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pre_process.py
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pre_process.py
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import pickle
import pandas as pd
from tqdm import tqdm
from ase.io import read
import numpy as np
import os
from tools import fingerprint_adslab, _concatenate_shell,fingerprint_element_attributes
def pre_process():
# combine full data
dir = './cif/'
data = []
result = pd.read_csv('./id_prop.csv')
os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE"
file=os.listdir(dir)
for index in tqdm(file):
index=str(index).strip('.cif')
atoms = read(dir + str(index)+'.cif')
case_energy = ((result.loc[result['name']==index])['energy']).values[0]
case_VT = fingerprint_adslab(atoms)
case_VT2 = _concatenate_shell(case_VT)
case = {'index': index,
'atoms': atoms,
'energy': case_energy,
'VT': case_VT,
'VT2': case_VT2
}
data.append(case)
for i in tqdm(range(len(data))):
try:
case_VT = fingerprint_adslab(data[i]['atoms'])
data[i]['VT'] = case_VT
except:
print('wrong ' + str(i))
with open('VT' + '.pkl', "wb") as f:
pickle.dump(data, f)
for case in tqdm(data):
shells = _concatenate_shell(case['VT'])
case['VT2'] = shells
f_num = []
s_num = []
t_num = []
for case in data:
f_num.append(case['VT2']['n_1_layer'])
s_num.append(case['VT2']['n_2_layer'])
t_num.append(case['VT2']['n_3_layer'])
error_index = []
for i in range(len(f_num)):
if f_num[i] == 0:
print(i)
error_index.append(i)
for index in tqdm(error_index):
VT = fingerprint_adslab(data[index]['atoms'])
shells = _concatenate_shell(VT)
if shells['n_1_layer'] == 0:
print(index)
check = []
for index in error_index:
check.append(data[index]['VT'])
# combine all sets
total_doc = []
############################ inspect number of cases by layer category###########################
import matplotlib.pyplot as plt
n_first = []
n_second = []
n_third = []
for case in data:
n_first.append(case['VT2']['n_1_layer'])
n_second.append(case['VT2']['n_2_layer'])
n_third.append(case['VT2']['n_3_layer'])
n_first = set(n_first)
n_second = set(n_second)
n_third = set(n_third)
data_layer = {
'111': [], '121': [], '131': [], '141': [],
'211': [], '221': [], '231': [], '241': [],
'311': [], '321': [], '331': [], '341': [],
'112': [], '122': [], '132': [], '142': [],
'212': [], '222': [], '232': [], '242': [],
'312': [], '322': [], '332': [], '342': [],
'113': [], '123': [], '133': [], '143': [],
'213': [], '223': [], '233': [], '243': [],
'313': [], '323': [], '333': [], '343': [],
'114': [], '124': [], '134': [], '144': [],
'214': [], '224': [], '234': [], '244': [],
'314': [], '324': [], '334': [], '344': [],
}
for case in data:
layers_index = str(case['VT2']['n_1_layer']) + str(case['VT2']['n_2_layer'])+ str(case['VT2']['n_3_layer'])
data_layer[layers_index].append(case)
x = np.array(list(data_layer.keys()))
y = []
for item in data_layer:
y.append(len(data_layer[item]))
################ calculate several average energy attributes (tricky) of element #################
# case_num is the number of case that include certain element
# atom num is total number of certain element in all cases
# total energy is sum energy of all cases that include certain element
# everage_atom_energy is ev/atom in a case
# total eae is total
# get name array
element_array = {'first_layer': [], 'second_layer': [], 'third_layer': [], 'all_layers': []}
for item in data:
for key1 in item['VT2']['first_layer']:
element_array['first_layer'].append(key1)
element_array['all_layers'].append(key1)
for key2 in item['VT2']['second_layer']:
element_array['second_layer'].append(key2)
element_array['all_layers'].append(key2)
for key3 in item['VT2']['third_layer']:
element_array['third_layer'].append(key3)
element_array['all_layers'].append(key3)
element_array = {'first_layer': set(element_array['first_layer']),
'second_layer': set(element_array['second_layer']),
'third_layer': set(element_array['third_layer']),
'all_layers': set(element_array['all_layers'])}
# first layer
first_layer_conclusion = {}
for element in element_array['first_layer']:
case_num = 0
atom_num = 0
total_energy = 0
total_eae = 0
all_energis = []
for item in data:
for atom in item['VT2']['first_layer']:
if str(element) == atom:
case_num = case_num + 1
atom_num = atom_num + item['VT2']['first_layer'][atom]['number']
total_energy = total_energy + item['energy']
all_energis.append(item['energy'])
everage_atom_energy = (item['energy']) / (item['VT2']['first_layer'][atom]['number'])
total_eae = total_eae + everage_atom_energy
conclusion = {'case_num': case_num, 'atom_num': atom_num, 'total_energy': total_energy,
'total_eae': total_eae, 'case_median': np.median(all_energis), 'aver_case': total_energy / case_num,
'aver_atom': total_energy / atom_num, 'aver_eae': total_eae / case_num}
first_layer_conclusion[str(element)] = conclusion
# second layer
second_layer_conclusion = {}
for element in element_array['second_layer']:
case_num = 0
atom_num = 0
total_energy = 0
total_eae = 0
all_energis = []
for item in data:
for atom in item['VT2']['second_layer']:
if str(element) == atom:
case_num = case_num + 1
atom_num = atom_num + item['VT2']['second_layer'][atom]['number']
total_energy = total_energy + item['energy']
all_energis.append(item['energy'])
everage_atom_energy = (item['energy']) / (item['VT2']['second_layer'][atom]['number'])
total_eae = total_eae + everage_atom_energy
conclusion = {'case_num': case_num, 'atom_num': atom_num, 'total_energy': total_energy,
'total_eae': total_eae, 'case_median': np.median(all_energis), 'aver_case': total_energy / case_num,
'aver_atom': total_energy / atom_num, 'aver_eae': total_eae / case_num}
second_layer_conclusion[str(element)] = conclusion
# third layer
third_layer_conclusion = {}
for element in element_array['third_layer']:
case_num = 0
atom_num = 0
total_energy = 0
total_eae = 0
all_energis = []
for item in data:
for atom in item['VT2']['third_layer']:
if str(element) == atom:
case_num = case_num + 1
atom_num = atom_num + item['VT2']['third_layer'][atom]['number']
total_energy = total_energy + item['energy']
all_energis.append(item['energy'])
everage_atom_energy = (item['energy']) / (item['VT2']['third_layer'][atom]['number'])
total_eae = total_eae + everage_atom_energy
conclusion = {'case_num': case_num, 'atom_num': atom_num, 'total_energy': total_energy,
'total_eae': total_eae, 'case_median': np.median(all_energis), 'aver_case': total_energy / case_num,
'aver_atom': total_energy / atom_num, 'aver_eae': total_eae / case_num}
third_layer_conclusion[str(element)] = conclusion
all_layers_conclusion = {}
for element in element_array['all_layers']:
case_num = 0
atom_num = 0
total_energy = 0
total_eae = 0
all_energis = []
for item in data:
if (element in item['VT2']['first_layer']) or (element in item['VT2']['second_layer']) or (element in item['VT2']['third_layer']):
case_num = case_num + 1
total_energy = total_energy + item['energy']
all_energis.append(item['energy'])
if (element in item['VT2']['first_layer']) and (element in item['VT2']['second_layer']) and (element in item['VT2']['third_layer']):
atom_num = atom_num + item['VT2']['first_layer'][element]['number'] + \
item['VT2']['second_layer'][element]['number'] + \
item['VT2']['third_layer'][element]['number']
everage_atom_energy = (item['energy']) / (
item['VT2']['second_layer'][element]['number'] + item['VT2']['first_layer'][element]['number'] + item['VT2']['third_layer'][element]['number'])
elif element in item['VT2']['first_layer']:
atom_num = atom_num + item['VT2']['first_layer'][element]['number']
everage_atom_energy = (item['energy']) / (item['VT2']['first_layer'][element]['number'])
elif element in item['VT2']['second_layer']:
atom_num = atom_num + item['VT2']['second_layer'][element]['number']
everage_atom_energy = (item['energy']) / (item['VT2']['second_layer'][element]['number'])
else:
atom_num = atom_num + item['VT2']['third_layer'][element]['number']
everage_atom_energy = (item['energy']) / (item['VT2']['third_layer'][element]['number'])
conclusion = {'case_num': case_num, 'atom_num': atom_num, 'total_energy': total_energy,
'total_eae': total_eae, 'case_median': np.median(all_energis), 'aver_case': total_energy / case_num,
'aver_atom': total_energy / atom_num, 'aver_eae': total_eae / case_num}
all_layers_conclusion[str(element)] = conclusion
trick_attributes = {'first_layer': first_layer_conclusion,
'second_layer': second_layer_conclusion,
'third_layer': third_layer_conclusion,
'all_layers': all_layers_conclusion}
with open("tricky_attributes.pkl", "wb") as f:
pickle.dump(trick_attributes, f)
# ######################## get other properties of each element################
element_properties = {}
for element in element_array['all_layers']:
element_properties[element] = fingerprint_element_attributes(element)
for item in element_properties:
if element_properties[item]['elemental properties']['electron_affinity'] is None:
element_properties[item]['elemental properties']['electron_affinity'] = 0
for item in element_properties:
del element_properties[item]['macro properties']['mineral_hardness']
with open("element_attributes.pkl", "wb") as f:
pickle.dump(element_properties, f)