Open editorialbot opened 4 weeks ago
editorialbot
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4 weeks ago Hello humans, I'm @editorialbot, a robot that can help you with some common editorial tasks.
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@editorialbot generate pdfSoftware report:
github.com/AlDanial/cloc v 1.90 T=0.02 s (2367.9 files/s, 179335.5 lines/s)
-------------------------------------------------------------------------------
Language files blank comment code
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C++ 17 133 10 1041
C/C++ Header 14 161 891 432
Markdown 4 53 0 418
YAML 3 7 17 187
CMake 8 36 29 170
TeX 1 4 0 46
HTML 1 3 19 45
CSS 1 1 2 6
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SUM: 49 398 968 2345
-------------------------------------------------------------------------------Commit count by author:
86 Jonathan M. Waldrop
1 Ryan RichardReference check summary (note 'MISSING' DOIs are suggestions that need verification):
β
OK DOIs
- 10.1063/5.0196384 is OK
π‘ SKIP DOIs
- No DOI given, and none found for title: Uncertainty propagation with functionally correlat...
- No DOI given, and none found for title: Uncertainties: a Python package for calculations w...
- No DOI given, and none found for title: CMake
- No DOI given, and none found for title: Eigen
β MISSING DOIs
- None
β INVALID DOIs
- None
Paper file info:
π Wordcount for paper.md is 1064
β
The paper includes a Statement of need section
editorialbot
commented
4 weeks ago License info:
β
License found: Apache License 2.0 (Valid open source OSI approved license)
editorialbot
commented
4 weeks ago :point_right::page_facing_up: Download article proof :page_facing_up: View article proof on GitHub :page_facing_up: :point_left:
YehorYudinIPP
commented
4 weeks ago 
baxmittens
commented
4 weeks ago Hi all,
Nice paper and software project. Congratulations! I wanted to look into this topic for a long time. I will therefore gladly take this opportunity.
I read the paper and one particular thing that caught my attention was
""" These element are defined as ππ = Μππ Β± π_ππ , where Μππ is the mean value of the variable and π_ππ is its standard deviation. """
I think the standard deviation is the square root of the variance of a random variable which is equipped with - or linked to - a probability distribution. Therefore I think π_ππ should rather be called 'uncertainty', since it does not own a probability measure. Surely, you can say something like 'π_ππ is called the uncertainty and is assumed to represent an error measure closely related to the standard deviation of a random variable' (or something like that). I think in one of your sources https://arxiv.org/abs/1610.08716 this is also more carefully phrased.
Next thing would be
""" The uncertainty of πΉ (π΄) can be determined as π_πΉ β ... """
I think this should also rather be described as the linear uncertainty (or something alike) since in general the pdf of F(A(Ο)) will be skewed, and, in that case, the standard deviation doesn't represent too much of the uncertainty of the outcome (see the example of a cantilever with uncertain tip displacement which I attache). In general, the difference between the method at hand and 'uncertainty quantification' which involves integration over a probability space should be briefly pointed out, I think.
Greetz Max
using Distributions
using ProgressMeter
import .Threads: @threads
using CairoMakie
using Measurements
using Statistics
function cantilever_displacement(E,X,Y,L,w,t)
# see https://www.sfu.ca/~ssurjano/canti.html
Dfact1 = 4*(L^3) / (E*w*t)
Dfact2 = sqrt((Y/(t^2))^2 + (X/(w^2))^2)
D = Dfact1 * Dfact2
return D
end
N_E = Normal{Float64}(2.9e7, 1.45e6) # Young's modulus
N_X = Normal{Float64}(500.0, 50.0) # horizontal load
N_Y = Normal{Float64}(1000.0, 100.0) # vertical load
N_L = Normal{Float64}(100.0, 10.0) # beam length
N_w = Normal{Float64}(4.0, 0.4) # beam width
N_t = Normal{Float64}(2.0, 0.2) # beam width
M_E = measurement(2.9e7, 1.45e6) # Young's modulus
M_X = measurement(500.0, 50.0) # horizontal load
M_Y = measurement(1000.0, 100.0) # vertical load
M_L = measurement(100.0, 10.0) # beam length
M_w = measurement(4.0, 0.4) # beam width
M_t = measurement(2.0, 0.2) # beam width
samplefunc() = [rand(N_E), rand(N_X), rand(N_Y), rand(N_L), rand(N_w), rand(N_t)]
N = 5_000_000
monte_carlo_resvec = Vector{Float64}(undef, N)
@showprogress @threads for i = 1:N
monte_carlo_resvec[i] = cantilever_displacement(samplefunc()...)
end
sample_mean = foldl(+, monte_carlo_resvec)/N
sample_var = foldl(+, map(x->(x-sample_mean)^2, monte_carlo_resvec))/(N-1)
sample_sqrt_var = sqrt(sample_var)
quantvals = map(x->cdf(Normal{Float64}(0.0,1.0),x), -3:3)
sample_quantiles = quantile(monte_carlo_resvec, quantvals)
Ο_F = cantilever_displacement(M_E,M_X,M_Y,M_L,M_w,M_t)
f = Figure(size=(600,300));
ax = Axis(f[1,1:4])
xlims!(ax, [0,20])
ylims!(ax, [0,.25])
band!(ax, [sample_quantiles[1], sample_quantiles[2]], 0, 5, color=(:red,0.1), label="99.7% quantile")
band!(ax, [sample_quantiles[end-1], sample_quantiles[end]], 0, 5, color=(:red,0.1))
band!(ax, [sample_quantiles[2], sample_quantiles[3]], 0, 5, color=(:yellow,0.2), label="95.4% quantile")
band!(ax, [sample_quantiles[end-2], sample_quantiles[end-1]], 0, 5, color=(:yellow,0.2))
band!(ax, [sample_quantiles[3], sample_quantiles[5]], 0, 5, color=(:green,0.2), label="68.2% quantile")
hist!(ax, monte_carlo_resvec, normalization = :pdf, bins = 500, color=(:red, 0.45), strokewidth=0.1, label="histogram")
lines!(ax, [sample_mean, sample_mean], [0.0,0.25], label="exp. value")
lines!(ax, [sample_mean+sample_sqrt_var, sample_mean+sample_sqrt_var], [0.0,0.25], label="mean+βvar")
lines!(ax, [sample_mean-sample_sqrt_var, sample_mean-sample_sqrt_var], [0.0,0.25], label="mean-βvar")
lines!(ax, [Ο_F.val, Ο_F.val], [0.0,0.25], label="Ο_F.val",linestyle=:dash)
lines!(ax, [Ο_F.val+Ο_F.err, Ο_F.val+Ο_F.err], [0.0,0.25], label="Ο_F.val+Ο_F.err",linestyle=:dash)
lines!(ax, [Ο_F.val-Ο_F.err, Ο_F.val-Ο_F.err], [0.0,0.25], label="Ο_F.val-Ο_F.err",linestyle=:dash)
Legend(f[1,5], ax, labelsize=8)
f@jwaldrop107
Edit: Thought about it and now I think you can call π_π a (empirical) standard deviation...but without knowledge of the pdf of a, this does not contain too much useful information, unless you assume your measurement error is normal distributed which does not necessarily needs to be true.
baxmittens
commented
2 weeks ago baxmittens
commented
1 week ago A statement of need: Do the authors clearly state what problems the software is designed to solve and who the target audience is?
There is a motivation section but it is a bit short. But a statement of need is given in the paper. This could be used as a template for the documentation.
Community guidelines: Are there clear guidelines for third parties wishing to 1. Contribute to the software 2. Report issues or problems with the software 3. Seek support
I think community guidelines are missing.
jwaldrop107
commented
1 day ago @editorialbot generate pdf
Hi all,
Sorry about the delayed response. I've pushed some changes based on the comments made so far (wording in the paper, statement of need in the documentation, and community guidelines).
Thanks for taking the time to review this, and I'm looking forward to any additional comments/recommendations.
editorialbot
commented
1 day ago :point_right::page_facing_up: Download article proof :page_facing_up: View article proof on GitHub :page_facing_up: :point_left:
Submitting author: !--author-handle-->@jwaldrop107<!--end-author-handle-- (Jonathan Waldrop) Repository: https://github.com/QCUncertainty/sigma Branch with paper.md (empty if default branch): joss_paper Version: v0.1 Editor: !--editor-->@vissarion<!--end-editor-- Reviewers: @baxmittens, @YehorYudinIPP Archive: Pending
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