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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.

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

  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

  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

  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

  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 fourierFreqD(windowLength: Int, fs: Double): Array[Double]

    See fourierFreq.

    See fourierFreq. shifted = false

  24. 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

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

    See fourierShiftD

  26. 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

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

    See fourierTrD

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

    See fourierTrD

  29. 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.

  30. final def getClass(): Class[_]
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  31. def haarTr2D(data: Array[Array[Double]]): Array[Array[Double]]

    See haarTrD

  32. 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

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  34. def iFourierShiftC(data: Array[Complex]): Array[Complex]

    See iFourierShiftD

  35. 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

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

    See fourierTrD

  37. final def isInstanceOf[T0]: Boolean
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  41. def rootMeanSquareD(data: Array[Double]): Double

    Root mean square of a vector.

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    (Since version ) see corresponding Javadoc for more information.

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