Hi. I'm trying to understand the python standard library modules. Seems that some are written in C and others are written in python? How does that work? How did they decide which is which. And also, what if you want to extend a built in python class that is written in C? How is a class even created in C which is not an OOP language? I know that's a lot of questions, but thanks if you can help me understand.
Edit: Also, I can find certain modules like os by looking in the directory /usr/lib/python3.9 (I'm on linux). The os module is in that directory named os.py. But I can't find others like for example the sys module, which I believe is written in C. Where can I find that?
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Although calling c++ libs from python is more normal - you can call a python module from c++ by bascially calling the python intepreter and have it execute the python source. This is called embedding
Alternatively the boost.python library makes it very easy.
You can also try the Boost.Python library; which has this capability. This library is mainly used to expose C++ to Python, but can be used the other way around.
Warning: a Cython core developer's opinion ahead.
I almost always recommend Cython over ctypes. The reason is that it has a much smoother upgrade path. If you use ctypes, many things will be simple at first, and it's certainly cool to write your FFI code in plain Python, without compilation, build dependencies and all that. However, at some point, you will almost certainly find that you have to call into your C library a lot, either in a loop or in a longer series of interdependent calls, and you would like to speed that up. That's the point where you'll notice that you can't do that with ctypes. Or, when you need callback functions and you find that your Python callback code becomes a bottleneck, you'd like to speed it up and/or move it down into C as well. Again, you cannot do that with ctypes. So you have to switch languages at that point and start rewriting parts of your code, potentially reverse engineering your Python/ctypes code into plain C, thus spoiling the whole benefit of writing your code in plain Python in the first place.
With Cython, OTOH, you're completely free to make the wrapping and calling code as thin or thick as you want. You can start with simple calls into your C code from regular Python code, and Cython will translate them into native C calls, without any additional calling overhead, and with an extremely low conversion overhead for Python parameters. When you notice that you need even more performance at some point where you are making too many expensive calls into your C library, you can start annotating your surrounding Python code with static types and let Cython optimise it straight down into C for you. Or, you can start rewriting parts of your C code in Cython in order to avoid calls and to specialise and tighten your loops algorithmically. And if you need a fast callback, just write a function with the appropriate signature and pass it into the C callback registry directly. Again, no overhead, and it gives you plain C calling performance. And in the much less likely case that you really cannot get your code fast enough in Cython, you can still consider rewriting the truly critical parts of it in C (or C++ or Fortran) and call it from your Cython code naturally and natively. But then, this really becomes the last resort instead of the only option.
So, ctypes is nice to do simple things and to quickly get something running. However, as soon as things start to grow, you'll most likely come to the point where you notice that you'd better used Cython right from the start.
ctypes is your best bet for getting it done quickly, and it's a pleasure to work with as you're still writing Python!
I recently wrapped an FTDI driver for communicating with a USB chip using ctypes and it was great. I had it all done and working in less than one work day. (I only implemented the functions we needed, about 15 functions).
We were previously using a third-party module, PyUSB, for the same purpose. PyUSB is an actual C/Python extension module. But PyUSB wasn't releasing the GIL when doing blocking reads/writes, which was causing problems for us. So I wrote our own module using ctypes, which does release the GIL when calling the native functions.
One thing to note is that ctypes won't know about #define constants and stuff in the library you're using, only the functions, so you'll have to redefine those constants in your own code.
Here's an example of how the code ended up looking (lots snipped out, just trying to show you the gist of it):
from ctypes import *
d2xx = WinDLL('ftd2xx')
OK = 0
INVALID_HANDLE = 1
DEVICE_NOT_FOUND = 2
DEVICE_NOT_OPENED = 3
...
def openEx(serial):
serial = create_string_buffer(serial)
handle = c_int()
if d2xx.FT_OpenEx(serial, OPEN_BY_SERIAL_NUMBER, byref(handle)) == OK:
return Handle(handle.value)
raise D2XXException
class Handle(object):
def __init__(self, handle):
self.handle = handle
...
def read(self, bytes):
buffer = create_string_buffer(bytes)
count = c_int()
if d2xx.FT_Read(self.handle, buffer, bytes, byref(count)) == OK:
return buffer.raw[:count.value]
raise D2XXException
def write(self, data):
buffer = create_string_buffer(data)
count = c_int()
bytes = len(data)
if d2xx.FT_Write(self.handle, buffer, bytes, byref(count)) == OK:
return count.value
raise D2XXException
Someone did some benchmarks on the various options.
I might be more hesitant if I had to wrap a C++ library with lots of classes/templates/etc. But ctypes works well with structs and can even callback into Python.
So NumPy “uses C underneath”. But what does this mean? How can a Python package (and therefore I assume Python code) be written in C?