wrapcco 0.1.0

A Python module to wrap C/C++ code and generate Python extensions.

---

Hey there! Welcome to WrapC.co ⚡ – your new shortcut to turbocharged Python extensions! 🚀 Born from the fiery forge of developing Statis.co, our bleeding-edge quantitative finance toolkit, WrapC.co is here to revolutionize how you build Python-C/C++ integrations.

When we first built Statis.co, we brute-forced our way through manual Python-C extensions and Numpy C API integrations. While it worked (and honestly, crushed Numpy-python in speed tests 💨), we quickly hit a wall. Endless boilerplate code, argument parsing gymnastics, and setup file headaches sucked the joy out of development. That's when we thought: "There's gotta be a better way..." ❄️

Enter WrapC.co – the developer experience upgrade you didn't know you needed. We automated the boring bits so you can focus on writing killer C/C++ code instead of wrestling with wrapper setup. Now you can seamlessly expose optimized numerical functions, process Numpy arrays at warp speed, and kiss 80% of that boilerplate junk goodbye 🗑️. Same native performance, 10x the developer zen. 🔮

Installation

Install from PyPi

pip install wrapcco

Install from Source

git clone https://github.com/H3cth0r/wrapc.co
cd wrapc.co
pip install -e .

Direct Install

python3 -m pip install git+https://github.com/H3cth0r/wrapc.co

Documentation

Requirements

• Python>=3.9.
• Extensions should be in c++17.
• MacOs and NixOs support.

Numpy Superpowers 🏗️⚡

Want to crush array operations at C++ speed? Our NumpyArrayRef struct is your new best friend – think of it as a nitro boost for Numpy interop 🚀. Nestled in wrapcco/resources/FunctionWrapper.hpp, this bad boy gives you two killer features right out the gate:

• array: Your direct line to Numpy's array soul
• owns_data: Memory management on autopilot ✈️

Check this lightning-round example – we're adding scalars to arrays faster than you can say "zero-copy":

#include <numpy/arrayobject.h>
#include "FunctionWrapper"

NumpyArrayRef addNtoArr(const NumpyArrayRef& a, int nToAdd) {
    // Store array specs ⚙️
    const npy_intp size = a.size();
    double* a_data = a.data<double>();

    // Create output array lightning-fast 🌩️
    npy_intp dims[] = {size};
    PyObject* result_obj = PyArray_SimpleNew(1, dims, NPY_DOUBLE);
    auto* result_arr = reinterpret_cast<PyArrayObject*>(result_obj);
    double* result_data = static_cast<double*>(PyArray_DATA(result_arr));

    // Magic happens here 🎩✨
    for (npy_intp i = 0; i < size; ++i) {
        result_data[i] = a_data[i] + nToAdd;
    }

    return NumpyArrayRef(result_arr);  // Automatic memory VIP treatment 🎟️
}

You can even pass a array as reference and modify the array directly.

void addScalarInPlace(NumpyArrayRef& arr_ref, int scalar) {
    if (arr_ref.dtype() != NPY_DOUBLE) {
        ...
    }
    
    double* data = arr_ref.data<double>();
    npy_intp size = arr_ref.size();
    
    #pragma omp parallel for
    for(npy_intp i = 0; i < size; ++i) {
        data[i] += scalar;
    }
}

Want to compile extensions like an absolute ninja? Grab our header directory with this one-liner:

from wrapcco import get_fw_include
fw_dir = get_fw_include()  # Boom – instant C++ toolkit access 🔧

Examples

For further testing examples, please check this directories:

• Test Wrapper (test/test_wrapper)
• Test Wrapper save files (test/test_wrapper_files)
• Test Wrapper generate library (test/test_wrapper_str_code)
• Test Wrapper generate library with files (test/test_wrapper_str_code_file)
• Test Extension and Test Extension save files(test/test_extension)
• Test Command Line (test/test_command_line)
• Test Command Line save files (test/test_command_line)

Command Line Usage

python -m wrapcco <library>.hpp --module-name <modulename> --save-script <true/false> --output-path "./"

• <library>: hpp file containing the library functions.
• --module-name: name or your output module executable.
• --save-script: choose if you want to generate the cpp extension file.

Get examples with

wrapcco --help-examples

TODO

• Add Piping, to streamline functions