from valuesets.enums.core import SexEnum s = SexEnum.MALE print(s.value) # "MALE" print(s.get_meaning()) # "NCIT:C20197" print(s.get_description())# "Male sex"

from valuesets.enums.bio.structural_biology import StructuralBiologyTechnique from valuesets.enums.spatial.spatial_qualifiers import AnatomicalSide # Rich enums with metadata and ontology mappings technique = StructuralBiologyTechnique.CRYO_EM print(technique.value) # "CRYO_EM" print(technique.get_description()) # "Cryo-electron microscopy" print(technique.get_meaning()) # "CHMO:0002413" (Chemical Methods Ontology) print(technique.get_annotations()) # {'resolution_range': '2-30 Å typical', ...} # Spatial relationships with BSPO mappings side = AnatomicalSide.LEFT print(side.get_meaning()) # "BSPO:0000000" (Biological Spatial Ontology) # Look up enums by their ontology terms found = AnatomicalSide.from_meaning("BSPO:0000000") # Returns LEFT

from valuesets.enums.statistics import StatisticalTest, PValueThreshold from valuesets.enums.data_science import DatasetSplitType, ModelType # Standardized statistical tests with STATO ontology mappings test = StatisticalTest.STUDENTS_T_TEST print(test.get_meaning()) # "STATO:0000176" print(test.get_description()) # "Student's t-test for comparing means" # ML pipeline with standard splits split = DatasetSplitType.TRAIN model = ModelType.RANDOM_FOREST # P-value thresholds with clear semantics threshold = PValueThreshold.SIGNIFICANT print(threshold.get_annotations()) # {'value': 0.05, 'symbol': '*'}

from valuesets.enums.bio.taxonomy import CommonOrganismTaxaEnum, BiologicalKingdom from valuesets.enums.bio.cell_biology import CellCyclePhase, CellType # Model organisms with NCBI Taxonomy IDs human = CommonOrganismTaxaEnum.HUMAN print(human.get_meaning()) # "NCBITaxon:9606" print(human.get_description()) # "Homo sapiens (human)" # Cell biology with CL and GO mappings phase = CellCyclePhase.S_PHASE print(phase.get_meaning()) # "GO:0000084" neuron = CellType.NEURON print(neuron.get_meaning()) # "CL:0000540" # Get all organisms at a specific taxonomic level mammals = [org for org in CommonOrganismTaxaEnum if 'MAMMALIA' in str(org)]

# Type-safe enums with ontology mappings status = VitalStatusEnum.ALIVE print(status.meaning) # "NCIT:C37987"

# Example: Different systems use different names for the same concept from valuesets.enums.medical import BloodTypeEnum from external_system import PatientBloodType # Third-party enum # Even though the enum values might be named differently: # BloodTypeEnum.A_POSITIVE vs PatientBloodType.A_POS # They map to the same SNOMED code: SNOMED:278149003 if blood_type.get_meaning() == patient_blood.get_meaning(): # Semantic interoperability - works across different naming conventions process_compatible_blood_type() # Or use the utility function if same_meaning_as(blood_type, patient_blood): process_compatible_blood_type()

# Some enums support hierarchical is_a relationships from valuesets.enums import ViralGenomeTypeEnum # Baltimore classification with hierarchy positive_rna = ViralGenomeTypeEnum.SSRNA_POSITIVE # Group IV # inherits from SSRNA (single-stranded RNA)

from valuesets.enums.bio.structural_biology import CryoEMGridType grid = CryoEMGridType.QUANTIFOIL metadata = grid.get_metadata() print(metadata) # { # 'name': 'QUANTIFOIL', # 'value': 'QUANTIFOIL', # 'description': 'Quantifoil holey carbon grid', # 'annotations': { # 'hole_sizes': '1.2/1.3, 2/1, 2/2 μm common', # 'manufacturer': 'Quantifoil' # } # } # Get all grid types with their descriptions at once all_grids = CryoEMGridType.get_all_descriptions() # {'C_FLAT': 'C-flat holey carbon grid', 'QUANTIFOIL': ...}

from valuesets.enums.spatial import AnatomicalPlane # Get all ontology mappings for an enum mappings = AnatomicalPlane.get_all_meanings() print(mappings) # {'SAGITTAL': 'BSPO:0000417', 'CORONAL': 'BSPO:0000019', ...} # List all metadata for every value in an enum all_metadata = AnatomicalPlane.list_metadata() for name, meta in all_metadata.items(): print(f"{name}: {meta.get('description', 'No description')}") # Find enum by ontology term (useful for data integration) plane = AnatomicalPlane.from_meaning("BSPO:0000417") # Returns SAGITTAL

# Future: Dynamically expand enum with all neuron subtypes cell_types = CellTypeEnum.expand_from_ontology() # Would add: MOTOR_NEURON, SENSORY_NEURON, INTERNEURON, etc.

# Future: Check maturity before use if enum_def.maturity_level == MaturityLevel.STABLE: use_in_production()