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> Size: 452kB Dimensions: (chain: 4, draw: 500, team: 6) Coordinates: * chain (chain) int64 32B 0 1 2 3 * draw (draw) int64 4kB 0 1 2 3 4 5 6 7 ... 493 494 495 496 497 498 499 * team (team) <U8 192B 'Wales' 'France' 'Ireland' ... 'Italy' 'England' Data variables: home (chain, draw) float64 16kB ... intercept (chain, draw) float64 16kB ... atts_star (chain, draw, team) float64 96kB ... defs_star (chain, draw, team) float64 96kB ... sd_att (chain, draw) float64 16kB ... sd_def (chain, draw) float64 16kB ... atts (chain, draw, team) float64 96kB ... defs (chain, draw, team) float64 96kB ... Attributes: created_at: 2024-03-06T20:46:23.841916 arviz_version: 0.17.0 inference_library: pymc inference_library_version: 5.10.4+7.g34d2a5d9 sampling_time: 8.503105401992798 tuning_steps: 1000
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<xarray.Dataset> Size: 2MB Dimensions: (chain: 4, draw: 500, match: 60) Coordinates: * chain (chain) int64 32B 0 1 2 3 * draw (draw) int64 4kB 0 1 2 3 4 5 6 ... 493 494 495 496 497 498 499 * match (match) <U16 4kB 'Wales Italy' ... 'Ireland England' home_team (match) <U8 2kB ... away_team (match) <U8 2kB ... Data variables: home_points (chain, draw, match) int64 960kB ... away_points (chain, draw, match) int64 960kB ... Attributes: created_at: 2024-03-06T20:46:25.689246 arviz_version: 0.17.0 inference_library: pymc inference_library_version: 5.10.4+7.g34d2a5d9
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<xarray.Dataset> Size: 2MB Dimensions: (chain: 4, draw: 500, match: 60) Coordinates: * chain (chain) int64 32B 0 1 2 3 * draw (draw) int64 4kB 0 1 2 3 4 5 6 ... 493 494 495 496 497 498 499 * match (match) <U16 4kB 'Wales Italy' ... 'Ireland England' home_team (match) <U8 2kB ... away_team (match) <U8 2kB ... Data variables: home_points (chain, draw, match) float64 960kB ... away_points (chain, draw, match) float64 960kB ... Attributes: created_at: 2024-03-06T20:46:24.120642 arviz_version: 0.17.0 inference_library: pymc inference_library_version: 5.10.4+7.g34d2a5d9
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<xarray.Dataset> Size: 260kB Dimensions: (chain: 4, draw: 500, team: 6) Coordinates: * chain (chain) int64 32B 0 1 2 3 * draw (draw) int64 4kB 0 1 2 3 4 5 6 7 ... 493 494 495 496 497 498 499 * team (team) <U8 192B 'Wales' 'France' 'Ireland' ... 'Italy' 'England' Data variables: home (chain, draw) float64 16kB ... sd_att (chain, draw) float64 16kB ... sd_def (chain, draw) float64 16kB ... intercept (chain, draw) float64 16kB ... atts_star (chain, draw, team) float64 96kB ... defs_star (chain, draw, team) float64 96kB ... Attributes: created_at: 2024-03-06T20:46:24.377610 arviz_version: 0.17.0 inference_library: pymc inference_library_version: 5.10.4+7.g34d2a5d9
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<xarray.Dataset> Size: 248kB Dimensions: (chain: 4, draw: 500) Coordinates: * chain (chain) int64 32B 0 1 2 3 * draw (draw) int64 4kB 0 1 2 3 4 5 ... 495 496 497 498 499 Data variables: (12/17) max_energy_error (chain, draw) float64 16kB ... index_in_trajectory (chain, draw) int64 16kB ... smallest_eigval (chain, draw) float64 16kB ... perf_counter_start (chain, draw) float64 16kB ... largest_eigval (chain, draw) float64 16kB ... step_size (chain, draw) float64 16kB ... ... ... reached_max_treedepth (chain, draw) bool 2kB ... perf_counter_diff (chain, draw) float64 16kB ... tree_depth (chain, draw) int64 16kB ... process_time_diff (chain, draw) float64 16kB ... step_size_bar (chain, draw) float64 16kB ... energy (chain, draw) float64 16kB ... Attributes: created_at: 2024-03-06T20:46:23.854033 arviz_version: 0.17.0 inference_library: pymc inference_library_version: 5.10.4+7.g34d2a5d9 sampling_time: 8.503105401992798 tuning_steps: 1000
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<xarray.Dataset> Size: 116kB Dimensions: (chain: 1, draw: 500, team: 6) Coordinates: * chain (chain) int64 8B 0 * draw (draw) int64 4kB 0 1 2 3 4 5 6 7 ... 493 494 495 496 497 498 499 * team (team) <U8 192B 'Wales' 'France' 'Ireland' ... 'Italy' 'England' Data variables: atts_star (chain, draw, team) float64 24kB ... sd_att (chain, draw) float64 4kB ... atts (chain, draw, team) float64 24kB ... sd_def (chain, draw) float64 4kB ... defs (chain, draw, team) float64 24kB ... intercept (chain, draw) float64 4kB ... home (chain, draw) float64 4kB ... defs_star (chain, draw, team) float64 24kB ... Attributes: created_at: 2024-03-06T20:46:09.475945 arviz_version: 0.17.0 inference_library: pymc inference_library_version: 5.10.4+7.g34d2a5d9
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<xarray.Dataset> Size: 492kB Dimensions: (chain: 1, draw: 500, match: 60) Coordinates: * chain (chain) int64 8B 0 * draw (draw) int64 4kB 0 1 2 3 4 5 6 ... 493 494 495 496 497 498 499 * match (match) <U16 4kB 'Wales Italy' ... 'Ireland England' home_team (match) <U8 2kB ... away_team (match) <U8 2kB ... Data variables: away_points (chain, draw, match) int64 240kB ... home_points (chain, draw, match) int64 240kB ... Attributes: created_at: 2024-03-06T20:46:09.479330 arviz_version: 0.17.0 inference_library: pymc inference_library_version: 5.10.4+7.g34d2a5d9
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<xarray.Dataset> Size: 9kB Dimensions: (match: 60) Coordinates: * match (match) <U16 4kB 'Wales Italy' ... 'Ireland England' home_team (match) <U8 2kB ... away_team (match) <U8 2kB ... Data variables: home_points (match) int64 480B ... away_points (match) int64 480B ... Attributes: created_at: 2024-03-06T20:46:09.480812 arviz_version: 0.17.0 inference_library: pymc inference_library_version: 5.10.4+7.g34d2a5d9
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<xarray.Dataset> Size: 36kB Dimensions: (chain: 4, draw: 500) Coordinates: * chain (chain) int64 32B 0 1 2 3 * draw (draw) int64 4kB 0 1 2 3 4 5 6 7 ... 493 494 495 496 497 498 499 Data variables: sd_att (chain, draw) float64 16kB ... sd_def (chain, draw) float64 16kB ... Attributes: sd_att: pymc.logprob.transforms.LogTransform sd_def: pymc.logprob.transforms.LogTransform
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> Size: 452kB Dimensions: (chain: 4, draw: 500, team_new: 6) Coordinates: * chain (chain) int64 32B 0 1 2 3 * draw (draw) int64 4kB 0 1 2 3 4 5 6 7 ... 493 494 495 496 497 498 499 * team_new (team_new) <U8 192B 'Wales' 'France' ... 'Italy' 'England' Data variables: home (chain, draw) float64 16kB ... intercept (chain, draw) float64 16kB ... atts_star (chain, draw, team_new) float64 96kB ... defs_star (chain, draw, team_new) float64 96kB ... sd_att (chain, draw) float64 16kB ... sd_def (chain, draw) float64 16kB ... atts (chain, draw, team_new) float64 96kB ... defs (chain, draw, team_new) float64 96kB ... Attributes: created_at: 2024-03-06T20:46:23.841916 arviz_version: 0.17.0 inference_library: pymc inference_library_version: 5.10.4+7.g34d2a5d9 sampling_time: 8.503105401992798 tuning_steps: 1000
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<xarray.Dataset> Size: 2MB Dimensions: (chain: 4, draw: 500, match_new: 60) Coordinates: * chain (chain) int64 32B 0 1 2 3 * draw (draw) int64 4kB 0 1 2 3 4 5 6 ... 493 494 495 496 497 498 499 * match_new (match_new) <U16 4kB 'Wales Italy' ... 'Ireland England' home_team (match_new) <U8 2kB ... away_team (match_new) <U8 2kB ... Data variables: home_points (chain, draw, match_new) int64 960kB ... away_points (chain, draw, match_new) int64 960kB ... Attributes: created_at: 2024-03-06T20:46:25.689246 arviz_version: 0.17.0 inference_library: pymc inference_library_version: 5.10.4+7.g34d2a5d9
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<xarray.Dataset> Size: 2MB Dimensions: (chain: 4, draw: 500, match_new: 60) Coordinates: * chain (chain) int64 32B 0 1 2 3 * draw (draw) int64 4kB 0 1 2 3 4 5 6 ... 493 494 495 496 497 498 499 * match_new (match_new) <U16 4kB 'Wales Italy' ... 'Ireland England' home_team (match_new) <U8 2kB ... away_team (match_new) <U8 2kB ... Data variables: home_points (chain, draw, match_new) float64 960kB ... away_points (chain, draw, match_new) float64 960kB ... Attributes: created_at: 2024-03-06T20:46:24.120642 arviz_version: 0.17.0 inference_library: pymc inference_library_version: 5.10.4+7.g34d2a5d9
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<xarray.Dataset> Size: 260kB Dimensions: (chain: 4, draw: 500, team_new: 6) Coordinates: * chain (chain) int64 32B 0 1 2 3 * draw (draw) int64 4kB 0 1 2 3 4 5 6 7 ... 493 494 495 496 497 498 499 * team_new (team_new) <U8 192B 'Wales' 'France' ... 'Italy' 'England' Data variables: home (chain, draw) float64 16kB ... sd_att (chain, draw) float64 16kB ... sd_def (chain, draw) float64 16kB ... intercept (chain, draw) float64 16kB ... atts_star (chain, draw, team_new) float64 96kB ... defs_star (chain, draw, team_new) float64 96kB ... Attributes: created_at: 2024-03-06T20:46:24.377610 arviz_version: 0.17.0 inference_library: pymc inference_library_version: 5.10.4+7.g34d2a5d9
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<xarray.Dataset> Size: 248kB Dimensions: (chain: 4, draw: 500) Coordinates: * chain (chain) int64 32B 0 1 2 3 * draw (draw) int64 4kB 0 1 2 3 4 5 ... 495 496 497 498 499 Data variables: (12/17) max_energy_error (chain, draw) float64 16kB ... index_in_trajectory (chain, draw) int64 16kB ... smallest_eigval (chain, draw) float64 16kB ... perf_counter_start (chain, draw) float64 16kB ... largest_eigval (chain, draw) float64 16kB ... step_size (chain, draw) float64 16kB ... ... ... reached_max_treedepth (chain, draw) bool 2kB ... perf_counter_diff (chain, draw) float64 16kB ... tree_depth (chain, draw) int64 16kB ... process_time_diff (chain, draw) float64 16kB ... step_size_bar (chain, draw) float64 16kB ... energy (chain, draw) float64 16kB ... Attributes: created_at: 2024-03-06T20:46:23.854033 arviz_version: 0.17.0 inference_library: pymc inference_library_version: 5.10.4+7.g34d2a5d9 sampling_time: 8.503105401992798 tuning_steps: 1000
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<xarray.Dataset> Size: 116kB Dimensions: (chain: 1, draw: 500, team_new: 6) Coordinates: * chain (chain) int64 8B 0 * draw (draw) int64 4kB 0 1 2 3 4 5 6 7 ... 493 494 495 496 497 498 499 * team_new (team_new) <U8 192B 'Wales' 'France' ... 'Italy' 'England' Data variables: atts_star (chain, draw, team_new) float64 24kB ... sd_att (chain, draw) float64 4kB ... atts (chain, draw, team_new) float64 24kB ... sd_def (chain, draw) float64 4kB ... defs (chain, draw, team_new) float64 24kB ... intercept (chain, draw) float64 4kB ... home (chain, draw) float64 4kB ... defs_star (chain, draw, team_new) float64 24kB ... Attributes: created_at: 2024-03-06T20:46:09.475945 arviz_version: 0.17.0 inference_library: pymc inference_library_version: 5.10.4+7.g34d2a5d9
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<xarray.Dataset> Size: 492kB Dimensions: (chain: 1, draw: 500, match_new: 60) Coordinates: * chain (chain) int64 8B 0 * draw (draw) int64 4kB 0 1 2 3 4 5 6 ... 493 494 495 496 497 498 499 * match_new (match_new) <U16 4kB 'Wales Italy' ... 'Ireland England' home_team (match_new) <U8 2kB ... away_team (match_new) <U8 2kB ... Data variables: away_points (chain, draw, match_new) int64 240kB ... home_points (chain, draw, match_new) int64 240kB ... Attributes: created_at: 2024-03-06T20:46:09.479330 arviz_version: 0.17.0 inference_library: pymc inference_library_version: 5.10.4+7.g34d2a5d9
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<xarray.Dataset> Size: 9kB Dimensions: (match_new: 60) Coordinates: * match_new (match_new) <U16 4kB 'Wales Italy' ... 'Ireland England' home_team (match_new) <U8 2kB ... away_team (match_new) <U8 2kB ... Data variables: home_points (match_new) int64 480B ... away_points (match_new) int64 480B ... Attributes: created_at: 2024-03-06T20:46:09.480812 arviz_version: 0.17.0 inference_library: pymc inference_library_version: 5.10.4+7.g34d2a5d9
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<xarray.Dataset> Size: 36kB Dimensions: (chain: 4, draw: 500) Coordinates: * chain (chain) int64 32B 0 1 2 3 * draw (draw) int64 4kB 0 1 2 3 4 5 6 7 ... 493 494 495 496 497 498 499 Data variables: sd_att (chain, draw) float64 16kB ... sd_def (chain, draw) float64 16kB ... Attributes: sd_att: pymc.logprob.transforms.LogTransform sd_def: pymc.logprob.transforms.LogTransform