Class StandardKernel1dShape

Method Details

• evaluate

  protected abstract double evaluate(double x)

  Returns the point value of the function defining this shape at a point a given absolute fraction of the nominal width from the center. Calling this method for values of x out of the range 0<=x<=getNormalisedExtent() has an undefined effect; the function value is assumed symmetric and zero for larger absolute values.

  Parameters:

  x - normalised absolute distance in range 0..normExtent

  Returns:

  function value at x

• getNormalisedExtent

  public double getNormalisedExtent()

  Returns the extent of a kernel with this shape of unit nominal width. The value of the evaluate(x) method for x greater than the value returned from this method is taken to be zero.

• isSquare

  public boolean isSquare()

  Indicates whether this shape has features which are intentionally non-smooth and should be portrayed as such. This non-smoothness applies either within the extent or at its edge.

  Returns:

  true iff there are non-smooth features that should be visible

• getName

  public String getName()

  Returns a one-word name for this shape.

  Specified by:

  getName in interface Kernel1dShape

  Returns:

  name

• getDescription

  public String getDescription()

  Returns a short description for this shape.

  Specified by:

  getDescription in interface Kernel1dShape

  Returns:

  description

• createFixedWidthKernel

  public Kernel1d createFixedWidthKernel(double width)

  Description copied from interface: Kernel1dShape

  Creates a fixed width kernel with a given nominal width. The width is some kind of characteristic half-width in one direction of the smoothing function. It is in units of grid points (array element spacing). It would generally be less than or equal to the kernel's extent.

  Specified by:

  createFixedWidthKernel in interface Kernel1dShape

  Parameters:

  width - half-width

  Returns:

  new kernel

• createMeanKernel

  public Kernel1d createMeanKernel(double width)

  Description copied from interface: Kernel1dShape

  Creates an averaging kernel with a given nominal fixed width. The 'convolution' it performs is not really a convolution, instead it's a sort of weighted moving average. This is a smoothing that's suitable for intensive quantities. Using proper convolution for intensive quantities like the mean or median is problematic if there may be blank values in the input array, since the smoothed value has to keep track of how many non-blank values it has encountered.

  Specified by:

  createMeanKernel in interface Kernel1dShape

  Parameters:

  width - half-width

  Returns:

  new kernel

• createKnnKernel

  public Kernel1d createKnnKernel(double k, boolean isSymmetric, int minWidth, int maxWidth)

  Description copied from interface: Kernel1dShape

  Creates an adaptive kernel that uses a K-nearest-neighbours algorithm to determine local smoothing width, so that the width of the kernel is determined by the distance (number of 1-pixel bins) within which the given number k of samples is found.

  The nearest neighbour search may be symmetric or asymmetric. In the asymmetric case, the kernel width is determined separately for the positive and negative directions along the axis.

  Minimum and maximum smoothing widths are also supplied as bounds on the smoothing width for the case that the samples are very dense or very spread out (the latter case covers the edge of the data region as well). If minWidth==maxWidth, the result is a fixed-width kernel.

  Specified by:

  createKnnKernel in interface Kernel1dShape

  Parameters:

  k - number of nearest neighbours included in the distance that characterises the smoothing

  isSymmetric - true for bidirectional KNN search, false for unidirectional

  minWidth - minimum smoothing width

  maxWidth - maximum smoothing width

  Returns:

  new kernel

• toString

  public String toString()

  Overrides:

  toString in class Object

• getStandardOptions

  public static Kernel1dShape[] getStandardOptions()

  Returns an array of the generally recommended kernel shape options.

  Returns:

  kernel shape options

• createTruncatedGaussian

  public static StandardKernel1dShape createTruncatedGaussian(double truncSigma)

  Returns a kernel shape based on the Gaussian function with truncation at a given number of standard deviations.

  Parameters:

  truncSigma - number of sigma at which to truncate the kernel

  Returns:

  new kernel shape

• createSymmetricNormalisedKernel

  public static Kernel1d createSymmetricNormalisedKernel(double[] levels, boolean isSquare)

  Creates a symmetric normalised kernel based on a fixed array of function values. The levels array gives a list of the values at x=0, 1 (and -1), 2 (and -2), ....

  Parameters:

  levels - kernel function values on 1d grid starting from 0

  isSquare - true iff the kernel is considered non-smooth

  Returns:

  new kernel

• createSymmetricMeanKernel

  public static Kernel1d createSymmetricMeanKernel(double[] levels, boolean isSquare)

  Creates a symmetric averabing kernel based on a fixed array of function values. The levels array gives a list of the values at x=0, 1 (and -1), 2 (and -2), ....

  Parameters:

  levels - kernel function values on 1d grid starting from 0

  isSquare - true iff the kernel is considered non-smooth

  Returns:

  new kernel