Merge pull request #3649 from heplesser/ticket-516

Port ticket-516 test from SLI to Py

Author: heplesser

testsuite/mpitests/ticket-516.sli

@@ -0,0 +1,62 @@
+# -*- coding: utf-8 -*-
+#
+# test_ticket_516.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/>.
+
+import pytest
+from mpi_test_wrapper import MPITestAssertEqual
+
+
+@pytest.mark.skipif_incompatible_mpi
+@pytest.mark.parametrize("on_source", [True, False])
+@MPITestAssertEqual([1, 2, 4], debug=False)
+def test_ticket_516(on_source):
+    """
+    Confirm that simulations of a spatial network yield consistent spike trains.
+
+    The test compares data written by spike_recorder to SPIKE_LABEL.
+    """
+
+    import nest
+
+    nest.rng_seed = 1234567
+    nest.total_num_virtual_procs = 4
+
+    # Drive network with DC current, random connectivity and weights
+    # lead to variation in spike times between neurons
+    layer = nest.Create("iaf_psc_exp", params={"I_e": 500}, positions=nest.spatial.grid(shape=[5, 5], edge_wrap=False))
+    sr = nest.Create(
+        "spike_recorder",
+        params={
+            "record_to": "ascii",
+            "time_in_steps": True,
+            "label": SPIKE_LABEL.format(nest.num_processes),  # noqa: F821
+        },
+    )
+
+    nest.Connect(
+        layer,
+        layer,
+        {"rule": "pairwise_bernoulli", "use_on_source": on_source, "mask": {"circular": {"radius": 0.5}}, "p": 0.7},
+        {"weight": nest.random.uniform(-5, 15), "delay": 1},
+    )
+
+    nest.Connect(layer, sr)
+
+    nest.Simulate(200)