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Component Library solution for developers
About NetworkX
NetworkX is a Python package for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks. Generators for classic graphs, random graphs, and synthetic networks. Additional benefits from Python include fast prototyping, easy to teach, and multi-platform. Network structure and analysis measures.
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Free Version available.
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NetworkX
networkx.org
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Scapy
Scapy is a powerful interactive packet manipulation program. It is able to forge or decode packets of a wide number of protocols, send them on the wire, capture them, match requests and replies, and much more. It can easily handle most classical tasks like scanning, tracerouting, probing, unit tests, attacks, or network discovery (it can replace hping, 85% of nmap, arpspoof, arp-sk, arping, tcpdump, tshark, p0f, etc.). It also performs very well at a lot of other specific tasks that most other tools can’t handle, like sending invalid frames, injecting your own 802.11 frames, combining technics (VLAN hopping+ARP cache poisoning, VOIP decoding on WEP encrypted channel), etc. Scapy runs natively on Linux, Windows, OSX, and on most Unixes with libpcap. The same code base now runs natively on both Python 2 and Python 3. Scapy development uses the Git version control system. Scapy reference repository is hosted on GitHub.
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PyQtGraph
PyQtGraph is a pure-python graphics and GUI library built on PyQt/PySide and NumPy. It is intended for use in mathematics/scientific/engineering applications. Despite being written entirely in python, the library is very fast due to its heavy leverage of NumPy for number crunching and Qt's GraphicsView framework for fast display. PyQtGraph is distributed under the MIT open-source license. Basic 2D plotting in interactive view boxes. Line and scatter plots. Data can be panned/scaled by mouse. Fast drawing for real-time data display and interaction. Displays most data types (int or float; any bit depth; RGB, RGBA, or luminance). Functions for slicing multidimensional images at arbitrary angles (great for MRI data). Rapid update for video display or real-time interaction. Image display with interactive lookup tables and level control. Mesh rendering with isosurface generation. Interactive viewports rotate/zoom with mouse. Basic 3D scenegraph for easier programming.
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Pylons
The Pylons web framework is designed for building web applications and sites in an easy and concise manner. They can range from as small as a single Python module, to a substantial directory layout for larger and more complex web applications. Pylons comes with project templates that help boot-strap a new web application project, or you can start from scratch and set things up exactly as desired. A framework to make writing web applications in Python easy. Utilizes a minimalist, component-based philosophy that makes it easy to expand on. Harness existing knowledge about Python. Extensible application design. Fast and efficient, an incredibly small per-request call stack provides top performance. Uses existing and well-tested Python packages. Pylons 1.0 series is stable and production-ready but in maintenance-only mode. The Pylons Project now maintains the Pyramid web framework for future development. Pylons 1.0 users should strongly consider using Pyramid for their next project.
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NumPy
Fast and versatile, the NumPy vectorization, indexing, and broadcasting concepts are the de-facto standards of array computing today. NumPy offers comprehensive mathematical functions, random number generators, linear algebra routines, Fourier transforms, and more. NumPy supports a wide range of hardware and computing platforms, and plays well with distributed, GPU, and sparse array libraries. The core of NumPy is well-optimized C code. Enjoy the flexibility of Python with the speed of compiled code. NumPy’s high level syntax makes it accessible and productive for programmers from any background or experience level. NumPy brings the computational power of languages like C and Fortran to Python, a language much easier to learn and use. With this power comes simplicity: a solution in NumPy is often clear and elegant.
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