derivkit.derivatives.adaptive.batch_eval module#

Batch evaluation utilities for derivative estimation.

Evaluate a user function over a 1D grid with optional parallelism and return a 2D array with consistent shape suitable for downstream polynomial fitting and diagnostics (e.g., in adaptive.adaptive_fit.AdaptiveFitDerivative).

derivkit.derivatives.adaptive.batch_eval.eval_function_batch(function: Callable[[float], Any], xs: ndarray, n_workers: int = 1) → ndarray#

Evaluate a function over 1D inputs and return a (n_points, n_comp) float array.

Evaluates function(x) for each x in xs. If n_workers > 1, uses a multiprocess.Pool; otherwise runs serially. Scalars become a single column. For array outputs, this routine coerces to a consistent 2D shape that downstream polynomial-fitting code expects.

Parameters:

  • function – Callable mapping a float to a scalar or array-like. Must be picklable if used with multiple processes.

  • xs – 1D array of abscissae.

  • n_workers – If > 1, evaluate in parallel using multiprocess.Pool.

Returns:

Array of shape (n_points, n_comp) with dtype float.

Return type:

np.ndarray

Raises:

ValueError – If xs is not 1D or outputs cannot be coerced consistently.

Examples

>>> import numpy as np
>>> from derivkit.derivatives.adaptive.batch_eval import eval_function_batch
>>> def f(x):
...     return np.array([x, x**2])
>>> xs = np.linspace(-1.0, 1.0, 5)
>>> y = eval_function_batch(f, xs)
>>> y.shape
(5, 2)