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package cdp

Type Members

final case class ARCWindow(in: D, size: I, lo: D = 0.0, hi: D = 1.0, lag: D = 0.96) extends SingleOut[Double] with Product with Serializable
Automatic range control UGen.
Automatic range control UGen. It traces the range of a windowed input signal.
If all values of a window are the same, the lo value is output.
in
signal to adjust
size
window size of input
lo
desired lower margin of output
hi
desired upper margin of output
lag
lag or feedback coefficient
final case class AffineTransform2D(in: D, widthIn: I, heightIn: I, widthOut: I = 0, heightOut: I = 0, m00: D, m10: D, m01: D, m11: D, m02: D, m12: D, wrap: I = 1, rollOff: D = 0.86, kaiserBeta: D = 7.5, zeroCrossings: I = 15) extends SingleOut[Double] with Product with Serializable
An affine transformation UGen for image rotation, scaling, translation, shearing.
An affine transformation UGen for image rotation, scaling, translation, shearing. It uses either a sinc-based band-limited resampling algorithm, or bicubic interpolation, depending on the zeroCrossings parameter.
All window defining parameters (widthIn, heightIn, widthOut, heightOut) are polled once per matrix. All matrix and filter parameters are polled one per output pixel.
in
the signal to resample
widthIn
the width (number of columns) of the input matrix
heightIn
the height (number of rows) of the input matrix
widthOut
the width (number of columns) of the output matrix. the special value zero (default) means it is the same as widthIn.
heightOut
the height (number of rows) of the output matrix. the special value zero (default) means it is the same as heightIn.
m00
coefficient of the first column of the first row (scale-x)
m10
coefficient of the first column of the second row (shear-y)
m01
coefficient of the second column of the first row (shear-x)
m11
coefficient of the second column of the second row (scale-y)
m02
coefficient of the third column of the first row (translate-x)
m12
coefficient of the third column of the second row (translate-y)
wrap
if non-zero, wraps coordinates around the input images boundaries. TODO: currently wrap = 0 is broken if using sinc interpolation!
rollOff
the FIR anti-aliasing roll-off width. Between zero and one.
kaiserBeta
the FIR windowing function's parameter
zeroCrossings
the number of zero-crossings in the truncated and windowed sinc FIR. If zero, algorithm uses bicubic interpolation instead.
See also
ScanImage
final case class ArithmSeq[A, L](start: GE[A] = 0, step: GE[A] = 1, length: GE[L] = Long.MaxValue)(implicit num: Num[A], numL: NumInt[L]) extends SingleOut[A] with ProductWithAdjuncts with Product with Serializable
A UGen that produces an arithmetic sequence of values.
A UGen that produces an arithmetic sequence of values. If both start and step are of integer type (Int or Long), this produces an integer output, otherwise it produces a floating point output.
E.g. ArithmSeq(start = 1, step = 2) will produce a series 1, 3, 5, 7, ....
start
the first output element
step
the amount added to each successive element
length
the number of elements to output
final case class AudioFileIn(file: URI, numChannels: Int) extends MultiOut[Double] with Product with Serializable
final case class AudioFileOut(in: D, file: URI, spec: AudioFileSpec) extends SingleOut[Long] with Product with Serializable
A UGen that reads in an audio file.
A UGen that reads in an audio file. The output signal is the monotonously increasing number of frames written, which can be used to monitor progress or determine the end of the writing process. The UGen keeps running until the in signal ends.
in
the signal to write.
file
the file to write to
spec
the spec for the audio file, including numbers of channels and sample-rate
final case class BinaryOp[A, B, C](op: Op[A, B, C], a: GE[A], b: GE[B]) extends SingleOut[C] with Product with Serializable
A binary operator UGen, for example two sum or multiply two signals.
A binary operator UGen, for example two sum or multiply two signals. The left or a input is "hot", i.e. it keeps the UGen running, while the right or b input may close early, and the last value will be remembered.
op
the operator (e.g. BinaryOp.Times)
a
the left operand which determines how long the UGen computes
b
the right operand.
final case class Biquad(in: D, b0: D = 0.0, b1: D = 0.0, b2: D = 0.0, a1: D = 0.0, a2: D = 0.0) extends SingleOut[Double] with Product with Serializable
A second order filter section (biquad) UGen.
A second order filter section (biquad) UGen. Filter coefficients are given directly rather than calculated for you. The formula is equivalent to:
```
y(n) = b0 * x(n) + b1 * x(n-1) + b2 * x(n-2) - a1 * y(n-1) - a2 * y(n-2)
```
where x is in, and y is the output. Note the naming and signum of the coefficients differs from SuperCollider's SOS.
final case class Bleach(in: D, filterLen: I = 256, feedback: D = 0.001, filterClip: D = 8.0) extends SingleOut[Double] with Product with Serializable
An adaptive filter UGen capable of removing resonances from an input signal.
An adaptive filter UGen capable of removing resonances from an input signal. It does so by continuously maintaining a filter kernel inverse to the input signal. The output is the continuous application of the current filter to the input, thus "whitening" the signal.
One can obtain a "colored" signal by subtracting the output of this UGen from the input signal.
in
the signal to analyze and to filter
filterLen
the length of the FIR filter
feedback
the adaptation speed of the filter. Lower values mean it takes longer for the filter to adjust to input signal, higher values mean it is faster changing according to the input. Typical values are in the order of -60 dB.
filterClip
a clipping threshold for the filter coefficients, to avoid that a filter "explodes". Each filter coefficient is clipped to have a magnitude no larger than the given clip value.
See also
BleachKernel
final case class BleachKernel(in: D, filterLen: I = 256, feedback: D = 0.001, filterClip: D = 8.0) extends SingleOut[Double] with Product with Serializable
An UGen that outputs an adaptive filter kernel based on an input signal.
An UGen that outputs an adaptive filter kernel based on an input signal. The finite impulse response kernel can be used to remove resonances from the input signal. This UGen operates like the Bleach UGen, but instead of filtering the input, it outputs the raw filter coefficients. For each input sample, filterLen output samples will be generated, representing the current kernel.
If one is only interested in a "final" kernel, one can just TakeRight the last filterLen output samples.
in
the signal to analyze
filterLen
the length of the FIR filter
feedback
the adaptation speed of the filter. Lower values mean it takes longer for the filter to adjust to input signal, higher values mean it is faster changing according to the input. Typical values are in the order of -60 dB.
filterClip
a clipping threshold for the filter coefficients, to avoid that a filter "explodes". Each filter coefficient is clipped to have a magnitude no larger than the given clip value.
See also
Bleach
final case class Blobs2D(in: D, width: I, height: I, thresh: D = 0.3, pad: I = 0) extends MultiOut[Any] with Product with Serializable
A blob detection UGen.
A blob detection UGen. It is based on the meta-balls algorithm by Julien Gachadoat (http://www.v3ga.net/processing/BlobDetection/).
Currently, we output four channels: - 0 - numBlobs - flushes for each window one element with the number of blobs detected - 1 - bounds - quadruplets of xMin, xMax, yMin, yMax - for each blob - 2 - numVertices - the number of vertices (contour coordinates) for each blob - 3 - vertices - tuples of x and y for the vertices of each blob
in
the image(s) to analyse
width
the width of the image
height
the height of the image
thresh
the threshold for blob detection between zero and one
pad
size of "border" to put around input matrices. If greater than zero, a border of that size is created internally, filled with the threshold value
final case class BufferDisk[A](in: GE[A])(implicit num: Num[A]) extends SingleOut[A] with ProductWithAdjuncts with Product with Serializable
final case class BufferMemory[A](in: GE[A], size: I) extends SingleOut[A] with Product with Serializable
Inserts a buffer into the stream of up to size frames.
final case class ChannelProxy[A](elem: GE[A], index: Int) extends GE.Lazy[A] with Product with Serializable
Straight outta ScalaCollider.
final case class Clip[A](in: GE[A], lo: GE[A], hi: GE[A])(implicit num: Num[A]) extends SingleOut[A] with ProductWithAdjuncts with Product with Serializable
final case class CombN(in: D, maxLength: I, length: I, decay: D = 44100.0) extends SingleOut[Double] with Product with Serializable
A comb filter delay line UGen with no interpolation.
A comb filter delay line UGen with no interpolation.
in
the signal to filter
maxLength
the maximum delay time in sample frames. this is only read once and constrains the values of length
length
the delay time in sample frames. The value can be obtained from the delay time in seconds by multiplication with the sampling rate.
decay
the feedback's -60 dB decay time in sample frames. The value can be obtained from the decay time in seconds by multiplication with the sampling rate. Negative numbers are allowed and mean that the feedback signal is phase inverted.
final case class Complex1FFT(in: D, size: I, padding: I = 0) extends FFTFullUGen with Product with Serializable
final case class Complex1IFFT(in: D, size: I, padding: I = 0) extends FFTFullUGen with Product with Serializable
final case class Complex2FFT(in: D, rows: I, columns: I = 0) extends FFT2FullUGen with Product with Serializable
Warning: window parameter modulation is currently not working correctly (issue #30)
final case class Complex2IFFT(in: D, rows: I, columns: I = 0) extends FFT2FullUGen with Product with Serializable
Warning: window parameter modulation is currently not working correctly (issue #30)
final case class ComplexBinaryOp(op: Int, a: D, b: D) extends SingleOut[Double] with Product with Serializable
Binary operation UGen between two complex signals.
Binary operation UGen between two complex signals. Complex signals are represented by interleaved real and imaginary components.
Note: Unlike the "normal" real-valued BinaryOp, this UGen terminates as soon as one of the two inputs a or b terminates.
final case class ComplexUnaryOp(op: Int, in: D) extends SingleOut[Double] with Product with Serializable
final case class Concat[A](a: GE[A], b: GE[A]) extends SingleOut[A] with Product with Serializable
Concatenates two signals.
sealed trait Const[A1] extends UGenIn[A1] with StreamIn
A scalar constant used as an input to a UGen.
final case class ConstB(value: Boolean) extends Const[Boolean] with StreamIn with Product with Serializable
final case class ConstD(value: Double) extends Const[Double] with DoubleLike with Product with Serializable
final case class ConstI(value: Int) extends Const[Int] with IntLike with Product with Serializable
final case class ConstL(value: Long) extends Const[Long] with LongLike with Product with Serializable
final case class ConstQ(in: D, fftSize: I, minFreqN: D = 0.0008, maxFreqN: D = 0.4096, numBands: I = 432) extends SingleOut[Double] with Product with Serializable
A UGen that performs a constant Q spectral analysis, using an already FFT'ed input signal.
A UGen that performs a constant Q spectral analysis, using an already FFT'ed input signal. For each input window, the output signal consists of numBands filter output values. These values are squared, since further processing might need to decimate them again or take the logarithm. Otherwise the caller needs to add .sqrt to get the actual filter outputs.
The default settings specify a range of nine octaves and numBands = 432 thus being 48 bands per octave. At 44.1 kHz, the default minimum frequency would be 35.28 Hz, the maximum would be 18063.36 Hz.
in
the input signal which has already been windowed and FFT'ed using Real1FFT in mode 0 or 2. The windows should have been rotated prior to the FFT so that the time signal starts in the middle of the window and then wraps around.
fftSize
the fft size which corresponds with the window size of the input
minFreqN
the normalized minimum frequency (frequency in Hz, divided by the sampling rate). should be greater than zero and less than or equal to 0.5.
maxFreqN
the normalized maximum frequency (frequency in Hz, divided by the sampling rate). should be greater than zero and less than or equal to 0.5.
numBands
the number of bands or kernels to apply, spreading them evenly (logarithmically) across the range from minimum to maximum frequency.
final case class ControlBlockSize() extends SingleOut[Int] with Product with Serializable
A scalar information UGen that reports the control block size.
final case class Convolution(in: D, kernel: D, kernelLen: I, kernelUpdate: B = false, mode: I = 0) extends SingleOut[Double] with Product with Serializable
A UGen that convolves an input signal with a fixed or changing filter kernel.
A UGen that convolves an input signal with a fixed or changing filter kernel. kernelUpdate is read synchronous with in, and while it is zero the most recent kernel is reused (making it possible to use more efficient calculation in the frequency domain). When kernelUpdate becomes 1, a new kernel is polled.
For example, if you want to update the kernel every ten sample frames, then kernelUpdate could be given as Metro(10).tail or Metro(10, 1). If the kernel is never updated, then kernelUpdate could be given as constant zero. If a new kernel is provided for each input sample, the value could be given as constant one.
in
the signal to be filtered
kernel
the filter kernel. This is read in initially and when kernelUpdate is one.
kernelLen
the filter length in sample frames. One value is polled whenever a new kernel is required.
kernelUpdate
a gate value read synchronous with in, specifying whether a new kernel is to be read in (non-zero) after the next frame, or if the previous kernel is to be reused (zero, default).
mode
currently unused; leave at zero
final case class DC[A](in: GE[A]) extends SingleOut[A] with Product with Serializable
Creates a constant infinite signal.
final case class DCT_II(in: D, size: I, numCoeffs: I, zero: I = 0) extends SingleOut[Double] with Product with Serializable
A UGen for type II discrete cosine transform.
A UGen for type II discrete cosine transform.
in
input signal
size
input signal window size
numCoeffs
number of coefficients output
zero
if zero (default), the zero'th coefficient is skipped in the output, if greater than zero, the zero'th coefficient is included. In any case, the output window size is numCoeffs.
final case class DEnvGen[L](levels: D, lengths: GE[L], shapes: I = 1, curvatures: D = 0.0)(implicit numL: NumInt[L]) extends SingleOut[Double] with ProductWithAdjuncts with Product with Serializable
An envelope generator UGen similar to SuperCollider's DemandEnvGen.
An envelope generator UGen similar to SuperCollider's DemandEnvGen.
For each parameter of the envelope (levels, durations and shapes), values are polled every time a new segment starts. The UGen keeps running as long as level inputs are provided. If any of the other inputs terminate early, their last values will be used for successive segments (e.g. one can use a constant length or shape).
Note that as a consequence of having a "hot" levels input, the target level of the last segment may not be reached, as the UGen would terminate one sample earlier. As a workaround one can duplicate the last level value with a length of 1.
The difference to DemandEnvGen is that this UGen does not duplicate functionality in the form of levelScale, levelBias, timeScale, and it does not provide reset and gate functionality. Durations are given as lengths in sample frames.
case class DebugAnyPromise[A](in: GE[A], p: Promise[IndexedSeq[Any]]) extends ZeroOut with Product with Serializable
A debugging UGen that completes a promise with the input values it sees.
A debugging UGen that completes a promise with the input values it sees.
in
the signal to monitor
case class DebugDoublePromise(in: D, p: Promise[IndexedSeq[Double]]) extends ZeroOut with Product with Serializable
A debugging UGen that completes a promise with the input values it sees.
A debugging UGen that completes a promise with the input values it sees.
in
the signal to monitor
case class DebugIntPromise(in: I, p: Promise[IndexedSeq[Int]]) extends ZeroOut with Product with Serializable
A debugging UGen that completes a promise with the input values it sees.
A debugging UGen that completes a promise with the input values it sees.
in
the signal to monitor
case class DebugSink[A](in: GE[A]) extends ZeroOut with Product with Serializable
A debugging UGen that installs a persistent no-op sink, allowing the in UGen to remain in the graph even if it does not have a side-effect and it is not connected to any other graph element.
A debugging UGen that installs a persistent no-op sink, allowing the in UGen to remain in the graph even if it does not have a side-effect and it is not connected to any other graph element.
in
the element to keep inside the graph
final case class DebugThrough[A](in: GE[A], label: String) extends SingleOut[A] with Product with Serializable
A UGen that passes through its input, and upon termination prints the number of frames seen.
A UGen that passes through its input, and upon termination prints the number of frames seen.
For convenience, import DebugThrough._ allows to write
```
val sin = SinOsc(freq) <| "my-sin"
```
in
the input to be pulled.
label
an identifying label to prepend to the printing.
final case class DelayN[A](in: GE[A], maxLength: I, length: I) extends SingleOut[A] with Product with Serializable
final case class DetectLocalMax[A](in: GE[A], size: I)(implicit ord: Ord[A]) extends SingleOut[Boolean] with ProductWithAdjuncts with Product with Serializable
A UGen that outputs triggers for local maxima within a sliding window.
A UGen that outputs triggers for local maxima within a sliding window. If multiple local maxima occur within the current window, only the one with the largest value will cause the trigger.
By definition, the first and last value in the input stream cannot qualify for local maxima.
If a local maximum spreads across multiple samples (adjacent values are the same), the location of the last sample is reported.
in
the signal to analyze for local maxima
size
the sliding window size. Each two emitted triggers are spaced apart at least by size frames.
final case class Differentiate[A](in: GE[A])(implicit num: Num[A]) extends SingleOut[A] with ProductWithAdjuncts with Product with Serializable
A UGen that outputs the differences between adjacent input samples.
A UGen that outputs the differences between adjacent input samples.
The length of the signal is the length of the input. The first output will be the first input (internal x1 state is zero).
final case class Distinct[A](in: GE[A])(implicit eq: Eq[A]) extends SingleOut[A] with ProductWithAdjuncts with Product with Serializable
A UGen that collects distinct values from the input and outputs them in the original order.
A UGen that collects distinct values from the input and outputs them in the original order.
Note: Currently keeps all seen values in memory.
final case class Done[A](in: GE[A]) extends SingleOut[Boolean] with Product with Serializable
A UGen that outputs a single frame of 1 when the input is finished.
final case class Drop[A, L](in: GE[A], length: GE[L])(implicit numL: NumInt[L]) extends SingleOut[A] with ProductWithAdjuncts with Product with Serializable
final case class DropRight[A, L](in: GE[A], length: GE[L])(implicit numL: NumInt[L]) extends SingleOut[A] with ProductWithAdjuncts with Product with Serializable
final case class DropWhile[A](in: GE[A], p: B) extends SingleOut[A] with Product with Serializable
final case class Elastic[A](in: GE[A], num: I = 1) extends SingleOut[A] with Product with Serializable
Inserts a buffer into the stream of num blocks.
final case class ElseGE[A](pred: IfOrElseIfThen[GE[A]], branch: Graph, result: GE[A]) extends ElseLike[GE[A]] with GE.Lazy[A] with Product with Serializable
final case class ElseIf[+A](pred: IfOrElseIfThen[A], cond: B) extends Product with Serializable
final case class ElseIfThen[+A](pred: IfOrElseIfThen[A], cond: B, branch: Graph, result: A) extends IfThenLike[A] with ElseOrElseIfThen[A] with Product with Serializable
sealed trait ElseLike[+A] extends ElseOrElseIfThen[A]
sealed trait ElseOrElseIfThen[+A] extends Then[A]
final case class ElseUnit(pred: IfOrElseIfThen[Any], branch: Graph) extends ElseLike[Any] with Expander[Unit] with Product with Serializable
final case class Empty[A]() extends SingleOut[A] with Product with Serializable
A single channel UGen of zero length.
final case class ExpExp(in: D, inLow: D, inHigh: D, outLow: D, outHigh: D) extends SingleOut[Double] with Product with Serializable
final case class ExpLin(in: D, inLow: D, inHigh: D, outLow: D, outHigh: D) extends SingleOut[Double] with Product with Serializable
sealed trait FFT2FullUGen extends SingleOut[Double]
sealed trait FFT2HalfUGen extends SingleOut[Double]
sealed trait FFTFullUGen extends SingleOut[Double]
sealed trait FFTHalfUGen extends SingleOut[Double]
final case class Feed[A](init: GE[A]) extends GE.Lazy[A] with Product with Serializable
First stage of a feedback process.
First stage of a feedback process. This UGen outputs the signal eventually fed to it via .back.
final case class FilterSeq[A](in: GE[A], gate: B) extends SingleOut[A] with Product with Serializable
A UGen that filters its input, retaining only those elements in the output sequence for which the predicate gate holds.
A UGen that filters its input, retaining only those elements in the output sequence for which the predicate gate holds.
You can think of it as Gate but instead of outputting zeroes when the gate is closed, no values are output at all. Consequently, the length of the output stream will differ from the length of the input stream.
See also
Gate
final case class Flatten[A](elem: GE[A]) extends GE.Lazy[A] with Product with Serializable
A graph element that flattens the channels from a nested multi-channel structure.
A graph element that flattens the channels from a nested multi-channel structure.
elem
the element to flatten
final case class Fold[A](in: GE[A], lo: GE[A], hi: GE[A])(implicit num: Num[A]) extends SingleOut[A] with ProductWithAdjuncts with Product with Serializable
final case class FoldCepstrum(in: D, size: I, crr: D, cri: D, clr: D, cli: D, ccr: D, cci: D, car: D, cai: D) extends SingleOut[Double] with Product with Serializable
We operate on a complex cepstrum (size is the number of complex frames).
We operate on a complex cepstrum (size is the number of complex frames). We distinguish a left L (causal) and right R (anti-causal) half. The formulas then are
```
reL_out = crr * reL + ccr * reR
reR_out = clr * reR + car * reL
imL_out = cri * imL + cci * imR
imR_out = cli * imR + cai * imL
```
Note that causal and anti-causal are misnamed in the coefficient.
For example, to make the signal causal for minimum phase reconstruction: We add the conjugate anti-causal (right) part to the causal (left) part: crr = 1, ccr = 1, cri = 1, cci = -1 and clear the anti-causal (right) part: clr = 0, car = 0, cli = 0, cai = 0 (you can just call FoldCepstrum.minPhase for this case)
final case class Fourier[L](in: D, size: GE[L], padding: GE[L] = 0, dir: I = 1, mem: I = 131072)(implicit num: NumInt[L]) extends SingleOut[Double] with ProductWithAdjuncts with Product with Serializable
Disk-buffered (large) Fourier transform.
Disk-buffered (large) Fourier transform. Output windows will have a complex size of (size + padding).nextPowerOfTwo
in
input signal to transform. This must be complex (Re, Im interleaved)
size
the (complex) window size
padding
the (complex) zero-padding size for each window
dir
the direction is 1 for forward and -1 for backward transform. other numbers will do funny things.
mem
the amount of frames (chunk size) to buffer in memory. this should be a power of two.
final case class Frames[A](in: GE[A]) extends SingleOut[Long] with Product with Serializable
A UGen that generates a signal that incrementally counts the frames of its input.
A UGen that generates a signal that incrementally counts the frames of its input. The first value output will be 1, and the last will correspond to the number of frames seen, i.e. Length(in).
final case class Gate[A](in: GE[A], gate: B) extends SingleOut[A] with Product with Serializable
A UGen that passes through its input while the gate is open, and outputs zero while the gate is closed.
A UGen that passes through its input while the gate is open, and outputs zero while the gate is closed.
See also
FilterSeq
Latch
final case class GenWindow[L](size: GE[L], shape: I, param: D = 0.0)(implicit numL: NumInt[L]) extends SingleOut[Double] with ProductWithAdjuncts with Product with Serializable
A repeated window generator UGen.
A repeated window generator UGen. It repeats the same window again and again (unless parameters are modulated). The parameters are demand-rate, polled once per window.
size
the window size
shape
the identifier of the window shape, such as GenWindow.Hann.
param
parameter used by some window shapes, such as GenWindow.Kaiser
final case class GeomSeq[A, L](start: GE[A] = 1, grow: GE[A] = 2, length: GE[L] = Long.MaxValue)(implicit num: Num[A], numL: NumInt[L]) extends SingleOut[A] with ProductWithAdjuncts with Product with Serializable
A UGen that produces a geometric sequence of values.
A UGen that produces a geometric sequence of values. If both start and grow are of integer type (Int or Long), this produces an integer output, otherwise it produces a floating point output.
E.g. GeomSeq(start = 1, grow = 2) will produce a series 1, 2, 4, 8, ....
start
the first output element
grow
the multiplicative factor by which each successive element will grow
length
the number of elements to output
final case class GimpSlur(in: D, width: I, height: I, kernel: D, kernelWidth: I, kernelHeight: I, repeat: I = 1, wrap: I = 0) extends SingleOut[Double] with Product with Serializable
A UGen similar to GIMP's Slur image filter.
A UGen similar to GIMP's Slur image filter. Instead of a hard-coded kernel, the probability table must be provided as a separate input. The kernel width and height should be odd, so that the kernel is considered to be symmetric around each input image's pixel. If they are odd, the centre corresponds to integer divisions kernelWidth/2 and kernelHeight/2.
in
image input
width
image width
height
image height
kernel
normalized and integrated probability table. Like the image, the cells are read horizontally first, every widthKernel cell begins a new cell. The cell probabilities must have been integrated from first to last, and must be normalized so that the last cell value equals one. A new kernel signal is read once per input image. (If the signal ends, the previous kernel will be used again).
kernelWidth
width of the kernel signal. Read once per input image.
kernelHeight
height of the kernel signal. Read once per input image.
repeat
number of recursive application of the displacement per image. Read once per input image.
wrap
if great than zero, wraps pixels around the image bounds, otherwise clips.
final case class GramSchmidtMatrix(in: D, rows: I, columns: I, normalize: I = 0) extends SingleOut[Double] with Product with Serializable
A UGen that orthogonalizes an input matrix using the stabilized Gram-Schmidt algorithm.
A UGen that orthogonalizes an input matrix using the stabilized Gram-Schmidt algorithm. It processes the row vectors per matrix by making them orthogonal to one another, optionally also orthonormal.
in
the input matrices
rows
the number of rows of the input
columns
the number of columns of the input
final case class HPF(in: D, freqN: D = 0.02) extends SingleOut[Double] with Product with Serializable
A second order high-pass filter UGen.
A second order high-pass filter UGen. It implements the same type of filter as the SuperCollider equivalent.
in
the signal to filter
freqN
the normalized cut-off frequency (frequency in Hertz divided by sampling rate)
final case class Hash[A](in: GE[A]) extends SingleOut[Long] with Product with Serializable
A UGen that produces a continuous hash value, useful for comparisons and debugging applications.
A UGen that produces a continuous hash value, useful for comparisons and debugging applications. The hash is produced using the 64-bit Murmur 2 algorithm, however the continuous output is given without "finalising" the hash, and it is not initialized with the input's length (as it is unknown at that stage).
Not yet implemented: Only after the input terminates, one additional sample of finalised hash is given (you can use .last to only retain the final hash).
in
the signal to hash.
final case class Histogram[A](in: GE[A], bins: I, lo: GE[A] = 0, hi: GE[A] = 1, mode: I = 0, reset: B = false)(implicit num: Num[A]) extends SingleOut[Int] with ProductWithAdjuncts with Product with Serializable
A UGen that calculates running histogram of an input signal with given boundaries and bin-size.
A UGen that calculates running histogram of an input signal with given boundaries and bin-size. The bins are divided linearly, if another mapping (e.g. exponential) is needed, it must be pre-applied to the input signal.
Note: currently parameter modulation (bin, lo, hi, mode, reset) is not working correctly.
in
the input signal
bins
the number of bins. this is read at initialization time only or when reset fires
lo
the lowest bin boundary. input values below this value are clipped. this value may be updated (although that is seldom useful).
hi
the highest bin boundary. input values above this value are clipped. this value may be updated (although that is seldom useful).
mode
if 0 (default), outputs only after in has finished, if 1 outputs the entire histogram for every input sample.
reset
when greater than zero, resets the count.
See also
NormalizeWindow
final case class If(cond: B) extends Product with Serializable
Beginning of a conditional block.
Beginning of a conditional block.
cond
the condition, which is treated as a boolean with zero being treated as false and non-zero as true. Note that multi-channel expansion does not apply here, because the branches may have side-effects which are not supposed to be repeated across channels. Instead, if cond expands to multiple channels, they are combined using logical | (OR).
See also
IfThen
sealed trait IfOrElseIfThen[+A] extends Then[A]
final case class IfThen[+A](cond: B, branch: Graph, result: A) extends IfThenLike[A] with Product with Serializable
A side effecting conditional block.
A side effecting conditional block. To turn it into a full if-then-else construction, call Else or ElseIf.
See also
Else
ElseIf
sealed trait IfThenLike[+A] extends IfOrElseIfThen[A] with Expander[Unit]
final case class ImageFileIn(file: URI, numChannels: Int) extends MultiOut[Double] with Product with Serializable
final case class ImageFileOut(in: GE[Double], file: URI, spec: Spec) extends ZeroOut with Product with Serializable
trait ImageFilePlatform extends AnyRef
final case class ImageFileSeqIn(template: URI, numChannels: Int, indices: GE[Int]) extends MultiOut[Double] with Product with Serializable
Reads a sequence of images, outputting them directly one after the other, determining their file names by formatting a template with a numeric argument given through indices.
Reads a sequence of images, outputting them directly one after the other, determining their file names by formatting a template with a numeric argument given through indices.
template
a file which contains a single placeholder for java.util.Formatter syntax, such as %d to insert an integer number. Alternatively, if the file name does not contain a % character but a digit or a sequence of digits, those digits will be replaced by %d to produce a valid template. Therefore, if the template is foo-123.jpg and the indices contain 4 and 5, then the UGen will read the images foo-4 and foo-5 (the placeholder 123 is irrelevant).
final case class ImageFileSeqOut(in: D, template: URI, spec: Spec, indices: I) extends ZeroOut with Product with Serializable
Writes a sequence of images, taken their data one by one from the input in, and writing them to individual files, determining their file names by formatting a template with a numeric argument given through indices.
Writes a sequence of images, taken their data one by one from the input in, and writing them to individual files, determining their file names by formatting a template with a numeric argument given through indices.
template
a file which contains a single placeholder for java.util.Formatter syntax, such as %d to insert an integer number. Alternatively, if the file name does not contain a % character but a digit or a sequence of digits, those digits will be replaced by %d to produce a valid template. Therefore, if the template is foo-123.jpg and the indices contain 4 and 5, then the UGen will write the images foo-4 and foo-5 (the placeholder 123 is irrelevant).
final case class Impulse(freqN: D, phase: D = 0.0) extends SingleOut[Boolean] with Product with Serializable
Impulse (repeated dirac) generator.
Impulse (repeated dirac) generator. For a single impulse that is never repeated, use zero.
freqN
normalized frequency (reciprocal of frame period)
phase
phase offset in cycles (0 to 1).
final case class Indices[A](in: GE[A]) extends SingleOut[Long] with Product with Serializable
A UGen that generates a signal that incrementally counts the frames of its input.
A UGen that generates a signal that incrementally counts the frames of its input. The first value output will be 0, and the last will correspond to the number of frames seen minutes one.
final case class LFSaw(freqN: D, phase: D = 0.0) extends SingleOut[Double] with Product with Serializable
Aliased sawtooth oscillator.
Aliased sawtooth oscillator. Note that the frequency is not in Hertz but the normalized frequency as we do not maintained one global sample rate. For a frequency in Hertz, freqN would be that frequency divided by the assumed sample rate.
Note: Unlike SuperCollider where LFSaw starts at zero for a zero phase, this oscillator begins at -1. To let it start at zero, use a phase of 0.5
freqN
normalized frequency (f/sr). Note: negative values are currently broken.
phase
phase offset from 0 to 1. Note: negative values are currently broken.
final case class LPF(in: D, freqN: D = 0.02) extends SingleOut[Double] with Product with Serializable
A second order low-pass filter UGen.
A second order low-pass filter UGen. It implements the same type of filter as the SuperCollider equivalent.
in
the signal to filter
freqN
the normalized cut-off frequency (frequency in Hertz divided by sampling rate)
final case class Latch[A](in: GE[A], gate: B) extends SingleOut[A] with Product with Serializable
A sample-and-hold UGen.
A sample-and-hold UGen. It passes through its input while the gate is open, and outputs the last held value while the gate is closed.
See also
Gate
final case class LeakDC(in: D, coeff: D = 0.995) extends GE.Lazy[Double] with Product with Serializable
A filter UGen to remove very low frequency content DC offset.
A filter UGen to remove very low frequency content DC offset.
This is a one-pole highpass filter implementing the formula
```
y[n] = x[n] - x[n-1] + coeff * y[n-1]
```
in
input signal to be filtered
coeff
the leak coefficient determines the filter strength. the value must be between zero and one (exclusive) for the filter to remain stable. values closer to one produce less bass attenuation.
final case class Length[A](in: GE[A]) extends SingleOut[Long] with Product with Serializable
Reports the length of the input as a single value one the input has terminated.
final case class Limiter(in: D, attack: I, release: I, ceiling: D = 1.0) extends SingleOut[Double] with Product with Serializable
A UGen that limits the amplitude of its input signal.
A UGen that limits the amplitude of its input signal. Modelled after FScape 1.0 limiter module. N.B.: The UGen outputs a gain control signal, so the input must be multiplied by this signal to obtain the actually limited signal.
in
input signal
attack
the attack duration in frames, as -60 dB point
release
the release duration in frames, as -60 dB point
ceiling
the maximum allowed amplitude
final case class LinExp(in: D, inLow: D, inHigh: D, outLow: D, outHigh: D) extends SingleOut[Double] with Product with Serializable
final case class LinKernighanTSP(init: I, weights: D, size: I, mode: I = 0, timeOut: D = 0.0) extends MultiOut[Any] with Product with Serializable
A UGen that solves the traveling salesman problem (TSP) using the Lin-Kernighan heuristic.
A UGen that solves the traveling salesman problem (TSP) using the Lin-Kernighan heuristic. For each input value of size, a corresponding initial tour and edge weight sequence are read, the tour is optimized and output along with the tour's cost.
Currently, we output two channels: - 0 - tour - the optimized tour - 1 - cost - the cost of the optimized tour, i.e. the sum of its edge weights
init
the initial tour, for example linear or randomized. Should consist of size zero-based vertex indices
weights
the symmetric edge weights, a sequence of length size * (size - 1) / 2, sorted as vertex connections (0,1), (0,2), (0,3), ... (0,size-1), (1,2), (1,3), ... (1,size-1), etc., until (size-2,size-1).
size
for each complete graph, the number of vertices.
mode
currently unused and should remain at the default value of zero.
timeOut
currently unused and should remain at the default value of zero.
final case class LinLin(in: D, inLow: D, inHigh: D, outLow: D, outHigh: D) extends SingleOut[Double] with Product with Serializable
final case class Line[L](start: D, end: D, length: GE[L])(implicit numL: NumInt[L]) extends SingleOut[Double] with ProductWithAdjuncts with Product with Serializable
A line segment generating UGen.
A line segment generating UGen. The UGen terminates when the segment has reached the end.
A line can be used to count integers (in the lower ranges, where floating point noise is not yet relevant), e.g. Line(a, b, b - a + 1) counts from a to b (inclusive).
start
starting value
end
ending value
length
length of the segment in sample frames
final case class Loudness(in: D, sampleRate: D, size: I, spl: D = 70, diffuse: I = 1) extends SingleOut[Double] with Product with Serializable
A loudness measurement UGen, using Zwicker bands.
A loudness measurement UGen, using Zwicker bands. One value in Phon per window is output.
The original algorithm outputs a minimum value of 3.0. This is still the case, but if a window is entirely silent (all values are zero), the output value is also 0.0. Thus one may either distinguish between these two cases, or just treat output value of <= 3.0 as silences.
in
the signal to analyse
sampleRate
sample rate of the input signal
size
the window size for which to calculate values
spl
the reference of 0 dBFS in decibels
diffuse
whether to assume diffuse field (1) or free field (0)
final case class Masking2D(fg: D, bg: D, rows: I, columns: I, threshNoise: D, threshMask: D, blurRows: I, blurColumns: I) extends SingleOut[Double] with Product with Serializable
final case class MatchLen[A, B](in: GE[A], ref: GE[B]) extends SingleOut[A] with Product with Serializable
A UGen that extends or truncates its first argument to match the length of the second argument.
A UGen that extends or truncates its first argument to match the length of the second argument. If in is shorter than ref, it will be padded with the zero element of its number type. If in is longer, the trailing elements will be dropped.
final case class MatrixInMatrix[A](in: GE[A], rowsOuter: I, columnsOuter: I, rowsInner: I, columnsInner: I, rowStep: I = 1, columnStep: I = 1, mode: I = 0) extends SingleOut[A] with Product with Serializable
Note: mode is not yet implemented.
final case class MatrixOutMatrix[A](in: GE[A], rowsInner: I, columnsInner: I, columnsOuter: I, rowOff: I = 0, columnOff: I = 0, rowNum: I = 1, columnNum: I = 1) extends SingleOut[A] with Product with Serializable
A UGen that stitches together sequences of sub-matrices to a larger matrix.
A UGen that stitches together sequences of sub-matrices to a larger matrix. The matrix dimensions and offsets are updated per "matrix chunk" which is are columnsOuter/columnNum input matrices of size rowsInner * columnsInner. In other words, the UGen has no intrinsic knowledge of the height of the output matrix.
For example, if the input matrices are of size (5, 6) (rows, columns), and we want to assemble the cells (1, 1), (1, 2), (1, 3), (2, 1), (2, 2), (2, 3), that is copped matrices of size (2, 3) beginning at the second row and second column, and we want the outer matrix to have 9 columns, so that each three input matrices appear horizontally next to each other, the settings would be rowsInner = 5, columnsInner = 6, columnsOuter = 9, rowOff = 1, columnOff = 1, rowNum = 2, columnNum = 3.
For more complex behaviour, such as skipping particular rows or columns, ScanImage can be used.
in
the sequence of smaller matrices
rowsInner
height of the input matrices
columnsInner
width of input matrices
columnsOuter
width of the output matrix. Must be an integer multiple of columnNum.
rowOff
offset in rows within the input matrices, where copying to the output matrix begins
columnOff
offset in columns within the input matrices, where copying to the output matrix begins
rowNum
number of rows to copy from each input matrix
columnNum
number of columns to copy from each input matrix.
final case class MelFilter(in: D, size: I, minFreq: D = 55.0, maxFreq: D = 18000.0, sampleRate: D = 44100.0, bands: I = 42) extends SingleOut[Double] with Product with Serializable
A UGen that maps short-time Fourier transformed spectra to the mel scale.
A UGen that maps short-time Fourier transformed spectra to the mel scale. To obtain the MFCC, one has to take the log of the output of this UGen and decimate it with a DCT.
Example:
```
def mfcc(in: GE) = {
  val fsz  = 1024
  val lap  = Sliding(in, fsz, fsz/2) * GenWindow(fsz, GenWindow.Hann)
  val fft  = Real1FFT(lap, fsz, mode = 1)
  val mag  = fft.complex.mag.max(-80)
  val mel  = MelFilter(mag, fsz/2, bands = 42)
  DCT_II(mel.log, 42, 13, zero = 0)
}
```
in
magnitudes of spectra, as output by Real1FFT(..., mode = 1).complex.abs
size
bands in input spectrum (assumed to be fft-size / 2). lowest band corresponds to DC and highest to (size - 1)/size * sampleRate/2.
minFreq
lower frequency to sample. Will be clipped between zero (inclusive) and Nyquist (exclusive).
maxFreq
upper frequency to sample. Will be clipped between minFreq (inclusive) and Nyquist (exclusive).
bands
number of filter bands output
final case class Metro[A](period: GE[A], phase: GE[A] = 0)(implicit num: NumInt[A]) extends SingleOut[Boolean] with ProductWithAdjuncts with Product with Serializable
Metronome (repeated dirac) generator.
Metronome (repeated dirac) generator. For a single impulse that is never repeated, use a period of zero. Unlike Impulse which uses a frequency and generates fractional phases prone to floating point noise, this is UGen is useful for exact sample frame spacing. Unlike Impulse, the phase cannot be modulated.
period
number of frames between impulses. Zero is short-hand for an infinitely long period. One value is read per output period.
phase
phase offset in frames. One value is read per output period.
final case class NormalizeWindow(in: D, size: I, mode: I = NormalizeWindow.Normalize) extends SingleOut[Double] with Product with Serializable
A UGen that normalizes each input window according to a mode.
A UGen that normalizes each input window according to a mode. It can be used for normalizing the value range or removing DC offset. If the last window is not entirely filled, the output will pad that window always to zero (no matter the normalization mode!)
A window size of 1 should be avoided (and does not really make sense), although the UGen makes efforts to not output NaN values.
in
the input signal
size
the input's window size
mode
0 for normalizing the amplitude to 1, 1 for fitting into the range of 0 to 1, 2 for fitting into the range of -1 to 1, 3 for removing DC (creating a mean of zero).
final case class NumChannels[A](in: GE[A]) extends SingleOut[Int] with Product with Serializable
A graph element that produces an integer with number-of-channels of the input element.
final case class OffsetOverlapAdd[A](in: GE[A], size: I, step: I, offset: I, minOffset: I)(implicit num: Num[A]) extends SingleOut[A] with ProductWithAdjuncts with Product with Serializable
Overlapping window summation with offset (fuzziness) that can be modulated.
final case class OnePole(in: D, coef: D) extends SingleOut[Double] with Product with Serializable
A one pole (IIR) filter UGen.
A one pole (IIR) filter UGen. Implements the formula :
```
out(i) = ((1 - abs(coef)) * in(i)) + (coef * out(i-1))
```
in
input signal to be processed
coef
feedback coefficient. Should be between -1 and +1
final case class OnePoleWindow(in: D, size: I, coef: D) extends SingleOut[Double] with Product with Serializable
A one pole (IIR) filter UGen applied to windowed data.
A one pole (IIR) filter UGen applied to windowed data. Implements the formula :
```
out(i) = ((1 - abs(coef)) * in(i)) + (coef * out(i-1))
```
This filter runs in parallel for all frames of the window (or matrix). That is, in the above formula out is replaced by each window element, and i is the window count.
in
input signal to be processed
size
window size
coef
feedback coefficient. Should be between -1 and +1
final case class OverlapAdd[A](in: GE[A], size: I, step: I)(implicit num: Num[A]) extends SingleOut[A] with ProductWithAdjuncts with Product with Serializable
A UGen that performs overlap-and-add operation on a stream of input windows.
A UGen that performs overlap-and-add operation on a stream of input windows. The size and step parameters are demand-rate, polled once per (input) window.
in
the non-overlapped input
size
the window size in the input
step
the step between successive windows in the output. when smaller than size, the overlapping portions are summed together. Currently step values larger than size are clipped to size. This may change in the future
See also
Sliding
final case class PeakCentroid1D(in: D, size: I, thresh1: D = 0.5, thresh2: D = 0.33, radius: I = 1) extends GE.Lazy[Double] with Product with Serializable
Warning: window parameter modulation is currently not working correctly (issue #30)
final case class PeakCentroid2D(in: D, width: I, height: I, thresh1: D = 0.5, thresh2: D = 0.33, radius: I = 1) extends MultiOut[Double] with Product with Serializable
Warning: window parameter modulation is currently not working correctly (issue #30)
final case class Pearson(x: D, y: D, size: I) extends SingleOut[Double] with Product with Serializable
A UGen that calculates the Pearson product-moment correlation coefficient of two input matrices.
A UGen that calculates the Pearson product-moment correlation coefficient of two input matrices.
x
first matrix
y
second matrix
size
matrix or window size
final case class PenImage(src: D = 1.0, alpha: D = 1.0, dst: D = 0.0, width: I, height: I, x: D = 0, y: D = 0, next: B = false, rule: I = PenImage.SrcOver, op: I = BinaryOp.Plus.id, wrap: I = 0, rollOff: D = 0.86, kaiserBeta: D = 7.5, zeroCrossings: I = 0) extends SingleOut[Double] with Product with Serializable
A UGen that writes the pixels of an image using an x and y input signal.
A UGen that writes the pixels of an image using an x and y input signal. It uses either a sinc-based band-limited resampling algorithm, or bicubic interpolation, depending on the zeroCrossings parameter.
All window defining parameters (width, height) are polled once per matrix. All writing and filter parameters are polled one per output pixel.
src
the source signal's amplitude or "pen color"
alpha
the alpha component of the source signal (0.0 transparent to 1.0 opaque).
dst
the "background" image to draw on. A DC(0.0) can be used, for example, to have a "black" background.
width
the width (number of columns) of the input and output matrix
height
the height (number of rows) of the input and output matrix
x
horizontal position of the dynamic pen signal
y
vertical position of the dynamic pen signal
next
a trigger that causes the UGen to emit the current image and begin a new one. An image of size width * height will be output, and new background data will be read from in.
rule
quasi-Porter-Duff rule id for composition between background (in) and pen foreground. It is assumed that A_r = A_d = 1, and instead of addition we use a custom binary operation op. Where the constrain leads to otherwise identical rules, we flip the operand order (e.g. SrcOver versus SrcAtop).
op
BinaryOp.Op identifier for the operand in the application of the Porter-Duff composition (+ in the standard definition).
wrap
if non-zero, wraps coordinates around the input images boundaries. TODO: currently wrap = 0 is broken if using sinc interpolation!
rollOff
the FIR anti-aliasing roll-off width. Between zero and one.
kaiserBeta
the FIR windowing function's parameter
zeroCrossings
the number of zero-crossings in the truncated and windowed sinc FIR. If zero (default), algorithm uses bicubic interpolation instead.
See also
ScanImage
final case class PitchAC(in: D, sampleRate: D, pitchMin: D = 75.0, pitchMax: D = 600.0, numCandidates: I = 15, silenceThresh: D = 0.03, voicingThresh: D = 0.45, octaveCost: D = 0.01, octaveJumpCost: D = 0.35, voicedUnvoicedCost: D = 0.14) extends GE.Lazy[Double] with Product with Serializable
A graph element that implements Boersma's auto-correlation based pitch tracking algorithm.
A graph element that implements Boersma's auto-correlation based pitch tracking algorithm.
cf. Paul Boersma, ACCURATE SHORT-TERM ANALYSIS OF THE FUNDAMENTAL FREQUENCY AND THE HARMONICS-TO-NOISE RATIO OF A SAMPLED SOUND, Institute of Phonetic Sciences, University of Amsterdam, Proceedings 17 (1993), 97-110
Note that this is currently implemented as a macro, thus being quite slow. A future version might implement it directly as a UGen. Currently stepSize is automatically given, and windowing is fixed to Hann.
final case class PitchesToViterbi(lags: D, strengths: D, numIn: I = 14, peaks: D, maxLag: I, voicingThresh: D = 0.45, silenceThresh: D = 0.03, octaveCost: D = 0.01, octaveJumpCost: D = 0.35, voicedUnvoicedCost: D = 0.03) extends SingleOut[Double] with Product with Serializable
A UGen that takes concurrent pitch tracker paths, and conditions them for the Viterbi algorithm.
A UGen that takes concurrent pitch tracker paths, and conditions them for the Viterbi algorithm. The inputs are typically taken from AutoCorrelationPitches, and from this a suitable add signal is produced to be used in the Viterbi UGen. The output are matrices of size (numIn + 1).squared.
Warning: This is still not thoroughly tested.
lags
pitches given as sample periods, such as returned by AutoCorrelationPitches.
strengths
strengths corresponding to the lags, such as returned by AutoCorrelationPitches.
numIn
number of paths / candidates. to this the unvoiced candidate is added
peaks
the peak amplitude of the underlying input signal, one sample per pitch frame, used for the unvoiced candidate.
maxLag
the maximum lag time, corresponding to the minimum pitch
voicingThresh
threshold for determining whether window is voiced or unvoiced.
silenceThresh
threshold for determining whether window is background or foreground.
octaveCost
weighting factor for low versus high frequency preference.
octaveJumpCost
costs for moving pitches up and down. to match the parameters in Praat, you should multiply the "literature" value by 0.01 * sampleRate / stepSize (typically in the order of 0.25)
voicedUnvoicedCost
cost for transitioning between voiced and unvoiced segments. to match the parameters in Praat, the "literature" value by 0.01 * sampleRate / stepSize (typically in the order of 0.25) see StrongestLocalMaxima see Viterbi
final case class Plot1D[A](in: GE[A], size: I, label: String = "plot")(implicit num: Num[A]) extends ZeroOut with ProductWithAdjuncts with Product with Serializable
Debugging utility that plots 1D "windows" of the input data.
final case class Poll[A](in: GE[A], gate: B, label: String = "poll") extends ZeroOut with Product with Serializable
A UGen that prints snapshots of its input to the console.
A UGen that prints snapshots of its input to the console. Note that arguments have different order than in ScalaCollider!
in
the input to be pulled. If this is a constant, the UGen will close after polling it. This is to prevent a dangling Poll whose trigger is infinite (such as Impulse). If you want to avoid that, you should wrap the input in a DC.
gate
gate that causes the UGen to print a snapshot of the input when open.
label
an identifying label to prepend to the printing.
final case class PriorityQueue[A, B](keys: GE[A], values: GE[B], size: I)(implicit ord: Ord[A]) extends SingleOut[B] with ProductWithAdjuncts with Product with Serializable
A sorting UGen that can be thought of as a bounded priority queue.
A sorting UGen that can be thought of as a bounded priority queue. It keeps all data in memory but limits the size to the top size items. By its nature, the UGen only starts outputting values once the input signal (keys) has finished.
Both inputs are "hot" and the queue filling ends when either of keys or values is finished.
keys
the sorting keys; higher values mean higher priority
values
the values corresponding with the keys and eventually output by the UGen. It is well possible to use the same signal both for keys and values.
size
the maximum size of the priority queue.
final case class Progress(in: D, trig: B, label: String = "render") extends ZeroOut with Product with Serializable
A UGen that contributes to the progress monitoring of a graph.
A UGen that contributes to the progress monitoring of a graph. It is possible to instantiate multiple instances of this UGen, in which cases their individual progress reports will simply be added up (and clipped to the range from zero to one).
in
progress fraction from zero to one
trig
trigger that causes the UGen to submit a snapshot of the progress to the control instance.
label
the label can be used to distinguish the contributions of different progress UGens
final case class ProgressFrames[A, L](in: GE[A], numFrames: GE[L], label: String = "render")(implicit numL: NumInt[L]) extends ZeroOut with ProductWithAdjuncts with Product with Serializable
A variant of progress UGen for the common case where one wants to count the incoming frames of a signal.
A variant of progress UGen for the common case where one wants to count the incoming frames of a signal.
It is possible to instantiate multiple instances of this UGen, in which cases their individual progress reports will simply be added up (and clipped to the range from zero to one).
The progress update is automatically triggered, using a combination where both the progress fraction (0.2%) and elapsed time (100ms) must have increased.
in
signal whose length to monitor
numFrames
the expected length of the input signal
label
the label can be used to distinguish the contributions of different progress UGens
final case class Real1FFT(in: D, size: I, padding: I = 0, mode: I = 0) extends FFTHalfUGen with Product with Serializable
Forward short-time Fourier transform UGen for a real-valued input signal.
Forward short-time Fourier transform UGen for a real-valued input signal. The FFT size is equal to size + padding. The output is a succession of complex half-spectra, i.e. from DC to Nyquist. Depending on mode, the output window size is either size + padding or size + padding + 2.
in
the real signal to transform. If overlapping windows are desired, a Sliding should already have been applied to this signal, as well as multiplication with a window function.
size
the time domain input window size
padding
amount of zero padding for each input window.
mode
packing mode. 0 (default) is standard "packed" mode, whereby the real part of the bin at Nyquist is stored in the imaginary slot of the DC. This mode allows perfect reconstruction with a Real1IFFT using the same mode. 1 is "unpacked" mode, whereby the output windows are made two samples longer, so that the Nyquist bin is included in the very end. By definition, the imaginary parts of DC and Nyquist are zero. This mode allows perfect reconstruction with a Real1IFFT using the same mode. 2 is "discarded" mode, whereby the Nyquist bin is omitted. While it doesn't allow a perfect reconstruction, this mode is useful for analysis, because the output window size is equal to the fft-size, and the imaginary part of DC is correctly zero'ed.
final case class Real1FullFFT(in: D, size: I, padding: I = 0) extends FFTFullUGen with Product with Serializable
final case class Real1FullIFFT(in: D, size: I, padding: I = 0) extends FFTFullUGen with Product with Serializable
final case class Real1IFFT(in: D, size: I, padding: I = 0, mode: I = 0) extends FFTHalfUGen with Product with Serializable
Backward or inverse short-time Fourier transform UGen for a real-valued output signal.
Backward or inverse short-time Fourier transform UGen for a real-valued output signal. The FFT size is equal to size. The output is a succession of time domain signals of length size - padding. Depending on mode, the output input size is supposed to be either size or size + 2.
in
the complex signal to transform.
size
the frequency domain input window size (fft size)
padding
amount of zero padding for each output window. These are the number of sample frames to drop from each output window after the FFT.
mode
packing mode of the input signal. 0 (default) is standard "packed" mode, whereby the real part of the bin at Nyquist is stored in the imaginary slot of the DC. This mode allows perfect reconstruction with a Real1IFFT using the same mode. 1 is "unpacked" mode, whereby the output windows are made two samples longer, so that the Nyquist bin is included in the very end. By definition, the imaginary parts of DC and Nyquist are zero. This mode allows perfect reconstruction with a Real1IFFT using the same mode. 2 is "discarded" mode, whereby the Nyquist bin is omitted. While it doesn't allow a perfect reconstruction, this mode is useful for analysis, because the output window size is equal to the fft-size, and the imaginary part of DC is correctly zero'ed.
final case class Real2FFT(in: D, rows: I, columns: I = 0, mode: I = 0) extends FFT2HalfUGen with Product with Serializable
Forward short-time Fourier transform UGen for a real-valued input signal.
Forward short-time Fourier transform UGen for a real-valued input signal. The FFT size is equal to rows * columns. The output is a succession of complex half-spectra, i.e. from DC to Nyquist.
XXX TODO: Depending on mode, the output window size is either size + padding or size + padding + 2.
Warning: window parameter modulation is currently not working correctly (issue #30)
in
the real signal to transform. If overlapping windows are desired, a Sliding should already have been applied to this signal, as well as multiplication with a window function.
rows
the input matrix number of rows
columns
the input matrix number of columns
mode
packing mode. 0 (default) is standard "packed" mode, whereby the real part of the bin at Nyquist is stored in the imaginary slot of the DC. This mode allows perfect reconstruction with a Real2IFFT using the same mode. 1 is "unpacked" mode, whereby the output windows are made two samples longer, so that the Nyquist bin is included in the very end. By definition, the imaginary parts of DC and Nyquist are zero. This mode allows perfect reconstruction with a Real2IFFT using the same mode. 2 is "discarded" mode, whereby the Nyquist bin is omitted. While it doesn't allow a perfect reconstruction, this mode is useful for analysis, because the output window size is equal to the fft-size, and the imaginary part of DC is correctly zero'ed.
final case class Real2FullFFT(in: D, rows: I, columns: I = 0) extends FFT2FullUGen with Product with Serializable
Warning: window parameter modulation is currently not working correctly (issue #30)
final case class Real2FullIFFT(in: D, rows: I, columns: I = 0) extends FFT2FullUGen with Product with Serializable
Warning: window parameter modulation is currently not working correctly (issue #30)
final case class Real2IFFT(in: D, rows: I, columns: I = 0, mode: I = 0) extends FFT2HalfUGen with Product with Serializable
Warning: window parameter modulation is currently not working correctly (issue #30)
final case class Reduce[A](elem: GE[A], op: Op[A, A, A]) extends GE.Lazy[A] with Product with Serializable
A UGen that reduces the channels of the input signal, so it become a single-channel signal.
final case class ReduceWindow[A](in: GE[A], size: I, op: Op[A, A, A]) extends SingleOut[A] with Product with Serializable
A UGen that reduces all elements in each window to single values, for example by calculating the sum or product.
final case class RepeatWindow[A, L](in: GE[A], size: I = 1, num: GE[L] = 2)(implicit numL: NumInt[L]) extends SingleOut[A] with ProductWithAdjuncts with Product with Serializable
A UGen that repeats the contents of input windows a number of times.
A UGen that repeats the contents of input windows a number of times.
in
the input signal to group into windows of size size and repeat num times. If the input ends before a full window is filled, the last window is padded with zeroes to obtain an input window if size size
size
the window size. one value is polled per iteration (outputting num windows of size size). the minimum size is one (it will be clipped to this).
num
the number of repetitions of the input windows. one value is polled per iteration (outputting num windows of size size). the minimum num is one (it will be clipped to this).
final case class Resample(in: D, factor: D, minFactor: D = 0, rollOff: D = 0.86, kaiserBeta: D = 7.5, zeroCrossings: I = 15) extends SingleOut[Double] with Product with Serializable
A band-limited resampling UGen.
A band-limited resampling UGen.
It uses an internal table for the anti-aliasing filter. Table resolution is currently fixed at 4096 filter samples per zero crossing and linear interpolation in the FIR table, but the total filter length can be specified through the zeroCrossings parameter. Note: If filter parameters are changed, the table must be recalculated which is very expensive. However, factor modulation is efficient.
Note: Unlike most other UGens, all parameters but in are read at "output rate". That is particular important for factor modulation. For each frame output, one frame from factor is consumed. Currently, modulating rollOff, kaiserBeta or zeroCrossings is not advised, as this case is not properly handled internally.
in
the signal to resample
factor
the resampling factor, where values greater than one mean the signal is stretched (sampling-rate increases or pitch lowers) and values less than one mean the signal is condensed (sampling-rate decreases or pitch rises)
minFactor
the minimum expected resampling factor, which controls the amount of buffering needed for the input signal. This is used at initialization time only. The default value of zero makes the UGen settles on the first factor value encountered. It is possible to use a value actually higher than the lowest provided factor, in order to limit the buffer usage. In that case, the FIR anti-aliasing filter will be truncated.
rollOff
the FIR anti-aliasing roll-off width
kaiserBeta
the FIR windowing function's parameter
zeroCrossings
the number of zero-crossings in the truncated and windowed sinc FIR.
final case class ResampleWindow(in: D, size: I, factor: D, minFactor: D = 0.0, rollOff: D = 0.86, kaiserBeta: D = 7.5, zeroCrossings: I = 15) extends SingleOut[Double] with Product with Serializable
A band-limited resampling UGen for images/matrices.
A band-limited resampling UGen for images/matrices. This works like Resample but processes each window cell across time. Thus is is not _resampling each window by itself_, but each "pixel" or matrix cell in a window across successive windows.
in
the signal to resample
size
the window size. Currently this is only read once upon initialization.
factor
the resampling factor, where values greater than one mean the signal is stretched (sampling-rate increases or pitch lowers) and values less than one mean the signal is condensed (sampling-rate decreases or pitch rises)
minFactor
the minimum expected resampling factor, which controls the amount of buffering needed for the input signal. This is used at initialization time only. The default value of zero makes the UGen settles on the first factor value encountered. It is possible to use a value actually higher than the lowest provided factor, in order to limit the buffer usage. In that case, the FIR anti-aliasing filter will be truncated.
rollOff
the FIR anti-aliasing roll-off width
kaiserBeta
the FIR windowing function's parameter
zeroCrossings
the number of zero-crossings in the truncated and windowed sinc FIR.
final case class ResizeWindow[A](in: GE[A], size: I, start: I = 0, stop: I = 0) extends SingleOut[A] with Product with Serializable
A UGen that resizes the windowed input signal by trimming each windows boundaries (if start is greater than zero or stop is less than zero) or padding the boundaries with zeroes (if start is less than zero or stop is greater than zero).
A UGen that resizes the windowed input signal by trimming each windows boundaries (if start is greater than zero or stop is less than zero) or padding the boundaries with zeroes (if start is less than zero or stop is greater than zero). The output window size is thus size - start + stop.
in
the signal to window and resize
size
the input window size
start
the delta window size at the output window's beginning
stop
the delta window size at the output window's ending
final case class ReverseWindow[A](in: GE[A], size: I, clump: I = 1) extends SingleOut[A] with Product with Serializable
Reverses the (clumped) elements of input windows.
Reverses the (clumped) elements of input windows.
E.g. ReverseWindow(ArithmSeq(1, 1, 9), 3) gives 7,8,9, 4,5,6, 1,2,3. The behavior when size % clump != 0 is to clump "from both sides" of the window, e.g. ReverseWindow(ArithmSeq(1, 1, 9), 4) gives 6,7,8,9, 5, 1,2,3,4.
in
the window'ed signal to reverse
size
the window size
clump
the grouping size of window elements
final case class RotateFlipMatrix[A](in: GE[A], rows: I, columns: I, mode: I) extends SingleOut[A] with Product with Serializable
A UGen that can apply horizontal and vertical flip and 90-degree step rotations to a matrix.
A UGen that can apply horizontal and vertical flip and 90-degree step rotations to a matrix.
Unless mode is 90-degree rotation (4 or 5) and the matrix is not square, this needs one internal matrix buffer, otherwise two internal matrix buffers are needed.
in
the matrix / matrices to rotate
rows
the number of rows in the input
columns
the number of columns in the input
mode
0: pass, 1: flip horizontally, 2: flip vertically, 3: rotate 180 degrees, 4: rotate clockwise, 8: rotate anti-clockwise. See the companion object for constants. If you combine flipping and rotation, flipping is performed first, so a mode of 5 means flip horizontally, followed by rotate clockwise.
final case class RotateWindow[A](in: GE[A], size: I, amount: I = 0) extends SingleOut[A] with Product with Serializable
A UGen that rotates the contents of a window, wrapping around its boundaries.
A UGen that rotates the contents of a window, wrapping around its boundaries. For example, it can be used to align the phases prior to FFT so that the sample that was formerly in the centre of the window moves to the beginning of the window.
in
the signal to window and resize
size
the input window size
amount
the rotation amount in sample frames. Positive values "move" the contents to the right, negative values "move" the contents to the left. The amount is taken modulus size.
final case class RunningMax[A](in: GE[A], gate: B = false)(implicit ord: Ord[A]) extends SingleOut[A] with ProductWithAdjuncts with Product with Serializable
A UGen that tracks the maximum value seen so far, until a trigger resets it.
A UGen that tracks the maximum value seen so far, until a trigger resets it.
Note: This is different from SuperCollider's RunningMax UGen which outputs the maximum of a sliding window instead of waiting for a reset trigger. To track a sliding maximum, you can use PriorityQueue.
in
the signal to track
gate
a gate that when greater than zero (and initially) sets the the internal state is set to negative infinity.
final case class RunningMin[A](in: GE[A], gate: B = false)(implicit ord: Ord[A]) extends SingleOut[A] with ProductWithAdjuncts with Product with Serializable
A UGen that tracks the minimum value seen so far, until a trigger resets it.
A UGen that tracks the minimum value seen so far, until a trigger resets it.
Note: This is different from SuperCollider's RunningMin UGen which outputs the maximum of a sliding window instead of waiting for a reset trigger. To track a sliding minimum, you can use PriorityQueue.
in
the signal to track
gate
a gate that when greater than zero (and initially) sets the the internal state is set to positive infinity.
final case class RunningProduct[A](in: GE[A], gate: B = false)(implicit num: Num[A]) extends SingleOut[A] with ProductWithAdjuncts with Product with Serializable
A UGen that tracks the maximum value seen so far, until a trigger resets it.
A UGen that tracks the maximum value seen so far, until a trigger resets it.
in
the signal to track
gate
a gate that when greater than zero (and initially) sets the the internal state is set to one.
final case class RunningSum[A](in: GE[A], gate: B = false)(implicit num: Num[A]) extends SingleOut[A] with ProductWithAdjuncts with Product with Serializable
A UGen that integrates the values seen so far, until a trigger resets it.
A UGen that integrates the values seen so far, until a trigger resets it.
Note: This is different from SuperCollider's RunningSum UGen which outputs the sum of a sliding window instead of waiting for a reset trigger. To track a sliding sum, you can subtract a delayed version of RunningSum from a non-delayed RunningSum.
in
the signal to track
gate
a gate that when greater than zero (an initially) sets the the internal state is set to zero.
final case class RunningWindowMax[A](in: GE[A], size: I, gate: B = false)(implicit ord: Ord[A]) extends SingleOut[A] with ProductWithAdjuncts with Product with Serializable
A UGen that like RunningMax calculates the maximum observed value of the running input.
A UGen that like RunningMax calculates the maximum observed value of the running input. However, it operates on entire windows, i.e. it outputs windows that contain the maximum elements of all the past windows observed.
in
the windowed signal to monitor
size
the window size. This should normally be a constant. If modulated, the internal buffer will be re-allocated, essentially causing a reset trigger.
gate
a gate signal that clears the internal state. When a gate is open (> 0), the currently processed window is reset altogether until its end, beginning accumulation again from the successive window.
final case class RunningWindowMin[A](in: GE[A], size: I, gate: B = false)(implicit ord: Ord[A]) extends SingleOut[A] with ProductWithAdjuncts with Product with Serializable
A UGen that like RunningMin calculates the minimum observed value of the running input.
A UGen that like RunningMin calculates the minimum observed value of the running input. However, it operates on entire windows, i.e. it outputs windows that contain the minimum elements of all the past windows observed.
in
the windowed signal to monitor
size
the window size. This should normally be a constant. If modulated, the internal buffer will be re-allocated, essentially causing a reset trigger.
gate
a gate signal that clears the internal state. When a gate is open (> 0), the currently processed window is reset altogether until its end, beginning accumulation again from the successive window.
final case class RunningWindowProduct[A](in: GE[A], size: I, gate: B = false)(implicit num: Num[A]) extends SingleOut[A] with ProductWithAdjuncts with Product with Serializable
A UGen that like RunningProduct calculates the product of the running input.
A UGen that like RunningProduct calculates the product of the running input. However, it operates on entire windows, i.e. it outputs windows that contain the sum elements of all the past windows observed.
in
the windowed signal to monitor
size
the window size. This should normally be a constant. If modulated, the internal buffer will be re-allocated, essentially causing a reset trigger.
gate
a gate signal that clears the internal state. When a gate is open (> 0), the currently processed window is reset altogether until its end, beginning accumulation again from the successive window.
final case class RunningWindowSum[A](in: GE[A], size: I, gate: B = false)(implicit num: Num[A]) extends SingleOut[A] with ProductWithAdjuncts with Product with Serializable
A UGen that like RunningSum calculates the sum of the running input.
A UGen that like RunningSum calculates the sum of the running input. However, it operates on entire windows, i.e. it outputs windows that contain the sum elements of all the past windows observed.
in
the windowed signal to monitor
size
the window size. This should normally be a constant. If modulated, the internal buffer will be re-allocated, essentially causing a reset trigger.
gate
a gate signal that clears the internal state. When a gate is open (> 0), the currently processed window is reset altogether until its end, beginning accumulation again from the successive window.
trait SampleRate extends GE[Double]
A sample rate element simply signalizes that it refers to a sampling rate in Hertz.
A sample rate element simply signalizes that it refers to a sampling rate in Hertz. When in implicit scope, it enables extension methods on graph elements, such as 440.hertz or 2.3.seconds.
final case class ScanImage(in: D, width: I, height: I, x: D = 0, y: D = 0, next: B = false, wrap: I = 0, rollOff: D = 0.86, kaiserBeta: D = 7.5, zeroCrossings: I = 0) extends SingleOut[Double] with Product with Serializable
A UGen that scans the pixels of an image using an x and y input signal.
A UGen that scans the pixels of an image using an x and y input signal. It uses either a sinc-based band-limited resampling algorithm, or bicubic interpolation, depending on the zeroCrossings parameter.
All window defining parameters (width, height) are polled once per matrix. All scanning and filter parameters are polled one per output pixel.
in
the image to scan
width
the width (number of columns) of the input matrix
height
the height (number of rows) of the input matrix
x
horizontal position of the dynamic scanning signal
y
vertical position of the dynamic scanning signal
next
a trigger that causes the UGen to read in a new image from in.
wrap
if non-zero, wraps coordinates around the input images boundaries. TODO: currently wrap = 0 is broken if using sinc interpolation!
rollOff
the FIR anti-aliasing roll-off width. Between zero and one.
kaiserBeta
the FIR windowing function's parameter
zeroCrossings
the number of zero-crossings in the truncated and windowed sinc FIR. If zero (default), algorithm uses bicubic interpolation instead.
See also
AffineTransform2D
Slices
PenImage
final case class SegModPhasor(freqN: D, phase: D = 0.0) extends SingleOut[Double] with Product with Serializable
A phasor UGen that takes new frequency values at the beginning of each cycle.
A phasor UGen that takes new frequency values at the beginning of each cycle. It can be used to implement the 'segmod' program of Döbereiner and Lorenz. In contrast to LFSaw which continuously reads frequency values, its output values go from zero to one, and the phase argument is only used internally (the output signal always starts at zero).
To turn this, for example, into a sine oscillator, you can use (SegModPhasor(...) * 2 * math.Pi).sin.
Warning: passing in zero frequencies will halt the process. XXX TODO
freqN
normalized frequency (f/sr). One value per output cycle is read. Also note that the UGen terminates when the freqN signal ends. Thus, to use a constant frequency, wrap it in a DC or DC(...).take.
phase
phase offset in radians. The phase offset is only used internally to determine when to change the frequency. For example, with a phase of 0.25, when the output phase reaches 0.25, the next frequency value is read. With the default phase of 0.0, a frequency value is read after one complete cycle (output reaches 1.0).
final case class SetResetFF(set: B, reset: B = false) extends SingleOut[Boolean] with Product with Serializable
A flip-flop UGen with two inputs, one (set) triggering an output of 1, the other (reset) triggering an output of 0.
A flip-flop UGen with two inputs, one (set) triggering an output of 1, the other (reset) triggering an output of 0. Subsequent triggers happening within the same input slot have no effect. If both inputs receive a trigger at the same time, the reset input takes precedence.
Both inputs are "hot" and the UGen runs until both have been terminated.
final case class Sheet1D[A](in: GE[A], size: I, label: String = "sheet") extends ZeroOut with Product with Serializable
Debugging utility that displays 1D "windows" of the input data as a spreadsheet or table view.
Debugging utility that displays 1D "windows" of the input data as a spreadsheet or table view.
Warning: window parameter modulation is currently not working correctly (issue #30)
final case class SinOsc(freqN: D, phase: D = 0.0) extends SingleOut[Double] with Product with Serializable
Sine oscillator.
Sine oscillator. Note that the frequency is not in Hertz but the normalized frequency as we do not maintained one global sample rate. For a frequency in Hertz, freqN would be that frequency divided by the assumed sample rate.
freqN
normalized frequency (f/sr).
phase
phase offset in radians
final case class Slices[A, L](in: GE[A], spans: GE[L])(implicit num: Num[A], numL: NumInt[L]) extends SingleOut[A] with ProductWithAdjuncts with Product with Serializable
A UGen that assembles slices of an input signal in random access fashion.
A UGen that assembles slices of an input signal in random access fashion. It does so by buffering the input to disk.
in
the signal to re-arrange.
spans
successive frame start (inclusive) and stop (exclusive) frame positions determining the spans that are output by the UGen. This parameter is read on demand. First, the first two values are read, specifying the first span. Only after this span has been output, the next two values from spans are read, and so on. Values are clipped to zero (inclusive) and the length of the input signal (exclusive). If a start position is greater than a stop position, the span is output in reversed order.
See also
ScanImage
final case class Sliding[A](in: GE[A], size: I, step: I) extends SingleOut[A] with Product with Serializable
A UGen that produces a sliding window over its input.
A UGen that produces a sliding window over its input.
When the input terminates and the last window is not full, it will be flushed with its partial content. Otherwise, all windows are guaranteed to be zero-padded to the window length if they had been only partially filled when the input ended.
Unlike the sliding operation of Scala collections, the UGen always performs steps for partial windows, e.g. Sliding(ArithmSeq(1, length = 4), size = 3, step = 1) will produce the flat output 1, 2, 3, 2, 3, 4, 3, 4, 0, 4, thus there are four windows, the first two of which are full, the third which is full by padding, and the last is partial.
in
the input to be repacked into windows
size
the window size. this is clipped to be at least one
step
the stepping factor in the input, between windows. This clipped to be at least one. If step size is larger than window size, frames in the input are skipped.
See also
OverlapAdd
final case class SlidingPercentile[A](in: GE[A], len: I = 3, frac: D = 0.5, interp: I = 0)(implicit num: Num[A]) extends SingleOut[A] with ProductWithAdjuncts with Product with Serializable
A UGen that reports a percentile of a sliding window across its input.
A UGen that reports a percentile of a sliding window across its input. The UGen starts outputting values immediately, even if the median window len is not yet reached. This is because len can be modulated. If one wants to discard the initial values, use a drop.
Note that for an even median length and no interpolation, the reported median may be either the value at index len/2 or len/2 + 1 in the sorted window.
All arguments are polled at the same rate. Changing the frac value may cause an internal table rebuild and can thus be expensive.
in
the input to analyze
len
the length of the sliding median window
frac
the percentile from zero to one. The default of 0.5 produces the median.
interp
if zero (default), uses nearest-rank, otherwise uses linear interpolation. Note: currently not implemented, must be zero
final case class SlidingWindowPercentile[A](in: GE[A], winSize: I, medianLen: I = 3, frac: D = 0.5, interp: I = 0)(implicit num: Num[A]) extends SingleOut[A] with ProductWithAdjuncts with Product with Serializable
A UGen that reports a percentile of a sliding window across every cell of a window'ed input (such as an image).
A UGen that reports a percentile of a sliding window across every cell of a window'ed input (such as an image).
The UGen starts outputting values immediately, even if the medianLen is not yet reached. This is because medianLen can be modulated (per input window). If one wants to discard the initial values, use a drop, for example for medianLen/2 * winSize frames.
Note that for an even median length and no interpolation, the reported median may be either the value at index medianLen/2 or medianLen/2 + 1 in the sorted window.
All arguments but in are polled per input window. Changing the frac value may cause an internal table rebuild and can thus be expensive.
in
the window'ed input to analyze
winSize
the size of the input windows
medianLen
the length of the sliding median window (the filter window applied to every cell of the input window)
frac
the percentile from zero to one. The default of 0.5 produces the median.
interp
if zero (default), uses nearest-rank, otherwise uses linear interpolation. Note: currently not implemented, must be zero
final case class SortWindow[A, B](keys: GE[A], values: GE[B], size: I)(implicit ord: Ord[A]) extends SingleOut[B] with ProductWithAdjuncts with Product with Serializable
A UGen that sorts the input data window by window.
A UGen that sorts the input data window by window.
keys
the sorting keys; output will be in ascending order
values
the values corresponding with the keys and eventually output by the UGen. It is well possible to use the same signal both for keys and values.
size
the window size.
final case class StrongestLocalMaxima(in: D, size: I, minLag: I, maxLag: I, thresh: D = 0.0, octaveCost: D = 0.0, num: I = 14) extends MultiOut[Double] with Product with Serializable
A peak detection UGen, useful for implementing the auto-correlation based pitch detection method of Paul Boersma (1993).
A peak detection UGen, useful for implementing the auto-correlation based pitch detection method of Paul Boersma (1993). Taking an already calculated auto-correlation of size size, the UGen looks for local maxima within a given range.
The UGen has two outputs. The first output gives the lag times or periods of the n strongest peaks per window (to obtain a frequency, divide the sampling rate by these lag times). The second output gives the intensities of these n candidates. If there are less than n candidates, the empty slots are output as zeroes.
in
the auto-correlation windows
size
the size of the auto-correlation windows. must be at least 2.
minLag
the minimum lag time in sample frames, corresponding to the maximum frequency accepted
maxLag
the maximum lag time in sample frames, corresponding to the minimum frequency accepted
thresh
the "voicing" threshold for considered for local maxima within minLag maxLag.
octaveCost
a factor for favouring higher frequencies. use zero to turn off this feature.
num
number of candidate periods output. This is clipped to be at least 1. see PitchesToViterbi see Viterbi
final case class Take[A, L](in: GE[A], length: GE[L])(implicit numL: NumInt[L]) extends SingleOut[A] with ProductWithAdjuncts with Product with Serializable
final case class TakeRight[A, L](in: GE[A], length: GE[L])(implicit numL: NumInt[L]) extends SingleOut[A] with ProductWithAdjuncts with Product with Serializable
final case class TakeWhile[A](in: GE[A], p: B) extends SingleOut[A] with Product with Serializable
sealed trait Then[+A] extends Lazy
final case class Timer(trig: B) extends SingleOut[Long] with Product with Serializable
A UGen that outputs the number of sample frames passed since last triggered.
A UGen that outputs the number of sample frames passed since last triggered. If no trigger is used, it simply outputs a linearly rising ramp.
trig
trigger signal to reset the counter. Note that the UGen shuts down when trig finishes, so to use a constant like 0, it has to be wrapped in a DC, for example.
final case class TransposeMatrix[A](in: GE[A], rows: I, columns: I) extends SingleOut[A] with Product with Serializable
A UGen that transposes 2-dimensional matrices.
A UGen that transposes 2-dimensional matrices. This is a 2-dimensional windowed process, where each window has length rows * columns. Input is assumed to be "left-to-right, top-to-bottom", so the first samples constitute the first row, the next samples constitute the second row, etc.
Note: The UGens takes up twice the matrix size of memory.
The output matrix is transposed (rows and columns exchanged). So an input of (a, b, c, d, e, f) with rows = 2 and columns = 3 is interpreted as ((a, b, c), (d, e, f)), transposed as ((a, d), (b, e), (c, f)) and output flat as (a, d, b, e, c, f).
To rotate an image ninety degrees clockwise, you would have rows = height and columns = width.
in
the input matrices
rows
the number of rows of the _input_
columns
the number of columns of the _input_
See also
RotateFlipMatrix
final case class Trig[A](in: GE[A])(implicit num: Num[A]) extends SingleOut[Boolean] with ProductWithAdjuncts with Product with Serializable
A UGen that creates a trigger when input changes from non-positive to positive.
A UGen that creates a trigger when input changes from non-positive to positive. This is useful when a gate argument of another UGen should indeed be used like a single trigger, as a constant positive input will not produce a held signal.
final case class TrigHold[L](in: B, length: GE[L], clear: B = false)(implicit numL: NumInt[L]) extends SingleOut[Boolean] with ProductWithAdjuncts with Product with Serializable
A UGen that holds an input trigger signal for a given duration.
A UGen that holds an input trigger signal for a given duration. When a trigger is received from in, the output changes from zero to one for the given length amount of frames. If a new trigger arrives within this period, the internal length counter is reset (the high state is kept for further length frames).
in
an input trigger or gate signal. Whenever the signal is positive, the internal hold state is reset. If a strict trigger is needed instead of a gate signal, a Trig UGen can be inserted.
length
the number of sample frames to hold the high output once a high input is received. A new value is polled whenever in is high.
clear
an auxiliary trigger or gate signal that resets the output to low if there is currently no high in. This signal is read synchronously with in. If both clear and in are high at a given point, then the UGen outputs high but has its length counter reset.
sealed trait UGenInGroup[A] extends UGenInLike[A]
final case class UGenOutProxy[A](ugen: MultiOut[A], outputIndex: Int) extends UGenProxy[A] with Product with Serializable
A UGenOutProxy refers to a particular output of a multi-channel UGen.
A UGenOutProxy refers to a particular output of a multi-channel UGen. A sequence of these form the representation of a multi-channel-expanded UGen.
sealed trait UGenProxy[A] extends UGenIn[A]
final case class UnaryOp[A, B](op: Op[A, B], in: GE[A]) extends SingleOut[B] with Product with Serializable
final case class UnzipWindow[A](in: GE[A], size: I = 1) extends GE.Lazy[A] with Product with Serializable
final case class UnzipWindowN[A](numOutputs: Int, in: GE[A], size: I = 1) extends MultiOut[A] with Product with Serializable
final case class ValueBooleanSeq(elems: Boolean*) extends SingleOut[Boolean] with Product with Serializable
Loops the given values.
final case class ValueDoubleSeq(elems: Double*) extends SingleOut[Double] with Product with Serializable
Loops the given values.
final case class ValueIntSeq(elems: Int*) extends SingleOut[Int] with Product with Serializable
Loops the given values.
final case class ValueLongSeq(elems: Long*) extends SingleOut[Long] with Product with Serializable
Loops the given values.
final case class Viterbi(mul: D = 1.0, add: D, numStates: I, numFrames: I = -1) extends SingleOut[Int] with Product with Serializable
A UGen performing a generalized Viterbi algorithm.
A UGen performing a generalized Viterbi algorithm. The Viterbi algorithm tries to find the best path among sequences of states, by evaluating transition probabilities. It runs over a predefined number of frames, accumulating data of different states. It maximizes the likelihood of the terminal state, and then backtracks to reconstruct the likely path (sequence of states). The output is the sequence of state indices (from zero inclusive to numStates exclusive).
Note: This UGen must run until numFrames or the inputs are exhausted, before it can begin outputting values.
This implementation is generalized in the sense that instead of the canonical matrices "sequences of observations", "initial probabilities", "transition matrix", and "emission matrix", it takes two large matrices mul and add that contain the equivalent information. These two matrices allow the UGen to operate in two different modes:
- multiplicative (as in the Wikipedia article) by damping the probabilities over time - accumulative (as used in Praat for pitch tracking) by adding up the weights (if you have "costs", feed in their negative values).
Basically the internal delta matrix is created by the update function delta = (delta_prev * mul) + add (with the corresponding matrix indices).
The initial delta state is zero. Therefore, in order to provide the initial state, you obtain an initial vector v by providing an add matrix of numStates x numStates cells, which is zero except for the first column filled by v (alternatively, each row filled with the next value of v, or a diagonal matrix of v; if v can take negative value, make sure to fill the initial numStates x numStates completely by duplicating v).
For the classical data, set add just to the initial matrix as explained above (multiplying emitted first observations with initial probabilities), and then use exclusively mul by passing in emitted observations multiplied by their transition probabilities:
```
mul[t][i][j] = transitionProb[i][j] * emissionProb[observation[t]][i]
```
See https://en.wikipedia.org/wiki/Viterbi_algorithm
mul
the generalized multiplicative matrix (combining transition probabilities, emission probabilities and observations). If only accumulation is used, set this to 1.0.
add
the generalized accumulative matrix (combining transition probabilities, emission probabilities and observations). If only multiplication is used, set this to provide the initial state (see above), followed either by zeroes or by terminating the signal.
numStates
the number of different states, as reflected by the inner dimensions of matrices mul and add.
numFrames
the number of observations. If -1, the UGen runs until the input is exhausted. This happens when both mul and add end. see StrongestLocalMaxima see PitchesToViterbi
final case class WPE_Dereverberate(in: D, fftSize: I = 512, winStep: I = 128, delay: I = 3, taps: I = 10, alpha: D = 0.9999, psdLen: I = 0) extends D with Product with Serializable
A graph element performing end-to-end blind de-reverberation of an input signal.
A graph element performing end-to-end blind de-reverberation of an input signal. It performs the FFT/IFFT setup around invocations of WPE_ReverbFrame.
Note: this does not yet work correctly with multi-channel input.
in
the reverberant time domain signal
fftSize
the fft-size
winStep
the step size for the sliding window; typically 1/4 of fftSize
delay
the delay in spectral frames to avoid suppression of early reflections
taps
the filter size in spectral frames to capture the late reverberation
alpha
the decay factor for the filter coefficients
psdLen
the number of preceding spectral frames to include as "context" in the psd
final case class WPE_ReverbFrame(in: D, psd: D, bins: I, delay: I = 3, taps: I = 10, alpha: D = 0.9999) extends MultiOut[Double] with Product with Serializable
A UGen implementation of a single frame Weighted Prediction Error (WPE) de-reverberation algorithm in the frequency domain.
A UGen implementation of a single frame Weighted Prediction Error (WPE) de-reverberation algorithm in the frequency domain. It takes a DFT'ed input signal frame by frame and returns the estimated reverberated components. To actually obtain the de-reverberated signal, subtract the output from the input signal, then perform inverse FFT and overlap-add reconstruction.
The algorithm closely follows the Python package described in L. Drude, J. Heymann, Ch. Boeddeker, R. Haeb-Umbach, 'NARA-WPE: A Python package for weighted prediction error dereverberation in Numpy and Tensorflow for online and offline processing' and its Numpy implementation (MIT licensed).
Note: this does not yet work correctly with multi-channel input.
in
the sequence of complex FFT'ed frames. Should have been obtained through Real1FFT with mode = 1.
psd
the power spectrum density estimation, frame by frame corresponding with in. It should correspond with the shape of in, however being monophonic instead of multi-channel and using real instead of complex numbers (half the signal window length).
bins
the number of frequency bins (should be fftSize / 2 + 1)
delay
the delay in spectral frames to avoid suppression of early reflections
taps
the filter size in spectral frames to capture the late reverberation
alpha
the decay factor for the filter coefficients
final case class WhiteNoise(mul: D = 1.0) extends SingleOut[Double] with Product with Serializable
Nominal signal range is -mul to +mul (exclusive).
final case class WindowApply[A](in: GE[A], size: I, index: I = 0, mode: I = 0) extends SingleOut[A] with Product with Serializable
A UGen that extracts for each input window the element at a given index.
A UGen that extracts for each input window the element at a given index.
For example, the first element per window can be extracted with index = 0, and the last element per window can be extracted with index = -1, mode = 1 (wrap).
If the input in terminates before a window of size is full, it will be padded with zeroes.
in
the window'ed signal to index
size
the window size.
index
the zero-based index into each window. One value per window is polled.
mode
wrap mode. 0 clips indices, 1 wraps them around, 2 folds them, 3 outputs zeroes when index is out of bounds.
final case class WindowIndexWhere(p: B, size: I) extends SingleOut[Int] with Product with Serializable
A UGen that determines for each input window the first index where a predicate holds.
A UGen that determines for each input window the first index where a predicate holds. It outputs one integer value per window; if the predicate does not hold across the entire window or if the window size is zero, the index will be -1.
p
a predicate to detect the index
size
the window size.
final case class WindowMaxIndex[A](in: GE[A], size: I)(implicit ord: Ord[A]) extends SingleOut[Int] with ProductWithAdjuncts with Product with Serializable
A UGen that determines for each input window the index of the maximum element.
A UGen that determines for each input window the index of the maximum element. It outputs one integer value per window; if multiple elements have the same value, the index of the first element is reported (notably zero if the window contains only identical elements).
in
the input signal.
size
the window size.
final case class Wrap[A](in: GE[A], lo: GE[A], hi: GE[A])(implicit num: Num[A]) extends SingleOut[A] with ProductWithAdjuncts with Product with Serializable
final case class Zip[A](a: GE[A], b: GE[A]) extends SingleOut[A] with Product with Serializable
final case class ZipWindow[A](a: GE[A], b: GE[A], size: I = 1) extends SingleOut[A] with Product with Serializable
final case class ZipWindowN[A](in: GE[A], size: I = 1) extends SingleOut[A] with Product with Serializable

Value Members

object ARCWindow extends ProductReader[ARCWindow] with Serializable
object AffineTransform2D extends ProductReader[AffineTransform2D] with Serializable
object ArithmSeq extends ProductReader[ArithmSeq[_, _]] with Serializable
object AudioFileIn extends ProductReader[AudioFileIn] with Serializable
object AudioFileOut extends ProductReader[AudioFileOut] with Serializable
object BinaryOp extends ProductReader[SingleOut[_]] with Serializable
object Biquad extends ProductReader[Biquad] with Serializable
object Bleach extends ProductReader[Bleach] with Serializable
object BleachKernel extends ProductReader[BleachKernel] with Serializable
object Blobs2D extends ProductReader[Blobs2D] with Serializable
object BlockSize
object BufferDisk extends ProductReader[BufferDisk[_]] with Serializable
object BufferMemory extends ProductReader[BufferMemory[_]] with Serializable
object ChannelProxy extends ProductReader[ChannelProxy[_]] with Serializable
object Clip extends ProductReader[Clip[_]] with Serializable
object CombN extends ProductReader[CombN] with Serializable
object Complex1FFT extends ProductReader[Complex1FFT] with Serializable
object Complex1IFFT extends ProductReader[Complex1IFFT] with Serializable
object Complex2FFT extends ProductReader[Complex2FFT] with Serializable
object Complex2IFFT extends ProductReader[Complex2IFFT] with Serializable
object ComplexBinaryOp extends ProductReader[ComplexBinaryOp] with Serializable
Binary operator assuming operands are complex signals (real and imaginary interleaved).
Binary operator assuming operands are complex signals (real and imaginary interleaved). Outputs another complex stream even if the operator yields a purely real-valued result.
XXX TODO - need more ops such as conjugate, polar-to-cartesian, ...
object ComplexUnaryOp extends ProductReader[ComplexUnaryOp] with Serializable
Unary operator assuming stream is complex signal (real and imaginary interleaved).
Unary operator assuming stream is complex signal (real and imaginary interleaved). Outputs another complex stream even if the operator yields a purely real-valued result (ex. abs).
XXX TODO - need more ops such as conjugate, polar-to-cartesian, ...
object Concat extends ProductReader[Concat[_]] with Serializable
object Const
object ConstB extends Serializable
object ConstD extends Serializable
object ConstI extends Serializable
object ConstL extends Serializable
object ConstQ extends ProductReader[ConstQ] with Serializable
object ControlBlockSize extends ProductReader[ControlBlockSize] with Serializable
object Convolution extends ProductReader[Convolution] with Serializable
object DC extends ProductReader[DC[_]] with Serializable
object DCT_II extends ProductReader[DCT_II] with Serializable
object DEnvGen extends ProductReader[DEnvGen[_]] with Serializable
object DebugSink extends ProductReader[DebugSink[_]] with Serializable
object DebugThrough extends ProductReader[DebugThrough[_]] with Serializable
object DelayN extends ProductReader[DelayN[_]] with Serializable
object DetectLocalMax extends ProductReader[DetectLocalMax[_]] with Serializable
object Differentiate extends ProductReader[Differentiate[_]] with Serializable
object Distinct extends ProductReader[Distinct[_]] with Serializable
object Done extends ProductReader[Done[_]] with Serializable
object Drop extends ProductReader[Drop[_, _]] with Serializable
object DropRight extends ProductReader[DropRight[_, _]] with Serializable
object DropWhile extends ProductReader[DropWhile[_]] with Serializable
object Elastic extends ProductReader[Elastic[_]] with Serializable
object Else
object ElseGE extends ProductReader[ElseGE[_]] with Serializable
object ElseIfThen extends ProductReader[ElseIfThen[_]] with Serializable
object ElseUnit extends ProductReader[ElseUnit] with Serializable
object Empty extends ProductReader[Empty[_]] with Serializable
object ExpExp extends ProductReader[ExpExp] with Serializable
object ExpLin extends ProductReader[ExpLin] with Serializable
object Feed extends ProductReader[Feed[_]] with Serializable
object FilterSeq extends ProductReader[FilterSeq[_]] with Serializable
object Flatten extends ProductReader[Flatten[_]] with Serializable
object Fold extends ProductReader[Fold[_]] with Serializable
object FoldCepstrum extends ProductReader[FoldCepstrum] with Serializable
object Fourier extends ProductReader[Fourier[_]] with Serializable
object Frames extends ProductReader[Frames[_]] with Serializable
object Gate extends ProductReader[Gate[_]] with Serializable
object GenWindow extends ProductReader[GenWindow[_]] with Serializable
object GeomSeq extends ProductReader[GeomSeq[_, _]] with Serializable
object GimpSlur extends ProductReader[GimpSlur] with Serializable
object GramSchmidtMatrix extends ProductReader[GramSchmidtMatrix] with Serializable
object HPF extends ProductReader[HPF] with Serializable
object Hash extends ProductReader[Hash[_]] with Serializable
object HilbertCurve
object Histogram extends ProductReader[Histogram[_]] with Serializable
object IfThen extends ProductReader[IfThen[_]] with Serializable
object ImageFile extends ImageFilePlatform
object ImageFileIn extends ProductReader[ImageFileIn] with Serializable
object ImageFileOut extends ProductReader[ImageFileOut] with Serializable
object ImageFileSeqIn extends ProductReader[ImageFileSeqIn] with Serializable
object ImageFileSeqOut extends ProductReader[ImageFileSeqOut] with Serializable
object Impulse extends ProductReader[Impulse] with Serializable
object Indices extends ProductReader[Indices[_]] with Serializable
object LFSaw extends ProductReader[LFSaw] with Serializable
object LPF extends ProductReader[LPF] with Serializable
object Latch extends ProductReader[Latch[_]] with Serializable
object LeakDC extends ProductReader[LeakDC] with Serializable
object Length extends ProductReader[Length[_]] with Serializable
object Limiter extends ProductReader[Limiter] with Serializable
object LinExp extends ProductReader[LinExp] with Serializable
object LinKernighanTSP extends ProductReader[LinKernighanTSP] with Serializable
object LinLin extends ProductReader[LinLin] with Serializable
object Line extends ProductReader[Line[_]] with Serializable
object Loudness extends ProductReader[Loudness] with Serializable
object Masking2D extends ProductReader[Masking2D] with Serializable
object MatchLen extends ProductReader[MatchLen[_, _]] with Serializable
object MatrixInMatrix extends ProductReader[MatrixInMatrix[_]] with Serializable
object MatrixOutMatrix extends ProductReader[MatrixOutMatrix[_]] with Serializable
object MelFilter extends ProductReader[MelFilter] with Serializable
object Metro extends ProductReader[Metro[_]] with Serializable
object Mix
object NormalizeWindow extends ProductReader[NormalizeWindow] with Serializable
object NumChannels extends ProductReader[NumChannels[_]] with Serializable
object OffsetOverlapAdd extends ProductReader[OffsetOverlapAdd[_]] with Serializable
object OnePole extends ProductReader[OnePole] with Serializable
object OnePoleWindow extends ProductReader[OnePoleWindow] with Serializable
object OverlapAdd extends ProductReader[OverlapAdd[_]] with Serializable
object PeakCentroid1D extends ProductReader[PeakCentroid1D] with Serializable
object PeakCentroid2D extends ProductReader[PeakCentroid2D] with Serializable
object Pearson extends ProductReader[Pearson] with Serializable
object PenImage extends ProductReader[PenImage] with Serializable
object PitchAC extends ProductReader[PitchAC] with Serializable
object PitchesToViterbi extends ProductReader[PitchesToViterbi] with Serializable
object Plot1D extends ProductReader[Plot1D[_]] with Serializable
object Poll extends ProductReader[Poll[_]] with Serializable
object PriorityQueue extends ProductReader[PriorityQueue[_, _]] with Serializable
object Progress extends ProductReader[Progress] with Serializable
object ProgressFrames extends ProductReader[ProgressFrames[_, _]] with Serializable
object Real1FFT extends ProductReader[Real1FFT] with Serializable
object Real1FullFFT extends ProductReader[Real1FullFFT] with Serializable
object Real1FullIFFT extends ProductReader[Real1FullIFFT] with Serializable
object Real1IFFT extends ProductReader[Real1IFFT] with Serializable
object Real2FFT extends ProductReader[Real2FFT] with Serializable
object Real2FullFFT extends ProductReader[Real2FullFFT] with Serializable
object Real2FullIFFT extends ProductReader[Real2FullIFFT] with Serializable
object Real2IFFT extends ProductReader[Real2IFFT] with Serializable
object Reduce extends ProductReader[GE[_]] with Serializable
object ReduceWindow extends ProductReader[GE[_]] with Serializable
object RepeatWindow extends ProductReader[RepeatWindow[_, _]] with Serializable
object Resample extends ProductReader[Resample] with Serializable
object ResampleWindow extends ProductReader[ResampleWindow] with Serializable
object ResizeWindow extends ProductReader[ResizeWindow[_]] with Serializable
object ReverseWindow extends ProductReader[ReverseWindow[_]] with Serializable
object RotateFlipMatrix extends ProductReader[RotateFlipMatrix[_]] with Serializable
object RotateWindow extends ProductReader[RotateWindow[_]] with Serializable
object RunningMax extends ProductReader[RunningMax[_]] with Serializable
object RunningMin extends ProductReader[RunningMin[_]] with Serializable
object RunningProduct extends ProductReader[RunningProduct[_]] with Serializable
object RunningSum extends ProductReader[RunningSum[_]] with Serializable
object RunningWindowMax extends ProductReader[RunningWindowMax[_]] with Serializable
object RunningWindowMin extends ProductReader[RunningWindowMin[_]] with Serializable
object RunningWindowProduct extends ProductReader[RunningWindowProduct[_]] with Serializable
object RunningWindowSum extends ProductReader[RunningWindowSum[_]] with Serializable
object SampleRate extends ProductReader[SampleRate]
object ScanImage extends ProductReader[ScanImage] with Serializable
object SegModPhasor extends ProductReader[SegModPhasor] with Serializable
object SetResetFF extends ProductReader[SetResetFF] with Serializable
object Sheet1D extends ProductReader[Sheet1D[_]] with Serializable
object SinOsc extends ProductReader[SinOsc] with Serializable
object Slices extends ProductReader[Slices[_, _]] with Serializable
object Sliding extends ProductReader[Sliding[_]] with Serializable
object SlidingPercentile extends ProductReader[SlidingPercentile[_]] with Serializable
object SlidingWindowPercentile extends ProductReader[SlidingWindowPercentile[_]] with Serializable
object SortWindow extends ProductReader[SortWindow[_, _]] with Serializable
object StrongestLocalMaxima extends ProductReader[StrongestLocalMaxima] with Serializable
object Take extends ProductReader[Take[_, _]] with Serializable
object TakeRight extends ProductReader[TakeRight[_, _]] with Serializable
object TakeWhile extends ProductReader[TakeWhile[_]] with Serializable
object Then
object Timer extends ProductReader[Timer] with Serializable
object TransposeMatrix extends ProductReader[TransposeMatrix[_]] with Serializable
object Trig extends ProductReader[Trig[_]] with Serializable
object TrigHold extends ProductReader[TrigHold[_]] with Serializable
object UGenInGroup
object UnaryOp extends ProductReader[SingleOut[_]] with Serializable
object UnzipWindow extends ProductReader[UnzipWindow[_]] with Serializable
object UnzipWindowN extends ProductReader[UnzipWindowN[_]] with Serializable
object ValueBooleanSeq extends ProductReader[ValueBooleanSeq] with Serializable
object ValueDoubleSeq extends ProductReader[ValueDoubleSeq] with Serializable
object ValueIntSeq extends ProductReader[ValueIntSeq] with Serializable
object ValueLongSeq extends ProductReader[ValueLongSeq] with Serializable
object ValueSeq
Loops the given values.
object Viterbi extends ProductReader[Viterbi] with Serializable
object WPE_Dereverberate extends ProductReader[WPE_Dereverberate] with Serializable
object WPE_ReverbFrame extends ProductReader[WPE_ReverbFrame] with Serializable
object WhiteNoise extends ProductReader[WhiteNoise] with Serializable
object WindowApply extends ProductReader[WindowApply[_]] with Serializable
object WindowIndexWhere extends ProductReader[WindowIndexWhere] with Serializable
object WindowMaxIndex extends ProductReader[WindowMaxIndex[_]] with Serializable
object Wrap extends ProductReader[Wrap[_]] with Serializable
object Zip extends ProductReader[Zip[_]] with Serializable
object ZipWindow extends ProductReader[ZipWindow[_]] with Serializable
object ZipWindowN extends ProductReader[ZipWindowN[_]] with Serializable

Packages

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