def analyse_bonds(model, A, B):
'''
Check A-B distances present in the model.
model: Atoms object or string. If string it will read a file
in the same folder, e.g. "name.traj"
A: string, chemical symbol, e.g. "H"
B: string, chemical symbol, e.g. "H"
'''
# Read file or Atoms object
if isinstance(model, str) is True:
model = read(model)
analysis = Analysis(model)
dash = "-" * 40
print_AB = A + "-" + B
# Retrieve bonds and values
AB_Bonds = analysis.get_bonds(A, B)
AB_BondsValues = analysis.get_values(AB_Bonds)
# Table header
print(dash)
print(print_AB+" Distance / Angstrom")
print(dash)
print('{:<6.5s}{:>4.10s}{:^13.10s}{:>4.10s}'.format(
"count", "average", "minimum", "maximum"))
# Table contents
print('{:<6.0f}{:>4.6f}{:^12.6f}{:>4.6f}'.format(
len(AB_BondsValues[0]), np.average(AB_BondsValues),
np.amin(AB_BondsValues), np.amax(AB_BondsValues)))
def search_abnormal_bonds(model):
'''
Check all bond lengths in the model for abnormally
short ones, ie. less than 0.74 Angstrom.
model: Atoms object or string. If string it will read a file
in the same folder, e.g. "name.traj"
'''
# Combination as AB = BA for bonds, avoiding redundancy
from itertools import combinations_with_replacement
# Read file or Atoms object
if isinstance(model, str) is True:
model = read(model)
# Define lists of variables
abnormal_bonds = []
list_of_abnormal_bonds = []
analysis = Analysis(model)
# set() to ensure unique chemical symbols list
list_of_symbols = list(set(model.get_chemical_symbols()))
all_bonds = combinations_with_replacement(list_of_symbols, 2)
# Iterate over all arrangements of chemical symbols
for bonds in all_bonds:
A = bonds[0]
B = bonds[1]
print_AB = A+'-'+B
AB_Bonds = analysis.get_bonds(A, B)
# Make sure bond exist before retrieving values
if not AB_Bonds == [[]]:
AB_BondsValues = analysis.get_values(AB_Bonds)
for i in range(0, len(AB_BondsValues)):
for values in AB_BondsValues[i]:
if values < 0.74:
abnormal_bonds += [1]
list_of_abnormal_bonds = list_of_abnormal_bonds + [print_AB]
# Abnormality check
if not len(abnormal_bonds) == 0:
print("A total of", len(abnormal_bonds),
"abnormal bond lengths observed (<0.74 A).")
print("Identities:", list_of_abnormal_bonds)
else:
print("OK")
def analyse_all_bonds(model):
'''
Returns a table of bond distance analysis for the supplied model.
Parameters:
model: Atoms object or string. If string it will read a file
in the same folder, e.g. "name.traj"
'''
# Combination as AB = BA for bonds, avoiding redundancy
from itertools import combinations_with_replacement
# Read file or Atoms object
if isinstance(model, str) is True:
model = read(model)
analysis = Analysis(model)
dash = "-" * 40
# set() to ensure unique chemical symbols list
list_of_symbols = list(set(model.get_chemical_symbols()))
all_bonds = combinations_with_replacement(list_of_symbols, 2)
# Table heading
print(dash)
print('{:<6.5s}{:<6.5s}{:>4.10s}{:^13.10s}{:>4.10s}'.format(
"Bond", "Count", "Average", "Minimum", "Maximum"))
print(dash)
# Iterate over all arrangements of chemical symbols
for bonds in all_bonds:
A = bonds[0]
B = bonds[1]
print_AB = A + '-' + B
AB_Bonds = analysis.get_bonds(A, B)
# Make sure bond exist before retrieving values, then print contents
if not AB_Bonds == [[]]:
AB_BondsValues = analysis.get_values(AB_Bonds)
print('{:<8.8s}{:<6.0f}{:>4.6f}{:^12.6f}{:>4.6f}'.format(
print_AB, len(AB_BondsValues[0]), np.average(AB_BondsValues),
np.amin(AB_BondsValues), np.amax(AB_BondsValues)))
def analyse_bonds(model, A, B, verbose=True, multirow=False):
'''
Check A-B distances present in the model.
Parameters:
model: Atoms object
XXX
A: string, chemical symbol, e.g. "H"
B: string, chemical symbol, e.g. "H"
verbose: Boolean
Whether to print information to screen
multirow: Boolean
Whether we are working with analyse_all_bonds, so the output is multirow,
or just one specific analysis of a bond, in which case the table header is needed.
'''
from ase.geometry.analysis import Analysis
analysis = Analysis(model)
print_AB = A + "-" + B
# Retrieve bonds and values
AB_Bonds = analysis.get_bonds(A, B)
if AB_Bonds == [[]]:
AB_BondsValues = None
else:
AB_BondsValues = analysis.get_values(AB_Bonds)
if verbose and AB_BondsValues is not None:
if not multirow:
print_bond_table_header()
# Table contents
import numpy as np
print('{:<8.8s}{:<6.0f}{:>4.6f}{:^12.6f}{:>4.6f}'.format(
print_AB, len(AB_BondsValues[0]), np.average(AB_BondsValues),
np.amin(AB_BondsValues), np.amax(AB_BondsValues)))
return print_AB, AB_Bonds, AB_BondsValues
AllSnIAngles = []
AllPbIPbAngles = []
AllSnISnAngles = []
for i in range(0, 16):
# VASP POSCAR
PEA2PbSnI4 = read('CONTCAR_PbSn' + str(i))
#print(PEA2PbSnI4)
ana = Analysis(PEA2PbSnI4)
# Bonds
PbIBonds = ana.get_bonds('Pb', 'I', unique=True)
SnIBonds = ana.get_bonds('Sn', 'I', unique=True)
#print("\nThere are {} Pb-I bonds in PEA2PbSnI4.".format(len(PbIBonds[0])))
#print("There are {} Pb-I bonds in PEA2PbSnI4.".format(len(SnIBonds[0])))
if len(PbIBonds[0]) > 0:
PbIBondValues = ana.get_values(PbIBonds)
if len(SnIBonds[0]) > 0:
SnIBondValues = ana.get_values(SnIBonds)
#print("The average Pb-I bond length is {}.".format(np.average(PbIBondValues)))
#print("The average Sn-I bond length is {}.".format(np.average(SnIBondValues)))
if len(PbIBonds[0]) > 0:
def search_abnormal_bonds(model, verbose=True):
'''
Check all bond lengths in the model for abnormally
short ones, ie. less than 0.74 Angstrom.
Parameters:
model: Atoms object or string. If string it will read a file
in the same folder, e.g. "name.traj"
'''
# Combination as AB = BA for bonds, avoiding redundancy
from itertools import combinations_with_replacement
# Imports necessary to work out accurate minimum bond distances
from ase.data import chemical_symbols, covalent_radii
# Read file or Atoms object
if isinstance(model, str) is True:
model = read(model)
# Define lists of variables
abnormal_bonds = []
list_of_abnormal_bonds = []
analysis = Analysis(model)
# set() to ensure unique chemical symbols list
list_of_symbols = list(set(model.get_chemical_symbols()))
all_bonds = combinations_with_replacement(list_of_symbols, 2)
# Iterate over all arrangements of chemical symbols
for bonds in all_bonds:
A = bonds[0]
B = bonds[1]
# For softcoded bond cutoff
sum_of_covalent_radii = covalent_radii[chemical_symbols.index(
A)] + covalent_radii[chemical_symbols.index(B)]
print_AB = A + '-' + B
AB_Bonds = analysis.get_bonds(A, B)
# Make sure bond exist before retrieving values
if not AB_Bonds == [[]]:
AB_BondsValues = analysis.get_values(AB_Bonds)
for i in range(0, len(AB_BondsValues)):
for values in AB_BondsValues[i]:
# TODO: move the 75% of sum_of_covalent_radii before the loops
if values < max(0.4, sum_of_covalent_radii * 0.75):
abnormal_bonds += [1]
list_of_abnormal_bonds = list_of_abnormal_bonds + [
print_AB
]
# Abnormality check
# is it possible to make a loop with different possible values instead of 0.75 and takes the average
if len(abnormal_bonds) > 0:
if verbose:
print(
"A total of", len(abnormal_bonds),
"abnormal bond lengths observed (<" +
str(max(0.4, sum_of_covalent_radii * 0.75)) + " A).")
print("Identities:", list_of_abnormal_bonds)
return False
else:
if verbose:
print("OK")
return True
from ase.io.trajectory import Trajectory
import numpy as np
from ase.io import read
from ase.geometry.analysis import Analysis
#old file see bonds.py for better version
file = read('tin_acetate.xyz')
traj = Trajectory('tin_acetate.traj', 'w')
traj.write(file)
file = read('tin_acetate.traj')
ana = Analysis(file)
SiOBonds = ana.get_bonds('Sn', 'O')
SiOSiAngles = ana.get_angles('O', 'Sn', 'O')
print("there are {} Si-O bonds in BETA".format(len(SiOBonds[0])))
print("there are {} Si-O-Si angles in BETA".format(len(SiOSiAngles[0])))
SiOBondsValues = ana.get_values(SiOBonds)
SiOSiAngleValues = ana.get_values(SiOSiAngles)
print("bond length data:")
print("the average Si-O bond length is {}.".format(np.average(SiOBondsValues)))
print("the minimum Si-O Distance is:", np.amin(SiOBondsValues))
print("the maximum Si-O Distance is:", np.amax(SiOBondsValues))
print("bond angle data:")
print("the average Si-O-Si angle is {}.".format(np.average(SiOSiAngleValues)))
print("the maximum Si-O-Si angle is:", np.amax(SiOSiAngleValues))
print("the minimum Si-O-Si angle is:", np.amin(SiOSiAngleValues))
