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