Port test_parallel_conn_and_rand from SLI to Py

testsuite/pytests/sli2py_mpi/test_parallel_conn_and_rand.py

@@ -0,0 +1,75 @@
+# -*- coding: utf-8 -*-
+#
+# test_parallel_conn_and_rand.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 numpy as np
+import pandas as pd
+import pytest
+from mpi_test_wrapper import MPITestAssertEqual
+
+# Functions to be passed via decorators must be in module namespace without qualifiers
+from numpy import meshgrid
+
+
+@pytest.mark.skipif_incompatible_mpi
+@pytest.mark.skipif_missing_threads
+@MPITestAssertEqual([1, 2, 4], debug=False)
+def test_parallel_cond_and_rand():
+    """
+    Parallel Connect and Randomize Test Script
+
+    This script creates a neuron population to itself, randomizing
+    weight, delay, receptor type and one synaptic parameter. It runs
+    with a fixed number of virtual processes, and checks that connections
+    are invariant on executing with a varying number of MPI processes.
+    """
+
+    import nest
+    import pandas as pd  # noqa: F811
+
+    nest.total_num_virtual_procs = 16
+    nest.rng_seed = 123
+
+    num_neurons = 100
+    tau_syns = np.linspace(0.2, 1.0, 9)
+    receptor_min = 1
+    receptor_max = len(tau_syns)
+
+    pop = nest.Create("iaf_psc_alpha_multisynapse", num_neurons, {"tau_syn": tau_syns})
+
+    nest.Connect(
+        pop,
+        pop,
+        {"rule": "fixed_indegree", "indegree": 23},
+        {
+            "synapse_model": "stdp_synapse",
+            "delay": nest.random.uniform(0.5, 1.5),
+            "weight": nest.math.max(nest.random.normal(10, 5), 0),
+            "receptor_type": 1 + nest.random.uniform_int(receptor_max + 1 - receptor_min),
+            "alpha": nest.random.uniform(0.1, 2.3),
+            "tau_plus": nest.random.uniform(1.5, 5.0),
+        },
+    )
+
+    conns = pd.DataFrame.from_dict(
+        nest.GetConnections().get(["source", "target", "weight", "delay", "alpha", "tau_plus"])
+    )
+
+    conns.to_csv(OTHER_LABEL.format(nest.num_processes, nest.Rank()), index=False, sep="\t")  # noqa: F821