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#include "../../structures/mask2d.h"
#include "../../algorithms/thresholdtools.h"
#include <boost/test/unit_test.hpp>
using algorithms::ThresholdTools;
BOOST_AUTO_TEST_SUITE(threshold_tools, *boost::unit_test::label("algorithms"))
BOOST_AUTO_TEST_CASE(winsorized_masked_mean_var) {
num_t mean, variance;
Mask2DCPtr fullMask = Mask2D::CreateSetMaskPtr<true>(100, 1);
Image2DPtr image1 = Image2D::CreateZeroImagePtr(100, 1);
// we just don't want it to crash
ThresholdTools::WinsorizedMeanAndStdDev(image1.get(), fullMask.get(), mean,
variance);
Mask2DCPtr emptyMask = Mask2D::CreateSetMaskPtr<false>(100, 1);
ThresholdTools::WinsorizedMeanAndStdDev(image1.get(), emptyMask.get(), mean,
variance);
BOOST_CHECK_CLOSE(mean, 0.0, 1e-5);
BOOST_CHECK_CLOSE(variance, 0.0, 1e-5);
image1->SetValue(0, 0, 1.0);
ThresholdTools::WinsorizedMeanAndStdDev(image1.get(), emptyMask.get(), mean,
variance);
BOOST_CHECK_CLOSE(mean, 0.0, 1e-5);
BOOST_CHECK_CLOSE(variance, 0.0, 1e-5);
image1->SetValue(1, 0, -2.0);
ThresholdTools::WinsorizedMeanAndStdDev(image1.get(), emptyMask.get(), mean,
variance);
BOOST_CHECK_CLOSE(mean, 0.0, 1e-5);
BOOST_CHECK_CLOSE(variance, 0.0, 1e-5);
Mask2DPtr mask1 = Mask2D::CreateSetMaskPtr<false>(100, 1);
for (unsigned x = 50; x < 100; ++x) mask1->SetValue(x, 0, true);
ThresholdTools::WinsorizedMeanAndStdDev(image1.get(), mask1.get(), mean,
variance);
BOOST_CHECK_CLOSE(mean, 0.0, 1e-5);
BOOST_CHECK_CLOSE(variance, 0.0, 1e-5);
for (unsigned x = 0; x < 50; ++x) image1->SetValue(x, 0, 1.0);
ThresholdTools::WinsorizedMeanAndStdDev(image1.get(), mask1.get(), mean,
variance);
BOOST_CHECK_CLOSE(mean, 1.0, 1e-5);
BOOST_CHECK_CLOSE(variance, 0.0, 1e-5);
for (unsigned x = 0; x < 25; ++x) image1->SetValue(x, 0, -1.0);
ThresholdTools::WinsorizedMeanAndStdDev(image1.get(), mask1.get(), mean,
variance);
BOOST_CHECK_CLOSE(mean, 0.0, 1e-5);
// Remember: since the distribution is not Gaussian, the variance does not
// correspond with the Winsorized variance. The sqrtn(1.54) should be the
// correction term.
BOOST_CHECK_CLOSE(variance, 1.0 * std::sqrt(1.54), 1e-5);
for (unsigned x = 0; x < 100; ++x) image1->SetValue(x, 0, x + 1.0);
ThresholdTools::WinsorizedMeanAndStdDev(image1.get(), mask1.get(), mean,
variance);
BOOST_CHECK_LT(fabs(mean - 25.5), 0.2);
// Since the distribution is not Gaussian, the variance does not correspond
// with the Winsorized variance. Therefore, don't test it here. TODO
}
BOOST_AUTO_TEST_CASE(winsorized_masked_mode) {
num_t mode;
Mask2DCPtr fullMask = Mask2D::CreateSetMaskPtr<true>(100, 1);
Image2DPtr image1 = Image2D::CreateZeroImagePtr(100, 1);
// we just don't want it to crash
ThresholdTools::WinsorizedMode(image1.get(), fullMask.get());
Mask2DCPtr emptyMask = Mask2D::CreateSetMaskPtr<false>(100, 1);
mode = ThresholdTools::WinsorizedMode(image1.get(), emptyMask.get());
BOOST_CHECK_CLOSE(mode, 0.0, 1e-5);
image1->SetValue(0, 0, 1.0);
mode = ThresholdTools::WinsorizedMode(image1.get(), emptyMask.get());
BOOST_CHECK_CLOSE(mode, 0.0, 1e-5);
Mask2DPtr mask1 = Mask2D::CreateSetMaskPtr<false>(100, 1);
for (unsigned x = 50; x < 100; ++x) mask1->SetValue(x, 0, true);
mode = ThresholdTools::WinsorizedMode(image1.get(), mask1.get());
BOOST_CHECK_CLOSE(mode, 0.0, 1e-5);
for (unsigned x = 0; x < 50; ++x) image1->SetValue(x, 0, 1.0);
mode = ThresholdTools::WinsorizedMode(image1.get(), mask1.get());
BOOST_CHECK_CLOSE(mode, sqrt(0.5) * 1.0541, 1e-5);
// for(unsigned x=0;x<100;++x)
// image1->SetValue(x, 0, x+1.0);
// mode = ThresholdTools::WinsorizedMeanAndStdDev(image1, mask1, mean,
// variance); BOOST_CHECK_CLOSE(mean, 25.5, "Mean of 50% flagged sequencial
// image");
// Since the distribution is not Gaussian, the variance does not correspond
// with the Winsorized variance. Therefore, don't test it here. TODO
}
BOOST_AUTO_TEST_SUITE_END()
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