def main():
args = parse_args()
fn_h5, grp_name = parse_h5(args.output, 'output')
# check if the group is already present (and not empty) in the output file
if check_output(fn_h5, grp_name, args.overwrite):
return
# Load the cost function from the HDF5 file
cost, used_volume = load_cost(args.cost)
# Find the optimal charges
results = {}
results['x'] = cost.solve(args.qtot, args.ridge)
results['charges'] = results['x'][:cost.natom]
# Related properties
results['cost'] = cost.value(results['x'])
if results['cost'] < 0:
results['rmsd'] = 0.0
else:
results['rmsd'] = (results['cost'] / used_volume)**0.5
# Worst case stuff
results['cost_worst'] = cost.worst(0.0)
if results['cost_worst'] < 0:
results['rmsd_worst'] = 0.0
else:
results['rmsd_worst'] = (results['cost_worst'] / used_volume)**0.5
# Write some things on screen
if log.do_medium:
log('Important parameters:')
log.hline()
log('RMSD charges: %10.5e' % np.sqrt(
(results['charges']**2).mean()))
log('RMSD ESP: %10.5e' % results['rmsd'])
log('Worst RMSD ESP: %10.5e' % results['rmsd_worst'])
log.hline()
# Perform a symmetry analysis if requested
if args.symmetry is not None:
mol_pot = IOData.from_file(args.symmetry[0])
mol_sym = IOData.from_file(args.symmetry[1])
if not hasattr(mol_sym, 'symmetry'):
raise ValueError('No symmetry information found in %s.' %
args.symmetry[1])
aim_results = {'charges': results['charges']}
sym_results = symmetry_analysis(mol_pot.coordinates, mol_pot.cell,
mol_sym.symmetry, aim_results)
results['symmetry'] = sym_results
# Store the results in an HDF5 file
write_script_output(fn_h5, grp_name, results, args)
def main():
args = parse_args()
fn_h5, grp_name = parse_h5(args.output, 'output')
# check if the group is already present (and not empty) in the output file
if check_output(fn_h5, grp_name, args.overwrite):
return
# Load the cost function from the HDF5 file
cost, used_volume = load_cost(args.cost)
# Find the optimal charges
results = {}
results['x'] = cost.solve(args.qtot, args.ridge)
results['charges'] = results['x'][:cost.natom]
# Related properties
results['cost'] = cost.value(results['x'])
if results['cost'] < 0:
results['rmsd'] = 0.0
else:
results['rmsd'] = (results['cost']/used_volume)**0.5
# Worst case stuff
results['cost_worst'] = cost.worst(0.0)
if results['cost_worst'] < 0:
results['rmsd_worst'] = 0.0
else:
results['rmsd_worst'] = (results['cost_worst']/used_volume)**0.5
# Write some things on screen
if log.do_medium:
log('Important parameters:')
log.hline()
log('RMSD charges: %10.5e' % np.sqrt((results['charges']**2).mean()))
log('RMSD ESP: %10.5e' % results['rmsd'])
log('Worst RMSD ESP: %10.5e' % results['rmsd_worst'])
log.hline()
# Perform a symmetry analysis if requested
if args.symmetry is not None:
sys = System.from_file(args.symmetry[0])
sys_sym = System.from_file(args.symmetry[1])
sym = sys_sym.extra.get('symmetry')
if sym is None:
raise ValueError('No symmetry information found in %s.' % args.symmetry[1])
sys_results = {'charges': results['charges']}
sym_results = symmetry_analysis(sys, sym, sys_results)
results['symmetry'] = sym_results
sys.extra['symmetry'] = sym
# Store the results in an HDF5 file
write_script_output(fn_h5, grp_name, results, args)
def main():
args = parse_args()
fn_h5, grp_name = parse_h5(args.output, 'output')
# check if the group is already present (and not empty) in the output file
if check_output(fn_h5, grp_name, args.overwrite):
return
# Load the cost function from the HDF5 file
cost, used_volume = load_cost(args.cost)
# Load the charges from the HDF5 file
charges = load_charges(args.charges)
# Fix total charge if requested
if args.qtot is not None:
charges -= (charges.sum() - args.qtot) / len(charges)
# Store parameters in output
results = {}
results['qtot'] = charges.sum()
# Fitness of the charges
results['cost'] = cost.value_charges(charges)
if results['cost'] < 0:
results['rmsd'] = 0.0
else:
results['rmsd'] = (results['cost'] / used_volume)**0.5
# Worst case stuff
results['cost_worst'] = cost.worst(0.0)
if results['cost_worst'] < 0:
results['rmsd_worst'] = 0.0
else:
results['rmsd_worst'] = (results['cost_worst'] / used_volume)**0.5
# Write some things on screen
if log.do_medium:
log('RMSD charges: %10.5e' % np.sqrt(
(charges**2).mean()))
log('RMSD ESP: %10.5e' % results['rmsd'])
log('Worst RMSD ESP: %10.5e' % results['rmsd_worst'])
log.hline()
# Store the results in an HDF5 file
write_script_output(fn_h5, grp_name, results, args)
def main():
args = parse_args()
fn_h5, grp_name = parse_h5(args.output, 'output')
# check if the group is already present (and not empty) in the output file
if check_output(fn_h5, grp_name, args.overwrite):
return
# Load the cost function from the HDF5 file
cost, used_volume = load_cost(args.cost)
# Find the optimal charges
results = {}
# MODIFICATION HERE
results['x'] = cost.solve(args.qtot, args.ridge)
results['charges'] = results['x'][:cost.natom]
# Related properties
results['cost'] = cost.value(results['x'])
if results['cost'] < 0:
results['rmsd'] = 0.0
else:
results['rmsd'] = (results['cost'] / used_volume)**0.5
# Worst case stuff
results['cost_worst'] = cost.worst(0.0)
if results['cost_worst'] < 0:
results['rmsd_worst'] = 0.0
else:
results['rmsd_worst'] = (results['cost_worst'] / used_volume)**0.5
# Write some things on screen
if log.do_medium:
log('Important parameters:')
log.hline()
log('RMSD charges: %10.5e' % np.sqrt(
(results['charges']**2).mean()))
log('RMSD ESP: %10.5e' % results['rmsd'])
log('Worst RMSD ESP: %10.5e' % results['rmsd_worst'])
log.hline()
# Store the results in an HDF5 file
write_script_output(fn_h5, grp_name, results, args)
def main():
args = parse_args()
fn_h5, grp_name = parse_h5(args.output, 'output')
# check if the group is already present (and not empty) in the output file
if check_output(fn_h5, grp_name, args.overwrite):
return
# Load the cost function from the HDF5 file
cost, used_volume = load_cost(args.cost)
# Load the charges from the HDF5 file
charges = load_charges(args.charges)
# Fix total charge if requested
if args.qtot is not None:
charges -= (charges.sum() - args.qtot)/len(charges)
# Store parameters in output
results = {}
results['qtot'] = charges.sum()
# Fitness of the charges
results['cost'] = cost.value_charges(charges)
if results['cost'] < 0:
results['rmsd'] = 0.0
else:
results['rmsd'] = (results['cost']/used_volume)**0.5
# Worst case stuff
results['cost_worst'] = cost.worst(0.0)
if results['cost_worst'] < 0:
results['rmsd_worst'] = 0.0
else:
results['rmsd_worst'] = (results['cost_worst']/used_volume)**0.5
# Write some things on screen
if log.do_medium:
log('RMSD charges: %10.5e' % np.sqrt((charges**2).mean()))
log('RMSD ESP: %10.5e' % results['rmsd'])
log('Worst RMSD ESP: %10.5e' % results['rmsd_worst'])
log.hline()
# Store the results in an HDF5 file
write_script_output(fn_h5, grp_name, results, args)
def main():
args = parse_args()
fn_h5, grp_name = parse_h5(args.output, 'output')
# check if the group is already present (and not empty) in the output file
if check_output(fn_h5, grp_name, args.overwrite):
return
# Load the cost function from the HDF5 file
cost, used_volume = load_cost(args.cost)
# Find the optimal charges
results = {}
results['x'] = cost.solve(args.qtot, args.ridge)
results['charges'] = results['x'][:cost.natom]
# Related properties
results['cost'] = cost.value(results['x'])
if results['cost'] < 0:
results['rmsd'] = 0.0
else:
results['rmsd'] = (results['cost']/used_volume)**0.5
# Worst case stuff
results['cost_worst'] = cost.worst(0.0)
if results['cost_worst'] < 0:
results['rmsd_worst'] = 0.0
else:
results['rmsd_worst'] = (results['cost_worst']/used_volume)**0.5
# Write some things on screen
if log.do_medium:
log('Important parameters:')
log.hline()
log('RMSD charges: %10.5e' % np.sqrt((results['charges']**2).mean()))
log('RMSD ESP: %10.5e' % results['rmsd'])
log('Worst RMSD ESP: %10.5e' % results['rmsd_worst'])
log.hline()
# Store the results in an HDF5 file
write_script_output(fn_h5, grp_name, results, args)
