def test_equilibrium_raises_when_no_phases_can_be_active():
"""Equliibrium raises when the components passed cannot give any active phases"""
# all phases contain AL and/or FE in a sublattice, so no phases can be active
equilibrium(ALFE_DBF, ['VA'], list(ALFE_DBF.phases.keys()), {
v.T: 300,
v.P: 101325
})
def test_equilibrium_raises_with_no_active_phases_passed():
"""Passing inactive phases to equilibrium raises a ConditionError."""
# the only phases passed are the disordered phases, which are inactive
equilibrium(ALNIFCC4SL_DBF, ['AL', 'NI', 'VA'], ['FCC_A1', 'BCC_A2'], {
v.T: 300,
v.P: 101325
})
def test_eq_overdetermined_comps():
"""
The number of composition conditions should yield exactly one dependent component.
This is the overdetermined case.
"""
equilibrium(ALFE_DBF, ['AL', 'FE'], 'LIQUID', {v.T: 2000, v.P: 101325,
v.X('FE'): 0.2, v.X('AL'): 0.8})
def test_eq_overdetermined_comps():
"""
The number of composition conditions should yield exactly one dependent component.
This is the overdetermined case.
"""
equilibrium(ALFE_DBF, ['AL', 'FE'], 'LIQUID', {v.T: 2000, v.P: 101325,
v.X('FE'): 0.2, v.X('AL'): 0.8}, pbar=False)
def test_eq_on_endmember():
"""
When the composition condition is right on top of an end-member
the convex hull is still correctly constructed (gh-28).
"""
equilibrium(ALFE_DBF, ['AL', 'FE', 'VA'], ['LIQUID', 'B2_BCC'],
{v.X('AL'): [0.4, 0.5, 0.6], v.T: [300, 600], v.P: 101325}, pbar=False)
def test_eq_b2_without_all_comps():
"""
All-vacancy endmembers are correctly excluded from the computation when fewer than
all components in a Database are selected for the calculation.
"""
equilibrium(Database(ALNIPT_TDB), ['AL', 'NI', 'VA'], 'BCC_B2', {v.X('NI'): 0.4, v.P: 101325, v.T: 1200},
verbose=True, pbar=False)
def test_eq_on_endmember():
"""
When the composition condition is right on top of an end-member
the convex hull is still correctly constructed (gh-28).
"""
equilibrium(ALFE_DBF, ['AL', 'FE', 'VA'], ['LIQUID', 'B2_BCC'],
{v.X('AL'): [0.4, 0.5, 0.6], v.T: [300, 600], v.P: 101325}, verbose=True)
def test_eq_output_property():
"""
Extra properties can be specified to `equilibrium`.
"""
equilibrium(ALFE_DBF, ['AL', 'FE', 'VA'], ['LIQUID', 'B2_BCC'],
{v.X('AL'): 0.25, v.T: (300, 2000, 500), v.P: 101325},
output=['heat_capacity', 'degree_of_ordering'], pbar=False)
def test_eq_output_property():
"""
Extra properties can be specified to `equilibrium`.
"""
equilibrium(ALFE_DBF, ['AL', 'FE', 'VA'], ['LIQUID', 'B2_BCC'],
{v.X('AL'): 0.25, v.T: (300, 2000, 500), v.P: 101325},
output=['heat_capacity', 'degree_of_ordering'])
def test_equlibrium_no_opt_solver():
"""Passing in a solver with `ignore_convergence = True` gives a result."""
class NoOptSolver(InteriorPointSolver):
ignore_convergence = True
comps = ['PB', 'SN', 'VA']
phases = list(PBSN_DBF.phases.keys())
conds = {v.T: 300, v.P: 101325, v.X('SN'): 0.50}
ipopt_solver_eq_res = equilibrium(PBSN_DBF,
comps,
phases,
conds,
solver=InteriorPointSolver(),
verbose=True)
no_opt_eq_res = equilibrium(PBSN_DBF,
comps,
phases,
conds,
solver=NoOptSolver(),
verbose=True)
ipopt_GM = ipopt_solver_eq_res.GM.values.squeeze()
no_opt_GM = no_opt_eq_res.GM.values.squeeze()
no_opt_MU = no_opt_eq_res.MU.values.squeeze()
assert ipopt_GM != no_opt_GM # global min energy is different from lower convex hull
assert np.allclose([-17452.5115967],
no_opt_GM) # energy from lower convex hull
assert np.allclose([-19540.6522632, -15364.3709302],
no_opt_MU) # chempots from lower convex hull
def time_equilibrium_al_fe(self):
equilibrium(self.db_alni, ['AL', 'NI', 'VA'], ['LIQUID', 'FCC_L12'], {
v.X('AL'): 0.10,
v.T: (300, 2500, 20),
v.P: 101325
},
pbar=False)
def time_equilibrium_al_ni(self):
equilibrium(self.db_alfe, ['AL', 'FE', 'VA'], ['LIQUID', 'B2_BCC'], {
v.X('AL'): 0.25,
v.T: (300, 2000, 50),
v.P: 101325
},
pbar=False)
def test_eq_unary_issue78():
"Unary equilibrium calculations work with property calculations."
eq = equilibrium(ALFE_DBF, ['AL', 'VA'], 'FCC_A1', {v.T: 1200, v.P: 101325}, output='SM')
np.testing.assert_allclose(eq.SM, 68.143273)
eq = equilibrium(ALFE_DBF, ['AL', 'VA'], 'FCC_A1', {v.T: 1200, v.P: 101325}, output='SM', parameters={'GHSERAL': 1000})
np.testing.assert_allclose(eq.GM, 1000)
np.testing.assert_allclose(eq.SM, 0)
def test_eq_b2_without_all_comps():
"""
All-vacancy endmembers are correctly excluded from the computation when fewer than
all components in a Database are selected for the calculation.
"""
equilibrium(Database(ALNIPT_TDB), ['AL', 'NI', 'VA'], 'BCC_B2', {v.X('NI'): 0.4, v.P: 101325, v.T: 1200},
verbose=True)
def test_eq_underdetermined_comps():
"""
The number of composition conditions should yield exactly one dependent component.
This is the underdetermined case.
"""
with pytest.raises(ValueError):
equilibrium(ALFE_DBF, ['AL', 'FE'], 'LIQUID', {v.T: 2000, v.P: 101325})
def test_eq_missing_component():
"""
Specifying a condition involving a non-existent component raises an error.
"""
# No Co or Cr in this database ; Co condition specification should cause failure
equilibrium(ALNIFCC4SL_DBF, ['AL', 'NI', 'VA'], ['LIQUID'],
{v.T: 1523, v.X('AL'): 0.88811111111111107,
v.X('CO'): 0.11188888888888888, v.P: 101325})
def test_eq_avoid_phase_cycling():
"""
Converge without getting stuck in an add/remove phase cycle.
"""
# This set of conditions is known to trigger the issue
my_phases_alfe = ['LIQUID', 'B2_BCC', 'FCC_A1', 'HCP_A3', 'AL5FE2', 'AL2FE', 'AL13FE4', 'AL5FE4']
equilibrium(ALFE_DBF, ['AL', 'FE', 'VA'], my_phases_alfe, {v.X('AL'): 0.44,
v.T: 1600, v.P: 101325}, verbose=True)
def test_eq_missing_component():
"""
Specifying a condition involving a non-existent component raises an error.
"""
# No Co or Cr in this database ; Co condition specification should cause failure
equilibrium(ALNIFCC4SL_DBF, ['AL', 'NI', 'VA'], ['LIQUID'],
{v.T: 1523, v.X('AL'): 0.88811111111111107,
v.X('CO'): 0.11188888888888888, v.P: 101325}, pbar=False)
def test_unused_equilibrium_kwarg_warns():
"Check that an unused keyword argument raises a warning"
with warnings.catch_warnings(record=True) as w:
equilibrium(ALFE_DBF, ['AL', 'FE', 'VA'], 'FCC_A1', {v.T: 1300, v.P: 101325, v.X('AL'): 0}, unused_kwarg='should raise a warning')
assert len(w) >= 1
categories = [warning.__dict__['_category_name'] for warning in w]
assert 'UserWarning' in categories
expected_string_fragment = 'keyword arguments were passed, but unused'
assert any([expected_string_fragment in str(warning.message) for warning in w])
def test_dilute_condition():
"""
'Zero' and dilute composition conditions are correctly handled.
"""
eq = equilibrium(ALFE_DBF, ['AL', 'FE', 'VA'], 'FCC_A1', {v.T: 1300, v.P: 101325, v.X('AL'): 0}, verbose=True)
assert_allclose(np.squeeze(eq.GM.values), -64415.84, atol=0.1)
eq = equilibrium(ALFE_DBF, ['AL', 'FE', 'VA'], 'FCC_A1', {v.T: 1300, v.P: 101325, v.X('AL'): 1e-12}, verbose=True)
assert_allclose(np.squeeze(eq.GM.values), -64415.841)
assert_allclose(np.squeeze(eq.MU.values), [-385499.682936, -64415.837878], atol=1.0)
def test_eq_some_phases_filtered():
"""
Phases are filtered out from equilibrium() when some cannot be built.
"""
# should not raise; AL13FE4 should be filtered out
equilibrium(ALFE_DBF, ['AL', 'VA'], ['FCC_A1', 'AL13FE4'], {
v.T: 1200,
v.P: 101325
})
def test_dilute_condition():
"""
'Zero' and dilute composition conditions are correctly handled.
"""
eq = equilibrium(ALFE_DBF, ["AL", "FE", "VA"], "FCC_A1", {v.T: 1300, v.P: 101325, v.X("AL"): 0}, pbar=False)
assert_allclose(np.squeeze(eq.GM.values), -64415.84, atol=0.1)
eq = equilibrium(ALFE_DBF, ["AL", "FE", "VA"], "FCC_A1", {v.T: 1300, v.P: 101325, v.X("AL"): 1e-8}, pbar=False)
assert_allclose(np.squeeze(eq.GM.values), -64415.84069827)
assert_allclose(eq.MU.values, [[[[-335723.04320981, -64415.8379852]]]], atol=0.1)
def test_equilibrium_raises_with_invalid_solver():
"""
SolverBase instances passed to equilibrium should raise an error.
"""
equilibrium(CUO_DBF, ['O'],
'GAS', {
v.T: 1000,
v.P: 1e5
},
solver=SolverBase())
def test_dilute_condition():
"""
'Zero' and dilute composition conditions are correctly handled.
"""
eq = equilibrium(ALFE_DBF, ['AL', 'FE', 'VA'], 'FCC_A1', {v.T: 1300, v.P: 101325, v.X('AL'): 0}, verbose=True)
assert_allclose(np.squeeze(eq.GM.values), -64415.84, atol=0.1)
eq = equilibrium(ALFE_DBF, ['AL', 'FE', 'VA'], 'FCC_A1', {v.T: 1300, v.P: 101325, v.X('AL'): 1e-8}, verbose=True)
# Checked in TC
assert_allclose(np.squeeze(eq.GM.values), -64415.841)
# We loosen the tolerance a bit here because our convergence tolerance is too low for the last digit
assert_allclose(np.squeeze(eq.MU.values), [-335723.28, -64415.838], atol=1.0)
def test_eq_underdetermined_comps():
"""
The number of composition conditions should yield exactly one dependent component.
This is the underdetermined case.
"""
equilibrium(ALFE_DBF, ['AL', 'FE'],
'LIQUID', {
v.T: 2000,
v.P: 101325
},
pbar=False)
def test_missing_models_with_phase_records_passed_to_equilibrium_raises():
"equilibrium should raise an error if all the active phases are not included in the phase_records"
my_phases = ['LIQUID', 'FCC_A1', 'HCP_A3', 'AL5FE2', 'AL2FE', 'AL13FE4', 'AL5FE4']
comps = ['AL', 'FE', 'VA']
conds = {v.T: 1400, v.P: 101325, v.N: 1.0, v.X('AL'): 0.55}
models = instantiate_models(ALFE_DBF, comps, my_phases)
phase_records = build_phase_records(ALFE_DBF, comps, my_phases, conds, models)
with pytest.raises(ValueError):
# model=models NOT passed
equilibrium(ALFE_DBF, comps, my_phases, conds, verbose=True, phase_records=phase_records)
def test_eq_output_property():
"""
Extra properties can be specified to `equilibrium`.
"""
equilibrium(
ALFE_DBF,
["AL", "FE", "VA"],
["LIQUID", "B2_BCC"],
{v.X("AL"): 0.25, v.T: (300, 2000, 500), v.P: 101325},
output=["heat_capacity", "degree_of_ordering"],
pbar=False,
)
def test_eq_on_endmember():
"""
When the composition condition is right on top of an end-member
the convex hull is still correctly constructed (gh-28).
"""
equilibrium(
ALFE_DBF,
["AL", "FE", "VA"],
["LIQUID", "B2_BCC"],
{v.X("AL"): [0.4, 0.5, 0.6], v.T: [300, 600], v.P: 101325},
pbar=False,
)
def test_eq_missing_component():
"""
Specifying a condition involving a non-existent component raises an error.
"""
# No Co or Cr in this database ; Co condition specification should cause failure
equilibrium(
ALNIFCC4SL_DBF,
["AL", "NI", "VA"],
["LIQUID"],
{v.T: 1523, v.X("AL"): 0.88811111111111107, v.X("CO"): 0.11188888888888888, v.P: 101325},
pbar=False,
)
def test_eq_gas_phase():
eq = equilibrium(CUO_DBF, ['O'],
'GAS', {
v.T: 1000,
v.P: 1e5
},
verbose=True)
np.testing.assert_allclose(eq.GM, -110380.61071, atol=0.1)
eq = equilibrium(CUO_DBF, ['O'],
'GAS', {
v.T: 1000,
v.P: 1e9
},
verbose=True)
np.testing.assert_allclose(eq.GM, -7.20909E+04, atol=0.1)
def test_eq_issue62_last_component_not_va():
"""
VA is not last when components are sorted alphabetically.
"""
test_tdb = """
ELEMENT VA VACUUM 0.0000E+00 0.0000E+00 0.0000E+00!
ELEMENT AL FCC_A1 2.6982E+01 4.5773E+03 2.8322E+01!
ELEMENT CO HCP_A3 5.8933E+01 4.7656E+03 3.0040E+00!
ELEMENT CR BCC_A2 5.1996E+01 4.0500E+03 2.3560E+01!
ELEMENT W BCC_A2 1.8385E+02 4.9700E+03 3.2620E+01!
PHASE FCC_A1 % 2 1 1 !
CONSTITUENT FCC_A1 :AL,CO,CR,W : VA% : !
"""
equilibrium(Database(test_tdb), ['AL', 'CO', 'CR', 'W', 'VA'], ['FCC_A1'],
{"T": 1248, "P": 101325, v.X("AL"): 0.081, v.X("CR"): 0.020, v.X("W"): 0.094})
def plot_convex_hull(dbf, comps, phases, conds, ax=None):
if ax is None:
ax = plt.gca()
result = equilibrium(dbf, comps, phases, conds)
unique_phase_sets = np.unique(result['Phase'].values.squeeze(), axis=0)
comp_cond = [c for c in conds.keys() if isinstance(c, v.X)]
if len(comp_cond) != 1:
raise ValueError(
f"Exactly one composition condition required for plotting convex hull. Got {len(comp_cond)} with conditions for {comp_cond}"
)
comp_cond = str(comp_cond[0])
for phase_set in unique_phase_sets:
label = '+'.join([ph for ph in phase_set if ph != ''])
# composition indices with the same unique phase
unique_phase_idx = np.nonzero(
np.all(result['Phase'].values.squeeze() == phase_set, axis=1))[0]
masked_result = result.isel(**{comp_cond: unique_phase_idx})
ax.plot(masked_result[comp_cond].squeeze(),
masked_result.GM.squeeze(),
color='limegreen',
linestyle=':',
lw=4,
label='convex hull',
zorder=3)
return ax
def test_eq_charge_ndzro():
"""Nd-Zr-O system (with charged species) are correctly calculated and charged balanced in equilibrium"""
comps = ['ND', 'ZR', 'O', 'VA']
phases = ['ND2O3_A', 'PYRO']
conds = {v.P: 101325, v.N: 1, v.T: 1400, v.X('ND'): 0.25, v.X('O'): 0.625}
# Higher point density is required for convergence. Lower point densities
# Can result in either no phases, or only FLUO phase (incorrect)
res = equilibrium(AL2O3_ND2O3_ZRO2_DBF, comps, phases, conds, verbose=True)
# Values below checked with Thermo-Calc
assert np.isclose(-432325.423784, res.GM.values.squeeze())
assert np.all(res.Phase.values.squeeze() == np.array(['ND2O3_A', 'PYRO', '', '']))
assert np.allclose(res.NP.values.squeeze()[:2], [0.30164254, 0.69835646])
# site fractions of ND2O3_A
Y_ND2O3_A = res.Y.values.squeeze()[0, :5]
Y_PYRO = res.Y.values.squeeze()[1, :]
SPEC_CHG_ND2O3_A = np.array([3, 4, -2, -2, 0]) # (ND+3,ZR+4):(O-2):(O-2,VA)
SITE_RATIO_ND2O3_A = np.array([2, 2, 3, 1, 1]) # 2:3:1
SPEC_CHG_PYRO = np.array([3, 4, 3, 4, -2, 0, -2, -2, 0]) # (ND+3,ZR+4):(ND+3,ZR+4):(O-2,VA):(O-2):(O-2,VA)
SITE_RATIO_PYRO = np.array([2, 2, 2, 2, 6, 6, 1, 1, 1]) # 2:2:6:1:1
CHG_ND2O3_A = np.dot(Y_ND2O3_A*SPEC_CHG_ND2O3_A, SITE_RATIO_ND2O3_A)
CHG_PYRO = np.dot(Y_PYRO*SPEC_CHG_PYRO, SITE_RATIO_PYRO)
print('CHARGE ND2O3_A', CHG_ND2O3_A)
print('CHARGE PYRO', CHG_PYRO)
assert np.isclose(CHG_ND2O3_A, 0)
assert np.isclose(CHG_PYRO, 0)
assert np.allclose(Y_ND2O3_A, [9.79497936e-01, 2.05020639e-02, 1.00000000e+00, 2.05020639e-02, 9.79497936e-01],
rtol=5e-4)
assert np.allclose(Y_PYRO, [9.99970071e-01, 2.99288042e-05, 3.83395063e-02, 9.61660494e-01, 9.93381787e-01,
6.61821340e-03, 1.00000000e+00, 1.39970285e-03, 9.98600297e-01], rtol=5e-4)
def test_eq_model_phase_name():
"""
Phase name is set in PhaseRecord when using Model-based JIT compilation.
"""
eq = equilibrium(ALFE_DBF, ['AL', 'FE', 'VA'], 'LIQUID',
{v.X('FE'): 0.3, v.T: 1000, v.P: 101325}, model=Model)
assert eq.Phase.sel(vertex=0).isel(T=0, P=0, X_FE=0) == 'LIQUID'
def test_eq_binary():
"Binary phase diagram point equilibrium calculation with magnetism."
my_phases = ["LIQUID", "FCC_A1", "HCP_A3", "AL5FE2", "AL2FE", "AL13FE4", "AL5FE4"]
comps = ["AL", "FE", "VA"]
conds = {v.T: 1400, v.P: 101325, v.X("AL"): 0.55}
eqx = equilibrium(ALFE_DBF, comps, my_phases, conds, pbar=False)
assert_allclose(eqx.GM.values.flat[0], -9.608807e4)
def estimate_hyperplane(dbf, comps, phases, current_statevars, comp_dicts, phase_models, parameters):
region_chemical_potentials = []
parameters = OrderedDict(sorted(parameters.items(), key=str))
for cond_dict, phase_flag in comp_dicts:
# We are now considering a particular tie vertex
for key, val in cond_dict.items():
if val is None:
cond_dict[key] = np.nan
cond_dict.update(current_statevars)
if np.any(np.isnan(list(cond_dict.values()))):
# This composition is unknown -- it doesn't contribute to hyperplane estimation
pass
else:
# Extract chemical potential hyperplane from multi-phase calculation
# Note that we consider all phases in the system, not just ones in this tie region
multi_eqdata = equilibrium(dbf, comps, phases, cond_dict, verbose=False,
model=phase_models, scheduler=dask.local.get_sync, parameters=parameters)
# Does there exist only a single phase in the result with zero internal degrees of freedom?
# We should exclude those chemical potentials from the average because they are meaningless.
num_phases = len(np.squeeze(multi_eqdata['Phase'].values != ''))
zero_dof = np.all((multi_eqdata['Y'].values == 1.) | np.isnan(multi_eqdata['Y'].values))
if (num_phases == 1) and zero_dof:
region_chemical_potentials.append(np.full_like(np.squeeze(multi_eqdata['MU'].values), np.nan))
else:
region_chemical_potentials.append(np.squeeze(multi_eqdata['MU'].values))
region_chemical_potentials = np.nanmean(region_chemical_potentials, axis=0, dtype=np.float)
return region_chemical_potentials
def test_eq_parameter_override():
"""
Check that overriding parameters works in equilibrium().
"""
comps = ["AL"]
dbf = AL_PARAMETER_DBF
phases = ['FCC_A1']
conds = {v.P: 101325, v.T: 500}
# Check that current database should work as expected
eq_res = equilibrium(dbf, comps, phases, conds)
np.testing.assert_allclose(eq_res.GM.values.squeeze(), 5000.0)
# Check that overriding parameters works
eq_res = equilibrium(dbf, comps, phases, conds, parameters={'VV0000': 10000})
np.testing.assert_allclose(eq_res.GM.values.squeeze(), 10000.0)
def ternplot(dbf,
comps,
phases,
conds,
x=None,
y=None,
eq_kwargs=None,
**plot_kwargs):
"""
Calculate the ternary isothermal, isobaric phase diagram.
This function is a convenience wrapper around equilibrium() and eqplot().
Parameters
----------
dbf : Database
Thermodynamic database containing the relevant parameters.
comps : list
Names of components to consider in the calculation.
phases : list
Names of phases to consider in the calculation.
conds : dict
Maps StateVariables to values and/or iterables of values.
For ternplot only one changing composition and one potential coordinate each is supported.
x : Composition
instance of a pycalphad.variables.composition to plot on the x-axis.
Must correspond to an independent condition.
y : Composition
instance of a pycalphad.variables.composition to plot on the y-axis.
Must correspond to an independent condition.
eq_kwargs : optional
Keyword arguments to equilibrium().
plot_kwargs : optional
Keyword arguments to eqplot().
Returns
-------
A phase diagram as a figure.
Examples
--------
None yet.
"""
eq_kwargs = eq_kwargs if eq_kwargs is not None else dict()
indep_comps = [
key for key, value in conds.items()
if isinstance(key, v.Composition) and len(np.atleast_1d(value)) > 1
]
indep_pots = [
key for key, value in conds.items()
if ((key == v.T) or (key == v.P)) and len(np.atleast_1d(value)) > 1
]
if (len(indep_comps) != 2) or (len(indep_pots) != 0):
raise ValueError(
'ternplot() requires exactly two composition coordinates')
full_eq = equilibrium(dbf, comps, phases, conds, **eq_kwargs)
# TODO: handle x and y as strings with #87
x = x if x in indep_comps else indep_comps[0]
y = y if y in indep_comps else indep_comps[1]
return eqplot(full_eq, x=x, y=y, **plot_kwargs)
def test_eq_binary():
"Binary phase diagram point equilibrium calculation with magnetism."
my_phases = ['LIQUID', 'FCC_A1', 'HCP_A3', 'AL5FE2',
'AL2FE', 'AL13FE4', 'AL5FE4']
comps = ['AL', 'FE', 'VA']
conds = {v.T: 1400, v.P: 101325, v.X('AL'): 0.55}
eqx = equilibrium(ALFE_DBF, comps, my_phases, conds, pbar=False)
assert_allclose(eqx.GM.values.flat[0], -9.608807e4)
def test_equilibrium_result_dataset_can_serialize_to_netcdf():
"""
The xarray Dataset returned by equilibrium should serializable to a netcdf file.
"""
fname = 'eq_result_netcdf_test.nc'
eq = equilibrium(ALFE_DBF, ['AL', 'VA'], 'FCC_A1', {v.T: 1200, v.P: 101325})
eq.to_netcdf(fname)
os.remove(fname) # cleanup
def test_eq_binary():
"Binary phase diagram point equilibrium calculation with magnetism."
my_phases = ['LIQUID', 'FCC_A1', 'HCP_A3', 'AL5FE2',
'AL2FE', 'AL13FE4', 'AL5FE4']
comps = ['AL', 'FE', 'VA']
conds = {v.T: 1400, v.P: 101325, v.X('AL'): 0.55}
eqx = equilibrium(ALFE_DBF, comps, my_phases, conds, verbose=True)
assert_allclose(eqx.GM.values.flat[0], -9.608807e4)
def test_degree_of_ordering():
"Degree of ordering should be calculated properly."
my_phases = ['B2_BCC']
comps = ['AL', 'FE', 'VA']
conds = {v.T: 300, v.P: 101325, v.X('AL'): 0.25}
eqx = equilibrium(ALFE_DBF, comps, my_phases, conds, output='degree_of_ordering', verbose=True)
print('Degree of ordering: {}'.format(eqx.degree_of_ordering.sel(vertex=0).values.flatten()))
assert np.isclose(eqx.degree_of_ordering.sel(vertex=0).values.flatten(), np.array([0.66663873]))
def test_rose_nine():
"Nine-component rose diagram point equilibrium calculation."
my_phases_rose = ["TEST"]
comps = ["H", "HE", "LI", "BE", "B", "C", "N", "O", "F"]
conds = dict({v.T: 1000, v.P: 101325})
for comp in comps[:-1]:
conds[v.X(comp)] = 1.0 / float(len(comps))
eqx = equilibrium(ROSE_DBF, comps, my_phases_rose, conds, pbar=False)
assert_allclose(eqx.GM.values.flat[0], -5.8351e3, atol=0.1)
def test_rose_nine():
"Nine-component rose diagram point equilibrium calculation."
my_phases_rose = ['TEST']
comps = ['H', 'HE', 'LI', 'BE', 'B', 'C', 'N', 'O', 'F']
conds = dict({v.T: 1000, v.P: 101325})
for comp in comps[:-1]:
conds[v.X(comp)] = 1.0/float(len(comps))
eqx = equilibrium(ROSE_DBF, comps, my_phases_rose, conds, pbar=False)
assert_allclose(eqx.GM.values.flat[0], -5.8351e3)
def test_eq_single_phase():
"Equilibrium energy should be the same as for a single phase with no miscibility gaps."
res = calculate(ALFE_DBF, ['AL', 'FE'], 'LIQUID', T=[1400, 2500], P=101325,
points={'LIQUID': [[0.1, 0.9], [0.2, 0.8], [0.3, 0.7],
[0.7, 0.3], [0.8, 0.2]]})
eq = equilibrium(ALFE_DBF, ['AL', 'FE'], 'LIQUID',
{v.T: [1400, 2500], v.P: 101325,
v.X('AL'): [0.1, 0.2, 0.3, 0.7, 0.8]}, verbose=True, pbar=False)
assert_allclose(eq.GM, res.GM, atol=0.1)
def test_eq_illcond_magnetic_hessian():
"""
Check equilibrium of a system with an ill-conditioned Hessian due to magnetism (Tc->0).
This is difficult to reproduce so we only include some known examples here.
"""
# This set of conditions is known to trigger the issue
eq = equilibrium(ALFE_DBF, ['AL', 'FE', 'VA'], ['FCC_A1', 'AL13FE4'],
{v.X('AL'): 0.8, v.T: 300, v.P: 1e5}, pbar=False)
assert_allclose(eq.GM.values, [[[-31414.46677]]])
def test_eq_four_sublattice():
"""
Balancing mass in a multi-sublattice phase in a single-phase configuration.
"""
eq = equilibrium(ALNIFCC4SL_DBF, ['AL', 'NI', 'VA'], 'FCC_L12',
{v.T: 1073, v.X('NI'): 0.7601, v.P: 101325}, pbar=False)
assert_allclose(np.squeeze(eq.X.sel(vertex=0).values), [1-.7601, .7601])
# Not a strict equality here because we can't yet reach TC's value of -87260.6
assert eq.GM.values < -87256.3
def test_eq_ternary_edge_case_mass():
"""
Equilibrium along an edge of composition space will still balance mass.
"""
eq = equilibrium(ALCOCRNI_DBF, ['AL', 'CO', 'CR', 'VA'], ['L12_FCC', 'BCC_B2', 'LIQUID'],
{v.T: 1523, v.X('AL'): 0.88811111111111107,
v.X('CO'): 0.11188888888888888, v.P: 101325}, pbar=False)
mass_error = np.nansum(np.squeeze(eq.NP * eq.X), axis=-2) - \
[0.88811111111111107, 0.11188888888888888, 0]
assert np.all(np.abs(mass_error) < 0.01)
def test_eqplot_ternary():
"""
eqplot should return an axes object that has a traingular projection when
two independent components and one independent potential are passed.
"""
eq = equilibrium(ALCOCRNI_DBF, ['AL', 'CO', 'CR', 'VA'], ['LIQUID'],
{v.T: 2500, v.X('AL'): (0,0.5,0.33), v.X('CO'): (0,0.5,0.3), v.P: 101325})
ax = eqplot(eq)
assert isinstance(ax, Axes)
assert ax.name == 'triangular'
def test_eq_ternary_edge_misc_gap():
"""
Equilibrium at edge of miscibility gap will still balance mass.
"""
eq = equilibrium(ALCOCRNI_DBF, ['AL', 'CO', 'CR', 'VA'], ['L12_FCC', 'BCC_B2', 'LIQUID'],
{v.T: 1523, v.X('AL'): 0.33366666666666667,
v.X('CO'): 0.44455555555555554, v.P: 101325}, pbar=False)
mass_error = np.nansum(np.squeeze(eq.NP * eq.X), axis=-2) - \
[0.33366666666666667, 0.44455555555555554, 0.22177777777777785]
assert np.all(np.abs(mass_error) < 0.001)
def test_eq_ternary_inside_mass():
"""
Equilibrium in interior of composition space will still balance mass.
"""
eq = equilibrium(ALCOCRNI_DBF, ['AL', 'CO', 'CR', 'VA'], ['L12_FCC', 'BCC_B2', 'LIQUID'],
{v.T: 1523, v.X('AL'): 0.44455555555555554,
v.X('CO'): 0.22277777777777777, v.P: 101325}, pbar=False)
mass_error = np.nansum(np.squeeze(eq.NP * eq.X), axis=-2) - \
[0.44455555555555554, 0.22277777777777777, 0.333]
assert np.all(np.abs(mass_error) < 0.01)
def test_eqplot_binary():
"""
eqplot should return an axes object when one independent component and one
independent potential are passed.
"""
my_phases = ['LIQUID', 'FCC_A1', 'HCP_A3', 'AL5FE2',
'AL2FE', 'AL13FE4', 'AL5FE4']
comps = ['AL', 'FE', 'VA']
conds = {v.T: (1400, 1500, 50), v.P: 101325, v.X('AL'): (0, 1, 0.5)}
eq = equilibrium(ALFE_DBF, comps, my_phases, conds)
ax = eqplot(eq)
assert isinstance(ax, Axes)
def test_eq_composition_cond_sorting():
"""
Composition conditions are correctly constructed when the dependent component does not
come last in alphabetical order (gh-21).
"""
eq = equilibrium(ALFE_DBF, ["AL", "FE"], "LIQUID", {v.T: 2000, v.P: 101325, v.X("FE"): 0.2}, pbar=False)
# Values computed by Thermo-Calc
tc_energy = -143913.3
tc_mu_fe = -184306.01
tc_mu_al = -133815.12
assert_allclose(eq.GM.values, tc_energy)
assert_allclose(eq.MU.values, [[[[tc_mu_al, tc_mu_fe]]]], rtol=1e-6)
def test_eq_illcond_hessian():
"""
Check equilibrium of a system with an ill-conditioned Hessian.
This is difficult to reproduce so we only include some known examples here (gh-23).
"""
# This set of conditions is known to trigger the issue
eq = equilibrium(ALFE_DBF, ['AL', 'FE', 'VA'], 'LIQUID',
{v.X('FE'): 0.73999999999999999, v.T: 401.5625, v.P: 1e5}, pbar=False)
assert_allclose(eq.GM.values, [[[-16507.22325998]]])
# chemical potentials were checked in TC and accurate to 1 J/mol
# pycalphad values used for more significant figures
# once again, py33 converges to a slightly different value versus every other python
assert_allclose(eq.MU.values, [[[[-55611.954141, -2767.72322]]]], atol=0.1)
def test_eq_ternary_edge_case_mass():
"""
Equilibrium along an edge of composition space will still balance mass.
"""
eq = equilibrium(
ALCOCRNI_DBF,
["AL", "CO", "CR", "VA"],
["L12_FCC", "BCC_B2", "LIQUID"],
{v.T: 1523, v.X("AL"): 0.88811111111111107, v.X("CO"): 0.11188888888888888, v.P: 101325},
pbar=False,
)
mass_error = np.nansum(np.squeeze(eq.NP * eq.X), axis=-2) - [0.88811111111111107, 0.11188888888888888, 0]
assert np.all(np.abs(mass_error) < 0.01)
def test_eq_ternary_inside_mass():
"""
Equilibrium in interior of composition space will still balance mass.
"""
eq = equilibrium(
ALCOCRNI_DBF,
["AL", "CO", "CR", "VA"],
["L12_FCC", "BCC_B2", "LIQUID"],
{v.T: 1523, v.X("AL"): 0.44455555555555554, v.X("CO"): 0.22277777777777777, v.P: 101325},
pbar=False,
verbose=True,
)
mass_error = np.nansum(np.squeeze(eq.NP * eq.X), axis=-2) - [0.44455555555555554, 0.22277777777777777, 0.333]
assert np.all(np.abs(mass_error) < 0.01)
def test_eq_issue43_chempots_misc_gap():
"""
Equilibrium for complex ternary miscibility gap (gh-43).
"""
eq = equilibrium(
ISSUE43_DBF,
["AL", "NI", "CR", "VA"],
"GAMMA_PRIME",
{v.X("AL"): 0.1246, v.X("CR"): 1e-9, v.T: 1273, v.P: 101325},
verbose=False,
pbar=False,
)
chempots = 8.31451 * np.squeeze(eq["T"].values) * np.array([[[[[-19.47631644, -25.71249032, -6.0706158]]]]])
assert_allclose(eq.GM.values, -81933.259)
assert_allclose(eq.MU.values, chempots, atol=1)
def test_eq_issue43_chempots_tricky_potentials():
"""
Ternary equilibrium with difficult convergence for chemical potentials (gh-43).
"""
eq = equilibrium(
ISSUE43_DBF,
["AL", "NI", "CR", "VA"],
["FCC_A1", "GAMMA_PRIME"],
{v.X("AL"): 0.1246, v.X("CR"): 0.6, v.T: 1273, v.P: 101325},
verbose=False,
pbar=False,
)
chempots = 8.31451 * np.squeeze(eq["T"].values) * np.array([[[[[-12.78777939, -4.42862046, -8.77499585]]]]])
assert_allclose(eq.GM.values, -70567.7329)
assert_allclose(eq.MU.values, chempots, atol=1)
def test_eq_illcond_magnetic_hessian():
"""
Check equilibrium of a system with an ill-conditioned Hessian due to magnetism (Tc->0).
This is difficult to reproduce so we only include some known examples here.
"""
# This set of conditions is known to trigger the issue
eq = equilibrium(
ALFE_DBF,
["AL", "FE", "VA"],
["FCC_A1", "AL13FE4"],
{v.X("AL"): 0.8, v.T: 300, v.P: 1e5},
pbar=False,
verbose=True,
)
assert_allclose(eq.GM.values, [[[-31414.46677]]])
assert_allclose(eq.MU.values, [[[[-8490.140, -123111.773]]]], atol=0.1)
def test_eq_ternary_edge_misc_gap():
"""
Equilibrium at edge of miscibility gap will still balance mass.
"""
eq = equilibrium(
ALCOCRNI_DBF,
["AL", "CO", "CR", "VA"],
["L12_FCC", "BCC_B2", "LIQUID"],
{v.T: 1523, v.X("AL"): 0.33366666666666667, v.X("CO"): 0.44455555555555554, v.P: 101325},
pbar=False,
)
mass_error = np.nansum(np.squeeze(eq.NP * eq.X), axis=-2) - [
0.33366666666666667,
0.44455555555555554,
0.22177777777777785,
]
assert np.all(np.abs(mass_error) < 0.001)
