Appendix: Technical Details

Measurement is a composite parametric type, whose parameter is the AbstractFloat subtype of the nominal value and the uncertainty of the measurement. Measurement type itself is subtype of AbstractFloat, thus Measurement objects can be used in any function taking AbstractFloat arguments without redefining it, and calculation of uncertainty will be exact.

In detail, this is the definition of the type:

The fields represent:

As already explained in the Usage section, every time you use one of the constructors

you define a new independent measurement. This happens because these contructors generate a new unique tag field, that is used to distinguish between really equal objects and measurements that only by chance share the same nominal value and uncertainty. For these reasons,

will produce two independent measurements and they will be treated as such when performing mathematical operations. In particular, you can also notice that they are not egal

If you instead intend to make a variable really the same thing as an independent measurement you have to use assignment:

Thanks to how the Julia language is designed, support for complex measurements, arbitrary precision calculations and array operations came with practically no effort during the development of the package. As explained by Steven G. Johnson, since in Julia a lot of nonlinear functions are internally implemented in terms of elementary operations on the real and imaginary parts it was natural to make the type subtype of Real in order to easily work with complex measurements. In particular, it was then chosen to select the AbstractFloat type because some functions of complex arguments (like sqrt and log) take Complex{AbstractFloat} arguments instead of generic Complex{Real}, and any operation on a Measurement{R} object, with R subtype of Real different from AbstractFloat, would turn it into Measurement{F}, with F subtype of AbstractFloat, anyway.

One must carefully take care of correlation between two measurements when propagating the uncertainty for an operation. Actually, the term "correlation" may refer to different kind of dependences between two or more quantities, what we mean by this term in Measurements.jl is explained in the Usage section of this manual.

Dealing with functional correlation between Measurement objects, when using functions with arity larger than one, is an important feature of this package. This is accomplished by keeping inside each Measurement object the list of its derivatives with respect to the independent variables from which the quantity comes. This role is played by the der field. This dictionary is useful in order to trace the contribution of each measurement and propagate the uncertainty in the case of functions with more than one argument.

The use of the list of derivatives has been inspired by Python package uncertainties, but the rest of the implementation of Measurements.jl is completely independent from that of uncertainties package.

For a function of real arguments with uncertainties , , and , …​, the linear error propagation theory prescribes that uncertainty is propagated as follows:

where the factors are the covariances defined as

] is the expected value, or mean, of . If uncertainties of the quantities , , , …​, are independent and normally distributed, the covariances are null and the above formula for uncertainty propagation simplifies to

In general, calculating the covariances is not an easy task. The trick adopted in Measurements.jl in order to deal with simple functional correlation is to propagate the uncertainty always using really independent variables. Thus, dealing with functional correlation boils down to finding the set of all the independent measurements on which an expression depends. If this set is made up of , it is possible to calculate the uncertainty of with

where all covariances due to functional correlation are null. This explains the purpose of keeping the list of derivatives with respect to independent variables in Measurement objects: by looking at the der fields of , , , …​, it is possible to determine the set of independent variables. If other types of correlation (not functional) between , , , …​, are present, they should be treated by calculating the covariances as shown above.

For a function of only one argument, , there is no problem of correlation and the uncertainty propagation formula in the linear approximation simply reads

even if is not an independent variable and comes from operations on really independent measurements.

For example, suppose you want to calculate the function of two arguments, and and are functionally correlated, because they come from some mathematical operations on really independent variables , , , say , . By using the chain rule, the uncertainty on is calculated as follows:

What Measurements.jl really does is to calculate the derivatives like and , and store them in the der field of and respectively in order to be able to perform further operations involving these quantities.

This method is also described in the paper by Giordano, M.

Measurements.jl defines new methods for mathematical operations in order to make them accept Measurement arguments. The single most important thing to know about how to define new methods in the package is the Measurements.result. This function, not exported because it is intended to be used only within the package, takes care of propagating the uncertainty as described in the section above. It has two methods: one for functions with arity equal to one, and the other for any other case. This is its syntax:

for functions of one argument, and

for functions of two or more arguments. The arguments are:

In the case of functions with arity larger than one, der and a tuples must have the same length.

For example, for a one-argument function like we have

Instead, the method for subtraction operation is defined as follows:

Thus, in order to support Measurement argument(s) for a new mathematical operation you have to calculate the result of the operation, the partial derivatives of the function with respect to all arguments and then pass this information to Measurements.result function.