Changeset 408

Timestamp:

07/10/08 12:45:18 (1 month ago)

Author:

pierre

Message:

Start to play with nest2.py in a distributed simulation framework. Add a method to have an estimate of the firing rate in a distributed context (frame/thread). More generally, after having done some code profiling, I noticed that setting the values of the parameters was very slow in nest2, due to the fact that in the common.py file, each time we want to set a parameter, we get all the native_parameters, translate them, and then only modify a single value. I've changed therefore the common.py file and the results speed up greatly the building of networks. Moreover, checking the delays in the connector should be an option. Because it also slows down notably the loops. What about a tag check_connectionc, added to the Connector object, that will be used to check either weights/delays but that can also skip it if the user know what he's doing ?

Files:

• trunk/src/common.py (modified) (6 diffs)
• trunk/src/nest1/__init__.py (modified) (1 diff)
• trunk/src/nest2/__init__.py (modified) (5 diffs)
• trunk/src/nest2/connectors.py (modified) (3 diffs)

trunk/src/common.py

@@ -125 +125 @@
-    def set_parameters(self, **parameters):
-        """Set cell parameters, given as a sequence of parameter=value arguments."""
-
-
+#
+#
-        if self.is_standard_cell():
-all_
+
-        self.set_native_parameters(parameters)
-    
@@ -315 +315 @@
-        parameters = cls.checkParameters(parameters, with_defaults=False)
-        native_parameters = {}
-
+
+
+
-            pname = D['translated_name']
-            try:
@@ -1228 +1230 @@
-    """Base class for Connector classes."""
-    
-
+, check_connections=False
-        self.w_index = 0 # should probably use a generator
-        self.d_index = 0 # rather than storing these values
-        self.weights = weights
-        self.delays = delays
+        self.check_connections = check_connections
-        if delays is None:
-            self.delays = get_min_delay()
@@ -1252 +1255 @@
-        else:
-            raise Exception("weights is of type %s" % type(self.weights))
-        #assert numpy.all(weights>=0), "Weight values must be positive"
-        self.w_index += N
-        return weights
@@ -1268 +1270 @@
-        else:
-            raise Exception("delays is of type %s" % type(self.delays))
-
-
-
-
+
+
+
+
+
-        self.d_index += N
-        return delays
@@ -1406 +1409 @@
-            d = 1e12; assert 0 <= eval(d_expression), eval(d_expression)
-        except ZeroDivisionError:
-
-
+
-        self.d_expression = d_expression
-        # We will use the numpy functions, so we need to parse the function

trunk/src/nest1/__init__.py

@@ -1263 +1263 @@
-        print "\n------- Projection description -------"
-        print "Projection %s from %s [%d cells] to %s [%d cells]" %(self.label, self.pre.label, len(self.pre.cell),self.post.label, len(self.post.cell))
-Connector used is %s
+\t| Connector
-        if isinstance(self._method.weights,RandomDistribution):
-are drawn from %s distribution with parameter
-
-
+: drawn from %s distribution with param
+
+ 
-        if isinstance(self._method.delays,RandomDistribution):
-are drawn from %s distribution with parameter
-
-
-
+: drawn from %s distribution with param
+
+ 
+ 
-        print "\t --> %d connections have been created for this projection" %len(self)
-
-
-
-
-
+
+
+
-        
-        print "---- End of Projection description -----"

trunk/src/nest2/__init__.py

@@ -10 +10 @@
-from pyNN.random import *
-from pyNN import recording
-
+, re
-from math import *
-from pyNN.nest2.cells import *
@@ -515 +515 @@
-            os.remove(nest_filename)
-    if gather and len(node_list) > 1:
+        #if rank() == 0:
-        raise Exception("gather not yet implemented")
-    return merged_filename
@@ -571 +572 @@
-            self.cell_local = self.cell[numpy.array(nest.GetStatus(self.cell.tolist(),'local'))]
-            self.first_id = self.cell.reshape(self.size,)[0]
-
-            for id in self.cell:
-                id.parent = self
@@ -864 +864 @@
-        Returns the mean number of spikes per neuron.
-        """
-
+
+
+
+
+
+
+
+
+
+
+
+
-        n_rec = len(self.recorders['spikes'].recorded)
-        return float(n_spikes)/n_rec
@@ -1361 +1372 @@
-        print "\n------- Projection description -------"
-        print "Projection %s from %s [%d cells] to %s [%d cells]" %(self.label, self.pre.label, len(self.pre.cell),self.post.label, len(self.post.cell))
-Connector used is %s : 
+\t| Connector : %s
-        if isinstance(self._method.weights,RandomDistribution):
-are drawn from %s distribution with parameter
-
-
+: drawn from %s distribution with param
+
+ 
-        if isinstance(self._method.delays,RandomDistribution):
-are drawn from %s distribution with parameter
+: drawn from %s distribution with param
-        else:
-          print "\t| Delays: ", self._method.delays
-
-
-
-
-
+
+
+
-        dict = nest.GetConnections([self.pre.cell.flat[0]], self._plasticity_model)[0]
-        for i in xrange(len(self._targets)):

trunk/src/nest2/connectors.py

@@ -87 +87 @@
-    
-    def connect(self, projection):
-        postsynaptic_neurons = projection.post.cell_loca
-projection.post.size
+       postsynaptic_neurons = projection.post.cel
+len(postsynaptic_neurons)
-        for pre in projection.pre.cell.flat:
-            if projection.rng:
@@ -120 +120 @@
-            print "Periodic boundaries activated and set to size ", periodic_boundaries
-        postsynaptic_neurons = projection.post.cell.flatten() # array
-
+
-        # what about NativeRNG?
-        if projection.rng:
@@ -130 +130 @@
-        else:
-            rng = numpy.random
-esynaptic_neurons
+ojection.pre.cell.flat
-            # We compute the distances from the post cell to all the others
-            distances = common.distances(pre, projection.post, self.mask,