pyMDO: An Object-Oriented Framework for Multidisciplinary Design Optimization
ACM Transactions on Mathematical Software (TOMS)
Run-time automatic instantiation of algorithms using C++ templates
International Journal of Computational Science and Engineering
High-performance parallel computations using python as high-level language
Euro-Par 2010 Proceedings of the 2010 conference on Parallel processing
Live python-based visualization laboratory
Edutainment'11 Proceedings of the 6th international conference on E-learning and games, edutainment technologies
pyOpt: a Python-based object-oriented framework for nonlinear constrained optimization
Structural and Multidisciplinary Optimization
Minimizing the ripple effect of web-centric software by using the pheromone extension
Information Sciences: an International Journal
Python scripting libraries for subsurface fluid and heat flow simulations with TOUGH2 and SHEMAT
Computers & Geosciences
Map algebra and model algebra for integrated model building
Environmental Modelling & Software
Python for scientific computing education: Modeling of queueing systems
Scientific Programming
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The goal of this book is to teach computational scientists how to develop tailored, flexible, and human-efficient working environments built from small programs (scripts) written in the easy-to-learn, high-level language Python. The focus is on examples and applications of relevance to computational scientists: gluing existing applications and tools, e.g. for automating simulation, data analysis, and visualization; steering simulations and computational experiments; equipping old programs with graphical user interfaces; making computational Web applications; and creating interactive interfaces with a Maple/Matlab-like syntax to numerical applications in C/C++ or Fortran. In short, scripting with Python makes you much more productive, increases the reliability of your scientific work and lets you have more fun - on Unix, Windows and Macintosh. All the tools and examples in this book are open source codes. The third edition is compatible with the new NumPy implementation and features updated information, correction of errors, and improved associated software tools.