len(indices))
to_remove.set_data(numpy.array(indices))
dummy_fluid.remove_particles(to_remove)
return dummy_fluid
def setUp(self):
""" The setup consists of points randomly distributed in a
cube [-1,1] X [-1.1] X [1-,1]
The smoothing length for the points is proportional to the
number of particles.
"""
self.cl_precision = cl_precision = "single"
self.np = np = 1<<14
self.x = x = random.random(np) * 2.0 - 1.0
self.y = y = random.random(np) * 2.0 - 1.0
self.z = z = random.random(np) * 2.0 - 1.0
vol_per_particle = numpy.power(8.0/np, 1.0/3.0)
self.h = h = numpy.ones_like(x) * vol_per_particle
self.cy_pa = base.get_particle_array(name='test', x=x, y=y, z=z, h=h)
self.cl_pa = base.get_particle_array(
name="test", cl_precision=self.cl_precision,
x=x, y=y, z=z, h=h)
# the scale factor for the cell sizes
self.kernel_scale_factor = kernel_scale_factor = 2.0
self._setup()
def t(self):
ret = {}
da = DoubleArray()
pa = ParticleArray()
kernel = kernels.CubicSplineKernel(3)
get_time = time.time
for N in Ns:
x = numpy.arange(N)
z = y = numpy.zeros(N)
mu = m = rho = numpy.ones(N)
h = 2*m
da = DoubleArray(N)
da2 = DoubleArray(N)
da.set_data(z)
da2.set_data(z)
pa = get_particle_array(x=x, y=y, z=z, h=h, mu=mu, rho=rho, m=m, tmp=z,
tx=z, ty=m, tz=z, nx=m, ny=z, nz=z, u=z, v=z, w=z,
ubar=z, vbar=z, wbar=z, q=m)
pb = get_particle_array(x=x+0.1**0.5, y=y, z=z, h=h, mu=mu, rho=rho, m=m, tmp=z,
tx=m, ty=z, tz=z, nx=z, ny=m, nz=z, u=z, v=z, w=z,
ubar=z, vbar=z, wbar=z, q=m)
particles = Particles(arrays=[pa, pb])
func = func_getter.get_func(pa, pb)
calc = SPHCalc(particles, [pa], pb, kernel, [func], ['tmp']*func.num_outputs)
print cls.__name__
t = get_time()
calc.sph('tmp', 'tmp', 'tmp')
t = get_time() - t
nam = '%s'%(cls.__name__)
ret[nam +' /%d'%(N)] = t/N
return ret
def setUp(self):
""" Setup for SPHOperationTestCase
Setup:
------
Create two particle arrays, one Fluid and one Solid.
Instantiate the class with a default function `SPHRho` with
various combinations of the from and on types and check for the
filtering of the arrays.
"""
x = numpy.linspace(0,1,11)
h = numpy.ones_like(x) * 2 * (x[1] - x[0])
#create the fluid particle array
self.fluid = base.get_particle_array(name='fluid', type=Fluid, x=x,h=h)
#create the solid particle array
self.solid = base.get_particle_array(name="solid", type=Solid, x=x,h=h)
#create the particles
self.particles = particles = base.Particles(arrays=[self.fluid,
self.solid])
#set the kernel
self.kernel = base.CubicSplineKernel()
def get_boundary_particles(**kwargs):
# left boundary
x = numpy.ones(50)
for i in range(50):
x[i] = -0.6 - (i + 1) * dxl
m = numpy.ones_like(x) * dxl
h = numpy.ones_like(x) * 2 * dxr
rho = numpy.ones_like(x)
u = numpy.zeros_like(x)
e = numpy.ones_like(x) * 2.5
p = (0.4) * rho * e
cs = numpy.sqrt(1.4 * p / rho)
left = base.get_particle_array(name="left", type=Boundary, x=x, m=m, h=h, rho=rho, u=u, e=e, cs=cs, p=p)
# right boundary
for i in range(50):
x[i] = 0.6 + (i + 1) * dxr
m = numpy.ones_like(x) * dxl
h = numpy.ones_like(x) * 2 * dxr
rho = numpy.ones_like(x) * 0.25
u = numpy.zeros_like(x)
e = numpy.ones_like(x) * 1.795
p = (0.4) * rho * e
# cs = numpy.sqrt(0.4*e)
cs = numpy.sqrt(1.4 * p / rho)
right = base.get_particle_array(name="right", type=Boundary, x=x, m=m, h=h, rho=rho, u=u, e=e, cs=cs, p=p)
return [left, right]
def setUp(self):
""" The setup consists of two fluid particle arrays, each
having one particle. The fluids are acted upon by an external
vector force and gravity.
Comparison is made with the PySPH integration of the system.
"""
x1 = numpy.array([-0.5])
y1 = numpy.array([1.0])
x2 = numpy.array([0.5])
y2 = numpy.array([1.0])
tmpx1 = numpy.ones_like(x1)
tmpx2 = numpy.ones_like(x2)
self.f1 = base.get_particle_array(name="fluid1", x=x1, y=y1, tmpx=tmpx1)
self.f2 = base.get_particle_array(name="fluid2", x=x2, y=y2, tmpx=tmpx2)
self.particles = base.Particles(arrays=[self.f1, self.f2])
self.kernel = kernel = base.CubicSplineKernel(dim=2)
gravity = solver.SPHIntegration(
sph.GravityForce.withargs(gy=-10.0), on_types=[Fluid], updates=["u", "v"], id="gravity"
)
force = solver.SPHIntegration(
sph.GravityForce.withargs(gx=-10.0), on_types=[Fluid], updates=["u", "v"], id="force"
)
position = solver.SPHIntegration(sph.PositionStepping, on_types=[Fluid], updates=["x", "y"], id="step")
gravity.calc_type = sph.CLCalc
force.calc_type = sph.CLCalc
position.calc_type = sph.CLCalc
gravity_calcs = gravity.get_calcs(self.particles, kernel)
force_calcs = force.get_calcs(self.particles, kernel)
position_calcs = position.get_calcs(self.particles, kernel)
self.calcs = calcs = []
calcs.extend(gravity_calcs)
calcs.extend(force_calcs)
calcs.extend(position_calcs)
self.integrator = CLIntegrator(self.particles, calcs)
self.ctx = ctx = cl.create_some_context()
self.integrator.setup_integrator(ctx)
self.queue = calcs[0].queue
self.dt = 0.1
self.nsteps = 10
def get_boundary_particles():
""" Get the particles corresponding to the dam and fluids """
# get the tank
xt1, yt1 = geom.create_2D_tank(x1=0, y1=0,
x2=tank_length, y2=tank_height,
dx=dx)
xt2, yt2 = geom.create_2D_tank(x1=-dx/2, y1=-dx/2,
x2=tank_length + dx/2, y2=tank_height+dx/2,
dx=dx)
x = numpy.concatenate( (xt1, xt2) )
y = numpy.concatenate( (yt1, yt2) )
h = numpy.ones_like(x) * h0
m = numpy.ones_like(x) * ro*dx*dx*0.5
rho = numpy.ones_like(x) * ro
cs = numpy.ones_like(x) * co
tank = base.get_particle_array(cl_precision="single", name="tank",
type=Solid, x=x,y=y,m=m,rho=rho,h=h,cs=cs)
np = tank.get_number_of_particles()
# create the gate
y1 = numpy.arange(dx/2, tank_height+1e-4, dx/2)
x1 = numpy.ones_like(y1)*(0.38-dx/2)
y2 = numpy.arange(dx/2+dx/4, tank_height+1e-4, dx/2)
x2 = numpy.ones_like(y2)*(0.38-dx)
y3 = numpy.arange(dx/2, tank_height+1e-4, dx/2)
x3 = numpy.ones_like(y3)*(0.38-1.5*dx)
x = numpy.concatenate( (x1, x2, x3) )
y = numpy.concatenate( (y1, y2, y3) )
h = numpy.ones_like(x) * h0
m = numpy.ones_like(x) * 0.5 * dx/2 * dx/2 * ro
rho = numpy.ones_like(x) * ro
cs = numpy.ones_like(x) * co
v = numpy.ones_like(x) * 1.5
gate = base.get_particle_array(cl_precision="single", name="gate",
x=x, y=y, m=m, rho=rho, h=h, cs=cs,
v=v,
type=Solid)
np += gate.get_number_of_particles()
print "Number of solid particles = %d"%(np)
return [tank, gate]
def setUp(self):
self.np = 25
self.x1 = x1 = numpy.array([-0.125, 0.125, 0.375, 0.625, 0.875,
-0.125, 0.125, 0.375, 0.625, 0.875,
-0.125, 0.125, 0.375, 0.625, 0.875,
-0.125, 0.125, 0.375, 0.625, 0.875,
-0.125, 0.125, 0.375, 0.625, 0.875,]
)
self.y1 = y1 = numpy.array([-0.125, -0.125, -0.125, -0.125, -0.125,
0.125, 0.125, 0.125, 0.125, 0.125,
0.375, 0.375, 0.375, 0.375, 0.375,
0.625, 0.625, 0.625, 0.625, 0.625,
0.875, 0.875, 0.875, 0.875, 0.875]
)
self.z1 = z1 = numpy.zeros_like(x1)
self.h1 = h1 = numpy.ones_like(x1) * 0.1
self.x2 = x2 = numpy.array([-0.125, 0.125, 0.375, 0.625, 0.875,
-0.125, 0.125, 0.375, 0.625, 0.875,
-0.125, 0.125, 0.375, 0.625, 0.875,
-0.125, 0.125, 0.375, 0.625, 0.875,
-0.125, 0.125, 0.375, 0.625, 0.875,]
)
self.y2 = y2 = numpy.array([-0.125, -0.125, -0.125, -0.125, -0.125,
0.125, 0.125, 0.125, 0.125, 0.125,
0.375, 0.375, 0.375, 0.375, 0.375,
0.625, 0.625, 0.625, 0.625, 0.625,
0.875, 0.875, 0.875, 0.875, 0.875]
)
self.z2 = z2 = numpy.zeros_like(x2)
self.h2 = h2 = numpy.ones_like(x2) * 0.15
self.pa1 = pa1 = base.get_particle_array(name='test1',
x=x1, y=y1, z=z1, h=h1)
self.pa2 = pa2 = base.get_particle_array(name='test2',
x=x2, y=y2, z=z2, h=h2)
self.periodic_domain = periodic_domain = PeriodicDomain(xmin=-0.2,
xmax=1.0)
def setUp(self):
self.np = np = 100
x = numpy.random.random(np)
y = numpy.random.random(np)
z = numpy.random.random(np)
m = numpy.ones_like(x)
cy_pa = base.get_particle_array(name="test", x=x, y=y, z=z, m=m)
cl_pa = base.get_particle_array(name="test", cl_precision="double",
x=x, y=y, z=z, m=m)
cy_particles = base.Particles(
[cy_pa,], locator_type=CYLoctor.NSquareNeighborLocator)
cl_particles = base.CLParticles( [cl_pa,] )
cy_solver = solver.Solver(
dim=3, integrator_type=solver.EulerIntegrator)
cl_solver = solver.Solver(
dim=3, integrator_type=solver.EulerIntegrator)
self.cy_solver = cy_solver
self.cl_solver = cl_solver
cy_solver.add_operation(solver.SPHIntegration(
sph.NBodyForce.withargs(), on_types=[0], from_types=[0],
updates=['u','v','w'], id='nbody_force')
)
cy_solver.add_operation_step([0,])
cl_solver.add_operation(solver.SPHIntegration(
sph.NBodyForce.withargs(), on_types=[0], from_types=[0],
updates=['u','v','w'], id='nbody_force')
)
cl_solver.add_operation_step([0,])
cl_solver.set_cl(True)
cy_solver.setup(cy_particles)
cl_solver.setup(cl_particles)
def standard_shock_tube_data(name="", type=0, cl_precision="double",
nl=320, nr=80, smoothing_length=None, **kwargs):
""" Standard 400 particles shock tube problem """
dxl = 0.6/nl
dxr = dxl*4
x = numpy.ones(nl+nr)
x[:nl] = numpy.arange(-0.6, -dxl+1e-10, dxl)
x[nl:] = numpy.arange(dxr, 0.6+1e-10, dxr)
m = numpy.ones_like(x)*dxl
h = numpy.ones_like(x)*2*dxr
if smoothing_length:
h = numpy.ones_like(x) * smoothing_length
rho = numpy.ones_like(x)
rho[nl:] = 0.25
u = numpy.zeros_like(x)
e = numpy.ones_like(x)
e[:nl] = 2.5
e[nl:] = 1.795
p = 0.4*rho*e
cs = numpy.sqrt(1.4*p/rho)
idx = numpy.arange(nl+nr)
return base.get_particle_array(name=name,x=x,m=m,h=h,rho=rho,p=p,e=e,
cs=cs,type=type, idx=idx,
cl_precision=cl_precision)
def get_fluid():
""" Get the fluid particle array """
x, y = numpy.mgrid[dx : box_length - 1e-10 : dx, dx : box_height - 1e-10 : dx]
xf, yf = x.ravel(), y.ravel()
mf = numpy.ones_like(xf) * m
hf = numpy.ones_like(xf) * h
rhof = numpy.ones_like(xf) * ro
cf = numpy.ones_like(xf) * co
pf = numpy.zeros_like(xf)
fluid = base.get_particle_array(name="fluid", type=Fluid, x=xf, y=yf, h=hf, rho=rhof, c=cf, p=pf)
# remove indices within the square
indices = []
np = fluid.get_number_of_particles()
x, y = fluid.get("x", "y")
for i in range(np):
if 1.0 - dx / 2 <= x[i] <= 2.0 + dx / 2:
if 2.0 - dx / 2 <= y[i] <= 3.0 + dx / 2:
indices.append(i)
to_remove = base.LongArray(len(indices))
to_remove.set_data(numpy.array(indices))
fluid.remove_particles(to_remove)
return fluid
def test_sph_calc():
x = numpy.array([0,])
y = numpy.array([0,])
z = numpy.array([0,])
h = numpy.ones_like(x)
pa = base.get_particle_array(name="test", x=x, y=y, z=z,h=h)
particles = base.Particles(arrays=[pa,])
kernel = base.CubicSplineKernel(dim=1)
vector_force1 = sph.VectorForce.withargs(force=base.Point(1,1,1))
vector_force2 = sph.VectorForce.withargs(force=base.Point(1,1,1))
func1 = vector_force1.get_func(pa,pa)
func2 = vector_force2.get_func(pa,pa)
calc = sph.SPHCalc(particles=particles, sources=[pa,pa], dest=pa,
kernel=kernel, funcs=[func1, func2],
updates=['u','v','w'], integrates=True)
# evaluate the calc. Accelerations are stored in _tmpx, _tmpy and _tmpz
calc.sph('_tmpx', '_tmpy', '_tmpz')
tmpx, tmpy, tmpz = pa.get('_tmpx', '_tmpy', '_tmpz')
# the acceleration should be 2 in each direction
assert ( abs(tmpx[0] - 2.0) < 1e-16 )
assert ( abs(tmpy[0] - 2.0) < 1e-16 )
assert ( abs(tmpz[0] - 2.0) < 1e-16 )
def setUp(self):
""" A Dummy fluid solver is created with the following operations
(i) -- Equation of State
(ii) -- Density Rate
(iii)-- Momentum Equation Pressure Gradient
(iv) -- Momentum Equation Viscosity
"""
self.kernel = kernel = base.CubicSplineKernel(dim=2)
self.solver = s = solver.Solver(dim=2,
integrator_type=solver.EulerIntegrator)
s.default_kernel = kernel
self.particles = base.Particles(arrays=[base.get_particle_array()])
# Create some replacement operations
self.visc = solver.SPHIntegration(sph.MorrisViscosity,
on_types=[Fluids],
from_types=[Fluids, Solids],
updates=['u', 'v'],
id='visc')
self.summation_density = solver.SPHOperation(sph.SPHRho,
on_types=[Fluids, Solids],
updates=['rho'],
id='sd')
def get_fluid_particles():
xf1, yf1 = geom.create_2D_filled_region(x1=dx, y1=dx,
x2=fluid_column_width,
y2=fluid_column_height,
dx=dx)
xf2, yf2 = geom.create_2D_filled_region(x1=dx/2, y1=dx/2,
x2=fluid_column_width,
y2=fluid_column_height,
dx=dx)
x = numpy.concatenate((xf1, xf2))
y = numpy.concatenate((yf1, yf2))
print 'Number of fluid particles: ', len(x)
hf = numpy.ones_like(x) * h
mf = numpy.ones_like(x) * dx * dy * ro * 0.5
rhof = numpy.ones_like(x) * ro
csf = numpy.ones_like(x) * co
fluid = base.get_particle_array(cl_precision="single",
name="fluid", type=Fluid,
x=x, y=y, h=hf, m=mf, rho=rhof,
cs=csf)
return fluid
def setUp(self):
"""
11 particles from 0~1 with dx = 0.1, h = 0.1
Particle 5 has h = 0.2
x x x x x O x x x x x
With a kernel scale factor of 2, particle 5 should have
neighbor indices [1,2,3,4,5,6,7,8,9]
"""
x = numpy.linspace(0, 1, 11)
dx = x[1] - x[0]
self._h = dx + 1e-10
h = numpy.ones_like(x) * self._h
h[5] = 2 * self._h
m = numpy.ones_like(x) * dx
rho = numpy.ones_like(x)
pa = base.get_particle_array(type=0,
name="test",
x=x,
h=h,
m=m,
rho=rho)
self.particles = base.Particles(arrays=[pa],
variable_h=True,
in_parallel=False)
def setUp(self):
"""
11 particles from 0~1 with dx = 0.1, h = 0.1
Particle 5 has h = 0.2
x x x x x O x x x x x
With a kernel scale factor of 2, particle 5 should have
neighbor indices [1,2,3,4,5,6,7,8,9]
"""
x = numpy.linspace(0,1,11)
dx = x[1] - x[0]
self._h = dx + 1e-10
h = numpy.ones_like(x) * self._h
h[5] = 2 * self._h
m = numpy.ones_like(x) * dx
rho = numpy.ones_like(x)
pa = base.get_particle_array(type=0, name="test",
x=x, h=h, m=m,rho=rho)
self.particles = base.Particles(arrays=[pa], variable_h=True,
in_parallel=False)
def standard_shock_tube_data(name="", type=0):
""" Standard 400 particles shock tube problem """
dxl = 0.001875
dxr = dxl*4
x = numpy.ones(400, float)
x[:320] = numpy.arange(-0.6, -dxl+1e-4, dxl)
x[320:] = numpy.arange(dxr, 0.6+1e-4, dxr)
m = numpy.ones_like(x)*dxl
h = numpy.ones_like(x)*2*dxr
rho = numpy.ones_like(x)
rho[320:] = 0.25
u = numpy.zeros_like(x)
e = numpy.ones_like(x)
e[:320] = 2.5
e[320:] = 1.795
p = 0.4*rho*e
cs = numpy.sqrt(1.4*p/rho)
idx = numpy.arange(400)
return base.get_particle_array(name=name,x=x,m=m,h=h,rho=rho,p=p,e=e,
cs=cs,type=type, idx=idx)
def setUp(self):
""" The setup consists of a 2D square ([0,1] X [0,1]) with 25
particles.
"""
xc = numpy.arange(0,1.0, 0.2)
x, y = numpy.meshgrid(xc,xc)
self.x = x = x.ravel()
self.y = y = y.ravel()
self.h = h = numpy.ones_like(x) * 0.25
dx = dy = 0.2
self.dx = dx
self.block_size = 0.5
self.cell_size = 0.5
self.pa = pa = base.get_particle_array(name="test", x=x, y=y, h=h)
self.cm = cm = parallel.ParallelCellManager(arrays_to_bin=[pa,],
max_radius_scale=2.0,
dimension=2.0,
load_balancing=False,
initialize=False)
self.block_000_indices = 0,1,2,5,6,7,10,11,12
self.block_100_indices = 3,4,8,9,13,14
self.block_010_indices = 15,16,17,20,21,22
self.block_110_indices = 18,19,23,24
def create_particles_3d(**kwargs):
x, y, z = numpy.mgrid[0.25:0.75+1e-10:dx,
0.25:0.75+1e-10:dx,
0.25:0.75+1e-10:dx]
x = x.ravel()
y = y.ravel()
z = z.ravel()
np = len(x)
u = random.random(np) * 0
v = random.random(np) * 0
w = random.random(np) * 0
m = numpy.ones_like(x) * dx**3
vol_per_particle = numpy.power(0.5**3/np ,1.0/3.0)
radius = 2 * vol_per_particle
print "Using smoothing length: ", radius
h = numpy.ones_like(x) * radius
fluid = base.get_particle_array(name="fluid", type=base.Fluid,
x=x, y=y, z=z,
u=u, v=v, w=w,
m=m,h=h)
print "Number of particles: ", fluid.get_number_of_particles()
return [fluid,]
def create_particles_2d(**kwargs):
x, y = numpy.mgrid[0.25:0.75+1e-10:dx, 0.25:0.75+1e-10:dx]
x = x.ravel()
y = y.ravel()
np = len(x)
u = numpy.zeros_like(x)
v = numpy.zeros_like(x)
m = numpy.ones_like(x) * dx**2
vol_per_particle = numpy.power(0.5**2/np ,1.0/2.0)
radius = 2 * vol_per_particle
print "Using smoothing length: ", radius
h = numpy.ones_like(x) * radius
fluid = base.get_particle_array(name="fluid", type=base.Fluid,
x=x, y=y,
u=u, v=v,
m=m,
h=h)
print "Number of particles: ", fluid.get_number_of_particles()
return [fluid,]
def get_circular_patch(name="", type=0, dx=0.05):
x,y = numpy.mgrid[-1.05:1.05+1e-4:dx, -1.05:1.05+1e-4:dx]
x = x.ravel()
y = y.ravel()
m = numpy.ones_like(x)*dx*dx
h = numpy.ones_like(x)*2*dx
rho = numpy.ones_like(x)
p = 0.5*1.0*100*100*(1 - (x**2 + y**2))
cs = numpy.ones_like(x) * 100.0
u = 0*x
v = 0*y
indices = []
for i in range(len(x)):
if numpy.sqrt(x[i]*x[i] + y[i]*y[i]) - 1 > 1e-10:
indices.append(i)
pa = base.get_particle_array(x=x, y=y, m=m, rho=rho, h=h, p=p, u=u, v=v,
cs=cs,name=name, type=type)
la = base.LongArray(len(indices))
la.set_data(numpy.array(indices))
pa.remove_particles(la)
pa.set(idx=numpy.arange(len(pa.x)))
return pa
def setUp(self):
# create a dummy particle array
self.pa = pa = base.get_particle_array(name="test",
cl_precision="single",
type=1)
# create a simple solver with one operation
self.s = s = solver.Solver(3, solver.EulerIntegrator)
# add the velocity gradient operation
s.add_operation(solver.SPHOperation(
sph.VelocityGradient3D.withargs(), on_types=[1,],
from_types=[1,], id="vgrad")
)
# add the stress rate function
s.add_operation(solver.SPHIntegration(
sph.HookesDeviatoricStressRate3D.withargs(),
on_types=[1,], id="stress_rate")
)
particles = base.Particles(arrays=[pa,])
s.setup(particles)
self.integrator = s.integrator
def setUp(self):
if not solver.HAS_CL:
try:
import nose.plugins.skip as skip
reason = "PyOpenCL not installed!"
raise skip.SkipTest(reason)
except ImportError:
pass
self.np = np = 101
self.x = x = numpy.linspace(0, 1, np)
self.m = m = numpy.ones_like(x) * (x[1] - x[0])
self.h = h = 2*self.m
pa = base.get_particle_array(name="test", cl_precision="single",
x=x, m=m, h=h)
particles = base.CLParticles([pa,])
kernel = base.CubicSplineKernel(dim=1)
func = sph.GravityForce.withargs(gx=-1, gy=-1, gz=-1).get_func(pa,pa)
self.calc = sph.CLCalc(particles, sources=[pa,], dest=pa,
updates=['u','v','w'], kernel=kernel,
funcs=[func,])
if solver.HAS_CL:
self.context = solver.create_some_context()
def setUp(self):
""" The setup consists of a 2D square ([0,1] X [0,1]) with 25
particles.
"""
xc = numpy.arange(0,1.0, 0.2)
x, y = numpy.meshgrid(xc,xc)
self.x = x = x.ravel()
self.y = y = y.ravel()
self.h = h = numpy.ones_like(x) * 0.25
dx = dy = 0.2
self.dx = dx
self.block_size = 0.5
self.cell_size = 0.5
self.pa = pa = base.get_particle_array(name="test", x=x, y=y, h=h)
self.cm = cm = parallel.ParallelCellManager(arrays_to_bin=[pa,],
max_radius_scale=2.0,
dimension=2.0,
load_balancing=False,
initialize=False)
self.block_000_indices = 0,1,2,5,6,7,10,11,12
self.block_100_indices = 3,4,8,9,13,14
self.block_010_indices = 15,16,17,20,21,22
self.block_110_indices = 18,19,23,24
def setUp(self):
""" A Dummy fluid solver is created with the following operations
(i) -- Equation of State
(ii) -- Density Rate
(iii)-- Momentum Equation Pressure Gradient
(iv) -- Momentum Equation Viscosity
"""
self.kernel = kernel = base.CubicSplineKernel(dim = 2)
self.solver = s = solver.Solver(dim=2,
integrator_type=solver.EulerIntegrator)
s.default_kernel = kernel
self.particles = base.Particles(arrays=[base.get_particle_array()])
# Create some replacement operations
self.visc = solver.SPHIntegration(
sph.MorrisViscosity, on_types=[Fluids],
from_types = [Fluids, Solids], updates=['u','v'], id='visc'
)
self.summation_density = solver.SPHOperation(
sph.SPHRho, on_types=[Fluids, Solids],
updates=['rho'], id='sd'
)
def get_boundary_particles():
""" Get the particles corresponding to the dam and fluids """
xb1, yb1 = geom.create_2D_tank(x1=0, y1=0,
x2=container_width, y2=container_height,
dx=dx)
xb2, yb2 = geom.create_2D_tank(x1=-dx/2, y1=-dx/2,
x2=container_width, y2=container_height,
dx=dx)
xb = numpy.concatenate((xb1, xb2))
yb = numpy.concatenate((yb1, yb2))
hb = numpy.ones_like(xb)*h
mb = numpy.ones_like(xb)*dx*dy*ro*0.5
rhob = numpy.ones_like(xb) * ro
cb = numpy.ones_like(xb)*co
boundary = base.get_particle_array(cl_precision="single",
name="boundary", type=Solid,
x=xb, y=yb, h=hb, rho=rhob, cs=cb,
m=mb)
print 'Number of Boundary particles: ', len(xb)
return boundary
def test_load_output(self):
self.setup_solver()
s = self.solver
s.particles = self.particles
d = tempfile.mkdtemp()
s.output_directory = d
s.detailed_output = True
s.fname = 'temp_solver'
s.dt = 0
x = numpy.arange(10)
pa = base.get_particle_array(name='pa', x=x)
#pa = base.ParticleArray(name='pa')
pa.add_property(dict(name='q', data=-x))
s.particles.arrays[0] = pa
old_props = {}
for name,prop in s.particles.arrays[0].properties.iteritems():
old_props[name] = prop.get_npy_array().copy()
s.dump_output(s.dt)
pa.q = pa.x = pa.z
ret = s.load_output('?')
self.assertEqual(ret, ["0"])
s.load_output('0')
try:
for name,prop in s.particles.arrays[0].properties.iteritems():
self.assertTrue(numpy.allclose(prop,old_props[name]),
msg='prop:%s\nold:%s, new:%s'%(name,old_props[name], pa.get(name)))
finally:
shutil.rmtree(d, True)
def setUp(self):
""" The setup consists of four particles placed at the
vertices of a unit square.
The function tested is
..math::
\frac{DU_a}{Dt} = \frac{1}{2}\sum_{b=1}^{N}m_b\left[
\left(\frac{p_a}{\rho_a^2} + \frac{p_b}{\rho_b^2}\right)\,(v_a -
v_b)\right]\,\nabla_a \cdot W_{ab}
The mass of each particle is 1
"""
self.precision = "single"
self.np = 4
x = numpy.array([0, 0, 1, 1], numpy.float64)
y = numpy.array([0, 1, 1, 0], numpy.float64)
z = numpy.zeros_like(x)
m = numpy.ones_like(x)
u = numpy.array([1, 0, 0, -1], numpy.float64)
p = numpy.array([0, 0, 1, 1], numpy.float64)
tmpx = numpy.zeros_like(x)
tmpy = numpy.zeros_like(x)
tmpz = numpy.zeros_like(x)
self.pa = pa = base.get_particle_array(name="test", x=x, y=y, z=z,
m=m, u=u, p=p,
tmpx=tmpx, tmpy=tmpy, tmpz=tmpz,
cl_precision=self.precision)
env = sph.EnergyEquationNoVisc.withargs()
ewv = sph.EnergyEquation.withargs(alpha=1.0, beta=1.0,
gamma=1.4, eta=0.1)
self.env = env.get_func(pa,pa)
self.ewv = ewv.get_func(pa,pa)
self.env.kernel = base.CubicSplineKernel(dim=2)
self.env.nbr_locator = \
base.Particles.get_neighbor_particle_locator(pa,
pa)
self.ewv.kernel = base.CubicSplineKernel(dim=2)
self.ewv.nbr_locator = \
base.Particles.get_neighbor_particle_locator(pa,
pa)
self.setup_cl()
def setUp(self):
""" The setup consists of four particles placed at the
vertices of a unit square. The force function to be tested is:
..math::
f_i = \sum_{j=1}^{4} \frac{m_j}{|x_j - x_i|^3 +
\eps}(x_j - x_i)
The mass of each particle is 1
"""
self.precision = "single"
self.np = 4
x = numpy.array([0, 0, 1, 1], numpy.float64)
y = numpy.array([0, 1, 1, 0], numpy.float64)
z = numpy.zeros_like(x)
m = numpy.ones_like(x)
tmpx = numpy.zeros_like(x)
tmpy = numpy.zeros_like(x)
tmpz = numpy.zeros_like(x)
self.pa = pa = base.get_particle_array(name="test",
x=x,
y=y,
z=z,
m=m,
tmpx=tmpx,
tmpy=tmpy,
tmpz=tmpz,
cl_precision=self.precision)
self.func = func = sph.NBodyForce.get_func(pa, pa)
self.eps = func.eps
if solver.HAS_CL:
self.ctx = ctx = cl.create_some_context()
self.q = q = cl.CommandQueue(ctx)
pa.setup_cl(ctx, q)
pysph_root = solver.get_pysph_root()
template = solver.cl_read(
path.join(pysph_root, "sph/funcs/external_force.clt"),
function_name=func.cl_kernel_function_name,
precision=self.precision)
prog_src = solver.create_program(template, func)
self.prog = cl.Program(ctx,
prog_src).build(solver.get_cl_include())
def setUp(self):
""" The setup consists of four particles placed at the
vertices of a unit square. The pressure gradient term to be
tested is
..math::
\frac{\nablaP}{\rho}_i = \sum_{j=1}^{4}
-m_j(\frac{Pa}{\rho_a^2} + \frac{Pb}{\rho_b^2})\nabla W_{ab}
The mass of each particle is 1
"""
self.precision = "single"
self.np = 4
x = numpy.array([0, 0, 1, 1], numpy.float64)
y = numpy.array([0, 1, 1, 0], numpy.float64)
z = numpy.zeros_like(x)
m = numpy.ones_like(x)
u = numpy.array([1, 0, 0, -1], numpy.float64)
p = numpy.array([0, 0, 1, 1], numpy.float64)
tmpx = numpy.zeros_like(x)
tmpy = numpy.zeros_like(x)
tmpz = numpy.zeros_like(x)
self.pa = pa = base.get_particle_array(name="test", x=x, y=y, z=z,
m=m, u=u, p=p,
tmpx=tmpx, tmpy=tmpy, tmpz=tmpz,
cl_precision=self.precision)
grad_func = sph.SPHPressureGradient.withargs()
mom_func = sph.MomentumEquation.withargs(alpha=1.0, beta=1.0,
gamma=1.4, eta=0.1)
self.grad_func = grad_func.get_func(pa,pa)
self.mom_func = mom_func.get_func(pa,pa)
self.grad_func.kernel = base.CubicSplineKernel(dim=2)
self.grad_func.nbr_locator = \
base.Particles.get_neighbor_particle_locator(pa,
pa)
self.mom_func.kernel = base.CubicSplineKernel(dim=2)
self.mom_func.nbr_locator = \
base.Particles.get_neighbor_particle_locator(pa,
pa)
self.setup_cl()
def setUp(self):
"""The setup consists of ten particles with the following cellids:
cellids = [7, 5, 6, 1, 8, 2, 5, 3, 8, 7]
Nine cells are created by a manager that uses cells for
binning. The constant cell size of the domain manager is used
to create this special structure.
"""
self.cl_precision = cl_precision = "single"
self.x = x = numpy.array( [0.3, 0.6, 0.1, 0.3, 0.6,
0.6, 0.6, 0.1, 0.6, 0.3] )
self.y = y = numpy.array( [0.6, 0.3, 0.6, 0.1, 0.6,
0.1, 0.3, 0.3, 0.6, 0.6] )
self.np = len(x)
# Construct the ParticleArray to be used with the Cython manager
self.cy_pa = base.get_particle_array(
name='test', cl_precision=cl_precision,
x=x, y=y)
# Construct the ParticleArray to be used with the OpenCL manager
self.cl_pa = base.get_particle_array(
name="test", cl_precision=self.cl_precision,
x=x, y=y)
# constant cell size to use
self.const_cell_size = 0.25
# constants based on the initial data
self.mx, self.my = 0.1, 0.1
self.Mx, self.My = 0.6, 0.6
self.ncells = 9
self.ncellsp1 = 10
# perform the individual manager setup
self._setup()
def setUp(self):
"""
Setup a default simulation in 2D. The setup consists of four particles
on a circle of radius 2/pi. The particles are constrained to move
along the circle with forcing functions defined in
pysph/sph/funcs/external_forces.pyx
With the choice of radius and unit magnitude of velocity, the particles
move by pi/2 radians in 1 second.
"""
self.r = r = 2. / numpy.pi
self.x = x = numpy.array([1.0, 0.0, -1.0, 0.0])
self.y = y = numpy.array([0.0, 1.0, 0.0, -1.0])
x *= r
y *= r
p = numpy.zeros_like(x)
e = numpy.zeros_like(x)
h = numpy.ones_like(x)
self.pa = pa = base.get_particle_array(x=x,
y=y,
p=p,
e=e,
h=h,
name='tmp')
self.particles = particles = base.Particles(arrays=[pa])
self.kernel = base.CubicSplineKernel(dim=2)
circlex = solver.SPHIntegration(sph.MoveCircleX,
from_types=[Fluids],
on_types=[Fluids],
updates=['x', 'y'],
id='circlex')
circley = solver.SPHIntegration(sph.MoveCircleY,
from_types=[Fluids],
on_types=[Fluids],
updates=['x', 'y'],
id='circley')
self.calcx = circlex.get_calcs(particles, self.kernel)
self.calcy = circley.get_calcs(particles, self.kernel)
self.calcs = []
self.calcs.extend(self.calcx)
self.calcs.extend(self.calcy)
self.integrator = Integrator(particles=particles, calcs=self.calcs)
self.setup()
def test_sph_calc():
x = numpy.array([
0,
])
y = numpy.array([
0,
])
z = numpy.array([
0,
])
h = numpy.ones_like(x)
pa = base.get_particle_array(name="test",
x=x,
y=y,
z=z,
h=h,
_tmpx=z,
_tmpy=z,
_tmpz=z)
particles = base.Particles(arrays=[
pa,
])
kernel = base.CubicSplineKernel(dim=1)
vector_force1 = sph.VectorForce.withargs(force=base.Point(1, 1, 1))
vector_force2 = sph.VectorForce.withargs(force=base.Point(1, 1, 1))
func1 = vector_force1.get_func(pa, pa)
func2 = vector_force2.get_func(pa, pa)
calc = sph.SPHCalc(particles=particles,
sources=[pa, pa],
dest=pa,
kernel=kernel,
funcs=[func1, func2],
updates=['u', 'v', 'w'],
integrates=True)
# evaluate the calc. Accelerations are stored in _tmpx, _tmpy and _tmpz
calc.sph('_tmpx', '_tmpy', '_tmpz')
tmpx, tmpy, tmpz = pa.get('_tmpx', '_tmpy', '_tmpz')
# the acceleration should be 2 in each direction
assert (abs(tmpx[0] - 2.0) < 1e-16)
assert (abs(tmpy[0] - 2.0) < 1e-16)
assert (abs(tmpz[0] - 2.0) < 1e-16)
def get_fluid_particles(name="fluid"):
x, y = get_2D_staggered_grid(base.Point(dx, dx), base.Point(dx/2, dx/2),
base.Point(1.0,2.0), dx)
hf = numpy.ones_like(x) * h
mf = numpy.ones_like(x) * dx * dy * ro
rhof = numpy.ones_like(x) * ro
csf = numpy.ones_like(x) * co
fluid = base.get_particle_array(name=name, type=Fluid,
x=x, y=y, h=hf, m=mf, rho=rhof, cs=csf)
return fluid
def setUp(self):
""" The setup consists of four particles placed at the
vertices of a unit square. The force function to be tested is:
..math::
f_i = \sum_{j=1}^{4} \frac{m_j}{|x_j - x_i|^3 +
\eps}(x_j - x_i)
The mass of each particle is 1
"""
self.precision = "single"
self.np = 4
x = numpy.array([0, 0, 1, 1], numpy.float64)
y = numpy.array([0, 1, 1, 0], numpy.float64)
z = numpy.zeros_like(x)
m = numpy.ones_like(x)
tmpx = numpy.zeros_like(x)
tmpy = numpy.zeros_like(x)
tmpz = numpy.zeros_like(x)
self.pa = pa = base.get_particle_array(name="test", x=x, y=y, z=z,
m=m, tmpx=tmpx, tmpy=tmpy,
tmpz=tmpz,
cl_precision=self.precision)
self.func = func = sph.NBodyForce.get_func(pa, pa)
self.eps = func.eps
if solver.HAS_CL:
self.ctx = ctx = cl.create_some_context()
self.q = q = cl.CommandQueue(ctx)
pa.setup_cl(ctx, q)
pysph_root = solver.get_pysph_root()
template = solver.cl_read(
path.join(pysph_root, "sph/funcs/external_force.clt"),
function_name=func.cl_kernel_function_name,
precision=self.precision)
prog_src = solver.create_program(template, func)
self.prog = cl.Program(ctx, prog_src).build(solver.get_cl_include())
def setUp(self):
""" Setup for SPHOperationTestCase
Setup:
------
Create two particle arrays, one Fluid and one Solid.
Instantiate the class with a default function `SPHRho` with
various combinations of the from and on types and check for the
filtering of the arrays.
"""
x = numpy.linspace(0, 1, 11)
h = numpy.ones_like(x) * 2 * (x[1] - x[0])
#create the fluid particle array
self.fluid = base.get_particle_array(name='fluid',
type=Fluid,
x=x,
h=h)
#create the solid particle array
self.solid = base.get_particle_array(name="solid",
type=Solid,
x=x,
h=h)
#create the particles
self.particles = particles = base.Particles(
arrays=[self.fluid, self.solid])
#set the kernel
self.kernel = base.CubicSplineKernel()
def create_particles(**kwargs):
x = numpy.zeros(0)
y = numpy.zeros(0)
u = numpy.zeros(0)
v = numpy.zeros(0)
m = numpy.zeros(0)
rad = 0.0
for j in range(1, n+1):
npnts = 4*j
dtheta = 2*pi/npnts
theta = numpy.arange(0, 2*pi-1e-10, dtheta)
rad = rad + dr
_x = rad*cos(theta)
_y = rad*sin(theta)
_u = -cos(theta)
_v = -sin(theta)
if j == 1:
_m = numpy.ones_like(_x) * m1
else:
_m = numpy.ones_like(_x) * (2.0*j - 1.0)/(j) * m1
x = numpy.concatenate( (x, _x) )
y = numpy.concatenate( (y, _y) )
m = numpy.concatenate( (m, _m) )
u = numpy.concatenate( (u, _u) )
v = numpy.concatenate( (v, _v) )
rho = numpy.ones_like(x) * 1.0
h = numpy.ones_like(x) * h0
p = numpy.ones_like(x) * 0.0
e = numpy.ones_like(x) * 0.0
rhop = numpy.ones_like(x)
div = numpy.zeros_like(x)
q = numpy.zeros_like(x)
fluid = base.get_particle_array(name="fluid", type=base.Fluid,
x=x,y=y,m=m,rho=rho, h=h,
u=u,v=v,p=p,e=e,
rhop=rhop, q=q, div=div)
print "Number of fluid particles = ", fluid.get_number_of_particles()
return fluid
def setUp(self):
parrays = []
for i in range(np):
x = numpy.array([x0[i]])
y = numpy.array([y0[i]])
z = numpy.array([z0[i]])
h = numpy.array([h0[i]])
mi = numpy.array([m[i]])
name = 'array' + str(i)
pa = base.get_particle_array(name=name, x=x, y=y, z=z, h=h, m=mi)
parrays.append(pa)
self.parrays = parrays
self.particles = base.Particles(arrays=parrays)
kernel = base.CubicSplineKernel(dim=3)
self.solver = s = solver.Solver(dim=3,
integrator_type=solver.EulerIntegrator)
# NBodyForce operation
s.add_operation(
solver.SPHIntegration(sph.NBodyForce.withargs(eps=eps),
on_types=[Fluids],
from_types=[Fluids],
updates=['u', 'v', 'w'],
id='nbody_force'))
# position stepping
s.add_operation_step([Fluids])
# time step and final time
s.set_final_time(tf)
s.set_time_step(dt)
# setup the integrator
s.setup_integrator(self.particles)
def setUp(self):
""" The setup consists of four particles placed at the
vertices of a unit square.
The function tested is
..math::
p_a = (\gamma - 1.0)\rho_a U_a
cs_a = \sqrt( (\gamma - 1.0) U_a )
"""
self.precision = "single"
self.np = 4
x = numpy.array([0, 0, 1, 1], numpy.float64)
y = numpy.array([0, 1, 1, 0], numpy.float64)
z = numpy.zeros_like(x)
m = numpy.ones_like(x)
e = numpy.array([1, 2, 1, 2], numpy.float64)
rho = numpy.array([2, 1, 2, 1], numpy.float64)
tmpx = numpy.zeros_like(x)
tmpy = numpy.zeros_like(x)
tmpz = numpy.zeros_like(x)
self.pa = pa = base.get_particle_array(name="test", x=x, y=y, z=z,
m=m, e=e, rho=rho,
tmpx=tmpx, tmpy=tmpy, tmpz=tmpz,
cl_precision=self.precision)
ideal = sph.IdealGasEquation.withargs(gamma=1.4)
self.ideal = ideal.get_func(pa,pa)
self.ideal.nbr_locator = \
base.Particles.get_neighbor_particle_locator(pa,
pa)
self.setup_cl()
def get_circular_patch(name="", type=0, dx=0.025):
x, y = numpy.mgrid[-1.05:1.05 + 1e-4:dx, -1.05:1.05 + 1e-4:dx]
x = x.ravel()
y = y.ravel()
m = numpy.ones_like(x) * dx * dx
h = numpy.ones_like(x) * 2 * dx
rho = numpy.ones_like(x)
p = 0.5 * 1.0 * 100 * 100 * (1 - (x**2 + y**2))
cs = numpy.ones_like(x) * 100.0
u = -100 * x
v = 100 * y
indices = []
for i in range(len(x)):
if numpy.sqrt(x[i] * x[i] + y[i] * y[i]) - 1 > 1e-10:
indices.append(i)
pa = base.get_particle_array(x=x,
y=y,
m=m,
rho=rho,
h=h,
p=p,
u=u,
v=v,
cs=cs,
name=name,
type=type)
la = base.LongArray(len(indices))
la.set_data(numpy.array(indices))
pa.remove_particles(la)
pa.set(idx=numpy.arange(len(pa.x)))
print 'Number of particles: ', len(pa.x)
return pa
def get_fluid():
""" Get the fluid particle array """
x, y = numpy.mgrid[dx:box_length - 1e-10:dx, dx:box_height - 1e-10:dx]
xf, yf = x.ravel(), y.ravel()
mf = numpy.ones_like(xf) * m
hf = numpy.ones_like(xf) * h
rhof = numpy.ones_like(xf) * ro
cf = numpy.ones_like(xf) * co
pf = numpy.zeros_like(xf)
fluid = base.get_particle_array(name="fluid",
type=Fluid,
x=xf,
y=yf,
h=hf,
rho=rhof,
c=cf,
p=pf)
# remove indices within the square
indices = []
np = fluid.get_number_of_particles()
x, y = fluid.get('x', 'y')
for i in range(np):
if 1.0 - dx / 2 <= x[i] <= 2.0 + dx / 2:
if 2.0 - dx / 2 <= y[i] <= 3.0 + dx / 2:
indices.append(i)
to_remove = base.LongArray(len(indices))
to_remove.set_data(numpy.array(indices))
fluid.remove_particles(to_remove)
return fluid
def get_dummy_particles():
x, y = numpy.mgrid[-5 * dx:box_length + 5 * dx + 1e-10:dx,
-5 * dx:box_height + 5 * dx + 1e-10:dx]
xd, yd = x.ravel(), y.ravel()
md = numpy.ones_like(xd) * m
hd = numpy.ones_like(xd) * h
rhod = numpy.ones_like(xd) * ro
cd = numpy.ones_like(xd) * co
pd = numpy.zeros_like(xd)
dummy_fluid = base.get_particle_array(name="dummy_fluid",
type=Fluid,
x=xd,
y=yd,
h=hd,
rho=rhod,
c=cd,
p=pd)
# remove indices within the square
indices = []
np = dummy_fluid.get_number_of_particles()
x, y = dummy_fluid.get('x', 'y')
for i in range(np):
if -dx / 2 <= x[i] <= box_length + dx / 2:
if -dx / 2 <= y[i] <= box_height + dx / 2:
indices.append(i)
to_remove = base.LongArray(len(indices))
to_remove.set_data(numpy.array(indices))
dummy_fluid.remove_particles(to_remove)
return dummy_fluid
def standard_shock_tube_data(name="", type=0):
""" Standard 400 particles shock tube problem """
dxl = 0.001875
dxr = dxl * 4
x = numpy.ones(400, float)
x[:320] = numpy.arange(-0.6, -dxl + 1e-4, dxl)
x[320:] = numpy.arange(dxr, 0.6 + 1e-4, dxr)
m = numpy.ones_like(x) * dxl
h = numpy.ones_like(x) * 2 * dxr
rho = numpy.ones_like(x)
rho[320:] = 0.25
u = numpy.zeros_like(x)
e = numpy.ones_like(x)
e[:320] = 2.5
e[320:] = 1.795
p = 0.4 * rho * e
cs = numpy.sqrt(1.4 * p / rho)
idx = numpy.arange(400)
return base.get_particle_array(name=name,
x=x,
m=m,
h=h,
rho=rho,
p=p,
e=e,
cs=cs,
type=type,
idx=idx)
def setUp(self):
""" Create some default particle properties """
x = numpy.linspace(0, 1, 11)
m = numpy.ones_like(x) * (x[1] - x[0])
rho = numpy.ones_like(x)
h = 2 * m
pa = base.get_particle_array(cl_precision="single",
x=x,
h=h,
m=m,
rho=rho)
self.pa = pa
platforms = cl.get_platforms()
devices = platforms[0].get_devices()
self.ctx = ctx = cl.Context(devices)
queue = cl.CommandQueue(ctx, devices[0])
self.queue = queue
def get_fluid_particles():
x, y = get_2D_staggered_grid(base.Point(dx, dx),
base.Point(dx / 2, dx / 2),
base.Point(1.0, 2.0), dx)
print 'Number of fluid particles: ', len(x)
hf = numpy.ones_like(x) * h
mf = numpy.ones_like(x) * dx * dy * ro * 0.5
rhof = numpy.ones_like(x) * ro
csf = numpy.ones_like(x) * co
fluid = base.get_particle_array(name="fluid",
type=Fluid,
x=x,
y=y,
h=hf,
m=mf,
rho=rhof,
cs=csf)
return fluid
print("Device compute units:", device.max_compute_units)
precision_types = ['single']
device_extensions = device.get_info(cl.device_info.EXTENSIONS)
if 'cl_khr_fp64' in device_extensions:
precision_types.append('double')
for prec in precision_types:
print "\n========================================================"
print "Summation Density Comparison using %s precision" % (prec)
pa = base.get_particle_array(cl_precision=prec,
name="test",
x=x,
h=h,
m=m,
rho=rho)
particles = base.Particles(arrays=[
pa,
],
locator_type=NSquareNeighborLocator)
kernel = base.CubicSplineKernel(dim=1)
# create the function
func = sph.SPHRho.get_func(pa, pa)
# create the CLCalc object
cl_calc = sph.CLCalc(particles=particles,
app.process_command_line()
#global variables
h = 2.097e-2
dx = dy = h/(1.3)
g = -9.81
xf = numpy.array([0])
yf = numpy.array([0.3])
hf = numpy.array([h])
mf = numpy.array([1.0])
vf = numpy.array([0.0])
cf = numpy.array([25.0])
rhof = numpy.array([1.0])
fluid = base.get_particle_array(name="fluid", type=Fluid, x=xf, y=yf,
h=hf, m=mf, rho=rhof, v=vf, cs=cf)
#generate the boundary
l = base.Line(base.Point(-.5), 1.0, 0)
g = base.Geometry('line', [l], False)
g.mesh_geometry(dx)
boundary = g.get_particle_array(re_orient=True)
boundary.m[:] = 1.0
particles = base.Particles(arrays=[fluid, boundary])
app.particles = particles
kernel = base.HarmonicKernel(dim=2, n=3)
def setUp(self):
""" The setup consists of two fluid particle arrays, each
having one particle. The fluids are acted upon by an external
vector force and gravity.
Comparison is made with the PySPH integration of the system.
"""
x1 = numpy.array([
-0.5,
])
y1 = numpy.array([
1.0,
])
x2 = numpy.array([
0.5,
])
y2 = numpy.array([
1.0,
])
tmpx1 = numpy.ones_like(x1)
tmpx2 = numpy.ones_like(x2)
self.f1 = base.get_particle_array(name="fluid1",
x=x1,
y=y1,
tmpx=tmpx1)
self.f2 = base.get_particle_array(name="fluid2",
x=x2,
y=y2,
tmpx=tmpx2)
self.particles = base.Particles(arrays=[self.f1, self.f2])
self.kernel = kernel = base.CubicSplineKernel(dim=2)
gravity = solver.SPHIntegration(sph.GravityForce.withargs(gy=-10.0),
on_types=[Fluid],
updates=['u', 'v'],
id='gravity')
force = solver.SPHIntegration(sph.GravityForce.withargs(gx=-10.0),
on_types=[Fluid],
updates=['u', 'v'],
id='force')
position = solver.SPHIntegration(
sph.PositionStepping,
on_types=[Fluid],
updates=['x', 'y'],
id='step',
)
gravity.calc_type = sph.CLCalc
force.calc_type = sph.CLCalc
position.calc_type = sph.CLCalc
gravity_calcs = gravity.get_calcs(self.particles, kernel)
force_calcs = force.get_calcs(self.particles, kernel)
position_calcs = position.get_calcs(self.particles, kernel)
self.calcs = calcs = []
calcs.extend(gravity_calcs)
calcs.extend(force_calcs)
calcs.extend(position_calcs)
self.integrator = CLIntegrator(self.particles, calcs)
self.ctx = ctx = cl.create_some_context()
self.integrator.setup_integrator(ctx)
self.queue = calcs[0].queue
self.dt = 0.1
self.nsteps = 10
vf = numpy.array([0.0])
cf = numpy.array([25.0])
rhof = numpy.array([1.0])
#define the staggered grid of boundary particles
xb = numpy.array([-dx, 0, dx, -dx / 2, dx / 2])
yb = numpy.array([0, 0, 0, -dy / 2, -dy / 2])
hb = numpy.ones_like(xb) * h
mb = numpy.ones_like(xb)
rhob = numpy.ones_like(xb)
fluid = base.get_particle_array(name="fluid",
type=0,
x=xf,
y=yf,
h=hf,
m=mf,
rho=rhof,
v=vf,
cf=cf)
boundary = base.get_particle_array(name="boundary",
type=1,
x=xb,
y=yb,
h=hb,
m=mb,
rho=rhob)
particles = base.Particles(arrays=[boundary, fluid])
app.particles = particles
def get_boundary_particles():
""" Get the particles corresponding to the dam and fluids """
#left wall
ylw = get_1D_grid(0, container_height, dy)
xlw = numpy.zeros_like(ylw)
nb1 = len(ylw)
#bottom
xbs = get_1D_grid(dx, container_width + dx, dx)
ybs = numpy.zeros_like(xbs)
nb3 = len(xbs)
max_xb = numpy.max(xbs)
#staggered left wall
yslw = get_1D_grid(-dx / 2, container_height, dx)
xslw = numpy.ones_like(yslw) * -dx / 2
nb4 = len(yslw)
#staggered bottom
xsb = get_1D_grid(dx / 2, container_width + dx + dx, dx)
ysb = numpy.ones_like(xsb) * -dy / 2
nb6 = len(xsb)
max_xsb = numpy.max(xsb)
#right wall
yrw = numpy.arange(dx, container_height, dx)
xrw = numpy.ones_like(yrw) * max_xb
nb2 = len(yrw)
#staggered right wall
ysrw = numpy.arange(dy / 2, container_height, dy)
xsrw = numpy.ones_like(ysrw) * max_xsb
nb5 = len(ysrw)
nb = nb1 + nb2 + nb3 + nb4 + nb5 + nb6
print "Number of Boundary Particles: ", nb
xb = numpy.zeros(nb, float)
yb = numpy.zeros(nb, float)
idx = 0
xb[:nb1] = xlw
yb[:nb1] = ylw
idx += nb1
xb[idx:idx + nb2] = xrw
yb[idx:idx + nb2] = yrw
idx += nb2
xb[idx:idx + nb3] = xbs
yb[idx:idx + nb3] = ybs
idx += nb3
xb[idx:idx + nb4] = xslw
yb[idx:idx + nb4] = yslw
idx += nb4
xb[idx:idx + nb5] = xsrw
yb[idx:idx + nb5] = ysrw
idx += nb5
xb[idx:] = xsb
yb[idx:] = ysb
hb = numpy.ones_like(xb) * h
mb = numpy.ones_like(xb) * dx * dy * ro * 0.5
rhob = numpy.ones_like(xb) * ro
cb = numpy.ones_like(xb) * co
boundary = base.get_particle_array(name="boundary",
type=Solid,
x=xb,
y=yb,
h=hb,
rho=rhob,
cs=cb,
m=mb)
return boundary
h = numpy.ones_like(x) * 0.2
m = numpy.ones_like(x) * 0.1
rho = numpy.ones_like(x)
fval = x * x
#create the particles on processor 1
if rank == 1:
x = numpy.linspace(1.1, 2, 10)
h = numpy.ones_like(x) * 0.2
m = numpy.ones_like(x) * 0.1
rho = numpy.ones_like(x)
fval = x * x
#create the particles in parallel without load balancing
kernel = CubicSplineKernel(dim=1)
pa = get_particle_array(x=x, h=h, m=m, fval=fval, rho=rho)
particles = Particles([pa], in_parallel=True, load_balancing=False)
#make sure the particles need updating
particles.update()
#choose the function and the sph calc
func = sph.SPHRho(pa, pa)
calc = sph.SPHCalc(particles=particles,
kernel=kernel,
func=func,
updates=['rho'],
integrates=False)
tmpx = pa.get('tmpx', only_real_particles=False)
logger.debug('tempx for all particles %s' % (tmpx))
def setUp(self):
""" The setup consists of four particles placed at the
vertices of a unit square.
The function tested is
..math::
\frac{DU_a}{Dt} = \frac{1}{2}\sum_{b=1}^{N}m_b\left[
\left(\frac{p_a}{\rho_a^2} + \frac{p_b}{\rho_b^2}\right)\,(v_a -
v_b)\right]\,\nabla_a \cdot W_{ab}
The mass of each particle is 1
"""
self.precision = "single"
self.np = 4
x = numpy.array([0, 0, 1, 1], numpy.float64)
y = numpy.array([0, 1, 1, 0], numpy.float64)
z = numpy.zeros_like(x)
m = numpy.ones_like(x)
u = numpy.array([1, 0, 0, -1], numpy.float64)
p = numpy.array([0, 0, 1, 1], numpy.float64)
tmpx = numpy.zeros_like(x)
tmpy = numpy.zeros_like(x)
tmpz = numpy.zeros_like(x)
self.pa = pa = base.get_particle_array(name="test",
x=x,
y=y,
z=z,
m=m,
u=u,
p=p,
tmpx=tmpx,
tmpy=tmpy,
tmpz=tmpz,
cl_precision=self.precision)
env = sph.EnergyEquationNoVisc.withargs()
ewv = sph.EnergyEquation.withargs(alpha=1.0,
beta=1.0,
gamma=1.4,
eta=0.1)
self.env = env.get_func(pa, pa)
self.ewv = ewv.get_func(pa, pa)
self.env.kernel = base.CubicSplineKernel(dim=2)
self.env.nbr_locator = \
base.Particles.get_neighbor_particle_locator(pa,
pa)
self.ewv.kernel = base.CubicSplineKernel(dim=2)
self.ewv.nbr_locator = \
base.Particles.get_neighbor_particle_locator(pa,
pa)
self.setup_cl()
h = h = numpy.ones_like(x) * 0.25
dx = dy = 0.2
dx = dx
block_size = 0.5
cell_size = 0.5
block_000_indices = 0, 1, 2, 5, 6, 7, 10, 11, 12
block_100_indices = 3, 4, 8, 9, 13, 14
block_010_indices = 15, 16, 17, 20, 21, 22
block_110_indices = 18, 19, 23, 24
name = "rank" + str(pid)
pa = base.get_particle_array(name="test", x=x, y=y, h=h)
pa.x += 1.0 * pid
pa.x += 1e-10
pa.y += 1.0 * (pid % 2)
pa.y += 1e-10
# create the cell manager
cm = parallel.ParallelCellManager(arrays_to_bin=[
pa,
],
max_radius_scale=2.0,
dimension=2.0,
load_balancing=False,
initialize=False,
def setUp(self):
""" The setup consists of four particles placed at the
vertices of a unit square.
"""
self.precision = "single"
self.np = 4
x = numpy.array([0, 0, 1, 1], numpy.float64)
y = numpy.array([0, 1, 1, 0], numpy.float64)
z = numpy.zeros_like(x)
m = numpy.ones_like(x)
u = numpy.array([1, 0, 0, -1], numpy.float64)
p = numpy.array([0, 0, 1, 1], numpy.float64)
self.pa = pa = base.get_particle_array(name="test",
x=x,
y=y,
z=z,
m=m,
u=u,
p=p,
cl_precision=self.precision)
self.particles = particles = base.Particles([
pa,
])
sphrho_func = sph.SPHRho.withargs()
density_rate_func = sph.SPHDensityRate.withargs()
self.sphrho_func = sphrho_func.get_func(pa, pa)
self.density_rate_func = density_rate_func.get_func(pa, pa)
self.sphrho_func.kernel = base.CubicSplineKernel(dim=2)
self.density_rate_func.kernel = base.CubicSplineKernel(dim=2)
self.rho_calc = sph.SPHCalc(particles, [
pa,
],
pa,
base.CubicSplineKernel(dim=2), [
self.sphrho_func,
],
updates=['rho'])
self.rho_calc_cl = sph.CLCalc(particles, [
pa,
],
pa,
base.CubicSplineKernel(dim=2), [
self.sphrho_func,
],
updates=['rho'])
if solver.HAS_CL:
self.ctx = ctx = cl.create_some_context()
self.q = q = cl.CommandQueue(ctx)
self.rho_calc_cl.setup_cl(ctx)
def setUp(self):
""" A simple simulation in 2D with boundary particles spaced with a
distance dp. The fluid particles are in a row just above as shown
in the fgure.
dx
o o o o o
x x x x x x x x x x x x x x x
Y
| Z
| /
|/___ X
Expected Behavior:
-------------------
The Monaghan Boundary force is defned such that a particle moving
parallel to the wall experiences a constant force.
Each fluid particle can be thought of successive instances of a
moving particle and hence each of them should experience the same
force.
"""
#fluid particle spacing
self.dx = dx = 0.1
#solid particle spacing
self.dp = dp = 0.05
#the fluid properties
xf = numpy.array([-.2, -.1, 0.0, 0.1, 0.2])
yf = numpy.array([dp, dp, dp, dp, dp])
hf = numpy.ones_like(xf) * 2 * dx
mf = numpy.ones_like(xf) * dx
cs = numpy.ones_like(xf)
rhof = numpy.ones_like(xf)
self.fluid = base.get_particle_array(x=xf,
y=yf,
h=hf,
m=mf,
rho=rhof,
cs=cs,
ax=mf,
ay=mf,
az=mf,
name='fluid',
type=Fluid)
l = base.Line(base.Point(-0.35), 0.70, 0.0)
g = base.Geometry('line', lines=[l], is_closed=False)
g.mesh_geometry(dp)
self.solid = g.get_particle_array(re_orient=True)
self.particles = particles = base.Particles(
arrays=[self.fluid, self.solid])
self.kernel = kernel = base.CubicSplineKernel(dim=2)
self.solver = solver.Solver(kernel.dim, solver.EulerIntegrator)
self.solver.add_operation(
solver.SPHIntegration(sph.MonaghanBoundaryForce.withargs(delp=dp),
from_types=[Solid],
on_types=[Fluid],
updates=['u', 'v', 'w'],
id='boundary'))
self.solver.setup_integrator(particles)
self.integrator = self.solver.integrator
_rho, _p
These variables should not be communicated with MPI
"""
import pysph.base.api as base
import numpy
from mpi4py import MPI
comm = MPI.COMM_WORLD
num_procs = comm.Get_size()
pid = comm.Get_rank()
x = numpy.linspace(0, 1, 11)
pa = base.get_particle_array(name='test' + str(pid), x=x)
pa.add_property({'name': '_rho'})
pa.add_property({'name': '_p'})
if pid == 0:
# make sure the sending data defines the underscored variables
assert pa.properties.has_key('_rho')
assert pa.properties.has_key('_p')
comm.send(obj=pa, dest=1)
if pid == 1:
pa2 = comm.recv(source=0)
