Ilya LavrenovandMichael Nosov
22233414f9
* Try to fix Windows Azure * Try static MSVC runtime * Fixed type * cnpy * Rename * Set CMAKE_MSVC_RUNTIME_LIBRARY * Fixed issues with oneDNN * Removed IB * Minimize a number of threads * Increase image size to avoid test failures on some platforms There is an assert 'length >= nlanes' and 'nlanes' value depends on machine architecture Set 320x320 test image to guarantee that image size >= nlanes Also increased image size for 'plugin shared tests' to ensure the same (even though tests do not work with legacy preprocessing) * Descreased to 160x160 * Specify toolchain for samples * Build samples and its tests even for static build * Used 3.21 to enable CMAKE_TOOLCHAIN_FILE env var usage * Fixed cmake download path * Set CMAKE_TOOLCHAIN_FILE conditionally * Fix Co-authored-by: Michael Nosov <mikhail.nosov@intel.com>
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:
Default installation directory is /usr/local.
To specify a different directory, add -DCMAKE_INSTALL_PREFIX=/path/to/install/dir to the cmake invocation in step 4.
- get cmake
- create a build directory, say $HOME/build
- cd $HOME/build
- cmake /path/to/cnpy
- make
- make install
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 --std=c++11
Description:
There are two functions for writing data: npy_save and npz_save.
There are 3 functions for reading:
npy_loadwill 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();
};