Scientific Plotting in Python
The document discusses advancements in scientific plotting in Python, highlighting the limitations of traditional libraries like Matplotlib and introducing a new project called mplexporter aimed at improving data transfer between plotting figures and a universal JSON format. It emphasizes the importance of interactive, shareable graphics leveraging web technologies like SVG and D3.js through platf
Frequentism and Bayesianism IV: How to be a Bayesian in Python | Pythonic Perambulations
I've been spending a lot of time recently writing about frequentism and Bayesianism. In Frequentism and Bayesianism I: a Practical Introduction I gave an introduction to the main philosophical differences between frequentism and Bayesianism, and showed that for many common problems the two methods give basically the same point estimates. In Frequentism and Bayesianism II: When Results Differ I wen
コマンドラインでの選択的インターフェイスについて考える - Qiita
# https://gist.github.com/mitukiii/4234173 import sys, commands from percol.command import SelectorCommand from percol.key import SPECIAL_KEYS from percol.finder import FinderMultiQueryMigemo, FinderMultiQueryRegex ## prompt # Case Insensitive / Match Method に応じてプロンプトに表示 def dynamic_prompt(): prompt = ur"" if percol.model.finder.__class__ == FinderMultiQueryMigemo: prompt += "[Migemo]" elif percol
Frequentism and Bayesianism III: Confidence, Credibility, and why Frequentism and Science do not Mix | Pythonic Perambulations
This post is part of a 5-part series: Part I Part II Part III Part IV Part V See also Frequentism and Bayesianism: A Python-driven Primer, a peer-reviewed article partially based on this content. In Douglas Adams' classic Hitchhiker's Guide to the Galaxy, hyper-intelligent pan-dimensional beings build a computer named Deep Thought in order to calculate "the Answer to the Ultimate Question of Life,
Welcome to Plan — Plan 0.1 documentation
Welcome to Plan¶ Welcome to Plan’s documentation. This documentation is mainly divided into two parts. You can get started with Installation and then have a look at Quickstart in order to have a feel about how plan looks like. You can head over to Tutorial if you want to know how to use Plan in real life. The rest part of the user’s guide is about how to define one specific job or plan object and
Python, Pitch shifting, and the Pianoputer - __del__( self )
Record a sound, change its pitch 50 times and assign each new sound to a key of your computer keyboard. You get a Pianoputer ! A sound can be encoded as an array (or list) of values, like this: To make this sound play twice faster, we remove every second value in the array: By doing so we didn’t only halved the sound’s duration, we also doubled its frequency, making it higher-pitched than the orig
Introducing ciso8601: A fast ISO8601 date time parser for Python | The Making of Close
Project link: https://github.com/closeio/ciso8601 Sometimes it's the little things that make web apps faster. We noticed that loading leads and opportunity view pages in Close wasn't as fast as we wanted it to be in some cases, so we pulled up the profiler. To our surprise, a large portion of time was spent parsing date times, sometimes up to a second. How did this happen? We serialize certain lea
Parallelism and Concurrency with Python
Presented May 2014. For more information on PyParallel, see: https://speakerdeck.com/trent/pyparallel-how-we-removed-the-gil-and-exploited-all-cores …
PythonJS now faster than CPython
pystone benchmark PythonJS using the dart backend is 6x faster than CPython. pystone.py nbody benchmark PythonJS using the fast javascript backend is faster than CPython and even 2x faster than PyPy. nbody.py richard's benchmark PythonJS using the dart backend is 7x faster than CPython. richards.py float benchmark PythonJS using the dart backend is 3x faster than CPython. float.py Micro-Benchmarks
Pythonのコマンドライン引数処理の決定版 docopt (あとJuliaに移植したよ) - りんごがでている
Pythonをよく使う人にはよく知ってる人も多いのですが、docoptという便利ライブラリがあります。 docoptはargparseやoptparseのようなコマンドライン引数をパースするライブラリなのですが、その発想がコロンブスの卵なのです。 例えばPython標準のargparseだと、argparseのAPIを組み合わせてパーサを組み立てるわけです。するとパーサと一緒にヘルプも作ってくれて、"program --help"などとすると自動生成されたヘルプを表示してくれるようになります。 しかし、そのAPIを覚えるのが大変で、毎回ドキュメントを読まないと忘れちゃうわけです。 import argparse parser = argparse.ArgumentParser(description='Process some integers.') parser.add_argument
Home | diesel
Poll Without Pain. The performance of nonblocking I/O meets the convenience of coroutines. Packed with a bunch of useful protocols and a first-class toolkit for concurrency, diesel is the best way to write network applications in Python. Try It (requires RFC 6455 WebSockets): from diesel.web import DieselFlask from diesel.util.queue import Fanout from diesel import first app = DieselFlask(__name__
blog.bjrn.se: TrueCrypt explained
The most popular cryptographic software for Windows is probably TrueCrypt. In this article I will explain how TrueCrypt works and as a by-product a working Python implementation will be provided. This article is written from a programmer perspective and the math will be kept to the minimum. The emphasis is on how TrueCrypt uses cryptographic primitives such as AES and SHA-1, not how the primitives
Frequentism and Bayesianism II: When Results Differ | Pythonic Perambulations
This post is part of a 5-part series: Part I Part II Part III Part IV Part V See also Frequentism and Bayesianism: A Python-driven Primer, a peer-reviewed article partially based on this content. In a previous post I gave a brief practical introduction to frequentism and Bayesianism as they relate to the analysis of scientific data. In it, I discussed the fundamental philosophical difference betwe
Python Tools for Machine Learning - CB Insights Research
Python is one of the best programming languages out there, with an extensive coverage in scientific computing: computer vision, artificial intelligence, mathematics, astronomy to name a few. Unsurprisingly, this holds true for machine learning as well. Of course, it has some disadvantages too; one of which is that the tools and libraries for Python are scattered. If you are a unix-minded person, t
Painting with Code
By day, I code mobile apps and design digital experiences. In my free time, I create art. I recently began exploring both at once with generative art, a sublime combination of computer programming and graphic art. Generative art, or art derived from computer code, is a relatively new format, in which the artist-programmer realizes a vision or idea by defining parameters and commands. But making ge
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Home — VisPy
GitHub - cathalgarvey/tinystatus: A peer to peer microstatus system written in 30 lines of pure python. (MOVED to Gitlab)
http://tavendo.com/blog/post/is-crossbar-the-future-of-python-web-apps/
PyParallel: How we removed the GIL and exploited all cores
GitHub - robclewley/compneuro: Computational Neuroscience class materials in PyDSTool based on Hugh Wilson's book (and some from Eugene Izhikevich's book). DRAFT VERSION! See files_info.txt for index. Please improve and discuss!
