Welcome to Enthought Canopy! Canopy provides Python 2.7 and 3.5, with easy installation and updates via a graphical package manager of over 450 pre-built and tested scientific and analytic Python packages from the Enthought Python Distribution. These include NumPy, Pandas, SciPy, matplotlib, scikit-learn, and Jupyter / IPython. Enthought Canopy is best suites for scripting data analytical concepts. It has a wide range of data analytical libraries and also is good for data visualization. I would not recommend using Enthought Canopy only as an IDE, there may be better options available. If you're looking for a good data simulation & visualization package, Canopy it is.
Enthought Canopy is a Python for scientific and analytic computing distribution and analysis environment. It is available for free for academic users. It includes Python, an integrated development environment, and libraries in the SciPy Software Stack (NumPy, SciPy, Mathplotlib, Pandas, …).
Canopy is available for MacOS, Windows, and Linux from Enthought at https://store.enthought.com/downloads/. Installation is rather straightforward: simply download the installer for your operating system, run the installer and follow the prompts. Installers are available for Python 2.7 or 3.5. Be sure to download version 3.5 for this course. Some more detailed instructions are available at the following links:
When you run Canopy you will be presented with a welcome screen that will have large buttons that allow you to access the various features of the environment. These include:
The Mayavi project includes two related <em>packages</em> for 3-dimensional visualization:
These libraries operate at different levels of abstraction. TVTK manipulates visualization objects, while Mayavi lets you operate on your data, and then see the results. Most users either use the Mayavi user interface or program to its scripting interface; you probably don't need to interact with TVTK unless you want to create a new Mayavi module.
Mayavi seeks to provide easy and interactive visualization of 3-D data. It offers:
Additionally Mayavi is a reusable tool that can be embedded in your applications in different ways or combined with the Envisage application-building framework to assemble domain-specific tools.
TVTK wraps VTK objects to provide a convenient, Pythonic API, while supporting Traits attributes and NumPy/SciPy arrays. TVTK is implemented mostly in pure Python, except for a small extension module.Developers typically use TVTK to write Mayavi modules, and then use Mayavi to interact with visualizations or create applications.
The Mayavi application.
Last updated: Tue 21 November 2017