SpectralEntropy

class of dascore.transform.spectral_descriptors inherits from: dascore.transform.spectral_descriptors._SpectralDescriptor, PatchProcessor, DascoreBaseModel, pydantic.main.BaseModel

source

SpectralEntropy(
    *args ,
    dim: Any = None,
    fmin: Any = None,
    fmax: Any = None,
    normalize: Any = True,
    spectral_format: Any = auto,
    negative_frequencies: Any = auto,
)-> None

Compute spectral entropy from a Fourier-domain patch.

Spectral entropy measures the disorder of the spectral power distribution. Low values indicate concentrated spectral energy, while high values indicate broadband or noisy spectra. The input patch must already be transformed with Patch.dft or Patch.stft.

Parameters

Parameter	Description
dim	Frequency dimension over which to compute the descriptor. This can be either the original dimension name, such as "time", or the Fourier dimension name, such as "ft_time". If omitted, a single Fourier dimension is inferred.
fmin	Optional lower frequency bound.
fmax	Optional upper frequency bound.
normalize	If True, normalize entropy to [0, 1]. Defaults to True.
spectral_format	Representation of the spectral data. "auto" uses DASCore DFT/STFT metadata when available. Other options are "fft" for complex Fourier coefficients, "amplitude" for amplitude spectra, "power" for power spectra, and "density" for power spectral densities.
negative_frequencies	How to handle negative frequency bins. "auto" drops negative bins when power is symmetric and raises otherwise, folding a verified negative Nyquist bin to positive frequency without changing its power. "drop" always uses non-negative frequencies, "raise" rejects spectra with negative bins, and "keep" includes them in the calculation.

Returns

PatchType Patch containing spectral entropy.

Methods

Name	Description
check	Refuse a patch which does not carry what the operation needs.
describe	Return the entropy of the power distribution.
get_metadata	Return the reduced metadata, and the bins and format to read.
model_copy	Copy the model, dropping cached values the update invalidates.
new	Create new instance with some attributed updated.
numpy_kernel	Return the descriptor of the power in the bins kept.
spectral_entropy	Compute spectral entropy from a Fourier-domain patch.
reconcile	Return metadata or a patch holding the final data; default as is.
result_dtype	Return the power’s dtype, at least double.
run	Run the operation: check, get_metadata, kernel, reconcile, record.
units	Return no units: entropy, in bits or normalized to [0, 1], is unitless.