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-In this paper I will show how the combination of Python programming language and Phoenix-M interface (created by IUAC, New Delhi) can be used as a low cost substitute for PLC (Programmable Logic Controllers). In Home Automation this combination can be used for a variety of purposes.
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+ + ++Development of scientific programs isn't much different than development of computer programs of any other kind. One of the key characteristic of computer programs is correctness. No matter whether we create programs for our own purpose or for other parties, we do not want to spent hours or days waiting for results of computations that will be flawed from the very beginning. As long as programs consist of few lines of code, we may be able to verify correctness of all cases in those programs manually after every change or even try to prove their correctness. However, real life programs consist of thousands, hundred thousands or even millions of lines of code, and even more states. In such a setup we need tools and methods that would allow to automate the process of software testing. +
++Python, a programming language with a weak dynamic type system, makes the use of automated software testing even more important because in this case test suites and the testing framework of choice have to accommodate for the weaknesses of the language. Also, agile software development techniques may intrinsically require automated testing as their core component to guarantee effectiveness of those methods. +
++In this talk I will show how to do automated testing of programs written in Python. Test automation tools will be described and common issues and pitfalls outlined. I will also discuss the notion of code coverage with tests and testing via examples (doctests). +
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+ ++ +Synchrotron X-ray tomography images the inner 3-D micro-structure of +objects. Recent progress bringing acquisition rates down to a few seconds +have opened the door to in-situ monitoring of material transformations +during, e.g., mechanical or heat treatments. However, this powerful +imaging technique presents many challenges, such as the huge size of +typical datasets, or the poor signal over noise ratio. In this talk, we +will present how the standard modules of the scientific Python stack, +combined with a few additional developments, are used to process and +visualize such 3-D tomography images for research purposes. The data +presented in this talk consist of 3-D images of window-glass raw +materials, that react together at high temperature to form liquids, and +images of glasses undergoing phase separation.3 +
++ +Using the Traits module, it was possible to write at minimal cost a +custom graphical application with an embedded Mayavi scene to perform +"4-D visualization", that is, to display cuts through a 3-D volume that +can be updated with the next or previous image of the dataset. Easy +interaction with the data (placing markers) could also be added at +minimal cost. Efficient state-of-the-art algorithms for denoising images +and segmenting (extracting) objects were implemented using scipy, and +PyAMG for multigrid resolution of linear systems. +
++ +Finally, we will show how this work led us "naturally" to take part in +development efforts of open-source Scientific-python packages. Improving +the documentation of scipy.ndimage on the documentation wiki was a first +easy contribution. Then, one segmentation algorithm as well as one +denoising algorithm were contributed to the scikits-image package. We +will finish the talk by a brief overview of scikits-image and its +development process. +
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