WIP: reviewing new api (#95)

* Equivalent to, not equivalent with

* Include dispersion entropy

* Clearer wording

* Symbolization routines need to be imported before estimators using them

* Export in-place version.

* Follow Julia's linting conventions

* Run CI on all pull requests

* Don't export in-place version

* Missing a letter

Author: kahaaga

.github/workflows/ci.yml

@@ -3,6 +3,7 @@ on:
   pull_request:
     branches:
       - main
+      - '**' # matches every branch
   push:
     branches:
       - main

docs/src/index.md

@@ -50,4 +50,4 @@ The input data type typically depend on the probability estimator chosen. In gen
 
 - _Timeseries_, which are `AbstractVector{<:Real}`, used in e.g. with [`WaveletOverlap`](@ref).
 - _Multi-dimensional timeseries, or datasets, or state space sets_, which are `Dataset`, used e.g. with [`NaiveKernel`](@ref).
-- _Spatial data_, which are higher dimensional standard `Array`, used e.g. with  [`SpatialSymbolicPermutation`](@ref).
+- _Spatial data_, which are higher dimensional standard `Array`s, used e.g. with  [`SpatialSymbolicPermutation`](@ref).

src/Entropies.jl

@@ -1,7 +1,7 @@
 """
 A Julia package that provides estimators for probabilities, entropies,
 and complexity measures for timeseries, nonlinear dynamics and complex systems.
-It can be used as standalone or part of several projects in the JuliaDynamics organization,
+It can be used as a standalone package, or as part of several projects in the JuliaDynamics organization,
 such as [DynamicalSystems.jl](https://juliadynamics.github.io/DynamicalSystems.jl/dev/)
 or [CausalityTools.jl](https://juliadynamics.github.io/CausalityTools.jl/dev/).
 """
@@ -12,10 +12,10 @@ using DelayEmbeddings: AbstractDataset, Dataset, dimension
 export AbstractDataset, Dataset
 const Array_or_Dataset = Union{<:AbstractArray, <:AbstractDataset}
 
+include("symbolization/symbolize.jl")
 include("probabilities.jl")
 include("probabilities_estimators/probabilities_estimators.jl")
 include("entropies/entropies.jl")
-include("symbolization/symbolize.jl")
 include("deprecations.jl")
 
 

src/entropies/entropies.jl

@@ -3,4 +3,6 @@ include("tsallis.jl")
 include("shannon.jl")
 include("convenience_definitions.jl")
 include("direct_entropies/nearest_neighbors/nearest_neighbors.jl")
-# TODO: What else is included here from direct entropies?
+include("direct_entropies/entropy_dispersion.jl")
+
+# TODO: What else is included here from direct entropies?

src/entropies/shannon.jl

@@ -2,7 +2,7 @@ export entropy_shannon
 
 """
     entropy_shannon(args...; base = MathConstants.e)
-Equivalent with `entropy_renyi(args...; base, q = 1)` and provided solely for convenience.
+Equivalent to `entropy_renyi(args...; base, q = 1)` and provided solely for convenience.
 Compute the Shannon entropy, given by
 ```math
 H(p) = - \\sum_i p[i] \\log(p[i])

test/runtests.jl

@@ -158,16 +158,16 @@ end
             @test sum(p2) ≈ 1.0
 
             # Entropies
-            @test entropy_renyi!(s, x, est, q = 1) ≈ 0  # Regular order-1 entropy
-            @test entropy_renyi!(s, y, est, q = 1) >= 0 # Regular order-1 entropy
-            @test entropy_renyi!(s, x, est, q = 2) ≈ 0  # Higher-order entropy
-            @test entropy_renyi!(s, y, est, q = 2) >= 0 # Higher-order entropy
+            @test Entropies.entropy_renyi!(s, x, est, q = 1) ≈ 0  # Regular order-1 entropy
+            @test Entropies.entropy_renyi!(s, y, est, q = 1) >= 0 # Regular order-1 entropy
+            @test Entropies.entropy_renyi!(s, x, est, q = 2) ≈ 0  # Higher-order entropy
+            @test Entropies.entropy_renyi!(s, y, est, q = 2) >= 0 # Higher-order entropy
 
             # For a time series
             sz = zeros(Int, N - (est.m-1)*est.τ)
             @test probabilities!(sz, z, est) isa Probabilities
             @test probabilities(z, est) isa Probabilities
-            @test entropy_renyi!(sz, z, est) isa Real
+            @test Entropies.entropy_renyi!(sz, z, est) isa Real
             @test entropy_renyi(z, est) isa Real
         end
 
@@ -290,7 +290,7 @@ end
             RectangularBinning([0.2, 0.3, 0.3])
         ]
 
-        @testset "Binning test $i" for i in 1:length(binnings)
+        @testset "Binning test $i" for i in eachindex(binnings)
             est = VisitationFrequency(binnings[i])
             @test probabilities(D, est) isa Probabilities
             @test entropy_renyi(D, est, q=1, base = 3) isa Real # Regular order-1 entropy
@@ -310,7 +310,7 @@ end
             RectangularBinning([0.2, 0.3, 0.3])
         ]
 
-        @testset "Binning test $i" for i in 1:length(binnings)
+        @testset "Binning test $i" for i in eachindex(binnings)
             to = Entropies.transferoperator(D, binnings[i])
             @test to isa Entropies.TransferOperatorApproximationRectangular