Changeset 1013 for trunk

Timestamp:

11/25/11 12:21:30 (6 months ago)

Author:

pierre

Message:

Start working with the new pyNN version since the common module is isolated. Fix som bugs in the nest/connectors.py file, and try to start to make NeMo? compatible with

Location:

trunk

Files:

• setup.py (modified) (1 diff)
• src/nemo/__init__.py (modified) (2 diffs)
• src/nemo/standardmodels/cells.py (modified) (1 diff)
• src/nest/connectors.py (modified) (5 diffs)

trunk/setup.py

@@ -44 +44 @@
     package_dir={'pyNN': 'src'},
     packages = ['pyNN','pyNN.nest', 'pyNN.pcsim', 'pyNN.neuron', 'pyNN.nineml',
-                'pyNN.brian', 'pyNN.common', 'pyNN.recording',
-                'pyNN.standardmodels', 'pyNN.descriptions',
+                'pyNN.brian','pyNN.nemo', 'pyNN.common',
+                'pyNN.recording', 'pyNN.standardmodels', 'pyNN.descriptions',
                 'pyNN.nest.standardmodels', 'pyNN.pcsim.standardmodels',
-                'pyNN.neuron.standardmodels', 'pyNN.brian.standardmodels'],
+                'pyNN.neuron.standardmodels', 'pyNN.brian.standardmodels', 'pyNN.nemo.standardmodels'],
     package_data = {'pyNN': ['neuron/nmodl/*.mod', "descriptions/templates/*/*"]},
     author = "The PyNN team",

trunk/src/nemo/__init__.py

@@ -120 +120 @@
                 player = SpikeSourcePoisson.spike_player(**params)
                 setattr(idx, 'player', player)
-                simulator.state.net.add_neuron(int(idx), 0., 0., -80., 0, 0., -80., 0.)
+                ntype = simulator.state.net.add_neuron_type('Input')
+                simulator.state.net.add_neuron(ntype, int(idx))
         elif isinstance(celltype, SpikeSourceArray):
             ### For the moment, we model spike_source_array and spike_source_poisson
@@ -131 +132 @@
                 player = SpikeSourceArray.spike_player(**params)
                 setattr(idx, 'player', player)
-                simulator.state.net.add_neuron(int(idx), 0., 0., -80., 0., -0., -80, 0)
+                ntype = simulator.state.net.add_neuron_type('Input')
+                simulator.state.net.add_neuron(ntype, int(idx))
+        elif isinstance(celltype, cells.IF_curr_exp):
+            init = celltype.default_initial_values
+            print params
+            for idx in self.all_cells:
+                ntype = simulator.state.net.add_neuron_type('IF_curr_exp')
+                simulator.state.net.add_neuron(ntype, int(idx),
+                        params['v_rest'],
+                        params['cm'],
+                        params['tau_m'],
+                        params['t_refrac'],
+                        params['tau_syn_E'],
+                        params['tau_syn_I'],
+                        params['i_offset'],
+                        params['v_reset'],
+                        params['v_thresh'],
+                        init['v'], 0., 0., 1000.)
         else:            
-            ## Currently, we only have the Izhikevitch model...
             init = celltype.default_initial_values
+            ntype = simulator.state.net.add_neuron_type('Izhikevich')
             for idx in self.all_cells:
-                simulator.state.net.add_neuron(int(idx), params['a'], params['b'], params['c'], params['d'], init['u'], init['v'], 0.)
+                simulator.state.net.add_neuron(ntype, int(idx), params['a'], params['b'], params['c'], params['d'], init['u'], init['v'], 0.)
        
         self._mask_local = numpy.ones((n,), bool) # all cells are local

trunk/src/nemo/standardmodels/cells.py

@@ -134 +134 @@
     pass
 
-class IF_curr_exp(ModelNotAvailable):
-    pass
+class IF_curr_exp(cells.IF_curr_exp):
+    """Leaky integrate and fire model with fixed threshold and
+    decaying-exponential post-synaptic current. (Separate synaptic currents for
+    excitatory and inhibitory synapses."""
+    translations = build_translations(
+        ('v_rest',     'v_rest'),
+        ('v_reset',    'v_reset'),
+        ('cm',         'cm'), 
+        ('tau_m',      'tau_m'),
+        ('tau_refrac', 't_refrac'),
+        ('tau_syn_E',  'tau_syn_E'),
+        ('tau_syn_I',  'tau_syn_I'),
+        ('v_thresh',   'v_thresh'),
+        ('i_offset',   'i_offset'), 
+    )
+
+    indices = {
+            'v_rest' : 0,
+            'cm' : 1,
+            'tau_m' : 2,
+            't_refrac' : 3,
+            'tau_syn_E' : 4,
+            'tau_syn_I' : 5,
+            'i_offset' : 6,
+            'v_reset' : 7,
+            'v_thresh' : 8
+        }
+    initial_indices = {'v' : 0}
+
 
 class IF_cond_alpha(ModelNotAvailable):

trunk/src/nest/connectors.py

@@ -7 +7 @@
 $Id$
 """
-from pyNN import random, core
+from pyNN import random, core, errors
 from pyNN.connectors import Connector, AllToAllConnector, FixedProbabilityConnector, \
                             DistanceDependentProbabilityConnector, FixedNumberPreConnector, \
@@ -109 +109 @@
         for count, tgt in enumerate(projection.post.local_cells):
             connector._probabilistic_connect(tgt, 1)
-            self.progression(count)        
+            self.progression(count, projection._simulator.state.mpi_rank)
     
 
@@ -139 +139 @@
                 proba = proba.astype(float)           
             connector._probabilistic_connect(tgt, proba, self.n_connections)
-            self.progression(count)
+            self.progression(count, projection._simulator.state.mpi_rank)
 
 
@@ -156 +156 @@
                 n = self.n
             connector._probabilistic_connect(tgt, 1, n)
-            self.progression(count)
-
+            self.progression(count, projection._simulator.state.mpi_rank)
+
+
+class FastFromListConnector(FromListConnector):
+    
+    __doc__ = FromListConnector.__doc__
+    
+    def connect(self, projection):
+        """Connect-up a Projection."""
+        idx     = numpy.argsort(self.conn_list[:, 1])
+        self.targets    = numpy.unique(self.conn_list[:, 1]).astype(int)
+        self.candidates = projection.pre.all_cells
+        self.conn_list  = self.conn_list[idx]
+        self.progressbar(len(self.targets))        
+        count = 0
+        left  = numpy.searchsorted(self.conn_list[:, 1], self.targets, 'left')
+        right = numpy.searchsorted(self.conn_list[:, 1], self.targets, 'right')
+        for tgt, l, r in zip(self.targets, left, right):
+            sources = self.conn_list[l:r, 0].astype(int)
+            weights = self.conn_list[l:r, 2]
+            delays  = self.conn_list[l:r, 3]
+        
+            srcs     = projection.pre.all_cells[sources]
+            try:
+                srcs     = projection.pre.all_cells[sources]
+            except IndexError:
+                raise errors.ConnectionError("invalid sources index or indices")
+            try:
+                tgt    = projection.post.all_cells[tgt]
+            except IndexError:
+                raise errors.ConnectionError("invalid target index %d" %tgt)
+            ## We need to exclude the non local cells. Fastidious, need maybe
+            ## to use a convergent_connect method, instead of a divergent_connect one
+            #idx     = eval(tests)
+            #projection.connection_manager.connect(src, tgts[idx].tolist(), weights[idx], delays[idx])
+            projection.connection_manager.convergent_connect(srcs.tolist(), tgt, weights, delays)
+            self.progression(count, projection._simulator.state.mpi_rank)
+            count += 1
 
 class FastSmallWorldConnector(SmallWorldConnector):
@@ -172 +208 @@
             proba  = proba_generator.get(connector.N, connector.distance_matrix).astype(float)
             connector._probabilistic_connect(tgt, proba, self.n_connections, self.rewiring)
-            self.progression(count)
+            self.progression(count, projection._simulator.state.mpi_rank)