breeze.signal
Type members
Classlikes
This class is a converter for using breeze.signal functions on Arrays of Double and Complex, from Java/Matlab/Mathematica.
This class is a converter for using breeze.signal functions on Arrays of Double and Complex, from Java/Matlab/Mathematica.
Option values: how to deal with convolution and filter padding.
Option values: how to deal with convolution and filter padding.
- Companion
- object
Option values: how to deal with convolution overhangs.
Option values: how to deal with convolution overhangs.
- Companion
- object
Option values: how to deal with convolution and filter padding.
Option values: how to deal with convolution and filter padding.
- Companion
- object
slices specific result ranges out of results for convolve, etc
slices specific result ranges out of results for convolve, etc
- Companion
- object
Option values: window function for filter design.
Option values: window function for filter design.
- Companion
- object
Shift the zero-frequency component to the center of the spectrum. 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. 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 Params
- dft
input array
Returns the discrete fourier transform of a DenseVector or DenseMatrix. Currently, DenseVector/DenseMatrix types of Double and Complex are supported. Scaling follows the common signal processing convention, i.e. <b>no scaling on forward DFT</b>, and 1/n scaling for the inverse DFT. Of note, fft(x: DenseMatrix[Double]) will perform the 2D fft in both row and column dimensions, as opposed to the MatLab toolbox syntax, which performs column-wise 1D fft. Implementation is via the implicit trait fft.Impl[ InputType, OutputType ], which is found in breeze.signal.support.fft.Impl.scala.
Returns the discrete fourier transform of a DenseVector or DenseMatrix. Currently, DenseVector/DenseMatrix types of Double and Complex are supported. Scaling follows the common signal processing convention, i.e. <b>no scaling on forward DFT</b>, and 1/n scaling for the inverse DFT. Of note, fft(x: DenseMatrix[Double]) will perform the 2D fft in both row and column dimensions, as opposed to the MatLab toolbox syntax, which performs column-wise 1D fft. Implementation is via the implicit trait fft.Impl[ InputType, OutputType ], which is found in breeze.signal.support.fft.Impl.scala.
Inverse shift the zero-frequency component to the center of the spectrum. For odd sequences, this is not equivalent to breeze.signal.fourierShift
Inverse shift the zero-frequency component to the center of the spectrum. For odd sequences, this is not equivalent to breeze.signal.fourierShift
- Value Params
- dft
input array
Returns the inverse fast fourier transform of a DenseVector or DenseMatrix. Currently, DenseVector/DenseMatrix types of Double and Complex are supported. Scaling follows the common signal processing convention, i.e. no scaling on forward DFT, and <b>1/n scaling for the inverse DFT</b>. Of note, ifft(x: DenseMatrix[Double]) will perform the 2D ifft in both row and column dimensions, as opposed to the MatLab toolbox syntax, which performs column-wise 1D ifft. Implementation is via the implicit trait CanIFFT[ InputType, OutputType ], which is found in breeze.signal.support.CanIFFT.scala.
Returns the inverse fast fourier transform of a DenseVector or DenseMatrix. Currently, DenseVector/DenseMatrix types of Double and Complex are supported. Scaling follows the common signal processing convention, i.e. no scaling on forward DFT, and <b>1/n scaling for the inverse DFT</b>. Of note, ifft(x: DenseMatrix[Double]) will perform the 2D ifft in both row and column dimensions, as opposed to the MatLab toolbox syntax, which performs column-wise 1D ifft. Implementation is via the implicit trait CanIFFT[ InputType, OutputType ], which is found in breeze.signal.support.CanIFFT.scala.
Value members
Concrete methods
Convolves DenseVectors. Implementation is via the implicit trait CanConvolve[ InputType, OutputType ], which is found in breeze.signal.support.CanConvolve.scala.
Convolves DenseVectors. Implementation is via the implicit trait CanConvolve[ InputType, OutputType ], which is found in breeze.signal.support.CanConvolve.scala.
- Value Params
- canConvolve
implicit delegate which is used for implementation. End-users should not use this argument.
- data
DenseVector or DenseMatrix to be convolved
- kernel
DenseVector or DenseMatrix kernel
Correlates DenseVectors. Implementation is via the implicit trait CanConvolve[ InputType, OutputType ], which is found in breeze.signal.support.CanConvolve.scala. See breeze.signal.convolve for options and other information.
Correlates DenseVectors. Implementation is via the implicit trait CanConvolve[ InputType, OutputType ], which is found in breeze.signal.support.CanConvolve.scala. See breeze.signal.convolve for options and other information.
FIR filter design using the window method.
FIR filter design using the window method.
This function computes the coefficients of a finite impulse response
filter. The filter will have linear phase; it will be Type I if
numtaps is odd and Type II if numtaps is even.
Type II filters always have zero response at the Nyquist rate, so a
ValueError exception is raised if firwin is called with numtaps even and
having a passband whose right end is at the Nyquist rate.
Portions of the code are translated from scipy (scipy.org) based on provisions of the BSD license.
- Value Params
- nyquist
The nyquist frequency, default is 1.
- omegas
Cutoff frequencies of the filter, specified in units of "nyquist." The frequencies should all be positive and monotonically increasing. The frequencies must lie between (0, nyquist). 0 and nyquist should not be included in this array.
- optWindow
Currently supports a hamming window breeze.signal.OptWindowFunction.Hamming, a specified window breeze.signal.OptWindowFunction.User, or no window breeze.signal.OptWindowFunction.None.
- scale
Whether to scale the coefficiency so that frequency response is unity at either (A) 0 if zeroPass is true or (B) at nyquist if the first passband ends at nyquist, or (C) the center of the first passband. Default is true.
- zeroPass
If true (default), the gain at frequency 0 (ie the "DC gain") is 1, if false, 0.
Filter input data with the specified kernel and options.
Filter input data with the specified kernel and options.
- Value Params
- canFilter
(implicit delegate to perform filtering on specific Input data types)
- data
data to be filtered
- kernel
filter kernel (argument of DenseVector[Double] will specify a FIR kernel with specified values).
- overhang
whether to have overhanging values. See breeze.signal.OptOverhang
- padding
how to pad the values. See breeze.signal.OptPadding
Bandpass filter the input data.
Bandpass filter the input data.
- Value Params
- canFilterBPBS
(implicit delegate to perform filtering on specific Input data types)
- data
data to be filtered
- kernelDesign
currently only supports OptKernelType.Firwin. See breeze.signal.OptDesignMethod
- omegas
sequence of two filter band parameters, in units of the Nyquist frequency, or in Hz if the sampleRate is set to a specific value other than 2d.
- overhang
whether to have overhanging values when filtering. See breeze.signal.OptOverhang
- padding
how to pad the values when filtering. See breeze.signal.OptPadding
- sampleRate
default of 2.0 means that the Nyquist frequency is 1.0
- taps
number of taps to use (default = 512)
Bandstop filter the input data.
Bandstop filter the input data.
- Value Params
- canFilterBPBS
(implicit delegate to perform filtering on specific Input data types)
- data
data to be filtered
- kernelDesign
currently only supports OptKernelType.Firwin. See breeze.signal.OptDesignMethod
- omegas
sequence of two filter band parameters, in units of the Nyquist frequency, or in Hz if the sampleRate is set to a specific value other than 2d.
- overhang
whether to have overhanging values when filtering. See breeze.signal.OptOverhang
- padding
how to pad the values when filtering. See breeze.signal.OptPadding
- sampleRate
default of 2.0 means that the Nyquist frequency is 1.0
- taps
number of taps to use (default = 512)
Highpass filter the input data.
Highpass filter the input data.
- Value Params
- canFilterLPHP
(implicit delegate to perform filtering on specific Input data types)
- data
data to be filtered
- kernelDesign
currently only supports OptKernelType.Firwin. See breeze.signal.OptDesignMethod
- omega
cutoff frequency, in units of the Nyquist frequency, or in Hz if the sampleRate is set to a specific value other than 2d.
- overhang
whether to have overhanging values when filtering. See breeze.signal.OptOverhang
- padding
how to pad the values when filtering. See breeze.signal.OptPadding
- sampleRate
default of 2.0 means that the Nyquist frequency is 1.0
- taps
number of taps to use (default = 512)
Lowpass filter the input data.
Lowpass filter the input data.
- Value Params
- canFilterLPHP
(implicit delegate to perform filtering on specific Input data types)
- data
data to be filtered
- kernelDesign
currently only supports OptKernelType.Firwin. See breeze.signal.OptDesignMethod
- omega
cutoff frequency, in units of the Nyquist frequency, or in Hz if the sampleRate is set to a specific value other than 2d.
- overhang
whether to have overhanging valueswhen filtering. See breeze.signal.OptOverhang
- padding
how to pad the values when filtering. See breeze.signal.OptPadding
- sampleRate
default of 2.0 means that the Nyquist frequency is 1.0
- taps
number of taps to use (default = 512)
Median filter the input data.
Median filter the input data.
- Value Params
- overhang
specify OptOverhang.PreserveLength (default) or OptOverhang.None (result will be (windowLength -1) shorter) for OptOverhang.PreserveLength, the edges will feature symmetrical odd windows of increasing size, ie ( median( {0} ), median( {0, 1, 2} ), median( {0, 1, 2, 3, 4} )... )
- 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
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 Params
- dt
time step (CAUTION: 1.0/fs; specify default of -1 if using fs)
- fs
sampling frequency (1.0/dt; specify default of -1 if using dt)
- shifted
whether to return fourierShift'ed frequencies, default=false
- windowLength
window length of discrete Fourier transform
Return the padded fast haar transformation of a DenseVector or DenseMatrix. 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 DenseVector or DenseMatrix. 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 Params
- canHaarTransform
implicit delegate which is used for implementation. End-users should not use this argument.
- v
DenseVector or DenseMatrix to be transformed.
- Returns
DenseVector or DenseMatrix
- See also
Returns the inverse fast haar transform for a DenseVector or DenseMatrix.
Returns the inverse fast haar transform for a DenseVector or DenseMatrix.