nim-pymod

hen compiles the whole thing as a shared C library, exposing the auto-generated functions in the shared library. Regarding runtime type-checking: Again, I can only talk about the C backend. The type checking provided by shared or static C libraries will be whatever the C compiler enforces... I haven't tested whether (or how much) this can be violated. Nim-Pymod does auto-generate the Python->C type-checking code for you, so you can't (for example) pass in a Python string when an int is expected by your Nim function.

treats from other languages mixed in (such as Lisp-like macros and some Ruby-like syntax). I find Nim much more readable than C or C++, especially for Numpy integration. I also find Nim much more efficient to code in than C or C++ (in terms of programmer time). And Nim is a very extensible language, which enables Nim-Pymod to be more than just a wrapper. For example: 1. Nim-Pymod uses Nim macros (which are like optionally-typed Lisp macros rather than text-munging C preprocessor macros) to auto-generate the C boilerplate functions around our Nim code to create Python extension modules. 2. Nim-Pymod provides statically-typed C++-like iterators to access the Numpy arrays; these iterators include automatic inline checks to catch the usual subtle array-access errors. Nim macros are themselves Nim code, which can be controlled via globals, which in turn can be set by compiler directives; by compiling the Nim code in "production" mode rather than "debug" mode after testing, we can switch off the slowest of these checks to get back to direct-access speed without needing to make any code changes. (And of course Nim's static typing catches type errors at compilation time regardless of the compilation mode.) 3. Nim exceptions have an informative stack trace like Python exceptions do, and Nim-Pymod converts Nim exceptions into Python exceptions at the interface, preserving the stack trace, meaning you have a Python stack trace all the way back to the exact location in your Nim code. Earlier on in our development of Nim-Pymod, there were some occasional headaches with Nim due to its in-development status. Occasionally the Nim syntax would change slightly and that would break our code (boo). We've also debugged a few problems in the Nim standard library. I suppose these problems are an unfortunate consequence of Nim having a small set of core devs contributing their time (rather than being supported by Microsoft, Sun, Google or Mozilla). Fortunately, these problems seem to have stabilised by now. The Nim standard library is reasonably large, somewhere between C++ STL (data structures & algos) & Python stdlib (task-specific functionality). I recall that the stdlib could use some standardisation for uniformity, but I haven't been watching it closely for the last year or so. Third party libraries are not abundant, aside from a handful of prolific Nim community-members who have produced dozens of fantastic libraries (eg, https://github.com/def- , https://github.com/dom96 , https://github.com/fowlmouth , https://github.com/yglukhov ). I'm happy to answer any other questions about using Nim in production!

ry we've created allows you to write Nim functions, compile them as standard CPython extension modules, and simply drop them into your existing Python code: https://github.com/jboy/nim-pymod The Pymod library even includes a type `ptr PyArrayObject` that provides native Nim access to Numpy ndarrays via the Numpy C-API [ https://github.com/jboy/nim-pymod#pyarrayobject-type ]. So you can bounce back and forth between your Python code and your Nim code for the cost of a Python extension module function call. All of Numpy, Scipy & Matplotlib are still available to you in Python, in addition to statically-typed C++-like iterators in Nim+Pymod [ https://github.com/jboy/nim-pymod#pyarrayiter-types , https://github.com/jboy/nim-pymod#pyarrayiter-loop-idioms ]. The Nim for-loops will be compiled to C code that the C compiler can then auto-vectorize.

nce that I've yet found between C++'s ethos of thoughtful, precise control and Python's user-friendliness. (And if you ever happen to need seamless Nim-Python compatibility, including native Nim support for Numpy arrays with C++-style iterators, my Pymod project may be of assistance: https://github.com/jboy/nim-pymod )

e functionality as modules on top of this fundamental Numpy array type. [2] Python for-loops are slow: https://jakevdp.github.io/blog/2014/05/09/why-python-is-slow... So the most expensive operations in a Python number-crunching program are likely already implemented using Numpy & Scipy operations, which run in compiled C (and additionally, often make use of Blas/Atlas/LAPACK/etc, for even greater speedups in sustained number-crunching). But unfortunately, Numpy & PyPy do not naturally work together. Being written in C, Numpy makes substantial use of the CPython C-API -- and in fact, Numpy provides its own C-API [3]! The official Numpy package doesn't work with PyPy; the PyPy project very thoughtfully provides its own PyPy-compatible Numpy package [4]. [3] Numpy C-API: http://docs.scipy.org/doc/numpy-1.10.0/reference/c-api.html [4] PyPy-compatible Numpy package: http://pypy.org/download.html#installing-numpy Furthermore, Numpy is fantastic, but it can't offer all possible permutations of matrix operations. In particular, there are certain image-processing operations that are awkward (and thus, computationally-inefficient) to express using Numpy operations. So you might ultimately need to go to the Numpy C-API anyway. This is why we created (and, just a few days ago, open-sourced) Pymod: https://github.com/jboy/nim-pymod Pymod is a Nim+Python project that auto-generates all the Python C-API boilerplate & auto-compiles a Python C extension module that wraps the functions in a Nim module. Pymod enables us to write our Numpy array-processing code in Nim, then compile it (for C++-like speeds) as a well-behaved Python module. Nim made this very easy, because it compiles to C. After considering our Python-integration options (CPython C-API, `ctypes` and `cffi`), we decided to go with the CPython C-API & Numpy C-API. We explained this decision in greater detail in the "Implementation details" section of the Pymod README [5]; the executive summary is that `ctypes` seems better suited to wrapping C types in Python, rather than exposing existing Python types in C, while the CPython C-API code could be generated & compiled with the C code that Nim was going to produce anyway. [5] Pymod implementation details: https://github.com/jboy/nim-pymod#implementation-details That said, we would be delighted for Pymod-produced Python modules to be able to run under PyPy. We've been strongly considering implementing a `cffi` back-end for Pymod, but this won't necessarily solve the Numpy issue. It would be even better if PyPy could support all the CPython C-API extension modules in the world in one fell swoop.

m-lang and haven't looked back. To me, Nim combines all the best features of Python & C++, while avoiding most of the worst: C++ speed, C++ static types, C++ generics, C++ operator overloading; combined with a clean Pythonic syntax & Lispy macros.)