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GainMatrixSmootherTests.cpp
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1 // This file is part of the Acts project.
2 //
3 // Copyright (C) 2019-2020 CERN for the benefit of the Acts project
4 //
5 // This Source Code Form is subject to the terms of the Mozilla Public
6 // License, v. 2.0. If a copy of the MPL was not distributed with this
7 // file, You can obtain one at http://mozilla.org/MPL/2.0/.
8 
9 #include <boost/test/unit_test.hpp>
10 
11 #include "Acts/Definitions/Algebra.hpp"
12 #include "Acts/Definitions/TrackParametrization.hpp"
13 #include "Acts/EventData/MultiTrajectory.hpp"
14 #include "Acts/EventData/TrackStatePropMask.hpp"
15 #include "Acts/EventData/VectorMultiTrajectory.hpp"
16 #include "Acts/Geometry/GeometryContext.hpp"
17 #include "Acts/Tests/CommonHelpers/FloatComparisons.hpp"
18 #include "Acts/TrackFitting/GainMatrixSmoother.hpp"
19 #include "Acts/Utilities/Result.hpp"
20 
21 #include <cmath>
22 #include <cstddef>
23 
24 namespace {
25 
26 using namespace Acts;
27 using namespace Acts::Test;
28 
29 using ParametersVector = Acts::BoundVector;
30 using CovarianceMatrix = Acts::BoundSquareMatrix;
31 using Jacobian = Acts::BoundMatrix;
32 
33 const Acts::GeometryContext tgContext;
34 
35 } // namespace
36 
37 BOOST_AUTO_TEST_SUITE(TrackFittingGainMatrixSmoother)
38 
39 BOOST_AUTO_TEST_CASE(Smooth) {
40  VectorMultiTrajectory traj;
41  size_t ts_idx = traj.addTrackState(TrackStatePropMask::All);
42  auto ts = traj.getTrackState(ts_idx);
43 
44  // Make dummy track parameter
45  CovarianceMatrix covTrk;
46  covTrk.setIdentity();
47  covTrk.diagonal() << 0.08, 0.3, 1, 1, 1, 1;
48  BoundVector parValues;
49  parValues << 0.3, 0.5, 0.5 * M_PI, 0., 1 / 100., 0.;
50 
51  ts.predicted() = parValues;
52  ts.predictedCovariance() = covTrk;
53 
54  parValues << 0.301, 0.503, 0.5 * M_PI, 0., 1 / 100., 0.;
55 
56  ts.filtered() = parValues;
57  ts.filteredCovariance() = covTrk;
58  ts.pathLength() = 1.;
59  ts.jacobian().setIdentity();
60 
61  ts_idx = traj.addTrackState(TrackStatePropMask::All, ts_idx);
62  ts = traj.getTrackState(ts_idx);
63 
64  parValues << 0.2, 0.5, 0.5 * M_PI, 0., 1 / 100., 0.;
65  ts.predicted() = parValues;
66  ts.predictedCovariance() = covTrk;
67 
68  parValues << 0.27, 0.53, 0.5 * M_PI, 0., 1 / 100., 0.;
69  ts.filtered() = parValues;
70  ts.filteredCovariance() = covTrk;
71  ts.pathLength() = 2.;
72  ts.jacobian().setIdentity();
73 
74  ts_idx = traj.addTrackState(TrackStatePropMask::All, ts_idx);
75  ts = traj.getTrackState(ts_idx);
76 
77  parValues << 0.35, 0.49, 0.5 * M_PI, 0., 1 / 100., 0.;
78  ts.predicted() = parValues;
79  ts.predictedCovariance() = covTrk;
80 
81  parValues << 0.33, 0.43, 0.5 * M_PI, 0., 1 / 100., 0.;
82  ts.filtered() = parValues;
83  ts.filteredCovariance() = covTrk;
84  ts.pathLength() = 3.;
85  ts.jacobian().setIdentity();
86 
87  // "smooth" these three track states
88  BOOST_CHECK(GainMatrixSmoother()(tgContext, traj, ts_idx).ok());
89 
90  // Regression tests, only tests very basic correctness of the math, but tests
91  // for regressions in the result.
92 
93  auto ts1 = traj.getTrackState(0);
94  BOOST_CHECK(ts1.hasSmoothed());
95  BOOST_CHECK_NE(ts1.filtered(), ts1.smoothed());
96 
97  double tol = 1e-6;
98 
99  ParametersVector expPars;
100  expPars << 0.3510000, 0.4730000, 1.5707963, 0.0000000, 0.0100000, 0.0000000;
101  CovarianceMatrix expCov;
102  expCov.setIdentity();
103  expCov.diagonal() << 0.0800000, 0.3000000, 1.0000000, 1.0000000, 1.0000000,
104  1.0000000;
105  CHECK_CLOSE_ABS(ts1.smoothed(), expPars, tol);
106  CHECK_CLOSE_ABS(ts1.smoothedCovariance(), expCov, tol);
107 
108  auto ts2 = traj.getTrackState(1);
109  BOOST_CHECK(ts2.hasSmoothed());
110  BOOST_CHECK_NE(ts2.filtered(), ts2.smoothed());
111 
112  expPars << 0.2500000, 0.4700000, 1.5707963, 0.0000000, 0.0100000, 0.0000000;
113  CHECK_CLOSE_ABS(ts2.smoothed(), expPars, tol);
114  CHECK_CLOSE_ABS(ts2.smoothedCovariance(), expCov, tol);
115 
116  auto ts3 = traj.getTrackState(2);
117  BOOST_CHECK(ts3.hasSmoothed());
118  // last one, smoothed == filtered
119  BOOST_CHECK_EQUAL(ts3.filtered(), ts3.smoothed());
120 
121  expPars << 0.3300000, 0.4300000, 1.5707963, 0.0000000, 0.0100000, 0.0000000;
122  CHECK_CLOSE_ABS(ts3.smoothed(), expPars, tol);
123  CHECK_CLOSE_ABS(ts3.smoothedCovariance(), expCov, tol);
124 }
125 
126 BOOST_AUTO_TEST_SUITE_END()