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 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
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).
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.
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,
Anaconda is a freemium open source distribution of the Python and R programming languages for large-scale data processing, predictive analytics, and scientific computing, that aims to simplify package management and deployment
The condacommand is the primary interface for managing installations of various packages. It can:
Query and search the Anaconda package index and current Anaconda installation.
Create new conda environments.
Install and update packages into existing conda environments.
Anaconda Cloud is where data scientists share their work. You can search and download popular Python and R packages and notebooks to jumpstart your data science work.
Anaconda is the world’s most popular Python data science platform. Anaconda, Inc. continues to lead open source projects like Anaconda, NumPy and SciPy that form the foundation of modern data science. Anaconda’s flagship product, Anaconda Enterprise, allows organizations to secure, govern, scale and extend Anaconda to deliver actionable insights that drive businesses and industries forward.
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.
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
Custom fields for issues, time-entries, projects and users
SCM integration (SVN, CVS, Git, Mercurial and Bazaar)
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.
- 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.