Issue with Maintaining Order When Flattening a 3D NumPy Array to 1D
I'm performance testing and Hey everyone, I'm running into an issue that's driving me crazy. This might be a silly question, but Hey everyone, I'm running into an issue that's driving me crazy. I have a 3D NumPy array that I need to flatten into a 1D array while preserving the order of elements, but it seems that the default behavior isn't what I expected. My array is structured as a shape of (2, 3, 4), representing 2 matrices, each containing 3 rows and 4 columns. However, when I use the `numpy.ravel()` function, the output is not in the order I anticipated. Here’s what I’ve tried: ```python import numpy as np # Create a 3D array array_3d = np.array([[[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]], [[13, 14, 15, 16], [17, 18, 19, 20], [21, 22, 23, 24]]]) # Flattening the array using ravel() flattened_array = array_3d.ravel() print(flattened_array) ``` The output I get is: ```plaintext [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24] ``` While this is correct, I expected a flattened result that represents the matrices row by row, then goes to the next matrix. Instead, after reviewing the documentation, I realized that `ravel()` flattens the array in 'C' (row-major) order by default, but I want to preserve the original structure of the data. To ensure the order I need, I tried using the `order` parameter in `ravel()` by setting it to 'F' (column-major) like this: ```python flattened_array_f = array_3d.ravel(order='F') print(flattened_array_f) ``` However, this also does not yield the correct sequence as I was hoping to maintain the original 2D slice order. Is there a function or method that allows me to achieve a flat array that respects the original row-wise order of each 2D slice without changing the structure? Or is there a more efficient way to achieve this without manually iterating through the array? Any advice would be greatly appreciated! My development environment is Linux. Am I missing something obvious? Any ideas what could be causing this? This issue appeared after updating to Python LTS. Thanks for taking the time to read this!