File indexing completed on 2025-01-12 03:30:51

 /*  KStars tests
     SPDX-FileCopyrightText: 2020 Eric Dejouhanet <eric.dejouhanet@gmail.com>
 
     SPDX-License-Identifier: GPL-2.0-or-later
 */
 
 #include <QTest>
 #include <memory>
 #include "testfitsdata.h"
 #include "Options.h"
 #include "ekos/auxiliary/solverutils.h"
 #include "ekos/auxiliary/stellarsolverprofile.h"
 #include <QtGlobal>
 
 Q_DECLARE_METATYPE(FITSMode);
 
 TestFitsData::TestFitsData(QObject *parent) : QObject(parent)
 {
 }
 
 void TestFitsData::initTestCase()
 {
     Options::setStellarSolverPartition(true);
 }
 
 void TestFitsData::cleanupTestCase()
 {
 }
 
 void TestFitsData::init()
 {
 
 }
 
 void TestFitsData::cleanup()
 {
 
 }
 
 void TestFitsData::testComputeHFR_data()
 {
 #if QT_VERSION < 0x050900
     QSKIP("Skipping fixture-based test on old QT version.");
 #else
     QTest::addColumn<QString>("NAME");
     QTest::addColumn<FITSMode>("MODE");
     QTest::addColumn<int>("NSTARS");
     QTest::addColumn<double>("HFR");
 
     // Normal HFR differs from focusHFR, in that the 'quickHFR' setting
     // defaults to true, so only the 25% in the center of the image
     // will have stars detected and HFRs computed. 'quickHFR' does not apply
     // to FITS_FOCUS images, only to FITS_NORMAL images.
 
     // Normal HFR tests
     QTest::newRow("NGC4535-1-FOCUS") << "ngc4535-autofocus1.fits" << FITS_FOCUS << 11 << 3.92;
     QTest::newRow("NGC4535-2-FOCUS") << "ngc4535-autofocus2.fits" << FITS_FOCUS << 17 << 2.13;
     QTest::newRow("NGC4535-3-FOCUS") << "ngc4535-autofocus3.fits" << FITS_FOCUS << 126 << 1.254911;
 
     // Focus HFR tests
     QTest::newRow("NGC4535-1-NORMAL") << "ngc4535-autofocus1.fits" << FITS_NORMAL << 3 << 3.17;
     QTest::newRow("NGC4535-2-NORMAL") << "ngc4535-autofocus2.fits" << FITS_NORMAL << 4 << 1.99;
     QTest::newRow("NGC4535-3-NORMAL") << "ngc4535-autofocus3.fits" << FITS_NORMAL << 30 << 1.22;
 #endif
 }
 
 void TestFitsData::testComputeHFR()
 {
 #if QT_VERSION < 0x050900
     QSKIP("Skipping fixture-based test on old QT version.");
 #else
     QFETCH(QString, NAME);
     QFETCH(FITSMode, MODE);
     QFETCH(int, NSTARS);
     QFETCH(double, HFR);
 
     if(!QFile::exists(NAME))
         QSKIP("Skipping load test because of missing fixture");
 
     std::unique_ptr<FITSData> d(new FITSData(MODE));
     QVERIFY(d != nullptr);
 
     QFuture<bool> worker = d->loadFromFile(NAME);
     QTRY_VERIFY_WITH_TIMEOUT(worker.isFinished(), 60000);
     QVERIFY(worker.result());
 
     worker = d->findStars(ALGORITHM_SEP);
     QTRY_VERIFY_WITH_TIMEOUT(worker.isFinished(), 10000);
     QVERIFY(worker.result());
 
     QCOMPARE(d->getDetectedStars(), NSTARS);
     QCOMPARE(d->getStarCenters().count(), NSTARS);
     QVERIFY2(abs(d->getHFR() - HFR) <= 0.1, qPrintable(QString("HFR expected(measured): %1(%2)").arg(HFR).arg(d->getHFR())));
 #endif
 }
 
 void TestFitsData::testBahtinovFocusHFR_data()
 {
 #if QT_VERSION < 0x050900
     QSKIP("Skipping fixture-based test on old QT version.");
 #else
     QTest::addColumn<QString>("NAME");
     QTest::addColumn<FITSMode>("MODE");
     QTest::addColumn<int>("NSTARS");
     QTest::addColumn<double>("HFR");
 
     QTest::newRow("BAHTINOV-1-NORMAL") << "bahtinov-focus.fits" << FITS_NORMAL << 1 << 1.544;
 #endif
 }
 
 void TestFitsData::testBahtinovFocusHFR()
 {
 #if QT_VERSION < 0x050900
     QSKIP("Skipping fixture-based test on old QT version.");
 #else
     QFETCH(QString, NAME);
     QFETCH(FITSMode, MODE);
     QFETCH(int, NSTARS);
     QFETCH(double, HFR);
 
     if(!QFile::exists(NAME))
         QSKIP("Skipping load test because of missing fixture");
 
     std::unique_ptr<FITSData> d(new FITSData(MODE));
     QVERIFY(d != nullptr);
 
     QFuture<bool> worker = d->loadFromFile(NAME);
     QTRY_VERIFY_WITH_TIMEOUT(worker.isFinished(), 10000);
     QVERIFY(worker.result());
 
     // The bahtinov algorithm depends on which star is selected and number of average rows - not sure how to fiddle with that yet
     const QRect trackingBox(204, 240, 128, 128);
 
     d->findStars(ALGORITHM_BAHTINOV, trackingBox).waitForFinished();
     QCOMPARE(d->getDetectedStars(), NSTARS);
     QCOMPARE(d->getStarCenters().count(), 1);
     QVERIFY(abs(d->getHFR() - HFR) < 0.01);
 #endif
 }
 
 void TestFitsData::initGenericDataFixture()
 {
 #if QT_VERSION < 0x050900
     QSKIP("Skipping fixture-based test on old QT version.");
 #else
     QTest::addColumn<QString>("NAME");
     QTest::addColumn<FITSMode>("MODE");
 
     // Star count
     QTest::addColumn<int>("NSTARS_CENTROID");
     QTest::addColumn<int>("NSTARS_STELLARSOLVER");
 
     // HFRs using variouls methods
     QTest::addColumn<double>("HFR_CENTROID");
     QTest::addColumn<double>("HFR_GRADIENT");
     QTest::addColumn<double>("HFR_THRESHOLD");
     QTest::addColumn<double>("HFR_STELLARSOLVER");
 
     // Statistics
     QTest::addColumn<double>("ADU");
     QTest::addColumn<double>("MEAN");
     QTest::addColumn<double>("STDDEV");
     QTest::addColumn<double>("SNR");
     QTest::addColumn<long>("MAXIMUM");
     QTest::addColumn<long>("MINIMUM");
     QTest::addColumn<double>("MEDIAN");
 
     // This tracking box should detect a single centered star using SEP
     QTest::addColumn<QRect>("TRACKING_BOX");
 
     QTest::newRow("M47-1-NORMAL")
             << "m47_sim_stars.fits"
             << FITS_NORMAL
             << 80       // Stars found with the Centroid detection
             << 104      // Stars found with the StellarSolver detection - default profile limits count
             << 1.49     // HFR found with the Centroid detection
             << 1.482291 // HFR found with the Gradient detection
             << 0.0      // HFR found with the Threshold detection - not used
             << 1.482291 // HFR found with the StellarSolver detection
             << 41.08    // ADU
             << 41.08    // Mean
             << 360.2932 // StdDev
             << 0.114    // SNR
             << 57832L   // Max
             << 21L      // Min
             << 31.0     // Median
             << QRect(591 - 16 / 2, 482 - 16 / 2, 16, 16);
 #endif
 }
 
 void TestFitsData::testLoadFits_data()
 {
 #if QT_VERSION < 0x050900
     QSKIP("Skipping fixture-based test on old QT version.");
 #else
     initGenericDataFixture();
 #endif
 }
 
 void TestFitsData::testLoadFits()
 {
 #if QT_VERSION < 0x050900
     QSKIP("Skipping fixture-based test on old QT version.");
 #else
     QFETCH(QString, NAME);
     QFETCH(FITSMode, MODE);
     QFETCH(int, NSTARS_CENTROID);
     QFETCH(int, NSTARS_STELLARSOLVER);
     QFETCH(double, HFR_CENTROID);
     QFETCH(double, HFR_GRADIENT);
     QFETCH(double, HFR_THRESHOLD);
     Q_UNUSED(HFR_THRESHOLD);
     QFETCH(double, HFR_STELLARSOLVER);
     QFETCH(double, ADU);
     QFETCH(double, MEAN);
     QFETCH(double, STDDEV);
     QFETCH(double, SNR);
     QFETCH(long, MAXIMUM);
     QFETCH(long, MINIMUM);
     QFETCH(double, MEDIAN);
     QFETCH(QRect, TRACKING_BOX);
 
     if(!QFile::exists(NAME))
         QSKIP("Skipping load test because of missing fixture");
 
     std::unique_ptr<FITSData> fd(new FITSData(MODE));
     QVERIFY(fd != nullptr);
 
     QFuture<bool> worker = fd->loadFromFile(NAME);
     QTRY_VERIFY_WITH_TIMEOUT(worker.isFinished(), 10000);
     QVERIFY(worker.result());
 
     // Statistics computation
     QVERIFY(abs(fd->getADU() - ADU) < 0.01);
     QVERIFY(abs(fd->getMean() - MEAN) < 0.01);
     fprintf(stderr, "%f vs %f (%f\n", fd->getStdDev(), STDDEV, abs(fd->getStdDev() - STDDEV));
     QVERIFY(abs(fd->getStdDev() - STDDEV) < 0.01);
     QVERIFY(abs(fd->getSNR() - SNR) < 0.001);
 
     // Minmax
     QCOMPARE((long)fd->getMax(), MAXIMUM);
     QCOMPARE((long)fd->getMin(), MINIMUM);
 
     QVERIFY(abs(fd->getMedian() - MEDIAN) < 0.01);
 
     // Without searching for stars, there are no stars found
     QCOMPARE(fd->getStarCenters().count(), 0);
     QCOMPARE(fd->getHFR(), -1.0);
 
     // Default algorithm is centroid, 80 stars with 1.495 as HFR
     fd->findStars().waitForFinished();
     QCOMPARE(fd->getDetectedStars(), NSTARS_CENTROID);
     QCOMPARE(fd->getStarCenters().count(), NSTARS_CENTROID);
     QVERIFY(abs(fd->getHFR() - HFR_CENTROID) < 0.01);
 
     // With the centroid algorithm, 80 stars with MEAN HFR 1.495
     fd->findStars(ALGORITHM_CENTROID).waitForFinished();
     QCOMPARE(fd->getDetectedStars(), NSTARS_CENTROID);
     QCOMPARE(fd->getStarCenters().count(), NSTARS_CENTROID);
     QVERIFY(abs(fd->getHFR() - HFR_CENTROID) < 0.01);
 
     // With the gradient algorithm, one single star found with HFR 1.801
     fd->findStars(ALGORITHM_GRADIENT).waitForFinished();
     QCOMPARE(fd->getDetectedStars(), 1);
     QCOMPARE(fd->getStarCenters().count(), 1);
     QVERIFY(abs(fd->getHFR() - HFR_GRADIENT) < 0.01);
 
     // The threshold algorithm depends on a global option - skip until we know how to fiddle with that
     //QCOMPARE(fd->findStars(ALGORITHM_THRESHOLD), -1);
     //QCOMPARE(fd->getDetectedStars(), 0);
     //QCOMPARE(fd->getStarCenters().count(), 0);
     //QCOMPARE(fd->getHFR(), -1.0);
 
     // With the SEP algorithm, 100 stars with MEAN HFR 2.08
     fd->findStars(ALGORITHM_SEP).waitForFinished();
     QCOMPARE(fd->getDetectedStars(), NSTARS_STELLARSOLVER);
     QCOMPARE(fd->getStarCenters().count(), NSTARS_STELLARSOLVER);
     QVERIFY(abs(fd->getHFR() - HFR_STELLARSOLVER) < 0.1);
 
     // Test the SEP algorithm with a tracking box, as used by the internal guider and subframe focus.
     fd->findStars(ALGORITHM_SEP, TRACKING_BOX).waitForFinished();
     auto centers = fd->getStarCenters();
     QCOMPARE(centers.count(), 1);
     // QWARN(QString("Center    %1,%2").arg(centers[0]->x).arg(centers[0]->y).toStdString().c_str());
     // QWARN(QString("TB Center %1,%2").arg(TRACKING_BOX.center().x()).arg(TRACKING_BOX.center().y()).toStdString().c_str());
     QVERIFY(abs(centers[0]->x - TRACKING_BOX.center().x()) <= 5);
     QVERIFY(abs(centers[0]->y - TRACKING_BOX.center().y()) <= 5);
 #endif
 }
 
 void TestFitsData::testCentroidAlgorithmBenchmark_data()
 {
 #if QT_VERSION < 0x050900
     QSKIP("Skipping fixture-based test on old QT version.");
 #else
     initGenericDataFixture();
 #endif
 }
 
 void TestFitsData::testCentroidAlgorithmBenchmark()
 {
 #if QT_VERSION < 0x050900
     QSKIP("Skipping fixture-based test on old QT version.");
 #else
     QFETCH(QString, NAME);
 
     if(!QFile::exists(NAME))
         QSKIP("Skipping load test because of missing fixture");
 
     std::unique_ptr<FITSData> d(new FITSData());
     QVERIFY(d != nullptr);
 
     QFuture<bool> worker = d->loadFromFile(NAME);
     QTRY_VERIFY_WITH_TIMEOUT(worker.isFinished(), 10000);
     QVERIFY(worker.result());
 
     QBENCHMARK { d->findStars(ALGORITHM_CENTROID).waitForFinished(); }
 #endif
 }
 
 void TestFitsData::testGradientAlgorithmBenchmark_data()
 {
 #if QT_VERSION < 0x050900
     QSKIP("Skipping fixture-based test on old QT version.");
 #else
     initGenericDataFixture();
 #endif
 }
 
 void TestFitsData::testGradientAlgorithmBenchmark()
 {
 #if QT_VERSION < 0x050900
     QSKIP("Skipping fixture-based test on old QT version.");
 #else
     QFETCH(QString, NAME);
 
     if(!QFile::exists(NAME))
         QSKIP("Skipping load test because of missing fixture");
 
     std::unique_ptr<FITSData> d(new FITSData());
     QVERIFY(d != nullptr);
 
     QFuture<bool> worker = d->loadFromFile(NAME);
     QTRY_VERIFY_WITH_TIMEOUT(worker.isFinished(), 10000);
     QVERIFY(worker.result());
 
     QBENCHMARK { d->findStars(ALGORITHM_GRADIENT).waitForFinished(); }
 #endif
 }
 
 void TestFitsData::testThresholdAlgorithmBenchmark_data()
 {
 #if QT_VERSION < 0x050900
     QSKIP("Skipping fixture-based test on old QT version.");
 #else
     initGenericDataFixture();
 #endif
 }
 
 void TestFitsData::testThresholdAlgorithmBenchmark()
 {
 #if QT_VERSION < 0x050900
     QSKIP("Skipping fixture-based test on old QT version.");
 #else
     QSKIP("Skipping benchmark of Threshold Algorithm, which requires fiddling with a global option");
 
     QFETCH(QString, NAME);
 
     if(!QFile::exists(NAME))
         QSKIP("Skipping load test because of missing fixture");
 
     std::unique_ptr<FITSData> d(new FITSData());
     QVERIFY(d != nullptr);
 
     QFuture<bool> worker = d->loadFromFile(NAME);
     QTRY_VERIFY_WITH_TIMEOUT(worker.isFinished(), 10000);
     QVERIFY(worker.result());
 
     QBENCHMARK { d->findStars(ALGORITHM_THRESHOLD).waitForFinished(); }
 #endif
 }
 
 void TestFitsData::testSEPAlgorithmBenchmark_data()
 {
 #if QT_VERSION < 0x050900
     QSKIP("Skipping fixture-based test on old QT version.");
 #else
     initGenericDataFixture();
 #endif
 }
 
 void TestFitsData::testSEPAlgorithmBenchmark()
 {
 #if QT_VERSION < 0x050900
     QSKIP("Skipping fixture-based test on old QT version.");
 #else
     QFETCH(QString, NAME);
 
     if(!QFile::exists(NAME))
         QSKIP("Skipping load test because of missing fixture");
 
     std::unique_ptr<FITSData> d(new FITSData());
     QVERIFY(d != nullptr);
 
     QFuture<bool> worker = d->loadFromFile(NAME);
     QTRY_VERIFY_WITH_TIMEOUT(worker.isFinished(), 10000);
     QVERIFY(worker.result());
 
     QBENCHMARK { d->findStars(ALGORITHM_SEP).waitForFinished(); }
 #endif
 }
 
 QString SolverLoop::status() const
 {
     return QString("%1/%2 %3% %4 %5")
            .arg(upto()).arg(repetitions).arg(upto() * 100.0 / repetitions, 2, 'f', 0)
            .arg(solver.get() && solver->isRunning() ? " running" : "")
            .arg(done() ? " Done" : "");
 }
 
 SolverLoop::SolverLoop(const QVector<QString> &files, const QString &dir, bool isDetecting, int numReps)
 {
     filenames = files;
     repetitions = numReps;
     directory = dir;
     detecting = isDetecting;
 
     // Preload the images.
     for (const QString &fname : files)
     {
         images.push_back(QSharedPointer<FITSData>(new FITSData));
         images.back().get()->loadFromFile(QString("%1/%2").arg(dir, fname)).waitForFinished();
     }
 }
 
 void SolverLoop::start()
 {
     startDetect(numDetects % filenames.size());
 }
 
 bool SolverLoop::done() const
 {
     return numDetects >= repetitions;
 }
 
 int SolverLoop::upto() const
 {
     return numDetects;
 }
 
 void SolverLoop::timeout()
 {
     qInfo() << QString("timed out on %1 %2 !!!!!!!!!!!!!!!!!!!!!").arg(currentIndex).arg(filenames[currentIndex]);
     if (detecting)
         watcher.cancel();
     else
         solver->abort();
 
     startDetect(currentIndex);
 }
 
 void SolverLoop::solverDone(bool timedOut, bool success, const FITSImage::Solution &solution,
                             double elapsedSeconds)
 {
     disconnect(&timer, &QTimer::timeout, this, &SolverLoop::timeout);
     timer.stop();
     disconnect(solver.get(), &SolverUtils::done, this, &SolverLoop::solverDone);
 
     const auto &filename = filenames[currentIndex];
     if (timedOut)
         qInfo() << QString("#%1: %2 Solver timed out: %3s").arg(numDetects).arg(filename).arg(elapsedSeconds, 0, 'f', 1);
     else if (!success)
         qInfo() << QString("#%1: %2 Solver failed: %3s").arg(numDetects).arg(filename).arg(elapsedSeconds, 0, 'f', 1);
     else
     {
         const double ra = solution.ra;
         const double dec = solution.dec;
         const double scale = solution.pixscale;
         qInfo() << QString("#%1: %2 Solver returned RA %3 DEC %4 Scale %5: %6s").arg(numDetects).arg(filename)
                 .arg(ra, 6, 'f', 3).arg(dec, 6, 'f', 3).arg(scale).arg(elapsedSeconds, 4, 'f', 1);
 
         if (numDetects++ < repetitions)
             startDetect(numDetects % filenames.size());
     }
 }
 
 void SolverLoop::randomTimeout()
 {
     disconnect(&watcher, &QFutureWatcher<bool>::finished, this, &SolverLoop::detectFinished);
     disconnect(&watcher, &QFutureWatcher<bool>::canceled, this, &SolverLoop::detectFinished);
     disconnect(&randomAbortTimer, &QTimer::timeout, this, &SolverLoop::randomTimeout);
     disconnect(&timer, &QTimer::timeout, this, &SolverLoop::timeout);
     timer.stop();
     randomAbortTimer.stop();
     qInfo() << QString("#%1: %2 random timeout was %3s (%4s)").arg(numDetects).arg(filenames[currentIndex])
             .arg(thisRandomTimeout, 3, 'f', 3).arg(dTimer.elapsed() / 1000.0, 3, 'f', 3);
     thisImage.reset();
     if (++numDetects < repetitions)
     {
         startDetect(numDetects % filenames.size());
     }
 }
 
 void SolverLoop::detectFinished()
 {
     disconnect(&timer, &QTimer::timeout, this, &SolverLoop::timeout);
     timer.stop();
     randomAbortTimer.stop();
     disconnect(&watcher, &QFutureWatcher<bool>::finished, this, &SolverLoop::detectFinished);
     disconnect(&watcher, &QFutureWatcher<bool>::canceled, this, &SolverLoop::detectFinished);
     bool result = watcher.result();
     if (result)
     {
         qInfo() << QString("#%1: %2 HFR %3 (%4s)").arg(numDetects).arg(filenames[currentIndex])
                 .arg(thisImage->getHFR(), 4, 'f', 2).arg(dTimer.elapsed() / 1000.0, 3, 'f', 3);
         if (++numDetects < repetitions)
         {
             startDetect(numDetects % filenames.size());
         }
     }
     else
     {
         QFAIL("Detect failed");
     }
 }
 
 void SolverLoop::startDetect(int index)
 {
     connect(&timer, &QTimer::timeout, this, &SolverLoop::timeout, Qt::UniqueConnection);
     timer.setSingleShot(true);
     timer.start(timeoutSecs * 1000);
 
     currentIndex = index;
     if (detecting)
     {
         thisImage.reset(new FITSData(images[currentIndex]));
         // detecting stars
         connect(&watcher, &QFutureWatcher<bool>::finished, this, &SolverLoop::detectFinished);
         connect(&watcher, &QFutureWatcher<bool>::canceled, this, &SolverLoop::detectFinished);
 
         if (randomAbortSecs > 0)
         {
             randomAbortTimer.setSingleShot(true);
             connect(&randomAbortTimer, &QTimer::timeout, this, &SolverLoop::randomTimeout, Qt::UniqueConnection);
             thisRandomTimeout = rand.generateDouble() * randomAbortSecs;
             randomAbortTimer.start(thisRandomTimeout * 1000);
 
         }
         dTimer.start();
         future = thisImage->findStars(ALGORITHM_SEP);
         watcher.setFuture(future);
     }
     else
     {
         // plate solving
         auto profiles = Ekos::getDefaultAlignOptionsProfiles();
         auto parameters = profiles.at(Options::solveOptionsProfile());
 
         // Double search radius
         Options::setSolverType(0); // Internal solver
         parameters.search_radius = parameters.search_radius * 2;
         solver.reset(new SolverUtils(parameters, 20), &QObject::deleteLater);
         connect(solver.get(), &SolverUtils::done, this, &SolverLoop::solverDone, Qt::UniqueConnection);
         solver->useScale(false, 0, 0);
         solver->usePosition(false, 0, 0);
         solver->runSolver(images[currentIndex]);
     }
 }
 
 // This tests how well we can detect stars and/or plate-solve a number of images
 // at the same time. Mostly a memory test--a failure would be a segv.
 // I have not provided the fits files as part of the source code, so you need to
 // provide them, add them the QVector<QString> variables, and make sure the directory
 // below points to the one holding your files.
 //
 // You may wish to reconfigure the section with SolverLoop at the bottom to have more
 // or less parallelism and switch between star detection and plate solves.
 //
 // You can want to run this as follows:
 //   export QTEST_FUNCTION_TIMEOUT=1000000000; testfitsdata testParallelSolvers -maxwarnings 1000000
 void TestFitsData::testParallelSolvers()
 {
 #define SKIP_PARALLEL_SOLVERS_TEST
 #ifdef SKIP_PARALLEL_SOLVERS_TEST
     QSKIP("Skipping testParallelSolvers");
     return;
 #else
     Options::setAutoDebayer(false);
 
     const QString dir = "/home/hy/Desktop/SharedFolder/DEBUG-solver";
     const QString dir1 = dir;
     const QVector<QString> files1 =
     {
         "guide_frame_00-20-08.fits",
         "guide_frame_00-20-12.fits",
         "guide_frame_00-20-15.fits",
         "guide_frame_00-20-18.fits",
         "guide_frame_00-20-21.fits",
         "guide_frame_00-20-24.fits",
         "guide_frame_00-20-27.fits",
     };
 
     const QString dir2 = dir;
     const QVector<QString> files2 =
     {
         "guide_frame_00-20-30.fits",
         "guide_frame_00-20-34.fits",
         "guide_frame_00-20-37.fits",
         "guide_frame_00-20-40.fits",
         "guide_frame_00-20-43.fits",
         "guide_frame_00-20-46.fits"
     };
 
     const QString dir3 = dir;
     const QVector<QString> files3 =
     {
         "m5_Light_LPR_120_secs_2022-03-12T04-44-56_201.fits",
         "m5_Light_LPR_120_secs_2022-03-12T04-47-02_202.fits",
         "m5_Light_LPR_120_secs_2022-03-12T04-49-04_203.fits",
         "m5_Light_LPR_120_secs_2022-03-12T04-51-06_204.fits",
         "m5_Light_LPR_120_secs_2022-03-12T04-53-07_205.fits"
     };
 
     const QString dir5 = dir;
     const QVector<QString> files5 =
     {
         "m74_Light_LPR_60_secs_2021-10-11T04-48-41_301.fits",
         "m74_Light_LPR_60_secs_2021-10-11T04-49-43_302.fits",
         "m74_Light_LPR_60_secs_2021-10-11T04-50-55_303.fits",
         "m74_Light_LPR_60_secs_2021-10-11T04-51-57_304.fits"
     };
 
     // Set the number of iterations here. THe more the better, e.g. 10000.
     constexpr int numIterations = 10000;
 
     // In the below declarations of SolverLoop,
     // for the 3rd arg: true means detect stars, false means plate solve them.
 
     // Detect stars in guide files
     SolverLoop loop1(files1, dir1, true, numIterations);
     loop1.setRandomAbort(1);
 
     // Detect stars in other guide files
     SolverLoop loop2(files2, dir2, true, numIterations);
     loop2.setRandomAbort(1);
 
     // Detect stars in subs
     SolverLoop loop3(files3, dir3, true, numIterations / 15);
     loop3.setRandomAbort(3);
 
     // This one solves the fits files
     SolverLoop loop4(files1, dir1, false, numIterations / 50);
 
     // This one solves the fits files
     SolverLoop loop5(files5, dir5, false, numIterations / 50);
 
     loop1.start();
     loop2.start();
     loop3.start();
     loop4.start();
     loop5.start();
 
     int iteration = 0;
     while(!loop1.done()
             || !loop2.done()
             || !loop3.done()
             || !loop4.done()
             || !loop5.done())
     {
         // The qWait is needed to allow message passing.
         QTest::qWait(10);
         if (iteration++ % 400 == 0)
             qInfo() << QString("%1 -- %2 -- %3 -- %4 -- %5")
                     .arg(loop1.status())
                     .arg(loop2.status())
                     .arg(loop3.status())
                     .arg(loop4.status())
                     .arg(loop5.status());
     }
 
     qInfo() << QString("%1 -- %2 -- %3 -- %4 -- %5")
             .arg(loop1.status())
             .arg(loop2.status())
             .arg(loop3.status())
             .arg(loop4.status())
             .arg(loop5.status());
 
     qInfo() << QString("Done!");
 #endif
 }
 
 QTEST_GUILESS_MAIN(TestFitsData)