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genx_script_standard_RAXS_new5.py
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genx_script_standard_RAXS_new5.py
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#consider two domains (both half layer but different sorbate environment, one with 1pb OH and one with none)
import models.raxs as model
from models.utils import UserVars
import numpy as np
import pickle
from operator import mul
from numpy.linalg import inv
from copy import deepcopy
import domain_creator
from domain_creator import add_atom,print_data,create_list,add_atom_in_slab,\
create_match_lib_before_fitting,create_match_lib_during_fitting,create_sorbate_ids
##script for RAXS data fitting
##update from version 1##
#the sorbate will be updated in sim function by rotation, so you need to specify the associated r and rotation angle and top angle
#range during fitting, so dx dy dz for sorbates will be set to 0
#and note the initial water position is anchored to the initial Pb position
##updata from version 2##
#now it can consider a clean domain (no sorbate), to do that simply set the pb_number to be 0
##update from version 3##
#when you consider more than one pair of water molecules, you can set different position constraint
##update from version 4##
#now the coordination configuration of sorbate is trigonal bipyramid with lone electron pair sit on the middle layer
#read hexahedra module for detail
'''
the forms of ids and group names
###########atm ids################
sorbate: Pb1_D1A, Pb2_D2B, HO1_D1A
water: Os1_D1A
surf atms: O1_1_0_D1A (half layer), O1_1_1_D1A(full layer atms)
############group names###########
gp_Pb1_D1(discrete grouping for sorbate, group u dx dy dz)
gp_O1O7_D1(discrete grouping for surface atms, group dx dy in symmetry)
gp_sorbates_set1_D1(discrete grouping for each set of sorbates (O and metal), group oc)
gp_HO_set1_D1(discrete grouping for each set of oxygen sorbates, group u)
gp_waters_set1_D1(discrete grouping for each set of water at same layer, group u, oc and dz)
gp_O1O2_O7O8_D1(sequence grouping for u, oc and dz)
some print samples
#print domain_creator.extract_coor(domain1A,'O1_1_0_D1A')
#print domain_creator.extract_coor(domain1B,'Pb1_D1B')
#print_data(N_sorbate=4,N_atm=40,domain=domain1A,z_shift=1,save_file='D://model.xyz')
#print domain_class_1.cal_bond_valence1(domain1A,'Pb1_D1A',3,False)
'''
###############################################global vars##################################################
##file paths##
batch_path_head='/u1/uaf/cqiu/batchfile/'
##pars for sorbates##
Pb_NUMBER=[1]#domain1 has 1 pb, sencond item represent the number of Pb in second domain
Pb_ATTACH_ATOM=[[['O1_1_0','O1_2_0','O1_3_0']]]#The initial lead postion (first one) for domain1 will form a bidentate mode with O1 AND O2
Pb_ATTACH_ATOM_OFFSET=[[[None,None,None]]]#consider the unit offsets of the above two atms
O_NUMBER=[[1]]#the number must fit in the trigonla bipyramid configuration (4 oxygen ligands in total including the anchors)
##adsorption configuration pars##
#monodentate mode#
R_S=[[2]]#vertical shiftment for monodentate mode (use R value in grid)
#bidentate mode#
THETA=[[1.4]]#open angel for the surface complex structure (the final theta value are defined in the grid table)
PHI=[[np.pi/2]]#rotation angle for the surface complex structure (final phi value in grid table)
FLAG=[['1_1+0_1']]#flag for bidentate mode (1_1+0_1 is more steric feasible than other cases)
ED_FLAG=[['type2']]#extend_flag for bidentate mode
MIRROR=False#mirror switch for bidentate mode
#note no pars for tridentate mode#
##pars for interfacial waters##
WATER_NUMBER=[2,2]#must be even number considering 2 atoms each layer
V_SHIFT=[[2],[2]]#vertical shiftment of water molecules, in unit of angstrom,two items each if consider two water pair
R=[[3],[3]]#distance bw two waters at each layer in unit of angstrom
ALPHA=[[3],[3]]#alpha angle used to cal the pos of water molecule
##chemically different domain type##
DOMAIN=[1]#1 for half layer and 2 for full layer
DOMAIN_NUMBER=len(DOMAIN)
##cal bond valence switch##
USE_BV=False
##want to output the data for plotting?##
PLOT=False
##############################################set up atm ids###############################################
for i in range(DOMAIN_NUMBER):
##text files
vars()['discrete_vars_file_domain'+str(int(i+1))]='new_varial_file_domain'+str(int(i+1))+'.txt'
vars()['sim_batch_file_domain'+str(int(i+1))]='sim_batch_file_domain'+str(int(i+1))+'.txt'
vars()['scale_operation_file_domain'+str(int(i+1))]='scale_operation_file_domain'+str(int(i+1))+'.txt'
##user defined variables
vars()['rgh_domain'+str(int(i+1))]=UserVars()
##sorbate list (HO is oxygen binded to pb and Os is water molecule)
vars()['pb_list_domain'+str(int(i+1))+'a']=create_sorbate_ids(el='Pb',N=Pb_NUMBER[i],tag='_D'+str(int(i+1))+'A')
vars()['pb_list_domain'+str(int(i+1))+'b']=create_sorbate_ids(el='Pb',N=Pb_NUMBER[i],tag='_D'+str(int(i+1))+'B')
vars()['HO_list_domain'+str(int(i+1))+'a']=create_sorbate_ids(el='HO',N=sum(O_NUMBER[i]),tag='_D'+str(int(i+1))+'A')
vars()['HO_list_domain'+str(int(i+1))+'b']=create_sorbate_ids(el='HO',N=sum(O_NUMBER[i]),tag='_D'+str(int(i+1))+'B')
vars()['Os_list_domain'+str(int(i+1))+'a']=create_sorbate_ids(el='Os',N=WATER_NUMBER[i],tag='_D'+str(int(i+1))+'A')
vars()['Os_list_domain'+str(int(i+1))+'b']=create_sorbate_ids(el='Os',N=WATER_NUMBER[i],tag='_D'+str(int(i+1))+'B')
vars()['sorbate_ids_domain'+str(int(i+1))+'a']=vars()['pb_list_domain'+str(int(i+1))+'a']+vars()['HO_list_domain'+str(int(i+1))+'a']+vars()['Os_list_domain'+str(int(i+1))+'a']
vars()['sorbate_ids_domain'+str(int(i+1))+'b']=vars()['pb_list_domain'+str(int(i+1))+'b']+vars()['HO_list_domain'+str(int(i+1))+'b']+vars()['Os_list_domain'+str(int(i+1))+'b']
vars()['sorbate_els_domain'+str(int(i+1))]=['Pb']*Pb_NUMBER[i]+['O']*(sum(O_NUMBER[i])+WATER_NUMBER[i])
##set up group name container(discrete:single atom from each domain, sequence:double atoms at same layer from each domain)
#atom ids for grouping(containerB must be the associated chemically equivalent atoms)
equivalent_atm_list_A_1=["O1_1_0","O1_2_0","Fe1_2_0","Fe1_3_0","O1_3_0","O1_4_0","Fe1_4_0","Fe1_6_0","O1_5_0","O1_6_0","O1_7_0","O1_8_0","Fe1_8_0","Fe1_9_0"]
equivalent_atm_list_A_2=["O1_11_t","O1_12_t","O1_1_0","O1_2_0","Fe1_2_0","Fe1_3_0","O1_3_0","O1_4_0","Fe1_4_0","Fe1_6_0","O1_5_0","O1_6_0","O1_7_0","O1_8_0"]
vars()['ids_domain'+str(int(i+1))+'A']=vars()['sorbate_ids_domain'+str(int(i+1))+'a']+map(lambda x:x+'_D'+str(int(i+1))+'A',vars()['equivalent_atm_list_A_'+str(int(DOMAIN[i]))])
equivalent_atm_list_B_1=["O1_7_0","O1_8_0","Fe1_8_0","Fe1_9_0","O1_9_0","O1_10_0","Fe1_10_0","Fe1_12_0","O1_11_0","O1_12_0","O1_1_1","O1_2_1","Fe1_2_1","Fe1_3_1"]
equivalent_atm_list_B_2=["O1_5_0","O1_6_0","O1_7_0","O1_8_0","Fe1_8_0","Fe1_9_0","O1_9_0","O1_10_0","Fe1_10_0","Fe1_12_0","O1_11_0","O1_12_0","O1_1_1","O1_2_1"]
vars()['ids_domain'+str(int(i+1))+'B']=vars()['sorbate_ids_domain'+str(int(i+1))+'b']+map(lambda x:x+'_D'+str(int(i+1))+'B',vars()['equivalent_atm_list_B_'+str(int(DOMAIN[i]))])
vars()['discrete_gp_names_domain'+str(int(i+1))]=map(lambda x:'gp_'+x.rsplit('_')[0]+'_D'+str(int(i+1)),vars()['sorbate_ids_domain'+str(int(i+1))+'a'])+\
map(lambda x:'gp_'+x[0].rsplit('_')[0][:-1]+x[0].rsplit('_')[1]+x[1].rsplit('_')[0][:-1]+x[1].rsplit('_')[1]+'_D'+str(int(i+1)),zip(vars()['equivalent_atm_list_A_'+str(int(DOMAIN[i]))],vars()['equivalent_atm_list_B_'+str(int(DOMAIN[i]))]))
#consider the top 10 atom layers
atm_sequence_gp_names_1=['O1O2_O7O8','Fe2Fe3_Fe8Fe9','O3O4_O9O10','Fe4Fe6_Fe10Fe12','O5O6_O11O12','O7O8_O1O2','Fe8Fe9_Fe2Fe3','O9O10_O3O4','Fe10Fe12_Fe4Fe6','O11O12_O5O6']
atm_sequence_gp_names_2=['O11O12_O5O6','O1O2_O7O8','Fe2Fe3_Fe8Fe9','O3O4_O9O10','Fe4Fe6_Fe10Fe12','O5O6_O11O12','O7O8_O1O2','Fe8Fe9_Fe2Fe3','O9O10_O3O4','Fe10Fe12_Fe4Fe6']
vars()['sequence_gp_names_domain'+str(int(i+1))]=map(lambda x:'gp_'+x+'_D'+str(int(i+1)),vars()['atm_sequence_gp_names_'+str(int(DOMAIN[i]))])
##atom ids being considered for bond valence check
atm_list_A_1=['O1_1_0','O1_2_0','O1_3_0','O1_4_0','O1_5_0','O1_6_0','Fe1_4_0','Fe1_6_0']
atm_list_A_2=['O1_1_0','O1_2_0','O1_3_0','O1_4_0','O1_11_t','O1_12_t','Fe1_2_0','Fe1_3_0']
atm_list_B_1=['O1_7_0','O1_8_0','O1_9_0','O1_10_0','O1_11_0','O1_12_0','Fe1_10_0','Fe1_12_0']
atm_list_B_2=['O1_7_0','O1_8_0','O1_9_0','O1_10_0','O1_5_0','O1_6_0','Fe1_8_0','Fe1_9_0']
vars()['atm_list_'+str(int(i+1))+'A']=map(lambda x:x+'_D'+str(int(i+1))+'A',vars()['atm_list_A_'+str(int(DOMAIN[i]))])
vars()['atm_list_'+str(int(i+1))+'B']=map(lambda x:x+'_D'+str(int(i+1))+'B',vars()['atm_list_B_'+str(int(DOMAIN[i]))])
if (sum(O_NUMBER[i])!=0)&(WATER_NUMBER[i]!=0):#if have both water and oxygen sorbate
vars()['match_order_'+str(int(i+1))+'A']=vars()['Os_list_domain'+str(int(i+1))+'a']+vars()['pb_list_domain'+str(int(i+1))+'a']+vars()['HO_list_domain'+str(int(i+1))+'a']+\
map(lambda x:x+'_D'+str(int(i+1))+'A',vars()['atm_list_A_'+str(int(DOMAIN[i]))])
vars()['match_order_'+str(int(i+1))+'B']=vars()['Os_list_domain'+str(int(i+1))+'b']+vars()['pb_list_domain'+str(int(i+1))+'b']+vars()['HO_list_domain'+str(int(i+1))+'b']+\
map(lambda x:x+'_D'+str(int(i+1))+'B',vars()['atm_list_B_'+str(int(DOMAIN[i]))])
elif (sum(O_NUMBER[i])!=0)&(WATER_NUMBER[i]==0):#if no water in the model
vars()['match_order_'+str(int(i+1))+'A']=vars()['pb_list_domain'+str(int(i+1))+'a']+vars()['HO_list_domain'+str(int(i+1))+'a']+\
map(lambda x:x+'_D'+str(int(i+1))+'A',vars()['atm_list_A_'+str(int(DOMAIN[i]))])
vars()['match_order_'+str(int(i+1))+'B']=vars()['pb_list_domain'+str(int(i+1))+'b']+vars()['HO_list_domain'+str(int(i+1))+'b']+\
map(lambda x:x+'_D'+str(int(i+1))+'B',vars()['atm_list_B_'+str(int(DOMAIN[i]))])
elif (sum(O_NUMBER[i])==0)&(WATER_NUMBER[i]!=0):#if no oxygen sorbate in the model
vars()['match_order_'+str(int(i+1))+'A']=vars()['pb_list_domain'+str(int(i+1))+'a']+vars()['Os_list_domain'+str(int(i+1))+'a']+\
map(lambda x:x+'_D'+str(int(i+1))+'A',vars()['atm_list_A_'+str(int(DOMAIN[i]))])
vars()['match_order_'+str(int(i+1))+'B']=vars()['pb_list_domain'+str(int(i+1))+'b']+vars()['Os_list_domain'+str(int(i+1))+'b']+\
map(lambda x:x+'_D'+str(int(i+1))+'B',vars()['atm_list_B_'+str(int(DOMAIN[i]))])
elif (sum(O_NUMBER[i])==0)&(WATER_NUMBER[i]==0):#if neither water nor oxygen sorbate
vars()['match_order_'+str(int(i+1))+'A']=vars()['pb_list_domain'+str(int(i+1))+'a']+\
map(lambda x:x+'_D'+str(int(i+1))+'A',vars()['atm_list_A_'+str(int(DOMAIN[i]))])
vars()['match_order_'+str(int(i+1))+'B']=vars()['pb_list_domain'+str(int(i+1))+'b']+\
map(lambda x:x+'_D'+str(int(i+1))+'B',vars()['atm_list_B_'+str(int(DOMAIN[i]))])
##the matching row Id information in the symfile, which will be used to group atms from chemically equivalent domain
sym_file_Fe=np.array(['Fe1_2_0','Fe1_3_0','Fe1_4_0','Fe1_6_0','Fe1_8_0','Fe1_9_0','Fe1_10_0','Fe1_12_0',\
'Fe1_2_1','Fe1_3_1','Fe1_4_1','Fe1_6_1','Fe1_8_1','Fe1_9_1','Fe1_10_1','Fe1_12_1'])
sym_file_O_1=np.array(['O1_1_0','O1_2_0','O1_3_0','O1_4_0','O1_5_0','O1_6_0','O1_7_0','O1_8_0','O1_9_0','O1_10_0','O1_11_0','O1_12_0',\
'O1_1_1','O1_2_1','O1_3_1','O1_4_1','O1_5_1','O1_6_1','O1_7_1','O1_8_1','O1_9_1','O1_10_1','O1_11_1','O1_12_1'])
sym_file_O_2=np.array(['O1_11_t','O1_12_t','O1_1_0','O1_2_0','O1_3_0','O1_4_0','O1_5_0','O1_6_0','O1_7_0','O1_8_0','O1_9_0','O1_10_0','O1_11_0','O1_12_0',\
'O1_1_1','O1_2_1','O1_3_1','O1_4_1','O1_5_1','O1_6_1','O1_7_1','O1_8_1','O1_9_1','O1_10_1','O1_11_1','O1_12_1'])
vars()['sym_file_Fe_domain'+str(int(i+1))]=np.append(map(lambda x:x+'_D'+str(int(i+1))+'A',sym_file_Fe), map(lambda x:x+'_D'+str(int(i+1))+'B',sym_file_Fe[4:]))
if (sum(O_NUMBER[i])!=0)&(WATER_NUMBER[i]!=0):#if have both water and oxygen sorbate
vars()['sym_file_O_domain'+str(int(i+1))]=np.append(map(lambda x:x+'_D'+str(int(i+1))+'A',vars()['sym_file_O_'+str(int(DOMAIN[i]))]), map(lambda x:x+'_D'+str(int(i+1))+'B',vars()['sym_file_O_'+str(int(DOMAIN[i]))][6:]))
vars()['sym_file_O_domain'+str(int(i+1))]=np.append(vars()['HO_list_domain'+str(int(i+1))+'a']+vars()['Os_list_domain'+str(int(i+1))+'a']+vars()['HO_list_domain'+str(int(i+1))+'b']+vars()['Os_list_domain'+str(int(i+1))+'b'],vars()['sym_file_O_domain'+str(int(i+1))])
elif (sum(O_NUMBER[i])!=0)&(WATER_NUMBER[i]==0):#if no water in the model
vars()['sym_file_O_domain'+str(int(i+1))]=np.append(map(lambda x:x+'_D'+str(int(i+1))+'A',vars()['sym_file_O_'+str(int(DOMAIN[i]))]), map(lambda x:x+'_D'+str(int(i+1))+'B',vars()['sym_file_O_'+str(int(DOMAIN[i]))][6:]))
vars()['sym_file_O_domain'+str(int(i+1))]=np.append(vars()['HO_list_domain'+str(int(i+1))+'a']+vars()['HO_list_domain'+str(int(i+1))+'b'],vars()['sym_file_O_domain'+str(int(i+1))])
elif (sum(O_NUMBER[i])==0)&(WATER_NUMBER[i]!=0):#if no oxygen sorbate in the model
vars()['sym_file_O_domain'+str(int(i+1))]=np.append(map(lambda x:x+'_D'+str(int(i+1))+'A',vars()['sym_file_O_'+str(int(DOMAIN[i]))]), map(lambda x:x+'_D'+str(int(i+1))+'B',vars()['sym_file_O_'+str(int(DOMAIN[i]))][6:]))
vars()['sym_file_O_domain'+str(int(i+1))]=np.append(vars()['Os_list_domain'+str(int(i+1))+'a']+vars()['Os_list_domain'+str(int(i+1))+'b'],vars()['sym_file_O_domain'+str(int(i+1))])
elif (sum(O_NUMBER[i])==0)&(WATER_NUMBER[i]==0):#if neither water nor oxygen sorbate
vars()['sym_file_O_domain'+str(int(i+1))]=np.append(map(lambda x:x+'_D'+str(int(i+1))+'A',vars()['sym_file_O_'+str(int(DOMAIN[i]))]), map(lambda x:x+'_D'+str(int(i+1))+'B',vars()['sym_file_O_'+str(int(DOMAIN[i]))][6:]))
vars()['sym_file_O_domain'+str(int(i+1))]=vars()['sym_file_O_domain'+str(int(i+1))]
vars()['sym_file_Pb_domain'+str(int(i+1))]=np.array(vars()['pb_list_domain'+str(int(i+1))+'a']+vars()['pb_list_domain'+str(int(i+1))+'b'])
vars()['id_match_in_sym_domain'+str(int(i+1))]={'Fe':vars()['sym_file_Fe_domain'+str(int(i+1))],'O':vars()['sym_file_O_domain'+str(int(i+1))],'Pb':vars()['sym_file_Pb_domain'+str(int(i+1))]}
##id list according to the order in the reference domain (used to set up ref domain)
ref_id_list_1=["O1_1_0","O1_2_0","Fe1_2_0","Fe1_3_0","O1_3_0","O1_4_0","Fe1_4_0","Fe1_6_0","O1_5_0","O1_6_0","O1_7_0","O1_8_0","Fe1_8_0","Fe1_9_0","O1_9_0","O1_10_0","Fe1_10_0","Fe1_12_0","O1_11_0","O1_12_0",\
'O1_1_1','O1_2_1','Fe1_2_1','Fe1_3_1','O1_3_1','O1_4_1','Fe1_4_1','Fe1_6_1','O1_5_1','O1_6_1','O1_7_1','O1_8_1','Fe1_8_1','Fe1_9_1','O1_9_1','O1_10_1','Fe1_10_1','Fe1_12_1','O1_11_1','O1_12_1']
ref_id_list_2=["O1_11_t","O1_12_t","O1_1_0","O1_2_0","Fe1_2_0","Fe1_3_0","O1_3_0","O1_4_0","Fe1_4_0","Fe1_6_0","O1_5_0","O1_6_0","O1_7_0","O1_8_0","Fe1_8_0","Fe1_9_0","O1_9_0","O1_10_0","Fe1_10_0","Fe1_12_0","O1_11_0","O1_12_0",\
'O1_1_1','O1_2_1','Fe1_2_1','Fe1_3_1','O1_3_1','O1_4_1','Fe1_4_1','Fe1_6_1','O1_5_1','O1_6_1','O1_7_1','O1_8_1','Fe1_8_1','Fe1_9_1','O1_9_1','O1_10_1','Fe1_10_1','Fe1_12_1','O1_11_1','O1_12_1']
###############################################setting slabs##################################################################
unitcell = model.UnitCell(5.038, 5.434, 7.3707, 90, 90, 90)
inst = model.Instrument(wavel = .833, alpha = 2.0)
bulk = model.Slab(T_factor='B')
ref_domain1 = model.Slab(c = 1.0,T_factor='B')
ref_domain2 = model.Slab(c = 1.0,T_factor='B')
rgh=UserVars()
rgh.new_var('beta', 0.0)
################################################build up ref domains############################################
#add atoms for bulk and two ref domains (ref_domain1<half layer> and ref_domain2<full layer>)
#In those two reference domains, the atoms are ordered according to first hight (z values), then y values
#it is a super surface structure by stacking the surface slab on bulk slab, the repeat vector was counted
#only two possible path(one for runing in pacman, the other in local laptop)
try:
add_atom_in_slab(bulk,batch_path_head+'bulk.str')
except:
batch_path_head='D:\\Github\\batchfile\\'
add_atom_in_slab(bulk,batch_path_head+'bulk.str')
add_atom_in_slab(ref_domain1,batch_path_head+'half_layer2.str')
add_atom_in_slab(ref_domain2,batch_path_head+'full_layer2.str')
##symmetry library
#note the Fe output file is the same, but O and Pb output file is different due to different sorbate configuration
#also note the nameing rule for O pb output file
for i in range(DOMAIN_NUMBER):
vars()['sym_file_domain'+str(int(i+1))]={'Fe':batch_path_head+'Fe output file for Genx reading.txt',\
'O':batch_path_head+'O output file for Genx reading'+str(sum(O_NUMBER[i])+WATER_NUMBER[i])+'_'+str(int(DOMAIN[i]))+'.txt',\
'Pb':batch_path_head+'Pb output file for Genx reading'+str(Pb_NUMBER[i])+'_'+str(int(DOMAIN[i]))+'.txt'}
###################create domain classes and initiate the chemical equivalent domains####################
#when change or create a new domain, make sure the terminated_layer (start from 0)set right
##setup domains
for i in range(DOMAIN_NUMBER):
vars()['domain_class_'+str(int(i+1))]=domain_creator.domain_creator(ref_domain=vars()['ref_domain'+str(int(DOMAIN[i]))],id_list=vars()['ref_id_list_'+str(int(DOMAIN[i]))],terminated_layer=0,domain_tag='_D'+str(int(i+1)),new_var_module=vars()['rgh_domain'+str(int(i+1))])
vars()['domain'+str(int(i+1))+'A']=vars()['domain_class_'+str(int(i+1))].domain_A
vars()['domain'+str(int(i+1))+'B']=vars()['domain_class_'+str(int(i+1))].domain_B
vars(vars()['domain_class_'+str(int(i+1))])['domainA']=vars()['domain'+str(int(i+1))+'A']
vars(vars()['domain_class_'+str(int(i+1))])['domainB']=vars()['domain'+str(int(i+1))+'B']
#Adding sorbates to domainA and domainB
for j in range(Pb_NUMBER[i]):
pb_coors_a=[]
O_coors_a=[]
if len(Pb_ATTACH_ATOM[i][j])==1:#monodentate case
ids=Pb_ATTACH_ATOM[i][j][0]+'_D'+str(int(i+1))+'A'
offset=Pb_ATTACH_ATOM_OFFSET[i][j][0]
pb_id=vars()['pb_list_domain'+str(int(i+1))+'a'][j]#pb_id is a str NOT list
O_index=[0]+[sum(O_NUMBER[i][0:ii+1]) for ii in range(len(O_NUMBER[i]))]
#for [1,2,2], which means inside one domain there are 1OH corresponding to pb1, 2 OH's corresponding to pb2 and so son.
#will return [0,1,3,5], O_id extract OH according to O_index
O_id=vars()['HO_list_domain'+str(int(i+1))+'a'][O_index[j]:O_index[j+1]]#O_ide is a list of str
sorbate_coors=vars()['domain_class_'+str(int(i+1))].adding_sorbate_bipyramid_monodentate(domain=vars()['domain'+str(int(i+1))+'A'],phi=PHI[i][j],r=R_S[i][j],attach_atm_id=ids,offset=offset,pb_id=pb_id,O_id=O_id)
pb_coors_a.append(sorbate_coors[0])
[O_coors_a.append(sorbate_coors[k]) for k in range(len(sorbate_coors))[1:]]
pb_id_B=vars()['pb_list_domain'+str(int(i+1))+'b'][j]
O_id_B=vars()['HO_list_domain'+str(int(i+1))+'b'][O_index[j]:O_index[j+1]]
#now put on sorbate on the symmetrically related domain
sorbate_ids=[pb_id_B]+O_id_B
sorbate_els=['Pb']+['O']*(len(O_id_B))
add_atom(domain=vars()['domain'+str(int(i+1))+'B'],ref_coor=np.array(pb_coors_a+O_coors_a)*[-1,1,1]-[-1.,0.06955,0.5],ids=sorbate_ids,els=sorbate_els)
#grouping sorbates (each set of Pb and HO, set the occupancy equivalent during fitting, looks like gp_sorbates_set1_D1)
#also group the oxygen sorbate to set equivalent u during fitting (looks like gp_HO_set1_D1)
sorbate_set_ids=[pb_id]+O_id+[pb_id_B]+O_id_B
HO_set_ids=O_id+O_id_B
N=len(sorbate_set_ids)/2
M=len(O_id)
vars()['gp_sorbates_set'+str(j+1)+'_D'+str(int(i+1))]=vars()['domain_class_'+str(int(i+1))].grouping_discrete_layer(domain=[vars()['domain'+str(int(i+1))+'A']]*N+[vars()['domain'+str(int(i+1))+'B']]*N,atom_ids=sorbate_set_ids)
vars()['gp_HO_set'+str(j+1)+'_D'+str(int(i+1))]=vars()['domain_class_'+str(int(i+1))].grouping_discrete_layer(domain=[vars()['domain'+str(int(i+1))+'A']]*M+[vars()['domain'+str(int(i+1))+'B']]*M,atom_ids=HO_set_ids)
elif len(Pb_ATTACH_ATOM[i][j])==2:#bidentate case
ids=[Pb_ATTACH_ATOM[i][j][0]+'_D'+str(int(i+1))+'A',Pb_ATTACH_ATOM[i][j][1]+'_D'+str(int(i+1))+'A']
offset=Pb_ATTACH_ATOM_OFFSET[i][j]
pb_id=vars()['pb_list_domain'+str(int(i+1))+'a'][j]
O_index=[0]+[sum(O_NUMBER[i][0:ii+1]) for ii in range(len(O_NUMBER[i]))]
O_id=vars()['HO_list_domain'+str(int(i+1))+'a'][O_index[j]:O_index[j+1]]
sorbate_coors=vars()['domain_class_'+str(int(i+1))].adding_sorbate_trigonal_bipyramid(domain=vars()['domain'+str(int(i+1))+'A'],theta=THETA[i][j],phi=PHI[i][j],flag=FLAG[i][j],extend_flag=ED_FLAG[i][j],attach_atm_ids=ids,offset=offset,pb_id=pb_id,O_id=O_id,mirror=MIRROR)
pb_coors_a.append(sorbate_coors[0])
[O_coors_a.append(sorbate_coors[k]) for k in range(len(sorbate_coors))[1:]]
pb_id_B=vars()['pb_list_domain'+str(int(i+1))+'b'][j]
O_id_B=vars()['HO_list_domain'+str(int(i+1))+'b'][O_index[j]:O_index[j+1]]
#now put on sorbate on the symmetrically related domain
sorbate_ids=[pb_id_B]+O_id_B
sorbate_els=['Pb']+['O']*(len(O_id_B))
add_atom(domain=vars()['domain'+str(int(i+1))+'B'],ref_coor=np.array(pb_coors_a+O_coors_a)*[-1,1,1]-[-1.,0.06955,0.5],ids=sorbate_ids,els=sorbate_els)
#grouping sorbates (each set of Pb and HO, set the occupancy equivalent during fitting, looks like gp_sorbates_set1_D1)
#also group the oxygen sorbate to set equivalent u during fitting (looks like gp_HO_set1_D1)
sorbate_set_ids=[pb_id]+O_id+[pb_id_B]+O_id_B
HO_set_ids=O_id+O_id_B
N=len(sorbate_set_ids)/2
M=len(O_id)
vars()['gp_sorbates_set'+str(j+1)+'_D'+str(int(i+1))]=vars()['domain_class_'+str(int(i+1))].grouping_discrete_layer(domain=[vars()['domain'+str(int(i+1))+'A']]*N+[vars()['domain'+str(int(i+1))+'B']]*N,atom_ids=sorbate_set_ids)
vars()['gp_HO_set'+str(j+1)+'_D'+str(int(i+1))]=vars()['domain_class_'+str(int(i+1))].grouping_discrete_layer(domain=[vars()['domain'+str(int(i+1))+'A']]*M+[vars()['domain'+str(int(i+1))+'B']]*M,atom_ids=HO_set_ids)
elif len(Pb_ATTACH_ATOM[i][j])==3:#tridentate case (no oxygen sorbate here considering it is a trigonal pyramid structure)
ids=[Pb_ATTACH_ATOM[i][j][0]+'_D'+str(int(i+1))+'A',Pb_ATTACH_ATOM[i][j][1]+'_D'+str(int(i+1))+'A',Pb_ATTACH_ATOM[i][j][2]+'_D'+str(int(i+1))+'A']
offset=Pb_ATTACH_ATOM_OFFSET[i][j]
pb_id=vars()['pb_list_domain'+str(int(i+1))+'a'][j]
O_index=[0]+[sum(O_NUMBER[i][0:ii+1]) for ii in range(len(O_NUMBER[i]))]
O_id=vars()['HO_list_domain'+str(int(i+1))+'a'][O_index[j]:O_index[j+1]]
sorbate_coors=vars()['domain_class_'+str(int(i+1))].adding_share_triple_trigonal_bipyramid(domain=vars()['domain'+str(int(i+1))+'A'],attach_atm_ids_ref=ids[0:2],attach_atm_id_third=[ids[-1]],offset=offset,sorbate_id=pb_id,sorbate_oxygen_ids=O_id)
pb_coors_a.append(sorbate_coors[0])
[O_coors_a.append(sorbate_coors[k]) for k in range(len(sorbate_coors))[1:]]
pb_id_B=vars()['pb_list_domain'+str(int(i+1))+'b'][j]
O_id_B=vars()['HO_list_domain'+str(int(i+1))+'b'][O_index[j]:O_index[j+1]]
#now put on sorbate on the symmetrically related domain
sorbate_ids=[pb_id_B]+O_id_B
sorbate_els=['Pb']+['O']*(len(O_id_B))
add_atom(domain=vars()['domain'+str(int(i+1))+'B'],ref_coor=np.array(pb_coors_a+O_coors_a)*[-1,1,1]-[-1.,0.06955,0.5],ids=sorbate_ids,els=sorbate_els)
#grouping sorbates (each set of Pb and HO, set the occupancy equivalent during fitting, looks like gp_sorbates_set1_D1)
#also group the oxygen sorbate to set equivalent u during fitting (looks like gp_HO_set1_D1)
sorbate_set_ids=[pb_id]+O_id+[pb_id_B]+O_id_B
HO_set_ids=O_id+O_id_B
N=len(sorbate_set_ids)/2
M=len(O_id)
vars()['gp_sorbates_set'+str(j+1)+'_D'+str(int(i+1))]=vars()['domain_class_'+str(int(i+1))].grouping_discrete_layer(domain=[vars()['domain'+str(int(i+1))+'A']]*N+[vars()['domain'+str(int(i+1))+'B']]*N,atom_ids=sorbate_set_ids)
vars()['gp_HO_set'+str(j+1)+'_D'+str(int(i+1))]=vars()['domain_class_'+str(int(i+1))].grouping_discrete_layer(domain=[vars()['domain'+str(int(i+1))+'A']]*M+[vars()['domain'+str(int(i+1))+'B']]*M,atom_ids=HO_set_ids)
if WATER_NUMBER[i]!=0:#add water molecules if any
for jj in range(WATER_NUMBER[i]/2):#note will add water pair (two oxygens) each time, and you can't add single water
O_ids_a=vars()['Os_list_domain'+str(int(i+1))+'a'][jj*2:jj*2+2]
O_ids_b=vars()['Os_list_domain'+str(int(i+1))+'b'][jj*2:jj*2+2]
#set the first pb atm to be the ref atm(if you have two layers, same ref point but different height)
H2O_coors_a=[]
try:#anchor the first sorbate
H2O_coors_a=vars()['domain_class_'+str(int(i+1))].add_oxygen_pair2(domain=vars()['domain'+str(int(i+1))+'A'],O_ids=O_ids_a,ref_id=vars()['pb_list_domain'+str(int(i+1))+'a'][0],v_shift=V_SHIFT[i][jj],r=R[i][jj],alpha=ALPHA[i][jj])
except:#anchor O1 if without sorbate
H2O_coors_a=vars()['domain_class_'+str(int(i+1))].add_oxygen_pair2(domain=vars()['domain'+str(int(i+1))+'A'],O_ids=O_ids_a,ref_id='O1_1_0_D'+str(i+1)+'A',v_shift=V_SHIFT[i][jj],r=R[i][jj],alpha=ALPHA[i][jj])
add_atom(domain=vars()['domain'+str(int(i+1))+'B'],ref_coor=H2O_coors_a*[-1,1,1]-[-1.,0.06955,0.5],ids=O_ids_b,els=['O','O'])
#group water molecules at each layer (set equivalent the oc and u during fitting)
M=len(O_ids_a)
vars()['gp_waters_set'+str(jj+1)+'_D'+str(int(i+1))]=vars()['domain_class_'+str(int(i+1))].grouping_discrete_layer(domain=[vars()['domain'+str(int(i+1))+'A']]*M+[vars()['domain'+str(int(i+1))+'B']]*M,atom_ids=O_ids_a+O_ids_b)
#set variables
vars()['domain_class_'+str(int(i+1))].set_new_vars(head_list=['u_o_n','u_Fe_n','oc_n'],N_list=[4,3,7])
vars()['domain_class_'+str(int(i+1))].set_discrete_new_vars_batch(batch_path_head+vars()['discrete_vars_file_domain'+str(int(i+1))])
######################################do grouping###############################################
for i in range(DOMAIN_NUMBER):
#note the grouping here is on a layer basis, ie atoms of same layer are groupped together (4 atms grouped together in sequence grouping)
#you may group in symmetry, then atoms of same layer are not independent (and know the use_sym set to false here)
if DOMAIN[i]==1:
vars()['atm_gp_list_domain'+str(int(i+1))]=vars()['domain_class_'+str(int(i+1))].grouping_sequence_layer(domain=[vars()['domain'+str(int(i+1))+'A'],vars()['domain'+str(int(i+1))+'B']], first_atom_id=['O1_1_0_D'+str(int(i+1))+'A','O1_7_0_D'+str(int(i+1))+'B'],\
sym_file=vars()['sym_file_domain'+str(int(i+1))], id_match_in_sym=vars()['id_match_in_sym_domain'+str(int(i+1))],layers_N=10,use_sym=False)
elif DOMAIN[i]==2:
vars()['atm_gp_list_domain'+str(int(i+1))]=vars()['domain_class_'+str(int(i+1))].grouping_sequence_layer(domain=[vars()['domain'+str(int(i+1))+'A'],vars()['domain'+str(int(i+1))+'B']], first_atom_id=['O1_11_t_D'+str(int(i+1))+'A','O1_5_0_D'+str(int(i+1))+'B'],\
sym_file=vars()['sym_file_domain'+str(int(i+1))], id_match_in_sym=vars()['id_match_in_sym_domain'+str(int(i+1))],layers_N=10,use_sym=False)
vars(vars()['domain_class_'+str(int(i+1))])['atm_gp_list_domain'+str(int(i+1))]=vars()['atm_gp_list_domain'+str(int(i+1))]
#assign name to each group
for j in range(len(vars()['sequence_gp_names_domain'+str(int(i+1))])):vars()[vars()['sequence_gp_names_domain'+str(int(i+1))][j]]=vars()['atm_gp_list_domain'+str(int(i+1))][j]
#you may also only want to group each chemically equivalent atom from two domains (the use_sym is set to true here)
vars()['atm_gp_discrete_list_domain'+str(int(i+1))]=[]
for j in range(len(vars()['ids_domain'+str(int(i+1))+'A'])):
vars()['atm_gp_discrete_list_domain'+str(int(i+1))].append(vars()['domain_class_'+str(int(i+1))].grouping_discrete_layer(domain=[vars()['domain'+str(int(i+1))+'A'],vars()['domain'+str(int(i+1))+'B']],\
atom_ids=[vars()['ids_domain'+str(int(i+1))+'A'][j],vars()['ids_domain'+str(int(i+1))+'B'][j]],sym_file=vars()['sym_file_domain'+str(int(i+1))],id_match_in_sym=vars()['id_match_in_sym_domain'+str(int(i+1))],use_sym=True))
vars(vars()['domain_class_'+str(int(i+1))])['atm_gp_discrete_list_domain'+str(int(i+1))]=vars()['atm_gp_discrete_list_domain'+str(int(i+1))]
for j in range(len(vars()['discrete_gp_names_domain'+str(int(i+1))])):vars()[vars()['discrete_gp_names_domain'+str(int(i+1))][j]]=vars()['atm_gp_discrete_list_domain'+str(int(i+1))][j]
#####################################do bond valence matching###################################
if USE_BV:
for i in range(DOMAIN_NUMBER):
if DOMAIN[i]==1:
vars()['match_lib_'+str(int(i+1))+'A']=create_match_lib_before_fitting(domain_class=vars()['domain_class_'+str(int(i+1))],domain=vars()['domain_class_'+str(int(i+1))].build_super_cell(ref_domain=vars()['domain_class_'+str(int(i+1))].create_equivalent_domains_2()[0],rem_atom_ids=['Fe1_2_0_D'+str(int(i+1))+'A','Fe1_3_0_D'+str(int(i+1))+'A']),atm_list=vars()['atm_list_'+str(int(i+1))+'A'],search_range=2.3)
vars()['match_lib_'+str(int(i+1))+'B']=create_match_lib_before_fitting(domain_class=vars()['domain_class_'+str(int(i+1))],domain=vars()['domain_class_'+str(int(i+1))].build_super_cell(ref_domain=vars()['domain_class_'+str(int(i+1))].create_equivalent_domains_2()[1],rem_atom_ids=['Fe1_8_0_D'+str(int(i+1))+'B','Fe1_9_0_D'+str(int(i+1))+'B']),atm_list=vars()['atm_list_'+str(int(i+1))+'B'],search_range=2.3)
elif DOMAIN[i]==2:
vars()['match_lib_'+str(int(i+1))+'A']=create_match_lib_before_fitting(domain_class=vars()['domain_class_'+str(int(i+1))],domain=vars()['domain_class_'+str(int(i+1))].build_super_cell(ref_domain=vars()['domain_class_'+str(int(i+1))].create_equivalent_domains_2()[0],rem_atom_ids=None),atm_list=vars()['atm_list_'+str(int(i+1))+'A'],search_range=2.3)
vars()['match_lib_'+str(int(i+1))+'B']=create_match_lib_before_fitting(domain_class=vars()['domain_class_'+str(int(i+1))],domain=vars()['domain_class_'+str(int(i+1))].build_super_cell(ref_domain=vars()['domain_class_'+str(int(i+1))].create_equivalent_domains_2()[1],rem_atom_ids=None),atm_list=vars()['atm_list_'+str(int(i+1))+'B'],search_range=2.3)
#####################################specify f1f2 here###################################
res_el='Pb'
f1f2_file='raxs_pb_formatted.f1f2'
f1f2=np.loadtxt(batch_path_head+f1f2_file)
VARS=vars()#pass local variables to sim function
###################################fitting function part##########################################
def Sim(data,VARS=VARS):
VARS=VARS
F =[]
beta=rgh.beta
total_wt=0
domain={}
for i in range(DOMAIN_NUMBER):
#do this part by fitting u and OC instead
#extract the fitting par values in the associated attribute and then do the scaling(initiation+processing, actually update the fitting parameter values)
#VARS['domain_class_'+str(int(i+1))].init_sim_batch(batch_path_head+VARS['sim_batch_file_domain'+str(int(i+1))])
#VARS['domain_class_'+str(int(i+1))].scale_opt_batch(batch_path_head+VARS['scale_operation_file_domain'+str(int(i+1))])
#create matching lib dynamically during fitting
if USE_BV:
vars()['match_lib_fitting_'+str(i+1)+'A'],vars()['match_lib_fitting_'+str(i+1)+'B']=deepcopy(VARS['match_lib_'+str(i+1)+'A']),deepcopy(VARS['match_lib_'+str(i+1)+'B'])
create_match_lib_during_fitting(domain_class=VARS['domain_class_'+str(int(i+1))],domain=VARS['domain'+str(int(i+1))+'A'],atm_list=VARS['atm_list_'+str(int(i+1))+'A'],pb_list=VARS['pb_list_domain'+str(int(i+1))+'a'],HO_list=VARS['HO_list_domain'+str(int(i+1))+'a'],match_lib=vars()['match_lib_fitting_'+str(int(i+1))+'A'])
#grap wt for each domain and cal the total wt
vars()['wt_domain'+str(int(i+1))]=VARS['rgh_domain'+str(int(i+1))].wt
total_wt=total_wt+vars()['wt_domain'+str(int(i+1))]
#update sorbates
for j in range(VARS['Pb_NUMBER'][i]):
pb_coors_a=[]
O_coors_a=[]
if len(VARS['Pb_ATTACH_ATOM'][i][j])==1:#monodentate case
phi=getattr(VARS['rgh_domain'+str(int(i+1))],'phi')
r=getattr(VARS['rgh_domain'+str(int(i+1))],'r')
ids=[VARS['Pb_ATTACH_ATOM'][i][j][0]+'_D'+str(int(i+1))+'A']
offset=VARS['Pb_ATTACH_ATOM_OFFSET'][i][j][0]
pb_id=VARS['pb_list_domain'+str(int(i+1))+'a'][j]#pb_id is a str NOT list
O_index=[0]+[sum(VARS['O_NUMBER'][i][0:ii+1]) for ii in range(len(VARS['O_NUMBER'][i]))]
#for [1,2,2], which means inside one domain there are 1OH corresponding to pb1, 2 OH's corresponding to pb2 and so son.
#will return [0,1,3,5], O_id extract OH according to O_index
O_id=VARS['HO_list_domain'+str(int(i+1))+'a'][O_index[j]:O_index[j+1]]#O_ide is a list of str
sorbate_coors=VARS['domain_class_'+str(int(i+1))].adding_sorbate_bipyramid_monodentate(domain=VARS['domain'+str(int(i+1))+'A'],phi=phi,r=r,attach_atm_id=ids,offset=offset,pb_id=pb_id,O_id=O_id)
pb_coors_a.append(sorbate_coors[0])
[O_coors_a.append(sorbate_coors[k]) for k in range(len(sorbate_coors))[1:]]
pb_id_B=VARS['pb_list_domain'+str(int(i+1))+'b'][j]
O_id_B=VARS['HO_list_domain'+str(int(i+1))+'b'][O_index[j]:O_index[j+1]]
#now put on sorbate on the symmetrically related domain
sorbate_ids=[pb_id_B]+O_id_B
sorbate_els=['Pb']+['O']*(len(O_id_B))
add_atom(domain=VARS['domain'+str(int(i+1))+'B'],ref_coor=np.array(pb_coors_a+O_coors_a)*[-1,1,1]-[-1.,0.06955,0.5],ids=sorbate_ids,els=sorbate_els)
elif len(VARS['Pb_ATTACH_ATOM'][i][j])==2:#bidentate case
theta=getattr(VARS['rgh_domain'+str(int(i+1))],'theta')
phi=getattr(VARS['rgh_domain'+str(int(i+1))],'phi')
ids=[VARS['Pb_ATTACH_ATOM'][i][j][0]+'_D'+str(int(i+1))+'A',VARS['Pb_ATTACH_ATOM'][i][j][1]+'_D'+str(int(i+1))+'A']
offset=VARS['Pb_ATTACH_ATOM_OFFSET'][i][j]
pb_id=VARS['pb_list_domain'+str(int(i+1))+'a'][j]
O_index=[0]+[sum(VARS['O_NUMBER'][i][0:ii+1]) for ii in range(len(VARS['O_NUMBER'][i]))]
O_id=VARS['HO_list_domain'+str(int(i+1))+'a'][O_index[j]:O_index[j+1]]
sorbate_coors=VARS['domain_class_'+str(int(i+1))].adding_sorbate_trigonal_bipyramid(domain=VARS['domain'+str(int(i+1))+'A'],theta=theta,phi=phi,flag=VARS['FLAG'][i][j],extend_flag=VARS['ED_FLAG'][i][j],attach_atm_ids=ids,offset=offset,pb_id=pb_id,O_id=O_id,mirror=VARS['MIRROR'])
pb_coors_a.append(sorbate_coors[0])
[O_coors_a.append(sorbate_coors[k]) for k in range(len(sorbate_coors))[1:]]
pb_id_B=VARS['pb_list_domain'+str(int(i+1))+'b'][j]
O_id_B=VARS['HO_list_domain'+str(int(i+1))+'b'][O_index[j]:O_index[j+1]]
#now put on sorbate on the symmetrically related domain
sorbate_ids=[pb_id_B]+O_id_B
sorbate_els=['Pb']+['O']*(len(O_id_B))
add_atom(domain=VARS['domain'+str(int(i+1))+'B'],ref_coor=np.array(pb_coors_a+O_coors_a)*[-1,1,1]-[-1.,0.06955,0.5],ids=sorbate_ids,els=sorbate_els)
elif len(VARS['Pb_ATTACH_ATOM'][i][j])==3:#tridentate case (no oxygen sorbate here considering it is a trigonal pyramid structure)
ids=[VARS['Pb_ATTACH_ATOM'][i][j][0]+'_D'+str(int(i+1))+'A',VARS['Pb_ATTACH_ATOM'][i][j][1]+'_D'+str(int(i+1))+'A',VARS['Pb_ATTACH_ATOM'][i][j][2]+'_D'+str(int(i+1))+'A']
offset=VARS['Pb_ATTACH_ATOM_OFFSET'][i][j]
pb_id=VARS['pb_list_domain'+str(int(i+1))+'a'][j]
O_index=[0]+[sum(VARS['O_NUMBER'][i][0:ii+1]) for ii in range(len(VARS['O_NUMBER'][i]))]
O_id=VARS['HO_list_domain'+str(int(i+1))+'a'][O_index[j]:O_index[j+1]]
sorbate_coors=VARS['domain_class_'+str(int(i+1))].adding_share_triple_trigonal_bipyramid(domain=VARS['domain'+str(int(i+1))+'A'],attach_atm_ids_ref=ids[0:2],attach_atm_id_third=[ids[-1]],offset=offset,sorbate_id=pb_id,sorbate_oxygen_ids=O_id)
pb_coors_a.append(sorbate_coors[0])
[O_coors_a.append(sorbate_coors[k]) for k in range(len(sorbate_coors))[1:]]
pb_id_B=VARS['pb_list_domain'+str(int(i+1))+'b'][j]
O_id_B=VARS['HO_list_domain'+str(int(i+1))+'b'][O_index[j]:O_index[j+1]]
#now put on sorbate on the symmetrically related domain
sorbate_ids=[pb_id_B]+O_id_B
sorbate_els=['Pb']+['O']*(len(O_id_B))
add_atom(domain=VARS['domain'+str(int(i+1))+'B'],ref_coor=np.array(pb_coors_a+O_coors_a)*[-1,1,1]-[-1.,0.06955,0.5],ids=sorbate_ids,els=sorbate_els)
#set up multiple domains
#note for each domain there are two sub domains which symmetrically related to each other, so have equivalent wt
for i in range(DOMAIN_NUMBER):
domain['domain'+str(int(i+1))+'A']={'slab':VARS['domain'+str(int(i+1))+'A'],'wt':0.5*vars()['wt_domain'+str(int(i+1))]/total_wt}
domain['domain'+str(int(i+1))+'B']={'slab':VARS['domain'+str(int(i+1))+'B'],'wt':0.5*vars()['wt_domain'+str(int(i+1))]/total_wt}
#set up sample
sample = model.Sample(inst, bulk, domain, unitcell,coherence=False,surface_parms={'delta1':0.,'delta2':0.1391})
#bond valence won't be integrated as part of data sets, but you can print out to check the bv as one of the post-fitting work
if USE_BV:
bond_valence=[domain_class_1.cal_bond_valence3(domain=VARS['domain'+str(i+1)+'A'],match_lib=vars()['match_lib_fitting_'+str(i+1)+'A']) for i in range(DOMAIN_NUMBER)]
for bv in bond_valence:
for key in bv.keys():
print "%s\t%5.3f\n"%(key,bv[key])
#cal structure factor for each dataset in this for loop
for data_set in data:
h = data_set.extra_data['h']
k = data_set.extra_data['k']
l = data_set.extra_data['l']
LB = data_set.extra_data['LB']
dL = data_set.extra_data['dL']
rough = (1-beta)/((1-beta)**2 + 4*beta*np.sin(np.pi*(l-LB)/dL)**2)**0.5#roughness model, double check LB and dL values are correctly set up in data file
f = rough*sample.calc_f(h, k, l,f1f2,res_el)
F.append(abs(f))
#print_data(N_sorbate=4,N_atm=40,domain=domain1A,z_shift=1,save_file='D://model.xyz')
#export the model results for plotting if PLOT set to true
if PLOT:
plot_data_container_experiment={}
plot_data_container_model={}
for data_set in data:
f=np.array([])
h = data_set.extra_data['h']
k = data_set.extra_data['k']
l = data_set.x
LB = data_set.extra_data['LB']
dL = data_set.extra_data['dL']
I=data_set.y
eI=data_set.error
#make dumy hkl and f to make the plot look smoother
l_dumy=np.arange(l[0],l[-1],0.1)
N=len(l_dumy)
h_dumy=np.array([h[0]]*N)
k_dumy=np.array([k[0]]*N)
LB_dumy=[]
dL_dumy=[]
for i in range(N):
index=list(l-l_dumy[i]).index(min(l-l_dumy[i]))
LB_dumy.append(LB[index])
dL_dumy.append(dL[index])
LB_dumy=np.array(LB_dumy)
dL_dumy=np.array(dL_dumy)
rough_dumy = (1-beta)/((1-beta)**2 + 4*beta*np.sin(np.pi*(l_dumy-LB_dumy)/dL_dumy)**2)**0.5
f_dumy = rough_dumy*sample.calc_f(h_dumy, k_dumy, l_dumy)
label=str(int(h[0]))+str(int(k[0]))+'L'
plot_data_container_experiment[label]=np.concatenate((l[:,np.newaxis],I[:,np.newaxis],eI[:,np.newaxis]),axis=1)
plot_data_container_model[label]=np.concatenate((l_dumy[:,np.newaxis],f_dumy[:,np.newaxis]),axis=1)
hkls=['00L','02L','10L','11L','20L','22L','30L','2-1L','21L']
plot_data_list=[]
for hkl in hkls:
plot_data_list.append([plot_data_container_experiment[hkl],plot_data_container_model[hkl]])
pickle.dump(plot_data_list,open("D:\\Google Drive\\useful codes\\plotting\\temp_plot","wb"))
return F