Changeset 322

Timestamp:

06/04/08 16:38:40 (5 years ago)

Author:

apdavison

Message:

Assorted minor bug-fixes related to parallel simulations

Location:

trunk/src

Files:

• common.py (modified) (6 diffs)
• nest2/__init__.py (modified) (4 diffs)
• nest2/connectors.py (modified) (2 diffs)
• neuron/__init__.py (modified) (4 diffs)
• neuron/connectors.py (modified) (1 diff)
• random.py (modified) (3 diffs)

trunk/src/common.py

@@ -100 +100 @@
 
     def __getattr__(self, name):
-        try:
-            val = self.get_parameters()[name]
-        except KeyError:
-            raise NonExistentParameterError(name, self.cellclass)
+        if name in IDMixin.non_parameter_attributes:
+            val = self.__getattribute__(name)
+        else:
+            try:
+                val = self.get_parameters()[name]
+            except KeyError:
+                raise NonExistentParameterError(name, self.cellclass)
         return val
     
@@ -368 +371 @@
         'tau_m'      :  20.0,   # Membrane time constant in ms.
         'tau_refrac' :   0.0,   # Duration of refractory period in ms. 
-        'tau_syn_E'  :   0.3,   # Rise time of the excitatory synaptic alpha function in ms.
+        'tau_syn_E'  :   0.5,   # Rise time of the excitatory synaptic alpha function in ms.
         'tau_syn_I'  :   0.5,   # Rise time of the inhibitory synaptic alpha function in ms.
         'i_offset'   :   0.0,   # Offset current in nA
@@ -624 +627 @@
 
 def get_min_delay():
-    pass
+    return 1e12
+
+def get_max_delay():
+    return -1e12
 
 def num_processes():
@@ -1164 +1170 @@
     # --- Methods for writing/reading information to/from file. ----------------
     
+    def getWeights(self, format='list', gather=True):
+        """
+        Possible formats are: a list of length equal to the number of connections
+        in the projection, a 2D weight array (with zero or None for non-existent
+        connections).
+        """
+        return _abstract_method(self)
+    
+    def getDelays(self, format='list', gather=True):
+        """
+        Possible formats are: a list of length equal to the number of connections
+        in the projection, a 2D delay array (with None or 1e12 for non-existent
+        connections).
+        """
+        return _abstract_method(self)
+    
     def saveConnections(self, filename, gather=False):
         """Save connections to file in a format suitable for reading in with the
@@ -1235 +1257 @@
         else:
             raise Exception("delays is of type %s" % type(self.delays))
-        assert numpy.all(delays >= get_min_delay()), "Delay values must be greater than the minimum delay %g" %get_min_delay()
+        assert numpy.all(delays >= get_min_delay()), \
+               "Delay values must be greater than or equal to the minimum delay %g. The smallest delay is %g." % (get_min_delay(), delays.min())
+        assert numpy.all(delays <= get_max_delay()), \
+               "Delay values must be less than or equal to the maximum delay %s. The largest delay is %s" % (get_max_delay(), delays.max())                                                                                              
         self.d_index += N
         return delays
@@ -1360 +1385 @@
             'xy': [0,1], 'yz': [1,2], 'xz': [0,2], 'xyz': None, None: None}
     
-    def __init__(self, d_expression, axes=None, scale_factor=1.0, offset=0.,
+    def __init__(self, d_expression, axes=None, scale_factor=1.0, offset=0.0,
                  periodic_boundaries=False, allow_self_connections=True,
                  weights=0.0, delays=None):

trunk/src/nest2/__init__.py

@@ -285 +285 @@
 def get_max_delay():
     return nest.GetSynapseDefaults('static_synapse')['max_delay']
+common.get_max_delay = get_max_delay
 
 def num_processes():
@@ -479 +480 @@
 
     if compatible_output:
-        if gather == False or nest.Rank() == 0: # if we gather, only do this on the master node
+        if gather == False:
+            user_filename += '.%d' % rank()
+        if gather == False or rank() == 0: # if we gather, only do this on the master node
             recording.write_compatible_output(nest_filename, user_filename,
                                               population, get_time_step())
@@ -711 +714 @@
             raise Exception('rset() not yet implemented for NativeRNG')
         else:
-            rarr = rand_distr.next(n=len(self.cell_local))
-            assert len(rarr) == len(self.cell_local)
+            #rarr = rand_distr.next(n=len(self.cell_local))
+            rarr = rand_distr.next(n=self.size)
+            print rank(), self.cell_local[:5], self.cell_local[-5:], len(rarr), len(self.cell_local)
+            assert len(rarr) >= len(self.cell_local), "The length of rarr (%d) must be greater than that of cell_local (%d)" % (len(rarr), len(self.cell_local))
+            rarr = rarr[:len(self.cell_local)]
             for cell,val in zip(self.cell_local, rarr):
                 setattr(cell, parametername, val)
@@ -1269 +1275 @@
         """Save connections to file in a format suitable for reading in with the
         'fromFile' method."""
+        if gather == True:
+            raise Exception("saveConnections(gather=True) not yet supported")
+        else:
+            filename += '.%d' % rank()
         f = open(filename, 'w', DEFAULT_BUFFER_SIZE)
         weights = []; delays = []

trunk/src/nest2/connectors.py

@@ -5 +5 @@
 
 from pyNN import common
-from pyNN.nest2.__init__ import nest, is_number
+from pyNN.nest2.__init__ import nest, is_number, get_max_delay, get_min_delay
 import numpy
 # note that WDManager is defined in __init__.py imported here, then imported
@@ -79 +79 @@
             return projection.pre.size
         else:
-            raise Exception("Connection method not yet implemented for the case where presynaptic and postsynaptic Populations have different sizes.")
+            raise Exception("OneToOneConnector does not support presynaptic and postsynaptic Populations of different sizes.")
     
 class FixedProbabilityConnector(common.FixedProbabilityConnector):
     
     def connect(self, projection):
-        postsynaptic_neurons  = projection.post.cell.flatten()
+        postsynaptic_neurons = projection.post.cell_local.flatten()
         npost = projection.post.size
-        for pre in projection.pre.cell.flat:
+        for pre in projection.pre.cell:
             if projection.rng:
                 rarr = projection.rng.uniform(0, 1, (npost,)) # what about NativeRNG?
             else:
                 rarr = numpy.random.uniform(0, 1, (npost,))
+            if len(rarr) > len(postsynaptic_neurons):
+                rarr = rarr[:len(postsynaptic_neurons)]
             target_list = numpy.compress(numpy.less(rarr, self.p_connect), postsynaptic_neurons).tolist()
             # if self connections are not allowed, check whether pre and post are the same

trunk/src/neuron/__init__.py

@@ -328 +328 @@
             'pc = new ParallelContext()',
             'dt = %f' % timestep,
+            'tstop = 0',
             'min_delay = %g' % min_delay,
             'create dummy_section',
@@ -407 +408 @@
     if not running:
         running = True
-        hoc_commands += ['tstop = 0',
-                         'local_minimum_delay = pc.set_maxstep(10)',
+        hoc_commands += ['local_minimum_delay = pc.set_maxstep(10)',
                          'tmp = finitialize()',]
         hoc_execute(hoc_commands,"--- run() ---")
@@ -752 +752 @@
             raise common.InvalidDimensionsError
         return coords
+
+    def index(self, n):
+        """Return the nth cell in the population (Indexing starts at 0)."""
+        if hasattr(n, '__len__'):
+            n = numpy.array(n)
+        return self.fullgidlist[n]
 
     def get(self, parameter_name, as_array=False):
@@ -1166 +1172 @@
         self.hoc_label = self.label.replace(" ","_")
         if not rng:
-            self.rng = numpy.random.RandomState()
+            self.rng = NumpyRNG()
         hoc_commands = ['objref %s' % self.hoc_label,
                         '%s = new List()' % self.hoc_label]

trunk/src/neuron/connectors.py

@@ -93 +93 @@
                 hoc_commands += self.singleConnect(projection, src, tgt, w, d)
         else:
-            raise Exception("Method '%s' not yet implemented for the case where presynaptic and postsynaptic Populations have different sizes." % sys._getframe().f_code.co_name)
+            raise Exception("OneToOneConnector does not support presynaptic and postsynaptic Populations of different sizes.")
         return hoc_commands
 

trunk/src/random.py

@@ -16 +16 @@
     print "Warning: GSL random number generators not available"
 import time
+import logging
 
 # The following two functions taken from
@@ -60 +61 @@
         self.rng = numpy.random.RandomState()
         if self.seed:
+            if not parallel_safe:
+                self.seed += rank # ensure different nodes get different sequences
+                if rank != 0:
+                    logging.warning("Changing the seed to %s on node %d" % (self.seed, rank))
             self.rng.seed(self.seed)
         else:
@@ -89 +94 @@
             raise ValueError, "The sample number must be positive"
         if self.parallel_safe and self.num_processes > 1:
-            # strip out the random numbers that should be used on other processors
+            # strip out the random numbers that should be used on other processors.
+            # This assumes that the first neuron in a population is always created on
+            # the node with rank 0, and that neurons are distributed in a round-robin
+            # This assumption is not true for NEST
             rarr = rarr[numpy.arange(self.rank, len(rarr), self.num_processes)]
         return rarr