Generate seeds before adding graph components

This means that if you do set the seed for the top-level
Network in your model, adding or removing graphs will not
affect the seed for the Ensembles in your model.

Fixes #855

Author: tcstewar

nengo_gui/page.py

@@ -12,6 +12,7 @@
 import nengo_gui
 import nengo_gui.user_action
 import nengo_gui.config
+import nengo_gui.seed_generation
 
 
 class PageSettings(object):
@@ -434,6 +435,12 @@ def build(self):
         with self.lock:
             self.building = True
 
+            # set all the seeds so that creating components doesn't affect
+            #  the neural model itself
+            seeds = nengo_gui.seed_generation.define_all_seeds(self.model)
+            for obj, s in seeds.items():
+                obj.seed = s
+
             # modify the model for the various Components
             for c in self.components:
                 c.add_nengo_objects(self)
@@ -456,6 +463,10 @@ def build(self):
                 line = nengo_gui.exec_env.determine_line_number()
                 self.error = dict(trace=traceback.format_exc(), line=line)
 
+            # set the defined seeds back to None
+            for obj in seeds:
+                obj.seed = None
+
             self.stdout += exec_env.stdout.getvalue()
 
             if self.sim is not None:

nengo_gui/seed_generation.py

@@ -0,0 +1,32 @@
+import nengo
+import nengo.utils.numpy as npext
+import numpy as np
+
+
+def define_all_seeds(net, seeds=None):
+    if seeds is None:
+        seeds = {}
+
+    if net.seed is None:
+        if net not in seeds:
+            # this only happens at the very top level
+            seeds[net] = np.random.randint(npext.maxint)
+        rng = np.random.RandomState(seed=seeds[net])
+    else:
+        rng = np.random.RandomState(seed=net.seed)
+
+    # let's use the same algorithm as the builder, just to be consistent
+    sorted_types = sorted(net.objects, key=lambda t: t.__name__)
+    for obj_type in sorted_types:
+        for obj in net.objects[obj_type]:
+            # generate a seed for each item, so that manually setting a seed
+            #  for a particular item doesn't change the generated seed for
+            #  other items
+            generated_seed = rng.randint(npext.maxint)
+            if obj.seed is None:
+                seeds[obj] = generated_seed
+
+    for subnet in net.networks:
+        define_all_seeds(subnet, seeds)
+
+    return seeds