3 Preface There s a famous old quote about writing maintainable software: Always code as if the guy who ends up maintaining your code will be a violent psychopath who knows where you live. --John Woods comp.lang.c++ While I m not usually one for aphorisms, this one strikes a chord with me. Maybe it s because I ve spent my professional career writing software at huge companies, but I have yet to inherit code that didn t eventually cause me to curse the original author at some point. Everyone (besides you, of course, dear reader) struggles to write code that s easy to maintain. When Python became popular, many thought that, because of its terseness, it would naturally lead to more maintainable software. Alas, maintainability is not an emergent property of using an expressive language. Badly written Python code is just as unmaintainable as badly written C++, Perl, Java and all the rest of the languages known for their, ahem, readability. Terse code is not a free lunch. So what do we do? Resign ourselves to maintaining code we can t understand? Rant on Twitter and The Daily WTF about the awful code we have to work on? What must we do to stop the pain? Write. Idiomatic. Code. It s that simple. Idioms in a programming language are a sort of lingua franca to let future readers know exactly what we re trying to accomplish. We may document our code extensively, write exhaustive unit tests, and hold code reviews three times a day, but the fact remains: when someone else needs to make changes, the code is king. If that someone is you, all the documentation in the world won t help you understand unreadable code. After all, how can you even be sure the code is doing what the documentation says? We re usually reading someone else s code because there s a problem. But idiomatic code helps here, too. Even if it s wrong, when code is written idiomatiii
The content of this book looks primarily at the various means for code organization in Python and provides user with a rigorous grounding in these. it dives into topics such as python object system, functions, decorators, metaprogramming and generators providing the reader with an in-depth knowledge of these topics that is essential for writing idiomatic and robust python code. It aims to provide a reader with not only a how-to but also a why for Python concepts. Concepts are well explained with abundance of code snippets.
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One of the special concepts in Python is the idea of writing idiomatic code that is most aligned with the language features and ideals. In Python, we call this idiomatic code Pythonic. When you write Pythonic code, you are leveraging over 25 years of experience of many thousands of developers. You are writing code that is expected and tune in the CPython runtime. Most importantly perhaps, you are writing code that is easily read and understood by your fellow and senior Python developers.
The Python classes are ultimately derived from the pydicom.Dataset class from the existing pydicom package and therefore inherit low-level behaviors, such as accessing, setting, iterating over data elements, and reading/writing to/from files that many developers are already familiar with. It further allows developers to retain low-level control over all data elements in order to add to or alter information in objects constructed by highdicom. Below pydicom.Dataset in the class hierarchy, there is a common abstract base class called highdicom.SOPClass (Fig. 2A), which abstracts the attributes that are required by all SOP classes. Specific SOP classes are then implemented by dedicated Python classes that are derived from the abstract base class (Fig. 2B). In this way, we aim to provide an idiomatic Python interface that abstracts as much of the low-level DICOM encoding and decoding rules as possible while staying close to the standard DICOM terminology to avoid potential ambiguities.
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