def runTest(self):
m=fmsh.MeshTri()
m.refine(4)
# split mesh into two sets of triangles
I1=np.arange(m.t.shape[1]/2)
I2=np.setdiff1d(np.arange(m.t.shape[1]),I1)
bix=m.boundary_facets()
bix1=bix[0:len(bix)/2]
bix2=np.setdiff1d(bix,bix1)
a=fasm.AssemblerElement(m,felem.ElementTriP1())
def dudv(du,dv):
return du[0]*dv[0]+du[1]*dv[1]
A=a.iasm(dudv)
A1=a.iasm(dudv,tind=I1)
A2=a.iasm(dudv,tind=I2)
B=a.fasm(dudv)
B1=a.fasm(dudv,find=bix1)
B2=a.fasm(dudv,find=bix2)
f=a.iasm(lambda v: 1*v)
I=m.interior_nodes()
x=np.zeros(A.shape[0])
x[I]=scipy.sparse.linalg.spsolve((A+B)[I].T[I].T,f[I])
X=np.zeros(A.shape[0])
X[I]=scipy.sparse.linalg.spsolve((A1+B1)[I].T[I].T+(A2+B2)[I].T[I].T,f[I])
self.assertAlmostEqual(np.linalg.norm(x-X),0.0,places=10)
def plot_refined(self, mesh, dofnum, sol, minval, maxval, nref=2):
"""Draw a discrete function on refined mesh."""
print "Plotting on a refined mesh. This is slowish due to loop over elements..."
import copy
import spfem.mesh as fmsh
plt.figure()
for itr in range(mesh.t.shape[1]):
m = fmsh.MeshTri(
np.array([[
mesh.p[0, mesh.t[0, itr]], mesh.p[0, mesh.t[1, itr]],
mesh.p[0, mesh.t[2, itr]]
],
[
mesh.p[1, mesh.t[0, itr]],
mesh.p[1, mesh.t[1, itr]], mesh.p[1, mesh.t[2,
itr]]
]]), np.array([[0], [1], [2]]))
M = copy.deepcopy(m)
m.refine(nref)
qps = {}
qps[0] = np.array([m.p[0, :]])
qps[1] = np.array([m.p[1, :]])
u, _, _, _ = self.evalbasis(M, qps, [0])
newu = u[0].flatten() * 0.0
for jtr in range(len(u)):
newu += sol[dofnum.t_dof[jtr, itr]] * u[jtr].flatten()
m.plot(newu, nofig=True, smooth=True, zlim=(minval, maxval))
plt.hold('on')
plt.colorbar()
def visualize_basis_tri(self, save_figures=False, draw_derivatives=False):
"""Draw the basis functions given by self.evalbasis.
Only for triangular elements. For debugging purposes."""
if self.dim != 2:
raise NotImplementedError("visualize_basis_tri supports "
"only triangular elements.")
import copy
import spfem.mesh as fmsh
m = fmsh.MeshTri(np.array([[0, 1.0, 0.0], [0.0, 0.0, 1.0]]),
np.array([[0], [1], [2]]))
M = copy.deepcopy(m)
m.refine(4)
qps = {}
qps[0] = np.array([m.p[0, :]])
qps[1] = np.array([m.p[1, :]])
u, du, ddu = self.evalbasis(M, qps, [0])
for itr in range(len(u)):
m.plot3(u[itr].flatten())
if save_figures:
plt.savefig('bfun' + str(itr) + '.pdf')
if draw_derivatives:
for itr in range(len(u)):
m.plot3(ddu[itr][0][0].flatten())
if save_figures:
plt.savefig('bfun' + str(itr) + '.pdf')
if not save_figures:
m.show()
def runTest(self, verbose=False):
m = fmsh.MeshTri()
m.refine(6)
e = felem.AbstractElementArgyris()
a = fasm.AssemblerAbstract(m, e)
A = a.iasm(lambda ddu, ddv: ddu[0][0] * ddv[0][0] + ddu[1][0] * ddv[1][
0] + ddu[0][1] * ddv[0][1] + ddu[1][1] * ddv[1][1])
# construct a loading that corresponds to the analytical solution uex
import sympy as sp
from sympy.abc import x, y, z
uex = (sp.sin(sp.pi * x) * sp.sin(sp.pi * y))**2
uexfun = sp.lambdify((x, y), uex, "numpy")
load = sp.diff(uex, x, 4) + sp.diff(
uex, y, 4) + 2 * sp.diff(sp.diff(uex, x, 2), y, 2)
loadfun = sp.lambdify((x, y), load, "numpy")
def F(x):
return loadfun(x[0], x[1])
f = a.iasm(lambda v, x: F(x) * v)
D = a.dofnum_u.getdofs(N=m.boundary_nodes(), F=m.boundary_facets())
I = np.setdiff1d(np.arange(a.dofnum_u.N), D)
x = futil.direct(A, f, I=I)
Linferror = np.max(
np.abs(x[a.dofnum_u.n_dof[0, :]] - uexfun(m.p[0, :], m.p[1, :])))
self.assertTrue(Linferror <= 5e-3)
def plot_lagmult(self,
mesh,
dofnum,
sol,
minval,
maxval,
lambdaeval,
nref=2,
cbar=True):
"""Draw plate obstacle lagrange multiplier on refined mesh."""
print "Plotting on a refined mesh. This is slowish due to loop over elements..."
import copy
import spfem.mesh as fmsh
plt.figure()
totmaxval = 0
for itr in range(mesh.t.shape[1]):
m = fmsh.MeshTri(
np.array([[
mesh.p[0, mesh.t[0, itr]], mesh.p[0, mesh.t[1, itr]],
mesh.p[0, mesh.t[2, itr]]
],
[
mesh.p[1, mesh.t[0, itr]],
mesh.p[1, mesh.t[1, itr]], mesh.p[1, mesh.t[2,
itr]]
]]), np.array([[0], [1], [2]]))
M = copy.deepcopy(m)
m.refine(nref)
qps = {}
qps[0] = np.array([m.p[0, :]])
qps[1] = np.array([m.p[1, :]])
u, _, _, d4u = self.evalbasis(M, qps, [0])
newu = u[0].flatten() * 0.0
#newd4u = d4u[0].flatten()*0.0
newd4u = const_cell(0 * qps[0], self.dim, self.dim)
for jtr in range(len(u)):
newu += sol[dofnum.t_dof[jtr, itr]] * u[jtr].flatten()
newd4u[0][0] += sol[dofnum.t_dof[
jtr, itr]] * d4u[jtr][0][0].flatten()
newd4u[0][1] += sol[dofnum.t_dof[
jtr, itr]] * d4u[jtr][0][1].flatten()
newd4u[1][0] += sol[dofnum.t_dof[
jtr, itr]] * d4u[jtr][1][0].flatten()
newd4u[1][1] += sol[dofnum.t_dof[
jtr, itr]] * d4u[jtr][1][1].flatten()
tmp = lambdaeval(newu, newd4u, qps, M.param())
#print tmp.shape
if np.max(tmp) > totmaxval:
totmaxval = np.max(tmp)
m.plot(tmp.flatten(),
nofig=True,
smooth=True,
zlim=(minval, maxval))
plt.hold('on')
print "Maximum value: " + str(totmaxval)
if cbar:
plt.colorbar()
def setUp(self):
self.mesh=fmsh.MeshTri()
self.mesh.refine(5)
# boundary and interior node sets
D1=np.nonzero(self.mesh.p[0,:]==0)[0]
D2=np.nonzero(self.mesh.p[1,:]==0)[0]
D3=np.nonzero(self.mesh.p[0,:]==1)[0]
D4=np.nonzero(self.mesh.p[1,:]==1)[0]
D=np.union1d(D1,D2);
D=np.union1d(D,D3);
self.D=np.union1d(D,D4);
self.I=np.setdiff1d(np.arange(0,self.mesh.p.shape[1]),self.D)
def runTest(self):
m=fmsh.MeshTri()
m.refine(5)
a=fasm.AssemblerAbstract(m,felem.AbstractElementTriPp(2))
b=fasm.AssemblerElement(m,felem.ElementTriP2())
A=a.iasm(lambda u,v: u*v)
B=b.iasm(lambda u,v: u*v)
self.assertAlmostEqual(np.sum(A.data),np.sum(B.data),places=10)
C=a.iasm(lambda du,v: du[0]*v)
D=b.iasm(lambda du,v: du[0]*v)
self.assertAlmostEqual(C.data[0],D.data[0],places=10)
def runTest(self):
m=fmsh.MeshTri()
m.refine(3)
a=fasm.AssemblerElement(m,felem.ElementTriP1())
def G(x,y):
return np.sin(np.pi*x)
u=G(m.p[0,:],m.p[1,:])
# interpolate just the values and compare
def v1(v,w):
return w[0]*v
def v2(u,v):
return u*v
f=a.fasm(v1,interp={0:u})
A=a.fasm(v2)
self.assertAlmostEqual(np.linalg.norm(f-A*u),0.0,places=10)
def runTest(self):
mesh = fmsh.MeshTri()
mesh.refine(2)
def G(x, y):
return x**3 * np.sin(20.0 * y)
def U(x):
return G(x[0], x[1])
def dUdx(x):
return 3.0 * x[0]**2 * np.sin(20.0 * x[1])
def dUdy(x):
return 20.0 * x[0]**3 * np.cos(20.0 * x[1])
dU = {0: dUdx, 1: dUdy}
def dudv(du, dv):
return du[0] * dv[0] + du[1] * dv[1]
gamma = 100
def uv(u, v, du, dv, x, h, n):
return gamma * 1 / h * u * v - du[0] * n[0] * v - du[1] * n[
1] * v - u * dv[0] * n[0] - u * dv[1] * n[1]
def fv(v, dv, x):
return (-6.0 * x[0] * np.sin(20.0 * x[1]) +
400.0 * x[0]**3 * np.sin(20.0 * x[1])) * v
def gv(v, dv, x, h, n):
return G(x[0], x[1]) * v + gamma * 1 / h * G(
x[0], x[1]) * v - dv[0] * n[0] * G(
x[0], x[1]) - dv[1] * n[1] * G(x[0], x[1])
hs = np.array([])
errs = np.array([])
for itr in range(4):
mesh.refine()
a = fasm.AssemblerElement(mesh, felem.ElementTriP1())
D = mesh.boundary_nodes()
I = mesh.interior_nodes()
K = a.iasm(dudv)
B = a.fasm(uv, normals=True)
f = a.iasm(fv)
g = a.fasm(gv, normals=True)
x = np.zeros(K.shape[0])
x = spsolve(K + B, f + g)
hs = np.append(hs, mesh.param())
errs = np.append(errs,
np.sqrt(a.L2error(x, U)**2 + a.H1error(x, dU)**2))
pfit = np.polyfit(np.log10(hs), np.log10(errs), 1)
# check that the convergence rate matches theory
self.assertTrue(pfit[0] >= 0.99)
def runTest(self):
def U(x):
return 1 + x[0] - x[0]**2 * x[1]**2
def dUdx(x):
return 1 - 2 * x[0] * x[1]**2
def dUdy(x):
return -2 * x[0]**2 * x[1]
def dudv(du, dv):
return du[0] * dv[0] + du[1] * dv[1]
def uv(u, v):
return u * v
def F(x, y):
return 2 * x**2 + 2 * y**2
def fv(v, x):
return F(x[0], x[1]) * v
def G(x, y):
return (x==1)*(3-3*y**2)+\
(x==0)*(0)+\
(y==1)*(1+x-3*x**2)+\
(y==0)*(1+x)
def gv(v, x):
return G(x[0], x[1]) * v
dexact = {}
dexact[0] = dUdx
dexact[1] = dUdy
hs = {}
H1errs = {}
L2errs = {}
for p in range(1, 3):
mesh = fmsh.MeshTri()
mesh.refine(1)
hs[p - 1] = np.array([])
H1errs[p - 1] = np.array([])
L2errs[p - 1] = np.array([])
for itr in range(4):
mesh.refine()
a = fasm.AssemblerElement(mesh, felem.ElementTriPp(p))
A = a.iasm(dudv)
f = a.iasm(fv)
B = a.fasm(uv)
g = a.fasm(gv)
u = np.zeros(a.dofnum_u.N)
u = spsolve(A + B, f + g)
hs[p - 1] = np.append(hs[p - 1], mesh.param())
L2errs[p - 1] = np.append(L2errs[p - 1], a.L2error(u, U))
H1errs[p - 1] = np.append(H1errs[p - 1], a.H1error(u, dexact))
pfit = np.polyfit(np.log10(hs[p - 1]), np.log10(H1errs[p - 1]), 1)
self.assertTrue(pfit[0] >= 0.95 * p)
def runTest(self):
mesh = fmsh.MeshTri()
mesh.refine(2)
a = fasm.AssemblerElement(mesh, felem.ElementTriRT0())
b = fasm.AssemblerElement(mesh, felem.ElementTriRT0(),
felem.ElementP0())
c = fasm.AssemblerElement(mesh, felem.ElementP0())
def sigtau(u, v):
sig = u
tau = v
return sig[0] * tau[0] + sig[1] * tau[1]
def divsigv(du, v):
divsig = du
return divsig * v
def fv(v, x):
return 2 * np.pi**2 * np.sin(np.pi * x[0]) * np.sin(
np.pi * x[1]) * v
def uv(u, v):
return u * v
def exact(x):
return np.sin(np.pi * x[0]) * np.sin(np.pi * x[1])
hs = np.array([])
L2err = np.array([])
for itr in range(1, 4):
mesh.refine()
a = fasm.AssemblerElement(mesh, felem.ElementTriRT0())
b = fasm.AssemblerElement(mesh, felem.ElementTriRT0(),
felem.ElementP0())
c = fasm.AssemblerElement(mesh, felem.ElementP0())
A = a.iasm(sigtau)
B = b.iasm(divsigv)
C = c.iasm(uv)
f = c.iasm(fv)
K1 = spsp.hstack((-A, -B.T))
K2 = spsp.hstack((-B, 0 * C))
K = spsp.vstack((K1, K2)).tocsr()
F = np.hstack((np.zeros(A.shape[0]), f))
u = np.zeros(a.dofnum_u.N + c.dofnum_u.N)
u = spsolve(K, F)
Iu = np.arange(C.shape[0], dtype=np.int64) + A.shape[0]
hs = np.append(hs, mesh.param())
L2err = np.append(L2err, c.L2error(u[Iu], exact))
pfit = np.polyfit(np.log10(hs), np.log10(L2err), 1)
self.assertTrue(pfit[0] >= 0.95)
self.assertTrue(pfit[0] <= 1.15)
