Merge pull request #2976 from nicolossus/port_test_sinusoidal_gamma_generator

Port sinusoidal generator tests (SLI-2-Py)

Author: heplesser

testsuite/pytests/sli2py_stimulating/test_sinusoidal_generators.py

@@ -0,0 +1,189 @@
+# -*- coding: utf-8 -*-
+#
+# test_sinusoidal_generators.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/>.
+
+"""
+Test basic properties of sinusoidal generators.
+"""
+
+import nest
+import numpy as np
+import numpy.testing as nptest
+import pytest
+
+# List of sinusoidal generator models
+gen_models = ["sinusoidal_poisson_generator", "sinusoidal_gamma_generator"]
+
+
+@pytest.fixture(autouse=True)
+def reset():
+    nest.ResetKernel()
+
+
+@pytest.mark.parametrize("gen_model", gen_models)
+def test_individual_spike_trains_true_by_default(gen_model):
+    """
+    Test that ``individual_spike_trains`` is true by default.
+    """
+
+    gen = nest.Create(gen_model)
+    assert gen.individual_spike_trains
+
+
+@pytest.mark.parametrize("gen_model", gen_models)
+def test_set_individual_spike_trains_on_set_defaults(gen_model):
+    """
+    Test whether ``individual_spike_trains`` can be set on ``SetDefaults``.
+    """
+
+    nest.SetDefaults(gen_model, {"individual_spike_trains": False})
+    gen = nest.Create(gen_model)
+    assert not gen.individual_spike_trains
+
+
+@pytest.mark.parametrize("gen_model", gen_models)
+def test_set_individual_spike_trains_on_creation(gen_model):
+    """
+    Test whether ``individual_spike_trains`` can be set on model creation.
+    """
+
+    gen = nest.Create(gen_model, params={"individual_spike_trains": False})
+    assert not gen.individual_spike_trains
+
+
+@pytest.mark.parametrize("gen_model", gen_models)
+def test_set_individual_spike_trains_on_copy_model(gen_model):
+    """
+    Test whether the set ``individual_spike_trains`` is inherited on ``CopyModel``.
+    """
+
+    nest.CopyModel(
+        gen_model,
+        "sinusoidal_generator_copy",
+        params={"individual_spike_trains": False},
+    )
+    gen = nest.Create("sinusoidal_generator_copy")
+    assert not gen.individual_spike_trains
+
+
+@pytest.mark.parametrize("gen_model", gen_models)
+def test_set_individual_spike_trains_on_instance(gen_model):
+    """
+    Test that ``individual_spike_trains`` cannot be set on an instance.
+    """
+
+    gen = nest.Create(gen_model)
+
+    with pytest.raises(nest.kernel.NESTErrors.BadProperty):
+        gen.individual_spike_trains = False
+
+
+@pytest.mark.skipif_missing_threads()
+@pytest.mark.parametrize("gen_model", gen_models)
+@pytest.mark.parametrize("individual_spike_trains", [False, True])
+@pytest.mark.parametrize("num_threads", [1, 2])
+def test_sinusoidal_generator_with_spike_recorder(gen_model, num_threads, individual_spike_trains):
+    """
+    Test spike recording with both true and false ``individual_spike_trains``.
+
+    The test builds a network with ``num_threads x 4`` parrot neurons that
+    receives spikes from the specified sinusoidal generator. A ``spike_recorder``
+    is connected to each parrot neuron. The test ensures that different targets
+    (on the same or different threads) receives identical spike trains if
+    ``individual_spike_trains`` is false and different spike trains otherwise.
+    """
+
+    nest.local_num_threads = num_threads
+    nrns_per_thread = 4
+    total_num_nrns = num_threads * nrns_per_thread
+
+    nest.SetDefaults(
+        gen_model,
+        {
+            "rate": 100,
+            "amplitude": 50.0,
+            "frequency": 10.0,
+            "individual_spike_trains": individual_spike_trains,
+        },
+    )
+
+    parrots = nest.Create("parrot_neuron", total_num_nrns)
+    gen = nest.Create(gen_model)
+    srecs = nest.Create("spike_recorder", total_num_nrns)
+
+    nest.Connect(gen, parrots)
+    nest.Connect(parrots, srecs, "one_to_one")
+
+    nest.Simulate(500.0)
+
+    # Nested list of recorded spike times from each sender
+    spikes_all_nrns = srecs.get("events", "times")
+
+    # Check that we actually obtained a spike times array for each neuron
+    assert len(spikes_all_nrns) == total_num_nrns
+
+    if individual_spike_trains:
+        # all trains must be pairwise different
+        assert all(
+            not np.array_equal(left, right)
+            for idx, left in enumerate(spikes_all_nrns[:-1])
+            for right in spikes_all_nrns[(idx + 1) :]
+        )
+    else:
+        # all trains should be equal
+        assert all(np.array_equal(spikes_all_nrns[0], right) for right in spikes_all_nrns[1:])
+
+
+@pytest.mark.parametrize("gen_model", gen_models)
+def test_sinusoidal_generator_rate_profile(gen_model):
+    """
+    Test recorded rate of provided sinusoidal generator against expectation.
+
+    The test checks that the recorded rate profile with ``multimeter`` is the
+    same as the analytical expectation.
+    """
+
+    dc = 1.0
+    ac = 0.5
+    freq = 10.0
+    phi = 2.0
+
+    nest.SetDefaults(
+        gen_model,
+        {"rate": dc, "amplitude": ac, "frequency": freq, "phase": phi / np.pi * 180},
+    )
+
+    parrots = nest.Create("parrot_neuron")
+    sspg = nest.Create(gen_model)
+    mm = nest.Create("multimeter", {"record_from": ["rate"]})
+
+    nest.Connect(sspg, parrots)
+    nest.Connect(mm, sspg)
+
+    nest.Simulate(100.0)
+
+    times = mm.events["times"]
+    scaled_times = times * 2 * np.pi * freq / 1000
+    shifted_times = scaled_times + phi
+    expected_rates = np.sin(shifted_times) * ac + dc
+
+    actual_rates = mm.events["rate"]
+
+    nptest.assert_allclose(actual_rates, expected_rates)