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Package is "python-arviz" Tue May 19 17:50:16 2026 rev:15 rq:1353969 version:1.1.0 Changes: -------- --- /work/SRC/openSUSE:Factory/python-arviz/python-arviz.changes 2026-03-03 15:33:02.230264251 +0100 +++ /work/SRC/openSUSE:Factory/.python-arviz.new.1966/python-arviz.changes 2026-05-19 17:50:44.089542149 +0200 @@ -1,0 +2,33 @@ +Tue May 19 08:07:23 UTC 2026 - Daniel Garcia <[email protected]> + +- Update to 1.1.0: + ArviZ 1.1 builds on the 1.0 release with incremental improvements + and no major breaking changes. + + The 1.0 ArviZ introduced many breaking changes: See the migration + guide for an overview of improvements and guidance on things that + will break and how to update them. + + The whole library has been refactored for extra flexibility and + modularity as well as reducing the cost of maintaining and adding + new features to the library. arviz is now a metapackage that exposes + all the common functions that are implemented in independent + packages of the ArviZverse. For a detailed list of changes you can + check the changelog of each of these packages. + + Take a look at the online book on Exploratory Analysis of Bayesian + Models to see it in action and at the docs of the 3 ArviZ packages: + arviz-base, arviz-stats and arviz-plots (note all the objects at the + top level namespace of these 3 libraries are also exposed as + arviz.xyz) + + ## Changes in the metapackage. + - Add Apache license headers by @ellatso in #2534 + - Schema updates by @OriolAbril in #2544 + - Replace gitter mentions with GitHub discussions by @cheelaakhil in + #2561 + - Check legacy imports by @symeneses in #2567 + - Emit prominent MigrationWarning instead of breaking InferenceData + import by @michaelosthege in #2575 + +------------------------------------------------------------------- Old: ---- arviz-1.0.0.tar.gz New: ---- arviz-1.1.0.tar.gz ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ Other differences: ------------------ ++++++ python-arviz.spec ++++++ --- /var/tmp/diff_new_pack.BhdFzz/_old 2026-05-19 17:50:44.945577519 +0200 +++ /var/tmp/diff_new_pack.BhdFzz/_new 2026-05-19 17:50:44.949577684 +0200 @@ -20,7 +20,7 @@ # Upstream supports Python 3.12+ %define skip_python311 1 Name: python-arviz -Version: 1.0.0 +Version: 1.1.0 Release: 0 Summary: Exploratory analysis of Bayesian models License: Apache-2.0 ++++++ arviz-1.0.0.tar.gz -> arviz-1.1.0.tar.gz ++++++ diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn' '--exclude=.svnignore' old/arviz-1.0.0/.github/workflows/publish.yml new/arviz-1.1.0/.github/workflows/publish.yml --- old/arviz-1.0.0/.github/workflows/publish.yml 2026-03-02 15:50:15.000000000 +0100 +++ new/arviz-1.1.0/.github/workflows/publish.yml 2026-04-24 07:01:01.000000000 +0200 @@ -41,7 +41,7 @@ id-token: write steps: - name: Download Distribution Artifacts - uses: actions/download-artifact@v7 + uses: actions/download-artifact@v8 with: # The build-and-inspect-python-package action invokes upload-artifact. # These are the correct arguments from that action. diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn' '--exclude=.svnignore' old/arviz-1.0.0/.pre-commit-config.yaml new/arviz-1.1.0/.pre-commit-config.yaml --- old/arviz-1.0.0/.pre-commit-config.yaml 2026-03-02 15:50:15.000000000 +0100 +++ new/arviz-1.1.0/.pre-commit-config.yaml 2026-04-24 07:01:01.000000000 +0200 @@ -23,3 +23,15 @@ rev: 0.4.1 hooks: - id: no-print-statements +- repo: https://github.com/Lucas-C/pre-commit-hooks + rev: v1.5.5 + hooks: + - id: insert-license + files: ^(src|tests)/.*\.py$ + args: + - --license-filepath + - tools/APACHE_HEADER.txt + - --comment-style + - "#" + - --detect-license-in-X-top-lines=30 + - --no-extra-eol diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn' '--exclude=.svnignore' old/arviz-1.0.0/CITATION.cff new/arviz-1.1.0/CITATION.cff --- old/arviz-1.0.0/CITATION.cff 2026-03-02 15:50:15.000000000 +0100 +++ new/arviz-1.1.0/CITATION.cff 2026-04-24 07:01:01.000000000 +0200 @@ -1,26 +1,69 @@ -cff-version: 1.2.0 -message: "If you use this software, please cite it as below." -title: "ArviZ" -url: "https://github.com/arviz-devs/arviz"; +cff-version: "1.2.0" +authors: +- family-names: Martin + given-names: Osvaldo A + orcid: "https://orcid.org/0000-0001-7419-8978"; +- family-names: Abril-Pla + given-names: Oriol + orcid: "https://orcid.org/0000-0002-1847-9481"; +- family-names: Deklerk + given-names: Jordan +- family-names: Axen + given-names: Seth D. + orcid: "https://orcid.org/0000-0003-3933-8247"; +- family-names: Carroll + given-names: Colin + orcid: "https://orcid.org/0000-0001-6977-0861"; +- family-names: Hartikainen + given-names: Ari + orcid: "https://orcid.org/0000-0002-4569-569X"; +- family-names: Vehtari + given-names: Aki + orcid: "https://orcid.org/0000-0003-2164-9469"; +contact: +- family-names: Martin + given-names: Osvaldo A + orcid: "https://orcid.org/0000-0001-7419-8978"; +- family-names: Abril-Pla + given-names: Oriol + orcid: "https://orcid.org/0000-0002-1847-9481"; +doi: 10.5281/zenodo.18837209 +message: If you use this software, please cite our article in the + Journal of Open Source Software. preferred-citation: - type: article authors: - - - family-names: Kumar - given-names: Ravin - orcid: "https://orcid.org/0000-0003-0501-6098"; - - - family-names: Carroll - given-names: Colin - orcid: "https://orcid.org/0000-0001-6977-0861"; - - - family-names: Hartikainen - given-names: Ari - orcid: "https://orcid.org/0000-0002-4569-569X"; - - - family-names: Osvaldo - given-names: Martin - orcid: "https://orcid.org/0000-0001-7419-8978"; - doi: "10.21105/joss.01143" - journal: "Journal of Open Source Software" - title: "ArviZ a unified library for exploratory analysis of Bayesian models in Python" + - family-names: Martin + given-names: Osvaldo A + orcid: "https://orcid.org/0000-0001-7419-8978"; + - family-names: Abril-Pla + given-names: Oriol + orcid: "https://orcid.org/0000-0002-1847-9481"; + - family-names: Deklerk + given-names: Jordan + - family-names: Axen + given-names: Seth D. + orcid: "https://orcid.org/0000-0003-3933-8247"; + - family-names: Carroll + given-names: Colin + orcid: "https://orcid.org/0000-0001-6977-0861"; + - family-names: Hartikainen + given-names: Ari + orcid: "https://orcid.org/0000-0002-4569-569X"; + - family-names: Vehtari + given-names: Aki + orcid: "https://orcid.org/0000-0003-2164-9469"; + date-published: 2026-03-06 + doi: 10.21105/joss.09889 + issn: 2475-9066 + issue: 119 + journal: Journal of Open Source Software + publisher: + name: Open Journals + start: 9889 + title: "ArviZ: a modular and flexible library for exploratory analysis + of Bayesian models" + type: article + url: "https://joss.theoj.org/papers/10.21105/joss.09889"; + volume: 11 +title: "ArviZ: a modular and flexible library for exploratory analysis + of Bayesian models" diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn' '--exclude=.svnignore' old/arviz-1.0.0/README.md new/arviz-1.1.0/README.md --- old/arviz-1.0.0/README.md 2026-03-02 15:50:15.000000000 +0100 +++ new/arviz-1.1.0/README.md 2026-04-24 07:01:01.000000000 +0200 @@ -85,23 +85,22 @@ ## Citation -If you use ArviZ and want to cite it please use [](https://doi.org/10.21105/joss.01143) +If you use ArviZ and want to cite it please use [](https://doi.org/10.21105/joss.09889) Here is the citation in BibTeX format ``` -@article{arviz_2019, - doi = {10.21105/joss.01143}, - url = {https://doi.org/10.21105/joss.01143}, - year = {2019}, - publisher = {The Open Journal}, - volume = {4}, - number = {33}, - pages = {1143}, - author = {Ravin Kumar and Colin Carroll and Ari Hartikainen and Osvaldo Martin}, - title = {ArviZ a unified library for exploratory analysis of Bayesian models in Python}, - journal = {Journal of Open Source Software} -} +@article{Martin2026, +doi = {10.21105/joss.09889}, +url = {https://doi.org/10.21105/joss.09889}, +year = {2026}, +publisher = {The Open Journal}, +volume = {11}, +number = {119}, +pages = {9889}, +author = {Martin, Osvaldo A. and Abril-Pla, Oriol and Deklerk, Jordan and Axen, Seth D. and Carroll, Colin and Hartikainen, Ari and Vehtari, Aki}, +title = {ArviZ: a modular and flexible library for exploratory analysis of Bayesian models}, +journal = {Journal of Open Source Software}} ``` diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn' '--exclude=.svnignore' old/arviz-1.0.0/docs/source/community.md new/arviz-1.1.0/docs/source/community.md --- old/arviz-1.0.0/docs/source/community.md 2026-03-02 15:50:15.000000000 +0100 +++ new/arviz-1.1.0/docs/source/community.md 2026-04-24 07:01:01.000000000 +0200 @@ -45,13 +45,9 @@ It is the best way to be up to date with the latest developments and events related to ArviZ. -### Gitter -The chat at [Gitter](https://gitter.im/arviz-devs/community) is a great space -to ask quick questions about ArviZ and to chat with other users and contributors. -For longer questions, the Discourse forums listed below are probably a better platform. - -### Affine forums -Many ArviZ contributors are also active in one of [PyMC Discourse](https://discourse.pymc.io/) +### GitHub Discussions and Affine forums +For questions and discussions, you can use [GitHub Discussions](https://github.com/arviz-devs/arviz/discussions) +or one of the affine forums: [PyMC Discourse](https://discourse.pymc.io/) or [Stan Discourse](https://discourse.mc-stan.org/) (and sometimes even in both!). ## Conferences diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn' '--exclude=.svnignore' old/arviz-1.0.0/docs/source/contributing/index.md new/arviz-1.1.0/docs/source/contributing/index.md --- old/arviz-1.0.0/docs/source/contributing/index.md 2026-03-02 15:50:15.000000000 +0100 +++ new/arviz-1.1.0/docs/source/contributing/index.md 2026-04-24 07:01:01.000000000 +0200 @@ -25,7 +25,7 @@ Before contributing to ArviZ, please make sure to read and observe the [Code of Conduct](https://github.com/arviz-devs/arviz/blob/main/CODE_OF_CONDUCT.md). ## Communication -Contact us on [Gitter](https://gitter.im/arviz-devs/community) +Contact us on [GitHub Discussions](https://github.com/arviz-devs/arviz/discussions) if you want to contribute to the project but you are not sure where you can contribute or how to start. ## Choosing a contribution type diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn' '--exclude=.svnignore' old/arviz-1.0.0/docs/source/index.md new/arviz-1.1.0/docs/source/index.md --- old/arviz-1.0.0/docs/source/index.md 2026-03-02 15:50:15.000000000 +0100 +++ new/arviz-1.1.0/docs/source/index.md 2026-04-24 07:01:01.000000000 +0200 @@ -248,13 +248,13 @@ Contributions and issue reports are very welcome at [the GitHub repository](https://github.com/arviz-devs/arviz). We have a {ref}`contributing guide <contributing_guide>` to help you through the process. -If you have any doubts, please do not hesitate to contact us on [gitter](https://gitter.im/arviz-devs/community). +If you have any doubts, please do not hesitate to contact us on [GitHub Discussions](https://github.com/arviz-devs/arviz/discussions). ::: :::{grid-item} <h3>Citation</h3> -If you use ArviZ, please cite it using <a class="reference external" href="https://doi.org/10.21105/joss.01143";><img alt="JOSS" src="https://joss.theoj.org/papers/10.21105/joss.01143/status.svg";></a>. +If you use ArviZ, please cite it using <a class="reference external" href="https://doi.org/10.21105/joss.09889";><img alt="JOSS" src="https://joss.theoj.org/papers/10.21105/joss.09889/status.svg)"></a>. See our {ref}`support page <arviz_org:cite>` for information on how to cite in BibTeX format. ::: diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn' '--exclude=.svnignore' old/arviz-1.0.0/docs/source/schema/schema.md new/arviz-1.1.0/docs/source/schema/schema.md --- old/arviz-1.0.0/docs/source/schema/schema.md 2026-03-02 15:50:15.000000000 +0100 +++ new/arviz-1.1.0/docs/source/schema/schema.md 2026-04-24 07:01:01.000000000 +0200 @@ -54,6 +54,10 @@ No metadata is _required_ to be present in order to be compliant with ArviZ's schema. However, it is recommended to store the following fields when relevant: * `name`: All the quantities stored in a DataTree are tied to a single model. The model identifier can be added as metadata to simplify the calls to model comparison functions. +* `sample_dims`: list of dimensions that were generated through a sampling process. Common examples + are `chain`, `draw` or `sample`. It will generally be taken as the default value for arguments + like `dim` or `sample_dims`. +* `sampled_variables`: list of variable names on which inference was performed. * `created_at`: the date of creation of the group. * `creation_library`: the library used to create the DataTree, does not necessary be ArviZ. * `creation_library_version`: the version of `creation_library` that generated the DataTree. @@ -62,6 +66,8 @@ * `inference_library_version`: version of the inference library used. Metadata can be stored at the whole `DataTree` level but also at group level when needed. +In particular, the `name` attribute is only taken into account at the `DataTree` level +whereas `sample_dims` or `sampled_variables` are only taken into account at the group level. ## Relations between groups @@ -115,6 +121,8 @@ * `tree_depth`: The number of tree doublings in the balanced binary tree. * `n_steps`: The number of leapfrog steps computed. It is related to `tree_depth` with `n_steps <= 2^tree_dept`. +* `reached_max_treedepth`: (boolean) Indicates the sampler reached the maximum allowed tree depth + at this iteration. * `diverging`: (boolean) Indicates the presence of leapfrog transitions with large energy deviation from starting and subsequent termination of the trajectory. "large" is defined as `max_energy_error` going over a threshold. * `energy`: The value of the Hamiltonian energy for the accepted proposal (up to an Binary files old/arviz-1.0.0/paper/figures/figure_0.png and new/arviz-1.1.0/paper/figures/figure_0.png differ diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn' '--exclude=.svnignore' old/arviz-1.0.0/paper/paper.md new/arviz-1.1.0/paper/paper.md --- old/arviz-1.0.0/paper/paper.md 1970-01-01 01:00:00.000000000 +0100 +++ new/arviz-1.1.0/paper/paper.md 2026-04-24 07:01:01.000000000 +0200 @@ -0,0 +1,142 @@ +--- +title: 'ArviZ: a modular and flexible library for exploratory analysis of Bayesian models' +tags: + - Python + - Bayesian statistics + - Bayesian workflow +authors: + - name: Osvaldo A Martin + orcid: 0000-0001-7419-8978 + equal-contrib: true + corresponding: true + affiliation: 1 + - name: Oriol Abril-Pla + orcid: 0000-0002-1847-9481 + equal-contrib: true + corresponding: true + affiliation: 2 + - name: Jordan Deklerk + affiliation: 3 + - name: Seth D. Axen + orcid: 0000-0003-3933-8247 + affiliation: 4 + - name: Colin Carroll + orcid: 0000-0001-6977-0861 + affiliation: 2 + - name: Ari Hartikainen + orcid: 0000-0002-4569-569X + affiliation: 2 + - name: Aki Vehtari + orcid: 0000-0003-2164-9469 + affiliation: "1, 5" +affiliations: + - name: Aalto University, Espoo, Finland + index: 1 + - name: arviz-devs + index: 2 + - name: DICK's Sporting Goods, Coraopolis, Pennsylvania + index: 3 + - name: University of Tübingen + index: 4 + - name: ELLIS Institute Finland + index: 5 +date: 17 January 2026 +bibliography: references.bib +--- + +# Summary + +When working with Bayesian models, a range of related tasks must be addressed beyond inference itself. These include diagnosing the quality of Markov chain Monte Carlo (MCMC) samples, model criticism, model comparison, etc. We collectively refer to these activities as exploratory analysis of Bayesian models. + +In this work, we present a redesigned version of ArviZ, a Python package for exploratory analysis of Bayesian models (EABM). The redesign emphasizes greater user control and modularity. This redesign delivers a more flexible and efficient toolkit for exploratory analysis of Bayesian models. With its renewed focus on modularity and usability, ArviZ is well-positioned to remain an essential tool for Bayesian modelers in both research and applied settings. + + +# Statement of need + +Probabilistic programming has emerged as a powerful paradigm for statistical modeling, accompanied by a growing ecosystem of tools for model specification and inference. Effective modeling requires robust support for uncertainty visualization, sampling diagnostics, model comparison, and model checking [@Gelman_2020; @Martin_2024; @Guo_2024]. ArviZ addresses this gap by providing a unified, backend-agnostic library to perform these tasks. The original ArviZ paper [@Kumar_2019] described the landscape of probabilistic programming tools at the time and the need for a unified, backend-agnostic library for exploratory analysis — a need that has only grown as the ecosystem has expanded. + +The methods implemented in ArviZ are grounded in well-established statistical principles and provide robust, interpretable diagnostics and visualizations [@Vehtari_2017; @Gelman_2019; @Dimitriadis_2021; @Paananen_2021; @Padilla_2021; @Vehtari_2021; @Sailynoja_2022; @Kallioinen_2023; @Sailynoja_2025]. Modern Bayesian practice is a rapidly advancing field in which new methodological developments continually extend the range and complexity of models that can be fit in practice. For instance, the methods to compute key ArviZ features such as `ess`, `rhat`, `loo` or `compare` have been improved between 2019 and now, and new implementations needed significant development effort to adapt to because it wasn't possible to change a part of ArviZ without also adapting everything that interacted with it. The redesign addresses these challenges by modularizing the codebase, allowing individual components to be updated or replaced without affecting the entire system. This modularity not only faci litates maintenance and updates but also encourages community contributions, as developers can focus on specific components without needing to understand the entire codebase. + + +# State of the field + +In the Python Bayesian ecosystem, ArviZ occupies a niche comparable to tools in the R/Stan community such as posterior [@gelman_2013;@Vehtari_2021], loo [@Vehtari_2017;@loo], bayesplot [@bayesplot0;@bayesplot1], priorsense [@Kallioinen_2023], and ggdist [@kay_2024] sharing similar goals while reflecting different language ecosystems and workflows. + +# Research impact statement + +ArviZ [@Kumar_2019] is a Python package for exploratory analysis of Bayesian models that has been widely used in academia and industry since its introduction in 2019, with over 700 citations and 75 million downloads. Its goal is to integrate seamlessly with established probabilistic programming languages and statistical interfaces, such as PyMC [@Abril-pla_2023], Stan (via the cmdstanpy interface) [@stan], Pyro, NumPyro [@Phan_2019; @Bingham_2019], emcee [@emcee], and Bambi [@Capretto_2022], among others. + +The maturity of ArviZ has also led to other initiatives, including ArviZ.jl [@arvizjl_2025] for Julia, PreliZ [@icazatti_2023] for prior elicitation and the development of educational resources [@eabm_2025]. + +# Software design + +The previous ArviZ design divided the package into three submodules, which are now available as three independent installable packages. This redesign emphasizes greater user control and modularity. The new architecture enables users to customize the installation and use of specific components. Key design changes include: + +General functionality, data processing, and data input/output (I/O) have been streamlined and enhanced for greater versatility. Previously, ArviZ used the custom `InferenceData` class to organize and store the high-dimensional outputs of Bayesian inference in a structured, labeled format, enabling efficient analysis, metadata persistence, and serialization. These have been replaced with the `DataTree` class from xarray [@Hoyer_2017], which, like the original `InferenceData`, supports grouping but is more flexible, enabling richer nesting and automatic support for all xarray I/O formats. Additionally, converters allow more flexibility in dimensionality, naming, and indexing of their generated outputs. + +Statistical functions are now accessible through two distinct interfaces: + +* A low-level array interface with only `numpy` [@harris_2020] and `scipy` [@virtanen_2020] as dependencies, intended for advanced users +and developers of third-party libraries. +* A higher-level xarray interface designed for end users, which simplifies usage by automating common tasks and handling metadata. + +Plotting functions have also been redesigned to support modularity at multiple levels: + +* At a high level, ArviZ offers a collection of “batteries-included” plots. These are built-in plotting functions providing sensible defaults for common tasks like MCMC sampling diagnostics, predictive checks, and model comparison. +* At an intermediate level, the application programming interface enables easier customization of batteries-included plots and simplifies the creation of new plots. This is achieved through the `PlotCollection` class, which enables developers and advanced users to focus solely on the plotting logic, delegating any faceting or aesthetic mappings to `PlotCollection`. +* At a lower level, we have improved the separation between computational and plotting logic, reducing code duplication and enhancing modular design. These changes also facilitate support for multiple plotting backends, improving extensibility and maintainability. Currently, ArviZ supports three plotting backends: matplotlib [@Hunter_2007], Bokeh [@Bokeh_2018], and plotly [@plotly_2015]. + +Thanks to this new design, the cost of adding "batteries-included" plots has reduced in more than half even though ArviZ now supports one extra backend. Consequently, redesigned ArviZ already has 37 "batteries-included", 10 more than the 0.x versions. + +## Examples + +For the first example, we use the low-level array interface to compute the effective sample sizes for some fake data. We construct an array resembling data from MCMC sampling with 4 chains and 1000 draws for two posterior variables. When using the array interface we need to specify which axes represent the chains and which the draws. + + import numpy as np + from arviz_stats.base import array_stats + + rng = np.random.default_rng() + samples = rng.normal(size=(4, 1000, 2)) # (chain, draw, variable) + array_stats.ess(samples, chain_axis=0, draw_axis=1) + +The array interface is lightweight and intended for advanced users and library developers. For most users, we instead recommend the xarray interface, as it is more user-friendly and automates many tasks. When converting the NumPy array to a `DataTree`, ArviZ assigns `chain` and `draw` as named dimensions based on the assumed dimension order, so this information is already encoded in the resulting object and does not need to be specified explicitly when calling other functions. + + import arviz as az + dt_samples = az.convert_to_datatree(samples) + az.ess(dt_samples) + +The only required argument for battery-included plots, like `plot_dist`, is the input data, typically a `DataTree` (`dt`). In this example we also apply optional customizations. + + az.style.use('arviz-variat') + dt = az.load_arviz_data("centered_eight") + pc = az.plot_dist( + dt, + kind="dot", + visuals={"dist":{"marker": "C6"}, + "point_estimate_text":False}, + aes={"color": ["school"]} + ); + pc.add_legend("school", loc="outside right upper") + +{width=4.5in} + +To create \autoref{fig:plot_dist} we change the default kind argument in `plot_dist` from "kde" to "dot" to produce quantile dot plots [@kay_2016], and map the school dimension to color so that each school is shown in a different hue. Variables that do not have a school dimension (such as mu and tau) are automatically assigned a neutral color. We also disable the point-estimate text and set a custom marker style for the dots, and finally add a legend for the school. + +For more examples and a more comprehensive overview, see the [ArviZ documentation](https://python.arviz.org/en/latest/) and the [EABM guide](https://arviz-devs.github.io/EABM/) [@eabm_2025]. These resources include a wide range of examples designed for all types of users, from casual users to advanced analysts and developers looking to use ArviZ in their projects or libraries. + +## AI usage disclosure + +Generative AI tools were used during software development and documentation in a limited capacity, primarily to assist with rewording and minor code suggestions. All AI-assisted contributions were reviewed and edited by the authors. Core design decisions, feature development, and scientific or technical judgment were carried out by the authors, and all code and claims were tested and manually verified to ensure correctness. + +## Acknowledgements + +We thank our fiscal sponsor, NumFOCUS, a nonprofit 501(c)(3) public charity, for their operational and financial support. We also thank all the contributors to `arviz`, `arviz-base`, `arviz-stats`, and `arviz-plots` repositories, including code contributors, documentation writers, issue reporters, and users who have provided feedback and suggestions. + +This research was supported by: + +* The Research Council of Finland Flagship Program "Finnish Center for Artificial Intelligence" (FCAI) +* Research Council of Finland grant 340721 +* Essential Open Source Software Round 4 grant by the Chan Zuckerberg Initiative (CZI) +* Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy – EXC number 2064/1 – Project number 390727645 + +# References diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn' '--exclude=.svnignore' old/arviz-1.0.0/paper/references.bib new/arviz-1.1.0/paper/references.bib --- old/arviz-1.0.0/paper/references.bib 1970-01-01 01:00:00.000000000 +0100 +++ new/arviz-1.1.0/paper/references.bib 2026-04-24 07:01:01.000000000 +0200 @@ -0,0 +1,413 @@ +@article{Kumar_2019, +doi = {10.21105/joss.01143}, +url = {10.21105/joss.01143}, +year = {2019}, publisher = {The Open Journal}, +volume = {4}, +number = {33}, +pages = {1143}, +author = {Ravin Kumar and Colin Carroll and Ari Hartikainen and Osvaldo Martin}, +title = {{ArviZ} a unified library for exploratory analysis of {Bayesian} models in {Python}}, +journal = {Journal of Open Source Software} +} + +@article{Abril-pla_2023, + title = {{PyMC}: a modern, and comprehensive probabilistic programming framework in {Python}}, + volume = {9}, + issn = {2376-5992}, + shorttitle = {{PyMC}}, + url = {https://peerj.com/articles/cs-1516}, + doi = {10.7717/peerj-cs.1516}, + language = {en}, + urldate = {2023-10-26}, + journal = {PeerJ Computer Science}, + author = {Abril-Pla, Oriol and Andreani, Virgile and Carroll, Colin and Dong, Larry and Fonnesbeck, Christopher J. and Kochurov, Maxim and Kumar, Ravin and Lao, Junpeng and Luhmann, Christian C. and Martin, Osvaldo A. and Osthege, Michael and Vieira, Ricardo and Wiecki, Thomas and Zinkov, Robert}, + month = sep, + year = {2023}, + note = {Publisher: PeerJ Inc.}, + pages = {e1516}, +} + +@article{stan, + title={Stan: A Probabilistic Programming Language}, + volume={76}, + url={https://www.jstatsoft.org/index.php/jss/article/view/v076i01}, + doi={10.18637/jss.v076.i01}, + number={1}, + journal={Journal of Statistical Software}, + author={Carpenter, Bob and Gelman, Andrew and Hoffman, Matthew D. and Lee, Daniel and Goodrich, Ben and Betancourt, Michael and Brubaker, Marcus and Guo, Jiqiang and Li, Peter and Riddell, Allen}, + year={2017}, + pages={1-32} +} + +@article{Phan_2019, + title={Composable Effects for Flexible and Accelerated Probabilistic Programming in {NumPyro}}, + author={Phan, Du and Pradhan, Neeraj and Jankowiak, Martin}, + journal={arXiv preprint arXiv:1912.11554}, + year={2019} +} + +@article{Bingham_2019, + author = {Eli Bingham and + Jonathan P. Chen and + Martin Jankowiak and + Fritz Obermeyer and + Neeraj Pradhan and + Theofanis Karaletsos and + Rohit Singh and + Paul A. Szerlip and + Paul Horsfall and + Noah D. Goodman}, + title = {Pyro: Deep Universal Probabilistic Programming}, + journal = {Journal of Machine Learning Research}, + volume = {20}, + pages = {28:1--28:6}, + year = {2019}, + url = {http://jmlr.org/papers/v20/18-403.html} +} + +@misc{Gelman_2020, + title={Bayesian Workflow}, + author={Andrew Gelman and Aki Vehtari and Daniel Simpson and Charles C. Margossian and Bob Carpenter and Yuling Yao and Lauren Kennedy and Jonah Gabry and Paul-Christian Bürkner and Martin Modrák}, + year={2020}, + eprint={2011.01808}, + archivePrefix={arXiv}, + doi={10.48550/arXiv.2011.01808}, + primaryClass={stat.ME} +} + +@article{Sailynoja_2025, + title={Recommendations for visual predictive checks in {Bayesian} workflow}, + author={S{\"a}ilynoja, Teemu and Johnson, Andrew R and Martin, Osvaldo A and Vehtari, Aki}, + journal={arXiv:2503.01509}, + doi={10.48550/arXiv.2503.01509}, + year={2025}, +} + +@article{Vehtari_2017, + title={Practical {Bayesian} model evaluation using leave-one-out cross-validation and {WAIC}}, + author={Vehtari, Aki and Gelman, Andrew and Gabry, Jonah}, + journal={Statistics and Computing}, + doi={10.1007/s11222-016-9696-4}, + volume={27}, + pages={1413--1432}, + year={2017}, +} + +@article{Vehtari_2021, + title={Rank-normalization, folding, and localization: An improved $\widehat{R}$ for assessing convergence of {MCMC}}, + author={Vehtari, Aki and Gelman, Andrew and Simpson, Daniel and Carpenter, Bob and B{\"u}rkner, Paul Christian}, + journal={Bayes Anal}, + year={2021}, + volume={16}, + doi={10.1214/20-BA1221}, + pages={667--718} +} + +@article{Sailynoja_2022, + title = {Graphical test for discrete uniformity and its applications in goodness-of-fit evaluation and multiple sample comparison}, + volume = {32}, + pages = {1573--1375}, + journal = {Statistics and Computing}, + author = {Säilynoja, Teemu and Bürkner, Paul Christian and Vehtari, Aki}, + doi = {10.1007/s11222-022-10090-6}, + year = {2022} +} + +@article{Kallioinen_2023, +title = {Detecting and diagnosing prior and likelihood sensitivity with power-scaling}, +author = {Noa Kallioinen and Topi Paananen and Paul Christian Bürkner and Aki Vehtari}, +year = {2023}, +journal = {Statistics and Computing}, +volume = {34}, +issue = {57}, +doi = {10.1007/s11222-023-10366-5}, +encoding = {UTF-8}, +} + +@article{Paananen_2021, + author = {Paananen, T. and Piironen, J. and Bürkner, P. C. and Vehtari, A.}, + year = 2021, + title = {Implicitly adaptive importance sampling}, + journal = {Statistics and Computing}, + volume = 31, + number = 16, + doi= {10.1007/s11222-020-09982-2}, +} + +@article{Gelman_2019, +author = {Andrew Gelman and Ben Goodrich and Jonah Gabry and Aki Vehtari}, +title = {R-squared for {Bayesian} regression models}, +journal = {The American Statistician}, +doi={10.1080/00031305.2018.1549100}, +volume = {73}, +number = {3}, +pages = {307-309}, +year = {2019} +} + +@article{Dimitriadis_2021, + title = {Stable reliability diagrams for probabilistic classifiers}, + volume = {118}, + issn = {0027-8424, 1091-6490}, + url = {https://pnas.org/doi/full/10.1073/pnas.2016191118}, + doi = {10.1073/pnas.2016191118}, + language = {en}, + number = {8}, + urldate = {2023-04-12}, + journal = {Proceedings of the National Academy of Sciences}, + author = {Dimitriadis, Timo and Gneiting, Tilmann and Jordan, Alexander I.}, + month = feb, + year = {2021}, + pages = {e2016191118}, +} + +@article{Capretto_2022, + title={Bambi: A Simple Interface for Fitting {Bayesian} Linear Models in {Python}}, + volume={103}, + number={15}, + journal={Journal of Statistical Software}, + author={Capretto, Tomás and Piho, Camen and Kumar, Ravin and Westfall, Jacob and Yarkoni, Tal and Martin, Osvaldo A}, + year={2022}, + pages={1–29} +} + +@article{Hoyer_2017, + title = {{xarray}: {N-D} labeled arrays and datasets in {Python}}, + author = {Hoyer, S. and J. Hamman}, + journal = {Journal of Open Research Software}, + volume = {5}, + number = {1}, + year = {2017}, + publisher = {Ubiquity Press}, + doi = {10.5334/jors.148}, + url = {https://doi.org/10.5334/jors.148} +} + +@article{Hunter_2007, + Author = {Hunter, J. D.}, + Title = {Matplotlib: A {2D} graphics environment}, + Journal = {Computing in Science \& Engineering}, + Volume = {9}, + Number = {3}, + Pages = {90--95}, + abstract = {Matplotlib is a 2D graphics package used for Python for + application development, interactive scripting, and publication-quality + image generation across user interfaces and operating systems.}, + publisher = {IEEE COMPUTER SOC}, + doi = {10.1109/MCSE.2007.55}, + year = 2007 +} + +@manual{Bokeh_2018, +title = {Bokeh: {Python} library for interactive visualization}, +author = {{Bokeh Development Team}}, +year = {2018}, +url = {https://bokeh.pydata.org/en/latest/}, +} + +@online{plotly_2015, +author = {{Plotly Technologies Inc.}}, +title = {Collaborative data science}, +publisher = {Plotly Technologies Inc.}, +address = {Montreal, QC}, +year = {2015}, +url = {https://plot.ly}, +} + +@misc{Guo_2024, + title={{VMC}: A Grammar for Visualizing Statistical Model Checks}, + author={Ziyang Guo and Alex Kale and Matthew Kay and Jessica Hullman}, + year={2024}, + eprint={2408.16702}, + archivePrefix={arXiv}, + primaryClass={cs.HC}, + url={https://arxiv.org/abs/2408.16702}, + doi={10.48550/arXiv.2408.16702}, +} + +@book{Martin_2024, + title = {Bayesian {Analysis} with {Python}: {A} {Practical} {Guide} to probabilistic modeling, 3rd {Edition}}, + isbn = {978-1-80512-716-1}, + shorttitle = {Bayesian {Analysis} with {Python}}, + language = {English}, + publisher = {Packt Publishing}, + author = {Martin, Osvaldo A}, + month = feb, + year = {2024}, +} + + +@article{emcee, + doi = {10.21105/joss.01864}, + url = {https://doi.org/10.21105/joss.01864}, + year = {2019}, + publisher = {The Open Journal}, + volume = {4}, + number = {43}, + pages = {1864}, + author = {Daniel Foreman-Mackey and Will M. Farr and Manodeep Sinha and Anne M. Archibald and David W. Hogg and Jeremy S. Sanders and Joe Zuntz and Peter K. g. Williams and Andrew R. j. Nelson and Miguel de Val-Borro and Tobias Erhardt and Ilya Pashchenko and Oriol Abril Pla}, + title = {{emcee} v3: A {Python} ensemble sampling toolkit for affine-invariant {MCMC}}, + journal = {Journal of Open Source Software} } + + +@book{eabm_2025, + author = {Osvaldo A Martin and Oriol Abril-Pla and Jordan Deklerk}, + title = {Exploratory analysis of {Bayesian} models}, + month = nov, + year = 2025, + publisher = {Zenodo}, + version = {v0.3.0}, + doi = {10.5281/zenodo.15127548}, + url = {https://doi.org/10.5281/zenodo.15127548}, + }, + +@software{arvizjl_2025, + author = {Axen, Seth D and Widmann, David}, + title = {{arviz-devs/ArviZ.jl}: v0.14.0}, + month = sep, + year = 2025, + publisher = {Zenodo}, + version = {v0.14.0}, + doi = {10.5281/zenodo.17194186}, + url = {https://doi.org/10.5281/zenodo.17194186}, +} + +@article{icazatti_2023, +author = {Icazatti, Alejandro and Abril-Pla, Oriol and Klami, Arto and Martin, Osvaldo A}, +doi = {10.21105/joss.05499}, +journal = {Journal of Open Source Software}, +month = sep, +number = {89}, +pages = {5499}, +title = {{PreliZ: A tool-box for prior elicitation}}, +url = {https://joss.theoj.org/papers/10.21105/joss.05499}, +volume = {8}, +year = {2023} +} + +@Misc{bayesplot0, + title = {bayesplot: Plotting for {Bayesian} Models}, + author = {Jonah Gabry and Tristan Mahr}, + year = {2025}, + note = {R package version 1.15.0}, + url = {https://mc-stan.org/bayesplot/}, + doi = {10.32614/cran.package.bayesplot}, +} + +@Article{bayesplot1, + title = {Visualization in {Bayesian} workflow}, + author = {Jonah Gabry and Daniel Simpson and Aki Vehtari and Michael Betancourt and Andrew Gelman}, + year = {2019}, + journal = {Journal of the Royal Statistical Society Series A}, + volume = {182}, + issue = {2}, + pages = {389-402}, + doi = {10.1111/rssa.12378}, +} + +@book{gelman_2013, + address = {Boca Raton}, + edition = {3 edition}, + title = {Bayesian {Data} {Analysis}, {Third} {Edition}}, + isbn = {978-1-4398-4095-5}, + publisher = {Chapman and Hall/CRC}, + author = {Gelman, Andrew and Carlin, John B. and Stern, Hal S. and Dunson, David B. and Vehtari, Aki and Rubin, Donald B.}, + month = nov, + year = {2013}, +} + +@Misc{loo, + title = {loo: Efficient leave-one-out cross-validation and WAIC for Bayesian models}, + year = {2025}, + note = {R package version 2.9.0}, + url = {https://mc-stan.org/loo/}, + doi = {10.32614/cran.package.loo}, +} + +@article{Padilla_2021, + title={Uncertainty Visualization}, + author={Padilla, Lace and Kay, Matthew and Hullman, Jessica}, + journal={Wiley StatsRef: Statistics Reference Online}, + year={2021}, + publisher={Wiley Online Library}, + doi={10.1002/9781118445112.stat08296}, + url={http://space.ucmerced.edu/Downloads/publications/Uncertainty_Visualization_Padilla_Kay_Hullman_2022.pdf} +} + + +@inproceedings{kay_2016, +author = {Kay, Matthew and Kola, Tara and Hullman, Jessica R. and Munson, Sean A.}, +title = {When (ish) is My Bus? User-centered Visualizations of Uncertainty in Everyday, Mobile Predictive Systems}, +year = {2016}, +isbn = {9781450333627}, +publisher = {Association for Computing Machinery}, +address = {New York, NY, USA}, +url = {https://doi.org/10.1145/2858036.2858558}, +doi = {10.1145/2858036.2858558}, +booktitle = {Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems}, +pages = {5092–5103}, +numpages = {12}, +keywords = {uncertainty visualization, transit predictions, mobile interfac-es, end-user visualization, dotplots}, +location = {San Jose, California, USA}, +series = {CHI '16} +} + +@article{kay_2024, + author = {Matthew Kay}, + title = {{ggdist}: Visualizations of Distributions and Uncertainty in the Grammar of Graphics}, + journal = {IEEE Transactions on Visualization and Computer Graphics}, + year = {2024}, + volume = {30}, + number = {1}, + pages = {414--424}, + doi = {10.1109/TVCG.2023.3327195}, +} + + +@article{harris_2020, + title = {Array programming with {NumPy}}, + author = {Charles R. Harris and K. Jarrod Millman and St{\'{e}}fan J. + van der Walt and Ralf Gommers and Pauli Virtanen and David + Cournapeau and Eric Wieser and Julian Taylor and Sebastian + Berg and Nathaniel J. Smith and Robert Kern and Matti Picus + and Stephan Hoyer and Marten H. van Kerkwijk and Matthew + Brett and Allan Haldane and Jaime Fern{\'{a}}ndez del + R{\'{i}}o and Mark Wiebe and Pearu Peterson and Pierre + G{\'{e}}rard-Marchant and Kevin Sheppard and Tyler Reddy and + Warren Weckesser and Hameer Abbasi and Christoph Gohlke and + Travis E. Oliphant}, + year = {2020}, + month = sep, + journal = {Nature}, + volume = {585}, + number = {7825}, + pages = {357--362}, + doi = {10.1038/s41586-020-2649-2}, + publisher = {Springer Science and Business Media {LLC}}, + url = {https://doi.org/10.1038/s41586-020-2649-2} +} + + +@ARTICLE{virtanen_2020, + author = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and + Haberland, Matt and Reddy, Tyler and Cournapeau, David and + Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and + Bright, Jonathan and {van der Walt}, St{\'e}fan J. and + Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and + Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and + Kern, Robert and Larson, Eric and Carey, C J and + Polat, {\.I}lhan and Feng, Yu and Moore, Eric W. and + {VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and + Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and + Harris, Charles R. and Archibald, Anne M. and + Ribeiro, Ant{\^o}nio H. and Pedregosa, Fabian and + {van Mulbregt}, Paul and {SciPy 1.0 Contributors}}, + title = {{{SciPy} 1.0: Fundamental Algorithms for Scientific + Computing in Python}}, + journal = {Nature Methods}, + year = {2020}, + volume = {17}, + pages = {261--272}, + adsurl = {https://rdcu.be/b08Wh}, + doi = {10.1038/s41592-019-0686-2}, +} diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn' '--exclude=.svnignore' old/arviz-1.0.0/pyproject.toml new/arviz-1.1.0/pyproject.toml --- old/arviz-1.0.0/pyproject.toml 2026-03-02 15:50:15.000000000 +0100 +++ new/arviz-1.1.0/pyproject.toml 2026-04-24 07:01:01.000000000 +0200 @@ -25,9 +25,9 @@ ] dynamic = ["version", "description"] dependencies = [ - "arviz_base>=1.0.0,<1.1.0", - "arviz_stats[xarray]>=1.0.0,<1.1.0", - "arviz_plots>=1.0.0,<1.1.0", + "arviz_base>=1.1.0,<1.2.0", + "arviz_stats[xarray]>=1.1.0,<1.2.0", + "arviz_plots>=1.1.0,<1.2.0", ] [tool.flit.module] diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn' '--exclude=.svnignore' old/arviz-1.0.0/src/arviz/__init__.py new/arviz-1.1.0/src/arviz/__init__.py --- old/arviz-1.0.0/src/arviz/__init__.py 2026-03-02 15:50:15.000000000 +0100 +++ new/arviz-1.1.0/src/arviz/__init__.py 2026-04-24 07:01:01.000000000 +0200 @@ -1,3 +1,16 @@ +# Copyright ArviZ contributors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. # pylint: disable=unused-import,unused-wildcard-import,wildcard-import,invalid-name """Expose features from _ArviZverse_ refactored packages together in the ``arviz`` namespace.""" @@ -13,6 +26,11 @@ info = "" + +class MigrationWarning(UserWarning, FutureWarning): + """Warning raised when a legacy name is accessed on the ``arviz`` namespace.""" + + try: from arviz_base import * import arviz_base as base @@ -60,7 +78,7 @@ info += _status # define version last so it isn't overwritten by the respective attribute in the imported libraries -__version__ = "1.0.0" +__version__ = "1.1.0" info = f"Status information for ArviZ {__version__}\n\n{info}" @@ -80,6 +98,25 @@ raise ImportError("\n".join(lines)) +_MIGRATION_GUIDE_URL = "https://python.arviz.org/en/latest/user_guide/migration_guide.html#datatree"; + + +def __getattr__(name): + """Guide users who expect legacy names on the ``arviz`` namespace.""" + if name == "InferenceData": + import warnings + from xarray import DataTree + + warnings.warn( + "arviz.InferenceData is no longer available on the " + "arviz package; ArviZ now uses xarray's DataTree for the same " + f"role. See the migration guide: {_MIGRATION_GUIDE_URL}", + MigrationWarning, + ) + + return DataTree + raise AttributeError(f"module {__name__!r} has no attribute {name!r}") + # clean namespace del functools, logging, matches, pat, re, _status, versions, unique_versions diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn' '--exclude=.svnignore' old/arviz-1.0.0/tests/__init__.py new/arviz-1.1.0/tests/__init__.py --- old/arviz-1.0.0/tests/__init__.py 2026-03-02 15:50:15.000000000 +0100 +++ new/arviz-1.1.0/tests/__init__.py 2026-04-24 07:01:01.000000000 +0200 @@ -1 +1,14 @@ +# Copyright ArviZ contributors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. """Tests for ArviZ metapackage, will check namespace availability only.""" diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn' '--exclude=.svnignore' old/arviz-1.0.0/tests/test_namespace.py new/arviz-1.1.0/tests/test_namespace.py --- old/arviz-1.0.0/tests/test_namespace.py 2026-03-02 15:50:15.000000000 +0100 +++ new/arviz-1.1.0/tests/test_namespace.py 2026-04-24 07:01:01.000000000 +0200 @@ -1,3 +1,16 @@ +# Copyright ArviZ contributors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. import importlib import re @@ -5,6 +18,7 @@ import arviz_plots import arviz_stats import pytest +from xarray import DataTree import arviz as az @@ -20,10 +34,10 @@ def test_aliases(): # These are xarray aliases exposed for user convenience - xarray_aliases = {"from_netcdf", "from_zarr"} + xarray_aliases = {"from_netcdf", "from_zarr", "InferenceData"} for obj_name in dir(az): - if not obj_name.startswith("_") and obj_name != "info": + if not obj_name.startswith("_") and obj_name not in ["info", "MigrationWarning"]: obj = getattr(az, obj_name) if obj_name in xarray_aliases: @@ -73,6 +87,25 @@ assert "must share the same minor version" in message +def test_inference_data_import_points_to_migration_guide(): + """Legacy arviz.InferenceData should error with a link to the migration guide.""" + with pytest.warns(az.MigrationWarning, match="https://python.arviz.org/.*/migration_guide.*";): + from arviz import InferenceData + + assert InferenceData is DataTree + + +def test_inference_data_getattr_points_to_migration_guide(): + """Legacy arviz.InferenceData should error with a link to the migration guide.""" + with pytest.warns(az.MigrationWarning, match="https://python.arviz.org/.*/migration_guide.*";): + az.InferenceData + + +def test_getattr_unknown_attribute(): + with pytest.raises(AttributeError, match="has no attribute"): + az.totally_missing_arviz_name_xyz + + def test_compatible_package_versions(monkeypatch): """ Compatible minor versions should not raise an ImportError and should be diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn' '--exclude=.svnignore' old/arviz-1.0.0/tools/APACHE_HEADER.txt new/arviz-1.1.0/tools/APACHE_HEADER.txt --- old/arviz-1.0.0/tools/APACHE_HEADER.txt 1970-01-01 01:00:00.000000000 +0100 +++ new/arviz-1.1.0/tools/APACHE_HEADER.txt 2026-04-24 07:01:01.000000000 +0200 @@ -0,0 +1,13 @@ +Copyright ArviZ contributors + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License.
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