Parameters

At their simplest, individual parameters consist of a name and a value. The value is either a simple type such as a numerical value or a string, or an aggregate of such simple types, such as a set, list or array.

However, we may also wish to specify the physical dimensions of the parameter, i.e., its units, and the range of permissible values.

It is also often useful to specify an object that generates numerical values or strings, such as a random number generator, and treat that object as the parameter.

To support all these uses, we define the Parameter and ParameterRange classes, and various subclasses of the ParameterDist abstract class, such as GammaDist, NormalDist and UniformDist.

The Parameter class

Here are some examples of creating Parameter objects:

>>> i1 = Parameter(3)
>>> f1 = Parameter(6.2)
>>> f2 = Parameter(-65.3, "mV")
>>> s1 = Parameter("hello", name="message_to_the_world")

The parameter name, units, value and type can be accessed as attributes:

>>> i1.value
3
>>> f1.type
<type 'float'>
>>> f2.units
'mV'
>>> s1.name
'message_to_the_world'

Parameter objects are not hugely useful at the moment. The units are not used for checking dimensional consistency, for example, and Parameter objects are not drop-in replacements for numerical values - you must always use the value attribute to access the value, whereas it might be nice to define, for example, a class IntegerParameter which was a subclass of the built-in int type.

The ParameterRange class

When investigating the behaviour of a model or in doing sensitivity analysis, it is often useful to run a model several times using a different value for a certain parameter each time (also see the iter_range_keys() and similar methods of the ParameterSet class, below). The ParameterRange class supports this. Some usage examples:

>>> tau_m_range = ParameterRange([10.0, 15.0, 20.0], "ms", "tau_m")
>>> tau_m_range.name
'tau_m'
>>> tau_m_range.next()
10.0
>>> tau_m_range.next()
15.0
>>> [2*tau_m for tau_m in tau_m_range]
[20.0, 30.0, 40.0]

The ParameterDist classes

As with taking parameter values from a series or range, it is often useful to pick values from a particular random distribution. Three classes are available: UniformDist, GammaDist and NormalDist. Examples:

>>> ud = UniformDist(min=-1.0, max=1.0)
>>> gd = GammaDist(mean=0.5, std=1.0)
>>> nd = NormalDist(mean=-70, std=5.0)
>>> ud.next()
array([-0.56342352])
>>> gd.next(3)
array([ 0.04061142,  0.05550265,  0.23469344])
>>> nd.next(2)
array([-76.18506715, -68.71229944])