Projects sharing features with Obspy

SciPy is a scientific computing library for Python that provides a comprehensive collection of mathematical algorithms and numerical tools for research and engineering. It functions as a high-performance numerical analysis framework, bridging high-level Python code with compiled C and Fortran routines to execute complex computations at hardware speeds. The library is built upon array-based data structures that utilize strided memory layouts to enable efficient data manipulation and slicing. By employing vectorized operation dispatch and linking to optimized hardware-specific linear algebra li

Astropy is a core Python library for astronomy, providing a unified framework for astronomical data analysis, coordinate and time computations, and physical constants and unit handling. It is designed as a standard tool for the astronomy community, enabling consistent reading, processing, and visualization of FITS files and ASCII tables through a modular subpackage system that shares common interfaces and conventions. The library distinguishes itself through broad interoperability across astronomy software, establishing shared protocols for data exchange and collaboration. It includes a coord

PyMC is a Bayesian probabilistic programming framework used for building probabilistic models and performing Bayesian inference. It provides a probabilistic graphical model library for specifying random variables, priors, and likelihood functions, supported by an MCMC sampling engine and variational inference tools to estimate posterior distributions. The framework features a GPU-accelerated inference backend that compiles models into machine code to increase execution speed. It utilizes a backend-agnostic tensor execution model and just-in-time graph compilation to optimize the computation o

NumPy is a foundational library for scientific computing in Python, providing a comprehensive framework for managing and manipulating large-scale numerical information. It centers on high-performance multidimensional array objects that serve as the primary data structure for complex mathematical operations and data analysis workflows. The library distinguishes itself through specialized mechanisms for handling multidimensional data, including advanced indexing, slicing, and broadcasting techniques that allow for efficient operations across arrays of varying shapes. It utilizes strided metadat

Numba is a just-in-time compiler that translates high-level Python functions into optimized machine code at runtime. By leveraging the LLVM compiler infrastructure, it provides a framework for accelerating numerical data processing and mathematical computations, enabling performance levels comparable to statically compiled languages. The project distinguishes itself through its ability to perform type-inference-based specialization, which generates machine instructions tailored to the specific data types used during execution. It employs a lazy compilation pipeline that defers translation unt

Multibody dynamics tool kit.

The official sources for the RDKit library

SymPy is a Python computer algebra system and symbolic mathematics library. It performs algebraic manipulations, calculus, and equation solving using symbolic representations to achieve exact computations rather than numerical approximations. The library includes a LaTeX expression parser that converts mathematical strings into symbolic representations for computation and formula manipulation. It also incorporates a mathematical benchmarking suite to measure execution speed and detect performance regressions across different software versions. The system provides capabilities for automated m

Parsers and algorithms for computational chemistry logfiles

Shapely is a library for the manipulation and analysis of planar geometric objects, serving as a Python wrapper for the GEOS C++ engine. It provides a framework for calculating geometric properties, evaluating spatial relationships, and performing topological predicates within a Cartesian plane. The project distinguishes itself through a vectorized geometry processor capable of executing spatial operations across large arrays of shapes to increase throughput. It also includes a spatial indexing system based on R-trees to accelerate the retrieval of intersecting geometries and nearest neighbor

PythonRobotics is a comprehensive collection of modular robotics algorithms and educational simulations designed for autonomous navigation, state estimation, and motion control. The project provides a library of standalone implementations for path planning, localization, mapping, and kinematics, serving as a resource for researchers and students to experiment with foundational and advanced robotic theories. The project distinguishes itself through an algorithm-centric design where each module functions as an isolated script, allowing for independent testing and clear pedagogical demonstration

Colour Science for Python

Manim is a scriptable, code-driven framework designed for generating precise technical visualizations and mathematical animations. By using a high-level programming interface, it allows users to define geometric shapes, motion paths, and animation logic that are compiled into high-quality video assets. The system functions as a specialized engine for creating reproducible, data-driven representations of complex mathematical concepts and geometric transformations. The framework distinguishes itself through an interpolation-based engine that calculates intermediate states between keyframes to e

Biopython is a bioinformatics library for Python providing tools to parse, manipulate, and analyze biological sequences, molecular structures, and phylogenetic trees. It serves as a biological sequence parser for genomic and proteomic data across multiple industry-standard file formats and acts as an interface for querying biological data and citations from NCBI Entrez repositories. The project distinguishes itself through specialized toolkits for protein structure analysis and phylogenetic tree construction. It includes a protein structure analyzer for processing PDB and mmCIF files to calcu

NetworkX is a Python library designed for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks. It provides a comprehensive framework for modeling relationships between entities as graphs, directed graphs, or multigraphs, allowing users to attach arbitrary metadata and properties to nodes and edges. The library distinguishes itself through a modular architecture that decouples graph analysis logic from data storage, utilizing nested dictionaries and adjacency lists to manage topology. It features a pluggable backend system that delegates computat

Practical PyTorch is a collection of deep learning tutorials and guides focused on implementing recurrent neural networks. The project provides practical code for building sequence models and sequence-to-sequence architectures using the PyTorch framework. The repository covers the implementation of models for neural machine translation, character-level text generation, and text classification. It includes examples for transforming input sequences into output sequences for machine translation and synthesizing new text. The project also extends to sequence data prediction and time series analy

GluonTS is a probabilistic time series library and deep learning forecasting framework. It provides a toolkit for building, training, and evaluating neural network architectures that predict future values as probability distributions to quantify uncertainty. The project distinguishes itself by supporting zero-shot forecasting and integrating diverse modeling approaches, including deep probabilistic neural networks and wrappers for external statistical libraries such as Prophet and R forecast. It implements specialized architectural primitives like causal convolutions and invertible residual n

GluonTS is a framework for probabilistic time series forecasting, designed to predict future values as probability distributions with confidence intervals. It supports both traditional model training and zero-shot forecasting, where pretrained models generate predictions for new series without additional training. The project distinguishes itself by integrating a wide variety of forecasting approaches into a unified workflow. This includes deep learning architectures such as recurrent neural networks and causal convolutions, as well as the integration of external statistical models, the Proph

Chronos-forecasting is a zero-shot time series forecasting framework based on a pretrained large language model. It enables the prediction of future values across diverse datasets without requiring task-specific training or optimization. The system functions as a probabilistic forecasting tool, producing multiple future trajectories and quantile forecasts to quantify uncertainty and potential prediction errors. It incorporates exogenous covariate integration to merge external variables and historical context into the input stream for increased precision. The project includes utilities for sy

Functional Models and Algorithms for Sparse Signal Processing

XLB is a fully differentiable 2D/3D Lattice Boltzmann Method (LBM) library that leverages hardware acceleration. It supports JAX and NVIDIA Warp backends, and is specifically designed to solve fluid dynamics problems in a computationally efficient and differentiable manner. Its unique…

.. docs-include-ref

A python library for time-series smoothing and outlier detection in a vectorized way.

Automatic discovery of non-trivial statistical truths from 500+ public time series — mutual information, Granger causality, FDR correction

A State-based Transformation System for Brain Modeling.

Time series analysis in the tidyverse