Comments (3)
The reason why that I never went on with this idea is that it creates lots of type-instabilities, or it makes it more likely to have inhomogeneous containers which include both IndependentMeasurement
s and DependentMeasurement
s, which is really not a good idea performance-wise.
If one wants to distinguish an "independent" measure from a derived quantity, I believe traits would be a better approach.
from measurements.jl.
Different approach in dispatch-on-constant
branch: no new type, the only change is the dispatch of result
for 1-arg functions is based on constants (Val{...}
), but this isn't very fast. The branch is left there only as a proof-of-concept.
from measurements.jl.
We could make
Measurement
type abstract, and define two concrete types,IndependentMeasurement
andDependentMeasurement
(the latter may be an abuse of language but gives the idea):
I support this evolution. As far as I understand, the Python uncertainties packages works in this way. I have written code to do elaborations of uncertain and correlated quantities in R and resorted to the same concepts. It seems to me the most straightforward way to easily track down correlations when doing elaborations using quantities that themselves depend on overlapping sets of uncertain variables.
from measurements.jl.
Related Issues (20)
- Error when hashing Measurement{Float64} HOT 5
- Adding measurement components back to a measurement after iteratively solving for a value HOT 7
- [FR] Plot recipe: ribbon plots option beside error bars HOT 11
- tryparse for Measurement type
- Can't use unique with measurements HOT 1
- Measurements with missing errors HOT 4
- Measurements.value(x::Missing) = missing HOT 1
- Integration with Zygote
- `weightedmean()` returns `NaN ± 0`? HOT 1
- Use auto-differentiation engine
- Bad integration with Plots' boxplot HOT 2
- Move to pkgextensions for Julia v1.9+
- one(measurement) should return 1, not 1 ± 0 HOT 8
- `Symbolics.jl` support? HOT 7
- Plotting mixture of measurements and missing data HOT 1
- Trying to use Measurements to differentiate respect to a unitful quantity. HOT 12
- Is there an autodiff package that is compatible? HOT 3
- Broken `MeasurementsJunoExt.jl` HOT 1
- Julia 1.6 incompatibility from stdlib compat bounds HOT 2
- Wrong values when multiplication with scalar? HOT 3
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from measurements.jl.