Strange behavior with np.interp for extrapolation with large input arrays
I keep running into I've searched everywhere and can't find a clear answer. I'm maintaining legacy code that I'm currently working on a project where I need to interpolate some data using NumPy's `np.interp` function. The question arises when I attempt to use it for extrapolation beyond the bounds of my `xp` array. I'm using NumPy version 1.24.3 and I have two arrays: `xp` as the x-coordinates and `fp` as the corresponding y-coordinates. However, when I pass large values for `x` that are outside the range of `xp`, I am not getting the expected results. Instead, I get results that seem to be capped at the maximum `fp` value, rather than extrapolating appropriately. Here's a snippet of my code: ```python import numpy as np # Given x-coordinates and corresponding y-coordinates xp = np.array([1, 2, 3, 4, 5]) fp = np.array([10, 20, 30, 40, 50]) # Trying to extrapolate for values beyond the range of xp x = np.array([0, 6]) # Values outside the bounds of xp result = np.interp(x, xp, fp) print(result) ``` The output I receive is `[10 50]`, which is not what I anticipated for extrapolated values. I expected `np.interp` to extend the linear trend, providing values below 10 for `x = 0` and above 50 for `x = 6`. I've looked through the documentation, and it seems `np.interp` should indeed perform linear interpolation and extrapolation, but the behavior is confusing. I've also experimented with different data types for `xp` and `fp`, but the scenario continues. Is there a limitation with `np.interp` when it comes to large input arrays or specific data types? Or is there an alternative approach I should consider for proper extrapolation? Would appreciate any insights or solutions! I recently upgraded to Python latest. My team is using Python for this REST API. Is there a better approach?