arviz.InferenceData.extend#

InferenceData.extend(other, join='left')[source]#

Extend InferenceData with groups from another InferenceData.

Parameters
otherInferenceData

InferenceData to be added

join{‘left’, ‘right’}, default ‘left’

Defines how the two decide which group to keep when the same group is present in both objects. ‘left’ will discard the group in other whereas ‘right’ will keep the group in other and discard the one in self.

See also

add_groups

Add new groups to InferenceData object.

concat

Concatenate InferenceData objects.

Examples

Take two InferenceData objects, and extend the first with the groups it doesn’t have but are present in the 2nd InferenceData object.

First InferenceData:

import arviz as az
idata = az.load_arviz_data("rugby")

Second InferenceData:

other_idata = az.load_arviz_data("radon")

Call the extend method:

idata.extend(other_idata)
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, obs_id: 919)
      Coordinates:
        * chain    (chain) int64 0 1 2 3
        * draw     (draw) int64 0 1 2 3 4 5 6 7 8 ... 492 493 494 495 496 497 498 499
        * obs_id   (obs_id) int64 0 1 2 3 4 5 6 7 ... 911 912 913 914 915 916 917 918
      Data variables:
          y        (chain, draw, obs_id) float64 ...
      Attributes:
          created_at:                 2020-07-24T18:15:12.448264
          arviz_version:              0.9.0
          inference_library:          pymc3
          inference_library_version:  3.9.2

    • <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:  (chain: 1, draw: 500, obs_id: 919)
      Coordinates:
        * chain    (chain) int64 0
        * draw     (draw) int64 0 1 2 3 4 5 6 7 8 ... 492 493 494 495 496 497 498 499
        * obs_id   (obs_id) int64 0 1 2 3 4 5 6 7 ... 911 912 913 914 915 916 917 918
      Data variables:
          y        (chain, draw, obs_id) float64 ...
      Attributes:
          created_at:                 2020-07-24T18:15:12.457652
          arviz_version:              0.9.0
          inference_library:          pymc3
          inference_library_version:  3.9.2

    • <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

    • <xarray.Dataset>
      Dimensions:     (obs_id: 919, County: 85)
      Coordinates:
        * obs_id      (obs_id) int64 0 1 2 3 4 5 6 7 ... 912 913 914 915 916 917 918
        * County      (County) object 'AITKIN' 'ANOKA' ... 'WRIGHT' 'YELLOW MEDICINE'
      Data variables:
          floor_idx   (obs_id) int32 ...
          county_idx  (obs_id) int32 ...
          uranium     (County) float64 ...
      Attributes:
          created_at:                 2020-07-24T18:15:12.459832
          arviz_version:              0.9.0
          inference_library:          pymc3
          inference_library_version:  3.9.2

See how now the first InferenceData has more groups, with the data from the second one, but the groups it originally had have not been modified, even if also present in the second InferenceData.