add/ adapt tests to run more binaires

spynnaker_integration_tests/test_external_devices/test_live_neuron_voltage.py

@@ -160,6 +160,8 @@ class TestLiveNeuronVoltage(BaseTestCase):
 
     def test_live_neuron_voltage(self) -> None:
         self.runsafe(live_neuron_voltage)
+        self.check_binaries_used(["external_device_lif_control.aplx",
+                                  "external_device_lif_control_neuron.aplx"])
 
 
 if __name__ == '__main__':

spynnaker_integration_tests/test_multi_call_examples/test_stdp_random_run.py

@@ -12,21 +12,26 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import numpy
 import pyNN.spiNNaker as sim
+
 from spinnaker_testbase import BaseTestCase
-import numpy
+from spynnaker.pyNN.models.abstract_pynn_model import AbstractPyNNModel
 
 
 class TestSTDPRandomRun(BaseTestCase):
     # Test that reset with STDP and using Random weights results in the
     # same thing being loaded twice, both with data generated off and on the
     # machine
 
-    def do_run(self) -> None:
+    def run_model(self, model: AbstractPyNNModel) -> None:
+        """
+        Runs the test with the requested model
+        """
         sim.setup(1.0)
         pop = sim.Population(1, sim.IF_curr_exp(i_offset=5.0), label="pop")
         pop.record("spikes")
-        pop_2 = sim.Population(1, sim.IF_curr_exp(), label="pop_2")
+        pop_2 = sim.Population(1, model, label="pop_2")
         proj = sim.Projection(
             pop, pop_2, sim.AllToAllConnector(),
             sim.STDPMechanism(
@@ -53,7 +58,28 @@ def do_run(self) -> None:
 
         assert numpy.array_equal(weights_1_1, weights_2_1)
         assert numpy.array_equal(weights_1_2, weights_2_2)
+        assert len(spikes_1[0]) > 0
         assert numpy.array_equal(spikes_1, spikes_2)
 
-    def test_do_run(self) -> None:
-        self.runsafe(self.do_run)
+    def _do_if_curr_exp(self) -> None:
+        self.run_model(sim.IF_curr_exp())
+
+    def test_check_if_curr_exp(self) -> None:
+        self.runsafe(self._do_if_curr_exp)
+        self.check_binary_used("IF_curr_exp_stdp_mad_pair_additive.aplx")
+
+    def _do_if_curr_exp_ca2_additive(self) -> None:
+        self.run_model(sim.extra_models.IFCurrExpCa2Adaptive())
+
+    def test_check_if_curr_exp_ca2_additive(self) -> None:
+        self.runsafe(self._do_if_curr_exp_ca2_additive)
+        self.check_binary_used(
+            "IF_curr_exp_ca2_adaptive_stdp_mad_pair_additive.aplx")
+
+    def _do_izk_cond_exp_dual(self) -> None:
+        self.run_model(sim.extra_models.Izhikevich_cond_dual())
+
+    def test_check_izk_cond_exp_dual(self) -> None:
+        self.runsafe(self._do_izk_cond_exp_dual)
+        self.check_binary_used(
+            "IZK_cond_exp_dual_stdp_mad_pair_additive.aplx")

spynnaker_integration_tests/test_stdp/test_STDP_nearest_pair_additive.py

@@ -20,7 +20,7 @@
 
 class TestSTDPNearestPairAdditive(BaseTestCase):
 
-    def potentiation_and_depression(self) -> None:
+    def potentiation_and_depression(self, n_synapse_cores: int) -> None:
         p.setup(1)
         runtime = 100
         initial_run = 1000  # to negate any initial conditions
@@ -54,7 +54,8 @@ def potentiation_and_depression(self) -> None:
                                  {'spike_times': extra_spikes}, label="extra")
 
         # Post-plastic-synapse population
-        post_pop = p.Population(1, p.IF_curr_exp(),  label="post")
+        post_pop = p.Population(1, p.IF_curr_exp(), label="post",
+                                n_synapse_cores=n_synapse_cores)
 
         # Create projections
         p.Projection(
@@ -136,8 +137,20 @@ def potentiation_and_depression(self) -> None:
         self.assertTrue(numpy.allclose(
                         weights[0], new_weight_exact, atol=0.001))
 
+    def do_synapse(self) -> None:
+        self.potentiation_and_depression(1)
+
     def test_potentiation_and_depression(self) -> None:
-        self.runsafe(self.potentiation_and_depression)
+        self.runsafe(self.do_synapse)
+        self.check_binary_used("synapses_stdp_mad_nearest_pair_additive.aplx")
+
+    def do_combined(self) -> None:
+        self.potentiation_and_depression(0)
+
+    def test_combined(self) -> None:
+        self.runsafe(self.do_combined)
+        self.check_binary_used(
+            "IF_curr_exp_stdp_mad_nearest_pair_additive.aplx")
 
 
 if __name__ == '__main__':

spynnaker_integration_tests/test_stdp/test_STDP_nearest_pair_multiplicative.py

@@ -20,7 +20,12 @@
 
 class TestSTDPNearestPairAdditive(BaseTestCase):
 
-    def potentiation_and_depression(self) -> None:
+    def potentiation_and_depression(self, n_synapse_cores: int) -> None:
+        """
+        Runs the test with or without synapse cores
+
+        :param n_synapse_cores: Numer of synapse cores to use. Zero = combined
+        """
         p.setup(1)
         runtime = 100
         initial_run = 1000  # to negate any initial conditions
@@ -54,7 +59,8 @@ def potentiation_and_depression(self) -> None:
                                  {'spike_times': extra_spikes}, label="extra")
 
         # Post-plastic-synapse population
-        post_pop = p.Population(1, p.IF_curr_exp(),  label="post")
+        post_pop = p.Population(1, p.IF_curr_exp(), label="post",
+                                n_synapse_cores=n_synapse_cores)
 
         # Create projections
         p.Projection(
@@ -136,8 +142,21 @@ def potentiation_and_depression(self) -> None:
         self.assertTrue(numpy.allclose(
                         weights[0], new_weight_exact, atol=0.001))
 
+    def do_synapse(self) -> None:
+        self.potentiation_and_depression(1)
+
     def test_potentiation_and_depression(self) -> None:
-        self.runsafe(self.potentiation_and_depression)
+        self.runsafe(self.do_synapse)
+        self.check_binary_used(
+            "synapses_stdp_mad_nearest_pair_multiplicative.aplx")
+
+    def do_combined(self) -> None:
+        self.potentiation_and_depression(0)
+
+    def test_combined(self) -> None:
+        self.runsafe(self.do_combined)
+        self.check_binary_used(
+            "IF_curr_exp_stdp_mad_nearest_pair_multiplicative.aplx")
 
 
 if __name__ == '__main__':

spynnaker_integration_tests/test_stdp/test_STDP_neuromodulation.py

@@ -12,17 +12,24 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import math
+from typing import cast, List
+import unittest
+
+import numpy
 import pyNN.spiNNaker as sim
+
 from spinnaker_testbase import BaseTestCase
-import numpy
-import unittest
-import math
 
+from spynnaker.pyNN.models.neuron import ConnectionHolder
+from spynnaker.pyNN.models.neuron.plasticity.stdp.weight_dependence import (
+    AbstractWeightDependence)
 
-class TestSTDPNeuromodulation(BaseTestCase):
 
-    def neuromodulation(self) -> None:
+class TestSTDPNeuromodulation(BaseTestCase):
 
+    def neuromodulation(self, weight_dependence: AbstractWeightDependence
+                        ) -> ConnectionHolder:
         """
         Simple test for neuromodulated STDP.
         Two pre-synaptic spikes are added, at times 1500 and 2400ms.
@@ -97,8 +104,7 @@ def neuromodulation(self) -> None:
             timing_dependence=sim.SpikePairRule(
                 tau_plus=10, tau_minus=12,
                 A_plus=1, A_minus=1),
-            weight_dependence=sim.AdditiveWeightDependence(
-                w_min=0, w_max=20),
+            weight_dependence=weight_dependence,
             weight=rewarded_syn_weight)
 
         # Create a plastic connection between pre and post neurons
@@ -127,6 +133,13 @@ def neuromodulation(self) -> None:
 
         print(spikes)
 
+        return weights
+
+    def do_additive(self) -> None:
+        weight_dependence = sim.AdditiveWeightDependence(w_min=0, w_max=20)
+        weights = self.neuromodulation(weight_dependence)
+
+        DA_concentration = 0.1
         pot = 1 * math.exp(-((1504 - 1500)/10))
         decay = math.exp(-((1601 - 1504)/1000))
         el = pot * decay
@@ -135,15 +148,30 @@ def neuromodulation(self) -> None:
         decay_e = math.exp(-((2400 - 1601)/1000))
         weight_exact = (
             ((el * DA_concentration) * const)*((decay_d * decay_e) - 1))
-
         print(f"Weight calculated: {weight_exact}")
-        print(f"Weight from SpiNNaker: {weights[0][2]}")
-
+        weights0 = cast(List[float], weights[0])
+        print(f"Weight from SpiNNaker: {weights0[2]}")
         self.assertTrue(numpy.allclose(
-                        weights[0][2], weight_exact, atol=0.02))
+                        weights0[2], weight_exact, atol=0.02))
+
+        self.check_binary_used(
+            "synapses_stdp_izhikevich_neuromodulation_pair_additive.aplx")
+
+    def test_additive(self) -> None:
+        self.runsafe(self.do_additive)
+
+    def do_multiplicative(self) -> None:
+        weight_dependence = sim.MultiplicativeWeightDependence(
+            w_min=0, w_max=20)
+        self.neuromodulation(weight_dependence)
+
+        # TODO Weights expected
+        self.check_binary_used(
+            "synapses_stdp_izhikevich_neuromodulation_"
+            "pair_multiplicative.aplx")
 
-    def test_neuromodulation(self) -> None:
-        self.runsafe(self.neuromodulation)
+    def test_multiplicative(self) -> None:
+        self.runsafe(self.do_multiplicative)
 
 
 if __name__ == '__main__':

spynnaker_integration_tests/test_various/test_binaries.py

@@ -0,0 +1,103 @@
+# Copyright (c) 2026 The University of Manchester
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import pyNN.spiNNaker as sim
+from spinnaker_testbase import BaseTestCase
+
+from spynnaker.pyNN.models.abstract_pynn_model import AbstractPyNNModel
+from spynnaker.pyNN.models.populations import Population
+
+INTERVAL = 50
+N_NEURONS = 5
+
+
+class TestBinaries(BaseTestCase):
+    """
+    Tests limited to checking binaries run and spike
+    """
+
+    def add_population(self, model: AbstractPyNNModel, weight: int,
+                       input_pop: Population) -> Population:
+        """
+        Add a Populations and input projection for this model in combined mode
+        """
+        population = sim.Population(
+            N_NEURONS, model, label=model.name)
+        sim.Projection(input_pop, population, sim.OneToOneConnector(),
+                       synapse_type=sim.StaticSynapse(weight=weight, delay=1))
+        population.record("spikes")
+        return population
+
+    def add_neuron_population(self, model: AbstractPyNNModel, weight: int,
+                              input_pop: Population) -> Population:
+        """
+        Add a Populations and input projection for this model in split mode
+        """
+        population = sim.Population(
+            N_NEURONS, model, label=model.name, n_synapse_cores=1)
+        sim.Projection(input_pop, population, sim.OneToOneConnector(),
+                       synapse_type=sim.StaticSynapse(weight=weight, delay=1))
+        population.record("spikes")
+        return population
+
+    def check_population(self, population: Population) -> None:
+        """
+        Check the population produced at least 1 spike
+        """
+        neo = population.get_data(variables="spikes")
+        spikes_trains = neo.segments[0].spiketrains
+        print(population.label, spikes_trains)
+        self.assertEqual(len(spikes_trains), N_NEURONS)
+        for spike_trains in spikes_trains:
+            self.assertGreaterEqual(len(spikes_trains), 1)
+
+    def check_binaries(self) -> None:
+        """
+        Run a toy script with binaries not tested better elsewhere
+        """
+
+        sim.setup(timestep=1.0)
+
+        spike_times = []
+        for i in range(N_NEURONS):
+            spike_times.append([i, i + INTERVAL, i + INTERVAL * 2])
+        input_pop = sim.Population(
+            N_NEURONS, sim.SpikeSourceArray(spike_times=spike_times),
+            label="input")
+
+        # Ideally there should be better tests for these modules
+        # remove models tested elsewhere!
+        populations = []
+
+        # IF_curr_delta_ca2_adaptive.aplx
+        populations.append(self.add_population(
+            sim.extra_models.IFCurrDeltaCa2Adaptive(), 15, input_pop))
+
+        # IF_curr_delta_ca2_adaptive_neuron.aplx
+        populations.append(self.add_neuron_population(
+            sim.extra_models.IFCurrDeltaCa2Adaptive(), 15, input_pop))
+
+        sim.run(N_NEURONS + INTERVAL * 3)
+
+        for population in populations:
+            self.check_population(population)
+        sim.end()
+
+        self.check_binaries_used([
+            "IF_curr_delta_ca2_adaptive.aplx",
+            "IF_curr_delta_ca2_adaptive_neuron.aplx",
+        ])
+
+    def test_binaries(self) -> None:
+        self.runsafe(self.check_binaries)