MedianFrequency

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

source

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

Compute the median frequency of a Fourier-domain patch.

This measure divides a signal’s power spectrum into two regions of equal total power. The input patch must already be transformed with Patch.dft or Patch.stft.

See for more detail: - (Phinyomark12?) - Online version of the Phinyomark publication - Matlab’s medfreq

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.
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

The Patch instance with the median-frequency as data.

Example

import dascore as dc
import matplotlib.pyplot as plt

patch = dc.examples.get_example_patch('example_event_2')

fig, axs = plt.subplots(1,2, layout='constrained', figsize=(12,4))
ax = patch.viz.waterfall(cmap='seismic', ax=axs[0])

spec = patch.stft(time=.02, overlap=.019, taper_window="boxcar")
med = spec.median_frequency(fmin=50, fmax=300)
ax = med.viz.waterfall(cmap='turbo', ax=axs[1], scale=[0,1])

Methods

Name	Description
check	Refuse a patch which does not carry what the operation needs.
describe	Return the frequency which halves the power.
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.
median_frequency	Compute the median frequency of a Fourier-domain patch.
reconcile	Return metadata or a patch holding the final data; default as is.
result_dtype	Return the dtype `describe` returns for power of dtype `power`.
run	Run the operation: check, get_metadata, kernel, reconcile, record.
units	Return the output’s data units: the frequencies’ by default.