# Python modules in C

August 03, 2012

Writing your own C extensions to Python can seem like a pretty daunting task when you first get started. If you take a look at the Python/C API docs the details of reference counting and compilation are enough to make you go crazy. This is the main reason why so many options exist for wrapping or compiling C code into Python without ever directly interacting with the API. That being said, I often find that all that I need to do is wrap a single C function that accepts a few doubles and returns another double. In this case, it seems crazy to generate the thousands of lines of C code required by automatic methods like Cython and SWIG. You might argue that these aesthetic issues don't provide sufficient reason for diving into the rabbit hole that the C API seems to be—and maybe you'd be right—but I'm a stubborn coder and I don't mind getting my hands a little dirty so I went for it. This was a few years ago and since then, I've developed a template module that suits my needs perfectly and it seems to make the extension writing process relatively painless so I thought that I'd share what I've learned here.

I'm not going to claim that what I say here is a general introduction to writing C extensions because I don't feel qualified to do that but it should be a sufficient tutorial for a scientific programmer (read: grad student) to get started and write a fully functional module for their research. In particular, this tutorial will be most useful for someone who already has a chunk of code written in C and just wants to be able to call a few of those functions directly from within Python. Several people have specifically asked me about how to do this when they have legacy data analysis code that they would like to use with my Markov chain Monte Carlo package emcee. In that context, the C code is expected to return the likelihood of some data given some model parameters passed as doubles to the C function. This is the same format that would be needed if you just wanted to find the minimum chi-squared (or maximum likelihood) solution to a problem using something like scipy.optimize.

## The Objective

To be concrete, let's consider a specific example: fitting a line (parameterized by a slope m and y intercept b) to some N noisy data points $\{ x_n, y_n, \sigma_n \}$. In this case, the chi-squared function is given by:

$$\chi^2 (m, b) = \sum_{n = 1} ^N \frac{[y_n - (m \, x_n + b)]^2}{\sigma_n^2} \quad .$$

It's probably overkill to write this function in C but it'll do for our purposes today. In C, the file chi2.c containing our function should look something like:

#include "chi2.h"

double chi2(double m, double b, double *x, double *y, double *yerr, int N) {
int n;
double result = 0.0, diff;

for (n = 0; n < N; n++) {
diff = (y[n] - (m * x[n] + b)) / yerr[n];
result += diff * diff;
}

return result;
}


And the corresponding header file chi2.h is simply:

double chi2(double m, double b, double *x, double *y, double *yerr, int N);


Now, our goal is to wrap this function so that we can call it from directly within Python.

## The Wrapper

The code needed to write the wrapper module is another C file containing a few special Python functions. Conventionally, the names of C extensions begin with an underscore so let's call our module _chi2 and write it in a file _chi2.c (not to be confused with the chi2.c file that we wrote just a minute ago).

In order to be able to access the C functions and types in the Python API, the first thing that we need to do is import the Python header. I also expect that we'll want to interact with numpy arrays and our chi2 function as well so let's import those headers too:

#include <Python.h>
#include <numpy/arrayobject.h>
#include "chi2.h"


Next, we should write the docstrings for our module and the function that we're wrapping:

static char module_docstring[] =
"This module provides an interface for calculating chi-squared using C.";
static char chi2_docstring[] =
"Calculate the chi-squared of some data given a model.";


and declare the function:

static PyObject *chi2_chi2(PyObject *self, PyObject *args);


This is the first time that we're seeing anything Python-specific. The type PyObject refers to all Python types. Any communication between the Python interpreter and your C code will be done by passing PyObjects so any function that you want to be able to call from Python must return one. Under the hood, PyObject is just a struct with a reference count and a pointer to the data contained within the object. This can be as simple as a double or int or as complicated as a fully functional Python class. Remember: everything is an object.

The name that I've given to the function (chi2_chi2) is also a matter of convention. From Python, we're going to call the function with the command _chi2.chi2 where _chi2 is the name of the module and chi2 is the name of the function. Since C doesn't have any concept of namespaces, the convention is to name your C functions with the form {module_name}_{function_name} and my preference is to leave out the leading underscore but it doesn't really matter either way.

The arguments for the function are pretty standard fare. In our case the self object points to the module and the args object is a Python tuple of input arguments—we'll see how to parse them soon. It is also possible to accept keyword arguments by including a third PyObject in the calling specification but let's not get into that here.

Now, we'll specify what the members of this module will be. In this case there is only going to be one function (called chi2) so the "method definition" looks like:

static PyMethodDef module_methods[] = {
{"chi2", chi2_chi2, METH_VARARGS, chi2_docstring},
{NULL, NULL, 0, NULL}
};


More functions can be added by adding more lines like the second one. This second line contains all the info that the interpreter needs to link a Python call to the correct C function and call it in the right way. The first string is the name of the function as it will be called from Python, the second object is the C function to link to and the last argument is the docstring for the function. The third argument METH_VARARGS means that the function only accepts positional arguments. If you wanted to support keyword arguments, you would need to change this to METH_VARARGS | METH_KEYWORDS.

The final step in initializing your new C module is to write an init{name} function. This function must be called init_chi2 where _chi2 is (of course) the name of the module.

PyMODINIT_FUNC init_chi2(void)
{
PyObject *m = Py_InitModule3("_chi2", module_methods, module_docstring);
if (m == NULL)
return;

/* Load numpy functionality. */
import_array();
}


Everything that's going on here should be fairly self explanatory by this point but it's important to note that if you want to use any of the functionality defined by numpy, you need to include the call to import_array() (a function defined in the numpy/arrayobject.h header).

## The Interface

Up to this point, we've written only about 25 lines of C code to set up a C extension module. All of these steps will be common between any modules that you write but as we continue, the details become somewhat less general because I will focus on building a wrapper for scientific code.

Now, it's time to write the chi2_chi2 function that we declared above. In this example, the args tuple will contain two doubles (the slope and y-intercept of our model) and three numpy arrays for the x, y and uncertainties that constitute the "data" that we're trying to model. Let's just throw down the whole function here and then dissect it line-by-line below:

static PyObject *chi2_chi2(PyObject *self, PyObject *args)
{
double m, b;
PyObject *x_obj, *y_obj, *yerr_obj;

/* Parse the input tuple */
if (!PyArg_ParseTuple(args, "ddOOO", &m, &b, &x_obj, &y_obj,
&yerr_obj))
return NULL;

/* Interpret the input objects as numpy arrays. */
PyObject *x_array = PyArray_FROM_OTF(x_obj, NPY_DOUBLE, NPY_IN_ARRAY);
PyObject *y_array = PyArray_FROM_OTF(y_obj, NPY_DOUBLE, NPY_IN_ARRAY);
PyObject *yerr_array = PyArray_FROM_OTF(yerr_obj, NPY_DOUBLE,
NPY_IN_ARRAY);

/* If that didn't work, throw an exception. */
if (x_array == NULL || y_array == NULL || yerr_array == NULL) {
Py_XDECREF(x_array);
Py_XDECREF(y_array);
Py_XDECREF(yerr_array);
return NULL;
}

/* How many data points are there? */
int N = (int)PyArray_DIM(x_array, 0);

/* Get pointers to the data as C-types. */
double *x    = (double*)PyArray_DATA(x_array);
double *y    = (double*)PyArray_DATA(y_array);
double *yerr = (double*)PyArray_DATA(yerr_array);

/* Call the external C function to compute the chi-squared. */
double value = chi2(m, b, x, y, yerr, N);

/* Clean up. */
Py_DECREF(x_array);
Py_DECREF(y_array);
Py_DECREF(yerr_array);

if (value < 0.0) {
PyErr_SetString(PyExc_RuntimeError,
"Chi-squared returned an impossible value.");
return NULL;
}

/* Build the output tuple */
PyObject *ret = Py_BuildValue("d", value);
return ret;
}


I know that that was a lot in one go so let's break things down a little bit. The first thing that we did was parse the input tuple using the PyArg_ParseTuple function. This function takes the tuple, a format and the list of pointers to the objects that you want to take the input values. This format should be familiar if you've ever used something like the sscanf function in C but the format characters are a little different. In our example, d indicates that the argument should be cast as a C double and O is just a catchall for PyObjects. There isn't a specific format character for numpy arrays so we have to parse them as raw PyObjects and then interpret them afterwards. If PyArg_ParseTuple fails, it will return NULL which is the C-API technique for propagating exceptions. That means that we should also return NULL immediately if parsing the tuple fails.

The next few lines (12-25) show how to load numpy arrays from the raw objects. The PyArray_FROM_OTF function is a fairly general method for converting an arbitrary Python object into a well-behaved numpy array that can be used in a standard C function. It is important to note that this creation mechanism only returns a copy of the object if necessary. Instead, the function normally only returns a pointer to the input object if it was already a numpy array satisfying various requirements that we won't discuss in detail here. The flags NPY_DOUBLE and NPY_IN_ARRAY ensure that the returned array object will be represented as contiguous arrays of C doubles. There are some other options available for different types, orderings and permissions but most of the time, this is probably what you'll need and the other options are described in the documentation.

Reference Counting: Memory management in Python works by keeping track of the number of "references" to a particular object and then deallocating the memory of that object when that count reaches zero. You can read more about the details of this system elsewhere but for now, you need to keep in mind that when you return a PyObject from a function you might want to run Py_INCREF on it to increment the reference count and when you create a new object within you function that you don't want to return, you should run Py_DECREF on it before the function returns (even if the execution failed) so that you don't end up with a memory leak. The documentation explaining this system makes the important comment that it is part of each function's "interface specification" whether or not it increases the reference count of an object before it returns or not. With this in mind, you need to keep careful track of which functions do what or the memory usage can become a little ugly.

In our example, the objects returned by PyArg_ParseTuple do not have their reference count incremented (the calling function still "owns" them) so you don't need to decrement the reference count of the *_obj objects before returning. Conversely, PyArray_FROM_OTF does return an object with a +1 reference count. This means that you must call Py_DECREF with the *_array objects as the first argument before returning from this function. If PyArray_FROM_OTF can't coerce the input object into a form digestible as a numpy array, it will return NULL so that's why on lines 19-21, I actually use Py_XDECREF. Py_XDECREF checks to make sure that the object isn't a NULL pointer before trying to decrease the reference count whereas Py_DECREF will explode if you try to call it on NULL.

If we successfully reach line 25 then all of the input arguments were as expected and we have the input numpy arrays arranged the way we want them to be. Now we can get on to the fun stuff. For simplicity, on line 26, I'm assuming that we received a 1D array (but I could check this using the PyArray_NDIM function) and getting the length of the array. Then, I'm getting pointers to the actual C array (which will be formatted properly as an array of doubles because of the flags that we used in PyArray_FROM_OTF above). Then, on line 34, we can finally call the C function that we wanted to wrap in the first place.

The conditional on line 41 in the example is probably unnecessary because the chi2 function (by definition) will always return a non-negative value but I wanted to include it anyways because it demonstrates how you would throw an exception if something went wrong in the execution of the C code. The Python interpreter has a global variable that contains a pointer to the most recent exception that has been thrown. Then if a function returns NULL it starts an upwards cascade where each function either catches the exception using a try-except statement or also returns NULL. When the interpreter receives a NULL return value, it stops execution of the current code and shows a representation of the value of the global Exception variable and the traceback. On line 42, if chi2 returned a number less than zero, the global Exception is being set to have the type RuntimeError and the description: "Chi-squared returned an impossible value." Then, by returning NULL we're indicating that something went wrong.

Finally, if value was non-negative (it'd better be), we can use Py_BuildValue to create the output tuple. If Py_ParseTuple has a syntax similar to sscanf then Py_BuildValue is the analog of sprintf with the same format characters as Py_ParseTuple. Here, we don't need to Py_INCREF the return object because Py_BuildValue does that for us but if you generated the output in a different way, you might have to.

That's it for the code required for our module. It might seem like a lot of work but you'll notice that we've only written about 120 lines of code and the vast majority of these lines will be exactly the same in every module that you need to write.

## Building

The last thing that we need to talk about is how you might compile and link this module so that it can actually be called from Python. The best way to do this is to use the built-in Python distribution utilities. Traditionally, the build script is called setup.py and for our example, the file is actually extremely simple:

from distutils.core import setup, Extension
import numpy.distutils.misc_util

setup(
ext_modules=[Extension("_chi2", ["_chi2.c", "chi2.c"])],
include_dirs=numpy.distutils.misc_util.get_numpy_include_dirs(),
)


and you can call it using the command:

python setup.py build_ext --inplace


which will compile and link you source code and create a shared object called _chi2.so in the same directory. Then, from Python, you can do the following:

>>> import _chi2
>>> print _chi2.chi2(2.0, 1.0, [-1.0, 4.2, 30.6],
...                            [-1.5, 8.0, 63.0],
...                            [1.0, 1.5, 0.6])
2.89888888889


## Summary

Hopefully, after going through this tutorial, you should be able to write your own C-extension module especially if it is just a single C function that you want to wrap. In general, I find that most of my time is spent copy-and-pasting when I'm writing a C extension and once you get the hang of it it shouldn't take too much effort to incorporate code like this into projects that you're working on. Since so much of this structure is the same across projects, it would be awesome if someone wanted to make an interactive tool for auto-generating skeleton code but I haven't seen anything like this yet.

To see all the source code for this tutorial in one place, you can check out the gist or clone the repository using git:

git clone git://gist.github.com/3247796.git c_ext
cd c_ext
python setup.py build_ext --inplace