libnpy is a multi-platform C++ library for reading and writing NPY and
NPZ files, with an additional .NET interface. It was built with the
intention of making it easier for multi-language projects to use NPZ and
NPY files for data storage, given their simplicity and support across
most Python deep learning frameworks.
The implementations in this library are based upon the following file format documents:
- NPY: The NPY file format is documented by the NumPy developers in this note
- NPZ: While not explicitly documented, the NPZ format is a a PKZIP archive of NPY files, and thus is documented here: https://pkware.cachefly.net/webdocs/casestudies/APPNOTE.TXT.
There are two main ways to use the library: as a statically linked C++ library, and as a .NET DLL (on Windows using Visual Studio). In this guide we will walk through how to compile the library and run the tests on our currently supported platforms. These directions will likely work for other platforms as well (the codebase is written to be clean, portable C++ 11). If you have problems on your platform, please raise it as an issue.
First, install all of the necessary dependencies:
sudo apt-get install git cmake build-essential
If you want to build the documentation, you will also need:
sudo apt-get install doxygen
You may also find that cmake is easier to use via the curses GUI:
sudo apt-get install cmake-curses-gui
Once everything is in place, you can clone the repository and generate the makefiles:
git clone https://github.com/matajoh/libnpy.git
mkdir libnpy/build
cd libnpy/build
cmake -DCMAKE_BUILD_TYPE=Debug ..
Your other build options are Release and RelWithDebInfo.
On Windows, you can download and install the dependencies from the following locations:
Download and run e.g. v3.19/cmake-3.19.0-win64-x64.msi from
https://cmake.org/files/.
Get the latest Windows git from https://git-scm.com/downloads. Download a version of Visual Studio from https://visualstudio.microsoft.com/vs/. You will need the C++ compiler (and C# compiler if needed).
Browse to http://swig.org/download.html and download the latest version of
swigwin. Unzip the directory and copy it to your C:\ drive. Add (e.g.)
C:\swigwin-4.0.2 to your PATH. CMake should then find swig automatically.
If you want to build the documentation, you should also download Doxygen.
Now that everything is ready, cmake can generate the MSBuild files necessary for the project. Run the following commands in a command prompt once you have navigated to your desired source code folder:
git clone https://github.com/matajoh/libnpy.git
mkdir libnpy\build
cd libnpy\build
cmake ..
If building the C# library, you will also need to do the following:
cmake --build . --target NumpyIONative
cmake ..
The reason for the above is that SWIG autogenerates the C# files for the interface in the first pass, after which CMake needs to scan the generated directory to build the wrapper library.
You are now able to build the test the library. Doing so is the same
regardless of your platform. First, navigate to the build folder you
created above. Then run the following commands:
cmake --build . --config <CONFIG>
Where <CONFIG> is one of Release|Debug|RelWithDebInfo. This will build
the project, including the tests and (if selected) the documentation. You
can then do the following:
ctest -C <CONFIG>
Where again you replace <CONFIG> as above will run all of the tests.
If you want to install the library, run:
cmake --build . --config <CONFIG> --target INSTALL
Once the library has been built and installed, you can begin to use it in your code. We have provided some sample programs (and naturally the tests as well) which show how to use the library, but the basic concepts are as follows. For the purpose of this sample code we will use the built-in tensor class, but you should use your own tensor class as appropriate.
#include "tensor.h"
#include "npy.h"
#include "npz.h"
...
// create a tensor object
std::vector<size_t> shape({32, 32, 3});
npy::tensor<std::uint8_t> color(shape);
// fill it with some data
for (int row = 0; row < color.shape(0); ++row)
{
for (int col = 0; col < color.shape(1); ++col)
{
color(row, col, 0) = static_cast<std::uint8_t>(row << 3);
color(row, col, 1) = static_cast<std::uint8_t>(col << 3);
color(row, col, 2) = 128;
}
}
// save it to disk as an NPY file
npy::save("color.npy", color);
// we can manually set the endianness to use
npy::save("color.npy", color, npy::endian_t::BIG);
// the built-in tensor class also has a save method
color.save("color.npy");
// we can peek at the header of the file
npy::header_info header = npy::peek("color.npy");
// we can load it back the same way
color = npy::load<std::uint8_t, npy::tensor>("color.npy");
// let's create a second tensor as well
shape = {32, 32};
npy::tensor<float> gray(shape);
for (int row = 0; row < gray.shape(0); ++row)
{
for (int col = 0; col < gray.shape(1); ++col)
{
gray(row, col) = 0.21f * color(row, col, 0) +
0.72f * color(row, col, 1) +
0.07f * color(row, col, 2);
}
}
// we can write them to an NPZ file
{
npy::onpzstream output("test.npz");
output.write("color.npy", color);
output.write("gray.npy", gray);
}
// and we can read them back out again
{
npy::inpzstream input("test.npz");
// we can test to see if the archive contains a file
if (input.contains("color.npy"))
{
// and peek at its header
header = input.peek("color.npy");
}
color = input.read<std::uint8_t>("color.npy");
gray = input.read<float>("gray.npy");
}The generated documentation contains more details on all of the functionality. We hope you find that the library fulfills your needs and is easy to use, but if you have any difficulties please create issues so the maintainers can make the library even better. Thanks!
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