florisvb/PyNumDiff

AI Summary: The `utility.slide_function` is currently inefficient, storing weights and results in a large array, leading to $\Omega(N^2)$ time complexity. This approach was chosen for its perceived simplicity in handling a complex combination step using NumPy. The issue proposes finding a more space-efficient method to improve performance, which would benefit `polydiff`, `lineardiff`, and `jerk_sliding`.

AI Summary: The issue proposes a fundamental change to the library's API to support non-equispaced data points, moving from a `(x, dt)` interface to `(x, t)`. This would require evaluating each method's compatibility, potentially re-implementing some or raising errors for methods that inherently rely on equispaced data (e.g., Fourier spectral, Savitsky-Golay).