class FancyDict(dict):
"""Dictionary with keys available as attributes also."""
def __getattr__(self, key):
if key not in self:
return dict.__getattribute__(self, key)
value = self[key]
if isinstance(value, dict):
return FancyDict(value)
return value
formula = property(lambda self: hill(self.numbers))
def __dir__(self):
return self.keys() # for tab-completion
def formula(self, d):
return hill(d.numbers)
def formula(self, d):
return hill(d.numbers)
def __init__(self, references, filter='', verbose=True):
"""Phase-diagram.
references: list of (name, energy) tuples
List of references. The energy must be the total energy and not
energy per atom. The names can also be dicts like
``{'Zn': 1, 'O': 2}`` which would be equivalent to ``'ZnO2'``.
filter: str or list of str
Use only those references that match the given filter.
Example: ``filter='ZnO'`` will select those that
contain zinc or oxygen.
verbose: bool
Write information.
"""
filter = parse_formula(filter)[0]
self.verbose = verbose
self.species = {}
self.references = []
for name, energy in references:
if isinstance(name, str):
count = parse_formula(name)[0]
else:
count = name
name = hill(count)
if filter and any(symbol not in filter for symbol in count):
continue
natoms = 0
for symbol, n in count.items():
natoms += n
if symbol not in self.species:
self.species[symbol] = len(self.species)
self.references.append((count, energy, name, natoms))
self.symbols = [None] * len(self.species)
for symbol, id in self.species.items():
self.symbols[id] = symbol
if verbose:
print('Species:', ', '.join(self.symbols))
print('References:', len(self.references))
for i, (count, energy, name, natoms) in enumerate(self.references):
print('{0:<5}{1:10}{2:10.3f}'.format(i, name, energy))
self.points = np.zeros((len(self.references), len(self.species) + 1))
for s, (count, energy, name, natoms) in enumerate(self.references):
for symbol, n in count.items():
self.points[s, self.species[symbol]] = n / natoms
self.points[s, -1] = energy / natoms
hull = ConvexHull(self.points[:, 1:])
# Find relevant simplices:
ok = hull.equations[:, -2] < 0
self.simplices = hull.simplices[ok]
# Create a mask for those points that are on the convex hull:
self.hull = np.zeros(len(self.points), bool)
for simplex in self.simplices:
self.hull[simplex] = True
if verbose:
print('Simplices:', len(self.simplices))
def __init__(self, references, filter='', verbose=True):
"""Phase-diagram.
references: list of (name, energy) tuples
List of references. The names can also be dicts like
``{'Zn': 1, 'O': 2}`` which would be equivalent to ``'ZnO2'``.
filter: str or list of str
Use only those references that match the given filter.
Example: ``filter='ZnO'`` will select those that
contain zinc or oxygen.
verbose: bool
Write information.
"""
filter = parse_formula(filter)[0]
self.verbose = verbose
self.species = {}
self.references = []
for name, energy in references:
if isinstance(name, str):
count = parse_formula(name)[0]
else:
count = name
name = hill(count)
if filter and any(symbol not in filter for symbol in count):
continue
natoms = 0
for symbol, n in count.items():
natoms += n
if symbol not in self.species:
self.species[symbol] = len(self.species)
self.references.append((count, energy, name, natoms))
if verbose:
print('Species:', ', '.join(self.species))
print('References:', len(self.references))
for i, (count, energy, name, natoms) in enumerate(self.references):
print('{0:<5}{1:10}{2:10.3f}'.format(i, name, energy))
self.points = np.zeros((len(self.references), len(self.species) + 1))
for s, (count, energy, name, natoms) in enumerate(self.references):
for symbol, n in count.items():
self.points[s, self.species[symbol]] = n / natoms
self.points[s, -1] = energy / natoms
hull = ConvexHull(self.points[:, 1:])
# Find relevant vertices:
ok = hull.equations[:, -2] < 0
vertices = set()
for simplex in hull.simplices[ok]:
vertices.update(simplex)
self.vertices = np.array(list(vertices))
if verbose:
print('Simplices:', ok.sum())
# Create triangulation:
if len(self.species) == 2:
D = Delaunay1D # scipy's Delaunay doesn't like 1-d!
else:
D = Delaunay
self.tri = D(self.points[self.vertices, 1:-1])
def formula(self):
"""Chemical formula string."""
return hill(self.numbers)
def __init__(self, dct, subscript=None):
self.dct = dct
self.cell = [['{0:.3f}'.format(a) for a in axis] for axis in dct.cell]
forces = dict2forces(dct)
if forces is None:
fmax = None
self.forces = None
else:
fmax = (forces**2).sum(1).max()**0.5
N = len(forces)
self.forces = []
for n, f in enumerate(forces):
if n < 5 or n >= N - 5:
f = tuple('{0:10.3f}'.format(x) for x in f)
symbol = chemical_symbols[dct.numbers[n]]
self.forces.append((n, symbol) + f)
elif n == 5:
self.forces.append(
(' ...', '', ' ...', ' ...', ' ...'))
self.stress = dct.get('stress')
if self.stress is not None:
self.stress = ', '.join('{0:.3f}'.format(s) for s in self.stress)
if 'masses' in dct:
mass = dct.masses.sum()
else:
mass = atomic_masses[dct.numbers].sum()
formula = hill(dct.numbers)
if subscript:
formula = subscript.sub(r'<sub>\1</sub>', formula)
table = [('id', dct.id),
('age', float_to_time_string(now() - dct.ctime, True)),
('formula', formula), ('user', dct.user),
('calculator', dct.get('calculator')),
('energy [eV]', dct.get('energy')), ('fmax [eV/Ang]', fmax),
('charge [|e|]', dct.get('charge')), ('mass [au]', mass),
('unique id', dct.unique_id),
('volume [Ang^3]', abs(np.linalg.det(dct.cell)))]
self.table = [(name, value) for name, value in table
if value is not None]
if 'key_value_pairs' in dct:
self.key_value_pairs = sorted(dct.key_value_pairs.items())
else:
self.key_value_pairs = None
if 'keywords' in dct:
self.keywords = ', '.join(sorted(dct.keywords))
else:
self.keywords = None
self.dipole = dct.get('dipole')
if self.dipole is not None:
self.dipole = ', '.join('{0:.3f}'.format(d) for d in self.dipole)
self.data = dct.get('data')
if self.data:
self.data = ', '.join(self.data.keys())
self.constraints = dct.get('constraints')
if self.constraints:
self.constraints = ', '.join(d['name'] for d in self.constraints)
def __init__(self, dct, subscript=None):
self.dct = dct
self.cell = [['{0:.3f}'.format(a) for a in axis] for axis in dct.cell]
forces = dict2forces(dct)
if forces is None:
fmax = None
self.forces = None
else:
fmax = (forces**2).sum(1).max()**0.5
N = len(forces)
self.forces = []
for n, f in enumerate(forces):
if n < 5 or n >= N - 5:
f = tuple('{0:10.3f}'.format(x) for x in f)
symbol = chemical_symbols[dct.numbers[n]]
self.forces.append((n, symbol) + f)
elif n == 5:
self.forces.append((' ...', '',
' ...',
' ...',
' ...'))
self.stress = dct.get('stress')
if self.stress is not None:
self.stress = ', '.join('{0:.3f}'.format(s) for s in self.stress)
if 'masses' in dct:
mass = dct.masses.sum()
else:
mass = atomic_masses[dct.numbers].sum()
formula = hill(dct.numbers)
if subscript:
formula = subscript.sub(r'<sub>\1</sub>', formula)
table = [
('id', '', dct.id),
('age', '', float_to_time_string(now() - dct.ctime, True)),
('formula', '', formula),
('user', '', dct.user),
('calculator', '', dct.get('calculator')),
('energy', 'eV', dct.get('energy')),
('fmax', 'eV/Ang', fmax),
('charge', '|e|', dct.get('charge')),
('mass', 'au', mass),
('unique id', '', dct.unique_id),
('volume', 'Ang^3', abs(np.linalg.det(dct.cell)))]
self.table = [(name, unit, value) for name, unit, value in table
if value is not None]
if 'key_value_pairs' in dct:
self.key_value_pairs = sorted(dct.key_value_pairs.items())
else:
self.key_value_pairs = None
self.dipole = dct.get('dipole')
if self.dipole is not None:
self.dipole = ', '.join('{0:.3f}'.format(d) for d in self.dipole)
self.data = dct.get('data')
if self.data:
self.data = ', '.join(self.data.keys())
self.constraints = dct.get('constraints')
if self.constraints:
self.constraints = ', '.join(d['name'] for d in self.constraints)
def formula(self):
"""Chemical formula string."""
return hill(self.numbers)
def __init__(self, row, subscript=None):
self.row = row
self.cell = [["{0:.3f}".format(a) for a in axis] for axis in row.cell]
forces = row.get("constrained_forces")
if forces is None:
fmax = None
self.forces = None
else:
fmax = (forces ** 2).sum(1).max() ** 0.5
N = len(forces)
self.forces = []
for n, f in enumerate(forces):
if n < 5 or n >= N - 5:
f = tuple("{0:10.3f}".format(x) for x in f)
symbol = chemical_symbols[row.numbers[n]]
self.forces.append((n, symbol) + f)
elif n == 5:
self.forces.append((" ...", "", " ...", " ...", " ..."))
self.stress = row.get("stress")
if self.stress is not None:
self.stress = ", ".join("{0:.3f}".format(s) for s in self.stress)
if "masses" in row:
mass = row.masses.sum()
else:
mass = atomic_masses[row.numbers].sum()
formula = hill(row.numbers)
if subscript:
formula = subscript.sub(r"<sub>\1</sub>", formula)
table = [
("id", "", row.id),
("age", "", float_to_time_string(now() - row.ctime, True)),
("formula", "", formula),
("user", "", row.user),
("calculator", "", row.get("calculator")),
("energy", "eV", row.get("energy")),
("fmax", "eV/Ang", fmax),
("charge", "|e|", row.get("charge")),
("mass", "au", mass),
("magnetic moment", "au", row.get("magmom")),
("unique id", "", row.unique_id),
("volume", "Ang^3", row.get("volume")),
]
self.table = [(name, unit, value) for name, unit, value in table if value is not None]
self.key_value_pairs = sorted(row.key_value_pairs.items()) or None
self.dipole = row.get("dipole")
if self.dipole is not None:
self.dipole = ", ".join("{0:.3f}".format(d) for d in self.dipole)
self.plots = []
self.data = row.get("data")
if self.data:
plots = []
for name, value in self.data.items():
if isinstance(value, dict) and "xlabel" in value:
plots.append((value.get("number"), name))
self.plots = [name for number, name in sorted(plots)]
self.data = ", ".join(self.data.keys())
self.constraints = row.get("constraints")
if self.constraints:
self.constraints = ", ".join(d["name"] for d in self.constraints)