From 40769156e327738598423aeece6468484b590a59 Mon Sep 17 00:00:00 2001 From: Datseris Date: Tue, 12 Dec 2023 21:58:55 +0000 Subject: [PATCH] update tutorial with change metric number --- docs/src/tutorial.md | 18 ++++++++---------- 1 file changed, 8 insertions(+), 10 deletions(-) diff --git a/docs/src/tutorial.md b/docs/src/tutorial.md index 4db96c03..d7a6c33b 100644 --- a/docs/src/tutorial.md +++ b/docs/src/tutorial.md @@ -186,10 +186,9 @@ Performing the step-by-step analysis of transition indicators is possible and mi ## [Tutorial -- TransitionsInTimeseries.jl] (@id example_fastforward) TransitionsInTimeseries.jl wraps this typical workflow into a simple, extendable, and modular API that researchers can use with little effort. In addition, it allows performing the same analysis for several indicators / change metrics in one go. +The interface is simple, and directly parallelizes the [Workflow](@ref). -The interface is simple, and directly parallelizes the [Workflow](@ref). It is based on the creation of a [`TransitionsSurrogatesConfig`](@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. - -The following blocks illustrate how the above extensive example is re-created in TransitionsInTimeseries.jl +The following blocks illustrate how the above extensive example is re-created in TransitionsInTimeseries.jl. But first, let's load the input timeseries: ````@example tutorial using TransitionsInTimeseries, CairoMakie @@ -205,17 +204,17 @@ ax.title = "input timeseries" fig ```` -To perform all of the above analysis we follow a 2-step process. +To perform all of the whole [Workflow](@ref) analysis we follow a 2-step process. -Step 1, we decide what indicators and change metrics to use in [`SlidingWindowConfig`](@ref) and apply those via -a sliding window to the input timeseries using [`transition_metrics`](@ref). +**Step 1** is to provide what indicators and change metrics to use in [`SlidingWindowConfig`](@ref) and apply those via +a sliding window to the input timeseries using [`estimate_indicator_changes`](@ref). ````@example tutorial # These indicators are suitable for Critical Slowing Down indicators = (var, ar1_whitenoise) # use the ridge regression slope for both indicators -change_metrics = RidgeRegressionSlope() +change_metrics = (RidgeRegressionSlope(), RidgeRegressionSlope()) # choices go into a configuration struct config = SlidingWindowConfig(indicators, change_metrics; @@ -238,8 +237,8 @@ scatter!(axs[3], results.t_change, results.x_change[:, 2]; fig ```` -Step 2 is to estimate significance using [`SurrogatesConfig`](@ref) -and the function [`estimate_significance!`](@ref). +**Step 2** is to estimate significance using [`SurrogatesSignificance`](@ref) +and the function [`significant_transitions`](@ref). ````@example tutorial signif = SurrogatesSignificance(n = 1000, tail = :right) @@ -264,4 +263,3 @@ vlines!(axs[1], results.t_change[flagsboth]; label = "flags", color = ("black", [xlims!(ax, 0, 50) for ax in axs] fig ```` -