void checkMargins(const QRect& window, const QSize& surfaceSize)
{
BOOST_CHECK_GE(window.top(), 0.05 * surfaceSize.height());
BOOST_CHECK_LE(window.bottom(), 0.95 * surfaceSize.height());
BOOST_CHECK_GE(window.left(), (int)(0.4 * window.width()));
BOOST_CHECK_LE(window.left(), 0.25 * surfaceSize.width());
BOOST_CHECK_LE(window.right(), 0.95 * surfaceSize.width());
}
void checkDimensions(const QRect& window, const QSize& surfaceSize)
{
BOOST_CHECK_LE(window.height(), 0.9 * surfaceSize.height());
BOOST_CHECK_LE(window.width(), 0.9 * surfaceSize.width());
BOOST_CHECK_LE(window.height(), maxSizePixels.height());
BOOST_CHECK_LE(window.width(), maxSizePixels.width());
BOOST_CHECK_GE(window.height(), minSizePixels.height());
BOOST_CHECK_GE(window.width(), minSizePixels.width());
}
static void streaming_up_down_test(const SRCParams ¶ms)
{
// Source noise
Samples noise(noise_size);
RNG(seed).fill_samples(noise, noise_size);
// Upsampled buffer
Samples buf1(size_t(double(noise_size + 1) * params.fd / params.fs) + 1);
// Downsampled buffer
Samples buf2(noise_size + 100);
StreamingSRC src;
BOOST_REQUIRE(src.open(params));
size_t buf1_data = resample(src, noise, noise_size, buf1, buf1.size());
BOOST_REQUIRE(src.open(SRCParams(params.fd, params.fs, params.a, params.q)));
size_t buf2_data = resample(src, buf1, buf1_data, buf2, buf2.size());
BOOST_CHECK(abs(int(buf2_data) - int(noise_size)) <= 1);
// Resample introduces not more than -A dB of noise.
// 2 resamples introduces twice more noise, -A + 6dB
sample_t diff = diff_resampled(params, noise, buf2, MIN(noise_size, buf2_data));
BOOST_MESSAGE("Transform: " << params.fs << "Hz <-> " << params.fd << "Hz Diff: " << value2db(diff) << " dB");
BOOST_CHECK_LE(value2db(diff), -params.a + 7);
}
static void
test_less_than_equal_to()
{
{
floating_point_emulation::tfloat<T, S> f(42), g(43);
BOOST_CHECK_LE(f, g);
}
{
floating_point_emulation::tfloat<T, S> f(42), g(43);
BOOST_CHECK_EQUAL(f <= g, true);
BOOST_CHECK_EQUAL(g <= f, false);
}
{
floating_point_emulation::tfloat<T, S> f(42), g(43.);
BOOST_CHECK_EQUAL(f <= g, true);
BOOST_CHECK_EQUAL(g <= f, false);
}
{
floating_point_emulation::tfloat<T, S> f(42.), g(43);
BOOST_CHECK_EQUAL(f <= g, true);
BOOST_CHECK_EQUAL(g <= f, false);
}
{
floating_point_emulation::tfloat<T, S> f(42.), g(43.);
BOOST_CHECK_EQUAL(f <= g, true);
BOOST_CHECK_EQUAL(g <= f, false);
}
{
floating_point_emulation::tfloat<T, S> f(42), g(42);
BOOST_CHECK_EQUAL(f <= g, true);
BOOST_CHECK_EQUAL(g <= f, true);
}
{
floating_point_emulation::tfloat<T, S> f(42), g(42.);
BOOST_CHECK_EQUAL(f <= g, true);
BOOST_CHECK_EQUAL(g <= f, true);
}
{
floating_point_emulation::tfloat<T, S> f(42.), g(42);
BOOST_CHECK_EQUAL(f <= g, true);
BOOST_CHECK_EQUAL(g <= f, true);
}
{
floating_point_emulation::tfloat<T, S> f(42.), g(42.);
BOOST_CHECK_EQUAL(f <= g, true);
BOOST_CHECK_EQUAL(g <= f, true);
}
}
void DiskFixture::testPosition(std::array<float,3> p)
{
// check that point lies in plane
BOOST_CHECK_SMALL(dot(m_normal,p-m_centre),1e-5f);
// check that point is sufficiently close to centre
BOOST_CHECK_LE(norm2(p-m_centre), m_radiusSquared);
}
static void equivalence_test(const SRCParams ¶ms)
{
// Source noise
Samples noise(noise_size);
RNG(seed).fill_samples(noise, noise_size);
// Upsampled buffer
Samples buf1(size_t(double(noise_size + 1) * params.fd / params.fs) + 1);
// Downsampled buffer
Samples buf2(noise_size + 100);
StreamingSRC src;
BufferSRC test;
BOOST_MESSAGE("Transform: " << params.fs << "Hz <-> " << params.fd);
/////////////////////////////////////////////////////////
// Resample using StreamingSRC
BOOST_REQUIRE(src.open(params));
size_t buf1_data = resample(src, noise, noise_size, buf1, buf1.size());
BOOST_REQUIRE(src.open(SRCParams(params.fd, params.fs, params.a, params.q)));
size_t buf2_data = resample(src, buf1, buf1_data, buf2, buf2.size());
/////////////////////////////////////////////////////////
// Resample using BufferSRC and check the difference
BOOST_REQUIRE(test.open(params));
test.process(noise, noise_size);
BOOST_REQUIRE_EQUAL(test.size(), buf1_data);
sample_t diff = peak_diff(test.result(), buf1, buf1_data);
BOOST_CHECK_LE(diff, SAMPLE_THRESHOLD);
BOOST_REQUIRE(test.open(SRCParams(params.fd, params.fs, params.a, params.q)));
test.process(buf1, buf1_data);
BOOST_REQUIRE_EQUAL(test.size(), buf2_data);
diff = peak_diff(test.result(), buf2, buf2_data);
BOOST_CHECK_LE(diff, SAMPLE_THRESHOLD);
}
BOOST_AUTO_TEST_CASE_TEMPLATE
( test_mapping_increment, Tp, int2_sets )
{
{ // test the invarient of the increment
Tp fix(100, randomize(-3, 3));
for (dimension_type mapping_dim = 0; mapping_dim < 2;
++mapping_dim)
{
mapping_iterator<typename Tp::container_type>
iter = mapping_begin(fix.container, mapping_dim),
end = mapping_end(fix.container, mapping_dim);
int count = 0;
double tmp = (*iter)[mapping_dim];
for (; iter != end; ++iter)
{
BOOST_CHECK_LE(tmp, (*iter)[mapping_dim]);
tmp = (*iter)[mapping_dim];
if (++count > 100) break;
}
BOOST_CHECK_EQUAL(count, 100);
}
}
{ // test at the limit: a tree where all elements are the same
Tp fix(100, same());
for (dimension_type mapping_dim = 0; mapping_dim < 2;
++mapping_dim)
{
mapping_iterator<typename Tp::container_type>
iter = mapping_begin(fix.container, mapping_dim),
end = mapping_end(fix.container, mapping_dim);
int count = 0;
for (; iter != end; ++iter)
{
BOOST_CHECK_EQUAL(100, (*iter)[mapping_dim]);
if (++count > 100) break;
}
BOOST_CHECK_EQUAL(count, 100);
}
}
{ // test at the limit: a tree with 2 elements
Tp fix(2, same());
for (dimension_type mapping_dim = 0; mapping_dim < 2;
++mapping_dim)
{
mapping_iterator<const typename Tp::container_type>
pre = mapping_cbegin(fix.container, mapping_dim),
post = mapping_cbegin(fix.container, mapping_dim),
end = mapping_cend(fix.container, mapping_dim);
BOOST_CHECK(pre != end);
BOOST_CHECK(++pre != post++);
BOOST_CHECK(pre == post);
BOOST_CHECK(post++ != end);
BOOST_CHECK(++pre == end);
BOOST_CHECK(post == end);
}
}
{ // test at the limit: a right-unbalanced tree (pre-increment)
Tp fix(100, increase());
for (dimension_type mapping_dim = 0; mapping_dim < 2;
++mapping_dim)
{
mapping_iterator<typename Tp::container_type>
iter = mapping_begin(fix.container, mapping_dim),
end = mapping_end(fix.container, mapping_dim);
int count = 0;
double tmp = (*iter)[mapping_dim];
for (; iter != end; ++iter)
{
BOOST_CHECK_LE(tmp, (*iter)[mapping_dim]);
tmp = (*iter)[mapping_dim];
if (++count > 100) break;
}
BOOST_CHECK_EQUAL(count, 100);
}
}
{ // test at the limit: a left-unbalanced tree (post-increment)
Tp fix(100, decrease());
for (dimension_type mapping_dim = 0; mapping_dim < 2;
++mapping_dim)
{
mapping_iterator<typename Tp::container_type>
iter = mapping_begin(fix.container, mapping_dim),
end = mapping_end(fix.container, mapping_dim);
int count = 0;
double tmp = (*iter)[mapping_dim];
for (; iter != end; iter++)
{
BOOST_CHECK_LE(tmp, (*iter)[mapping_dim]);
tmp = (*iter)[mapping_dim];
if (++count > 100) break;
}
BOOST_CHECK_EQUAL(count, 100);
}
}
}
BOOST_AUTO_TEST_CASE_TEMPLATE(cmp_le4, mp_int_type, mp_int_types)
{
const mp_int_type x("11");
const mp_int_type y("49941");
BOOST_CHECK_LE(x, y);
}
BOOST_AUTO_TEST_CASE_TEMPLATE(cmp_le3, mp_int_type, mp_int_types)
{
const mp_int_type x("-123456789");
const mp_int_type y("-123456789");
BOOST_CHECK_LE(x, y);
}
BOOST_AUTO_TEST_CASE_TEMPLATE(cmp_le2, mp_int_type, mp_int_types)
{
const mp_int_type x("-1");
const mp_int_type y("0");
BOOST_CHECK_LE(x, y);
}
BOOST_AUTO_TEST_CASE_TEMPLATE
( test_neighbor_decrement, Tp, int2_maps )
{
// Prove that you can iterate N nodes, down to 1 node
{
Tp fix(100, randomize(-20, 20));
int2 target;
typedef typename neighbor_iterator<typename Tp::container_type>
::distance_type distance_type;
while (!fix.container.empty())
{
randomize(-22, 22)(target, 0, 0);
size_t countdown = fix.container.size();
std::reverse_iterator<neighbor_iterator<typename Tp::container_type> >
iter(--neighbor_end(fix.container, target)),
end(neighbor_begin(fix.container, target));
distance_type max_dist = distance(iter.base());
for(; --countdown != 0 && iter != end; ++iter)
{
distance_type tmp = distance(iter.base());
BOOST_CHECK_LE(tmp, max_dist);
max_dist = tmp;
}
BOOST_CHECK_EQUAL(countdown, 0u);
BOOST_CHECK(iter == end);
fix.container.erase(fix.container.begin());
}
}
// Prove that you can iterate a very unbalanced tree
{
Tp fix(40, increase());
int2 target;
typedef typename neighbor_iterator<typename Tp::container_type>
::distance_type distance_type;
while (!fix.container.empty())
{
randomize(0, 40)(target, 0, 0);
size_t countdown = fix.container.size();
std::reverse_iterator<neighbor_iterator<typename Tp::container_type> >
iter(--neighbor_end(fix.container, target)),
end(neighbor_begin(fix.container, target));
distance_type max_dist = distance(iter.base());
for(; --countdown != 0 && iter != end; ++iter)
{
distance_type tmp = distance(iter.base());
BOOST_CHECK_LE(tmp, max_dist);
max_dist = tmp;
}
BOOST_CHECK_EQUAL(countdown, 0u);
BOOST_CHECK(iter == end);
fix.container.erase(fix.container.begin());
}
}
// Prove that you can iterate an opposite unbalanced tree
{
Tp fix(40, decrease());
int2 target;
typedef typename neighbor_iterator<typename Tp::container_type>
::distance_type distance_type;
while (!fix.container.empty())
{
randomize(0, 40)(target, 0, 0);
size_t countdown = fix.container.size();
std::reverse_iterator<neighbor_iterator<typename Tp::container_type> >
iter(--neighbor_end(fix.container, target)),
end(neighbor_begin(fix.container, target));
distance_type max_dist = distance(iter.base());
for(; --countdown != 0 && iter != end; ++iter)
{
distance_type tmp = distance(iter.base());
BOOST_CHECK_LE(tmp, max_dist);
max_dist = tmp;
}
BOOST_CHECK_EQUAL(countdown, 0u);
BOOST_CHECK(iter == end);
fix.container.erase(fix.container.begin());
}
}
// Prove that you can iterate equivalent nodes
{
int2 target;
same()(target, 0, 100);
Tp fix(100, same());
std::reverse_iterator<neighbor_iterator<typename Tp::container_type> >
iter(neighbor_end(fix.container, target)),
end(neighbor_begin(fix.container, target));
size_t count = 1;
++iter;
for(; iter != end && count < fix.container.size(); ++iter, ++count)
{
BOOST_CHECK_CLOSE(distance(iter.base()), 0.0, 0.000000001);
}
BOOST_CHECK(iter == end);
BOOST_CHECK_EQUAL(count, fix.container.size());
}
}
BOOST_AUTO_TEST_CASE_TEMPLATE(cmp_mp_int_le_unsigned_integral_type, mp_int_type, mp_int_types)
{
const mp_int_type x("0");
BOOST_CHECK_LE(x, 32101U);
}
BOOST_AUTO_TEST_CASE_TEMPLATE(cmp_mp_int_le_integral_type1, mp_int_type, mp_int_types)
{
const mp_int_type x("0");
BOOST_CHECK_LE(x, 123456789);
}
BOOST_AUTO_TEST_CASE_TEMPLATE(cmp_mp_int_lt_integral_type2, mp_int_type, mp_int_types)
{
const mp_int_type x("0x100000000");
BOOST_CHECK_LE(1, x);
}
typename bg::default_area_result<G1>::type
check_result(
std::vector<OutputType> const& intersection_output,
std::string const& caseid,
std::size_t expected_count = 0, int expected_point_count = 0,
double expected_length_or_area = 0,
double percentage = 0.0001,
bool debug = false)
{
bool const is_line = bg::geometry_id<OutputType>::type::value == 2;
typename bg::default_area_result<G1>::type length_or_area = 0;
int n = 0;
for (typename std::vector<OutputType>::const_iterator it = intersection_output.begin();
it != intersection_output.end();
++it)
{
if (expected_point_count > 0)
{
// here n should rather be of type std::size_t, but expected_point_count
// is set to -1 in some test cases so type int was left for now
n += static_cast<int>(bg::num_points(*it, true));
}
// instead of specialization we check it run-time here
length_or_area += is_line
? bg::length(*it)
: bg::area(*it);
if (debug)
{
std::cout << std::setprecision(20) << bg::wkt(*it) << std::endl;
}
}
#if ! defined(BOOST_GEOMETRY_NO_BOOST_TEST)
#if ! defined(BOOST_GEOMETRY_NO_ROBUSTNESS)
if (expected_point_count > 0)
{
BOOST_CHECK_MESSAGE(bg::math::abs(n - expected_point_count) < 3,
"intersection: " << caseid
<< " #points expected: " << expected_point_count
<< " detected: " << n
<< " type: " << (type_for_assert_message<G1, G2>())
);
}
#endif
if (expected_count > 0)
{
BOOST_CHECK_MESSAGE(intersection_output.size() == expected_count,
"intersection: " << caseid
<< " #outputs expected: " << expected_count
<< " detected: " << intersection_output.size()
<< " type: " << (type_for_assert_message<G1, G2>())
);
}
double const detected_length_or_area = boost::numeric_cast<double>(length_or_area);
if (percentage > 0.0)
{
BOOST_CHECK_CLOSE(detected_length_or_area, expected_length_or_area, percentage);
}
else
{
// In some cases (geos_2) the intersection is either 0, or a tiny rectangle,
// depending on compiler/settings. That cannot be tested by CLOSE
BOOST_CHECK_LE(detected_length_or_area, expected_length_or_area);
}
#endif
return length_or_area;
}
void do_check_le()
{
int i = 62;
BOOST_CHECK_LE( i, 16 );
}
BOOST_AUTO_TEST_CASE_TEMPLATE(cmp_integral_type_le_mp_int, mp_int_type, mp_int_types)
{
const mp_int_type x("32101");
BOOST_CHECK_LE(x, 32102);
}
BOOST_AUTO_TEST_CASE_TEMPLATE
( test_neighbor_upper_bound, Tp, quad_sets )
{
typedef quadrance<typename Tp::container_type, int, quad_diff> metric_type;
typedef typename metric_type::distance_type distance_type;
typedef neighbor_iterator<typename Tp::container_type, metric_type>
neighbor_iterator_type;
// Prove that you can find lower bound with N nodes, down to 1 node
{
Tp fix(100, randomize(-20, 20));
metric_type metric;
quad target;
while (!fix.container.empty())
{
randomize(-22, 22)(target, 0, 0);
// Find min and max dist first
distance_type min_dist;
distance_type max_dist;
typename Tp::container_type::iterator it = fix.container.begin();
min_dist = max_dist
= metric.distance_to_key(fix.container.rank()(), *it, target);
++it;
for (; it != fix.container.end(); ++it)
{
distance_type tmp
= metric.distance_to_key(fix.container.rank()(), *it, target);
if (tmp < min_dist) min_dist = tmp;
if (tmp > max_dist) max_dist = tmp;
}
distance_type avg_dist = (min_dist + max_dist) / 2;
// use this knowledge to test the upper bound
neighbor_iterator_type i
= neighbor_upper_bound(fix.container, metric, target, min_dist);
BOOST_CHECK(i != neighbor_begin(fix.container, metric, target));
if (i != neighbor_end(fix.container, metric, target))
{ BOOST_CHECK_LT(min_dist, distance(i)); }
BOOST_CHECK_EQUAL(min_dist, distance(--i));
i = neighbor_upper_bound(fix.container, metric, target, max_dist);
BOOST_CHECK(i == neighbor_end(fix.container, metric, target));
i = neighbor_upper_bound(fix.container, metric, target, avg_dist);
if (i != neighbor_end(fix.container, metric, target))
{ BOOST_CHECK_GT(distance(i), avg_dist); }
if (i == neighbor_end(fix.container, metric, target))
{ BOOST_CHECK_LE(distance(--i), avg_dist); }
fix.container.erase(i);
}
}
// Prove that you can find the upper bound when node and target are same
{
Tp fix(100, same());
metric_type metric;
quad target;
same()(target, 0, 100);
// All points and targets are the same.
while (!fix.container.empty())
{
neighbor_iterator_type i
= neighbor_upper_bound(fix.container, metric, target, 1);
BOOST_CHECK(i == neighbor_end(fix.container, metric, target));
i = neighbor_upper_bound(fix.container, metric, target, 0);
BOOST_CHECK(i == neighbor_end(fix.container, metric, target));
fix.container.erase(--i);
}
}
// Prove that you can find the upper bound in an unbalanced tree
{
Tp fix(100, increase());
metric_type metric;
quad target;
while (!fix.container.empty())
{
randomize(0, 100)(target, 0, 0);
// Find min and max dist first
distance_type min_dist;
distance_type max_dist;
typename Tp::container_type::iterator it = fix.container.begin();
min_dist = max_dist
= metric.distance_to_key(fix.container.rank()(), *it, target);
++it;
for (; it != fix.container.end(); ++it)
{
distance_type tmp
= metric.distance_to_key(fix.container.rank()(), *it, target);
if (tmp < min_dist) min_dist = tmp;
if (tmp > max_dist) max_dist = tmp;
}
distance_type avg_dist = (min_dist + max_dist) / 2;
// Use this knowledge to test the upper bound
neighbor_iterator_type i
= neighbor_upper_bound(fix.container, metric, target, min_dist);
BOOST_CHECK(i != neighbor_begin(fix.container, metric, target));
if (i != neighbor_end(fix.container, metric, target))
{ BOOST_CHECK_LT(min_dist, distance(i)); }
BOOST_CHECK_EQUAL(min_dist, distance(--i));
i = neighbor_upper_bound(fix.container, metric, target, max_dist);
BOOST_CHECK(i == neighbor_end(fix.container, metric, target));
i = neighbor_upper_bound(fix.container, metric, target, avg_dist);
if (i != neighbor_end(fix.container, metric, target))
{ BOOST_CHECK_GT(distance(i), avg_dist); }
if (i == neighbor_end(fix.container, metric, target))
{ BOOST_CHECK_LE(distance(--i), avg_dist); }
fix.container.erase(i);
}
}
// Prove that you can find the upper bound in an unbalanced tree
{
Tp fix(100, decrease());
metric_type metric;
quad target;
while (!fix.container.empty())
{
randomize(0, 100)(target, 0, 0);
// Find min and max dist first
distance_type min_dist;
distance_type max_dist;
typename Tp::container_type::iterator it = fix.container.begin();
min_dist = max_dist
= metric.distance_to_key(fix.container.rank()(), *it, target);
++it;
for (; it != fix.container.end(); ++it)
{
distance_type tmp
= metric.distance_to_key(fix.container.rank()(), *it, target);
if (tmp < min_dist) min_dist = tmp;
if (tmp > max_dist) max_dist = tmp;
}
distance_type avg_dist = (min_dist + max_dist) / 2;
// Use this knowledge to test the upper bound
neighbor_iterator_type i
= neighbor_upper_bound(fix.container, metric, target, min_dist);
BOOST_CHECK(i != neighbor_begin(fix.container, metric, target));
if (i != neighbor_end(fix.container, metric, target))
{ BOOST_CHECK_LT(min_dist, distance(i)); }
BOOST_CHECK_EQUAL(min_dist, distance(--i));
i = neighbor_upper_bound(fix.container, metric, target, max_dist);
BOOST_CHECK(i == neighbor_end(fix.container, metric, target));
i = neighbor_upper_bound(fix.container, metric, target, avg_dist);
if (i != neighbor_end(fix.container, metric, target))
{ BOOST_CHECK_GT(distance(i), avg_dist); }
if (i == neighbor_end(fix.container, metric, target))
{ BOOST_CHECK_LE(distance(--i), avg_dist); }
fix.container.erase(i);
}
}
}
