Open Source Python Mathematics Software for Linux

SymPy is an open source Python library for symbolic mathematics. Its goal is to become a full-featured computer algebra system (CAS) while maintaining the simplicity of its code. Written entirely in Python, SymPy is easy to use, comprehensible and easily extensible. It’s also very lightweight as it solely depends on mpmath, a pure Python library for arbitrary floating point arithmetic. SymPy has participated in every Google Summer of Code since 2007 and because of this has continuously improved. A lot more people have also contributed to it, and used it in their projects. Beyond being an interactive tool, it can be embedded in other apps and extended with custom functions.

This is the main repository for the SciPy library, one of the core packages that make up the SciPy stack. SciPy is an open source software used in the fields of mathematics, science, and engineering, with modules for statistics, optimization, integration, linear algebra, signal and image processing, and many more. The SciPy library contains many of the user-friendly and efficient numerical routines, including those for numerical integration, interpolation, and optimization. SciPy is built to work with NumPy, a software that provides convenient and fast N-dimensional array manipulation. Both SciPy and NumPy run on all popular operating systems, are fast and easy to install, and are powerful yet easy to use. They’re currently depended upon by numerous leading scientists and engineers all over the world. Try them for yourself!

PyTensor is a fork of Aesara, a Python library for defining, optimizing, and efficiently evaluating mathematical expressions involving multi-dimensional arrays. PyTensor is based on Theano, which has been powering large-scale computationally intensive scientific investigations since 2007. A hackable, pure-Python codebase. Extensible graph framework is suitable for rapid development of custom operators and symbolic optimizations. Implements an extensible graph transpilation framework that currently provides compilation via C, JAX, and Numba. Based on one of the most widely-used Python tensor libraries: Theano.

RPy is a very simple, yet robust, Python interface to the R Programming Language. It can manage all kinds of R objects and can execute arbitrary R functions (including the graphic functions).

It consists of various methods for deep learning on graphs and other irregular structures, also known as geometric deep learning, from a variety of published papers. In addition, it consists of an easy-to-use mini-batch loader for many small and single giant graphs, a large number of common benchmark datasets (based on simple interfaces to create your own), and helpful transforms, both for learning on arbitrary graphs as well as on 3D meshes or point clouds. We have outsourced a lot of functionality of PyTorch Geometric to other packages, which needs to be additionally installed. These packages come with their own CPU and GPU kernel implementations based on C++/CUDA extensions. We do not recommend installation as root user on your system python. Please setup an Anaconda/Miniconda environment or create a Docker image. We provide pip wheels for all major OS/PyTorch/CUDA combinations.

This collection of Python3 modules provides a large range of implemented decision aiding algorithms useful in the field of outranking digraphs based Multiple Criteria Decision Aid (MCDA), especially best choice, linear ranking and absolute or relative rating algorithms with multiple incommensurable criteria. Technical documentation and tutorials are available under the following link: https://digraph3.readthedocs.io/en/latest/ The tutorials introduce the main objects like digraphs, outranking digraphs and performance tableaux. There is also a tutorial provided on undirected graphs. Some tutorials are problem oriented and show how to compute the winner of an election, how to build a best choice recommendation, or how to linearly rank or rate with multiple incommensurable performance criteria. Other tutorials concern more specifically operational aspects of computing maximal independent sets (MISs) and kernels in graphs and digraphs.

1. Create an object-oriented python script that can represent mathematical concepts and their properties. 2. Represent all numeric values exactly. 3. Provide a variety of formats to export or embed representations of the mathematical concepts.

The Generic Graphics Toolkit is a library of generic programming primitives that allow applications to be written that adapt to whatever underlying graphics system the developer is using.

The Modular toolkit for Data Processing (MDP) is a Python data processing framework. From the user's perspective, MDP is a collection of supervised and unsupervised learning algorithms and other data processing units that can be combined into data processing sequences and more complex feed-forward network architectures. From the scientific developer's perspective, MDP is a modular framework, which can easily be expanded. The implementation of new algorithms is easy and intuitive. The new implemented units are then automatically integrated with the rest of the library. The base of available algorithms is steadily increasing and includes signal processing methods (Principal Component Analysis, Independent Component Analysis, Slow Feature Analysis), manifold learning methods ([Hessian] Locally Linear Embedding), several classifiers, probabilistic methods (Factor Analysis, RBM), data pre-processing methods, and many others.

AirTSP is also named AirTSP (TSP standing for Travel Service Provider). That project aims at providing a clean API and a simple implementation, as a C++ library, of an Airline Schedule Management System. It is intended to be used in simulated environments only: it is not designed to work in the real-world of Airline IT operations. AirTSP/AirTSP makes an extensive use of existing open-source libraries for increased functionality, speed and accuracy. In particular the Boost (C++ Standard Extensions: http://www.boost.org) library is used. AirTSP/AirTSP is the one of the components of the Travel Market Simulator (http://www.travel-market-simulator). However, it may be used in a stand-alone mode.

APLEpy stands for Algebraic Programming Language Extension for Python. It is an open source alternative to commercial products such as AMPL and GAMS. It offers the same high level of abstraction while keeping the advantages of using Python environment.

This project develops a simple, fast and easy to use Python graph library using NumPy, Scipy and PySparse.

This is a Python script for Blender which uses short (quaternion-based, floretion-based) algorithms to draw curves in space. The user can create new shapes and curves by setting a variety of parameters.

That project aims at providing a clean API and a simple implementation, as a C++ library, of an Airline-related Inventory Management system. That library uses the Standard Airline IT C++ object model (http://sf.net/projects/stdair).

That project aims at studying and comparing typical airline IT methods, for instance RM-related algorithms. It works from a Unix/Linux/Mac command-line, and exposes basic APIs. It is being developed in C++, with Python wrappers for some components.

That project aims at providing a clean API and a simple implementation, as a C++ library, of a Travel-oriented Distribution System. It corresponds to the simulated version of the real-world Computerized Reservation Systems (CRS).

A NEW GUI VERSION WILL BE RELEASED SOON! Thank you for visiting the Physics Calculator, provided by PIezoSystems Inc., please visit their company website at https://piezosystems.org. For other downloads, please visit our github page, https://github.com/PiezoSystems. We hope that you find this program useful and easy to use, for troubleshooting, please email us, contact@piezosystems.org. If any bugs were found, please email us at bugs@piezosystems.org.

Tim Peters' FixedPoint.py + Write docs for the Library Reference manual. I expect the existing module docstring will be a good start. + Create a test driver for Python's regression suite. + Have fun modernizing it, if you like (for example,

This project hosts tools used for analysis of Gaussian Mixture Distributions (GMDs) which are used for statistical signal processing. The tools are libraries for implementing GMD operations and programs used to analyze properties of GMDs.

GluCat is a generic library of C++ templates that implement universal Clifford algebras over the field of real numbers. The PyClical extension module for Python gives users an easy Python scripting interface for calculations in Clifford algebras. The name PyClical is an homage to Pertti Lounesto's CLICAL.

This program generates customizable hyper-surfaces (multi-dimensional input and output) and samples data from them to be used further as benchmark for response surface modeling tasks or optimization algorithms.

math lib for .NET. n-dim arrays, complex numbers, linear algebra, FFT, sorting, cells- and logical arrays as well as 3D plotting classes help developing algorithms on every platform supporting .NET. Sources from SVN, binaries: http://ilnumerics.net

MLPACK is a C++ machine learning library with emphasis on scalability, speed, and ease-of-use. Its aim is to make machine learning possible for novice users by means of a simple, consistent API, while simultaneously exploiting C++ language features to provide maximum performance and flexibility for expert users. * More info + downloads: https://mlpack.org * Git repo: https://github.com/mlpack/mlpack

Multi-categorization Recommendation Adjusting (MRA) is to optimize the results of recommendation based on traditional(basic) recommendation models, through introducing objective category information and taking use of the feature that users always get the habits of preferring certain categories. Besides this, there are two advantages of this improved model: 1) it can be easily applied to any kind of existing recommendation models. And 2) a controller is set in this improved model to provide controllable adjustment range, which thereby makes it possible to provide optional modes of recommendation aiming different kinds of users.

Math tools in Python to tackle down problems in Operational Research fields. Comes with a Django based web interface to allow remote access to complex simulation means.