JOSS address editor comments (#305)

* DOIs, resolved except RR2007, ScikitLearn, NUTS * add Andre ORCID * addressed e.g.s, reseearch list, and CES pipeline list * replot sinusoid figures with larger fonts * add trailing commas * julia format * trailing comma * pdf update * formatting bullets * capitalization * grammar... * reduce trimmed fonts * new pdf
CliMA · Apr 23, 2024 · 332101c · 332101c
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diff --git a/examples/Sinusoid/calibrate.jl b/examples/Sinusoid/calibrate.jl
@@ -115,6 +115,9 @@ p = plot(
     legend = :bottomright,
     xlims = (0, 6),
     ylims = (-6, 10),
+    guidefontsize = 14,
+    tickfontsize = 12,
+    legendfontsize = 12,
 )
 vline!([theta_true[1]], color = :red, style = :dash, label = :false)
 hline!([theta_true[2]], color = :red, style = :dash, label = :false)

diff --git a/examples/Sinusoid/emulate.jl b/examples/Sinusoid/emulate.jl
@@ -14,7 +14,7 @@
 # First, we load the packages we need:
 using LinearAlgebra, Random
 
-using Distributions, Plots
+using Distributions, Plots, Plots.PlotMeasures
 using JLD2
 
 using CalibrateEmulateSample
@@ -179,7 +179,7 @@ p2 = contour(
     ylabel = "Vertical Shift",
     title = "True Sinusoid Mean",
 )
-p = plot(p1, p2, size = (600, 300), layout = (1, 2))
+p = plot(p1, p2, size = (600, 300), layout = (1, 2), guidefontsize = 12, tickfontsize = 10, legendfontsize = 10)
 savefig(p, joinpath(data_save_directory, "sinusoid_groundtruth_contours.png"))
 # The first panel shows how the range varies with respect to the two parameters in the Gaussian process
 # emulator. The contours show the range is mostly dependent on the amplitude, with little variation with
@@ -212,7 +212,17 @@ p2 = contour(
     title = "GP Sinusoid Mean",
 )
 plot!(inputs[1, :], inputs[2, :]; seriestype = :scatter, zcolor = outputs[2, :], label = :false)
-p = plot(p1, p2, size = (600, 300), layout = (1, 2))
+p = plot(
+    p1,
+    p2,
+    right_margin = 3mm,
+    bottom_margin = 3mm,
+    size = (600, 300),
+    layout = (1, 2),
+    guidefontsize = 12,
+    tickfontsize = 10,
+    legendfontsize = 10,
+)
 savefig(p, joinpath(data_save_directory, "sinusoid_GP_emulator_contours.png"))
 
 # Plot RF emulator contours
@@ -238,7 +248,17 @@ p2 = contour(
     title = "RF Sinusoid Mean",
 )
 plot!(inputs[1, :], inputs[2, :]; seriestype = :scatter, zcolor = outputs[2, :], label = :false)
-p = plot(p1, p2, size = (600, 300), layout = (1, 2))
+p = plot(
+    p1,
+    p2,
+    right_margin = 3mm,
+    bottom_margin = 3mm,
+    size = (600, 300),
+    layout = (1, 2),
+    guidefontsize = 12,
+    tickfontsize = 10,
+    legendfontsize = 10,
+)
 savefig(p, joinpath(data_save_directory, "sinusoid_RF_emulator_contours.png"))
 
 # Both the GP and RF emulator give similar results to the ground truth G(θ), indicating they are correctly
@@ -272,7 +292,17 @@ p2 = contour(
     ylabel = "Vertical Shift",
     title = "GP 1σ in Sinusoid Mean",
 )
-p = plot(p1, p2, size = (600, 300), layout = (1, 2))
+p = plot(
+    p1,
+    p2,
+    size = (600, 300),
+    layout = (1, 2),
+    right_margin = 3mm,
+    bottom_margin = 3mm,
+    guidefontsize = 12,
+    tickfontsize = 10,
+    legendfontsize = 10,
+)
 savefig(p, joinpath(data_save_directory, "sinusoid_GP_emulator_std_contours.png"))
 
 
@@ -299,7 +329,7 @@ p2 = contour(
     ylabel = "Vertical Shift",
     title = "RF 1σ in Sinusoid Mean",
 )
-p = plot(p1, p2, size = (600, 300), layout = (1, 2))
+p = plot(p1, p2, size = (600, 300), layout = (1, 2), guidefontsize = 12, tickfontsize = 10, legendfontsize = 10)
 savefig(p, joinpath(data_save_directory, "sinusoid_RF_emulator_std_contours.png"))
 # The GP and RF uncertainty predictions are similar and show lower uncertainties around the region of interest
 # where we have more training points.
@@ -331,7 +361,7 @@ p2 = contour(
     ylabel = "Vertical Shift",
     title = "GP error in Sinusoid Mean",
 )
-p = plot(p1, p2, size = (600, 300), layout = (1, 2))
+p = plot(p1, p2, size = (600, 300), layout = (1, 2), guidefontsize = 12, tickfontsize = 10, legendfontsize = 10)
 savefig(p, joinpath(data_save_directory, "sinusoid_GP_errors_contours.png"))
 
 rf_diff_grid = abs.(rf_grid - g_true_grid)
@@ -357,7 +387,7 @@ p2 = contour(
     ylabel = "Vertical Shift",
     title = "RF error in Sinusoid Mean",
 )
-p = plot(p1, p2, size = (600, 300), layout = (1, 2))
+p = plot(p1, p2, size = (600, 300), layout = (1, 2), guidefontsize = 12, tickfontsize = 10, legendfontsize = 10)
 savefig(p, joinpath(data_save_directory, "sinusoid_RF_errors_contours.png"))
 
 # Here, we want the emulator to show the low errors in the region around the true parameter values near θ = (3, 6),

diff --git a/examples/Sinusoid/sample.jl b/examples/Sinusoid/sample.jl
@@ -172,9 +172,9 @@ plot_all = plot(
     layout = layout,
     size = (600, 600),
     legend = :true,
-    guidefontsize = 8,
-    tickfontsize = 6,
-    legendfontsize = 6,
+    guidefontsize = 14,
+    tickfontsize = 12,
+    legendfontsize = 12,
 )
 
 savefig(plot_all, joinpath(data_save_directory, "sinusoid_MCMC_hist_GP.png"))
@@ -293,9 +293,9 @@ plot_all = plot(
     layout = layout,
     size = (600, 600),
     legend = :true,
-    guidefontsize = 8,
-    tickfontsize = 6,
-    legendfontsize = 6,
+    guidefontsize = 14,
+    tickfontsize = 12,
+    legendfontsize = 12,
 )
 
 savefig(plot_all, joinpath(data_save_directory, "sinusoid_MCMC_hist_RF.png"))

diff --git a/examples/Sinusoid/sinusoid_setup.jl b/examples/Sinusoid/sinusoid_setup.jl
@@ -74,7 +74,15 @@ function generate_obs(amplitude_true, vert_shift_true, Γ; rng = Random.GLOBAL_R
     # Generate the "true" signal for these parameters
     signal_true = model(amplitude_true, vert_shift_true; rng = rng)
     # Plot
-    p = plot(signal_true, color = :black, linewidth = 3, label = "True signal")
+    p = plot(
+        signal_true,
+        color = :black,
+        linewidth = 3,
+        label = "True signal",
+        guidefontsize = 14,
+        tickfontsize = 12,
+        legendfontsize = 12,
+    )
 
     # We will observe properties of the signal that inform us about the amplitude and vertical 
     # position. These properties will be the range (the difference between the maximum and the minimum),

diff --git a/paper.bib b/paper.bib
@@ -1,6 +1,5 @@
 @article{julia,
   doi       = {10.1137/141000671},
-  url       = {https://doi.org/10.1137%2F141000671},
   year      = 2017,
   month     = {jan},
   publisher = {Society for Industrial {\&} Applied Mathematics ({SIAM})},
@@ -17,7 +16,8 @@ @book{Sisson:2018
 isbn = {978-1-351-64346-7},
 publisher = {CRC Press},
 author = {Sisson, Scott A. and Fan, Yanan and Beaumont, Mark},
-year = {2018}
+year = {2018},
+doi = {10.1201/9781315117195}
 }
 
 @incollection{Nott:2018,
@@ -29,6 +29,7 @@ @incollection{Nott:2018
   pages   = {211-241},
   year    = {2018},
   publisher = {CRC Press},
+  doi = {10.1201/9781315117195-8}
 }
 
 @article{Cleary:2021,
@@ -38,14 +39,12 @@ @article{Cleary:2021
 pages = {109716},
 year = {2021},
 issn = {0021-9991},
-doi = {https://doi.org/10.1016/j.jcp.2020.109716},
-url = {https://www.sciencedirect.com/science/article/pii/S0021999120304903},
+doi = {10.1016/j.jcp.2020.109716},
 author = {Emmet Cleary and Alfredo Garbuno-Inigo and Shiwei Lan and Tapio Schneider and Andrew M. Stuart},
 }
 
 @article{Dunbar:2022a,
 doi = {10.21105/joss.04869},
-url = {https://doi.org/10.21105/joss.04869},
 year = {2022},
 publisher = {The Open Journal},
 volume = {7},
@@ -72,7 +71,7 @@ @mastersthesis{Hillier:2022
     school   = {Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science},
     author   = {Adeline Hillier},
     year     = {2022},
-    url = {https://hdl.handle.net/1721.1/145140}
+    doi = {1721.1/145140}
 }
 
 
@@ -82,7 +81,8 @@ @book{Rasmussen:2006
   volume={2},
   number={3},
   year={2006},
-  publisher={MIT press Cambridge, MA}
+  publisher={MIT press Cambridge, MA},
+  doi = {10.1142/S0129065704001899}
 }
 
 @article{Iglesias:2013,
@@ -93,7 +93,8 @@ @article{Iglesias:2013
   number={4},
   pages={045001},
   year={2013},
-  publisher={IOP Publishing}
+  publisher={IOP Publishing},
+  doi={10.1088/0266-5611/29/4/045001}
 }
 
 @inproceedings{Rahimi:2007,
@@ -104,7 +105,7 @@ @inproceedings{Rahimi:2007
   number={4},
   pages={5},
   year={2007},
-  organization={Citeseer}
+  url = {https://proceedings.neurips.cc/paper_files/paper/2007/file/013a006f03dbc5392effeb8f18fda755-Paper.pdf},
 }
 
 @inproceedings{Rahimi:2008,
@@ -113,7 +114,8 @@ @inproceedings{Rahimi:2008
   booktitle={2008 46th Annual Allerton Conference on Communication, Control, and Computing},
   pages={555--561},
   year={2008},
-  organization={IEEE}
+  organization={IEEE},
+  doi = {10.1109/allerton.2008.4797607}
 }
 
 @article{Liu:2022,
@@ -138,21 +140,19 @@ @article{Cotter:2013
 keywords = {algorithms, Bayesian inverse problems, Bayesian nonparametrics, Gaussian random field, MCMC},
 year = {2013},
 doi = {10.1214/13-STS421},
-URL = {https://doi.org/10.1214/13-STS421}
 }
 
 @article{Sherlock:2010,
  ISSN = {08834237},
- URL = {http://www.jstor.org/stable/41058939},
  author = {Chris Sherlock and Paul Fearnhead and Gareth O. Roberts},
  journal = {Statistical Science},
  number = {2},
  pages = {172--190},
  publisher = {Institute of Mathematical Statistics},
  title = {The Random Walk Metropolis: Linking Theory and Practice Through a Case Study},
- urldate = {2023-12-06},
  volume = {25},
- year = {2010}
+ year = {2010},
+ doi = {10.1214/10-sts327}
 }
 
 @article{Dunbar:2021,
@@ -163,7 +163,7 @@ @article{Dunbar:2021
 number = {9},
 pages = {e2020MS002454},
 keywords = {uncertainty quantification, model calibration, machine learning, general circulation model, parametric uncertainty, inverse problem},
-doi = {https://doi.org/10.1029/2020MS002454},
+doi = {10.1029/2020MS002454},
 year = {2021}
 }
 
@@ -177,7 +177,7 @@ @article{Howland:2022
 number = {3},
 pages = {e2021MS002735},
 keywords = {uncertainty quantification, Bayesian learning, GCM, seasonal cycle},
-doi = {https://doi.org/10.1029/2021MS002735},
+doi = {10.1029/2021MS002735},
 year = {2022}
 }
 
@@ -189,7 +189,7 @@ @article{Dunbar:2022b
 number = {9},
 pages = {e2022MS002997},
 keywords = {optimal design, model calibration, uncertainty quantification, general circulation model, optimal placement, machine learning},
-doi = {https://doi.org/10.1029/2022MS002997},
+doi = {10.1029/2022MS002997},
 year = {2022}
 }
 
@@ -212,12 +212,12 @@ @article{Fairbrother:2022
   journal={Journal of Statistical Software},
   volume={102},
   pages={1--36},
-  year={2022}
+  year={2022},
+  doi={10.18637/jss.v102.i01}
 }
 
 @article{Dixit:2022,
 doi = {10.21105/joss.04561},
-url = {https://doi.org/10.21105/joss.04561},
 year = {2022},
 publisher = {The Open Journal},
 volume = {7},
@@ -248,14 +248,12 @@ @article{Tankhilevich:2020
     month = {02},
     issn = {1367-4803},
     doi = {10.1093/bioinformatics/btaa078},
-    url = {https://doi.org/10.1093/bioinformatics/btaa078},
     note = {btaa078},
     eprint = {https://academic.oup.com/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btaa078/32353462/btaa078.pdf},
 }
 
 @article{Huang:2022,
 doi = {10.1088/1361-6420/ac99fa},
-url = {https://dx.doi.org/10.1088/1361-6420/ac99fa},
 year = {2022},
 month = {oct},
 publisher = {IOP Publishing},
@@ -271,7 +269,6 @@ @article{Mansfield:2022
         title = {Calibration and Uncertainty Quantification of a Gravity Wave Parameterization: A Case Study of the {Quasi}-{Biennial} {Oscillation} in an Intermediate Complexity Climate Model},
         volume = {14},
         issn = {1942-2466},
-        url = {https://onlinelibrary.wiley.com/doi/abs/10.1029/2022MS003245},
         doi = {10.1029/2022MS003245},
         language = {en},
         number = {11},
@@ -283,8 +280,6 @@ @article{Mansfield:2022
 @article{King:2023,
         type = {preprint},
         title = {Bayesian History Matching applied to the calibration of a gravity wave parameterization},
-        url = {https://essopenarchive.org/users/709594/articles/693996-bayesian-history-matching-applied-to-the-calibration-of-a-gravity-wave-parameterization?commit=613feab171223df3237efc786346eacd545733f1},
-urldate = {2024-01-05},
         institution = {Preprints},
         author = {King, Robert C and Mansfield, Laura A and Sheshadri, Aditi},
         month = dec,
@@ -301,12 +296,12 @@ @article{Metropolis:1953
   number={6},
   pages={1087--1092},
   year={1953},
-  publisher={American Institute of Physics}
+  publisher={American Institute of Physics},
+  doi = {10.1063/1.1699114}
 }
 
 @article{Huggins:2023,
 doi = {10.21105/joss.05428},
-url = {https://doi.org/10.21105/joss.05428},
 year = {2023},
 publisher = {The Open Journal},
 volume = {8},
@@ -317,13 +312,17 @@ @article{Huggins:2023
 journal = {Journal of Open Source Software}
 }
 
-@article{Gammal:2022,
-      title={Fast and robust Bayesian Inference using Gaussian Processes with GPry}, 
-      author={Jonas El Gammal and Nils Schöneberg and Jesús Torrado and Christian Fidler},
-      year={2022},
-      eprint={2211.02045},
-      archivePrefix={arXiv},
-      primaryClass={astro-ph.CO}
+@article{Gammal:2023,
+doi = {10.1088/1475-7516/2023/10/021},
+year = {2023},
+month = {oct},
+publisher = {IOP Publishing},
+volume = {2023},
+number = {10},
+pages = {021},
+author = {Jonas El Gammal and Nils Schöneberg and Jesús Torrado and Christian Fidler},
+title = {Fast and robust Bayesian inference using Gaussian processes with GPry},
+journal = {Journal of Cosmology and Astroparticle Physics},
 }
 
 @article{livingstone:2022,
@@ -334,7 +333,8 @@ @article{livingstone:2022
   number={2},
   pages={496--523},
   year={2022},
-  publisher={Oxford University Press}
+  publisher={Oxford University Press},
+  doi={10.1111/rssb.12482}
 }
 
 @article{hoffman:2014,