sidResultTypes¶
Central reference for all sid result struct types.
sidResultTypes help sidResultTypes
This file is a documentation-only reference. It lists every result struct returned by the sid public API, together with its fields, dimensions, and the functions that produce or consume it.
Use this file as a single lookup point when writing code that reads fields from sid result structs.
See the Python equivalent in python/sid/_results.py (frozen dataclasses with per-field type annotations and docstrings).
Inputs¶
| Name | Description |
|---|---|
| (none — this function takes no arguments) |
Outputs¶
| Name | Description |
|---|---|
| (none — prints a help reminder to the console) |
Examples¶
help sidResultTypes % view all result struct definitions
sidResultTypes % prints a reminder to use help
=========================================================================
1. FreqResult (sidFreqBT, sidFreqBTFDR, sidFreqETFE)
=========================================================================
Produced by: sidFreqBT, sidFreqBTFDR, sidFreqETFE
Consumed by: sidBodePlot, sidSpectrumPlot, sidCompare, sidResidual,
sidLTVdiscTune (frequency method)
Field Dimensions Description
.................................................................
.Frequency (nf x 1) Frequency vector, rad/sample
.FrequencyHz (nf x 1) Frequency vector, Hz
.Response (nf x ny x nu) Complex frequency response.
[] in time-series mode.
.ResponseStd (nf x ny x nu) Standard deviation of Response.
[] in time-series mode.
.NoiseSpectrum (nf x ny x ny) Noise spectrum (or output
spectrum in time-series mode).
.NoiseSpectrumStd (nf x ny x ny) Standard deviation of
NoiseSpectrum.
.Coherence (nf x 1) Squared coherence (SISO only).
[] for MIMO or time-series.
.SampleTime scalar Sample time in seconds.
.WindowSize scalar or (nf x 1) Lag window size M. Scalar for
BT/ETFE; per-freq vector for
BTFDR.
.DataLength scalar Number of samples N.
.NumTrajectories scalar Number of trajectories L.
.Method char 'sidFreqBT', 'sidFreqBTFDR',
or 'sidFreqETFE'.
=========================================================================
2. FreqMapResult (sidFreqMap)
=========================================================================
Field Dimensions Description
.................................................................
.Time (K x 1) Center time of each segment
(seconds).
.Frequency (nf x 1) Frequency vector, rad/sample.
.FrequencyHz (nf x 1) Frequency vector, Hz.
.Response (nf x K [x ny x nu]) Time-varying complex response.
[] in time-series mode.
.ResponseStd (nf x K [x ny x nu]) Std dev of Response.
[] in time-series mode.
.NoiseSpectrum (nf x K [x ny x ny]) Noise spectrum.
.NoiseSpectrumStd (nf x K [x ny x ny]) Std dev of NoiseSpectrum.
.Coherence (nf x K) Squared coherence (SISO).
[] for MIMO or time-series.
.SampleTime scalar Sample time in seconds.
.SegmentLength scalar Segment length L.
.Overlap scalar Overlap P.
.WindowSize scalar BT lag window M, or []
for Welch.
.Algorithm char 'bt' or 'welch'.
.NumTrajectories scalar Number of trajectories.
.Method char 'sidFreqMap'.
=========================================================================
3. SpectrogramResult (sidSpectrogram)
=========================================================================
Field Dimensions Description
.................................................................
.Time (K x 1) Center time of each segment
(seconds).
.Frequency (n_bins x 1) Frequency vector, Hz.
.FrequencyRad (n_bins x 1) Frequency vector, rad/s.
.Power (n_bins x K x n_ch) Power spectral density.
.PowerDB (n_bins x K x n_ch) 10*log10(Power).
.Complex (n_bins x K x n_ch) Complex STFT coefficients.
.SampleTime scalar Sample time in seconds.
.WindowLength scalar Segment length L.
.Overlap scalar Overlap P.
.NFFT scalar FFT length.
.NumTrajectories scalar Number of trajectories.
.Method char 'sidSpectrogram'.
=========================================================================
4. LTVResult (sidLTVdisc)
=========================================================================
Produced by: sidLTVdisc, sidLTVdiscTune (bestResult output)
Consumed by: sidLTVdiscFrozen, sidCompare, sidResidual
Field Dimensions Description
.................................................................
.A (p x p x N) Time-varying dynamics matrices.
.B (p x q x N) Time-varying input matrices.
.Lambda (N-1 x 1) Regularization values used.
.Cost (1 x 3) [total, data_fidelity,
regularization].
.DataLength scalar Number of time steps N.
.StateDim scalar State dimension p.
.InputDim scalar Input dimension q.
.NumTrajectories scalar Number of trajectories L.
.Algorithm char 'cosmic'.
.Preconditioned logical Preconditioning flag.
.Method char 'sidLTVdisc'.
.AStd (p x p x N) Std dev of A(k) entries.
.BStd (p x q x N) Std dev of B(k) entries.
.P (d x d x N) Row-wise posterior covariance,
d = p + q.
.NoiseCov (p x p) Noise covariance (provided or
estimated).
.NoiseCovEstimated logical true if estimated from
residuals.
.NoiseVariance scalar trace(NoiseCov) / p.
.DegreesOfFreedom scalar Effective d.o.f. (NaN if
NoiseCov was provided).
=========================================================================
5. LTVIOResult (sidLTVdiscIO)
=========================================================================
Field Dimensions Description
.................................................................
.A (n x n x N) Estimated dynamics matrices.
.B (n x q x N) Estimated input matrices.
.X (N+1 x n x L) or Estimated state trajectories.
cell {L x 1}
.H (py x n) Observation matrix (copy).
.R (py x py) Noise covariance used.
.Cost (n_iter x 1) Cost J at each iteration.
.Iterations scalar Number of alternating iters.
.Lambda (N-1 x 1) Regularisation used.
.DataLength scalar Number of time steps N.
.StateDim scalar State dimension n.
.OutputDim scalar Output dimension py.
.InputDim scalar Input dimension q.
.NumTrajectories scalar Number of trajectories L.
.Algorithm char 'cosmic'.
.Method char 'sidLTVdiscIO'.
=========================================================================
6. FrozenResult (sidLTVdiscFrozen)
=========================================================================
Field Dimensions Description
.................................................................
.Frequency (nf x 1) Frequency vector, rad/sample.
.FrequencyHz (nf x 1) Frequency vector, Hz.
.TimeSteps (nk x 1) Selected time step indices
(1-based).
.Response (nf x p x q x nk) Complex frozen transfer
function G(w, k).
.ResponseStd (nf x p x q x nk) Std dev of Response.
[] if no uncertainty.
.SampleTime scalar Sample time in seconds.
.Method char 'sidLTVdiscFrozen'.
=========================================================================
7. CompareResult (sidCompare)
=========================================================================
Field Dimensions Description
.................................................................
.Predicted (N x ny) Model-predicted output.
.Measured (N x ny) Measured output (copy).
.Fit (1 x ny) NRMSE fit percentage per
channel (100% = perfect).
.Residual (N x ny) Measured - Predicted.
.Method char Method of the source model.
=========================================================================
8. ResidualResult (sidResidual)
=========================================================================
Field Dimensions Description
.................................................................
.Residual (N x ny) Residual time series e(t).
.AutoCorr (M+1 x 1) Normalised autocorrelation
r_ee(tau).
.CrossCorr (2M+1 x 1) Normalised cross-correlation
r_eu(tau). [] for time-series.
.ConfidenceBound scalar 99% bound 2.58 / sqrt(N).
.WhitenessPass logical true if autocorrelation
test passes.
.IndependencePass logical true if cross-correlation
test passes.
.DataLength scalar Number of samples N.
See also¶
sidFreqBT, sidFreqBTFDR, sidFreqETFE, sidFreqMap, sidSpectrogram, sidLTVdisc, sidLTVdiscIO, sidLTVdiscFrozen, sidCompare, sidResidual
Changelog¶
- 2026-04-09: First version by Pedro Lourenco.