top button
Flag Notify
    Connect to us
      Site Registration

Site Registration

Small Introduction About Apache Wicket?

0 votes
252 views

What is Apache Wicket?

Apache Wicket, commonly referred to as Wicket, is a lightweight component-based web application framework for the Java programming language conceptually similar to JavaServer Faces and Tapestry. It was originally written by Jonathan Locke in April 2004. Version 1.0 was released in June 2005. It graduated into an Apache top-level project in June 2007.

Invented in 2004, Wicket is one of the few survivors of the Java serverside web framework wars of the mid 2000's. Wicket is an open source, component oriented, serverside, Java web application framework. With a history of over a decade, it is still going strong and has a solid future ahead. Learn why you should consider Wicket for your next web application.

Wicket uses plain XHTML for templating (which enforces a clear separation of presentation and business logic and allows templates to be edited with conventional WYSIWYG design tools). Each component is bound to a named element in the XHTML and becomes responsible for rendering that element in the final output. The page is simply the top-level containing component and is paired with exactly one XHTML template. Using a special tag, a group of individual components may be abstracted into a single component called a panel, which can then be reused whole in that page, other pages, or even other panels.

Each component is backed by its own model, which represents the state of the component. The framework does not have knowledge of how components interact with their models, which are treated as opaque objects automatically serialized and persisted between requests. More complex models, however, may be made detachable and provide hooks to arrange their own storage and restoration at the beginning and end of each request cycle. Wicket does not mandate any particular object-persistence or ORM layer, so applications often use some combination of Hibernate objects, EJBs or POJOs as models.

In Wicket, all server side state is automatically managed. You should never directly use an HttpSession object or similar wrapper to store state. Instead, state is associated with components. Each server-side page component holds a nested hierarchy of stateful components, where each component’s model is, in the end, a POJO (Plain Old Java Object)

Wicket is all about simplicity. There are no configuration files to learn in Wicket. Wicket is a simple class library with a consistent approach to component structure.

Apache Wicket is a simple and features rich component-based web framework, the real reusable components is the main selling point of this framework. However, due to the big different between component-based and MVC architecture, it makes Wicket hard to learn, especially for those classic MVC developers.

Video for Apache Wicket

posted Jan 31, 2018 by anonymous

  Promote This Article
Facebook Share Button Twitter Share Button LinkedIn Share Button


Related Articles

What is aiohttp?
Asynchronous HTTP client/server framework for asyncio and Python 

Features:

  • Supports both client and server side of HTTP protocol.
  • Supports both client and server Web-Sockets out-of-the-box and avoids Callback Hell.
  • Provides Web-server with middlewares and pluggable routing.

Commands

pip install aiohttp

You may want to install optional cchardet library as faster replacement for chardet:

pip install cchardet

For speeding up DNS resolving by client API you may install aiodns as well. This option is highly recommended:

pip install aiodns

Example

import aiohttp
import asyncio

async def fetch(session, url):
    async with session.get(url) as response:
        return await response.text()

async def main():
    async with aiohttp.ClientSession() as session:
        html = await fetch(session, 'http://python.org')
        print(html)

if __name__ == '__main__':
    loop = asyncio.get_event_loop()
    loop.run_until_complete(main())

Video for aiohttp

https://www.youtube.com/watch?v=Z784Mwm4VBg

 

 

READ MORE

What is Seaborn?
Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics.

Features

  • A dataset-oriented API for examining relationships between multiple variables
  • Specialized support for using categorical variables to show observations or aggregate statistics
  • Options for visualizing univariate or bivariate distributions and for comparing them between subsets of data
  • Automatic estimation and plotting of linear regression models for different kinds dependent variables
  • Convenient views onto the overall structure of complex datasets
  • High-level abstractions for structuring multi-plot grids that let you easily build complex visualizations
  • Concise control over matplotlib figure styling with several built-in themes
  • Tools for choosing color palettes that faithfully reveal patterns in your data

Seaborn aims to make visualization a central part of exploring and understanding data. Its dataset-oriented plotting functions operate on dataframes and arrays containing whole datasets and internally perform the necessary semantic mapping and statistical aggregation to produce informative plots.

Example Code

import seaborn as sns
sns.set()
tips = sns.load_dataset("tips")
sns.relplot(x="total_bill", y="tip", col="time",
            hue="smoker", style="smoker", size="size",
            data=tips);

Video for Seaborn
https://www.youtube.com/watch?v=eMkEL7gdVV0

READ MORE

What is Mlpack Library?

mlpack is a C++ machine learning library with emphasis on scalability, speed, and ease-of-use. Its aim is to make machine learning possible for novice users by means of a simple, consistent API, while simultaneously exploiting C++ language features to provide maximum performance and maximum flexibility for expert users. 

This is done by providing a set of command-line executables which can be used as black boxes, and a modular C++ API for expert users and researchers to easily make changes to the internals of the algorithms.

As a result of this approach, mlpack outperforms competing machine learning libraries by large margins; see the BigLearning workshop paper and the benchmarks for details.

mlpack is developed by contributors from around the world. It is released free of charge, under the 3-clause BSD License (more information). (Versions older than 1.0.12 were released under the GNU Lesser General Public License: LGPL, version 3.)

mlpack was originally presented at the BigLearning workshop of NIPS 2011 [pdf] and later published in the Journal of Machine Learning Research [pdf], with version 3 being published in the Journal of Open Source Software [pdf]. Please cite mlpack in your work using this citation.

mlpack bindings for R are provided by the RcppMLPACK project.

Currently mlpack supports the following algorithms:

  • Collaborative Filtering
  • Decision stumps (one-level decision trees)
  • Density Estimation Trees
  • Euclidean Minimum Spanning Trees
  • Gaussian Mixture Models (GMMs)
  • Hidden Markov Models (HMMs)
  • Kernel Principal Component Analysis (KPCA)
  • K-Means Clustering
  • Least-Angle Regression (LARS/LASSO)
  • Linear Regression
  • Local Coordinate Coding
  • Locality-Sensitive Hashing (LSH)
  • Logistic regression
  • Max-Kernel Search
  • Naive Bayes Classifier
  • Nearest neighbor search with dual-tree algorithms
  • Neighbourhood Components Analysis (NCA)
  • Non-negative Matrix Factorization (NMF)
  • Principal Components Analysis (PCA)
  • Independent component analysis (ICA)
  • Rank-Approximate Nearest Neighbor (RANN)
  • Simple Least-Squares Linear Regression (and Ridge Regression)
  • Sparse Coding, Sparse dictionary learning

For more detail visit here - http://mlpack.org/docs.html

Video for Mlpack

https://www.youtube.com/watch?v=yQtp3gf5wtY

READ MORE

What is PyShark?

PyShark is a wrapper for the Wireshark CLI interface, tshark, so all of the Wireshark decoders are available to PyShark!

Python wrapper for tshark, allowing python packet parsing using wireshark dissectors.

There are quite a few python packet parsing modules, this one is different because it doesn't actually parse any packets, it simply uses tshark's (wireshark command-line utility) ability to export XMLs to use its parsing.

This package allows parsing from a capture file or a live capture, using all wireshark dissectors you have installed. Tested on windows/linux.

Example Code for Reading a File

import pyshark
cap = pyshark.FileCapture('/tmp/mycapture.cap')
cap
>>> <FileCapture /tmp/mycapture.cap>
print cap[0]
Packet (Length: 698)
Layer ETH:
        Destination: aa:bb:cc:dd:ee:ff
        Source: 00:de:ad:be:ef:00
        Type: IP (0x0800)
Layer IP:
        Version: 4
        Header Length: 20 bytes
        Differentiated Services Field: 0x00 (DSCP 0x00: Default; ECN: 0x00: Not-ECT (Not ECN-Capable Transport))
        Total Length: 684
        Identification: 0x254f (9551)
        Flags: 0x00
        Fragment offset: 0
        Time to live: 1
        Protocol: UDP (17)
        Header checksum: 0xe148 [correct]
        Source: 192.168.0.1
        Destination: 192.168.0.2​

Video for PyShark

https://www.youtube.com/watch?v=gstHeldo61w

READ MORE

What is FastText?

FastText is an open-source, free, lightweight library that allows users to learn text representations and text classifiers. It works on standard, generic hardware. Models can later be reduced in size to even fit on mobile devices.
FastText builds on modern Mac OS and Linux distributions. Since it uses C++11 features, it requires a compiler with good C++11 support.

Steps for Installing

- git clone https://github.com/facebookresearch/fastText.git
- cd fastText
- make

Text classification is a core problem to many applications, like spam detection, sentiment analysis or smart replies. In this tutorial, we describe how to build a text classifier with the fastText tool.

What is text classification?
The goal of text classification is to assign documents (such as emails, posts, text messages, product reviews, etc...) to one or multiple categories. Such categories can be review scores, spam v.s. non-spam, or the language in which the document was typed. 

Nowadays, the dominant approach to build such classifiers is machine learning, that is learning classification rules from examples. In order to build such classifiers, we need labeled data, which consists of documents and their corresponding categories (or tags, or labels).

Video for FastText

https://www.youtube.com/watch?v=tQvghqdefTM

READ MORE

 

What is Django CMS?

Django CMS is a modern web publishing platform built with Django, the web application framework “for perfectionists with deadlines”.

django CMS offers out-of-the-box support for the common features you’d expect from a CMS, but can also be easily customized and extended by developers to create a site that is tailored to their precise needs.

Integrate Django applications painlessly; build sophisticated sites with easy-to-use tools.

$ pip install --upgrade virtualenv
$ virtualenv env
$ source env/bin/activate
(env) $ pip install djangocms-installer
(env) $ djangocms mysite

Features

  • Frontend-editing 
  • Reusable plugins 
  • Flexible Plugin Architecture 
  • Search Engine Optimization 
  • Editorial workflow 
  • Permission Management 
  • Versioning 
  • Multisites 
  • Multilanguage 
  • Applications (Apps) 
  • Media Asset Manager (MAM) 

Video for Django CMS

https://www.youtube.com/watch?v=NbsRVfLCE1U
READ MORE

What is Nagare?

Nagare is a free and open-source web framework for developing web applications in Stackless Python. This allows web applications to be developed in much the same way as desktop applications, for rapid application development.

Nagare is a components based framework: a Nagare application is a composition of interacting components each one with its own state and workflow kept on the server. 

Each component can have one or several views that are composed to generate the final web page. This enables the developers to reuse or write highly reusable components easily and quickly.

Nagare is also a continuation-based web framework which enables to code a web application like a desktop application, with no need to split its control flow in a multitude of controllers and with the automatic handling of the back, fork and refresh actions from the browser.

Its component model and use of the continuation come from the famous Seaside Smalltalk framework.

Furthermore, Nagare integrates the best tools and standard from the Python world. For example:

  • WSGI: binds the application to several possible publishers,
  • lxml: generates the DOM trees and brings to Nagare the full set of XML features (XSL, XPath, Schemas …),
  • setuptools: installs, deploys and extends the Nagare framework and the Nagare applications too,
  • PEAK Rules: generic methods are heavily used in Nagare, to associate views to components, to define security rules, to translate Python code to Javascript
  • WebOb: for its Request and Response Objects.

 

READ MORE
...