arviz.InferenceData.extend — ArviZ dev documentation (original) (raw)

InferenceData.extend(other, join='left', warn_on_custom_groups=False)[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.

warn_on_custom_groupsbool, default False

Emit a warning when custom groups are present in the InferenceData. “custom group” means any group whose name isn’t defined in InferenceData schema specification

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("radon")

Second InferenceData:

other_idata = az.load_arviz_data("rugby")

Call the extend method:

idata.extend(other_idata) idata

• posterior
  <xarray.Dataset> Size: 4MB
  Dimensions: (chain: 4, draw: 500, g_coef: 2, County: 85)
  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
  • g_coef (g_coef) <U9 72B 'intercept' 'slope'
  • County (County) <U17 6kB 'AITKIN' 'ANOKA' ... 'WRIGHT' 'YELLOW MEDICINE'
    Data variables:
    g (chain, draw, g_coef) float64 32kB ...
    za_county (chain, draw, County) float64 1MB ...
    b (chain, draw) float64 16kB ...
    sigma_a (chain, draw) float64 16kB ...
    a (chain, draw, County) float64 1MB ...
    a_county (chain, draw, County) float64 1MB ...
    sigma (chain, draw) float64 16kB ...
    Attributes:
    created_at: 2020-07-24T18:15:12.191355
    arviz_version: 0.9.0
    inference_library: pymc3
    inference_library_version: 3.9.2
    sampling_time: 18.096983432769775
    tuning_steps: 1000
    • Dimensions:
      * chain: 4
      * draw: 500
      * g_coef: 2
      * County: 85
    • Coordinates: (4)
      * chain
      (chain)
      int64
      0 1 2 3
      * draw
      (draw)
      int64
      0 1 2 3 4 5 ... 495 496 497 498 499
      array([ 0, 1, 2, ..., 497, 498, 499], shape=(500,))
      * g_coef
      (g_coef)
      <U9
      'intercept' 'slope'
      array(['intercept', 'slope'], dtype='<U9')
      * County
      (County)
      <U17
      'AITKIN' ... 'YELLOW MEDICINE'
      array(['AITKIN', 'ANOKA', 'BECKER', 'BELTRAMI', 'BENTON', 'BIG STONE',
      'BLUE EARTH', 'BROWN', 'CARLTON', 'CARVER', 'CASS', 'CHIPPEWA',
      'CHISAGO', 'CLAY', 'CLEARWATER', 'COOK', 'COTTONWOOD', 'CROW WING',
      'DAKOTA', 'DODGE', 'DOUGLAS', 'FARIBAULT', 'FILLMORE', 'FREEBORN',
      'GOODHUE', 'HENNEPIN', 'HOUSTON', 'HUBBARD', 'ISANTI', 'ITASCA',
      'JACKSON', 'KANABEC', 'KANDIYOHI', 'KITTSON', 'KOOCHICHING',
      'LAC QUI PARLE', 'LAKE', 'LAKE OF THE WOODS', 'LE SUEUR', 'LINCOLN',
      'LYON', 'MAHNOMEN', 'MARSHALL', 'MARTIN', 'MCLEOD', 'MEEKER',
      'MILLE LACS', 'MORRISON', 'MOWER', 'MURRAY', 'NICOLLET', 'NOBLES',
      'NORMAN', 'OLMSTED', 'OTTER TAIL', 'PENNINGTON', 'PINE', 'PIPESTONE',
      'POLK', 'POPE', 'RAMSEY', 'REDWOOD', 'RENVILLE', 'RICE', 'ROCK',
      'ROSEAU', 'SCOTT', 'SHERBURNE', 'SIBLEY', 'ST LOUIS', 'STEARNS',
      'STEELE', 'STEVENS', 'SWIFT', 'TODD', 'TRAVERSE', 'WABASHA', 'WADENA',
      'WASECA', 'WASHINGTON', 'WATONWAN', 'WILKIN', 'WINONA', 'WRIGHT',
      'YELLOW MEDICINE'], dtype='<U17')
    • Data variables: (7)
      * g
      (chain, draw, g_coef)
      float64
      ...
      [4000 values with dtype=float64]
      * za_county
      (chain, draw, County)
      float64
      ...
      [170000 values with dtype=float64]
      * b
      (chain, draw)
      float64
      ...
      [2000 values with dtype=float64]
      * sigma_a
      (chain, draw)
      float64
      ...
      [2000 values with dtype=float64]
      * a
      (chain, draw, County)
      float64
      ...
      [170000 values with dtype=float64]
      * a_county
      (chain, draw, County)
      float64
      ...
      [170000 values with dtype=float64]
      * sigma
      (chain, draw)
      float64
      ...
      [2000 values with dtype=float64]
    • Indexes: (4)
      * PandasIndex
      PandasIndex(Index([0, 1, 2, 3], dtype='int64', name='chain'))
      * PandasIndex
      PandasIndex(Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,
      ...
      490, 491, 492, 493, 494, 495, 496, 497, 498, 499],
      dtype='int64', name='draw', length=500))
      * PandasIndex
      PandasIndex(Index(['intercept', 'slope'], dtype='object', name='g_coef'))
      * PandasIndex
      PandasIndex(Index(['AITKIN', 'ANOKA', 'BECKER', 'BELTRAMI', 'BENTON', 'BIG STONE',
      'BLUE EARTH', 'BROWN', 'CARLTON', 'CARVER', 'CASS', 'CHIPPEWA',
      'CHISAGO', 'CLAY', 'CLEARWATER', 'COOK', 'COTTONWOOD', 'CROW WING',
      'DAKOTA', 'DODGE', 'DOUGLAS', 'FARIBAULT', 'FILLMORE', 'FREEBORN',
      'GOODHUE', 'HENNEPIN', 'HOUSTON', 'HUBBARD', 'ISANTI', 'ITASCA',
      'JACKSON', 'KANABEC', 'KANDIYOHI', 'KITTSON', 'KOOCHICHING',
      'LAC QUI PARLE', 'LAKE', 'LAKE OF THE WOODS', 'LE SUEUR', 'LINCOLN',
      'LYON', 'MAHNOMEN', 'MARSHALL', 'MARTIN', 'MCLEOD', 'MEEKER',
      'MILLE LACS', 'MORRISON', 'MOWER', 'MURRAY', 'NICOLLET', 'NOBLES',
      'NORMAN', 'OLMSTED', 'OTTER TAIL', 'PENNINGTON', 'PINE', 'PIPESTONE',
      'POLK', 'POPE', 'RAMSEY', 'REDWOOD', 'RENVILLE', 'RICE', 'ROCK',
      'ROSEAU', 'SCOTT', 'SHERBURNE', 'SIBLEY', 'ST LOUIS', 'STEARNS',
      'STEELE', 'STEVENS', 'SWIFT', 'TODD', 'TRAVERSE', 'WABASHA', 'WADENA',
      'WASECA', 'WASHINGTON', 'WATONWAN', 'WILKIN', 'WINONA', 'WRIGHT',
      'YELLOW MEDICINE'],
      dtype='object', name='County'))
    • Attributes: (6)
      created_at :
      2020-07-24T18:15:12.191355
      arviz_version :
      0.9.0
      inference_library :
      pymc3
      inference_library_version :
      3.9.2
      sampling_time :
      18.096983432769775
      tuning_steps :
      1000
• posterior_predictive
  <xarray.Dataset> Size: 15MB
  Dimensions: (chain: 4, draw: 500, obs_id: 919)
  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
  • obs_id (obs_id) int64 7kB 0 1 2 3 4 5 6 7 ... 912 913 914 915 916 917 918
    Data variables:
    y (chain, draw, obs_id) float64 15MB ...
    Attributes:
    created_at: 2020-07-24T18:15:12.449843
    arviz_version: 0.9.0
    inference_library: pymc3
    inference_library_version: 3.9.2
    • Dimensions:
      * chain: 4
      * draw: 500
      * obs_id: 919
    • Coordinates: (3)
      * chain
      (chain)
      int64
      0 1 2 3
      * draw
      (draw)
      int64
      0 1 2 3 4 5 ... 495 496 497 498 499
      array([ 0, 1, 2, ..., 497, 498, 499], shape=(500,))
      * obs_id
      (obs_id)
      int64
      0 1 2 3 4 5 ... 914 915 916 917 918
      array([ 0, 1, 2, ..., 916, 917, 918], shape=(919,))
    • Data variables: (1)
      * y
      (chain, draw, obs_id)
      float64
      ...
      [1838000 values with dtype=float64]
    • Indexes: (3)
      * PandasIndex
      PandasIndex(Index([0, 1, 2, 3], dtype='int64', name='chain'))
      * PandasIndex
      PandasIndex(Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,
      ...
      490, 491, 492, 493, 494, 495, 496, 497, 498, 499],
      dtype='int64', name='draw', length=500))
      * PandasIndex
      PandasIndex(Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,
      ...
      909, 910, 911, 912, 913, 914, 915, 916, 917, 918],
      dtype='int64', name='obs_id', length=919))
    • Attributes: (4)
      created_at :
      2020-07-24T18:15:12.449843
      arviz_version :
      0.9.0
      inference_library :
      pymc3
      inference_library_version :
      3.9.2
• log_likelihood
  <xarray.Dataset> Size: 15MB
  Dimensions: (chain: 4, draw: 500, obs_id: 919)
  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
  • obs_id (obs_id) int64 7kB 0 1 2 3 4 5 6 7 ... 912 913 914 915 916 917 918
    Data variables:
    y (chain, draw, obs_id) float64 15MB ...
    Attributes:
    created_at: 2020-07-24T18:15:12.448264
    arviz_version: 0.9.0
    inference_library: pymc3
    inference_library_version: 3.9.2
    • Dimensions:
      * chain: 4
      * draw: 500
      * obs_id: 919
    • Coordinates: (3)
      * chain
      (chain)
      int64
      0 1 2 3
      * draw
      (draw)
      int64
      0 1 2 3 4 5 ... 495 496 497 498 499
      array([ 0, 1, 2, ..., 497, 498, 499], shape=(500,))
      * obs_id
      (obs_id)
      int64
      0 1 2 3 4 5 ... 914 915 916 917 918
      array([ 0, 1, 2, ..., 916, 917, 918], shape=(919,))
    • Data variables: (1)
      * y
      (chain, draw, obs_id)
      float64
      ...
      [1838000 values with dtype=float64]
    • Indexes: (3)
      * PandasIndex
      PandasIndex(Index([0, 1, 2, 3], dtype='int64', name='chain'))
      * PandasIndex
      PandasIndex(Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,
      ...
      490, 491, 492, 493, 494, 495, 496, 497, 498, 499],
      dtype='int64', name='draw', length=500))
      * PandasIndex
      PandasIndex(Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,
      ...
      909, 910, 911, 912, 913, 914, 915, 916, 917, 918],
      dtype='int64', name='obs_id', length=919))
    • Attributes: (4)
      created_at :
      2020-07-24T18:15:12.448264
      arviz_version :
      0.9.0
      inference_library :
      pymc3
      inference_library_version :
      3.9.2
• log_prior
  <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
    • Dimensions:
      * chain: 4
      * draw: 500
      * team: 6
    • Coordinates: (3)
      * chain
      (chain)
      int64
      0 1 2 3
      * draw
      (draw)
      int64
      0 1 2 3 4 5 ... 495 496 497 498 499
      array([ 0, 1, 2, ..., 497, 498, 499], shape=(500,))
      * team
      (team)
      <U8
      'Wales' 'France' ... 'England'
      array(['Wales', 'France', 'Ireland', 'Scotland', 'Italy', 'England'],
      dtype='<U8')
    • Data variables: (6)
      * home
      (chain, draw)
      float64
      ...
      [2000 values with dtype=float64]
      * sd_att
      (chain, draw)
      float64
      ...
      [2000 values with dtype=float64]
      * sd_def
      (chain, draw)
      float64
      ...
      [2000 values with dtype=float64]
      * intercept
      (chain, draw)
      float64
      ...
      [2000 values with dtype=float64]
      * atts_star
      (chain, draw, team)
      float64
      ...
      [12000 values with dtype=float64]
      * defs_star
      (chain, draw, team)
      float64
      ...
      [12000 values with dtype=float64]
    • Indexes: (3)
      * PandasIndex
      PandasIndex(Index([0, 1, 2, 3], dtype='int64', name='chain'))
      * PandasIndex
      PandasIndex(Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,
      ...
      490, 491, 492, 493, 494, 495, 496, 497, 498, 499],
      dtype='int64', name='draw', length=500))
      * PandasIndex
      PandasIndex(Index(['Wales', 'France', 'Ireland', 'Scotland', 'Italy', 'England'], dtype='object', name='team'))
    • Attributes: (4)
      created_at :
      2024-03-06T20:46:24.377610
      arviz_version :
      0.17.0
      inference_library :
      pymc
      inference_library_version :
      5.10.4+7.g34d2a5d9
• sample_stats
  <xarray.Dataset> Size: 150kB
  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 ... 494 495 496 497 498 499
    Data variables:
    step_size_bar (chain, draw) float64 16kB ...
    diverging (chain, draw) bool 2kB ...
    energy (chain, draw) float64 16kB ...
    tree_size (chain, draw) float64 16kB ...
    mean_tree_accept (chain, draw) float64 16kB ...
    step_size (chain, draw) float64 16kB ...
    depth (chain, draw) int64 16kB ...
    energy_error (chain, draw) float64 16kB ...
    lp (chain, draw) float64 16kB ...
    max_energy_error (chain, draw) float64 16kB ...
    Attributes:
    created_at: 2020-07-24T18:15:12.197697
    arviz_version: 0.9.0
    inference_library: pymc3
    inference_library_version: 3.9.2
    sampling_time: 18.096983432769775
    tuning_steps: 1000
    • Dimensions:
      * chain: 4
      * draw: 500
    • Coordinates: (2)
      * chain
      (chain)
      int64
      0 1 2 3
      * draw
      (draw)
      int64
      0 1 2 3 4 5 ... 495 496 497 498 499
      array([ 0, 1, 2, ..., 497, 498, 499], shape=(500,))
    • Data variables: (10)
      * step_size_bar
      (chain, draw)
      float64
      ...
      [2000 values with dtype=float64]
      * diverging
      (chain, draw)
      bool
      ...
      [2000 values with dtype=bool]
      * energy
      (chain, draw)
      float64
      ...
      [2000 values with dtype=float64]
      * tree_size
      (chain, draw)
      float64
      ...
      [2000 values with dtype=float64]
      * mean_tree_accept
      (chain, draw)
      float64
      ...
      [2000 values with dtype=float64]
      * step_size
      (chain, draw)
      float64
      ...
      [2000 values with dtype=float64]
      * depth
      (chain, draw)
      int64
      ...
      [2000 values with dtype=int64]
      * energy_error
      (chain, draw)
      float64
      ...
      [2000 values with dtype=float64]
      * lp
      (chain, draw)
      float64
      ...
      [2000 values with dtype=float64]
      * max_energy_error
      (chain, draw)
      float64
      ...
      [2000 values with dtype=float64]
    • Indexes: (2)
      * PandasIndex
      PandasIndex(Index([0, 1, 2, 3], dtype='int64', name='chain'))
      * PandasIndex
      PandasIndex(Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,
      ...
      490, 491, 492, 493, 494, 495, 496, 497, 498, 499],
      dtype='int64', name='draw', length=500))
    • Attributes: (6)
      created_at :
      2020-07-24T18:15:12.197697
      arviz_version :
      0.9.0
      inference_library :
      pymc3
      inference_library_version :
      3.9.2
      sampling_time :
      18.096983432769775
      tuning_steps :
      1000
• prior
  <xarray.Dataset> Size: 1MB
  Dimensions: (chain: 1, draw: 500, County: 85, g_coef: 2)
  Coordinates:
  • chain (chain) int64 8B 0
  • draw (draw) int64 4kB 0 1 2 3 4 5 6 ... 494 495 496 497 498 499
  • County (County) <U17 6kB 'AITKIN' 'ANOKA' ... 'YELLOW MEDICINE'
  • g_coef (g_coef) <U9 72B 'intercept' 'slope'
    Data variables:
    a_county (chain, draw, County) float64 340kB ...
    sigma_log__ (chain, draw) float64 4kB ...
    sigma_a (chain, draw) float64 4kB ...
    a (chain, draw, County) float64 340kB ...
    b (chain, draw) float64 4kB ...
    za_county (chain, draw, County) float64 340kB ...
    sigma (chain, draw) float64 4kB ...
    g (chain, draw, g_coef) float64 8kB ...
    sigma_a_log__ (chain, draw) float64 4kB ...
    Attributes:
    created_at: 2020-07-24T18:15:12.454586
    arviz_version: 0.9.0
    inference_library: pymc3
    inference_library_version: 3.9.2
    • Dimensions:
      * chain: 1
      * draw: 500
      * County: 85
      * g_coef: 2
    • Coordinates: (4)
      * chain
      (chain)
      int64
      0
      * draw
      (draw)
      int64
      0 1 2 3 4 5 ... 495 496 497 498 499
      array([ 0, 1, 2, ..., 497, 498, 499], shape=(500,))
      * County
      (County)
      <U17
      'AITKIN' ... 'YELLOW MEDICINE'
      array(['AITKIN', 'ANOKA', 'BECKER', 'BELTRAMI', 'BENTON', 'BIG STONE',
      'BLUE EARTH', 'BROWN', 'CARLTON', 'CARVER', 'CASS', 'CHIPPEWA',
      'CHISAGO', 'CLAY', 'CLEARWATER', 'COOK', 'COTTONWOOD', 'CROW WING',
      'DAKOTA', 'DODGE', 'DOUGLAS', 'FARIBAULT', 'FILLMORE', 'FREEBORN',
      'GOODHUE', 'HENNEPIN', 'HOUSTON', 'HUBBARD', 'ISANTI', 'ITASCA',
      'JACKSON', 'KANABEC', 'KANDIYOHI', 'KITTSON', 'KOOCHICHING',
      'LAC QUI PARLE', 'LAKE', 'LAKE OF THE WOODS', 'LE SUEUR', 'LINCOLN',
      'LYON', 'MAHNOMEN', 'MARSHALL', 'MARTIN', 'MCLEOD', 'MEEKER',
      'MILLE LACS', 'MORRISON', 'MOWER', 'MURRAY', 'NICOLLET', 'NOBLES',
      'NORMAN', 'OLMSTED', 'OTTER TAIL', 'PENNINGTON', 'PINE', 'PIPESTONE',
      'POLK', 'POPE', 'RAMSEY', 'REDWOOD', 'RENVILLE', 'RICE', 'ROCK',
      'ROSEAU', 'SCOTT', 'SHERBURNE', 'SIBLEY', 'ST LOUIS', 'STEARNS',
      'STEELE', 'STEVENS', 'SWIFT', 'TODD', 'TRAVERSE', 'WABASHA', 'WADENA',
      'WASECA', 'WASHINGTON', 'WATONWAN', 'WILKIN', 'WINONA', 'WRIGHT',
      'YELLOW MEDICINE'], dtype='<U17')
      * g_coef
      (g_coef)
      <U9
      'intercept' 'slope'
      array(['intercept', 'slope'], dtype='<U9')
    • Data variables: (9)
      * a_county
      (chain, draw, County)
      float64
      ...
      [42500 values with dtype=float64]
      * sigma_log__
      (chain, draw)
      float64
      ...
      [500 values with dtype=float64]
      * sigma_a
      (chain, draw)
      float64
      ...
      [500 values with dtype=float64]
      * a
      (chain, draw, County)
      float64
      ...
      [42500 values with dtype=float64]
      * b
      (chain, draw)
      float64
      ...
      [500 values with dtype=float64]
      * za_county
      (chain, draw, County)
      float64
      ...
      [42500 values with dtype=float64]
      * sigma
      (chain, draw)
      float64
      ...
      [500 values with dtype=float64]
      * g
      (chain, draw, g_coef)
      float64
      ...
      [1000 values with dtype=float64]
      * sigma_a_log__
      (chain, draw)
      float64
      ...
      [500 values with dtype=float64]
    • Indexes: (4)
      * PandasIndex
      PandasIndex(Index([0], dtype='int64', name='chain'))
      * PandasIndex
      PandasIndex(Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,
      ...
      490, 491, 492, 493, 494, 495, 496, 497, 498, 499],
      dtype='int64', name='draw', length=500))
      * PandasIndex
      PandasIndex(Index(['AITKIN', 'ANOKA', 'BECKER', 'BELTRAMI', 'BENTON', 'BIG STONE',
      'BLUE EARTH', 'BROWN', 'CARLTON', 'CARVER', 'CASS', 'CHIPPEWA',
      'CHISAGO', 'CLAY', 'CLEARWATER', 'COOK', 'COTTONWOOD', 'CROW WING',
      'DAKOTA', 'DODGE', 'DOUGLAS', 'FARIBAULT', 'FILLMORE', 'FREEBORN',
      'GOODHUE', 'HENNEPIN', 'HOUSTON', 'HUBBARD', 'ISANTI', 'ITASCA',
      'JACKSON', 'KANABEC', 'KANDIYOHI', 'KITTSON', 'KOOCHICHING',
      'LAC QUI PARLE', 'LAKE', 'LAKE OF THE WOODS', 'LE SUEUR', 'LINCOLN',
      'LYON', 'MAHNOMEN', 'MARSHALL', 'MARTIN', 'MCLEOD', 'MEEKER',
      'MILLE LACS', 'MORRISON', 'MOWER', 'MURRAY', 'NICOLLET', 'NOBLES',
      'NORMAN', 'OLMSTED', 'OTTER TAIL', 'PENNINGTON', 'PINE', 'PIPESTONE',
      'POLK', 'POPE', 'RAMSEY', 'REDWOOD', 'RENVILLE', 'RICE', 'ROCK',
      'ROSEAU', 'SCOTT', 'SHERBURNE', 'SIBLEY', 'ST LOUIS', 'STEARNS',
      'STEELE', 'STEVENS', 'SWIFT', 'TODD', 'TRAVERSE', 'WABASHA', 'WADENA',
      'WASECA', 'WASHINGTON', 'WATONWAN', 'WILKIN', 'WINONA', 'WRIGHT',
      'YELLOW MEDICINE'],
      dtype='object', name='County'))
      * PandasIndex
      PandasIndex(Index(['intercept', 'slope'], dtype='object', name='g_coef'))
    • Attributes: (4)
      created_at :
      2020-07-24T18:15:12.454586
      arviz_version :
      0.9.0
      inference_library :
      pymc3
      inference_library_version :
      3.9.2
• prior_predictive
  <xarray.Dataset> Size: 4MB
  Dimensions: (chain: 1, draw: 500, obs_id: 919)
  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
  • obs_id (obs_id) int64 7kB 0 1 2 3 4 5 6 7 ... 912 913 914 915 916 917 918
    Data variables:
    y (chain, draw, obs_id) float64 4MB ...
    Attributes:
    created_at: 2020-07-24T18:15:12.457652
    arviz_version: 0.9.0
    inference_library: pymc3
    inference_library_version: 3.9.2
    • Dimensions:
      * chain: 1
      * draw: 500
      * obs_id: 919
    • Coordinates: (3)
      * chain
      (chain)
      int64
      0
      * draw
      (draw)
      int64
      0 1 2 3 4 5 ... 495 496 497 498 499
      array([ 0, 1, 2, ..., 497, 498, 499], shape=(500,))
      * obs_id
      (obs_id)
      int64
      0 1 2 3 4 5 ... 914 915 916 917 918
      array([ 0, 1, 2, ..., 916, 917, 918], shape=(919,))
    • Data variables: (1)
      * y
      (chain, draw, obs_id)
      float64
      ...
      [459500 values with dtype=float64]
    • Indexes: (3)
      * PandasIndex
      PandasIndex(Index([0], dtype='int64', name='chain'))
      * PandasIndex
      PandasIndex(Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,
      ...
      490, 491, 492, 493, 494, 495, 496, 497, 498, 499],
      dtype='int64', name='draw', length=500))
      * PandasIndex
      PandasIndex(Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,
      ...
      909, 910, 911, 912, 913, 914, 915, 916, 917, 918],
      dtype='int64', name='obs_id', length=919))
    • Attributes: (4)
      created_at :
      2020-07-24T18:15:12.457652
      arviz_version :
      0.9.0
      inference_library :
      pymc3
      inference_library_version :
      3.9.2
• observed_data
  <xarray.Dataset> Size: 15kB
  Dimensions: (obs_id: 919)
  Coordinates:
  • obs_id (obs_id) int64 7kB 0 1 2 3 4 5 6 7 ... 912 913 914 915 916 917 918
    Data variables:
    y (obs_id) float64 7kB ...
    Attributes:
    created_at: 2020-07-24T18:15:12.458415
    arviz_version: 0.9.0
    inference_library: pymc3
    inference_library_version: 3.9.2
    • Dimensions:
      * obs_id: 919
    • Coordinates: (1)
      * obs_id
      (obs_id)
      int64
      0 1 2 3 4 5 ... 914 915 916 917 918
      array([ 0, 1, 2, ..., 916, 917, 918], shape=(919,))
    • Data variables: (1)
      * y
      (obs_id)
      float64
      ...
      [919 values with dtype=float64]
    • Indexes: (1)
      * PandasIndex
      PandasIndex(Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,
      ...
      909, 910, 911, 912, 913, 914, 915, 916, 917, 918],
      dtype='int64', name='obs_id', length=919))
    • Attributes: (4)
      created_at :
      2020-07-24T18:15:12.458415
      arviz_version :
      0.9.0
      inference_library :
      pymc3
      inference_library_version :
      3.9.2
• constant_data
  <xarray.Dataset> Size: 21kB
  Dimensions: (obs_id: 919, County: 85)
  Coordinates:
  • obs_id (obs_id) int64 7kB 0 1 2 3 4 5 6 ... 912 913 914 915 916 917 918
  • County (County) <U17 6kB 'AITKIN' 'ANOKA' ... 'YELLOW MEDICINE'
    Data variables:
    floor_idx (obs_id) int32 4kB ...
    county_idx (obs_id) int32 4kB ...
    uranium (County) float64 680B ...
    Attributes:
    created_at: 2020-07-24T18:15:12.459832
    arviz_version: 0.9.0
    inference_library: pymc3
    inference_library_version: 3.9.2
    • Dimensions:
      * obs_id: 919
      * County: 85
    • Coordinates: (2)
      * obs_id
      (obs_id)
      int64
      0 1 2 3 4 5 ... 914 915 916 917 918
      array([ 0, 1, 2, ..., 916, 917, 918], shape=(919,))
      * County
      (County)
      <U17
      'AITKIN' ... 'YELLOW MEDICINE'
      array(['AITKIN', 'ANOKA', 'BECKER', 'BELTRAMI', 'BENTON', 'BIG STONE',
      'BLUE EARTH', 'BROWN', 'CARLTON', 'CARVER', 'CASS', 'CHIPPEWA',
      'CHISAGO', 'CLAY', 'CLEARWATER', 'COOK', 'COTTONWOOD', 'CROW WING',
      'DAKOTA', 'DODGE', 'DOUGLAS', 'FARIBAULT', 'FILLMORE', 'FREEBORN',
      'GOODHUE', 'HENNEPIN', 'HOUSTON', 'HUBBARD', 'ISANTI', 'ITASCA',
      'JACKSON', 'KANABEC', 'KANDIYOHI', 'KITTSON', 'KOOCHICHING',
      'LAC QUI PARLE', 'LAKE', 'LAKE OF THE WOODS', 'LE SUEUR', 'LINCOLN',
      'LYON', 'MAHNOMEN', 'MARSHALL', 'MARTIN', 'MCLEOD', 'MEEKER',
      'MILLE LACS', 'MORRISON', 'MOWER', 'MURRAY', 'NICOLLET', 'NOBLES',
      'NORMAN', 'OLMSTED', 'OTTER TAIL', 'PENNINGTON', 'PINE', 'PIPESTONE',
      'POLK', 'POPE', 'RAMSEY', 'REDWOOD', 'RENVILLE', 'RICE', 'ROCK',
      'ROSEAU', 'SCOTT', 'SHERBURNE', 'SIBLEY', 'ST LOUIS', 'STEARNS',
      'STEELE', 'STEVENS', 'SWIFT', 'TODD', 'TRAVERSE', 'WABASHA', 'WADENA',
      'WASECA', 'WASHINGTON', 'WATONWAN', 'WILKIN', 'WINONA', 'WRIGHT',
      'YELLOW MEDICINE'], dtype='<U17')
    • Data variables: (3)
      * floor_idx
      (obs_id)
      int32
      ...
      [919 values with dtype=int32]
      * county_idx
      (obs_id)
      int32
      ...
      [919 values with dtype=int32]
      * uranium
      (County)
      float64
      ...
      [85 values with dtype=float64]
    • Indexes: (2)
      * PandasIndex
      PandasIndex(Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,
      ...
      909, 910, 911, 912, 913, 914, 915, 916, 917, 918],
      dtype='int64', name='obs_id', length=919))
      * PandasIndex
      PandasIndex(Index(['AITKIN', 'ANOKA', 'BECKER', 'BELTRAMI', 'BENTON', 'BIG STONE',
      'BLUE EARTH', 'BROWN', 'CARLTON', 'CARVER', 'CASS', 'CHIPPEWA',
      'CHISAGO', 'CLAY', 'CLEARWATER', 'COOK', 'COTTONWOOD', 'CROW WING',
      'DAKOTA', 'DODGE', 'DOUGLAS', 'FARIBAULT', 'FILLMORE', 'FREEBORN',
      'GOODHUE', 'HENNEPIN', 'HOUSTON', 'HUBBARD', 'ISANTI', 'ITASCA',
      'JACKSON', 'KANABEC', 'KANDIYOHI', 'KITTSON', 'KOOCHICHING',
      'LAC QUI PARLE', 'LAKE', 'LAKE OF THE WOODS', 'LE SUEUR', 'LINCOLN',
      'LYON', 'MAHNOMEN', 'MARSHALL', 'MARTIN', 'MCLEOD', 'MEEKER',
      'MILLE LACS', 'MORRISON', 'MOWER', 'MURRAY', 'NICOLLET', 'NOBLES',
      'NORMAN', 'OLMSTED', 'OTTER TAIL', 'PENNINGTON', 'PINE', 'PIPESTONE',
      'POLK', 'POPE', 'RAMSEY', 'REDWOOD', 'RENVILLE', 'RICE', 'ROCK',
      'ROSEAU', 'SCOTT', 'SHERBURNE', 'SIBLEY', 'ST LOUIS', 'STEARNS',
      'STEELE', 'STEVENS', 'SWIFT', 'TODD', 'TRAVERSE', 'WABASHA', 'WADENA',
      'WASECA', 'WASHINGTON', 'WATONWAN', 'WILKIN', 'WINONA', 'WRIGHT',
      'YELLOW MEDICINE'],
      dtype='object', name='County'))
    • Attributes: (4)
      created_at :
      2020-07-24T18:15:12.459832
      arviz_version :
      0.9.0
      inference_library :
      pymc3
      inference_library_version :
      3.9.2
• unconstrained_posterior
  <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
    • Dimensions:
      * chain: 4
      * draw: 500
    • Coordinates: (2)
      * chain
      (chain)
      int64
      0 1 2 3
      * draw
      (draw)
      int64
      0 1 2 3 4 5 ... 495 496 497 498 499
      array([ 0, 1, 2, ..., 497, 498, 499], shape=(500,))
    • Data variables: (2)
      * sd_att
      (chain, draw)
      float64
      ...
      [2000 values with dtype=float64]
      * sd_def
      (chain, draw)
      float64
      ...
      [2000 values with dtype=float64]
    • Indexes: (2)
      * PandasIndex
      PandasIndex(Index([0, 1, 2, 3], dtype='int64', name='chain'))
      * PandasIndex
      PandasIndex(Index([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,
      ...
      490, 491, 492, 493, 494, 495, 496, 497, 498, 499],
      dtype='int64', name='draw', length=500))
    • Attributes: (2)
      sd_att :
      pymc.logprob.transforms.LogTransform
      sd_def :
      pymc.logprob.transforms.LogTransform

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.