def __init__(self, struc, min_domain_size=1, max_domain_size=100):
"""
Args:
struc: structure to simulate augmented xrd spectra from
min_domain_size: smallest domain size (in nm) to be sampled,
leading to the broadest peaks
max_domain_size: largest domain size (in nm) to be sampled,
leading to the most narrow peaks
"""
self.calculator = xrd.XRDCalculator()
self.struc = struc
self.pattern = self.calculator.get_pattern(struc, two_theta_range=(0,80))
self.possible_domains = np.linspace(min_domain_size, max_domain_size, 100)
def __init__(self, struc, max_texture=0.6):
"""
Args:
struc: pymatgen structure object from which xrd
spectra are simulated
max_texture: maximum strength of texture applied.
For example, max_texture=0.6 implies peaks will be
scaled by as much as +/- 60% of their original
intensities.
"""
self.calculator = xrd.XRDCalculator()
self.struc = struc
self.max_texture = max_texture
def __init__(self,
spectra_dir,
spectrum_fname,
predicted_phases,
reference_dir='References'):
"""
Args:
spectrum_fname: name of file containing the
xrd spectrum (in xy format)
reference_dir: path to directory containing the
reference phases (CIF files)
"""
self.spectra_dir = spectra_dir
self.spectrum_fname = spectrum_fname
self.pred_phases = predicted_phases
self.ref_dir = reference_dir
self.calculator = xrd.XRDCalculator()
def __init__(self,
spectra_dir,
spectrum_fname,
max_phases,
cutoff_intensity,
wavelen='CuKa',
reference_dir='References'):
"""
Args:
spectrum_fname: name of file containing the
xrd spectrum (in xy format)
reference_dir: path to directory containing the
reference phases (CIF files)
wavelen: wavelength used for diffraction (angstroms).
Defaults to Cu K-alpha radiation (1.5406 angstroms).
"""
self.spectra_dir = spectra_dir
self.spectrum_fname = spectrum_fname
self.ref_dir = reference_dir
self.calculator = xrd.XRDCalculator()
self.max_phases = max_phases
self.cutoff = cutoff_intensity
self.wavelen = wavelen
# energy of per atom used to calcualte the binding enegy
E_Sn = -3.980911
E_S = -2.696603
E_Ca = -1.250692
E_O = -0.867783
###########################################################
############################################################
#parameter in the GCN
#about torch
torch.set_default_dtype(torch.float64)
torch.set_printoptions(precision=8)
#about pymatgen
patt_xrd = xrd.XRDCalculator('CuKa')
#about inputs of NN layers in GCN
global sample_num, rmat_num, series_num
sample_num = 1 #output of G
rmat_num = 28 #row nums of the matrix for the input of CNN
########################################################
#get energy from the OUTCAR
def get_total_energy(folder):
energy_string = os.popen('grep TOTEN ' + folder +
'/OUTCAR | tail -1').read().split(' ')[-2]
energy_slab = round(np.float64(float(energy_string)), 5)
return energy_slab
A_pos = [0, 16, 26, 36, 52, 62]
B_pos = [2, 12, 28, 38, 48, 64]
#C_pos = [4, 14, 24, 40, 50, 60] #reference only, not actually used
#poly_dict = {'A':A_pos, 'B':B_pos, 'C':C_pos}
#laer_z = {0:4, 1:12, 2:20}
ra = np.random.randint
expt = np.array([[11.6093069, 0.33422539],
[20.2156104, 0.45120426],
[23.3406177, 0.42613735],
[35.472999, 0.3342209],
[36.1080272, 0.2172465],
[40.5365135, 0.434493],
[47.7724929, 0.52640495],
[63.1969942, 0.3175141],
[64.4336281, 0.4679155]])
xrds = xrd.XRDCalculator()
generation = 0
dna_index = {}
fitdex = pd.DataFrame(columns=['matchfit', 'matchtot', 'fitsum'])
#Define DNA as nested namedtuples
DNA = namedtuple('DNA', 'Polytope L1 L2 L3 Lattice')
POL = namedtuple('POL', 'Layers Symmetry')
LAY = namedtuple('LAY', 'N Mineral') #Mineral is b or g, might affect certain things about M+ layer
SYM = namedtuple('SYM', 'Cn, Sz, Sxy, Tx, Ty') #Sym elements affect M+ layer stacking
LN = namedtuple('LN', 'C W1 W2')
M = namedtuple('M', 'pos phi theta')
#Sym contents:
#Cn = number indicating n subscript, note a C2 = sx + sy
#Sz = 0 or 1, an xy plane running through metal atoms
#Sxy = (-1:1); -1 indicates a zx plane at b = 0.5; 1 for zy plane, 0 means no plane, both is a C2