Getting Started with Scikit-beam

Importing scikit-beam

In order to encourage consistency amongst users in importing and using Scikit-beam functionality, we have put together the following guidelines.

Since most of the functionality in Scikit-beam resides in sub-packages, importing scikit-beam as:

>>> import skbeam

is not very useful. Instead, it is best to import the desired sub-package with the syntax:

>>> from skbeam import subpackage  

For example, to access the correlation-related functionality, you can import skbeam.core.correlation with:

>>> from skbeam.core import correlation as corr
>>> g2 = corr.multi_tau_auto_corr(5, 3, labels, img_seq)

Note that for clarity, and to avoid any issues, we recommend to never import any skbeam functionality using *, for example:

>>> from skbeam.core.correlation import *  # NOT recommended

Some components of Scikit-Beam started off as standalone packages (e.g. PyFITS, PyWCS), so in cases where Scikit-Beam needs to be used as a drop-in replacement, the following syntax is also acceptable:

>>> from skbeam.io import foo as bar

Getting started with subpackages

Warning

This is not implemented in skbeam yet

Because different subpackages have very different functionality, further suggestions for getting started are in the documentation for the subpackages, which you can reach by browsing the sections listed in the Scientific Algorithms.

Or, if you want to dive right in, you can either look at docstrings for particular a package or object, or access their documentation using the find_api_page() function. For example, doing this:

>>> from skbeam import find_api_page
>>> find_api_page(corr.multi_tau_auto_corr)  

Will bring up the documentation for the multi_tau_auto_corr() in your browser.