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()