`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 result generator 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.
`FiniteTimeOut`	Time-domain block that requires a source with a known length.
`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#: Sampling frequency of the signal, defaults to 1.0

num_samples#: Number of signal samples. -1 denotes an indefinite stream.

num_channels#: Number of channels

digest#: 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, …).

Notes

Yielded blocks remain stable after the generator advances. Implementations must not modify blocks received from their source. Callers should treat blocks as read-only because some generators return views to avoid copying.

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#: 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).

sample_freq#: Sampling frequency of the signal, defaults to 1.0

num_samples#: Number of signal samples. -1 denotes an indefinite stream.

num_channels#: Number of 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#: Number of frequencies

freqs#: 1-D array of frequencies

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

digest#: 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:

numint, optional: Maximum number of spectral snapshots per block to yield. Defaults to 1.

Yields:

numpy.ndarray

Two-dimensional spectral data block with shape (n, num_freqs * num_channels), where n is the number of snapshots in the yielded block and is at most num. The final block may contain fewer than num snapshots.

The second axis stores the spectra in frequency-major order with channels interlaced for each frequency bin. Reshagping a block with block.reshape(n, num_freqs, num_channels) gives direct access to the data by snapshot, frequency, and channel.

sample_freq#: Sampling frequency of the signal, defaults to 1.0

num_samples#: Number of signal samples. -1 denotes an indefinite stream.

num_channels#: Number of channels

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 result() generator in block-wise manner.

source#: Data source; Generator or derived object.

sample_freq#: Sampling frequency of output signal, as given by source.

num_channels#: Number of channels in output, as given by source.

num_samples#: Number of samples in output, as given by 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.FiniteTimeOut#

Bases: TimeOut

Time-domain block that requires a source with a known length.

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)

source#: Data source; Generator or derived object.

sample_freq#: Sampling frequency of output signal, as given by source.

num_channels#: Number of channels in output, as given by source.

num_samples#: Number of samples in output, as given by source.

digest#: A unique identifier for the generator, based on its properties. (read-only)

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 result() generator in block-wise manner.

source#: Data source; Generator or derived object.

sample_freq#: Sampling frequency of output signal, as given by source.

num_channels#: Number of channels in output, as given by source.

num_samples#: Number of snapshots in output, as given by source.

num_freqs#: Number of frequencies in output, as given by source.

freqs#: 1-D array of frequencies, as given by source.

block_size#: The size of the block used to calculate the spectra

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.

Parameters:

numint, optional: Maximum number of spectral snapshots per block to yield. Defaults to 1.

Yields:

numpy.ndarray

Two-dimensional spectral data block with shape (n, num_freqs * num_channels), where n is the number of snapshots in the yielded block and is at most num. The final block may contain fewer than num snapshots.

The second axis stores the spectra in frequency-major order with channels interlaced for each frequency bin. Reshaping a block with block.reshape(n, num_freqs, num_channels) gives direct access to the data by snapshot, frequency, and channel.

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 result() generator in block-wise manner.

source#: Data source; Generator or derived object.

sample_freq#: Sampling frequency of output signal, as given by source.

num_channels#: Number of channels in output, as given by source.

num_freqs#: Number of frequencies in output, as given by source.

num_samples#: Number of samples / snapshots in output, as given by 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, …)

freqs#: 1-D array of frequencies

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

base#

`base`#