Stratax

Stratax is a modern scientific computing library built around modern C++20 containers, shape metadata, and element-wise array operations. The core library is header-first, with Python bindings powered by pybind11.

The project is still young: vectors, matrices, tensors, shape/stride metadata, indexing, slicing, reshaping, conversions, creation helpers, printing, comparison, and arithmetic are active. Linear algebra, calculus, random, and statistics modules are currently reserved API areas.

Current Features

• C++20 Vector, Matrix, and Tensor containers
• Contiguous Buffer storage with shape and stride metadata
• Bounds-checked at(...) and multidimensional operator(...) access
• Element-wise arithmetic and comparison operators
• Reshape, flatten, slicing, and container conversion helpers
• Tensor creation helpers such as zeros, ones, full, and identity
• Stream printing for vectors, matrices, tensors, shapes, and strides
• Python bindings for Shape, Vector, Matrix, and Tensor
• Doxygen API documentation

Requirements

• C++20 compiler
• CMake 3.20 or newer
• Python 3.10 or newer for bindings
• pybind11 2.12 or newer
• scikit-build-core 0.10 or newer
• pytest 8 or newer for Python tests
• Doxygen 1.17 or newer for API docs

Quick C++ Example

#include <stratax.h>
#include <iostream>

int main()
{
    stratax::container::Vector<double> a{1.0, 2.0, 3.0};
    stratax::container::Vector<double> b{4.0, 5.0, 6.0};

    auto c = a + b;

    std::cout << c << '\n'; // [5, 7, 9]
}

Quick Python Example

from stratax import Matrix, Shape, Tensor, Vector, to_matrix, to_tensor, to_vector

shape = Shape([2, 2])
vector = Vector([1.0, 2.0, 3.0])
matrix = Matrix([[1.0, 2.0], [3.0, 4.0]])
tensor = Tensor([2, 2], 1.0)

vector_as_tensor = to_tensor(vector)
tensor_as_vector = to_vector(tensor)
tensor_as_matrix = to_matrix(Tensor([2, 2], 5.0))

tensor[1, 1] = 9.0

print(shape.elements)
print(vector.tolist())
print(matrix.tolist())
print(tensor.tolist())
print(vector_as_tensor.tolist())
print(tensor_as_vector.tolist())
print(tensor_as_matrix.tolist())

Python bindings currently expose double-based Shape, Vector, Matrix, and Tensor wrappers plus free-function conversions (to_vector, to_matrix, to_tensor). The C++ API remains the primary interface while the binding surface grows.

Build From Source

Configure and build with CMake:

cmake -S . -B build
cmake --build build

For editable Python installation, use pip from an environment with the build dependencies available:

python -m pip install -e .

Run Tests

The C++ tests live under tests/cpp/ and are registered with CTest through GoogleTest:

cmake -S . -B build
cmake --build build
ctest --test-dir build --output-on-failure

The Python tests live under tests/python/ and use pytest:

python -m pytest tests/python

Generate Documentation

Generate API documentation with Doxygen:

doxygen Doxyfile

The generated HTML entry point is:

docs/cpp/html/index.html

If you configure through CMake and Doxygen is available, you can also run:

cmake --build build --target docs

Repository Layout

include/stratax/      Public C++ headers
bindings/             pybind11 binding sources
python/stratax/       Python package
tests/cpp/            C++ tests
tests/python/         Python tests
examples/cpp/         C++ examples
examples/python/      Python examples
docs/cpp/             C++ API documentation
docs/python/          Python documentation

Installation

Python

pip install stratax

C++

Stratax is header-first for C++. Include the repository include/ directory in your build and include the umbrella header:

#include <stratax.h>

License

Stratax is licensed under the MIT License.

Contributing

Contributions, bug reports, and feature requests are welcome through GitHub Issues and Pull Requests.

Project Status

Implemented:

• Core storage and metadata: Buffer, Shape, Strides, Slice
• Containers: Vector, Matrix, Tensor
• Operations: arithmetic, comparison, indexing, reshape, slicing
• Container helpers: creation and conversions
• I/O: stream printing
• Python bindings: Shape, Vector, Matrix, Tensor, conversion free functions

Roadmap

• Broadcasting
• Logical operations
• Linear algebra algorithms
• Calculus helpers
• Random sampling and distributions
• Statistics routines
• CSV and binary I/O