def analyse(self):
OptimizeTask.analyse(self)
for name, data in self.data.items():
if 'distances' in data:
distances = data['distances']
energies = data['energies']
fit0 = np.poly1d(np.polyfit(1 / distances, energies, 3))
fit1 = np.polyder(fit0, 1)
fit2 = np.polyder(fit1, 1)
dmin = None
for t in np.roots(fit1):
if t > 0 and fit2(t) > 0:
dmin = 1 / t
break
if dmin is None:
raise ValueError('No minimum!')
if abs(dmin) < min(distances) or abs(dmin) > max(distances):
raise ValueError('Fit outside of range! ' + \
str(abs(dmin)) + ' not in ' + \
str(distances))
emin = fit0(t)
k = fit2(t) * t**4
m1, m2 = self.create_system(name).get_masses()
m = m1 * m2 / (m1 + m2)
hnu = units._hbar * 1e10 * sqrt(k / units._e / units._amu / m)
data['minimum energy'] = emin
self.results[name][1:] = [energies[2] - emin, dmin, 1000 * hnu]
else:
self.results[name].extend([None, None])
for name, data in self.data.items():
atoms = self.create_system(name)
if len(atoms) == 1:
self.results[name].extend([None, None])
continue
eatoms = 0.0
for symbol in atoms.get_chemical_symbols():
if symbol in self.data and symbol != name:
eatoms += self.data[symbol]['energy']
else:
eatoms = None
break
ea = None
ea0 = None
if eatoms is not None:
ea = eatoms - data['energy']
if 'minimum energy' in data:
ea0 = eatoms - data['minimum energy']
self.results[name].extend([ea, ea0])
def analyse(self):
OptimizeTask.analyse(self)
for name, data in self.data.items():
if 'distances' in data:
distances = data['distances']
energies = data['energies']
fit0 = np.poly1d(np.polyfit(1 / distances, energies, 3))
fit1 = np.polyder(fit0, 1)
fit2 = np.polyder(fit1, 1)
dmin = None
for t in np.roots(fit1):
if t > 0 and fit2(t) > 0:
dmin = 1 / t
break
if dmin is None:
raise ValueError('No minimum!')
if abs(dmin) < min(distances) or abs(dmin) > max(distances):
raise ValueError('Fit outside of range! ' + \
str(abs(dmin)) + ' not in ' + \
str(distances))
emin = fit0(t)
k = fit2(t) * t**4
m1, m2 = self.create_system(name).get_masses()
m = m1 * m2 / (m1 + m2)
hnu = units._hbar * 1e10 * sqrt(k / units._e / units._amu / m)
data['minimum energy'] = emin
self.results[name][1:] = [energies[2] - emin, dmin, 1000 * hnu]
else:
self.results[name].extend([None, None])
for name, data in self.data.items():
atoms = self.create_system(name)
if len(atoms) == 1:
self.results[name].extend([None, None])
continue
eatoms = 0.0
for symbol in atoms.get_chemical_symbols():
if symbol in self.data and symbol != name:
eatoms += self.data[symbol]['energy']
else:
eatoms = None
break
ea = None
ea0 = None
if eatoms is not None:
ea = eatoms - data['energy']
if 'minimum energy' in data:
ea0 = eatoms - data['minimum energy']
self.results[name].extend([ea, ea0])
def analyse(self):
OptimizeTask.analyse(self)
for name, data in self.data.items():
if 'strains' in data:
atoms = self.create_system(name)
volumes = data['strains']**3 * atoms.get_volume()
energies = data['energies']
eos = EquationOfState(volumes, energies)
try:
v, e, B = eos.fit()
except ValueError:
self.results[name].extend([None, None])
else:
self.results[name][1:] = [energies[2] - e, v,
B * 1e24 / units.kJ]
else:
self.results[name].extend([None, None])
def analyse(self):
OptimizeTask.analyse(self)
for name, data in self.data.items():
if 'strains' in data:
atoms = self.create_system(name)
volumes = data['strains']**3 * atoms.get_volume()
energies = data['energies']
eos = EquationOfState(volumes, energies)
try:
v, e, B = eos.fit()
except ValueError:
self.results[name].extend([None, None])
else:
self.results[name][1:] = [
energies[2] - e, v, B * 1e24 / units.kJ
]
else:
self.results[name].extend([None, None])
