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Initialize repository (#1)
* Add dependencies * Squashed 'deps/data/example_data/' content from commit 594a6ad git-subtree-dir: deps/data/example_data git-subtree-split: 594a6addd3aeddbd5d03e1a59300a1dcf0491a68 * Add more dependencies * Run JuliaFormatter * Define constants * Define metadata * Load metadata * Add data registration * Add data loading * Update documentation * Remove docs manifest from version history * Preserve order of datasets in file * Add function for listing example data * Print example datasets in docs * Update docstring * Update docs * Update CI workflow * Rename function * Replace show with describe * Check datasets are abstract dict * Depend on InferenceObjectsNetCDF * Add InferenceObjects as extras dep * Remove git subtree * Get example data from artifact
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‎.JuliaFormatter.toml

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style = "blue"

‎.github/workflows/CI.yml

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- main
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tags: ['*']
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pull_request:
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schedule:
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- cron: "0 0 * * *"
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concurrency:
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# Skip intermediate builds: always.
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# Cancel intermediate builds: only if it is a pull request build.
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fail-fast: false
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matrix:
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version:
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- '1.0'
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- '1.8'
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- '1.6'
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- '1'
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- 'nightly'
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os:
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- ubuntu-latest
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arch:
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- x64
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include:
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- version: '1'
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os: windows-latest
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arch: x64
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- version: '1'
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os: macOS-latest
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arch: x64
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steps:
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- uses: actions/checkout@v2
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- uses: julia-actions/setup-julia@v1

‎Artifacts.toml

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[arviz_example_data]
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git-tree-sha1 = "1ea01419c2c0cd6cf2b2daffb235fc17091e30bc"
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[[arviz_example_data.download]]
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sha256 = "eae35685807a0dcbbf405b21eaf5fa7aff6729c5a33f8f800f093c5a8318aa2b"
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url = "https://github.com/arviz-devs/arviz_example_data/archive/refs/tags/v0.1.0.tar.gz"

‎Project.toml

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authors = ["Seth Axen <seth.axen@gmail.com> and contributors"]
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version = "0.1.0"
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[deps]
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Artifacts = "56f22d72-fd6d-98f1-02f0-08ddc0907c33"
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DataDeps = "124859b0-ceae-595e-8997-d05f6a7a8dfe"
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InferenceObjectsNetCDF = "7cb6d088-77df-42c3-8f05-5ca8d42599d1"
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JSON3 = "0f8b85d8-7281-11e9-16c2-39a750bddbf1"
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OrderedCollections = "bac558e1-5e72-5ebc-8fee-abe8a469f55d"
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StructTypes = "856f2bd8-1eba-4b0a-8007-ebc267875bd4"
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[compat]
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julia = "1"
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DataDeps = "0.7"
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InferenceObjects = "0.1"
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InferenceObjectsNetCDF = "0.1"
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JSON3 = "1"
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OrderedCollections = "1"
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StructTypes = "1"
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julia = "1.6"
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[extras]
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InferenceObjects = "b5cf5a8d-e756-4ee3-b014-01d49d192c00"
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Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
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[targets]
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test = ["Test"]
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test = ["InferenceObjects", "Test"]

‎README.md

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# ArviZExampleData
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[![Stable](https://img.shields.io/badge/docs-stable-blue.svg)](https://arviz-devs.github.io/ArviZExampleData.jl/stable/)
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[![Dev](https://img.shields.io/badge/docs-dev-blue.svg)](https://arviz-devs.github.io/ArviZExampleData.jl/dev/)
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[![Stable](https://img.shields.io/badge/docs-stable-blue.svg)](https://exampledata.julia.arviz.org/stable/)
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[![Dev](https://img.shields.io/badge/docs-dev-blue.svg)](https://exampledata.julia.arviz.org/dev/)
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[![Build Status](https://github.com/arviz-devs/ArviZExampleData.jl/actions/workflows/CI.yml/badge.svg?branch=main)](https://github.com/arviz-devs/ArviZExampleData.jl/actions/workflows/CI.yml?query=branch%3Amain)
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[![Coverage](https://codecov.io/gh/arviz-devs/ArviZExampleData.jl/branch/main/graph/badge.svg)](https://codecov.io/gh/arviz-devs/ArviZExampleData.jl)
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[![Code Style: Blue](https://img.shields.io/badge/code%20style-blue-4495d1.svg)](https://github.com/invenia/BlueStyle)
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[![ColPrac: Contributor's Guide on Collaborative Practices for Community Packages](https://img.shields.io/badge/ColPrac-Contributor's%20Guide-blueviolet)](https://github.com/SciML/ColPrac)
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[![Powered by NumFOCUS](https://img.shields.io/badge/powered%20by-NumFOCUS-orange.svg?style=flat&colorA=E1523D&colorB=007D8A)](https://numfocus.org)
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Utilities for loading data include in the [`arviz_example_data`](https://github.com/arviz-devs/arviz_example_data) repository.
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The resulting objects are [InferenceObjects.jl](https://github.com/arviz-devs/InferenceObjects.jl)'s `InferenceData`, and these utilities are used in [ArviZ.jl](https://github.com/arviz-devs/ArviZ.jl).

‎docs/Manifest.toml

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‎docs/make.jl

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using ArviZExampleData
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using Documenter
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DocMeta.setdocmeta!(ArviZExampleData, :DocTestSetup, :(using ArviZExampleData); recursive=true)
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DocMeta.setdocmeta!(
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ArviZExampleData, :DocTestSetup, :(using ArviZExampleData); recursive=true
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)
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makedocs(;
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modules=[ArviZExampleData],
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),
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pages=[
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"Home" => "index.md",
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"Datasets" => "datasets.md",
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"API" => "api.md",
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"For developers" => "for_developers.md",
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],
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)
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deploydocs(;
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repo="github.com/arviz-devs/ArviZExampleData.jl",
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devbranch="main",
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)
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deploydocs(; repo="github.com/arviz-devs/ArviZExampleData.jl", devbranch="main")

‎docs/src/api.md

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# API
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```@autodocs
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Modules = [ArviZExampleData]
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Private = false
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```

‎docs/src/datasets.md

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# Datasets
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The following shows the currently available example datasets:
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```@example
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using ArviZExampleData
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println(describe_example_data())
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```

‎docs/src/for_developers.md

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# For developers
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This package has [`arviz_example_data`](https://github.com/arviz-devs/arviz_example_data) as a data dependency, which is included as an [artifact](https://pkgdocs.julialang.org/v1/artifacts/).
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When `arviz_example_data` is updated, and a new release is made, `Artifacts.toml` should be updated to point to the new tarball corresponding to the release:
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```julia
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julia> using ArtifactUtils
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julia> version = v"0.1.0";
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julia> tarball_url = "https://github.com/arviz-devs/arviz_example_data/archive/refs/tags/v$version.tar.gz";
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julia> add_artifact!("Artifacts.toml", "arviz_example_data", tarball_url; force=true);
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```

‎docs/src/index.md

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# ArviZExampleData
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Documentation for [ArviZExampleData](https://github.com/arviz-devs/ArviZExampleData.jl).
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[![Build Status](https://github.com/arviz-devs/ArviZExampleData.jl/actions/workflows/CI.yml/badge.svg?branch=main)](https://github.com/arviz-devs/ArviZExampleData.jl/actions/workflows/CI.yml?query=branch%3Amain)
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[![Coverage](https://codecov.io/gh/arviz-devs/ArviZExampleData.jl/branch/main/graph/badge.svg)](https://codecov.io/gh/arviz-devs/ArviZExampleData.jl)
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[![ColPrac: Contributor's Guide on Collaborative Practices for Community Packages](https://img.shields.io/badge/ColPrac-Contributor's%20Guide-blueviolet)](https://github.com/SciML/ColPrac)
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[![Powered by NumFOCUS](https://img.shields.io/badge/powered%20by-NumFOCUS-orange.svg?style=flat&colorA=E1523D&colorB=007D8A)](https://numfocus.org)
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```@index
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```
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```@autodocs
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Modules = [ArviZExampleData]
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```
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Utilities for loading data include in the [`arviz_example_data`](https://github.com/arviz-devs/arviz_example_data) repository.
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The resulting objects are [InferenceObjects.jl](https://github.com/arviz-devs/InferenceObjects.jl)'s `InferenceData`, and these utilities are used in [ArviZ.jl](https://github.com/arviz-devs/ArviZ.jl).

‎src/ArviZExampleData.jl

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module ArviZExampleData
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# Write your package code here.
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using Artifacts: @artifact_str
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using DataDeps: DataDeps
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using InferenceObjectsNetCDF: InferenceObjectsNetCDF
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using JSON3: JSON3
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using OrderedCollections: OrderedDict
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using StructTypes: StructTypes
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export describe_example_data, load_example_data
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const EXAMPLE_DATA_DIR = only(readdir(artifact"arviz_example_data"; join=true))
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const LOCAL_DATA_DIR = joinpath(EXAMPLE_DATA_DIR, "data")
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const LOCAL_DATA_FILE = joinpath(EXAMPLE_DATA_DIR, "data_local.json")
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const REMOTE_DATA_FILE = joinpath(EXAMPLE_DATA_DIR, "data_remote.json")
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const DATA_HTTP_PROTOCOL = "https"
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include("metadata.jl")
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const LOCAL_EXAMPLE_DATA = OrderedDict(
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md.name => md for md in JSON3.read(read(LOCAL_DATA_FILE), Vector{LocalFileMetadata})
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)
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const REMOTE_EXAMPLE_DATA = OrderedDict(
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md.name => md for md in JSON3.read(read(REMOTE_DATA_FILE), Vector{RemoteFileMetadata})
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)
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include("register.jl")
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include("load.jl")
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include("describe.jl")
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function __init__()
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return register_all_data()
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end
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end

‎src/describe.jl

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"""
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describe_example_data() -> String
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Return a string containing descriptions of all available datasets.
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# Examples
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```jldoctest
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julia> describe_example_data("radon") |> println
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radon
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=====
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Radon is a radioactive gas that enters homes through contact points with the ground. It is a carcinogen that is the primary cause of lung cancer in non-smokers. Radon levels vary greatly from household to household.
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This example uses an EPA study of radon levels in houses in Minnesota to construct a model with a hierarchy over households within a county. The model includes estimates (gamma) for contextual effects of the uranium per household.
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See Gelman and Hill (2006) for details on the example, or https://docs.pymc.io/notebooks/multilevel_modeling.html by Chris Fonnesbeck for details on this implementation.
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remote: http://ndownloader.figshare.com/files/24067472
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```
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"""
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function describe_example_data end
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function describe_example_data(name::AbstractString)
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haskey(LOCAL_EXAMPLE_DATA, name) && return describe(LOCAL_EXAMPLE_DATA[name])
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haskey(REMOTE_EXAMPLE_DATA, name) && return describe(REMOTE_EXAMPLE_DATA[name])
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throw(
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ArgumentError(
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"Dataset $name not found. Valid datasets are: $(keys(load_example_data()))"
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),
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)
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end
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function describe_example_data()
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data = load_example_data()
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return join(map(describe, values(data)), "\n\n")
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end
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"""
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describe(md::AbstractFileMetadata) -> String
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Return description of the metadata of a dataset.
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"""
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describe
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function describe(md::LocalFileMetadata)
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io = IOBuffer()
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println(io, md.name)
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println(io, repeat("=", length(md.name)))
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println(io)
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if md.description !== nothing
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println(io, md.description)
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println(io)
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end
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print(io, "local: ", joinpath(LOCAL_DATA_DIR, md.filename))
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return String(take!(io))
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end
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function describe(md::RemoteFileMetadata)
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io = IOBuffer()
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println(io, md.name)
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println(io, repeat("=", length(md.name)))
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println(io)
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if md.description !== nothing
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println(io, md.description)
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println(io)
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end
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print(io, "remote: ", md.url)
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return String(take!(io))
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end

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