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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- def convolve(data: Array[Double], kernel: Array[Double]): Array[Double]
- def correlate(data: Array[Double], kernel: Array[Double]): Array[Double]
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- def filterBP(data: Array[Double], omegaLow: Double, omegaHigh: Double): Array[Double]
See filterBP
- def filterBP(data: Array[Double], omegaLow: Double, omegaHigh: Double, sampleRate: Double): Array[Double]
See filterBP
- 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
- def filterBS(data: Array[Double], omegaLow: Double, omegaHigh: Double): Array[Double]
See filterBS
- def filterBS(data: Array[Double], omegaLow: Double, omegaHigh: Double, sampleRate: Double): Array[Double]
See filterBS
- 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
- def filterHP(data: Array[Double], omega: Double): Array[Double]
See filterHP
- def filterHP(data: Array[Double], omega: Double, sampleRate: Double): Array[Double]
See filterHP
- 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
- def filterLP(data: Array[Double], omega: Double): Array[Double]
See filterLP
- def filterLP(data: Array[Double], omega: Double, sampleRate: Double): Array[Double]
See filterLP
- 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
- 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
- def fourierFreqD(windowLength: Int, fs: Double): Array[Double]
See fourierFreq.
See fourierFreq. shifted = false
- 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
- def fourierShiftC(data: Array[Complex]): Array[Complex]
See fourierShiftD
- 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
- def fourierTr2C(data: Array[Array[Complex]]): Array[Array[Complex]]
See fourierTrD
- def fourierTrC(data: Array[Complex]): Array[Complex]
See fourierTrD
- 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.
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- def haarTr2D(data: Array[Array[Double]]): Array[Array[Double]]
See haarTrD
- 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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- def iFourierShiftC(data: Array[Complex]): Array[Complex]
See iFourierShiftD
- 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
- def iFourierTrC(data: Array[Complex]): Array[Complex]
See fourierTrD
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- def rootMeanSquareD(data: Array[Double]): Double
Root mean square of a vector.
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