Scientific measurements with shared calibration

// CPU Float64 measurements with independent reading noise and shared calibration.
import aner.tensor
import aner.science

let readings = science.calibrated([[20.0], [22.0]], "degC", 0.2, 0.3)
let first = readings.gather_rows(List(0))
let second = readings.gather_rows(List(1))
let fused = science.mean(first, second)
let difference = first.subtract(second)

print("Fused temperature")
print(fused.item())
print(fused.unit())
print("Fused variance is 0.11 within tolerance")
print(fused.variance() > 0.109999999999 && fused.variance() < 0.110000000001)
print("Shared covariance is 0.09 within tolerance")
let shared = science.covariance(readings, 0, 0, readings, 1, 0)
print(shared > 0.089999999999 && shared < 0.090000000001)
print("Difference temperature")
print(difference.item())
print(difference.unit())
print("Difference variance is 0.08 within tolerance")
print(difference.variance() > 0.079999999999 && difference.variance() < 0.080000000001)
print("A reading minus itself has zero variance")
print(first.subtract(first).variance())
print("Kelvin conversion retains the source model")
let kelvin = fused.convert("K")
print(kelvin.item() > 294.149999999 && kelvin.item() < 294.150000001)
print(kelvin.variance() > 0.109999999999 && kelvin.variance() < 0.110000000001)