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<section id="torch-dimensionality-reduction">
<h1>Torch Dimensionality Reduction<a class="headerlink" href="#torch-dimensionality-reduction" title="Link to this heading">#</a></h1>
<p align="center">
<img src="https://github.com/torchdr/torchdr/raw/main/docs/source/figures/torchdr_logo.png" width="800" alt="torchdr logo">
</p>
<p><a class="reference external" href="https://torchdr.github.io/"><img alt="Documentation" src="https://img.shields.io/badge/Documentation-blue.svg" /></a>
<a class="reference external" href="https://github.com/TorchDR/TorchDR/tree/main/benchmarks"><img alt="Benchmark" src="https://img.shields.io/badge/Benchmarks-blue.svg" /></a>
<a class="reference external" href="https://github.com/TorchDR/TorchDR/releases"><img alt="Version" src="https://img.shields.io/github/v/release/TorchDR/TorchDR.svg?color=blue&cacheSeconds=3600" /></a>
<a class="reference external" href="https://opensource.org/licenses/BSD-3-Clause"><img alt="License" src="https://img.shields.io/badge/License-BSD_3--Clause-blue.svg" /></a>
<a class="reference external" href="https://www.python.org/downloads/release/python-3100/"><img alt="Python 3.8+" src="https://img.shields.io/badge/python-3.8%2B-blue.svg" /></a>
<a class="reference external" href="https://pytorch.org/get-started/locally/"><img alt="Pytorch" src="https://img.shields.io/badge/PyTorch-ee4c2c?logo=pytorch&logoColor=white" /></a>
<a class="reference external" href="https://github.com/astral-sh/ruff"><img alt="Ruff" src="https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json" /></a>
<a class="reference internal" href="#"><span class="xref myst"><img alt="Test Status" src="https://github.com/torchdr/torchdr/actions/workflows/testing.yml/badge.svg" /></span></a>
<a class="reference external" href="https://dl.circleci.com/status-badge/redirect/gh/TorchDR/TorchDR/tree/main"><img alt="CircleCI" src="https://dl.circleci.com/status-badge/img/gh/TorchDR/TorchDR/tree/main.svg?style=svg" /></a>
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<p><strong>TorchDR</strong> is a high-performance dimensionality reduction library built on PyTorch. It provides GPU and multi-GPU accelerated DR methods in a unified framework with a simple, scikit-learn-compatible API.</p>
<section id="key-features">
<h2>Key Features<a class="headerlink" href="#key-features" title="Link to this heading">#</a></h2>
<div class="pst-scrollable-table-container"><table class="table">
<thead>
<tr class="row-odd"><th class="head"><p>Feature</p></th>
<th class="head"><p>Description</p></th>
</tr>
</thead>
<tbody>
<tr class="row-even"><td><p><strong>High Performance</strong></p></td>
<td><p>Engineered for speed with GPU acceleration, <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code> support, and optimized algorithms leveraging sparsity and negative sampling.</p></td>
</tr>
<tr class="row-odd"><td><p><strong>Multi-GPU Support</strong></p></td>
<td><p>Scale to massive datasets with built-in distributed computing. Use the <code class="docutils literal notranslate"><span class="pre">torchdr</span></code> CLI or <code class="docutils literal notranslate"><span class="pre">torchrun</span></code> for easy multi-GPU execution of compatible modules and methods.</p></td>
</tr>
<tr class="row-even"><td><p><strong>Modular by Design</strong></p></td>
<td><p>Every component is designed to be easily customized, extended, or replaced to fit your specific needs.</p></td>
</tr>
<tr class="row-odd"><td><p><strong>Memory-Efficient</strong></p></td>
<td><p>Natively handles sparsity and memory-efficient symbolic operations. Supports PyTorch DataLoader for streaming large datasets.</p></td>
</tr>
<tr class="row-even"><td><p><strong>Seamless Integration</strong></p></td>
<td><p>Fully compatible with the scikit-learn and PyTorch ecosystems. Use familiar APIs and integrate effortlessly into your existing workflows.</p></td>
</tr>
<tr class="row-odd"><td><p><strong>Minimal Dependencies</strong></p></td>
<td><p>Requires only PyTorch, NumPy, and scikit‑learn; optionally add Faiss for fast k‑NN or KeOps for symbolic computation.</p></td>
</tr>
</tbody>
</table>
</div>
</section>
<section id="getting-started">
<h2>Getting Started<a class="headerlink" href="#getting-started" title="Link to this heading">#</a></h2>
<p><strong>TorchDR</strong> offers a <strong>user-friendly API similar to scikit-learn</strong> where dimensionality reduction modules can be called with the <code class="docutils literal notranslate"><span class="pre">fit_transform</span></code> method. It seamlessly accepts both NumPy arrays and PyTorch tensors as input, ensuring that the output matches the type and backend of the input.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.datasets</span><span class="w"> </span><span class="kn">import</span> <span class="n">fetch_openml</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">torchdr</span><span class="w"> </span><span class="kn">import</span> <span class="n">UMAP</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">fetch_openml</span><span class="p">(</span><span class="s2">"mnist_784"</span><span class="p">)</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s2">"float32"</span><span class="p">)</span>
<span class="n">z</span> <span class="o">=</span> <span class="n">UMAP</span><span class="p">(</span><span class="n">n_neighbors</span><span class="o">=</span><span class="mi">30</span><span class="p">)</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
</pre></div>
</div>
<p><strong>GPU Acceleration</strong>: Set <code class="docutils literal notranslate"><span class="pre">device="cuda"</span></code> to run on GPU. By default (<code class="docutils literal notranslate"><span class="pre">device="auto"</span></code>), TorchDR uses the input data’s device.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">z</span> <span class="o">=</span> <span class="n">UMAP</span><span class="p">(</span><span class="n">n_neighbors</span><span class="o">=</span><span class="mi">30</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="s2">"cuda"</span><span class="p">)</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
</pre></div>
</div>
<p><strong>Multi-GPU</strong>: Use the <code class="docutils literal notranslate"><span class="pre">torchdr</span></code> CLI to parallelize across GPUs with no code changes:</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>torchdr<span class="w"> </span>my_script.py<span class="w"> </span><span class="c1"># Use all available GPUs</span>
torchdr<span class="w"> </span>--gpus<span class="w"> </span><span class="m">4</span><span class="w"> </span>my_script.py<span class="w"> </span><span class="c1"># Use 4 GPUs</span>
</pre></div>
</div>
<p><strong>torch.compile</strong>: Enable <code class="docutils literal notranslate"><span class="pre">compile=True</span></code> for additional speed on PyTorch 2.0+.</p>
<p><strong>Backends</strong>: The <code class="docutils literal notranslate"><span class="pre">backend</span></code> parameter controls k-NN and memory-efficient computations:</p>
<div class="pst-scrollable-table-container"><table class="table">
<thead>
<tr class="row-odd"><th class="head"><p>Backend</p></th>
<th class="head"><p>Description</p></th>
</tr>
</thead>
<tbody>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">"faiss"</span></code></p></td>
<td><p>Fast approximate k-NN via <a class="reference external" href="https://github.com/facebookresearch/faiss">Faiss</a> <strong>(Recommended)</strong></p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">"keops"</span></code></p></td>
<td><p>Exact symbolic computation via <a class="reference external" href="https://www.kernel-operations.io/keops/index.html">KeOps</a> with linear memory</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">None</span></code></p></td>
<td><p>Raw PyTorch</p></td>
</tr>
</tbody>
</table>
</div>
<p><strong>DataLoader for Large Datasets</strong>: Pass a PyTorch <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code> instead of a tensor to stream data batch-by-batch. <strong>Requires <code class="docutils literal notranslate"><span class="pre">backend="faiss"</span></code></strong>.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">torch.utils.data</span><span class="w"> </span><span class="kn">import</span> <span class="n">DataLoader</span><span class="p">,</span> <span class="n">TensorDataset</span>
<span class="n">dataloader</span> <span class="o">=</span> <span class="n">DataLoader</span><span class="p">(</span><span class="n">TensorDataset</span><span class="p">(</span><span class="n">X</span><span class="p">),</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">10000</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="n">z</span> <span class="o">=</span> <span class="n">UMAP</span><span class="p">(</span><span class="n">backend</span><span class="o">=</span><span class="s2">"faiss"</span><span class="p">)</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">dataloader</span><span class="p">)</span>
</pre></div>
</div>
</section>
<section id="methods">
<h2>Methods<a class="headerlink" href="#methods" title="Link to this heading">#</a></h2>
<section id="neighbor-embedding">
<h3>Neighbor Embedding<a class="headerlink" href="#neighbor-embedding" title="Link to this heading">#</a></h3>
<p><strong>TorchDR</strong> provides a suite of neighbor embedding methods, optimal for data visualization.</p>
<div class="pst-scrollable-table-container"><table class="table">
<thead>
<tr class="row-odd"><th class="head"><p>Method</p></th>
<th class="head text-center"><p>Complexity</p></th>
<th class="head text-center"><p>Multi-GPU</p></th>
<th class="head text-center"><p>Paper</p></th>
</tr>
</thead>
<tbody>
<tr class="row-even"><td><p><a class="reference external" href="https://torchdr.github.io/dev/gen_modules/torchdr.UMAP.html"><code class="docutils literal notranslate"><span class="pre">UMAP</span></code></a></p></td>
<td class="text-center"><p>O(n)</p></td>
<td class="text-center"><p>✅</p></td>
<td class="text-center"><p><a class="reference external" href="https://arxiv.org/abs/1802.03426">↗</a></p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference external" href="https://torchdr.github.io/dev/gen_modules/torchdr.LargeVis.html"><code class="docutils literal notranslate"><span class="pre">LargeVis</span></code></a></p></td>
<td class="text-center"><p>O(n)</p></td>
<td class="text-center"><p>✅</p></td>
<td class="text-center"><p><a class="reference external" href="https://arxiv.org/abs/1602.00370">↗</a></p></td>
</tr>
<tr class="row-even"><td><p><a class="reference external" href="https://torchdr.github.io/dev/gen_modules/torchdr.InfoTSNE.html"><code class="docutils literal notranslate"><span class="pre">InfoTSNE</span></code></a></p></td>
<td class="text-center"><p>O(n)</p></td>
<td class="text-center"><p>✅</p></td>
<td class="text-center"><p><a class="reference external" href="https://arxiv.org/abs/2206.01816">↗</a></p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference external" href="https://torchdr.github.io/dev/gen_modules/torchdr.PACMAP.html"><code class="docutils literal notranslate"><span class="pre">PACMAP</span></code></a></p></td>
<td class="text-center"><p>O(n)</p></td>
<td class="text-center"><p>❌</p></td>
<td class="text-center"><p><a class="reference external" href="https://arxiv.org/abs/2012.04456">↗</a></p></td>
</tr>
<tr class="row-even"><td><p><a class="reference external" href="https://torchdr.github.io/dev/gen_modules/torchdr.SNE.html"><code class="docutils literal notranslate"><span class="pre">SNE</span></code></a></p></td>
<td class="text-center"><p>O(n²)</p></td>
<td class="text-center"><p>❌</p></td>
<td class="text-center"><p><a class="reference external" href="https://papers.nips.cc/paper/2002/hash/6150ccc6069bea6b5716254057a194ef-Abstract.html">↗</a></p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference external" href="https://torchdr.github.io/dev/gen_modules/torchdr.TSNE.html"><code class="docutils literal notranslate"><span class="pre">TSNE</span></code></a></p></td>
<td class="text-center"><p>O(n²)</p></td>
<td class="text-center"><p>❌</p></td>
<td class="text-center"><p><a class="reference external" href="https://jmlr.org/papers/v9/vandermaaten08a.html">↗</a></p></td>
</tr>
<tr class="row-even"><td><p><a class="reference external" href="https://torchdr.github.io/dev/gen_modules/torchdr.TSNEkhorn.html"><code class="docutils literal notranslate"><span class="pre">TSNEkhorn</span></code></a></p></td>
<td class="text-center"><p>O(n²)</p></td>
<td class="text-center"><p>❌</p></td>
<td class="text-center"><p><a class="reference external" href="https://arxiv.org/abs/2305.13797">↗</a></p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference external" href="https://torchdr.github.io/dev/gen_modules/torchdr.COSNE.html"><code class="docutils literal notranslate"><span class="pre">COSNE</span></code></a></p></td>
<td class="text-center"><p>O(n²)</p></td>
<td class="text-center"><p>❌</p></td>
<td class="text-center"><p><a class="reference external" href="https://arxiv.org/abs/2111.15037">↗</a></p></td>
</tr>
</tbody>
</table>
</div>
<blockquote>
<div><p><em>Note:</em> Quadratic methods support <code class="docutils literal notranslate"><span class="pre">backend="keops"</span></code> for exact computation with linear memory usage.</p>
</div></blockquote>
</section>
<section id="spectral-embedding">
<h3>Spectral Embedding<a class="headerlink" href="#spectral-embedding" title="Link to this heading">#</a></h3>
<p><strong>TorchDR</strong> provides various <strong>spectral embedding</strong> methods: <a class="reference external" href="https://torchdr.github.io/dev/gen_modules/torchdr.PCA.html"><code class="docutils literal notranslate"><span class="pre">PCA</span></code></a>, <a class="reference external" href="https://torchdr.github.io/dev/gen_modules/torchdr.IncrementalPCA.html"><code class="docutils literal notranslate"><span class="pre">IncrementalPCA</span></code></a>, <a class="reference external" href="https://torchdr.github.io/dev/gen_modules/torchdr.ExactIncrementalPCA.html"><code class="docutils literal notranslate"><span class="pre">ExactIncrementalPCA</span></code></a>, <a class="reference external" href="https://torchdr.github.io/dev/gen_modules/torchdr.KernelPCA.html"><code class="docutils literal notranslate"><span class="pre">KernelPCA</span></code></a>, <a class="reference external" href="https://torchdr.github.io/dev/gen_modules/torchdr.PHATE.html"><code class="docutils literal notranslate"><span class="pre">PHATE</span></code></a>. <code class="docutils literal notranslate"><span class="pre">PCA</span></code> and <code class="docutils literal notranslate"><span class="pre">ExactIncrementalPCA</span></code> support multi-GPU distributed training via the <code class="docutils literal notranslate"><span class="pre">distributed="auto"</span></code> parameter.</p>
</section>
</section>
<section id="benchmarks">
<h2>Benchmarks<a class="headerlink" href="#benchmarks" title="Link to this heading">#</a></h2>
<p>Relying on <strong>TorchDR</strong> enables an <strong>orders-of-magnitude improvement in runtime performance</strong> compared to CPU-based implementations. <a class="reference external" href="https://github.com/TorchDR/TorchDR/blob/main/benchmarks/benchmark_umap_single_cell.py">See the code</a>.</p>
<p align="center">
<img src="https://github.com/torchdr/torchdr/raw/main/docs/source/figures/umap_benchmark_single_cell.png" width="1024" alt="UMAP benchmark on single cell data">
</p>
</section>
<section id="examples">
<h2>Examples<a class="headerlink" href="#examples" title="Link to this heading">#</a></h2>
<p>See the <a class="reference external" href="https://github.com/TorchDR/TorchDR/tree/main/examples/">examples</a> folder for all examples.</p>
<p><strong>MNIST.</strong> (<a class="reference external" href="https://github.com/TorchDR/TorchDR/tree/main/examples/images/panorama_readme.py">Code</a>)
A comparison of various neighbor embedding methods on the MNIST digits dataset.</p>
<p align="center">
<img src="https://github.com/torchdr/torchdr/raw/main/docs/source/figures/mnist_readme.png" width="800" alt="various neighbor embedding methods on MNIST">
</p>
<!-- **Single-cell genomics.** ([Code](https://github.com/TorchDR/TorchDR/tree/main/examples/single_cell/single_cell_readme.py))
Visualizing cells using `LargeVis` from `TorchDR`.
<p align="center">
<img src="https://github.com/torchdr/torchdr/raw/main/docs/source/figures/single_cell.gif" width="700" alt="single cell embeddings">
</p> -->
<p><strong>CIFAR100.</strong> (<a class="reference external" href="https://github.com/TorchDR/TorchDR/tree/main/examples/images/cifar100.py">Code</a>)
Visualizing the CIFAR100 dataset using DINO features and <code class="docutils literal notranslate"><span class="pre">TSNE</span></code>.</p>
<p align="center">
<img src="https://github.com/torchdr/torchdr/raw/main/docs/source/figures/cifar100_tsne.png" width="1024" alt="TSNE on CIFAR100 DINO features">
</p>
</section>
<section id="advanced-features">
<h2>Advanced Features<a class="headerlink" href="#advanced-features" title="Link to this heading">#</a></h2>
<section id="affinities">
<h3>Affinities<a class="headerlink" href="#affinities" title="Link to this heading">#</a></h3>
<p><strong>TorchDR</strong> features a <strong>wide range of affinities</strong> which can then be used as a building block for DR algorithms. It includes:</p>
<ul class="simple">
<li><p>Affinities based on k-NN normalizations: <a class="reference external" href="https://torchdr.github.io/dev/gen_modules/torchdr.SelfTuningAffinity.html"><code class="docutils literal notranslate"><span class="pre">SelfTuningAffinity</span></code></a>, <a class="reference external" href="https://torchdr.github.io/dev/gen_modules/torchdr.MAGICAffinity.html"><code class="docutils literal notranslate"><span class="pre">MAGICAffinity</span></code></a>, <a class="reference external" href="https://torchdr.github.io/dev/gen_modules/torchdr.UMAPAffinity.html"><code class="docutils literal notranslate"><span class="pre">UMAPAffinity</span></code></a>, <a class="reference external" href="https://torchdr.github.io/dev/gen_modules/torchdr.PHATEAffinity.html"><code class="docutils literal notranslate"><span class="pre">PHATEAffinity</span></code></a>, <a class="reference external" href="https://torchdr.github.io/dev/gen_modules/torchdr.PACMAPAffinity.html"><code class="docutils literal notranslate"><span class="pre">PACMAPAffinity</span></code></a>.</p></li>
<li><p>Doubly stochastic affinities: <a class="reference external" href="https://torchdr.github.io/dev/gen_modules/torchdr.SinkhornAffinity.html"><code class="docutils literal notranslate"><span class="pre">SinkhornAffinity</span></code></a>, <a class="reference external" href="https://torchdr.github.io/dev/gen_modules/torchdr.DoublyStochasticQuadraticAffinity.html"><code class="docutils literal notranslate"><span class="pre">DoublyStochasticQuadraticAffinity</span></code></a>.</p></li>
<li><p>Adaptive affinities with entropy control: <a class="reference external" href="https://torchdr.github.io/dev/gen_modules/torchdr.EntropicAffinity.html"><code class="docutils literal notranslate"><span class="pre">EntropicAffinity</span></code></a>, <a class="reference external" href="https://torchdr.github.io/dev/gen_modules/torchdr.SymmetricEntropicAffinity.html"><code class="docutils literal notranslate"><span class="pre">SymmetricEntropicAffinity</span></code></a>.</p></li>
</ul>
</section>
<section id="evaluation-metrics">
<h3>Evaluation Metrics<a class="headerlink" href="#evaluation-metrics" title="Link to this heading">#</a></h3>
<p><strong>TorchDR</strong> provides efficient GPU-compatible evaluation metrics: <a class="reference external" href="https://torchdr.github.io/dev/gen_modules/torchdr.silhouette_score.html"><code class="docutils literal notranslate"><span class="pre">silhouette_score</span></code></a>, <a class="reference external" href="https://torchdr.github.io/dev/gen_modules/torchdr.knn_label_accuracy.html"><code class="docutils literal notranslate"><span class="pre">knn_label_accuracy</span></code></a>, <a class="reference external" href="https://torchdr.github.io/dev/gen_modules/torchdr.neighborhood_preservation.html"><code class="docutils literal notranslate"><span class="pre">neighborhood_preservation</span></code></a>, <a class="reference external" href="https://torchdr.github.io/dev/gen_modules/torchdr.kmeans_ari.html"><code class="docutils literal notranslate"><span class="pre">kmeans_ari</span></code></a>.</p>
</section>
</section>
<section id="installation">
<h2>Installation<a class="headerlink" href="#installation" title="Link to this heading">#</a></h2>
<p>Install the core <code class="docutils literal notranslate"><span class="pre">torchdr</span></code> library from PyPI:</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>pip<span class="w"> </span>install<span class="w"> </span>torchdr<span class="w"> </span><span class="c1"># or: uv pip install torchdr</span>
</pre></div>
</div>
<p><strong>Note:</strong> <code class="docutils literal notranslate"><span class="pre">torchdr</span></code> does not install <code class="docutils literal notranslate"><span class="pre">faiss-gpu</span></code> or <code class="docutils literal notranslate"><span class="pre">pykeops</span></code> by default. You need to install them separately to use the corresponding backends.</p>
<ul>
<li><p><strong>Faiss (Recommended)</strong>: For the fastest k-NN computations, install <a class="reference external" href="https://github.com/facebookresearch/faiss">Faiss</a>. Please follow their <a class="reference external" href="https://github.com/facebookresearch/faiss/blob/main/INSTALL.md">official installation guide</a>. A common method is using <code class="docutils literal notranslate"><span class="pre">conda</span></code>:</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>conda<span class="w"> </span>install<span class="w"> </span>-c<span class="w"> </span>pytorch<span class="w"> </span>-c<span class="w"> </span>nvidia<span class="w"> </span>faiss-gpu
</pre></div>
</div>
</li>
<li><p><strong>KeOps</strong>: For memory-efficient symbolic computations, install <a class="reference external" href="https://www.kernel-operations.io/keops/index.html">PyKeOps</a>.</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>pip<span class="w"> </span>install<span class="w"> </span>pykeops
</pre></div>
</div>
</li>
</ul>
<section id="installation-from-source">
<h3>Installation from Source<a class="headerlink" href="#installation-from-source" title="Link to this heading">#</a></h3>
<p>If you want to use the latest, unreleased version of <code class="docutils literal notranslate"><span class="pre">torchdr</span></code>, you can install it directly from GitHub:</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>pip<span class="w"> </span>install<span class="w"> </span>git+https://github.com/torchdr/torchdr
</pre></div>
</div>
</section>
</section>
<section id="finding-help">
<h2>Finding Help<a class="headerlink" href="#finding-help" title="Link to this heading">#</a></h2>
<p>If you have any questions or suggestions, feel free to open an issue on the <a class="reference external" href="https://github.com/torchdr/torchdr/issues">issue tracker</a> or contact <a class="reference external" href="https://huguesva.github.io/">Hugues Van Assel</a> directly.</p>
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