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pyionic_liquid.py
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pyionic_liquid.py
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#!/home/mdi0316/anaconda3/bin/python
### common input start
import os, sys, re
import numpy as np
import pandas as pd
import shutil
import subprocess as sp
import datetime
import time
import math
scripts_dir = '/home/mdi0316/FUNCTIONS'
classes_dir = '/home/mdi0316/CLASSES'
zmat_converter_dir = '/home/mdi0316/CLASSES/zmatrix-master'
sys.path.insert(0, scripts_dir)
sys.path.insert(0, classes_dir)
sys.path.insert(0, zmat_converter_dir)
import filecmp
import warnings
import GAMESS
import IONIC_LIQUID
import SLURM
from converter import Converter
from Functions import print_tab, running_jobs, find_last_log, calculate_center_of_mass, calculate_center_of_charge, Coulomb_Energy
from IONIC_LIQUID import R_LIST, T_LIST, P_LIST
import numpy.linalg as LA
import scipy.constants as const
Ha2eV = const.value('hartree-electron volt relationship') #27.211
Ang2Bohr = 1.8897259886
from IONIC_LIQUID import mono_dict
dft_dict = { 'N311' : { 'gbasis' : 'N311' , 'ngauss' : 6 } }
"""
Theta = BEND
Phi = TORSION (or dihedral)
bn20 = Radius [~7]
bcn20 = Theta [0,180]
dih20 = Phi [0,360]
"""
inp_dir = '/home/mdi0316/Inputfiles/GAMESS'
work_dir = '/data/mdi0316/WORK'
mono_dir = os.path.join( work_dir, 'MONOMERS' )
## define calculation labels and dictionaries ##
global IL_LABEL
IL_LABEL = sys.argv[1].split('/')[0]
global POST_SCF
global BASIS
global FUNCTIONAL
POST_SCF = 'DFTTYP'
#POST_SCF = 'NONE'
#BASIS = 'APCseg-1'
BASIS = 'N311'
#BASIS = 'STO'
FUNCTIONAL = 'B3LYP'
## define IL from CLASS
IL_dir = os.path.join( work_dir, IL_LABEL )
cat_label, ani_label = IL_LABEL.split('_')
cat_atnum, cat_type, cat_inp, cat_composition, cat_log_dict = mono_dict[cat_label].values()
ani_atnum, ani_type, ani_inp, ani_composition, ani_log_dict = mono_dict[ani_label].values()
cat_log_file = cat_log_dict[POST_SCF][FUNCTIONAL][BASIS]
ani_log_file = ani_log_dict[POST_SCF][FUNCTIONAL][BASIS]
global N_ATOMS
N_ATOMS = cat_atnum + ani_atnum
cat_dir = os.path.join( mono_dir, cat_label, POST_SCF, BASIS, FUNCTIONAL )
ani_dir = os.path.join( mono_dir, ani_label, POST_SCF, BASIS, FUNCTIONAL )
cation = GAMESS.GAMESS_calculation( cat_dir, cat_inp, cat_log_file,
runtyp = 'OPTIMIZE', post_scf = POST_SCF, basis = BASIS, functional = FUNCTIONAL,
natoms = cat_atnum )
anion = GAMESS.GAMESS_calculation( ani_dir, ani_inp, ani_log_file,
runtyp = 'OPTIMIZE', post_scf = POST_SCF, basis = BASIS, functional = FUNCTIONAL,
natoms = ani_atnum )
global ZERO_ENERGY
#try:
cat_ener = cation.get_out_dict()['OPT']['TOT.EN.']
ani_ener = anion.get_out_dict()['OPT']['TOT.EN.']
cat_atoms_list = cation.get_atoms_list()
ani_atoms_list = anion.get_atoms_list( shift = cat_atnum )
ZERO_ENERGY = cat_ener + ani_ener # -20924.916677969093 eV
#except(FileNotFoundError):
# warnings.warn( 'Cation/Anion not completed' )
# ZERO_ENERGY = 0
IL = IONIC_LIQUID.IONIC_LIQUID( IL_LABEL, IL_dir , zero_energy=ZERO_ENERGY, cation_label=cat_label, anion_label=ani_label,
post_scf = POST_SCF, basis = BASIS, functional = FUNCTIONAL )
for fold in [ IL.scan_dir, IL.csv_dir ]:
os.makedirs(fold, exist_ok = True)
#print(cation)
#print(anion)
#print(IL)
## default
input_csv = IL.inp_scan_csv
result_csv = IL.opt_scan_csv
scan_type = 'SCAN'
runtyp = 'OPTIMIZE'
if len( sys.argv ) == 3:
scan_type = sys.argv[2]
if scan_type == 'SCAN':
runtyp = sys.argv[3]
if runtyp == 'ENERGY':
result_csv = IL.ene_scan_csv
elif runtyp == 'EDA':
result_csv = IL.eda_scan_csv
elif scan_type == 'EQUILIBRIUM':
input_csv = IL.equil_inp_csv
result_csv = IL.equil_out_csv
elif scan_type == 'MIN.SURF.':
input_csv = IL.min_surf_inp_csv
result_csv = IL.min_surf_out_csv
elif scan_type == 'GPR':
gpr_idx = str(sys.argv[3])
input_csv = os.path.join( IL.csv_dir, 'gpr_{}_inp.csv'.format(gpr_idx) )
result_csv = IL.opt_gpr_csv
elif scan_type == 'RANDOM':
input_csv = IL.inp_rndm_csv
result_csv = IL.opt_rndm_csv
else:
print( "possible values: 'EQUILIBRIUM', 'MIN.SURF.', 'GPR', 'RANDOM'")
print( unrecognized_scan_type )
## read running jobs
run_ids, run_job_labels = running_jobs()
#def resubmit(resumbit_obj, error, close_R_tuple=None):
# gms_inp_name = resumbit_obj.inp_name
# os.chdir( resumbit_obj.run_dir )
# # save failed files
# skip = False
# if os.path.exists( 'FAILED' ):
# print_tab( 2, ['WARNING: FAILED folder exists', resumbit_obj.run_dir] )
# all_failed = [ int(item) for item in os.listdir('FAILED') ]
# last_one = os.path.join( resumbit_obj.run_dir, 'FAILED', str(max(all_failed)), gms_inp_name )
# if filecmp.cmp( last_one, resumbit_obj.inp_file ):
# print_tab( 2, 'WARNING: no change has been made to the input file' )
# skip = True
#
# if not skip:
# #print_tab( 2 , [ error, gms_inp_name, resumbit_obj.run_dir ] )
# os.makedirs( 'FAILED', exist_ok=True )
# fail_fold = os.path.join( resumbit_obj.run_dir, 'FAILED', str(len(os.listdir( 'FAILED' )) ) )
# os.makedirs( fail_fold )
# shutil.copy2( resumbit_obj.inp_file, fail_fold )
# shutil.copy2( resumbit_obj.opt_file, fail_fold )
# shutil.copy2( resumbit_obj.dat_file, fail_fold )
# with open( os.path.join(fail_fold, 'fail.txt'), 'w+' ) as fail_txt:
# now = datetime.datetime.now()
# fail_txt.write( 'FAILED on {}\nERROR: {}'.format(now, error) )
#
# ## write new files
# if error == 'TOO MANY STEPS TAKEN':
# ## new_Zmat from same directory
# new_Zmat = resumbit_obj.get_last_geometry()[2]
# elif error in ['SCF.Failed', '-ABNORMALLY-']:
# ## new_Zmat from closest converged R directory
# [tmp_R, tmp_T, tmp_P], tmp_closest_R = close_R_tuple
# new_obj = get_gms_info( tmp_r = tmp_closest_R, tmp_t = tmp_T, tmp_p = tmp_P )
# new_Zmat = new_obj.get_equilibrium_geometry()[2]
# print_tab( 2, [ 'Failed RTP = {}, {}, {}'.format( tmp_R, tmp_T, tmp_P ),
# 'CONVERGED RTP = {}, {}, {}'.format( tmp_closest_R, tmp_T, tmp_P ) ] )
# ## set RTP back to tmp_values
# new_Zmat[51][5] = float(tmp_R)*Ang2Bohr
# new_Zmat[51][6] = tmp_R
# new_Zmat[52][6] = tmp_T
# new_Zmat[53][6] = tmp_P
# else:
# print( unknown_error )
# ## resubmit
# resumbit_obj.write_input_from_Zmat(new_Zmat)
# sp.call( 'sbatch -J {} submit_gamess.sh {}'.format(gms_inp_name, gms_inp_name), shell=True )
def get_gms_info( tmp_r, tmp_t, tmp_p, runtyp, scan_type ):
tmp_r = tmp_r.strip()
tmp_t = tmp_t.strip()
tmp_p = tmp_p.strip()
if scan_type == 'SCAN':
run_label = 'SCAN_{}'.format(runtyp[:3])
head_dir = IL.scan_dir
elif scan_type == 'GPR':
run_label = 'GPR_{}'.format(runtyp[:3])
tmp_r = '{:4.4f}'.format(float(tmp_r))
tmp_t = '{:4.4f}'.format(float(tmp_t))
tmp_p = '{:4.4f}'.format(float(tmp_p))
head_dir = IL.gpr_dir
elif scan_type == 'RANDOM':
tmp_r = '{:4.4f}'.format(float(tmp_r))
tmp_t = '{:4.4f}'.format(float(tmp_t))
tmp_p = '{:4.4f}'.format(float(tmp_p))
run_label = 'RND_{}'.format(runtyp[:3])
head_dir = IL.rnd_dir
elif scan_type == 'MIN.SURF.':
run_label = 'MIN_{}'.format(runtyp[:3])
head_dir = IL.min_surf_dir
elif scan_type == 'EQUILIBRIUM':
run_label = 'EQU_{}'.format(runtyp[:3])
head_dir = IL.equil_dir
gms_dir = os.path.join( head_dir, POST_SCF, BASIS, FUNCTIONAL,
'T_{}'.format(tmp_t), 'P_{}'.format(tmp_p), 'R_{}'.format(tmp_r) )
run_dir = os.path.join( gms_dir, runtyp )
gms_inp = '{}_{}_{}_{}_{}_T_{}_P_{}_R_{}.inp'.format(IL_LABEL,POST_SCF[:3],BASIS,FUNCTIONAL,run_label,tmp_t,tmp_p,tmp_r)
gms_log = None ## new calc
if os.path.exists( run_dir ):
all_logs = [ff for ff in os.listdir(run_dir) if ff.startswith('log') ]
if len( all_logs ) == 0:
gms_log = None
elif len( all_logs ) == 1:
gms_log = all_logs[-1]
else:
print( run_dir )
print( stop_more_than_one_log )
gms_obj = GAMESS.GAMESS_calculation( gms_dir, gms_inp, gms_log,
zero_en = ZERO_ENERGY,
runtyp = runtyp, post_scf = POST_SCF, basis = BASIS, functional = FUNCTIONAL,
natoms = N_ATOMS )
return( gms_obj )
def read_df_line( R_read, T_read, P_read, df_read, run_label, print_opt = False ):
df_line = df_read.loc[ df_read[('Coordinates', 'Radius')] == str(R_read) ].loc[
df_read[('Coordinates', 'Theta')] == str(T_read) ].loc[
df_read[('Coordinates', 'Phi')] == str(P_read) ]
if len(df_line) > 1:
print( error_more_than_one_line_in_df )
if df_line.empty:
df_index = None
new_line = True
prev_stat = 'New.Line'
else:
df_index = df_line.index.values[0]
new_line = False
prev_stat = df_line[(run_label,'STATUS')].values[0]
if print_opt:
print_tab( 1, 'R={:5s},T={:3s},P={:3s}: {}:{}'.format(R_read, T_read, P_read, run_label, prev_stat) )
return( df_line, prev_stat, new_line, df_index )
def write_and_run_new( run_dir, inp_name, runtyp, scan_type = 'SCAN',
run_zmat = None, nat_cat = None, nat_ani = None ):
warnings.warn( 'will skip new files till ATOMATE is correctly installed' )
## define new zmatrix from relaxed anion and cation according to R,T,P if not explicitely in input
#if run_zmat == None:
# run_zmat = IL.get_cation_anion_gamess_zmat_dict(radius=R, theta=T, phi=P)
if scan_type == 'SCAN':
run_ifreeze = IL.ifreeze_coords
elif scan_type == 'MIN.SURF.':
run_ifreeze = ','.join( IL.ifreeze_coords.split(',')[1:] )
elif scan_type == 'EQUILIBRIUM':
if not os.path.exists( run_dir ):
os.makedirs( run_dir )
run_ifreeze = None
## read relaxed anion/cation and create guess coordinates BEGINS
cat_cart_coords = IL.CATION.cart_dict_to_dat()
ani_cart_coords = IL.ANION.cart_dict_to_dat()
IL_guess_cart_coords = os.path.join( run_dir, 'guess_cart_coords.dat' )
IL_guess_zmat_coords = os.path.join( run_dir, 'guess_zmat_coords.dat' )
Z = float(R)*np.sin(np.deg2rad(float(T)))
XY = Z/np.tan(np.deg2rad(float(T)))
X = XY*np.cos(np.deg2rad(float(P)))
Y = XY*np.sin(np.deg2rad(float(P)))
if not os.path.exists( IL_guess_cart_coords ):
with open( IL_guess_cart_coords, 'w+' ) as IL_cc:
IL_cc.write( '{}\n\n'.format(IL.cation_nat+IL.anion_nat))
for cat_line in open( cat_cart_coords, 'r' ).readlines()[2:]:
IL_cc.write(cat_line)
for ani_line in open( ani_cart_coords, 'r' ).readlines()[2:]:
sym,x,y,z = ani_line.split()
IL_cc.write('{:2s} {:1.8f} {:1.8f} {:1.8f}\n'.format( sym, float(x) + X, float(y) + Y, float(z) + Z ) )
a = Converter()
a.run_cartesian( input_file = IL_guess_cart_coords, output_file=IL_guess_zmat_coords )
run_zmat = {}
run_zmat[0] = { 'elem.':open(cat_cart_coords,'r').readlines()[2].split()[0], 'idx.':'1' }
IL_zm_lines = open( IL_guess_zmat_coords, 'r' ).readlines()
for zm_idx, zm_line in enumerate(IL_zm_lines[3:]):
if len(zm_line.split()) == 3:
elem, str_ref, str_val = zm_line.split()
line_dict = { 'elem.':elem, 'idx.':zm_idx+2,
'STR' : {'ref':str_ref, 'label': 'r{}'.format(zm_idx), 'val':float(str_val)} }
elif len(zm_line.split()) == 5:
elem, str_ref, str_val, ben_ref, ben_val = zm_line.split()
line_dict = {'elem.':elem, 'idx.':zm_idx+2,
'STR' : {'ref':str_ref, 'label': 'r{}'.format(zm_idx), 'val':float(str_val)},
'BEN' : {'ref':ben_ref, 'label': 'b{}'.format(zm_idx), 'val':float(ben_val)} }
elif len(zm_line.split()) == 7:
elem, str_ref, str_val, ben_ref, ben_val, tor_ref, tor_val = zm_line.split()
line_dict = {'elem.':elem, 'idx.':zm_idx+2,
'STR' : {'ref':str_ref, 'label': 'r{}'.format(zm_idx), 'val':float(str_val)},
'BEN' : {'ref':ben_ref, 'label': 'b{}'.format(zm_idx), 'val':float(ben_val)},
'TOR' : {'ref':tor_ref, 'label': 't{}'.format(zm_idx), 'val':float(tor_val)} }
else:
print(error_unkonwn_coordinate_type)
run_zmat[zm_idx+1] = line_dict
## read relaxed anion/cation and create guess coordinates ENDS
## define new GAMESS object and run
new_gms = GAMESS.GAMESS_calculation( run_dir, inp_name, '',
zero_en = ZERO_ENERGY,
runtyp = runtyp, post_scf = POST_SCF, basis = BASIS, functional = FUNCTIONAL,
natoms = N_ATOMS, ifreeze = run_ifreeze, nat_cat = nat_cat, nat_ani = nat_ani )
new_gms.write_input_file(run_zmat, msg=inp_name)
## submit
queue = 'nodesloq'
if runtyp in ['EDA', 'ENERGY'] or scan_type in ['EQUILIBRIUM', 'MIN.SURF.']:
queue = 'nodeshiq'
slurm_obj = SLURM.SLURM( new_gms.run_dir, 'GAMESS', job_name = new_gms.inp_name, job_queue = queue )
slurm_obj.write_batch()
slurm_obj.submit_batch()
return( 'Running' )
return( 'NEW' )
def resubmit_jobs( folder ):
os.chdir( folder )
inp_name = [ ff for ff in os.listdir( os.getcwd() ) if ff.endswith('inp') ][0]
warnings.warn( 'missing log, will resubmit' )
shutil.copy( '/home/mdi0316/scripts/submit_gamess.sh', './' )
sp.call( 'sbatch --partition=nodesloq -J {} submit_gamess.sh {}'.format(inp_name, inp_name), shell=True)
return( 'Running' )
### common input end
## !!ALL energies in EV!!
def main():
global R
global T
global P
## READ previously written results
print_tab( 0, 'input_csv {}'.format(input_csv) )
print_tab( 0, 'result_csv {}'.format(result_csv) )
print_tab( 0, 'scan_type {}'.format(scan_type) )
if not os.path.exists( input_csv ):
IL.write_inp_csv( scan_type = scan_type )
input_df = pd.read_csv( input_csv, header = [0,1], dtype=object ) #index_col=0
coords_columns = [ ('Coordinates', ii) for ii in [ 'Radius', 'Theta', 'Phi' ]]
equil_columns = [ ('OPT', ii) for ii in [ 'STATUS','EXEC.','SCF','GEOM.','TOT.EN.','INT.EN.','Rlx.R','Rlx.T','Rlx.P' ]]
min_surf_columns = [ ('OPT', ii) for ii in [ 'STATUS','EXEC.','SCF','GEOM.','TOT.EN.','INT.EN.','Rlx.R' ]]
opt_columns = [ ('OPT', ii) for ii in [ 'STATUS','EXEC.','SCF','GEOM.','TOT.EN.','INT.EN.',
'MULL.CHAR.CAT.','MULL.CHAR.AN.','D.COM.', 'D.COC.',
'OLD.COUL.EN.','NEW.COUL.EN.' ]]
eda_columns = [ ('EDA', ii) for ii in [ 'STATUS','EXEC.','SCF','ES.','EX.','REP.','POL.','INT.EN.' ]]
ene_columns = [ ('ENE', ii) for ii in [ 'STATUS','EXEC.','SCF','TOT.EN.','INT.EN.' ]]
scan_dict = { 'OPTIMIZE' : { 'csv' : IL.opt_scan_csv, 'res_cols' : opt_columns},
'EDA' : { 'csv' : IL.eda_scan_csv, 'res_cols' : eda_columns},
'ENERGY' : { 'csv' : IL.ene_scan_csv, 'res_cols' : ene_columns} }
opt_csv = scan_dict[runtyp]['csv']
opt_res_cols = scan_dict[runtyp]['res_cols']
opt_label = runtyp[:3]
reduced_coordinates = False
if scan_type == 'SCAN':
results_columns = opt_res_cols
reduced_coordinates = False #True
reduced_coordinates = True
if reduced_coordinates:
#input_df = input_df.sample(n=100)
reduced_R_list = [str(r) for r in R_LIST]
reduced_T_list = [str(t) for t in T_LIST]
reduced_P_list = [str(p) for p in P_LIST]
reduced_R_list = [ '3.0', '4.0', '5.0', '6.0', '7.0', '8.0', '10.0', '12.0', '15.0', '20.0' ]
#reduced_R_list = [ '5.0' ] #'5.0', '5.5', '6.0', '6.5', '7.0', '10.0', '15.0', '20.0' ]
reduced_T_list = [ '90' ]#
reduced_P_list = [ '90' ]#
elif scan_type == 'EQUILIBRIUM':
results_columns = equil_columns
elif scan_type == 'MIN.SURF.':
results_columns = min_surf_columns
elif scan_type == 'GPR':
reduced_coordinates = True
pass
elif scan_type == 'RANDOM':
reduced_coordinates = True
if reduced_coordinates:
input_df = input_df.loc[input_df[('Coordinates','Radius')].isin( reduced_R_list ) ]
input_df = input_df.loc[input_df[('Coordinates','Theta')].isin( reduced_T_list ) ]
input_df = input_df.loc[input_df[('Coordinates','Phi')].isin( reduced_P_list ) ]
print_tab( 0, 'reduced R_list: {}'.format(reduced_R_list) )
print_tab( 0, 'reduced R_list: {}'.format(reduced_T_list) )
print_tab( 0, 'reduced P_list: {}'.format(reduced_P_list) )
## read out CSV
if os.path.exists( result_csv ):
result_df = pd.read_csv( result_csv, header = [0,1], dtype=object )
else:
result_df = pd.DataFrame( columns = pd.MultiIndex.from_tuples(coords_columns + results_columns), dtype=object )
print_tab(1, 'NEW file: {}'.format( result_csv ) )
## READ previously written results ends
print_tab( 1, ['scan_type = {}, runtyp = {}, opt_label = {}'.format( scan_type, runtyp, opt_label),
'reduced_coordinates = {}, result_csv = {}'.format( reduced_coordinates, result_csv )] )
print( input_df )
for index, row in input_df.iterrows():
R, T, P = str(row[('Coordinates','Radius')]), str(row[('Coordinates','Theta')]), str(row[('Coordinates','Phi')])
run_dict = {('Coordinates','Radius'):str(R), ('Coordinates','Theta'):str(T), ('Coordinates','Phi'):str(P) }
prev_out_line, prev_out_stat, new_out_line, df_out_index = read_df_line(R,T,P,result_df,opt_label,print_opt=True )
run_obj = get_gms_info( R, T, P, runtyp , scan_type )
print(run_obj)
proceed = True
### system is running
#if run_obj.inp_name in run_labels:
# run_exec = 'Running'
# print_tab(1, 'Running')
# proceed = False
## read previous result R,T,P for optimize case
if runtyp in ['EDA', 'ENERGY']:
opt_obj = get_gms_info( R, T, P, 'OPTIMIZE', 'SCAN' )
print( 'read opt' )
prev_opt_line, prev_opt_stat, new_opt_line, df_opt_index = read_df_line(R,T,P,opt_df,'OPT')
if prev_opt_stat == 'FAIL.':
## Do not calculate EDA and ENERGY if OPT did not converge
print_tab(1, 'skipping')
proceed = False
## Has the calculation already succeede/failed
if prev_out_stat in ['FAIL.', 'SUCC.']:
proceed = False
#print( 'proceed:', proceed )
if proceed:
if os.path.exists( run_obj.run_dir ):
run_exec = run_obj.get_execution(run_job_labels)
print( run_exec )
## check whether input and output have the same parameters
# run_obj.read_RTP_io( RADIUS_STR, THETA_STR, PHI_STR, CHECK_LINE )
## READ OUTPUT BEGINS
if run_exec == 'Running':
run_stat = 'Running'
print_tab(3 , run_stat )
elif run_exec == 'Missing':
## folder exists but no out file
run_exec = resubmit_jobs( run_obj.run_dir )
elif run_exec in ['unknown.exec.', '-ABNORMALLY-']:
run_stat = run_obj.read_error() # 'FAIL.'
elif run_exec == 'NORMALLY':
run_stat = 'unknown.stat.'
gamess_out_dict = run_obj.get_out_dict()[opt_label]
if runtyp == 'OPTIMIZE':
opt_scf = gamess_out_dict['SCF']
opt_geom = gamess_out_dict['GEOM.']
run_dict[('OPT','SCF' )] = opt_scf
run_dict[('OPT','GEOM.')] = opt_geom
run_dict[('OPT','TOT.EN.')] = gamess_out_dict['TOT.EN.']
run_dict[('OPT','INT.EN.')] = gamess_out_dict['INT.EN.']
cart_dict = gamess_out_dict['CART.COORDS.']
atoms_dict = gamess_out_dict['ATOMS']
mulliken_dict = gamess_out_dict['MULL.CHARGES']
internuc_dict = gamess_out_dict['INTERNUCL.DISTANCES']
## calculate Distance center of masses (com) and charges (coc)
Z_cat, xyz_cat, charge_cat = [], [], []
for at_index in range(0, cat_atnum):
Z_cat.append( float(atoms_dict[at_index]['Z']) )
xyz_cat.append( [ float(cart_dict[at_index][idx]) for idx in ['x','y','z'] ] )
charge_cat.append( float(mulliken_dict[at_index]['charge']) )
com_cat = calculate_center_of_mass( Z_cat, xyz_cat )
coc_cat = calculate_center_of_charge( Z_cat, xyz_cat, charge_cat )
Z_ani, xyz_ani, charge_ani = [], [], []
for at_index in range(cat_atnum, cat_atnum+ani_atnum):
Z_ani.append( float(atoms_dict[at_index]['Z']) )
xyz_ani.append( [ float(cart_dict[at_index][idx]) for idx in ['x','y','z'] ] )
charge_ani.append( float(mulliken_dict[at_index]['charge']) )
com_ani = calculate_center_of_mass( Z_ani, xyz_ani )
coc_ani = calculate_center_of_charge( Z_ani, xyz_ani, charge_ani )
distance_center_of_masses = LA.norm( com_ani - com_cat )
distance_center_of_charges = LA.norm( coc_ani - coc_cat )
run_dict[('OPT','D.COM.')] = distance_center_of_masses
run_dict[('OPT','D.COC.')] = distance_center_of_charges
## OLD Mulliken charges
cat_charges_dict = { k:v for (k,v) in mulliken_dict.items() if k < cat_atnum }
ani_charges_dict = { k:v for (k,v) in mulliken_dict.items() if k >= cat_atnum }
cat_charges_sum = sum([v['charge'] for v in cat_charges_dict.values() ])
ani_charges_sum = sum([v['charge'] for v in ani_charges_dict.values() ])
run_dict[('OPT','MULL.CHAR.CAT.')] = cat_charges_sum
run_dict[('OPT','MULL.CHAR.AN.')] = ani_charges_sum
run_dict[('OPT','OLD.COUL.EN.')] = Coulomb_Energy(distance_center_of_masses, cat_charges_sum, ani_charges_sum)
## NEW Mulliken charges
tot_int = 0
tot_coul_energy = 0
for int_key, int_dict in internuc_dict.items():
ii_idx = int(int_dict['at.1']['idx.1'])
ii_sym = int_dict['at.1']['elem.1']
jj_idx = int(int_dict['at.2']['idx.2'])
jj_sym = int_dict['at.2']['elem.2']
ii_label = '{}{}'.format( ii_sym, ii_idx )
jj_label = '{}{}'.format( jj_sym, jj_idx )
print( cat_atoms_list )
print( ani_atoms_list )
if ii_label in cat_atoms_list and jj_label in ani_atoms_list:
ii_dict = [v for v in mulliken_dict.values() if int(v['idx'])==int(ii_idx) and v['elem.']==ii_sym ][0]
jj_dict = [v for v in mulliken_dict.values() if int(v['idx'])==int(jj_idx) and v['elem.']==jj_sym ][0]
ii_charge = ii_dict['charge']
jj_charge = jj_dict['charge']
ii_jj_dist = int_dict['dist.']
ii_jj_coul_energy = Coulomb_Energy( ii_jj_dist, q1 = ii_charge, q2 = jj_charge )
tot_coul_energy += ii_jj_coul_energy
tot_int += 1
run_dict[('OPT','NEW.COUL.EN.')] = tot_coul_energy
#run_dict[('OPT','NEW.COUL.EN.')] = run_obj.internal_coulomb_energy()
if run_exec == 'NORMALLY' and opt_scf == 'CONVERGED' and opt_geom == 'LOCATED':
run_stat = 'SUCC.'
else:
run_stat = 'FAIL.'
elif runtyp == 'EDA':
run_dict[ ('EDA','INT.EN.')] = gamess_out_dict['ALL.BS']['INT.EN.']
run_dict[ ('EDA','ES.')] = gamess_out_dict['ALL.BS']['ES.']
run_dict[ ('EDA','EX.')] = gamess_out_dict['ALL.BS']['EX.']
run_dict[ ('EDA','REP.')] = gamess_out_dict['ALL.BS']['REP.']
run_dict[ ('EDA','POL.')] = gamess_out_dict['ALL.BS']['POL.']
if run_exec == 'NORMALLY':
run_stat = 'SUCC.'
else:
run_stat = 'FAIL.'
elif runtyp == 'ENERGY':
ene_scf = gamess_out_dict['SCF']
run_dict[('ENE','SCF' )] = ene_scf
run_dict[('ENE','TOT.EN.')] = gamess_out_dict['TOT.EN.']
run_dict[('ENE','INT.EN.')] = gamess_out_dict['INT.EN.']
if run_exec == 'NORMALLY' and ene_scf == 'CONVERGED':
run_stat = 'SUCC.'
else:
run_stat = 'FAIL.'
run_dict[(opt_label,'STATUS')] = run_stat
## READ OUTPUT ENDS
else:
print( scan_type )
## folder does not exists
if runtyp == 'OPTIMIZE':
run_exec = write_and_run_new( run_obj.gms_dir, run_obj.inp_name, runtyp, scan_type = scan_type )
else:
prev_opt_line, prev_opt_stat, new_opt_line, df_opt_index = read_df_line( R, T, P, opt_df, 'OPT' )
if prev_opt_stat == 'SUCC.':
opt_zmat = opt_obj.get_out_dict()['OPT']['ZMAT']
run_exec = write_and_run_new( run_obj.gms_dir, run_obj.inp_name, runtyp, scan_type = scan_type,
run_zmat = opt_zmat, nat_cat = cat_atnum, nat_ani = ani_atnum )
else:
run_exec = 'OPT.FAIL.'
run_dict[(opt_label,'EXEC.')] = run_exec
if new_out_line:
result_df = result_df.append( pd.Series( run_dict ), ignore_index=True )
else:
result_df.replace( result_df.loc[df_out_index], pd.Series( run_dict ) )
### copy reduced grid results from scan csv file
#if scan_type == 'GPR':
# if gpr_idx == '0':
# link_r = '{:3.1f}'.format(float(R))
# link_p = int(P)
# link_t = int(T)
# run_obj = get_gms_info(tmp_r=link_r,tmp_p=link_p,tmp_t=link_t)
### copy reduced grid results from scan csv file
print( 'writing {} reuslts to {}'.format(runtyp, result_csv) )
result_df.to_csv( result_csv, index=False )
### subtract ElectroStatic Contribution
#tot_df = pd.concat([opt_df, eda_df], axis=1)
#print( tot_df )
#tot_df[('OPT','NON.COUL.EN.')] = tot_df[('OPT', 'TOT.EN.')] - tot_df[('EDA','ES.')]
#print( tot_df[('OPT','NON.COUL.EN.')] )
#print( here2 )
#opt_df[('OPT','NON.COUL.EN.')] = tot_df[('OPT','NON.COUL.EN.')]
#print( opt_df[('OPT','NON.COUL.EN.')] ) #= opt_df[('OPT', 'TOT.EN.')] - eda_df[('EDA','ES.')]
#opt_df.to_csv( result_csv, index=False )
## Check error in OPT DF
if os.path.exists( IL.opt_scan_csv ):
all_opt_df = pd.read_csv( IL.opt_scan_csv, header = [0,1], dtype=object )
suc_opt_df = all_opt_df.loc[ all_opt_df[('OPT','STATUS')] == 'SUCC.' ]
run_opt_df = all_opt_df.loc[ all_opt_df[('OPT','EXEC.')] == 'Running' ]
abn_opt_df = all_opt_df.loc[ all_opt_df[('OPT','EXEC.')] == '-ABNORMALLY-' ]
#unknown_opt_df = all_opt_df.loc[ all_opt_df[('OPT','STATUS')] == 'unknown.stat.' ]
print( '{:4d} Total configurations'.format(len(all_opt_df)))
print( '{:4d} Succeeded jobs'.format(len(suc_opt_df)))
print( '{:4d} Running jobs'.format(len(run_opt_df)))
print( '{:4d} Abnormally jobs'.format(len(abn_opt_df)))
#print( '{:4d} Unknown status jobs'.format(len(unknown_opt_df)))
## fix errors
##for fail_df in [abnormal_opt_df, unknown_opt_df]:
#for index, fail_row in abn_opt_df.iterrows():
# fail_stat = fail_row[('OPT', 'STATUS')]
# fail_exex = fail_row[('OPT', 'EXEC.')]
# fail_R = fail_row[('Coordinates', 'Radius')]
# fail_T = fail_row[('Coordinates', 'Theta')]
# fail_P = fail_row[('Coordinates', 'Phi')]
# fail_out_line, fail_out_stat, fail_out_line, df_fail_index = read_df_line( fail_R, fail_T, fail_P, abn_opt_df, 'OPT', print_opt=True )
# fail_obj = get_gms_info( fail_R, fail_T, fail_P, 'OPTIMIZE', 'SCAN' )
# ## double check for execution
# fail_exec = fail_obj.get_execution(run_job_labels)
# if fail_exec == '-ABNORMALLY-':
# fail_stat = fail_obj.read_error()
# if fail_stat in ['reduce QMTTOL']:
# # find closest succeeded configuration with same T,P
# fixed_angles_df = suc_opt_df.loc[ suc_opt_df[('Coordinates', 'Theta')] == fail_T
# ].loc[ suc_opt_df[('Coordinates','Phi')] == fail_P ]
# suc_R_list = fixed_angles_df[('Coordinates','Radius')].values
# delta = 1e2
# for suc_R in suc_R_list:
# tmp_delta = abs(float(suc_R) - float(fail_R))
# if tmp_delta < delta:
# closest_R = suc_R
# delta = tmp_delta
# print( fail_R, closest_R, delta )
# else:
# print( fail_stat )
# else:
# print( porca_merda_df_file_corrotto )
if __name__ == '__main__':
main()