Nengo comes with a variety of templates: pre-built components that can be used to build your models. These are the various icons on the left side of the screen that can be dragged in to your model.
These components are defined in python/nef/templates. There is one file for each item, and the following example uses thalamus.py.
The file starts with basic information, including the full name (title) of the component, the text to be used in the interface (label), and an image to use as an icon. The image should be stored in /images/nengoIcons:
title='Thalamus' label='Thalamus' icon='thalamus.png'
Next, we define the parameters that should be set for the component. These can be strings (str), integers (int), real numbers (float), or checkboxes (bool). For each one, we must indicate the name of the parameter, the label text, the type, and the help text:
params=[ ('name','Name',str,'Name of thalamus'), ('neurons','Neurons per dimension',int,'Number of neurons to use'), ('D','Dimensions',int,'Number of actions the thalamus can represent'), ('useQuick', 'Quick mode', bool, 'If true, the same distribution of neurons will be used for each action'), ]
Next, we need a function that will test if the parameters are valid. This function will be given the parameters as a dictionary and should return a string containing the error message if there is an error, or not return anything if there is no error:
def test_params(net,p): try: net.network.getNode(p['name']) return 'That name is already taken' except: pass
Finally, we define the function that actually makes the component. This function will be passed in a nef.Network object that corresponds to the network we have dragged the template into, along with all of the parameters specified in the params list above. This script can now do any scripting calculations desired to build the model:
def make(net,name='Network Array', neurons=50, D=2, useQuick=True): thal = net.make_array(name, neurons, D, max_rate=(100,300), intercept=(-1, 0), radius=1, encoders=[], quick=useQuick) def addOne(x): return [x+1] net.connect(thal, None, func=addOne, origin_name='xBiased', create_projection=False)
The last step to make the template appear in the Nengo interface is to add it to the list in python/nef/templates/__init__.py.