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2 changes: 1 addition & 1 deletion .github/workflows/mypy.yml
Original file line number Diff line number Diff line change
Expand Up @@ -21,7 +21,7 @@ jobs:
with:
environment-file: conda-envs/environment-test.yml
create-args: >-
python=3.11
python=3.12
environment-name: pymc-test
init-shell: bash
cache-environment: true
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2 changes: 1 addition & 1 deletion .github/workflows/publish-release-notes-to-discourse.yml
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Expand Up @@ -20,7 +20,7 @@ jobs:
- name: Set up Python
uses: actions/setup-python@e797f83bcb11b83ae66e0230d6156d7c80228e7c # v6.0.0
with:
python-version: "3.11"
python-version: "3.12"

- name: Install dependencies
run: pip install requests
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2 changes: 1 addition & 1 deletion .github/workflows/tests.yml
Original file line number Diff line number Diff line change
Expand Up @@ -182,7 +182,7 @@ jobs:
strategy:
matrix:
linker: [cvm, numba]
python-version: ["3.11"]
python-version: ["3.12"]
test-subset:
- tests/variational/test_approximations.py tests/variational/test_callbacks.py tests/variational/test_inference.py tests/variational/test_opvi.py tests/test_initial_point.py
- tests/model/test_core.py tests/sampling/test_mcmc.py
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2 changes: 1 addition & 1 deletion ARCHITECTURE.md
Original file line number Diff line number Diff line change
Expand Up @@ -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
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2 changes: 1 addition & 1 deletion conda-envs/environment-alternative-backends.yml
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Expand Up @@ -5,7 +5,7 @@ channels:
- nodefaults
dependencies:
# Base dependencies
- arviz>=0.13.0
- arviz>=1.0.0rc0
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if we need to run the test suite before we make the stable 1.0 release we can add arviz-plots here in the conda section and then this arviz indicator with the release candidate on the pip section. If we use the rc part in the version pin for pip it should automatically take pre-releases into account.

- blas
- cachetools>=4.2.1
- cloudpickle
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3 changes: 2 additions & 1 deletion conda-envs/environment-dev.yml
Original file line number Diff line number Diff line change
Expand Up @@ -5,7 +5,7 @@ channels:
- nodefaults
dependencies:
# Base dependencies
- arviz>=0.13.0
- arviz>=1.0.0rc0
- blas
- cachetools>=4.2.1
- cloudpickle
Expand All @@ -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
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2 changes: 1 addition & 1 deletion conda-envs/environment-docs.yml
Original file line number Diff line number Diff line change
Expand Up @@ -5,7 +5,7 @@ channels:
- nodefaults
dependencies:
# Base dependencies
- arviz>=0.13.0
- arviz>=1.0.0rc0
- cachetools>=4.2.1
- cloudpickle
- numpy>=1.25.0
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2 changes: 1 addition & 1 deletion conda-envs/environment-test.yml
Original file line number Diff line number Diff line change
Expand Up @@ -5,7 +5,7 @@ channels:
- nodefaults
dependencies:
# Base dependencies
- arviz>=0.13.0
- arviz>=1.0.0rc0
- blas
- cachetools>=4.2.1
- cloudpickle
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2 changes: 1 addition & 1 deletion conda-envs/windows-environment-dev.yml
Original file line number Diff line number Diff line change
Expand Up @@ -5,7 +5,7 @@ channels:
- nodefaults
dependencies:
# Base dependencies (see install guide for Windows)
- arviz>=0.13.0
- arviz>=1.0.0rc0
- blas
- cachetools>=4.2.1
- cloudpickle
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2 changes: 1 addition & 1 deletion conda-envs/windows-environment-test.yml
Original file line number Diff line number Diff line change
Expand Up @@ -5,7 +5,7 @@ channels:
- nodefaults
dependencies:
# Base dependencies (see install guide for Windows)
- arviz>=0.13.0
- arviz>=1.0.0rc0
- blas
- cachetools>=4.2.1
- cloudpickle
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2 changes: 1 addition & 1 deletion docs/source/conf.py
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Expand Up @@ -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`",
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This is also something I am unsure about for arviz docs but we might want to keep the alias around for InferenceData and maybe point to the schema page or a page about the schema implementation in python arviz. All "InferenceData" objects are compatible with DataTree but not the other way around so I think there is still value in using "InferenceData" in docs as in addition to saying the python type is xarray.DataTree it also says it will follow the schema (i.e. if a posterior group is present it has a specific meaning, log_likelihood group has that other meaning, fit_info group could also be there but has no convention agreed-on meaning)

"MultiTrace": ":class:`~pymc.backends.base.MultiTrace`",
"BaseTrace": ":class:`~pymc.backends.base.BaseTrace`",
"Point": ":class:`~pymc.Point`",
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285 changes: 158 additions & 127 deletions docs/source/learn/core_notebooks/dimensionality.ipynb

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1,115 changes: 627 additions & 488 deletions docs/source/learn/core_notebooks/dims_module.ipynb

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240 changes: 91 additions & 149 deletions docs/source/learn/core_notebooks/model_comparison.ipynb

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2,322 changes: 1,438 additions & 884 deletions docs/source/learn/core_notebooks/posterior_predictive.ipynb

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1,970 changes: 1,167 additions & 803 deletions docs/source/learn/core_notebooks/pymc_overview.ipynb

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