arviz.InferenceData.rename#
- InferenceData.rename(name_dict=None, groups=None, filter_groups=None, inplace=False)[source]#
Perform xarray renaming of variable and dimensions on all groups.
Loops groups to perform Dataset.rename(name_dict) for every key in name_dict if key is a dimension/data_vars of the dataset. The renaming is performed on all relevant groups (like posterior, prior, sample stats) while non relevant groups like observed data are omitted. See
xarray.Dataset.rename()
- Parameters
- name_dict
dict
Dictionary whose keys are current variable or dimension names and whose values are the desired names.
- 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.
- name_dict
- Returns
InferenceData
A new InferenceData object by default. When
inplace==True
perform renaming in-place and returnNone
See also
xarray.Dataset.rename
Returns a new object with renamed variables and dimensions.
rename_vars
Perform xarray renaming of variable or coordinate names on all groups of an InferenceData object.
rename_dims
Perform xarray renaming of dimensions on all groups of InferenceData object.
Examples
Use
rename
to renaming of variable and dimensions on all groups of the InferenceData object. We first check the original object:import arviz as az idata = az.load_arviz_data("rugby") idata
arviz.InferenceData-
<xarray.Dataset> Dimensions: (chain: 4, draw: 500, team: 6) Coordinates: * chain (chain) int64 0 1 2 3 * draw (draw) int64 0 1 2 3 4 5 6 7 ... 492 493 494 495 496 497 498 499 * team (team) object 'Wales' 'France' 'Ireland' ... 'Italy' 'England' Data variables: home (chain, draw) float64 ... intercept (chain, draw) float64 ... atts_star (chain, draw, team) float64 ... defs_star (chain, draw, team) float64 ... sd_att (chain, draw) float64 ... sd_def (chain, draw) float64 ... atts (chain, draw, team) float64 ... defs (chain, draw, team) float64 ... Attributes: created_at: 2019-07-12T20:31:53.545143 inference_library: pymc3 inference_library_version: 3.7
-
<xarray.Dataset> Dimensions: (chain: 4, draw: 500, match: 60) Coordinates: * chain (chain) int64 0 1 2 3 * draw (draw) int64 0 1 2 3 4 5 6 7 ... 493 494 495 496 497 498 499 * match (match) object 'Wales Italy' ... 'Ireland England' Data variables: home_points (chain, draw, match) int64 ... away_points (chain, draw, match) int64 ... Attributes: created_at: 2019-07-12T20:31:53.563854 inference_library: pymc3 inference_library_version: 3.7
-
<xarray.Dataset> Dimensions: (chain: 4, draw: 500) Coordinates: * chain (chain) int64 0 1 2 3 * draw (draw) int64 0 1 2 3 4 5 6 ... 493 494 495 496 497 498 499 Data variables: energy_error (chain, draw) float64 ... energy (chain, draw) float64 ... tree_size (chain, draw) float64 ... tune (chain, draw) bool ... mean_tree_accept (chain, draw) float64 ... lp (chain, draw) float64 ... depth (chain, draw) int64 ... max_energy_error (chain, draw) float64 ... step_size (chain, draw) float64 ... step_size_bar (chain, draw) float64 ... diverging (chain, draw) bool ... Attributes: created_at: 2019-07-12T20:31:53.555203 inference_library: pymc3 inference_library_version: 3.7
-
<xarray.Dataset> Dimensions: (chain: 1, draw: 500, team: 6, match: 60) Coordinates: * chain (chain) int64 0 * draw (draw) int64 0 1 2 3 4 5 6 7 ... 493 494 495 496 497 498 499 * team (team) object 'Wales' 'France' 'Ireland' ... 'Italy' 'England' * match (match) object 'Wales Italy' ... 'Ireland England' Data variables: sd_att_log__ (chain, draw) float64 ... intercept (chain, draw) float64 ... atts_star (chain, draw, team) float64 ... defs_star (chain, draw, team) float64 ... away_points (chain, draw, match) int64 ... sd_att (chain, draw) float64 ... sd_def_log__ (chain, draw) float64 ... home (chain, draw) float64 ... atts (chain, draw, team) float64 ... sd_def (chain, draw) float64 ... home_points (chain, draw, match) int64 ... defs (chain, draw, team) float64 ... Attributes: created_at: 2019-07-12T20:31:53.573731 inference_library: pymc3 inference_library_version: 3.7
-
<xarray.Dataset> Dimensions: (match: 60) Coordinates: * match (match) object 'Wales Italy' ... 'Ireland England' Data variables: home_points (match) float64 ... away_points (match) float64 ... Attributes: created_at: 2019-07-12T20:31:53.581293 inference_library: pymc3 inference_library_version: 3.7
In order to rename the dimensions and variable, we use:
idata.rename({"team": "team_new", "match":"match_new"}, inplace=True) idata
arviz.InferenceData-
<xarray.Dataset> Dimensions: (chain: 4, draw: 500, team_new: 6) Coordinates: * chain (chain) int64 0 1 2 3 * draw (draw) int64 0 1 2 3 4 5 6 7 ... 492 493 494 495 496 497 498 499 * team_new (team_new) object 'Wales' 'France' ... 'Italy' 'England' Data variables: home (chain, draw) float64 ... intercept (chain, draw) float64 ... atts_star (chain, draw, team_new) float64 ... defs_star (chain, draw, team_new) float64 ... sd_att (chain, draw) float64 ... sd_def (chain, draw) float64 ... atts (chain, draw, team_new) float64 ... defs (chain, draw, team_new) float64 ... Attributes: created_at: 2019-07-12T20:31:53.545143 inference_library: pymc3 inference_library_version: 3.7
-
<xarray.Dataset> Dimensions: (chain: 4, draw: 500, match_new: 60) Coordinates: * chain (chain) int64 0 1 2 3 * draw (draw) int64 0 1 2 3 4 5 6 7 ... 493 494 495 496 497 498 499 * match_new (match_new) object 'Wales Italy' ... 'Ireland England' Data variables: home_points (chain, draw, match_new) int64 ... away_points (chain, draw, match_new) int64 ... Attributes: created_at: 2019-07-12T20:31:53.563854 inference_library: pymc3 inference_library_version: 3.7
-
<xarray.Dataset> Dimensions: (chain: 4, draw: 500) Coordinates: * chain (chain) int64 0 1 2 3 * draw (draw) int64 0 1 2 3 4 5 6 ... 493 494 495 496 497 498 499 Data variables: energy_error (chain, draw) float64 ... energy (chain, draw) float64 ... tree_size (chain, draw) float64 ... tune (chain, draw) bool ... mean_tree_accept (chain, draw) float64 ... lp (chain, draw) float64 ... depth (chain, draw) int64 ... max_energy_error (chain, draw) float64 ... step_size (chain, draw) float64 ... step_size_bar (chain, draw) float64 ... diverging (chain, draw) bool ... Attributes: created_at: 2019-07-12T20:31:53.555203 inference_library: pymc3 inference_library_version: 3.7
-
<xarray.Dataset> Dimensions: (chain: 1, draw: 500, team_new: 6, match_new: 60) Coordinates: * chain (chain) int64 0 * draw (draw) int64 0 1 2 3 4 5 6 7 ... 493 494 495 496 497 498 499 * team_new (team_new) object 'Wales' 'France' ... 'Italy' 'England' * match_new (match_new) object 'Wales Italy' ... 'Ireland England' Data variables: sd_att_log__ (chain, draw) float64 ... intercept (chain, draw) float64 ... atts_star (chain, draw, team_new) float64 ... defs_star (chain, draw, team_new) float64 ... away_points (chain, draw, match_new) int64 ... sd_att (chain, draw) float64 ... sd_def_log__ (chain, draw) float64 ... home (chain, draw) float64 ... atts (chain, draw, team_new) float64 ... sd_def (chain, draw) float64 ... home_points (chain, draw, match_new) int64 ... defs (chain, draw, team_new) float64 ... Attributes: created_at: 2019-07-12T20:31:53.573731 inference_library: pymc3 inference_library_version: 3.7
-
<xarray.Dataset> Dimensions: (match_new: 60) Coordinates: * match_new (match_new) object 'Wales Italy' ... 'Ireland England' Data variables: home_points (match_new) float64 ... away_points (match_new) float64 ... Attributes: created_at: 2019-07-12T20:31:53.581293 inference_library: pymc3 inference_library_version: 3.7