Module `audian.buffereddata`

Base class for computed data.

Classes

class BufferedData (name, source_name, tbefore=0, tafter=0, panel='none', panel_type='trace', color='#00ee00', lw_thin=1.1, lw_thick=2)

Random access to time-series data of which only a part is held in memory.

This is a base class for accessing large audio recordings either from a file (class AudioLoader) or by computing its contents on the fly (e.g. filtered data, envelopes or spectrograms). The BufferedArray behaves like a single big ndarray with first dimension indexing the frames and second dimension indexing the channels of the data. Higher dimensions are also supported. For example, a third dimension for frequencies needed for spectrograms. Internally the class holds only a part of the data in memory. The size of this buffer is set to bufferframes frames. If more data are requested, the buffer is enlarged accordingly.

Classes inheriting BufferedArray just need to implement

self.load_buffer(offset, nsamples, pbuffer)

This function needs to load the supplied pbuffer with nframes frames of data starting at frame offset.

In the constructor or some kind of opening function, you need to set the following member variables, followed by a call to init_buffer():

self.rate            # number of frames per second
self.channels        # number of channels per frame
self.frames          # total number of frames
self.shape = (self.frames, self.channels, ...)        
self.bufferframes    # number of frames the buffer should hold
self.backframes      # number of frames kept for moving back
self.init_buffer()

or provide all this information via the constructor:

Parameters

rate : float: The sampling rate of the data in seconds.
channels : int: The number of channels.
frames : int: The number of frames.
bufferframes : int: Number of frames the curent data buffer holds.
backframes : int: Number of frames the curent data buffer should keep before requested data ranges.
verbose : int: If larger than zero show detailed error/warning messages.

Attributes

rate : float: The sampling rate of the data in seconds.
channels : int: The number of channels.
frames : int: The number of frames. Same as len().
shape : tuple: Frames and channels of the data. Optional higher dimensions.
ndim : int: Number of dimensions: 2 (frames and channels) or higher.
size : int: Total number of samples: frames times channels.
offset : int: Index of first frame in the current buffer.
buffer : ndarray of floats: The curently available data. First dimension is time, second channels. Optional higher dimensions according to ndim and shape.
bufferframes : int: Number of samples the curent data buffer holds.
backframes : int: Number of samples the curent data buffer should keep before requested data ranges.
follow : int: If zero (default), move buffer position only for requests outside the current buffer. If larger than zero then buffer position follows requested data ranges if buffer can be moved by more than followframes. This results in more frequent but smaller buffer updates. Set it after calling the constructor or init_buffer().
buffer_changed : ndarray of bool: For each channel a flag, whether the buffer content has been changed. Set to True, whenever load_buffer() was called.

Methods

len(): Number of frames.
__getitem__: Access data.
blocks(): Generator for blockwise processing of AudioLoader data.
update_buffer(): make sure that the buffer contains data of a range of indices.
update_time(): make sure that the buffer contains data of a given time range.
reload_buffer(): reload the current buffer.
load_buffer(): load a range of samples into a buffer.
move_buffer(): move and resize buffer.
buffer_position(): compute position and size of buffer.
recycle_buffer(): move buffer to new position and recycle content if possible.

Notes

Access via __getitem__ or __next__ is slow! Even worse, using numpy functions on this class first converts it to a numpy array - that is something we actually do not want! We should subclass directly from numpy.ndarray . For details see http://docs.scipy.org/doc/numpy/user/basics.subclassing.html When subclassing, there is an offset argument, that might help to speed up __getitem__ .

Construtor for initializing 2D arrays (times x channels).

Expand source code

class BufferedData(BufferedArray):

    def __init__(self, name, source_name, tbefore=0, tafter=0,
                 panel='none', panel_type='trace',
                 color='#00ee00', lw_thin=1.1, lw_thick=2):
        super().__init__(verbose=0)
        self.name = name
        self.source_name = source_name
        self.tbefore = 0
        self.tafter = 0
        self.panel = panel
        self.panel_type = panel_type
        self.plot_items = []
        self.color = color
        self.lw_thin = lw_thin
        self.lw_thick = lw_thick
        self.source = None
        self.source_tbefore = tbefore
        self.source_tafter = tafter
        self.dests = []
        self.need_update = False


    def expand_times(self, tbefore, tafter):
        self.tbefore += tbefore
        self.tafter += tafter
        return self.source_tbefore + tbefore, self.source_tafter + tafter


    def update_step(self, step=1, more_shape=None):
        tbuffer = self.bufferframes/self.rate
        if step < 1:
            step = 1
        self.rate = self.source.rate/step
        self.frames = (self.source.frames + step - 1)//step
        if more_shape is None:
            self.shape = (self.frames, self.channels)
        else:
            self.shape = (self.frames, self.channels) + more_shape
        self.ndim = len(self.shape)
        self.size = self.frames * self.channels
        if self.source.bufferframes == self.source.frames:
            self.bufferframes = self.frames
        else:
            self.bufferframes = int(tbuffer*self.rate)
        self.offset = (self.source.offset + step - 1)//step
        self.follow = 0

        
    def open(self, source, step=1, more_shape=None):
        self.source = source
        self.source.dests.append(self)
        self.ampl_min = source.ampl_min
        self.ampl_max = source.ampl_max
        self.unit = source.unit
        self.bufferframes = 0
        self.backframes = 0
        self.channels = self.source.channels
        self.rate = self.source.rate
        self.buffer_changed = np.zeros(self.channels, dtype=bool)
        self.buffer = np.zeros((0, self.channels))
        self.plot_items = [None]*self.channels
        self.update_step(step, more_shape)

        
    def align_buffer(self):
        soffset = self.source.offset
        snframes = len(self.source.buffer)
        if soffset > 0:
            n = floor(self.source_tbefore*self.source.rate)
            soffset += n
            snframes -= n
        if self.source.offset + len(self.source.buffer) < self.source.frames:
            n = floor(self.source_tafter*self.source.rate)
            snframes -= n
        offset = ceil(soffset*self.rate/self.source.rate)
        nframes = floor((soffset + snframes)*self.rate/self.source.rate) - offset
        self.move_buffer(offset, nframes)
        self.bufferframes = len(self.buffer)


    def load_buffer(self, offset, nframes, buffer):
        print(f'load {self.name} {offset/self.rate:.3f} - {(offset + nframes)/self.rate:.3f}')
        # transform to rate of source buffer:
        soffset = floor(offset*self.source.rate/self.rate)
        snframes = ceil((offset + nframes)*self.source.rate/self.rate) - soffset
        nbefore = floor(self.source_tbefore/self.source.rate)
        soffset -= nbefore
        snframes += nbefore
        nafter = ceil(self.source_tafter/self.source.rate)
        snframes += nafter
        soffset -= self.source.offset
        if soffset < 0:
            nbefore += soffset
            snframes += soffset
            soffset = 0
        if soffset + snframes > len(self.source.buffer):
            snframes = len(self.source.buffer) - soffset
        source = self.source.buffer[soffset:soffset + snframes]
        self.process(source, buffer, nbefore)


    def recompute(self):
        if len(self.source.buffer) > 0:
            self.allocate_buffer()
        self.reload_buffer()


    def is_visible(self):
        for pi in self.plot_items:
            if pi is not None and pi.isVisible():
                return True
        return False


    def set_visible(self, show):
        for pi in self.plot_items:
            if pi is not None:
                pi.setVisible(show)


    def set_need_update(self):
        self.need_update = False
        for pi in self.plot_items:
            if pi is not None and pi.isVisible():
                self.need_update = True
                break
        for d in self.dests:
            d.set_need_update()
        # end of dependency chain:
        if len(self.dests) == 0:
            # go to sources and propagate needed update:
            trace = self
            while hasattr(trace, 'source'):
                s = trace.source
                s.need_update = trace.need_update or s.need_update
                trace = s
            

    def recompute_all(self):
        if self.need_update:
            self.recompute()
            for d in self.dests:
                d.recompute_all()

Ancestors

audioio.bufferedarray.BufferedArray

Subclasses

Methods

def expand_times(self, tbefore, tafter)
def update_step(self, step=1, more_shape=None)
def open(self, source, step=1, more_shape=None)
def align_buffer(self)
def load_buffer(self, offset, nframes, buffer)
def recompute(self)
def is_visible(self)
def set_visible(self, show)
def set_need_update(self)
def recompute_all(self)