Article Network

Python Multimedia: Video Format Conversion, Manipulations and Effects

by Ninad Sathaye | December 2010 | Open Source

Photographs capture the moment, but it is the video that helps us relive that moment! Video has become a major part of our lives. We preserve our memories by capturing the family vacation on a camcorder. When it comes to digitally preserving those recorded memories, the digital video processing plays an important role. We will use GStreamer for learning the fundamentals of video processing..

In this article by Ninad Sathaye, author of the book Python Multimedia Beginner's Guide, we shall:

  • Develop a simple command-line video player
  • Perform basic video manipulations such as cropping, resizing, and tweaking the parameters such as brightness, contrast, and saturation levels of a streaming video
  • Learn how to convert video between different video formats

So let's get on with it.

Read Python Multimedia: Video Format Conversion, Manipulations and Effects in full

Python Multimedia: Working with Audios

by Ninad Sathaye | August 2010 | Open Source

Decades ago, silent movies lit up the screen—but later, it was audio effect that brought life into them. We deal with digital audio processing quite frequently—when just playing a CD track, recording your own voice or converting songs into a different audio format. There are many libraries or multimedia frameworks available for audio processing. This article teaches some common digital audio processing techniques using Python bindings of a popular multimedia framework called GStreamer.

In this article by Ninad Sathaye, author of Python Multimedia, we shall:

  • Learn basic concepts behind GStreamer multimedia framework
  • Use GStreamer API for audio processing
  • Develop some simple audio processing tools for 'everyday use'. We will develop tools that will batch convert audio file formats, record an audio, and play audio files
Read Python Multimedia: Working with Audios in full

Python Testing: Coverage Analysis

by Greg L. Turnquist | June 2011 | Open Source

Coverage analysis is measuring which lines in a program are run and which lines aren't. This type of analysis is also known as 'code coverage', or more simply 'coverage'.

In this article by Greg Lee Turnquist, author of Python Testing Cookbook, we will cover:

  • Building a network management application
  • Installing and running coverage on your test suite
  • Generating an HTML report using coverage
  • Generating an XML report using coverage
  • Getting nosy with coverage
Read Python Testing: Coverage Analysis in full

Python Testing: Installing the Robot Framework

by Greg L. Turnquist | June 2011 | Open Source

The Robot Framework is a useful framework for writing acceptance tests using the keyword approach. Keywords are short-hand commands that are provided by various libraries and can also be user defined. This easily supports BDD-style Given-When-Then keywords. It also opens the door to third-party libraries defining custom keywords to integrate with other test tools, such as Selenium. It also means acceptance tests written using Robot Framework aren't confined to web applications.

This article by Greg Lee Turnquist, author of Python Testing Cookbook, shows all the steps needed to install the Robot Framework as well as the third party Robot Framework Selenium Library.

Read Python Testing: Installing the Robot Framework in full

Python Testing: Mock Objects

by Daniel Arbuckle | December 2010 | Beginner's Guides

In this article by Daniel Arbuckle, author of Python Testing, we shall:

  • Examine the ideas of mock objects in general
  • Learn how to use Python Mocker
  • Learn how to mock the "self" parameter of a method
Read Python Testing: Mock Objects in full

Python Text Processing with NLTK 2.0: Creating Custom Corpora

by Jacob Perkins | November 2010 | Open Source

Natural Language Processing is used everywhere—in search engines, spell checkers, mobile phones, computer games, and even in your washing machine. Python's Natural Language Toolkit (NLTK) suite of libraries has rapidly emerged as one of the most efficient tools for Natural Language Processing.

In this article by Jacob Perkins, author of the book Python Text Processing with NLTK 2.0 Cookbook, we will cover:

  • Setting up a custom corpus
  • Creating a word list corpus
  • Creating a part-of-speech tagged word corpus
  • Creating a chunked phrase corpus
  • Creating a categorized text corpus
  • Creating a categorized chunk corpus reader
  • Lazy corpus loading
  • Creating a custom corpus view
  • Creating a MongoDB backed corpus reader
  • Corpus editing with file locking
Read Python Text Processing with NLTK 2.0: Creating Custom Corpora in full

Python Text Processing with NLTK 2: Transforming Chunks and Trees

by Jacob Perkins | December 2010 | Open Source

Natural Language Processing is used everywhere—in search engines, spell checkers, mobile phones, computer games, and even in your washing machine. Python's Natural Language Toolkit (NLTK) suite of libraries has rapidly emerged as one of the most efficient tools for Natural Language Processing.

In this by Jacob Perkins, author of the book Python Text Processing with NLTK 2.0 Cookbook, we will cover:

  • Filtering insignificant words
  • Correcting verb forms
  • Swapping verb phrases
  • Swapping noun cardinals
  • Swapping infinitive phrases
  • Singularizing plural nouns
  • Chaining chunk transformations
  • Converting a chunk tree to text
  • Flattening a deep tree
  • Creating a shallow tree
  • Converting tree nodes
Read Python Text Processing with NLTK 2: Transforming Chunks and Trees in full

Python Text Processing with NLTK: Storing Frequency Distributions in Redis

by Jacob Perkins | November 2010 | Open Source

Natural Language Processing is used everywhere—in search engines, spell checkers, mobile phones, computer games, and even in your washing machine. Python's Natural Language Toolkit (NLTK) suite of libraries has rapidly emerged as one of the most efficient tools for Natural Language Processing.

In the previous article by Jacob Perkins, author of the book Python Text Processing with NLTK 2.0 Cookbook, we saw how to use execnet to do parallel and distributed processing with NLTK.

In this article, we will cover:

  • Storing a frequency distribution in Redis
  • Storing a conditional frequency distribution in Redis
  • Storing an ordered dictionary in Redis
  • Distributed word scoring with Redis and execnet
Read Python Text Processing with NLTK: Storing Frequency Distributions in Redis in full

Python: Unit Testing with Doctest

by Daniel Arbuckle | September 2010 | Beginner's Guides Open Source

In this article by Daniel Arbuckle, author of Python Testing, we shall:

  • Discuss in detail what Unit testing is
  • Talk about the ways in which Unit testing helps various stages of development
  • Work with examples that illustrate Unit testing and its advantages
Read Python: Unit Testing with Doctest in full

Python: Using doctest for Documentation

by Greg L. Turnquist | May 2011 | Open Source

Python provides the useful ability to embed comments inside functions that are accessible from a Python shell. These are known as docstrings. A docstring provides the ability to embed not only information, but also code samples that are runnable.In this article, we will explore different ways to use doctest to develop documentation.

In this article by Greg Lee Turnquist, author of Python Testing Cookbook, we will cover:

  • Documenting the basics
  • Catching stack traces
  • Running doctests from the command line
  • Printing out all your documentation including a status report
Read Python: Using doctest for Documentation in full

Q Replication Components in IBM Replication Server

by Pav Kumar-Chatterjee | August 2010 | Enterprise Articles IBM

In this article by Pav Kumar-Chatterjee, author of IBM InfoSphere Replication Server and Data Event Publisher, we will discuss three layers—The DB2 database layer, the WebSphere MQ layer, and the Q replication layer that make up a Q replication solution, and the relationship between Replication/Publication Queue Map, Q subscription, and subscription group. We will also take a look at the internals of the Q Capture and Q Apply programs.

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Q Subscription Maintenance in IBM Infosphere

by Pav Kumar-Chatterjee | November 2010 | Enterprise Articles IBM

In this article by Pav Kumar-Chatterjee, author of IBM InfoSphere Replication Server and Data Event Publisher, we will cover the following topics:

  • Checking the state of a Q subscription
  • Stop, drop, alter or start a Q subscription
  • Sending a signal using ASNCLP
Read Q Subscription Maintenance in IBM Infosphere in full

Qmail Quickstarter: Virtualization

by Kyle Wheeler | July 2008 | Linux Servers Networking & Telephony

One of the most interesting extensions to the standard mail setup is that of virtualization. In this article by Kyle Wheeler, let's see the many reasons for wanting to virtualize email services, from hosting multiple domains with different users to simply extending the ability to apply policies to different sets of email. There are three basic techniques that are used with a standard qmail system for attaining different forms of virtualization: qmail's control/virtualdomains file, user-definable address extensions, and running multiple qmail instances on the same system.

Read Qmail Quickstarter: Virtualization in full

qooxdoo: Working with Layouts

by Mohamed Raffi Rajesh Kumar Bachu | December 2011 | Beginner's Guides Open Source

Over the past few years, all the major internet or enterprise applications are developed or migrated to Rich Internet Application to support all the features that are provided in the desktop applications. This helps organizations keep the end users happy and also improves application deployment and maintenance. qooxdoo is a stable, open source RIA framework. If you are waiting and watching for the right time to migrate your application to qooxdoo, this is the right time!

In this article by Rajesh Kumar Bachu and Mohamed Raffi, authors of qooxdoo Beginner's Guide, we'll cover the following topics:

  • Widgets
  • Containers
  • Panels
  • Layout managers
  • Layouts
Read qooxdoo: Working with Layouts in full

QR Codes, Geolocation, Google Maps API, and HTML5 Video

by Shane Gliser | June 2013 | Open Source

We have discussed many of the core concerns of small and big business. Let's turn our eyes now to other concepts that would concern media companies. In this article by Shane Gliser from the book Creating Mobile Apps with jQuery Mobile, we'll look at a movie theater chain, but really, these concepts could be applied to any business that has multiple physical locations.

In this article, we'll cover:

  • QR Codes
  • Basic geolocation
  • Integrating Google Maps API
  • Linking and embedding video
Read QR Codes, Geolocation, Google Maps API, and HTML5 Video in full

Quality Assurance in Asterisk 1.6

by Barrie Dempster David Gomillion David Merel | September 2009 | Networking & Telephony

Quality Assurance tells us everything regarding monitoring calls, recording calls, and capturing detailed call logs. In this article by Barrie Dempster, David Gomillion, and David Merel you learn how to install and use these features in Asterisk 1.6.

Read Quality Assurance in Asterisk 1.6 in full

Query Performance Tuning

by Alberto Ferrari Chris Webb Marco Russo | February 2014 | Enterprise Articles

In this article by Chris Webb, Alberto Ferrari, and Marco Russo, authors of Expert Cube Development with SSAS Multidimensional Models, we will learn about the steps that you'll need to go through in order to ensure your cube is as responsive as possible.

Read Query Performance Tuning in full

Query Performance Tuning in Microsoft Analysis Services: Part 1

by Chris Webb | July 2009 | .NET Microsoft MySQL PHP

In this two-part article by Chris Webb, we will cover query performance tuning, including how to design aggregations and partitions and how to write efficient MDX. The first part will cover performance-specific design features, along with the concepts of partitions and aggregations.

One of the main reasons for building Analysis Services cubes as part of a BI solution is because it should mean you get better query performance than if you were querying your relational database directly. While it's certainly the case that Analysis Services is very fast it would be naive to think that all of our queries, however complex, will return in seconds without any tuning being necessary. This article will describe the steps you'll need to go through in order to ensure your cube is as responsive as possible.

Read Query Performance Tuning in Microsoft Analysis Services: Part 1 in full

Query Performance Tuning in Microsoft Analysis Services: Part 2

by Chris Webb | July 2009 | .NET Microsoft MySQL PHP

In the previous part of the article by Chris Webb, we covered performance-specific design features such as partitions and aggregations. In this part, we will cover MDX calculation performance and caching. We'll see how important caching is to overall query performance.

Read Query Performance Tuning in Microsoft Analysis Services: Part 2 in full

Querying and Selecting Data

by Eric Pimpler | April 2013 | Cookbooks

Selecting features from a geographic layer or rows from a standalone attribute table is one of the most common GIS operations. Queries are created to enable these selections, and can be either attribute or spatial queries. Attribute queries use SQL statements to select features or rows through the use of one or more fields or columns in a dataset. An example attribute query would be "Select all land parcels with a property value greater than $500,000". Spatial queries are used to select features based on some type of spatial relationship. An example might be "Select all land parcels that intersect a 100 year floodplain" or perhaps "Select all streets that are completely within Travis County, Texas". It is also possible to combine attribute and spatial queries. An example might be "Select all land parcels that intersect the 100 year floodplain and have a property value greater than $500,000".

In this article by Eric Pimpler, author of Programming ArcGIS 10.1 with Python Cookbook, we will cover the following recipes:

  • Constructing proper attribute query syntax

  • Creating feature layers and table views

  • Selecting features and rows with the Select Layer by Attribute tool

  • Selecting features with the Select by Location tool

  • Combining spatial and attribute queries with the Select by Location tool

Read Querying and Selecting Data in full
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