`base`#

Implements base classes for signal processing blocks in Acoular.

The classes in this module are abstract base classes that provide a common interface for all classes that generate an output via the generator result() in block-wise manner. They are not intended to be used directly, but to be subclassed by classes that implement the actual signal processing.

`Generator`	Interface for any generating signal processing block.
`SamplesGenerator`	Interface for any generating multi-channel time domain signal processing block.
`SpectraGenerator`	Interface for any generating multi-channel signal frequency domain processing block.
`InOut`	Abstract base class receiving from a `source` and returning signals in the same domain.
`TimeOut`	Abstract base class receiving from a `source` and returning time domain signals.
`SpectraOut`	Abstract base class receiving from a `source` and returning frequency domain signals.

class acoular.base.Generator#

Bases: ABCHasStrictTraits

Interface for any generating signal processing block.

It provides a common interface for all classes, which generate an output via the generator result() in block-wise manner. It has a common set of traits that are used by all classes that implement this interface. This includes the sampling frequency of the signal (sample_freq), the number of samples (num_samples), and the number of channels (num_channels). A private trait digest is used to store the internal identifier of the object, which is a hash of the object’s attributes. This is used to check if the object’s internal state has changed.

sample_freq = Float(1.0)#: Sampling frequency of the signal, defaults to 1.0

num_samples = CInt#: Number of signal samples

num_channels = CInt#: Number of channels

digest = Property(depends_on=['sample_freq', 'num_samples', 'num_channels'])#: A unique identifier for the generator, based on its properties. (read-only)

abstractmethod result(num)#

Python generator that yields the output block-wise.

This method needs to be implemented by the derived classes.

Parameters:

numint: The size of the first dimension of the blocks to be yielded

Yields:

numpy.ndarray: Two-dimensional output data block of shape (num, …)

class acoular.base.SamplesGenerator#

Bases: Generator

Interface for any generating multi-channel time domain signal processing block.

It provides a common interface for all SamplesGenerator classes, which generate an output via the generator result() in block-wise manner. This class has no real functionality on its own and should not be used directly.

digest = Property(depends_on=['sample_freq', 'num_samples', 'num_channels'])#: A unique identifier for the generator, based on its properties. (read-only)

abstractmethod result(num)#

Python generator that yields the output block-wise.

Parameters:

numint: This parameter defines the size of the blocks to be yielded (i.e. the number of samples per block)

Yields:

numpy.ndarray: The two-dimensional time-data block of shape (num, num_channels).

class acoular.base.SpectraGenerator#

Bases: Generator

Interface for any generating multi-channel signal frequency domain processing block.

It provides a common interface for all SpectraGenerator classes, which generate an output via the generator result() in block-wise manner. This class has no real functionality on its own and should not be used directly.

num_freqs = CInt#: Number of frequencies

freqs = CArray#: 1-D array of frequencies

block_size = CInt#: The length of the block used to calculate the spectra

digest = Property(depends_on=['sample_freq', 'num_samples', 'num_channels', 'num_freqs', 'block_size'])#: A unique identifier for the generator, based on its properties. (read-only)

abstractmethod result(num=1)#

Python generator that yields the output block-wise.

Parameters:

numinteger: This parameter defines the size of the number of snapshots to be yielded. Defaults to 1.

Yields:

numpy.ndarray: A two-dimensional block of shape (num, num_channels * num_freqs).

class acoular.base.TimeOut#

Bases: SamplesGenerator

Abstract base class receiving from a source and returning time domain signals.

It provides a base class that can be used to create signal processing blocks that receive data from any generating source and generates a time signal output via the generator result() in block-wise manner.

source = Instance(Generator)#: Data source; Generator or derived object.

sample_freq = Delegate('source')#: Sampling frequency of output signal, as given by source.

num_channels = Delegate('source')#: Number of channels in output, as given by source.

num_samples = Delegate('source')#: Number of samples in output, as given by source.

digest = Property(depends_on=['source.digest'])#: A unique identifier for the generator, based on its properties. (read-only)

abstractmethod result(num)#

Python generator that processes the source data and yields the time-signal block-wise.

This method needs to be implemented by the derived classes.

Parameters:

numint: This parameter defines the size of the blocks to be yielded (i.e. the number of samples per block)

Yields:

numpy.ndarray: Two-dimensional output data block of shape (num, num_channels)

class acoular.base.SpectraOut#

Bases: SpectraGenerator

Abstract base class receiving from a source and returning frequency domain signals.

It provides a base class that can be used to create signal processing blocks that receive data from any generating source domain and generates a frequency domain output via the generator result() in block-wise manner.

source = Instance(Generator)#: Data source; Generator or derived object.

sample_freq = Delegate('source')#: Sampling frequency of output signal, as given by source.

num_channels = Delegate('source')#: Number of channels in output, as given by source.

num_samples = Delegate('source')#: Number of snapshots in output, as given by source.

num_freqs = Delegate('source')#: Number of frequencies in output, as given by source.

freqs = Delegate('source')#: 1-D array of frequencies, as given by source.

block_size = Delegate('source')#: The size of the block used to calculate the spectra

digest = Property(depends_on=['source.digest'])#: A unique identifier for the generator, based on its properties. (read-only)

abstractmethod result(num=1)#

Python generator that processes the source data and yields the output block-wise.

This method needs to be implemented by the derived classes.

numinteger: This parameter defines the the number of snapshots to be yielded. Defaults to 1.

Yields:

numpy.ndarray: A two-dimensional block of shape (num, num_channels * num_freqs).

class acoular.base.InOut#

Bases: SamplesGenerator, SpectraGenerator

Abstract base class receiving from a source and returning signals in the same domain.

It provides a base class that can be used to create signal processing blocks that receive data from any generating source and generates an output via the generator result() in block-wise manner.

source = Instance(Generator)#: Data source; Generator or derived object.

sample_freq = Delegate('source')#: Sampling frequency of output signal, as given by source.

num_channels = Delegate('source')#: Number of channels in output, as given by source.

num_freqs = Delegate('source')#: Number of frequencies in output, as given by source.

num_samples = Delegate('source')#: Number of samples / snapshots in output, as given by source.

digest = Property(depends_on=['source.digest'])#: A unique identifier for the generator, based on its properties. (read-only)

abstractmethod result(num)#

Python generator that processes the source data and yields the output block-wise.

This method needs to be implemented by the derived classes.

Parameters:

numint: The size of the first dimension of the blocks to be yielded

Yields:

numpy.ndarray: Two-dimensional output data block of shape (num, …)

base#

`base`#