Merge 3709339 into 44d1500

NeuralEnsemble · Sep 5, 2017 · 3e1aaad · 3e1aaad
2 parents 44d1500 + 3709339
commit 3e1aaad
Show file tree

Hide file tree

Showing 2 changed files with 11 additions and 7 deletions.
diff --git a/pyNN/nest/projections.py b/pyNN/nest/projections.py
@@ -318,23 +318,18 @@ def _get_attributes_as_list(self, names):
                 nest_names.append('target')
             else:
                 nest_names.append(name)
-        values = nest.GetStatus(self.nest_connections, nest_names)
+        values = numpy.array(nest.GetStatus(self.nest_connections, nest_names))  # ought to preserve int type for source, target
         if 'weight' in names:  # other attributes could also have scale factors - need to use translation mechanisms
-            values = numpy.array(values)  # ought to preserve int type for source, target
             scale_factors = numpy.ones(len(names))
             scale_factors[names.index('weight')] = 0.001
             if self.receptor_type == 'inhibitory' and self.post.conductance_based:
                 scale_factors[names.index('weight')] *= -1  # NEST uses negative values for inhibitory weights, even if these are conductances
             values *= scale_factors
-            values = values.tolist()
         if 'presynaptic_index' in names:
-            values = numpy.array(values)
             values[:, names.index('presynaptic_index')] -= self.pre.first_id
-            values = values.tolist()
         if 'postsynaptic_index' in names:
-            values = numpy.array(values)
             values[:, names.index('postsynaptic_index')] -= self.post.first_id
-            values = values.tolist()
+        values = values.tolist()
         for i in xrange(len(values)):
             values[i] = tuple(values[i])
         return values

diff --git a/test/system/test_nest.py b/test/system/test_nest.py
@@ -185,5 +185,14 @@ def test_tsodyks_markram_synapse():
     assert_arrays_equal(tau_psc, numpy.arange(0.2, 0.7, 0.1))
 
 
+def test_issue505():
+    sim = pyNN.nest
+    sim.setup()
+    p = sim.Population(2, sim.IF_cond_exp())
+    projection = sim.Projection(p, p, sim.AllToAllConnector(), sim.TsodyksMarkramSynapse(U=0.543))
+    U = projection.get('U', format='list', with_address=False)
+    assert_equal(U, [0.543, 0.543, 0.543, 0.543])
+
+
 if __name__ == '__main__':
     data = test_random_seeds()