Unexpected results when using np.log1p on large arrays with negative values in NumPy 1.23
I'm sure I'm missing something obvious here, but I'm relatively new to this, so bear with me. I'm working with unexpected results when using `np.log1p` on a large array that contains negative values. I thought `np.log1p(x)` computes the natural logarithm of one plus the input array, but when I apply it to an array that contains negative values, I get NaNs instead of the expected results. Here's a simplified version of my code: ```python import numpy as np data = np.array([-1.0, 0.0, 1.0, 2.0, 3.0]) result = np.log1p(data) print(result) ``` When I run this, I see the output: ``` [nan, 0.0, 0.69314718, 1.09861229, 1.38629436] ``` I was under the impression that `np.log1p` should handle the calculation for `0.0` correctly and return `0.0`, which it does, but I'm puzzled by the `NaN` for `-1.0`. I understand that the logarithm of zero or negative numbers is undefined, but I was hoping for some form of clarification or warning instead of simply getting `NaN`. Is there a way to preprocess the data to avoid this scenario or is there a better function that could handle such cases more gracefully? I've also tried using `np.clip` to limit the values, but that feels like a workaround rather than a solution. Any insights on how to handle negative values in this context would be much appreciated! I'm working on a API that needs to handle this. My development environment is Linux. Has anyone else encountered this? For context: I'm using Python on Windows 10. Is there a simpler solution I'm overlooking?