Specify, run, and analyze emergent experiments in Python.

Emergent is a neural network simulator. http://grey.colorado.edu/emergent/index.php/Main_Page

This was mainly used for my own simulations. While I think this has the potential to be useful to others, I think it would require more work.

The main action is in emergent.py which has the base class one would inherit from to define a new experiment. That could look as follows:

@pools.register('my_experiment')
class MyExperiment(emergent.Base):
   def __init__(self):
       self.proj_name = 'my_proj' # emergent proj file
       self.prefix = '/home/wiecki/emergent' # directory
       self.tags = ['intact', 'lesioned']

       self.flag['tag'] = 'intact'
       self.flag['lesioned'] = False
       self.flags.append(self.flag)

       self.flag['tag'] = 'lesioned'
       self.flag['lesioned'] = True
       self.flags.append(self.flag)

   def analyze(self):
       for tag in self.tags:
           pylab.plot(self.data[tag]['minus_cycles'])

You could then run this model as follows:

mpirun -n 40 python emergent.py --run --mpi --batches 20 --group my_experiment

This would then launch 40 emergent processes (20 batches * 2 conditions (intact and lesioned)).

If you call it afterwards with --analyze set, it would aggregate all log-files into one numpy array and call the analyze() method defined above. I also recoded the groupby function of emergent. However, only later I learned about a package called pandas which does this and much more, much better. So in the future I might change the data structure to be a pandas dataframe instead of a numpy array which gives it more flexibility.

Copyright (c) 2012, Thomas Wiecki All rights reserved.

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