Support for SpyNNaker8

examples/cmp_australia.py

@@ -3,7 +3,7 @@
 #
 # "Using Stochastic Spiking Neural Networks on SpiNNaker to Solve Constraint Satisfaction Problems"
 # Submitted to the journal Frontiers in Neuroscience| Neuromorphic Engineering
-#-----------------------------------------------------------------------------------------------------------------------
+# -----------------------------------------------------------------------------------------------------------------------
 """
 Coloring map problem for the map of australia.
 
@@ -23,63 +23,75 @@
 
 The coloring uses 3 colors red -> 0, green->1, blue->2
 """
-import spynnaker7.pyNN as p                                           # simulator
+import spynnaker8 as p  # simulator
 import argparse
+
 try:
     from spinnaker_csp import CSP, plot_entropy
 except:
     import sys
     import os
-    sys.path.append(os.getcwd()+'/..')
+
+    sys.path.append(os.getcwd() + "/..")
     from spinnaker_csp import CSP, plot_entropy
 
-parser = argparse.ArgumentParser(description='''This script creates a spiking neural network representation of the 
+parser = argparse.ArgumentParser(
+    description="""This script creates a spiking neural network representation of the
                                               problem of 3-colouring the map of australia whose dynamics implements a
-                                              stochastic search for satisfiability.''')
-parser.add_argument("--i_offset",   help='offset current',                   type=float, default=0.7)
-parser.add_argument("--phase",      help='phase shift for depression',       type=float, default=100)
-parser.add_argument("--w_inh",      help='inhibition weight for depression', type=float, default=-1.5)
-parser.add_argument("-n", "--name", help='name to use for naming files',     type=str,   default='world_cmp')
+                                              stochastic search for satisfiability."""
+)
+parser.add_argument("--i_offset", help="offset current", type=float, default=0.7)
+parser.add_argument(
+    "--phase", help="phase shift for depression", type=float, default=100
+)
+parser.add_argument(
+    "--w_inh", help="inhibition weight for depression", type=float, default=-1.5
+)
+parser.add_argument(
+    "-n", "--name", help="name to use for naming files", type=str, default="world_cmp"
+)
 args = parser.parse_args()
 
 name = args.name
 
-bordering_states = [{'source':1, 'target':2},
-                     {'source':1, 'target':6},
-                     {'source':2, 'target':3},
-                     {'source':2, 'target':6},
-                     {'source':3, 'target':4},
-                     {'source':3, 'target':6},
-                     {'source':4, 'target':5},
-                     {'source':4, 'target':6},
-                     {'source':5, 'target':6}]
+bordering_states = [
+    {"source": 1, "target": 2},
+    {"source": 1, "target": 6},
+    {"source": 2, "target": 3},
+    {"source": 2, "target": 6},
+    {"source": 3, "target": 4},
+    {"source": 3, "target": 6},
+    {"source": 4, "target": 5},
+    {"source": 4, "target": 6},
+    {"source": 5, "target": 6},
+]
 
 # SpiNNaker setup.
 run_time = 30000
 p.setup(timestep=1.0)
 # Build spiking neural network.
-map = CSP(variables_number = 7, domain_size = 3, constraints=bordering_states, core_size=10)
-map.cell_params_lif['i_offset']= 0.2
-map.clues_inhibition=False
+map = CSP(variables_number=7, domain_size=3, constraints=bordering_states, core_size=10)
+map.cell_params_lif["i_offset"] = 0.2
+map.clues_inhibition = False
 map.build_domains_pops()
 map.build_stimulation_pops(1, stim_ratio=1, full=True)
 map.build_dissipation_pops()
-map.internal_inhibition(w_range=[-1.2, -1.5], d_range=[1.0,1.0])
-map.stimulate_cores(w_range=[0.05,  0.3], d_range=[1.0,1.0])
-map.depress_cores(w_range=[-1.5, -2.0], d_range=[1.0,1.0])
-map.apply_constraints(w_range=[ 1.2,  1.4], d_range=[1.0,1.2])
+map.internal_inhibition(w_range=[-1.2, -1.5], d_range=[1.0, 1.0])
+map.stimulate_cores(w_range=[0.05, 0.3], d_range=[1.0, 1.0])
+map.depress_cores(w_range=[-1.5, -2.0], d_range=[1.0, 1.0])
+map.apply_constraints(w_range=[1.2, 1.4], d_range=[1.0, 1.2])
 # Record spikes from variable populations.
 map.recording()
 # Run simulation.
 p.run(run_time)
 # Save recorded spikes.
-map.save('australia')
-map.report_network_params('report_cmp_australia')
+map.save("australia")
+map.report_network_params("report_cmp_australia")
 # End simulation.
 p.end()
 # Plot entropy.
-sol=plot_entropy('australia', 200, preprint=False)
+sol = plot_entropy("australia", 200, preprint=False)
 formatted = []
 for i in sol:
-    formatted.append('{{%d}}'%(i+1))
-print "".join(formatted)
+    formatted.append("{{%d}}" % (i + 1))
+print("".join(formatted))