👀 Views: 100 💬 Answers: 1 📅 Created: 2025-06-23

numpy data-analysis numerical-methods python

I've searched everywhere and can't find a clear answer. I'm deploying to production and I'm stuck on something that should probably be simple. I'm working with a dataset where the x-values are not uniformly spaced, and I'm trying to compute the numerical derivatives using `np.diff()` in NumPy 1.24.3. However, I'm getting unexpected results for the derivative calculations, particularly with regard to the scaling of each segment. Here's a simplified version of my implementation: ```python import numpy as np x = np.array([0, 1, 3, 4, 7]) # non-uniformly spaced f = np.array([0, 1, 4, 2, 5]) # corresponding y-values delta_x = np.diff(x) delta_f = np.diff(f) derivatives = delta_f / delta_x print(derivatives) ``` The output I'm getting is `array([1. , 1.5, -1. , 1. ]),` which seems misleading because it doesn't account for the changes in spacing between `x` values. For example, between `1` and `3`, the spacing is `2`, but my derivative calculation outputs `1.5`, which makes it seem like the change in `f` is relative to a uniform spacing. I expected the derivative to reflect the actual distances in `x`, not just the differences in `f`. I tried normalizing the derivatives by dividing by the corresponding `delta_x`, but that didn't yield the expected results either. ```python direct_derivative = delta_f / delta_x corrected_derivative = direct_derivative / np.repeat(delta_x, 2)[:len(direct_derivative)] print(corrected_derivative) ``` This approach resulted in an `IndexError` because of mismatched lengths. Is there a best practice for calculating numerical derivatives for non-uniformly spaced data using NumPy? Am I missing a key function or method that could simplify this process? Any insights would be greatly appreciated! I'm working on a web app that needs to handle this.