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)
def main():
client, db = mongo.connect()
parser = argparse.ArgumentParser(description='PLOTTING')
parser.add_argument('-d', '--dataset', nargs='+', required=True, default=[], help='The dataset(s) to draw from')
parser.add_argument('-m', '--molecules', type = argparse.FileType('r'), default = [])
parser.add_argument('-s', '--savefig', help='Location to save the figure')
parser.add_argument('-t', '--title', required=True, help='The title of the figure - enclose in ""')
parser.add_argument('-p', '--plot_title', action='store_true', help='Will try to create plot titles from name of dataset')
parser.add_argument('-l', '--legend', action='store_true', help='Includes legends on plot')
parser.add_argument('--size', nargs=2, default=[14,8], type=float, help='The size of the plot to draw')
parser.add_argument('--cols', default=4, type=float, help='The number of columns to display')
parser.set_defaults(which='main')
subparser = parser.add_subparsers(help='Plot types')
scatter = subparser.add_parser('scatter', help="Plot a scatter graph")
scatter.add_argument('-e', '--errors', action='store_true', help='Includes error plots')
scatter.set_defaults(which='scatter')
error = subparser.add_parser('error', help="Plot an error graph")
error.set_defaults(which='error')
hybrid = subparser.add_parser('hybrid', help="Plot a hybrid error graph")
hybrid.set_defaults(which='hybrid')
charge = subparser.add_parser('charge', help="Plot a charge error graph")
charge.set_defaults(which='charge')
charge3D = subparser.add_parser('charge_3D', help="Plot a 3D charge error graph")
charge3D.set_defaults(which='charge_3D')
args = parser.parse_args()
dataset_names = glob_datasets(db.shifts.distinct('spectra.dataset'), args.dataset)
figure = plotting.Figure(dimensions=args.size, main_title=args.title, cols=args.cols)
if isinstance(args.molecules, file):
lines = args.molecules.readlines()
args.molecules = [x.rstrip() for x in lines]
for i,name in enumerate(dataset_names):
if args.plot_title:
axis_title = name.split('/')[-1]
functional, basis = axis_title.split('_')
basis = '6-311+G(2d,p)'if basis == '631+G' else '6-31G(d,p)'
axis_title = functional + ' // ' + basis
else:
axis_title = name
#axis_title = ''
if args.molecules:
molecules_of_interest = args.molecules
else:
molecules_of_interest = []
stream = tensor_analysis.TensorStream.build(db, name, molecules_of_interest)
exp, calc = stream.exp, stream.calc
scaled_tensors = stream.scale_stream()
if args.which == 'scatter':
plotting.plot_tensor_scatter(exp, calc, figure, plot_title=axis_title, legend=args.legend)
if args.errors:
plotting.plot_scaled_shift_errors(scaled_tensors, figure, plot_title=axis_title, legend=args.legend)
if args.which == 'error':
plotting.plot_scaled_shift_errors(scaled_tensors, figure, plot_title=axis_title, legend=args.legend)
if args.which == 'hybrid':
figure = plotting.plot_scaled_tensors_by_hybridisation(stream.hybridisations, scaled_tensors, plot_title=args.title)
if args.which == 'charge':
figure = plotting.plot_charge_chart(stream.hybridisations, stream.charges, scaled_tensors, plot_title=args.title)
if args.which == 'charge_3D':
print args.which
figure = plotting.plot_3D_scatter(stream.hybridisations, stream.charges, scaled_tensors, plot_title=args.title)
if args.savefig:
figure.save_figure(args.savefig)
else:
figure.show_figure()
