public void testOverflowHandling() throws Exception {
Logger logger = Logger.getLogger("AppenderTest.overflowTest");
AsyncCoalescingStatisticsAppender appender = new AsyncCoalescingStatisticsAppender();
appender.setName("overflowTestAppender");
appender.setTimeSlice(1000);
appender.setQueueSize(2); // set low queue size so we overflow
logger.addAppender(appender);
logger.setAdditivity(false);
logger.setLevel(Level.INFO);
appender.activateOptions();
for (int i = 0; i < 1000; i++) {
StopWatch stopWatch = new StopWatch("testOverflow");
Math
.random(); // this should happen super fast, faster than the appender's Dispatcher thread
// can drain
logger.info(stopWatch.stop());
}
// again close the appender
appender.close();
assertTrue(
"Expected some stop watch messages to get discarded",
appender.getNumDiscardedMessages() > 0);
}
private void accept(WebDriver driver, String message) {
StopWatch stopWatch = new Log4JStopWatch();
stopWatch.start();
WebElement img = driver.findElement(By.xpath("//img[@alt='Click to sign']"));
driver.manage().timeouts().implicitlyWait(waitTimeout, TimeUnit.SECONDS);
img.click();
List<WebElement> signed_imgs = driver.findElements(By.xpath("//img[@alt='Signed']"));
driver.manage().timeouts().implicitlyWait(waitTimeout, TimeUnit.SECONDS);
if (signed_imgs.size() != 0) {
stopWatch.stop("accept", message);
} else {
randomDelay();
stopWatch.start();
WebElement awsForm = driver.findElement(By.name("awsForm"));
driver.manage().timeouts().implicitlyWait(waitTimeout, TimeUnit.SECONDS);
awsForm.submit();
signed_imgs = driver.findElements(By.xpath("//img[@alt='Signed']"));
driver.manage().timeouts().implicitlyWait(waitTimeout, TimeUnit.SECONDS);
stopWatch.stop("accept'", message);
}
}
// test for http://jira.codehaus.org/browse/PERFFORJ-22
public void testFlushOnShutdown() throws Exception {
DOMConfigurator.configure(getClass().getResource("log4j-shutdownbug.xml"));
// make a bunch of logs, but not enough to go over the timeslice.
for (int i = 0; i < 5; i++) {
StopWatch stopWatch = new Log4JStopWatch("tag1");
Thread.sleep(10 * i);
stopWatch.stop();
}
// at this point none of the file appenders will have written anything because they haven't been
// flushed
assertEquals("", FileUtils.readFileToString(new File("target/stats-shutdownbug.log")));
assertEquals("", FileUtils.readFileToString(new File("target/graphs-shutdownbug.log")));
// now, to simulate shutdown, get the async appender and run the shutdown hook. We need to use
// reflection
// because the shutdown hook is private.
AsyncCoalescingStatisticsAppender appender =
(AsyncCoalescingStatisticsAppender)
Logger.getLogger(StopWatch.DEFAULT_LOGGER_NAME).getAppender("coalescingStatistics");
Field shutdownField = appender.getClass().getDeclaredField("shutdownHook");
shutdownField.setAccessible(true);
Thread shutdownHook = (Thread) shutdownField.get(appender);
shutdownHook.run();
// now there should be data in the files
assertFalse("".equals(FileUtils.readFileToString(new File("target/stats-shutdownbug.log"))));
assertFalse("".equals(FileUtils.readFileToString(new File("target/graphs-shutdownbug.log"))));
}
/**
* Will only ever be accessed by a single thread. Rechecks the target update time again in case a
* second write thread was blocking the current one. {@link #lastUpdateRun} gets set to a negative
* value to specify that this method is currently running.
*/
public void doUpdate() {
// Check whether entry is required.
if (!state.get().checkNeedsUpdate(forceUpdateInterval)) {
return;
}
// Prevent recursion!
// ------------------
// To prevent another call from entering this block it's
// necessary to set active
if (!active.compareAndSet(false, true)) {
return;
}
try {
final Set<String> ids = sessions.keySet();
log.info("Synchronizing session cache. Count = " + ids.size());
final StopWatch sw = new Slf4JStopWatch();
for (String id : ids) {
reload(id);
}
sw.stop("omero.sessions.synchronization");
log.info(String.format("Synchronization took %s ms.", sw.getElapsedTime()));
} catch (Exception e) {
log.error("Error synchronizing cache", e);
} finally {
active.set(false);
}
}
public void handleComponentEvent(
ComponentEventRequestParameters parameters, ComponentRequestHandler handler)
throws IOException {
StopWatch watch = profilePage(parameters.getActivePageName());
handler.handleComponentEvent(parameters);
watch.stop(watch.getTag(), "Component event handled");
}
@Override
public void postHandle(
HttpServletRequest request,
HttpServletResponse response,
Object handler,
ModelAndView modelAndView)
throws Exception {
StopWatch stopWatch = (StopWatch) request.getAttribute(ATTRIBUTE);
if (stopWatch != null) {
stopWatch.stop();
}
}
public void testCsvRenderer() throws Exception {
DOMConfigurator.configure(getClass().getResource("log4jWCsv.xml"));
Logger logger = Logger.getLogger("org.perf4j.CsvAppenderTest");
for (int i = 0; i < 20; i++) {
StopWatch stopWatch = new Log4JStopWatch(logger);
Thread.sleep(i * 10);
stopWatch.stop("csvTest");
}
// close the appender
logger.getAppender("coalescingStatistics").close();
// verify the statisticsLog.csv file
DateFormat dateFormat = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss");
for (Object line : FileUtils.readLines(new File("./target/statisticsLog.csv"))) {
String[] values = line.toString().split(",");
// first column is the tag
assertEquals("\"csvTest\"", values[0]);
// next 2 columns are the dates - ensure they can be parsed
assertTrue(dateFormat.parse(values[1]).before(dateFormat.parse(values[2])));
// next 3 columns are mean, min and max
double mean = Double.parseDouble(values[3]);
int min = Integer.parseInt(values[4]);
int max = Integer.parseInt(values[5]);
assertTrue(mean >= min && mean <= max);
// next column is stddev - ust make sure it's parseable
Double.parseDouble(values[6]);
// next column is count
assertTrue(Integer.parseInt(values[7]) < 20);
// final column is TPS - just make sure it's parseable
Double.parseDouble(values[8]);
}
// verify the pivotedStatisticsLog.csv file
for (Object line : FileUtils.readLines(new File("./target/pivotedStatisticsLog.csv"))) {
String[] values = line.toString().split(",");
// first 2 columns are the dates - ensure they can be parsed
assertTrue(dateFormat.parse(values[0]).before(dateFormat.parse(values[1])));
// next column is mean, ensure it can be parsed
Double.parseDouble(values[2]);
// last column should be empty, so make sure last char on string is comma
assertEquals(',', line.toString().charAt(line.toString().length() - 1));
}
}
public static void main(String[] args) throws LPException {
final int rows = 40;
final int cols = rows;
logger.trace("Start Unwrapping");
logger.info("Simulate Data");
SimulateData simulateData = new SimulateData(rows, cols);
simulateData.peaks();
DoubleMatrix Phi = simulateData.getSimulatedData();
DoubleMatrix Psi = UnwrapUtils.wrapDoubleMatrix(Phi);
StopWatch clockFull = new StopWatch();
clockFull.start();
Unwrapper unwrapper = new Unwrapper(Psi);
unwrapper.unwrap();
clockFull.stop();
logger.info("Total processing time {} [sec]", (double) (clockFull.getElapsedTime()) / 1000);
}
public void run() {
String loggingTag = "tag" + index.getAndIncrement();
for (int i = 0; i < STOP_WATCH_COUNT; i++) {
StopWatch stopWatch = new Log4JStopWatch(loggingTag);
int sleepTime = (int) (Math.random() * 400);
try {
Thread.sleep(sleepTime);
} catch (Exception e) {
/*do nothing*/
}
stopWatch.stop();
}
// this last sleep here seems necessary because otherwise Thread.join() above is returning
// before
// we're really done.
try {
Thread.sleep(10);
} catch (Exception e) {
/*ignore*/
}
}
public void testAwsppHtmlUtil(int docs, int times) throws InterruptedException {
init();
String message = Thread.currentThread().getName() + " loop:(%d/%d), docs(%d/%d) ";
String message1 = Thread.currentThread().getName() + " loop:(%d/%d)";
HtmlUnitDriver driver = new HtmlUnitDriver(true);
for (int j = 0; j < times; j++) {
StopWatch stopWatch = new Log4JStopWatch();
stopWatch.start();
goToTestPage(driver);
stopWatch.stop("TestPage", String.format(message1, j, times));
randomDelay();
stopWatch.start();
createTransaction(driver);
stopWatch.stop("Create/Resume", String.format(message, j, times, i, docs));
for (int i = 0; i < docs; i++) {
accept(driver, String.format(message, j, times, i, docs));
randomDelay();
stopWatch.start();
submit(driver);
if (i == docs - 1) {
WebElement link = driver.findElement(By.linkText("Go Back to Parent Page"));
driver.manage().timeouts().implicitlyWait(waitTimeout, TimeUnit.SECONDS);
} else {
WebElement img = driver.findElement(By.xpath("//img[@alt='Click to sign']"));
driver.manage().timeouts().implicitlyWait(waitTimeout, TimeUnit.SECONDS);
}
stopWatch.stop("Submit", message);
}
WebElement link = driver.findElement(By.linkText("Go Back to Parent Page"));
driver.manage().timeouts().implicitlyWait(waitTimeout, TimeUnit.SECONDS);
stopWatch.stop("Submit-f", String.format(message, j, times, i, docs));
link.click();
}
driver.close();
}
private void costantiniUnwrap() throws LPException {
final int ny = wrappedPhase.rows - 1; // start from Zero!
final int nx = wrappedPhase.columns - 1; // start from Zero!
if (wrappedPhase.isVector()) throw new IllegalArgumentException("Input must be 2D array");
if (wrappedPhase.rows < 2 || wrappedPhase.columns < 2)
throw new IllegalArgumentException("Size of input must be larger than 2");
// Default weight
DoubleMatrix w1 = DoubleMatrix.ones(ny + 1, 1);
w1.put(0, 0.5);
w1.put(w1.length - 1, 0.5);
DoubleMatrix w2 = DoubleMatrix.ones(1, nx + 1);
w2.put(0, 0.5);
w2.put(w2.length - 1, 0.5);
DoubleMatrix weight = w1.mmul(w2);
DoubleMatrix i, j, I_J, IP1_J, I_JP1;
DoubleMatrix Psi1, Psi2;
DoubleMatrix[] ROWS;
// Compute partial derivative Psi1, eqt (1,3)
i = intRangeDoubleMatrix(0, ny - 1);
j = intRangeDoubleMatrix(0, nx);
ROWS = grid2D(i, j);
I_J = JblasUtils.sub2ind(wrappedPhase.rows, ROWS[0], ROWS[1]);
IP1_J = JblasUtils.sub2ind(wrappedPhase.rows, ROWS[0].add(1), ROWS[1]);
Psi1 =
JblasUtils.getMatrixFromIdx(wrappedPhase, IP1_J)
.sub(JblasUtils.getMatrixFromIdx(wrappedPhase, I_J));
Psi1 = UnwrapUtils.wrapDoubleMatrix(Psi1);
// Compute partial derivative Psi2, eqt (2,4)
i = intRangeDoubleMatrix(0, ny);
j = intRangeDoubleMatrix(0, nx - 1);
ROWS = grid2D(i, j);
I_J = JblasUtils.sub2ind(wrappedPhase.rows, ROWS[0], ROWS[1]);
I_JP1 = JblasUtils.sub2ind(wrappedPhase.rows, ROWS[0], ROWS[1].add(1));
Psi2 =
JblasUtils.getMatrixFromIdx(wrappedPhase, I_JP1)
.sub(JblasUtils.getMatrixFromIdx(wrappedPhase, I_J));
Psi2 = UnwrapUtils.wrapDoubleMatrix(Psi2);
// Compute beq
DoubleMatrix beq = DoubleMatrix.zeros(ny, nx);
i = intRangeDoubleMatrix(0, ny - 1);
j = intRangeDoubleMatrix(0, nx - 1);
ROWS = grid2D(i, j);
I_J = JblasUtils.sub2ind(Psi1.rows, ROWS[0], ROWS[1]);
I_JP1 = JblasUtils.sub2ind(Psi1.rows, ROWS[0], ROWS[1].add(1));
beq.addi(JblasUtils.getMatrixFromIdx(Psi1, I_JP1).sub(JblasUtils.getMatrixFromIdx(Psi1, I_J)));
I_J = JblasUtils.sub2ind(Psi2.rows, ROWS[0], ROWS[1]);
I_JP1 = JblasUtils.sub2ind(Psi2.rows, ROWS[0].add(1), ROWS[1]);
beq.subi(JblasUtils.getMatrixFromIdx(Psi2, I_JP1).sub(JblasUtils.getMatrixFromIdx(Psi2, I_J)));
beq.muli(-1 / (2 * Constants._PI));
for (int k = 0; k < beq.length; k++) {
beq.put(k, Math.round(beq.get(k)));
}
beq.reshape(beq.length, 1);
logger.debug("Constraint matrix");
i = intRangeDoubleMatrix(0, ny - 1);
j = intRangeDoubleMatrix(0, nx - 1);
ROWS = grid2D(i, j);
DoubleMatrix ROW_I_J = JblasUtils.sub2ind(i.length, ROWS[0], ROWS[1]);
int nS0 = nx * ny;
// Use by S1p, S1m
DoubleMatrix[] COLS;
COLS = grid2D(i, j);
DoubleMatrix COL_IJ_1 = JblasUtils.sub2ind(i.length, COLS[0], COLS[1]);
COLS = grid2D(i, j.add(1));
DoubleMatrix COL_I_JP1 = JblasUtils.sub2ind(i.length, COLS[0], COLS[1]);
int nS1 = (nx + 1) * (ny);
// SOAPBinding.Use by S2p, S2m
COLS = grid2D(i, j);
DoubleMatrix COL_IJ_2 = JblasUtils.sub2ind(i.length + 1, COLS[0], COLS[1]);
COLS = grid2D(i.add(1), j);
DoubleMatrix COL_IP1_J = JblasUtils.sub2ind(i.length + 1, COLS[0], COLS[1]);
int nS2 = nx * (ny + 1);
// Equality constraint matrix (Aeq)
/*
S1p = + sparse(ROW_I_J, COL_I_JP1,1,nS0,nS1) ...
- sparse(ROW_I_J, COL_IJ_1,1,nS0,nS1);
S1m = -S1p;
S2p = - sparse(ROW_I_J, COL_IP1_J,1,nS0,nS2) ...
+ sparse(ROW_I_J, COL_IJ_2,1,nS0,nS2);
S2m = -S2p;
*/
// ToDo: Aeq matrix should be sparse from it's initialization, look into JblasMatrix factory for
// howto
// ...otherwise even a data set of eg 40x40 pixels will exhaust heap:
// ... dimension of Aeq (equality constraints) matrix for 30x30 input is 1521x6240 matrix
// ... dimension of Aeq ( ) matrix for 50x50 input is 2401x9800
// ... dimension of Aeq ( ) matrix for 512x512 input is 261121x1046528
DoubleMatrix S1p =
JblasUtils.setUpMatrixFromIdx(nS0, nS1, ROW_I_J, COL_I_JP1)
.sub(JblasUtils.setUpMatrixFromIdx(nS0, nS1, ROW_I_J, COL_IJ_1));
DoubleMatrix S1m = S1p.neg();
DoubleMatrix S2p =
JblasUtils.setUpMatrixFromIdx(nS0, nS2, ROW_I_J, COL_IP1_J)
.neg()
.add(JblasUtils.setUpMatrixFromIdx(nS0, nS2, ROW_I_J, COL_IJ_2));
DoubleMatrix S2m = S2p.neg();
DoubleMatrix Aeq =
concatHorizontally(concatHorizontally(S1p, S1m), concatHorizontally(S2p, S2m));
final int nObs = Aeq.columns;
final int nUnkn = Aeq.rows;
DoubleMatrix c1 = JblasUtils.getMatrixFromRange(0, ny, 0, weight.columns, weight);
DoubleMatrix c2 = JblasUtils.getMatrixFromRange(0, weight.rows, 0, nx, weight);
c1.reshape(c1.length, 1);
c2.reshape(c2.length, 1);
DoubleMatrix cost =
DoubleMatrix.concatVertically(
DoubleMatrix.concatVertically(c1, c1), DoubleMatrix.concatVertically(c2, c2));
logger.debug("Minimum network flow resolution");
StopWatch clockLP = new StopWatch();
LinearProgram lp = new LinearProgram(cost.data);
lp.setMinProblem(true);
boolean[] integerBool = new boolean[nObs];
double[] lowerBound = new double[nObs];
double[] upperBound = new double[nObs];
for (int k = 0; k < nUnkn; k++) {
lp.addConstraint(new LinearEqualsConstraint(Aeq.getRow(k).toArray(), beq.get(k), "cost"));
}
for (int k = 0; k < nObs; k++) {
integerBool[k] = true;
lowerBound[k] = 0;
upperBound[k] = 99999;
}
// setup bounds and integer nature
lp.setIsinteger(integerBool);
lp.setUpperbound(upperBound);
lp.setLowerbound(lowerBound);
LinearProgramSolver solver = SolverFactory.newDefault();
// double[] solution;
// solution = solver.solve(lp);
DoubleMatrix solution = new DoubleMatrix(solver.solve(lp));
clockLP.stop();
logger.debug("Total GLPK time: {} [sec]", (double) (clockLP.getElapsedTime()) / 1000);
// Displatch the LP solution
int offset;
int[] idx1p = JblasUtils.intRangeIntArray(0, nS1 - 1);
DoubleMatrix x1p = solution.get(idx1p);
x1p.reshape(ny, nx + 1);
offset = idx1p[nS1 - 1] + 1;
int[] idx1m = JblasUtils.intRangeIntArray(offset, offset + nS1 - 1);
DoubleMatrix x1m = solution.get(idx1m);
x1m.reshape(ny, nx + 1);
offset = idx1m[idx1m.length - 1] + 1;
int[] idx2p = JblasUtils.intRangeIntArray(offset, offset + nS2 - 1);
DoubleMatrix x2p = solution.get(idx2p);
x2p.reshape(ny + 1, nx);
offset = idx2p[idx2p.length - 1] + 1;
int[] idx2m = JblasUtils.intRangeIntArray(offset, offset + nS2 - 1);
DoubleMatrix x2m = solution.get(idx2m);
x2m.reshape(ny + 1, nx);
// Compute the derivative jumps, eqt (20,21)
DoubleMatrix k1 = x1p.sub(x1m);
DoubleMatrix k2 = x2p.sub(x2m);
// (?) Round to integer solution
if (roundK == true) {
for (int idx = 0; idx < k1.length; idx++) {
k1.put(idx, FastMath.floor(k1.get(idx)));
}
for (int idx = 0; idx < k2.length; idx++) {
k2.put(idx, FastMath.floor(k2.get(idx)));
}
}
// Sum the jumps with the wrapped partial derivatives, eqt (10,11)
k1.reshape(ny, nx + 1);
k2.reshape(ny + 1, nx);
k1.addi(Psi1.div(Constants._TWO_PI));
k2.addi(Psi2.div(Constants._TWO_PI));
// Integrate the partial derivatives, eqt (6)
// cumsum() method in JblasTester -> see cumsum_demo() in JblasTester.cumsum_demo()
DoubleMatrix k2_temp = DoubleMatrix.concatHorizontally(DoubleMatrix.zeros(1), k2.getRow(0));
k2_temp = JblasUtils.cumsum(k2_temp, 1);
DoubleMatrix k = DoubleMatrix.concatVertically(k2_temp, k1);
k = JblasUtils.cumsum(k, 1);
// Unwrap - final solution
unwrappedPhase = k.mul(Constants._TWO_PI);
}
private void estimateCPM() {
logger.info("Start EJML Estimation");
numIterations = 0;
boolean estimationDone = false;
DenseMatrix64F eL_hat = null;
DenseMatrix64F eP_hat = null;
DenseMatrix64F rhsL = null;
DenseMatrix64F rhsP = null;
// normalize master coordinates for stability -- only master!
TDoubleArrayList yMasterNorm = new TDoubleArrayList();
TDoubleArrayList xMasterNorm = new TDoubleArrayList();
for (int i = 0; i < yMaster.size(); i++) {
yMasterNorm.add(PolyUtils.normalize2(yMaster.getQuick(i), normWin.linelo, normWin.linehi));
xMasterNorm.add(PolyUtils.normalize2(xMaster.getQuick(i), normWin.pixlo, normWin.pixhi));
}
// helper variables
int winL;
int winP;
int maxWSum_idx = 0;
while (!estimationDone) {
String codeBlockMessage = "LS ESTIMATION PROCEDURE";
StopWatch stopWatch = new StopWatch();
StopWatch clock = new StopWatch();
clock.start();
stopWatch.setTag(codeBlockMessage);
stopWatch.start();
logger.info("Start iteration: {}" + numIterations);
/** Remove identified outlier from previous estimation */
if (numIterations != 0) {
logger.info(
"Removing observation {}, idxList {}, from observation vector."
+ index.getQuick(maxWSum_idx)
+ maxWSum_idx);
index.removeAt(maxWSum_idx);
yMasterNorm.removeAt(maxWSum_idx);
xMasterNorm.removeAt(maxWSum_idx);
yOffset.removeAt(maxWSum_idx);
xOffset.removeAt(maxWSum_idx);
// only for outlier removal
yMaster.removeAt(maxWSum_idx);
xMaster.removeAt(maxWSum_idx);
ySlave.removeAt(maxWSum_idx);
xSlave.removeAt(maxWSum_idx);
coherence.removeAt(maxWSum_idx);
// also take care of slave pins
slaveGCPList.remove(maxWSum_idx);
// if (demRefinement) {
// ySlaveGeometry.removeAt(maxWSum_idx);
// xSlaveGeometry.removeAt(maxWSum_idx);
// }
}
/** Check redundancy */
numObservations = index.size(); // Number of points > threshold
if (numObservations < numUnknowns) {
logger.severe(
"coregpm: Number of windows > threshold is smaller than parameters solved for.");
throw new ArithmeticException(
"coregpm: Number of windows > threshold is smaller than parameters solved for.");
}
// work with normalized values
DenseMatrix64F A =
new DenseMatrix64F(
SystemOfEquations.constructDesignMatrix_loop(
yMasterNorm.toArray(), xMasterNorm.toArray(), cpmDegree));
logger.info("TIME FOR SETUP of SYSTEM : {}" + stopWatch.lap("setup"));
RowD1Matrix64F Qy_1; // vector
double meanValue;
switch (cpmWeight) {
case "linear":
logger.info("Using sqrt(coherence) as weights");
Qy_1 = DenseMatrix64F.wrap(numObservations, 1, coherence.toArray());
// Normalize weights to avoid influence on estimated var.factor
logger.info("Normalizing covariance matrix for LS estimation");
meanValue = CommonOps.elementSum(Qy_1) / numObservations;
CommonOps.divide(meanValue, Qy_1); // normalize vector
break;
case "quadratic":
logger.info("Using coherence as weights.");
Qy_1 = DenseMatrix64F.wrap(numObservations, 1, coherence.toArray());
CommonOps.elementMult(Qy_1, Qy_1);
// Normalize weights to avoid influence on estimated var.factor
meanValue = CommonOps.elementSum(Qy_1) / numObservations;
logger.info("Normalizing covariance matrix for LS estimation.");
CommonOps.divide(meanValue, Qy_1); // normalize vector
break;
case "bamler":
// TODO: see Bamler papers IGARSS 2000 and 2004
logger.warning("Bamler weighting method NOT IMPLEMENTED, falling back to None.");
Qy_1 = onesEJML(numObservations);
break;
case "none":
logger.info("No weighting.");
Qy_1 = onesEJML(numObservations);
break;
default:
Qy_1 = onesEJML(numObservations);
break;
}
logger.info("TIME FOR SETUP of VC diag matrix: {}" + stopWatch.lap("diag VC matrix"));
/** tempMatrix_1 matrices */
final DenseMatrix64F yL_matrix = DenseMatrix64F.wrap(numObservations, 1, yOffset.toArray());
final DenseMatrix64F yP_matrix = DenseMatrix64F.wrap(numObservations, 1, xOffset.toArray());
logger.info("TIME FOR SETUP of TEMP MATRICES: {}" + stopWatch.lap("Temp matrices"));
/** normal matrix */
final DenseMatrix64F N =
new DenseMatrix64F(numUnknowns, numUnknowns); // = A_transpose.mmul(Qy_1_diag.mmul(A));
/*
// fork/join parallel implementation
RowD1Matrix64F result = A.copy();
DiagXMat dd = new DiagXMat(Qy_1, A, 0, A.numRows, result);
ForkJoinPool pool = new ForkJoinPool();
pool.invoke(dd);
CommonOps.multAddTransA(A, dd.result, N);
*/
CommonOps.multAddTransA(A, diagxmat(Qy_1, A), N);
DenseMatrix64F Qx_hat = N.copy();
logger.info("TIME FOR SETUP of NORMAL MATRIX: {}" + stopWatch.lap("Normal matrix"));
/** right hand sides */
// azimuth
rhsL = new DenseMatrix64F(numUnknowns, 1); // A_transpose.mmul(Qy_1_diag.mmul(yL_matrix));
CommonOps.multAddTransA(1d, A, diagxmat(Qy_1, yL_matrix), rhsL);
// range
rhsP = new DenseMatrix64F(numUnknowns, 1); // A_transpose.mmul(Qy_1_diag.mmul(yP_matrix));
CommonOps.multAddTransA(1d, A, diagxmat(Qy_1, yP_matrix), rhsP);
logger.info("TIME FOR SETUP of RightHand Side: {}" + stopWatch.lap("Right-hand-side"));
LinearSolver<DenseMatrix64F> solver = LinearSolverFactory.leastSquares(100, 100);
/** compute solution */
if (!solver.setA(Qx_hat)) {
throw new IllegalArgumentException("Singular Matrix");
}
solver.solve(rhsL, rhsL);
solver.solve(rhsP, rhsP);
logger.info("TIME FOR SOLVING of System: {}" + stopWatch.lap("Solving System"));
/** inverting of Qx_hat for stability check */
solver.invert(Qx_hat);
logger.info("TIME FOR INVERSION OF N: {}" + stopWatch.lap("Inversion of N"));
/** test inversion and check stability: max(abs([N*inv(N) - E)) ?= 0 */
DenseMatrix64F tempMatrix_1 = new DenseMatrix64F(N.numRows, N.numCols);
CommonOps.mult(N, Qx_hat, tempMatrix_1);
CommonOps.subEquals(
tempMatrix_1, CommonOps.identity(tempMatrix_1.numRows, tempMatrix_1.numCols));
double maxDeviation = CommonOps.elementMaxAbs(tempMatrix_1);
if (maxDeviation > .01) {
logger.severe(
"COREGPM: maximum deviation N*inv(N) from unity = {}. This is larger than 0.01"
+ maxDeviation);
throw new IllegalStateException("COREGPM: maximum deviation N*inv(N) from unity)");
} else if (maxDeviation > .001) {
logger.warning(
"COREGPM: maximum deviation N*inv(N) from unity = {}. This is between 0.01 and 0.001"
+ maxDeviation);
}
logger.info("TIME FOR STABILITY CHECK: {}" + stopWatch.lap("Stability Check"));
logger.info("Coeffs in Azimuth direction: {}" + rhsL.toString());
logger.info("Coeffs in Range direction: {}" + rhsP.toString());
logger.info("Max Deviation: {}" + maxDeviation);
logger.info("System Quality: {}" + solver.quality());
/** some other stuff if the scale is okay */
DenseMatrix64F Qe_hat = new DenseMatrix64F(numObservations, numObservations);
DenseMatrix64F tempMatrix_2 = new DenseMatrix64F(numObservations, numUnknowns);
CommonOps.mult(A, Qx_hat, tempMatrix_2);
CommonOps.multTransB(-1, tempMatrix_2, A, Qe_hat);
scaleInputDiag(Qe_hat, Qy_1);
// solution: Azimuth
DenseMatrix64F yL_hat = new DenseMatrix64F(numObservations, 1);
eL_hat = new DenseMatrix64F(numObservations, 1);
CommonOps.mult(A, rhsL, yL_hat);
CommonOps.sub(yL_matrix, yL_hat, eL_hat);
// solution: Range
DenseMatrix64F yP_hat = new DenseMatrix64F(numObservations, 1);
eP_hat = new DenseMatrix64F(numObservations, 1);
CommonOps.mult(A, rhsP, yP_hat);
CommonOps.sub(yP_matrix, yP_hat, eP_hat);
logger.info("TIME FOR DATA preparation for TESTING: {}" + stopWatch.lap("Testing Setup"));
/** overal model test (variance factor) */
double overAllModelTest_L = 0;
double overAllModelTest_P = 0;
for (int i = 0; i < numObservations; i++) {
overAllModelTest_L += FastMath.pow(eL_hat.get(i), 2) * Qy_1.get(i);
overAllModelTest_P += FastMath.pow(eP_hat.get(i), 2) * Qy_1.get(i);
}
overAllModelTest_L =
(overAllModelTest_L / FastMath.pow(SIGMA_L, 2)) / (numObservations - numUnknowns);
overAllModelTest_P =
(overAllModelTest_P / FastMath.pow(SIGMA_P, 2)) / (numObservations - numUnknowns);
logger.info("Overall Model Test Lines: {}" + overAllModelTest_L);
logger.info("Overall Model Test Pixels: {}" + overAllModelTest_P);
logger.info("TIME FOR OMT: {}" + stopWatch.lap("OMT"));
/** ---------------------- DATASNOPING ----------------------------------- * */
/** Assumed Qy diag */
/** initialize */
DenseMatrix64F wTest_L = new DenseMatrix64F(numObservations, 1);
DenseMatrix64F wTest_P = new DenseMatrix64F(numObservations, 1);
for (int i = 0; i < numObservations; i++) {
wTest_L.set(i, eL_hat.get(i) / (Math.sqrt(Qe_hat.get(i, i)) * SIGMA_L));
wTest_P.set(i, eP_hat.get(i) / (Math.sqrt(Qe_hat.get(i, i)) * SIGMA_P));
}
/** find maxima's */
// azimuth
winL = absArgmax(wTest_L);
double maxWinL = Math.abs(wTest_L.get(winL));
logger.info(
"maximum wtest statistic azimuth = {} for window number: {} "
+ maxWinL
+ index.getQuick(winL));
// range
winP = absArgmax(wTest_P);
double maxWinP = Math.abs(wTest_P.get(winP));
logger.info(
"maximum wtest statistic range = {} for window number: {} "
+ maxWinP
+ index.getQuick(winP));
/** use summed wTest in Azimuth and Range direction for outlier detection */
DenseMatrix64F wTestSum = new DenseMatrix64F(numObservations);
for (int i = 0; i < numObservations; i++) {
wTestSum.set(i, FastMath.pow(wTest_L.get(i), 2) + FastMath.pow(wTest_P.get(i), 2));
}
maxWSum_idx = absArgmax(wTest_P);
double maxWSum = wTest_P.get(winP);
logger.info(
"Detected outlier: summed sqr.wtest = {}; observation: {}"
+ maxWSum
+ index.getQuick(maxWSum_idx));
/** Test if we are estimationDone yet */
// check on number of observations
if (numObservations <= numUnknowns) {
logger.warning("NO redundancy! Exiting iterations.");
estimationDone = true; // cannot remove more than this
}
// check on test k_alpha
if (Math.max(maxWinL, maxWinP) <= criticalValue) {
// all tests accepted?
logger.info("All outlier tests accepted! (final solution computed)");
estimationDone = true;
}
if (numIterations >= maxIterations) {
logger.info("max. number of iterations reached (exiting loop).");
estimationDone = true; // we reached max. (or no max_iter specified)
}
/** Only warn if last iteration has been estimationDone */
if (estimationDone) {
if (overAllModelTest_L > 10) {
logger.warning(
"COREGPM: Overall Model Test, Lines = {} is larger than 10. (Suggest model or a priori sigma not correct.)"
+ overAllModelTest_L);
}
if (overAllModelTest_P > 10) {
logger.warning(
"COREGPM: Overall Model Test, Pixels = {} is larger than 10. (Suggest model or a priori sigma not correct.)"
+ overAllModelTest_P);
}
/** if a priori sigma is correct, max wtest should be something like 1.96 */
if (Math.max(maxWinL, maxWinP) > 200.0) {
logger.warning(
"Recommendation: remove window number: {} and re-run step COREGPM. max. wtest is: {}."
+ index.get(winL)
+ Math.max(maxWinL, maxWinP));
}
}
logger.info("TIME FOR wTestStatistics: {}" + stopWatch.lap("WTEST"));
logger.info("Total Estimation TIME: {}" + clock.getElapsedTime());
numIterations++; // update counter here!
} // only warn when iterating
yError = eL_hat.getData();
xError = eP_hat.getData();
yCoef = rhsL.getData();
xCoef = rhsP.getData();
}
private StopWatch profilePage(String pageName) {
String tag = "page." + pageName.replace('/', '.');
StopWatch watch = new Slf4JStopWatch(tag);
watch.start();
return watch;
}
public void handlePageRender(
PageRenderRequestParameters parameters, ComponentRequestHandler handler) throws IOException {
StopWatch watch = profilePage(parameters.getLogicalPageName());
handler.handlePageRender(parameters);
watch.stop(watch.getTag(), "Page rendered");
}
/**
* matrix test
*
* @throws Exception exception
*/
public void testMatrix() throws Exception {
ChatMessage msg = constructMessage();
int count = 100000;
// hot swap
for (int i = 0; i < 10000; i++) {
hessian(msg);
jackson(msg);
jackson2(msg);
msgpack(msg);
}
System.out.println(count + " loop:");
StopWatch stopWatch = new StopWatch();
stopWatch.start("hessian");
for (int i = 0; i < count; i++) {
hessian(msg);
}
stopWatch.stop("hessian");
System.out.println(stopWatch.getTag() + ":" + stopWatch.getElapsedTime());
stopWatch.start("jackson");
for (int i = 0; i < count; i++) {
jackson(msg);
}
stopWatch.stop("jackson");
System.out.println(stopWatch.getTag() + ":" + stopWatch.getElapsedTime());
stopWatch.start("msgpack");
for (int i = 0; i < count; i++) {
msgpack(msg);
}
stopWatch.stop("msgpack");
System.out.println(stopWatch.getTag() + ":" + stopWatch.getElapsedTime());
stopWatch.start("jackson2");
for (int i = 0; i < count; i++) {
jackson2(msg);
}
stopWatch.stop("jackson2");
System.out.println(stopWatch.getTag() + ":" + stopWatch.getElapsedTime());
}
public void setOverlays_async(
AMD_RenderingEngine_setOverlays __cb,
RLong tableId,
RLong imageId,
Map<Long, Integer> rowColorMap,
Current __current)
throws ServerError {
try {
final IceMapper mapper = new IceMapper(IceMapper.VOID);
// Sanity check so that we do not attempt to slice the entire table
if (rowColorMap.size() == 0) {
callInvokerOnMappedArgs(mapper, __cb, __current, new Object[] {null});
return;
}
// Translate our set of rows to an array for table slicing
Set<Long> rowsAsSet = rowColorMap.keySet();
long[] rows = new long[rowsAsSet.size()];
int rowIndex = 0;
for (Long row : rowsAsSet) {
rows[rowIndex] = row.longValue();
rowIndex++;
}
// Load the table and find the index of the mask column, throwing an
// exception if the mask column does not exist.
StopWatch s1 = new CommonsLogStopWatch("omero.getTable");
TablePrx table = roiService.getTable(tableId.getValue());
s1.stop();
s1 = new CommonsLogStopWatch("omero.getHeaders");
Column[] columns = table.getHeaders();
s1.stop();
int maskColumnIndex = 0;
for (; maskColumnIndex < columns.length; maskColumnIndex++) {
if (columns[maskColumnIndex] instanceof MaskColumn) {
break;
}
}
if (maskColumnIndex == columns.length) {
throw new IllegalArgumentException(
"Unable to find mask column in table: " + tableId.getValue());
}
// Slice the table and feed the byte array encoded bit masks to the
// rendering engine servant.
s1 = new CommonsLogStopWatch("omero.sliceAndBuildREMap");
Data data = table.slice(new long[] {maskColumnIndex}, rows);
MaskColumn maskColumn = (MaskColumn) data.columns[0];
final Map<byte[], Integer> forRenderingEngine = new LinkedHashMap<byte[], Integer>();
for (int i = 0; i < rows.length; i++) {
forRenderingEngine.put(maskColumn.bytes[i], rowColorMap.get(rows[i]));
}
s1.stop();
callInvokerOnMappedArgs(mapper, __cb, __current, forRenderingEngine);
} catch (Throwable t) {
if (!(t instanceof Exception)) {
__cb.ice_exception(new InternalException());
} else {
__cb.ice_exception((Exception) t);
}
}
}
