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 charges from the HDF5 file
charges = load_charges(args.charges)
# Load the uniform grid and the coordintes
ugrid, coordinates = load_ugrid_coordinates(args.grid)
ugrid.pbc[:] = 1 # enforce 3D periodic
# 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()
# Determine the grid specification
results['ugrid'] = ugrid
# Ewald parameters
rcut, alpha, gcut = parse_ewald_args(args)
# Some screen info
if log.do_medium:
log('Important parameters:')
log.hline()
log('Number of grid points: %12i' % ugrid.size)
log('Grid shape: [%8i, %8i, %8i]' % tuple(ugrid.shape))
log('Ewald real cutoff: %12.5e' % rcut)
log('Ewald alpha: %12.5e' % alpha)
log('Ewald reciprocal cutoff: %12.5e' % gcut)
log.hline()
# TODO: add summation ranges
log('Computing ESP (may take a while)')
# Allocate and compute ESP grid
esp = np.zeros(ugrid.shape, float)
compute_esp_grid_cube(ugrid, esp, coordinates, charges, rcut, alpha, gcut)
results['esp'] = esp
# 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 charges from the HDF5 file
charges = load_charges(args.charges)
# Load the uniform grid and the coordintes
ugrid, coordinates = load_ugrid_coordinates(args.grid)
ugrid.pbc[:] = 1 # enforce 3D periodic
# 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()
# Determine the grid specification
results['ugrid'] = ugrid
# Ewald parameters
rcut, alpha, gcut = parse_ewald_args(args)
# Some screen info
if log.do_medium:
log('Important parameters:')
log.hline()
log('Number of grid points: %12i' % ugrid.size)
log('Grid shape: [%8i, %8i, %8i]' % tuple(ugrid.shape))
log('Ewald real cutoff: %12.5e' % rcut)
log('Ewald alpha: %12.5e' % alpha)
log('Ewald reciprocal cutoff: %12.5e' % gcut)
log.hline()
# TODO: add summation ranges
log('Computing ESP (may take a while)')
# Allocate and compute ESP grid
esp = np.zeros(ugrid.shape, float)
compute_esp_grid_cube(ugrid, esp, coordinates, charges, rcut, alpha, gcut)
results['esp'] = esp
# 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)
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 potential data
if log.do_medium:
log('Loading potential array')
mol_pot = IOData.from_file(args.cube)
if not isinstance(mol_pot.grid, UniformGrid):
raise TypeError('The specified file does not contain data on a rectangular grid.')
mol_pot.grid.pbc[:] = parse_pbc(args.pbc) # correct pbc
esp = mol_pot.cube_data
# Reduce the grid if required
if args.stride > 1:
esp, mol_pot.grid = reduce_data(esp, mol_pot.grid, args.stride, args.chop)
# Fix sign
if args.sign:
esp *= -1
# Some screen info
if log.do_medium:
log('Important parameters:')
log.hline()
log('Number of grid points: %12i' % np.product(mol_pot.grid.shape))
log('Grid shape: [%8i, %8i, %8i]' % tuple(mol_pot.grid.shape))
log('PBC: [%8i, %8i, %8i]' % tuple(mol_pot.grid.pbc))
log.hline()
# Construct the weights for the ESP Cost function.
wdens = parse_wdens(args.wdens)
if wdens is not None:
if log.do_medium:
log('Loading density array')
# either the provided density or a built-in prodensity
rho = load_rho(mol_pot.coordinates, mol_pot.numbers, wdens[0], mol_pot.grid, args.stride, args.chop)
wdens = (rho,) + wdens[1:]
if log.do_medium:
log('Constructing weight function')
weights = setup_weights(mol_pot.coordinates, mol_pot.numbers, mol_pot.grid,
dens=wdens,
near=parse_wnear(args.wnear),
far=parse_wnear(args.wfar),
)
# write the weights to a cube file if requested
if args.wsave is not None:
if log.do_medium:
log(' Saving weights array ')
# construct a new data dictionary that contains all info for the cube file
mol_weights = mol_pot.copy()
mol_weights.cube_data = weights
mol_weights.to_file(args.wsave)
# rescale weights such that the cost function is the mean-square-error
if weights.max() == 0.0:
raise ValueError('No points with a non-zero weight were found')
wmax = weights.min()
wmin = weights.max()
used_volume = mol_pot.grid.integrate(weights)
# Some screen info
if log.do_medium:
log('Important parameters:')
log.hline()
log('Used number of grid points: %12i' % (weights>0).sum())
log('Used volume: %12.5f' % used_volume)
log('Used volume/atom: %12.5f' % (used_volume/mol_pot.natom))
log('Lowest weight: %12.5e' % wmin)
log('Highest weight: %12.5e' % wmax)
log('Max weight at edge: %12.5f' % max_at_edge(weights, mol_pot.grid.pbc))
# Ewald parameters
rcut, alpha, gcut = parse_ewald_args(args)
# Some screen info
if log.do_medium:
log('Ewald real cutoff: %12.5e' % rcut)
log('Ewald alpha: %12.5e' % alpha)
log('Ewald reciprocal cutoff: %12.5e' % gcut)
log.hline()
# Construct the cost function
if log.do_medium:
log('Setting up cost function (may take a while) ')
cost = ESPCost.from_grid_data(mol_pot.coordinates, mol_pot.grid, esp, weights, rcut, alpha, gcut)
# Store cost function info
results = {}
results['cost'] = cost
results['used_volume'] = used_volume
# Store cost function properties
results['evals'] = np.linalg.eigvalsh(cost._A)
abs_evals = abs(results['evals'])
if abs_evals.min() == 0.0:
results['cn'] = 0.0
else:
results['cn'] = abs_evals.max()/abs_evals.min()
# Report some on-screen info
if log.do_medium:
log('Important parameters:')
log.hline()
log('Lowest abs eigen value: %12.5e' % abs_evals.min())
log('Highest abs eigen value: %12.5e' % abs_evals.max())
log('Condition number: %12.5e' % results['cn'])
log.hline()
# Store the results in an HDF5 file
write_script_output(fn_h5, grp_name, results, args)
