In order to achieve good temporal precision, while updating stimuli in real-time from an interpreted language like Python or Matlab, it has been essential to make good use of the hardware accelerated graphics capabilities of modern computers. Where Matlab has, in the past, benefited from its large user base and wide variety of applications to science, Python stands to benefit even more. By nature of its clean, readable, and powerful syntax combined with its free and open-source release model Python is clearly a very popular language that is continuously growing and developing further. The fact that Python can be used in such a wide variety of ways (for example, in the author’s own lab Python is used not only for stimulus presentation but also for data analysis, for the generation of publication-quality figures, for computational modelling and for various general purpose scripts to manipulate files) means that in many cases this is likely to be the only programming language that a scientist need learn, with the obvious benefits in time that result. The fact that Python now has such a large user base means that there is a large community of excellent programmers developing libraries that PsychoPy can make use of. The platform independence that PsychoPy enjoys is based very much on the fact that it is based on pure Python code, using libraries such as wxPython, pyglet and numpy that have been written to be as platform independent as is technically possible. The high-level functions and libraries available in Python make it an ideal language in which to develop such software. One of the strengths of PsychoPy is its use of Python.
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