Python Installation

The uLib Python package exposes the Core and Math C++ libraries via pybind11 bindings. There are two ways to install it: as an end user (pre-built wheel / pip) or as a developer (editable build from source).

Prerequisites

uLib depends on native C++ libraries that must be compiled. Ensure the following are available in your environment before installing:

Dependency	Minimum version	Notes
Python	3.9
CMake	3.12
pybind11	2.6.0
Conan	2.x	for Eigen3 / Boost
micromamba / conda	any	recommended – provides ROOT, VTK

Creating the `mutom` Conda/Micromamba Environment

A ready-to-use environment definition is provided as condaenv.yml at the repository root.

=== "Micromamba" bash micromamba env create -f condaenv.yml micromamba activate mutom

=== "Conda" bash conda env create -f condaenv.yml conda activate mutom

The environment installs CMake, Conan, ROOT, VTK, and the compiler toolchain.

CUDA (optional)
If you want GPU-accelerated voxel filtering, you also need NVCC inside the environment:
micromamba install cuda-nvcc -c conda-forge

User Installation (wheel / pip)

Once the native dependencies are present in your environment, install the package with Poetry or pip:

# Activate your environment first
micromamba activate mutom

# Build and install (CUDA disabled by default)
poetry install

# Build and install with CUDA support
USE_CUDA=ON poetry install

After installation the module is importable from anywhere in the environment:

import uLib
print(dir(uLib.Core))
print(dir(uLib.Math))

Developer Installation (editable / in-tree build)

For development you typically want to skip the packaging layer and work directly against the CMake build tree.

Step 1 – Install Conan dependencies

conan profile detect          # first time only
conan install . --output-folder=build --build=missing

Step 2 – Configure and build

# Standard release build
cmake --preset conan-release

# …or manually
cmake -B build \
      -DCMAKE_TOOLCHAIN_FILE=build/conan_toolchain.cmake \
      -DCMAKE_BUILD_TYPE=Release \
      -DUSE_CUDA=OFF          # set to ON when a GPU is available

cmake --build build --target uLib_python -j$(nproc)

The shared library (uLib_python*.so) is written to build/src/Python/.

Step 3 – Make the module importable

Point PYTHONPATH at the build output and the Python source directory (the latter carries the uLib/__init__.py that stitches sub-modules together):

export PYTHONPATH="$(pwd)/build/src/Python:$(pwd)/src/Python:$PYTHONPATH"
python -c "import uLib; print(uLib.__version__)"

Or, for a one-shot check:

PYTHONPATH="build/src/Python:src/Python" python src/Python/testing/pybind_test.py

Step 4 – Run the tests

CMake registers the Python tests alongside the C++ ones; use ctest from the build directory:

cd build
ctest --output-on-failure -R pybind

Individual test scripts can also be run directly once PYTHONPATH is set:

python src/Python/testing/core_pybind_test.py
python src/Python/testing/math_pybind_test.py
python src/Python/testing/math_filters_test.py

Verifying the Installation

import uLib

# Core module
obj    = uLib.Core.Object()
timer  = uLib.Core.Timer()
timer.Start()
elapsed = timer.StopWatch()   # float, seconds

# Math module
v3 = uLib.Math.Vector3f([1.0, 0.0, 0.0])
print(v3[0])  # 1.0

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