top button
Flag Notify
    Connect to us
      Facebook Login
      Site Registration Why to Join

    Get Free Article Updates

Facebook Login
Site Registration

Small Overview about Redmine?

0 votes
58 views

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

 

posted Nov 29 by anonymous

  Promote This Article
Facebook Share Button Twitter Share Button Google+ Share Button LinkedIn Share Button Multiple Social Share Button


Related Articles

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

READ MORE

What is Dash?

Dash is a Python framework for building analytical web applications. No JavaScript required.

Written on top of Flask, Plotly.js, and React.js, Dash is ideal for building data visualization apps with highly custom user interfaces in pure Python. It's particularly suited for anyone who works with data in Python.

Through a couple of simple patterns, Dash abstracts away all of the technologies and protocols that are required to build an interactive web-based application. Dash is simple enough that you can bind a user interface around your Python code in an afternoon.

Dash apps are rendered in the web browser. You can deploy your apps to servers and then share them through URLs. Since Dash apps are viewed in the web browser, Dash is inherently cross-platform and mobile-ready.

Benefits

1) Lightweight 
   - Dash apps require very little boilerplate to get started: An app like this weighs in at just 40 lines of pure Python. Dash provides direct control
2) Direct Control
   - Dash provides a simple interface for tying UI controls, like sliders, dropdowns, and graphs, with your Python data analysis code. Dash is Composable and Modular
3) Completely Customizable
   - Every aesthetic element of a Dash app is customizable. Dash apps are built and published in the Web, so the full power of CSS is available.

The video for Dash in Python

https://www.youtube.com/watch?v=e4ti2fCpXMI​

READ MORE

What is PyTorch?

PyTorch is an open source machine learning library for Python, based on Torch, used for applications such as natural language processing. It is primarily developed by Facebook's artificial-intelligence research group, and Uber's "Pyro" software for probabilistic programming is built on it.

PyTorch is a python package that provides two high-level features:

  • Tensor computation (like numpy) with strong GPU acceleration
  • Deep Neural Networks built on a tape-based autodiff system

PyTorch has a unique way of building neural networks: using and replaying a tape recorder.

Most frameworks such as TensorFlow, Theano, Caffe and CNTK have a static view of the world. One has to build a neural network, and reuse the same structure again and again. Changing the way the network behaves means that one has to start from scratch.

With PyTorch, we use a technique called Reverse-mode auto-differentiation, which allows you to change the way your network behaves arbitrarily with zero lag or overhead. 

Our inspiration comes from several research papers on this topic, as well as current and past work such as autograd, autograd, Chainer, etc.

Video for PyTorch

https://www.youtube.com/watch?v=nbJ-2G2GXL0

READ MORE

What is Plone?

Plone CMS is an open source Content Management System for managing information and administering content. Plone is backed by Plone Foundation - international non-profit organization. The organization holds the copyright, and Plone Content Management System is available under a dual licensing scheme, GPL and a commercial license.

Plone Content Management System was founded in 1999 by Alan Runyan (USA), Alexander Limi (Norway) and Vidar Andersen (Norway). Plone has 200 core developers and more than 300 solution providers in 57 different countries.

Plone CMS is built on top of the Zope web application server and Zope's Content Management Framework, written in Python. Plone Content Management System is ideal as an intranet server, as a document publishing system and as a groupware tool for collaboration between separately located entities. A versatile software product like Plone Content Management System can be used in a myriad of ways. Plone works on top of Linux, Windows, Mac OSX, and other Unix variants.

Features:

  • ENTERPRISE INTEGRATION
  • FLEXIBLE WORKFLOWS
  • INDUSTRIAL STRENGTH SECURITY
  • LIMITLESS EXTENSIBILITY
  • ROBUST SCALABILITY

 

Main Benefits

Object-oriented navigation – Plone is an object-oriented system that uses folder-based navigation with human-readable URLs. Customizable navigation portlets offer flexible user guidance.

Search engine optimization – The compliance to web standards, as well as the automatic production of machine-readable sitemaps make Plone a search engine-optimized system.

Multilingual – Plone is designed for international use, featuring over 50 different languages, including Arabic, Hebrew and Chinese.

Internal search engine – An internal search engine, featuring advanced options facilitates finding specific information instantaneously. Various search engines (e. g. Solr GSA) can be plugged in via add-ons.

Social networking – Plone supports social networking by automatically generating feeds out of search results and folder contents. A wide range of extensions and add-on products integrate Plone into other social networks.

Accessibility – Plone is accessible and complies to WAI-AA standard and the U.S. Government Section 508. Since public institutions are legally obliged to offering barrier-free websites, Plone can perfectly assist on these efforts – including a barrier-free UI for editors as well.

 

Video for Plone CMS

https://www.youtube.com/watch?v=HSHE9d5gzFE

 

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 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

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
Contact Us
+91 9880187415
sales@queryhome.net
support@queryhome.net
#280, 3rd floor, 5th Main
6th Sector, HSR Layout
Bangalore-560102
Karnataka INDIA.
QUERY HOME
...