def test_z_mean_override(self, kwargs, expected):
"""Test overriding behavior of kw 'z_mean'."""
ws = hollweg(**kwargs)
ws_expected = hollweg(**expected)
for mode in ws:
assert np.isclose(ws[mode], ws_expected[mode], atol=1e-5, rtol=1.7e-4)
def test_hollweg1999_vals(self, kwargs, expected, desired_beta):
"""
Test calculated values based on Figure 2 of Hollweg1999
(DOI: https://doi.org/10.1029/1998JA900132) using eqn 3 of
Bellan 2012
(DOI: https://doi.org/10.1029/2012JA017856).
The WebPlotDigitizer software was used to determine the test
parameters for k, B, and expected omega from Figure 2 of
Hollweg1999.
- GitHub: https://github.com/ankitrohatgi/WebPlotDigitizer
- Web Version: https://automeris.io/WebPlotDigitizer/
"""
# k values need to be single valued for this test to function correctly
cs = speeds.cs_(kwargs["T_e"], kwargs["T_i"], kwargs["ion"]).value
va = speeds.va_(kwargs["B"], kwargs["n_i"], ion=kwargs["ion"]).value
beta = (cs / va)**2
if not np.isclose(beta, desired_beta, atol=2e-4):
pytest.fail(
f"The Holweg 1999 paper requires a 'beta' value of {desired_beta:0.5f} "
f"and the test parameters yielded {beta:.6f}.")
kz = (np.cos(kwargs["theta"]) * kwargs["k"]).value
w_alfven = (hollweg(**kwargs)["alfven_mode"]).value
big_omega = np.abs(w_alfven / (kz * va))
assert np.allclose(big_omega, expected, atol=1e-2)
def test_return_structure(self, kwargs, expected):
"""Test the structure of the returned values."""
ws = hollweg(**kwargs)
assert isinstance(ws, dict)
assert len({"acoustic_mode", "alfven_mode", "fast_mode"} - set(ws.keys())) == 0
for mode, val in ws.items():
assert isinstance(val, u.Quantity)
assert val.unit == u.rad / u.s
assert val.shape == expected["shape"]
def test_warning(self, kwargs, _warning):
"""Test scenarios that raise a `Warning`."""
with pytest.warns(_warning):
hollweg(**kwargs)
def test_raises(self, kwargs, _error):
"""Test scenarios that raise an `Exception`."""
with pytest.raises(_error):
hollweg(**kwargs)
def test_handle_k_theta_arrays(self, kwargs, expected):
"""Test scenarios involving k and theta arrays."""
ws = hollweg(**kwargs)
for mode, val in ws.items():
assert np.allclose(val.value, expected[mode])
