Prevent unwanted printing of matrices that arises due to disabled plo…

…tting

docs/src/manual/denoising.md

@@ -35,6 +35,7 @@ y = bestbasiscoef(x, wt, bt)
 x̂ = denoise(y, :wpt, wt, tree=bt)
 
 # plot results
+nothing # hide
 # plot([x₀ x x̂], title="Denoising Example", label=["original" "noisy" "denoised"],
 #     lw=[3 1 2], lc=[:black :grey :red])
 ```
@@ -64,6 +65,7 @@ Y = bestbasiscoef(coef, bt)
 X̂ = denoiseall(Y, :wpt, wt, tree=bt)
 
 # plot results
+nothing # hide
 # wiggle(X₀, sc=0.7, FaceColor=:white, ZDir=:reverse)
 # wiggle!(X, sc=0.7, EdgeColor=:grey, FaceColor=:white, ZDir=:reverse)
 # wiggle!(X̂, sc=0.7, EdgeColor=:red, FaceColor=:white, ZDir=:reverse)

docs/src/manual/localdiscriminantbasis.md

@@ -19,6 +19,7 @@ using Wavelets, WaveletsExt, Plots
 X, y = generateclassdata(ClassData(:cbf, 100, 100, 100));
 
 # view sample signals and how each class differs from one another
+nothing # hide
 # cylinder = wiggle(X[:,1:5], sc=0.3)
 # plot!(cylinder, title="Cylinder signals")
 # bell = wiggle(X[:,101:105], sc=0.3)
@@ -95,6 +96,7 @@ function get_basisvectors(n::Integer, wt::DiscreteWavelet, tree::BitVector,
 end
 
 bases = get_basisvectors(128, ldb.wt, ldb.tree, ldb.order[1:10]);
+nothing # hide
 # wiggle(bases, sc=0.3, ZDir=:reverse);
 # plot!(title="Top 10 LDB vectors")
 ```
@@ -110,6 +112,7 @@ If we are curious, we can use the `inverse_transform` function to observe how th
 X̃  = inverse_transform(ldb, X̂);
 
 # view sample signals and how each class differs from one another
+nothing # hide
 # cylinder = wiggle(X̃[:,1:5], sc=0.3)
 # plot!(cylinder, title="Cylinder signals")
 # bell = wiggle(X̃[:,101:105], sc=0.3)

docs/src/manual/transforms.md

@@ -43,13 +43,15 @@ We can also view the JBB and LSDB trees using a similar syntax. Unlike the previ
 ```@example wt
 # joint best basis
 tree = bestbasistree(xw, JBB())
+nothing # hide
 # plot_tfbdry(tree)
 ```
 
 * Least Statistically Dependent Basis (LSDB)
 ```@example wt
 # least statistically dependent basis
 tree = bestbasistree(xw, LSDB())
+nothing # hide
 # plot_tfbdry(tree)
 ```
 
@@ -61,6 +63,7 @@ The [Stationary Wavelet Transform (SWT)](https://link.springer.com/chapter/10.10
 y = sdwt(x, wt)
 
 # view the transform
+nothing # hide
 # wiggle(y, sc=0.7)
 ```
 
@@ -72,20 +75,23 @@ xw = cat([swpd(X[:,i], wt) for i in axes(X,2)]..., dims=3)
 
 # best basis trees, each column corresponds to 1 tree
 trees = bestbasistree(xw, BB(redundant=true)); 
+nothing # hide
 # plot_tfbdry(trees[:,1])
 ```
 
 * Joint Best Basis (JBB)
 ```@example wt
 # best basis trees, each column corresponds to 1 tree
 tree = bestbasistree(xw, JBB(redundant=true)); 
+nothing # hide
 # plot_tfbdry(tree)
 ```
 
 * Least Statistically Dependent Basis (LSDB)
 ```@example wt
 # best basis trees, each column corresponds to 1 tree
 tree = bestbasistree(xw, LSDB(redundant=true)); 
+nothing # hide
 # plot_tfbdry(tree)
 ```