def dev__write_kde_file():
kde_data_fn = 'pyposmat.kde.test.out'
try:
os.remove(kde_data_fn)
except FileNotFoundError as e:
pass
testing_set = get_testing_set()
o = PyposmatDataAnalyzer()
o.initialize_configuration(config_fn=testing_set['config_fn'])
o.initialize_results_data(results_data_fn=testing_set['results_data_fn'])
is_survive_idx, filter_info = o.analyze_results(
i_iteration=testing_set['i_iteration'])
o.write_kde_file(filename=kde_data_fn)
print(o.kde_data.df['sim_id'])
def analyze_results(self,
i_iteration,
data_fn=None,
config_fn=None,
kde_fn=None,
analysis_fn=None):
""" analyze the results of the simulation
this method analyzes the results of the simulation, and does post simulation
tasks, such as filtering by qoi performance, pareto optimization, etc.
Args:
data_fn(str): the path of the data file. By default this is set to none
where the the file will be determine by i_iteration and internal
attributes
config_fn(str): the path of the data file. By default this is set to none
where the the file will be determine by i_iteration and internal
attributes
kde_fn(str): the path of the data file. By default this is set to none
where the the file will be determine by i_iteration and internal
attributes
"""
if data_fn is None:
data_fn = os.path.join(\
self.root_directory,
self.data_directory,
'pyposmat.results.{}.out'.format(i_iteration))
if config_fn is None:
config_fn = os.path.join(\
self.root_directory,
self.configuration_filename)
if kde_fn is None:
kde_fn = os.path.join(\
self.root_directory,
self.data_directory,
'pyposmat.kde.{}.out'.format(i_iteration+1))
if analysis_fn is None:
analysis_fn = os.path.join(self.root_directory,
self.data_directory,
'pyposmat.analysis.out')
data_analyzer = PyposmatDataAnalyzer()
data_analyzer.initialize_configuration(config_fn=config_fn)
data_analyzer.analyze_results_data(i_iteration, filename=data_fn)
assert isinstance(data_analyzer.results_statistics, OrderedDict)
if os.path.isfile(analysis_fn):
data_analyzer.read_analysis_file(filename=analysis_fn)
self.log(
data_analyzer.str__results_descriptive_statistics(
statistics=data_analyzer.results_statistics))
self.log(data_analyzer.str__qoi_filtering_summary())
data_analyzer.write_kde_file(filename=kde_fn)
data_analyzer.analyze_kde_data(i_iteration, filename=kde_fn)
assert isinstance(data_analyzer.kde_statistics, OrderedDict)
self.log(
data_analyzer.str__kde_descriptive_statistics(
statistics=data_analyzer.kde_statistics))
data_analyzer.update_analysis(i_iteration)
data_analyzer.write_analysis_file(filename=analysis_fn)
import pypospack.utils
from pypospack.io.filesystem import OrderedDictYAMLLoader
from pypospack.pyposmat.data import PyposmatConfigurationFile
from pypospack.pyposmat.data import PyposmatDataFile
from pypospack.pyposmat.data import PyposmatDataAnalyzer
data_directory = os.path.join(pypospack.utils.get_pypospack_root_directory(),
'data', 'Si__sw__data', 'pareto_optimization_1')
config_fn = os.path.join(data_directory, 'pyposmat.config.in')
analysis_fn = 'pyposmat.analysis.out'
assert os.path.isdir(data_directory)
assert os.path.isfile(config_fn)
o = PyposmatDataAnalyzer()
o.initialize_configuration(config_fn=config_fn)
n_iterations = o.configuration.n_iterations
print("n_iterations:{}".format(n_iterations))
# read analysis results to file system
if os.path.isfile(analysis_fn):
with open(analysis_fn, 'r') as f:
analysis_results = yaml.load(f, OrderedDictYAMLLoader)
else:
analysis_results = OrderedDict()
for i in range(n_iterations):
if i not in analysis_results:
print('i_iteration:{}'.format(i))
analysis_results[i] = OrderedDict()
