Minors changes for JOSS paper (#76)

* ignore paper

* remove csic from affiliations

* remove abstracts

* fix link to docs

* replace Figure kwarg `resolution` by  `size`

codecov.yml

@@ -3,4 +3,5 @@ ignore:
   - "ext/*"
   - "src/misc/load_data.jl"
   - "src/misc/params.jl"
-  - "src/visualizations.jl"
+  - "src/visualizations.jl"
+  - "paper/*"

docs/src/examples/do-events.jl

@@ -124,7 +124,7 @@ function kyr_xticks(tticks_yr)
 end
 
 function plot_do(traw, xraw, tfilt, xfilt, t, r, t_transitions, xlims, xticks)
-    fig = Figure(resolution = (1600, 1200), fontsize = 24)
+    fig = Figure(size = (1600, 1200), fontsize = 24)
 
     ## Original timeseries with transition marked by vertical lines
     ax1 = Axis(fig[1, 1], xlabel = L"Time (kyr) $\,$", ylabel = L"$\delta^{18}$O (permil)",

docs/src/examples/logistic.jl

@@ -81,7 +81,7 @@ results = estimate_changes(config, x, rs)
 # Let's now plot the change metrics of the indicators
 
 function plot_change_metrics()
-    fig, ax = lines(rs, x; axis = (ylabel = "input",), figure = (resolution = (600, 600),))
+    fig, ax = lines(rs, x; axis = (ylabel = "input",), figure = (size = (600, 600),))
     hidexdecorations!(ax; grid = false)
     ## plot all change metrics
     for (i, c) in enumerate(eachcol(results.x_change))

paper/paper.bib

@@ -5,7 +5,6 @@ @article{wolff_changes_2010
 	issn = {02773791},
 	url = {https://linkinghub.elsevier.com/retrieve/pii/S027737910900208X},
 	doi = {10.1016/j.quascirev.2009.06.013},
-	abstract = {The EPICA ice core from Dome C extends 3259 m in depth, and encompasses 800 ka of datable and sequential ice. Numerous chemical species have been measured along the length of the cores. Here we concentrate on interpreting the main low-resolution patterns of major ions. We extend the published record for non-sea-salt calcium, sea-salt sodium and non-sea-salt sulfate flux to 800 ka. The non-sea-salt calcium record confirms that terrestrial dust originating from South America closely mirrored Antarctic climate, both at orbital and millennial timescales. A major cause of the main trends is most likely climate in southern South America, which could be sensitive to subtle changes in atmospheric circulation. Seasalt sodium also follows temperature, but with a threshold at low temperature. We re-examine the use of sodium as a sea ice proxy, concluding that it is probably reflecting extent, with high salt concentrations reflecting larger ice extents. With this interpretation, the sodium flux record indicates low ice extent operating as an amplifier in warm interglacials. Non-sea-salt sulfate flux is almost constant along the core, confirming the lack of change in marine productivity (for sulfur-producing organisms) in the areas of the Southern Ocean contributing to the flux at Dome C. For the first time we also present long records of reversible species such as nitrate and chloride, and show that the pattern of post-depositional losses described for shallower ice is maintained in older ice. It appears possible to use these concentrations to constrain snow accumulation rates in interglacial ice at this site, and the results suggest a possible correction to accumulation rates in one early interglacial. Taken together the chemistry records offer a number of constraints on the way the Earth system combined to give the major climate fluctuations of the late Quaternary period.},
 	language = {en},
 	number = {1-2},
 	urldate = {2022-06-14},
@@ -23,7 +22,6 @@ @article{dakos_methods_2012
 	issn = {1932-6203},
 	url = {https://dx.plos.org/10.1371/journal.pone.0041010},
 	doi = {10.1371/journal.pone.0041010},
-	abstract = {Many dynamical systems, including lakes, organisms, ocean circulation patterns, or financial markets, are now thought to have tipping points where critical transitions to a contrasting state can happen. Because critical transitions can occur unexpectedly and are difficult to manage, there is a need for methods that can be used to identify when a critical transition is approaching. Recent theory shows that we can identify the proximity of a system to a critical transition using a variety of so-called ‘early warning signals’, and successful empirical examples suggest a potential for practical applicability. However, while the range of proposed methods for predicting critical transitions is rapidly expanding, opinions on their practical use differ widely, and there is no comparative study that tests the limitations of the different methods to identify approaching critical transitions using time-series data. Here, we summarize a range of currently available early warning methods and apply them to two simulated time series that are typical of systems undergoing a critical transition. In addition to a methodological guide, our work offers a practical toolbox that may be used in a wide range of fields to help detect early warning signals of critical transitions in time series data.},
 	language = {en},
 	number = {7},
 	urldate = {2022-09-29},
@@ -42,10 +40,6 @@ @article{bury_deep_2021
 	issn = {0027-8424, 1091-6490},
 	url = {https://pnas.org/doi/full/10.1073/pnas.2106140118},
 	doi = {10.1073/pnas.2106140118},
-	abstract = {Significance
-            Early warning signals (EWS) of tipping points are vital to anticipate system collapse or other sudden shifts. However, existing generic early warning indicators designed to work across all systems do not provide information on the state that lies beyond the tipping point. Our results show how deep learning algorithms (artificial intelligence) can provide EWS of tipping points in real-world systems. The algorithm predicts certain qualitative aspects of the new state, and is also more sensitive and generates fewer false positives than generic indicators. We use theory about system behavior near tipping points so that the algorithm does not require data from the study system but instead learns from a universe of possible models.
-          , 
-            Many natural systems exhibit tipping points where slowly changing environmental conditions spark a sudden shift to a new and sometimes very different state. As the tipping point is approached, the dynamics of complex and varied systems simplify down to a limited number of possible “normal forms” that determine qualitative aspects of the new state that lies beyond the tipping point, such as whether it will oscillate or be stable. In several of those forms, indicators like increasing lag-1 autocorrelation and variance provide generic early warning signals (EWS) of the tipping point by detecting how dynamics slow down near the transition. But they do not predict the nature of the new state. Here we develop a deep learning algorithm that provides EWS in systems it was not explicitly trained on, by exploiting information about normal forms and scaling behavior of dynamics near tipping points that are common to many dynamical systems. The algorithm provides EWS in 268 empirical and model time series from ecology, thermoacoustics, climatology, and epidemiology with much greater sensitivity and specificity than generic EWS. It can also predict the normal form that characterizes the oncoming tipping point, thus providing qualitative information on certain aspects of the new state. Such approaches can help humans better prepare for, or avoid, undesirable state transitions. The algorithm also illustrates how a universe of possible models can be mined to recognize naturally occurring tipping points.},
 	language = {en},
 	number = {39},
 	urldate = {2022-10-20},
@@ -81,7 +75,6 @@ @article{bury_ewstools_2023
 	shorttitle = {ewstools},
 	url = {https://joss.theoj.org/papers/10.21105/joss.05038},
 	doi = {10.21105/joss.05038},
-	abstract = {Many systems in nature and society have the capacity to undergo critical transitions: sudden and profound changes in dynamics that are hard to reverse. Examples include the outbreak of disease, the collapse of an ecosystem, and the onset of a cardiac arrhythmia. From a mathematical perspective, these transitions may be understood as the crossing of a bifurcation (tipping point) in an appropriate dynamical system model. In 2009, Scheffer and colleagues proposed early warning signals (EWS) for bifurcations based on statistics of noisy fluctuations in time series data (Scheffer et al., 2009). This spurred massive interest in the subject, resulting in a multitude of different EWS for anticipating bifurcations (Clements \& Ozgul, 2018). More recently, EWS from deep learning classifiers have outperformed conventional EWS on several model and empirical datasets, whilst also providing information on the type of bifurcation (Bury et al., 2021). Software packages for EWS can facilitate the development and testing of EWS, whilst also providing the scientific community with tools to rapidly apply EWS to their own data.},
 	language = {en},
 	number = {82},
 	urldate = {2023-02-19},
@@ -99,7 +92,6 @@ @article{ismail_detecting_2020
 	issn = {2169-3536},
 	url = {https://ieeexplore.ieee.org/document/9250440/},
 	doi = {10.1109/ACCESS.2020.3036370},
-	abstract = {This study explores persistent homology to detect early warning signals of the 2017 and 2019 major financial crashes in Bitcoin. Sliding window is used to obtain point cloud datasets from a multidimensional time series (Bitcoin, Ethereum, Litecoin and Ripple). We apply persistent homology to quantify transient loops that appear in multiscale topological spaces, which associated on each point cloud dataset and encode the quantified information in a persistence landscape. Temporal changes in persistence landscapes are measured via their L1-norms. Consequently, a new representative is attained, called L1-norms time series. The L1-norms is associated with indicators: autocorrelation function at lag 1, variance and mean power spectrum at low frequencies to detect the signals. By using Kendall’s tau correlation and significance test, significant rising trend events that occur before major financial crashes in Bitcoin are defined as the signals. A threshold is determined to scan entire data and record all the significant rising trend events. Lastly, we compare L1-norms with residuals time series, which is another representative obtained from de-trending approach. Our result portrays that autocorrelation function at lag 1 and variance of the L1-norms successfully detect early warning signals before the 2017 and 2019 major financial crashes. However, variance of the L1-norms is better since it able to signal another 2018 major financial crash. For the residuals, no early warning signals are detected. Hence, persistent homology provides a better representative than de-trending approach. Overall, persistent homology is a promising method to detect early warning signals of major financial crashes in Bitcoin.},
 	language = {en},
 	urldate = {2023-03-14},
 	journal = {IEEE Access},
@@ -133,7 +125,6 @@ @article{tse_mechanisms_2016
 	issn = {1880-4276, 1883-2148},
 	url = {https://onlinelibrary.wiley.com/doi/10.1016/j.joa.2015.11.003},
 	doi = {10.1016/j.joa.2015.11.003},
-	abstract = {Blood circulation is the result of the beating of the heart, which provides the mechanical force to pump oxygenated blood to, and deoxygenated blood away from, the peripheral tissues. This depends critically on the preceding electrical activation. Disruptions in the orderly pattern of this propagating cardiac excitation wave can lead to arrhythmias. Understanding of the mechanisms underlying their generation and maintenance requires knowledge of the ionic contributions to the cardiac action potential, which is discussed in the first part of this review. A brief outline of the different classification systems for arrhythmogenesis is then provided, followed by a detailed discussion for each mechanism in turn, highlighting recent advances in this area.},
 	language = {en},
 	number = {2},
 	urldate = {2024-01-04},
@@ -151,8 +142,6 @@ @article{ditlevsen_warning_2023
 	issn = {2041-1723},
 	url = {https://www.nature.com/articles/s41467-023-39810-w},
 	doi = {10.1038/s41467-023-39810-w},
-	abstract = {Abstract
-            The Atlantic meridional overturning circulation (AMOC) is a major tipping element in the climate system and a future collapse would have severe impacts on the climate in the North Atlantic region. In recent years weakening in circulation has been reported, but assessments by the Intergovernmental Panel on Climate Change (IPCC), based on the Climate Model Intercomparison Project (CMIP) model simulations suggest that a full collapse is unlikely within the 21st century. Tipping to an undesired state in the climate is, however, a growing concern with increasing greenhouse gas concentrations. Predictions based on observations rely on detecting early-warning signals, primarily an increase in variance (loss of resilience) and increased autocorrelation (critical slowing down), which have recently been reported for the AMOC. Here we provide statistical significance and data-driven estimators for the time of tipping. We estimate a collapse of the AMOC to occur around mid-century under the current scenario of future emissions.},
 	language = {en},
 	number = {1},
 	urldate = {2024-02-13},
@@ -162,23 +151,4 @@ @article{ditlevsen_warning_2023
 	year = {2023},
 	pages = {4254},
 	file = {Ditlevsen and Ditlevsen - 2023 - Warning of a forthcoming collapse of the Atlantic .pdf:/home/jan/Zotero/storage/82KUXTX4/Ditlevsen and Ditlevsen - 2023 - Warning of a forthcoming collapse of the Atlantic .pdf:application/pdf},
-}
-
-@article{ben-yami_uncertainties_2023,
-	title = {Uncertainties in critical slowing down indicators of observation-based fingerprints of the {Atlantic} {Overturning} {Circulation}},
-	volume = {14},
-	issn = {2041-1723},
-	url = {https://www.nature.com/articles/s41467-023-44046-9},
-	doi = {10.1038/s41467-023-44046-9},
-	abstract = {Abstract
-            Observations are increasingly used to detect critical slowing down (CSD) to measure stability changes in key Earth system components. However, most datasets have non-stationary missing-data distributions, biases and uncertainties. Here we show that, together with the pre-processing steps used to deal with them, these can bias the CSD analysis. We present an uncertainty quantification method to address such issues. We show how to propagate uncertainties provided with the datasets to the CSD analysis and develop conservative, surrogate-based significance tests on the CSD indicators. We apply our method to three observational sea-surface temperature and salinity datasets and to fingerprints of the Atlantic Meridional Overturning Circulation derived from them. We find that the properties of these datasets and especially the specific gap filling procedures can in some cases indeed cause false indication of CSD. However, CSD indicators in the North Atlantic are still present and significant when accounting for dataset uncertainties and non-stationary observational coverage.},
-	language = {en},
-	number = {1},
-	urldate = {2024-02-13},
-	journal = {Nature Communications},
-	author = {Ben-Yami, Maya and Skiba, Vanessa and Bathiany, Sebastian and Boers, Niklas},
-	month = dec,
-	year = {2023},
-	pages = {8344},
-	file = {Ben-Yami et al. - 2023 - Uncertainties in critical slowing down indicators .pdf:/home/jan/Zotero/storage/SBRVGKQE/Ben-Yami et al. - 2023 - Uncertainties in critical slowing down indicators .pdf:application/pdf},
-}
+}

paper/paper.md

@@ -12,17 +12,15 @@ tags:
 authors:
  - name: Jan Swierczek-Jereczek
    orcid: 0000-0003-2213-0423
-   affiliation: "1, 2"
+   affiliation: 1
  - name: George Datseris
    orcid: 0000-0003-0872-7098
-   affiliation: 3
+   affiliation: 2
 affiliations:
 - name: Department of Earth Physics and Astrophysics, Complutense University of Madrid.
   index: 1
-- name: Geosciences Institute, CSIC-UCM.
-  index: 2
 - name: Department of Mathematics and Statistics, University of Exeter.
-  index: 3
+  index: 2
 date: 13 February 2024
 bibliography: paper.bib
 ---
@@ -238,7 +236,7 @@ multidimensional timeseries, are part of future developments of TransitionsInTim
 # Documentation
 
 The documentation of TransitionsInTimseries.jl is available at
-[https://docs.juliahub.com/General/TransitionsInTimeseries/stable/]
+<https://docs.juliahub.com/General/TransitionsInTimeseries/stable/>
 
 # Acknowledgements