Plan an active-learning lab campaign

from apex import Aether

simos = Aether(api_key=os.environ["APEX_API_KEY"])

# Plan an active-learning wet-lab campaign. The agent binds reagents,
# schedules plates, allocates instruments, and writes the run protocol with
# chain-of-custody. Refusal corpus blocks dual-use precursors.
plan = simos.lab_plan(
    objective="antibody developability screening · n=96 candidates",
    assays=[
        "thermal_stability",
        "aggregation_propensity",
        "hERG",
        "polyspecificity",
    ],
    instruments=[
        "plate_reader_01",
        "liquid_handler_03",
        "biacore_02",
        "cellomics_01",
    ],
    constraints={
        "weeks": 4,
        "max_plates_per_day": 12,
        "operator_shifts": "8h_business_days",
    },
    chain_of_custody=True,
    safety_review="gxp",
)

print(f"plates       = {len(plan.plates)}")
print(f"instrument_h = {plan.total_instrument_hours:.1f}")
print(f"operator_h   = {plan.total_operator_hours:.1f}")

# Each plate has a map and a runbook.
for p in plan.plates[:3]:
    print(p.id, p.layout_uri, p.runbook_uri)