Use Julia v1.6 syntax simplifications (#171)

* Use Julia 1.6 simplified keyword syntax

* Increment patch version number

* Require at least one argument

* Don't commit log files

.gitignore

@@ -4,5 +4,7 @@
 /deps/deps.jl
 /docs/src/assets/logo*.png
 /docs/src/assets/favicon.ico
+/docs/src/*.log
 /docs/build
+/test/*.log
 .DS_Store

Project.toml

@@ -1,7 +1,7 @@
 name = "ArviZ"
 uuid = "131c737c-5715-5e2e-ad31-c244f01c1dc7"
 authors = ["Seth Axen <[email protected]>"]
-version = "0.5.15"
+version = "0.5.16"
 
 [deps]
 Conda = "8f4d0f93-b110-5947-807f-2305c1781a2d"

docs/src/mpl_examples.md

@@ -422,7 +422,7 @@ using ArviZ
 ArviZ.use_style("arviz-darkgrid")
 
 idata = load_arviz_data("non_centered_eight")
-plot_loo_pit(; idata=idata, y="obs", color="indigo")
+plot_loo_pit(; idata, y="obs", color="indigo")
 
 gcf()
 ```
@@ -475,7 +475,7 @@ ArviZ.use_style("arviz-darkgrid")
 centered = load_arviz_data("centered_eight")
 coords = Dict("school" => ["Choate", "Deerfield"])
 plot_pair(
-    centered; var_names=["theta", "mu", "tau"], coords=coords, divergences=true, textsize=22
+    centered; var_names=["theta", "mu", "tau"], coords, divergences=true, textsize=22
 )
 
 gcf()
@@ -498,7 +498,7 @@ plot_pair(
     centered;
     var_names=["theta", "mu", "tau"],
     kind="hexbin",
-    coords=coords,
+    coords,
     colorbar=true,
     divergences=true,
 )
@@ -523,7 +523,7 @@ plot_pair(
     centered;
     var_names=["theta", "mu", "tau"],
     kind="kde",
-    coords=coords,
+    coords,
     divergences=true,
     textsize=22,
 )
@@ -550,7 +550,7 @@ plot_pair(
     kind=["scatter", "kde"],
     kde_kwargs=Dict("fill_last" => false),
     marginals=true,
-    coords=coords,
+    coords,
     point_estimate="median",
     figsize=(10, 8),
 )
@@ -590,7 +590,7 @@ ArviZ.use_style("arviz-darkgrid")
 
 data = load_arviz_data("centered_eight")
 coords = Dict("school" => ["Choate"])
-plot_posterior(data; var_names=["mu", "theta"], coords=coords, rope=(-1, 1))
+plot_posterior(data; var_names=["mu", "theta"], coords, rope=(-1, 1))
 
 gcf()
 ```

src/data.jl

@@ -115,7 +115,7 @@ function _from_dict(posterior=nothing; attrs=Dict(), coords=nothing, dims=nothin
     datasets = []
     for (name, dict) in pairs(dicts)
         (dict === nothing || isempty(dict)) && continue
-        dataset = dict_to_dataset(dict; attrs=attrs, coords=coords, dims=dims)
+        dataset = dict_to_dataset(dict; attrs, coords, dims)
         push!(datasets, name => dataset)
     end
 

src/dataset.jl

@@ -86,7 +86,7 @@ convert_to_dataset
 
 function convert_to_dataset(obj; group=:posterior, kwargs...)
     group = Symbol(group)
-    idata = convert_to_inference_data(obj; group=group, kwargs...)
+    idata = convert_to_inference_data(obj; group, kwargs...)
     dataset = getproperty(idata, group)
     return dataset
 end
@@ -126,7 +126,7 @@ function convert_to_constant_dataset(
         vals = _asarray(vals)
         val_dims = get(dims, key, nothing)
         (val_dims, val_coords) = base.generate_dims_coords(
-            size(vals), key; dims=val_dims, coords=coords
+            size(vals), key; dims=val_dims, coords
         )
         data[key] = xarray.DataArray(vals; dims=val_dims, coords=val_coords)
     end
@@ -136,7 +136,7 @@ function convert_to_constant_dataset(
         default_attrs = merge(default_attrs, Dict("inference_library" => string(library)))
     end
     attrs = merge(default_attrs, attrs)
-    return Dataset(; data_vars=data, coords=coords, attrs=attrs)
+    return Dataset(; data_vars=data, coords, attrs)
 end
 
 @doc doc"""
@@ -167,7 +167,7 @@ function dict_to_dataset(data; library=nothing, attrs=Dict(), kwargs...)
     if library !== nothing
         attrs = merge(attrs, Dict("inference_library" => string(library)))
     end
-    return arviz.dict_to_dataset(data; attrs=attrs, kwargs...)
+    return arviz.dict_to_dataset(data; attrs, kwargs...)
 end
 
 @doc doc"""

src/mcmcchains.jl

@@ -177,7 +177,7 @@ function from_mcmcchains(
         stats_dict = nothing
     else
         post_dict = convert_to_eltypes(chains_to_dict(posterior), eltypes)
-        stats_dict = chains_to_dict(posterior; section=:internals, rekey_fun=rekey_fun)
+        stats_dict = chains_to_dict(posterior; section=:internals, rekey_fun)
         stats_dict = enforce_stat_eltypes(stats_dict)
         stats_dict = convert_to_eltypes(stats_dict, Dict("is_accept" => Bool))
     end
@@ -199,9 +199,9 @@ function from_mcmcchains(
             group_data = popsubdict!(post_dict, group_data)
         end
         group_dataset = if group_data isa Chains
-            convert_to_dataset(group_data; library=library, eltypes=eltypes, kwargs...)
+            convert_to_dataset(group_data; library, eltypes, kwargs...)
         else
-            convert_to_dataset(group_data; library=library, kwargs...)
+            convert_to_dataset(group_data; library, kwargs...)
         end
         setattribute!(group_dataset, "inference_library", library)
         concat!(all_idata, InferenceData(; group => group_dataset))
@@ -213,7 +213,7 @@ function from_mcmcchains(
     else
         attrs = merge(attributes_dict(posterior), attrs_library)
     end
-    kwargs = Dict(pairs(merge((; attrs=attrs, dims=Dict()), kwargs)))
+    kwargs = Dict(pairs(merge((; attrs, dims=Dict()), kwargs)))
     post_idata = _from_dict(post_dict; sample_stats=stats_dict, kwargs...)
     concat!(all_idata, post_idata)
     return all_idata
@@ -246,11 +246,7 @@ function from_mcmcchains(
 
     if prior !== nothing
         pre_prior_idata = convert_to_inference_data(
-            prior;
-            posterior_predictive=prior_predictive,
-            library=library,
-            eltypes=eltypes,
-            kwargs...,
+            prior; posterior_predictive=prior_predictive, library, eltypes, kwargs...
         )
         prior_idata = rekey(
             pre_prior_idata,
@@ -263,7 +259,7 @@ function from_mcmcchains(
         concat!(all_idata, prior_idata)
     elseif prior_predictive !== nothing
         pre_prior_predictive_idata = convert_to_inference_data(
-            prior_predictive; eltypes=eltypes, kwargs...
+            prior_predictive; eltypes, kwargs...
         )
         concat!(
             all_idata,
@@ -278,7 +274,7 @@ function from_mcmcchains(
     ]
         group_data === nothing && continue
         group_data = convert_to_eltypes(group_data, eltypes)
-        group_dataset = convert_to_constant_dataset(group_data; library=library, kwargs...)
+        group_dataset = convert_to_constant_dataset(group_data; library, kwargs...)
         concat!(all_idata, InferenceData(; group => group_dataset))
     end
 

src/namedtuple.jl

@@ -186,7 +186,7 @@ function from_namedtuple(
             prior;
             posterior_predictive=prior_predictive,
             sample_stats=sample_stats_prior,
-            library=library,
+            library,
             kwargs...,
         )
         prior_idata = rekey(
@@ -207,7 +207,7 @@ function from_namedtuple(
     ]
         group_data === nothing && continue
         group_dict = Dict(pairs(group_data))
-        group_dataset = convert_to_constant_dataset(group_dict; library=library, kwargs...)
+        group_dataset = convert_to_constant_dataset(group_dict; library, kwargs...)
         concat!(all_idata, InferenceData(; group => group_dataset))
     end
 

src/plots.jl

@@ -78,7 +78,7 @@ for f in (:plot_density, :plot_forest, :plot_rank)
             ::typeof($(f)), data, args...; transform=identity, group=:posterior, kwargs...
         )
             tdata = transform(data)
-            dataset = convert_to_dataset(tdata; group=group)
+            dataset = convert_to_dataset(tdata; group)
             return tuple(dataset, args...), kwargs
         end
     end
@@ -96,7 +96,7 @@ for f in (:plot_density, :plot_forest)
         )
             tdata = transform(data)
             datasets = map(tdata) do datum
-                return convert_to_dataset(datum; group=group)
+                return convert_to_dataset(datum; group)
             end
             return tuple(datasets, args...), kwargs
         end
@@ -109,7 +109,7 @@ for f in (:plot_density, :plot_forest)
             kwargs...,
         )
             tdata = transform(data)
-            dataset = convert_to_dataset(tdata; group=group)
+            dataset = convert_to_dataset(tdata; group)
             return tuple(dataset, args...), kwargs
         end
     end

src/samplechains.jl

@@ -20,7 +20,9 @@ _maybe_multichain(x::SampleChains.AbstractChain) = SampleChains.MultiChain(x)
 function _maybe_multichain(x::AbstractVector{<:SampleChains.AbstractChain})
     return SampleChains.MultiChain(x)
 end
-function _maybe_multichain(x::Tuple{Vararg{<:SampleChains.AbstractChain}})
+function _maybe_multichain(
+    x::Tuple{<:SampleChains.AbstractChain,Vararg{<:SampleChains.AbstractChain}}
+)
     return SampleChains.MultiChain(x...)
 end
 
@@ -88,12 +90,7 @@ function from_samplechains(
         sample_stats_prior = _samplechains_info(prior_mc)
     end
     return from_namedtuple(
-        posterior_mc;
-        prior=prior_mc,
-        sample_stats=sample_stats,
-        sample_stats_prior=sample_stats_prior,
-        library=library,
-        kwargs...,
+        posterior_mc; prior=prior_mc, sample_stats, sample_stats_prior, library, kwargs...
     )
 end
 
@@ -120,7 +117,7 @@ function convert_to_inference_data(
         SampleChains.AbstractChain,
         SampleChains.MultiChain,
         AbstractVector{<:SampleChains.AbstractChain},
-        Tuple{Vararg{<:SampleChains.AbstractChain}},
+        Tuple{<:SampleChains.AbstractChain,Vararg{<:SampleChains.AbstractChain}},
     },
 }
     group = Symbol(group)

src/stats.jl

@@ -46,7 +46,7 @@ function psislw(logw, reff=1)
     log_weights = result.log_weights
     d = ndims(log_weights)
     dims = d == 1 ? Colon() : ntuple(Base.Fix1(+, 1), d - 1)
-    log_norm_exp = logsumexp(log_weights; dims=dims)
+    log_norm_exp = logsumexp(log_weights; dims)
     log_weights .-= log_norm_exp
     return log_weights, result.pareto_shape
 end
@@ -143,10 +143,10 @@ function StatsBase.summarystats(data::InferenceData; group=:posterior, kwargs...
     return summarystats(dataset; kwargs...)
 end
 function StatsBase.summarystats(data::Dataset; fmt=:wide, kwargs...)
-    s = arviz.summary(data; fmt=fmt, kwargs...)
+    s = arviz.summary(data; fmt, kwargs...)
     s isa Dataset && return s
     index_name = Symbol(fmt) == :long ? :statistic : :variable
-    return todataframes(s; index_name=index_name)
+    return todataframes(s; index_name)
 end
 
 """
@@ -168,6 +168,6 @@ Compute summary statistics on any object that can be passed to [`convert_to_data
   - `kwargs`: Keyword arguments passed to [`summarystats`](@ref).
 """
 function summary(data; group=:posterior, coords=nothing, dims=nothing, kwargs...)
-    dataset = convert_to_dataset(data; group=group, coords=coords, dims=dims)
+    dataset = convert_to_dataset(data; group, coords, dims)
     return summarystats(dataset; kwargs...)
 end

src/utils.jl

@@ -142,7 +142,7 @@ macro forwardplotfun(f)
                 backend_val = Val(backend)
                 load_backend(backend_val)
                 args, kwargs = convert_arguments($(f), args...; kwargs...)
-                result = arviz.$(f)(args...; kwargs..., backend=backend)
+                result = arviz.$(f)(args...; kwargs..., backend)
                 return convert_result($(f), result, backend_val)
             end
 
@@ -280,13 +280,13 @@ function topandas(::Val{:Series}, df)
     initialize_pandas()
     df = DataFrames.DataFrame(df)
     rownames = names(df)
-    colvals = Array(first(eachrow(df)))
+    colvals = Array(only(eachrow(df)))
     return pandas.Series(colvals, rownames)
 end
 function topandas(::Val{:ELPDData}, df)
     initialize_pandas()
     df = DataFrames.DataFrame(df)
     rownames = names(df)
-    colvals = Array(first(eachrow(df)))
+    colvals = Array(only(eachrow(df)))
     return ArviZ.arviz.stats.ELPDData(colvals, rownames)
 end

test/helpers.jl

@@ -49,12 +49,12 @@ function create_model(seed=10)
         "diverging" => Int.(randn(rng, nchains, ndraws) .> 0.95),
     )
     model = from_dict(;
-        posterior=posterior,
-        posterior_predictive=posterior_predictive,
-        sample_stats=sample_stats,
-        prior=prior,
-        prior_predictive=prior_predictive,
-        sample_stats_prior=sample_stats_prior,
+        posterior,
+        posterior_predictive,
+        sample_stats,
+        prior,
+        prior_predictive,
+        sample_stats_prior,
         observed_data=Dict("y" => data["y"]),
         dims=Dict("y" => ["obs_dim"], "log_likelihood" => ["obs_dim"]),
         coords=Dict("obs_dim" => 1:J),

test/test_data.jl

@@ -162,12 +162,12 @@ end
     posterior = Dict("A" => randn(rng, 2, 10, 2), "B" => randn(rng, 2, 10, 5, 2))
     prior = Dict("C" => randn(rng, 2, 10, 2), "D" => randn(rng, 2, 10, 5, 2))
 
-    idata = from_dict(posterior; prior=prior)
+    idata = from_dict(posterior; prior)
     @test check_multiple_attrs(
         Dict(:posterior => ["A", "B"], :prior => ["C", "D"]), idata
     ) == []
 
-    idata2 = from_dict(; prior=prior)
+    idata2 = from_dict(; prior)
     @test check_multiple_attrs(Dict(:prior => ["C", "D"]), idata2) == []
 end
 

test/test_dataset.jl

@@ -16,8 +16,8 @@
             "dimx" => [1, 2, 3], "dimy_1" => ["a", "b", "c"], "dimy_2" => ["d", "e"]
         )
         attrs = Dict("prop1" => 1, "prop2" => "propval")
-        @inferred ArviZ.Dataset(; data_vars=vars, coords=coords, attrs=attrs)
-        ds = ArviZ.Dataset(; data_vars=vars, coords=coords, attrs=attrs)
+        @inferred ArviZ.Dataset(; data_vars=vars, coords, attrs)
+        ds = ArviZ.Dataset(; data_vars=vars, coords, attrs)
         @test ds isa ArviZ.Dataset
         vars2, kwargs = ArviZ.dataset_to_dict(ds)
         for (k, v) in vars
@@ -113,9 +113,7 @@ end
         dataset = ArviZ.convert_to_constant_dataset(data)
         attrs = Dict("prop" => "propval")
 
-        dataset = ArviZ.convert_to_constant_dataset(
-            data; coords=coords, dims=dims, library=library, attrs=attrs
-        )
+        dataset = ArviZ.convert_to_constant_dataset(data; coords, dims, library, attrs)
         @test dataset isa ArviZ.Dataset
         @test "x" ∈ dataset.keys()
         @test "y" ∈ dataset.keys()
@@ -139,9 +137,7 @@ end
         dataset = ArviZ.convert_to_constant_dataset(data)
         attrs = (prop="propval",)
 
-        dataset = ArviZ.convert_to_constant_dataset(
-            data; coords=coords, dims=dims, library=library, attrs=attrs
-        )
+        dataset = ArviZ.convert_to_constant_dataset(data; coords, dims, library, attrs)
         @test dataset isa ArviZ.Dataset
         @test "x" ∈ dataset.keys()
         @test "y" ∈ dataset.keys()
@@ -171,7 +167,7 @@ end
     dims = Dict("b" => ["bi", "bj"])
     attrs = Dict("mykey" => 5)
 
-    ds = ArviZ.dict_to_dataset(vars; library=:MyLib, coords=coords, dims=dims, attrs=attrs)
+    ds = ArviZ.dict_to_dataset(vars; library=:MyLib, coords, dims, attrs)
     @test ds isa ArviZ.Dataset
     vars2, kwargs = ArviZ.dataset_to_dict(ds)
     for (k, v) in vars