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Python to C++ 14 transpiler

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:warning: This repository is no longer actively maintained by Lukas Martinelli.

This is a little experiment that shows how far you can go with the C++ 14 auto return type and templates. C++14 has such powerful type deduction that it is possible to transpile Python into C++ without worrying about the missing type annotations in python. Only a small subset of Python is working and you will encounter many bugs. The goal is to showcase the power of C++14 templates and not to create a fully functional transpiler.

Example

Original Python version.

def factorial(num):
	if num <= 1:
		return num
	return factorial(num-1) * num

Transpiled C++ template.

template <typename T1> auto factorial(T1 num) {
	if (num <= 1) {
		return num;
	}
	return factorial(num - 1) * num;
}

How it works

Consider a map implementation.

def map(values, fun):
	results = []
	for v in values:
		results.append(fun(v))
	return results

This can be transpiled into the following C++ template.

template <typename T1, typename T2>
auto map(T1 values, T2 fun) {
	std::vector<decltype(
		fun(std::declval<typename decltype(values)::value_type>()))> results{};
	for (auto v : values) {
		results.push_back(fun(v));
	}
	return results;
}

The parameters and the return types are deduced automatically In order to define the results vector we need to:

  1. Deduce the type for v returned from the values range using v_type = typename decltype(values)::value_type
  2. Deduce the return type of fun for call with parameter v decltype(fun(v))
  3. Because we dont know v at the time of definition we need to fake it std::declval<v_type>()
  4. This results in the fully specified value type of the results vector decltype(fun(std::declval<typename decltype(values)::value_type>()))

Trying it out

Requirements:

Transpiling:

./py14.py fib.py > fib.cpp

Compiling:

clang++ -Wall -Wextra -std=c++14 -Ipy14/runtime fib.cpp

Run regression tests:

cd regtests
make

Run tests

pip install -r requirements.txt
py.test --cov=py14

More Examples

Probability Density Function (PDF)

def pdf(x, mean, std_dev):
	term1 = 1.0 / ((2 * math.pi) ** 0.5)
	term2 = (math.e ** (-1.0 * (x-mean) ** 2.0 / 2.0 * (std_dev ** 2.0)))
	return term1 * term2

template <typename T1, typename T2, typename T3>
auto pdf(T1 x, T2 mean, T3 std_dev) {
	auto term1 = 1.0 / std::pow(2 * py14::math::pi, 0.5);
	auto term2 = std::pow(py14::math::e, -1.0 * std::pow(x - mean, 2.0) / 2.0 *
												std::pow(std_dev, 2.0));
	return term1 * term2;
}

Fibonacci

def fib(n):
	if n == 1:
		return 1
	elif n == 0:
		return 0
	else:
		return fib(n-1) + fib(n-2)

template <typename T1> auto fib(T1 n) {
	if (n == 1) {
		return 1;
	} else {
		if (n == 0) {
			return 0;
		} else {
			return fib(n - 1) + fib(n - 2);
		}
	}
}

Bubble Sort

def sort(seq):
	L = len(seq)
	for _ in range(L):
		for n in range(1, L):
			if seq[n] < seq[n - 1]:
				seq[n - 1], seq[n] = seq[n], seq[n - 1]
	return seq

template <typename T1> auto sort(T1 seq) {
	auto L = seq.size();
	for (auto _ : rangepp::range(L)) {
		for (auto n : rangepp::range(1, L)) {
			if (seq[n] < seq[n - 1]) {
				std::tie(seq[n - 1], seq[n]) = std::make_tuple(seq[n], seq[n - 1]);
			}
		}
	}
	return seq;
}

Working Features

Only bare functions using the basic language features are supported.

Language Keywords

Builtins

Data Structures