Use arviz 1.0

Author: aloctavodia

ARCHITECTURE.md

@@ -35,7 +35,7 @@ of the topics below refer to that specific library
 * Plotting e.g. Trace plots, rank plots, posterior plots
 * MCMC sampling diagnostics e.g. Rhat, Effective Sample Size.
 * Model comparison, particularly efficient leave-one-out cross-validation approximation
-* Inference Data structure
+* Data structures for Bayesian inference data storage and manipulation
 
 
 # Modules

conda-envs/environment-alternative-backends.yml

@@ -5,7 +5,7 @@ channels:
 - defaults
 dependencies:
 # Base dependencies
-- arviz>=0.13.0
+- arviz>=1.0.0rc0
 - blas
 - cachetools>=4.2.1
 - cloudpickle

conda-envs/environment-dev.yml

@@ -5,7 +5,7 @@ channels:
 - defaults
 dependencies:
 # Base dependencies
-- arviz>=0.13.0
+- arviz>=1.0.0rc0
 - blas
 - cachetools>=4.2.1
 - cloudpickle
@@ -19,6 +19,7 @@ dependencies:
 - typing-extensions>=3.7.4
 - threadpoolctl>=3.1.0
 - zarr>=2.5.0,<3
+- netCDF4
 # Extra dependencies for dev, testing and docs build
 - ipython>=7.16
 - jax

conda-envs/environment-docs.yml

@@ -5,7 +5,7 @@ channels:
 - defaults
 dependencies:
 # Base dependencies
-- arviz>=0.13.0
+- arviz>=1.0.0rc0
 - cachetools>=4.2.1
 - cloudpickle
 - numpy>=1.25.0

conda-envs/environment-test.yml

@@ -5,7 +5,7 @@ channels:
 - defaults
 dependencies:
 # Base dependencies
-- arviz>=0.13.0
+- arviz>=1.0.0rc0
 - blas
 - cachetools>=4.2.1
 - cloudpickle

conda-envs/windows-environment-dev.yml

@@ -5,7 +5,7 @@ channels:
 - defaults
 dependencies:
 # Base dependencies (see install guide for Windows)
-- arviz>=0.13.0
+- arviz>=1.0.0rc0
 - blas
 - cachetools>=4.2.1
 - cloudpickle

conda-envs/windows-environment-test.yml

@@ -5,7 +5,7 @@ channels:
 - defaults
 dependencies:
 # Base dependencies (see install guide for Windows)
-- arviz>=0.13.0
+- arviz>=1.0.0rc0
 - blas
 - cachetools>=4.2.1
 - cloudpickle

docs/source/conf.py

@@ -66,7 +66,7 @@
     "ndarray": ":class:`~numpy.ndarray`",
     "Covariance": ":mod:`Covariance <pymc.gp.cov>`",
     "Mean": ":mod:`Mean <pymc.gp.mean>`",
-    "InferenceData": ":class:`~arviz.InferenceData`",
+    "DataTree": ":class:`~xarray.DataTree`",
     "MultiTrace": ":class:`~pymc.backends.base.MultiTrace`",
     "BaseTrace": ":class:`~pymc.backends.base.BaseTrace`",
     "Point": ":class:`~pymc.Point`",