Sliding window of M-by-N shape numpy.ndarray

Sliding window of M-by-N shape numpy.ndarray

A sliding window operation on a NumPy array involves extracting smaller subarrays (windows) from a larger array and applying some operation on each of these windows. Here's how you can create a sliding window of an M-by-N shape on a NumPy array:

import numpy as np

def sliding_window(arr, window_shape):
    """
    Generate a sliding window of a specified shape on a NumPy array.
    
    Parameters:
    - arr: Input NumPy array.
    - window_shape: Shape (M, N) of the sliding window.
    
    Returns:
    - Generator yielding each window as a NumPy subarray.
    """
    M, N = window_shape
    for i in range(arr.shape[0] - M + 1):
        for j in range(arr.shape[1] - N + 1):
            yield arr[i:i+M, j:j+N]

# Example usage:
if __name__ == "__main__":
    # Create a sample NumPy array
    data = np.array([[1, 2, 3, 4],
                     [5, 6, 7, 8],
                     [9, 10, 11, 12]])

    # Define the shape of the sliding window
    window_shape = (2, 2)

    # Iterate over the sliding windows and print them
    for window in sliding_window(data, window_shape):
        print(window)

In this code:

  1. We define a sliding_window function that takes an input NumPy array (arr) and a desired window shape (window_shape) as input.

  2. Inside the function, we iterate through the rows and columns of the input array using nested loops. For each position (i, j) in the input array, we extract a subarray of shape (M, N) where M and N are the dimensions of the window.

  3. We yield each extracted window using a generator, allowing you to process each window one by one.

  4. In the example usage section, we create a sample NumPy array and specify a window shape of (2, 2). We then iterate over the sliding windows and print them.

You can modify the sliding_window function to perform specific operations on each window as needed for your application.

Examples

  1. "Sliding window numpy array Python" Description: Sliding window technique involves moving a window of specified shape across a numpy array to process data in overlapping segments. This can be useful for tasks like convolution, feature extraction, or image processing. Code:

    import numpy as np

def sliding_window(arr, shape):
    """Generate sliding windows of specified shape over a numpy array."""
    for i in range(arr.shape[0] - shape[0] + 1):
        for j in range(arr.shape[1] - shape[1] + 1):
            yield arr[i:i+shape[0], j:j+shape[1]]

# Example usage:
arr = np.random.rand(5, 5)  # Example 5x5 array
window_shape = (3, 3)  # Example window shape
for window in sliding_window(arr, window_shape):
    print(window)

  2. "Numpy sliding window implementation" Description: Numpy's array manipulation capabilities can be utilized to efficiently implement sliding window operations. This implementation utilizes array slicing to extract windowed segments from the array. Code:

    import numpy as np

def sliding_window(arr, shape):
    """Generate sliding windows of specified shape over a numpy array."""
    shape_arr = np.array(shape)
    arr_shape = np.array(arr.shape)
    for i in range(arr_shape[0] - shape_arr[0] + 1):
        for j in range(arr_shape[1] - shape_arr[1] + 1):
            yield arr[i:i+shape_arr[0], j:j+shape_arr[1]]

# Example usage:
arr = np.random.rand(5, 5)  # Example 5x5 array
window_shape = (3, 3)  # Example window shape
for window in sliding_window(arr, window_shape):
    print(window)

  3. "Python numpy sliding window example" Description: Demonstrates a basic example of using numpy to implement a sliding window over a 2D numpy array, useful for various tasks including image processing and signal analysis. Code:

    import numpy as np

def sliding_window(arr, shape):
    """Generate sliding windows of specified shape over a numpy array."""
    for i in range(arr.shape[0] - shape[0] + 1):
        for j in range(arr.shape[1] - shape[1] + 1):
            yield arr[i:i+shape[0], j:j+shape[1]]

# Example usage:
arr = np.random.rand(5, 5)  # Example 5x5 array
window_shape = (3, 3)  # Example window shape
for window in sliding_window(arr, window_shape):
    print(window)

  4. "2D sliding window numpy" Description: Provides an implementation for generating sliding windows over a 2D numpy array, enabling efficient processing of data in segments. Code:

    import numpy as np

def sliding_window(arr, shape):
    """Generate sliding windows of specified shape over a 2D numpy array."""
    for i in range(arr.shape[0] - shape[0] + 1):
        for j in range(arr.shape[1] - shape[1] + 1):
            yield arr[i:i+shape[0], j:j+shape[1]]

# Example usage:
arr = np.random.rand(5, 5)  # Example 5x5 array
window_shape = (3, 3)  # Example window shape
for window in sliding_window(arr, window_shape):
    print(window)

  5. "numpy moving window function" Description: Shows how to create a function in numpy to generate moving windows over a 2D array, useful for tasks like local statistics calculation or convolution operations. Code:

    import numpy as np

def moving_window(arr, shape):
    """Generate moving windows of specified shape over a numpy array."""
    return np.lib.stride_tricks.sliding_window_view(arr, shape)

# Example usage:
arr = np.random.rand(5, 5)  # Example 5x5 array
window_shape = (3, 3)  # Example window shape
windows = moving_window(arr, window_shape)
for window in windows:
    print(window)

  6. "numpy sliding window 2D array" Description: Illustrates how to implement a sliding window operation over a 2D numpy array, allowing for efficient processing of data in localized segments. Code:

    import numpy as np

def sliding_window(arr, shape):
    """Generate sliding windows of specified shape over a 2D numpy array."""
    for i in range(arr.shape[0] - shape[0] + 1):
        for j in range(arr.shape[1] - shape[1] + 1):
            yield arr[i:i+shape[0], j:j+shape[1]]

# Example usage:
arr = np.random.rand(5, 5)  # Example 5x5 array
window_shape = (3, 3)  # Example window shape
for window in sliding_window(arr, window_shape):
    print(window)

  7. "numpy sliding window array processing" Description: Demonstrates how to process a numpy array using a sliding window technique, enabling localized data analysis or manipulation. Code:

    import numpy as np

def sliding_window(arr, shape):
    """Generate sliding windows of specified shape over a numpy array."""
    for i in range(arr.shape[0] - shape[0] + 1):
        for j in range(arr.shape[1] - shape[1] + 1):
            yield arr[i:i+shape[0], j:j+shape[1]]

# Example usage:
arr = np.random.rand(5, 5)  # Example 5x5 array
window_shape = (3, 3)  # Example window shape
for window in sliding_window(arr, window_shape):
    print(window)

  8. "numpy sliding window convolution" Description: Shows how to perform convolution using a sliding window approach with numpy arrays, which is fundamental in tasks like image processing and signal filtering. Code:

    import numpy as np

def sliding_window_convolution(arr, kernel):
    """Perform convolution using sliding window technique."""
    kernel_shape = kernel.shape
    result = np.zeros_like(arr)
    for i in range(arr.shape[0] - kernel_shape[0] + 1):
        for j in range(arr.shape[1] - kernel_shape[1] + 1):
            result[i:i+kernel_shape[0], j:j+kernel_shape[1]] += kernel
    return result

# Example usage:
arr = np.random.rand(5, 5)  # Example 5x5 array
kernel = np.ones((3, 3))  # Example kernel for convolution
result = sliding_window_convolution(arr, kernel)
print(result)

  9. "numpy sliding window image processing" Description: Provides a method for implementing sliding window operations on images using numpy arrays, which is commonly used in tasks like edge detection or object detection in computer vision. Code:

    import numpy as np

def sliding_window_image_processing(image, window_shape):
    """Apply sliding window operation on an image."""
    for i in range(image.shape[0] - window_shape[0] + 1):
        for j in range(image.shape[1] - window_shape[1] + 1):
            window = image[i:i+window_shape[0], j:j+window_shape[1]]
            # Perform processing on the window
            # Example: edge detection, object detection
            processed_window = process_window(window)
            yield processed_window

# Example usage:
image = np.random.rand(100, 100)  # Example image
window_shape = (10, 10)  # Example window shape
for processed_window in sliding_window_image_processing(image, window_shape):
    print(processed_window)

  10. "numpy sliding window for feature extraction" Description: Demonstrates how to use a sliding window approach with numpy arrays to extract features from data, which can be beneficial in tasks like time series analysis or speech recognition. Code:

    import numpy as np

def sliding_window_feature_extraction(data, window_size):
    """Extract features using sliding window technique."""
    features = []
    for i in range(len(data) - window_size + 1):
        window = data[i:i+window_size]
        # Extract features from the window
        # Example: mean, standard deviation, max value
        window_features = [np.mean(window), np.std(window), np.max(window)]
        features.append(window_features)
    return np.array(features)

# Example usage:
data = np.random.rand(100)  # Example data
window_size = 10  # Example window size
extracted_features = sliding_window_feature_extraction(data, window_size)
print(extracted_features)

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