About CubicWeb ?

What is CubicWeb ?

CubicWeb is a semantic web application framework, licensed under the LGPL, that empowers developers to efficiently build web applications by reusing components (called cubes ) and following the well known object-oriented design principles.
Cubic Web Solutions was founded in 2011, a pioneer of modern Open Source Internet solutions, started with a vision of providing their customers, a better quality solutions, and today we lead the world in Web/Software Development in open source technology, with an experience of working with clients all over the world to change the way we execute and communicate.

Features

an engine driven by the explicit data model of the application,
a query language named RQL similar to W3C’s SPARQL,
a selection+view mechanism for semi-automatic XHTML/XML/JSON/text generation,
a library of reusable components (data model and views) that fulfill common needs,
the power and flexibility of the Python programming language,
the reliability of SQL databases, LDAP directories, Subversion and Mercurial for storage backends.

The framework is entirely driven by a data model. Once the data model is defined, one gets a functional web application and can further customize the views. A cube is a reusable component defining specific features.

A cube is a software component made of three parts:

its data model (schema),
its logic (entities) and
its user interface (views).

A cube can use other cubes as building blocks and assemble them to provide a whole with richer functionalities than its parts.

Video for Cubic

Small Introduction about aiohttp in python?

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

Small Overview about Mezzanine?

What is Mezzanine?

Mezzanine is a powerful, consistent, and flexible content management platform. Built using the Django framework, Mezzanine provides a simple yet highly extensible architecture that encourages diving in and hacking on the code. Mezzanine is BSD licensed and supported by a diverse and active community.

In some ways, Mezzanine resembles tools such as Wordpress, providing an intuitive interface for managing pages, blog posts, form data, store products, and other types of content. But Mezzanine is also different. Unlike many other platforms that make extensive use of modules or reusable applications, Mezzanine provides most of its functionality by default. This approach yields a more integrated and efficient platform.

Features

Hierarchical page navigation
Save as draft and preview on site
Scheduled publishing
Drag-and-drop page ordering
WYSIWYG editing
In-line page editing
Drag-and-drop HTML5 forms builder with CSV export
SEO friendly URLs and metadata
E-commerce / Shopping cart module (Cartridge)
Configurable dashboard widgets
Blog engine
Tagging
Free Themes, and a Premium Themes Marketplace
User accounts and profiles with email verification
Translated to over 35 languages
Sharing via Facebook or Twitter
Multi-lingual sites
Custom templates per page or blog post
Twitter Bootstrap integration
API for custom content types
Search engine and API

Mezzanine is an open source project managed using both the Git and Mercurial version control systems.

Video for Mezzanine blogging

https://www.youtube.com/watch?v=3I5nrcsy7RI

Small Introduction About Seaborn?

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

Small Overview about Redmine?

What is Redmine?
Redmine is a flexible project management web application. Written using the Ruby on Rails framework, it is cross-platform and cross-database.

Redmine is open source and released under the terms of the GNU General Public License v2 (GPL).

It is a cross-platform, cross-database, and open source tool that also has issue-tracking features. Users can manage multiple projects and subprojects, and have access to many planning, tracking, and documenting features available from similar commercial products.

Redmine has a news area where members can publish news items. It allows the creation of documents, such as user documentation or technical documentation, which can be downloaded by others. A Files module is a table that lists all uploaded files and its details.

Users can easily create project wikis with the help of a toolbar. Other features include custom fields for creating additional information, and a Repository to view a given revision and the latest commits. The software can be configured to receive emails for issue creation and comments. It also supports particular versions of different databases, such as MySQL, PostgreSQL, MS SQL Server, and SQLite. API and plug-ins are also available.

Features

Multiple projects support
Flexible role based access control
Flexible issue tracking system
Gantt chart and calendar
News, documents & files management
Feeds & email notifications
Per project wiki
Per project forums
Time tracking
Custom fields for issues, time-entries, projects and users
SCM integration (SVN, CVS, Git, Mercurial and Bazaar)
Issue creation via email
Multiple LDAP authentication support
User self-registration support
Multilanguage support
Multiple databases support

Video for Redmine Introduction

https://www.youtube.com/watch?v=sAobJ1kmyqA

Small Overview about WSGI?

What is WSGI?

WSGI is the Web Server Gateway Interface.

It is a specification that describes how a web server communicates with web applications, and how web applications can be chained together to process one request.

WSGI is a specification, laid out in PEP 333, for a standardized interface between Web servers and Python Web frameworks/applications.

The goal is to provide a relatively simple yet comprehensive interface capable of supporting all (or most) interactions between a Web server and a Web framework. (Think "CGI" but programmatic rather than I/O based.)

An additional goal is to support "middleware" components for pre- and post-processing of requests: think gzip, recording, proxy, load-balancing.

Main Features

- WSGI gives you flexibility.

Application developers can swap out web stack components for others. For example, a developer can switch from Green Unicorn to uWSGI without modifying the application or framework that implements WSGI. From PEP 3333:

- WSGI servers promote scaling.

Serving thousands of requests for dynamic content at once is the domain of WSGI servers, not frameworks. WSGI servers handle processing requests from the web server and deciding how to communicate those requests to an application framework's process. The segregation of responsibilities is important for efficiently scaling web traffic.

Video for WSGI

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

Small Discussion About Statsmodels ?

What is Statsmodels ?

Statsmodels is a Python package that allows users to explore data, estimate statistical models, and perform statistical tests. Statsmodels is built on top of the numerical libraries NumPy and SciPy, integrates with Pandas for data handling and uses Patsy for an R-like formula interface.

Statsmodels is part of the scientific Python stack that is oriented towards data analysis, data science and statistics. Statsmodels is built on top of the numerical libraries NumPy and SciPy, integrates with Pandas for data handling and uses Patsy[3] for an R-like formula interface. Graphical functions are based on the Matplotlib library. Statsmodels provides the statistical backend for other Python libraries. Statmodels in free software released under the Modified BSD (3-clause) license.

Features

Linear regression models:
Mixed Linear Model with mixed effects and variance components
GLM: Generalized linear models with support for all of the one-parameter exponential family distributions
Bayesian Mixed GLM for Binomial and Poisson
GEE: Generalized Estimating Equations for one-way clustered or longitudinal data
Discrete models:
Multivariate:
Nonparametric statistics: Univariate and multivariate kernel density estimators
Datasets: Datasets used for examples and in testing
Statistics: a wide range of statistical tests
Imputation with MICE, regression on order statistic and Gaussian imputation
Mediation analysis
Tools for reading Stata .dta files, but pandas has a more recent version
Table output to ascii, latex, and html
Miscellaneous models

Video for Statsmodels

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

Small Introduction About PyShark?

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

About CubicWeb ?

References

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