Defer NESTML compilation to sim.setup()

Previously each nestml_cell_type() / nestml_synapse_type() call compiled and
installed a NEST module immediately, causing a collision when called more than
once (second nest.Install() fails; single module name overwrites earlier models).

Now both functions register a stub class in a module-level list and return it
immediately — no compilation occurs. sim.setup() calls _compile_and_resolve(),
which gathers all pending descriptions, invokes generate_nest_target() once for
all of them, calls nest.Install() once, then resolves each stub class in place
so it behaves identically to a native_cell_type() / native_synapse_type() class.

Calling nestml_cell_type() or nestml_synapse_type() after setup() raises
RuntimeError. Examples updated to register NESTML types before setup().

Author: apdavison

examples/nestml/stdp.py

@@ -41,36 +41,27 @@
 if options.debug:
     init_logging(None, debug=True)
 
-sim.setup(timestep=0.01, min_delay=1.0)
-
-import pyNN
-nest = pyNN.nest
-from pyNN.utility import init_logging
-
-# === Create the cell type from a NESTML definition
+# === Register the NESTML synapse (and co-generated neuron) before sim.setup()
 
 current_dir = os.path.dirname(os.path.abspath(__file__))
-input_path = os.path.join(current_dir, "wb_cond_exp_neuron.nestml")
-# celltype_cls = sim.nestml.nestml_cell_type("wb_cond_exp_neuron", input_path)
-
-synapse_type = sim.nestml.nestml_synapse_type("stdp_synapse", "stdp_synapse.nestml", "iaf_psc_exp_neuron.nestml")
+synapse_type = sim.nestml.nestml_synapse_type(
+    "stdp_synapse",
+    os.path.join(current_dir, "stdp_synapse.nestml"),
+    postsynaptic_neuron_nestml_description=os.path.join(current_dir, "iaf_psc_exp_neuron.nestml"),
+)
 post_cell_type = synapse_type.postsynaptic_cell_type
 
+sim.setup(timestep=0.01, min_delay=1.0)
 
-# === Build and instrument the network =======================================
-
-init_logging(logfile=None, debug=True)
 
-# nest.setup()
+# === Build and instrument the network =======================================
 
-p2 = nest.Population(10, nest.SpikeSourcePoisson())
-p1 = nest.Population(10, post_cell_type())
+p2 = sim.Population(10, sim.SpikeSourcePoisson())
+p1 = sim.Population(10, post_cell_type())
 
-connector = nest.AllToAllConnector()
+connector = sim.AllToAllConnector()
 
-# stdp_params = {'Wmax': 50.0, 'lambda': 0.015, 'w': 0.001}
-stdp_params = {}
-prj = nest.Projection(p2, p1, connector, receptor_type='excitatory', synapse_type=synapse_type(**stdp_params))
+prj = sim.Projection(p2, p1, connector, receptor_type='excitatory', synapse_type=synapse_type())
 
 p1.record(["V_m", "I_syn_exc", "I_syn_inh"])
 

examples/nestml/wang_buzsaki_current_injection.py

@@ -40,10 +40,7 @@
 if options.debug:
     init_logging(None, debug=True)
 
-sim.setup(timestep=0.01, min_delay=1.0)
-
-
-# === Create the cell type from a NESTML definition
+# === Register the NESTML cell type (must happen before sim.setup())
 
 # note that the NESTML definition can also be stored in a separate file
 
@@ -141,6 +138,8 @@
 
 celltype_cls = sim.nestml.nestml_cell_type("wb_cond_exp", nestml_description)
 
+sim.setup(timestep=0.01, min_delay=1.0)
+
 # add some variability between neurons
 rng = NumpyRNG(seed=1309463846)
 rnd = lambda min, max: RandomDistribution("uniform", (min, max), rng=rng)

examples/nestml/wang_buzsaki_synaptic_input.py

@@ -41,15 +41,14 @@
 if options.debug:
     init_logging(None, debug=True)
 
-sim.setup(timestep=0.01, min_delay=1.0)
-
-
-# === Create the cell type from a NESTML definition
+# === Register the NESTML cell type (must happen before sim.setup())
 
 current_dir = os.path.dirname(os.path.abspath(__file__))
 input_path = os.path.join(current_dir, "wb_cond_exp_neuron.nestml")
 celltype_cls = sim.nestml.nestml_cell_type("wb_cond_exp_neuron", input_path)
 
+sim.setup(timestep=0.01, min_delay=1.0)
+
 # add some variability between neurons
 rng = NumpyRNG(seed=1309463846)
 rnd = lambda min, max: RandomDistribution("uniform", (min, max), rng=rng)

pyNN/nest/control.py

@@ -79,6 +79,8 @@ def setup(timestep=DEFAULT_TIMESTEP, min_delay=DEFAULT_MIN_DELAY,
     # Set min_delay and max_delay
     simulator.state.set_delays(min_delay, max_delay)
     nest.SetDefaults('spike_generator', {'precise_times': True})
+    from . import nestml
+    nestml._compile_and_resolve()
     return rank()