copying to personal repo

cnmodel/util/tests/test_expfitting.py

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+from __future__ import print_function
+from cnmodel.util import ExpFitting
+import numpy as np
+
+
+def test_fit1():
+    fit = ExpFitting(nexp=1)
+    x = np.linspace(0.0, 50, 500)
+    p = [-50.0, 4.0, 5.0]
+    y = p[0] + p[1] * np.exp(-x / p[2])
+    res = fit.fit(x, y, fit.fitpars)
+    pr = [float(res[k].value) for k in res.keys()]
+    print("\noriginal: ", p)
+    print("fit res:  ", pr)
+    for i, v in enumerate(p):
+        assert np.allclose(v, pr[i])
+
+
+def test_fit2():
+    fit = ExpFitting(nexp=2)
+    x = np.linspace(0.0, 50, 500)
+    p = [
+        -50.0,
+        4.0,
+        5.0,
+        1.0,
+        4.5,
+    ]  # last term is ratio of the two time constants (t2 = delta*t1)
+    y = p[0] + p[1] * np.exp(-x / p[2]) + p[3] * np.exp(-x / (p[2] * p[4]))
+    res = fit.fit(x, y, fit.fitpars)
+    pr = [float(res[k].value) for k in res.keys()]
+    print("\noriginal: ", p)
+    print("fit res:  ", pr)
+    # we can only do this approximately for 2 exp fits
+    for i, v in enumerate(p):  # test each one individually
+        assert np.allclose(pr[i] / v, 1.0, atol=1e-4, rtol=1e-2)

cnmodel/util/tests/test_matlab.py

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+import pytest
+import numpy as np
+
+# from cnmodel.util.matlab_proc import MatlabProcess
+import matlab.engine
+
+
+def test_matlab():
+    global proc
+    try:
+        # proc = MatlabProcess()
+        proc = matlab.engine.start_matlab()
+    except RuntimeError:
+        # no matlab available; skip this test
+        pytest.skip("MATLAB unavailable")
+
+    base_vcount = proc.who(nargout=1)  # .shape[0]
+    assert len(base_vcount) == 0
+
+    e4 = np.array(proc.eye(4, nargout=1))
+
+    assert isinstance(e4, np.ndarray)
+    assert np.all(e4 == np.eye(4))
+    # assert proc.who().shape[0] == base_vcount
+
+    o6_ref = np.array(proc.ones(6, nargout=1))  #  _transfer=False))
+    # o6 = o6_ref.get()
+    o6 = np.array(proc.ones(6, nargout=1))
+    assert np.all(o6 == np.ones(6))
+    # print(proc.who(nargout=1))
+    # assert proc.who(nargout=1) == base_vcount + 1
+
+    del o6_ref
+    # assert proc.who().shape[0] == base_vcount
+
+    proc.close()
+
+
+if __name__ == "__main__":
+    test_matlab()

cnmodel/util/tests/test_sound.py

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+import numpy as np
+from cnmodel.util import sound
+
+
+def test_conversions():
+    pa = np.array([3990.5, 20, 0.3639, 2e-5])
+    db = np.array([166, 120, 85.2, 0])
+
+    assert np.allclose(sound.pa_to_dbspl(pa), db, atol=0.1, rtol=0.002)
+    assert np.allclose(sound.dbspl_to_pa(db), pa, atol=0.1, rtol=0.002)
+
+
+def test_tonepip():
+    rate = 100000
+    dur = 0.1
+    ps = 0.01
+    rd = 0.02
+    pd = 0.08
+    db = 60
+    s1 = sound.TonePip(
+        rate=rate,
+        duration=dur,
+        f0=5321,
+        dbspl=db,
+        pip_duration=pd,
+        pip_start=[ps],
+        ramp_duration=rd,
+    )
+
+    # test array sizes
+    assert s1.sound.size == s1.time.size == int(dur * rate) + 1
+
+    # test for consistency
+    assert np.allclose(
+        [s1.sound.min(), s1.sound.mean(), s1.sound.max()],
+        [-0.028284253158247834, -1.0954891976168953e-10, 0.028284270354167296],
+    )
+
+    # test that we got the requested amplitude
+    assert np.allclose(s1.measure_dbspl(ps + rd, ps + pd - rd), db, atol=0.1, rtol=0.01)
+
+    # test for quiet before and after pip
+    assert np.all(s1.sound[: int(ps * rate) - 1] == 0)
+    assert np.all(s1.sound[int((ps + pd) * rate) + 1 :] == 0)
+
+    # test the sound can be recreated from its key
+    key = s1.key()
+    s2 = sound.create(**key)
+    assert np.all(s1.time == s2.time)
+    assert np.all(s1.sound == s2.sound)
+
+
+def test_noisepip():
+    rate = 100000
+    dur = 0.1
+    ps = 0.01
+    rd = 0.02
+    pd = 0.08
+    db = 60
+    s1 = sound.NoisePip(
+        rate=rate,
+        duration=dur,
+        seed=184724,
+        dbspl=db,
+        pip_duration=pd,
+        pip_start=[ps],
+        ramp_duration=rd,
+    )
+
+    # test array sizes
+    assert s1.sound.size == s1.time.size == int(dur * rate) + 1
+
+    # test for consistency
+    assert np.allclose(
+        [s1.sound.min(), s1.sound.mean(), s1.sound.max()],
+        [-0.082260796003197786, -0.00018484322982972046, 0.069160217220832404],
+    )
+
+    # test that we got the requested amplitude
+    assert np.allclose(s1.measure_dbspl(ps + rd, ps + pd - rd), db, atol=0.1, rtol=0.01)
+
+    # test for quiet before and after pip
+    assert np.all(s1.sound[: int(ps * rate) - 1] == 0)
+    assert np.all(s1.sound[int((ps + pd) * rate) + 1 :] == 0)
+
+    # test the sound can be recreated from its key
+    key = s1.key()
+    s2 = sound.create(**key)
+    # also test new seed works, and does not affect other sounds
+    key["seed"] += 1
+    s3 = sound.create(**key)
+    s3.sound  # generate here to advance rng before generating s2
+
+    assert np.all(s1.time == s2.time)
+    assert np.all(s1.sound == s2.sound)
+    start = int(ps * rate) + 1
+    end = int((ps + pd) * rate) - 1
+    assert not np.any(s1.sound[start:end] == s3.sound[start:end])

cnmodel/util/tests/test_stim.py

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+import numpy as np
+from numpy.testing import assert_raises
+
+from cnmodel.util import stim
+from neuron import h
+
+h.dt = 0.025
+
+
+def test_make_pulse():
+    params = dict(delay=10, Sfreq=50, dur=1, amp=15, PT=0, NP=5)
+
+    assert_raises(Exception, lambda: stim.make_pulse(params))
+    params["dt"] = 0.025
+
+    w, maxt, times = stim.make_pulse(params)
+
+    assert w.min() == 0.0
+    assert w.max() == 15
+    assert w.dtype == np.float64
+
+    triggers = np.argwhere(np.diff(w) > 0)[:, 0] + 1
+    assert np.all(triggers == times)
+    assert w.sum() == 15 * len(times) * int(1 / h.dt)
+
+    params["PT"] = 100
+    w, maxt, times = stim.make_pulse(params)
+    triggers = np.argwhere(np.diff(w) > 0)[:, 0] + 1
+    assert triggers[-1] - triggers[-2] == 100 / h.dt