This package aims at giving low-level general tools to the extraction of spectrum of an unresolved object with slit spectroscopy.

Implemented in Python 3 for the analysis of large quantitative databases, this software introduces a novel high‑utility pattern‑sampling algorithm, along with an on‑disk variant, both grounded in two original theorems. The approach ensures the interactivity crucial for user‑centric methods while providing strong statistical guarantees through randomized sampling. Users can instantly discover relevant and representative patterns, enabling fast and efficient exploration of the database. A use case in discovering sub‑profiles within Cultural heritage knowledge graphs demonstrates the superiority of this method over state‑of‑the‑art approaches.

Implemented in Python 3 for processing complex data streams, this tool applies a weighted reservoir‐sampling approach to directly sample patterns from batches derived from those streams. Dubbed "A Generic Reservoir Patterns Sampler", the algorithm compensates for temporal biases and adapts to a variety of pattern types (sequential, weighted, and unweighted). Experiments on real‑world datasets have shown its ability to build incremental online classifiers for sequential data, achieving performance on par with offline models.

Implemented in Python 3, TPSampling samples set‑based patterns from a transactional database that has been compressed into a trie. It draws patterns according to a utility measure based on the norm, directly from an occurrence trie built by an internal algorithm called TPSpace (Trie‑based Pattern Space). As output, TPSpace returns a weighted occurrence trie. TPSampling then takes as input this occurrence trie, the index of a specific occurrence, and its size, and returns the corresponding pattern.

Implemented in Java using the Jena library, TTProfiler enables the automatic construction of knowledge‐graph profiles. The resulting graph is built from an online knowledge base and has been tested on several knowledge graphs related to cultural heritage.