1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
|
"""Unit tests for dP assembly"""
# Copyright (C) 2014 Johan Hake
#
# This file is part of DOLFIN.
#
# DOLFIN is free software: you can redistribute it and/or modify
# it under the terms of the GNU Lesser General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# DOLFIN is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Lesser General Public License for more details.
#
# You should have received a copy of the GNU Lesser General Public License
# along with DOLFIN. If not, see <http://www.gnu.org/licenses/>.
#
import pytest
import os
import numpy as np
from dolfin import *
from dolfin_utils.test import *
@pytest.fixture(params=[1, 2, 3])
def dim(request):
return request.param
def _create_dp_problem(dim):
assert dim in [1, 2, 3]
if dim == 1:
mesh = UnitIntervalMesh(20)
elif dim == 2:
mesh = UnitSquareMesh(10, 10)
else:
mesh = UnitCubeMesh(4, 4, 4)
P1 = FiniteElement("P", mesh.ufl_cell(), 1)
V = FunctionSpace(mesh, P1)
VV = FunctionSpace(mesh, P1*P1)
# Create expression used to interpolate initial data
y_dim = 1 if dim > 1 else 0
z_dim = 2 if dim > 2 else (1 if dim > 1 else 0)
e = Expression("x[0] + 2*x[{}] + x[{}]".format(y_dim, z_dim), degree=2)
ee = Expression(["x[0]+x[{}]".format(z_dim), "x[0]*x[{}]+x[{}]".format(y_dim, z_dim)], degree=2)
# Create coefficients
u = interpolate(e, V)
uu = interpolate(ee, VV)
# Test and trial spaces
v = TestFunction(V)
vv = TestFunction(VV)
U = TrialFunction(V)
UU = TrialFunction(VV)
# Subdomains
subdomain = AutoSubDomain(lambda x, on_boundary: x[0] <= 0.5)
disjoint_subdomain = AutoSubDomain(lambda x, on_boundary: x[0] > 0.5)
vertex_domain = MeshFunction("size_t", mesh, 0, 0)
subdomain.mark(vertex_domain, 1)
bc = DirichletBC(VV, Constant((0, 0)), disjoint_subdomain)
dPP = dP(subdomain_data=vertex_domain)
return (u, uu), (v, vv), (U, UU), dPP, bc
def test_scalar_assemble(dim):
eps = 1000*DOLFIN_EPS
(u, uu), (v, vv), (U, UU), dPP, bc = _create_dp_problem(dim)
scalar_value = assemble(u*dP)
assert abs(scalar_value-u.vector().sum()) < eps
scalar_value = assemble((uu[0]+uu[1])*dPP)
assert abs(scalar_value-uu.vector().sum()) < eps
scalar_value = assemble((uu[0]+uu[1])*dPP(1))
bc.apply(uu.vector())
assert abs(scalar_value-uu.vector().sum()) < eps
def test_vector_assemble(dim):
eps = 1000*DOLFIN_EPS
(u, uu), (v, vv), (U, UU), dPP, bc = _create_dp_problem(dim)
# In parallel vec.get_local() will return only local to process values
vec = assemble(u*v*dPP)
assert sum(np.absolute(vec.get_local() - u.vector().get_local())) < eps
vec = assemble(inner(uu, vv)*dP)
assert sum(np.absolute(vec.get_local() - uu.vector().get_local())) < eps
vec = assemble(inner(uu, vv)*dPP(1))
bc.apply(uu.vector())
assert sum(np.absolute(vec.get_local() - uu.vector().get_local())) < eps
def test_matrix_assemble(dim):
eps = 1000*DOLFIN_EPS
(u, uu), (v, vv), (U, UU), dPP, bc = _create_dp_problem(dim)
# Scalar assemble
mat = assemble(u*v*U*dPP)
# Create a numpy matrix based on the local size of the vector
# and populate it with values from local vector
loc_range = u.vector().local_range()
vec_mat = np.zeros_like(mat.array())
vec_mat[range(loc_range[1] - loc_range[0]),
range(loc_range[0], loc_range[1])] = u.vector().get_local()
assert np.sum(np.absolute(mat.array() - vec_mat)) < eps
# Vector assemble
mat = assemble((uu[0]*vv[0]*UU[0] + uu[1]*vv[1]*UU[1])*dPP)
# Create a numpy matrix based on the local size of the vector
# and populate it with values from local vector
loc_range = uu.vector().local_range()
vec_mat = np.zeros_like(mat.array())
vec_mat[range(loc_range[1] - loc_range[0]),
range(loc_range[0], loc_range[1])] = uu.vector().get_local()
assert np.sum(np.absolute(mat.array() - vec_mat)) < eps
|
