Source code for pyo.lib.fourier

"""
Fast Fourier Transform.

A Fast Fourier Transform (FFT) is an efficient algorithm to compute
the discrete Fourier transform (DFT) and its inverse (IFFT).

The objects below can be used to perform sound processing in the
spectral domain.

"""

"""
Copyright 2009-2015 Olivier Belanger

This file is part of pyo, a python module to help digital signal
processing script creation.

pyo is free software: you can redistribute it and/or modify
it under the terms of the GNU Lesser General Public License as
published by the Free Software Foundation, either version 3 of the
License, or (at your option) any later version.

pyo is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
GNU Lesser General Public License for more details.

You should have received a copy of the GNU Lesser General Public
License along with pyo.  If not, see <http://www.gnu.org/licenses/>.
"""

from ._core import *
from ._maps import *


[docs]class FFT(PyoObject): """ Fast Fourier Transform. FFT analyses an input signal and converts it into the spectral domain. Three audio signals are sent out of the object, the `real` part, from bin 0 (DC) to bin size/2 (Nyquist), the `imaginary` part, from bin 0 to bin size/2-1, and the bin number, an increasing count from 0 to size-1. `real` and `imaginary` buffer's left samples up to size-1 are filled with zeros. See notes below for an example of how to retrieve each signal component. :Parent: :py:class:`PyoObject` :Args: input: PyoObject Input signal to process. size: int {pow-of-two > 4}, optional FFT size. Must be a power of two greater than 4. The FFT size is the number of samples used in each analysis frame. Defaults to 1024. overlaps: int, optional The number of overlaped analysis block. Must be a positive integer. More overlaps can greatly improved sound quality synthesis but it is also more CPU expensive. Defaults to 4. wintype: int, optional Shape of the envelope used to filter each input frame. Possible shapes are : 0. rectangular (no windowing) 1. Hamming 2. Hanning 3. Bartlett (triangular) 4. Blackman 3-term 5. Blackman-Harris 4-term 6. Blackman-Harris 7-term 7. Tuckey (alpha = 0.66) 8. Sine (half-sine window) .. note:: FFT has no `out` method. Signal must be converted back to time domain, with IFFT, before being sent to output. FFT has no `mul` and `add` attributes. Real, imaginary and bin_number parts are three separated set of audio streams. The user should call : | FFT['real'] to retrieve the real part. | FFT['imag'] to retrieve the imaginary part. | FFT['bin'] to retrieve the bin number part. >>> s = Server().boot() >>> s.start() >>> a = Noise(.25).mix(2) >>> fin = FFT(a, size=1024, overlaps=4, wintype=2) >>> t = ExpTable([(0,0),(3,0),(10,1),(20,0),(30,.8),(50,0),(70,.6),(150,0),(512,0)], size=512) >>> amp = TableIndex(t, fin["bin"]) >>> re = fin["real"] * amp >>> im = fin["imag"] * amp >>> fout = IFFT(re, im, size=1024, overlaps=4, wintype=2).mix(2).out() """ def __init__(self, input, size=1024, overlaps=4, wintype=2): pyoArgsAssert(self, "oiIi", input, size, overlaps, wintype) PyoObject.__init__(self) self._real_dummy = [] self._imag_dummy = [] self._bin_dummy = [] self._input = input self._size = size self._overlaps = overlaps self._wintype = wintype self._in_fader = InputFader(input) in_fader, size, wintype, lmax = convertArgsToLists(self._in_fader, size, wintype) self._base_players = [] for j in range(overlaps): for i in range(lmax): hopsize = wrap(size, i) * j // overlaps self._base_players.append(FFTMain_base(wrap(in_fader, i), wrap(size, i), hopsize, wrap(wintype, i))) self._real_objs = [] self._imag_objs = [] self._bin_objs = [] for j in range(len(self._base_players)): self._real_objs.append(FFT_base(wrap(self._base_players, j), 0, self._mul, self._add)) self._imag_objs.append(FFT_base(wrap(self._base_players, j), 1, self._mul, self._add)) self._bin_objs.append(FFT_base(wrap(self._base_players, j), 2, self._mul, self._add)) self._base_objs = [Sig(0)] # Dummy objs to prevent PyoObjectBase methods to fail. self._init_play() def __len__(self): return len(self._real_objs) def __getitem__(self, str): if str == "real": self._real_dummy.append(Dummy([obj for i, obj in enumerate(self._real_objs)])) return self._real_dummy[-1] if str == "imag": self._imag_dummy.append(Dummy([obj for i, obj in enumerate(self._imag_objs)])) return self._imag_dummy[-1] if str == "bin": self._bin_dummy.append(Dummy([obj for i, obj in enumerate(self._bin_objs)])) return self._bin_dummy[-1]
[docs] def get(self, identifier="real", all=False): """ Return the first sample of the current buffer as a float. Can be used to convert audio stream to usable Python data. "real", "imag" or "bin" must be given to `identifier` to specify which stream to get value from. :Args: identifier: string {"real", "imag", "bin"} Address string parameter identifying audio stream. Defaults to "real". all: boolean, optional If True, the first value of each object's stream will be returned as a list. Otherwise, only the value of the first object's stream will be returned as a float. Defaults to False. """ if not all: return self.__getitem__(identifier)[0]._getStream().getValue() else: return [obj._getStream().getValue() for obj in self.__getitem__(identifier).getBaseObjects()]
[docs] def setInput(self, x, fadetime=0.05): """ Replace the `input` attribute. :Args: x: PyoObject New signal to process. fadetime: float, optional Crossfade time between old and new input. Default to 0.05. """ pyoArgsAssert(self, "oN", x, fadetime) self._input = x self._in_fader.setInput(x, fadetime)
[docs] def play(self, dur=0, delay=0): dur, delay, lmax = convertArgsToLists(dur, delay) self._autoplay(dur, delay) self._in_fader.play(dur, delay) self._base_players = [obj.play(wrap(dur, i), wrap(delay, i)) for i, obj in enumerate(self._base_players)] self._real_objs = [obj.play(wrap(dur, i), wrap(delay, i)) for i, obj in enumerate(self._real_objs)] self._imag_objs = [obj.play(wrap(dur, i), wrap(delay, i)) for i, obj in enumerate(self._imag_objs)] self._bin_objs = [obj.play(wrap(dur, i), wrap(delay, i)) for i, obj in enumerate(self._bin_objs)] return self
[docs] def stop(self, wait=0): self._autostop(wait) self._in_fader.stop(wait) [obj.stop(wait) for obj in self._base_players] [obj.stop(wait) for obj in self._real_objs] [obj.stop(wait) for obj in self._imag_objs] [obj.stop(wait) for obj in self._bin_objs] return self
[docs] def out(self, chnl=0, inc=1, dur=0, delay=0): return self.play(dur, delay)
[docs] def setSize(self, x): """ Replace the `size` attribute. :Args: x: int new `size` attribute. """ pyoArgsAssert(self, "i", x) self._size = x x, lmax = convertArgsToLists(x) poly = len(self._base_players) // self._overlaps for j in range(self._overlaps): for i in range(poly): hopsize = wrap(x, i) * j // self._overlaps self._base_players[j * poly + i].setSize(wrap(x, i), hopsize)
[docs] def setWinType(self, x): """ Replace the `wintype` attribute. :Args: x: int new `wintype` attribute. """ pyoArgsAssert(self, "i", x) self._wintype = x x, lmax = convertArgsToLists(x) [obj.setWinType(wrap(x, i)) for i, obj in enumerate(self._base_players)]
@property def input(self): """PyoObject. Input signal to process.""" return self._input @input.setter def input(self, x): self.setInput(x) @property def size(self): """int. FFT size.""" return self._size @size.setter def size(self, x): self.setSize(x) @property def wintype(self): """int. Windowing method.""" return self._wintype @wintype.setter def wintype(self, x): self.setWinType(x)
[docs]class IFFT(PyoObject): """ Inverse Fast Fourier Transform. IFFT takes a signal in the spectral domain and converts it to a real audio signal using an inverse fast fourier transform. IFFT takes two signals in input, the `real` and `imaginary` parts of an FFT analysis and returns the corresponding real signal. These signals must correspond to `real` and `imaginary` parts from an FFT object. :Parent: :py:class:`PyoObject` :Args: inreal: PyoObject Input `real` signal. inimag: PyoObject Input `imaginary` signal. size: int {pow-of-two > 4}, optional FFT size. Must be a power of two greater than 4. The FFT size is the number of samples used in each analysis frame. This value must match the `size` attribute of the former FFT object. Defaults to 1024. overlaps: int, optional The number of overlaped analysis block. Must be a positive integer. More overlaps can greatly improved sound quality synthesis but it is also more CPU expensive. This value must match the `overlaps` atribute of the former FFT object. Defaults to 4. wintype: int, optional Shape of the envelope used to filter each output frame. Possible shapes are : 0. rectangular (no windowing) 1. Hamming 2. Hanning 3. Bartlett (triangular) 4. Blackman 3-term 5. Blackman-Harris 4-term 6. Blackman-Harris 7-term 7. Tuckey (alpha = 0.66) 8. Sine (half-sine window) .. note:: The number of streams in `inreal` and `inimag` attributes must be egal to the output of the former FFT object. In most case, it will be `channels of processed sound` * `overlaps`. The output of IFFT must be mixed to reconstruct the real signal from the overlapped streams. It is left to the user to call the mix(channels of the processed sound) method on an IFFT object. >>> s = Server().boot() >>> s.start() >>> a = Noise(.25).mix(2) >>> fin = FFT(a, size=1024, overlaps=4, wintype=2) >>> t = ExpTable([(0,0),(3,0),(10,1),(20,0),(30,.8),(50,0),(70,.6),(150,0),(512,0)], size=512) >>> amp = TableIndex(t, fin["bin"]) >>> re = fin["real"] * amp >>> im = fin["imag"] * amp >>> fout = IFFT(re, im, size=1024, overlaps=4, wintype=2).mix(2).out() """ def __init__(self, inreal, inimag, size=1024, overlaps=4, wintype=2, mul=1, add=0): pyoArgsAssert(self, "ooiIiOO", inreal, inimag, size, overlaps, wintype, mul, add) PyoObject.__init__(self, mul, add) self._inreal = inreal self._inimag = inimag self._size = size self._overlaps = overlaps self._wintype = wintype self._in_fader = InputFader(inreal) self._in_fader2 = InputFader(inimag) in_fader, in_fader2, size, wintype, mul, add, lmax = convertArgsToLists( self._in_fader, self._in_fader2, size, wintype, mul, add ) self._base_objs = [] ratio = lmax // overlaps for i in range(lmax): hopsize = wrap(size, i) * ((i // ratio) % overlaps) // overlaps self._base_objs.append( IFFT_base( wrap(in_fader, i), wrap(in_fader2, i), wrap(size, i), hopsize, wrap(wintype, i), wrap(mul, i), wrap(add, i), ) ) self._init_play() def __len__(self): return len(self._inreal)
[docs] def setInReal(self, x, fadetime=0.05): """ Replace the `inreal` attribute. :Args: x: PyoObject New input `real` signal. fadetime: float, optional Crossfade time between old and new input. Default to 0.05. """ pyoArgsAssert(self, "oN", x, fadetime) self._inreal = x self._in_fader.setInput(x, fadetime)
[docs] def setInImag(self, x, fadetime=0.05): """ Replace the `inimag` attribute. :Args: x: PyoObject New input `imag` signal. fadetime: float, optional Crossfade time between old and new input. Default to 0.05. """ pyoArgsAssert(self, "oN", x, fadetime) self._inimag = x self._in_fader2.setInput(x, fadetime)
[docs] def setSize(self, x): """ Replace the `size` attribute. :Args: x: int new `size` attribute. """ pyoArgsAssert(self, "i", x) self._size = x x, lmax = convertArgsToLists(x) ratio = len(self._base_objs) // self._overlaps for i, obj in enumerate(self._base_objs): hopsize = wrap(x, i) * ((i // ratio) % self._overlaps) // self._overlaps self._base_objs[i].setSize(wrap(x, i), hopsize)
[docs] def setWinType(self, x): """ Replace the `wintype` attribute. :Args: x: int new `wintype` attribute. """ pyoArgsAssert(self, "i", x) self._wintype = x x, lmax = convertArgsToLists(x) [obj.setWinType(wrap(x, i)) for i, obj in enumerate(self._base_objs)]
[docs] def ctrl(self, map_list=None, title=None, wxnoserver=False): self._map_list = [SLMapMul(self._mul)] PyoObject.ctrl(self, map_list, title, wxnoserver)
@property def inreal(self): """PyoObject. Real input signal.""" return self._inreal @inreal.setter def inreal(self, x): self.setInReal(x) @property def inimag(self): """PyoObject. Imaginary input signal.""" return self._inimag @inimag.setter def inimag(self, x): self.setInImag(x) @property def size(self): """int. FFT size.""" return self._size @size.setter def size(self, x): self.setSize(x) @property def wintype(self): """int. Windowing method.""" return self._wintype @wintype.setter def wintype(self, x): self.setWinType(x)
[docs]class CarToPol(PyoObject): """ Performs the cartesian to polar conversion. The Cartesian system locates points on a plane by measuring the horizontal and vertical distances from an arbitrary origin to a point. These are usually denoted as a pair of values (X,Y). The Polar system locates the point by measuring the straight line distance, usually denoted by R, from the origin to the point and the angle of an imaginary line from the origin to the point measured counterclockwise from the positive X axis. :Parent: :py:class:`PyoObject` :Args: inreal: PyoObject Real input signal. inimag: PyoObject Imaginary input signal. .. note:: Polar coordinates can be retrieve by calling : | CarToPol['mag'] to retrieve the magnitude part. | CarToPol['ang'] to retrieve the angle part. CarToPol has no `out` method. Signal must be converted back to time domain, with IFFT, before being sent to output. >>> s = Server().boot() >>> snd1 = SfPlayer(SNDS_PATH+"/transparent.aif", loop=True, mul=.7).mix(2) >>> snd2 = FM(carrier=[75,100,125,150], ratio=[.499,.5,.501,.502], index=20, mul=.1).mix(2) >>> fin1 = FFT(snd1, size=1024, overlaps=4) >>> fin2 = FFT(snd2, size=1024, overlaps=4) >>> # get magnitudes and phases of input sounds >>> pol1 = CarToPol(fin1["real"], fin1["imag"]) >>> pol2 = CarToPol(fin2["real"], fin2["imag"]) >>> # times magnitudes and adds phases >>> mag = pol1["mag"] * pol2["mag"] * 100 >>> pha = pol1["ang"] + pol2["ang"] >>> # converts back to rectangular >>> car = PolToCar(mag, pha) >>> fout = IFFT(car["real"], car["imag"], size=1024, overlaps=4).mix(2).out() >>> s.start() """ def __init__(self, inreal, inimag, mul=1, add=0): pyoArgsAssert(self, "ooOO", inreal, inimag, mul, add) PyoObject.__init__(self, mul, add) self._mag_dummy = [] self._ang_dummy = [] self._inreal = inreal self._inimag = inimag self._in_fader = InputFader(inreal) self._in_fader2 = InputFader(inimag) in_fader, in_fader2, mul, add, lmax = convertArgsToLists(self._in_fader, self._in_fader2, mul, add) self._base_objs = [] for i in range(lmax): self._base_objs.append(CarToPol_base(wrap(in_fader, i), wrap(in_fader2, i), 0, wrap(mul, i), wrap(add, i))) self._base_objs.append(CarToPol_base(wrap(in_fader, i), wrap(in_fader2, i), 1, wrap(mul, i), wrap(add, i))) self._init_play() def __len__(self): return len(self._inreal) def __getitem__(self, str): if str == "mag": self._mag_dummy.append(Dummy([obj for i, obj in enumerate(self._base_objs) if i % 2 == 0])) return self._mag_dummy[-1] if str == "ang": self._ang_dummy.append(Dummy([obj for i, obj in enumerate(self._base_objs) if i % 2 == 1])) return self._ang_dummy[-1]
[docs] def get(self, identifier="mag", all=False): """ Return the first sample of the current buffer as a float. Can be used to convert audio stream to usable Python data. "mag" or "ang" must be given to `identifier` to specify which stream to get value from. :Args: identifier: string {"mag", "ang"} Address string parameter identifying audio stream. Defaults to "mag". all: boolean, optional If True, the first value of each object's stream will be returned as a list. Otherwise, only the value of the first object's stream will be returned as a float. Defaults to False. """ if not all: return self.__getitem__(identifier)[0]._getStream().getValue() else: return [obj._getStream().getValue() for obj in self.__getitem__(identifier).getBaseObjects()]
[docs] def setInReal(self, x, fadetime=0.05): """ Replace the `inreal` attribute. :Args: x: PyoObject New signal to process. fadetime: float, optional Crossfade time between old and new input. Default to 0.05. """ pyoArgsAssert(self, "oN", x, fadetime) self._inreal = x self._in_fader.setInput(x, fadetime)
[docs] def setInImag(self, x, fadetime=0.05): """ Replace the `inimag` attribute. :Args: x: PyoObject New signal to process. fadetime: float, optional Crossfade time between old and new input. Default to 0.05. """ pyoArgsAssert(self, "oN", x, fadetime) self._inimag = x self._in_fader2.setInput(x, fadetime)
@property def inreal(self): """PyoObject. Real input signal.""" return self._inreal @inreal.setter def inreal(self, x): self.setInReal(x) @property def inimag(self): """PyoObject. Imaginary input signal.""" return self._inimag @inimag.setter def inimag(self, x): self.setInImag(x)
[docs]class PolToCar(PyoObject): """ Performs the polar to cartesian conversion. The Polar system locates the point by measuring the straight line distance, usually denoted by R, from the origin to the point and the angle of an imaginary line from the origin to the point measured counterclockwise from the positive X axis. The Cartesian system locates points on a plane by measuring the horizontal and vertical distances from an arbitrary origin to a point. These are usually denoted as a pair of values (X,Y). :Parent: :py:class:`PyoObject` :Args: inmag: PyoObject Magintude input signal. inang: PyoObject Angle input signal. .. note:: Cartesians coordinates can be retrieve by calling : | PolToCar['real'] to retrieve the real part. | CarToPol['imag'] to retrieve the imaginary part. PolToCar has no `out` method. Signal must be converted back to time domain, with IFFT, before being sent to output. >>> s = Server().boot() >>> snd1 = SfPlayer(SNDS_PATH+"/transparent.aif", loop=True, mul=.7).mix(2) >>> snd2 = FM(carrier=[75,100,125,150], ratio=[.499,.5,.501,.502], index=20, mul=.1).mix(2) >>> fin1 = FFT(snd1, size=1024, overlaps=4) >>> fin2 = FFT(snd2, size=1024, overlaps=4) >>> # get magnitudes and phases of input sounds >>> pol1 = CarToPol(fin1["real"], fin1["imag"]) >>> pol2 = CarToPol(fin2["real"], fin2["imag"]) >>> # times magnitudes and adds phases >>> mag = pol1["mag"] * pol2["mag"] * 100 >>> pha = pol1["ang"] + pol2["ang"] >>> # converts back to rectangular >>> car = PolToCar(mag, pha) >>> fout = IFFT(car["real"], car["imag"], size=1024, overlaps=4).mix(2).out() >>> s.start() """ def __init__(self, inmag, inang, mul=1, add=0): pyoArgsAssert(self, "ooOO", inmag, inang, mul, add) PyoObject.__init__(self, mul, add) self._real_dummy = [] self._imag_dummy = [] self._inmag = inmag self._inang = inang self._in_fader = InputFader(inmag) self._in_fader2 = InputFader(inang) in_fader, in_fader2, mul, add, lmax = convertArgsToLists(self._in_fader, self._in_fader2, mul, add) self._base_objs = [] for i in range(lmax): self._base_objs.append(PolToCar_base(wrap(in_fader, i), wrap(in_fader2, i), 0, wrap(mul, i), wrap(add, i))) self._base_objs.append(PolToCar_base(wrap(in_fader, i), wrap(in_fader2, i), 1, wrap(mul, i), wrap(add, i))) self._init_play() def __len__(self): return len(self._inmag) def __getitem__(self, str): if str == "real": self._real_dummy.append(Dummy([obj for i, obj in enumerate(self._base_objs) if i % 2 == 0])) return self._real_dummy[-1] if str == "imag": self._imag_dummy.append(Dummy([obj for i, obj in enumerate(self._base_objs) if i % 2 == 1])) return self._imag_dummy[-1]
[docs] def get(self, identifier="real", all=False): """ Return the first sample of the current buffer as a float. Can be used to convert audio stream to usable Python data. "real" or "imag" must be given to `identifier` to specify which stream to get value from. :Args: identifier: string {"real", "imag"} Address string parameter identifying audio stream. Defaults to "mag". all: boolean, optional If True, the first value of each object's stream will be returned as a list. Otherwise, only the value of the first object's stream will be returned as a float. Defaults to False. """ if not all: return self.__getitem__(identifier)[0]._getStream().getValue() else: return [obj._getStream().getValue() for obj in self.__getitem__(identifier).getBaseObjects()]
[docs] def setInMag(self, x, fadetime=0.05): """ Replace the `inmag` attribute. :Args: x: PyoObject New signal to process. fadetime: float, optional Crossfade time between old and new input. Default to 0.05. """ pyoArgsAssert(self, "oN", x, fadetime) self._inmag = x self._in_fader.setInput(x, fadetime)
[docs] def setInAng(self, x, fadetime=0.05): """ Replace the `inang` attribute. :Args: x: PyoObject New signal to process. fadetime: float, optional Crossfade time between old and new input. Default to 0.05. """ pyoArgsAssert(self, "oN", x, fadetime) self._inang = x self._in_fader2.setInput(x, fadetime)
@property def inmag(self): """PyoObject. Magnitude input signal.""" return self._inmag @inmag.setter def inmag(self, x): self.setInMag(x) @property def inang(self): """PyoObject. Angle input signal.""" return self._inang @inang.setter def inang(self, x): self.setInAng(x)
[docs]class FrameDelta(PyoObject): """ Computes the phase differences between successive frames. The difference between the phase values of successive FFT frames for a given bin determines the exact frequency of the energy centered in that bin. This is often known as the phase difference (and sometimes also referred to as phase derivative or instantaneous frequency if it's been subjected to a few additional calculations). In order to reconstruct a plausible playback of re-ordered FFT frames, we need to calculate the phase difference between successive frames and use it to construct a `running phase` (by simply summing the successive differences with FrameAccum) for the output FFT frames. :Parent: :py:class:`PyoObject` :Args: input: PyoObject Phase input signal, usually from an FFT analysis. framesize: int, optional Frame size in samples. Usually the same as the FFT size. Defaults to 1024. overlaps: int, optional Number of overlaps in incomming signal. Usually the same as the FFT overlaps. Defaults to 4. .. note:: FrameDelta has no `out` method. Signal must be converted back to time domain, with IFFT, before being sent to output. >>> s = Server().boot() >>> s.start() >>> snd = SNDS_PATH + '/transparent.aif' >>> size, hop = 1024, 256 >>> nframes = int(sndinfo(snd)[0] / size) + 1 >>> a = SfPlayer(snd, mul=.3) >>> m_mag = [NewMatrix(width=size, height=nframes) for i in range(4)] >>> m_pha = [NewMatrix(width=size, height=nframes) for i in range(4)] >>> fin = FFT(a, size=size, overlaps=4) >>> pol = CarToPol(fin["real"], fin["imag"]) >>> delta = FrameDelta(pol["ang"], framesize=size, overlaps=4) >>> m_mag_rec = MatrixRec(pol["mag"], m_mag, 0, [i*hop for i in range(4)]).play() >>> m_pha_rec = MatrixRec(delta, m_pha, 0, [i*hop for i in range(4)]).play() >>> m_mag_read = MatrixPointer(m_mag, fin["bin"]/size, Sine(freq=0.25, mul=.5, add=.5)) >>> m_pha_read = MatrixPointer(m_pha, fin["bin"]/size, Sine(freq=0.25, mul=.5, add=.5)) >>> accum = FrameAccum(m_pha_read, framesize=size, overlaps=4) >>> car = PolToCar(m_mag_read, accum) >>> fout = IFFT(car["real"], car["imag"], size=size, overlaps=4).mix(1).out() >>> right = Delay(fout, delay=0.013).out(1) """ def __init__(self, input, framesize=1024, overlaps=4, mul=1, add=0): pyoArgsAssert(self, "oiiOO", input, framesize, overlaps, mul, add) PyoObject.__init__(self, mul, add) self._input = input self._framesize = framesize self._overlaps = overlaps self._in_fader = InputFader(input) in_fader, framesize, overlaps, mul, add, lmax = convertArgsToLists( self._in_fader, framesize, overlaps, mul, add ) num_of_mains = len(self._in_fader) // self._overlaps self._base_players = [] for j in range(num_of_mains): objs_list = [] for i in range(len(self._in_fader)): if (i % num_of_mains) == j: objs_list.append(self._in_fader[i]) self._base_players.append(FrameDeltaMain_base(objs_list, wrap(framesize, j), wrap(overlaps, j))) self._base_objs = [] for i in range(lmax): base_player = i % num_of_mains overlap = i // num_of_mains self._base_objs.append( FrameDelta_base(self._base_players[base_player], overlap, wrap(mul, i), wrap(add, i)) ) self._init_play()
[docs] def out(self, chnl=0, inc=1, dur=0, delay=0): return self.play(dur, delay)
[docs] def setInput(self, x, fadetime=0.05): """ Replace the `input` attribute. :Args: x: PyoObject New signal to process. fadetime: float, optional Crossfade time between old and new input. Default to 0.05. """ pyoArgsAssert(self, "oN", x, fadetime) self._input = x self._in_fader.setInput(x, fadetime)
[docs] def setFrameSize(self, x): """ Replace the `framesize` attribute. :Args: x: int new `framesize` attribute. """ pyoArgsAssert(self, "i", x) self._framesize = x x, lmax = convertArgsToLists(x) [obj.setFrameSize(wrap(x, i)) for i, obj in enumerate(self._base_players)]
@property def input(self): """PyoObject. Phase input signal.""" return self._input @input.setter def input(self, x): self.setInput(x) @property def framesize(self): """PyoObject. Frame size in samples.""" return self._framesize @framesize.setter def framesize(self, x): self.setFrameSize(x)
[docs]class FrameAccum(PyoObject): """ Accumulates the phase differences between successive frames. The difference between the phase values of successive FFT frames for a given bin determines the exact frequency of the energy centered in that bin. This is often known as the phase difference (and sometimes also referred to as phase derivative or instantaneous frequency if it's been subjected to a few additional calculations). In order to reconstruct a plausible playback of re-ordered FFT frames, we need to calculate the phase difference between successive frames, with FrameDelta, and use it to construct a `running phase` (by simply summing the successive differences) for the output FFT frames. :Parent: :py:class:`PyoObject` :Args: input: PyoObject Phase input signal. framesize: int, optional Frame size in samples. Usually same as the FFT size. Defaults to 1024. overlaps: int, optional Number of overlaps in incomming signal. Usually the same as the FFT overlaps. Defaults to 4. .. note:: FrameAccum has no `out` method. Signal must be converted back to time domain, with IFFT, before being sent to output. >>> s = Server().boot() >>> s.start() >>> snd = SNDS_PATH + '/transparent.aif' >>> size, hop = 1024, 256 >>> nframes = int(sndinfo(snd)[0] / size) + 1 >>> a = SfPlayer(snd, mul=.3) >>> m_mag = [NewMatrix(width=size, height=nframes) for i in range(4)] >>> m_pha = [NewMatrix(width=size, height=nframes) for i in range(4)] >>> fin = FFT(a, size=size, overlaps=4) >>> pol = CarToPol(fin["real"], fin["imag"]) >>> delta = FrameDelta(pol["ang"], framesize=size, overlaps=4) >>> m_mag_rec = MatrixRec(pol["mag"], m_mag, 0, [i*hop for i in range(4)]).play() >>> m_pha_rec = MatrixRec(delta, m_pha, 0, [i*hop for i in range(4)]).play() >>> m_mag_read = MatrixPointer(m_mag, fin["bin"]/size, Sine(freq=0.25, mul=.5, add=.5)) >>> m_pha_read = MatrixPointer(m_pha, fin["bin"]/size, Sine(freq=0.25, mul=.5, add=.5)) >>> accum = FrameAccum(m_pha_read, framesize=size, overlaps=4) >>> car = PolToCar(m_mag_read, accum) >>> fout = IFFT(car["real"], car["imag"], size=size, overlaps=4).mix(1).out() >>> right = Delay(fout, delay=0.013).out(1) """ def __init__(self, input, framesize=1024, overlaps=4, mul=1, add=0): pyoArgsAssert(self, "oiiOO", input, framesize, overlaps, mul, add) PyoObject.__init__(self, mul, add) self._input = input self._framesize = framesize self._overlaps = overlaps self._in_fader = InputFader(input) in_fader, framesize, overlaps, mul, add, lmax = convertArgsToLists( self._in_fader, framesize, overlaps, mul, add ) num_of_mains = len(self._in_fader) // self._overlaps self._base_players = [] for j in range(num_of_mains): objs_list = [] for i in range(len(self._in_fader)): if (i % num_of_mains) == j: objs_list.append(self._in_fader[i]) self._base_players.append(FrameAccumMain_base(objs_list, wrap(framesize, j), wrap(overlaps, j))) self._base_objs = [] for i in range(lmax): base_player = i % num_of_mains overlap = i // num_of_mains self._base_objs.append( FrameAccum_base(self._base_players[base_player], overlap, wrap(mul, i), wrap(add, i)) ) self._init_play()
[docs] def out(self, chnl=0, inc=1, dur=0, delay=0): return self.play(dur, delay)
[docs] def setInput(self, x, fadetime=0.05): """ Replace the `input` attribute. :Args: x: PyoObject New signal to process. fadetime: float, optional Crossfade time between old and new input. Default to 0.05. """ pyoArgsAssert(self, "oN", x, fadetime) self._input = x self._in_fader.setInput(x, fadetime)
[docs] def setFrameSize(self, x): """ Replace the `framesize` attribute. :Args: x: int new `framesize` attribute. """ pyoArgsAssert(self, "i", x) self._framesize = x x, lmax = convertArgsToLists(x) [obj.setFrameSize(wrap(x, i)) for i, obj in enumerate(self._base_players)]
@property def input(self): """PyoObject. Phase input signal.""" return self._input @input.setter def input(self, x): self.setInput(x) @property def framesize(self): """PyoObject. Frame size in samples.""" return self._framesize @framesize.setter def framesize(self, x): self.setFrameSize(x)
[docs]class Vectral(PyoObject): """ Performs magnitude smoothing between successive frames. Vectral applies filter with different coefficients for increasing and decreasing magnitude vectors, bin by bin. :Parent: :py:class:`PyoObject` :Args: input: PyoObject Magnitude input signal, usually from an FFT analysis. framesize: int, optional Frame size in samples. Usually the same as the FFT size. Defaults to 1024. overlaps: int, optional Number of overlaps in incomming signal. Usually the same as the FFT overlaps. Defaults to 4. up: float or PyoObject, optional Filter coefficient for increasing bins, between 0 and 1. Lower values results in a longer ramp time for bin magnitude. Defaults to 1. down: float or PyoObject, optional Filter coefficient for decreasing bins, between 0 and 1. Lower values results in a longer decay time for bin magnitude. Defaults to 0.7 damp: float or PyoObject, optional High frequencies damping factor, between 0 and 1. Lower values mean more damping. Defaults to 0.9. .. note:: Vectral has no `out` method. Signal must be converted back to time domain, with IFFT, before being sent to output. >>> s = Server().boot() >>> snd = SNDS_PATH + '/accord.aif' >>> size, olaps = 1024, 4 >>> snd = SfPlayer(snd, speed=[.75,.8], loop=True, mul=.3) >>> fin = FFT(snd, size=size, overlaps=olaps) >>> pol = CarToPol(fin["real"], fin["imag"]) >>> vec = Vectral(pol["mag"], framesize=size, overlaps=olaps, down=.2, damp=.6) >>> car = PolToCar(vec, pol["ang"]) >>> fout = IFFT(car["real"], car["imag"], size=size, overlaps=olaps).mix(2).out() >>> s.start() """ def __init__(self, input, framesize=1024, overlaps=4, up=1.0, down=0.7, damp=0.9, mul=1, add=0): pyoArgsAssert(self, "oiiOOOOO", input, framesize, overlaps, up, down, damp, mul, add) PyoObject.__init__(self, mul, add) self._input = input self._framesize = framesize self._overlaps = overlaps self._up = up self._down = down self._damp = damp self._in_fader = InputFader(input) in_fader, framesize, overlaps, up, down, damp, mul, add, lmax = convertArgsToLists( self._in_fader, framesize, overlaps, up, down, damp, mul, add ) num_of_mains = len(self._in_fader) // self._overlaps self._base_players = [] for j in range(num_of_mains): objs_list = [] for i in range(len(self._in_fader)): if (i % num_of_mains) == j: objs_list.append(self._in_fader[i]) self._base_players.append( VectralMain_base( objs_list, wrap(framesize, j), wrap(overlaps, j), wrap(up, j), wrap(down, j), wrap(damp, j) ) ) self._base_objs = [] for i in range(lmax): base_player = i % num_of_mains overlap = i // num_of_mains self._base_objs.append(Vectral_base(self._base_players[base_player], overlap, wrap(mul, i), wrap(add, i))) self._init_play()
[docs] def out(self, chnl=0, inc=1, dur=0, delay=0): return self.play(dur, delay)
[docs] def setInput(self, x, fadetime=0.05): """ Replace the `input` attribute. :Args: x: PyoObject New signal to process. fadetime: float, optional Crossfade time between old and new input. Default to 0.05. """ pyoArgsAssert(self, "oN", x, fadetime) self._input = x self._in_fader.setInput(x, fadetime)
[docs] def setFrameSize(self, x): """ Replace the `framesize` attribute. :Args: x: int new `framesize` attribute. """ pyoArgsAssert(self, "i", x) self._framesize = x x, lmax = convertArgsToLists(x) [obj.setFrameSize(wrap(x, i)) for i, obj in enumerate(self._base_players)]
[docs] def setUp(self, x): """ Replace the `up` attribute. :Args: x: float or PyoObject new `up` attribute. """ pyoArgsAssert(self, "O", x) self._up = x x, lmax = convertArgsToLists(x) [obj.setUp(wrap(x, i)) for i, obj in enumerate(self._base_players)]
[docs] def setDown(self, x): """ Replace the `down` attribute. :Args: x: float or PyoObject new `down` attribute. """ pyoArgsAssert(self, "O", x) self._down = x x, lmax = convertArgsToLists(x) [obj.setDown(wrap(x, i)) for i, obj in enumerate(self._base_players)]
[docs] def setDamp(self, x): """ Replace the `damp` attribute. :Args: x: float or PyoObject new `damp` attribute. """ pyoArgsAssert(self, "O", x) self._damp = x x, lmax = convertArgsToLists(x) [obj.setDamp(wrap(x, i)) for i, obj in enumerate(self._base_players)]
[docs] def ctrl(self, map_list=None, title=None, wxnoserver=False): self._map_list = [ SLMap(0.0, 1.0, "lin", "up", self._up), SLMap(0.0, 1.0, "lin", "down", self._down), SLMap(0.0, 1.0, "lin", "damp", self._damp), SLMapMul(self._mul), ] PyoObject.ctrl(self, map_list, title, wxnoserver)
@property def input(self): """PyoObject. Magnitude input signal.""" return self._input @input.setter def input(self, x): self.setInput(x) @property def framesize(self): """int. Frame size in samples.""" return self._framesize @framesize.setter def framesize(self, x): self.setFrameSize(x) @property def up(self): """float or PyoObject. Filter coefficient for increasing bins.""" return self._up @up.setter def up(self, x): self.setUp(x) @property def down(self): """float or PyoObject. Filter coefficient for decreasing bins.""" return self._down @down.setter def down(self, x): self.setDown(x) @property def damp(self): """float or PyoObject. High frequencies damping factor.""" return self._damp @damp.setter def damp(self, x): self.setDamp(x)
[docs]class CvlVerb(PyoObject): """ Convolution based reverb. CvlVerb implements convolution based on a uniformly partitioned overlap-save algorithm. This object can be used to convolve an input signal with an impulse response soundfile to simulate real acoustic spaces. :Parent: :py:class:`PyoObject` :Args: input: PyoObject Input signal to process. impulse: string, optional Path to the impulse response soundfile. The file must have the same sampling rate as the server to get the proper convolution. Available at initialization time only. Defaults to 'IRMediumHallStereo.wav', located in pyo SNDS_PATH folder. size: int {pow-of-two}, optional The size in samples of each partition of the impulse file. Small size means smaller latency but more computation time. If not a power-of-2, the object will find the next power-of-2 greater and use that as the actual partition size. This value must also be greater or equal than the server's buffer size. Available at initialization time only. Defaults to 1024. bal: float or PyoObject, optional Balance between wet and dry signal, between 0 and 1. 0 means no reverb. Defaults to 0.25. .. seealso:: :py:class:`WGVerb`, :py:class:`STRev`, :py:class:`Freeverb` >>> s = Server().boot() >>> s.start() >>> sf = SfPlayer(SNDS_PATH+"/transparent.aif", loop=True, mul=0.5) >>> cv = CvlVerb(sf, SNDS_PATH+"/IRMediumHallStereo.wav", size=1024, bal=0.4).out() """ def __init__(self, input, impulse=SNDS_PATH + "/IRMediumHallStereo.wav", bal=0.25, size=1024, mul=1, add=0): pyoArgsAssert(self, "osOiOO", input, impulse, bal, size, mul, add) PyoObject.__init__(self, mul, add) self._input = input self._impulse = impulse self._bal = bal self._size = size self._in_fader = InputFader(input) in_fader, bal, size, mul, add, lmax = convertArgsToLists(self._in_fader, bal, size, mul, add) impulse, lmax2 = convertArgsToLists(impulse) self._base_objs = [] for file in impulse: _size, _dur, _snd_sr, _snd_chnls, _format, _type = sndinfo(file) lmax3 = max(lmax, _snd_chnls) self._base_objs.extend( [ CvlVerb_base( wrap(in_fader, i), stringencode(file), wrap(bal, i), wrap(size, i), i % _snd_chnls, wrap(mul, i), wrap(add, i), ) for i in range(lmax3) ] ) self._init_play()
[docs] def setInput(self, x, fadetime=0.05): """ Replace the `input` attribute. :Args: x: PyoObject New signal to process. fadetime: float, optional Crossfade time between old and new input. Default to 0.05. """ pyoArgsAssert(self, "oN", x, fadetime) self._input = x self._in_fader.setInput(x, fadetime)
[docs] def setBal(self, x): """ Replace the `bal` attribute. :Args: x: float or PyoObject new `bal` attribute. """ pyoArgsAssert(self, "O", x) self._bal = x x, lmax = convertArgsToLists(x) [obj.setBal(wrap(x, i)) for i, obj in enumerate(self._base_objs)]
[docs] def ctrl(self, map_list=None, title=None, wxnoserver=False): self._map_list = [SLMap(0.0, 1.0, "lin", "bal", self._bal), SLMapMul(self._mul)] PyoObject.ctrl(self, map_list, title, wxnoserver)
@property def input(self): """PyoObject. Input signal to process.""" return self._input @input.setter def input(self, x): self.setInput(x) @property def bal(self): """float or PyoObject. Wet / dry balance.""" return self._bal @bal.setter def bal(self, x): self.setBal(x)
[docs]class IFFTMatrix(PyoObject): """ Inverse Fast Fourier Transform with a PyoMatrixObject as input. IFFTMatrix takes a matrix as input and read it as it is a sonogram. On the current column, given by the `index` argument, the cells at the bottom represent the lower frequencies of the spectrum and the cells at the top, the higher frequencies of the spectrum. Because a matrix is usually used to store bipolar signals (with the amplitude between -1 and 1), a cell value of -1 represent a frequency bin with no amplitude and a cell value of 1 represents the maximum amplitude for the given frequency bin. The instantaneous angle value (in polar coordinates) of each frequency bin is given by the current sample in the audio signal given to the `phase` argument. Generally speaking, the more noisy this signal is, the more energy a bin with a positive amplitude value will have. :Parent: :py:class:`PyoObject` :Args: matrix: PyoMatrixObject The matrix used like a sonogram. index: PyoObject Normalized horizontal position in the matrix. 0 is the first column and 1 is the last. Positions between two columns are interpolated. If this signal is a Phasor, the matrix is read from left to right. phase: PyoObject Instantaneous angle value used to compute the inverse FFT. Try different signals like white noise or an oscillator with a frequency slightly detuned in relation to the frequency of the FFT (sr / fftsize). size: int {pow-of-two > 4}, optional FFT size. Must be a power of two greater than 4. The FFT size is the number of samples used in each analysis frame. This value must match the `size` attribute of the former FFT object. Defaults to 1024. overlaps: int, optional The number of overlaped analysis block. Must be a positive integer. More overlaps can greatly improved sound quality synthesis but it is also more CPU expensive. This value must match the `overlaps` atribute of the former FFT object. Defaults to 4. wintype: int, optional Shape of the envelope used to filter each output frame. Possible shapes are : 0. rectangular (no windowing) 1. Hamming 2. Hanning 3. Bartlett (triangular) 4. Blackman 3-term 5. Blackman-Harris 4-term 6. Blackman-Harris 7-term 7. Tuckey (alpha = 0.66) 8. Sine (half-sine window) .. note:: The output of IFFTMatrix must be mixed to reconstruct the real signal from the overlapped streams. It is left to the user to call the mix(number of channels) method on an IFFTMatrix object. >>> s = Server().boot() >>> s.start() >>> m = NewMatrix(512, 512) >>> m.genSineTerrain(1, 0.15) >>> index = Phasor([0.4, 0.5]) >>> phase = Noise(0.7) >>> fout = IFFTMatrix(m, index, phase, size=2048, overlaps=16, wintype=2).mix(2).out() """ def __init__(self, matrix, index, phase, size=1024, overlaps=4, wintype=2, mul=1, add=0): pyoArgsAssert(self, "mooiIiOO", matrix, index, phase, size, overlaps, wintype, mul, add) PyoObject.__init__(self, mul, add) self._matrix = matrix self._index = index self._phase = phase self._size = size self._overlaps = overlaps self._wintype = wintype matrix, index, phase, size, wintype, mul, add, self._lmax = convertArgsToLists( matrix, index, phase, size, wintype, mul, add ) self._base_objs = [] for j in range(overlaps): for i in range(self._lmax): hopsize = int(wrap(size, i) / overlaps) * j self._base_objs.append( IFFTMatrix_base( wrap(matrix, i), wrap(index, i), wrap(phase, i), wrap(size, i), hopsize, wrap(wintype, i), wrap(mul, i), wrap(add, i), ) ) self._init_play() def __len__(self): return int(len(self._base_objs) / self._overlaps)
[docs] def setIndex(self, x): """ Replace the `index` attribute. :Args: x: PyoObject new `index` attribute. """ pyoArgsAssert(self, "o", x) self._index = x x, lmax = convertArgsToLists(x) for j in range(overlaps): for i in range(self._lmax): self._base_objs[j * self._overlaps + i].setIndex(wrap(x, i))
[docs] def setPhase(self, x): """ Replace the `phase` attribute. :Args: x: PyoObject new `phase` attribute. """ pyoArgsAssert(self, "o", x) self._phase = x x, lmax = convertArgsToLists(x) for j in range(overlaps): for i in range(self._lmax): self._base_objs[j * self._overlaps + i].setPhase(wrap(x, i))
[docs] def setSize(self, x): """ Replace the `size` attribute. :Args: x: int new `size` attribute. """ pyoArgsAssert(self, "i", x) self._size = x x, lmax = convertArgsToLists(x) for j in range(overlaps): for i in range(self._lmax): hopsize = int(wrap(x, i) / self._overlaps) * j self._base_objs[j * self._overlaps + i].setSize(wrap(x, i), hopsize)
[docs] def setWinType(self, x): """ Replace the `wintype` attribute. :Args: x: int new `wintype` attribute. """ pyoArgsAssert(self, "i", x) self._wintype = x x, lmax = convertArgsToLists(x) for j in range(overlaps): for i in range(self._lmax): self._base_objs[j * self._overlaps + i].setWinType(wrap(x, i))
[docs] def ctrl(self, map_list=None, title=None, wxnoserver=False): self._map_list = [SLMapMul(self._mul)] PyoObject.ctrl(self, map_list, title, wxnoserver)
@property def index(self): """PyoObject. Normalized horizontal position.""" return self._index @index.setter def index(self, x): self.setIndex(x) @property def phase(self): """PyoObject. Instantaneous bin angle value.""" return self._phase @phase.setter def phase(self, x): self.setPhase(x) @property def size(self): """int. FFT size.""" return self._size @size.setter def size(self, x): self.setSize(x) @property def wintype(self): """int. Windowing method.""" return self._wintype @wintype.setter def wintype(self, x): self.setWinType(x)