Requires Python 3.5. See http://nlesc.github.io/noodles/ for more information.

Install the following in a virtualenv:

pip install .

To enable Xenon for remote execution, Java must be installed, and Xenon can be installed with

pip install '.[xenon]'

If Java cannot be found (needed by Xenon), run

export JAVA_HOME="/usr/lib/jvm/default-java"  # or similar...

in your shell initialization script (like ~/.bashrc).

To enable the TinyDB based job database, run

pip install '.[prov]'

This is needed if you want to interrupt a running workflow and resume where you left of, or to reuse results over multiple runs.

To run unit tests, run

pip install '.[test]'
nosetests test

Some tests depend on the optional modules being installed. Those are skipped if if imports fail. If you want to test everything, make sure you have NumPy installed as well.

The prototype is very simple. It should support the definition of function objects that are manageable in the workflow engine and give output of the workflow as a graph. The only dependency of this prototype should be the graph plotting library: pygraphviz. To keep the interface clean, we avoid the use of Fireworks specific functionality at this point. The abstract concepts in this context are: workflow, node, link.

Questions:

• What does a developer adding functionality to the workflow engine need to know?
• How do we specify the surrounding context of functions in terms of types and monadic context?

The user should have it easy. From the spirit of wishful programming, we may give here some examples of how the user would use the workflow engine.

The developer has prepared some nice functions for the user:

@schedule
def f(a, b):
    return a+b

@schedule
def g(a, b):
    return a-b

@schedule
def h(a, b):
    return a*b

The user then uses these in a workflow:

u = f(5, 4)
v = g(u, 3)
w = g(u, 2)
x = h(v, w)

draw_graph("graph-example1.svg", x)

Resulting in the graph: