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Python for Scientific Computing

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Pythonista - omz:software. PyDDE: Python/C DDE solver. PyDDE is an open source numerical solver for systems of delay differential equations (DDEs), implemented as a Python package and written in both Python and C.

PyDDE: Python/C DDE solver

PyDDE is built around the back-end of ddesolve (now called PBSddesolve), an R package with the same functionality, which in turn is built on the numerical routines of Simon Wood's Solv95, a C-based DDE solver for Microsoft Windows systems. There is now also a Mac port for Solv95, with Cocoa frontend by Ashley Buckner. If you use a different OS or prefer that your simulations are scriptable within a nice environment like Python, you might wish to read on! On-going development of PyDDE has ceased. Development does appear to be continuing for PBSddesolve. Contact I welcome any questions or suggestions about PyDDE--particulary if you had problems with the package, and "especially particularly" if you also have the solution!

PyDSTool. PyDSTool is a sophisticated & integrated simulation and analysis environment for dynamical systems models of physical systems (ODEs, DAEs, maps, and hybrid systems).


PyDSTool is platform independent, written primarily in Python with some underlying C and Fortran legacy code for fast solving. It makes extensive use of the numpy and scipy libraries. PyDSTool supports symbolic math, optimization, phase plane analysis, continuation and bifurcation analysis, data analysis, and other tools for modeling -- particularly for biological applications. The project is fully open source with a BSD license, and welcomes contributions from the community. Please visit the support pages at to post questions and feedback. These documentation pages are still under development. If you use PyDSTool in your published work, please cite it in the following way: Clewley RH, Sherwood WE, LaMar MD, Guckenheimer JM (2007) PyDSTool, a software environment for dynamical systems modeling. Complex Networks.

SimPy: Simulating Systems in Python. By Klaus Müller and Tony Vignaux 02/27/2003 Simulating complex real-world systems is now possible with SimPy , an open source simulation package.

SimPy: Simulating Systems in Python

SimPy, originally developed by the authors of this article, has been developed to production quality by a small team of enthusiastic open sourcerers around the world. As far as we know, it is the only existing discrete event Python simulation package. Actually, it is one of a very small number of fully object oriented simulation systems. This article is written with the explicit goal of whetting the appetite of simulation newbies to play with this powerful problem solving technique and to get even more users and developers for SimPy. Why consider simulation? To demonstrate using SimPy for simulation we will study a simple scenario.

When customers call they may have to wait for a free line. To model "quality of service", we label a customer "happy" or "unhappy" depending on wait time. SimPy yield hold,self,t. Epigrass: a tool to study disease spread in complex networks. SciPy - Python Programming Language – Official Website. Array creation — NumPy v1.6 Manual. Introduction There are 5 general mechanisms for creating arrays: Conversion from other Python structures (e.g., lists, tuples)Intrinsic numpy array array creation objects (e.g., arange, ones, zeros, etc.)Reading arrays from disk, either from standard or custom formatsCreating arrays from raw bytes through the use of strings or buffersUse of special library functions (e.g., random) This section will not cover means of replicating, joining, or otherwise expanding or mutating existing arrays.

Array creation — NumPy v1.6 Manual

Nor will it cover creating object arrays or record arrays. Both of those are covered in their own sections. Converting Python array_like Objects to Numpy Arrays In general, numerical data arranged in an array-like structure in Python can be converted to arrays through the use of the array() function. Examples: Cookbook - Pyplot — Matplotlib v1.0.1 documentation. Thumbnail gallery — Matplotlib v1.0.1 documentation. How not to write Python code » Ikke’s blog. Lately I’ve been reading some rather unclean Python code.

How not to write Python code » Ikke’s blog

Maybe this is mainly because the author(s) of the code had no in-depth knowledge of the Python language itself, the ‘platform’ delivered with cPython,… Here’s a list of some of the mistakes you should really try to avoid when writing Python code: Some days ago RealNitro pointed me at this list of essential Python readings. “Idiomatic Python” is a must-read, even for experienced Python developers. That’s about it for now, maybe I’ll add some more items to this list later on. If you have some other hints, comments!

NumPy for Matlab Users - MATLAB® and NumPy/SciPy have a lot in common.

NumPy for Matlab Users -

But there are many differences. NumPy and SciPy were created to do numerical and scientific computing in the most natural way with Python, not to be MATLAB® clones. This page is intended to be a place to collect wisdom about the differences, mostly for the purpose of helping proficient MATLAB® users become proficient NumPy and SciPy users. NumPyProConPage is another page for curious people who are thinking of adopting Python with NumPy and SciPy instead of MATLAB® and want to see a list of pros and cons. Short answer.