Core/TrackFitting/GainMatrixSmootherTests.cpp

0001 // This file is part of the ACTS project.
0002 //
0003 // Copyright (C) 2016 CERN for the benefit of the ACTS project
0004 //
0005 // This Source Code Form is subject to the terms of the Mozilla Public
0006 // License, v. 2.0. If a copy of the MPL was not distributed with this
0007 // file, You can obtain one at https://mozilla.org/MPL/2.0/.
0008
0009 #include <boost/test/unit_test.hpp>
0010
0011 #include "Acts/Definitions/Algebra.hpp"
0012 #include "Acts/Definitions/TrackParametrization.hpp"
0013 #include "Acts/EventData/MultiTrajectory.hpp"
0014 #include "Acts/EventData/TrackStatePropMask.hpp"
0015 #include "Acts/EventData/VectorMultiTrajectory.hpp"
0016 #include "Acts/Geometry/GeometryContext.hpp"
0017 #include "Acts/Tests/CommonHelpers/FloatComparisons.hpp"
0018 #include "Acts/TrackFitting/GainMatrixSmoother.hpp"
0019 #include "Acts/Utilities/Result.hpp"
0020
0021 #include <cmath>
0022 #include <cstddef>
0023 #include <numbers>
0024
0025 namespace {
0026
0027 using namespace Acts;
0028 using namespace Acts::Test;
0029
0030 using ParametersVector = Acts::BoundVector;
0031 using CovarianceMatrix = Acts::BoundSquareMatrix;
0032 using Jacobian = Acts::BoundMatrix;
0033
0034 const Acts::GeometryContext tgContext;
0035
0036 }  // namespace
0037
0038 BOOST_AUTO_TEST_SUITE(TrackFittingGainMatrixSmoother)
0039
0040 BOOST_AUTO_TEST_CASE(Smooth) {
0041   VectorMultiTrajectory traj;
0042   std::size_t ts_idx = traj.addTrackState(TrackStatePropMask::All);
0043   auto ts = traj.getTrackState(ts_idx);
0044
0045   // Make dummy track parameter
0046   CovarianceMatrix covTrk;
0047   covTrk.setIdentity();
0048   covTrk.diagonal() << 0.08, 0.3, 1, 1, 1, 1;
0049   BoundVector parValues;
0050   parValues << 0.3, 0.5, std::numbers::pi / 2., 0., 1 / 100., 0.;
0051
0052   ts.predicted() = parValues;
0053   ts.predictedCovariance() = covTrk;
0054
0055   parValues << 0.301, 0.503, std::numbers::pi / 2., 0., 1 / 100., 0.;
0056
0057   ts.filtered() = parValues;
0058   ts.filteredCovariance() = covTrk;
0059   ts.pathLength() = 1.;
0060   ts.jacobian().setIdentity();
0061
0062   ts_idx = traj.addTrackState(TrackStatePropMask::All, ts_idx);
0063   ts = traj.getTrackState(ts_idx);
0064
0065   parValues << 0.2, 0.5, std::numbers::pi / 2., 0., 1 / 100., 0.;
0066   ts.predicted() = parValues;
0067   ts.predictedCovariance() = covTrk;
0068
0069   parValues << 0.27, 0.53, std::numbers::pi / 2., 0., 1 / 100., 0.;
0070   ts.filtered() = parValues;
0071   ts.filteredCovariance() = covTrk;
0072   ts.pathLength() = 2.;
0073   ts.jacobian().setIdentity();
0074
0075   ts_idx = traj.addTrackState(TrackStatePropMask::All, ts_idx);
0076   ts = traj.getTrackState(ts_idx);
0077
0078   parValues << 0.35, 0.49, std::numbers::pi / 2., 0., 1 / 100., 0.;
0079   ts.predicted() = parValues;
0080   ts.predictedCovariance() = covTrk;
0081
0082   parValues << 0.33, 0.43, std::numbers::pi / 2., 0., 1 / 100., 0.;
0083   ts.filtered() = parValues;
0084   ts.filteredCovariance() = covTrk;
0085   ts.pathLength() = 3.;
0086   ts.jacobian().setIdentity();
0087
0088   // "smooth" these three track states
0089   BOOST_CHECK(GainMatrixSmoother()(tgContext, traj, ts_idx).ok());
0090
0091   // Regression tests, only tests very basic correctness of the math, but tests
0092   // for regressions in the result.
0093
0094   auto ts1 = traj.getTrackState(0);
0095   BOOST_CHECK(ts1.hasSmoothed());
0096   BOOST_CHECK_NE(ts1.filtered(), ts1.smoothed());
0097
0098   double tol = 1e-6;
0099
0100   ParametersVector expPars;
0101   expPars << 0.3510000, 0.4730000, 1.5707963, 0.0000000, 0.0100000, 0.0000000;
0102   CovarianceMatrix expCov;
0103   expCov.setIdentity();
0104   expCov.diagonal() << 0.0800000, 0.3000000, 1.0000000, 1.0000000, 1.0000000,
0105       1.0000000;
0106   CHECK_CLOSE_ABS(ts1.smoothed(), expPars, tol);
0107   CHECK_CLOSE_ABS(ts1.smoothedCovariance(), expCov, tol);
0108
0109   auto ts2 = traj.getTrackState(1);
0110   BOOST_CHECK(ts2.hasSmoothed());
0111   BOOST_CHECK_NE(ts2.filtered(), ts2.smoothed());
0112
0113   expPars << 0.2500000, 0.4700000, 1.5707963, 0.0000000, 0.0100000, 0.0000000;
0114   CHECK_CLOSE_ABS(ts2.smoothed(), expPars, tol);
0115   CHECK_CLOSE_ABS(ts2.smoothedCovariance(), expCov, tol);
0116
0117   auto ts3 = traj.getTrackState(2);
0118   BOOST_CHECK(ts3.hasSmoothed());
0119   // last one, smoothed == filtered
0120   BOOST_CHECK_EQUAL(ts3.filtered(), ts3.smoothed());
0121
0122   expPars << 0.3300000, 0.4300000, 1.5707963, 0.0000000, 0.0100000, 0.0000000;
0123   CHECK_CLOSE_ABS(ts3.smoothed(), expPars, tol);
0124   CHECK_CLOSE_ABS(ts3.smoothedCovariance(), expCov, tol);
0125 }
0126
0127 BOOST_AUTO_TEST_SUITE_END()