Euroscipy 2017 tutorial requirements

Tutorials are hands-on sessions, therefore you should bring your own laptop.

You can either install a selection of packages necessary for the tutorials (using pip or conda, or your OS package manager), or install a distribution which ships a large number of Python packages (which is recommended if you're using Windows), such as Anaconda or Canopy.

Required packages for all tutorials

• Python
• NumPy
• SciPy
• Matplotlib
• Jupyter notebook

Intro tutorials

NumPy

For the NumPy tutorial, it will be sufficient to install the packages mentioned on the top of this page.

Teaching material is available from Github, but some last-minute changes are likely.

Git

Apart from Python you will need the Git software to follow the examples yourself. See git-scm.com/downloads for binaries for Mac OS X and Windows as well as installation instructions for Linux and Solaris. There is no need to install a Git GUI as all examples will be demonstrated on the command line.

The tutorial (presently still under development) can be obtained from Github. You can get the LaTeX source and the images by downloading a zip file from Github or by cloning the repository once you have Git installed:

  git clone https://github.com/gertingold/euroscipy-git-tutorial.git

After the tutorial has been given, a PDF file of the presentation will be made available on Github.

Matplotlib

The basic packages listed on top of this page will be sufficient. We work along the SciPy lecture note but will devlope or own notebooks.

Advanced tutorials

scikit-image

If you installed Anaconda or Canopy, you already have scikit-image installed. Otherwise, see http://scikit-image.org/docs/stable/install.html for installation instructions.

scikit-image version 0.12 or 0.13 is preferable.

Test code

    >>> from skimage import io, data
    >>> camera = data.camera()
    >>> io.imshow(camera)

Cython

To follow the Cython tutorial, please have Cython 0.25 or later (pip-)installed, in addition to the general packages listed above.

The Jupyter notebook(s) used in the tutorial will be made available at

http://kurs.behnel.de/espy2017/