use of gdsc.smlm.model.MaskDistribution in project GDSC-SMLM by aherbert.
the class FilterResults method filterResults.
/**
* Apply the filters to the data
*/
private void filterResults() {
checkLimits();
MemoryPeakResults newResults = new MemoryPeakResults();
newResults.copySettings(results);
newResults.setName(results.getName() + " Filtered");
// Initialise the mask
ByteProcessor mask = getMask(filterSettings.maskTitle);
MaskDistribution maskFilter = null;
final float centreX = results.getBounds().width / 2.0f;
final float centreY = results.getBounds().height / 2.0f;
if (mask != null) {
double scaleX = (double) results.getBounds().width / mask.getWidth();
double scaleY = (double) results.getBounds().height / mask.getHeight();
maskFilter = new MaskDistribution((byte[]) mask.getPixels(), mask.getWidth(), mask.getHeight(), 0, scaleX, scaleY);
}
int i = 0;
final int size = results.size();
final double maxVariance = filterSettings.maxPrecision * filterSettings.maxPrecision;
for (PeakResult result : results.getResults()) {
if (i % 64 == 0)
IJ.showProgress(i, size);
if (getDrift(result) > filterSettings.maxDrift)
continue;
if (result.getSignal() < filterSettings.minSignal)
continue;
if (getSNR(result) < filterSettings.minSNR)
continue;
if (getVariance(result) > maxVariance)
continue;
final float width = getWidth(result);
if (width < filterSettings.minWidth || width > filterSettings.maxWidth)
continue;
if (maskFilter != null) {
// Check the coordinates are inside the mask
double[] xy = new double[] { result.getXPosition() - centreX, result.getYPosition() - centreY };
if (!maskFilter.isWithinXY(xy))
continue;
}
// Passed all filters. Add to the results
newResults.add(result);
}
IJ.showProgress(1);
IJ.showStatus(newResults.size() + " Filtered localisations");
MemoryPeakResults.addResults(newResults);
}
use of gdsc.smlm.model.MaskDistribution in project GDSC-SMLM by aherbert.
the class CreateData method createMaskDistribution.
private SpatialDistribution createMaskDistribution(ImagePlus imp, double sliceDepth, boolean updateArea) {
// Calculate the scale of the mask
final int w = imp.getWidth();
final int h = imp.getHeight();
final double scaleX = (double) settings.size / w;
final double scaleY = (double) settings.size / h;
// Use an image for the distribution
if (imp.getStackSize() > 1) {
ImageStack stack = imp.getImageStack();
List<int[]> masks = new ArrayList<int[]>(stack.getSize());
int[] maxMask = new int[w * h];
for (int slice = 1; slice <= stack.getSize(); slice++) {
int[] mask = extractMask(stack.getProcessor(slice));
if (updateArea) {
for (int i = 0; i < mask.length; i++) if (mask[i] != 0)
maxMask[i] = 1;
}
masks.add(mask);
}
if (updateArea)
updateArea(maxMask, w, h);
if (sliceDepth == 0)
// Auto configure to the full depth of the simulation
sliceDepth = settings.depth / masks.size();
return new MaskDistribution3D(masks, w, h, sliceDepth / settings.pixelPitch, scaleX, scaleY, createRandomGenerator());
} else {
int[] mask = extractMask(imp.getProcessor());
if (updateArea)
updateArea(mask, w, h);
return new MaskDistribution(mask, w, h, settings.depth / settings.pixelPitch, scaleX, scaleY, createRandomGenerator());
}
}
use of gdsc.smlm.model.MaskDistribution in project GDSC-SMLM by aherbert.
the class PCPALMMolecules method runSimulation.
private void runSimulation(boolean resultsAvailable) {
if (resultsAvailable && !showSimulationDialog())
return;
startLog();
log("Simulation parameters");
if (blinkingDistribution == 3) {
log(" - Clusters = %d", nMolecules);
log(" - Simulation size = %s um", Utils.rounded(simulationSize, 4));
log(" - Molecules/cluster = %s", Utils.rounded(blinkingRate, 4));
log(" - Blinking distribution = %s", BLINKING_DISTRIBUTION[blinkingDistribution]);
log(" - p-Value = %s", Utils.rounded(p, 4));
} else {
log(" - Molecules = %d", nMolecules);
log(" - Simulation size = %s um", Utils.rounded(simulationSize, 4));
log(" - Blinking rate = %s", Utils.rounded(blinkingRate, 4));
log(" - Blinking distribution = %s", BLINKING_DISTRIBUTION[blinkingDistribution]);
}
log(" - Average precision = %s nm", Utils.rounded(sigmaS, 4));
log(" - Clusters simulation = " + CLUSTER_SIMULATION[clusterSimulation]);
if (clusterSimulation > 0) {
log(" - Cluster number = %s +/- %s", Utils.rounded(clusterNumber, 4), Utils.rounded(clusterNumberSD, 4));
log(" - Cluster radius = %s nm", Utils.rounded(clusterRadius, 4));
}
final double nmPerPixel = 100;
double width = simulationSize * 1000.0;
// Allow a border of 3 x sigma for +/- precision
//if (blinkingRate > 1)
width -= 3 * sigmaS;
RandomGenerator randomGenerator = new Well19937c(System.currentTimeMillis() + System.identityHashCode(this));
RandomDataGenerator dataGenerator = new RandomDataGenerator(randomGenerator);
UniformDistribution dist = new UniformDistribution(null, new double[] { width, width, 0 }, randomGenerator.nextInt());
molecules = new ArrayList<Molecule>(nMolecules);
// Create some dummy results since the calibration is required for later analysis
results = new MemoryPeakResults();
results.setCalibration(new gdsc.smlm.results.Calibration(nmPerPixel, 1, 100));
results.setSource(new NullSource("Molecule Simulation"));
results.begin();
int count = 0;
// Generate a sequence of coordinates
ArrayList<double[]> xyz = new ArrayList<double[]>((int) (nMolecules * 1.1));
Statistics statsRadius = new Statistics();
Statistics statsSize = new Statistics();
String maskTitle = TITLE + " Cluster Mask";
ByteProcessor bp = null;
double maskScale = 0;
if (clusterSimulation > 0) {
// Simulate clusters.
// Note: In the Veatch et al. paper (Plos 1, e31457) correlation functions are built using circles
// with small radii of 4-8 Arbitrary Units (AU) or large radii of 10-30 AU. A fluctuations model is
// created at T = 1.075 Tc. It is not clear exactly how the particles are distributed.
// It may be that a mask is created first using the model. The particles are placed on the mask using
// a specified density. This simulation produces a figure to show either a damped cosine function
// (circles) or an exponential (fluctuations). The number of particles in each circle may be randomly
// determined just by density. The figure does not discuss the derivation of the cluster size
// statistic.
//
// If this plugin simulation is run with a uniform distribution and blinking rate of 1 then the damped
// cosine function is reproduced. The curve crosses g(r)=1 at a value equivalent to the average
// distance to the centre-of-mass of each drawn cluster, not the input cluster radius parameter (which
// is a hard upper limit on the distance to centre).
final int maskSize = lowResolutionImageSize;
int[] mask = null;
// scale is in nm/pixel
maskScale = width / maskSize;
ArrayList<double[]> clusterCentres = new ArrayList<double[]>();
int totalSteps = 1 + (int) Math.ceil(nMolecules / clusterNumber);
if (clusterSimulation == 2 || clusterSimulation == 3) {
// Clusters are non-overlapping circles
// Ensure the circles do not overlap by using an exclusion mask that accumulates
// out-of-bounds pixels by drawing the last cluster (plus some border) on an image. When no
// more pixels are available then stop generating molecules.
// This is done by cumulatively filling a mask and using the MaskDistribution to select
// a new point. This may be slow but it works.
// TODO - Allow clusters of different sizes...
mask = new int[maskSize * maskSize];
Arrays.fill(mask, 255);
MaskDistribution maskDistribution = new MaskDistribution(mask, maskSize, maskSize, 0, maskScale, maskScale, randomGenerator);
double[] centre;
IJ.showStatus("Computing clusters mask");
int roiRadius = (int) Math.round((clusterRadius * 2) / maskScale);
if (clusterSimulation == 3) {
// Generate a mask of circles then sample from that.
// If we want to fill the mask completely then adjust the total steps to be the number of
// circles that can fit inside the mask.
totalSteps = (int) (maskSize * maskSize / (Math.PI * Math.pow(clusterRadius / maskScale, 2)));
}
while ((centre = maskDistribution.next()) != null && clusterCentres.size() < totalSteps) {
IJ.showProgress(clusterCentres.size(), totalSteps);
// The mask returns the coordinates with the centre of the image at 0,0
centre[0] += width / 2;
centre[1] += width / 2;
clusterCentres.add(centre);
// Fill in the mask around the centre to exclude any more circles that could overlap
double cx = centre[0] / maskScale;
double cy = centre[1] / maskScale;
fillMask(mask, maskSize, (int) cx, (int) cy, roiRadius, 0);
//Utils.display("Mask", new ColorProcessor(maskSize, maskSize, mask));
try {
maskDistribution = new MaskDistribution(mask, maskSize, maskSize, 0, maskScale, maskScale, randomGenerator);
} catch (IllegalArgumentException e) {
// This can happen when there are no more non-zero pixels
log("WARNING: No more room for clusters on the mask area (created %d of estimated %d)", clusterCentres.size(), totalSteps);
break;
}
}
IJ.showProgress(1);
IJ.showStatus("");
} else {
// Pick centres randomly from the distribution
while (clusterCentres.size() < totalSteps) clusterCentres.add(dist.next());
}
if (showClusterMask || clusterSimulation == 3) {
// Show the mask for the clusters
if (mask == null)
mask = new int[maskSize * maskSize];
else
Arrays.fill(mask, 0);
int roiRadius = (int) Math.round((clusterRadius) / maskScale);
for (double[] c : clusterCentres) {
double cx = c[0] / maskScale;
double cy = c[1] / maskScale;
fillMask(mask, maskSize, (int) cx, (int) cy, roiRadius, 1);
}
if (clusterSimulation == 3) {
// We have the mask. Now pick points at random from the mask.
MaskDistribution maskDistribution = new MaskDistribution(mask, maskSize, maskSize, 0, maskScale, maskScale, randomGenerator);
// Allocate each molecule position to a parent circle so defining clusters.
int[][] clusters = new int[clusterCentres.size()][];
int[] clusterSize = new int[clusters.length];
for (int i = 0; i < nMolecules; i++) {
double[] centre = maskDistribution.next();
// The mask returns the coordinates with the centre of the image at 0,0
centre[0] += width / 2;
centre[1] += width / 2;
xyz.add(centre);
// Output statistics on cluster size and number.
// TODO - Finding the closest cluster could be done better than an all-vs-all comparison
double max = distance2(centre, clusterCentres.get(0));
int cluster = 0;
for (int j = 1; j < clusterCentres.size(); j++) {
double d2 = distance2(centre, clusterCentres.get(j));
if (d2 < max) {
max = d2;
cluster = j;
}
}
// Assign point i to cluster
centre[2] = cluster;
if (clusterSize[cluster] == 0) {
clusters[cluster] = new int[10];
}
if (clusters[cluster].length <= clusterSize[cluster]) {
clusters[cluster] = Arrays.copyOf(clusters[cluster], (int) (clusters[cluster].length * 1.5));
}
clusters[cluster][clusterSize[cluster]++] = i;
}
// Generate real cluster size statistics
for (int j = 0; j < clusterSize.length; j++) {
final int size = clusterSize[j];
if (size == 0)
continue;
statsSize.add(size);
if (size == 1) {
statsRadius.add(0);
continue;
}
// Find centre of cluster and add the distance to each point
double[] com = new double[2];
for (int n = 0; n < size; n++) {
double[] xy = xyz.get(clusters[j][n]);
for (int k = 0; k < 2; k++) com[k] += xy[k];
}
for (int k = 0; k < 2; k++) com[k] /= size;
for (int n = 0; n < size; n++) {
double dx = xyz.get(clusters[j][n])[0] - com[0];
double dy = xyz.get(clusters[j][n])[1] - com[1];
statsRadius.add(Math.sqrt(dx * dx + dy * dy));
}
}
}
if (showClusterMask) {
bp = new ByteProcessor(maskSize, maskSize);
for (int i = 0; i < mask.length; i++) if (mask[i] != 0)
bp.set(i, 128);
Utils.display(maskTitle, bp);
}
}
// Use the simulated cluster centres to create clusters of the desired size
if (clusterSimulation == 1 || clusterSimulation == 2) {
for (double[] clusterCentre : clusterCentres) {
int clusterN = (int) Math.round((clusterNumberSD > 0) ? dataGenerator.nextGaussian(clusterNumber, clusterNumberSD) : clusterNumber);
if (clusterN < 1)
continue;
//double[] clusterCentre = dist.next();
if (clusterN == 1) {
// No need for a cluster around a point
xyz.add(clusterCentre);
statsRadius.add(0);
statsSize.add(1);
} else {
// Generate N random points within a circle of the chosen cluster radius.
// Locate the centre-of-mass and the average distance to the centre.
double[] com = new double[3];
int j = 0;
while (j < clusterN) {
// Generate a random point within a circle uniformly
// http://stackoverflow.com/questions/5837572/generate-a-random-point-within-a-circle-uniformly
double t = 2.0 * Math.PI * randomGenerator.nextDouble();
double u = randomGenerator.nextDouble() + randomGenerator.nextDouble();
double r = clusterRadius * ((u > 1) ? 2 - u : u);
double x = r * Math.cos(t);
double y = r * Math.sin(t);
double[] xy = new double[] { clusterCentre[0] + x, clusterCentre[1] + y };
xyz.add(xy);
for (int k = 0; k < 2; k++) com[k] += xy[k];
j++;
}
// Add the distance of the points from the centre of the cluster.
// Note this does not account for the movement due to precision.
statsSize.add(j);
if (j == 1) {
statsRadius.add(0);
} else {
for (int k = 0; k < 2; k++) com[k] /= j;
while (j > 0) {
double dx = xyz.get(xyz.size() - j)[0] - com[0];
double dy = xyz.get(xyz.size() - j)[1] - com[1];
statsRadius.add(Math.sqrt(dx * dx + dy * dy));
j--;
}
}
}
}
}
} else {
// Random distribution
for (int i = 0; i < nMolecules; i++) xyz.add(dist.next());
}
// The Gaussian sigma should be applied so the overall distance from the centre
// ( sqrt(x^2+y^2) ) has a standard deviation of sigmaS?
final double sigma1D = sigmaS / Math.sqrt(2);
// Show optional histograms
StoredDataStatistics intraDistances = null;
StoredData blinks = null;
if (showHistograms) {
int capacity = (int) (xyz.size() * blinkingRate);
intraDistances = new StoredDataStatistics(capacity);
blinks = new StoredData(capacity);
}
Statistics statsSigma = new Statistics();
for (int i = 0; i < xyz.size(); i++) {
int nOccurrences = getBlinks(dataGenerator, blinkingRate);
if (showHistograms)
blinks.add(nOccurrences);
final int size = molecules.size();
// Get coordinates in nm
final double[] moleculeXyz = xyz.get(i);
if (bp != null && nOccurrences > 0) {
bp.putPixel((int) Math.round(moleculeXyz[0] / maskScale), (int) Math.round(moleculeXyz[1] / maskScale), 255);
}
while (nOccurrences-- > 0) {
final double[] localisationXy = Arrays.copyOf(moleculeXyz, 2);
// Add random precision
if (sigma1D > 0) {
final double dx = dataGenerator.nextGaussian(0, sigma1D);
final double dy = dataGenerator.nextGaussian(0, sigma1D);
localisationXy[0] += dx;
localisationXy[1] += dy;
if (!dist.isWithinXY(localisationXy))
continue;
// Calculate mean-squared displacement
statsSigma.add(dx * dx + dy * dy);
}
final double x = localisationXy[0];
final double y = localisationXy[1];
molecules.add(new Molecule(x, y, i, 1));
// Store in pixels
float[] params = new float[7];
params[Gaussian2DFunction.X_POSITION] = (float) (x / nmPerPixel);
params[Gaussian2DFunction.Y_POSITION] = (float) (y / nmPerPixel);
results.addf(i + 1, (int) x, (int) y, 0, 0, 0, params, null);
}
if (molecules.size() > size) {
count++;
if (showHistograms) {
int newCount = molecules.size() - size;
if (newCount == 1) {
//intraDistances.add(0);
continue;
}
// Get the distance matrix between these molecules
double[][] matrix = new double[newCount][newCount];
for (int ii = size, x = 0; ii < molecules.size(); ii++, x++) {
for (int jj = size + 1, y = 1; jj < molecules.size(); jj++, y++) {
final double d2 = molecules.get(ii).distance2(molecules.get(jj));
matrix[x][y] = matrix[y][x] = d2;
}
}
// Get the maximum distance for particle linkage clustering of this molecule
double max = 0;
for (int x = 0; x < newCount; x++) {
// Compare to all-other molecules and get the minimum distance
// needed to join at least one
double linkDistance = Double.POSITIVE_INFINITY;
for (int y = 0; y < newCount; y++) {
if (x == y)
continue;
if (matrix[x][y] < linkDistance)
linkDistance = matrix[x][y];
}
// Check if this is larger
if (max < linkDistance)
max = linkDistance;
}
intraDistances.add(Math.sqrt(max));
}
}
}
results.end();
if (bp != null)
Utils.display(maskTitle, bp);
// Used for debugging
//System.out.printf(" * Molecules = %d (%d activated)\n", xyz.size(), count);
//if (clusterSimulation > 0)
// System.out.printf(" * Cluster number = %s +/- %s. Radius = %s +/- %s\n",
// Utils.rounded(statsSize.getMean(), 4), Utils.rounded(statsSize.getStandardDeviation(), 4),
// Utils.rounded(statsRadius.getMean(), 4), Utils.rounded(statsRadius.getStandardDeviation(), 4));
log("Simulation results");
log(" * Molecules = %d (%d activated)", xyz.size(), count);
log(" * Blinking rate = %s", Utils.rounded((double) molecules.size() / xyz.size(), 4));
log(" * Precision (Mean-displacement) = %s nm", (statsSigma.getN() > 0) ? Utils.rounded(Math.sqrt(statsSigma.getMean()), 4) : "0");
if (showHistograms) {
if (intraDistances.getN() == 0) {
log(" * Mean Intra-Molecule particle linkage distance = 0 nm");
log(" * Fraction of inter-molecule particle linkage @ 0 nm = 0 %%");
} else {
plot(blinks, "Blinks/Molecule", true);
double[][] intraHist = plot(intraDistances, "Intra-molecule particle linkage distance", false);
// Determine 95th and 99th percentile
int p99 = intraHist[0].length - 1;
double limit1 = 0.99 * intraHist[1][p99];
double limit2 = 0.95 * intraHist[1][p99];
while (intraHist[1][p99] > limit1 && p99 > 0) p99--;
int p95 = p99;
while (intraHist[1][p95] > limit2 && p95 > 0) p95--;
log(" * Mean Intra-Molecule particle linkage distance = %s nm (95%% = %s, 99%% = %s, 100%% = %s)", Utils.rounded(intraDistances.getMean(), 4), Utils.rounded(intraHist[0][p95], 4), Utils.rounded(intraHist[0][p99], 4), Utils.rounded(intraHist[0][intraHist[0].length - 1], 4));
if (distanceAnalysis) {
performDistanceAnalysis(intraHist, p99);
}
}
}
if (clusterSimulation > 0) {
log(" * Cluster number = %s +/- %s", Utils.rounded(statsSize.getMean(), 4), Utils.rounded(statsSize.getStandardDeviation(), 4));
log(" * Cluster radius = %s +/- %s nm (mean distance to centre-of-mass)", Utils.rounded(statsRadius.getMean(), 4), Utils.rounded(statsRadius.getStandardDeviation(), 4));
}
}
use of gdsc.smlm.model.MaskDistribution in project GDSC-SMLM by aherbert.
the class PCPALMAnalysis method cropToRoi.
/**
* Extract all the PC-PALM molecules that are within the image ROI region. The coordinates bounds are
* converted using relative scaling to the limits of the PC-PALM molecules. If a non-rectangular ROI is used then a
* mask is extracted and used for the crop. If no image is provided then the full set of molecules is returned.
* <p>
* Set the area property to the region covered by the molecules.
*
* @param imp
* @return
*/
ArrayList<Molecule> cropToRoi(ImagePlus imp) {
croppedArea = PCPALMMolecules.area;
if (PCPALMMolecules.molecules == null || PCPALMMolecules.molecules.isEmpty()) {
return PCPALMMolecules.molecules;
}
double pcw = PCPALMMolecules.maxx - PCPALMMolecules.minx;
double pch = PCPALMMolecules.maxy - PCPALMMolecules.miny;
Roi roi = (imp == null) ? null : imp.getRoi();
if (roi == null || !roi.isArea()) {
log("Roi = %s nm x %s nm = %s um^2", Utils.rounded(pcw, 3), Utils.rounded(pch, 3), Utils.rounded(croppedArea, 3));
minx = PCPALMMolecules.minx;
maxx = PCPALMMolecules.maxx;
miny = PCPALMMolecules.miny;
maxy = PCPALMMolecules.maxy;
return PCPALMMolecules.molecules;
}
int w = imp.getWidth();
int h = imp.getHeight();
Rectangle bounds = roi.getBounds();
// Construct relative coordinates
minx = PCPALMMolecules.minx + pcw * ((double) bounds.x / w);
miny = PCPALMMolecules.miny + pch * ((double) bounds.y / h);
maxx = PCPALMMolecules.minx + pcw * ((double) (bounds.x + bounds.width) / w);
maxy = PCPALMMolecules.miny + pch * ((double) (bounds.y + bounds.height) / h);
final double roix = maxx - minx;
final double roiy = maxy - miny;
ArrayList<Molecule> newMolecules = new ArrayList<Molecule>(PCPALMMolecules.molecules.size() / 2);
// Support non-rectangular ROIs
if (roi.getMask() != null) {
MaskDistribution dist = createMaskDistribution(roi.getMask(), roix, roiy);
double fraction = (double) dist.getSize() / (roi.getMask().getWidth() * roi.getMask().getHeight());
log("Roi is a mask of %d pixels", dist.getSize());
croppedArea = fraction * roix * roiy / 1e6;
log("Roi area %s x %s nm x %s nm = %s um^2", Utils.rounded(fraction), Utils.rounded(roix, 3), Utils.rounded(roiy, 3), Utils.rounded(croppedArea, 3));
double[] xyz = new double[3];
// The mask is 0,0 in the centre so offset by this as well
double xoffset = minx + roix / 2;
double yoffset = miny + roiy / 2;
for (Molecule m : PCPALMMolecules.molecules) {
xyz[0] = m.x - xoffset;
xyz[1] = m.y - yoffset;
if (dist.isWithinXY(xyz))
newMolecules.add(m);
}
} else {
croppedArea = roix * roiy / 1e6;
log("Roi = %s nm x %s nm = %s um^2", Utils.rounded(roix, 3), Utils.rounded(roiy, 3), Utils.rounded(croppedArea, 3));
for (Molecule m : PCPALMMolecules.molecules) {
if (m.x < minx || m.x >= maxx || m.y < miny || m.y >= maxy)
continue;
newMolecules.add(m);
}
}
return newMolecules;
}
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