I am currently rewriting this to use modern C++ and best practices. So this README is currently out of date.

NumPy offers the save method for easy saving of arrays into .npy and savez for zipping multiple .npy arrays together into a .npz file.

cnpy lets you read and write to these formats in C++.

The motivation comes from scientific programming where large amounts of data are generated in C++ and analyzed in Python.

Writing to .npy has the advantage of using low-level C++ I/O (fread and fwrite) for speed and binary format for size. The .npy file header takes care of specifying the size, shape, and data type of the array, so specifying the format of the data is unnecessary.

Loading data written in numpy formats into C++ is equally simple, but requires you to type-cast the loaded data to the type of your choice.

There are two ways to use the library:

Just include this into your CMakeLists.txt.

include(FetchContent)
FetchContent_Declare(cnpy
        GIT_REPOSITORY "https://github.com/TomSchammo/cnpy"
        GIT_SHALLOW True
)
FetchContent_MakeAvailable(cnpy)

This library needs support for C++20 features, so at least GCC 13 and setting the CMAKE_CXX_STANDARD to 20 or above are required.

1. Install cmake and git
2. Run git clone https://github.com/TomSchammo/cnpy and cd cnpy
3. Create a build directory and change into it using mkdir build and cd build
4. Run CMake: cmake ... This library uses the new dual ABI by default. If you need support for the legacy ABI, don't forget to set the flags using -DCMAKE_CXX_FLAGS="-D_GLIBCXX_USE_CXX11_ABI=0"
5. Run make to build and make install to install the project.

To use the library, include the cnpy/cnpy.hpp header in your source code. It is necessary to link against zlib when building your library. The flag for that should be -lz.

There are two functions for writing data: npy_save and npz_save.

There are 3 functions for reading:

• npy_load will load a .npy file.
• npz_load(fname) will load a .npz and return a dictionary of NpyArray structures.
• npz_load(fname,varname) will load and return the NpyArray for data varname from the specified .npz file.

The data structure for loaded data is below. Data is accessed via the data<T>()-method, which returns a pointer of the specified type (which must match the underlying datatype of the data). The array shape and word size are read from the npy header.

struct npy_array {
    std::vector<size_t> shape;
    size_t word_size;
    template<typename T> T* data();
};