np.lib.stride_tricks.as_strided is a NumPy function that allows you to create a new view of an array with a different shape and stride. This function can be useful for manipulating and accessing data in more efficient ways, especially when working with large arrays.

Parameters

The as_strided function takes the following parameters:

x: The input array, which you want to create a new view of.
shape: The desired shape of the output view.
strides: The desired strides of the output view.
subok (optional): If True, subclasses of x are preserved in the output. Default is True.
writeable (optional): If False, the returned array will be read-only. Default is True.

The shape parameter specifies the shape of the output view. It defines the number of elements along each dimension of the view. The strides parameter determines the number of bytes to step in each dimension when accessing elements of the view. By manipulating the shape and strides parameters, you can create views with different dimensions and access patterns.

Here’s a general step-by-step process of using np.lib.stride_tricks.as_strided:

Import the NumPy library: import numpy as np
Create or obtain an input array x that you want to create a new view of.
Determine the desired shape and strides for the output view.
Call np.lib.stride_tricks.as_strided with the input array x, shape, and strides as arguments. python：output_view = np.lib.stride_tricks.as_strided(x, shape, strides)
The output_view will be a new array that represents a view of x with the desired shape and strides.

It’s important to note that np.lib.stride_tricks.as_strided does not perform any checks for the validity of the shape and strides parameters. Therefore, you should ensure that the parameters are compatible with the underlying data in the input array to avoid unexpected behavior or memory access issues.

Here’s a simple example to demonstrate how to use np.lib.stride_tricks.as_strided:

import numpy as np

# Create an input array
x = np.array([1, 2, 3, 4, 5, 6])

# Define the desired shape and strides for the output view
shape = (4, 2)
strides = (8, 4)  # 8 bytes (2 elements) along the first dimension, 4 bytes (1 element) along the second dimension

# Create a new view using as_strided
output_view = np.lib.stride_tricks.as_strided(x, shape, strides)

print(output_view)

Output:

[[1 2]
 [3 4]
 [5 6]
 [0 0]]

In this example, we created a new view of the input array x with a shape of (4, 2) and strides of (8, 4). The resulting view is a 2-dimensional array where each row corresponds to a 2-element window in the original array x. The 0 elements in the last row are a result of accessing elements beyond the original array’s size.

Remember to use np.lib.stride_tricks.as_strided with caution, as incorrect usage of shape and strides can lead to unexpected results or memory issues.

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Python教程--numpy as_strided function

Parameters