1. import numpy as np
  3. def merge_array_datasets(datasets):
  4. """
  5. Merges a list of NumPy arrays into a single array.
  6. Performs basic sanity checks to ensure compatibility.
  8. Args:
  9. datasets (list): A list of NumPy arrays to merge.
  11. Returns:
  12. numpy.ndarray: The merged array, or None if merging fails.
  13. """
  14. if not datasets:
  15. return None # Handle empty list
  17. # Check if all elements are NumPy arrays
  18. for dataset in datasets:
  19. if not isinstance(dataset, np.ndarray):
  20. print("Error: All elements must be NumPy arrays.")
  21. return None
  23. # Check if arrays have the same shape
  24. first_shape = datasets[0].shape
  25. for dataset in datasets:
  26. if dataset.shape != first_shape:
  27. print("Error: Arrays must have the same shape.")
  28. return None
  30. try:
  31. merged_array = np.concatenate(datasets) #Concatenate all arrays
  32. return merged_array
  33. except ValueError as e:
  34. print(f"Error during concatenation: {e}")
  35. return None
  38. if __name__ == '__main__':
  39. # Example Usage
  40. array1 = np.array([[1, 2], [3, 4]])
  41. array2 = np.array([[5, 6], [7, 8]])
  42. array3 = np.array([[9, 10], [11, 12]])
  44. datasets = [array1, array2, array3]
  45. merged_array = merge_array_datasets(datasets)
  47. if merged_array is not None:
  48. print("Merged array shape:", merged_array.shape)
  50. # Example with incompatible shapes
  51. array4 = np.array([[1, 2, 3], [4, 5, 6]])
  52. datasets2 = [array1, array4]
  53. merged_array2 = merge_array_datasets(datasets2)
  54. if merged_array2 is None:
  55. print("Incompatible shapes test passed")
  57. #Example with non-array element
  58. datasets3 = [array1, [1,2]]
  59. merged_array3 = merge_array_datasets(datasets3)
  60. if merged_array3 is None:
  61. print("Non-array element test passed")

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