Port regression test 433 to Pytests

testsuite/pytests/sli2py_regressions/test_ticket_433.py

@@ -0,0 +1,156 @@
+# -*- coding: utf-8 -*-
+#
+# test_ticket_433.py
+#
+# This file is part of NEST.
+#
+# Copyright (C) 2004 The NEST Initiative
+#
+# NEST is free software: you can redistribute it and/or modify
+# it under the terms of the GNU General Public License as published by
+# the Free Software Foundation, either version 2 of the License, or
+# (at your option) any later version.
+#
+# NEST is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU General Public License for more details.
+#
+# You should have received a copy of the GNU General Public License
+# along with NEST.  If not, see <http://www.gnu.org/licenses/>.
+
+"""
+Regression test for Ticket #433.
+
+Test against receptor_type mishandling in ht_neuron.
+
+This test ensures that:
+- ht_neuron rejects connections with invalid or missing receptor_type.
+- receptor_type is properly passed in spatial connections.
+- All models with receptor_types reject connections with plain static_synapse.
+
+Author: Hans Ekkehard Plesser, 2010-06-28
+"""
+
+import nest
+import pytest
+
+
+@pytest.fixture(autouse=True)
+def prepare_kernel():
+    nest.ResetKernel()
+    nest.set_verbosity("M_INFO")
+
+
+@pytest.mark.skipif("ht_neuron" not in nest.node_models, reason="ht_neuron not available")
+def test_explicit_receptor_type():
+    """
+    Test that explicit receptor_type works for ht_neuron.
+    """
+    nest.ResetKernel()
+    nest.CopyModel("ht_neuron", "MyHT")
+    nest.CopyModel("poisson_generator", "RetinaGen", {"rate": 10.0})
+    nest.CopyModel("parrot_neuron", "RetinaNode")
+    ampa = nest.GetDefaults("MyHT")["receptor_types"]["AMPA"]
+    shape = [10, 10]
+    extent = [8.0, 8.0]
+    retina_gen = nest.Create("RetinaGen", positions=nest.spatial.grid(shape=shape, extent=extent))
+    retina = nest.Create("RetinaNode", positions=nest.spatial.grid(shape=shape, extent=extent))
+    Tp = nest.Create("MyHT", positions=nest.spatial.grid(shape=shape, extent=extent))
+    nest.Connect(retina_gen, retina, conn_spec="one_to_one")
+    # Explicit receptor_type
+    nest.Connect(retina_gen, Tp, conn_spec={"rule": "one_to_one"}, syn_spec={"receptor_type": ampa})
+    nest.Simulate(10.0)
+
+
+@pytest.mark.skipif("ht_neuron" not in nest.node_models, reason="ht_neuron not available")
+def test_implicit_receptor_type_fails():
+    """
+    Test that missing receptor_type raises an error for ht_neuron.
+    """
+    nest.ResetKernel()
+    nest.CopyModel("ht_neuron", "MyHT")
+    nest.CopyModel("poisson_generator", "RetinaGen", {"rate": 10.0})
+    nest.CopyModel("parrot_neuron", "RetinaNode")
+    shape = [10, 10]
+    extent = [8.0, 8.0]
+    retina_gen = nest.Create("RetinaGen", positions=nest.spatial.grid(shape=shape, extent=extent))
+    retina = nest.Create("RetinaNode", positions=nest.spatial.grid(shape=shape, extent=extent))
+    Tp = nest.Create("MyHT", positions=nest.spatial.grid(shape=shape, extent=extent))
+    nest.Connect(retina_gen, retina, conn_spec="one_to_one")
+    # Missing receptor_type should fail
+    with pytest.raises(nest.NESTError):
+        nest.Connect(retina_gen, Tp, conn_spec={"rule": "one_to_one"})
+
+
+@pytest.mark.skipif("ht_neuron" not in nest.node_models, reason="ht_neuron not available")
+def test_connectlayers_receptor_type():
+    """
+    Test that receptor_type is properly passed in spatial Connect.
+    """
+    nest.ResetKernel()
+    nest.CopyModel("ht_neuron", "MyHT")
+    nest.CopyModel("poisson_generator", "RetinaGen", {"rate": 10.0})
+    nest.CopyModel("parrot_neuron", "RetinaNode")
+    ampa = nest.GetDefaults("MyHT")["receptor_types"]["AMPA"]
+    shape = [10, 10]
+    extent = [8.0, 8.0]
+    retina = nest.Create("RetinaNode", positions=nest.spatial.grid(shape=shape, extent=extent))
+    Tp = nest.Create("MyHT", positions=nest.spatial.grid(shape=shape, extent=extent))
+    # receptor_type passed in syn_spec
+    nest.Connect(
+        retina,
+        Tp,
+        conn_spec={
+            "rule": "pairwise_bernoulli",
+            "p": 1.0,
+            "mask": {"circular": {"radius": 2.0}},
+        },
+        syn_spec={"receptor_type": ampa, "synapse_model": "ht_synapse"},
+    )
+    nest.Simulate(10.0)
+
+
+@pytest.mark.skipif("ht_neuron" not in nest.node_models, reason="ht_neuron not available")
+def test_connectlayers_receptor_type_in_synapse():
+    """
+    Test that receptor_type in custom synapse model works in spatial Connect.
+    """
+    nest.ResetKernel()
+    nest.CopyModel("ht_neuron", "MyHT")
+    nest.CopyModel("poisson_generator", "RetinaGen", {"rate": 10.0})
+    nest.CopyModel("parrot_neuron", "RetinaNode")
+    ampa = nest.GetDefaults("MyHT")["receptor_types"]["AMPA"]
+    # Create a custom synapse model with receptor_type set
+    nest.CopyModel("ht_synapse", "ht_syn_ampa", {"receptor_type": ampa})
+    shape = [10, 10]
+    extent = [8.0, 8.0]
+    retina = nest.Create("RetinaNode", positions=nest.spatial.grid(shape=shape, extent=extent))
+    Tp = nest.Create("MyHT", positions=nest.spatial.grid(shape=shape, extent=extent))
+    nest.Connect(
+        retina,
+        Tp,
+        conn_spec={
+            "rule": "pairwise_bernoulli",
+            "p": 1.0,
+            "mask": {"circular": {"radius": 2.0}},
+        },
+        syn_spec={"synapse_model": "ht_syn_ampa"},
+    )
+    nest.Simulate(10.0)
+
+
+def test_all_models_with_receptor_types_reject_static_synapse():
+    """
+    Assert that all models with receptor_types reject connections with plain static_synapse.
+    """
+    for mod in nest.node_models:
+        dflts = nest.GetDefaults(mod)
+        if "receptor_types" in dflts:
+            # Avoid models with auto-generated ports (length 0 or less)
+            if len(dflts["receptor_types"]) > 0:
+                nest.ResetKernel()
+                src = nest.Create(mod)
+                tgt = nest.Create(mod)
+                with pytest.raises(nest.NESTError):
+                    nest.Connect(src, tgt, syn_spec="static_synapse")