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|
"""Unit tests for the Vector interface"""
# Copyright (C) 2011-2014 Garth N. Wells
#
# 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/>.
#
# Modified by Anders Logg 2011
import pytest
import numpy
from copy import copy
from dolfin import *
from dolfin_utils.test import *
# TODO: Use the fixture setup from matrix in a shared conftest.py when
# we move tests to one flat folder.
# Lists of backends supporting or not supporting GenericVector::data()
# access
data_backends = []
no_data_backends = ["PETSc", "Tpetra"]
# Add serial only backends
if MPI.size(MPI.comm_world) == 1:
# TODO: What about "Dense" and "Sparse"? The sub_backend wasn't
# used in the old test.
data_backends += ["Eigen"]
# Remove backends we haven't built with
data_backends = list(filter(has_linear_algebra_backend, data_backends))
no_data_backends = list(filter(has_linear_algebra_backend, no_data_backends))
any_backends = data_backends + no_data_backends
# Fixtures setting up and resetting the global linear algebra backend
# for a list of backends
data_backend = set_parameters_fixture("linear_algebra_backend", data_backends)
no_data_backend = set_parameters_fixture("linear_algebra_backend",
no_data_backends)
any_backend = set_parameters_fixture("linear_algebra_backend", any_backends)
class TestVectorForAnyBackend:
def test_create_empty_vector(self, any_backend):
v0 = Vector()
info(v0)
info(v0, True)
assert v0.size() == 0
def test_create_vector(self, any_backend):
n = 301
v1 = Vector(MPI.comm_world, n)
assert v1.size() == n
def test_copy_vector(self, any_backend):
n = 301
v0 = Vector(MPI.comm_world, n)
v1 = Vector(v0)
assert v0.size() == n
del v0
assert v1.size() == n
def test_assign_and_copy_vector(self, any_backend):
n = 301
v0 = Vector(MPI.comm_world, n)
v0[:] = 1.0
assert v0.sum() == n
v1 = Vector(v0)
del v0
assert v1.sum() == n
def test_zero(self, any_backend):
v0 = Vector(MPI.comm_world, 301)
v0.zero()
assert v0.sum() == 0.0
def test_apply(self, any_backend):
v0 = Vector(MPI.comm_world, 301)
v0.apply("insert")
v0.apply("add")
def test_str(self, any_backend):
v0 = Vector(MPI.comm_world, 13)
tmp = v0.str(False)
tmp = v0.str(True)
def test_init_range(self, any_backend):
n = 301
local_range = MPI.local_range(MPI.comm_world, n)
v0 = Vector(MPI.comm_world)
v0.init(local_range)
assert v0.local_range() == local_range
def test_size(self, any_backend):
n = 301
v0 = Vector(MPI.comm_world, 301)
assert v0.size() == n
def test_local_size(self, any_backend):
n = 301
local_range = MPI.local_range(MPI.comm_world, n)
v0 = Vector(MPI.comm_world)
v0.init(local_range)
assert v0.local_size() == local_range[1] - local_range[0]
def test_owns_index(self, any_backend):
m, n = 301, 25
v0 = Vector(MPI.comm_world, m)
local_range = v0.local_range()
in_range = local_range[0] <= n < local_range[1]
assert v0.owns_index(n) == in_range
#def test_set(self, any_backend):
#def test_add(self, any_backend):
def test_get_local(self, any_backend):
from numpy import empty
n = 301
v0 = Vector(MPI.comm_world, n)
data = v0.get_local()
def test_set_local(self, any_backend):
from numpy import zeros
n = 301
v0 = Vector(MPI.comm_world, n)
data = zeros((v0.local_size()), dtype='d')
v0.set_local(data)
data = zeros((v0.local_size()*2), dtype='d')
def test_add_local(self, any_backend):
from numpy import zeros
n = 301
v0 = Vector(MPI.comm_world, n)
data = zeros((v0.local_size()), dtype='d')
v0.add_local(data)
data = zeros((v0.local_size()*2), dtype='d')
def test_gather(self, any_backend):
# Gather not implemented in Eigen
if any_backend == "Eigen" or any_backend == "Tpetra":
return
# Create distributed vector of local size 1
x = DefaultFactory().create_vector(MPI.comm_world)
r = MPI.rank(x.mpi_comm())
x.init((r, r+1))
# Create local vector
y = DefaultFactory().create_vector(MPI.comm_self)
# Do the actual test across all rank permutations
for target_rank in range(MPI.size(x.mpi_comm())):
# Set nonzero value on single rank
if r == target_rank:
x[0] = 42.0 # Set using local index
else:
x[0] = 0.0 # Set using local index
assert numpy.isclose(x.sum(), 42.0)
# Gather (using global index) and check the result
x.gather(y, numpy.array([target_rank], dtype=la_index_dtype()))
assert numpy.isclose(y[0], 42.0)
# NumPy array version
out = x.gather(numpy.array([target_rank], dtype=la_index_dtype()))
assert out.shape == (1,) and numpy.isclose(out[0], 42.0)
# Test gather on zero
out = x.gather_on_zero()
if r == 0:
expected = numpy.array([42.0 if i == target_rank else 0.0
for i in range(x.size())])
else:
expected = numpy.array([])
assert out.shape == expected.shape and numpy.allclose(out, expected)
# Test also the corner case of empty indices on one process
if r == target_rank:
out = x.gather(numpy.array([], dtype=la_index_dtype()))
expected = numpy.array([])
else:
out = x.gather(numpy.array([target_rank], dtype=la_index_dtype()))
expected = numpy.array([42.0])
assert out.shape == expected.shape and numpy.allclose(out, expected)
# Check that distributed gather vector is not accepted
if MPI.size(MPI.comm_world) > 1:
z = DefaultFactory().create_vector(MPI.comm_world)
with pytest.raises(RuntimeError):
x.gather(z, numpy.array([0], dtype=la_index_dtype()))
# Check that gather vector of wrong size is not accepted
z = DefaultFactory().create_vector(MPI.comm_self)
z.init(3)
with pytest.raises(RuntimeError):
x.gather(z, numpy.array([0], dtype=la_index_dtype()))
def test_axpy(self, any_backend):
n = 301
v0 = Vector(MPI.comm_world, n)
v0[:] = 1.0
v1 = Vector(v0)
v0.axpy(2.0, v1)
assert v0.sum() == 2*n + n
def test_abs(self, any_backend):
n = 301
v0 = Vector(MPI.comm_world, n)
v0[:] = -1.0
v0.abs()
assert v0.sum() == n
def test_inner(self, any_backend):
n = 301
v0 = Vector(MPI.comm_world, n)
v0[:] = 2.0
v1 = Vector(MPI.comm_world, n)
v1[:] = 3.0
assert v0.inner(v1) == 6*n
def test_norm(self, any_backend):
n = 301
v0 = Vector(MPI.comm_world, n)
v0[:] = -2.0
assert v0.norm("l1") == 2.0*n
assert v0.norm("l2") == sqrt(4.0*n)
assert v0.norm("linf") == 2.0
def test_min(self, any_backend):
v0 = Vector(MPI.comm_world, 301)
v0[:] = 2.0
assert v0.min() == 2.0
def test_max(self, any_backend):
v0 = Vector(MPI.comm_world,301)
v0[:] = -2.0
assert v0.max() == -2.0
def test_sum(self, any_backend):
n = 301
v0 = Vector(MPI.comm_world, n)
v0[:] = -2.0
assert v0.sum() == -2.0*n
def test_sum_entries(self, any_backend):
from numpy import zeros
n = 301
v0 = Vector(MPI.comm_world, n)
v0[:] = -2.0
entries = zeros(5, dtype='uintp')
assert v0.sum(entries) == -2.0
entries[0] = 2
entries[1] = 1
entries[2] = 236
entries[3] = 123
entries[4] = 97
assert v0.sum(entries) == -2.0*5
def test_scalar_mult(self, any_backend):
n = 301
v0 = Vector(MPI.comm_world, n)
v0[:] = -1.0
v0 *= 2.0
assert v0.sum() == -2.0*n
def test_vector_element_mult(self, any_backend):
n = 301
v0 = Vector(MPI.comm_world, n)
v1 = Vector(MPI.comm_world, n)
v0[:] = -2.0
v1[:] = 3.0
v0 *= v1
assert v0.sum() == -6.0*n
def test_scalar_divide(self, any_backend):
n = 301
v0 = Vector(MPI.comm_world, n)
v0[:] = -1.0
v0 /= -2.0
assert v0.sum() == 0.5*n
def test_vector_add(self, any_backend):
n = 301
v0 = Vector(MPI.comm_world, n)
v1 = Vector(MPI.comm_world, n)
v0[:] = -1.0
v1[:] = 2.0
v0 += v1
assert v0.sum() == n
def test_scalar_add(self, any_backend):
n = 301
v0 = Vector(MPI.comm_world, n)
v1 = Vector(MPI.comm_world, n)
v0[:] = -1.0
v0 += 2.0
assert v0.sum() == n
v0 -= 2.0
assert v0.sum() == -n
v0 = v0 + 3.0
assert v0.sum() == 2*n
v0 = v0 - 1.0
assert v0.sum() == n
def test_vector_subtract(self, any_backend):
n = 301
v0 = Vector(MPI.comm_world, n)
v1 = Vector(MPI.comm_world, n)
v0[:] = -1.0
v1[:] = 2.0
v0 -= v1
assert v0.sum() == -3.0*n
def test_vector_assignment(self, any_backend):
m, n = 301, 345
v0 = Vector(MPI.comm_world, m)
v1 = Vector(MPI.comm_world, n)
v0[:] = -1.0
v1[:] = 2.0
v0 = v1
assert v0.sum() == 2.0*n
def test_vector_assignment_length(self, any_backend):
# Test that assigning vectors of different lengths fails
m, n = 301, 345
v0 = Vector(MPI.comm_world, m)
v1 = Vector(MPI.comm_world, n)
def wrong_assignment(v0, v1):
v0[:] = v1
with pytest.raises(RuntimeError):
wrong_assignment(v0, v1)
def test_vector_assignment_length(self, any_backend):
# Test that assigning with diffrent parallel layouts fails
if MPI.size(MPI.comm_world) > 1:
m = 301
local_range0 = MPI.local_range(MPI.comm_world, m)
print("local range", local_range0[0], local_range0[1])
# Shift parallel partitiong but preserve global size
if MPI.rank(MPI.comm_world) == 0:
local_range1 = (local_range0[0], local_range0[1] + 1)
elif MPI.rank(MPI.comm_world) == MPI.size(MPI.comm_world) - 1:
local_range1 = (local_range0[0] + 1, local_range0[1])
else:
local_range1 = (local_range0[0] + 1, local_range0[1] + 1)
v0 = Vector(MPI.comm_world)
v0.init(local_range0)
v1 = Vector(MPI.comm_world)
v1.init(local_range1)
assert v0.size() == v1.size()
def wrong_assignment(v0, v1):
v0[:] = v1
with pytest.raises(RuntimeError):
wrong_assignment(v0, v1)
# Test the access of the raw data through pointers
# This is only available for Eigen backend
def test_vector_data(self, data_backend):
# Test for ordinary Vector
v = Vector(MPI.comm_world, 301)
v = as_backend_type(v)
rw_array = v.array_view()
assert rw_array.flags.owndata == False
with pytest.raises(Exception):
rw_array.resize([10])
# Check that the array is a writable view
rw_array[0] = 42
ro_array = v.get_local()
assert ro_array[0] == 42
# Test for as_backend_type Vector
v = as_backend_type(v)
rw_array2 = v.array_view()
assert (rw_array2 == ro_array).all()
# xfail on TypeError
xfail_type = pytest.mark.xfail(strict=True, raises=TypeError)
xfail_type_py3 = pytest.mark.xfail(strict=True, raises=TypeError)
# some systems (32 bit arches) do not have numpy.float128
try:
test_float128 = numpy.float128(42.0)
except AttributeError:
test_float128 = numpy.longdouble(42.0)
@pytest.mark.parametrize("operand",
[int(42), 42.0, numpy.sin(1.0), numpy.float(42.0),
numpy.float64(42.0), numpy.float_(42.0),
numpy.int(42.0), numpy.long(42.0),
numpy.float16(42.0), numpy.float16(42.0),
numpy.float32(42.0), test_float128,
numpy.longfloat(42.0), numpy.int8(42.0),
numpy.int16(42.0), numpy.int32(42.0),
numpy.intc(42.0), numpy.longdouble(42.0),
numpy.int0(42.0), numpy.int64(42.0),
numpy.int_(42.0), numpy.longlong(42.0),
])
def test_vector_type_priority_with_numpy(self, any_backend, operand):
"""Test that DOLFIN return types are prefered over NumPy types for
binary operations on NumPy objects
"""
def _test_binary_ops(v, operand):
assert isinstance(v + operand, cpp.la.GenericVector)
assert isinstance(v - operand, cpp.la.GenericVector)
assert isinstance(v*operand, cpp.la.GenericVector)
assert isinstance(v/operand, cpp.la.GenericVector)
assert isinstance(operand + v, cpp.la.GenericVector)
assert isinstance(operand - v, cpp.la.GenericVector)
assert isinstance(operand*v, cpp.la.GenericVector)
assert isinstance(v+v, cpp.la.GenericVector)
assert isinstance(v-v, cpp.la.GenericVector)
assert isinstance(v*v, cpp.la.GenericVector)
v += v.copy(); assert isinstance(v, cpp.la.GenericVector)
v -= v.copy(); assert isinstance(v, cpp.la.GenericVector)
v *= v.copy(); assert isinstance(v, cpp.la.GenericVector)
v += operand; assert isinstance(v, cpp.la.GenericVector)
v -= operand; assert isinstance(v, cpp.la.GenericVector)
v *= operand; assert isinstance(v, cpp.la.GenericVector)
v /= operand; assert isinstance(v, cpp.la.GenericVector)
op = copy(operand); op += v; assert isinstance(op, cpp.la.GenericVector)
op = copy(operand); op -= v; assert isinstance(op, cpp.la.GenericVector)
op = copy(operand); op *= v; assert isinstance(op, cpp.la.GenericVector)
# Test with vector wrapper
v = Vector(MPI.comm_world, 8)
_test_binary_ops(v, operand)
# Test with vector casted to backend type
v = as_backend_type(v)
_test_binary_ops(v, operand)
|
