Some fixes for the notebook output (#248)

Author: Datseris

Project.toml

@@ -1,7 +1,7 @@
 name = "DynamicalSystems"
 uuid = "61744808-ddfa-5f27-97ff-6e42cc95d634"
 repo = "https://github.com/JuliaDynamics/DynamicalSystems.jl.git"
-version = "3.3.25"
+version = "3.3.26"
 
 [deps]
 Attractors = "f3fd9213-ca85-4dba-9dfd-7fc91308fec7"

docs/Project.toml

@@ -11,7 +11,6 @@ DocumenterTools = "35a29f4d-8980-5a13-9543-d66fff28ecb8"
 DynamicalSystems = "61744808-ddfa-5f27-97ff-6e42cc95d634"
 DynamicalSystemsBase = "6e36e845-645a-534a-86f2-f5d4aa5a06b4"
 FractalDimensions = "4665ce21-e117-4649-aed8-08bbe5ccbead"
-GLMakie = "e9467ef8-e4e7-5192-8a1a-b1aee30e663a"
 Literate = "98b081ad-f1c9-55d3-8b20-4c87d4299306"
 ModelingToolkit = "961ee093-0014-501f-94e3-6117800e7a78"
 OrdinaryDiffEq = "1dea7af3-3e70-54e6-95c3-0bf5283fa5ed"

docs/src/tutorial.jl

@@ -31,8 +31,8 @@ import Pkg
 
 #nb # Activate an environment in the folder containing the notebook
 #nb Pkg.activate(dirname(@__DIR__))
-#nb Pkg.add(["DynamicalSystems", "CairoMakie", "GLMakie", "OrdinaryDiffEq", "BenchmarkTools"])
-Pkg.status(["DynamicalSystems", "CairoMakie", "GLMakie", "OrdinaryDiffEq", "BenchmarkTools"]; mode = Pkg.PKGMODE_MANIFEST)
+#nb Pkg.add(["DynamicalSystems", "CairoMakie", "OrdinaryDiffEq", "BenchmarkTools"])
+Pkg.status(["DynamicalSystems", "CairoMakie", "OrdinaryDiffEq", "BenchmarkTools"]; mode = Pkg.PKGMODE_MANIFEST)
 
 #nb # ## **DynamicalSystems.jl** summary
 
@@ -359,7 +359,7 @@ xg = yg = range(-1, 1; length = 101)
 
 sampler, _ = statespace_sampler((xg, yg))
 
-fs = basins_fractions(mapper, sampler)
+fs = basins_fractions(mapper, sampler; show_progress = false)
 
 # and we can see the stored "attractors"
 
@@ -509,7 +509,7 @@ pex, sey
 
 # Alternatively, you could use [`FractalDimensions`](@ref) to get the fractal dimensions of the chaotic attractor of the henon map using the Grassberger-Procaccia algorithm:
 
-grassberger_proccacia_dim(X)
+grassberger_proccacia_dim(X; show_progress = false)
 
 # Or, you could obtain a recurrence matrix of a state space set with [`RecurrenceAnalysis`](@ref)
 
@@ -548,15 +548,15 @@ using Random: Xoshiro
 rng = Xoshiro(1234)
 x .+= randn(rng, length(x))/100
 ## compute noise-contaminated fractal dim.
-Δ_orig = generalized_dim(embed(x, 2, 1))
+Δ_orig = generalized_dim(embed(x, 2, 1); show_progress = false)
 
 # And we do the surrogate test
 
 surrogate_method = RandomFourier()
 sgen = surrogenerator(x, surrogate_method, rng)
 Δ_surr = map(1:1000) do i
     s = sgen()
-    generalized_dim(embed(s, 2, 1))
+    generalized_dim(embed(s, 2, 1); show_progress = false)
 end
 
 # and visualize the test result