Examples with Location org.apache.commons.math3.geometry.partitioning.Region.Location used on opensource projects

Example 1 with Location

use of org.apache.commons.math3.geometry.partitioning.Region.Location in project SeqMonk by s-andrews.

the class CodonBiasPipeline method startPipeline.

protected void startPipeline() {
    // We first need to generate probes over all of the features listed in
    // the feature types.  The probes should cover the whole area of the
    // feature regardless of where it splices.
    Vector<Probe> probes = new Vector<Probe>();
    double pValue = optionsPanel.pValue();
    String libraryType = optionsPanel.libraryType();
    Chromosome[] chrs = collection().genome().getAllChromosomes();
    for (int c = 0; c < chrs.length; c++) {
        if (cancel) {
            progressCancelled();
            return;
        }
        progressUpdated("Making probes for chr" + chrs[c].name(), c, chrs.length * 2);
        Feature[] features = collection().genome().annotationCollection().getFeaturesForType(chrs[c], optionsPanel.getSelectedFeatureType());
        for (int f = 0; f < features.length; f++) {
            if (cancel) {
                progressCancelled();
                return;
            }
            Probe p = new Probe(chrs[c], features[f].location().start(), features[f].location().end(), features[f].location().strand(), features[f].name());
            probes.add(p);
        }
    }
    allProbes = probes.toArray(new Probe[0]);
    collection().setProbeSet(new ProbeSet("Features over " + optionsPanel.getSelectedFeatureType(), allProbes));
    // Now we can quantitate each individual feature and test for whether it is significantly
    // showing codon bias
    ArrayList<Vector<ProbeTTestValue>> significantProbes = new ArrayList<Vector<ProbeTTestValue>>();
    // data contains the data stores that this pipeline is going to use. We need to test each data store.
    for (int d = 0; d < data.length; d++) {
        significantProbes.add(new Vector<ProbeTTestValue>());
    }
    int probeCounter = 0;
    for (int c = 0; c < chrs.length; c++) {
        if (cancel) {
            progressCancelled();
            return;
        }
        progressUpdated("Quantitating features on chr" + chrs[c].name(), chrs.length + c, chrs.length * 2);
        Feature[] features = collection().genome().annotationCollection().getFeaturesForType(chrs[c], optionsPanel.getSelectedFeatureType());
        for (int p = 0; p < features.length; p++) {
            // Get the corresponding feature and work out the mapping between genomic position and codon sub position.
            int[] mappingArray = createGenomeMappingArray(features[p]);
            DATASTORE_LOOP: for (int d = 0; d < data.length; d++) {
                if (cancel) {
                    progressCancelled();
                    return;
                }
                long[] reads = data[d].getReadsForProbe(allProbes[probeCounter]);
                // TODO: make this configurable
                if (reads.length < 5) {
                    data[d].setValueForProbe(allProbes[probeCounter], Float.NaN);
                    continue DATASTORE_LOOP;
                }
                int pos1Count = 0;
                int pos2Count = 0;
                int pos3Count = 0;
                READ_LOOP: for (int r = 0; r < reads.length; r++) {
                    int genomicReadStart = SequenceRead.start(reads[r]);
                    int genomicReadEnd = SequenceRead.end(reads[r]);
                    int readStrand = SequenceRead.strand(reads[r]);
                    int relativeReadStart = -1;
                    // forward reads
                    if (readStrand == 1) {
                        if (libraryType == "Same strand specific") {
                            if (features[p].location().strand() == Location.FORWARD) {
                                // The start of the read needs to be within the feature
                                if (genomicReadStart - features[p].location().start() < 0) {
                                    continue READ_LOOP;
                                } else {
                                    // look up the read start pos in the mapping array
                                    relativeReadStart = mappingArray[genomicReadStart - features[p].location().start()];
                                }
                            }
                        } else if (libraryType == "Opposing strand specific") {
                            if (features[p].location().strand() == Location.REVERSE) {
                                // The "start" of a reverse read/probe is actually the end
                                if (features[p].location().end() - genomicReadEnd < 0) {
                                    continue READ_LOOP;
                                } else {
                                    relativeReadStart = mappingArray[features[p].location().end() - genomicReadEnd];
                                }
                            }
                        }
                    }
                    // reverse reads
                    if (readStrand == -1) {
                        if (libraryType == "Same strand specific") {
                            if (features[p].location().strand() == Location.REVERSE) {
                                if (features[p].location().end() - genomicReadEnd < 0) {
                                    continue READ_LOOP;
                                } else {
                                    // System.out.println("features[p].location().end() is " + features[p].location().end() + ", genomicReadEnd is " + genomicReadEnd);
                                    // System.out.println("mapping array[0] is " + mappingArray[0]);
                                    relativeReadStart = mappingArray[features[p].location().end() - genomicReadEnd];
                                }
                            }
                        } else if (libraryType == "Opposing strand specific") {
                            if (features[p].location().strand() == Location.FORWARD) {
                                // The start of the read needs to be within the feature
                                if (genomicReadStart - features[p].location().start() < 0) {
                                    continue READ_LOOP;
                                } else {
                                    // look up the read start position in the mapping array
                                    relativeReadStart = mappingArray[genomicReadStart - features[p].location().start()];
                                }
                            }
                        }
                    }
                    // find out which position the read is in
                    if (relativeReadStart == -1) {
                        continue READ_LOOP;
                    } else if (relativeReadStart % 3 == 0) {
                        pos3Count++;
                        continue READ_LOOP;
                    } else if ((relativeReadStart + 1) % 3 == 0) {
                        pos2Count++;
                        continue READ_LOOP;
                    } else if ((relativeReadStart + 2) % 3 == 0) {
                        pos1Count++;
                    }
                }
                // closing bracket for read loop
                // System.out.println("pos1Count for "+ features[p].name() + " is " + pos1Count);
                // System.out.println("pos2Count for "+ features[p].name() + " is " + pos2Count);
                // System.out.println("pos3Count for "+ features[p].name() + " is " + pos3Count);
                int interestingCodonCount = 0;
                int otherCodonCount = 0;
                if (optionsPanel.codonSubPosition() == 1) {
                    interestingCodonCount = pos1Count;
                    otherCodonCount = pos2Count + pos3Count;
                } else if (optionsPanel.codonSubPosition() == 2) {
                    interestingCodonCount = pos2Count;
                    otherCodonCount = pos1Count + pos3Count;
                } else if (optionsPanel.codonSubPosition() == 3) {
                    interestingCodonCount = pos3Count;
                    otherCodonCount = pos1Count + pos2Count;
                }
                int totalCount = interestingCodonCount + otherCodonCount;
                // BinomialDistribution bd = new BinomialDistribution(interestingCodonCount+otherCodonCount, 1/3d);
                BinomialDistribution bd = new BinomialDistribution(totalCount, 1 / 3d);
                // Since the binomial distribution gives the probability of getting a value higher than
                // this we need to subtract one so we get the probability of this or higher.
                double thisPValue = 1 - bd.cumulativeProbability(interestingCodonCount - 1);
                if (interestingCodonCount == 0)
                    thisPValue = 1;
                // We have to add all results at this stage so we don't mess up the multiple
                // testing correction later on.
                significantProbes.get(d).add(new ProbeTTestValue(allProbes[probeCounter], thisPValue));
                float percentageCount;
                if (totalCount == 0) {
                    percentageCount = 0;
                } else {
                    percentageCount = ((float) interestingCodonCount / (float) totalCount) * 100;
                }
                data[d].setValueForProbe(allProbes[probeCounter], percentageCount);
            // System.out.println("totalCount = " + totalCount);
            // System.out.println("interestingCodonCount " + interestingCodonCount);
            // System.out.println("pValue = " + thisPValue);
            // System.out.println("percentageCount = " + percentageCount);
            // System.out.println("");
            }
            probeCounter++;
        }
    }
    // filtering those which pass our p-value cutoff
    for (int d = 0; d < data.length; d++) {
        ProbeTTestValue[] ttestResults = significantProbes.get(d).toArray(new ProbeTTestValue[0]);
        BenjHochFDR.calculateQValues(ttestResults);
        ProbeList newList = new ProbeList(collection().probeSet(), "Codon bias < " + pValue + " in " + data[d].name(), "Probes showing significant codon bias for position " + optionsPanel.codonSubPosition() + " with a cutoff of " + pValue, "FDR");
        for (int i = 0; i < ttestResults.length; i++) {
            if (ttestResults[i].q < pValue) {
                newList.addProbe(ttestResults[i].probe, (float) ttestResults[i].q);
            }
        }
    }
    StringBuffer quantitationDescription = new StringBuffer();
    quantitationDescription.append("Codon bias pipeline using codon position " + optionsPanel.codonSubPosition() + " for " + optionsPanel.libraryType() + " library.");
    collection().probeSet().setCurrentQuantitation(quantitationDescription.toString());
    quantitatonComplete();
}

Example 2 with Location

use of org.apache.commons.math3.geometry.partitioning.Region.Location in project j6dof-flight-sim by chris-ali.

the class Engine method calculateEngMoments.

/**
 * Calculates the moment generated by the engine as a function of its thrust and location
 * relative to the aircraft's center of gravity. Used in {@link Engine#updateEngineState(EnumMap, EnumMap, double[])}
 */
protected void calculateEngMoments() {
    Vector3D forceVector = new Vector3D(engineThrust);
    Vector3D armVector = new Vector3D(enginePosition);
    this.engineMoment = Vector3D.crossProduct(forceVector, armVector).toArray();
}

Example 3 with Location

use of org.apache.commons.math3.geometry.partitioning.Region.Location in project MindsEye by SimiaCryptus.

the class ObjectLocation method renderAlpha.

/**
 * Render alpha tensor.
 *
 * @param alphaPower     the alpha power
 * @param img            the img
 * @param locationResult the location result
 * @param classification the classification
 * @param category       the category
 * @return the tensor
 */
public Tensor renderAlpha(final double alphaPower, final Tensor img, final Result locationResult, final Tensor classification, final int category) {
    TensorArray tensorArray = TensorArray.wrap(new Tensor(classification.getDimensions()).set(category, 1));
    DeltaSet<Layer> deltaSet = new DeltaSet<>();
    locationResult.accumulate(deltaSet, tensorArray);
    double[] rawDelta = deltaSet.getMap().entrySet().stream().filter(x -> x.getValue().target == img.getData()).findAny().get().getValue().getDelta();
    Tensor deltaColor = new Tensor(rawDelta, img.getDimensions()).mapAndFree(x -> Math.abs(x));
    Tensor delta1d = blur(reduce(deltaColor), 3);
    return TestUtil.normalizeBands(TestUtil.normalizeBands(delta1d, 1).mapAndFree(x -> Math.pow(x, alphaPower)));
}

Example 4 with Location

use of org.apache.commons.math3.geometry.partitioning.Region.Location in project chordatlas by twak.

the class Concarnie method removeOverlaps.

private void removeOverlaps(List<Problem> current) {
    Closer<Problem> closer = new Closer();
    for (Problem a : current) {
        try {
            Region<Euclidean2D> ar = a.chull.createRegion();
            b: for (Problem b : current) if (a != b)
                for (Vector2D v : b.chull.getVertices()) {
                    Location vInA = ar.checkPoint(v);
                    if (vInA == Location.BOUNDARY || vInA == Location.INSIDE) {
                        closer.add(a, b);
                        continue b;
                    }
                }
        } catch (InsufficientDataException th) {
        } catch (MathIllegalArgumentException th) {
        }
    }
    for (Set<Problem> close : closer.close()) {
        List<Problem> intersecting = new ArrayList<Problem>(close);
        Collections.sort(intersecting, (a, b) -> -Double.compare(a.area(), b.area()));
        for (int i = 1; i < intersecting.size(); i++) {
            Problem togo = intersecting.get(i);
            togo.addPortal();
            current.remove(togo);
        }
    }
}

Example 5 with Location

use of org.apache.commons.math3.geometry.partitioning.Region.Location in project SeqMonk by s-andrews.

the class AntisenseTranscriptionPipeline method startPipeline.

protected void startPipeline() {
    // We first need to generate probes over all of the features listed in
    // the feature types.  The probes should cover the whole area of the
    // feature regardless of where it splices.
    Vector<Probe> probes = new Vector<Probe>();
    double pValue = optionsPanel.pValue();
    QuantitationStrandType readFilter = optionsPanel.readFilter();
    long[] senseCounts = new long[data.length];
    long[] antisenseCounts = new long[data.length];
    Chromosome[] chrs = collection().genome().getAllChromosomes();
    for (int c = 0; c < chrs.length; c++) {
        if (cancel) {
            progressCancelled();
            return;
        }
        progressUpdated("Getting total antisense rate for chr" + chrs[c].name(), c, chrs.length * 2);
        Feature[] features = getValidFeatures(chrs[c]);
        for (int f = 0; f < features.length; f++) {
            if (cancel) {
                progressCancelled();
                return;
            }
            Probe p = new Probe(chrs[c], features[f].location().start(), features[f].location().end(), features[f].location().strand(), features[f].name());
            probes.add(p);
            for (int d = 0; d < data.length; d++) {
                long[] reads = data[d].getReadsForProbe(p);
                for (int r = 0; r < reads.length; r++) {
                    if (readFilter.useRead(p, reads[r])) {
                        senseCounts[d] += SequenceRead.length(reads[r]);
                    } else {
                        antisenseCounts[d] += SequenceRead.length(reads[r]);
                    }
                }
            }
        }
    }
    Probe[] allProbes = probes.toArray(new Probe[0]);
    collection().setProbeSet(new ProbeSet("Features over " + optionsPanel.getSelectedFeatureType(), allProbes));
    // Now we can work out the overall antisense rate
    double[] antisenseProbability = new double[data.length];
    for (int d = 0; d < data.length; d++) {
        System.err.println("Antisense counts are " + antisenseCounts[d] + " sense counts are " + senseCounts[d]);
        antisenseProbability[d] = antisenseCounts[d] / (double) (antisenseCounts[d] + senseCounts[d]);
        System.err.println("Antisense probability for " + data[d].name() + " is " + antisenseProbability[d]);
    }
    // Now we can quantitate each individual feature and test for whether it is significantly
    // showing antisense expression
    ArrayList<Vector<ProbeTTestValue>> significantProbes = new ArrayList<Vector<ProbeTTestValue>>();
    for (int d = 0; d < data.length; d++) {
        significantProbes.add(new Vector<ProbeTTestValue>());
    }
    int[] readLengths = new int[data.length];
    for (int d = 0; d < readLengths.length; d++) {
        readLengths[d] = data[d].getMaxReadLength();
        System.err.println("For " + data[d].name() + " max read len is " + readLengths[d]);
    }
    for (int c = 0; c < chrs.length; c++) {
        if (cancel) {
            progressCancelled();
            return;
        }
        progressUpdated("Quantitating features on chr" + chrs[c].name(), chrs.length + c, chrs.length * 2);
        Probe[] thisChrProbes = collection().probeSet().getProbesForChromosome(chrs[c]);
        for (int p = 0; p < thisChrProbes.length; p++) {
            for (int d = 0; d < data.length; d++) {
                if (cancel) {
                    progressCancelled();
                    return;
                }
                long senseCount = 0;
                long antisenseCount = 0;
                long[] reads = data[d].getReadsForProbe(thisChrProbes[p]);
                for (int r = 0; r < reads.length; r++) {
                    if (readFilter.useRead(thisChrProbes[p], reads[r])) {
                        // TODO: Just count overlap?
                        senseCount += SequenceRead.length(reads[r]);
                    } else {
                        antisenseCount += SequenceRead.length(reads[r]);
                    }
                }
                // if (thisChrProbes[p].name().equals("RP4-798A10.2")) {
                // System.err.println("Raw base counts are sense="+senseCount+" anti="+antisenseCount+" from "+reads.length+" reads");
                // }
                int senseReads = (int) (senseCount / readLengths[d]);
                int antisenseReads = (int) (antisenseCount / readLengths[d]);
                // if (thisChrProbes[p].name().equals("RP4-798A10.2")) {
                // System.err.println("Raw read counts are sense="+senseReads+" anti="+antisenseReads+" from "+reads.length+" reads");
                // }
                BinomialDistribution bd = new BinomialDistribution(senseReads + antisenseReads, antisenseProbability[d]);
                // Since the binomial distribution gives the probability of getting a value higher than
                // this we need to subtract one so we get the probability of this or higher.
                double thisPValue = 1 - bd.cumulativeProbability(antisenseReads - 1);
                if (antisenseReads == 0)
                    thisPValue = 1;
                // We have to add all results at this stage so we don't mess up the multiple
                // testing correction later on.
                significantProbes.get(d).add(new ProbeTTestValue(thisChrProbes[p], thisPValue));
                double expected = ((senseReads + antisenseReads) * antisenseProbability[d]);
                if (expected < 1)
                    expected = 1;
                float obsExp = antisenseReads / (float) expected;
                data[d].setValueForProbe(thisChrProbes[p], obsExp);
            }
        }
    }
    // filtering those which pass our p-value cutoff
    for (int d = 0; d < data.length; d++) {
        ProbeTTestValue[] ttestResults = significantProbes.get(d).toArray(new ProbeTTestValue[0]);
        BenjHochFDR.calculateQValues(ttestResults);
        ProbeList newList = new ProbeList(collection().probeSet(), "Antisense < " + pValue + " in " + data[d].name(), "Probes showing significant antisense transcription from a basal level of " + antisenseProbability[d] + " with a cutoff of " + pValue, "FDR");
        for (int i = 0; i < ttestResults.length; i++) {
            if (ttestResults[i].probe.name().equals("RP4-798A10.2")) {
                System.err.println("Raw p=" + ttestResults[i].p + " q=" + ttestResults[i].q);
            }
            if (ttestResults[i].q < pValue) {
                newList.addProbe(ttestResults[i].probe, (float) ttestResults[i].q);
            }
        }
    }
    StringBuffer quantitationDescription = new StringBuffer();
    quantitationDescription.append("Antisense transcription pipeline quantitation ");
    quantitationDescription.append(". Directionality was ");
    quantitationDescription.append(optionsPanel.libraryTypeBox.getSelectedItem());
    if (optionsPanel.ignoreOverlaps()) {
        quantitationDescription.append(". Ignoring existing overlaps");
    }
    quantitationDescription.append(". P-value cutoff was ");
    quantitationDescription.append(optionsPanel.pValue());
    collection().probeSet().setCurrentQuantitation(quantitationDescription.toString());
    quantitatonComplete();
}