Use arviz_example_data submodule (#2096)

* Remove local datasets

* Add example data as git submodule

* Explicitly specify package data

* Add name to metadata

* Replace dataset constant writing code

* Add missing newline to file

* Update example_data

* Update setup.py

* Update package data

* Include all package data

* Checkout with submodules on Azure

* Add missing keyword to RemoteFileMetadata

* Build docs with submodules

* Specify encoding

* Update branch to main

* Update submodule commit

* Specify example data files to include

* Don't run pydocstyle on example_data

* Ignore example_data using black

* Revert "Ignore example_data using black"

This reverts commit 296f279.

* Ignore example_data using black

* Update CHANGELOG.md

.azure-pipelines/azure-pipelines-base.yml

@@ -29,6 +29,10 @@ jobs:
 
 
   steps:
+
+  - checkout: self
+    submodules: true
+
   - task: UsePythonVersion@0
     inputs:
       versionSpec: '$(python.version)'

.azure-pipelines/azure-pipelines-benchmarks.yml

@@ -11,6 +11,9 @@ jobs:
 
   steps:
 
+  - checkout: self
+    submodules: true
+
   - task: UsePythonVersion@0
     inputs:
       versionSpec: '$(python.version)'

.azure-pipelines/azure-pipelines-docs.yml

@@ -16,6 +16,8 @@ jobs:
         name: "Python 3.9 - docs"
 
   steps:
+  - checkout: self
+    submodules: true
   - script: |
       PRTitle=$(curl -s https://api.github.com/repos/arviz-devs/arviz/pulls/${PULLREQUESTNUMBER} | grep title | cut -d'"' -f4)
       echo "##vso[task.setvariable variable=PRTitle]${PRTitle}"

.azure-pipelines/azure-pipelines-external.yml

@@ -27,6 +27,8 @@ jobs:
         name: "External special"
 
   steps:
+  - checkout: self
+    submodules: true
   - task: UsePythonVersion@0
     inputs:
       versionSpec: '$(python.version)'

.azure-pipelines/azure-pipelines-wheel.yml

@@ -18,6 +18,9 @@ jobs:
 
   steps:
 
+  - checkout: self
+    submodules: true
+
   - task: UsePythonVersion@0
     inputs:
       versionSpec: '$(python.version)'

.gitmodules

@@ -0,0 +1,4 @@
+[submodule "arviz/data/example_data"]
+	path = arviz/data/example_data
+	url = https://github.com/arviz-devs/arviz_example_data
+	branch = main

.pydocstyle.ini

@@ -1,2 +1,3 @@
 [pydocstyle]
 convention = numpy
+match-dir = '^(?!example_data).*'

.readthedocs.yml

@@ -1,5 +1,8 @@
 version: 2
 
+submodules:
+  include: all
+
 sphinx:
     configuration: doc/source/conf.py
 

CHANGELOG.md

@@ -8,6 +8,7 @@
 ### Maintenance and fixes
 * Add exception in `az.plot_hdi` for `x` of type `np.datetime64` and `smooth=True` ([2016](https://github.com/arviz-devs/arviz/pull/2016))
 * Change `ax.plot` usage to `ax.scatter` in `plot_pair` ([1990](https://github.com/arviz-devs/arviz/pull/1990))
+* Example data has been moved to the [arviz_example_data](https://github.com/arviz-devs/arviz_example_data) repository and is now included as a git submodule. ([2096](https://github.com/arviz-devs/arviz/pull/2096))
 
 ### Deprecation
 * Removed `fill_last`, `contour` and `plot_kwargs` arguments from `plot_pair` function ([2085](https://github.com/arviz-devs/arviz/pull/2085))

MANIFEST.in

@@ -5,5 +5,5 @@ include LICENSE
 include arviz/py.typed
 
 recursive-include arviz *.mplstyle
-recursive-include arviz *.nc
 recursive-include arviz/static *
+recursive-include arviz/data/example_data *.nc *.json

arviz/data/datasets.py

@@ -1,6 +1,7 @@
 """Base IO code for all datasets. Heavily influenced by scikit-learn's implementation."""
 import hashlib
 import itertools
+import json
 import os
 import shutil
 from collections import namedtuple
@@ -9,141 +10,26 @@
 from ..rcparams import rcParams
 from .io_netcdf import from_netcdf
 
-LocalFileMetadata = namedtuple("LocalFileMetadata", ["filename", "description"])
+LocalFileMetadata = namedtuple("LocalFileMetadata", ["name", "filename", "description"])
 
 RemoteFileMetadata = namedtuple(
-    "RemoteFileMetadata", ["filename", "url", "checksum", "description"]
+    "RemoteFileMetadata", ["name", "filename", "url", "checksum", "description"]
 )
-_DATASET_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), "_datasets")
-
-# Models for local datasets are located at https://github.com/arviz-devs/arviz_example_data
-
-LOCAL_DATASETS = {
-    "centered_eight": LocalFileMetadata(
-        filename=os.path.join(_DATASET_DIR, "centered_eight.nc"),
-        description="""
-A centered parameterization of the eight schools model. Provided as an example of a
-model that NUTS has trouble fitting. Compare to `load_arviz_data("non_centered_eight")`.
-
-The eight schools model is a hierarchical model used for an analysis of the effectiveness
-of classes that were designed to improve students' performance on the Scholastic Aptitude Test.
-
-See Bayesian Data Analysis (Gelman et. al.) for more details.
-""",
-    ),
-    "non_centered_eight": LocalFileMetadata(
-        filename=os.path.join(_DATASET_DIR, "non_centered_eight.nc"),
-        description="""
-A non-centered parameterization of the eight schools model. This is a hierarchical model
-where sampling problems may be fixed by a non-centered parametrization. Compare to
-`load_arviz_data("centered_eight")`.
-
-The eight schools model is a hierarchical model used for an analysis of the effectiveness
-of classes that were designed to improve students' performance on the Scholastic Aptitude Test.
-
-See Bayesian Data Analysis (Gelman et. al.) for more details.
-""",
-    ),
-}
-
-REMOTE_DATASETS = {
-    "radon": RemoteFileMetadata(
-        filename="radon_hierarchical.nc",
-        url="http://ndownloader.figshare.com/files/24067472",
-        checksum="a9b2b4adf1bf9c5728e5bdc97107e69c4fc8d8b7d213e9147233b57be8b4587b",
-        description="""
-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.
-
-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.
-
-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.
-""",
-    ),
-    "rugby": RemoteFileMetadata(
-        filename="rugby.nc",
-        url="http://ndownloader.figshare.com/files/16254359",
-        checksum="9eecd2c6317e45b0388dd97ae6326adecf94128b5a7d15a52c9fcfac0937e2a6",
-        description="""
-The Six Nations Championship is a yearly rugby competition between Italy, Ireland,
-Scotland, England, France and Wales. Fifteen games are played each year, representing
-all combinations of the six teams.
-
-This example uses and includes results from 2014 - 2017, comprising 60 total
-games. It models latent parameters for each team's attack and defense, as well
-as a parameter for home team advantage.
-
-See https://docs.pymc.io/notebooks/rugby_analytics.html by Peader Coyle
-for more details and references.
-""",
-    ),
-    "regression1d": RemoteFileMetadata(
-        filename="regression1d.nc",
-        url="http://ndownloader.figshare.com/files/16254899",
-        checksum="909e8ffe344e196dad2730b1542881ab5729cb0977dd20ba645a532ffa427278",
-        description="""
-A synthetic one dimensional linear regression dataset with latent slope,
-intercept, and noise ("eps"). One hundred data points, fit with PyMC3.
-
-True slope and intercept are included as deterministic variables.
-""",
-    ),
-    "regression10d": RemoteFileMetadata(
-        filename="regression10d.nc",
-        url="http://ndownloader.figshare.com/files/16255736",
-        checksum="c6716ec7e19926ad2a52d6ae4c1d1dd5ddb747e204c0d811757c8e93fcf9f970",
-        description="""
-A synthetic multi-dimensional (10 dimensions) linear regression dataset with
-latent weights ("w"), intercept, and noise ("eps"). Five hundred data points,
-fit with PyMC3.
-
-True weights and intercept are included as deterministic variables.
-""",
-    ),
-    "classification1d": RemoteFileMetadata(
-        filename="classification1d.nc",
-        url="http://ndownloader.figshare.com/files/16256678",
-        checksum="1cf3806e72c14001f6864bb69d89747dcc09dd55bcbca50aba04e9939daee5a0",
-        description="""
-A synthetic one dimensional logistic regression dataset with latent slope and
-intercept, passed into a Bernoulli random variable. One hundred data points,
-fit with PyMC3.
-
-True slope and intercept are included as deterministic variables.
-""",
-    ),
-    "classification10d": RemoteFileMetadata(
-        filename="classification10d.nc",
-        url="http://ndownloader.figshare.com/files/16256681",
-        checksum="16c9a45e1e6e0519d573cafc4d266d761ba347e62b6f6a79030aaa8e2fde1367",
-        description="""
-A synthetic multi dimensional (10 dimensions) logistic regression dataset with
-latent weights ("w") and intercept, passed into a Bernoulli random variable.
-Five hundred data points, fit with PyMC3.
-
-True weights and intercept are included as deterministic variables.
-""",
-    ),
-    "glycan_torsion_angles": RemoteFileMetadata(
-        filename="glycan_torsion_angles.nc",
-        url="http://ndownloader.figshare.com/files/22882652",
-        checksum="4622621fe7a1d3075c18c4c34af8cc57c59eabbb3501b20c6e2d9c6c4737034c",
-        description="""
-Torsion angles phi and psi are critical for determining the three dimensional
-structure of bio-molecules. Combinations of phi and psi torsion angles that
-produce clashes between atoms in the bio-molecule result in high energy, unlikely structures.
-
-This model uses a Von Mises distribution to propose torsion angles for the
-structure of a glycan molecule (pdb id: 2LIQ), and a Potential to estimate
-the proposed structure's energy. Said Potential is bound by Boltzman's law.
-""",
-    ),
-}
+_EXAMPLE_DATA_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), "example_data")
+_LOCAL_DATA_DIR = os.path.join(_EXAMPLE_DATA_DIR, "data")
+
+with open(os.path.join(_EXAMPLE_DATA_DIR, "data_local.json"), "r", encoding="utf-8") as f:
+    LOCAL_DATASETS = {
+        entry["name"]: LocalFileMetadata(
+            name=entry["name"],
+            filename=os.path.join(_LOCAL_DATA_DIR, entry["filename"]),
+            description=entry["description"],
+        )
+        for entry in json.load(f)
+    }
+
+with open(os.path.join(_EXAMPLE_DATA_DIR, "data_remote.json"), "r", encoding="utf-8") as f:
+    REMOTE_DATASETS = {entry["name"]: RemoteFileMetadata(**entry) for entry in json.load(f)}
 
 
 def get_data_home(data_home=None):
@@ -277,6 +163,6 @@ def list_datasets():
             location = f"remote: {resource.url}"
         else:
             location = "unknown"
-        lines.append(f"{name}\n{'=' * len(name)}\n{resource.description}\n{location}")
+        lines.append(f"{name}\n{'=' * len(name)}\n{resource.description}\n\n{location}")
 
     return f"\n\n{10 * '-'}\n\n".join(lines)

arviz/tests/base_tests/test_data.py

@@ -58,6 +58,7 @@ def no_remote_data(monkeypatch, tmpdir):
         REMOTE_DATASETS,
         "test_remote",
         RemoteFileMetadata(
+            name="test_remote",
             filename=filename,
             url=url,
             checksum="2c5501a9f5d7b6998fc7e6a4651030b9765032b2e5a1d7331f5b1f3df6c632a5",
@@ -68,7 +69,11 @@ def no_remote_data(monkeypatch, tmpdir):
         REMOTE_DATASETS,
         "bad_checksum",
         RemoteFileMetadata(
-            filename=filename, url=url, checksum="bad!", description=centered.description
+            name="bad_checksum",
+            filename=filename,
+            url=url,
+            checksum="bad!",
+            description=centered.description,
         ),
     )
     UnknownFileMetaData = namedtuple(

pyproject.toml

@@ -1,5 +1,6 @@
 [tool.black]
 line-length = 100
+extend_exclude = 'example_data'
 
 [tool.isort]
 multi_line_output = 3