// An explicit coefficient matrix declares which independent sources affect each value.
import aner.tensor
import aner.science

let nominal = tensor.semantic([[20.0], [22.0]], "sensor", List("A", "B"), "degC")
let measured = science.measurements(nominal,
    List("calibration", "reading_A", "reading_B"), List(0.3, 0.2, 0.2),
    [[1.0, 1.0, 0.0], [1.0, 0.0, 1.0]], "delta_degC", "independent")
print("Variance at sensor A is 0.13 within tolerance")
let variance = measured.variance(0, 0)
print(variance > 0.129999999999 && variance < 0.130000000001)
print("Shared covariance is 0.09 within tolerance")
let covariance = science.covariance(measured, 0, 0, measured, 1, 0)
print(covariance > 0.089999999999 && covariance < 0.090000000001)
print("Each value retains two active source coefficients")
print(measured.source_entries())
print("Exact and unknown declarations preserve the nominal value")
let exact = science.exact(nominal.gather_rows(List(0)))
let unknown = science.unknown(nominal.gather_rows(List(0)))
print(exact.item())
print(exact.variance())
print(unknown.item())
// unknown.variance() would fail: unknown uncertainty cannot be interpreted as zero.
let exported = measured.export_values("Inspect nominal values with a values-only numerical tool")
print("The acknowledged export has two numeric rows")
print(tensor.rows(exported.values()))
print(tensor.value(exported.values(), 1, 0))