Alternatives to NPY_LOGE10 for Log10 in NumPy C-API

Understanding NPY_LOGE10

• C-API
  The NumPy C-API provides functions and structures to interact with NumPy arrays from C code. This allows for performance-critical operations or integration with existing C libraries.

• Function
  NPY_LOGE10 is not an actual function within the standard NumPy C-API. It likely refers to a macro or constant that represents the base-10 logarithm function (log10) exposed by the C-API.

Equivalent Functionality

• numpy.log10()
  The recommended approach for calculating base-10 logarithms in Python using NumPy is the numpy.log10() function. This function operates element-wise on NumPy arrays, providing efficient vectorized computation.

Example (Python)

import numpy as np

data = np.array([1, 10, 100])
log10_results = np.log10(data)
print(log10_results)  # Output: [0. 1. 2.]

Key Points

• It's generally preferable to use numpy.log10() in Python code for clarity and compatibility. The C-API is typically used for advanced scenarios where performance is paramount.
• If you're working in C and need the base-10 logarithm functionality, you might encounter a macro or constant named NPY_LOGE10 within the NumPy C-API headers. However, the exact implementation details would depend on the specific NumPy version you're using.

Additional Considerations

• The NumPy C-API offers various other mathematical functions (e.g., exponentiation, trigonometric functions) that can be used for element-wise array operations in C code.
• For complex-valued inputs, numpy.log10() has a defined branch cut and continuity behavior. Consult the NumPy documentation for more details if you're working with complex numbers.

---

#include <Python.h>
#include <numpy/arrayobject.h>
#include <math.h>

static PyObject* calculate_log10(PyObject* self, PyObject* args) {
  PyObject* input_array = NULL;
  PyObject* output_array = NULL;
  PyArrayObject* input_array_obj = NULL;
  PyArrayObject* output_array_obj = NULL;
  int i, size;
  double* input_data, *output_data;

  // Parse arguments
  if (!PyArg_ParseTuple(args, "O", &input_array)) {
    return NULL;
  }

  // Ensure input is a NumPy array
  if (!PyArray_Check(input_array)) {
    PyErr_SetString(PyExc_TypeError, "Input must be a NumPy array");
    return NULL;
  }

  // Get input array object
  input_array_obj = (PyArrayObject*)PyArray_FROM_OTF(input_array, NPY_DOUBLE, NPY_ARRAY_CARRAY);
  if (!input_array_obj) {
    PyErr_SetString(PyExc_ValueError, "Failed to convert input to double array");
    return NULL;
  }

  // Get array size and data pointers
  size = PyArray_SIZE(input_array_obj);
  input_data = (double*)PyArray_DATA(input_array_obj);

  // Create output array (assuming same size and type as input)
  output_array_obj = (PyArrayObject*)PyArray_NewCopy(input_array_obj, NPY_ANYORDER);
  if (!output_array_obj) {
    Py_DECREF(input_array_obj);
    return NULL;
  }
  output_data = (double*)PyArray_DATA(output_array_obj);

  // Calculate log10 for each element (with error handling)
  for (i = 0; i < size; ++i) {
    if (input_data[i] <= 0) {
      PyErr_SetString(PyExc_ValueError, "log10 is undefined for non-positive values");
      goto cleanup;
    }
    output_data[i] = log10(input_data[i]);
  }

  // Cleanup (release memory)
cleanup:
  Py_DECREF(input_array_obj);
  if (PyErr_Occurred()) {
    Py_DECREF(output_array_obj);
    return NULL;
  }

  // Return the output array
  return PyArray_Return(output_array_obj);
}

static PyMethodDef methods[] = {
  {"calculate_log10", calculate_log10, METH_VARARGS, "Calculates base-10 logarithm of a NumPy array"},
  {NULL, NULL, 0, NULL} /* Sentinel */
};

static struct PyModuleDef moduledef = {
  PyModuleDef_HEAD_INIT,
  "my_log10_module", /* Module name */
  "Example module for calculating log10 in C",
  -1,
  methods,
  NULL,
  NULL,
  NULL,
  NULL
};

PyMODINIT_FUNC PyInit_my_log10_module(void) {
  return PyModule_Create(&moduledef);
}

1. Includes necessary headers (Python.h, numpy/arrayobject.h, and math.h).
2. Defines a C function calculate_log10 that takes a single argument (the input NumPy array).
3. Parses arguments and ensures the input is a NumPy array.
4. Converts the input array to a C double array for efficient element-wise operations.
5. Creates a new NumPy array (output) with the same size and type as the input.
6. Iterates through each element and calculates the log10 using math.h's log10 function.
7. Handles potential errors (log10 is undefined for non-positive values).
8. Releases memory using Py_DECREF and returns the output array.
9. Defines module initialization details (PyModuleDef and `Py

---

Using PyArray_IterObject (More Flexible)

This approach iterates over the NumPy array element-wise and performs calculations in C, potentially offering more control over the processing.

#include <Python.h>
#include <numpy/arrayobject.h>

static PyObject* calculate_log10_iter(PyObject* self, PyObject* args) {
  PyObject* input_array = NULL;
  PyObject* output_array = NULL;
  PyArrayObject* input_array_obj = NULL;
  PyArrayIterObject* iter = NULL;
  npy_intp size;
  double* input_data, *output_data;
  Py_ssize_t i;

  // Parse arguments
  if (!PyArg_ParseTuple(args, "O", &input_array)) {
    return NULL;
  }

  // Ensure input is a NumPy array
  if (!PyArray_Check(input_array)) {
    PyErr_SetString(PyExc_TypeError, "Input must be a NumPy array");
    return NULL;
  }

  // Get input array object and size
  input_array_obj = (PyArrayObject*)PyArray_FROM_OTF(input_array, NPY_DOUBLE, NPY_ARRAY_CARRAY);
  if (!input_array_obj) {
    PyErr_SetString(PyExc_ValueError, "Failed to convert input to double array");
    return NULL;
  }
  size = PyArray_SIZE(input_array_obj);

  // Create output array (assuming same size and type as input)
  output_array = PyArray_NewCopy(input_array_obj, NPY_ANYORDER);
  if (!output_array) {
    Py_DECREF(input_array_obj);
    return NULL;
  }
  output_data = (double*)PyArray_DATA(output_array);

  // Get iterator for input array
  iter = (PyArrayIterObject*)PyArray_IterNew(input_array_obj);
  if (!iter) {
    Py_DECREF(input_array_obj);
    Py_DECREF(output_array);
    return NULL;
  }

  // Iterate and calculate log10 for each element
  for (i = 0; PyArray_ITER_NOTDONE(iter); ++i) {
    input_data = (double*)PyArray_ITER_DATA(iter);
    if (*input_data <= 0) {
      PyErr_SetString(PyExc_ValueError, "log10 is undefined for non-positive values");
      goto cleanup;
    }
    *output_data = log10(*input_data);
    PyArray_ITER_NEXT(iter);
    output_data++;
  }

  // Cleanup (release memory)
cleanup:
  Py_DECREF(iter);
  Py_DECREF(input_array_obj);
  if (PyErr_Occurred()) {
    Py_DECREF(output_array);
    return NULL;
  }

  // Return the output array
  return PyArray_Return(output_array);
}

// ... rest of module definition code (similar to previous example)

1. Similar includes (Python.h and numpy/arrayobject.h).
2. Defines calculate_log10_iter that takes an input NumPy array.
3. Parses arguments and checks for a NumPy array.
4. Converts input to a C double array and creates an output array.
5. Creates a PyArrayIterObject for iterating over the input array.
6. Loops through elements using PyArray_ITER_NOTDONE and PyArray_ITER_NEXT.
7. Performs calculations and error handling within the loop.
8. Releases memory using Py_DECREF and returns the output array.

Leveraging numpy.log10 from Python (Simpler)

For simpler scenarios, you can call numpy.log10 from Python within your C code using the Python C API. This approach might be less performant but is easier to implement.

#include <Python.h>

static PyObject* calculate_log10_python(PyObject* self, PyObject* args) {
  PyObject* input_array = NULL;
  PyObject* log10_func = NULL