Module audian.bufferedfilter

Filter data on the fly.

Classes

class BufferedFilter (name='filtered',
source='data',
panel='trace',
color='#00ee00',
lw_thin=1.1,
lw_thick=2)

Expand source code

class BufferedFilter(BufferedData):

    def __init__(self, name='filtered', source='data', panel='trace',
                 color='#00ee00', lw_thin=1.1, lw_thick=2):
        super().__init__(name, source, tbefore=10, panel=panel,
                         panel_type='trace', color=color,
                         lw_thin=lw_thin, lw_thick=lw_thick)
        self.highpass_cutoff = 0
        self.lowpass_cutoff = 1
        self.filter_order = 2
        self.sos = None

        
    def open(self, source):
        super().open(source)
        self.highpass_cutoff = 0
        self.lowpass_cutoff = self.rate/2
        self.filter_order = 2
        self.sos = None
        self.update()


    def process(self, source, dest, nbefore):
        if self.sos is None:
            dest[:, :] = source[nbefore:, :]
        else:
            for c in range(self.channels):
                dest[:, c] = sosfilt(self.sos, source[:, c],)[nbefore:]

            
    def update(self):
        if self.highpass_cutoff < 0.001*self.rate/2 and \
           self.lowpass_cutoff >= self.rate/2 - 1e-8:
            self.sos = None
        elif self.highpass_cutoff < 0.001*self.rate/2:
            self.sos = butter(self.filter_order, self.lowpass_cutoff,
                              'lowpass', fs=self.rate, output='sos')
        elif self.lowpass_cutoff >= self.rate/2 - 1e-8:
            self.sos = butter(self.filter_order, self.highpass_cutoff,
                              'highpass', fs=self.rate, output='sos')
        else:
            self.sos = butter(self.filter_order,
                              (self.highpass_cutoff, self.lowpass_cutoff),
                              'bandpass', fs=self.rate, output='sos')
        self.recompute_all()

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.

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 the 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.
• move_buffer(): move and resize buffer (called by update_buffer()).
• load_buffer(): load a range of samples into a buffer (called by reload_buffer() and move_buffer()).
• _buffer_position(): compute position and size of buffer (used by update_buffer()).
• _recycle_buffer(): move buffer to new position and recycle content if possible (called by move_buffer()).
• allocate_buffer(): reallocate the buffer to have the right size (called by _recycle_buffer()).

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).

Ancestors

• BufferedData
• audioio.bufferedarray.BufferedArray

Methods

def open(self, source)

Expand source code

def open(self, source):
    super().open(source)
    self.highpass_cutoff = 0
    self.lowpass_cutoff = self.rate/2
    self.filter_order = 2
    self.sos = None
    self.update()

def process(self, source, dest, nbefore)

Expand source code

def process(self, source, dest, nbefore):
    if self.sos is None:
        dest[:, :] = source[nbefore:, :]
    else:
        for c in range(self.channels):
            dest[:, c] = sosfilt(self.sos, source[:, c],)[nbefore:]

def update(self)

Expand source code

def update(self):
    if self.highpass_cutoff < 0.001*self.rate/2 and \
       self.lowpass_cutoff >= self.rate/2 - 1e-8:
        self.sos = None
    elif self.highpass_cutoff < 0.001*self.rate/2:
        self.sos = butter(self.filter_order, self.lowpass_cutoff,
                          'lowpass', fs=self.rate, output='sos')
    elif self.lowpass_cutoff >= self.rate/2 - 1e-8:
        self.sos = butter(self.filter_order, self.highpass_cutoff,
                          'highpass', fs=self.rate, output='sos')
    else:
        self.sos = butter(self.filter_order,
                          (self.highpass_cutoff, self.lowpass_cutoff),
                          'bandpass', fs=self.rate, output='sos')
    self.recompute_all()