How to: Use Python to Solve Optimization Problems via reddit.com. Sleepyti.me bedtime calculator. Python for Informatics: Exploring Information, an open textbook. Charles is currently a Clinical Assistant Professor and teaches in the School of Information at the University of Michigan. Charles also works with the IMS Global Learning Consortium as the IMS Affiliate Coordinator. Previously he was the Executive Director of the Sakai Foundation and the Chief Architect of the Sakai Project. Charles is the author of the book, "Using Google App Engine" from O'Reilly and Associates. He also wrote the O'Reilly book on High Performance Computing. Charles has a background in standards including serving as the vice-chair for the IEEE Posix P1003 standards effort and edited the Standards Column in IEEE Computer Magazine from 1995-1999.
Charles is active in television and radio as a hobby, he has co-hosted several television shows including "Nothin but Net" produced by MediaOne and a nationally televised program about the Internet called "Internet:TCI". Chuck's hobbies include off-road motorcycle riding, karaoke and playing hockey. Ph.D. European Supergrid Slowly Coming into Focus. Color Scheme Designer 3. YQafj.png (773×938) Scratch. PyBrain. 10 Reasons Python Rocks for Research — Hoyt Koepke. The following is an account of my own experience with Python.
Because that experience has been so positive, it is an unabashed attempt to promote the use of Python for general scientific research and development. About four years ago, I dropped MATLAB in favor of Python as my primary language for coding research projects. This article is a personal account of how rewarding I have found that experience. As I describe in the next sections, the variety and quality of Python’s features has spoiled me. Even in small scripts, I now rely on Python’s numerous data structures, classes, nested functions, iterators, the flexible function calling syntax, an extensive kitchen-sink-included standard library, great scientific libraries, and outstanding documentation.
1. An Introduction to Distutils — Python v2.7 documentation. This document covers using the Distutils to distribute your Python modules, concentrating on the role of developer/distributor: if you’re looking for information on installing Python modules, you should refer to the Installing Python Modules chapter. 1.1.
Concepts & Terminology Using the Distutils is quite simple, both for module developers and for users/administrators installing third-party modules. As a developer, your responsibilities (apart from writing solid, well-documented and well-tested code, of course!) Are: write a setup script (setup.py by convention)(optional) write a setup configuration filecreate a source distribution(optional) create one or more built (binary) distributions Each of these tasks is covered in this document. Not all module developers have access to a multitude of platforms, so it’s not always feasible to expect them to create a multitude of built distributions.
10 Reasons Python Rocks for Research — Hoyt Koepke. The following is an account of my own experience with Python.
Because that experience has been so positive, it is an unabashed attempt to promote the use of Python for general scientific research and development. About four years ago, I dropped MATLAB in favor of Python as my primary language for coding research projects. From python import podcast. PySnippet. Flask (A Python Microframework) Using the Cython Compiler to write fast Python code. Passionate Python developer since 2002after Basic, Logo, Pascal, Prolog, Scheme, Java, C, ...CS studies in Germany, Ireland, FrancePhD in distributed systems in 2007Language design for self-organising systemsDarmstadt University of Technologies, GermanyCurrent occupations:Employed by Senacor Technologies AG, GermanyIT transformations, SOA design, Java-Development, ...»lxml« OpenSource XML toolkit for Part 1: Intro to CythonPart 2: Building Cython modulesPart 3: Writing fast codePart 4: Talking to other extensions Cython is the missing linkbetween the simplicity of Pythonand the speed of C / C++ / Fortran.
Package Index : Theano 0.3.0. Optimizing compiler for evaluating mathematical expressions on CPUs and GPUs.
Latest Version: 0.6.0 Theano is a Python library that allows you to define, optimize, and efficiently evaluate mathematical expressions involving multi-dimensional arrays. It is built on top of NumPy. Theano features: John Anderson ( sontek ) - Debugging Python with pdb. Being a great debugger is almost as important as being great at writing the software.
I think I spend just as much time debugging programs as I do writing them. pdb (python debugger) is a very powerful tool for interactively debugging the state of your application and inspecting portions of your code. For the rest of this post we are going to be debugging a file called fib.py with the following code: # fib.pydef main(): low = 0 high = 1 for i in range(10): print high new_high = get_new_high(low, high) low = high high = new_high def get_new_high(low, high): return low + high if __name__ == '__main__': main() There are many ways to get your application to drop into pdb, the first way is to import pdb in the file you want to debug and put pdb.set_trace() on any lines you want to debug.
So we are going to change our file to look like this: You will see it shows you what method its in and the next line its going to run: Features of the Standard Library.