Open Source Python Mathematics Software

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.

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 delivering a reference implementation of a library, estimating and serving average prices paid for air travel products. It is not intended for use by an actual airline, but rather by simulators or other airline-related modules of

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 studying the impact of IT systems interactions on traveller demand and airline revenues. Passenger demand is generated (Monte Carlo) and injected into simulated CRS and airline IT systems. Differential analysis is then performed on various changes compared to a bottom line scenario.

This is Slycat - a web-based data science analysis and visualization platform, created at Sandia National Laboratories. The goal of the Slycat project is to develop processes, tools and techniques to support data science, particularly analysis of large, high-dimensional data.