在说到自己对这句话的理解时，王晨提高了声调，高声说：“我觉得胡锦涛总书记在这样一个严肃的大会上，代表党中央这样一个重要的讲话中，总结 30年的经验、体会，说到‘不折腾’，我个人的理解就是表达了全党、全国人民总结30年的改革开放巨大成就，最根本的就是坚定不移地走中国特色社会主义道路，坚定不移地坚持中国特色社会主义理论体系，就是这样一个道路，不是别的什么道路，就是这样一个理论体系不是别的什么理论，我们中国在下一个30年、下一个50年就一定能够取得更大的成就，就一定能够发展得更好，屹立于世界民族之林。”

　　王晨总结称，“不折腾”就是要我们沿着这条道路、这样一个方向继续奋勇前进，确实说出了全国人民的心声、全党的共同意志。

　　值得一提的是，翻译在翻译“不折腾”这个词时，直接使用了汉语拼音的直译，引来会场全体记者的笑声。“不折腾”这个词，或许将会成为英语当中的一个专有名词。

Comparing MATLAB’s stability with Scipy is not a fair comparison since MATLAB has had 20 more years to fix its bugs. Many years ago when I was an avid MATLAB user, I contributed some bug reports to MathWorks that caused me some headaches. But most of my headaches with MATLAB have nothing to do with bugs. First, its a horribly designed language created by electrical engineers who should have taken a course in language design prior to writing the language. Second, it promotes bad programming style; some users write clean code but most just learn enough to quickly hack together a filter that no one else can read. Third, its hard to spread computation across machines due to licensing limitations, causing needless tension among colleagues who are all fighting for licenses. People end up cutting experiments and moving on so less science gets done. Fourth, it is harder to collaborate with others because your collaborators must purchase MATLAB licenses to use your code. Fifth, it is primarily an interactive environment rather than a batch environment, encouraging experimentation and statistical analysis which is not reproducible. Sixth, it is difficult to work with any data structure other than a matrix (e.g. a graph, hash map, tree, lists, heaps). This is frustrating when needing to write algorithms requiring richer data structures.

Have you ever tried writing a large application in MATLAB? Are you familiar with MATLAB’s object oriented interface? It is unusable. Modifying a data member causes the object to be copied prior to modification. At some point, prototype code needs to make its way into a larger application. While most people intend to rewrite their prototype code in C or C++, it is often too costly to do so they end up writing their application in the same language as their prototype code, i.e. MATLAB.

You should not be surprised if there is symbol overlap in packages. It would be very difficult for two projects to maintain mutual exclusion in their namespaces. In general, it is bad programming practice to import everything. It should only be done at the python prompt, not in programs.

With regard to memory management, I was unable to handle large data sets with MATLAB. When Scipy came into the picture, I could handle much larger data sets. Also, I can pass very large arrays into functions and modify their contents without any copying. This is particularly helpful when dealing with a memory footprint that approaches the 2GB limit.

You raise a good point about complex data. However, MATLAB has its data type headaches as well. For example, if I have an int8 array (say an image), not all arithmetic operators are supported in MATLAB. Images often need to be converted to a double prior to doing arithmetic on them. That’s annoying, especially when you’re working with large images. I can do most arithmetic operations on int8 arrays in Scipy.

Often, the semantics of Python/Scipy can be quite different from MATLAB. This causes some frustration when migrating from MATLAB but this is temporary. Eventually, I believe the committed learner should find the Python/Scipy framework to be much more flexible.