Field Detail

• psfRadius

  protected double[] psfRadius

• maxErrorRetries

  protected final int maxErrorRetries

  See Also:

  Constant Field Values

Constructor Detail

• CostesSignificanceTest

  public CostesSignificanceTest(PearsonsCorrelation<T> pc,
                                int psfRadiusInPixels,
                                int nrRandomizations,
                                boolean showShuffledImages)

  Creates a new Costes significance test object by using a cube block with the given edge length.

  Parameters:

  psfRadiusInPixels - The edge width of the 3D cube block.

Method Detail

• execute

  public void execute(DataContainer<T> container)
               throws MissingPreconditionException

  Builds a list of blocks that represent the images. To do so we create a list image ROI cursors. If a block does not fit into the image it will get a out-of-bounds strategy.

  Specified by:

  execute in class Algorithm<T extends RealType<T> & NativeType<T>>

  Throws:

  MissingPreconditionException

• generateBlocks

  protected void generateBlocks(RandomAccessible<T> img,
                                List<IterableInterval<T>> blockList,
                                double[] offset,
                                double[] size)
                         throws MissingPreconditionException

  This method drives the creation of RegionOfInterest-Cursors on the given image. It does not matter if those generated blocks are used for reading and/or writing. The resulting blocks are put into the given list and are in the responsibility of the caller, i.e. he or she must make sure the cursors get closed on some point in time.

  Parameters:

  img - The image to create cursors on.

  blockList - The list to put newly created cursors into

  offset -

  size -

  Throws:

  MissingPreconditionException

• generateBlocksXY

  protected void generateBlocksXY(RandomAccessible<T> img,
                                  List<IterableInterval<T>> blockList,
                                  double[] offset,
                                  double[] size)

  Goes stepwise through the y-dimensions of the image data and adds cursors for each row to the given list. The method does not check if there is a y-dimensions, so this should be made sure before. you can enforce to create all cursors as out-of-bounds one.

  Parameters:

  img - The image to get the data and cursors from.

  blockList - The list to put the blocks into.

  offset - The current offset configuration. Only [0] and [1] will be changed.

  size -

• generateBlocksX

  protected void generateBlocksX(RandomAccessible<T> img,
                                 List<IterableInterval<T>> blockList,
                                 double[] offset,
                                 double[] size)

  Goes stepwise through a row of image data and adds cursors to the given list. If there is not enough image data for a whole block, an out-of-bounds cursor is generated. The creation of out-of-bound cursors could be enforced as well.

  Parameters:

  img - The image to get the data and cursors from.

  blockList - The list to put the blocks into.

  offset - The current offset configuration. Only [0] of it will be changed.

  size -

• calculateStatistics

  protected void calculateStatistics(List<Double> compareValues,
                                     double originalVal)

• processResults

  public void processResults(ResultHandler<T> handler)

  Description copied from class: Algorithm
  A method to give the algorithm the opportunity to let its results being processed by the passed handler. By default this methods passes the collected warnings to the handler and sub-classes should make use of this by adding custom behavior and call the super class.

  Overrides:

  processResults in class Algorithm<T extends RealType<T> & NativeType<T>>

  Parameters:

  handler - The ResultHandler to process the results.

• getCostesPValue

  public double getCostesPValue()

• getShuffledMean

  public double getShuffledMean()

• getShuffledStdDerivation

  public double getShuffledStdDerivation()

• getShuffledPearsonsNotLessOriginal

  public double getShuffledPearsonsNotLessOriginal()