arviz.InferenceData.unstack#
- InferenceData.unstack(dim=None, groups=None, filter_groups=None, inplace=False)[source]#
Perform an xarray unstacking on all groups.
Unstack existing dimensions corresponding to MultiIndexes into multiple new dimensions. Loops groups to perform Dataset.unstack(key=value). The selection is performed on all relevant groups (like posterior, prior, sample stats) while non relevant groups like observed data are omitted. See
xarray.Dataset.unstack()
- Parameters:
- dim
Hashable
or iterable ofHashable
, optional Dimension(s) over which to unstack. By default unstacks all MultiIndexes.
- groups
str
orlist
ofstr
, optional Groups where the selection is to be applied. Can either be group names or metagroup names.
- filter_groups{
None
, “like”, “regex”}, optional If
None
(default), interpret groups as the real group or metagroup names. If “like”, interpret groups as substrings of the real group or metagroup names. If “regex”, interpret groups as regular expressions on the real group or metagroup names. A lapandas.filter
.- inplacebool, optional
If
True
, modify the InferenceData object inplace, otherwise, return the modified copy.
- dim
- Returns:
InferenceData
A new InferenceData object by default. When
inplace==True
perform selection in place and returnNone
See also
xarray.Dataset.unstack
Unstack existing dimensions corresponding to MultiIndexes into multiple new dimensions.
stack
Perform an xarray stacking on all groups of InferenceData object.
Examples
Use
unstack
to unstack existing dimensions corresponding to MultiIndexes into multiple new dimensions. We first stack two dimensionsc1
andc99
toz
:import arviz as az import numpy as np datadict = { "a": np.random.randn(100), "b": np.random.randn(1, 100, 10), "c": np.random.randn(1, 100, 3, 4), } coords = { "c1": np.arange(3), "c99": np.arange(4), "b1": np.arange(10), } dims = {"c": ["c1", "c99"], "b": ["b1"]} idata = az.from_dict( posterior=datadict, posterior_predictive=datadict, coords=coords, dims=dims ) idata.stack(z=["c1", "c99"], inplace=True) idata
arviz.InferenceData-
<xarray.Dataset> Dimensions: (chain: 1, draw: 100, b1: 10, z: 12) Coordinates: * chain (chain) int64 0 * draw (draw) int64 0 1 2 3 4 5 6 7 8 9 ... 90 91 92 93 94 95 96 97 98 99 * b1 (b1) int64 0 1 2 3 4 5 6 7 8 9 * z (z) object MultiIndex * c1 (z) int64 0 0 0 0 1 1 1 1 2 2 2 2 * c99 (z) int64 0 1 2 3 0 1 2 3 0 1 2 3 Data variables: a (chain, draw) float64 -2.048 1.316 1.614 ... -1.129 0.4292 0.3217 b (chain, draw, b1) float64 0.8102 1.8 2.007 ... 0.001562 -1.844 c (chain, draw, z) float64 -0.3673 0.673 0.3847 ... 0.1952 -0.778 Attributes: created_at: 2023-11-17T19:39:37.486672 arviz_version: 0.17.0.dev0
-
<xarray.Dataset> Dimensions: (chain: 1, draw: 100, b1: 10, z: 12) Coordinates: * chain (chain) int64 0 * draw (draw) int64 0 1 2 3 4 5 6 7 8 9 ... 90 91 92 93 94 95 96 97 98 99 * b1 (b1) int64 0 1 2 3 4 5 6 7 8 9 * z (z) object MultiIndex * c1 (z) int64 0 0 0 0 1 1 1 1 2 2 2 2 * c99 (z) int64 0 1 2 3 0 1 2 3 0 1 2 3 Data variables: a (chain, draw) float64 -2.048 1.316 1.614 ... -1.129 0.4292 0.3217 b (chain, draw, b1) float64 0.8102 1.8 2.007 ... 0.001562 -1.844 c (chain, draw, z) float64 -0.3673 0.673 0.3847 ... 0.1952 -0.778 Attributes: created_at: 2023-11-17T19:39:37.490526 arviz_version: 0.17.0.dev0
In order to unstack the dimension
z
, we use:idata.unstack(inplace=True) idata
arviz.InferenceData-
<xarray.Dataset> Dimensions: (c1: 3, c99: 4, chain: 1, draw: 100, b1: 10) Coordinates: * c1 (c1) int64 0 1 2 * c99 (c99) int64 0 1 2 3 * chain (chain) int64 0 * draw (draw) int64 0 1 2 3 4 5 6 7 8 9 ... 90 91 92 93 94 95 96 97 98 99 * b1 (b1) int64 0 1 2 3 4 5 6 7 8 9 Data variables: a (chain, draw) float64 -2.048 1.316 1.614 ... -1.129 0.4292 0.3217 b (chain, draw, b1) float64 0.8102 1.8 2.007 ... 0.001562 -1.844 c (chain, draw, c1, c99) float64 -0.3673 0.673 ... 0.1952 -0.778 Attributes: created_at: 2023-11-17T19:39:37.486672 arviz_version: 0.17.0.dev0
-
<xarray.Dataset> Dimensions: (c1: 3, c99: 4, chain: 1, draw: 100, b1: 10) Coordinates: * c1 (c1) int64 0 1 2 * c99 (c99) int64 0 1 2 3 * chain (chain) int64 0 * draw (draw) int64 0 1 2 3 4 5 6 7 8 9 ... 90 91 92 93 94 95 96 97 98 99 * b1 (b1) int64 0 1 2 3 4 5 6 7 8 9 Data variables: a (chain, draw) float64 -2.048 1.316 1.614 ... -1.129 0.4292 0.3217 b (chain, draw, b1) float64 0.8102 1.8 2.007 ... 0.001562 -1.844 c (chain, draw, c1, c99) float64 -0.3673 0.673 ... 0.1952 -0.778 Attributes: created_at: 2023-11-17T19:39:37.490526 arviz_version: 0.17.0.dev0