signals.miso

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Multiple and partial coherence of a multiple-input/single-output system.

When several partially correlated sources drive one response, the ordinary coherence of each source with the output is misleading: a source that only correlates with the true cause inherits a spurious coherence through it. Bendat & Piersol, Random Data: Analysis and Measurement Procedures (4th ed., 2010, Chapter 7), resolve this with the multiple-input/output (MISO) coherence functions, computed here from the Welch cross-spectral machinery of phonometry.signals.spectra:

the ordinary coherence $γ_{i y}^{2} = ∣ G_{i y} ∣^{2} / (G_{ii} G_{y y})$ (Eq. 7.109) of each input with the output, taken on its own;
the multiple coherence $γ_{y : x}^{2} = G_{v v} / G_{y y} = 1 - G_{nn} / G_{y y}$ (Eq. 7.35): the fraction of the output autospectrum linearly explained by all inputs jointly, obtained from the input cross-spectral matrix $G_{xx}$ and the input-output vector $G_{i y}$ (matrix form $γ_{y : x}^{2} = G_{i y}^{H} G_{xx}^{- 1} G_{i y} / G_{y y}$ , Eqs. 7.170-7.192). For additive uncorrelated output noise of per-band signal-to-noise ratio $SNR = G_{v v} / G_{nn}$ this is exactly $SNR / (1 + SNR)$ ;
the partial coherence $γ_{i y \cdot (i - 1)!}^{2} = ∣ G_{i y \cdot (i - 1)!} ∣^{2} / (G_{ii \cdot (i - 1)!} G_{y y})$ (Eq. 7.87): the coherence of input i with the output once the linear effect of the inputs before it in the conditioning order has been removed. The 4th-edition definition uses the total output $G_{y y}$ in the denominator (not the conditioned output), which makes the partial coherences of the ordered inputs add up to the multiple coherence, $γ_{y : x}^{2} = \sum γ_{i y \cdot (i - 1)!}^{2}$ (Eq. 7.116), and reduce exactly to the ordinary coherences when the inputs are mutually uncorrelated (Eq. 7.117);
the conditioned (residual) spectra $G_{ij \cdot r!}$ computed by the Gaussian-elimination recursion $G_{ij \cdot r!} = G_{ij \cdot (r - 1)!} - G_{r j \cdot (r - 1)!} G_{i r \cdot (r - 1)!} / G_{r r \cdot (r - 1)!}$ (Eq. 7.94, base case Eq. 7.95), the Schur complement that removes the linear effect of the pivot input r from every remaining record;
the partial (cumulative) coherent output spectra $G_{v_{i}} = ∣ L_{i y} ∣^{2} G_{ii \cdot (i - 1)!} = γ_{i y \cdot (i - 1)!}^{2} G_{y y}$ (Eq. 7.86): the share of output power the i-th ordered input contributes, so that $G_{y y} = \sum G_{v_{i}} + G_{nn}$ (Eqs. 7.88-7.89, 7.121). Comparing them band by band answers “which source dominates here?”.

The random errors follow Bendat & Piersol Section 9.3: conditioning on the $i - 1$ preceding inputs costs $i - 1$ degrees of freedom, so the i-th ordered input carries $n_{d} - (i - 1)$ effective averages (Eqs. 9.100/9.101) and the q-input multiple coherence carries $n_{d} - (q - 1)$ (Eqs. 9.98/9.99).

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miso_coherence

miso_coherence(
    inputs: Sequence[NDArray[np.float64] | list[float]] | NDArray[np.float64],
    output: NDArray[np.float64] | list[float],
    fs: float,
    *,
    order: Sequence[int] | None = None,
    window: str = 'hann',
    nperseg: int | None = None,
    overlap: float = 0.5,
    scaling: Literal['density', 'spectrum'] = 'density',
) -> MISOCoherenceResult

Multiple and partial coherence of a MISO system (Bendat & Piersol 7).

Estimates every auto- and cross-spectrum of the q inputs and the output by the shared Welch core of cross_spectral_density (Hann taper and 50 % overlap by default, no detrending), then:

reports the ordinary coherence of each input with the output (Eq. 7.109);
forms the multiple coherence $γ_{y : x}^{2}$ (Eq. 7.35) from the residual output spectrum left by the Gaussian-elimination conditioning of Section 7.3;
conditions the inputs in order to get the partial coherences $γ_{i y \cdot (i - 1)!}^{2}$ (Eq. 7.87) and the partial coherent output spectra $G_{v_{i}}$ (Eq. 7.86), which decompose the output power source by source ( $\sum_{i} G_{v_{i}} + G_{nn} = G_{y y}$ ).

The partial coherences and the coherent-output decomposition depend on the conditioning order; the ordinary and multiple coherences do not. Absent a physical basis, Bendat & Piersol (Section 7.2.4) recommend ordering the inputs by descending ordinary coherence with the output.

Parameters

Name	Description
`inputs`	The `q` input records (`q >= 2`), a sequence of equal-length 1-D arrays or a 2-D `(q, n)` array.
`output`	The output record, 1-D, same length as the inputs.
`fs`	Sample rate, in Hz.
`order`	Conditioning order as input indices (default `0..q-1`).
`window`	Segment taper (default Hann).
`nperseg`	Welch segment length; `None` picks a default.
`overlap`	Segment overlap fraction in [0, 1) (default 0.5).
`scaling`	`'density'` (units²/Hz) or `'spectrum'` (units²).

Returns: A MISOCoherenceResult.

Raises

Exception	When
ValueError	If the inputs or parameters are invalid.

MISOCoherenceResult

MISOCoherenceResult(
    frequencies: NDArray[np.float64],
    n_inputs: int,
    order: tuple[int, ...],
    ordinary_coherence: NDArray[np.float64],
    multiple_coherence: NDArray[np.float64],
    partial_coherence: NDArray[np.float64],
    coherent_output_spectra: NDArray[np.float64],
    output_psd: NDArray[np.float64],
    noise_psd: NDArray[np.float64],
    multiple_coherence_random_error: NDArray[np.float64],
    coherent_output_random_error: NDArray[np.float64],
    n_segments: int,
    n_averages: float,
    resolution_bandwidth: float,
    window: str,
    nperseg: int,
    overlap: float,
    scaling: str,
)

Multiple and partial coherence of a MISO system (B&P Chapter 7).

Every per-input array is indexed by the original input index (the order in which the records were passed), so ordinary_coherence[i] and coherent_output_spectra[i] refer to the same physical source; the conditioning that produced the partial coherences is recorded in order.

Attributes

Name	Description
`frequencies`	One-sided frequency axis, in Hz.
`n_inputs`	Number of inputs `q` (`q >= 2`).
`order`	Conditioning order actually applied, as original input indices; `partial_coherence[order[k]]` is conditioned on the inputs `order[:k]`.
`ordinary_coherence`	$γ_{i y}^{2} (f) \in [0, 1]$ per input (Eq. 7.109), shape `(q, F)`: each input against the output on its own.
`multiple_coherence`	$γ_{y : x}^{2} (f) \in [0, 1]$ (Eq. 7.35): the fraction of output power explained by all inputs jointly. Equals the sum of the partial coherences (Eq. 7.116) and `1 - noise_psd/output_psd`.
`partial_coherence`	$γ_{i y \cdot (i - 1)!}^{2} (f) \in [0, 1]$ per input (Eq. 7.87, total-output denominator), shape `(q, F)`: the coherence of the input with the output once the linear effect of the inputs preceding it in `order` is removed.
`coherent_output_spectra`	Partial coherent output spectrum $G_{v_{i}}$ per input (Eq. 7.86), shape `(q, F)`: the output power the input contributes, with `sum(coherent_output_spectra) + noise_psd = output_psd`.
`output_psd`	Measured output autospectrum $\hat{G}_{y y} (f)$ .
`noise_psd`	Residual (uncorrelated) output spectrum $\hat{G}_{nn} = \hat{G}_{y y \cdot q!}$ after removing every input (Eq. 7.121).
`multiple_coherence_random_error`	Normalized random error of $γ_{y : x}^{2}$ (Eq. 9.98), using $n_{d} - (q - 1)$ effective averages.
`coherent_output_random_error`	Normalized random error of each $G_{v_{i}}$ (Eq. 9.100), shape `(q, F)`, using $n_{d} - (i - 1)$ effective averages for the `i`-th ordered input.
`n_segments`	Raw number of (possibly overlapped) segments averaged.
`n_averages`	Effective number of independent averages $n_{d}$ .
`resolution_bandwidth`	Effective noise bandwidth $B_{e}$ , in Hz.
`window`	Taper name.
`nperseg`	Segment length, in samples.
`overlap`	Segment overlap fraction.
`scaling`	`'density'` or `'spectrum'`.

MISOCoherenceResult.dominant_input()

MISOCoherenceResult.dominant_input() -> NDArray[np.intp]

Index of the input contributing the most output power per bin.

Returns, for every frequency, the original input index whose partial coherent output spectrum coherent_output_spectra is largest - the source that dominates that band. Bins where every contribution is zero report the first input (index 0).

Returns: Integer array of length len(frequencies).

MISOCoherenceResult.plot()

MISOCoherenceResult.plot(
    ax: Axes | None = None,
    *,
    language: str = 'en',
    **kwargs: Any,
) -> Axes | NDArray[Any]

Plot the per-input coherent output spectra and multiple coherence.

Parameters

Name	Description
`language`	Label language, `"en"` (default) or `"es"`.

Creado y mantenido por José Manuel Requena Plens ORCID Todos los proyectos