Class Measurement

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        Measurement

Basic method for uncertainty propagation:
Say we have a few measurements: x1, x2, x3, ..., and they each has a uncertainty: d1, d2, d3, ..., respectively.
Now we have a function: f( x1, x2, x3, ... ), we want to get the value of this function with given measurements x1, x2, x3,
... and also the uncertainty of the result of f.
A way to do this is the following:
    1. We need to get the contribution to the uncertainty of f result due to each measurement: fd1, fd2, fd3, ...
       This can be given by the following equations:
           fd1 = df / dx1 * d1
           fd2 = df / dx2 * d2
           fd3 = df / dx3 * d3
           ...
       where `df / dx1' is a partial derivative of f with respect to x1.
    2. With fd1, fd2, fd3, ..., we can get the uncertainty of f using this equation:
           fd  = math.sqrt( fd1 * fd1 + fd2 * fd2 + fd3 * fd3 + ... )