Documentation |
Package Evaluator |
Build Status |
Code Coverage |
---|---|---|---|

`Measurements.jl`

is a package that allows you to define numbers with
uncertainties, perform
calculations involving them, and easily get the uncertainty of the result
according to
linear error propagation theory.
This library is written in Julia, a modern high-level,
high-performance dynamic programming language designed for technical computing.

When used in the Julia interactive session, it can serve also as an easy-to-use calculator.

- Support for most mathematical operations available in Julia standard library
and special functions
from
`SpecialFunctions.jl`

package, involving real and complex numbers. All existing functions that accept`AbstractFloat`

(and`Complex{AbstractFloat}`

as well) arguments and internally use already supported functions can in turn perform calculations involving numbers with uncertainties without being redefined. This greatly enhances the power of`Measurements.jl`

without effort for the users - Functional correlation between variables is correctly handled, so
`x-x ≈ zero(x)`

,`x/x ≈ one(x)`

,`tan(x) ≈ sin(x)/cos(x)`

,`cis(x) ≈ exp(im*x)`

, etc... - Support for arbitrary precision (also called multiple precision) numbers with uncertainties. This is useful for measurements with very low relative error
- Define arrays of measurements and perform calculations with them. Some linear algebra functions work out-of-the-box
- Propagate uncertainty for any function of real arguments (including functions
based on
C/Fortran calls),
using
`@uncertain`

macro - Function to get the derivative and the gradient of an expression with respect to one or more independent measurements
- Functions to calculate standard score and weighted mean
- Parse strings to create measurement objects
- Easy way to attach the uncertainty to a number using the
`±`

sign as infix operator. This syntactic sugar makes the code more readable and visually appealing - Extensible in combination with external packages: you can propagate errors of
measurements with their physical units, perform numerical integration
with
`QuadGK.jl`

, numerical and automatic differentiation, and much more. - Integration with
`Plots.jl`

.

Further features are expected to come in the future, see the section "How Can I Help?" and the TODO list below.

The method used to handle functional correlation is described in this paper:

- M. Giordano, 2016, "Uncertainty propagation with functionally correlated
quantities", arXiv:1610.08716
(Bibcode:
`2016arXiv161008716G`

)

If you use use this package for your research, please cite it.

The complete manual of `Measurements.jl`

is available at
http://measurementsjl.readthedocs.io. There, people interested in the details
of the package, in order integrate the package in their workflow, can can find a
technical appendix explaining how the package internally works. You can also
download the PDF version of the manual from
https://media.readthedocs.org/pdf/measurementsjl/latest/measurementsjl.pdf.

`Measurements.jl`

is available for Julia 0.6 and later versions, and can be
installed with
Julia built-in package manager.
In a Julia session run the commands

```
julia> Pkg.update()
julia> Pkg.add("Measurements")
```

Older versions are also available for Julia 0.4 and 0.5.

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
```

where

`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 with zero 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))`

.

```
measurement(string)
```

`measurement`

function has also a method that enables you to create a
`Measurement`

object from a string.

This module extends many methods defined in Julia’s mathematical standard
library, and some methods from widespread third-party packages as well. This is
the case for most special functions
in `SpecialFunctions.jl`

package, and the `quadgk`

integration routine
from `QuadGK.jl`

package. See the
full manual for details.

`±`

SignThe `±`

infix operator 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:

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

```
julia> using Measurements
julia> a = measurement(4.5, 0.1)
4.5 ± 0.1
julia> b = 3.8 ± 0.4
3.8 ± 0.4
julia> 2a + b
12.8 ± 0.4472135954999579
julia> a - 1.2b
-0.05999999999999961 ± 0.49030602688525043
julia> l = measurement(0.936, 1e-3);
julia> T = 1.942 ± 4e-3;
julia> P = 4pi^2*l/T^2
9.797993213510699 ± 0.041697817535336676
julia> c = measurement(4)
4.0 ± 0.0
julia> a*c
18.0 ± 0.4
julia> sind(94 ± 1.2)
0.9975640502598242 ± 0.0014609761696991563
julia> x = 5.48 ± 0.67;
julia> y = 9.36 ± 1.02;
julia> log(2x^2 - 3.4y)
3.3406260917568824 ± 0.5344198747546611
julia> atan2(y, x)
1.0411291003154137 ± 0.07141014208254456
```

You can construct `Measurement`

objects from strings. Within parentheses there
is the uncertainty on the last digits.

```
julia> measurement("-12.34(56)")
-12.34 ± 0.56
julia> measurement("+1234(56)e-2")
12.34 ± 0.56
julia> measurement("123.4e-1 +- 0.056e1")
12.34 ± 0.56
julia> measurement("(-1.234 ± 0.056)e1")
-12.34 ± 0.56
julia> measurement("1234e-2 +/- 0.56e0")
12.34 ± 0.56
julia> measurement("-1234e-2")
-12.34 ± 0.0
```

Here you can see examples of how functionally correlated variables are treated within the package:

```
julia> x = 8.4 ± 0.7
julia> x - x
0.0 ± 0.0
julia> x/x
1.0 ± 0.0
julia> x*x*x - x^3
0.0 ± 0.0
julia> sin(x)/cos(x) - tan(x)
-2.220446049250313e-16 ± 0.0 # They are equal within numerical accuracy
```

`@uncertain`

MacroMacro `@uncertain`

can be used to propagate uncertainty in arbitrary real- or
complex-valued functions of any number of real arguments, even in functions not
natively supported by this package.

```
julia> @uncertain zeta(2 ± 0.13)
1.6449340668482273 ± 0.12188127308075564
julia> @uncertain log(9.4 ± 1.3, 58.8 ± 3.7)
1.8182372640255153 ± 0.11568300475873611
julia> log(9.4 ± 1.3, 58.8 ± 3.7)
1.8182372640255153 ± 0.11568300475593848
```

Here are a few examples about uncertainty propagation of complex-valued measurements.

```
julia> u = complex(32.7 ± 1.1, -3.1 ± 0.2)
julia> v = complex(7.6 ± 0.9, 53.2 ± 3.4)
julia> 2u + v
(73.0 ± 2.3769728648009427) + (47.0 ± 3.4234485537247377)im
julia> sqrt(u * v)
(33.004702573592 ± 1.0831254428098636) + (25.997507418428984 ± 1.1082833691607152)im
```

You can create arrays of `Measurement`

objects and perform mathematical
operations on them:

```
julia> A = [1.03 ± 0.14, 2.88 ± 0.35, 5.46 ± 0.97]
3-element Array{Measurements.Measurement{Float64},1}:
1.03±0.14
2.88±0.35
5.46±0.97
julia> log.(A)
3-element Array{Measurements.Measurement{Float64},1}:
0.0295588±0.135922
1.05779±0.121528
1.69745±0.177656
julia> cos.(A) .^ 2 .+ sin.(A) .^ 2
3-element Array{Measurements.Measurement{Float64},1}:
1.0±0.0
1.0±0.0
1.0±0.0
julia> B = measurement.([174.8, 253.7, 626.6], [12.2, 19.4, 38.5])
3-element Array{Measurements.Measurement{Float64},1}:
174.8±12.2
253.7±19.4
626.6±38.5
julia> sum(B)
1055.1 ± 44.80457565918909
julia> mean(B)
351.7 ± 14.93485855306303
```

The package provides a convenient function, `Measurements.derivative`

, that
returns the total derivative and the gradient of an expression with respect to
independent measurements.

```
julia> x = 98.1 ± 12.7
98.1 ± 12.7
julia> y = 105.4 ± 25.6
105.4 ± 25.6
julia> z = 78.3 ± 14.1
78.3 ± 14.1
julia> Measurements.derivative(2x - 4y, x)
2.0
julia> Measurements.derivative(2x - 4y, y)
-4.0
julia> Measurements.derivative.(log1p(x) + y^2 - cos(x/y), [x, y, z])
3-element Array{Float64,1}:
0.0177005
210.793
0.0 # The expression does not depend on z
```

`stdscore`

FunctionYou can get the distance in number of standard deviations between a real
measurement and its expected value (not a `Measurement`

) using `stdscore`

:

```
julia> stdscore(1.3 ± 0.12, 1)
2.5000000000000004
```

You can also test the consistency of two real measurements by measuring the
standard score of their difference and zero. This is what `stdscore`

does if
both arguments are `Measurement`

objects:

```
julia> stdscore((4.7 ± 0.58) - (5 ± 0.01), 0)
-0.5171645175253433
julia> stdscore(4.7 ± 0.58, 5 ± 0.01)
-0.5171645175253433
```

`weightedmean`

FunctionCalculate the weighted and arithmetic means of your set of measurements with
`weightedmean`

and `mean`

respectively:

```
julia> weightedmean((3.1±0.32, 3.2±0.38, 3.5±0.61, 3.8±0.25))
3.4665384454054498 ± 0.16812474090663868
julia> mean((3.1±0.32, 3.2±0.38, 3.5±0.61, 3.8±0.25))
3.4000000000000004 ± 0.2063673908348894
```

`SIUnits.jl`

and `Unitful.jl`

Used together with third-party packages, `Measurements.jl`

enables you to
perform calculations involving numbers with both uncertainty and physical unit.
For example, you can use `SIUnits.jl`

or
`Unitful.jl`

.

```
julia> using Measurements, SIUnits, SIUnits.ShortUnits
julia> hypot((3 ± 1)*m, (4 ± 2)*m) # Pythagorean theorem
5.0 ± 1.7088007490635064 m
julia> (50 ± 1)Ω * (13 ± 2.4)*1e-2*A # Ohm's Law
6.5 ± 1.20702112657567 kg m²s⁻³A⁻¹
julia> 2pi*sqrt((5.4 ± 0.3)*m / ((9.81 ± 0.01)*m/s^2)) # Pendulum's period
4.661677707464357 ± 0.1295128435999655 s
julia> using Measurements, Unitful
julia> hypot((3 ± 1)*u"m", (4 ± 2)*u"m") # Pythagorean theorem
5.0 ± 1.7088007490635064 m
julia> (50 ± 1)*u"Ω" * (13 ± 2.4)*1e-2*u"A" # Ohm's Law
6.5 ± 1.20702112657567 A Ω
julia> 2pi*sqrt((5.4 ± 0.3)*u"m" / ((9.81 ± 0.01)*u"m/s^2")) # Pendulum's period
4.661677707464357 ± 0.12951284359996548 s
```

The package is developed at https://github.com/JuliaPhysics/Measurements.jl. There you can submit bug reports, make suggestions, and propose pull requests.

Have a look at the TODO list below and the bug list at
https://github.com/JuliaPhysics/Measurements.jl/issues, pick-up a task, write great
code to accomplish it and send a pull request. In addition, you can instruct
more mathematical functions to accept `Measurement`

type arguments. Please,
read the
technical appendix
of the complete documentation in order to understand the design of this package.
Bug reports and wishlists are welcome as well.

- Add pretty printing: optionally print only the relevant significant digits (issue #5)
- Other suggestions welcome
`:-)`

The ChangeLog of the package is available in NEWS.md file in top directory. There have been some breaking changes from time to time, beware of them when upgrading the package.

The `Measurements.jl`

package is licensed under the MIT "Expat" License. The
original author is Mosè Giordano.

Please, cite the paper Giordano 2016 (http://arxiv.org/abs/1610.08716) if you employ this package in your research work.

05/17/2016

7 days ago

274 commits