The built-in script interpreter

Conedy does not only ship with a Python module, but also with its own script interpreter.

Why use the script interpreter?

The Python module and the script interpreter both provide everything, Conedy has to offer. Beyond this, however, the script interpreter’s functionality is rather limited, whereas Python offers a vast amount of libraries. On the other hand, the interpreter can easily be linked statically, which can be very useful, if you want to distribute computations onto a cluster. Furthermore supports the Condor job management system (see below).

So, if you are only using Conedy on a single computer, you will usually want to use its Python module. If you want your calculations to run on other computers but a full install of Conedy on all of them is impossible or significantly troublesome, you should take a look at the script interpreter—even more, if your job management software is Condor.

Differences to the Python module

We illustrate the differences between both ways of using Conedy with an example Python script and its corresponding script for use with the script interpreter:

#! /usr/bin/env python

import conedy as co

N =

for p in [i*0.1 for i in range(11)]:
        for k in range(4,11):
                N.torusNearestNeighbors(100, 100, k, co.lorenz(), co.staticWeightedEdge(0.1))
                N.randomizeStates(co.lorenz(), co.gaussian(0.0, 2.0), co.gaussian(0.0, 2.0), co.gaussian(0.0, 2.0))

                co.set("samplingTime", 1.0)
                co.set("odeStepType", "gsl_rk8pd")

                N.observeTime("sw_%G_%G" % (p,k))
                N.observeSum("sw_%G_%G" % (p,k), co.component(0))



(This example script generates small-world networks based on a 100×100 torus of lorenz oscillators with rewiring probabilities p and mean degrees k. P is varied between 0.1 and 1.0, k between 4 and 10. The dynamics on each of these networks is integrated for 10000 time units and the sum over all nodes of the first component is written to a file, whose name contains the current values of p and k.)

The following script performs the same operations, if run with conedy:

network N;

for (double p = 0.0; p <= 1.0; p += 0.1)
        for (int k = 4; k <= 10; k++)
                N.torusNearestNeighbors(100, 100, k, lorenz(), staticWeightedEdge(0.1));
                N.randomizeStates(lorenz, gaussian(0.0, 2.0), gaussian(0.0, 2.0), gaussian(0.0, 2.0));

                samplingTime = 1.0;
                odeStepType = "gsl_rk8pd";

                N.observeTime("sw_" + p + "_" + k);
                N.observeSum("sw_" + p + "_" + k, component(0));



The following differences can be spotted:

  • Commands are separated by semicola instead of newlines.
  • The name-space declaration co. has vanished.
  • Loops are done in C-style, except for a semicolon at the end of each loop.
  • Calls of co.set have been replaced by direct assignments.
  • Strings are handled differently.

Note that most commands in the Reference have an example for use with the script interpreter. If you are familiar with Bison/Flex grammar files, you may also look into the files Parser.yy and Scanner.ll of Conedy’s source code. Although the built-in interpreter supports some C-constructs, it may still be limited in some cases.

Vectorising Loops

In the above example, it is not neccessary to compute the bodies of the inner loop in a specific order. Instead each one may be issued independently; the loop is vectorisable. With a Conedy script this can easily be done in the following way:

  • Replace the for of the loop you want to vectorise by vectorFor.
  • Pass the numbers of the iteration, you want to compute, as an additional argument to the conedy script interpreter.

In the above example both loops are vectorisable. If you replace both occurences of for by vectorFor, you can issue the second iteration of the inner loop in the first iteration of the outer loop with:

conedy 0 1

assuming, that the script is stored in Note the zero-based enumeration.

At the moment, Conedy only supports to vectorise two nested loops. Note, however, that you can still use a regular loop in the innermost vectorised loop.

Having vectorised your loops, distributed computing is quite straightforward, since only the conedy executable is needed to run such a script.


Condor is a job management system developed at the Computer Science Department of the University of Wisconsin.

conedyCondor is a tool, that automatically generates a DAG file from a script with vectorized loops (see above). All you need to do to distribute computations is calling this file with condor_submit_dag.

In addition to vectorFor, conedyCondor also interpretes the command chainFor, which causes the bodies of the respective loop to be processed one after another—but possibly on different machines. “Communication” between these different iterations has to happen via files, however.

conedyCondor is not installed by default; you need to add condor to the todo list in your config.h and than recompile conedy.

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