Algorithms - (how to) write simulations that run faster? - Computational Science Beta - Stack Exchange. I will try to answer your question considering that you are asking for Python specifically. I will describe my own method of tackling a simulation problem. Strategies for faster simulations are given in this description. First, I prototype new simulations in Python. Of course, I try to make use of NumPy and SciPy as much as I can. Whereas NumPy provides a suitable array data type for numerical simulations, SciPy offers a wide numerical routines working with NumPy arrays. Once the prototypes work more or less, I try to learn which parts of the program or script are the bottleneck.
There are typical candidates for that: Loops in Python are slow. I use a simple profiling strategy to learn where all the run time is spent. %timeit script.py This "magic command" will do the profiling (using timeit) for you and present you a list with times once your script has terminated. Once you nailed down the parts that need to be speeded up, you may consider using compiled languages. Performance - What tools or approaches are available to speed up code written in Python? - Computational Science Beta - Stack Exchange. I'm going to break up my answer into three parts. Profiling, speeding up the python code via c, and speeding up python via python.
It is my view that Python has some of the best tools for looking at what your code's performance is then drilling down to the actual bottle necks. Speeding up code without profiling is about like trying to kill a deer with an uzi. If you are really only interested in mat-vec products, I would recommend scipy.sparse. Python tools for profiling profile and cProfile modules: These modules will give you your standard run time analysis and function call stack. Kernprof: this tool puts together many routines for doing things like line by line code timing memory_profiler: this tool produces line by line memory foot print of your code.
IPython timers: The timeit function is quite nice for seeing the differences in functions in a quick interactive way. Speeding up Python Cython: cython is the quickest way to take a few functions in python and get faster code. Python Course: Generators. Introduction Generators are a simple and powerful possibility to create or to generate iterators. On the surface they look like functions, but there is both a syntactical and a semantical difference. Instead of return statements you will find inside of the body of a generator only yield statements, i.e. one or more yield statements. Another important feature of generators is that the local variables and the execution start is automatically saved between calls. This is necessary, because unlike an ordinary function successive calls to a generator function don't start execution at the beginning of the function. Instead, the new call to a generator function will resume execution right after the yield statement in the code, where the last call exited.
Everything what can be done with a generator can be implemented with a class based iterator as well. The following is a simple example of a generator, which is capable of producing four city names: Method of Operation Method of working: #! Charming Python: Iterators and simple generators. Welcome to the world of exotic flow control. With Python 2.2 (now in its third alpha release -- see Resources later in this article), programmers will get some new options for making programs tick that were not available -- or at least not as convenient -- in earlier Python versions.
While what Python 2.2 gives us is not quite as mind-melting as the full continuations and microthreads that are possible in Stackless Python, generators and iterators do something a bit different from traditional functions and classes. Let's consider iterators first, since they are simpler to understand. Basically, an iterator is an object that has a .next() method. Well, that's not quite true; but it's close. A generator is a little more complicated and general. In some ways, a generator is like the closures which were discussed in previous installments of this column discussing functional programming (see Resources). Taking a random walk Utilizing this function is as simple as: Back to top New ways of walking. AdvancedBooks. Mastering Python Regular Expressions By Félix López, Víctor Romero ISBN 13: 9781783283156 Packt Publishing 110 pages (February 2014) Book overview: Explore the workings of Regular Expressions in Python Learn all about optimizing regular expressions using RegexBuddy Full of practical and step-by-step examples, tips for performance, and solutions for performance-related problems faced by users all over the world Who this book was written for This book is aimed at Python developers who want to learn how to leverage Regular Expressions in Python.
Publisher's page Python in Practice By Mark Summerfield ISBN 13: 978-0321905635 Addison-Wesley Professional 336 pages (Aug 2013) This book is aimed at existing Python programmers who want to take their Python programming to the next level. The book is entirely Python 3-based and the topics it covers include Design Patterns in Python, Concurrency, Extending Python, High-Level Networking, GUI Programming with Tkinter, and 3D Graphics.
By Erik Westra By Greg L. Pratiques avancées et méconnues en Python. From scripting to object-oriented Python programming. Introduction Python has begun soaring in popularity in recent years, and part of the reason is that the language is very flexible, yet incredibly powerful. Python can be used for systems administration, Web development, GUI programming, scientific computing, and more. The main aim of this article is to introduce people who are used to scripting procedural code in Bash, PHP, or some other language, and to assist them in moving into object-oriented Python developing. This rising popularity of Python means that developers currently using other programming languages might be called upon to do some of their projects in Python, in addition to their favorite language.
Procedural programming certainly has its place, and it can be a highly effective way to solve a problem. On a very basic level, procedural programmming can be defined as a list of instructions, and Bash and PHP often are written in such a manner. Writing a disk-monitoring function in PHP and Bash PHP disk-monitoring example #! #! Iterator - The Python yield keyword explained. Understanding Python decorators. Python’s super() considered super! « Deep Thoughts by Raymond Hettinger. If you aren’t wowed by Python’s super() builtin, chances are you don’t really know what it is capable of doing or how to use it effectively. Much has been written about super() and much of that writing has been a failure.
This article seeks to improve on the situation by: providing practical use casesgiving a clear mental model of how it worksshowing the tradecraft for getting it to work every timeconcrete advice for building classes that use super()favoring real examples over abstract ABCD diamond diagrams. The examples for this post are available in both Python 2 syntax and Python 3 syntax. Using Python 3 syntax, let’s start with a basic use case, a subclass for extending a method from one of the builtin classes: class LoggingDict(dict): def __setitem__(self, key, value): logging.info('Setting %r to %r' % (key, value)) super(). This class has all the same capabilities as its parent, dict, but it extends the __setitem__ method to make log entries whenever a key is updated. Search Order. Tiago Cogumbreiro - Creating Python Instance Methods.
DecoratorLibrary. This page is meant to be a central repository of decorator code pieces, whether useful or not <wink>. It is NOT a page to discuss decorator syntax! Feel free to add your suggestions. Please make sure example code conforms with PEP 8. Creating Well-Behaved Decorators / "Decorator decorator" Note: This is only one recipe. Others include inheritance from a standard decorator (link?) Toggle line numbers 1 def simple_decorator(decorator): 2 '''This decorator can be used to turn simple functions 3 into well-behaved decorators, so long as the decorators 4 are fairly simple.
Property Definition These decorators provide a readable way to define properties: 1 import sys 2 3 def propget(func): 4 locals = sys. Here's a way that doesn't require any new decorators: 1 class Example(object): 2 @apply 3 def myattr(): 4 doc = '''This is the doc string.''' 5 6 def fget(self): 7 return self. Yet another property decorator: Memoize Here's a memoizing class. Alternate memoize as nested functions Cached Properties Retry.