growthcurves.non_parametric module

growthcurves.non_parametric module#

Non-parametric fitting methods for growth curves.

This module provides non-parametric methods for growth curve analysis, including sliding window fitting and no-growth detection.

All methods operate in linear OD space (not log-transformed).

growthcurves.non_parametric.fit_non_parametric(t, N, method='sliding_window', window_points=15, smooth='fast', use_weights=True, **kwargs)[source]#

Calculate growth statistics using non-parametric methods.

This unified function supports multiple methods for calculating the maximum specific growth rate (Umax): - “sliding_window”: Finds maximum slope in log-transformed OD across windows - “spline”: Fits spline to entire curve and calculates from derivative

Parameters:

  • t – Time array (hours)

  • N – OD values (baseline-corrected, must be positive)

  • method – Method for calculating Umax (“sliding_window” or “spline”)

  • window_points – Number of points in sliding window (for sliding_window method)

  • smooth – Smoothing mode/value for spline method. - “fast”: notebook auto-default rule mapped to lam - “slow”: GCV-selected smoothing (auto GCV) - float: manual lam value

  • use_weights – Whether to apply OD-dependent weighting for spline method

  • **kwargs – Additional arguments (for compatibility)

Returns:

  • params: Model parameters (includes fit_t_min, fit_t_max, and other

    method-specific values)

  • model_type: Method used for fitting

Return type:

Dict containing

growthcurves.non_parametric.fit_sliding_window(t, N, window_points=15, step=None, n_fits=None, **kwargs)[source]#

Calculate maximum specific growth rate using the sliding window method.

Finds the maximum specific growth rate by fitting a line to log-transformed OD N in consecutive windows using the Theil-Sen estimator, selecting the window with the steepest slope.

Parameters:

  • t – Time array (hours)

  • N – OD values (baseline-corrected, must be positive)

  • window_points – Number of points in each sliding window

  • step – Step size for sliding window (default: 1 if step is None and n_fits is None)

  • n_fits – Approximate number of fits to perform (default: None). Ignored if step is provided.

Returns:

  • slope: Slope of the linear fit in log space

    (equals specific growth rate, h⁻¹)

  • intercept: Intercept of the linear fit in log space

  • time_at_umax: Time at maximum growth rate (hours)

  • model_type: “sliding_window”

Returns None if calculation fails.

Return type:

Dict with model parameters

growthcurves.non_parametric.fit_spline(t, N, smooth='fast', use_weights=True)[source]#

Calculate maximum specific growth rate using spline fitting.

Fits a smoothing spline to log-transformed OD N and calculates the maximum specific growth rate from the spline’s derivative.

Parameters:

  • t – Time array (hours)

  • N – OD values

  • smooth – Smoothing mode/value. - “fast”: notebook auto-default rule mapped to lam - “slow”: GCV-selected smoothing (lam=None) - float: manual lam value

  • use_weights – Whether to apply OD-dependent weighting (default: True)

Returns:

  • t_knots: Spline knot points (t values)

    • spline_coeffs: Spline coefficients

    • spline_k: Spline degree (3)

    • time_at_umax: Time at maximum growth rate (hours)

    • model_type: “spline”

Returns None if calculation fails.

Return type:

Dict with model parameters

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