List of usage examples for org.apache.commons.lang ArrayUtils toString
public static String toString(Object array)
Outputs an array as a String, treating null as an empty array.
From source file:pt.webdetails.cda.dataaccess.XPathDataAccess.java
private static boolean legacyFallbackInvoke(Object object, String methodName, Class<?>[] argTypes, Object[] args, Class<?>[] argTypesFallback, Object[] argsFallback) { Method method = null;/*from w ww . j av a 2 s . co m*/ try { try { method = object.getClass().getMethod(methodName, argTypes); } catch (NoSuchMethodException e1) { logger.debug(String.format("failed to find %s(%s): ", methodName, ArrayUtils.toString(argTypes), e1.getLocalizedMessage())); try { method = object.getClass().getMethod(methodName, argTypesFallback); args = argsFallback; } catch (NoSuchMethodException e2) { logger.error(String.format("failed to find %1$s(%2$s) or %1$s(%3$s) ", methodName, ArrayUtils.toString(argTypes), ArrayUtils.toString(argTypesFallback))); throw e2; } } method.invoke(object, args); return true; } catch (Exception e) { logger.error(String.format("%s call failed ", methodName), e); } return false; }
From source file:tudresden.ocl20.pivot.essentialocl.expressions.factory.EssentialOclFactory.java
/** * <p>/*from w w w . j a v a2 s .c o m*/ * Creates a new {@link ExpressionInOcl}. The body expression and the context * variable must not be <code>null</code>. The result and parameter variables * are optional since they are only required for constraints whose context is * an operation. * </p> * * @param body * The body expression as a {@link String} in OCL concrete syntax. * @param bodyExpression * The {@link OclExpression} that is the body of the * {@link ExpressionInOcl}. * @param context * The {@link Variable} representing the contextual classifier. * @param result * The result {@link Variable} of an operation {@link Constraint} . * @param parameter * The parameters of an operation {@link Constraint}. * * @return An {@link ExpressionInOcl} instance. */ public ExpressionInOcl createExpressionInOcl(String body, OclExpression bodyExpression, Variable context, Variable result, Variable... parameter) { if (LOGGER.isDebugEnabled()) { LOGGER.debug("createExpressionInOcl(bodyExpression=" + bodyExpression //$NON-NLS-1$ + ", context=" + context + ", result=" + result + ", parameter=" //$NON-NLS-1$//$NON-NLS-2$ //$NON-NLS-3$ + ArrayUtils.toString(parameter) + ") - enter"); //$NON-NLS-1$ } if (bodyExpression == null || context == null) { throw new NullArgumentException("bodyExpression or context"); //$NON-NLS-1$ } ExpressionInOcl expressionInOcl; expressionInOcl = ExpressionsFactory.INSTANCE.createExpressionInOcl(); expressionInOcl.setBodyExpression(bodyExpression); expressionInOcl.setContext(context); if (StringUtils.isNotEmpty(body)) { expressionInOcl.setBody(body); } if (result != null) { expressionInOcl.setResult(result); } if (parameter != null) { expressionInOcl.getParameter().addAll(Arrays.asList(parameter)); } if (LOGGER.isDebugEnabled()) { LOGGER.debug("createExpressionInOcl() - exit - return value=" //$NON-NLS-1$ + expressionInOcl); } return expressionInOcl; }
From source file:ubic.gemma.analysis.expression.coexpression.links.AbstractMatrixRowPairAnalysis.java
/** * populate geneToProbeMap and gather stats. Probes that do not map to any genes are still counted. *///from w ww .j a va2 s . co m private void initGeneToProbeMap() { int[] stats = new int[20]; // how many genes per probe this.numUniqueGenes = 0; this.flatProbe2GeneMap = new HashMap<CompositeSequence, Collection<Gene>>(); this.geneToProbeMap = new HashMap<Gene, Collection<CompositeSequence>>(); for (CompositeSequence cs : probeToGeneMap.keySet()) { if (!this.flatProbe2GeneMap.containsKey(cs)) { this.flatProbe2GeneMap.put(cs, new HashSet<Gene>()); } Collection<Collection<Gene>> genes = probeToGeneMap.get(cs); /* * Genes will be empty if the probe does not map to any genes. */ if (genes == null) continue; // defensive. for (Collection<Gene> cluster : genes) { if (cluster.isEmpty()) continue; numUniqueGenes++; for (Gene g : cluster) { if (!geneToProbeMap.containsKey(g)) { geneToProbeMap.put(g, new HashSet<CompositeSequence>()); } this.geneToProbeMap.get(g).add(cs); this.flatProbe2GeneMap.get(cs).add(g); } if (cluster.size() >= stats.length) { stats[stats.length - 1]++; } else { stats[cluster.size()]++; } } } if (numUniqueGenes == 0) { log.warn("There are no genes for this data set, " + this.flatProbe2GeneMap.size() + " probes."); } log.info("Mapping Stats: " + numUniqueGenes + " unique genes; genes per probe distribution summary: " + ArrayUtils.toString(stats)); }
From source file:uk.ac.diamond.scisoft.analysis.io.numpy.NumPyTest.java
public NumPyTest(int index, String numpyDataType, int abstractDatasetDataType, int[] shape, boolean addInf, boolean unsigned) { this.index = index; this.numpyDataType = numpyDataType; this.abstractDatasetDataType = abstractDatasetDataType; this.shape = shape; this.addInf = addInf; this.unsigned = unsigned; this.len = 1; for (int i = 0; i < shape.length; i++) { this.len *= shape[i]; }/*from www . j av a 2 s . co m*/ this.shapeStr = ArrayUtils.toString(shape); this.shapeStr = this.shapeStr.substring(1, shapeStr.length() - 1); // System.out.println(this.toString()); }
From source file:uk.ac.diamond.scisoft.analysis.io.numpy.NumPyTest.java
@Override public String toString() { return String.format("TEST %d: numpyType=%s datasetType=%d len=%d shape=%s", index, numpyDataType, abstractDatasetDataType, len, ArrayUtils.toString(shape)); }
From source file:uk.ac.diamond.scisoft.ncd.core.data.plots.GuinierPlotData.java
public Object[] getGuinierPlotParameters(IDataset data, IDataset axis) { Dataset guinierData = getSaxsPlotDataset(data, axis); Dataset guinierAxis = getSaxsPlotAxis(axis); /* int sliceSize = data.getSize() / 10; int[] step = new int[] {sliceSize}; IndexIterator dataSliceIter = guinierData.getSliceIterator(null, null, null); IndexIterator axisSliceIter = guinierAxis.getSliceIterator(null, null, null); // Iterate over data slices//w w w.j a v a2 s . c o m int[] minPosition = new int[] {-1}; double minError = Double.MAX_VALUE; double slope = Double.NaN; double intercept = Double.NaN; Map<DoublePoint, Double> clusterInputMap = new HashMap<DoublePoint, Double>(); while (dataSliceIter.hasNext() && axisSliceIter.hasNext()) { SimpleRegression regression = new SimpleRegression(); int[] pos = dataSliceIter.getPos(); // Iterate over data values for linear regression IndexIterator dataIter = new SliceIterator( guinierData.getShape(), guinierData.getSize(), pos, step); pos = axisSliceIter.getPos(); IndexIterator axisIter = new SliceIterator( guinierAxis.getShape(), guinierAxis.getSize(), pos, step); int points = 0; while (dataIter.hasNext() && axisIter.hasNext()) { double dataVal = guinierData.getDouble(dataIter.getPos()); double axisVal = guinierAxis.getDouble(axisIter.getPos()); regression.addData(axisVal, dataVal); points++; } if (points == sliceSize) { regression.regress(); double err = regression.getMeanSquareError(); if (err < minError) { minError = err; minPosition = Arrays.copyOf(pos, pos.length); slope = regression.getSlope(); intercept = regression.getIntercept(); double I0 = Math.exp(intercept); double Rg = Math.sqrt(-3.0*slope); System.out.println(" Min Pos : " + Arrays.toString(minPosition)); System.out.println(" Min Error : " + Double.toString(minError)); System.out.println(" Slope : " + Double.toString(slope) + " Intercept : " + Double.toString(intercept)); System.out.println(" I(0) : " + Double.toString(I0) + " Rg : " + Double.toString(Rg)); clusterInputMap.put(new DoublePoint(minPosition), minError); } } else { break; } } DBSCANClusterer<DoublePoint> clusterer = new DBSCANClusterer<DoublePoint>(5, 5); List<Cluster<DoublePoint>> clusterResults = clusterer.cluster(clusterInputMap.keySet()); // output the clusters for (int i = 0; i < clusterResults.size(); i++) { System.out.println("Cluster " + i); double[] minPoint = null; double minVal = Double.MAX_VALUE; for (DoublePoint point : clusterResults.get(i).getPoints()) { System.out.println(Arrays.toString(point.getPoint())); Double val = clusterInputMap.get(point); if (val < minVal) { minVal = val; minPoint = Arrays.copyOf(point.getPoint(), point.getPoint().length); } minVal = (val < minVal ? val : minVal); } System.out.println("Min cluster point : " + Arrays.toString(minPoint)); System.out.println("Min cluster value : " + Double.toString(minVal)); System.out.println(); } */ ConvergenceChecker<PointValuePair> cmaesChecker = new SimplePointChecker<PointValuePair>(1e-6, 1e-8); RandomDataGenerator rnd = new RandomDataGenerator(); CMAESOptimizer optimizer = new CMAESOptimizer(cmaesMaxIterations, 0.0, true, 0, cmaesCheckFeasableCount, rnd.getRandomGenerator(), false, cmaesChecker); GuinierLineFitFunction function = new GuinierLineFitFunction(guinierData, guinierAxis); Amount<Dimensionless> I0 = Amount.valueOf(Double.NaN, Double.NaN, Dimensionless.UNIT); Amount<Dimensionless> Rg = Amount.valueOf(Double.NaN, Double.NaN, Dimensionless.UNIT); double[] qvals = new double[] { Double.NaN, Double.NaN }; double q0 = guinierAxis.getDouble(0); double qMin = guinierAxis.getDouble(1); double qMax = guinierAxis.getDouble(guinierAxis.getSize() - 1); double[] startPosition = new double[] { guinierAxis.getDouble(0), guinierAxis.getDouble(GuinierLineFitFunction.MIN_POINTS) }; double[] cmaesInputSigma = new double[] { (qMin - q0) * 0.1, qMax * 0.1 }; try { final PointValuePair res = optimizer.optimize(new MaxEval(cmaesMaxIterations), new ObjectiveFunction(function), GoalType.MAXIMIZE, new CMAESOptimizer.PopulationSize(cmaesLambda), new CMAESOptimizer.Sigma(cmaesInputSigma), new SimpleBounds(new double[] { q0, q0 }, new double[] { qMin, qMax }), new InitialGuess(startPosition)); qvals = res.getPoint(); function.value(qvals); I0 = getI0(function.regression); Rg = getRg(function.regression); System.out.println("Final Result"); String msg = StringUtils.join(new String[] { " I(0) ", I0.toString(), " Rg ", Rg.toString() }, " : "); System.out.println(msg); msg = StringUtils.join(new String[] { "Slice", ArrayUtils.toString(res.getPoint()), "R", Double.toString(function.regression.getR()) }, " : "); System.out.println(msg); /* // Run Monte-Carlo simulation to generate error estimates Rg values //double finalR = function.regression.getR(); int maxSample = 10000; int minSample = 10; int totalSample = 100000; int counter = 0; int totalCounter = 0; GuinierLineFitFunction mcFunction = new GuinierLineFitFunction(guinierData, guinierAxis); DescriptiveStatistics statsR = new DescriptiveStatistics(); List<Pair<Double, Amount<Dimensionless>>> listI0 = new ArrayList<Pair<Double,Amount<Dimensionless>>>(); List<Pair<Double, Amount<Dimensionless>>> listRg = new ArrayList<Pair<Double,Amount<Dimensionless>>>(); while ((counter < maxSample && totalCounter < totalSample) || (counter < minSample && totalCounter >= totalSample)) { double q1 = rnd.nextUniform(q0, qMin); double q2 = rnd.nextUniform(q0, qMax); if (!(q2 > q1)) { continue; } totalCounter++; mcFunction.value(new double[] {q1, q2}); double tmpR = Math.abs(mcFunction.regression.getR()); //boolean equalsR = Precision.equalsWithRelativeTolerance(tmpR, finalR, 0.1); if (!(Double.isNaN(tmpR) || Double.isInfinite(tmpR))) { statsR.addValue(tmpR); Amount<Dimensionless> tmpI0 = getI0(mcFunction.regression); Amount<Dimensionless> tmpRg = getRg(mcFunction.regression); if (Double.isNaN(tmpI0.getEstimatedValue()) || Double.isInfinite(tmpI0.getEstimatedValue()) || Double.isNaN(tmpRg.getEstimatedValue()) || Double.isInfinite(tmpRg.getEstimatedValue())) { continue; } listI0.add(new Pair<Double, Amount<Dimensionless>>(tmpR, tmpI0)); listRg.add(new Pair<Double, Amount<Dimensionless>>(tmpR, tmpRg)); counter++; } } double threshold = statsR.getPercentile(90); //double threshold = 0.95*statsR.getMax(); SummaryStatistics statsI0 = new SummaryStatistics(); SummaryStatistics statsRg = new SummaryStatistics(); for (Pair<Double, Amount<Dimensionless>> tmpVal : listRg) { if (tmpVal.getFirst() > threshold) { statsRg.addValue(tmpVal.getSecond().getEstimatedValue()); } } for (Pair<Double, Amount<Dimensionless>> tmpVal : listI0) { if (tmpVal.getFirst() > threshold) { statsI0.addValue(tmpVal.getSecond().getEstimatedValue()); } } double meanI0 = statsI0.getMean(); double stdI0 = statsI0.getStandardDeviation(); I0 = Amount.valueOf(meanI0, stdI0, Dimensionless.UNIT); double meanRg = statsRg.getMean(); double stdRg = statsRg.getStandardDeviation(); Rg = Amount.valueOf(meanRg, stdRg, Dimensionless.UNIT); String msg = StringUtils.join(new String[] { "Monte-Carlo Rg", Rg.toString() }, " : "); System.out.println(msg); */ } catch (MaxCountExceededException e) { System.out.println("Maximum counts exceeded"); return null; } return new Object[] { I0, Rg, qvals[0], qvals[1] }; }
From source file:uk.ac.diamond.scisoft.ncd.core.data.plots.PorodPlotData.java
public SimpleRegression getPorodPlotParameters(IDataset data, IDataset axis) { Dataset porodData = getSaxsPlotDataset(data, axis); Dataset porodAxis = getSaxsPlotAxis(axis); CMAESOptimizer optimizer = new CMAESOptimizer(cmaesMaxIterations, 0.0, true, 0, cmaesCheckFeasableCount, new Well19937a(), false, cmaesChecker); PorodLineFitFunction function = new PorodLineFitFunction(porodData, porodAxis); int dataSize = porodAxis.getSize(); double q0 = porodAxis.getDouble(0); double qMax = porodAxis.getDouble(dataSize - 1); double[] startPosition = new double[] { porodAxis.getDouble(dataSize - PorodLineFitFunction.MIN_POINTS - 1), porodAxis.getDouble(dataSize - 1) }; double[] cmaesInputSigma = new double[] { qMax * 0.1, qMax * 0.1 }; try {//from ww w .j av a 2s . c o m final PointValuePair res = optimizer.optimize(new MaxEval(cmaesMaxIterations), new ObjectiveFunction(function), GoalType.MAXIMIZE, new CMAESOptimizer.PopulationSize(cmaesLambda), new CMAESOptimizer.Sigma(cmaesInputSigma), new SimpleBounds(new double[] { q0, q0 }, new double[] { qMax, qMax }), new InitialGuess(startPosition)); function.value(res.getPoint()); double slope = function.regression.getSlope(); Amount<Dimensionless> c4 = getC4(function.regression); System.out.println("Final Result"); String msg = StringUtils .join(new String[] { " c4 ", c4.toString(), " slope ", Double.toString(slope) }, " : "); System.out.println(msg); msg = StringUtils.join(new String[] { "Slice", ArrayUtils.toString(res.getPoint()), "R", Double.toString(function.regression.getR()) }, " : "); System.out.println(msg); } catch (MaxCountExceededException e) { return null; } return function.regression; }
From source file:uk.ac.ebi.embl.api.storage.DataRow.java
@Override public String toString() { return ArrayUtils.toString(values); }
From source file:uk.ac.tgac.rampart.RampartConfig.java
@Override public String toString() { StringJoiner sj = new StringJoiner("\n"); sj.add("Job Configuration File: " + this.getConfigFile().getAbsolutePath()); sj.add("Output Directory: " + this.getOutputDir().getAbsolutePath()); sj.add("Job Prefix: " + this.getJobPrefix()); sj.add("Stages: " + ArrayUtils.toString(stages)); return sj.toString(); }
From source file:uk.ac.tgac.rampart.stage.analyse.asm.selector.DefaultAssemblySelector.java
@Override public AssemblyStats selectAssembly(AssemblyStatsTable table, Organism organism, QuastV23.AssemblyStats refStats, boolean cegmaEnabled) { boolean referenceProvided = refStats != null; boolean estimatesProvided = organism.getEstimated() != null; // Acquire metric groups ContiguityMetrics.Matrix contiguity = new ContiguityMetrics.Matrix(table, referenceProvided); ProblemMetrics.Matrix problems = new ProblemMetrics.Matrix(table, referenceProvided); ConservationMetrics.Matrix conservation = new ConservationMetrics.Matrix(table, referenceProvided ? refStats.getTotalLengthGt0() : estimatesProvided ? organism.getEstimated().getEstGenomeSize() : 0, referenceProvided ? refStats.getGcPc() : estimatesProvided ? organism.getEstimated().getEstGcPercentage() : 0.0, referenceProvided ? refStats.getNbGenes() : estimatesProvided ? organism.getEstimated().getEstNbGenes() : 0, cegmaEnabled);/* ww w . ja v a2s .c om*/ log.info("Acquired metrics"); log.debug("Contiguity"); log.debug(contiguity.toString()); log.debug("Problems"); log.debug(problems.toString()); log.debug("Conservation"); log.debug(conservation.toString()); // Normalise matrices contiguity.normalise(); problems.normalise(); conservation.normalise(); log.info("Scaled metrics"); log.debug("Contiguity"); log.debug(contiguity.toString()); log.debug("Problems"); log.debug(problems.toString()); log.debug("Conservation"); log.debug(conservation.toString()); // Apply weightings contiguity.weight(contiguityWeightings); problems.weight(problemWeightings); conservation.weight(conservationWeightings); log.info("Applied weightings"); log.debug("Contiguity"); log.debug(contiguity.toString()); log.debug("Problems"); log.debug(problems.toString()); log.debug("Conservation"); log.debug(conservation.toString()); // Calc scores for groups double[] contiguityScores = contiguity.calcScores(); double[] problemScores = problems.calcScores(); double[] conservationsScores = conservation.calcScores(); log.info("Calculated scores for each metric group"); log.debug("Contiguity: " + Arrays.toString(contiguityScores)); log.debug("Problems: " + Arrays.toString(problemScores)); log.debug("Conservation: " + Arrays.toString(conservationsScores)); table.addGroupScores(contiguityScores, problemScores, conservationsScores); AssemblyGroupStats.Matrix finalScores = new AssemblyGroupStats.Matrix(contiguityScores, problemScores, conservationsScores); finalScores.weight(groupWeightings); double[] scores = finalScores.calcScores(); log.info("Weightings applied to group scores. Final scores calculated."); // Save merged matrix with added scores table.addScores(scores); log.debug("Final scores are: " + ArrayUtils.toString(scores)); // Report best assembly stats log.info("Best assembly stats: " + table.getBest().toString()); return table.getBest(); }