def test_get_lattices(self):
sm = StructureMatcher(ltol=0.2, stol=0.3, angle_tol=5, primitive_cell=True, scale=True, attempt_supercell=False)
l1 = Lattice.from_lengths_and_angles([1, 2.1, 1.9], [90, 89, 91])
l2 = Lattice.from_lengths_and_angles([1.1, 2, 2], [89, 91, 90])
s1 = Structure(l1, [], [])
s2 = Structure(l2, [], [])
lattices = list(sm._get_lattices(s=s1, target_lattice=s2.lattice))
self.assertEqual(len(lattices), 16)
l3 = Lattice.from_lengths_and_angles([1.1, 2, 20], [89, 91, 90])
s3 = Structure(l3, [], [])
lattices = list(sm._get_lattices(s=s1, target_lattice=s3.lattice))
self.assertEqual(len(lattices), 0)
def material_properties(result):
"""
"""
atoms = []
for i, species in enumerate(result.species):
for j in range(result.composition[i]):
# Since AFLOW return value like ['Al', 'Si/n'], /n is needed to be gone!
if len(species) > 2:
atoms.append(species[:-2])
else:
atoms.append(species)
lat = Lattice.from_lengths_and_angles(result.geometry[:3],
result.geometry[3:])
mat_property = {
'catalog': result.catalog,
'formula': result.compound,
'lattice': lat.matrix,
'coordinates': result.positions_fractional,
'atom_array': atoms,
'form_energy_cell': result.enthalpy_formation_cell,
'n_atoms': result.natoms,
'volume': result.volume_cell,
'space_group': result.spacegroup_relax
}
return mat_property
def test_get_lattices(self):
sm = StructureMatcher(ltol=0.2, stol=0.3, angle_tol=5,
primitive_cell=True, scale=True,
attempt_supercell=False)
l1 = Lattice.from_lengths_and_angles([1, 2.1, 1.9] , [90, 89, 91])
l2 = Lattice.from_lengths_and_angles([1.1, 2, 2] , [89, 91, 90])
s1 = Structure(l1, [], [])
s2 = Structure(l2, [], [])
lattices = list(sm._get_lattices(s = s1, target_s = s2))
self.assertEqual(len(lattices), 16)
l3 = Lattice.from_lengths_and_angles([1.1, 2, 20] , [89, 91, 90])
s3 = Structure(l3, [], [])
lattices = list(sm._get_lattices(s = s1, target_s = s3))
self.assertEqual(len(lattices), 0)
def lattice(self):
# lattice matrix: basis vectors are rows
si = self["=.in"].structure_information
# todo: convert non-angstrom units
assert si.lengthunit.type == 2
lattice = Lattice.from_lengths_and_angles(abc=si.lattice_constants,
ang=si.axis_angles)
return lattice
def material_properties(result, dos):
"""
"""
atoms = []
for i, species in enumerate(result.species):
for j in range(result.composition[i]):
atoms.append(species)
lat = Lattice.from_lengths_and_angles(result.geometry[:3],
result.geometry[3:])
mat_property = {
'formula': result.compound,
'lattice': lat.matrix,
'coordinates': result.positions_fractional,
'atom_array': atoms,
'form_energy_cell': result.enthalpy_formation_cell,
'n_atoms': result.natoms,
'volume': result.volume_cell,
'space_group': result.spacegroup_relax,
'dos_fermi': dos
}
return mat_property
data = np.genfromtxt("./pcu_2.log", skip_footer=29, skip_header=17)
a_mean = []
b_mean = []
c_mean = []
alpha_mean = []
beta_mean = []
gamma_mean = []
vol_mean = []
mechanical_eig_max = []
mechanical_eig_min = []
temp = []
h = []
for frame in data:
lattice = Lattice.from_lengths_and_angles(
[frame[14], frame[15], frame[16]], [frame[17], frame[18], frame[19]])
hmatrix = lattice.matrix
h.append(hmatrix)
a_mean.append(np.mean(data[:, 14]))
b_mean.append(np.mean(data[:, 15]))
c_mean.append(np.mean(data[:, 16]))
alpha_mean.append(np.mean(data[:, 17]))
beta_mean.append(np.mean(data[:, 18]))
gamma_mean.append(np.mean(data[:, 19]))
vol_mean.append(np.mean(data[:, 13]))
temp.append(np.mean(data[:, 11]))
h = np.array(h)
h0 = np.mean(h, axis=0)
h0_std = np.std(h, axis=0)
def FromString(self, contents):
ScaleConstant = {'Ang': 1, 'Bohr': 0.5291772083}
params_pattern = re.compile('^([A-Za-z]*) (.*)\n', re.M)
block_pattern = re.compile(
'^%block (?P<n>[A-Za-z_]*)(.*)%endblock *(?P=n)', re.DOTALL | re.M)
params = params_pattern.findall(contents)
blocks = block_pattern.findall(contents)
# Making Dictionary from raw list, deliting all \n entries
block_dict = {
blocks[i][0]: blocks[i][1].strip().replace('\n', ' ')
for i in range(0, len(blocks))
}
param_dict = {
params[i][0]: params[i][1].strip()
for i in range(0, len(params))
}
# print "Full FDF Dictionary:\n", block_dict
if 'LatticeVectors' in block_dict.keys():
lattice_list = np.array(
block_dict['LatticeVectors'].strip().split()).astype(float)
lattice = Lattice(lattice_list)
elif 'LatticeParameters' in block_dict.keys():
lattice_list = np.array(
block_dict['LatticeParameters'].strip().split()).astype(float)
lattice = Lattice.from_lengths_and_angles(lattice_list[:3],
lattice_list[3:])
print lattice_list[:3], '\n', lattice_list[3:]
# print "Lattice Matrix:\n" , lattice.matrix
# print "Siesta input parameters:\n", param_dict
# Getting Coordinates matrix
if 'AtomicCoordinatesAndAtomicSpecies' in block_dict.keys():
Coords = block_dict['AtomicCoordinatesAndAtomicSpecies']
npCoord = np.array(Coords.split()).astype(float)
if npCoord.size % 4 == 0:
npCoord = npCoord.reshape(npCoord.size / 4, 4)
# Sorting table by element number
npCoord = npCoord[np.lexsort(npCoord.T)]
# Getting labels of Chemical elements
ChemSpec = np.array(block_dict['ChemicalSpeciesLabel'].split())
if ChemSpec.size % 3 == 0:
ChemSpec = ChemSpec.reshape(ChemSpec.size / 3, 3)
Elems = []
for x in npCoord:
Elems.append(ChemSpec[x[3] - 1][2])
# Checking Coordinates format and converting bohr to ang if necessery
LatticeConstant = param_dict['LatticeConstant'].strip().split()
inCartesian = False
CoordFormat = param_dict['AtomicCoordinatesFormat']
if CoordFormat:
if CoordFormat in ('Ang', 'NotScaledCartesianAng'):
inCartesian = True
elif CoordFormat in ('Bohr', 'NotScaledCartesianBohr'):
inCartesian = True
npCoord[:, :-1] *= ScaleConstant['Bohr']
elif CoordFormat == 'ScaledCartesian':
inCartesian = True
npCoord[:, :-1] *= ScaleConstant[LatticeConstant[1]] * float(
LatticeConstant[0])
struct = Structure(lattice, Elems, npCoord[:, :-1], inCartesian)
print '---------------\n', struct.to_dict, '\n---------------\n'
return contents
