git
Hiberfil.sys
Hiberfil.sys and Windows Hibernate function
To understand why hiberfil.sys exists, we must look at the Windows Hibernate function. When you activate Hibernate, Windows takes a snap shot of your current session (all your running programs, open files, etc.) and writes that information to your hard drive. Hibernate was designed to speedup shut downs and restarts and save power on laptops.
DSPL Tools
DSPL Tools is a small suite of command-line utilities designed to help generate, organize, and validate DSPL datasets. The suite currently includes the following components:
- DSPL Check: Checks a dataset against a variety of criteria including adherence to the official DSPL schema, consistency of internal references, and CSV layout.
- DSPL Gen: Generates a simple, DSPL dataset “template” from an input CSV file
This software is released under a BSD license; the full source code is available for browsing and download on the DSPL open source site. Release notes are provided in the DSPL Tools README file.
DSPL Developer Guide
DSPL stands for Dataset Publishing Language. It is a representation format for both the metadata (information about the dataset, such as its name and provider, as well as the concepts it contains and displays) and actual data of datasets. Datasets described in this format can be imported into the Google Public Data Explorer, a tool that allows for rich, visual exploration of the data.
Note: To upload data to Google Public Data using the Public Data upload tool, you must have a Google Account.
This document is intended for data owners who want their content to be available in the Public Data Explorer. It goes beyond the Tutorial by diving deeper into the details of the DSPL schema and supported features. Only a basic familiarity of XML is assumed, although knowledge of relational databases is also useful.
Although not a requirement, we suggest reading through the Tutorial, which is shorter and easier to digest, before looking at this document.
Reproducible research
OpenCPU
An API for Embedded Scientific Computing
OpenCPU is a system for embedded scientific computing and reproducible research.
dplyr
dplyr is a new package which provides a set of tools for efficiently manipulating datasets in R. dplyr is the next iteration of plyr, focussing on only data frames. dplyr is faster, has a more consistent API and should be easier to use. There are three key ideas that underlie dplyr:
- Your time is important, so Romain Francois has written the key pieces in Rcpp to provide blazing fast performance. Performance will only get better over time, especially once we figure out the best way to make the most of multiple processors.
- Tabular data is tabular data regardless of where it lives, so you should use the same functions to work with it. With
dplyr, anything you can do to a local data frame you can also do to a remote database table. PostgreSQL, MySQL, SQLite and Google bigquery support is built-in; adding a new backend is a matter of implementing a handful of S3 methods. - The bottleneck in most data analyses is the time it takes for you to figure out what to do with your data, and dplyr makes this easier by having individual functions that correspond to the most common operations (
group_by,summarise,mutate,filter,selectandarrange). Each function does one only thing, but does it well.
Dalvik VM Internals
Dalvik — the virtual machine with the unusual name — runs your code on Android. Join us to learn about the motivation for its design and get
some details about how it works. You’ll also walk away with a few tips for how to write code that works well with the platform. Be prepared
for a deep dive into technical details. Questions encouraged!
Three ways of creating dictionaries in Python
March 30, 2012
Dictionaries are the fundamental data structure in Python, and a key tool in any Python programmer’s arsenal. They allow O(1) lookup speed, and have been heavily optimized for memory overhead and lookup speed efficiency.
Free Podcasting Software
A vast array of software is available to support your podcasting requirements; some programs offer one stop integrated solutions to all your podcasting needs, while others fill more specific niches. However, every piece of software that you need in order to record and produce a professional, quality podcast is readily available free of charge.
Acquiring the Audio
No matter what software you choose to help create and produce your podcasts, you will first have to get your audio into the computer. How you choose to do this is entirely up to you, but is likely to depend on what level of audio quality you want and your budget. At the most basic level you could use your computer’s in-built microphone, but for a reasonable degree of quality you will want a semi-decent external microphone.
1. Audacity – Audio Recorder and Editor
2. WinLAME – Audio Encoder
Once your audio has been recorded and edited (preferably in WAV format) you will need to convert it into a suitable podcast format. The universally accepted audio file format for podcasts is MP3. WinLAME is an excellent piece of free podcasting software designed to convert WAV files into the podcast friendly MP3 format.
3. Mp3Tag – ID3 Tag Editor
Now that you have your podcast MP3 audio file and some suitable artwork for its cover, you will need to embed the artwork into the MP3 file and add some additional tags that can be displayed by the listener’s playback device.