The documentation has copious examples and helpful pointers to other functions which may be useful (See also:). When Joe is implementing code, he finds the interactive help invaluable, provided by typing any object or function with a '?' after it in the interactive prompt. The tutorial is clearly written, and covers the basics of array computation and 2D graphing. The tutorial covers the basics of PyLab, explaining some of the philosophy. Joe clicks the tutorial link, which his terminal automatically pops up in a browser. PyLab notices that it is the first time it is run, and suggests he read the tutorial, and provides a link. Joe is happy that there was no hassle over dependencies on his older university computer, and that installing directly into his home directory (he does not have root access on the university computers) is not a problem. After downloading, the program installs with no hassles, and Joe can launch Pylab by typing 'pylab' and pressing enter in a terminal.The page also has a small number of big, clear links to promotional materials (screencasts, testimonials), documentation, and community information (how to get involved).
He notes that must have determined he is running Linux automatically.
#Pyhton pylab module download#
He finds a page with minimal clutter, showing a couple pictures of PyLab, and a direct download link to a binary for his operating system. He finds PyLab as the first search result on Google He hears about PyLab from a friend, who recommends it as an alternative. Joe is frustrated with Matlab, because he finds it is slow when running his neural network experiments. people without root access or spare time.īuild Process - Make the build process simple for the combination of the five core components (Python, NumPy, SciPy, Matplotlib, and IPython). Installation - Make the installation process trivial, especially for, e.g.
#Pyhton pylab module how to#
Unfortunately, for those who are not already familiar with Python and the intricacies of how to build your own Python environment, or for those not familiar with the details of how there are conflicting names exported by different modules, or how the best list of NumPy examples is found on the wiki in a non-obvious place (and that the docstrings are not the best documentation), or that the speed of linear algebra operators is dependent on a carefully compiled combination of LAPACK, ATLAS, and Goto BLAS, or a host of other reasons (some outlined below), the picture is not nearly so rosy.ĭocumentation - Dramatically enhance the standard documentation by consistently adopting the new DocstringStandard for all functions in the NumPy API.ĪPI Consistency - Create an official API for the PyLab system such that there is an official way to import the PyLab packages, and such that there are not multiple functions with very similar names in different packages. The philosophy behind this vision is to consider Rails and Ruby while Ruby was somewhat popular beforehand, it was Rails which propelled it to the forefront.Īt the moment, the current combination of Python, NumPy, SciPy, Matplotlib, and IPython provide a compelling environment for numerical analysis and computation. To make PyLab an easy to use, well packaged, well integrated, and well documented, numeric computation environment so compelling that instead of having people go to Python and discovering that it is suitable for numeric computation, they will find PyLab first and then fall in love with Python. See the following post for further discussion of the difference between the vision for a new PyLab expressed on this page, and the existing pylab package which is part of matplotlib: By integrating consensus from mailing list discussions, I will refine and polish this vision and form a plan of action such that the community can move the numpy+scipy+ipython+matplotlib ensemble closer to the vision outlined below. Type '?' for help.Currently this page reflects the vision of KeirMierle, and not necessarily the community as a whole. IPython 7.13.0 - An enhanced Interactive Python. Type 'copyright', 'credits' or 'license' for more information It pops up the plot in a separate window: $ ipython3 The following shows how the numpy and matplotlib functions can be used directly. iPython is the kernel used in Jupyter Notebook, or it can be run standalone. iPython has a shortcut for that, anyway: %pylabĪlthough iPython does actually allow you to enter Python block structures interactively, I wouldn't use it for that I think it's at its best where it's possible to do a quick test with single-line entries.
Generally, you'd only do that at the iPython interactive console if you wanted to do an immediate test rather than create a Python script.