xllim 2.0.6

xLLiM is a C++ with Python bindings implementation of Gaussian Locally-Linear Mapping.

xLLiM

xLLiM is a C++ with Python bindings implementation of Gaussian Locally-Linear Mapping. It can be used to perform efficient inversion of high-dimensional models. xLLiM integrates features such as:

• Forward model functionals. These can be implemented as C++ or as pure Python functions. The forward model can be used to generate data following a distribution and refine GLLiM results by sampling the PDF using various strategies.
• Multi-initialization options
• Post-GLLiM refinement methods such as Importance Sampling (IS) and Iterative Mixture Importance Sampling (IMIS)
• Post-processing analysis, including confidence quantification on predictions, detection of multiple solutions, and permutation of predictions in case of signal regularity. Currently, xLLiM supports only Gaussian probability distributions, but other distributions may be added to it in the future.

xLLiM is distributed as a compiled shared library and can be installed via conda, pip, or as a Docker container. It is integrated in the Planet-GLLiM astrophysics application, that is distributed as a Docker image for local use and as a data processing service on Allgo-18.

Repository migration: xLLiM has moved to GitHub. The current repository is https://github.com/xllim-tools/xllim. The former GitLab repository (https://gitlab.inria.fr/xllim/xllim) is no longer maintained.

The xLLiM method was previously implemented in R and is available on CRAN. Other implementations of GLLiM also exist:

• A pure Python implementation: pyGLLiM
• A Julia implementation with IS and IMIS: Fast Bayesian Inversion

This xLLiM implementation is derived from Kernelo. Kernelo is obsolete and is no longer maintained.

Table of Contents

• Documentation and API reference
• Runtime Dependencies
  • Mandatory
  • Optional
• xLLiM installation options
  • 1. Conda-forge (recommended)
  • 2. PyPI
  • 3. Docker
  • 4. Manual installation (optional)
• Licence

Documentation and API reference

The API reference of the xLLiM module is available at xLLiM API reference. For more information you can find a complete scientific documentation in the Planet-GLLiM documentation.

Runtime Dependencies

Mandatory:

These packages are required for the core functionality of the library.

Name	version
Python	>=3.11, < 3.13
Numpy	>=2.0, < 3
h5py	>=3.8, < 4

For other python versions, see Other python versions.

Optional:

ENVI / GDAL Support

xllim requires GDAL specifically for ENVI hyperspectral file support. Because GDAL depends on native system libraries, installation can be complex.

Recommended (Most reliable)

Using Conda is the simplest way to handle linked C libraries:

conda install gdal

Alternative: pip + system package (Advanced users)

If installing via apt or brew, the Python GDAL version must match the system GDAL version. If the versions do not match, the installation will fail.

Example on Ubuntu:

1. Install system libraries

sudo apt install libgdal-dev gdal-bin

2. Check the system version

gdal-config --version

3. Install the matching python wrapper (e.g., if version is 3.8.4)

pip install "gdal==3.8.4"

xLLiM installation options

xLLiM can be installed and used in several ways, depending on your environment and preference.

1. Conda-forge (recommended)

The easiest way to install xLLiM is via conda.

First create and activate an environment if you don't already have one:

conda create -n <my-env> python=3.12
conda activate <my-env>

(you can use python 3.11 or 3.12)

Then install xLLiM. This will also install the required dependencies needed.

conda install xllim

2. PyPI

Available wheels

We've built and tested wheels for Linux x86_64 (manylinux_2_28) - Python 3.11 and 3.12. If you're in one of these configurations, you can install xllim easily with:

pip install xllim

Mac/Windows

If you're on Mac or Windows, and are fine with Python 3.11/12, we recommend you either:

• install with conda (Section 1.)
• use our pre-built docker image (Section 3.)

Other Python versions

If you want to use a different Python version (3.10, 3.13, 3.14...): They've not been tested as some of xLLiM dependencies exclude these versions. It might be possible though. Then you may try:

• Building from the sdist (pip install xllim): when no pre-built wheel matches your Python version or platform, pip automatically falls back to downloading the source distribution and compiling the C++ extension on your machine. The Python-side build tools (scikit-build-core, cmake, ninja, numpy) are installed automatically. However, you must first install the native system libraries manually (compiler, Armadillo, OpenBLAS, Boost, LAPACK — see Section 4. for details). If those are present, the build should succeed. .
• Compiling, building and installing from scratch (advanced users) (Section 4.)

3. Docker

A minimal Docker image is available based on python:3.11-slim, which already includes xLLiM installed via the wheel generated by our CI pipeline:

Prerequisite

Docker Engine is available on a variety of Linux platforms, macOS and Windows 10. You can find the installation documentation on Docker's documentation website

Linux & MacOs

• Docker Engine (Installation instructions)
• Docker Compose (Installation instructions)

Windows

• Docker Desktop that contains everything to run Docker (Installation instructions).

Using the Docker image

Pull the image:

docker pull ghcr.io/xllim-tools/xllim/xllim:latest

Note: you can also pull specific versions of xLLiM. Please refer to xLLiM GHCR.

Run your project by mounting your source directory:

docker run -it \
  -v $(pwd):/workspace \
  -w /workspace \
  ghcr.io/xllim-tools/xllim/xllim:latest \
  python main.py

This allows you to use xLLiM without manually managing dependencies or Python environments.

You can also open an interactive shell:

docker run -it -v $(pwd):/workspace -w /workspace ghcr.io/xllim-tools/xllim/xllim:latest bash

4. Manual installation (optional)

If you need to compile xLLiM from source (e.g., for an unsupported platform or Python version, or for development), please refer to the requirements below.

Build Dependencies

These are required only if you are compiling the library from source.

Name	Version	Notes
C++ compiler	C++17 support	System-installed (e.g., g++ >= 9, clang++ >= 5)
CMake	>= 3.21	Auto-installed by pip when using pip install .
Ninja	any	Auto-installed by pip when using pip install .
Python	>= 3.11, < 3.13	System-installed; see Other Python versions for other versions
Armadillo	>= 12.6, < 13	System-installed
Boost	>= 1.78, < 2	System-installed; components: system, thread, random
OpenBLAS	>= 0.3.15, < 1	System-installed (BLAS + LAPACK backends)
Pybind11	2.13.6	Vendored as Git submodule — no manual install needed
Carma	0.8.0	Vendored as Git submodule — no manual install needed

Build from source

Since xLLiM contains C++ extensions with Python bindings, building from source requires native system libraries that pip cannot install automatically. The instructions below use a Debian/Ubuntu-based system as example - adapt package names for your distribution.

Note: Pybind11 and Carma are included as Git submodules in extern/ and do not need to be installed separately.

1. Clone the project with submodules

git clone --recurse-submodules https://github.com/xllim-tools/xllim.git
cd xllim

2. Install system dependencies

sudo apt update

# Compilation tools
sudo apt-get install -y --no-install-recommends g++ cmake ninja-build

# Armadillo and linear algebra backends
sudo apt-get install -y --no-install-recommends libopenblas-dev liblapack-dev libarpack2-dev libsuperlu-dev libarmadillo-dev

# Python development headers
sudo apt-get install -y --no-install-recommends python3-dev

# Boost (components: system, thread, random)
sudo apt-get install -y --no-install-recommends libboost-dev libboost-system-dev libboost-thread-dev libboost-random-dev

⚠️ Boost version: xLLiM requires Boost >= 1.78. Ubuntu 24.04+ ships a compatible version out of the box. On older distributions (e.g., Ubuntu 22.04 ships 1.74), you will need to build Boost >= 1.78 from source — see .github/workflows/build_publish.yml for an example.

3. Build and install

pip install .

This uses scikit-build-core to drive the CMake build automatically. The Python-side build tools (scikit-build-core, cmake, ninja, numpy) are fetched by pip as needed.

4. Verify the installation

python3
>>> import xllim

Manual CMake build (for development)

For C++ development or debugging, you can drive CMake directly:

cmake -S . -B build -G Ninja -DCMAKE_BUILD_TYPE=Debug -DXLLIM_BUILD_TESTS=ON
cmake --build build
ctest --test-dir build

The build type can be either Debug (-O0 -g) or Release (-O2 -DNDEBUG). To install the compiled module into your Python environment, use pip install . as described above.

Licence

This software is licensed under the GNU GPL-compatible CeCILL-C License. While the software is free, we would appreciate it if you send us an email at [e-mail] to let us know how you use it. Also, please contact us if the licence does not meet your needs.