First draft for JOSS paper

.github/workflows/draft-pdf.yml

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+on: [push]
+
+jobs:
+  paper:
+    runs-on: ubuntu-latest
+    name: Paper Draft
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+      - name: Build draft PDF
+        uses: openjournals/openjournals-draft-action@master
+        with:
+          journal: joss
+          # This should be the path to the paper within your repo.
+          paper-path: paper/paper.md
+      - name: Upload
+        uses: actions/upload-artifact@v1
+        with:
+          name: paper
+          # This is the output path where Pandoc will write the compiled
+          # PDF. Note, this should be the same directory as the input
+          # paper.md
+          path: paper/paper.pdf

paper/code/Project.toml

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+[deps]
+CairoMakie = "13f3f980-e62b-5c42-98c6-ff1f3baf88f0"
+DelimitedFiles = "8bb1440f-4735-579b-a4ab-409b98df4dab"

paper/code/ewstools-tuto-1.py

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+import time
+import numpy as np
+import matplotlib.pyplot as plt
+import ewstools
+from ewstools.models import simulate_ricker
+
+# Set seed for reproducibility
+np.random.seed(0)
+
+# Initialize time series and spectrum computation
+series = simulate_ricker(tmax=1000, F=[0,2.7])
+ts = ewstools.TimeSeries(data=series, transition=860)
+ts.detrend(method='Lowess', span=0.2)
+ts.state[['state','smoothing']].plot()
+ts.compute_spectrum(rolling_window=0.5, ham_length=40)
+
+# Initialize parameters for timing functions
+rw = 0.5
+n = 100
+t_elapsed = np.zeros(10)
+
+# Time functions (in a not very elegant way)
+t0 = time.time()
+for i in range(n):
+    ts.compute_var(rolling_window=rw)
+t_elapsed[0] = time.time() - t0
+
+t0 = time.time()
+for i in range(n):
+    ts.compute_cv()
+t_elapsed[1] = time.time() - t0
+
+t0 = time.time()
+for i in range(n):
+    ts.compute_skew(rolling_window=rw)
+t_elapsed[2] = time.time() - t0
+
+t0 = time.time()
+for i in range(n):
+    ts.compute_kurt()
+t_elapsed[3] = time.time() - t0
+
+t0 = time.time()
+for i in range(n):
+    ts.compute_auto(rolling_window=rw, lag=1)
+t_elapsed[4] = time.time() - t0
+
+t0 = time.time()
+for i in range(n):
+    ts.compute_smax()
+t_elapsed[5] = time.time() - t0
+
+t0 = time.time()
+for i in range(n):
+    ts.compute_ktau()
+t_elapsed[6] = time.time() - t0
+
+t0 = time.time()
+surro = ewstools.core.block_bootstrap(ts.state.residuals, n, bs_type='Stationary', block_size=10)
+t_elapsed[7] = time.time() - t0

paper/code/figure1.jl

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+using TransitionsInTimeseries, DelimitedFiles, CairoMakie, Random
+
+x = readdlm("ewstools-tuto-1.csv", ',')[:, end]
+t = eachindex(x)
+
+# Choose the indicators and how to measure their change over time
+indicators = (var, ar1_whitenoise)
+change_metrics = (kendalltau, kendalltau)
+config = SegmentedWindowConfig(indicators, change_metrics, [t[1]], [t[end]];
+    width_ind = length(x) ÷ 2, whichtime = last, min_width_cha = 50)
+results = estimate_indicator_changes(config, x, t)
+signif = SurrogatesSignificance(n = 1000, tail = [:right, :right], rng = Xoshiro(1995))
+flags = significant_transitions(results, signif)
+fig = plot_changes_significance(results, signif)
+ylims!(contents(fig[2, 1])[2], (0.037, 0.045))
+save("../figures/figure1.png", fig)

paper/code/figure2.jl

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+using TransitionsInTimeseries, DelimitedFiles, CairoMakie, Random
+
+coefficient_of_variation(x) = std(x) / mean(x)
+
+function main()
+    x = readdlm("ewstools-tuto-1.csv", ',')[:, end]
+    t = eachindex(x)
+
+    # Choose the indicators and how to measure their change over time
+    indicators = (var, coefficient_of_variation, skewness, kurtosis,
+        ar1_whitenoise, LowfreqPowerSpectrum())
+    stride = [1, 1, 1, 1, 1, 40]
+    n, m = 100, length(indicators)
+    t_elapsed = zeros(m+2)
+
+
+    for (i, ind) in enumerate(indicators)
+        # Build configuration with adequate parameters of the sliding window
+        config = SegmentedWindowConfig((ind, ind), (nothing, nothing), [t[1]], [t[end]];
+            width_ind = length(x) ÷ 2, stride_ind = stride[i], whichtime = last,
+            min_width_cha = 1)
+
+        t0 = time()
+        for i in 1:n
+            # Compute the metrics over sliding windows and their significance
+            results = estimate_indicator_changes(config, x, t)
+        end
+        t_elapsed[i] = (time() - t0) / 2
+    end
+
+    config = SegmentedWindowConfig((nothing, nothing), (kendalltau, kendalltau), [t[1]], [t[end]];
+        width_ind = length(x) ÷ 2, stride_ind = 1, whichtime = last, min_width_cha = 1)
+    t0 = time()
+    for i in 1:n
+        results = estimate_indicator_changes(config, x, t)
+    end
+    t_elapsed[m+1] = (time() - t0) / 2
+
+    sgen = surrogenerator(x, BlockShuffle(), Xoshiro(1995))
+    t0 = time()
+    for i in 1:n
+        s = sgen()
+    end
+    t_elapsed[m+2] = time() - t0
+
+    return t_elapsed
+end
+
+t_tt = main()
+t_et = [0.03840542, 0.05554581, 0.03895116, 0.04029274, 7.96556187,
+    2.73067856, 0.39529872, 0.02751493]
+
+# [0.04681492, 8.13679838, 0.04035759, 0.09219241]
+inds = eachindex(t_et)
+w = 0.4
+
+fig, ax = barplot(inds .- 0.5*w, t_et, label = L"ewstools $\,$", width = w,
+    fillto = 1e-5)
+barplot!(ax, inds .+ 0.5*w, t_tt, label = L"TransitionsInTimeseries.jl $\,$",
+    width = w, fillto = 1e-5)
+ax.yscale = log10
+ax.xticks = (1:8, [L"Variance $\,$", L"Coeff. of variation $\,$", L"Skewness $\,$",
+    L"Kurtosis $\,$", L"Lag-1 autocorr. $\,$", L"Spectral $\,$",
+    L"Kendall $\tau$ corr. coeff.", L"Block bootstrap $\,$"])
+ax.ylabel = L"Run time (s) of 100 computations on Ricker model data $\,$"
+ax.yticks = (10.0 .^ (-5:1), [L"10^{%$e}" for e in -5:1])
+ax.xgridvisible = false
+ax.ygridvisible = false
+ax.xticklabelrotation = π / 4
+axislegend(ax, position = :lt)
+save("../figures/figure2.png", fig)