def test_calculate(self):
xshiftval = 0
yshiftval = 0
deltaX = {'Range': [0, 10], 'Label': 'O'}
deltaY = {'Range': [0, 10], 'Label': 'H_2O'}
bulk = data.ReferenceDataSet(cation=1,
anion=2,
energy=-100.00,
funits=1)
pure = data.DataSet(cation=24,
x=48,
y=0,
area=60.22,
energy=-575.00,
label="Stoich",
nspecies=1)
H2O = data.DataSet(cation=24,
x=48,
y=2,
area=60.22,
energy=-600.00,
label="One",
nspecies=1)
dataset = [pure, H2O]
system, SE = mu_vs_mu.calculate(dataset, bulk, deltaX, deltaY, 0, 0)
expected_phase = np.zeros(
np.arange(0, 10, 0.025).size * np.arange(0, 10, 0.025).size)
expected_phase = np.reshape(
expected_phase,
(np.arange(0, 10, 0.025).size, np.arange(0, 10, 0.025).size))
assert_almost_equal(system.z, expected_phase)
def test_reference_data_1(self):
bulk = data.ReferenceDataSet(cation=1,
anion=2,
energy=-90.00,
funits=1)
assert bulk.energy == -90.00
assert bulk.cation == 1
assert bulk.anion == 2
def test_calculete_normalisation(self):
bulk = data.ReferenceDataSet(cation=1,
anion=2,
energy=-100.00,
funits=1)
x = mu_vs_mu.calculate_normalisation(1, 2, bulk, 3)
expected = 33.5
assert_almost_equal(expected, x, decimal=4)
def test_reference_data_2(self):
bulk = data.ReferenceDataSet(cation=1,
anion=2,
energy=-90.00,
entropy=True,
file=test_data,
funits=10,
temp_range=[100, 120])
assert_almost_equal(bulk.svib[0], 0.00049717)
def test_calculate_excess(self):
bulk = data.ReferenceDataSet(cation=1,
anion=2,
energy=-100.00,
funits=1)
x1 = mu_vs_mu.calculate_excess(1, 2, 3, bulk)
x2 = mu_vs_mu.calculate_excess(2, 2, 3, bulk, nspecies=1, check=True)
expected1 = 0.16666666666
expected2 = -0.3333333333
assert_almost_equal(expected1, x1, decimal=4)
assert_almost_equal(expected2, x2, decimal=4)
def test_normalise_phase_energy(self):
bulk = data.ReferenceDataSet(cation=1,
anion=2,
energy=-100.00,
funits=1)
phase_1 = data.DataSet(cation=10,
x=0,
y=0,
energy=-90.0,
label="Periclase")
calculated = bulk_mu_vs_t.normalise_phase_energy(phase_1, bulk)
assert calculated == 910.0
def test_calculate_bulk_energy(self):
bulk = data.ReferenceDataSet(cation=1,
anion=2,
energy=-100.00,
funits=1)
phase_1 = data.DataSet(cation=10,
x=0,
y=0,
energy=-90.0,
label="Periclase")
calculated = bulk_mu_vs_t.calculate_bulk_energy(
10, 10, 10, 10, 10, phase_1, bulk, 10, 10, 10, 10, 10)
assert calculated == 110.0
def test_evaluate_phases(self):
bulk = data.ReferenceDataSet(cation=1,
anion=2,
energy=-100.00,
funits=1)
phase_1 = data.DataSet(cation=10,
x=0,
y=0,
energy=-90.0,
label="Periclase")
calculated = bulk_mu_vs_mu.evaluate_phases([phase_1], bulk,
np.arange(0, 10, 1),
np.arange(0, 10, 1), 1, 10,
10)[0]
assert calculated[0] == 1
def test_calculate(self):
bulk = data.ReferenceDataSet(cation=1,
anion=2,
energy=-100.00,
funits=1)
phase_1 = data.DataSet(cation=10,
x=0,
y=10,
energy=-90.0,
label="Periclase")
phase_2 = data.DataSet(cation=10,
x=0,
y=10,
energy=-100.0,
label="Periclase")
ref = {'Range': [-3, 2], 'Label': 'test'}
calculated = bulk_mu_vs_mu.calculate([phase_1, phase_2], bulk, ref,
ref, -10, -10)
assert calculated.z[0, 0] == 0
def chemical_potential_1(self):
bulk = data.ReferenceDataSet(cation=1,
anion=2,
energy=-100.00,
funits=1)
phase_1 = data.DataSet(cation=10,
x=0,
y=10,
energy=-90.0,
label="Periclase")
phase_2 = data.DataSet(cation=10,
x=0,
y=10,
energy=-100.0,
label="Periclase")
ref = {'Range': [-3, 2], 'Label': 'test'}
calculated = bulk_mu_vs_mu.calculate([phase_1, phase_2], bulk, ref,
ref, -10, -10)
ax = calculated.plot_phase()
assert_almost_equal(calculated.x, ax.lines[0].get_xydata().T[0])
def test_evaluate_phases(self):
xshiftval = 0
yshiftval = 0
deltaX = {'Range': [0, 10], 'Label': 'O'}
deltaY = {'Range': [0, 10], 'Label': 'H_2O'}
bulk = data.ReferenceDataSet(cation=1,
anion=2,
energy=-100.00,
funits=1)
pure = data.DataSet(cation=24,
x=48,
y=0,
area=60.22,
energy=-575.00,
label="Stoich",
nspecies=1)
H2O = data.DataSet(cation=24,
x=48,
y=2,
area=60.22,
energy=-600.00,
label="One",
nspecies=1)
dataset = [pure, H2O]
nsurfaces = len(dataset)
X = np.arange(deltaX['Range'][0],
deltaX['Range'][1],
0.025,
dtype="float")
Y = np.arange(deltaY['Range'][0],
deltaY['Range'][1],
0.025,
dtype="float")
X = X - xshiftval
Y = Y - yshiftval
phase, SE = mu_vs_mu.evaluate_phases(dataset, bulk, X, Y, nsurfaces,
xshiftval, yshiftval)
expected_phase = np.zeros(X.size * Y.size)
expected_phase = expected_phase + 2
assert_almost_equal(phase, expected_phase)
