nlsq.precision.bound_inference.infer_bounds

nlsq.precision.bound_inference.infer_bounds(xdata, ydata, p0, safety_factor=10.0, enforce_positivity=True)[source]

Infer reasonable parameter bounds from data and initial guess.

This is a convenience function that creates a BoundsInference instance and returns the inferred bounds.

Parameters:

  • xdata (array_like) – Independent variable data

  • ydata (array_like) – Dependent variable data

  • p0 (array_like) – Initial parameter guess

  • safety_factor (float, optional) – Multiplier for bound ranges (larger = more conservative). Default: 10.0

  • enforce_positivity (bool, optional) – Force all bounds to be non-negative if p0 is positive. Default: True

Returns:

  • lower_bounds (ndarray) – Lower bounds for each parameter

  • upper_bounds (ndarray) – Upper bounds for each parameter

Return type:

tuple[ndarray, ndarray]

Examples

Basic usage:

>>> import numpy as np
>>> x = np.linspace(0, 10, 100)
>>> y = 2.5 * np.exp(-0.5 * x) + 1.0
>>> p0 = [2.0, 0.5, 1.0]
>>> bounds = infer_bounds(x, y, p0)
>>> print(f"Lower: {bounds[0]}")
>>> print(f"Upper: {bounds[1]}")

With custom safety factor:

>>> bounds = infer_bounds(x, y, p0, safety_factor=20.0)  # More conservative

Allow negative parameters:

>>> bounds = infer_bounds(x, y, p0, enforce_positivity=False)