def test_unequal_arrays(self):
r"""Test errors upon unequal array lengths"""
with pytest.raises(ValueError):
langmuir.Characteristic(self.bias_longarr, current_arr)
with pytest.raises(ValueError):
langmuir.Characteristic(bias_arr, self.current_longarr)
def test_addition(self):
r"""Test addition of characteristic objects"""
a = langmuir.Characteristic(bias_arr, current_arr)
b = langmuir.Characteristic(bias_arr, self.current_arr2)
ab_sum = a + b
errStr = f"Addition of characteristic objects is not behaving as it " f"should."
assert (a.current + b.current == ab_sum.current).all(), errStr
def test_subtraction(self):
r"""Test addition of characteristic objects"""
a = langmuir.Characteristic(bias_arr, current_arr)
b = langmuir.Characteristic(bias_arr, self.current_arr2)
ab_sub = a - b
errStr = f"Subtraction of characteristic objects is not behaving as it should."
assert (a.current - b.current == ab_sub.current).all(), errStr
def test_subtraction(self):
r"""Test addition of characteristic objects"""
with pytest.warns(FutureWarning):
a = langmuir.Characteristic(bias_arr, current_arr)
with pytest.warns(FutureWarning):
b = langmuir.Characteristic(bias_arr, self.current_arr2)
ab_sub = a - b
errStr = "Subtraction of characteristic objects is not behaving as it should."
assert (a.current - b.current == ab_sub.current).all(), errStr
def characteristic_simulated():
r""""Create a simulated probe characteristic (provisional)"""
T_e_sim = 1 * u.eV
n_e_sim = 1e18 * u.m**-3
probe_area_sim = 1 * u.cm**2
I_es_sim = (n_e_sim * probe_area_sim * const.e *
np.sqrt(T_e_sim / (2 * np.pi * const.m_e)))
# Create bias array
bias_simarr = np.arange(-20, 15, 0.1) * u.V
# Calculate electron current and limit to electron saturation current
current_simarr = np.exp(const.e * bias_simarr / T_e_sim) * u.A
current_simarr[current_simarr > I_es_sim] = I_es_sim
# Add simulated linear sheath expansion current
current_simarr[current_simarr == I_es_sim] += (
bias_simarr[current_simarr == I_es_sim] * 5e-4 * u.A / u.V)
# Add simulated linear ion collection current
current_simarr[current_simarr < I_es_sim] += (
bias_simarr[current_simarr < I_es_sim] * 1e-4 * u.A / u.V)
return langmuir.Characteristic(bias_simarr, current_simarr)
def test_invalid_dimensions(self):
r"""Test 2D arrays work with Characteristic class"""
# `Characteristic.get_unique_bias` runs during initialization
# which prevents an exception from being raised..
with pytest.warns(FutureWarning):
langmuir.Characteristic(self.bias_2darr, self.current_2darr)
def shuffle_characteristic(characteristic):
r""""Shuffle a given characteristic"""
_shuffle = sorted(np.arange(len(characteristic.bias)),
key=lambda k: np.random.random())
U_shuffled = characteristic.bias[_shuffle]
I_shuffled = characteristic.current[_shuffle]
return langmuir.Characteristic(U_shuffled, I_shuffled)
def test_get_EEDF():
"""Test the obtained EEDF"""
char = langmuir.Characteristic(bias_arr[:17], current_arr[:17])
energy, probability = langmuir.get_EEDF(char, visualize=False)
expect_energy = (0.14118696, 0.05293109, 0.00709731)
expect_probability = (8.64838751, 8.05295503, 3.42348436)
assert np.allclose(energy.to(u.eV).value, np.array(expect_energy))
assert np.allclose(probability, np.array(expect_probability))
def test_infinite_U(self):
r"""Test error upon an infinite bias value"""
with pytest.raises(ValueError):
langmuir.Characteristic(self.bias_infarr, current_arr)
def test_infinite_I(self):
r"""Test error upon an infinite current value"""
with pytest.raises(ValueError):
langmuir.Characteristic(bias_arr, self.current_infarr)
def characteristic():
r""""Create a dummy characteristic with random values"""
return langmuir.Characteristic(bias_arr, current_arr)
def characteristic():
r"""Create a dummy characteristic with random values"""
with pytest.warns(FutureWarning):
return langmuir.Characteristic(bias_arr, current_arr)
