def __init__(self, model_file, mother=mother, super_cell_flag=False):
'''
loading the regression results
'''
self.mother = mother
# The flag to inluce 1NN and edges shorter than 1NN
NN1 = 1
[
self.Gcv, self.J, self.intercept, self.RMSE_test_atom,
self.RMSE_test_site
] = pickle.load(open(model_file, "rb"))
self.super_cell_flag = super_cell_flag
# Initialize graph object
self.Graphs = lf.initialize_graph_object(self.mother, dz, NN1=1)
# Initialize calculation object
empty = 'grey'
filled = 'r'
occ = [empty, filled]
self.Cal = lf.calculations(occ)
self.Gm = self.Graphs.Gm
#%%
'''
Read the json files
Creat pi matrix
size of number of configuration * numbers of clusters
'''
empty = 'grey'
filled = 'r'
occ = [empty, filled]
# Initialize the cluster object and isomorphs object
Graphs = lf.initialize_graph_object(mother, dz)
with open('clusters.json') as f:
Gcv = json.load(f)['Gcv']
with open(json_name) as f:
ES_data = json.load(f)
config_batch_i = ES_data['config_iso']
Graphs.get_configs(config_batch_i)
Gsv = Graphs.Gsv
Cal = lf.calculations(occ)
#pi = Cal.get_pi_matrix_l(Gsv ,Gcv)
#np.save(pi_name, pi, allow_pickle = True)
pi = np.load('pi_iso_1.npy')