def latticeDistribution(self,
nx,
ny,
rho,
xmin=(0.0, 0.0),
xmax=(1.0, 1.0),
rmin=None,
rmax=None,
nNodePerh=2.01,
xlmin=1e30,
xlmax=-1e30):
dx = (xmax[0] - xmin[0]) / nx
dy = (xmax[1] - xmin[1]) / ny
hx = 1.0 / (nNodePerh * dx)
hy = 1.0 / (nNodePerh * dy)
H0 = SymTensor2d(hx, 0.0, 0.0, hy)
x = []
y = []
m = []
H = []
for j in xrange(ny):
for i in xrange(nx):
if i * dx < xlmin or i * dx > xlmax:
xx = xmin[0] + (i + 0.5) * dx
yy = xmin[1] + (j + 0.5) * dy
r = sqrt(xx * xx + yy * yy)
m0 = dx * dy * rho(Vector2d(xx, yy))
if ((r >= rmin or rmin is None)
and (r <= rmax or rmax is None)):
x.append(xx)
y.append(yy)
m.append(m0)
H.append(H0)
dx = dx * 0.5
dy = dy * 0.5
xx = xlmin + 0.5 * dx
while (xx <= xlmax):
for j in range(ny * 2):
yy = xmin[1] + (j + 0.5) * dy
r = sqrt(xx * xx + yy * yy)
m0 = dx * dy * rho(Vector2d(xx, yy))
if ((r >= rmin or rmin is None)
and (r <= rmax or rmax is None)):
x.append(xx)
y.append(yy)
m.append(m0)
H.append(H0)
xx += dx
return x, y, m, H
def __init__(
self,
boundary, # Some object that has "xmin", "xmax", & "contains"
dx, # Nominal linear resolution
rho, # initial mass density: constant, list, or function
nNodePerh=2.01, # desired nPerh for H tensors
jitter=0.0, # (fraction of dx) any randomness to initial positions
SPH=False, # Should we force round H tensors?
):
assert dx > 0.0
assert nNodePerh > 0.0
self.nNodePerh = nNodePerh
# Start by clipping a lattice.
xmin, xmax = boundary.xmin, boundary.xmax
nx = max(1, int((xmax.x - xmin.x) / dx + 0.5))
ny = max(1, int((xmax.y - xmin.y) / dx + 0.5))
self.x, self.y = [], []
for iy in xrange(ny):
for ix in xrange(nx):
posi = Vector2d(
xmin.x + (ix + 0.5 + jitter * rangen.uniform(0, 1)) * dx,
xmin.y + (iy + 0.5 + jitter * rangen.uniform(0, 1)) * dx)
if boundary.contains(posi):
self.x.append(posi.x)
self.y.append(posi.y)
n = len(self.x)
assert len(self.y) == n
# Density and mass.
if type(rho) is float:
self.rho = ConstantRho(rho)
else:
self.rho = rho
# Mass per node.
self.m = [self.rho(Vector2d(self.x[i], self.y[i])) for i in xrange(n)]
# Set H.
h0 = nNodePerh * dx
H0 = SymTensor2d(1.0 / h0, 0.0, 0.0, 1.0 / h0)
self.H = [H0] * len(self.x)
# Have the base class break up the serial node distribution
# for parallel cases.
NodeGeneratorBase.__init__(self, True, self.x, self.y, self.m, self.H)
return
def computeWeightedCentroids(self, tessellation):
barycenters = self.computeBarycenters(tessellation)
centroids = []
for icell, cell in enumerate(tessellation.cells):
mass = 0.0
xc = 0.0
yc = 0.0
for ftmp in cell:
if ftmp < 0:
n0 = tessellation.faces[~ftmp][1]
n1 = tessellation.faces[~ftmp][0]
else:
n0 = tessellation.faces[ftmp][0]
n1 = tessellation.faces[ftmp][1]
x0 = tessellation.nodes[2 * n0]
y0 = tessellation.nodes[2 * n0 + 1]
x1 = tessellation.nodes[2 * n1]
y1 = tessellation.nodes[2 * n1 + 1]
xb = barycenters[icell][0]
yb = barycenters[icell][1]
xt = (x0 + x1 + xb) / 3.0
yt = (y0 + y1 + yb) / 3.0
xe = (x0 + x1) / 2.0
ye = (y0 + y1) / 2.0
d = 0.5 * ((x0 - xb) * (y1 - yb) - (x1 - xb) *
(y0 - yb)) * self.rhofunc(Vector2d(xe, ye))
mass += d
xc += d * xt
yc += d * yt
xc /= mass
yc /= mass
centroids.append(xc)
centroids.append(yc)
return centroids
def computeMasses(self, tessellation):
barycenters = self.computeBarycenters(tessellation)
result = []
for icell, cell in enumerate(tessellation.cells):
mass = 0.0
for ftmp in cell:
if ftmp < 0:
n0 = tessellation.faces[~ftmp][1]
n1 = tessellation.faces[~ftmp][0]
else:
n0 = tessellation.faces[ftmp][0]
n1 = tessellation.faces[ftmp][1]
x0 = tessellation.nodes[2 * n0]
y0 = tessellation.nodes[2 * n0 + 1]
x1 = tessellation.nodes[2 * n1]
y1 = tessellation.nodes[2 * n1 + 1]
xb = barycenters[icell][0]
yb = barycenters[icell][1]
xt = (x0 + x1 + xb) / 3.0
yt = (y0 + y1 + yb) / 3.0
xe = (x0 + x1) / 2.0
ye = (y0 + y1) / 2.0
d = 0.5 * ((x0 - xb) * (y1 - yb) - (x1 - xb) *
(y0 - yb)) * self.rhofunc(Vector2d(xe, ye))
mass += d
result.append(mass)
return result
def _string2Vector2d(x):
return Vector2d(*tuple(x.split()))
def __init__(self, nr, densityProfileMethod,
rmin = 0.0,
rmax = 1.0,
thetaMin = 0.0,
thetaMax = pi,
nNodePerh = 2.01,
offset=None,
m0ForMassMatching=None):
assert nr > 0
assert rmin >= 0
assert rmin < rmax
assert thetaMin < thetaMax
assert thetaMin >= 0.0 and thetaMin <= 2.0*pi
assert thetaMax >= 0.0 and thetaMax <= 2.0*pi
assert nNodePerh > 0.0
assert offset is None or len(offset)==3
if offset is None:
self.offset = Vector2d(0,0)
else:
self.offset = Vector2d(offset[0],offset[1])
self.nr = nr
self.rmin = rmin
self.rmax = rmax
self.thetaMin = thetaMin
self.thetaMax = thetaMax
self.nNodePerh = nNodePerh
self.xmin = Vector2d(-2.0*rmax,-2.0*rmax)
self.xmax = Vector2d(2.0*rmax,2.0*rmax)
# no reason to support a constant density method here, just use a regular lattice for that
self.densityProfileMethod = densityProfileMethod
# Determine how much total mass there is in the system.
targetMass = self.integrateTotalMass(self.densityProfileMethod,
rmin, rmax,
thetaMin, thetaMax)
#targetMass = self.integrateTotalMass(self.densityProfileMethod,
# rmax)
targetN = pi*(nr**2)
self.m0 = targetMass/targetN
self.vol = pi*(rmax**2)
# what this means is this currently only supports creating a full sphere and then
# cutting out the middle to rmin if rmin > 0
if m0ForMassMatching is None:
self.rho0 = targetMass/self.vol
else:
self.m0 = m0ForMassMatching
self.rho0 = targetN*self.m0/self.vol
print "Found total mass = {0:3.3e} with rho0 = {1:3.3e}".format(targetMass,self.rho0)
# compute kappa first
# k = 3/(self.rho0*rmax**3) * targetMass/(4.0*pi)
# print "Found kappa={0:3.3f}. Was that what you expected?".format(k)
nlat = nr
# create the unstretched lattice
self.xl, self.yl, self.ml, self.Hl = \
self.latticeDistribution(nlat,
self.rho0,
self.m0,
self.xmin, # (xmin, ymin, zmin)
self.xmax, # (xmax, ymax, zmax)
self.rmax,
self.nNodePerh)
self.rl = []
for i in xrange(len(self.xl)):
self.rl.append(sqrt(self.xl[i]**2+self.yl[i]**2))
print "Sorting unstretched lattice... %d elements" % len(self.rl)
multiSort(self.rl,self.xl,self.yl)
self.x = []
self.y = []
self.m = []
self.H = []
nx = 2*nlat+1
eta = (self.xmax[0] - self.xmin[0])/nx
print "Stretching lattice..."
dr = eta * 0.01 # this will essentially be the error in the new dumb way
r0p = 0
rp = 0
rn = 0
for i in xrange(1,len(self.rl)):
#print "%d / %d" % (i,len(self.rl))
r0 = self.rl[i]
if (abs(r0-r0p)/r0>1e-10):
sol = r0**2*self.rho0/2.0
iter = int(10*rmax // dr)
fn = 0
for j in xrange(iter+1):
rj = dr*j
rjj = dr*(j+1)
fj = rj * densityProfileMethod(rj)
fjj = rjj * densityProfileMethod(rjj)
fn = fn + 0.5*(fj+fjj)*dr
if (fn>=sol):
rn = rj
break
r0p = r0
if (rn <= rmax and rn > rmin):
self.x.append(self.xl[i] * rn/r0)
self.y.append(self.yl[i] * rn/r0)
self.m.append(self.ml[i])
self.H.append(self.Hl[i])
seededMass = sum(self.m)
mAdj = targetMass / seededMass
for i in xrange(len(self.m)):
self.m[i] = self.m[i] * mAdj
# Initialize the base class. If "serialInitialization" is True, this
# is where the points are broken up between processors as well.
serialInitialization = True
NodeGeneratorBase.__init__(self, serialInitialization,
self.x, self.y, self.m, self.H)
return
def localMassDensity(self, i):
loc = Vector2d(0,0)
loc = self.localPosition(i) - self.offset
return self.densityProfileMethod(loc.magnitude())
def refineNodes2d(gen,
deta = 0.25):
n = gen.localNumNodes()
x = []
y = []
m = []
rho = []
vx = []
vy = []
eps = []
H = []
extras = {}
for name in gen.extraFields:
extras[name] = []
etas = [Vector2d(-1, -1) * deta,
Vector2d(-1, 1) * deta,
Vector2d( 1, -1) * deta,
Vector2d( 1, 1) * deta]
for i in xrange(n):
ri = gen.localPosition(i)
mi = gen.localMass(i)
Hi = gen.localHtensor(i)
Hinv = Hi.Inverse()
eigen = Hinv.eigenVectors()
R = rotationMatrix2d(eigen.eigenVectors.getColumn(0))
T = SymTensor2d(eigen.eigenValues.x, 0.0,
0.0, eigen.eigenValues.y)
T.rotationalTransform(eigen.eigenVectors)
T = T*R.Inverse()
mj = 0.25*mi
Hj = Hi*2.0
for eta in etas:
rj = ri + T*eta
x.append(rj.x)
y.append(rj.y)
m.append(mj)
rho.append(gen.rho[i])
vx.append(gen.vx[i])
vy.append(gen.vy[i])
eps.append(gen.eps[i])
H.append(Hj)
for name in gen.extraFields:
extras[name].append(gen.__dict__[name][i])
gen.x = x
gen.y = y
gen.m = m
gen.rho = rho
gen.vx = vx
gen.vy = vy
gen.eps = eps
gen.H = H
for name in gen.extraFields:
gen.__dict__[name] = extras[name]
for f in ([gen.x, gen.y, gen.m, gen.vx, gen.vy, gen.eps, gen.H] +
[gen.__dict__[x] for x in gen.extraFields]):
assert len(f) == len(etas)*n
return
def localMassDensity(self, i):
return self.gen2d.rhofunc((Vector2d(self.x[i], self.y[i]) - self.center).magnitude())
def __init__(self,
drCenter, drRatio,
rho,
rmin,
rmax,
startFromCenter = True,
thetamin = 0.0,
thetamax = 0.5*pi,
phi = pi,
ntheta = 1,
center = (0.0, 0.0, 0.0),
distributionType = "constantDTheta", # one of (constantDTheta, constantNTheta)
aspectRatio = 1.0, # only for constantDTheta
nNodePerh = 2.01,
SPH = False,
rejecter = None):
assert thetamax <= pi
self.gen2d = GenerateRatioSphere2d(drStart = drCenter,
drRatio = drRatio,
rho = rho,
rmin = rmin,
rmax = rmax,
startFromCenter = startFromCenter,
thetamin = thetamin,
thetamax = thetamax,
ntheta = ntheta,
center = (0.0, 0.0),
distributionType = distributionType,
aspectRatio = aspectRatio,
nNodePerh = nNodePerh,
SPH = SPH)
# The 2D class already split the nodes up between processors, but
# we want to handle that ourselves. Distribute the full set of RZ
# nodes to every process, then redecompose them below.
self.x = mpi.allreduce(self.gen2d.x[:], mpi.SUM)
self.y = mpi.allreduce(self.gen2d.y[:], mpi.SUM)
self.m = mpi.allreduce(self.gen2d.m[:], mpi.SUM)
self.H = mpi.allreduce(self.gen2d.H[:], mpi.SUM)
n = len(self.x)
self.z = [0.0]*n
self.globalIDs = [0]*n
# Convert the 2-D H tensors to 3-D, and correct the masses.
for i in xrange(n):
xi = self.x[i]
yi = self.y[i]
H2d = SymTensor2d(self.H[i])
H2dinv = H2d.Inverse()
hxy0 = 0.5*(H2dinv.Trace())
dphi = CylindricalBoundary.angularSpacing(yi, hxy0, nNodePerh, 2.0)
assert dphi > 0.0
nsegment = max(1, int(phi/dphi + 0.5))
dphi = phi/nsegment
hz = dphi*yi*nNodePerh
self.H[i] = SymTensor3d(H2d.xx, H2d.xy, 0.0,
H2d.yx, H2d.yy, 0.0,
0.0, 0.0, 1.0/hz)
if SPH:
h0 = self.H[i].Determinant()**(1.0/3.0)
self.H[-1] = SymTensor3d(h0, 0.0, 0.0,
0.0, h0, 0.0,
0.0, 0.0, h0)
# Convert the mass to the full hoop mass, which will then be used in
# generateCylDistributionFromRZ to compute the actual nodal masses.
mi = self.m[i]
circ = 2.0*pi*yi
mhoop = mi*circ
self.m[i] = mhoop
assert len(self.m) == n
assert len(self.H) == n
# Duplicate the nodes from the xy-plane, creating rings of nodes about
# the x-axis. We use a C++ helper method for the sake of speed.
kernelExtent = 2.0
extras = []
xvec = self.vectorFromList(self.x, vector_of_double)
yvec = self.vectorFromList(self.y, vector_of_double)
zvec = self.vectorFromList(self.z, vector_of_double)
mvec = self.vectorFromList(self.m, vector_of_double)
Hvec = self.vectorFromList(self.H, vector_of_SymTensor3d)
globalIDsvec = self.vectorFromList(self.globalIDs, vector_of_int)
extrasVec = vector_of_vector_of_double()
for extra in extras:
extrasVec.append(self.vectorFromList(extra, vector_of_double))
generateCylDistributionFromRZ(xvec, yvec, zvec, mvec, Hvec, globalIDsvec,
extrasVec,
nNodePerh, kernelExtent, phi,
mpi.rank, mpi.procs)
self.x = [x + center[0] for x in xvec]
self.y = [x + center[1] for x in yvec]
self.z = [z + center[2] for z in zvec]
self.m = list(mvec)
self.H = [SymTensor3d(x) for x in Hvec]
self.globalIDs = list(globalIDsvec)
for i in xrange(len(extras)):
extras[i] = list(extrasVec[i])
self.center = Vector2d(*center)
# If the user provided a "rejecter", give it a pass
# at the nodes.
if rejecter:
self.x, self.y, self.z, self.m, self.H = rejecter(self.x,
self.y,
self.z,
self.m,
self.H)
# Initialize the base class.
NodeGeneratorBase.__init__(self, False,
self.x, self.y, self.z, self.m, self.H)
return
def localMassDensity(self, i):
return self.rho(Vector2d(self.x[i], self.y[i]))
def __init__(self,
n,
rho,
boundary,
holes=[],
maxIterations=100,
fracTol=1.0e-3,
tessellationFileName=None,
nNodePerh=2.01,
offset=(0.0, 0.0),
rejecter=None):
assert n > 0
# Did we get passed a function or a constant for the density?
if type(rho) == type(1.0):
def rhofunc(posi):
return rho
else:
rhofunc = rho
self.rhofunc = rhofunc
# Build a polytope PLC version of the boundary.
plc = poly.polytope.PLC2d()
plc_coords = poly.vector_of_double()
edges = boundary.edges
vertices = boundary.vertices()
plc.facets.resize(len(edges))
for i, edge in enumerate(edges):
plc.facets[i].append(edge.first)
plc.facets[i].append(edge.second)
assert len(plc.facets[i]) == 2
for p in vertices:
plc_coords.append(p[0])
plc_coords.append(p[1])
assert len(plc_coords) == 2 * len(vertices)
# Add any holes to the boundary PLC.
plc.holes.resize(len(holes))
for ihole, hole in enumerate(holes):
offlast = len(plc_coords) / 2
edges = hole.edges
vertices = hole.vertices()
plc.holes[ihole].resize(len(edges))
for i, edge in enumerate(edges):
plc.holes[ihole][i].append(offlast + edge.first)
plc.holes[ihole][i].append(offlast + edge.second)
assert len(plc.holes[ihole][i]) == 2
for p in vertices:
plc_coords.append(p[0])
plc_coords.append(p[1])
assert len(plc_coords) % 2 == 0
# Initialize the desired number of generators in the boundary using the Sobol sequence.
generators = poly.vector_of_double()
seed = 0
length = max(boundary.xmax.x - boundary.xmin.x,
boundary.xmax.y - boundary.xmin.y)
while len(generators) < 2 * n:
[coords, seed] = i4_sobol(2, seed)
p = boundary.xmin + length * Vector2d(coords[0], coords[1])
ihole = 0
use = boundary.contains(p, False)
if use:
while use and ihole < len(holes):
use = not holes[ihole].contains(p, False)
ihole += 1
if use:
generators.append(p.x)
generators.append(p.y)
assert len(generators) == 2 * n
# Iterate the points toward centroidal relaxation.
self.tessellation = poly.polytope.Tessellation2d()
tessellator = poly.polytope.BoostTessellator2d()
iteration = 0
maxDelta = 2.0 * fracTol
while iteration < maxIterations and maxDelta > fracTol:
tessellator.tessellate(points=generators,
PLCpoints=plc_coords,
geometry=plc,
mesh=self.tessellation)
new_generators = self.computeWeightedCentroids(self.tessellation)
assert len(new_generators) == len(generators)
maxDelta = 0.0
for i in xrange(len(generators) / 2):
deltai = sqrt((generators[2 * i] - new_generators[2 * i])**2 +
(generators[2 * i + 1] -
new_generators[2 * i + 1])**2)
maxDelta = max(maxDelta, deltai / length)
generators[2 * i] = 0.5 * (generators[2 * i] +
new_generators[2 * i])
generators[2 * i + 1] = 0.5 * (generators[2 * i + 1] +
new_generators[2 * i + 1])
iteration += 1
print "CentroidalGenerator2d: Iteration %i, maxDelta=%g" % (
iteration, maxDelta)
# If requested, write out the final tessellation to a silo file.
if tessellationFileName:
poly.polytope.writeTessellation2d(mesh=self.tessellation,
filePrefix=tessellationFileName,
nodeFields=None,
edgeFields=None,
faceFields=None,
cellFields=None,
cycle=iteration)
# Now we can fill out the usual Spheral generator info.
assert len(self.tessellation.cells) == n
self.x, self.y, self.m, self.H = [], [], [], []
centroids = self.computeWeightedCentroids(self.tessellation)
masses = self.computeMasses(self.tessellation)
areas = self.computeAreas(self.tessellation)
assert len(centroids) == 2 * n
assert len(masses) == n
assert len(areas) == n
for i in xrange(n):
self.x.append(centroids[2 * i] + offset[0])
self.y.append(centroids[2 * i + 1] + offset[1])
self.m.append(masses[i])
hi = nNodePerh * sqrt(areas[i] / pi)
assert hi > 0.0
self.H.append(SymTensor2d(1.0 / hi, 0.0, 0.0, 1.0 / hi))
assert len(self.x) == n
assert len(self.y) == n
assert len(self.m) == n
assert len(self.H) == n
# If the user provided a "rejecter", give it a pass
# at the nodes.
if rejecter:
self.x, self.y, self.m, self.H = rejecter(self.x, self.y, self.m,
self.H)
# Have the base class break up the serial node distribution
# for parallel cases.
NodeGeneratorBase.__init__(self, True, self.x, self.y, self.m, self.H)
return
def latticeCylindricalDistribution(self,
nx,
ny,
rho,
xmin=(0.0, 0.0),
xmax=(1.0, 1.0),
rmin=None,
rmax=None,
nNodePerh=2.01):
k = 0
np = 0
deltar = rmax - rmin
dx = (xmax[0] - xmin[0]) / nx
dy = (xmax[1] - xmin[1]) / ny
hx = 1.0 / (nNodePerh * dx)
hy = 1.0 / (nNodePerh * dy)
H0 = SymTensor2d(hx, 0.0, 0.0, hy)
x = []
y = []
m = []
H = []
ml = []
Hl = []
xl = []
yl = []
xc = []
yc = []
mc = []
Hc = []
for j in xrange(ny):
for i in xrange(nx):
xx = xmin[0] + (i + 0.5) * dx
yy = xmin[1] + (j + 0.5) * dy
r = sqrt(xx * xx + yy * yy)
m0 = dx * dy * rho(Vector2d(xx, yy))
if (r >= rmin * 0.8):
xl.append(xx)
yl.append(yy)
ml.append(m0)
Hl.append(H0)
k = k + 1
if (r >= rmax):
x.append(xx)
y.append(yy)
m.append(m0)
H.append(H0)
np = np + 1
# Start at the outermost radius, and work our way inward.
theta = 2 * 3.14159
ri = rmax + 2.0 * nNodePerh / nx
#import random
#random.seed()
while ri > 0:
# Get the nominal delta r, delta theta, number of nodes, and mass per
# node at this radius.
rhoi = rho(Vector2d(ri, 0.0))
dr = sqrt(m0 / rhoi)
arclength = theta * ri
arcmass = arclength * dr * rhoi
nTheta = max(1, int(arcmass / m0))
dTheta = theta / nTheta
mi = arcmass / nTheta
hi = nNodePerh * 0.5 * (dr + ri * dTheta)
Hi = SymTensor2d(1.0 / hi, 0.0, 0.0, 1.0 / hi)
# Now assign the nodes for this radius.
for i in xrange(nTheta):
thetai = (i + 0.5) * dTheta
xc.append(ri * cos(thetai))
yc.append(ri * sin(thetai))
mc.append(mi)
Hc.append(Hi)
xi = ri * cos(thetai)
yi = ri * sin(thetai)
if (ri < rmin):
x.append(xi)
y.append(yi)
m.append(mi)
H.append(Hi)
np = np + 1
elif (ri >= rmin):
#eps = random.random()
#func = ((ri-rmin)/deltar)**2
func = 1 - ri / (rmin - rmax) - rmax / (rmax - rmin)
if (func > 1.0):
func = 1.0
if (func < 0.0):
func = 0.0
#if (eps <= func):
#x.append(ri*cos(thetai))
#y.append(ri*sin(thetai))
#m.append(mi)
#H.append(Hi)
#else:
minddr = nx
mink = 2 * k
for j in xrange(k):
ddr = sqrt((xl[j] - xi)**2 + (yl[j] - yi)**2)
if (ddr < minddr):
minddr = ddr
mink = j
xi = xi + (xl[mink] - xi) * func
yi = yi + (yl[mink] - yi) * func
minddr = nx
for j in xrange(np):
ddr = sqrt((x[j] - xi)**2 + (y[j] - yi)**2)
if (ddr < minddr):
minddr = ddr
if (minddr > (1.0 / hx) * 0.5):
x.append(xi + (xl[mink] - xi) * func)
y.append(yi + (yl[mink] - yi) * func)
m.append(ml[mink])
H.append(Hl[mink])
# Decrement to the next radial bin inward.
ri = max(0.0, ri - dr)
return x, y, m, H
def __init__(self,
nRadial,
nTheta,
rho,
xmin=None,
xmax=None,
rmin=None,
rmax=None,
nNodePerh=2.01,
theta=pi / 2.0,
azimuthalOffsetFraction=0.0,
SPH=False,
rotation=0.0,
offset=None,
xminreject=None,
xmaxreject=None,
rreject=None,
originreject=None,
reversereject=False,
relaxation=None,
rejecter=None,
xlmin=None,
xlmax=None):
assert nRadial > 0
assert nTheta > 0
assert nNodePerh > 0.0
assert offset is None or len(offset) == 2
assert ((xminreject is None and xmaxreject is None)
or (len(xminreject) == 2 and len(xmaxreject) == 2))
assert ((rreject is None and originreject is None)
or (rreject and len(originreject) == 2))
assert azimuthalOffsetFraction == 0.0 or theta == 2.0 * pi
self.nRadial = nRadial
self.nTheta = nTheta
self.xmin = xmin
self.xmax = xmax
self.rmin = rmin
self.rmax = rmax
self.nNodePerh = nNodePerh
self.theta = theta
self.azimuthalOffsetFraction = azimuthalOffsetFraction
self.xlmin = xlmin
self.xlmax = xlmax
# If the user provided a constant for rho, then use the constantRho
# class to provide this value.
if type(rho) == type(1.0):
self.rho = ConstantRho(rho)
else:
self.rho = rho
self.x, self.y, self.m, self.H = \
self.latticeDistribution(self.nRadial, # nx
self.nTheta, # ny
self.rho,
self.xmin,
self.xmax,
self.rmin,
self.rmax,
self.nNodePerh,
self.xlmin,
self.xlmax)
# If SPH has been specified, make sure the H tensors are round.
if SPH:
self.makeHround()
# If requested, apply a rotation (in radians).
if rotation:
nhat = Vector2d(cos(rotation), -sin(rotation))
R = rotationMatrix2d(nhat)
for i in xrange(len(self.x)):
v = R * Vector2d(self.x[i], self.y[i])
self.x[i], self.y[i] = v.x, v.y
self.H[i].rotationalTransform(R)
# If requested, shift the nodes.
if offset:
for i in xrange(len(self.x)):
self.x[i] += offset[0]
self.y[i] += offset[1]
# Did the user request to reject nodes based on a box?
if xminreject:
assert len(xminreject) == 2 and len(xmaxreject) == 2
xminreject = Vector2d(*xminreject)
xmaxreject = Vector2d(*xmaxreject)
x = []
y = []
m = []
H = []
for xi, yi, mi, Hi in zip(self.x, self.y, self.m, self.H):
t = testPointInBox2d(Vector2d(xi, yi), xminreject, xmaxreject)
if ((t and (not reversereject))
or ((not t) and reversereject)):
x.append(xi)
y.append(yi)
m.append(mi)
H.append(Hi)
self.x = x
self.y = y
self.m = m
self.H = H
# Did the user request to reverse nodes based on a cylinder?
if rreject:
assert len(originreject) == 2
originreject = Vector2d(*originreject)
x = []
y = []
m = []
H = []
for xi, yi, mi, Hi in zip(self.x, self.y, self.m, self.H):
t = (Vector2d(xi, yi) - originreject).magnitude() <= rreject
if ((t and (not reversereject))
or ((not t) and reversereject)):
x.append(xi)
y.append(yi)
m.append(mi)
H.append(Hi)
self.x = x
self.y = y
self.m = m
self.H = H
# If the user provided a "rejecter", give it a pass
# at the nodes.
if rejecter:
self.x, self.y, self.m, self.H = rejecter(self.x, self.y, self.m,
self.H)
# Have the base class break up the serial node distribution
# for parallel cases.
NodeGeneratorBase.__init__(self, True, self.x, self.y, self.m, self.H)
# If requested, employ some relaxation on the NodeDistribution.
if relaxation:
relaxation(self)
return
def __init__(self,
dxSurface,
xratio,
dySurface,
yratio,
rho,
xmin,
xmax,
nNodePerh=2.01,
SPH=False,
flipx=False,
flipy=False):
assert dxSurface > 0.0
assert xratio > 0.0
assert dySurface > 0.0
assert yratio > 0.0
assert xmin[0] < xmax[0]
assert xmin[1] < xmax[1]
assert nNodePerh > 0.0
# If the user provided a constant for rho, then use the constantRho
# class to provide this value.
if type(rho) == type(1.0):
self.rhofunc = ConstantRho(rho)
else:
self.rhofunc = rho
self.x, self.y, self.m, self.H, self.rho = [], [], [], [], []
# Decide the actual ratios we're going to use to arrive at an integer number of radial bins.
def adjustRatio(drStart, drRatio, rmin, rmax):
if abs(drRatio - 1.0) > 1e-4:
neff = max(
1,
int(
log(1.0 - (rmax - rmin) *
(1.0 - drRatio) / drStart) / log(drRatio) + 0.5))
drStart = (rmax - rmin) * (1.0 - drRatio) / (1.0 -
drRatio**neff)
else:
neff = max(1, int((rmax - rmin) / drStart + 0.5))
drStart = (rmax - rmin) / neff
return drStart, neff
dxSurface, nxeff = adjustRatio(dxSurface, xratio, xmin[0], xmax[0])
dySurface, nyeff = adjustRatio(dySurface, yratio, xmin[1], xmax[1])
print "Adjusting initial spacing to (%g, %g) in order to create integer numbers of bins (%i, %i) to edges." % (
dxSurface, dySurface, nxeff, nyeff)
def flipcoord(xi, x0, x1):
return x0 + x1 - xi
# Work our way in from the y surface.
y1 = xmax[1]
dy = dySurface
while y1 > xmin[1] + 0.1 * dy:
y0 = max(xmin[1], y1 - dy)
yi = 0.5 * (y0 + y1)
hy = nNodePerh * dy
yi0 = y0
yi1 = y1
if flipy:
yi = flipcoord(yi, xmin[1], xmax[1])
yi0 = flipcoord(yi0, xmin[1], xmax[1])
yi1 = flipcoord(yi1, xmin[1], xmax[1])
# Work our way in from the x surface.
x1 = xmax[0]
dx = dxSurface
while x1 > xmin[0] + 0.1 * dx:
x0 = max(xmin[0], x1 - dx)
xi = 0.5 * (x0 + x1)
hx = nNodePerh * dx
xi0 = x0
xi1 = x1
if flipx:
xi = flipcoord(xi, xmin[0], xmax[0])
xi0 = flipcoord(xi0, xmin[0], xmax[0])
xi1 = flipcoord(xi1, xmin[0], xmax[0])
self.x.append(xi)
self.y.append(yi)
rhoi, mi = computeRhoAndMass2d(Vector2d(xi0, yi0),
Vector2d(xi1, yi0),
Vector2d(xi1, yi1),
Vector2d(xi0, yi1),
Vector2d(xi, yi), self.rhofunc)
self.m.append(mi)
self.rho.append(rhoi)
self.H.append(SymTensor2d(1.0 / hx, 0.0, 0.0, 1.0 / hy))
x1 = x0
dx *= xratio
y1 = y0
dy *= yratio
# # Make sure the total mass is what we intend it to be, by applying
# # a multiplier to the particle masses.
# M0 = 0.0
# for m in self.m:
# M0 += m
# assert M0 > 0.0
# M1 = (xmax[0] - xmin[0]) * (xmax[1] - xmin[1]) * rho
# massCorrection = M1/M0
# for i in xrange(len(self.m)):
# self.m[i] *= massCorrection
# print "Applied a mass correction of %f to ensure total mass is %f." % (massCorrection, M1)
# Have the base class break up the serial node distribution
# for parallel cases.
NodeGeneratorBase.__init__(self, True, self.x, self.y, self.m, self.H,
self.rho)
return
def localPosition(self, i):
assert i >= 0 and i < len(self.x)
assert len(self.x) == len(self.y)
return Vector2d(self.x[i], self.y[i])
def localMassDensity(self, i):
assert i >= 0 and i < len(self.H)
return self.rhofunc(Vector2d(self.x[i], self.y[i]))
def __init__(self,
drStart, drRatio,
rho,
rmin,
rmax,
startFromCenter = True,
thetamin = 0.0,
thetamax = 0.5*pi,
ntheta = 1,
center = (0.0, 0.0),
distributionType = "constantDTheta", # one of (constantDTheta, constantNTheta)
aspectRatio = 1.0, # only for constantDTheta
nNodePerh = 2.01,
SPH = False,
rejecter = None,
perturbFunc = None):
assert drStart > 0.0
assert drRatio > 0.0
assert nNodePerh > 0.0
assert rmin >= 0.0
assert rmax > rmin
assert thetamax > thetamin
assert distributionType.lower() in ("constantdtheta", "constantntheta")
self.center = center
# Did we get passed a function or a constant for the density?
if type(rho) == type(1.0):
def rhofunc(posi):
return rho
else:
rhofunc = rho
self.rhofunc = rhofunc
# Do we have a perturbation function?
def zeroPerturbation(posi):
return posi
if not perturbFunc:
perturbFunc = zeroPerturbation
self.x, self.y, self.m, self.H = [], [], [], []
constantN = (distributionType.lower() == "constantntheta")
Dtheta = thetamax - thetamin
nthetamin = max(2, int(Dtheta/(0.5*pi) + 0.5)*2)
# Decide the actual drStart we're going to use to arrive at an integer number of radial bins.
if abs(drRatio - 1.0) > 1e-4:
neff = max(1, int(log(1.0 - (rmax - rmin)*(1.0 - drRatio)/drStart)/log(drRatio) + 0.5))
drStart = (rmax - rmin)*(1.0 - drRatio)/(1.0 - drRatio**neff)
else:
neff = max(1, int((rmax - rmin)/drStart + 0.5))
drStart = (rmax - rmin)/neff
print "Adjusting initial radial spacing to %g in order to create an integer radial number of bins %i." % (drStart, neff)
# Step in radius (in or out) until we span the full radial range.
dr = drStart
for i in xrange(neff):
if abs(drRatio - 1.0) > 1e-4:
if startFromCenter:
r0 = min(rmax, rmin + drStart*(1.0 - drRatio**i)/(1.0 - drRatio))
r1 = min(rmax, rmin + drStart*(1.0 - drRatio**(i + 1))/(1.0 - drRatio))
else:
r0 = max(rmin, rmax - drStart*(1.0 - drRatio**(i + 1))/(1.0 - drRatio))
r1 = max(rmin, rmax - drStart*(1.0 - drRatio**i)/(1.0 - drRatio))
else:
r0 = min(rmax, rmin + i*drStart)
r1 = min(rmax, rmin + (i + 1)*drStart)
dr = r1 - r0
ri = 0.5*(r0 + r1)
li = Dtheta*ri
if constantN:
ntheta = ntheta
else:
ntheta = max(nthetamin, int(li/dr*aspectRatio))
dtheta = Dtheta/ntheta
hr = nNodePerh * dr
ha = nNodePerh * ri*dtheta
for j in xrange(ntheta):
theta0 = thetamin + j*dtheta
theta1 = thetamin + (j + 1)*dtheta
pos0 = perturbFunc(Vector2d(r0*cos(theta0), r0*sin(theta0)))
pos1 = perturbFunc(Vector2d(r1*cos(theta0), r1*sin(theta0)))
pos2 = perturbFunc(Vector2d(r1*cos(theta1), r1*sin(theta1)))
pos3 = perturbFunc(Vector2d(r0*cos(theta1), r0*sin(theta1)))
areai = 0.5*((pos1 - pos0).cross(pos2 - pos0).z +
(pos2 - pos0).cross(pos3 - pos0).z)
posi = 0.25*(pos0 + pos1 + pos2 + pos3)
mi = areai*self.rhofunc(posi)
xi = posi.x
yi = posi.y
self.x.append(xi + center[0])
self.y.append(yi + center[1])
self.m.append(mi)
if SPH:
hi = sqrt(hr*ha)
self.H.append(SymTensor2d(1.0/hi, 0.0, 0.0, 1.0/hi))
else:
self.H.append(SymTensor2d(1.0/hr, 0.0, 0.0, 1.0/ha))
runit = Vector2d(xi, yi).unitVector()
T = rotationMatrix2d(runit).Transpose()
self.H[-1].rotationalTransform(T)
# # Do a numerical integral to get the expected total mass.
# class integfunc(ScalarFunctor):
# def __init__(self, rho, Dtheta):
# ScalarFunctor.__init__(self)
# self.rho = rho
# self.Dtheta = Dtheta
# return
# def __call__(self, ri):
# return Dtheta*ri*self.rho(ri)
# M1 = simpsonsIntegrationDouble(integfunc(rhofunc, Dtheta), rmin, rmax, 10000)
# # Make sure the total mass is what we intend it to be, by applying
# # a multiplier to the particle masses.
# M0 = sum(self.m)
# assert M0 > 0.0
# massCorrection = M1/M0
# for i in xrange(len(self.m)):
# self.m[i] *= massCorrection
# print "Applied a mass correction of %f to ensure total mass is %f." % (massCorrection, M1)
# If the user provided a "rejecter", give it a pass
# at the nodes.
if rejecter:
self.x, self.y, self.m, self.H = rejecter(self.x,
self.y,
self.m,
self.H)
# Have the base class break up the serial node distribution
# for parallel cases.
NodeGeneratorBase.__init__(self, True,
self.x, self.y, self.m, self.H)
return
