ezmsg.sigproc.wavelets#

Functions

cwt(frequencies, wavelet, min_phase=MinPhaseMode.NONE, axis='time', scales=None)[source]#

Perform a continuous wavelet transform. The function is equivalent to the pywt.cwt function, but is designed to work with streaming data.

Parameters:

  • frequencies (list | tuple | ndarray[tuple[Any, ...], dtype[_ScalarT]] | None) – The wavelet frequencies to use in Hz. If None provided then the scales will be used. Note: frequencies will be sorted from smallest to largest.

  • wavelet (str | ContinuousWavelet | Wavelet) – Wavelet object or name of wavelet to use.

  • min_phase (MinPhaseMode) – See filterbank MinPhaseMode for details.

  • axis (str) – The target axis for operation. Note that this will be moved to the -1th dimension because fft and matrix multiplication is much faster on the last axis. This axis must be in the msg.axes and it must be of type AxisArray.LinearAxis.

  • scales (list | tuple | ndarray[tuple[Any, ...], dtype[_ScalarT]] | None) –

    The scales to use. If None, the scales will be calculated from the frequencies. Note: Scales will be sorted from largest to smallest. Note: Use of scales is deprecated in favor of frequencies. Convert scales to frequencies using

    pywt.scale2frequency(wavelet, scales, precision=10) * fs where fs is the sampling frequency.

Returns:

A primed Generator object that expects an AxisArray via .send(axis_array) of continuous data and yields an AxisArray with a continuous wavelet transform in its data.

Return type:

CWTTransformer

Classes

class CWT(*args, settings=None, **kwargs)[source]#

Bases: BaseTransformerUnit[CWTSettings, AxisArray, AxisArray, CWTTransformer]

Parameters:

settings (Settings | None)

SETTINGS#

alias of CWTSettings

class CWTSettings(frequencies, wavelet, min_phase=MinPhaseMode.NONE, axis='time', scales=None)[source]#

Bases: Settings

Settings for CWT See cwt for argument details.

Parameters:
frequencies: list | tuple | ndarray[tuple[Any, ...], dtype[_ScalarT]] | None#
wavelet: str | ContinuousWavelet | Wavelet#
min_phase: MinPhaseMode = 'No kernel modification'#
axis: str = 'time'#
scales: list | tuple | ndarray[tuple[Any, ...], dtype[_ScalarT]] | None = None#
__init__(frequencies, wavelet, min_phase=MinPhaseMode.NONE, axis='time', scales=None)#
Parameters:
Return type:

None

class CWTState[source]#

Bases: object

neg_rt_scales: ndarray[tuple[Any, ...], dtype[_ScalarT]] | None = None#
int_psi_scales: list[ndarray[tuple[Any, ...], dtype[_ScalarT]]] | None = None#
template: AxisArray | None = None#
fbgen: Generator[AxisArray, AxisArray, None] | None = None#
last_conv_samp: ndarray[tuple[Any, ...], dtype[_ScalarT]] | None = None#
class CWTTransformer(*args, **kwargs)[source]#

Bases: BaseStatefulTransformer[CWTSettings, AxisArray, AxisArray, CWTState]

class CWTSettings(frequencies, wavelet, min_phase=MinPhaseMode.NONE, axis='time', scales=None)[source]#

Bases: Settings

Settings for CWT See cwt for argument details.

Parameters:
frequencies: list | tuple | ndarray[tuple[Any, ...], dtype[_ScalarT]] | None#
wavelet: str | ContinuousWavelet | Wavelet#
min_phase: MinPhaseMode = 'No kernel modification'#
axis: str = 'time'#
scales: list | tuple | ndarray[tuple[Any, ...], dtype[_ScalarT]] | None = None#
__init__(frequencies, wavelet, min_phase=MinPhaseMode.NONE, axis='time', scales=None)#
Parameters:
Return type:

None

class CWTState[source]#

Bases: object

neg_rt_scales: ndarray[tuple[Any, ...], dtype[_ScalarT]] | None = None#
int_psi_scales: list[ndarray[tuple[Any, ...], dtype[_ScalarT]]] | None = None#
template: AxisArray | None = None#
fbgen: Generator[AxisArray, AxisArray, None] | None = None#
last_conv_samp: ndarray[tuple[Any, ...], dtype[_ScalarT]] | None = None#
class CWTTransformer(*args, **kwargs)[source]#

Bases: BaseStatefulTransformer[CWTSettings, AxisArray, AxisArray, CWTState]

class CWT(*args, settings=None, **kwargs)[source]#

Bases: BaseTransformerUnit[CWTSettings, AxisArray, AxisArray, CWTTransformer]

Parameters:

settings (Settings | None)

SETTINGS#

alias of CWTSettings

cwt(frequencies, wavelet, min_phase=MinPhaseMode.NONE, axis='time', scales=None)[source]#

Perform a continuous wavelet transform. The function is equivalent to the pywt.cwt function, but is designed to work with streaming data.

Parameters:

  • frequencies (list | tuple | ndarray[tuple[Any, ...], dtype[_ScalarT]] | None) – The wavelet frequencies to use in Hz. If None provided then the scales will be used. Note: frequencies will be sorted from smallest to largest.

  • wavelet (str | ContinuousWavelet | Wavelet) – Wavelet object or name of wavelet to use.

  • min_phase (MinPhaseMode) – See filterbank MinPhaseMode for details.

  • axis (str) – The target axis for operation. Note that this will be moved to the -1th dimension because fft and matrix multiplication is much faster on the last axis. This axis must be in the msg.axes and it must be of type AxisArray.LinearAxis.

  • scales (list | tuple | ndarray[tuple[Any, ...], dtype[_ScalarT]] | None) –

    The scales to use. If None, the scales will be calculated from the frequencies. Note: Scales will be sorted from largest to smallest. Note: Use of scales is deprecated in favor of frequencies. Convert scales to frequencies using

    pywt.scale2frequency(wavelet, scales, precision=10) * fs where fs is the sampling frequency.

Returns:

A primed Generator object that expects an AxisArray via .send(axis_array) of continuous data and yields an AxisArray with a continuous wavelet transform in its data.

Return type:

CWTTransformer