use of uk.ac.sussex.gdsc.smlm.model.UniformIllumination in project GDSC-SMLM by aherbert.
the class BlinkEstimatorTest method estimateBlinking.
private TIntHashSet estimateBlinking(UniformRandomProvider rg, double blinkingRate, double ton, double toff, int particles, double fixedFraction, boolean timeAtLowerBound, boolean doAssert) {
Assumptions.assumeTrue(TestSettings.allow(TestComplexity.MAXIMUM));
final SpatialIllumination activationIllumination = new UniformIllumination(100);
int totalSteps = 100;
final double eAct = totalSteps * 0.3 * activationIllumination.getAveragePhotons();
final ImageModel imageModel = new ActivationEnergyImageModel(eAct, activationIllumination, ton, 0, toff, 0, blinkingRate, rg);
final double[] max = new double[] { 256, 256, 32 };
final double[] min = new double[3];
final SpatialDistribution distribution = new UniformDistribution(min, max, rg.nextInt());
final List<CompoundMoleculeModel> compounds = new ArrayList<>(1);
final CompoundMoleculeModel c = new CompoundMoleculeModel(1, 0, 0, 0, Arrays.asList(new MoleculeModel(0, 0, 0, 0)));
c.setDiffusionRate(diffusionRate);
c.setDiffusionType(DiffusionType.RANDOM_WALK);
compounds.add(c);
final List<CompoundMoleculeModel> molecules = imageModel.createMolecules(compounds, particles, distribution, false);
// Activate fluorophores
final List<? extends FluorophoreSequenceModel> fluorophores = imageModel.createFluorophores(molecules, totalSteps);
totalSteps = checkTotalSteps(totalSteps, fluorophores);
final List<LocalisationModel> localisations = imageModel.createImage(molecules, fixedFraction, totalSteps, photons, 0.5, false);
// // Remove localisations to simulate missed counts.
// List<LocalisationModel> newLocalisations = new
// ArrayList<LocalisationModel>(localisations.size());
// boolean[] id = new boolean[fluorophores.size() + 1];
// Statistics photonStats = new Statistics();
// for (LocalisationModel l : localisations)
// {
// photonStats.add(l.getIntensity());
// // Remove by intensity threshold and optionally at random.
// if (l.getIntensity() < minPhotons || rand.nextDouble() < pDelete)
// continue;
// newLocalisations.add(l);
// id[l.getId()] = true;
// }
// localisations = newLocalisations;
// logger.info("Photons = %f", photonStats.getMean());
//
// List<FluorophoreSequenceModel> newFluorophores = new
// ArrayList<FluorophoreSequenceModel>(fluorophores.size());
// for (FluorophoreSequenceModel f : fluorophores)
// {
// if (id[f.getId()])
// newFluorophores.add(f);
// }
// fluorophores = newFluorophores;
final MemoryPeakResults results = new MemoryPeakResults();
final CalibrationWriter calibration = new CalibrationWriter();
calibration.setNmPerPixel(pixelPitch);
calibration.setExposureTime(msPerFrame);
calibration.setCountPerPhoton(1);
results.setCalibration(calibration.getCalibration());
results.setPsf(PsfHelper.create(PSFType.ONE_AXIS_GAUSSIAN_2D));
final float b = 0;
float intensity;
final float z = 0;
for (final LocalisationModel l : localisations) {
// Remove by intensity threshold and optionally at random.
if (l.getIntensity() < minPhotons || rg.nextDouble() < probabilityDelete) {
continue;
}
final int frame = l.getTime();
intensity = (float) l.getIntensity();
final float x = (float) l.getX();
final float y = (float) l.getY();
final float[] params = Gaussian2DPeakResultHelper.createParams(b, intensity, x, y, z, psfWidth);
results.add(frame, 0, 0, 0, 0, 0, 0, params, null);
}
// Add random localisations
// Intensity doesn't matter at the moment for tracing
intensity = (float) photons;
for (int i = (int) (localisations.size() * probabilityAdd); i-- > 0; ) {
final int frame = 1 + rg.nextInt(totalSteps);
final float x = (float) (rg.nextDouble() * max[0]);
final float y = (float) (rg.nextDouble() * max[1]);
final float[] params = Gaussian2DPeakResultHelper.createParams(b, intensity, x, y, z, psfWidth);
results.add(frame, 0, 0, 0, 0, 0, 0, params, null);
}
// Get actual simulated stats ...
final Statistics statsNBlinks = new Statistics();
final Statistics statsTOn = new Statistics();
final Statistics statsTOff = new Statistics();
final Statistics statsSampledNBlinks = new Statistics();
final Statistics statsSampledTOn = new Statistics();
final StoredDataStatistics statsSampledTOff = new StoredDataStatistics();
for (final FluorophoreSequenceModel f : fluorophores) {
statsNBlinks.add(f.getNumberOfBlinks());
statsTOn.add(f.getOnTimes());
statsTOff.add(f.getOffTimes());
final int[] on = f.getSampledOnTimes();
statsSampledNBlinks.add(on.length);
statsSampledTOn.add(on);
statsSampledTOff.add(f.getSampledOffTimes());
}
logger.info(FunctionUtils.getSupplier("N = %d (%d), N-blinks = %f, tOn = %f, tOff = %f, Fixed = %f", fluorophores.size(), localisations.size(), blinkingRate, ton, toff, fixedFraction));
logger.info(FunctionUtils.getSupplier("Actual N-blinks = %f (%f), tOn = %f (%f), tOff = %f (%f), 95%% = %f, max = %f", statsNBlinks.getMean(), statsSampledNBlinks.getMean(), statsTOn.getMean(), statsSampledTOn.getMean(), statsTOff.getMean(), statsSampledTOff.getMean(), statsSampledTOff.getStatistics().getPercentile(95), statsSampledTOff.getStatistics().getMax()));
logger.info("-=-=--=-");
final BlinkEstimator be = new BlinkEstimator();
be.setMaxDarkTime((int) (toff * 10));
be.setMsPerFrame(msPerFrame);
be.setRelativeDistance(false);
final double d = ImageModel.getRandomMoveDistance(diffusionRate);
be.setSearchDistance((fixedFraction < 1) ? Math.sqrt(2 * d * d) * 3 : 0);
be.setTimeAtLowerBound(timeAtLowerBound);
// Assertions.assertTrue("Max dark time must exceed the dark time of the data (otherwise no
// plateau)",
// be.maxDarkTime > statsSampledTOff.getStatistics().getMax());
final int nMolecules = fluorophores.size();
if (usePopulationStatistics) {
blinkingRate = statsNBlinks.getMean();
toff = statsTOff.getMean();
} else {
blinkingRate = statsSampledNBlinks.getMean();
toff = statsSampledTOff.getMean();
}
// See if any fitting regime gets a correct answer
final TIntHashSet ok = new TIntHashSet();
for (int numberOfFittedPoints = MIN_FITTED_POINTS; numberOfFittedPoints <= MAX_FITTED_POINTS; numberOfFittedPoints++) {
be.setNumberOfFittedPoints(numberOfFittedPoints);
be.computeBlinkingRate(results, true);
final double moleculesError = DoubleEquality.relativeError(nMolecules, be.getNMolecules());
final double blinksError = DoubleEquality.relativeError(blinkingRate, be.getNBlinks());
final double offError = DoubleEquality.relativeError(toff * msPerFrame, be.getTOff());
logger.info(FunctionUtils.getSupplier("Error %d: N = %f, blinks = %f, tOff = %f : %f", numberOfFittedPoints, moleculesError, blinksError, offError, (moleculesError + blinksError + offError) / 3));
if (moleculesError < relativeError && blinksError < relativeError && offError < relativeError) {
ok.add(numberOfFittedPoints);
logger.info("-=-=--=-");
logger.info(FunctionUtils.getSupplier("*** Correct at %d fitted points ***", numberOfFittedPoints));
if (doAssert) {
break;
}
}
// if (!be.isIncreaseNFittedPoints())
// break;
}
logger.info("-=-=--=-");
if (doAssert) {
Assertions.assertFalse(ok.isEmpty());
}
// relativeError);
return ok;
}
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