arviz-base

library1.2.0pypypi✓ verified 85d ago

arviz-base is a Python package that provides core ArviZ features and converters, serving as a fundamental component of the modular ArviZ 1.0 ecosystem. It focuses on data structures, primarily leveraging `xarray.DataTree`, and conversion utilities from various probabilistic programming languages (PPLs). This library, currently at version 1.0.0, is under active development as part of the broader ArviZ refactoring, aiming for increased flexibility and a minimal dependency footprint.

data ai-ml

pip install arviz-base

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INSTALL

IMPORT

SIG · ARVIZ-BASE

arviz-base

datapythonv1.2.0

Install

8.8s avg

Import

1548ms

Disk

182MB

Pass rate

10/ 10

Env Coverage10 / 10

glibc

3.9–3.13

musl

3.9–3.13

Install & Compatibility

Where this runs

tested against v0.3.0 · pip install

no network on importno background threads

Install × environment matrix

Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux

musl

py 3.10–3.920 runs

installs and imports cleanly · install 0.0s · import 1.582s · 179.7MB

glibc

py 3.10–3.920 runs

installs and imports cleanly · install 8.8s · import 1.514s · 172MB

182MB installed

● package 182MB

Code

Verified usage

Verified import paths — ran on the pinned version, not inferred.

arviz_base

✓ import arviz_base as az

✗ import arviz as az_old; az_old.InferenceData()

arviz-base uses its own namespace; the old `arviz.InferenceData` has been replaced by `xarray.DataTree` in 1.0.

load_arviz_data

✓ from arviz_base import load_arviz_data

Used for loading pre-packaged ArviZ example datasets.

from_dict

✓ from arviz_base import from_dict

Used for converting Python dictionaries to `xarray.DataTree` following ArviZ conventions.

This quickstart demonstrates loading an example dataset using `load_arviz_data` and then extracting data with `extract`. It also shows how to convert raw Python dictionaries containing NumPy arrays into an ArviZ-compatible `xarray.DataTree` using `from_dict`.

import arviz_base as az import numpy as np # Load an example dataset provided by ArviZ Base idata = az.load_arviz_data("centered_eight") print(f"Loaded DataTree with groups: {list(idata.keys())}") # Extract a specific group, e.g., 'posterior', combining chain and draw dimensions extracted_data = az.extract(idata, group="posterior") print(f"\nExtracted posterior data shape: {extracted_data.sizes}") # Convert a simple dictionary to a DataTree my_data = { "posterior": { "alpha": np.random.normal(0, 1, size=(4, 500)), "beta": np.random.normal(0, 0.5, size=(4, 500)) } } custom_idata = az.from_dict(my_data) print(f"\nCustom DataTree with groups: {list(custom_idata.keys())}")

Debug

Known issues

breakingArviZ 1.0, of which arviz-base is a part, introduces a major refactoring. The central `arviz.InferenceData` object has been replaced by `xarray.DataTree`. Code relying on `InferenceData` attributes or methods will break.

fix

Migrate your data handling to use `xarray.DataTree` structures and `arviz_base`'s new conversion and extraction functions. Consult the ArviZ migration guide.

affects: 0.x to 1.0.0+

gotchaarviz-base is a minimal package focused on data structures and converters. For statistical computations or plotting, you will need `arviz-stats` and `arviz-plots` respectively, or install the overarching `arviz` package with necessary extras.

fix

If you require statistical summaries or visualizations, install `arviz-stats` (`pip install arviz-stats`) and `arviz-plots` (`pip install arviz-plots`), or install `arviz` with relevant I/O and plotting extras (e.g., `pip install "arviz[netcdf4,matplotlib]"`).

affects: 1.0.0+

gotchaI/O functionalities for formats like NetCDF, HDF5, or Zarr are provided via optional dependencies. `arviz-base` itself does not have these as hard requirements to maintain a minimal footprint.

fix

Install the specific I/O backend you need separately, e.g., `pip install netcdf4` or `pip install zarr`. When installing `arviz` (the meta-package), these can be specified as extras, e.g., `pip install "arviz[netcdf4]"`.

affects: 1.0.0+

Errors

Common errors & fixes

ModuleNotFoundError: No module named 'xarray'

The core dependency `xarray` is not installed, which is essential for `arviz-base`'s data structures like `DataTree`.

fix

Install xarray: `pip install xarray` or `pip install "arviz-base"` (which should pull `xarray` as a dependency).

AttributeError: module 'arviz' has no attribute 'InferenceData'

Attempting to use the deprecated `arviz.InferenceData` object directly with an ArviZ 1.0+ installation (or `arviz-base`). The `InferenceData` object has been replaced by `xarray.DataTree`.

fix

Update your code to use `xarray.DataTree` or `arviz_base`'s conversion functions like `from_dict` or `from_cmdstanpy` to create `DataTree` objects. Refer to the ArviZ migration guide for detailed steps.

ValueError: setting an array element with a sequence. The above exception was the direct cause of the following exception: ValueError: could not broadcast input array from shape (X,Y) into shape (Z,)

This error typically occurs when the input data (e.g., NumPy arrays or dictionaries) provided to conversion functions like `from_dict` or other ArviZ functions do not conform to the expected dimensionality or shape for creating a `DataTree` group.

fix

Ensure that the input arrays have consistent shapes and the correct number of dimensions corresponding to the `chain`, `draw`, and variable-specific dimensions expected by ArviZ. For instance, posterior samples usually require `(chain, draw, *variable_dimensions)`.

Upgrade

Version history

1.2.0latest on PyPI · released Jun 12, 2026

Audit

Dependencies

xarrayrequiredCore data structure (DataTree) and data manipulation.

numpyrequiredFundamental numerical computing.

typing-extensionsrequiredType hinting support.

netcdf4optionalOptional I/O backend for NetCDF files.

h5netcdfoptionalOptional I/O backend for HDF5-backed NetCDF files.

zarroptionalOptional I/O backend for Zarr arrays.

Agent activity