Fix tests and docs after introducing possibility of indicator=nothing
#66
Conversation
This reverts commit bf3501d.
Project.toml
Outdated
| TimeseriesSurrogates = "2" | ||
| TimeseriesSurrogates = "^2.6.4" |
There was a problem hiding this comment.
This compat change doesn't make sense, unless you explicitly use in the source code a function that did not exist in version v2.0 and only exists in v2.6. The latest version will anyways be installed.
docs/Project.toml
Outdated
| [compat] | ||
| Documenter = "0.27" | ||
| ComplexityMeasures = "^2.7" | ||
| Documenter = "1" |
There was a problem hiding this comment.
We should remove all compat entries from the docs.
docs/make.jl
Outdated
| "George Datseris <[email protected]>", warnonly = true, bib) # draft = true, |
There was a problem hiding this comment.
what does warnonly do? Also remove the commented out draft.
There was a problem hiding this comment.
warnonly = true prevents the building of the docs from failing if some references are not found (in earlier versions of Documenter.jl this was the default). In our case, only few refs are not found and I do not understand why. We should fix this (your help is required here) and remove warnonly=true.
| tseg_start = t_rasmussen[1:end-1] .+ 200 | ||
| tseg_end = t_rasmussen[2:end] .- 200 | ||
| config = SegmentedWindowConfig(indicators, change_metrics, | ||
| tseg_start, tseg_end; whichtime = last, width_ind = Int(200÷dt), | ||
| min_width_cha = 100) # require >=100 data points to estimate change metric | ||
| results = estimate_indicator_changes(config, r, t) | ||
| signif = SurrogatesSignificance(n = 10_000, tail = :right, rng = Xoshiro(1995)) | ||
| signif = SurrogatesSignificance(n = 1000, tail = [:right, :right], rng = Xoshiro(1995)) |
There was a problem hiding this comment.
since tail is the same for both you can just keep it as is right? No need to make [:tail, :tail].
There was a problem hiding this comment.
a, ok, you enforced that tail must be the same length as the indicators.
| @@ -30,15 +30,15 @@ Let us load data from a bistable nonlinear model subject to noise and to a gradu | |||
|
|
|||
| with $x_{l}$ the state of the linear model, $x_{nl}$ the state of the bistable model, $f$ the forcing and $n$ the noise. For $f=0$ they both display an equilibrium point at $x=-1$. However, the bistable model also displays a further equilibrium point at $x=1$. Loading (and visualizing with [Makie](https://docs.makie.org/stable/)) such prototypical data to test some indicators can be done by simply running: | |||
|
|
|||
| ```@example tutorial | |||
| ````@example tutorial | |||
| using TransitionsInTimeseries, CairoMakie | ||
|
|
||
| t, x_linear, x_nlinear = load_linear_vs_doublewell() | ||
| fig, ax = lines(t, x_linear) | ||
| lines!(ax, t, x_nlinear) | ||
| ax.title = "raw data" | ||
| fig | ||
| ``` | ||
| ```` |
| @@ -49,7 +49,7 @@ fig | |||
|
|
|||
| The nonlinear system clearly displays a transition between two stability regimes. To forecast such transition, we analyze the fluctuations of the timeseries around the attractor, assumed to be tracked. Therefore, a detrending step is needed - here simply obtained by building the difference of the timeseries with lag 1. | |||
|
|
|||
| ```@example tutorial | |||
| ````@example tutorial | |||
| @@ -58,7 +58,7 @@ fig, ax = lines(tfluct, x_l_fluct) | |||
| lines!(ax, tfluct, x_nl_fluct .+ 0.05) | |||
| ax.title = "input timeseries" | |||
| fig | |||
| ``` | |||
| ```` | |||
There was a problem hiding this comment.
incorrect amount of `. Everywhere in the file this repeats.
| =# | ||
|
|
||
| signif = SurrogatesSignificance(n = 1000, tail = :right) | ||
| signif = SurrogatesSignificance(n = 1000, tail = [:right, :right]) |
There was a problem hiding this comment.
here we need only one tail, right?
|
|
||
| The following blocks illustrate how the above extensive example is re-created in TransitionsInTimeseries.jl. But first, let's load the input timeseries: | ||
| The interface is simple, and directly parallelizes the [Workflow](@ref). It is based on the creation of a [`SurrogatesSignificance`](@ref), which contains a list of indicators, and corresponding metrics, to use for doing the above analysis. It also specifies what kind of surrogates to generate. |
There was a problem hiding this comment.
This isn't correct. it should say SlidingWindowConfig.
There was a problem hiding this comment.
Actually, repeating information in the documentation is bad. This whole paragraph should become the sentence it originally was: The interface is simple, and directly parallelizes the [Workflow](@ref).
Codecov ReportAttention:
Additional details and impacted files@@ Coverage Diff @@
## main #66 +/- ##
==========================================
- Coverage 41.69% 40.35% -1.35%
==========================================
Files 17 19 +2
Lines 331 399 +68
==========================================
+ Hits 138 161 +23
- Misses 193 238 +45 ☔ View full report in Codecov by Sentry. |
This PR mainly fixes the tests and docs that broke after the introduction of passing
indicators = nothing(#65).Majors:
nothingas an indicator, the need of complementing anif-statement insignificant_transitionswas overseen. This is now corrected such that the surrogate is correctly passed to the analysis pipeline regardless of the case.full_analysis.jlneeds@test count(flags) == 2since both indicators are positive at the transition.tailto be of the same size asindicatorsandchange_metricstoo. This would be more consistent and easier to maintain. So far, I applied this to the surrogate analysis. If you agree, we can make the same change across all types of significance analysis.Minors:
warnonly = truesince some referencing were not found. I tried to solve this, which only partially succeeded (I don't understand why the ref to e.g.PrecomputedLowfreqPowerSpectrumis not found).meanandvar?docs/make.jl, I simplified loading of example files. Before that we were loading by reading the directory, looping over it and then permute the vector to have the desired order. This is actually simpler to just write manually.difference_of_maxestosrc/for potential future use.