After installing the package, you can start using it with

using Measurements

The module defines a new Measurement data type. Measurement objects can be created with the two following constructors:

Measurement(value, uncertainty)
value ± uncertainty


  • value is the nominal value of the measurement
  • uncertainty is its uncertainty, assumed to be a standard deviation.

They are both subtype of AbstractFloat. Some keyboard layouts provide an easy way to type the ± sign, if your does not, remember you can insert it in Julia REPL with \pm followed by TAB key. You can provide value and uncertainty of any subtype of Real that can be converted to AbstractFloat. Thus, Measurement(42, 33//12) and pi ± 0.1 are valid.

Measurement(value) creates a Measurement object that doesn’t have uncertainty, like mathematical constants. See below for further examples.


Every time you use one of the constructors above you define a new independent measurement. Instead, when you perform mathematical operations involving Measurement objects you create a quantity that is not independent, but rather depends on really independent measurements.

Most mathematical operations are instructed, by operator overloading, to accept Measurement type, and uncertainty is calculated exactly using analityc expressions of functions’ derivatives.

In addition, it is possible to create a Complex measurement with complex(Measurement(a, b), Measurement(c, d)).

Those interested in the technical details of the package, in order integrate the package in their workflow, can have a look at the technical appendix.


The ± sign is a convenient symbol to define quantities with uncertainty, but can lead to unexpected results if used in elaborate expressions involving many ±s. Use parantheses where appropriate to avoid confusion. See for example the following cases:

7.5±1.2 + 3.9±0.9 # This is wrong!
# => 11.4 ± 1.2 ± 0.9 ± 0.0
(7.5±1.2) + (3.9±0.9) # This is correct
# => 11.4 ± 1.5

Correlation Between Variables

The fact that two or more measurements are correlated means that there is some sort of relationship beetween them. In the context of measurements and error propagation theory, the term “correlation” is very broad and can indicate different things. Among others, there may be some dependence between uncertainties of different measurements with different values, or a dependence between the values of two measurements while their uncertainties are different.

Here, for correlation we mean the most simple case of functional relationship: if \(x = \bar{x} \pm \sigma_x\) is an independent measurement, a quantity \(y = f(x) = \bar{y} \pm \sigma_y\) that is function of \(x\) is not like an independent measurement but is a quantity that depends on \(x\), so we say that \(y\) is correlated with \(x\). The package Measurements.jl is able to handle this type of correlation when propagating the uncertainty for operations and functions taking two or more arguments. As a result, \(x - x = 0 \pm 0\) and \(x/x = 1 \pm 0\). If this correlation was not accounted for, you would always get non-zero uncertainties even for these operations that have exact results. Two truly different measurements that only by chance share the same nominal value and uncertainty are not treated as correlated.

Propagate Uncertainty for Arbitrary Functions

@uncertain f(x, ...)

Existing functions implemented exclusively in Julia that accept AbstractFloat arguments will work out-of-the-box with Measurement objects as long as they internally use functions already supported by this package. However, there are functions that take arguments that are specific subtypes of AbstractFloat, or are implemented in such a way that does not play nicely with Measurement variables.

The package provides the @uncertain macro that overcomes this limitation and further extends the power of Measurements.jl.

This macro allows you to propagate uncertainty in arbitrary functions, including those based on C/Fortran calls, that accept any number of real arguments. The macro exploits derivative and gradient functions from Calculus package in order to perform numerical differentiation.

Derivative and Gradient

Measurements.derivative(y::Measurement, x::Measurement)
Measurements.gradient(y::Measurement, x::AbstractArray{Measurement})

In order to propagate the uncertainties, Measurements.jl keeps track of the partial derivative of an expression with respect to all independent measurements from which the expression comes. For this reason, the package provides two convenient functions, Measurements.derivative and Measurements.gradient, to get the partial derivative and the gradient of an expression with respect to independent measurements.

Standard Score

stdscore(measure::Measurement, expected_value::Real) → standard_score

The stdscore function is available to calculate the standard score between a measurement and its expected value.

Weighted Average

weightedmean(iterable) → weighted_mean

weightedmean function gives the weighted mean of a set of measurements using inverses of variances as weights. Use mean for the simple arithmetic mean.