Changeset 713 for trunk/src/pcsim/__init__.py
- Timestamp:
- 02/17/10 22:45:18 (2 years ago)
- Files:
-
- 1 modified
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trunk/src/pcsim/__init__.py (modified) (7 diffs)
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trunk/src/pcsim/__init__.py
r712 r713 15 15 16 16 import pyNN.random 17 from pyNN import common, recording, errors, space, __doc__17 from pyNN import common, recording, errors, space, core, __doc__ 18 18 from pyNN.pcsim import simulator 19 19 common.simulator = simulator … … 74 74 """Return a list of all the StandardCellType classes available for this simulator.""" 75 75 setup() 76 standard_cell_types = [obj for obj in globals().values() if isinstance(obj, type) and issubclass(obj, common.StandardCellType)]76 standard_cell_types = [obj for obj in globals().values() if isinstance(obj, type) and issubclass(obj, standardmodels.StandardCellType)] 77 77 for cell_class in standard_cell_types: 78 78 try: … … 261 261 # if type(source) != types.ListType and type(target) != types.ListType: 262 262 # connections = simulator.net.connect(source, target, syn_factory) 263 # if not co mmon.is_listlike(connections):263 # if not core.is_listlike(connections): 264 264 # connections = [connections] 265 265 # return connections … … 304 304 e.g., (10,10) will create a two-dimensional population of size 10x10. 305 305 cellclass should either be a standardized cell class (a class inheriting 306 from common.StandardCellType) or a string giving the name of the306 from standardmodels.StandardCellType) or a string giving the name of the 307 307 simulator-specific model that makes up the population. 308 308 cellparams should be a dict which is passed to the neuron model … … 336 336 cellclass = getattr(pypcsim, cellclass) 337 337 self.celltype = cellclass 338 if issubclass(cellclass, common.StandardCellType):338 if issubclass(cellclass, standardmodels.StandardCellType): 339 339 self.celltype = cellclass(cellparams) 340 340 self.cellfactory = self.celltype.simObjFactory … … 582 582 ## 583 583 # handle synapse dynamics 584 if co mmon.is_listlike(method.weights):584 if core.is_listlike(method.weights): 585 585 w = method.weights[0] 586 586 elif hasattr(method.weights, "next"): # random distribution … … 588 588 else: 589 589 w = method.weights 590 if co mmon.is_listlike(method.delays):590 if core.is_listlike(method.delays): 591 591 d = min(method.delays) 592 592 elif hasattr(method.delays, "next"): # random distribution
