Changeset 398
- Timestamp:
- 06/26/08 14:05:31 (5 months ago)
- Files:
-
- trunk/src/common.py (modified) (2 diffs)
- trunk/src/nest2/connectors.py (modified) (1 diff)
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trunk/src/common.py
r397 r398 245 245 dims = diff2.shape 246 246 diff2 = diff2.flatten() 247 #diff2 = numpy.minimum(diff2, periodic_boundaries[i]-diff2)248 diff2 = numpy.array(map(min, ((x_i, y_i) for (x_i, y_i) in zip(diff2, periodic_boundaries[i]-diff2))))247 diff2 = numpy.minimum(diff2, periodic_boundaries[i]-diff2) 248 #diff2 = numpy.array(map(min, ((x_i, y_i) for (x_i, y_i) in zip(diff2, periodic_boundaries[i]-diff2)))) 249 249 diff2 = diff2.reshape(dims) 250 250 diff2 **= 2 … … 1411 1411 raise 1412 1412 self.d_expression = d_expression 1413 # We will use the numpy functions, so we need to parse the function 1414 # given by the user to look for some key function and add numpy 1415 # in front of them (or add from numpy import *) 1416 func = ['exp','log','sin','cos','cosh','sinh','tan','tanh'] 1417 for item in func: 1418 self.d_expression = self.d_expression.replace(item,"numpy.%s" %item) 1413 1419 self.allow_self_connections = allow_self_connections 1414 1420 self.mask = DistanceDependentProbabilityConnector.AXES[axes] trunk/src/nest2/connectors.py
r397 r398 136 136 self.scale_factor, self.offset, 137 137 periodic_boundaries) 138 func = eval("lambda d: %s" %self.d_expression) 139 distances[0] = func(distances[0]) 138 140 for post in postsynaptic_neurons: 139 func = eval("lambda d: %s" %self.d_expression)140 distances[0] = func(distances[0])141 141 #probabilities = map(func,distances[0]) 142 143 142 if self.allow_self_connections or pre != post: 144 143 # calculate the distance between the two cells :

