Modify generic LPU example to generate LPUs containing output graded …

…potential neurons.
neurokernel · Dec 7, 2015 · 2bc7d94 · 2bc7d94
1 parent f0e67b1
commit 2bc7d94
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Showing 2 changed files with 6 additions and 4 deletions.
diff --git a/examples/generic/data/gen_generic_lpu.py b/examples/generic/data/gen_generic_lpu.py
@@ -53,7 +53,7 @@ def create_lpu(file_name, lpu_name, N_sensory, N_local, N_proj):
             name = t+"_"+str(i)
 
             # All local neurons are graded potential only:
-            if t != 'local' or np.random.rand() < 0.5:
+            if t != 'local' and np.random.rand() < 0.5:
                 G.node[idx] = {
                     'model': 'LeakyIAF',
                     'name': name+'_s',
@@ -93,7 +93,7 @@ def create_lpu(file_name, lpu_name, N_sensory, N_local, N_proj):
                 # ports (which are not represented as separate nodes):
                 if t == 'proj':
                     G.node[idx]['selector'] = '/%s/out/gpot/%s' % (lpu_name, str(gpot_out_id))
-                    gpot_out_id = 1
+                    gpot_out_id += 1
 
             idx += 1
 

diff --git a/examples/generic/visualize_output.py b/examples/generic/visualize_output.py
@@ -20,9 +20,11 @@
 nx.readwrite.gexf.GEXF.convert_bool = {'false':False, 'False':False,
                                        'true':True, 'True':True}
 
-# Select IDs of projection neurons:
+# Select IDs of spiking projection neurons:
 G = nx.read_gexf('./data/generic_lpu.gexf.gz')
-neu_proj = sorted([int(k) for k, n in G.node.items() if n['name'][:4] == 'proj'])
+neu_proj = sorted([int(k) for k, n in G.node.items() if \
+                   n['name'][:4] == 'proj' and \
+                   n['spiking']])
 
 V = vis.visualizer()
 V.add_LPU('./data/generic_input.h5', LPU='Sensory')