cnpy

C++11 library to save/load arrays to/from NumPy binary format.

Purpose

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.

Installation

cnpy depends on Zlib and CMake. The default installation directory is /usr/local but you can change that with the --prefix option to the setup.py script.

>>> python setup.py  # Configure
>>> cd build
>>> make             # Build
>>> ctest            # Run tests
>>> make install

make install will also install CMake scripts to detect the library as a target in other projects using a find_package(cnpy) command. To enable this feature, set -Dcnpy_DIR=${prefix}/share/cmake/cnpy in the most appropriate location.

Using

To use, #include "cnpy.h" in your source code. Compile the source code mycode.cpp as

g++ -o mycode mycode.cpp -L/path/to/install/dir -lcnpy -lz

Description

There are two functions for writing data: npy_save, 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 structues. 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 NpyArray {
    std::vector<size_t> shape;
    size_t word_size;
    template<typename T> T* data();
};

See the sample tests for examples of how to use the library.