suboptimumg.log_analysis.yaw_plots#

suboptimumg.log_analysis.yaw_plots.eval_lpv_bode(chassis, irl_car, V_grid, f_grid)[source]#

Evaluate the analytic Bode surface over a (speed, frequency) grid.

Parameters:

  • chassis (ChassisParams) – Chassis scalars to evaluate the transfer function at.

  • irl_car (IrlCar) – Source of the mass, wheelbase, and CG split.

  • V_grid (NDArray[np.float64]) – Speed grid (m/s), length nV.

  • f_grid (NDArray[np.float64]) – Frequency grid (Hz), length nf.

Returns:

  • mag (NDArray[np.float64]) – Dimensionless magnitude |H(j 2 pi f; V)|, shape (nV, nf).

  • phase_deg (NDArray[np.float64]) – Phase, unwrapped along the frequency axis (deg), shape (nV, nf).

Return type:

tuple[NDArray[float64], NDArray[float64]]

suboptimumg.log_analysis.yaw_plots.plot_bode_slices(fits, V_picks, f_grid, linear_scale=False)[source]#

Two-panel magnitude/phase Bode of one or more fits, at fixed speeds.

Every (fit, speed) pair becomes its own series. Fits after the first are drawn dashed, so a comparison model reads clearly against the headline one.

Parameters:

  • fits (dict[str, YawFit]) – Fits keyed by display name.

  • V_picks (NDArray[np.float64] or list of float) – Speeds to slice at (m/s).

  • f_grid (NDArray[np.float64]) – Frequency grid (Hz).

  • linear_scale (bool, optional) – Plot magnitude linearly rather than in dB.

Return type:

go.Figure

suboptimumg.log_analysis.yaw_plots.plot_bode_surface(fit, V_grid, f_grid, log_mag=True)[source]#

Magnitude Bode surface of a fit over (speed, frequency).

Parameters:

  • fit (YawFit) – Supplies the linear-region chassis parameters and geometry.

  • V_grid (NDArray[np.float64]) – Speed grid (m/s).

  • f_grid (NDArray[np.float64]) – Frequency grid (Hz).

  • log_mag (bool, optional) – Plot magnitude in dB rather than linear.

Return type:

go.Figure

suboptimumg.log_analysis.yaw_plots.plot_fit_overlay(window_arrays, prediction, subwindow_indices=None, max_display_hz=50.0, height_per_subplot=220, error_ylim=(-1.0, 1.0), relative_time=True)[source]#

Measured vs simulated yaw rate per sub-window, residual on the second axis.

Works for a training fit (fit.train) or a held-out evaluation, since both are a Prediction.

Parameters:

  • window_arrays (list of WindowArrays) – Source sub-windows.

  • prediction (Prediction) – Predictions to overlay; must be aligned with window_arrays.

  • subwindow_indices (list of int, range, slice, or None, optional) – Which sub-windows to draw. None draws all of them.

  • max_display_hz (float, optional) – Downsample the traces to at most this rate, for browser responsiveness.

  • height_per_subplot (int, optional) – Pixel height per sub-window row.

  • error_ylim (tuple[float, float], optional) – Fixed initial range for the residual axis (rad/s).

  • relative_time (bool, optional) – Start each sub-window’s time axis at zero.

Return type:

go.Figure

Raises:

ValueError – If subwindow_indices selects nothing.

suboptimumg.log_analysis.yaw_plots.plot_residual_vs(window_arrays, prediction, x_of='amplitude', n_bins=24)[source]#

Residual structure against steering, steering amplitude, or speed.

A flat binned mean means the model has extracted everything systematic. Residual that grows with "amplitude" is the classic tire-saturation signature, and the reason the Ca-decay scheduler exists.

Parameters:

  • window_arrays (list of WindowArrays) – Source sub-windows.

  • prediction (Prediction) – Supplies the residuals; must be aligned with window_arrays.

  • x_of ({"input", "amplitude", "speed"}, optional) – What to put on the x axis: signed steering (deg), absolute steering (deg), or speed (m/s).

  • n_bins (int, optional) – Number of quantile bins for the mean/sigma overlay.

Return type:

go.Figure

Raises:

ValueError – If x_of is unrecognised.