Installation

SciNumTools v3 can be installed in several ways, depending on how it is intended to be used. Python users can install the Python bindings directly from PyPI or Conda, while C++ users can use vcpkg, Conan, Homebrew, or build the library directly from source.

For most users, installing a pre-built package is recommended. Building from source is useful when developing SciNumTools itself, requiring specific build options, or integrating a development version of the library.

Python

The Python bindings are available as a regular Python package on PyPI:

pip install scinumtools3

SciNumTools is also available through conda-forge:

conda install conda-forge::scinumtools3

After installation, the Python interface can be imported directly:

from scinumtools3.puq import Quantity
from scinumtools3.dip import DIP

C++

For C++ projects, SciNumTools can be installed using several package managers, including vcpkg and Conan.

vcpkg

SciNumTools is available as a vcpkg package:

git clone https://github.com/microsoft/vcpkg.git
cd vcpkg

./bootstrap-vcpkg.sh

./vcpkg install scinumtools3

On Windows, use bootstrap-vcpkg.bat instead of bootstrap-vcpkg.sh.

Conan

SciNumTools can also be created as a local Conan package directly from the source repository:

git clone --recurse-submodules https://github.com/vrtulka23/scinumtools3.git
cd scinumtools3

conan create .

macOS / Homebrew

On macOS, SciNumTools can be installed using the project Homebrew tap:

brew tap vrtulka23/tap
brew install scinumtools3

Building from Source

SciNumTools is built using CMake and requires a C++17-compatible compiler. Building from source provides full access to the C++ library, command-line applications, tests, and other components of the project.

With DIP enabled (the default), install HDF5 development headers and the HDF5 C library before configuring. The HDF5 C++ library is not required. If HDF5 is installed outside the standard search paths, pass its installation prefix as -DHDF5_ROOT=/path/to/hdf5 when running CMake. The commands below also require the Ninja build tool.

Clone the repository with its submodules and configure the build. The briefpp submodule provides the header-only renderer for DIP reports. Alternatively, set SNT_BRIEFPP_INCLUDE_DIR to a directory containing briefpp/report.hpp. The cpp-httplib submodule provides the optional REST server. Alternatively, install cpp-httplib 0.46.0 or newer and set SNT_HTTPLIB_INCLUDE_DIR to its header directory. Builds with -DENABLE_SNT_SERVER=OFF do not need cpp-httplib.

git clone --recurse-submodules https://github.com/vrtulka23/scinumtools3.git
cd scinumtools3

cmake -G Ninja -B build
cmake --build build

The test suite can then be executed using CTest:

ctest --test-dir build

Finally, install the compiled library and associated components:

cmake --install build

Using the Setup Script

The repository also provides a convenience setup script for building, testing, and installing SciNumTools:

sudo ./setup.sh -b -t -i

Here -b builds the project, -t runs the tests, and -i installs the resulting components.

Using SciNumTools from CMake

Once installed, SciNumTools can be discovered from another CMake project using find_package:

find_package(snt REQUIRED)

Individual SciNumTools modules can then be linked to an executable:

add_executable(${EXEC_NAME} ${SOURCE_FILES})

target_link_libraries(
    ${EXEC_NAME}
    PRIVATE
    snt-exs
    snt-puq
    snt-dip
)

This modular approach allows an application to link only the SciNumTools components it requires.

Choosing an Installation Method

The recommended installation method depends on the intended use:

Python application: install scinumtools3 from PyPI or Conda.
C++ application: use vcpkg, Conan, Homebrew, or a system installation.
SciNumTools development: build directly from source.
Reproducible development environment: use the Docker integration.

After installation, continue with Quick Start to start using SciNumTools.