Merge branch 'dw/bluestyle'

Author: zsteve

.JuliaFormatter.toml

@@ -0,0 +1 @@
+style="blue"

.github/workflows/Format.yml

@@ -0,0 +1,24 @@
+name: Format
+
+on:
+  pull_request:
+
+jobs:
+  format:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v2
+      - uses: julia-actions/setup-julia@latest
+        with:
+          version: 1
+      - name: Format code
+        run: |
+          using Pkg
+          Pkg.add(; name="JuliaFormatter", uuid="98e50ef6-434e-11e9-1051-2b60c6c9e899")
+          using JuliaFormatter
+          format("."; verbose=true)
+        shell: julia --color=yes {0}
+      - uses: reviewdog/action-suggester@v1
+        with:
+          tool_name: JuliaFormatter
+          fail_on_error: true

README.md

@@ -8,6 +8,7 @@
 [![CI](https://github.com/zsteve/OptimalTransport.jl/workflows/CI/badge.svg?branch=master)](https://github.com/zsteve/OptimalTransport.jl/actions?query=workflow%3ACI+branch%3Amaster)
 [![Codecov](https://codecov.io/gh/zsteve/OptimalTransport.jl/branch/master/graph/badge.svg)](https://codecov.io/gh/zsteve/OptimalTransport.jl)
 [![Coveralls](https://coveralls.io/repos/github/zsteve/OptimalTransport.jl/badge.svg?branch=master)](https://coveralls.io/github/zsteve/OptimalTransport.jl?branch=master)
+[![Code Style: Blue](https://img.shields.io/badge/code%20style-blue-4495d1.svg)](https://github.com/invenia/BlueStyle)
 
 This package provides some [Julia](https://julialang.org/) implementations of algorithms for computational [optimal transport](https://optimaltransport.github.io/), including the Earth-Mover's (Wasserstein) distance, Sinkhorn scaling algorithm for entropy-regularised transport as well as some variants or extensions. 
 

docs/make.jl

@@ -1,6 +1,6 @@
 using OptimalTransport
-import Literate
-import Pkg
+using Literate: Literate
+using Pkg: Pkg
 
 if haskey(ENV, "GITHUB_ACTIONS")
     # Print `@debug` statements (https://github.com/JuliaDocs/Documenter.jl/issues/955)
@@ -75,6 +75,4 @@ makedocs(;
     checkdocs=:exports,
 )
 
-deploydocs(;
-    repo="github.com/zsteve/OptimalTransport.jl", push_preview=true,       
-)
+deploydocs(; repo="github.com/zsteve/OptimalTransport.jl", push_preview=true)

examples/basic/script.jl

@@ -21,11 +21,11 @@ Random.seed!(1234);
 # and $\nu$ (target measure) in 1D:
 
 M = 200
-μ = fill(1/M, M)
+μ = fill(1 / M, M)
 μsupport = rand(M)
 
 N = 250
-ν = fill(1/N, N)
+ν = fill(1 / N, N)
 νsupport = rand(N);
 
 # Now we compute the quadratic cost matrix $C_{ij} = \| x_i - x_j \|_2^2$:
@@ -133,11 +133,11 @@ norm(γ - γpot, Inf)
 # We construct two random measures, now with different total masses:
 
 M = 100
-μ = fill(1/M, M)
+μ = fill(1 / M, M)
 μsupport = rand(M)
 
 N = 200
-ν = fill(1/M, N)
+ν = fill(1 / M, N)
 νsupport = rand(N);
 
 # We compute the quadratic cost matrix:
@@ -164,9 +164,9 @@ norm(γ - γpot, Inf)
 
 μsupport = νsupport = range(-2, 2; length=100)
 C = pairwise(SqEuclidean(), μsupport', νsupport')
-μ = exp.(- μsupport.^2 ./ 0.5^2)
+μ = exp.(-μsupport .^ 2 ./ 0.5^2)
 μ ./= sum(μ)
-ν = νsupport.^2 .* exp.(- νsupport.^2 ./ 0.5^2)
+ν = νsupport .^ 2 .* exp.(-νsupport .^ 2 ./ 0.5^2)
 ν ./= sum(ν)
 
 plot(μsupport, μ; label=raw"$\mu$", size=(600, 400))
@@ -176,8 +176,11 @@ plot!(νsupport, ν; label=raw"$\nu$")
 
 γ = sinkhorn(μ, ν, C, 0.01)
 heatmap(
-    μsupport, νsupport, γ;
-    title="Entropically regularised optimal transport", size=(600, 600)
+    μsupport,
+    νsupport,
+    γ;
+    title="Entropically regularised optimal transport",
+    size=(600, 600),
 )
 
 # ### Quadratically regularised transport
@@ -187,8 +190,11 @@ heatmap(
 
 γquad = Matrix(quadreg(μ, ν, C, 5; maxiter=500))
 heatmap(
-    μsupport, νsupport, γquad;
-    title="Quadratically regularised optimal transport", size=(600, 600)
+    μsupport,
+    νsupport,
+    γquad;
+    title="Quadratically regularised optimal transport",
+    size=(600, 600),
 )
 
 # ### Sinkhorn barycenters
@@ -208,9 +214,9 @@ heatmap(
 # $\lambda_1 \in \{0.25, 0.5, 0.75\}$.
 
 support = range(-1, 1; length=250)
-mu1 = exp.(- (support .+ 0.5).^2 ./ 0.1^2)
+mu1 = exp.(-(support .+ 0.5) .^ 2 ./ 0.1^2)
 mu1 ./= sum(mu1)
-mu2 = exp.(- (support .- 0.5).^2 ./ 0.1^2)
+mu2 = exp.(-(support .- 0.5) .^ 2 ./ 0.1^2)
 mu2 ./= sum(mu2)
 
 plt = plot(; size=(800, 400), legend=:outertopright)