def test_call_of_check_sweep(self):
"""
Test `find_floating_potential` appropriately calls
`plasmapy.analysis.swept_langmuir.helpers.check_sweep` so we can relay on
the `check_sweep` tests.
"""
varr = np.linspace(-20.0, 20.0, 100)
carr = np.linspace(-20.0, 20.0, 100)
assert _sl.helpers.check_sweep is _sl.floating_potential.check_sweep
with mock.patch(_sl.floating_potential.__name__ +
".check_sweep") as mock_cs:
mock_cs.return_value = varr, carr
find_floating_potential(voltage=varr,
current=carr,
fit_type="linear")
assert mock_cs.call_count == 1
# passed args
assert len(mock_cs.call_args[0]) == 2
assert np.array_equal(mock_cs.call_args[0][0], varr)
assert np.array_equal(mock_cs.call_args[0][1], carr)
# passed kwargs
assert mock_cs.call_args[1] == {"strip_units": True}
def test_kwarg_min_points(self, min_points, fit_type, islands, indices):
"""
Test functionality of keyword `min_points` and how it affects the
size of the crossing-point island.
"""
voltage = self._voltage
current = self._linear_current if fit_type == "linear" else self._exp_current
results = find_floating_potential(
voltage,
current,
min_points=min_points,
fit_type=fit_type,
)
assert isinstance(results, FloatingPotentialResults)
assert results.islands == islands
assert results.indices == indices
def test_perfect_exponential(self, a, alpha, b):
"""Test calculated fit parameters on a few perfectly exponential cases."""
voltage = self._voltage
current = a * np.exp(alpha * voltage) + b
results = find_floating_potential(
voltage=voltage,
current=current,
fit_type="exponential",
min_points=0.8,
)
assert isinstance(results, FloatingPotentialResults)
assert np.isclose(results.vf, np.log(-b / a) / alpha)
assert np.isclose(results.vf_err, 0.0, 1e-7)
assert np.isclose(results.rsq, 1.0)
assert isinstance(results.func, ffuncs.ExponentialPlusOffset)
assert np.allclose(results.func.params, (a, alpha, b))
assert np.allclose(results.func.param_errors, (0.0, 0.0, 0.0), atol=2e-8)
def test_perfect_linear(self, m, b):
"""Test calculated fit parameters on a few perfectly linear cases."""
voltage = self._voltage
current = m * voltage + b
results = find_floating_potential(
voltage=voltage,
current=current,
fit_type="linear",
min_points=0.8,
)
assert isinstance(results, FloatingPotentialResults)
assert np.isclose(results.vf, -b / m)
assert np.isclose(results.vf_err, 0.0)
assert np.isclose(results.rsq, 1.0)
assert isinstance(results.func, ffuncs.Linear)
assert np.allclose(results.func.params, (m, b))
assert np.allclose(results.func.param_errors, (0.0, 0.0), atol=2e-8)
def test_warnings(self, kwargs, expected, _warning):
"""Test scenarios that issue warnings."""
with pytest.warns(_warning):
results = find_floating_potential(**kwargs)
assert isinstance(results, FloatingPotentialResults)
for key, val in expected.items():
rtn_val = getattr(results, key)
if val is None:
assert rtn_val is None
elif key == "func":
assert isinstance(rtn_val, val.__class__)
elif np.isscalar(val):
if np.isnan(val):
assert np.isnan(rtn_val)
else:
assert np.isclose(rtn_val, val)
else:
assert rtn_val == val
def test_island_finding(self, kwargs, expected):
"""
Test scenarios related to the identification of crossing-point islands.
"""
results = find_floating_potential(**kwargs)
assert isinstance(results, FloatingPotentialResults)
for key, val in expected.items():
rtn_val = getattr(results, key)
if val is None:
assert rtn_val is None
elif key == "func":
assert isinstance(rtn_val, val.__class__)
elif np.isscalar(val):
if np.isnan(val):
assert np.isnan(rtn_val)
else:
assert np.isclose(rtn_val, val, atol=1e-7)
else:
assert rtn_val == val
def test_raises(self, kwargs, _error):
"""Test scenarios that raise `Exception`s."""
with pytest.raises(_error):
find_floating_potential(**kwargs)