(api_reference)= # API reference ## Data reorganization ```{eval-rst} .. autosummary:: :toctree: generated/ arviz_base.extract arviz_base.dataset_to_dataarray arviz_base.dataset_to_dataframe arviz_base.explode_dataset_dims arviz_base.references_to_dataset ``` ## User facing converters ```{eval-rst} .. autosummary:: :toctree: generated/ arviz_base.convert_to_datatree arviz_base.from_dict ``` ## Library specific converters ```{eval-rst} .. autosummary:: :toctree: generated/ arviz_base.from_cmdstanpy arviz_base.from_emcee arviz_base.from_numpyro ``` More coming soon... ## Iteration and subsetting ```{eval-rst} .. autosummary:: :toctree: generated/ arviz_base.xarray_sel_iter arviz_base.xarray_var_iter ``` ## How to cite ArviZ and implemented methods ```{eval-rst} .. autosummary:: :toctree: generated/ arviz_base.citations ``` ## Example datasets The behaviour of the functions in this section is partially controlled by the following environment variable: :::{envvar} ARVIZ_DATA If present, store remote datasets after downloading in the location indicated there. Otherwise, datasets are stored at `~/arviz_data/` ::: ```{eval-rst} .. autosummary:: :toctree: generated/ arviz_base.load_arviz_data arviz_base.list_datasets arviz_base.get_data_home arviz_base.clear_data_home ``` ## Conversion utilities ```{eval-rst} .. autosummary:: :toctree: generated/ arviz_base.convert_to_dataset arviz_base.dict_to_dataset arviz_base.generate_dims_coords arviz_base.make_attrs arviz_base.ndarray_to_dataarray arviz_base.NumPyroInferenceAdapter arviz_base.MCMCAdapter arviz_base.SVIAdapter ``` (labeller_api)= ## Labels ```{eval-rst} .. autosummary:: :toctree: generated/ arviz_base.labels.BaseLabeller arviz_base.labels.DimCoordLabeller arviz_base.labels.IdxLabeller arviz_base.labels.DimIdxLabeller arviz_base.labels.MapLabeller arviz_base.labels.NoVarLabeller arviz_base.labels.mix_labellers ``` (arviz_configuration)= ## Configuration Most ArviZ default values are regulated by {class}`arviz_base.rcParams`, a class similar to a dictionary storing key-value pairs inspired by the one in matplotlib. It is similar to a dictionary and not a dictionary though because all keys are fixed, and each key has associated a validation function to help prevent setting nonsensical defaults. ### ArviZ configuration file The `rcParams` class is generated and populated at import time. ArviZ checks several locations for a file named `arvizrc` and, if found, prefers those settings over the library ones. The locations checked are the following: 1. Current working directory, {func}`os.getcwd` 1. Location indicated by {envvar}`ARVIZ_DATA` environment variable 1. The third and last location checked is OS dependent: * On Linux: `$XDG_CONFIG_HOME/arviz` if exists, otherwise `~/.config/arviz/` * Elsewhere: `~/.arviz/` :::{dropdown} Example `arvizrc` file :name: arvizrc :open: ```none data.index_origin : 1 plot.backend : bokeh stats.ci_kind : hdi stats.ci_prob : 0.95 ``` All available keys are listed below. The `arvizrc` file can have any subset of the keys, it isn't necessary to include them all. For those keys without a user defined default, the library one is used. ::: ### Context manager A context manager is also available to temporarily change the default settings. ```{eval-rst} .. autosummary:: :toctree: generated/ arviz_base.rc_context ``` ## rcParams Below, all keys available within `rcParams` are listed, along with their library default. Keys can be accessed or modified via ``arviz_base.rcParams[key]``, for example, ``arviz_base.rcParams["data.sample_dims"]``. :::{important} These defaults are subject to change. If you rely on a specific default value, you should create an {ref}`arvizrc ` file with the key-value pairs you rely on. The goal of the ArviZ team is to try and adapt to best practices as they evolve, which sometimes requires updating default values, for example to use new algorithms. ::: ### data ```{eval-rst} .. py:data:: data.http_protocol :type: str :value: "https" Protocol for loading remote datasets. Can be "https" or "http". .. py:data:: data.index_origin :type: int :value: 0 Index origin. By default, ArviZ adds coordinate values to all dimensions. If no coordinate values were provided, ArviZ generates integer indices as coordinate values starting at `index_origin`. .. py:data:: data.sample_dims :type: list :value: ["chain", "draw"] What the sampling dimensions are named. These are the dimensions that will be reduced by default when computing or plotting, therefore, they should be always present. .. py:data:: data.save_warmup :type: bool :value: False Whether to save warmup iterations. ``` ### stats ```{eval-rst} .. py:data:: stats.module :type: str or object :value: "base" Preferred module for :doc:`stats computations `. It accepts a custom user-created statistics class, which must have the necessary functions for stats and plots to work properly. When validating the input though, only the ``eti`` and ``rhat`` methods are checked. .. py:data:: stats.ci_kind :type: str :value: "eti" Type of credible interval to compute by default, one of "eti" or "hdi". .. py:data:: stats.ci_prob :type: float :value: 0.89 The default probability of computed credible intervals. Its default value here is also a friendly reminder of the arbitrary nature of commonly values like 95% .. py:data:: stats.envelope_prob :type: float :value: 0.99 The default probability of envelopes used in diagnostic plots. The relatively high default value reflects their role in highlighting systematic departures from the model while reducing false positives due to random variation. .. py:data:: stats.round_to :type: int or str :value: "2g" Default rounding for statistical summaries. If an integer, specifies decimal places. If a string ending in 'g', specifies significant digits. Integers can be negative. Use the strings "None" or "none" for no rounding. .. py:data:: stats.ic_compare_method :type: str :value: "stacking" Method for comparing multiple models using their information criteria values, one of "stacking", "bb-pseudo-bma" or "pseudo-mba". .. py:data:: stats.ic_pointwise :type: bool :value: True Whether to return pointwise values when computing PSIS-LOO-CV. .. py:data:: stats.ic_scale :type: str :value: "log" The scale in which to return the PSIS-LOO-CV values, one of "deviance" (common in the past and reason of the information criterion naming), "log" or "negative_log". .. py:data:: stats.point_estimate :type: str :value: "mean" Default statistical summary for centrality, one of "mean", "median" or "mode". ``` ### plots ```{eval-rst} .. py:data:: plot.backend :type: str :value: "auto" Default plotting backend for :mod:`arviz_plots`, one of "matplotlib", "plotly", "bokeh", "none" or "auto". If "auto" when setting the value (or when importing the library if using "auto" in a template) the available backends will be checked in the order above, the first one that is found to be available is set as the default plotting backend. .. py:data:: plot.density_kind :type: str :value: "auto" Default representation for 1D marginal densities, one of "auto", "kde", "hist", "ecdf" or "dot". "auto" means a set of heuristic rules chooses between "ecdf", "hist" and "kde" on a variable-per-variable basis. .. py:data:: plot.max_subplots :type: int :value: 40 Maximum number of :term:`arviz_plots:plots` that can be generated at once. ```