def do_analysis(self):
log.info("Performing complete analysis")
partnum = len(self.cfg.partitions)
scheme_count = submodels.count_all_schemes(partnum)
subset_count = submodels.count_all_subsets(partnum)
self.cfg.progress.begin(scheme_count, subset_count)
# Iterate over submodels, which we can turn into schemes afterwards in the loop
model_iterator = submodels.submodel_iterator([], 1, partnum)
scheme_name = 1
for m in model_iterator:
s = scheme.model_to_scheme(m, scheme_name, self.cfg)
scheme_name = scheme_name + 1
res = self.analyse_scheme(s)
# Write out the scheme
self.cfg.reporter.write_scheme_summary(s, res)
self.cfg.reporter.write_best_scheme(self.results)
def do_analysis(self):
log.info("Performing complete analysis")
partnum = len(self.cfg.partitions)
scheme_count = submodels.count_all_schemes(partnum)
subset_count = submodels.count_all_subsets(partnum)
self.cfg.progress.begin(scheme_count, subset_count)
# Iterate over submodels, which we can turn into schemes afterwards in the loop
model_iterator = submodels.submodel_iterator([], 1, partnum)
scheme_name = 1
for m in model_iterator:
s = scheme.model_to_scheme(m, scheme_name, self.cfg)
scheme_name = scheme_name + 1
res = self.analyse_scheme(s)
# Write out the scheme
self.cfg.reporter.write_scheme_summary(s, res)
self.cfg.reporter.write_best_scheme(self.results)
def do_analysis(self):
models = self.cfg.models
partnum = len(self.cfg.partitions)
self.total_scheme_num = submodels.count_all_schemes(partnum)
log.info("Analysing all possible schemes for %d starting partitions", partnum)
log.info("This will result in %s schemes being created", self.total_scheme_num)
self.total_subset_num = submodels.count_all_subsets(partnum)
log.info("PartitionFinder will have to analyse %d subsets to complete this analysis" % (self.total_subset_num))
if self.total_subset_num > 10000:
log.warning("%d is a lot of subsets, this might take a long time to analyse", self.total_subset_num)
log.warning("Perhaps consider using a different search scheme instead (see Manual)")
# clear any schemes that are currently loaded
self.cfg.schemes.clear_schemes()
# iterate over submodels, which we can turn into schemes afterwards in the loop
model_iterator = submodels.submodel_iterator([], 1, partnum)
scheme_name = 1
for m in model_iterator:
s = scheme.model_to_scheme(m, scheme_name, self.cfg)
scheme_name = scheme_name + 1
self.analyse_scheme(s, models)