Reimplement Dataset and InferenceData using DimensionalData

Project.toml

@@ -1,13 +1,16 @@
 name = "ArviZ"
 uuid = "131c737c-5715-5e2e-ad31-c244f01c1dc7"
 authors = ["Seth Axen <seth.axen@gmail.com>"]
-version = "0.5.22"
+version = "0.6.0-DEV"
 
 [deps]
 Conda = "8f4d0f93-b110-5947-807f-2305c1781a2d"
 DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
+Dates = "ade2ca70-3891-5945-98fb-dc099432e06a"
+DimensionalData = "0703355e-b756-11e9-17c0-8b28908087d0"
 LogExpFunctions = "2ab3a3ac-af41-5b50-aa03-7779005ae688"
 Markdown = "d6f4376e-aef5-505a-96c1-9c027394607a"
+OrderedCollections = "bac558e1-5e72-5ebc-8fee-abe8a469f55d"
 PSIS = "ce719bf2-d5d0-4fb9-925d-10a81b42ad04"
 PyCall = "438e738f-606a-5dbb-bf0a-cddfbfd45ab0"
 PyPlot = "d330b81b-6aea-500a-939a-2ce795aea3ee"
@@ -18,7 +21,9 @@ StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
 [compat]
 Conda = "1.0"
 DataFrames = "0.20, 0.21, 0.22, 1.0"
+DimensionalData = "0.20"
 LogExpFunctions = "0.2.0, 0.3"
+OrderedCollections = "1"
 PSIS = "0.2, 0.3, 0.4, 0.5"
 PyCall = "1.91.2"
 PyPlot = "2.8.2"

docs/Project.toml

@@ -16,7 +16,7 @@ Soss = "8ce77f84-9b61-11e8-39ff-d17a774bf41c"
 Turing = "fce5fe82-541a-59a6-adf8-730c64b5f9a0"
 
 [compat]
-ArviZ = "0.5"
+ArviZ = "0.6"
 CmdStan = "6.0"
 Distributions = "0.25"
 Documenter = "0.27"

docs/src/api.md

@@ -78,7 +78,6 @@
 | [`from_mcmcchains`](@ref)           | Convert `MCMCChains` data into an `InferenceData`.                      |
 | [`from_samplechains`](@ref)         | Convert `SampleChains` data into an `InferenceData`.                    |
 | [`concat`](@ref)                    | Concatenate `InferenceData` objects.                                    |
-| [`concat!`](@ref)                   | Concatenate `InferenceData` objects in-place.                           |
 
 ## [Utils](@id utils-api)
 

docs/src/index.md

@@ -57,8 +57,9 @@ These functions are not exported to reduce namespace clutter.
 For `InferenceData` inputs, [`summarystats`](@ref) replaces `arviz.summary` to avoid confusion with `Base.summary`.
 For arbitrary inputs and the full functionality of `arviz.summary`, use [`ArviZ.summary`](@ref), which is not exported.
 
-ArviZ.jl transparently interconverts between `arviz.InferenceData` and our own [`InferenceData`](@ref), used for dispatch.
-`InferenceData` has identical usage to its Python counterpart.
+While Python ArviZ is built on xarray, and `InferenceData` groups are `xarray.Dataset`s, ArviZ.jl is built on [DimensionalData.jl](https://rafaqz.github.io/DimensionalData.jl/stable/), and `InferenceData` groups are `DimensionalData.AbstractDimStack`s that have identical usage to [`DimensionalData.DimStack`](https://rafaqz.github.io/DimensionalData.jl/stable/api/#DimensionalData.DimStack).
+When ArviZ.jl uses functionality implemented in Python, it transparently interconverts `InferenceData` to/from `arviz.InferenceData`.
+Going to Python, this is non-allocating.
 
 Functions that in ArviZ return Pandas types here return [DataFrames.jl](https://github.com/JuliaData/DataFrames.jl) types.
 

docs/src/mpl_examples.md

@@ -14,7 +14,7 @@ using ArviZ
 ArviZ.use_style("arviz-darkgrid")
 
 data = load_arviz_data("centered_eight")
-plot_autocorr(data; var_names=("tau", "mu"))
+plot_autocorr(data; var_names=[:tau, :mu])
 
 gcf()
 ```
@@ -48,7 +48,7 @@ using ArviZ
 ArviZ.use_style("arviz-darkgrid")
 
 data = load_arviz_data("regression1d")
-plot_bpv(data; kind="t_stat", t_stat="0.5")
+plot_bpv(data; kind=:t_stat, t_stat="0.5")
 
 gcf()
 ```
@@ -91,7 +91,7 @@ non_centered_data = load_arviz_data("non_centered_eight")
 plot_density(
     [centered_data, non_centered_data];
     data_labels=["Centered", "Non Centered"],
-    var_names=["theta"],
+    var_names=[:theta],
     shade=0.1,
 )
 
@@ -169,7 +169,7 @@ using ArviZ
 ArviZ.use_style("arviz-darkgrid")
 
 idata = load_arviz_data("radon")
-plot_ess(idata; var_names=["b"], kind="evolution")
+plot_ess(idata; var_names=[:b], kind=:evolution)
 
 gcf()
 ```
@@ -186,7 +186,7 @@ using ArviZ
 ArviZ.use_style("arviz-darkgrid")
 
 idata = load_arviz_data("non_centered_eight")
-plot_ess(idata; var_names=["mu"], kind="local", marker="_", ms=20, mew=2, rug=true)
+plot_ess(idata; var_names=[:mu], kind=:local, marker="_", ms=20, mew=2, rug=true)
 
 gcf()
 ```
@@ -203,7 +203,7 @@ using ArviZ
 ArviZ.use_style("arviz-darkgrid")
 
 idata = load_arviz_data("radon")
-plot_ess(idata; var_names=["sigma"], kind="quantile", color="C4")
+plot_ess(idata; var_names=[:sigma], kind=:quantile, color="C4")
 
 gcf()
 ```
@@ -224,7 +224,7 @@ non_centered_data = load_arviz_data("non_centered_eight")
 plot_forest(
     [centered_data, non_centered_data];
     model_names=["Centered", "Non Centered"],
-    var_names=["mu"],
+    var_names=[:mu],
 )
 title("Estimated theta for eight schools model")
 
@@ -245,12 +245,12 @@ ArviZ.use_style("arviz-darkgrid")
 rugby_data = load_arviz_data("rugby")
 plot_forest(
     rugby_data;
-    kind="ridgeplot",
-    var_names=["defs"],
+    kind=:ridgeplot,
+    var_names=[:defs],
     linewidth=4,
     combined=true,
     ridgeplot_overlap=1.5,
-    colors="blue",
+    colors=:blue,
     figsize=(9, 4),
 )
 title("Relative defensive strength\nof Six Nation rugby teams")
@@ -276,7 +276,7 @@ x_data = randn(100)
 y_data = 2 .+ x_data .* 0.5
 y_data_rep = 0.5 .* randn(200, 100) .+ transpose(y_data)
 plot(x_data, y_data; color="C6")
-plot_hdi(x_data, y_data_rep; color="k", plot_kwargs=Dict("ls" => "--"))
+plot_hdi(x_data, y_data_rep; color=:k, plot_kwargs=Dict(:ls => "--"))
 
 gcf()
 ```
@@ -295,9 +295,9 @@ ArviZ.use_style("arviz-darkgrid")
 data = load_arviz_data("non_centered_eight")
 plot_pair(
     data;
-    var_names=["theta"],
-    coords=Dict("school" => ["Choate", "Phillips Andover"]),
-    kind="hexbin",
+    var_names=[:theta],
+    coords=Dict(:school => ["Choate", "Phillips Andover"]),
+    kind=:hexbin,
     marginals=true,
     figsize=(10, 10),
 )
@@ -319,16 +319,16 @@ ArviZ.use_style("arviz-darkgrid")
 data = load_arviz_data("centered_eight")
 
 ## Combine different posterior draws from different chains
-obs = data.posterior_predictive["obs"].values
+obs = data.posterior_predictive.obs
 size_obs = size(obs)
 y_hat = reshape(obs, prod(size_obs[1:2]), size_obs[3:end]...)
 
 plot_kde(
     y_hat;
     label="Estimated Effect\n of SAT Prep",
     rug=true,
-    plot_kwargs=Dict("linewidth" => 2, "color" => "black"),
-    rug_kwargs=Dict("color" => "black"),
+    plot_kwargs=Dict(:linewidth => 2, :color => :black),
+    rug_kwargs=Dict(:color => :black),
 )
 
 gcf()
@@ -405,7 +405,7 @@ ArviZ.use_style("arviz-darkgrid")
 
 idata = load_arviz_data("radon")
 
-plot_loo_pit(idata; y="y", ecdf=true, color="maroon")
+plot_loo_pit(idata; y=:y, ecdf=true, color=:maroon)
 
 gcf()
 ```
@@ -422,7 +422,7 @@ using ArviZ
 ArviZ.use_style("arviz-darkgrid")
 
 idata = load_arviz_data("non_centered_eight")
-plot_loo_pit(; idata, y="obs", color="indigo")
+plot_loo_pit(; idata, y=:obs, color=:indigo)
 
 gcf()
 ```
@@ -439,7 +439,7 @@ using ArviZ
 ArviZ.use_style("arviz-darkgrid")
 
 data = load_arviz_data("centered_eight")
-plot_mcse(data; var_names=["tau", "mu"], rug=true, extra_methods=true)
+plot_mcse(data; var_names=[:tau, :mu], rug=true, extra_methods=true)
 
 gcf()
 ```
@@ -456,7 +456,7 @@ using ArviZ
 ArviZ.use_style("arviz-darkgrid")
 
 data = load_arviz_data("radon")
-plot_mcse(data; var_names=["sigma_a"], color="C4", errorbar=true)
+plot_mcse(data; var_names=[:sigma_a], color="C4", errorbar=true)
 
 gcf()
 ```
@@ -473,9 +473,9 @@ using ArviZ
 ArviZ.use_style("arviz-darkgrid")
 
 centered = load_arviz_data("centered_eight")
-coords = Dict("school" => ["Choate", "Deerfield"])
+coords = Dict(:school => ["Choate", "Deerfield"])
 plot_pair(
-    centered; var_names=["theta", "mu", "tau"], coords, divergences=true, textsize=22
+    centered; var_names=[:theta, :mu, :tau], coords, divergences=true, textsize=22
 )
 
 gcf()
@@ -493,11 +493,11 @@ using ArviZ
 ArviZ.use_style("arviz-darkgrid")
 
 centered = load_arviz_data("centered_eight")
-coords = Dict("school" => ["Choate", "Deerfield"])
+coords = Dict(:school => ["Choate", "Deerfield"])
 plot_pair(
     centered;
-    var_names=["theta", "mu", "tau"],
-    kind="hexbin",
+    var_names=[:theta, :mu, :tau],
+    kind=:hexbin,
     coords,
     colorbar=true,
     divergences=true,
@@ -518,11 +518,11 @@ using ArviZ
 ArviZ.use_style("arviz-darkgrid")
 
 centered = load_arviz_data("centered_eight")
-coords = Dict("school" => ["Choate", "Deerfield"])
+coords = Dict(:school => ["Choate", "Deerfield"])
 plot_pair(
     centered;
-    var_names=["theta", "mu", "tau"],
-    kind="kde",
+    var_names=[:theta, :mu, :tau],
+    kind=:kde,
     coords,
     divergences=true,
     textsize=22,
@@ -543,15 +543,15 @@ using ArviZ
 ArviZ.use_style("arviz-darkgrid")
 
 centered = load_arviz_data("centered_eight")
-coords = Dict("school" => ["Choate", "Deerfield"])
+coords = Dict(:school => ["Choate", "Deerfield"])
 plot_pair(
     centered;
-    var_names=["mu", "theta"],
-    kind=["scatter", "kde"],
-    kde_kwargs=Dict("fill_last" => false),
+    var_names=[:mu, :theta],
+    kind=[:scatter, :kde],
+    kde_kwargs=Dict(:fill_last => false),
     marginals=true,
     coords,
-    point_estimate="median",
+    point_estimate=:median,
     figsize=(10, 8),
 )
 
@@ -570,7 +570,7 @@ using ArviZ
 ArviZ.use_style("arviz-darkgrid")
 
 data = load_arviz_data("centered_eight")
-ax = plot_parallel(data; var_names=["theta", "tau", "mu"])
+ax = plot_parallel(data; var_names=[:theta, :tau, :mu])
 ax.set_xticklabels(ax.get_xticklabels(); rotation=70)
 draw()
 
@@ -589,8 +589,8 @@ using ArviZ
 ArviZ.use_style("arviz-darkgrid")
 
 data = load_arviz_data("centered_eight")
-coords = Dict("school" => ["Choate"])
-plot_posterior(data; var_names=["mu", "theta"], coords, rope=(-1, 1))
+coords = Dict(:school => ["Choate"])
+plot_posterior(data; var_names=[:mu, :theta], coords, rope=(-1, 1))
 
 gcf()
 ```
@@ -607,7 +607,7 @@ using ArviZ
 ArviZ.use_style("arviz-darkgrid")
 
 data = load_arviz_data("non_centered_eight")
-plot_ppc(data; data_pairs=Dict("obs" => "obs"), alpha=0.03, figsize=(12, 6), textsize=14)
+plot_ppc(data; data_pairs=Dict(:obs => :obs), alpha=0.03, figsize=(12, 6), textsize=14)
 
 gcf()
 ```
@@ -624,7 +624,7 @@ using ArviZ
 ArviZ.use_style("arviz-darkgrid")
 
 data = load_arviz_data("non_centered_eight")
-plot_ppc(data; alpha=0.3, kind="cumulative", figsize=(12, 6), textsize=14)
+plot_ppc(data; alpha=0.3, kind=:cumulative, figsize=(12, 6), textsize=14)
 
 gcf()
 ```
@@ -641,7 +641,7 @@ using ArviZ
 ArviZ.use_style("arviz-darkgrid")
 
 data = load_arviz_data("centered_eight")
-plot_rank(data; var_names=("tau", "mu"))
+plot_rank(data; var_names=[:tau, :mu])
 
 gcf()
 ```
@@ -658,7 +658,7 @@ using ArviZ
 ArviZ.use_style("arviz-darkgrid")
 
 data = load_arviz_data("classification10d")
-plot_separation(data; y="outcome", y_hat="outcome", figsize=(8, 1))
+plot_separation(data; y=:outcome, y_hat=:outcome, figsize=(8, 1))
 
 gcf()
 ```
@@ -675,7 +675,7 @@ using ArviZ
 ArviZ.use_style("arviz-darkgrid")
 
 data = load_arviz_data("non_centered_eight")
-plot_trace(data; var_names=("tau", "mu"))
+plot_trace(data; var_names=[:tau, :mu])
 
 gcf()
 ```
@@ -692,7 +692,7 @@ using ArviZ
 ArviZ.use_style("arviz-darkgrid")
 
 data = load_arviz_data("non_centered_eight")
-plot_violin(data; var_names=["mu", "tau"])
+plot_violin(data; var_names=[:mu, :tau])
 
 gcf()
 ```