def main ():
client, db = mongo.connect()
parser = argparse.ArgumentParser(description='UTILITIES')
parser.add_argument('molecule', help='the name of the molecule/molecues', nargs='?')
parser.set_defaults(which='main')
subparser = parser.add_subparsers(help='TEST SET COMMANDS')
plotter = subparser.add_parser('plot', help='PLOTTING OPERATIONS')
plotter.add_argument('-d', '--dataset', required=True, help='Dataset to plot')
plotter.add_argument('-s', '--savefig', help='Location to save the figure')
plotter.set_defaults(which='plotting')
converter = subparser.add_parser('convert', help='CONVERSION OPERATIONS')
converter.add_argument('-s', '--save', help='Name the com file')
converter.set_defaults(which='convert')
spectra = subparser.add_parser('spectra', help='SPECTRA GRAB')
spectra.add_argument('-s', '--savefig', help='Location to save the figure')
spectra.add_argument('-d', '--dataset', help='dataset to examine, otherwise all', default=None)
spectra.set_defaults(which='spectra')
args = parser.parse_args()
mol = molecule.Molecule(os.getcwd()).pull_record(db, {'g_safename':args.molecule})
if args.which == 'plotting':
if args.savefig:
loc = args.savefig
else:
loc = None
main_title = mol.g_safename + ' ' + args.dataset
shift_sets = tensor_analysis.molecule_tensor_analysis(mol, args.dataset)
exp, calc, names = tensor_analysis.extract_data(shift_sets)
fig = plotting.Figure(dimensions=(12,5), cols=1, main_title=main_title)
plotting.plot_tensor_scatter(calc, exp, fig, loc, legend=False)
fig.show_figure()
# plotting.plot_tensors(tensors, title=args.dataset, loc=loc)
elif args.which == 'convert':
params = molecule.input_gaussian_params()
molecule.convert_to_com(mol, **params)
mol.cleanup()
if args.save:
if not args.save.endswith('.com'):
args.save += '.com'
old = os.path.join(os.getcwd(), mol.g_safename, mol.g_safename+'.com')
new = os.path.join(os.path.dirname(old), args.save)
os.rename(old, new)
elif args.which == 'spectra':
shift_list = []
for x in mol.get_spectra(nuclei='13C'):
if hasattr(x, 'dataset'):
name = "(C) " + x.dataset
else:
name = "(E) " + str(x.assignment_method)
shifts = sorted(x.shifts, key=lambda y: y[0])
shifts.insert(0, name)
shift_list.append(shifts)
print '\n'
for sl in sublist(shift_list, 5):
for line in zip(*sl):
# print shifts
print ''.join(['{:<30}'.format(x) for x in line])
print '\n'
exp_spectra = mol.get_spectra(computed=False, nuclei='13C')
if args.dataset:
comp_spectra = mol.get_spectra(computed=True, dataset=args.dataset, assignment_method='Computed')
else:
comp_spectra = mol.get_spectra(computed=True, assignment_method='Computed')
if comp_spectra:
print '{:<30}{:<25}{:<20}{:<20}{:<20}'.format('Experimental', 'Computed', 'slope', 'intercept', 'R')
for exp_spec in exp_spectra:
for comp_spec in comp_spectra:
stats = tensor_analysis.regres_stats(comp_spec, exp_spec)
print '{:<30}{:<25}{:<20}{:<20}{:<20}'.format(exp_spec.assignment_method, comp_spec.dataset, round(stats[0],4),
round(stats[1],4), round(stats[2],4))
if args.savefig:
print 'Saving figure to {0}'.format(saveto)
t = tensor_analysis.molecule_tensor_analysis(mol, comp_spec.dataset)
plotting.plot_tensors(t, title=mol.g_safename, loc=saveto)
