ezmsg.sigproc.adaptive_lattice_notch#

Adaptive lattice notch filter for narrow-band interference removal.

Classes

class AdaptiveLatticeNotchFilterSettings(gamma=0.995, mu=0.99, eta=0.99, axis=None, init_notch_freq=None, chunkwise=False)[source]#

Bases: Settings

Settings for the Adaptive Lattice Notch Filter.

Parameters:
gamma: float = 0.995#

Pole-zero contraction factor

mu: float = 0.99#

Smoothing factor

eta: float = 0.99#

Forgetting factor

axis: str | None = None#

Deprecated since version 3.8.

Scheduled for removal in 4.0. The dimension messages accumulate along now comes from stream_dim; see ezmsg.sigproc.util.deprecation.

init_notch_freq: float | None = None#

Initial notch frequency. Should be < nyquist.

chunkwise: bool = False#

Speed up processing by updating the target freq once per chunk only.

__init__(gamma=0.995, mu=0.99, eta=0.99, axis=None, init_notch_freq=None, chunkwise=False)#
Parameters:
Return type:

None

class AdaptiveLatticeNotchFilterState[source]#

Bases: object

axis: str = ''#

The resolved stream dimension, fixed at reset so every later use agrees.

s_history: NDArray | None = None#

Historical s values for the adaptive filter.

p: NDArray | None = None#

Accumulated product for reflection coefficient update

q: NDArray | None = None#

Accumulated product for reflection coefficient update

k1: NDArray | None = None#

Reflection coefficient

freq_template: CoordinateAxis | None = None#

Template for the frequency axis on the output

zi: NDArray | None = None#

Initial conditions for the filter, updated after every chunk

class AdaptiveLatticeNotchFilterTransformer(*args, **kwargs)[source]#

Bases: BaseStatefulTransformer[AdaptiveLatticeNotchFilterSettings, AxisArray, AxisArray, AdaptiveLatticeNotchFilterState]

Adaptive Lattice Notch Filter implementation as a stateful transformer.

https://biomedical-engineering-online.biomedcentral.com/articles/10.1186/1475-925X-13-170

The filter automatically tracks and removes frequency components from the input signal. It outputs the estimated frequency (in Hz) and the filtered sample.

NONRESET_SETTINGS_FIELDS: ClassVar[frozenset[str]] = frozenset({'chunkwise', 'eta', 'gamma', 'mu'})#