substr_name=options['substr'])
# Remove parameters not currently being optimized.
dic['FF'].remove_other_params(dic['Params.'])
else:
logger.info('Loaded initial FF: {} ({})'.format(
dic['FF'].substr_name, dic['FF'].filename))
# Calculate initial FF data if necessary.
if dic['FF'].data == []:
logger.info('Calculating initial FF data.')
dic['FF'].calculate_data(options['calc'].split(), backup=True)
assert len(dic['FF'].data) == len(dic['Ref. Data']), \
'Num. data points for ref. ({}) '.format(len(dic['Ref. Data'])) + \
'and FF data ({}) '.format(len(dic['FF'].data)) + \
'is unequal.'
# Determine X2 if necessary.
if dic['FF'].x2 is None:
logger.info('Calculating initial X2.')
dic['FF'].calculate_x2(dic['Ref. Data'])
return dic
if __name__ == '__main__':
# Setup logs.
remove_logs()
sys.excepthook = log_uncaught_exceptions
with open('options/logging.yaml', 'r') as f:
log_config = yaml.load(f)
logging.config.dictConfig(log_config)
# Process arguments.
run(sys.argv[1:])
if options['output']:
lines = ['X2: {}'.format(x2), '']
head = '{0:^20}{1:^20}{2:^10}{3:^22}{4:^22}'.format(
'Ref. Name', 'Calc. Name', 'Ref. Weight', 'Ref. Value',
'Calc. Value')
lines.append(head)
lines.append('-' * len(head))
for r_d, c_d in zip(
sorted(dic['Ref. Data'], key=calculate.sort_datum),
sorted(dic['Calc. Data'], key=calculate.sort_datum)):
lines.append('{0:<20}{1:<20}{2:>10.4f}{3:>22.6f}{4:>22.6f}'.format(
r_d.name, c_d.name, r_d.weight, r_d.value, c_d.value))
if options['output'] == 'print':
for line in lines:
print line
else:
with open(options['output'], 'w') as f:
for line in lines:
f.write(line + '\n')
if __name__ == '__main__':
# Setup logs.
remove_logs()
sys.excepthook = log_uncaught_exceptions
with open('options/logging.yaml', 'r') as f:
config = yaml.load(f)
logging.config.dictConfig(config)
# Execute.
process_args(sys.argv[1:])