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DynamicQuantities defines a simple statically-typed Quantity type for Julia. Physical dimensions are stored as a value, as opposed to a parametric type, as in Unitful.jl. This can greatly improve both runtime performance, by avoiding type instabilities, and startup time, as it avoids overspecializing methods.


DynamicQuantities can greatly outperform Unitful when the compiler cannot infer dimensions in a function:

julia> using BenchmarkTools, DynamicQuantities; import Unitful

julia> dyn_uni = 0.2u"m/s"
0.2 m s⁻¹

julia> unitful = convert(Unitful.Quantity, dyn_uni)
0.2 m s⁻¹

julia> f(x, i) = x ^ i * 0.3;

julia> @btime f($dyn_uni, 1);
  2.708 ns (0 allocations: 0 bytes)

julia> @btime f($unitful, 1);
  2.597 μs (30 allocations: 1.33 KiB)

Note the μ and n: this is a 1000x speedup! Here, the DynamicQuantities quantity object allows the compiler to build a function that is type stable, while the Unitful quantity object, which stores its dimensions in the type, requires type inference at runtime.

However, if the dimensions in your function can be inferred by the compiler, then you can get better speeds with Unitful:

julia> g(x) = x ^ 2 * 0.3;

julia> @btime g($dyn_uni);
  1.791 ns (0 allocations: 0 bytes)

julia> @btime g($unitful);
  1.500 ns (0 allocations: 0 bytes)

While both of these are type stable, because Unitful parametrizes the type on the dimensions, functions can specialize to units and the compiler can optimize away units from the code.


You can create a Quantity object by using the convenience macro u"...":

julia> x = 0.3u"km/s"
300.0 m s⁻¹

julia> y = 42 * u"kg"
42.0 kg

or by importing explicitly:

julia> using DynamicQuantities: kPa

julia> room_temp = 100kPa
100000.0 m⁻¹ kg s⁻²

Note that Units is an exported submodule, so you can also access this as Units.kPa. You may like to define

julia> const U = Units

so that you can simply write, say, U.kPa or C.m_e.

This supports a wide range of SI base and derived units, with common prefixes.

You can also construct values explicitly with the Quantity type, with a value and keyword arguments for the powers of the physical dimensions (mass, length, time, current, temperature, luminosity, amount):

julia> x = Quantity(300.0, length=1, time=-1)
300.0 m s⁻¹

Elementary calculations with +, -, *, /, ^, sqrt, cbrt, abs are supported:

julia> x * y
12600.0 m kg s⁻¹

julia> x / y
7.142857142857143 m kg⁻¹ s⁻¹

julia> x ^ 3
2.7e7  s⁻³

julia> x ^ -1
0.0033333333333333335 m⁻¹ s

julia> sqrt(x)
17.320508075688775 m¹ᐟ² s⁻¹ᐟ²

julia> x ^ 1.5
5196.152422706632 m³ᐟ² s⁻³ᐟ²

Each of these values has the same type, which means we don't need to perform type inference at runtime.

Furthermore, we can do dimensional analysis by detecting DimensionError:

julia> x + 3 * x
1.2 m¹ᐟ² kg

julia> x + y
ERROR: DimensionError: 0.3 m¹ᐟ² kg and 10.2 kg² s⁻² have incompatible dimensions

The dimensions of a Quantity can be accessed either with dimension(quantity) for the entire Dimensions object:

julia> dimension(x)
m¹ᐟ² kg

or with umass, ulength, etc., for the various dimensions:

julia> umass(x)

julia> ulength(x)

Finally, you can strip units with ustrip:

julia> ustrip(x)


There are a variety of physical constants accessible via the Constants submodule:

julia> Constants.c
2.99792458e8 m s⁻¹

which you may like to define as

julia> const C = Constants

These can also be used inside the u"..." macro:

julia> u"Constants.c * Hz"
2.99792458e8 m s⁻²

Similarly, you can just import each individual constant:

julia> using DynamicQuantities.Constants: h

For the full list, see the docs.

Symbolic Units

You can also choose to not eagerly convert to SI base units, instead leaving the units as the user had written them. For example:

julia> q = 100us"cm * kPa"
100.0 cm kPa

julia> q^2
10000.0 cm² kPa²

You can convert to regular SI base units with uexpand:

julia> uexpand(q^2)
1.0e6 kg² s⁻⁴

This also works with constants:

julia> x = us"Constants.c * Hz"
1.0 Hz c

julia> x^2
1.0 Hz² 

julia> uexpand(x^2)
8.987551787368176e16  s⁻⁴

You can also convert a quantity in regular base SI units to symbolic units with the |> infix operator

julia> 5e-9u"m" |> us"nm"
5.0 nm

You can also convert between different symbolic units. (Note that you can write this more explicitly with uconvert(us"nm", 5e-9u"m").)

Finally, you can also import these directly:

julia> using DynamicQuantities.SymbolicUnits: cm

or constants:

julia> using DynamicQuantities.SymbolicConstants: h

Note that SymbolicUnits and SymbolicConstants are exported, so you can simply access these as and SymbolicConstants.h, respectively.

Custom Units

You can create custom units with the @register_unit macro:

julia> @register_unit OneFiveV 1.5u"V"

and then use it in calculations normally:

julia> x = us"OneFiveV"
1.0 OneFiveV

julia> x * 10u"A" |> us"W"
15.0 W

julia> 3us"V" |> us"OneFiveV"
2.0 OneFiveV


For working with an array of quantities that have the same dimensions, you can use a QuantityArray:

julia> ar = QuantityArray(rand(3), u"m/s")
3-element QuantityArray(::Vector{Float64}, ::Quantity{Float64, Dimensions{FixedRational{Int32, 25200}}}):
 0.2729202669351497 m s⁻¹
 0.992546340360901 m s⁻¹
 0.16863543422972482 m s⁻¹

This QuantityArray is a subtype <:AbstractArray{Quantity{Float64,Dimensions{...}},1}, meaning that indexing a specific element will return a Quantity:

julia> ar[2]
0.992546340360901 m s⁻¹

julia> ar[2] *= 2
1.985092680721802 m s⁻¹

julia> ar[2] += 0.5u"m/s"
2.485092680721802 m s⁻¹

This also has a custom broadcasting interface which allows the compiler to avoid redundant dimension calculations, relative to if you had simply used an array of quantities:

julia> f(v) = v^2 * 1.5;

julia> @btime $f.(xa) setup=(xa = randn(100000) .* u"km/s");
  109.500 μs (2 allocations: 3.81 MiB)

julia> @btime $f.(qa) setup=(xa = randn(100000) .* u"km/s"; qa = QuantityArray(xa));
  50.917 μs (3 allocations: 781.34 KiB)

So we can see the QuantityArray version saves on both time and memory.


DynamicQuantities allows you to convert back and forth from Unitful.jl:

julia> using Unitful: Unitful, @u_str; import DynamicQuantities

julia> x = 0.5u"km/s"
0.5 km s⁻¹

julia> y = convert(DynamicQuantities.Quantity, x)
500.0 m s⁻¹

julia> y2 = y^2 * 0.3
75000.0  s⁻²

julia> x2 = convert(Unitful.Quantity, y2)
75000.0  s⁻²

julia> x^2*0.3 == x2


Both a Quantity's values and dimensions are of arbitrary type. The default Dimensions (for the u"..." macro) performs exponent tracking for SI units, and SymbolicDimensions (for the us"..." macro) performs exponent tracking for all known unit and constant symbols, using a sparse array.

You can create custom spaces dimension spaces by simply creating a Julia struct subtyped to AbstractDimensions:

julia> struct CookiesAndMilk{R} <: AbstractDimensions{R}

julia> cookie_rate = Quantity(0.9, CookiesAndMilk(cookies=1, milk=-1))
0.9 cookies milk⁻¹

julia> total_milk = Quantity(103, CookiesAndMilk(milk=1))
103 milk

julia> total_cookies = cookie_rate * total_milk
92.7 cookies

Exponents are tracked by default with the type R = FixedRational{Int32,C}, which represents rational numbers with a fixed denominator C. This is much faster than Rational.

julia> typeof(0.5u"kg")
Quantity{Float64, Dimensions{FixedRational{Int32, 25200}}}

You can change the type of the value field by initializing with a value explicitly of the desired type.

julia> typeof(Quantity(Float16(0.5), mass=1, length=1))
Quantity{Float16, Dimensions{FixedRational{Int32, 25200}}}

or by conversion:

julia> typeof(convert(Quantity{Float16}, 0.5u"m/s"))
Quantity{Float16, Dimensions{FixedRational{Int32, 25200}}}

For many applications, FixedRational{Int8,6} will suffice, and can be faster as it means the entire Dimensions struct will fit into 64 bits. You can change the type of the dimensions field by passing the type you wish to use as the second argument to Quantity:

julia> using DynamicQuantities

julia> R8 = Dimensions{FixedRational{Int8,6}};

julia> R32 = Dimensions{FixedRational{Int32,2^4 * 3^2 * 5^2 * 7}};  # Default

julia> q8 = [Quantity{Float64,R8}(randn(), length=rand(-2:2)) for i in 1:1000];

julia> q32 = [Quantity{Float64,R32}(randn(), length=rand(-2:2)) for i in 1:1000];

julia> f(x) = @. x ^ 2 * 0.5;

julia> @btime f($q8);
  1.433 μs (3 allocations: 15.77 KiB)

julia> @btime f($q32);
  1.883 μs (4 allocations: 39.12 KiB)