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package graph

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Package Members

  1. package cdp

Type Members

  1. 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

  2. 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

  3. 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

  4. final case class AudioFileIn(file: URI, numChannels: Int) extends MultiOut[Double] with Product with Serializable
  5. 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

  6. 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.

  7. 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.

  8. final case class Bleach(in: D, filterLen: I = 256, feedback: D = 0.001, filterClip: D = 8.0) extends SingleOut[Double] with Product with Serializable
  9. 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

  10. final case class BufferDisk[A](in: GE[A])(implicit num: Num[A]) extends SingleOut[A] with ProductWithAdjuncts with Product with Serializable
  11. 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.

  12. final case class ChannelProxy[A](elem: GE[A], index: Int) extends GE.Lazy[A] with Product with Serializable

    Straight outta ScalaCollider.

  13. 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
  14. 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.

  15. final case class Complex1FFT(in: D, size: I, padding: I = 0) extends FFTFullUGen with Product with Serializable
  16. final case class Complex1IFFT(in: D, size: I, padding: I = 0) extends FFTFullUGen with Product with Serializable
  17. 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)

  18. 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)

  19. 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.

  20. final case class ComplexUnaryOp(op: Int, in: D) extends SingleOut[Double] with Product with Serializable
  21. final case class Concat[A](a: GE[A], b: GE[A]) extends SingleOut[A] with Product with Serializable

    Concatenates two signals.

  22. sealed trait Const[A1] extends UGenIn[A1] with StreamIn

    A scalar constant used as an input to a UGen.

  23. final case class ConstB(value: Boolean) extends Const[Boolean] with StreamIn with Product with Serializable
  24. final case class ConstD(value: Double) extends Const[Double] with DoubleLike with Product with Serializable
  25. final case class ConstI(value: Int) extends Const[Int] with IntLike with Product with Serializable
  26. final case class ConstL(value: Long) extends Const[Long] with LongLike with Product with Serializable
  27. 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.

  28. final case class ControlBlockSize() extends SingleOut[Int] with Product with Serializable

    A scalar information UGen that reports the control block size.

  29. 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

  30. final case class DC[A](in: GE[A]) extends SingleOut[A] with Product with Serializable

    Creates a constant infinite signal.

  31. 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.

  32. 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.

  33. 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

  34. 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

  35. 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

  36. 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

  37. 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.

  38. final case class DelayN[A](in: GE[A], maxLength: I, length: I) extends SingleOut[A] with Product with Serializable
  39. 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.

  40. 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).

  41. 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.

  42. 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.

  43. 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
  44. 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
  45. final case class DropWhile[A](in: GE[A], p: B) extends SingleOut[A] with Product with Serializable
  46. 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.

  47. 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
  48. final case class ElseIf[+A](pred: IfOrElseIfThen[A], cond: B) extends Product with Serializable
  49. final case class ElseIfThen[+A](pred: IfOrElseIfThen[A], cond: B, branch: Graph, result: A) extends IfThenLike[A] with ElseOrElseIfThen[A] with Product with Serializable
  50. sealed trait ElseLike[+A] extends ElseOrElseIfThen[A]
  51. sealed trait ElseOrElseIfThen[+A] extends Then[A]
  52. final case class ElseUnit(pred: IfOrElseIfThen[Any], branch: Graph) extends ElseLike[Any] with Expander[Unit] with Product with Serializable
  53. final case class Empty[A]() extends SingleOut[A] with Product with Serializable

    A single channel UGen of zero length.

  54. final case class ExpExp(in: D, inLow: D, inHigh: D, outLow: D, outHigh: D) extends SingleOut[Double] with Product with Serializable
  55. final case class ExpLin(in: D, inLow: D, inHigh: D, outLow: D, outHigh: D) extends SingleOut[Double] with Product with Serializable
  56. sealed trait FFT2FullUGen extends SingleOut[Double]
  57. sealed trait FFT2HalfUGen extends SingleOut[Double]
  58. sealed trait FFTFullUGen extends SingleOut[Double]
  59. sealed trait FFTHalfUGen extends SingleOut[Double]
  60. 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.

  61. 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

  62. 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

  63. 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
  64. 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)

  65. 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.

  66. 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).

  67. 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

  68. 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

  69. 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

  70. 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.

  71. 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

  72. 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)

  73. 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.

  74. 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

  75. 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

  76. sealed trait IfOrElseIfThen[+A] extends Then[A]
  77. 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

  78. sealed trait IfThenLike[+A] extends IfOrElseIfThen[A] with Expander[Unit]
  79. final case class ImageFileIn(file: URI, numChannels: Int) extends MultiOut[Double] with Product with Serializable
  80. final case class ImageFileOut(in: GE[Double], file: URI, spec: Spec) extends ZeroOut with Product with Serializable
  81. trait ImageFilePlatform extends AnyRef
  82. 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).

  83. 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).

  84. 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).

  85. 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.

  86. 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.

  87. 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)

  88. 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

  89. 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.

  90. 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.

  91. 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

  92. final case class LinExp(in: D, inLow: D, inHigh: D, outLow: D, outHigh: D) extends SingleOut[Double] with Product with Serializable
  93. 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.

  94. final case class LinLin(in: D, inLow: D, inHigh: D, outLow: D, outHigh: D) extends SingleOut[Double] with Product with Serializable
  95. 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

  96. 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)

  97. 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
  98. 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.

  99. 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.

  100. 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.

  101. 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

  102. 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.

  103. 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).

  104. 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.

  105. 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.

  106. 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

  107. 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

  108. 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

  109. 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)

  110. 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)

  111. 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

  112. 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 Ar = Ad = 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

  113. 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.

  114. 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

  115. 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.

  116. 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.

  117. 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.

  118. 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

  119. 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

  120. 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.

  121. final case class Real1FullFFT(in: D, size: I, padding: I = 0) extends FFTFullUGen with Product with Serializable
  122. final case class Real1FullIFFT(in: D, size: I, padding: I = 0) extends FFTFullUGen with Product with Serializable
  123. 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.

  124. 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.

  125. 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)

  126. 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)

  127. 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)

  128. 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.

  129. 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.

  130. 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).

  131. 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.

  132. 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.

  133. 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

  134. 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

  135. 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.

  136. 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.

  137. 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.

  138. 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.

  139. 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.

  140. 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.

  141. 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.

  142. 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.

  143. 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.

  144. 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.

  145. 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.

  146. 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

  147. 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).

  148. 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.

  149. 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)

  150. 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

  151. 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

  152. 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

  153. 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

  154. 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

  155. 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.

  156. 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

  157. 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
  158. 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
  159. final case class TakeWhile[A](in: GE[A], p: B) extends SingleOut[A] with Product with Serializable
  160. sealed trait Then[+A] extends Lazy
  161. 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.

  162. 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

  163. 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.

  164. 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.

  165. sealed trait UGenInGroup[A] extends UGenInLike[A]
  166. 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.

  167. sealed trait UGenProxy[A] extends UGenIn[A]
  168. final case class UnaryOp[A, B](op: Op[A, B], in: GE[A]) extends SingleOut[B] with Product with Serializable
  169. final case class UnzipWindow[A](in: GE[A], size: I = 1) extends GE.Lazy[A] with Product with Serializable
  170. final case class UnzipWindowN[A](numOutputs: Int, in: GE[A], size: I = 1) extends MultiOut[A] with Product with Serializable
  171. final case class ValueBooleanSeq(elems: Boolean*) extends SingleOut[Boolean] with Product with Serializable

    Loops the given values.

  172. final case class ValueDoubleSeq(elems: Double*) extends SingleOut[Double] with Product with Serializable

    Loops the given values.

  173. final case class ValueIntSeq(elems: Int*) extends SingleOut[Int] with Product with Serializable

    Loops the given values.

  174. final case class ValueLongSeq(elems: Long*) extends SingleOut[Long] with Product with Serializable

    Loops the given values.

  175. 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

  176. 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

  177. 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

  178. final case class WhiteNoise(mul: D = 1.0) extends SingleOut[Double] with Product with Serializable

    Nominal signal range is -mul to +mul (exclusive).

  179. 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.

  180. 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.

  181. 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.

  182. 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
  183. final case class Zip[A](a: GE[A], b: GE[A]) extends SingleOut[A] with Product with Serializable
  184. final case class ZipWindow[A](a: GE[A], b: GE[A], size: I = 1) extends SingleOut[A] with Product with Serializable
  185. final case class ZipWindowN[A](in: GE[A], size: I = 1) extends SingleOut[A] with Product with Serializable

Value Members

  1. object ARCWindow extends ProductReader[ARCWindow] with Serializable
  2. object AffineTransform2D extends ProductReader[AffineTransform2D] with Serializable
  3. object ArithmSeq extends ProductReader[ArithmSeq[_, _]] with Serializable
  4. object AudioFileIn extends ProductReader[AudioFileIn] with Serializable
  5. object AudioFileOut extends ProductReader[AudioFileOut] with Serializable
  6. object BinaryOp extends ProductReader[SingleOut[_]] with Serializable
  7. object Biquad extends ProductReader[Biquad] with Serializable
  8. object Bleach extends ProductReader[Bleach] with Serializable
  9. object Blobs2D extends ProductReader[Blobs2D] with Serializable
  10. object BlockSize
  11. object BufferDisk extends ProductReader[BufferDisk[_]] with Serializable
  12. object BufferMemory extends ProductReader[BufferMemory[_]] with Serializable
  13. object ChannelProxy extends ProductReader[ChannelProxy[_]] with Serializable
  14. object Clip extends ProductReader[Clip[_]] with Serializable
  15. object CombN extends ProductReader[CombN] with Serializable
  16. object Complex1FFT extends ProductReader[Complex1FFT] with Serializable
  17. object Complex1IFFT extends ProductReader[Complex1IFFT] with Serializable
  18. object Complex2FFT extends ProductReader[Complex2FFT] with Serializable
  19. object Complex2IFFT extends ProductReader[Complex2IFFT] with Serializable
  20. 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, ...

  21. 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, ...

  22. object Concat extends ProductReader[Concat[_]] with Serializable
  23. object Const
  24. object ConstB extends Serializable
  25. object ConstD extends Serializable
  26. object ConstI extends Serializable
  27. object ConstL extends Serializable
  28. object ConstQ extends ProductReader[ConstQ] with Serializable
  29. object ControlBlockSize extends ProductReader[ControlBlockSize] with Serializable
  30. object Convolution extends ProductReader[Convolution] with Serializable
  31. object DC extends ProductReader[DC[_]] with Serializable
  32. object DCT_II extends ProductReader[DCT_II] with Serializable
  33. object DEnvGen extends ProductReader[DEnvGen[_]] with Serializable
  34. object DebugSink extends ProductReader[DebugSink[_]] with Serializable
  35. object DebugThrough extends ProductReader[DebugThrough[_]] with Serializable
  36. object DelayN extends ProductReader[DelayN[_]] with Serializable
  37. object DetectLocalMax extends ProductReader[DetectLocalMax[_]] with Serializable
  38. object Differentiate extends ProductReader[Differentiate[_]] with Serializable
  39. object Distinct extends ProductReader[Distinct[_]] with Serializable
  40. object Done extends ProductReader[Done[_]] with Serializable
  41. object Drop extends ProductReader[Drop[_, _]] with Serializable
  42. object DropRight extends ProductReader[DropRight[_, _]] with Serializable
  43. object DropWhile extends ProductReader[DropWhile[_]] with Serializable
  44. object Elastic extends ProductReader[Elastic[_]] with Serializable
  45. object Else
  46. object ElseGE extends ProductReader[ElseGE[_]] with Serializable
  47. object ElseIfThen extends ProductReader[ElseIfThen[_]] with Serializable
  48. object ElseUnit extends ProductReader[ElseUnit] with Serializable
  49. object Empty extends ProductReader[Empty[_]] with Serializable
  50. object ExpExp extends ProductReader[ExpExp] with Serializable
  51. object ExpLin extends ProductReader[ExpLin] with Serializable
  52. object Feed extends ProductReader[Feed[_]] with Serializable
  53. object FilterSeq extends ProductReader[FilterSeq[_]] with Serializable
  54. object Flatten extends ProductReader[Flatten[_]] with Serializable
  55. object Fold extends ProductReader[Fold[_]] with Serializable
  56. object FoldCepstrum extends ProductReader[FoldCepstrum] with Serializable
  57. object Fourier extends ProductReader[Fourier[_]] with Serializable
  58. object Frames extends ProductReader[Frames[_]] with Serializable
  59. object Gate extends ProductReader[Gate[_]] with Serializable
  60. object GenWindow extends ProductReader[GenWindow[_]] with Serializable
  61. object GeomSeq extends ProductReader[GeomSeq[_, _]] with Serializable
  62. object GimpSlur extends ProductReader[GimpSlur] with Serializable
  63. object GramSchmidtMatrix extends ProductReader[GramSchmidtMatrix] with Serializable
  64. object HPF extends ProductReader[HPF] with Serializable
  65. object Hash extends ProductReader[Hash[_]] with Serializable
  66. object HilbertCurve
  67. object Histogram extends ProductReader[Histogram[_]] with Serializable
  68. object IfThen extends ProductReader[IfThen[_]] with Serializable
  69. object ImageFile extends ImageFilePlatform
  70. object ImageFileIn extends ProductReader[ImageFileIn] with Serializable
  71. object ImageFileOut extends ProductReader[ImageFileOut] with Serializable
  72. object ImageFileSeqIn extends ProductReader[ImageFileSeqIn] with Serializable
  73. object ImageFileSeqOut extends ProductReader[ImageFileSeqOut] with Serializable
  74. object Impulse extends ProductReader[Impulse] with Serializable
  75. object Indices extends ProductReader[Indices[_]] with Serializable
  76. object LFSaw extends ProductReader[LFSaw] with Serializable
  77. object LPF extends ProductReader[LPF] with Serializable
  78. object Latch extends ProductReader[Latch[_]] with Serializable
  79. object LeakDC extends ProductReader[LeakDC] with Serializable
  80. object Length extends ProductReader[Length[_]] with Serializable
  81. object Limiter extends ProductReader[Limiter] with Serializable
  82. object LinExp extends ProductReader[LinExp] with Serializable
  83. object LinKernighanTSP extends ProductReader[LinKernighanTSP] with Serializable
  84. object LinLin extends ProductReader[LinLin] with Serializable
  85. object Line extends ProductReader[Line[_]] with Serializable
  86. object Loudness extends ProductReader[Loudness] with Serializable
  87. object Masking2D extends ProductReader[Masking2D] with Serializable
  88. object MatchLen extends ProductReader[MatchLen[_, _]] with Serializable
  89. object MatrixInMatrix extends ProductReader[MatrixInMatrix[_]] with Serializable
  90. object MatrixOutMatrix extends ProductReader[MatrixOutMatrix[_]] with Serializable
  91. object MelFilter extends ProductReader[MelFilter] with Serializable
  92. object Metro extends ProductReader[Metro[_]] with Serializable
  93. object Mix
  94. object NormalizeWindow extends ProductReader[NormalizeWindow] with Serializable
  95. object NumChannels extends ProductReader[NumChannels[_]] with Serializable
  96. object OffsetOverlapAdd extends ProductReader[OffsetOverlapAdd[_]] with Serializable
  97. object OnePole extends ProductReader[OnePole] with Serializable
  98. object OnePoleWindow extends ProductReader[OnePoleWindow] with Serializable
  99. object OverlapAdd extends ProductReader[OverlapAdd[_]] with Serializable
  100. object PeakCentroid1D extends ProductReader[PeakCentroid1D] with Serializable
  101. object PeakCentroid2D extends ProductReader[PeakCentroid2D] with Serializable
  102. object Pearson extends ProductReader[Pearson] with Serializable
  103. object PenImage extends ProductReader[PenImage] with Serializable
  104. object PitchAC extends ProductReader[PitchAC] with Serializable
  105. object PitchesToViterbi extends ProductReader[PitchesToViterbi] with Serializable
  106. object Plot1D extends ProductReader[Plot1D[_]] with Serializable
  107. object Poll extends ProductReader[Poll[_]] with Serializable
  108. object PriorityQueue extends ProductReader[PriorityQueue[_, _]] with Serializable
  109. object Progress extends ProductReader[Progress] with Serializable
  110. object ProgressFrames extends ProductReader[ProgressFrames[_, _]] with Serializable
  111. object Real1FFT extends ProductReader[Real1FFT] with Serializable
  112. object Real1FullFFT extends ProductReader[Real1FullFFT] with Serializable
  113. object Real1FullIFFT extends ProductReader[Real1FullIFFT] with Serializable
  114. object Real1IFFT extends ProductReader[Real1IFFT] with Serializable
  115. object Real2FFT extends ProductReader[Real2FFT] with Serializable
  116. object Real2FullFFT extends ProductReader[Real2FullFFT] with Serializable
  117. object Real2FullIFFT extends ProductReader[Real2FullIFFT] with Serializable
  118. object Real2IFFT extends ProductReader[Real2IFFT] with Serializable
  119. object Reduce extends ProductReader[GE[_]] with Serializable
  120. object ReduceWindow extends ProductReader[GE[_]] with Serializable
  121. object RepeatWindow extends ProductReader[RepeatWindow[_, _]] with Serializable
  122. object Resample extends ProductReader[Resample] with Serializable
  123. object ResampleWindow extends ProductReader[ResampleWindow] with Serializable
  124. object ResizeWindow extends ProductReader[ResizeWindow[_]] with Serializable
  125. object ReverseWindow extends ProductReader[ReverseWindow[_]] with Serializable
  126. object RotateFlipMatrix extends ProductReader[RotateFlipMatrix[_]] with Serializable
  127. object RotateWindow extends ProductReader[RotateWindow[_]] with Serializable
  128. object RunningMax extends ProductReader[RunningMax[_]] with Serializable
  129. object RunningMin extends ProductReader[RunningMin[_]] with Serializable
  130. object RunningProduct extends ProductReader[RunningProduct[_]] with Serializable
  131. object RunningSum extends ProductReader[RunningSum[_]] with Serializable
  132. object RunningWindowMax extends ProductReader[RunningWindowMax[_]] with Serializable
  133. object RunningWindowMin extends ProductReader[RunningWindowMin[_]] with Serializable
  134. object RunningWindowProduct extends ProductReader[RunningWindowProduct[_]] with Serializable
  135. object RunningWindowSum extends ProductReader[RunningWindowSum[_]] with Serializable
  136. object SampleRate extends ProductReader[SampleRate]
  137. object ScanImage extends ProductReader[ScanImage] with Serializable
  138. object SegModPhasor extends ProductReader[SegModPhasor] with Serializable
  139. object SetResetFF extends ProductReader[SetResetFF] with Serializable
  140. object Sheet1D extends ProductReader[Sheet1D[_]] with Serializable
  141. object SinOsc extends ProductReader[SinOsc] with Serializable
  142. object Slices extends ProductReader[Slices[_, _]] with Serializable
  143. object Sliding extends ProductReader[Sliding[_]] with Serializable
  144. object SlidingPercentile extends ProductReader[SlidingPercentile[_]] with Serializable
  145. object SlidingWindowPercentile extends ProductReader[SlidingWindowPercentile[_]] with Serializable
  146. object SortWindow extends ProductReader[SortWindow[_, _]] with Serializable
  147. object StrongestLocalMaxima extends ProductReader[StrongestLocalMaxima] with Serializable
  148. object Take extends ProductReader[Take[_, _]] with Serializable
  149. object TakeRight extends ProductReader[TakeRight[_, _]] with Serializable
  150. object TakeWhile extends ProductReader[TakeWhile[_]] with Serializable
  151. object Then
  152. object Timer extends ProductReader[Timer] with Serializable
  153. object TransposeMatrix extends ProductReader[TransposeMatrix[_]] with Serializable
  154. object Trig extends ProductReader[Trig[_]] with Serializable
  155. object TrigHold extends ProductReader[TrigHold[_]] with Serializable
  156. object UGenInGroup
  157. object UnaryOp extends ProductReader[SingleOut[_]] with Serializable
  158. object UnzipWindow extends ProductReader[UnzipWindow[_]] with Serializable
  159. object UnzipWindowN extends ProductReader[UnzipWindowN[_]] with Serializable
  160. object ValueBooleanSeq extends ProductReader[ValueBooleanSeq] with Serializable
  161. object ValueDoubleSeq extends ProductReader[ValueDoubleSeq] with Serializable
  162. object ValueIntSeq extends ProductReader[ValueIntSeq] with Serializable
  163. object ValueLongSeq extends ProductReader[ValueLongSeq] with Serializable
  164. object ValueSeq

    Loops the given values.

  165. object Viterbi extends ProductReader[Viterbi] with Serializable
  166. object WPE_Dereverberate extends ProductReader[WPE_Dereverberate] with Serializable
  167. object WPE_ReverbFrame extends ProductReader[WPE_ReverbFrame] with Serializable
  168. object WhiteNoise extends ProductReader[WhiteNoise] with Serializable
  169. object WindowApply extends ProductReader[WindowApply[_]] with Serializable
  170. object WindowIndexWhere extends ProductReader[WindowIndexWhere] with Serializable
  171. object WindowMaxIndex extends ProductReader[WindowMaxIndex[_]] with Serializable
  172. object Wrap extends ProductReader[Wrap[_]] with Serializable
  173. object Zip extends ProductReader[Zip[_]] with Serializable
  174. object ZipWindow extends ProductReader[ZipWindow[_]] with Serializable
  175. object ZipWindowN extends ProductReader[ZipWindowN[_]] with Serializable

Ungrouped