np.array_repr behaves unexpectedly with large floating-point numbers in NumPy 1.25
Does anyone know how to I'm wondering if anyone has experience with I'm working with an scenario with how `np.array_repr` handles large floating-point numbers in NumPy 1.25. When I create a numpy array with very high values, `array_repr` seems to truncate the numbers, which leads to misinterpretation of the data. For instance, consider the following code: ```python import numpy as np # Create an array with large floating-point numbers large_values = np.array([1e+100, 2e+100, 3e+100]) # Use np.array_repr to print the array array_representation = np.array_repr(large_values) print(array_representation) ``` When I run this, I get the output: ``` array([1.e+100, 2.e+100, 3.e+100]) ``` This behavior is expected, but when I attempt to print the array with a specific precision using: ```python print(np.array_repr(large_values, precision=3)) ``` I expected to see `1e+100` styled consistently, but it seems to be rounding off or displaying in scientific notation without adhering to my precision request. Instead, it gives me: ``` array([1.000e+100, 2.000e+100, 3.000e+100]) ``` Is there a way to enforce a consistent format for large floating-point numbers using `np.array_repr` with a specified precision? I want to ensure that my large numbers are represented clearly without losing their significance. Any insights or alternative approaches would be greatly appreciated! Thanks in advance! The stack includes Python and several other technologies.