def generate_all_schemes(cfg):
"""
Convert the abstract schema given by the algorithm into subsets
"""
log.info("Generating all possible schemes for the partitions...")
subset_count = len(cfg.user_subsets)
# Now generate the pattern for this many partitions
all_schemes = submodels.get_submodels(subset_count)
scheme_name = 1
scheme_list = []
for scheme in all_schemes:
subs = {}
# We use the numbers returned to group the different subsets
for sub_index, grouping in enumerate(scheme):
insub = subs.setdefault(grouping, [])
insub.append(sub_index)
# We now have what we need to create a subset. Each entry will have a
# set of values which are the index for the partition
created_subsets = []
for sub_indexes in subs.values():
sub = subset_ops.merge_subsets(
[cfg.user_subsets[i] for i in sub_indexes])
created_subsets.append(sub)
scheme_list.append(Scheme(cfg, str(scheme_name), created_subsets))
log.debug("Created scheme %d of %d" % (scheme_name, len(all_schemes)))
scheme_name += 1
return scheme_list
def create_scheme(cfg, scheme_name, scheme_description):
"""
Generate a single scheme given a list of numbers that represent the
indexes of the partitions e.g. [0,1,2,3,4,5,6,7]
"""
subset_count = len(cfg.user_subsets)
# Check that the correct number of items are in the list
if len(scheme_description) != subset_count:
log.error("There's a problem with the description of scheme %s" %
scheme_name)
raise SchemeError
# Now generate the pattern
subs = {}
# We use the numbers returned to group the different subsets
for sub_index, grouping in enumerate(scheme_description):
insub = subs.setdefault(grouping, [])
insub.append(sub_index)
# We now have what we need to create a subset. Each entry will have a
# set of values which are the index for the partition
created_subsets = []
for sub_indexes in subs.values():
subs_to_merge = [cfg.user_subsets[i] for i in sub_indexes]
sub = subset_ops.merge_subsets(subs_to_merge)
created_subsets.append(sub)
return Scheme(cfg, str(scheme_name), created_subsets, description=scheme_description)
def model_to_scheme(model, scheme_name, cfg):
"""Turn a model definition e.g. [0, 1, 2, 3, 4] into a scheme"""
subs = {}
# We use the numbers returned to group the different subsets
for sub_index, grouping in enumerate(model):
insub = subs.setdefault(grouping, [])
insub.append(sub_index)
# We now have what we need to create a subset. Each entry will have a
# set of values which are the index for the partition
created_subsets = []
for sub_indexes in subs.values():
subs_to_merge = [cfg.user_subsets[i] for i in sub_indexes]
sub = subset_ops.merge_subsets(subs_to_merge)
created_subsets.append(sub)
return Scheme(cfg, str(scheme_name), created_subsets)
