def fit_modulus(self,
moduli: numpy.ndarray,
order: int = 2) -> numpy.ndarray:
'''Interpolate static elastic constants :math:`c^\\text{st}_{ij}(V)` as
a function of volume with polynomial least square fitting
:param moduli: The static elastic moduli :math:`c^\\text{st}_{ij}(V)`
to be fitted
:param order: The order for least square fitting
:returns: The interpolated modulus :math:`c^\\text{st}_{ij}(V)`
'''
# c_of_v = scipy.interpolate.UnivariateSpline(
# numpy.flip(self.volumes, axis=0),
# numpy.flip(moduli, axis=0)
# )
# return c_of_v(self.v_array)
strains = calculate_eulerian_strain(self.volumes[0], self.volumes)
strain_array = calculate_eulerian_strain(self.volumes[0], self.v_array)
_, modulus_array = polynomial_least_square_fitting(strains,
moduli,
strain_array,
order=order)
return modulus_array
def calibrate_energy_on_reference(volumes_before_calibration: Matrix,
energies_before_calibration: Matrix,
order: Optional[int] = 3):
"""
In multi-configuration system calculation, the volume set of each calculation may vary a little,
This function would make the volume set of the first configuration (normally, the most populated configuration)
as a reference volume set, then calibrate the energies of all configurations to this reference volume set.
:param volumes_before_calibration: Original volume sets of all configurations
:param energies_before_calibration: Free energies of all configurations on the original volume sets.
:param order: The order of Birch--Murnaghan EOS fitting.
:return: Free energies of each configuration on the
referenced volumes (usually the volumes of the first configuration).
"""
configurations_amount, _ = volumes_before_calibration.shape
volumes_for_reference: Vector = volumes_before_calibration[0]
energies_after_calibration = np.empty(volumes_before_calibration.shape)
for i in range(configurations_amount):
strains_before_calibration = calculate_eulerian_strain(
volumes_before_calibration[i, 0], volumes_before_calibration[i])
strains_after_calibration = calculate_eulerian_strain(
volumes_before_calibration[i, 0], volumes_for_reference)
_, energies_after_calibration[i, :] = polynomial_least_square_fitting(
strains_before_calibration,
energies_before_calibration[i],
strains_after_calibration,
order=order)
return energies_after_calibration
def get_static_p_of_v(v_sparse,
f_sparse,
v0=None,
N=100,
order=3,
v_ratio=1.2) -> callable:
v0 = v0 if v0 != None else v_sparse[numpy.argmin(f_sparse)]
x_sparse = calculate_eulerian_strain(v0, v_sparse)
x_dense = numpy.linspace(
calculate_eulerian_strain(v0,
numpy.max(v_sparse) * v_ratio),
calculate_eulerian_strain(v0,
numpy.min(v_sparse) / v_ratio), N + 1)
_, f_dense = polynomial_least_square_fitting(x_sparse,
f_sparse,
x_dense,
order=order)
v_dense = from_eulerian_strain(v0, x_dense)
p_dense = -numpy.gradient(f_dense) / numpy.gradient(v_dense)
def static_p_of_v(v_new: numpy.array) -> numpy.array:
x_new = calculate_eulerian_strain(v0, v_new)
return InterpolatedUnivariateSpline(x_dense, p_dense)(x_new)
return static_p_of_v
def _calculate_pressure_static(self, order: int = 3):
volumes = numpy.array(
[volume.volume for volume in self.qha_input.volumes])
static_energies = numpy.array(
[volume.energy for volume in self.qha_input.volumes])
strains = calculate_eulerian_strain(volumes[0], volumes)
strain_array = calculate_eulerian_strain(volumes[0], self.v_array)
_, static_energy_array = polynomial_least_square_fitting(
strains, static_energies, strain_array, order=order)
self.static_p_array = -numpy.gradient(
static_energy_array) / numpy.gradient(self.v_array)
def fit_modulus(self, moduli: numpy.ndarray, order: int = 2) -> numpy.ndarray:
'''Interpolate static elastic constants :math:`c^\\text{st}_{ij}(V)` as
a function of volume with polynomial least square fitting
:param moduli: The static elastic moduli :math:`c^\\text{st}_{ij}(V)`
to be fitted
:param order: The order for least square fitting
:returns: The interpolated modulus :math:`c^\\text{st}_{ij}(V)`
'''
strains = calculate_eulerian_strain(self.volumes[0], self.volumes)
strain_array = calculate_eulerian_strain(self.volumes[0], self.v_array)
p = numpy.polyfit(strains, self.volumes * moduli, deg = order + 1)
modulus_array = numpy.polyval(p, strain_array) / self.v_array
return modulus_array
def fit_modulus(volumes: numpy.ndarray,
v_array: numpy.ndarray,
moduli: numpy.ndarray,
order: int = 2) -> numpy.ndarray:
'''Interpolate static elastic constants :math:`c^\\text{st}_{ij}(V)` as
a function of volume with polynomial least square fitting
:param moduli: The static elastic moduli :math:`c^\\text{st}_{ij}(V)`
to be fitted
:param order: The order for least square fitting
:returns: The interpolated modulus :math:`c^\\text{st}_{ij}(V)`
'''
strains = calculate_eulerian_strain(volumes[0], volumes)
strain_array = calculate_eulerian_strain(volumes[0], v_array)
_, modulus_array = polynomial_least_square_fitting(strains,
moduli,
strain_array,
order=order)
return modulus_array
def static_p_of_v(v_new: numpy.array) -> numpy.array:
x_new = calculate_eulerian_strain(v0, v_new)
return InterpolatedUnivariateSpline(x_dense, p_dense)(x_new)
