Use JuliaFormatter

.JuliaFormatter.toml

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

.github/workflows/Format.yml

@@ -0,0 +1,33 @@
+name: Format
+
+on:
+ push:
+ branches:
+ # This is where pull requests from "bors r+" are built.
+ - staging
+ # This is where pull requests from "bors try" are built.
+ - trying
+ # Build the master branch.
+ - master
+ 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
+ if: github.event_name == 'pull_request'
+ with:
+ tool_name: JuliaFormatter
+ fail_on_error: true

README.md

@@ -3,6 +3,7 @@
 [![Build Status](https://github.com/TuringLang/AdvancedPS.jl/workflows/CI/badge.svg?branch=master)](https://github.com/TuringLang/AdvancedPS.jl/actions?query=workflow%3ACI%20branch%3Amaster)
 [![Coverage](https://codecov.io/gh/TuringLang/AdvancedPS.jl/branch/master/graph/badge.svg)](https://codecov.io/gh/TuringLang/AdvancedPS.jl)
 [![Coverage](https://coveralls.io/repos/github/TuringLang/AdvancedPS.jl/badge.svg?branch=master)](https://coveralls.io/github/TuringLang/AdvancedPS.jl?branch=master)
+[![Code Style: Blue](https://img.shields.io/badge/code%20style-blue-4495d1.svg)](https://github.com/invenia/BlueStyle)
 
 ### Reference
 

src/AdvancedPS.jl

@@ -1,10 +1,10 @@
 module AdvancedPS
 
-import AbstractMCMC
-import Distributions
-import Libtask
-import Random
-import StatsFuns
+using AbstractMCMC: AbstractMCMC
+using Distributions: Distributions
+using Libtask: Libtask
+using Random: Random
+using StatsFuns: StatsFuns
 
 include("resampling.jl")
 include("container.jl")

src/container.jl

@@ -6,7 +6,7 @@ end
 const Particle = Trace
 
 function Trace(f)
- ctask = let f=f
+ ctask = let f = f
  Libtask.CTask() do
  res = f()
  Libtask.produce(nothing)
@@ -34,7 +34,7 @@ reset_model(f) = deepcopy(f)
 delete_retained!(f) = nothing
 
 # Task copying version of fork for Trace.
-function fork(trace::Trace, isref::Bool = false)
+function fork(trace::Trace, isref::Bool=false)
  newtrace = copy(trace)
  isref && delete_retained!(newtrace.f)
 
@@ -48,7 +48,7 @@ end
 # Create new task and copy randomness
 function forkr(trace::Trace)
  newf = reset_model(trace.f)
- ctask = let f=trace.ctask.task.code
+ ctask = let f = trace.ctask.task.code
  Libtask.CTask() do
  res = f()
  Libtask.produce(nothing)
@@ -105,7 +105,7 @@ end
 function Base.push!(pc::ParticleContainer, p::Particle)
  push!(pc.vals, p)
  push!(pc.logWs, 0.0)
- pc
+ return pc
 end
 
 # clones a theta-particle
@@ -116,7 +116,7 @@ function Base.copy(pc::ParticleContainer)
  # copy weights
  logWs = copy(pc.logWs)
 
- ParticleContainer(vals, logWs)
+ return ParticleContainer(vals, logWs)
 end
 
 """
@@ -183,9 +183,9 @@ of the particle `weights`. For Particle Gibbs sampling, one can provide a refere
 function resample_propagate!(
  rng::Random.AbstractRNG,
  pc::ParticleContainer,
- randcat = resample_systematic,
- ref::Union{Particle, Nothing} = nothing;
- weights = getweights(pc)
+ randcat=resample_systematic,
+ ref::Union{Particle,Nothing}=nothing;
+ weights=getweights(pc),
 )
  # check that weights are not NaN
  @assert !any(isnan, weights)
@@ -232,24 +232,24 @@ function resample_propagate!(
  pc.vals = children
  reset_logweights!(pc)
 
- pc
+ return pc
 end
 
 function resample_propagate!(
  rng::Random.AbstractRNG,
  pc::ParticleContainer,
  resampler::ResampleWithESSThreshold,
- ref::Union{Particle,Nothing} = nothing;
- weights = getweights(pc)
+ ref::Union{Particle,Nothing}=nothing;
+ weights=getweights(pc),
 )
  # Compute the effective sample size ``1 / ∑ wᵢ²`` with normalized weights ``wᵢ``
  ess = inv(sum(abs2, weights))
 
  if ess ≤ resampler.threshold * length(pc)
- resample_propagate!(rng, pc, resampler.resampler, ref; weights = weights)
+ resample_propagate!(rng, pc, resampler.resampler, ref; weights=weights)
  end
 
- pc
+ return pc
 end
 
 """
@@ -290,9 +290,13 @@ function reweight!(pc::ParticleContainer)
 
  # The posterior for models with random number of observations is not well-defined.
  if numdone != 0
- error("mis-aligned execution traces: # particles = ", n,
- " # completed trajectories = ", numdone,
- ". Please make sure the number of observations is NOT random.")
+ error(
+ "mis-aligned execution traces: # particles = ",
+ n,
+ " # completed trajectories = ",
+ numdone,
+ ". Please make sure the number of observations is NOT random.",
+ )
  end
 
  return false