Clusters to Terms Mapping

>>> #
>>> # TEST PREPARATION
>>> #
>>> from techminer2.thesaurus.descriptors import ApplyThesaurus, CreateThesaurus
>>> CreateThesaurus(root_directory="example/", quiet=True).run()
>>> ApplyThesaurus(root_directory="example/", quiet=True).run()

>>> #
>>> # CODE TESTED
>>> #
>>> from techminer2.packages.networks.co_occurrence.author_keywords import ClustersToTermsMapping
>>> mapping = (
...     ClustersToTermsMapping()
...     #
...     # FIELD:
...     .having_terms_in_top(10)
...     .having_terms_ordered_by("OCC")
...     .having_term_occurrences_between(None, None)
...     .having_term_citations_between(None, None)
...     .having_terms_in(None)
...     #
...     # COUNTERS:
...     .using_term_counters(True)
...     #
...     # NETWORK:
...     .using_clustering_algorithm_or_dict("louvain")
...     .using_association_index("association")
...     #
...     # DATABASE:
...     .where_root_directory_is("example/")
...     .where_database_is("main")
...     .where_record_years_range_is(None, None)
...     .where_record_citations_range_is(None, None)
...     .where_records_match(None)
...     #
...     .run()
... )
>>> from pprint import pprint
>>> pprint(mapping)
{0: ['FINTECH 31:5168',
     'INNOVATION 07:0911',
     'FINANCIAL_INCLUSION 03:0590',
     'BLOCKCHAIN 03:0369',
     'CROWDFUNDING 03:0335',
     'MARKETPLACE_LENDING 03:0317',
     'FINANCIAL_INSTITUTION 02:0484'],
 1: ['FINANCIAL_SERVICE 04:0667',
     'BUSINESS_MODEL 03:0896',
     'FINANCIAL_TECHNOLOGIES 03:0461']}

>>> #
>>> # CODE TESTED
>>> #
>>> from techminer2.packages.networks.co_occurrence.author_keywords import ClustersToTermsMapping
>>> mapping = (
...     ClustersToTermsMapping()
...     #
...     # FIELD:
...     .having_terms_in_top(10)
...     .having_terms_ordered_by("OCC")
...     .having_term_occurrences_between(None, None)
...     .having_term_citations_between(None, None)
...     .having_terms_in(None)
...     #
...     # COUNTERS:
...     .using_term_counters(False)
...     #
...     # NETWORK:
...     .using_clustering_algorithm_or_dict("louvain")
...     .using_association_index("association")
...     #
...     # DATABASE:
...     .where_root_directory_is("example/")
...     .where_database_is("main")
...     .where_record_years_range_is(None, None)
...     .where_record_citations_range_is(None, None)
...     .where_records_match(None)
...     #
...     .run()
... )
>>> from pprint import pprint
>>> pprint(mapping)
{0: ['FINTECH',
     'INNOVATION',
     'FINANCIAL_INCLUSION',
     'BLOCKCHAIN',
     'CROWDFUNDING',
     'MARKETPLACE_LENDING',
     'FINANCIAL_INSTITUTION'],
 1: ['FINANCIAL_SERVICE', 'BUSINESS_MODEL', 'FINANCIAL_TECHNOLOGIES']}