Core/TrackFitting/MbfSmootherTests.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/TrackParametrization.hpp"
0012 #include "Acts/EventData/TrackStatePropMask.hpp"
0013 #include "Acts/EventData/TrackStateType.hpp"
0014 #include "Acts/EventData/VectorMultiTrajectory.hpp"
0015 #include "Acts/Geometry/GeometryContext.hpp"
0016 #include "Acts/TrackFitting/MbfSmoother.hpp"
0017 #include "Acts/Utilities/Result.hpp"
0018 #include "ActsTests/CommonHelpers/FloatComparisons.hpp"
0019
0020 #include <cstddef>
0021 #include <numbers>
0022
0023 namespace {
0024
0025 using namespace Acts;
0026
0027 using ParametersVector = Acts::BoundVector;
0028 using CovarianceMatrix = Acts::BoundMatrix;
0029 using Jacobian = Acts::BoundMatrix;
0030
0031 const Acts::GeometryContext tgContext =
0032     Acts::GeometryContext::dangerouslyDefaultConstruct();
0033
0034 }  // namespace
0035
0036 namespace ActsTests {
0037
0038 BOOST_AUTO_TEST_SUITE(TrackFittingSuite)
0039
0040 BOOST_AUTO_TEST_CASE(Smooth) {
0041   VectorMultiTrajectory traj;
0042
0043   std::array<BoundIndices, 2> projector{eBoundLoc0, eBoundLoc1};
0044
0045   // Make dummy track parameter
0046   CovarianceMatrix covTrk;
0047   covTrk.setIdentity();
0048   covTrk.diagonal() << 0.08, 0.3, 1, 1, 1, 1;
0049
0050   std::size_t ts_idx = traj.addTrackState(TrackStatePropMask::All);
0051   auto ts = traj.getTrackState(ts_idx);
0052   ts.typeFlags().setIsMeasurement();
0053
0054   ts.predicted() << 0.3, 0.5, std::numbers::pi / 2., 0., 1 / 100., 0.;
0055   ts.predictedCovariance() = covTrk;
0056
0057   ts.allocateCalibrated(2);
0058   ts.calibrated<2>() << 0.351, 0.473;
0059   ts.calibratedCovariance<2>() << 1e+8, 0., 0., 1e+8;
0060   ts.setProjectorSubspaceIndices(projector);
0061
0062   ts.filtered() << 0.301, 0.503, std::numbers::pi / 2., 0., 1 / 100., 0.;
0063   ts.filteredCovariance() = covTrk;
0064   ts.pathLength() = 1.;
0065   ts.jacobian().setIdentity();
0066
0067   ts_idx = traj.addTrackState(TrackStatePropMask::All, ts_idx);
0068   ts = traj.getTrackState(ts_idx);
0069   ts.typeFlags().setIsMeasurement();
0070
0071   ts.predicted() << 0.2, 0.5, std::numbers::pi / 2., 0., 1 / 100., 0.;
0072   ts.predictedCovariance() = covTrk;
0073
0074   ts.allocateCalibrated(2);
0075   ts.calibrated<2>() << 0.351, 0.473;
0076   ts.calibratedCovariance<2>() << 1e+8, 0., 0., 1e+8;
0077   ts.setProjectorSubspaceIndices(projector);
0078
0079   ts.filtered() << 0.27, 0.53, std::numbers::pi / 2., 0., 1 / 100., 0.;
0080   ts.filteredCovariance() = covTrk;
0081   ts.pathLength() = 2.;
0082   ts.jacobian().setIdentity();
0083
0084   ts_idx = traj.addTrackState(TrackStatePropMask::All, ts_idx);
0085   ts = traj.getTrackState(ts_idx);
0086   ts.typeFlags().setIsMeasurement();
0087
0088   ts.predicted() << 0.35, 0.49, std::numbers::pi / 2., 0., 1 / 100., 0.;
0089   ts.predictedCovariance() = covTrk;
0090
0091   ts.allocateCalibrated(2);
0092   ts.calibrated<2>() << 0.351, 0.473;
0093   ts.calibratedCovariance<2>() << 1e+8, 0., 0., 1e+8;
0094   ts.setProjectorSubspaceIndices(projector);
0095
0096   ts.filtered() << 0.33, 0.43, std::numbers::pi / 2., 0., 1 / 100., 0.;
0097   ts.filteredCovariance() = covTrk;
0098   ts.pathLength() = 3.;
0099   ts.jacobian().setIdentity();
0100
0101   // "smooth" these three track states
0102   BOOST_CHECK(MbfSmoother()(tgContext, traj, ts_idx).ok());
0103
0104   // Regression tests, only tests very basic correctness of the math, but tests
0105   // for regressions in the result.
0106
0107   auto ts1 = traj.getTrackState(0);
0108   BOOST_CHECK(ts1.hasSmoothed());
0109   BOOST_CHECK_NE(ts1.filtered(), ts1.smoothed());
0110
0111   double tol = 1e-6;
0112
0113   ParametersVector expPars;
0114   expPars << 0.301, 0.503, 1.5707963, 0.0, 0.01, 0.0;
0115   CovarianceMatrix expCov;
0116   expCov.setIdentity();
0117   expCov.diagonal() << 0.08, 0.3, 1.0, 1.0, 1.0, 1.0;
0118   CHECK_CLOSE_ABS(ts1.smoothed(), expPars, tol);
0119   CHECK_CLOSE_ABS(ts1.smoothedCovariance(), expCov, tol);
0120
0121   auto ts2 = traj.getTrackState(1);
0122   BOOST_CHECK(ts2.hasSmoothed());
0123   BOOST_CHECK_NE(ts2.filtered(), ts2.smoothed());
0124
0125   expPars << 0.27, 0.53, 1.5707963, 0.0, 0.01, 0.0;
0126   CHECK_CLOSE_ABS(ts2.smoothed(), expPars, tol);
0127   CHECK_CLOSE_ABS(ts2.smoothedCovariance(), expCov, tol);
0128
0129   auto ts3 = traj.getTrackState(2);
0130   BOOST_CHECK(ts3.hasSmoothed());
0131   // last one, smoothed == filtered
0132   BOOST_CHECK_EQUAL(ts3.filtered(), ts3.smoothed());
0133
0134   expPars << 0.33, 0.43, 1.5707963, 0.0, 0.01, 0.0;
0135   CHECK_CLOSE_ABS(ts3.smoothed(), expPars, tol);
0136   CHECK_CLOSE_ABS(ts3.smoothedCovariance(), expCov, tol);
0137 }
0138
0139 BOOST_AUTO_TEST_SUITE_END()
0140
0141 }  // namespace ActsTests