JavaCompatible

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

class Object
trait Matchable
class Any

Value members

Concrete methods

def convolve(data: Array[Double], kernel: Array[Double]): Array[Double]
def correlate(data: Array[Double], kernel: Array[Double]): Array[Double]
def filterBP(data: Array[Double], omegaLow: Double, omegaHigh: Double, sampleRate: Double, taps: Int): Array[Double]

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

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

Value parameters:
data

data to filter

omegaHigh

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

omegaLow

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

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

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

Value parameters:
data

data to filter

omegaHigh

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

omegaLow

low 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, sampleRate: Double): Array[Double]
def filterBS(data: Array[Double], omegaLow: Double, omegaHigh: Double): Array[Double]
def filterHP(data: Array[Double], omega: Double, sampleRate: Double, taps: Int): Array[Double]

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

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

Value parameters:
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 filterHP(data: Array[Double], omega: Double, sampleRate: Double): Array[Double]
def filterHP(data: Array[Double], omega: Double): Array[Double]
def filterLP(data: Array[Double], omega: Double, sampleRate: Double, taps: Int): Array[Double]

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

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

Value parameters:
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, sampleRate: Double): Array[Double]
def filterLP(data: Array[Double], omega: Double): Array[Double]
def filterMedianD(data: Array[Double], windowLength: Int): Array[Double]

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

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

Value parameters:
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, shifted: Boolean): Array[Double]

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.

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

Value parameters:
fs

sampling frequency (Hz)

shifted

whether to return fourierShift'ed frequencies, default=false

windowLength

window length of discrete Fourier transform

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

See fourierFreq. shifted = false

See fourierFreq. shifted = false

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.

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.

Value parameters:
data

input array

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

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

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.

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.

Value parameters:
data

data to be transformed.

See also:

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.

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.

Value parameters:
data

input array

Root mean square of a vector.

Root mean square of a vector.