breeze.signal

JavaCompatible

object JavaCompatible

This class is a converter for using breeze.signal functions on Arrays of Double and Complex, from Java/Matlab/Mathematica.

Linear Supertypes
AnyRef, Any
Ordering
  1. Alphabetic
  2. By inheritance
Inherited
  1. JavaCompatible
  2. AnyRef
  3. Any
  1. Hide All
  2. Show all
Learn more about member selection
Visibility
  1. Public
  2. All

Value Members

  1. final def !=(arg0: Any): Boolean

    Definition Classes
    AnyRef → Any
  2. final def ##(): Int

    Definition Classes
    AnyRef → Any
  3. final def ==(arg0: Any): Boolean

    Definition Classes
    AnyRef → Any
  4. final def asInstanceOf[T0]: T0

    Definition Classes
    Any
  5. def clone(): AnyRef

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  6. def convolve(data: Array[Double], kernel: Array[Double]): Array[Double]

  7. def correlate(data: Array[Double], kernel: Array[Double]): Array[Double]

  8. final def eq(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef
  9. def equals(arg0: Any): Boolean

    Definition Classes
    AnyRef → Any
  10. def filterBP(data: Array[Double], omegaLow: Double, omegaHigh: Double): Array[Double]

    See filterBP

  11. def filterBP(data: Array[Double], omegaLow: Double, omegaHigh: Double, sampleRate: Double): Array[Double]

    See filterBP

  12. def filterBP(data: Array[Double], omegaLow: Double, omegaHigh: Double, sampleRate: Double, taps: Int): Array[Double]

    Bandpass filter the data using a windowed FIR filter.

    Bandpass filter the data using a windowed FIR filter. See/use breeze.signal.filterBP() for more details, and to set advanced options.

    data

    data to filter

    omegaLow

    low frequency (in units of Nyquist frequency or Hz if sampleRate is set to specific value other than 2d)

    omegaHigh

    high frequency (in units of Nyquist frequency or Hz if sampleRate is set to specific value other than 2d)

    sampleRate

    in Hz, default 2d (omegaLow/High will then be in units of Nyquist frequency)

    taps

    number of taps to use, default 512

    returns

  13. def filterBS(data: Array[Double], omegaLow: Double, omegaHigh: Double): Array[Double]

    See filterBS

  14. def filterBS(data: Array[Double], omegaLow: Double, omegaHigh: Double, sampleRate: Double): Array[Double]

    See filterBS

  15. def filterBS(data: Array[Double], omegaLow: Double, omegaHigh: Double, sampleRate: Double, taps: Int): Array[Double]

    Bandstop filter the data using a windowed FIR filter.

    Bandstop filter the data using a windowed FIR filter. See/use breeze.signal.filterBS() for more details, and to set advanced options.

    data

    data to filter

    omegaLow

    low frequency (in units of Nyquist frequency or Hz if sampleRate is set to specific value other than 2d)

    omegaHigh

    high frequency (in units of Nyquist frequency or Hz if sampleRate is set to specific value other than 2d)

    sampleRate

    in Hz, default 2d (omegaLow/High will then be in units of Nyquist frequency)

    taps

    number of taps to use, default 512

    returns

  16. def filterHP(data: Array[Double], omega: Double): Array[Double]

    See filterHP

  17. def filterHP(data: Array[Double], omega: Double, sampleRate: Double): Array[Double]

    See filterHP

  18. def filterHP(data: Array[Double], omega: Double, sampleRate: Double, taps: Int): Array[Double]

    High pass filter the data using a windowed FIR filter.

    High pass filter the data using a windowed FIR filter. See/use breeze.signal.filterHP() for more details, and to set advanced options.

    data

    data to filter

    omega

    cutoff frequency (in units of Nyquist frequency or Hz if sampleRate is set to specific value other than 2d)

    sampleRate

    in Hz, default 2d (omega will then be in units of Nyquist frequency)

    taps

    number of taps to use, default 512

    returns

  19. def filterLP(data: Array[Double], omega: Double): Array[Double]

    See filterLP

  20. def filterLP(data: Array[Double], omega: Double, sampleRate: Double): Array[Double]

    See filterLP

  21. def filterLP(data: Array[Double], omega: Double, sampleRate: Double, taps: Int): Array[Double]

    Low pass filter the data using a windowed FIR filter.

    Low pass filter the data using a windowed FIR filter. See/use breeze.signal.filterLP() for more details, and to set advanced options.

    data

    data to filter

    omega

    cutoff frequency (in units of Nyquist frequency or Hz if sampleRate is set to specific value other than 2d)

    sampleRate

    in Hz, default 2d (omega will then be in units of Nyquist frequency)

    taps

    number of taps to use, default 512

    returns

  22. def filterMedianD(data: Array[Double], windowLength: Int): Array[Double]

    Median filter the input data.

    Median filter the input data. Edge values are median-filtered with shorter windows, in order to preserve the total length of the input.

    windowLength

    only supports odd windowLength values, since even values would cause half-frame time shifts in one or the other direction, and would also lead to floating point values even for integer input

  23. def finalize(): Unit

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  24. def fourierFreqD(windowLength: Int, fs: Double): Array[Double]

    See fourierFreq.

    See fourierFreq. shifted = false

  25. def fourierFreqD(windowLength: Int, fs: Double, shifted: Boolean): Array[Double]

    Returns the frequencies for each tap in a discrete Fourier transform, useful for plotting.

    Returns the frequencies for each tap in a discrete Fourier transform, useful for plotting. You must specify either an fs or a dt argument. If you specify both, which is redundant, fs == 1.0/dt must be true.

    f = [0, 1, ..., n/2-1, -n/2, ..., -1] / (dt*n) if n is even f = [0, 1, ..., (n-1)/2, -(n-1)/2, ..., -1] / (dt*n) if n is odd

    windowLength

    window length of discrete Fourier transform

    fs

    sampling frequency (Hz)

    shifted

    whether to return fourierShift'ed frequencies, default=false

  26. def fourierShiftC(data: Array[Complex]): Array[Complex]

    See fourierShiftD

  27. def fourierShiftD(data: Array[Double]): Array[Double]

    Shift the zero-frequency component to the center of the spectrum.

    Shift the zero-frequency component to the center of the spectrum. Use fourierShiftC instead for complex array input. This function swaps half-spaces for all axes listed (defaults to all). Note that y[0] is the Nyquist component only if len(x) is even.

    data

    input array

    returns

  28. def fourierTr2C(data: Array[Array[Complex]]): Array[Array[Complex]]

    See fourierTrD

  29. def fourierTrC(data: Array[Complex]): Array[Complex]

    See fourierTrD

  30. def fourierTrD(data: Array[Double]): Array[Complex]

    Returns the discrete fourier transform.

    Returns the discrete fourier transform. Use fourierTrC instead for complex array imput. Use fourierTr2/2C instead for 2D Fourier tranform.

    returns
  31. final def getClass(): Class[_]

    Definition Classes
    AnyRef → Any
  32. def haarTr2D(data: Array[Array[Double]]): Array[Array[Double]]

    See haarTrD

  33. def haarTrD(data: Array[Double]): Array[Double]

    Return the padded fast haar transformation of a vector or matrix.

    Return the padded fast haar transformation of a vector or matrix. Note that the output will always be padded to a power of 2. A matrix will cause a 2D fht. The 2D haar transformation is defined for squared power of 2 matrices. A new matrix will thus be created and the old matrix will be placed in the upper-left part of the new matrix. Avoid calling this method with a matrix that has few cols / many rows or many cols / few rows (e.g. 1000000 x 3) as this will cause a very high memory consumption.

    data

    data to be transformed.

    See also

    https://en.wikipedia.org/wiki/Haar_wavelet

  34. def hashCode(): Int

    Definition Classes
    AnyRef → Any
  35. def iFourierShiftC(data: Array[Complex]): Array[Complex]

    See iFourierShiftD

  36. def iFourierShiftD(data: Array[Double]): Array[Double]

    Shift the zero-frequency component to the center of the spectrum.

    Shift the zero-frequency component to the center of the spectrum. Use fourierShiftC instead for complex array input. This function swaps half-spaces for all axes listed (defaults to all). Note that y[0] is the Nyquist component only if len(x) is even.

    data

    input array

    returns

  37. def iFourierTrC(data: Array[Complex]): Array[Complex]

    See fourierTrD

  38. final def isInstanceOf[T0]: Boolean

    Definition Classes
    Any
  39. final def ne(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef
  40. final def notify(): Unit

    Definition Classes
    AnyRef
  41. final def notifyAll(): Unit

    Definition Classes
    AnyRef
  42. def rootMeanSquareD(data: Array[Double]): Double

    Root mean square of a vector.

  43. final def synchronized[T0](arg0: ⇒ T0): T0

    Definition Classes
    AnyRef
  44. def toString(): String

    Definition Classes
    AnyRef → Any
  45. final def wait(): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  46. final def wait(arg0: Long, arg1: Int): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  47. final def wait(arg0: Long): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Inherited from AnyRef

Inherited from Any

Ungrouped