import os, copy, argparse
from collections import OrderedDict
from pypospack.pyposmat.data import PyposmatDataFile
from pypospack.pyposmat.data import PyposmatConfigurationFile
from pypospack.pyposmat.data import PyposmatDataAnalyzer
if __name__ == "__main__":
_fn_config = os.path.join("resources", "pyposmat.config.in")
_fn_data = os.path.join("resources", "pyposmat.results.0.out")
_fn_pareto_out = os.path.join("pyposmat.pareto.out")
pda = PyposmatDataAnalyzer(fn_config=_fn_config, fn_data=_fn_data)
pareto_df = pda.calculate_pareto_set()
datafile = PyposmatDataFile()
datafile.df = pareto_df
datafile.parameter_names = pda.parameter_names
datafile.qoi_names = pda.qoi_names
datafile.error_names = pda.error_names
datafile.names = ['sim_id'] \
+datafile.parameter_names\
+datafile.qoi_names\
+datafile.error_names
datafile.types = ['sim_id']\
+len(datafile.parameter_names)*['param']\
+len(datafile.qoi_names)*['qoi_names']\
+len(datafile.error_names)*['error_names']
datafile.write(_fn_pareto_out)
datafile.read(_fn_pareto_out)
def test__calculate_pareto_set__df_equals_DataFrame():
o = PyposmatDataAnalyzer(fn_config=config_fn, fn_data=data_fn)
o.calculate_pareto_set(df=o.data.df)
def test__calculate_pareto_set__df_equals_None():
o = PyposmatDataAnalyzer(fn_config=config_fn, fn_data=data_fn)
o.calculate_pareto_set()
'examples', 'Ni__eam__born_exp_rose',
'01_preconditioning_3.5NN', 'data',
'pyposmat.results.0.out')
# check if files exist
if os.path.isfile(config_fn):
print('config_fn:{}'.format(config_fn))
else:
print('cannot find config_fn:{}'.format(config_fn))
exit()
if os.path.isfile(data_fn):
print('data_fn:{}'.format(data_fn))
else:
print('cannot find data_fn:{}'.format(data_fn))
exit()
# initialization
o = PyposmatDataAnalyzer(fn_config=config_fn, fn_data=data_fn)
print(o.parameter_names)
results_nrows, results_ncols = o.data.df.shape
# calculate_pareto_set
pareto_df = o.calculate_pareto_set(df=o.data.df)
pareto_nrows, pareto_ncols = pareto_df.loc[pareto_df["is_pareto"] ==
True].shape
print("n_results:{}".format(results_nrows))
print("n_pareto:{}".format(pareto_nrows))
print(pareto_df["is_pareto"])