Subclassing Front¶
Every NeuroDevSim model requires at least one Front subclass definition with its own manage_front method:
class MyFront(Front):
def manage_front(self,constellation):
This is how specific models are defined, as explained in Getting started. In this subsection additional, optional aspects of subclassing are described in detail.
Additional attributes¶
It may be useful to store data that is needed to control the behavior of fronts or that introduces new dynamics to the model as an additional Front attribute. There are strict rules on using additional attributes for a Front subclass:
They need to be declared in the subclass definition using a special syntax shown below.
They are all typed using data types defined in the Python ctypes library or as NeuroDevSim classes. Trying to set an attribute to a value of a different type will throw an error.
They are all fixed size: no dictionaries, lists, strings,…
They will be present in every instance of the subclass, instance specific attributes are not supported (see the Important dos and don’ts). So even if the attribute is functionally needed in only one or a few instances of the subclass, it will be present in all of them. Consider carefully whether an extra attribute is really needed.
The syntax to specify additional attributes is unusual, it is specific to how Python supports memory shared arrays. They are defined as a list of tuples, with each tuple containing the attribute name and and a ctypes data type:
[('attribute1_name', ctypes_data_type), ('attribute2_name', ctypes_data_type)]
For example, to define an int attribute foo:
class MyFront(Front):
_fields_ = Front._fields_ + [('foo', c_int)]
and a second float attribute bar:
class MyFront(Front):
_fields_ = Front._fields_ + [('foo', c_int), ('bar', c_double)]
Different from standard Python behavior, it is important to respect the types of the additional attributes. The following code will cause a ‘TypeError: int expected instead of float’ because foo has been declared to be an integer:
class MyFront(Front):
_fields_ = Front._fields_ + [('foo', c_int)]
def manage_front(self,constellation):
...
# this will cause an error because 1.5 is a float
self.foo = 1.5
# this is fine
self.foo = int(1.5)
The following ctypes are imported in NeuroDevSim and can be used in attribute definitions:
ctypes type |
Python type |
|---|---|
c_bool |
bool |
c_char |
1-character bytes object |
c_short |
int |
c_int |
int |
c_long |
int |
c_double |
float |
The differences between c_short, c_int and c_long is in the number of bytes used (2, 4, 8 bytes on 64 bit operating systems) and the corresponding range of numbers encoded (−32,768 through 32,767; −2,147,483,648 through 2,147,483,647 and -9,223,372,036,854,775,808 through 9,223,372,036,854,775,807). Additional ctypes data types exist and can be imported by knowledgeable users.
In addition, one can also use a class type, which has been defined elsewhere. At present we recommend using only the predefined Point class or the ID class, which is used to identify Fronts and Substrate:
class MyFront(Front):
_fields_ = Front._fields_ + [('friend', ID)]
def manage_front(self,constellation):
...
# get a list of fronts with neuron_name beginning with "friend"
my_friends = self.get_fronts(constellation,what='name',name="friend",returnID=True)
# store the first front returned as an ID
self.friend = my_friends[0][0]
Using Fronts or Substrate as an additional attribute is not recommended because it makes a copy of the original instance and this copy will not be updated. Moreover, for Front the specific subclass would have to be specified.
Attribute initialization¶
Additional attributes are automatically initialized to a zero value, in the case of a Structure all its fields are set to zero. An additional attribute can be given a starting value immediately after its instantiation by add_child:
class MyFront(Front):
_fields_ = Front._fields_ + [('foo', c_int)]
def manage_front(self,constellation):
...
new_front = self.add_child(constellation,new_pos)
new_front.foo = 7
...