Re: [spyder] Spyder install questions.

[David Verelst]

As mentioned by Todd, installing the different python packages on Linux shouldn't be hard. The important ones (numpy, matplotlib, scipy, etc) are all available in the repositories of all the major distributions. And dependencies are taken care of by the package manager so you don't need to worry about that (at least not for the mainstream packages we are considering here). It might be a little more work if you want to have a complete clone of the Python(x,y) environment you have on Windows. I haven't checked up on Python(x,y) for a while so I don't know exactly what's in there; it will be more than just numpy, scipy, matplotlib and spyder. But than again, it will strongly depend on what you exactly want to do with your Python environment. Although a bit comprehensive, I can surely recommend following literature: Python Scientific Lecture Notes http://scipy-lectures.github.com/ Numpy Guide for Matlab users http://www.scipy.org/NumPy_for_Matlab_Users The Python g-node also organises a yearly summer school on scientific programming with Python. All the lecture presentations are available on the website. Advanced Scientific Programming in Python (St Andrews 2011 Course) https://python.g-node.org/wiki/schedule MIT open course ware 6.189 A Gentle Introduction to Programming Using Python http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-189-a-gentle-introduction-to-programming-using-python-january-iap-2008/ How to Think Like a Computer Scientist: Learning with Python 2nd Edition http://openbookproject.net/thinkCSpy/ There are also some books on scientific Python programming published by Springer: http://www.springerlink.com/content/978-3-642-02474-0 http://www.springerlink.com/content/978-3-540-73915-9 And this list goes on...google, duckduckgo, and others are your friends ;-) Hope this helps you anyway forward with you Python adoption! Regards, David On 05/12/11 19:10, todd rme wrote: On Mon, Dec 5, 2011 at 5:29 PM, Aaron <johnson.aaron.anth...@gmail.com> wrote: Hello everyone, Our mechanical engineering department at our university is currently working to replace MATLAB with a Python Suite and I've recently discovered Spyder along the way.  In order for this project to go forward, we need to have install procedures prepared for Windows, Ubuntu, and Mac OS X.  The situation with Windows is quite simple with the install of python(x,y), but this masks a lot of details that need to be addressed when doing an Ubuntu install (have not tried Mac yet).  I was hoping that someone in this group could please tell me the bare minimums that need to be installed for Spyder to operate as a suitable MATLAB replacement.  For example, using the Synaptic Package Manager in Ubuntu 11.10, I was able to complete a Spyder install. Afterwards, I opened the program and began using commands listed in the numpy tutorial.  The question is, have all dependencies been taken care of, allowing me to begin running Spyder after installing via Synaptic Package Manager? Or do I need to follow a more detailed procedure?  In case you need to know, I have moderate experience in Ubuntu and I am a decent MATLAB user trying to break through to the Python world.  I don't have a learned Python community in my department and any help that you could give would be great. Unlike MatLab, python isn't really monolothic, which means you can have as many or as few capabilities as you want. For a Matlab replacement, absolutely essential modules would be: numpy - this provides a matlab-like multidimensional array and some other matlab-like capabilities like basic signal processing. matplotlib - this provides matlab-like 2D plotting (and experimental 3D plotting). You can't use this without numpy. To make a more complete matlab replacement, these would be very useful: scipy - provides a lot of advanced scientific and engineering functions and tools, built on top of numpy. Numpy provides the equivalent of the core matlab installation, while scipy provides the equivalent of many of the more specialized matlab toolboxes sympy - provides symbolic manipulation, like maple or mathematica pylint - a source code style and error checker, useful if you use mlint in matlab mpi4py - if you are using supercomputer, clusters, or other obscenely parallel systems or use mpi, this provides a good python interface for them. rpy - provides a python interface for the R statistics language. There are other modules in these categories (for instance there are a bunch of plotting packages), and many more specialized modules. If we knew more about what you wanted to do, it might make it easier. -Todd -- You received this message because you are subscribed to the Google Groups "spyder" group. To post to this group, send email to spyderlib@googlegroups.com. To unsubscribe from this group, send email to spyderlib+unsubscr...@googlegroups.com. For more options, visit this group at http://groups.google.com/group/spyderlib?hl=en.