PyQtGraph - Scientific Graphics and GUI Library for Python Orange – Data Mining Fruitful & Fun VPython Monte - machine learning in Python News Overview — SimPy 3.0.2 documentation Event discrete simulation for Python. News | PyPI | Bitbucket | Issues | Mailing list >>> import simpy>>>>>> def clock(env, name, tick):... while True:... print(name, env.now)... yield env.timeout(tick)...>>> env = simpy.Environment()>>> env.process(clock(env, 'fast', 0.5))<Process(clock) object at 0x...>>>> env.process(clock(env, 'slow', 1))<Process(clock) object at 0x...>>>> env.run(until=2)fast 0slow 0fast 0.5slow 1fast 1.0fast 1.5 SimPy is a process-based discrete-event simulation framework based on standard Python. Its event dispatcher is based on Python’s generators and can also be used for asynchronous networking or to implement multi-agent systems (with both, simulated and real communication). Processes in SimPy are simple Python generator functions and are used to model active components like customers, vehicles or agents. Simulations can be performed “as fast as possible”, in real time (wall clock time) or by manually stepping through the events.
Model Builder Python Library From OSGeo Wiki Motivation Several OSGeo software projects support Python. However, a global abstraction layer is lacking which would help to do "OSGeo Python programming". We think of well documented bindings to the various software projects which are handled as plugins (or whatever appropriate). Functionality Shared functions may include Import/Export of data Linking to external data sources Reprojection tools GIS analysis Plotting and printing Programming Language Python SWIG ... Existing code to be recycled Efforts outside the OSGEO world Architecture Draft concept: License It is proposed to license the OSGeo Python Library under ... Communication A new mailing list is created: Unofficial Python GIS SIG Interested people
plotexplorer_gui 0.3.2 Package Index > plotexplorer_gui > 0.3.2 Not Logged In plotexplorer_gui 0.3.2 Downloads ↓ A wxpython/matplotlib script for plotting and contrasting a collection of graphs This script creates a matplotlib window next to the sortable list of checkboxes from which graphs can be selected. This script depends upon wxpython and matplotlib. Downloads (All Versions): 7 downloads in the last day 84 downloads in the last week 555 downloads in the last month Website maintained by the Python community Real-time CDN by Fastly / hosting by Rackspace / design by Tim Parkin
Intro and Overview - Vision Egg Homepage — Modular toolkit for Data Processing (MDP) Topical Software — SciPy.org This page indexes add-on software and other resources relevant to SciPy, categorized by scientific discipline or computational topic. It is intended to be exhaustive. If you know of an unlisted resource, see About This Page, below. You may also want to take a look at the list of Scikits, Python packages oriented specifically at scientific computation tasks. The listings are roughly organized by topic, with introductory resources first, more general topics next, and discipline-specific resources last. Unless otherwise indicated, all packages listed here are provided under some form of open source license. If you distribute or know of a resource that is not listed here, please add a listing. Please include a description — be as brief as you can, but make sure you include in your text a link to the resource’s home page and some keywords that potential users might search for to find the resource. In addition, please also list your software on
Introduction to machine learning using Python tools Summary: Have you ever wondered how recommendation engines work? Or how you can predict house prices based on historical real-estate data? Talk outline: 1) Introduction to machine learning: supervised, unsupervised and reinforcement learning 2) Feature extraction: examples from a few real world data sets, including text and image data. 3) Supervised learning methods: regression, naive bayes, support vector machines. 4) Unsupervised learning methods: clustering and dimensionality reduction 5) Designing a recommendation engine: collaborative filtering. 6) Debugging your machine learning algorithm: learning curves and the bias-variance tradeoff. All concepts will be illustrated with real world data sets, drawn from the web. Goals of the talk: 1) Have an systematic way to think about machine learning. 2) Understand the major different classes of machine learning algorithms so that you know when to use what. Intended audience: Prerequisites: An intermediate-level programming background is assumed.