Looping Beyond the Default: Alternatives to NpyIter_EnableExternalLoop in NumPy

NumPy C-API and Iterating over Arrays

NumPy's C-API provides functions for low-level manipulation of NumPy arrays. One such function is NpyIter, which allows you to create an iterator object for efficient traversal over multi-dimensional arrays.

Default Iteration Order

By default, NpyIter follows what's called a C-style iteration order. This means it iterates over the elements in the array, visiting elements within the innermost dimension first, then moving on to the next element in the previous dimension, and so on.

External Loop Control with NpyIter_EnableExternalLoop

The NpyIter_EnableExternalLoop function is used to break the default C-style iteration and enable you to control the looping order from outside the NpyIter object. This gives you more flexibility in how you want to iterate over the array elements.

1. Call NpyIter_EnableExternalLoop
   Before creating the iterator using NpyIter_New, you call NpyIter_EnableExternalLoop with the iterator object to be used. This sets a flag indicating that external looping will be used.

2. Iterate Manually
   You then implement your own loops to control the iteration pattern. You can iterate over any dimension or combination of dimensions as needed.

Example (Illustrative, not actual C code)

// (Assuming you have initialized your array 'arr' and iterator 'it')

// Enable external loop
NpyIter_EnableExternalLoop(it);

// Iterate over the first dimension (assuming 'arr' has shape (2, 3, 4))
for (int i = 0; i < NpyIter_GetShape(it)[0]; i++) {
  // Move the iterator to the beginning of the i-th element in the first dimension
  NpyIter_GotoMultiIndex(it, &i);

  // Inner loop (can be customized for other dimensions)
  for (int j = 0; j < NpyIter_GetShape(it)[1]; j++) {
    NpyIter_MoveToNext(it);  // Move to the next element in the current row
    // Access and process the element using NpyIter_GetDataPtrArray(it)
  }
}

Key Points

• Consider using this function when you need a specific non-default iteration pattern or when integrating NumPy arrays with other C libraries that might have their own iteration mechanisms.
• It requires manual loop management within your C code.
• NpyIter_EnableExternalLoop provides more control over iteration order compared to the default C-style iteration.

---

#include <numpy/arrayobject.h>

int main() {
  // Create a 3D NumPy array
  npy_intp dims[] = {2, 3, 4};
  PyObject* arr = PyArray_New(&PyArray_Type, 3, dims, NPY_INT32, NULL, NULL, 0, 0, NULL);

  // Fill the array with sample data (replace with your data population logic)
  int* data = (int*)PyArray_GetData(arr);
  for (int i = 0; i < PyArray_SIZE(arr); i++) {
    data[i] = i;
  }

  // Create an iterator
  NpyIter* it = NpyIter_New(1, &arr, NPY_ITER_READONLY, NULL);
  if (it == NULL) {
    PyErr_Print();
    return -1;
  }

  // Enable external loop
  NpyIter_EnableExternalLoop(it);

  // Iterate over elements, printing every other element
  int count = 0;
  while (NpyIter_NOT_FINISHED(it)) {
    if (count % 2 == 0) {
      // Access data using NpyIter_GetDataPtrArray
      int* current_data = *(int**)NpyIter_GetDataPtrArray(it);
      printf("Element (%ld, %ld, %ld): %d\n",
             NpyIter_GetIndex(it, 0), NpyIter_GetIndex(it, 1), NpyIter_GetIndex(it, 2),
             *current_data);
    }
    count++;
    NpyIter_MoveToNext(it);
  }

  // Clean up
  NpyIter_Deallocate(it);
  Py_DECREF(arr);
  return 0;
}

This code creates a 3D NumPy array, fills it with data, and then iterates through it using NpyIter_EnableExternalLoop. The loop visits every other element and prints its value along with its indices.

---

1. • The most common approach is to leverage NumPy's built-in iteration mechanisms. You can use constructs like for loops directly on NumPy arrays:

   import numpy as np
   
   arr = np.arange(12).reshape(3, 4)
   for element in arr.flat:  # Flattened iteration
       print(element)
   

   • This offers a concise and Pythonic way to iterate over arrays, often without needing C-level control.

2. nditer Function
   

   • NumPy's nditer function provides a more advanced iterator object for efficient multi-dimensional array traversal. It offers features like broadcasting and buffering, handling complex iteration patterns:

   import numpy as np
   
   arr = np.arange(6).reshape(2, 3)
   for x in np.nditer(arr):
       print(x)
   

   nditer can be more efficient than basic loops, especially for complex array operations.

3. Custom C Loops (Without NpyIter_EnableExternalLoop)
   

   • If you need fine-grained control over iteration order in C, you can write custom loops without relying on NpyIter_EnableExternalLoop. This approach involves manually calculating strides and offsets to access elements based on the desired order.

   # (Assuming you have initialized your array 'arr' with dimensions 'd1', 'd2')
   for (int i = 0; i < d1; i++) {
       for (int j = 0; j < d2; j++) {
           int index = i * d2 + j;  // Calculate index based on your order
           int* data_ptr = (int*)PyArray_GETPTR1(arr, index);
           // Access and process data using data_ptr
       }
   }
   

   This approach requires more C programming expertise but gives you maximum control over the iteration.