From 09af86e8cdf8fe35f5eb3d81b36df3bfe95f6386 Mon Sep 17 00:00:00 2001 From: Neha S <39901160+neha-shah99@users.noreply.github.com> Date: Fri, 5 Jun 2020 01:11:30 +0530 Subject: [PATCH 01/24] Fix inconsistent capitalization in plot_hdi docstring (#1221) * Fix inconsistent capitalization in plot_hdi docstring * Update Changelog --- CHANGELOG.md | 2 +- arviz/plots/hdiplot.py | 12 ++++++------ 2 files changed, 7 insertions(+), 7 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 8a4d1fb21a..3476406e9c 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -13,6 +13,7 @@ ### Deprecation ### Documentation +* Fixed inconsistent capitalization in `plot_hdi` docstring (#1221) ## v0.8.3 (2020 May 28) ### Maintenance and fixes @@ -281,4 +282,3 @@ ## v0.3.0 (2018 Dec 14) * First Beta Release - diff --git a/arviz/plots/hdiplot.py b/arviz/plots/hdiplot.py index ba5be26b8c..f9dbcd5932 100644 --- a/arviz/plots/hdiplot.py +++ b/arviz/plots/hdiplot.py @@ -33,13 +33,13 @@ def plot_hdi( Parameters ---------- x : array-like - Values to plot + Values to plot. y : array-like - values from which to compute the hdi. Assumed shape (chain, draw, \*shape). + Values from which to compute the hdi. Assumed shape (chain, draw, \*shape). hdi_prob : float, optional Probability for the highest density interval. Defaults to 0.94. color : str - Color used for the limits of the hdi and fill. Should be a valid matplotlib color + Color used for the limits of the hdi and fill. Should be a valid matplotlib color. circular : bool, optional Whether to compute the hdi taking into account `x` is a circular variable (in the range [-np.pi, np.pi]) or not. Defaults to False (i.e non-circular variables). @@ -49,11 +49,11 @@ def plot_hdi( Defaults to True. smooth_kwargs : dict, optional Additional keywords modifying the Savitzky-Golay filter. See Scipy's documentation for - details + details. fill_kwargs : dict Keywords passed to `fill_between` (use fill_kwargs={'alpha': 0} to disable fill). plot_kwargs : dict - Keywords passed to hdi limits + Keywords passed to hdi limits. ax: axes, optional Matplotlib axes or bokeh figures. backend: str, optional @@ -64,7 +64,7 @@ def plot_hdi( show : bool, optional Call backend show function. credible_interval: float, optional - deprecated: Please see hdi_prob + Deprecated: Please see hdi_prob Returns ------- From 99cfef769d7e01804aea4e4542a0b97c76ffe645 Mon Sep 17 00:00:00 2001 From: Ravin Kumar Date: Sat, 6 Jun 2020 19:59:53 -0700 Subject: [PATCH 02/24] [WIP] Election 2020 (#1216) * Add README * Add initial ArviZ 2020 election * Add election.md * Update elections/ArviZ_2020.md Co-authored-by: Colin * Update elections/ArviZ_2020.md Co-authored-by: Colin * Update elections/ArviZ_2020.md Co-authored-by: Colin * Update elections/ArviZ_2020.md Co-authored-by: Colin * Update elections/ArviZ_2020.md Co-authored-by: Colin * Update elections/ArviZ_2020.md Co-authored-by: Colin * Add section on nominations and fix typos * Update elections/ArviZ_2020.md Co-authored-by: Alexandre ANDORRA * Update elections/ArviZ_2020.md Co-authored-by: Alexandre ANDORRA * Update elections/ArviZ_2020.md Co-authored-by: Alexandre ANDORRA * change who can make council nominations * Update election * Add where results will be posted * Update timing Co-authored-by: Colin Co-authored-by: Alexandre ANDORRA Co-authored-by: Oriol (Prodesk) --- elections/ArviZ_2020.md | 58 +++++++++++++++++++++++++++++++++++++++++ elections/README.md | 2 ++ 2 files changed, 60 insertions(+) create mode 100644 elections/ArviZ_2020.md create mode 100644 elections/README.md diff --git a/elections/ArviZ_2020.md b/elections/ArviZ_2020.md new file mode 100644 index 0000000000..1342855169 --- /dev/null +++ b/elections/ArviZ_2020.md @@ -0,0 +1,58 @@ +# Election 2020 + +## Summary of Process +This is the first election cycle for ArviZ. During this cycle we are aiming +for the following outcomes + +* Ratification of GOVERNANCE.md +* Identification and listing of Core Contributors +* Nominations for Council Candidates +* Council Elections. Particularly + * Size of Council + * Inaugural ArviZ Council + +This being the first election means that the process will have more steps than +the ones listed in GOVERNANCE.md. + +The moderators for these tasks live in the PST and CST timezone so the dates are reflective of their localities. + +## Process Details +### Identification of Core Contributors (June 7th) +1. A Google Form will be shared over Slack to get core contributor names, emails, and optionally Github username. + * Emails are needed for Elections steps so NumFOCUS can mail and track votes + * The Google Form is moderated by Ravin (@canyon289) +2. Ravin will open a Pull Request with all names and update the PR periodically until June 12th 8pm PST, which is also the cutoff time for the form. + * On June 12th 8pm PST Ravin will make the form's responses public as well, with exception of email +3. The pull request will stay open 24 hours at a minimum. If there is no dissent the pull request will be merged. If there is discussion the timeline below will be shifted. + +Note: This is a one time process for identifying core contributors. After this cycle the core contributor acceptance process will performed by the ArviZ Council once elected + +### Nominations for Council (June 14th) +1. A github issue labeled *Arviz 2020 Council Elections* will be opened for nominations +2. Core contributors can nominate themselves or other core contributors. +3. Core Contributor must accept acknowledgement with thumbs up on nomination + * Without an acknowledgement, they will not be a candidate in the election. + * A nomination can be explicitly declined as well in a comment, if that individual + feels they will not be able to serve as a Council Member for any reason. +4. Nominations end at June 20th anywhere on Earth. On June 21st nominations will be compiled in the last comment and the issue will be closed. + + +### First Round Election for Council and Council size (Week of June 21) +The first election will be for council and council size. Elections will take place over +a Google Form owned by NumFOCUS. The tallying will be performed in accordance with `GOVERNANCE.md` + +The election period will be exactly 7 days from when NumFOCUS email timestamp for the Google Form. + +1. NumFOCUS will email all registered voters a ballot for a vote on + * Council Size + * Council Members +2. At conclusion of election period NumFOCUS will remove the voters identifying information and share the results (most likely in Google Sheets) + * A table of results will be added to this document + +### Tie Votes (1 week) +In the event of a tie there will be a run-off election. The specific timing will depend on the results of the election above. +The tallying will be performed in accordance with `GOVERNANCE.md` + +1. NumFOCUS will email all registered voters a ballot for a vote on tied candidates +2. At conclusion of election period NumFOCUS will remove the voters identifying information and share the results (most likely in Google Sheets) + * A table of results will be added to this document diff --git a/elections/README.md b/elections/README.md new file mode 100644 index 0000000000..eea9049f9c --- /dev/null +++ b/elections/README.md @@ -0,0 +1,2 @@ +# Elections +Repository for ArviZ elections From 1521304881701546e4758fe39b6ef433dff6838f Mon Sep 17 00:00:00 2001 From: Oriol Abril Date: Sun, 7 Jun 2020 15:06:53 +0200 Subject: [PATCH 03/24] add MultiObservedRVs to observed_data (#1098) * add MultiObservedRVs to observed_data * typo * update tests * lint * update changelog --- CHANGELOG.md | 2 + arviz/data/io_pymc3.py | 29 +++++++----- arviz/tests/external_tests/test_data_pymc.py | 50 +++++++++++++++++--- 3 files changed, 61 insertions(+), 20 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 3476406e9c..da7e8928f0 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -6,6 +6,8 @@ * loo-pit plot. The kde is computed over the data interval (this could be shorter than [0, 1]). The hdi is computed analitically (#1215) ### Maintenance and fixes +* Include data from `MultiObservedRV` to `observed_data` when using + `from_pymc3` (#1098) * Added a note on `plot_pair` when trying to use `plot_kde` on `InferenceData` objects. (#1218) diff --git a/arviz/data/io_pymc3.py b/arviz/data/io_pymc3.py index 879d24fe35..891210a27d 100644 --- a/arviz/data/io_pymc3.py +++ b/arviz/data/io_pymc3.py @@ -1,7 +1,7 @@ """PyMC3-specific conversion code.""" import logging import warnings -from typing import Dict, List, Any, Optional, Iterable, Union, TYPE_CHECKING, Tuple +from typing import Dict, List, Tuple, Any, Optional, Iterable, Union, TYPE_CHECKING from types import ModuleType import numpy as np @@ -149,18 +149,21 @@ def arbitrary_element(dct: Dict[Any, np.ndarray]) -> np.ndarray: self.coords = coords self.dims = dims - self.observations = self.find_observations() + self.observations, self.multi_observations = self.find_observations() - def find_observations(self) -> Optional[Dict[str, Var]]: + def find_observations(self) -> Tuple[Optional[Dict[str, Var]], Optional[Dict[str, Var]]]: """If there are observations available, return them as a dictionary.""" - has_observations = False - if self.model is not None: - if any((hasattr(obs, "observations") for obs in self.model.observed_RVs)): - has_observations = True - if has_observations: - assert self.model is not None - return {obs.name: obs.observations for obs in self.model.observed_RVs} - return None + if self.model is None: + return (None, None) + observations = {} + multi_observations = {} + for obs in self.model.observed_RVs: + if hasattr(obs, "observations"): + observations[obs.name] = obs.observations + elif hasattr(obs, "data"): + for key, val in obs.data.items(): + multi_observations[key] = val.eval() if hasattr(val, "eval") else val + return observations, multi_observations def split_trace(self) -> Tuple[Union[None, MultiTrace], Union[None, MultiTrace]]: """Split MultiTrace object into posterior and warmup. @@ -361,7 +364,7 @@ def priors_to_xarray(self): ) return priors_dict - @requires("observations") + @requires(["observations", "multi_observations"]) @requires("model") def observed_data_to_xarray(self): """Convert observed data to xarray.""" @@ -372,7 +375,7 @@ def observed_data_to_xarray(self): else: dims = self.dims observed_data = {} - for name, vals in self.observations.items(): + for name, vals in {**self.observations, **self.multi_observations}.items(): if hasattr(vals, "get_value"): vals = vals.get_value() vals = utils.one_de(vals) diff --git a/arviz/tests/external_tests/test_data_pymc.py b/arviz/tests/external_tests/test_data_pymc.py index 8abfba5825..cf8e093a93 100644 --- a/arviz/tests/external_tests/test_data_pymc.py +++ b/arviz/tests/external_tests/test_data_pymc.py @@ -227,7 +227,7 @@ def test_multiple_observed_rv(self, log_likelihood): "posterior": ["x"], "observed_data": ["y1", "y2"], "log_likelihood": ["y1", "y2"], - "sample_stats": ["diverging", "lp"], + "sample_stats": ["diverging", "lp", "~log_likelihood"], } if not log_likelihood: test_dict.pop("log_likelihood") @@ -237,7 +237,6 @@ def test_multiple_observed_rv(self, log_likelihood): fails = check_multiple_attrs(test_dict, inference_data) assert not fails - assert not hasattr(inference_data.sample_stats, "log_likelihood") @pytest.mark.skipif( version_info < (3, 6), reason="Requires updated PyMC3, which needs Python 3.6" @@ -250,11 +249,48 @@ def test_multiple_observed_rv_without_observations(self): ) trace = pm.sample(100, chains=2) inference_data = from_pymc3(trace=trace) - assert inference_data - assert not hasattr(inference_data, "observed_data") - assert hasattr(inference_data, "posterior") - assert hasattr(inference_data, "sample_stats") - assert hasattr(inference_data, "log_likelihood") + test_dict = { + "posterior": ["mu"], + "sample_stats": ["lp"], + "log_likelihood": ["x"], + "observed_data": ["value", "~x"], + } + fails = check_multiple_attrs(test_dict, inference_data) + assert not fails + assert inference_data.observed_data.value.dtype.kind == "f" + + def test_multiobservedrv_to_observed_data(self): + # fake regression data, with weights (W) + np.random.seed(2019) + N = 100 + X = np.random.uniform(size=N) + W = 1 + np.random.poisson(size=N) + a, b = 5, 17 + Y = a + np.random.normal(b * X) + + with pm.Model(): + a = pm.Normal("a", 0, 10) + b = pm.Normal("b", 0, 10) + mu = a + b * X + sigma = pm.HalfNormal("sigma", 1) + + def weighted_normal(y, w): + return w * pm.Normal.dist(mu=mu, sd=sigma).logp(y) + + y_logp = pm.DensityDist( # pylint: disable=unused-variable + "y_logp", weighted_normal, observed={"y": Y, "w": W} + ) + trace = pm.sample(20, tune=20) + idata = from_pymc3(trace) + test_dict = { + "posterior": ["a", "b", "sigma"], + "sample_stats": ["lp"], + "log_likelihood": ["y_logp"], + "observed_data": ["y", "w"], + } + fails = check_multiple_attrs(test_dict, idata) + assert not fails + assert idata.observed_data.y.dtype.kind == "f" def test_single_observation(self): with pm.Model(): From 198cca535f433165d75782d481f8bd64f50833cd Mon Sep 17 00:00:00 2001 From: Ravin Kumar Date: Sun, 7 Jun 2020 06:10:12 -0700 Subject: [PATCH 04/24] Add finances section (#1220) * Add finances section * Update GOVERNANCE.md Co-authored-by: Alexandre ANDORRA * Update GOVERNANCE.md Co-authored-by: Alexandre ANDORRA * Update funding line * Add caveat to disbursing funds Co-authored-by: Oriol Abril Co-authored-by: Alexandre ANDORRA --- GOVERNANCE.md | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/GOVERNANCE.md b/GOVERNANCE.md index c57f771a13..6264fbf66b 100644 --- a/GOVERNANCE.md +++ b/GOVERNANCE.md @@ -93,8 +93,12 @@ Council Members will have the responsibility of * Make decisions when regular community discussion doesn’t produce consensus on an issue in a reasonable time frame. * Make decisions about strategic collaborations with other organizations or individuals. * Make decisions about the overall scope, vision and direction of the project. +* Developing funding sources +* Deciding how to disburse funds with consultation from Core Contributors -Note that each individual council member does not have the power to unilaterally wield these responsibilities, but the council as a whole must jointly make these decisions. In other words, Council Members are first and foremost Core Contributors, but only when needed they can collectively make decisions for the health of the project. +The council may choose to delegate these responsibilities to sub-committees. If so, Council members must update this document to make the delegation clear. + +Note that individual council member does not have the power to unilaterally wield these responsibilities, but the council as a whole must jointly make these decisions. In other words, Council Members are first and foremost Core Contributors, but only when needed they can collectively make decisions for the health of the project. ArviZ will be holding its first election to determine its initial council in the coming weeks and this document will be updated. @@ -202,4 +206,3 @@ can make the determination for acceptance with a process of their choosing. #### Core Contributor Responsibilities * Enforce code of conduct * Maintain a check against Council - From d8b14cf2bc386a8ab56c59c6bc89c6b64a28b2e2 Mon Sep 17 00:00:00 2001 From: Michael Nowotny Date: Tue, 9 Jun 2020 10:20:28 -0700 Subject: [PATCH 05/24] Added support for PyJAGS in ArviZ (#1219) Co-authored-by: Oriol Abril --- .azure-pipelines/azure-pipelines-external.yml | 2 + CHANGELOG.md | 2 + arviz/data/__init__.py | 2 + arviz/data/io_pyjags.py | 323 ++ .../tests/external_tests/test_data_pyjags.py | 101 + doc/api.rst | 1 + doc/notebooks/InferenceDataCookbook.ipynb | 2745 ++++++++++------- requirements-external.txt | 1 + 8 files changed, 2036 insertions(+), 1141 deletions(-) create mode 100644 arviz/data/io_pyjags.py create mode 100644 arviz/tests/external_tests/test_data_pyjags.py diff --git a/.azure-pipelines/azure-pipelines-external.yml b/.azure-pipelines/azure-pipelines-external.yml index 47f144a985..8842a39577 100644 --- a/.azure-pipelines/azure-pipelines-external.yml +++ b/.azure-pipelines/azure-pipelines-external.yml @@ -43,6 +43,8 @@ jobs: displayName: 'Debug information' - script: | + sudo apt-get update + sudo apt-get install jags python -m pip install --upgrade pip if [ "$(pytorch.version)" = "latest" ]; then diff --git a/CHANGELOG.md b/CHANGELOG.md index da7e8928f0..b9b8dcfd58 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -4,6 +4,7 @@ ### New features * loo-pit plot. The kde is computed over the data interval (this could be shorter than [0, 1]). The hdi is computed analitically (#1215) +* Added support for PyJAGS via the function `from_pyjags` in the module arviz.data.io_pyjags. (#1219) ### Maintenance and fixes * Include data from `MultiObservedRV` to `observed_data` when using @@ -15,6 +16,7 @@ ### Deprecation ### Documentation +* A section has been added to the documentation at InferenceDataCookbook.ipynb illustrating the use of ArviZ in conjunction with PyJAGS. (#1219) * Fixed inconsistent capitalization in `plot_hdi` docstring (#1221) ## v0.8.3 (2020 May 28) diff --git a/arviz/data/__init__.py b/arviz/data/__init__.py index 96372455d3..a2bb51b1bd 100644 --- a/arviz/data/__init__.py +++ b/arviz/data/__init__.py @@ -7,6 +7,7 @@ from .io_cmdstan import from_cmdstan from .io_cmdstanpy import from_cmdstanpy from .io_dict import from_dict +from .io_pyjags import from_pyjags from .io_pymc3 import from_pymc3, from_pymc3_predictions from .io_pystan import from_pystan from .io_emcee import from_emcee @@ -24,6 +25,7 @@ "dict_to_dataset", "convert_to_dataset", "convert_to_inference_data", + "from_pyjags", "from_pymc3", "from_pymc3_predictions", "from_pystan", diff --git a/arviz/data/io_pyjags.py b/arviz/data/io_pyjags.py new file mode 100644 index 0000000000..88c3575364 --- /dev/null +++ b/arviz/data/io_pyjags.py @@ -0,0 +1,323 @@ +"""Convert PyJAGS sample dictionaries to ArviZ inference data objects.""" +from collections import OrderedDict +from collections.abc import Iterable +import typing as tp + +import numpy as np +import xarray + +from arviz.data.inference_data import InferenceData + +from .base import requires, dict_to_dataset +from ..rcparams import rcParams + + +class PyJAGSConverter: + """Encapsulate PyJAGS specific logic.""" + + def __init__( + self, + *, + posterior: tp.Optional[tp.Mapping[str, np.ndarray]] = None, + prior: tp.Optional[tp.Mapping[str, np.ndarray]] = None, + coords=None, + dims=None, + save_warmup: bool = None, + warmup_iterations: int = 0 + ): + self.posterior = posterior + self.prior = prior + self.coords = coords + self.dims = dims + self.save_warmup = rcParams["data.save_warmup"] if save_warmup is None else save_warmup + self.warmup_iterations = warmup_iterations + + import pyjags + + self.pyjags = pyjags + + def _pyjags_samples_to_xarray( + self, pyjags_samples: tp.Mapping[str, np.ndarray] + ) -> tp.Tuple[xarray.Dataset, xarray.Dataset]: + data, data_warmup = get_draws( + pyjags_samples=pyjags_samples, + warmup_iterations=self.warmup_iterations, + warmup=self.save_warmup, + ) + + return ( + dict_to_dataset(data, library=self.pyjags, coords=self.coords, dims=self.dims), + dict_to_dataset(data_warmup, library=self.pyjags, coords=self.coords, dims=self.dims,), + ) + + @requires("posterior") + def posterior_to_xarray(self) -> tp.Tuple[xarray.Dataset, xarray.Dataset]: + """Extract posterior samples from fit.""" + return self._pyjags_samples_to_xarray(self.posterior) + + @requires("prior") + def prior_to_xarray(self) -> tp.Tuple[xarray.Dataset, xarray.Dataset]: + """Extract posterior samples from fit.""" + return self._pyjags_samples_to_xarray(self.prior) + + def to_inference_data(self): + """Convert all available data to an InferenceData object.""" + # obs_const_dict = self.observed_and_constant_data_to_xarray() + # predictions_const_data = self.predictions_constant_data_to_xarray() + save_warmup = self.save_warmup and self.warmup_iterations > 0 + # self.posterior is not None + + idata_dict = { + "posterior": self.posterior_to_xarray(), + "prior": self.prior_to_xarray(), + "save_warmup": save_warmup, + } + + return InferenceData(**idata_dict) + + +def get_draws( + pyjags_samples: tp.Mapping[str, np.ndarray], + variables: tp.Optional[tp.Union[str, tp.Iterable[str]]] = None, + warmup: bool = False, + warmup_iterations: int = 0, +) -> tp.Tuple[tp.Mapping[str, np.ndarray], tp.Mapping[str, np.ndarray]]: + """ + Convert PyJAGS samples dictionary to ArviZ format and split warmup samples. + + Parameters + ---------- + pyjags_samples: a dictionary mapping variable names to NumPy arrays of MCMC + chains of samples with shape + (parameter_dimension, chain_length, number_of_chains) + + variables: the variables to extract from the samples dictionary + warmup: whether or not to return warmup draws in data_warmup + warmup_iterations: the number of warmup iterations if any + + Returns + ------- + A tuple of two samples dictionaries in ArviZ format + """ + data_warmup = OrderedDict() + + if variables is None: + variables = list(pyjags_samples.keys()) + elif isinstance(variables, str): + variables = [variables] + + if not isinstance(variables, Iterable): + raise TypeError("variables must be of type Sequence or str") + + variables = tuple(variables) + + if warmup_iterations > 0: + (warmup_samples, actual_samples,) = _split_pyjags_dict_in_warmup_and_actual_samples( + pyjags_samples=pyjags_samples, + warmup_iterations=warmup_iterations, + variable_names=variables, + ) + + data = _convert_pyjags_dict_to_arviz_dict(samples=actual_samples, variable_names=variables) + + if warmup: + data_warmup = _convert_pyjags_dict_to_arviz_dict( + samples=warmup_samples, variable_names=variables + ) + else: + data = _convert_pyjags_dict_to_arviz_dict(samples=pyjags_samples, variable_names=variables) + + return data, data_warmup + + +def _split_pyjags_dict_in_warmup_and_actual_samples( + pyjags_samples: tp.Mapping[str, np.ndarray], + warmup_iterations: int, + variable_names: tp.Optional[tp.Tuple[str, ...]] = None, +) -> tp.Tuple[tp.Mapping[str, np.ndarray], tp.Mapping[str, np.ndarray]]: + """ + Split a PyJAGS samples dictionary into actual samples and warmup samples. + + Parameters + ---------- + pyjags_samples: a dictionary mapping variable names to NumPy arrays of MCMC + chains of samples with shape + (parameter_dimension, chain_length, number_of_chains) + + warmup_iterations: the number of draws to be split off for warmum + variable_names: the variables in the dictionary to use; if None use all + + Returns + ------- + A tuple of two pyjags samples dictionaries in PyJAGS format + """ + if variable_names is None: + variable_names = tuple(pyjags_samples.keys()) + + warmup_samples: tp.Dict[str, np.ndarray] = {} + actual_samples: tp.Dict[str, np.ndarray] = {} + + for variable_name, chains in pyjags_samples.items(): + if variable_name in variable_names: + warmup_samples[variable_name] = chains[:, :warmup_iterations, :] + actual_samples[variable_name] = chains[:, warmup_iterations:, :] + + return warmup_samples, actual_samples + + +def _convert_pyjags_dict_to_arviz_dict( + samples: tp.Mapping[str, np.ndarray], variable_names: tp.Optional[tp.Tuple[str, ...]] = None, +) -> tp.Mapping[str, np.ndarray]: + """ + Convert a PyJAGS dictionary to an ArviZ dictionary. + + Takes a python dictionary of samples that has been generated by the sample + method of a model instance and returns a dictionary of samples in ArviZ + format. + + Parameters + ---------- + samples: a dictionary mapping variable names to P arrays with shape + (parameter_dimension, chain_length, number_of_chains) + + Returns + ------- + a dictionary mapping variable names to NumPy arrays with shape + (number_of_chains, chain_length, parameter_dimension) + """ + # pyjags returns a dictionary of NumPy arrays with shape + # (parameter_dimension, chain_length, number_of_chains) + # but arviz expects samples with shape + # (number_of_chains, chain_length, parameter_dimension) + + variable_name_to_samples_map = {} + + if variable_names is None: + variable_names = tuple(samples.keys()) + + for variable_name, chains in samples.items(): + if variable_name in variable_names: + parameter_dimension, _, _ = chains.shape + if parameter_dimension == 1: + variable_name_to_samples_map[variable_name] = chains[0, :, :].transpose() + else: + variable_name_to_samples_map[variable_name] = np.swapaxes(chains, 0, 2) + + return variable_name_to_samples_map + + +def _extract_arviz_dict_from_inference_data(idata,) -> tp.Mapping[str, np.ndarray]: + """ + Extract the samples dictionary from an ArviZ inference data object. + + Extracts a dictionary mapping parameter names to NumPy arrays of samples + with shape (number_of_chains, chain_length, parameter_dimension) from an + ArviZ inference data object. + + Parameters + ---------- + idata: InferenceData + + Returns + ------- + a dictionary mapping variable names to NumPy arrays with shape + (number_of_chains, chain_length, parameter_dimension) + + """ + variable_name_to_samples_map = {} + + for key, value in idata.posterior.to_dict()["data_vars"].items(): + variable_name_to_samples_map[key] = np.array(value["data"]) + + return variable_name_to_samples_map + + +def _convert_arviz_dict_to_pyjags_dict( + samples: tp.Mapping[str, np.ndarray] +) -> tp.Mapping[str, np.ndarray]: + """ + Convert and ArviZ dictionary to a PyJAGS dictionary. + + Takes a python dictionary of samples in ArviZ format and returns the samples + as a dictionary in PyJAGS format. + + Parameters + ---------- + samples: a dictionary mapping variable names to NumPy arrays with shape + (number_of_chains, chain_length, parameter_dimension) + + Returns + ------- + a dictionary mapping variable names to NumPy arrays with shape + (parameter_dimension, chain_length, number_of_chains) + + """ + # pyjags returns a dictionary of NumPy arrays with shape + # (parameter_dimension, chain_length, number_of_chains) + # but arviz expects samples with shape + # (number_of_chains, chain_length, parameter_dimension) + + variable_name_to_samples_map = {} + + for variable_name, chains in samples.items(): + if chains.ndim == 2: + number_of_chains, chain_length = chains.shape + chains = chains.reshape((number_of_chains, chain_length, 1)) + + variable_name_to_samples_map[variable_name] = np.swapaxes(chains, 0, 2) + + return variable_name_to_samples_map + + +def from_pyjags( + posterior: tp.Optional[tp.Mapping[str, np.ndarray]] = None, + prior: tp.Optional[tp.Mapping[str, np.ndarray]] = None, + coords=None, + dims=None, + save_warmup=None, + warmup_iterations: int = 0, +) -> InferenceData: + """ + Convert PyJAGS posterior samples to an ArviZ inference data object. + + Takes a python dictionary of samples that has been generated by the sample + method of a model instance and returns an Arviz inference data object. + For a usage example read the + :doc:`Cookbook section on from_pyjags ` + + Parameters + ---------- + posterior: dict of {str : array_like}, optional + a dictionary mapping variable names to NumPy arrays containing + posterior samples with shape + (parameter_dimension, chain_length, number_of_chains) + + prior: dict of {str : array_like}, optional + a dictionary mapping variable names to NumPy arrays containing + prior samples with shape + (parameter_dimension, chain_length, number_of_chains) + + coords: dict[str, iterable] + A dictionary containing the values that are used as index. The key + is the name of the dimension, the values are the index values. + + dims: dict[str, List(str)] + A mapping from variables to a list of coordinate names for the variable. + + save_warmup : bool, optional + Save warmup iterations in InferenceData. If not defined, use default defined by the rcParams. + warmup_iterations: int, optional + Number of warmup iterations + + Returns + ------- + InferenceData + """ + return PyJAGSConverter( + posterior=posterior, + prior=prior, + dims=dims, + coords=coords, + save_warmup=save_warmup, + warmup_iterations=warmup_iterations, + ).to_inference_data() diff --git a/arviz/tests/external_tests/test_data_pyjags.py b/arviz/tests/external_tests/test_data_pyjags.py new file mode 100644 index 0000000000..4f6864d584 --- /dev/null +++ b/arviz/tests/external_tests/test_data_pyjags.py @@ -0,0 +1,101 @@ +import typing as tp +import numpy as np +import pytest + +from arviz.data.io_pyjags import ( + _convert_pyjags_dict_to_arviz_dict, + _convert_arviz_dict_to_pyjags_dict, + _extract_arviz_dict_from_inference_data, + from_pyjags, +) + +from arviz.tests.helpers import check_multiple_attrs + + +PYJAGS_POSTERIOR_DICT = {"b": np.random.randn(3, 10, 3), "int": np.random.randn(1, 10, 3)} +PYJAGS_PRIOR_DICT = {"b": np.random.randn(3, 10, 3), "int": np.random.randn(1, 10, 3)} + + +def verify_equality_of_numpy_values_dictionaries( + dict_1: tp.Mapping[tp.Any, np.ndarray], dict_2: tp.Mapping[tp.Any, np.ndarray] +) -> bool: + if dict_1.keys() != dict_2.keys(): + return False + + for key in dict_1.keys(): + if not np.all(dict_1[key] == dict_2[key]): + return False + + return True + + +def test_convert_pyjags_samples_dictionary_to_arviz_samples_dictionary(): + arviz_samples_dict_from_pyjags_samples_dict = _convert_pyjags_dict_to_arviz_dict( + PYJAGS_POSTERIOR_DICT + ) + + pyjags_dict_from_arviz_dict_from_pyjags_dict = _convert_arviz_dict_to_pyjags_dict( + arviz_samples_dict_from_pyjags_samples_dict + ) + + assert verify_equality_of_numpy_values_dictionaries( + PYJAGS_POSTERIOR_DICT, pyjags_dict_from_arviz_dict_from_pyjags_dict, + ) + + +def test_extract_samples_dictionary_from_arviz_inference_data(): + arviz_samples_dict_from_pyjags_samples_dict = _convert_pyjags_dict_to_arviz_dict( + PYJAGS_POSTERIOR_DICT + ) + + arviz_inference_data_from_pyjags_samples_dict = from_pyjags(PYJAGS_POSTERIOR_DICT) + arviz_dict_from_idata_from_pyjags_dict = _extract_arviz_dict_from_inference_data( + arviz_inference_data_from_pyjags_samples_dict + ) + + assert verify_equality_of_numpy_values_dictionaries( + arviz_samples_dict_from_pyjags_samples_dict, arviz_dict_from_idata_from_pyjags_dict, + ) + + +def test_roundtrip_from_pyjags_via_arviz_to_pyjags(): + arviz_inference_data_from_pyjags_samples_dict = from_pyjags(PYJAGS_POSTERIOR_DICT) + arviz_dict_from_idata_from_pyjags_dict = _extract_arviz_dict_from_inference_data( + arviz_inference_data_from_pyjags_samples_dict + ) + + pyjags_dict_from_arviz_idata = _convert_arviz_dict_to_pyjags_dict( + arviz_dict_from_idata_from_pyjags_dict + ) + + assert verify_equality_of_numpy_values_dictionaries( + PYJAGS_POSTERIOR_DICT, pyjags_dict_from_arviz_idata + ) + + +@pytest.mark.parametrize("posterior", [None, PYJAGS_POSTERIOR_DICT]) +@pytest.mark.parametrize("prior", [None, PYJAGS_PRIOR_DICT]) +@pytest.mark.parametrize("save_warmup", [True, False]) +@pytest.mark.parametrize("warmup_iterations", [0, 5]) +def test_inference_data_attrs(posterior, prior, save_warmup, warmup_iterations: int): + arviz_inference_data_from_pyjags_samples_dict = from_pyjags( + posterior=posterior, + prior=prior, + save_warmup=save_warmup, + warmup_iterations=warmup_iterations, + ) + posterior_warmup_prefix = ( + "" if save_warmup and warmup_iterations > 0 and posterior is not None else "~" + ) + prior_warmup_prefix = "" if save_warmup and warmup_iterations > 0 and prior is not None else "~" + print(f'posterior_warmup_prefix="{posterior_warmup_prefix}"') + test_dict = { + f'{"~" if posterior is None else ""}posterior': ["b", "int"], + f'{"~" if prior is None else ""}prior': ["b", "int"], + f"{posterior_warmup_prefix}warmup_posterior": ["b", "int"], + f"{prior_warmup_prefix}warmup_prior": ["b", "int"], + } + + fails = check_multiple_attrs(test_dict, arviz_inference_data_from_pyjags_samples_dict) + print(fails) + assert not fails diff --git a/doc/api.rst b/doc/api.rst index c8acbc73f4..6c916adf9d 100644 --- a/doc/api.rst +++ b/doc/api.rst @@ -104,6 +104,7 @@ Data from_numpyro from_pystan from_tfp + from_pyjags concat Utils diff --git a/doc/notebooks/InferenceDataCookbook.ipynb b/doc/notebooks/InferenceDataCookbook.ipynb index 9a292e0721..db9ea346e3 100644 --- a/doc/notebooks/InferenceDataCookbook.ipynb +++ b/doc/notebooks/InferenceDataCookbook.ipynb @@ -33,14 +33,33 @@ "
  • From CmdStanPy
    • \n", "
  • From CmdStan
    • \n", "
  • From NumPyro
    • \n", + "
  • From PyJAGS
    • \n", "" ] }, { "cell_type": "code", "execution_count": 1, - "metadata": {}, - "outputs": [], + "metadata": { + "ExecuteTime": { + "end_time": "2020-06-05T06:47:07.825895Z", + "start_time": "2020-06-05T06:47:05.567299Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "Bad key \"text.kerning_factor\" on line 4 in\n", + "/Users/michaelnowotny/anaconda3/envs/continuous_time_mcmc/lib/python3.7/site-packages/matplotlib/mpl-data/stylelib/_classic_test_patch.mplstyle.\n", + "You probably need to get an updated matplotlibrc file from\n", + "https://github.com/matplotlib/matplotlib/blob/v3.1.3/matplotlibrc.template\n", + "or from the matplotlib source distribution\n" + ] + } + ], "source": [ "import arviz as az\n", "import numpy as np" @@ -56,7 +75,12 @@ { "cell_type": "code", "execution_count": 2, - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2020-06-05T06:47:07.834217Z", + "start_time": "2020-06-05T06:47:07.827892Z" + } + }, "outputs": [ { "data": { @@ -79,7 +103,12 @@ { "cell_type": "code", "execution_count": 3, - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2020-06-05T06:47:07.849191Z", + "start_time": "2020-06-05T06:47:07.836546Z" + } + }, "outputs": [ { "data": { @@ -418,31 +447,31 @@ " stroke: currentColor;\n", " fill: currentColor;\n", "}\n", - "
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-       "\n",
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-       "            0.83775208],\n",
-       "          [-1.24754965,  0.03186646, -1.17173012, -0.25164439,\n",
-       "           -0.97814632],\n",
-       "          [ 1.76993695,  0.87525555,  0.47367291,  0.13530509,\n",
-       "           -1.55133306],\n",
-       "          [ 1.60352578,  0.0033755 ,  1.01254228,  0.69500592,\n",
-       "            0.34745531]],\n",
-       "\n",
-       "         [[-0.68808907,  1.277161  ,  2.30646473,  2.031739  ,\n",
-       "           -0.07374876],\n",
-       "          [-0.59114479, -0.30621512,  0.16989577, -0.50583495,\n",
-       "            0.30695075],\n",
-       "          [ 0.86958083, -0.23970463, -0.70407376,  0.11897558,\n",
-       "            1.19036389],\n",
-       "          [ 0.44322182, -1.73639962,  0.34135547, -1.7563068 ,\n",
-       "           -1.14795004]]],\n",
-       "\n",
-       "\n",
-       "        [[[ 0.97245749, -0.05014608, -0.12538573,  1.29271907,\n",
-       "            0.54534332],\n",
-       "          [-0.67816675,  1.14111945,  0.90715142,  0.83863591,\n",
-       "            1.58558454],\n",
-       "          [ 0.35619794,  1.80515797,  1.81719894, -1.07813151,\n",
-       "            0.24617304],\n",
-       "          [-0.09727719, -0.28463861, -0.4518747 , -0.08660062,\n",
-       "            0.72210784]],\n",
-       "\n",
-       "         [[-0.74390093, -0.14939757,  0.37617852,  1.14944149,\n",
-       "            1.50406514],\n",
-       "          [-1.13525365,  2.40258395, -1.12464939,  0.89162206,\n",
-       "           -1.60676311],\n",
-       "          [ 0.10804639,  0.0979568 , -0.19591481,  0.91671561,\n",
-       "            0.87937073],\n",
-       "          [-0.58392584, -0.04417706,  0.85360259, -0.13219749,\n",
-       "           -0.80365311]],\n",
-       "\n",
-       "         [[ 0.66419822,  1.09618762, -0.7372364 , -0.48354843,\n",
-       "           -1.25719753],\n",
-       "          [-0.12966296, -0.964358  ,  0.87522067, -2.00175619,\n",
-       "            0.12083439],\n",
-       "          [ 0.70092816, -0.64578291,  0.57031347,  0.20656673,\n",
-       "           -0.61951346],\n",
-       "          [-1.2668886 , -0.35512118,  0.27409265,  0.05177446,\n",
-       "            1.07283329]]]]])
    • created_at :
      2020-05-22T18:37:44.104273
      arviz_version :
      0.7.0
    " + "
    xarray.Dataset
      • chain: 1
      • draw: 2
      • x_dim_0: 3
      • x_dim_1: 4
      • x_dim_2: 5
      • chain
        (chain)
        int64
        0
        array([0])
      • draw
        (draw)
        int64
        0 1
        array([0, 1])
      • x_dim_0
        (x_dim_0)
        int64
        0 1 2
        array([0, 1, 2])
      • x_dim_1
        (x_dim_1)
        int64
        0 1 2 3
        array([0, 1, 2, 3])
      • x_dim_2
        (x_dim_2)
        int64
        0 1 2 3 4
        array([0, 1, 2, 3, 4])
      • x
        (chain, draw, x_dim_0, x_dim_1, x_dim_2)
        float64
        -2.531 0.02934 ... 0.3143 1.476
        array([[[[[-2.53141338,  0.0293362 , -0.31797349, -0.38416052,\n",
+       "            0.65699797],\n",
+       "          [ 0.73642501, -0.33947601,  1.25816597, -0.54142323,\n",
+       "           -0.34398848],\n",
+       "          [-1.79511926, -1.84445   , -0.97113704, -0.12678947,\n",
+       "            0.32444704],\n",
+       "          [-0.47438221,  0.36690041, -0.26187114, -1.19070916,\n",
+       "            0.85294391]],\n",
+       "\n",
+       "         [[-0.41815669,  0.1957752 ,  0.6886881 ,  0.63300147,\n",
+       "           -1.89064   ],\n",
+       "          [ 1.39771593, -1.37922292, -0.42467395,  0.36615158,\n",
+       "           -0.53448955],\n",
+       "          [ 1.87240442,  0.87341995, -0.46510392,  2.02606476,\n",
+       "            0.52217645],\n",
+       "          [-2.32110378,  0.17556565,  0.95855116,  0.22715783,\n",
+       "           -1.59268363]],\n",
+       "\n",
+       "         [[-0.99935492,  0.03826597,  1.01987852,  0.00570501,\n",
+       "           -0.60340024],\n",
+       "          [ 0.44653992, -0.31397169,  0.17854584,  1.44793585,\n",
+       "           -0.09069965],\n",
+       "          [ 1.06170312, -0.67144881, -0.22251256, -0.50621294,\n",
+       "           -1.33514321],\n",
+       "          [ 0.88686166,  0.40737944, -0.99517113, -1.25562153,\n",
+       "           -0.63561923]]],\n",
+       "\n",
+       "\n",
+       "        [[[ 1.36571452, -2.29912971, -1.00403962, -1.42428011,\n",
+       "           -1.41196551],\n",
+       "          [ 0.26538569,  0.54212232,  0.70934791,  1.08697315,\n",
+       "           -0.91302377],\n",
+       "          [-0.9892173 , -0.55414038, -0.40352794, -0.6559312 ,\n",
+       "           -0.07250926],\n",
+       "          [ 1.07151292, -0.06069678, -0.58923782,  2.01437447,\n",
+       "            0.2254075 ]],\n",
+       "\n",
+       "         [[ 0.52728233,  1.84723873,  0.35972655,  0.17523724,\n",
+       "           -1.21429218],\n",
+       "          [ 0.56766558,  0.08161915,  1.62533143, -0.05491164,\n",
+       "           -0.96536941],\n",
+       "          [ 0.59570011,  0.84365485,  0.08222788,  0.44461705,\n",
+       "           -0.75669282],\n",
+       "          [ 1.98290187, -0.54792439,  0.7715102 ,  0.09412406,\n",
+       "           -0.58288696]],\n",
+       "\n",
+       "         [[-1.31553959, -0.06921722, -1.26205366, -1.18785697,\n",
+       "           -0.12261834],\n",
+       "          [ 0.95509663, -0.1493733 , -1.37067232, -0.51907106,\n",
+       "            1.61435864],\n",
+       "          [-0.3016619 , -2.41799613,  0.49895458, -0.0383496 ,\n",
+       "           -0.3007486 ],\n",
+       "          [ 0.40954147,  0.72124904, -0.13965946,  0.3143309 ,\n",
+       "            1.47596637]]]]])
    • created_at :
      2020-06-05T06:47:07.854091
      arviz_version :
      0.8.3
    " ], "text/plain": [ "\n", @@ -903,10 +942,10 @@ " * x_dim_1 (x_dim_1) int64 0 1 2 3\n", " * x_dim_2 (x_dim_2) int64 0 1 2 3 4\n", "Data variables:\n", - " x (chain, draw, x_dim_0, x_dim_1, x_dim_2) float64 -0.7137 ... 1.073\n", + " x (chain, draw, x_dim_0, x_dim_1, x_dim_2) float64 -2.531 ... 1.476\n", "Attributes:\n", - " created_at: 2020-05-22T18:37:44.104273\n", - " arviz_version: 0.7.0" + " created_at: 2020-06-05T06:47:07.854091\n", + " arviz_version: 0.8.3" ] }, "execution_count": 5, @@ -928,7 +967,12 @@ { "cell_type": "code", "execution_count": 6, - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2020-06-05T06:47:07.890310Z", + "start_time": "2020-06-05T06:47:07.882462Z" + } + }, "outputs": [ { "data": { @@ -955,7 +999,12 @@ { "cell_type": "code", "execution_count": 7, - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2020-06-05T06:47:07.920814Z", + "start_time": "2020-06-05T06:47:07.891906Z" + } + }, "outputs": [ { "data": { @@ -1294,468 +1343,354 @@ " stroke: currentColor;\n", " fill: currentColor;\n", "}\n", - "
    xarray.Dataset
      • b_dim_0: 10
      • c_dim_0: 3
      • c_dim_1: 4
      • chain: 1
      • draw: 100
      • chain
        (chain)
        int64
        0
        array([0])
      • draw
        (draw)
        int64
        0 1 2 3 4 5 6 ... 94 95 96 97 98 99
        array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
+       "
        xarray.Dataset
          • b_dim_0: 10
          • c_dim_0: 3
          • c_dim_1: 4
          • chain: 1
          • draw: 100
          • chain
            (chain)
            int64
            0
            array([0])
          • draw
            (draw)
            int64
            0 1 2 3 4 5 6 ... 94 95 96 97 98 99
            array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
        "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
        "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
        "       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,\n",
        "       72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89,\n",
-       "       90, 91, 92, 93, 94, 95, 96, 97, 98, 99])
          • b_dim_0
            (b_dim_0)
            int64
            0 1 2 3 4 5 6 7 8 9
            array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
          • c_dim_0
            (c_dim_0)
            int64
            0 1 2
            array([0, 1, 2])
          • c_dim_1
            (c_dim_1)
            int64
            0 1 2 3
            array([0, 1, 2, 3])
          • a
            (chain, draw)
            float64
            -0.5718 0.1517 ... 1.629 -2.573
            array([[-5.71791583e-01,  1.51730543e-01, -5.05459631e-01,\n",
-       "        -1.06467888e+00, -6.98745700e-01, -3.37020579e-01,\n",
-       "        -1.74228501e-01,  1.04270446e+00,  7.77975066e-01,\n",
-       "        -1.38196910e+00,  3.17101875e-02,  7.93898466e-01,\n",
-       "         6.85941798e-01,  7.57706692e-01,  2.55028919e-01,\n",
-       "         4.05399689e-01,  1.29306896e+00, -1.02850914e+00,\n",
-       "        -8.29752303e-01, -4.31316250e-01,  3.56329743e-02,\n",
-       "         6.43909818e-01,  4.43610135e-01,  2.09309287e-01,\n",
-       "         1.88429950e-01,  1.76838246e-02,  2.29132034e+00,\n",
-       "         9.09739448e-05,  1.88760002e-01,  9.65156248e-01,\n",
-       "         1.39564226e+00,  6.11758984e-01, -5.38831405e-01,\n",
-       "         1.78472418e-01,  7.18118829e-01, -1.41314548e+00,\n",
-       "         8.24896212e-01, -1.28118243e+00, -3.88540413e-01,\n",
-       "         2.39674137e+00,  7.92698437e-01, -1.43353283e+00,\n",
-       "        -6.56072224e-01, -3.90387838e-01,  5.66535579e-01,\n",
-       "        -2.07716434e-01, -1.28477141e+00, -1.12515304e+00,\n",
-       "        -3.47644702e-01, -9.79563306e-01,  6.14335666e-01,\n",
-       "        -5.32912908e-01, -4.73263658e-01, -7.48243977e-01,\n",
-       "         2.51175314e+00, -2.48506397e+00, -9.76805870e-01,\n",
-       "         8.29099795e-01,  3.76158145e-01,  6.87496426e-01,\n",
-       "        -4.78721708e-01,  4.97867990e-01, -1.16580577e-01,\n",
-       "         1.55926008e-01,  2.71825147e-01, -4.80759333e-01,\n",
-       "         2.31981429e+00, -4.27321766e-01,  2.46173069e-01,\n",
-       "         5.38772939e-01, -2.13418715e+00,  2.09078748e+00,\n",
-       "        -1.45733338e+00, -1.23329771e+00, -7.65530602e-01,\n",
-       "        -6.17785366e-01,  1.79866787e+00, -1.19479804e+00,\n",
-       "         1.16590303e+00,  3.81030294e-02,  3.95749357e-01,\n",
-       "        -3.84523461e-01,  7.73802734e-02,  1.10870019e+00,\n",
-       "         2.19088727e+00, -6.53154287e-01, -9.60974022e-01,\n",
-       "        -4.74257945e-02,  1.12396953e+00, -1.57166652e-01,\n",
-       "         1.90400937e+00, -6.34237152e-01, -1.73810050e+00,\n",
-       "        -1.10621010e+00,  7.22525119e-01,  7.81256999e-01,\n",
-       "         5.59576784e-01, -1.81929305e-01,  1.62907319e+00,\n",
-       "        -2.57251582e+00]])
          • b
            (chain, draw, b_dim_0)
            float64
            -0.3169 -0.6537 ... -0.5796 -0.1467
            array([[[-3.16860483e-01, -6.53664260e-01,  1.85572977e+00,\n",
-       "         -5.14435128e-02, -8.43679004e-01, -2.34451581e+00,\n",
-       "          2.61330480e+00, -4.61179798e-01,  9.87700947e-02,\n",
-       "          3.75889388e-01],\n",
-       "        [-1.26347318e+00,  2.65150186e-01,  5.50435579e-01,\n",
-       "         -1.26936866e-01, -6.70363489e-01, -3.59857611e-01,\n",
-       "         -3.34206968e-01,  8.23534321e-01,  2.22666990e-02,\n",
-       "          1.84439880e+00],\n",
-       "        [ 1.08721405e+00,  3.60870181e-01, -1.56719231e+00,\n",
-       "         -1.43206498e+00,  2.41268204e-01,  3.56766260e-01,\n",
-       "         -2.12829156e+00,  1.97384021e+00,  2.82524276e-01,\n",
-       "          2.66097129e-01],\n",
-       "        [ 1.16785768e+00, -8.34963287e-01,  2.17487717e-01,\n",
-       "         -1.35734917e-03,  7.75696943e-02,  1.12215513e+00,\n",
-       "          8.89939691e-01, -8.57260362e-02, -5.39923623e-01,\n",
-       "         -3.69315413e-01],\n",
-       "        [-8.23490366e-01, -1.62716263e+00,  2.56725486e-01,\n",
-       "         -1.24706686e+00,  5.26670022e-02,  3.34397034e-01,\n",
-       "          1.05660491e+00, -2.51293330e-01, -1.10510125e+00,\n",
-       "          1.12342016e+00],\n",
-       "        [-1.47398313e+00, -4.64075113e-01, -3.29435966e-02,\n",
-       "         -7.17995124e-01,  9.11980696e-01, -3.05241259e-01,\n",
-       "         -2.71838511e-01, -2.29105439e-01, -3.06884204e-01,\n",
-       "          7.72175599e-01],\n",
-       "        [ 4.35511314e-01,  7.99212717e-01,  1.73623745e+00,\n",
-       "         -1.04708315e+00, -7.28464807e-01, -2.50961249e+00,\n",
-       "          1.57206680e-01, -7.82369680e-01, -1.22046598e+00,\n",
-       "          3.51981644e-01],\n",
-       "        [ 2.23029715e+00,  3.24552971e-01,  9.56383021e-01,\n",
-       "          5.86212895e-03, -2.56575232e-01, -1.27865011e-01,\n",
-       "          4.95216461e-01, -1.50309987e-01,  5.75945710e-01,\n",
-       "          1.14987141e+00],\n",
-       "        [ 2.02339595e-01,  8.54137043e-01,  3.99113185e-02,\n",
-       "         -1.12321971e-01,  1.79019215e+00, -4.65258468e-01,\n",
-       "          3.34339331e-01,  9.82227826e-01, -1.23322699e+00,\n",
-       "          7.08383153e-01],\n",
-       "        [ 8.21774187e-01, -4.62826576e-01,  2.87416429e-01,\n",
-       "          2.22906962e+00, -8.77595555e-01,  7.23585025e-01,\n",
-       "          1.10233506e-01,  3.19714957e-01,  7.83381815e-01,\n",
-       "          1.98885826e+00],\n",
-       "        [-2.94982881e+00,  7.09793709e-01,  7.32343867e-01,\n",
-       "          4.94521884e-01,  1.11639913e+00,  3.67221876e-01,\n",
-       "         -1.23368721e+00, -6.36545541e-01, -1.76193444e+00,\n",
-       "          1.13301534e+00],\n",
-       "        [-2.60706433e-01,  1.19586589e+00,  1.60963673e+00,\n",
-       "         -1.79448273e+00, -9.36463115e-01, -1.49208617e+00,\n",
-       "         -8.99267503e-01, -1.40926358e-01, -7.05341899e-01,\n",
-       "          7.25827860e-01],\n",
-       "        [ 1.57204152e-01, -1.73833008e-02, -5.00137602e-01,\n",
-       "          2.84764473e-01,  7.87515461e-01, -5.72528703e-01,\n",
-       "          3.84710250e-02,  1.13291082e+00,  6.35590237e-01,\n",
-       "         -3.07127332e-01],\n",
-       "        [-1.31547598e+00,  1.16825713e+00, -1.10453877e+00,\n",
-       "         -1.38270392e+00,  1.23594341e+00, -9.37878139e-01,\n",
-       "         -1.89864512e+00, -2.35619316e-01, -4.19829968e-01,\n",
-       "         -2.82671338e-01],\n",
-       "        [ 2.19625455e+00, -1.93216365e-01, -1.18200045e+00,\n",
-       "          8.49904145e-02,  1.37097594e+00,  3.02598964e-01,\n",
-       "         -6.27317948e-01,  3.49273253e+00,  1.85330337e+00,\n",
-       "          2.44841071e-02],\n",
-       "        [-2.71511932e-01,  1.28066865e+00,  3.03477141e-01,\n",
-       "         -9.28277135e-01,  2.41389740e-02,  1.49997380e-01,\n",
-       "         -6.65695217e-01, -6.43508188e-02,  1.61538861e+00,\n",
-       "         -4.22446683e-02],\n",
-       "        [-1.08706373e+00, -6.38179374e-01,  2.65257933e-01,\n",
-       "          3.86645256e-01, -2.60727257e-01,  3.36721128e-01,\n",
-       "          4.20001725e-01, -1.07174102e+00,  1.50946330e+00,\n",
-       "          8.45316703e-01],\n",
-       "        [-2.85484064e-01, -3.05747867e-01, -8.24650064e-01,\n",
-       "         -5.32447718e-01, -1.07286378e+00, -9.31526957e-01,\n",
-       "          2.54986749e-01, -7.48860753e-01,  9.95092932e-02,\n",
-       "          1.94996724e+00],\n",
-       "        [-7.44221901e-01,  4.74012336e-01, -1.15035263e+00,\n",
-       "         -1.14066508e+00, -6.61185364e-02,  7.68987833e-01,\n",
-       "         -1.40408687e+00, -1.82423679e+00,  2.39632210e-01,\n",
-       "         -6.25720223e-01],\n",
-       "        [ 9.77893338e-02, -1.99288823e-01, -1.81368104e-01,\n",
-       "         -6.26555181e-01, -1.10423809e+00,  6.00990722e-02,\n",
-       "         -1.12624305e+00, -2.59025976e-01, -1.19868123e+00,\n",
-       "         -1.32595575e+00],\n",
-       "        [-5.24592460e-01,  1.50936922e-01,  1.16292229e-01,\n",
-       "         -1.70279357e+00, -2.81507198e-01, -7.02196399e-01,\n",
-       "          1.09622568e+00,  1.00348309e+00, -2.37049093e-02,\n",
-       "         -4.95316061e-01],\n",
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-       "         -1.14594123e+00],\n",
-       "        [ 8.43042470e-01, -1.03935817e+00,  5.59916726e-01,\n",
-       "         -1.83162715e-01,  6.48581493e-01, -1.32797827e+00,\n",
-       "          2.54470369e-01, -1.66479792e+00, -9.26045258e-01,\n",
-       "          1.28628312e+00],\n",
-       "        [-1.37572561e+00,  1.37869833e+00, -2.01948964e-01,\n",
-       "         -3.90810617e-01,  3.81280021e-01,  1.93517334e+00,\n",
-       "          3.93450261e-01, -7.89391271e-01,  2.88379924e-01,\n",
-       "          7.43513585e-01],\n",
-       "        [ 2.69264350e+00,  9.79395161e-01, -8.50354553e-02,\n",
-       "         -1.09525727e+00,  5.88844551e-01, -1.24817682e-01,\n",
-       "         -2.03337088e-01, -4.64399874e-01,  6.14324573e-01,\n",
-       "          1.20915648e+00],\n",
-       "        [-5.98932633e-01, -5.75527528e-01,  4.89516229e-01,\n",
-       "         -1.34510667e+00, -2.71792325e+00, -1.02121827e+00,\n",
-       "         -8.42087926e-01, -2.42532853e+00,  3.51761458e-01,\n",
-       "          2.30399552e+00],\n",
-       "        [-3.87927558e-02, -5.90323988e-02,  8.28079602e-01,\n",
-       "          1.67142524e-01, -8.07133900e-01,  2.26677121e+00,\n",
-       "         -1.12823923e+00,  9.14944097e-01, -9.48507061e-01,\n",
-       "         -1.87396219e-01],\n",
-       "        [-1.46249664e+00,  2.11550380e-01, -1.45921771e+00,\n",
-       "          1.10926228e+00,  1.67936779e-02,  1.33854847e+00,\n",
-       "         -4.38008734e-01,  2.86601165e-02,  5.52226873e-02,\n",
-       "         -4.98063364e-01],\n",
-       "        [ 7.28799046e-02,  1.67343113e+00,  7.08275251e-02,\n",
-       "         -8.59636154e-02,  1.71834985e+00,  3.50565129e-01,\n",
-       "         -1.96335302e-01,  4.34326966e-01,  3.13544134e-01,\n",
-       "         -1.82278145e+00],\n",
-       "        [ 5.28552306e-01, -2.34845935e-01,  5.62204066e-01,\n",
-       "          1.03957936e-01,  2.09555570e+00, -4.56152765e-01,\n",
-       "          7.93881145e-01, -9.74499281e-01,  2.59837507e+00,\n",
-       "         -4.62507782e-02],\n",
-       "        [ 4.69044520e-01, -1.27555573e+00, -8.11927813e-02,\n",
-       "          1.14078554e+00,  3.64793277e-01,  7.83894076e-01,\n",
-       "         -1.35422742e+00,  1.07771968e+00, -1.46444850e-01,\n",
-       "         -9.27699852e-01],\n",
-       "        [-1.32361163e+00, -3.06738107e-01,  1.13726952e+00,\n",
-       "          6.42783766e-01, -2.26057049e+00, -2.72846326e+00,\n",
-       "         -1.05847861e+00, -3.07878281e-01, -1.18016897e-01,\n",
-       "          1.98553376e+00],\n",
-       "        [-1.31017431e+00,  1.40756533e+00,  1.47354194e+00,\n",
-       "          6.30790181e-01, -1.55075245e+00, -2.66183142e-01,\n",
-       "          4.16764430e-01, -5.89096473e-01,  1.09521769e+00,\n",
-       "         -3.37087595e-01],\n",
-       "        [ 1.34878198e-01, -3.22443985e-01, -1.38217589e+00,\n",
-       "         -9.21582257e-01, -1.83178419e-01, -1.76608528e+00,\n",
-       "         -1.21317499e+00, -1.30350694e+00, -3.00699769e+00,\n",
-       "          1.96063545e+00],\n",
-       "        [ 8.12042295e-01, -1.04980664e-01, -1.31537562e-01,\n",
-       "         -1.04842789e+00, -1.32695186e+00, -4.93829335e-01,\n",
-       "         -1.27367041e-01, -6.10190044e-01, -1.05175293e+00,\n",
-       "         -9.14673183e-01],\n",
-       "        [ 5.57496639e-01,  3.33745995e-01, -1.41207288e+00,\n",
-       "         -1.80832680e-01,  6.54362236e-01,  1.09561194e+00,\n",
-       "         -1.36987888e+00,  9.28661342e-01, -1.29174479e+00,\n",
-       "         -4.34919129e-01],\n",
-       "        [ 1.50890685e+00,  1.71248829e-01, -6.71378128e-01,\n",
-       "          1.61511216e-02, -1.01576400e+00,  6.46946460e-02,\n",
-       "          8.37867559e-01,  3.99508824e-01, -1.38123456e+00,\n",
-       "         -2.08199109e+00],\n",
-       "        [-1.37180117e+00,  5.28779551e-01,  9.63192550e-01,\n",
-       "         -1.13204752e+00,  4.95014138e-01, -1.34241797e+00,\n",
-       "          9.80372437e-01, -1.72488214e-01,  3.15447749e-01,\n",
-       "         -6.36720910e-01],\n",
-       "        [ 5.99154599e-01, -1.20326407e+00, -7.28013646e-01,\n",
-       "          1.92672362e+00,  1.01858907e+00,  4.01546179e-01,\n",
-       "          1.24038274e-01,  1.80450982e+00,  7.78510702e-02,\n",
-       "         -5.42282163e-01],\n",
-       "        [ 1.42441435e-02,  1.61374208e+00,  3.36836376e-01,\n",
-       "         -1.10707512e-01,  1.59962811e+00, -4.44767611e-01,\n",
-       "         -7.99417778e-01,  3.46562090e-01,  1.67375229e+00,\n",
-       "          4.55401592e-01],\n",
-       "        [-1.10800269e+00, -3.29520973e+00,  1.69310571e+00,\n",
-       "          1.18005276e+00,  1.21199847e+00,  1.25933438e+00,\n",
-       "          1.96121772e+00, -2.39489144e+00, -5.79627563e-01,\n",
-       "         -1.46748595e-01]]])
          • c
            (chain, draw, c_dim_0, c_dim_1)
            float64
            0.1671 0.1605 ... 0.08688 0.6655
            array([[[[ 0.16705418,  0.16047865, -1.04401799, -0.1658331 ],\n",
-       "         [ 0.42170658, -0.64402441,  1.34854433,  1.13197454],\n",
-       "         [ 0.53111123, -0.10079094, -0.81106121,  2.0199374 ]],\n",
-       "\n",
-       "        [[ 1.54671771,  0.88588517, -2.07622664, -1.45881461],\n",
-       "         [ 1.61776299, -2.71176385,  0.0593579 ,  0.74271534],\n",
-       "         [ 1.51647953, -0.29674889, -0.4149073 ,  0.25030481]],\n",
-       "\n",
-       "        [[-0.62550656, -0.3416603 , -1.01134301, -0.64108836],\n",
-       "         [-0.88581356, -0.46053341,  0.44253639, -1.06501789],\n",
-       "         [ 0.96862958, -0.47046163,  1.09882401,  0.06555265]],\n",
+       "       90, 91, 92, 93, 94, 95, 96, 97, 98, 99])
          • b_dim_0
            (b_dim_0)
            int64
            0 1 2 3 4 5 6 7 8 9
            array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
          • c_dim_0
            (c_dim_0)
            int64
            0 1 2
            array([0, 1, 2])
          • c_dim_1
            (c_dim_1)
            int64
            0 1 2 3
            array([0, 1, 2, 3])
          • a
            (chain, draw)
            float64
            0.4088 1.503 ... 2.206 1.027
            array([[ 0.4087575 ,  1.50284003, -0.32110297, -0.5081788 , -0.40220769,\n",
+       "        -0.87170109,  0.58234101, -1.2532232 , -1.27802523,  0.39957152,\n",
+       "        -0.49446914,  0.08184825, -1.09241227, -0.11821264, -0.71780162,\n",
+       "         0.0259301 ,  0.77956171,  1.35580445,  0.38844641, -0.16342852,\n",
+       "         0.53165113,  2.06913002,  0.92034131,  1.73366827, -0.5568965 ,\n",
+       "        -0.02064945, -0.18267666, -2.30062119, -0.64253516, -0.51288127,\n",
+       "         0.50325295,  0.17290704, -1.77467825,  0.17853849,  1.12904415,\n",
+       "        -0.43143987, -0.92632142, -0.01645762,  0.81464567, -0.69047524,\n",
+       "         0.31237736,  1.03805261,  0.74668786,  0.49091469, -0.24580843,\n",
+       "         1.63408641,  0.28925712,  0.25804162,  0.46606289, -0.32438678,\n",
+       "         0.89096089, -0.50119217,  0.9399042 ,  1.53970956,  1.89230283,\n",
+       "         2.04098941,  0.70703458, -0.25777736,  1.5909036 , -0.51204827,\n",
+       "         1.13309954, -0.54244491, -1.05233313,  1.37228423, -1.40729348,\n",
+       "        -0.18392637,  1.3075593 ,  0.43090685,  1.73409776,  0.26104879,\n",
+       "        -1.56477891, -0.15001929,  0.08645028, -2.18003407, -1.04873406,\n",
+       "         0.2809008 ,  1.51876126,  1.56841936, -1.63249759, -0.32400191,\n",
+       "         0.74173753, -0.03056765,  0.60011698,  1.17327571,  1.55188347,\n",
+       "        -0.29661   , -0.66575001,  0.17150096, -0.89358152,  1.18756685,\n",
+       "        -0.53369329,  0.61222564, -2.413132  , -0.62118833, -1.20883966,\n",
+       "        -0.25263999,  0.64636472, -1.46738947,  2.20584409,  1.02661146]])
          • b
            (chain, draw, b_dim_0)
            float64
            1.476 -1.619 ... -0.234 -0.4691
            array([[[ 1.47583113, -1.61850772, -1.00567701,  1.59130143,\n",
+       "         -2.70939073, -0.32208833, -0.8290444 ,  0.95591707,\n",
+       "         -2.54974249, -1.2783129 ],\n",
+       "        [-0.39069582,  0.05163113,  0.16583261, -1.60243147,\n",
+       "          0.79297067,  0.4710381 ,  0.3588299 , -0.84003121,\n",
+       "         -1.86692259, -0.6474608 ],\n",
+       "        [-1.96175341, -1.09400964, -2.21993972,  0.21706801,\n",
+       "          0.76956733,  0.3444925 ,  0.81878462,  0.9932153 ,\n",
+       "          0.31274786, -1.6314479 ],\n",
+       "        [-0.03952375, -2.36662827,  0.65765405,  1.27566284,\n",
+       "         -1.45247659,  0.45645506,  0.36085082, -1.13979315,\n",
+       "         -1.67802767, -0.42547025],\n",
+       "        [-0.07688113,  1.15958334,  1.90242608, -0.4757569 ,\n",
+       "          1.03134424, -1.07496819,  1.45626625, -1.66599655,\n",
+       "          0.53816622, -0.85455336],\n",
+       "        [ 0.24342553,  1.92788425, -1.14099635,  1.02660438,\n",
+       "          0.84570118,  0.52583678, -0.57034698,  0.29374723,\n",
+       "          0.29201958, -1.70399906],\n",
+       "        [-1.60161943,  0.35876593,  1.74052249,  0.2746904 ,\n",
+       "          0.63780419, -0.00697206,  0.48420847,  0.53321361,\n",
+       "          0.33917488, -0.57243425],\n",
+       "        [-0.05396134, -0.22006373,  1.17210683, -1.37060005,\n",
+       "          0.08053576, -1.46767508,  0.78608646, -1.34038302,\n",
+       "         -1.31355684,  0.86910461],\n",
+       "        [ 0.23228371,  1.57107215, -1.29847331, -1.55540464,\n",
+       "          0.66886013,  0.18779141, -0.72266311, -0.26396283,\n",
+       "         -0.47957717,  0.17685023],\n",
+       "        [ 0.32024396,  1.25630042, -1.36619242, -2.17431584,\n",
+       "          0.88105417, -0.40696917,  0.75849999, -0.50962728,\n",
+       "          0.60923359, -0.28743051],\n",
+       "        [-0.67991084, -0.16668529, -2.01916804,  0.66978664,\n",
+       "          0.55195307, -0.75698972,  0.3731393 ,  3.0321424 ,\n",
+       "          0.19768966,  0.32245512],\n",
+       "        [ 0.24265546,  0.31484513, -0.54093232,  0.55076068,\n",
+       "          1.70801073,  1.55515304,  1.45947265,  0.79756442,\n",
+       "         -0.9327435 , -0.14492943],\n",
+       "        [-0.09547235,  2.15238349, -0.24629559,  0.69213711,\n",
+       "          0.79880295,  1.67473797, -0.6721388 , -0.61702302,\n",
+       "          1.42743035,  0.62481683],\n",
+       "        [-1.01113829, -1.17778151, -1.10051484,  0.66303279,\n",
+       "          0.13521376,  0.90119668,  0.90157469, -0.96421418,\n",
+       "          0.36368006, -0.29937799],\n",
+       "        [-0.9764022 ,  0.6219051 , -1.06031713,  1.79369019,\n",
+       "         -0.44426712, -1.05036791, -1.14154439,  0.56378153,\n",
+       "         -1.78828805,  0.47315994],\n",
+       "        [-1.68982688, -0.32025535,  0.36022152, -0.8370535 ,\n",
+       "         -0.14332632,  0.06815294, -1.61260231,  0.71768138,\n",
+       "         -1.76568865, -0.87783555],\n",
+       "        [-0.26703028,  1.85926017, -1.59001067,  1.33126187,\n",
+       "         -0.24028955, -0.66836802, -1.58913159, -0.06353482,\n",
+       "          0.40493389,  0.0543821 ],\n",
+       "        [-0.69066605, -0.50403279, -2.06433044, -0.46782886,\n",
+       "          0.73085315, -0.64970474,  0.09500286, -0.42257805,\n",
+       "          0.01164576,  1.16617444],\n",
+       "        [-0.83110593,  0.65110698,  0.38459692,  1.39669437,\n",
+       "          0.52470097,  0.63845948, -0.57165764,  0.05179001,\n",
+       "         -1.12804426, -0.16622764],\n",
+       "        [ 0.53669977,  2.5467846 , -0.12930673, -1.27013447,\n",
+       "          0.79202548, -0.17326274, -0.93079713,  1.0694834 ,\n",
+       "          0.97966753, -0.16657571],\n",
+       "        [ 0.13875546,  0.93897615, -1.42084175, -3.20996515,\n",
+       "         -0.14070185,  1.33908636,  0.06599638,  0.56749995,\n",
+       "         -0.56354363,  1.35076449],\n",
+       "        [-1.03731276,  0.77057101, -0.40229643,  0.70569859,\n",
+       "          0.55371961, -1.64145607,  0.63700429, -0.41165661,\n",
+       "          1.2533469 , -0.70317559],\n",
+       "        [-0.38602661,  0.14817244, -0.54863005,  0.96320631,\n",
+       "          2.39237437, -1.19731112,  0.93472987,  1.82761474,\n",
+       "         -0.7135787 , -0.9490069 ],\n",
+       "        [ 1.85201013, -2.80408529, -1.6690351 , -0.03107612,\n",
+       "         -0.97019559,  0.81377753, -0.999108  , -1.51672843,\n",
+       "         -0.29147867, -0.85245037],\n",
+       "        [-0.68813791,  1.31641459,  0.04396029, -1.55601145,\n",
+       "         -0.27597517,  0.29970841, -0.70182393, -0.30392192,\n",
+       "          2.21230113, -0.82231998],\n",
+       "        [-0.34788156,  0.66450364,  0.08479376,  0.83612302,\n",
+       "         -1.55370097, -0.74227218, -0.74354936,  0.38806982,\n",
+       "         -0.86233554,  0.12006202],\n",
+       "        [-0.83056635,  1.39474473,  0.69762114,  2.55218776,\n",
+       "         -1.04326242, -0.74644726, -1.52779528, -1.31936875,\n",
+       "         -0.92264893,  0.03338227],\n",
+       "        [ 1.87615721,  0.87830095, -0.22633912,  0.64324966,\n",
+       "         -0.16252636,  0.83944054,  0.75616369,  0.61202454,\n",
+       "          1.35962185, -0.14221056],\n",
+       "        [ 0.17752984, -0.84081234, -0.79361584, -0.33528855,\n",
+       "         -0.22953828, -0.02818515,  0.39538595, -0.76546093,\n",
+       "          0.78379839,  0.34211069],\n",
+       "        [ 0.61994025,  0.39283943, -0.55824111, -1.17934189,\n",
+       "         -1.39210166, -0.46341491, -0.73424565, -0.64638396,\n",
+       "          0.79830344,  0.96934562],\n",
+       "        [-1.41343163,  1.06006244,  1.49947771, -0.46270976,\n",
+       "          0.09943798, -0.4684736 ,  1.63438025,  0.60625227,\n",
+       "         -1.24457114, -0.15914475],\n",
+       "        [ 0.31092562,  0.06237202, -2.17317246, -1.23788166,\n",
+       "          1.12866779,  0.22058525, -0.11456525,  1.62085958,\n",
+       "          1.6471402 ,  0.29539675],\n",
+       "        [-1.09866097, -0.33973742,  0.67295376,  0.12236121,\n",
+       "         -0.43719718,  1.30570356, -0.87106353, -1.12429554,\n",
+       "         -1.02080603,  0.10773611],\n",
+       "        [-1.67634003, -0.09640028, -0.81937802,  1.0737298 ,\n",
+       "          0.39097488,  0.92458839,  0.08745057,  0.40238264,\n",
+       "          2.33396032,  0.25287253],\n",
+       "        [ 0.08028501,  0.22134174,  0.23821156,  0.11769623,\n",
+       "         -0.80955053,  0.68560598,  0.49980855,  1.95763562,\n",
+       "         -0.31829725,  0.7332504 ],\n",
+       "        [-0.72295893,  1.44327766, -0.0350902 , -0.88471094,\n",
+       "         -0.23629482, -0.85177673, -0.35337343,  0.50791563,\n",
+       "         -0.7629206 ,  1.16651663],\n",
+       "        [ 0.04220982,  0.59047676,  0.27004635, -1.22493657,\n",
+       "          1.15157604,  0.57732437,  0.04382566,  0.55716482,\n",
+       "          0.96143061, -0.63606422],\n",
+       "        [ 1.38552519,  0.16376554, -0.24523257,  0.23125256,\n",
+       "         -1.27936312, -0.23548808,  0.06133936, -0.3260571 ,\n",
+       "          1.38444306,  0.12396926],\n",
+       "        [-0.4995823 ,  0.6292016 , -0.4680013 , -0.03105802,\n",
+       "          0.29273029,  1.18582937,  0.14881861, -0.39289098,\n",
+       "          0.58210808, -1.47714574],\n",
+       "        [-2.10597548,  0.09329549,  1.57466225, -0.58014967,\n",
+       "          1.75605606, -0.09969959,  1.29415947, -0.30958269,\n",
+       "          1.28360668, -0.98792531],\n",
+       "        [ 0.90265702, -0.60011735,  0.85975947, -0.51132463,\n",
+       "         -0.3357568 ,  0.55994565, -0.12669934,  0.02272814,\n",
+       "         -1.39120186,  1.1383934 ],\n",
+       "        [ 1.19034463, -0.07055557, -1.05209418, -1.00414357,\n",
+       "          0.54120888,  0.08094923,  1.43973462,  1.11460386,\n",
+       "          0.65139892,  1.65825563],\n",
+       "        [-0.77948511,  0.43616853,  0.56607436, -0.16718011,\n",
+       "          0.86358309,  0.77057734,  0.37729457,  0.53706068,\n",
+       "         -1.59622079, -0.65367978],\n",
+       "        [-0.49302913,  3.47199611,  0.23443268, -0.9648753 ,\n",
+       "         -0.90606348, -1.56987161,  1.25379429, -0.77142778,\n",
+       "         -0.81211117,  1.58446648],\n",
+       "        [-0.93664028, -0.35946751, -1.35453698, -0.90006223,\n",
+       "         -0.07992358, -0.10977378, -0.64657695,  0.48964445,\n",
+       "         -0.18508663, -0.0652289 ],\n",
+       "        [ 2.17034487, -0.68073163,  0.18163993,  0.18136038,\n",
+       "          0.47382237, -0.78593273, -1.79251513, -0.40372338,\n",
+       "          1.44832726, -1.49375153],\n",
+       "        [ 0.16076516,  0.16656596, -0.54037385,  0.37436071,\n",
+       "          0.93987897, -0.67401711, -1.72258259, -2.05423046,\n",
+       "          1.24212687,  1.32136291],\n",
+       "        [ 1.40518381, -0.02561162,  0.30404393, -0.37102864,\n",
+       "         -0.7402975 , -1.62410763, -1.98787609,  0.67915757,\n",
+       "          1.8906322 ,  0.06139485],\n",
+       "        [ 0.84460199,  1.43915938, -1.1833213 ,  0.32430687,\n",
+       "         -0.6518108 ,  1.69764697,  1.14469428,  0.14454147,\n",
+       "          0.18554629, -0.168191  ],\n",
+       "        [ 0.27189222,  1.14009847, -0.5143344 , -0.65669111,\n",
+       "          0.39329444, -3.02136179, -0.90879585, -1.14166753,\n",
+       "          0.22430043, -0.64583285],\n",
+       "        [-1.22246939, -1.12336937,  0.02286542,  0.94257172,\n",
+       "         -0.31216443, -1.34974873,  1.54879704,  0.5647002 ,\n",
+       "         -0.6612776 ,  0.01187873],\n",
+       "        [ 0.40468703, -0.03636206,  1.75994033,  0.69585188,\n",
+       "          1.14862922,  0.33034829,  2.73638075, -0.40265489,\n",
+       "         -0.77341395, -0.97350195],\n",
+       "        [ 1.24834806, -0.28942914,  0.24920516,  1.12153164,\n",
+       "         -1.12047366,  0.84398082,  0.4118804 ,  0.13090905,\n",
+       "          0.998463  , -1.88064252],\n",
+       "        [-0.43912864,  1.23907654, -0.52769809, -0.12803379,\n",
+       "          1.73266102,  0.16833375,  0.09682839,  1.56214235,\n",
+       "          0.06487009, -0.66254781],\n",
+       "        [-1.53424335, -1.75275629, -0.10023282, -0.96723118,\n",
+       "          1.96204167, -0.39458072,  0.20871515, -2.20779714,\n",
+       "         -1.0057085 , -0.8908652 ],\n",
+       "        [-0.09913704, -0.55359486,  0.61926756, -0.84209803,\n",
+       "          1.07419138,  0.21700486,  0.13920164, -1.24174736,\n",
+       "          0.20579289, -0.75516259],\n",
+       "        [ 0.74605859,  0.99646065, -1.08974663,  1.23169524,\n",
+       "         -1.21459234,  0.83548719,  0.46503283, -0.11858287,\n",
+       "         -2.39990035, -0.33174063],\n",
+       "        [-0.62121768,  0.11619578,  0.48822156, -1.5998897 ,\n",
+       "         -0.09038936,  0.37117214,  0.96308831, -1.42578582,\n",
+       "         -0.67643394, -0.66744808],\n",
+       "        [ 1.71090278,  2.08360667, -0.4225552 , -0.92160635,\n",
+       "          0.84203467,  0.1091858 ,  0.31465002, -0.30966054,\n",
+       "          0.36207452,  0.2070106 ],\n",
+       "        [ 0.94717714,  0.08834919, -0.33153505,  0.87502294,\n",
+       "          0.87774687, -0.21422208,  0.71621029, -0.21813208,\n",
+       "         -1.87474591, -1.32441086],\n",
+       "        [-0.47100905,  0.03435122,  0.71261576, -0.41954572,\n",
+       "         -0.99971291, -1.71945308,  1.43650446,  0.55123926,\n",
+       "          0.38743478, -0.45572081],\n",
+       "        [ 0.38879971, -0.1596689 , -0.41217246, -1.29495426,\n",
+       "          2.43453146,  0.02029317, -2.20788623, -0.17873662,\n",
+       "         -1.94992635, -0.44402022],\n",
+       "        [-2.0959677 ,  1.17830027, -0.30270294, -0.64615306,\n",
+       "          1.64156   , -0.01018129,  1.02935399, -0.21858383,\n",
+       "         -0.75983239, -0.03257432],\n",
+       "        [-1.32910976,  1.74883827, -0.3229067 , -0.68173192,\n",
+       "         -0.10916739, -0.00434904, -1.98907853, -1.16671081,\n",
+       "          0.47725859, -1.42823431],\n",
+       "        [ 0.31851469,  3.01310136, -0.97815949, -0.99689906,\n",
+       "         -0.42348215, -0.25101005,  0.99199831,  1.16762275,\n",
+       "          0.65359341, -0.70280763],\n",
+       "        [-1.23073466,  0.60758245, -0.54220073, -1.20184626,\n",
+       "         -0.27897645,  0.85166489,  0.76495273,  1.42307555,\n",
+       "          0.58915061, -0.84833981],\n",
+       "        [-0.77529214,  0.72864915,  0.48530567,  0.68724795,\n",
+       "          0.09164434, -0.28523593,  0.55716607, -0.38010051,\n",
+       "         -0.30689402, -0.26176055],\n",
+       "        [ 0.02982609, -1.02482835,  0.39417403, -0.2793383 ,\n",
+       "          0.59988294, -0.81255426, -1.13297374,  0.51053813,\n",
+       "         -1.4637327 , -3.32950448],\n",
+       "        [-0.37036542,  1.16521838,  1.398283  , -0.55711498,\n",
+       "         -1.87476573, -1.83784818,  0.77255495, -0.12403687,\n",
+       "         -0.71000502,  0.78458304],\n",
+       "        [ 0.00393788,  0.07055419, -0.29660186,  0.03357786,\n",
+       "         -0.42857192,  0.27139745, -0.75916369, -0.02803492,\n",
+       "         -2.37509829,  0.7573789 ],\n",
+       "        [-1.50055421, -0.365724  ,  0.01514847, -0.11596194,\n",
+       "         -0.76819565,  0.70419409, -1.27260212,  2.10537012,\n",
+       "         -0.85139193, -0.48917308],\n",
+       "        [ 0.68722981, -1.11982167, -1.17072139, -0.22265587,\n",
+       "         -0.64586133,  1.421204  , -0.1650338 ,  1.35906552,\n",
+       "          1.66161069, -1.14594215],\n",
+       "        [ 0.23639301, -2.21011861,  1.67690895,  1.6117799 ,\n",
+       "         -0.10538148,  1.82665295,  0.15384634,  0.9102795 ,\n",
+       "         -0.92302891,  0.4738414 ],\n",
+       "        [-0.39901383,  0.4713246 , -0.73676913, -0.70300932,\n",
+       "          0.76507028, -1.33110286, -1.59768726, -1.07778394,\n",
+       "         -1.67093264, -0.08567017],\n",
+       "        [ 1.03837021,  0.53820892, -0.91348001,  0.06532431,\n",
+       "          0.46730774, -0.95854977,  0.18859863,  0.87536922,\n",
+       "         -0.98546513, -0.61036688],\n",
+       "        [ 0.09462368,  1.42040267,  0.44277283,  0.9672926 ,\n",
+       "          0.2394287 , -0.88888528,  0.25319636,  0.8628376 ,\n",
+       "         -0.13429954, -0.06736821],\n",
+       "        [-1.10652619, -0.68550688, -0.89707797,  0.28861809,\n",
+       "          0.70157342,  0.13603496, -0.57774116, -0.7306075 ,\n",
+       "         -0.24524038,  0.25256936],\n",
+       "        [ 0.44857724,  1.83835211, -1.25145111, -0.33050593,\n",
+       "         -0.42872811, -0.90102964, -1.65514419, -0.13525644,\n",
+       "          0.30343377,  1.44231529],\n",
+       "        [-0.44092134, -0.68813406, -0.46920367,  0.44730345,\n",
+       "          0.61270319,  1.90897761, -0.01191331, -1.8166567 ,\n",
+       "          0.13100133,  0.28159205],\n",
+       "        [ 1.83482828, -0.23362668, -0.60654732,  1.58296373,\n",
+       "         -0.43294588,  2.86150541,  0.61033781, -0.28819459,\n",
+       "         -0.7428619 , -0.95832671],\n",
+       "        [-0.4641641 , -0.69377076, -1.05199909, -1.00439363,\n",
+       "         -1.28484386,  0.53498276, -0.3767258 , -0.62944392,\n",
+       "         -0.62892174,  0.62367184],\n",
+       "        [-0.47960615,  0.83862729,  0.38209951,  0.48804628,\n",
+       "          1.25285071, -0.70501126, -1.13808358,  1.24436977,\n",
+       "          0.1343849 , -1.64352074],\n",
+       "        [-0.75413586, -0.25835879, -1.18326665,  0.42529959,\n",
+       "         -0.29532952,  0.41030383,  0.64174792,  1.990364  ,\n",
+       "         -0.7145188 , -0.22973732],\n",
+       "        [ 0.49104546,  1.74798501, -0.01567247,  1.26094097,\n",
+       "          0.73055094,  1.20725117, -0.72169009,  1.88142408,\n",
+       "         -1.75872706,  1.14676545],\n",
+       "        [-0.46658432,  1.36844217,  1.80622632,  0.41630248,\n",
+       "          0.3579169 ,  0.63120228, -1.20243591,  0.57654537,\n",
+       "         -1.84240287,  1.27009568],\n",
+       "        [-0.24366607, -0.88445978, -0.3636501 , -0.89821684,\n",
+       "          0.68951694, -0.06386171,  1.39500081,  1.20184981,\n",
+       "          2.39544895,  0.89550972],\n",
+       "        [ 0.15242367, -1.23084778, -1.08164855, -0.51743519,\n",
+       "         -0.72274606,  1.59652417, -1.03722895,  0.05965752,\n",
+       "          0.86449152, -0.52185899],\n",
+       "        [-2.10288082, -1.34181694,  0.1533846 ,  0.7981945 ,\n",
+       "         -0.18419771, -0.72076238,  2.79993884,  0.2766844 ,\n",
+       "          0.32125412,  1.99233887],\n",
+       "        [-0.83756302,  1.32435262, -1.15151501, -0.45478924,\n",
+       "         -0.0380437 ,  1.01389659, -0.38360173,  0.04593002,\n",
+       "         -1.67314883,  0.76107315],\n",
+       "        [ 0.28664064,  0.19577641, -0.36499579,  0.15812154,\n",
+       "          1.07155452, -0.99255685, -1.30725076,  0.73864051,\n",
+       "          0.39314181, -0.10817879],\n",
+       "        [-0.28412102,  0.01055585,  0.02351819, -0.3818316 ,\n",
+       "          1.34194109,  0.52821611, -1.45351116,  1.26910084,\n",
+       "          1.03286574, -0.85091732],\n",
+       "        [ 0.76658407,  0.45298155,  1.17236988,  1.49296971,\n",
+       "         -0.86080561,  0.7830452 , -1.18019225,  0.04014171,\n",
+       "         -0.2439888 , -0.28891682],\n",
+       "        [ 2.39252097,  0.11763426,  1.71953541, -0.38594126,\n",
+       "          0.54640727,  0.47610705, -0.77441705, -0.12780554,\n",
+       "         -0.84585647, -1.03872811],\n",
+       "        [-0.92517281, -1.46206904,  0.94346589,  2.15323477,\n",
+       "          1.71204618,  0.41852599, -1.02678493,  0.00684078,\n",
+       "         -0.90444402, -0.75351707],\n",
+       "        [ 1.15746181, -0.54314825,  0.58927014, -0.29607625,\n",
+       "         -0.31875024,  0.05611806, -1.33931667,  0.64054206,\n",
+       "          1.20274762,  0.63497818],\n",
+       "        [ 1.02606806,  0.24731296,  1.09443807, -2.9686459 ,\n",
+       "         -0.65211653, -0.49871233,  1.88351013, -0.06799509,\n",
+       "          0.57002699, -0.56179467],\n",
+       "        [ 0.88163277, -0.51283766, -0.02129781,  1.74594465,\n",
+       "         -0.00437275,  0.60638676,  0.55630893,  0.69052337,\n",
+       "          0.83378663, -0.75891006],\n",
+       "        [ 0.44577217,  0.97412345,  1.28856365,  0.60390408,\n",
+       "         -1.79502655, -0.61766774,  1.01743531, -0.56012918,\n",
+       "         -0.26204686, -0.76476869],\n",
+       "        [ 1.20949467, -0.196461  ,  1.08501425, -0.42779261,\n",
+       "          1.79577772,  1.17864942,  0.26969297, -0.78404253,\n",
+       "         -0.71966673,  1.03127274],\n",
+       "        [ 2.50222227,  0.46094406,  0.33207066, -0.95603468,\n",
+       "         -0.47991149,  1.23441534,  0.69248441,  0.91406389,\n",
+       "         -0.23401162, -0.4690532 ]]])
          • c
            (chain, draw, c_dim_0, c_dim_1)
            float64
            -0.703 -0.4777 ... -0.6178 0.34
            array([[[[-0.70295144, -0.47769958,  0.83339221, -1.13238531],\n",
+       "         [ 3.07763421,  1.76203663,  0.51726531, -0.18522201],\n",
+       "         [-0.11274144,  0.36530569, -1.12617178, -1.2879944 ]],\n",
+       "\n",
+       "        [[-0.71366689,  0.45517522,  0.19207493,  0.8582474 ],\n",
+       "         [-0.16947168,  0.46780349,  0.81728124,  0.8487605 ],\n",
+       "         [ 1.01930005, -0.98207195,  0.76484748, -0.68116289]],\n",
+       "\n",
+       "        [[ 2.866716  ,  0.23486823,  0.49692715,  1.03331798],\n",
+       "         [-0.03404072, -1.40102939, -0.49063134,  1.30965462],\n",
+       "         [-0.91686508, -0.26013333, -0.23745687,  0.38117166]],\n",
        "\n",
        "        ...,\n",
        "\n",
-       "        [[ 0.62673748, -1.17745086,  0.13021733, -0.29328622],\n",
-       "         [-0.16972871,  2.34141874, -0.15779152,  0.53371202],\n",
-       "         [-0.76215612,  0.8340473 , -1.0724748 , -0.21852644]],\n",
+       "        [[-0.26675049,  0.51526067, -1.71930161,  0.14597503],\n",
+       "         [ 0.21683451,  0.27233914,  0.63131215, -2.14824951],\n",
+       "         [-0.95930595, -0.83879845,  2.00595171, -1.46179157]],\n",
        "\n",
-       "        [[ 0.02718433,  0.69267516, -2.12772701, -1.07388436],\n",
-       "         [-0.42648033,  1.620098  ,  0.00845212, -1.50349176],\n",
-       "         [ 2.03480941,  1.29875184, -0.01895534, -0.57372993]],\n",
+       "        [[ 1.04718495, -0.00514249,  2.15201396,  0.40880395],\n",
+       "         [ 1.3599958 ,  1.35873101,  0.98481853, -0.40102083],\n",
+       "         [-1.70085248,  0.04483807,  0.10493523,  1.21906874]],\n",
        "\n",
-       "        [[-1.16794039, -1.34014998,  0.64416874, -0.17673203],\n",
-       "         [-0.45691506,  0.33035933,  1.52023497,  0.08582065],\n",
-       "         [-0.81817882,  0.75011982,  0.08688425,  0.66549062]]]])
        • created_at :
          2020-05-22T18:37:44.138117
          arviz_version :
          0.7.0
      " + " [[-0.76008122, -1.07032246, 0.74947097, 1.2425853 ],\n", + " [ 0.22337831, 1.45601438, 0.3239558 , -1.73507268],\n", + " [ 0.68339758, 0.27008985, -0.61782048, 0.34003194]]]])
  • created_at :
    2020-06-05T06:47:07.886685
    arviz_version :
    0.8.3
    • " ], "text/plain": [ "\n", @@ -1767,12 +1702,12 @@ " * c_dim_0 (c_dim_0) int64 0 1 2\n", " * c_dim_1 (c_dim_1) int64 0 1 2 3\n", "Data variables:\n", - " a (chain, draw) float64 -0.5718 0.1517 -0.5055 ... 1.629 -2.573\n", - " b (chain, draw, b_dim_0) float64 -0.3169 -0.6537 ... -0.5796 -0.1467\n", - " c (chain, draw, c_dim_0, c_dim_1) float64 0.1671 0.1605 ... 0.6655\n", + " a (chain, draw) float64 0.4088 1.503 -0.3211 ... -1.467 2.206 1.027\n", + " b (chain, draw, b_dim_0) float64 1.476 -1.619 ... -0.234 -0.4691\n", + " c (chain, draw, c_dim_0, c_dim_1) float64 -0.703 -0.4777 ... 0.34\n", "Attributes:\n", - " created_at: 2020-05-22T18:37:44.138117\n", - " arviz_version: 0.7.0" + " created_at: 2020-06-05T06:47:07.886685\n", + " arviz_version: 0.8.3" ] }, "execution_count": 7, @@ -1794,7 +1729,12 @@ { "cell_type": "code", "execution_count": 8, - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2020-06-05T06:47:07.931892Z", + "start_time": "2020-06-05T06:47:07.924238Z" + } + }, "outputs": [ { "data": { @@ -1824,7 +1764,12 @@ { "cell_type": "code", "execution_count": 9, - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2020-06-05T06:47:07.959122Z", + "start_time": "2020-06-05T06:47:07.934319Z" + } + }, "outputs": [ { "data": { @@ -2163,454 +2108,468 @@ " stroke: currentColor;\n", " fill: currentColor;\n", "}\n", - "
    xarray.Dataset
      • b1: 10
      • c1: 3
      • c2: 4
      • chain: 1
      • draw: 100
      • chain
        (chain)
        int64
        0
        array([0])
      • draw
        (draw)
        int64
        0 1 2 3 4 5 6 ... 94 95 96 97 98 99
        array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
+       "
        xarray.Dataset
          • b1: 10
          • c1: 3
          • c2: 4
          • chain: 1
          • draw: 100
          • chain
            (chain)
            int64
            0
            array([0])
          • draw
            (draw)
            int64
            0 1 2 3 4 5 6 ... 94 95 96 97 98 99
            array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
        "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
        "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
        "       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,\n",
        "       72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89,\n",
-       "       90, 91, 92, 93, 94, 95, 96, 97, 98, 99])
          • b1
            (b1)
            int64
            0 1 2 3 4 5 6 7 8 9
            array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
          • c1
            (c1)
            int64
            0 1 2
            array([0, 1, 2])
          • c2
            (c2)
            int64
            0 1 2 3
            array([0, 1, 2, 3])
          • a
            (chain, draw)
            float64
            0.01982 -0.9719 ... 2.027 -1.217
            array([[ 0.01981882, -0.97185541, -1.80884986, -0.59277742,  0.37340979,\n",
-       "        -0.71225583, -0.40587328,  1.16856281, -0.31366583, -0.5518511 ,\n",
-       "        -0.04656   ,  0.56815774, -1.2439348 ,  2.08522934,  0.45574688,\n",
-       "        -1.06895637, -0.85352385, -0.62793174, -0.27053372,  0.46962001,\n",
-       "         1.36078145, -1.04298814,  0.84862272, -0.19586287, -0.98416101,\n",
-       "         2.18967279, -0.93084148, -0.24505302,  0.39832258,  1.80304636,\n",
-       "        -1.5605025 ,  0.51551749,  0.18062967, -0.13854656,  1.7074158 ,\n",
-       "         0.45348735,  1.09081428, -1.27068969, -1.87007864, -0.32599561,\n",
-       "         0.6138246 ,  0.90059247,  0.86095623,  0.64027537,  2.58137628,\n",
-       "        -1.66263597, -0.10828307, -0.85208544,  1.469295  , -0.96892902,\n",
-       "        -1.0060098 , -0.27220176, -1.53540495, -1.09158364, -0.73929302,\n",
-       "        -2.6088808 , -1.05342755, -1.18775392, -0.20334327,  0.91857726,\n",
-       "        -0.15580377,  0.86976088,  0.34050427, -0.56160685, -0.39398077,\n",
-       "         1.12261784, -0.05721846,  1.09312616,  1.47940088,  1.10283684,\n",
-       "        -0.58480539,  0.80183034,  0.69937769, -0.20500447,  0.85980215,\n",
-       "         1.81163469, -0.57004559,  0.2441397 ,  0.55092788, -0.6361768 ,\n",
-       "         1.15880538, -0.39865237,  1.3109803 , -0.97937057,  2.39662052,\n",
-       "         0.81854036,  0.86500015, -0.59281159, -0.22316877,  1.08161263,\n",
-       "        -0.15720139, -3.22564007, -0.26462126, -0.63789931,  1.07377188,\n",
-       "        -0.4470352 ,  0.40825174, -1.69726242,  2.02667323, -1.21737953]])
          • b
            (chain, draw, b1)
            float64
            0.1776 0.07881 ... -1.019 0.2414
            array([[[ 1.77645461e-01,  7.88146535e-02, -1.17042605e+00,\n",
-       "         -3.28978426e-02, -2.59725675e-01, -9.07524518e-01,\n",
-       "         -3.20125819e-01,  8.62649214e-01,  7.88056338e-01,\n",
-       "          4.29109029e-01],\n",
-       "        [-1.03662188e+00, -6.97937173e-01, -9.51388592e-01,\n",
-       "          6.95767619e-01, -2.22356206e-01,  1.18100698e+00,\n",
-       "         -1.54694923e+00, -3.28187705e-01, -7.62050666e-02,\n",
-       "          1.26522560e-01],\n",
-       "        [ 8.35671692e-01, -1.74937186e+00,  6.64597592e-01,\n",
-       "         -1.28110892e+00,  5.15395656e-01, -1.06396867e-01,\n",
-       "          1.92040193e-01, -1.25114546e+00, -2.27276063e+00,\n",
-       "         -9.48077172e-01],\n",
-       "        [-4.23986369e-01,  1.05174873e+00,  1.63910716e+00,\n",
-       "         -4.49827730e-01,  1.89549586e-01,  1.95265732e-01,\n",
-       "         -1.25752750e+00,  1.24931864e-01,  8.88343928e-01,\n",
-       "          1.51963713e+00],\n",
-       "        [-3.27407298e-01, -8.88977027e-02,  1.72289708e+00,\n",
-       "         -8.76716083e-01, -4.45771450e-01,  3.97679824e-01,\n",
-       "          4.44343622e-01, -1.25635342e+00,  5.03058803e-01,\n",
-       "          7.60352743e-01],\n",
-       "        [-2.11394801e-01, -6.58191848e-01,  1.11957330e+00,\n",
-       "         -5.08071026e-01,  2.96528302e-01,  1.41208372e-01,\n",
-       "          8.00276416e-01, -8.72566954e-01,  5.05142696e-01,\n",
-       "         -7.95801756e-01],\n",
-       "        [-9.70919924e-02, -1.64727521e+00, -9.61843841e-01,\n",
-       "          1.73899530e-01,  1.04157683e-01, -8.58182724e-01,\n",
-       "          6.74244955e-01,  9.55909156e-01, -2.78068313e-01,\n",
-       "         -4.74082042e-01],\n",
-       "        [ 7.60248039e-01,  9.82445913e-01,  1.12112510e+00,\n",
-       "          6.01678640e-01, -3.81749244e-01, -1.13766040e+00,\n",
-       "          9.54625691e-02, -2.47636586e-01,  1.68853899e+00,\n",
-       "          1.38482772e+00],\n",
-       "        [-1.34218544e+00,  1.19602188e+00, -3.31004078e-01,\n",
-       "          2.10968091e+00, -8.80712801e-01,  5.64646402e-01,\n",
-       "          5.79665066e-01, -1.92550338e-01,  1.15161250e+00,\n",
-       "         -8.81247804e-01],\n",
-       "        [-7.28878874e-01, -3.12026958e-02, -7.77530509e-01,\n",
-       "         -1.36035963e+00, -5.88883057e-01,  2.42833938e-01,\n",
-       "         -7.25828224e-01,  1.94901332e-01,  9.22911051e-01,\n",
-       "         -4.60307541e-01],\n",
-       "        [-9.28372599e-02, -1.39654867e+00, -3.71284934e-01,\n",
-       "         -1.65128556e-01,  3.42337250e-01, -4.83243584e-01,\n",
-       "          1.00428520e+00,  2.03382551e-01,  1.42196847e-01,\n",
-       "          1.39153823e+00],\n",
-       "        [ 2.56077174e+00, -1.58562428e+00, -8.70117447e-01,\n",
-       "          1.62853569e-01, -9.62924357e-01,  6.68887865e-01,\n",
-       "          9.19836783e-01,  6.13722118e-01,  6.66355237e-01,\n",
-       "          1.39854714e-01],\n",
-       "        [ 1.34381443e+00, -1.56915289e-01,  5.59855456e-01,\n",
-       "          6.59198665e-01, -4.73661911e-01,  7.64191061e-01,\n",
-       "          9.46435656e-01, -5.17993887e-01,  3.43898355e-02,\n",
-       "         -3.02233456e-01],\n",
-       "        [ 5.21939505e-01, -9.49575217e-01, -1.44548784e+00,\n",
-       "         -2.06623253e-01,  1.30266623e+00,  1.94242084e+00,\n",
-       "         -1.35415764e-01, -8.41563890e-01,  1.60146633e-01,\n",
-       "         -1.07282088e+00],\n",
-       "        [ 5.20631515e-01,  1.42796159e+00, -7.46426856e-01,\n",
-       "          2.77420766e+00,  2.21255736e-01, -4.61213672e-01,\n",
-       "         -4.37371899e-01,  1.51369199e+00, -1.27792743e-01,\n",
-       "          1.34145353e+00],\n",
-       "        [ 9.34875754e-01, -9.90991144e-01, -5.07048628e-01,\n",
-       "          6.48914651e-02, -1.98210575e+00,  9.80345469e-01,\n",
-       "         -3.76952338e-01,  1.86935273e+00,  1.85813384e+00,\n",
-       "         -1.06437669e+00],\n",
-       "        [ 2.33084653e-01,  8.50911851e-02, -5.86567868e-01,\n",
-       "          2.09616110e+00, -5.34806091e-01, -3.64525097e-01,\n",
-       "         -2.35046006e+00, -9.50350299e-01, -2.58864638e+00,\n",
-       "          1.20848594e+00],\n",
-       "        [ 1.64355130e-01,  2.33817235e+00,  4.27671294e-01,\n",
-       "          1.49204584e-01, -2.26483577e+00,  2.37553474e-01,\n",
-       "         -1.01249817e+00,  1.25293913e+00, -1.37406165e+00,\n",
-       "         -9.58708309e-01],\n",
-       "        [-7.02940657e-01,  4.20586564e-01, -1.24819147e+00,\n",
-       "          1.27980007e+00,  4.06186366e-01, -1.31682831e+00,\n",
-       "          1.97227693e-01, -4.44543285e-01,  5.99286996e-01,\n",
-       "         -2.35068320e-01],\n",
-       "        [-1.01840781e+00,  1.40312672e+00,  4.10716732e-01,\n",
-       "          1.20646941e+00, -3.42574163e-01, -3.10677103e-01,\n",
-       "         -1.90769597e-01,  1.51816105e+00,  5.54940116e-01,\n",
-       "         -7.95306663e-01],\n",
-       "        [-7.17739741e-01,  1.19204800e+00, -1.36128757e+00,\n",
-       "          8.38921325e-01,  7.11370138e-01,  8.30909062e-01,\n",
-       "         -6.39327765e-01,  1.06214266e+00,  2.35018429e-01,\n",
-       "          1.29555837e+00],\n",
-       "        [-5.27676258e-01,  3.05885785e-01, -7.26139632e-01,\n",
-       "          6.15832894e-01,  3.45404405e-01, -2.08665967e-01,\n",
-       "          1.43815423e+00,  2.28141552e+00, -1.22110104e+00,\n",
-       "         -5.21541377e-01],\n",
-       "        [ 2.60197138e-01,  1.45638719e+00,  1.66244165e-01,\n",
-       "         -7.44324173e-01, -1.62884997e+00, -2.45126139e-01,\n",
-       "          7.09687151e-01,  1.39893013e+00,  1.14517667e-01,\n",
-       "         -3.32908027e-02],\n",
-       "        [ 1.37201115e+00,  1.09144845e+00, -4.05103680e-01,\n",
-       "          1.93262103e+00, -1.09300763e+00,  2.53709411e+00,\n",
-       "         -9.70895833e-01, -3.41615891e-01,  2.13701391e+00,\n",
-       "         -1.40406044e+00],\n",
-       "        [-1.28625558e+00, -6.75428197e-01,  1.84138888e-02,\n",
-       "          7.39428872e-01,  4.36714953e-01,  2.70964204e-01,\n",
-       "          3.18396117e-02, -1.03632009e+00,  4.65406959e-01,\n",
-       "         -8.13064411e-01],\n",
-       "        [-7.81534619e-01,  1.73787751e+00, -4.66956412e-01,\n",
-       "          7.97705643e-01, -1.43578525e-01,  6.59155451e-01,\n",
-       "         -3.43612174e-01,  1.58302846e+00,  2.88927035e-01,\n",
-       "          1.41067635e+00],\n",
-       "        [-2.34930781e-01,  7.71306383e-01,  8.54388855e-01,\n",
-       "         -6.95086216e-01,  1.48984568e+00,  1.14450435e+00,\n",
-       "          3.07432619e-01, -1.27727053e+00, -4.07226334e-02,\n",
-       "         -1.10187691e+00],\n",
-       "        [ 3.24428123e-01, -2.34207578e-01, -1.69117913e-01,\n",
-       "          4.82073810e-01,  4.21152426e-01, -7.98758881e-01,\n",
-       "         -1.24458276e+00,  4.99752159e-01,  1.05268539e-01,\n",
-       "          2.46421491e-01],\n",
-       "        [-3.85032317e-01, -1.15221282e+00,  1.25673349e+00,\n",
-       "          3.33186046e-01,  2.20598119e+00,  5.33681234e-01,\n",
-       "         -1.37861053e+00,  5.32815897e-01, -3.19600634e-01,\n",
-       "         -6.14058497e-01],\n",
-       "        [-1.83881558e-01, -3.93315931e-01, -9.04903615e-01,\n",
-       "         -3.38544506e-03,  3.80987096e-01, -7.99505446e-01,\n",
-       "         -4.45695633e-01,  5.30461618e-01, -2.69301426e-02,\n",
-       "          7.56054213e-01],\n",
-       "        [-1.50539901e+00, -1.88151617e-01, -8.42567232e-01,\n",
-       "         -3.14055450e-01,  5.92045546e-02,  2.65563890e-01,\n",
-       "         -1.71881534e+00,  1.91233277e+00,  3.71511757e-01,\n",
-       "         -1.04868504e+00],\n",
-       "        [ 4.68770791e-01,  1.15604525e+00, -9.04127129e-01,\n",
-       "          7.33777081e-01, -4.99889318e-01,  1.98738620e+00,\n",
-       "          1.75025801e+00,  2.60728191e+00, -1.08045097e-01,\n",
-       "          8.14284311e-01],\n",
-       "        [-1.35141251e+00, -1.21996688e-02,  6.17008624e-01,\n",
-       "          1.91442896e+00,  8.40271802e-01,  7.14118829e-01,\n",
-       "         -2.92512134e-01, -3.69678645e-01, -3.89745428e-01,\n",
-       "          5.86398402e-01],\n",
-       "        [ 9.69577327e-01, -9.66289517e-01, -1.09203844e-01,\n",
-       "         -1.47715864e-01,  4.26128983e-01,  8.58730424e-01,\n",
-       "         -1.31344688e+00,  7.15309923e-01,  1.60397664e+00,\n",
-       "         -9.13461456e-02],\n",
-       "        [-6.73216498e-01, -9.26368869e-01,  8.91369978e-01,\n",
-       "          7.66236113e-01, -9.00128047e-01,  1.04703737e+00,\n",
-       "         -5.55103944e-01,  1.76209097e+00,  9.43596152e-01,\n",
-       "          5.95717391e-01],\n",
-       "        [ 1.34369466e+00,  3.36503646e+00,  5.19808272e-02,\n",
-       "         -5.94261345e-01, -4.21049011e-01,  4.48940417e-01,\n",
-       "          1.99244232e-01, -5.12257812e-01,  7.78079557e-01,\n",
-       "         -2.32754446e+00],\n",
-       "        [-7.96450801e-01, -1.18029155e+00,  5.42901913e-01,\n",
-       "          7.74730657e-01, -7.35405166e-01, -1.94426972e+00,\n",
-       "         -3.33206466e-01, -8.97923604e-01, -2.12511484e-01,\n",
-       "         -3.31498541e-01],\n",
-       "        [-1.01536314e+00, -4.42561687e-02,  1.61735758e-01,\n",
-       "          5.80281256e-01,  1.06112848e-01, -1.84934830e+00,\n",
-       "          9.66450884e-01, -1.34119684e+00, -7.08124456e-01,\n",
-       "         -6.38789817e-01],\n",
-       "        [-1.28578916e+00,  3.87345725e-01,  1.75775079e+00,\n",
-       "          8.44142408e-01, -7.18797696e-01,  3.75856425e-01,\n",
-       "          2.03969360e-01,  2.64374869e+00, -2.88174164e-01,\n",
-       "          1.09386770e-01],\n",
-       "        [ 1.47011611e+00, -1.87445861e-01, -3.26844350e-01,\n",
-       "         -4.08377543e-01,  1.56911791e-01, -4.22548582e-01,\n",
-       "          4.46550560e-01,  2.61376967e-01,  7.18573723e-01,\n",
-       "         -1.03415994e+00],\n",
-       "        [-1.60006610e+00, -1.65364514e+00, -1.58160230e+00,\n",
-       "         -7.03660254e-01, -2.12902608e+00, -3.50118545e-01,\n",
-       "         -7.62117669e-01, -1.44812372e+00, -1.71822003e+00,\n",
-       "         -1.92800397e+00],\n",
-       "        [-7.62296063e-01, -1.35680207e+00, -4.83911476e-01,\n",
-       "         -6.67017624e-01,  1.54363150e+00, -6.63792243e-01,\n",
-       "         -7.61111903e-02, -1.19247231e+00, -4.84754939e-01,\n",
-       "         -1.15458443e+00],\n",
-       "        [ 6.27612842e-01, -2.19399486e-01,  3.74306828e-02,\n",
-       "          9.91244711e-01,  3.18561314e-02,  1.71714247e-01,\n",
-       "          1.97531291e+00, -6.88594212e-01, -6.35083585e-02,\n",
-       "          1.15634547e-01],\n",
-       "        [ 1.64588056e-01, -2.03453683e-01,  4.52974491e-03,\n",
-       "         -3.56098248e-01, -8.61838076e-01, -4.23795734e-01,\n",
-       "          2.24468645e-02,  9.11969549e-01,  1.82087442e-01,\n",
-       "         -1.35136559e+00],\n",
-       "        [-1.18654082e+00,  5.29000191e-02, -3.65754039e-01,\n",
-       "          4.43584288e-01,  1.03963940e+00, -4.31728563e-01,\n",
-       "          4.45921868e-01, -1.93144108e+00,  7.79359344e-02,\n",
-       "         -1.96243082e-01],\n",
-       "        [-1.42804697e-01, -5.68985256e-01,  2.86480955e-01,\n",
-       "          4.43181232e-01, -5.57978607e-01,  1.64612573e-01,\n",
-       "          1.90808257e-01, -1.22603574e-01, -1.68192299e+00,\n",
-       "         -2.95194312e-01],\n",
-       "        [ 3.89504288e-01, -1.10083239e-01,  1.43978128e+00,\n",
-       "          9.84036656e-01, -8.55429919e-01,  4.64853046e-01,\n",
-       "          7.51119517e-02, -1.59627361e+00,  5.82009883e-01,\n",
-       "          1.89744131e+00],\n",
-       "        [-2.15368935e-01,  1.44894875e+00,  2.92920102e-01,\n",
-       "          1.56730347e+00,  3.14154823e-01, -6.83120265e-01,\n",
-       "         -1.54124953e+00, -1.26658822e-01,  9.40149606e-01,\n",
-       "          1.33185916e+00],\n",
-       "        [-3.74504512e-01, -1.51055563e+00,  1.19883515e+00,\n",
-       "          6.90992297e-01,  7.98357652e-01,  2.78255111e-01,\n",
-       "         -4.52789731e-01,  3.24317529e-01,  1.70282199e+00,\n",
-       "         -1.58254406e+00],\n",
-       "        [ 1.24688693e-01,  1.02697786e+00, -1.07774572e+00,\n",
-       "         -3.52269811e-01, -1.06108526e+00, -4.07765339e-01,\n",
-       "         -1.69673067e-01,  8.75315198e-01, -1.18683297e+00,\n",
-       "          6.93246311e-01],\n",
-       "        [-4.40460024e-02,  5.13526124e-01,  3.76132723e-01,\n",
-       "          2.58601643e-01, -1.58686151e+00,  6.72800465e-01,\n",
-       "          5.13487166e-01, -1.81382132e-01,  2.69372016e-01,\n",
-       "          6.74870902e-03],\n",
-       "        [ 2.75377900e-02,  1.22435570e+00, -1.92979528e+00,\n",
-       "         -1.51232663e-01,  1.50525778e+00, -3.04237042e-01,\n",
-       "          2.01145235e+00, -1.25508503e-02, -8.39006547e-01,\n",
-       "          2.65128972e-01],\n",
-       "        [-4.45149453e-01,  7.57881426e-01, -3.74903548e-01,\n",
-       "         -4.97799122e-01,  1.36078037e+00, -8.13620618e-01,\n",
-       "          4.84657539e-01, -6.09069540e-01,  6.69617070e-01,\n",
-       "         -1.68079951e-01],\n",
-       "        [-9.97368053e-01,  3.46731895e-01,  1.06219680e+00,\n",
-       "         -1.26053853e+00,  1.82743479e+00, -1.38478751e+00,\n",
-       "          5.89350883e-01, -1.85052191e-01,  1.54942864e-01,\n",
-       "          2.22789404e-01],\n",
-       "        [ 7.03513863e-01, -2.10936363e-01, -4.80521273e-01,\n",
-       "         -4.22316656e-01,  1.58449925e+00, -1.02983783e+00,\n",
-       "         -1.32111309e+00,  1.06684293e+00,  4.73961206e-01,\n",
-       "          1.29218249e+00],\n",
-       "        [-5.10738687e-01,  1.25891183e-01, -2.16609599e-01,\n",
-       "         -5.16912441e-01,  1.41987365e+00, -4.09151237e-01,\n",
-       "          9.09191476e-02, -7.36144260e-01,  1.45766417e+00,\n",
-       "          1.80305558e-01],\n",
-       "        [ 1.68251414e-01,  2.31310881e-01, -4.68545719e-01,\n",
-       "          2.64488020e-01,  1.12548426e+00, -7.83379512e-01,\n",
-       "          1.14592648e+00,  2.45184215e-01, -1.82206749e+00,\n",
-       "          7.23966935e-01],\n",
-       "        [ 1.33047828e+00, -5.20348042e-01, -1.56023198e+00,\n",
-       "         -4.64789145e-01, -1.13185281e+00,  6.76249330e-01,\n",
-       "          3.16678457e-01,  9.53320221e-01, -1.91906170e+00,\n",
-       "          5.81676904e-01],\n",
-       "        [ 8.11791641e-01,  1.51918929e+00,  1.34302841e+00,\n",
-       "          4.94275300e-02,  4.84758802e-02, -8.61595407e-01,\n",
-       "          1.33598164e-01,  1.21920640e+00, -1.13828031e+00,\n",
-       "          1.31509948e+00],\n",
-       "        [ 4.98405238e-01, -7.05551738e-01, -2.55316367e-01,\n",
-       "         -7.03569727e-02,  1.99683614e-01, -7.06777571e-01,\n",
-       "          1.06476978e+00,  1.97225406e+00, -1.17103100e-01,\n",
-       "          6.17318160e-01],\n",
-       "        [ 4.98524041e-01, -4.35297610e-01,  2.32904248e+00,\n",
-       "          1.96198510e+00,  7.02060805e-01, -5.90328783e-01,\n",
-       "          1.95598796e-01,  4.60439231e-01, -1.54083410e+00,\n",
-       "          8.22152504e-01],\n",
-       "        [ 3.81344430e-01, -1.06785798e+00, -1.20040333e+00,\n",
-       "          1.12287264e+00,  9.62299665e-01,  3.47181087e-03,\n",
-       "         -2.22317529e-01,  7.87317097e-01,  1.14967168e+00,\n",
-       "          1.35406981e+00],\n",
-       "        [ 4.53034061e-02, -2.15828981e+00, -1.11644314e-02,\n",
-       "         -3.42280911e-01,  2.45849559e-01, -1.25542590e+00,\n",
-       "          6.07707003e-01,  1.54698300e+00,  3.59054953e-01,\n",
-       "         -1.58955653e-02],\n",
-       "        [ 3.23814605e-01, -6.56059271e-01,  9.93045209e-01,\n",
-       "         -2.05687871e-01,  1.65400291e+00, -1.87941531e+00,\n",
-       "         -8.18073065e-01,  3.85394193e-01, -8.55296960e-01,\n",
-       "         -1.60779417e-02],\n",
-       "        [-2.80729336e+00,  1.02519245e+00,  1.89250883e-01,\n",
-       "         -5.45124202e-01, -8.28903493e-01,  7.39424520e-01,\n",
-       "          9.26038760e-01, -3.53467812e-01, -1.15106387e+00,\n",
-       "         -1.61852113e+00],\n",
-       "        [-7.24932021e-01,  1.95939187e+00, -7.49913568e-01,\n",
-       "         -5.53403916e-01, -9.22165706e-01,  4.09298269e-01,\n",
-       "          7.51507697e-01,  1.43968412e-01,  1.78338995e+00,\n",
-       "         -5.33042121e-01],\n",
-       "        [-9.99312525e-01,  3.82981895e-01,  2.65076215e-01,\n",
-       "          3.37281784e-01, -9.74510884e-01, -1.34044124e-01,\n",
-       "         -1.13215195e+00, -3.97670730e-01, -5.42567849e-01,\n",
-       "         -1.13894616e+00],\n",
-       "        [ 1.10047452e-01,  6.13562160e-01, -3.14261722e-01,\n",
-       "         -4.54337572e-01, -7.77573578e-02, -9.64570699e-01,\n",
-       "         -1.82085437e+00,  8.59758835e-01, -9.35036017e-01,\n",
-       "         -2.27867797e-01],\n",
-       "        [ 7.68461896e-01, -4.13573611e-01,  2.44078874e+00,\n",
-       "         -4.09155337e-01,  1.89906192e+00,  2.50995459e-01,\n",
-       "          1.55163330e-01,  1.03651035e-01, -1.23931484e+00,\n",
-       "          1.06960911e-01],\n",
-       "        [ 1.86322513e-01, -6.61777226e-01, -1.84912795e-01,\n",
-       "          5.86924362e-01,  6.94610539e-02, -5.78232111e-01,\n",
-       "          1.17747344e+00, -2.43161081e-01, -6.71739663e-01,\n",
-       "         -3.91790630e-01],\n",
-       "        [-6.54090195e-01, -2.07395829e+00,  7.93831300e-01,\n",
-       "         -8.45067547e-01, -1.16592680e+00, -3.31542945e-01,\n",
-       "         -5.13086452e-01, -1.09672337e-01,  1.39591445e-02,\n",
-       "          9.28282162e-01],\n",
-       "        [ 4.45325049e-01,  2.84751982e-01,  3.95034136e-01,\n",
-       "         -1.07752546e+00,  2.39788495e-01, -9.70949858e-01,\n",
-       "          1.11400120e+00, -1.60862482e+00, -1.85619947e-01,\n",
-       "          7.52726152e-01],\n",
-       "        [-7.65308665e-01, -3.36100099e-01, -5.57173010e-02,\n",
-       "         -6.85094946e-02, -4.34509396e-01,  1.51885775e-01,\n",
-       "          1.25255724e+00, -4.40324288e-02,  5.41795032e-01,\n",
-       "         -1.73348735e-01],\n",
-       "        [ 2.17580378e+00,  4.33054437e-01, -1.32238706e+00,\n",
-       "         -6.45409143e-03,  1.14784382e+00, -1.87582131e-01,\n",
-       "          6.95460008e-01,  4.17304234e-01,  5.09058101e-01,\n",
-       "         -8.64411699e-01],\n",
-       "        [-9.16112181e-01, -9.46340251e-01, -1.23799850e+00,\n",
-       "          1.44948271e+00,  7.56320802e-02, -7.14496677e-01,\n",
-       "         -1.09290187e+00, -1.09295436e+00,  6.12971659e-01,\n",
-       "         -6.98510986e-01],\n",
-       "        [-6.72508620e-01, -1.82870199e+00,  1.18706894e+00,\n",
-       "         -3.92328588e-02, -1.94148349e+00,  3.90212827e-01,\n",
-       "          8.93266724e-01,  8.97900012e-01,  3.16138209e-01,\n",
-       "         -6.41208120e-01],\n",
-       "        [-1.35719819e+00,  1.41320804e+00,  8.33185146e-01,\n",
-       "          6.83250580e-01,  1.55044503e+00,  1.56922369e-01,\n",
-       "         -1.07119421e-01, -7.61873094e-02,  1.72312913e+00,\n",
-       "          6.47784911e-01],\n",
-       "        [ 1.22671980e-01,  4.84712927e-01,  9.39168337e-01,\n",
-       "          9.10838268e-01, -3.79246941e-01,  7.54594518e-01,\n",
-       "          3.55363501e-01,  3.32017041e-01,  3.85805908e-02,\n",
-       "          1.44465002e+00],\n",
-       "        [-6.04247305e-01, -4.78223505e-01, -3.91950649e-01,\n",
-       "         -1.82351976e+00, -1.01147911e-01, -8.63374247e-01,\n",
-       "          4.74903304e-01, -2.09153962e-01, -2.20634319e-01,\n",
-       "          1.27862972e-01],\n",
-       "        [ 1.05610670e+00, -1.60068919e+00,  2.01009393e+00,\n",
-       "         -4.97932360e-02, -2.89726221e-01,  6.52833728e-01,\n",
-       "         -1.01856233e+00, -9.58422457e-01, -1.36403746e+00,\n",
-       "          9.88404165e-01],\n",
-       "        [-3.18984853e-01, -3.61803187e-01, -4.73822593e-01,\n",
-       "          6.95014381e-01, -6.55922768e-01, -1.44032898e+00,\n",
-       "          1.85330066e+00,  2.86224334e-01, -1.03807223e+00,\n",
-       "         -1.28138340e+00],\n",
-       "        [-8.91055976e-01,  4.65191800e-01, -6.20896314e-01,\n",
-       "          1.21430944e+00,  1.49802361e+00,  4.24164129e-01,\n",
-       "          1.02138071e+00, -2.65604913e-01, -5.03118329e-02,\n",
-       "          6.29302968e-01],\n",
-       "        [ 2.53267774e-01,  1.02530742e-01, -5.91166726e-01,\n",
-       "          1.42786740e+00,  4.98264284e-01, -1.43429097e+00,\n",
-       "         -5.02773607e-01,  1.67250452e+00,  5.94200618e-01,\n",
-       "         -6.33544411e-01],\n",
-       "        [-7.09789221e-01, -1.92129555e+00,  1.07768282e-01,\n",
-       "          1.31397923e+00, -2.80660278e-02, -1.12618313e+00,\n",
-       "          1.32394771e+00,  7.65582351e-01, -5.59287054e-01,\n",
-       "          3.79322556e-01],\n",
-       "        [-3.51647588e-01, -1.46011427e-01, -6.93183510e-01,\n",
-       "          3.93048458e-01, -1.07123330e-01,  1.80152607e+00,\n",
-       "          1.90042742e-02, -1.27937872e+00,  6.54379500e-01,\n",
-       "         -4.69003655e-01],\n",
-       "        [ 1.04881672e+00,  4.44625229e-01, -7.71402029e-02,\n",
-       "         -8.40106685e-01,  5.08351358e-01, -8.37934815e-01,\n",
-       "         -9.29665467e-01,  4.38977970e-01,  1.45401452e+00,\n",
-       "          1.95528270e+00],\n",
-       "        [ 1.31412422e+00, -4.88505861e-01,  6.75837560e-02,\n",
-       "         -2.27401501e-02, -6.48205479e-02, -2.43022646e-01,\n",
-       "          3.35484204e-02, -2.37374370e-01, -8.08487172e-02,\n",
-       "         -3.93211432e-01],\n",
-       "        [ 2.05387407e+00,  8.43713550e-01, -3.18514938e-01,\n",
-       "          9.22791039e-01,  8.51452834e-01,  3.16421654e-01,\n",
-       "         -2.01274581e-01,  3.05957219e-01,  2.22734179e-01,\n",
-       "         -9.43422096e-01],\n",
-       "        [ 1.58224865e+00, -1.03033747e+00, -4.21518501e-02,\n",
-       "          1.40896281e+00, -1.16365021e+00,  2.31679902e+00,\n",
-       "         -4.67419221e-01,  3.77422633e-01, -8.79261439e-01,\n",
-       "          2.58954879e-02],\n",
-       "        [ 4.23142610e-01, -5.87311959e-02, -1.92665127e+00,\n",
-       "         -2.36840785e+00,  2.82117055e-01,  9.47822675e-01,\n",
-       "         -4.63357569e-01,  7.50067454e-01,  9.38139678e-01,\n",
-       "         -1.97651428e+00],\n",
-       "        [-1.25426835e+00,  1.36221903e+00, -1.53282758e+00,\n",
-       "         -1.39113601e+00, -1.76829632e+00, -2.96142734e-01,\n",
-       "         -1.42331571e+00,  2.20173337e-02, -1.39875835e+00,\n",
-       "         -1.37081250e-01],\n",
-       "        [ 3.72633240e-01,  8.64578500e-01, -7.67856009e-01,\n",
-       "          5.47304326e-01, -1.07351247e+00,  1.45602800e+00,\n",
-       "         -4.06864437e-01,  3.72741147e-01,  1.03914224e-01,\n",
-       "          2.05972702e-01],\n",
-       "        [-4.94679655e-01, -1.82525522e-01,  1.77904213e+00,\n",
-       "         -1.83567647e+00,  1.92268666e-01, -2.07188128e-03,\n",
-       "         -9.66714073e-02,  1.95650101e+00, -2.05428420e-01,\n",
-       "          6.94831043e-01],\n",
-       "        [-8.55925060e-01,  3.69664007e-03,  8.41574384e-02,\n",
-       "          5.60487566e-01,  1.93991399e+00, -8.64795166e-01,\n",
-       "         -2.09053791e-01,  2.94752793e-01,  1.45188699e+00,\n",
-       "         -9.31164522e-01],\n",
-       "        [-2.42597283e-01, -3.81109787e-01, -7.30342101e-01,\n",
-       "         -2.87312960e-01, -1.22726779e-01,  4.52854438e-01,\n",
-       "         -1.36241424e-01, -2.38480553e+00,  6.87501729e-01,\n",
-       "         -1.06112412e+00],\n",
-       "        [-8.61101842e-01,  1.54109750e+00, -8.86959048e-01,\n",
-       "          7.03948288e-01, -1.00950843e+00,  1.72912484e-01,\n",
-       "         -3.43084548e-01,  9.13378511e-01, -1.79469799e+00,\n",
-       "         -9.08089452e-01],\n",
-       "        [-2.76325030e-01,  2.56302951e-02,  1.00371616e+00,\n",
-       "          2.55068834e-01, -4.67144661e-01,  1.51063087e+00,\n",
-       "         -7.42985435e-01,  5.20079749e-03,  1.43255897e+00,\n",
-       "         -8.67823513e-01],\n",
-       "        [-4.07789831e-01, -7.64042004e-01, -9.21548029e-02,\n",
-       "          3.74484978e-01,  6.86990080e-02, -6.42321712e-02,\n",
-       "          1.48879194e+00, -3.22833772e-01, -1.43687335e+00,\n",
-       "         -4.66612351e-01],\n",
-       "        [ 7.16780279e-01,  9.96177401e-01,  1.33592793e+00,\n",
-       "         -1.40515484e+00, -4.08714396e-01,  3.25727497e-01,\n",
-       "          2.57469554e+00,  2.22885162e+00,  2.27186724e-01,\n",
-       "         -1.23005726e-02],\n",
-       "        [ 5.77131457e-01, -1.74247441e+00, -8.55070048e-01,\n",
-       "         -1.19430904e+00,  1.12967263e+00,  9.79381852e-01,\n",
-       "          4.36955757e-01,  1.58699908e+00, -1.01852044e+00,\n",
-       "          2.41415032e-01]]])
          • c
            (chain, draw, c1, c2)
            float64
            1.02 -0.4499 ... -2.762 -0.9751
            array([[[[ 1.02005349, -0.4498801 ,  0.43856617,  0.8734847 ],\n",
-       "         [-0.92132587,  0.74262351,  0.33308393, -0.56148552],\n",
-       "         [ 1.66644011,  1.70566648, -0.4596628 , -1.96391805]],\n",
-       "\n",
-       "        [[ 0.65433018, -2.17152847, -0.92701914,  1.35694109],\n",
-       "         [-2.02054413, -1.28996295, -0.22409921, -0.41672461],\n",
-       "         [-0.96793892, -1.80701375,  0.04605529,  1.19433595]],\n",
-       "\n",
-       "        [[ 0.54743149, -0.47487852, -1.2753375 ,  1.17706101],\n",
-       "         [ 1.29083006,  2.04867009,  0.11308128, -0.28884669],\n",
-       "         [ 0.11518327,  0.15988808, -0.81723452,  1.65931404]],\n",
+       "       90, 91, 92, 93, 94, 95, 96, 97, 98, 99])
          • b1
            (b1)
            int64
            0 1 2 3 4 5 6 7 8 9
            array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
          • c1
            (c1)
            int64
            0 1 2
            array([0, 1, 2])
          • c2
            (c2)
            int64
            0 1 2 3
            array([0, 1, 2, 3])
          • a
            (chain, draw)
            float64
            -0.3753 -1.343 ... 0.2744 1.353
            array([[-3.75346014e-01, -1.34279885e+00, -1.81966145e-01,\n",
+       "        -3.56027148e-01, -4.91391126e-01, -1.77717530e-01,\n",
+       "        -1.06402332e+00, -1.18231990e+00,  1.72532825e+00,\n",
+       "         5.24281952e-01,  5.77555211e-01,  2.91337464e-01,\n",
+       "        -8.37998249e-01,  8.48762904e-02, -1.09796865e+00,\n",
+       "        -9.81763196e-01,  7.28671071e-01, -1.81745148e+00,\n",
+       "         8.09745894e-01, -9.40200839e-01,  7.31322483e-01,\n",
+       "         2.94313910e-01, -6.72534458e-02, -6.87359517e-01,\n",
+       "         1.27262226e+00,  9.40409456e-01,  4.46302483e-01,\n",
+       "         1.38721683e-01, -3.91378150e-01,  2.97032305e-01,\n",
+       "        -6.08816792e-01,  1.14561872e+00,  9.02347668e-01,\n",
+       "         1.18282099e+00,  1.11689919e-01, -1.03027132e+00,\n",
+       "         9.73148302e-01, -1.20490575e-03,  1.45153366e-01,\n",
+       "        -7.06944130e-01,  1.29471707e+00, -8.59920407e-01,\n",
+       "         1.57687082e-01,  6.93504104e-01,  5.53264762e-01,\n",
+       "        -1.09333662e+00,  2.11740967e-01, -2.76497632e-01,\n",
+       "         1.22886080e+00, -5.10891653e-01, -8.18504801e-01,\n",
+       "         9.92668370e-01, -8.16829237e-01, -1.14691651e-02,\n",
+       "         3.59445865e-01,  1.21180953e+00, -2.11572313e-01,\n",
+       "        -5.48715078e-02, -4.57762612e-01, -2.17555942e-01,\n",
+       "         1.66085921e-02,  2.25531279e-01,  4.36644816e-01,\n",
+       "        -1.43999288e-01,  2.49844987e-01,  7.38767224e-01,\n",
+       "        -2.11630403e-01,  4.35409621e-01, -1.20065321e+00,\n",
+       "         5.91633821e-01, -1.83674365e+00, -1.47862654e+00,\n",
+       "         1.41080588e+00,  1.11046576e+00, -3.63604683e-01,\n",
+       "         7.09826828e-01, -1.24470396e+00,  4.12014472e-01,\n",
+       "        -7.56505493e-01,  8.47744142e-01,  4.50065923e-01,\n",
+       "        -1.67920301e-01, -8.58652124e-01, -7.08302508e-01,\n",
+       "        -8.22896186e-02,  1.08820668e-01,  2.39990899e+00,\n",
+       "        -9.64984269e-01,  1.98025631e+00, -5.63937986e-01,\n",
+       "         4.79969662e-01, -1.35120678e-01, -2.05635299e+00,\n",
+       "        -8.10160991e-02,  8.54539325e-02, -7.29010226e-01,\n",
+       "         9.89029011e-02,  4.03121712e-01,  2.74395661e-01,\n",
+       "         1.35305973e+00]])
          • b
            (chain, draw, b1)
            float64
            0.7681 1.076 ... -0.7853 0.05745
            array([[[ 7.68136098e-01,  1.07561765e+00,  9.10586329e-01,\n",
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+       "          3.92373217e-01, -3.75640224e-01, -1.94163633e+00,\n",
+       "          3.74350597e-01],\n",
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+       "         -2.19797709e-01, -7.21600779e-01,  2.71613355e+00,\n",
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+       "         -8.37791983e-01],\n",
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+       "         -9.69957478e-02, -9.70864027e-01, -8.36588613e-01,\n",
+       "         -3.11743240e-01],\n",
+       "        [ 6.88186108e-01,  5.98385772e-01, -5.97026112e-01,\n",
+       "          1.42851886e+00,  8.48137205e-01,  2.10094975e+00,\n",
+       "         -1.39977142e+00,  9.00597548e-02, -3.32103419e-01,\n",
+       "          1.00096181e+00],\n",
+       "        [-5.75493146e-01, -3.03378656e-02,  6.63252919e-01,\n",
+       "          1.12336409e+00, -1.40282882e+00, -4.73099614e-02,\n",
+       "          1.20252584e+00, -1.12931811e+00, -2.51504773e-01,\n",
+       "          1.04238905e+00],\n",
+       "        [ 2.05908536e+00, -6.73223564e-01, -8.28433667e-01,\n",
+       "          1.42059000e+00,  1.18108136e-01, -8.49054932e-01,\n",
+       "          9.29292459e-01,  3.07204803e-01,  2.13973646e-01,\n",
+       "          1.34638560e-01],\n",
+       "        [ 2.92877236e-01, -1.80840456e+00, -1.93953588e+00,\n",
+       "          5.77596624e-01, -8.39459288e-01,  6.00684260e-01,\n",
+       "         -4.41878956e-01, -9.35274669e-01,  6.54620416e-01,\n",
+       "         -1.33994904e+00],\n",
+       "        [-3.45382615e-01,  1.78441951e+00, -1.86387122e+00,\n",
+       "         -1.57407691e+00, -9.18080424e-01, -2.97663828e-01,\n",
+       "         -1.07019644e+00,  2.72095831e-01, -1.01488386e+00,\n",
+       "         -1.04330639e+00],\n",
+       "        [-2.06182101e+00, -4.35465044e-01,  1.17828779e+00,\n",
+       "         -3.15474204e-01, -7.34544509e-01,  4.17702043e-01,\n",
+       "         -1.01855704e+00,  4.07705284e-01, -1.69794386e+00,\n",
+       "          7.58022453e-01],\n",
+       "        [ 3.18949420e+00,  9.86866683e-01,  9.19029444e-01,\n",
+       "          3.70346127e-01, -1.00609383e-01,  2.50634986e-01,\n",
+       "         -2.66066453e-01,  7.68503000e-01, -4.84721848e-01,\n",
+       "          6.64463545e-01],\n",
+       "        [-7.70486579e-02,  1.77854896e+00,  1.05041812e+00,\n",
+       "         -1.16871035e+00, -5.49272667e-01,  4.90860526e-01,\n",
+       "         -6.65684325e-01,  3.19016242e-01, -3.08419867e-02,\n",
+       "          3.83336937e-01],\n",
+       "        [ 5.38446634e-01,  3.84648737e-02,  8.34928722e-01,\n",
+       "          1.66875872e+00, -2.74772488e-01, -1.95327441e-01,\n",
+       "         -1.90873997e-01, -7.24974748e-01,  1.55935511e-01,\n",
+       "          5.15091003e-01],\n",
+       "        [ 2.61995897e+00,  4.27156216e-01,  1.67796339e+00,\n",
+       "          1.49522003e+00, -7.42639758e-01,  5.89963092e-01,\n",
+       "          6.92495839e-01, -3.30882647e-01, -2.05346621e-01,\n",
+       "          6.46917732e-02],\n",
+       "        [-6.10844536e-01,  1.29325755e+00,  1.97670270e+00,\n",
+       "         -2.10314694e-01,  5.02810444e-01,  8.13566688e-02,\n",
+       "          6.17377858e-01, -2.18846962e+00,  1.64405874e+00,\n",
+       "          2.00712909e-01],\n",
+       "        [-6.35040237e-01, -7.38362877e-01,  4.55427554e-01,\n",
+       "          5.82199736e-01, -3.51577851e-01, -4.80669054e-01,\n",
+       "          1.53673742e+00, -4.26302769e-01,  3.03403439e-01,\n",
+       "          3.80990155e-01],\n",
+       "        [ 2.31035054e+00, -1.33327629e+00,  7.57442360e-01,\n",
+       "          1.43731251e+00, -3.20197965e-01, -7.28043718e-01,\n",
+       "          2.41117068e+00, -1.42493238e-01,  1.20961182e+00,\n",
+       "         -2.39712520e-01],\n",
+       "        [-7.47062377e-02, -7.10758562e-01, -7.59662534e-01,\n",
+       "          4.60120419e-01,  8.18082306e-01,  9.36053104e-01,\n",
+       "          1.88414518e-01, -8.05855712e-01, -1.81044032e-01,\n",
+       "         -1.44335737e-01],\n",
+       "        [ 1.80637700e+00,  5.52588860e-02, -7.61655157e-01,\n",
+       "         -2.98208537e-01,  1.81032166e-01,  3.56062940e-01,\n",
+       "         -3.95832933e-01, -2.67323533e-01,  8.65167789e-01,\n",
+       "         -8.79764539e-01],\n",
+       "        [-3.64116879e-01, -5.34883748e-01, -1.78526312e+00,\n",
+       "          5.58735140e-01, -1.30261404e+00,  9.38324544e-01,\n",
+       "          6.56705109e-03, -1.02323151e-01, -4.85686688e-01,\n",
+       "          3.15624126e-01],\n",
+       "        [-9.90323181e-01, -1.59132745e+00, -3.12422896e-01,\n",
+       "         -2.18984942e-01,  1.13719454e-01,  1.97342568e-01,\n",
+       "         -2.10654688e-02, -1.22284948e+00, -1.05032467e-01,\n",
+       "          5.72509090e-01],\n",
+       "        [ 8.41189077e-01,  1.35995100e+00,  1.74515425e-01,\n",
+       "         -6.13907711e-01,  3.58761207e-01,  8.66666542e-01,\n",
+       "          2.55003366e-01,  4.29695741e-01, -4.25406947e-01,\n",
+       "         -5.85776012e-01],\n",
+       "        [ 1.49207302e+00,  3.01100643e-01, -3.57757069e-01,\n",
+       "          1.81734848e+00,  1.92244970e+00, -3.21185224e-01,\n",
+       "          5.61633371e-03,  1.49762418e+00, -1.77086966e+00,\n",
+       "         -9.27834088e-01],\n",
+       "        [-1.23953142e+00, -1.11627959e+00, -2.79716711e-01,\n",
+       "         -1.35294210e-01, -4.37315328e-01, -1.48715013e-01,\n",
+       "          9.71068417e-01, -1.19546973e-01,  1.67494845e-01,\n",
+       "         -2.70729006e-01],\n",
+       "        [-2.10646244e-01,  6.35055249e-01, -9.18012310e-01,\n",
+       "          4.25266371e-01, -3.53126393e-01,  5.31174151e-01,\n",
+       "         -4.42584660e-01, -9.40463110e-01, -5.40683105e-01,\n",
+       "          8.62053071e-01],\n",
+       "        [-1.47947499e+00,  6.61564994e-01,  1.03992587e-01,\n",
+       "         -9.52106996e-01, -1.26348051e+00,  7.08744024e-01,\n",
+       "          2.31831311e-01,  1.60670955e-01,  3.17776787e-01,\n",
+       "         -5.98191886e-01],\n",
+       "        [-9.39024827e-01, -2.66596517e-01,  7.28922695e-01,\n",
+       "          8.26072732e-01, -2.28778336e-01,  2.64038874e-01,\n",
+       "          1.52406675e+00, -1.28824724e+00,  5.50972862e-01,\n",
+       "          6.48486368e-01],\n",
+       "        [ 6.21937647e-01, -2.88335046e-01, -2.03556573e+00,\n",
+       "          7.22697275e-01,  2.97198251e-01, -7.98297618e-01,\n",
+       "          8.37846652e-02, -9.28889209e-01,  2.92330213e-01,\n",
+       "         -5.93527191e-01],\n",
+       "        [ 3.29998724e-02,  7.14343701e-01, -3.93449519e-01,\n",
+       "          1.55183313e+00, -3.18196673e-01, -1.57433911e-01,\n",
+       "          2.27338509e+00,  1.83091486e+00, -1.04692504e+00,\n",
+       "         -5.12453642e-01],\n",
+       "        [ 1.09281572e-01, -9.85697115e-01, -1.78253165e-01,\n",
+       "         -7.18241579e-01,  2.86056354e-01,  1.65448389e+00,\n",
+       "         -6.94737517e-01, -1.39011571e+00, -1.88211758e+00,\n",
+       "         -6.52848266e-01],\n",
+       "        [ 1.40088791e+00,  9.35446371e-02,  2.05642719e-01,\n",
+       "          9.96662344e-01, -1.46894561e-01, -7.02579645e-01,\n",
+       "         -4.10302265e-01,  7.44005015e-01, -1.35258201e-01,\n",
+       "         -4.80352483e-01],\n",
+       "        [-1.75769336e-01, -1.20582629e+00, -6.73920193e-01,\n",
+       "         -5.52306005e-01,  4.50176062e-02,  2.11762468e-01,\n",
+       "          1.19777680e-02,  1.13114892e+00, -2.19765472e+00,\n",
+       "         -8.05261974e-01],\n",
+       "        [ 2.16068541e+00,  2.74861757e+00,  1.20476504e-01,\n",
+       "          3.83409668e-01, -1.10493045e-01, -2.34331735e-01,\n",
+       "          1.35258601e+00, -9.13122928e-01,  7.06968970e-01,\n",
+       "          1.03873328e+00],\n",
+       "        [ 2.42190877e-01, -1.00404666e+00, -1.39894963e+00,\n",
+       "         -1.75276682e-01,  4.87709753e-01,  1.68330541e-01,\n",
+       "          4.65352039e-01, -1.38792439e+00, -5.97778456e-01,\n",
+       "          2.32355844e+00],\n",
+       "        [-1.71818503e+00,  2.15131919e+00, -7.02717501e-01,\n",
+       "          1.75314476e+00,  8.98620763e-02, -8.29092428e-01,\n",
+       "          4.60949872e-01,  4.30103864e-01, -2.15114063e+00,\n",
+       "          8.48320835e-01],\n",
+       "        [-8.97510400e-01,  9.61220196e-01, -1.04136133e+00,\n",
+       "          1.86398889e-01,  7.70143659e-01,  4.67537123e-01,\n",
+       "         -7.02437299e-01, -4.88163675e-01, -6.97337907e-01,\n",
+       "         -5.40521649e-01],\n",
+       "        [ 1.08731377e+00, -1.13182063e+00,  4.80661417e-01,\n",
+       "          1.00749703e-01, -3.29861589e-03,  4.34322695e-01,\n",
+       "         -4.41401664e-01,  1.00217087e-01, -6.83182571e-02,\n",
+       "          5.22441977e-01],\n",
+       "        [ 6.75950437e-01, -4.48878137e-01,  1.51184390e+00,\n",
+       "          1.08274433e-01,  2.53869474e-05,  6.72970755e-01,\n",
+       "         -4.35120214e-01, -7.93727814e-01,  4.51607815e-01,\n",
+       "         -6.41357683e-01],\n",
+       "        [-5.90070098e-01,  1.65401585e+00,  1.24955796e+00,\n",
+       "         -1.27789633e+00, -6.91232103e-01, -1.15134353e+00,\n",
+       "          1.31197545e+00,  8.81769529e-01,  5.75884885e-01,\n",
+       "          1.07521914e+00],\n",
+       "        [ 1.94031350e+00,  2.13298568e-01, -7.04536654e-01,\n",
+       "         -7.88909079e-01,  1.53007861e+00, -7.97437758e-01,\n",
+       "          1.36856363e+00,  7.73068118e-02,  2.31809847e-01,\n",
+       "         -6.46430656e-02],\n",
+       "        [-3.51771194e-01,  1.03392986e-01,  1.64376653e+00,\n",
+       "         -8.99541521e-01,  1.08867979e+00, -5.24664009e-01,\n",
+       "         -1.09892011e+00,  1.18881878e+00, -8.84780340e-01,\n",
+       "          3.45179451e-01],\n",
+       "        [ 1.02187525e+00,  8.01564112e-01, -3.51037986e-01,\n",
+       "          2.11031426e-01,  1.40994313e-01,  7.09967748e-01,\n",
+       "         -1.06269498e+00, -9.73849912e-01, -1.84571572e+00,\n",
+       "         -2.45978621e+00],\n",
+       "        [-7.58400248e-01,  2.03737641e+00,  1.83910268e+00,\n",
+       "          3.50225259e-01, -1.11016105e+00, -5.53083929e-02,\n",
+       "          1.04439557e+00, -3.11171069e-01, -1.10583178e+00,\n",
+       "          4.88404853e-01],\n",
+       "        [ 2.30582393e+00,  6.41402263e-01,  1.27565207e+00,\n",
+       "          1.48470354e+00, -1.06293487e+00, -4.92018540e-01,\n",
+       "         -7.22529322e-01, -2.98084261e-01,  8.12775081e-01,\n",
+       "          2.36634526e+00],\n",
+       "        [ 2.36693262e-01, -1.73127374e+00, -6.08210650e-01,\n",
+       "          3.64829398e+00, -8.11572940e-01, -5.18213130e-01,\n",
+       "         -1.42171526e+00, -2.99249399e-01, -7.85268793e-01,\n",
+       "          5.74472635e-02]]])
          • c
            (chain, draw, c1, c2)
            float64
            1.226 0.4789 ... -0.1108 -0.984
            array([[[[ 1.22649718,  0.47893663,  2.0413824 , -0.55030657],\n",
+       "         [-1.95116998,  0.01010687,  0.35432766, -1.29626763],\n",
+       "         [-0.14866476, -0.74223465,  0.75565304, -0.42292514]],\n",
+       "\n",
+       "        [[-0.44381607,  0.74929562, -1.3168288 , -0.44077197],\n",
+       "         [-0.16409627,  1.17079294, -0.82892697, -0.949321  ],\n",
+       "         [ 1.28950286,  1.87127791, -1.49458565, -0.55805979]],\n",
+       "\n",
+       "        [[-0.71525592, -0.45653018, -0.63288416, -0.07811683],\n",
+       "         [ 0.25913129,  1.06400024,  0.80976502,  0.49944962],\n",
+       "         [ 0.19796366,  0.21067087, -0.15476082,  1.03569289]],\n",
        "\n",
        "        ...,\n",
        "\n",
-       "        [[-0.28540463, -1.28584019,  0.87720325, -1.07269883],\n",
-       "         [ 0.356084  ,  0.32729327,  0.75095435,  0.6184187 ],\n",
-       "         [-1.217892  , -0.71936379, -2.04673678, -0.82102186]],\n",
+       "        [[-0.11238804, -1.03611087,  0.30879005,  0.51892315],\n",
+       "         [ 0.15313038,  0.07670505,  0.23467951, -0.05199881],\n",
+       "         [-0.04757508,  2.40812467,  1.82961157,  0.5802526 ]],\n",
        "\n",
-       "        [[-0.27846377,  0.48934981, -0.68583754, -0.3273201 ],\n",
-       "         [ 0.34772057, -1.25103354, -0.57521346, -1.16746275],\n",
-       "         [ 0.73566763, -0.65645491,  0.01928188,  1.14569043]],\n",
+       "        [[ 2.31332637, -0.43235048,  0.25731847, -0.80461147],\n",
+       "         [ 0.51664674, -1.4117265 ,  0.78968602,  0.62016636],\n",
+       "         [-0.32006963, -1.27597271, -0.25757998, -0.20061661]],\n",
        "\n",
-       "        [[ 1.38519414,  1.27254177,  0.7439506 , -0.32102153],\n",
-       "         [-2.92455703, -1.01257742, -0.57167905, -1.7494546 ],\n",
-       "         [ 1.38689731, -0.34624132, -2.76234387, -0.97510035]]]])
        • created_at :
          2020-05-22T18:37:44.177562
          arviz_version :
          0.7.0
      " + " [[-0.04375878, 0.90268547, 0.20359463, -1.09686883],\n", + " [ 0.77290891, -1.78355972, 0.90514859, 0.12455105],\n", + " [ 1.90092155, -0.11419447, -0.11078289, -0.98397723]]]])
  • created_at :
    2020-06-05T06:47:07.928390
    arviz_version :
    0.8.3
    • " ], "text/plain": [ "\n", @@ -2622,12 +2581,12 @@ " * c1 (c1) int64 0 1 2\n", " * c2 (c2) int64 0 1 2 3\n", "Data variables:\n", - " a (chain, draw) float64 0.01982 -0.9719 -1.809 ... 2.027 -1.217\n", - " b (chain, draw, b1) float64 0.1776 0.07881 -1.17 ... -1.019 0.2414\n", - " c (chain, draw, c1, c2) float64 1.02 -0.4499 ... -2.762 -0.9751\n", + " a (chain, draw) float64 -0.3753 -1.343 -0.182 ... 0.4031 0.2744 1.353\n", + " b (chain, draw, b1) float64 0.7681 1.076 0.9106 ... -0.7853 0.05745\n", + " c (chain, draw, c1, c2) float64 1.226 0.4789 2.041 ... -0.1108 -0.984\n", "Attributes:\n", - " created_at: 2020-05-22T18:37:44.177562\n", - " arviz_version: 0.7.0" + " created_at: 2020-06-05T06:47:07.928390\n", + " arviz_version: 0.8.3" ] }, "execution_count": 9, @@ -2649,7 +2608,12 @@ { "cell_type": "code", "execution_count": 10, - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2020-06-05T06:47:09.286020Z", + "start_time": "2020-06-05T06:47:07.961226Z" + } + }, "outputs": [], "source": [ "import pymc3 as pm\n", @@ -2667,7 +2631,12 @@ { "cell_type": "code", "execution_count": 11, - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2020-06-05T06:47:16.996019Z", + "start_time": "2020-06-05T06:47:09.288094Z" + } + }, "outputs": [ { "name": "stderr", @@ -2676,75 +2645,11 @@ "Auto-assigning NUTS sampler...\n", "Initializing NUTS using jitter+adapt_diag...\n", "Multiprocess sampling (2 chains in 4 jobs)\n", - "NUTS: [theta_tilde, tau, mu]\n" + "NUTS: [theta_tilde, tau, mu]\n", + "Sampling 2 chains, 0 divergences: 100%|██████████| 2000/2000 [00:00<00:00, 2065.44draws/s]\n", + "100%|██████████| 1000/1000 [00:00<00:00, 1713.18it/s]\n" ] }, - { - "data": { - "text/html": [ - "\n", - "
    \n", - " \n", - " \n", - " 100.00% [3000/3000 00:01<00:00 Sampling 2 chains, 7 divergences]\n", - "
    \n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Sampling 2 chains for 1_000 tune and 500 draw iterations (2_000 + 1_000 draws total) took 2 seconds.\n", - "There were 7 divergences after tuning. Increase `target_accept` or reparameterize.\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "
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"ExecuteTime": { + "end_time": "2020-06-05T06:48:14.965999Z", + "start_time": "2020-06-05T06:48:11.404455Z" + } + }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "INFO:cmdstanpy:compiling stan program, exe file: /Users/alex_andorra/tptm_alex/arviz/doc/notebooks/eight_school\n", + "INFO:cmdstanpy:compiling stan program, exe file: /Volumes/GIT/PycharmProjects/arviz/doc/notebooks/eight_school\n", "INFO:cmdstanpy:compiler options: stanc_options=None, cpp_options=None\n", - "INFO:cmdstanpy:compiled model file: /Users/alex_andorra/tptm_alex/arviz/doc/notebooks/eight_school\n", - "INFO:cmdstanpy:found newer exe file, not recompiling\n", - "INFO:cmdstanpy:compiled model file: /Users/alex_andorra/tptm_alex/arviz/doc/notebooks/eight_school\n", - "INFO:cmdstanpy:start chain 1\n", - "INFO:cmdstanpy:start chain 2\n", - "INFO:cmdstanpy:start chain 3\n", - "INFO:cmdstanpy:start chain 4\n", - "INFO:cmdstanpy:finish chain 2\n", - "INFO:cmdstanpy:finish chain 1\n", - "INFO:cmdstanpy:finish chain 3\n", - "INFO:cmdstanpy:finish chain 4\n" + "ERROR:cmdstanpy:file /Volumes/GIT/PycharmProjects/arviz/doc/notebooks/eight_school.stan, exception ERROR\n", + " error: PCH file uses a newer PCH format that cannot be read\n", + "1 error generated.\n", + "make: *** [/Volumes/GIT/PycharmProjects/arviz/doc/notebooks/eight_school] Error 1 \n", + "ERROR:cmdstanpy:model compilation failed\n" ] }, { - "data": { - "text/plain": [ - "Inference data with groups:\n", - "\t> posterior\n", - "\t> posterior_predictive\n", - "\t> log_likelihood\n", - "\t> sample_stats\n", - "\t> observed_data" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" + "ename": "ValueError", + "evalue": "Unable to compile Stan model file: /Volumes/GIT/PycharmProjects/arviz/doc/notebooks/eight_school.stan.", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 41\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 42\u001b[0m \u001b[0mstan_file\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"./eight_school.stan\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 43\u001b[0;31m \u001b[0mstan_model\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mCmdStanModel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstan_file\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstan_file\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 44\u001b[0m \u001b[0mstan_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 45\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/anaconda3/envs/continuous_time_mcmc/lib/python3.7/site-packages/cmdstanpy/model.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, model_name, stan_file, exe_file, compile, stanc_options, cpp_options, logger)\u001b[0m\n\u001b[1;32m 145\u001b[0m raise ValueError(\n\u001b[1;32m 146\u001b[0m 'Unable to compile Stan model file: {}.'.format(\n\u001b[0;32m--> 147\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stan_file\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 148\u001b[0m )\n\u001b[1;32m 149\u001b[0m )\n", + "\u001b[0;31mValueError\u001b[0m: Unable to compile Stan model file: /Volumes/GIT/PycharmProjects/arviz/doc/notebooks/eight_school.stan." + ] } ], "source": [ @@ -3192,8 +3108,13 @@ }, { "cell_type": "code", - "execution_count": 16, - "metadata": {}, + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2020-06-05T06:48:14.969410Z", + "start_time": "2020-06-05T06:47:05.603Z" + } + }, "outputs": [], "source": [ "# save for CmdStan example (needs CmdStanPy run)\n", @@ -3202,8 +3123,13 @@ }, { "cell_type": "code", - "execution_count": 17, - "metadata": {}, + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2020-06-05T06:48:14.970867Z", + "start_time": "2020-06-05T06:47:05.605Z" + } + }, "outputs": [], "source": [ "schools_code = \"\"\"\n", @@ -3248,8 +3174,13 @@ }, { "cell_type": "code", - "execution_count": 18, - "metadata": {}, + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2020-06-05T06:48:14.972663Z", + "start_time": "2020-06-05T06:47:05.607Z" + } + }, "outputs": [], "source": [ "eight_school_data = {\n", @@ -3261,8 +3192,13 @@ }, { "cell_type": "code", - "execution_count": 19, - "metadata": {}, + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2020-06-05T06:48:14.974211Z", + "start_time": "2020-06-05T06:47:05.609Z" + } + }, "outputs": [], "source": [ "import pystan\n", @@ -3272,8 +3208,13 @@ }, { "cell_type": "code", - "execution_count": 20, - "metadata": {}, + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2020-06-05T06:48:14.975638Z", + "start_time": "2020-06-05T06:47:05.611Z" + } + }, "outputs": [], "source": [ "# Bash shell\n", @@ -3293,41 +3234,14 @@ }, { "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "arviz.data.io_cmdstan - INFO - glob found 4 files for 'posterior':\n", - "1: sample_data/eight_school-202005222038-1.csv\n", - "2: sample_data/eight_school-202005222038-2.csv\n", - "3: sample_data/eight_school-202005222038-3.csv\n", - "4: sample_data/eight_school-202005222038-4.csv\n", - "INFO:arviz.data.io_cmdstan:glob found 4 files for 'posterior':\n", - "1: sample_data/eight_school-202005222038-1.csv\n", - "2: sample_data/eight_school-202005222038-2.csv\n", - "3: sample_data/eight_school-202005222038-3.csv\n", - "4: sample_data/eight_school-202005222038-4.csv\n" - ] - }, - { - "data": { - "text/plain": [ - "Inference data with groups:\n", - "\t> posterior\n", - "\t> posterior_predictive\n", - "\t> log_likelihood\n", - "\t> sample_stats\n", - "\t> observed_data" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2020-06-05T06:48:14.977219Z", + "start_time": "2020-06-05T06:47:05.614Z" } - ], + }, + "outputs": [], "source": [ "# Let's use .stan and .csv files created/saved by the CmdStanPy procedure\n", "\n", @@ -3370,8 +3284,13 @@ }, { "cell_type": "code", - "execution_count": 22, - "metadata": {}, + "execution_count": 16, + "metadata": { + "ExecuteTime": { + "end_time": "2020-06-05T06:48:56.834305Z", + "start_time": "2020-06-05T06:48:46.413022Z" + } + }, "outputs": [ { "data": { @@ -3386,7 +3305,7 @@ "\t> observed_data" ] }, - "execution_count": 22, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -3443,14 +3362,545 @@ ")\n", "numpyro_data" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## From PyJAGS" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Import Packages" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "ExecuteTime": { + "end_time": "2020-06-05T06:48:59.842990Z", + "start_time": "2020-06-05T06:48:59.556800Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:numexpr.utils:Note: NumExpr detected 12 cores but \"NUMEXPR_MAX_THREADS\" not set, so enforcing safe limit of 8.\n", + "INFO:numexpr.utils:NumExpr defaulting to 8 threads.\n" + ] + } + ], + "source": [ + "# import arviz as az\n", + "# import numpy as np\n", + "import pyjags\n", + "# import xarray as xr\n", + "# xr.set_options(display_style=\"html\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### JAGS Model Code" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Prior Model" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "ExecuteTime": { + "end_time": "2020-06-05T06:49:04.048517Z", + "start_time": "2020-06-05T06:49:04.045625Z" + } + }, + "outputs": [], + "source": [ + "eight_school_prior_model_code = ''' \n", + "model {\n", + " mu ~ dnorm(0.0, 1.0/25)\n", + " tau ~ dt(0.0, 1.0/25, 1.0) T(0, )\n", + " for (i in 1:J) {\n", + " theta_tilde[i] ~ dnorm(0.0, 1.0)\n", + " }\n", + "}\n", + "'''" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Posterior Model" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "ExecuteTime": { + "end_time": "2020-06-05T06:49:05.084881Z", + "start_time": "2020-06-05T06:49:05.082448Z" + } + }, + "outputs": [], + "source": [ + "eight_school_posterior_model_code = ''' \n", + "model {\n", + " mu ~ dnorm(0.0, 1.0/25)\n", + " tau ~ dt(0.0, 1.0/25, 1.0) T(0, )\n", + " for (i in 1:J) {\n", + " theta_tilde[i] ~ dnorm(0.0, 1.0)\n", + " y[i] ~ dnorm(mu + tau * theta_tilde[i], 1.0/(sigma[i]^2))\n", + " }\n", + "}\n", + "'''" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "ExecuteTime": { + "end_time": "2020-06-05T06:49:05.479290Z", + "start_time": "2020-06-05T06:49:05.476663Z" + } + }, + "outputs": [], + "source": [ + "parameters = ['mu', 'tau', 'theta_tilde']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Construct JAGS Model and Run Adaptation Steps" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Prior Model" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "ExecuteTime": { + "end_time": "2020-06-05T06:49:06.671164Z", + "start_time": "2020-06-05T06:49:06.636850Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:pyjags:Using JAGS library located in /usr/local/lib/libjags.4.dylib.\n", + "INFO:pyjags:Loading module basemod from /usr/local/lib/JAGS/modules-4/basemod.so\n", + "INFO:pyjags:Loading module bugs from /usr/local/lib/JAGS/modules-4/bugs.so\n", + "INFO:pyjags:Loading module lecuyer from /usr/local/lib/JAGS/modules-4/lecuyer.so\n" + ] + } + ], + "source": [ + "jags_prior_model = pyjags.Model(\n", + " code=eight_school_prior_model_code, \n", + " data={\"J\": 8}, \n", + " chains=4, \n", + " threads=4,\n", + " chains_per_thread=1\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Posterior Model" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "ExecuteTime": { + "end_time": "2020-06-05T06:49:07.305091Z", + "start_time": "2020-06-05T06:49:07.286068Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "adapting: iterations 4000 of 4000, elapsed 0:00:00, remaining 0:00:00\n" + ] + } + ], + "source": [ + "jags_posterior_model = pyjags.Model(\n", + " code=eight_school_posterior_model_code, \n", + " data=eight_school_data, \n", + " chains=4, \n", + " threads=4,\n", + " chains_per_thread=1\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Draw 1000 Burn-In Samples and 5000 Actual Samples per Chain" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "ExecuteTime": { + "end_time": "2020-06-05T06:49:07.978300Z", + "start_time": "2020-06-05T06:49:07.909931Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "sampling: iterations 24000 of 24000, elapsed 0:00:00, remaining 0:00:00\n", + "sampling: iterations 24000 of 24000, elapsed 0:00:00, remaining 0:00:00\n" + ] + } + ], + "source": [ + "jags_prior_samples = jags_prior_model.sample(5000 + 1000, vars=parameters)\n", + "jags_posterior_samples = jags_posterior_model.sample(5000 + 1000, vars=parameters)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Convert PyJAGS Samples Dictionary to ArviZ Inference Data Object" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "ExecuteTime": { + "end_time": "2020-06-05T06:49:08.360698Z", + "start_time": "2020-06-05T06:49:08.347208Z" + } + }, + "outputs": [], + "source": [ + "pyjags_data = az.from_pyjags(\n", + " posterior=jags_posterior_samples, \n", + " prior=jags_prior_samples, \n", + " save_warmup=True, \n", + " warmup_iterations=1000\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Trace Plot" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "ExecuteTime": { + "end_time": "2020-06-05T06:49:12.381634Z", + "start_time": "2020-06-05T06:49:08.854544Z" + }, + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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\n", 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    " + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "az.plot_trace(pyjags_data);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Display Estimation Summary" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "ExecuteTime": { + "end_time": "2020-06-05T06:49:12.556274Z", + "start_time": "2020-06-05T06:49:12.383282Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
    meansdhdi_3%hdi_97%mcse_meanmcse_sdess_meaness_sdess_bulkess_tailr_hat
    mu4.4203.338-1.74010.7200.0270.01915675.015675.015747.017914.01.0
    tau3.6143.3050.0009.3270.0530.0413961.03198.05245.04677.01.0
    theta_tilde[0]0.3110.990-1.6052.1280.0080.00616104.016104.016117.019148.01.0
    theta_tilde[1]0.1040.941-1.6781.8770.0070.00519509.018216.019495.019244.01.0
    theta_tilde[2]-0.0760.964-1.8791.7400.0070.00519621.019090.019660.019388.01.0
    theta_tilde[3]0.0610.930-1.7181.8250.0070.00518988.018988.018999.019073.01.0
    theta_tilde[4]-0.1660.930-1.9911.5590.0070.00518997.018419.018998.019344.01.0
    theta_tilde[5]-0.0680.945-1.8451.7250.0070.00518916.018916.018914.019186.01.0
    theta_tilde[6]0.3480.959-1.4092.1890.0080.00515849.015849.015898.019764.01.0
    theta_tilde[7]0.0830.985-1.8421.8850.0070.00519629.019629.019633.019391.01.0
    \n", + "
    " + ], + "text/plain": [ + " mean sd hdi_3% hdi_97% mcse_mean mcse_sd ess_mean \\\n", + "mu 4.420 3.338 -1.740 10.720 0.027 0.019 15675.0 \n", + "tau 3.614 3.305 0.000 9.327 0.053 0.041 3961.0 \n", + "theta_tilde[0] 0.311 0.990 -1.605 2.128 0.008 0.006 16104.0 \n", + "theta_tilde[1] 0.104 0.941 -1.678 1.877 0.007 0.005 19509.0 \n", + "theta_tilde[2] -0.076 0.964 -1.879 1.740 0.007 0.005 19621.0 \n", + "theta_tilde[3] 0.061 0.930 -1.718 1.825 0.007 0.005 18988.0 \n", + "theta_tilde[4] -0.166 0.930 -1.991 1.559 0.007 0.005 18997.0 \n", + "theta_tilde[5] -0.068 0.945 -1.845 1.725 0.007 0.005 18916.0 \n", + "theta_tilde[6] 0.348 0.959 -1.409 2.189 0.008 0.005 15849.0 \n", + "theta_tilde[7] 0.083 0.985 -1.842 1.885 0.007 0.005 19629.0 \n", + "\n", + " ess_sd ess_bulk ess_tail r_hat \n", + "mu 15675.0 15747.0 17914.0 1.0 \n", + "tau 3198.0 5245.0 4677.0 1.0 \n", + "theta_tilde[0] 16104.0 16117.0 19148.0 1.0 \n", + "theta_tilde[1] 18216.0 19495.0 19244.0 1.0 \n", + "theta_tilde[2] 19090.0 19660.0 19388.0 1.0 \n", + "theta_tilde[3] 18988.0 18999.0 19073.0 1.0 \n", + "theta_tilde[4] 18419.0 18998.0 19344.0 1.0 \n", + "theta_tilde[5] 18916.0 18914.0 19186.0 1.0 \n", + "theta_tilde[6] 15849.0 15898.0 19764.0 1.0 \n", + "theta_tilde[7] 19629.0 19633.0 19391.0 1.0 " + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "az.summary(pyjags_data)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "celltoolbar": "Raw Cell Format", "kernelspec": { - "display_name": "arviz-devs", + "display_name": "Python 3", "language": "python", - "name": "arviz-devs" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -3462,7 +3912,20 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.7" + "version": "3.7.6" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": {}, + "toc_section_display": true, + "toc_window_display": false } }, "nbformat": 4, diff --git a/requirements-external.txt b/requirements-external.txt index e9f72e3349..e1f481e2d2 100644 --- a/requirements-external.txt +++ b/requirements-external.txt @@ -1,4 +1,5 @@ emcee +pyjags pymc3 @ git+https://github.com/pymc-devs/pymc3 pystan cmdstanpy From 661e057d3281a119db0a5a06b6090d3b6b58de3a Mon Sep 17 00:00:00 2001 From: Piyush Gautam Date: Wed, 10 Jun 2020 00:50:53 +0530 Subject: [PATCH 06/24] html representation for jupyter notebooks (#1217) * added html representation for jupyter notebooks * black change * used xarray's default and changed formatting * pylint changes * styling changes and pydocstyle corrections * updated the cookbook notebook * updated the other notebooks * minor changes * added test hyml repr * deleted unwanted files * rerun the notebooks * pylint correction * pydocstyle correction * again pydocstyle correction * black correction * pylint correction * group_id correction and rerun the notebooks * add docstring to test * pylint solved * added more tests * minor nits * add change to changelog * minor fix --- CHANGELOG.md | 1 + arviz/data/inference_data.py | 32 +- arviz/tests/base_tests/test_data.py | 24 + arviz/utils.py | 30 + doc/notebooks/InferenceDataCookbook.ipynb | 28098 +++++++++++++++++++- doc/notebooks/Introduction.ipynb | 8156 +++++- doc/notebooks/Numba.ipynb | 70 +- doc/notebooks/XarrayforArviZ.ipynb | 2208 +- doc/notebooks/pystan_refitting.ipynb | 49 +- 9 files changed, 37789 insertions(+), 879 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index b9b8dcfd58..762a8e6a40 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -4,6 +4,7 @@ ### New features * loo-pit plot. The kde is computed over the data interval (this could be shorter than [0, 1]). The hdi is computed analitically (#1215) +* Added `html_repr` of InferenceData objects for jupyter notebooks. (#1217) * Added support for PyJAGS via the function `from_pyjags` in the module arviz.data.io_pyjags. (#1219) ### Maintenance and fixes diff --git a/arviz/data/inference_data.py b/arviz/data/inference_data.py index 4ad2b54180..bf53a9a069 100644 --- a/arviz/data/inference_data.py +++ b/arviz/data/inference_data.py @@ -3,13 +3,16 @@ from collections.abc import Sequence from copy import copy as ccopy, deepcopy from datetime import datetime +from html import escape import warnings +import uuid import netCDF4 as nc import numpy as np import xarray as xr +from xarray.core.options import OPTIONS -from ..utils import _subset_list +from ..utils import _subset_list, HtmlTemplate from ..rcparams import rcParams SUPPORTED_GROUPS = [ @@ -125,7 +128,7 @@ def __init__(self, **kwargs): self._groups_warmup.append(key) def __repr__(self): - """Make string representation of object.""" + """Make string representation of InferenceData object.""" msg = "Inference data with groups:\n\t> {options}".format( options="\n\t> ".join(self._groups) ) @@ -133,6 +136,31 @@ def __repr__(self): msg += "\n\nWarmup iterations saved ({}*).".format(WARMUP_TAG) return msg + def _repr_html_(self): + """Make html representation of InferenceData object.""" + display_style = OPTIONS["display_style"] + if display_style == "text": + html_repr = f"
    {escape(repr(self))}
    " + else: + elements = "".join( + [ + HtmlTemplate.element_template.format( + group_id=group + str(uuid.uuid4()), + group=group, + xr_data=getattr( # pylint: disable=protected-access + self, group + )._repr_html_(), + ) + for group in self._groups_all + ] + ) + formatted_html_template = HtmlTemplate.html_template.format( # pylint: disable=possibly-unused-variable + elements + ) + css_template = HtmlTemplate.css_template # pylint: disable=possibly-unused-variable + html_repr = "%(formatted_html_template)s%(css_template)s" % locals() + return html_repr + def __delattr__(self, group): """Delete a group from the InferenceData object.""" if group in self._groups: diff --git a/arviz/tests/base_tests/test_data.py b/arviz/tests/base_tests/test_data.py index dcd8b61c5b..d619eaa09c 100644 --- a/arviz/tests/base_tests/test_data.py +++ b/arviz/tests/base_tests/test_data.py @@ -4,10 +4,12 @@ import os from typing import Dict from urllib.parse import urlunsplit +from html import escape import numpy as np import pytest import xarray as xr +from xarray.core.options import OPTIONS from arviz import ( concat, @@ -456,6 +458,28 @@ def test_map(self, use): ) assert np.allclose(idata_map.posterior.mu, idata.posterior.mu) + def test_repr_html(self): + """Test if the function _repr_html is generating html.""" + idata = load_arviz_data("centered_eight") + display_style = OPTIONS["display_style"] + xr.set_options(display_style="html") + html = idata._repr_html_() # pylint: disable=protected-access + + assert html is not None + assert " +
    +
    arviz.InferenceData
    +
    +
      + {} +
    + + """ + element_template = """ +
  • + + +
    +
    +
      +
      {xr_data}
      +
    +
    +
    • + """ + specific_style = ".xr-wrap{width:700px!important;}" + css_template = f"" diff --git a/doc/notebooks/InferenceDataCookbook.ipynb b/doc/notebooks/InferenceDataCookbook.ipynb index db9ea346e3..74c2311a6f 100644 --- a/doc/notebooks/InferenceDataCookbook.ipynb +++ b/doc/notebooks/InferenceDataCookbook.ipynb @@ -84,6 +84,8 @@ "outputs": [ { "data": { +<<<<<<< HEAD +======= "text/plain": [ "Inference data with groups:\n", "\t> posterior" @@ -112,8 +114,22 @@ "outputs": [ { "data": { +>>>>>>> master "text/html": [ - "
    \n", + "\n", + "
    \n", + "
    \n", + "
    arviz.InferenceData
    \n", + "
    \n", + "
      \n", + " \n", + "
    • \n", + " \n", + " \n", + "
      \n", + "
      \n", + "
        \n", + "
        \n", "\n", "\n", "Show/Hide data repr\n", @@ -447,11 +463,44 @@ " stroke: currentColor;\n", " fill: currentColor;\n", "}\n", +<<<<<<< HEAD + "
        xarray.Dataset
          • chain: 1
          • draw: 100
          • chain
            (chain)
            int64
            0
            array([0])
          • draw
            (draw)
            int64
            0 1 2 3 4 5 6 ... 94 95 96 97 98 99
            array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
+=======
        "
            xarray.Dataset
              • chain: 1
              • draw: 100
              • chain
                (chain)
                int64
                0
                array([0])
              • draw
                (draw)
                int64
                0 1 2 3 4 5 6 ... 94 95 96 97 98 99
                array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
+>>>>>>> master
        "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
        "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
        "       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,\n",
        "       72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89,\n",
+<<<<<<< HEAD
+       "       90, 91, 92, 93, 94, 95, 96, 97, 98, 99])
              • x
                (chain, draw)
                float64
                0.9861 -1.73 ... 1.181 -0.6773
                array([[ 0.98605975, -1.73031229, -0.3438436 , -1.2854882 ,  2.15283021,\n",
+       "         0.7697191 ,  1.20059217,  0.56791817,  0.24699331,  1.45072424,\n",
+       "         0.56309706,  0.4916484 ,  1.11851184,  0.60836583,  0.53459243,\n",
+       "        -0.24027367, -0.08113917,  1.09965117, -0.5250582 , -0.14018375,\n",
+       "         0.00877254,  0.29289487,  0.53529345,  0.5686672 , -0.50405256,\n",
+       "         1.21049436,  1.92970939,  0.6451438 , -1.90297465, -2.20664022,\n",
+       "         0.94825718,  0.65380712,  0.14873348,  2.2369029 , -0.00634083,\n",
+       "         0.47998524,  0.66608171,  0.84612559, -0.87434128, -1.19747381,\n",
+       "        -0.13200142, -0.17078229,  0.21824332, -0.62744278, -1.25989103,\n",
+       "        -0.71730994,  0.13367653,  1.8664674 ,  0.48494957,  0.44275805,\n",
+       "         2.3221074 ,  0.44674722, -0.25826249, -0.89009015, -0.26787942,\n",
+       "        -0.8918638 ,  1.17021567, -0.45076478, -0.46388847,  0.74750357,\n",
+       "         0.49044061,  1.37903498,  0.15152647, -1.72086452,  2.37496625,\n",
+       "        -0.87244294,  1.80472576, -0.10907982,  0.02654398, -0.47168779,\n",
+       "        -0.11076243,  0.50385984, -0.54184399,  1.51314286,  1.12160445,\n",
+       "         0.56574961,  0.86476562,  1.09279521, -0.38281333, -0.17650365,\n",
+       "        -1.44046758,  0.34415934,  1.42436337, -1.30452653, -1.29635651,\n",
+       "        -1.12254422, -0.28325058,  0.76951173, -0.28846291, -1.21583151,\n",
+       "        -0.71471947, -0.19685318,  0.37733054,  0.60330507,  1.59981284,\n",
+       "         0.2089567 , -1.16980006,  0.26503182,  1.18126789, -0.67725935]])
            • created_at :
              2020-06-06T18:07:29.075877
              arviz_version :
              0.8.3

        \n", + "
      \n", + "
      \n", + "
      • \n", + " \n", + "
    \n", + "
    \n", + " " + ], + "text/plain": [ +======= "
    xarray.Dataset
      • chain: 1
      • draw: 2
      • x_dim_0: 3
      • x_dim_1: 4
      • x_dim_2: 5
      • chain
        (chain)
        int64
        0
        array([0])
      • draw
        (draw)
        int64
        0 1
        array([0, 1])
      • x_dim_0
        (x_dim_0)
        int64
        0 1 2
        array([0, 1, 2])
      • x_dim_1
        (x_dim_1)
        int64
        0 1 2 3
        array([0, 1, 2, 3])
      • x_dim_2
        (x_dim_2)
        int64
        0 1 2 3 4
        array([0, 1, 2, 3, 4])
      • x
        (chain, draw, x_dim_0, x_dim_1, x_dim_2)
        float64
        -2.531 0.02934 ... 0.3143 1.476
        array([[[[[-2.53141338,  0.0293362 , -0.31797349, -0.38416052,\n",
        "            0.65699797],\n",
        "          [ 0.73642501, -0.33947601,  1.25816597, -0.54142323,\n",
@@ -977,27 +1032,28 @@
     {
      "data": {
       "text/plain": [
+>>>>>>> master
        "Inference data with groups:\n",
        "\t> posterior"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "datadict = {\n",
-    "    \"a\": np.random.randn(100),\n",
-    "    \"b\": np.random.randn(1, 100, 10),\n",
-    "    \"c\": np.random.randn(1, 100, 3, 4),\n",
-    "}\n",
-    "dataset = az.convert_to_inference_data(datadict)\n",
+    "size = 100\n",
+    "dataset = az.convert_to_inference_data(np.random.randn(size))\n",
     "dataset"
    ]
   },
   {
    "cell_type": "code",
+<<<<<<< HEAD
+   "execution_count": 3,
+   "metadata": {},
+=======
    "execution_count": 7,
    "metadata": {
     "ExecuteTime": {
@@ -1005,6 +1061,7 @@
      "start_time": "2020-06-05T06:47:07.891906Z"
     }
    },
+>>>>>>> master
    "outputs": [
     {
      "data": {
@@ -1343,11 +1400,37 @@
        "  stroke: currentColor;\n",
        "  fill: currentColor;\n",
        "}\n",
+<<<<<<< HEAD
+       "
        xarray.Dataset
          • chain: 1
          • draw: 100
          • chain
            (chain)
            int64
            0
            array([0])
          • draw
            (draw)
            int64
            0 1 2 3 4 5 6 ... 94 95 96 97 98 99
            array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
+=======
        "
            xarray.Dataset
              • b_dim_0: 10
              • c_dim_0: 3
              • c_dim_1: 4
              • chain: 1
              • draw: 100
              • chain
                (chain)
                int64
                0
                array([0])
              • draw
                (draw)
                int64
                0 1 2 3 4 5 6 ... 94 95 96 97 98 99
                array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
+>>>>>>> master
        "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
        "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
        "       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,\n",
        "       72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89,\n",
+<<<<<<< HEAD
+       "       90, 91, 92, 93, 94, 95, 96, 97, 98, 99])
              • x
                (chain, draw)
                float64
                0.9861 -1.73 ... 1.181 -0.6773
                array([[ 0.98605975, -1.73031229, -0.3438436 , -1.2854882 ,  2.15283021,\n",
+       "         0.7697191 ,  1.20059217,  0.56791817,  0.24699331,  1.45072424,\n",
+       "         0.56309706,  0.4916484 ,  1.11851184,  0.60836583,  0.53459243,\n",
+       "        -0.24027367, -0.08113917,  1.09965117, -0.5250582 , -0.14018375,\n",
+       "         0.00877254,  0.29289487,  0.53529345,  0.5686672 , -0.50405256,\n",
+       "         1.21049436,  1.92970939,  0.6451438 , -1.90297465, -2.20664022,\n",
+       "         0.94825718,  0.65380712,  0.14873348,  2.2369029 , -0.00634083,\n",
+       "         0.47998524,  0.66608171,  0.84612559, -0.87434128, -1.19747381,\n",
+       "        -0.13200142, -0.17078229,  0.21824332, -0.62744278, -1.25989103,\n",
+       "        -0.71730994,  0.13367653,  1.8664674 ,  0.48494957,  0.44275805,\n",
+       "         2.3221074 ,  0.44674722, -0.25826249, -0.89009015, -0.26787942,\n",
+       "        -0.8918638 ,  1.17021567, -0.45076478, -0.46388847,  0.74750357,\n",
+       "         0.49044061,  1.37903498,  0.15152647, -1.72086452,  2.37496625,\n",
+       "        -0.87244294,  1.80472576, -0.10907982,  0.02654398, -0.47168779,\n",
+       "        -0.11076243,  0.50385984, -0.54184399,  1.51314286,  1.12160445,\n",
+       "         0.56574961,  0.86476562,  1.09279521, -0.38281333, -0.17650365,\n",
+       "        -1.44046758,  0.34415934,  1.42436337, -1.30452653, -1.29635651,\n",
+       "        -1.12254422, -0.28325058,  0.76951173, -0.28846291, -1.21583151,\n",
+       "        -0.71471947, -0.19685318,  0.37733054,  0.60330507,  1.59981284,\n",
+       "         0.2089567 , -1.16980006,  0.26503182,  1.18126789, -0.67725935]])
            • created_at :
              2020-06-06T18:07:29.075877
              arviz_version :
              0.8.3
          "
+=======
        "       90, 91, 92, 93, 94, 95, 96, 97, 98, 99])
      • b_dim_0
        (b_dim_0)
        int64
        0 1 2 3 4 5 6 7 8 9
        array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
      • c_dim_0
        (c_dim_0)
        int64
        0 1 2
        array([0, 1, 2])
      • c_dim_1
        (c_dim_1)
        int64
        0 1 2 3
        array([0, 1, 2, 3])
      • a
        (chain, draw)
        float64
        0.4088 1.503 ... 2.206 1.027
        array([[ 0.4087575 ,  1.50284003, -0.32110297, -0.5081788 , -0.40220769,\n",
        "        -0.87170109,  0.58234101, -1.2532232 , -1.27802523,  0.39957152,\n",
        "        -0.49446914,  0.08184825, -1.09241227, -0.11821264, -0.71780162,\n",
@@ -1691,26 +1774,30 @@
        "        [[-0.76008122, -1.07032246,  0.74947097,  1.2425853 ],\n",
        "         [ 0.22337831,  1.45601438,  0.3239558 , -1.73507268],\n",
        "         [ 0.68339758,  0.27008985, -0.61782048,  0.34003194]]]])
    • created_at :
      2020-06-05T06:47:07.886685
      arviz_version :
      0.8.3
    " +>>>>>>> master ], "text/plain": [ "\n", - "Dimensions: (b_dim_0: 10, c_dim_0: 3, c_dim_1: 4, chain: 1, draw: 100)\n", + "Dimensions: (chain: 1, draw: 100)\n", "Coordinates:\n", " * chain (chain) int64 0\n", " * draw (draw) int64 0 1 2 3 4 5 6 7 8 9 ... 90 91 92 93 94 95 96 97 98 99\n", - " * b_dim_0 (b_dim_0) int64 0 1 2 3 4 5 6 7 8 9\n", - " * c_dim_0 (c_dim_0) int64 0 1 2\n", - " * c_dim_1 (c_dim_1) int64 0 1 2 3\n", "Data variables:\n", +<<<<<<< HEAD + " x (chain, draw) float64 0.9861 -1.73 -0.3438 ... 0.265 1.181 -0.6773\n", + "Attributes:\n", + " created_at: 2020-06-06T18:07:29.075877\n", +======= " a (chain, draw) float64 0.4088 1.503 -0.3211 ... -1.467 2.206 1.027\n", " b (chain, draw, b_dim_0) float64 1.476 -1.619 ... -0.234 -0.4691\n", " c (chain, draw, c_dim_0, c_dim_1) float64 -0.703 -0.4777 ... 0.34\n", "Attributes:\n", " created_at: 2020-06-05T06:47:07.886685\n", +>>>>>>> master " arviz_version: 0.8.3" ] }, - "execution_count": 7, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -1723,6 +1810,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ +<<<<<<< HEAD + "## From nd numpy array\n" +======= "## From dictionary with coords and dims" ] }, @@ -1759,10 +1849,15 @@ "\n", "dataset = az.convert_to_inference_data(datadict, coords=coords, dims=dims)\n", "dataset" +>>>>>>> master ] }, { "cell_type": "code", +<<<<<<< HEAD + "execution_count": 4, + "metadata": {}, +======= "execution_count": 9, "metadata": { "ExecuteTime": { @@ -1770,11 +1865,25 @@ "start_time": "2020-06-05T06:47:07.934319Z" } }, +>>>>>>> master "outputs": [ { "data": { "text/html": [ - "
    \n", + "\n", + "
    \n", + "
    \n", + "
    arviz.InferenceData
    \n", + "
    \n", + "
      \n", + " \n", + "
    • \n", + " \n", + " \n", + "
      \n", + "
      \n", + "
        \n", + "
        \n", "\n", "\n", "Show/Hide data repr\n", @@ -2108,550 +2217,17547 @@ " stroke: currentColor;\n", " fill: currentColor;\n", "}\n", - "
        xarray.Dataset
          • b1: 10
          • c1: 3
          • c2: 4
          • chain: 1
          • draw: 100
          • chain
            (chain)
            int64
            0
            array([0])
          • draw
            (draw)
            int64
            0 1 2 3 4 5 6 ... 94 95 96 97 98 99
            array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
-       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
-       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
-       "       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,\n",
-       "       72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89,\n",
-       "       90, 91, 92, 93, 94, 95, 96, 97, 98, 99])
          • b1
            (b1)
            int64
            0 1 2 3 4 5 6 7 8 9
            array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
          • c1
            (c1)
            int64
            0 1 2
            array([0, 1, 2])
          • c2
            (c2)
            int64
            0 1 2 3
            array([0, 1, 2, 3])
          • a
            (chain, draw)
            float64
            -0.3753 -1.343 ... 0.2744 1.353
            array([[-3.75346014e-01, -1.34279885e+00, -1.81966145e-01,\n",
-       "        -3.56027148e-01, -4.91391126e-01, -1.77717530e-01,\n",
-       "        -1.06402332e+00, -1.18231990e+00,  1.72532825e+00,\n",
-       "         5.24281952e-01,  5.77555211e-01,  2.91337464e-01,\n",
-       "        -8.37998249e-01,  8.48762904e-02, -1.09796865e+00,\n",
-       "        -9.81763196e-01,  7.28671071e-01, -1.81745148e+00,\n",
-       "         8.09745894e-01, -9.40200839e-01,  7.31322483e-01,\n",
-       "         2.94313910e-01, -6.72534458e-02, -6.87359517e-01,\n",
-       "         1.27262226e+00,  9.40409456e-01,  4.46302483e-01,\n",
-       "         1.38721683e-01, -3.91378150e-01,  2.97032305e-01,\n",
-       "        -6.08816792e-01,  1.14561872e+00,  9.02347668e-01,\n",
-       "         1.18282099e+00,  1.11689919e-01, -1.03027132e+00,\n",
-       "         9.73148302e-01, -1.20490575e-03,  1.45153366e-01,\n",
-       "        -7.06944130e-01,  1.29471707e+00, -8.59920407e-01,\n",
-       "         1.57687082e-01,  6.93504104e-01,  5.53264762e-01,\n",
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-       "         1.22886080e+00, -5.10891653e-01, -8.18504801e-01,\n",
-       "         9.92668370e-01, -8.16829237e-01, -1.14691651e-02,\n",
-       "         3.59445865e-01,  1.21180953e+00, -2.11572313e-01,\n",
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-       "        -2.11630403e-01,  4.35409621e-01, -1.20065321e+00,\n",
-       "         5.91633821e-01, -1.83674365e+00, -1.47862654e+00,\n",
-       "         1.41080588e+00,  1.11046576e+00, -3.63604683e-01,\n",
-       "         7.09826828e-01, -1.24470396e+00,  4.12014472e-01,\n",
-       "        -7.56505493e-01,  8.47744142e-01,  4.50065923e-01,\n",
-       "        -1.67920301e-01, -8.58652124e-01, -7.08302508e-01,\n",
-       "        -8.22896186e-02,  1.08820668e-01,  2.39990899e+00,\n",
-       "        -9.64984269e-01,  1.98025631e+00, -5.63937986e-01,\n",
-       "         4.79969662e-01, -1.35120678e-01, -2.05635299e+00,\n",
-       "        -8.10160991e-02,  8.54539325e-02, -7.29010226e-01,\n",
-       "         9.89029011e-02,  4.03121712e-01,  2.74395661e-01,\n",
-       "         1.35305973e+00]])
          • b
            (chain, draw, b1)
            float64
            0.7681 1.076 ... -0.7853 0.05745
            array([[[ 7.68136098e-01,  1.07561765e+00,  9.10586329e-01,\n",
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-       "          3.92373217e-01, -3.75640224e-01, -1.94163633e+00,\n",
-       "          3.74350597e-01],\n",
-       "        [ 5.69076079e-01, -5.57230047e-02, -2.42731868e+00,\n",
-       "          1.72719231e-01, -1.04277979e+00, -2.39913671e+00,\n",
-       "         -2.19797709e-01, -7.21600779e-01,  2.71613355e+00,\n",
-       "          5.58153306e-01],\n",
-       "        [ 1.40359134e-01, -2.07505739e+00,  2.44758630e+00,\n",
-       "         -1.33072224e+00, -2.79030741e-01,  1.16279627e+00,\n",
-       "         -5.14899836e-03,  5.28927847e-01, -8.88655403e-01,\n",
-       "         -8.37791983e-01],\n",
-       "        [ 1.07464038e+00, -9.84440089e-01, -4.39809375e-01,\n",
-       "          9.41709724e-01,  1.00624537e+00, -1.57890650e-01,\n",
-       "          1.16553486e+00,  3.07942196e-01, -1.10206858e+00,\n",
-       "         -5.15514901e-01],\n",
-       "        [-6.48613253e-01, -8.53594888e-01, -1.59185984e-01,\n",
-       "          3.50664933e-01, -1.05422229e+00,  3.79168150e-02,\n",
-       "          4.92574361e-01,  6.57106753e-01, -4.30607697e-01,\n",
-       "         -1.14009230e+00],\n",
-       "        [ 5.95914967e-01, -1.11158589e-01, -1.67480208e+00,\n",
-       "          6.72059856e-01,  1.85113208e-01, -1.05265893e+00,\n",
-       "          1.11688161e+00, -6.91981288e-01,  1.42654251e+00,\n",
-       "         -3.32065578e-01],\n",
-       "        [-5.75919886e-01, -1.28962628e+00,  7.27974128e-01,\n",
-       "          1.67930127e+00,  1.43216941e+00,  7.64068864e-01,\n",
-       "         -1.76969651e-01,  7.51692510e-01, -1.76763633e+00,\n",
-       "         -1.13014402e-01],\n",
-       "        [ 7.16019100e-01,  2.91425330e-01,  5.17573139e-01,\n",
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-       "          4.13504258e-01],\n",
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-       "          1.00806030e-01],\n",
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-       "          1.29120281e+00, -6.78971114e-01, -8.35262296e-01,\n",
-       "          5.65649350e-01],\n",
-       "        [-3.44249274e-01, -4.22369533e-01,  7.83459061e-01,\n",
-       "          8.89774081e-04,  3.11037248e-01,  1.34980216e+00,\n",
-       "          3.06052324e-01, -3.21098480e-01, -5.70835363e-01,\n",
-       "          2.21334061e-01],\n",
-       "        [-1.70488321e-01, -3.29323021e-01, -1.29960096e+00,\n",
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-       "         -1.69681488e+00,  9.85868996e-01, -3.41153538e-01,\n",
-       "          1.35007239e-02],\n",
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-       "          3.22927768e-01,  8.00120287e-02, -7.81225106e-01,\n",
-       "          4.79028167e-01, -5.74317725e-01, -9.21461384e-02,\n",
-       "          2.17733422e+00],\n",
-       "        [ 1.11365533e+00, -1.20376477e+00,  1.88441546e-01,\n",
-       "          6.96615817e-01, -1.70984662e+00,  4.72098121e-01,\n",
-       "          6.43126919e-01, -3.65735489e-02,  1.11218349e+00,\n",
-       "          5.86920344e-01],\n",
-       "        [ 6.15389122e-01,  1.03039966e+00,  1.12458617e+00,\n",
-       "          9.00623262e-01, -5.01107093e-01,  4.96895953e-01,\n",
-       "          1.63461296e-01,  9.78801047e-01,  6.60324198e-02,\n",
-       "          4.64788088e-01],\n",
-       "        [ 5.11928035e-01, -3.60092970e-01,  1.88257804e+00,\n",
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-       "         -1.77377633e+00,  1.41624759e+00,  8.45724969e-01,\n",
-       "         -1.79599145e+00],\n",
-       "        [-1.44373864e+00, -8.02041381e-01,  5.87918622e-01,\n",
-       "          1.03562862e+00, -6.70812295e-01,  1.12582313e+00,\n",
-       "         -1.43828200e+00, -4.98170782e-01,  1.15316411e+00,\n",
-       "         -6.39421099e-01],\n",
-       "        [ 2.01474701e-01,  6.55774434e-01,  2.06062809e+00,\n",
-       "         -1.26293888e-01, -7.64049874e-01,  1.43503101e+00,\n",
-       "         -1.10854814e-01,  7.79795885e-01, -2.66797538e-01,\n",
-       "         -4.88411970e-01],\n",
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-       "         -6.17343986e-01, -3.05023188e-01,  6.36418557e-01,\n",
-       "          4.14258776e-02],\n",
-       "        [-8.55579889e-01,  6.80456691e-01,  1.75000600e-01,\n",
-       "         -2.49803645e+00,  5.23769683e-01,  8.75899639e-01,\n",
-       "         -2.19618627e-01,  5.85126779e-01, -6.59788209e-01,\n",
-       "          7.00126748e-02],\n",
-       "        [ 1.20724413e+00, -2.53278218e-01,  1.82195992e-01,\n",
-       "          6.39024434e-01,  6.32527222e-02,  2.83268564e-01,\n",
-       "         -1.84600977e-01, -1.01947500e+00,  1.12476191e+00,\n",
-       "          2.41964122e-02],\n",
-       "        [-3.32231672e-01,  2.81288927e-01, -8.35379862e-01,\n",
-       "          2.09179948e-01,  7.69408139e-01,  3.75007048e-01,\n",
-       "         -2.54286781e+00,  1.35103707e-01,  6.57572419e-01,\n",
-       "          3.57452309e-01],\n",
-       "        [-3.82472812e+00, -5.28736649e-01, -2.92992932e-01,\n",
-       "         -1.02755806e+00, -1.66254721e-01,  2.32914923e-01,\n",
-       "         -9.36666506e-01,  2.00301799e-01,  6.60730372e-02,\n",
-       "          2.24850800e+00],\n",
-       "        [-1.51672375e+00, -1.92884534e+00, -9.37706918e-01,\n",
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-       "          1.61132195e+00],\n",
-       "        [-1.83702169e+00,  5.15440697e-01,  1.36624937e+00,\n",
-       "          6.80366375e-01, -1.03124790e-01,  1.25449247e+00,\n",
-       "          2.12373577e-01, -1.58864966e+00,  4.33120342e-01,\n",
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-       "        [-1.33568555e+00, -3.48191181e-01,  2.40334397e-01,\n",
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-       "          3.53408673e-01],\n",
-       "        [ 1.40516589e+00,  2.20098629e-01,  1.45140487e-01,\n",
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-       "          6.96700714e-01],\n",
-       "        [ 4.15145938e-01,  1.46605880e+00,  1.33786480e+00,\n",
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-       "        [ 1.94031350e+00,  2.13298568e-01, -7.04536654e-01,\n",
-       "         -7.88909079e-01,  1.53007861e+00, -7.97437758e-01,\n",
-       "          1.36856363e+00,  7.73068118e-02,  2.31809847e-01,\n",
-       "         -6.46430656e-02],\n",
-       "        [-3.51771194e-01,  1.03392986e-01,  1.64376653e+00,\n",
-       "         -8.99541521e-01,  1.08867979e+00, -5.24664009e-01,\n",
-       "         -1.09892011e+00,  1.18881878e+00, -8.84780340e-01,\n",
-       "          3.45179451e-01],\n",
-       "        [ 1.02187525e+00,  8.01564112e-01, -3.51037986e-01,\n",
-       "          2.11031426e-01,  1.40994313e-01,  7.09967748e-01,\n",
-       "         -1.06269498e+00, -9.73849912e-01, -1.84571572e+00,\n",
-       "         -2.45978621e+00],\n",
-       "        [-7.58400248e-01,  2.03737641e+00,  1.83910268e+00,\n",
-       "          3.50225259e-01, -1.11016105e+00, -5.53083929e-02,\n",
-       "          1.04439557e+00, -3.11171069e-01, -1.10583178e+00,\n",
-       "          4.88404853e-01],\n",
-       "        [ 2.30582393e+00,  6.41402263e-01,  1.27565207e+00,\n",
-       "          1.48470354e+00, -1.06293487e+00, -4.92018540e-01,\n",
-       "         -7.22529322e-01, -2.98084261e-01,  8.12775081e-01,\n",
-       "          2.36634526e+00],\n",
-       "        [ 2.36693262e-01, -1.73127374e+00, -6.08210650e-01,\n",
-       "          3.64829398e+00, -8.11572940e-01, -5.18213130e-01,\n",
-       "         -1.42171526e+00, -2.99249399e-01, -7.85268793e-01,\n",
-       "          5.74472635e-02]]])
          • c
            (chain, draw, c1, c2)
            float64
            1.226 0.4789 ... -0.1108 -0.984
            array([[[[ 1.22649718,  0.47893663,  2.0413824 , -0.55030657],\n",
-       "         [-1.95116998,  0.01010687,  0.35432766, -1.29626763],\n",
-       "         [-0.14866476, -0.74223465,  0.75565304, -0.42292514]],\n",
+<<<<<<< HEAD
+       "
            xarray.Dataset
              • chain: 1
              • draw: 2
              • x_dim_0: 3
              • x_dim_1: 4
              • x_dim_2: 5
              • chain
                (chain)
                int64
                0
                array([0])
              • draw
                (draw)
                int64
                0 1
                array([0, 1])
              • x_dim_0
                (x_dim_0)
                int64
                0 1 2
                array([0, 1, 2])
              • x_dim_1
                (x_dim_1)
                int64
                0 1 2 3
                array([0, 1, 2, 3])
              • x_dim_2
                (x_dim_2)
                int64
                0 1 2 3 4
                array([0, 1, 2, 3, 4])
              • x
                (chain, draw, x_dim_0, x_dim_1, x_dim_2)
                float64
                0.4887 0.1041 ... 1.812 -0.1801
                array([[[[[ 0.48871812,  0.104059  ,  2.52839358,  0.34683312,\n",
+       "           -1.64163885],\n",
+       "          [ 0.13362705, -0.37274136,  1.57671058, -1.40591077,\n",
+       "           -0.61797283],\n",
+       "          [-2.21051946, -0.63569656,  0.35856112,  1.19773328,\n",
+       "           -0.78210076],\n",
+       "          [ 0.38410654,  1.21098546, -1.05257952, -1.13003428,\n",
+       "           -0.32335117]],\n",
        "\n",
-       "        [[-0.44381607,  0.74929562, -1.3168288 , -0.44077197],\n",
-       "         [-0.16409627,  1.17079294, -0.82892697, -0.949321  ],\n",
-       "         [ 1.28950286,  1.87127791, -1.49458565, -0.55805979]],\n",
+       "         [[ 0.18496352,  0.98486652,  0.21531806, -0.33013583,\n",
+       "           -0.65043716],\n",
+       "          [ 0.12486439, -0.23435485, -0.89305091,  0.52130207,\n",
+       "            1.2360257 ],\n",
+       "          [-0.01976296, -0.69663875, -0.22386098,  0.72681712,\n",
+       "           -0.29705774],\n",
+       "          [ 0.33742164, -0.12607612, -0.86499256, -0.50236806,\n",
+       "           -0.42588285]],\n",
        "\n",
-       "        [[-0.71525592, -0.45653018, -0.63288416, -0.07811683],\n",
-       "         [ 0.25913129,  1.06400024,  0.80976502,  0.49944962],\n",
-       "         [ 0.19796366,  0.21067087, -0.15476082,  1.03569289]],\n",
+       "         [[-0.32033675, -0.51853466, -0.49995089,  0.74906308,\n",
+       "           -1.24181623],\n",
+       "          [ 1.00986665, -0.76900377, -0.7368668 , -0.90870827,\n",
+       "            2.07551017],\n",
+       "          [ 0.07275855,  0.13140076,  0.42146223, -0.37905082,\n",
+       "           -0.87708245],\n",
+       "          [ 0.07107187, -0.3310002 , -0.47052444, -2.21177506,\n",
+       "            0.50585727]]],\n",
        "\n",
-       "        ...,\n",
        "\n",
-       "        [[-0.11238804, -1.03611087,  0.30879005,  0.51892315],\n",
-       "         [ 0.15313038,  0.07670505,  0.23467951, -0.05199881],\n",
-       "         [-0.04757508,  2.40812467,  1.82961157,  0.5802526 ]],\n",
+       "        [[[ 0.74804504,  0.5077915 , -1.02414587,  0.17942489,\n",
+       "           -0.95321125],\n",
+       "          [-1.1681952 ,  0.2595123 ,  0.55414018, -0.07742311,\n",
+       "            0.23504554],\n",
+       "          [-1.67093376,  3.05178503,  1.13881084,  1.52687156,\n",
+       "            0.39957633],\n",
+       "          [-0.77348054,  0.75746532, -0.18462232, -1.55404678,\n",
+       "            0.43196616]],\n",
        "\n",
-       "        [[ 2.31332637, -0.43235048,  0.25731847, -0.80461147],\n",
-       "         [ 0.51664674, -1.4117265 ,  0.78968602,  0.62016636],\n",
-       "         [-0.32006963, -1.27597271, -0.25757998, -0.20061661]],\n",
+       "         [[ 0.72685196,  0.55040329,  2.98781832,  0.50645443,\n",
+       "           -1.59038574],\n",
+       "          [ 0.63023112,  1.06417812, -0.0097851 ,  0.73082959,\n",
+       "            0.1795614 ],\n",
+       "          [ 1.13175455, -0.22850091, -0.35148136,  1.41284534,\n",
+       "            1.14989438],\n",
+       "          [ 1.15694763,  0.25082223,  0.63699894,  0.94669835,\n",
+       "            2.04569458]],\n",
        "\n",
-       "        [[-0.04375878,  0.90268547,  0.20359463, -1.09686883],\n",
-       "         [ 0.77290891, -1.78355972,  0.90514859,  0.12455105],\n",
-       "         [ 1.90092155, -0.11419447, -0.11078289, -0.98397723]]]])
            • created_at :
              2020-06-05T06:47:07.928390
              arviz_version :
              0.8.3
          " - ], - "text/plain": [ - "\n", - "Dimensions: (b1: 10, c1: 3, c2: 4, chain: 1, draw: 100)\n", - "Coordinates:\n", - " * chain (chain) int64 0\n", - " * draw (draw) int64 0 1 2 3 4 5 6 7 8 9 ... 90 91 92 93 94 95 96 97 98 99\n", - " * b1 (b1) int64 0 1 2 3 4 5 6 7 8 9\n", - " * c1 (c1) int64 0 1 2\n", - " * c2 (c2) int64 0 1 2 3\n", - "Data variables:\n", - " a (chain, draw) float64 -0.3753 -1.343 -0.182 ... 0.4031 0.2744 1.353\n", - " b (chain, draw, b1) float64 0.7681 1.076 0.9106 ... -0.7853 0.05745\n", - " c (chain, draw, c1, c2) float64 1.226 0.4789 2.041 ... -0.1108 -0.984\n", - "Attributes:\n", - " created_at: 2020-06-05T06:47:07.928390\n", - " arviz_version: 0.8.3" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dataset.posterior" - ] + " [[ 0.71797671, 0.01496838, 2.60191956, -0.19663734,\n", + " 0.96659497],\n", + " [ 0.71500337, 0.57580205, 1.45620172, -2.07584805,\n", + " 1.05644411],\n", + " [ 0.62634854, 0.4360448 , -0.30319726, -0.61445416,\n", + " -0.43261106],\n", + " [ 0.17185218, -0.86328598, 0.03175343, 1.81219514,\n", + " -0.18008911]]]]])
      • created_at :
        2020-06-06T18:07:29.148885
        arviz_version :
        0.8.3

    \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " " + ], + "text/plain": [ + "Inference data with groups:\n", + "\t> posterior" + ] + }, + "execution_count": 4, +======= + "
    xarray.Dataset
      • b1: 10
      • c1: 3
      • c2: 4
      • chain: 1
      • draw: 100
      • chain
        (chain)
        int64
        0
        array([0])
      • draw
        (draw)
        int64
        0 1 2 3 4 5 6 ... 94 95 96 97 98 99
        array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
+       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
+       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
+       "       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,\n",
+       "       72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89,\n",
+       "       90, 91, 92, 93, 94, 95, 96, 97, 98, 99])
      • b1
        (b1)
        int64
        0 1 2 3 4 5 6 7 8 9
        array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
      • c1
        (c1)
        int64
        0 1 2
        array([0, 1, 2])
      • c2
        (c2)
        int64
        0 1 2 3
        array([0, 1, 2, 3])
      • a
        (chain, draw)
        float64
        -0.3753 -1.343 ... 0.2744 1.353
        array([[-3.75346014e-01, -1.34279885e+00, -1.81966145e-01,\n",
+       "        -3.56027148e-01, -4.91391126e-01, -1.77717530e-01,\n",
+       "        -1.06402332e+00, -1.18231990e+00,  1.72532825e+00,\n",
+       "         5.24281952e-01,  5.77555211e-01,  2.91337464e-01,\n",
+       "        -8.37998249e-01,  8.48762904e-02, -1.09796865e+00,\n",
+       "        -9.81763196e-01,  7.28671071e-01, -1.81745148e+00,\n",
+       "         8.09745894e-01, -9.40200839e-01,  7.31322483e-01,\n",
+       "         2.94313910e-01, -6.72534458e-02, -6.87359517e-01,\n",
+       "         1.27262226e+00,  9.40409456e-01,  4.46302483e-01,\n",
+       "         1.38721683e-01, -3.91378150e-01,  2.97032305e-01,\n",
+       "        -6.08816792e-01,  1.14561872e+00,  9.02347668e-01,\n",
+       "         1.18282099e+00,  1.11689919e-01, -1.03027132e+00,\n",
+       "         9.73148302e-01, -1.20490575e-03,  1.45153366e-01,\n",
+       "        -7.06944130e-01,  1.29471707e+00, -8.59920407e-01,\n",
+       "         1.57687082e-01,  6.93504104e-01,  5.53264762e-01,\n",
+       "        -1.09333662e+00,  2.11740967e-01, -2.76497632e-01,\n",
+       "         1.22886080e+00, -5.10891653e-01, -8.18504801e-01,\n",
+       "         9.92668370e-01, -8.16829237e-01, -1.14691651e-02,\n",
+       "         3.59445865e-01,  1.21180953e+00, -2.11572313e-01,\n",
+       "        -5.48715078e-02, -4.57762612e-01, -2.17555942e-01,\n",
+       "         1.66085921e-02,  2.25531279e-01,  4.36644816e-01,\n",
+       "        -1.43999288e-01,  2.49844987e-01,  7.38767224e-01,\n",
+       "        -2.11630403e-01,  4.35409621e-01, -1.20065321e+00,\n",
+       "         5.91633821e-01, -1.83674365e+00, -1.47862654e+00,\n",
+       "         1.41080588e+00,  1.11046576e+00, -3.63604683e-01,\n",
+       "         7.09826828e-01, -1.24470396e+00,  4.12014472e-01,\n",
+       "        -7.56505493e-01,  8.47744142e-01,  4.50065923e-01,\n",
+       "        -1.67920301e-01, -8.58652124e-01, -7.08302508e-01,\n",
+       "        -8.22896186e-02,  1.08820668e-01,  2.39990899e+00,\n",
+       "        -9.64984269e-01,  1.98025631e+00, -5.63937986e-01,\n",
+       "         4.79969662e-01, -1.35120678e-01, -2.05635299e+00,\n",
+       "        -8.10160991e-02,  8.54539325e-02, -7.29010226e-01,\n",
+       "         9.89029011e-02,  4.03121712e-01,  2.74395661e-01,\n",
+       "         1.35305973e+00]])
      • b
        (chain, draw, b1)
        float64
        0.7681 1.076 ... -0.7853 0.05745
        array([[[ 7.68136098e-01,  1.07561765e+00,  9.10586329e-01,\n",
+       "          8.18055061e-01, -1.52584506e+00, -4.46789810e-01,\n",
+       "          3.92373217e-01, -3.75640224e-01, -1.94163633e+00,\n",
+       "          3.74350597e-01],\n",
+       "        [ 5.69076079e-01, -5.57230047e-02, -2.42731868e+00,\n",
+       "          1.72719231e-01, -1.04277979e+00, -2.39913671e+00,\n",
+       "         -2.19797709e-01, -7.21600779e-01,  2.71613355e+00,\n",
+       "          5.58153306e-01],\n",
+       "        [ 1.40359134e-01, -2.07505739e+00,  2.44758630e+00,\n",
+       "         -1.33072224e+00, -2.79030741e-01,  1.16279627e+00,\n",
+       "         -5.14899836e-03,  5.28927847e-01, -8.88655403e-01,\n",
+       "         -8.37791983e-01],\n",
+       "        [ 1.07464038e+00, -9.84440089e-01, -4.39809375e-01,\n",
+       "          9.41709724e-01,  1.00624537e+00, -1.57890650e-01,\n",
+       "          1.16553486e+00,  3.07942196e-01, -1.10206858e+00,\n",
+       "         -5.15514901e-01],\n",
+       "        [-6.48613253e-01, -8.53594888e-01, -1.59185984e-01,\n",
+       "          3.50664933e-01, -1.05422229e+00,  3.79168150e-02,\n",
+       "          4.92574361e-01,  6.57106753e-01, -4.30607697e-01,\n",
+       "         -1.14009230e+00],\n",
+       "        [ 5.95914967e-01, -1.11158589e-01, -1.67480208e+00,\n",
+       "          6.72059856e-01,  1.85113208e-01, -1.05265893e+00,\n",
+       "          1.11688161e+00, -6.91981288e-01,  1.42654251e+00,\n",
+       "         -3.32065578e-01],\n",
+       "        [-5.75919886e-01, -1.28962628e+00,  7.27974128e-01,\n",
+       "          1.67930127e+00,  1.43216941e+00,  7.64068864e-01,\n",
+       "         -1.76969651e-01,  7.51692510e-01, -1.76763633e+00,\n",
+       "         -1.13014402e-01],\n",
+       "        [ 7.16019100e-01,  2.91425330e-01,  5.17573139e-01,\n",
+       "         -5.87202549e-01, -2.24393392e-01,  1.58194760e+00,\n",
+       "          1.97700475e+00,  7.21719307e-01,  3.56365845e-01,\n",
+       "          4.13504258e-01],\n",
+       "        [ 2.55518679e-01,  1.87780453e-01,  1.07923704e-01,\n",
+       "         -1.13015150e+00,  6.22121321e-01, -8.70984605e-01,\n",
+       "          7.05908259e-01, -1.22293028e+00, -4.13651163e-02,\n",
+       "          1.00806030e-01],\n",
+       "        [-2.03400078e-01,  1.36444725e+00,  8.13152648e-01,\n",
+       "          4.97547187e-01, -5.27292488e-01,  2.09404437e+00,\n",
+       "          1.29120281e+00, -6.78971114e-01, -8.35262296e-01,\n",
+       "          5.65649350e-01],\n",
+       "        [-3.44249274e-01, -4.22369533e-01,  7.83459061e-01,\n",
+       "          8.89774081e-04,  3.11037248e-01,  1.34980216e+00,\n",
+       "          3.06052324e-01, -3.21098480e-01, -5.70835363e-01,\n",
+       "          2.21334061e-01],\n",
+       "        [-1.70488321e-01, -3.29323021e-01, -1.29960096e+00,\n",
+       "          1.49419871e-01,  3.14145477e-01,  5.26980681e-01,\n",
+       "         -1.69681488e+00,  9.85868996e-01, -3.41153538e-01,\n",
+       "          1.35007239e-02],\n",
+       "        [ 1.05281617e+00, -6.22224184e-01, -1.61429480e-01,\n",
+       "          3.22927768e-01,  8.00120287e-02, -7.81225106e-01,\n",
+       "          4.79028167e-01, -5.74317725e-01, -9.21461384e-02,\n",
+       "          2.17733422e+00],\n",
+       "        [ 1.11365533e+00, -1.20376477e+00,  1.88441546e-01,\n",
+       "          6.96615817e-01, -1.70984662e+00,  4.72098121e-01,\n",
+       "          6.43126919e-01, -3.65735489e-02,  1.11218349e+00,\n",
+       "          5.86920344e-01],\n",
+       "        [ 6.15389122e-01,  1.03039966e+00,  1.12458617e+00,\n",
+       "          9.00623262e-01, -5.01107093e-01,  4.96895953e-01,\n",
+       "          1.63461296e-01,  9.78801047e-01,  6.60324198e-02,\n",
+       "          4.64788088e-01],\n",
+       "        [ 5.11928035e-01, -3.60092970e-01,  1.88257804e+00,\n",
+       "          1.24409726e-01,  3.15532617e+00, -3.26778768e-01,\n",
+       "         -1.77377633e+00,  1.41624759e+00,  8.45724969e-01,\n",
+       "         -1.79599145e+00],\n",
+       "        [-1.44373864e+00, -8.02041381e-01,  5.87918622e-01,\n",
+       "          1.03562862e+00, -6.70812295e-01,  1.12582313e+00,\n",
+       "         -1.43828200e+00, -4.98170782e-01,  1.15316411e+00,\n",
+       "         -6.39421099e-01],\n",
+       "        [ 2.01474701e-01,  6.55774434e-01,  2.06062809e+00,\n",
+       "         -1.26293888e-01, -7.64049874e-01,  1.43503101e+00,\n",
+       "         -1.10854814e-01,  7.79795885e-01, -2.66797538e-01,\n",
+       "         -4.88411970e-01],\n",
+       "        [-6.19119194e-01, -4.93687654e-02, -1.90882318e-01,\n",
+       "          4.77769813e-01, -8.09502423e-01, -2.89051816e+00,\n",
+       "         -9.70475649e-01, -2.08798212e-01, -4.93020626e-01,\n",
+       "         -7.21046268e-01],\n",
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      arviz_version :
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    " + ], + "text/plain": [ + "\n", + "Dimensions: (chain: 1, draw: 2, x_dim_0: 3, x_dim_1: 4, x_dim_2: 5)\n", + "Coordinates:\n", + " * chain (chain) int64 0\n", + " * draw (draw) int64 0 1\n", + " * x_dim_0 (x_dim_0) int64 0 1 2\n", + " * x_dim_1 (x_dim_1) int64 0 1 2 3\n", + " * x_dim_2 (x_dim_2) int64 0 1 2 3 4\n", + "Data variables:\n", + " x (chain, draw, x_dim_0, x_dim_1, x_dim_2) float64 0.4887 ... -0.1801\n", + "Attributes:\n", + " created_at: 2020-06-06T18:07:29.148885\n", + " arviz_version: 0.8.3" + ] + }, + "execution_count": 5, +======= + "execution_count": 10, + "metadata": { + "ExecuteTime": { + "end_time": "2020-06-05T06:47:09.286020Z", + "start_time": "2020-06-05T06:47:07.961226Z" + } + }, + "outputs": [], + "source": [ + "import pymc3 as pm\n", + "\n", + "draws = 500\n", + "chains = 2\n", + "\n", + "eight_school_data = {\n", + " \"J\": 8,\n", + " \"y\": np.array([28.0, 8.0, -3.0, 7.0, -1.0, 1.0, 18.0, 12.0]),\n", + " \"sigma\": np.array([15.0, 10.0, 16.0, 11.0, 9.0, 11.0, 10.0, 18.0]),\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "ExecuteTime": { + "end_time": "2020-06-05T06:47:16.996019Z", + "start_time": "2020-06-05T06:47:09.288094Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Auto-assigning NUTS sampler...\n", + "Initializing NUTS using jitter+adapt_diag...\n", + "Multiprocess sampling (2 chains in 4 jobs)\n", + "NUTS: [theta_tilde, tau, mu]\n", + "Sampling 2 chains, 0 divergences: 100%|██████████| 2000/2000 [00:00<00:00, 2065.44draws/s]\n", + "100%|██████████| 1000/1000 [00:00<00:00, 1713.18it/s]\n" + ] + }, + { + "data": { + "text/plain": [ + "Inference data with groups:\n", + "\t> posterior\n", + "\t> posterior_predictive\n", + "\t> log_likelihood\n", + "\t> sample_stats\n", + "\t> prior\n", + "\t> prior_predictive\n", + "\t> observed_data" + ] + }, + "execution_count": 11, +>>>>>>> master + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ +<<<<<<< HEAD + "dataset.posterior" +======= + "with pm.Model() as model:\n", + " mu = pm.Normal(\"mu\", mu=0, sd=5)\n", + " tau = pm.HalfCauchy(\"tau\", beta=5)\n", + " theta_tilde = pm.Normal(\"theta_tilde\", mu=0, sd=1, shape=eight_school_data[\"J\"])\n", + " theta = pm.Deterministic(\"theta\", mu + tau * theta_tilde)\n", + " pm.Normal(\n", + " \"obs\", mu=theta, sd=eight_school_data[\"sigma\"], observed=eight_school_data[\"y\"]\n", + " )\n", + "\n", + " trace = pm.sample(draws, chains=chains)\n", + " prior = pm.sample_prior_predictive()\n", + " posterior_predictive = pm.sample_posterior_predictive(trace)\n", + "\n", + " pm_data = az.from_pymc3(\n", + " trace=trace,\n", + " prior=prior,\n", + " posterior_predictive=posterior_predictive,\n", + " coords={\"school\": np.arange(eight_school_data[\"J\"])},\n", + " dims={\"theta\": [\"school\"], \"theta_tilde\": [\"school\"]},\n", + " )\n", + "pm_data" +>>>>>>> master + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ +<<<<<<< HEAD + "## From a dictionary" +======= + "## From PyStan" +>>>>>>> master + ] + }, + { + "cell_type": "code", +<<<<<<< HEAD + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
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        xarray.Dataset
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+       "        -1.12345666, -0.24305322, -0.3447775 , -0.81779847,  0.2375271 ,\n",
+       "        -0.62111339,  0.57584957, -0.77529001,  0.42162154, -0.79075283,\n",
+       "        -0.48893675, -0.19220573,  0.4611616 ,  0.54313755, -2.36706096,\n",
+       "        -0.56875632, -1.44190913,  0.92496636,  1.01274784,  1.27662421,\n",
+       "         2.54281675,  0.66455272, -0.21618373, -0.20878461, -0.59426284,\n",
+       "         1.36383277,  0.22091957, -0.9332109 , -0.78613826, -0.56383727,\n",
+       "        -0.25611629, -0.46299795, -0.89960577, -0.40653695,  0.34247082,\n",
+       "         0.92588544,  1.71109405, -1.66670151, -0.85590094, -2.01899857,\n",
+       "        -1.80837008, -0.40595888, -0.19649084, -1.21913057, -0.14738896,\n",
+       "        -0.64461526, -1.20750942, -0.54507745, -0.62939225,  0.44823848,\n",
+       "         1.1800143 , -0.93698394, -0.11068548, -1.29358602, -0.9128787 ,\n",
+       "        -1.11518176,  1.73361458, -0.83401513, -0.27121065,  0.21397022,\n",
+       "         0.11951771, -1.20583004, -1.25714942,  1.4872156 ,  1.00344769,\n",
+       "         0.63906445, -1.01471355,  0.11675147,  0.89560963, -0.60301298,\n",
+       "        -0.45956725, -1.29658997,  0.86000723, -0.54165219, -0.55998307,\n",
+       "        -0.26992935, -2.18324991,  2.08447852, -0.49793872, -2.043601  ,\n",
+       "         1.22161564, -1.26097956,  0.24968684,  0.25852879, -0.2715059 ]])
          • b
            (chain, draw, b_dim_0)
            float64
            -0.4105 -0.1523 ... 1.901 -1.437
            array([[[-4.10480871e-01, -1.52326994e-01,  1.18903773e-01,\n",
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+       "         -3.07991478e-01, -1.20627967e+00,  6.10669110e-01,\n",
+       "          4.75650675e-01],\n",
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+       "         -5.54663853e-01,  1.01013341e+00,  2.48372571e-01,\n",
+       "         -1.65297574e+00],\n",
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+       "         -1.12750668e+00,  3.82920054e-01,  1.42412087e+00,\n",
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+       "         -2.49419007e+00],\n",
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+       "         -1.60672830e+00],\n",
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+       "         -1.11120067e+00],\n",
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+       "          4.06107089e-01],\n",
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+       "          1.21748351e+00,  9.17667802e-01,  1.42942340e+00,\n",
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+       "         -1.65111269e+00],\n",
+       "        [-5.09575687e-02, -9.61150117e-01,  2.77548037e-01,\n",
+       "         -5.42005658e-01,  1.68012151e-01, -2.12159150e-01,\n",
+       "         -8.27127783e-01, -3.00307265e-01,  1.04215489e+00,\n",
+       "          1.71028758e+00],\n",
+       "        [ 9.87570380e-01, -1.11759079e+00, -7.17478366e-01,\n",
+       "         -5.17187497e-02,  8.18351150e-02, -9.48860280e-01,\n",
+       "         -8.31270063e-01,  2.68092475e-01, -4.07260052e-01,\n",
+       "          9.01999076e-01],\n",
+       "        [-3.76504906e-02, -1.37971542e-01,  7.55129171e-01,\n",
+       "          1.44786580e-03, -6.87303162e-01, -1.89148590e-01,\n",
+       "          1.47595373e+00,  8.04670163e-01,  6.52582883e-01,\n",
+       "         -1.07906956e+00],\n",
+       "        [-6.47928641e-01, -2.76026331e-01,  7.57559828e-01,\n",
+       "          1.70338228e-01,  3.35391141e-01,  2.88025695e-01,\n",
+       "          1.71584580e+00, -1.07483247e+00,  1.36925171e-01,\n",
+       "         -1.47718278e+00],\n",
+       "        [-4.12450193e-01,  1.83945097e+00,  8.89794089e-01,\n",
+       "          7.13612449e-01,  4.82162349e-01, -4.24203521e-01,\n",
+       "         -7.28282455e-01, -3.15548781e-01, -1.57312914e+00,\n",
+       "         -1.16086929e+00],\n",
+       "        [ 4.00804885e-01, -1.81290229e+00,  2.32103941e+00,\n",
+       "         -9.57086521e-01, -9.60132118e-01,  8.18356375e-02,\n",
+       "          5.77512023e-01, -1.49896450e+00, -1.27637634e+00,\n",
+       "          6.47515731e-02],\n",
+       "        [ 1.33662654e+00, -1.31983956e-02, -7.82694835e-01,\n",
+       "         -5.92274534e-01, -7.41020211e-01,  8.20059395e-01,\n",
+       "         -7.11278077e-01, -1.47428258e-01, -9.83640598e-01,\n",
+       "         -1.32516586e+00],\n",
+       "        [-3.37978720e-01,  1.98221516e+00, -4.71458892e-01,\n",
+       "          9.67317917e-01, -2.28452557e-01, -1.41679352e+00,\n",
+       "         -6.93882004e-01,  6.38205057e-01,  1.37433318e-01,\n",
+       "         -8.29680317e-01],\n",
+       "        [-1.97948434e-01,  5.22154138e-01,  1.12876096e+00,\n",
+       "         -6.86808246e-01, -5.36343048e-01,  5.54262182e-01,\n",
+       "          1.61485265e-01, -3.68313557e-01, -4.54748197e-01,\n",
+       "          3.89753658e-01],\n",
+       "        [ 3.64232981e-01,  8.10020794e-01, -6.24296298e-01,\n",
+       "         -2.64049993e-01, -9.08804764e-01,  3.46641432e-02,\n",
+       "         -4.33526037e-01,  1.67290161e-01, -1.01782402e-01,\n",
+       "          1.41206345e-01],\n",
+       "        [ 8.55091526e-01,  6.19533658e-01,  9.13841099e-01,\n",
+       "          1.62989624e+00, -8.28210350e-02,  1.78531996e+00,\n",
+       "          2.37024460e-01,  8.54996779e-01,  8.49176167e-02,\n",
+       "         -1.15311353e+00],\n",
+       "        [ 6.29776172e-01, -1.24156716e+00,  6.39625183e-01,\n",
+       "         -1.00828153e+00,  4.11249207e-02, -1.72599323e-01,\n",
+       "          1.31975761e+00,  3.81221395e-01,  4.44803514e-01,\n",
+       "          3.78114548e-01],\n",
+       "        [-1.05583440e+00,  7.47216568e-01,  1.09689723e+00,\n",
+       "         -8.35949649e-01, -3.59913184e-01,  1.13090944e+00,\n",
+       "         -8.27497774e-02,  2.96331630e-03,  2.84283963e-01,\n",
+       "          8.07604902e-01],\n",
+       "        [-1.11506725e+00, -1.37372796e+00,  1.94552165e-01,\n",
+       "         -1.17197524e+00,  1.09293610e+00,  8.60059141e-01,\n",
+       "         -4.81480023e-02,  5.30950795e-01, -1.74398771e+00,\n",
+       "          7.24308411e-01],\n",
+       "        [-1.80234036e+00, -1.62740545e+00,  1.63979435e+00,\n",
+       "         -6.26738408e-01, -5.32551748e-01, -5.09361960e-01,\n",
+       "          6.47488706e-01, -8.66502356e-01, -3.54139391e-01,\n",
+       "          1.27826927e+00],\n",
+       "        [ 2.47952012e+00, -8.74131876e-01,  5.05003737e-01,\n",
+       "          2.03072296e+00, -7.39885280e-01, -1.34586177e-01,\n",
+       "          3.42639949e-01,  3.02809799e-01,  5.30834699e-01,\n",
+       "          6.58747556e-01],\n",
+       "        [-1.36899589e-01, -1.32271031e-01,  8.52293304e-01,\n",
+       "         -6.31564641e-01,  7.75550762e-01, -2.24162172e+00,\n",
+       "          1.72462883e+00, -1.27738406e+00, -4.87915709e-01,\n",
+       "         -1.70778195e+00],\n",
+       "        [-8.20310834e-01, -9.20607681e-01,  7.30956400e-01,\n",
+       "          1.44798831e+00,  7.27420471e-01, -4.59446500e-01,\n",
+       "         -4.94813259e-01,  6.66482225e-01, -9.80362021e-01,\n",
+       "          2.43591413e-01],\n",
+       "        [ 9.76763863e-02,  2.88188072e-01,  1.89577361e+00,\n",
+       "         -2.27114323e-01,  1.64763708e+00,  1.20321048e-01,\n",
+       "         -4.40267665e-02, -1.47887078e-01,  8.01149020e-02,\n",
+       "         -2.77389039e-01],\n",
+       "        [-1.01237765e+00,  5.67782981e-02, -4.38683450e-01,\n",
+       "          3.44769675e-01,  8.25628076e-01,  6.89376574e-01,\n",
+       "          2.35672502e-02,  6.25443258e-01,  1.46707276e+00,\n",
+       "         -6.85269873e-01],\n",
+       "        [-1.05525632e+00, -1.02739878e+00, -3.31028608e-01,\n",
+       "         -1.70091503e+00, -4.07926479e-01,  5.31102117e-01,\n",
+       "          8.06173830e-01,  1.91851045e-01, -1.05976203e-01,\n",
+       "         -8.65871078e-03],\n",
+       "        [ 1.83609776e-01,  7.20184155e-01, -1.26043653e+00,\n",
+       "         -3.10450489e-01,  1.40688823e+00, -1.31090763e+00,\n",
+       "          3.52115122e-01, -1.20578435e+00,  3.34896601e-01,\n",
+       "         -8.11448655e-01],\n",
+       "        [ 9.36997095e-01, -6.89774333e-02,  1.82323312e-01,\n",
+       "          1.18874235e+00, -1.93475508e+00, -1.97259570e-01,\n",
+       "         -4.01805129e-01,  1.56519719e-01,  7.20284772e-01,\n",
+       "         -1.53685647e+00],\n",
+       "        [-6.45985070e-01, -7.42697032e-01,  2.87562403e+00,\n",
+       "         -1.07277721e+00, -3.62236615e-01, -2.94318060e-01,\n",
+       "         -7.25182328e-01, -1.70563655e-01, -1.18736218e+00,\n",
+       "         -6.45296851e-01],\n",
+       "        [-1.60023179e+00, -1.19415277e+00, -7.91282026e-01,\n",
+       "         -1.13260994e+00,  5.49423718e-01,  3.03478893e-01,\n",
+       "          7.92048680e-01,  6.25290380e-01,  8.74379780e-01,\n",
+       "          4.36688308e-01],\n",
+       "        [ 8.51012084e-01, -1.83146490e+00, -1.86077181e-01,\n",
+       "         -3.69655835e-01,  1.03910492e+00, -2.51607407e+00,\n",
+       "         -1.11522402e-01, -5.54494197e-01, -2.55004155e+00,\n",
+       "         -3.53517009e-01],\n",
+       "        [-1.03797040e+00, -4.76380291e-01,  7.02052916e-01,\n",
+       "         -1.66906878e+00, -2.89958113e-03,  5.36159096e-01,\n",
+       "         -8.29569427e-01,  7.40179298e-01,  3.63797566e-01,\n",
+       "          1.56341992e+00],\n",
+       "        [-1.57537578e+00, -5.20559786e-01, -7.27432492e-01,\n",
+       "          7.71677019e-01,  5.48647428e-01,  1.41156547e+00,\n",
+       "          1.49595207e+00, -2.82577827e-01, -1.98455727e-01,\n",
+       "         -4.88550101e-01],\n",
+       "        [ 9.54153065e-01, -3.42524165e-01, -7.19864339e-02,\n",
+       "         -5.13491821e-01, -7.85754124e-01, -4.52257659e-02,\n",
+       "         -2.08017148e-02, -1.43624649e+00,  5.11142839e-01,\n",
+       "         -8.87633078e-01],\n",
+       "        [-1.41590491e-01, -1.16176491e+00, -1.44355079e+00,\n",
+       "          1.99734620e+00,  5.50875149e-01,  1.75354673e-01,\n",
+       "         -1.83293396e-01,  3.80751173e-01,  9.70215514e-02,\n",
+       "         -8.69527016e-02],\n",
+       "        [ 6.68853722e-01, -1.43495637e+00,  6.32481136e-01,\n",
+       "          6.19208375e-01,  1.72485630e+00, -4.48701279e-01,\n",
+       "         -5.87827145e-01,  1.17639731e+00, -1.77408371e+00,\n",
+       "         -2.51830933e-01],\n",
+       "        [-1.55729952e+00, -1.58866119e+00,  2.63960443e-01,\n",
+       "          8.81333365e-01, -2.50260581e-01, -4.52841504e-01,\n",
+       "          6.30461667e-01, -2.43398269e+00, -1.34262790e+00,\n",
+       "         -2.52949828e+00],\n",
+       "        [-9.27477066e-01, -3.03438443e-01,  1.43821828e+00,\n",
+       "         -1.29496358e+00,  7.62432532e-01, -5.96522418e-01,\n",
+       "         -1.45502923e+00,  1.36355478e+00,  2.17632209e-01,\n",
+       "          9.65861276e-01],\n",
+       "        [-2.40225456e-01, -4.02848251e-02,  6.54526613e-01,\n",
+       "         -2.09261789e-01, -1.64800098e-01, -4.75481902e-01,\n",
+       "          1.21937632e+00, -1.14866961e-01, -5.13479561e-01,\n",
+       "         -3.30640785e-01],\n",
+       "        [ 1.29402459e+00, -1.60698294e-01, -7.92434912e-01,\n",
+       "         -1.07742986e+00,  2.43178713e-01,  1.00656953e-01,\n",
+       "          1.36373102e-01,  2.58918163e-01,  1.89809614e-01,\n",
+       "         -6.30684459e-02],\n",
+       "        [ 7.75252323e-01, -2.60870609e-01, -5.97804654e-01,\n",
+       "          1.24670604e+00,  7.96899525e-01, -3.27239237e-01,\n",
+       "          1.03952056e+00,  8.87511794e-01,  9.85236298e-01,\n",
+       "         -1.05767898e+00],\n",
+       "        [-1.44699183e-01,  9.11422880e-01, -6.29496869e-01,\n",
+       "         -4.13543427e-01,  7.60975390e-02,  4.70630985e-01,\n",
+       "          1.11392353e+00,  2.62117186e+00, -2.73881904e+00,\n",
+       "          8.29593648e-01],\n",
+       "        [ 1.21278933e+00, -2.50603472e-01,  7.31591703e-01,\n",
+       "          1.91026743e+00,  9.43079363e-01,  6.73031399e-01,\n",
+       "          1.63807920e-01,  1.84092750e+00,  1.36988211e+00,\n",
+       "          6.76178900e-01],\n",
+       "        [-9.43631589e-01, -8.32594075e-01,  9.67736749e-02,\n",
+       "         -9.74210548e-01,  1.63433456e+00,  1.37967517e+00,\n",
+       "         -3.18047718e-01,  5.59277747e-01,  2.09180627e-01,\n",
+       "         -2.38439923e-01],\n",
+       "        [-1.00381236e+00, -2.43858940e-02, -6.76335057e-01,\n",
+       "          2.88015338e-01,  1.29484002e+00, -1.16994525e+00,\n",
+       "         -2.24838843e+00,  6.98694368e-01, -1.76020375e+00,\n",
+       "         -1.00754451e+00],\n",
+       "        [-9.24827848e-01, -4.52282412e-02, -2.83507390e-01,\n",
+       "         -8.83829828e-02, -4.49698973e-01, -2.77632281e-01,\n",
+       "         -1.99532247e-01,  2.86744691e-01,  5.57449988e-01,\n",
+       "          5.78371576e-01],\n",
+       "        [-8.95433556e-02, -8.78745785e-01,  1.38576703e+00,\n",
+       "          4.00941311e-02, -2.44365258e-01,  8.69340948e-01,\n",
+       "         -6.46151791e-01,  1.05331451e-01,  4.36569316e-01,\n",
+       "         -4.82321089e-01],\n",
+       "        [-1.18093161e+00,  1.11004838e+00, -2.96104564e-01,\n",
+       "         -1.15458694e+00,  4.99279854e-01, -5.38178798e-01,\n",
+       "         -1.27968202e+00, -1.40496506e+00, -6.61347545e-02,\n",
+       "         -2.72885541e-01],\n",
+       "        [ 7.54951942e-01, -1.00795823e+00, -1.65064634e-01,\n",
+       "         -3.61276495e-02, -1.39992830e+00, -1.71150207e-01,\n",
+       "          3.93172662e-01,  1.12326414e+00,  5.33252515e-01,\n",
+       "         -4.34410142e-01],\n",
+       "        [ 4.51626690e-01,  5.66246816e-01,  9.85985984e-01,\n",
+       "          2.08673518e-03, -1.80460669e+00,  2.25401170e+00,\n",
+       "         -1.19596303e+00,  4.44254458e-01,  3.49840707e-01,\n",
+       "         -1.38226433e+00],\n",
+       "        [-1.22310129e-01, -1.35818909e-01, -7.89870597e-01,\n",
+       "         -9.57954557e-01, -1.09182931e-01, -2.05273094e-01,\n",
+       "         -1.97952364e+00, -4.98298028e-01,  1.90078737e+00,\n",
+       "         -1.43712870e+00]]])
          • c
            (chain, draw, c_dim_0, c_dim_1)
            float64
            -1.041 0.4122 ... -0.9928 0.7617
            array([[[[-1.04127041,  0.41217704, -1.03400048, -0.12264575],\n",
+       "         [ 1.04107835, -0.26339671,  0.96054064, -0.22215247],\n",
+       "         [ 1.04423588,  1.08154076, -1.0384382 ,  1.2484653 ]],\n",
+       "\n",
+       "        [[-1.95494131,  0.49214263,  0.47497275,  0.30189806],\n",
+       "         [-2.32052569,  0.83418501, -0.70571508, -0.01323852],\n",
+       "         [ 1.28823264,  1.54410576,  0.91816028,  0.30587703]],\n",
+       "\n",
+       "        [[-1.52475937, -1.02624381, -1.58744789,  1.06811567],\n",
+       "         [ 2.28969475, -0.59806168,  0.30152991, -0.87979082],\n",
+       "         [ 0.66695957, -0.37827421, -1.19831325, -0.67450885]],\n",
+       "\n",
+       "        ...,\n",
+       "\n",
+       "        [[ 0.218554  , -0.60661969,  1.34206727,  1.42814161],\n",
+       "         [-0.18270282, -0.23375035, -1.86696975, -1.14066794],\n",
+       "         [-0.7107148 ,  0.56726087,  1.30172505, -1.34156061]],\n",
+       "\n",
+       "        [[-0.36618216,  0.63246716,  0.93517297,  0.68838127],\n",
+       "         [ 0.63987735,  0.39818941, -1.14925665,  0.5878074 ],\n",
+       "         [ 1.02279209, -0.04997891, -0.03299896,  0.92170792]],\n",
+       "\n",
+       "        [[-1.95153135,  0.39313075, -1.09503186, -0.63834405],\n",
+       "         [ 0.39769657, -0.61514386,  0.09034792, -1.00511881],\n",
+       "         [-1.016085  , -0.62807559, -0.99275874,  0.76174708]]]])
        • created_at :
          2020-06-06T18:07:29.245680
          arviz_version :
          0.8.3

        \n", + "
      \n", + "
      \n", + "
      • \n", + " \n", + "
    \n", + "
    \n", + " " + ], + "text/plain": [ + "Inference data with groups:\n", + "\t> posterior" + ] + }, + "execution_count": 6, +======= + "execution_count": 12, + "metadata": { + "ExecuteTime": { + "end_time": "2020-06-05T06:47:57.347055Z", + "start_time": "2020-06-05T06:47:16.997392Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:pystan:COMPILING THE C++ CODE FOR MODEL anon_model_9d743830ec4a29fb58eb4660d4b9417f NOW.\n" + ] + }, + { + "data": { + "text/plain": [ + "Inference data with groups:\n", + "\t> posterior\n", + "\t> posterior_predictive\n", + "\t> log_likelihood\n", + "\t> sample_stats\n", + "\t> observed_data" + ] + }, + "execution_count": 12, +>>>>>>> master + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ +<<<<<<< HEAD + "datadict = {\n", + " \"a\": np.random.randn(100),\n", + " \"b\": np.random.randn(1, 100, 10),\n", + " \"c\": np.random.randn(1, 100, 3, 4),\n", + "}\n", + "dataset = az.convert_to_inference_data(datadict)\n", + "dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + "Show/Hide data repr\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Show/Hide attributes\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    xarray.Dataset
      • b_dim_0: 10
      • c_dim_0: 3
      • c_dim_1: 4
      • chain: 1
      • draw: 100
      • chain
        (chain)
        int64
        0
        array([0])
      • draw
        (draw)
        int64
        0 1 2 3 4 5 6 ... 94 95 96 97 98 99
        array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
+       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
+       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
+       "       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,\n",
+       "       72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89,\n",
+       "       90, 91, 92, 93, 94, 95, 96, 97, 98, 99])
      • b_dim_0
        (b_dim_0)
        int64
        0 1 2 3 4 5 6 7 8 9
        array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
      • c_dim_0
        (c_dim_0)
        int64
        0 1 2
        array([0, 1, 2])
      • c_dim_1
        (c_dim_1)
        int64
        0 1 2 3
        array([0, 1, 2, 3])
      • a
        (chain, draw)
        float64
        -1.127 0.2247 ... 0.2585 -0.2715
        array([[-1.12732068,  0.22474725,  0.94396769,  0.42085979,  0.55230194,\n",
+       "         0.81605924,  2.70808522,  1.41957694, -0.61711058,  0.56834516,\n",
+       "        -0.22410035,  0.5276792 , -0.1116566 ,  0.13772934,  0.10561586,\n",
+       "        -1.12345666, -0.24305322, -0.3447775 , -0.81779847,  0.2375271 ,\n",
+       "        -0.62111339,  0.57584957, -0.77529001,  0.42162154, -0.79075283,\n",
+       "        -0.48893675, -0.19220573,  0.4611616 ,  0.54313755, -2.36706096,\n",
+       "        -0.56875632, -1.44190913,  0.92496636,  1.01274784,  1.27662421,\n",
+       "         2.54281675,  0.66455272, -0.21618373, -0.20878461, -0.59426284,\n",
+       "         1.36383277,  0.22091957, -0.9332109 , -0.78613826, -0.56383727,\n",
+       "        -0.25611629, -0.46299795, -0.89960577, -0.40653695,  0.34247082,\n",
+       "         0.92588544,  1.71109405, -1.66670151, -0.85590094, -2.01899857,\n",
+       "        -1.80837008, -0.40595888, -0.19649084, -1.21913057, -0.14738896,\n",
+       "        -0.64461526, -1.20750942, -0.54507745, -0.62939225,  0.44823848,\n",
+       "         1.1800143 , -0.93698394, -0.11068548, -1.29358602, -0.9128787 ,\n",
+       "        -1.11518176,  1.73361458, -0.83401513, -0.27121065,  0.21397022,\n",
+       "         0.11951771, -1.20583004, -1.25714942,  1.4872156 ,  1.00344769,\n",
+       "         0.63906445, -1.01471355,  0.11675147,  0.89560963, -0.60301298,\n",
+       "        -0.45956725, -1.29658997,  0.86000723, -0.54165219, -0.55998307,\n",
+       "        -0.26992935, -2.18324991,  2.08447852, -0.49793872, -2.043601  ,\n",
+       "         1.22161564, -1.26097956,  0.24968684,  0.25852879, -0.2715059 ]])
      • b
        (chain, draw, b_dim_0)
        float64
        -0.4105 -0.1523 ... 1.901 -1.437
        array([[[-4.10480871e-01, -1.52326994e-01,  1.18903773e-01,\n",
+       "         -5.15526776e-01, -4.88829917e-01,  3.60527638e-03,\n",
+       "         -3.07991478e-01, -1.20627967e+00,  6.10669110e-01,\n",
+       "          4.75650675e-01],\n",
+       "        [ 1.80658209e+00,  8.03745003e-02,  8.08133239e-01,\n",
+       "         -7.79504732e-01,  4.94626289e-01, -6.37253723e-02,\n",
+       "         -5.54663853e-01,  1.01013341e+00,  2.48372571e-01,\n",
+       "         -1.65297574e+00],\n",
+       "        [-4.93419509e-01,  1.25872282e+00,  8.40311035e-01,\n",
+       "         -2.02307680e+00, -2.06469854e-01, -1.05892853e+00,\n",
+       "         -1.12750668e+00,  3.82920054e-01,  1.42412087e+00,\n",
+       "          1.86512880e-01],\n",
+       "        [-1.26751378e+00, -1.12009701e+00, -5.89554225e-01,\n",
+       "         -2.41330004e-02, -5.15588571e-01, -1.91449281e-01,\n",
+       "         -5.17195127e-01, -7.88493913e-01, -8.18935006e-01,\n",
+       "          7.18765942e-01],\n",
+       "        [ 2.02456394e-02, -1.30775178e+00, -1.68587291e-01,\n",
+       "         -1.07524427e+00,  7.26948618e-01,  1.30404790e+00,\n",
+       "          1.58097029e+00, -8.66411341e-01,  8.70885311e-01,\n",
+       "         -7.01514030e-01],\n",
+       "        [-9.89756628e-01, -1.22009412e+00,  7.93777157e-03,\n",
+       "          9.08534517e-01, -1.91995248e+00, -9.26509576e-01,\n",
+       "          3.13036421e-01, -1.32919691e+00, -1.07938222e+00,\n",
+       "         -2.49419007e+00],\n",
+       "        [ 1.38406019e+00, -4.02997385e-01,  1.19009527e-01,\n",
+       "         -4.42122614e-01,  4.27467018e-01, -2.62159950e-01,\n",
+       "          3.35292851e-01, -2.30354860e+00,  9.02801744e-01,\n",
+       "         -1.54311556e+00],\n",
+       "        [-3.34014295e-01, -1.97806027e+00,  1.04707794e+00,\n",
+       "          1.12552471e+00, -1.21997589e+00, -1.14062585e+00,\n",
+       "          1.37689625e+00, -2.64318351e-01,  8.49212332e-01,\n",
+       "          5.70582173e-01],\n",
+       "        [-6.42535743e-01, -2.18190754e-01,  1.02111077e-01,\n",
+       "         -1.14361445e+00,  2.61195960e-01,  1.52097026e-01,\n",
+       "         -1.28179837e+00, -1.94317336e-01, -1.23937319e+00,\n",
+       "          2.47585757e-01],\n",
+       "        [-1.73268574e+00, -2.67945036e-01,  5.55403786e-01,\n",
+       "         -5.26391649e-01, -4.54841609e-01, -4.53981872e-01,\n",
+       "          7.56959815e-01,  5.07298186e-01,  1.31536058e+00,\n",
+       "          4.56526594e-01],\n",
+       "        [ 2.02591570e+00,  1.03309281e+00, -2.49892588e+00,\n",
+       "          7.34853256e-02,  9.76801778e-01,  7.62989008e-01,\n",
+       "         -1.35201246e-01,  1.54234502e+00,  4.23651277e-01,\n",
+       "          1.24763713e-01],\n",
+       "        [-4.49606221e-01,  3.08133485e-01, -5.78542257e-01,\n",
+       "          5.31707558e-01,  2.28288315e+00,  1.00187616e+00,\n",
+       "          3.72957072e-01,  1.21382151e+00, -3.70784401e-01,\n",
+       "          3.81886703e-01],\n",
+       "        [-8.64670869e-01,  1.03353682e-01, -4.05538645e-01,\n",
+       "         -5.87200524e-02, -9.71082230e-01,  2.13877315e-01,\n",
+       "          6.05710672e-01,  1.22261467e+00, -2.31697018e-01,\n",
+       "         -1.60672830e+00],\n",
+       "        [ 1.26427819e+00, -8.18070411e-01, -1.60046005e+00,\n",
+       "          2.43160242e+00, -6.84788523e-01, -2.77262854e-01,\n",
+       "         -3.44713364e-01, -4.96236011e-02,  9.62855080e-01,\n",
+       "          9.87611138e-01],\n",
+       "        [-1.74291874e+00,  3.88501231e-02,  4.16979002e-02,\n",
+       "          7.83776043e-02, -1.14251866e+00,  1.31901970e-01,\n",
+       "          8.48722134e-02,  9.17506507e-01, -1.06310171e+00,\n",
+       "         -1.11120067e+00],\n",
+       "        [-4.85356279e-01, -4.72966874e-01,  2.33127502e-01,\n",
+       "          1.25272771e+00, -3.53104283e-01,  2.07209566e+00,\n",
+       "         -5.27783349e-01,  2.67675451e-01, -8.95412495e-02,\n",
+       "          4.27709126e-01],\n",
+       "        [ 7.55793475e-01,  4.85963528e-02,  1.13041327e+00,\n",
+       "         -7.00816787e-01,  2.60968711e-01,  7.71287124e-01,\n",
+       "         -4.93150276e-01,  1.42899951e+00, -6.21045390e-01,\n",
+       "         -2.57806979e-01],\n",
+       "        [ 2.71929022e-01,  1.56673007e+00, -2.75793847e-01,\n",
+       "         -1.98172012e+00,  4.98135229e-01, -1.77372218e+00,\n",
+       "         -4.29970825e-01, -3.56193468e-01,  5.42588139e-01,\n",
+       "         -7.91464258e-01],\n",
+       "        [ 9.81331855e-01, -6.66281665e-01,  1.35651070e+00,\n",
+       "         -8.24223597e-01,  7.32726085e-01,  2.27873232e-01,\n",
+       "         -1.71966483e+00,  9.90555090e-01, -1.31219529e-01,\n",
+       "         -1.73041936e+00],\n",
+       "        [ 6.37523178e-01, -9.56116139e-01, -7.34328551e-01,\n",
+       "         -8.11264360e-02,  6.55580374e-01,  4.29345495e-01,\n",
+       "         -7.45094189e-02,  4.37701047e-01, -6.61555954e-01,\n",
+       "          7.65103469e-01],\n",
+       "        [-1.40177307e+00,  4.61065490e-01, -7.36204644e-01,\n",
+       "         -5.83157506e-01, -1.24216628e+00,  1.31100361e+00,\n",
+       "          4.71273611e-01,  2.17177481e+00, -4.92067225e-01,\n",
+       "          4.06107089e-01],\n",
+       "        [ 7.15234123e-01,  1.23471562e+00,  1.79990381e-02,\n",
+       "          6.93433631e-01, -7.94267361e-01,  1.43338811e+00,\n",
+       "          8.61151547e-01,  8.85704255e-01, -9.66632035e-01,\n",
+       "          4.01380654e-02],\n",
+       "        [ 8.81043056e-01, -1.51126827e-01,  3.00355107e-01,\n",
+       "         -9.22554705e-01, -1.75515496e-01, -1.55777884e+00,\n",
+       "          1.21748351e+00,  9.17667802e-01,  1.42942340e+00,\n",
+       "         -1.67878838e-01],\n",
+       "        [ 1.72882678e+00, -1.30970849e+00,  1.25337257e+00,\n",
+       "          2.74493087e-01,  8.37109424e-01,  1.81265412e+00,\n",
+       "         -1.62310480e-01, -3.42021451e-01, -1.26820504e+00,\n",
+       "          3.08106807e-01],\n",
+       "        [-7.39874519e-01, -1.50716037e+00, -6.89941463e-01,\n",
+       "          1.53972070e+00, -1.14359643e-01, -2.02456310e+00,\n",
+       "          1.47855946e-01,  1.32973846e+00,  2.78651297e-01,\n",
+       "         -9.86358396e-01],\n",
+       "        [-6.07458292e-01, -3.87725081e-01,  1.09838454e+00,\n",
+       "         -1.36176727e+00, -1.30907582e+00, -1.99649225e-01,\n",
+       "          2.14846505e-01, -1.45133811e-01, -8.18289388e-01,\n",
+       "         -1.29977583e+00],\n",
+       "        [ 9.51943978e-01,  1.34648305e+00,  6.68482110e-01,\n",
+       "          5.61646039e-02, -1.13141811e-01, -8.90735884e-01,\n",
+       "         -7.74115612e-01,  1.00717444e+00, -2.23041682e+00,\n",
+       "          1.15615224e+00],\n",
+       "        [-3.86497680e-01,  1.29395214e+00,  5.66237744e-01,\n",
+       "         -3.34047434e-01, -8.61049133e-01, -7.59301143e-01,\n",
+       "          2.38490521e-01, -1.47146340e+00, -2.38634961e-01,\n",
+       "         -4.94128450e-01],\n",
+       "        [-1.47524732e-02,  1.51460201e+00, -4.87076342e-01,\n",
+       "         -6.77472637e-01, -1.23581285e+00,  1.04684947e+00,\n",
+       "         -3.32549578e-01,  7.01950755e-01,  1.66814192e+00,\n",
+       "          3.63630856e-01],\n",
+       "        [ 6.51060663e-01, -6.86769542e-01, -2.06559845e-01,\n",
+       "          4.95863467e-01, -3.94917217e-01, -7.86520435e-01,\n",
+       "         -2.60507772e+00,  1.22467019e+00, -5.95281986e-01,\n",
+       "         -2.56881630e+00],\n",
+       "        [ 2.56063828e+00, -7.92523761e-01,  5.13125257e-02,\n",
+       "         -9.75967263e-02, -6.41516230e-01, -2.12719592e+00,\n",
+       "         -3.08406176e-01,  2.17573104e+00,  7.83110498e-01,\n",
+       "         -7.66648899e-02],\n",
+       "        [-7.99935214e-01, -1.29323632e+00,  6.74778794e-01,\n",
+       "         -7.23077835e-01, -2.74167630e-01,  5.67607549e-01,\n",
+       "         -7.40084659e-01,  1.41266345e+00, -6.93479404e-01,\n",
+       "         -7.20044183e-01],\n",
+       "        [ 3.10997632e-01, -1.59603081e-01, -1.34720678e+00,\n",
+       "         -3.74387926e+00, -7.52116730e-01,  6.55978086e-01,\n",
+       "         -1.70711044e-01,  1.07777654e-01,  1.45241524e-01,\n",
+       "         -4.26560295e-01],\n",
+       "        [-1.89958429e+00,  2.18533956e+00,  8.32232192e-02,\n",
+       "         -1.14868938e+00,  9.99680833e-01, -1.99669947e+00,\n",
+       "          6.78483820e-01, -2.10363082e+00, -4.98576248e-02,\n",
+       "          4.85089850e-01],\n",
+       "        [-8.77035692e-01, -1.04972672e-01, -1.01961753e-01,\n",
+       "         -4.58336541e-01, -6.41340785e-02, -1.85724604e-01,\n",
+       "         -9.54651977e-02,  5.70632647e-01,  6.98287675e-01,\n",
+       "         -1.35524272e+00],\n",
+       "        [ 4.75724874e-01,  6.60606142e-01, -1.37162799e-01,\n",
+       "         -5.91151672e-01,  7.36924824e-01,  4.25140845e-01,\n",
+       "          1.78476625e+00, -1.62690254e+00,  7.74943830e-01,\n",
+       "          1.13505046e+00],\n",
+       "        [ 4.47934942e-01,  1.53380720e+00,  1.44888386e+00,\n",
+       "         -1.54095810e+00, -1.27281911e+00,  1.54034525e-02,\n",
+       "          9.32918668e-01, -4.83068371e-01, -8.66531516e-01,\n",
+       "          3.72754418e-02],\n",
+       "        [ 1.41112501e-01,  8.53672889e-01, -6.84609374e-01,\n",
+       "         -5.16441795e-02,  1.25730020e+00,  9.07275967e-01,\n",
+       "         -5.28039608e-01, -4.37604093e-01, -3.34821543e-01,\n",
+       "         -9.03179566e-01],\n",
+       "        [-6.30587181e-01,  5.62825785e-01, -1.41479164e+00,\n",
+       "          1.22863878e+00,  1.25920491e-01, -1.26333403e+00,\n",
+       "         -3.45244594e-01,  1.61327667e+00, -7.29641478e-01,\n",
+       "         -1.02167280e+00],\n",
+       "        [ 3.97391132e-01,  1.13979457e+00, -2.52475483e-02,\n",
+       "          2.23918320e-01, -1.02622548e+00,  4.93484606e-01,\n",
+       "         -1.37062353e-01,  4.94500901e-01,  3.46856261e-01,\n",
+       "         -1.60015501e+00],\n",
+       "        [-5.02617372e-02,  1.48472903e-01,  3.53047214e-01,\n",
+       "          2.11793401e-01, -1.79881932e+00,  3.91979010e-02,\n",
+       "         -6.33930728e-01,  2.94991887e-01, -4.19299501e-01,\n",
+       "         -1.80021076e+00],\n",
+       "        [ 2.46577576e-01,  1.09722937e+00, -1.69909137e+00,\n",
+       "         -7.64495779e-01, -3.20746313e-01,  1.51753448e+00,\n",
+       "          6.44955466e-01,  5.26443928e-01, -1.38258344e+00,\n",
+       "         -1.17873643e+00],\n",
+       "        [ 7.28406464e-01,  2.36556806e+00, -1.55961124e+00,\n",
+       "         -5.11067503e-01, -6.29440399e-01, -1.39747186e-01,\n",
+       "         -1.61089349e-01, -4.16005150e-01,  2.26286603e+00,\n",
+       "         -4.99195471e-01],\n",
+       "        [-6.45847748e-01,  9.14957542e-01,  1.99209466e+00,\n",
+       "         -1.68318074e+00, -7.32301286e-02, -9.95194696e-01,\n",
+       "          1.52640554e+00,  1.39398992e+00, -1.45529442e+00,\n",
+       "          1.04971330e+00],\n",
+       "        [-1.15308407e-01,  8.08000016e-01, -6.23910300e-01,\n",
+       "          5.80028618e-01, -7.34909847e-01,  9.06922965e-01,\n",
+       "         -2.40949798e-01,  8.95802682e-01,  4.84587562e-01,\n",
+       "         -3.42216833e-01],\n",
+       "        [ 5.72391863e-01, -5.76927175e-01, -3.99330734e-02,\n",
+       "          1.90200912e-01, -1.13050346e-01, -1.87607540e+00,\n",
+       "          7.51310474e-01,  5.71923293e-01, -4.64373266e-01,\n",
+       "          4.39916461e-01],\n",
+       "        [ 1.36617529e+00,  1.72098897e-01,  4.04839700e-02,\n",
+       "         -5.71288712e-01,  1.66793413e+00,  6.56617293e-01,\n",
+       "          1.40948385e+00, -4.59214248e-01,  7.77691741e-01,\n",
+       "          4.68344436e-01],\n",
+       "        [-1.13228113e-01, -6.68152059e-01, -2.22281674e-01,\n",
+       "         -7.65836285e-01, -1.78373125e-01,  1.25152340e+00,\n",
+       "         -1.48588060e-01, -7.56471244e-01,  5.64220987e-01,\n",
+       "          1.39476167e-01],\n",
+       "        [-1.47067099e+00, -1.16185755e+00,  2.21050469e-01,\n",
+       "          3.02132123e+00, -1.11371844e+00, -1.06585744e+00,\n",
+       "          1.39999386e+00, -6.00297727e-01, -5.36869913e-01,\n",
+       "         -7.63330906e-01],\n",
+       "        [ 8.68007836e-01,  2.61169481e-01,  1.02598158e+00,\n",
+       "          2.74267912e+00,  5.62441579e-01, -8.77154727e-02,\n",
+       "         -1.05116044e+00,  4.49346789e-01,  5.51281926e-01,\n",
+       "         -1.94522073e-01],\n",
+       "        [ 1.48805242e+00, -6.36849160e-01,  2.72908752e-01,\n",
+       "         -5.23404699e-01, -1.75790257e+00, -1.07634668e+00,\n",
+       "          8.49887906e-01,  2.84220040e-01,  3.88195791e-01,\n",
+       "         -1.01650363e+00],\n",
+       "        [-1.01467838e+00, -3.09935228e-01, -4.09510631e-02,\n",
+       "          2.27560638e-01, -1.75719433e-01, -5.89289512e-01,\n",
+       "         -2.58136567e-01,  5.92096894e-01,  9.00351741e-01,\n",
+       "         -4.92830010e-01],\n",
+       "        [-2.80804208e+00,  1.61278692e+00, -5.97996057e-01,\n",
+       "          2.67522455e-02,  5.30441642e-01, -9.86061091e-01,\n",
+       "         -1.99792630e+00, -1.34509168e+00,  3.62422914e-01,\n",
+       "          1.01858683e+00],\n",
+       "        [-7.84235558e-01, -9.77885919e-01,  4.34384667e-01,\n",
+       "         -5.14927970e-01,  5.47335015e-01,  1.18908836e+00,\n",
+       "         -8.78122116e-01,  1.07589323e+00,  1.20783439e-01,\n",
+       "         -1.65111269e+00],\n",
+       "        [-5.09575687e-02, -9.61150117e-01,  2.77548037e-01,\n",
+       "         -5.42005658e-01,  1.68012151e-01, -2.12159150e-01,\n",
+       "         -8.27127783e-01, -3.00307265e-01,  1.04215489e+00,\n",
+       "          1.71028758e+00],\n",
+       "        [ 9.87570380e-01, -1.11759079e+00, -7.17478366e-01,\n",
+       "         -5.17187497e-02,  8.18351150e-02, -9.48860280e-01,\n",
+       "         -8.31270063e-01,  2.68092475e-01, -4.07260052e-01,\n",
+       "          9.01999076e-01],\n",
+       "        [-3.76504906e-02, -1.37971542e-01,  7.55129171e-01,\n",
+       "          1.44786580e-03, -6.87303162e-01, -1.89148590e-01,\n",
+       "          1.47595373e+00,  8.04670163e-01,  6.52582883e-01,\n",
+       "         -1.07906956e+00],\n",
+       "        [-6.47928641e-01, -2.76026331e-01,  7.57559828e-01,\n",
+       "          1.70338228e-01,  3.35391141e-01,  2.88025695e-01,\n",
+       "          1.71584580e+00, -1.07483247e+00,  1.36925171e-01,\n",
+       "         -1.47718278e+00],\n",
+       "        [-4.12450193e-01,  1.83945097e+00,  8.89794089e-01,\n",
+       "          7.13612449e-01,  4.82162349e-01, -4.24203521e-01,\n",
+       "         -7.28282455e-01, -3.15548781e-01, -1.57312914e+00,\n",
+       "         -1.16086929e+00],\n",
+       "        [ 4.00804885e-01, -1.81290229e+00,  2.32103941e+00,\n",
+       "         -9.57086521e-01, -9.60132118e-01,  8.18356375e-02,\n",
+       "          5.77512023e-01, -1.49896450e+00, -1.27637634e+00,\n",
+       "          6.47515731e-02],\n",
+       "        [ 1.33662654e+00, -1.31983956e-02, -7.82694835e-01,\n",
+       "         -5.92274534e-01, -7.41020211e-01,  8.20059395e-01,\n",
+       "         -7.11278077e-01, -1.47428258e-01, -9.83640598e-01,\n",
+       "         -1.32516586e+00],\n",
+       "        [-3.37978720e-01,  1.98221516e+00, -4.71458892e-01,\n",
+       "          9.67317917e-01, -2.28452557e-01, -1.41679352e+00,\n",
+       "         -6.93882004e-01,  6.38205057e-01,  1.37433318e-01,\n",
+       "         -8.29680317e-01],\n",
+       "        [-1.97948434e-01,  5.22154138e-01,  1.12876096e+00,\n",
+       "         -6.86808246e-01, -5.36343048e-01,  5.54262182e-01,\n",
+       "          1.61485265e-01, -3.68313557e-01, -4.54748197e-01,\n",
+       "          3.89753658e-01],\n",
+       "        [ 3.64232981e-01,  8.10020794e-01, -6.24296298e-01,\n",
+       "         -2.64049993e-01, -9.08804764e-01,  3.46641432e-02,\n",
+       "         -4.33526037e-01,  1.67290161e-01, -1.01782402e-01,\n",
+       "          1.41206345e-01],\n",
+       "        [ 8.55091526e-01,  6.19533658e-01,  9.13841099e-01,\n",
+       "          1.62989624e+00, -8.28210350e-02,  1.78531996e+00,\n",
+       "          2.37024460e-01,  8.54996779e-01,  8.49176167e-02,\n",
+       "         -1.15311353e+00],\n",
+       "        [ 6.29776172e-01, -1.24156716e+00,  6.39625183e-01,\n",
+       "         -1.00828153e+00,  4.11249207e-02, -1.72599323e-01,\n",
+       "          1.31975761e+00,  3.81221395e-01,  4.44803514e-01,\n",
+       "          3.78114548e-01],\n",
+       "        [-1.05583440e+00,  7.47216568e-01,  1.09689723e+00,\n",
+       "         -8.35949649e-01, -3.59913184e-01,  1.13090944e+00,\n",
+       "         -8.27497774e-02,  2.96331630e-03,  2.84283963e-01,\n",
+       "          8.07604902e-01],\n",
+       "        [-1.11506725e+00, -1.37372796e+00,  1.94552165e-01,\n",
+       "         -1.17197524e+00,  1.09293610e+00,  8.60059141e-01,\n",
+       "         -4.81480023e-02,  5.30950795e-01, -1.74398771e+00,\n",
+       "          7.24308411e-01],\n",
+       "        [-1.80234036e+00, -1.62740545e+00,  1.63979435e+00,\n",
+       "         -6.26738408e-01, -5.32551748e-01, -5.09361960e-01,\n",
+       "          6.47488706e-01, -8.66502356e-01, -3.54139391e-01,\n",
+       "          1.27826927e+00],\n",
+       "        [ 2.47952012e+00, -8.74131876e-01,  5.05003737e-01,\n",
+       "          2.03072296e+00, -7.39885280e-01, -1.34586177e-01,\n",
+       "          3.42639949e-01,  3.02809799e-01,  5.30834699e-01,\n",
+       "          6.58747556e-01],\n",
+       "        [-1.36899589e-01, -1.32271031e-01,  8.52293304e-01,\n",
+       "         -6.31564641e-01,  7.75550762e-01, -2.24162172e+00,\n",
+       "          1.72462883e+00, -1.27738406e+00, -4.87915709e-01,\n",
+       "         -1.70778195e+00],\n",
+       "        [-8.20310834e-01, -9.20607681e-01,  7.30956400e-01,\n",
+       "          1.44798831e+00,  7.27420471e-01, -4.59446500e-01,\n",
+       "         -4.94813259e-01,  6.66482225e-01, -9.80362021e-01,\n",
+       "          2.43591413e-01],\n",
+       "        [ 9.76763863e-02,  2.88188072e-01,  1.89577361e+00,\n",
+       "         -2.27114323e-01,  1.64763708e+00,  1.20321048e-01,\n",
+       "         -4.40267665e-02, -1.47887078e-01,  8.01149020e-02,\n",
+       "         -2.77389039e-01],\n",
+       "        [-1.01237765e+00,  5.67782981e-02, -4.38683450e-01,\n",
+       "          3.44769675e-01,  8.25628076e-01,  6.89376574e-01,\n",
+       "          2.35672502e-02,  6.25443258e-01,  1.46707276e+00,\n",
+       "         -6.85269873e-01],\n",
+       "        [-1.05525632e+00, -1.02739878e+00, -3.31028608e-01,\n",
+       "         -1.70091503e+00, -4.07926479e-01,  5.31102117e-01,\n",
+       "          8.06173830e-01,  1.91851045e-01, -1.05976203e-01,\n",
+       "         -8.65871078e-03],\n",
+       "        [ 1.83609776e-01,  7.20184155e-01, -1.26043653e+00,\n",
+       "         -3.10450489e-01,  1.40688823e+00, -1.31090763e+00,\n",
+       "          3.52115122e-01, -1.20578435e+00,  3.34896601e-01,\n",
+       "         -8.11448655e-01],\n",
+       "        [ 9.36997095e-01, -6.89774333e-02,  1.82323312e-01,\n",
+       "          1.18874235e+00, -1.93475508e+00, -1.97259570e-01,\n",
+       "         -4.01805129e-01,  1.56519719e-01,  7.20284772e-01,\n",
+       "         -1.53685647e+00],\n",
+       "        [-6.45985070e-01, -7.42697032e-01,  2.87562403e+00,\n",
+       "         -1.07277721e+00, -3.62236615e-01, -2.94318060e-01,\n",
+       "         -7.25182328e-01, -1.70563655e-01, -1.18736218e+00,\n",
+       "         -6.45296851e-01],\n",
+       "        [-1.60023179e+00, -1.19415277e+00, -7.91282026e-01,\n",
+       "         -1.13260994e+00,  5.49423718e-01,  3.03478893e-01,\n",
+       "          7.92048680e-01,  6.25290380e-01,  8.74379780e-01,\n",
+       "          4.36688308e-01],\n",
+       "        [ 8.51012084e-01, -1.83146490e+00, -1.86077181e-01,\n",
+       "         -3.69655835e-01,  1.03910492e+00, -2.51607407e+00,\n",
+       "         -1.11522402e-01, -5.54494197e-01, -2.55004155e+00,\n",
+       "         -3.53517009e-01],\n",
+       "        [-1.03797040e+00, -4.76380291e-01,  7.02052916e-01,\n",
+       "         -1.66906878e+00, -2.89958113e-03,  5.36159096e-01,\n",
+       "         -8.29569427e-01,  7.40179298e-01,  3.63797566e-01,\n",
+       "          1.56341992e+00],\n",
+       "        [-1.57537578e+00, -5.20559786e-01, -7.27432492e-01,\n",
+       "          7.71677019e-01,  5.48647428e-01,  1.41156547e+00,\n",
+       "          1.49595207e+00, -2.82577827e-01, -1.98455727e-01,\n",
+       "         -4.88550101e-01],\n",
+       "        [ 9.54153065e-01, -3.42524165e-01, -7.19864339e-02,\n",
+       "         -5.13491821e-01, -7.85754124e-01, -4.52257659e-02,\n",
+       "         -2.08017148e-02, -1.43624649e+00,  5.11142839e-01,\n",
+       "         -8.87633078e-01],\n",
+       "        [-1.41590491e-01, -1.16176491e+00, -1.44355079e+00,\n",
+       "          1.99734620e+00,  5.50875149e-01,  1.75354673e-01,\n",
+       "         -1.83293396e-01,  3.80751173e-01,  9.70215514e-02,\n",
+       "         -8.69527016e-02],\n",
+       "        [ 6.68853722e-01, -1.43495637e+00,  6.32481136e-01,\n",
+       "          6.19208375e-01,  1.72485630e+00, -4.48701279e-01,\n",
+       "         -5.87827145e-01,  1.17639731e+00, -1.77408371e+00,\n",
+       "         -2.51830933e-01],\n",
+       "        [-1.55729952e+00, -1.58866119e+00,  2.63960443e-01,\n",
+       "          8.81333365e-01, -2.50260581e-01, -4.52841504e-01,\n",
+       "          6.30461667e-01, -2.43398269e+00, -1.34262790e+00,\n",
+       "         -2.52949828e+00],\n",
+       "        [-9.27477066e-01, -3.03438443e-01,  1.43821828e+00,\n",
+       "         -1.29496358e+00,  7.62432532e-01, -5.96522418e-01,\n",
+       "         -1.45502923e+00,  1.36355478e+00,  2.17632209e-01,\n",
+       "          9.65861276e-01],\n",
+       "        [-2.40225456e-01, -4.02848251e-02,  6.54526613e-01,\n",
+       "         -2.09261789e-01, -1.64800098e-01, -4.75481902e-01,\n",
+       "          1.21937632e+00, -1.14866961e-01, -5.13479561e-01,\n",
+       "         -3.30640785e-01],\n",
+       "        [ 1.29402459e+00, -1.60698294e-01, -7.92434912e-01,\n",
+       "         -1.07742986e+00,  2.43178713e-01,  1.00656953e-01,\n",
+       "          1.36373102e-01,  2.58918163e-01,  1.89809614e-01,\n",
+       "         -6.30684459e-02],\n",
+       "        [ 7.75252323e-01, -2.60870609e-01, -5.97804654e-01,\n",
+       "          1.24670604e+00,  7.96899525e-01, -3.27239237e-01,\n",
+       "          1.03952056e+00,  8.87511794e-01,  9.85236298e-01,\n",
+       "         -1.05767898e+00],\n",
+       "        [-1.44699183e-01,  9.11422880e-01, -6.29496869e-01,\n",
+       "         -4.13543427e-01,  7.60975390e-02,  4.70630985e-01,\n",
+       "          1.11392353e+00,  2.62117186e+00, -2.73881904e+00,\n",
+       "          8.29593648e-01],\n",
+       "        [ 1.21278933e+00, -2.50603472e-01,  7.31591703e-01,\n",
+       "          1.91026743e+00,  9.43079363e-01,  6.73031399e-01,\n",
+       "          1.63807920e-01,  1.84092750e+00,  1.36988211e+00,\n",
+       "          6.76178900e-01],\n",
+       "        [-9.43631589e-01, -8.32594075e-01,  9.67736749e-02,\n",
+       "         -9.74210548e-01,  1.63433456e+00,  1.37967517e+00,\n",
+       "         -3.18047718e-01,  5.59277747e-01,  2.09180627e-01,\n",
+       "         -2.38439923e-01],\n",
+       "        [-1.00381236e+00, -2.43858940e-02, -6.76335057e-01,\n",
+       "          2.88015338e-01,  1.29484002e+00, -1.16994525e+00,\n",
+       "         -2.24838843e+00,  6.98694368e-01, -1.76020375e+00,\n",
+       "         -1.00754451e+00],\n",
+       "        [-9.24827848e-01, -4.52282412e-02, -2.83507390e-01,\n",
+       "         -8.83829828e-02, -4.49698973e-01, -2.77632281e-01,\n",
+       "         -1.99532247e-01,  2.86744691e-01,  5.57449988e-01,\n",
+       "          5.78371576e-01],\n",
+       "        [-8.95433556e-02, -8.78745785e-01,  1.38576703e+00,\n",
+       "          4.00941311e-02, -2.44365258e-01,  8.69340948e-01,\n",
+       "         -6.46151791e-01,  1.05331451e-01,  4.36569316e-01,\n",
+       "         -4.82321089e-01],\n",
+       "        [-1.18093161e+00,  1.11004838e+00, -2.96104564e-01,\n",
+       "         -1.15458694e+00,  4.99279854e-01, -5.38178798e-01,\n",
+       "         -1.27968202e+00, -1.40496506e+00, -6.61347545e-02,\n",
+       "         -2.72885541e-01],\n",
+       "        [ 7.54951942e-01, -1.00795823e+00, -1.65064634e-01,\n",
+       "         -3.61276495e-02, -1.39992830e+00, -1.71150207e-01,\n",
+       "          3.93172662e-01,  1.12326414e+00,  5.33252515e-01,\n",
+       "         -4.34410142e-01],\n",
+       "        [ 4.51626690e-01,  5.66246816e-01,  9.85985984e-01,\n",
+       "          2.08673518e-03, -1.80460669e+00,  2.25401170e+00,\n",
+       "         -1.19596303e+00,  4.44254458e-01,  3.49840707e-01,\n",
+       "         -1.38226433e+00],\n",
+       "        [-1.22310129e-01, -1.35818909e-01, -7.89870597e-01,\n",
+       "         -9.57954557e-01, -1.09182931e-01, -2.05273094e-01,\n",
+       "         -1.97952364e+00, -4.98298028e-01,  1.90078737e+00,\n",
+       "         -1.43712870e+00]]])
      • c
        (chain, draw, c_dim_0, c_dim_1)
        float64
        -1.041 0.4122 ... -0.9928 0.7617
        array([[[[-1.04127041,  0.41217704, -1.03400048, -0.12264575],\n",
+       "         [ 1.04107835, -0.26339671,  0.96054064, -0.22215247],\n",
+       "         [ 1.04423588,  1.08154076, -1.0384382 ,  1.2484653 ]],\n",
+       "\n",
+       "        [[-1.95494131,  0.49214263,  0.47497275,  0.30189806],\n",
+       "         [-2.32052569,  0.83418501, -0.70571508, -0.01323852],\n",
+       "         [ 1.28823264,  1.54410576,  0.91816028,  0.30587703]],\n",
+       "\n",
+       "        [[-1.52475937, -1.02624381, -1.58744789,  1.06811567],\n",
+       "         [ 2.28969475, -0.59806168,  0.30152991, -0.87979082],\n",
+       "         [ 0.66695957, -0.37827421, -1.19831325, -0.67450885]],\n",
+       "\n",
+       "        ...,\n",
+       "\n",
+       "        [[ 0.218554  , -0.60661969,  1.34206727,  1.42814161],\n",
+       "         [-0.18270282, -0.23375035, -1.86696975, -1.14066794],\n",
+       "         [-0.7107148 ,  0.56726087,  1.30172505, -1.34156061]],\n",
+       "\n",
+       "        [[-0.36618216,  0.63246716,  0.93517297,  0.68838127],\n",
+       "         [ 0.63987735,  0.39818941, -1.14925665,  0.5878074 ],\n",
+       "         [ 1.02279209, -0.04997891, -0.03299896,  0.92170792]],\n",
+       "\n",
+       "        [[-1.95153135,  0.39313075, -1.09503186, -0.63834405],\n",
+       "         [ 0.39769657, -0.61514386,  0.09034792, -1.00511881],\n",
+       "         [-1.016085  , -0.62807559, -0.99275874,  0.76174708]]]])
    • created_at :
      2020-06-06T18:07:29.245680
      arviz_version :
      0.8.3
    " + ], + "text/plain": [ + "\n", + "Dimensions: (b_dim_0: 10, c_dim_0: 3, c_dim_1: 4, chain: 1, draw: 100)\n", + "Coordinates:\n", + " * chain (chain) int64 0\n", + " * draw (draw) int64 0 1 2 3 4 5 6 7 8 9 ... 90 91 92 93 94 95 96 97 98 99\n", + " * b_dim_0 (b_dim_0) int64 0 1 2 3 4 5 6 7 8 9\n", + " * c_dim_0 (c_dim_0) int64 0 1 2\n", + " * c_dim_1 (c_dim_1) int64 0 1 2 3\n", + "Data variables:\n", + " a (chain, draw) float64 -1.127 0.2247 0.944 ... 0.2497 0.2585 -0.2715\n", + " b (chain, draw, b_dim_0) float64 -0.4105 -0.1523 ... 1.901 -1.437\n", + " c (chain, draw, c_dim_0, c_dim_1) float64 -1.041 0.4122 ... 0.7617\n", + "Attributes:\n", + " created_at: 2020-06-06T18:07:29.245680\n", + " arviz_version: 0.8.3" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dataset.posterior" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## From dictionary with coords and dims" +======= + "import pystan\n", + "\n", + "schools_code = \"\"\"\n", + "data {\n", + " int J;\n", + " real y[J];\n", + " real sigma[J];\n", + "}\n", + "\n", + "parameters {\n", + " real mu;\n", + " real tau;\n", + " real theta_tilde[J];\n", + "}\n", + "\n", + "transformed parameters {\n", + " real theta[J];\n", + " for (j in 1:J)\n", + " theta[j] = mu + tau * theta_tilde[j];\n", + "}\n", + "\n", + "model {\n", + " mu ~ normal(0, 5);\n", + " tau ~ cauchy(0, 5);\n", + " theta_tilde ~ normal(0, 1);\n", + " y ~ normal(theta, sigma);\n", + "}\n", + "\n", + "generated quantities {\n", + " vector[J] log_lik;\n", + " vector[J] y_hat;\n", + " for (j in 1:J) {\n", + " log_lik[j] = normal_lpdf(y[j] | theta[j], sigma[j]);\n", + " y_hat[j] = normal_rng(theta[j], sigma[j]);\n", + " }\n", + "}\n", + "\"\"\"\n", + "\n", + "eight_school_data = {\n", + " \"J\": 8,\n", + " \"y\": np.array([28.0, 8.0, -3.0, 7.0, -1.0, 1.0, 18.0, 12.0]),\n", + " \"sigma\": np.array([15.0, 10.0, 16.0, 11.0, 9.0, 11.0, 10.0, 18.0]),\n", + "}\n", + "\n", + "stan_model = pystan.StanModel(model_code=schools_code)\n", + "fit = stan_model.sampling(data=eight_school_data, control={\"adapt_delta\": 0.9})\n", + "\n", + "stan_data = az.from_pystan(\n", + " posterior=fit,\n", + " posterior_predictive=\"y_hat\",\n", + " observed_data=[\"y\"],\n", + " log_likelihood={\"y\": \"log_lik\"},\n", + " coords={\"school\": np.arange(eight_school_data[\"J\"])},\n", + " dims={\n", + " \"theta\": [\"school\"],\n", + " \"y\": [\"school\"],\n", + " \"log_lik\": [\"school\"],\n", + " \"y_hat\": [\"school\"],\n", + " \"theta_tilde\": [\"school\"],\n", + " },\n", + ")\n", + "\n", + "stan_data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## From Pyro" +>>>>>>> master + ] + }, + { + "cell_type": "code", +<<<<<<< HEAD + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
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    arviz.InferenceData
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+       "         -5.17905951e-02],\n",
+       "        [ 4.57408303e-02, -1.13200690e+00, -5.76430396e-01,\n",
+       "         -4.03125387e-01, -1.23968589e-01, -1.17913451e+00,\n",
+       "          1.90922718e-01, -1.26507180e+00,  1.07226062e-01,\n",
+       "         -1.81417687e+00]]])
          • c
            (chain, draw, c1, c2)
            float64
            0.2361 0.1921 ... -0.722 -0.6994
            array([[[[ 0.23612495,  0.19209144,  1.73643296,  0.32152596],\n",
+       "         [ 0.6624001 , -0.70653542, -0.38179504, -0.40551882],\n",
+       "         [-0.57635498,  0.63250108, -0.32027305, -1.38916566]],\n",
+       "\n",
+       "        [[-1.11897017,  0.58815242, -0.58968597, -0.65490374],\n",
+       "         [ 0.04078477,  0.11616236, -0.60324171, -0.30388797],\n",
+       "         [ 1.45114249,  0.08002404, -0.72766003,  0.66437293]],\n",
+       "\n",
+       "        [[ 0.34177152,  0.47166011,  0.23152757,  0.84419889],\n",
+       "         [ 0.02070863,  2.98960753, -1.6101033 ,  1.00311769],\n",
+       "         [ 0.15683503, -2.11034956, -0.16731209,  1.07437286]],\n",
+       "\n",
+       "        ...,\n",
+       "\n",
+       "        [[-0.92695133,  0.29513187, -0.88513231, -1.345392  ],\n",
+       "         [-0.02382486, -0.30252184, -0.60326201, -0.30289022],\n",
+       "         [ 0.16873816,  0.49046628, -1.06360509,  0.0283932 ]],\n",
+       "\n",
+       "        [[-0.28788494, -0.59228032, -0.85666178, -0.57441519],\n",
+       "         [-0.05765098,  0.79718082, -0.77759788, -0.04799847],\n",
+       "         [ 1.37334172,  0.36906373, -0.13126941,  1.20878831]],\n",
+       "\n",
+       "        [[ 0.12294222, -0.53413433,  1.70802865, -0.2050028 ],\n",
+       "         [-1.09157998,  0.56964328, -0.66168397, -2.51403759],\n",
+       "         [-0.17579789,  2.12954436, -0.72202585, -0.69935395]]]])
        • created_at :
          2020-06-06T18:07:29.430311
          arviz_version :
          0.8.3

        \n", + "
      \n", + "
      \n", + "
      • \n", + " \n", + "
    \n", + "
    \n", + " " + ], + "text/plain": [ + "Inference data with groups:\n", + "\t> posterior" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "datadict = {\n", + " \"a\": np.random.randn(100),\n", + " \"b\": np.random.randn(1, 100, 10),\n", + " \"c\": np.random.randn(1, 100, 3, 4),\n", + "}\n", + "coords = {\"c1\": np.arange(3), \"c2\": np.arange(4), \"b1\": np.arange(10)}\n", + "dims = {\"b\": [\"b1\"], \"c\": [\"c1\", \"c2\"]}\n", + "\n", + "dataset = az.convert_to_inference_data(datadict, coords=coords, dims=dims)\n", + "dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + "Show/Hide data repr\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Show/Hide attributes\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    xarray.Dataset
      • b1: 10
      • c1: 3
      • c2: 4
      • chain: 1
      • draw: 100
      • chain
        (chain)
        int64
        0
        array([0])
      • draw
        (draw)
        int64
        0 1 2 3 4 5 6 ... 94 95 96 97 98 99
        array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
+       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
+       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
+       "       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,\n",
+       "       72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89,\n",
+       "       90, 91, 92, 93, 94, 95, 96, 97, 98, 99])
      • b1
        (b1)
        int64
        0 1 2 3 4 5 6 7 8 9
        array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
      • c1
        (c1)
        int64
        0 1 2
        array([0, 1, 2])
      • c2
        (c2)
        int64
        0 1 2 3
        array([0, 1, 2, 3])
      • a
        (chain, draw)
        float64
        1.388 -0.435 ... -0.02538 0.4237
        array([[ 1.38830379, -0.43501265, -1.33190295, -1.41406417,  0.16199589,\n",
+       "         0.51785049, -1.6173349 , -0.86856443, -1.04148202,  0.45262536,\n",
+       "         0.04212587,  0.69100054,  0.21114523,  0.34253538, -0.03343265,\n",
+       "         1.037077  , -0.24286319, -0.1167344 ,  1.6590027 ,  0.32700689,\n",
+       "         1.1623474 , -1.84436443, -1.35460971, -0.66132992,  0.77032251,\n",
+       "         0.93694648,  1.38257997, -0.69085695,  1.34625778,  1.83124689,\n",
+       "         0.56922388, -0.16049682, -2.08624686,  1.15367138,  0.62911656,\n",
+       "        -0.97548518,  0.9520221 , -0.74840271, -1.09028329,  0.40747891,\n",
+       "         0.30226566,  0.51866642,  1.80876867, -1.11795652, -1.36475722,\n",
+       "         0.07815867, -0.9690401 ,  0.64219237, -0.42278462, -1.80964916,\n",
+       "         0.39444554, -0.80730441,  0.56727989, -0.33355779, -0.40628526,\n",
+       "        -1.59803928, -1.0435544 ,  1.67447346, -0.87995185,  0.80716309,\n",
+       "        -0.49476011, -0.44018119,  0.09406969,  1.30699081,  0.78742038,\n",
+       "        -0.53732592,  1.20071111,  0.06567662, -0.00651144,  0.98783025,\n",
+       "         0.36537125,  1.21336023,  0.25457246,  0.17375063,  0.30920106,\n",
+       "        -1.22422813, -0.49061882, -0.16515283,  0.9196351 ,  1.4863149 ,\n",
+       "        -0.24464703, -0.1622031 ,  0.8447644 , -0.18530109,  0.29724243,\n",
+       "        -1.82665651, -0.27167164,  1.14502589,  1.73467347,  1.43562209,\n",
+       "        -1.3906193 , -0.69945332, -1.6929867 ,  1.84201555,  1.34464875,\n",
+       "         0.92803875,  0.8052289 ,  0.54302172, -0.02538094,  0.42370527]])
      • b
        (chain, draw, b1)
        float64
        1.924 1.801 ... 0.1072 -1.814
        array([[[ 1.92391253e+00,  1.80108000e+00, -7.60429102e-01,\n",
+       "          2.04034449e+00,  3.76075921e-01,  1.25217407e-01,\n",
+       "          9.75207522e-02,  1.06997096e+00,  2.85450437e-01,\n",
+       "         -6.82304483e-01],\n",
+       "        [ 3.48509165e-01,  1.00458128e-01,  1.65088650e+00,\n",
+       "         -3.39920713e-01,  1.16888685e+00,  7.61983988e-01,\n",
+       "          9.33126526e-02, -8.67201513e-01, -5.94461695e-01,\n",
+       "          3.60390010e-01],\n",
+       "        [ 1.22256924e+00,  1.84802049e+00,  4.61874423e-01,\n",
+       "         -1.83144069e-02,  1.52285735e+00,  4.87822163e-01,\n",
+       "         -7.94476233e-01, -3.69569713e-01,  4.79913752e-01,\n",
+       "         -6.77562688e-01],\n",
+       "        [-1.10573125e+00, -6.23825004e-02, -5.31393753e-01,\n",
+       "         -8.97124043e-02,  1.91075671e-01, -6.89308578e-01,\n",
+       "          9.26698177e-01, -1.67841463e+00, -4.49912124e-01,\n",
+       "         -2.40952915e-01],\n",
+       "        [ 1.29272063e+00, -8.82709162e-01, -1.46037590e-01,\n",
+       "         -6.53131318e-01, -4.89414354e-01, -2.82620822e-01,\n",
+       "         -1.66586339e-01,  9.32175514e-01, -2.03390765e-02,\n",
+       "         -5.03801841e-02],\n",
+       "        [ 3.74297340e-01, -5.56503628e-01,  1.91915985e+00,\n",
+       "          3.92747732e-01, -1.62708902e+00, -5.66360190e-01,\n",
+       "         -4.88078731e-01, -7.59406762e-01,  7.81492007e-01,\n",
+       "          2.32593614e+00],\n",
+       "        [ 7.14882179e-01,  6.62091932e-01, -1.07335376e+00,\n",
+       "         -3.91815417e-01,  2.60702798e-01,  8.24525553e-01,\n",
+       "         -6.05867698e-01, -1.24943960e+00,  1.04199904e-01,\n",
+       "         -2.03927440e-01],\n",
+       "        [ 1.20274308e+00, -3.41970559e-01,  3.70511393e-01,\n",
+       "         -1.42110894e+00, -5.46644514e-01,  6.91634567e-01,\n",
+       "          1.02584573e+00, -1.47139724e-01,  3.06292736e-01,\n",
+       "          7.00123981e-01],\n",
+       "        [ 7.44392159e-01, -7.50067878e-01, -3.41249157e+00,\n",
+       "          7.00132730e-01,  1.20121092e+00,  4.08633161e-01,\n",
+       "          5.63824351e-02, -1.16944822e+00,  9.32967153e-01,\n",
+       "         -1.95445708e-01],\n",
+       "        [ 1.21496867e-01,  6.59458919e-01, -3.89861537e-01,\n",
+       "         -5.58697786e-01,  1.33573293e+00, -6.44471334e-01,\n",
+       "          1.54147805e+00,  5.92119076e-01, -8.86584226e-02,\n",
+       "          1.95274105e-01],\n",
+       "        [ 1.06098608e+00, -2.82765769e-01,  1.08213644e+00,\n",
+       "          3.04537601e-01,  1.08725779e+00,  3.22904694e-02,\n",
+       "         -6.59831892e-01, -5.57167899e-02,  9.18853605e-01,\n",
+       "          5.36449632e-01],\n",
+       "        [-4.97447020e-01, -4.86300929e-01,  9.34420724e-01,\n",
+       "         -6.19971173e-01, -1.75987924e+00,  6.44141128e-01,\n",
+       "         -9.70392685e-01, -2.91947763e-01,  1.23733883e+00,\n",
+       "         -5.37270004e-01],\n",
+       "        [ 2.25221108e+00, -9.90566736e-01, -8.98790776e-01,\n",
+       "          9.00995573e-01,  4.73845053e-01, -1.25481546e+00,\n",
+       "          4.96640412e-01,  1.14010088e+00, -3.78053521e-01,\n",
+       "         -2.84238712e-01],\n",
+       "        [-1.10473462e+00, -1.62181603e+00,  4.03782875e-01,\n",
+       "          2.09176633e+00,  1.36890161e+00, -4.01728907e-01,\n",
+       "          3.48384706e-01, -6.14815762e-01,  2.25576200e-01,\n",
+       "          1.19383199e-02],\n",
+       "        [-2.97305059e-01, -2.32629437e+00, -6.98556866e-01,\n",
+       "         -7.16542658e-01, -3.27182009e-01, -9.79554603e-01,\n",
+       "         -1.68728074e+00,  7.40939791e-01, -5.50000563e-01,\n",
+       "          5.56878344e-01],\n",
+       "        [ 5.34706054e-01, -3.14891175e-01, -1.47422531e+00,\n",
+       "          2.15918622e-01,  8.17389124e-02, -7.80917520e-01,\n",
+       "         -6.06612151e-01,  7.93798206e-01, -1.30556308e+00,\n",
+       "         -1.70321869e-01],\n",
+       "        [ 9.59223957e-02,  4.96341269e-01,  6.63282293e-01,\n",
+       "         -3.95359264e-01,  5.36241057e-01, -1.18265554e+00,\n",
+       "         -5.27047169e-01,  6.71221524e-01, -4.81323133e-01,\n",
+       "         -7.31154040e-01],\n",
+       "        [-2.23902462e-01,  6.22276687e-02, -1.32284493e+00,\n",
+       "          4.34040941e-01,  3.31576004e-01, -6.42562729e-01,\n",
+       "          8.62576398e-01,  8.81783504e-01, -1.22598154e-01,\n",
+       "         -2.11848571e+00],\n",
+       "        [-3.16485679e-01,  1.53293150e+00, -1.28428061e+00,\n",
+       "          8.11371289e-01, -6.19634130e-01, -7.36023308e-01,\n",
+       "          1.32804948e+00, -1.29011346e+00, -4.40420160e-01,\n",
+       "         -1.91320854e+00],\n",
+       "        [-1.03175649e+00, -4.49615068e-01,  5.34820114e-01,\n",
+       "          3.36993617e-04, -1.40843313e-01, -1.22657004e+00,\n",
+       "          3.82503086e-01,  7.05558278e-01, -8.14730205e-02,\n",
+       "          9.16947313e-01],\n",
+       "        [-1.69357491e-01,  1.07358766e+00, -1.90090858e+00,\n",
+       "         -4.55947375e-01,  2.03251914e+00, -6.80382404e-01,\n",
+       "         -2.35649358e-01, -2.32739095e-02, -1.25150521e+00,\n",
+       "         -6.05312611e-01],\n",
+       "        [-9.08760587e-01,  2.43106599e+00, -1.44580502e+00,\n",
+       "          1.04744946e+00, -1.35920420e+00, -2.71115195e-01,\n",
+       "         -2.39854115e-01,  7.78360895e-01, -1.09722589e+00,\n",
+       "         -1.15268874e-01],\n",
+       "        [ 9.76881010e-01,  2.68883643e+00, -3.24975958e-01,\n",
+       "         -8.06842598e-01, -5.49401370e-01, -1.43952910e+00,\n",
+       "         -7.45745334e-02,  7.37760401e-02,  1.04568257e+00,\n",
+       "          7.82707287e-01],\n",
+       "        [ 1.69222347e+00,  7.84248680e-01,  6.59246959e-01,\n",
+       "          1.41868131e+00, -2.55093275e-01,  1.49597414e-01,\n",
+       "         -1.75880840e+00,  1.05808482e+00, -1.83229982e+00,\n",
+       "         -4.17680320e-01],\n",
+       "        [-1.35725745e+00, -5.25880928e-01, -6.08473794e-01,\n",
+       "         -1.52841920e-01,  6.39393526e-01,  3.52195509e-01,\n",
+       "          6.04446580e-01, -1.74781983e+00,  1.73508552e+00,\n",
+       "         -2.42250316e-01],\n",
+       "        [ 1.99530098e-01, -1.77155126e+00, -7.07213902e-01,\n",
+       "         -4.72761521e-01,  5.92876975e-01,  2.67389458e+00,\n",
+       "         -1.51831938e+00,  1.30288115e+00, -4.55473351e-01,\n",
+       "         -1.97223024e+00],\n",
+       "        [-4.37225905e-01,  1.46374001e+00, -2.32583483e+00,\n",
+       "          3.69061132e-01, -6.93579207e-01, -9.72149794e-01,\n",
+       "         -2.07717717e+00, -1.26175902e-03, -6.15444095e-01,\n",
+       "          1.62329265e+00],\n",
+       "        [-1.69541097e+00, -2.16832056e+00,  3.48101501e-01,\n",
+       "          1.06321028e+00, -4.22168000e-01, -5.93960571e-01,\n",
+       "          4.29741616e-01, -9.27936449e-01,  1.12970822e+00,\n",
+       "          1.24338646e-02],\n",
+       "        [-9.19781965e-02, -1.33717565e+00,  8.92016704e-01,\n",
+       "         -1.17763238e+00,  3.84561419e-01, -1.24024158e+00,\n",
+       "          8.89486620e-01, -1.00956651e+00,  5.11035823e-01,\n",
+       "         -1.37453708e+00],\n",
+       "        [-2.74008636e-01,  1.71072575e-01,  7.78803367e-01,\n",
+       "         -1.50740259e+00,  3.68612664e-02, -1.36147897e+00,\n",
+       "          1.21106372e-01, -1.13181057e+00,  2.00434950e-01,\n",
+       "         -8.29565977e-01],\n",
+       "        [ 4.37852105e-01,  5.81100511e-01, -1.20866178e-01,\n",
+       "          2.22737432e+00, -1.19461450e+00,  7.57124910e-02,\n",
+       "          8.84631171e-01,  1.30825341e+00,  8.21468578e-01,\n",
+       "          2.03324210e+00],\n",
+       "        [ 6.79502654e-01,  9.29875055e-01, -2.93311345e-01,\n",
+       "         -8.75088857e-01, -8.25360972e-01,  1.18442005e-01,\n",
+       "          2.12524452e+00,  7.67879289e-01, -1.43858559e+00,\n",
+       "         -6.43196980e-01],\n",
+       "        [-2.18751942e+00, -5.56348525e-01, -5.14506590e-01,\n",
+       "          2.91135315e-01,  3.32739559e-01,  9.48011583e-01,\n",
+       "         -6.88693887e-01,  5.38616450e-02,  4.45741015e-01,\n",
+       "          1.23069340e+00],\n",
+       "        [ 8.55213358e-01,  3.65347132e-02,  6.94298968e-01,\n",
+       "          4.01009366e-01,  4.46410395e-01,  9.36488498e-01,\n",
+       "         -1.94413269e+00,  2.93608443e-01, -6.33947867e-01,\n",
+       "         -1.94314919e-01],\n",
+       "        [-5.28696387e-02, -1.03806503e+00,  7.26542237e-01,\n",
+       "          9.30517222e-01, -1.01358069e+00, -5.52573275e-01,\n",
+       "          5.72649372e-01,  1.04461535e+00,  1.31734552e-01,\n",
+       "          4.47659649e-01],\n",
+       "        [ 4.55887740e-02,  1.74668844e-01,  9.95158254e-01,\n",
+       "          2.29885559e+00, -7.16418753e-01,  6.66169558e-01,\n",
+       "         -1.10431242e+00,  1.50949008e-01, -7.07084426e-01,\n",
+       "          3.14615809e-01],\n",
+       "        [-1.23419998e+00,  1.41173145e-02,  1.27841932e+00,\n",
+       "         -1.21467892e+00, -1.78769952e-01, -8.74227333e-01,\n",
+       "          8.99781420e-01,  6.84570766e-02,  2.31322046e+00,\n",
+       "         -1.27958653e+00],\n",
+       "        [-7.55721916e-02,  3.81398189e-01, -6.95703267e-01,\n",
+       "          1.79565801e-01, -1.99418664e+00,  1.03922105e+00,\n",
+       "         -3.81260082e-01,  8.98086236e-01, -1.34892603e+00,\n",
+       "          4.24810557e-01],\n",
+       "        [ 6.38429428e-01,  6.25171689e-01, -1.32450927e+00,\n",
+       "         -8.81213752e-02,  3.71802554e-01, -7.14984918e-01,\n",
+       "          7.40837763e-01, -1.39655176e+00, -1.99782703e+00,\n",
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+       "          1.90922718e-01, -1.26507180e+00,  1.07226062e-01,\n",
+       "         -1.81417687e+00]]])
      • c
        (chain, draw, c1, c2)
        float64
        0.2361 0.1921 ... -0.722 -0.6994
        array([[[[ 0.23612495,  0.19209144,  1.73643296,  0.32152596],\n",
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+       "         [-0.57635498,  0.63250108, -0.32027305, -1.38916566]],\n",
+       "\n",
+       "        [[-1.11897017,  0.58815242, -0.58968597, -0.65490374],\n",
+       "         [ 0.04078477,  0.11616236, -0.60324171, -0.30388797],\n",
+       "         [ 1.45114249,  0.08002404, -0.72766003,  0.66437293]],\n",
+       "\n",
+       "        [[ 0.34177152,  0.47166011,  0.23152757,  0.84419889],\n",
+       "         [ 0.02070863,  2.98960753, -1.6101033 ,  1.00311769],\n",
+       "         [ 0.15683503, -2.11034956, -0.16731209,  1.07437286]],\n",
+       "\n",
+       "        ...,\n",
+       "\n",
+       "        [[-0.92695133,  0.29513187, -0.88513231, -1.345392  ],\n",
+       "         [-0.02382486, -0.30252184, -0.60326201, -0.30289022],\n",
+       "         [ 0.16873816,  0.49046628, -1.06360509,  0.0283932 ]],\n",
+       "\n",
+       "        [[-0.28788494, -0.59228032, -0.85666178, -0.57441519],\n",
+       "         [-0.05765098,  0.79718082, -0.77759788, -0.04799847],\n",
+       "         [ 1.37334172,  0.36906373, -0.13126941,  1.20878831]],\n",
+       "\n",
+       "        [[ 0.12294222, -0.53413433,  1.70802865, -0.2050028 ],\n",
+       "         [-1.09157998,  0.56964328, -0.66168397, -2.51403759],\n",
+       "         [-0.17579789,  2.12954436, -0.72202585, -0.69935395]]]])
    • created_at :
      2020-06-06T18:07:29.430311
      arviz_version :
      0.8.3
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    arviz.InferenceData
    \n", + "
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    • \n", + " \n", + " \n", + "
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      \n", + "
        \n", + "
        \n", + "\n", + "\n", + "Show/Hide data repr\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Show/Hide attributes\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
        xarray.Dataset
          • chain: 2
          • draw: 500
          • school: 8
          • chain
            (chain)
            int64
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          • draw
            (draw)
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          • school
            (school)
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            array([0, 1, 2, 3, 4, 5, 6, 7])
          • mu
            (chain, draw)
            float64
            -2.014 12.94 ... -0.8899 8.764
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+       "         2.73834767e+00,  2.99774173e+00,  4.77927333e+00,\n",
+       "         6.39428461e+00,  5.69730364e+00,  1.52260956e+00,\n",
+       "         4.10697981e+00,  5.33571259e+00, -4.60291816e+00,\n",
+       "         1.39106478e+01, -3.48205386e+00,  2.65984393e+00,\n",
+       "         4.34544847e+00,  4.05353436e+00,  4.60805329e+00,\n",
+       "         4.49838320e+00,  6.18288161e+00,  4.68321170e+00,\n",
+       "         8.22636757e+00,  7.46305616e+00,  1.86797925e+00,\n",
+       "         3.52775807e+00,  4.58553352e+00,  6.00165852e+00,\n",
+       "         2.30795423e+00,  3.87745479e+00,  4.71956275e+00,\n",
+       "         3.59765270e+00,  8.76541367e-01,  8.58855749e+00,\n",
+       "         5.99659884e+00,  5.55868477e+00,  7.35063826e+00,\n",
+       "         1.61952520e+00,  5.39007963e+00,  7.54568528e+00,\n",
+       "        -1.25064746e+00, -1.49232899e+00,  9.69553929e+00,\n",
+       "         7.87543087e+00,  2.72304397e+00,  6.54930464e+00,\n",
+       "         4.94699722e+00,  4.03119470e+00,  4.99513608e+00,\n",
+       "         7.46867238e+00,  5.99313640e+00,  2.03972134e+00,\n",
+       "         6.13349077e+00,  7.95710680e+00, -2.52523101e-01,\n",
+       "         1.01077198e+01,  6.24455164e+00],\n",
+       "       [ 3.63182329e+00,  1.16127698e-01,  8.09666052e+00,\n",
+       "         7.82761062e+00,  1.57256034e+00,  2.00517013e+00,\n",
+       "         1.47236299e+00,  1.05582436e+00,  4.88947678e+00,\n",
+       "         1.80111426e+00,  1.40419986e+00,  2.76427283e+00,\n",
+       "         5.83658212e+00,  6.18640939e+00,  4.07693873e+00,\n",
+       "         5.20826372e+00,  8.67895992e-01,  8.67895992e-01,\n",
+       "         8.67895992e-01,  4.50709642e+00,  3.75812126e+00,\n",
+       "         2.01963570e+00,  1.57946870e+00,  1.03519705e+01,\n",
+       "        -6.80918329e-01,  4.19504847e+00,  5.89265198e+00,\n",
+       "         7.04142601e+00,  2.65166061e+00,  4.55965090e+00,\n",
+       "         4.98098662e+00, -1.41242051e+00,  9.24548261e+00,\n",
+       "         1.33612926e+00,  3.11084566e+00,  6.98920653e+00,\n",
+       "        -2.82079639e-01,  6.90342577e+00,  2.01360741e+00,\n",
+       "         7.49924593e+00,  4.94728585e-01,  5.82592975e+00,\n",
+       "         3.75817989e+00,  4.51834412e+00,  4.45205537e+00,\n",
+       "         7.39982769e+00, -4.51493691e-01,  8.03093481e+00,\n",
+       "         3.28889538e+00,  5.07396812e+00,  5.04783745e+00,\n",
+       "         7.33357262e+00,  2.68984364e+00,  2.68984364e+00,\n",
+       "         2.68984364e+00,  3.88464406e+00,  3.50674967e+00,\n",
+       "         3.99780914e+00,  5.44631133e+00,  3.48467368e+00,\n",
+       "         6.96535325e-01,  7.50323418e+00,  7.42606945e+00,\n",
+       "         9.01061262e+00,  1.73951112e-01,  2.75395359e+00,\n",
+       "         4.23162979e+00,  2.57694637e+00,  8.74410366e+00,\n",
+       "         3.34738928e-01,  1.24126592e+01, -4.31561076e-01,\n",
+       "         5.32190068e+00,  1.73620969e+00,  4.82885082e+00,\n",
+       "         1.67598185e+00,  4.15054922e-02,  9.76188731e+00,\n",
+       "         1.98399792e-01,  8.86614813e+00,  3.47358256e-01,\n",
+       "        -1.03321936e+00,  9.08184457e+00,  7.08575048e+00,\n",
+       "         6.63469814e-03,  3.66084649e+00,  5.46949336e+00,\n",
+       "         2.82356744e+00,  7.11227267e+00,  6.15832928e+00,\n",
+       "         1.44705232e+00,  1.27331272e+00,  1.27668689e+00,\n",
+       "        -2.07074118e+00,  9.94535731e+00,  4.30643664e+00,\n",
+       "         7.06316227e+00, -4.02548368e-02,  5.80709818e+00,\n",
+       "         6.54451596e+00,  7.98235447e-01,  2.76517848e+00,\n",
+       "         3.51958143e+00,  3.56557505e+00,  6.93569050e+00,\n",
+       "         8.48417483e+00,  9.14823315e+00,  8.78306229e+00,\n",
+       "         3.91181008e+00,  6.15000155e+00,  3.36671297e+00,\n",
+       "         3.45080693e+00,  7.30877790e-01,  1.19442357e+01,\n",
+       "        -1.99296699e+00, -2.16334777e+00,  2.47145000e+00,\n",
+       "         8.39188900e+00,  1.07556824e+00,  6.44136824e+00,\n",
+       "        -3.35134408e-01,  9.17762762e+00,  3.49880307e-01,\n",
+       "         7.43540694e+00,  5.33983821e+00,  3.53668628e+00,\n",
+       "         3.94563223e+00,  3.68023927e+00,  4.95603253e+00,\n",
+       "         4.95565905e+00,  7.52868379e+00,  4.05359789e+00,\n",
+       "         6.51337803e+00,  2.17470570e+00,  2.82369479e+00,\n",
+       "         4.72679418e+00,  8.76814010e+00,  2.59209152e+00,\n",
+       "         5.34561118e+00,  3.25733119e+00,  4.00246781e+00,\n",
+       "         8.33770665e-01,  8.33770665e-01,  6.93765272e+00,\n",
+       "         4.67603385e+00, -3.14363717e+00,  4.00875281e+00,\n",
+       "         5.52725970e+00,  2.28182710e+00,  6.22018116e+00,\n",
+       "         4.60331530e+00,  5.25234580e+00,  9.35664907e+00,\n",
+       "         3.33644417e+00,  3.04256648e+00,  4.29581525e+00,\n",
+       "         7.09411767e+00,  4.18825261e+00,  3.42085778e-01,\n",
+       "         7.64849325e+00, -1.02864862e+00,  6.59021603e+00,\n",
+       "         5.84483119e+00,  3.46257013e+00,  5.67994974e+00,\n",
+       "         3.35052316e+00,  2.06091491e-01,  6.86894119e+00,\n",
+       "         5.95501433e+00,  5.62506991e+00,  2.95817362e+00,\n",
+       "         4.99052044e+00,  2.72847236e+00,  4.60628690e+00,\n",
+       "         2.05854151e+00,  1.07062316e+00,  1.39619469e+00,\n",
+       "         5.57847406e+00,  8.02701729e+00, -9.35793196e-01,\n",
+       "        -9.35793196e-01, -3.22726099e+00, -1.48219571e+00,\n",
+       "        -3.80568552e+00, -3.80568552e+00,  7.26775999e+00,\n",
+       "         5.62384441e+00,  3.30589252e+00, -2.56406166e-02,\n",
+       "        -2.56406166e-02,  6.70605312e+00,  1.92064094e+00,\n",
+       "         4.52476866e+00,  4.26482568e+00,  5.19830097e+00,\n",
+       "         5.19830097e+00,  5.55040698e+00,  1.81262705e+00,\n",
+       "         6.71000446e+00,  7.07744062e-01,  1.67881858e+00,\n",
+       "        -2.38625473e-01, -3.57065274e+00,  1.38559926e+01,\n",
+       "        -2.28209203e-01,  4.47903555e+00,  3.21300503e+00,\n",
+       "         5.13248886e+00,  9.50953062e-01,  7.16870518e+00,\n",
+       "         5.30866449e+00,  6.48691675e+00,  8.03583264e+00,\n",
+       "         2.07918943e+00,  8.43284860e+00,  1.34922701e+00,\n",
+       "         5.60138396e+00,  5.30520091e+00,  2.21739558e+00,\n",
+       "         3.49633793e-01, -1.67193360e+00,  2.52573580e+00,\n",
+       "         1.64023599e+00,  1.16665122e+01, -2.70149725e+00,\n",
+       "         5.51160292e+00,  8.08524320e+00,  2.36268002e+00,\n",
+       "         7.69472187e-01,  1.10185893e+00,  1.02930030e+00,\n",
+       "         4.69574622e+00,  6.21451900e+00,  1.43855979e+00,\n",
+       "         5.15178386e+00,  3.47037061e+00,  9.07197410e-01,\n",
+       "         7.27561427e+00,  4.22993705e+00,  4.45173546e+00,\n",
+       "         9.10095947e+00,  5.06862059e+00,  1.18420526e+01,\n",
+       "         5.75966035e+00,  4.28062288e+00,  5.41981440e+00,\n",
+       "         2.68764727e-01,  8.86716838e+00,  2.90370388e+00,\n",
+       "         1.86803527e+00,  1.66670293e+00,  3.53592646e+00,\n",
+       "        -2.59908648e+00,  4.97636704e+00,  2.73148089e+00,\n",
+       "         4.98443447e+00,  4.43251932e+00,  2.27586171e+00,\n",
+       "         8.51825947e+00,  2.70128407e+00,  7.42228409e+00,\n",
+       "         1.84197854e+00,  4.89308990e+00,  6.71476014e+00,\n",
+       "         4.35871251e+00,  4.90341871e+00,  8.55451386e+00,\n",
+       "         8.83161112e+00,  7.63174447e+00,  1.73827144e+00,\n",
+       "         4.66630769e+00,  5.72731355e+00,  2.05832550e+00,\n",
+       "         2.26153045e-01, -5.05932553e-01, -4.40159946e-01,\n",
+       "         5.15489443e+00,  2.11073823e+00,  9.03111392e+00,\n",
+       "         1.05037104e+01,  6.32332159e-01,  4.30934591e+00,\n",
+       "         1.75419767e+00,  3.45794993e+00,  6.77938944e+00,\n",
+       "         2.00533410e+00,  7.36668278e+00,  6.44714693e+00,\n",
+       "         2.98229449e+00,  9.24720183e-01,  7.61612287e+00,\n",
+       "         7.80184167e+00, -9.30019559e-01,  4.83817314e+00,\n",
+       "         4.75614030e+00,  1.53631516e+00,  5.04871078e+00,\n",
+       "        -3.41107244e+00,  3.37376756e+00,  4.88491984e+00,\n",
+       "         4.76829625e+00,  1.07617850e+00,  2.58363091e+00,\n",
+       "         6.72453206e+00,  3.10857689e-01,  3.26555748e+00,\n",
+       "         2.42017504e+00, -2.70434482e+00,  1.27860820e+01,\n",
+       "        -5.89766522e+00,  9.29991946e+00,  2.69483415e+00,\n",
+       "         9.59170156e-01,  5.22648939e+00,  2.61609296e+00,\n",
+       "         1.63909192e+00,  7.15393600e+00,  3.98140083e-01,\n",
+       "         6.77108738e+00,  3.44501960e+00,  6.35264019e+00,\n",
+       "         5.46625032e+00,  6.08815565e+00,  1.27657528e+01,\n",
+       "        -5.49494620e+00, -5.26490749e+00,  1.20871587e+01,\n",
+       "         8.82335649e+00,  2.15223573e+00,  6.10690930e+00,\n",
+       "         2.80734833e+00,  9.84399774e-01,  7.45513402e+00,\n",
+       "        -3.03749322e-01, -2.54957720e-01,  8.97050550e+00,\n",
+       "         5.74973240e+00,  5.92051518e+00,  8.02790650e+00,\n",
+       "         1.70115394e+00,  3.69946604e+00,  1.20848051e+01,\n",
+       "         1.56507102e+00,  3.57798898e+00,  6.38666543e+00,\n",
+       "         1.09798168e+01, -1.39209318e+00,  6.28904449e+00,\n",
+       "         3.45750558e+00,  3.49307287e+00,  6.70722261e+00,\n",
+       "         6.46570727e+00,  3.00276605e+00,  2.47001584e+00,\n",
+       "         6.50015446e+00,  7.03574332e+00,  7.58653886e+00,\n",
+       "         3.05218394e+00,  6.56525097e+00,  6.34355111e+00,\n",
+       "         3.65181062e+00,  5.33285836e+00,  7.07323552e+00,\n",
+       "         3.35555328e+00,  2.40593926e+00,  4.07821907e+00,\n",
+       "         6.06723093e+00,  3.33924324e+00,  8.47540996e+00,\n",
+       "         6.55184354e+00,  6.55660461e+00, -1.14269772e+00,\n",
+       "         9.63969256e+00, -3.69739235e-01,  9.08980641e+00,\n",
+       "         2.50180730e+00,  6.08788503e+00,  2.19677092e+00,\n",
+       "        -2.50227530e+00,  8.67061611e+00,  2.73386047e+00,\n",
+       "         6.31004030e-01,  6.75261061e+00,  1.09043881e-01,\n",
+       "         4.62472473e+00,  7.83564219e+00,  9.43946808e+00,\n",
+       "         2.35089570e+00,  8.46985192e+00,  5.53873482e-02,\n",
+       "         5.70011030e+00,  3.55368634e+00,  1.32208110e+00,\n",
+       "         4.16607240e+00,  5.23832166e+00,  4.44486991e+00,\n",
+       "         2.39799074e+00,  7.21911508e+00,  2.61416964e+00,\n",
+       "         1.04011497e+01,  8.99363387e-01,  7.87929247e+00,\n",
+       "         5.25277333e+00,  4.05454429e+00, -7.67597958e-01,\n",
+       "         1.22858748e+01,  2.12609570e+00,  4.76117851e+00,\n",
+       "         8.81131135e+00,  2.07380643e+00,  1.94965783e+00,\n",
+       "         2.52679689e+00,  6.89766849e+00,  7.30306016e+00,\n",
+       "         4.56272319e+00,  5.04595827e+00,  2.28520315e+00,\n",
+       "         2.87285340e+00,  5.39241893e+00,  7.34739992e+00,\n",
+       "        -3.94029815e+00,  7.49830997e+00,  3.93360667e+00,\n",
+       "         3.45142936e+00,  3.32736762e+00,  4.72749177e+00,\n",
+       "        -2.39550651e-01,  7.75951894e+00,  1.20204052e+01,\n",
+       "         8.17845384e+00,  2.94663687e+00, -3.41178691e-02,\n",
+       "         1.03215604e+01,  7.22553598e+00,  1.89647201e+00,\n",
+       "         3.59294445e+00,  6.85970806e+00,  6.93376911e+00,\n",
+       "         3.34471286e+00, -9.94745318e-01,  5.05311688e+00,\n",
+       "         4.59779894e+00,  4.38326175e+00,  4.29569742e+00,\n",
+       "         6.42009886e+00,  6.69922156e+00,  7.93760269e+00,\n",
+       "         8.34813849e+00,  3.27325638e+00,  5.96929247e-01,\n",
+       "         1.45148552e-01,  1.46789715e+00,  6.83929042e+00,\n",
+       "        -7.72780650e-01, -2.76181563e+00,  2.26786750e+00,\n",
+       "         7.42635084e+00,  9.39800329e+00,  2.63870315e+00,\n",
+       "         5.04170832e+00,  4.79083118e+00,  3.61426592e+00,\n",
+       "         5.67166560e+00,  3.71122494e+00,  3.75664619e+00,\n",
+       "         1.84995002e+00,  8.86199510e+00,  6.97006007e+00,\n",
+       "        -1.11277678e+00,  3.80226186e+00,  7.60335053e+00,\n",
+       "         1.10310962e+01, -2.49337704e+00,  4.38962708e+00,\n",
+       "         3.10041806e+00,  4.21846266e+00,  1.72960085e+00,\n",
+       "         9.57291473e-01,  8.34342016e+00,  1.03275477e+00,\n",
+       "         8.49323013e+00,  1.49981154e+00,  5.65829498e+00,\n",
+       "         1.12839848e+00,  1.06607200e+01,  1.25955293e+01,\n",
+       "        -2.45129853e+00,  4.47863662e-01,  5.47505883e+00,\n",
+       "         5.47505883e+00,  4.58310176e+00,  5.68337171e+00,\n",
+       "         3.74157903e-01,  1.16414656e+01, -4.12836944e+00,\n",
+       "         1.95064943e+00,  8.10956530e-03,  8.76235476e+00,\n",
+       "        -8.89935230e-01,  8.76389087e+00]])
          • theta_tilde
            (chain, draw, school)
            float64
            -0.9319 1.08 ... 0.5706 -0.4123
            array([[[-0.93193184,  1.07955863, -0.63944692, ..., -1.15699353,\n",
+       "         -1.72731201, -0.80842907],\n",
+       "        [ 1.33210719, -0.43188553,  1.73382788, ...,  0.8361501 ,\n",
+       "          1.27725948, -0.22564279],\n",
+       "        [ 1.04270809,  0.31605842,  0.91628183, ...,  0.11904838,\n",
+       "          0.94874201, -0.89329465],\n",
+       "        ...,\n",
+       "        [ 0.60455847,  0.41670938,  0.30380914, ..., -0.5236711 ,\n",
+       "         -0.66643775, -0.23138039],\n",
+       "        [-0.17076808, -0.43805528, -0.83658994, ...,  0.13940144,\n",
+       "          1.43255767,  0.52613097],\n",
+       "        [ 0.34137494,  1.92113419,  0.91367552, ...,  1.19301448,\n",
+       "          2.80274532, -0.85711612]],\n",
+       "\n",
+       "       [[ 0.28056653, -0.61760326,  1.14611145, ..., -0.49222139,\n",
+       "          1.47608045,  1.28578082],\n",
+       "        [ 0.67172511, -0.1482499 , -0.25078999, ..., -0.86408684,\n",
+       "          0.20942027,  0.76079871],\n",
+       "        [ 0.66347005, -1.28144782, -0.19553371, ...,  1.00912494,\n",
+       "          0.81284581, -0.16367622],\n",
+       "        ...,\n",
+       "        [ 0.3410791 , -0.49307311, -0.03897836, ..., -0.10989548,\n",
+       "          0.36688885, -0.89893107],\n",
+       "        [ 0.08130106,  0.60077994, -0.32559539, ..., -0.7244612 ,\n",
+       "          0.08748465,  1.12986153],\n",
+       "        [ 1.37718068,  0.93452373, -1.05378636, ...,  0.35680436,\n",
+       "          0.57057624, -0.41225969]]])
          • tau
            (chain, draw)
            float64
            0.4474 0.3094 7.001 ... 3.59 3.348
            array([[4.47364225e-01, 3.09438937e-01, 7.00139614e+00, 2.33802782e+00,\n",
+       "        1.73686868e+01, 2.08647159e+00, 2.60153730e+00, 9.22392577e+00,\n",
+       "        3.05042468e-01, 7.73965895e+00, 4.25382653e+00, 9.21054520e+00,\n",
+       "        3.74595066e+00, 5.95613546e+00, 1.63005028e+01, 3.73426479e+00,\n",
+       "        3.36030603e+00, 3.03750109e+00, 1.12203823e+01, 1.12203823e+01,\n",
+       "        1.12203823e+01, 1.46593997e+00, 1.47899905e+00, 5.89254966e+00,\n",
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+       "        5.64819158e+00, 3.57773526e+00, 1.41417949e+00, 1.71880538e+00,\n",
+       "        9.83310333e-01, 4.29973283e-01, 8.73379592e+00, 3.40346058e-02,\n",
+       "        5.58780246e-01, 5.63595146e+00, 1.89344044e+00, 5.26521667e-01,\n",
+       "        1.24020722e-01, 3.43118031e-01, 8.15294798e-01, 2.22562616e+00,\n",
+       "        1.96053959e+00, 1.00477194e+00, 1.28844802e+00, 1.03524805e+00,\n",
+       "        3.82352347e+00, 1.65549026e+00, 1.13413404e+00, 3.30382828e+00,\n",
+       "        3.11654378e+00, 1.07337999e+00, 1.74766323e+00, 1.59181656e+00,\n",
+       "        8.73204474e-01, 2.60520344e+00, 2.58107388e+00, 1.79853904e+00,\n",
+       "        1.74254114e+00, 7.09498658e+00, 2.44155456e+00, 4.81585929e+00,\n",
+       "        9.44713304e-01, 2.46345663e+00, 4.31741576e+00, 1.85545907e+00,\n",
+       "        4.72384442e+00, 1.24268345e+00, 1.38138794e+01, 9.25892650e+00,\n",
+       "        3.06933572e+00, 4.55101936e-01, 6.93529221e+00, 3.79349981e+00,\n",
+       "        4.82202579e+00, 2.51395292e-01, 3.55535105e-01, 2.58909892e-01,\n",
+       "        1.07153345e+01, 2.47792734e-01, 1.08696079e+01, 3.02154438e+00,\n",
+       "        2.86200527e+00, 6.30021175e+00, 1.57405444e+00, 2.29769893e+00,\n",
+       "        4.49354761e+00, 4.39634462e+00, 1.80450375e+00, 1.83029690e+00,\n",
+       "        2.76441303e+00, 1.26655376e+00, 1.49865721e+00, 2.73035589e+00,\n",
+       "        1.93292798e+00, 5.54069486e+00, 1.54714571e+00, 2.13829935e+00,\n",
+       "        1.49585263e+00, 7.40522102e-01, 1.68360311e-01, 4.79390967e+00,\n",
+       "        6.50254533e-01, 9.04501958e+00, 3.12013209e-01, 2.96999747e-01,\n",
+       "        1.83501724e+00, 4.36912044e+00, 2.72487425e+00, 3.77268275e+00,\n",
+       "        1.10981008e+00, 7.99307178e+00, 3.35016274e+00, 3.17178644e+00,\n",
+       "        2.32950017e+00, 4.18132908e+00, 4.79112685e-01, 1.47763067e+00,\n",
+       "        1.07190517e+01, 1.39635550e+01, 3.00060190e+00, 4.47950122e+00,\n",
+       "        2.22658995e+00, 7.21854913e-01, 6.83162572e-01, 8.12546775e+00,\n",
+       "        7.06258523e-01, 6.89297367e-01, 1.14443101e+00, 3.43857900e+00,\n",
+       "        1.06245562e+00, 1.62537435e+01, 5.70309018e+00, 6.11469540e+00,\n",
+       "        3.72576009e+00, 3.25810480e+00, 4.27772314e+00, 5.89278817e+00,\n",
+       "        2.22181081e+00, 3.93991749e+00, 2.02401123e+00, 2.33095514e+00,\n",
+       "        8.96349762e+00, 1.34896286e+00, 2.23285432e+00, 2.92054712e+00,\n",
+       "        5.03323365e+00, 2.56937806e+00, 5.83991827e+00, 1.26629283e+00,\n",
+       "        2.71944192e+00, 8.64273275e-01, 5.13509834e+00, 2.16662949e+00,\n",
+       "        3.73543341e+00, 1.23590938e+00, 8.38816919e-01, 4.32691843e+00,\n",
+       "        6.84451135e-01, 7.87623750e+00, 4.07666681e+00, 2.03034439e+00,\n",
+       "        1.91531604e+00, 2.03931989e+00, 2.43319104e+00, 2.37632731e+00,\n",
+       "        3.98497067e+00, 1.08112193e+00, 2.28505613e-01, 5.04936183e+00,\n",
+       "        2.35111739e+00, 2.56936794e+00, 6.16206404e+00, 2.81945294e+00,\n",
+       "        3.39413072e+00, 2.53205822e+00, 2.93143251e+00, 3.36514543e+00,\n",
+       "        3.49593763e+00, 3.64690141e+00, 3.87323691e+00, 2.36820489e+00,\n",
+       "        1.78590999e+00, 6.66913023e+00, 2.04351672e-01, 3.25617715e+00,\n",
+       "        1.69566962e+00, 5.22082863e+00, 5.75458429e-01, 5.72599583e-01,\n",
+       "        2.36868952e-01, 3.91244681e+00, 2.44401483e+00, 2.79059691e+00,\n",
+       "        5.81409310e+00, 2.33397323e+00, 1.25845195e+00, 3.19021135e+00,\n",
+       "        6.43856860e+00, 3.48948376e-01, 7.84800632e+00, 1.10693551e+00,\n",
+       "        3.90908300e+00, 3.01883998e+00, 4.94049869e+00, 4.26376196e+00,\n",
+       "        2.06695193e+00, 6.80840729e-01, 6.80254898e-01, 2.62736665e-01,\n",
+       "        2.44253940e-01, 4.56976646e-01, 3.27086696e-01, 3.16178367e+00,\n",
+       "        5.53333311e+00, 5.77166186e-01, 1.53568825e+01, 3.14842353e+00,\n",
+       "        1.02436591e+00, 4.05365926e+00, 4.30120418e+00, 1.46895264e+00,\n",
+       "        2.42124275e+00, 2.10170979e+00, 1.20549422e+00, 1.04554736e+00,\n",
+       "        8.06833503e+00, 1.39633068e+00, 4.26784293e+00, 6.57140020e+00,\n",
+       "        2.25899927e+00, 2.26050382e+00, 5.49584507e+00, 5.74142152e+00,\n",
+       "        7.36952793e-01, 2.08972215e+00, 1.05970662e+01, 3.75780715e-01,\n",
+       "        6.49231216e+00, 6.19779903e+00, 2.93787993e-01, 2.02701926e-01,\n",
+       "        1.22254480e-01, 6.18160836e+00, 3.62063400e+00, 3.03089951e+00,\n",
+       "        6.02108211e+00, 2.73807712e+00, 1.82459257e+00, 5.81289768e+00,\n",
+       "        1.84169495e+00, 5.98573839e+00, 5.43515618e+00, 3.88177270e+00,\n",
+       "        5.34525798e+00, 1.57388007e+00, 4.86141429e+00, 1.62002469e+00,\n",
+       "        3.97558795e+00, 3.91428266e+00, 1.22684292e+00, 2.49578917e+00,\n",
+       "        3.77988421e-01, 9.45057529e-01, 1.34607746e+01, 1.54449278e+00,\n",
+       "        7.00132221e+00, 3.18040289e+00, 8.49637203e+00, 6.04267265e+00,\n",
+       "        3.50384327e+00, 2.28607710e+00, 5.71702334e-02, 4.06466158e-01,\n",
+       "        1.40679483e+00, 2.02046348e+00, 7.35370938e+00, 3.84766993e+00,\n",
+       "        3.73974270e+00, 1.12633672e+00, 1.88998167e+00, 2.25342685e+00,\n",
+       "        9.28228536e+00, 9.28228536e+00, 4.55933960e+00, 4.92305697e+00,\n",
+       "        2.16809838e+00, 1.32786414e+00, 8.01367473e+00, 4.06748670e+00,\n",
+       "        9.89595479e+00, 8.60362678e+00, 3.58966421e+00, 3.34788273e+00]])
          • theta
            (chain, draw, school)
            float64
            -2.431 -1.531 -2.3 ... 10.67 7.384
            array([[[-2.43086409, -1.53099522, -2.3000168 , ..., -2.53154864,\n",
+       "         -2.78668873, -2.37561337],\n",
+       "        [13.3524455 , 12.80659747, 13.47675353, ..., 13.19897707,\n",
+       "         13.33547349, 12.870417  ],\n",
+       "        [17.85746416, 12.76990198, 16.97230384, ..., 11.39055666,\n",
+       "         17.19957045,  4.30274202],\n",
+       "        ...,\n",
+       "        [ 0.30439584,  0.13134936,  0.02734572, ..., -0.73492864,\n",
+       "         -0.8664452 , -0.46567059],\n",
+       "        [ 9.81155452,  9.3479949 ,  8.65681113, ..., 10.3494855 ,\n",
+       "         12.59222294, 11.02019548],\n",
+       "        [ 6.42851041,  7.27980482,  6.73690948, ...,  6.88743856,\n",
+       "          7.75488394,  5.7826723 ]],\n",
+       "\n",
+       "       [[ 5.81071064, -1.16450057, 12.53255621, ..., -0.19078149,\n",
+       "         15.09510453, 13.61723243],\n",
+       "        [ 4.50925169, -0.85343585, -1.52405445, ..., -5.53505401,\n",
+       "          1.48574927,  5.09179856],\n",
+       "        [ 9.81987137,  4.76839455,  7.58880667, ..., 10.71763045,\n",
+       "         10.20784054,  7.67154919],\n",
+       "        ...,\n",
+       "        [11.69687206,  4.52013777,  8.42699946, ...,  7.81685503,\n",
+       "         11.91892949,  1.02828731],\n",
+       "        [-0.59809173,  1.26666303, -2.05871336, ..., -3.49050769,\n",
+       "         -0.5758947 ,  3.16588827],\n",
+       "        [13.37453029, 11.89256674,  5.2359377 , ...,  9.95843003,\n",
+       "         10.6741132 ,  7.38369378]]])
        • created_at :
          2020-06-06T18:08:05.191460
          arviz_version :
          0.8.3
          inference_library :
          pymc3
          inference_library_version :
          3.8

        \n", + "
      \n", + "
      \n", + "
      • \n", + " \n", + "
    • \n", + " \n", + " \n", + "
      \n", + "
      \n", + "
        \n", + "
        \n", + "\n", + "\n", + "Show/Hide data repr\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Show/Hide attributes\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
        xarray.Dataset
          • chain: 2
          • draw: 500
          • obs_dim_0: 8
          • chain
            (chain)
            int64
            0 1
            array([0, 1])
          • draw
            (draw)
            int64
            0 1 2 3 4 5 ... 495 496 497 498 499
            array([  0,   1,   2, ..., 497, 498, 499])
          • obs_dim_0
            (obs_dim_0)
            int64
            0 1 2 3 4 5 6 7
            array([0, 1, 2, 3, 4, 5, 6, 7])
          • obs
            (chain, draw, obs_dim_0)
            float64
            -16.98 -1.64 1.798 ... 25.1 19.28
            array([[[-16.97645538,  -1.64038568,   1.79770041, ...,  14.40859199,\n",
+       "         -23.09960687, -18.13050863],\n",
+       "        [ 23.62847574,  28.00510263,  29.16764597, ..., -18.97816711,\n",
+       "           9.95080714,  16.89268424],\n",
+       "        [ 37.02276389,   4.9645585 ,   8.9831761 , ...,   3.46762858,\n",
+       "          21.48353196, -22.54091146],\n",
+       "        ...,\n",
+       "        [ 19.95419475,   8.99518895,  -2.7647318 , ...,   0.69631123,\n",
+       "          -2.61307142,  21.69863335],\n",
+       "        [ -1.58701966,   2.72730288,   0.22424347, ...,   6.45413578,\n",
+       "          13.83444615,  16.59758946],\n",
+       "        [ 29.57892186,  28.30755109,  10.86959693, ...,  -4.0969694 ,\n",
+       "          18.96384652, -21.49265517]],\n",
+       "\n",
+       "       [[  7.47752947,  -4.37000202, -14.8472858 , ..., -14.50545683,\n",
+       "           3.12202882,  -5.73977302],\n",
+       "        [  0.7952108 ,  -9.05784386,   7.57644261, ..., -14.4017723 ,\n",
+       "           1.63382119,  18.30680557],\n",
+       "        [ 16.81443595,   8.89175516,   5.43731547, ...,  17.45147949,\n",
+       "          -3.19802734,  -4.75950592],\n",
+       "        ...,\n",
+       "        [-10.19634347,  22.66826828,  26.12711621, ...,  10.35126659,\n",
+       "          24.84716965,  14.48155495],\n",
+       "        [ -4.16541154,  11.29841989, -26.00202243, ...,   0.55060509,\n",
+       "           7.44766052,  31.05157162],\n",
+       "        [  5.95437579,   6.34268575,   8.62670338, ...,  12.31584594,\n",
+       "          25.10061593,  19.27639116]]])
        • created_at :
          2020-06-06T18:08:05.349112
          arviz_version :
          0.8.3
          inference_library :
          pymc3
          inference_library_version :
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        \n", + "
      \n", + "
      \n", + "
      • \n", + " \n", + "
    • \n", + " \n", + " \n", + "
      \n", + "
      \n", + "
        \n", + "
        \n", + "\n", + "\n", + "Show/Hide data repr\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Show/Hide attributes\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
        xarray.Dataset
          • chain: 2
          • draw: 500
          • obs_dim_0: 8
          • chain
            (chain)
            int64
            0 1
            array([0, 1])
          • draw
            (draw)
            int64
            0 1 2 3 4 5 ... 495 496 497 498 499
            array([  0,   1,   2, ..., 497, 498, 499])
          • obs_dim_0
            (obs_dim_0)
            int64
            0 1 2 3 4 5 6 7
            array([0, 1, 2, 3, 4, 5, 6, 7])
          • obs
            (chain, draw, obs_dim_0)
            float64
            -5.685 -3.676 ... -3.49 -3.842
            array([[[-5.68484982, -3.67572298, -3.69248424, ..., -3.36837032,\n",
+       "         -5.38195577, -4.12822736],\n",
+       "        [-4.10376841, -3.33704052, -4.22176828, ..., -3.93177199,\n",
+       "         -3.33031266, -3.81047947],\n",
+       "        [-3.85559103, -3.33528345, -4.47061499, ..., -3.76296466,\n",
+       "         -3.22472706, -3.90074205],\n",
+       "        ...,\n",
+       "        [-5.33153649, -3.53110194, -3.7094273 , ..., -3.32927173,\n",
+       "         -5.0012374 , -4.04911422],\n",
+       "        [-4.36214329, -3.23060908, -3.95692031, ..., -3.67804405,\n",
+       "         -3.36774389, -3.8107918 ],\n",
+       "        [-4.66105354, -3.22411703, -3.87669797, ..., -3.46006493,\n",
+       "         -3.74633564, -3.86896332]],\n",
+       "\n",
+       "       [[-4.72113221, -3.64146398, -4.16273878, ..., -3.32269315,\n",
+       "         -3.26371571, -3.81334647],\n",
+       "        [-4.85324486, -3.61344026, -3.69578197, ..., -3.49330873,\n",
+       "         -4.58512601, -3.88295728],\n",
+       "        [-4.36147113, -3.27374   , -3.91051715, ..., -3.70705009,\n",
+       "         -3.52511237, -3.83822308],\n",
+       "        ...,\n",
+       "        [-4.21763758, -3.28207083, -3.94655912, ..., -3.50885658,\n",
+       "         -3.40642072, -3.99507955],\n",
+       "        [-5.44443507, -3.44821276, -3.69325776, ..., -3.40015884,\n",
+       "         -4.94684294, -3.92974475],\n",
+       "        [-4.10233177, -3.29728401, -3.82400903, ..., -3.64845971,\n",
+       "         -3.48986671, -3.84219653]]])
        • created_at :
          2020-06-06T18:08:05.346903
          arviz_version :
          0.8.3
          inference_library :
          pymc3
          inference_library_version :
          3.8

        \n", + "
      \n", + "
      \n", + "
      • \n", + " \n", + "
    • \n", + " \n", + " \n", + "
      \n", + "
      \n", + "
        \n", + "
        \n", + "\n", + "\n", + "Show/Hide data repr\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Show/Hide attributes\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
        xarray.Dataset
          • chain: 2
          • draw: 500
          • chain
            (chain)
            int64
            0 1
            array([0, 1])
          • draw
            (draw)
            int64
            0 1 2 3 4 5 ... 495 496 497 498 499
            array([  0,   1,   2, ..., 497, 498, 499])
          • energy
            (chain, draw)
            float64
            54.52 54.96 54.27 ... 48.71 52.74
            array([[54.52160643, 54.95687483, 54.27067055, 49.10964855, 50.72976279,\n",
+       "        46.15553381, 48.17393693, 44.47687278, 47.87431257, 46.21133132,\n",
+       "        50.66542988, 48.47734258, 49.5352183 , 47.03373166, 46.50687766,\n",
+       "        46.65056654, 51.02033901, 51.34946622, 50.97514503, 52.62398149,\n",
+       "        54.11538063, 49.38704282, 52.448772  , 58.85837468, 56.19848921,\n",
+       "        54.62420629, 53.95110432, 51.11996596, 50.57471821, 51.68038428,\n",
+       "        53.74084637, 50.19865079, 53.28808161, 54.89180583, 53.76867527,\n",
+       "        49.32524811, 47.08445233, 49.1243453 , 48.63807178, 45.20603424,\n",
+       "        52.99634132, 51.89072421, 47.71031214, 49.77615397, 48.20182985,\n",
+       "        49.25869024, 52.75251238, 51.1598453 , 48.25773604, 53.5598244 ,\n",
+       "        51.86943369, 58.59089918, 56.25740159, 49.48317636, 52.60384042,\n",
+       "        55.70528335, 54.08445635, 51.16553814, 53.09058361, 51.6535333 ,\n",
+       "        52.56935218, 55.23281544, 55.21947663, 57.23267929, 55.65247884,\n",
+       "        50.69957387, 48.20132423, 47.20210464, 47.46505534, 47.70952866,\n",
+       "        48.35531293, 51.08039718, 52.21331115, 51.16269634, 46.19248757,\n",
+       "        44.42095393, 49.470161  , 59.08761687, 52.05040137, 50.22388005,\n",
+       "        49.97409896, 51.87596621, 52.28301697, 47.99711269, 47.44711831,\n",
+       "        50.12178028, 49.73906133, 49.11136314, 48.35579577, 51.18917448,\n",
+       "        51.44566784, 50.39035018, 47.75678021, 48.47188683, 49.61341197,\n",
+       "        48.80640695, 50.27044123, 49.20724321, 47.81334872, 47.83239105,\n",
+       "        45.86280003, 50.68591711, 53.00304482, 50.30159952, 47.4708534 ,\n",
+       "        45.72613178, 47.06700367, 46.76972905, 52.36562995, 51.73090985,\n",
+       "        52.06586998, 52.66878118, 53.24908252, 57.23098017, 50.39790665,\n",
+       "        58.78099295, 49.06070421, 49.65475022, 49.58557107, 49.37204057,\n",
+       "        49.76708232, 45.26738951, 46.65781403, 47.34593169, 51.28649564,\n",
+       "        52.05116934, 57.12687705, 56.55121196, 53.97339635, 59.74400108,\n",
+       "        56.65847724, 53.00157087, 47.87992086, 48.72122558, 48.74976971,\n",
+       "        49.99931321, 51.37939305, 53.66377036, 52.40574199, 54.28425081,\n",
+       "        54.27756375, 48.62355588, 54.78227227, 56.89009561, 49.3998884 ,\n",
+       "        53.01421857, 59.14194416, 55.4769736 , 54.04591133, 48.51438803,\n",
+       "        51.51576145, 50.36912028, 48.85034295, 46.67157008, 47.50172218,\n",
+       "        51.33977961, 52.06635977, 51.64532305, 55.35540336, 54.5257091 ,\n",
+       "        54.04430903, 52.09718445, 50.87275206, 49.87873006, 48.17603978,\n",
+       "        47.60321542, 47.4497779 , 47.90886782, 50.92138271, 59.28103152,\n",
+       "        53.47716696, 50.46358368, 52.74687336, 53.85013681, 49.02239218,\n",
+       "        50.62123458, 50.21995491, 51.56848987, 52.20053707, 49.60123845,\n",
+       "        58.26423451, 57.51115144, 60.022103  , 55.89578116, 51.69176363,\n",
+       "        54.21693777, 51.6907195 , 50.49074369, 48.3769583 , 45.83136183,\n",
+       "        45.58907585, 47.4179835 , 49.84640292, 50.72474095, 52.51297121,\n",
+       "        47.33749676, 49.71804704, 50.22070501, 46.84124769, 49.38667317,\n",
+       "        47.33984659, 47.4314463 , 47.44635807, 45.51996513, 45.73395308,\n",
+       "        51.0030376 , 52.78370397, 51.66420403, 46.83508104, 49.39586429,\n",
+       "        50.19837873, 49.32080493, 47.17603073, 47.25434393, 47.74170705,\n",
+       "        49.40680436, 49.52699711, 50.84088062, 51.81092264, 50.33764575,\n",
+       "        50.44769977, 49.09420585, 53.22611751, 52.25917315, 46.86964895,\n",
+       "        46.36395158, 53.33477778, 55.82903514, 52.47133715, 51.0821753 ,\n",
+       "        50.55080747, 47.44256052, 48.48089976, 48.20652204, 49.78358086,\n",
+       "        52.42374909, 53.16344726, 53.25020103, 50.03293047, 49.73035208,\n",
+       "        48.92927453, 53.04114137, 57.93004039, 55.88141386, 54.25877798,\n",
+       "        49.04335994, 47.24083672, 51.45499411, 51.2990735 , 49.25146863,\n",
+       "        51.97573023, 51.30177488, 52.81859714, 55.08607418, 53.64896044,\n",
+       "        47.90562078, 47.37685531, 49.85905465, 52.54778476, 51.82132266,\n",
+       "        50.16566838, 49.82662244, 48.34319432, 48.25357395, 45.86624667,\n",
+       "        49.88291044, 49.34682388, 51.67856221, 45.54401496, 45.15925283,\n",
+       "        49.18105106, 47.79485295, 49.68857038, 48.742197  , 50.21175167,\n",
+       "        52.11092666, 53.99036219, 50.96319513, 54.12115636, 49.10965022,\n",
+       "        50.46237483, 48.58837103, 47.19636347, 47.11606613, 47.63753637,\n",
+       "        51.85565863, 52.90922418, 51.74390654, 52.76424441, 49.43350663,\n",
+       "        49.88200565, 49.58332475, 49.31126017, 53.93426356, 49.00841865,\n",
+       "        50.26790555, 56.6599792 , 54.49004485, 54.28881061, 54.68177041,\n",
+       "        54.47260288, 50.56304132, 52.09744814, 51.98245774, 51.19833937,\n",
+       "        50.07368621, 51.7632764 , 47.55513182, 45.37128629, 45.48234658,\n",
+       "        52.82297329, 49.21264152, 46.77243442, 49.95021056, 46.31865094,\n",
+       "        45.00932632, 46.58200528, 48.31752065, 46.29755461, 51.31405935,\n",
+       "        54.53919087, 52.20612455, 52.51279961, 53.87558256, 52.51613071,\n",
+       "        50.00421059, 52.56374857, 50.14562315, 54.95559643, 53.99484075,\n",
+       "        53.65264922, 52.24483306, 53.96513596, 51.92346592, 54.76463265,\n",
+       "        50.86503348, 48.71759475, 50.90548694, 49.42953537, 46.6970215 ,\n",
+       "        47.50267852, 46.28910759, 46.44191692, 51.4278663 , 49.50166175,\n",
+       "        50.36172026, 52.07050733, 49.99101015, 55.28676316, 53.28986807,\n",
+       "        56.03496623, 56.29910687, 52.94953976, 49.35067424, 50.12602492,\n",
+       "        53.13307434, 49.65117122, 50.40163346, 48.43488376, 46.95089029,\n",
+       "        49.81443666, 49.85240688, 54.86953323, 53.30949774, 50.420449  ,\n",
+       "        49.58389367, 53.58314139, 51.17776022, 51.60390155, 50.65734825,\n",
+       "        48.7501096 , 46.81571634, 46.59459091, 50.26756745, 54.42956098,\n",
+       "        50.33095827, 47.61830288, 52.80981098, 46.28437746, 47.78734214,\n",
+       "        47.86573256, 46.4227254 , 49.26119556, 50.06877899, 49.99082197,\n",
+       "        52.02296758, 49.42796761, 51.74261644, 53.36213415, 58.7402137 ,\n",
+       "        63.86784408, 59.20534414, 53.72144415, 51.0537658 , 51.27862467,\n",
+       "        53.38818183, 49.70313386, 51.87580152, 47.87567439, 48.69384443,\n",
+       "        53.68863693, 51.81363142, 49.6301598 , 52.13528675, 48.59869587,\n",
+       "        48.62901133, 49.45844042, 51.29091375, 51.207433  , 50.51757778,\n",
+       "        54.0953048 , 53.32710778, 50.21251521, 49.42815968, 49.35374968,\n",
+       "        46.81854429, 51.07617748, 52.19232418, 53.89082781, 47.5229164 ,\n",
+       "        51.01525914, 52.53535814, 53.00577307, 48.05771004, 46.40661989,\n",
+       "        46.54041088, 47.29801764, 47.44293976, 45.97106231, 49.11697927,\n",
+       "        51.33693395, 52.29346454, 53.11575722, 51.94422578, 49.89876913,\n",
+       "        47.48160038, 50.27766838, 49.34196258, 47.04818695, 48.87148726,\n",
+       "        46.72607847, 51.28395888, 54.63257457, 48.3686483 , 53.93040581,\n",
+       "        51.04002976, 54.77188972, 52.31058797, 50.32725151, 49.74455668,\n",
+       "        51.78741102, 50.94116801, 50.13931055, 51.86309955, 54.44276321,\n",
+       "        60.81698034, 58.82589071, 68.98475798, 62.02564084, 51.67527227,\n",
+       "        50.93162536, 48.56519192, 46.13648073, 52.98461519, 53.26008866,\n",
+       "        52.37621147, 51.928506  , 58.24021831, 56.84817852, 54.26597904,\n",
+       "        51.95699584, 48.12764737, 46.67075138, 52.68650677, 48.77241963,\n",
+       "        49.58681376, 50.60597679, 50.67549612, 56.16169128, 53.34365291,\n",
+       "        52.20767402, 49.2856608 , 48.55052149, 48.34306771, 52.93056148,\n",
+       "        53.11813376, 50.08108645, 48.54791324, 46.09753605, 51.74122273,\n",
+       "        50.81805106, 53.50201984, 52.78495898, 48.38661816, 52.35353566,\n",
+       "        46.82668261, 46.55487301, 50.52384383, 50.41186977, 56.2319735 ],\n",
+       "       [50.16361331, 46.62711183, 47.23610145, 53.28699413, 51.68517652,\n",
+       "        50.71092413, 48.84499357, 49.28761862, 49.07832709, 50.0026781 ,\n",
+       "        54.473093  , 49.81008283, 50.42967236, 50.35692724, 48.03328994,\n",
+       "        50.34253745, 52.90368169, 54.69412291, 61.30327169, 49.81797419,\n",
+       "        49.42008667, 53.45581835, 49.57166189, 47.53825733, 51.15944002,\n",
+       "        55.45530541, 52.46549332, 55.99315182, 58.15823896, 50.57708104,\n",
+       "        48.23112112, 46.14427454, 46.15555848, 45.86897627, 48.06362451,\n",
+       "        55.37724044, 51.25873221, 50.56404492, 52.34211184, 50.5790094 ,\n",
+       "        56.67642973, 52.37489375, 51.68770331, 50.05394278, 47.43000344,\n",
+       "        48.99996225, 51.84228733, 53.52030955, 52.20226346, 51.40431476,\n",
+       "        49.93526844, 50.2412656 , 47.87525523, 51.19718934, 48.50887948,\n",
+       "        46.69185387, 45.82492472, 51.92282536, 50.78224511, 49.08186821,\n",
+       "        53.02115435, 52.21497235, 53.48573797, 54.26253189, 55.10082421,\n",
+       "        48.37982005, 50.78795593, 50.00850187, 47.15201007, 50.88519269,\n",
+       "        50.52842056, 52.59706885, 54.79765573, 58.75673192, 56.07990983,\n",
+       "        50.17064295, 50.01403143, 55.46511548, 53.56169562, 55.78343   ,\n",
+       "        50.58496765, 46.80098036, 48.46870195, 49.37411999, 51.26253781,\n",
+       "        50.39171053, 52.11800727, 52.36046736, 51.88421593, 59.72325384,\n",
+       "        60.26365237, 50.42358178, 47.43044029, 53.43757059, 52.83789805,\n",
+       "        56.89871957, 55.89007065, 51.29782883, 50.18322282, 45.61733947,\n",
+       "        50.25498539, 47.7412606 , 46.99173779, 51.78903718, 54.64552474,\n",
+       "        50.11778611, 54.25325274, 50.74366548, 50.6189566 , 50.31884918,\n",
+       "        53.41866326, 51.37557933, 49.73311384, 50.92521916, 55.0110074 ,\n",
+       "        57.87976538, 52.11626432, 48.34714342, 52.1572418 , 49.39154052,\n",
+       "        51.41006686, 50.84410363, 51.63156232, 55.98060126, 52.60006144,\n",
+       "        53.25099525, 52.45160735, 51.02107816, 48.8719842 , 50.44148341,\n",
+       "        54.50682044, 53.83339673, 52.12124849, 50.99928885, 49.98763877,\n",
+       "        49.1167665 , 48.01986316, 52.54095181, 49.4448884 , 48.32118984,\n",
+       "        48.68426386, 48.64869427, 51.58569747, 46.84291499, 49.06818037,\n",
+       "        49.06143119, 50.2935445 , 50.95657384, 48.93952925, 52.39005283,\n",
+       "        48.14024611, 47.74244761, 52.83891496, 50.05359436, 48.45608875,\n",
+       "        46.88888672, 50.35639365, 49.5827445 , 50.14388507, 48.10339369,\n",
+       "        51.31134925, 49.10766527, 48.40981561, 50.37257662, 54.92827969,\n",
+       "        53.50996636, 51.0524846 , 52.88557845, 50.17599291, 48.05067162,\n",
+       "        47.32281387, 51.21157513, 47.88826716, 49.35217251, 47.74508622,\n",
+       "        45.78640373, 44.96304527, 44.83036983, 47.85837555, 48.11190504,\n",
+       "        45.96838354, 44.78054104, 45.50590579, 46.28860841, 51.57033615,\n",
+       "        46.27261294, 50.85013639, 51.36191921, 49.16891255, 52.90882732,\n",
+       "        50.11020028, 53.69319776, 51.05818504, 49.09453219, 48.58376521,\n",
+       "        50.48633757, 52.30657094, 50.83004693, 47.58598347, 46.87434153,\n",
+       "        52.80156077, 54.32281916, 56.63400343, 60.14647556, 55.5551745 ,\n",
+       "        50.96233167, 52.20627371, 50.14777941, 50.07189745, 49.50738056,\n",
+       "        43.61955396, 47.19130644, 45.87847293, 48.40355917, 57.20701945,\n",
+       "        52.4640959 , 50.00610371, 52.06522054, 48.54096965, 48.13252367,\n",
+       "        49.0540721 , 51.01149709, 52.22185385, 48.86740714, 59.85684606,\n",
+       "        49.29640578, 51.33610271, 57.5423779 , 56.95048125, 54.42944528,\n",
+       "        56.29316444, 50.82885804, 54.73348345, 49.8828759 , 47.92376588,\n",
+       "        47.08091943, 46.65393507, 47.2700024 , 47.35102919, 51.59700031,\n",
+       "        52.32507354, 49.2463373 , 50.9421696 , 52.5838049 , 52.32398206,\n",
+       "        56.60898915, 54.19820497, 55.37097894, 47.5878352 , 55.33051654,\n",
+       "        57.87566945, 56.99238213, 52.81039001, 51.83468992, 47.61445005,\n",
+       "        50.62151663, 49.48161058, 49.81566045, 48.47118224, 48.90544177,\n",
+       "        48.50138485, 50.77296886, 53.42497227, 53.54253052, 49.12463406,\n",
+       "        47.10187062, 46.96902581, 46.99307649, 52.79486624, 53.17867497,\n",
+       "        53.61593314, 51.92242719, 54.74017959, 55.19403312, 53.55261023,\n",
+       "        49.78942286, 49.32181498, 53.48483924, 50.67824242, 49.20817846,\n",
+       "        54.97083816, 56.90733229, 48.73670308, 52.52129619, 54.37887144,\n",
+       "        49.67886094, 51.62623028, 46.87845354, 47.64509709, 49.50568421,\n",
+       "        54.04141636, 53.46924566, 52.34363354, 52.54602226, 50.87070369,\n",
+       "        54.27171166, 50.85737295, 52.60456831, 52.22785809, 51.22073634,\n",
+       "        54.45416768, 49.60988916, 54.03086345, 50.81633277, 49.1759403 ,\n",
+       "        57.28296778, 58.00043749, 57.55881371, 54.28515851, 57.83085008,\n",
+       "        60.50043823, 59.84233904, 58.09981761, 53.91635361, 52.42784599,\n",
+       "        52.11891725, 49.40698604, 51.91230645, 50.69844503, 46.56379199,\n",
+       "        49.85581051, 50.38931426, 55.27506369, 56.78967254, 53.72319514,\n",
+       "        51.62231677, 51.38114536, 54.08585269, 52.07600622, 49.27245129,\n",
+       "        48.67716728, 50.97580786, 52.94129447, 51.66422519, 55.05177848,\n",
+       "        49.83151102, 51.78222044, 48.39042383, 51.6837263 , 50.34324913,\n",
+       "        49.24587202, 50.90767277, 49.0761185 , 45.22067981, 45.86828864,\n",
+       "        47.92226604, 55.3933141 , 51.78165197, 47.42942187, 46.34758806,\n",
+       "        46.27937182, 47.06945341, 48.07631717, 48.64136057, 48.60104402,\n",
+       "        48.92043787, 48.75950125, 49.03056725, 48.9863847 , 46.54140617,\n",
+       "        50.37052683, 49.1000927 , 54.13045593, 53.31355225, 51.10786248,\n",
+       "        50.5786586 , 51.59270521, 51.70552394, 53.57964988, 51.39551801,\n",
+       "        52.20901097, 51.6691176 , 49.26969105, 48.34712697, 53.06810819,\n",
+       "        51.08668493, 53.34469259, 48.77663216, 50.00880335, 52.06967102,\n",
+       "        49.53356853, 49.84954767, 50.09647902, 52.298723  , 52.37315436,\n",
+       "        46.84418757, 48.28373161, 51.65315392, 51.26151914, 46.10317657,\n",
+       "        51.0752779 , 53.52364518, 47.10127022, 50.17919388, 50.67331798,\n",
+       "        50.79745595, 51.82939783, 51.19550031, 47.35310722, 48.88104462,\n",
+       "        51.64116275, 55.12778675, 54.52507266, 48.6645698 , 54.66492622,\n",
+       "        59.79460973, 62.53382595, 50.61594094, 51.16259492, 49.39072387,\n",
+       "        53.41974223, 52.20458553, 49.58302975, 50.84521256, 53.67738506,\n",
+       "        52.9114082 , 52.14819655, 49.79335011, 49.73268089, 50.21881039,\n",
+       "        54.87169655, 53.48516861, 52.15133133, 46.20838179, 47.33302538,\n",
+       "        48.64542243, 51.72815427, 51.31186398, 53.10805374, 51.3545924 ,\n",
+       "        50.0502054 , 50.62532944, 55.60556536, 50.35195813, 46.49968076,\n",
+       "        46.8752375 , 47.51800046, 47.96951129, 45.8935262 , 49.86334493,\n",
+       "        59.72949754, 50.21179489, 46.64978192, 50.93392219, 48.73325085,\n",
+       "        47.60897864, 48.472894  , 50.61908245, 53.18742165, 54.92709782,\n",
+       "        50.97256895, 52.88435214, 53.45757804, 53.82922186, 51.22351512,\n",
+       "        49.55881708, 48.48040192, 58.07752335, 49.4645017 , 48.3900203 ,\n",
+       "        47.40693374, 49.66786186, 51.17144266, 52.08011617, 49.56480158,\n",
+       "        48.09154352, 52.80015354, 51.98205341, 49.39633219, 55.25431742,\n",
+       "        49.33217678, 51.29631635, 48.77383339, 51.88027475, 48.46659297,\n",
+       "        46.90945915, 46.521153  , 50.47392065, 53.23085225, 54.37782031,\n",
+       "        57.04687178, 54.67376368, 52.55679396, 49.15966109, 54.06733307,\n",
+       "        59.32341409, 59.59345131, 49.4827867 , 46.84919157, 44.47654981,\n",
+       "        46.3358912 , 45.60667653, 48.78847678, 55.51683424, 52.78370861,\n",
+       "        52.83145838, 46.60974623, 45.62709565, 48.71086868, 52.74363305]])
          • step_size_bar
            (chain, draw)
            float64
            0.5827 0.5827 ... 0.6018 0.6018
            array([[0.58273571, 0.58273571, 0.58273571, 0.58273571, 0.58273571,\n",
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+       "        0.58273571, 0.58273571, 0.58273571, 0.58273571, 0.58273571,\n",
+       "        0.58273571, 0.58273571, 0.58273571, 0.58273571, 0.58273571,\n",
+       "        0.58273571, 0.58273571, 0.58273571, 0.58273571, 0.58273571,\n",
+       "        0.58273571, 0.58273571, 0.58273571, 0.58273571, 0.58273571,\n",
+       "        0.58273571, 0.58273571, 0.58273571, 0.58273571, 0.58273571,\n",
+       "        0.58273571, 0.58273571, 0.58273571, 0.58273571, 0.58273571,\n",
+       "        0.58273571, 0.58273571, 0.58273571, 0.58273571, 0.58273571,\n",
+       "        0.58273571, 0.58273571, 0.58273571, 0.58273571, 0.58273571,\n",
+       "        0.58273571, 0.58273571, 0.58273571, 0.58273571, 0.58273571,\n",
+       "        0.58273571, 0.58273571, 0.58273571, 0.58273571, 0.58273571,\n",
+       "        0.58273571, 0.58273571, 0.58273571, 0.58273571, 0.58273571,\n",
+       "        0.58273571, 0.58273571, 0.58273571, 0.58273571, 0.58273571,\n",
+       "        0.58273571, 0.58273571, 0.58273571, 0.58273571, 0.58273571,\n",
+       "        0.58273571, 0.58273571, 0.58273571, 0.58273571, 0.58273571,\n",
+       "        0.58273571, 0.58273571, 0.58273571, 0.58273571, 0.58273571,\n",
+       "        0.58273571, 0.58273571, 0.58273571, 0.58273571, 0.58273571,\n",
+       "        0.58273571, 0.58273571, 0.58273571, 0.58273571, 0.58273571,\n",
+       "        0.58273571, 0.58273571, 0.58273571, 0.58273571, 0.58273571,\n",
+       "        0.58273571, 0.58273571, 0.58273571, 0.58273571, 0.58273571,\n",
+       "        0.58273571, 0.58273571, 0.58273571, 0.58273571, 0.58273571,\n",
+       "        0.58273571, 0.58273571, 0.58273571, 0.58273571, 0.58273571,\n",
+       "        0.58273571, 0.58273571, 0.58273571, 0.58273571, 0.58273571,\n",
+       "        0.58273571, 0.58273571, 0.58273571, 0.58273571, 0.58273571,\n",
+       "        0.58273571, 0.58273571, 0.58273571, 0.58273571, 0.58273571,\n",
+       "        0.58273571, 0.58273571, 0.58273571, 0.58273571, 0.58273571,\n",
+       "        0.58273571, 0.58273571, 0.58273571, 0.58273571, 0.58273571,\n",
+       "        0.58273571, 0.58273571, 0.58273571, 0.58273571, 0.58273571,\n",
+       "        0.58273571, 0.58273571, 0.58273571, 0.58273571, 0.58273571,\n",
+       "        0.58273571, 0.58273571, 0.58273571, 0.58273571, 0.58273571,\n",
+       "        0.58273571, 0.58273571, 0.58273571, 0.58273571, 0.58273571,\n",
+       "        0.58273571, 0.58273571, 0.58273571, 0.58273571, 0.58273571,\n",
+       "        0.58273571, 0.58273571, 0.58273571, 0.58273571, 0.58273571,\n",
+       "        0.58273571, 0.58273571, 0.58273571, 0.58273571, 0.58273571,\n",
+       "        0.58273571, 0.58273571, 0.58273571, 0.58273571, 0.58273571,\n",
+       "        0.58273571, 0.58273571, 0.58273571, 0.58273571, 0.58273571,\n",
+       "        0.58273571, 0.58273571, 0.58273571, 0.58273571, 0.58273571,\n",
+       "        0.58273571, 0.58273571, 0.58273571, 0.58273571, 0.58273571,\n",
+       "        0.58273571, 0.58273571, 0.58273571, 0.58273571, 0.58273571,\n",
+       "        0.58273571, 0.58273571, 0.58273571, 0.58273571, 0.58273571,\n",
+       "        0.58273571, 0.58273571, 0.58273571, 0.58273571, 0.58273571,\n",
+       "        0.58273571, 0.58273571, 0.58273571, 0.58273571, 0.58273571,\n",
+       "        0.58273571, 0.58273571, 0.58273571, 0.58273571, 0.58273571,\n",
+       "        0.58273571, 0.58273571, 0.58273571, 0.58273571, 0.58273571,\n",
+       "        0.58273571, 0.58273571, 0.58273571, 0.58273571, 0.58273571,\n",
+       "        0.58273571, 0.58273571, 0.58273571, 0.58273571, 0.58273571,\n",
+       "        0.58273571, 0.58273571, 0.58273571, 0.58273571, 0.58273571,\n",
+       "        0.58273571, 0.58273571, 0.58273571, 0.58273571, 0.58273571,\n",
+       "        0.58273571, 0.58273571, 0.58273571, 0.58273571, 0.58273571,\n",
+       "        0.58273571, 0.58273571, 0.58273571, 0.58273571, 0.58273571,\n",
+       "        0.58273571, 0.58273571, 0.58273571, 0.58273571, 0.58273571,\n",
+       "        0.58273571, 0.58273571, 0.58273571, 0.58273571, 0.58273571,\n",
+       "        0.58273571, 0.58273571, 0.58273571, 0.58273571, 0.58273571,\n",
+       "        0.58273571, 0.58273571, 0.58273571, 0.58273571, 0.58273571,\n",
+       "        0.58273571, 0.58273571, 0.58273571, 0.58273571, 0.58273571,\n",
+       "        0.58273571, 0.58273571, 0.58273571, 0.58273571, 0.58273571,\n",
+       "        0.58273571, 0.58273571, 0.58273571, 0.58273571, 0.58273571,\n",
+       "        0.58273571, 0.58273571, 0.58273571, 0.58273571, 0.58273571,\n",
+       "        0.58273571, 0.58273571, 0.58273571, 0.58273571, 0.58273571,\n",
+       "        0.58273571, 0.58273571, 0.58273571, 0.58273571, 0.58273571,\n",
+       "        0.58273571, 0.58273571, 0.58273571, 0.58273571, 0.58273571,\n",
+       "        0.58273571, 0.58273571, 0.58273571, 0.58273571, 0.58273571,\n",
+       "        0.58273571, 0.58273571, 0.58273571, 0.58273571, 0.58273571,\n",
+       "        0.58273571, 0.58273571, 0.58273571, 0.58273571, 0.58273571,\n",
+       "        0.58273571, 0.58273571, 0.58273571, 0.58273571, 0.58273571,\n",
+       "        0.58273571, 0.58273571, 0.58273571, 0.58273571, 0.58273571,\n",
+       "        0.58273571, 0.58273571, 0.58273571, 0.58273571, 0.58273571,\n",
+       "        0.58273571, 0.58273571, 0.58273571, 0.58273571, 0.58273571,\n",
+       "        0.58273571, 0.58273571, 0.58273571, 0.58273571, 0.58273571,\n",
+       "        0.58273571, 0.58273571, 0.58273571, 0.58273571, 0.58273571,\n",
+       "        0.58273571, 0.58273571, 0.58273571, 0.58273571, 0.58273571,\n",
+       "        0.58273571, 0.58273571, 0.58273571, 0.58273571, 0.58273571,\n",
+       "        0.58273571, 0.58273571, 0.58273571, 0.58273571, 0.58273571,\n",
+       "        0.58273571, 0.58273571, 0.58273571, 0.58273571, 0.58273571,\n",
+       "        0.58273571, 0.58273571, 0.58273571, 0.58273571, 0.58273571,\n",
+       "        0.58273571, 0.58273571, 0.58273571, 0.58273571, 0.58273571,\n",
+       "        0.58273571, 0.58273571, 0.58273571, 0.58273571, 0.58273571,\n",
+       "        0.58273571, 0.58273571, 0.58273571, 0.58273571, 0.58273571,\n",
+       "        0.58273571, 0.58273571, 0.58273571, 0.58273571, 0.58273571,\n",
+       "        0.58273571, 0.58273571, 0.58273571, 0.58273571, 0.58273571,\n",
+       "        0.58273571, 0.58273571, 0.58273571, 0.58273571, 0.58273571,\n",
+       "        0.58273571, 0.58273571, 0.58273571, 0.58273571, 0.58273571,\n",
+       "        0.58273571, 0.58273571, 0.58273571, 0.58273571, 0.58273571,\n",
+       "        0.58273571, 0.58273571, 0.58273571, 0.58273571, 0.58273571,\n",
+       "        0.58273571, 0.58273571, 0.58273571, 0.58273571, 0.58273571,\n",
+       "        0.58273571, 0.58273571, 0.58273571, 0.58273571, 0.58273571,\n",
+       "        0.58273571, 0.58273571, 0.58273571, 0.58273571, 0.58273571,\n",
+       "        0.58273571, 0.58273571, 0.58273571, 0.58273571, 0.58273571,\n",
+       "        0.58273571, 0.58273571, 0.58273571, 0.58273571, 0.58273571,\n",
+       "        0.58273571, 0.58273571, 0.58273571, 0.58273571, 0.58273571,\n",
+       "        0.58273571, 0.58273571, 0.58273571, 0.58273571, 0.58273571,\n",
+       "        0.58273571, 0.58273571, 0.58273571, 0.58273571, 0.58273571,\n",
+       "        0.58273571, 0.58273571, 0.58273571, 0.58273571, 0.58273571,\n",
+       "        0.58273571, 0.58273571, 0.58273571, 0.58273571, 0.58273571,\n",
+       "        0.58273571, 0.58273571, 0.58273571, 0.58273571, 0.58273571,\n",
+       "        0.58273571, 0.58273571, 0.58273571, 0.58273571, 0.58273571,\n",
+       "        0.58273571, 0.58273571, 0.58273571, 0.58273571, 0.58273571,\n",
+       "        0.58273571, 0.58273571, 0.58273571, 0.58273571, 0.58273571,\n",
+       "        0.58273571, 0.58273571, 0.58273571, 0.58273571, 0.58273571],\n",
+       "       [0.60178576, 0.60178576, 0.60178576, 0.60178576, 0.60178576,\n",
+       "        0.60178576, 0.60178576, 0.60178576, 0.60178576, 0.60178576,\n",
+       "        0.60178576, 0.60178576, 0.60178576, 0.60178576, 0.60178576,\n",
+       "        0.60178576, 0.60178576, 0.60178576, 0.60178576, 0.60178576,\n",
+       "        0.60178576, 0.60178576, 0.60178576, 0.60178576, 0.60178576,\n",
+       "        0.60178576, 0.60178576, 0.60178576, 0.60178576, 0.60178576,\n",
+       "        0.60178576, 0.60178576, 0.60178576, 0.60178576, 0.60178576,\n",
+       "        0.60178576, 0.60178576, 0.60178576, 0.60178576, 0.60178576,\n",
+       "        0.60178576, 0.60178576, 0.60178576, 0.60178576, 0.60178576,\n",
+       "        0.60178576, 0.60178576, 0.60178576, 0.60178576, 0.60178576,\n",
+       "        0.60178576, 0.60178576, 0.60178576, 0.60178576, 0.60178576,\n",
+       "        0.60178576, 0.60178576, 0.60178576, 0.60178576, 0.60178576,\n",
+       "        0.60178576, 0.60178576, 0.60178576, 0.60178576, 0.60178576,\n",
+       "        0.60178576, 0.60178576, 0.60178576, 0.60178576, 0.60178576,\n",
+       "        0.60178576, 0.60178576, 0.60178576, 0.60178576, 0.60178576,\n",
+       "        0.60178576, 0.60178576, 0.60178576, 0.60178576, 0.60178576,\n",
+       "        0.60178576, 0.60178576, 0.60178576, 0.60178576, 0.60178576,\n",
+       "        0.60178576, 0.60178576, 0.60178576, 0.60178576, 0.60178576,\n",
+       "        0.60178576, 0.60178576, 0.60178576, 0.60178576, 0.60178576,\n",
+       "        0.60178576, 0.60178576, 0.60178576, 0.60178576, 0.60178576,\n",
+       "        0.60178576, 0.60178576, 0.60178576, 0.60178576, 0.60178576,\n",
+       "        0.60178576, 0.60178576, 0.60178576, 0.60178576, 0.60178576,\n",
+       "        0.60178576, 0.60178576, 0.60178576, 0.60178576, 0.60178576,\n",
+       "        0.60178576, 0.60178576, 0.60178576, 0.60178576, 0.60178576,\n",
+       "        0.60178576, 0.60178576, 0.60178576, 0.60178576, 0.60178576,\n",
+       "        0.60178576, 0.60178576, 0.60178576, 0.60178576, 0.60178576,\n",
+       "        0.60178576, 0.60178576, 0.60178576, 0.60178576, 0.60178576,\n",
+       "        0.60178576, 0.60178576, 0.60178576, 0.60178576, 0.60178576,\n",
+       "        0.60178576, 0.60178576, 0.60178576, 0.60178576, 0.60178576,\n",
+       "        0.60178576, 0.60178576, 0.60178576, 0.60178576, 0.60178576,\n",
+       "        0.60178576, 0.60178576, 0.60178576, 0.60178576, 0.60178576,\n",
+       "        0.60178576, 0.60178576, 0.60178576, 0.60178576, 0.60178576,\n",
+       "        0.60178576, 0.60178576, 0.60178576, 0.60178576, 0.60178576,\n",
+       "        0.60178576, 0.60178576, 0.60178576, 0.60178576, 0.60178576,\n",
+       "        0.60178576, 0.60178576, 0.60178576, 0.60178576, 0.60178576,\n",
+       "        0.60178576, 0.60178576, 0.60178576, 0.60178576, 0.60178576,\n",
+       "        0.60178576, 0.60178576, 0.60178576, 0.60178576, 0.60178576,\n",
+       "        0.60178576, 0.60178576, 0.60178576, 0.60178576, 0.60178576,\n",
+       "        0.60178576, 0.60178576, 0.60178576, 0.60178576, 0.60178576,\n",
+       "        0.60178576, 0.60178576, 0.60178576, 0.60178576, 0.60178576,\n",
+       "        0.60178576, 0.60178576, 0.60178576, 0.60178576, 0.60178576,\n",
+       "        0.60178576, 0.60178576, 0.60178576, 0.60178576, 0.60178576,\n",
+       "        0.60178576, 0.60178576, 0.60178576, 0.60178576, 0.60178576,\n",
+       "        0.60178576, 0.60178576, 0.60178576, 0.60178576, 0.60178576,\n",
+       "        0.60178576, 0.60178576, 0.60178576, 0.60178576, 0.60178576,\n",
+       "        0.60178576, 0.60178576, 0.60178576, 0.60178576, 0.60178576,\n",
+       "        0.60178576, 0.60178576, 0.60178576, 0.60178576, 0.60178576,\n",
+       "        0.60178576, 0.60178576, 0.60178576, 0.60178576, 0.60178576,\n",
+       "        0.60178576, 0.60178576, 0.60178576, 0.60178576, 0.60178576,\n",
+       "        0.60178576, 0.60178576, 0.60178576, 0.60178576, 0.60178576,\n",
+       "        0.60178576, 0.60178576, 0.60178576, 0.60178576, 0.60178576,\n",
+       "        0.60178576, 0.60178576, 0.60178576, 0.60178576, 0.60178576,\n",
+       "        0.60178576, 0.60178576, 0.60178576, 0.60178576, 0.60178576,\n",
+       "        0.60178576, 0.60178576, 0.60178576, 0.60178576, 0.60178576,\n",
+       "        0.60178576, 0.60178576, 0.60178576, 0.60178576, 0.60178576,\n",
+       "        0.60178576, 0.60178576, 0.60178576, 0.60178576, 0.60178576,\n",
+       "        0.60178576, 0.60178576, 0.60178576, 0.60178576, 0.60178576,\n",
+       "        0.60178576, 0.60178576, 0.60178576, 0.60178576, 0.60178576,\n",
+       "        0.60178576, 0.60178576, 0.60178576, 0.60178576, 0.60178576,\n",
+       "        0.60178576, 0.60178576, 0.60178576, 0.60178576, 0.60178576,\n",
+       "        0.60178576, 0.60178576, 0.60178576, 0.60178576, 0.60178576,\n",
+       "        0.60178576, 0.60178576, 0.60178576, 0.60178576, 0.60178576,\n",
+       "        0.60178576, 0.60178576, 0.60178576, 0.60178576, 0.60178576,\n",
+       "        0.60178576, 0.60178576, 0.60178576, 0.60178576, 0.60178576,\n",
+       "        0.60178576, 0.60178576, 0.60178576, 0.60178576, 0.60178576,\n",
+       "        0.60178576, 0.60178576, 0.60178576, 0.60178576, 0.60178576,\n",
+       "        0.60178576, 0.60178576, 0.60178576, 0.60178576, 0.60178576,\n",
+       "        0.60178576, 0.60178576, 0.60178576, 0.60178576, 0.60178576,\n",
+       "        0.60178576, 0.60178576, 0.60178576, 0.60178576, 0.60178576,\n",
+       "        0.60178576, 0.60178576, 0.60178576, 0.60178576, 0.60178576,\n",
+       "        0.60178576, 0.60178576, 0.60178576, 0.60178576, 0.60178576,\n",
+       "        0.60178576, 0.60178576, 0.60178576, 0.60178576, 0.60178576,\n",
+       "        0.60178576, 0.60178576, 0.60178576, 0.60178576, 0.60178576,\n",
+       "        0.60178576, 0.60178576, 0.60178576, 0.60178576, 0.60178576,\n",
+       "        0.60178576, 0.60178576, 0.60178576, 0.60178576, 0.60178576,\n",
+       "        0.60178576, 0.60178576, 0.60178576, 0.60178576, 0.60178576,\n",
+       "        0.60178576, 0.60178576, 0.60178576, 0.60178576, 0.60178576,\n",
+       "        0.60178576, 0.60178576, 0.60178576, 0.60178576, 0.60178576,\n",
+       "        0.60178576, 0.60178576, 0.60178576, 0.60178576, 0.60178576,\n",
+       "        0.60178576, 0.60178576, 0.60178576, 0.60178576, 0.60178576,\n",
+       "        0.60178576, 0.60178576, 0.60178576, 0.60178576, 0.60178576,\n",
+       "        0.60178576, 0.60178576, 0.60178576, 0.60178576, 0.60178576,\n",
+       "        0.60178576, 0.60178576, 0.60178576, 0.60178576, 0.60178576,\n",
+       "        0.60178576, 0.60178576, 0.60178576, 0.60178576, 0.60178576,\n",
+       "        0.60178576, 0.60178576, 0.60178576, 0.60178576, 0.60178576,\n",
+       "        0.60178576, 0.60178576, 0.60178576, 0.60178576, 0.60178576,\n",
+       "        0.60178576, 0.60178576, 0.60178576, 0.60178576, 0.60178576,\n",
+       "        0.60178576, 0.60178576, 0.60178576, 0.60178576, 0.60178576,\n",
+       "        0.60178576, 0.60178576, 0.60178576, 0.60178576, 0.60178576,\n",
+       "        0.60178576, 0.60178576, 0.60178576, 0.60178576, 0.60178576,\n",
+       "        0.60178576, 0.60178576, 0.60178576, 0.60178576, 0.60178576,\n",
+       "        0.60178576, 0.60178576, 0.60178576, 0.60178576, 0.60178576,\n",
+       "        0.60178576, 0.60178576, 0.60178576, 0.60178576, 0.60178576,\n",
+       "        0.60178576, 0.60178576, 0.60178576, 0.60178576, 0.60178576,\n",
+       "        0.60178576, 0.60178576, 0.60178576, 0.60178576, 0.60178576,\n",
+       "        0.60178576, 0.60178576, 0.60178576, 0.60178576, 0.60178576,\n",
+       "        0.60178576, 0.60178576, 0.60178576, 0.60178576, 0.60178576,\n",
+       "        0.60178576, 0.60178576, 0.60178576, 0.60178576, 0.60178576,\n",
+       "        0.60178576, 0.60178576, 0.60178576, 0.60178576, 0.60178576,\n",
+       "        0.60178576, 0.60178576, 0.60178576, 0.60178576, 0.60178576]])
          • diverging
            (chain, draw)
            bool
            False False False ... False False
            array([[False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False],\n",
+       "       [False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False]])
          • max_energy_error
            (chain, draw)
            float64
            -0.346 -0.5386 ... 0.4909 0.4531
            array([[-3.45958861e-01, -5.38593755e-01,  3.87242922e+00,\n",
+       "         9.57583755e-01,  5.07626701e+00,  5.52380031e-01,\n",
+       "         4.68629912e-01, -1.16402606e-01,  3.68614326e-01,\n",
+       "        -1.85091125e-01,  8.34590176e-01,  3.87514015e-01,\n",
+       "        -3.94672419e-01, -1.77613405e-01, -7.41839644e-01,\n",
+       "         2.22435718e-01,  1.44818710e+00,  3.21114703e-01,\n",
+       "         2.96626158e-01,  5.72171739e+01,  4.98954948e+01,\n",
+       "         2.04823143e-01,  3.15203861e-01,  2.74642553e+00,\n",
+       "         2.65229052e+00,  4.14126688e-01, -3.81097391e-01,\n",
+       "        -1.84114480e-01,  2.43827998e+00, -5.54991328e-01,\n",
+       "        -3.15189018e-01, -2.79553770e-01,  2.96661431e-01,\n",
+       "         4.68424687e-01, -3.50867174e-01,  4.87995769e-01,\n",
+       "         1.18923972e+00,  2.64310047e-01, -2.87177938e-01,\n",
+       "        -2.15095091e-01,  1.55408096e+00,  2.83338457e-01,\n",
+       "         2.27377424e-01,  2.00592786e-01, -3.56319604e-01,\n",
+       "         5.13778985e-01,  5.61689079e-01,  5.29556535e+00,\n",
+       "         8.64533383e-02,  4.46890667e-01, -4.60951704e-01,\n",
+       "         2.88829323e-01,  2.32782149e+00, -1.87442701e-01,\n",
+       "        -3.05865485e-01,  3.94343813e-01, -7.43098576e-01,\n",
+       "         3.52842448e+00,  7.11474053e-01, -6.04827307e-01,\n",
+       "         4.98747287e+00,  1.38038384e+00,  1.08947220e-01,\n",
+       "        -5.58079586e-01,  6.70782431e-01, -2.67905008e-01,\n",
+       "        -5.73830782e-01,  4.39329801e-01, -6.27351991e-01,\n",
+       "         4.69701754e+00,  2.12199292e+00, -1.91756184e-01,\n",
+       "        -1.12054542e+00, -3.40460768e-01, -8.74683455e-01,\n",
+       "         4.38390666e-01,  4.03745567e-01,  6.40257352e-01,\n",
+       "         7.27694589e+01,  1.82214826e+00,  1.25206824e+00,\n",
+       "         2.37610413e-01, -8.51191057e-01, -8.56125154e-01,\n",
+       "         3.43945179e-01,  1.11968072e+00, -4.03164442e-01,\n",
+       "         2.34874650e+00,  4.86789244e-01,  8.78210286e-01,\n",
+       "        -6.81684652e-01,  3.43494807e+00,  3.34020624e-01,\n",
+       "        -1.45782737e+00, -2.57551957e-01,  4.63244467e-01,\n",
+       "        -1.76187185e-01,  1.10541542e-01, -2.97745732e-01,\n",
+       "        -2.28084973e-01,  2.84097898e-01,  1.99394884e+00,\n",
+       "        -5.86414864e-01, -8.92857362e-01, -1.55566886e-01,\n",
+       "         1.81163196e+00,  2.18869096e+01, -1.22828623e-01,\n",
+       "         6.84918446e-01,  7.64573434e-01,  2.96271035e-01,\n",
+       "        -2.16494135e-01, -1.92511678e-01,  6.61524764e-01,\n",
+       "         2.76567260e-01,  2.72532306e+01,  4.09988106e+00,\n",
+       "         1.79526877e-01,  2.39160589e+00,  3.29531963e-01,\n",
+       "         3.77012181e-01, -5.30442772e-01,  3.34952660e-01,\n",
+       "        -2.51363046e-01,  2.94234221e+00,  1.08715736e+00,\n",
+       "         8.87629706e+00, -2.42188910e-01, -2.81862870e-01,\n",
+       "         4.29960903e-01, -8.89114939e-01,  1.38210400e+00,\n",
+       "        -2.81010363e-01,  2.57112713e-01, -2.02113331e-01,\n",
+       "         2.73401806e-01, -5.19386797e-01, -3.32498710e-01,\n",
+       "         1.22419450e-01, -5.87969136e-01,  6.52980826e-01,\n",
+       "        -3.41636664e-01,  8.33565797e-01,  5.46158002e-01,\n",
+       "        -4.89995490e-01,  3.05843116e-01,  6.74488721e-01,\n",
+       "        -6.13043148e-01,  4.73435477e-01, -1.74653101e-01,\n",
+       "        -4.64788693e-01, -4.45702670e-01, -2.61850364e-01,\n",
+       "         8.25777345e-01, -4.07749141e-01, -2.03927470e+00,\n",
+       "         1.33266369e+00, -1.84351289e+00,  3.98830356e-01,\n",
+       "        -5.25674437e-01,  4.87700548e-01,  2.06091070e-01,\n",
+       "        -2.76063358e-01,  4.17932477e-01, -1.70656151e-01,\n",
+       "         8.26321577e-01, -1.95782693e-01,  3.04544000e-01,\n",
+       "        -5.55784364e-01, -1.04941716e+00, -1.15189583e+00,\n",
+       "         1.09051227e+01, -3.73903307e-01,  6.11502980e-01,\n",
+       "        -3.26614022e-01, -1.17154802e+00, -2.54743498e-01,\n",
+       "        -1.12064638e-01,  5.84723198e+01,  4.19541665e-01,\n",
+       "         9.50197607e-01,  9.10532062e+00, -8.71837824e-01,\n",
+       "        -7.41036755e-01, -7.95553696e-01,  1.57441161e+01,\n",
+       "        -4.06270268e-01, -2.07463142e-01, -1.86113374e-01,\n",
+       "         1.68600695e-01,  2.51509982e-01,  3.42941256e+00,\n",
+       "        -1.66978469e-01, -4.85169809e-01, -6.43272033e-01,\n",
+       "        -3.90656025e-01,  4.02474938e-01, -3.33851319e-01,\n",
+       "        -1.36425053e-01,  2.30238759e-01, -4.80080034e-01,\n",
+       "        -8.68792483e-01,  9.06635899e-01, -2.08750612e-01,\n",
+       "         3.34781700e-01,  6.09199686e-01,  3.82453188e-01,\n",
+       "         2.24654044e-01, -1.83904392e-01,  3.89753040e-01,\n",
+       "        -2.78985712e-01, -4.61756078e-01, -5.68271816e-01,\n",
+       "         7.25465465e-01,  1.09642167e+00,  1.90095457e-01,\n",
+       "         2.68869674e-01, -1.34546228e-01,  5.23307781e+00,\n",
+       "         1.22299511e-01, -4.51630746e-01,  3.88597953e-01,\n",
+       "         1.47048551e-01, -2.19712416e-01, -3.46909419e-01,\n",
+       "         1.92230343e-01,  4.70321935e-01,  1.43562409e+00,\n",
+       "        -5.11738645e-02,  4.40793936e+01, -5.94418106e-01,\n",
+       "        -1.31497099e-01, -9.60106690e-01,  2.78226544e+00,\n",
+       "         7.83837820e-02,  8.04756639e-01,  6.00107045e-01,\n",
+       "        -4.61190472e-01,  3.35813374e-01,  2.80688169e-01,\n",
+       "         1.88144934e-01,  1.22720026e+00,  9.52216026e-01,\n",
+       "        -4.70740556e-01,  7.45188005e+00, -2.72084889e-01,\n",
+       "         3.53891233e-01,  1.98597238e+00,  1.00441644e+00,\n",
+       "        -3.16637076e-01, -2.44622330e-01, -3.15419756e-01,\n",
+       "         4.19142451e+01,  2.01465062e-01, -3.08469777e-01,\n",
+       "         1.85172793e-01,  4.76193408e+00,  3.41702342e-01,\n",
+       "         3.17949165e-01, -3.77482793e-01,  1.52292994e-01,\n",
+       "        -2.10162413e-01, -1.36280586e-01,  2.29911124e-01,\n",
+       "         1.57823173e-01,  2.52310025e+01,  1.46499986e+01,\n",
+       "         5.58195847e+00,  1.53320126e-01, -1.21797399e-01,\n",
+       "         5.98759494e+00, -1.39560248e-01,  4.82997546e-01,\n",
+       "         2.34932480e-01,  7.47489104e+00, -2.79421418e-01,\n",
+       "         5.67911567e-01,  8.33676216e-02,  2.48527674e+01,\n",
+       "        -4.20994583e-01, -3.74318548e-01,  5.01901366e-01,\n",
+       "         2.68992629e-01,  2.65042107e-01,  9.12942084e-02,\n",
+       "         2.64254609e-01,  8.68700373e+00, -9.12322599e-01,\n",
+       "        -1.06435455e+00, -1.83136871e-01,  1.40308729e+00,\n",
+       "         4.01946900e+00, -1.22493441e+00,  7.96484862e-01,\n",
+       "        -8.14299594e-02,  2.28111476e-01,  1.16201663e+00,\n",
+       "        -6.25584924e-01, -5.59565372e-01,  7.93134531e-01,\n",
+       "         5.38416313e-01, -3.76822134e-01, -3.75287530e-01,\n",
+       "        -3.91811217e-01, -4.80554508e-01, -9.50555682e-01,\n",
+       "         8.41922847e+00,  7.05759628e+00, -9.60696759e-02,\n",
+       "        -3.44708282e-01,  4.97313379e+01,  2.37843288e-01,\n",
+       "         6.20794357e-01,  4.23444213e-01,  2.27299881e-01,\n",
+       "        -2.96824083e-01,  1.35702174e-01,  1.63460942e-01,\n",
+       "         3.56947757e-01,  7.81433937e-01, -2.07313741e-01,\n",
+       "        -2.34499127e-01, -1.79140209e-01,  1.68680144e-01,\n",
+       "         8.21306225e-01,  5.31096207e-01,  3.38146173e-01,\n",
+       "         2.00327120e-01,  6.19240100e+00,  2.35866046e+00,\n",
+       "         4.73014539e-01,  3.60178353e-01,  2.69348137e+00,\n",
+       "         4.00284900e-01,  3.74672777e-01, -4.34558454e-01,\n",
+       "         3.68570269e-01,  8.27030547e-01, -4.82800816e-01,\n",
+       "         1.66651527e-01,  6.54896257e-01, -8.27095607e-02,\n",
+       "         2.05153450e-01,  1.45968693e+00,  1.70633048e-01,\n",
+       "         1.73906012e-01, -2.30375480e-01, -5.14583700e-02,\n",
+       "         2.42191383e+00, -4.41326738e-01, -2.85642855e-01,\n",
+       "         3.28916381e-01,  1.94729141e+00,  8.30503940e-01,\n",
+       "         2.93553867e-01,  4.80879157e+00, -2.09987098e-01,\n",
+       "         2.22344228e-01, -3.78549903e-01,  2.23875941e-01,\n",
+       "        -9.88241560e-01,  8.82151599e+00,  1.04577074e+00,\n",
+       "         2.47943158e-01, -2.36955478e-01,  1.19315417e-01,\n",
+       "         8.22909072e-01, -1.06303182e+00,  2.88289256e-01,\n",
+       "        -3.34857050e-01,  4.39420111e-01,  2.76678402e-01,\n",
+       "         2.23271731e-01,  3.09168634e-01,  2.05877148e+01,\n",
+       "        -5.27679779e-01,  4.11145995e-01,  1.10066992e+01,\n",
+       "        -1.61494321e-01,  2.56431566e+00,  3.12585705e-01,\n",
+       "        -2.71384616e-01,  8.39416238e+00,  6.56507997e+00,\n",
+       "         4.34441692e-01,  2.73074894e-01,  1.73137288e-01,\n",
+       "         3.49980365e-01,  8.94331756e+00,  1.78057467e-01,\n",
+       "         3.42761137e-01, -2.41547970e+00, -8.78237010e-01,\n",
+       "         2.50783032e-01,  8.73289230e-01, -5.38794104e-01,\n",
+       "        -2.21614747e-01,  2.97970338e+00, -4.98041339e-01,\n",
+       "        -4.19266567e-01,  6.46738300e+00, -4.32932690e-01,\n",
+       "         1.81948972e-01,  3.53034094e-01,  8.70805744e-02,\n",
+       "         1.75605464e-01, -1.50994507e-01,  3.16111246e-01,\n",
+       "         3.84961109e+00,  3.43004680e-01,  7.51170898e+00,\n",
+       "        -1.95908557e-01, -3.06939525e-01,  1.09661325e+00,\n",
+       "         2.47756801e-01, -1.01923190e-01,  1.16807112e+00,\n",
+       "         3.77260597e-01,  1.89571085e+01, -3.59560390e-01,\n",
+       "         5.56406060e-01, -4.64857220e-01,  1.05632205e+01,\n",
+       "        -3.14585118e-01, -2.58622841e-01, -6.55867014e-01,\n",
+       "         2.55142898e-01,  4.24843845e-01, -1.87978628e-01,\n",
+       "        -7.96849259e-02,  2.59235133e-01,  6.34086560e+01,\n",
+       "         7.81503284e-01, -5.65717930e-01,  2.59882717e-01,\n",
+       "        -2.28647723e-01, -2.24880373e-01,  3.80942380e+00,\n",
+       "         1.39224462e-01,  4.30664630e-01,  2.14031697e-01,\n",
+       "         1.40841338e+00, -2.65590359e-01, -1.02214986e-01,\n",
+       "         4.80570096e-01, -3.01539289e-01,  2.92197983e+00,\n",
+       "        -5.02933108e-01, -7.28240773e-01, -2.34828288e-01,\n",
+       "        -2.64087873e-01,  4.41070048e-01,  1.47014823e-01,\n",
+       "         3.08336830e-01,  2.59153654e-01,  1.77255475e+01,\n",
+       "         2.17456568e-01,  6.42754622e-01, -8.26128334e-01,\n",
+       "         3.26236035e+00,  7.94454486e-01,  8.35032638e-01,\n",
+       "        -1.00066040e-01,  3.16367687e-01,  3.87681436e-01,\n",
+       "        -1.06656528e+00,  5.63192498e-01,  2.85351880e-01,\n",
+       "        -2.05559024e-01,  9.90147286e+00, -6.59674045e-01,\n",
+       "        -2.75452872e-01,  2.14810293e-01,  1.30824509e+01,\n",
+       "         3.63799465e-01,  2.68919598e+00, -4.93900642e-01,\n",
+       "        -1.84805855e-01, -2.14156063e-01,  1.03759560e+00,\n",
+       "         5.62219053e-01, -4.82588960e-01,  2.12586587e-01,\n",
+       "        -3.52038943e-01,  2.66059224e+00,  4.79437017e-01,\n",
+       "        -1.47579725e-01,  3.51092649e-01, -1.22472509e-01,\n",
+       "        -1.34034660e+00, -9.13082002e-01,  9.60122771e-01,\n",
+       "        -7.74678142e-01,  4.79667601e-01,  6.53077799e-01,\n",
+       "        -2.00878835e-01,  4.01282335e-01,  9.69058470e-01,\n",
+       "        -4.07795688e-01,  9.75788732e+00],\n",
+       "       [ 1.79860139e-01,  2.57749327e-01,  2.06620668e-01,\n",
+       "         5.24780963e-01, -4.44786433e-01,  5.33808842e-01,\n",
+       "        -1.97703417e-01, -4.78780043e-01,  7.02168147e-01,\n",
+       "         3.92322718e-01,  5.84171048e-01, -3.06658289e-01,\n",
+       "         3.77004855e-01,  9.69939884e-02, -1.92274140e-01,\n",
+       "         3.73763327e-01,  2.15874824e-01,  3.42594685e+00,\n",
+       "         1.49468999e+02, -4.84675991e-01,  8.52539351e-01,\n",
+       "         4.94385242e-01,  5.82395728e-01,  1.13119778e-01,\n",
+       "        -6.04476805e-01,  4.35070945e-01, -6.35107530e-01,\n",
+       "         4.81996847e-01, -2.54631442e-01, -4.51432312e-01,\n",
+       "         8.34066785e+00,  8.28768212e-01, -3.55524897e-01,\n",
+       "        -4.12397313e-01,  7.88740131e+01,  1.09933259e+00,\n",
+       "         2.54380183e-01, -3.49554892e-01,  1.62513975e-01,\n",
+       "        -1.19771232e-01,  4.97793173e-01, -4.23941084e-01,\n",
+       "         2.14389241e-01,  6.08426446e-01,  2.06800294e-01,\n",
+       "        -1.45216515e-01, -4.79884896e-01, -2.11866654e-01,\n",
+       "         1.37523425e-01,  3.74533374e+01,  5.68415731e-01,\n",
+       "         2.85875080e+00, -4.05025328e-01,  3.79227342e+01,\n",
+       "         3.63158625e+00,  3.58718116e-01, -1.12933153e-01,\n",
+       "         6.89334980e-01, -2.68342347e-01, -2.15851806e-01,\n",
+       "        -2.99624337e-01, -4.39105973e-01,  3.46531664e-01,\n",
+       "        -3.06174454e-01,  3.45847569e-01, -4.05139068e-01,\n",
+       "         9.96094177e-01,  5.80203033e-01,  6.74221338e-01,\n",
+       "        -2.07705255e+00,  4.52474761e-01, -3.55197721e-01,\n",
+       "         3.77985046e-01,  6.27543908e+00, -4.81141424e-01,\n",
+       "         6.36341124e-01,  2.98708243e-01,  1.74152964e-01,\n",
+       "        -3.05865414e-01,  1.86821691e-01, -4.98983644e-01,\n",
+       "        -1.94021934e-01,  4.53315514e-01,  3.26685454e-01,\n",
+       "         1.41211741e+00, -2.49670722e-01,  3.23204014e-01,\n",
+       "         4.25557367e-01, -2.15044088e-01,  9.02963769e-01,\n",
+       "        -3.82430144e-01, -4.15788712e-01, -3.64887463e-01,\n",
+       "         2.50792843e+00,  8.18705926e-01,  2.23390920e-01,\n",
+       "        -1.63053189e-01, -5.47179947e-01, -7.63028649e-01,\n",
+       "        -2.37482874e-01,  1.54210743e+00,  6.54381740e-01,\n",
+       "         1.73754759e-01,  3.12250675e-01,  5.42658382e-01,\n",
+       "        -2.65303647e-01, -5.68167147e-01, -3.43746145e-01,\n",
+       "        -4.51142776e-01,  2.19192229e-01,  3.23391244e-01,\n",
+       "        -1.10505959e-01, -3.30013126e-01,  7.60311276e-01,\n",
+       "        -3.53148871e-01, -9.42147547e-01, -1.25580833e+00,\n",
+       "         4.58550849e-01,  3.33075524e-01,  2.53154980e-01,\n",
+       "         2.22866857e-01, -2.39490677e-01, -2.96397625e-01,\n",
+       "         2.79725105e-01, -2.09311035e-01,  1.16047409e-01,\n",
+       "         1.54082228e-01,  7.79763043e-01, -3.77346737e-01,\n",
+       "         4.63933280e-01,  4.28535496e-01, -1.89024197e-01,\n",
+       "         1.52572973e-01, -2.90130809e-01, -5.04256816e-01,\n",
+       "        -5.04471627e-01,  3.90582453e-01,  2.24551819e-01,\n",
+       "        -2.54122083e-01, -2.66988657e-01, -1.41051592e-01,\n",
+       "        -3.52526782e-01,  2.77246365e+01,  3.16792916e-01,\n",
+       "         7.12643878e-01, -1.72328231e+00,  4.28980632e-01,\n",
+       "         2.90896925e-01,  1.97114123e+01,  2.70253917e-01,\n",
+       "        -1.76361420e-01,  2.37243142e-01,  3.33496475e-01,\n",
+       "        -3.98098280e-01,  2.86361117e-01,  2.58629396e-01,\n",
+       "         4.27549059e-01, -2.73191925e-01, -1.59408733e-01,\n",
+       "        -9.07684445e-02, -3.63686430e-01, -4.25483583e-01,\n",
+       "        -8.41948985e-01,  3.57700683e-01,  6.35300850e-01,\n",
+       "         9.69392194e+00,  1.63622778e+00,  2.08872914e+01,\n",
+       "        -3.97835606e-01, -1.76402531e-01,  1.80774883e-01,\n",
+       "         9.78057258e+00, -6.30791812e-01,  3.46317475e-01,\n",
+       "        -4.54440031e-01, -3.58954736e-01, -2.68658117e-01,\n",
+       "        -1.30632252e-01,  2.04265266e-01, -7.49423282e-01,\n",
+       "         2.83819558e+00,  3.50051265e-01, -6.58239637e-02,\n",
+       "         3.26444743e-01,  2.31561040e+01,  3.07045191e-01,\n",
+       "         2.71552848e-01,  2.27180959e-01,  2.15186980e-01,\n",
+       "         3.57000979e+00,  1.98350650e-01,  3.88377102e-01,\n",
+       "        -1.90270260e-01, -3.06137622e-01, -3.54989176e-01,\n",
+       "         1.19358374e+00,  2.96456861e-01, -3.23815684e-01,\n",
+       "        -1.13041793e-01, -1.86206518e-01,  1.20614578e+00,\n",
+       "        -1.10262050e-01,  2.91638248e+00, -3.24372767e-01,\n",
+       "        -4.55969135e-01,  2.60173875e-01,  2.97251709e-01,\n",
+       "        -2.54866618e-01,  3.53753826e-01, -3.10201956e-01,\n",
+       "         6.54483616e-02,  5.96420979e+00, -2.69245515e-01,\n",
+       "         4.51668756e-01,  5.85961475e-01, -9.93423023e-02,\n",
+       "        -3.40058688e-01,  2.52270919e+00, -1.27451983e+00,\n",
+       "         5.57460146e-01, -2.95833472e-01,  7.71680386e-01,\n",
+       "         6.10288598e-01, -3.12518914e-01,  1.15064550e+00,\n",
+       "        -4.45365452e-01,  5.08594188e-01, -2.34755812e-01,\n",
+       "         7.82575721e+00, -5.30065847e-01,  4.08784319e+00,\n",
+       "        -5.77140785e-01,  1.49980801e+01,  1.26534564e-01,\n",
+       "         1.83895169e-01, -8.82823608e-02,  1.08122585e-01,\n",
+       "         1.10012264e-01,  5.07735899e-01, -8.01486480e-01,\n",
+       "        -5.46104213e-01,  6.59353067e-01,  1.34599417e+00,\n",
+       "         4.78756457e+01, -1.82288448e-01,  2.88275377e-01,\n",
+       "         4.51144595e+00, -9.52218729e-01, -1.12027185e-01,\n",
+       "         7.41114602e-01,  3.56897620e-01, -3.50634407e-01,\n",
+       "        -1.52626842e-01, -3.31329260e-01, -3.41326935e-01,\n",
+       "         1.87132535e+00,  4.23750496e-01,  3.26390227e+01,\n",
+       "         2.24571080e-01, -4.25066590e-01, -1.04817550e-01,\n",
+       "         1.68097357e-01,  5.63460952e-01,  9.10214293e-01,\n",
+       "         9.85814228e-01,  8.09785661e-01,  7.65993076e-01,\n",
+       "         2.04838229e-01,  6.71732965e-01, -3.72004934e-01,\n",
+       "        -4.30615958e-01,  2.11320934e+00,  4.09809231e-01,\n",
+       "        -3.72958377e-01,  1.11659596e+00,  7.58676653e-01,\n",
+       "        -5.74167469e-01,  3.83524103e-01, -4.64160426e-01,\n",
+       "         1.14066044e+00,  8.50022177e-01,  1.01334198e+01,\n",
+       "        -1.17975922e-01,  1.14889315e+00,  1.48870027e+00,\n",
+       "        -3.27642931e-01,  1.94769974e-01, -6.28140526e-01,\n",
+       "        -1.95256503e-01,  1.67805498e-01,  5.97920849e-01,\n",
+       "        -1.95009948e-01,  3.09144389e+01,  2.82972383e-01,\n",
+       "         1.23466035e+00,  2.18070124e+00,  5.39783852e-01,\n",
+       "         5.63705408e-01, -4.86858657e-01,  4.04569664e-01,\n",
+       "         1.20159276e-01, -2.10626474e-01,  3.04233177e+01,\n",
+       "         2.81251749e-01, -3.85626289e-01,  5.86986983e-01,\n",
+       "         5.47170281e+00, -9.94974670e-01,  6.86066646e-01,\n",
+       "         5.46518119e-01, -1.47599907e+00,  1.96056001e-01,\n",
+       "        -4.54718962e-01, -5.77235620e-01,  4.10137993e-01,\n",
+       "         5.09502960e-01, -5.30135726e-01,  4.45355269e-01,\n",
+       "         3.96742466e+00, -1.55841458e-01,  3.47470058e-01,\n",
+       "        -2.53872090e-01,  8.43270788e-01,  7.59072577e+00,\n",
+       "        -3.81672211e-01, -4.04803303e-01,  4.90014233e-01,\n",
+       "         1.14564023e+00,  3.70610923e-01,  4.82841982e-01,\n",
+       "        -3.56606068e-01,  7.84232005e-01,  7.63924363e-01,\n",
+       "         9.59218934e-01,  4.59299406e-01, -1.93037175e-01,\n",
+       "         3.78654420e-01,  4.63502510e-01,  1.09189024e+00,\n",
+       "         3.28719248e-01,  2.16164142e-01,  1.18603943e-01,\n",
+       "        -3.07858018e-01,  2.17432835e-01,  3.08015511e-01,\n",
+       "         3.75806208e-01,  5.97356377e-01,  3.13421016e-01,\n",
+       "        -2.05441059e-01, -5.60110830e-01, -7.32063796e-01,\n",
+       "         5.95150978e-01,  1.33400714e-01, -1.10698425e-01,\n",
+       "        -1.83111379e-01,  7.85180666e-01,  1.50793722e+00,\n",
+       "         2.23055469e+00, -1.04951308e+00,  1.56619728e-01,\n",
+       "         4.72729184e-01,  1.98840468e-01,  2.43956027e+00,\n",
+       "        -6.44417944e-01, -2.13045459e-01,  1.62714681e-01,\n",
+       "        -7.17335891e-01, -4.91982666e-01, -3.01744648e-01,\n",
+       "        -5.36660288e-01, -3.63805153e-01, -7.92199462e-01,\n",
+       "        -2.77626719e-01, -2.27987211e-01,  1.13479811e-01,\n",
+       "        -3.94811423e-01,  4.44073418e-01, -3.58262551e-01,\n",
+       "         3.61968642e-01, -3.18856486e-01,  2.60103669e-01,\n",
+       "        -4.82655997e-01,  6.05578826e-01,  3.11054975e-01,\n",
+       "         1.53421880e+00, -1.90645779e-01,  4.27744397e-01,\n",
+       "         1.75730626e-01,  4.38024739e-01,  2.06957738e-01,\n",
+       "         2.46515010e-01,  6.38127228e+00,  1.53893890e-01,\n",
+       "         1.59020989e-01,  2.02399792e-01,  3.46873036e-01,\n",
+       "        -1.30905252e-01, -3.19647948e-01, -8.13670395e-02,\n",
+       "         3.67275950e-01, -2.64462424e-01,  4.69255756e-01,\n",
+       "         1.24663598e+00,  1.33252606e-01,  7.45836559e-01,\n",
+       "         2.15101285e+00,  1.99968249e+00, -1.75338313e+00,\n",
+       "         7.74248279e-01, -1.99829589e-01,  3.61171169e-01,\n",
+       "        -2.74385046e-01, -3.47980997e-01,  1.12769899e+00,\n",
+       "         1.34639349e-01, -1.03709348e+00,  2.94511142e+00,\n",
+       "        -2.18812769e-01,  1.45991406e-01, -2.74716445e+00,\n",
+       "         6.86877738e-01, -4.99807762e-01,  3.55803118e+01,\n",
+       "        -6.31042132e-01,  2.52738661e-01,  1.60547883e-01,\n",
+       "         3.06600501e-01, -4.33417286e-01,  1.13550421e-01,\n",
+       "        -1.76201166e-01, -2.05353893e-01,  2.37089600e-01,\n",
+       "         1.39924298e+00,  6.60022922e-01,  4.61475883e-01,\n",
+       "         2.96027657e-01,  3.08107786e-01, -1.25267013e-01,\n",
+       "        -5.54496970e-01,  3.55440627e-01,  1.90019505e+01,\n",
+       "        -4.82243969e-01, -3.08325763e-01,  1.02328270e+00,\n",
+       "        -4.49753630e-01,  3.28038109e-01,  1.67902450e+01,\n",
+       "         2.77578453e-01, -2.50234872e-01, -4.42939531e-01,\n",
+       "         7.99652573e-01, -2.61988124e-01,  4.04839449e-01,\n",
+       "         3.51167998e-01,  3.21536818e-01, -3.52745152e-01,\n",
+       "        -1.67741214e-01,  2.44523829e+01,  9.63196553e-02,\n",
+       "         5.62037402e-01, -3.37046660e-01,  1.06746699e-01,\n",
+       "         4.68680801e-01,  1.75659107e-01, -4.12512463e-01,\n",
+       "         3.95325057e-01,  7.21339091e-01,  4.04488548e+00,\n",
+       "        -5.51312779e-01,  1.95791327e+00, -1.20314207e+00,\n",
+       "         1.73148481e+00, -3.17581093e-01,  6.50544647e-01,\n",
+       "        -2.15041484e-01,  1.24453392e-01, -1.23538429e-01,\n",
+       "         4.45132215e-01,  1.70787436e-01, -6.07080597e-01,\n",
+       "         3.66084740e-01, -1.59701918e+00, -9.76927104e-01,\n",
+       "        -4.25384829e-01,  3.43367597e-01,  6.54513636e-01,\n",
+       "        -3.35890328e-01, -3.59805317e-01,  4.31611098e-01,\n",
+       "         1.53287150e+00,  1.66437679e-01,  1.09101852e-01,\n",
+       "         3.40450009e-01,  7.97478560e-01, -3.09409879e-01,\n",
+       "         6.03390464e-01, -4.31516610e-01,  4.24302891e-01,\n",
+       "         4.90853702e-01,  4.53074760e-01]])
          • depth
            (chain, draw)
            int64
            3 3 3 2 2 3 3 3 ... 3 3 3 3 3 3 3 3
            array([[3, 3, 3, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3,\n",
+       "        3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3,\n",
+       "        3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3,\n",
+       "        3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2,\n",
+       "        2, 3, 3, 3, 3, 3, 2, 2, 3, 3, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
+       "        3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
+       "        3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
+       "        3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
+       "        3, 3, 2, 3, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
+       "        3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
+       "        3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
+       "        3, 3, 3, 3, 3, 3, 3, 2, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
+       "        3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
+       "        2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3,\n",
+       "        3, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3,\n",
+       "        3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
+       "        3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3,\n",
+       "        3, 2, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
+       "        3, 3, 3, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
+       "        1, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
+       "        3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
+       "        2, 3, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 3, 3,\n",
+       "        3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n",
+       "       [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3,\n",
+       "        3, 3, 3, 3, 2, 3, 3, 2, 2, 3, 3, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
+       "        3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
+       "        3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
+       "        3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
+       "        3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
+       "        3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
+       "        3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3,\n",
+       "        3, 3, 3, 3, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 2, 3, 3,\n",
+       "        3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 3, 2, 3, 3,\n",
+       "        3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
+       "        3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
+       "        3, 3, 2, 3, 3, 3, 3, 2, 3, 3, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
+       "        3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
+       "        3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
+       "        3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 3, 2,\n",
+       "        3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3,\n",
+       "        3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
+       "        3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2,\n",
+       "        3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
+       "        2, 2, 3, 3, 3, 3, 4, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
+       "        3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
+       "        3, 3, 3, 2, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3]])
          • mean_tree_accept
            (chain, draw)
            float64
            0.9997 0.9675 ... 0.7485 0.7574
            array([[9.99669151e-01, 9.67536833e-01, 4.45610613e-01, 7.15858088e-01,\n",
+       "        6.02150780e-01, 7.06296880e-01, 7.82306370e-01, 9.99950624e-01,\n",
+       "        7.63598090e-01, 9.54663408e-01, 5.82583289e-01, 8.27639789e-01,\n",
+       "        9.32793675e-01, 9.25906322e-01, 9.10434203e-01, 9.37227778e-01,\n",
+       "        7.16665392e-01, 8.63247253e-01, 8.63051201e-01, 8.64190394e-02,\n",
+       "        8.01842992e-02, 9.70276708e-01, 8.33499610e-01, 4.15721508e-01,\n",
+       "        5.52153293e-01, 9.07259671e-01, 9.02984889e-01, 9.47466962e-01,\n",
+       "        4.87296148e-01, 1.00000000e+00, 9.64025757e-01, 9.86136712e-01,\n",
+       "        8.11129813e-01, 8.06586061e-01, 1.00000000e+00, 9.20298392e-01,\n",
+       "        8.59983088e-01, 8.89827171e-01, 9.98933781e-01, 1.00000000e+00,\n",
+       "        7.10108867e-01, 9.00163878e-01, 9.05094628e-01, 8.96993845e-01,\n",
+       "        1.00000000e+00, 8.64833106e-01, 7.79829396e-01, 7.28576891e-01,\n",
+       "        9.66945567e-01, 7.88128877e-01, 9.78385318e-01, 8.13144613e-01,\n",
+       "        4.02506367e-01, 9.68001003e-01, 9.40833180e-01, 8.72633875e-01,\n",
+       "        8.98789791e-01, 4.96900179e-02, 6.89556904e-01, 8.55714761e-01,\n",
+       "        5.65311527e-01, 7.08729983e-01, 9.57455837e-01, 8.87411825e-01,\n",
+       "        9.30186908e-01, 9.83535693e-01, 9.91331502e-01, 9.27156608e-01,\n",
+       "        1.00000000e+00, 5.73672579e-01, 5.03596033e-01, 9.52937765e-01,\n",
+       "        1.00000000e+00, 9.80758411e-01, 9.95588735e-01, 6.66046649e-01,\n",
+       "        8.10273552e-01, 7.45358410e-01, 4.28571429e-01, 3.64615823e-01,\n",
+       "        6.17664332e-01, 8.81580580e-01, 9.71025452e-01, 1.00000000e+00,\n",
+       "        8.64023658e-01, 6.17437819e-01, 9.40712829e-01, 1.82967601e-01,\n",
+       "        7.13348951e-01, 6.23110875e-01, 1.00000000e+00, 4.33684600e-01,\n",
+       "        8.45468822e-01, 9.82746951e-01, 8.78404105e-01, 6.99126735e-01,\n",
+       "        9.97330997e-01, 9.61296889e-01, 1.00000000e+00, 9.77987819e-01,\n",
+       "        8.94091792e-01, 3.62442326e-01, 9.69663387e-01, 1.00000000e+00,\n",
+       "        1.00000000e+00, 6.19884668e-01, 2.75184682e-01, 9.87315537e-01,\n",
+       "        7.47608709e-01, 8.99585366e-01, 8.46624288e-01, 9.78839624e-01,\n",
+       "        1.00000000e+00, 7.18889274e-01, 8.97315466e-01, 2.65415836e-01,\n",
+       "        3.27069733e-01, 8.85223982e-01, 5.40560183e-01, 8.78297089e-01,\n",
+       "        8.65685255e-01, 1.00000000e+00, 7.85710879e-01, 1.00000000e+00,\n",
+       "        2.19160211e-01, 5.09061423e-01, 5.24157872e-01, 9.97757045e-01,\n",
+       "        1.00000000e+00, 7.96156113e-01, 9.87517247e-01, 8.23169711e-01,\n",
+       "        9.16032474e-01, 8.82632349e-01, 9.82289587e-01, 8.88135626e-01,\n",
+       "        9.90411662e-01, 9.98066301e-01, 9.50804495e-01, 8.32884873e-01,\n",
+       "        9.31231292e-01, 9.31257290e-01, 7.36514389e-01, 7.93686205e-01,\n",
+       "        9.90420231e-01, 9.43967559e-01, 7.54620647e-01, 8.24516187e-01,\n",
+       "        8.07707769e-01, 9.90346885e-01, 9.56856024e-01, 1.00000000e+00,\n",
+       "        9.87316714e-01, 7.12472858e-01, 9.82794602e-01, 9.66245780e-01,\n",
+       "        5.73565506e-01, 6.66182690e-01, 8.23275215e-01, 1.00000000e+00,\n",
+       "        8.73815560e-01, 9.24082694e-01, 1.00000000e+00, 9.43085186e-01,\n",
+       "        9.37936276e-01, 8.90531752e-01, 9.53556748e-01, 8.46541899e-01,\n",
+       "        8.50951329e-01, 8.05770558e-01, 1.00000000e+00, 8.36522015e-01,\n",
+       "        9.75002616e-01, 6.51330247e-01, 9.60608407e-01, 8.19329785e-01,\n",
+       "        9.56922146e-01, 9.85224551e-01, 6.66666667e-01, 8.00205041e-01,\n",
+       "        5.90200070e-01, 6.87389165e-03, 9.71815521e-01, 1.00000000e+00,\n",
+       "        9.86550913e-01, 2.38498301e-01, 9.95379735e-01, 9.91286696e-01,\n",
+       "        9.53286835e-01, 9.20814472e-01, 9.17281156e-01, 3.98668550e-01,\n",
+       "        9.75808315e-01, 9.49703381e-01, 9.27754282e-01, 1.00000000e+00,\n",
+       "        8.13636307e-01, 9.99363739e-01, 9.91594358e-01, 8.11593336e-01,\n",
+       "        9.96394457e-01, 9.44039876e-01, 8.07716095e-01, 9.96642412e-01,\n",
+       "        8.17032485e-01, 8.33323365e-01, 8.27777137e-01, 9.34964047e-01,\n",
+       "        1.00000000e+00, 7.87823278e-01, 9.92504322e-01, 8.75609471e-01,\n",
+       "        1.00000000e+00, 8.43413816e-01, 5.81960450e-01, 9.38644106e-01,\n",
+       "        9.23483108e-01, 9.89090912e-01, 1.55730554e-01, 9.66931128e-01,\n",
+       "        1.00000000e+00, 8.38175102e-01, 9.32860258e-01, 9.04686195e-01,\n",
+       "        1.00000000e+00, 8.98862146e-01, 7.70706848e-01, 8.84796999e-01,\n",
+       "        9.93865453e-01, 6.93441040e-01, 9.97967222e-01, 9.83704543e-01,\n",
+       "        9.73041142e-01, 2.13539494e-01, 9.76744261e-01, 7.36494251e-01,\n",
+       "        7.57125538e-01, 9.81418450e-01, 9.11283886e-01, 8.97840717e-01,\n",
+       "        9.19928607e-01, 7.97436313e-01, 5.12179152e-01, 9.94475463e-01,\n",
+       "        6.89545608e-01, 9.72558156e-01, 8.21393786e-01, 5.46007463e-01,\n",
+       "        8.61094739e-01, 1.00000000e+00, 9.70757934e-01, 9.85294848e-01,\n",
+       "        4.40616384e-01, 8.78989264e-01, 1.00000000e+00, 9.75851527e-01,\n",
+       "        8.36043710e-01, 8.19504390e-01, 7.89036613e-01, 9.92964663e-01,\n",
+       "        9.50317450e-01, 9.85320354e-01, 9.70027898e-01, 9.23040944e-01,\n",
+       "        9.76088733e-01, 2.06662362e-01, 2.63275736e-01, 3.38115196e-01,\n",
+       "        9.07595755e-01, 9.58724775e-01, 1.18351265e-01, 9.61380958e-01,\n",
+       "        7.59769840e-01, 9.17499932e-01, 1.74196328e-01, 9.53916116e-01,\n",
+       "        7.37537800e-01, 9.63853723e-01, 4.18341238e-01, 9.92866870e-01,\n",
+       "        8.42023952e-01, 7.76855514e-01, 8.69345054e-01, 8.79531898e-01,\n",
+       "        9.62421065e-01, 8.88181014e-01, 2.18710007e-01, 9.56695419e-01,\n",
+       "        5.74377055e-01, 9.90650811e-01, 6.63293727e-01, 8.49446194e-01,\n",
+       "        9.06523624e-01, 6.04614267e-01, 9.90012218e-01, 8.87273150e-01,\n",
+       "        8.06088376e-01, 9.46772229e-01, 9.62467550e-01, 7.96275294e-01,\n",
+       "        8.64292793e-01, 1.00000000e+00, 9.76627816e-01, 9.88058421e-01,\n",
+       "        9.90806244e-01, 9.76568828e-01, 1.38088850e-02, 5.71824040e-01,\n",
+       "        9.97524170e-01, 9.79233202e-01, 8.81966673e-02, 9.33737664e-01,\n",
+       "        8.63246362e-01, 8.15158371e-01, 9.00201816e-01, 1.00000000e+00,\n",
+       "        9.48170549e-01, 9.14056830e-01, 9.55164508e-01, 6.07352768e-01,\n",
+       "        9.94780019e-01, 9.74235372e-01, 9.60089408e-01, 9.56527529e-01,\n",
+       "        7.04426918e-01, 6.69107532e-01, 8.82200557e-01, 9.21637261e-01,\n",
+       "        4.54245406e-01, 7.71926278e-01, 8.03501676e-01, 8.19795636e-01,\n",
+       "        8.26799873e-01, 9.39116575e-01, 8.41046546e-01, 1.00000000e+00,\n",
+       "        8.87613905e-01, 7.67465318e-01, 9.64006869e-01, 9.54793161e-01,\n",
+       "        6.79442909e-01, 9.75797361e-01, 8.71503582e-01, 4.03630689e-01,\n",
+       "        9.28623223e-01, 9.40542072e-01, 9.75596640e-01, 1.00000000e+00,\n",
+       "        4.21989975e-01, 9.73248270e-01, 1.00000000e+00, 8.53801850e-01,\n",
+       "        4.62391187e-01, 8.14866220e-01, 7.93092324e-01, 7.66520293e-01,\n",
+       "        9.61340683e-01, 8.39801800e-01, 9.74966688e-01, 9.20949312e-01,\n",
+       "        1.00000000e+00, 2.36061799e-02, 5.33887841e-01, 9.07736975e-01,\n",
+       "        9.57128820e-01, 9.78237542e-01, 8.29347789e-01, 1.00000000e+00,\n",
+       "        9.18793292e-01, 9.97760341e-01, 8.28389211e-01, 8.69578116e-01,\n",
+       "        9.15164065e-01, 8.97291669e-01, 1.68775094e-01, 9.92747805e-01,\n",
+       "        7.98342501e-01, 4.10686436e-01, 1.00000000e+00, 2.38207930e-01,\n",
+       "        7.95451694e-01, 9.76179672e-01, 4.08014953e-01, 1.50126336e-01,\n",
+       "        7.61067202e-01, 9.18736090e-01, 9.26504008e-01, 8.53667949e-01,\n",
+       "        8.10335755e-01, 9.37099683e-01, 8.64344363e-01, 1.00000000e+00,\n",
+       "        9.89772181e-01, 8.83195231e-01, 8.07675655e-01, 1.00000000e+00,\n",
+       "        9.82758368e-01, 4.37184190e-01, 9.30361588e-01, 9.99910492e-01,\n",
+       "        5.87761061e-01, 9.99535630e-01, 9.11415132e-01, 8.75387627e-01,\n",
+       "        9.57437275e-01, 9.28885437e-01, 1.00000000e+00, 8.41879050e-01,\n",
+       "        3.37403242e-01, 7.77459318e-01, 4.17908390e-01, 9.85969102e-01,\n",
+       "        1.00000000e+00, 7.31566547e-01, 8.80044278e-01, 9.84491213e-01,\n",
+       "        6.76265949e-01, 8.59396992e-01, 5.84833722e-09, 9.84977278e-01,\n",
+       "        7.49713881e-01, 9.03075096e-01, 2.83433493e-01, 9.99645996e-01,\n",
+       "        9.98893352e-01, 1.00000000e+00, 8.68609454e-01, 8.43897606e-01,\n",
+       "        9.08679909e-01, 9.96102879e-01, 9.23745682e-01, 4.13444284e-01,\n",
+       "        6.46466285e-01, 9.99281514e-01, 9.19056663e-01, 9.89414434e-01,\n",
+       "        9.30038118e-01, 6.51184462e-01, 9.28430929e-01, 8.24554452e-01,\n",
+       "        9.43345157e-01, 5.60702722e-01, 9.59784073e-01, 1.00000000e+00,\n",
+       "        7.45595616e-01, 9.94861641e-01, 3.63337047e-01, 9.35986096e-01,\n",
+       "        9.35925058e-01, 9.37988487e-01, 9.45121374e-01, 8.91874307e-01,\n",
+       "        9.40600531e-01, 8.58882723e-01, 8.83153525e-01, 5.29237569e-01,\n",
+       "        9.47016217e-01, 6.78716717e-01, 9.61738909e-01, 2.35331675e-01,\n",
+       "        6.12545788e-01, 7.74339518e-01, 9.41058522e-01, 8.75471711e-01,\n",
+       "        8.59864277e-01, 9.79334309e-01, 7.38395610e-01, 8.33104799e-01,\n",
+       "        9.87239573e-01, 5.48944602e-01, 9.87363150e-01, 9.36353882e-01,\n",
+       "        8.99248745e-01, 1.25103263e-01, 9.56432937e-01, 2.32186414e-01,\n",
+       "        9.73968438e-01, 9.51086577e-01, 8.86983240e-01, 7.07964772e-01,\n",
+       "        7.69851252e-01, 9.19880988e-01, 9.25399893e-01, 9.95678115e-01,\n",
+       "        3.25670951e-01, 8.41936443e-01, 9.88321175e-01, 9.21866558e-01,\n",
+       "        9.95829248e-01, 9.58900806e-01, 9.46218750e-01, 5.29751842e-01,\n",
+       "        9.88712758e-01, 8.44182599e-01, 7.85880513e-01, 9.31338014e-01,\n",
+       "        7.88418432e-01, 4.68678307e-01, 9.44128006e-01, 7.66132700e-01],\n",
+       "       [9.64680476e-01, 8.70118500e-01, 8.95496804e-01, 7.51530343e-01,\n",
+       "        9.28313065e-01, 7.81126264e-01, 9.65450670e-01, 1.00000000e+00,\n",
+       "        7.62825588e-01, 9.16390101e-01, 8.22674769e-01, 9.72758935e-01,\n",
+       "        8.28739017e-01, 9.49632563e-01, 9.87500091e-01, 9.43016491e-01,\n",
+       "        9.23274815e-01, 2.99319776e-01, 7.12201832e-02, 1.00000000e+00,\n",
+       "        6.85681407e-01, 7.53926556e-01, 9.13339719e-01, 9.50988065e-01,\n",
+       "        9.01091752e-01, 8.51524058e-01, 1.00000000e+00, 7.52317833e-01,\n",
+       "        9.61744343e-01, 9.66656049e-01, 3.45706221e-01, 7.70008073e-01,\n",
+       "        9.72378540e-01, 9.92843927e-01, 3.00799601e-01, 7.85352521e-01,\n",
+       "        8.79043771e-01, 9.55372774e-01, 9.48665753e-01, 9.77168341e-01,\n",
+       "        7.03934810e-01, 1.00000000e+00, 9.50922625e-01, 8.04133100e-01,\n",
+       "        8.99784441e-01, 9.90167506e-01, 8.80979497e-01, 1.00000000e+00,\n",
+       "        9.52080044e-01, 5.71432092e-01, 8.65345460e-01, 6.14800833e-01,\n",
+       "        1.00000000e+00, 3.79867171e-06, 6.76522406e-02, 8.38931721e-01,\n",
+       "        9.77145077e-01, 6.79544193e-01, 9.82369730e-01, 1.00000000e+00,\n",
+       "        9.07325385e-01, 9.82981984e-01, 8.29772673e-01, 9.94145815e-01,\n",
+       "        8.14912981e-01, 9.66234260e-01, 6.42252418e-01, 9.09108469e-01,\n",
+       "        7.77871975e-01, 9.96511917e-01, 8.06147636e-01, 9.87633076e-01,\n",
+       "        8.22157432e-01, 7.05420281e-01, 9.57169180e-01, 8.02730701e-01,\n",
+       "        8.91717112e-01, 9.25370863e-01, 9.52007785e-01, 9.52870377e-01,\n",
+       "        1.00000000e+00, 1.00000000e+00, 7.55438083e-01, 9.28898038e-01,\n",
+       "        3.15814146e-01, 9.69232438e-01, 8.85219496e-01, 7.66253495e-01,\n",
+       "        9.74507315e-01, 6.15316650e-01, 9.92541112e-01, 9.72318191e-01,\n",
+       "        9.91588010e-01, 5.15056557e-01, 9.18195549e-01, 9.13405242e-01,\n",
+       "        1.00000000e+00, 8.86365722e-01, 9.85541460e-01, 1.00000000e+00,\n",
+       "        7.30625337e-01, 9.31394759e-01, 9.33510321e-01, 8.55785286e-01,\n",
+       "        9.11906188e-01, 9.67187800e-01, 8.97045012e-01, 9.62171951e-01,\n",
+       "        1.00000000e+00, 9.13081940e-01, 8.44301795e-01, 9.83597768e-01,\n",
+       "        9.07855797e-01, 6.72462846e-01, 8.89833955e-01, 9.95791675e-01,\n",
+       "        9.89010333e-01, 7.59197137e-01, 8.51797342e-01, 8.84579503e-01,\n",
+       "        9.17023878e-01, 9.81264609e-01, 9.82160551e-01, 8.43639455e-01,\n",
+       "        9.85922003e-01, 9.64804283e-01, 9.35366861e-01, 7.96308269e-01,\n",
+       "        1.00000000e+00, 7.74874769e-01, 8.75298255e-01, 9.73738803e-01,\n",
+       "        9.31061885e-01, 9.87314156e-01, 9.60046100e-01, 9.93031423e-01,\n",
+       "        7.87545060e-01, 9.21851905e-01, 1.00000000e+00, 9.83076224e-01,\n",
+       "        9.64948689e-01, 9.90709211e-01, 1.42847364e-04, 8.77712072e-01,\n",
+       "        7.72103250e-01, 9.97404978e-01, 8.33295097e-01, 8.87554810e-01,\n",
+       "        5.70246157e-01, 8.99519214e-01, 9.89723895e-01, 8.97844863e-01,\n",
+       "        8.15157489e-01, 9.95917047e-01, 8.89577981e-01, 8.88683240e-01,\n",
+       "        7.60846371e-01, 1.00000000e+00, 1.00000000e+00, 9.81543003e-01,\n",
+       "        8.65965540e-01, 9.62464199e-01, 8.64633483e-01, 8.13599894e-01,\n",
+       "        6.74923861e-01, 6.46620365e-01, 5.74170228e-01, 2.74578001e-01,\n",
+       "        9.75102138e-01, 9.66287450e-01, 9.13966939e-01, 3.38734643e-01,\n",
+       "        1.00000000e+00, 9.49886047e-01, 9.98869387e-01, 9.65362821e-01,\n",
+       "        9.81524205e-01, 9.62586708e-01, 9.05227388e-01, 9.79599354e-01,\n",
+       "        1.16192406e-01, 8.59618022e-01, 9.97754100e-01, 8.62728112e-01,\n",
+       "        1.28201521e-04, 8.82274250e-01, 8.62846747e-01, 8.92309796e-01,\n",
+       "        9.41271495e-01, 5.93921551e-02, 9.52081859e-01, 8.86050765e-01,\n",
+       "        9.83113696e-01, 1.00000000e+00, 9.79127203e-01, 3.99769519e-01,\n",
+       "        8.93860596e-01, 9.98342869e-01, 1.00000000e+00, 9.80443293e-01,\n",
+       "        4.36603107e-01, 9.63108328e-01, 6.49686353e-01, 9.90675533e-01,\n",
+       "        9.86432550e-01, 8.92691367e-01, 8.19770152e-01, 1.00000000e+00,\n",
+       "        9.08824818e-01, 9.10013358e-01, 9.70388795e-01, 2.64048163e-01,\n",
+       "        9.92333807e-01, 7.02849907e-01, 7.42803047e-01, 9.99724870e-01,\n",
+       "        9.87499521e-01, 5.65459114e-01, 9.74932906e-01, 8.38138745e-01,\n",
+       "        9.91087942e-01, 4.86650724e-01, 8.16015308e-01, 9.78118851e-01,\n",
+       "        5.57666594e-01, 9.97906693e-01, 7.36913742e-01, 9.22404769e-01,\n",
+       "        5.80207353e-01, 9.53256851e-01, 3.37925279e-01, 1.00000000e+00,\n",
+       "        3.51688704e-01, 9.44433550e-01, 9.39953753e-01, 1.00000000e+00,\n",
+       "        9.65964392e-01, 9.47339086e-01, 9.39748618e-01, 8.74652329e-01,\n",
+       "        8.75945135e-01, 8.42046108e-01, 3.93981922e-01, 5.71428897e-01,\n",
+       "        9.83232015e-01, 8.90965164e-01, 6.01301766e-01, 9.69038248e-01,\n",
+       "        9.98467679e-01, 5.63979245e-01, 8.99796196e-01, 9.18908762e-01,\n",
+       "        9.67446748e-01, 9.89407011e-01, 9.79522111e-01, 6.50271976e-01,\n",
+       "        8.42276783e-01, 4.47860962e-01, 9.36244847e-01, 9.57043952e-01,\n",
+       "        9.82426844e-01, 9.45751322e-01, 8.31901817e-01, 8.41770280e-01,\n",
+       "        6.20311179e-01, 8.71556577e-01, 6.24491906e-01, 9.16785805e-01,\n",
+       "        7.23660886e-01, 9.94213928e-01, 1.00000000e+00, 3.91301981e-01,\n",
+       "        8.66753369e-01, 9.53355925e-01, 8.43098998e-01, 7.43260968e-01,\n",
+       "        1.00000000e+00, 8.25671218e-01, 9.81180612e-01, 5.40031205e-01,\n",
+       "        6.93753305e-01, 2.00007729e-01, 9.86755671e-01, 7.33568459e-01,\n",
+       "        7.25534265e-01, 1.00000000e+00, 8.97708603e-01, 1.00000000e+00,\n",
+       "        1.00000000e+00, 9.42621499e-01, 6.90601320e-01, 9.91758321e-01,\n",
+       "        5.38415051e-01, 9.35984567e-01, 8.54238618e-01, 7.44655464e-01,\n",
+       "        8.15484503e-01, 7.16628683e-01, 9.82993383e-01, 8.66955901e-01,\n",
+       "        9.60372891e-01, 9.96999033e-01, 1.72694267e-01, 9.38385186e-01,\n",
+       "        9.84498163e-01, 7.00659390e-01, 6.43341044e-01, 9.36028659e-01,\n",
+       "        7.06101158e-01, 8.97089479e-01, 1.00000000e+00, 9.41702204e-01,\n",
+       "        9.26190793e-01, 1.00000000e+00, 7.74098363e-01, 8.41470634e-01,\n",
+       "        9.94545350e-01, 7.86799665e-01, 5.73853969e-01, 9.87390217e-01,\n",
+       "        8.29951881e-01, 1.00000000e+00, 6.07337529e-01, 7.63329917e-01,\n",
+       "        1.00000000e+00, 9.31438917e-01, 8.01199071e-01, 6.72603428e-01,\n",
+       "        8.98868539e-01, 9.12952560e-01, 9.75497218e-01, 8.90757477e-01,\n",
+       "        6.55694651e-01, 9.00566882e-01, 7.81465767e-01, 9.93733859e-01,\n",
+       "        9.27274640e-01, 9.40431319e-01, 6.14627579e-01, 8.77231346e-01,\n",
+       "        8.83817447e-01, 9.31342350e-01, 9.82097832e-01, 9.43982700e-01,\n",
+       "        8.90105956e-01, 7.43541021e-01, 8.32962010e-01, 8.97851364e-01,\n",
+       "        9.96309044e-01, 1.00000000e+00, 9.83526638e-01, 7.65753715e-01,\n",
+       "        9.36610227e-01, 9.88482389e-01, 9.99590902e-01, 6.37266456e-01,\n",
+       "        8.00144345e-01, 8.06286214e-01, 9.84370608e-01, 8.95825951e-01,\n",
+       "        8.06642696e-01, 9.33213267e-01, 5.47686065e-01, 9.86403225e-01,\n",
+       "        9.44142035e-01, 9.54307292e-01, 1.00000000e+00, 1.00000000e+00,\n",
+       "        9.54965746e-01, 1.00000000e+00, 9.74866623e-01, 9.53262050e-01,\n",
+       "        9.69054550e-01, 9.69823641e-01, 9.55690056e-01, 9.85159768e-01,\n",
+       "        8.24015558e-01, 1.00000000e+00, 8.77526234e-01, 9.96112280e-01,\n",
+       "        8.69366292e-01, 9.71500123e-01, 9.11659973e-01, 9.02653122e-01,\n",
+       "        7.47484423e-01, 9.39944520e-01, 8.01030316e-01, 9.11739988e-01,\n",
+       "        8.93808925e-01, 9.41175993e-01, 8.32031388e-01, 7.56327925e-01,\n",
+       "        9.59032065e-01, 9.38117867e-01, 9.16527720e-01, 8.83929615e-01,\n",
+       "        9.93390099e-01, 9.84216611e-01, 9.91804551e-01, 7.98252578e-01,\n",
+       "        9.04113572e-01, 8.04211709e-01, 8.45184780e-01, 9.43762841e-01,\n",
+       "        6.85280242e-01, 4.54058006e-01, 7.33619318e-01, 1.00000000e+00,\n",
+       "        8.63755912e-01, 9.61737558e-01, 9.26847674e-01, 9.37143960e-01,\n",
+       "        1.00000000e+00, 8.30306796e-01, 9.47945870e-01, 1.00000000e+00,\n",
+       "        8.45482467e-01, 1.00000000e+00, 9.38352625e-01, 1.00000000e+00,\n",
+       "        7.52506203e-01, 9.92334169e-01, 5.98785321e-01, 1.00000000e+00,\n",
+       "        8.17532724e-01, 9.10114719e-01, 8.38578649e-01, 9.90111452e-01,\n",
+       "        9.72325509e-01, 9.62727303e-01, 9.88664969e-01, 9.26411457e-01,\n",
+       "        6.54483875e-01, 7.31785191e-01, 9.18091614e-01, 8.75140654e-01,\n",
+       "        8.26533210e-01, 9.89344176e-01, 1.00000000e+00, 8.01242396e-01,\n",
+       "        3.48569208e-01, 1.00000000e+00, 9.82261741e-01, 4.50298442e-01,\n",
+       "        9.18078110e-01, 7.83087259e-01, 4.33459418e-01, 8.96780466e-01,\n",
+       "        9.47771188e-01, 8.30018409e-01, 6.85268779e-01, 9.80820473e-01,\n",
+       "        8.32889596e-01, 7.89644171e-01, 9.59267927e-01, 9.51638203e-01,\n",
+       "        9.95525852e-01, 1.43253023e-01, 9.28380239e-01, 8.56500602e-01,\n",
+       "        9.93508997e-01, 9.66548814e-01, 8.47635168e-01, 8.90136989e-01,\n",
+       "        9.80332270e-01, 8.18794991e-01, 7.54317683e-01, 6.84700966e-01,\n",
+       "        9.70690084e-01, 5.88369292e-01, 9.90501732e-01, 4.17444913e-01,\n",
+       "        1.00000000e+00, 6.88926318e-01, 9.84168971e-01, 9.66083127e-01,\n",
+       "        9.96442866e-01, 8.18654320e-01, 9.31052325e-01, 9.69606490e-01,\n",
+       "        9.10227572e-01, 1.00000000e+00, 9.58218303e-01, 1.00000000e+00,\n",
+       "        9.01875514e-01, 7.46084919e-01, 9.81425729e-01, 1.00000000e+00,\n",
+       "        8.66783502e-01, 2.48881302e-01, 9.26570163e-01, 9.42201394e-01,\n",
+       "        8.04121672e-01, 7.28021968e-01, 1.00000000e+00, 8.75694592e-01,\n",
+       "        1.00000000e+00, 7.52301900e-01, 7.48515470e-01, 7.57397517e-01]])
          • step_size
            (chain, draw)
            float64
            0.539 0.539 0.539 ... 0.4737 0.4737
            array([[0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
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+       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
+       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
+       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
+       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
+       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
+       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
+       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
+       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
+       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
+       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
+       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
+       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
+       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
+       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
+       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
+       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
+       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
+       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
+       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
+       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
+       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
+       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
+       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
+       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
+       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
+       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
+       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
+       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
+       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
+       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
+       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
+       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
+       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
+       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
+       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
+       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
+       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
+       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
+       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
+       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
+       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
+       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
+       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
+       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
+       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
+       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
+       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
+       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
+       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
+       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
+       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
+       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
+       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
+       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
+       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
+       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
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+       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
+       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
+       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
+       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
+       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
+       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
+       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
+       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
+       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
+       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
+       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
+       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
+       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
+       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
+       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
+       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
+       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
+       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
+       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
+       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
+       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
+       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
+       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
+       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
+       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
+       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
+       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
+       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
+       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
+       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
+       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
+       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
+       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
+       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
+       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
+       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
+       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
+       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
+       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
+       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
+       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556],\n",
+       "       [0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
+       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
+       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
+       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
+       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
+       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
+       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
+       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
+       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
+       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
+       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
+       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
+       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
+       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
+       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
+       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
+       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
+       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
+       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
+       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
+       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
+       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
+       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
+       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
+       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
+       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
+       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
+       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
+       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
+       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
+       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
+       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
+       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
+       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
+       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
+       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
+       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
+       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
+       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
+       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
+       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
+       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
+       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
+       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
+       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
+       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
+       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
+       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
+       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
+       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
+       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
+       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
+       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
+       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
+       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
+       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
+       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
+       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
+       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
+       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
+       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
+       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
+       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
+       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
+       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
+       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
+       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
+       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
+       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
+       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
+       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
+       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
+       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
+       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
+       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
+       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
+       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
+       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
+       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
+       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
+       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
+       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
+       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
+       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
+       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
+       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
+       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
+       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
+       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
+       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
+       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
+       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
+       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
+       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
+       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
+       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
+       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
+       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
+       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
+       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459]])
          • tree_size
            (chain, draw)
            float64
            7.0 7.0 7.0 3.0 ... 7.0 7.0 7.0 7.0
            array([[ 7.,  7.,  7.,  3.,  3.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
+       "         7.,  7.,  7.,  7.,  7.,  7.,  3.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
+       "         7.,  3.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
+       "         3.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
+       "         7.,  7.,  7.,  7.,  7.,  3.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
+       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
+       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  3.,  3.,  7.,  7.,\n",
+       "         7.,  7.,  7.,  3.,  3.,  7.,  7.,  7.,  3.,  7.,  7.,  7.,  7.,\n",
+       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
+       "         7.,  7.,  7.,  7.,  3.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
+       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
+       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
+       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
+       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  3.,  7.,  7.,  3.,\n",
+       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
+       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
+       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
+       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
+       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
+       "         7.,  7.,  3.,  7.,  3.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
+       "         7.,  7.,  7.,  7.,  7.,  3.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
+       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
+       "         3.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
+       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  3.,  7.,  7.,  7.,  3.,  7.,\n",
+       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
+       "         7.,  7.,  7.,  3.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
+       "         7.,  7.,  3.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
+       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  3.,  7.,  7.,\n",
+       "         7.,  7.,  7.,  7.,  7.,  3.,  7.,  7.,  7.,  7.,  7.,  3.,  7.,\n",
+       "         3.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
+       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  3.,  7.,  7.,\n",
+       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
+       "         7.,  7.,  1.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  3.,\n",
+       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
+       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
+       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  3.,  7.,  7.,  3.,  7.,  7.,\n",
+       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  3.,  7.,  7.,  7.,\n",
+       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
+       "         7.,  7.,  7.,  7.,  7.,  7.],\n",
+       "       [ 7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
+       "         7.,  7.,  7.,  7.,  7.,  3.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
+       "         3.,  7.,  7.,  3.,  3.,  7.,  7.,  7.,  3.,  7.,  7.,  7.,  7.,\n",
+       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
+       "         7.,  3.,  3.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
+       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  3.,  7.,  7.,  7.,\n",
+       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
+       "         7.,  7.,  3.,  7.,  7.,  7.,  7.,  7.,  3.,  7.,  7.,  7.,  7.,\n",
+       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  3.,\n",
+       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
+       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  3.,\n",
+       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
+       "         7.,  7.,  7.,  7.,  7.,  7.,  3.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
+       "         7.,  7.,  3.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  3.,  3.,\n",
+       "         3.,  3.,  3.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  3.,  7.,\n",
+       "         3.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
+       "         7.,  7.,  7.,  3.,  7.,  7.,  7.,  7.,  7.,  3.,  7.,  7.,  7.,\n",
+       "         3.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  3.,  7.,  7.,\n",
+       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
+       "         7.,  7.,  3.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
+       "         7.,  7.,  7.,  7.,  7.,  7.,  3.,  7.,  7.,  7.,  7.,  3.,  7.,\n",
+       "         7.,  7.,  3.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
+       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
+       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
+       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
+       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
+       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  3.,  7.,  7.,  7.,  7.,  7.,\n",
+       "         3.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
+       "         7.,  7.,  7.,  7.,  7.,  3.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
+       "         7.,  7.,  7.,  7.,  3.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
+       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
+       "         7.,  3.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
+       "         7.,  3.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
+       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  3.,  3.,\n",
+       "         7.,  7.,  7.,  7., 15.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
+       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
+       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
+       "         7.,  7.,  7.,  7.,  7.,  7.,  3.,  7.,  3.,  7.,  7.,  7.,  7.,\n",
+       "         7.,  7.,  7.,  7.,  7.,  7.]])
          • lp
            (chain, draw)
            float64
            -49.28 -50.75 ... -44.16 -47.04
            array([[-49.27724736, -50.75483014, -45.03889794, -44.14666445,\n",
+       "        -44.20643755, -42.04409755, -43.42401873, -41.95079896,\n",
+       "        -44.56420491, -41.92039609, -43.42754446, -45.75749707,\n",
+       "        -43.65325698, -42.57066678, -43.34737833, -44.25702578,\n",
+       "        -45.5226202 , -44.68080407, -45.77286064, -45.77286064,\n",
+       "        -45.77286064, -45.54949284, -49.68773836, -49.63873553,\n",
+       "        -47.88171341, -47.30985942, -48.47575842, -44.69927806,\n",
+       "        -47.23816847, -48.33028575, -47.61713966, -45.51120227,\n",
+       "        -47.6853604 , -50.0831773 , -46.84647395, -43.84111464,\n",
+       "        -44.82183851, -46.32165484, -44.7721744 , -43.89467872,\n",
+       "        -46.67713672, -43.53761563, -45.38915379, -46.11211378,\n",
+       "        -44.04877696, -45.08911736, -47.84263453, -44.11592021,\n",
+       "        -45.84884196, -49.57423043, -48.8834551 , -51.7607259 ,\n",
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+       "        -44.66588846, -48.04025208, -49.01715434, -47.79214687,\n",
+       "        -48.98043333, -45.53184009, -47.2041232 , -44.72420136,\n",
+       "        -49.46424811, -46.79983041, -44.04227394, -42.91863568,\n",
+       "        -43.97746027, -44.83141731, -48.69877082, -47.92290948,\n",
+       "        -46.88932515, -46.34584215, -46.02981906, -47.87252249,\n",
+       "        -46.80307986, -44.1145662 , -42.64508742, -42.91383116,\n",
+       "        -45.02425086, -44.35691943, -43.76217641, -47.62601932,\n",
+       "        -49.73236691, -43.71502801, -43.9914042 , -44.74107947,\n",
+       "        -42.79125375, -43.78222773, -45.72450659, -48.1444365 ,\n",
+       "        -48.36796618, -44.02322612, -48.34768799, -45.87548176,\n",
+       "        -46.13475633, -47.04243703, -46.93408111, -45.91561447,\n",
+       "        -45.83474124, -45.60341281, -45.60403331, -45.14878158,\n",
+       "        -45.49676377, -44.85116172, -46.24093578, -47.28413239,\n",
+       "        -42.81540653, -45.18919048, -46.23582176, -47.23391066,\n",
+       "        -47.00239762, -44.88804043, -45.56847132, -46.75637296,\n",
+       "        -46.33416959, -45.63682029, -44.04723861, -44.08935247,\n",
+       "        -43.8915887 , -47.07546292, -50.30273426, -49.29015696,\n",
+       "        -50.11082403, -47.13236617, -47.04854802, -46.907683  ,\n",
+       "        -48.75568422, -52.39003808, -48.33335131, -43.83065183,\n",
+       "        -42.72258118, -42.72258118, -42.98527806, -42.38734039,\n",
+       "        -46.6733164 , -49.31505795, -48.56876539, -45.81689736,\n",
+       "        -42.175127  , -43.39571478, -44.16453369, -47.04446667]])
          • energy_error
            (chain, draw)
            float64
            -0.06193 0.1016 ... 0.1739 0.1582
            array([[-6.19325448e-02,  1.01645444e-01, -4.14474785e-01,\n",
+       "         1.08876669e-01, -1.03233219e+00,  2.22737310e-01,\n",
+       "         9.19175585e-02, -1.16402606e-01,  1.68847246e-01,\n",
+       "        -1.85091125e-01,  2.89957614e-01,  3.87514015e-01,\n",
+       "        -2.16643775e-01, -1.77613405e-01, -3.19980180e-01,\n",
+       "         9.94336111e-02,  1.44818710e+00, -1.21480846e-01,\n",
+       "        -7.32093341e-02,  0.00000000e+00,  0.00000000e+00,\n",
+       "         1.45951374e-02,  3.15203861e-01, -1.99938001e-01,\n",
+       "         3.02324538e-01, -1.95728134e-01,  2.17657308e-02,\n",
+       "        -1.84114480e-01,  6.69728148e-01, -1.87114648e-01,\n",
+       "        -7.54352204e-02, -2.59994811e-01,  2.07277244e-01,\n",
+       "         2.50204424e-01, -3.25135936e-01, -2.78307797e-01,\n",
+       "         1.15292246e-01,  1.69880890e-01, -2.64840578e-02,\n",
+       "        -6.32399396e-02,  1.94434954e-01, -2.45446724e-01,\n",
+       "         1.79428718e-01,  1.17880142e-01, -3.14028346e-01,\n",
+       "        -1.18922502e-02,  1.66781129e-01, -2.23591491e-01,\n",
+       "         2.14786739e-02,  3.86498486e-01, -9.37422616e-02,\n",
+       "         2.49256023e-01,  2.16215471e+00,  1.52081760e-01,\n",
+       "        -2.32054691e-03,  5.31131062e-02, -7.43098576e-01,\n",
+       "         0.00000000e+00,  4.39532360e-02,  2.26273697e-01,\n",
+       "         5.43610070e-02,  5.84198994e-02, -7.79212207e-02,\n",
+       "         3.38947074e-01, -5.83557754e-01, -2.67905008e-01,\n",
+       "        -4.78446150e-01,  4.39329801e-01, -5.20336503e-01,\n",
+       "         7.80195279e-02, -6.49516780e-01,  1.60437770e-01,\n",
+       "        -5.37036268e-01, -3.40460768e-01, -8.74683455e-01,\n",
+       "         4.38390666e-01,  3.07325382e-01,  4.77269288e-01,\n",
+       "        -1.03498030e+00,  7.88383319e-01, -1.94021835e-01,\n",
+       "         9.14665650e-02, -1.90778203e-01, -8.22549636e-01,\n",
+       "         3.43945179e-01,  4.19095292e-01, -1.15867937e-01,\n",
+       "         0.00000000e+00,  4.86789244e-01,  7.94809573e-01,\n",
+       "        -6.81684652e-01,  4.61737965e-01,  5.17861573e-02,\n",
+       "        -9.73478560e-01, -2.57551957e-01,  3.67240936e-01,\n",
+       "        -1.51522173e-01,  1.10541542e-01, -1.48109916e-02,\n",
+       "        -2.28084973e-01,  2.08137973e-02,  8.62346826e-01,\n",
+       "         1.56395841e-01, -5.69867459e-01, -1.38321358e-01,\n",
+       "        -1.97555138e-01,  4.39857362e-01,  2.85106577e-02,\n",
+       "         2.49326204e-01, -5.13695003e-02,  2.96271035e-01,\n",
+       "         9.48347494e-03, -7.55208761e-02,  9.45350973e-02,\n",
+       "        -1.17068048e-01,  6.00427447e-01, -3.18490444e-01,\n",
+       "         1.27538393e-01, -3.44864320e-02, -1.51723962e-01,\n",
+       "        -2.91325668e-01, -5.30442772e-01,  3.18580581e-01,\n",
+       "        -9.30570327e-03, -5.84173928e-01,  7.29047209e-01,\n",
+       "         2.27952828e-01, -5.21965958e-02, -1.47273477e-01,\n",
+       "         2.96237310e-01, -1.41363612e-01, -3.87567346e-01,\n",
+       "        -4.65921986e-03,  1.08538515e-01, -2.76345949e-02,\n",
+       "         2.73401806e-01,  2.89237794e-02, -3.00218808e-01,\n",
+       "         2.77857973e-02,  2.64365772e-01, -1.77286312e-01,\n",
+       "         1.79518053e-01,  1.39292359e-01,  1.49086139e-01,\n",
+       "        -2.46507317e-01,  3.05843116e-01,  1.59516259e-01,\n",
+       "        -6.13043148e-01,  1.54765968e-02,  7.74893922e-03,\n",
+       "        -6.14285297e-02, -3.22696158e-01, -2.61850364e-01,\n",
+       "         1.67286852e-01,  3.37900366e-02, -1.76210785e+00,\n",
+       "         1.21221555e+00, -1.36200503e+00,  3.98830356e-01,\n",
+       "        -4.42289113e-01, -1.45287614e-01,  1.45342151e-01,\n",
+       "        -1.34669426e-01, -2.72580559e-02,  1.05588575e-01,\n",
+       "        -4.56947314e-02, -1.06466971e-01,  1.99700315e-01,\n",
+       "         2.50670250e-01, -1.04941716e+00, -1.15189583e+00,\n",
+       "         6.88387948e-02, -3.73903307e-01,  2.30790617e-01,\n",
+       "        -9.57335046e-02, -1.17154802e+00,  8.24373695e-02,\n",
+       "         2.31224647e-03, -1.40453125e-01,  2.82701450e-01,\n",
+       "         3.60449226e-01,  0.00000000e+00,  1.36131820e-01,\n",
+       "        -2.95469966e-01, -5.44148290e-01,  6.12427640e-01,\n",
+       "        -9.84534546e-02, -8.25439515e-02, -1.86113374e-01,\n",
+       "        -5.83618294e-03, -1.03350255e-01,  3.09172966e-01,\n",
+       "         1.33605114e-02, -6.90201931e-02, -1.54675571e-01,\n",
+       "        -1.24395964e-01,  2.62452006e-01, -1.41160503e-01,\n",
+       "         5.72642032e-02,  2.00826302e-01, -4.80080034e-01,\n",
+       "        -7.25395068e-01,  1.18797715e-01, -2.08750612e-01,\n",
+       "         2.14592245e-01,  6.46166449e-02,  2.39236522e-01,\n",
+       "        -1.77269858e-01, -1.57113855e-01,  3.35531182e-01,\n",
+       "         5.38964148e-02,  2.93116092e-01, -2.79557552e-01,\n",
+       "         4.92498010e-01,  7.41592670e-03,  6.55750286e-03,\n",
+       "         1.04057873e-01, -5.40523204e-03,  0.00000000e+00,\n",
+       "         1.22299511e-01, -2.44040808e-01,  3.17916816e-01,\n",
+       "        -2.67871380e-02, -4.56975267e-02, -3.04432274e-01,\n",
+       "         1.92230343e-01,  2.72201800e-01, -9.99144135e-02,\n",
+       "         2.68856128e-02,  4.89649419e-02, -5.94418106e-01,\n",
+       "        -6.43494060e-02, -9.60106690e-01,  2.49370329e-01,\n",
+       "         1.57904841e-02,  4.51471272e-01,  3.74295457e-01,\n",
+       "        -4.61190472e-01,  9.72190030e-02, -9.99436562e-02,\n",
+       "         1.13102412e-01,  1.22720026e+00,  9.52216026e-01,\n",
+       "        -1.80203520e-01, -6.21434329e-01, -2.72084889e-01,\n",
+       "         2.88104469e-01,  8.36175522e-02,  1.47149351e-01,\n",
+       "        -1.08615994e-01,  1.15773365e-01, -3.15419756e-01,\n",
+       "         2.01142582e-01,  1.97679813e-01, -2.62292479e-01,\n",
+       "        -4.59820592e-02, -2.62411676e-01,  3.28161948e-01,\n",
+       "         2.31241983e-01, -2.79127442e-01,  1.21257330e-01,\n",
+       "        -1.82602701e-01, -9.32502189e-02,  1.80439138e-02,\n",
+       "        -1.24389267e-01,  4.79446754e-01, -4.41142579e-01,\n",
+       "         0.00000000e+00,  4.38779548e-02, -9.74350949e-02,\n",
+       "         3.41028899e-01, -1.39560248e-01,  1.57203088e-01,\n",
+       "        -1.50164053e-01,  1.79054992e+00, -2.63418591e-01,\n",
+       "         9.77289650e-02,  7.31885449e-02, -2.62430965e-01,\n",
+       "        -2.09769682e-01, -3.74318548e-01,  3.03119432e-02,\n",
+       "         1.12912174e-01,  1.29520801e-01,  9.08624084e-02,\n",
+       "         2.64254609e-01,  4.72613207e-01,  3.61159364e-01,\n",
+       "        -1.06435455e+00, -7.97654937e-02,  1.73738580e-01,\n",
+       "        -1.82572925e-01, -2.15989532e-01,  3.28076006e-01,\n",
+       "        -8.14299594e-02,  2.28111476e-01,  3.19707312e-01,\n",
+       "         1.62981815e-01, -3.97363902e-01, -3.11034354e-02,\n",
+       "         9.68067068e-02, -2.37160402e-02,  1.46876214e-01,\n",
+       "        -2.46389714e-01, -7.07524485e-02, -9.50555682e-01,\n",
+       "         3.19511926e+00, -3.59198013e+00,  3.50012130e-03,\n",
+       "        -3.44708282e-01,  1.32957716e+00, -1.07582568e-01,\n",
+       "        -1.46924895e-01,  1.45051305e-01,  2.27299881e-01,\n",
+       "        -1.93985507e-01,  6.13013728e-02,  4.98133640e-02,\n",
+       "         1.37516251e-02,  5.97513910e-01, -2.73294721e-02,\n",
+       "        -4.38914963e-02,  6.60112237e-03, -6.08085935e-02,\n",
+       "        -5.20856208e-01,  4.43335587e-01, -4.01110404e-02,\n",
+       "        -2.99324873e-03,  4.08212045e-01, -1.01940250e-01,\n",
+       "        -1.78016594e-01,  3.23187240e-01, -1.31426883e-01,\n",
+       "         3.18597764e-02, -1.27468134e-01, -3.86218887e-01,\n",
+       "         3.68570269e-01,  4.52298726e-01, -4.82800816e-01,\n",
+       "         4.51559152e-02,  2.74832239e-01, -7.21776216e-02,\n",
+       "         2.05153450e-01,  1.33271927e+00, -3.12781117e-02,\n",
+       "         1.73906012e-01, -1.14024584e-01, -1.14444483e-02,\n",
+       "         3.12349513e-01,  9.60686235e-02, -2.08058658e-01,\n",
+       "        -5.69258576e-02,  2.45411865e-01,  5.72665589e-01,\n",
+       "         2.37804194e-01, -1.69386803e-01, -2.09987098e-01,\n",
+       "         1.65463001e-01, -3.78549903e-01,  1.00985869e-01,\n",
+       "        -7.78165407e-01,  0.00000000e+00,  1.04577074e+00,\n",
+       "        -1.47984851e-01,  2.82372106e-02, -3.22800505e-04,\n",
+       "         8.22909072e-01, -8.87892422e-01,  2.88289256e-01,\n",
+       "        -3.34857050e-01, -8.03350855e-02, -8.26935217e-03,\n",
+       "         1.03249195e-01, -2.08238401e-01,  5.34411245e-01,\n",
+       "        -5.27679779e-01,  1.02648723e-01,  4.09146981e-01,\n",
+       "        -1.61494321e-01,  0.00000000e+00,  2.46431255e-01,\n",
+       "        -2.71384616e-01,  4.14982319e-01, -3.40172801e-01,\n",
+       "         2.17275739e-01, -9.12700028e-02,  1.29403601e-01,\n",
+       "         1.73007937e-01,  1.64103200e-01,  7.65986045e-02,\n",
+       "         1.39835675e-01, -1.94553361e+00, -3.96114788e-01,\n",
+       "         2.27604210e-01,  8.73289230e-01, -2.66365006e-01,\n",
+       "         3.58175467e-02,  4.08497163e-02, -4.98041339e-01,\n",
+       "        -1.00511162e-01, -1.34636019e-01, -4.32932690e-01,\n",
+       "        -1.24901005e-02, -1.15132101e-02,  4.76294939e-02,\n",
+       "         1.38035283e-01, -8.75114261e-02,  2.50459043e-01,\n",
+       "        -5.71264958e-01,  3.43004680e-01,  1.13316498e-01,\n",
+       "         1.79049155e-02, -7.42889148e-02,  1.05883991e+00,\n",
+       "        -5.51281380e-02, -4.30754726e-02,  7.76899793e-02,\n",
+       "         1.15725565e-01,  0.00000000e+00, -1.23746918e-01,\n",
+       "         1.75246508e-01,  2.31973508e-01, -3.73159985e-01,\n",
+       "        -2.01645002e-01,  7.77669497e-03, -2.29996434e-01,\n",
+       "         9.53130068e-02,  2.57757486e-01,  1.54036030e-01,\n",
+       "        -5.17293688e-02,  1.63111654e-01, -2.01352431e-01,\n",
+       "         5.48000631e-01, -4.02016428e-01, -5.14913226e-03,\n",
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+       "        -5.93353720e-02,  1.87531911e-01, -1.32716389e-01,\n",
+       "         7.08694494e-01, -2.65590359e-01, -4.66673049e-02,\n",
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+       "        -1.94302943e-01, -3.82521455e-01,  1.56775935e-01,\n",
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+       "        -1.06656528e+00,  4.10919362e-01,  1.42798662e-01,\n",
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+       "        -3.06876831e-02,  1.74483155e-01,  1.57663542e-01,\n",
+       "        -2.62688751e-01,  2.60746299e+00,  6.49989623e-02,\n",
+       "         3.95926410e-02,  2.04150169e-01, -5.63714181e-01,\n",
+       "         1.93427440e-01, -4.82588960e-01,  8.44911148e-02,\n",
+       "        -1.45229463e-01,  3.54538678e-01,  2.68343145e-01,\n",
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+       "        -1.03660663e+00, -5.02906634e-01,  9.56500212e-01,\n",
+       "        -7.74678142e-01,  1.80061413e-01, -6.66819619e-02,\n",
+       "        -2.00878835e-01, -8.78614339e-02,  8.38030722e-01,\n",
+       "         1.19672434e-02,  2.00593489e-01],\n",
+       "       [-1.35880111e-01,  5.25120085e-02,  1.61231190e-01,\n",
+       "         1.36244504e-01, -4.44786433e-01,  1.49745530e-01,\n",
+       "        -1.00922049e-01, -1.27469009e-01,  3.04203619e-01,\n",
+       "         5.97928068e-02, -4.24696571e-02, -1.81210314e-01,\n",
+       "         1.42347073e-01,  2.17761349e-02, -1.56034411e-01,\n",
+       "         9.10460369e-02,  2.15874824e-01,  0.00000000e+00,\n",
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+       "         1.28230261e-01, -5.19428406e-01,  5.16259933e-02,\n",
+       "         1.05062073e-01,  4.35070945e-01, -6.35107530e-01,\n",
+       "         4.55716899e-01, -2.07682403e-01, -4.51432312e-01,\n",
+       "        -3.79746629e-01,  6.97116057e-03, -1.54710241e-01,\n",
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+       "        -1.52443341e-02,  3.66967464e-01, -3.42058248e-01,\n",
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+       "        -1.36121486e-01,  2.95184334e-01, -1.28113285e-01,\n",
+       "         4.14554618e-02, -1.50768052e-02,  4.07387611e-01,\n",
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+       "         3.84877474e-01,  1.01875328e-04, -1.54442151e-02,\n",
+       "         6.78984051e-02, -8.40659645e-02,  3.03344861e-01,\n",
+       "        -3.06174454e-01,  8.03315085e-02, -2.06009954e-01,\n",
+       "         3.53523056e-01,  5.80203033e-01,  1.90971829e-01,\n",
+       "        -1.89701666e+00,  3.75985031e-01, -2.15483574e-01,\n",
+       "         3.28914750e-01,  1.42427391e-02, -4.81141424e-01,\n",
+       "        -3.10218289e-02,  2.98708243e-01,  1.07453142e-01,\n",
+       "        -7.82188051e-02, -1.43544096e-01, -4.98983644e-01,\n",
+       "        -1.28181989e-01,  3.02861957e-01,  1.86702457e-01,\n",
+       "         1.18801086e+00, -2.06377042e-01, -7.49406133e-02,\n",
+       "         1.71146702e-01, -1.06800518e-01,  6.46141879e-01,\n",
+       "        -3.08251858e-01, -1.85257826e-01, -5.25163044e-03,\n",
+       "         2.35080516e-01,  1.37269138e-02,  5.98141493e-02,\n",
+       "        -1.06285434e-01,  9.99668633e-02, -6.55607783e-01,\n",
+       "        -5.06597855e-02,  1.21399760e-01, -2.08101749e-01,\n",
+       "         1.50946601e-01,  3.12250675e-01, -2.43809765e-01,\n",
+       "         1.01987838e-01, -1.16564719e-01,  1.84671608e-01,\n",
+       "        -3.27854208e-01, -2.30495508e-03,  4.23871564e-03,\n",
+       "        -1.60167947e-02, -3.30013126e-01,  6.66388831e-01,\n",
+       "         2.89417728e-01, -1.96302168e-01, -1.25580833e+00,\n",
+       "         4.00302504e-01,  1.56602947e-02,  1.63945724e-01,\n",
+       "        -7.35834259e-02,  1.13208101e-02,  3.82428642e-02,\n",
+       "         2.56484131e-02, -3.22967779e-02, -8.16311283e-02,\n",
+       "        -2.57237658e-02, -8.50465596e-02, -2.47585661e-01,\n",
+       "         1.91188712e-01,  1.32146816e-01, -1.57571515e-01,\n",
+       "         1.34123069e-01,  6.70073173e-02, -5.23877357e-02,\n",
+       "        -5.04471627e-01,  3.19888829e-01,  6.99284322e-02,\n",
+       "        -7.48688543e-02, -3.37358426e-03,  9.69838981e-02,\n",
+       "        -3.52526782e-01,  0.00000000e+00,  5.14884252e-02,\n",
+       "         2.84294253e-01, -1.72328231e+00,  1.86407665e-01,\n",
+       "         7.54230704e-03, -1.82935156e-03, -2.35493662e-02,\n",
+       "        -1.67955990e-01,  8.67240453e-02,  2.45418322e-01,\n",
+       "        -3.04808647e-01, -4.58186733e-02,  8.74711183e-02,\n",
+       "         3.26658371e-01, -2.57366808e-02, -1.14383095e-01,\n",
+       "         3.78023799e-02,  2.29451558e-01, -3.63674862e-01,\n",
+       "        -7.01908567e-01,  2.60524092e-01,  2.92926608e-01,\n",
+       "        -7.47623273e-01, -3.94217458e-01,  4.76634832e-01,\n",
+       "        -3.97835606e-01, -1.74856229e-01, -1.88582853e-02,\n",
+       "        -1.11128837e+00, -3.18171527e-01,  5.98408509e-02,\n",
+       "        -2.01288377e-01, -5.92564540e-02, -6.96559847e-02,\n",
+       "         3.59526827e-02,  1.80686992e-01, -7.49423282e-01,\n",
+       "         0.00000000e+00,  3.50051265e-01, -5.29017129e-02,\n",
+       "         1.43062274e-01,  0.00000000e+00,  2.53086347e-01,\n",
+       "         7.69728504e-02, -3.23075924e-02,  9.38408666e-02,\n",
+       "         0.00000000e+00, -1.41711369e-02,  3.52541309e-02,\n",
+       "         1.25794688e-01, -1.73643016e-01,  2.69250945e-03,\n",
+       "         0.00000000e+00,  7.91636185e-02, -2.11842594e-01,\n",
+       "        -1.39297049e-02, -1.49766000e-01,  1.20614578e+00,\n",
+       "        -9.07562797e-04,  3.19048998e-01, -1.66830915e-01,\n",
+       "        -4.55969135e-01, -8.24094269e-03,  2.56682477e-01,\n",
+       "        -2.20020132e-01, -7.55985601e-02, -3.10201956e-01,\n",
+       "        -6.41264318e-03,  3.17054773e-01, -1.56306270e-01,\n",
+       "         4.16216769e-01,  8.55316768e-02, -2.58944178e-02,\n",
+       "        -4.22231465e-02,  1.61499683e-01, -1.27451983e+00,\n",
+       "        -1.36935611e-01,  6.44152248e-02,  7.71680386e-01,\n",
+       "         7.57751857e-03, -8.10765226e-02,  2.14395023e-01,\n",
+       "        -4.45365452e-01,  4.60698117e-01,  2.23092702e-01,\n",
+       "        -6.08244965e-02,  7.79968824e-02,  1.23921546e-01,\n",
+       "        -5.77140785e-01, -2.97711811e-01, -5.20976288e-02,\n",
+       "        -2.61906284e-02, -3.95787832e-02, -4.88305441e-03,\n",
+       "         5.38022094e-02,  5.07735899e-01,  6.22555341e-02,\n",
+       "         5.69387108e-02, -2.26767590e-01,  4.68939653e-01,\n",
+       "        -5.17950238e-02, -4.52815171e-02,  1.30823425e-01,\n",
+       "        -9.94106298e-02, -9.52218729e-01, -5.76197961e-02,\n",
+       "         4.21173023e-01,  7.29184750e-02, -2.81983498e-01,\n",
+       "         1.39725106e-01, -3.31329260e-01, -4.55448335e-02,\n",
+       "        -1.48355884e-02, -1.19709392e-01,  2.61561941e-01,\n",
+       "         2.24571080e-01, -8.35785285e-02,  4.09880925e-02,\n",
+       "         5.98319135e-02,  1.96498783e-01, -2.64319044e-01,\n",
+       "         6.68666125e-01, -1.97497805e-01,  4.03413396e-01,\n",
+       "         1.87026249e-01,  4.25417671e-01,  4.13455721e-02,\n",
+       "        -1.92226755e-01,  1.08757707e-01,  3.45871079e-01,\n",
+       "        -1.73321429e-01, -6.28527184e-01, -6.35191020e-01,\n",
+       "        -2.43279755e-01,  1.52174320e-01, -4.64160426e-01,\n",
+       "         3.18635681e-01,  8.50022177e-01,  6.94410797e-01,\n",
+       "         3.58173587e-02,  2.21584449e-01,  5.47268331e-01,\n",
+       "        -2.46124528e-01,  1.59268645e-01, -2.24725626e-01,\n",
+       "        -9.09499432e-02, -6.69322584e-02,  3.86144686e-01,\n",
+       "        -1.34154491e-01, -5.10498048e-02,  5.64116519e-02,\n",
+       "         1.13122556e-01, -4.06446541e-02, -2.53786341e-01,\n",
+       "         4.95383157e-01, -4.86858657e-01,  3.45092497e-01,\n",
+       "        -8.94980519e-02, -8.25640326e-02,  8.00429036e-01,\n",
+       "        -1.10946099e-01,  2.78223157e-02,  4.07123667e-01,\n",
+       "        -2.61988020e-01,  3.90022105e-02,  5.39488093e-01,\n",
+       "         5.46518119e-01, -4.59704641e-01, -1.11071838e-01,\n",
+       "         1.94601314e-01, -5.77235620e-01, -3.49685219e-02,\n",
+       "         3.82190837e-02, -1.13788451e-01, -1.08570815e-01,\n",
+       "         2.13016537e-01, -1.08701048e-01,  2.76998523e-01,\n",
+       "        -1.47971707e-01,  5.11839245e-01,  1.78040239e-01,\n",
+       "        -3.08576511e-01, -3.94029609e-01,  1.73607903e-01,\n",
+       "        -1.95279957e-02, -2.33186979e-01,  3.86745726e-02,\n",
+       "         6.39577734e-02, -6.71149955e-01,  3.22971818e-01,\n",
+       "        -1.03611001e-01,  9.90263283e-02, -1.82235045e-02,\n",
+       "        -1.21147633e-01,  3.16300177e-02,  1.55236778e-01,\n",
+       "        -8.67855666e-02,  1.17469627e-01,  9.97291146e-02,\n",
+       "        -3.07858018e-01, -1.20887504e-01, -1.08704527e-02,\n",
+       "         3.75806208e-01, -1.74311165e-02, -1.79990670e-02,\n",
+       "        -2.05441059e-01, -1.67302784e-01, -8.49807409e-02,\n",
+       "         1.15543726e-01,  1.04033127e-01,  7.62800836e-02,\n",
+       "        -1.56923378e-01,  5.72582132e-01, -2.21091838e-01,\n",
+       "         1.67001301e-01, -1.04951308e+00,  8.67166201e-02,\n",
+       "         8.42169433e-02,  1.00910499e-01,  3.05148835e-01,\n",
+       "        -6.44417944e-01,  6.32367502e-02, -3.16509054e-02,\n",
+       "        -8.19838022e-02, -3.67572922e-02,  1.65033748e-01,\n",
+       "        -1.41250500e-01, -1.38991976e-01, -4.87795744e-01,\n",
+       "        -1.20164344e-01,  1.14937961e-01,  4.27377425e-02,\n",
+       "        -3.94811423e-01,  6.62417672e-02, -2.37190077e-01,\n",
+       "         3.61968642e-01, -3.15778272e-01,  2.60103669e-01,\n",
+       "        -1.74161220e-01,  2.98980280e-02,  2.39174956e-02,\n",
+       "        -6.81515326e-02, -1.31398660e-01,  2.43697209e-01,\n",
+       "         3.59533606e-02, -2.02178247e-01,  2.06957738e-01,\n",
+       "         1.88466643e-01, -1.72017377e-01,  7.04907310e-02,\n",
+       "         1.28427322e-01,  4.14626613e-02,  1.36114937e-01,\n",
+       "        -4.67616687e-02, -2.30417672e-01, -8.13670395e-02,\n",
+       "         1.97737743e-01,  2.21529890e-01,  2.32036114e-01,\n",
+       "        -2.54443038e-01, -1.14729382e-01,  7.45836559e-01,\n",
+       "         6.98889941e-01,  2.22140037e-01, -1.66752289e+00,\n",
+       "        -2.02239626e-01, -6.55772304e-02,  1.57773767e-01,\n",
+       "         2.34692473e-01, -2.46405243e-01,  1.38387262e-01,\n",
+       "         8.74101993e-02, -1.17462988e-01,  7.37042805e-02,\n",
+       "        -2.18812769e-01,  1.29967636e-01, -2.34436559e+00,\n",
+       "         5.15678408e-01, -2.22568198e-01, -1.15041664e+00,\n",
+       "        -2.95431203e-01,  1.74905844e-01,  8.38763665e-02,\n",
+       "         2.67711130e-01, -2.24708839e-03,  1.59411295e-02,\n",
+       "        -6.17296723e-02, -6.62890478e-02,  1.57858166e-01,\n",
+       "        -4.02907179e-01, -7.11124058e-03, -7.95632769e-02,\n",
+       "        -3.34033997e-02,  2.74042192e-01, -6.11967427e-02,\n",
+       "        -2.50164580e-01,  2.85495385e-01,  8.06406509e-01,\n",
+       "        -4.82243969e-01,  4.33664649e-02,  6.81393928e-01,\n",
+       "        -1.72558109e-02,  1.65410539e-01,  1.01854871e-01,\n",
+       "         1.79057603e-01, -6.62667191e-02, -4.42939531e-01,\n",
+       "         4.80457544e-01, -2.61988124e-01,  2.13968169e-02,\n",
+       "         1.65440852e-01,  1.02103945e-02, -2.04515019e-01,\n",
+       "         1.82951152e-03, -7.23124537e-02,  9.63196553e-02,\n",
+       "         8.27339205e-02, -1.39613041e-01, -1.85279015e-02,\n",
+       "         4.16841840e-02,  1.16537313e-01, -4.12512463e-01,\n",
+       "         2.52440503e-01,  1.74719837e-01,  1.75164205e-01,\n",
+       "         2.13962792e-01, -1.19278937e-01, -1.20314207e+00,\n",
+       "         5.85693749e-01, -2.41117179e-04,  3.83402058e-01,\n",
+       "        -2.15041484e-01,  9.66155788e-03, -6.80138978e-02,\n",
+       "         3.01506344e-01,  1.63918553e-01,  7.99789646e-02,\n",
+       "         1.08817339e-01, -1.23661525e+00, -9.76927104e-01,\n",
+       "        -1.08533793e-01,  1.04051419e-01,  4.10992576e-01,\n",
+       "        -2.91584665e-01, -3.59805317e-01, -2.21677952e-01,\n",
+       "         0.00000000e+00,  3.57145758e-02, -7.31860186e-02,\n",
+       "         2.74190711e-01,  3.11028523e-01, -9.69822057e-02,\n",
+       "         5.39725764e-01, -4.31516610e-01,  2.96922845e-01,\n",
+       "         1.73932468e-01,  1.58197902e-01]])
        • created_at :
          2020-06-06T18:08:05.200362
          arviz_version :
          0.8.3
          inference_library :
          pymc3
          inference_library_version :
          3.8

        \n", + "
      \n", + "
      \n", + "
      • \n", + " \n", + "
    • \n", + " \n", + " \n", + "
      \n", + "
      \n", + "
        \n", + "
        \n", + "\n", + "\n", + "Show/Hide data repr\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Show/Hide attributes\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
        xarray.Dataset
          • chain: 1
          • draw: 500
          • school: 8
          • chain
            (chain)
            int64
            0
            array([0])
          • draw
            (draw)
            int64
            0 1 2 3 4 5 ... 495 496 497 498 499
            array([  0,   1,   2, ..., 497, 498, 499])
          • school
            (school)
            int64
            0 1 2 3 4 5 6 7
            array([0, 1, 2, 3, 4, 5, 6, 7])
          • theta
            (chain, draw, school)
            float64
            -0.06145 -0.1634 ... -20.97 -16.97
            array([[[-6.14501022e-02, -1.63365997e-01, -4.13644815e-02, ...,\n",
+       "         -4.63283674e-02, -7.75680442e-01, -6.98667408e-01],\n",
+       "        [ 6.13220767e+00, -2.30667156e+01,  4.65098938e+00, ...,\n",
+       "         -2.23623379e+01,  6.54679323e+00,  2.69077777e+00],\n",
+       "        [ 4.69451708e+00,  4.94303445e+00,  7.79012703e+00, ...,\n",
+       "         -3.96463931e+00,  9.76373781e+00,  2.20580563e+01],\n",
+       "        ...,\n",
+       "        [ 2.29990703e+00,  6.15991006e+00, -2.25355857e+00, ...,\n",
+       "          1.23139385e+01,  2.41692832e+00,  1.43921436e+01],\n",
+       "        [ 1.82986665e+04,  1.17716775e+04,  2.72781885e+03, ...,\n",
+       "          1.33645653e+04,  9.02494707e+03,  7.20704177e+03],\n",
+       "        [-1.41328100e+01,  2.64878173e+00, -2.10570634e+00, ...,\n",
+       "          1.18699567e+01, -2.09679409e+01, -1.69682186e+01]]])
          • tau
            (chain, draw)
            float64
            0.3528 9.909 ... 1.355e+04 14.48
            array([[3.52758223e-01, 9.90921652e+00, 8.82268310e+00, 1.09506829e+01,\n",
+       "        2.82781227e+00, 7.05636649e+00, 4.34661467e+00, 3.81757701e+01,\n",
+       "        2.52909559e+00, 1.24109602e+00, 3.69661235e+00, 1.85534615e+00,\n",
+       "        7.99305183e+00, 3.58047378e+00, 1.93410919e-02, 1.23341118e+01,\n",
+       "        1.85582662e+02, 1.81814135e-01, 1.36015776e+01, 1.88326305e+00,\n",
+       "        4.28833380e+01, 4.73379709e-01, 1.45705561e+00, 1.95489617e+00,\n",
+       "        1.69556957e+01, 4.69383217e-01, 1.02456969e+01, 5.89584004e+00,\n",
+       "        3.64301191e+00, 3.12110928e+01, 1.57666008e+01, 3.36118654e+00,\n",
+       "        3.10994379e+00, 2.48015080e+00, 7.94695777e-02, 1.11550224e+01,\n",
+       "        2.59944706e+00, 2.21700090e+00, 4.52298934e-01, 8.58965387e+00,\n",
+       "        2.71031784e-01, 1.42845197e+00, 2.44995264e-01, 6.22967827e+00,\n",
+       "        1.38837200e+02, 1.13099926e-01, 4.92721722e-01, 1.30898249e+01,\n",
+       "        2.35794873e+01, 2.64121203e-01, 3.78933248e+00, 6.56492829e-01,\n",
+       "        2.98140962e+00, 1.53587841e+01, 6.98058918e+00, 1.47787227e+02,\n",
+       "        1.48526964e+00, 8.59682664e-01, 2.83839393e+01, 4.02707073e+00,\n",
+       "        5.11753973e+00, 7.05691781e-01, 5.04432948e+00, 4.81262958e+00,\n",
+       "        2.91581954e+00, 4.17704463e-01, 3.13419925e-01, 9.00808247e+00,\n",
+       "        4.76669830e+01, 1.04939289e+01, 5.36119948e+00, 8.77849980e-01,\n",
+       "        3.96020355e+00, 7.63709077e+01, 2.55095149e+00, 1.13287460e+00,\n",
+       "        1.67426833e+01, 6.13177566e+00, 5.48656718e+00, 3.17413565e+00,\n",
+       "        8.50072291e+00, 6.94156931e-01, 8.32983298e+00, 7.45127555e-01,\n",
+       "        5.44255434e+00, 1.35820365e+00, 1.02721820e+01, 3.20084312e-01,\n",
+       "        2.98734019e+00, 2.22231323e+01, 3.21937710e+00, 2.74295513e+00,\n",
+       "        3.57539588e+00, 3.02013057e+00, 7.33324857e+00, 4.00746781e-01,\n",
+       "        1.02458048e-01, 1.71510849e+01, 2.95998509e+01, 1.89275077e+00,\n",
+       "        1.05976920e+01, 3.50916220e+00, 3.93048556e+00, 6.08174590e+00,\n",
+       "        1.42364784e+01, 4.07021810e+00, 1.90530058e+00, 2.61457004e+01,\n",
+       "        1.73703104e+00, 9.73443581e+01, 4.56783696e+01, 5.40013717e+00,\n",
+       "        1.36196779e+01, 1.32687151e+00, 2.01672828e+00, 8.03541095e+00,\n",
+       "        1.43248625e+01, 6.73507227e+01, 1.42853703e+01, 9.77510867e+01,\n",
+       "        2.51928541e+00, 1.05741412e+01, 7.90138593e-01, 2.32535617e-01,\n",
+       "        2.46350082e+00, 8.71937109e+00, 6.68236503e-01, 3.46282932e+02,\n",
+       "        6.53859713e+00, 6.54254558e+03, 1.23126409e+01, 4.31687520e+00,\n",
+       "        3.63062670e+00, 2.91367673e+00, 2.16899778e+00, 3.87215837e+00,\n",
+       "        8.33874856e-01, 1.93531458e+00, 5.18779082e+00, 1.07962825e+01,\n",
+       "        4.30146316e+00, 3.70785288e+01, 1.18055319e+01, 2.35960519e-01,\n",
+       "        2.42052211e-01, 1.80993804e+00, 2.55626443e+01, 1.04628207e+01,\n",
+       "        2.18022917e+00, 2.44838425e+01, 6.54002023e+00, 7.74241198e+00,\n",
+       "        2.12054906e+01, 2.47703499e+00, 2.62944750e-01, 2.45108870e+01,\n",
+       "        5.63068713e+00, 2.62561589e+00, 2.06773173e+00, 3.62805122e+00,\n",
+       "        2.09593853e+01, 3.57842130e+00, 5.44429125e+01, 1.99575943e+00,\n",
+       "        1.36439385e+00, 1.06974026e+01, 2.74675936e+01, 3.35200695e+01,\n",
+       "        4.32666956e+00, 7.30704505e+00, 6.87665159e-01, 2.13611691e+00,\n",
+       "        2.39506808e+00, 9.43924918e+00, 2.36976082e+00, 6.77674697e-01,\n",
+       "        8.81320821e-01, 8.72607624e+00, 8.26009804e+00, 5.40046357e+00,\n",
+       "        5.23898713e-01, 2.26243574e+01, 7.23313667e-01, 3.15445459e+00,\n",
+       "        7.70204442e-01, 3.37540745e+00, 6.67879684e+01, 6.29514730e+00,\n",
+       "        5.88679525e-01, 1.14752886e+00, 9.28747492e-01, 1.51401617e+01,\n",
+       "        7.95191397e-01, 1.82152907e+00, 1.25600247e+00, 3.71828532e+00,\n",
+       "        9.55040222e-01, 1.09833657e+00, 3.98945095e+00, 4.06461225e+00,\n",
+       "        2.01970616e+01, 3.70041270e+00, 3.80595680e+00, 3.62783507e+00,\n",
+       "        7.64816391e+00, 8.16913042e-01, 4.28972804e-01, 2.83445006e+00,\n",
+       "        2.11733157e+00, 2.23289951e+01, 4.80169463e+00, 8.22022937e+00,\n",
+       "        2.64196851e+00, 3.88991278e+00, 8.05525967e+00, 5.35754138e+00,\n",
+       "        4.87824565e+00, 8.14449225e+00, 1.67698639e+00, 6.35486925e+00,\n",
+       "        2.32427968e+01, 2.80670151e+00, 1.91974470e+01, 5.90191448e+01,\n",
+       "        4.89478839e+01, 1.36560975e+01, 3.39931007e+01, 6.36144813e+00,\n",
+       "        6.15409795e+00, 5.67951613e+00, 2.77445601e+01, 2.92503318e+01,\n",
+       "        1.62196911e+01, 6.65151195e+00, 4.93148797e+00, 3.13059608e+00,\n",
+       "        4.91283671e+01, 1.50905657e+01, 3.88341201e+00, 6.82982279e+00,\n",
+       "        9.96691600e+00, 2.86407451e+01, 1.53074614e-01, 6.05986177e+00,\n",
+       "        1.07614965e+00, 6.07179884e-01, 6.13187986e+00, 6.22489208e+00,\n",
+       "        6.49108304e+01, 8.83969684e-01, 4.85528891e+00, 3.76781673e+01,\n",
+       "        2.80524863e+00, 3.26332348e+00, 2.01498656e+00, 6.75727305e+01,\n",
+       "        2.79340018e+00, 1.29749365e+01, 3.17507764e+01, 1.18233399e+00,\n",
+       "        2.22744355e-01, 9.00986459e+00, 3.15524609e+00, 4.44751680e+00,\n",
+       "        2.49752098e+00, 1.88972302e+00, 1.44930701e+00, 1.56431846e-01,\n",
+       "        2.32909351e+00, 2.58421215e+00, 5.28552999e+01, 3.24968245e+01,\n",
+       "        5.87146125e-01, 5.60367662e+00, 3.94160925e+00, 5.78097813e+00,\n",
+       "        6.38677851e-01, 5.69493188e+01, 9.91033835e+00, 1.10463092e+00,\n",
+       "        1.26223657e+01, 1.76633084e+01, 1.09007683e+01, 3.38350350e+00,\n",
+       "        2.64325991e+00, 2.48731758e+00, 4.71416415e+00, 2.01027943e+00,\n",
+       "        3.84705358e+01, 1.39555830e+02, 1.74141176e+00, 6.24924839e+01,\n",
+       "        1.10679458e+01, 2.35486442e-01, 5.23525679e+00, 3.41810903e+01,\n",
+       "        4.78649023e+01, 7.69021824e+01, 5.38513032e+00, 9.12823082e+00,\n",
+       "        2.41101297e-01, 2.84705077e+00, 8.90029138e+00, 7.09028758e+00,\n",
+       "        1.02979760e+01, 1.58491619e-02, 1.69321641e+01, 1.24476376e+01,\n",
+       "        2.80940998e+00, 1.85492698e+00, 4.13510089e+00, 2.24718612e+00,\n",
+       "        1.85401390e+00, 9.85505851e+00, 9.38592610e+00, 2.24683589e-01,\n",
+       "        9.33634016e+00, 8.65138771e+00, 2.04458133e+01, 1.00994291e+00,\n",
+       "        9.27041587e+00, 3.43223369e+00, 1.02010766e+01, 6.88501094e+00,\n",
+       "        4.90507048e+00, 6.89075857e+00, 9.62478873e+00, 1.37631145e+00,\n",
+       "        1.97459230e+00, 4.86381546e+00, 4.20344080e-01, 1.13379655e+01,\n",
+       "        6.39187347e-01, 2.14116645e+01, 7.66738562e+00, 1.42350174e+00,\n",
+       "        8.72931704e-01, 2.84816715e-01, 9.55117314e-01, 7.96152413e+00,\n",
+       "        9.98974147e+00, 4.97977080e+00, 1.51003551e+00, 4.88371246e+00,\n",
+       "        1.41427115e+01, 2.64638862e+00, 3.88439911e+01, 6.65434165e+00,\n",
+       "        1.41092636e+01, 1.15806290e+01, 4.87140788e+00, 4.61743687e+00,\n",
+       "        7.17728146e+01, 7.73459578e+00, 8.11124814e-01, 1.74685951e+02,\n",
+       "        1.01568566e+01, 1.17072536e+00, 5.06288247e+00, 4.71549862e+00,\n",
+       "        8.24201449e+00, 4.70745065e+00, 1.07473466e+01, 9.72499125e+00,\n",
+       "        1.90146619e+00, 2.21627599e+01, 2.09931393e+00, 1.74031995e+01,\n",
+       "        1.23133563e+01, 4.85510860e+00, 4.63090501e+00, 3.03037437e+00,\n",
+       "        2.14604415e+00, 7.44831662e+00, 1.34290090e+00, 5.85868643e-01,\n",
+       "        1.23938210e+01, 5.57274181e+00, 2.64953210e+02, 1.58262210e+00,\n",
+       "        5.79513430e+00, 4.73585808e+00, 8.54088794e-01, 3.46144173e+00,\n",
+       "        7.54440541e+00, 1.79717803e+01, 8.10607763e+00, 3.16925981e+00,\n",
+       "        6.98091571e+00, 1.04008324e+02, 2.24098601e+00, 4.79639261e+00,\n",
+       "        9.49134038e+00, 2.15830748e+01, 1.78897543e+00, 4.50568026e+00,\n",
+       "        1.41835763e+00, 5.02905089e+00, 5.91282522e+00, 7.76090458e+00,\n",
+       "        6.40009532e+00, 1.27458410e+01, 2.40173308e+00, 6.25820838e+00,\n",
+       "        1.43987374e+00, 3.67206434e+01, 7.98520119e+01, 1.94812595e+00,\n",
+       "        5.94039608e+00, 9.82385477e-01, 1.20624902e+01, 2.68192869e+01,\n",
+       "        3.11915047e+00, 3.19687718e+01, 1.65737073e+00, 2.63042770e-01,\n",
+       "        2.39211783e+00, 6.18256584e-01, 9.19936167e+01, 5.28611929e+00,\n",
+       "        7.88175429e+00, 8.41720240e+00, 1.27098316e+00, 1.03838514e+01,\n",
+       "        3.66092713e+00, 5.01054311e+01, 3.10574286e+00, 1.57029298e+01,\n",
+       "        1.37374817e+01, 8.22505822e-01, 2.22768280e+00, 1.53703513e+01,\n",
+       "        9.82911107e-02, 4.87695059e+01, 5.98224541e-01, 1.01791519e+01,\n",
+       "        1.20383410e+01, 1.41243456e+01, 7.31441573e+00, 2.20305277e+00,\n",
+       "        3.23221081e+01, 1.04430626e+01, 7.53170699e+00, 2.00741075e+00,\n",
+       "        3.92624753e+01, 6.76830027e+01, 6.20349670e+01, 2.69677988e+00,\n",
+       "        2.34019637e+00, 1.85327911e+00, 1.03456889e+01, 5.48704958e+00,\n",
+       "        5.49283122e+00, 1.19471977e+01, 2.87804398e+00, 2.03968797e-01,\n",
+       "        2.57428290e+01, 1.93861156e+01, 1.25433354e+01, 2.90134161e+00,\n",
+       "        1.38895600e+01, 1.23272959e+00, 8.56331400e+00, 1.56926967e+00,\n",
+       "        1.92846051e+01, 6.38268661e+01, 1.35855922e+01, 1.39593627e+00,\n",
+       "        5.62517866e+00, 4.43004650e+00, 1.77784757e+00, 8.40728490e+00,\n",
+       "        1.42646867e+00, 9.50323267e-01, 2.50319125e+01, 1.31136059e+00,\n",
+       "        2.86052340e+00, 1.00988074e+01, 6.06426417e+00, 1.96013766e+00,\n",
+       "        1.36298326e+02, 4.00464263e+00, 7.64631635e+00, 2.63277976e+01,\n",
+       "        1.13937686e+00, 9.76896485e+01, 7.57151767e+01, 2.05133601e+00,\n",
+       "        4.22665734e+00, 6.26429373e-01, 2.64633926e+00, 2.07625781e+01,\n",
+       "        5.19932135e+00, 9.88862999e-01, 3.39815977e-01, 1.66964133e+00,\n",
+       "        1.79500281e+00, 1.41019974e+01, 1.35463294e+04, 1.44842777e+01]])
          • tau_log__
            (chain, draw)
            float64
            -1.042 2.293 2.177 ... 9.514 2.673
            array([[-1.04197238,  2.29346529,  2.17732603,  2.39340182,  1.03950336,\n",
+       "         1.95393026,  1.46939731,  3.64220102,  0.92786177,  0.21599488,\n",
+       "         1.30741682,  0.61807128,  2.07857264,  1.27549513, -3.94552333,\n",
+       "         2.51236874,  5.2235004 , -1.70477035,  2.61018579,  0.63300594,\n",
+       "         3.75848336, -0.74785744,  0.3764177 ,  0.67033708,  2.83060381,\n",
+       "        -0.75633575,  2.3268578 ,  1.77424702,  1.29281079,  3.44077357,\n",
+       "         2.75789383,  1.21229405,  1.13460465,  0.90831937, -2.532381  ,\n",
+       "         2.41188984,  0.95529875,  0.79615534, -0.79341196,  2.15055844,\n",
+       "        -1.30551918,  0.35659132, -1.4065164 ,  1.82932469,  4.93330202,\n",
+       "        -2.17948355, -0.70781072,  2.57183521,  3.16037715, -1.33134718,\n",
+       "         1.33218988, -0.42084351,  1.09239622,  2.73168756,  1.94313332,\n",
+       "         4.99577358,  0.39559633, -0.15119195,  3.34582347,  1.39303925,\n",
+       "         1.6326738 , -0.34857671,  1.61826474,  1.57124363,  1.07015093,\n",
+       "        -0.87298112, -1.16021137,  2.19812223,  3.86423898,  2.35079689,\n",
+       "         1.67918773, -0.13027957,  1.37629543,  4.33560183,  0.93646642,\n",
+       "         0.1247583 ,  2.81796134,  1.81348438,  1.70230277,  1.15503536,\n",
+       "         2.14015121, -0.36505722,  2.11984341, -0.29419986,  1.6942485 ,\n",
+       "         0.30616298,  2.32943947, -1.13917084,  1.09438342,  3.10113374,\n",
+       "         1.16918789,  1.00903585,  1.27407591,  1.10530006,  1.99241861,\n",
+       "        -0.91442552, -2.27830185,  2.84206143,  3.38776932,  0.63803121,\n",
+       "         2.36063624,  1.25537732,  1.36876297,  1.80529181,  2.65580757,\n",
+       "         1.40369659,  0.64463978,  3.26368476,  0.55217736,  4.57825478,\n",
+       "         3.82162487,  1.68642435,  2.61151565,  0.28282392,  0.70147653,\n",
+       "         2.08385814,  2.66199666,  4.20991363,  2.65923596,  4.58242432,\n",
+       "         0.92397529,  2.35841151, -0.23554692, -1.45871187,  0.90158344,\n",
+       "         2.16554711, -0.40311312,  5.84725616,  1.87772264,  8.7860816 ,\n",
+       "         2.51062645,  1.46253181,  1.28940528,  1.06941577,  0.77426521,\n",
+       "         1.35381207, -0.18167194,  0.66026989,  1.64630795,  2.37920187,\n",
+       "         1.45895523,  3.61303806,  2.46856823, -1.44409078, -1.41860183,\n",
+       "         0.59329261,  3.24113208,  2.34782808,  0.77943   ,  3.19801341,\n",
+       "         1.87794026,  2.04671326,  3.05426014,  0.90706228, -1.33581134,\n",
+       "         3.19911739,  1.72823148,  0.96531549,  0.72645222,  1.28869565,\n",
+       "         3.04258653,  1.27492172,  3.99715268,  0.69102465,  0.31071027,\n",
+       "         2.37000097,  3.3130069 ,  3.51214435,  1.46479809,  1.98883896,\n",
+       "        -0.37445325,  0.75898965,  0.87341165,  2.24487644,  0.86278903,\n",
+       "        -0.3890879 , -0.12633356,  2.16631581,  2.11143646,  1.6864848 ,\n",
+       "        -0.64645691,  3.11902709, -0.32391231,  1.14881561, -0.26109929,\n",
+       "         1.21651604,  4.20152295,  1.83977907, -0.52987334,  0.13761081,\n",
+       "        -0.07391838,  2.71735093, -0.22917244,  0.5996763 ,  0.22793404,\n",
+       "         1.31326263, -0.04600182,  0.09379683,  1.38365362,  1.40231835,\n",
+       "         3.00553713,  1.30844436,  1.33656742,  1.28863607,  2.03446561,\n",
+       "        -0.20222263, -0.84636176,  1.04184794,  0.7501566 ,  3.10588606,\n",
+       "         1.5689689 ,  2.10659811,  0.97152429,  1.35838674,  2.08632525,\n",
+       "         1.67850517,  1.58478566,  2.0973419 ,  0.51699837,  1.84922133,\n",
+       "         3.14599527,  1.03200995,  2.9547773 ,  4.07786188,  3.89075614,\n",
+       "         2.61418613,  3.52615758,  1.85025604,  1.81711819,  1.73686604,\n",
+       "         3.32303979,  3.37589092,  2.78622601,  1.89484419,  1.59564076,\n",
+       "         1.14122343,  3.89443661,  2.71406976,  1.35671415,  1.92129873,\n",
+       "         2.29927121,  3.35483036, -1.8768298 ,  1.80168699,  0.07338953,\n",
+       "        -0.49893018,  1.81350137,  1.82855611,  4.17301449, -0.12333251,\n",
+       "         1.58006861,  3.62908081,  1.03149217,  1.18274615,  0.70061252,\n",
+       "         4.21320451,  1.02725956,  2.56301953,  3.45791718,  0.16749044,\n",
+       "        -1.50173056,  2.19832004,  1.14906649,  1.49234592,  0.91529863,\n",
+       "         0.63643027,  0.37108552, -1.85513485,  0.84547914,  0.94942068,\n",
+       "         3.96755799,  3.48114238, -0.53248155,  1.72342292,  1.37158908,\n",
+       "         1.75457289, -0.4483551 ,  4.04216173,  2.29357849,  0.09951127,\n",
+       "         2.5354703 ,  2.87148952,  2.38883327,  1.21891171,  0.97201297,\n",
+       "         0.91120485,  1.55057163,  0.69827373,  3.64989264,  4.93846473,\n",
+       "         0.55469614,  4.13504629,  2.40405316, -1.44610194,  1.6554159 ,\n",
+       "         3.53167258,  3.86838251,  4.34253426,  1.68364151,  2.2113719 ,\n",
+       "        -1.42253811,  1.04628364,  2.18608402,  1.9587259 ,  2.33194737,\n",
+       "        -4.14463865,  2.82921502,  2.52153085,  1.03297449,  0.61784533,\n",
+       "         1.41951173,  0.80967882,  0.61735296,  2.28798488,  2.23921134,\n",
+       "        -1.49306214,  2.23391433,  2.15771974,  3.01777813,  0.00989381,\n",
+       "         2.22682824,  1.23321127,  2.32249326,  1.92934672,  1.59026946,\n",
+       "         1.93018118,  2.26434193,  0.31940706,  0.68036195,  1.5818232 ,\n",
+       "        -0.86668167,  2.42815688, -0.44755768,  3.06393584,  2.0369757 ,\n",
+       "         0.35311985, -0.13589796, -1.25590941, -0.0459211 ,  2.07462046,\n",
+       "         2.30155871,  1.60538387,  0.41213317,  1.58590568,  2.64919941,\n",
+       "         0.97319593,  3.65955339,  1.89526952,  2.64683158,  2.44933379,\n",
+       "         1.58338299,  1.52983976,  4.27350578,  2.04570322, -0.20933334,\n",
+       "         5.1629898 ,  2.318149  ,  0.15762353,  1.62193598,  1.55085466,\n",
+       "         2.10924479,  1.5491465 ,  2.37465889,  2.27469899,  0.64262527,\n",
+       "         3.0984134 ,  0.74161059,  2.85665407,  2.51068455,  1.58003147,\n",
+       "         1.53275232,  1.10868617,  0.76362622,  2.00798805,  0.29483213,\n",
+       "        -0.53465967,  2.51719804,  1.71788718,  5.57955324,  0.45908303,\n",
+       "         1.75701865,  1.55516293, -0.15772012,  1.24168519,  2.02080628,\n",
+       "         2.88880277,  2.09261411,  1.15349806,  1.9431801 ,  4.64447093,\n",
+       "         0.80691595,  1.5678641 ,  2.25037984,  3.07190943,  0.58164307,\n",
+       "         1.50533888,  0.34949961,  1.61523128,  1.77712376,  2.0490989 ,\n",
+       "         1.85631288,  2.54520502,  0.87619059,  1.83389394,  0.36455543,\n",
+       "         3.60333909,  4.38017507,  0.66686786,  1.78177581, -0.0177715 ,\n",
+       "         2.49010065,  3.28912129,  1.13756068,  3.46475954,  0.50523245,\n",
+       "        -1.33543863,  0.87217909, -0.48085172,  4.52171919,  1.66508438,\n",
+       "         2.0645505 ,  2.13027752,  0.23979074,  2.34025185,  1.29771643,\n",
+       "         3.91412941,  1.13325293,  2.75384731,  2.62012799, -0.19539972,\n",
+       "         0.80096194,  2.73244041, -2.31982169,  3.88710524, -0.51378911,\n",
+       "         2.3203417 ,  2.48809664,  2.64789994,  1.98984716,  0.78984402,\n",
+       "         3.47575146,  2.34593789,  2.01912171,  0.69684571,  3.67026924,\n",
+       "         4.21483508,  4.12769821,  0.99205842,  0.85023484,  0.61695656,\n",
+       "         2.3365699 ,  1.70239069,  1.70344383,  2.48049675,  1.05711089,\n",
+       "        -1.58978825,  3.2481561 ,  2.96455712,  2.52918948,  1.06517326,\n",
+       "         2.63113748,  0.20923089,  2.14748726,  0.45061033,  2.95930712,\n",
+       "         4.1561742 ,  2.60900984,  0.33356535,  1.72725271,  1.48841008,\n",
+       "         0.5754034 ,  2.12909858,  0.35520193, -0.05095307,  3.22015151,\n",
+       "         0.27106522,  1.05100462,  2.31241734,  1.80241321,  0.6730147 ,\n",
+       "         4.91484605,  1.38745434,  2.03422401,  3.27062532,  0.1304815 ,\n",
+       "         4.5817956 ,  4.32697862,  0.71849129,  1.44141145, -0.46771924,\n",
+       "         0.97317727,  3.03315224,  1.64852811, -0.01119948, -1.07935105,\n",
+       "         0.51260883,  0.58500659,  2.64631645,  9.51387089,  2.67306376]])
          • mu
            (chain, draw)
            float64
            -0.575 -4.24 ... 6.441 -0.1923
            array([[-5.74954439e-01, -4.24026973e+00,  1.06399686e+01,\n",
+       "         1.91456322e+00,  4.29695877e-01, -2.01048011e+00,\n",
+       "         9.83590495e+00, -1.65934652e+00, -4.50888000e+00,\n",
+       "        -2.64479169e+00, -6.54382162e-01,  6.68568578e+00,\n",
+       "        -2.37345586e+00, -1.00224692e+00,  3.72748135e+00,\n",
+       "        -3.81813612e+00, -8.79383199e+00, -1.38768333e+00,\n",
+       "         4.94204733e+00, -2.76050004e+00, -3.66244864e+00,\n",
+       "         4.46486418e+00,  4.76278811e+00, -4.19818364e+00,\n",
+       "        -1.62970974e+01, -1.88728519e+00,  1.68572759e-01,\n",
+       "         7.05875142e-01, -2.28460344e+00, -1.29060203e+00,\n",
+       "        -6.77952511e+00, -1.76806101e+00,  9.90307217e+00,\n",
+       "        -5.08191417e+00, -2.77955936e+00, -1.26552198e-01,\n",
+       "         1.64346376e+00, -1.14847267e+01, -4.88870442e+00,\n",
+       "         1.65330247e+00, -7.10146375e+00,  1.84563814e+00,\n",
+       "         5.26597073e+00,  4.04741491e+00, -5.48389781e+00,\n",
+       "        -1.79354982e+00,  5.16019352e-01, -4.31502392e+00,\n",
+       "        -5.87991929e-01,  4.61685537e-01, -5.40508477e+00,\n",
+       "         1.09498247e+01,  3.45775140e+00,  1.49258284e+00,\n",
+       "         3.65787080e+00,  1.63590297e+00, -7.12138162e+00,\n",
+       "        -3.69376332e+00, -3.46716192e+00,  3.00012488e+00,\n",
+       "         1.40545775e+00, -2.01390642e+00,  1.48216348e+00,\n",
+       "        -4.92331232e+00,  3.48236754e+00, -7.20020825e+00,\n",
+       "        -3.44613220e-01,  2.75874260e+00,  1.08676430e+01,\n",
+       "         1.49758422e+00,  2.66957196e+00,  5.89560838e+00,\n",
+       "         4.25193649e+00, -9.55325417e-01, -6.80641874e+00,\n",
+       "        -4.05416966e+00,  2.82086177e+00, -2.55402781e+00,\n",
+       "        -3.51058648e+00,  2.37721291e+00,  8.26518462e-01,\n",
+       "        -3.18520105e+00, -5.16332273e+00, -3.12271009e+00,\n",
+       "         3.93310136e+00, -2.59256990e+00,  2.31691345e+00,\n",
+       "        -2.88824091e+00, -8.96081434e+00, -5.86659549e+00,\n",
+       "         9.64150577e-01, -5.29518057e-01,  8.07233244e-01,\n",
+       "         3.46797817e-01, -1.22774808e+00,  6.92011381e+00,\n",
+       "        -1.60441560e-01, -1.27536333e+00, -2.00232765e+00,\n",
+       "        -1.01317842e+00,  4.32083123e+00, -2.75420656e+00,\n",
+       "         4.38470391e+00, -7.03065667e+00,  1.59631173e+00,\n",
+       "         3.24310246e+00, -6.74995978e+00,  5.14451266e-02,\n",
+       "        -3.83024909e+00,  3.47873122e+00,  3.85596436e+00,\n",
+       "         2.16062643e+00, -7.65296478e-01, -5.72504062e+00,\n",
+       "        -6.78614816e+00, -2.55111928e-01, -3.51953327e+00,\n",
+       "        -8.12987952e+00,  2.84455946e+00, -4.08802220e+00,\n",
+       "         3.93285016e+00,  2.34029335e+00, -6.28521891e+00,\n",
+       "        -1.99753259e+00,  8.52259672e+00, -4.70316695e+00,\n",
+       "        -5.84544108e+00, -9.77174364e-01,  3.35560384e+00,\n",
+       "        -2.64715720e+00,  1.39209304e+00, -5.85492165e+00,\n",
+       "         2.09293379e+00,  7.12267020e-01,  6.53179008e+00,\n",
+       "        -7.75205559e+00, -3.72766190e+00, -7.78152504e-01,\n",
+       "        -3.58276238e-01, -6.75348299e+00,  2.60416320e-01,\n",
+       "         8.54374355e-01,  2.31704904e+00, -2.26825906e+00,\n",
+       "         4.98542876e-01,  1.44839537e+00, -2.89322375e+00,\n",
+       "         3.63089543e+00,  3.34314447e+00, -1.28886082e+01,\n",
+       "        -6.48330066e+00, -7.71711742e+00,  3.46089879e+00,\n",
+       "         3.87306842e-01, -4.22001171e+00,  8.79130960e+00,\n",
+       "         1.09366708e+00,  7.45316905e+00, -6.67264980e+00,\n",
+       "        -4.74778053e+00, -1.85626573e+00, -2.29836995e+00,\n",
+       "        -2.63968432e+00,  1.11591877e+00, -5.85554129e+00,\n",
+       "        -7.34007162e+00,  1.26131351e+01,  2.26555517e+00,\n",
+       "        -6.64153593e+00, -2.93997210e+00, -5.79436329e+00,\n",
+       "        -2.23246389e+00, -7.25228661e+00, -1.27507950e+00,\n",
+       "         4.93854306e+00,  6.24543773e+00,  2.58163273e-01,\n",
+       "        -6.48269015e+00, -6.70598543e+00,  5.11213269e+00,\n",
+       "        -4.45154132e+00, -9.60513233e+00,  5.53992814e+00,\n",
+       "         3.03982430e-01, -4.59488174e+00,  3.90511028e+00,\n",
+       "         3.90015474e-01, -2.00087666e+00, -1.10626317e+01,\n",
+       "         7.44471214e+00,  9.13013556e+00, -4.92737128e+00,\n",
+       "        -1.24050284e+01, -7.26431728e+00,  8.20564458e+00,\n",
+       "        -1.63773883e-01,  6.59385336e+00,  3.70476835e+00,\n",
+       "         2.99163710e+00,  1.32498665e-02, -3.75484141e+00,\n",
+       "        -1.84710639e+00,  1.05882620e+00,  6.22453006e+00,\n",
+       "         1.14053974e+01,  9.59385076e-01,  6.08189258e-01,\n",
+       "         6.18600649e+00, -2.42357484e+00,  7.25813496e+00,\n",
+       "         5.56186618e+00,  1.79100581e+00,  6.23296561e+00,\n",
+       "        -5.33407644e+00,  7.06255531e-01,  1.13015609e+01,\n",
+       "         1.63792528e+00,  2.82502835e+00, -4.70103311e+00,\n",
+       "         3.84265500e+00, -1.18879727e+01,  5.26878406e+00,\n",
+       "         7.97558088e+00,  4.00421126e+00, -5.44253622e+00,\n",
+       "         1.69866138e+00,  5.20107807e+00,  2.69582082e+00,\n",
+       "        -2.78873509e+00,  4.24818709e+00,  4.14761768e+00,\n",
+       "         2.11280220e+00,  4.35557936e+00, -1.73022204e+00,\n",
+       "        -4.75580583e+00,  1.97110601e+00, -2.11293834e+00,\n",
+       "        -8.23098306e-01, -9.12397500e-01,  2.55408354e+00,\n",
+       "         1.20535882e+01,  9.24326322e-01, -2.49886416e+00,\n",
+       "        -9.41658523e+00,  7.28848467e+00, -3.27620347e+00,\n",
+       "        -6.32840076e+00,  4.51099044e+00, -2.88037503e-01,\n",
+       "        -1.15249688e+01,  1.02842513e+01,  5.98595192e+00,\n",
+       "         3.91480222e+00, -5.23579242e+00, -1.01907311e+00,\n",
+       "         7.90259887e+00, -2.61138178e+00,  2.70138717e+00,\n",
+       "        -6.54034677e+00, -7.33292575e-01,  3.35193883e+00,\n",
+       "        -1.00662987e+01,  1.13133910e+01,  1.63133211e+00,\n",
+       "        -5.97649216e-01, -3.43753219e+00,  2.62528512e+00,\n",
+       "        -5.48916477e-01, -4.95239283e+00,  1.59525171e+00,\n",
+       "        -6.02360489e+00,  5.54256689e+00,  4.80234764e+00,\n",
+       "         4.97973550e+00, -3.88758642e-01, -4.14082880e+00,\n",
+       "        -3.03871231e-01,  4.14140894e+00, -3.63169177e-01,\n",
+       "        -1.24659063e+00,  6.60138102e+00, -2.36554926e+00,\n",
+       "         1.17834340e+00,  7.08476390e+00,  2.43243312e+00,\n",
+       "         2.48999013e+00, -3.94362956e+00, -2.96990171e-01,\n",
+       "         1.09785176e+01, -2.96038733e-01,  4.26555017e-01,\n",
+       "        -4.43663959e+00, -5.29452181e+00,  8.34274612e+00,\n",
+       "         9.36572451e+00,  1.78986899e+00,  4.24125687e+00,\n",
+       "         3.73966793e-01, -2.41163818e+00, -2.81739347e-01,\n",
+       "         9.20753304e-01, -2.89688528e+00,  2.13607991e+00,\n",
+       "         2.71058287e-01, -1.40881176e+00, -4.08375618e+00,\n",
+       "         6.82899675e-01, -6.34703504e-01, -1.02501378e+00,\n",
+       "         7.85180987e+00,  8.54695614e+00,  1.15353216e+00,\n",
+       "         6.54015280e+00,  7.36857180e+00, -4.51503641e+00,\n",
+       "        -2.95239939e+00,  1.11210319e+00, -1.61629210e-01,\n",
+       "        -7.06651246e+00, -8.07195302e+00, -3.64787782e+00,\n",
+       "        -3.89153835e+00,  4.18964576e+00,  9.83545724e-01,\n",
+       "         5.85175963e+00, -2.71517319e+00,  2.14758933e+00,\n",
+       "        -3.11505604e+00, -2.11089507e+00,  8.33082205e+00,\n",
+       "        -9.30346077e+00,  2.65814263e+00, -5.84972402e+00,\n",
+       "         5.20245535e+00, -5.22117856e+00, -2.93227625e+00,\n",
+       "        -4.11580937e+00, -2.38075135e+00, -4.10498402e+00,\n",
+       "         2.29445652e+00, -2.89219952e+00,  7.64805245e+00,\n",
+       "        -2.50643035e+00,  5.40051296e+00,  4.35823418e+00,\n",
+       "         3.07154388e+00, -8.61670387e-01,  1.13360388e+00,\n",
+       "        -4.46882155e-01, -5.58529799e-01, -3.80805709e+00,\n",
+       "        -8.91082567e+00,  2.69448963e+00,  5.09920163e+00,\n",
+       "        -5.55844853e+00, -7.33447751e+00, -5.29797016e+00,\n",
+       "        -3.85955169e+00,  5.40390149e+00, -5.19253557e+00,\n",
+       "         4.03001184e+00, -1.03083231e+01, -4.83364819e+00,\n",
+       "         5.45598464e+00,  6.89069883e+00,  3.53798299e+00,\n",
+       "        -1.20538031e+00,  3.31367839e+00,  1.54345012e+00,\n",
+       "        -1.87607115e+00,  9.65115423e+00, -1.35614050e+00,\n",
+       "         5.15456542e+00, -3.98056439e+00,  5.98079994e+00,\n",
+       "        -4.94423753e-01,  7.85514879e+00, -4.01675607e-01,\n",
+       "         1.44682527e-01, -6.32632519e+00,  9.44279191e-01,\n",
+       "        -5.98906726e+00, -5.00447375e+00,  2.87108872e+00,\n",
+       "        -1.23985185e+01,  1.09257174e+01,  4.76783047e+00,\n",
+       "        -1.65910417e+00, -2.88933327e+00, -4.26062338e+00,\n",
+       "         1.40116303e+00,  2.05250233e+00, -9.83774194e+00,\n",
+       "         8.88282832e-01,  5.59870174e+00,  2.06126702e-01,\n",
+       "         1.75220503e+00,  1.55238763e+00, -6.41206793e+00,\n",
+       "        -5.95490195e+00,  7.54701210e+00, -7.08338838e+00,\n",
+       "        -1.64454505e+00, -3.21769522e+00,  4.25740122e+00,\n",
+       "         3.17253991e+00, -1.21037656e+00, -1.41074699e+00,\n",
+       "         1.70737547e+00,  1.04044288e+01, -3.73849906e+00,\n",
+       "         2.01868622e+00,  3.95275715e+00, -3.15534434e+00,\n",
+       "         1.72878864e+00,  7.16740348e-01,  3.43928313e+00,\n",
+       "        -6.34318674e+00,  1.96481449e+00,  3.95974226e-01,\n",
+       "         1.61958478e+00,  4.98405837e+00, -1.42666813e+00,\n",
+       "         4.51790955e+00,  9.72885645e+00,  5.04937847e+00,\n",
+       "        -1.14385591e+01, -2.18296607e+00, -6.95637064e+00,\n",
+       "        -6.89022740e+00,  9.07483833e+00,  1.85239967e-01,\n",
+       "        -8.19522122e+00, -3.60942719e+00,  1.02134677e+00,\n",
+       "         7.79410914e+00,  1.63428520e+00,  9.87169062e-01,\n",
+       "        -9.12466407e+00, -6.52297691e+00,  5.16446630e-01,\n",
+       "         5.07924422e+00,  2.20501292e+00,  9.32103768e-01,\n",
+       "         4.80006148e+00, -1.32677228e+00,  2.07389508e+00,\n",
+       "        -1.17281550e+00, -3.81909802e+00,  5.75339726e+00,\n",
+       "         1.11600336e+00, -1.67001808e-01,  7.69815193e+00,\n",
+       "        -1.74344014e+00, -7.25369494e+00,  5.92978250e+00,\n",
+       "        -7.62064968e+00, -3.63957253e+00, -6.27079590e+00,\n",
+       "        -3.13278119e+00, -2.33465879e+00, -1.57118049e+00,\n",
+       "        -3.11722479e+00,  5.56277437e+00,  6.22231427e+00,\n",
+       "        -3.37466696e+00, -6.21777050e-01,  5.36544714e+00,\n",
+       "         1.88478718e+00,  3.80942623e+00, -2.00864174e-01,\n",
+       "         2.96686717e+00, -1.00017482e-01,  1.14620586e+01,\n",
+       "        -1.87290006e+00,  1.73808164e-02,  2.23790361e-01,\n",
+       "         2.55540631e+00, -5.01070688e+00, -8.24809919e-01,\n",
+       "        -3.11352896e+00,  2.40932940e-01, -1.28041169e+00,\n",
+       "        -5.59773808e+00,  7.89826664e-01, -1.91503484e+00,\n",
+       "        -1.05019253e+01, -1.60414316e+00,  5.68381752e+00,\n",
+       "         1.77658728e+00, -7.06087811e+00,  6.53479672e+00,\n",
+       "         1.84916843e-01, -4.82146892e+00, -6.93503993e+00,\n",
+       "        -7.07402871e+00,  8.79637795e+00,  4.97401950e+00,\n",
+       "         6.44051400e+00, -1.92319958e-01]])
          • theta_tilde
            (chain, draw, school)
            float64
            1.456 1.167 1.513 ... -1.434 -1.158
            array([[[ 1.45568353,  1.16677207,  1.51262231, ...,  1.49855067,\n",
+       "         -0.56901864, -0.35070187],\n",
+       "        [ 1.04675051, -1.89989247,  0.89727166, ..., -1.82880939,\n",
+       "          1.08858889,  0.69945464],\n",
+       "        [-0.67388247, -0.64571447, -0.32301302, ..., -1.65534767,\n",
+       "         -0.09931568,  1.29417408],\n",
+       "        ...,\n",
+       "        [-0.1896265 ,  0.0840938 , -0.51252159, ...,  0.5204879 ,\n",
+       "         -0.1813283 ,  0.66785746],\n",
+       "        [ 1.35034558,  0.86851845,  0.20089415, ...,  0.9861066 ,\n",
+       "          0.66575279,  0.53155368],\n",
+       "        [-0.9624567 ,  0.19615073, -0.13210092, ...,  0.83278413,\n",
+       "         -1.43435672, -1.15821438]]])
        • created_at :
          2020-06-06T18:08:05.352686
          arviz_version :
          0.8.3
          inference_library :
          pymc3
          inference_library_version :
          3.8

        \n", + "
      \n", + "
      \n", + "
      • \n", + " \n", + "
    • \n", + " \n", + " \n", + "
      \n", + "
      \n", + "
        \n", + "
        \n", + "\n", + "\n", + "Show/Hide data repr\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Show/Hide attributes\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
        xarray.Dataset
          • chain: 1
          • draw: 500
          • obs_dim_0: 8
          • chain
            (chain)
            int64
            0
            array([0])
          • draw
            (draw)
            int64
            0 1 2 3 4 5 ... 495 496 497 498 499
            array([  0,   1,   2, ..., 497, 498, 499])
          • obs_dim_0
            (obs_dim_0)
            int64
            0 1 2 3 4 5 6 7
            array([0, 1, 2, 3, 4, 5, 6, 7])
          • obs
            (chain, draw, obs_dim_0)
            float64
            -3.605 4.575 ... -16.14 -5.657
            array([[[-3.60548654e+00,  4.57547736e+00, -1.11284126e+01, ...,\n",
+       "         -9.56906087e+00,  4.14705229e-01,  2.84360259e+00],\n",
+       "        [ 4.89112106e+00, -1.33537335e+01,  2.29098812e+00, ...,\n",
+       "         -1.61614666e+01,  3.69916753e+00,  1.47268137e+01],\n",
+       "        [-1.16073417e+01, -5.70038634e+00,  1.52872633e+01, ...,\n",
+       "         -3.80733807e+01,  2.27064666e+01,  2.88979444e+01],\n",
+       "        ...,\n",
+       "        [ 5.19297425e+00,  7.69150333e+00,  8.00622289e+00, ...,\n",
+       "          7.08637173e+00, -4.87507692e+00,  3.16607702e+01],\n",
+       "        [ 1.83152225e+04,  1.17753769e+04,  2.71083927e+03, ...,\n",
+       "          1.33705972e+04,  9.00167536e+03,  7.19006049e+03],\n",
+       "        [-1.97014446e+01,  2.98840905e+00, -4.85452806e+00, ...,\n",
+       "          5.82130895e+00, -1.61363982e+01, -5.65737657e+00]]])
        • created_at :
          2020-06-06T18:08:05.356439
          arviz_version :
          0.8.3
          inference_library :
          pymc3
          inference_library_version :
          3.8

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        xarray.Dataset
          • obs_dim_0: 8
          • obs_dim_0
            (obs_dim_0)
            int64
            0 1 2 3 4 5 6 7
            array([0, 1, 2, 3, 4, 5, 6, 7])
          • obs
            (obs_dim_0)
            float64
            28.0 8.0 -3.0 7.0 ... 1.0 18.0 12.0
            array([28.,  8., -3.,  7., -1.,  1., 18., 12.])
        • created_at :
          2020-06-06T18:08:05.357843
          arviz_version :
          0.8.3
          inference_library :
          pymc3
          inference_library_version :
          3.8

        \n", + "
      \n", + "
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      • \n", + " \n", + "
    \n", + "
    \n", + " " + ], + "text/plain": [ + "Inference data with groups:\n", + "\t> posterior\n", + "\t> posterior_predictive\n", + "\t> log_likelihood\n", + "\t> sample_stats\n", + "\t> prior\n", + "\t> prior_predictive\n", + "\t> observed_data" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "with pm.Model() as model:\n", + " mu = pm.Normal(\"mu\", mu=0, sd=5)\n", + " tau = pm.HalfCauchy(\"tau\", beta=5)\n", + " theta_tilde = pm.Normal(\"theta_tilde\", mu=0, sd=1, shape=eight_school_data[\"J\"])\n", + " theta = pm.Deterministic(\"theta\", mu + tau * theta_tilde)\n", + " pm.Normal(\n", + " \"obs\", mu=theta, sd=eight_school_data[\"sigma\"], observed=eight_school_data[\"y\"]\n", + " )\n", + "\n", + " trace = pm.sample(draws, chains=chains)\n", + " prior = pm.sample_prior_predictive()\n", + " posterior_predictive = pm.sample_posterior_predictive(trace)\n", + "\n", + " pm_data = az.from_pymc3(\n", + " trace=trace,\n", + " prior=prior,\n", + " posterior_predictive=posterior_predictive,\n", + " coords={\"school\": np.arange(eight_school_data[\"J\"])},\n", + " dims={\"theta\": [\"school\"], \"theta_tilde\": [\"school\"]},\n", + " )\n", + "pm_data" + ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## From PyMC3" + "## From PyStan" ] }, { "cell_type": "code", - "execution_count": 10, - "metadata": { - "ExecuteTime": { - "end_time": "2020-06-05T06:47:09.286020Z", - "start_time": "2020-06-05T06:47:07.961226Z" + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:pystan:COMPILING THE C++ CODE FOR MODEL anon_model_9d743830ec4a29fb58eb4660d4b9417f NOW.\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "
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    arviz.InferenceData
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        xarray.Dataset
          • chain: 4
          • draw: 1000
          • school: 8
          • chain
            (chain)
            int64
            0 1 2 3
            array([0, 1, 2, 3])
          • draw
            (draw)
            int64
            0 1 2 3 4 5 ... 995 996 997 998 999
            array([  0,   1,   2, ..., 997, 998, 999])
          • school
            (school)
            int64
            0 1 2 3 4 5 6 7
            array([0, 1, 2, 3, 4, 5, 6, 7])
          • mu
            (chain, draw)
            float64
            7.136 4.809 3.715 ... 9.668 1.005
            array([[ 7.13647713,  4.80878256,  3.71456426, ...,  4.59832792,\n",
+       "         2.61858011,  7.71184613],\n",
+       "       [ 2.85614299,  2.93917185,  4.54687519, ...,  4.77567137,\n",
+       "         5.81110368,  2.75432175],\n",
+       "       [ 5.38414147,  0.03029178,  1.20891239, ...,  5.55392541,\n",
+       "        -0.11888032,  7.14845165],\n",
+       "       [ 4.83046053,  3.485902  ,  9.15602531, ...,  2.64168638,\n",
+       "         9.66764308,  1.00491627]])
          • tau
            (chain, draw)
            float64
            3.235 5.204 1.977 ... 1.96 3.162
            array([[3.23509663, 5.20424104, 1.97695143, ..., 1.83067557, 6.74139156,\n",
+       "        0.68903071],\n",
+       "       [5.75947041, 1.68197019, 5.29099056, ..., 0.04472656, 0.59257567,\n",
+       "        0.93668379],\n",
+       "       [0.77299688, 1.51050476, 1.30755851, ..., 1.60030628, 0.56242846,\n",
+       "        0.8348308 ],\n",
+       "       [6.29899008, 1.25855614, 2.17400965, ..., 1.29833941, 1.95973216,\n",
+       "        3.16169546]])
          • theta_tilde
            (chain, draw, school)
            float64
            2.324 -0.7764 ... 0.4021 -1.033
            array([[[ 2.32379123e+00, -7.76418571e-01, -5.01022113e-01, ...,\n",
+       "          1.19049238e+00, -1.48404796e+00, -2.08798281e-01],\n",
+       "        [-2.77386924e-01,  1.54767321e+00,  3.15376703e-01, ...,\n",
+       "         -1.36228981e+00,  1.78175896e+00, -4.93907464e-01],\n",
+       "        [ 2.64105004e+00,  5.35997803e-01, -8.90539506e-01, ...,\n",
+       "          6.57794882e-01, -5.64605164e-01,  2.36985615e-03],\n",
+       "        ...,\n",
+       "        [-3.15678175e-02, -3.87417276e-02, -5.58340680e-01, ...,\n",
+       "         -4.65353543e-01, -1.09751383e+00,  5.09319375e-02],\n",
+       "        [ 8.32965205e-01,  6.48222367e-01, -3.90991116e-01, ...,\n",
+       "          5.25179247e-01,  1.82321932e+00, -4.60474289e-01],\n",
+       "        [-1.53204318e-01, -3.87665960e-01,  9.31997670e-02, ...,\n",
+       "         -9.76083835e-01, -7.66930923e-01,  1.06999894e+00]],\n",
+       "\n",
+       "       [[ 5.77676190e-01, -1.50966398e+00, -6.19455736e-01, ...,\n",
+       "          1.14525937e+00,  1.54350676e+00, -2.07929704e-01],\n",
+       "        [-3.43948517e-01,  1.17132661e-01, -5.50675852e-01, ...,\n",
+       "          1.00058737e+00, -1.66125455e+00,  5.08106406e-02],\n",
+       "        [ 1.46112443e+00,  3.38328510e-01,  2.25898376e-01, ...,\n",
+       "         -1.10698408e+00,  1.44374163e+00,  1.13869599e-01],\n",
+       "        ...,\n",
+       "        [-3.30736969e-01, -3.33683083e-03, -1.88341415e+00, ...,\n",
+       "          1.73407550e+00, -9.64094311e-01, -3.26539032e-01],\n",
+       "        [ 6.68216215e-01,  6.26043865e-01,  1.95043754e+00, ...,\n",
+       "         -1.10676754e+00,  8.18153965e-01,  1.06553262e+00],\n",
+       "        [-2.27309201e+00, -3.77663513e-01,  1.15103591e+00, ...,\n",
+       "          4.58309592e-01, -1.37554228e+00,  4.53414105e-01]],\n",
+       "\n",
+       "       [[-3.85989935e-01, -8.03879920e-01, -3.88217932e-01, ...,\n",
+       "          1.39905894e-01,  1.59325937e-01, -1.71527073e-01],\n",
+       "        [ 3.62197623e-01,  6.61213573e-01, -1.21496208e+00, ...,\n",
+       "         -1.18914239e+00,  1.06584352e+00,  6.47365331e-01],\n",
+       "        [ 1.61322730e+00,  4.13485480e-01, -1.68128280e+00, ...,\n",
+       "          1.06886144e-01, -9.47703569e-01, -8.95436956e-01],\n",
+       "        ...,\n",
+       "        [ 1.27754470e+00,  8.23942845e-01, -8.58297611e-01, ...,\n",
+       "          1.97596280e+00, -8.47224301e-01,  9.81683929e-01],\n",
+       "        [ 1.11932977e+00, -6.66114769e-01,  1.58278413e-02, ...,\n",
+       "         -9.58635064e-02, -4.49391408e-01, -3.11846845e-01],\n",
+       "        [-1.90416914e-01, -4.43275790e-01,  1.51870062e+00, ...,\n",
+       "         -1.80770713e-01, -2.90641862e-01, -1.25523226e+00]],\n",
+       "\n",
+       "       [[-1.47189064e+00,  3.38261505e-01,  8.75363603e-01, ...,\n",
+       "         -2.12825340e+00,  6.92338462e-02,  3.03061317e-01],\n",
+       "        [-1.70498312e+00,  1.23927425e+00, -1.13002724e-01, ...,\n",
+       "         -1.11902291e+00,  4.77301514e-01,  6.57273497e-01],\n",
+       "        [ 1.57051641e+00, -1.03781767e+00, -2.51307144e-01, ...,\n",
+       "          3.01850309e-01,  5.89430598e-01,  4.77979603e-01],\n",
+       "        ...,\n",
+       "        [ 7.57588988e-01, -2.41586074e-01,  8.26638715e-01, ...,\n",
+       "          4.31405786e-01,  2.68134996e-01,  5.41040260e-01],\n",
+       "        [ 1.37956246e+00,  9.46952150e-01, -3.49853455e-01, ...,\n",
+       "         -2.23551741e-01,  3.58858360e-01, -1.29738334e+00],\n",
+       "        [ 6.20848787e-01,  1.02202002e+00, -1.10013506e+00, ...,\n",
+       "         -1.44705474e+00,  4.02059914e-01, -1.03299979e+00]]])
          • theta
            (chain, draw, school)
            float64
            14.65 4.625 5.516 ... 2.276 -2.261
            array([[[14.6541663 ,  4.62468804,  5.51562219, ..., 10.987835  ,\n",
+       "          2.3354386 ,  6.46099452],\n",
+       "        [ 3.36519415, 12.86324702,  6.45007894, ..., -2.28090198,\n",
+       "         14.0814857 ,  2.23836906],\n",
+       "        [ 8.93579191,  4.77420588,  1.95401091, ...,  5.01499279,\n",
+       "          2.59836727,  3.71924935],\n",
+       "        ...,\n",
+       "        [ 4.54053749,  4.52740439,  3.57618728, ...,  3.74641656,\n",
+       "          2.58913617,  4.69156778],\n",
+       "        [ 8.23392472,  6.98850091, -0.0172441 , ...,  6.15901906,\n",
+       "         14.90961546, -0.48565738],\n",
+       "        [ 7.60628365,  7.44473238,  7.77606364, ...,  7.03929439,\n",
+       "          7.18340717,  8.44910827]],\n",
+       "\n",
+       "       [[ 6.18325191, -5.83872201, -0.71159399, ...,  9.45223042,\n",
+       "         11.74592451,  1.65857801],\n",
+       "        [ 2.3606607 ,  3.13618549,  2.01295148, ...,  4.62212997,\n",
+       "          0.14499122,  3.02463383],\n",
+       "        [12.27767076,  6.33696815,  5.74210137, ..., -1.31016711,\n",
+       "         12.18569852,  5.14935817],\n",
+       "        ...,\n",
+       "        [ 4.76087864,  4.77552213,  4.69143273, ...,  4.85323061,\n",
+       "          4.73255075,  4.7610664 ],\n",
+       "        [ 6.20707235,  6.18208204,  6.96688552, ...,  5.15526015,\n",
+       "          6.29592181,  6.44251239],\n",
+       "        [ 0.6251533 ,  2.40057046,  3.83247843, ...,  3.18361292,\n",
+       "          1.46587359,  3.17902739]],\n",
+       "\n",
+       "       [[ 5.08577245,  4.76274479,  5.08405022, ...,  5.49228829,\n",
+       "          5.50729992,  5.25155157],\n",
+       "        [ 0.57739302,  1.02905803, -1.80491422, ..., -1.76591346,\n",
+       "          1.64025349,  1.00814019],\n",
+       "        [ 3.31830147,  1.74956884, -0.98946324, ...,  1.34867227,\n",
+       "         -0.03026548,  0.03807618],\n",
+       "        ...,\n",
+       "        [ 7.59838821,  6.87248632,  4.18038635, ...,  8.71607108,\n",
+       "          4.19810704,  7.12492037],\n",
+       "        [ 0.5106626 , -0.49352223, -0.10997829, ..., -0.17279669,\n",
+       "         -0.37163084, -0.29427186],\n",
+       "        [ 6.98948574,  6.77839136,  8.41630969, ...,  6.99753869,\n",
+       "          6.90581487,  6.1005451 ]],\n",
+       "\n",
+       "       [[-4.44096402,  6.96116639, 10.34436718, ..., -8.57538648,\n",
+       "          5.26656384,  6.73944076],\n",
+       "        [ 1.34008503,  5.04559822,  3.34368173, ...,  2.07754885,\n",
+       "          4.08661275,  4.3131176 ],\n",
+       "        [12.57034315,  6.89979968,  8.60968115, ...,  9.8122508 ,\n",
+       "         10.43745312, 10.19515758],\n",
+       "        ...,\n",
+       "        [ 3.62529402,  2.32802566,  3.714944  , ...,  3.20179751,\n",
+       "          2.98981661,  3.34414027],\n",
+       "        [12.371216  , 11.52341567,  8.98202402, ...,  9.22954155,\n",
+       "         10.37090935,  7.12511922],\n",
+       "        [ 2.96785106,  4.23623232, -2.47337574, ..., -3.57023011,\n",
+       "          2.27610728, -2.26111447]]])
        • created_at :
          2020-06-06T18:09:40.337581
          arviz_version :
          0.8.3
          inference_library :
          pystan
          inference_library_version :
          2.19.1.1

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        xarray.Dataset
          • chain: 4
          • draw: 1000
          • school: 8
          • chain
            (chain)
            int64
            0 1 2 3
            array([0, 1, 2, 3])
          • draw
            (draw)
            int64
            0 1 2 3 4 5 ... 995 996 997 998 999
            array([  0,   1,   2, ..., 997, 998, 999])
          • school
            (school)
            int64
            0 1 2 3 4 5 6 7
            array([0, 1, 2, 3, 4, 5, 6, 7])
          • y_hat
            (chain, draw, school)
            float64
            34.8 19.28 2.068 ... -4.68 -29.99
            array([[[ 3.48048527e+01,  1.92757538e+01,  2.06824326e+00, ...,\n",
+       "          1.65816291e+01,  7.20235910e-02,  7.95096646e+00],\n",
+       "        [ 1.85775714e+01, -4.43324480e+00, -3.05770368e+01, ...,\n",
+       "          8.42976543e+00,  1.37226945e+01, -4.00326916e+00],\n",
+       "        [ 6.28236785e+00,  8.93194288e+00,  9.21632858e+00, ...,\n",
+       "          2.98329599e+00,  8.90041129e-01,  1.29937275e+01],\n",
+       "        ...,\n",
+       "        [ 6.49485425e-01,  1.32389663e+01,  3.14424443e+00, ...,\n",
+       "          9.30828012e-01,  6.15663541e+00, -1.39732975e+01],\n",
+       "        [ 9.99887892e+00,  2.23380619e+01, -9.53097078e+00, ...,\n",
+       "          8.06037911e+00,  4.06551816e+00,  3.84731971e+00],\n",
+       "        [ 2.47197758e+01,  9.86092974e+00, -1.68509054e+01, ...,\n",
+       "          5.45291637e+00, -1.26471028e+00,  3.54839713e+01]],\n",
+       "\n",
+       "       [[-9.16576433e+00,  1.44090144e+00, -5.62980916e-01, ...,\n",
+       "          2.18937216e+01,  2.60324690e+01, -1.14729870e+01],\n",
+       "        [ 2.56332048e+00, -1.19960802e+01, -2.32269985e+01, ...,\n",
+       "          8.24150112e+00, -3.14522724e+00, -1.02591329e+00],\n",
+       "        [ 3.24804089e+00,  8.41704129e-01,  1.05827029e+01, ...,\n",
+       "          1.71709061e+01,  2.19578482e+01,  2.02425986e+01],\n",
+       "        ...,\n",
+       "        [-7.76720819e+00,  5.05484127e+00,  3.93395466e+01, ...,\n",
+       "         -3.04267161e+00, -6.29672152e+00,  2.18037055e+01],\n",
+       "        [ 3.03826832e+01,  1.99055248e+01,  9.78954704e-01, ...,\n",
+       "          2.24195277e+01, -1.68734750e+00,  3.82229593e+01],\n",
+       "        [ 2.58120017e+00,  2.93309398e+01,  1.18486680e+01, ...,\n",
+       "         -8.11685705e+00, -1.96778665e+01, -2.05111408e+01]],\n",
+       "\n",
+       "       [[ 1.04745948e+01,  1.81267858e-01, -5.18772531e+00, ...,\n",
+       "          2.28899434e+01, -1.01485739e+01, -9.18437576e+00],\n",
+       "        [-1.02080210e+01,  1.61975154e+01,  2.33181742e-02, ...,\n",
+       "         -2.22974602e+01,  1.13096213e+01, -1.06498392e+01],\n",
+       "        [-1.62270711e+01,  4.85258854e+00, -3.52789049e-01, ...,\n",
+       "          2.38982218e+01, -1.43721659e+01,  4.39459127e+01],\n",
+       "        ...,\n",
+       "        [ 7.52941391e+00,  6.56295817e+00, -2.04005120e+00, ...,\n",
+       "          1.14073372e+01, -6.44289722e+00,  3.70561430e+00],\n",
+       "        [-3.47024104e+01,  4.61400769e-02, -1.13586839e+01, ...,\n",
+       "         -1.29018351e+00, -2.08509962e+00, -1.58749584e+01],\n",
+       "        [ 2.09926355e+01,  6.37251824e+00, -3.88045666e+00, ...,\n",
+       "          1.45280931e+00,  2.46328670e+00, -2.05572561e+01]],\n",
+       "\n",
+       "       [[-7.52198096e+00,  1.53606496e+01,  6.20581496e+00, ...,\n",
+       "          2.27688345e+00,  1.34336562e+01,  1.01406904e+00],\n",
+       "        [ 3.65598969e+00, -2.64648778e+00, -3.98093582e+00, ...,\n",
+       "          1.42580030e+01,  5.06085178e+00,  3.31112552e+01],\n",
+       "        [ 5.88888063e-01, -7.31787807e+00,  2.00544588e+01, ...,\n",
+       "          2.21847833e+01,  8.46356441e+00, -1.78468823e+00],\n",
+       "        ...,\n",
+       "        [ 2.01461681e+01,  5.81207430e+00,  3.79895275e+00, ...,\n",
+       "          1.67763881e+01,  5.68953911e+00,  3.66570228e+01],\n",
+       "        [ 4.25844083e+00,  8.20607960e+00,  1.44014300e+01, ...,\n",
+       "          1.26573016e+01,  2.99075662e+00,  2.42526939e+01],\n",
+       "        [ 7.69120055e+00,  4.82073044e+00,  1.43329590e+00, ...,\n",
+       "         -2.99816348e+00, -4.67994662e+00, -2.99865249e+01]]])
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          2020-06-06T18:09:40.355005
          arviz_version :
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          inference_library :
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        xarray.Dataset
          • chain: 4
          • draw: 1000
          • school: 8
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            (chain)
            int64
            0 1 2 3
            array([0, 1, 2, 3])
          • draw
            (draw)
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            0 1 2 3 4 5 ... 995 996 997 998 999
            array([  0,   1,   2, ..., 997, 998, 999])
          • school
            (school)
            int64
            0 1 2 3 4 5 6 7
            array([0, 1, 2, 3, 4, 5, 6, 7])
          • y
            (chain, draw, school)
            float64
            -4.023 -3.278 ... -4.458 -4.123
            array([[[-4.02279157, -3.27848728, -3.83315972, ..., -3.72905219,\n",
+       "         -4.44841605, -3.85665687],\n",
+       "        [-4.97559687, -3.33977948, -3.86594911, ..., -3.36131446,\n",
+       "         -3.2982974 , -3.95636189],\n",
+       "        [-4.43464213, -3.27355236, -3.73946129, ..., -3.38344607,\n",
+       "         -4.40757508, -3.91512948],\n",
+       "        ...,\n",
+       "        [-4.84998069, -3.28181823, -3.77599257, ..., -3.34800242,\n",
+       "         -4.40899725, -3.89173804],\n",
+       "        [-4.49520592, -3.22663928, -3.70890388, ..., -3.42681512,\n",
+       "         -3.26927601, -4.04988381],\n",
+       "        [-4.5512191 , -3.22306524, -3.91833106, ..., -3.467549  ,\n",
+       "         -3.80651703, -3.82876837]],\n",
+       "\n",
+       "       [[-4.68470095, -4.17907476, -3.70175538, ..., -3.61204124,\n",
+       "         -3.41709093, -3.97434888],\n",
+       "        [-5.08782367, -3.33980708, -3.74060867, ..., -3.37104796,\n",
+       "         -4.81553032, -3.93362695],\n",
+       "        [-4.17630348, -3.235352  , -3.84079354, ..., -3.338887  ,\n",
+       "         -3.39055413, -3.88173513],\n",
+       "        ...,\n",
+       "        [-4.82711487, -3.27350991, -3.80707049, ..., -3.37818664,\n",
+       "         -4.10164967, -3.89017782],\n",
+       "        [-4.6823925 , -3.23804775, -3.88554836, ..., -3.38818169,\n",
+       "         -3.90645086, -3.85697336],\n",
+       "        [-5.29228258, -3.37829168, -3.78270452, ..., -3.33653697,\n",
+       "         -4.58841031, -3.92938677]],\n",
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+       "         -4.55973016, -3.99576242],\n",
+       "        [-4.98073594, -3.41686307, -3.69942229, ..., -3.31733617,\n",
+       "         -4.84697599, -4.03012452],\n",
+       "        ...,\n",
+       "        [-4.55193488, -3.22788006, -3.79222637, ..., -3.56285758,\n",
+       "         -4.17398487, -3.84598684],\n",
+       "        [-5.30624134, -3.58222323, -3.7078402 , ..., -3.32251749,\n",
+       "         -4.90910772, -4.04256511],\n",
+       "        [-4.60797031, -3.22898526, -3.94608219, ..., -3.46547211,\n",
+       "         -3.83692834, -3.8630195 ]],\n",
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+       "         -4.03222561, -3.85201628],\n",
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+       "         -4.18943535, -3.90049572],\n",
+       "        [-4.15604276, -3.22757583, -3.95477862, ..., -3.63772539,\n",
+       "         -3.5074842 , -3.81433723],\n",
+       "        ...,\n",
+       "        [-4.94726938, -3.38238009, -3.77959459, ..., -3.3368665 ,\n",
+       "         -4.34805165, -3.92493361],\n",
+       "        [-4.16978627, -3.28359592, -3.97193526, ..., -3.59669064,\n",
+       "         -3.51253875, -3.84598384],\n",
+       "        [-5.01945202, -3.29235336, -3.69206892, ..., -3.40314374,\n",
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            array([[0.99579955, 0.99997379, 0.93992614, ..., 0.99624142, 0.97568498,\n",
+       "        0.9369229 ],\n",
+       "       [0.97832672, 0.99349373, 0.99722256, ..., 0.9805275 , 0.66544124,\n",
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+       "        0.66272908],\n",
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            array([[0.28338441, 0.28338441, 0.28338441, ..., 0.28338441, 0.28338441,\n",
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+       "       [0.33586788, 0.33586788, 0.33586788, ..., 0.33586788, 0.33586788,\n",
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            4 4 4 4 3 3 3 4 ... 3 3 3 3 4 3 3 3
            array([[4, 4, 4, ..., 3, 4, 4],\n",
+       "       [4, 4, 4, ..., 4, 3, 4],\n",
+       "       [4, 3, 3, ..., 4, 4, 4],\n",
+       "       [3, 3, 3, ..., 3, 3, 3]])
          • n_leapfrog
            (chain, draw)
            int64
            15 15 15 15 7 7 7 ... 15 7 15 7 7 7
            array([[15, 15, 15, ...,  7, 15, 15],\n",
+       "       [15, 15, 15, ..., 15, 15, 15],\n",
+       "       [15, 15, 15, ..., 15, 15, 15],\n",
+       "       [15,  7,  7, ...,  7,  7,  7]])
          • diverging
            (chain, draw)
            bool
            False False False ... False False
            array([[False, False, False, ..., False, False, False],\n",
+       "       [False, False, False, ..., False, False, False],\n",
+       "       [False, False, False, ..., False, False, False],\n",
+       "       [False, False, False, ..., False, False, False]])
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            float64
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            array([[13.44645842, 11.99665989, 13.69804473, ...,  9.47143089,\n",
+       "         7.16629642, 10.67200903],\n",
+       "       [ 9.60154795, 10.94666316,  6.87490628, ..., 16.32734631,\n",
+       "        14.64064544, 15.29534917],\n",
+       "       [ 9.36936729, 12.29084989, 13.71656332, ..., 15.59482811,\n",
+       "        14.1985079 , 11.18395816],\n",
+       "       [12.92992346, 12.50820152, 12.27994555, ..., 10.04009466,\n",
+       "        15.00120291, 17.84393059]])
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            array([[ -8.78560155,  -6.30221316,  -7.71007656, ...,  -4.02162552,\n",
+       "         -4.1154919 ,  -6.29556094],\n",
+       "       [ -6.28739162,  -5.5135253 ,  -3.52534459, ..., -11.32215542,\n",
+       "         -9.42216618,  -9.47624511],\n",
+       "       [ -4.57956447,  -6.89073912,  -7.12165559, ...,  -7.61427701,\n",
+       "         -6.53577978,  -6.48605742],\n",
+       "       [ -8.08265987,  -6.56502669,  -5.84788307, ...,  -6.06854386,\n",
+       "         -6.06377373,  -6.65130787]])
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        xarray.Dataset
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          • school
            (school)
            int64
            0 1 2 3 4 5 6 7
            array([0, 1, 2, 3, 4, 5, 6, 7])
          • y
            (school)
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        \n", + "
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    \n", + " " + ], + "text/plain": [ + "Inference data with groups:\n", + "\t> posterior\n", + "\t> posterior_predictive\n", + "\t> log_likelihood\n", + "\t> sample_stats\n", + "\t> observed_data" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" } - }, - "outputs": [], + ], "source": [ - "import pymc3 as pm\n", + "import pystan\n", "\n", - "draws = 500\n", - "chains = 2\n", + "schools_code = \"\"\"\n", + "data {\n", + " int J;\n", + " real y[J];\n", + " real sigma[J];\n", + "}\n", + "\n", + "parameters {\n", + " real mu;\n", + " real tau;\n", + " real theta_tilde[J];\n", + "}\n", + "\n", + "transformed parameters {\n", + " real theta[J];\n", + " for (j in 1:J)\n", + " theta[j] = mu + tau * theta_tilde[j];\n", + "}\n", + "\n", + "model {\n", + " mu ~ normal(0, 5);\n", + " tau ~ cauchy(0, 5);\n", + " theta_tilde ~ normal(0, 1);\n", + " y ~ normal(theta, sigma);\n", + "}\n", + "\n", + "generated quantities {\n", + " vector[J] log_lik;\n", + " vector[J] y_hat;\n", + " for (j in 1:J) {\n", + " log_lik[j] = normal_lpdf(y[j] | theta[j], sigma[j]);\n", + " y_hat[j] = normal_rng(theta[j], sigma[j]);\n", + " }\n", + "}\n", + "\"\"\"\n", "\n", "eight_school_data = {\n", " \"J\": 8,\n", " \"y\": np.array([28.0, 8.0, -3.0, 7.0, -1.0, 1.0, 18.0, 12.0]),\n", " \"sigma\": np.array([15.0, 10.0, 16.0, 11.0, 9.0, 11.0, 10.0, 18.0]),\n", - "}" + "}\n", + "\n", + "stan_model = pystan.StanModel(model_code=schools_code)\n", + "fit = stan_model.sampling(data=eight_school_data, control={\"adapt_delta\": 0.9})\n", + "\n", + "stan_data = az.from_pystan(\n", + " posterior=fit,\n", + " posterior_predictive=\"y_hat\",\n", + " observed_data=[\"y\"],\n", + " log_likelihood={\"y\": \"log_lik\"},\n", + " coords={\"school\": np.arange(eight_school_data[\"J\"])},\n", + " dims={\n", + " \"theta\": [\"school\"],\n", + " \"y\": [\"school\"],\n", + " \"log_lik\": [\"school\"],\n", + " \"y_hat\": [\"school\"],\n", + " \"theta_tilde\": [\"school\"],\n", + " },\n", + ")\n", + "\n", + "stan_data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## From Pyro" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
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          • theta_tilde_dim_0: 8
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            array([0, 1])
          • draw
            (draw)
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            (theta_tilde_dim_0)
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            0 1 2 3 4 5 6 7
            array([0, 1, 2, 3, 4, 5, 6, 7])
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            (chain, draw)
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            array([[ 6.783849  ,  3.0570068 , -0.5898714 ,  7.692052  ,  5.978842  ,\n",
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+       "        10.008863  ,  6.0008774 ,  5.048078  ,  1.7424315 ,  2.6212542 ,\n",
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+       "         3.0202703 ,  2.4996028 ,  3.3100448 ,  3.2369874 ,  5.179661  ,\n",
+       "         1.4572364 , -0.03877497, -1.1503081 ,  1.7937964 ,  1.7937964 ,\n",
+       "         6.6380773 ,  1.5952408 ,  7.8001666 ,  6.2870593 ,  3.2218194 ,\n",
+       "         6.0440335 ,  3.8866324 ,  4.218601  ,  4.218601  ,  7.2325306 ,\n",
+       "         7.5369225 , 12.167865  ,  7.2303514 ,  7.0910425 ,  2.2544699 ,\n",
+       "        13.885392  ,  2.3582263 , -0.71636176,  4.0944424 ,  8.234982  ,\n",
+       "         4.7547674 ,  7.5123987 ,  1.0010346 ,  1.463587  ,  5.0094767 ,\n",
+       "         6.3490815 ,  4.3477235 ,  2.9339297 ,  5.3828955 ,  1.4211316 ,\n",
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+       "         4.8213353 ,  5.420148  ,  4.641708  ,  5.0392356 ,  3.5072942 ,\n",
+       "         4.488197  ,  5.82481   ,  3.192687  ,  7.916957  ,  2.268492  ,\n",
+       "         5.9801583 ,  6.9189725 , 12.165862  , -2.8586159 ,  9.570917  ,\n",
+       "         7.068532  ,  2.0816557 ,  6.644463  , -1.9028682 , 10.164826  ,\n",
+       "         2.5184588 , 11.621914  , -3.3529563 , 10.299711  ,  4.5834465 ,\n",
+       "         4.585137  ,  7.91378   ,  2.8814616 ,  5.1396675 ,  4.668864  ,\n",
+       "         3.4275546 ,  4.9457726 ,  3.6325824 ,  5.336408  ,  6.201971  ,\n",
+       "         6.2212915 ,  0.6339596 ,  7.687422  , 12.570242  , 12.476558  ,\n",
+       "        10.869892  ,  6.915905  ,  5.5935144 ,  6.058858  ,  1.7806189 ,\n",
+       "         9.691803  ,  1.2325222 , 10.002609  ,  6.168408  ,  9.982995  ,\n",
+       "        -0.51718354,  6.5104423 ,  2.9149487 ,  1.4453427 ,  4.3771286 ,\n",
+       "         4.527439  ,  1.4286865 ,  3.5170612 ,  7.5207043 ,  6.6093435 ,\n",
+       "         0.9016738 ,  8.855167  ,  4.44285   ,  6.2064247 , 11.12649   ,\n",
+       "        10.901761  , 10.992046  ,  1.6697562 ,  9.535236  , -1.4199173 ,\n",
+       "         0.04521966,  0.24071217,  1.0354244 ,  3.8727565 ,  6.546321  ,\n",
+       "         7.4994783 ,  5.1963906 ,  2.2472491 , -1.4566408 ,  3.2481778 ,\n",
+       "         1.5054731 ,  5.427044  ,  0.7663537 , 12.421476  , 13.937447  ,\n",
+       "         9.55212   ,  8.892584  ,  4.5370684 ,  6.597247  ,  3.521164  ,\n",
+       "         6.200644  , -0.6878598 , -0.5076442 , -1.1443609 , -2.024046  ,\n",
+       "         7.497966  ,  3.9299464 ,  2.1074817 ,  7.6489606 , -0.5676765 ,\n",
+       "         8.88996   ,  1.7325966 ,  3.6853108 ,  9.022504  ,  3.554699  ,\n",
+       "         6.0461593 , 10.99902   , -4.534336  , -1.3497578 ,  3.4103231 ,\n",
+       "         3.4103231 ,  5.511337  ,  1.5036378 ,  6.0223374 ,  5.0781465 ,\n",
+       "         7.4995213 ,  2.2854679 ,  5.5526514 ,  4.916196  ,  2.576561  ,\n",
+       "         5.576095  ,  4.371717  , 13.234244  , 13.360312  , -0.45095968,\n",
+       "         2.989245  ,  0.6203029 ,  7.7407403 ,  9.276141  ,  4.255246  ,\n",
+       "         6.0797963 ,  7.8773675 ,  2.5528507 ,  2.1499953 ,  6.2999554 ,\n",
+       "         2.9320016 ,  5.145282  ,  4.0042167 ,  4.7150197 ,  2.9001827 ,\n",
+       "         3.637968  ,  3.0349693 ,  3.6715143 ,  1.2648342 ,  2.0227237 ,\n",
+       "         1.0630711 ,  5.546765  ,  3.8676581 ,  3.0002105 ,  2.6321206 ,\n",
+       "         4.190834  ,  5.7021546 ,  5.8634467 ,  0.2976514 , -0.47097135,\n",
+       "        -1.0752056 ,  4.7460985 , -0.6505427 ,  5.6235523 , -0.9203634 ,\n",
+       "        -1.8839953 , 11.195079  , -2.0718043 , 11.193365  ,  6.1056333 ,\n",
+       "         8.325111  ,  9.075426  ,  2.9479647 ,  7.128322  ,  4.3343573 ,\n",
+       "         3.1772745 ,  5.592222  ,  7.5314674 , -3.0730538 ,  3.9833744 ,\n",
+       "         9.110682  ,  4.5988865 ,  2.8390033 ,  3.4741888 ,  4.0355644 ,\n",
+       "         3.4970965 ,  0.71459496,  7.2869844 ,  3.6135454 ,  9.620527  ,\n",
+       "         1.0082316 ,  7.475091  ,  1.0405822 ,  4.5348783 ,  3.8841453 ,\n",
+       "         4.7387238 ,  4.521943  ,  4.521943  ,  3.539055  ,  6.8544626 ,\n",
+       "        11.370565  ,  4.756797  ,  4.6440043 ,  6.966434  ,  7.002037  ,\n",
+       "         9.16461   ,  5.50093   ,  6.000969  ,  1.3224025 ,  6.746578  ,\n",
+       "         3.1069808 ,  4.3904467 ,  5.1858315 , -0.6927078 ,  7.3248353 ,\n",
+       "         0.03497177,  2.7320523 ,  4.0992765 ,  1.4401541 ,  3.696537  ,\n",
+       "         6.4522524 ,  4.343199  , -1.7796711 ,  9.775927  ,  8.519873  ,\n",
+       "         7.5724382 ,  2.1690316 ,  0.6314293 ,  2.6643667 ,  7.1365185 ,\n",
+       "         8.02526   ,  0.5847881 ,  6.0416284 ,  1.4256818 ,  8.6619425 ,\n",
+       "         2.8416598 ,  3.5279734 ,  5.7481217 ,  5.685895  ,  3.817007  ,\n",
+       "         3.817007  ,  5.1601176 ,  4.495685  ,  2.047713  ,  2.5250795 ,\n",
+       "         6.463599  ,  7.9371533 , -3.1092994 ,  1.6143876 ,  1.0753083 ,\n",
+       "         8.186201  ,  5.4840565 ,  3.3782108 ,  6.6359487 ,  0.73560786,\n",
+       "        -2.5476358 ,  2.1959705 ,  0.595925  , -0.6245699 ,  8.363325  ,\n",
+       "         7.2956586 ,  1.3452858 ,  7.595389  ,  1.9837761 , -2.1932921 ,\n",
+       "         5.130442  ,  3.8775177 ,  3.8775177 ,  3.9545794 ,  3.3309066 ,\n",
+       "         4.0000944 ,  7.8284636 ,  9.910145  ,  0.29044223,  0.4511162 ,\n",
+       "         4.23416   ,  6.625504  ,  6.7739162 ,  5.979492  ,  2.0962138 ,\n",
+       "         7.1208596 ,  1.2915809 ,  2.0792012 ,  6.7928405 ,  4.659316  ,\n",
+       "         3.5058026 ,  2.5944214 ,  7.936224  ,  7.7682953 ,  7.1779675 ,\n",
+       "         6.921358  ,  0.8594191 , -0.21582747,  0.5738251 ,  2.7562642 ,\n",
+       "         3.674602  ,  0.5121915 ,  1.0169942 ,  7.307401  ,  6.4692693 ,\n",
+       "         8.035346  ,  6.3669305 ,  5.8459167 ,  4.03193   ,  7.025049  ,\n",
+       "         6.2521224 ,  1.1102605 ,  3.404097  , -1.5265621 ,  9.016952  ,\n",
+       "        11.493106  , -0.57146204, -0.26515374,  7.4198837 ,  6.505427  ,\n",
+       "         7.2936172 ,  9.671284  ,  2.9167275 ,  2.2982335 ,  7.3781447 ,\n",
+       "         2.1675684 ,  5.190227  ,  5.3436894 ,  3.0521636 ,  6.544801  ,\n",
+       "         4.743841  ,  5.7023606 ,  6.8151307 ,  5.6628094 ,  3.9610717 ,\n",
+       "         5.13286   ,  1.6667626 ,  6.377818  ,  2.9189663 ,  5.914687  ,\n",
+       "         5.556989  , 12.396165  ,  0.9704962 ,  7.037617  ,  8.90337   ,\n",
+       "         4.53032   ,  5.2621    ,  6.3007636 ,  1.6994746 , -4.1169577 ,\n",
+       "         0.44233608,  8.0539665 ,  4.4645185 ,  1.6229515 ,  6.553206  ,\n",
+       "         7.863461  ,  2.2408123 ,  6.6904135 ,  3.169836  ,  5.7995133 ,\n",
+       "         5.9354954 ,  4.294053  ,  3.3903077 ,  8.167379  ,  0.7955775 ,\n",
+       "         4.042009  ,  7.1713643 ,  2.5641007 ,  9.766011  ,  1.7654216 ,\n",
+       "         6.607266  ,  5.0007186 ,  9.228579  ,  0.11804408,  4.6933355 ,\n",
+       "         8.756182  ,  7.6457686 , 11.668673  , -2.2822227 ,  5.744774  ,\n",
+       "        -0.12102944,  5.706559  ,  6.738437  ,  3.9768252 ,  0.6605184 ,\n",
+       "         1.1702331 ,  7.049793  ,  4.7348275 ,  3.3859143 ,  5.335453  ,\n",
+       "         5.6728067 ,  8.422111  ,  0.6059617 ,  8.701169  ,  1.3406711 ,\n",
+       "         0.9726557 ,  2.9391463 ,  8.847219  ,  0.65883195, -1.1129549 ,\n",
+       "         4.2620173 ,  4.603801  ,  6.1635494 ,  4.814609  ,  4.5336676 ,\n",
+       "        10.157522  ,  2.3478928 ,  8.260862  ,  4.5819983 ,  5.691238  ,\n",
+       "         5.3385615 ,  3.9145231 ,  3.366712  ,  4.9146166 ,  4.2341347 ,\n",
+       "         4.2132525 ,  0.06960184,  5.7912407 ,  7.7237253 ,  8.238167  ,\n",
+       "        -1.5925429 , -1.8350666 ,  2.7601335 ,  5.2639394 ,  1.3687866 ,\n",
+       "         1.4656156 ,  4.4150705 ,  0.8827308 ,  1.5304792 ,  7.2156863 ,\n",
+       "         7.9426675 ,  4.7031536 ,  2.7878287 ,  3.0513043 ,  1.1879033 ,\n",
+       "         3.9434485 ,  0.61285603, -1.3296754 ,  2.951462  , -0.25293034],\n",
+       "       [ 5.01953   ,  6.86166   ,  6.6249776 ,  2.2893884 ,  2.501866  ,\n",
+       "         8.285073  ,  3.7216325 ,  4.7185307 ,  5.471057  ,  2.2488348 ,\n",
+       "         5.539174  ,  6.0150375 ,  2.0376778 ,  3.660337  ,  1.6074322 ,\n",
+       "         2.8387644 ,  4.8960958 ,  3.3996067 ,  7.467748  ,  4.651307  ,\n",
+       "         6.6853914 ,  8.318576  ,  7.023429  ,  6.5320916 ,  7.9992576 ,\n",
+       "         7.2837024 ,  4.562793  ,  6.547352  , -0.11292058,  7.4648447 ,\n",
+       "         0.98815346,  4.535407  ,  5.8668675 ,  4.8986874 ,  2.8843725 ,\n",
+       "         3.232449  ,  2.6490922 , -2.0015569 , -2.0015569 , -0.9957659 ,\n",
+       "        -0.9957659 ,  0.3136965 ,  3.6559486 ,  3.5627646 ,  1.8010818 ,\n",
+       "         6.344411  ,  4.7709913 ,  5.2192707 ,  3.2569563 ,  3.880964  ,\n",
+       "         4.206388  ,  3.2617676 ,  4.079238  ,  2.3552814 ,  3.5103083 ,\n",
+       "         4.422537  ,  7.6496224 ,  1.7483644 ,  5.4506264 ,  3.694887  ,\n",
+       "         2.9585373 ,  3.606896  ,  2.6271925 ,  2.4373152 ,  3.0032928 ,\n",
+       "         2.8940313 ,  7.236042  ,  5.4723234 ,  5.602808  ,  4.8854675 ,\n",
+       "         3.133181  , -1.9972157 , -5.426025  ,  0.74136853,  2.1391473 ,\n",
+       "         7.446571  ,  3.438631  ,  4.4135585 ,  2.515184  ,  7.4213567 ,\n",
+       "         1.4862064 ,  9.58293   ,  2.3712258 ,  9.991231  , 11.811281  ,\n",
+       "        -3.8299305 , 10.42727   ,  4.5649595 ,  4.0661554 , 12.225602  ,\n",
+       "         5.1727004 ,  5.384991  ,  2.5262818 ,  2.6618812 ,  4.545309  ,\n",
+       "         5.200153  ,  4.7776184 ,  0.37785053,  2.9929814 ,  5.422091  ,\n",
+       "         6.1752453 ,  7.7365675 ,  1.2166401 ,  3.3863912 ,  6.6130266 ,\n",
+       "         8.373728  ,  7.0688004 ,  5.1593804 ,  8.469555  ,  4.330571  ,\n",
+       "        -1.7626251 , 12.750014  ,  9.7615    ,  6.4595337 ,  8.6939125 ,\n",
+       "         2.2231188 ,  5.390884  ,  0.97161454,  4.505579  ,  6.609976  ,\n",
+       "         4.9074917 ,  4.2184486 ,  6.618556  ,  2.004937  ,  5.4137464 ,\n",
+       "         9.630217  ,  9.768252  ,  0.30616486,  3.3912468 ,  7.838382  ,\n",
+       "         9.576023  ,  7.655735  ,  0.18133971, -3.5502794 , 12.7816515 ,\n",
+       "         6.2193003 ,  6.8971252 ,  2.8146944 ,  6.2655287 ,  2.7793994 ,\n",
+       "         6.990551  ,  4.122216  ,  7.902957  ,  2.0129812 ,  3.2262692 ,\n",
+       "         2.7485383 ,  9.244148  ,  7.6056643 ,  5.710777  ,  2.5418003 ,\n",
+       "         3.4473598 ,  4.393044  ,  3.6912231 ,  6.887758  ,  7.915879  ,\n",
+       "         8.873672  ,  5.941959  ,  2.1800964 ,  0.72718906,  6.024666  ,\n",
+       "         2.1498783 ,  1.6291099 ,  9.088835  , -0.6355481 ,  8.986781  ,\n",
+       "        13.685892  , -4.462116  , 12.183017  , -2.3599992 ,  6.3404007 ,\n",
+       "         2.0727506 ,  4.6059237 ,  2.2072875 ,  4.4966164 ,  8.327364  ,\n",
+       "         9.140227  ,  4.9690924 ,  1.7394838 ,  0.86819786,  5.5950966 ,\n",
+       "         2.6679792 ,  2.9878035 ,  1.293262  ,  5.688295  ,  2.998148  ,\n",
+       "         7.8103237 ,  2.1600375 ,  0.8984237 ,  3.8629336 ,  7.250372  ,\n",
+       "         5.4487286 ,  9.825451  ,  9.6626625 ,  3.5370805 ,  2.28778   ,\n",
+       "         0.7985786 ,  6.5033493 ,  5.7602005 ,  2.1282008 ,  0.7583219 ,\n",
+       "        -0.2883258 ,  4.04663   ,  4.0531526 ,  7.637555  ,  4.522324  ,\n",
+       "         1.8140192 ,  6.2982025 ,  0.8498249 ,  4.9770107 , 10.625928  ,\n",
+       "         2.4482574 ,  1.7317593 ,  2.473702  ,  3.3864214 ,  5.2274213 ,\n",
+       "         7.1590877 ,  4.082163  ,  4.655895  ,  4.341129  ,  0.72216916,\n",
+       "         7.632001  ,  5.273179  ,  7.0966406 ,  3.9848332 ,  6.7652764 ,\n",
+       "        -1.8530495 ,  4.807021  ,  3.7115476 ,  7.7180634 ,  5.495058  ,\n",
+       "         3.5686898 ,  8.432459  , -3.9393754 ,  3.5627987 ,  1.3835332 ,\n",
+       "         3.4305634 ,  5.2384458 ,  4.813102  ,  3.7107148 ,  5.8590145 ,\n",
+       "         7.921546  ,  2.628134  ,  2.35359   ,  4.0655026 ,  3.800044  ,\n",
+       "         3.1461186 ,  5.714904  ,  5.1543646 ,  3.2590818 ,  3.7851493 ,\n",
+       "         4.40026   ,  2.7656972 ,  1.0225668 ,  7.9855137 ,  2.9293344 ,\n",
+       "         4.256064  ,  5.404146  ,  7.5405574 ,  9.850468  , 10.078182  ,\n",
+       "         6.9839487 ,  8.5010395 , 11.371491  ,  3.3040867 ,  3.5877137 ,\n",
+       "         1.6272161 ,  1.6272161 ,  5.909365  ,  5.780271  ,  5.5829563 ,\n",
+       "         4.555645  ,  4.3025413 ,  4.9703617 ,  3.4080243 ,  0.05362892,\n",
+       "         5.641428  ,  1.9199136 ,  0.4426855 ,  9.342329  ,  2.4715855 ,\n",
+       "         0.20299262,  7.053833  ,  3.064495  ,  3.7879605 ,  3.0975132 ,\n",
+       "         4.8171186 ,  4.1754603 ,  1.8580515 ,  3.4130657 ,  1.8592646 ,\n",
+       "         5.972606  ,  2.600781  ,  5.1370234 ,  3.590805  ,  1.6839093 ,\n",
+       "         1.1044543 , 11.325514  , 11.263687  ,  3.0054102 ,  4.2464194 ,\n",
+       "        -1.2235668 ,  0.78690815,  8.217627  ,  8.289513  ,  1.818387  ,\n",
+       "         7.193574  ,  5.6828175 ,  9.229635  ,  1.8125782 ,  6.0123167 ,\n",
+       "         3.310267  ,  3.2328024 ,  8.0518055 ,  0.5462723 ,  5.6560984 ,\n",
+       "         7.22637   ,  8.11613   ,  3.7949753 ,  7.096421  , 10.765428  ,\n",
+       "         0.62853765,  6.8455167 ,  1.8135126 ,  4.342347  ,  5.897304  ,\n",
+       "         3.0075347 ,  1.4480636 ,  6.296116  ,  7.323115  ,  2.1111772 ,\n",
+       "         3.2788057 ,  4.3963056 ,  2.1767483 ,  4.343244  ,  1.7362591 ,\n",
+       "         8.032246  ,  7.7730284 ,  2.500092  ,  7.870913  , -3.3032596 ,\n",
+       "         4.2814445 ,  3.4301393 , 10.24017   , -0.06418872,  5.7352066 ,\n",
+       "         5.756984  ,  5.756984  ,  2.188118  ,  5.0771995 ,  1.7392151 ,\n",
+       "        10.904758  ,  9.412046  ,  7.786703  ,  8.769625  , 10.503181  ,\n",
+       "         8.57885   ,  6.9199967 ,  5.4401836 , -0.05953297,  8.981047  ,\n",
+       "         9.556315  ,  3.1879342 ,  6.2667456 ,  7.8490686 ,  7.600093  ,\n",
+       "         7.9399824 ,  0.6109712 ,  6.932536  ,  2.7567782 ,  1.5435878 ,\n",
+       "         4.8659782 ,  2.2375255 ,  8.436639  ,  9.785013  ,  8.592904  ,\n",
+       "         8.077721  , -0.98542786,  0.47609437,  4.7859526 , -1.187131  ,\n",
+       "         7.878492  ,  9.416552  ,  1.2292858 ,  5.719812  ,  7.2965436 ,\n",
+       "         1.962739  , -0.49165836,  5.951705  ,  1.9709172 ,  0.49937028,\n",
+       "         1.8703003 ,  5.446128  ,  4.893553  ,  4.086196  ,  6.650732  ,\n",
+       "         2.2123806 ,  2.0845377 ,  7.724782  ,  7.0939484 ,  9.294254  ,\n",
+       "         0.98116875, 11.221153  , -0.876735  ,  1.3594122 ,  7.842224  ,\n",
+       "         8.8228245 ,  8.8228245 , 10.224634  ,  7.027872  ,  6.7001348 ,\n",
+       "         7.376293  ,  9.412687  , -2.8747458 ,  1.0567542 ,  7.480833  ,\n",
+       "        10.921535  ,  6.2435284 ,  5.193436  ,  6.8472857 ,  3.895755  ,\n",
+       "         6.2523193 ,  3.9274454 ,  2.3286366 ,  4.988546  ,  4.2868986 ,\n",
+       "         7.0591617 ,  2.0112956 ,  6.0083036 ,  0.5485797 ,  2.927389  ,\n",
+       "         8.296615  ,  0.8524151 ,  8.771065  ,  0.8508061 ,  7.5617332 ,\n",
+       "         4.0908194 ,  6.449942  ,  5.5285096 , -1.5314658 ,  4.6953344 ,\n",
+       "         4.931533  ,  6.3189793 ,  4.447262  ,  4.547436  ,  6.949565  ,\n",
+       "         2.027462  ,  4.97022   ,  4.3807526 , 10.430753  ,  3.2305171 ,\n",
+       "         0.36104214, 10.798279  , -1.6254423 ,  4.7359886 ,  2.0681913 ,\n",
+       "         3.7673833 ,  1.9625635 ,  4.2347145 ,  7.492998  ,  6.4289722 ,\n",
+       "         4.327282  , -0.13930517,  2.281067  , -1.5606796 , -0.6715053 ,\n",
+       "        -2.6402383 ,  6.4653087 ,  1.9133025 ,  5.9235144 ,  8.07356   ,\n",
+       "         5.0766296 ,  5.6559167 , 10.879023  , -0.92014146,  2.5594158 ,\n",
+       "         5.5791693 ,  2.8021357 ,  1.2819654 , -2.5959618 , -0.8181571 ,\n",
+       "         1.8510228 ,  4.257959  ,  9.037647  ,  1.8694059 ,  9.915788  ,\n",
+       "         5.596039  ,  2.743896  ,  7.2139883 ,  4.4983315 ,  3.314092  ,\n",
+       "        10.760844  , -1.5691249 ,  7.673089  ,  2.6870704 ,  2.4335454 ,\n",
+       "        -0.2063955 , -0.75785524, -1.10085   ,  8.008105  ,  0.92949176]],\n",
+       "      dtype=float32)
          • tau
            (chain, draw)
            float32
            2.8592596 7.631564 ... 4.101541
            array([[ 2.8592596 ,  7.631564  ,  2.2398646 ,  2.2167501 ,  3.1840062 ,\n",
+       "         7.012798  ,  3.243187  ,  6.973709  ,  5.5824986 ,  0.7016568 ,\n",
+       "         5.86172   , 19.530476  ,  0.25236988,  3.668568  ,  1.5498195 ,\n",
+       "         0.8107503 ,  1.0509298 ,  7.2098413 ,  1.8983476 ,  5.2955256 ,\n",
+       "         7.7060432 ,  0.43670726,  3.6594412 ,  6.322126  ,  0.20263568,\n",
+       "         3.1838627 ,  2.1906252 ,  3.6247058 ,  3.6476884 ,  8.569651  ,\n",
+       "         0.8154066 ,  0.52646595,  8.347277  ,  5.3391485 ,  5.3391485 ,\n",
+       "         4.3533516 ,  2.2721968 ,  4.044897  , 12.670698  ,  3.6306577 ,\n",
+       "         1.7800876 ,  0.8172901 , 10.408458  , 10.408458  ,  8.752845  ,\n",
+       "         2.7025979 ,  1.8058288 ,  0.77591455,  4.6208324 ,  1.3864799 ,\n",
+       "         4.8308764 ,  5.2640085 ,  2.2747922 ,  2.1527889 ,  0.6709348 ,\n",
+       "         0.40966344,  0.76452506,  1.7523602 ,  9.632993  ,  0.71305096,\n",
+       "         1.2565391 ,  7.3918915 ,  1.9825102 ,  4.3520317 ,  4.191029  ,\n",
+       "         4.46019   ,  6.5468745 ,  1.7655234 ,  2.4890428 ,  0.13036194,\n",
+       "         4.1576633 ,  3.6138592 ,  2.5137105 ,  7.9432316 ,  1.4701631 ,\n",
+       "         4.3312316 ,  3.2743263 ,  6.1689467 ,  0.81546783,  2.3457444 ,\n",
+       "        14.765036  ,  2.7969723 ,  0.6444865 ,  5.056386  ,  6.6326447 ,\n",
+       "         3.3019314 ,  3.4695005 , 14.173468  ,  1.0817696 ,  1.9358197 ,\n",
+       "         6.57442   ,  0.21757841,  1.6629736 ,  2.837197  ,  8.087209  ,\n",
+       "         0.923576  ,  1.5801088 ,  3.2785625 ,  1.1708685 ,  5.65828   ,\n",
+       "         2.544188  ,  0.97468114,  0.85679036,  0.4686036 ,  1.2682633 ,\n",
+       "         1.0238816 ,  1.0111494 ,  3.2353783 ,  5.850848  ,  2.6748486 ,\n",
+       "         6.8615456 ,  1.0207653 ,  5.1171284 ,  4.7231007 ,  0.9388937 ,\n",
+       "         1.1121522 ,  0.19136462,  2.5782864 ,  6.664866  ,  1.035229  ,\n",
+       "         1.2018548 ,  3.5486271 ,  0.26484078,  0.5413486 , 10.383554  ,\n",
+       "         6.841315  ,  1.663987  ,  1.40886   ,  1.4120247 ,  6.9056168 ,\n",
+       "         1.4446397 ,  2.328224  ,  2.377081  ,  3.0902762 ,  1.1421281 ,\n",
+       "         1.1183867 ,  0.9663997 ,  0.72247726,  0.4770038 ,  9.449483  ,\n",
+       "         3.4366267 ,  1.8293209 ,  5.91921   ,  1.5030367 ,  1.8295302 ,\n",
+       "         1.9259436 ,  1.0454125 ,  1.1833336 ,  1.635451  ,  3.1804576 ,\n",
+       "         7.3813477 ,  0.15226473,  9.872404  ,  0.6245333 ,  2.2530851 ,\n",
+       "         3.5460145 ,  4.784728  ,  3.6828964 ,  1.7373091 ,  1.3222244 ,\n",
+       "         1.2164129 ,  3.2084877 ,  5.9633555 ,  8.61425   ,  4.7127013 ,\n",
+       "         1.0490998 ,  2.031085  ,  0.84674907,  1.4061344 ,  0.20833626,\n",
+       "         1.1506217 ,  8.719571  ,  0.5209326 ,  3.2316248 ,  4.3853264 ,\n",
+       "         4.2003636 ,  2.0493677 , 10.459623  , 10.027447  ,  5.8418946 ,\n",
+       "         5.8418946 ,  3.1374278 ,  3.4132574 ,  3.4001408 ,  6.9404526 ,\n",
+       "         0.6055962 ,  2.6735137 ,  5.0053363 ,  2.393575  ,  4.3127356 ,\n",
+       "         7.9282527 ,  4.0160623 ,  6.3807807 ,  4.9094033 ,  5.3358846 ,\n",
+       "         1.5355688 ,  2.814463  ,  0.20154344,  0.16425201,  2.7684138 ,\n",
+       "         0.79762113,  0.29586637,  8.217145  ,  2.5801165 ,  0.62110734,\n",
+       "         5.5564575 ,  0.03571094,  9.594197  ,  1.6323905 ,  6.1980906 ,\n",
+       "         5.9835644 , 10.406311  ,  1.9029803 ,  4.983942  ,  1.1461568 ,\n",
+       "         8.874212  ,  2.818826  ,  1.3390121 ,  1.8619214 ,  6.0745525 ,\n",
+       "         5.101248  ,  1.0643479 ,  1.7463428 , 13.184822  ,  6.307802  ,\n",
+       "         6.2804346 ,  1.8698411 ,  1.3450637 ,  5.679282  ,  3.7104957 ,\n",
+       "         1.4926912 ,  1.3450874 ,  1.6143674 ,  3.5736926 ,  4.833857  ,\n",
+       "         0.38555366,  1.7992418 ,  7.924101  ,  0.5311662 ,  1.0728621 ,\n",
+       "         6.104986  ,  0.41169518,  9.873746  ,  0.6301795 ,  0.6971753 ,\n",
+       "         0.433702  ,  1.6750252 ,  4.7702184 ,  1.1450185 ,  9.231825  ,\n",
+       "         0.65097904,  0.07083836,  7.7014275 ,  1.1763483 ,  3.9216068 ,\n",
+       "         0.7965546 ,  7.643338  ,  0.3307356 ,  1.5628933 ,  8.430889  ,\n",
+       "         2.0809162 ,  9.241798  ,  9.241798  ,  4.945677  ,  1.0078614 ,\n",
+       "         1.0361626 , 10.885583  , 12.564192  ,  0.33511108,  4.6200314 ,\n",
+       "         4.940873  ,  0.5206075 ,  0.07736349,  0.08582618,  1.9981915 ,\n",
+       "         1.7438189 ,  1.5340894 ,  0.7772387 ,  6.601632  ,  2.115645  ,\n",
+       "         8.213202  ,  5.2723393 , 11.72069   ,  0.9106035 ,  0.85000837,\n",
+       "        10.724954  ,  3.2172287 ,  2.224444  ,  4.3728848 ,  6.769486  ,\n",
+       "        12.659085  ,  6.2303095 ,  1.7800417 ,  0.41352934,  4.2141705 ,\n",
+       "         2.3804212 ,  1.9563856 ,  2.9715116 ,  1.8923243 ,  3.1873477 ,\n",
+       "         3.2488613 , 11.12932   ,  1.2553946 ,  0.72405314,  8.201361  ,\n",
+       "         8.201361  ,  2.594084  ,  4.2437234 ,  1.8843505 ,  1.5556631 ,\n",
+       "         9.879009  , 13.189886  ,  0.6889656 ,  4.8971453 ,  2.709153  ,\n",
+       "         5.6291595 ,  3.035018  ,  8.095402  ,  2.2264924 ,  6.9823604 ,\n",
+       "         8.809837  ,  2.8444104 ,  7.2972403 ,  4.9063    ,  8.416786  ,\n",
+       "         6.3838606 ,  3.103317  ,  3.3709526 ,  3.7451754 ,  1.5255952 ,\n",
+       "         6.1873636 , 18.791533  , 18.791533  ,  0.5587105 ,  4.3095994 ,\n",
+       "         6.199546  ,  0.7963446 ,  3.250815  ,  4.6577425 ,  3.8594632 ,\n",
+       "         0.39367265,  0.94611126,  0.6230297 ,  0.35022599,  3.6903803 ,\n",
+       "         2.337941  ,  5.383348  , 12.14523   ,  1.4398934 ,  2.1742728 ,\n",
+       "         1.6072241 ,  5.467194  ,  1.2596793 ,  2.1560652 ,  1.1517068 ,\n",
+       "         5.9963036 ,  1.9717857 ,  1.7716382 ,  1.9160262 ,  2.8552551 ,\n",
+       "         4.745581  ,  7.348962  ,  3.2277074 ,  2.984302  ,  2.2140386 ,\n",
+       "         1.8019764 ,  0.59824824,  1.8449849 ,  3.216473  ,  6.51493   ,\n",
+       "         3.411695  ,  7.4430933 ,  6.659884  ,  5.054224  ,  1.388572  ,\n",
+       "         4.7616067 ,  3.9080114 ,  4.5838494 , 20.273996  ,  3.7882788 ,\n",
+       "         6.1818533 ,  1.1102964 ,  1.5883152 ,  0.45917422,  8.478489  ,\n",
+       "         0.09718665,  2.6336462 ,  7.2664666 ,  2.434935  ,  2.8858519 ,\n",
+       "         0.71751714,  3.0329962 ,  6.130552  ,  0.860823  ,  0.25771728,\n",
+       "         0.42584717,  0.5142102 ,  0.8449509 ,  4.968134  ,  3.1147995 ,\n",
+       "         2.4746912 ,  2.0156538 ,  3.6191094 ,  1.2793202 ,  0.7103939 ,\n",
+       "         3.7820547 ,  6.9056473 ,  4.0495677 ,  1.7817774 ,  2.3426752 ,\n",
+       "         4.633561  ,  2.070319  ,  1.5363076 ,  0.95820147,  3.6127422 ,\n",
+       "         0.3730284 ,  0.28459108,  1.723181  ,  4.1297984 ,  2.3784215 ,\n",
+       "         2.6407814 ,  4.4255576 ,  3.8354557 ,  3.4539526 ,  0.68732715,\n",
+       "        12.028891  ,  2.09528   ,  3.4303656 ,  3.6795819 ,  2.233653  ,\n",
+       "         9.531067  ,  2.4739041 ,  1.3033855 ,  1.8244535 ,  2.7561784 ,\n",
+       "         3.35038   ,  2.4797575 ,  1.2056069 ,  1.42102   ,  3.5945454 ,\n",
+       "         1.5078974 ,  1.030959  ,  2.032822  ,  1.9084902 ,  0.71502614,\n",
+       "         0.24140915,  1.3988683 ,  2.2474515 ,  5.052353  ,  0.88052756,\n",
+       "         3.1064644 ,  5.4538717 ,  0.08182631,  2.2336638 ,  5.490027  ,\n",
+       "         3.8521888 ,  1.7020229 ,  0.31675732,  0.54662824,  0.5233591 ,\n",
+       "         7.528719  ,  0.5788549 ,  2.3658106 ,  7.823274  ,  9.688509  ,\n",
+       "         5.4737873 ,  2.0360384 ,  8.556991  ,  1.9897192 ,  1.9756216 ,\n",
+       "         4.789227  ,  2.9638944 ,  3.9432104 ,  4.5745883 ,  2.8481593 ,\n",
+       "         2.703546  ,  0.4868355 ,  0.81143165, 11.803428  ,  2.3433266 ,\n",
+       "         7.462632  ,  3.5330765 ,  1.769893  ,  3.4632206 ,  6.0739894 ,\n",
+       "         2.1918657 ,  3.4950104 ,  3.3037906 ,  1.5210805 ,  3.043605  ,\n",
+       "         4.5550523 ,  1.3057604 ,  2.3426685 ,  0.89967585,  1.4859152 ,\n",
+       "         0.45666224,  0.29178444,  7.5672245 ,  1.3531511 ,  1.6870949 ],\n",
+       "       [ 0.9909044 ,  1.5977436 ,  1.5915296 ,  2.5393262 ,  2.087789  ,\n",
+       "         4.243172  ,  7.001409  ,  9.290381  ,  0.91922265,  7.900055  ,\n",
+       "         7.7129283 ,  0.6525277 ,  6.4433904 ,  8.98685   ,  3.415953  ,\n",
+       "         4.779433  ,  4.0793686 ,  7.4854875 ,  1.1678308 ,  4.129944  ,\n",
+       "         3.0361595 ,  2.4230134 ,  1.9914376 ,  2.428689  ,  2.7493334 ,\n",
+       "         4.6773024 ,  3.6110651 ,  1.139311  ,  1.1700116 ,  1.7576461 ,\n",
+       "         4.4287186 ,  1.3367376 ,  1.6895905 ,  6.6807804 ,  3.783243  ,\n",
+       "         6.1245394 ,  3.9788265 , 13.16905   , 13.16905   , 15.664255  ,\n",
+       "        15.664255  ,  2.3494203 ,  7.6555758 ,  2.4532216 ,  0.5315145 ,\n",
+       "         8.000637  ,  3.0699728 ,  1.4240437 ,  3.2925067 ,  2.53016   ,\n",
+       "         5.606144  ,  1.4063253 ,  4.0349874 ,  5.277519  ,  9.044592  ,\n",
+       "         3.3025165 ,  6.7250423 ,  4.524832  ,  1.3821722 ,  6.007044  ,\n",
+       "         2.626573  ,  0.5769744 ,  8.237151  ,  8.070817  ,  1.7475601 ,\n",
+       "         1.1638703 ,  3.0113087 ,  2.7498362 ,  5.914818  ,  6.1232486 ,\n",
+       "         0.29810682,  5.1201816 ,  0.563086  ,  1.8486384 ,  4.001296  ,\n",
+       "         1.2235026 ,  1.8340818 ,  7.513959  ,  0.7324949 ,  2.7174852 ,\n",
+       "         5.1975183 ,  2.0456045 ,  4.820118  ,  5.1033993 ,  5.1648126 ,\n",
+       "         3.173502  ,  2.104412  ,  1.9142842 , 11.029547  ,  0.78433573,\n",
+       "         1.357436  ,  3.3496656 ,  1.1371782 ,  5.2250524 ,  2.2853363 ,\n",
+       "         1.2432598 ,  2.9474394 ,  2.2762651 , 11.131979  ,  0.03542077,\n",
+       "         0.09264842,  0.6235244 ,  3.4893827 ,  2.3070836 ,  0.25985214,\n",
+       "         0.22785841,  0.39597985,  1.5273579 ,  1.6996378 ,  2.936499  ,\n",
+       "         4.2797337 ,  1.6371739 ,  4.6433635 ,  0.43203235,  0.7139715 ,\n",
+       "         4.613933  ,  0.9789104 ,  0.0981968 ,  0.53859735,  0.4859575 ,\n",
+       "         6.3171396 ,  1.0921121 ,  1.5136721 ,  1.112587  ,  2.716591  ,\n",
+       "         2.309645  ,  0.57045245,  6.38959   ,  4.577489  ,  1.3076562 ,\n",
+       "         1.4818728 , 13.748591  ,  1.4261547 ,  4.716801  ,  2.5566208 ,\n",
+       "         3.2431536 ,  2.4402661 ,  0.07903078,  1.96319   ,  3.2732992 ,\n",
+       "         1.5368414 ,  0.45010495,  0.19129893, 16.928976  ,  0.5482865 ,\n",
+       "         8.09678   ,  2.225882  ,  3.1512907 ,  4.057416  ,  2.610217  ,\n",
+       "         2.1332395 ,  9.60669   ,  2.2393262 ,  2.651483  ,  3.3116841 ,\n",
+       "         1.6829383 ,  2.4401853 ,  1.5279691 ,  3.227448  ,  1.6379778 ,\n",
+       "         2.4299464 ,  2.075777  ,  3.1862235 ,  3.2390447 ,  2.329407  ,\n",
+       "         2.201795  ,  5.703568  ,  0.40667385,  0.87402445,  3.3419642 ,\n",
+       "         4.641601  ,  1.6191708 ,  4.8605385 ,  6.7923694 ,  4.4612546 ,\n",
+       "         0.7880722 ,  3.642695  ,  5.7544723 ,  2.404113  ,  6.683872  ,\n",
+       "         0.81219465,  1.7379894 ,  1.7376539 ,  5.8059955 ,  4.278271  ,\n",
+       "         1.2685912 ,  4.5402994 ,  5.0245523 ,  2.5448208 ,  1.2269168 ,\n",
+       "         1.2570435 ,  0.09639393,  0.41742703,  2.19767   , 11.426054  ,\n",
+       "         6.1925087 ,  1.6211317 ,  2.6454413 ,  4.8318114 ,  7.5347095 ,\n",
+       "         4.0129    ,  4.6609755 ,  3.0834131 ,  1.8212253 ,  0.15747103,\n",
+       "         0.34305257,  0.50059736,  0.20459786,  0.02735537,  0.47397   ,\n",
+       "         0.05234714,  0.190357  ,  0.5601471 ,  1.4498736 ,  5.3912153 ,\n",
+       "         3.1650052 ,  2.2476134 ,  1.4172924 ,  3.296812  ,  4.6089897 ,\n",
+       "         2.8611212 ,  5.402524  ,  4.947583  ,  1.0548209 ,  0.7901154 ,\n",
+       "         0.9943615 ,  3.0772898 ,  1.2716869 ,  2.432948  ,  3.8804119 ,\n",
+       "         1.2244211 ,  0.8237403 ,  4.9685516 ,  1.316479  ,  1.0661899 ,\n",
+       "         0.16147617,  6.15918   ,  2.6841154 ,  1.4046829 ,  1.5815251 ,\n",
+       "         4.5101395 ,  4.211768  ,  3.2184887 ,  2.2674942 , 11.533861  ,\n",
+       "         1.5308465 ,  1.5567675 ,  3.4679332 ,  0.9563234 ,  2.6597273 ,\n",
+       "         4.113006  ,  5.94164   ,  4.1099057 ,  0.7565336 ,  9.429806  ,\n",
+       "         2.4552286 ,  0.53369445,  3.3575032 ,  4.5981975 ,  0.05409329,\n",
+       "         2.4364839 ,  1.5215359 ,  2.9032164 ,  1.1249074 ,  1.1532617 ,\n",
+       "        19.157143  , 19.157143  ,  1.3512957 ,  0.65963423,  1.5522751 ,\n",
+       "         0.5923748 , 14.26093   ,  2.619348  ,  7.663063  ,  1.520335  ,\n",
+       "         1.7601604 ,  4.5279403 ,  5.6468053 ,  2.8692524 ,  1.6483278 ,\n",
+       "         5.082613  ,  3.348174  ,  7.963322  ,  5.7132883 ,  7.863322  ,\n",
+       "         1.2458255 ,  2.605769  , 11.989439  ,  0.9381594 ,  1.8971261 ,\n",
+       "         2.5842643 ,  2.3395038 ,  4.5372963 ,  2.8475537 ,  2.8704324 ,\n",
+       "         0.7754769 ,  1.4568441 ,  1.3712978 ,  2.1131537 ,  1.5345919 ,\n",
+       "         0.9531036 ,  0.7739705 ,  1.5121455 ,  9.828634  ,  7.4287105 ,\n",
+       "         2.9388795 ,  0.2816141 ,  2.3579755 ,  0.14984295,  0.24800135,\n",
+       "         0.15856981,  1.0296572 ,  2.5968904 ,  7.6706834 ,  0.50782156,\n",
+       "         8.095074  ,  8.709511  ,  0.73007673,  3.476412  ,  3.0747168 ,\n",
+       "         1.3265907 ,  0.31783798,  5.4608517 ,  1.749261  ,  1.7294059 ,\n",
+       "         1.438313  ,  3.280551  ,  0.24643764,  0.66403604,  1.1227971 ,\n",
+       "         3.8661366 ,  2.7161083 ,  3.560833  ,  4.763618  ,  1.1688248 ,\n",
+       "         2.1935544 ,  3.8671257 ,  2.203893  ,  1.477727  ,  2.7158074 ,\n",
+       "         6.5069942 ,  0.456299  ,  1.5456046 ,  4.563259  ,  4.4469385 ,\n",
+       "         5.397097  ,  5.397097  ,  2.1064    ,  5.4984164 ,  1.3417419 ,\n",
+       "         1.709095  ,  7.2107835 ,  1.4702858 ,  1.2010643 ,  3.3940632 ,\n",
+       "         1.6871938 ,  8.092438  ,  1.875149  ,  9.6689    ,  4.6120315 ,\n",
+       "         3.9952703 ,  1.4235352 ,  8.582107  ,  6.6970954 ,  7.282295  ,\n",
+       "        14.207127  ,  2.9826918 ,  1.5618287 ,  1.7103666 ,  1.6702276 ,\n",
+       "         3.8603537 ,  2.7703676 ,  2.6307878 ,  2.504591  , 11.882615  ,\n",
+       "         3.8029606 , 10.591273  ,  4.6216316 ,  3.2560668 ,  3.2578232 ,\n",
+       "         3.8841403 ,  8.381626  ,  6.4172707 ,  4.9437313 ,  2.496393  ,\n",
+       "         2.5616014 ,  5.484207  ,  3.86267   ,  8.52895   ,  1.520958  ,\n",
+       "         3.0663316 ,  5.64784   ,  0.93371737,  1.988599  ,  3.3644938 ,\n",
+       "         3.9448767 ,  8.905218  ,  1.6713282 ,  1.7212286 ,  2.0422182 ,\n",
+       "         3.4921372 ,  3.8627925 ,  0.26233172,  0.43868166,  3.3021283 ,\n",
+       "         3.651146  ,  3.651146  ,  2.4456723 ,  9.569137  ,  2.3459058 ,\n",
+       "         0.2361946 ,  2.2865353 , 16.966942  ,  9.5944605 ,  3.9430783 ,\n",
+       "         7.300621  ,  8.237884  ,  1.1408961 ,  3.106974  ,  5.3557086 ,\n",
+       "         4.113908  ,  0.73947203,  0.8736652 ,  2.8615105 ,  4.674794  ,\n",
+       "         1.346915  ,  0.6225802 ,  0.19296533,  6.3390613 ,  2.802343  ,\n",
+       "         3.9072082 ,  1.5721794 ,  4.805414  ,  0.9118719 ,  6.6454163 ,\n",
+       "         0.35046232,  8.147577  ,  5.0826526 ,  3.0514736 ,  1.8530624 ,\n",
+       "         1.573253  ,  2.814517  ,  3.2968137 ,  3.1585622 ,  2.970489  ,\n",
+       "         4.581018  ,  1.5554717 ,  0.6137824 ,  2.1317117 ,  5.246378  ,\n",
+       "         4.361018  ,  2.8805962 ,  2.4948945 ,  6.1967874 ,  0.5377769 ,\n",
+       "         0.37031844,  0.7325885 ,  1.9997089 ,  2.398744  ,  2.0033321 ,\n",
+       "         1.6943733 ,  5.4950047 ,  5.6807523 ,  2.5063794 ,  3.8039002 ,\n",
+       "        11.003864  ,  0.7521229 ,  7.85719   ,  1.0913641 ,  2.1949823 ,\n",
+       "         8.510656  ,  3.865408  ,  2.2440512 ,  4.94407   , 13.895223  ,\n",
+       "         0.26907384,  1.5775707 ,  2.8049283 ,  1.0583861 ,  1.3993121 ,\n",
+       "         2.1294248 ,  3.9265387 ,  3.4271214 ,  1.6736029 ,  2.3646955 ,\n",
+       "         0.47015515,  4.9279428 ,  3.7552726 ,  3.7249236 ,  1.6924706 ,\n",
+       "         0.331155  ,  2.1292431 ,  0.959191  ,  2.2490025 ,  9.622374  ,\n",
+       "         5.5982914 ,  4.0080676 ,  2.5344353 ,  2.7411196 ,  4.101541  ]],\n",
+       "      dtype=float32)
          • theta_tilde
            (chain, draw, theta_tilde_dim_0)
            float32
            0.6061655 1.8185308 ... -1.4317532
            array([[[ 0.6061655 ,  1.8185308 , -0.09561205, ..., -1.3342378 ,\n",
+       "          0.70666367,  0.04662064],\n",
+       "        [ 1.0919316 ,  0.7878127 ,  0.51768357, ..., -0.86000085,\n",
+       "          1.1659694 ,  1.070951  ],\n",
+       "        [-0.44649038, -1.1334856 , -0.08350085, ..., -0.79331404,\n",
+       "          0.80516696,  0.05294776],\n",
+       "        ...,\n",
+       "        [ 0.27960604, -0.6067694 ,  0.63655376, ...,  1.3932375 ,\n",
+       "         -0.6413138 , -0.09322856],\n",
+       "        [ 0.65896815,  1.2729853 , -0.16130266, ...,  0.538792  ,\n",
+       "          2.0496454 ,  0.28260455],\n",
+       "        [ 0.6073655 ,  0.49289384,  0.3575558 , ..., -0.46220857,\n",
+       "          0.6597414 ,  1.7932637 ]],\n",
+       "\n",
+       "       [[-0.05533975,  1.5325787 , -0.02127528, ..., -0.8900326 ,\n",
+       "         -0.39435595, -0.55493706],\n",
+       "        [ 1.0245105 ,  1.0371798 , -0.12512936, ...,  1.3573611 ,\n",
+       "          0.12283397,  0.33178735],\n",
+       "        [ 1.2065461 ,  0.14588034, -0.02969379, ...,  0.61200786,\n",
+       "         -0.50436723,  0.29426408],\n",
+       "        ...,\n",
+       "        [ 0.5336305 , -0.36422867,  0.607937  , ..., -0.33750185,\n",
+       "          0.75881875, -0.354092  ],\n",
+       "        [ 0.11554712, -0.7284259 ,  1.029543  , ..., -0.9233731 ,\n",
+       "          0.17342997,  0.47577518],\n",
+       "        [-0.5292633 ,  0.8391605 , -1.8189237 , ..., -0.04487276,\n",
+       "          1.7539048 , -1.4317532 ]]], dtype=float32)
        • created_at :
          2020-06-06T18:10:09.951965
          arviz_version :
          0.8.3
          inference_library :
          pyro
          inference_library_version :
          1.1.0

        \n", + "
      \n", + "
      \n", + "
      • \n", + " \n", + "
    • \n", + " \n", + " \n", + "
      \n", + "
      \n", + "
        \n", + "
        \n", + "\n", + "\n", + "Show/Hide data repr\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Show/Hide attributes\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
        xarray.Dataset
          • chain: 2
          • draw: 500
          • obs_dim_0: 8
          • chain
            (chain)
            int64
            0 1
            array([0, 1])
          • draw
            (draw)
            int64
            0 1 2 3 4 5 ... 495 496 497 498 499
            array([  0,   1,   2, ..., 497, 498, 499])
          • obs_dim_0
            (obs_dim_0)
            int64
            0 1 2 3 4 5 6 7
            array([0, 1, 2, 3, 4, 5, 6, 7])
          • obs
            (chain, draw, obs_dim_0)
            float32
            17.056637 13.765715 ... -33.684814
            array([[[ 17.056637  ,  13.765715  ,  28.1891    , ...,  11.842781  ,\n",
+       "          11.978273  ,   0.3044815 ],\n",
+       "        [ 21.057343  ,  -7.351166  ,  11.830295  , ...,  -5.1788282 ,\n",
+       "          11.68743   ,  22.859253  ],\n",
+       "        [  7.56715   ,   6.8423553 ,   5.7562637 , ...,   3.304874  ,\n",
+       "           5.348268  ,  16.123114  ],\n",
+       "        ...,\n",
+       "        [ 32.053898  ,  -9.957651  ,  -0.8588064 , ...,   7.980326  ,\n",
+       "           3.5410204 , -14.456598  ],\n",
+       "        [ 20.119667  ,  -7.6601057 ,  -2.9862726 , ...,  -0.10599685,\n",
+       "           1.5778246 , -16.948988  ],\n",
+       "        [ 25.463974  , -14.382548  ,  19.056568  , ..., -14.948351  ,\n",
+       "         -15.824716  ,   7.1326447 ]],\n",
+       "\n",
+       "       [[ -4.4520526 ,   3.186278  ,   4.188992  , ...,   1.6511054 ,\n",
+       "           8.9940605 ,   1.2203226 ],\n",
+       "        [ 23.332848  ,  13.669164  ,  -0.15677023, ...,  -6.408621  ,\n",
+       "          22.05013   ,   1.6746373 ],\n",
+       "        [ 29.850473  ,   0.35601377,  31.621748  , ...,  24.21464   ,\n",
+       "           8.574121  ,  -4.5073214 ],\n",
+       "        ...,\n",
+       "        [ 10.046104  ,  -9.08149   ,  24.914795  , ...,   0.8853679 ,\n",
+       "          -3.557653  ,   1.4098097 ],\n",
+       "        [-16.446758  ,  21.567833  ,  17.841633  , ...,  11.072165  ,\n",
+       "          19.724937  ,  -5.2535686 ],\n",
+       "        [ -4.726616  ,   4.298817  ,  10.874061  , ...,   0.40810192,\n",
+       "           5.026056  , -33.684814  ]]], dtype=float32)
        • created_at :
          2020-06-06T18:10:10.302667
          arviz_version :
          0.8.3
          inference_library :
          pyro
          inference_library_version :
          1.1.0

        \n", + "
      \n", + "
      \n", + "
      • \n", + " \n", + "
    • \n", + " \n", + " \n", + "
      \n", + "
      \n", + "
        \n", + "
        \n", + "\n", + "\n", + "Show/Hide data repr\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Show/Hide attributes\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
        xarray.Dataset
          • chain: 2
          • draw: 500
          • obs_dim_0: 8
          • chain
            (chain)
            int64
            0 1
            array([0, 1])
          • draw
            (draw)
            int64
            0 1 2 3 4 5 ... 495 496 497 498 499
            array([  0,   1,   2, ..., 497, 498, 499])
          • obs_dim_0
            (obs_dim_0)
            int64
            0 1 2 3 4 5 6 7
            array([0, 1, 2, 3, 4, 5, 6, 7])
          • obs
            (chain, draw, obs_dim_0)
            float32
            -4.4705133 -3.3008652 ... -4.252307
            array([[[-4.4705133, -3.3008652, -3.8681855, ..., -3.3328528,\n",
+       "         -3.6443205, -3.8491797],\n",
+       "        [-4.2400713, -3.22724  , -3.8871422, ..., -3.4007401,\n",
+       "         -3.404223 , -3.810225 ],\n",
+       "        [-5.572689 , -3.8407664, -3.70118  , ..., -3.3636737,\n",
+       "         -4.6304407, -4.0493298],\n",
+       "        ...,\n",
+       "        [-5.2727504, -4.190528 , -3.773724 , ..., -3.5955849,\n",
+       "         -6.145524 , -4.1133003],\n",
+       "        [-4.9237747, -3.276835 , -3.7557254, ..., -3.3465247,\n",
+       "         -3.9749088, -3.925208 ],\n",
+       "        [-5.274494 , -3.4969072, -3.71345  , ..., -3.333908 ,\n",
+       "         -4.6904016, -3.9407105]],\n",
+       "\n",
+       "       [[-4.806156 , -3.2322083, -3.8164787, ..., -3.3575134,\n",
+       "         -4.1154737, -3.89682  ],\n",
+       "        [-4.472113 , -3.2228694, -3.8738499, ..., -3.5833087,\n",
+       "         -3.8201694, -3.8420815],\n",
+       "        [-4.4680734, -3.228054 , -3.8706927, ..., -3.4967794,\n",
+       "         -3.9630103, -3.8464642],\n",
+       "        ...,\n",
+       "        [-5.3380413, -3.7239227, -3.7146387, ..., -3.3529468,\n",
+       "         -4.696886 , -4.111705 ],\n",
+       "        [-4.487238 , -3.2412963, -4.0651107, ..., -3.3996596,\n",
+       "         -3.6743426, -3.8204584],\n",
+       "        [-5.527108 , -3.2873592, -3.7158775, ..., -3.3171015,\n",
+       "         -3.709279 , -4.252307 ]]], dtype=float32)
        • created_at :
          2020-06-06T18:10:10.252687
          arviz_version :
          0.8.3
          inference_library :
          pyro
          inference_library_version :
          1.1.0

        \n", + "
      \n", + "
      \n", + "
      • \n", + " \n", + "
    • \n", + " \n", + " \n", + "
      \n", + "
      \n", + "
        \n", + "
        \n", + "\n", + "\n", + "Show/Hide data repr\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Show/Hide attributes\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
        xarray.Dataset
          • chain: 2
          • draw: 500
          • chain
            (chain)
            int64
            0 1
            array([0, 1])
          • draw
            (draw)
            int64
            0 1 2 3 4 5 ... 495 496 497 498 499
            array([  0,   1,   2, ..., 497, 498, 499])
          • diverging
            (chain, draw)
            bool
            False False False ... False False
            array([[False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False],\n",
+       "       [False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False]])
        • created_at :
          2020-06-06T18:10:10.186572
          arviz_version :
          0.8.3
          inference_library :
          pyro
          inference_library_version :
          1.1.0

        \n", + "
      \n", + "
      \n", + "
      • \n", + " \n", + "
    • \n", + " \n", + " \n", + "
      \n", + "
      \n", + "
        \n", + "
        \n", + "\n", + "\n", + "Show/Hide data repr\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Show/Hide attributes\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
        xarray.Dataset
          • chain: 1
          • draw: 500
          • theta_tilde_dim_0: 8
          • chain
            (chain)
            int64
            0
            array([0])
          • draw
            (draw)
            int64
            0 1 2 3 4 5 ... 495 496 497 498 499
            array([  0,   1,   2, ..., 497, 498, 499])
          • theta_tilde_dim_0
            (theta_tilde_dim_0)
            int64
            0 1 2 3 4 5 6 7
            array([0, 1, 2, 3, 4, 5, 6, 7])
          • mu
            (chain, draw)
            float32
            1.8310521 7.369206 ... -0.07544144
            array([[ 1.83105206e+00,  7.36920595e+00, -1.19266405e+01,\n",
+       "        -8.94674808e-02,  6.69769812e+00,  2.36118245e+00,\n",
+       "        -5.29538059e+00, -5.94624472e+00,  2.89671206e+00,\n",
+       "        -1.86073518e+00,  6.28713250e-01,  6.81693649e+00,\n",
+       "         7.71915436e+00,  7.51500702e+00,  6.99194527e+00,\n",
+       "         6.81107426e+00, -6.58409548e+00, -8.57430363e+00,\n",
+       "         8.83453548e-01,  6.32625198e+00, -2.42169404e+00,\n",
+       "         1.10239995e+00,  2.38961601e+00, -3.36049986e+00,\n",
+       "        -2.06537867e+00,  4.25744951e-01, -6.23024511e+00,\n",
+       "        -5.16256762e+00, -9.30563509e-01,  1.11181331e+00,\n",
+       "        -1.45188928e+00,  1.81846488e+00,  4.33571339e+00,\n",
+       "        -1.40784955e+00, -3.79042912e+00,  5.96538925e+00,\n",
+       "        -2.45449328e+00,  6.48085117e+00,  1.84746668e-01,\n",
+       "         9.50277996e+00,  1.59419513e+00, -7.20801449e+00,\n",
+       "        -5.50850153e+00, -2.64637923e+00, -1.01556320e+01,\n",
+       "         4.47114372e+00, -3.56763458e+00, -4.17484492e-01,\n",
+       "         2.15950990e+00,  1.10670459e+00, -4.33139133e+00,\n",
+       "         1.62531209e+00,  3.99615616e-01,  3.86136270e+00,\n",
+       "        -1.66640115e+00,  8.03124046e+00,  6.59306240e+00,\n",
+       "        -1.21368408e+01, -1.73939645e+00, -1.23819363e+00,\n",
+       "         7.29799569e-01, -2.25639677e+00, -2.72025943e+00,\n",
+       "         1.38648021e+00, -9.59429359e+00, -7.46370316e-01,\n",
+       "        -3.87315184e-01,  1.04348350e+00,  2.42776895e+00,\n",
+       "        -1.55527663e+00, -9.87819314e-01,  1.33469507e-01,\n",
+       "        -1.15047801e+00, -2.34605980e+00, -9.89660168e+00,\n",
+       "        -6.90545464e+00,  6.10778809e+00,  7.69460106e+00,\n",
+       "        -1.39867568e+00,  7.19076538e+00, -4.95351934e+00,\n",
+       "        -1.97004724e+00,  3.65625173e-01, -2.02283001e+00,\n",
+       "         9.23406219e+00,  8.91490579e-01, -2.46193695e+00,\n",
+       "        -1.28320193e+00,  3.88940901e-01,  6.97067678e-01,\n",
+       "         3.08503330e-01,  2.66331816e+00, -2.39172482e+00,\n",
+       "         1.02955704e+01,  4.05393171e+00,  4.36696005e+00,\n",
+       "        -2.88689470e+00, -2.59753019e-01,  1.83168995e+00,\n",
+       "        -5.59702826e+00, -5.45363486e-01,  4.74622154e+00,\n",
+       "         7.84159184e+00,  7.90888882e+00, -1.89434886e+00,\n",
+       "        -4.59797335e+00, -5.81234455e+00, -1.44287062e+00,\n",
+       "        -1.03176842e+01,  1.72845376e+00, -4.21865988e+00,\n",
+       "        -2.41528332e-01, -3.51904678e+00, -3.86472702e+00,\n",
+       "        -8.33917618e+00, -2.86969948e+00,  9.17288303e+00,\n",
+       "         9.08611679e+00, -7.91800928e+00,  6.45593882e+00,\n",
+       "        -5.40709019e+00,  1.03506794e+01, -3.90159106e+00,\n",
+       "        -5.60358238e+00,  3.02379179e+00,  5.49144506e+00,\n",
+       "         1.07119732e+01,  4.13225114e-01, -5.58654308e+00,\n",
+       "         2.38638973e+00, -2.92863822e+00, -9.32064176e-01,\n",
+       "        -5.47271252e+00,  1.19561327e+00,  1.26454878e+01,\n",
+       "        -7.34495687e+00,  8.26337337e+00, -1.26257706e+00,\n",
+       "         4.92097187e+00,  2.12870312e+00, -2.17358923e+00,\n",
+       "        -3.96695042e+00,  6.61581874e-01,  8.85077190e+00,\n",
+       "        -1.16093235e+01,  8.39181244e-02, -1.67453897e+00,\n",
+       "        -7.13899088e+00,  8.08746433e+00,  2.99629211e+00,\n",
+       "         3.96617866e+00, -4.35207939e+00,  3.00484705e+00,\n",
+       "         9.48771238e-01,  4.28298569e+00, -1.19457293e+00,\n",
+       "         2.45752597e+00, -2.69014406e+00, -5.79377270e+00,\n",
+       "        -8.44127178e+00, -2.69214272e+00,  6.68628025e+00,\n",
+       "         2.48715353e+00, -1.07650578e+00,  2.27392578e+00,\n",
+       "        -1.51223516e+00,  3.04859757e+00, -1.00483437e+01,\n",
+       "        -1.69506609e+00,  1.46986410e-01, -1.91448200e+00,\n",
+       "        -9.90330100e-01,  1.42848074e+00, -1.89951634e+00,\n",
+       "        -5.19370317e+00,  9.64916134e+00,  1.89337349e+00,\n",
+       "        -1.90529120e+00,  5.24329305e-01,  3.37787271e+00,\n",
+       "         1.24935162e+00, -3.98928595e+00,  1.06664634e+00,\n",
+       "        -2.32655501e+00,  3.89265680e+00, -5.16304207e+00,\n",
+       "         1.64964437e+00,  2.15085730e-01, -5.77951670e+00,\n",
+       "         5.68300152e+00, -4.04687309e+00, -1.45483112e+00,\n",
+       "         1.94204617e+00, -3.63496733e+00,  4.26030397e+00,\n",
+       "        -7.30518246e+00, -7.07409573e+00, -2.52731180e+00,\n",
+       "         5.80309927e-01, -3.93980503e+00,  5.90484381e+00,\n",
+       "        -7.15272367e-01, -1.15752685e+00,  4.38277149e+00,\n",
+       "        -2.20936552e-01,  2.76147127e+00, -2.84579486e-01,\n",
+       "        -2.42135954e+00,  5.01637459e+00, -1.76355731e+00,\n",
+       "        -8.02004623e+00,  2.04684105e+01,  9.89901161e+00,\n",
+       "        -3.71212363e+00, -8.04001808e-01,  1.52333260e+01,\n",
+       "        -2.71955824e+00, -3.53012061e+00,  5.94824219e+00,\n",
+       "         2.87859440e+00, -6.98200035e+00, -1.31001401e+00,\n",
+       "         3.54433417e-01,  7.41048288e+00, -8.93629551e+00,\n",
+       "        -6.54948139e+00,  1.54468322e+00, -5.30149841e+00,\n",
+       "         6.27821970e+00,  8.57540369e-01, -2.00245172e-01,\n",
+       "         8.83830130e-01, -3.87067080e-01, -5.35265970e+00,\n",
+       "         1.17853045e+00,  4.24543810e+00, -9.52724099e-01,\n",
+       "         1.05860481e+01,  2.68119621e+00,  3.16103578e-01,\n",
+       "         1.02651558e+01,  8.70150447e-01,  5.10061455e+00,\n",
+       "        -8.74845922e-01,  1.06127477e+00, -1.07085204e+00,\n",
+       "        -1.34933448e+00,  6.79564047e+00,  4.95760679e+00,\n",
+       "        -3.85755599e-01,  1.58938322e+01,  3.82075429e+00,\n",
+       "         8.43453884e+00, -1.87526774e+00,  2.94839168e+00,\n",
+       "        -5.34259796e+00, -1.37222636e+00,  1.56460536e+00,\n",
+       "        -1.82807553e+00, -3.93960208e-01, -2.65401840e+00,\n",
+       "        -8.08656883e+00, -2.96218753e+00, -8.40376091e+00,\n",
+       "         6.10560703e+00, -4.93199682e+00,  5.52653193e-01,\n",
+       "        -1.50517826e+01, -5.32551575e+00,  2.01356936e+00,\n",
+       "         8.41543484e+00,  1.65440190e+00, -2.26092815e+00,\n",
+       "        -2.43353653e+00, -5.91755152e+00, -4.91763067e+00,\n",
+       "         4.21300697e+00, -6.90289450e+00, -9.91271877e+00,\n",
+       "         3.00915432e+00,  1.23764837e+00, -2.11345482e+00,\n",
+       "        -1.16648540e-01,  4.33283758e+00, -2.26752186e+00,\n",
+       "        -1.16003406e+00,  3.81865025e+00, -3.34965992e+00,\n",
+       "        -8.85756016e+00,  5.39200974e+00,  1.10907221e+01,\n",
+       "         7.02498484e+00,  2.91726619e-01,  2.56532478e+00,\n",
+       "        -1.08591070e+01, -6.92411947e+00, -7.71028900e+00,\n",
+       "        -2.64707375e+00, -8.07448292e+00, -1.52650061e+01,\n",
+       "         5.43098688e+00, -3.78360510e+00,  5.23834610e+00,\n",
+       "        -9.35549498e-01,  7.61654472e+00, -1.33892345e+00,\n",
+       "         6.66223812e+00,  5.61331797e+00, -9.75874043e+00,\n",
+       "        -1.04858646e+01,  1.77814394e-01,  9.24438357e-01,\n",
+       "         2.80017948e+00, -4.24891996e+00,  7.73771095e+00,\n",
+       "        -9.24943149e-01, -1.84135449e+00, -6.52403879e+00,\n",
+       "        -1.71091104e+00, -3.77612948e+00,  6.72239494e+00,\n",
+       "        -2.76906681e+00, -2.94944644e+00,  1.83637130e+00,\n",
+       "         6.14798260e+00, -4.28675127e+00, -4.43767452e+00,\n",
+       "        -2.81541675e-01, -1.00980639e+00,  1.40076780e+00,\n",
+       "         2.90011930e+00, -2.16282582e+00, -1.81636465e+00,\n",
+       "         1.30194211e+00, -2.62355518e+00,  6.56885815e+00,\n",
+       "        -3.61158371e+00, -7.70886707e+00,  4.06852394e-01,\n",
+       "        -9.12503910e+00, -3.99473369e-01, -2.63025022e+00,\n",
+       "         6.63814926e+00, -6.40740514e-01, -9.53052616e+00,\n",
+       "         1.57841671e+00, -4.32275355e-01, -8.56765866e-01,\n",
+       "        -3.21094418e+00, -1.37355804e+01,  2.75833344e+00,\n",
+       "        -4.30860519e+00,  7.86440325e+00,  1.00282073e+00,\n",
+       "         2.19119835e+00, -1.11399639e+00, -6.83745909e+00,\n",
+       "         1.79168391e+00,  4.85345078e+00,  1.77308023e+00,\n",
+       "         3.60162592e+00,  4.91117096e+00,  3.42620850e+00,\n",
+       "         1.86812174e+00,  4.39406633e-01,  4.64404249e+00,\n",
+       "         5.62924576e+00,  7.49305201e+00, -5.01564980e-01,\n",
+       "         7.47700691e-01, -2.45133638e+00, -4.43825531e+00,\n",
+       "         2.54469180e+00, -4.52198124e+00,  1.93659878e+00,\n",
+       "         5.14554024e+00, -1.54210615e+00,  1.13418598e+01,\n",
+       "        -2.53370434e-01,  9.94487667e+00, -7.87288380e+00,\n",
+       "         1.02964675e+00, -3.00911784e+00, -5.36752272e+00,\n",
+       "        -2.14944553e+00,  1.41393316e+00,  2.45506263e+00,\n",
+       "        -1.77195096e+00, -2.79614496e+00, -1.34936082e+00,\n",
+       "        -1.16544704e+01, -1.95253313e+00,  8.18402481e+00,\n",
+       "         7.21612549e+00, -4.91288090e+00,  2.19248319e+00,\n",
+       "         9.97444451e-01,  2.07281208e+00, -3.99678826e+00,\n",
+       "         3.54919732e-01,  4.24775451e-01, -9.11628842e-01,\n",
+       "         6.72282791e+00,  4.29300356e+00,  3.48318577e+00,\n",
+       "        -1.90434575e+00, -3.69519830e+00, -5.08857298e+00,\n",
+       "         6.64628863e-01,  1.08672094e+00, -4.15021467e+00,\n",
+       "         5.20118999e+00, -4.28416538e+00,  6.84449625e+00,\n",
+       "         9.84103584e+00,  9.43293095e+00, -6.22304058e+00,\n",
+       "        -8.38033295e+00,  2.60023832e+00,  2.90164185e+00,\n",
+       "        -9.26851559e+00, -5.69589674e-01,  4.89514112e+00,\n",
+       "         2.62224793e+00,  6.37408924e+00, -9.53890514e+00,\n",
+       "         3.70149899e+00,  7.25612068e+00,  4.77210331e+00,\n",
+       "        -5.49839735e+00,  3.61553311e-01,  1.88503504e+00,\n",
+       "         1.35543537e+00,  5.75249720e+00,  6.40857279e-01,\n",
+       "         2.82977223e+00,  1.47245991e+00, -2.95132542e+00,\n",
+       "         6.06460047e+00,  5.75195503e+00, -4.99901915e+00,\n",
+       "        -3.38064694e+00, -8.64464951e+00, -1.02621114e+00,\n",
+       "        -4.99272537e+00,  8.79884422e-01, -2.89354038e+00,\n",
+       "         7.75743103e+00, -2.95966125e+00,  4.15148163e+00,\n",
+       "        -4.49703503e+00,  2.09148836e+00,  1.21093464e+00,\n",
+       "        -2.56159282e+00, -4.02910739e-01,  3.59068131e+00,\n",
+       "        -1.49127707e-01, -1.03146482e+00, -4.02073812e+00,\n",
+       "         6.10921812e+00, -1.11715519e+00, -1.03993597e+01,\n",
+       "         6.32133198e+00,  9.38441157e-02, -5.48374295e-01,\n",
+       "         3.23091269e+00,  2.65178537e+00,  3.63729024e+00,\n",
+       "         5.70872593e+00,  3.31697512e+00,  8.44650936e+00,\n",
+       "         7.31386691e-02,  3.26802731e+00,  2.44168830e+00,\n",
+       "        -7.86440313e-01, -8.20612526e+00, -4.75589561e+00,\n",
+       "        -2.73538065e+00,  2.09568596e+00,  4.75485802e+00,\n",
+       "         1.26565361e+00,  5.04449546e-01, -4.07291603e+00,\n",
+       "         5.41869581e-01,  4.77750123e-01, -1.77831125e+00,\n",
+       "        -2.42855740e+00,  5.80212212e+00,  2.78317261e+00,\n",
+       "        -8.38098049e-01, -1.13215065e+01,  1.04781361e+01,\n",
+       "        -1.98796690e-02, -8.12773609e+00,  3.65236521e+00,\n",
+       "         4.78217268e+00,  5.68206692e+00,  1.17952776e+00,\n",
+       "         3.48964882e+00, -7.54414424e-02]], dtype=float32)
          • tau
            (chain, draw)
            float32
            233.58215 994.4366 ... 27.182827
            array([[2.33582153e+02, 9.94436584e+02, 2.88996196e+00, 4.46266115e-01,\n",
+       "        4.96314669e+00, 5.80443611e+01, 6.78751850e+00, 1.49578667e+00,\n",
+       "        7.38433151e+01, 4.20371771e+00, 3.13775492e+00, 8.71614647e+00,\n",
+       "        3.26564932e+00, 4.71219480e-01, 3.88621855e+00, 5.17096996e+00,\n",
+       "        6.44069195e+00, 2.11158514e+00, 8.10319036e-02, 4.32135868e+00,\n",
+       "        3.56079245e+00, 7.56645775e+00, 3.47996688e+00, 8.96701574e-01,\n",
+       "        8.81987810e-01, 6.37050915e+00, 1.29537487e+00, 2.42891556e+02,\n",
+       "        1.45000434e+00, 3.44764090e+00, 4.36339569e+00, 6.93003559e+00,\n",
+       "        1.21201019e+01, 1.22789011e+01, 6.61703014e+00, 3.75274944e+00,\n",
+       "        4.36765718e+00, 1.34682770e+01, 4.79594326e+00, 6.00994720e+01,\n",
+       "        2.38157487e+00, 8.64460068e+01, 1.70366192e+00, 3.13302803e+00,\n",
+       "        1.08464298e+01, 1.89743690e+01, 1.00153017e+01, 4.93525600e+00,\n",
+       "        2.10727382e+00, 2.61057854e+00, 4.94686413e+00, 2.40696449e+01,\n",
+       "        2.96104956e+00, 1.18571396e+01, 2.97007084e+01, 6.49375916e+00,\n",
+       "        5.76199579e+00, 9.28328037e+00, 2.05416012e+00, 4.67196894e+00,\n",
+       "        1.94539279e-01, 1.69849586e+00, 4.11854172e+00, 9.30481796e+01,\n",
+       "        1.30114019e+00, 6.92282200e+00, 2.10049458e+01, 1.03697910e+01,\n",
+       "        1.64222889e+01, 7.37519503e-01, 8.64608002e+00, 1.58246374e+00,\n",
+       "        1.15422897e+01, 1.25697498e+01, 2.78301773e+01, 2.34611492e+01,\n",
+       "        4.61381073e+01, 8.49647427e+00, 2.38395095e+00, 3.06760292e+01,\n",
+       "        8.38161111e-01, 8.70039749e+00, 3.20347100e-01, 2.86008606e+01,\n",
+       "        6.00329304e+00, 1.99586225e+00, 1.01666679e+01, 8.67040217e-01,\n",
+       "        1.96274054e+00, 8.22034546e+02, 3.25686569e+01, 7.47367191e+00,\n",
+       "        5.82090683e+01, 6.80117130e-01, 1.32837164e+00, 2.87984238e+01,\n",
+       "        2.78351212e+00, 6.56047773e+00, 7.62254834e-01, 1.19701965e+02,\n",
+       "        3.16393661e+00, 9.53789806e+00, 1.14697514e+01, 9.03164291e+00,\n",
+       "        1.99453759e+00, 5.68666697e+00, 1.65687599e+01, 1.67806506e-01,\n",
+       "        7.41473317e-01, 3.13937008e-01, 2.65185714e+00, 3.46124039e+01,\n",
+       "        1.43749255e+03, 4.64936829e+00, 1.42269874e+00, 3.32906990e+01,\n",
+       "        1.39783907e+00, 1.89604580e+00, 6.28229370e+01, 4.18284950e+01,\n",
+       "        7.72986352e-01, 2.69628406e+00, 2.78242207e+00, 2.73950160e-01,\n",
+       "        5.61358929e+00, 3.10696411e+00, 4.68212414e+00, 1.69084892e-01,\n",
+       "        1.76831853e+00, 3.58425570e+00, 1.66552944e+01, 6.66814518e+00,\n",
+       "        2.22552757e+01, 2.18585186e+01, 4.84984636e+00, 2.61942062e+01,\n",
+       "        6.71889210e+00, 3.07793331e+01, 7.65414143e+00, 6.03538275e+00,\n",
+       "        1.08152752e+01, 1.53035221e+01, 1.82176857e+01, 5.10415316e+00,\n",
+       "        2.33483219e+01, 9.00594652e-01, 1.42888498e+01, 1.11383905e+01,\n",
+       "        2.80002928e+00, 1.97191668e+00, 9.47514629e+00, 1.59550037e+01,\n",
+       "        1.24129760e+00, 3.00389910e+00, 7.33918142e+00, 2.03434730e+00,\n",
+       "        1.40971959e+00, 2.60195613e+00, 5.19892263e+00, 4.85553789e+00,\n",
+       "        4.01490164e+00, 5.50108337e+00, 1.95370235e+01, 3.57205272e+00,\n",
+       "        3.22123230e-01, 3.18224182e+01, 5.46338511e+00, 3.92666698e+00,\n",
+       "        1.19204788e+01, 3.34672689e+00, 1.28777447e+01, 7.79884398e-01,\n",
+       "        6.61139846e-01, 1.53919148e+00, 2.90023565e+00, 2.00248814e+00,\n",
+       "        5.53505516e+00, 6.83246183e+00, 4.38924694e+00, 8.34967804e+00,\n",
+       "        5.10579491e+00, 1.58182859e+01, 1.78390675e+01, 5.87143898e+00,\n",
+       "        8.79881096e+00, 2.89170361e+00, 1.12620888e+01, 1.57768428e+00,\n",
+       "        2.32329521e+01, 5.88128757e+00, 3.41677904e+00, 7.61884537e+01,\n",
+       "        5.26621222e-01, 2.92264342e+00, 1.46880138e+00, 1.58016682e+00,\n",
+       "        3.81652985e+01, 2.46611953e+00, 4.79778290e+00, 1.88320708e+00,\n",
+       "        1.90092564e+00, 7.12588310e-01, 9.07944775e+00, 7.06309557e+00,\n",
+       "        2.61442447e+00, 1.78211820e+00, 3.95346403e+00, 2.53750610e+00,\n",
+       "        8.14840674e-01, 2.00210166e+00, 2.62024078e+01, 5.12925744e-01,\n",
+       "        6.95824099e+00, 2.35959935e+00, 1.71262836e+00, 5.10069351e+01,\n",
+       "        3.19263029e+00, 1.15136087e-01, 1.57397881e+01, 9.10933018e-01,\n",
+       "        2.54393845e+01, 1.22716546e+00, 6.51532364e+00, 2.91248016e+01,\n",
+       "        6.27013743e-02, 7.57493925e+00, 1.00060427e+00, 1.94976521e+00,\n",
+       "        1.05164957e+01, 1.20389929e+01, 7.51705074e+00, 1.34320679e+02,\n",
+       "        4.60176182e+00, 5.06012535e+00, 3.81312418e+00, 3.13340473e+00,\n",
+       "        1.12241850e+01, 6.68661475e-01, 9.63961029e+00, 1.80393448e+01,\n",
+       "        1.27726388e+00, 8.14500600e-02, 1.34037886e+01, 4.43923569e+00,\n",
+       "        9.86487961e+00, 4.39872217e+00, 4.16832018e+00, 1.09472620e+00,\n",
+       "        2.91746311e+01, 1.49736700e+01, 9.17174101e-01, 1.25601950e+01,\n",
+       "        5.54890394e-01, 5.42279053e+00, 1.89136982e+00, 3.33140159e+00,\n",
+       "        5.18533659e+00, 8.20474243e+00, 3.74159360e+00, 6.61919546e+00,\n",
+       "        9.13305569e+00, 1.16696537e+00, 6.32780876e+01, 4.78538799e+00,\n",
+       "        9.15983295e+00, 1.19233716e+00, 4.15419197e+00, 3.01625061e+00,\n",
+       "        1.33225393e+01, 8.89955699e-01, 3.94315934e+00, 1.25022098e-01,\n",
+       "        1.56112921e+00, 2.40924430e+00, 5.45164061e+00, 3.19068031e+01,\n",
+       "        2.74245787e+00, 5.39334259e+01, 2.68976002e+01, 3.05201454e+01,\n",
+       "        1.01681805e+00, 3.24505115e+00, 3.51270258e-01, 3.39455485e+00,\n",
+       "        1.03448999e+00, 6.52295971e+00, 8.54770851e+00, 3.17444348e+00,\n",
+       "        1.41029816e+01, 8.06216145e+00, 4.02719927e+00, 8.40551949e+00,\n",
+       "        7.55664349e+00, 5.41498232e+00, 2.03295345e+01, 1.33403766e+00,\n",
+       "        3.10369968e+00, 9.22239971e+00, 1.33640230e+00, 3.04194498e+00,\n",
+       "        1.49802998e-01, 7.67462845e+01, 1.00229049e+00, 9.41823196e+00,\n",
+       "        2.40813103e+01, 5.96192837e-01, 1.24422464e+01, 6.29803009e+01,\n",
+       "        9.61070156e+00, 3.14717960e+00, 3.35008736e+01, 1.62203751e+02,\n",
+       "        4.78771782e+00, 9.57639408e+00, 2.18938899e+00, 6.93462944e+00,\n",
+       "        1.47582645e+01, 1.59039507e+01, 3.55825844e+01, 1.57970297e+00,\n",
+       "        6.86061192e+00, 1.46212173e+00, 1.21917558e+00, 5.43149567e+00,\n",
+       "        1.26152265e+00, 2.14529729e+00, 3.60484076e+00, 3.20548248e+00,\n",
+       "        8.68775845e+00, 4.69007778e+00, 6.97661924e+00, 2.86727786e+00,\n",
+       "        6.55940008e+00, 3.04591227e+00, 6.40091038e+00, 7.69913483e+01,\n",
+       "        1.76605213e+00, 1.69695389e+00, 1.49314392e+02, 1.74520826e+00,\n",
+       "        2.10937786e+00, 1.85648233e-01, 7.94631481e+00, 4.20290565e+00,\n",
+       "        1.58578515e+00, 2.86422396e+00, 2.78503656e+00, 1.90926838e+01,\n",
+       "        2.03964019e+00, 2.16318626e+01, 9.85763626e+01, 8.25113773e+00,\n",
+       "        7.35772908e-01, 4.36113882e+00, 1.37380188e+02, 4.84867334e+00,\n",
+       "        1.83547437e+00, 7.29557495e+01, 1.18845024e+01, 6.21232643e+01,\n",
+       "        8.89026070e+00, 8.67310905e+00, 9.10755172e-02, 4.66671085e+00,\n",
+       "        7.80379639e+01, 4.44808617e+01, 1.78106499e+01, 1.36136341e+01,\n",
+       "        1.11453638e+01, 6.47962141e+00, 3.14819312e+00, 3.80757451e+00,\n",
+       "        6.99266315e-01, 5.32106857e+01, 2.26561832e+00, 1.17912173e+00,\n",
+       "        1.61539803e+01, 1.64006214e+01, 6.56036139e+00, 2.02290916e+01,\n",
+       "        1.35110483e+01, 1.61717999e+00, 4.25558233e+00, 1.24771166e+00,\n",
+       "        8.42467189e-01, 3.71366239e+00, 1.12391367e+01, 7.85699415e+00,\n",
+       "        1.17313776e+01, 2.07943416e+00, 1.07938728e+01, 1.71249676e+01,\n",
+       "        4.25358963e+00, 3.37078810e+00, 2.89949465e+00, 6.30591965e+01,\n",
+       "        8.41671944e-01, 2.24199390e+00, 2.29184461e+00, 1.12583256e+01,\n",
+       "        1.59082878e+00, 1.61483455e+00, 8.51412296e+00, 2.94279718e+00,\n",
+       "        1.55814886e+00, 1.94127309e+00, 3.83025789e+00, 7.02682590e+00,\n",
+       "        2.06390142e+00, 1.34827960e+00, 2.81168457e+02, 6.22805953e-01,\n",
+       "        8.24851453e-01, 2.11877942e-01, 3.92178574e+01, 9.66570312e+02,\n",
+       "        3.94462729e+00, 7.99840927e-01, 1.70189846e+00, 1.00781441e+01,\n",
+       "        2.87433743e-01, 1.20128322e+00, 2.29061928e+01, 3.59614491e+00,\n",
+       "        4.05278158e+00, 1.58789263e+01, 1.11763752e+00, 1.74107971e+01,\n",
+       "        1.57513487e+00, 1.86496060e-02, 7.02734985e+01, 3.25848503e+01,\n",
+       "        3.71071362e+00, 1.89383469e+01, 4.06730556e+00, 1.25093555e+00,\n",
+       "        1.31658208e+00, 6.87853718e+00, 4.67559671e+00, 2.59621410e+01,\n",
+       "        2.93781424e+00, 8.28132534e+00, 2.93957710e+00, 2.71064987e+01,\n",
+       "        1.96016669e-01, 7.85356760e-01, 3.60274577e+00, 2.52142549e+00,\n",
+       "        6.00418663e+00, 1.30085669e+01, 4.41197300e+00, 4.23312283e+00,\n",
+       "        1.43678570e+00, 9.44298446e-01, 1.23984262e-01, 4.08745956e+01,\n",
+       "        3.59652996e-01, 4.51054305e-01, 2.02281017e+01, 5.19320641e+01,\n",
+       "        2.71144605e+00, 2.10441933e+01, 2.76381893e+01, 2.75218344e+00,\n",
+       "        1.07174101e+01, 1.65728331e+00, 1.40117636e+01, 5.70923004e+01,\n",
+       "        2.04354548e+00, 7.14082837e-01, 1.98984873e+00, 2.52286911e+01,\n",
+       "        2.85708785e+00, 8.33650780e+00, 3.08708382e+00, 4.20598221e+00,\n",
+       "        3.83560791e+01, 6.60561275e+00, 3.31780434e+00, 1.28404593e+00,\n",
+       "        1.66767464e+01, 6.89008808e+00, 5.83668041e+00, 1.42359662e+00,\n",
+       "        6.85395598e-02, 9.49191093e-01, 1.09593658e+01, 1.75060701e+00,\n",
+       "        1.01909657e+01, 4.41580963e+00, 1.00245590e+01, 8.76809508e-02,\n",
+       "        5.11244965e+00, 3.84632111e+00, 7.63232660e+00, 4.04119635e+00,\n",
+       "        2.86264753e+00, 5.07223177e+00, 9.76756382e+00, 2.71828270e+01]],\n",
+       "      dtype=float32)
          • theta_tilde
            (chain, draw, theta_tilde_dim_0)
            float32
            0.6022393 ... -0.31073767
            array([[[ 0.6022393 , -0.39827886,  0.19862896, ..., -0.95424646,\n",
+       "         -1.1647658 ,  0.3821102 ],\n",
+       "        [-1.497994  ,  0.74775034, -0.78757095, ..., -1.6560527 ,\n",
+       "         -1.613713  , -0.46137986],\n",
+       "        [-0.16146933,  0.2763805 , -0.8783446 , ...,  0.56389153,\n",
+       "         -0.01581344, -0.8957549 ],\n",
+       "        ...,\n",
+       "        [-1.1428025 , -1.1540538 , -1.0932158 , ...,  1.6082281 ,\n",
+       "         -2.009276  , -0.5227252 ],\n",
+       "        [ 1.8399812 ,  0.33280653,  0.45785332, ...,  1.834717  ,\n",
+       "          0.8924277 ,  1.2369446 ],\n",
+       "        [-0.14975677,  0.8303282 , -0.00824529, ..., -1.6754906 ,\n",
+       "          0.26279444, -0.31073767]]], dtype=float32)
        • created_at :
          2020-06-06T18:10:10.351586
          arviz_version :
          0.8.3
          inference_library :
          pyro
          inference_library_version :
          1.1.0

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        xarray.Dataset
          • chain: 1
          • draw: 500
          • obs_dim_0: 8
          • chain
            (chain)
            int64
            0
            array([0])
          • draw
            (draw)
            int64
            0 1 2 3 4 5 ... 495 496 497 498 499
            array([  0,   1,   2, ..., 497, 498, 499])
          • obs_dim_0
            (obs_dim_0)
            int64
            0 1 2 3 4 5 6 7
            array([0, 1, 2, 3, 4, 5, 6, 7])
          • obs
            (chain, draw, obs_dim_0)
            float32
            152.4126 -104.43863 ... -19.538307
            array([[[ 1.5241260e+02, -1.0443863e+02,  5.3149067e+01, ...,\n",
+       "         -2.1432968e+02, -2.7346640e+02,  9.8980553e+01],\n",
+       "        [-1.4595604e+03,  7.5740137e+02, -7.9331067e+02, ...,\n",
+       "         -1.6279233e+03, -1.6046724e+03, -4.5491840e+02],\n",
+       "        [-1.2688635e+01, -1.5242083e+01,  6.5625114e+00, ...,\n",
+       "          7.7777863e-01,  1.3528487e+01, -4.4868240e+01],\n",
+       "        ...,\n",
+       "        [ 6.5915680e+00, -8.9369183e+00, -1.1899769e+01, ...,\n",
+       "         -1.4138508e-01, -2.4801119e+01, -1.1125523e+00],\n",
+       "        [ 1.1234647e+01,  9.1552639e+00,  1.3359819e+01, ...,\n",
+       "          6.3331509e+00, -6.9926414e+00,  2.9034739e+01],\n",
+       "        [ 3.9082799e+00,  2.4938337e+01,  1.8535004e+01, ...,\n",
+       "         -4.8968292e+01,  1.1919480e+01, -1.9538307e+01]]], dtype=float32)
        • created_at :
          2020-06-06T18:10:10.400546
          arviz_version :
          0.8.3
          inference_library :
          pyro
          inference_library_version :
          1.1.0

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        xarray.Dataset
          • obs_dim_0: 8
          • obs_dim_0
            (obs_dim_0)
            int64
            0 1 2 3 4 5 6 7
            array([0, 1, 2, 3, 4, 5, 6, 7])
          • obs
            (obs_dim_0)
            float32
            28.0 8.0 -3.0 7.0 ... 1.0 18.0 12.0
            array([28.,  8., -3.,  7., -1.,  1., 18., 12.], dtype=float32)
        • created_at :
          2020-06-06T18:10:10.445994
          arviz_version :
          0.8.3
          inference_library :
          pyro
          inference_library_version :
          1.1.0

        \n", + "
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    \n", + " " + ], +======= + "execution_count": 13, "metadata": { "ExecuteTime": { - "end_time": "2020-06-05T06:47:16.996019Z", - "start_time": "2020-06-05T06:47:09.288094Z" + "end_time": "2020-06-05T06:48:09.602373Z", + "start_time": "2020-06-05T06:47:57.349110Z" } }, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Auto-assigning NUTS sampler...\n", - "Initializing NUTS using jitter+adapt_diag...\n", - "Multiprocess sampling (2 chains in 4 jobs)\n", - "NUTS: [theta_tilde, tau, mu]\n", - "Sampling 2 chains, 0 divergences: 100%|██████████| 2000/2000 [00:00<00:00, 2065.44draws/s]\n", - "100%|██████████| 1000/1000 [00:00<00:00, 1713.18it/s]\n" - ] - }, { "data": { +>>>>>>> master "text/plain": [ "Inference data with groups:\n", "\t> posterior\n", @@ -2663,113 +19769,1598 @@ "\t> observed_data" ] }, - "execution_count": 11, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "with pm.Model() as model:\n", - " mu = pm.Normal(\"mu\", mu=0, sd=5)\n", - " tau = pm.HalfCauchy(\"tau\", beta=5)\n", - " theta_tilde = pm.Normal(\"theta_tilde\", mu=0, sd=1, shape=eight_school_data[\"J\"])\n", - " theta = pm.Deterministic(\"theta\", mu + tau * theta_tilde)\n", - " pm.Normal(\n", - " \"obs\", mu=theta, sd=eight_school_data[\"sigma\"], observed=eight_school_data[\"y\"]\n", - " )\n", + "import pyro\n", + "import pyro.distributions as dist\n", + "import torch\n", + "from pyro.infer import MCMC, NUTS, Predictive\n", "\n", - " trace = pm.sample(draws, chains=chains)\n", - " prior = pm.sample_prior_predictive()\n", - " posterior_predictive = pm.sample_posterior_predictive(trace)\n", + "pyro.enable_validation(True)\n", + "pyro.set_rng_seed(0)\n", "\n", - " pm_data = az.from_pymc3(\n", - " trace=trace,\n", - " prior=prior,\n", - " posterior_predictive=posterior_predictive,\n", - " coords={\"school\": np.arange(eight_school_data[\"J\"])},\n", - " dims={\"theta\": [\"school\"], \"theta_tilde\": [\"school\"]},\n", - " )\n", - "pm_data" + "draws = 500\n", + "chains = 2\n", + "eight_school_data = {\n", + " \"J\": 8,\n", + " \"y\": torch.tensor([28.0, 8.0, -3.0, 7.0, -1.0, 1.0, 18.0, 12.0]),\n", + " \"sigma\": torch.tensor([15.0, 10.0, 16.0, 11.0, 9.0, 11.0, 10.0, 18.0]),\n", + "}\n", + "\n", + "\n", + "def model(J, sigma, y=None):\n", + " mu = pyro.sample(\"mu\", dist.Normal(0, 5))\n", + " tau = pyro.sample(\"tau\", dist.HalfCauchy(5))\n", + " with pyro.plate(\"J\", J):\n", + " theta_tilde = pyro.sample(\"theta_tilde\", dist.Normal(0, 1))\n", + " theta = mu + tau * theta_tilde\n", + " return pyro.sample(\"obs\", dist.Normal(theta, sigma), obs=y)\n", + "\n", + "\n", + "nuts_kernel = NUTS(model, jit_compile=True, ignore_jit_warnings=True)\n", + "mcmc = MCMC(\n", + " nuts_kernel,\n", + " num_samples=draws,\n", + " warmup_steps=draws,\n", + " num_chains=chains,\n", + " disable_progbar=True,\n", + ")\n", + "mcmc.run(**eight_school_data)\n", + "posterior_samples = mcmc.get_samples()\n", + "posterior_predictive = Predictive(model, posterior_samples).get_samples(\n", + " eight_school_data[\"J\"], eight_school_data[\"sigma\"]\n", + ")\n", + "prior = Predictive(model, num_samples=500).get_samples(\n", + " eight_school_data[\"J\"], eight_school_data[\"sigma\"]\n", + ")\n", + "\n", + "pyro_data = az.from_pyro(\n", + " mcmc,\n", + " prior=prior,\n", + " posterior_predictive=posterior_predictive,\n", + " coords={\"school\": np.arange(eight_school_data[\"J\"])},\n", + " dims={\"theta\": [\"school\"]},\n", + ")\n", + "pyro_data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## From PyStan" + "## From emcee" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 14, "metadata": { "ExecuteTime": { - "end_time": "2020-06-05T06:47:57.347055Z", - "start_time": "2020-06-05T06:47:16.997392Z" + "end_time": "2020-06-05T06:48:11.402298Z", + "start_time": "2020-06-05T06:48:09.604316Z" } }, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:pystan:COMPILING THE C++ CODE FOR MODEL anon_model_9d743830ec4a29fb58eb4660d4b9417f NOW.\n" - ] - }, { "data": { + "text/html": [ + "\n", + "
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    arviz.InferenceData
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        xarray.Dataset
          • chain: 40
          • draw: 1500
          • chain
            (chain)
            int64
            0 1 2 3 4 5 6 ... 34 35 36 37 38 39
            array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
+       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
+       "       36, 37, 38, 39])
          • draw
            (draw)
            int64
            0 1 2 3 4 ... 1496 1497 1498 1499
            array([   0,    1,    2, ..., 1497, 1498, 1499])
          • mu
            (chain, draw)
            float64
            0.9284 0.7497 ... -1.109 -1.109
            array([[ 0.92841914,  0.74969441,  0.74969441, ...,  4.72566447,\n",
+       "         4.72221834,  4.72221834],\n",
+       "       [ 0.04751325,  0.04751325,  0.04751325, ...,  6.30209019,\n",
+       "         6.30209019,  6.30209019],\n",
+       "       [-2.55298982, -2.55298982, -2.55298982, ...,  9.28990943,\n",
+       "         9.28990943,  7.96917791],\n",
+       "       ...,\n",
+       "       [ 0.75359111,  0.75359111,  0.75359111, ...,  0.62855896,\n",
+       "         0.62855896,  0.62855896],\n",
+       "       [-1.64097945, -1.64097945, -1.41262278, ...,  4.42408698,\n",
+       "         4.60329133,  3.89910876],\n",
+       "       [ 0.56729028,  0.56729028,  0.56729028, ..., -1.10909274,\n",
+       "        -1.10909274, -1.10909274]])
          • tau
            (chain, draw)
            float64
            0.7789 0.9465 ... 8.763 8.763
            array([[ 0.77890285,  0.94646991,  0.94646991, ...,  3.06241616,\n",
+       "         3.06435583,  3.06435583],\n",
+       "       [ 1.32485136,  1.32485136,  1.32485136, ...,  0.74095528,\n",
+       "         0.74095528,  0.74095528],\n",
+       "       [ 0.6536186 ,  0.6536186 ,  0.6536186 , ...,  0.57241746,\n",
+       "         0.57241746,  3.8804493 ],\n",
+       "       ...,\n",
+       "       [ 0.93276919,  0.93276919,  0.93276919, ..., 20.11266279,\n",
+       "        20.11266279, 20.11266279],\n",
+       "       [ 0.37813884,  0.37813884,  0.42137438, ...,  7.74062311,\n",
+       "         6.91967957,  4.92296393],\n",
+       "       [ 0.2226751 ,  0.2226751 ,  0.2226751 , ...,  8.76313578,\n",
+       "         8.76313578,  8.76313578]])
          • eta0
            (chain, draw)
            float64
            0.5965 0.356 ... 0.9787 0.9787
            array([[ 0.59650655,  0.35598681,  0.35598681, ...,  0.71466293,\n",
+       "         0.71523086,  0.71523086],\n",
+       "       [ 0.5749315 ,  0.5749315 ,  0.5749315 , ..., -0.12368357,\n",
+       "        -0.12368357, -0.12368357],\n",
+       "       [ 0.8644362 ,  0.8644362 ,  0.8644362 , ..., -0.40901378,\n",
+       "        -0.40901378, -0.06287993],\n",
+       "       ...,\n",
+       "       [ 0.82396593,  0.82396593,  0.82396593, ...,  0.77767883,\n",
+       "         0.77767883,  0.77767883],\n",
+       "       [-2.17958654, -2.17958654, -2.07240863, ...,  0.26757447,\n",
+       "         0.3684135 ,  0.23850609],\n",
+       "       [-0.35343175, -0.35343175, -0.35343175, ...,  0.97868557,\n",
+       "         0.97868557,  0.97868557]])
          • eta1
            (chain, draw)
            float64
            1.395 0.9969 0.9969 ... 2.189 2.189
            array([[ 1.39536064,  0.99693975,  0.99693975, ..., -0.18711784,\n",
+       "        -0.18694776, -0.18694776],\n",
+       "       [-0.0967978 , -0.0967978 , -0.0967978 , ...,  0.59447501,\n",
+       "         0.59447501,  0.59447501],\n",
+       "       [-0.74216502, -0.74216502, -0.74216502, ...,  0.50398771,\n",
+       "         0.50398771,  0.4091619 ],\n",
+       "       ...,\n",
+       "       [ 0.08496028,  0.08496028,  0.08496028, ...,  0.35080969,\n",
+       "         0.35080969,  0.35080969],\n",
+       "       [-0.99002989, -0.99002989, -0.86249973, ..., -0.71346413,\n",
+       "        -0.6690839 , -1.30316671],\n",
+       "       [-1.61647419, -1.61647419, -1.61647419, ...,  2.1885378 ,\n",
+       "         2.1885378 ,  2.1885378 ]])
          • eta2
            (chain, draw)
            float64
            1.428 1.154 1.154 ... 0.1311 0.1311
            array([[ 1.42788977,  1.15418066,  1.15418066, ...,  1.3167935 ,\n",
+       "         1.31822332,  1.31822332],\n",
+       "       [ 0.41236399,  0.41236399,  0.41236399, ..., -0.34438568,\n",
+       "        -0.34438568, -0.34438568],\n",
+       "       [ 2.26975462,  2.26975462,  2.26975462, ...,  0.74946974,\n",
+       "         0.74946974,  0.11741706],\n",
+       "       ...,\n",
+       "       [-0.11300394, -0.11300394, -0.11300394, ...,  0.01968209,\n",
+       "         0.01968209,  0.01968209],\n",
+       "       [ 0.03733591,  0.03733591, -0.02665422, ...,  0.08855891,\n",
+       "         0.09957141,  0.873668  ],\n",
+       "       [-0.29183736, -0.29183736, -0.29183736, ...,  0.13107062,\n",
+       "         0.13107062,  0.13107062]])
          • eta3
            (chain, draw)
            float64
            -0.7293 -0.7175 ... 0.3588 0.3588
            array([[-0.72926825, -0.71747056, -0.71747056, ..., -0.24082616,\n",
+       "        -0.24043765, -0.24043765],\n",
+       "       [ 0.24672591,  0.24672591,  0.24672591, ..., -0.89020229,\n",
+       "        -0.89020229, -0.89020229],\n",
+       "       [-1.45436567, -1.45436567, -1.45436567, ..., -1.31071147,\n",
+       "        -1.31071147, -0.68573358],\n",
+       "       ...,\n",
+       "       [ 0.39412955,  0.39412955,  0.39412955, ...,  0.38296835,\n",
+       "         0.38296835,  0.38296835],\n",
+       "       [-1.61338832, -1.61338832, -1.39419799, ..., -0.20925129,\n",
+       "        -0.08789249, -0.44863267],\n",
+       "       [-0.76149221, -0.76149221, -0.76149221, ...,  0.35875544,\n",
+       "         0.35875544,  0.35875544]])
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            float64
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            array([[ 0.27536896,  0.42802117,  0.42802117, ..., -0.64697444,\n",
+       "        -0.647279  , -0.647279  ],\n",
+       "       [ 1.1247147 ,  1.1247147 ,  1.1247147 , ..., -0.3792712 ,\n",
+       "        -0.3792712 , -0.3792712 ],\n",
+       "       [ 0.04575852,  0.04575852,  0.04575852, ..., -0.04265142,\n",
+       "        -0.04265142, -0.03572373],\n",
+       "       ...,\n",
+       "       [ 0.97692625,  0.97692625,  0.97692625, ...,  0.19855731,\n",
+       "         0.19855731,  0.19855731],\n",
+       "       [-0.96656389, -0.96656389, -0.92286515, ..., -0.7191441 ,\n",
+       "        -0.66556347, -1.11350372],\n",
+       "       [ 0.85792392,  0.85792392,  0.85792392, ...,  0.18125692,\n",
+       "         0.18125692,  0.18125692]])
          • eta5
            (chain, draw)
            float64
            -0.5001 -0.6041 ... -0.1164 -0.1164
            array([[-0.50013399, -0.60410533, -0.60410533, ..., -1.08707667,\n",
+       "        -1.08765507, -1.08765507],\n",
+       "       [-0.12619171, -0.12619171, -0.12619171, ..., -0.30565393,\n",
+       "        -0.30565393, -0.30565393],\n",
+       "       [-0.18718385, -0.18718385, -0.18718385, ..., -0.98486991,\n",
+       "        -0.98486991, -0.44800379],\n",
+       "       ...,\n",
+       "       [-2.04134322, -2.04134322, -2.04134322, ...,  0.95433456,\n",
+       "         0.95433456,  0.95433456],\n",
+       "       [-1.44616313, -1.44616313, -1.38958095, ...,  0.07653725,\n",
+       "         0.00765534, -0.34260937],\n",
+       "       [ 1.14110187,  1.14110187,  1.14110187, ..., -0.11644015,\n",
+       "        -0.11644015, -0.11644015]])
          • eta6
            (chain, draw)
            float64
            0.1888 -0.05081 ... 0.6959 0.6959
            array([[ 1.88821093e-01, -5.08071132e-02, -5.08071132e-02, ...,\n",
+       "         2.20885330e-01,  2.21008935e-01,  2.21008935e-01],\n",
+       "       [ 1.68791224e-01,  1.68791224e-01,  1.68791224e-01, ...,\n",
+       "        -7.62386375e-01, -7.62386375e-01, -7.62386375e-01],\n",
+       "       [ 1.53277921e+00,  1.53277921e+00,  1.53277921e+00, ...,\n",
+       "        -8.44490237e-01, -8.44490237e-01, -3.47285167e-01],\n",
+       "       ...,\n",
+       "       [ 1.35927742e-02,  1.35927742e-02,  1.35927742e-02, ...,\n",
+       "         7.83259115e-01,  7.83259115e-01,  7.83259115e-01],\n",
+       "       [ 1.60246678e+00,  1.60246678e+00,  1.58314291e+00, ...,\n",
+       "         1.25047974e-01,  2.21093345e-01, -1.19317876e-03],\n",
+       "       [ 1.46657872e+00,  1.46657872e+00,  1.46657872e+00, ...,\n",
+       "         6.95921870e-01,  6.95921870e-01,  6.95921870e-01]])
          • eta7
            (chain, draw)
            float64
            0.2937 0.1625 ... 1.533 1.533
            array([[ 0.29370708,  0.16252705,  0.16252705, ..., -1.50049936,\n",
+       "        -1.50147066, -1.50147066],\n",
+       "       [-0.74697999, -0.74697999, -0.74697999, ...,  0.30721301,\n",
+       "         0.30721301,  0.30721301],\n",
+       "       [ 1.46935877,  1.46935877,  1.46935877, ...,  0.3629416 ,\n",
+       "         0.3629416 ,  0.13814133],\n",
+       "       ...,\n",
+       "       [ 0.89145609,  0.89145609,  0.89145609, ...,  0.3007356 ,\n",
+       "         0.3007356 ,  0.3007356 ],\n",
+       "       [ 0.13342492,  0.13342492,  0.17922369, ..., -0.26854499,\n",
+       "        -0.16240555, -0.0922921 ],\n",
+       "       [ 0.85255194,  0.85255194,  0.85255194, ...,  1.53291263,\n",
+       "         1.53291263,  1.53291263]])
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          arviz_version :
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          inference_library :
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          inference_library_version :
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            array([[-19.02258213, -17.53015266, -17.53015266, ..., -17.8496425 ,\n",
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+       "        -12.51696217, -17.11064559],\n",
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            int64
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          • arg_1_dim_0
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          • arg_2_dim_0
            (arg_2_dim_0)
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            array([0, 1, 2, 3, 4, 5, 6, 7])
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          • arg_2
            (arg_2_dim_0)
            float64
            15.0 10.0 16.0 ... 11.0 10.0 18.0
            array([15., 10., 16., 11.,  9., 11., 10., 18.])
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        \n", + "
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      • \n", + " \n", + "
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    \n", + " " + ], "text/plain": [ "Inference data with groups:\n", "\t> posterior\n", - "\t> posterior_predictive\n", - "\t> log_likelihood\n", "\t> sample_stats\n", "\t> observed_data" ] }, - "execution_count": 12, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "import pystan\n", - "\n", - "schools_code = \"\"\"\n", - "data {\n", - " int J;\n", - " real y[J];\n", - " real sigma[J];\n", - "}\n", - "\n", - "parameters {\n", - " real mu;\n", - " real tau;\n", - " real theta_tilde[J];\n", - "}\n", - "\n", - "transformed parameters {\n", - " real theta[J];\n", - " for (j in 1:J)\n", - " theta[j] = mu + tau * theta_tilde[j];\n", - "}\n", - "\n", - "model {\n", - " mu ~ normal(0, 5);\n", - " tau ~ cauchy(0, 5);\n", - " theta_tilde ~ normal(0, 1);\n", - " y ~ normal(theta, sigma);\n", - "}\n", - "\n", - "generated quantities {\n", - " vector[J] log_lik;\n", - " vector[J] y_hat;\n", - " for (j in 1:J) {\n", - " log_lik[j] = normal_lpdf(y[j] | theta[j], sigma[j]);\n", - " y_hat[j] = normal_rng(theta[j], sigma[j]);\n", - " }\n", - "}\n", - "\"\"\"\n", + "import emcee\n", "\n", "eight_school_data = {\n", " \"J\": 8,\n", @@ -2777,226 +21368,2411 @@ " \"sigma\": np.array([15.0, 10.0, 16.0, 11.0, 9.0, 11.0, 10.0, 18.0]),\n", "}\n", "\n", - "stan_model = pystan.StanModel(model_code=schools_code)\n", - "fit = stan_model.sampling(data=eight_school_data, control={\"adapt_delta\": 0.9})\n", - "\n", - "stan_data = az.from_pystan(\n", - " posterior=fit,\n", - " posterior_predictive=\"y_hat\",\n", - " observed_data=[\"y\"],\n", - " log_likelihood={\"y\": \"log_lik\"},\n", - " coords={\"school\": np.arange(eight_school_data[\"J\"])},\n", - " dims={\n", - " \"theta\": [\"school\"],\n", - " \"y\": [\"school\"],\n", - " \"log_lik\": [\"school\"],\n", - " \"y_hat\": [\"school\"],\n", - " \"theta_tilde\": [\"school\"],\n", - " },\n", - ")\n", "\n", - "stan_data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## From Pyro" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "ExecuteTime": { - "end_time": "2020-06-05T06:48:09.602373Z", - "start_time": "2020-06-05T06:47:57.349110Z" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Inference data with groups:\n", - "\t> posterior\n", - "\t> posterior_predictive\n", - "\t> log_likelihood\n", - "\t> sample_stats\n", - "\t> prior\n", - "\t> prior_predictive\n", - "\t> observed_data" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import pyro\n", - "import pyro.distributions as dist\n", - "import torch\n", - "from pyro.infer import MCMC, NUTS, Predictive\n", + "def log_prior_8school(theta, J):\n", + " mu = theta[0]\n", + " tau = theta[1]\n", + " eta = theta[2:]\n", + " # Half-cauchy prior\n", + " if tau < 0:\n", + " return -np.inf\n", + " hwhm = 25\n", + " prior_tau = -np.log(tau ** 2 + hwhm ** 2)\n", + " prior_mu = -((mu / 10) ** 2) # normal prior, loc=0, scale=10\n", + " prior_eta = -np.sum(eta ** 2) # normal prior, loc=0, scale=1\n", + " return prior_mu + prior_tau + prior_eta\n", "\n", - "pyro.enable_validation(True)\n", - "pyro.set_rng_seed(0)\n", "\n", - "draws = 500\n", - "chains = 2\n", - "eight_school_data = {\n", - " \"J\": 8,\n", - " \"y\": torch.tensor([28.0, 8.0, -3.0, 7.0, -1.0, 1.0, 18.0, 12.0]),\n", - " \"sigma\": torch.tensor([15.0, 10.0, 16.0, 11.0, 9.0, 11.0, 10.0, 18.0]),\n", - "}\n", + "def log_likelihood_8school(theta, y, sigma):\n", + " mu = theta[0]\n", + " tau = theta[1]\n", + " eta = theta[2:]\n", + " return -np.sum(((mu + tau * eta - y) / sigma) ** 2)\n", "\n", "\n", - "def model(J, sigma, y=None):\n", - " mu = pyro.sample(\"mu\", dist.Normal(0, 5))\n", - " tau = pyro.sample(\"tau\", dist.HalfCauchy(5))\n", - " with pyro.plate(\"J\", J):\n", - " theta_tilde = pyro.sample(\"theta_tilde\", dist.Normal(0, 1))\n", - " theta = mu + tau * theta_tilde\n", - " return pyro.sample(\"obs\", dist.Normal(theta, sigma), obs=y)\n", + "def lnprob_8school(theta, J, y, sigma):\n", + " prior = log_prior_8school(theta, J)\n", + " if prior <= -np.inf:\n", + " return -np.inf\n", + " like = log_likelihood_8school(theta, y, sigma)\n", + " return like + prior\n", "\n", "\n", - "nuts_kernel = NUTS(model, jit_compile=True, ignore_jit_warnings=True)\n", - "mcmc = MCMC(\n", - " nuts_kernel,\n", - " num_samples=draws,\n", - " warmup_steps=draws,\n", - " num_chains=chains,\n", - " disable_progbar=True,\n", - ")\n", - "mcmc.run(**eight_school_data)\n", - "posterior_samples = mcmc.get_samples()\n", - "posterior_predictive = Predictive(model, posterior_samples).get_samples(\n", - " eight_school_data[\"J\"], eight_school_data[\"sigma\"]\n", - ")\n", - "prior = Predictive(model, num_samples=500).get_samples(\n", - " eight_school_data[\"J\"], eight_school_data[\"sigma\"]\n", + "nwalkers = 40\n", + "ndim = eight_school_data[\"J\"] + 2\n", + "draws = 1500\n", + "pos = np.random.normal(size=(nwalkers, ndim))\n", + "pos[:, 1] = np.absolute(pos[:, 1])\n", + "sampler = emcee.EnsembleSampler(\n", + " nwalkers,\n", + " ndim,\n", + " lnprob_8school,\n", + " args=(eight_school_data[\"J\"], eight_school_data[\"y\"], eight_school_data[\"sigma\"]),\n", ")\n", + "sampler.run_mcmc(pos, draws)\n", "\n", - "pyro_data = az.from_pyro(\n", - " mcmc,\n", - " prior=prior,\n", - " posterior_predictive=posterior_predictive,\n", - " coords={\"school\": np.arange(eight_school_data[\"J\"])},\n", - " dims={\"theta\": [\"school\"]},\n", - ")\n", - "pyro_data" + "# define variable names, it cannot be inferred from emcee\n", + "var_names = [\"mu\", \"tau\"] + [\"eta{}\".format(i) for i in range(eight_school_data[\"J\"])]\n", + "emcee_data = az.from_emcee(sampler, var_names=var_names)\n", + "emcee_data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## From emcee" + "## From CmdStanPy" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 15, "metadata": { "ExecuteTime": { - "end_time": "2020-06-05T06:48:11.402298Z", - "start_time": "2020-06-05T06:48:09.604316Z" + "end_time": "2020-06-05T06:48:14.965999Z", + "start_time": "2020-06-05T06:48:11.404455Z" } }, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ +<<<<<<< HEAD + "INFO:cmdstanpy:compiling c++\n", + "INFO:cmdstanpy:compiled model file: /Users/percy/PycharmProjects/arviz/doc/notebooks/eight_school\n", + "INFO:cmdstanpy:found newer exe file, not recompiling\n", + "INFO:cmdstanpy:compiled model file: /Users/percy/PycharmProjects/arviz/doc/notebooks/eight_school\n", + "INFO:cmdstanpy:start chain 1\n", + "INFO:cmdstanpy:start chain 2\n", + "INFO:cmdstanpy:finish chain 1\n", + "INFO:cmdstanpy:start chain 3\n", + "INFO:cmdstanpy:finish chain 2\n", + "INFO:cmdstanpy:start chain 4\n", + "INFO:cmdstanpy:finish chain 3\n", + "INFO:cmdstanpy:finish chain 4\n" + ] + }, { "data": { + "text/html": [ + "\n", + "
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          • draw
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          • school
            (school)
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          • mu
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            float64
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            array([[ 0.860351, -0.637211,  4.62776 , ...,  5.56301 ,  2.55374 ,\n",
+       "         2.31729 ],\n",
+       "       [ 0.732634, -0.220275,  0.615834, ...,  7.16887 ,  8.03808 ,\n",
+       "         4.4595  ],\n",
+       "       [ 5.91379 ,  3.70948 ,  4.26014 , ..., -3.40594 ,  9.58868 ,\n",
+       "         0.366119],\n",
+       "       [ 3.88691 ,  3.18823 ,  5.80652 , ...,  2.068   ,  8.69849 ,\n",
+       "         6.69092 ]])
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            float64
            5.038 3.547 2.764 ... 1.098 4.68
            array([[5.03848 , 3.54695 , 2.76361 , ..., 8.59895 , 0.33265 , 0.427009],\n",
+       "       [9.96193 , 5.76789 , 0.800563, ..., 2.45978 , 6.35597 , 1.18186 ],\n",
+       "       [1.86466 , 5.26644 , 2.46189 , ..., 4.01369 , 0.966623, 2.22911 ],\n",
+       "       [6.751   , 2.61096 , 9.75554 , ..., 6.4743  , 1.09751 , 4.67988 ]])
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+       "        [ 5.56617e-01,  1.77470e-01, -1.04413e+00, ...,  1.65032e+00,\n",
+       "         -6.74706e-01, -1.86848e-01],\n",
+       "        ...,\n",
+       "        [ 1.31558e+00, -2.21541e-01,  3.70592e-02, ...,  6.68264e-01,\n",
+       "         -3.41076e-01, -1.91695e+00],\n",
+       "        [-1.32237e+00, -5.24289e-01, -4.04783e-01, ...,  6.25155e-01,\n",
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+       "        [-6.74953e-01, -1.30215e+00, -1.58508e-01, ...,  8.47776e-01,\n",
+       "          6.84311e-01,  9.77449e-01]],\n",
+       "\n",
+       "       [[-2.00545e-01, -2.10582e-01, -9.40322e-02, ...,  2.10087e-02,\n",
+       "          2.37020e-01,  1.29188e-01],\n",
+       "        [-5.38384e-01, -9.47796e-01,  7.59535e-01, ...,  4.92083e-01,\n",
+       "          7.14972e-01, -5.74193e-01],\n",
+       "        [ 1.05020e-02, -1.43597e-01,  5.72359e-02, ...,  3.59991e-01,\n",
+       "         -1.34135e-02, -7.78741e-01],\n",
+       "        ...,\n",
+       "        [ 1.25372e+00,  9.10764e-01, -3.08858e+00, ..., -1.96722e+00,\n",
+       "          1.12084e-02,  2.52069e-01],\n",
+       "        [ 1.93895e+00,  2.57758e-01, -2.47922e+00, ..., -8.94723e-01,\n",
+       "         -1.26788e-01,  4.69993e-01],\n",
+       "        [ 2.97201e+00,  6.67406e-02, -1.94356e-01, ...,  1.00890e-01,\n",
+       "         -3.81350e-01,  2.11749e-01]],\n",
+       "\n",
+       "       [[ 1.21786e+00,  1.23069e+00, -5.52686e-01, ..., -1.48749e+00,\n",
+       "          7.83702e-01,  1.85699e+00],\n",
+       "        [ 1.72311e+00,  7.67163e-01, -2.34386e-02, ..., -2.42422e+00,\n",
+       "          3.66557e-01,  1.29801e+00],\n",
+       "        [ 1.92775e+00,  7.48698e-01,  3.09622e-01, ..., -1.77732e+00,\n",
+       "          1.59023e-01,  1.52893e+00],\n",
+       "        ...,\n",
+       "        [ 1.23452e-01,  1.16843e+00,  1.00505e-02, ...,  1.31982e+00,\n",
+       "         -5.95969e-01, -4.30592e-01],\n",
+       "        [ 2.62933e-01, -1.08506e+00, -1.75313e+00, ..., -7.69045e-01,\n",
+       "          2.01635e-01, -1.55323e+00],\n",
+       "        [ 4.78519e-01,  4.44985e-01,  1.39678e+00, ...,  4.84171e-02,\n",
+       "          5.20688e-01,  1.69244e+00]],\n",
+       "\n",
+       "       [[-1.31033e+00, -7.85428e-03, -3.17845e-01, ..., -6.09675e-01,\n",
+       "          1.70432e+00,  1.16530e+00],\n",
+       "        [ 2.02721e+00,  5.59707e-01, -2.80847e-02, ...,  6.90420e-01,\n",
+       "         -5.35285e-01, -1.54980e-01],\n",
+       "        [ 1.26965e+00, -8.16178e-01,  7.36237e-01, ..., -3.99220e-03,\n",
+       "          7.63763e-01, -7.12747e-01],\n",
+       "        ...,\n",
+       "        [ 9.42448e-01, -7.24387e-04,  1.05740e+00, ...,  3.66910e-02,\n",
+       "          6.49015e-01, -5.97576e-01],\n",
+       "        [ 1.04661e+00,  1.81675e+00,  2.53350e-01, ..., -1.45018e-01,\n",
+       "         -7.19568e-01, -1.61255e+00],\n",
+       "        [ 2.12768e+00, -3.70835e-01,  1.35789e+00, ..., -9.19882e-02,\n",
+       "         -1.23920e+00,  3.21054e-01]]])
          • theta
            (chain, draw, school)
            float64
            2.699 3.425 9.285 ... 0.8916 8.193
            array([[[ 2.69949e+00,  3.42481e+00,  9.28470e+00, ...,  9.34520e+00,\n",
+       "          7.47338e+00, -3.06906e+00],\n",
+       "        [-9.28944e-01,  5.04228e+00, -1.94239e+00, ..., -2.93536e+00,\n",
+       "         -4.58497e-01, -2.97207e+00],\n",
+       "        [ 6.16603e+00,  5.11822e+00,  1.74220e+00, ...,  9.18859e+00,\n",
+       "          2.76314e+00,  4.11139e+00],\n",
+       "        ...,\n",
+       "        [ 1.68756e+01,  3.65800e+00,  5.88168e+00, ...,  1.13094e+01,\n",
+       "          2.63012e+00, -1.09207e+01],\n",
+       "        [ 2.11385e+00,  2.37933e+00,  2.41909e+00, ...,  2.76170e+00,\n",
+       "          2.64032e+00,  2.76664e+00],\n",
+       "        [ 2.02908e+00,  1.76127e+00,  2.24961e+00, ...,  2.67930e+00,\n",
+       "          2.60950e+00,  2.73467e+00]],\n",
+       "\n",
+       "       [[-1.26518e+00, -1.36517e+00, -2.04107e-01, ...,  9.41921e-01,\n",
+       "          3.09381e+00,  2.01959e+00],\n",
+       "        [-3.32561e+00, -5.68706e+00,  4.16063e+00, ...,  2.61801e+00,\n",
+       "          3.90360e+00, -3.53216e+00],\n",
+       "        [ 6.24242e-01,  5.00876e-01,  6.61655e-01, ...,  9.04029e-01,\n",
+       "          6.05096e-01, -7.59738e-03],\n",
+       "        ...,\n",
+       "        [ 1.02527e+01,  9.40915e+00, -4.28361e-01, ...,  2.32995e+00,\n",
+       "          7.19644e+00,  7.78891e+00],\n",
+       "        [ 2.03620e+01,  9.67638e+00, -7.71979e+00, ...,  2.35124e+00,\n",
+       "          7.23222e+00,  1.10253e+01],\n",
+       "        [ 7.97201e+00,  4.53838e+00,  4.22980e+00, ...,  4.57874e+00,\n",
+       "          4.00880e+00,  4.70976e+00]],\n",
+       "\n",
+       "       [[ 8.18468e+00,  8.20861e+00,  4.88322e+00, ...,  3.14013e+00,\n",
+       "          7.37513e+00,  9.37645e+00],\n",
+       "        [ 1.27841e+01,  7.74970e+00,  3.58604e+00, ..., -9.05752e+00,\n",
+       "          5.63993e+00,  1.05454e+01],\n",
+       "        [ 9.00605e+00,  6.10335e+00,  5.02239e+00, ..., -1.15431e-01,\n",
+       "          4.65164e+00,  8.02420e+00],\n",
+       "        ...,\n",
+       "        [-2.91044e+00,  1.28378e+00, -3.36560e+00, ...,  1.89143e+00,\n",
+       "         -5.79797e+00, -5.13420e+00],\n",
+       "        [ 9.84283e+00,  8.53983e+00,  7.89406e+00, ...,  8.84530e+00,\n",
+       "          9.78358e+00,  8.08729e+00],\n",
+       "        [ 1.43279e+00,  1.35804e+00,  3.47971e+00, ...,  4.74046e-01,\n",
+       "          1.52679e+00,  4.13875e+00]],\n",
+       "\n",
+       "       [[-4.95910e+00,  3.83388e+00,  1.74114e+00, ..., -2.29009e-01,\n",
+       "          1.53928e+01,  1.17538e+01],\n",
+       "        [ 8.48119e+00,  4.64960e+00,  3.11490e+00, ...,  4.99089e+00,\n",
+       "          1.79062e+00,  2.78358e+00],\n",
+       "        [ 1.81926e+01, -2.15574e+00,  1.29889e+01, ...,  5.76757e+00,\n",
+       "          1.32574e+01, -1.14672e+00],\n",
+       "        ...,\n",
+       "        [ 8.16969e+00,  2.06331e+00,  8.91392e+00, ...,  2.30555e+00,\n",
+       "          6.26992e+00, -1.80089e+00],\n",
+       "        [ 9.84716e+00,  1.06924e+01,  8.97655e+00, ...,  8.53934e+00,\n",
+       "          7.90876e+00,  6.92871e+00],\n",
+       "        [ 1.66482e+01,  4.95545e+00,  1.30457e+01, ...,  6.26042e+00,\n",
+       "          8.91628e-01,  8.19341e+00]]])
        • created_at :
          2020-06-06T18:10:32.329048
          arviz_version :
          0.8.3
          inference_library :
          cmdstanpy
          inference_library_version :
          0.8.0

        \n", + "
      \n", + "
      \n", + "
      • \n", + " \n", + "
    • \n", + " \n", + " \n", + "
      \n", + "
      \n", + "
        \n", + "
        \n", + "\n", + "\n", + "Show/Hide data repr\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Show/Hide attributes\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
        xarray.Dataset
          • chain: 4
          • draw: 1000
          • school: 8
          • chain
            (chain)
            int64
            0 1 2 3
            array([0, 1, 2, 3])
          • draw
            (draw)
            int64
            0 1 2 3 4 5 ... 995 996 997 998 999
            array([  0,   1,   2, ..., 997, 998, 999])
          • school
            (school)
            int64
            0 1 2 3 4 5 6 7
            array([0, 1, 2, 3, 4, 5, 6, 7])
          • y_hat
            (chain, draw, school)
            float64
            17.82 -12.27 15.19 ... -11.52 28.27
            array([[[ 17.8214   , -12.2736   ,  15.1864   , ...,  10.6945   ,\n",
+       "          11.1511   , -25.1836   ],\n",
+       "        [  9.80558  ,   2.64404  ,  -0.126269 , ..., -22.818    ,\n",
+       "          15.7038   ,   7.63275  ],\n",
+       "        [ -0.307212 ,   5.86482  , -17.3024   , ...,  18.2618   ,\n",
+       "           1.18445  ,   4.95961  ],\n",
+       "        ...,\n",
+       "        [ 18.9973   ,  -2.61467  ,  10.0733   , ...,   6.23517  ,\n",
+       "          20.3914   , -31.1207   ],\n",
+       "        [  7.63424  ,  -9.18872  , -17.4574   , ...,  22.6733   ,\n",
+       "          11.1596   , -13.6902   ],\n",
+       "        [ -4.94321  ,  16.7564   ,  -7.47152  , ...,  -0.874168 ,\n",
+       "          16.0975   , -15.7978   ]],\n",
+       "\n",
+       "       [[-13.8236   ,   3.30856  ,  -9.71613  , ...,   1.07714  ,\n",
+       "          24.3845   , -37.759    ],\n",
+       "        [-19.5816   ,   9.47864  ,   7.43956  , ...,   8.73732  ,\n",
+       "          15.1482   ,  20.7514   ],\n",
+       "        [ -3.2081   ,  -5.29835  , -23.4598   , ...,  -7.25993  ,\n",
+       "          15.408    , -12.4758   ],\n",
+       "        ...,\n",
+       "        [ 17.2876   ,   9.47491  ,   9.11321  , ...,  -0.844266 ,\n",
+       "          23.7267   ,  13.1609   ],\n",
+       "        [ 42.8214   , -11.115    , -28.3195   , ...,   9.54406  ,\n",
+       "           6.86596  ,  24.7486   ],\n",
+       "        [ 31.5371   ,  -9.2391   ,  -5.19494  , ...,  16.0107   ,\n",
+       "          18.2181   ,  19.2444   ]],\n",
+       "\n",
+       "       [[-22.2079   ,  16.09     ,  -1.43103  , ...,  -2.81685  ,\n",
+       "         -11.7201   ,  26.1408   ],\n",
+       "        [  7.84245  ,  12.1987   ,  12.4391   , ...,  -8.31294  ,\n",
+       "          16.7167   ,  24.0476   ],\n",
+       "        [ -5.47622  , -10.7056   , -13.119    , ...,  -9.98965  ,\n",
+       "           1.99784  ,  12.6135   ],\n",
+       "        ...,\n",
+       "        [ 10.498    ,  -6.5292   ,  -1.95445  , ...,   6.90389  ,\n",
+       "           9.90309  ,  -9.53769  ],\n",
+       "        [-22.7585   ,  16.791    ,   3.64741  , ...,  16.2263   ,\n",
+       "          13.1702   ,  -2.93753  ],\n",
+       "        [ 16.2394   ,   3.08554  ,   9.0992   , ...,  -7.29659  ,\n",
+       "          16.6593   ,  -2.51172  ]],\n",
+       "\n",
+       "       [[ 23.8207   ,   6.97066  , -13.2277   , ...,  -4.33379  ,\n",
+       "          16.7229   ,  -4.24901  ],\n",
+       "        [ 19.6784   ,  10.6246   ,  15.6069   , ...,   8.62725  ,\n",
+       "          -0.49937  ,  -4.91235  ],\n",
+       "        [-23.3668   ,   8.97635  ,  22.8516   , ...,  16.1906   ,\n",
+       "          18.5997   ,  -0.0484115],\n",
+       "        ...,\n",
+       "        [  1.07142  ,   2.08327  ,  28.3587   , ...,  18.9032   ,\n",
+       "          11.3534   ,  -3.75202  ],\n",
+       "        [ 11.1992   ,   7.75921  ,  11.0927   , ...,   2.09711  ,\n",
+       "           4.61789  ,  31.6442   ],\n",
+       "        [ 27.53     ,  19.7944   ,  16.7601   , ..., -15.1517   ,\n",
+       "         -11.5217   ,  28.2679   ]]])
        • created_at :
          2020-06-06T18:10:32.340884
          arviz_version :
          0.8.3
          inference_library :
          cmdstanpy
          inference_library_version :
          0.8.0

        \n", + "
      \n", + "
      \n", + "
      • \n", + " \n", + "
    • \n", + " \n", + " \n", + "
      \n", + "
      \n", + "
        \n", + "
        \n", + "\n", + "\n", + "Show/Hide data repr\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Show/Hide attributes\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
        xarray.Dataset
          • chain: 4
          • draw: 1000
          • school: 8
          • chain
            (chain)
            int64
            0 1 2 3
            array([0, 1, 2, 3])
          • draw
            (draw)
            int64
            0 1 2 3 4 5 ... 995 996 997 998 999
            array([  0,   1,   2, ..., 997, 998, 999])
          • school
            (school)
            int64
            0 1 2 3 4 5 6 7
            array([0, 1, 2, 3, 4, 5, 6, 7])
          • log_lik
            (chain, draw, school)
            float64
            -5.049 -3.326 ... -4.685 -3.832
            array([[[-5.04947, -3.32619, -3.98628, ..., -3.60461, -3.77557,\n",
+       "         -4.15974],\n",
+       "        [-5.48673, -3.26526, -3.69371, ..., -3.38083, -4.9251 ,\n",
+       "         -4.15524],\n",
+       "        [-4.68637, -3.26305, -3.73545, ..., -3.59391, -4.38233,\n",
+       "         -3.90534],\n",
+       "        ...,\n",
+       "        [-3.90199, -3.31579, -3.8456 , ..., -3.75602, -4.40269,\n",
+       "         -4.62005],\n",
+       "        [-5.11608, -3.37948, -3.74888, ..., -3.32966, -4.40112,\n",
+       "         -3.94088],\n",
+       "        [-5.12585, -3.41613, -3.74535, ..., -3.32849, -4.40586,\n",
+       "         -3.94179]],\n",
+       "\n",
+       "       [[-5.53021, -3.66006, -3.70679, ..., -3.31685, -4.3325 ,\n",
+       "         -3.96303],\n",
+       "        [-5.80764, -4.1582 , -3.79167, ..., -3.32765, -4.21507,\n",
+       "         -4.18161],\n",
+       "        [-5.29239, -3.50271, -3.71771, ..., -3.31687, -4.73444,\n",
+       "         -4.03181],\n",
+       "        ...,\n",
+       "        [-4.32691, -3.23145, -3.70444, ..., -3.32414, -3.80511,\n",
+       "         -3.83668],\n",
+       "        [-3.75663, -3.23557, -3.73504, ..., -3.32438, -3.80125,\n",
+       "         -3.81078],\n",
+       "        [-4.51837, -3.28144, -3.79362, ..., -3.36976, -4.20029,\n",
+       "         -3.89133]],\n",
+       "\n",
+       "       [[-4.49954, -3.22174, -3.8129 , ..., -3.33576, -3.78596,\n",
+       "         -3.81993],\n",
+       "        [-4.14148, -3.22184, -3.77625, ..., -3.73482, -3.98538,\n",
+       "         -3.81258],\n",
+       "        [-4.4287 , -3.23951, -3.81723, ..., -3.32198, -4.11242,\n",
+       "         -3.8337 ],\n",
+       "        ...,\n",
+       "        [-5.75022, -3.44706, -3.69179, ..., -3.32012, -6.05324,\n",
+       "         -4.26237],\n",
+       "        [-4.35962, -3.22298, -3.92333, ..., -3.57117, -3.55907,\n",
+       "         -3.83294],\n",
+       "        [-5.19547, -3.4421 , -3.77353, ..., -3.31798, -4.57836,\n",
+       "         -3.90468]],\n",
+       "\n",
+       "       [[-6.04099, -3.30831, -3.73543, ..., -3.32308, -3.25551,\n",
+       "         -3.8094 ],\n",
+       "        [-4.47362, -3.27765, -3.76456, ..., -3.38265, -4.53524,\n",
+       "         -3.94039],\n",
+       "        [-3.84073, -3.73722, -4.19083, ..., -3.41076, -3.33398,\n",
+       "         -4.07603],\n",
+       "        ...,\n",
+       "        [-4.50086, -3.39775, -3.96876, ..., -3.32388, -3.9095 ,\n",
+       "         -4.10324],\n",
+       "        [-4.35927, -3.25777, -3.97168, ..., -3.55172, -3.73069,\n",
+       "         -3.849  ],\n",
+       "        [-3.91335, -3.26787, -4.19439, ..., -3.43118, -4.68501,\n",
+       "         -3.83167]]])
        • created_at :
          2020-06-06T18:10:32.343924
          arviz_version :
          0.8.3
          inference_library :
          cmdstanpy
          inference_library_version :
          0.8.0

        \n", + "
      \n", + "
      \n", + "
      • \n", + " \n", + "
    • \n", + " \n", + " \n", + "
      \n", + "
      \n", + "
        \n", + "
        \n", + "\n", + "\n", + "Show/Hide data repr\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Show/Hide attributes\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
        xarray.Dataset
          • chain: 4
          • draw: 1000
          • chain
            (chain)
            int64
            0 1 2 3
            array([0, 1, 2, 3])
          • draw
            (draw)
            int64
            0 1 2 3 4 5 ... 995 996 997 998 999
            array([  0,   1,   2, ..., 997, 998, 999])
          • lp
            (chain, draw)
            float64
            -7.826 -6.066 ... -8.303 -8.039
            array([[ -7.82575,  -6.06571,  -5.37794, ...,  -7.80535,  -8.74137,\n",
+       "         -9.8017 ],\n",
+       "       [ -3.36045,  -5.68201,  -4.56247, ..., -13.8115 , -10.5851 ,\n",
+       "         -7.94476],\n",
+       "       [ -6.90788,  -6.99938,  -6.69535, ..., -11.5595 ,  -7.91042,\n",
+       "         -5.89621],\n",
+       "       [ -6.73536,  -5.18604,  -4.06661, ...,  -3.82035,  -8.3025 ,\n",
+       "         -8.03949]])
          • accept_stat
            (chain, draw)
            float64
            0.9451 0.9838 ... 0.8485 0.9904
            array([[0.945142, 0.983825, 0.99608 , ..., 0.996257, 0.864367, 0.979232],\n",
+       "       [0.99676 , 0.785461, 0.941576, ..., 0.805343, 1.      , 0.751312],\n",
+       "       [0.897601, 0.97537 , 0.817992, ..., 0.872228, 0.920333, 0.980318],\n",
+       "       [0.901122, 0.984381, 0.986321, ..., 1.      , 0.848503, 0.990407]])
          • stepsize
            (chain, draw)
            float64
            0.5043 0.5043 ... 0.3523 0.3523
            array([[0.504285, 0.504285, 0.504285, ..., 0.504285, 0.504285, 0.504285],\n",
+       "       [0.39744 , 0.39744 , 0.39744 , ..., 0.39744 , 0.39744 , 0.39744 ],\n",
+       "       [0.379572, 0.379572, 0.379572, ..., 0.379572, 0.379572, 0.379572],\n",
+       "       [0.352264, 0.352264, 0.352264, ..., 0.352264, 0.352264, 0.352264]])
          • treedepth
            (chain, draw)
            int64
            3 3 3 3 3 3 3 3 ... 3 3 3 4 4 3 3 3
            array([[3, 3, 3, ..., 3, 3, 3],\n",
+       "       [3, 3, 3, ..., 3, 3, 3],\n",
+       "       [4, 3, 3, ..., 3, 3, 4],\n",
+       "       [3, 3, 3, ..., 3, 3, 3]])
          • n_leapfrog
            (chain, draw)
            int64
            7 7 7 7 7 7 7 ... 15 15 15 7 7 15
            array([[ 7,  7,  7, ...,  7,  7,  7],\n",
+       "       [ 7,  7,  7, ...,  7,  7,  7],\n",
+       "       [15,  7,  7, ...,  7, 15, 15],\n",
+       "       [ 7,  7,  7, ...,  7,  7, 15]])
          • diverging
            (chain, draw)
            bool
            False False False ... False False
            array([[False, False, False, ..., False, False, False],\n",
+       "       [False, False, False, ..., False, False, False],\n",
+       "       [False, False, False, ..., False, False, False],\n",
+       "       [False, False, False, ..., False, False, False]])
          • energy
            (chain, draw)
            float64
            10.34 12.39 9.315 ... 12.34 12.99
            array([[10.341  , 12.3946 ,  9.31486, ..., 13.4174 , 13.6243 , 12.9495 ],\n",
+       "       [11.0993 , 14.047  , 10.7643 , ..., 17.0451 , 17.1117 , 15.7323 ],\n",
+       "       [15.9822 , 10.2966 , 10.7779 , ..., 13.6941 , 16.5774 , 12.7611 ],\n",
+       "       [12.1458 ,  8.06191,  8.33783, ..., 10.2761 , 12.3444 , 12.995  ]])
        • created_at :
          2020-06-06T18:10:32.336070
          arviz_version :
          0.8.3
          inference_library :
          cmdstanpy
          inference_library_version :
          0.8.0

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        xarray.Dataset
          • school: 8
          • school
            (school)
            int64
            0 1 2 3 4 5 6 7
            array([0, 1, 2, 3, 4, 5, 6, 7])
          • y
            (school)
            float64
            28.0 8.0 -3.0 7.0 ... 1.0 18.0 12.0
            array([28.,  8., -3.,  7., -1.,  1., 18., 12.])
        • created_at :
          2020-06-06T18:10:32.342119
          arviz_version :
          0.8.3
          inference_library :
          cmdstanpy
          inference_library_version :
          0.8.0

        \n", + "
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    \n", + " " + ], "text/plain": [ "Inference data with groups:\n", "\t> posterior\n", + "\t> posterior_predictive\n", + "\t> log_likelihood\n", "\t> sample_stats\n", "\t> observed_data" ] }, - "execution_count": 14, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" - } - ], - "source": [ - "import emcee\n", - "\n", - "eight_school_data = {\n", - " \"J\": 8,\n", - " \"y\": np.array([28.0, 8.0, -3.0, 7.0, -1.0, 1.0, 18.0, 12.0]),\n", - " \"sigma\": np.array([15.0, 10.0, 16.0, 11.0, 9.0, 11.0, 10.0, 18.0]),\n", - "}\n", - "\n", - "\n", - "def log_prior_8school(theta, J):\n", - " mu = theta[0]\n", - " tau = theta[1]\n", - " eta = theta[2:]\n", - " # Half-cauchy prior\n", - " if tau < 0:\n", - " return -np.inf\n", - " hwhm = 25\n", - " prior_tau = -np.log(tau ** 2 + hwhm ** 2)\n", - " prior_mu = -((mu / 10) ** 2) # normal prior, loc=0, scale=10\n", - " prior_eta = -np.sum(eta ** 2) # normal prior, loc=0, scale=1\n", - " return prior_mu + prior_tau + prior_eta\n", - "\n", - "\n", - "def log_likelihood_8school(theta, y, sigma):\n", - " mu = theta[0]\n", - " tau = theta[1]\n", - " eta = theta[2:]\n", - " return -np.sum(((mu + tau * eta - y) / sigma) ** 2)\n", - "\n", - "\n", - "def lnprob_8school(theta, J, y, sigma):\n", - " prior = log_prior_8school(theta, J)\n", - " if prior <= -np.inf:\n", - " return -np.inf\n", - " like = log_likelihood_8school(theta, y, sigma)\n", - " return like + prior\n", - "\n", - "\n", - "nwalkers = 40\n", - "ndim = eight_school_data[\"J\"] + 2\n", - "draws = 1500\n", - "pos = np.random.normal(size=(nwalkers, ndim))\n", - "pos[:, 1] = np.absolute(pos[:, 1])\n", - "sampler = emcee.EnsembleSampler(\n", - " nwalkers,\n", - " ndim,\n", - " lnprob_8school,\n", - " args=(eight_school_data[\"J\"], eight_school_data[\"y\"], eight_school_data[\"sigma\"]),\n", - ")\n", - "sampler.run_mcmc(pos, draws)\n", - "\n", - "# define variable names, it cannot be inferred from emcee\n", - "var_names = [\"mu\", \"tau\"] + [\"eta{}\".format(i) for i in range(eight_school_data[\"J\"])]\n", - "emcee_data = az.from_emcee(sampler, var_names=var_names)\n", - "emcee_data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## From CmdStanPy" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "ExecuteTime": { - "end_time": "2020-06-05T06:48:14.965999Z", - "start_time": "2020-06-05T06:48:11.404455Z" - } - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ +======= "INFO:cmdstanpy:compiling stan program, exe file: /Volumes/GIT/PycharmProjects/arviz/doc/notebooks/eight_school\n", "INFO:cmdstanpy:compiler options: stanc_options=None, cpp_options=None\n", "ERROR:cmdstanpy:file /Volumes/GIT/PycharmProjects/arviz/doc/notebooks/eight_school.stan, exception ERROR\n", @@ -3017,6 +23793,7 @@ "\u001b[0;32m~/anaconda3/envs/continuous_time_mcmc/lib/python3.7/site-packages/cmdstanpy/model.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, model_name, stan_file, exe_file, compile, stanc_options, cpp_options, logger)\u001b[0m\n\u001b[1;32m 145\u001b[0m raise ValueError(\n\u001b[1;32m 146\u001b[0m 'Unable to compile Stan model file: {}.'.format(\n\u001b[0;32m--> 147\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stan_file\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 148\u001b[0m )\n\u001b[1;32m 149\u001b[0m )\n", "\u001b[0;31mValueError\u001b[0m: Unable to compile Stan model file: /Volumes/GIT/PycharmProjects/arviz/doc/notebooks/eight_school.stan." ] +>>>>>>> master } ], "source": [ @@ -3234,11 +24011,2517 @@ }, { "cell_type": "code", +<<<<<<< HEAD + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "arviz.data.io_cmdstan - INFO - glob found 8 files for 'posterior':\n", + "1: sample_data/eight_school-202006050131-1.csv\n", + "2: sample_data/eight_school-202006050131-2.csv\n", + "3: sample_data/eight_school-202006050131-3.csv\n", + "4: sample_data/eight_school-202006050131-4.csv\n", + "5: sample_data/eight_school-202006062340-1.csv\n", + "6: sample_data/eight_school-202006062340-2.csv\n", + "7: sample_data/eight_school-202006062340-3.csv\n", + "8: sample_data/eight_school-202006062340-4.csv\n", + "INFO:arviz.data.io_cmdstan:glob found 8 files for 'posterior':\n", + "1: sample_data/eight_school-202006050131-1.csv\n", + "2: sample_data/eight_school-202006050131-2.csv\n", + "3: sample_data/eight_school-202006050131-3.csv\n", + "4: sample_data/eight_school-202006050131-4.csv\n", + "5: sample_data/eight_school-202006062340-1.csv\n", + "6: sample_data/eight_school-202006062340-2.csv\n", + "7: sample_data/eight_school-202006062340-3.csv\n", + "8: sample_data/eight_school-202006062340-4.csv\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "
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    arviz.InferenceData
    \n", + "
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    • \n", + " \n", + " \n", + "
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        xarray.Dataset
          • chain: 8
          • draw: 1000
          • school: 8
          • chain
            (chain)
            int64
            0 1 2 3 4 5 6 7
            array([0, 1, 2, 3, 4, 5, 6, 7])
          • draw
            (draw)
            int64
            0 1 2 3 4 5 ... 995 996 997 998 999
            array([  0,   1,   2, ..., 997, 998, 999])
          • school
            (school)
            int64
            0 1 2 3 4 5 6 7
            array([0, 1, 2, 3, 4, 5, 6, 7])
          • mu
            (chain, draw)
            float64
            5.754 5.437 3.104 ... 8.698 6.691
            array([[ 5.75411 ,  5.43667 ,  3.10354 , ...,  8.40989 ,  6.88816 ,\n",
+       "         4.84188 ],\n",
+       "       [ 3.46458 ,  4.61087 ,  4.40147 , ...,  4.67511 ,  4.14192 ,\n",
+       "         3.22894 ],\n",
+       "       [ 2.82112 ,  2.54198 ,  0.871348, ...,  5.75993 ,  6.32959 ,\n",
+       "         4.04523 ],\n",
+       "       ...,\n",
+       "       [ 0.732634, -0.220275,  0.615834, ...,  7.16887 ,  8.03808 ,\n",
+       "         4.4595  ],\n",
+       "       [ 5.91379 ,  3.70948 ,  4.26014 , ..., -3.40594 ,  9.58868 ,\n",
+       "         0.366119],\n",
+       "       [ 3.88691 ,  3.18823 ,  5.80652 , ...,  2.068   ,  8.69849 ,\n",
+       "         6.69092 ]])
          • tau
            (chain, draw)
            float64
            1.462 1.613 3.111 ... 1.098 4.68
            array([[ 1.46249 ,  1.61302 ,  3.11057 , ...,  0.425836,  4.6845  ,\n",
+       "         4.10248 ],\n",
+       "       [ 1.81714 ,  6.89331 ,  3.70876 , ..., 10.6449  ,  0.962066,\n",
+       "         1.94808 ],\n",
+       "       [ 3.01982 ,  1.65352 ,  9.04967 , ..., 10.7062  ,  1.59843 ,\n",
+       "         2.85751 ],\n",
+       "       ...,\n",
+       "       [ 9.96193 ,  5.76789 ,  0.800563, ...,  2.45978 ,  6.35597 ,\n",
+       "         1.18186 ],\n",
+       "       [ 1.86466 ,  5.26644 ,  2.46189 , ...,  4.01369 ,  0.966623,\n",
+       "         2.22911 ],\n",
+       "       [ 6.751   ,  2.61096 ,  9.75554 , ...,  6.4743  ,  1.09751 ,\n",
+       "         4.67988 ]])
          • theta_tilde
            (chain, draw, school)
            float64
            -0.6214 0.9452 ... -1.239 0.3211
            array([[[-6.21370e-01,  9.45155e-01, -1.36712e+00, ...,  1.58131e+00,\n",
+       "          3.09975e+00,  4.29483e-01],\n",
+       "        [-1.52821e+00, -1.72430e+00,  1.43802e+00, ..., -3.74407e-01,\n",
+       "         -1.02713e+00, -3.46431e-01],\n",
+       "        [ 2.21221e+00,  1.09807e+00, -1.79456e+00, ...,  6.13786e-01,\n",
+       "          4.77244e-01,  8.50358e-01],\n",
+       "        ...,\n",
+       "        [ 5.00555e-01,  1.80918e-01,  2.09223e+00, ...,  1.43732e+00,\n",
+       "         -7.30622e-01, -3.05692e-01],\n",
+       "        [ 1.45083e+00, -1.79060e-01, -6.24895e-01, ...,  5.03847e-01,\n",
+       "          2.68425e-01,  6.35325e-01],\n",
+       "        [ 1.92402e+00, -1.30168e+00, -1.11849e+00, ...,  9.00814e-01,\n",
+       "         -1.30922e-01,  1.36532e+00]],\n",
+       "\n",
+       "       [[-4.28034e-01, -9.60212e-01,  5.96710e-01, ..., -7.73785e-01,\n",
+       "          7.33206e-01, -4.75392e-01],\n",
+       "        [ 1.49771e+00,  3.52741e-01, -9.06776e-01, ...,  1.63798e-01,\n",
+       "          7.72913e-01,  5.42626e-01],\n",
+       "        [-3.57775e-01,  2.69583e-01,  5.84750e-01, ..., -3.71687e-01,\n",
+       "          6.64695e-01, -1.64340e-01],\n",
+       "        ...,\n",
+       "        [ 1.39126e+00, -3.91738e-01, -1.91612e+00, ..., -7.01897e-01,\n",
+       "          8.46013e-02, -4.69081e-01],\n",
+       "        [ 9.73856e-02, -7.23720e-01,  4.66254e-01, ...,  1.41110e+00,\n",
+       "          6.15712e-01, -3.28810e-01],\n",
+       "        [-5.40356e-01, -2.34899e-01,  9.06709e-01, ...,  1.68489e+00,\n",
+       "         -1.44686e-01,  2.26657e-02]],\n",
+       "\n",
+       "       [[ 6.40529e-01, -4.76495e-01,  1.11577e+00, ..., -1.12361e+00,\n",
+       "         -3.58983e-01, -3.49613e-02],\n",
+       "        [ 1.42522e-01,  7.29873e-01, -2.50795e-01, ...,  4.42624e-02,\n",
+       "          9.92563e-02,  4.24022e-02],\n",
+       "        [ 1.70180e+00, -3.27843e-02, -5.23013e-01, ..., -1.85521e-01,\n",
+       "          1.15851e+00, -3.43518e-01],\n",
+       "        ...,\n",
+       "        [ 1.23330e+00,  6.60788e-01, -8.08756e-01, ...,  2.03739e-02,\n",
+       "          5.41245e-01,  3.66303e-01],\n",
+       "        [ 1.27968e+00,  7.37817e-01, -1.59119e+00, ..., -4.11977e-01,\n",
+       "          9.20530e-01, -3.43531e-01],\n",
+       "        [ 9.14241e-01,  8.80481e-01,  3.02754e-01, ...,  3.47260e-01,\n",
+       "          7.66575e-01, -1.21809e+00]],\n",
+       "\n",
+       "       ...,\n",
+       "\n",
+       "       [[-2.00545e-01, -2.10582e-01, -9.40322e-02, ...,  2.10087e-02,\n",
+       "          2.37020e-01,  1.29188e-01],\n",
+       "        [-5.38384e-01, -9.47796e-01,  7.59535e-01, ...,  4.92083e-01,\n",
+       "          7.14972e-01, -5.74193e-01],\n",
+       "        [ 1.05020e-02, -1.43597e-01,  5.72359e-02, ...,  3.59991e-01,\n",
+       "         -1.34135e-02, -7.78741e-01],\n",
+       "        ...,\n",
+       "        [ 1.25372e+00,  9.10764e-01, -3.08858e+00, ..., -1.96722e+00,\n",
+       "          1.12084e-02,  2.52069e-01],\n",
+       "        [ 1.93895e+00,  2.57758e-01, -2.47922e+00, ..., -8.94723e-01,\n",
+       "         -1.26788e-01,  4.69993e-01],\n",
+       "        [ 2.97201e+00,  6.67406e-02, -1.94356e-01, ...,  1.00890e-01,\n",
+       "         -3.81350e-01,  2.11749e-01]],\n",
+       "\n",
+       "       [[ 1.21786e+00,  1.23069e+00, -5.52686e-01, ..., -1.48749e+00,\n",
+       "          7.83702e-01,  1.85699e+00],\n",
+       "        [ 1.72311e+00,  7.67163e-01, -2.34386e-02, ..., -2.42422e+00,\n",
+       "          3.66557e-01,  1.29801e+00],\n",
+       "        [ 1.92775e+00,  7.48698e-01,  3.09622e-01, ..., -1.77732e+00,\n",
+       "          1.59023e-01,  1.52893e+00],\n",
+       "        ...,\n",
+       "        [ 1.23452e-01,  1.16843e+00,  1.00505e-02, ...,  1.31982e+00,\n",
+       "         -5.95969e-01, -4.30592e-01],\n",
+       "        [ 2.62933e-01, -1.08506e+00, -1.75313e+00, ..., -7.69045e-01,\n",
+       "          2.01635e-01, -1.55323e+00],\n",
+       "        [ 4.78519e-01,  4.44985e-01,  1.39678e+00, ...,  4.84171e-02,\n",
+       "          5.20688e-01,  1.69244e+00]],\n",
+       "\n",
+       "       [[-1.31033e+00, -7.85428e-03, -3.17845e-01, ..., -6.09675e-01,\n",
+       "          1.70432e+00,  1.16530e+00],\n",
+       "        [ 2.02721e+00,  5.59707e-01, -2.80847e-02, ...,  6.90420e-01,\n",
+       "         -5.35285e-01, -1.54980e-01],\n",
+       "        [ 1.26965e+00, -8.16178e-01,  7.36237e-01, ..., -3.99220e-03,\n",
+       "          7.63763e-01, -7.12747e-01],\n",
+       "        ...,\n",
+       "        [ 9.42448e-01, -7.24387e-04,  1.05740e+00, ...,  3.66910e-02,\n",
+       "          6.49015e-01, -5.97576e-01],\n",
+       "        [ 1.04661e+00,  1.81675e+00,  2.53350e-01, ..., -1.45018e-01,\n",
+       "         -7.19568e-01, -1.61255e+00],\n",
+       "        [ 2.12768e+00, -3.70835e-01,  1.35789e+00, ..., -9.19882e-02,\n",
+       "         -1.23920e+00,  3.21054e-01]]])
          • theta
            (chain, draw, school)
            float64
            4.845 7.136 3.755 ... 0.8916 8.193
            array([[[ 4.84536e+00,  7.13639e+00,  3.75472e+00, ...,  8.06676e+00,\n",
+       "          1.02875e+01,  6.38223e+00],\n",
+       "        [ 2.97165e+00,  2.65535e+00,  7.75622e+00, ...,  4.83275e+00,\n",
+       "          3.77990e+00,  4.87787e+00],\n",
+       "        [ 9.98476e+00,  6.51918e+00, -2.47856e+00, ...,  5.01277e+00,\n",
+       "          4.58804e+00,  5.74864e+00],\n",
+       "        ...,\n",
+       "        [ 8.62304e+00,  8.48693e+00,  9.30084e+00, ...,  9.02195e+00,\n",
+       "          8.09876e+00,  8.27971e+00],\n",
+       "        [ 1.36846e+01,  6.04935e+00,  3.96083e+00, ...,  9.24843e+00,\n",
+       "          8.14559e+00,  9.86434e+00],\n",
+       "        [ 1.27351e+01, -4.98243e-01,  2.53307e-01, ...,  8.53746e+00,\n",
+       "          4.30478e+00,  1.04431e+01]],\n",
+       "\n",
+       "       [[ 2.68678e+00,  1.71974e+00,  4.54888e+00, ...,  2.05851e+00,\n",
+       "          4.79691e+00,  2.60073e+00],\n",
+       "        [ 1.49351e+01,  7.04241e+00, -1.63982e+00, ...,  5.73997e+00,\n",
+       "          9.93879e+00,  8.35135e+00],\n",
+       "        [ 3.07457e+00,  5.40129e+00,  6.57017e+00, ...,  3.02297e+00,\n",
+       "          6.86666e+00,  3.79197e+00],\n",
+       "        ...,\n",
+       "        [ 1.94849e+01,  5.05102e-01, -1.57218e+01, ..., -2.79649e+00,\n",
+       "          5.57568e+00, -3.18205e-01],\n",
+       "        [ 4.23561e+00,  3.44565e+00,  4.59049e+00, ...,  5.49949e+00,\n",
+       "          4.73428e+00,  3.82558e+00],\n",
+       "        [ 2.17629e+00,  2.77134e+00,  4.99528e+00, ...,  6.51124e+00,\n",
+       "          2.94708e+00,  3.27310e+00]],\n",
+       "\n",
+       "       [[ 4.75540e+00,  1.38219e+00,  6.19053e+00, ..., -5.71994e-01,\n",
+       "          1.73705e+00,  2.71554e+00],\n",
+       "        [ 2.77764e+00,  3.74884e+00,  2.12728e+00, ...,  2.61517e+00,\n",
+       "          2.70610e+00,  2.61209e+00],\n",
+       "        [ 1.62720e+01,  5.74661e-01, -3.86174e+00, ..., -8.07558e-01,\n",
+       "          1.13555e+01, -2.23737e+00],\n",
+       "        ...,\n",
+       "        [ 1.89639e+01,  1.28344e+01, -2.89876e+00, ...,  5.97805e+00,\n",
+       "          1.15546e+01,  9.68164e+00],\n",
+       "        [ 8.37506e+00,  7.50893e+00,  3.78619e+00, ...,  5.67108e+00,\n",
+       "          7.80099e+00,  5.78048e+00],\n",
+       "        [ 6.65769e+00,  6.56122e+00,  4.91035e+00, ...,  5.03753e+00,\n",
+       "          6.23573e+00,  5.64515e-01]],\n",
+       "\n",
+       "       ...,\n",
+       "\n",
+       "       [[-1.26518e+00, -1.36517e+00, -2.04107e-01, ...,  9.41921e-01,\n",
+       "          3.09381e+00,  2.01959e+00],\n",
+       "        [-3.32561e+00, -5.68706e+00,  4.16063e+00, ...,  2.61801e+00,\n",
+       "          3.90360e+00, -3.53216e+00],\n",
+       "        [ 6.24242e-01,  5.00876e-01,  6.61655e-01, ...,  9.04029e-01,\n",
+       "          6.05096e-01, -7.59738e-03],\n",
+       "        ...,\n",
+       "        [ 1.02527e+01,  9.40915e+00, -4.28361e-01, ...,  2.32995e+00,\n",
+       "          7.19644e+00,  7.78891e+00],\n",
+       "        [ 2.03620e+01,  9.67638e+00, -7.71979e+00, ...,  2.35124e+00,\n",
+       "          7.23222e+00,  1.10253e+01],\n",
+       "        [ 7.97201e+00,  4.53838e+00,  4.22980e+00, ...,  4.57874e+00,\n",
+       "          4.00880e+00,  4.70976e+00]],\n",
+       "\n",
+       "       [[ 8.18468e+00,  8.20861e+00,  4.88322e+00, ...,  3.14013e+00,\n",
+       "          7.37513e+00,  9.37645e+00],\n",
+       "        [ 1.27841e+01,  7.74970e+00,  3.58604e+00, ..., -9.05752e+00,\n",
+       "          5.63993e+00,  1.05454e+01],\n",
+       "        [ 9.00605e+00,  6.10335e+00,  5.02239e+00, ..., -1.15431e-01,\n",
+       "          4.65164e+00,  8.02420e+00],\n",
+       "        ...,\n",
+       "        [-2.91044e+00,  1.28378e+00, -3.36560e+00, ...,  1.89143e+00,\n",
+       "         -5.79797e+00, -5.13420e+00],\n",
+       "        [ 9.84283e+00,  8.53983e+00,  7.89406e+00, ...,  8.84530e+00,\n",
+       "          9.78358e+00,  8.08729e+00],\n",
+       "        [ 1.43279e+00,  1.35804e+00,  3.47971e+00, ...,  4.74046e-01,\n",
+       "          1.52679e+00,  4.13875e+00]],\n",
+       "\n",
+       "       [[-4.95910e+00,  3.83388e+00,  1.74114e+00, ..., -2.29009e-01,\n",
+       "          1.53928e+01,  1.17538e+01],\n",
+       "        [ 8.48119e+00,  4.64960e+00,  3.11490e+00, ...,  4.99089e+00,\n",
+       "          1.79062e+00,  2.78358e+00],\n",
+       "        [ 1.81926e+01, -2.15574e+00,  1.29889e+01, ...,  5.76757e+00,\n",
+       "          1.32574e+01, -1.14672e+00],\n",
+       "        ...,\n",
+       "        [ 8.16969e+00,  2.06331e+00,  8.91392e+00, ...,  2.30555e+00,\n",
+       "          6.26992e+00, -1.80089e+00],\n",
+       "        [ 9.84716e+00,  1.06924e+01,  8.97655e+00, ...,  8.53934e+00,\n",
+       "          7.90876e+00,  6.92871e+00],\n",
+       "        [ 1.66482e+01,  4.95545e+00,  1.30457e+01, ...,  6.26042e+00,\n",
+       "          8.91628e-01,  8.19341e+00]]])
        • created_at :
          2020-06-06T18:10:33.158160
          arviz_version :
          0.8.3

        \n", + "
      \n", + "
      \n", + "
      • \n", + " \n", + "
    • \n", + " \n", + " \n", + "
      \n", + "
      \n", + "
        \n", + "
        \n", + "\n", + "\n", + "Show/Hide data repr\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Show/Hide attributes\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
        xarray.Dataset
          • chain: 8
          • draw: 1000
          • school: 8
          • chain
            (chain)
            int64
            0 1 2 3 4 5 6 7
            array([0, 1, 2, 3, 4, 5, 6, 7])
          • draw
            (draw)
            int64
            0 1 2 3 4 5 ... 995 996 997 998 999
            array([  0,   1,   2, ..., 997, 998, 999])
          • school
            (school)
            int64
            0 1 2 3 4 5 6 7
            array([0, 1, 2, 3, 4, 5, 6, 7])
          • y_hat
            (chain, draw, school)
            float64
            19.05 -1.305 10.61 ... -11.52 28.27
            array([[[ 19.0496   ,  -1.30496  ,  10.6111   , ...,   3.43562  ,\n",
+       "          15.013    ,   4.59842  ],\n",
+       "        [-14.611    ,  11.6403   ,   2.91314  , ...,  -3.46515  ,\n",
+       "         -25.2819   ,  20.9299   ],\n",
+       "        [ 14.5244   ,   4.84974  ,   3.61749  , ...,  -1.81476  ,\n",
+       "          -6.17733  , -10.6538   ],\n",
+       "        ...,\n",
+       "        [ -3.66121  ,   4.88421  ,  -5.88003  , ...,  -9.1894   ,\n",
+       "           8.20073  ,   4.98755  ],\n",
+       "        [ 38.035    ,   2.42776  ,  23.6846   , ...,  20.0096   ,\n",
+       "          10.1016   ,   9.22392  ],\n",
+       "        [ 24.2337   ,  16.5773   ,  -8.03439  , ...,   4.93551  ,\n",
+       "         -22.9208   , -13.6367   ]],\n",
+       "\n",
+       "       [[  0.212848 ,   4.96483  ,   7.24908  , ...,   0.823218 ,\n",
+       "           0.840567 ,  10.0623   ],\n",
+       "        [ 21.0565   ,  24.0836   ,  -2.36259  , ...,   1.7789   ,\n",
+       "           3.11908  ,  26.3255   ],\n",
+       "        [  6.86017  ,   6.56333  , -14.5155   , ...,  -1.54344  ,\n",
+       "          30.2473   , -14.6159   ],\n",
+       "        ...,\n",
+       "        [ 38.5073   ,  -0.233819 ,  13.8765   , ..., -12.4776   ,\n",
+       "          13.0071   ,   4.49981  ],\n",
+       "        [ -9.76525  ,  -2.31316  ,   2.28414  , ...,  16.0648   ,\n",
+       "          16.6526   ,  -3.44407  ],\n",
+       "        [  6.07809  ,  -6.72812  ,   1.97262  , ...,   2.60334  ,\n",
+       "           2.23987  ,  -6.51931  ]],\n",
+       "\n",
+       "       [[ 16.4241   ,   0.344368 ,  28.502    , ...,  -7.54104  ,\n",
+       "         -14.1077   ,   6.46194  ],\n",
+       "        [  0.55366  , -12.8928   , -21.3035   , ...,   8.0707   ,\n",
+       "           7.93818  ,   5.92858  ],\n",
+       "        [ 24.945    ,  13.2419   ,  -6.48804  , ...,  -0.57189  ,\n",
+       "           5.65947  ,   8.71675  ],\n",
+       "        ...,\n",
+       "        [ 11.0441   ,   6.66108  ,  18.1153   , ...,  13.6414   ,\n",
+       "          18.9915   ,  18.0794   ],\n",
+       "        [ -3.01072  ,  -3.66957  ,  11.0674   , ..., -18.295    ,\n",
+       "          -2.24387  ,  15.3192   ],\n",
+       "        [ 17.8048   ,  22.1956   ,   0.977643 , ...,  20.2169   ,\n",
+       "          16.6958   ,  15.2811   ]],\n",
+       "\n",
+       "       ...,\n",
+       "\n",
+       "       [[-13.8236   ,   3.30856  ,  -9.71613  , ...,   1.07714  ,\n",
+       "          24.3845   , -37.759    ],\n",
+       "        [-19.5816   ,   9.47864  ,   7.43956  , ...,   8.73732  ,\n",
+       "          15.1482   ,  20.7514   ],\n",
+       "        [ -3.2081   ,  -5.29835  , -23.4598   , ...,  -7.25993  ,\n",
+       "          15.408    , -12.4758   ],\n",
+       "        ...,\n",
+       "        [ 17.2876   ,   9.47491  ,   9.11321  , ...,  -0.844266 ,\n",
+       "          23.7267   ,  13.1609   ],\n",
+       "        [ 42.8214   , -11.115    , -28.3195   , ...,   9.54406  ,\n",
+       "           6.86596  ,  24.7486   ],\n",
+       "        [ 31.5371   ,  -9.2391   ,  -5.19494  , ...,  16.0107   ,\n",
+       "          18.2181   ,  19.2444   ]],\n",
+       "\n",
+       "       [[-22.2079   ,  16.09     ,  -1.43103  , ...,  -2.81685  ,\n",
+       "         -11.7201   ,  26.1408   ],\n",
+       "        [  7.84245  ,  12.1987   ,  12.4391   , ...,  -8.31294  ,\n",
+       "          16.7167   ,  24.0476   ],\n",
+       "        [ -5.47622  , -10.7056   , -13.119    , ...,  -9.98965  ,\n",
+       "           1.99784  ,  12.6135   ],\n",
+       "        ...,\n",
+       "        [ 10.498    ,  -6.5292   ,  -1.95445  , ...,   6.90389  ,\n",
+       "           9.90309  ,  -9.53769  ],\n",
+       "        [-22.7585   ,  16.791    ,   3.64741  , ...,  16.2263   ,\n",
+       "          13.1702   ,  -2.93753  ],\n",
+       "        [ 16.2394   ,   3.08554  ,   9.0992   , ...,  -7.29659  ,\n",
+       "          16.6593   ,  -2.51172  ]],\n",
+       "\n",
+       "       [[ 23.8207   ,   6.97066  , -13.2277   , ...,  -4.33379  ,\n",
+       "          16.7229   ,  -4.24901  ],\n",
+       "        [ 19.6784   ,  10.6246   ,  15.6069   , ...,   8.62725  ,\n",
+       "          -0.49937  ,  -4.91235  ],\n",
+       "        [-23.3668   ,   8.97635  ,  22.8516   , ...,  16.1906   ,\n",
+       "          18.5997   ,  -0.0484115],\n",
+       "        ...,\n",
+       "        [  1.07142  ,   2.08327  ,  28.3587   , ...,  18.9032   ,\n",
+       "          11.3534   ,  -3.75202  ],\n",
+       "        [ 11.1992   ,   7.75921  ,  11.0927   , ...,   2.09711  ,\n",
+       "           4.61789  ,  31.6442   ],\n",
+       "        [ 27.53     ,  19.7944   ,  16.7601   , ..., -15.1517   ,\n",
+       "         -11.5217   ,  28.2679   ]]])
        • created_at :
          2020-06-06T18:10:33.514389
          arviz_version :
          0.8.3

        \n", + "
      \n", + "
      \n", + "
      • \n", + " \n", + "
    • \n", + " \n", + " \n", + "
      \n", + "
      \n", + "
        \n", + "
        \n", + "\n", + "\n", + "Show/Hide data repr\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Show/Hide attributes\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
        xarray.Dataset
          • chain: 8
          • draw: 1000
          • school: 8
          • chain
            (chain)
            int64
            0 1 2 3 4 5 6 7
            array([0, 1, 2, 3, 4, 5, 6, 7])
          • draw
            (draw)
            int64
            0 1 2 3 4 5 ... 995 996 997 998 999
            array([  0,   1,   2, ..., 997, 998, 999])
          • school
            (school)
            int64
            0 1 2 3 4 5 6 7
            array([0, 1, 2, 3, 4, 5, 6, 7])
          • log_lik
            (chain, draw, school)
            float64
            -4.818 -3.225 ... -4.685 -3.832
            array([[[-4.8184 , -3.22525, -3.78064, ..., -3.52319, -3.51894,\n",
+       "         -3.85801],\n",
+       "        [-5.01903, -3.36435, -3.9175 , ..., -3.37754, -4.23258,\n",
+       "         -3.88759],\n",
+       "        [-4.34821, -3.23249, -3.69206, ..., -3.38337, -4.12093,\n",
+       "         -3.86962],\n",
+       "        ...,\n",
+       "        [-4.46136, -3.22271, -3.98706, ..., -3.58275, -3.7117 ,\n",
+       "         -3.83067],\n",
+       "        [-4.08239, -3.24055, -3.78616, ..., -3.59798, -3.70707,\n",
+       "         -3.81635],\n",
+       "        [-4.1448 , -3.58262, -3.7122 , ..., -3.5516 , -4.15932,\n",
+       "         -3.81305]],\n",
+       "\n",
+       "       [[-5.0509 , -3.41873, -3.80283, ..., -3.32146, -4.09313,\n",
+       "         -3.94565],\n",
+       "        [-4.00631, -3.22611, -3.69514, ..., -3.40967, -3.54644,\n",
+       "         -3.82985],\n",
+       "        [-5.0076 , -3.25529, -3.87041, ..., -3.33374, -3.84128,\n",
+       "         -3.91328],\n",
+       "        ...,\n",
+       "        [-3.78811, -3.50239, -4.00763, ..., -3.37639, -3.99334,\n",
+       "         -4.04347],\n",
+       "        [-4.88198, -3.32523, -3.80406, ..., -3.40049, -4.10142,\n",
+       "         -3.91243],\n",
+       "        [-5.10891, -3.35822, -3.81638, ..., -3.44235, -4.35448,\n",
+       "         -3.92684]],\n",
+       "\n",
+       "       [[-4.82768, -3.4405 , -3.8565 , ..., -3.32705, -4.54394,\n",
+       "         -3.94234],\n",
+       "        [-5.04069, -3.31189, -3.74287, ..., -3.32761, -4.39104,\n",
+       "         -3.94532],\n",
+       "        [-3.93264, -3.4972 , -3.69298, ..., -3.33033, -3.44227,\n",
+       "         -4.12212],\n",
+       "        ...,\n",
+       "        [-3.80844, -3.33838, -3.69155, ..., -3.41923, -3.42924,\n",
+       "         -3.8176 ],\n",
+       "        [-4.48285, -3.22273, -3.78147, ..., -3.40699, -3.74162,\n",
+       "         -3.86901],\n",
+       "        [-4.6392 , -3.23187, -3.81374, ..., -3.3842 , -3.91351,\n",
+       "         -4.01112]],\n",
+       "\n",
+       "       ...,\n",
+       "\n",
+       "       [[-5.53021, -3.66006, -3.70679, ..., -3.31685, -4.3325 ,\n",
+       "         -3.96303],\n",
+       "        [-5.80764, -4.1582 , -3.79167, ..., -3.32765, -4.21507,\n",
+       "         -4.18161],\n",
+       "        [-5.29239, -3.50271, -3.71771, ..., -3.31687, -4.73444,\n",
+       "         -4.03181],\n",
+       "        ...,\n",
+       "        [-4.32691, -3.23145, -3.70444, ..., -3.32414, -3.80511,\n",
+       "         -3.83668],\n",
+       "        [-3.75663, -3.23557, -3.73504, ..., -3.32438, -3.80125,\n",
+       "         -3.81078],\n",
+       "        [-4.51837, -3.28144, -3.79362, ..., -3.36976, -4.20029,\n",
+       "         -3.89133]],\n",
+       "\n",
+       "       [[-4.49954, -3.22174, -3.8129 , ..., -3.33576, -3.78596,\n",
+       "         -3.81993],\n",
+       "        [-4.14148, -3.22184, -3.77625, ..., -3.73482, -3.98538,\n",
+       "         -3.81258],\n",
+       "        [-4.4287 , -3.23951, -3.81723, ..., -3.32198, -4.11242,\n",
+       "         -3.8337 ],\n",
+       "        ...,\n",
+       "        [-5.75022, -3.44706, -3.69179, ..., -3.32012, -6.05324,\n",
+       "         -4.26237],\n",
+       "        [-4.35962, -3.22298, -3.92333, ..., -3.57117, -3.55907,\n",
+       "         -3.83294],\n",
+       "        [-5.19547, -3.4421 , -3.77353, ..., -3.31798, -4.57836,\n",
+       "         -3.90468]],\n",
+       "\n",
+       "       [[-6.04099, -3.30831, -3.73543, ..., -3.32308, -3.25551,\n",
+       "         -3.8094 ],\n",
+       "        [-4.47362, -3.27765, -3.76456, ..., -3.38265, -4.53524,\n",
+       "         -3.94039],\n",
+       "        [-3.84073, -3.73722, -4.19083, ..., -3.41076, -3.33398,\n",
+       "         -4.07603],\n",
+       "        ...,\n",
+       "        [-4.50086, -3.39775, -3.96876, ..., -3.32388, -3.9095 ,\n",
+       "         -4.10324],\n",
+       "        [-4.35927, -3.25777, -3.97168, ..., -3.55172, -3.73069,\n",
+       "         -3.849  ],\n",
+       "        [-3.91335, -3.26787, -4.19439, ..., -3.43118, -4.68501,\n",
+       "         -3.83167]]])
        • created_at :
          2020-06-06T18:10:33.488126
          arviz_version :
          0.8.3

        \n", + "
      \n", + "
      \n", + "
      • \n", + " \n", + "
    • \n", + " \n", + " \n", + "
      \n", + "
      \n", + "
        \n", + "
        \n", + "\n", + "\n", + "Show/Hide data repr\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Show/Hide attributes\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
        xarray.Dataset
          • chain: 8
          • draw: 1000
          • chain
            (chain)
            int64
            0 1 2 3 4 5 6 7
            array([0, 1, 2, 3, 4, 5, 6, 7])
          • draw
            (draw)
            int64
            0 1 2 3 4 5 ... 995 996 997 998 999
            array([  0,   1,   2, ..., 997, 998, 999])
          • lp
            (chain, draw)
            float64
            -10.36 -8.78 ... -8.303 -8.039
            array([[-10.3645 ,  -8.77989,  -8.25432, ...,  -8.8717 ,  -3.50969,\n",
+       "         -6.82756],\n",
+       "       [ -4.22274,  -2.63855,  -2.69171, ...,  -5.31493,  -5.41059,\n",
+       "         -6.71709],\n",
+       "       [ -4.48958,  -3.36304,  -2.94072, ...,  -5.73727,  -7.05069,\n",
+       "         -4.78392],\n",
+       "       ...,\n",
+       "       [ -3.36045,  -5.68201,  -4.56247, ..., -13.8115 , -10.5851 ,\n",
+       "         -7.94476],\n",
+       "       [ -6.90788,  -6.99938,  -6.69535, ..., -11.5595 ,  -7.91042,\n",
+       "         -5.89621],\n",
+       "       [ -6.73536,  -5.18604,  -4.06661, ...,  -3.82035,  -8.3025 ,\n",
+       "         -8.03949]])
          • accept_stat
            (chain, draw)
            float64
            0.9435 1.0 0.9498 ... 0.8485 0.9904
            array([[0.94354 , 1.      , 0.949822, ..., 0.905146, 0.93289 , 0.869462],\n",
+       "       [1.      , 0.982365, 0.962294, ..., 0.722748, 0.361011, 0.978471],\n",
+       "       [0.99881 , 0.985447, 0.971381, ..., 0.970813, 1.      , 0.808264],\n",
+       "       ...,\n",
+       "       [0.99676 , 0.785461, 0.941576, ..., 0.805343, 1.      , 0.751312],\n",
+       "       [0.897601, 0.97537 , 0.817992, ..., 0.872228, 0.920333, 0.980318],\n",
+       "       [0.901122, 0.984381, 0.986321, ..., 1.      , 0.848503, 0.990407]])
          • stepsize
            (chain, draw)
            float64
            0.4298 0.4298 ... 0.3523 0.3523
            array([[0.429759, 0.429759, 0.429759, ..., 0.429759, 0.429759, 0.429759],\n",
+       "       [0.488728, 0.488728, 0.488728, ..., 0.488728, 0.488728, 0.488728],\n",
+       "       [0.362846, 0.362846, 0.362846, ..., 0.362846, 0.362846, 0.362846],\n",
+       "       ...,\n",
+       "       [0.39744 , 0.39744 , 0.39744 , ..., 0.39744 , 0.39744 , 0.39744 ],\n",
+       "       [0.379572, 0.379572, 0.379572, ..., 0.379572, 0.379572, 0.379572],\n",
+       "       [0.352264, 0.352264, 0.352264, ..., 0.352264, 0.352264, 0.352264]])
          • treedepth
            (chain, draw)
            int64
            3 3 4 3 3 3 3 3 ... 3 3 3 4 4 3 3 3
            array([[3, 3, 4, ..., 3, 3, 2],\n",
+       "       [3, 3, 3, ..., 3, 3, 3],\n",
+       "       [4, 3, 3, ..., 3, 3, 3],\n",
+       "       ...,\n",
+       "       [3, 3, 3, ..., 3, 3, 3],\n",
+       "       [4, 3, 3, ..., 3, 3, 4],\n",
+       "       [3, 3, 3, ..., 3, 3, 3]])
          • n_leapfrog
            (chain, draw)
            int64
            7 7 15 7 7 7 7 ... 15 15 15 7 7 15
            array([[ 7,  7, 15, ..., 15,  7,  3],\n",
+       "       [ 7,  7, 15, ...,  7,  7,  7],\n",
+       "       [15,  7,  7, ...,  7,  7, 15],\n",
+       "       ...,\n",
+       "       [ 7,  7,  7, ...,  7,  7,  7],\n",
+       "       [15,  7,  7, ...,  7, 15, 15],\n",
+       "       [ 7,  7,  7, ...,  7,  7, 15]])
          • diverging
            (chain, draw)
            bool
            False False False ... False False
            array([[False, False, False, ..., False, False, False],\n",
+       "       [False, False, False, ..., False, False, False],\n",
+       "       [False, False, False, ..., False, False, False],\n",
+       "       ...,\n",
+       "       [False, False, False, ..., False, False, False],\n",
+       "       [False, False, False, ..., False, False, False],\n",
+       "       [False, False, False, ..., False, False, False]])
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      • \n", + " \n", + "
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    \n", + " " + ], + "text/plain": [ + "Inference data with groups:\n", + "\t> posterior\n", + "\t> posterior_predictive\n", + "\t> log_likelihood\n", + "\t> sample_stats\n", + "\t> observed_data" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" +======= "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2020-06-05T06:48:14.977219Z", "start_time": "2020-06-05T06:47:05.614Z" +>>>>>>> master } }, "outputs": [], @@ -3294,6 +26577,3257 @@ "outputs": [ { "data": { + "text/html": [ + "\n", + "
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          • depth
            (chain, draw)
            int64
            3 4 3 3 3 4 3 3 ... 3 3 3 3 3 3 3 3
            array([[3, 4, 3, ..., 3, 4, 3],\n",
+       "       [3, 4, 4, ..., 3, 3, 3],\n",
+       "       [3, 3, 3, ..., 2, 3, 3],\n",
+       "       [3, 3, 3, ..., 3, 3, 3]])
        • created_at :
          2020-06-06T18:10:52.913185
          arviz_version :
          0.8.3
          inference_library :
          numpyro
          inference_library_version :
          0.2.3

        \n", + "
      \n", + "
      \n", + "
      • \n", + " \n", + "
    • \n", + " \n", + " \n", + "
      \n", + "
      \n", + "
        \n", + "
        \n", + "\n", + "\n", + "Show/Hide data repr\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Show/Hide attributes\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
        xarray.Dataset
          • chain: 1
          • draw: 500
          • school: 8
          • chain
            (chain)
            int64
            0
            array([0])
          • draw
            (draw)
            int64
            0 1 2 3 4 5 ... 495 496 497 498 499
            array([  0,   1,   2, ..., 497, 498, 499])
          • school
            (school)
            int64
            0 1 2 3 4 5 6 7
            array([0, 1, 2, 3, 4, 5, 6, 7])
          • mu
            (chain, draw)
            float32
            1.8776392 3.322776 ... -0.5562178
            array([[ 1.87763917e+00,  3.32277608e+00, -9.43409824e+00,\n",
+       "        -6.36535287e-01,  4.97456253e-01, -7.59930372e+00,\n",
+       "        -1.73702741e+00, -6.81911755e+00, -5.64398336e+00,\n",
+       "        -2.23989189e-01,  5.17267179e+00,  8.54710960e+00,\n",
+       "         3.31850290e+00,  1.21073082e-01,  7.83620691e+00,\n",
+       "         4.69881475e-01, -1.08328164e+00,  8.50991058e+00,\n",
+       "        -8.41802776e-01, -4.04251289e+00,  3.32294011e+00,\n",
+       "         5.99611139e+00, -6.17558670e+00,  6.03334332e+00,\n",
+       "        -5.70485020e+00, -2.03827691e+00, -1.12319574e+01,\n",
+       "         4.22157764e+00,  2.38109422e+00, -4.61673450e+00,\n",
+       "         5.93315411e+00,  4.55151653e+00, -7.10970700e-01,\n",
+       "        -6.31284094e+00, -4.34699202e+00,  6.09261799e+00,\n",
+       "        -6.14266682e+00,  9.26569998e-01,  1.61548471e+00,\n",
+       "        -4.15261239e-01, -7.68356943e+00, -2.56281686e+00,\n",
+       "        -4.79618311e+00,  7.81891727e+00, -3.24380898e+00,\n",
+       "         1.65736473e+00, -5.14156342e+00,  1.10072966e+01,\n",
+       "        -1.44031830e+01,  5.42257833e+00,  5.59339571e+00,\n",
+       "         1.49750960e+00, -3.32892269e-01, -4.46871185e+00,\n",
+       "         2.56567287e+00,  2.47131181e+00, -1.37173009e+00,\n",
+       "        -1.49122763e+00, -1.84207225e+00,  9.69244099e+00,\n",
+       "        -3.78764582e+00, -6.92075205e+00, -2.02182364e+00,\n",
+       "        -2.73029757e+00,  9.31008577e-01,  7.28544283e+00,\n",
+       "         1.54594302e+00,  5.64335871e+00, -3.37919688e+00,\n",
+       "         4.13902712e+00,  2.63738465e+00,  1.40324092e+00,\n",
+       "         1.34920418e+00,  4.90401697e+00,  3.16824293e+00,\n",
+       "         1.47920895e+00,  2.66602039e-01,  2.51417804e+00,\n",
+       "        -5.36913395e+00, -6.41616249e+00,  4.33436871e+00,\n",
+       "        -3.89514852e+00,  5.18284702e+00, -3.32038426e+00,\n",
+       "         1.16524143e+01, -6.82530260e+00, -3.53052795e-01,\n",
+       "         1.58044920e+01, -2.88200164e+00,  7.18814087e+00,\n",
+       "         2.98873329e+00, -2.26602387e+00,  4.08212948e+00,\n",
+       "         3.42252636e+00,  8.58223617e-01,  6.26113534e-01,\n",
+       "        -7.87266970e+00,  2.15487385e+00, -6.07770061e+00,\n",
+       "        -2.48635459e+00,  2.68985540e-01,  3.20517206e+00,\n",
+       "        -7.47953987e+00,  4.08663481e-01,  8.99567795e+00,\n",
+       "         7.04715443e+00,  7.23012495e+00, -7.46469927e+00,\n",
+       "        -1.33649683e+00,  1.10470068e+00, -2.52437449e+00,\n",
+       "        -3.95289946e+00,  2.47145557e+00, -1.09172354e+01,\n",
+       "         4.06590843e+00,  1.29628773e+01,  4.18381977e+00,\n",
+       "        -2.29971838e+00,  8.35384464e+00, -1.05598001e+01,\n",
+       "        -3.01278502e-01,  5.95889282e+00, -9.29296911e-01,\n",
+       "        -7.44931984e+00,  1.46392691e+00, -5.03668404e+00,\n",
+       "         5.68537998e+00, -2.34223104e+00, -3.17319202e+00,\n",
+       "        -1.99956322e+00, -2.91206074e+00,  3.08447218e+00,\n",
+       "         6.02147937e-01, -2.49044871e+00, -3.42613077e+00,\n",
+       "        -3.59730273e-01,  4.51586843e-01, -9.63892519e-01,\n",
+       "         2.33508396e+00, -2.13027024e+00,  3.09376287e+00,\n",
+       "        -7.35646665e-01, -8.27977657e+00,  2.87267494e+00,\n",
+       "        -2.67699122e+00, -8.89165998e-02, -2.52230263e+00,\n",
+       "        -5.46618128e+00, -8.53099227e-01,  1.64677703e+00,\n",
+       "         3.64264667e-01, -6.47672355e-01,  1.26347458e+00,\n",
+       "        -6.80529928e+00, -3.41742945e+00,  3.55985022e+00,\n",
+       "        -2.55946088e+00,  5.54295111e+00,  9.42449033e-01,\n",
+       "         1.64480090e-01, -8.02109167e-02,  3.24023986e+00,\n",
+       "        -4.53600079e-01,  1.37038574e+01,  5.71575737e+00,\n",
+       "         5.76857448e-01,  2.17014074e+00,  8.14787924e-01,\n",
+       "        -2.32475162e+00, -1.25709087e-01,  5.28375340e+00,\n",
+       "         9.62919891e-01,  6.91391659e+00,  2.62722158e+00,\n",
+       "        -2.74987268e+00,  2.25159264e+00,  1.91202760e+00,\n",
+       "         3.22131705e+00, -5.74864435e+00, -6.94986010e+00,\n",
+       "         5.20943463e-01, -5.00131655e+00, -3.07738566e+00,\n",
+       "         3.08097601e+00,  2.67554283e+00,  3.52074385e+00,\n",
+       "         6.54691935e+00, -9.24593830e+00, -3.36103296e+00,\n",
+       "         4.60509825e+00, -2.21546078e+00,  2.92350554e+00,\n",
+       "        -5.45947027e+00, -2.05733395e+00,  4.57703733e+00,\n",
+       "        -1.06910706e+00,  7.19312000e+00, -2.47113132e+00,\n",
+       "         3.12669230e+00, -1.83465838e+00,  4.28496313e+00,\n",
+       "         5.37801313e+00,  5.02111244e+00,  7.15357685e+00,\n",
+       "        -3.89565319e-01,  3.89020848e+00, -3.05091119e+00,\n",
+       "        -4.23250914e+00, -2.70682287e+00,  5.63752937e+00,\n",
+       "         1.95974433e+00,  1.16094184e+00,  5.58682775e+00,\n",
+       "        -1.27820683e+00,  1.79363227e+00,  4.05184221e+00,\n",
+       "        -7.00801432e-01, -8.40434170e+00, -8.00249863e+00,\n",
+       "         3.10724616e+00,  1.68522644e+00, -7.73088264e+00,\n",
+       "        -6.81490719e-01,  4.33881950e+00, -2.85863638e+00,\n",
+       "        -6.09480000e+00,  6.74300861e+00, -4.61606646e+00,\n",
+       "        -5.85084915e+00,  6.28509665e+00,  1.95886517e+00,\n",
+       "         1.37964690e+00, -1.37427435e+01,  2.82988310e-01,\n",
+       "         5.19354916e+00, -2.49803233e+00,  4.12895918e+00,\n",
+       "         3.44766212e+00,  2.19018960e+00, -3.72761774e+00,\n",
+       "        -2.44066030e-01, -1.30070686e+00, -7.02205515e+00,\n",
+       "         5.58520174e+00, -3.75585365e+00,  5.79257107e+00,\n",
+       "        -5.56449747e+00, -2.05371952e+00,  5.01734138e-01,\n",
+       "        -2.25151634e+00, -7.47076082e+00, -2.79890943e+00,\n",
+       "         4.27494675e-01, -7.40755653e+00,  1.63190055e+00,\n",
+       "         5.20384073e+00,  2.63987112e+00, -5.63552999e+00,\n",
+       "        -1.90767086e+00,  2.34675384e+00,  7.09238386e+00,\n",
+       "        -1.90808758e-01, -3.74278712e+00, -4.35007524e+00,\n",
+       "         4.41096497e+00,  5.99206209e+00,  9.79538822e+00,\n",
+       "         3.85714984e+00, -7.91006833e-02, -6.17025375e-01,\n",
+       "         9.57570493e-01, -2.43118548e+00,  3.27787709e+00,\n",
+       "         1.25472581e+00, -6.39819193e+00,  5.21416187e+00,\n",
+       "        -8.07223260e-01,  5.50955153e+00,  3.49549317e+00,\n",
+       "        -1.64368260e+00, -4.73124313e+00,  2.40179896e-01,\n",
+       "         5.83935118e+00,  4.42518854e+00, -6.38878107e+00,\n",
+       "        -4.94022989e+00,  1.35768807e+00,  3.12588358e+00,\n",
+       "        -9.07020473e+00,  4.31778097e+00, -4.70105934e+00,\n",
+       "         8.42939377e-01,  4.88915968e+00, -2.72294712e+00,\n",
+       "        -8.45176220e-01,  4.62164545e+00, -2.80933213e+00,\n",
+       "         4.71438694e+00, -2.46584034e+00, -7.28257990e+00,\n",
+       "         4.51298285e+00,  2.38831258e+00,  7.25005722e+00,\n",
+       "         1.79590631e+00,  5.94742823e+00,  7.22948265e+00,\n",
+       "         3.35202265e+00, -4.28335810e+00, -1.37254620e+00,\n",
+       "        -1.73780191e+00,  2.67699456e+00, -5.62134886e+00,\n",
+       "         9.09809971e+00,  5.02816010e+00,  9.05102825e+00,\n",
+       "         7.36941040e-01,  1.68737400e+00,  8.23375225e+00,\n",
+       "         8.10452104e-01, -6.44127703e+00,  1.16090810e+00,\n",
+       "        -1.34760213e+00, -4.49384308e+00, -3.41933179e+00,\n",
+       "        -4.81171131e+00, -1.84972250e+00,  1.02269201e+01,\n",
+       "        -1.26457906e+00, -2.97555542e+00,  5.53493214e+00,\n",
+       "        -1.07800531e+00,  9.11830544e-01,  1.52422190e+00,\n",
+       "         1.16673994e+00, -1.45918512e+00, -8.07522106e+00,\n",
+       "         7.53456783e+00,  8.10562551e-01,  1.59508491e+00,\n",
+       "        -1.25905542e+01, -2.68738222e+00, -5.06097794e+00,\n",
+       "        -5.67802286e+00, -2.19069934e+00,  4.38740063e+00,\n",
+       "         1.46200097e+00, -5.58933640e+00,  6.96885443e+00,\n",
+       "         9.52366066e+00, -5.29678154e+00,  3.33105922e+00,\n",
+       "         1.38898647e+00, -3.12741876e+00,  2.34334993e+00,\n",
+       "        -2.98348856e+00, -1.02783978e+00,  3.65240884e+00,\n",
+       "        -3.50431395e+00, -2.64991903e+00,  5.53679609e+00,\n",
+       "         5.90298462e+00,  3.79234552e+00, -4.90194845e+00,\n",
+       "        -2.69498777e+00,  4.49044991e+00,  5.99002552e+00,\n",
+       "         6.21761417e+00, -1.55535388e+00, -1.58084214e+00,\n",
+       "        -5.09701395e+00,  4.06920147e+00,  2.25836396e+00,\n",
+       "         1.25704491e+00,  6.84968662e+00,  7.44565535e+00,\n",
+       "        -1.87951326e+00, -9.00892019e-01,  4.42775679e+00,\n",
+       "         6.54110003e+00,  3.14750051e+00,  1.95613086e+00,\n",
+       "        -3.91129422e+00,  1.61477637e+00,  1.50569409e-01,\n",
+       "         9.52131367e+00,  2.35001040e+00, -6.31063700e+00,\n",
+       "        -6.33711386e+00,  4.53157854e+00,  2.09618174e-02,\n",
+       "        -2.14906359e+00, -9.78138298e-02, -1.86061585e+00,\n",
+       "         4.90669966e+00,  1.24480343e+00,  7.89691955e-02,\n",
+       "        -5.58514953e-01, -2.23632979e+00,  1.30819321e+00,\n",
+       "        -3.82723236e+00, -5.53356695e+00, -2.46174788e+00,\n",
+       "        -3.10814881e+00,  3.28143239e+00, -8.99871159e+00,\n",
+       "        -3.80512625e-02, -1.40937150e+00, -1.05609441e+00,\n",
+       "        -1.55699253e+00, -1.06937952e+01,  7.26015615e+00,\n",
+       "         7.25563622e+00, -8.90508771e-01,  3.33858395e+00,\n",
+       "        -4.63448620e+00,  2.72201848e+00,  2.69444180e+00,\n",
+       "         7.78187931e-01,  7.55690718e+00,  2.75735259e-01,\n",
+       "        -2.91637230e+00, -6.81530666e+00, -1.57308030e+00,\n",
+       "        -7.58241272e+00,  1.43748093e+00, -7.55627155e+00,\n",
+       "         3.19303679e+00, -5.36505556e+00, -1.15430202e+01,\n",
+       "         4.40225661e-01,  5.46767759e+00, -8.20215797e+00,\n",
+       "         3.56679022e-01,  4.81598663e+00,  1.69988811e-01,\n",
+       "         1.18402758e+01,  8.49781418e+00, -5.40500975e+00,\n",
+       "         2.09457254e+00, -1.63413680e+00,  2.38487005e+00,\n",
+       "        -5.24984980e+00, -7.08062840e+00, -4.49847698e+00,\n",
+       "         4.75647640e+00, -6.63694525e+00, -4.31203794e+00,\n",
+       "         3.76967698e-01, -4.72881651e+00, -3.61989594e+00,\n",
+       "         7.30160332e+00, -2.82294011e+00,  5.45915794e+00,\n",
+       "         5.97896147e+00,  1.42480135e+00,  1.71769476e+00,\n",
+       "         1.83458173e+00, -2.03421044e+00, -1.21695495e+00,\n",
+       "        -2.88737923e-01,  1.16490376e+00,  2.53098369e+00,\n",
+       "         3.88871217e+00, -2.18504024e+00,  3.27239442e+00,\n",
+       "         7.90604830e+00,  1.32355189e+00, -1.46008480e+00,\n",
+       "        -1.72825086e+00,  1.21700764e-01,  5.27798367e+00,\n",
+       "         8.14821243e+00,  3.93104291e+00, -6.05948031e-01,\n",
+       "         3.23243809e+00,  3.96863151e+00, -6.16936684e+00,\n",
+       "         4.38937044e+00,  3.05740547e+00,  3.85804129e+00,\n",
+       "         1.32996821e+00,  3.84759259e+00,  6.66850865e-01,\n",
+       "         9.07183945e-01,  1.48809273e-02, -3.71322846e+00,\n",
+       "         1.65257967e+00,  2.42728853e+00, -9.26358759e-01,\n",
+       "         2.15981793e+00, -1.40861428e+00, -3.81361461e+00,\n",
+       "        -1.52566981e+00, -4.59913254e+00,  6.24776423e-01,\n",
+       "         9.58626366e+00,  3.67797637e+00, -4.09784746e+00,\n",
+       "         1.54376483e+00, -5.56217790e-01]], dtype=float32)
          • tau
            (chain, draw)
            float32
            1.142396 0.82828474 ... 27.683926
            array([[1.14239597e+00, 8.28284740e-01, 3.78205299e+00, 9.73977280e+00,\n",
+       "        8.45564842e+00, 4.66784382e+00, 5.73158979e+00, 1.75484061e+00,\n",
+       "        9.52065066e-02, 8.36029129e+01, 3.42491865e-01, 2.32345676e+00,\n",
+       "        5.29000092e+00, 1.06360044e+01, 2.88606501e+00, 1.34007835e+01,\n",
+       "        2.24463615e+01, 2.03689718e+00, 6.03032410e-02, 4.65588999e+00,\n",
+       "        6.22869492e-01, 9.02841854e+00, 1.86696701e+01, 4.86623240e+00,\n",
+       "        3.72949719e+00, 3.86954117e+00, 6.17830658e+00, 1.61912191e+00,\n",
+       "        1.82664406e+00, 2.62900055e+02, 2.35055580e+01, 6.12994671e+00,\n",
+       "        3.77730465e+00, 7.43117332e+00, 4.83659286e+01, 6.05498195e-01,\n",
+       "        3.65242934e+00, 5.45612240e+00, 4.06896925e+00, 3.69497752e+00,\n",
+       "        2.64012794e+01, 1.59782636e+00, 3.76606345e+00, 3.25247431e+00,\n",
+       "        9.63840961e+00, 1.81573849e+01, 3.03462267e+00, 4.15390015e+00,\n",
+       "        3.40319290e+01, 3.98135281e+00, 3.63751745e+00, 1.45697870e+01,\n",
+       "        3.92402229e+01, 1.08645380e+00, 5.30041790e+00, 1.26662433e+00,\n",
+       "        1.36301699e+01, 6.51986897e-01, 3.13314438e-01, 1.57919002e+00,\n",
+       "        4.63338089e+00, 4.35344458e-01, 1.36403275e+01, 2.81532745e+01,\n",
+       "        3.47195888e+00, 3.76934004e+00, 1.01469460e+01, 1.81859837e+01,\n",
+       "        4.16979980e+00, 3.12015228e-02, 3.77084970e+00, 6.29084778e+00,\n",
+       "        7.24869251e-01, 9.34037704e+01, 1.16617656e+00, 9.01426554e-01,\n",
+       "        5.80987453e+00, 3.58328152e+00, 1.74854606e-01, 2.08160877e+01,\n",
+       "        1.23795118e+01, 2.25147705e+01, 4.57677698e+00, 1.01074677e+01,\n",
+       "        1.59020786e+01, 6.53844118e+00, 1.44939363e+00, 4.70238876e+00,\n",
+       "        2.56741619e+01, 7.94446766e-01, 4.58743191e+00, 3.10524321e+00,\n",
+       "        2.20093393e+00, 7.25855780e+00, 2.31834793e+00, 3.17246890e+00,\n",
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+       "        1.78776817e+01, 2.79887152e+00, 2.63987331e+01, 6.75481272e+00,\n",
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+       "        5.93189776e-01, 2.26463776e+01, 4.93218708e+00, 3.72727633e+00,\n",
+       "        7.28081894e+00, 2.83549607e-01, 4.21679544e+00, 1.81901665e+01,\n",
+       "        1.21280432e+00, 4.17735249e-01, 1.08885217e+00, 3.38204895e+02,\n",
+       "        5.14020615e+01, 4.05303240e+00, 3.31265783e+00, 2.31162834e+00,\n",
+       "        7.68731642e+00, 2.76660309e+01, 2.93328056e+01, 2.63904119e+00,\n",
+       "        6.05144310e+00, 1.70312095e+00, 2.34002411e+02, 2.32598419e+01,\n",
+       "        3.39389496e+01, 3.65918040e+00, 9.16184068e-01, 4.22204465e-01,\n",
+       "        1.32080593e+01, 7.56184769e+00, 5.85528135e+00, 5.01661110e+00,\n",
+       "        4.70766020e+00, 5.80670738e+00, 6.71747208e+00, 1.51498508e+01,\n",
+       "        1.18939209e+01, 8.12217522e+00, 1.05186157e+02, 1.21349227e+00,\n",
+       "        4.31718903e+01, 2.86553049e+00, 4.41276360e+00, 2.33716488e+00,\n",
+       "        2.49567938e+00, 2.59115744e+00, 1.09836912e+01, 6.47885036e+00,\n",
+       "        9.83264732e+00, 1.60747280e+01, 1.71704245e+00, 7.54926252e+00,\n",
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+       "        5.02637024e+01, 2.45705509e+01, 1.05007780e+00, 6.42584753e+00,\n",
+       "        3.04220700e+00, 6.73474884e+00, 6.40137005e+00, 9.73407059e+01,\n",
+       "        6.12944651e+00, 5.05982876e+00, 2.88817406e-01, 3.43511552e-01,\n",
+       "        1.27810249e+01, 5.02977324e+00, 4.91624737e+00, 2.71376348e+00,\n",
+       "        3.26131096e+01, 1.79950600e+01, 2.51596594e+00, 8.24217129e+00,\n",
+       "        1.70410385e+01, 5.17923450e+00, 1.21298075e+00, 8.87988853e+00,\n",
+       "        5.11569262e+00, 9.66544056e+00, 1.56618862e+01, 1.24250488e+01,\n",
+       "        1.28370821e-01, 7.94004345e+00, 1.31174731e+00, 5.52232218e+00,\n",
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+       "        7.75093746e+00, 2.88230634e+00, 7.61907697e-01, 4.07307373e+03,\n",
+       "        3.73773336e+00, 4.47626781e+00, 4.07136250e+00, 3.17902966e+01,\n",
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+       "        1.62638676e+00, 2.41606688e+00, 5.14008570e+00, 4.28319126e-01,\n",
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+       "        4.14515066e+00, 1.52046785e+01, 1.11286020e+01, 1.22764349e+01,\n",
+       "        3.71391237e-01, 7.82304049e+00, 1.60837650e+00, 4.45431185e+00,\n",
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+       "        2.07663870e+00, 1.57697382e+01, 2.48631897e+01, 9.37386990e-01,\n",
+       "        2.68251038e+00, 4.97329235e+00, 5.82806396e+00, 1.17972302e+00,\n",
+       "        7.03372908e+00, 5.78110352e+01, 4.18201790e+01, 2.00440907e+00,\n",
+       "        5.15675128e-01, 6.46189737e+00, 4.04254258e-01, 1.52897298e+00,\n",
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+       "        4.21542892e+01, 3.06898892e-01, 4.91473770e+00, 8.65565777e-01,\n",
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+       "        8.30045283e-01, 5.00013924e+00, 1.27071619e+00, 1.05359161e+00,\n",
+       "        8.96452725e-01, 1.43233871e+01, 1.01140821e+00, 3.89470482e+00,\n",
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+       "        3.65321350e+00, 1.51140106e+00, 6.32374535e+01, 4.83192253e+00,\n",
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+       "        1.19185162e+01, 1.38340578e+01, 4.10225754e+01, 2.93181000e+01,\n",
+       "        1.13749208e+01, 6.11714888e+00, 7.03764915e-01, 1.31945190e+01,\n",
+       "        1.42294288e+00, 3.44097424e+00, 1.10320222e+00, 2.76839256e+01]],\n",
+       "      dtype=float32)
          • theta
            (chain, draw, school)
            float32
            0.61433244 1.9731792 ... -21.27477
            array([[[  0.61433244,   1.9731792 ,   2.8793137 , ...,   3.1135025 ,\n",
+       "           0.36174998,   2.3965962 ],\n",
+       "        [  2.855525  ,   1.7712725 ,   4.221959  , ...,   1.8995284 ,\n",
+       "           2.4188023 ,   4.186133  ],\n",
+       "        [-10.665205  ,  -3.991663  ,  -3.7031555 , ..., -13.287186  ,\n",
+       "         -10.481701  ,  -3.5811882 ],\n",
+       "        ...,\n",
+       "        [ -3.4689064 ,  -2.3196993 ,  -1.73383   , ...,  -7.962269  ,\n",
+       "          -9.47191   ,  -6.03683   ],\n",
+       "        [  0.13607061,  -0.9721953 ,   2.647256  , ...,   0.971618  ,\n",
+       "           2.8553798 ,   2.584463  ],\n",
+       "        [ -4.5051947 ,   3.6043565 , -46.242397  , ..., -29.500881  ,\n",
+       "          39.389797  , -21.27477   ]]], dtype=float32)
        • created_at :
          2020-06-06T18:10:53.014131
          arviz_version :
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          inference_library_version :
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      • \n", + " \n", + "
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        \n", + "
        \n", + "\n", + "\n", + "Show/Hide data repr\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Show/Hide attributes\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
        xarray.Dataset
          • chain: 1
          • draw: 500
          • y_dim_0: 8
          • chain
            (chain)
            int64
            0
            array([0])
          • draw
            (draw)
            int64
            0 1 2 3 4 5 ... 495 496 497 498 499
            array([  0,   1,   2, ..., 497, 498, 499])
          • y_dim_0
            (y_dim_0)
            int64
            0 1 2 3 4 5 6 7
            array([0, 1, 2, 3, 4, 5, 6, 7])
          • y
            (chain, draw, y_dim_0)
            float32
            19.690388 -0.3481581 ... -34.692043
            array([[[ 19.690388  ,  -0.3481581 , -22.367306  , ...,  16.622826  ,\n",
+       "         -15.768808  ,  -6.8451705 ],\n",
+       "        [  9.477534  ,  -9.470677  ,   8.480469  , ...,  -3.1395423 ,\n",
+       "           8.926895  ,  21.148169  ],\n",
+       "        [-12.141539  ,  20.385231  ,  14.313728  , ...,  -8.920135  ,\n",
+       "          -8.540663  ,  10.570219  ],\n",
+       "        ...,\n",
+       "        [  6.1951447 , -16.812447  , -21.583776  , ...,  -6.104438  ,\n",
+       "         -19.764782  , -15.566424  ],\n",
+       "        [ 23.94809   , -19.047083  ,  -6.228318  , ...,   9.324339  ,\n",
+       "          15.134213  ,   0.15730432],\n",
+       "        [ 18.83905   ,  -3.9360547 , -30.911896  , ..., -25.75918   ,\n",
+       "          44.763027  , -34.692043  ]]], dtype=float32)
        • created_at :
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          arviz_version :
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        \n", + "
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      • \n", + " \n", + "
    • \n", + " \n", + " \n", + "
      \n", + "
      \n", + "
        \n", + "
        \n", + "\n", + "\n", + "Show/Hide data repr\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Show/Hide attributes\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
        xarray.Dataset
          • y_dim_0: 8
          • y_dim_0
            (y_dim_0)
            int64
            0 1 2 3 4 5 6 7
            array([0, 1, 2, 3, 4, 5, 6, 7])
          • y
            (y_dim_0)
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            28.0 8.0 -3.0 7.0 ... 1.0 18.0 12.0
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          arviz_version :
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        \n", + "
      \n", + "
      \n", + "
      • \n", + " \n", + "
    \n", + "
    \n", + " " + ], "text/plain": [ "Inference data with groups:\n", "\t> posterior\n", @@ -3912,6 +30446,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", +<<<<<<< HEAD + "version": "3.6.10" +======= "version": "3.7.6" }, "toc": { @@ -3926,6 +30463,7 @@ "toc_position": {}, "toc_section_display": true, "toc_window_display": false +>>>>>>> master } }, "nbformat": 4, diff --git a/doc/notebooks/Introduction.ipynb b/doc/notebooks/Introduction.ipynb index 23d7791891..b403f4e276 100644 --- a/doc/notebooks/Introduction.ipynb +++ b/doc/notebooks/Introduction.ipynb @@ -43,7 +43,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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98sorV3Tus7Ky9NNPPxXq4wQAV2NmG4BPOnbM1MZN1vouD+9qS9Jnn30mSZo0aZIzaEtSVFSUevbsqfj4eK1bV7gh6qysLM2fP1+SNGXKFGfQlqRRo0apYcOGiouL048//phvDWPHjtW4sXUkSevWS5UqtdTw4cN18eJFLVy48IrH1KpVS//3f/+n4cOHq2nTpoX+ZeC1116TYRiaNm3aVSMyZcqUUZMmTQr1PADgaoRtALY4ceKEunTpookTJyotLU3Tp0/X7bffrsjISI0ZM0br16933nf16tUaN26cevfurUGDBum1115Tenr6Vc+Znp6uDz74QGPGjFGfPn3Up08fjR8/XkuWLLnqvl8sMGXm7lTViNf0tyfvVf/+/RUZGamRI0dq5syZunjx4lWP2bFjh7p06aIXXnhBSUlJ+te//qUhQ4aoZ8+eGjVqlBYtsme/uxMnTujo0aMKCgpS9+7dr7o9KipKkhQbG1uo59u9e7cuXbqkWrVq5RtaHc/367+j9PR07dixw3l7vbqGOneSTNPamaSoNVzLrl27dOTIEbVr104NGzYs9vMBgCsxRgLAVtnZ2Zo0aZJOnDihtm3bKjExUTt37tSTTz6pN954Q4cPH9aMGTPUtm1bde7cWTt37tS8efOUlJSkF154wfk858+f1yOPPKJDhw4pIiJCbdu2lWma2rNnj/7xj39o3759mjJliiTrOPGly6ScrJk6k3BQjRo1VIcOHZSZman9+/fro48+cs4l/7qr63Dp0iVNmDBBaWlpatOmjbPmF198Ubm5ubrjjjtK7fMnWRcJSlKDBg3y7Qw7AvOhQ4eK9HwFdYfze75jx44pMzNT4eHhzosn77nb0Jatpr5dJv1hdGNJ0uHDhwtVw7U4Qv0tt9yi9PR0rVq1Sj/++KP8/PzUtGlTRUVFKTg4uNjvBwBKAmEbgK327NmjDh066Msvv1TZsmUlSYsXL9bUqVP18ssvKzk5WbNnz1azZs0kSWfOnNGYMWO0YsUKPfjgg86L9KZOnapDhw5p5MiR+tOf/qTAwEBJ0rlz5zRlyhTNnz9f3bt3V9euXfXNIikjQ6pdZ5zen93qihnizMxMvf766/r666/12Wefafz48VfVvG7dOvXv319///vfne8nJiZGTzzxhObMmXNV2J44caLi4uKK9Hl5+umnC32B36lTpyRJVapUyfd2R/h13M8Vz5ffYzq0l+rUtrZWjFlfVmFhYUpOTlZKSorKlStXqFryc+TIEUnWRaH33Xeffv755ytuf/fdd/Xqq6/S9QbgFgjbAGzl5+enxx9/3Bm0JWnw4MGaMWOG4uPjNXbsWGfQlqwwN3DgQH3++eeKi4tTzZo1deDAAW3cuFHNmzfXn//8Z/n5XZ6Qi4iI0JNPPqn77rtPX375pTp06KIFX1kXRo4b101hYVfOawcGBurRRx/VokWLtG7dunzDdrly5TRlyhRn0JakyMhINWjQQIcPH9aJEydUo0YN521du3ZV9erVi/R5qVWrVqHvm5aWJkkFdnMdb09NTXXZ8+X3GMMwNHyY9Oa/TX35tang4GBdvHhRqampxQrbjhGfjz/+WBEREXr99dfVunVrnTp1Sm+++aa2bt2qKVOm6PPPP6fDDcB2hG0Atqpevbrq1Klzxdv8/PxUrVo1JSYmqnPnzlc9xtHNPnfunCRpy5YtkqRevXpdEbQdmjRpopCQEP3www9aEyOdPStFVJL6RUkJCQmKjY3Vzz//rJSUFOXm5kqyLrI7fvx4vjU3bdpUFSpUuOrttWvX1uHDh3Xu3LkrwvaYMWMK86nwSoMGSu/+Vzp6VAotodzr+DvKycnRSy+95NzSsEGDBnr11Vc1YsQInTp1SsuWLdOwYcNK5p0CwA0ibAOwVUGjCo5Od363O27LzMyUJJ08eVKSNGvWLM2aNavA95WRkaGvvra62sPuMDRv3md6++23lZ2dXaSaCzrQxTHf7airpOzcuVMLFy5UUFCQMjIynG9/5JFHVLFiRefnI7+LRn/99vzmz/NzI89X0GNCQw0N6G/qm0XSpUtFq+N69dWrV++qvcMDAwM1YMAAffzxx4qLiyNsA7AdYRuArQzj2tvuXe92yToJUpJat26d70ErDskXpc3bJH9/qVHDvZryl7cUGhqqyZMnq127doqIiHCOhgwdOlRnz5694Zp+7cMPP9TRo0eL9Jjbb79dbdq0kSTFx8fr22+/veo+999/vypWrKhq1apJsubZ85OQkCBJzvtdz40837Uec+dwQ18vTFVO9kWFhoYVa4REknMkp6DRHMfbL1y4UKz3AwAlgbANwOM5ut+9evXS6NGjC7zfK69Z4weRvaSdcTGSpIceekhDhgy54n7p6enOEZWSsGnTpiJfINmuXTtn2B46dKiGDh2q8PDwfANko0aNJFk7fWRnZ1+1I8n+/fslqdAXDDqez/G438rv+erUqaPAwEBduHBBCQkJV3T/G9Q31LD+fu3fJ4WGFf+ixcaNrZ1N8tueUZKSk5Ml6YrrAADALoRtAB6vU6dOevfddxUTE1Ng2L50ydSKldZ6+B2Gli21glp+IyHR0dHObnlJ+O1JiyWtRo0aqlu3ro4ePaoNGzZcdSR6dHS0JKlHjx6Fer5WrVopNDRU8fHxOnDggDPc/vb5evbs6XxbcHCw2rdvr02bNik6Olr33HPPFY+pFL5WknTpUndlZZkqU+bGDxLq1q2b/P39dfjwYSUlJV01P+/4xYaDbQC4Aw61AeDxWrZsqU6dOmn37t165ZVXlJKSctV9Pvz4oFJSNqteXalNazkvyly4cOEVM9tHjhzRzJkzS6nykvP73/9ekjRjxgydP3/e+fY1a9Zo/fr1qlWrlnr16nXFY9auXauRI0fq+eefv+LtZcqU0YgRIyRJr7zyinOnEck6rv3QoUNq27atmjZtmm8Nc+bM0bFjx5xv37Nnj3Zs/1qGEaa0zNu0YWPxPtaKFStq6NChSk1N1euvv66srCznbUuWLNHWrVsVFBR01b9YAIAd6GwD8ArPPfecHn30US1YsEArVqxQo0aNVLlyZaWkpOjQoUM6ffq0DP+7NXxYVxmGoaFDh+rTTz9VbGys7r77bjVr1kzJycmKi4tTZGSkvv/++0LvS+0ObrvtNm3cuFExMTG655571KFDByUmJiouLk5BQUF67rnnrhovuXTpkn7++WdVqlTpqucbO3astm3bpj179uiuu+5ybq33/fffKzw8XE8//fRVj+nUqZNGjhypuXPnasyYMerUqZOysrK0detWSVKfvk9pXWyYFi421Tvycmf7iSeecM7HO/5cuHChNm/eLEmqXLmy/vWvf13xvh5++GHt3btXy5cv165du9S0aVOdPn1a+/btk7+/v5588knddNNNxfiMAkDJIGwD8AqVKlXSf//7X33zzTdauXKlDhw4oD179qhSpUoqX76Gzpy7S2XL9dPA/tb9K1SooDlz5mjGjBmKi4tTbGysqlevrgceeECjR4/W7373O3s/oCLy8/PTP//5T82dO1eLFy/Whg0bFBwcrD59+mjChAmqV69ekZ4vKChIM2fO1IcffqgVK1Zo3bp1Kl++vIYMGaIHH3ywwB1ZJk+erEaNGmn+/PnaunWrypQpo44dO2rcuHGqXOUWrYs1tXWbdOKkqRrVrcC9f//+q36xOXPmjPNiy/wu7AwNDdXs2bP1v//9T6tXr9aGDRtUtmxZde/eXWPGjFHr1q2L9PECgKsYZiEHEz3pqu6CLiICXIHXm/v7+3O5WrNWGnaHNGWy507PecNr7bG/5mrrNukP90oP3u+5fxe+wBteb/AMnvxaCw8Pv+59+E4HwKtdSDS1PtZa33HbjV+Uh5Jx+1Dr7+Dbb6Xs7JK7CBUA3BVhG4BXW7Zcys6WmjWVGjUkbNutR3epUrh07ry0YZPd1QCA6xG2AXgt0zS1eInVPR06hKDtDgICDA0eZK0XLqKzDcD7EbYBeK3de6Sfj0llg6X+fe2uBg635f3is3WbdPIkgRuAdyNsA/Bai/K62lFRUkgInW13UbOmofbtJNOUli63uxoAcC3CNgCvdPGiqTVrrfVtjJC4nSGDrL+TpctN5ebS3QbgvQjbALzSytVSRoZUr67Uornd1eC3evWUypWTTp6Udu22uxoAcB3CNgCv5Lgw8rahhgyDzra7CQ42FNXHWi9dRmcbgPcibAPwOgcPmTpwUAoIkPPESLifwbdavwStWSulphK4AXgnwjYAr7NsuRXcenSTKlSgq+2uWraQatWS0tKltTF2VwMArkHYBuBVsrNNLV9prQfdStB2Z4ZhOLvb3zJKAsBLEbYBeJXNW6TEROuUws6d7K4G1zNwgGQY0s5d0i8nCNwAvA9hG4BXcXRIB/S3TiuEe7upqqGOHay1Y/wHALwJYRuA10hMNLVxk7VmhMRzOP6uli4Te24D8DqEbQBeY1W0lJ0tNWksNahP2PYUvXpYe26fOm2NkwCANyFsA/AajhGSwXS1PUpQkKG+UdaaCyUBeBvCNgCv8NNPpg4csPbW7tfX7mpQVI5fkNbGsOc2AO9C2AbgFVassgJa187sre2JWjSX6tSW0tOl6LV2VwMAJYewDcDj5eaaWrXaWvfvT9D2RIZh/OpCSTrbALwHYRuAx9uz17q4LiRE6t7V7mpwo24dIPn5Sbt2S/HxBG4A3oGwDcDjrcwbIYnsaV1sB89UpYqhDu2t9bIVhG0A3oGwDcCjZWWZzhnfAYyQeDzHhZLLlrPnNgDvQNgG4NG2bpOSk6WISlK7tnZXg+Lq0d0aBzp12hoPAgBPR9gG4NEcu5D0jZL8/else7rgYEORPa21YzwIADwZYRuAx0pNNRW7wVr370fQ9haOcaDotdaYEAB4MsI2AI+1LlbKyJBq1ZKaNrG7GpSUdm2tsaDkZGnLVrurAYDiIWwD8FiOMYMB/QwZBp1tb+HvbzhPAV3BKAkAD0fYBuCRLlwwtX27te7fz95aUPIcoySxG6SUFAI3AM9F2AbgkVavkXJypWZNpdq16Gp7m8aNpJvrSJmZUsw6u6sBgBtH2AbgkVastLqdXBjpnQzDcP7dMkoCwJMRtgF4nPh4Uz/ss4727tvH7mrgKo7xoB3fSWfPErgBeCbCNgCPsyra+rNDeykigs62t6pZw9AtLSXTvPx3DgCehrANwKOYpskIiQ9hlASApyNsA/Ao+w9Ix45LgYFSrx52VwNXi+ot+ftLBw5IR38mcAPwPIRtAB5l5WorcPXoLpUrR2fb21WsaKhzJ2vt+BcNAPAkhG0AHiM319SaNda6XxRB21cMyBslWbnKGiMCAE9C2AbgMfZ+LyWckcqVk7PbCe/Xo7tUtqx08pS0Z6/d1QBA0RC2AXiM6DWXR0iCguhs+4rgYEORvaw1F0oC8DSEbQAeISfH1Jq11rpvH4K2r3GMkkSvkbKyCNwAPAdhG4BH2L1HOndeCg2VOnawuxqUtnZtpYhKUnKytGWb3dUAQOERtgF4hNV5IyS9ekplytDZ9jUBAYb6RlnrVavpbAPwHIRtAG4vO9vU2hhrzQiJ7+rX1/q7j90gpaYSuAF4BsI2ALcXt1NKTJQqlJfat7O7GtilWVOpZg0pPV2K3Wh3NQBQOIRtAG4veq3VxYyMtMYJ4JsMw1D/ftZ6FbuSAPAQhG0Abi0721TMOmvNCAkcoyRbtklJSQRuAO6PsA3ArW3fYe1AER4utW5ldzWwW92bDTVuJOXkSGti7K4GAK6PsA3ArTkOsunNCAnyOLrb7EoCwBMQtgG4rawsU+vWW2tGSODg2AJw5y7pdAKBG4B7I2wDcFtbt0mXUqSICKnVLXZXA3dxU1VDbVpb69XR9tYCANdD2AbgthwjJH16S35+dLZxGaMkADwFYRuAW8rIMLV+g7VmhAS/1SdS8veXDhyUjv5M4AbgvgjbANzSlq1SaqpUtarUornd1cDdVKhgqHNHa013G4A7I2wDcEur80ZIonozQoL89etnvS5WrpJMk8ANwD0RtgG4nfR0UxvzjuPuG0XQRv56dJOCg6VfTkg/7re7GgDIH2EbgNvZtFlKS5eqV5OaNrG7GrirkBBDPbpb65Uc3w7ATXL/WiMAACAASURBVBG2Abid6LV5u5D0kQyDzjYK5tiVZHW0lJND4AbgfgjbANxKWpqpTZutdVRvgjaurXNHKSxMOndeittpdzUAcDXCNgC3smmLlJ4u1aghNWlsdzVwd2XKGOrT21qvZFcSAG6IsA3ArUT/ahcSRkhQGP3zRkliYqTMTAI3APdC2AbgNlJTfzVCwkE2KKTWraQqlaVLKdLmLXZXAwBXImwDcBubNksZGVKtmlKjhnZXA0/h52eob5S1ZpQEgLshbANwG+xCghvVP++Amw0bpZQUAjcA90HYBuAWrhghYRcSFFHjRlKd2lJmprQu1u5qAOAywjYAt7BhkxWUateWGjawuxp4GsMwnN3tVYySAHAjhG0AboFdSFBc/fpaf27fLl24QOAG4B4I2wBsl5JiakveLhLsQoIbVbuWoaZNpJxcKXqt3dUAgIWwDcB2GzZJmVnWzG39enZXA0/GKAkAd0PYBmC7NY4REnYhQTH17SMZhrRnr3TyJIEbgP0I2wBslZJiavNWa80uJCiuypUNtWtrrVdF21sLAEiEbQA2i90gZWVJdW+W6jFCghLgOL6dURIA7oCwDcBWzoNsejNCgpLRq5cUECAd/kk6/BOBG4C9CNsAbHPxoqmt26x1H0ZIUELKhxnq2tla090GYDfCNgDbxG60Rkjq1ZXq1yNso+T0c+5KIpkmgRuAfQjbAGxzeRcSgjZKVveuUtmy0slT0vc/2F0NAF9G2AZgi+SLprZut9Z9ettaCrxQcLChXj2s9cpVdLYB2IewDcAWsbFSdrbUoL5U92Y62yh5jlGS1Wuk7GwCNwB7ELYB2OLyLiQEbbhGx/ZSxQpSYqK04zu7qwHgqwjbAEpd8kVT2/JGSKJ621kJvFlAgKE+faw1u5IAsAthG0CpWx8r5eRIDRpIderQ2YbrOA64iVkvZWQQuAGUPsI2gFIX7diFhBESuFjLFtJNN0mpqdLGTXZXA8AXEbYBlKqkJFPbd1hrdiGBq/n5GeoXZa1XMkoCwAaEbQClyjFC0qihVKc2nW24Xv+8XUk2bbZOLQWA0kTYBlCqHLuQcJANSkuD+tYppVlZ0rpYu6sB4GsI2wBKTWKiqR2OEZJIe2uB7zAMw9nd5oAbAKWNsA2g1KyLlXJypcaNpVq16Gyj9PTNm9v+Lk46e47ADaD0ELYBlBp2IYFdatYw1KK5lJsrRa+xuxoAvoSwDaBUXEg09V2ctWYXEtjBMUrCATcAShNhG0CpWLfe6io2bWJ1GYHSFtVb8vOTftgn/fILgRtA6SBsAygVjhGSPoyQwCaVKhnq0N5ar1xtby0AfAdhG4DLXbhgKm6ntWaEBHbqF3V5VxLTpLsNwPUI2wBcLmadNULSrKlUozqdbdinV08psIz08zHp0CG7qwHgCwjbAFzOcZANIySwW2iooW5drfXKaDrbAFyPsA3Apc6dM7Vzl7WO6m1nJYDl8q4kUm4ugRuAaxG2AbjUmhhrhKR5M6laNTrbsF+XzlK5clJCgrRnr93VAPB2hG0ALuXY07hfX4I23ENQkKHIntaa49sBuBphG4DLnDxpau/31t7GUX3srga4zDFKsmatlJ1N4AbgOoRtAC6zOu9Y7LZtpMoRdLbhPtq2kSqFS0nJ0rbtdlcDwJsRtgG4jHOEJIqgDfcSEGAoKspaM0oCwJUI2wBc4ujPpg4dlgICpMhedlcDXM3xS+D6WCktjcANwDUI2wBcYnXeHsadOkrly9PZhvtp0VyqXl1KS5c2bLS7GgDeirANoMSZpqmVq601u5DAXRmGof59rfUqDrgB4CKEbQAlbv8BKT5eCgqSenSzuxqgYI5dSTZvkZKTCdwASh5hG0CJc4yQdO8mhYTQ2Yb7qlfXUMMGUna2tHad3dUA8EaEbQAlKjfX1Opoa80uJPAEjlEndiUB4AqEbQAlas9eKeGMdRx25052VwNcX7+8LQB37pLOnCFwAyhZhG0AJWpl3t7akT2tY7EBd1etmqFWt0imefkgJgAoKYRtACUmO9vU2rXWml1I4EkcF0oySgKgpBG2AZSYHd9JiUlSxYpSu7Z2VwMUXp9Iyd/f2knn2DECN4CSQ9gGUGIcx7P36W0dhw14iooVDXXsYK0do1AAUBII2wBKREaGqXWx1ppdSOCJ+jt2JVltHcwEACWBsA2gRGzeKqWkSFWrSLe0tLsaoOh69rAOYoqPt8ZJAKAkELYBlAjHCEnfKMnPj842PE9IiOE88ZRREgAlhbANoNhSU01t2Git2YUEnsyxK8nqaCknh8ANoPgI2wCKLXaDlJkp1aolNW5kdzXAjevUUQoNlc6etQ65AYDiImwDKDbHCEn/vpJh0NmG5woMNNSnt7VevoLONoDiI2wDKJakJFNbtllrdiGBN7h1gPU6XhMjpacTuAEUD2EbQLHErJdycqRGDaWbbyZsw/O1ukWqXl1KS5PWb7C7GgCejrANoFgu70JC0IZ3MAxDA/tb62XL6WwDKB7CNoAbdvasqbid1rpflL21ACVpYN4oybbt0rlzBG4AN46wDeCGRa+RTNM6xKZaNTrb8B61axlq0VzKzZVWRdtdDQBPRtgGcMNWRjNCAu/l6G6zKwmA4iBsA7ghx+NN7dsn+flJUb3trgYoeX37SAEB0oGD0k8/EbgB3BjCNoAbsnKV9WeH9lKlSnS24X0qVDDUtYu1XraSsA3gxhC2ARSZaZpakRc+BvYnaMN7OfbcXrmS49sB3BjCNoAi2/ejFP+LFBws9exhdzWA63TtIoWFSWfOSt/F2V0NAE9E2AZQZI6uds8eUkgInW14r8BAQ337WOvljJIAuAGEbQBFkp1tOrdCG8AICXyAY1eSmBgpLY3ADaBoCNsAimTbDikxUapYUerY3u5qANdr2UKqWUNKS5fWrbe7GgCehrANoEgcIyT9oqSAADrb8H6GYVzec5tREgBFRNgGUGipqabWx1prRkjgSwb2t/7cvkM6e5bADaDwCNsACm19rJSeLtWqJTVranc1QOmpWdPQLS2t49tXrLK7GgCehLANoNAc/4Q+oJ8hw6CzDd/CKAmAG0HYBlAo58+b2r7DWg/oZ28tgB2i+khlykiHD0uHDhO4ARQOYRtAoayKtv4JvXkzqVYtutrwPeXDDHXraq2XryBsAygcwjaAQuF4duDy8e0rVnF8O4DCIWwDuK5jx0z9uF/y95OiouyuBrBPl85ShfLSuXPSju/srgaAJyBsA7iuFausDl6nTlJ4RTrb8F1lyhjOXzgZJQFQGIRtANdkmqZzqzP21gYuj5LErLf2ngeAayFsA7im73+QTpyQypaVena3uxrAftZFwtae8zEc3w7gOgjbAK5p2XKrc9erpxQcTGcbMAzD2d1mlATA9RC2ARQoM9PUqmhrPWggQRtwGJB3fPuO76TTCQRuAAUjbAMo0MZN0qVLUtUqUts2dlcDuI8a1Q21aS2ZprR8hd3VAHBnhG0ABVqW90/kA/pL/v50toFfG3yr9TXx7TJTpkl3G0D+CNsA8nUh0dSmzdZ64ACCNvBbvSOlssFSfLy093u7qwHgrgjbAPK1OlrKyZGaNpHq1SVsA78VEmKod6S1/nYZnW0A+SNsA8iXYxeSW7kwEijQ4EHW10f0Gik9ncAN4GqEbQBXOXI073h2f6kvx7MDBWrdSqpeXUpJkdax5zaAfBC2AVzFsXdw1y4czw5ci5+f4dwWk1ESAPkhbAO4Qk6OqRUrrfWtXBgJXNetA60/d3wnnTpN4AZwJcI2gCvE7ZQSzkihoVK3rnZXA7i/GtUNtW3DntsA8kfYBnAFx97a/aKkwEA620BhOPbcXsqe2wB+g7ANwCk11VRMjLVmFxKg8HpHSmXLSvG/SHv22l0NAHdC2AbgtC5WSkuXatWUWjS3uxrAc5Qta6hPb2v97VI62wAuI2wDcHLsrT1wgCHDoLMNFIVjlCR6rZSWRuAGYCFsA5AkJSSY2vGdtR7Q395aAE/U6hapRg0pNZU9twFcRtgGIElascraTaF1K6lmDbraQFGx5zaA/BC2Acg0zcvHs7O3NnDDbh1g/fldnHTqFIEbAGEbgKQf9klHf5YCA+W8yAtA0VWvbqh9O+tfiZYut7saAO6AsA3AuXtC715SaCidbaA4HBdKLvnWVG4u3W3A1xG2AR+XkWFqdbS1HjyIoA0UV+9IKbScdOq0nBcdA/BdhG3Ax8Wsly6lSNVuktq1tbsawPMFBRnOHX0WLaGzDfg6wjbg4xwjJINutXZTAFB8tw2xvpbWx0qJiQRuwJcRtgEfdurU5b21B91K0AZKSqNGhpo0lrKypBUr7a4GgJ0I24APW7rc2jWhXVupRnXCNlCSHN3tRUtMmSbdbcBXEbYBH5WbazoP3uDCSKDk9esrBQVJR45K3/9gdzUA7ELYBnzUzl3SyZNSSIi15R+AkhUaaiiqt7VezIWSgM8ibAM+ytHV7ttHCg6msw24wtC8UZLV0VJqKoEb8EWEbcAHpaaaWhtjrYcMJmgDrtLqFqlObSktXc797AH4FsI24IOi10jp6VYIaNHc7moA72UYhrO7zZ7bgG8ibAM+aMnSyxdGGgadbcCVbh0g+ftLP+yTDv9E4AZ8DWEb8DHHjpvas1fy87NCAADXqlTJUI/u1poLJQHfQ9gGfMzSvAsjO3eSKlemqw2UBscoyfKVUmYmgRvwJYRtwIfk5JhautxaD+bESKDUdOogVa0iJSdbR7gD8B2EbcCHbN0unT0rVSgvde9mdzWA7/D3NzRksLXmQknAtxC2AR+yaLH1Q35AfykwkM42UJoG32rIMKTtO6T4eAI34CsI24CPOHvO1IYN1vq2oQRtoLRVr26ocydr/c0iwjbgKwjbgI9YukzKyZVuaSnVr0fYBuww/A7ra2/JUikjg8AN+ALCNuADcnNNLcwbIbmdrjZgmy6dpWo3WRdKRq+1uxoApYGwDfiAHd9JJ09KoeWkPr3trgbwXf7+hm6/zfqF96uv6WwDvoCwDfgAx3zowAFScDCdbcBOQwdLAQHWiZIHDhK4AW9H2Aa83PnzpnNfXy6MBOxXqZKhyF7W+qtvCNuAtyNsA15u6XIpJ0dq3kxq2ICwDbiDO4dZX4srV0mXLhG4AW9G2Aa8WG6u6dxb2zEnCsB+rW6R6tWV0tOlZSvsrgaAKxG2AS8Wt1OK/0UKCZH69rG7GgAOhmFoeF53++tvTJkm3W3AWxG2AS+2cNHlEyPLlqWznZ8jR47omWee0ZAhQ9SzZ08NGzZMr776qhITEwv9HC+++KK6dOmiLl26aOfOnVfdnpubq3fffVdDhw5VZGSkJk6cqIMHD+b7XNnZ2Ro9erQmTJhwQwHMUce1LF68WF26dNELL7yQ79t//V/v3r01dOhQTZw4UTNmzNBPP/1U5OdF/gb2l8oGS0d/tn4xBuCdCNuAl7qQaCpmvbVmb+38bd++XWPHjtWKFSsUGhqq7t27KzAwUPPnz9eYMWOUkJBw3efYsWOHFi1aJMMo+HP80Ucf6f3331e5cuXUsWNH7d27V4888ohSUlKuuu+8efN05MgRTZky5ZrP6Uq1atXS4MGDNXjwYPXq1Uv169fXkSNH9PHHH2vUqFF69tln860dRVOunKEB/a01F0oC3ivA7gIAuMay5VJ2ttS0idS4EWH7t9LT0/XMM88oPT1d48eP14QJEyRJpmlqxowZ+uSTT/Tiiy/qzTffLPA5MjIyNG3aNNWvX1/lypXTnj17rrpPdna2Pv74YzVq1EjvvfeeAgMDtWzZMj333HP6+uuvNXr0aOd9z507p9mzZ2vYsGFq0qRJyX/QhdSqVSs988wzV7zNNE1t2LBBr732mpYvX66EhAT9+9//VkAAP0aKY9gdhr5ZZGrdeunsOVOVI/haBbwNnW3AC5nmr06M5MLIfK1Zs0bnz5/XzTffrPHjxzvfbhiGJk6cqOrVq2vLli0FjntI0vvvv6/4+Hg9/vjjBYbOEydO6OLFi+rfv78CAwMlSQMGDFBQUJAOHDhwxX1nzpypgIAAPfjggyXwEZYswzDUo0cPvffee6pSpYri4uK0YMECu8vyeI0aGrqlpbVj0OIldlcDwBUI24AX2rlLOn5cKltW6hdldzXuaf/+/ZKkNm3ayM/vym+FAQEBatWqlSRp3bp1+T7+0KFD+uSTTzR06FC1adOmwPdz8eJFSVJYWJjzbX5+fipXrpzzNknavXu3li5dqokTJ6pChQo39kGVgkqVKjn/FWDevHk2V+Mdht9h/UK8cJGp7GzGSQBvQ9gGvJCjq92vrxQSQmc7P2lpaZKuDMG/5gi8+XW2c3NzNW3aNIWFhWnSpEnXfD/VqlWTJB07dsz5tuTkZCUmJuqmm25yPt+rr76qpk2b6vbbby/6B1PK+vbtKz8/P8XHxxdqrh3X1jtSqlhBSjgjbdxkdzUAShphG/AyFy6YWhtjre/gwsgCVaxYUZJ06tSpfG8/ceJEgbfPnz9fe/fu1cMPP3zdLnRERISaNGmiJUuWaOfOnUpOTtabb76p3Nxcde/eXZL05Zdf6uDBg5oyZcpVXXZ3VK5cOdWoUUOStZsLiicw0NCQwdZ6wVd0tgFvw5UtgJdZuFjKypKaNZOaNiVsF6Rt27b64IMPtHHjRiUmJjrDtyQlJCRo27ZtkqTU1NQrHpeQkKBZs2apXbt2Gjx4cKHe1yOPPKJHH31UDz30kPNt3bp1U48ePZSUlOTcFrBFixbO2zMyMlSmTJkbDt/X2/6vuCpWrKj4+HglJye79P34iuHDDH0+19SO76SDh0w1asjXLuAtCNuAF8nONvV13hZiI4bzw/paOnfurCZNmmj//v2aPHmypkyZonr16unw4cOaNm2asrOzJemq7fdeeeUVZWVl6fHHHy/0+2rfvr0++OADLV26VJcuXVKLFi106623SpLefvttSdKf/vQnSdK2bdv0+uuv68iRIwoKCtKgQYM0efJkBQUFFenju9YvAvHx8dq9e3eRnu+3HHuA27U9obepdpOhyEhT0WukeQtMPfUEn1fAWxC2AS+yPlY6c1aqWFHq09vuatybYRiaNm2a/vKXv2jfvn1X7EhSqVIl3X///XrnnXdUvnx559ujo6O1fv16jRs3TnXr1i3S+6tfv74zUDvs27dPixYt0mOPPaaKFSsqISFBU6ZMUYMGDfTSSy/pyJEjeu+99xQcHKxHH320SO/vt1v3/drixYuLHbaTkpIk6YrPD4rn7hGGoteYWrlKemiCqUqVCNyANyBsA17EMe95+1BrDhTXVr16dX344YeKiYnRnj17lJGRoXr16mngwIFau3atJKlevXrO+8fGxkqStm7dqri4uCuey3Eh5euvv65y5cppyJAhGjp0aIHv2zRNvfLKK2rYsKGGDx8uSVqwYIEyMzM1depU1ahRQ3369FF8fLwWLFhwxQiK3VJSUvTLL79IuvLzg+Jp2cJQ82amftgnfb1QGvf/7K4IQEkgbANe4vBPpnbukvz9pGG3E7QLKyAgQH379lXfvn2veLvjgJp27dpd9Zi9e/cW+HyOvbPze9yvLVq0SPv27dOsWbPk7+8vSTp69KgqVqzovPhQkpo3b65vv/1Wx48fV/Xq1Qv3QbnYqlWrZJqm6tSpoypVqthdjlcZeZehZ18w9dU3pkb/XgoK4msZ8HSEbcBLOLraPXtKVavyA7o4zp07p+joaFWoUEG9e/d2vv2ZZ54pcDxj4sSJiouL06xZs66577Zk7b39n//8R4MGDVLr1q2vuC0jI+OK/09PT5ckt9ml5Pz58/rvf/8rSbr77rttrsb7RPaSqlaxtgFcHS0NHmR3RQCKyz2+ewMoluSLplastNa/48LIQjt8+PBV4TYhIUF//etflZqaqkceeUTBwcEl/n7feecdZWZmXjXDXb9+faWmpjoP0snOzlZ0dLQCAwNVs2bNEq+jKEzT1MaNGzV+/HidPXtWHTp00LBhw2ytyRsFBBi6M+9reO5803khKgDPRWcb8AJLl0np6VKD+lKb1te/PyyffPKJYmJi1KRJE1WuXFnnz5/X7t27lZmZqXHjxmnIkCEl/j4PHjyor776Sg8//LAiIiKuuG3EiBGaO3eunn76aXXu3Fnx8fE6cuSIxowZ45LQX5Ddu3frhRdekGQF/qSkJO3fv1+JiYmSpEGDBmnKlCkFHlGP4rl9qPS/D6XDh6W4nVK7tnZXBKA4+E4JeLjcXFNf5o2Q3DncYCu2IoiMjNT58+d18OBB7d69W2FhYerSpYtGjhyp9u3bu+R9vvbaa6pbt65GjBhx1W0RERGaPn26/v3vf2vz5s0KDQ3V6NGj9cADD7ikloLEx8crPj5ekhQUFKSwsDDVq1dPLVq00ODBg1W/fv1SrcfXlC9v6NaBpr7+Rpo7z1S7tnxNA57MMAv5b1QXLlxwdS0lJjw83KPqhWez+/W2abOpvz5pKjRU+mqeobJl+cHsrex+raH0HDtmatQYU4YhffqRodq1Sv/rmtcbSosnv9bCw8Ovex9mtgEP57gwcshgEbQBL1GnjqGuXSTTlOYvYG4b8GSEbcCDHY83tXmLZBjSnXcQtAFvcvcI62v626XSxYsEbsBTEbYBD/bV19YP4K6dpZo1CduAN+nQXqpXV0pLlxYtsbsaADeKsA14qNRUU0uWWus72e4P8DqGYTi72wu+MpWdTXcb8ESEbcBDLVshpaRItWpJnTraXQ0AVxjQX6pYUTp92jrkBoDnIWwDHignx9QX86wu14g7Dfn50dkGvFFQ0OXu9sefmsrNpbsNeBrCNuCBNm6S4n+RQkOlwbfaXQ0AVxp+hxQSIh05an3tA/AshG3AA33+hdXdGna7FBJCVxvwZmFhhobfYa0/+oQj3AFPQ9gGPMy+H03t2i0FBFgjJAC8390jDAWWkb7/wTrCHYDnIGwDHubzuVZXq2+UVLkyYRvwBRERhgYPttYff0pnG/AkhG3Ag5w6ZWptjLW+526CNuBLRt1jyN9P2rpN+nE/gRvwFIRtwIPMW2AqJ1dq305q1JCwDfiSGtUN9Y2y1nS3Ac9B2AY8RHKyqYWLrPXvRxK0AV907yjraz9mnXTsOIEb8ASEbcBDfPm1dWxzwwZS5052VwPADvXrG+reTTJN6dPPCNuAJyBsAx4gLc3UvPnWD9Z7RxkyDDrbgK/6w2jr63/ZCikhgcANuDvCNuABliyVkpKlGjWk3pF2VwPATi1bGGrTWsrOlubOI2wD7o6wDbi57GxTn+Vt9zdqpKGAALragK9zdLcXLpKSkgjcgDsjbANublW0dPq0VClcGsTR7AAkdeooNW5kXccxbwFhG3BnhG3AjeXmms4tvu4aYSgoiK42AMkwDI251/p+MG+BlHyRwA24K8I24MZi1klHj0qh5aRht9tdDQB30qun1KC+lJIi5wXUANwPYRtwU7m5pv73kfUDdMTvpLAwutoALvPzMzT2Puv7whfz6W4D7oqwDbipDRulw4elsmWlu0cQtAFcrVdPqUEDq7v9BTuTAG6JsA24IdM0NefDvK72nVL58oRtAFfz8zM07r5fzW4nE7gBd0PYBtzQ5i3SgQNScLB0910EbQAF69njcnebfbcB90PYBtyMaZqa84H1A3P4HVJ4RcI2gIL9urv9xXwpMZHADbgTwjbgZjZvlX7YJwUGSvfcTdAGcH09e0iNG0tpaXJuFwrAPRC2ATdimqZmv2f9oLxzmBQRQdgGcH1+foYmjLe+X3z5tXTmDIEbcBeEbcCNrI+V9h+QygZLo0cRtAEUXpdOUqtbpMxM6YOPCNuAuyBsA24iN9fU7Pcdp0Uyqw2gaAzD0IMTrO8bi5ZIv5wgcAPugLANuIk1a6WfjlinRd4zkqANoOhatzLUqaOUkyO9/z/CNuAOCNuAG8jONvXeHOsH4z0jDZXntEgAN+iB+63vHytWSod/InADdiNsA25g+Urp2HGpQnnp7hF2VwPAkzVtYqhPb8k0pVnvErYBuxG2AZtlZJh6L29W+97RhkJC6GoDKJ4H7jfk7y9t2ix9F0fgBuxE2AZsNm+BlHBGuukma7s/ACiu2rUM3X6btX57lqncXAI3YBfCNmCj5GRTH39i/RC8f5yhoCC62gBKxtgxhsqWlX7cb12ADcAehG3ARh9+bOpSitSggTSgn93VAPAmlSoZ+n3ezkbvzDaVlUV3G7ADYRuwyalTphZ8Za0nPmDI35+uNoCSdc/dUqVw6cQJ6auv7a4G8E2EbcAm7842lZUltW8nde5kdzUAvFFIiKHx46xf5N//wFRSEt1toLQRtgEb7P3e1IpVkmFIEx80ZBh0tQG4xtDB1qjapUvSnA8I20BpI2wDpcw0Tb01w/qBN+hWa09cAHAVf39DD//R+j7z1dfS0Z8J3EBpImwDpWzlaumHfVLZ4MsnvQGAK3Vob6hHdyknV5r5H8I2UJoI20ApSkszNesd6wfdH+41VDmCsA2gdPzxocsH3WzZSuAGSgthGyhFn821DrCpdpM08i67qwHgS+rUNjTiTmv95r/ZChAoLYRtoJScPGnqk8+sH24TH+IAGwCl7/+NMRQeLh07bp1eC8D1CNtAKXlzhqmMDKldWymqt93VAPBFYWGG/vig9Yv+nP+ZOnOG7jbgaoRtoBRs2GgqdoPk7y9N/jNb/QGwz8ABUssWUlq6NHMWYRtwNcI24GIZGaam/9v6gTbyLqleXYI2APv4+Rl5v/RLq1ZLcTsJ3IArEbYBF/v4U1MnT0pVKlvzkgBgtyaNDd1xm7V+fToXSwKuRNgGXOh4vKlPPrXWD08yFBJC2AbgHiaMN1SxgnTkqPT5F3ZXA3gvwjbgIrm5pv71iqnMLKlTR6lPpN0VAcBlFSoYmvSnvIslPzD1yy90twFXIGwDLrLkW2nnLik4WJryGBdFAnA/A/tL7dtJkgG+igAAHnpJREFUmZnSa9NNmSaBGyhphG3ABc6eM51X+d8/zlCN6gRtAO7HMAxNecxQYBlp6zZpVbTdFQHeh7ANuMD0t0xduiQ1aSzniW0A4I5q1zI05g9WQ+CtGaaSkuhuAyWJsA2UsHXrTa2Nkfz9pCf/aigggK428P/bu/PwKKpE/ePfyk4WoAHZhZDBsIMsIq7MCCKyeGVGyIwOqIC4MVwVBAR12BRBgyiy6o876h2vgXGYUVBUhiWCgoDMhD2IJigYIBIgJCTppM/vj0oCCGgIvaS738/z9FPdp3o58JxUvVV16hyp2u75A8THQ06OHbhFxH0UtkXc6Phxw4uz7B3VH34PV12loC0iVV94uMVTYy1CQuDjT+2JuETEPRS2RdwoebYhJweaxcPQ+xS0RcR/tGltkTTQfv7iLMPJky7fVkgkQChsi7jJv1Yb1qy1p2Sf+JRFRITCtoj4l+FDLRo3huxseGlWvq+rIxIQFLZF3ODHHw3Js+3LrkP+CC1bKGiLiP+JjLS7k1gWvLeskI2b1J1E5HIpbItcJmMML7xoOHkSEq+CewcraIuI/+rQ3uJ3paMoTZ+p0UlELpfCtshl+us7BXyxESLC4emnNPqIiPi/hx6wSGgWyo8/wksva7IbkcuhsC1yGb7eb3jpZbtf46OPWCQkKGiLiP+LirKY/lwsoaGwZi18usrXNRLxXwrbIpVUUGCYNMXgdML118Fv7/R1jURE3KdtmzDuv9c+gTBrtiHrsM5ui1SGwrZIJc2Za8jIhDp1LJ4aZ2FZOqstIoHlj3dDm9ZwKg+mPmcoLlbgFrlUCtsilfDJp4Z/fmA/nz4tFkdNBW0RCTxhYRbPTrSIjob/pMFf3lLYFrlUCtsil+ibbw0zk+0dzr2D4frrInxcIxERz2nUyGLsaPuEwptvw9avFLhFLoXCtsglyM83PP2soaAAunTWLJEiEhx69rDo1weMgSnTDDk5CtwiFaWwLVJBxhhmvGg48B1cUQf+/IxFaKjCtogEh8dGWcTHw4/HYOrzhpISBW6RilDYFqmgd96Ff62xp2OfMslSP20RCSpRURZTnrWIjIQvN8Pivyhsi1SEwrZIBXz+hWHBInvHMmqkRbu2CtoiEnwSEizGjTnTf/uz9QrcIr9EYVvkF3ybYZg01WAM/Fd/jactIsGt160Wd5VO5z5tuuHAdwrcIj9HYVvkZ5w8aRg/wZCfD1d3sPssajxtEQl2jz5s0a4t5OXBxGcM+fkK3CIXo7AtchGFhYbxEw0HD0GD+jB1skV4uIK2iEh4uMXUyRa1a8O3GTBpim6YFLkYhW2RC3C5DM+9YEjbDrExMON53RApInK2OrUtpk+ziIiAzzfC/IUK2yIXorAtcgHzFhhWr4GwMHh+mkVCgoK2iMhPtW5lMXG8vX18dwksX6HALfJTCtsiP7Hkb4Z3l9jPJ4yz6NRRQVtE5GJ63GJx/7328xdnGbZsVeAWOVuYrysgUpWs+Mjw6mv2juLBByx63XrhoH369GnWrl3Lzp07SU9PZ/fu3TidToYNG8YDDzxQqd8uKCjgnXfe4dNPP+XQoUNERkbSunVrBg8eTOfOnSv9bxKRwLNnzx6+/PJLdu7cya5duzh69CgAGzdurNT37dixgzfffJO0tDROnz5NvXr16Nu3L0lJSVSrVu0XPz/0PosD3xn+tRomPGN47RVIvEonKkRAYVuk3Oq19gyRAL8fBH+8++Lv/e6775g8ebLbfjs/P59HH32U3bt3U716dbp06UJeXh5btmxh06ZNTJgwgf79+7vt90TEvy1evJjU1FS3fNfKlSuZOnUqJSUltGjRgvr167N3714WLFjAqlWrWLhwITExMT/7HZZlMXE8HDtm2PZvGDPWsGAeNGygwC2isC0CfLHJMGWaweWC/v3sYa1+boi/6Oho+vfvT+vWrbn22mtZuXIlixYtqvTvz5s3j927d9OyZUtefvllHA4HAGlpaTz22GPMnDmTLl260KBBg0r/hogEjrZt29K8eXNatWpF69atGTBgAEVFRZf8PUeOHGH69OmUlJQwceLE8oN6p9PJjBkzWL58OXPmzGH8+PG/+F0RERbTp8Gj/23Yvx+eeNIw/zV0c7kEPfXZlqD3xSbDxKcNxcXQ4xYY8/gvj6XduHFjJk6cyIABA2jTpg1hYZU/bnU6nSxfvhyAJ554ojxoA7Rv356BAwfidDpJSUmp9G+ISGAZMmQII0aM4KabbqJ27dqV/p7ly5dTWFhI165dz7l6Fh4ezjPPPEN0dDQffPABJ06cqND3xcZaJM+wqF8Pvv8eRj9pOJmrPtwS3BS2Jaht+Nww4WlDkRNuuhGemWARGurdszAZGRkUFBQQERFBu3btzltf1l/bXZeMRUTK7N27F4BOnTqdt65mzZo0b96ckpISNmzYUOHvrFPHYtaLFg4HpO+zu5Ro0hsJZgrbErQ+W2+Y+KzB6YRf3wxTJ1mEhXn/cufp06cBiImJueAZ9Ro1agBw6NAh8vLyvFo3EQlsZdufuLi4C64v2/7s27fvkr63SROL2ckW1avDrt0w9ilDQYECtwQnhW0JSp98anj6z3bXkVt+A5Oe9U3QBvvsEcDx48cpKCg4b/2hQ4fKn2dlZXmtXiIS+Mq2PxfbtpRtfyqz7flVgn2GOyYG/v0fGDfBcPq0ArcEH4VtCTpL/maY8pyhpAR69YRnJ/ouaANceeWV1KlTB2MMH3744Xnry/pzgz1qiYiIu3Ts2BGATz/9FKfTec667du3s3//fqDy256WLSySZ1pUqwZbv4LRYw15eQrcElwUtiVoGGNY+IarfBztu34HT0/wbdAGe8iswYMHA/Daa6+xfPlyTpw4waFDh5g1axYbNmwgNDS0/L0iIu5y2223UbduXbKysnjyySfZv38/eXl5bNq0iVGjRrll29O2jcXLL1nExkDadnhsjG6alOCiof8kKBQVGWa8ZPj4E/v1iOEWg++5+A5kypQp55V1796d7t27e6R+gwYN4vvvv2fp0qVMmzatvNyyLB588EGWLFlCTk4O1atX98jvi0hwio6OJjk5mdGjR7Nx48ZzJsVp2rQpd999N2+//fZlb3vatrF45WV4Yoxh924Y9ZjhpZlQp7ZOIEjgU9iWgHf8uH0j5H/SIDQERj9hcUe/n9/AX6g7R4MGDTwWti3LYvTo0fTv35/U1FSOHj2Kw+HgN7/5Dc2aNeONN94gMjKShg0beuT3RSR4XXXVVSxZsoRVq1axd+9eXC4XLVq0YODAgbzyyisANGvW7LJ/p0WixZzZ8Nhow9f74eFHDckz7ZspRQKZwrYEtMxMw9inDAcPQUwMTJtscU2XX96wV3bK48uVmJhIYmLiOWXbtm2jpKSETp06XdZ43iIiFxMVFUW/fv3o169feVl0dDTbt28HLjw0YGUkJFjMn2uPv/39QXhopGHmdPvMt0igUp9tCVipnxkeeNgO2g3qw4K5FQvaVc3SpUsBuPPOO31cExEJJnv27GHbtm0kJCTQoUMHt31vo4YW8+datGoFJ0/CqMcN/1qtPtwSuBS2JeCUlBgWveFiwjOG/Hy4ugMsmm/RLN73QTspKYmkpCSOHDlyTvmxY8fOG1qruLiY119/ndWrV9O5c2d69OjhzaqKSABZunQpSUlJzJs377x16enpFBcXn1P27bffMmrUKIwxjB492u31cdS0eHWWxfXdoKgI/jzF8MZiFy6XQrcEHl2TloCSk2OY+rzhy83260F3wSMPeWbEkXHjxpGdnU1YWFh5UH7//ffLu6DUqVOHGTNmnPOZzMxMgAvu2EaOHEliYiINGzbEGMOOHTvIzs4mMTGR559/3u31FxH/tWHDBhYvXlz+umzYvmHDhpWXDR06lBtuuAGwx/HPzMwkOzv7vO96+eWXycjIoHnz5jgcDg4fPsyOHTuwLItx48aVz2LrbtWqWUx/DuYvNLy7BP7yFnzzreHppyA62vcnR0TcRWFbAsaWrYapzxl+PAaRkTBujEWvWz23wd67d+95Z6OPHj3K0aNHAahfv36Fv6tx48b06dOHtLQ0vvjiC0JCQmjSpAl33303AwcOJDw83K11FxH/lpOTw86dO88rP7ssJyenQt/Vu3dvVq5cyddff01ubi4Oh4MePXrwyCOPXNJ2rDJCQy1GPmLxqwTDzGRD6mfwQKZh6mRIaKbALYHBMsZU6JpNRf9oqwKHw+FX9ZXLU1xsWPwXw9t/BWOgWTxM/rPltQ212pt4i9qaeJO329uOnYZn/mw4mm2fMBnzuMXtvRW4g4E/b9scDscvvkd9tsWvffONYcTDhrf+1w7ad/SH1xd4L2iLiIh7tG1jsfgNi67XQGEhPPeCYdp0l2acFL+nsC1+qaTE8M67hmEPGtL3QfXqMGWSxdjRIURFKWiLiPgjR02Ll2ZYDB9qERICKz+Ge4ca/v0fBW7xXwrb4nf2fW146FHDvAUGpxOu7wZv/Y/FLb9WyBYR8XchIRb3DbGYM9uiQX3IOgx/eswwd76LwkKFbvE/CtviNwoLDQtfdzH8QcPuPRAbA+PHWsyYbmnKXxGRANOhvcVf/p9F3z52N8H/S4Eh9xu2bFXgFv+i0UikyjPGsHotzF9gyDpsl/36ZnjsvxWyRUQCWUyMxVNjLW660ZA8y56k7LHRhj69DQ8/aOFwaB8gVZ/CtlRpe/YYXnnNsH2H/bpuXXh8lMVNN2oDKyISLG683qJjB1j4umHZP+HDlfYswfffB78bgEfmUhBxF4VtqZKysw2L3jB8uNJ+HRUFf7zb4veD0A2QIiJBKCbG4onHLHrdapj1iiE9HebMNbz/ATz8INxwPViW9g9S9ShsS5WSn29Y+h78718Npwvsstt6wYPDLerW1UZURCTYtW1j8fp8+PAjWPiGIfMAjJ9oaNsGRgyHTh21r5CqRWFbqoSCAvvS4F//z3D8uF3WpjWMGmnRprU2nCIickZoqEX/fvDr7vZ+Y+l7sGMnjHrc0LmT4d7BFh2v1pluqRoUtsWncnMN//wAlv7NnmYdoHEjGDbUouct2lCKiMjFxcVZPDTC4q7fGd582+5SsvUr2PqVoV1bGHwPdLvWHk5QxFc0Xbv4xOEjhiV/szeMp0/bZQ3qw71DLHr38q+bXdTexFvU1sSb/LG9ZWUZ/vquYcUKKHLaZVdeCQN/Z3H7bVCtmv/sW4KJP7a1MhWZrl1hW7xq39eGd1MMq1ZDSYldltAM/pBk0bMHhIf734ZQ7U28RW1NvMmf21v2j4aUJYb3l0Nenl0WGwO33gp9b7dokagrp1WJP7c1hW2pEgoLDes+g+UrDF9tO1PeqSP84fcW3br690ZP7U28RW1NvCkQ2lt+vj2q1dK/2WN0l/nVr+zQ3asn1Kzpv/ufQOHPbU1hW3wqfZ9h+QrDJ6vg1Cm7LCQEftPdPpPdsmVgbODU3sRb1NbEmwKpvblchq1fwYqPDKmpZ7qYhIXBjTfAbbdadL0GIiMDY7/kb/y5rVUkbOsGSXGrY8cMa9bZG7T09DPl9erZZxH63A7162ljJiIi3hMSYnFNF7imi8XJXMOqf8GKDw1702HtOli7zlAtCrp1M/z6ZovrukF0tPZV4h46sy2X7fARw7pUWJdqSNsOZS0qPBxuuhH69bHo3MkeqikQ/VJ7y87O9mJtJJDVrFmT42VjY4pUUp06dSr0vmDYl+772rDyY8PaVDh8+Ex5RDhccw3cfJPFtddAnTqBuf+qKvy5rakbiXiEMYaMTNjwuR2wd+85d33LFtCrp0WvW4OjL9wvtbdatWp5sTYiIj/v2LFjFXpfMO1LjTHs3QtrU+3g/f33565PaGaH765dLDq010zG7ubPbU1hW9wm67Dd323rVsPWbfDjj2fWWRa0bwfdb7a4+abg6yaisC0i/kRh++cZY/g2A9alwudfGPbsPXPFFuyz3u3b2zNVtm8HrVqqr/fl8ue2pj7bUiklJfb0t7v3wM5dhq++gu8PnvueiAi4uoN9ie3mG6FWLW1oRETE/1mWRUIz+2z2/fdanDhh2PIVbN5s+HIzHDkKW7bClq12Ag8LgxaJhvbtoF1bi3btwBEEV3Wl4nRmO8i5XIYffoD0fbB7j2HXbtibfmaimTKhIdCyJXTuBF06W7RprSP5MjqzLSL+RGe2K88Y+2TU5i3wnzTD9u2Uz358trp1ocVVcNVVFomJ9vM6dfx7mFtP8ue2pjPbUq6kxHD4CGRkwDffwrcZ9mWyzEwoLDz//dWioEUL+/JYhw4WHTtATIw2EpWRfvawLCKXQTdIiviWZVnEN4X4pvaslMYYDv0Aadth+w57kICMDDhyxH58tuHM+cyaNeFXCYYmTSC+iWUvmyqEBwOF7QBhjCH3lP3HnZUFBw/BwYP2IP4HD9llxcUX/mxEOMQ3g9YtoVUri1YtoGnTwB09xNsqeue/yC9xOByEhWmzLVJVWJZFo4bQqCHcfpu9z8zLM+z72r5inL7PsG+fHcCPH8e+9+krgDMhPDoamlxpaNAAGtSH+vWt0iXUr6cp5gOBttpVnMtlyM2FnOOQk3PWMsdw5Kgdro+WLk8X/Px3hYdDkyshPh6axVs0K+2T1rCBgrWIiIg7xMRYXN3Bvq8J7H1rYaHhm2/t0J15wJCZCRkH4NBByM+HPXvth+3c3r2xsYbataBW6aN2LXA4rPKy2rXAUQuqx0FEhPblVZHCtoeVlBgKC6GwyO4HnXcKTuVBXl7psvy1KX994qQdqo/n2EfCJa6K/16N6lCvPuVH2o0aWjRqBA0bwhV1FKpFRES8LTLSolVLu2tmWQAHcDoN3x+E776DrMOQlWX4IQt++AF+yCrNCqfsR+aBs7/xwrfbRUYaYmMhLg7ifrqMg9hYi6gou6toVBRUq2Y/floWFWVPBCTu4Rdh+/Rpw5at9vSqxmU3MWMAYy9dZz03QLWoAvLyTPn7zv5M2edcZz0vcdldLEpK7HBcXEz5o6QEiktKn1+grOxRWGg/CkqXhQV2wHY63fN/EBcHjprgcNjLmjXtQfbr1bVvxKhbF+peoZsWRURE/EV4uEWzeGgWX1Zy7j48N9fw4zE4dozy5bFj5iev4fgJcLnOZJGzh+c9V4XGxADs4F4tCiKj7Cvj4WEQHlG6DL/AIwzCwu2uqWFhEBJS+gi1B1kICbHOlJU+QkPtZWxsAQUFxi4rXWdZZ/47LOvM/0xZuWXZZ/U7Xl31+7z7RdhesMjw3rJL+USep6pyWSIjITYGYmIgJvbM89jy1xYx0VCjxplQ7XDYr8PDq3ZDEhEREfeKi7OIi7NvpDzj/Dzgchny8iC39Cx4bq79OHUKTuZC7ilTfoa84LTd7bSgwL7ifrrg3LKyMerKgjsn3PWv+bmgX/ncNvdVe6Khqswvwnb3my0yMg0lJaVHN6UPOOvoB3sZEgIR4eE4i53267OOgMqOjKyQs46SLPsoKiwMQsPso6ywMAgrXdqvrbOen/Wes94XGQWREfall8hIiIqEiNJlZOlDl2RERETE3UJCrPKuIhdWsfxhjN319ewAXlgIRUX2VfwiJxQ7wVkMzqLSpfOnD4Oz2O5V4HLZvQdcJfZzlznzvKR0vasEwsIjKCgowlVypry8N0J53c4vq1ULmjWr9H+b1/hF2O7U0aJTx4oHVYejut+O1ygiIiLiC5Zl9+mOigJqVvpbLvkTDkdcQOe2EF9XQEREREQkUClsi4iIiIh4iMK2iIiIiIiHKGyLiIiIiHiIwraIiIiIiIcobIuIiIiIeIjCtoiIiIiIhyhsi4iIiIh4iMK2iIiIiIiHKGyLiIiIiHiIwraIiIiIiIcobIuIiIiIeIjCtoiIiIiIhyhsi4iIiIh4iMK2iIiIiIiHKGyLiIiIiHiIwraIiIiIiIdYxhjj60q4U25uLlu3bqVz587ExcX5ujoS4NTexFvU1sSb1N7EW4KhrQXcme1Tp06xbt06Tp065euqSBBQexNvUVsTb1J7E28JhrYWcGFbRERERKSqUNgWEREREfGQ0EmTJk3ydSXcLSIigvj4eCIjI31dFQkCam/iLWpr4k1qb+Itgd7WAu4GSRERERGRqkLdSEREREREPERhW0RERETEQxS2RUREREQ8RGFbRERERMRDFLZFRERERDwkzNcV8LbvvvuOO+64g/z8fJKSkpgyZYqvqyQBwOl0snr1alavXk1aWhpZWVkANG/enAEDBpCUlERoaKiPayn+KC0tjTlz5rBt2zaKi4tJTEzkvvvuo0+fPr6umgSQw4cP89FHH5Gamso333xDdnY2NWrUoFOnTgwfPpwOHTr4uooS4BYtWkRycjIAKSkpXH311T6ukfsEVdh2uVyMHz/e19WQAHTgwAFGjRpFdHQ01113Hbfccgu5ubmsWbOGyZMnk5qayvz587Esy9dVFT+yceNGhg8fTkREBH379iUmJoZPPvmExx9/nKysLIYOHerrKkqAePvtt3n99ddp0qQJN9xwA7Vq1SIzM5NVq1axatUqkpOTdYAnHpOens6cOXOIjo4mPz/f19Vxu6AaZ3vx4sUkJyfz5JNPMn36dJ3ZFrc5fPgwq1atYsCAAURHR5eX5+fnM3jwYHbs2MHs2bO5/fbbfVhL8SfFxcXcfvvtZGVlsWTJElq1agVAbm4ud911FwcPHuTjjz+mUaNGPq6pBIJPPvmEmjVr0rVr13PKt2zZwn333Ud0dDTr168nIiLCRzWUQOV0OklKSiIsLIymTZvy/vvvB9yZ7aDps71//35mz57NiBEjyndaIu5Sr1497rnnnnOCNkB0dDT3338/AJs3b/ZF1cRPbdy4kQMHDtCvX79ztllxcXE89NBDOJ1Oli1b5sMaSiDp1avXeUEboEuXLlx77bWcOHGCvXv3+qBmEugWLFjAvn37eP755wO2u2VQhO2SkhLGjx9P06ZNefjhh31dHQkyYWF2b61A3YiIZ3z55ZcA3HjjjeetKyvTAZx4Q9k2rGwp4i47d+5kwYIFjBw5kubNm/u6Oh4TFH85CxcuZNeuXaSkpOgSmHjde++9B1w4NIlcTEZGBgBNmzY9b90VV1xBdHQ0mZmZXq6VBJtDhw7x+eefc8UVV5CYmOjr6kgAKSoqYty4cbRs2ZLhw4f7ujoeFfBhe8+ePcybN49hw4bRtm1bX1dHgkxKSgqpqal069aN7t27+7o64kdOnToF2N1GLiQ2Npbc3FxvVkmCjNPpZOzYsRQVFTFmzBhdnRO3euWVV8jIyODvf/97wLctvwjbL7zwAkVFRRV+/5AhQ4iPjy8/amrSpAkjR470YA0lUFS2rV3ImjVrmDp1Ko0aNeLFF190Uw1FRDyvbPSuzZs3M2jQIO68805fV0kCyLZt21i8eDEjR44MiismfhG2U1JSLmkomNtuu434+HgWLVpEeno67777rrqPSIVUtq391Lp16xg1ahS1a9fmzTffpG7dum6spQSD2NhYgIuevT516hQ1atTwZpUkSLhcLiZMmMDy5cu54447mDx5sq+rJAGkuLiY8ePH06JFC0aMGOHr6niFX4Ttbdu2Vepzu3btwuVyMWjQoAuuT0lJISUlhR49ejBv3rzLqaIEiMq2tbOtXbuWP/3pTzgcDt566y2uvPJKN9RMgk3ZQVxmZuZ5XeCOHj1Kfn4+7du390HNJJC5XC6eeuop/vGPf9CvXz9eeOEFQkKCYiwF8ZL8/Pzye1Iu1r03KSkJgLlz59KzZ09vVc1j/CJsV9YNN9yAw+E4r/zo0aOsW7eOhIQEOnXqROvWrX1QOwlEZUG7Ro0avPXWWxe8uU2kIq655hoWLlzI+vXr6du37znr1q9fX/4eEXc5O2j36dOHmTNnBnxfWvG+iIgI7rrrrguu27JlCxkZGdxyyy3UqlUrYOYRCKpJbcps2rSJIUOGaFIbcat169YxcuTI8qCdkJDg6yqJHysuLqZ3794cPnz4opParFy5ksaNG/u4phIIyrqOLFu2jN69e5OcnKyh/sTrxo8fz7JlywJuUhv9JYm4wf79+xk5ciRFRUV07dqVFStWnPeeRo0a8dvf/tYHtRN/FBYWxrRp0xg+fDj33HPPOdO1Hzx4kHHjxiloi9vMnTuXZcuWER0dTXx8PPPnzz/vPT179tSkcCKVoLAt4gbZ2dnlo5hcKGgDdO3aVWFbLkm3bt145513ePXVV/nwww8pLi4mMTGRMWPG0KdPH19XTwLIwYMHAbs/7YIFCy74nkaNGilsi1RCUHYjERERERHxBt1iLCIiIiLiIQrbIiIiIiIeorAtIiIiIuIhCtsiIiIiIh6isC0iIiIi4iEK2yIiIiIiHqKwLSIiIiLiIQrbIiIiIiIeorAtIiIiIuIhCtsiIiIiIh6isC0iIiIi4iEK2yIiIiIiHvL/AQp1q3fIk+b/AAAAAElFTkSuQmCC\n", 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    " ] @@ -70,7 +70,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "
    " ] @@ -138,7 +138,7 @@ "text": [ "Auto-assigning NUTS sampler...\n", "Initializing NUTS using jitter+adapt_diag...\n", - "Multiprocess sampling (4 chains in 4 jobs)\n", + "Multiprocess sampling (2 chains in 2 jobs)\n", "NUTS: [theta, tau, mu]\n" ] }, @@ -159,8 +159,8 @@ " background: #F44336;\n", " }\n", " \n", - " \n", - " 100.00% [8000/8000 00:03<00:00 Sampling 4 chains, 108 divergences]\n", + " \n", + " 100.00% [2000/2000 00:04<00:00 Sampling 2 chains, 7 divergences]\n", " \n", " " ], @@ -175,14 +175,10 @@ "name": "stderr", "output_type": "stream", "text": [ - "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 3 seconds.\n", - "There were 27 divergences after tuning. Increase `target_accept` or reparameterize.\n", - "The acceptance probability does not match the target. It is 0.6478974611182154, but should be close to 0.8. Try to increase the number of tuning steps.\n", - "There were 12 divergences after tuning. Increase `target_accept` or reparameterize.\n", - "The acceptance probability does not match the target. It is 0.7166147876408784, but should be close to 0.8. Try to increase the number of tuning steps.\n", - "There were 49 divergences after tuning. Increase `target_accept` or reparameterize.\n", - "There were 20 divergences after tuning. Increase `target_accept` or reparameterize.\n", - "The acceptance probability does not match the target. It is 0.6579601587628714, but should be close to 0.8. Try to increase the number of tuning steps.\n", + "There were 3 divergences after tuning. Increase `target_accept` or reparameterize.\n", + "The acceptance probability does not match the target. It is 0.7032617674641958, but should be close to 0.8. Try to increase the number of tuning steps.\n", + "There were 4 divergences after tuning. Increase `target_accept` or reparameterize.\n", + "The acceptance probability does not match the target. It is 0.6593200895612787, but should be close to 0.8. Try to increase the number of tuning steps.\n", "The estimated number of effective samples is smaller than 200 for some parameters.\n" ] }, @@ -203,8 +199,8 @@ " background: #F44336;\n", " }\n", " \n", - " \n", - " 100.00% [4000/4000 00:01<00:00]\n", + " \n", + " 100.00% [1000/1000 00:00<00:00]\n", " \n", " " ], @@ -243,9 +239,9 @@ "outputs": [ { "data": { - "image/png": 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"text/plain": [ - "
    " + "
    " ] }, "metadata": {}, @@ -274,6 +270,5840 @@ "outputs": [ { "data": { + "text/html": [ + "\n", + "
    \n", + "
    \n", + "
    arviz.InferenceData
    \n", + "
    \n", + "
      \n", + " \n", + "
    • \n", + " \n", + " \n", + "
      \n", + "
      \n", + "
        \n", + "
        \n", + "\n", + "\n", + "Show/Hide data repr\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Show/Hide attributes\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
        xarray.Dataset
          • chain: 2
          • draw: 500
          • school: 8
          • chain
            (chain)
            int64
            0 1
            array([0, 1])
          • draw
            (draw)
            int64
            0 1 2 3 4 5 ... 495 496 497 498 499
            array([  0,   1,   2, ..., 497, 498, 499])
          • school
            (school)
            <U16
            'Choate' ... 'Mt. Hermon'
            array(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
+       "       'Hotchkiss', 'Lawrenceville', "St. Paul's", 'Mt. Hermon'], dtype='<U16')
          • mu
            (chain, draw)
            float64
            4.45 3.18 -5.307 ... 4.155 6.853
            array([[ 4.44989359e+00,  3.18026068e+00, -5.30729597e+00,\n",
+       "        -1.77919561e+00, -2.45517955e+00,  9.06611876e+00,\n",
+       "         1.32318478e+01,  1.91589793e+00,  3.42242348e+00,\n",
+       "         6.57171154e+00,  4.57052379e+00,  5.94723510e+00,\n",
+       "         9.27924171e+00,  5.12448203e+00,  6.45848835e+00,\n",
+       "         4.18543262e+00,  4.01683793e+00,  1.99123267e+00,\n",
+       "         4.52100602e+00,  9.86194846e+00,  9.22788162e+00,\n",
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+       "         2.39144928e+00,  1.17576025e+00,  2.03454305e+00,\n",
+       "         2.02583062e+00,  1.58663837e+00,  3.25593415e+00,\n",
+       "         2.34720839e+00,  2.41180546e+00,  3.33769084e+00,\n",
+       "         1.06717051e+00,  5.88155744e+00,  2.72138599e+00,\n",
+       "         2.43865647e+00,  8.97710605e+00,  9.12417214e+00,\n",
+       "         8.71388504e+00,  9.75962979e+00,  9.41780287e+00,\n",
+       "         9.81909844e+00,  1.08491833e+01,  1.00721956e+01,\n",
+       "         1.17280060e+01,  1.08642070e+01,  2.90649629e+00,\n",
+       "         2.90649629e+00,  4.28881112e+00,  9.79629949e-01,\n",
+       "         1.45678889e+00, -1.75896128e+00, -7.29632608e-01,\n",
+       "         2.15153601e-01,  2.15153601e-01,  2.15153601e-01,\n",
+       "         2.15153601e-01,  2.15153601e-01,  2.15153601e-01,\n",
+       "         2.15153601e-01,  2.15153601e-01,  2.15153601e-01,\n",
+       "         2.15153601e-01,  2.15153601e-01,  2.15153601e-01,\n",
+       "         2.15153601e-01,  2.15153601e-01,  2.15153601e-01,\n",
+       "         2.15153601e-01,  2.15153601e-01,  2.15153601e-01,\n",
+       "         2.15153601e-01,  2.15153601e-01,  2.15153601e-01,\n",
+       "         2.15153601e-01,  2.15153601e-01,  2.15153601e-01,\n",
+       "         2.15153601e-01,  2.15153601e-01,  2.15153601e-01,\n",
+       "         2.15153601e-01,  2.15153601e-01,  2.15153601e-01,\n",
+       "         2.15153601e-01,  2.15153601e-01,  2.15153601e-01,\n",
+       "         2.15153601e-01,  2.15153601e-01,  2.15153601e-01,\n",
+       "         2.15153601e-01,  2.15153601e-01,  2.15153601e-01,\n",
+       "         2.15153601e-01,  2.15153601e-01,  2.15153601e-01,\n",
+       "         2.15153601e-01,  2.15153601e-01,  2.15153601e-01,\n",
+       "         2.15153601e-01,  2.15153601e-01,  2.15153601e-01,\n",
+       "         2.15153601e-01,  2.15153601e-01,  2.15153601e-01,\n",
+       "         2.15153601e-01,  2.15153601e-01,  2.15153601e-01,\n",
+       "         2.15153601e-01,  2.15153601e-01,  2.15153601e-01,\n",
+       "         2.15153601e-01,  2.15153601e-01,  2.15153601e-01,\n",
+       "         2.15153601e-01,  2.15153601e-01,  2.15153601e-01,\n",
+       "         2.15153601e-01,  2.15153601e-01,  2.15153601e-01,\n",
+       "         2.15153601e-01,  2.15153601e-01,  2.15153601e-01,\n",
+       "         2.15153601e-01,  2.15153601e-01,  2.15153601e-01,\n",
+       "         2.15153601e-01,  2.15153601e-01,  2.15153601e-01,\n",
+       "         2.15153601e-01,  2.15153601e-01,  2.15153601e-01,\n",
+       "         2.15153601e-01,  8.85490803e-01, -3.76383171e-01,\n",
+       "        -3.76383171e-01, -1.51217090e+00, -9.01704689e-01,\n",
+       "        -9.01704689e-01, -1.10350417e+00,  1.86768746e+00,\n",
+       "         2.54420475e+00,  2.17560719e+00,  2.43332664e+00,\n",
+       "         3.85816574e+00,  5.65749841e+00,  5.65749841e+00,\n",
+       "         6.52021252e+00,  6.52021252e+00,  6.52021252e+00,\n",
+       "         4.38862682e+00,  6.99179020e+00,  7.68057239e+00,\n",
+       "         5.21462609e+00,  4.38336721e+00,  9.08430229e+00,\n",
+       "         8.31259046e+00,  8.54765328e+00,  9.96800991e-01,\n",
+       "         4.64428095e+00,  2.76491329e+00,  3.29384060e+00,\n",
+       "         3.43290603e+00,  2.33411260e+00,  4.04978603e+00,\n",
+       "         2.52232796e+00,  2.49728803e+00,  1.21402275e+00,\n",
+       "        -9.70964197e-01, -2.58545748e+00,  1.21982171e-02,\n",
+       "         1.78386793e-01,  1.02593465e+00,  1.62020732e+00,\n",
+       "         4.33743804e+00,  4.30129985e+00,  3.15367039e+00,\n",
+       "         2.52893265e+00,  3.74185464e+00,  4.42967750e+00,\n",
+       "         5.57173537e+00,  5.17724929e+00,  5.17317637e+00,\n",
+       "         4.70664269e+00,  2.29912944e+00,  9.39708927e-01,\n",
+       "        -1.44797067e+00, -4.09857129e+00, -2.72204674e+00,\n",
+       "         4.09844910e+00,  5.05053253e+00,  3.77033126e+00,\n",
+       "         2.89548750e+00,  4.13148338e+00,  4.64594238e+00,\n",
+       "         2.29947497e+00,  2.37928354e+00,  2.87668343e+00,\n",
+       "         1.87369717e+00,  4.12354082e+00,  2.00836069e+00,\n",
+       "         3.35848175e+00,  5.33537202e+00,  8.20564574e+00,\n",
+       "         3.84176840e+00,  6.48125420e+00,  3.49829288e+00,\n",
+       "         3.16115800e+00,  3.39842439e+00,  4.10880024e+00,\n",
+       "         2.89236604e+00,  2.65674539e+00,  3.05789045e+00,\n",
+       "         4.84541078e+00,  5.64916108e-01,  2.08247102e+00,\n",
+       "         4.47823226e+00,  5.50659778e+00,  5.78140606e+00,\n",
+       "         7.64585809e+00,  6.64957557e+00,  4.44631326e+00,\n",
+       "         5.50676658e+00,  1.32742846e+00,  1.32742846e+00,\n",
+       "         2.34147980e+00,  1.11317217e+00,  1.95399233e+00,\n",
+       "         6.09756763e-01,  2.65575863e+00,  7.30784812e-01,\n",
+       "         4.58600594e+00,  3.38011586e+00,  4.19146468e+00,\n",
+       "         1.28174431e+00,  2.59785916e+00,  1.65116570e+00,\n",
+       "         1.21538878e+00,  6.91060701e-01,  1.70503784e+00,\n",
+       "         2.57290694e+00,  1.41955763e-01,  7.30495210e+00,\n",
+       "         7.21041717e+00,  7.72685850e+00,  7.69352667e+00,\n",
+       "         9.51180226e+00,  9.51180226e+00,  1.05224928e+01,\n",
+       "         1.05224928e+01,  3.50610501e+00,  4.97703545e+00,\n",
+       "         4.78188634e+00,  5.66423882e+00,  2.64516025e+00,\n",
+       "         8.46411249e+00,  4.33935652e+00,  5.79290618e+00,\n",
+       "         5.29772240e+00,  1.77997789e+00, -7.89502916e-01,\n",
+       "        -7.89502916e-01,  1.89459389e-01,  2.10804139e-01,\n",
+       "         2.10804139e-01,  2.10804139e-01, -3.70647858e+00,\n",
+       "        -3.70647858e+00, -6.14595145e+00, -2.64546649e+00,\n",
+       "        -5.40805365e+00, -5.59699030e+00,  4.55012800e+00,\n",
+       "         4.01018193e+00,  3.35255882e-01,  5.72391565e+00,\n",
+       "         5.11969696e+00,  6.00684172e+00,  3.86700407e+00,\n",
+       "         3.20174801e+00,  2.20497401e+00,  3.88707024e+00,\n",
+       "         4.71300382e+00,  3.56637950e+00,  3.56637950e+00,\n",
+       "         3.56637950e+00,  3.95083472e+00,  6.98945578e+00,\n",
+       "         6.38678250e+00,  4.92703146e+00,  5.21385572e+00,\n",
+       "         5.21017162e+00,  1.86211043e+00,  7.06000855e+00,\n",
+       "         4.48080314e-01,  8.92343403e+00,  3.76589045e+00,\n",
+       "         6.28458191e+00,  5.86258325e+00,  1.55463644e+00,\n",
+       "         7.03526407e-01,  1.00716205e+01,  1.02124868e+00,\n",
+       "        -1.52171327e+00, -7.08577079e-01,  3.08197456e+00,\n",
+       "         5.52617842e+00,  8.10740104e+00,  4.41898267e+00,\n",
+       "         7.42988921e+00,  3.45356122e+00,  3.55781077e+00,\n",
+       "         2.47671273e+00,  4.01632627e-02,  8.13332610e+00,\n",
+       "         6.25128458e+00, -2.35106696e+00, -1.64337858e+00,\n",
+       "        -1.39070801e+00,  5.05690057e+00,  5.16553349e+00,\n",
+       "         7.02382286e+00,  8.11526581e+00,  5.32712890e+00,\n",
+       "         4.83653591e+00,  6.83335454e+00,  6.21671254e+00,\n",
+       "         5.73363757e+00,  4.03813834e+00,  7.00381831e+00,\n",
+       "         4.43721639e+00,  4.97099686e+00,  1.93116785e+00,\n",
+       "         4.53273408e+00,  5.14232072e+00, -2.73422213e-01,\n",
+       "         2.21614469e+00,  2.04942952e+00,  1.59207173e+00,\n",
+       "         4.18236452e+00,  1.44309854e+00,  8.15213312e+00,\n",
+       "         6.09602907e+00,  8.21894946e+00, -5.28458365e-01,\n",
+       "        -9.46442321e-01,  8.99322950e-01,  4.09829629e+00,\n",
+       "         5.70905761e-01,  1.51823751e+00,  4.28432748e+00,\n",
+       "         7.44705499e+00,  2.28817336e+00,  1.87115137e+00,\n",
+       "         3.74452986e+00, -7.28791068e-01,  5.37969558e+00,\n",
+       "         1.78502381e-01,  3.60809610e+00,  3.41595543e+00,\n",
+       "         1.44503013e-01,  4.07566705e+00,  5.28026267e+00,\n",
+       "         3.52025904e+00,  5.23642322e+00,  4.72214365e+00,\n",
+       "         5.47383457e+00,  3.38143272e+00,  1.82024422e+00,\n",
+       "         8.56132936e-01,  3.10472083e-02,  2.56439446e+00,\n",
+       "         2.56439446e+00,  3.89013037e+00,  5.63164049e+00,\n",
+       "         4.15479956e+00,  6.85276017e+00]])
          • theta
            (chain, draw, school)
            float64
            11.18 1.053 4.916 ... 6.669 12.67
            array([[[ 11.17881673,   1.05250176,   4.91645367, ...,  -2.4378686 ,\n",
+       "          11.81281447,  -0.93787861],\n",
+       "        [ 12.82872622,   0.17362158,   3.85817533, ...,  -2.78749748,\n",
+       "          14.47379589,  -0.63999867],\n",
+       "        [ -7.45664925,   3.66670299,  -8.16612314, ...,  -4.44612383,\n",
+       "         -10.8963984 ,  -0.98104479],\n",
+       "        ...,\n",
+       "        [  5.84407761,   5.35767678,   1.76958013, ...,   7.48542798,\n",
+       "           5.72098469,   4.78733115],\n",
+       "        [  5.84407761,   5.35767678,   1.76958013, ...,   7.48542798,\n",
+       "           5.72098469,   4.78733115],\n",
+       "        [  6.65047641,   4.14180457,   4.96096063, ...,   3.24494081,\n",
+       "           5.51854655,   5.64576333]],\n",
+       "\n",
+       "       [[  1.89808065,   9.14860898,   6.13166834, ...,   4.85267978,\n",
+       "           7.2228535 ,   6.36015008],\n",
+       "        [  2.97074505,   8.88566785,   6.56350757, ...,   4.66457925,\n",
+       "           5.66100623,   7.59144271],\n",
+       "        [  3.50207861,   9.3857739 ,   7.02957254, ...,   3.36536962,\n",
+       "           5.77176979,   7.54132391],\n",
+       "        ...,\n",
+       "        [  1.30754901,   6.38862991,   1.34619061, ...,   8.88757138,\n",
+       "           4.28924647,   9.33627892],\n",
+       "        [  9.1337481 ,   4.64669861,   7.18699457, ...,   0.96324186,\n",
+       "           5.15415303,   1.72465413],\n",
+       "        [  9.3525312 ,   2.22558717,   2.69395287, ...,   9.62450399,\n",
+       "           6.66915754,  12.67340736]]])
          • tau
            (chain, draw)
            float64
            4.875 4.042 5.603 ... 1.978 5.472
            array([[ 4.87496253,  4.04231931,  5.60307949,  8.49194572, 11.22309181,\n",
+       "         4.45691021,  5.62230469, 11.25287806,  4.65097855, 18.83350879,\n",
+       "        13.07520504,  6.7250893 ,  7.31484769,  7.44425768, 12.50222817,\n",
+       "         9.92165035,  8.59585189,  8.02889298,  7.10117701,  8.8152386 ,\n",
+       "        10.60101071,  6.46562512,  6.40437874,  9.24420649,  9.16837519,\n",
+       "         6.01522946,  3.02543206,  3.08620636,  3.21877972,  4.06757889,\n",
+       "         4.30389901,  7.64980013,  4.41086201,  3.76535086,  3.05572346,\n",
+       "         3.14695677,  1.87867643,  1.87867643,  1.87867643,  1.4802188 ,\n",
+       "         1.4802188 ,  1.4802188 ,  1.4802188 ,  1.14034044,  1.14034044,\n",
+       "         1.14034044,  1.14034044,  1.24730824,  1.24730824,  1.36243814,\n",
+       "         1.36243814,  1.39607106,  1.39607106,  1.3146174 ,  1.3146174 ,\n",
+       "         1.3146174 ,  1.3146174 ,  1.3146174 ,  1.57949811,  1.57949811,\n",
+       "         1.57949811,  1.57949811,  1.57949811,  1.57949811,  1.4762983 ,\n",
+       "         1.4762983 ,  1.4762983 ,  1.4762983 ,  1.4762983 ,  1.4762983 ,\n",
+       "         1.4762983 ,  1.4762983 ,  1.4762983 ,  1.4762983 ,  1.4762983 ,\n",
+       "         3.4795356 ,  4.45955648,  3.24422015,  4.81186998,  3.83207645,\n",
+       "         3.75951973,  5.56786242,  3.19423906,  2.96834418,  5.73596382,\n",
+       "         4.14801082,  3.00783828,  2.96763948,  2.96763948,  5.7139748 ,\n",
+       "         2.61057127,  2.02544637,  2.18149657,  2.03369641,  2.03369641,\n",
+       "         2.03369641,  2.03369641,  3.44633136,  2.19518172,  3.94691052,\n",
+       "         2.41811967,  2.65200769,  2.93609091,  3.61755089,  1.83585577,\n",
+       "         7.3317217 , 11.19986311,  4.56032089,  5.12596377,  4.66103387,\n",
+       "         3.3039563 ,  3.01541756,  3.51718201,  3.45853742,  5.88552971,\n",
+       "         6.25679393,  2.45387111,  2.48047082,  4.53779842,  3.50496513,\n",
+       "         6.58740593,  7.03344961,  4.34756764,  4.42061511,  6.02529631,\n",
+       "         4.8510639 ,  3.30852096,  2.97906621,  3.52480301,  2.15507047,\n",
+       "         2.6565469 ,  2.6565469 ,  2.57168477,  1.94016799,  2.4079637 ,\n",
+       "         1.82129769,  3.731645  ,  2.69064499,  7.28969875,  4.75672599,\n",
+       "         5.86141111,  4.8828992 ,  4.24994287,  7.90934153,  4.75785837,\n",
+       "         4.09248835,  3.18837062,  2.82887176,  2.186934  ,  3.6329454 ,\n",
+       "         2.64612391,  3.42646444,  4.8989631 ,  3.34481006,  4.87723302,\n",
+       "         3.58793707,  2.71359674,  6.71745793,  5.66453176,  6.06417964,\n",
+       "         3.17945383,  2.4218607 ,  3.24178259,  6.94463062,  4.51318007,\n",
+       "         4.99008876,  9.51964181,  6.68139307,  8.79497529,  7.97599983,\n",
+       "         7.02952304,  4.91564349,  7.35894225,  7.11806619, 14.87781029,\n",
+       "         4.66101978,  4.81566044,  6.58593275,  6.27415556,  6.52480026,\n",
+       "         9.88628246, 10.9204201 ,  6.63864591,  7.86736638,  4.77690931,\n",
+       "         2.05790061,  2.17154982,  3.01060546,  6.05360404,  2.93831871,\n",
+       "         7.12663449,  4.65725984,  6.69596564,  3.77264673,  4.40081357,\n",
+       "         4.84742253,  3.05207211,  3.41325165,  3.60522879,  2.73592948,\n",
+       "         5.6497371 ,  5.22535245,  6.21638125,  7.72683913,  6.14983861,\n",
+       "         3.28418158,  6.67596636,  3.41216567, 11.68884104,  7.64978151,\n",
+       "        11.49645968, 11.67045139,  9.95108334,  9.95858952,  4.39870374,\n",
+       "         6.86235695, 12.4998046 ,  6.48855143,  6.70167462,  7.13658725,\n",
+       "         8.2241664 ,  8.05565566,  5.68940774,  4.52365306,  4.99538326,\n",
+       "         6.11906042,  3.08320925,  2.43877911,  2.75837591,  1.99275939,\n",
+       "         2.29589066,  4.31018467,  2.49790497,  2.43175719,  2.43175719,\n",
+       "         2.01590244,  2.08760064,  2.08760064,  2.08760064,  2.81845   ,\n",
+       "         2.62153974,  3.4469418 ,  3.42587678,  2.19143621,  2.90003653,\n",
+       "         1.99333611,  2.02417773,  2.25729832,  3.86444472,  4.73307823,\n",
+       "         6.39363009, 12.45687313,  8.71287367,  7.69794219, 10.10418408,\n",
+       "         7.82922646, 10.20266255, 10.54219669,  3.41197402,  2.60352715,\n",
+       "         2.60352715, 12.58778938,  2.75778429,  2.79802492,  2.17596369,\n",
+       "         4.17220828,  4.47532229,  3.86308523,  6.08054973,  5.76725119,\n",
+       "        10.67219304,  7.96794757, 11.72062025,  8.67784178,  5.94012673,\n",
+       "        13.49521296,  9.90656917, 11.69740997,  6.95010722,  5.31818544,\n",
+       "         5.85342394,  5.49534874,  6.79021335,  8.37543598, 10.04384941,\n",
+       "         8.70424743,  7.76772369,  3.27065597,  4.40243612,  2.58753254,\n",
+       "         3.79659933,  4.36879   ,  3.04545264,  2.03493727,  2.44723498,\n",
+       "         3.02032485,  2.36533772,  2.92120372,  2.05631978,  2.05631978,\n",
+       "         3.38141159,  4.4208667 ,  2.99375515,  2.8219202 ,  1.9614063 ,\n",
+       "         2.06883636,  2.56189374,  3.54617512,  3.77003013,  6.58012038,\n",
+       "         6.7528589 ,  4.1527212 ,  3.06445609,  2.64407309,  1.6300495 ,\n",
+       "         1.6300495 ,  2.22277656,  2.22277656,  2.77183819,  3.83383279,\n",
+       "         3.00224368,  2.36450496,  2.36450496,  2.56447717,  1.8531181 ,\n",
+       "         1.53955738,  1.53955738,  1.53955738,  1.53955738,  1.63318296,\n",
+       "         1.63318296,  1.86483123,  1.71473354,  1.71473354,  1.71473354,\n",
+       "         4.97037934,  3.94362623,  2.9375983 ,  3.02948305,  2.20092591,\n",
+       "         4.43361573,  4.06182104,  4.07520591,  5.86357241,  5.10280445,\n",
+       "         7.95464106,  4.24533354, 10.09956754,  7.75836039,  6.77897924,\n",
+       "         7.00013197,  6.71323564,  4.87593999,  4.42279034,  3.06272572,\n",
+       "         9.11314565,  5.48396773,  5.01431862,  4.57372532,  2.73857071,\n",
+       "         2.97316641,  4.11649565,  4.49427571,  3.49558235,  3.84727526,\n",
+       "         4.78666986,  9.55395169,  5.51265341,  6.07249793,  5.97814298,\n",
+       "         3.55710265,  3.05518133,  2.82772287,  2.6101243 ,  3.54320645,\n",
+       "         3.26741004,  4.35501377,  8.8741689 ,  4.12293718,  2.21146418,\n",
+       "         2.21146418,  4.01652416,  4.68654566,  8.9458965 ,  8.62796228,\n",
+       "         6.33722841,  3.51037977,  4.91343102,  7.4969136 ,  7.88795451,\n",
+       "         8.20635343,  8.6045245 ,  9.55319517,  6.05797904,  6.78788887,\n",
+       "         5.44103743,  3.64234149,  4.73429753,  3.29833437,  2.53663688,\n",
+       "         3.19554695,  2.99510638,  2.22752818,  1.62690097,  1.62690097,\n",
+       "         1.62690097,  1.62690097,  1.62690097,  1.62690097,  1.62690097,\n",
+       "         2.53448114,  4.31246123,  4.28436666,  5.41616242,  4.18616144,\n",
+       "         5.15089087,  4.7545822 ,  6.4621661 ,  4.00183778,  2.9318403 ,\n",
+       "         3.07038642,  2.21211423,  3.22664098,  4.22065387,  5.27946285,\n",
+       "         5.17703026,  6.86096583,  7.55456002,  7.09609819,  8.98180754,\n",
+       "         9.17903342,  7.99839898,  2.5808782 ,  2.68511384,  1.65040858,\n",
+       "         1.81305252,  2.83857541,  4.20120328,  3.07032147,  4.69393137,\n",
+       "         4.99027302,  5.26603236,  9.53374607,  8.36143544,  6.04169344,\n",
+       "         4.02095243,  7.19417661,  6.37331177,  8.65107954,  6.37907358,\n",
+       "         4.5948025 ,  4.07958377,  4.63372478,  3.33275935,  5.93185029,\n",
+       "         3.94392161,  2.53421463,  5.59209648,  6.36802256,  9.08673942,\n",
+       "        15.24740266,  6.23535928,  5.29695598,  6.73717916,  8.63444508,\n",
+       "         8.04699313, 10.14847935, 12.59830117,  5.86185149,  6.69107613,\n",
+       "         2.59540414,  2.59540414,  3.72209586,  3.67377285,  3.35780411,\n",
+       "         7.7391137 ,  2.8561222 ,  2.8561222 ,  2.12710679,  2.65584051,\n",
+       "         6.46154462,  6.88907122, 10.47113525,  6.59860185,  2.54124757,\n",
+       "         2.64894125,  1.87236325,  1.87236325,  1.47696233,  1.59960475,\n",
+       "         1.59052016,  1.59052016,  1.59052016,  1.59052016,  1.59052016,\n",
+       "         1.63530062,  1.63530062,  1.63530062,  1.63530062,  1.73896123],\n",
+       "       [ 3.68058571,  3.98835094,  4.00653335,  7.12995266,  8.88464598,\n",
+       "         9.87694375,  8.00012551,  6.57826567,  6.23145172,  8.99871129,\n",
+       "         8.30986917,  5.60278998,  2.69506012,  2.11833131,  2.22341129,\n",
+       "         7.10648025,  3.82089677,  3.23520269,  3.60881317,  4.86492815,\n",
+       "         5.59531431,  5.87249984, 15.76906223,  8.07478884, 15.15627806,\n",
+       "        10.88622552,  5.33981537,  6.0355375 ,  3.7184077 ,  2.30117836,\n",
+       "         1.99388771,  3.21680712,  5.95833757,  7.16734591,  3.91826548,\n",
+       "         3.721883  ,  4.45786938, 14.08678209,  9.83794706,  7.60619316,\n",
+       "         6.29818741,  4.13245266,  6.65339305,  7.3531982 ,  8.39148647,\n",
+       "         5.23604933,  3.35443268,  3.09524081,  2.63862977,  5.79054141,\n",
+       "         2.61669811,  3.33467065,  1.80305957,  2.19878329,  2.04913517,\n",
+       "         1.61392393,  2.53023287,  1.65723719,  1.91614851,  1.91614851,\n",
+       "         2.6900256 ,  2.28342952,  2.3213357 ,  5.62990987,  3.09605967,\n",
+       "         4.52853882,  4.90607228,  5.29139322,  1.82916693,  1.91375308,\n",
+       "         2.75860738,  2.54737611,  2.26570442,  2.26570442, 10.00251836,\n",
+       "        11.65403485,  8.01674904,  4.90701486,  5.97366373,  5.61449849,\n",
+       "         8.92405076,  6.12368642,  4.53119728,  6.32384921,  3.186594  ,\n",
+       "         8.85717818, 11.72565475,  8.04523993,  9.85183144,  4.17854066,\n",
+       "         7.26263296,  6.34533848,  7.7529397 ,  4.74927783,  7.29995169,\n",
+       "        17.05022905, 14.39066025,  8.2716974 ,  8.86228057,  5.09613427,\n",
+       "         6.80742174,  3.97193948,  7.76896957,  4.53281161,  5.55462894,\n",
+       "         6.75140051,  4.5540024 ,  6.37258694,  6.38138748,  6.81715024,\n",
+       "        12.47588898,  8.61367875, 14.61363126,  8.57321443,  9.31199682,\n",
+       "         9.68276537, 20.14962898,  8.84903168,  6.81074148,  7.69394216,\n",
+       "        14.99601254, 19.49034852,  8.01280388,  3.49321859,  4.40901561,\n",
+       "         8.29146907, 11.25795621,  8.07520442,  8.81167727, 10.35024528,\n",
+       "         8.83549645, 11.23250324,  4.71037598,  3.11059188,  1.94147401,\n",
+       "         2.64398957,  1.84357368,  1.04231641,  1.04231641,  1.04231641,\n",
+       "         1.04231641,  2.27734912,  2.84084214,  3.80466762,  2.52963663,\n",
+       "         6.0448761 ,  3.65424953,  2.16203352,  2.06603412,  4.04522644,\n",
+       "         2.85735886,  2.59570572,  3.14150309,  1.93184138,  2.68553313,\n",
+       "         2.74890581,  6.46515568,  4.47180628,  7.7857996 ,  5.68396781,\n",
+       "         5.97654343,  3.15436246,  2.04675168,  3.87995185,  3.7674744 ,\n",
+       "         2.72755363,  2.18860477,  2.51155024,  3.17261972,  1.86003889,\n",
+       "         1.94611756,  1.94611756,  1.53340791,  2.8465656 ,  2.69934624,\n",
+       "         3.46900868,  1.16260469,  0.89025164,  0.89025164,  0.89025164,\n",
+       "         0.89025164,  0.89025164,  0.89025164,  0.89025164,  0.89025164,\n",
+       "         0.89025164,  0.89025164,  0.89025164,  0.89025164,  0.89025164,\n",
+       "         0.89025164,  0.89025164,  0.89025164,  0.89025164,  0.89025164,\n",
+       "         0.89025164,  0.89025164,  0.89025164,  0.89025164,  0.89025164,\n",
+       "         0.89025164,  0.89025164,  0.89025164,  0.89025164,  0.89025164,\n",
+       "         0.89025164,  0.89025164,  0.89025164,  0.89025164,  0.89025164,\n",
+       "         0.89025164,  0.89025164,  0.89025164,  0.89025164,  0.89025164,\n",
+       "         0.89025164,  0.89025164,  0.89025164,  0.89025164,  0.89025164,\n",
+       "         0.89025164,  0.89025164,  0.89025164,  0.89025164,  0.89025164,\n",
+       "         0.89025164,  0.89025164,  0.89025164,  0.89025164,  0.89025164,\n",
+       "         0.89025164,  0.89025164,  0.89025164,  0.89025164,  0.89025164,\n",
+       "         0.89025164,  0.89025164,  0.89025164,  0.89025164,  0.89025164,\n",
+       "         0.89025164,  0.89025164,  0.89025164,  0.89025164,  0.89025164,\n",
+       "         0.89025164,  0.89025164,  0.89025164,  0.89025164,  0.89025164,\n",
+       "         0.89025164,  0.89025164,  0.89025164,  0.89025164,  0.89025164,\n",
+       "         0.89025164,  1.39741164,  1.85055526,  1.85055526,  1.8063058 ,\n",
+       "         1.77631559,  1.77631559,  3.14244762,  2.53581528,  3.30317252,\n",
+       "         3.04499075,  2.01535912,  2.46757594,  1.13556478,  1.13556478,\n",
+       "         1.31060591,  1.31060591,  1.31060591,  1.66159731,  4.67486523,\n",
+       "         3.29575092,  5.06715114, 14.89454029,  8.64901009,  3.45460307,\n",
+       "         4.8444278 ,  3.01704247,  4.44211449,  4.27306405,  7.26996471,\n",
+       "         2.4174295 ,  4.95287373,  5.69476681,  4.29024753,  2.07505887,\n",
+       "         2.88567513,  5.45253664,  4.28999277,  3.02660678,  2.89658259,\n",
+       "         2.23391401,  2.35319701,  2.01834026,  1.84944283,  2.39301948,\n",
+       "         3.50126631,  5.61987146,  5.0467372 ,  3.87564631,  2.49117048,\n",
+       "         2.76372834,  5.98181795,  6.57987405,  5.22099862,  5.32114379,\n",
+       "        12.02953004, 10.74107535,  4.51628093,  4.53442064,  3.13430919,\n",
+       "         4.15070363,  5.25001249,  3.67790502,  2.85371634,  3.9655607 ,\n",
+       "         3.95842465,  3.39224695,  4.78328468,  3.50555728,  3.54316531,\n",
+       "         3.56192299,  2.4086793 ,  3.24999207,  3.87396415,  4.3015363 ,\n",
+       "         4.09947785,  4.41166489,  1.90674931,  2.7857621 ,  1.61579323,\n",
+       "         2.16062806,  2.89428751,  4.36301683,  3.13433252,  2.85845788,\n",
+       "         3.35136686,  4.36834967,  4.21891717,  6.237099  ,  2.46886981,\n",
+       "         3.60183221,  1.64789396,  1.64789396,  2.62619208,  3.78384289,\n",
+       "         4.2487973 ,  3.10591684,  5.29879151,  2.3390641 ,  3.32814866,\n",
+       "         5.23064623,  2.88204004,  3.2211747 ,  2.33917384,  1.90868073,\n",
+       "         2.0897423 ,  3.52719169,  2.50264623,  2.94314953,  6.21822876,\n",
+       "         3.40026927,  3.32267357,  2.83092867,  2.22867153,  1.4320662 ,\n",
+       "         1.4320662 ,  1.88340074,  1.88340074,  2.53790621,  3.08110739,\n",
+       "         3.50776967,  2.95263694,  3.40174344,  4.30299304,  4.17848193,\n",
+       "         3.65962812,  2.5625635 ,  5.22713894,  1.454944  ,  1.454944  ,\n",
+       "         1.44661112,  1.57387184,  1.57387184,  1.57387184,  1.77731444,\n",
+       "         1.77731444,  3.35352387,  3.61086199,  4.62611915,  6.78805825,\n",
+       "         4.85921988,  5.32440748,  3.79311876,  1.61842578,  2.16939738,\n",
+       "         1.94883509,  3.78585679,  2.06063432,  1.46332122,  2.89104514,\n",
+       "         2.59260804,  1.28789494,  1.28789494,  1.28789494,  1.5347956 ,\n",
+       "         1.81076556,  2.61448915,  2.49005258,  2.28011899,  5.37991104,\n",
+       "         4.69467013,  7.07492047,  4.13285415,  3.48519835, 10.36564179,\n",
+       "         6.14205765,  4.34976974,  3.22963625,  6.67580331,  6.29589744,\n",
+       "         7.13630463,  9.26705636, 12.10112489, 10.85385668, 13.0618523 ,\n",
+       "         5.17758538,  5.17318677, 10.77114341, 15.69148084, 11.85922615,\n",
+       "        23.0824281 , 18.04529926,  6.40422475, 19.53455177, 22.3395412 ,\n",
+       "        14.88067599, 11.5453065 ,  8.19508928,  6.17770704,  9.0367138 ,\n",
+       "         5.81164284, 10.34669445,  5.01375078,  9.62552993,  8.01095911,\n",
+       "        10.34391011,  3.26719948,  5.76486505,  7.07202004,  2.78463822,\n",
+       "         2.65961869,  2.67584155,  4.04659973,  7.48074929,  5.03220862,\n",
+       "         9.72486755,  5.9301018 ,  6.13858229, 10.47549883,  8.39049105,\n",
+       "         3.82084296,  4.86738473,  4.75321925,  4.93722273,  6.47429181,\n",
+       "         6.16519566,  6.20777739,  9.02180929,  9.12616752,  8.83803857,\n",
+       "         9.29829992,  9.40421014,  8.322127  , 10.27295156, 10.7127193 ,\n",
+       "        11.6481489 ,  5.46446215, 19.51476896, 12.52936608, 10.28003481,\n",
+       "        12.97497294,  8.48966032,  5.08797595,  5.69033171,  6.23472778,\n",
+       "         4.8519205 ,  4.31145894,  4.42425785,  3.7120722 ,  1.87113772,\n",
+       "         1.87113772,  3.98138188,  3.14460592,  1.97758402,  5.4722307 ]])
        • created_at :
          2020-06-06T18:07:53.669221
          arviz_version :
          0.8.3
          inference_library :
          pymc3
          inference_library_version :
          3.8

        \n", + "
      \n", + "
      \n", + "
      • \n", + " \n", + "
    • \n", + " \n", + " \n", + "
      \n", + "
      \n", + "
        \n", + "
        \n", + "\n", + "\n", + "Show/Hide data repr\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Show/Hide attributes\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
        xarray.Dataset
          • chain: 2
          • draw: 500
          • school: 8
          • chain
            (chain)
            int64
            0 1
            array([0, 1])
          • draw
            (draw)
            int64
            0 1 2 3 4 5 ... 495 496 497 498 499
            array([  0,   1,   2, ..., 497, 498, 499])
          • school
            (school)
            <U16
            'Choate' ... 'Mt. Hermon'
            array(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
+       "       'Hotchkiss', 'Lawrenceville', "St. Paul's", 'Mt. Hermon'], dtype='<U16')
          • obs
            (chain, draw, school)
            float64
            9.27 -3.737 13.58 ... 5.565 -37.82
            array([[[ 9.27019662e+00, -3.73664108e+00,  1.35793629e+01, ...,\n",
+       "          1.49786990e+01,  1.22912334e+01, -2.46182885e+00],\n",
+       "        [ 1.29656615e+01, -1.30706639e+01,  6.79264057e+00, ...,\n",
+       "          7.34811060e+00,  1.24558827e+01, -5.94944893e+00],\n",
+       "        [-7.98628907e+00,  1.06890297e+01, -2.06272346e+01, ...,\n",
+       "         -1.80913609e+00, -1.63333482e+01,  1.18830812e+01],\n",
+       "        ...,\n",
+       "        [ 9.18730201e+00,  7.21862428e+00, -1.43830827e+01, ...,\n",
+       "         -4.93565579e+00,  1.92571973e+00,  6.38756653e+00],\n",
+       "        [-9.43512049e+00,  9.15788289e+00,  3.01401239e+00, ...,\n",
+       "         -5.07793293e+00, -8.28412816e-01, -5.94208127e+00],\n",
+       "        [ 1.62514124e+01,  1.57190243e+01,  1.94852387e+01, ...,\n",
+       "          1.63508206e+01, -5.91218542e+00, -1.33013406e+01]],\n",
+       "\n",
+       "       [[ 2.26087456e+01,  6.42799448e+00, -3.52925241e+00, ...,\n",
+       "          1.80076407e+01, -3.36020807e-01,  1.92479390e+01],\n",
+       "        [ 3.92165407e+01,  6.77626143e+00,  5.90141542e+00, ...,\n",
+       "          6.22275580e+00,  1.82726353e+01,  1.10532285e+00],\n",
+       "        [ 8.06194773e+00, -4.44753888e+00,  5.10370518e+00, ...,\n",
+       "         -1.19490499e-01,  1.54966090e+01,  7.08856045e+00],\n",
+       "        ...,\n",
+       "        [-3.03534251e+00,  2.24378707e+00, -8.44986049e+00, ...,\n",
+       "          2.12971871e-03,  1.49181986e+01,  6.61268146e+00],\n",
+       "        [ 3.72829526e+00, -8.56749669e+00,  1.81610302e+01, ...,\n",
+       "          7.69364146e+00, -3.01472311e+00, -1.46863809e+01],\n",
+       "        [ 1.50901701e+01,  4.68583065e+00, -2.08895476e+01, ...,\n",
+       "         -3.62125889e+00,  5.56507786e+00, -3.78249384e+01]]])
        • created_at :
          2020-06-06T18:07:53.815773
          arviz_version :
          0.8.3
          inference_library :
          pymc3
          inference_library_version :
          3.8

        \n", + "
      \n", + "
      \n", + "
      • \n", + " \n", + "
    • \n", + " \n", + " \n", + "
      \n", + "
      \n", + "
        \n", + "
        \n", + "\n", + "\n", + "Show/Hide data repr\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Show/Hide attributes\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
        xarray.Dataset
          • chain: 2
          • draw: 500
          • school: 8
          • chain
            (chain)
            int64
            0 1
            array([0, 1])
          • draw
            (draw)
            int64
            0 1 2 3 4 5 ... 495 496 497 498 499
            array([  0,   1,   2, ..., 497, 498, 499])
          • school
            (school)
            <U16
            'Choate' ... 'Mt. Hermon'
            array(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
+       "       'Hotchkiss', 'Lawrenceville', "St. Paul's", 'Mt. Hermon'], dtype='<U16')
          • obs
            (chain, draw, school)
            float64
            -4.256 -3.463 ... -3.863 -3.81
            array([[[-4.25577142, -3.46286229, -3.81393007, ..., -3.3656724 ,\n",
+       "         -3.41292995, -4.06762619],\n",
+       "        [-4.13847217, -3.52778462, -3.78339165, ..., -3.37611123,\n",
+       "         -3.2836942 , -4.05586826],\n",
+       "        [-6.42070868, -3.31541094, -3.74365387, ..., -3.43939688,\n",
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+       "         -4.00045703, -3.87161943]],\n",
+       "\n",
+       "       [[-5.14101139, -3.22812014, -3.85439321, ..., -3.3781691 ,\n",
+       "         -3.80225806, -3.85839657],\n",
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+       "         -3.9691717 , -3.83998898],\n",
+       "        ...,\n",
+       "        [-5.21029305, -3.23450619, -3.72842056, ..., -3.57391555,\n",
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+       "        [-4.41795643, -3.27774678, -3.89421253, ..., -3.31683939,\n",
+       "         -4.04660255, -3.97224661],\n",
+       "        [-4.39971783, -3.38824284, -3.75484971, ..., -3.62419773,\n",
+       "         -3.86346358, -3.8100101 ]]])
        • created_at :
          2020-06-06T18:07:53.811286
          arviz_version :
          0.8.3
          inference_library :
          pymc3
          inference_library_version :
          3.8

        \n", + "
      \n", + "
      \n", + "
      • \n", + " \n", + "
    • \n", + " \n", + " \n", + "
      \n", + "
      \n", + "
        \n", + "
        \n", + "\n", + "\n", + "Show/Hide data repr\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Show/Hide attributes\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
        xarray.Dataset
          • chain: 2
          • draw: 500
          • chain
            (chain)
            int64
            0 1
            array([0, 1])
          • draw
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+       "        0.27148071, 0.27148071, 0.27148071, 0.27148071, 0.27148071,\n",
+       "        0.27148071, 0.27148071, 0.27148071, 0.27148071, 0.27148071,\n",
+       "        0.27148071, 0.27148071, 0.27148071, 0.27148071, 0.27148071,\n",
+       "        0.27148071, 0.27148071, 0.27148071, 0.27148071, 0.27148071,\n",
+       "        0.27148071, 0.27148071, 0.27148071, 0.27148071, 0.27148071,\n",
+       "        0.27148071, 0.27148071, 0.27148071, 0.27148071, 0.27148071,\n",
+       "        0.27148071, 0.27148071, 0.27148071, 0.27148071, 0.27148071,\n",
+       "        0.27148071, 0.27148071, 0.27148071, 0.27148071, 0.27148071,\n",
+       "        0.27148071, 0.27148071, 0.27148071, 0.27148071, 0.27148071,\n",
+       "        0.27148071, 0.27148071, 0.27148071, 0.27148071, 0.27148071,\n",
+       "        0.27148071, 0.27148071, 0.27148071, 0.27148071, 0.27148071,\n",
+       "        0.27148071, 0.27148071, 0.27148071, 0.27148071, 0.27148071,\n",
+       "        0.27148071, 0.27148071, 0.27148071, 0.27148071, 0.27148071,\n",
+       "        0.27148071, 0.27148071, 0.27148071, 0.27148071, 0.27148071,\n",
+       "        0.27148071, 0.27148071, 0.27148071, 0.27148071, 0.27148071,\n",
+       "        0.27148071, 0.27148071, 0.27148071, 0.27148071, 0.27148071,\n",
+       "        0.27148071, 0.27148071, 0.27148071, 0.27148071, 0.27148071,\n",
+       "        0.27148071, 0.27148071, 0.27148071, 0.27148071, 0.27148071,\n",
+       "        0.27148071, 0.27148071, 0.27148071, 0.27148071, 0.27148071,\n",
+       "        0.27148071, 0.27148071, 0.27148071, 0.27148071, 0.27148071,\n",
+       "        0.27148071, 0.27148071, 0.27148071, 0.27148071, 0.27148071,\n",
+       "        0.27148071, 0.27148071, 0.27148071, 0.27148071, 0.27148071,\n",
+       "        0.27148071, 0.27148071, 0.27148071, 0.27148071, 0.27148071,\n",
+       "        0.27148071, 0.27148071, 0.27148071, 0.27148071, 0.27148071]])
          • mean_tree_accept
            (chain, draw)
            float64
            0.6358 0.7715 1.0 ... 0.9569 0.7325
            array([[6.35830176e-01, 7.71505320e-01, 1.00000000e+00, 8.46071160e-01,\n",
+       "        9.24817279e-01, 9.84356429e-01, 7.78027888e-01, 9.35127805e-01,\n",
+       "        9.64571524e-01, 9.68018339e-01, 9.38390011e-01, 9.95204578e-01,\n",
+       "        7.58419298e-01, 9.93836673e-01, 6.09990637e-01, 9.84023426e-01,\n",
+       "        9.87597576e-01, 9.82328649e-01, 8.75260426e-01, 9.34604991e-01,\n",
+       "        9.66898214e-01, 9.44579706e-01, 9.66158053e-01, 8.70667657e-01,\n",
+       "        9.89489286e-01, 9.24373579e-01, 9.78868311e-01, 9.29241307e-01,\n",
+       "        6.49699175e-01, 8.74888513e-01, 7.93295228e-01, 6.47752583e-01,\n",
+       "        9.78119098e-01, 9.90398245e-01, 7.96357250e-01, 9.90363095e-01,\n",
+       "        8.95125681e-01, 4.94648200e-01, 7.99248163e-02, 1.45365235e-01,\n",
+       "        2.40444904e-01, 5.85962480e-02, 3.17197472e-02, 5.00099173e-02,\n",
+       "        6.28823857e-05, 7.42632323e-19, 9.37098121e-18, 1.42857179e-01,\n",
+       "        2.16798372e-05, 3.50036787e-02, 2.19553084e-02, 6.66666667e-01,\n",
+       "        6.83774693e-02, 1.26072442e-01, 8.83125771e-07, 1.71086674e-12,\n",
+       "        7.92416706e-03, 7.25506480e-09, 3.33333333e-01, 5.77357742e-12,\n",
+       "        4.09780284e-04, 2.42187010e-03, 1.97029008e-02, 5.66676213e-08,\n",
+       "        2.37687456e-01, 1.61842548e-02, 1.33929269e-05, 2.77844841e-05,\n",
+       "        1.86466911e-04, 4.97456024e-07, 5.41309967e-04, 4.38841424e-06,\n",
+       "        6.48764872e-03, 3.42329261e-03, 5.19548943e-03, 9.41502411e-02,\n",
+       "        6.53902514e-01, 8.76723384e-01, 9.16268639e-01, 9.63876555e-01,\n",
+       "        1.00000000e+00, 8.78731553e-01, 9.45224249e-01, 8.65842680e-01,\n",
+       "        7.97354563e-01, 9.96736984e-01, 8.48348008e-01, 6.59404760e-01,\n",
+       "        1.16290051e-01, 8.19989558e-01, 9.94341513e-01, 9.49692605e-01,\n",
+       "        2.96554033e-01, 7.63212217e-01, 5.06486671e-05, 1.07369278e-01,\n",
+       "        1.16561001e-08, 2.29556677e-01, 5.85884313e-01, 2.15825571e-01,\n",
+       "        7.52825129e-01, 6.82095139e-01, 7.23625274e-01, 4.93411570e-01,\n",
+       "        6.63251395e-01, 1.86558287e-01, 9.42056650e-01, 9.79638905e-01,\n",
+       "        1.00000000e+00, 8.17921724e-01, 9.45621176e-01, 9.99590929e-01,\n",
+       "        9.31345011e-01, 7.14199383e-01, 9.43678778e-01, 9.18425898e-01,\n",
+       "        8.41301457e-01, 3.62558481e-01, 8.88025010e-01, 9.83381719e-01,\n",
+       "        8.40685345e-01, 9.70519154e-01, 6.40520704e-01, 9.95874420e-01,\n",
+       "        8.91613143e-01, 9.14819388e-01, 9.69047220e-01, 9.46499382e-01,\n",
+       "        6.72827399e-01, 7.14285714e-01, 1.43533146e-01, 2.33365513e-01,\n",
+       "        6.54596199e-01, 6.37306900e-01, 1.51032855e-01, 9.71657429e-01,\n",
+       "        5.73343782e-01, 7.98320423e-01, 8.10690510e-01, 9.83220822e-01,\n",
+       "        8.93890370e-01, 9.57113211e-01, 7.86379847e-01, 6.07290412e-01,\n",
+       "        9.45562918e-01, 9.93848592e-01, 9.26733416e-01, 9.68412594e-01,\n",
+       "        9.17513240e-01, 2.28349138e-01, 8.19728204e-01, 9.37054763e-01,\n",
+       "        9.15931138e-01, 8.66790249e-01, 9.24365159e-01, 9.31208170e-01,\n",
+       "        7.12286662e-01, 3.02663830e-01, 9.74593912e-01, 9.14888200e-01,\n",
+       "        8.89767568e-01, 7.33060886e-01, 1.56026526e-01, 7.18051876e-01,\n",
+       "        9.49134451e-01, 9.24301339e-01, 7.87213976e-01, 9.17845226e-01,\n",
+       "        9.98379582e-01, 9.70336401e-01, 8.51049897e-01, 9.45245557e-01,\n",
+       "        9.92687878e-01, 9.98461519e-01, 8.39334669e-01, 9.91373331e-01,\n",
+       "        6.57099977e-01, 9.81009860e-01, 9.86236042e-01, 8.94604927e-01,\n",
+       "        9.59254684e-01, 9.42107855e-01, 9.80012590e-01, 7.42330951e-01,\n",
+       "        9.89682519e-01, 9.85802853e-01, 4.92898495e-01, 1.00000000e+00,\n",
+       "        6.24277517e-01, 9.95633969e-01, 5.71728807e-01, 9.47781371e-01,\n",
+       "        9.43403453e-01, 9.91543626e-01, 8.73521443e-01, 7.88567471e-01,\n",
+       "        5.71428692e-01, 8.56842367e-01, 7.44513310e-01, 9.41422107e-01,\n",
+       "        1.00000000e+00, 9.89986183e-01, 9.44365278e-01, 8.12478625e-01,\n",
+       "        9.63772733e-01, 9.49021276e-01, 9.95017685e-01, 9.63490169e-01,\n",
+       "        8.37292290e-01, 9.85737595e-01, 8.97936693e-01, 9.48617304e-01,\n",
+       "        9.51239007e-01, 9.91718445e-01, 9.21868326e-01, 1.00000000e+00,\n",
+       "        6.10683989e-01, 9.83112737e-01, 9.51407730e-01, 1.00000000e+00,\n",
+       "        6.61147275e-01, 9.97873646e-01, 9.91524610e-01, 9.49981238e-01,\n",
+       "        9.36977460e-01, 5.81213152e-01, 9.46721884e-01, 9.58244786e-01,\n",
+       "        9.01239750e-01, 5.90530878e-01, 9.21075754e-01, 8.37413598e-02,\n",
+       "        1.44321464e-01, 8.56703034e-01, 4.99465718e-02, 2.72501393e-01,\n",
+       "        1.47919370e-01, 4.26910284e-02, 1.71756213e-04, 3.10361704e-01,\n",
+       "        5.35665202e-01, 2.71367243e-01, 8.24517668e-01, 8.94465524e-01,\n",
+       "        8.72527609e-01, 4.04274340e-01, 9.53130102e-01, 1.00000000e+00,\n",
+       "        6.92074493e-01, 9.75144337e-01, 7.82363220e-01, 9.31081739e-01,\n",
+       "        9.64379797e-01, 9.75989817e-01, 8.56326658e-01, 9.59078960e-01,\n",
+       "        9.98854836e-01, 8.15994121e-01, 8.25077248e-01, 8.69949552e-01,\n",
+       "        1.61240182e-01, 8.47981978e-01, 9.91304869e-01, 6.02690696e-01,\n",
+       "        6.51185631e-01, 3.54308044e-01, 7.21258137e-01, 9.66211261e-01,\n",
+       "        6.59391383e-01, 9.16577546e-01, 8.86581478e-01, 9.97942780e-01,\n",
+       "        9.93051630e-01, 9.97479777e-01, 9.53352477e-01, 8.54382453e-01,\n",
+       "        9.19192345e-01, 9.70211936e-01, 9.79501177e-01, 9.46017259e-01,\n",
+       "        7.74285764e-01, 9.61133063e-01, 9.60164876e-01, 9.01123744e-01,\n",
+       "        9.70757573e-01, 9.44250400e-01, 9.13811153e-01, 9.44510123e-01,\n",
+       "        1.00000000e+00, 9.26996509e-01, 9.31537351e-01, 8.37625401e-01,\n",
+       "        9.44609461e-01, 4.30666451e-01, 2.64404380e-01, 3.50776947e-01,\n",
+       "        9.11105401e-01, 1.00000000e+00, 8.23087560e-01, 2.64654691e-01,\n",
+       "        3.10686915e-01, 1.00000000e+00, 8.82310012e-01, 1.00000000e+00,\n",
+       "        6.03261770e-01, 4.57025232e-01, 1.00000000e+00, 5.29236064e-01,\n",
+       "        6.58433194e-01, 9.25432093e-01, 9.39036226e-01, 9.14282239e-01,\n",
+       "        9.52394661e-01, 7.01577195e-01, 7.03571828e-01, 7.58442338e-05,\n",
+       "        3.62571694e-01, 1.53594242e-01, 4.08010675e-01, 7.18105506e-01,\n",
+       "        8.53151763e-01, 8.46435666e-01, 4.32228638e-02, 6.73501437e-01,\n",
+       "        3.31341097e-01, 3.33333334e-01, 1.06508111e-18, 2.62121915e-02,\n",
+       "        9.93028513e-03, 1.95081901e-01, 3.76784596e-08, 2.20574841e-01,\n",
+       "        3.46673086e-01, 5.22873112e-14, 5.32418416e-02, 1.40212629e-02,\n",
+       "        8.30976098e-01, 7.77255783e-01, 8.23309825e-01, 9.46163470e-01,\n",
+       "        1.00000000e+00, 8.57251543e-01, 1.00000000e+00, 8.72140689e-01,\n",
+       "        9.52961406e-01, 7.58347135e-01, 9.55093474e-01, 9.49992801e-01,\n",
+       "        9.66398856e-01, 9.83053486e-01, 9.90394088e-01, 8.84835270e-01,\n",
+       "        8.15762233e-01, 9.82337903e-01, 8.70596999e-01, 4.69069905e-01,\n",
+       "        9.66124839e-01, 9.95204686e-01, 6.82719145e-01, 9.50554346e-01,\n",
+       "        1.00000000e+00, 9.15299246e-01, 9.03584162e-01, 9.54075218e-01,\n",
+       "        9.24021518e-01, 7.93500915e-01, 8.69728630e-01, 1.00000000e+00,\n",
+       "        9.57289833e-01, 8.51581225e-01, 9.18279429e-01, 9.38774422e-01,\n",
+       "        4.90713023e-01, 7.95008383e-01, 7.26604575e-01, 1.00000000e+00,\n",
+       "        8.84065187e-01, 9.41699807e-01, 8.65693116e-01, 9.83549452e-01,\n",
+       "        1.05739112e-01, 6.02028160e-01, 9.52399965e-01, 7.62573793e-01,\n",
+       "        8.79962829e-01, 9.85565321e-01, 9.85484918e-01, 9.98763548e-01,\n",
+       "        9.54649200e-01, 1.00000000e+00, 9.79285585e-01, 9.47995013e-01,\n",
+       "        1.00000000e+00, 9.64668940e-01, 9.84162777e-01, 8.88319117e-01,\n",
+       "        1.00000000e+00, 5.79571430e-01, 9.48481447e-01, 7.94709010e-01,\n",
+       "        1.00000000e+00, 7.57712860e-01, 7.05164852e-01, 8.43020163e-01,\n",
+       "        7.45207316e-03, 7.78301166e-04, 2.29822909e-02, 6.33874279e-03,\n",
+       "        2.41291691e-01, 1.29497839e-02, 6.80797541e-01, 1.47371519e-01,\n",
+       "        8.14615011e-01, 9.59771485e-01, 9.27096593e-01, 9.95889801e-01,\n",
+       "        8.51512412e-01, 8.98359704e-01, 9.92150096e-01, 8.79073704e-01,\n",
+       "        9.92316872e-01, 6.81752424e-01, 2.55571093e-01, 7.81939633e-01,\n",
+       "        8.52121805e-01, 9.78145901e-01, 5.90364428e-01, 9.97847159e-01,\n",
+       "        9.69774337e-01, 9.98592673e-01, 9.90703719e-01, 1.00000000e+00,\n",
+       "        7.07492222e-01, 5.36711467e-01, 7.33162625e-01, 2.37872269e-01,\n",
+       "        1.46922513e-01, 9.20546783e-01, 8.82365627e-01, 1.00000000e+00,\n",
+       "        9.54486306e-01, 7.53832590e-01, 9.07772399e-01, 9.99943104e-01,\n",
+       "        9.90796151e-01, 8.37224888e-01, 5.40600141e-01, 9.82562633e-01,\n",
+       "        8.43276508e-01, 9.62295473e-01, 9.85204391e-01, 9.56475791e-01,\n",
+       "        8.68839234e-01, 9.75673901e-01, 5.63219570e-01, 7.03804818e-01,\n",
+       "        9.45051959e-01, 8.07903293e-01, 7.69776572e-01, 6.41282261e-01,\n",
+       "        9.94378721e-01, 9.67022724e-01, 9.84395861e-01, 9.73451597e-01,\n",
+       "        9.87136825e-01, 9.61283073e-01, 9.47345221e-01, 9.82729571e-01,\n",
+       "        9.04276624e-01, 8.26439821e-01, 8.97750726e-01, 6.29074708e-05,\n",
+       "        1.95868842e-01, 9.81680119e-01, 8.41014552e-01, 1.00000000e+00,\n",
+       "        9.32278825e-01, 1.39832465e-01, 3.39300135e-01, 9.38378273e-01,\n",
+       "        1.22531477e-01, 9.32919168e-01, 9.52046699e-01, 9.84703861e-01,\n",
+       "        8.41170307e-01, 7.46785504e-01, 9.56733034e-01, 2.13812322e-02,\n",
+       "        3.47623481e-01, 3.10272741e-01, 6.03396095e-02, 1.78752379e-05,\n",
+       "        2.75244613e-05, 7.65245633e-02, 1.15026086e-03, 3.56371084e-01,\n",
+       "        4.06841238e-02, 1.12615490e-01, 1.06597475e-01, 1.08105694e-01],\n",
+       "       [3.28317478e-01, 9.66129702e-01, 5.61032696e-01, 8.62497030e-01,\n",
+       "        9.63550683e-01, 8.45961194e-01, 7.20158054e-01, 9.88748018e-01,\n",
+       "        9.77474325e-01, 9.55736441e-01, 9.98737008e-01, 9.89761407e-01,\n",
+       "        6.29297273e-01, 8.91289356e-01, 5.01859641e-01, 3.54513736e-01,\n",
+       "        8.25423014e-01, 9.76254647e-01, 7.43818065e-01, 8.46674546e-01,\n",
+       "        9.91487258e-01, 8.65014618e-01, 7.62045317e-01, 9.84391947e-01,\n",
+       "        8.00838622e-01, 8.91455295e-01, 8.80087031e-01, 7.83411087e-01,\n",
+       "        9.70090353e-01, 7.73950511e-01, 9.66804261e-01, 7.87718577e-01,\n",
+       "        8.90655420e-01, 9.58954339e-01, 9.27871038e-01, 9.76030652e-01,\n",
+       "        8.22833260e-01, 7.39852563e-01, 9.09729742e-01, 9.63413192e-01,\n",
+       "        9.70448973e-01, 9.86417429e-01, 6.85710546e-01, 9.88766341e-01,\n",
+       "        9.65876322e-01, 9.17723041e-01, 9.22653501e-01, 1.00000000e+00,\n",
+       "        9.71965730e-01, 6.17201497e-01, 9.71333291e-01, 9.31186818e-01,\n",
+       "        9.75606867e-01, 9.25439842e-01, 7.19943858e-01, 3.33388205e-01,\n",
+       "        3.54787369e-01, 7.35441537e-01, 8.50560458e-01, 3.11910764e-01,\n",
+       "        4.03196672e-01, 6.86363427e-01, 7.28145300e-01, 3.65737000e-01,\n",
+       "        9.23417651e-01, 7.71167178e-01, 8.52920710e-01, 9.42535433e-01,\n",
+       "        7.60381223e-01, 6.16768344e-01, 8.07773432e-01, 7.15335886e-01,\n",
+       "        8.70692382e-01, 1.68695429e-01, 1.96374055e-01, 9.50552110e-01,\n",
+       "        9.91420152e-01, 8.55439088e-01, 9.83948180e-01, 9.93279091e-01,\n",
+       "        8.77848179e-01, 9.87161719e-01, 9.98924808e-01, 7.68595015e-01,\n",
+       "        9.97389576e-01, 8.37650979e-01, 9.79633127e-01, 9.86222784e-01,\n",
+       "        9.27366947e-01, 2.60967334e-01, 9.71555813e-01, 9.94333089e-01,\n",
+       "        5.54240061e-01, 9.81142809e-01, 9.84143813e-01, 8.66206576e-01,\n",
+       "        9.88895733e-01, 9.93197526e-01, 8.33165386e-01, 5.72917446e-01,\n",
+       "        7.78478046e-01, 1.00000000e+00, 9.24815703e-01, 7.55930499e-01,\n",
+       "        3.24447748e-01, 9.96775602e-01, 9.87068329e-01, 1.00000000e+00,\n",
+       "        9.22285083e-01, 9.53581355e-01, 6.34094814e-01, 8.10731624e-01,\n",
+       "        9.42476352e-01, 9.98516726e-01, 9.89911296e-01, 9.78146336e-01,\n",
+       "        8.86368568e-01, 8.39175265e-01, 7.31570105e-01, 9.92964364e-01,\n",
+       "        9.49213700e-01, 9.66109640e-01, 9.94040711e-01, 8.82833567e-01,\n",
+       "        9.18563078e-01, 9.97679189e-01, 9.95760829e-01, 9.18202694e-01,\n",
+       "        5.71130122e-01, 9.86475396e-01, 9.98532757e-01, 9.76214779e-01,\n",
+       "        9.41627770e-01, 8.81715016e-01, 9.98214377e-01, 3.27053067e-01,\n",
+       "        9.37947128e-01, 1.33474387e-01, 5.53093888e-05, 1.73707320e-05,\n",
+       "        7.86566149e-04, 6.66852451e-01, 5.11656045e-01, 8.02728761e-01,\n",
+       "        6.70270256e-01, 7.55408520e-01, 9.76378683e-01, 8.67729926e-01,\n",
+       "        8.56485398e-01, 7.36080414e-01, 8.41170083e-01, 8.80129718e-01,\n",
+       "        1.00000000e+00, 8.49872616e-01, 8.97181631e-01, 2.73316217e-01,\n",
+       "        8.29831687e-01, 9.25997458e-01, 9.13097328e-01, 9.93382146e-01,\n",
+       "        9.77683315e-01, 9.83663761e-01, 1.00000000e+00, 5.92413824e-01,\n",
+       "        6.01233693e-01, 7.27004996e-01, 8.68141209e-01, 8.00898438e-01,\n",
+       "        9.46962288e-02, 9.26626083e-01, 5.78934227e-01, 2.53925247e-02,\n",
+       "        4.97805786e-01, 8.47119971e-01, 7.19704654e-01, 1.00000000e+00,\n",
+       "        8.31395932e-01, 5.52711192e-01, 7.23888278e-13, 7.02431527e-15,\n",
+       "        2.22120039e-01, 2.45354002e-08, 9.93690223e-03, 6.02651058e-07,\n",
+       "        8.20222634e-23, 1.16148088e-01, 9.47394946e-04, 1.74355268e-08,\n",
+       "        1.02187591e-13, 3.82173043e-03, 4.40092209e-29, 1.06459310e-49,\n",
+       "        9.87165694e-13, 2.41152795e-13, 1.28628214e-02, 7.20955460e-03,\n",
+       "        4.18662802e-07, 4.65905541e-20, 7.61083815e-10, 3.15346755e-57,\n",
+       "        6.25942833e-05, 8.58774302e-13, 1.61609198e-05, 7.97577071e-43,\n",
+       "        4.89153659e-13, 2.30161414e-78, 1.08973302e-03, 1.64556387e-06,\n",
+       "        4.60179596e-03, 2.21139956e-11, 6.82660500e-15, 7.25573202e-04,\n",
+       "        4.77334428e-26, 1.24860683e-13, 1.75216529e-07, 1.65928841e-06,\n",
+       "        4.19316314e-08, 9.97877927e-05, 1.46440459e-14, 4.84822675e-10,\n",
+       "        5.04602104e-06, 8.48172250e-70, 1.69276145e-08, 1.08177180e-07,\n",
+       "        1.82229117e-33, 8.93571603e-06, 1.99919287e-04, 3.12574975e-05,\n",
+       "        3.58079042e-10, 1.57551731e-22, 7.76208456e-40, 9.85663317e-05,\n",
+       "        4.72816823e-08, 4.24944803e-09, 3.07556883e-65, 1.68889371e-02,\n",
+       "        1.94676587e-11, 9.40089278e-08, 5.46085240e-04, 1.12643422e-02,\n",
+       "        1.65607468e-06, 3.15611739e-14, 1.65027257e-03, 9.31757021e-03,\n",
+       "        1.51075828e-03, 4.62701323e-08, 6.14088778e-14, 1.04659598e-17,\n",
+       "        1.74965485e-03, 5.81221173e-11, 1.67197624e-09, 1.44140273e-04,\n",
+       "        3.20303237e-15, 8.82233299e-03, 1.45611831e-01, 4.11729638e-22,\n",
+       "        3.33333333e-01, 9.18228518e-01, 1.33054366e-05, 7.90596092e-01,\n",
+       "        8.73606888e-01, 8.06629800e-02, 1.47386986e-01, 7.95318988e-01,\n",
+       "        9.95422317e-01, 6.68091487e-01, 9.40017988e-01, 8.16788925e-01,\n",
+       "        1.00000000e+00, 5.27691571e-02, 6.58832667e-01, 1.04819615e-02,\n",
+       "        5.21902286e-11, 5.31010257e-01, 1.88853323e-01, 9.72975942e-01,\n",
+       "        9.54156962e-01, 7.47769513e-01, 9.75685507e-01, 9.80248166e-01,\n",
+       "        9.13099813e-01, 9.90062454e-01, 6.76155432e-01, 9.81197899e-01,\n",
+       "        9.49488040e-01, 8.90050581e-01, 8.55282414e-01, 9.84358295e-01,\n",
+       "        9.87714231e-01, 9.29813433e-01, 7.18269006e-01, 8.79496412e-01,\n",
+       "        9.80997415e-01, 9.20556228e-01, 9.94683676e-01, 6.06881740e-01,\n",
+       "        8.15102282e-01, 9.33681143e-01, 7.33319604e-01, 4.18669813e-01,\n",
+       "        4.43530907e-01, 8.53498115e-01, 9.95094585e-01, 9.67750983e-01,\n",
+       "        8.35234316e-01, 5.99037705e-01, 7.19723846e-01, 8.82242415e-01,\n",
+       "        9.76041723e-01, 9.60625313e-01, 9.50161554e-01, 9.75100548e-01,\n",
+       "        9.84396115e-01, 9.08799134e-01, 9.59338249e-01, 9.78655499e-01,\n",
+       "        7.22702455e-01, 9.67692530e-01, 7.33598267e-01, 1.00000000e+00,\n",
+       "        6.80322932e-01, 9.11160046e-01, 4.21998291e-01, 8.08628893e-01,\n",
+       "        1.00000000e+00, 9.93538335e-01, 8.89043622e-01, 1.00000000e+00,\n",
+       "        9.66584766e-01, 9.57050352e-01, 9.17710099e-01, 9.67982541e-01,\n",
+       "        1.00000000e+00, 8.51864172e-01, 3.86500814e-01, 4.15570577e-01,\n",
+       "        2.75790686e-01, 7.82339921e-01, 9.39116390e-01, 7.67758015e-01,\n",
+       "        8.18186270e-01, 7.54171923e-01, 9.45562890e-01, 9.08453599e-01,\n",
+       "        8.67214913e-01, 3.86623106e-01, 6.60280811e-01, 1.25896999e-01,\n",
+       "        4.76803208e-02, 6.79827398e-01, 7.54969787e-01, 9.40134530e-01,\n",
+       "        6.72694715e-02, 9.94135747e-01, 9.72825016e-01, 7.19768919e-01,\n",
+       "        4.68938875e-01, 6.59086188e-01, 8.66596560e-01, 6.99744742e-01,\n",
+       "        9.39497706e-01, 7.69580937e-01, 9.21299326e-01, 9.26321441e-01,\n",
+       "        3.29397217e-01, 8.51985624e-01, 9.62059533e-01, 8.59720078e-01,\n",
+       "        8.46246568e-01, 7.27065383e-01, 5.30119328e-04, 8.76603508e-01,\n",
+       "        6.02103657e-03, 5.32063432e-01, 7.88731334e-01, 8.65131033e-01,\n",
+       "        9.17056289e-01, 8.26591134e-01, 5.79765014e-01, 9.25492859e-01,\n",
+       "        9.28805256e-01, 9.25607428e-01, 5.90688682e-01, 9.21889029e-01,\n",
+       "        9.07933904e-03, 5.02462752e-01, 7.43302547e-01, 2.76556063e-01,\n",
+       "        6.20511785e-04, 2.21657532e-01, 1.64775864e-01, 2.57952633e-01,\n",
+       "        9.09790743e-01, 9.74324207e-01, 7.12348670e-01, 1.00000000e+00,\n",
+       "        9.81143865e-01, 5.66699365e-01, 6.95892360e-01, 2.49945675e-01,\n",
+       "        4.55036549e-01, 3.75109768e-01, 9.82393048e-01, 3.91449760e-01,\n",
+       "        4.04897359e-01, 5.65347873e-01, 9.03759270e-01, 1.23042520e-02,\n",
+       "        2.56927810e-02, 6.74238591e-01, 5.80052253e-01, 4.38106358e-01,\n",
+       "        8.62018724e-01, 7.24606225e-01, 4.84984929e-01, 7.33641817e-01,\n",
+       "        7.67533010e-01, 9.43255306e-01, 9.95268554e-01, 6.65772611e-01,\n",
+       "        9.88583727e-01, 7.40327086e-01, 7.42856950e-01, 9.44927485e-01,\n",
+       "        9.95302356e-01, 8.81587933e-01, 9.37009697e-01, 8.70092522e-01,\n",
+       "        7.88120774e-01, 8.66067385e-01, 9.73837586e-01, 4.11599496e-01,\n",
+       "        1.00000000e+00, 9.03347643e-01, 9.93971630e-01, 7.43625942e-01,\n",
+       "        8.36864886e-01, 9.74818024e-01, 9.48203792e-01, 8.97371738e-01,\n",
+       "        9.46193902e-01, 9.51345656e-01, 9.95239808e-01, 1.00000000e+00,\n",
+       "        9.73637539e-01, 9.96913568e-01, 9.86171705e-01, 9.79415197e-01,\n",
+       "        9.38998203e-01, 9.88178466e-01, 9.72033053e-01, 9.99690747e-01,\n",
+       "        6.78148717e-01, 9.44390665e-01, 9.99947354e-01, 1.00000000e+00,\n",
+       "        9.58954340e-01, 3.15497028e-01, 8.51243636e-01, 7.58355192e-01,\n",
+       "        8.56612957e-01, 9.81419663e-01, 7.83065878e-01, 3.03864875e-01,\n",
+       "        9.52472335e-01, 6.03559504e-01, 1.00000000e+00, 9.82264917e-01,\n",
+       "        9.54284565e-01, 9.28579165e-01, 9.84370590e-01, 9.90204857e-01,\n",
+       "        9.99234846e-01, 9.93735725e-01, 9.81541925e-01, 9.38543061e-01,\n",
+       "        1.00000000e+00, 8.42416545e-01, 1.00000000e+00, 1.00000000e+00,\n",
+       "        9.80311056e-01, 9.76776818e-01, 4.82655800e-01, 8.56521160e-01,\n",
+       "        1.00000000e+00, 9.24998077e-01, 8.56898573e-01, 8.56008218e-01,\n",
+       "        8.83100007e-01, 1.00000000e+00, 9.73975530e-01, 6.54868709e-01,\n",
+       "        9.65251390e-01, 9.58764214e-01, 8.26141154e-01, 1.87973610e-02,\n",
+       "        4.17840686e-01, 9.83369254e-01, 9.56921425e-01, 7.32532745e-01]])
          • tree_size
            (chain, draw)
            float64
            7.0 3.0 15.0 7.0 ... 7.0 7.0 7.0
            array([[ 7.,  3., 15.,  7., 15., 15.,  7., 15.,  7., 15., 15., 15., 15.,\n",
+       "        15., 15.,  7., 23., 15.,  7., 15., 15., 15., 15., 15., 15., 15.,\n",
+       "        15.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  3.,  3.,  3.,  3.,\n",
+       "         3.,  3.,  3.,  3.,  3.,  7.,  1.,  1.,  7.,  7.,  3.,  7.,  3.,\n",
+       "         3.,  3.,  3.,  3.,  3.,  1.,  3.,  1.,  3.,  3., 15.,  1.,  7.,\n",
+       "         3.,  1.,  3.,  3.,  1.,  3.,  3.,  3.,  3.,  3.,  7.,  7.,  7.,\n",
+       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7., 15.,  7.,  7.,\n",
+       "         3.,  7., 15.,  3.,  7.,  1.,  7., 15.,  3.,  7.,  7.,  7., 15.,\n",
+       "         7.,  7.,  7., 15.,  7., 15.,  7.,  7.,  7., 15.,  7.,  7.,  7.,\n",
+       "         7.,  7.,  7.,  7.,  7., 15.,  7.,  7.,  7.,  7.,  7., 15.,  7.,\n",
+       "        30.,  3., 11.,  3.,  7.,  7.,  7.,  7.,  7.,  7.,  7., 15.,  7.,\n",
+       "        11.,  7., 15.,  7.,  7., 15.,  7.,  7., 23., 15., 47.,  7.,  7.,\n",
+       "         3., 31.,  7.,  7., 23., 15.,  7.,  7., 15.,  7., 15., 15., 15.,\n",
+       "        15.,  7., 15., 15., 15., 15.,  7., 15.,  7., 15., 15., 15., 15.,\n",
+       "        15., 15., 15.,  7.,  7., 15.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
+       "         7.,  7.,  7.,  7.,  7., 15.,  7., 15.,  7., 31.,  7.,  7., 15.,\n",
+       "         7.,  7., 15., 15., 15., 15., 15., 15., 15.,  7., 15.,  7., 15.,\n",
+       "        15., 15.,  7.,  7., 15.,  7.,  7.,  3.,  3., 11.,  7.,  7.,  3.,\n",
+       "         3.,  7.,  7.,  7.,  3., 15., 11.,  7., 11.,  7.,  3.,  7.,  7.,\n",
+       "         7.,  7.,  7., 15., 15., 15., 15., 15.,  7., 15., 15.,  7., 15.,\n",
+       "         7.,  7.,  7.,  7.,  3., 15., 15.,  7.,  7., 15., 15., 15., 15.,\n",
+       "        15., 15.,  7., 15., 15.,  7.,  7.,  7., 15., 15., 15., 15., 15.,\n",
+       "        15.,  7.,  7., 15.,  7.,  7.,  7.,  7.,  7.,  7.,  3.,  3.,  7.,\n",
+       "         7., 15.,  7.,  7.,  7.,  3.,  3.,  3.,  7.,  7.,  7., 15., 15.,\n",
+       "         7.,  3.,  7.,  1.,  7.,  7.,  3.,  7.,  7.,  7.,  7.,  3., 15.,\n",
+       "         3.,  1.,  3.,  3.,  3.,  3.,  7.,  7.,  1.,  3., 15., 15.,  7.,\n",
+       "        31.,  7.,  7.,  7., 15.,  7., 15., 15., 15., 15., 15., 15.,  7.,\n",
+       "        15., 15., 15.,  7.,  7., 15., 15., 15.,  7.,  7.,  7.,  7.,  7.,\n",
+       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7., 11.,  7., 15.,  7., 15.,\n",
+       "        15., 15., 15.,  3.,  7.,  7., 15., 15., 15.,  7.,  7., 15.,  7.,\n",
+       "         7., 15.,  7., 15., 15., 31.,  7., 15.,  7.,  7.,  7.,  7.,  7.,\n",
+       "        11.,  5.,  7.,  7.,  7.,  7.,  1.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
+       "        15.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7., 15., 15., 15.,\n",
+       "        15., 15., 15.,  7.,  7., 15.,  7., 11., 15.,  7.,  7.,  7.,  7.,\n",
+       "         7., 15., 15., 15., 15.,  7., 15., 15., 15., 15., 15.,  7.,  7.,\n",
+       "         7.,  7.,  7., 15., 15., 15.,  7., 15., 15., 15., 15., 15., 15.,\n",
+       "        15.,  7.,  7.,  3.,  3.,  7.,  7.,  7., 15.,  7.,  3.,  7., 31.,\n",
+       "        15., 15., 15.,  7., 11.,  7.,  3.,  3.,  3., 31.,  3.,  3.,  3.,\n",
+       "         3.,  3.,  7.,  3.,  3., 15.],\n",
+       "       [15.,  7.,  7., 15., 23., 23., 15., 15., 15., 15., 15., 15., 15.,\n",
+       "         7.,  7., 15.,  7., 23.,  7., 15., 15., 15., 31., 15., 15., 15.,\n",
+       "        15.,  7., 23.,  7., 15.,  7., 15., 15.,  7., 23., 15., 15., 15.,\n",
+       "        15., 15., 15.,  7., 23., 15., 15.,  7., 15., 15., 15.,  7.,  7.,\n",
+       "        15.,  3., 15.,  3.,  7.,  3.,  3.,  3.,  7., 15., 15.,  7., 15.,\n",
+       "        15.,  7., 15.,  7., 15., 15.,  7., 15.,  3., 15., 15., 15., 15.,\n",
+       "        15.,  7., 15., 15., 15., 15.,  7., 15., 15., 15., 15., 15., 15.,\n",
+       "        15., 15., 15.,  7., 15., 15., 15., 15., 15., 15.,  7., 15.,  3.,\n",
+       "        31., 15., 15., 15.,  7., 15., 15., 15., 15., 15., 15., 15., 15.,\n",
+       "        31., 15., 15., 15., 15., 31., 31.,  7., 15., 15., 15., 15., 31.,\n",
+       "        31., 15., 15., 23.,  7., 23.,  7., 15.,  3.,  3.,  3.,  3.,  7.,\n",
+       "         7.,  3.,  7., 15.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  3.,  7.,\n",
+       "         7., 15., 15.,  7., 31., 15.,  7.,  7., 15., 15.,  7.,  3., 15.,\n",
+       "         3., 15.,  3.,  7., 11.,  7., 15.,  7.,  7.,  1.,  1.,  3.,  3.,\n",
+       "         3.,  3.,  1.,  3.,  3.,  7.,  1.,  7.,  1.,  1.,  1.,  1.,  3.,\n",
+       "         3.,  3.,  1.,  6.,  1.,  7.,  1.,  1.,  1.,  1.,  1.,  3.,  1.,\n",
+       "        15.,  1.,  3.,  1.,  1.,  3.,  7.,  1.,  3.,  7.,  3.,  3.,  7.,\n",
+       "         1.,  3.,  7.,  1.,  3.,  3., 11.,  3.,  1.,  3.,  3.,  2.,  1.,\n",
+       "         1.,  7.,  3.,  3.,  3.,  3.,  1.,  1.,  1.,  3.,  9.,  1.,  3.,\n",
+       "         3.,  3.,  3.,  1.,  3.,  1.,  3.,  3.,  1.,  3.,  7.,  1., 11.,\n",
+       "         7.,  3., 15., 11.,  7.,  7.,  7.,  7.,  7.,  3., 15.,  3.,  1.,\n",
+       "         5.,  7.,  7.,  7., 15., 15.,  7.,  7., 23.,  7.,  7., 15.,  7.,\n",
+       "         7., 15., 15.,  7.,  7.,  7., 15.,  7.,  7.,  7.,  7., 15.,  3.,\n",
+       "         7.,  3.,  7., 15., 15.,  7.,  3.,  7., 15., 15., 15., 15., 15.,\n",
+       "        15., 15., 15.,  7.,  7.,  7., 15.,  7.,  7.,  7., 15., 15.,  7.,\n",
+       "        15.,  7., 15.,  7.,  7.,  7.,  7.,  7.,  7.,  3.,  3., 15., 15.,\n",
+       "         7.,  7., 11.,  7., 15., 15.,  7.,  3., 23.,  3.,  3.,  7.,  7.,\n",
+       "        15.,  3.,  7.,  7.,  7., 15.,  7.,  7.,  7.,  3.,  7.,  7.,  7.,\n",
+       "        15., 31.,  7.,  7.,  7., 15.,  3.,  7.,  3., 15.,  7.,  7.,  7.,\n",
+       "         7., 15.,  7.,  7.,  7., 23.,  7.,  1.,  3.,  3.,  3.,  7., 15.,\n",
+       "         3.,  7.,  7.,  7., 15., 15., 15., 23., 15.,  3.,  3.,  7.,  7.,\n",
+       "         7.,  7.,  7.,  7.,  3.,  3.,  3., 15.,  6.,  7., 15.,  7.,  7.,\n",
+       "        15., 15., 23., 15., 15., 15., 15., 11., 15., 15., 15., 15., 15.,\n",
+       "        31., 15., 15., 15., 15., 15., 15., 31., 15., 15., 15., 15., 31.,\n",
+       "        15., 15., 15., 15., 15., 15., 15., 15., 15., 15., 15., 15.,  7.,\n",
+       "         7.,  7., 15., 23., 15., 15., 15., 15., 15., 15.,  7., 15., 31.,\n",
+       "         7., 15., 15., 15., 15., 15., 15., 15., 15., 15., 15., 31., 15.,\n",
+       "        15., 15., 15., 15., 15., 15., 15., 15., 15., 15., 15., 23.,  7.,\n",
+       "         7.,  3.,  7.,  7.,  7.,  7.]])
          • lp
            (chain, draw)
            float64
            -57.32 -60.39 ... -54.0 -56.9
            array([[-57.31822473, -60.39028527, -63.4471001 , -60.4192723 ,\n",
+       "        -65.14291022, -60.51097039, -63.29861849, -64.79104548,\n",
+       "        -62.48782882, -65.546425  , -63.50560839, -60.80924214,\n",
+       "        -62.10517054, -60.88355767, -62.82318898, -64.17644169,\n",
+       "        -61.83957224, -62.01280074, -60.47774168, -66.38766238,\n",
+       "        -65.47092464, -61.50030614, -60.58986324, -65.19850783,\n",
+       "        -63.17967943, -57.75363717, -54.39465126, -52.17360757,\n",
+       "        -55.42435425, -54.49940055, -58.98795015, -59.10199397,\n",
+       "        -57.43561372, -56.85720261, -55.46539305, -52.10582096,\n",
+       "        -48.8581283 , -48.8581283 , -48.8581283 , -49.70577235,\n",
+       "        -49.70577235, -49.70577235, -49.70577235, -50.79466371,\n",
+       "        -50.79466371, -50.79466371, -50.79466371, -47.79467063,\n",
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+       "        -59.16271799, -59.75818277, -64.89989039, -59.35155945,\n",
+       "        -59.00395553, -54.23133503, -46.69813902, -49.42962761,\n",
+       "        -52.52929999, -54.85470405, -51.45557864, -52.39301525,\n",
+       "        -51.85084174, -50.8470161 , -48.92502782, -48.92502782,\n",
+       "        -48.92502782, -46.96672035, -47.55557601, -51.71932018,\n",
+       "        -52.1931534 , -53.29156574, -56.06022891, -56.3562971 ,\n",
+       "        -59.88355965, -57.25684725, -55.76390732, -63.44496078,\n",
+       "        -58.40492879, -55.57409489, -64.61032336, -58.74141265,\n",
+       "        -59.7182531 , -60.70113546, -67.56289592, -67.32513934,\n",
+       "        -64.5197525 , -64.14072553, -60.42614942, -68.01914335,\n",
+       "        -66.57324058, -64.95708042, -66.82015203, -69.75318428,\n",
+       "        -65.68378338, -62.6453411 , -67.48450504, -67.35459006,\n",
+       "        -68.53276157, -62.7233112 , -61.7876735 , -60.76344011,\n",
+       "        -61.37535398, -63.36436164, -61.76409978, -64.12513892,\n",
+       "        -61.53974995, -62.80173375, -62.18994181, -55.88199927,\n",
+       "        -60.61293574, -60.46281098, -56.87452303, -55.40178531,\n",
+       "        -55.20934504, -54.31942004, -59.4018247 , -58.55145913,\n",
+       "        -61.24638225, -61.9967568 , -60.47705385, -66.62658636,\n",
+       "        -61.29270712, -59.39128641, -60.33390385, -58.94550258,\n",
+       "        -58.57613915, -60.50909689, -62.09806463, -64.85267852,\n",
+       "        -66.09652891, -62.16290385, -64.21702599, -65.30820455,\n",
+       "        -64.62563924, -67.96047408, -68.52681529, -64.04456827,\n",
+       "        -67.52329538, -59.04466885, -68.86210462, -70.92480017,\n",
+       "        -66.77136507, -66.62378945, -59.41939926, -57.61630917,\n",
+       "        -59.4959824 , -57.43603643, -55.99678804, -58.25798526,\n",
+       "        -56.94759814, -54.57090867, -51.36750123, -51.36750123,\n",
+       "        -55.8462047 , -55.4976664 , -53.99871956, -56.89758441]])
          • depth
            (chain, draw)
            int64
            3 2 4 3 4 4 3 4 ... 5 3 3 2 3 3 3 3
            array([[3, 2, 4, 3, 4, 4, 3, 4, 3, 4, 4, 4, 4, 4, 4, 3, 5, 4, 3, 4, 4, 4,\n",
+       "        4, 4, 4, 4, 4, 3, 3, 3, 3, 3, 3, 3, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
+       "        3, 1, 1, 3, 3, 2, 3, 2, 2, 2, 2, 2, 2, 1, 2, 1, 2, 2, 4, 1, 3, 2,\n",
+       "        1, 2, 2, 1, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
+       "        4, 3, 3, 2, 3, 4, 2, 3, 1, 3, 4, 2, 3, 3, 3, 4, 3, 3, 3, 4, 3, 4,\n",
+       "        3, 3, 3, 4, 3, 3, 3, 3, 3, 3, 3, 3, 4, 3, 3, 3, 3, 3, 4, 3, 5, 2,\n",
+       "        4, 2, 3, 3, 3, 3, 3, 3, 3, 4, 3, 4, 3, 4, 3, 3, 4, 3, 3, 5, 4, 6,\n",
+       "        3, 3, 2, 5, 3, 3, 5, 4, 3, 3, 4, 3, 4, 4, 4, 4, 3, 4, 4, 4, 4, 3,\n",
+       "        4, 3, 4, 4, 4, 4, 4, 4, 4, 3, 3, 4, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
+       "        3, 3, 4, 3, 4, 3, 5, 3, 3, 4, 3, 3, 4, 4, 4, 4, 4, 4, 4, 3, 4, 3,\n",
+       "        4, 4, 4, 3, 3, 4, 3, 3, 2, 2, 4, 3, 3, 2, 2, 3, 3, 3, 2, 4, 4, 3,\n",
+       "        4, 3, 2, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 3, 4, 4, 3, 4, 3, 3, 3, 3,\n",
+       "        2, 4, 4, 3, 3, 4, 4, 4, 4, 4, 4, 3, 4, 4, 3, 3, 3, 4, 4, 4, 4, 4,\n",
+       "        4, 3, 3, 4, 3, 3, 3, 3, 3, 3, 2, 2, 3, 3, 4, 3, 3, 3, 2, 2, 2, 3,\n",
+       "        3, 3, 4, 4, 3, 2, 3, 1, 3, 3, 2, 3, 3, 3, 3, 2, 4, 2, 1, 2, 2, 2,\n",
+       "        2, 3, 3, 1, 2, 4, 4, 3, 5, 3, 3, 3, 4, 3, 4, 4, 4, 4, 4, 4, 3, 4,\n",
+       "        4, 4, 3, 3, 4, 4, 4, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 3,\n",
+       "        4, 3, 4, 4, 4, 4, 2, 3, 3, 4, 4, 4, 3, 3, 4, 3, 3, 4, 3, 4, 4, 5,\n",
+       "        3, 4, 3, 3, 3, 3, 3, 4, 3, 3, 3, 3, 3, 1, 3, 3, 3, 3, 3, 3, 4, 3,\n",
+       "        3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 3, 3, 4, 3, 4, 4, 3, 3,\n",
+       "        3, 3, 3, 4, 4, 4, 4, 3, 4, 4, 4, 4, 4, 3, 3, 3, 3, 3, 4, 4, 4, 3,\n",
+       "        4, 4, 4, 4, 4, 4, 4, 3, 3, 2, 2, 3, 3, 3, 4, 3, 2, 3, 5, 4, 4, 4,\n",
+       "        3, 4, 3, 2, 2, 2, 5, 2, 2, 2, 2, 2, 3, 2, 2, 4],\n",
+       "       [4, 3, 3, 4, 5, 5, 4, 4, 4, 4, 4, 4, 4, 3, 3, 4, 3, 5, 3, 4, 4, 4,\n",
+       "        5, 4, 4, 4, 4, 3, 5, 3, 4, 3, 4, 4, 3, 5, 4, 4, 4, 4, 4, 4, 3, 5,\n",
+       "        4, 4, 3, 4, 4, 4, 3, 3, 4, 2, 4, 2, 3, 2, 2, 2, 3, 4, 4, 3, 4, 4,\n",
+       "        3, 4, 3, 4, 4, 3, 4, 2, 4, 4, 4, 4, 4, 3, 4, 4, 4, 4, 3, 4, 4, 4,\n",
+       "        4, 4, 4, 4, 4, 4, 3, 4, 4, 4, 4, 4, 4, 3, 4, 2, 5, 4, 4, 4, 3, 4,\n",
+       "        4, 4, 4, 4, 4, 4, 4, 5, 4, 4, 4, 4, 5, 5, 3, 4, 4, 4, 4, 5, 5, 4,\n",
+       "        4, 5, 3, 5, 3, 4, 2, 2, 2, 2, 3, 3, 2, 3, 4, 3, 3, 3, 3, 3, 3, 3,\n",
+       "        2, 3, 3, 4, 4, 3, 5, 4, 3, 3, 4, 4, 3, 2, 4, 2, 4, 2, 3, 4, 3, 4,\n",
+       "        3, 3, 1, 1, 2, 2, 2, 2, 1, 2, 2, 3, 1, 3, 1, 1, 1, 1, 2, 2, 2, 1,\n",
+       "        3, 1, 3, 1, 1, 1, 1, 1, 2, 1, 4, 1, 2, 1, 1, 2, 3, 1, 2, 3, 2, 2,\n",
+       "        3, 1, 2, 3, 1, 2, 2, 4, 2, 1, 2, 2, 2, 1, 1, 3, 2, 2, 2, 2, 1, 1,\n",
+       "        1, 2, 4, 1, 2, 2, 2, 2, 1, 2, 1, 2, 2, 1, 2, 3, 1, 4, 3, 2, 4, 4,\n",
+       "        3, 3, 3, 3, 3, 2, 4, 2, 1, 3, 3, 3, 3, 4, 4, 3, 3, 5, 3, 3, 4, 3,\n",
+       "        3, 4, 4, 3, 3, 3, 4, 3, 3, 3, 3, 4, 2, 3, 2, 3, 4, 4, 3, 2, 3, 4,\n",
+       "        4, 4, 4, 4, 4, 4, 4, 3, 3, 3, 4, 3, 3, 3, 4, 4, 3, 4, 3, 4, 3, 3,\n",
+       "        3, 3, 3, 3, 2, 2, 4, 4, 3, 3, 4, 3, 4, 4, 3, 2, 5, 2, 2, 3, 3, 4,\n",
+       "        2, 3, 3, 3, 4, 3, 3, 3, 2, 3, 3, 3, 4, 5, 3, 3, 3, 4, 2, 3, 2, 4,\n",
+       "        3, 3, 3, 3, 4, 3, 3, 3, 5, 3, 1, 2, 2, 2, 3, 4, 2, 3, 3, 3, 4, 4,\n",
+       "        4, 5, 4, 2, 2, 3, 3, 3, 3, 3, 3, 2, 2, 2, 4, 3, 3, 4, 3, 3, 4, 4,\n",
+       "        5, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 4, 4, 4, 4, 4, 4, 5, 4, 4, 4,\n",
+       "        4, 5, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 3, 3, 3, 4, 5, 4, 4, 4,\n",
+       "        4, 4, 4, 3, 4, 5, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 4, 4, 4, 4,\n",
+       "        4, 4, 4, 4, 4, 4, 4, 4, 5, 3, 3, 2, 3, 3, 3, 3]])
          • step_size_bar
            (chain, draw)
            float64
            0.3176 0.3176 ... 0.2718 0.2718
            array([[0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
+       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
+       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
+       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
+       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
+       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
+       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
+       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
+       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
+       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
+       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
+       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
+       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
+       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
+       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
+       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
+       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
+       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
+       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
+       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
+       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
+       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
+       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
+       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
+       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
+       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
+       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
+       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
+       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
+       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
+       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
+       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
+       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
+       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
+       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
+       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
+       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
+       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
+       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
+       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
+       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
+       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
+       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
+       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
+       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
+       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
+       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
+       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
+       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
+       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
+       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
+       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
+       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
+       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
+       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
+       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
+       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
+       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
+       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
+       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
+       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
+       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
+       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
+       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
+       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
+       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
+       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
+       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
+       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
+       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
+       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
+       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
+       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
+       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
+       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
+       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
+       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
+       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
+       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
+       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
+       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
+       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
+       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
+       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
+       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
+       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
+       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
+       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
+       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
+       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
+       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
+       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
+       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
+       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
+       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
+       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
+       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
+       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
+       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
+       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196],\n",
+       "       [0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
+       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
+       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
+       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
+       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
+       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
+       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
+       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
+       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
+       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
+       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
+       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
+       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
+       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
+       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
+       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
+       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
+       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
+       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
+       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
+       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
+       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
+       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
+       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
+       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
+       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
+       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
+       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
+       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
+       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
+       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
+       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
+       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
+       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
+       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
+       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
+       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
+       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
+       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
+       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
+       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
+       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
+       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
+       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
+       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
+       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
+       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
+       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
+       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
+       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
+       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
+       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
+       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
+       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
+       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
+       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
+       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
+       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
+       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
+       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
+       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
+       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
+       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
+       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
+       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
+       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
+       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
+       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
+       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
+       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
+       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
+       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
+       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
+       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
+       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
+       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
+       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
+       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
+       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
+       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
+       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
+       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
+       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
+       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
+       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
+       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
+       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
+       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
+       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
+       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
+       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
+       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
+       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
+       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
+       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
+       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
+       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
+       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
+       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
+       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612]])
          • diverging
            (chain, draw)
            bool
            False False False ... False False
            array([[False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "         True, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False,  True, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False,  True,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False],\n",
+       "       [False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False,  True, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "         True, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False,  True, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False,  True, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False]])
          • energy_error
            (chain, draw)
            float64
            0.01684 0.6038 ... 0.06367 0.4126
            array([[ 1.68399133e-02,  6.03806531e-01, -2.03937381e-01,\n",
+       "         1.89366291e-01, -6.86793141e-02, -9.04721835e-02,\n",
+       "         3.53770748e-01,  1.11664054e-01, -3.26447518e-01,\n",
+       "        -9.54710814e-02, -2.02420776e-01, -1.46485446e-01,\n",
+       "         2.87348127e-02, -2.76057108e-02,  4.96012860e-01,\n",
+       "        -1.96727346e-01,  1.67060523e-02, -3.60310256e-03,\n",
+       "        -2.94016199e-02,  3.31364152e-02, -6.07079070e-03,\n",
+       "         6.68741170e-02, -9.05157533e-02,  2.17865523e-01,\n",
+       "        -6.20298344e-02, -3.17620352e-02, -2.88531112e-01,\n",
+       "        -2.13967225e-01,  4.31026720e-01,  5.99071481e-02,\n",
+       "         4.00405405e-01,  4.75997505e-01, -1.80449714e-01,\n",
+       "        -4.91082764e-02, -2.48423117e-01, -4.37327129e-01,\n",
+       "        -1.08690374e+00,  0.00000000e+00,  0.00000000e+00,\n",
+       "         1.05882780e+00,  0.00000000e+00,  0.00000000e+00,\n",
+       "         0.00000000e+00,  1.89692166e+00,  0.00000000e+00,\n",
+       "         0.00000000e+00,  0.00000000e+00, -3.44566898e+00,\n",
+       "         0.00000000e+00,  2.25368983e+00,  0.00000000e+00,\n",
+       "        -1.42393819e-02,  0.00000000e+00,  9.72286308e-01,\n",
+       "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
+       "         0.00000000e+00, -2.54985822e+00,  0.00000000e+00,\n",
+       "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
+       "         0.00000000e+00, -2.86117236e-01,  0.00000000e+00,\n",
+       "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
+       "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
+       "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
+       "         2.20723978e+00,  5.09745798e-01,  6.59015930e-02,\n",
+       "         2.11389816e-01, -2.13738659e-01, -8.54751013e-01,\n",
+       "        -1.46488827e-01, -8.57041353e-02,  3.67172938e-02,\n",
+       "         2.95796620e-01, -7.44105775e-02, -8.70043594e-02,\n",
+       "        -1.36839405e-01,  0.00000000e+00,  2.60212139e-01,\n",
+       "        -5.53976078e-01, -4.94104238e-01,  8.88190461e-01,\n",
+       "         2.87805001e-01,  0.00000000e+00,  0.00000000e+00,\n",
+       "         0.00000000e+00,  8.51443417e-01, -1.05608700e+00,\n",
+       "         4.34678850e-01, -1.16118271e+00,  2.14850671e-02,\n",
+       "         4.18638120e-01,  5.63245581e-01,  3.44154213e-02,\n",
+       "         1.95279012e+00,  1.00679554e-01, -2.36931357e-01,\n",
+       "        -2.05140736e-01,  4.08885895e-01, -1.10876498e+00,\n",
+       "        -1.78620442e-01,  3.39285810e-02,  2.95115348e-01,\n",
+       "         8.44339347e-02, -4.94107314e-04, -1.30008330e+00,\n",
+       "         1.35682461e+00, -7.03536873e-01,  1.23669287e-01,\n",
+       "         7.38907045e-02,  1.16890070e-02,  1.89266966e-01,\n",
+       "        -1.22063621e-01,  9.54248569e-02, -1.98412388e-02,\n",
+       "        -6.74123400e-02, -1.14408984e+00,  1.60103428e-01,\n",
+       "        -1.07075090e-02,  2.79437655e-02,  0.00000000e+00,\n",
+       "         5.91516550e-03,  9.22022457e-02,  8.86322548e-01,\n",
+       "        -8.87515677e-01,  3.84951696e-01, -1.17177512e-01,\n",
+       "         3.69503347e-01, -1.09712792e-01,  5.81769961e-02,\n",
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+       "         3.33430237e-02, -1.28970527e-01, -2.76969904e-01,\n",
+       "        -4.42610379e-02, -1.50055621e-01, -2.07778285e-01,\n",
+       "        -1.57702724e-01, -4.11320887e-01,  2.71148779e-01,\n",
+       "         7.12639078e-01,  1.38460439e-01, -3.03290278e-02,\n",
+       "        -4.35888405e-02, -9.12020231e-01, -2.57262172e-01,\n",
+       "         2.01837350e-01,  1.02098454e-01,  2.58860849e-02,\n",
+       "         1.03506068e-01,  5.32560491e-02,  1.18359415e-02,\n",
+       "        -2.35523350e-01, -9.72157134e-02, -1.12373771e-01,\n",
+       "        -3.48134358e-02,  3.57649869e-01, -4.75650120e-01,\n",
+       "        -4.92359751e-02, -3.66304622e-02,  4.35404119e-01,\n",
+       "        -5.66021890e-02,  1.54473318e-01,  3.59199872e-02,\n",
+       "        -6.50541432e-02, -4.59867561e-02,  2.38925686e-01,\n",
+       "        -3.94325046e-02, -2.38569234e-01,  4.24551138e-02,\n",
+       "         5.96091419e-02, -1.18375227e-02, -2.40576083e+00,\n",
+       "         1.59722848e-01, -1.29718135e+00,  9.05971745e-01,\n",
+       "         5.51460653e-01,  2.53648908e-01, -2.71358149e-01,\n",
+       "         4.87557109e-03, -2.45809178e-01,  1.47998749e-01,\n",
+       "        -6.97896048e-02,  1.06621671e-01,  2.52925702e-01,\n",
+       "         6.78879448e-01, -3.06330075e+00,  0.00000000e+00,\n",
+       "         2.55714674e+00,  4.10520115e-01,  1.96484000e-01,\n",
+       "        -7.87476969e-02,  1.60063927e+00, -2.13933103e+00,\n",
+       "        -1.31891407e-01,  3.28348649e-01, -2.80568682e-01,\n",
+       "         4.28800813e-01, -7.16088354e-01, -8.41627265e-01,\n",
+       "         1.34248711e-02,  1.70488587e-01, -5.60519926e-01,\n",
+       "        -3.90304378e-02,  5.07437020e-01, -1.71892810e-01,\n",
+       "        -1.80010021e-01,  3.00583723e-02, -3.04176564e-01,\n",
+       "        -9.74006220e-01,  0.00000000e+00, -1.53618278e-01,\n",
+       "         0.00000000e+00,  4.65628427e-01,  2.08876926e-01,\n",
+       "         8.56574179e-02,  3.08704513e-02,  1.90736412e-01,\n",
+       "         1.43810679e-01, -2.73651356e-01, -1.86551374e-01,\n",
+       "        -6.20426263e-01,  5.71539565e-01,  7.91370560e-01,\n",
+       "         0.00000000e+00, -3.24780902e+00,  2.85696123e-01,\n",
+       "         0.00000000e+00,  0.00000000e+00,  5.51885730e-01,\n",
+       "         0.00000000e+00,  1.03508372e+00, -8.55615022e-02,\n",
+       "         6.08082302e-02,  2.17618372e-01, -7.20598863e-02,\n",
+       "        -2.67548460e-02,  5.03131569e-01, -1.69724220e+00,\n",
+       "         1.26776754e+00, -2.50365687e-01,  1.00148079e+00,\n",
+       "        -6.51734834e-01, -1.45713873e+00,  7.97346823e-01,\n",
+       "        -4.22285277e-02,  2.80142580e-02,  0.00000000e+00,\n",
+       "         0.00000000e+00, -2.32245334e+00, -3.52456926e-01,\n",
+       "         1.91604565e-01,  3.10279070e-01, -8.17320960e-02,\n",
+       "         7.37873285e-01, -3.45285605e-01,  1.94778069e-01,\n",
+       "        -4.08724625e-01, -1.91741670e-01,  5.96881578e-01,\n",
+       "        -8.96115085e-02,  4.29812978e-02,  1.88578580e+00,\n",
+       "        -4.17822358e-01,  1.38582200e-02,  1.04753253e-02,\n",
+       "         1.16756897e-01,  1.88576182e-01,  3.19855532e-01,\n",
+       "         3.25266285e-01, -4.88613894e-01,  1.13991147e+00,\n",
+       "        -3.73941928e-01,  8.15795361e-02, -6.50723876e-02,\n",
+       "         2.85939466e-01,  9.72053556e-03, -2.85995133e-01,\n",
+       "         1.02396216e-01, -3.01045267e-02, -8.54626194e-02,\n",
+       "        -5.33878196e-02,  5.91150434e-03, -1.32480432e-02,\n",
+       "         4.43221471e-02,  2.90127004e-02, -6.10698275e-02,\n",
+       "         3.06346356e-01, -1.66737281e-01, -2.91606857e-02,\n",
+       "         2.52191688e-02, -3.38478029e-01,  3.64546929e-01,\n",
+       "        -5.21785148e-02, -2.24346539e-02, -1.67156361e+00,\n",
+       "         1.57256339e-01,  1.98533735e+00,  2.13669169e-01,\n",
+       "         1.90361756e-01, -2.23806858e-01,  3.91125140e-02,\n",
+       "        -1.08001610e-01,  4.28989130e-01,  1.31900193e-02,\n",
+       "        -5.91239021e-01, -1.28409387e-01, -1.79454716e-01,\n",
+       "        -1.37073630e-01,  6.49816073e-02,  8.56948164e-02,\n",
+       "         8.23426789e-03, -1.20269595e-01, -1.03510698e-01,\n",
+       "         2.07221068e-02,  9.81322526e-02, -1.90556891e-02,\n",
+       "         3.50243500e-01, -1.34025966e-01, -1.50620772e-01,\n",
+       "        -5.70488877e-02, -1.21475215e-01,  3.81194747e-01,\n",
+       "        -5.45611051e-01, -6.99153997e-02,  9.90190618e-02,\n",
+       "        -5.83950807e-03, -1.89917495e-01,  2.54407046e-02,\n",
+       "        -1.77037706e-02, -6.40364961e-02,  1.33227854e-01,\n",
+       "        -2.26396523e-01, -1.19989479e-01, -2.69158594e-01,\n",
+       "         0.00000000e+00,  6.45903340e-01,  4.79948762e-02,\n",
+       "         6.36738968e-02,  4.12639841e-01]])
          • max_energy_error
            (chain, draw)
            float64
            274.8 0.6038 ... -0.4008 0.5045
            array([[ 2.74816935e+02,  6.03806531e-01, -7.79957979e-01,\n",
+       "        -1.01594416e+00,  3.61564830e-01, -4.76151095e-01,\n",
+       "         3.53770748e-01, -1.55901633e-01, -4.76963696e-01,\n",
+       "        -1.54701681e+00,  6.36295155e-01, -2.18384866e-01,\n",
+       "         4.89861101e-01, -5.22662460e-01,  1.39887527e+00,\n",
+       "        -6.36442192e-01, -5.24107166e-01, -1.03965493e-01,\n",
+       "        -2.19282952e+00,  1.37863613e-01,  9.68812405e-02,\n",
+       "        -5.45971892e-01,  1.39468345e-01,  2.93938594e-01,\n",
+       "        -4.10017907e-01, -2.53134003e-01, -3.33474473e-01,\n",
+       "         2.71870499e-01,  8.88863105e-01,  4.06997701e-01,\n",
+       "         4.00405405e-01,  1.23555038e+00, -1.13156119e+00,\n",
+       "        -4.35367079e-01,  6.85429580e-01, -4.37327129e-01,\n",
+       "        -1.08690374e+00,  7.18973832e+01,  3.17473859e+02,\n",
+       "         1.51221120e+01,  4.51671386e+01,  3.69437768e+01,\n",
+       "         5.53388183e+00,  2.09631827e+02,  5.28610150e+01,\n",
+       "         4.17440859e+01,  3.92089139e+01,  7.57092302e+01,\n",
+       "         1.10626828e+02,  2.52821793e+02,  2.07985066e+01,\n",
+       "         2.39929310e+01,  3.69329571e+00,  1.99049854e+02,\n",
+       "         1.61569236e+02,  1.33901013e+02,  6.57489468e+02,\n",
+       "         1.87415660e+01,  4.12862595e+02,  2.58777292e+01,\n",
+       "         6.19407129e+01,  4.01712417e+02,  6.71732509e+01,\n",
+       "         1.66860628e+01,  7.39115236e+02,  1.96760384e+01,\n",
+       "         1.12207838e+01,  4.63150768e+01,  6.23095217e+02,\n",
+       "         1.45137587e+01,  1.05063078e+01,  4.81671164e+01,\n",
+       "         1.43265962e+03,  1.17892095e+02,  1.89705742e+01,\n",
+       "         1.11336729e+01,  1.99906908e+00, -2.91741126e-01,\n",
+       "        -3.38405295e-01, -3.25519249e-01, -8.54751013e-01,\n",
+       "         2.47383319e-01,  1.71321722e-01, -4.56920766e-01,\n",
+       "         3.37135769e-01, -1.29235578e-01,  2.97105180e+01,\n",
+       "         2.25477800e+00,  4.63758963e+00,  2.62172163e-01,\n",
+       "        -5.53976078e-01, -4.94104238e-01,  2.28777087e+02,\n",
+       "         1.21970591e+00,  1.17630978e+02,  3.53057921e+01,\n",
+       "         1.82674362e+01,  7.36356118e+00,  1.96351236e+00,\n",
+       "         1.50845177e+02,  1.38834120e+00,  1.13560841e+00,\n",
+       "         5.63575563e-01,  1.55970990e+00,  3.38699017e+00,\n",
+       "         2.29059238e+00, -1.17135288e-01, -8.58819289e-01,\n",
+       "        -2.37618177e-01,  1.31337508e+00, -1.10876498e+00,\n",
+       "        -3.10564141e-01,  3.91527588e-01,  1.08928545e+00,\n",
+       "         1.04891633e-01,  4.54714625e-01,  1.32068726e+00,\n",
+       "         1.35682461e+00,  1.53166670e+00, -4.02366798e-01,\n",
+       "         4.69063970e-01,  1.36313677e-01,  4.67336814e+00,\n",
+       "        -1.80956270e-01,  3.98740301e-01,  3.11976152e-01,\n",
+       "        -1.75007997e-01, -1.14408984e+00,  1.11914575e+00,\n",
+       "         2.79545098e+02,  1.01515658e+04,  2.66506964e+02,\n",
+       "         1.00013499e+01,  3.27597377e+01,  5.49354397e+01,\n",
+       "        -8.87515677e-01,  6.77010541e-01,  7.20986988e-01,\n",
+       "         3.69503347e-01, -1.47803455e-01,  3.10412059e-01,\n",
+       "         4.01450723e-01,  1.31979427e+00,  1.67512242e+00,\n",
+       "         4.05438676e-01, -3.73017420e-01, -5.96949277e-01,\n",
+       "        -1.74461027e-01, -8.02832118e-01,  2.73475371e+00,\n",
+       "        -6.40042713e-01, -2.75914443e-01,  1.88242095e-01,\n",
+       "         5.45203689e-01,  3.56418639e-01,  3.50460312e-01,\n",
+       "         1.20237393e+00,  2.04058152e+02,  1.67389029e-01,\n",
+       "        -3.17397460e-01,  1.01532973e+00,  8.98980673e-01,\n",
+       "         1.22501442e+01,  4.10980200e-01, -3.39884627e-01,\n",
+       "         1.57941000e-01,  4.51856366e-01, -3.61596888e-01,\n",
+       "        -8.31244912e-02,  4.66086854e-01,  2.92338355e-01,\n",
+       "         1.76261245e-01, -2.38452316e-01, -6.80804449e-01,\n",
+       "        -8.87409920e-01, -5.97562096e-01,  6.12440462e+01,\n",
+       "        -1.30966029e-01, -4.65535423e-01,  3.68842011e-01,\n",
+       "         1.33014249e-01,  1.53263864e+00, -2.47650894e-01,\n",
+       "         1.11743041e+00, -5.55410758e-01, -1.41625563e+00,\n",
+       "         1.60658328e+00, -7.74709717e-01,  6.91096694e-01,\n",
+       "        -5.18126775e-01,  8.24534180e-01, -7.13463873e-01,\n",
+       "         1.42210136e-01, -1.33389698e-01,  5.00115697e-01,\n",
+       "         6.88623101e-01,  4.47660489e+02,  3.93017586e-01,\n",
+       "         4.44351612e-01, -7.23064770e-01, -7.48860820e-01,\n",
+       "        -5.28616097e-01, -3.47947217e-01,  2.86059579e-01,\n",
+       "        -7.35920574e-01, -2.62511613e-01, -3.79130139e-01,\n",
+       "        -4.83382033e-01,  3.36714347e-01, -5.58944771e-01,\n",
+       "         2.58370830e-01, -1.85254417e-01, -1.27738419e-01,\n",
+       "        -6.12756376e-01, -4.09926570e-01, -7.15677408e-01,\n",
+       "         1.30080272e+00, -1.27241783e-01, -2.22738112e-01,\n",
+       "        -1.21805276e-01,  5.57399223e+01, -3.69704913e-01,\n",
+       "        -4.60629613e-01,  2.43762096e-01, -2.71232940e-01,\n",
+       "         2.59882033e+00, -9.06621914e-01, -6.07032057e-01,\n",
+       "        -8.02620523e-01,  3.82316402e+00, -6.94845166e-01,\n",
+       "         9.99234956e+01,  1.15843083e+01, -6.25235618e-01,\n",
+       "         1.02557335e+02,  1.15435497e+02,  1.27422620e+02,\n",
+       "         3.58198692e+00,  2.27971086e+01,  7.74205152e+01,\n",
+       "         5.29831690e+01,  2.64241648e+00,  4.10786692e+00,\n",
+       "        -4.93728024e-01,  3.56610810e-01,  5.98900130e+02,\n",
+       "        -3.41468178e-01, -7.50520524e-01,  5.65502713e-01,\n",
+       "        -6.57526937e-01, -4.69609447e-01, -2.71518847e-01,\n",
+       "        -4.77539114e-01, -3.73230146e-01,  4.90937311e-01,\n",
+       "        -3.92455367e-01, -3.58845789e-01,  3.28811736e-01,\n",
+       "         6.46266604e+00,  5.06064192e-01,  8.58688369e+00,\n",
+       "         2.62959697e-01, -1.73167468e+00,  7.38169084e-01,\n",
+       "         3.38464555e+00,  3.25505675e+00,  4.37195582e-01,\n",
+       "        -5.89159727e-01,  6.39559626e-01,  1.94771729e-01,\n",
+       "         4.61234276e-01, -5.01068291e-02, -6.98315390e-02,\n",
+       "        -1.23489307e-01, -4.39980004e-01,  3.40511094e-01,\n",
+       "        -6.67826179e-01, -3.08586901e-01, -1.74256869e-01,\n",
+       "        -4.04950537e-01,  3.96560392e-01, -3.48919654e-01,\n",
+       "         2.28528345e-01,  1.78802500e-01, -8.49974994e-01,\n",
+       "         1.32029181e-01,  2.91792616e-01, -4.77121095e-01,\n",
+       "        -1.30661793e+00,  8.64807677e-01,  5.24362509e-01,\n",
+       "         3.44901181e-01, -1.88410348e-01,  1.81696914e+01,\n",
+       "         3.24854006e+00,  2.12498041e+00,  2.37735147e-01,\n",
+       "        -5.81724645e-01,  3.93272659e-01,  1.37559392e+00,\n",
+       "         1.56258986e+01, -1.77441790e-01, -6.12443426e-01,\n",
+       "        -8.54471664e-01,  1.84254226e+00,  2.08877966e+00,\n",
+       "        -8.83306442e-01,  1.45929886e+00,  1.44722917e+02,\n",
+       "         1.20525012e-01, -6.37490192e-01, -3.97001233e-01,\n",
+       "        -1.41920112e+00,  1.19794888e+00, -1.28904881e+00,\n",
+       "         9.48682888e+00,  4.48958225e+02,  1.10040152e+02,\n",
+       "         1.16087965e+00,  4.32035723e+00,  3.32363950e+00,\n",
+       "        -9.53000331e-01,  3.34663217e+01,  8.35439425e-01,\n",
+       "         3.69000327e+02,  4.09901876e+02,  4.13834807e+01,\n",
+       "         9.52595977e+01,  1.16148588e+02,  5.08124362e+00,\n",
+       "         5.15443360e+01,  3.42733371e+02,  9.43168196e+02,\n",
+       "         3.05820227e+01,  2.87515471e+01,  8.89199594e+00,\n",
+       "         3.70919015e-01,  2.04339059e+00,  6.87954518e-01,\n",
+       "        -5.73870640e-01, -9.40070042e-01,  3.28477967e-01,\n",
+       "        -3.10922470e-01,  2.37570514e-01,  1.94586791e-01,\n",
+       "         4.73664734e-01, -3.41281210e-01, -8.83256757e-01,\n",
+       "         2.76783291e-01, -3.42582786e-01, -3.38226602e-01,\n",
+       "         4.08789430e-01,  5.97557633e-01, -2.91575781e-01,\n",
+       "         1.62451342e+00,  9.47538548e-01, -3.93596817e-01,\n",
+       "        -8.18703126e-02,  1.73745819e+00, -3.99315411e-01,\n",
+       "        -4.76546415e-01,  4.12969173e-01, -1.05603427e+00,\n",
+       "        -5.60489626e-01,  3.50771148e-01,  4.88442078e-01,\n",
+       "         1.57849391e+00, -2.09788801e+00, -4.67941266e-01,\n",
+       "         1.10778968e+02, -7.80452883e-01,  2.58882819e-01,\n",
+       "         2.30662632e+01,  7.98352229e-01,  1.00725847e+00,\n",
+       "        -5.06318289e-01,  3.07202710e-01, -2.76370562e-01,\n",
+       "         7.04766631e-01, -6.92144734e-01,  7.72262554e+02,\n",
+       "         3.00600613e+00, -1.91086494e+00,  7.72282465e-01,\n",
+       "         1.99757185e-01, -1.56880397e-01, -3.94109829e-01,\n",
+       "        -5.32218915e-01, -2.64702360e-01, -1.15481279e-01,\n",
+       "         4.54622337e-02, -3.87793830e-01, -2.97006820e-01,\n",
+       "        -7.35658605e-01, -1.45310406e-01, -5.72510713e-01,\n",
+       "        -4.80920319e-01,  1.28426782e+00, -3.16255990e-01,\n",
+       "        -1.15633642e+00, -1.07972315e+00,  1.03878854e+00,\n",
+       "        -2.12137840e+00,  1.45149425e+00,  1.18028005e+03,\n",
+       "         1.15191338e+01,  8.04099080e+01,  1.22724524e+01,\n",
+       "         9.30187938e+02,  4.34667618e+00,  6.92432150e-01,\n",
+       "         6.27915897e+00,  1.52571772e+00,  1.52524031e-01,\n",
+       "         1.80968731e-01, -5.93907465e-02,  4.18429361e-01,\n",
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+       "        -2.51498398e+00,  3.74736126e-01, -8.35740930e-02,\n",
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+       "        -4.94389974e-01, -5.90676171e-01, -1.67156361e+00,\n",
+       "         1.57256339e-01,  2.47128017e+00,  5.00816290e-01,\n",
+       "         6.32187291e-01,  1.22673622e+00, -1.57700483e-01,\n",
+       "         7.50737057e-01,  3.41594158e+00,  1.63572860e-01,\n",
+       "         1.74936789e+00, -3.23055635e-01, -3.38663943e-01,\n",
+       "        -2.70496997e-01,  1.66150120e-01, -9.06414679e-02,\n",
+       "        -2.84482195e-01, -2.47790675e-01, -1.56296934e-01,\n",
+       "         4.90751560e-02,  1.02569235e-01, -1.45240799e-01,\n",
+       "         6.44771848e-01, -5.95465332e-01, -3.45859829e-01,\n",
+       "        -2.04345082e-01, -1.21475215e-01,  2.97449225e+00,\n",
+       "        -7.20610508e-01, -2.29381889e-01,  2.23588295e-01,\n",
+       "         5.78109238e-01,  1.00836572e+00,  4.70697541e-01,\n",
+       "        -5.58601458e-01, -5.97103590e-01,  1.88859762e+00,\n",
+       "        -3.18466730e-01,  2.92485619e-01,  2.18456799e+00,\n",
+       "         1.86875136e+01,  4.03541467e+00, -5.28061373e-01,\n",
+       "        -4.00775092e-01,  5.04518205e-01]])
          • energy
            (chain, draw)
            float64
            59.28 61.6 68.81 ... 57.61 59.47
            array([[59.27755265, 61.60428906, 68.81393494, 67.96547627, 69.59668787,\n",
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+       "        68.12351623, 67.69661007, 69.52164951, 65.37835184, 67.9433699 ,\n",
+       "        67.4053013 , 66.60483517, 68.1719148 , 63.22713128, 72.14156854,\n",
+       "        73.40809155, 68.81619856, 67.86756678, 67.75927911, 69.54985144,\n",
+       "        66.6656891 , 61.01788234, 57.44602307, 58.28895004, 59.20846697,\n",
+       "        62.31573332, 64.27864243, 61.42901099, 61.53281116, 62.1794804 ,\n",
+       "        56.75531736, 54.59292309, 52.33175456, 51.69961802, 52.96110897,\n",
+       "        51.62635206, 52.10651111, 54.9149328 , 56.44193506, 62.67477221,\n",
+       "        56.71613589, 56.60921523, 51.25789804, 55.11287632, 54.2826413 ,\n",
+       "        57.82655158, 53.0590694 , 57.99436176, 52.18972895, 53.72280126,\n",
+       "        52.19210185, 56.04235291, 53.80421171, 49.65308361, 51.16558334,\n",
+       "        52.24735255, 50.32328633, 51.98664813, 51.34752679, 49.19003318,\n",
+       "        51.7556987 , 50.27472774, 52.62781551, 49.64031548, 49.84132598,\n",
+       "        53.02334304, 53.08867659, 48.93239839, 49.61539116, 51.70158476,\n",
+       "        54.70439655, 61.08962201, 63.66750548, 63.83570059, 65.80721437,\n",
+       "        64.97316561, 66.26285188, 61.50403086, 61.7331435 , 61.57135352,\n",
+       "        63.23560033, 56.46823783, 56.37614552, 62.31947162, 58.57641107,\n",
+       "        59.90447796, 54.22652469, 53.33914291, 56.87998449, 63.95543601,\n",
+       "        59.91269804, 60.84098431, 58.35956285, 57.82317964, 64.59777245,\n",
+       "        59.47045596, 59.69334986, 59.12136489, 61.35630456, 61.9002042 ,\n",
+       "        60.62341356, 64.29347034, 63.61830848, 63.33143854, 63.10446381,\n",
+       "        61.67760593, 57.99190217, 57.40830119, 60.01185956, 60.65420298,\n",
+       "        62.94050369, 61.11796869, 61.20628663, 61.22106669, 62.05332502,\n",
+       "        64.52692016, 61.04632283, 62.18952185, 63.16760736, 60.94717331,\n",
+       "        63.08727099, 63.44908437, 59.15404706, 60.7938029 , 56.97332377,\n",
+       "        56.27190342, 55.08538288, 54.71625966, 54.86290568, 59.99178182,\n",
+       "        54.99784358, 59.17889203, 59.07729872, 60.37583061, 63.43966426,\n",
+       "        62.28916106, 62.35961663, 61.19577467, 62.94122153, 62.78560743,\n",
+       "        65.15837961, 59.36464195, 57.15256222, 59.41182107, 63.10305956,\n",
+       "        63.46197305, 60.88100304, 64.34714619, 65.21537387, 62.38379215,\n",
+       "        60.0109194 , 59.18023043, 65.34988246, 63.65299843, 63.00498817,\n",
+       "        61.71123113, 57.07145173, 56.80917763, 61.69750841, 63.62885294,\n",
+       "        65.49122903, 68.90964537, 69.96742291, 67.47442022, 68.53782234,\n",
+       "        66.95091402, 69.75965528, 68.50414714, 66.11694241, 72.08661841,\n",
+       "        68.87603584, 60.10886407, 61.26829091, 65.93425295, 68.50729944,\n",
+       "        68.94186004, 67.08396743, 67.92159778, 70.29989741, 64.63663539,\n",
+       "        59.22791433, 58.51647374, 59.84942876, 62.57697579, 62.46261665,\n",
+       "        63.99398735, 65.44636577, 64.088037  , 60.710703  , 57.7533779 ,\n",
+       "        60.9405356 , 58.54757413, 56.71333648, 59.63869062, 63.16815298,\n",
+       "        63.46618175, 62.80320643, 64.23522719, 65.9361868 , 65.24991724,\n",
+       "        61.31294058, 62.81466502, 66.02117514, 69.78553939, 66.36733953,\n",
+       "        69.93221915, 70.19058261, 70.30707794, 68.4101062 , 68.17957957,\n",
+       "        67.94484698, 71.09155565, 66.18099928, 65.98422753, 62.00834378,\n",
+       "        64.29715403, 64.39333177, 63.59235363, 63.25910214, 61.26322901,\n",
+       "        61.01054167, 64.49121758, 59.3419948 , 59.74628057, 56.85070756,\n",
+       "        55.20724922, 58.88473353, 65.5434591 , 57.19626735, 53.07252511,\n",
+       "        52.47873605, 52.65513881, 57.33011462, 55.18593142, 54.65490101,\n",
+       "        55.81445227, 58.36093544, 57.62244311, 55.47450836, 53.03075914,\n",
+       "        56.04367289, 58.17261449, 55.50750768, 58.43502576, 60.63416138,\n",
+       "        67.12792959, 69.47503095, 70.17231475, 69.31869398, 66.5613466 ,\n",
+       "        69.73139871, 72.4269507 , 74.72194806, 67.51168047, 60.79181098,\n",
+       "        59.8630236 , 66.7771781 , 65.71695545, 57.31487694, 60.75159053,\n",
+       "        62.55863237, 64.60947425, 59.83196929, 65.7423465 , 70.30576559,\n",
+       "        70.50554567, 70.28433679, 69.95349959, 70.05210515, 67.36893744,\n",
+       "        66.7115498 , 75.29620702, 66.64632336, 64.80205821, 60.69672762,\n",
+       "        62.68326124, 63.93442167, 65.57205526, 66.05085711, 64.72602862,\n",
+       "        68.68273833, 68.09502013, 68.25228842, 64.0973677 , 62.02130466,\n",
+       "        59.52066701, 58.14349493, 59.01298376, 58.38311497, 58.10441094,\n",
+       "        58.10415662, 59.0882375 , 57.59350472, 55.43278986, 58.89155992,\n",
+       "        61.50807435, 57.59358382, 59.13613471, 55.63173111, 60.21159347,\n",
+       "        62.1464299 , 56.79492463, 59.29978101, 57.68226313, 62.4680459 ,\n",
+       "        64.25341181, 64.96708658, 60.46989868, 57.04864163, 54.57194284,\n",
+       "        55.0445196 , 51.28616385, 53.1932304 , 53.50378412, 56.49444659,\n",
+       "        56.49595139, 55.67280487, 55.90007246, 56.83736279, 53.97027766,\n",
+       "        52.24062141, 55.10697214, 53.74775853, 55.42501658, 51.79313598,\n",
+       "        54.13238245, 51.87752898, 51.49787351, 62.20918697, 55.69644405,\n",
+       "        62.12014024, 65.61315544, 58.91753716, 59.31358213, 61.21104013,\n",
+       "        63.20268388, 65.49041221, 64.11349164, 62.10107222, 63.1251592 ,\n",
+       "        67.35942789, 65.87491401, 63.82916493, 68.34568608, 67.06671325,\n",
+       "        62.30427878, 71.50415613, 64.57224241, 64.70464562, 63.14032799,\n",
+       "        66.26422778, 66.02553061, 64.3906122 , 62.24124086, 61.25424163,\n",
+       "        60.34501254, 60.08877255, 59.50967893, 63.46193262, 61.92310931,\n",
+       "        66.05030566, 64.69519516, 66.17888859, 62.1764268 , 63.30257367,\n",
+       "        60.15777565, 56.54094029, 57.06309142, 59.0150358 , 59.26704312,\n",
+       "        59.047265  , 61.43390075, 65.71886842, 65.84249992, 59.95650416,\n",
+       "        57.13258385, 56.40297066, 59.07542049, 66.4236414 , 70.84366923,\n",
+       "        68.21272343, 69.20878752, 67.70515794, 67.31334598, 64.58266075,\n",
+       "        64.04787788, 67.84525113, 67.35247877, 70.96507757, 68.7466543 ,\n",
+       "        73.00768621, 65.76211889, 65.31616126, 60.52836871, 63.75715507,\n",
+       "        57.0549859 , 56.62126907, 57.92130145, 53.01766113, 53.41388227,\n",
+       "        56.5889997 , 57.22262721, 57.80903837, 54.45982047, 51.39107904,\n",
+       "        54.07622405, 59.60959177, 61.31637639, 61.5064331 , 61.14339599,\n",
+       "        62.04417511, 63.50497502, 61.73271807, 62.01084165, 60.52209599,\n",
+       "        57.66322325, 55.64667379, 60.70470007, 56.96637842, 60.15046628,\n",
+       "        60.59961648, 69.13434704, 65.1182145 , 72.58971409, 68.85419848,\n",
+       "        69.70439875, 69.00152325, 63.90852289, 62.04366856, 60.65237661,\n",
+       "        56.68609365, 57.79038523, 59.27191781, 60.10503995, 60.5958649 ,\n",
+       "        60.62887754, 65.7243344 , 65.51633466, 68.00308785, 65.22321952,\n",
+       "        65.19586295, 62.11984873, 62.04020232, 64.29770614, 66.2066779 ,\n",
+       "        62.13127348, 64.92537346, 63.56421047, 62.45530006, 59.27782617,\n",
+       "        59.1819017 , 59.52616561, 62.29392213, 65.21386383, 71.7680218 ,\n",
+       "        73.18364797, 71.19181391, 65.37180265, 64.99442467, 64.23501693,\n",
+       "        68.28962528, 71.31481947, 72.1457408 , 74.32054488, 68.34760682,\n",
+       "        60.67457972, 61.95386699, 61.72224185, 63.08031637, 66.41047122,\n",
+       "        64.90822286, 62.18398768, 62.3059217 , 59.56133188, 56.73635953,\n",
+       "        64.54227787, 66.73055321, 68.20896576, 68.60994984, 62.45795515,\n",
+       "        54.76102261, 58.08857048, 54.03689141, 51.4455256 , 51.40543957,\n",
+       "        53.65514285, 59.13266515, 53.48209641, 53.76278727, 55.59589605,\n",
+       "        54.0276669 , 57.13607244, 57.07477496, 55.26017509, 54.57012118],\n",
+       "       [61.75972154, 59.66353545, 57.49981308, 62.35040496, 63.55054989,\n",
+       "        69.11928204, 70.85077282, 72.50395326, 68.1929167 , 65.99828308,\n",
+       "        66.54494732, 66.24887287, 60.45191822, 56.57705918, 56.5225119 ,\n",
+       "        58.75401056, 60.3864738 , 62.41669047, 60.44600446, 68.42613923,\n",
+       "        65.549628  , 65.41783826, 75.25604901, 70.91689766, 67.79104609,\n",
+       "        68.91432359, 67.01430298, 64.80070336, 63.36892049, 59.9243634 ,\n",
+       "        56.42637586, 56.39503211, 59.8040183 , 64.78440332, 63.2828307 ,\n",
+       "        61.51319333, 60.90721445, 70.29239434, 73.15593339, 72.74057052,\n",
+       "        69.79931711, 62.189592  , 61.57897598, 64.07317629, 64.14695226,\n",
+       "        65.42875135, 62.09765438, 61.00507083, 57.43361771, 62.45181912,\n",
+       "        61.07292959, 60.42219447, 59.64920872, 59.20474811, 54.52694328,\n",
+       "        55.5857527 , 54.28443337, 52.89467201, 53.94411753, 54.94217496,\n",
+       "        55.96819768, 58.61153463, 59.09165145, 64.75465816, 65.83370265,\n",
+       "        62.38642841, 63.09665799, 61.55663655, 59.03354255, 54.26831736,\n",
+       "        55.38134108, 59.83650615, 61.5386833 , 61.19323905, 67.29275122,\n",
+       "        73.40992671, 70.32428112, 68.1763955 , 68.3775828 , 59.08638128,\n",
+       "        68.13070797, 67.40920968, 61.86471024, 63.08910701, 61.2378988 ,\n",
+       "        65.47597137, 66.73154319, 66.25656569, 68.27007843, 68.21766013,\n",
+       "        65.70307091, 64.58937908, 67.24776665, 62.28277731, 62.96796616,\n",
+       "        72.05730687, 74.60323926, 76.03706896, 70.70071148, 68.52945376,\n",
+       "        61.47129512, 59.55580056, 61.34272434, 63.05010404, 64.94494333,\n",
+       "        64.75834256, 64.01381073, 63.27804542, 61.91558562, 64.98016928,\n",
+       "        68.37911747, 73.34095762, 77.76833281, 73.83605416, 71.00202439,\n",
+       "        72.10503174, 76.96140398, 75.10955745, 73.8252217 , 69.22841299,\n",
+       "        72.83492001, 73.11491653, 73.30319689, 68.51454432, 60.66782089,\n",
+       "        64.38205431, 67.50928313, 68.94763517, 73.60695031, 75.05474481,\n",
+       "        75.10046857, 68.35719733, 69.44133582, 64.64980347, 57.85048044,\n",
+       "        59.60218515, 56.95958089, 53.43278465, 54.91049774, 55.06193243,\n",
+       "        55.97969888, 54.32400804, 53.84511977, 58.24069579, 60.52930384,\n",
+       "        62.4078568 , 64.60064804, 56.7617469 , 56.29077606, 59.13946369,\n",
+       "        59.09730894, 59.300532  , 58.48935952, 59.47170595, 57.65668227,\n",
+       "        61.34796128, 60.0307865 , 63.16801209, 61.93288061, 62.0875022 ,\n",
+       "        63.61879072, 62.52159621, 58.08557717, 59.04493446, 60.51568822,\n",
+       "        60.02911748, 58.73009371, 58.19537049, 60.29449924, 58.33126949,\n",
+       "        55.9430416 , 55.65526377, 52.34981818, 55.09881132, 59.62897362,\n",
+       "        58.80088211, 54.16249913, 52.73036741, 54.00698062, 50.65531644,\n",
+       "        51.58255433, 52.22327119, 51.42436734, 53.50446129, 53.16742909,\n",
+       "        51.67768845, 53.0184032 , 55.16980732, 52.21763375, 53.40502475,\n",
+       "        55.76298006, 52.48357358, 53.36478014, 53.64830473, 51.61276366,\n",
+       "        51.88523906, 53.5570293 , 54.21260759, 53.53278073, 58.82813742,\n",
+       "        55.27356907, 52.80697999, 54.13721657, 52.69731068, 52.40214519,\n",
+       "        60.98136394, 52.74349271, 52.95223095, 52.09598228, 52.82814899,\n",
+       "        63.26585278, 52.04410148, 55.04496797, 58.14121934, 54.29574749,\n",
+       "        51.95370723, 55.50594377, 53.68418579, 53.31710545, 55.00316904,\n",
+       "        50.82752248, 55.03361665, 55.06940964, 53.02313131, 54.46348895,\n",
+       "        54.71509609, 51.51066799, 51.93468713, 57.31699402, 54.7027475 ,\n",
+       "        57.14287077, 52.18485417, 62.27061615, 50.10384409, 59.11805049,\n",
+       "        50.26607794, 57.46023568, 50.71739829, 53.30897685, 51.7087591 ,\n",
+       "        54.54348347, 52.9541625 , 51.62980664, 56.78231024, 53.16814843,\n",
+       "        55.23629625, 56.38042243, 54.6856571 , 52.27679106, 52.61414751,\n",
+       "        52.08614295, 55.08488847, 51.21766208, 51.21141158, 50.22572936,\n",
+       "        51.73950858, 53.26195444, 52.04240294, 55.78156868, 55.01809158,\n",
+       "        53.92531117, 53.45936453, 57.47500076, 57.89308419, 54.33544648,\n",
+       "        55.73111145, 56.16602124, 55.94494158, 53.9673364 , 57.53497424,\n",
+       "        54.51298877, 52.84873038, 56.59928663, 50.3611805 , 57.194914  ,\n",
+       "        60.03166681, 61.69876314, 68.59326673, 68.10521522, 65.522795  ,\n",
+       "        59.19671135, 60.50091588, 60.47069313, 62.2985155 , 62.00692781,\n",
+       "        64.54936371, 59.98009005, 60.57109195, 62.50629335, 58.4786275 ,\n",
+       "        59.91693124, 61.76023422, 62.05876171, 60.74158048, 55.4236545 ,\n",
+       "        55.23689984, 55.21627656, 54.54812164, 53.25618186, 53.95277046,\n",
+       "        56.29759349, 59.3891934 , 62.02151807, 62.60243162, 57.79576453,\n",
+       "        55.51405755, 61.50730979, 64.49195859, 62.41013659, 64.52407955,\n",
+       "        69.40412721, 69.74452062, 69.15707073, 64.37088277, 60.01229866,\n",
+       "        58.15784975, 58.22465477, 64.190562  , 57.38118093, 56.65310474,\n",
+       "        59.09530909, 59.57796805, 60.18179536, 60.24641329, 58.8283106 ,\n",
+       "        57.98520068, 58.46284851, 59.39062361, 60.03559243, 59.80669786,\n",
+       "        60.72497739, 61.12160722, 58.92817736, 55.82231153, 55.54665777,\n",
+       "        51.47888795, 55.60545311, 59.60610091, 59.97179279, 61.64092428,\n",
+       "        56.48271182, 57.44077119, 60.88777288, 61.13258615, 60.34184969,\n",
+       "        59.32917979, 55.19047978, 57.14427728, 60.72565578, 60.78316336,\n",
+       "        61.24280172, 60.52313367, 65.17474454, 60.3592334 , 57.32547148,\n",
+       "        62.3444173 , 64.65492383, 58.16095379, 56.47969437, 53.88697572,\n",
+       "        52.54146409, 55.09767929, 58.61888157, 56.15177373, 63.98283004,\n",
+       "        66.18647794, 61.40224296, 59.53731227, 55.86021195, 54.32572967,\n",
+       "        56.38028718, 54.82063951, 57.01532838, 59.12713585, 56.56992883,\n",
+       "        55.22339868, 54.95625705, 59.4972452 , 61.59469424, 60.93193206,\n",
+       "        60.16980007, 60.49769702, 60.50573728, 60.95709972, 59.86467672,\n",
+       "        54.47967712, 51.90946438, 56.18029482, 54.25592915, 56.50206978,\n",
+       "        62.70349816, 62.61076813, 64.32250184, 63.14104117, 71.08705887,\n",
+       "        68.66277705, 62.45864579, 64.53646597, 61.60529587, 52.08311886,\n",
+       "        57.62972994, 58.89277718, 56.09624969, 59.12332317, 57.13480274,\n",
+       "        53.82489475, 54.49087954, 56.78443359, 54.36270641, 50.77614435,\n",
+       "        51.20168729, 52.30758685, 56.01648629, 59.97410184, 59.58585269,\n",
+       "        60.65961771, 61.68360202, 64.0616123 , 61.83006365, 66.25739524,\n",
+       "        65.4842293 , 61.04647162, 66.83446153, 66.49770836, 61.71830044,\n",
+       "        67.18516212, 69.93979927, 77.53042036, 74.32554481, 69.47430167,\n",
+       "        69.6811975 , 72.26148529, 73.47568165, 68.23260503, 71.42457804,\n",
+       "        73.86350532, 74.34256122, 69.17489515, 70.92631156, 72.79820804,\n",
+       "        71.27271527, 72.20063612, 66.55722904, 64.41740619, 67.19631915,\n",
+       "        66.71337973, 69.84735781, 66.81219833, 69.19790391, 67.39046936,\n",
+       "        67.14191653, 64.52037675, 63.3771655 , 65.57663768, 63.22017156,\n",
+       "        61.09306924, 58.0141334 , 63.63313624, 62.79813765, 66.37465262,\n",
+       "        63.30535865, 67.66966419, 67.85799631, 72.38688022, 72.18259748,\n",
+       "        65.66471688, 64.22121474, 67.66077736, 65.53695715, 64.94865148,\n",
+       "        65.39932177, 68.13458477, 71.79926772, 69.99927421, 68.8460682 ,\n",
+       "        70.14407313, 69.43893793, 73.42124096, 73.03261908, 73.27447781,\n",
+       "        72.5476956 , 70.30477282, 71.5530811 , 77.97534693, 75.82370071,\n",
+       "        71.17147136, 72.46836871, 61.17692805, 62.94293203, 61.59532094,\n",
+       "        59.86086201, 61.31595507, 63.51426421, 59.80557813, 56.53464162,\n",
+       "        57.97918521, 57.37275784, 58.65622268, 57.60969511, 59.47142816]])
        • created_at :
          2020-06-06T18:07:53.678348
          arviz_version :
          0.8.3
          inference_library :
          pymc3
          inference_library_version :
          3.8

        \n", + "
      \n", + "
      \n", + "
      • \n", + " \n", + "
    • \n", + " \n", + " \n", + "
      \n", + "
      \n", + "
        \n", + "
        \n", + "\n", + "\n", + "Show/Hide data repr\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Show/Hide attributes\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
        xarray.Dataset
          • chain: 1
          • draw: 500
          • school: 8
          • chain
            (chain)
            int64
            0
            array([0])
          • draw
            (draw)
            int64
            0 1 2 3 4 5 ... 495 496 497 498 499
            array([  0,   1,   2, ..., 497, 498, 499])
          • school
            (school)
            <U16
            'Choate' ... 'Mt. Hermon'
            array(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
+       "       'Hotchkiss', 'Lawrenceville', "St. Paul's", 'Mt. Hermon'], dtype='<U16')
          • tau_log__
            (chain, draw)
            float64
            0.382 1.72 2.366 ... 0.4214 4.288
            array([[ 3.81956740e-01,  1.72017157e+00,  2.36613252e+00,\n",
+       "         2.21322638e+00,  5.45568150e-02,  2.28413932e+00,\n",
+       "         3.72101077e+00,  1.13856770e+00,  1.26315304e+00,\n",
+       "         8.59837379e-01,  2.35466672e+00,  4.16560159e+00,\n",
+       "         1.88168401e+00,  2.77346565e+00, -1.10713653e+00,\n",
+       "         1.11982044e+00,  1.53306616e+00, -1.17178131e+00,\n",
+       "         2.22554535e+00,  1.13918932e+00,  1.40434288e+00,\n",
+       "         1.01747959e+00,  2.24995942e+00, -1.15095020e+00,\n",
+       "         3.67459175e+00,  1.64369882e+00,  3.27750841e-01,\n",
+       "         3.38027219e+00,  1.85838671e+00, -1.82781262e+00,\n",
+       "        -3.76318214e-02, -3.36451058e-01,  2.62459648e+00,\n",
+       "         3.20374710e+00, -1.12322393e+00,  3.93836030e+00,\n",
+       "         1.82987635e+00,  1.83706381e+00,  2.53377477e+00,\n",
+       "         1.01983708e-01, -2.13527451e-01,  4.19319265e-01,\n",
+       "         2.62655736e+00,  1.00938113e+00,  3.01326505e+00,\n",
+       "         1.95801917e+00,  1.80246843e+00,  1.31244564e-01,\n",
+       "         8.77658786e-01,  1.25290558e+00,  2.74441582e+00,\n",
+       "         2.10346008e+00,  2.49912192e+00,  3.75078264e+00,\n",
+       "         3.31012086e+00,  1.78302729e+00,  3.18899239e+00,\n",
+       "         1.53881885e+00, -8.18271092e-01,  1.65882264e+00,\n",
+       "         3.11117573e+00,  2.26435877e-01,  6.60006689e-01,\n",
+       "         2.28289411e+00,  1.92364607e+00, -9.67064972e-01,\n",
+       "         2.21073561e+00,  1.65504284e+00,  2.76685547e+00,\n",
+       "         9.87511105e-01, -1.78164972e+00,  3.92850502e+00,\n",
+       "         1.98878464e+00,  3.99652233e+00,  3.93530577e+00,\n",
+       "        -1.73532757e+00,  2.75578820e+00,  4.19235772e+00,\n",
+       "         2.45269874e+00,  1.31178114e-01,  9.50847007e-02,\n",
+       "         3.15489529e+00,  2.81722234e+00,  3.15439791e+00,\n",
+       "        -1.05968972e+00,  1.61102276e+00,  2.31500794e-01,\n",
+       "         9.15788275e-01,  2.92298276e+00,  3.96988335e+00,\n",
+       "         3.37205669e+00,  2.33626329e+00,  5.58619423e+00,\n",
+       "         2.48744108e-01,  6.05017793e-01,  1.38619150e+00,\n",
+       "         1.07531626e+00,  4.81067913e-02,  1.90040586e+00,\n",
+       "         3.00004250e+00,  2.47021789e+00,  5.75910980e-01,\n",
+       "         3.86507751e+00,  1.23044163e+00,  6.36866523e-01,\n",
+       "        -1.18738298e+00,  1.49887427e+00,  3.29433308e+00,\n",
+       "         1.37949168e+00,  1.70534244e-01,  2.71825671e+00,\n",
+       "         1.52742172e+00,  2.30724880e+00,  2.43348940e+00,\n",
+       "        -6.61249291e-01,  4.10597059e+00,  4.00824352e+00,\n",
+       "         3.42113833e+00,  1.33846944e+00,  6.19311690e+00,\n",
+       "         2.11530582e+00,  1.07358434e+00,  6.57344667e+00,\n",
+       "         1.62867175e+00,  4.70732797e-01,  2.23648977e+00,\n",
+       "         2.78323131e+00,  2.62608213e+00,  2.54174532e+00,\n",
+       "         9.80920153e-01,  2.36174593e+00, -5.26223206e-03,\n",
+       "         3.76638410e+00, -3.35111535e+00,  4.76491046e+00,\n",
+       "         3.34036108e+00,  3.86775754e-01,  3.64896094e+00,\n",
+       "        -2.97491941e+00,  3.01401622e+00,  1.36629560e+00,\n",
+       "         8.60462344e-01,  3.64040812e+00,  1.32765830e+00,\n",
+       "         3.28973696e+00,  1.71392103e+00,  1.49585925e+00,\n",
+       "         1.41659404e+00,  3.84941377e+00,  1.02199448e+00,\n",
+       "         5.79837996e+00,  1.19491889e+00,  2.38713140e+00,\n",
+       "        -5.88914908e-01, -1.25711468e+00,  4.55065682e+00,\n",
+       "         1.61752809e+00, -1.26793858e+00,  1.10293342e+00,\n",
+       "         3.11956648e+00,  6.74587592e-01,  3.73153840e+00,\n",
+       "         2.64264834e+00,  2.87732238e+00,  1.42784715e+00,\n",
+       "         3.16187891e+00,  2.84984613e+00,  3.99349859e-01,\n",
+       "         4.21229313e-02,  1.31316882e-02,  2.81846060e+00,\n",
+       "         1.78817094e+00,  8.19043563e-01,  2.12348381e+00,\n",
+       "         1.53495129e+00,  1.77455095e-01,  2.51505193e+00,\n",
+       "         1.43267579e+00,  2.02461366e+00,  5.15265611e-01,\n",
+       "         5.65448776e-01,  6.98550229e-02,  6.88763903e-01,\n",
+       "         1.46603537e+00,  2.22545545e-01,  1.82169397e+00,\n",
+       "         1.16468003e+00,  1.84846499e-01,  1.13614855e+00,\n",
+       "         3.61147040e+00,  2.83117271e+00,  1.77836884e+00,\n",
+       "         6.04025172e-01,  3.41828921e+00,  3.03597545e+00,\n",
+       "         3.25066685e+00, -6.84654483e-01,  3.05782344e+00,\n",
+       "         4.00089323e+00,  1.02001460e+00,  4.03665277e+00,\n",
+       "        -2.43215389e-01, -1.01520914e+00,  2.27874419e+00,\n",
+       "         1.88097128e-01, -6.54833180e-01, -7.30144923e-01,\n",
+       "         2.44821840e+00,  1.21627433e+00,  2.70873486e+00,\n",
+       "         4.56913615e+00,  1.73570168e-01,  2.20823316e+00,\n",
+       "         1.52637495e+00,  1.00448181e+00,  7.90576273e-01,\n",
+       "         2.79172514e+00,  3.56711673e+00,  4.92862601e+00,\n",
+       "         6.17710016e-01,  1.58221947e+00,  1.79173055e+00,\n",
+       "         2.06459119e+00, -3.19964315e-01,  3.12960678e+00,\n",
+       "         1.90196134e+00,  1.77491402e+00,  7.53781036e-01,\n",
+       "         9.11717704e-01, -1.16860740e+00,  7.41648760e-01,\n",
+       "         6.87536119e-01,  3.36493227e-01,  1.34512634e-01,\n",
+       "         1.08426907e+00, -3.21090558e-01, -1.11501205e+00,\n",
+       "         5.92669826e+00,  1.99888057e+00,  5.44009783e+00,\n",
+       "         3.18551576e-01,  2.07038147e+00, -3.98913034e-01,\n",
+       "         6.60657911e-01,  3.01790439e-01,  1.27411668e+00,\n",
+       "         2.89682129e+00,  1.29021997e+00,  2.70745199e+00,\n",
+       "         1.64707105e-01,  3.09783652e+00,  7.96326294e-01,\n",
+       "         2.31836237e-03,  1.63630020e+00, -3.87364517e+00,\n",
+       "         5.19354627e+00,  2.72511926e+00, -8.17124240e-02,\n",
+       "         3.58556939e+00,  9.62387411e-01,  1.98005245e+00,\n",
+       "         2.49852523e+00,  7.39346255e-01,  1.48394698e+00,\n",
+       "         3.81570686e+00,  4.66838198e-01,  1.98379520e+00,\n",
+       "         1.47826410e+00,  3.37622910e+00,  2.01858377e+00,\n",
+       "         2.72603102e+00,  1.49826528e+00,  2.33006284e+00,\n",
+       "         8.69486722e-01,  3.53858775e+00,  1.89644299e+00,\n",
+       "         9.75618631e-01,  1.28445670e+00,  1.18901962e+00,\n",
+       "         7.93562547e-01,  2.17195897e+00,  3.38658041e+00,\n",
+       "         2.46778716e+00,  1.96295238e+00,  1.13108766e+00,\n",
+       "         7.38902758e-01,  2.15176746e+00, -4.00921490e-01,\n",
+       "         3.72831311e+00,  4.20211212e-01, -7.53667689e-01,\n",
+       "         3.19189472e-01,  1.78475108e+00,  2.31636756e+00,\n",
+       "         1.21672028e+00,  1.36880110e-01, -8.65697164e-01,\n",
+       "        -3.04359997e-01,  5.76399984e+00,  1.52426011e+00,\n",
+       "         1.71844398e+00, -2.14422368e-02,  2.61599301e-01,\n",
+       "         9.09009123e-01,  6.15412524e-01,  2.15080924e+00,\n",
+       "         9.71664471e-01,  2.07587597e+00, -6.50568078e-01,\n",
+       "         2.37282527e+00,  2.79697476e+00,  2.12923776e+00,\n",
+       "         2.92067727e+00,  2.39701549e-01,  3.43289017e+00,\n",
+       "         4.60929158e-01,  1.88543441e+00,  4.68253900e+00,\n",
+       "         7.03524912e-01,  1.57255884e+00,  1.91996366e+00,\n",
+       "         3.30492515e+00,  1.99207922e+00,  2.31784685e+00,\n",
+       "         1.31210517e+00,  1.90862377e+00,  1.40063154e+00,\n",
+       "         1.87671169e+00,  2.38295408e+00,  2.71621094e+00,\n",
+       "         1.55823346e+00,  2.25278911e+00,  1.84337692e+00,\n",
+       "        -1.67755019e+00,  1.86962269e+00,  2.52675306e+00,\n",
+       "         4.75431988e-01,  2.46041624e-01,  9.56420842e-01,\n",
+       "         1.17851358e-02,  3.26466409e-01,  1.86319901e+00,\n",
+       "         1.56629696e+00,  2.50096279e+00,  4.37828378e+00,\n",
+       "         1.47008368e+00,  1.23312724e+00,  7.43866328e-01,\n",
+       "         1.84859490e+00,  1.24492494e+00, -3.17538053e-01,\n",
+       "         2.67113740e+00,  1.75446406e+00,  6.95958467e-01,\n",
+       "         2.33097215e+00,  2.74076372e+00,  2.72851617e+00,\n",
+       "         1.42617587e+00,  6.64878538e-01,  1.53443807e+00,\n",
+       "         2.21416633e+00,  1.84954030e+00, -1.49033943e+00,\n",
+       "         1.50720485e+00, -1.98746592e-01,  2.82241148e+00,\n",
+       "         2.41642852e+00,  2.40732167e+00, -1.43715936e+00,\n",
+       "         1.86529949e+00,  1.41337844e+00, -7.38407388e-01,\n",
+       "         7.10919436e-01,  1.52296845e+00,  2.34597461e+00,\n",
+       "         1.08445641e+00,  2.06129897e+00,  2.90500627e-01,\n",
+       "         1.19422894e+00,  1.17030336e+00,  3.82574555e+00,\n",
+       "         2.07649462e+00,  1.25461088e+00,  2.89423283e+00,\n",
+       "         8.55336029e-01, -4.98450382e-01,  4.20407798e+00,\n",
+       "         1.85933044e+00,  1.14529484e+00,  1.24076825e+00,\n",
+       "         1.12401798e+00,  3.50081904e-01,  1.28061855e-02,\n",
+       "         9.71951044e-01,  2.60400972e+00,  1.16092015e+00,\n",
+       "         2.52403462e+00, -1.76715151e+00, -7.29251414e-01,\n",
+       "         2.08687174e+00, -1.21973669e+00,  2.62830862e+00,\n",
+       "         7.41934605e-01,  1.56355570e+00,  1.96593181e+00,\n",
+       "         2.30365824e+00,  2.93314791e+00,  1.65625774e+00,\n",
+       "         1.86017209e+00,  1.12422576e+00,  6.02755287e-01,\n",
+       "         3.08187059e+00, -7.35404816e-01,  2.42199367e+00,\n",
+       "         1.66327174e+00,  1.84229520e+00,  2.20457470e+00,\n",
+       "         3.02005809e+00,  3.23165972e-02,  2.96547606e+00,\n",
+       "         1.62277636e+00,  1.80826891e-01,  1.91767137e+00,\n",
+       "         1.74637651e+00,  2.33323467e+00,  1.67212945e+00,\n",
+       "        -4.46174072e-01,  6.45141451e+00,  3.00698052e+00,\n",
+       "         3.58969718e+00,  1.83776838e+00,  2.94462935e+00,\n",
+       "        -1.35572121e+00, -1.24001726e+00, -6.15629328e-02,\n",
+       "         5.75155192e-01,  1.99419322e-01,  3.08858492e+00,\n",
+       "         2.17508175e+00,  2.75376090e+00,  2.15036506e+00,\n",
+       "         1.90639312e+00,  4.70062848e-01,  2.36016117e+00,\n",
+       "         1.18084770e+00,  3.13646643e+00,  9.92346634e-01,\n",
+       "         8.22246310e-01,  1.97386172e+00,  1.48168888e+00,\n",
+       "         2.76256229e+00, -1.31707112e+00,  1.38440112e+00,\n",
+       "         3.24838342e+00,  1.33169054e+00, -8.13532972e-01,\n",
+       "         1.76410414e+00, -1.43997240e+00, -2.92450068e-01,\n",
+       "         4.76019298e-01,  5.43690622e-01,  1.04686822e+00,\n",
+       "         6.53237599e-01,  1.85537232e+00,  3.25496311e+00,\n",
+       "         2.61678347e+00,  1.15210603e+00,  1.87065717e+00,\n",
+       "         9.60005833e-01,  2.17615347e+00, -1.50339752e+00,\n",
+       "         3.25910670e+00,  1.90464856e+00,  2.02648150e-01,\n",
+       "         3.91800478e-01,  3.12336779e+00,  1.90728221e+00,\n",
+       "         4.21212143e+00, -5.72960579e-01,  3.38704757e+00,\n",
+       "         3.85323987e-01,  3.50724056e+00,  1.91253534e+00,\n",
+       "         1.03131380e+00,  1.40438187e+00,  2.74963104e+00,\n",
+       "         1.84279474e+00, -9.84820036e-01,  1.63613490e+00,\n",
+       "        -8.73614971e-02,  6.38398704e-01,  1.01392164e+00,\n",
+       "        -1.02334577e+00,  1.24569941e+00,  2.86791384e+00,\n",
+       "         5.40156998e-01,  8.84890095e-02,  3.06078995e+00,\n",
+       "         4.21358998e-01,  4.28841398e+00]])
          • tau
            (chain, draw)
            float64
            1.465 5.585 10.66 ... 1.524 72.85
            array([[1.46514870e+00, 5.58548666e+00, 1.06561002e+01, 9.14517465e+00,\n",
+       "        1.05607248e+00, 9.81723312e+00, 4.13061240e+01, 3.12229310e+00,\n",
+       "        3.53655482e+00, 2.36277643e+00, 1.05346173e+01, 6.44314321e+01,\n",
+       "        6.56455033e+00, 1.60140369e+01, 3.30503995e-01, 3.06430393e+00,\n",
+       "        4.63235861e+00, 3.09814575e-01, 9.25853061e+00, 3.12423457e+00,\n",
+       "        4.07284953e+00, 2.76621398e+00, 9.48735085e+00, 3.16336045e-01,\n",
+       "        3.94325550e+01, 5.17427284e+00, 1.38784314e+00, 2.93787665e+01,\n",
+       "        6.41338178e+00, 1.60764838e-01, 9.63067456e-01, 7.14300842e-01,\n",
+       "        1.37990049e+01, 2.46246285e+01, 3.25229586e-01, 5.13343594e+01,\n",
+       "        6.23311586e+00, 6.27807756e+00, 1.26009823e+01, 1.10736543e+00,\n",
+       "        8.07729987e-01, 1.52092586e+00, 1.38260896e+01, 2.74390236e+00,\n",
+       "        2.03537475e+01, 7.08527845e+00, 6.06459907e+00, 1.14024661e+00,\n",
+       "        2.40526188e+00, 3.50049917e+00, 1.55555241e+01, 8.19447443e+00,\n",
+       "        1.21718014e+01, 4.25543738e+01, 2.73884356e+01, 5.94783499e+00,\n",
+       "        2.42639665e+01, 4.65908394e+00, 4.41193779e-01, 5.25312237e+00,\n",
+       "        2.24474209e+01, 1.25412219e+00, 1.93480528e+00, 9.80501617e+00,\n",
+       "        6.84587359e+00, 3.80197292e-01, 9.12242448e+00, 5.23330412e+00,\n",
+       "        1.59085305e+01, 2.68454460e+00, 1.68360170e-01, 5.08309295e+01,\n",
+       "        7.30664817e+00, 5.44086053e+01, 5.11777961e+01, 1.76342426e-01,\n",
+       "        1.57334371e+01, 6.61786382e+01, 1.16196629e+01, 1.14017084e+00,\n",
+       "        1.09975200e+00, 2.34505815e+01, 1.67303149e+01, 2.34389204e+01,\n",
+       "        3.46563325e-01, 5.00793052e+00, 1.26049033e+00, 2.49874417e+00,\n",
+       "        1.85966743e+01, 5.29783503e+01, 2.91383942e+01, 1.03425173e+01,\n",
+       "        2.66718617e+02, 1.28241383e+00, 1.83128479e+00, 3.99958857e+00,\n",
+       "        2.93091968e+00, 1.04928270e+00, 6.68860851e+00, 2.00863906e+01,\n",
+       "        1.18250231e+01, 1.77875020e+00, 4.77069702e+01, 3.42274079e+00,\n",
+       "        1.89054760e+00, 3.05018461e-01, 4.47664672e+00, 2.69594284e+01,\n",
+       "        3.97288164e+00, 1.18593826e+00, 1.51538816e+01, 4.60628520e+00,\n",
+       "        1.00467460e+01, 1.13985870e+01, 5.16206040e-01, 6.07016323e+01,\n",
+       "        5.50500913e+01, 3.06042330e+01, 3.81320272e+00, 4.89369044e+02,\n",
+       "        8.29212131e+00, 2.92584797e+00, 7.15832835e+02, 5.09710001e+00,\n",
+       "        1.60116709e+00, 9.36041632e+00, 1.61711907e+01, 1.38195206e+01,\n",
+       "        1.27018203e+01, 2.66690908e+00, 1.06094587e+01, 9.94751589e-01,\n",
+       "        4.32234901e+01, 3.50452447e-02, 1.17320612e+02, 2.82293179e+01,\n",
+       "        1.47222631e+00, 3.84347094e+01, 5.10515479e-02, 2.03690423e+01,\n",
+       "        3.92079956e+00, 2.36425354e+00, 3.81073859e+01, 3.77219967e+00,\n",
+       "        2.68358038e+01, 5.55068323e+00, 4.46316988e+00, 4.12305356e+00,\n",
+       "        4.69655227e+01, 2.77873136e+00, 3.29764894e+02, 3.30328984e+00,\n",
+       "        1.08822324e+01, 5.54929107e-01, 2.84473640e-01, 9.46945855e+01,\n",
+       "        5.04061496e+00, 2.81411131e-01, 3.01299146e+00, 2.26365642e+01,\n",
+       "        1.96322316e+00, 4.17432767e+01, 1.40503645e+01, 1.77666371e+01,\n",
+       "        4.16971278e+00, 2.36149246e+01, 1.72851220e+01, 1.49085512e+00,\n",
+       "        1.04302269e+00, 1.01321829e+00, 1.67510442e+01, 5.97850743e+00,\n",
+       "        2.26832929e+00, 8.36021220e+00, 4.64109948e+00, 1.19417443e+00,\n",
+       "        1.23672510e+01, 4.18989547e+00, 7.57318453e+00, 1.67408310e+00,\n",
+       "        1.76023756e+00, 1.07235270e+00, 1.99125263e+00, 4.33202615e+00,\n",
+       "        1.24925272e+00, 6.18232225e+00, 3.20489725e+00, 1.20303376e+00,\n",
+       "        3.11474895e+00, 3.70204478e+01, 1.69653445e+01, 5.92019174e+00,\n",
+       "        1.82946792e+00, 3.05171620e+01, 2.08212781e+01, 2.58075438e+01,\n",
+       "        5.04264431e-01, 2.12811869e+01, 5.46469406e+01, 2.77323525e+00,\n",
+       "        5.66364500e+01, 7.84102608e-01, 3.62326645e-01, 9.76441043e+00,\n",
+       "        1.20695074e+00, 5.19528723e-01, 4.81839156e-01, 1.15677193e+01,\n",
+       "        3.37459168e+00, 1.50102734e+01, 9.64607461e+01, 1.18954415e+00,\n",
+       "        9.09962460e+00, 4.60146599e+00, 2.73049199e+00, 2.20466655e+00,\n",
+       "        1.63091311e+01, 3.54143365e+01, 1.38189511e+02, 1.85467600e+00,\n",
+       "        4.86574319e+00, 5.99982648e+00, 7.88207495e+00, 7.26174950e-01,\n",
+       "        2.28649867e+01, 6.69902065e+00, 5.89977385e+00, 2.12501962e+00,\n",
+       "        2.48859353e+00, 3.10799459e-01, 2.09939406e+00, 1.98880930e+00,\n",
+       "        1.40002939e+00, 1.14397911e+00, 2.95727748e+00, 7.25357561e-01,\n",
+       "        3.27911328e-01, 3.74914596e+02, 7.38078923e+00, 2.30464728e+02,\n",
+       "        1.37513454e+00, 7.92784677e+00, 6.71049057e-01, 1.93606567e+00,\n",
+       "        1.35227781e+00, 3.57554168e+00, 1.81164668e+01, 3.63358576e+00,\n",
+       "        1.49910296e+01, 1.17904773e+00, 2.21499784e+01, 2.21737994e+00,\n",
+       "        1.00232105e+00, 5.13613164e+00, 2.07824755e-02, 1.80106128e+02,\n",
+       "        1.52582335e+01, 9.21536932e-01, 3.60738921e+01, 2.61793912e+00,\n",
+       "        7.24312286e+00, 1.21645408e+01, 2.09456576e+00, 4.41031882e+00,\n",
+       "        4.54088429e+01, 1.59494332e+00, 7.27028284e+00, 4.38532659e+00,\n",
+       "        2.92602254e+01, 7.52765653e+00, 1.52721517e+01, 4.47392133e+00,\n",
+       "        1.02785874e+01, 2.38568602e+00, 3.44182776e+01, 6.66215488e+00,\n",
+       "        2.65280781e+00, 3.61270465e+00, 3.28386019e+00, 2.21126013e+00,\n",
+       "        8.77545805e+00, 2.95646801e+01, 1.17963146e+01, 7.12031794e+00,\n",
+       "        3.09902536e+00, 2.09363703e+00, 8.60004517e+00, 6.69702637e-01,\n",
+       "        4.16088594e+01, 1.52228305e+00, 4.70637233e-01, 1.37601202e+00,\n",
+       "        5.95809670e+00, 1.01387789e+01, 3.37609691e+00, 1.14669066e+00,\n",
+       "        4.20758113e-01, 7.37595287e-01, 3.18620212e+02, 4.59174492e+00,\n",
+       "        5.57584557e+00, 9.78786014e-01, 1.29900593e+00, 2.48186209e+00,\n",
+       "        1.85041978e+00, 8.59180842e+00, 2.64233890e+00, 7.97152625e+00,\n",
+       "        5.21749298e-01, 1.07276581e+01, 1.63949729e+01, 8.40845511e+00,\n",
+       "        1.85538492e+01, 1.27086980e+00, 3.09660105e+01, 1.58554652e+00,\n",
+       "        6.58921625e+00, 1.08044049e+02, 2.02086353e+00, 4.81896341e+00,\n",
+       "        6.82071063e+00, 2.72465020e+01, 7.33076017e+00, 1.01537881e+01,\n",
+       "        3.71398405e+00, 6.74380141e+00, 4.05776180e+00, 6.53199028e+00,\n",
+       "        1.08368686e+01, 1.51229119e+01, 4.75042200e+00, 9.51423509e+00,\n",
+       "        6.31783711e+00, 1.86831117e-01, 6.48584878e+00, 1.25128117e+01,\n",
+       "        1.60870899e+00, 1.27895281e+00, 2.60236551e+00, 1.01185485e+00,\n",
+       "        1.38606169e+00, 6.44431926e+00, 4.78888188e+00, 1.21942288e+01,\n",
+       "        7.97011314e+01, 4.34959911e+00, 3.43194527e+00, 2.10405477e+00,\n",
+       "        6.35088962e+00, 3.47267415e+00, 7.27938980e-01, 1.44564026e+01,\n",
+       "        5.78034898e+00, 2.00563048e+00, 1.02879381e+01, 1.54988173e+01,\n",
+       "        1.53101526e+01, 4.16274982e+00, 1.94425435e+00, 4.63871816e+00,\n",
+       "        9.15377467e+00, 6.35689658e+00, 2.25296171e-01, 4.51409559e+00,\n",
+       "        8.19757601e-01, 1.68173566e+01, 1.12057666e+01, 1.11041806e+01,\n",
+       "        2.37601741e-01, 6.45786966e+00, 4.10981675e+00, 4.77874378e-01,\n",
+       "        2.03586224e+00, 4.58581780e+00, 1.04434461e+01, 2.95783153e+00,\n",
+       "        7.85616810e+00, 1.33709671e+00, 3.30101152e+00, 3.22297021e+00,\n",
+       "        4.58669836e+01, 7.97645933e+00, 3.50647367e+00, 1.80696336e+01,\n",
+       "        2.35216464e+00, 6.07471279e-01, 6.69588316e+01, 6.41943714e+00,\n",
+       "        3.14336800e+00, 3.45826925e+00, 3.07719350e+00, 1.41918378e+00,\n",
+       "        1.01288854e+00, 2.64309623e+00, 1.35178322e+01, 3.19286983e+00,\n",
+       "        1.24788426e+01, 1.70818872e-01, 4.82269875e-01, 8.05966298e+00,\n",
+       "        2.95307914e-01, 1.38503240e+01, 2.09999425e+00, 4.77577231e+00,\n",
+       "        7.14156406e+00, 1.00107372e+01, 1.87866763e+01, 5.23966592e+00,\n",
+       "        6.42484230e+00, 3.07783296e+00, 1.82714618e+00, 2.17991416e+01,\n",
+       "        4.79311387e-01, 1.12683022e+01, 5.27654610e+00, 6.31100669e+00,\n",
+       "        9.06639480e+00, 2.04924821e+01, 1.03284445e+00, 1.94039385e+01,\n",
+       "        5.06713899e+00, 1.19820774e+00, 6.80509343e+00, 5.73378864e+00,\n",
+       "        1.03112411e+01, 5.32349183e+00, 6.40072344e-01, 6.33597891e+02,\n",
+       "        2.02262349e+01, 3.62231050e+01, 6.28250246e+00, 1.90036175e+01,\n",
+       "        2.57761327e-01, 2.89379224e-01, 9.40293769e-01, 1.77740635e+00,\n",
+       "        1.22069372e+00, 2.19460006e+01, 8.80290475e+00, 1.57015731e+01,\n",
+       "        8.58799297e+00, 6.72877509e+00, 1.60009475e+00, 1.05926585e+01,\n",
+       "        3.25713411e+00, 2.30223718e+01, 2.69755723e+00, 2.27560582e+00,\n",
+       "        7.19842117e+00, 4.40037110e+00, 1.58403786e+01, 2.67918857e-01,\n",
+       "        3.99243421e+00, 2.57486814e+01, 3.78744079e+00, 4.43289168e-01,\n",
+       "        5.83634147e+00, 2.36934297e-01, 7.46432515e-01, 1.60965408e+00,\n",
+       "        1.72235170e+00, 2.84871558e+00, 1.92175263e+00, 6.39407847e+00,\n",
+       "        2.59186585e+01, 1.36916132e+01, 3.16485116e+00, 6.49256169e+00,\n",
+       "        2.61171171e+00, 8.81234400e+00, 2.22373357e-01, 2.60262776e+01,\n",
+       "        6.71704658e+00, 1.22464150e+00, 1.47964246e+00, 2.27227764e+01,\n",
+       "        6.73476021e+00, 6.74995835e+01, 5.63853632e-01, 2.95784949e+01,\n",
+       "        1.47009053e+00, 3.33560964e+01, 6.77023190e+00, 2.80474830e+00,\n",
+       "        4.07300830e+00, 1.56368615e+01, 6.31416006e+00, 3.73506439e-01,\n",
+       "        5.13528272e+00, 9.16345779e-01, 1.89344648e+00, 2.75638940e+00,\n",
+       "        3.59390489e-01, 3.47536467e+00, 1.76002630e+01, 1.71627629e+00,\n",
+       "        1.09252225e+00, 2.13444115e+01, 1.52403130e+00, 7.28508336e+01]])
          • mu
            (chain, draw)
            float64
            6.105 -2.431 3.098 ... 1.05 -5.157
            array([[ 6.10461318e+00, -2.43095703e+00,  3.09780419e+00,\n",
+       "         1.83106258e+00, -7.18266693e+00, -9.55728135e-01,\n",
+       "        -5.40574607e+00,  1.80684159e+00,  7.58894837e-01,\n",
+       "         2.76978477e+00,  5.56357684e+00, -7.30274110e+00,\n",
+       "        -2.40679891e+00, -1.38899140e+00, -1.41510393e+00,\n",
+       "        -6.84395772e+00, -3.84489999e+00, -2.34962863e-01,\n",
+       "         6.84570680e+00,  4.21321274e+00,  8.41885056e+00,\n",
+       "         2.37475307e+00,  4.83537220e+00,  4.57327280e-01,\n",
+       "        -2.99668549e+00, -6.52887844e+00, -1.13966690e+01,\n",
+       "        -7.16159201e+00,  4.88050846e+00, -2.24253733e+00,\n",
+       "         7.78873939e+00, -7.48503135e+00, -5.96469427e+00,\n",
+       "         4.63071803e-01,  2.29108785e+00,  4.78036335e+00,\n",
+       "        -9.25869531e-01,  1.07955480e+00,  2.54365620e+00,\n",
+       "        -2.30864603e+00,  6.92057349e+00, -3.98187035e+00,\n",
+       "         6.16278112e+00, -4.50119410e+00, -2.44871038e+00,\n",
+       "        -7.64472243e+00, -3.42241931e+00,  8.26385307e+00,\n",
+       "         6.94069343e+00,  3.86206505e+00, -1.42184935e+00,\n",
+       "         7.06254445e-01,  3.16946543e+00,  2.12237926e+00,\n",
+       "         4.82057314e+00, -8.89843841e+00,  3.72708176e+00,\n",
+       "         1.28951829e+01, -3.10897458e+00,  9.60296251e+00,\n",
+       "         7.68633765e+00, -9.81777327e-01,  3.77644854e+00,\n",
+       "        -8.28438658e+00,  1.12098547e+00,  7.10526792e-01,\n",
+       "         1.19344213e+00, -4.11668624e+00,  7.91465430e+00,\n",
+       "         5.30149172e-01,  1.10193702e-02,  7.96461740e+00,\n",
+       "        -1.77562023e+00, -6.30224988e+00, -9.54534101e+00,\n",
+       "         1.04260871e+01, -1.78970689e+00, -6.92263567e+00,\n",
+       "        -5.48127379e+00,  5.33381294e+00, -1.67050899e+00,\n",
+       "         2.12241081e+00,  1.86546235e+00,  8.76413167e+00,\n",
+       "        -2.22334769e-01,  1.97025578e+00,  3.73439386e+00,\n",
+       "         5.78829227e-01,  6.94462256e-01, -2.86157410e+00,\n",
+       "        -3.25348196e+00,  2.67116897e+00, -7.27848887e+00,\n",
+       "         9.90857631e+00,  4.10291161e+00,  4.14593158e-01,\n",
+       "        -3.73111376e+00, -1.98033553e+00,  3.88696767e+00,\n",
+       "        -6.01074805e+00,  1.77249332e+01, -5.40626448e+00,\n",
+       "         2.00982940e+00,  2.07311341e+00,  6.32584325e-01,\n",
+       "        -3.22200753e+00, -6.27150736e+00,  9.74141209e-01,\n",
+       "         1.85342324e+00,  4.94313892e+00, -5.96432428e+00,\n",
+       "        -5.22061433e+00,  5.28945288e+00, -4.47466642e+00,\n",
+       "         2.70427191e+00,  8.39589608e+00, -1.29784464e+00,\n",
+       "        -2.53901747e-01, -8.33983895e-01,  1.09098225e+01,\n",
+       "        -1.70708788e+00, -8.15508803e+00,  1.15594806e+00,\n",
+       "        -6.17839028e+00, -4.41594225e+00, -1.20242529e+00,\n",
+       "         5.45853791e-01, -3.54656266e+00,  1.97459097e+00,\n",
+       "         2.60314539e+00,  3.34833850e+00,  2.42614395e+00,\n",
+       "         1.64971931e+00, -5.35206692e+00, -1.19687291e+00,\n",
+       "         8.33452601e+00, -3.99572996e-01,  6.95733714e-01,\n",
+       "        -5.48492941e+00,  1.86224989e+00, -2.78790112e+00,\n",
+       "        -2.67602455e+00, -2.57539136e+00, -3.00743896e+00,\n",
+       "         1.09423224e+01,  8.65889025e+00, -3.47254760e+00,\n",
+       "        -1.66606567e-01,  5.23655106e+00, -7.28311387e+00,\n",
+       "         1.06022463e+01, -1.29491652e+00, -3.90553638e+00,\n",
+       "        -2.18161053e+00,  6.74555039e+00, -4.87192737e+00,\n",
+       "         2.84164120e+00, -1.96705024e-01, -8.38343448e-01,\n",
+       "        -4.12381122e+00,  4.63133878e+00,  5.56794176e+00,\n",
+       "         1.59449878e-01,  6.81777277e+00,  1.66205859e+00,\n",
+       "        -2.01319885e-01,  3.12452492e+00,  8.17913333e+00,\n",
+       "         4.63514198e-01,  3.65556955e-01, -3.52332392e+00,\n",
+       "         3.98246392e+00, -4.01837606e+00, -8.84605267e-02,\n",
+       "         6.72074301e+00, -5.10885882e+00,  1.29127028e+01,\n",
+       "        -7.29365947e+00,  3.50185951e+00,  2.33832877e+00,\n",
+       "        -1.58650211e+00, -4.38140924e+00,  6.86875569e+00,\n",
+       "         4.50090702e+00,  2.67297441e+00, -8.70702981e+00,\n",
+       "        -1.29468974e+01,  7.39048348e+00, -2.10706153e+00,\n",
+       "         1.90498136e+00, -5.26587938e-01, -1.00204577e+00,\n",
+       "        -2.52325614e+00, -4.12591567e+00,  8.29509093e+00,\n",
+       "         1.40201087e+00, -2.62710953e-01, -3.23699415e+00,\n",
+       "         5.71245932e+00, -5.72711337e+00, -7.37220877e+00,\n",
+       "         3.76415653e+00,  3.13836907e+00, -5.48937615e+00,\n",
+       "        -3.50369700e+00, -8.90795239e+00, -4.92142502e+00,\n",
+       "         3.89900745e+00,  3.16882071e+00,  6.74776704e+00,\n",
+       "        -3.95844026e+00,  9.46581468e+00, -2.75711875e+00,\n",
+       "        -4.28907640e+00,  3.25591558e+00,  1.64991183e+00,\n",
+       "        -2.57412883e+00,  8.89107649e+00, -6.39302789e+00,\n",
+       "         3.80471510e+00, -8.03137967e+00, -1.12055010e+00,\n",
+       "         4.81215638e+00,  2.63677581e+00,  1.31704174e-01,\n",
+       "        -5.97214144e+00, -6.33138388e+00, -2.34017019e+00,\n",
+       "        -7.40757496e+00,  9.31633265e+00, -3.49219915e+00,\n",
+       "        -8.38836783e+00, -1.27551521e+01, -3.77378798e+00,\n",
+       "         2.44227859e+00, -3.97258035e+00, -2.79443644e+00,\n",
+       "        -3.30408373e+00,  2.32235044e+00,  3.01869121e-01,\n",
+       "         1.55957545e+00, -3.61417160e+00,  4.42869659e+00,\n",
+       "         2.62605797e+00, -1.60848231e+00, -5.15568328e+00,\n",
+       "         2.45294236e+00,  9.85891764e+00, -2.51902808e+00,\n",
+       "        -8.97948083e+00, -1.57069385e+00, -2.52030725e+00,\n",
+       "         2.33865288e+00, -6.72096205e+00, -1.15249542e+01,\n",
+       "        -1.17448875e+01, -7.29651065e+00, -5.21036443e+00,\n",
+       "        -4.22198246e+00, -4.33994727e+00,  9.92112763e-02,\n",
+       "         2.79222049e+00, -2.90944702e-01, -2.91509281e-01,\n",
+       "         9.47838122e-01, -1.36025692e+00,  5.20453360e+00,\n",
+       "        -3.75954169e+00,  3.17455956e+00,  5.66196984e+00,\n",
+       "         3.79799213e+00,  4.18275393e+00,  1.13376035e+00,\n",
+       "         1.51236149e+00,  2.26602946e-01, -2.81223280e+00,\n",
+       "        -7.73702813e-01, -6.13225321e+00, -1.23877222e+00,\n",
+       "         7.47157761e+00, -3.32435868e+00, -3.11139519e+00,\n",
+       "         2.86101290e+00,  2.25179071e+00, -4.94590335e+00,\n",
+       "         3.95076962e+00,  2.51639712e+00,  5.05233209e+00,\n",
+       "        -5.10050447e+00, -1.87173477e+00,  7.86431163e+00,\n",
+       "        -3.64053963e+00, -1.62767248e+00,  1.29182084e+00,\n",
+       "         7.06091862e+00, -1.62154732e+00, -1.58920242e+00,\n",
+       "        -9.02267863e+00, -1.54778642e+01, -3.12849418e+00,\n",
+       "        -4.16828512e+00,  2.00850404e+00, -5.91585387e+00,\n",
+       "         3.51609713e+00,  1.07281480e+01, -1.38106125e+00,\n",
+       "         1.19021413e+01, -1.21612028e+00, -4.61738392e+00,\n",
+       "         4.46621829e-01, -3.87304084e+00,  5.93047313e+00,\n",
+       "        -7.68851595e+00,  5.56535688e-01, -8.16820428e+00,\n",
+       "         9.38087296e+00, -1.95479677e+00,  5.48608897e-01,\n",
+       "        -2.25319704e+00,  4.19779107e+00, -6.96641711e+00,\n",
+       "        -6.42739918e-02, -9.93631520e+00, -4.35202011e-01,\n",
+       "        -2.42691102e+00,  3.45015397e+00,  1.09550720e+01,\n",
+       "         9.01992240e-01, -3.65297294e+00, -2.77788170e+00,\n",
+       "        -8.71586677e-01,  1.10170942e+00, -4.52034090e+00,\n",
+       "        -4.49942355e+00, -1.09076988e+00, -9.30883906e+00,\n",
+       "        -2.52038076e-01, -8.32724663e+00,  1.26759811e+00,\n",
+       "        -2.63838254e+00,  4.73354362e+00, -8.49606476e+00,\n",
+       "         2.15634858e+00, -6.91420037e+00, -4.92392985e+00,\n",
+       "         1.33582874e+01,  4.51233090e+00, -8.40229353e-01,\n",
+       "         3.10320665e+00, -3.47934374e+00,  5.13291067e+00,\n",
+       "         6.44021828e+00, -1.79008706e+00,  8.17000431e+00,\n",
+       "        -1.85262654e+00,  2.13672804e-01,  5.27828426e+00,\n",
+       "        -7.83876271e-01, -1.46835856e+00, -4.17876468e+00,\n",
+       "        -4.72005275e+00, -6.11144321e+00,  5.65519630e+00,\n",
+       "        -2.95172668e+00,  4.87096256e+00, -1.29554456e+00,\n",
+       "        -1.07085198e+00,  2.66577932e+00,  4.45230112e+00,\n",
+       "         2.47224277e+00, -3.34469304e+00, -9.12472920e-01,\n",
+       "         6.46535452e+00,  4.98125902e+00, -1.75634939e+00,\n",
+       "        -5.66494413e-02,  8.74882989e+00,  3.96983270e+00,\n",
+       "         1.19525264e+00, -7.60443249e+00, -7.43736160e+00,\n",
+       "        -2.24597488e+00, -9.72890524e+00, -5.32319480e+00,\n",
+       "         1.04548621e+01, -6.69722738e+00,  2.93238311e+00,\n",
+       "        -1.41996245e+00, -7.60820826e+00,  6.13578930e-01,\n",
+       "         3.64583269e+00, -2.40676924e+00,  9.10186694e+00,\n",
+       "        -3.59248384e+00, -7.91829301e+00, -5.16806194e+00,\n",
+       "        -9.85236563e+00,  3.35433590e+00,  4.17672477e+00,\n",
+       "        -2.63623675e+00,  8.62152797e-01, -6.35240297e+00,\n",
+       "         5.10085952e+00, -4.82514775e+00, -4.15042574e+00,\n",
+       "         4.47675857e-01,  1.23949298e+00,  6.72899486e+00,\n",
+       "        -4.67040325e+00, -1.45363751e+00, -2.16074435e-01,\n",
+       "        -2.71220988e+00, -8.21402591e-01,  4.13340022e+00,\n",
+       "         3.62680831e+00,  2.27187449e+00, -8.56780512e+00,\n",
+       "         6.17273755e+00, -3.50324768e+00, -2.06397400e+00,\n",
+       "        -3.99067379e+00, -1.04594369e+01,  6.99575781e-01,\n",
+       "         3.71457603e+00,  4.15606622e+00, -7.12088527e+00,\n",
+       "        -5.47793870e+00, -1.49061352e+00,  2.27965009e+00,\n",
+       "        -1.39605965e+01,  8.82621426e+00,  1.18897533e+00,\n",
+       "        -2.13081852e+00,  3.83038476e+00, -3.28699747e+00,\n",
+       "         3.94895191e+00, -3.65121987e+00, -3.85754648e-01,\n",
+       "        -1.70562641e+00, -7.64663697e+00,  8.46238228e-01,\n",
+       "         4.36911568e+00, -2.60483749e+00, -5.47060629e+00,\n",
+       "        -1.59165280e+00,  1.28506433e+00, -1.24080830e+00,\n",
+       "         1.31869452e+01,  4.94556732e+00, -5.80519430e+00,\n",
+       "         1.81276614e-01,  5.68727272e-01,  7.79758984e+00,\n",
+       "         4.93661068e+00, -5.52983850e+00, -1.43839294e+00,\n",
+       "        -1.86329206e-01,  4.45355601e+00,  9.26846003e+00,\n",
+       "         3.37296870e+00, -3.21616681e+00,  1.28557762e+00,\n",
+       "        -4.01190231e+00, -1.10919823e+00, -3.42190415e-01,\n",
+       "         2.95364357e+00, -1.13065902e+01, -1.93222647e+00,\n",
+       "         3.31126326e+00, -5.69073905e+00,  3.66376286e+00,\n",
+       "         2.40116460e-01, -2.18509970e+00, -8.29587183e+00,\n",
+       "         2.75375405e+00,  8.29158184e+00, -1.63312285e+00,\n",
+       "         1.61160098e+00, -8.77431444e+00, -2.72220583e+00,\n",
+       "        -4.08842089e+00, -2.70289106e+00, -3.83512351e+00,\n",
+       "         8.53151599e-01, -4.15625805e+00, -2.83367161e+00,\n",
+       "        -2.02393434e+00,  7.32914693e+00, -8.60206054e-01,\n",
+       "         2.72387993e+00, -8.80165332e+00,  1.16310157e+01,\n",
+       "         7.10573557e+00,  5.66091358e+00, -3.56531449e+00,\n",
+       "         3.34983256e+00, -2.69474758e+00, -9.75370111e-01,\n",
+       "         1.04966087e+00, -5.15684061e+00]])
          • theta
            (chain, draw, school)
            float64
            5.709 4.931 7.942 ... -148.0 57.13
            array([[[   5.70930385,    4.93074682,    7.94221514, ...,\n",
+       "            6.73393175,    5.25945377,    6.71540983],\n",
+       "        [  -6.36604437,    1.52313554,   -0.79141875, ...,\n",
+       "            1.72653154,    2.06565064,   -8.15200509],\n",
+       "        [  -3.18277146,   -6.73190541,    5.15401446, ...,\n",
+       "            4.48181387,  -11.19717704,   -4.63201673],\n",
+       "        ...,\n",
+       "        [ -19.94619218,   27.40876925,  -42.73590311, ...,\n",
+       "           35.40238934,   23.77915239,  -14.63741318],\n",
+       "        [  -0.7240705 ,    1.54887044,    2.47469385, ...,\n",
+       "            0.32691075,    1.82527134,    3.32844101],\n",
+       "        [   2.55515435,  -83.0924991 , -155.13210932, ...,\n",
+       "          -34.62676607, -148.00278481,   57.12632604]]])
        • created_at :
          2020-06-06T18:07:53.820305
          arviz_version :
          0.8.3
          inference_library :
          pymc3
          inference_library_version :
          3.8

        \n", + "
      \n", + "
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      • \n", + " \n", + "
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      \n", + "
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        \n", + "
        \n", + "\n", + "\n", + "Show/Hide data repr\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Show/Hide attributes\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
        xarray.Dataset
          • chain: 1
          • draw: 500
          • school: 8
          • chain
            (chain)
            int64
            0
            array([0])
          • draw
            (draw)
            int64
            0 1 2 3 4 5 ... 495 496 497 498 499
            array([  0,   1,   2, ..., 497, 498, 499])
          • school
            (school)
            <U16
            'Choate' ... 'Mt. Hermon'
            array(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
+       "       'Hotchkiss', 'Lawrenceville', "St. Paul's", 'Mt. Hermon'], dtype='<U16')
          • obs
            (chain, draw, school)
            float64
            -23.64 -2.679 ... -146.5 45.63
            array([[[ -23.64269622,   -2.67865008,   15.82882134, ...,\n",
+       "           13.81705825,   12.10308274,   33.08634734],\n",
+       "        [   7.74811804,    4.08411641,    6.99514875, ...,\n",
+       "           17.60264742,   -3.10707413,  -17.35223334],\n",
+       "        [  30.89420722,   -9.47794527,   17.28683682, ...,\n",
+       "           16.62098736,  -12.96357793,   12.89178226],\n",
+       "        ...,\n",
+       "        [ -17.98653277,   30.87852257,  -23.8373408 , ...,\n",
+       "           34.28047699,   35.02174102,  -10.1270695 ],\n",
+       "        [  20.9044711 ,  -13.53326351,    4.23200315, ...,\n",
+       "            7.8311032 ,  -23.31239724,   -3.14891481],\n",
+       "        [ -14.5990443 ,  -73.21184919, -151.68195527, ...,\n",
+       "          -48.30995401, -146.54737617,   45.63204153]]])
        • created_at :
          2020-06-06T18:07:53.822942
          arviz_version :
          0.8.3
          inference_library :
          pymc3
          inference_library_version :
          3.8

        \n", + "
      \n", + "
      \n", + "
      • \n", + " \n", + "
    • \n", + " \n", + " \n", + "
      \n", + "
      \n", + "
        \n", + "
        \n", + "\n", + "\n", + "Show/Hide data repr\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Show/Hide attributes\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
        xarray.Dataset
          • school: 8
          • school
            (school)
            <U16
            'Choate' ... 'Mt. Hermon'
            array(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
+       "       'Hotchkiss', 'Lawrenceville', "St. Paul's", 'Mt. Hermon'], dtype='<U16')
          • obs
            (school)
            float64
            28.0 8.0 -3.0 7.0 ... 1.0 18.0 12.0
            array([28.,  8., -3.,  7., -1.,  1., 18., 12.])
        • created_at :
          2020-06-06T18:07:53.824403
          arviz_version :
          0.8.3
          inference_library :
          pymc3
          inference_library_version :
          3.8

        \n", + "
      \n", + "
      \n", + "
      • \n", + " \n", + "
    \n", + "
    \n", + " " + ], "text/plain": [ "Inference data with groups:\n", "\t> posterior\n", @@ -309,7 +6139,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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    " ] @@ -341,11 +6171,8 @@ "output_type": "stream", "text": [ "INFO:pystan:COMPILING THE C++ CODE FOR MODEL anon_model_b0eada194ad938e30ed47926216f98ca NOW.\n", - "WARNING:pystan:90 of 2000 iterations ended with a divergence (4.5 %).\n", - "WARNING:pystan:Try running with adapt_delta larger than 0.8 to remove the divergences.\n", - "WARNING:pystan:Chain 2: E-BFMI = 0.189\n", - "WARNING:pystan:Chain 3: E-BFMI = 0.165\n", - "WARNING:pystan:E-BFMI below 0.2 indicates you may need to reparameterize your model\n" + "WARNING:pystan:39 of 2000 iterations ended with a divergence (1.95 %).\n", + "WARNING:pystan:Try running with adapt_delta larger than 0.8 to remove the divergences.\n" ] } ], @@ -399,7 +6226,7 @@ "outputs": [ { "data": { - "image/png": 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\n", "text/plain": [ "
    " ] @@ -426,6 +6253,2277 @@ "outputs": [ { "data": { + "text/html": [ + "\n", + "
    \n", + "
    \n", + "
    arviz.InferenceData
    \n", + "
    \n", + "
      \n", + " \n", + "
    • \n", + " \n", + " \n", + "
      \n", + "
      \n", + "
        \n", + "
        \n", + "\n", + "\n", + "Show/Hide data repr\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Show/Hide attributes\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
        xarray.Dataset
          • chain: 4
          • draw: 500
          • school: 8
          • chain
            (chain)
            int64
            0 1 2 3
            array([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])
          • school
            (school)
            <U16
            'Choate' ... 'Mt. Hermon'
            array(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
+       "       'Hotchkiss', 'Lawrenceville', "St. Paul's", 'Mt. Hermon'], dtype='<U16')
          • mu
            (chain, draw)
            float64
            10.74 -2.383 5.259 ... 8.929 6.259
            array([[10.73560243, -2.38325525,  5.25875081, ...,  5.11437518,\n",
+       "         4.48284624,  3.32088798],\n",
+       "       [ 5.18801686,  5.32356994,  5.77854836, ..., 10.12895743,\n",
+       "         5.10477044,  2.85072762],\n",
+       "       [ 5.279449  ,  1.27221624,  2.57758297, ...,  4.10260339,\n",
+       "         6.22640205,  4.69036724],\n",
+       "       [ 4.59031447,  3.53194292,  2.79784828, ...,  8.92907436,\n",
+       "         8.92907436,  6.25877939]])
          • tau
            (chain, draw)
            float64
            4.142 5.617 6.222 ... 1.42 3.102
            array([[ 4.14169088,  5.61671325,  6.22228848, ...,  3.69125991,\n",
+       "         2.33348628,  2.88903472],\n",
+       "       [ 4.81112853, 10.15731502,  9.12975583, ...,  4.25321054,\n",
+       "         3.74593616,  6.87348279],\n",
+       "       [ 7.51319567, 10.34112694,  9.51897377, ...,  3.9415122 ,\n",
+       "         3.19851804,  2.93427324],\n",
+       "       [ 2.7178844 ,  6.1721701 ,  8.92141693, ...,  1.41969807,\n",
+       "         1.41969807,  3.10210589]])
          • theta
            (chain, draw, school)
            float64
            11.95 4.371 4.869 ... 5.986 6.58
            array([[[11.9501699 ,  4.37131255,  4.86887542, ...,  9.8655532 ,\n",
+       "          6.54410133,  5.83685503],\n",
+       "        [ 4.59141419,  2.10357746, -0.58655321, ..., -6.77760948,\n",
+       "          5.82721013, -0.32546978],\n",
+       "        [11.62216439,  2.16057134,  2.03699279, ..., -1.4545384 ,\n",
+       "          8.10261439,  6.59891615],\n",
+       "        ...,\n",
+       "        [ 2.37446043,  1.24055826, 12.01508412, ...,  3.5001716 ,\n",
+       "          6.3168732 ,  6.35331336],\n",
+       "        [ 2.83406092,  8.61145377,  4.64653249, ...,  2.58655352,\n",
+       "          5.41444545,  1.23786006],\n",
+       "        [ 3.86118907, -0.72155858,  2.70786742, ...,  3.66516606,\n",
+       "          6.51863001,  6.73247587]],\n",
+       "\n",
+       "       [[ 7.20108117, -0.24667291,  7.71796254, ...,  6.87783395,\n",
+       "         11.10952912, -0.93810294],\n",
+       "        [21.75297374, -1.7932725 , -5.99911136, ..., 14.39207158,\n",
+       "         14.10770337,  4.85065602],\n",
+       "        [21.79201881, -1.66953615, -5.87256947, ..., 14.52951082,\n",
+       "         12.10126285,  4.87010732],\n",
+       "        ...,\n",
+       "        [ 9.84913684, 12.61656189,  5.99493273, ...,  1.9998071 ,\n",
+       "         16.01197517,  4.00927407],\n",
+       "        [ 6.3076968 , -2.3953748 ,  2.83320521, ...,  7.58584913,\n",
+       "         -2.21027024,  6.66849414],\n",
+       "        [ 4.98667269,  1.41754423,  9.06969537, ...,  4.58315199,\n",
+       "         -0.35288648, 10.86997631]],\n",
+       "\n",
+       "       [[ 9.68339397,  7.92652044,  2.75662029, ...,  0.58491494,\n",
+       "          7.58748997, 10.66222327],\n",
+       "        [ 7.96772519,  1.01062828,  7.96311474, ..., 10.82538315,\n",
+       "         10.06341431,  0.5685898 ],\n",
+       "        [ 1.74205603, -0.81737683,  5.88044354, ..., -0.40865625,\n",
+       "         19.14354768, -8.94377808],\n",
+       "        ...,\n",
+       "        [ 7.63158265,  8.19852305,  7.97905964, ...,  3.12752754,\n",
+       "          8.72762295,  7.67904376],\n",
+       "        [ 8.61682529,  5.51484249, 10.37399828, ...,  5.62767154,\n",
+       "          2.4985643 ,  2.08868689],\n",
+       "        [ 2.82445182,  5.33050637,  4.17582426, ...,  5.68025905,\n",
+       "         10.29503981,  5.40365662]],\n",
+       "\n",
+       "       [[10.41592456,  2.45697186,  2.32434962, ...,  4.75782383,\n",
+       "          4.16351619,  5.15781812],\n",
+       "        [ 2.20970154,  8.27852353,  9.83442088, ..., -1.20015954,\n",
+       "         -0.23708244,  6.79111646],\n",
+       "        [12.0692386 ,  4.16968353, -4.70381733, ...,  4.94694984,\n",
+       "         19.27750736,  2.32004447],\n",
+       "        ...,\n",
+       "        [ 9.84647679,  7.83930697,  9.97567288, ...,  9.88766553,\n",
+       "         11.42396199,  9.91282334],\n",
+       "        [ 9.84647679,  7.83930697,  9.97567288, ...,  9.88766553,\n",
+       "         11.42396199,  9.91282334],\n",
+       "        [ 7.61647612,  7.90795379,  5.96991115, ...,  5.27262613,\n",
+       "          5.98567544,  6.58025061]]])
        • created_at :
          2020-06-06T18:09:41.101731
          arviz_version :
          0.8.3
          inference_library :
          pystan
          inference_library_version :
          2.19.1.1

        \n", + "
      \n", + "
      \n", + "
      • \n", + " \n", + "
    • \n", + " \n", + " \n", + "
      \n", + "
      \n", + "
        \n", + "
        \n", + "\n", + "\n", + "Show/Hide data repr\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Show/Hide attributes\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
        xarray.Dataset
          • chain: 4
          • draw: 500
          • school: 8
          • chain
            (chain)
            int64
            0 1 2 3
            array([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])
          • school
            (school)
            <U16
            'Choate' ... 'Mt. Hermon'
            array(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
+       "       'Hotchkiss', 'Lawrenceville', "St. Paul's", 'Mt. Hermon'], dtype='<U16')
          • y_hat
            (chain, draw, school)
            float64
            20.64 10.5 -5.046 ... 16.35 -1.61
            array([[[ 20.63738522,  10.5012043 ,  -5.04566929, ...,   8.7332285 ,\n",
+       "          18.53008278,  -6.54725452],\n",
+       "        [ 19.86461026,   6.62318248,  -5.16408634, ...,   6.46381206,\n",
+       "          16.18426053,  29.27370301],\n",
+       "        [ 25.57913754,   2.22890159,  -8.40633281, ...,   7.28818881,\n",
+       "           5.94816768,  -0.20388646],\n",
+       "        ...,\n",
+       "        [ 20.02968866,  -2.81686571,   5.56223197, ...,   1.86975107,\n",
+       "           6.09389285,  27.74978625],\n",
+       "        [ -9.33973004,   0.43626395,   6.20234702, ...,  -7.71907314,\n",
+       "           7.57063463, -25.31062602],\n",
+       "        [ 23.52022983, -13.0388224 ,   8.75791964, ...,   0.1890194 ,\n",
+       "          19.38994636,   4.85142004]],\n",
+       "\n",
+       "       [[  8.95009592,   4.67016211,  17.29135465, ...,   4.56425409,\n",
+       "          18.90306893, -38.52786218],\n",
+       "        [  9.97064254, -20.52182613,   6.93995894, ...,  23.9960411 ,\n",
+       "          24.35766188, -15.34114179],\n",
+       "        [ 39.00427132,  -4.04256156,  -4.3293367 , ...,  -6.52798623,\n",
+       "          13.62374189,  -6.06476977],\n",
+       "        ...,\n",
+       "        [ 44.00963558,  17.65730702,  -4.53842242, ...,   5.53252057,\n",
+       "          -2.21877838,  18.33090945],\n",
+       "        [ -5.65146845,  -1.96411568, -10.31057671, ...,  15.84395016,\n",
+       "           4.8827069 ,  -5.70862416],\n",
+       "        [ -7.59652088,  -0.46991629,  -6.77197062, ...,   9.53299806,\n",
+       "          20.54637027,  23.65904017]],\n",
+       "\n",
+       "       [[ 27.06095661,  -9.89143179,  14.66885259, ...,  14.89931058,\n",
+       "          17.37310674,  10.88580544],\n",
+       "        [ 25.57992053,   0.09263637,   5.09162713, ...,  12.19685867,\n",
+       "          16.63640799,  16.74110778],\n",
+       "        [-17.80800298,   8.83651926,   4.02413155, ...,  -3.70058381,\n",
+       "          12.27771294, -11.08581052],\n",
+       "        ...,\n",
+       "        [-22.57910137, -10.50751024,   2.75168063, ...,  11.49365198,\n",
+       "          20.48522522, -22.09707402],\n",
+       "        [-12.40623774,  16.46567152,   0.81208005, ...,  -9.92078422,\n",
+       "          10.27931093, -18.01526668],\n",
+       "        [ -6.19770232,  27.65023023,  -6.95229655, ...,  13.09059215,\n",
+       "          10.62995982,  14.17440678]],\n",
+       "\n",
+       "       [[ 13.93668777,  16.30080221,  -2.5076843 , ...,   0.14416439,\n",
+       "          -2.85743274,  22.75883983],\n",
+       "        [ 11.7757269 ,  29.09355261,   2.48489421, ...,  -7.05970019,\n",
+       "         -10.78054183,   1.02652982],\n",
+       "        [ 18.33159454,  -0.37400684,  12.44023163, ...,  14.05119661,\n",
+       "          11.52528276, -14.49717182],\n",
+       "        ...,\n",
+       "        [ 13.38099709,  11.34656755,  29.57080529, ...,  23.56179887,\n",
+       "          -3.89251974,  22.43742056],\n",
+       "        [ 14.4924457 ,  25.78438465,   3.47125027, ..., -11.23972541,\n",
+       "           5.87609827,  12.62363297],\n",
+       "        [ -2.15357302,  -0.35889516,  11.70752398, ..., -16.31756139,\n",
+       "          16.35276023,  -1.6100444 ]]])
        • created_at :
          2020-06-06T18:09:41.118198
          arviz_version :
          0.8.3
          inference_library :
          pystan
          inference_library_version :
          2.19.1.1

        \n", + "
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        \n", + "
        \n", + "\n", + "\n", + "Show/Hide data repr\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Show/Hide attributes\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
        xarray.Dataset
          • chain: 4
          • draw: 500
          • school: 8
          • chain
            (chain)
            int64
            0 1 2 3
            array([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])
          • school
            (school)
            <U16
            'Choate' ... 'Mt. Hermon'
            array(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
+       "       'Hotchkiss', 'Lawrenceville', "St. Paul's", 'Mt. Hermon'], dtype='<U16')
          • y
            (chain, draw, school)
            float64
            -4.199 -3.287 ... -3.943 -3.855
            array([[[-4.19942661, -3.28736049, -3.81246319, ..., -3.64161907,\n",
+       "         -3.8777117 , -3.86792812],\n",
+       "        [-4.84468182, -3.39536262, -3.70290367, ..., -3.56679748,\n",
+       "         -3.96240769, -4.04375042],\n",
+       "        [-4.22306318, -3.39201826, -3.74108057, ..., -3.3417295 ,\n",
+       "         -3.71131484, -3.85432836],\n",
+       "        ...,\n",
+       "        [-5.08625157, -3.44997389, -4.13186466, ..., -3.3426638 ,\n",
+       "         -3.90400089, -3.85851565],\n",
+       "        [-5.03437649, -3.223393  , -3.80572542, ..., -3.32723526,\n",
+       "         -4.01350454, -3.9880505 ],\n",
+       "        [-4.92183805, -3.60185155, -3.75515958, ..., -3.3461855 ,\n",
+       "         -3.88063291, -3.85212944]],\n",
+       "\n",
+       "       [[-4.58831101, -3.5615617 , -3.91589194, ..., -3.45959799,\n",
+       "         -3.45891657, -4.06763515],\n",
+       "        [-3.71371171, -3.70106456, -3.70909497, ..., -4.05793951,\n",
+       "         -3.29727349, -3.88818856],\n",
+       "        [-3.71263102, -3.68902327, -3.70764377, ..., -4.07322911,\n",
+       "         -3.39549913, -3.88775994],\n",
+       "        ...,\n",
+       "        [-4.35910836, -3.32808684, -3.84955228, ..., -3.32096444,\n",
+       "         -3.24128484, -3.90784687],\n",
+       "        [-4.67266878, -3.76184271, -3.75798484, ..., -3.49606277,\n",
+       "         -5.26379874, -3.85317596],\n",
+       "        [-4.80390703, -3.43816725, -3.97605371, ..., -3.36988743,\n",
+       "         -4.90566584, -3.8112809 ]],\n",
+       "\n",
+       "       [[-4.37253997, -3.22155062, -3.75625123, ..., -3.31754577,\n",
+       "         -3.76362545, -3.81207209],\n",
+       "        [-4.51874881, -3.46578021, -3.92627312, ..., -3.7157518 ,\n",
+       "         -3.53647059, -4.01097254],\n",
+       "        [-5.15916567, -3.6102543 , -3.84555514, ..., -3.32503344,\n",
+       "         -3.22806213, -4.48622671],\n",
+       "        ...,\n",
+       "        [-4.54892746, -3.22172068, -3.92695646, ..., -3.33553783,\n",
+       "         -3.65140851, -3.83812304],\n",
+       "        [-4.46189421, -3.25240367, -4.04087067, ..., -3.40532696,\n",
+       "         -4.42299617, -3.96090617],\n",
+       "        [-5.03545146, -3.25715461, -3.79209845, ..., -3.40734961,\n",
+       "         -3.51835568, -3.87645805]],\n",
+       "\n",
+       "       [[-4.3140992 , -3.37514943, -3.74689581, ..., -3.37518604,\n",
+       "         -4.17876505, -3.88155636],\n",
+       "        [-5.1050765 , -3.2219115 , -4.01325061, ..., -3.33683671,\n",
+       "         -4.88447951, -3.85118138],\n",
+       "        [-4.19096464, -3.29488025, -3.69719716, ..., -3.38120741,\n",
+       "         -3.22968375, -3.95391143],\n",
+       "        ...,\n",
+       "        [-4.35932297, -3.22165274, -4.02037117, ..., -3.64324124,\n",
+       "         -3.43774501, -3.81603299],\n",
+       "        [-4.35932297, -3.22165274, -4.02037117, ..., -3.64324124,\n",
+       "         -3.43774501, -3.81603299],\n",
+       "        [-4.5502955 , -3.22156599, -3.84867434, ..., -3.39226907,\n",
+       "         -3.9432436 , -3.85464005]]])
        • created_at :
          2020-06-06T18:09:41.115181
          arviz_version :
          0.8.3
          inference_library :
          pystan
          inference_library_version :
          2.19.1.1

        \n", + "
      \n", + "
      \n", + "
      • \n", + " \n", + "
    • \n", + " \n", + " \n", + "
      \n", + "
      \n", + "
        \n", + "
        \n", + "\n", + "\n", + "Show/Hide data repr\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Show/Hide attributes\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
        xarray.Dataset
          • chain: 4
          • draw: 500
          • chain
            (chain)
            int64
            0 1 2 3
            array([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])
          • accept_stat
            (chain, draw)
            float64
            0.5882 0.6414 ... 0.0009974 0.48
            array([[5.88179346e-01, 6.41389529e-01, 9.37678207e-01, ...,\n",
+       "        9.23515478e-01, 9.61821485e-01, 8.14423583e-01],\n",
+       "       [9.61075139e-01, 9.92479385e-01, 9.99167555e-01, ...,\n",
+       "        9.79263419e-01, 9.58289464e-01, 9.60851272e-01],\n",
+       "       [9.86301496e-01, 9.84425192e-01, 9.54469155e-01, ...,\n",
+       "        8.41860803e-01, 9.27362741e-01, 9.94523221e-01],\n",
+       "       [9.52283483e-01, 6.46438496e-01, 9.75633981e-01, ...,\n",
+       "        6.27646170e-02, 9.97401915e-04, 4.80044753e-01]])
          • stepsize
            (chain, draw)
            float64
            0.2606 0.2606 ... 0.3035 0.3035
            array([[0.26064335, 0.26064335, 0.26064335, ..., 0.26064335, 0.26064335,\n",
+       "        0.26064335],\n",
+       "       [0.12223536, 0.12223536, 0.12223536, ..., 0.12223536, 0.12223536,\n",
+       "        0.12223536],\n",
+       "       [0.13518281, 0.13518281, 0.13518281, ..., 0.13518281, 0.13518281,\n",
+       "        0.13518281],\n",
+       "       [0.30353082, 0.30353082, 0.30353082, ..., 0.30353082, 0.30353082,\n",
+       "        0.30353082]])
          • treedepth
            (chain, draw)
            int64
            3 4 4 4 4 4 4 4 ... 2 3 2 2 3 3 3 3
            array([[3, 4, 4, ..., 4, 3, 4],\n",
+       "       [5, 5, 5, ..., 4, 5, 5],\n",
+       "       [5, 5, 5, ..., 4, 4, 4],\n",
+       "       [3, 4, 4, ..., 3, 3, 3]])
          • n_leapfrog
            (chain, draw)
            int64
            7 31 15 15 15 15 ... 7 3 15 7 7 11
            array([[ 7, 31, 15, ..., 23, 15, 23],\n",
+       "       [31, 31, 31, ..., 31, 31, 31],\n",
+       "       [31, 31, 31, ..., 31, 31, 15],\n",
+       "       [ 7, 23, 15, ...,  7,  7, 11]])
          • diverging
            (chain, draw)
            bool
            False False False ... False True
            array([[False, False, False, ..., False, False, False],\n",
+       "       [False, False, False, ..., False, False, False],\n",
+       "       [False, False, False, ..., False, False, False],\n",
+       "       [False, False, False, ..., False, False,  True]])
          • energy
            (chain, draw)
            float64
            22.7 24.35 21.51 ... 16.79 15.34
            array([[22.6967612 , 24.35094062, 21.50814304, ..., 20.17096719,\n",
+       "        20.26238974, 18.81485421],\n",
+       "       [23.8733572 , 25.89740769, 25.50088525, ..., 21.75214566,\n",
+       "        24.28654179, 25.63605085],\n",
+       "       [24.69925974, 26.74046019, 28.26096462, ..., 19.13939105,\n",
+       "        22.05673876, 18.04213339],\n",
+       "       [17.98317811, 21.89451151, 27.42787245, ..., 13.97427495,\n",
+       "        16.78600581, 15.34467776]])
          • lp
            (chain, draw)
            float64
            -18.89 -18.93 ... -9.633 -12.74
            array([[-18.89038713, -18.92823648, -17.61766361, ..., -16.39906054,\n",
+       "        -13.64755409, -15.67874189],\n",
+       "       [-17.28562197, -23.52929685, -23.24128558, ..., -19.26429636,\n",
+       "        -19.80764195, -20.13200941],\n",
+       "       [-19.56630339, -22.10420601, -22.90628681, ..., -15.59532748,\n",
+       "        -16.30184041, -12.72595726],\n",
+       "       [-13.17454693, -19.65660382, -22.36275391, ...,  -9.6329489 ,\n",
+       "         -9.6329489 , -12.74013837]])
        • created_at :
          2020-06-06T18:09:41.109561
          arviz_version :
          0.8.3
          inference_library :
          pystan
          inference_library_version :
          2.19.1.1

        \n", + "
      \n", + "
      \n", + "
      • \n", + " \n", + "
    • \n", + " \n", + " \n", + "
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      \n", + "
        \n", + "
        \n", + "\n", + "\n", + "Show/Hide data repr\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Show/Hide attributes\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
        xarray.Dataset
          • school: 8
          • school
            (school)
            <U16
            'Choate' ... 'Mt. Hermon'
            array(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
+       "       'Hotchkiss', 'Lawrenceville', "St. Paul's", 'Mt. Hermon'], dtype='<U16')
          • y
            (school)
            int64
            28 8 -3 7 -1 1 18 12
            array([28,  8, -3,  7, -1,  1, 18, 12])
        • created_at :
          2020-06-06T18:09:41.092802
          arviz_version :
          0.8.3
          inference_library :
          pystan
          inference_library_version :
          2.19.1.1

        \n", + "
      \n", + "
      \n", + "
      • \n", + " \n", + "
    \n", + "
    \n", + " " + ], "text/plain": [ "Inference data with groups:\n", "\t> posterior\n", @@ -465,9 +8563,9 @@ "outputs": [ { "data": { - "image/png": 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7TU888YR22WUX7bPPPpKkl19+WZK01157+T7Huf2ll15qwhYiy0btb/WmuXFYln17LQoFo1VPpzMDe8gQe5udiz0nZU87pkJI8zgDaG35vKWZ062yttLbD7RzICJO++l3rNq1XwGAZovSxiZ9rdLufv9M9OsDricAAMC2aW9Au3j//fc1c+ZMvffee5oxY4a22WYbSdKmTZskSTvssIPv85zb33nnneZsKDIrn7f0rxdL116v3mKc086rrWj0suVG192g3sKf559rNPaE5l9A3fkf0qvrLA3drRS4aSdZOc4AWlfnOEuf+qSdJnLobtJTq+RpV2rrB7KulvZzu+2kAQOs3j5yu+2atLEA0Ad4+yPvuXuS1yrt7JFHjU4ZL132Xendnur9G9cTAABAkixjvJllEVexWNQFF1ygJUuW6LTTTtN3v/vd3vt+/vOfa8aMGerq6tK0adMqnnvttdequ7tbP/zhD3X88ccHvn6uWj4UAAC2ot8AAMRBvwEAAACkh5U0dSoWi7rkkku0ZMkSfe5zn9Nll11Wdv/AgQMlBa+UcW4PWmkjSW+99VZCW5uMjo4Obdy4Me3NyJQsHZPfP2M08+LK26+6UjrowGizsipndCn2jK4sHZM4NmwwgTMI3Wo5zo04JlG3N6uSPiYdHR2JvVYri9tvZOH3mpXvcprHIugYJNEm16JRx6KW9jOJvq0eWfiNZEW7HQv6DVvWrjfaVSN/P2n1FUnLyvlALX7/jNFl35Uee3gnHXPcm9q8xb49qK+q1re18rFoZ/QbAIBGIEhTh2KxqIsvvlj333+/Ojs7NXv27IoZaHvuuaek4Jozzu1BNWsAt0LBaN16uz5BUGqBXT8qvfuuUbFYui2Xk3b9aLQT+0LB6IrZ5c+/YrZ08MfaP53BkqVGV801vRe3M6fbqR/81HuckxBne4Es47scfAxaoU2O0je51dJ+ZqHNBYCsaoW+IopWPx/Y9aN2ijNJ2rxF2rzZhPZVYX1bqx8LAAAQD2vaa+QO0Jx00km66qqreuvQuO21117K5/NatWqVtmzZUnbfli1btGrVKg0bNky77rprszYdLWrJUqNTJhqdN83+d8lS/0yFUQp/hhWnXLdeZRcKkv33uvXxt3nDhtYpglkolC6EJHufr5obvO1pF7GOu71AVvFdDj8GSbbJjShMHLVvcqul/cznLXVNUm/R6ma3uQCQZUn2FY0Qpf/JwvlAvf1kPm/p/HPLbzNGWrEy+PF+/aGk1I8FAABoLlbS1MBJcXb//ffrM5/5jH7wgx/4BmgkybIsnXrqqZo3b55uvPFGzZgxo/e+G2+8UVu2bFFXV1ezNh0tKuiiZfSh/rPjOsdZGn2ofGc2V5uVNWyofbt3RtewofG3+4yvSps2mYbM/oo7c7uasIvbfN7/OWHHOapa96OW7QWyqJnf5aTbjaSEHYOk2uRGzMiN2ze5xW0/lyw16p5fKlbdNam00iiLnykANFOS5+9JKhSM7l1stOAeu/0O63/SPrdNqp/8+CHlfxsT/7pt1dOG83wAAPoYgjQ1mDdvnn76059qwIAB2muvvXTTTTdVPOa4447TiBEjJElnn322HnnkEd1yyy1au3atRo4cqTVr1ujxxx/XqFGj9M///M/N3gVkRNTBpVouWvJ5S/l8aUaYc5FWbUDNntElz0VKvNnKGzYYdXSopoG7KBox2Fjrxa1znGtRz35k9WIciKtZ3+Uspw0JOwb2ChKjm252D3DFa5PrCaaEqXdALWr76d1+Y6Tu+ZJUVPd8ZfIzBYBminP+Xm9wO+rzlyw1mnO1kXEt/gjrf9I8t02yn1z/mrT33uW3Rb1uc3CeDwBA30OQpgbr19vrxrds2aLu7m7fxwwdOrQ3SDNgwADdcccduuGGG7R8+XL99re/1S677KKzzjpLU6dOVb9+/Zq27ciOOAOGz79QubQ9yom69z0mnBptQK3eFSK1XJxEVSiUX/AVi9Kcq42GDzcaOaL2DI5JBKfiqPdisNnbCzRKM77LUX5vhYLR629IRx+V2NtGFnYMglaQxBEUTLl3sdHUKbUd50LB6OFHa+ub4grafidw5fyd5GQAAGg1Uc7f652wEPX5Tr9rfLJzea8J3EGfscdLDy4rPXbs8c1p05NcxRN0ad+vn5EUbQUo5/kAAPQ9BGlqMHv2bM2ePTvWcwYOHKhLLrlEl1xySYO2Cq0kzgB9oWC2zhgu1zUp/KLF7z0W3GMP8rkvmIIG1OKuEHFfcAzdrfL+pAbu7l1cecFnjDT5HOnCGaauWdRJpC+LKomLwWZuL9BIjf4uV/u9OYNO/ftbWvGEtGy50dgTmvt78jsGQStIjhtjYh2jYUMr235JunuhdMzRxdgBbr/Z0Y5qfVMt/GYU++0PqWAA9HVh5+/1ThCK83y/fteRy0kb/2JUKNi1WpzXtHw2YdlD0te/Fq/Pq0WSK1d6eoJut2IFyTjPBwCgb6l92jmAmlUr7ukuWhl0kbPfvuEn6n7PM0aaeJoSL3bvLRz91KrS6yf5PoWCndPaj5Pvud6Cmvm8pUMObvxMNedi0K2Wi8FmbS/QaI38Lof93ryDTpJ03Q1KpTiv9xgkVQg6n7c08TT/+yafY7fhUYXNjpaq90218CusPGVyMm0oAKSp3kL1cdTbp8R5vl+/K5UC7LMuk8ZPsAP+7okIQcH3RvPrZ2q9dgmarNavn3+QK+yz5zwfAIC+g5U0QArCZmt5Z1h1TaptZlfQe5w63tKp45ObleU3q+66G6SJE6Tbb5VeedVKbPbXuvWVF29urTSLmjQGQPOE/d6yXJw3yZm9p463tOAe/5WIcWZSV5sd3aggid+M4kEDvTOSaUMBtI5m10qrt0+J83xvv2tZ0uc6pf9cUjqXDzunr2X76pXUypUhQ+zneQM+PT1SsVi+01k53wAAAOkjSAOkIGjAUFJFwKN7vh2oKS+OXH0gqloQIKmLgaBZdZJ9kTJkSDLvI/lfHLolcSEXJU90vQVXHaQxAJrH/Xvr18+op8f+LTeqOG8S7USSwdx83tKUyUY3+pTSizNIFNQONyNI4k3jQxsKoFUlWag+qnr7FL/AS1CKy0LBaLfdpO55dpqvYUPttvpnPw+PzDgpz4xJJ/geN91zGO9ktULBNOR8AwAAtAeCNEBK/AaXgmZ077evpUUL4g9ENWMAK2iAsxH8Lg6l6hdyUQdLo8xoTHrWY5IXgwDC5fOWVqw0umquPZvVaTdmTrd01dzSwNFZZ9bXXibZTiTZjtupyCoHyCwr+iCRXzs88TR7pU4aQRLaUACtKMlC9XGE9SlRzpc7x1l6e5PRTTeX6qQNGlheE9KvDzzkYLv/8asvZlnlE9HaJfjunazGKnoAABCGIA2QIu/gUtiM7loHopIcwPK7ePO74Dj/3GTez4/34lIKv5CLOlgaZUZjErMef/+M0a4fbe2LTqBVBf2GFy2w1DVJ+skd9u0/vk3q38/UFFhJana0t73N50u1C2oduApaBTNlcrzXYwULANSnUas4o/C7Nohzvtw9v5SqzNvHVesDuyaVAjxhQZlmB9+TWiVfDf0nAAAIQpAGaKC4J/xZmGEVtM1hF2/1XHAUCkarnzOSkUYdEG1fvReeGc8sAAAgAElEQVSXQRdycQZLo8xoDC+YGv5ZL1tuNHGCNPNi6d13TejM+mZdKAJ9TdBv+Nnn7EGn/v1Lt9UaWHn0F/XXuLlrQdEziGXf7m2D47a7fqtgpkyWTp9YWv7otD977C51dIS/FitYapeldj5L2wK0qlY853dEPV+O0seFnSuvWFkK8Dip0pxz4TT6E+cze/4F40krXX31q9/nHbX/BAAA8EOQBmiQWtPdpDnDKmibo1y81TJgt2Sp0ZyrS4WsLcvowhnJFU2Nk0oiyozGoMc8/4LR+dODL+4KBaPrbpAmTihtQ9AAcLOLyAJ9SdBv2Kj+tDPu365XnNnRdy0oltWNKRalOVfbjaR75rL7tqT6GPc+DBxo6cnHo20z4slSO5+lbQFaVSue87tFOV+O2scF9bP9+jmpRu3bnFRpx40xqex30P5EmaTh93lLitR/0uYCAIAgDaocAfRtQUGNQiG8WKYjn7d0yMHRZ9M5KXCivn7QawRtc/gKktrfzx2gkewLtjjHqRrnQtEtaLDUntFo9T7eb0aj32O6Jql39p1UGjx95NFi735EPX71fm8AhAv6nY/a3+qtceWIE1jx/na9rxM2O9rdfhcKdhoYL2NU1lZ6bwtqK4L6Br8+xq/9kaQNG2h/kpSldj5L2wK0qmaf8zdCtfPlNWuLmnN1tD4uqJ/t6bECz4WTuI6JI6zPdm9XlOc6n7f7+AT1n7S5AAAgDCtpgAZoZjHQpGZkhW1zI/Jmr1tfOejofs8kjlPcVBJRZjR6H2Mft/IdMUaadbmUy5nelETei1/JXoFjF1K1pVVEFuhL/H7nS5aW/4YtK17aGb/friSdO1U65qjg1/G23xNO9W8XnW0Kuk8Kn/UcpW8I2of1r6ms8DHqk6V2PkvbArSqdvgdhZ0v26ve/fufoD7Or58tFExNq9EbIai/c29X0DVO0Oftx9t/tsN3BQAANA5BGqABmlUMNKkC1VL4Njcib/awof6DjkkfpzipJKLmEy9P7VZ50elwPo9FCyyddWbl/d40D2kWkQX6Evdv2GlHvW3R6EPDX8PdXgT9dsMCNH7t94J7/NvFc7qkQQOtsloyUvnj3G1FLX2D3z5I0tDdwo8D4slSO5+lbQFaVbv8joICK379o2Tv46gDSnUZvX1LZf3IymsJv9XotV7HxBF0DeLsV9g1TmDaVJ8Vr97+s12+KwAAoDFIdwY0QJTUWUlIMg1ZtW3uHGdp0QJL119r/1vvLLd83tKFM8pTDMWdvR7nvaqlkliy1OiUiUbnTbP/9c6sD3tt93Hzcj6PffYOvi/otdIsIgv0FX7tqDHh7ai3vVixUrF/u0HvO/G00so7y7IDNKdPzJW1wYsX2u1n0PvV0jf4tT+SNGQI7U+SstTOZ2lbgFbVTr8j7/ly0IoTy5LGHi91TVWs82bvtcR++wanQGukfN7SxNP875v1rfCVPEGft7dPlir7z3b6rgAAgORZxoQlz0AWbNy4Me1NKNPR0ZG5bUpb0DFxz7SWki8MWijYF0beGVmLFtR+wh91NUk1Ub8nhYLR6ueMLEkH7J/OhUoSx9HZj+9cXjm7fdEC+4Js77130ujD39TmzSb0PcI+g2qfj9/9a9YW9cxq6cBR0sgR2YrNJ92edHR0JPZarSzuMW1Uu+5893YfJvXvb6VaGDmI+/e//faWVjyxkz55xJv6yY+Df2NB7YUUvZ1P4nWC2oN62jTnNffY3W6z+kp/X61tTfo3klRfm8a2tNt5IP2GrZ0+0zRE/R210u/Hry+xLGn2FdLF/1q5IiTu9UeUvso5rv36GfX0WImd3xYKRuMnGN9zdu97Rz0fj9p/Zqn9R23oNwAAjUC6M6AJHn7U9C7n9+ZbrvVE3XmeO1VAEjOyvOkJGi2ft3RsyhcoQbPOn33OaEzEbXP2490t3joQ5Z9HlNlzQZ9BtRoTfvf//hmjB5eVXuPEsUVdenG2AjVoT1dcWSz77jmpAZuRbz6uCadKdy8sv23FSqlzXOVjw1ap2DOQo72nN/WLZdntean4crTX8D6u3r6hmX1AVgaqkqrtFkez+9owWdoWIExW2gw/7fg7Ckp33L9/ZT3G0gqY6J9RUAo053VWrFRZ6k73eUS957f2iv7gVM7V+gW/zzvqdyDt70qWf0cAAPRlrKRpAVmbbdVKM8Caxe+YuE/uvZyZWu6LD+fiIMrAkPfC4SunS4MGZWulRCt9T/xm8kn2oOmFM+Kndgu6+Ono6NCLL76pV16tLSgXNtswaB/8zL+pfb8nzGyzpb2SZs3aoiZN8b+v3tV+SfK2085KmtGHv6l33zWBK92SXMF414KibrrZXoEXpx/wbtO69XYBZndgpmuStN++ta9gamQ7XjkAVn8azVpE/TxbqU9rtHY7FvQbtqx/pllpM+qVhd9P3EF67+OD2k2/yQFRPiO/Psyv9lo1tZzfBq2Iqbefz8Ln7Kddfkdpo98AADRCNkbpgBZTKBitetqoUPC/cvAWbfZyVml4CzvPudrokUeLga/r99rFovSTO6Qf3mjnho5aSyVp1Y5J1l7XLaiujDF2EG3N2mLvNkTZnrAaOEOGVK+P46dajYmgvOF+Vj8b662B2J5ZHXxfWL75Zvze3e9VrZ32284kc8oXCvaAlDMI5RRNfuSx8H7AzV0f58bu8gLMN91sp4jxCzT5HedmHX+/fuyquc353J33d/YzydpuABoj7TajndRSg9F7XuvXD7oDNFLlZ+TtXwoF+5rnkcfsJwwbWv58Y+IFaKTazm/9ztmT6hd+/0y2vqNJ/Y6aea4GAEBfQrozICbvDKRLL5ImTih/TLUB81xOMvIvGj3rcimXM4HpVsJe2znZHn1oc5evRzkmSbxuI1PQdI6ztPZ5o5/9vPz2YlGafI5kjCmb1deI7Qmb2ThsqP2e3ll9Tr0jv/uDjDqgvm1c/ayRLGlUSjWEkH0DBwbf5/7eujU75VSUwObzLxgdcnDlNnSOszT60PrrjAUNBM26LLwfcFQLNBljt18XzjC9rxN0nBvVjvsJGwBrdAoY7352TQpvWwGkL802o50EDdLXct3g7Qftz8g/BdqKleXt7tjjjf5reSkIY1lGE0+LPtkoyLCh0qqnTdmKn1r66Wrn3NUsW240cYI082Lp3Xer9+XNksTvKI30oAAA9BWspAFi8Lu4ue6Gysc5J/d+nFnXo/avXLnhCJvZFPbaznObOQM46jFJ4nWDjkkSM7oKBaP/XOJ/n3MR6Z7Vl/QszrsWFDV+QvDMxmqz9/3uv+gCSyeOLX+fE8fWnupsyVK7yOqsy+1B5PETos3ARN9yxZVFXTnH/76gVSeNniXt10ZUa0sle1Zv0DaErZiLKmwborR5q5+tnuLQWRHorAT0O85r1hYb0o4H8dvvZgRG/Pa/e34pUONsR7213QAkK602Iw2NXKWQ9MpBdz84bGgpRZnDsuzVnN5298Fl5atkjJEW3BPtPS3L//z2gP2li/9VvefRV1xZ9F0xFHVFfK0rZgsFU9Z/ZmnVV72/I1a0AQDQWKykAWIIurjxCiqE6a0N4H6MV9DMJu9rezX7ojXqMYnKmfW2cWPl/vkdk6RmdK1bHz+tQlKzOO9aUNSN3eWv6zezsdrsfb/7O8dZ+uIXilr9rL2CptYATaFgNOdqU3FRPefq5q/cQnatWVssK+TrmDpF2nef4NoojZwlHdRG5POWuiaZst+eV6Nnaldrz4tF6d7FRlOnlI6Ze38sy/6vWtvlHoTzO87PrE62HQ/jtPF+tQsa3Y4Efc/229fSogX1r4wC0BhBBezb7bfa6FUK9a4QqcXrr0frT6Keg1/2bWnMMfYx+a/lpfPS59aUT6Ryn4s459VvbzKefif4+LrPqfv1M+rpsfuvat+5LK/6qvd3lOV9AwCgHRCkAWIIurjxEyUdjvOYx39jdM21la/Rr5+RFJ5qx1soutkXrXGOSTXVBh+9F5Jr1hY15+rK1S1xggbOgGG/fsY3XVjYAGgSF7aFgtFNN1feHhakC7sQ8rt/5IicRo6obzuDgljGcHGGkqBaNLmcfNOGOeIOHEVNYVIttct++1qyk08Gb7ezDe62oqcnOOAUR6FgtNtuUvc86Y03pG9fVvmYBfdIp443valb3PtjjN1GOccul5O+crp0+53BbaffcT5wVHLteBi/VGPeyQuNFPY9q9a2AkhXUmkmsyrJVGRhJpwqLbw3+esGv/NEs/UyJko6XndK4SC5nHTA/qW+0DtxKEyxaNdp814zDB8e3Kfn89bWVG12KjdvYMfvXCQrq76CzpPq+R2lEeQDAKAvIUgDxOA3A+n8c8MfX23QJ5+3tNcekt9AYU9PcD5l57UPOdjScWPi5VyuNUdz0PbHOSZh21Rt8LFrkjMb3GjFSpUFaBxxZnR5BwzHHi8te6j84sP9+pU1aeq/sA0KflhWti56nDQW3m3N2nYiXQeO8r+9Wh2kOLM748w0rjbrM+h7Ldm3O9vgfk9b5WBNXJX7YelLE4zuXlj+OHcg1G9/jJGmnS+9/15ptdxuu1a+tt8KTue+kSMszZxuYrfjcfqSoFRjixY0b7A1ydn4SfajAKJp52BqrasUorZF3n5szDHSxNOkkSNqb7/c7x00gD9q/8p2d+zx8tSkkS6cYW+H+3Gf/kfpl7+qPO9e9XT1VJ9efn19sViqO+nu092TMuwATenxTuBsxUpvX1JapXv+uaU3SmMCXbXzpFp/R31lRRsAAGnJTJDmn/7pn2p+rmVZ+o//+I8EtwYI1oiZfH4DhbmcvUrm/OnVl+WHnWx7L94akUohiWMSNPj4nW9LHTtarhVDJpHVLX4Dhssekq78nnTRpf6vf/ON0pCdrcQ/e78Zhkd9uvkBt2qvfeEMlaU8cy6quTiDY+SInE4cW57yLGodpM5xloYPN3pmtR3sGbJzeQFgKf5M42qzPlesrL5P3vd01DPLOWg/uufZK2eCVsH49RWWJV17nXsQy4S2yUH3xW3H4/YlWUmTkkR/1Y6Fkwk6AemqZZVC1LbIrx979DHpsV9IF84wNbVffhMNZk63es8TLas0uWr0odKiBeXnz1//mtGzzxkZ2YEcp91x2uffPWV6V4U6r+Vsp9+xclbg+028ciZ6OZkH3Nwra+ZcbfTa60Z33KXeVf1+gZ1nnwsO3uTzlsaesDXgdKW060ebe57c6BVZ7b6iDQCANGUmSLNixQrf2y3LkgkYjXXus7xVCoEGizsDqdrgh3eg0LkYcV9M1HKSXZlaxtT9mkHqnd0YdME1an97u5xglRSe0iDqjK6wAcOg1+/psRKfxRlUF+NX/10993UjBwqDXnv0ofK9qAYcl16cq6kOkv2dKw2MSMYVeLC/f3EH+sNmffqlS3Ezxn7erG8Fp2mpNcgQtB+vv2EHPoNmqQYFlYJSPgZtV9B9Udu3aoNA3j6vUDDauLEywJ5WmpR62vFmpSRqpnYMOgGtxjkfdFJyVVulEKct8utzpFI/F7f9Cnrvrknlr23vi+ndF+9qjjE+75nPW3r40aJ+ckf5a3XPl44bY7a23/59uzd48PWvlfdFgwaWp1X2S8/mfV+vXM7OfRDlXOSgA+3+zzvhpJGSnBBRLZsDAABIVmaCNI888kjFbbfeeqvuvvtujR07VuPGjdPQofaV/GuvvaalS5dq2bJlmjhxor761a82e3OByKoNfgQNFH7kI/WdZPtdQLlzMdfymo3kXHC5V2oYYxfN3m/faEVHp0+LPrD0/AuVV165nD2oHJTWa+NfjAqF5C+y/OpiVPtcGjlQWO21/S6qAbe4dZD80h063N+/qDON3QMLQbM+gwat3IpFuypYUD79WoMMQSvovnO5dOGMyhnHzj55+4qgWb6NbtPDBoHs/P3utDamdzazu9ZYq6ZJycqKoKS0Y9AJaEVLltoTqfxWjviJ0xYF9TlhzwkT9N7e64xaakZGrdUY1Le798MbTHA/p18/o66p0a4vvP3WqP0lyzJVJx0sW250xezmBsCTqhtD8B4AgOZrQFnY2gwdOrTsv6eeekp33XWX5s2bp2uuuUbHHnus9ttvP+23334aM2aM5s6dq3nz5umuu+7S7373u7Q3H/AVNPhRKJTO6oPSfDkDg25xTrIDX9dzfp2Fgo/OLLPhwysDJ3cvlGZdXrndXpYlHXFYtIuHQsG+EPbqmmQPLk+Z7P+8WZdJp0w0WrLUf+q9sx/uzzeKWoqMhl2c16uRrw34qRYwKQ3O2OlUnN9LLmcXQXZbstTolIlG500zvb/XfN7SIQeXBwT8fndeTpFi93u67/O+t1S9HXACSF2TKts1Z1azpIrtzVKbHnTsfvdUZZ/34LLyVZCWJV02yw5EteKAT1aKQieF9h5In99Ehe75Cj2f9GuLLMsOPnh5+063XM5+Tpzz16D3DlvpHrVdCavV6N03v749jHsCx8gROc2cblW9vsjl7HTH119r9fZbK1ZWph31m3Rw3Q2V2QviXiPE5XeeFHdCRJTrVwAAkLzMBGm8brvtNn384x/X0UcfHfiYo446Sh//+MepR4PMijL4ETTg4x0YjHuSHfS6UyYr9mvWGnyIwj2gahfv9H+c92LoxLGlwcm4tVGCBoT329eu2eM3g887G9B7LPwGht1e+F9T9q9UOq6SYn/WjRwoTGsQspHfM6QnyudaLWDi/v51jrMHSr40wf493r1QGj/B/s3FGVjwG8g4/tjy93R+h+73dDjv7f69V2sH3Pd3z5c+21m5r0EDWUGDYmd8OX6bHiTos/LebqflqXz+T+6ItjpJsvexFX/rSQyAZUm7BZ2AVlRLsNQv8GKM1DVVvpOJ3P2Yc/7s1G7pmqrAfkvy7wO87eAZXw6fUOVuV8LOC4LOB8L2LYolS43GT7D73/ETjObdVNToQ+0ATNg2z5xuaeSIXG8wqFAwmnN1+TYYY9fScfZrwwb7/qifadLnv85n7Q4sxUHwHgCAdGQm3ZnXH/7wBx177LFVH5fP531TpQFJq6WobpQl5/m8pbHHm7JC22OP19aBQdVcnDEoX3PnOEvHjYm+L41c7h6W4qiar3/N0te/VkpZ0NNTGvCrtm9Bn0u/fkZzrq6+HcWitPo5o2NDipnPudpeGTRyRE5XXFnUrx63tOIJ6bxp0qePLOqgAyvrTvilOAoSVmujXo187SCkVWhPUT/XoNpMUvD3b8E9pf83xv7NfefbYQMLle2et87SQaPs5wQV+114b+X2OYGg4cP9CwkPH27U02NVtC/ForRkaeXsY+9Alnub3b9LZ79vv9MOvu+3r1VXznvvZ3XpRdLECcHpWvzSNEZhWXZat1Kdgtb7rbdT4eQ02nsA5WpNUdU5ztLw4eWTnMJSi+XzlqZOsXTqeOOb8sv9XMlu455/oVTP0t1mu9tB5zFB58/udsXd11iWNGWy0ekTS1EZv77O4Zxf11JDx5tO+e6F0t0LjS66wNKXJhjdvbDyebO+JR17TPn7rH7Ov5bdbbcbLVlqb+PAgZaefDxaALxR57/11I1JKmUaAACIJ7NBmv79+2v16tUqFovKBUyvLRaLWr16tfr379/krUNfU+sJdNDgh6TeIpKStOyh8ucte0j6+tdKxTFrPckOztecTHHoekWpCeHHGPu5hxxsba1/IBWLpncGn7fYuFfQ59LTYw8cRjHrMundLSawmLkx0uRzpDO+XNSDy6Ttty/d9+Ay6b+Wm4oL6kUL7LQNUTVyoLCZg5AbNlAToR3FbT+CBv2jDpIYI739tv/AwvMvGJ0/vXKQqVAwunex0YJ77OcPHCg9+bhd7NcrrL0qFqUnfuMfILIHz/zblWJR+tIEO/jjHSAP6neGDzeaNKV8v2+6WVq8sPbfi99ndd0NdpDGL11LUH0gL8uSPnOCymrSONvsfb1W+623U+HkOO39hg1GHR32v0OGtNZnBmRVPcHSnh6roo+pVmfGab9WPW2fP3ufe+9i09svee9zt9n29pX61yBOP+43OcuenFEsC9Q4bdJjvzS6YV75axljB0SOG1PZXgVNqFv9rH9gRbKDPjffqN7zAMf/Z++8w6Oo1j/+PZteKIGwISEiokgTFVAQQZAmCtGLxAC2K1ggEMUCl+ZVkCtSVPRKi/xQuYIaqoUQBRVFgkGQoCA1ooDUJRAI6WXO74+T2Z1yZnZ2swlJPJ/n4QF2Z2fO7M5pb/m+NhvQoT3n+zc4zxfrXf+W7+/xEcD8hfr5XdleK+skbwIFK4Nw3gsEAoFAcGWosU6aHj16IDU1FdOmTcOkSZMQHh6uej8/Px9z5szBX3/9hbg4jl6IQOAjKuuo0Bo/duxk8jjyondYAn8TpMzU8Lbd8jU9MfwrcVeUVLtpkP/f/CogIsL9+WObudew5iFHc1ktNs77nXhGKYeDetQeOZrPyFhJKZP/4WFW7NuTzZi3hkIr16guI+TJU3WrEPffEd7z5GmBdaPITa2RxOGg+PMPfjsa1GeGBKVhIXEUnFHAchvmvkmRe5nqihzLx2gN0A4HRU6O+fiw7EP+eGY2nthsQEI8QUK8fiwymndOn9afh1KWDdTHZLww6/NGv5Xyb+XrspPcKNpZ5pWXgT69bXjqCXbtnByKaTP45xN9/crgyVohdQPFwmTmyHx0JJCUSGtdFpRAUF14alj3NjiGt5a2mvVgNO9qHRZKtGO2u4AreR53OCi+WM+fLxa/C/TrQ1X3bLcT9O4FLFikd7B8sR74Yr06G9M0oM7kq6QUOHOGySZbcUpERxufS8v1rWCaJW9lneRJoKAvnTl1KWNUIBAIBILaQo110owfPx4ZGRlYs2YNvvrqK9xxxx2IiYkBAJw6dQpbt25FXl4eGjdujBdeeOEKt1ZQl/HU0MhDNnbzDG8rV/MNe9NnuDI1PMVXqfNm6e7aawzoT53R0nKavzvsdoL74ig+X298DCFAr57AD1v1kWiZu42Ng4D1SEJZCzq2GdukKSURzKC0wpnW24aJ42H5czyMvteJ472XvDOipkmLNYsRsgq1GaPnyVO5DCuRm8praSGEBbh2udVlFAkOptizlz+Gax00SvYdAHrdob8mIe4dy/I9W3H4DktQZji6XjfVgzfoqmaXctfneUY+Zc0C7f0p6wN1uZWNg0zCTH3cDe3V2ZsOB2CzUdHXawieGv/mvkkREsLer81ZUAJBVeNO0ssIb4JjduxU/19bxN7McM+bd4clgCv9JaMds82yKuV5fMdO8zUypSxrpncv9n+5vQDQuxew+Xv+55Syojy5UXl86tCegBCT68O6U6KoyLrUZ7MYIDLS+Dd1t07yJFCwKtb2dSljVCAQCASC2kCNddI0bdoUKSkpmD59OtLT05GWlqY7pnv37pg+fTqiPQlpEQg8xJe6vEaGty636jdZlHpnAPGlRJlRvRwAumsojzGKRteSuoHii1Tj67ONLfDQcBs3ayfnonlku1xnJnO38YZLv6kheHcRky5q1BgoKlRH4evaWPE3Txdcvgd3hlqlDB6vtg3gXsLNKlUtYecNkZFCVqG24u558vR3NTOSaK+lhVImQ2izUUV/4vddd/1y1hzgci4z/CjryMj9cPrLwKVcYN5b+jY8/xzQojnRaf3z2pAQz/8uDh7SN06ed2Kb6Y1NskPF4aC677eyff65Z4CZs11O8sRRrho/stxNXztBYYF+LLViEBR9/crg6XPhi4AVgeDvgFVJL0/PaTYvaucDuaaMFcO9dt4FgJWr+XMtb8zWjuuEAI8+DFx7LQAKREfr18Y85i9kWTMAO1Yrj2mEJAF7fzMfn+x2gkkTgNmv609GiCtj14pTwkwFQN4vysEN7iQhzeZEh4Ni8/f8ujzacbcmru0FAoFAIBB4To110gBAbGwsli5dir/++gu7du2Cw+EAANjtdnTu3BlXXXXVFW6hoK5iVqy5MkYlnsOHEGDnz/zjvTGAGBlS5Ag1o8g63gbQ4aDcejm3dTXPYJE5eQqIjITh9bQbSy2UMgeJLIGgTP1XRuQZbZZaXeeqB0EIMGmCenPK29TonSIEa1KYlNDLr6jPT4grUhwA2rW1YdIE9YbYSmZNvz6sXbzMIE8k3KxQUw1tQlahduLuefLmdzUyklitYSX3E0r5/Y8QYMhgYO2nxueg1DjqV25Dg3r8seett11R0xPH8x1LSmdH9nkJRUVENTYmL9FfN3GU6/ubNEHfPpeDSj3OWenzJ04ay7QNuIug483aAtJUZ/CTHdV7fwM63AC0a8v/rZXPRHAwRVER37kkqFq8lSNUIrKgBAI9RnPVomSgWQzF4H+oX3c4KPb+RoEKZ4EnGRKmWZewbrjXzrvaAK07ewFDBhPdHmHvbxSXLrP58JGHgOUfsblj+Ufsc7KzxWqWufI4TzLTO9zA318FB1MAbF4NCeGfcMxo9recUW9FZlg7B7M9hmtua+6BiYK3TjLLGuaNuzV1bS8QCAQCgcAzarSTRuaqq64SDhlBtWG0GXJnaPx1D0V0U/PFPS/arNttLGuDhzcGECPZgfkLgYWLmQEPUDudlFJlynveu48fwQXivmA0ABzOAm66kf+dxsRYN7gqNxkOB9UZJykFxj8PzHtbvak7dFh9jFxDBmDnzLlozSmyJoWgT28bCgr0196xE4gb5Pq/bKjcsxcIDNJH2vPY9A3QtKmEa1u638xa2XSZSVv4MjPM1whZhdqHledJLizMjFBUZ4SyqqFupVC9jNkxlJo7aJTH8SAETmkvwmmuHDWdlyfhls4E9//DdT1CgOFDWc0u2dlR8Sm3Y2Ob1q6LyXOS7DyW28ozwln5jYyOkeEVh9Zei43zynnEJdep/Y3tdoIdO+Xj9Q4fQdVjFDQiGzW1yOuXhcns/yILSiDgY5Zp8docYPA/gI2bKAbcRXRBRwDF2ESKfn1ckp08Ga+WLSmKiljGptH47q3hnheg9cNWYPdQ49kAACAASURBVFwS+/e330nIzISpVLFVZ4s3dSm1/ONeFiSlDYqgFEhMAgb0l5x7HB7fbwEWJbNGWJ2LlHMwhdq55s06Vrn+NcsaNhp3a/LaXiAQCAQCgXVqhZNGIKgu3KWL8xbeGzdRDB8GTJwCFBbqo5i1yAv71WspUlYZO2i0mtJW0TqClGgzReTXtFJlyqLaWuQCoNrsop53sI2OkveXAe3bSdwNZvJCawZX7SZj7z5+dPv58+43epQC/1tOsX6Dq2aEO5QbWtnBozyf1iD6cYrkrHVhpX6FzIcr2N/Kz/CkHtxtupQbfl72kJAbEvgSq3Vk1BGn1PlcupNiURr3AaaVb1bQuLpQyp8Z9fEPVwAfrqC6z6WsUp9Dxmxs5PV7u53gwFr9hbVGOCu/Ee+Y555Rn9fbiO0dO7XXZmOpkGa5svDWCrJRU+lgUxI3iOC2ruyZW/6Beymf6sKXxbIFgspitxOMGU0rJM74vD0fuOoqSSWnKbMoGVj8LjWcXyTJlSXOAq2gCbQiTse6p4b7/QckpH3FH+vlfYsv59/KnosQYNBAlgXT5VYgeSFU0mraPQ6P/Qdc//ZkLrLbCfoYHJOdTRER4V72mYdRJtYzSUDvXvz1uljbCwQCgUBQN6gxTprPPvusUp8fPHiwj1oi+DvjadSZw0Hx9nxg+DDXsVYX90YbHTnSOiHe+8W17Aj6bgvF/IXq96xsiCTJuKi2LLkTN0hd0H7vPqpz0kgSc0LxvtOiIn29Gy3KWi2yDMG2bfxjAwOtOX2UkX9G37+RU4QnCaR8Pj5OkVSbcqWjxirKmhcd2hOOgVP9fcjySHv3UVy6xLKJlOfSZg/FNhPSYgLf4q6ODC/zbc4bxoV+5Qjhn3dRp3SKltu7GTu4ZR4c5nuHznXXAb//rn7N0/O7y5QrKrJmbHE4qNPho4RnhLPS57WRwTd1UL9vFqlrNHfu3cf/jae9dGWlWeq6Ud/q/fFqqblbx8gGx5rioKmKYtmCukd193lWe0YyXEtLEqujYljInqr/NnpfkpiDJnkhVLKZgOeG+39Pk3TreBmbDfhkpcHN+gArAU3x9wOnz6jn/l491bLGw4dWfs7nzUWePD+pGygWJgPb04FHRwJJiXyntxG8TCybzdhBIyPW9gKBQCAQ1H5qjJNm8uTJIJ5YMitgizIinDR/I+SFMtOyJ1yjoLcLVE/Txb2VEuAZ+2VeeRno09u7wqJK7HaC3r2YxJlWzgSwlnXCQym5I2cXsUh5/vHLPuRvNoKD9XIKSuSIsR07gQeGu+ThjNrVrBnbfBrVkbACIWzDp9ykDujveo6YDIye4GAKhwPcqElKge7dgF/2WG+HJAERDYnOGRYcTPHd965C5rJU3VebzDfyy5ZTpG7Qy9n5whha142dAmuY1ZHhPZuUGhf6lQ0uZrhz0ABA7zuBNq2BaTPcH2sVrYNG5rbbgO3brZ/HrOhwbDOgU0f3xhaj7/beQa7sFm22jLs+z5zC7HeoV48ZmZSfNzb48SO2Qfm/MYHvpFk8HYPqulHf0/srKiK6/lZbahmIYtkCK5j1iapcwzw03IZ+fVgQjSyRKeNLKSrZud+po7Hz3eweWR00faCVkr59gK+/8axdnkiZPfowcO21rLaaETyZUmWb5UxVX0ioKX8bT8ZUeUwKCWHvezMm7dipf01Zk84MIRssEAgEAkHtpsY4aZKSkrxy0gj+XugLKaq17CtrfPE06szbQrpGetXaQvTeotx0ThxPuJkYRnrHAHDzTcAvv/Lf0+rVyxsSdxsi2SAnt6GoSFmPQX9s7176dppdQ846kTH6fs3OQSmw5Qf1axu/Bp56ghk7v/ue/7miIoL0H41PvC0DCAszvq4Wm43Vy3E41PUbtLIYVmQcAGB9qvUoaU+o68ZOgXcox5+Dh4z7RUAA/3VPjSsj/smcwVqKigjOOqpHF+2nn4B/PgLDzB8tY0bLNWlcr2nnG3fGFqPv9otU4PP1ntd54Rm8AbVci5x5sfc3Vqi5XVvziG1WzJnq7vMGjmSmN9Isno5Bdd2o78391eZaBqJYtsAdZn2CJ8Xo6zWM3U7Q105QWMDaIfPcM6gw5Hs+R5llfBu1wUgNwKqEmScOGkKAdxcBkY0JN6Ofx4qPgTUpBHf2MncWuYNSVqNmvSIwiSfHbEb8/a7x0sqYqlzzVHZMMtpTLX4XqF/Ps4wcgUAgEAgEtY8a46R55pln3B8k+FtjVEhRKZHDk1Xx1PjiSbq43U7w3DOulbRVQ5PdTjBpAjR1GoBJEzwzUvEiAPVGK4I1KUR3XJdbgW0ZFG9yCtv/uodtcnhFQYuKiNsNiRZKgeefA0pLXIY9h4MaOk2GJbB2Zu42diQpGZvI/lZubORMk+efBerXB0CtRdQbyZkB1FDqITiY4sIF9+fWQgj7XjIz1RtISWLRhIRQDB9K0ftOFt3ubWSgmUSbt9R1Y6fAO7Q1mYwgRC3NVxkOHNAbreQC6MlLfHMNd1DKooDXrmRj7cFDVOeEUZKTA+e4bJQVagaLfDZuC2C9T8rjec5F/nh78hQQGcn+zeYXpcPdZTQymjsTR1HnM6GcI7WSmZ6OG96MQXXdqO/N/dXmWga12cEkqB6M+sRvBlKMvlrDaNfn8vh4+gx7f8BdbB3Mk+k1y3i32VhWhTy/yP/nZU+aoa0VVxluvhn49VfXvP/ow2yvALCAq/kL3V9ElsfUBkp5Q6dOwGOPqvc9yrWJO3r1dH2H7sZU7Z4rcZR3wXtm1wP4NTAFAoFAIBDUPWqMk0YgcIeZM0DWdvaV8cWTdPEBd1VkfMwCoptaN2xo9f87tPfMKKLcGDAdZpcxX7vpXJOil0HYsRN467/8c1PKNjlfpOqj9VI3UGz6xvV/eUNi5kwhBHjrbaWRjhn2jAqrJsSztvIMMFrGJjJJCZ5DR5KAq5uze2dOIc83pPLmajWnQLfMocNAfj77N6XlKMjPQFnpWfgHRCE0rBsI8eN+btIE9gy8/V/+uSllGuDe1tXQbua191QZ6rqxU8AoLy9HRkYGzp49i6ioKHTr1g1+fvznmVeTyQhf1on5iSMNQimw9D33DmRfMn0GMGkCG9s7dSS4+SYJP2bws3xWrgYS4lExLntucLHiHAfc90ntPMKjWQz724pTRDt3pm5gziTZeJc4Sh2pXhlpFm/GoLpu1Pf2/mprLYPa7GASVA9GfYKi6tYw2nF1zGiKh4bbdOOd9vlV1qQEoHP4y8933CCCfn2o5n3r2ZNWM+CtUloCSJJr7bs42bX2jYqydg5CgJMnKr82IES5n2JSc/iNol8fgn59wJWe035eOV6ajam8OTF5CZvnln/kOtaTMcls3yPW2AKBQCAQ1H1qhZPm4sWL2LdvH3JychATE4NOnTpd6SYJrgBmC1ebDRWyKlfO+HLTjZ4bBex2gj5eGBO0GwMzYz5vUc8r5q3l7FnmRFBuHiUJTgeNfG55Q6LcRPbv6zpGGxGoNOz160OwKFndCPl4OQqR52RQHtuvj7FDR659k7mbvT98qOeFT4clsL95Bbpl5Gyk3EupyD4zFUXFp5zvBQfFILLpawgLu1f1GZvNFUVuJROJByHA3XfpJc/u7AUMGeyKzK9fT59dVVkjVl03dgqA9evX46UXp+D4Cdfz3Dw2Bv+ZOQv33qt+nh0Oli1Rk9jxs/VjWX0n4Id098caoYx0VdZ24VFZY4sVBzZg3id584gSeSyWpc48dYrwzp+8BOjXx3q0txnejEF13ahfmfurrbUMaquDSVA9GEoxtudLMVZ2DcMb91jwgoSHhuvrTZo9v3Y7cOqUq0abcoyWnRDPjfc8G8iqk98qGRnGa18gztI5KAWWvOf5tZVZtEpFAm2mECEUkyYQxA2yOaXneBlMWkUDszHVKDisTWuC5R+w/y//wDWHWkF7PSVijS0QCAQCQd3HJ06a3Nxc5OfnGxb6jYmJ8eq8Fy5cwMyZM7Fx40aUl5cDAAYPHux00qxevRqvv/46Fi1ahFtuucW7xnvB559/jl27duG3337D4cOHUVpailmzZmHIkCHc4/Py8jB//nxs2rQJ586dg91ux4ABA/D0008jzJNCFX9zjBau8oK5XVuCieN9b4y+ErgrZGqWDq9Flv1RRmuvXus+gi55CTDtJYrkhcCZM8DLBsU85Q3JmhT9JnPuLODIH3pZI1lqomFD/b1RCpVGtiyd8OEK/v2uXkuRNIZwN1ID+gOJSa4Iw8RRnhUUJYRFNBoV6FaSeykVJ46NxMBWgZjSPQLt7f7Y5yjDrG0XkJY1EkFBHwB4TPUdyN+X1TbJx9lszHkkR1t+tUn9e/6wFRiX5PodqsKIVdeNnX931q9fjxEjRmDgdYH4aITreZ794wWMGDECy5YtUzlqrPQRb7mrP9C9GwDCHJLbt/vu3PH3AzExzMkPeOakiRsIpKapX+PJ6PCQo3W9LVptZsiRMeqTTnmzHHM5SW1mjadOESOnzndbKHr3qvw45O0YVNeN+r6+v6osrO4raquDSVA9GPWJqljDGK3PF79r7KA2qxmjlfGd8waTd27X1mYq5aYNAFP2Y6tOfiu4W/vGXv0B6jew5qgxgxBgyGBg7aeu1x4cps46kn9bXiCaMohC+TwEB1OcOQNDRQOHgyImBkheCJ0sKe97lPdckZEuh1zmburR+Cm3T7kXEmtsgUAgEAj+HnjtpDl37hzefvttbN68GRcvXjQ8jhCC/fv3e3z+ixcvYvjw4Th+/Djatm2LTp064aOPPlId079/f0yfPh0bN26sVifNf//7X5w8eRIRERGw2+04efKk4bEFBQV45JFHcODAAfTo0QODBg3CgQMH8P7772Pnzp346KOPEBQUVG1tr+1oF9baBXNdML5YKYTsboOlNPpTyhwVssSYw0FNs0Jk5LooskPAjOBgCrvd5txkZmdTREQAh7OAdxbwPzNtBpOA4NWSUGYEUcp30MikrAKio9kX0aCBayMVHEwrHDSu+0leYuzw0aLeEPF1wxPigdVrmcRZ9pmpGNgqEOuGNoCNEMz85ixeURiTjxwegfLyR1Tnj23GIu6tGLdtNv4mMXO33uHGi26vCiNWXehvAj3l5eV46cUpiKEUG7KKsSGrGAAw7TZgbYId8atz8fK/p2LgwIFO6TNfGn20fP0NsOlrV58koMjwkaNm3WcuA8j1rTz77K23AmlfWZPR0UIp8NkXFMs/UhpgPCtaLfc/o8LM014C+vZWn08rw2PmINbeg6dOEaNnYv5CYOFi6pMi3d6OQXXdqO+r+7OyHhEIagO8PlEVaxijwBtKPc+e5DlhKAVGjwUmTWAOB94YO20GUFDgqheml19jQUtWa7QYwVv7AkDX2ECsGxqAIatykZY1QvOpiWh340QvrgV8+jmTOG7TWl/DTfm9GgWNKNfGrueBoF1b/jV5459SOpoXLCHvuV6cTDF8GPDoSODyZetSdMpzJ40hSIiv+U5ygUAgEAgEvkOfd20Bh8OB+Ph4rF27FoGBgWjUqBEopbjpppvQuHFjZ0bNzTffjM6dO3vVsOTkZBw/fhxJSUn49NNP8dJLL+mOadiwIVq3bo2dOzmC9FXIq6++is2bN2P79u0YPny46bFLly7FgQMH8NRTT+G9997DhAkT8N577+Gpp57C3r17sWzZsupptIc4HBSZuykcjioKja4EdjvT+2/X1oZOHfUGIvn92riYNdL81/4ObGNAuDUEbDZg9kx1FLTyPHv3eaZDLUmsfoIZo8eyehQA29Q8OpK9vvR94w0gpfoNIiHAfXGebRopZZk6895mTqXRY5k8xOnT/AjDWzoTjE10X9Q8eaFrM2X0fYeEsL8L8jNQVHwKU7qHwkYIAl89i5k71Mf62wB/f+YXl42bAIuK5F3/vntdxUdd2WL6Z142hCqpTkmE2tzfBHwyMjJw/MQpnNU8VzN3AMEzHZh0ewiO/XUSGRkZzvfkPqJ9Fn2BUipxzhsU23+y9rkb2pv3c+25Dx6y3iZZ9155z3I/jW7q/roAcxZrZSA9nXPtdoLevfTfu/x/+XwOB8W3myXMeUMvb2b0m/FejxtEsCaF4J23CJIXsiwkozabPRPe3q/RdcQY5HuM1iPZ2TVvXSgQeIuvxw+7nWDMaP3r2nXZr3vcj3+89R3gygoBwB1j5fcdDmoov7Yo2ZWt3u02T+7QhXbtq8RGCNKyiuCvaZu/bS7274n06npysJU7h4XsKNOi/A32H5CQskrC/gP8iAp3+zF5n9zlVrZf0O653p7v+jfv81YR85tAIBAIBH8vvDKnLF68GA6HA+PGjcOWLVvQs2dPEEKQkpKC9PR0LF++HC1btgQhBEuXLvWqYd988w1atGiBZ555xvS4q666CmfPnvXqGt5y++23o1kz9xZQSilWr16N0NBQjB07VvXe2LFjERoaitWr3Vi/rwCpGygeGE4x7nn2d+oGsSH3NUZOMDPNfy1xgwjWriR4cJjamD8sASgq4temWb2WFczUYrMB/7hX/7rys21aG78vb/qWLJVM5Xd4n9NyzTXlyM9Lx6WctcjPSwel5dZOpjjn7NcpphncZ2wz4KHhNqxdyQyNLzzHP8fX36iNj11u1bd3+Ues/ktZKRuD2tv9Efgq+/eAawOxdUQELkxsgq0jInDXtYEAgJ0ZjbEmhRV+NYr2o5RJzMnGUPl4HlpDqJBEEFSW++67D4DxM9xzWQ4AOOdeeTzbvsN639dCqbV+T6k1J+6UScD+A1UnwSajdFqsSSHIvUwxeqx31zUa62WM5g2eM0TOhHxgOMXMWRIeGM7GRF5097SXgHfeYs5r5TjyXMXyKztbfV27neDUKRYt7G6dIH8/zyR5fr+CK4vReuTkKf7xAoGA8dBwmyoYSLku27ipwrkyBW73WHY7wYD+/Pfk8TNuEME0fRyj8/29v5nPy5TCcuCDdp4uLTkNgK19tbhbCxs5am5ob94Gs3lDniMB6BxlhLh+g5mzJIwaAyxYBIwaA7wwQfJoP6bdJ3/3PX/P5UnbBQKBQCAQCAAv5c62bt2K2NhYneNB5tZbb8X777+Pu+++G4sWLcLzzz/v8TXOnj2Lvn37uj2OEIK8vDyPz18dHD16FA6HAz169EBoaKjqvdDQUHTq1Anp6ek4ffo0oqOjr1Ar1RhFDrkrQimwjpl8iKea/8p0eFm7+JOV/GO1MmLK1yeOZ5IPX6QaZ9lYiTK3IiNmxqWLqZjw3FTk5umLj/pC0/rOnkD2eVohc8CkDnIu8m949VpgzTqKSRPY77N3n/44SoHedwJtro/CuGeAp1PPwd/GNqVrhzbkSD9cxKYjJfjPq3Mw/53JFbWC+OzYydraqaN7X3rcIIKWLSl+zAAaNWIOJYHAG+bMmWPpGS6TgKioKNV45i2XL6WiIHcqzuf4rt8fOFg10msylLp0/+Wx5OMUqaJAtHfIdWp4uJOdkmV7/rec4vP1rs9JEqvjY4TN5tLh79SRoF8flm0JCtx0IztGK9fS5VZ4tE5g2T7AgkXq+cXTjL/aUBulLmG0HmnmXZlJgaDW48kY9NBwG/r1obpaKW/PB4YPY8e4GzsdDoqNX/PPrxw/O7QnsNn09ToPHqJY/K77+7ISVJB7KRXZZ6aiqNg1TwcFMEfLPkcZusYGOl+f+c1ZS+uI/Xvm6qTPftvnvi2Fheo6m4Be0k1GztB/7FE2z+0/IOnmxB0/A/HDXOt9wHj8Cw5W15yTJLa3slJbsjJZ7nVx/quL9yQQCAQCQWXxyklz9uxZ3Hnnnc7/2ypCL0tKShAYyBZpUVFR6Nq1K7788kuvnDTh4eE4d+6c2+OOHz+ORo0aeXz+6uDYsWMAgBYtWnDfb9GiBdLT03H06NEa46Q5fQYICdEvlE6f8UxHWcAnO5tiYbL6O16YDNzWlSIykm0gXpzM0uRlY9xzz7hfvNpsQGoagcYXqHp/yGBgzTr9ey9OBtq1ZdGx454meH9Z5YybhAChFTJgYaGAdiNlxIXz6w2Lj36ZNRIhbT5AwwiTdB8L/LST/enckeKF54DISIKbOhCEhxtvrhYsZr+Pvx9BWJj+/V27gLhBt6N5bAw+3s82r5O7h3GlHyZ3D0NaVgk+WjEX+YWTcOo0/5wyBw/BUCtbycZNFPP+67rekqXA8+MoBtzl201PdjbFyVPMSBcZKTZUdZE5c+YAcP8MA0Cjxrfh1dn8OYPHg8P0TuQL59fjr2MjMahVICb/Q190+NrrP0DjyHs9zkz5+huY9i0Z2bBCCNDqOuAUCwy2NHbNfRMoLWX9LDub4sMV5v3ZHU8+zh/r3c0b8jE/7QS+/c56G3jzy+5fgLfnE0gSEB4GbP4aCAkGJIk4r1uvvufrhN2/AGFhxPk7EgI8P866YWbjJqqZF30/vgnUGK1HxNgv+DvizRikrYNz+gwbTwH1HCOPnco1FgBs3cYfa5XjZ3Y2xekzwF39Cbamu465owfL9jbaF2jpeiuwc5dr/d/nTuC7LWx+NF6f5yEtC3h2Yx5+fDzCuWaQ6zG6X0fMRVjYJGsNVHDWof4/b45U8u13TELYbgcOHDSeI63sx0pLXVLHZoSFKv8mHs95Suri/FcX70kgEAgEAl9AKPVclKNbt2645ZZbMH8+E1ydM2cOli1bhk2bNuGqq65yHifLof36668eN2zUqFHYvn07NmzY4DxnmzZtcP/992PWrFkAgIMHD2LIkCEYMGAA3nrrLY+v4QuWLFmCN998E7NmzcKQIUNU761fvx4TJkxAYmIi11H11ltvITk5GQsWLED//gb57AAkSXI6wgSCukh5eTmua9kC7YPPYW1CA9WmTqIU8atzsb+4CbKO/OksVF7TWLduHeLj4wEAFyY2QXigvs9eLpbQ+HXmfPZi6BUILOPtvEEq+l51PMN1od8LBAJBXUHsNwRa3M7Tqy5h45Fi3HVtECZ3D8UDqy7CUcDWBmItLBAIBAKBQOAZXmXSREdH49QpV7pzq1atAABbtmzBI488AgAoLCxEZmYmmjRp4lXDHnnkEfzwww94+umnMW/ePFx77bWq948dO4Z//etfoJTi4Ycf9uoatYVLly5V6/X00S1QRbdEREQgJyenWtvkK6oqE0D5nZhdIzub4tGR+vT55R/w22O1vdnZFI+MME+1l+vVGMmhKY97+03gufHmGTWEAMOH8s8XFgp8900j9O53AfkF5tcDgNxL6Th6/ASWj4jgRt1Nuj0EPZf9hfY3bkD9Bj3cn9ADnnwcSIgnOHSYYpxB0h8hwH/nAUePAm+9Y/Q93+n8l1b6wfn6uTLnv7vcfsG0Xf37AhNecH0XRs/Clq0Ur83mn2PuLOCmGyv/nFt9bt2NHTx8PZ5ERET47Fy1GU/nDe3vYOUZvvmW3xEY6H0ma3X2+weHMS14ZaQxoJcoMRq7tLInSubOAg5nAUvf9759UycB7dvxx/mfdlC8/Ir+MzOmAde2hNux/44ewLYf3ffLX/dQTJzi+j/vu5D7/a5M9VgoRwlbOa+M1fHJ3ee9GXe8oTavfXxNXfsuxLzBqO79Rm2hsmOYkm83UzwQz8bVwiI2XnXuBN0ayxdYkeByh9t5unsoNmQV49uj4UjLOg9/hU/GF2thHtrvnbdG5SHPX8s+BL7+1vh9s/3Wxk1UN/fxvmPl/FlY5Jo3PZ2rfPns1RTqyj2JeUMgEAgEVYFXTprbbrsNH374IS5cuIBGjRqhT58+CAkJwdy5c3HmzBlERUXhiy++wPnz5/Hggw961bCePXviySefxNKlSxEXF4err74ahBCkp6fjvvvuw5EjR1BeXo7ExETccsstXl2jqqlXrx4AGNbMkV8PDw+vtjZZYcBdBB1vrr06sUYat+40/X2Bu2tERhIkJWqPIdwNgSftjYwkuHeQ5NYBk30eyM93fx/5+QSPPkzd1lc4dw4oKOBtUFg78wuA/Hz3O8S8y2cA8IuPAkD7Jv7O4/z8fRt1984CoNcdwK5MavrdPDEKGJsIuCuB5W8DZm3Lx7qhAbqIw9nb8uFvA8ok/fdiswFPPA6UlQLdbgPatXXtdM2ehbJS43ZHN/XN8338L1aTQv86QWRF7VeHg2LmbLUm+szZQMeba98Y8ndm0qRJePP1OZae4f174tCq7Tbn+54ahKqz37/3gdW28ceuLreyOlG6owkQEADMX6g3EN12G7BjhzXD24svq/u2w+GqDfPnUf64ffQYEBBAkJfHvzFCWPHkh4bbLGm/RzdlWv+u9rLjCgrZd6GcrzreTJGfr64xY9Tf9edl92o0PmnbavZ5Me4IBIKqxtMxzAy51tdz44A2rdk49e13Ei5f9lFjFe175CHrtSLLywvhOD0NJcV/IDCoJezRr8DPL8TyPN246as4e/o1lJX9hYfaAasOWlkLT7S0R1Aiz7mZu11zhHZvZbYWOf4XwYQXCAbeIyFlFfD9Fuj2Y0bzJW++kW/NbB/UsweFJBG8OsvanKnEl89eTaEu3pNAIBAIBL7CKyfNfffdhzNnzuD3339Hly5d0LBhQ8yYMQNTpkzB0qVLQQgBpRStWrXyqh6NzIQJE9C+fXskJyfj0CFWtfzcuXM4d+4cWrZsibFjxyIurvLFxKuKq6++GgBw9OhR7vvy60Y1a64kWh3lmop2IW1kzHY4qEeFjr0hO9vaNeQCz9oNgPJeAM8KMwMsGyRlFTU1RH72ufv7cBW2JADMN0/KAtWVwT8gCoD7qDv5OF8iFwC3UlzVzGlFaTlCQuqhsPAy0rJKMGTVRUzuHob2Tfyx71wZZm/Ld9bysNkactvxf0vZ9x9lJ85aNO6e3Q43EBCi/93HJuqfFW+LdBoVUFUWQD1xUm+MliT2em0YSwSMSZMmYc6cOW6fYQAoLT2E40cfRfMWywF4HrFbnf2+stHEO3/mv96rJ1BURCBJ+gs8NIxg4gusDxw8RJG8D9QsbAAAIABJREFUxNxhI/ft3MtsPHJG6hoc3+EGILKxsUHq3UUuZ6+VOd1uJ5g4Hqo5FABWLGOGLeW4ceKk/ppG/Z133onjCXcMMprDjT6fuZuKcUcgEFQpnoxhZqRuYLVTtqcDs+YCSYkAQDHnDd+2lxAgcZRrDHfH8aOPoijvS5RVjKX5+d/j8sX3ERx+DxpHjgbgfp4OCIgGlZinaUFcE3y8/5zbdUS7Gyd6fG+9egKJSYAkUdUcod1bZZ+nGD1WPU8p163t2towY5r1fSTAX+dSyjJ1V66GoYPoq01AYKB+nW5lrqrMs+ftmr+q8VV/EggEAoGgLuKVk6ZNmzaYN2+e6rW4uDh06tQJW7ZsQW5uLlq0aIE+ffogICCgUg285557cM899+DChQs4ceIEKKVo2rQpoqJ8b6z1NS1atIDdbkdmZiYKCgoQqqjeWFBQgMzMTMTGxiI6OvoKtrL2ol1IJ45SG8GUxuzqMCCfPGX9GlqDmfZehiV43l67nWDSBLVzxyrypkK9UKamEj++JDSsGwL8gjErPR/rhnGi7tLzEeAXjNCwbj6/ts3GXFGUMkdLQX4GykrPwj8gCqFh3UCI+1oYuZdSkXNuKgoLXaGQm46UqAzaSgkIP/+munPImzetE8bds2u3E4wZ7TLqKqPnlVQmk8zKhsqKI0dQO2jbti0OHDhg+Az/MCICPZflICwAyMv9UuWo8YTQsG4IDorBrG0XDKJtCxES1AyUluNSzlqP+qSvMXLy/LAVeGi4fqyUn315rO/UkSAqSsI0jmyZEknSO4N5l75ngMsBM2kCMOcNqpJfmTSBoF1bzw0eSkNX84oSg5GRrow5GU/7u1FwghIzh7TR58W4IxAIqgMrY5gZ8vgmF7eXJDZuA+ZBBMaZGsZQCpWj34zjRx9FXu6XGNQqkDlT7P7Y5yjDrG35SMv6EpRSS/N0aFg3+Pk3hVRyEfscZSj5dxQCXz1ruI5od2O29Ruq4J67gY2b1Pu8OW9QtGxJ0a6tzTnfOhwURUVsbSzvC40cAcr9mLugKKP5JiGeICGePRtHj1HMe1vdbkr5QW2EWJurvHn2qkM9ojJUtj8JBAKBQFBX8Wl1yJiYGDz44IMYPXo0BgwYUGkHjZJGjRrhxhtvxE033VQrHDQAK8CckJCAgoICLFq0SPXeokWLUFBQgKFDh16h1tVueAvpxe8aG7PlhbUSXxtymsV4dw3evaxc7dqYeXKuuEEEyQuBPnd60nJ2rVemAWtSiHMRzwzzxHIkXmWQpBKUS0X48vcSxK+6iO0nSnC5WML2E+z/X/7O3pekEvcn85D7BwMd2hNcvpSKPw93xLE/BuPkX6Nx7I/B+PNwR+ReSjX9fO6lVJw4NhK9mp3H5w+Ewd8GDGwViKld1MdN7cJe97cBV7f8wvSc8nMLuH92UzewTaiZg8Zo4+lwWN/1xw0iWJNC8M5bRPWcyGifFxEZV3tZv349/G2AXTOevtgFKHrR7pQqyRrbEANbBaIo70uUlxd6fB1C/BDZ9DWkZRVjyKpcVb8fsioXaVlFKCsvwPE/4z3qk9WJJLFMGrNn3+GgyNxNceRI5a41/nngmSRgyWLgxSmuPh43iGDtSoIZ09g4/u4iICYGHvVvJXY7QaeOfBlO5TGe9nf5vEbHmDmkjT4vxh2BQFBduBvDzDDKwnDnSJHXdZ6uxa04aMrLC1GUxxw0a4c2RNfYQIQH2tA1NhDrhrL5vTj/KzSyTzeZp4vRuOlMEOKHiMZPOyV/JUpR8u8ovKhZC5dJwHVtDnt2MwDatQXatuF/h6PHsrUwwP6OH0Yx7nkWvJQ4CobrVi1W5iCj+UZ+Nho0sH5Pw4dad0548uz5Ys1fHVSmPwkEAoFAUFfxKpPmn//8J+644w489dRTpse999572LJlCz788EOvGldTWb16NXbt2gUAOHz4sPO1HTt2AAA6d+6MhIQEAMCTTz6Jb7/9Fv/3f/+HAwcOoF27dti/fz/S09PRoUMHPPbYY1fmJmo5RpsdbZq5Mpp54nhXtDEhLFvFl0RGepe+bbQpUKbP887FS2NP3cBkEzyV9pEkIDeXnUd53rhBBKdOU0ua1t5E+8k4Tk+DRIFXe4dhaWYhei5zFSS+pqEfZvQOw0vf5cNxehqiY+d6fgEFERGAst7x2nXAt9+sx1/HRqBrswDcfW0YOsf4o34gwdyMC0jLGonYqz9A/QZ6aUVKy5F9ZioGtgrEuqENYCMEjYMLKqIGA/HDCL3EQ6NGdrfF1pVOGF4Wi/zsajdilALJS4B+fajqWfFVJpk7ySQRGVc3aNCgASQQnALFQDm6tuI5HrLqItKySkAA1A8JwOTuYUjLKvG6b9ZvEIfYqz/A5uNTkZZ1yvl6UEAjAEXof00BpnSPUET3mvfJ6kbuq7HNgGn/BkCY01c5JnuT3ci7TvduxDlGZ+6mqj5mtxP0qZBqSUyiOikYX6Cdc3zd373NihHjjkBQc7kSkks1UeaJF3BjBUkCmkaxIKr/Lac+kxkG2Nq7TAImdw9TZcgAgI0Q5/xemL+dO0+HBDVD7NUzUb9BHCgtR+6F12ADVDJn4+5ogr5tXGtgm62+2zUwj/0H2B8elLI1csuW6j2QnFG0dqV+f5N9nmLPXuDGDq6sVN4cBDC50k4d2ffjbr7p0J5JEGvh7U8T4qvm2RTywwKBQCAQ1F68ctLs2LEDzZq5T0H4888/sXMnp9IuhylTpoAQghdeeAGRkZGYMmWK5fYQQvDaa69ZPr6y7Nq1C59++qnqtczMTGRmZjr/LztpQkNDsWLFCsyfPx+bNm3CTz/9hCZNmuDxxx9HUlISgoODq63dNRGzjZTZe0bGnMRRcJvaDrCF8icrgZWrqU+NWN4Yi8zS53vfSbH3N1Z7oF1b1wZDWd9ANsR1uZVtUqxE5QF6Z8qbbwEbN0nYf8B13sRR1hw0N98E/LrH+7oPJcV/AADG3hqK8d3CkH68FKfzyhEd7ocezQNQUErx0nf5KC4+gvy8dI/lyJQoHTQAc7Ts3P48wgMJfjpZip9OlgIAWjSwYXa/cADAd8dfRL369+iuVZCfgaLiU5jSPcK5wf3rBTuumufgSjxERUXhwIEDeP1NyXCjzXtu5edq9VqKlFWuZ9eqLF51SgLVlnpWAmMyMjIgSRSNgvmyfY2CgQtFwLXzszGnXz0Arj7sDfUbxKFe/XucUoN+/k1w9mQSBrYKcjo/AVRE9wZgyKpcwz5Znch9dcdOrXMeiBukd6J6CyGuMYEnYSLPOcHBFHPf5Et+VtZQaSSd4sv+XhmdejHu1H1qouFdYM6VkFyqqTJP8vi20KS2oREvvwLc2Yvi+y3eXdtmA+7/B7BWvXV1ztvt7XxzQPsm/s7jwur1BNUs8iXqmtzk9fDWERF4YNVFQ5kzf79AUFru87lbkoAfM/T7EEqBvfsoCncay0HfM0DCi1OYXFriKKqTHNUGP7mbb4YPBdZvYP8mBJj8L/Y5+fpyzaCqGseEDKhAIBAIBLUXr5w0VikuLoafn7VF2KeffgpCCJ566ilERkbqnCBmVLeTZvbs2Zg9e7bl4+vVq4epU6di6tSpVdiq2ofZRsrdJsvImBM3iKBfH/1GXjaW8Yo2emvEkg0Gza9i2RnKtnmancC7F2b4czlMBvSXsPFrvlF+7psU0/5tXj/GZgOeexZoUA848gew/CP9Zua3ferzajcqRvzyq7XjjAgMaon8/O+dhUl7tVAXJ913jjlOigp34tgfg52vBwfFILLpa5WKqM92zENpWQ7u0uhxz96WjwfX5mJG7zCkZZ1EQX4GwsJ7qD5bVnoWgH6D+9cLdlzIK8Gdyy/h4HkJfn4xaH/zFvzy83XIycnBY48SfL5e/eUTAkx/WR2Fr2XlanXtmpRVxtljSkSRToEnnD3LnuvfxzVBSUkZ+qy4hDN5EpqG27D5kQYICPBH49fPIbqeDSM/zwUAUFI5XURC/Jz9Kz8vHcUlp1XOTxkW3Rti2Cerk759gZYtaUURY/aau1poWkY9Cfzfe+o+rO3TY0az+Y8nYTL7dcotVCzjafQszxCenW2u0+9LRFaMgEdNNbwLjHFX36OuXNOoHbwxLG4QQatWxtFMCfHA6rX897x10ADsezh2TP+6du2tZd+5MgBAaXk2ThwbiYGtAjWZrTnOzFYqFQNg62F5DaxcO3yeUA+tFl9CSWl2lczdNhvQyCBBJzcXeOu/xgETX24E7h8soV1bG9q0ZjU5lSjnUTNnsXKcCmcxXlixDE7p0NzLrvqRyUuA+vVolYxjYs0vEAgEAkHtpcqcNHl5edi9ezfsFi0DsiRaTEyM6v+CuonZRgqApU2WkTGH5yQxM5a59IatR2kqF+L16hFsT/fwC9CgvRcAeGC4+jv4cqPx5yUJANEb97THvPW299kuVYk9+hVcvvg+Zm3LNyhMympg9G1Rgn/39J30EZNnWIKBFXrcyoj9tUMDEL/qIpZmslobskNGiX8Aq4/F2+A2Cg/EknsboOeyHMRevUgl72C3E0z+l1p+b9IEgr69jZ87I4k/d7J4MsL4KbCKXPdNfq5/SWyien/7CRYdO6dfPfz3pwJsOlKCgrzNyL2U6hMJMiPnp4wc3cvrk9XJ11+zP1rkOSU42P1gu/R94O674HTA22z6MVqO4jWax8zGdKXT1l0mwscpktOAZLMBL04Ghg8DTp7iBwf8to+iYRWMJyIrRqCkphjeBZ5xJSSXjK753RaK3r1887x4Oo5qHYpnzvDPS4ixg8YX/Jypf83K2tuPAFLJb24zW5vEzAfgWjc0ClevHeR1A+D7uVte+7ZsyZ8MKdwHTGRsZ3VveFkohAA5ORQfp+hVDOTflic/rMThcNWPBKp+HBNrfoFAIBAIaieWnTR9+/ZV/X/jxo3OGixaysvLkZ2djfLycjz88MOWzh8bG4vQ0FAEBQUBALp06eLmE4LajLvijFY3dlaNOUY6wwB7/eAhiufG8xfeWngGA4BFG5sVWvYEKxHYSggBgoPcO2BqooMGAPz8QhAcfg/Ssr506lhra7nYQwk+f7CRT6WPCvIzUFyaYxixP6l7mLM+juyQURIa1g3BQTGYte2CwQa3ECFBzRAa1s35+qHDFK2v97yeg5ksXkK8tfMI46fACt26dUPz2BjM/vEC1ibon+s52/JxTUM/9GweiBB/grSsEnRtFoA9Z3wjQWbm/ARc0b28PllTCA6mKCrSR+RqkSTmoEleCBQVEeRcpJj2iv4YuX+bOeK1GMmksULUFA8Nd2U/fZwiqTInJQl4ez5z0jSL4Rutps0AKPV97RurCPmrvweivkLt5EpILhmt9ecvBBYurry8sbuMLt44qjPEG1z+SqzPray9u8X6I+NEGaZ0DzXNbAUoggIiMSs9F+uG8dcNMfVsOHVZ8uncTQibP9u1JcjcDfDm3CO/uz+PnIWjzUKRb2PaDPXx2t/WaN+27wBQL5wiJ0efyVPV45hY8wsEAoFAUPuwrE9y8uRJ5x9CCAoKClSvKf9kZ2fDbrfj4Ycfxvjx4y2dv2/fvpg711V0eMqUKVizZo3ndySoFfAKaCoLMBu9Z4ZcTNnh0C/Q2aKbcM+rrGMDuBbe8nm05zVaiJ901dI0bQuP1A0UDwynGPc8+/vgIQriwT6SUmDyi9aPr4k0b7Ec4fXvwaYjJei5LAeNXz+HnstysOkIi75TZrrIyBvEwmImfeQpViP2A/wjVI4WGUL8ENn0NaRlFWPIqlxsP1GCy8UStp8owZBVuUjLKkbjpjNVRutxz7PfG2DPZaeO1msuKJ9hZdaM9jyePn8CgRI/Pz/8Z+YspGUVI37VJdVzHb/qItKySjC7Xzj8bMTZRwZcG+h1P9Ticn4WQtJp4POdn76gcWPfnauoiODgIWv9T5KA02eATh0JOrTnz1OyI2L4UOtteOVlvkwapUzK8uMU9oLDwSRYeO0CmFSLduyRzyMfN+cNim83S9U25mjnTHlMFdQ9vF0TCq4sZmuW6rqmEu3a3lOMMrqUewWjcVQOQAOA9m29unyV0bzFcoSEdtGtvb8+UoJ7rw/EmFtCAbhfJ5eXZSMyei7Sfi/BkFUXueuGpuF+Pp+7KUVFQAR/rCAE+CLV/Xm6d1MrNaxJIXhlmusaPJS/bXAwf982aw4w7nmKaTOge1+MYwKBQCAQCLRYzqQ5ePCg899t2rTB/fffj1mzZvmsIZRSVUFCuSbNAw884LNrCGoO7vRyPdXStaJXrsxckKOcY5vJThe9/vBv+ygOVBRqV8oWdLmVH6nXLMZ6W5TwNn68jZ6WTh2B3b+4Ng81NUvGiJKSCzj2x30oLzsDP/+muLrlF2jeYjnKywvhOD0NJcV/IDCoJYKCO+LMqWeqRPrIasR+g8ajDLMD6jeIQ+zVH2Dz8alIy3J56kKCmiH26plc+SdvJQ6sZN8I7X5BZZCzE7p2jcOyZcvw3Ljx6Lks2/n+NQ39kPJAA9zfJhiAq490jvadBJnL+TkSQ1blYnL3EEV0byHSsooRe/XMSmfsaDl/HggL8825ft5FseJj68dPnwEUFjB9+sRRVCOX45r/EuIJUlbp66tpsdmAG9qzzxgFFix+1yWjxjuf0qCkHHt42T6Uskhjm40fre7LrBchf/X3QtRXqL1cCckl+ZrfbaGYv1D9XmUyF9xldJmNo0z6kt27rzLuK4tyDU5IKMol4Imbg3HsUjmui/DDnH7hCAn0w5ajLFDKSmZrWHgPFBU+jU1HFiAtyyVvFlPPho7RAcg8XWY6d3uSKSqjdXQMS1BLAN87CPh8vfnneeOJ3U4QcRIq24TRtdm6W19bDlC/Jssby38njhJzlkAgEAgEAjVe1aSZNWsWmjdv7tOG1KtXD6dPn/bpOQU1G7PNmycbO08MNq7Ub+XrlCvl8jJHcmbumxRrUlikntJgALCNF68tc94wNh45HBSbv9enwFvZpLC0fmsQwuoefLWpZjhzDu1vByI5UCZ/TyUXcfTw9aA2O1q324/oWFdWXX4eK/hTFdJHYeFu5MrS8xEU0AhN7C+Ynqd+gzjUq38PCvIzUFZ6tmKz2g0AfyNaGUOBmXyBMF4KKsPGTRQzZ7skPnr1HISoq/ojr6AD2je+hDn9wtGzeSD8bOxZUkqf1Q9ir/lKxsQb52dNYvlHno21lLK+mnvZpVtPCBA3CM5abQDrx5MmqKVYHn0YKC01rk1lJJNGqWuO5QUePDFS/X957HE4mDOG5/jhjTm+dhwL+au/H6K+Qu3lSkgu2e0EvXsxiTNfya25k28zGkcpBUaPBSZNcF8k3hsnhTdo1+DARQDAiJtDdOvsHs0DEB1uw6z0fK6MmTazNSp6OoJDOsFx+l8oLT0PADh1WUJOSbTp3C3PW1m/U6z91Pq9JI5i48I3myWnKgIhrF5jQjzB3n3U1EnzyEPG85E7qeyJ49nnlOtugF0/aQz/esrAuuQlQP167p8LgUAgEAgEfx+8ctLcf//9vm4HOnTogO3bt2PKlClo1oyteA8ePIgFCxa4/SwhBElJST5vk8D3aKNpzTZvVjd2lTHYyO1RSp7xCjcrz8sKkLJU+BMngeZXmbeFUmD1WoqkMepFuNJw5S1WNnTt2gKvvsK+6/sHSxg99so6ag7tb4fyMgcGtQpk+td2f+xzlGHWtnykZTlwaH87tG6333m8N3VfrOOHgXGvYd1aXsR+AdJ+L0Xs1fMsRewT4oew8B4AgFemAR3aE+zYqd+8AVUncSCMl4LK8PZ81/NDKfD9FsBmC0RUzJv45dgI/Hd7AUL8ibOPzKnQrP8kvj5e/7EAgf4NEBzS2Wft4Tk/Q8O6+TyDxipPPQnccxfB5u8pFiwyP1YZMatEGUWrfU/OolQacb5YD6xPpZg0wWVIihtEkHvZlW2z4mM2h037NwDCxh6lEdtuJ3j0YYoPV+jbGRxMYbfbdBr8Y0YzAxcPbWaDFjkTtWFF1urcN/VyomaOY3dZN0aFnWObXfk6NVf6+nUZUV9B4Am+zsBydz6zcZFS84AtgI1hzz8LXLgALPvQqyZagrcG33OmFHEpl7iOGEKA6Hp+FTJm1jJbGzS8D/UbDLI8dz+TBPTuxdbM6z6zfi//uFfeu6knU0pZ0EJCPJsPCTHOPl3+ETD4Psr9Xex2ggH9Kb7c6Hrtzl7AkMHEOcZn7ub/3lYkq+X5MCSU6uZtb+DNP2JOEggEAoGgdkGoWR6vG9LT0/HJJ59gz549yMnJwX333YfXXnsNALB161akp6fj8ccfR1SU+8jaffv2ITExEefOnfO4HYQQHDhwwOPP1RZycnKudBNUREREeNUmfTQt8Un0kMPBNOm10W1rUjyTSEscBbRpzS/crIUQYNIEV/vl72T/AQmjONFThABrV6prhsQP428aeCnyPIOetp6OzQbc2AH45Vf9OccmAv36MKfSwUNUZQisCsLCCHb82Ahdbr+A/HzXhUpKLuDo4esx4NpAZ42ZkjIJyT8X4veL5dhytBiHsiW0bHMYgYGNnJ/LvZSKE8dGYuB1gZjcI9S1QUzPR9rvJQivfw+at1jucTvl7zX3Uiqyz0xFUbE6Yr9xU88j9scmQlWQ2+GgWL2WYv0Ggp+2NcJtPS4gKdF9JLk3Gytv+8KVxNvxxOx8As/njYiICF1/lSkpuYA/Dl2PID+gsMz1+tUNbHiyUwgy/ipF2u9M2iTA5of6jccgKnp6ZZpf5Rg5uI3GLgCY8xrQ/XYbt59p4Y3PE8erJTcTk/SOBqNxWdmPza5vNLdm7mb1W7S88xarZwXwxxyz/ulwUOzdRzF9hrHMi9E9vTINiGhIdOOb1XVC6gaKOW+45lA5W3Tj1/D5GkPG3VhVVWucmoivx+0rjZg3GHXpN5XxtZHa3fkcDoovUinX0TJjGnBjB4JWrfhzDFC12TS8NbjMqt8K8MhnlyvW2WG6dXZwSEdAOuuTdbISeW7LPk+5+ycjeHslLfL8lrqBYvbrxgcq50ElVtbUH6dIWJSsP2d4OFvzG/3OWio7Z/DmH0DvVKyrc9KVQMwbAoFAIKgKvMqkAYBXX30VH330ESilCA0NRVlZmUq3tUmTJvjf//6H6OhojBgxwu352rdvj6+++gp79+7FmTNnMHnyZHTu3FnUpKnFKDcyAKpMhsmbaDmeLFTyEmBNChDbzDzqCjCOimPFK/UflGVl5CjQ1Wv55x/5GNCiBXD2rN64p4ycVhr8oqIoMjOZ5jLPQQOwItGLkrWRZuXVHqF+7I/7UCYBk7uHgVLg4U8v4tMDxShTNM3fBvxx+Da0ueGw87X6DeIQXv9ubPrjS6T9Xux8PaaeDZ2i/ZF5+kucPT3dY8Ow/Bv4ImJfjj5XOmgA9nwkjSEYlsAutvwD95rkegciRZvWeoOmFqHdL6gKjv1xHyQKfPlQQ2w+WopFOwuQXUhx7JKEl77LR4X6GfxtQLN6Eo6eYxmw1eWo8WYs88YIJhcI5vWzAf3VDgLZ6Z+8kOL0GQAU6HCDWjJsWAJVSZQpnTpatLUPjBxERnOrkWzLwUPUaZzyNFPBbifoaycoLFCPV5Sa10ojBBWOHaqSP/NErlEpASdfRxntXN1Sj0JqUiComfg6A8vd+ex2gmuu4U8wFMDJU0CrVsafN5qb2rUF9luMR5SkEuScfw8lxUcRGNQCEY2fgM0WqFqDKx00k7+9jIU7CgAAm/4ocQZdAMp19m40ihyLqGZ3+XTfIEnA/71H8dUmzz7X+nrg4CHj95UZ67JcYvISik3fGB+nxV12usPB5El5yL/jg8OA9z5QZ9fwfuPKzBlG849yLhZzkkAgEAgEtQOvnDSfffYZVqxYgRtuuAH/+c9/0LZtW7Rp00Z1TJs2bRAdHY3NmzdbctIAQFhYGG677TYAwOTJk9G8efMqkVYTVD1aI/OwhKqVYfJUr9xs4d2pI8HwoRSfrDS/JqXA3n0UfTWGMKMoONkY5nBQpKzSv08I8MH/2L+VRj7X/RBnkefCQoqNmyhmv27eRiN4mSPBQTGIbPpaldV6oLQcZaXMyvnHxXLcm3IRF4tcX1TzBjaM6hSCH0+UIi3rgsrpIkklKM7bhLtaBuDZ28JwNk9CdLgfejQPACHAkFUX8fWRxWgSNRU2m75ujRWUcmWjngSaNQOOHAFXIkj/WeDdRUC7ti4HzbYfJWT8BHTryqLvZceM/LdRNCZvs8Wi9NQGTSOEdr/A15SXnQEA3Ng0ALc3D8LFwnK8s7MILRrakFdMkV3I+nGZBJy4TGEPJcg5X7n+aJXqHMuUhhxeP3vqCdanf95FnX0WcM0JNhvFxPHs80ppMVk7324nqF/PWAZTnkPMdPIB/txqtxMkjqK6iN/F7wJRUVKlpFaU34VRJqpS5g3gG448kWs0KtLN+yxQ9XIvQmpSIKi9+DLbxuGgvFgtACwA66YO3p3XqoPm7OnpyD2/GKVSufO1C2eno37jMc65vL3dtf2f/O1lzMsocMqftYn0wyd7i/D+r4X45Uw5hrYPwuy+9SrW2e/C3vTfPp/XlQ52qxw8ZLzf4gUo7dgJfLPZ+DjeM+CuDpFZwIRMx5uZkoJ8biMZZMD7OcNo/vHV+QUCgUAgEFQfNveH6Pnkk09Qv359LFmyBG3btjU8rnXr1jhx4oRXDZsyZQq6du3q1WcFVxaekXnlar0+r6/rctjtLF3dygZLXnhr2xMcTJG5m6L3ndb0hHNz9a8NH8r/bPKSCnmY3/hZNMrX5NoEOTkU2edZmxwOppn85VcUk6YCm7933z5umyukw/o0P4+tIyJwYWITbB0RgT7NL+DEsZHIvZTq3YndXPPPwx1B6WUAwIjPcnH7VQGq63ew++Ol7/LxzxuDMbBVIHLPL0Z5eSHy89Jx6q9nUSqVY0qPcPRuEYThN4SgVwtWwNxGCCZ3D0OpVI6c8+/5pL3/9x5w8CC6+nTTAAAgAElEQVQw+D6CeAt+YkrlLCpGYpKESVOBzz4HJk0FHhkhITvb9QOnbmASCuOeZ3+nbnC9ZyVK3uEwt0560hcEAnf4+TcFAOxzlDF5wl1F6BTtj2MXJXSNVffju64NhKOAolQqx9lT/wal5W7O7j3VPZZN+TdUfdVuZw6TEyfhHJ8PHtLXflE6JOa8wWRXlLV/Vq52HdvlVuC5Z/nXX/wunH1/WIJ+DpMxmlujOIYZSoFpr0A3DhnhcLjmIyXymNOhPeHOre8uYpIy01/i1+KRDVi8z/LuhXcsj3WfGY+1vsSTtgsEgpqD2XrMUz5OkRA/jGLaDP77i991/dvK+OUpZ09Px/lzC3DXtX6qObH/tX44f24B5G3/PkcZyiWKr38vwoIdzEGzdmhDdI0NRINgPyTeGobtTzTGwFaBSN5ZiLJy6vN1ti8YPtT1PdpsTG74nbdYzVBlMJN2Xyrz3Dg25xo9Ayxrlqj2dJQyRwtgbR5qFqNek8cNYu2bMc13+2Kj+aeq990CgUAgEAh8j1dLxMOHD6Njx45o1KiR6XHh4eHIzs72qmGzZ89GaqrvjcWCqscooke7mL6SMkzywlvZngH9gcQkYNzzrFbA3Xe52mvksJn3NrBxE1vMb9zEFvefrDROZV+9lmL6f6y1kVJg2gxg1Bg4Nw5LlkpeRZy5zlmO7DNTMbBVINYNbYCusYEID7Sha2wg1g2tj4GtgnD+zIs+NawqDanr4kPhbwPuaRWIdRUbQvn6a4c2xMBWgZjybT7+1S0UpVI5fj/YHsf+GIzcS8yKqYz+U9K+CXu9pPioT9pMKfDJSiB+GEVBgbXPBAezH33bjxJ+26d+7+hR4JER7N/Z2XxZAtno6W7T54oOrxqMjLCCvy9Xt/wC/jZg1rZ8LPq5ACUScPqyhIEKw45rHGmIgdcFwt8G5Fx4H38e7lgljt8rMZbJTpb9B1jnlQ1y8vhspE2vbjf/vCdOugxF894y/uzqtep55sFhzDBlaW41mW6tOICtGDN5c+vE8QTt2tqYE+cGvhNHjl7mfdaooLP22Dt76dv8/RYYjrW+xJO2CwSCmoGRTJQ3Y4Q8/ruTSj5ZkfS5/AM2fvsKSSpB7vnFKoeLal5uFQg/XIINwLMbL6PNwmwMSrmEknK9/BkAZwBUUTmQ/HOhz9fZviAigtWHkR0zDw23qQKU5PXs3n38zJV5b7N1vjJwQvsM8KQ15fe14z4hrv2i2Trebifo09uGSRN8M2cYzT++Or9AIBAIBILqw+uaNMRCmoHD4UBwcLBX52/UqBEaNGjg1WcFVxaj9PCEeIKE+Csnw6RNZVdKtGiLOEsSqy+QvJBlSBilqFMKvD0fGD4MeOsd87R3mw1IWeV9QVBJsia9BbBNwh09gB+2ql8vyM9AUfEpTOkeYbAhC0Fa1km0uT4Dh7J6eNdQBVpD6tZjpSiTCjDFYEM4qXsYei7LQW5xhcMiPA8r7o/AD8dK8OJ3+djnKEPXWL3Mwr5zrJp5YFCLSrdZ3X7rMgxyJk3GT8bnAoB9B8xlcbT1LrRUZSScvvCoubSa4O9BQEADlEkEaVkl2OcoBQCczpOMDTs9wpD2ewnubx2AYukC0rJGIvbqD3wqP2Z1LCvIz3DKGPoCSoHRY4FePSV8v8X1ukuW0HPkLM65b7qXTlHOIZSy/69dSdCvj/u5tUN783prZlIontRciRtE0LIlxZ69wI0dgHZtXe+7q5vliVyj9tgTJ4Hvt5hPsFUp9yKkJgWC2oWvZAodDr2UJA9CWGYFwKRvk8YQ9L5Twqgx1q9lRM7591AqlWNy9/qGDpe0rBwAQcg8XYxBrQLROZpg3cFitwFQRy6WV9k6uzIkLwH69YGzrppyn6fcs5mZLMwCJ+QacEbZn3a7ftwHWDDF+g3s34+OBJISKXct7cs5w+hcYk4SCAQCgaB24VUmTYsWLbBv3z6UlpYaHpOXl4eDBw/iuuuu86phXbp0wZ49e0C9tWgLrhi8yKLEUXKxzSsjw2SWyt6pI0FREeFu1IqK1Cnq017Sn1spW6NF3hgQAtzZ03sHjadER+sdNABQVnoWgPuMlJ07z/qkHZdzZUNqKGyE4HReuaXr7zrNNoNJt4aga2wgnu0aiiA/Fskvab5EiVLM3paPAJsfIho/4ZN2ewohQOZuFmHfto2bY2EutedwsM1c4ii+VEFVRcL5MqJUUDu56Ub+6wX5GQAoGgUDxy+5ngd3/fje1sFVltVidSyTj/MllELloKks9w4Cdw7S8o979XOInF1jZW6121lkrZHBSnYAy9HHSolGM2OmltQNLOhhwSLm0Po4Rf1BWe6FJ0sjt9PqOkF5rBXpmaqWexFSkwJB7cGqTKFRhrH8enqG+3USIcCkCcRZl1BGKZVbGeQMF3dzop/Nz5kFe3tsAAAmf8ZDdsxc09B2xdfZPCQJSF7CvnvlPi9+GMWcN6jp/swM5TNg5RnRjvsrV+vrrhmtpX05Z/DOJeYkgUAgEAhqF145ae6++26cO3cOb775puEx8+bNw+XLlzFo0CCvGvbss88iJycHr732GoqLi706h+DKoTQyU8qinapKC94dVozPvEU4IS4JK5noptavK+vwy3IGvBoyhFSNLvWpU/zX/QOiALjfkPn5R/mkHaUaQ2p0uJ+l6288UowgP2DsLaEAgEB/G5K6hCItqwRDVl3E9hMluFwsYfsJ9v+0rBLUbzymyouUG0EpsOxDJk337WbzY9u1hanUniyblLxEvbEkhGV2VVVmiydGWEHdZO4sguho/euyo+P3cU1w7OkG8Kt4BN3146vq+zuzWgqLT1Y4e3yD1bFMPq4m07kTm4PMon3HJgKPPco/IGUVnBJs7ogbRPDuIuDpscA/H9HLpO3YCaeh69GRrs95YsxUzreUsgwjraOmKgxHPMmXewZUXmbV4aD4dY9wVgsEtQFPJFutyBQaBXkpXzeSqOzcCRj/PPDKNLYfiImByvkNsH2GlRqY7pAzXNzNieWKbPbEW0LcBkAF+QGb/yy+4utsIzZ9Azz8T0lX660yQXHDElxZJ55KWYq1tEAgEAgEgsrgldzZY489hg0bNuB///sfdu/ejb59+wIA/vrrLyxbtgxff/01du3ahXbt2iEhIcGrhqWmpqJXr15YsWIF0tLS0K1bN8TExCAwUL84JIQgKSnJq+sIqgaHg6qMzGbSKFWNFTkDu51gQH+qkrailBnOJ453pamziDdrK/+J4wkiGwMrV/PlZeSFvlJy7dBh4E2DzZ4vCA3rhuCgGMzadgHrhgaoJBHYhqwQIUHNEBrWzSfXC1AYUrvGBqJH8wC0aMAi8tZyrj9nWz7CAwl+OlmGZ7qEINDfZRWc3bceAGDhjgKkZZW4rmHzQ+MmTyMqerrH7SOEGfFyLzN5uGtawJLsxKgngZwcICAQ+PgT9XtyQVEt8gYvMpIgbhBMpfYWv8uPmPdVxCUPI5lCUWT078UrL+v7gL+qHwfjma4lWLCjELO25RuMI/m4pqEfejRnUbpVkdVS3WNZVdK0KZuDxozWS+YQwox77draDI2OlLLfbPK/+JIqSpikIZxSY4mjgDatibOfPzBcHdAAMKOiO5kyGd58C7AxrV8fWuXzP0/y5aknqNdyL7IEZEgIwY4fWe25AXeJiGSBoCbijWSrmeSUUZBXy5bW5Cl3ZQK7f2GBOBu/BiSJol49gu3pbFxduZpWSgZZSUTjJ3Dh7HTTeTnAZkOpJDkDp+QAqHkZBRiy6iImdw9D+yb+2HeuDLO35TvX2t/+Sb1eZ1cHx4777lyEMHluJZ7IklkNaBAIBAKBQCDg4ZWTJjg4GMuWLcPkyZPxww8/YM+ePQCAn3/+GT///DMAoHv37nj99de5ThUrLFiwAIQQUEpx/vx5pKYaFx4WTpqah690niuDrE0cHEzdGp/3H5Dw1Sb9OeRC0S1bUrRra9Nl1hhBiMsIz9vEPZME9O5FFJFaAECwZ6+1aGhvIcQPkU1fQ1rWSAxZlYvJ3UMUG7JCpGUVI/b/2Tvv8KiKto3fc5JAQgpCQujVRhdQmiBNBJGgSI2IvFYIon74ggRsCIqADTvFhmIJVdQQpOtLBwEFKRoQQSEQAiGBFJKw8/0xzO7p52zNJszvurwwu2fPmS3nmZmn3E/9aSAkxCfXi45ROlJDJIKZd0QjcUkOBi46j2T5hnBTHtIOFyHkysZySFNtP6sZt0fjrmvD0PPLHETH3IVKkR1RJfYRjzP7KAV+XM0aj8bHE+zeQ2EnCPfRJ+y1RtmPPbq7GlYTAiQOYZl5cpj8H7B7D9u4q8fFq9A4rsou/zgI7TphBeUbvUA0D4hM25iFHg2LcKkEuCE2hFW2LTyPiZ1d9/HMzXlYmV6ElEGVESKx344/qloCbcv8CU8GGJYoAXA4g7T8HuQ9Xawyca0SIfQcjnPmAUtS4LR/evPViZNAXJw9RxWvCNILMgdq/ue21ehvu6g/L4D1oGvdyv/BJoFA4B7u9M1SY2QjjPYyW7bqr+31bJ/DoextyF83/EHg4kXz9+QOklQBMbGjkZb+vmHAJaZyfxTnLFf0dzRKgJIASCHVUbFiA0RF90FstZG+G2yAIQS4sxcPlJkfO3qU/u/F7jzC19IfXEm4EGtpgUAgEAgE7uBRkAYAqlatinnz5uHQoUPYtGkTTpw4AYfDgRo1aqBTp05o2dJA3N4m06dP9+r1gtKltLPy1dl0riw27YI5dQXFzDeMM9l4o+jk8RS1atmrpKEU2LyV4sYb9J9v0Vy7CcjMpPAwpukWMZUTUKf+Z1h//Fmkpbt00SIq1kad+tPcbu5N6WXk521FSfFphIZVv5K5zkyLniO1Z8MKuLVuKFYdKcIK2YYwVAIqVGiMhtevwbHD7Q0z5N/afunKWD/ziQNWHjw0cjBq37PyXzWJQ4AnRhO3su7U90rSSOZAlcs3qCu7fI1ofC3Q+z0SEoLQijdh7dGV+PGI8p5dc6QIaYddj9WvTJAyqDLubcyCrP6savG1LSst5M7EYYkSevbQr/ywsk9WiRBWyRN63z3ganQNWDuqjCqCymImcTAkmwgEAnv4437Vnw+ZvK0aSWKStHv3sX5cVvijRyWvdFlzZDbS0rOdj/OK8/gaL6Dozx3O9TWlwKbjxWhVPRTLhsRg3OoLOHyOooQCkiSh5PJpFOSfRkH+dpw/8zJiYkcHbTWNGVNeBHp0l9CwoUMzN6lpfKP3696EvgQd2rMveMFn0PQhEggEAoFAIDDC4yANp3Hjxmjc2KJTtgfce++9Pj+nwP/w6pU6tVkgpDSy8vWy6VatYZunwkKicHzxY+045V97k2LOB/pOLD3enMWqafQoLCTIzKTYt58CFDidqXTIy+nWlfXC+Wah9TXtElM5AdExfTTBFXeDHrk5qcg69SwKL7kcpOEVa6F2/VcB/Md5LT1H6l3XheHOaysiIoygoJhi5ZFLWHn4ELIyZwY0Q55XqGRmss18vwTg+x+Mj7X6rfTpzaSJAHuOAaMKloS+BK1ucmDU44GVDfQ061xQPlD/HgHgdMZLuJi7En2vr8Cyc+NDsT+zBP+36gL2ni5RxK3PFgB/ni3BhUuOgFS1+MqWBQpJAv7zAPDZ58rH7TgT4+MJksez6k4jO2RWbWeVPKFniwD3HUxGFUFlLehb2skmAoHAPv64X/Vsop7tJYQl1jRtIiEuluLDOfpViYGges2XUK36s8g++wmKLv2NChUbKCrO+fr61k+zkXHhMjIuugYaKgGXaRiAYvS+NlQx30/fnIe09PcBAHf1fQm7dpfGu/OMihVdMtxm+NK+83lTBGgEAoFAIBC4g9dBGoGAo9WCJliSYq+awJcYZdMVFrJGxbypaJ3axnJkevBzJI2kTucTlzUzwqg/ySefUfy21/h13JHXsYOrF0HKIutgkjsQEoLIqM4evz43JxX/HnsId11fAZM6VZFt5M4h7c+HsGxZNIBuAFyO1IsXfkbG8UT0vjYUS4dco6iSGXlLJQxYdB5rjsxGter/BCxDnvdzMKuQkiRg8gssWCbvH8Ofm/4K+y21aO4K0LiDUQVLYSGTfJQjMrkF/kb+e5SkS7jzjtnoe30FxT178qIDezJKcJcqcDN9Ux5e2MD+AwJT1eKtLQskw4cB/foSfL6A6joTrXoqtGsLvPQCsP0XIC1Ne351AkCL5nJZTWtJQ/l3X6+u+++PJ2r07EHQs0fZrsqTf16csU+WzfciEJR3/CXZKreJ2ecpJk/RHkMpS7SKiWaVzupxyKv5jWRyfYkkVUBsNf0GizGVExBbbQx2Z7yPu66vgEWK+TsfaYcvoU1N5Rq9fZ0KWDYkzLlG3/nLsx7LDPuSWjWBkxnWx018DkgcYh44K6vJBAKBQCAQCMoXhKo9gG7wzz//YNeuXcjMzERRUZHuMd72iykpKcFPP/2Effv2ITs7Gy1btsSgQYMAAKdPn0Z2djauu+46hIaW33hTdna29UEBpEqVKpoxZWZSRcNhgC14ea+PQGI2lh07lRun9u2ArduUrycEmDgBmPGaMmNOT4IKACIjCXZsqYp2t55DXp7voijvznIFlf49Afyyi2LBV76TSNCTKbOTfR4eDhQUXMbRP1ujR72zWDakskaSbMCiXBwsqobYmruQn+8KWpw9MxunM17AxgerOPWw5Wz7twhd5mejes2XEVtttMdj9AW8akZe2QLoByPtyo/p3TtmBNN95S/c/UzsnE/g/rxh9D3Mnj0bzz33nOKeveygaPJBFprFa4Ot7P7Pwbq/K6F6nY8RGdU5aKta9PCXPecQAiSPZ5+X2o40akQVlXOA8n6X2x6jcz9wPxTzBL+e3EbJK16t7Ig796c3ttHfuPOe9V6bcQro1rVq0K3HSgtf2+3SRswbjGD9Tt25f7251+2MY+BQ44QpQoClC5m9lvfFLCwkzn8jI4EO7T2bY3yxJqbUYv2+8Dx+PV2CI0/GOfvKcdRr9GDAToU7Pw7QHjspGahZg/jl91Le7KRAiZg3BAKBQOAPPIpsFBUV4fnnn8cPPzBNILM4jzdBml9++QUTJkxARkYGKKUghKC4uNgZpPn1118xduxYvPPOO+jVq5dH1yjv+HKz4nJUaJ8LJu12o2w6ABoZNHWABmBNI/v2kUAdSoeTXoDGX/Cs6nkfO/DFl8rn7G5IzDCSKYur8aplxnthIZCftxWFl05iUqcqig0eAEiEYGKnCHSZ/w8qRG5FSGgn53NFl/4GADSL1zc9zaqFKo4rzQx5SoGpk4HmzYwzzf2dIe7rzFB/Oi8E5ZOjR48CUN6zm44X4+8cBxbcG2lw/1dCWno28vN3ICpaZ8K4iuHSmUtSlJWma9fTKxV9Svg8ClBTmTN+bvV8wa8XUYmixRVb5g9JQ2+advsbs+okOzZRSEAKBKWHVXWhGjv3q6drofh4gsQh1FB+mFJg8VKKMaOZnd2xk+K1NwGHg16pqKHYuJlg+2bbl3TizbpdjuX6vXMkuszPxqbjxejaQJlMpV6j+4NWNwG//mb/eK5qIE9M4I+rj7tvKLBwsXIfN/N19ptq01qsiQUCgUAgEJQ+HgVp3n33XXz//feIiYnB3XffjQYNGiAyMtKnAzt8+DAee+wxFBcXY/jw4bj55psxduxYxTHdu3dHeHg4Vq1aJYI0Ori7sbFzrogIgh1bgFWrKXr3UjY0ttKCDqSDWM+RvnuPPY1o3jRSfQ4WiLIfHVFLHLjzugnjCN79gOKnn7XP+yJAYyhTlv4Q6tT/zHLDV1J8GoB1sKW4+DRCZIdUqNgAALA/s0S3kmb/mRLFccGA3m/VX05OvfvDV0EhX9oDwdVDw4YNASjv2YyLlwFY3/9Zp2ciPLyJX6XOyiI88NKmNbMjX6cYNzPm8+jnCzyXu3Q4gMlTAEmiPr/vud3KPq+dX4NBmlEveDTzDRY8klfWEsLkcAYPLD9VigJBWcfT4K/ZfsPbtdDggcRUfjhlEVCzJhvwrHeUPQVXrgI82S77Yt3Osbt+5/O8nECs0c2koI2QfxcP3A/EVQPemqU8RpLYd9e9G9X0epz5BkWjRtQtuWKR9CQQCAQCgcAfeBSkSU1NRUxMDL799lvUru2fDqoffvghLl26hHnz5qFzZ/1M+goVKqBZs2Y4ePCgX8ZQlvFlVqv6XADw9ntA61bUttZ9aTiI1Y50vUCSHof+oM6MKuU5qOXrJYn1HKhc2dWfpGFDYwecHnM+YNea8br2OW+lDii9jKxTz+Ku6ysoZA5cetO52HD8OUTH9DE9b2hYdQDWwZawK8dxqsQ+gnOnX8L0zXlYNiRMI7MwY3MewqQQVIl9xPZ78ie+FzvSx+r+8DYoFMxZ7oLghDsg+vV7GFNfelFxz9aMYrbB6v5vXzsMv54cj6joXkGhXx8sEMIkcAAmiTN7rvFxE8YRrF1P8d0P3l9Xfd/LnUyA+4Fgud0iRFvl6csmzJ6iV+VLKTB/AUXqCtdzlALfLARSFlEkjxcBbIEgGPCkSt+qcs7btZCdapq33rZ1Kls4HEU4c3K81+t2jt31e/UoZcDC32t0FiiH4edqF3U1KT8335MuXqoNsFEKjHocSB5Pbdl+kfQkEAgEAoHAX7jf4RrA2bNncfPNN/stQAMA27dvR8uWLQ0DNJzq1asjMzPTb+Moq5htbPx1roS+TL7l3VnsX6tNUWam5y7wzEyKdRscWLfeYfs8LJBELJt2zpkH3XPy10uqu4b//dp0Jon25dfAex+wBvNfpzgwZ56t4TkpLCTYu0/7eG5OKo7+2RrH/uqPE/+MwrG/+uPon62Rm5Nq+9wumYNKhjJlBZdOID9vq+l5KkV2RHjFWpi+uQAOdWN7SjFjcwEa1q+L6JiOymtIFRATOxpp6UUYsOg8tv1bhAuXHNj2L/s7Lb0IMbGjg8KhSwjQopn/N13+uD/U+NIeCMo/qStYL6SnnqYYNiIMnbsq79mbqoegZpSE6ZvzdO//mZvz0PCaEMy4PRKXirNw5I8WbtmpYEaSgBHD9Z8beK+9ptCUsvkhdQULkhhlZM/9EGjXFoZBHE9wOIDf91PFdzxwKPuP/3/qCmvbo7ZbaqmZYGnCXKe2/nfyQ6p+wgWXh/Ol/RUIBJ7Bk6vkmAV/rdZTvloLde/m3vGekpuTiiN/tMCl4iyv1+0cy/X7pnyESsDb2/ICskaXJGDKZNbLZ/BA7R7LFxDC5tLMTIqURfrH2LX9gVizCwQCgUAguHrxqJKmVq1avh6HhtzcXNSoUcPyuPz8fJSUlPh9PGUNO/Jj/jiXXsa/r/vVpK5QavMTYj/ztV1b6/ObjU0uPcWbgNary56rXQt4ciwUC3d3Kmg4fx+jyMhQPuYrqQO7Mgf8OD14NU+lqH5IS5+LAYuAiZ0i0KxaKPafKcGMzQVIS7+EpUvfxow3QqCuR6le8yUAwJojs5GW7mqoGSaFILbaE87nA8m4p4GCAuYM5frWyeP1HYy+ljgIRD8nX9oDQflGzwGRkfkSYqtp79mM9CIMWHgeEztHOu//mZvzkJZehJRBldGyehgAoFlsLn495p4kSzDBK0QIYZWSxcX6xy1b7jquQ3v9nmcc7tiZ84FxhedffxEUFronccn7p1WvDuTmsoxu9etfnMLmTf64/HlKXXJgZvbNqEJl6uQrFp+yalIzAiEXY5T1ru5jICcYZNoEgrKEv+5ld/vyWa2nrNZCdt4Hq6Jw/71wCeRNW1x/390POH8e+N9G7fF83d+qRgh+PeXdul0OISGIq/Eq0tIfwoBFudr1++EiRMX0wdq/VmPlYeUavWrc44iK7omc7KUeVfOr4d/n7d1dn7X8+1aOmyUu7N0HvP+he9eRB+LM5lM7tj+YerAKBAKBQCAof3gUpBk4cCDmzp2Lc+fOoWrVqr4eEwAgNjYWx48ftzzur7/+shXMudrwZcNx+bk4Y5+0vxHzpYM4M1PbPNmuUwmAadayHLnkmRpXIEr5/ImT7vWe4agdRWqZBF9JlAH2ZQ5CVTJlnJzz3yMrYwIuFWc5H1tzpBhp6YXOvyMq1sa1N0zDgAEDMOONc7rnqV7zJVSr/iyyz36Cokt/o0LFBqgS+0jAK2h4MCahL5P+qR5PgSsVNHq/pa9THM5Ajq8kDgIRQPGlPRCUb/QcEID+PVtSkoPVf72BtMNFzuMaXhOClEGVcW/jcGz7lz0+s2ck3tleiA3Hn0NUdC8U5O/wWLIx0MjtM6X6Uioc+XFmARqOw8EqJyeMg2ZeA9j9Ov0V++Oc8iLQXGW7wkK1cqXysRq9j9/3U/QwsQ9GduvUaVaNyuyMcQ8cK7kYXzp99XpI8GAWt+dyRABbILCPv6Wf3OnLZ7WeMlsL2XkfevLPhAAzpgGH/gA++1x/XIQwKeO4WILOndhjCz4DVq9lVX1q5Ov+/2sfgd5fnvd43a5HTOUE1Kn/GdYffxZp6Sedj0dUrI069achOqYPLl7YiJzzC+G4nIdKke0RFlYbZzNfxLksV4QkvGItxNV41ePki8kvQBGgAdhn3qgRq3hZv4E9xtfqcbFAtWrUMMBuhPw3YCZbbcf28+pM+fUJ0fZgzTgFdOtqf4wCgUAgEAgEgIdBmkcffRQHDx7EiBEj8Pzzz6N9+/YgdjQ+3KBDhw747rvvsG3bNnTo0EH3mDVr1uDYsWO4//77fXrt8oKvGo7Lz5Vxiv3du5f9c/nSQWwUZLHjVAL0F9d6zJkH9OxB3Rpj7Vr2et7IeTwJuHjR3PHnkiirYih1kJbOpA4io8zlAV0yB+cMesIUIKJibVSK7Kh57emMl3D2zPuaap5XN+Vh5eHLqBqXhOiYO1EpsiOioqxNiyRVQGy10ZbH+Rq+oY6IIM77Qt1bYfQoimGJys9a3dzbHS1zsw1boAIovrQHgvLLoT+MjaP6nqX0Mi5kf4L2NS7g8XaVUCsqBGuwk/wAACAASURBVJ3rhSFEIldkU/JQPVJCl3oVEBFKkJZ+Akf/aKkI8nrr5PE37jiCPCE8nCKhr4SICIrJU5XPORzAP//qv+6+ocDCxZDZLKBHd61OTEJfggOHKL53s6eN1dvWs1tJI10BGj5+PRtp1RfC107f+HiC5PFaG5vQl6BnD4rFS6nzsxQBbIHAPoHqd2e3L5+d9ZTeWsju+zCqIIyIIOjXF5j/hTbYzo9ZkQZ8n0pRqRJB/3uAd94HNvyk/z7k6/5baoWhQWUJMzbnYamb63YzYionIDqmj6bP5YXclTj6Z2sUXnIFby4VbEVxSTbuur6iV9X8ak7rFP/wSiU+tyUOYYH25d9TDBxqPifzj0YdkJf/BuS/D/nxvrL9fP6KiCDYsQVYtZqa7pkDUVEaKMrTexEIBAKBoLSwFaS5/fbbdR8/efIkHnroIYSGhqJatWq6gRpCCNauXev2wB577DGkpaVhzJgxGDduHO644w7nczk5OVizZg1ee+01RERE4MEHH3T7/FcL3jYc99W5fOUgNstwsuNLi48nGD2KWsqQeVK6HhdnnBFtRHiEtSSaLyTKOJYyB+mXUKf+NE1me87575Fz9n30vb4Clg65RlHN8+3QMAxYlIMNx39A9ZpT/J4VzwNh3CkYEsJ6ANmFb6h5pZRebwX2nTgwLJE5PQ8cdOh+T2a/E75ZOfQHxZx5MN2wBSqA4kt7ICh/ZGVRt3toUQA7TpYgbn8hkjtFIr+YYv+ZYszcnIeVh4tQrRK7h/7KvgwAuL3BBZ86eco6hYXsXm/RnCgkyADmQGrZQj8zfPBAgipVqLMSZM48ICaaaqpR9u2nupnaZhAC1KwB/LaXmmYCq+0Wc2Cqehzo2EjzvhD+cfoa2dj4eIIxowkGDxTOJYHAXYJR+snOekq9FrL7PswqdXgw2GgP8J0qUL5jp/F7kK/7QySCmXdEI3FJDgYuOo/kTpGydXs+0tKLdNftdiAkRJHcpSetvPdUMRJSzuMOH1Tzq1EnxOmtxxcuBo4eo9hmUZ3K5dDiYolCkloeiPv3BJO9XpJCnL8PQC5hzcZgJfWp/n4p1Z+/AODt94DWrfTP6e8qtEBSnt6LQCAQCASlia0gzYkT5h0Vi4uLcfLkSdNj3OXaa6/FW2+9hWeeeQYvv/wyXn75ZRBCsHz5cixfvhwAULFiRbz55puoW7euT68t8E82jC8cxPHxBI8naYMs7jR5Z453h67MCcdOyTv/jOrVBapUYQ7OWrVYlcbE5+xlYM+6Im3G+7zoSQCFVfBOokxNdEwfVKs+Aev+nqfoL8FlDtSOUkovIzPjGZQ4gImdIg2qeSrZrubxhvuGMufk7/spKJgjMSPDXnWUc7yq79ZI3mn2XKDVTQ5s+AmGjUbVEgcc+WZFjXzDpr7PRABFUJoYSTbq3V+UXsa5rHkoLsnGK90j8fHuAnSZ77InDa8JwdTukXhhQx7e3Z6HmZvzfSLZGGgiI4G8PO/OYWSfjOyHnLhY/YqVffupYg5zOJhzMCKCokVzgh079XX99binH/B9qqtPy529gKQxQEQELDOBlXaL2pJuNHN0+tPpa2ZjjZ5Tz/MCgcBFsPa7c3c9Zfd98EodeSCGUhZwSegL5F6wn6Rlhlqa+N7G4UgZBCSvuaCYZyuEVkbterMRUznBdB9hByNp5WIHwcUiikmG63/71fxq1LbdyP4bBWj43MqrYOJi5cE5yWm/l3/vwIKvlMfKAwhr1/M9IbUMMPhq/gpUFVogKE/vRSAQCASC0sZWkObQoUP+HocuPXv2RGpqKubPn48tW7bgxIkTcDgcqFGjBm699VY8/PDDqFevXqmMrTwT7NkwPMjCAzVmTd6N6NmDYPZc/Z2UVcl7ZiaTR0lZxBb80dEE2zYBDzwEXLjAFvh39gJWrXFVfBg5yihlmWtZp55VSAtwCaAe3ROwc5fnEmVq9K4VFnoNKseOQrX4/+pu6PLztqK4+CwA3zUu5dSpA/xrIOejhmePM+ej8WfK5RHUjaL5c+rv1kgCj1JgpIUa2+hR2g2Inma5HL5h27EzuO8zwdWHnmSjJDEt/bffBQ4cZI+p7cjjbSthXMdIbDpejIyLl1HziuxZfjHFCxvykLyORTn84eTxN94GaCQJ6HIb8NPP2ufk9sMoO/eLryjuutOV+csr8/TsC6W4Ipnmnpewdm1g6UJX9nHSGOX5zTKB5diVblQfRwgLPLHj7AV6AoF8LcTneYFA4KK89Lszt0lK2rVV/k0pe93JDGoqXewOetLE9zYOh4NSjP3xAk7nMRtfVJKDs6efR2HBbuTlLtPdR9itUDWSVs64yKpgfb3+d44znIL3+DSTW1XDK2d4tcyOncCgRNfvsPcd1LkPk6MOILgrZWz+m7c/fwVjFZqnlKf3IhAIBAJBaeNRT5pJkybhlltuwcCBA02P+/bbb7Fz5068+uqrHg0OAGrXro3nnnvO49cL3KOsZMMMS5TQsweTcgEFWjS3fo28asGot82TY4DuXfU3mDw4o3b+yz8r/u+qNcyxuSJNK3FACNC8ObBvn760gFwCaP0GJgHkiUSZGuNrFSAt/TWEhzfR3czJN16+bFwKAD26mffj4fBNEGAvO7xKFa2zmW/omjbROgztSOCpeTwJTjk0OUaVORxJYptSeaApWO8zwdVFXBxB7zsoVq5yPdblNlbNceAgM5hyO3Jnoyg8teqi0y50baC0DfvPFAMA/tMyHJ/vLfSbkydYIQSY/gow6XntcyOGK+1HeLh+M+Tl37H/+vSmeOwRgrHj3Ot7pjcm9TWY5AzQpjXB7j32JMuMqm3tSjcm9CXIvaAv1xYMTl+9tRDAKmbj4oSNFgg4ZbXfndqGmdkkOXr7B4fD3lrWLnrSxH9lX8ZD3+XirusrYGKnSNka/izS0t9Hm5qheKe351KiRtLKNaPY3sLX638Ol/zMzHRPbvWB+4GmTSTna+XVTQ4HFOsYNXJ5zdlzjZ/nc57eb8VIPpPPX5yxT+rfE8FaheYJ5em9CAQCgUBQ2ngUpPn2228BwDJIs2vXLnz77bdeBWkEgaUsZcPIKyokiZpWIqgbw9+doHVWSZJxgCZ1BXWr14zDAaSmQdOsmdLLyLu4FZv+dxohoXG60gJ6EkAxlRNQp/5nWH/8WaSluzLljCTK1BjJGNiRG+Ibr5pRJo1LN+WhYlic241LrTa1jRoCY59yaUrv3mMdoOGba3kTa+7oUwdoOGoJPDP5NFewRxugAfQ3K3LGPsk2pXYcoXJEQ06Bv8nKYpmncn76GQirwH6rajtCKfDWtnyThsZ5aHCNhPuasyCNv5w8gcQdaUWuU69nC2652fVZ8UbJZudduQq4pqo9+TI9CAGmvMhqbCZPUT4ntz12nC1W1bZ2pIa4Q07uVOOB6mBw+hp9bydOAnFxAR+OQBDUlDW5Vj0b1q4tDG2SuvpaLwnIG5kzPaky9bo/VAL66PSFZGv48zhwpgS31ApDiEQ8khJVS6xxOtcLQ4PKJut/N6r51cglP60SnNTUqukax+Kl7snM8esaJezJx2U03xn95vn8lXGK/W0mFRoMCQm+oDy9F4FAIBAISht9L6OPKC4uRkiId/ry2dnZ+PzzzzFu3Dg88sgj+Oijj5zPpaenY926dSgoKPB2qIIr8M2HnGDMhjGq+MnM1K629RpRfveDyxkPmC8o+evd3YCpmzXn5qTi6J+tceyv/jjxzygcPzrwirRAJUMJoIJLTAIIAGIqJ6DhDXtQv9Fy1K47F/UbLUeDG3bbypBzyRjYu5YcLrtQMzoEaelFGLjoPLb9W4QLlxzY9m8RBiw6j7TDRYitMQP5eVuRk70UuTmbcPnyZfc+MB3+PsYyzP89wb4Hvd+nHg4H0PhGgiUpBFMnA5Nf0EpUqBmWKGHpQoJ3ZxHM/VD/OpLEpPWMAjQA36wQ5+sliVXdvDad/d27F3H7PktdQTEokeKpp9m/qSt8IHouEKgw6kmzdi37l9uRCR0jsPFYMRYfKMSImyKwQs8uLDyPlelFmNkzGl0bVHA6eRwqQ+qtkyeQ8H4txKbfgRBW5alnS7isi5U8opxvvtG/xrinzcfEJUF7dJfQohkxtT1q+wUoM4HdmXvNMEsI4eNo05o4e3ft3uP+NbzBaK6pXStgQxAIBH7AyIbt+11rh+U2iaO3xhs9St8GEwJ062o+HvXe4Nhf/XH0z9bIzUl1rvur13wZJQ4zydBI/H3egU3Hi1WPs7X9uax5yMleiryLm0ApW5uzpLFNzscjKrW7IrFWoJinQySCGT2jkJbO5nXl+j8XaemXEFvDVc3fq6e9dTpnx072r931PYfPO5mZ1LBvpBFcatTomvx5T+e7+HiCm1paLxQS+rJ9yruz2L9lWfK4PL0XgUAgEAhKE48qaexAKcWBAwdQxYtOqytXrsTzzz+P/Px8UEpBCEG8LG3l9OnTeOKJJzBjxgzcc889vhj2VU9ZyYZxp+LHKjtr6mSgeTPj9+hudhdH7ovUkxp7Z1s+pvwvzy0JIEJCPOrZYCRjYHYt+TXjaryK3cceQpuaYdidUaJoXBoqARKpiLOnJuBS8Tnn49c1qoOQ8FdQMdyeHrYeDgfvC0Odv8UJ44ilU5M7Hd2ptgLk2aAEE8Ypq68Sh7CeOHbuBatMcHXzWb1eOZyyIkEoKPvUrmXcnwlw2YcR3+XieI7rBoyPlLDjRLHGLszvH4N7G4cDAGbeEY3EJTkYuOg8kjtFeiTZWNoQAvy42n7G9N0JrGpu+DBtrwImMUY9nl8AZueSRgL16xE8cL9+PwS1zKOdOT6hL0GjRhQHr7QjlGcC+6ra1q48Smn1yNP7nAAIqTOBoIxjZMNA7Es26a3xDh92YPVa5XGUMllfvZ5kANsbnDj2EPoYSB5zqbLQUGZcrdbwvH+M+vHTGS84HwuvWAuRMQN0e9hExgxAWvoHGmnlL/YWgQJYdywKaYdd63y9av4161iQw6h3mvrzka9n1XKrZsgDaHpzcru2wC+7tBVPo0expCxena7uGdetq0uKNBDqEmWtCs2M8vReBAKBQCAoLWwHaUaMGKH4e+PGjZrHOJcvX8bx48eRlZXlcfBkz549GD9+PKKiopCcnIybb74ZgwcPVhzTsWNHREdHY82aNSJI40OCQWrECnf0b83kpygFrrnG3PFuJV+lh9zRaSQ11rleBQB5AZEAMpIxsHstLruw/+R/FYGY6pEE3RuEYeH+S7i9AVVsMmdsycKKP+3rYVvBgxNLUliW1u/7KV6coj3OqH+NXnDDTELM2/vAV5uVsiRBKCjbxMXp92fiwYApU/4CALSID8VX97o08WdszkNaehEebhWOT38txKg24Zi7uxCNrnEFXe5tHI6UQUDymguKYI5dycZgwN1gyvepwHc/UN0Ma34PezK/PPQfoHUrgkN/0CuOMKWHis8/cplHua0zs22891rKIqBSJeDRh4FVqyl692LnyM7W9s7xpNrWKliUmcky22e+YS0/5C/kn1O9un6/nEAg8BB35GCN9g8tmrmXpCZf402brg3QAMwWb9yiPw753mCphQyx3TU87x+jfvzd3lEYflME9meW4P9WncHujPdx1/UVdQJDHyC22hisP75MV1o5OqaPRpZNnVxBKa9kh+E6XQ6fC7POOvDjavNj5cjnHb3vc+Iz7PP89wSryD91isl9tmhGFMF/Nf/byH5P8kob0WtFIBAIBAJBoLAdpNmxY4fz/wkhyMrKQlZWlvGJQ0PRrVs3TJgwwaOBzZ07F4QQfPrpp2jWrJnuMSEhIWjatCnS09M9uobAmLKQDTN0MLBwMZyVDkkj9Tdn8fEESSP1G8PbWWyrnUlWSBLQ+w7gx1VsQ+CSGquikCnwp86zGi5ZNn3zOSzz8FrRMX1wJmMS2tcOw+NtI1ArKgQd64Si+eyzVzaZSp3spYPd08O2A9/MtWlN0COeID9f2SvovqGuahe9/jXy4IadDG1+H3C5HV8FLdUSeupsQjlikygIJF/pSGpNGEfQ504HXnp+gf69PiQMAxedx6IDl1AziuDNXtFYdaRIY9vubRyOhOsroMfn2didGY2adT9BZFSnoK2g8b7PgPJf9bnDwykAgqGDgZRF9q/VsQMQFwuMHWcc3JFXiH6douy3NXoUxbBESdMU+dAfrqbZct5+Dzh7zqGbGe1Nta1RsMjMgRboAHVZWAsJBFcr8qAyD0wnjaRofCMxXK+ZBYgT+sJ2cg63mwUFxtUflAJr1ug/dyGX7Q0mqvYGgEuqLC2dyRBbr+FZ/7fO9cJ0H3/s5koIkQhuqRWGs/mX0dc0MPQtGlz/Cwryd+gGY6yq+SUJyD5PER4O7NpteqiTZcspfv6f/TlQPe+YBdfYeh+yynjeY0//3PI5pqyoSwgEAoFAICg/2A7SrFu3DgCTMevZsyd69+5tGIAJCwtDlSpVEBYWpvu8Hfbs2YPWrVsbBmg4cXFx+O233zy+jqDsoefA4c3iY6KpxtGeuoI5ntToLbaNsvG4M8lOVljPHlBs2IykxkIk4pQAGrDwPCZ29p8EEJcsS0t/SCNjYPda+XlbcakoA2/cUcWZyffz30X4O8eBBfca6WS7NplGGzv+MtcGGwgJAd77QP9YeXDCLCPcKEP90B8s2GJXQswfcjvuVMeITaIgUCxZRpGTo318+04K6tiCrHMnMelufYdScqdIrEjPxhNtK6FCqGQobzZzcx62nyxBteojERXdJUDvzDNatgT8tbygFBj1OAC4JA/t0K0rEBdLsP4n46QBeYXo1ykORYICpbjytwPDEiXTgAjH4YBu8ObufsCDD9izRUZzqzoIYtWjRwSo/Ys7FQkCQWmSukKZpAMwW8XsGzVdr5mtHe0EZu3YTSuK3ZAhNl/D5yMtvQhtaoZh58li1+Ob8rDycBFSBlVGiMTe36bjxTiW48CXFmv2gvwdHkkr88SGyRb7JDVGcnDq85pJDxslRej1JTVDPceUBXWJQCPmCYFAIBAI/IftIE3t2q4VyxNPPIEmTZooHvM1BQUFtvrZ5Obm+m0MguDDzIGjdrRnZlLs208x83VW0aJmzgcunX4AmPexw6npzzd36oU5r96Y8brxKl8teWAmU3Bv43BM7V6CKT/nIe1wkfNxf0gAccmy9cef1ZUxsLqWXrCJ61970utm4L1A1y7EuRmSf86ZmRQfzNZ+z6NHsX/lVS1GG2qjCqo584Dq8eZVNhx/9YNxtzpGbBIFgWCTgSzLhp+Ac2dPAbC+1yuGuKpm9OTNakQxrfcKFRr5aNT+w9/5H3Jnkd0M4qZNgEGJ5s5BQlh297r1+hWkAAu6tLrJ4ezZ5c5YOT+kAg8+YP1adwLdZj165FKW6srGYHUaBeu49Cit/j8Cgbuoq5H1sFqveVold+CgQyHD6ClhbsoQm63hY6vdiwPnlqHLfNfj6r5wgHdrdivGPQ289bb3n4sRRv1DrSrT3en7ZpQEFWwVlaU5r4h5QiAQCAQC/2I7SCPniSee8PU4NFSvXh2HDx82PYZSivT0dNSpU8fv4xF4hq8XklaLbe5o37HTOsvt1Cnm8AKA5yc7FJlcDgcUgRhCgOTxbCGae8H+DoTSy6DUgbDQazB+zUWsH3ENwkIk13UoxdZ/LyM0tBZq1X4fl0vOGOo8+4KYygm2NKX10As2cf1rd3vdjBgOjHxUUjwm3wCpq0d4s8+YaOJ0UNrZHDS+kUAdonOnQazdihd3f+eeVMcE2yZRUP7ofCvwyy/6z23Zas+h9OORIky6jUIiBPc2DsfdN1TE/44XIXntBZy6QPFl/2j0/DLHJ722OLVqASdPmh9DCLP3+w/47LKlgl5FixpKgeRnrY/Z97v7fXbU57CSHnM30G0UwJ78AuslsGMnNHMAoLalweE0KkvOLH8lJAgE/sCu491TeUSe5AUKtGjuWpux6h17gQhKL5uutaNj3JchNlvD0xovOB8vKcnE6YwXFH3hAM/X7FZMfIagVi22L/cHlAIHDwHNm2kD9FbrdL05RV7Bz/82qtAJNkpzXhHzhEAgEAgE/keyPqR0uO2223D06FGsWLHC8JjFixcjIyMD3bp1C9zAvGDv3r147LHHcMstt6BVq1YYMmQI0tLSSntYfiN1BcWgRIqnnmb/pq7wfvHOF9tGcJ1/OzIEk6cCX6c4sGy5w7LUnlJg5hsUBw46dKXT9MjNScXRP1vj+NEBKC45j+0nilH9zSzM3HwRFy45sO3fIgxYlIu09EuIq/kqoqK7oHKVgYiM6uzXHg2EhCAyqrPb13JpYhfAcWVnI++r41BtzvQ2mYQAjydpAzR6JPQlWJJC8O4sgqULCXr2ILqbg8xMdl3eN4b/DbDfi1pGyNUgljh/S0ZBEr3fmzqY4+nvXP7+lqSQoHXeCa4eBg0giI7Wf65SZEdUDKuK6ZuM7/WKYVWx/UQxBiw8j23/FuHCJQd2nizGu9vz8dupy3j7zii8tf2Sz3ptcawCNADr41LaARpJsi9rZoSvfGCSBLRobj6f2oH11THGzIGmBwtga23z7d3ZA+o5YOYbTPLIaF4oLYycWaU9LiPc/Z4EgtLEai/A8UQeMXUFxcChFJOnsH3CwKFsXWeneofD1//H/uqPE/+MwrG/+uPon62Rm5PqPMYlYXYJAxblOudM+d4gtoZWhthoDS9/vGrcSM16HWBr9vqVJUy3uWa3ghBg3my2nvWVDKXR9/rNQvZdqNfaVut0vTmFV+VzKGU9ToOd0p5XxDwhEAgEAoH/CdogzciRIxEdHY3k5GS8/vrr+PXXXwEwGbQDBw7gnXfewcsvv4yqVaviwQcfLN3B2mDbtm0YNmwYdu3ahT59+iAxMRFZWVl4+umn8emnn5b28HyOvxaS6sW2HhkZ9uVbPpzDyvPtQCmwZas9J1luTir+PfYQetQ7i40PVsG5CdWw8cEq6FIvDC9syEPs62fQZX42NhyPRZ36n/lU1sxf6G0o84spHmkTgbT0IoVjdtu/RRi4+EoAqsY0/OeBEGewZViifbMTH0/QpjUxlCzgmwOjQMmOner3IG8Qax0kMXIYyiV2vPmdy9+fQBAMrPxBQvdu2scv5K7EpeJzWHm4CAMXKe/1AQvPIy29ENVqvYU69edj3bEodJmf7bRzv2eWYGr3SHyxt8jQ8eRvtmz1/hz39GMOqf73mB/HAzGEuP6f247k8S57In/e6P/9AR9L0yaSxr49ngRMmWz/+oWF5gfaCXSrMbLNenMApdo5ORicRmXNmeXJ9yQQlBbqtRkhwH1Dmf2SP5Y00r0M/8xMbZ8bLp+173d7PWiM1v896p3Dv8cewrmzPziPdUmYVVXMmd7uDYwCQDtPFiO2Ughbsy/KsR0YYucE+vSGYr5IHs/mEUC73vaEJ8cAS1II7huq/zz/XuRrbat1OqCdUxrfSIJy3rCitOcVMU8IBAKBQOB/PJI7CwQ1atTA3Llz8eSTT+KTTz7Bp59+CkIIVq1ahVWrVoFSitjYWHz44YeIjY0t7eGaUlJSghdeeAGEEHz11Vdo0oRpbI0ZMwaDBg3CW2+9hd69e/u1x0+gcacxurvw/hwbfqaa5vKUArv3uJpM+pqqsfrN6JVjuIysU8/irusrYNmQyk4Jg/Z1KmDZ0DAMWJSDdX9Honqdj/xeNeNrjDSxK4RWxfYsoMv8c87HGtaviylTX8bQIf0UQQ0uVQC412OFV8XIv1dJ4pVT0ARKGjVij6s32+3auv62IyFm1g/Gn79zgaC0ePklCZmZFL/vp5g8FXA4uE2riBEtK2Li2ouKPjNRFQgqhFZFdEwfEBKC6Jg+OJP5FnLOzkVxyXkcy3HghQ15fum1FUh+WAE0aUxw150Uy78zPm7GNKDwEpCTw/6uXJlV73Hb0agRxd59QMsWQFwsC0CHh1NkZAC4otA4earvxs3lwmrWYEEVuR0zsm+nTzvw+QLr81o5Z/SkK+04TvVssx3ZGrvj8jfu9h0rbTyR4BQIShPjtZnDKQs5Zx4QE01tVyr/e0J/72Akk6vGdP0/JAwDFuXip+PP4fLl4c7XeCNDbIZVD5v1x5fZ6k858lGgdm3XHPbYI2xtQAG0aMbW9Zu2UNsJb0YQAnTvyj6vxo2tj5evte30bVTOKTRg9jkri+L4P76R/S7teUXMEwKBQCAQ+J+gDdIAQOvWrfHjjz9iyZIl2LJlC06cOAGHw4EaNWrg1ltvRWJiIqKNtFmCiG3btuH48eMYMGCAM0ADANHR0UhKSsLEiRPx7bffBqTXT6Dw1ULSqNdHfDxB967A+x9qpQe++0GZBUwI0LUL8PP/vA/cVI4Ghg8DvvjS+Jj8vK0ovHQSkzpVUWhMA4BECCZ2qoS09HMgJKRMBWg4RhvKl14AIsK34/Tp06hevTruuusu5ObmOl8n11GWO9YkCUgaSdH4RmK6iTGqiiksBBwObd8Zo34Li5dSjBnt3obCKJhT2hsmgcBfxMcT9IgnyM+nePEll01rX6cC7rkxHJuOFyPj4mXUjApBqETR/YtzyM/b6gw8x1d/BtXi/+tzx1NpwgPAS1II+vSmWLlK/7gtW9k8JGfiM0BCXyaxyR2IrNcWRUw0cQaamT00TjSQJGD6K8DE57SBiaSRzCmptkdMLsy9flfDEiWEhbkuIElA7zuAVWvgtnOG93Lz1HEqH6eegwgIPqdRWXRm2XF0CgTBhNp2ZWZSzJmnrbgw65kh32foJQMBcplc5T2dNBKIiADenMWOs17/RyBt/gls3LgRQEvnc1yqzNfY7WFjNj9XrMjeO6vWoNixE875ihBz+TdCgBtvAA79oX2uU0dgs6rC9aNPqHOO0QvAq8/N5DbZge70bTSzz77upfrAQ8CFC9Qn/WOCYV4R84RAIBAIBP4lqIM0ABAVFYUHH3ywTEiaGbFjxw4AQOfO2gU4f2zn/UxlZQAAIABJREFUTh/UiQcRvlhIWjVHjI8n6JdA8f0P2tfKF/WE+CZAQwjLbraSgSkpPg0AaBavf3s1qxaqOK4sorehnPIKMPGZThg4kH1AISEhzgwydbWL/LtwOJjsHM9smzAOmg2AnhY4Ia6qGL1ASYvm+pvthYuBwQOpTzYWwbBhEgj8SUJfgszTmRg/3mXTQiSCrg1cTYcvXGI3n9qm+cvx5A3eVlk6HMD3qRQdOgCtWgHTZ2qPUQdoAGDG6xQnM6giwM8lNwHXgBwOOIMZegwfBnS6VULyePX8yGTBevZgDa9zcrQVPMCVhti/U4Bon1Mz+Iotf206ULOGK4vaXeeMJ45TI4wcRJ44jXztjLM71mDGHUenQBBsuFvdrN1nMElKPcmzHTvZPd2oEcW+39ka86+/2LEcu+v/jIwMyIM0/oSQELTv0BlNbgQWL1U+bmd+PnwEeO8DeuU17DH+2VjNpZTqB2gkCbi7H7Blm/Jc8sQHnsgw7mkmS62+FqVA0hhgwjjjgL+Zjdezz+rfg50EMiOysiiqVNFW+Xsy71mN28779SVinhAIBAKBwH8EfZCmPPD3338DAOrXr695rlq1aqhUqRKOHTsW4FH5n4S+BB3aAydOArVrAXFx9heMWVkUH8wBIiJcr/lgDtChPXWeZ9VqivUbCCIjfT50AMCdvdi/lAKr1xpvRiIryf8luFxSAwCwP7ME7etU0By//0wJACAqugYiI4PfaeMO789WfkejxgAXLwKEEERE2D8H/dD196MPU9xwvfK3wMk4BdzUkuC5icDb77kyvMc+CTRtQjBiOMWSZdprZJzynRyZN79zgaAscMMNwW3TfCVvSQgwZBD7f27P9eCOLkKA/ncD6zbY64O29Ft4PV8tWw7Ur0fRoT3wzDiC8+dZgOLGG9hY9/wKvP0eUdjC3lfmslWrKWa9C1BKnON/+imK3r3Mv7ObWiqTI+S2MyuLWtq+jFPG9tsTO6znIHLXabRqNVXNGdafgycIZ5ZAEDjq1QWio4kmaadeXe2xRvuMBZ8BH88leOpp5fEfzAEKCik+nU8UlR6VKrleb3f9X7NmTdM5xtccPQr8fdR6/rmpJfDbXtff8fHA/zZ6P2+puf464OVXgUqVrI+9cMH8OPXekGPHxsvts97vgUt+ql9vZ947dRq4/nrtWsIX+w+9eSVQc5pAIBAIBAL/Qij1R+cO79m9eze2b9+OI0eOIDc3F4QQVK5cGddddx3at2+Pm266qbSHaJuHH34YmzdvxurVq3UDNbfddhvy8/Oxa9cu3dc7HA5I6k59gqDl8uXLuK5RAzQLz8LSwTEKyQMHpRi4OBcHLlVD+pGjCAkpu9I/AoEgePHlvCFsmkAgEJR/xH7DO8RcKRAIBAKBQCDwhqCrpDl06BCeffZZHDx4EACgjiGRKwveli1bYtq0abjuuusCPsZAk8O7DwcJVapUQXZ2tvWBXpCVRfHAQ1oJqwWfsayl3/ZSTJikfd211wJHjigfu+N2oH594JPPXOXzd93Jnlux0vuxRlYCNqytiu49zyEvnz0WEv4KVvz5EAYsysXEThFoVi0U+8+UYMbmAqSlX8K1N7yMjrfloGZNsGbRQYY3melffc4yyG7rrPxMvDkvIcAjDwGfzocqQ9w6S0ybXWbvdf4gEPdOWcPXn0mVKlV8dq6yjLvzhtX3MOXlaXjwwQctbRoA9O8HjE4iyMqi+CoFWPmjbypdAgG35z3uOIeLefZeM+oxYO5H5sdY6evrHe/OZ0YIMGkC8KqO/Npr09m/enMmf15eLSNH/ruQZw8DMJ2j5WRlUQx/UCtD+uV84yxkT+y23dcYrR/MPgdA2G855e2zEPMGI9j2G55ip9Jh8VKKjz9VPiZJwNtvAqdOAdNf09osOzbZav3frMXLCAkJ0ayPr3YIAXr2ANaud3/uU88lnth4vd+DmmcnAjNeszfvAcyu9Oh1Dhcv+nf/4emcJvAOMW8IBAKBwB8EVZBm7969+M9//oOCggJERESgS5cuaNKkCapUqQJKKbKzs3Hw4EFs3LgRv/32G4YOHYoFCxagadOmpT10U6KiogAAFy5c0H3+4sWLqFy5ciCHFPTExRGMSdJqRfNFcM0aQH6+tmHl3r3acy3/Xrm5IgRYtMSXo2VjyssH8vLYRSqGJ6BO/c+w/vizSEs/6TwyomJt1Kk/DRXDE5CXR3H4sC/HYWOkFptMQoDEIUBYGBS9E+zyeBIQFyeB91iQfyaSBIx9imlLezK+axsRfP6J+/r+vXsRtG4VGJ1mgaC80q9fPzz5f59h3mxzmwYAX6UA57Ipnpsk4b4hFNdUpvjok9Iaubsw+9C1C7BoiT1PUfV4oKDAWPKMECB5vLLBva2REOCxR4CPPrHntDqbDeTpBJbCwoC4WKI7ZxLC+s0A5nr2aq3+oYOZDI2a4/8QxMWpHwMuXtS+Ab1j+TimzVB+TtNmAK1bmTf/NnoNoJw3atYACgqoxtHGP4fySqD6FQgEpU1cnL5t4WRmUrz7vtYedusKPJakbVzP+pOY9wvjWK3/K0X1A6BcH5dlCAH+O5at+mfp9I6xe465HwJNm0ho3cqByVPtv9a173ChZ+Plc52azEyK9z4wn5slCSgptj/vceZ+wOZAud31tS12d04Tc4FAIBAIBMFL0ARpLl++jGeeeQYFBQUYNGgQJk6c6AxuqLl48SKmT5+OpUuXYty4cUhLS3NW2AQjDRo0AAAcO3YMzZs3Vzx35swZ5Ofno2XLwDSQLEvoNUeULywThwDfLLR3LnUD0EAQUzkB0TF9kJ+3FSXFpxEaVh2VIjuCkNKTOLAK0IweBbS6CRj1uPvnHjEcaHwj+474op+rZvAgW7u2wNvvajdCI4YDxcXG3ychQHg4RXy85LMeBgKBwD0KChPQ8AZ7Nm3lKqBaNQcWfFV2qmjkpP1oP3N60vNA7zuAVWtcgZoe3YD27ZlDp2UL1iMLYP1j9u2nmDxFeQ69a1EKNG9GkDzeFdwxqsghBKgcrT++wkKC+Hh2HnlDbB480muYPGEcnM2Ys7KoIrjkcAApi7TXkSQ2N8vnaQDIPk81748fq4e7zb/NXrN4KcXCxdC8rwnjoEkCKc/OKrPvVyC42ti3XxugAYCffnb9Pw/OvPQi0KIZwY6dAE9AsiIY1/9qenQD1v9k/DwhLFlB/pl06wp0aEc0tpPbkgqhSjuTNJLtC37ZRZ1rAUlSzpd879G0iYTMTAoQ+3PvrR3YnkVNfDyz8fL5DgB27AQS+mqP15s/+GfAxzxhHEGL5oAkaYMhRnMZoA0Y+sMW8/drZ04Tc4FAIBAIBMFN0ARp1q1bh2PHjuGuu+7CK6+8YnpsVFQUpk2bhry8PKxatQrr16/H7bffHqCRuk/btm0xd+5cbNq0CX37KleHmzZtch4j0CJ3rqsXlkkjfdcw2hfoxQkJCUFkVOfAD8YDKLWXJajHPf2AL78GvviSQpKA5yYCiUOZbMSeX5VOSvlGgm/OevYgGJRofGFKgaQxwIRxtNxuJkRmmyDYaXUTsHmLfZvmSTVesEApcHcC8N0P1sc6HMzhNP0VYNVq5vji/wE8GMJsV3w8we3xBAX52vlMbX+586dNa2XCwo6d+sEWKwcST3z4fT8FBXM88uQHdRDmtTcpOrQHqlQBTpzUOrDk15Y7sXbs1A8oEaI91sjO1anNjnHHEWb0mpRFrrHy99WurX4SiL8obdtu9P22ayvmGsFVis11rsMBVLnGVQXpngxXYNf/7uyHJAno3cs8SDN6FDAsUcKBgw7s+x1o0ZwFUgAY2k4ju9qmNUH/u5V2sGFDh3POmzMPOHrUoQjcGL0fQoAG9YGjfwNbtrH/+vR24LlJksLWtlNtqyk1tntG88ecD1iSg/y9eBPg96cttjOniblASWnPzQKBQCAQ6BE0QZoNGzZAkiQ8/fTTtl8zbtw4rFq1CmvXrg3qIE3Hjh1Rt25dpKamYsSIEWjSpAkAJn82Z84chIWFoX///qU8yuBGb2E5Zx7wwP3WjkBPtP0fe4QFHfLd0Ivufzd7TVnG7HOalAyEhwMvTdXqdH+fqnSEvf0eC9KMHccyydlGhjkp9TYSu/dYSwCV582EyGwTlAVuvIHAtnerjMPtml0cDmDic/o2lFIWVJHbLrUdVGdpE6J0/sgTFhL6QjfYAlg7kOLjCXqo7KdRFcqJk8D117O+DmoHlnycPNMcAAYlumy5uoKVEGDqZFYdZGbD3ckKNnvN0MHa6kx5RU4gKiyDwbZ7UpkkEJRnWjQnIMQ66MKDw0aVFsHC1MlAfj7RVI4Y4XAAi01kn+/pxwI0AAvMNG3ies7KsW1kV+WPZ2ZSzJmn3DesXOU61ug9EAJMnABMV/Ve45W7X34Nhf1Xn8fI7hnNOTy5TI43AX5/22KrOU3MBS6CYW4WCAQCgUCPoAnS/P7772jYsCHq1q1r+zV169ZFo0aNsH//fj+OzHtCQ0Pxyiuv4NFHH8X999+Pvn37IjIyEqtXr8aJEyeQnJyMOnXqlPYwgxqjheUtNxNERVHDChBCgDt7KRf/crjMFpdDAdh5PvqEZSZfuEixdj3w55/WgZ5vv3P/fblLaVYOzXiNfSajR1F8OMf1uN545ME0/q88wKLerGVna+Vw9N5redxMiMw2QVlBL9sUAOrWAf75t3TGZIWnNpNS919ndjylLKjSQxUwiY932QD169WZwHL0gi2AZw4koyzi2rXY/8fF6UvHAK5MczvBdkqBa66xl3XsyftQvwYAFi52T5rGlwSLbfekMkkgKM/ExxMkDqGmksnKQDnV3EPBUslPiCvw3agRxcjR9l73y27j5/repfybB2YO/cGCK946tu0Gve4bCpVcJcGFC/ofulxa1eFgr3NHZtOdOcfTAH9p2+LSvn6wECxzs0AgEAgEekjWhwSGM2fOOHu3uEODBg2QmZnp+wH5mA4dOuDrr79GmzZtkJaWhm+++QaxsbGYNWsWHn744dIeXsDIzKTYvYcy3WE34AtLOZLEtO579iBYupDg3VkEM19Vyo5RymRojLj2WmDMaAnTVQp7lAIzXqe4vTvBJ3MlJI8nmuu7Q4P6nr+W8+QY1lhTbxz39NOXW/MlPBtcHqAxQm8sPMAiJ3UFxaBE6mwSyl8nSUxqQe87L+ubid/2Kn//ZpltAkEwwbJNieb+DrYADSFAnztdDhounVLaGM16ejaAUs9tQHw8QZvW9iVY+Peq7iEWF6eUsZn7ofZzlNtkvXna6Fh/vA/1a4zeV6CcMMFi20vrc/B0vScQBILBA7VzGYc3sucBCPU9FAzzCYdSXonJpLl8gfw8fJ3+1NNs/a92bB846HD7PreaKwD2/OCBBEtS2P5uSQqrxG/ZQv94vQSCxCHa3phWwRd35xx38LcttrK5pT0nBgvBMjcLBAKBQKBH0FTSXLx4EdHRBl1vTYiKisLFixf9MCLf07JlS3z88celPYxSw5vSYnUpOne+TZ7CNPh5U/r3P9RfqBtBroxr5hv6z89fQJ1NMXmGVXg4RcYp4Pf95nIBcu5LZJUonmbdEeLSg54wTv05srG1aU2RcwGY9bb/svvMzivvN/DIQ9rn1Q46dSaTuklrfDxBTLT2vZbVzcSq1RSJQ4EJk4CCAur8/YvMNkFZIjXNPV1+d2l1E/Drb96dg1Jg5Y/Kv7nU1m/7gGXfBj4DmhCXJJgco0pCgM01bJZSHq+X6WtXW119HP+7XVtgSQoxPUfTJhKSxxvbZL15GrDXh8ZfBLL3jJpgsu2B/hyElIzAl/ijd0R8PEHyeCjWoYCx1JW8p9eLU3wyBJ/BqwB8YVsIcZ1HvU5X43AAox4HKKVu3ed68mJdbgN+/p/+fCGvWmnaREKf3g6FQsKNNwB//Km8Bg/yDB6otXul2YvEG1tsNm67Nrc058RgIZjmZoFAIBAI1ARNkKakpATEg9QkSZJw+fJlP4xI4Et8UVos3yBNnqosa5/5BvvDXcfb4SPAl18bOx2//wF48AGqkugiaNoEaNGMYuky6wtGRQF9+0jIyXEYyrJZQSmQNMbV10Xdy4D3AfBEfoEQVrXCJQw8wajBplm2llEmUxWZHE552UxkZlJnnx5A+/v3phGpQBAoNm9x4Hc/qosSAtx7j/dBGj0oBVLTXBnH3uBJn7PRo3iWJnXe23Knit7yZ9TjQPJ46nS0qJ0wSSMpGt9IbEvQqF/f+w7qbNTM7Y6Vg83KJutJjpW2/Q5E7xmj6waTbQ/U5yCkZAS+xJ8BP3UC1h9/AmfPAo0aaQPkAPv9HlwafJVhvAqgTWuCa6+lOHLE83ONHuW6T+3Iksn3Yu7c5/LPns9hPKEiaaT5d/zcJAn39ndg3+9sbpn0vPaYpJHQDfIEQwBZLflsZ440G7e7Nre05sRgIdjmZoFAIBAI5ARNkEZQfsnMpFj/kzYTy5P+IvHxBNecYFlbcjzNipZrGBuxbz/F7QbZV0kjKT5fYP76ixeBeR+zhpZ8A/LA/XA2uLQLX3Q3akSdwRAAmmoUT+jZg6BnD2DlaoqP3Cz2ckrjxGpLxRd8Bhz/h+huPuxmMpXFzYT6d2LVrLO8BKME5Zut2313rtq1gWsbARs3uTJnk0ZCzy/mM/QCNHo2yAwug7PhJ21Tej2eHAPk5OBKgN6VbdyurbXt5hKT7doCWWdZxafcIcakJ5UvNHLO6Dlx5JnI7jjYrGyy+vmyZr99ydVo20VzaoGvCETAj9uradOp0ybO/wLo09uBxx5RVhdmZlKkLPLJZd2CEODRh4HtO4C9+/Sfr1Mb+DrF4VWA5vEkYFiiaxIsKHBvU+FwaHuvmcH7/Ywdp+wLOmce0LMHNf2OmzaR0LQJsG69Q3cv1fhG/crVYAog2w0YWY1b2Fz3uRrnZoFAIBCUDYIqSLN8+XIsX768tIch8CHyBagaT0uLmQSMb7AT1ODLNr3FdPXq9q7zxZfKa375NXNI8uxnu5nZcmkBQoDuXfUDPe5kevPeBydPwjJAI2/iSQjTex48kCiqeSQJeG4iqxqJiyOIi9M/V3nNZNL7nbRra91fpywGowRXFx3bA8u/8825BvQHhg6WdBsS+4PWNwF7dCp0OrQHEocQhIez4PcFE/VUpQyOwzJII0ksQCO3/9y5MvkFe++VUmDxUuYYtGvT9ZwzdjKihVPHP1xttl1IyQj08ERiKlDO5wMHlfJZAAtir1zFjC5fy9WqFXipTIBd86NPjHvh3J3A/p091/w8PElML0GtV0+WsCXHk35zk6cC+fnUdnWKN9+xkVy1kb0JpmCGOwEjq3ELm+sZV9vcLBAIBIKygRet0H0PpdSj/wTBiZmWsTcOeXcbY5plRttR2GvejOgupme+QfGSh7rUDgfL8uINMZ8ea/+1/CdPKbD+J+3zksQyvadOtt/c9Jdd1CkZZ4S6iefShQRjRrMPV/3ZzHrX3nUT+mqbggY7Zo05jTZdADD2Sddx5SUgJbi66HSrhObNlI/deAMwxcTWdOig/3iL5uzf+HhWaefPAI0kAXfeqf/ctu3MkdG0iYQ2rQmaNdEew/vZcBuVuoIiaYz1NYcPUwZoOA4HkJtr3z5/s9A9x6Cec8Zuo2bh1BF4i2hOLVAjbz4/KJEidYU9g6Znt/xhp/SqU+TwtVx4OLVttwE21onPEDye5N34OIbSzKksmG81TwzoD9xyM9E9bvVaYOBQ5XdTt45nY3ztTe0a2Wjt7Ol3zNfb6vfC5dL07E2gfk92cKd5vdW4hc0VCAQCgaD8EDSVNIcOHSrtIQh8jFHm7pNjgO5d7S8e1dl3dWrbqxSZ+SoQEUGQfZ5iskEwxU4vlrXrme6/+hi7TrOOHZhEgV6GE8/iqVMbmPW2drNxSxvgl932rsNxOIC//mLOxPx886afHD1Hohztgp86+yvofc/uOBStMplKs8GnGitpArNNV+9e7LjXpgM1a4jNk6BsMucDCZu3OLBtB9ChHQvcAEBBvvLe6NaVVdo1bSJh2nRllnKf3uxxzr791jbKG265GZjxmv5zXJ7lGpWNUTs7enTX137Xo/OtrMpywVf6zxMCvPW20k7yxsk//ezuu1Ni5Jxxt1GzgMHnn3p1gSpVSns0WoJpfpQjpGQEHG8kpgJVcd2yhfUxDgdw6hSb19SVjc2aAfuv9GvjPciqVwdAWUKCmSzyiOGsdyWTr/QMSu3Jby5bDvTuRQ33UFxis1EjiqZNJEREMDkyNfz1kgS0aAH8pqpSVVenmK2dPf2OjfaYXC4tJlpbzWPnWnKbyq/jDxvmTvWLnXELmysQCAQCQfkgaII0gvKH0QJUHaAxczLoLewB8yAAIUDyeIJOt3KnGmC0yYiJVi589Zg9F5j7of2og3zzM2I4MPJRCV+nOK70JNBfXMfHE9zZi2rkFtwN0HB4HwP1on3599RWHx7OpGQWUJB/N+rvZPgw49cvXkoxeKDnG4VgaPDJseNosLPpuqml2DgJyjadbpXQ6VblY2YOAnmT3xbNlQGar1McXjmn7LDzF3Ob9+IUAKAKqca332T9cqpWZXKFHDuyYZu2mD+vl/k75wNWJfrTz+5XB3OnYOMb9ft/cbxp1Hw1Ip9/oqMJtm0q7REpCab5UQ8hJSMAvJeYCoTzuWkTCX16ayXP5BDCpLy4zbynH9CmDdCiGZP8PXCAJVpRCvyyS94DzdymL/iK7VmWLXJf2tJdKGXzTL8Eiu9/MD5m1ONA8njqlOpVr2n5fBUeTjHqce05eI8cwN7a2d3vODOTIjvb+EMyCwSaXUtuU3nFlGvf5lv76m5wys5nJGyuQCAQCARlHxGkEfgNOwtQMyeD0cJeb/NCCNNk5hsm+TXWrtdfyPOS/CUpTGrr9/30irNOe9wvu+y9Z0KAGdPYIpo7I+UBGiNn2IGDDvy42t417MD7zMTHKxftIx8laNbUgeRnrc9x31Cgbx9lfb3ed2KULQ4An3wGdL3NvPmnEUbff0QlqvmOA4EdR0N57bMjENjBzEHAm/zKmfexw7KKzxe408vl7fdYkOb//gtcvNKf5q23KSY+w+w2l7vx1Il2Tz/gO5VzjFKWoX3qtGcBmrkfAnGxRFcmRY03jZqDCX9XkOjNPwCQlUURF1f6n1GwNcAWCIzwRb8MfzqfuS157BGCe/tTZzLBX38RxVqOB2AA9u8PK4D/PHClQlq1N3EFaKyR70UGDyRIWeQ/GW9CWF/Pm9vAMEijHtOEcUSzpmV92YDde6ArO972FpcdshukU3/HRjZevqcywywQqPd7UttU+fn9ZV/dDU6JIIxAIBAIBOUfEaQR+BWzBaiVk8FoYW/Edz+wTRML9LiuYdZIky/i27QmaA7gnn5U40ADgHkf23u/d/YCJj7nCsh07eJQyNfoOcN440tfZs7Js9jUGMkXyOH9Z9S4K20mDxa5i9H3P3kKIEk04FnDdh0NQnJAIDAnM5Pi8wX6ttbXyLNh7aDnoAFYdWLuBVf1iSfj4MGUH1ZoKzdfnGK/R40cSoENPwELF9uvqAim5smeEIgKEqOKqRMngbg4n17KI8r6dyi4egjm5BWtLSEYOpj1oSwsdFWM6MkmOxxMqvPMGe/7qTkcwPepbGIxml969QTWrPNur9C1C5A0Bs5KEbNzcXtitqY1kp/e+Qub57lEtXrtDLBqzjat9X8D6oqWxCGsKn/temq78tbdQKBVlay/7KsIvAgEAoFAIJBj0UZWIPCe+HiCNq21GzKrpolGjRL1HFl8g8ADPbwx5b8nrKXR6tR2NTX97gfA020jIcDKVcpMO73+AvL3aNT40luSxxtvgPU+V0Jcn6vZBtroO3k8Sf978aYhp1mja/X3HAjcacxp9JsXCK52UldQDBxqL0AjScB/x3p+LS59mTye2A6AGB1HKZO+lM9Z7pwzeTxB0yaSxo6or6EHbz49b7b2moSw/gjqZAduG/WaNZdG82SjptGenEcvucPXc4HR/FO7lk8v4zHB1ABbILAioS+rWH93Fvs3GGT5jGzJ1ykODEqkeOppiqQxwMmTrEJfb9380lTg/Q99M575X7D/jEjoy+Yyo3WxFSOGA//bqExEIASYOpn18FTPLerG9Hpr2vh4gsQh2mvxBC1Ov77aY+bMg67d1qto+WYhMGCI/QANIcDQwfaO5ZjtOQBhXwUCgUAgEAQGEaQRlBpWTgY9p3jSSNa0k28m9JxkVoEeNVlnVRsCz96O7UCLvMrFTn8DQNkTwYp5s82zivU+1+TxBEsXWm+gjQIVwxIlLF1IcE8/5fFjn3Rl3amddFZOOzNnJqD8ngNFMDoaBIKygjtBaUKYbWl8o71z680FhMDZm2vpQoKpk4Epk1lQmdsVdYD6kYeMr6EeN6VMFtJdp1lCX4LJL1gfJ0lsvNzWNG0iKZx0ksTmQ/W4uG3kyQdPPc3+TV3BDnQn4OwLjMZhhd4cYZXc4Sv0PiMAQSF1BgT+OxQIvCXYkleMbIk8GM8DNwAU95u7FZoAMO5pNl94UjHJ90byNah6HjPjv08Dt9xMdCvhr7mGoNOt2rnFrj0ZPFCbBCFJQEEBxYsvOTBgiH5SBq8eWrfeYWnj7cATxu4byv7+ZiHcmm/UNtVu8ppAIBAIBAKBLxFyZ4JSw0wCgWsRt2vLHFTyRse8/P2+oUD3bq7SfY420ANFEEYOpcC+3+1tCCQJSH4GmPGad3IDo0fB+R6zz2v7G3BZHABObey4WIJBifrvQU1hof4mQq7vbCRf4E0T1/h4giaNXZINAJB7Adi9hyq+O0kCet9BsWoNZN+7fmCJX+v3/dTZsJVjJ6vNH30LhDSBQGDNgYMO7N0HtGzB+tEA9p0vvLdXRASQccre9f7zgDYLWS5PEh9P0ENmA3r2cNkGPjYrO6G21VwWsns3Vx8DAPj8C2DzVtdxXN9frmfPMrPNbfrQwcDt3ZXnK6euAAAgAElEQVQRoHZtgckvsIrP5s3YubjUmZxfdlF8+bXW2cjHwG3r5q0UZ88CjRpReF5HaoynvVOMJM180d/CLvK5rl5d35/fW4S0pkDg+TpPz5boSXfpyX79fYzirbe15+zRnclPqs9BCNCpI7myfrS3geBj4XujrLMU63+iqFuHyRb37EHQswcbT0EBNew1SQjQuSP7XMxspzf2JHEIq+jk423aBLZ6X/I5mxCK5PHGNt6MEcNZAIq/j0GJVKOuYLeXjPozAIR9FQgEAoFAEFhEkEYAwP9NeI3Q2xToaUS3awtNo+OFi5mDzErrWu6MenOWdgybtmgf0wucTBhHULOGfpNMOxDCAjQ9exB8MNvh3NDwbC35Zow35ZQ32jYLNsmvoeesMnJ46QUb7PwWzJpuyj+ejz8F8vKUn5fDwWTh5H+bbaK4czU/X/u78MTJJxAI/Mu06Q7FPd6ntwPPTZJsO1/u7AVMeh5wOKjCPhohScCtHYEvvrTvvFfbMDuB19GjoAg2TxhHsGMn8NqbrseSRgJbtmlfq9az5wkEM9/QrywihCUh7N5DTeZGZtOSRmplYBZ8Zexs5GP46BPq/J7mf+H6nnyJJ71TrAI7gexvEexB+WAfn0DgT7xZ5+nZkqSRLhsvh/dPiY8n2LGTYtY72vNJEvDEaIInRgOLl1LFGp9LEGdmUnw6Hxj5qPnYeLJWYSELPnz0CcWM1+VHUOd4G99IUFhofC65/LGV7ZTbEzt7AXXvmPuGAq3+n737j5uqrvP//zyzmhco6kU4KuCP7GYGiAYJ8lldK1KxBc0y5ArU3U2TX+lqEEquoe7WIuknV0XQ7IegiaH90Is1TE0LW4OE23qJ6Jp9zYRyQNBU5EPr9f7+cTzXnDnzPmfOzJyZMz8e99utmzHX/DhzZub1Ou9fr/ex8QZoCt6NKY7xttzoXQ94r3futPwATTbraP2G4jZSuXvJVHJtAAAAkBQGaZB6Z3awUWDrnFlwZXhHT5zZX9mso8MOlWzFzDZsKD4mr2F1yielwUOkvx0nDR/mNrDKmeEVbGitXSedNaWw4eGvC33QQdKuXfkNN/289/n95Ub3h+zn4K3S8StnJnM134VKSxR4x1SqEVXOLL9KZ28DqM6zmwoHaCR3UPYzZ/Zq+LBMUSdRMGY4jvSzhwr39oriDaAPH+Zo3hxT0LFjjBtzJ1nq4Vdix478ys6ODqM//dnoKt8KP69UTtigS3DAaOwY+33d1YbeKlGvM84UdB76Y9qHj3IUzG1eXglbfVjqc6pUsGOvkpUvpQZ2WEECIInrPHss6S0a9F56m7v6UpK1ZKeXh7zXnT3T0eSzigc54lwn5/cwcx9ji9Xe+3WPMzxJXvsN6YS/LZ60Vs7gS7At4MX4jg7TN0FByk+e22uv6PcXxhbjV95ndM/K/KCMN9HNX11h2Z3mvb8ZnTzeqdtKSwAAgFpgkKbN1bIzu5LVOWGdM46iO3qCM59srx23tIHHGOmhh93/v2y5NG+O0aSJ0bOfg/wNraj9GIyRNj0nXfWv+U65eXNkbUw90G1/rU+fLk3tKu5cizuTudrvQrklCvziNqLizhquZPY2gOo93WO/vecZd2Wgv5Nox+tGC64uvF85CxW9QXAvxgb37rKVGStl9UNGXVPsf3NXb7obSfs7p4KvacsrtgH0no32N3v+F6TvfLdwQMY2+NPb63ZiTT7L3jHlnxUenDUd9TkNer/pK/HV2Wm/n01Yx165K1/iDOywgqQ9pbXqG40nqeu8YCyxDXr7972yxf2rvyaN/0Th99EWo4YOid4/5tSTpRkXFsbHsFgdx//7f4WrMcOOyy+qLfDwo6YvF4WVhnv/+ys7VluMnz3TUWdnb99rLr1N2neArNUVvAGreXOcuq20RGWI4wAAhGOQps3VqjO70hUZYZ0zR4+I39ETXtqr+DnOmeqWyCnF30gJdgSGCTbEombQZTL5es7e6117nfuPfBk0afDg8E7MfzjXfn47OuwPcG/PP6ba74L//JajFo2oeu5bACDvmJH22739WqR8J1Eup6J9WeJuyBycbSy5sapUia8ouZzRt25U6CBNb6+7P1bYAI13XP6yaN6/bQPoT623P8dfdxc/f9j5WPFDd+DI1jE1aaJTsPeOP8aGfU5vvKG+/c8GDHD05Br7/YKiOvbKXflS75JmaA5pr/pGY6nVdV6p5w1ro8SRzTo66zPhye3nj7iD635/+UvcIy/kOHpvL0dT1u8lrC1wx3Kjn/pW8YetAj3h/zh69lljXf0Tday2GJ/LuatlgnvM2KorSO5khvvuya94ZRCg8RDHAQCIxiBNm6tFI6eaFRm2zpkpk92/xenoKfXatufYutVeSiDIP5Muzmzv4CzusJUm3nu8+57ix/tfe9H1RksX259j1gx7mbNXNrslemx27Sq8fxLfBe/8ept9Z97rlwxbsXTRbOkTH0u+841OPiAdw4dl9KkJhTH14x9z402wjGPY71QqvM2Y4j3C/CtoPJXEMP+MTtsgj18m486vLrVa0L+hc1SuClsVGba/zukTVdBJJrnHG1X2M2zWdNjndOcPClfwSNK2bUaDBkXHzlKD/OWufKGkGfwoYYqg4N4lto7+Smbsl7p+rPba8jOfDv+bF8/9JaDv/EG85/Xv1xLMm+X8XsLyaDD3+F/Xv6dmNuvoivmOPnNmrx78mfTjn4Y/7oxJ0kdHu4NctuOKqq5ga1d458/dPyjybSIFxHEAAEpjkKbN1aIzu9oVGf5axCt+6A5e3LPSRG52X85rBzuLrpif0WfO7FXPM+4sYn8nlV/UTDpJ+tIs6dpvFt4WfO0pk1VQX7nrbGnyWe65vmdl8YaXwefatavw8wqbpR3czDNqbwJPUt8F//ld/j3p5T866ugw7+2vUHgMtRig8dDJB6TDFlMfe9w+mzfsd+q/be264rgUHKCRyo9h3atMQQffzOmSE1KLxusAHDmiePWPX9xOorABoc9P0Xt79wRnm7rn6f5uExrLyx0I8X9OI49288tjjxcf1OYt0qBB0c9ViwkflDSDhxKmKFc1M/ajrh+rvbb0BrxtqSYYM8NW4F80W3r33eJyllGlRMPKHNsG9m2T5YITybz34N93038uhg/LaND7jX76gCnKCwuulEaGDMz4RVVXmDndFO0dxIr5xkYcBwCgNAZpkHhndlKdNfesLH8WWKWvPXxYRsOHuf//zDNMwaaUcWbSSdL/OT78tYODJp+f4g7O+N9L8DmDs8e95xo9KvrzCs5U8jogvWOL6rhM+rswaJCj3l6jVzY7mnFhYS3reqxsoZMPSIfXQeOVzpLC47jtd+q/bdJE+/5cNnFjWC5nCvYWM8YtlXLxlwrv5x9M957LH8uC4ua6sFzlDdr730dHh9GuXe59LpubbL19f+7L5Yx1AsKQwaWfh9WLqCVKmCIouM+jf/W6pKpn7EddPyZxbXnn96Xv3ZFfoWKLmWHfe2+Ck62cZVgp0eDvJWoQK5h//vTn8L3Whg8rLuXpCcsLn/xE/M8gLK9M7XIk5ferIec0PuI4AAClMUgDScl2ZlfSWROczVVqtk1UCYMpk/P7u1Ry0e6di9GjChtAUn4DTn8D5tBD3L8NGmQvvSCpaNDE24DaL9i5aJs9HmfjT9u5M0a66mtS5/7Fs93C3n8SVj9k9PWF+cGpcjYFB9DcKp01GTa7N/iYsDwQJ4b1PGOspVIOfO9xX71cevd/i8uwdK/K18h3HOljJ0mP/7J48Nl/bN65KDVbOZirsllHa9d5e+CYvvvUqt6+7ZgklSx15mH1ImqFQUAEReUX7//b/lbt9W1Sm54PGuToK3Mc/cO54c9n+97PuNB7jyY015X6vQQnKdgGsQrzT/HKn1kz8qv4S50T/0BauaLyytSuTOi+a2g8xHEAAEpjkAY1UU5nTXCVSdfZRp/4eLxVKf7ZX7bVKp/4uLRrl4r2QvCUalh4DaCw14zT2Cuno7LU7PE4jcOwmUpxSgsk7YabVDA45Qnu1wOg9YTFoo4Oo/Ub7HEsGMdnTjdF5RyD96to89mQu3ph6mN/5xTdybZK8fFfFj9H8D149w2+n1J5Mqx++70rHI0eVfsykd4EBP/xlMo/rF5ErTAICE8uZ7TjdRNZyreaGfthsa4Wm55HxcxczmjwYGnpYrekWH6Fv718qF/U72XlfcWTFILtElu+8ziOtO8A9/mizknUaqdKJs+V+zc0HuI4AADRGKRBzcS5cLY1Au6+x10Jc9qp0uqfF5bokuwlDI44Ij/by3ueFT/0VtTYGzPBjrRzpxkd99HilSZxNzrcts3eGFm6uPLGov8cxm0c1mumUpwOu1J77FCHGGhdtlg04RS9tz9VcVy25QO35nxvwUCNLSZfe115HT8jRzhyHFPU8TRiWPhjwlYp+v+/lwNsM4dt76fcVZH1iJu2Y7LlnyQ6WpKalY72QIcswvZcLFWWOO51cNi1djmbnpcb12z3Dx7HjAvzJZhLvb4nbAXqih8W39dx4u2HI+VzXbDtFTwm9iAJ1865jzgOAEA4BmmQqrBGgDHuAI03e8y7iF2/oXjD5t5e6eme6M6zYMPB1hm47E5p2Z3FHYdxGxmbt9jvt2tX9YMm5TQOpdrPVIo7YJQpngBf8LdGqEPczg0loNaCte3dARr3b3E6dCR3r5iTx+dXQ4YNlqy8z2j2zPi17i+bW1ie8rK5TmRpL9vKoKCov3mC76ec10sjbnoTEIKDYpK/czR6RnmcTsgkZqUDaF1hey5es6C4NGUl18FR19px2wLlxrWwAfDgcdj2QatkwOOVzfayY11nF56jUvkurO3lP6aODnt9M/f29o31leY+2isAALS+iC5UoPa8RoCNN8AxelS+4WW7fyYjHTMyekDAez6vVnXUDDGvUZbLmcjXDHaUDRkcfr9JE919BG78lvtf/8zx9RvyrxUmqnEY9hzZbOG5S0pYI9b2Hi65KH9OvBmPUu3rEMc9r92r3I3NL77U/W/3KjbNAZLmxaJdu5zIPQSGDimuey+5HUrefaLud89KlfzN+02a6Oi+e9y4fN89TslOEndlkGONaR7bcQUF349kj1nB10urfrttAoJ/tVBUDpDscbacPFKuuPEfQHMJG6Dff397XCz3OjjqWjtOW6CcuPbfTxs9u6nXev+eZ4onpHkDUlGvH0fY+5h8lm0/HCe0bRXW9vIf065d9vMednszKjffVJr72rm9Qk4HALQTBmmQqqhGgK3xEdZpNXxYpuj2qMZMWCefx99xaHtNb+NO/wXjoEHRHWrBxmI5F9xhjarnnjc6a4r7HGdNqc9Fe6nNWv0mnJofnLrvnnyHqH+gKmlxz2stOwkBFCvVyZXNOpo5vfhxwVyQzTrqOrv4fqUGrm3K7cTzD7jfd4+jy+YW5i+vI83LL7Y8E3w/UTErbIC/nmwTEILCckBYnLV1QoY9RznauSMLaHVxJ03V4vnjDJrHuT5e/ZAbk+bNl6bPst//jTftbZiZ0xX5+nHEbdNIhfln1ozi17a1vfzHVOvPK22V5Jty2lCedm6vkNMBAO2GcmctrFmWRXslCVbeZ97bQya68RFWwiB4+9p14SXG3M5A897+AMWCjQj/c//2KfNe2QG3NNoVl0tdU6KPLajc8mW2vR1mXFhY/sCY8vdlqES5JXiCtYdrWYe4nPNKrWygvuLsl+Xu1dLbF9vCcsHksxyt+GHhnjLewPU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"text/plain": [ - "
    " + "
    " ] }, "metadata": {}, @@ -485,9 +8583,9 @@ ], "metadata": { "kernelspec": { - "display_name": "arviz-devs", + "display_name": "Python 3", "language": "python", - "name": "arviz-devs" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -499,7 +8597,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.7" + "version": "3.6.10" } }, "nbformat": 4, diff --git a/doc/notebooks/Numba.ipynb b/doc/notebooks/Numba.ipynb index fbadc2b008..ccdf0b1c9b 100644 --- a/doc/notebooks/Numba.ipynb +++ b/doc/notebooks/Numba.ipynb @@ -71,7 +71,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "327 ms ± 15 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" + "2.61 s ± 569 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" ] } ], @@ -105,7 +105,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "1.23 ms ± 68.9 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" + "1.54 ms ± 383 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" ] } ], @@ -122,14 +122,14 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "2.13 ms ± 196 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n" + "8.68 ms ± 435 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n" ] } ], @@ -146,7 +146,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -155,7 +155,7 @@ "False" ] }, - "execution_count": 9, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -167,7 +167,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -177,14 +177,14 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "14.5 ms ± 2.03 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n" + "74.7 ms ± 3.53 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n" ] } ], @@ -194,14 +194,14 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "908 µs ± 25.2 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n" + "3.85 ms ± 158 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n" ] } ], @@ -211,7 +211,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -220,7 +220,7 @@ "True" ] }, - "execution_count": 13, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -232,14 +232,14 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "12.5 ms ± 982 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n" + "43.8 ms ± 22.4 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" ] } ], @@ -249,14 +249,14 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "470 µs ± 16.8 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n" + "2.51 ms ± 584 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n" ] } ], @@ -266,7 +266,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -275,7 +275,7 @@ "True" ] }, - "execution_count": 16, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -294,7 +294,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -304,7 +304,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -313,7 +313,7 @@ "False" ] }, - "execution_count": 18, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -325,14 +325,14 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "52.6 ms ± 765 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n" + "182 ms ± 66.3 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" ] } ], @@ -342,14 +342,14 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "1.06 ms ± 89.6 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n" + "10.4 ms ± 2.97 ms per loop (mean ± std. dev. of 7 runs, 100 loops each)\n" ] } ], @@ -359,7 +359,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -368,7 +368,7 @@ "True" ] }, - "execution_count": 21, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -380,14 +380,14 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "8.51 ms ± 859 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" + "19.9 ms ± 1.37 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" ] } ], @@ -397,14 +397,14 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "1.18 ms ± 79.2 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" + "3.97 ms ± 574 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" ] } ], @@ -422,9 +422,9 @@ ], "metadata": { "kernelspec": { - "display_name": "arviz-devs", + "display_name": "Python 3", "language": "python", - "name": "arviz-devs" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -436,7 +436,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.7" + "version": "3.6.10" } }, "nbformat": 4, diff --git a/doc/notebooks/XarrayforArviZ.ipynb b/doc/notebooks/XarrayforArviZ.ipynb index 8782bd3c01..f6961be081 100644 --- a/doc/notebooks/XarrayforArviZ.ipynb +++ b/doc/notebooks/XarrayforArviZ.ipynb @@ -67,6 +67,2186 @@ "outputs": [ { "data": { + "text/html": [ + "\n", + "
    \n", + "
    \n", + "
    arviz.InferenceData
    \n", + "
    \n", + "
      \n", + " \n", + "
    • \n", + " \n", + " \n", + "
      \n", + "
      \n", + "
        \n", + "
        \n", + "\n", + "\n", + "Show/Hide data repr\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Show/Hide attributes\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
        xarray.Dataset
          • chain: 4
          • draw: 500
          • school: 8
          • chain
            (chain)
            int64
            0 1 2 3
            array([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])
          • school
            (school)
            object
            'Choate' ... 'Mt. Hermon'
            array(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
+       "       'Hotchkiss', 'Lawrenceville', "St. Paul's", 'Mt. Hermon'], dtype=object)
          • mu
            (chain, draw)
            float64
            ...
            array([[-3.476986, -2.455871, -2.826254, ...,  3.392022,  8.46255 , -0.238516],\n",
+       "       [ 8.250863,  8.250863,  8.250863, ...,  2.527095,  0.276589,  5.655297],\n",
+       "       [10.51707 ,  9.887949,  8.500833, ..., -1.571177, -4.435385,  9.762948],\n",
+       "       [ 4.532296,  4.532296,  3.914097, ...,  4.597058,  5.898506,  0.161389]])
          • theta
            (chain, draw, school)
            float64
            ...
            array([[[ 1.668654, -8.537401, ...,  0.155234, -6.818251],\n",
+       "        [-6.239359,  1.071411, ..., -4.462528, -1.110761],\n",
+       "        ...,\n",
+       "        [ 9.292977, 13.691033, ...,  8.176874,  5.888367],\n",
+       "        [11.715418,  4.492172, ..., 12.300712,  9.22107 ]],\n",
+       "\n",
+       "       [[ 8.096212,  7.756517, ...,  6.465884,  5.472468],\n",
+       "        [ 8.096212,  7.756517, ...,  6.465884,  5.472468],\n",
+       "        ...,\n",
+       "        [14.735501,  7.546139, ..., 15.732696, -4.697359],\n",
+       "        [-4.837035,  8.501408, ...,  5.850945, -0.426543]],\n",
+       "\n",
+       "       [[14.570919, 15.029668, ..., 11.798422,  8.519339],\n",
+       "        [12.686667,  7.679173, ..., 13.514133, 10.295221],\n",
+       "        ...,\n",
+       "        [ 5.361653,  2.78173 , ...,  7.224553, -7.416111],\n",
+       "        [13.439111,  9.614329, ..., 12.008359, 16.673157]],\n",
+       "\n",
+       "       [[ 4.326388,  5.198464, ...,  5.339654,  3.422931],\n",
+       "        [ 4.326388,  5.198464, ...,  5.339654,  3.422931],\n",
+       "        ...,\n",
+       "        [-1.420946, -4.034405, ..., 15.850648,  4.013397],\n",
+       "        [-0.050159,  0.063538, ..., 10.592933,  4.523389]]])
          • tau
            (chain, draw)
            float64
            ...
            array([[ 3.730101,  2.075383,  3.702993, ..., 10.107925,  8.079994,  7.728861],\n",
+       "       [ 1.193334,  1.193334,  1.193334, ..., 13.922048,  8.869919,  4.763175],\n",
+       "       [ 5.137247,  4.264381,  2.141432, ...,  2.811842, 12.179657,  4.452967],\n",
+       "       [ 0.50007 ,  0.50007 ,  0.902267, ...,  8.345631,  7.71079 ,  5.406798]])
        • created_at :
          2019-06-21T17:36:34.398087
          inference_library :
          pymc3
          inference_library_version :
          3.7

        \n", + "
      \n", + "
      \n", + "
      • \n", + " \n", + "
    • \n", + " \n", + " \n", + "
      \n", + "
      \n", + "
        \n", + "
        \n", + "\n", + "\n", + "Show/Hide data repr\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Show/Hide attributes\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
        xarray.Dataset
          • chain: 4
          • draw: 500
          • school: 8
          • chain
            (chain)
            int64
            0 1 2 3
            array([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])
          • school
            (school)
            object
            'Choate' ... 'Mt. Hermon'
            array(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
+       "       'Hotchkiss', 'Lawrenceville', "St. Paul's", 'Mt. Hermon'], dtype=object)
          • obs
            (chain, draw, school)
            float64
            ...
            array([[[ 7.850329e+00, -1.902792e+01, ..., -3.547030e+00,  1.619463e+01],\n",
+       "        [ 2.931985e+00,  1.919950e-01, ..., -8.065696e-01,  1.518667e+01],\n",
+       "        ...,\n",
+       "        [-7.248618e-01,  5.924768e+00, ...,  1.173805e+01, -1.422732e+01],\n",
+       "        [ 2.220263e+01,  1.548817e+01, ...,  8.783500e+00,  2.019629e+01]],\n",
+       "\n",
+       "       [[-1.202312e+01,  1.233019e+01, ...,  2.131579e+01,  8.356886e+00],\n",
+       "        [ 4.996825e+00,  1.506829e+01, ..., -1.342830e+00, -2.743757e+01],\n",
+       "        ...,\n",
+       "        [ 3.666123e+01,  1.349807e+01, ...,  4.540989e+01, -2.117575e+00],\n",
+       "        [ 1.791875e+00,  1.501421e+01, ..., -2.182083e+00, -6.630969e+00]],\n",
+       "\n",
+       "       [[ 3.377648e+01,  3.088294e+01, ...,  2.182889e+01,  4.625301e+00],\n",
+       "        [-5.600531e-01,  5.228436e+00, ...,  9.387947e+00,  3.665830e+00],\n",
+       "        ...,\n",
+       "        [ 3.279823e+00, -1.301396e+01, ...,  1.089418e+01, -1.149742e+01],\n",
+       "        [ 3.424522e+01,  2.320377e+01, ...,  9.892069e+00,  1.729264e+01]],\n",
+       "\n",
+       "       [[-1.517826e-02, -5.597241e-01, ..., -2.986433e+00,  1.075464e+01],\n",
+       "        [ 7.538687e+00,  2.524281e+01, ..., -8.230382e+00, -2.109873e+01],\n",
+       "        ...,\n",
+       "        [ 2.180411e+00, -1.861976e+01, ...,  2.564547e+01, -7.993703e+00],\n",
+       "        [-2.096968e+01,  5.474909e+00, ...,  4.697547e+00, -1.506955e+01]]])
        • created_at :
          2019-06-21T17:36:34.489022
          inference_library :
          pymc3
          inference_library_version :
          3.7

        \n", + "
      \n", + "
      \n", + "
      • \n", + " \n", + "
    • \n", + " \n", + " \n", + "
      \n", + "
      \n", + "
        \n", + "
        \n", + "\n", + "\n", + "Show/Hide data repr\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Show/Hide attributes\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
        xarray.Dataset
          • chain: 4
          • draw: 500
          • school: 8
          • chain
            (chain)
            int64
            0 1 2 3
            array([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])
          • school
            (school)
            object
            'Choate' ... 'Mt. Hermon'
            array(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
+       "       'Hotchkiss', 'Lawrenceville', "St. Paul's", 'Mt. Hermon'], dtype=object)
          • tune
            (chain, draw)
            bool
            ...
            array([[ True, False, False, ..., False, False, False],\n",
+       "       [ True, False, False, ..., False, False, False],\n",
+       "       [ True, False, False, ..., False, False, False],\n",
+       "       [ True, False, False, ..., False, False, False]])
          • depth
            (chain, draw)
            int64
            ...
            array([[5, 3, 3, ..., 5, 5, 4],\n",
+       "       [6, 3, 2, ..., 4, 4, 4],\n",
+       "       [3, 5, 3, ..., 4, 4, 5],\n",
+       "       [3, 4, 3, ..., 5, 5, 5]])
          • tree_size
            (chain, draw)
            float64
            ...
            array([[31.,  7.,  7., ..., 31., 31., 15.],\n",
+       "       [39.,  7.,  3., ..., 15., 15., 15.],\n",
+       "       [ 7., 31.,  7., ..., 15., 15., 31.],\n",
+       "       [ 7., 11.,  7., ..., 31., 31., 31.]])
          • lp
            (chain, draw)
            float64
            ...
            array([[-59.048452, -56.192829, -56.739609, ..., -63.171891, -62.871221,\n",
+       "        -59.67573 ],\n",
+       "       [-51.16655 , -51.16655 , -51.16655 , ..., -62.242981, -60.962775,\n",
+       "        -61.120349],\n",
+       "       [-57.1196  , -54.709673, -49.854318, ..., -58.202845, -63.100613,\n",
+       "        -61.906641],\n",
+       "       [-43.11603 , -43.11603 , -44.766386, ..., -60.530643, -63.616474,\n",
+       "        -58.345072]])
          • energy_error
            (chain, draw)
            float64
            ...
            array([[ 0.073872, -0.184094,  0.301398, ..., -0.024763,  0.015377,  0.011884],\n",
+       "       [ 0.542861,  0.      ,  0.      , ...,  0.035578, -0.144987, -0.023558],\n",
+       "       [ 1.30834 , -0.068309, -0.343327, ..., -0.480097,  1.118238, -0.505195],\n",
+       "       [-0.232345,  0.      ,  2.427791, ..., -0.007677, -0.087005, -0.003652]])
          • step_size_bar
            (chain, draw)
            float64
            ...
            array([[0.241676, 0.241676, 0.241676, ..., 0.241676, 0.241676, 0.241676],\n",
+       "       [0.233163, 0.233163, 0.233163, ..., 0.233163, 0.233163, 0.233163],\n",
+       "       [0.25014 , 0.25014 , 0.25014 , ..., 0.25014 , 0.25014 , 0.25014 ],\n",
+       "       [0.150248, 0.150248, 0.150248, ..., 0.150248, 0.150248, 0.150248]])
          • max_energy_error
            (chain, draw)
            float64
            ...
            array([[ 1.310060e-01, -2.066764e-01,  6.362023e-01, ...,  1.272182e-01,\n",
+       "        -3.155631e-01, -6.702092e-02],\n",
+       "       [ 2.089505e+00,  3.848563e+01,  6.992369e+01, ..., -3.713299e-01,\n",
+       "        -2.177462e-01, -1.621819e-01],\n",
+       "       [ 1.458063e+00,  4.335779e+02,  2.788723e+00, ..., -4.800969e-01,\n",
+       "         4.380251e+00, -5.051946e-01],\n",
+       "       [ 3.226553e-01,  2.736452e+02,  2.202908e+02, ..., -1.224747e-01,\n",
+       "        -1.009818e-01, -1.756579e-01]])
          • energy
            (chain, draw)
            float64
            ...
            array([[60.756731, 62.756232, 64.398717, ..., 67.394493, 66.923554, 65.031815],\n",
+       "       [53.535435, 56.914649, 54.576739, ..., 63.760659, 64.405753, 66.210544],\n",
+       "       [62.504616, 61.998659, 56.945798, ..., 64.477622, 68.892486, 67.322436],\n",
+       "       [50.115409, 46.916088, 52.915592, ..., 66.27361 , 67.768307, 67.209852]])
          • mean_tree_accept
            (chain, draw)
            float64
            ...
            array([[0.950641, 0.990596, 0.725287, ..., 0.971847, 0.979623, 0.986629],\n",
+       "       [0.78913 , 0.014034, 0.035809, ..., 0.989669, 0.987006, 0.991768],\n",
+       "       [0.26802 , 0.392567, 0.839235, ..., 0.969229, 0.105422, 0.979116],\n",
+       "       [0.909964, 0.157585, 0.061793, ..., 0.999467, 0.987537, 0.996704]])
          • step_size
            (chain, draw)
            float64
            ...
            array([[0.127504, 0.127504, 0.127504, ..., 0.127504, 0.127504, 0.127504],\n",
+       "       [0.12298 , 0.12298 , 0.12298 , ..., 0.12298 , 0.12298 , 0.12298 ],\n",
+       "       [0.207479, 0.207479, 0.207479, ..., 0.207479, 0.207479, 0.207479],\n",
+       "       [0.106445, 0.106445, 0.106445, ..., 0.106445, 0.106445, 0.106445]])
          • diverging
            (chain, draw)
            bool
            ...
            array([[False, False, False, ..., False, False, False],\n",
+       "       [False, False, False, ..., False, False, False],\n",
+       "       [False, False, False, ..., False, False, False],\n",
+       "       [False, False, False, ..., False, False, False]])
          • log_likelihood
            (chain, draw, school)
            float64
            ...
            array([[[-5.167744, -4.588952, ..., -4.813702, -4.355802],\n",
+       "        [-6.232175, -3.46155 , ..., -5.744349, -4.074576],\n",
+       "        ...,\n",
+       "        [-4.404661, -3.383463, ..., -3.703993, -3.866952],\n",
+       "        [-4.216295, -3.283048, ..., -3.383933, -3.821228]],\n",
+       "\n",
+       "       [[-4.507346, -3.22182 , ..., -3.886703, -3.875064],\n",
+       "        [-4.507346, -3.22182 , ..., -3.886703, -3.875064],\n",
+       "        ...,\n",
+       "        [-4.017982, -3.222554, ..., -3.247227, -4.23956 ],\n",
+       "        [-6.023146, -3.222781, ..., -3.959521, -4.047611]],\n",
+       "\n",
+       "       [[-4.027745, -3.468605, ..., -3.413821, -3.828006],\n",
+       "        [-4.148096, -3.222038, ..., -3.322139, -3.813795],\n",
+       "        ...,\n",
+       "        [-4.765866, -3.357675, ..., -3.802075, -4.391078],\n",
+       "        [-4.098143, -3.234554, ..., -3.401022, -3.843012]],\n",
+       "\n",
+       "       [[-4.872411, -3.260767, ..., -4.022945, -3.922838],\n",
+       "        [-4.872411, -3.260767, ..., -4.022945, -3.922838],\n",
+       "        ...,\n",
+       "        [-5.550527, -3.945658, ..., -3.244622, -3.907745],\n",
+       "        [-5.375459, -3.536461, ..., -3.495847, -3.895575]]])
        • created_at :
          2019-06-21T17:36:34.485802
          inference_library :
          pymc3
          inference_library_version :
          3.7

        \n", + "
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        \n", + "\n", + "\n", + "Show/Hide data repr\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Show/Hide attributes\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
        xarray.Dataset
          • chain: 1
          • draw: 500
          • school: 8
          • chain
            (chain)
            int64
            0
            array([0])
          • draw
            (draw)
            int64
            0 1 2 3 4 5 ... 495 496 497 498 499
            array([  0,   1,   2, ..., 497, 498, 499])
          • school
            (school)
            object
            'Choate' ... 'Mt. Hermon'
            array(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
+       "       'Hotchkiss', 'Lawrenceville', "St. Paul's", 'Mt. Hermon'], dtype=object)
          • tau
            (chain, draw)
            float64
            ...
            array([[ 6.560633,  1.016055, 68.91391 , ...,  1.560098,  5.948734,  0.763063]])
          • tau_log__
            (chain, draw)
            float64
            ...
            array([[ 1.881087,  0.015927,  4.232858, ...,  0.444748,  1.783178, -0.270415]])
          • mu
            (chain, draw)
            float64
            ...
            array([[ 5.29345 ,  0.813724,  0.712223, ..., -0.979857, -1.657547, -3.272668]])
          • theta
            (chain, draw, school)
            float64
            ...
            array([[[ 2.357357,  7.371371, ...,  6.135082,  3.984435],\n",
+       "        [ 0.258399, -0.752515, ...,  1.73084 , -0.034163],\n",
+       "        ...,\n",
+       "        [-4.353289,  2.194643, ..., -7.819076, -6.21613 ],\n",
+       "        [-4.131344, -4.093318, ..., -3.775218, -3.555126]]])
          • obs
            (chain, draw, school)
            float64
            ...
            array([[[ -3.539971,   6.769448, ...,   8.26964 ,  -8.569042],\n",
+       "        [-21.166369,   1.14605 , ..., -13.157913,  -8.5424  ],\n",
+       "        ...,\n",
+       "        [ 29.354582,  -5.511382, ..., -17.892521,  46.28878 ],\n",
+       "        [ -6.379747,   6.538907, ..., -21.155214,  -6.070767]]])
        • created_at :
          2019-06-21T17:36:34.490387
          inference_library :
          pymc3
          inference_library_version :
          3.7

        \n", + "
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        \n", + "
        \n", + "\n", + "\n", + "Show/Hide data repr\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Show/Hide attributes\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
        xarray.Dataset
          • school: 8
          • school
            (school)
            object
            'Choate' ... 'Mt. Hermon'
            array(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
+       "       'Hotchkiss', 'Lawrenceville', "St. Paul's", 'Mt. Hermon'], dtype=object)
          • obs
            (school)
            float64
            ...
            array([28.,  8., -3.,  7., -1.,  1., 18., 12.])
        • created_at :
          2019-06-21T17:36:34.491909
          inference_library :
          pymc3
          inference_library_version :
          3.7

        \n", + "
      \n", + "
      \n", + "
      • \n", + " \n", + "
    \n", + "
    \n", + " " + ], "text/plain": [ "Inference data with groups:\n", "\t> posterior\n", @@ -438,11 +2618,11 @@ " stroke: currentColor;\n", " fill: currentColor;\n", "}\n", - "
    xarray.Dataset
      • chain: 4
      • draw: 500
      • school: 8
      • chain
        (chain)
        int64
        0 1 2 3
        array([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])
      • school
        (school)
        object
        'Choate' ... 'Mt. Hermon'
        array(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
-       "       'Hotchkiss', 'Lawrenceville', "St. Paul's", 'Mt. Hermon'], dtype=object)
      • mu
        (chain, draw)
        float64
        ...
        array([[-3.476986, -2.455871, -2.826254, ...,  3.392022,  8.46255 , -0.238516],\n",
+       "
        xarray.Dataset
          • chain: 4
          • draw: 500
          • school: 8
          • chain
            (chain)
            int64
            0 1 2 3
            array([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])
          • school
            (school)
            object
            'Choate' ... 'Mt. Hermon'
            array(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
+       "       'Hotchkiss', 'Lawrenceville', "St. Paul's", 'Mt. Hermon'], dtype=object)
          • mu
            (chain, draw)
            float64
            -3.477 -2.456 ... 5.899 0.1614
            array([[-3.476986, -2.455871, -2.826254, ...,  3.392022,  8.46255 , -0.238516],\n",
        "       [ 8.250863,  8.250863,  8.250863, ...,  2.527095,  0.276589,  5.655297],\n",
        "       [10.51707 ,  9.887949,  8.500833, ..., -1.571177, -4.435385,  9.762948],\n",
-       "       [ 4.532296,  4.532296,  3.914097, ...,  4.597058,  5.898506,  0.161389]])
          • theta
            (chain, draw, school)
            float64
            ...
            array([[[ 1.668654, -8.537401, ...,  0.155234, -6.818251],\n",
+       "       [ 4.532296,  4.532296,  3.914097, ...,  4.597058,  5.898506,  0.161389]])
          • theta
            (chain, draw, school)
            float64
            1.669 -8.537 -2.623 ... 10.59 4.523
            array([[[ 1.668654, -8.537401, ...,  0.155234, -6.818251],\n",
        "        [-6.239359,  1.071411, ..., -4.462528, -1.110761],\n",
        "        ...,\n",
        "        [ 9.292977, 13.691033, ...,  8.176874,  5.888367],\n",
@@ -464,10 +2644,10 @@
        "        [ 4.326388,  5.198464, ...,  5.339654,  3.422931],\n",
        "        ...,\n",
        "        [-1.420946, -4.034405, ..., 15.850648,  4.013397],\n",
-       "        [-0.050159,  0.063538, ..., 10.592933,  4.523389]]])
          • tau
            (chain, draw)
            float64
            ...
            array([[ 3.730101,  2.075383,  3.702993, ..., 10.107925,  8.079994,  7.728861],\n",
+       "        [-0.050159,  0.063538, ..., 10.592933,  4.523389]]])
          • tau
            (chain, draw)
            float64
            3.73 2.075 3.703 ... 7.711 5.407
            array([[ 3.730101,  2.075383,  3.702993, ..., 10.107925,  8.079994,  7.728861],\n",
        "       [ 1.193334,  1.193334,  1.193334, ..., 13.922048,  8.869919,  4.763175],\n",
        "       [ 5.137247,  4.264381,  2.141432, ...,  2.811842, 12.179657,  4.452967],\n",
-       "       [ 0.50007 ,  0.50007 ,  0.902267, ...,  8.345631,  7.71079 ,  5.406798]])
        • created_at :
          2019-06-21T17:36:34.398087
          inference_library :
          pymc3
          inference_library_version :
          3.7
      " + " [ 0.50007 , 0.50007 , 0.902267, ..., 8.345631, 7.71079 , 5.406798]])
  • created_at :
    2019-06-21T17:36:34.398087
    inference_library :
    pymc3
    inference_library_version :
    3.7
    • " ], "text/plain": [ "\n", @@ -477,9 +2657,9 @@ " * draw (draw) int64 0 1 2 3 4 5 6 7 8 ... 492 493 494 495 496 497 498 499\n", " * school (school) object 'Choate' 'Deerfield' ... \"St. Paul's\" 'Mt. Hermon'\n", "Data variables:\n", - " mu (chain, draw) float64 ...\n", - " theta (chain, draw, school) float64 ...\n", - " tau (chain, draw) float64 ...\n", + " mu (chain, draw) float64 -3.477 -2.456 -2.826 ... 4.597 5.899 0.1614\n", + " theta (chain, draw, school) float64 1.669 -8.537 -2.623 ... 10.59 4.523\n", + " tau (chain, draw) float64 3.73 2.075 3.703 4.146 ... 8.346 7.711 5.407\n", "Attributes:\n", " created_at: 2019-06-21T17:36:34.398087\n", " inference_library: pymc3\n", @@ -850,8 +3030,8 @@ " stroke: currentColor;\n", " fill: currentColor;\n", "}\n", - "
    xarray.Dataset
      • school: 8
      • school
        (school)
        object
        'Choate' ... 'Mt. Hermon'
        array(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
-       "       'Hotchkiss', 'Lawrenceville', "St. Paul's", 'Mt. Hermon'], dtype=object)
      • obs
        (school)
        float64
        ...
        array([28.,  8., -3.,  7., -1.,  1., 18., 12.])
    • created_at :
      2019-06-21T17:36:34.491909
      inference_library :
      pymc3
      inference_library_version :
      3.7
    " + "
    xarray.Dataset
      • school: 8
      • school
        (school)
        object
        'Choate' ... 'Mt. Hermon'
        array(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
+       "       'Hotchkiss', 'Lawrenceville', "St. Paul's", 'Mt. Hermon'], dtype=object)
      • obs
        (school)
        float64
        28.0 8.0 -3.0 7.0 ... 1.0 18.0 12.0
        array([28.,  8., -3.,  7., -1.,  1., 18., 12.])
    • created_at :
      2019-06-21T17:36:34.491909
      inference_library :
      pymc3
      inference_library_version :
      3.7
    " ], "text/plain": [ "\n", @@ -859,7 +3039,7 @@ "Coordinates:\n", " * school (school) object 'Choate' 'Deerfield' ... \"St. Paul's\" 'Mt. Hermon'\n", "Data variables:\n", - " obs (school) float64 ...\n", + " obs (school) float64 28.0 8.0 -3.0 7.0 -1.0 1.0 18.0 12.0\n", "Attributes:\n", " created_at: 2019-06-21T17:36:34.491909\n", " inference_library: pymc3\n", @@ -952,9 +3132,9 @@ ], "metadata": { "kernelspec": { - "display_name": "arviz-devs", + "display_name": "Python 3", "language": "python", - "name": "arviz-devs" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -966,7 +3146,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.7" + "version": "3.6.10" } }, "nbformat": 4, diff --git a/doc/notebooks/pystan_refitting.ipynb b/doc/notebooks/pystan_refitting.ipynb index 59b03b5598..3423f446c0 100644 --- a/doc/notebooks/pystan_refitting.ipynb +++ b/doc/notebooks/pystan_refitting.ipynb @@ -58,7 +58,7 @@ { "data": { "text/plain": [ - "[]" + "[]" ] }, "execution_count": 3, @@ -263,8 +263,8 @@ "Computed from 2000 by 100 log-likelihood matrix\n", "\n", " Estimate SE\n", - "elpd_loo -250.85 7.15\n", - "p_loo 3.03 -\n", + "elpd_loo -250.66 7.17\n", + "p_loo 2.85 -\n", "------\n", "\n", "Pareto k diagnostic values:\n", @@ -272,7 +272,13 @@ "(-Inf, 0.5] (good) 100 100.0%\n", " (0.5, 0.7] (ok) 0 0.0%\n", " (0.7, 1] (bad) 0 0.0%\n", - " (1, Inf) (very bad) 0 0.0%" + " (1, Inf) (very bad) 0 0.0%\n", + "\n", + "\n", + "The scale is now log by default. Use 'scale' argument or 'stats.ic_scale' rcParam if\n", + "you rely on a specific value.\n", + "A higher log-score (or a lower deviance) indicates a model with better predictive\n", + "accuracy." ] }, "execution_count": 9, @@ -335,7 +341,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/oriol/venvs/arviz-dev/lib/python3.6/site-packages/arviz/stats/stats_refitting.py:98: UserWarning: reloo is an experimental and untested feature\n", + "/Users/percy/anaconda3/envs/arviz/lib/python3.6/site-packages/arviz/stats/stats_refitting.py:98: UserWarning: reloo is an experimental and untested feature\n", " warnings.warn(\"reloo is an experimental and untested feature\", UserWarning)\n", "arviz.stats.stats_refitting - INFO - Refitting model excluding observation 13\n", "INFO:arviz.stats.stats_refitting:Refitting model excluding observation 13\n", @@ -363,8 +369,8 @@ "Computed from 2000 by 100 log-likelihood matrix\n", "\n", " Estimate SE\n", - "elpd_loo -250.85 7.15\n", - "p_loo 3.03 -\n", + "elpd_loo -250.67 7.17\n", + "p_loo 2.86 -\n", "------\n", "\n", "Pareto k diagnostic values:\n", @@ -372,7 +378,13 @@ "(-Inf, 0.5] (good) 100 100.0%\n", " (0.5, 0.7] (ok) 0 0.0%\n", " (0.7, 1] (bad) 0 0.0%\n", - " (1, Inf) (very bad) 0 0.0%" + " (1, Inf) (very bad) 0 0.0%\n", + "\n", + "\n", + "The scale is now log by default. Use 'scale' argument or 'stats.ic_scale' rcParam if\n", + "you rely on a specific value.\n", + "A higher log-score (or a lower deviance) indicates a model with better predictive\n", + "accuracy." ] }, "execution_count": 13, @@ -395,8 +407,8 @@ "Computed from 2000 by 100 log-likelihood matrix\n", "\n", " Estimate SE\n", - "elpd_loo -250.85 7.15\n", - "p_loo 3.03 -\n", + "elpd_loo -250.66 7.17\n", + "p_loo 2.85 -\n", "------\n", "\n", "Pareto k diagnostic values:\n", @@ -404,7 +416,13 @@ "(-Inf, 0.5] (good) 96 96.0%\n", " (0.5, 0.7] (ok) 0 0.0%\n", " (0.7, 1] (bad) 2 2.0%\n", - " (1, Inf) (very bad) 2 2.0%" + " (1, Inf) (very bad) 2 2.0%\n", + "\n", + "\n", + "The scale is now log by default. Use 'scale' argument or 'stats.ic_scale' rcParam if\n", + "you rely on a specific value.\n", + "A higher log-score (or a lower deviance) indicates a model with better predictive\n", + "accuracy." ] }, "execution_count": 14, @@ -415,13 +433,6 @@ "source": [ "loo_orig" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { @@ -440,7 +451,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.9" + "version": "3.6.10" } }, "nbformat": 4, From a32424e38c4be4eeaa575ce3c9c27f90b2500fdd Mon Sep 17 00:00:00 2001 From: Oriol Abril Date: Wed, 10 Jun 2020 17:10:57 +0200 Subject: [PATCH 07/24] Maintenance on io_pymc3, io_pyro and tests (#1227) * change warning from pendingdeprecation to futurewarning * skip matplotlib animation tests if ffmpeg not installed * add warning when from pyro cannot get log likelihood * black * fix skipif * update changelog * increase test coverage * update changelog * fix changelog --- CHANGELOG.md | 7 ++- arviz/data/io_pymc3.py | 2 +- arviz/data/io_pyro.py | 13 ++++++ .../tests/base_tests/test_plots_matplotlib.py | 23 ++++++++++ arviz/tests/external_tests/test_data_pymc.py | 2 +- arviz/tests/external_tests/test_data_pyro.py | 46 +++++++++++++++---- 6 files changed, 79 insertions(+), 14 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 762a8e6a40..4761652ed6 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -3,18 +3,21 @@ ## v0.x.x Unreleased ### New features -* loo-pit plot. The kde is computed over the data interval (this could be shorter than [0, 1]). The hdi is computed analitically (#1215) +* loo-pit plot. The kde is computed over the data interval (this could be shorter than [0, 1]). The hdi is computed analitically (#1215) * Added `html_repr` of InferenceData objects for jupyter notebooks. (#1217) * Added support for PyJAGS via the function `from_pyjags` in the module arviz.data.io_pyjags. (#1219) ### Maintenance and fixes * Include data from `MultiObservedRV` to `observed_data` when using `from_pymc3` (#1098) - * Added a note on `plot_pair` when trying to use `plot_kde` on `InferenceData` objects. (#1218) +* Added `log_likelihood` argument to `from_pyro` and a warning if log likelihood cannot be obtained (#1227) +* Skip tests on matplotlib animations if ffmpeg is not installed (#1227) ### Deprecation +* Using `from_pymc3` without a model context available now raises a + `FutureWarning` and will be deprecated in a future version (#1227) ### Documentation * A section has been added to the documentation at InferenceDataCookbook.ipynb illustrating the use of ArviZ in conjunction with PyJAGS. (#1219) diff --git a/arviz/data/io_pymc3.py b/arviz/data/io_pymc3.py index 891210a27d..08583e83dd 100644 --- a/arviz/data/io_pymc3.py +++ b/arviz/data/io_pymc3.py @@ -86,7 +86,7 @@ def __init__( "Using `from_pymc3` without the model will be deprecated in a future release. " "Not using the model will return less accurate and less useful results. " "Make sure you use the model argument or call from_pymc3 within a model context.", - PendingDeprecationWarning, + FutureWarning, ) # This next line is brittle and may not work forever, but is a secret diff --git a/arviz/data/io_pyro.py b/arviz/data/io_pyro.py index 7d048886ee..f3698f84b8 100644 --- a/arviz/data/io_pyro.py +++ b/arviz/data/io_pyro.py @@ -1,5 +1,6 @@ """Pyro-specific conversion code.""" import logging +import warnings import numpy as np from packaging import version import xarray as xr @@ -26,6 +27,7 @@ def __init__( posterior=None, prior=None, posterior_predictive=None, + log_likelihood=True, predictions=None, constant_data=None, predictions_constant_data=None, @@ -62,6 +64,7 @@ def __init__( self.posterior = posterior self.prior = prior self.posterior_predictive = posterior_predictive + self.log_likelihood = log_likelihood self.predictions = predictions self.constant_data = constant_data self.predictions_constant_data = predictions_constant_data @@ -130,6 +133,8 @@ def sample_stats_to_xarray(self): @requires("model") def log_likelihood_to_xarray(self): """Extract log likelihood from Pyro posterior.""" + if not self.log_likelihood: + return None data = {} if self.observations is not None: try: @@ -143,6 +148,10 @@ def log_likelihood_to_xarray(self): data[obs_name] = np.reshape(log_like, shape) except: # pylint: disable=bare-except # cannot get vectorized trace + warnings.warn( + "Could not get vectorized trace, log_likelihood group will be omitted. " + "Check your model vectorization or set log_likelihood=False" + ) return None return dict_to_dataset(data, library=self.pyro, coords=self.coords, dims=self.dims) @@ -273,6 +282,7 @@ def from_pyro( *, prior=None, posterior_predictive=None, + log_likelihood=True, predictions=None, constant_data=None, predictions_constant_data=None, @@ -294,6 +304,8 @@ def from_pyro( Prior samples from a Pyro model posterior_predictive : dict Posterior predictive samples for the posterior + log_likelihood : bool, optional + Calculate and store pointwise log likelihood values. predictions: dict Out of sample predictions constant_data: dict @@ -313,6 +325,7 @@ def from_pyro( posterior=posterior, prior=prior, posterior_predictive=posterior_predictive, + log_likelihood=log_likelihood, predictions=predictions, constant_data=constant_data, predictions_constant_data=predictions_constant_data, diff --git a/arviz/tests/base_tests/test_plots_matplotlib.py b/arviz/tests/base_tests/test_plots_matplotlib.py index 9a157ec937..3e8d6cb3c1 100644 --- a/arviz/tests/base_tests/test_plots_matplotlib.py +++ b/arviz/tests/base_tests/test_plots_matplotlib.py @@ -3,6 +3,7 @@ import os from copy import deepcopy import matplotlib.pyplot as plt +from matplotlib import animation from pandas import DataFrame from scipy.stats import gaussian_kde import numpy as np @@ -497,6 +498,8 @@ def test_plot_pair_shapes(marginals, max_subplots): @pytest.mark.parametrize("alpha", [None, 0.2, 1]) @pytest.mark.parametrize("animated", [False, True]) def test_plot_ppc(models, kind, alpha, animated): + if animation and not animation.writers.is_available("ffmpeg"): + pytest.skip("matplotlib animations within ArviZ require ffmpeg") animation_kwargs = {"blit": False} axes = plot_ppc( models.model_1, @@ -516,6 +519,8 @@ def test_plot_ppc(models, kind, alpha, animated): @pytest.mark.parametrize("jitter", [None, 0, 0.1, 1, 3]) @pytest.mark.parametrize("animated", [False, True]) def test_plot_ppc_multichain(kind, jitter, animated): + if animation and not animation.writers.is_available("ffmpeg"): + pytest.skip("matplotlib animations within ArviZ require ffmpeg") data = from_dict( posterior_predictive={ "x": np.random.randn(4, 100, 30), @@ -543,6 +548,8 @@ def test_plot_ppc_multichain(kind, jitter, animated): @pytest.mark.parametrize("kind", ["kde", "cumulative", "scatter"]) @pytest.mark.parametrize("animated", [False, True]) def test_plot_ppc_discrete(kind, animated): + if animation and not animation.writers.is_available("ffmpeg"): + pytest.skip("matplotlib animations within ArviZ require ffmpeg") data = from_dict( observed_data={"obs": np.random.randint(1, 100, 15)}, posterior_predictive={"obs": np.random.randint(1, 300, (1, 20, 15))}, @@ -556,6 +563,10 @@ def test_plot_ppc_discrete(kind, animated): assert axes +@pytest.mark.skipif( + not animation.writers.is_available("ffmpeg"), + reason="matplotlib animations within ArviZ require ffmpeg", +) @pytest.mark.parametrize("kind", ["kde", "cumulative", "scatter"]) def test_plot_ppc_save_animation(models, kind): animation_kwargs = {"blit": False} @@ -577,6 +588,10 @@ def test_plot_ppc_save_animation(models, kind): assert os.path.getsize(path) +@pytest.mark.skipif( + not animation.writers.is_available("ffmpeg"), + reason="matplotlib animations within ArviZ require ffmpeg", +) @pytest.mark.parametrize("kind", ["kde", "cumulative", "scatter"]) def test_plot_ppc_discrete_save_animation(kind): data = from_dict( @@ -602,6 +617,10 @@ def test_plot_ppc_discrete_save_animation(kind): assert os.path.getsize(path) +@pytest.mark.skipif( + not animation.writers.is_available("ffmpeg"), + reason="matplotlib animations within ArviZ require ffmpeg", +) @pytest.mark.parametrize("system", ["Windows", "Darwin"]) def test_non_linux_blit(models, monkeypatch, system, caplog): import platform @@ -657,6 +676,10 @@ def test_plot_ppc_ax(models, kind, fig_ax): assert axes[0] is ax +@pytest.mark.skipif( + not animation.writers.is_available("ffmpeg"), + reason="matplotlib animations within ArviZ require ffmpeg", +) def test_plot_ppc_bad_ax(models, fig_ax): _, ax = fig_ax _, ax2 = plt.subplots(1, 2) diff --git a/arviz/tests/external_tests/test_data_pymc.py b/arviz/tests/external_tests/test_data_pymc.py index cf8e093a93..0551a76b90 100644 --- a/arviz/tests/external_tests/test_data_pymc.py +++ b/arviz/tests/external_tests/test_data_pymc.py @@ -425,7 +425,7 @@ def test_no_model_deprecation(self): obs = pm.Normal("obs", x * beta, 1, observed=y) # pylint: disable=unused-variable prior = pm.sample_prior_predictive() - with pytest.warns(PendingDeprecationWarning, match="without the model"): + with pytest.warns(FutureWarning, match="without the model"): inference_data = from_pymc3(prior=prior) test_dict = { "prior": ["beta", "obs"], diff --git a/arviz/tests/external_tests/test_data_pyro.py b/arviz/tests/external_tests/test_data_pyro.py index e21eb435c0..b59d725877 100644 --- a/arviz/tests/external_tests/test_data_pyro.py +++ b/arviz/tests/external_tests/test_data_pyro.py @@ -17,6 +17,7 @@ torch = importorskip("torch") pyro = importorskip("pyro") Predictive = pyro.infer.Predictive +dist = pyro.distributions class TestDataPyro: @@ -164,9 +165,6 @@ def test_inference_data_only_posterior_has_log_likelihood(self, data): assert not fails def test_multiple_observed_rv(self): - import pyro.distributions as dist - from pyro.infer import MCMC, NUTS - y1 = torch.randn(10) y2 = torch.randn(10) @@ -175,8 +173,8 @@ def model_example_multiple_obs(y1=None, y2=None): pyro.sample("y1", dist.Normal(x, 1), obs=y1) pyro.sample("y2", dist.Normal(x, 1), obs=y2) - nuts_kernel = NUTS(model_example_multiple_obs) - mcmc = MCMC(nuts_kernel, num_samples=10) + nuts_kernel = pyro.infer.NUTS(model_example_multiple_obs) + mcmc = pyro.infer.MCMC(nuts_kernel, num_samples=10) mcmc.run(y1=y1, y2=y2) inference_data = from_pyro(mcmc) test_dict = { @@ -190,9 +188,6 @@ def model_example_multiple_obs(y1=None, y2=None): assert not hasattr(inference_data.sample_stats, "log_likelihood") def test_inference_data_constant_data(self): - import pyro.distributions as dist - from pyro.infer import MCMC, NUTS - x1 = 10 x2 = 12 y1 = torch.randn(10) @@ -201,8 +196,8 @@ def model_constant_data(x, y1=None): _x = pyro.sample("x", dist.Normal(1, 3)) pyro.sample("y1", dist.Normal(x * _x, 1), obs=y1) - nuts_kernel = NUTS(model_constant_data) - mcmc = MCMC(nuts_kernel, num_samples=10) + nuts_kernel = pyro.infer.NUTS(model_constant_data) + mcmc = pyro.infer.MCMC(nuts_kernel, num_samples=10) mcmc.run(x=x1, y1=y1) posterior = mcmc.get_samples() posterior_predictive = Predictive(model_constant_data, posterior)(x1) @@ -232,3 +227,34 @@ def test_inference_data_num_chains(self, predictions_data, chains): inference_data = from_pyro(predictions=predictions, num_chains=chains) nchains = inference_data.predictions.dims["chain"] assert nchains == chains + + @pytest.mark.parametrize("log_likelihood", [True, False]) + def test_log_likelihood(self, log_likelihood): + """Test behaviour when log likelihood cannot be retrieved. + + If log_likelihood=True there is a warning to say log_likelihood group is skipped, + if log_likelihood=False there is no warning and log_likelihood is skipped. + """ + x = torch.randn((10, 2)) + y = torch.randn(10) + + def model_constant_data(x, y=None): + beta = pyro.sample("beta", dist.Normal(torch.ones(2), 3)) + pyro.sample("y", dist.Normal(x.matmul(beta), 1), obs=y) + + nuts_kernel = pyro.infer.NUTS(model_constant_data) + mcmc = pyro.infer.MCMC(nuts_kernel, num_samples=10) + mcmc.run(x=x, y=y) + if log_likelihood: + with pytest.warns(UserWarning, match="Could not get vectorized trace"): + inference_data = from_pyro(mcmc, log_likelihood=log_likelihood) + else: + inference_data = from_pyro(mcmc, log_likelihood=log_likelihood) + test_dict = { + "posterior": ["beta"], + "sample_stats": ["diverging"], + "~log_likelihood": [], + "observed_data": ["y"], + } + fails = check_multiple_attrs(test_dict, inference_data) + assert not fails From 34c3b1376ee90ad0810d9538f56e034fa2326aa4 Mon Sep 17 00:00:00 2001 From: Piyush Gautam Date: Thu, 11 Jun 2020 00:27:17 +0530 Subject: [PATCH 08/24] Fix render issue with notebooks (#1234) * rerun the notebooks and minor Changes * remove cache files * modified cookbook --- doc/notebooks/InferenceDataCookbook.ipynb | 17698 +++++++++----------- doc/notebooks/Introduction.ipynb | 6466 +++---- doc/notebooks/XarrayforArviZ.ipynb | 108 +- 3 files changed, 11146 insertions(+), 13126 deletions(-) diff --git a/doc/notebooks/InferenceDataCookbook.ipynb b/doc/notebooks/InferenceDataCookbook.ipynb index 74c2311a6f..94444a3764 100644 --- a/doc/notebooks/InferenceDataCookbook.ipynb +++ b/doc/notebooks/InferenceDataCookbook.ipynb @@ -33,7 +33,7 @@ "
  • From CmdStanPy
    • \n", "
  • From CmdStan
    • \n", "
  • From NumPyro
    • \n", - "
  • From PyJAGS
    • \n", + "
  • From PyJAGS
    • \n", "" ] }, @@ -46,20 +46,7 @@ "start_time": "2020-06-05T06:47:05.567299Z" } }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - "Bad key \"text.kerning_factor\" on line 4 in\n", - "/Users/michaelnowotny/anaconda3/envs/continuous_time_mcmc/lib/python3.7/site-packages/matplotlib/mpl-data/stylelib/_classic_test_patch.mplstyle.\n", - "You probably need to get an updated matplotlibrc file from\n", - "https://github.com/matplotlib/matplotlib/blob/v3.1.3/matplotlibrc.template\n", - "or from the matplotlib source distribution\n" - ] - } - ], + "outputs": [], "source": [ "import arviz as az\n", "import numpy as np" @@ -84,37 +71,6 @@ "outputs": [ { "data": { -<<<<<<< HEAD -======= - "text/plain": [ - "Inference data with groups:\n", - "\t> posterior" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "size = 100\n", - "dataset = az.convert_to_inference_data(np.random.randn(size))\n", - "dataset" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "ExecuteTime": { - "end_time": "2020-06-05T06:47:07.849191Z", - "start_time": "2020-06-05T06:47:07.836546Z" - } - }, - "outputs": [ - { - "data": { ->>>>>>> master "text/html": [ "\n", "
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        \n", + "
        \n", "\n", "\n", "Show/Hide data repr\n", @@ -1400,532 +1152,92 @@ " stroke: currentColor;\n", " fill: currentColor;\n", "}\n", -<<<<<<< HEAD - "
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->>>>>>> master
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-       "         -0.7402975 , -1.62410763, -1.98787609,  0.67915757,\n",
-       "          1.8906322 ,  0.06139485],\n",
-       "        [ 0.84460199,  1.43915938, -1.1833213 ,  0.32430687,\n",
-       "         -0.6518108 ,  1.69764697,  1.14469428,  0.14454147,\n",
-       "          0.18554629, -0.168191  ],\n",
-       "        [ 0.27189222,  1.14009847, -0.5143344 , -0.65669111,\n",
-       "          0.39329444, -3.02136179, -0.90879585, -1.14166753,\n",
-       "          0.22430043, -0.64583285],\n",
-       "        [-1.22246939, -1.12336937,  0.02286542,  0.94257172,\n",
-       "         -0.31216443, -1.34974873,  1.54879704,  0.5647002 ,\n",
-       "         -0.6612776 ,  0.01187873],\n",
-       "        [ 0.40468703, -0.03636206,  1.75994033,  0.69585188,\n",
-       "          1.14862922,  0.33034829,  2.73638075, -0.40265489,\n",
-       "         -0.77341395, -0.97350195],\n",
-       "        [ 1.24834806, -0.28942914,  0.24920516,  1.12153164,\n",
-       "         -1.12047366,  0.84398082,  0.4118804 ,  0.13090905,\n",
-       "          0.998463  , -1.88064252],\n",
-       "        [-0.43912864,  1.23907654, -0.52769809, -0.12803379,\n",
-       "          1.73266102,  0.16833375,  0.09682839,  1.56214235,\n",
-       "          0.06487009, -0.66254781],\n",
-       "        [-1.53424335, -1.75275629, -0.10023282, -0.96723118,\n",
-       "          1.96204167, -0.39458072,  0.20871515, -2.20779714,\n",
-       "         -1.0057085 , -0.8908652 ],\n",
-       "        [-0.09913704, -0.55359486,  0.61926756, -0.84209803,\n",
-       "          1.07419138,  0.21700486,  0.13920164, -1.24174736,\n",
-       "          0.20579289, -0.75516259],\n",
-       "        [ 0.74605859,  0.99646065, -1.08974663,  1.23169524,\n",
-       "         -1.21459234,  0.83548719,  0.46503283, -0.11858287,\n",
-       "         -2.39990035, -0.33174063],\n",
-       "        [-0.62121768,  0.11619578,  0.48822156, -1.5998897 ,\n",
-       "         -0.09038936,  0.37117214,  0.96308831, -1.42578582,\n",
-       "         -0.67643394, -0.66744808],\n",
-       "        [ 1.71090278,  2.08360667, -0.4225552 , -0.92160635,\n",
-       "          0.84203467,  0.1091858 ,  0.31465002, -0.30966054,\n",
-       "          0.36207452,  0.2070106 ],\n",
-       "        [ 0.94717714,  0.08834919, -0.33153505,  0.87502294,\n",
-       "          0.87774687, -0.21422208,  0.71621029, -0.21813208,\n",
-       "         -1.87474591, -1.32441086],\n",
-       "        [-0.47100905,  0.03435122,  0.71261576, -0.41954572,\n",
-       "         -0.99971291, -1.71945308,  1.43650446,  0.55123926,\n",
-       "          0.38743478, -0.45572081],\n",
-       "        [ 0.38879971, -0.1596689 , -0.41217246, -1.29495426,\n",
-       "          2.43453146,  0.02029317, -2.20788623, -0.17873662,\n",
-       "         -1.94992635, -0.44402022],\n",
-       "        [-2.0959677 ,  1.17830027, -0.30270294, -0.64615306,\n",
-       "          1.64156   , -0.01018129,  1.02935399, -0.21858383,\n",
-       "         -0.75983239, -0.03257432],\n",
-       "        [-1.32910976,  1.74883827, -0.3229067 , -0.68173192,\n",
-       "         -0.10916739, -0.00434904, -1.98907853, -1.16671081,\n",
-       "          0.47725859, -1.42823431],\n",
-       "        [ 0.31851469,  3.01310136, -0.97815949, -0.99689906,\n",
-       "         -0.42348215, -0.25101005,  0.99199831,  1.16762275,\n",
-       "          0.65359341, -0.70280763],\n",
-       "        [-1.23073466,  0.60758245, -0.54220073, -1.20184626,\n",
-       "         -0.27897645,  0.85166489,  0.76495273,  1.42307555,\n",
-       "          0.58915061, -0.84833981],\n",
-       "        [-0.77529214,  0.72864915,  0.48530567,  0.68724795,\n",
-       "          0.09164434, -0.28523593,  0.55716607, -0.38010051,\n",
-       "         -0.30689402, -0.26176055],\n",
-       "        [ 0.02982609, -1.02482835,  0.39417403, -0.2793383 ,\n",
-       "          0.59988294, -0.81255426, -1.13297374,  0.51053813,\n",
-       "         -1.4637327 , -3.32950448],\n",
-       "        [-0.37036542,  1.16521838,  1.398283  , -0.55711498,\n",
-       "         -1.87476573, -1.83784818,  0.77255495, -0.12403687,\n",
-       "         -0.71000502,  0.78458304],\n",
-       "        [ 0.00393788,  0.07055419, -0.29660186,  0.03357786,\n",
-       "         -0.42857192,  0.27139745, -0.75916369, -0.02803492,\n",
-       "         -2.37509829,  0.7573789 ],\n",
-       "        [-1.50055421, -0.365724  ,  0.01514847, -0.11596194,\n",
-       "         -0.76819565,  0.70419409, -1.27260212,  2.10537012,\n",
-       "         -0.85139193, -0.48917308],\n",
-       "        [ 0.68722981, -1.11982167, -1.17072139, -0.22265587,\n",
-       "         -0.64586133,  1.421204  , -0.1650338 ,  1.35906552,\n",
-       "          1.66161069, -1.14594215],\n",
-       "        [ 0.23639301, -2.21011861,  1.67690895,  1.6117799 ,\n",
-       "         -0.10538148,  1.82665295,  0.15384634,  0.9102795 ,\n",
-       "         -0.92302891,  0.4738414 ],\n",
-       "        [-0.39901383,  0.4713246 , -0.73676913, -0.70300932,\n",
-       "          0.76507028, -1.33110286, -1.59768726, -1.07778394,\n",
-       "         -1.67093264, -0.08567017],\n",
-       "        [ 1.03837021,  0.53820892, -0.91348001,  0.06532431,\n",
-       "          0.46730774, -0.95854977,  0.18859863,  0.87536922,\n",
-       "         -0.98546513, -0.61036688],\n",
-       "        [ 0.09462368,  1.42040267,  0.44277283,  0.9672926 ,\n",
-       "          0.2394287 , -0.88888528,  0.25319636,  0.8628376 ,\n",
-       "         -0.13429954, -0.06736821],\n",
-       "        [-1.10652619, -0.68550688, -0.89707797,  0.28861809,\n",
-       "          0.70157342,  0.13603496, -0.57774116, -0.7306075 ,\n",
-       "         -0.24524038,  0.25256936],\n",
-       "        [ 0.44857724,  1.83835211, -1.25145111, -0.33050593,\n",
-       "         -0.42872811, -0.90102964, -1.65514419, -0.13525644,\n",
-       "          0.30343377,  1.44231529],\n",
-       "        [-0.44092134, -0.68813406, -0.46920367,  0.44730345,\n",
-       "          0.61270319,  1.90897761, -0.01191331, -1.8166567 ,\n",
-       "          0.13100133,  0.28159205],\n",
-       "        [ 1.83482828, -0.23362668, -0.60654732,  1.58296373,\n",
-       "         -0.43294588,  2.86150541,  0.61033781, -0.28819459,\n",
-       "         -0.7428619 , -0.95832671],\n",
-       "        [-0.4641641 , -0.69377076, -1.05199909, -1.00439363,\n",
-       "         -1.28484386,  0.53498276, -0.3767258 , -0.62944392,\n",
-       "         -0.62892174,  0.62367184],\n",
-       "        [-0.47960615,  0.83862729,  0.38209951,  0.48804628,\n",
-       "          1.25285071, -0.70501126, -1.13808358,  1.24436977,\n",
-       "          0.1343849 , -1.64352074],\n",
-       "        [-0.75413586, -0.25835879, -1.18326665,  0.42529959,\n",
-       "         -0.29532952,  0.41030383,  0.64174792,  1.990364  ,\n",
-       "         -0.7145188 , -0.22973732],\n",
-       "        [ 0.49104546,  1.74798501, -0.01567247,  1.26094097,\n",
-       "          0.73055094,  1.20725117, -0.72169009,  1.88142408,\n",
-       "         -1.75872706,  1.14676545],\n",
-       "        [-0.46658432,  1.36844217,  1.80622632,  0.41630248,\n",
-       "          0.3579169 ,  0.63120228, -1.20243591,  0.57654537,\n",
-       "         -1.84240287,  1.27009568],\n",
-       "        [-0.24366607, -0.88445978, -0.3636501 , -0.89821684,\n",
-       "          0.68951694, -0.06386171,  1.39500081,  1.20184981,\n",
-       "          2.39544895,  0.89550972],\n",
-       "        [ 0.15242367, -1.23084778, -1.08164855, -0.51743519,\n",
-       "         -0.72274606,  1.59652417, -1.03722895,  0.05965752,\n",
-       "          0.86449152, -0.52185899],\n",
-       "        [-2.10288082, -1.34181694,  0.1533846 ,  0.7981945 ,\n",
-       "         -0.18419771, -0.72076238,  2.79993884,  0.2766844 ,\n",
-       "          0.32125412,  1.99233887],\n",
-       "        [-0.83756302,  1.32435262, -1.15151501, -0.45478924,\n",
-       "         -0.0380437 ,  1.01389659, -0.38360173,  0.04593002,\n",
-       "         -1.67314883,  0.76107315],\n",
-       "        [ 0.28664064,  0.19577641, -0.36499579,  0.15812154,\n",
-       "          1.07155452, -0.99255685, -1.30725076,  0.73864051,\n",
-       "          0.39314181, -0.10817879],\n",
-       "        [-0.28412102,  0.01055585,  0.02351819, -0.3818316 ,\n",
-       "          1.34194109,  0.52821611, -1.45351116,  1.26910084,\n",
-       "          1.03286574, -0.85091732],\n",
-       "        [ 0.76658407,  0.45298155,  1.17236988,  1.49296971,\n",
-       "         -0.86080561,  0.7830452 , -1.18019225,  0.04014171,\n",
-       "         -0.2439888 , -0.28891682],\n",
-       "        [ 2.39252097,  0.11763426,  1.71953541, -0.38594126,\n",
-       "          0.54640727,  0.47610705, -0.77441705, -0.12780554,\n",
-       "         -0.84585647, -1.03872811],\n",
-       "        [-0.92517281, -1.46206904,  0.94346589,  2.15323477,\n",
-       "          1.71204618,  0.41852599, -1.02678493,  0.00684078,\n",
-       "         -0.90444402, -0.75351707],\n",
-       "        [ 1.15746181, -0.54314825,  0.58927014, -0.29607625,\n",
-       "         -0.31875024,  0.05611806, -1.33931667,  0.64054206,\n",
-       "          1.20274762,  0.63497818],\n",
-       "        [ 1.02606806,  0.24731296,  1.09443807, -2.9686459 ,\n",
-       "         -0.65211653, -0.49871233,  1.88351013, -0.06799509,\n",
-       "          0.57002699, -0.56179467],\n",
-       "        [ 0.88163277, -0.51283766, -0.02129781,  1.74594465,\n",
-       "         -0.00437275,  0.60638676,  0.55630893,  0.69052337,\n",
-       "          0.83378663, -0.75891006],\n",
-       "        [ 0.44577217,  0.97412345,  1.28856365,  0.60390408,\n",
-       "         -1.79502655, -0.61766774,  1.01743531, -0.56012918,\n",
-       "         -0.26204686, -0.76476869],\n",
-       "        [ 1.20949467, -0.196461  ,  1.08501425, -0.42779261,\n",
-       "          1.79577772,  1.17864942,  0.26969297, -0.78404253,\n",
-       "         -0.71966673,  1.03127274],\n",
-       "        [ 2.50222227,  0.46094406,  0.33207066, -0.95603468,\n",
-       "         -0.47991149,  1.23441534,  0.69248441,  0.91406389,\n",
-       "         -0.23401162, -0.4690532 ]]])
        • c
          (chain, draw, c_dim_0, c_dim_1)
          float64
          -0.703 -0.4777 ... -0.6178 0.34
          array([[[[-0.70295144, -0.47769958,  0.83339221, -1.13238531],\n",
-       "         [ 3.07763421,  1.76203663,  0.51726531, -0.18522201],\n",
-       "         [-0.11274144,  0.36530569, -1.12617178, -1.2879944 ]],\n",
-       "\n",
-       "        [[-0.71366689,  0.45517522,  0.19207493,  0.8582474 ],\n",
-       "         [-0.16947168,  0.46780349,  0.81728124,  0.8487605 ],\n",
-       "         [ 1.01930005, -0.98207195,  0.76484748, -0.68116289]],\n",
-       "\n",
-       "        [[ 2.866716  ,  0.23486823,  0.49692715,  1.03331798],\n",
-       "         [-0.03404072, -1.40102939, -0.49063134,  1.30965462],\n",
-       "         [-0.91686508, -0.26013333, -0.23745687,  0.38117166]],\n",
+       "
          xarray.Dataset
            • chain: 1
            • draw: 2
            • x_dim_0: 3
            • x_dim_1: 4
            • x_dim_2: 5
            • chain
              (chain)
              int64
              0
              array([0])
            • draw
              (draw)
              int64
              0 1
              array([0, 1])
            • x_dim_0
              (x_dim_0)
              int64
              0 1 2
              array([0, 1, 2])
            • x_dim_1
              (x_dim_1)
              int64
              0 1 2 3
              array([0, 1, 2, 3])
            • x_dim_2
              (x_dim_2)
              int64
              0 1 2 3 4
              array([0, 1, 2, 3, 4])
            • x
              (chain, draw, x_dim_0, x_dim_1, x_dim_2)
              float64
              -0.921 0.4821 ... -1.388 -0.2951
              array([[[[[-0.92101852,  0.48214847, -1.34675845,  0.80791591,\n",
+       "            0.13442397],\n",
+       "          [ 1.55928147,  0.72467009, -0.26329447,  0.77020337,\n",
+       "           -0.79385986],\n",
+       "          [ 2.56014158, -0.22522851,  1.50429511,  0.98422616,\n",
+       "            0.87701818],\n",
+       "          [ 0.2033265 , -0.46213057,  1.83291516,  0.92185653,\n",
+       "            0.02246557]],\n",
+       "\n",
+       "         [[ 0.58975218,  0.2109381 , -0.25165215,  1.07386412,\n",
+       "            0.22433061],\n",
+       "          [-0.38069638,  0.17451069, -0.91240345, -1.14499426,\n",
+       "            0.2878055 ],\n",
+       "          [-1.75288879,  0.81279251, -0.92868769,  0.805726  ,\n",
+       "           -0.79796633],\n",
+       "          [-0.73333509, -0.1039633 , -1.59234066, -0.72387975,\n",
+       "            0.73062838]],\n",
+       "\n",
+       "         [[ 0.71570935,  0.52509819, -0.36251886, -1.60016203,\n",
+       "           -0.23893154],\n",
+       "          [-0.06835913, -1.21852009,  0.82192727, -1.10977168,\n",
+       "           -2.08662438],\n",
+       "          [-0.20764243, -0.14951083, -0.60938158,  1.3045079 ,\n",
+       "            1.49413921],\n",
+       "          [-0.77821166, -0.88093741,  0.3519268 ,  0.20628867,\n",
+       "           -1.56463733]]],\n",
+       "\n",
+       "\n",
+       "        [[[ 0.70988028, -0.71212251,  0.25541438,  0.05158433,\n",
+       "            0.75728211],\n",
+       "          [-0.10089313, -2.0769755 , -3.32968542,  0.88509685,\n",
+       "            0.53247162],\n",
+       "          [ 0.73857681, -1.82011301,  0.86925206,  0.70125327,\n",
+       "           -0.35396124],\n",
+       "          [-0.49242067,  0.25316   ,  0.18691584,  0.63272798,\n",
+       "           -0.87052449]],\n",
+       "\n",
+       "         [[ 0.16249541, -0.76002895, -0.57382345,  0.31514025,\n",
+       "            0.05763214],\n",
+       "          [-0.73349012,  0.373515  , -1.79050181,  0.14774867,\n",
+       "           -0.65746071],\n",
+       "          [ 1.55594162, -0.14762212, -2.85498527,  1.43403387,\n",
+       "           -0.74873244],\n",
+       "          [ 1.0050118 ,  0.44536177, -0.15660396,  0.65484298,\n",
+       "           -0.84545377]],\n",
+       "\n",
+       "         [[ 1.78973096, -0.3529071 , -0.73334952,  0.64730247,\n",
+       "            0.21085732],\n",
+       "          [-0.87333169,  1.23147537, -0.09859236,  1.002286  ,\n",
+       "           -1.4012664 ],\n",
+       "          [-0.85472506, -0.24004629, -0.04136821, -0.90713131,\n",
+       "            0.22470241],\n",
+       "          [-0.2516493 ,  1.97247898, -0.16846266, -1.38770486,\n",
+       "           -0.29507746]]]]])
          • created_at :
            2020-06-10T18:19:03.796075
            arviz_version :
            0.8.3

        • \n", + "
      \n", + "
      \n", + "
      • \n", + " \n", + "
    \n", + "
    \n", + "
    xarray.Dataset
      • chain: 1
      • draw: 2
      • x_dim_0: 3
      • x_dim_1: 4
      • x_dim_2: 5
      • chain
        (chain)
        int64
        0
        array([0])
      • draw
        (draw)
        int64
        0 1
        array([0, 1])
      • x_dim_0
        (x_dim_0)
        int64
        0 1 2
        array([0, 1, 2])
      • x_dim_1
        (x_dim_1)
        int64
        0 1 2 3
        array([0, 1, 2, 3])
      • x_dim_2
        (x_dim_2)
        int64
        0 1 2 3 4
        array([0, 1, 2, 3, 4])
      • x
        (chain, draw, x_dim_0, x_dim_1, x_dim_2)
        float64
        0.4887 0.1041 ... 1.812 -0.1801
        array([[[[[ 0.48871812,  0.104059  ,  2.52839358,  0.34683312,\n",
-       "           -1.64163885],\n",
-       "          [ 0.13362705, -0.37274136,  1.57671058, -1.40591077,\n",
-       "           -0.61797283],\n",
-       "          [-2.21051946, -0.63569656,  0.35856112,  1.19773328,\n",
-       "           -0.78210076],\n",
-       "          [ 0.38410654,  1.21098546, -1.05257952, -1.13003428,\n",
-       "           -0.32335117]],\n",
-       "\n",
-       "         [[ 0.18496352,  0.98486652,  0.21531806, -0.33013583,\n",
-       "           -0.65043716],\n",
-       "          [ 0.12486439, -0.23435485, -0.89305091,  0.52130207,\n",
-       "            1.2360257 ],\n",
-       "          [-0.01976296, -0.69663875, -0.22386098,  0.72681712,\n",
-       "           -0.29705774],\n",
-       "          [ 0.33742164, -0.12607612, -0.86499256, -0.50236806,\n",
-       "           -0.42588285]],\n",
-       "\n",
-       "         [[-0.32033675, -0.51853466, -0.49995089,  0.74906308,\n",
-       "           -1.24181623],\n",
-       "          [ 1.00986665, -0.76900377, -0.7368668 , -0.90870827,\n",
-       "            2.07551017],\n",
-       "          [ 0.07275855,  0.13140076,  0.42146223, -0.37905082,\n",
-       "           -0.87708245],\n",
-       "          [ 0.07107187, -0.3310002 , -0.47052444, -2.21177506,\n",
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    • created_at :
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      arviz_version :
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        xarray.Dataset
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          • draw
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            xarray.Dataset
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                int64
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-       "          3.45179451e-01],\n",
-       "        [ 1.02187525e+00,  8.01564112e-01, -3.51037986e-01,\n",
-       "          2.11031426e-01,  1.40994313e-01,  7.09967748e-01,\n",
-       "         -1.06269498e+00, -9.73849912e-01, -1.84571572e+00,\n",
-       "         -2.45978621e+00],\n",
-       "        [-7.58400248e-01,  2.03737641e+00,  1.83910268e+00,\n",
-       "          3.50225259e-01, -1.11016105e+00, -5.53083929e-02,\n",
-       "          1.04439557e+00, -3.11171069e-01, -1.10583178e+00,\n",
-       "          4.88404853e-01],\n",
-       "        [ 2.30582393e+00,  6.41402263e-01,  1.27565207e+00,\n",
-       "          1.48470354e+00, -1.06293487e+00, -4.92018540e-01,\n",
-       "         -7.22529322e-01, -2.98084261e-01,  8.12775081e-01,\n",
-       "          2.36634526e+00],\n",
-       "        [ 2.36693262e-01, -1.73127374e+00, -6.08210650e-01,\n",
-       "          3.64829398e+00, -8.11572940e-01, -5.18213130e-01,\n",
-       "         -1.42171526e+00, -2.99249399e-01, -7.85268793e-01,\n",
-       "          5.74472635e-02]]])
              • c
                (chain, draw, c1, c2)
                float64
                1.226 0.4789 ... -0.1108 -0.984
                array([[[[ 1.22649718,  0.47893663,  2.0413824 , -0.55030657],\n",
-       "         [-1.95116998,  0.01010687,  0.35432766, -1.29626763],\n",
-       "         [-0.14866476, -0.74223465,  0.75565304, -0.42292514]],\n",
-       "\n",
-       "        [[-0.44381607,  0.74929562, -1.3168288 , -0.44077197],\n",
-       "         [-0.16409627,  1.17079294, -0.82892697, -0.949321  ],\n",
-       "         [ 1.28950286,  1.87127791, -1.49458565, -0.55805979]],\n",
-       "\n",
-       "        [[-0.71525592, -0.45653018, -0.63288416, -0.07811683],\n",
-       "         [ 0.25913129,  1.06400024,  0.80976502,  0.49944962],\n",
-       "         [ 0.19796366,  0.21067087, -0.15476082,  1.03569289]],\n",
+       "       90, 91, 92, 93, 94, 95, 96, 97, 98, 99])
              • b_dim_0
                (b_dim_0)
                int64
                0 1 2 3 4 5 6 7 8 9
                array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
              • c_dim_0
                (c_dim_0)
                int64
                0 1 2
                array([0, 1, 2])
              • c_dim_1
                (c_dim_1)
                int64
                0 1 2 3
                array([0, 1, 2, 3])
              • a
                (chain, draw)
                float64
                -0.9868 -0.9059 ... -0.08585 0.2419
                array([[-0.98680087, -0.9059364 , -0.14165402, -0.58265096,  0.82603876,\n",
+       "        -2.46304762, -0.68751032, -0.44797211,  0.4767166 ,  1.20762507,\n",
+       "         1.7683901 ,  0.49919964,  0.39169713,  0.24818252,  0.76896913,\n",
+       "         1.35602941,  0.16012735, -0.40869246,  1.57454222, -0.88422323,\n",
+       "        -0.8032874 ,  0.97983047,  0.62862413,  0.86092572,  1.28611767,\n",
+       "         0.47059445, -1.94828812,  0.77238202,  1.34715668,  0.5508723 ,\n",
+       "         0.37690772,  0.36441709,  1.00628099, -0.25510614, -0.15580706,\n",
+       "        -2.12785677,  1.51691814, -1.11203406,  0.15787474,  0.49807416,\n",
+       "         0.59557061,  1.31281552,  0.81375214, -0.23155098,  0.33571588,\n",
+       "        -0.57863365, -0.28264525, -0.09610144, -0.54420598, -0.6172331 ,\n",
+       "        -0.7610177 , -0.46448357, -0.35348013,  0.14209789, -1.24727987,\n",
+       "         0.01894142,  1.5554319 , -1.58654114, -1.04792336,  1.60558452,\n",
+       "        -1.55497944,  0.42248955, -0.01063844,  0.37625908,  0.32187246,\n",
+       "        -0.90741871,  0.70642742,  1.4723044 , -1.05279937,  0.48145175,\n",
+       "        -0.70126199, -0.93931202, -0.48064917,  0.0822739 , -1.60523997,\n",
+       "         1.33511812,  0.80697787, -0.43798262, -1.12562896,  0.99484913,\n",
+       "        -1.57580105,  1.12748292, -1.16916992,  0.26178148,  0.10459207,\n",
+       "         1.53168163,  0.43148325, -0.05799433,  0.48346788,  0.34411697,\n",
+       "         0.00835765, -0.99452364, -2.11526315,  1.13121207, -0.30909674,\n",
+       "         0.43876463, -0.3636197 , -0.3043917 , -0.08585393,  0.2418645 ]])
              • b
                (chain, draw, b_dim_0)
                float64
                -0.6033 0.6579 ... 0.09994 -0.8692
                array([[[-6.03344744e-01,  6.57856984e-01,  3.44548919e-01,\n",
+       "          3.00142169e-01, -5.26833619e-01,  3.29689332e-01,\n",
+       "         -1.31465481e-01, -1.11881896e+00,  1.18846301e+00,\n",
+       "         -1.21813909e+00],\n",
+       "        [ 1.17352705e+00,  4.49529097e-01, -7.83719405e-01,\n",
+       "          7.05153489e-01,  7.20493088e-01, -1.65224704e-01,\n",
+       "          9.02912425e-01,  8.19731087e-01, -3.36621748e-01,\n",
+       "         -5.87045111e-01],\n",
+       "        [-9.96082427e-01,  4.77531075e-01, -2.34510302e+00,\n",
+       "          1.68160162e+00,  6.22797427e-03,  1.77374788e+00,\n",
+       "         -1.50265600e+00,  3.11192598e-01,  1.91409288e-01,\n",
+       "          7.48532726e-01],\n",
+       "        [-2.18461121e+00,  1.50224313e+00, -8.48418441e-02,\n",
+       "          1.56111055e-01, -6.28023349e-01,  2.44065494e+00,\n",
+       "          1.34358128e+00,  6.45792256e-02, -5.37334375e-01,\n",
+       "         -1.58412656e+00],\n",
+       "        [-1.33605723e+00,  1.53919334e+00,  9.89398732e-01,\n",
+       "          7.74029783e-02, -2.20688937e+00,  1.18267626e-01,\n",
+       "         -5.69714810e-03, -1.15067825e+00,  5.68043096e-01,\n",
+       "          7.70411480e-01],\n",
+       "        [-1.13852033e+00,  3.47448503e-01,  1.52309065e+00,\n",
+       "         -1.32490172e+00, -2.32465532e-01,  8.68848166e-01,\n",
+       "         -6.16837275e-02,  1.33329233e+00,  7.29225621e-02,\n",
+       "          7.24641776e-01],\n",
+       "        [-1.15838246e+00, -8.26426337e-01, -4.57894659e-01,\n",
+       "          4.56794856e-01,  3.10055489e-01, -1.79734037e-02,\n",
+       "         -9.84080320e-01, -1.29296979e+00, -8.40349545e-02,\n",
+       "          2.58786373e-01],\n",
+       "        [ 5.51656105e-01,  1.43825410e+00, -4.36959135e-01,\n",
+       "         -5.78931413e-02,  1.29019244e+00,  4.36264408e-01,\n",
+       "          1.33306016e+00,  2.58902126e+00,  2.18743344e+00,\n",
+       "         -4.36414636e-01],\n",
+       "        [-1.15194329e+00,  5.74085856e-01, -6.80884765e-01,\n",
+       "          1.38353054e+00, -2.72955555e-01, -1.32859425e+00,\n",
+       "          2.24159658e+00, -1.85937277e+00, -6.25979461e-01,\n",
+       "          2.98351415e-01],\n",
+       "        [ 7.03720099e-01,  1.44173056e+00, -6.92558561e-02,\n",
+       "          2.95877859e-01, -2.98021556e-01, -7.70862124e-01,\n",
+       "         -1.08625934e+00,  1.04516406e+00, -1.85273444e+00,\n",
+       "          2.06092575e+00],\n",
+       "        [ 1.04401520e-01,  7.39694607e-01, -1.15940584e+00,\n",
+       "         -5.81278186e-01, -1.38478532e+00,  1.83985938e+00,\n",
+       "          1.85276646e+00,  1.14389132e+00, -8.46548159e-01,\n",
+       "         -8.97060931e-01],\n",
+       "        [-7.03963313e-01, -2.14852744e+00, -4.67196755e-01,\n",
+       "         -1.28214529e+00,  1.56616160e+00, -2.29603438e+00,\n",
+       "         -8.71054829e-01, -1.57125278e+00, -1.27717725e-01,\n",
+       "          3.90472950e-01],\n",
+       "        [ 9.32857554e-01, -1.62350199e+00, -1.18949692e-01,\n",
+       "         -2.04093561e+00,  2.89166449e-01,  5.04334491e-01,\n",
+       "         -8.08487490e-01, -6.45941332e-01, -3.10636191e-03,\n",
+       "         -1.76777806e-02],\n",
+       "        [ 3.28726794e-04,  7.64485340e-01,  1.17277547e+00,\n",
+       "         -1.52697903e+00, -5.09176289e-01, -4.82164179e-01,\n",
+       "         -2.26069420e-01, -6.12613334e-01, -6.19346794e-01,\n",
+       "         -1.29346515e+00],\n",
+       "        [-2.05227275e+00, -3.65561981e-02,  4.18985471e-01,\n",
+       "          1.48795235e-01,  1.29697774e-01, -3.10792226e-01,\n",
+       "         -2.66639794e-01, -8.91297615e-01, -5.62512864e-01,\n",
+       "         -1.09868049e+00],\n",
+       "        [ 4.23890956e-01, -9.35524984e-01, -4.25062985e-01,\n",
+       "          3.30534938e-01,  1.36676232e+00,  1.00625155e+00,\n",
+       "         -1.51675944e-02, -1.90166945e+00,  9.34924334e-01,\n",
+       "         -1.22422149e+00],\n",
+       "        [ 1.27950941e-01,  9.86967420e-01,  3.69754064e-01,\n",
+       "         -1.06276533e+00, -4.18378711e-01, -7.63597916e-01,\n",
+       "          1.90183960e-01,  9.90085182e-02,  1.08525123e+00,\n",
+       "          2.17597127e-01],\n",
+       "        [-9.63262514e-01,  7.81149102e-02, -3.20795189e-01,\n",
+       "          4.05531512e-01, -7.26173288e-01, -1.01466534e+00,\n",
+       "          2.03474395e+00, -1.24271091e-01,  1.93436249e+00,\n",
+       "         -1.17909975e+00],\n",
+       "        [-5.70649376e-01, -8.34619821e-01,  8.45542524e-01,\n",
+       "         -7.42262280e-01,  1.14077178e-01,  4.97437579e-01,\n",
+       "          2.20796720e+00, -1.42905324e-01, -8.24939696e-01,\n",
+       "          1.40557906e+00],\n",
+       "        [-6.95666653e-01, -1.74886824e-01,  1.71599123e+00,\n",
+       "          5.86052743e-01, -2.16556634e-02, -4.10865757e-01,\n",
+       "          1.49435198e+00, -8.70780433e-01,  2.85899854e-01,\n",
+       "          8.68184593e-01],\n",
+       "        [ 2.73823061e-01, -4.98582368e-01,  1.53582947e+00,\n",
+       "         -9.14754419e-01,  9.71505598e-01,  9.92993972e-01,\n",
+       "          5.96438243e-01, -2.86586036e-01,  1.95743478e+00,\n",
+       "         -2.20829155e-01],\n",
+       "        [ 1.63850220e+00,  2.95440786e-01,  1.30065186e+00,\n",
+       "         -9.93511299e-02, -3.76079329e-01, -8.25564643e-01,\n",
+       "         -1.61999376e-01, -1.14248430e-01,  1.27566333e+00,\n",
+       "         -3.48046368e-01],\n",
+       "        [-1.45762416e-01,  1.39621941e+00,  1.92237888e+00,\n",
+       "          5.89155632e-01,  1.46429119e+00,  1.53810630e-01,\n",
+       "          1.58447573e-01, -2.43321209e-01, -7.38333316e-01,\n",
+       "         -1.23821968e+00],\n",
+       "        [ 9.76117826e-01,  3.29990313e-01,  1.15123880e-01,\n",
+       "          4.99346096e-01, -1.40186745e+00, -2.01628854e+00,\n",
+       "          7.64935235e-01, -1.44727437e+00,  1.75402206e-01,\n",
+       "         -7.43535801e-01],\n",
+       "        [-1.19678596e+00,  8.15575342e-01,  3.45959027e-01,\n",
+       "         -1.55267128e-01,  1.00092325e+00,  1.07173246e+00,\n",
+       "         -8.76878757e-01, -1.65442667e+00, -1.94738497e+00,\n",
+       "         -7.25719719e-01],\n",
+       "        [-7.31290552e-03,  4.25956663e-01,  1.41045243e-02,\n",
+       "         -9.38274013e-01, -1.44788155e+00, -3.44116787e-01,\n",
+       "         -1.48523306e-01, -1.44559454e+00, -2.18867427e-01,\n",
+       "          3.28646495e-01],\n",
+       "        [ 5.42897524e-01,  1.94972122e-01, -2.91695697e-01,\n",
+       "          7.66816314e-02,  7.58577867e-01,  1.63945182e-01,\n",
+       "          3.83678569e-01, -1.74527148e-02,  9.51222174e-01,\n",
+       "         -1.15197652e-03],\n",
+       "        [-8.08403556e-01, -1.80543885e+00, -5.38675846e-02,\n",
+       "         -1.09748280e+00, -3.38943085e-01, -8.89970856e-01,\n",
+       "         -2.03799415e+00,  1.01699459e+00, -1.48642805e+00,\n",
+       "         -4.51014926e-01],\n",
+       "        [ 7.51352276e-01,  2.22660335e-01, -1.42399694e-01,\n",
+       "          4.49104552e-01,  1.03711348e+00, -1.54627325e-01,\n",
+       "         -9.89487696e-01, -1.11269767e+00,  9.21655993e-01,\n",
+       "         -3.53273843e-01],\n",
+       "        [-2.29251295e+00,  5.44184518e-02,  6.62783314e-01,\n",
+       "         -7.95233752e-02, -4.51309564e-01,  3.92000090e-02,\n",
+       "         -5.78584216e-01,  5.49292384e-01,  1.79005634e+00,\n",
+       "         -2.44758103e-02],\n",
+       "        [ 8.03690663e-01, -1.02272529e+00, -1.11828509e-01,\n",
+       "          1.18464169e+00, -2.15790068e-01,  8.70460501e-01,\n",
+       "         -1.27119386e+00,  9.25915153e-01,  1.33622293e+00,\n",
+       "         -2.51928764e-01],\n",
+       "        [-8.90111272e-01, -7.93194826e-01,  6.48713202e-02,\n",
+       "          5.91572345e-01,  1.52077272e+00,  1.09985185e+00,\n",
+       "         -5.59315595e-01, -1.31593971e-01,  3.16769364e-01,\n",
+       "         -4.94721812e-02],\n",
+       "        [ 2.54369506e+00,  2.30949292e-01,  7.45563348e-01,\n",
+       "         -1.75457566e+00,  2.59847986e-01, -1.58300186e+00,\n",
+       "          1.61763219e-01,  5.86030617e-01, -3.52109618e-01,\n",
+       "         -6.28293444e-02],\n",
+       "        [-5.68713296e-01, -7.81448240e-01,  5.15461288e-01,\n",
+       "         -1.17411110e+00,  6.21615625e-01, -2.90792649e-02,\n",
+       "          3.97006524e-01, -6.39514419e-01, -7.50392889e-01,\n",
+       "         -2.79750746e-01],\n",
+       "        [ 1.72889034e-01, -1.97982806e-01,  1.52583598e+00,\n",
+       "         -1.81315590e+00,  2.35141661e+00, -3.28095238e-01,\n",
+       "         -8.22759414e-01,  3.78414247e-01,  3.04574866e-01,\n",
+       "          5.50304395e-01],\n",
+       "        [ 6.24635508e-01,  6.71712017e-01, -8.85141907e-01,\n",
+       "         -2.27334856e-01,  1.05295625e+00, -3.66685300e-01,\n",
+       "         -5.19790215e-01, -1.71297264e-01, -3.39302375e-01,\n",
+       "         -2.19899778e+00],\n",
+       "        [ 7.50469499e-01,  1.04234173e+00, -2.40317608e-01,\n",
+       "         -1.66288696e+00,  5.56633942e-01,  9.15179879e-01,\n",
+       "         -2.80424899e-01, -2.96306753e-01, -1.73490181e+00,\n",
+       "         -7.85773754e-01],\n",
+       "        [ 1.03282216e+00,  2.11585166e-01,  4.19046543e-01,\n",
+       "          5.30035878e-01,  5.76861633e-01, -1.28673367e+00,\n",
+       "          6.67434351e-01, -1.84240162e+00,  8.70674586e-01,\n",
+       "         -3.64595569e-01],\n",
+       "        [ 2.14885629e-01,  6.97504946e-02, -3.86208835e-01,\n",
+       "         -1.88037799e-01, -9.82338184e-01,  2.69590303e+00,\n",
+       "         -8.61690252e-02,  9.02908623e-01, -1.41021222e-01,\n",
+       "         -1.10885378e+00],\n",
+       "        [-1.82248996e+00, -8.98623971e-01, -9.96564208e-01,\n",
+       "         -1.19166582e+00, -3.08062239e-01, -1.17450276e+00,\n",
+       "          6.13779650e-01,  5.43977484e-02, -1.14477672e+00,\n",
+       "          3.16025195e-01],\n",
+       "        [ 1.93945496e+00, -9.37060487e-01,  5.54166202e-01,\n",
+       "          3.39138860e-01, -7.29067989e-01,  7.30360602e-01,\n",
+       "          1.43562425e+00, -4.94229988e-01, -1.70282859e-01,\n",
+       "         -2.88888190e-02],\n",
+       "        [-2.35357055e+00,  1.92570372e+00,  7.72192165e-01,\n",
+       "          2.46928562e+00, -3.17044114e-01, -7.24939088e-01,\n",
+       "          7.86879886e-01, -1.57938639e+00, -2.46990993e-01,\n",
+       "          1.55628847e-01],\n",
+       "        [-1.22929978e+00,  4.01089301e-01,  1.43012373e+00,\n",
+       "         -2.29381273e+00, -4.34980671e-01, -2.06277743e-01,\n",
+       "         -2.75172133e-02, -1.58550271e+00,  6.77293375e-01,\n",
+       "         -3.39666747e-01],\n",
+       "        [ 2.24603726e+00, -7.74774855e-01, -2.01070836e+00,\n",
+       "          7.40415400e-01, -3.18803111e-01,  9.46612796e-01,\n",
+       "          8.44652752e-01, -1.15179317e+00, -7.18727575e-01,\n",
+       "          2.87404152e-01],\n",
+       "        [ 4.54477614e-01, -4.85167977e-01, -7.92691833e-01,\n",
+       "         -1.34472187e-01,  3.37482612e-01,  6.79856864e-01,\n",
+       "         -1.13424369e+00,  4.39552684e-01, -4.04148831e-01,\n",
+       "         -1.75102811e+00],\n",
+       "        [-1.09035650e-01, -8.22962613e-01,  1.21837568e+00,\n",
+       "         -2.03396612e-01,  1.77164371e+00, -1.40503738e-01,\n",
+       "         -8.92262667e-02,  1.74430919e-01,  9.40570888e-01,\n",
+       "         -1.31919437e+00],\n",
+       "        [ 5.18201610e-01,  9.84784226e-01, -2.16946906e-01,\n",
+       "         -9.01160075e-01, -3.68841343e-01, -1.07503772e+00,\n",
+       "          1.06549941e-01, -2.17556257e-01,  4.59867485e-01,\n",
+       "         -9.42197967e-01],\n",
+       "        [-7.09849240e-01,  5.09311652e-01,  1.07440911e+00,\n",
+       "         -1.44736722e+00, -1.74730973e+00, -1.11585053e+00,\n",
+       "         -1.23565515e+00, -4.91550263e-01,  3.49279298e-01,\n",
+       "          1.79562131e-01],\n",
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+       "         -1.47313461e+00, -5.97154267e-01, -4.55353692e-01,\n",
+       "          4.11862471e-01,  1.29079229e+00,  2.37288868e-01,\n",
+       "          1.60198965e+00],\n",
+       "        [ 5.27139340e-01,  1.01802986e+00,  1.56954395e+00,\n",
+       "          2.95846433e-02,  7.87965037e-01,  7.60929985e-02,\n",
+       "         -1.10018427e+00,  6.31978969e-01, -5.73600346e-01,\n",
+       "         -6.62761723e-01],\n",
+       "        [-2.49885209e-01,  4.55945186e-01, -2.03508264e-01,\n",
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+       "         -4.30889625e-02,  1.72152085e+00,  3.97182799e-01,\n",
+       "          3.53575775e-02],\n",
+       "        [-3.81518364e-03,  6.54190057e-01,  5.50339083e-01,\n",
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+       "         -2.45353006e-01, -1.75969658e+00,  6.78991278e-01,\n",
+       "          6.25657815e-01],\n",
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+       "         -5.18068610e-01,  2.40620283e-01, -8.71807066e-01,\n",
+       "         -1.43612255e-01],\n",
+       "        [-1.10106376e+00,  3.97549645e-01,  2.00401129e+00,\n",
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+       "         -1.10453345e+00,  3.00348670e-01,  1.00185621e+00,\n",
+       "         -6.46622301e-01],\n",
+       "        [-1.19687823e+00, -1.38475043e+00,  1.13679486e-01,\n",
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+       "         -3.07382830e-02, -8.20097252e-01, -3.37131272e-01,\n",
+       "         -8.80811227e-02],\n",
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+       "          1.14013739e+00, -3.33805178e-01,  1.46836514e+00,\n",
+       "          4.43356972e-01],\n",
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+       "         -1.44498698e+00,  7.03015805e-01, -2.55268436e+00,\n",
+       "         -4.60478458e-01],\n",
+       "        [-8.45011272e-01,  6.34993822e-01,  8.08524113e-01,\n",
+       "         -3.90423327e-03,  1.04922445e+00,  3.65106953e-01,\n",
+       "         -1.40018595e+00,  7.53522660e-01, -3.19113699e-01,\n",
+       "         -2.52358504e-02],\n",
+       "        [-3.29678037e-01, -1.24339995e+00, -5.75499095e-01,\n",
+       "          4.86062342e-01, -1.38038769e+00,  1.07591195e+00,\n",
+       "         -2.45327566e-01,  8.88655335e-01,  5.43969492e-01,\n",
+       "         -4.40850238e-01],\n",
+       "        [ 1.00682658e+00, -7.30802660e-01,  5.95269764e-01,\n",
+       "         -4.75925572e-01,  1.11996064e-01,  1.37787877e+00,\n",
+       "          8.69259641e-01,  4.77820471e-01,  1.40742636e+00,\n",
+       "          3.16368165e-01],\n",
+       "        [ 1.12138660e+00,  5.60507472e-01, -2.08866446e-01,\n",
+       "         -4.80239818e-01,  1.65659766e-01,  7.94888131e-01,\n",
+       "          1.29508058e-01,  1.54886383e+00, -1.37161881e+00,\n",
+       "         -3.08584072e-01],\n",
+       "        [-1.25345168e+00,  3.96782152e-02, -4.13920092e-01,\n",
+       "         -7.49085529e-03,  3.06447557e-01,  2.23443530e-01,\n",
+       "         -3.40244842e-01,  6.77448331e-01, -3.36543496e-01,\n",
+       "          6.80560314e-01],\n",
+       "        [-3.35381614e-01,  6.95560434e-01, -2.09844097e+00,\n",
+       "         -1.48140570e-01,  1.06206977e+00,  4.83670858e-01,\n",
+       "          1.37422713e+00,  2.39302467e-02, -1.44550523e+00,\n",
+       "         -4.00853417e-01],\n",
+       "        [ 1.38346888e+00,  2.46196976e-01, -1.38661231e+00,\n",
+       "         -4.20661480e-01, -2.17158519e+00,  3.07335846e+00,\n",
+       "          8.66320838e-01, -1.72027291e-01, -9.30506940e-01,\n",
+       "          5.68491948e-01],\n",
+       "        [ 1.06352608e+00,  2.81620386e-01, -1.97817740e-01,\n",
+       "         -2.14577532e+00,  2.29294270e-01,  3.12411147e+00,\n",
+       "          7.62559840e-01,  1.81468594e+00,  1.43288098e+00,\n",
+       "          5.32036252e-01],\n",
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+       "         -7.28565581e-01,  6.02154831e-01, -1.57540076e+00,\n",
+       "          9.00097407e-01],\n",
+       "        [-8.87564777e-01, -3.37687525e-01, -1.30373687e+00,\n",
+       "         -9.25264876e-01,  4.57674318e-01,  5.71552621e-01,\n",
+       "          2.51754343e-01, -4.64932263e-01, -4.72336574e-01,\n",
+       "          1.28690059e+00],\n",
+       "        [ 1.02980926e+00,  1.78406543e+00,  1.38812486e-01,\n",
+       "         -5.39937070e-01, -1.21971756e+00,  2.35179228e-01,\n",
+       "          1.24813394e+00, -1.24967058e-01, -2.50217608e-01,\n",
+       "          1.49065306e+00],\n",
+       "        [ 1.56241734e+00, -7.81406889e-01,  1.69282258e-01,\n",
+       "          2.00136890e-02,  6.46874187e-01, -5.53988599e-01,\n",
+       "         -7.71398502e-01, -1.65553790e-01,  1.41919080e+00,\n",
+       "          1.83966288e+00],\n",
+       "        [-9.70244301e-01, -1.50092407e-01, -6.91250822e-01,\n",
+       "         -9.06007541e-02, -5.81764871e-01, -1.58418757e+00,\n",
+       "         -2.66046527e-01,  8.53074072e-01, -4.97415255e-01,\n",
+       "         -1.29947986e+00],\n",
+       "        [ 1.99111421e+00,  1.23112274e+00, -1.78580677e+00,\n",
+       "          2.16137216e+00, -1.17897327e+00,  7.25677361e-01,\n",
+       "          1.01507866e+00,  1.88055973e+00, -1.30712310e+00,\n",
+       "          5.53177858e-01],\n",
+       "        [ 5.71461316e-02,  5.68856855e-01,  2.88807926e+00,\n",
+       "         -1.92675717e+00,  1.34578245e+00,  1.53407411e+00,\n",
+       "          1.42921355e+00,  2.52360499e-01, -6.75064473e-01,\n",
+       "          5.50440103e-01],\n",
+       "        [ 1.93773586e+00, -1.65924138e+00,  1.31565550e-01,\n",
+       "         -5.01845167e-01, -7.52207811e-01,  1.13312168e+00,\n",
+       "         -8.19930360e-01,  1.16956479e+00,  4.89560102e-01,\n",
+       "         -2.45021515e-01],\n",
+       "        [-1.24447459e+00, -8.24150251e-01, -4.02139271e-01,\n",
+       "         -1.79798795e-01,  9.54804719e-02,  2.09432890e-01,\n",
+       "         -8.17885396e-01,  1.66873702e-02, -4.70928568e-01,\n",
+       "         -3.95689545e-01],\n",
+       "        [ 2.94331755e+00, -7.41260735e-01, -1.02319782e+00,\n",
+       "          7.62810656e-01, -4.06377583e-01, -9.88152610e-01,\n",
+       "         -7.57353383e-01,  3.54717129e-01,  3.96599643e-01,\n",
+       "         -9.43634206e-01],\n",
+       "        [-9.72298335e-01, -2.21009164e-01, -1.50039749e+00,\n",
+       "         -1.19028865e+00,  1.23098588e+00,  8.24026217e-01,\n",
+       "          9.89718193e-01, -1.08755983e+00, -7.23422655e-01,\n",
+       "         -5.55824284e-01],\n",
+       "        [-2.01336961e+00, -1.18368155e+00,  6.07639897e-01,\n",
+       "         -6.21729584e-01,  8.93307247e-01,  8.90775341e-01,\n",
+       "          1.62274563e+00,  1.32730613e-01,  6.85716699e-01,\n",
+       "          8.69952474e-02],\n",
+       "        [-8.10719963e-02, -5.82244926e-01, -9.69985962e-01,\n",
+       "         -1.17045115e+00, -1.21138420e+00, -1.70122241e-02,\n",
+       "         -2.37457374e+00,  9.35439412e-01,  1.10825128e+00,\n",
+       "         -3.47995753e-01],\n",
+       "        [ 1.33937444e+00, -9.35798276e-01,  1.59561402e-01,\n",
+       "         -4.02179819e-01, -9.76486640e-01, -1.06721930e+00,\n",
+       "         -8.43611920e-01, -1.18128172e+00, -6.39559238e-01,\n",
+       "          1.62521906e-01],\n",
+       "        [-1.43312551e-01, -1.04899410e-01, -1.68039437e-01,\n",
+       "         -2.57146952e-01,  5.13336071e-01, -2.48813418e+00,\n",
+       "         -4.84536874e-02,  1.48673137e-01, -5.85951279e-02,\n",
+       "          1.45592643e-01],\n",
+       "        [-1.75932522e+00,  3.03800033e-01, -5.04784587e-01,\n",
+       "         -1.43869784e+00,  1.01865411e+00, -1.67030075e+00,\n",
+       "          1.17725759e-01,  1.17619272e+00, -2.00344371e+00,\n",
+       "         -3.17190387e-01],\n",
+       "        [ 3.66239160e-01, -5.47566042e-01,  4.50547053e-01,\n",
+       "         -1.25180845e-01,  1.49339980e+00, -1.34963027e+00,\n",
+       "          7.67871627e-01, -1.87758006e-01, -1.59258526e-02,\n",
+       "          1.07390467e-01],\n",
+       "        [ 3.11521091e-01, -1.18972440e+00, -8.59059410e-01,\n",
+       "         -5.85899068e-01, -4.82552116e-01, -1.26256390e-02,\n",
+       "          1.15691018e+00, -1.21316822e+00, -1.40787253e+00,\n",
+       "         -3.19397863e-01],\n",
+       "        [ 1.36859047e+00,  1.78506695e+00, -1.15740938e+00,\n",
+       "          2.39921706e+00, -1.71503695e-01,  3.61854266e-01,\n",
+       "          1.57023463e-01,  7.17949728e-01,  1.12866189e+00,\n",
+       "         -1.01301265e+00],\n",
+       "        [-2.09989671e-01, -2.06284735e-01, -8.75787865e-01,\n",
+       "         -1.66506882e+00, -4.20230031e-01, -5.52696516e-01,\n",
+       "          1.07186780e+00,  1.16156231e+00,  1.65461615e+00,\n",
+       "         -1.52416303e+00],\n",
+       "        [-8.46637659e-02, -6.51837425e-01, -6.24534211e-01,\n",
+       "         -2.76999607e+00, -9.86229599e-01, -1.41828203e-01,\n",
+       "         -2.89176599e-01, -6.24649233e-02,  7.53189297e-01,\n",
+       "         -2.34088044e-01],\n",
+       "        [-3.36843106e-01,  7.88345196e-01,  1.92022029e-01,\n",
+       "         -1.09731124e+00, -1.42687165e-01, -2.05082193e-01,\n",
+       "          1.31160565e+00,  3.33380332e-01, -7.40996133e-01,\n",
+       "          1.69567078e-01],\n",
+       "        [-9.48397746e-01,  2.70348537e-01,  5.62477221e-01,\n",
+       "          5.58138565e-01, -2.15826773e-01,  2.15577478e+00,\n",
+       "          2.17412607e+00, -7.49010535e-01, -1.36451263e+00,\n",
+       "          7.12519586e-01],\n",
+       "        [ 1.56516891e-02, -5.21686997e-01,  7.22239695e-01,\n",
+       "         -9.37438397e-01,  1.59894256e-01, -5.96482530e-01,\n",
+       "         -2.19030354e+00,  5.46120049e-01, -6.60551232e-01,\n",
+       "          1.59589131e-02],\n",
+       "        [ 3.37102184e-01, -5.65722248e-01,  4.96497440e-01,\n",
+       "         -4.68222937e-01,  8.36226993e-01,  2.94908812e-01,\n",
+       "         -4.90975260e-01,  1.19590527e+00, -7.82249210e-01,\n",
+       "          3.59347055e-01],\n",
+       "        [ 9.61275614e-01, -5.13502622e-01,  1.01460589e+00,\n",
+       "          2.83716582e-01, -5.39229538e-01,  8.45856484e-01,\n",
+       "         -1.63622278e+00, -9.49820775e-01, -6.45886085e-01,\n",
+       "          1.11738800e+00],\n",
+       "        [ 9.97949389e-02,  1.12308263e+00,  8.41011812e-03,\n",
+       "          4.31484885e-01,  7.82874480e-02,  2.29647167e-01,\n",
+       "         -1.19929310e-01, -6.22903746e-01, -2.63618426e+00,\n",
+       "         -1.31847579e+00],\n",
+       "        [-5.78308065e-01, -1.84016526e-01,  1.81146195e-01,\n",
+       "         -8.75167273e-01,  8.92967631e-02, -4.88247804e-01,\n",
+       "         -1.26484259e+00, -1.62428317e+00, -2.47432140e+00,\n",
+       "          2.86863092e-01],\n",
+       "        [ 1.45782588e+00,  1.22470928e+00,  1.37059960e+00,\n",
+       "          5.15895151e-01,  5.67213541e-01, -8.12625513e-01,\n",
+       "         -1.05703323e+00,  1.23446732e+00,  5.72544579e-01,\n",
+       "          1.16824142e+00],\n",
+       "        [ 7.91618805e-01, -1.51390117e+00, -9.58717632e-01,\n",
+       "         -1.33186174e+00,  2.44159781e-01,  1.42667075e+00,\n",
+       "         -1.93043316e-01,  1.13969662e+00,  1.14080836e+00,\n",
+       "         -7.47563604e-01],\n",
+       "        [-1.25234755e+00, -4.89458697e-01, -1.05419689e+00,\n",
+       "         -3.61371620e-01, -1.35641263e-01,  9.26537840e-01,\n",
+       "          1.94874715e+00,  3.53143290e-01, -1.00025902e+00,\n",
+       "         -8.37607504e-01],\n",
+       "        [ 9.43426257e-01, -1.41564714e+00,  1.45535179e-01,\n",
+       "         -2.46613292e+00, -9.92801870e-01,  1.77217495e+00,\n",
+       "         -6.32787481e-01,  3.53400806e-01,  5.75950370e-02,\n",
+       "         -5.78596946e-01],\n",
+       "        [-6.32177724e-01,  1.04906198e+00,  1.15932725e+00,\n",
+       "         -6.95320997e-01, -2.09687542e+00,  1.13367200e+00,\n",
+       "         -1.07877931e-03, -9.22808422e-02, -6.24162045e-01,\n",
+       "         -4.88116120e-01],\n",
+       "        [-2.89103638e-01,  1.46969037e+00,  1.50983980e+00,\n",
+       "         -2.35424926e-01,  7.09090381e-02,  4.89436311e-01,\n",
+       "          1.37263363e+00,  1.46094496e-01, -7.05583452e-01,\n",
+       "          3.06267245e-01],\n",
+       "        [ 1.11293488e+00, -1.25152498e+00,  1.74525215e-01,\n",
+       "          4.06811364e-01, -1.83488761e-03,  1.81938683e-01,\n",
+       "          1.29059447e+00,  1.17073440e+00,  9.99376107e-02,\n",
+       "         -8.69229686e-01]]])
              • c
                (chain, draw, c_dim_0, c_dim_1)
                float64
                -0.1873 0.08068 ... -0.7178 -0.4593
                array([[[[-0.18733941,  0.08068124, -0.74981289,  1.14908319],\n",
+       "         [-0.23887625,  0.77923397, -1.09384671,  0.85300368],\n",
+       "         [ 0.11497364, -0.79956367, -0.65401389, -0.6553763 ]],\n",
+       "\n",
+       "        [[-0.0640023 , -0.3453749 , -0.21576098,  0.98122925],\n",
+       "         [-0.39585305,  0.49347018,  0.63004415,  0.85404986],\n",
+       "         [ 1.18751139, -0.85181631,  0.11780613,  0.80907842]],\n",
+       "\n",
+       "        [[ 1.65335708,  0.53322719,  0.30460134, -0.41337594],\n",
+       "         [ 1.76889868,  0.8506907 ,  1.42290637,  1.08454681],\n",
+       "         [ 1.37409373, -0.99599541, -0.12550746,  0.9409458 ]],\n",
        "\n",
        "        ...,\n",
        "\n",
-       "        [[-0.11238804, -1.03611087,  0.30879005,  0.51892315],\n",
-       "         [ 0.15313038,  0.07670505,  0.23467951, -0.05199881],\n",
-       "         [-0.04757508,  2.40812467,  1.82961157,  0.5802526 ]],\n",
+       "        [[ 2.03811654,  0.96546731, -0.39226401,  0.74229003],\n",
+       "         [ 0.19321198, -1.24549524, -1.60397554, -0.34112993],\n",
+       "         [ 0.96758733, -1.13541144,  0.7103624 , -0.39037624]],\n",
        "\n",
-       "        [[ 2.31332637, -0.43235048,  0.25731847, -0.80461147],\n",
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          • created_at :
            2020-06-06T18:07:29.148885
            arviz_version :
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          " + ".xr-wrap{width:700px!important;} " ], "text/plain": [ - "\n", - "Dimensions: (chain: 1, draw: 2, x_dim_0: 3, x_dim_1: 4, x_dim_2: 5)\n", - "Coordinates:\n", - " * chain (chain) int64 0\n", - " * draw (draw) int64 0 1\n", - " * x_dim_0 (x_dim_0) int64 0 1 2\n", - " * x_dim_1 (x_dim_1) int64 0 1 2 3\n", - " * x_dim_2 (x_dim_2) int64 0 1 2 3 4\n", - "Data variables:\n", - " x (chain, draw, x_dim_0, x_dim_1, x_dim_2) float64 0.4887 ... -0.1801\n", - "Attributes:\n", - " created_at: 2020-06-06T18:07:29.148885\n", - " arviz_version: 0.8.3" + "Inference data with groups:\n", + "\t> posterior" ] }, - "execution_count": 5, -======= - "execution_count": 10, - "metadata": { - "ExecuteTime": { - "end_time": "2020-06-05T06:47:09.286020Z", - "start_time": "2020-06-05T06:47:07.961226Z" + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" } - }, - "outputs": [], + ], "source": [ - "import pymc3 as pm\n", - "\n", - "draws = 500\n", - "chains = 2\n", - "\n", - "eight_school_data = {\n", - " \"J\": 8,\n", - " \"y\": np.array([28.0, 8.0, -3.0, 7.0, -1.0, 1.0, 18.0, 12.0]),\n", - " \"sigma\": np.array([15.0, 10.0, 16.0, 11.0, 9.0, 11.0, 10.0, 18.0]),\n", - "}" + "datadict = {\n", + " \"a\": np.random.randn(100),\n", + " \"b\": np.random.randn(1, 100, 10),\n", + " \"c\": np.random.randn(1, 100, 3, 4),\n", + "}\n", + "dataset = az.convert_to_inference_data(datadict)\n", + "dataset" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## From dictionary with coords and dims" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 5, "metadata": { "ExecuteTime": { - "end_time": "2020-06-05T06:47:16.996019Z", - "start_time": "2020-06-05T06:47:09.288094Z" + "end_time": "2020-06-05T06:47:07.931892Z", + "start_time": "2020-06-05T06:47:07.924238Z" } }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Auto-assigning NUTS sampler...\n", - "Initializing NUTS using jitter+adapt_diag...\n", - "Multiprocess sampling (2 chains in 4 jobs)\n", - "NUTS: [theta_tilde, tau, mu]\n", - "Sampling 2 chains, 0 divergences: 100%|██████████| 2000/2000 [00:00<00:00, 2065.44draws/s]\n", - "100%|██████████| 1000/1000 [00:00<00:00, 1713.18it/s]\n" - ] - }, - { - "data": { - "text/plain": [ - "Inference data with groups:\n", - "\t> posterior\n", - "\t> posterior_predictive\n", - "\t> log_likelihood\n", - "\t> sample_stats\n", - "\t> prior\n", - "\t> prior_predictive\n", - "\t> observed_data" - ] - }, - "execution_count": 11, ->>>>>>> master - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ -<<<<<<< HEAD - "dataset.posterior" -======= - "with pm.Model() as model:\n", - " mu = pm.Normal(\"mu\", mu=0, sd=5)\n", - " tau = pm.HalfCauchy(\"tau\", beta=5)\n", - " theta_tilde = pm.Normal(\"theta_tilde\", mu=0, sd=1, shape=eight_school_data[\"J\"])\n", - " theta = pm.Deterministic(\"theta\", mu + tau * theta_tilde)\n", - " pm.Normal(\n", - " \"obs\", mu=theta, sd=eight_school_data[\"sigma\"], observed=eight_school_data[\"y\"]\n", - " )\n", - "\n", - " trace = pm.sample(draws, chains=chains)\n", - " prior = pm.sample_prior_predictive()\n", - " posterior_predictive = pm.sample_posterior_predictive(trace)\n", - "\n", - " pm_data = az.from_pymc3(\n", - " trace=trace,\n", - " prior=prior,\n", - " posterior_predictive=posterior_predictive,\n", - " coords={\"school\": np.arange(eight_school_data[\"J\"])},\n", - " dims={\"theta\": [\"school\"], \"theta_tilde\": [\"school\"]},\n", - " )\n", - "pm_data" ->>>>>>> master - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ -<<<<<<< HEAD - "## From a dictionary" -======= - "## From PyStan" ->>>>>>> master - ] - }, - { - "cell_type": "code", -<<<<<<< HEAD - "execution_count": 6, - "metadata": {}, "outputs": [ { "data": { @@ -3642,8 +2735,8 @@ "
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-       "         -8.11448655e-01],\n",
-       "        [ 9.36997095e-01, -6.89774333e-02,  1.82323312e-01,\n",
-       "          1.18874235e+00, -1.93475508e+00, -1.97259570e-01,\n",
-       "         -4.01805129e-01,  1.56519719e-01,  7.20284772e-01,\n",
-       "         -1.53685647e+00],\n",
-       "        [-6.45985070e-01, -7.42697032e-01,  2.87562403e+00,\n",
-       "         -1.07277721e+00, -3.62236615e-01, -2.94318060e-01,\n",
-       "         -7.25182328e-01, -1.70563655e-01, -1.18736218e+00,\n",
-       "         -6.45296851e-01],\n",
-       "        [-1.60023179e+00, -1.19415277e+00, -7.91282026e-01,\n",
-       "         -1.13260994e+00,  5.49423718e-01,  3.03478893e-01,\n",
-       "          7.92048680e-01,  6.25290380e-01,  8.74379780e-01,\n",
-       "          4.36688308e-01],\n",
-       "        [ 8.51012084e-01, -1.83146490e+00, -1.86077181e-01,\n",
-       "         -3.69655835e-01,  1.03910492e+00, -2.51607407e+00,\n",
-       "         -1.11522402e-01, -5.54494197e-01, -2.55004155e+00,\n",
-       "         -3.53517009e-01],\n",
-       "        [-1.03797040e+00, -4.76380291e-01,  7.02052916e-01,\n",
-       "         -1.66906878e+00, -2.89958113e-03,  5.36159096e-01,\n",
-       "         -8.29569427e-01,  7.40179298e-01,  3.63797566e-01,\n",
-       "          1.56341992e+00],\n",
-       "        [-1.57537578e+00, -5.20559786e-01, -7.27432492e-01,\n",
-       "          7.71677019e-01,  5.48647428e-01,  1.41156547e+00,\n",
-       "          1.49595207e+00, -2.82577827e-01, -1.98455727e-01,\n",
-       "         -4.88550101e-01],\n",
-       "        [ 9.54153065e-01, -3.42524165e-01, -7.19864339e-02,\n",
-       "         -5.13491821e-01, -7.85754124e-01, -4.52257659e-02,\n",
-       "         -2.08017148e-02, -1.43624649e+00,  5.11142839e-01,\n",
-       "         -8.87633078e-01],\n",
-       "        [-1.41590491e-01, -1.16176491e+00, -1.44355079e+00,\n",
-       "          1.99734620e+00,  5.50875149e-01,  1.75354673e-01,\n",
-       "         -1.83293396e-01,  3.80751173e-01,  9.70215514e-02,\n",
-       "         -8.69527016e-02],\n",
-       "        [ 6.68853722e-01, -1.43495637e+00,  6.32481136e-01,\n",
-       "          6.19208375e-01,  1.72485630e+00, -4.48701279e-01,\n",
-       "         -5.87827145e-01,  1.17639731e+00, -1.77408371e+00,\n",
-       "         -2.51830933e-01],\n",
-       "        [-1.55729952e+00, -1.58866119e+00,  2.63960443e-01,\n",
-       "          8.81333365e-01, -2.50260581e-01, -4.52841504e-01,\n",
-       "          6.30461667e-01, -2.43398269e+00, -1.34262790e+00,\n",
-       "         -2.52949828e+00],\n",
-       "        [-9.27477066e-01, -3.03438443e-01,  1.43821828e+00,\n",
-       "         -1.29496358e+00,  7.62432532e-01, -5.96522418e-01,\n",
-       "         -1.45502923e+00,  1.36355478e+00,  2.17632209e-01,\n",
-       "          9.65861276e-01],\n",
-       "        [-2.40225456e-01, -4.02848251e-02,  6.54526613e-01,\n",
-       "         -2.09261789e-01, -1.64800098e-01, -4.75481902e-01,\n",
-       "          1.21937632e+00, -1.14866961e-01, -5.13479561e-01,\n",
-       "         -3.30640785e-01],\n",
-       "        [ 1.29402459e+00, -1.60698294e-01, -7.92434912e-01,\n",
-       "         -1.07742986e+00,  2.43178713e-01,  1.00656953e-01,\n",
-       "          1.36373102e-01,  2.58918163e-01,  1.89809614e-01,\n",
-       "         -6.30684459e-02],\n",
-       "        [ 7.75252323e-01, -2.60870609e-01, -5.97804654e-01,\n",
-       "          1.24670604e+00,  7.96899525e-01, -3.27239237e-01,\n",
-       "          1.03952056e+00,  8.87511794e-01,  9.85236298e-01,\n",
-       "         -1.05767898e+00],\n",
-       "        [-1.44699183e-01,  9.11422880e-01, -6.29496869e-01,\n",
-       "         -4.13543427e-01,  7.60975390e-02,  4.70630985e-01,\n",
-       "          1.11392353e+00,  2.62117186e+00, -2.73881904e+00,\n",
-       "          8.29593648e-01],\n",
-       "        [ 1.21278933e+00, -2.50603472e-01,  7.31591703e-01,\n",
-       "          1.91026743e+00,  9.43079363e-01,  6.73031399e-01,\n",
-       "          1.63807920e-01,  1.84092750e+00,  1.36988211e+00,\n",
-       "          6.76178900e-01],\n",
-       "        [-9.43631589e-01, -8.32594075e-01,  9.67736749e-02,\n",
-       "         -9.74210548e-01,  1.63433456e+00,  1.37967517e+00,\n",
-       "         -3.18047718e-01,  5.59277747e-01,  2.09180627e-01,\n",
-       "         -2.38439923e-01],\n",
-       "        [-1.00381236e+00, -2.43858940e-02, -6.76335057e-01,\n",
-       "          2.88015338e-01,  1.29484002e+00, -1.16994525e+00,\n",
-       "         -2.24838843e+00,  6.98694368e-01, -1.76020375e+00,\n",
-       "         -1.00754451e+00],\n",
-       "        [-9.24827848e-01, -4.52282412e-02, -2.83507390e-01,\n",
-       "         -8.83829828e-02, -4.49698973e-01, -2.77632281e-01,\n",
-       "         -1.99532247e-01,  2.86744691e-01,  5.57449988e-01,\n",
-       "          5.78371576e-01],\n",
-       "        [-8.95433556e-02, -8.78745785e-01,  1.38576703e+00,\n",
-       "          4.00941311e-02, -2.44365258e-01,  8.69340948e-01,\n",
-       "         -6.46151791e-01,  1.05331451e-01,  4.36569316e-01,\n",
-       "         -4.82321089e-01],\n",
-       "        [-1.18093161e+00,  1.11004838e+00, -2.96104564e-01,\n",
-       "         -1.15458694e+00,  4.99279854e-01, -5.38178798e-01,\n",
-       "         -1.27968202e+00, -1.40496506e+00, -6.61347545e-02,\n",
-       "         -2.72885541e-01],\n",
-       "        [ 7.54951942e-01, -1.00795823e+00, -1.65064634e-01,\n",
-       "         -3.61276495e-02, -1.39992830e+00, -1.71150207e-01,\n",
-       "          3.93172662e-01,  1.12326414e+00,  5.33252515e-01,\n",
-       "         -4.34410142e-01],\n",
-       "        [ 4.51626690e-01,  5.66246816e-01,  9.85985984e-01,\n",
-       "          2.08673518e-03, -1.80460669e+00,  2.25401170e+00,\n",
-       "         -1.19596303e+00,  4.44254458e-01,  3.49840707e-01,\n",
-       "         -1.38226433e+00],\n",
-       "        [-1.22310129e-01, -1.35818909e-01, -7.89870597e-01,\n",
-       "         -9.57954557e-01, -1.09182931e-01, -2.05273094e-01,\n",
-       "         -1.97952364e+00, -4.98298028e-01,  1.90078737e+00,\n",
-       "         -1.43712870e+00]]])
                    • c
                      (chain, draw, c_dim_0, c_dim_1)
                      float64
                      -1.041 0.4122 ... -0.9928 0.7617
                      array([[[[-1.04127041,  0.41217704, -1.03400048, -0.12264575],\n",
-       "         [ 1.04107835, -0.26339671,  0.96054064, -0.22215247],\n",
-       "         [ 1.04423588,  1.08154076, -1.0384382 ,  1.2484653 ]],\n",
-       "\n",
-       "        [[-1.95494131,  0.49214263,  0.47497275,  0.30189806],\n",
-       "         [-2.32052569,  0.83418501, -0.70571508, -0.01323852],\n",
-       "         [ 1.28823264,  1.54410576,  0.91816028,  0.30587703]],\n",
-       "\n",
-       "        [[-1.52475937, -1.02624381, -1.58744789,  1.06811567],\n",
-       "         [ 2.28969475, -0.59806168,  0.30152991, -0.87979082],\n",
-       "         [ 0.66695957, -0.37827421, -1.19831325, -0.67450885]],\n",
+       "       90, 91, 92, 93, 94, 95, 96, 97, 98, 99])
                    • b1
                      (b1)
                      int64
                      0 1 2 3 4 5 6 7 8 9
                      array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
                    • c1
                      (c1)
                      int64
                      0 1 2
                      array([0, 1, 2])
                    • c2
                      (c2)
                      int64
                      0 1 2 3
                      array([0, 1, 2, 3])
                    • a
                      (chain, draw)
                      float64
                      0.05449 -0.7435 ... 1.77 -0.06081
                      array([[ 0.05449388, -0.743533  , -0.32473799, -0.76209616, -0.15318108,\n",
+       "        -0.05848062,  0.97717731, -0.48588421, -0.8726501 , -0.07552945,\n",
+       "         0.83003715, -1.19688506, -1.1247065 , -0.6013013 , -0.49520942,\n",
+       "        -1.84118918, -2.59702905, -1.30659788,  0.98327226,  0.27655716,\n",
+       "        -1.82151708, -0.63585987, -0.2902761 , -1.12612464, -2.07791993,\n",
+       "        -0.13276421,  0.62298463, -0.48540343, -0.00994651, -0.11829631,\n",
+       "         1.01228631, -1.46444786, -0.78597604,  0.51325285,  0.13163611,\n",
+       "         1.09484645,  1.68969085, -0.52343559,  1.026338  ,  1.43407182,\n",
+       "        -0.7722721 , -0.57958438,  0.20528191,  0.61321184,  1.68564395,\n",
+       "        -0.30374717,  0.71144957, -0.65677733,  0.84996284, -1.30093387,\n",
+       "        -1.70843071,  0.74581651,  0.86280279,  2.27569422, -2.35786222,\n",
+       "        -0.86084899,  1.21739243,  1.5936106 ,  1.09724853, -0.26861585,\n",
+       "         2.61918066, -1.05169746, -1.0950662 ,  0.86920039, -0.45478166,\n",
+       "        -1.16591212,  0.72980447, -1.74191861, -2.17641695,  0.41770966,\n",
+       "         0.35722643,  0.30221771, -0.44295916, -0.2569641 ,  0.87502338,\n",
+       "        -0.61832058, -0.48042925, -0.6697292 , -0.42490819, -0.6803645 ,\n",
+       "         0.93372907, -0.94644329, -0.66789602, -0.60290496, -1.2474643 ,\n",
+       "        -0.38570061,  2.14155655,  1.42725836, -0.17612467, -0.34821489,\n",
+       "        -0.44672739, -1.71950312,  0.00706469,  0.45771954,  1.98270075,\n",
+       "        -0.19073369,  0.89296637,  1.2797043 ,  1.76970708, -0.0608144 ]])
                    • b
                      (chain, draw, b1)
                      float64
                      0.3839 -0.4249 ... -0.1143 0.9299
                      array([[[ 3.83949782e-01, -4.24922581e-01, -7.52450072e-01,\n",
+       "          1.21234248e+00, -9.22548373e-01,  1.68237742e-01,\n",
+       "          1.99966773e+00, -3.26202020e-01,  6.07073687e-01,\n",
+       "          1.02603162e+00],\n",
+       "        [ 5.99675466e-02, -6.68527204e-02,  1.53698961e+00,\n",
+       "          7.30218204e-01,  9.35546946e-01,  3.33506001e-01,\n",
+       "         -2.87332730e+00, -9.29787572e-01, -1.17752033e+00,\n",
+       "          1.06869258e+00],\n",
+       "        [-4.07316192e-01,  5.26275934e-01,  1.43789696e+00,\n",
+       "          3.38223434e-01,  8.74268720e-01, -1.12870206e+00,\n",
+       "         -1.58333161e-01,  2.40482598e-01,  6.66687355e-01,\n",
+       "         -1.51603941e+00],\n",
+       "        [-1.06800357e+00,  2.41715171e+00,  1.04449716e-01,\n",
+       "         -2.25170807e+00, -1.05611451e-01, -2.02149474e+00,\n",
+       "         -1.45532700e+00, -3.88297474e-01, -1.97361060e+00,\n",
+       "         -1.56830162e+00],\n",
+       "        [ 1.76291146e+00, -1.21929963e+00,  5.54212069e-01,\n",
+       "          2.22470001e-01, -8.45363510e-01, -1.96532800e+00,\n",
+       "         -1.69154874e+00, -3.15075022e-01, -6.70247876e-01,\n",
+       "         -1.96827011e+00],\n",
+       "        [-1.10074526e+00, -3.28861345e-02, -1.54599206e-01,\n",
+       "         -5.87159767e-01, -5.68027773e-01, -3.39073687e+00,\n",
+       "          8.23872261e-01, -1.74575654e+00,  2.07647066e+00,\n",
+       "          5.67036800e-01],\n",
+       "        [ 4.56109764e-01, -7.52974728e-02,  3.68002622e-01,\n",
+       "          4.22306025e-01,  1.42035086e-01, -4.71555345e-01,\n",
+       "         -1.40617288e-02,  2.13677626e-02, -1.24304685e+00,\n",
+       "          6.89790239e-01],\n",
+       "        [ 8.52533818e-01,  6.23537996e-01, -5.15118924e-01,\n",
+       "         -5.29511347e-01,  1.12812095e+00,  1.57289578e+00,\n",
+       "          1.10968372e+00,  1.65095343e-01,  1.64284234e-01,\n",
+       "         -4.92346994e-02],\n",
+       "        [-1.10327112e+00, -1.30902913e+00,  2.53922155e+00,\n",
+       "         -1.03140054e+00,  2.86071931e-01, -1.09701784e+00,\n",
+       "         -3.69774002e-01,  1.41517836e+00, -1.72944423e-01,\n",
+       "         -2.12026122e-01],\n",
+       "        [-1.50219780e+00, -2.91957306e-02, -7.52128963e-01,\n",
+       "          1.58525273e+00, -4.15595218e-01,  1.02651538e+00,\n",
+       "         -7.11375007e-01, -2.09104023e+00,  3.18268460e-01,\n",
+       "         -5.82647576e-01],\n",
+       "        [ 3.73359615e-01, -1.03221499e+00, -1.42464375e+00,\n",
+       "          8.21063294e-01, -1.30223298e+00,  6.04896419e-01,\n",
+       "          1.72032411e+00,  1.95756800e+00, -1.47355600e+00,\n",
+       "         -1.08295782e+00],\n",
+       "        [-2.09053829e-01, -9.20789263e-01,  6.90538387e-01,\n",
+       "         -2.24909099e-01,  4.80351972e-01,  2.24447864e-01,\n",
+       "          2.04021679e+00,  2.36314287e-01,  9.31508635e-01,\n",
+       "          1.70587886e+00],\n",
+       "        [ 8.23512231e-01, -4.11601643e-01,  7.40975886e-01,\n",
+       "          5.34362638e-01, -8.41619744e-01,  3.10125279e-01,\n",
+       "         -1.42290698e+00,  3.10667627e-01,  3.93114301e-01,\n",
+       "         -7.12339482e-01],\n",
+       "        [ 1.08797616e+00,  7.20099070e-01, -4.29989221e-01,\n",
+       "         -1.00040414e+00,  5.23271947e-01, -1.26459219e+00,\n",
+       "          5.76655277e-01, -1.50134399e-01,  2.27101382e-01,\n",
+       "          3.22129696e-01],\n",
+       "        [-5.54169108e-01,  8.76852061e-01, -1.49156442e+00,\n",
+       "          1.31718085e+00, -6.62791250e-01,  1.88165336e+00,\n",
+       "         -5.98252681e-01,  1.40909948e+00, -7.98402584e-01,\n",
+       "          4.21543289e-01],\n",
+       "        [-1.04607570e+00,  4.30123259e-01, -2.69978635e-02,\n",
+       "         -5.42491021e-01, -7.92985426e-03, -8.34306048e-01,\n",
+       "         -3.18437357e-01,  3.91265333e+00, -9.36230817e-02,\n",
+       "          1.37540300e+00],\n",
+       "        [ 3.74529640e-01,  1.28236224e+00, -8.52009056e-01,\n",
+       "          8.68775144e-01, -7.57826344e-01,  1.15150197e+00,\n",
+       "         -8.90333915e-01, -2.24407457e-01,  2.11361661e-01,\n",
+       "          1.43793929e+00],\n",
+       "        [ 7.54543106e-01, -1.85232222e-01, -5.83625517e-01,\n",
+       "          7.43067821e-01,  1.29570911e+00, -1.55280742e-01,\n",
+       "          2.22127015e-01,  1.52300482e-01, -1.96691075e-01,\n",
+       "         -6.90925328e-01],\n",
+       "        [ 1.55943492e+00,  9.79636059e-02,  9.24298490e-01,\n",
+       "         -2.38505258e-01,  1.49451569e-01,  5.16898436e-01,\n",
+       "          7.25136633e-01,  4.06703017e-01, -1.16765186e+00,\n",
+       "          1.79479576e-01],\n",
+       "        [ 1.13826388e+00, -2.11730429e+00,  3.91150465e-01,\n",
+       "          1.00826341e+00, -1.67066715e+00, -6.92940666e-01,\n",
+       "          3.95891782e-01,  4.87964854e-01,  4.76176214e-01,\n",
+       "         -1.05981270e+00],\n",
+       "        [ 1.27354503e+00,  1.50298815e+00,  3.06781617e+00,\n",
+       "          5.41638316e-01, -6.38883941e-03, -2.10172402e+00,\n",
+       "         -3.40211461e-01,  1.32993622e+00,  3.64584065e-02,\n",
+       "          8.93081220e-01],\n",
+       "        [ 1.28027306e+00,  2.87912392e-01,  1.85406734e+00,\n",
+       "         -1.31126694e+00, -1.77439477e+00,  1.17038342e+00,\n",
+       "         -1.55335582e+00, -9.70662966e-01, -4.14694856e-01,\n",
+       "          6.56108555e-01],\n",
+       "        [ 6.70757537e-01,  8.67990577e-01,  1.48922300e+00,\n",
+       "          2.17002319e+00,  2.14355502e-01,  4.04583299e-01,\n",
+       "         -1.87168157e-01,  4.54775098e-01,  1.34697528e-01,\n",
+       "         -8.34087427e-01],\n",
+       "        [ 4.24119098e-01,  5.66633209e-01, -1.19861504e+00,\n",
+       "         -1.31563848e+00, -7.25982516e-01, -5.17331148e-01,\n",
+       "          8.84768951e-01, -4.85917354e-01, -8.54276881e-01,\n",
+       "          1.36941018e+00],\n",
+       "        [-3.09892947e-01, -1.58699551e+00,  3.97868866e-02,\n",
+       "         -3.10165049e+00, -2.80230898e+00, -1.15420832e+00,\n",
+       "          8.88099145e-01,  1.36779300e+00,  3.44006608e-01,\n",
+       "         -1.18284855e+00],\n",
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+       "         -1.02470557e+00,  1.21560450e+00,  9.20955146e-01,\n",
+       "         -1.09237377e+00,  5.84158500e-01, -1.16055340e+00,\n",
+       "          8.37327161e-02],\n",
+       "        [ 8.65162098e-01, -5.05677085e-01,  1.82949868e-01,\n",
+       "         -5.92753364e-01, -5.62332911e-01, -1.08210662e-01,\n",
+       "         -1.31862879e+00,  1.03101919e+00,  7.38045897e-02,\n",
+       "         -4.71621984e-01],\n",
+       "        [ 7.83383547e-02,  1.91456640e+00, -9.74402295e-01,\n",
+       "          1.55854320e+00,  2.59809274e+00,  7.83316418e-02,\n",
+       "         -1.40941942e+00,  1.19892060e+00, -9.31637829e-01,\n",
+       "         -3.53693428e-02],\n",
+       "        [ 7.17526486e-01, -9.69508312e-01,  3.24199919e-01,\n",
+       "         -8.95324253e-01, -1.03194979e+00, -4.32445892e-01,\n",
+       "         -4.33561500e-01,  3.21475394e-01,  3.68854551e-01,\n",
+       "          1.08531537e+00],\n",
+       "        [-4.15527077e-01, -5.16091939e-01,  3.99410114e-01,\n",
+       "          4.63791972e-02,  1.37200838e+00, -1.17613794e+00,\n",
+       "          3.54701520e-01,  9.20563172e-01,  6.19535628e-01,\n",
+       "         -2.73494371e-01],\n",
+       "        [-1.28388793e+00,  2.01759201e-01,  2.40823245e+00,\n",
+       "         -6.14183274e-01, -2.10437195e-01,  9.40762801e-01,\n",
+       "          1.00825258e+00,  1.01788415e+00, -8.42117567e-02,\n",
+       "         -3.20454626e+00],\n",
+       "        [-1.42784282e+00,  9.84805601e-01,  6.40402245e-01,\n",
+       "         -5.69869434e-01,  4.37650067e-01,  9.12624333e-01,\n",
+       "         -9.61378102e-01, -1.04562022e+00, -1.67912791e+00,\n",
+       "          3.76781052e-01],\n",
+       "        [-6.60135138e-01, -2.18287191e+00, -4.93030369e-01,\n",
+       "         -1.76235979e-01,  9.35579631e-01,  1.64793682e-01,\n",
+       "         -1.40587603e+00, -6.81806964e-01,  1.02215879e+00,\n",
+       "          4.31489160e-01],\n",
+       "        [ 1.62515156e-01,  9.15907575e-01,  1.15927657e+00,\n",
+       "         -8.10727657e-01,  2.78226054e-01,  1.42864561e-01,\n",
+       "         -8.35643232e-01,  1.64703107e-01, -1.34467718e+00,\n",
+       "         -8.68861845e-01],\n",
+       "        [-2.44030875e-01, -3.66378408e-01, -1.10882172e+00,\n",
+       "         -9.86625978e-01, -9.90647599e-01, -1.93271978e+00,\n",
+       "          3.19268525e-01,  6.24626362e-01,  4.23577939e-01,\n",
+       "         -3.05202156e-01],\n",
+       "        [-6.60997240e-01, -8.21343516e-01, -1.02020096e-01,\n",
+       "          2.31683252e-01,  4.95402344e-01, -3.08681280e-01,\n",
+       "         -9.60064049e-01,  4.74289652e-01,  4.29826030e-01,\n",
+       "         -3.02252591e-01],\n",
+       "        [ 1.32019648e+00, -1.42493591e+00,  1.00049789e+00,\n",
+       "          1.74671065e+00,  9.75798761e-01,  1.08280211e+00,\n",
+       "          1.66381266e+00,  4.79047112e-01,  1.02944596e+00,\n",
+       "          1.44188697e+00],\n",
+       "        [ 1.41356761e+00, -1.29860859e+00,  3.21765217e-01,\n",
+       "          1.37592461e+00, -2.28733972e-01,  6.07310301e-01,\n",
+       "         -9.91045263e-01, -1.13200899e-01,  1.14816888e+00,\n",
+       "         -5.85060490e-01],\n",
+       "        [ 7.87384986e-01,  1.02690164e-01,  7.30967387e-01,\n",
+       "          1.55640681e+00,  3.66062745e-01,  8.35093179e-01,\n",
+       "         -9.52345842e-01, -4.77972470e-01,  1.72310542e+00,\n",
+       "         -2.62599445e-01],\n",
+       "        [-8.01348275e-01, -4.17957485e-01, -1.30991555e+00,\n",
+       "         -2.65776168e-01, -4.23852828e-01,  7.86504260e-01,\n",
+       "         -1.30354920e-02,  1.48565581e-01, -1.14283710e-01,\n",
+       "          9.29870682e-01]]])
                    • c
                      (chain, draw, c1, c2)
                      float64
                      -1.969 -0.3835 ... -0.6989 -0.393
                      array([[[[-1.9685806 , -0.38345697, -1.14734465, -0.08337334],\n",
+       "         [ 0.769021  ,  1.11133546,  1.91498295, -0.46834211],\n",
+       "         [-0.23264243, -1.05010379, -2.14064243, -1.07390044]],\n",
+       "\n",
+       "        [[-0.3422121 ,  0.11486382, -0.24465889,  0.18087892],\n",
+       "         [-0.0338139 , -0.60712889,  0.07562642,  0.56905369],\n",
+       "         [-0.18189297,  1.7870949 , -1.57350046, -0.50810089]],\n",
+       "\n",
+       "        [[-0.0510513 , -0.36662106, -0.8270934 , -1.26042414],\n",
+       "         [ 0.93017305,  0.65638302,  2.30087812, -0.57078071],\n",
+       "         [-0.56215181,  1.19480847,  1.10890094, -0.87102009]],\n",
        "\n",
        "        ...,\n",
        "\n",
-       "        [[ 0.218554  , -0.60661969,  1.34206727,  1.42814161],\n",
-       "         [-0.18270282, -0.23375035, -1.86696975, -1.14066794],\n",
-       "         [-0.7107148 ,  0.56726087,  1.30172505, -1.34156061]],\n",
+       "        [[ 0.40663338,  0.5611835 ,  0.39908726, -0.29270602],\n",
+       "         [ 0.58947034,  0.26671407, -0.57288192, -0.50354393],\n",
+       "         [ 1.51137947,  0.09171223, -0.91077266,  0.2399483 ]],\n",
        "\n",
-       "        [[-0.36618216,  0.63246716,  0.93517297,  0.68838127],\n",
-       "         [ 0.63987735,  0.39818941, -1.14925665,  0.5878074 ],\n",
-       "         [ 1.02279209, -0.04997891, -0.03299896,  0.92170792]],\n",
+       "        [[-1.46379518, -1.87590622,  0.51953765, -0.10717415],\n",
+       "         [ 1.32766471,  0.0645871 ,  0.0359231 ,  2.3641054 ],\n",
+       "         [ 0.64254228,  0.77359929,  0.08771938,  0.93903909]],\n",
        "\n",
-       "        [[-1.95153135,  0.39313075, -1.09503186, -0.63834405],\n",
-       "         [ 0.39769657, -0.61514386,  0.09034792, -1.00511881],\n",
-       "         [-1.016085  , -0.62807559, -0.99275874,  0.76174708]]]])
                  • created_at :
                    2020-06-06T18:07:29.245680
                    arviz_version :
                    0.8.3

              \n", + " [[-1.04082777, 0.17818282, 0.27089952, -1.47930329],\n", + " [ 0.29680156, 0.64995149, -0.5202781 , -0.01071339],\n", + " [-0.551221 , -0.40814589, -0.69886247, -0.39295165]]]])
          • created_at :
            2020-06-10T18:19:03.966946
            arviz_version :
            0.8.3

        \n", "
      \n", "
      \n", "
      • \n", @@ -4759,60 +3852,156 @@ "\t> posterior" ] }, - "execution_count": 6, -======= - "execution_count": 12, - "metadata": { - "ExecuteTime": { - "end_time": "2020-06-05T06:47:57.347055Z", - "start_time": "2020-06-05T06:47:16.997392Z" - } - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:pystan:COMPILING THE C++ CODE FOR MODEL anon_model_9d743830ec4a29fb58eb4660d4b9417f NOW.\n" - ] - }, - { - "data": { - "text/plain": [ - "Inference data with groups:\n", - "\t> posterior\n", - "\t> posterior_predictive\n", - "\t> log_likelihood\n", - "\t> sample_stats\n", - "\t> observed_data" - ] - }, - "execution_count": 12, ->>>>>>> master + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ -<<<<<<< HEAD "datadict = {\n", " \"a\": np.random.randn(100),\n", " \"b\": np.random.randn(1, 100, 10),\n", " \"c\": np.random.randn(1, 100, 3, 4),\n", "}\n", - "dataset = az.convert_to_inference_data(datadict)\n", + "coords = {\"c1\": np.arange(3), \"c2\": np.arange(4), \"b1\": np.arange(10)}\n", + "dims = {\"b\": [\"b1\"], \"c\": [\"c1\", \"c2\"]}\n", + "\n", + "dataset = az.convert_to_inference_data(datadict, coords=coords, dims=dims)\n", "dataset" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## From PyMC3" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "ExecuteTime": { + "end_time": "2020-06-05T06:47:09.286020Z", + "start_time": "2020-06-05T06:47:07.961226Z" + } + }, + "outputs": [], + "source": [ + "import pymc3 as pm\n", + "\n", + "draws = 500\n", + "chains = 2\n", + "\n", + "eight_school_data = {\n", + " \"J\": 8,\n", + " \"y\": np.array([28.0, 8.0, -3.0, 7.0, -1.0, 1.0, 18.0, 12.0]),\n", + " \"sigma\": np.array([15.0, 10.0, 16.0, 11.0, 9.0, 11.0, 10.0, 18.0]),\n", + "}" + ] + }, { "cell_type": "code", "execution_count": 7, - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2020-06-05T06:47:16.996019Z", + "start_time": "2020-06-05T06:47:09.288094Z" + } + }, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Auto-assigning NUTS sampler...\n", + "Initializing NUTS using jitter+adapt_diag...\n", + "Multiprocess sampling (2 chains in 2 jobs)\n", + "NUTS: [theta_tilde, tau, mu]\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "
      \n", + " \n", + " \n", + " 100.00% [2000/2000 00:02<00:00 Sampling 2 chains, 3 divergences]\n", + "
      \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "There were 3 divergences after tuning. Increase `target_accept` or reparameterize.\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "
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      arviz.InferenceData
      \n", + "
      \n", + "
        \n", + " \n", + "
      • \n", + " \n", + " \n", + "
        \n", + "
        \n", + "
          \n", + "
          \n", "\n", "\n", "Show/Hide data repr\n", @@ -5146,614 +4335,682 @@ " stroke: currentColor;\n", " fill: currentColor;\n", "}\n", - "
          xarray.Dataset
            • b_dim_0: 10
            • c_dim_0: 3
            • c_dim_1: 4
            • chain: 1
            • draw: 100
            • chain
              (chain)
              int64
              0
              array([0])
            • draw
              (draw)
              int64
              0 1 2 3 4 5 6 ... 94 95 96 97 98 99
              array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
-       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
-       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
-       "       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,\n",
-       "       72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89,\n",
-       "       90, 91, 92, 93, 94, 95, 96, 97, 98, 99])
            • b_dim_0
              (b_dim_0)
              int64
              0 1 2 3 4 5 6 7 8 9
              array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
            • c_dim_0
              (c_dim_0)
              int64
              0 1 2
              array([0, 1, 2])
            • c_dim_1
              (c_dim_1)
              int64
              0 1 2 3
              array([0, 1, 2, 3])
            • a
              (chain, draw)
              float64
              -1.127 0.2247 ... 0.2585 -0.2715
              array([[-1.12732068,  0.22474725,  0.94396769,  0.42085979,  0.55230194,\n",
-       "         0.81605924,  2.70808522,  1.41957694, -0.61711058,  0.56834516,\n",
-       "        -0.22410035,  0.5276792 , -0.1116566 ,  0.13772934,  0.10561586,\n",
-       "        -1.12345666, -0.24305322, -0.3447775 , -0.81779847,  0.2375271 ,\n",
-       "        -0.62111339,  0.57584957, -0.77529001,  0.42162154, -0.79075283,\n",
-       "        -0.48893675, -0.19220573,  0.4611616 ,  0.54313755, -2.36706096,\n",
-       "        -0.56875632, -1.44190913,  0.92496636,  1.01274784,  1.27662421,\n",
-       "         2.54281675,  0.66455272, -0.21618373, -0.20878461, -0.59426284,\n",
-       "         1.36383277,  0.22091957, -0.9332109 , -0.78613826, -0.56383727,\n",
-       "        -0.25611629, -0.46299795, -0.89960577, -0.40653695,  0.34247082,\n",
-       "         0.92588544,  1.71109405, -1.66670151, -0.85590094, -2.01899857,\n",
-       "        -1.80837008, -0.40595888, -0.19649084, -1.21913057, -0.14738896,\n",
-       "        -0.64461526, -1.20750942, -0.54507745, -0.62939225,  0.44823848,\n",
-       "         1.1800143 , -0.93698394, -0.11068548, -1.29358602, -0.9128787 ,\n",
-       "        -1.11518176,  1.73361458, -0.83401513, -0.27121065,  0.21397022,\n",
-       "         0.11951771, -1.20583004, -1.25714942,  1.4872156 ,  1.00344769,\n",
-       "         0.63906445, -1.01471355,  0.11675147,  0.89560963, -0.60301298,\n",
-       "        -0.45956725, -1.29658997,  0.86000723, -0.54165219, -0.55998307,\n",
-       "        -0.26992935, -2.18324991,  2.08447852, -0.49793872, -2.043601  ,\n",
-       "         1.22161564, -1.26097956,  0.24968684,  0.25852879, -0.2715059 ]])
            • b
              (chain, draw, b_dim_0)
              float64
              -0.4105 -0.1523 ... 1.901 -1.437
              array([[[-4.10480871e-01, -1.52326994e-01,  1.18903773e-01,\n",
-       "         -5.15526776e-01, -4.88829917e-01,  3.60527638e-03,\n",
-       "         -3.07991478e-01, -1.20627967e+00,  6.10669110e-01,\n",
-       "          4.75650675e-01],\n",
-       "        [ 1.80658209e+00,  8.03745003e-02,  8.08133239e-01,\n",
-       "         -7.79504732e-01,  4.94626289e-01, -6.37253723e-02,\n",
-       "         -5.54663853e-01,  1.01013341e+00,  2.48372571e-01,\n",
-       "         -1.65297574e+00],\n",
-       "        [-4.93419509e-01,  1.25872282e+00,  8.40311035e-01,\n",
-       "         -2.02307680e+00, -2.06469854e-01, -1.05892853e+00,\n",
-       "         -1.12750668e+00,  3.82920054e-01,  1.42412087e+00,\n",
-       "          1.86512880e-01],\n",
-       "        [-1.26751378e+00, -1.12009701e+00, -5.89554225e-01,\n",
-       "         -2.41330004e-02, -5.15588571e-01, -1.91449281e-01,\n",
-       "         -5.17195127e-01, -7.88493913e-01, -8.18935006e-01,\n",
-       "          7.18765942e-01],\n",
-       "        [ 2.02456394e-02, -1.30775178e+00, -1.68587291e-01,\n",
-       "         -1.07524427e+00,  7.26948618e-01,  1.30404790e+00,\n",
-       "          1.58097029e+00, -8.66411341e-01,  8.70885311e-01,\n",
-       "         -7.01514030e-01],\n",
-       "        [-9.89756628e-01, -1.22009412e+00,  7.93777157e-03,\n",
-       "          9.08534517e-01, -1.91995248e+00, -9.26509576e-01,\n",
-       "          3.13036421e-01, -1.32919691e+00, -1.07938222e+00,\n",
-       "         -2.49419007e+00],\n",
-       "        [ 1.38406019e+00, -4.02997385e-01,  1.19009527e-01,\n",
-       "         -4.42122614e-01,  4.27467018e-01, -2.62159950e-01,\n",
-       "          3.35292851e-01, -2.30354860e+00,  9.02801744e-01,\n",
-       "         -1.54311556e+00],\n",
-       "        [-3.34014295e-01, -1.97806027e+00,  1.04707794e+00,\n",
-       "          1.12552471e+00, -1.21997589e+00, -1.14062585e+00,\n",
-       "          1.37689625e+00, -2.64318351e-01,  8.49212332e-01,\n",
-       "          5.70582173e-01],\n",
-       "        [-6.42535743e-01, -2.18190754e-01,  1.02111077e-01,\n",
-       "         -1.14361445e+00,  2.61195960e-01,  1.52097026e-01,\n",
-       "         -1.28179837e+00, -1.94317336e-01, -1.23937319e+00,\n",
-       "          2.47585757e-01],\n",
-       "        [-1.73268574e+00, -2.67945036e-01,  5.55403786e-01,\n",
-       "         -5.26391649e-01, -4.54841609e-01, -4.53981872e-01,\n",
-       "          7.56959815e-01,  5.07298186e-01,  1.31536058e+00,\n",
-       "          4.56526594e-01],\n",
-       "        [ 2.02591570e+00,  1.03309281e+00, -2.49892588e+00,\n",
-       "          7.34853256e-02,  9.76801778e-01,  7.62989008e-01,\n",
-       "         -1.35201246e-01,  1.54234502e+00,  4.23651277e-01,\n",
-       "          1.24763713e-01],\n",
-       "        [-4.49606221e-01,  3.08133485e-01, -5.78542257e-01,\n",
-       "          5.31707558e-01,  2.28288315e+00,  1.00187616e+00,\n",
-       "          3.72957072e-01,  1.21382151e+00, -3.70784401e-01,\n",
-       "          3.81886703e-01],\n",
-       "        [-8.64670869e-01,  1.03353682e-01, -4.05538645e-01,\n",
-       "         -5.87200524e-02, -9.71082230e-01,  2.13877315e-01,\n",
-       "          6.05710672e-01,  1.22261467e+00, -2.31697018e-01,\n",
-       "         -1.60672830e+00],\n",
-       "        [ 1.26427819e+00, -8.18070411e-01, -1.60046005e+00,\n",
-       "          2.43160242e+00, -6.84788523e-01, -2.77262854e-01,\n",
-       "         -3.44713364e-01, -4.96236011e-02,  9.62855080e-01,\n",
-       "          9.87611138e-01],\n",
-       "        [-1.74291874e+00,  3.88501231e-02,  4.16979002e-02,\n",
-       "          7.83776043e-02, -1.14251866e+00,  1.31901970e-01,\n",
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-       "         -1.11120067e+00],\n",
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-       "          8.06173830e-01,  1.91851045e-01, -1.05976203e-01,\n",
-       "         -8.65871078e-03],\n",
-       "        [ 1.83609776e-01,  7.20184155e-01, -1.26043653e+00,\n",
-       "         -3.10450489e-01,  1.40688823e+00, -1.31090763e+00,\n",
-       "          3.52115122e-01, -1.20578435e+00,  3.34896601e-01,\n",
-       "         -8.11448655e-01],\n",
-       "        [ 9.36997095e-01, -6.89774333e-02,  1.82323312e-01,\n",
-       "          1.18874235e+00, -1.93475508e+00, -1.97259570e-01,\n",
-       "         -4.01805129e-01,  1.56519719e-01,  7.20284772e-01,\n",
-       "         -1.53685647e+00],\n",
-       "        [-6.45985070e-01, -7.42697032e-01,  2.87562403e+00,\n",
-       "         -1.07277721e+00, -3.62236615e-01, -2.94318060e-01,\n",
-       "         -7.25182328e-01, -1.70563655e-01, -1.18736218e+00,\n",
-       "         -6.45296851e-01],\n",
-       "        [-1.60023179e+00, -1.19415277e+00, -7.91282026e-01,\n",
-       "         -1.13260994e+00,  5.49423718e-01,  3.03478893e-01,\n",
-       "          7.92048680e-01,  6.25290380e-01,  8.74379780e-01,\n",
-       "          4.36688308e-01],\n",
-       "        [ 8.51012084e-01, -1.83146490e+00, -1.86077181e-01,\n",
-       "         -3.69655835e-01,  1.03910492e+00, -2.51607407e+00,\n",
-       "         -1.11522402e-01, -5.54494197e-01, -2.55004155e+00,\n",
-       "         -3.53517009e-01],\n",
-       "        [-1.03797040e+00, -4.76380291e-01,  7.02052916e-01,\n",
-       "         -1.66906878e+00, -2.89958113e-03,  5.36159096e-01,\n",
-       "         -8.29569427e-01,  7.40179298e-01,  3.63797566e-01,\n",
-       "          1.56341992e+00],\n",
-       "        [-1.57537578e+00, -5.20559786e-01, -7.27432492e-01,\n",
-       "          7.71677019e-01,  5.48647428e-01,  1.41156547e+00,\n",
-       "          1.49595207e+00, -2.82577827e-01, -1.98455727e-01,\n",
-       "         -4.88550101e-01],\n",
-       "        [ 9.54153065e-01, -3.42524165e-01, -7.19864339e-02,\n",
-       "         -5.13491821e-01, -7.85754124e-01, -4.52257659e-02,\n",
-       "         -2.08017148e-02, -1.43624649e+00,  5.11142839e-01,\n",
-       "         -8.87633078e-01],\n",
-       "        [-1.41590491e-01, -1.16176491e+00, -1.44355079e+00,\n",
-       "          1.99734620e+00,  5.50875149e-01,  1.75354673e-01,\n",
-       "         -1.83293396e-01,  3.80751173e-01,  9.70215514e-02,\n",
-       "         -8.69527016e-02],\n",
-       "        [ 6.68853722e-01, -1.43495637e+00,  6.32481136e-01,\n",
-       "          6.19208375e-01,  1.72485630e+00, -4.48701279e-01,\n",
-       "         -5.87827145e-01,  1.17639731e+00, -1.77408371e+00,\n",
-       "         -2.51830933e-01],\n",
-       "        [-1.55729952e+00, -1.58866119e+00,  2.63960443e-01,\n",
-       "          8.81333365e-01, -2.50260581e-01, -4.52841504e-01,\n",
-       "          6.30461667e-01, -2.43398269e+00, -1.34262790e+00,\n",
-       "         -2.52949828e+00],\n",
-       "        [-9.27477066e-01, -3.03438443e-01,  1.43821828e+00,\n",
-       "         -1.29496358e+00,  7.62432532e-01, -5.96522418e-01,\n",
-       "         -1.45502923e+00,  1.36355478e+00,  2.17632209e-01,\n",
-       "          9.65861276e-01],\n",
-       "        [-2.40225456e-01, -4.02848251e-02,  6.54526613e-01,\n",
-       "         -2.09261789e-01, -1.64800098e-01, -4.75481902e-01,\n",
-       "          1.21937632e+00, -1.14866961e-01, -5.13479561e-01,\n",
-       "         -3.30640785e-01],\n",
-       "        [ 1.29402459e+00, -1.60698294e-01, -7.92434912e-01,\n",
-       "         -1.07742986e+00,  2.43178713e-01,  1.00656953e-01,\n",
-       "          1.36373102e-01,  2.58918163e-01,  1.89809614e-01,\n",
-       "         -6.30684459e-02],\n",
-       "        [ 7.75252323e-01, -2.60870609e-01, -5.97804654e-01,\n",
-       "          1.24670604e+00,  7.96899525e-01, -3.27239237e-01,\n",
-       "          1.03952056e+00,  8.87511794e-01,  9.85236298e-01,\n",
-       "         -1.05767898e+00],\n",
-       "        [-1.44699183e-01,  9.11422880e-01, -6.29496869e-01,\n",
-       "         -4.13543427e-01,  7.60975390e-02,  4.70630985e-01,\n",
-       "          1.11392353e+00,  2.62117186e+00, -2.73881904e+00,\n",
-       "          8.29593648e-01],\n",
-       "        [ 1.21278933e+00, -2.50603472e-01,  7.31591703e-01,\n",
-       "          1.91026743e+00,  9.43079363e-01,  6.73031399e-01,\n",
-       "          1.63807920e-01,  1.84092750e+00,  1.36988211e+00,\n",
-       "          6.76178900e-01],\n",
-       "        [-9.43631589e-01, -8.32594075e-01,  9.67736749e-02,\n",
-       "         -9.74210548e-01,  1.63433456e+00,  1.37967517e+00,\n",
-       "         -3.18047718e-01,  5.59277747e-01,  2.09180627e-01,\n",
-       "         -2.38439923e-01],\n",
-       "        [-1.00381236e+00, -2.43858940e-02, -6.76335057e-01,\n",
-       "          2.88015338e-01,  1.29484002e+00, -1.16994525e+00,\n",
-       "         -2.24838843e+00,  6.98694368e-01, -1.76020375e+00,\n",
-       "         -1.00754451e+00],\n",
-       "        [-9.24827848e-01, -4.52282412e-02, -2.83507390e-01,\n",
-       "         -8.83829828e-02, -4.49698973e-01, -2.77632281e-01,\n",
-       "         -1.99532247e-01,  2.86744691e-01,  5.57449988e-01,\n",
-       "          5.78371576e-01],\n",
-       "        [-8.95433556e-02, -8.78745785e-01,  1.38576703e+00,\n",
-       "          4.00941311e-02, -2.44365258e-01,  8.69340948e-01,\n",
-       "         -6.46151791e-01,  1.05331451e-01,  4.36569316e-01,\n",
-       "         -4.82321089e-01],\n",
-       "        [-1.18093161e+00,  1.11004838e+00, -2.96104564e-01,\n",
-       "         -1.15458694e+00,  4.99279854e-01, -5.38178798e-01,\n",
-       "         -1.27968202e+00, -1.40496506e+00, -6.61347545e-02,\n",
-       "         -2.72885541e-01],\n",
-       "        [ 7.54951942e-01, -1.00795823e+00, -1.65064634e-01,\n",
-       "         -3.61276495e-02, -1.39992830e+00, -1.71150207e-01,\n",
-       "          3.93172662e-01,  1.12326414e+00,  5.33252515e-01,\n",
-       "         -4.34410142e-01],\n",
-       "        [ 4.51626690e-01,  5.66246816e-01,  9.85985984e-01,\n",
-       "          2.08673518e-03, -1.80460669e+00,  2.25401170e+00,\n",
-       "         -1.19596303e+00,  4.44254458e-01,  3.49840707e-01,\n",
-       "         -1.38226433e+00],\n",
-       "        [-1.22310129e-01, -1.35818909e-01, -7.89870597e-01,\n",
-       "         -9.57954557e-01, -1.09182931e-01, -2.05273094e-01,\n",
-       "         -1.97952364e+00, -4.98298028e-01,  1.90078737e+00,\n",
-       "         -1.43712870e+00]]])
            • c
              (chain, draw, c_dim_0, c_dim_1)
              float64
              -1.041 0.4122 ... -0.9928 0.7617
              array([[[[-1.04127041,  0.41217704, -1.03400048, -0.12264575],\n",
-       "         [ 1.04107835, -0.26339671,  0.96054064, -0.22215247],\n",
-       "         [ 1.04423588,  1.08154076, -1.0384382 ,  1.2484653 ]],\n",
-       "\n",
-       "        [[-1.95494131,  0.49214263,  0.47497275,  0.30189806],\n",
-       "         [-2.32052569,  0.83418501, -0.70571508, -0.01323852],\n",
-       "         [ 1.28823264,  1.54410576,  0.91816028,  0.30587703]],\n",
-       "\n",
-       "        [[-1.52475937, -1.02624381, -1.58744789,  1.06811567],\n",
-       "         [ 2.28969475, -0.59806168,  0.30152991, -0.87979082],\n",
-       "         [ 0.66695957, -0.37827421, -1.19831325, -0.67450885]],\n",
-       "\n",
+       "
              xarray.Dataset
                • chain: 2
                • draw: 500
                • school: 8
                • chain
                  (chain)
                  int64
                  0 1
                  array([0, 1])
                • draw
                  (draw)
                  int64
                  0 1 2 3 4 5 ... 495 496 497 498 499
                  array([  0,   1,   2, ..., 497, 498, 499])
                • school
                  (school)
                  int64
                  0 1 2 3 4 5 6 7
                  array([0, 1, 2, 3, 4, 5, 6, 7])
                • mu
                  (chain, draw)
                  float64
                  6.638 4.791 5.564 ... 5.818 2.299
                  array([[ 6.63799683e+00,  4.79134072e+00,  5.56424218e+00,\n",
+       "         5.57420954e+00,  3.22669739e+00,  4.09925197e+00,\n",
+       "         6.77486926e+00,  7.78605439e+00, -2.52944163e-01,\n",
+       "         6.61229871e+00,  4.18502958e+00,  1.76376618e+00,\n",
+       "         4.62694426e+00,  3.50036596e+00,  3.05630576e+00,\n",
+       "         6.62692262e+00,  4.14235573e+00,  6.16275502e+00,\n",
+       "         6.22938258e+00,  6.15304590e+00,  3.22079869e+00,\n",
+       "         7.23136829e-01,  7.23136829e-01,  5.34538848e+00,\n",
+       "         2.39661416e+00,  3.69046096e+00,  2.48955251e+00,\n",
+       "         7.46552620e+00,  9.03574415e+00,  6.72969686e+00,\n",
+       "         6.34335555e+00,  4.04220266e+00,  2.19778203e+00,\n",
+       "         1.37076499e-02,  1.74828963e+00,  7.67516122e+00,\n",
+       "         7.67516122e+00,  6.85639862e+00,  4.64813775e+00,\n",
+       "         2.65791301e+00,  5.01452937e+00,  1.83039053e+00,\n",
+       "         1.78045535e+00,  2.17111590e+00,  2.00686198e+00,\n",
+       "         5.41209154e+00,  4.34875837e+00,  4.91332931e+00,\n",
+       "         3.53930002e+00,  7.51604256e+00,  7.19161274e+00,\n",
+       "         2.03515930e+00,  9.42214963e+00,  7.09555156e+00,\n",
+       "         1.93087165e+00,  5.59001258e+00,  7.17291829e+00,\n",
+       "         5.13261847e+00,  5.86915693e+00,  2.57897255e+00,\n",
+       "         6.61818370e+00,  1.39310780e+00,  3.59564785e+00,\n",
+       "         4.32735112e+00,  7.85800197e+00,  1.60693679e+00,\n",
+       "         5.56027445e+00,  7.71824903e+00,  7.52958298e+00,\n",
+       "         2.17221759e+00,  2.61447368e+00,  1.35683982e+01,\n",
+       "         9.20871183e-02,  3.19997426e+00,  4.76437822e+00,\n",
+       "         5.54460105e+00,  6.67417392e+00,  5.16285638e+00,\n",
+       "        -8.19217157e-01,  1.26674178e+01, -5.89391857e-01,\n",
+       "        -5.89391857e-01, -2.70426811e+00,  9.43770323e+00,\n",
+       "         1.01234303e+01,  1.54012544e+00,  1.75412845e+00,\n",
+       "         4.47119156e+00,  4.97567517e+00,  1.84977782e+00,\n",
+       "         6.91461195e+00,  6.60657308e+00,  3.56008090e-01,\n",
+       "         6.93045204e+00, -1.25531329e-01, -1.25531329e-01,\n",
+       "         6.39259731e-01,  7.94587362e+00,  2.63018477e+00,\n",
+       "         5.63533128e+00,  3.86413926e+00,  4.56795589e+00,\n",
+       "         9.47234622e+00,  2.10143003e+00,  6.41839518e+00,\n",
+       "         3.83593844e+00,  8.59218468e+00,  2.27599500e+00,\n",
+       "         1.03086797e+01,  9.70552286e+00,  8.29190773e+00,\n",
+       "         7.70075129e-01,  7.70075129e-01, -1.80722476e+00,\n",
+       "         1.27386142e+01,  1.10855042e+01, -1.83874233e+00,\n",
+       "         5.03200899e+00,  3.79758372e+00,  3.21167374e+00,\n",
+       "         4.00223089e+00,  5.59012552e+00,  4.42937022e-01,\n",
+       "        -9.02381508e-01,  1.39209741e+01,  1.34006214e+01,\n",
+       "         1.22582214e+01,  1.16139064e+01, -6.53463404e-01,\n",
+       "         8.28525744e+00,  6.71494093e+00,  4.30594107e+00,\n",
+       "         1.06352087e+01,  1.06352087e+01,  9.34798044e-01,\n",
+       "         4.83439889e+00,  6.67060779e+00,  2.96651765e+00,\n",
+       "         1.51050127e-01,  6.61488225e+00,  1.35537300e+00,\n",
+       "         6.11452815e+00,  2.55743030e-03,  9.60590259e+00,\n",
+       "         8.76400612e+00,  4.63887209e+00,  8.66921267e+00,\n",
+       "         2.67414535e+00,  1.80939866e+00,  9.33687656e+00,\n",
+       "         3.21858895e+00,  6.46408966e+00,  2.49444981e+00,\n",
+       "         4.47436161e+00,  7.78333476e+00,  7.78333476e+00,\n",
+       "         1.91596858e+00,  1.34305869e-01,  7.88337283e+00,\n",
+       "         6.27562216e+00,  1.09570353e-01,  6.79647532e+00,\n",
+       "         7.45428378e+00,  5.91458976e+00,  3.96425644e+00,\n",
+       "         2.62864052e+00,  1.15880725e+01, -3.32958366e+00,\n",
+       "         1.22327127e+01,  4.55520976e+00,  5.65784063e+00,\n",
+       "         4.54399956e+00,  5.03478565e+00,  8.00220717e+00,\n",
+       "        -1.75237165e+00, -1.75237165e+00,  2.32928948e+00,\n",
+       "         1.16578999e+00,  9.38175578e+00,  5.58779336e+00,\n",
+       "         4.23843024e+00,  3.56550551e+00,  2.64287938e+00,\n",
+       "         6.31014056e+00,  3.39482162e+00,  1.46637751e+00,\n",
+       "         1.19727378e+00,  9.66674650e-01,  8.26702272e+00,\n",
+       "         9.53981693e+00,  6.40840350e+00,  2.61593886e+00,\n",
+       "         2.07214421e+00,  4.65990128e+00,  4.25202286e+00,\n",
+       "         3.49441521e+00,  3.18946961e+00,  5.31226307e+00,\n",
+       "         1.08201178e+01, -1.00265861e+00,  2.25268677e+00,\n",
+       "        -9.62701753e-01, -9.62701753e-01, -2.11114255e-01,\n",
+       "         2.08764265e+00,  1.02389970e+01,  5.05899290e+00,\n",
+       "         2.36038803e+00,  4.95046599e+00,  4.95046599e+00,\n",
+       "         5.50740944e+00,  5.05636826e+00,  5.05636826e+00,\n",
+       "         9.60963715e+00,  4.61532038e+00,  5.42485359e+00,\n",
+       "         4.23735723e+00,  7.19722486e+00,  4.80428582e+00,\n",
+       "         4.47200166e+00,  3.69703914e+00,  1.18735821e+00,\n",
+       "         3.88651322e+00,  7.18029090e+00,  3.37142575e+00,\n",
+       "         8.68892257e+00,  6.66567919e+00,  1.30531602e+00,\n",
+       "         2.12611574e+00,  2.52999503e+00,  1.14474721e+01,\n",
+       "         4.83272902e+00,  1.68596107e+00,  5.12616851e+00,\n",
+       "         4.62294476e+00,  1.57243937e+00,  8.94093940e+00,\n",
+       "         1.47305979e+00,  9.54717416e+00,  1.64331074e+00,\n",
+       "         9.09267978e+00,  7.95892546e+00,  2.73838051e+00,\n",
+       "         3.07380739e+00,  8.98818222e+00,  6.75491684e+00,\n",
+       "         2.15593227e+00, -1.47968755e+00, -3.04206257e+00,\n",
+       "        -3.04206257e+00, -2.00154781e+00,  6.74796336e+00,\n",
+       "         2.67234856e+00,  4.66409829e+00,  1.36469845e+01,\n",
+       "         4.41043730e+00,  1.87968450e+00,  8.48862751e+00,\n",
+       "         7.79359849e+00,  4.51405914e+00,  3.31569900e+00,\n",
+       "         1.13330419e+01,  3.08027387e+00,  3.14450558e+00,\n",
+       "         6.91518938e+00,  5.67012505e+00,  1.03377765e+01,\n",
+       "        -3.89340427e-01,  1.00805809e+01, -2.61932552e+00,\n",
+       "        -2.24101303e+00,  1.44110340e+01, -2.74644470e+00,\n",
+       "         1.14785803e+01, -9.14825041e-01,  4.97224257e+00,\n",
+       "         7.62871402e+00,  7.62871402e+00,  2.02161427e+00,\n",
+       "         4.46448127e+00,  4.24876571e+00,  1.64324694e+00,\n",
+       "         6.65373991e+00,  2.62945829e+00,  1.02979618e+01,\n",
+       "         8.81343336e+00,  4.77039931e+00, -7.90872152e-01,\n",
+       "        -2.22345626e+00,  5.17857938e+00,  4.88992195e+00,\n",
+       "         3.88931051e+00,  9.14627339e+00,  3.86560414e+00,\n",
+       "         5.50023277e+00,  2.74963035e+00,  5.54309756e+00,\n",
+       "         5.43677281e+00,  7.41340765e+00,  1.43905079e+00,\n",
+       "         6.13843878e+00,  3.96232306e+00,  4.82713674e+00,\n",
+       "        -3.21119648e-01, -3.21119648e-01,  7.48970624e+00,\n",
+       "         2.27479797e+00,  1.44371456e+00,  1.11767981e+00,\n",
+       "         3.12323417e+00,  6.63809623e-01,  7.27583706e+00,\n",
+       "         2.77215578e+00,  8.38458426e+00,  9.19398465e+00,\n",
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+       "         2.19646582e+00,  5.56067421e+00,  3.04828461e+00,\n",
+       "         5.92827468e+00,  6.21724225e+00,  2.51280189e+00,\n",
+       "         9.57306721e+00,  7.59353227e+00,  2.09202476e+00,\n",
+       "         9.88059803e+00,  4.54680518e+00, -4.30483067e+00,\n",
+       "         8.92024468e+00,  1.26309327e+00,  9.46959872e+00,\n",
+       "        -1.17827288e+00, -3.66655282e-01, -9.16874577e-01,\n",
+       "         6.03922031e+00,  1.88068890e+00,  5.80097792e+00,\n",
+       "         4.10859484e+00,  7.52813003e+00, -1.11027182e-01,\n",
+       "         1.37308819e+00,  1.19388966e+00,  8.58912431e-03,\n",
+       "         2.22575099e+00,  7.20720195e+00,  5.48023988e+00,\n",
+       "         1.28400000e+00, -2.20449686e-01,  5.18856371e+00,\n",
+       "         6.01845550e+00,  4.77155051e+00,  4.18297145e+00,\n",
+       "         5.43830940e+00,  2.46429298e+00,  7.28660900e+00,\n",
+       "         7.21764793e+00,  5.36153164e+00,  5.04585974e+00,\n",
+       "         1.40262626e+00,  1.40262626e+00,  7.26603051e+00,\n",
+       "         2.59531892e+00,  6.01218155e+00,  6.23284317e+00,\n",
+       "         2.91248685e+00,  5.84996530e+00,  7.94833070e+00,\n",
+       "         5.81831499e+00,  2.29860647e+00]])
                • theta_tilde
                  (chain, draw, school)
                  float64
                  -0.3253 -0.6165 ... 1.222 0.2978
                  array([[[-0.32529772, -0.61651454, -0.1667071 , ...,  1.03328716,\n",
+       "          2.1823746 ,  0.47316842],\n",
+       "        [ 2.02958114,  1.38140157,  0.75320748, ..., -0.76415401,\n",
+       "         -0.26376613,  2.59527651],\n",
+       "        [-1.29974382, -0.93838206, -1.66930843, ...,  0.45610521,\n",
+       "          1.16510883, -1.90862339],\n",
        "        ...,\n",
+       "        [ 0.76405243,  1.57497136, -0.19382101, ..., -0.90719718,\n",
+       "          0.13818502, -0.53287742],\n",
+       "        [-0.01956823, -0.44742285,  0.22391893, ...,  1.26672305,\n",
+       "          1.48674961,  0.59971362],\n",
+       "        [-1.31647166,  0.85301164,  0.48705457, ...,  1.10464535,\n",
+       "         -0.20971009,  0.16073751]],\n",
+       "\n",
+       "       [[ 0.39528795,  2.42188047, -0.63226669, ..., -1.08800486,\n",
+       "         -1.18916527, -1.15738863],\n",
+       "        [-1.14188208,  1.13112039,  0.07025925, ..., -0.71775304,\n",
+       "          0.42484354,  0.43962524],\n",
+       "        [ 0.71622022, -1.36901772,  0.34158932, ...,  1.14499937,\n",
+       "         -0.08757469, -1.25347347],\n",
+       "        ...,\n",
+       "        [ 0.18628521,  1.50088211, -0.65171658, ...,  0.48396343,\n",
+       "          0.77830976, -0.36442883],\n",
+       "        [ 0.86143906, -1.5081053 ,  0.16205563, ..., -1.43705565,\n",
+       "         -0.382151  , -0.01870404],\n",
+       "        [ 0.70965545,  1.59856161, -0.47552168, ...,  0.91627404,\n",
+       "          1.22218512,  0.29782471]]])
                • tau
                  (chain, draw)
                  float64
                  3.1 1.632 1.529 ... 2.741 4.724
                  array([[3.09988894e+00, 1.63207672e+00, 1.52902197e+00, 2.30099852e+00,\n",
+       "        8.31268078e-01, 1.88898817e+00, 5.85874168e+00, 4.71824548e+00,\n",
+       "        2.36699580e+00, 2.34732021e+00, 2.05737377e+00, 2.22388599e+00,\n",
+       "        1.26633038e+00, 3.10758565e+00, 2.43266266e+00, 2.49894565e+00,\n",
+       "        3.62801031e+00, 2.66434647e+00, 6.03401501e+00, 4.20103917e+00,\n",
+       "        2.87235863e+00, 9.61176630e+00, 9.61176630e+00, 4.47341001e+00,\n",
+       "        5.66040767e+00, 9.82792942e-01, 1.62994744e+00, 6.81143684e+00,\n",
+       "        1.42101541e+00, 3.99436157e+00, 1.06113735e+00, 6.66151684e+00,\n",
+       "        1.44678348e+00, 9.29213043e-01, 6.00298896e-01, 8.65854134e+00,\n",
+       "        8.65854134e+00, 7.49195541e+00, 3.92008212e-01, 4.67262667e-01,\n",
+       "        1.14031719e+01, 1.91770387e-01, 1.13407014e-01, 2.31341743e-01,\n",
+       "        1.87226273e+01, 3.07049024e-01, 3.79160657e+00, 2.29027053e+00,\n",
+       "        4.54164481e+00, 3.06000216e+00, 1.61702924e+00, 1.42514668e+00,\n",
+       "        2.69966316e+00, 7.76593714e+00, 9.35329781e-01, 4.80484917e+00,\n",
+       "        4.60629543e-02, 3.78059536e-02, 1.46668064e-01, 7.85845041e-02,\n",
+       "        2.45290474e+00, 3.99644466e+00, 2.81231960e+00, 3.45085186e+00,\n",
+       "        1.00796319e+00, 2.72362547e+00, 1.61928447e+00, 2.86957381e+00,\n",
+       "        3.01123031e+00, 7.49851917e-01, 9.57235307e-01, 2.35480165e+00,\n",
+       "        9.53584743e+00, 1.50321215e+00, 8.66088994e+00, 3.95555017e+00,\n",
+       "        8.28477536e-01, 1.16039960e+00, 5.29813451e+00, 1.82651411e-01,\n",
+       "        1.50293359e+01, 1.50293359e+01, 1.70908463e+00, 4.49037167e+00,\n",
+       "        3.72729985e+00, 4.20267362e-02, 2.15497907e-01, 1.44651372e-01,\n",
+       "        8.97409661e+00, 3.95659504e+00, 1.74384378e+00, 3.01505721e+00,\n",
+       "        8.89669163e+00, 6.33618449e+00, 3.36126815e+00, 3.36126815e+00,\n",
+       "        1.03556605e+01, 8.41171251e-01, 3.08236370e+00, 7.71365841e+00,\n",
+       "        3.40615951e+00, 5.29949043e+00, 3.89052725e+00, 2.97310920e-01,\n",
+       "        3.79674976e-01, 5.40723457e+00, 3.21220675e-01, 3.39422976e+00,\n",
+       "        5.62340485e+00, 7.33074407e+00, 2.03764109e+00, 5.68137917e+00,\n",
+       "        5.68137917e+00, 2.83686052e+00, 4.91676473e+00, 6.62090533e+00,\n",
+       "        6.64313421e+00, 3.60767832e+00, 2.64339476e+00, 5.23419542e+00,\n",
+       "        6.54119359e-01, 9.90659523e-01, 1.26385767e+00, 9.46167070e-01,\n",
+       "        3.42942204e+00, 1.17157162e+01, 1.51882166e+00, 7.75894868e+00,\n",
+       "        1.86186982e+00, 1.05676321e+01, 8.81156578e+00, 2.97112722e+00,\n",
+       "        4.97560335e+00, 4.97560335e+00, 2.37615802e+00, 4.85582620e-01,\n",
+       "        1.75115034e-01, 2.24492144e-02, 5.19194864e-03, 2.44152982e-03,\n",
+       "        4.96016077e-04, 9.81960781e-04, 2.61456184e-01, 3.04503815e+00,\n",
+       "        1.49740455e+00, 3.09210525e+00, 2.38372922e+00, 1.07710929e+00,\n",
+       "        7.37178789e-01, 1.44477609e+00, 4.44958349e-01, 7.56283868e+00,\n",
+       "        1.30693621e+01, 6.00919393e+00, 3.27627074e+00, 3.27627074e+00,\n",
+       "        3.61464843e+00, 1.42101331e+00, 4.23921964e-01, 8.45466943e+00,\n",
+       "        4.63349584e+00, 4.46047362e+00, 8.78396465e+00, 3.68844510e+00,\n",
+       "        6.55355892e+00, 2.16129532e+00, 2.77333940e+00, 3.76898084e+00,\n",
+       "        9.47836834e-01, 1.15779865e+00, 1.08542059e+01, 7.84930686e-01,\n",
+       "        2.53342726e+00, 9.22488199e-01, 6.24873580e+00, 6.24873580e+00,\n",
+       "        5.37298831e+00, 2.12673184e+00, 1.43190109e+00, 3.72911093e+00,\n",
+       "        1.67789722e+00, 4.10229845e+00, 2.91020442e+00, 4.75219278e+00,\n",
+       "        2.05454858e+00, 7.02387879e+00, 1.92291069e+00, 3.57246023e-01,\n",
+       "        2.83701174e+00, 7.95061579e+00, 3.87302365e+00, 6.27845736e+00,\n",
+       "        7.65529077e-01, 2.85486857e+00, 4.27274083e+00, 1.63762758e+00,\n",
+       "        7.98990100e-01, 1.20889927e+00, 3.59308318e+00, 3.23508196e+00,\n",
+       "        5.48955086e+00, 3.47140380e+00, 3.47140380e+00, 2.14376772e+00,\n",
+       "        5.27427110e-01, 3.29333098e+00, 1.75016375e+00, 1.22541172e+00,\n",
+       "        1.15733505e+01, 1.15733505e+01, 2.77463974e+00, 3.07338819e+00,\n",
+       "        3.07338819e+00, 1.70827704e+00, 4.18041312e+00, 7.70963663e-01,\n",
+       "        8.35031192e-01, 6.53935957e+00, 3.63566215e-01, 9.72246066e+00,\n",
+       "        1.40166669e+00, 3.38578921e+00, 3.07436946e+00, 9.76477570e-01,\n",
+       "        2.11253628e+00, 2.11351930e+00, 2.46627791e-01, 1.65123263e+00,\n",
+       "        1.29966369e+00, 2.56888919e+00, 1.00424894e+01, 3.52674417e+00,\n",
+       "        6.82369555e+00, 2.63738500e+00, 7.83608447e+00, 2.70102342e+00,\n",
+       "        4.45264351e+00, 3.08078679e-01, 4.49708000e+00, 9.99135072e+00,\n",
+       "        3.55835310e+00, 1.16907517e+00, 2.02513622e+00, 1.02567962e-01,\n",
+       "        5.16058441e+00, 6.94456578e-01, 3.04556772e+00, 8.53439537e+00,\n",
+       "        4.36448122e+00, 4.36448122e+00, 2.58496665e+00, 1.00182772e+00,\n",
+       "        3.81383379e+00, 3.74173366e+00, 4.48486880e+00, 2.82288845e+00,\n",
+       "        7.22636068e-01, 3.56734285e+00, 1.67506117e+00, 2.02941736e+00,\n",
+       "        4.12335849e+00, 7.57508195e+00, 3.25739237e+00, 3.14910194e+00,\n",
+       "        4.83292307e+00, 3.78441995e+00, 1.01299820e+00, 1.98906741e+00,\n",
+       "        3.41574334e+00, 2.06843286e+00, 1.55608621e+00, 1.92423040e-01,\n",
+       "        5.08025259e+00, 6.16125429e+00, 7.36498515e-01, 1.24789864e+00,\n",
+       "        6.27956089e+00, 6.27956089e+00, 7.49902395e-01, 2.11452399e+00,\n",
+       "        2.24293487e-01, 3.35055904e-01, 1.68753300e-02, 4.18807137e-01,\n",
+       "        4.58889059e+00, 1.54259092e+00, 1.03803681e+00, 5.31534450e+00,\n",
+       "        2.60984305e+00, 2.99648210e+00, 4.91208069e+00, 9.90204033e+00,\n",
+       "        1.35299652e+00, 8.37925328e+00, 3.34839656e+00, 5.28114427e+00,\n",
+       "        2.21231385e+00, 7.81986140e-01, 2.68384539e+00, 1.82893404e+00,\n",
+       "        2.51582305e+00, 2.22615720e+00, 7.25987045e+00, 8.99140943e+00,\n",
+       "        8.99140943e+00, 7.81139000e+00, 4.75357076e+00, 9.80665484e-01,\n",
+       "        6.26918311e-01, 4.63030324e-02, 7.68659875e-01, 2.10636907e+00,\n",
+       "        5.41121322e+00, 9.55072350e-01, 3.78895489e+00, 1.53556405e+00,\n",
+       "        1.36718670e+00, 5.32069899e+00, 5.32069899e+00, 2.10949104e+00,\n",
+       "        2.10949104e+00, 4.48217733e+00, 3.45878976e+00, 5.23224937e+00,\n",
+       "        7.23491941e-01, 1.09630159e+01, 1.90895106e+00, 1.94941130e+00,\n",
+       "        8.02391448e+00, 2.85057302e+00, 5.51741597e+00, 2.33330862e+00,\n",
+       "        5.63950929e+00, 2.00352029e+00, 4.18123136e+00, 7.96439656e+00,\n",
+       "        1.25523482e+00, 4.27480930e+00, 8.66754251e-01, 1.33945808e+00,\n",
+       "        1.96201914e+00, 1.33772085e+00, 1.78255793e+00, 2.23659524e+00,\n",
+       "        1.94135128e+00, 2.79944847e+00, 1.10391683e-01, 1.56316513e+00,\n",
+       "        3.72869234e+00, 9.55436772e-01, 2.13150808e+00, 4.47963252e+00,\n",
+       "        3.78356295e+00, 5.10899353e-01, 4.26619097e+00, 1.87941457e+01,\n",
+       "        1.87941457e+01, 3.77009222e+00, 6.41488833e-01, 6.80390052e-01,\n",
+       "        1.99263496e+00, 5.13747821e+00, 5.13747821e+00, 3.14078111e+00,\n",
+       "        5.88111221e+00, 3.29812183e+00, 5.75164624e+00, 2.69121554e+00,\n",
+       "        1.56347605e+00, 5.27496678e+00, 2.87100899e+00, 2.31302906e+00,\n",
+       "        1.68828031e+00, 1.09680422e+00, 2.91573241e-01, 1.42346712e+00,\n",
+       "        9.79412468e-01, 3.49815706e+00, 4.95950372e+00, 7.14087628e+00,\n",
+       "        1.62857484e+01, 4.72245450e-01, 5.43036556e+00, 3.80175888e-01,\n",
+       "        2.74008719e-01, 5.03512295e-01, 2.38120067e+00, 3.01954806e-01,\n",
+       "        2.33082112e-01, 6.65830660e-02, 3.33221349e+00, 1.93945827e+00,\n",
+       "        3.12383685e+00, 8.70943321e+00, 1.52682644e+00, 3.68808675e+00,\n",
+       "        5.24348750e+00, 1.95509466e+00, 3.66047354e+00, 5.97797944e+00,\n",
+       "        5.97797944e+00, 7.97725643e-01, 1.44672287e+00, 1.86453076e+00,\n",
+       "        2.15691321e+00, 1.40205795e+00, 1.28605987e+00, 6.35787524e+00,\n",
+       "        3.15948844e+00, 1.94095239e+00, 8.69195133e+00, 8.38406149e-01,\n",
+       "        5.38691304e+00, 1.15395625e+00, 9.96413349e-01, 4.18266814e+00,\n",
+       "        3.76753069e+00, 1.38748987e+00, 4.47522270e+00, 1.59775810e+00,\n",
+       "        2.60969544e+00, 2.33253345e-01, 2.34764683e+00, 1.44320625e+00,\n",
+       "        3.33586202e+00, 4.94987324e+00, 4.97271936e+00, 2.10739974e+00,\n",
+       "        2.33071428e+00, 6.63438674e-01, 4.57847467e+00, 2.01055839e+00,\n",
+       "        3.69642715e+00, 1.17259406e+01, 1.17259406e+01, 3.66305533e+00,\n",
+       "        8.64030696e-01, 2.50151607e+00, 9.32171449e+00, 4.08032473e+00,\n",
+       "        4.08032473e+00, 1.77127432e+00, 1.16114849e+01, 1.31348481e+00,\n",
+       "        9.71958059e-01, 3.26610629e+00, 9.46118073e+00, 5.62409928e+00,\n",
+       "        6.30274536e+00, 5.42199958e+00, 1.34460486e+01, 1.34460486e+01,\n",
+       "        2.41417679e+00, 3.57253639e+00, 3.57253639e+00, 9.10126440e+00,\n",
+       "        6.56418646e+00, 1.31892758e+00, 1.71368899e+00, 3.49349047e+00,\n",
+       "        3.22679402e+00, 3.22679402e+00, 5.40345503e+00, 1.01589129e+01,\n",
+       "        4.15664338e-02, 2.18942477e-02, 2.86514992e-02, 2.21497457e-02,\n",
+       "        1.79285582e+01, 1.52018745e-01, 2.01387018e+00, 5.44963070e-02,\n",
+       "        4.47199619e-02, 8.83507675e-01, 1.80266425e+00, 1.90345940e+00,\n",
+       "        2.63387518e-01, 2.40564145e+00, 9.87268294e-01, 3.78376592e+00,\n",
+       "        6.91571313e-01, 7.74075349e+00, 7.85526840e-01, 5.25342514e+00,\n",
+       "        2.59837379e+00, 3.78014832e+00, 1.65762665e+00, 2.03766737e+00,\n",
+       "        9.48164296e+00, 3.18067452e+00, 8.12671270e+00, 4.65211529e+00,\n",
+       "        1.35087314e+01, 7.04136640e+00, 1.79561310e+00, 1.30703893e+00,\n",
+       "        5.40688975e+00, 2.92931379e+00, 1.50542743e+00, 5.34863668e+00],\n",
+       "       [1.24679522e+00, 2.69730666e+00, 3.06708473e+00, 6.92341103e+00,\n",
+       "        7.26717041e+00, 9.72414460e+00, 1.35577385e+00, 1.82326047e+00,\n",
+       "        3.96073369e+00, 1.81744113e+00, 3.72269348e+00, 4.77067056e+00,\n",
+       "        1.56675436e+00, 1.02068900e+00, 4.81841901e+00, 9.12498146e+00,\n",
+       "        7.25951080e-02, 1.10271479e+01, 7.45875275e+00, 2.88800003e-01,\n",
+       "        1.81995149e-01, 5.12095187e+00, 9.87610764e-01, 3.69307794e+00,\n",
+       "        4.84798220e+00, 4.77871157e+00, 9.68037949e-01, 3.06196230e+00,\n",
+       "        1.15477283e+00, 6.61843048e+00, 5.59888635e+00, 4.14011897e+00,\n",
+       "        2.86431779e+00, 2.42811202e+00, 4.20164971e+00, 1.70820078e+00,\n",
+       "        3.75174586e+00, 3.37788148e+00, 5.25038077e+00, 2.60211647e+00,\n",
+       "        8.50346222e-01, 1.60489849e+00, 6.17231562e+00, 2.05356030e+00,\n",
+       "        8.92248648e+00, 1.06028349e+00, 2.38133402e+00, 2.00470276e+00,\n",
+       "        4.26467659e+00, 9.47934506e-01, 2.76079372e+00, 1.41926392e+00,\n",
+       "        4.82552494e+00, 4.41810884e+00, 2.49454359e+00, 5.94035090e+00,\n",
+       "        3.60946912e+00, 2.36784597e+00, 6.04802347e+00, 3.26922634e+00,\n",
+       "        1.84098669e+00, 2.75330362e+00, 3.40776199e-01, 4.93352194e+00,\n",
+       "        8.42721389e-01, 1.88560352e+00, 8.80146862e-01, 1.19693262e+01,\n",
+       "        3.23015199e+00, 6.10118786e+00, 6.10118786e+00, 6.10118786e+00,\n",
+       "        1.63457029e+00, 2.44627539e+00, 2.15039182e+00, 9.54263677e-01,\n",
+       "        4.59581704e+00, 1.27970621e+00, 8.90999077e-01, 2.59444082e+00,\n",
+       "        8.07765671e+00, 2.06489348e+00, 7.30716624e+00, 1.55705724e+00,\n",
+       "        1.95116251e-01, 6.26895788e+00, 3.16500180e+00, 2.64899286e+00,\n",
+       "        6.64882542e-01, 1.01369373e+01, 1.01369373e+01, 5.09688647e-01,\n",
+       "        8.00733988e+00, 8.00733988e+00, 5.51367313e+00, 1.75578277e+00,\n",
+       "        2.88077781e+00, 9.73438615e-01, 1.61544464e+00, 1.03220369e+01,\n",
+       "        3.08368988e+00, 1.61839634e+00, 6.21381470e+00, 2.73901378e+00,\n",
+       "        2.77239718e+00, 3.12358931e+00, 1.55532528e+00, 6.42751955e+00,\n",
+       "        2.50515300e+00, 2.07590623e+00, 9.79913098e-01, 9.64721831e-01,\n",
+       "        8.65572921e+00, 8.86250923e-01, 2.25136603e+00, 7.90066660e+00,\n",
+       "        1.85265675e-01, 6.44594794e+00, 1.18805200e+00, 2.82900436e+00,\n",
+       "        1.23349105e+00, 1.66764318e+00, 2.13600705e+00, 8.04333702e+00,\n",
+       "        3.09176790e+00, 3.63177716e+00, 1.98310717e+00, 1.10368125e+00,\n",
+       "        7.33181299e+00, 1.97166437e+00, 2.63493410e+00, 1.32731395e+00,\n",
+       "        2.85142135e+00, 3.22263124e+00, 6.69244299e+00, 3.98443734e+00,\n",
+       "        4.57490957e+00, 4.57490957e+00, 4.74879850e+00, 2.49749062e+00,\n",
+       "        5.08948541e+00, 4.79488230e+00, 5.33025905e+00, 2.13044289e+00,\n",
+       "        3.87433140e+00, 4.08152956e+00, 4.09627289e+00, 4.85782329e+00,\n",
+       "        4.85782329e+00, 1.89622359e+00, 3.55417008e+00, 4.20390028e+00,\n",
+       "        4.86505593e+00, 1.31719623e+00, 4.25118287e-01, 5.83370979e+00,\n",
+       "        1.60922560e+00, 1.74464914e+01, 1.15075290e+00, 2.30082422e+00,\n",
+       "        4.95258190e+00, 6.13898545e+00, 6.38753560e+00, 5.17571061e+00,\n",
+       "        7.47838700e-01, 6.50878033e+00, 2.91724933e+00, 3.36413301e+00,\n",
+       "        3.36413301e+00, 7.35672099e-01, 1.25595543e+00, 6.12928584e+00,\n",
+       "        3.70693918e-01, 3.27598876e-01, 4.28171982e+00, 1.35918814e+00,\n",
+       "        1.11209190e+01, 9.21687292e-02, 3.52776547e-01, 1.99177769e+00,\n",
+       "        2.73369093e+00, 2.09983715e+00, 2.05467612e+00, 2.98848085e+00,\n",
+       "        2.25755942e+00, 6.90530023e+00, 4.64255983e-01, 5.19906252e+00,\n",
+       "        6.09129927e-01, 9.18894282e+00, 5.10508085e-01, 5.11227240e+00,\n",
+       "        1.14972647e+00, 3.23907639e+00, 3.80036903e+00, 3.68816283e+00,\n",
+       "        1.91118207e+00, 2.60459123e+00, 1.63845804e+00, 5.97985216e+00,\n",
+       "        1.12298067e+00, 4.83394632e+00, 4.46988058e+00, 1.57787079e+00,\n",
+       "        4.71094053e+00, 3.00208086e+00, 3.81412747e+00, 3.08637257e-01,\n",
+       "        2.09300990e+00, 3.32846560e+00, 6.73214087e+00, 3.39545994e+00,\n",
+       "        7.46830145e+00, 2.53471019e+00, 7.43741906e-01, 6.87311468e-01,\n",
+       "        2.14268092e+00, 3.17597248e+00, 7.54868597e+00, 9.06028264e-01,\n",
+       "        8.03780173e+00, 3.49124570e+00, 2.02696755e+01, 2.24750423e+00,\n",
+       "        3.12782239e+00, 4.00393556e+00, 1.67990775e+00, 2.97917781e+00,\n",
+       "        5.67224459e+00, 1.05155587e+00, 8.34811215e-01, 5.59192368e+00,\n",
+       "        6.33305409e-01, 8.08629911e-01, 9.28525167e+00, 1.47331217e+00,\n",
+       "        7.35836287e+00, 4.28213077e+00, 4.41875045e+00, 5.43760749e+00,\n",
+       "        2.68760635e+00, 4.00252254e+00, 5.82620396e+00, 9.36370466e-01,\n",
+       "        8.44636664e+00, 2.70484604e+00, 4.75798008e+00, 1.13899875e+01,\n",
+       "        6.73646121e+00, 8.61142964e+00, 8.00575364e+00, 2.61346423e+00,\n",
+       "        2.64181585e+00, 1.47889937e+00, 8.39047969e+00, 1.64261744e-01,\n",
+       "        1.26094910e+00, 4.73942146e+00, 9.42158659e-01, 5.09953245e+00,\n",
+       "        3.04515105e+00, 6.92398732e+00, 9.15475583e+00, 9.34055603e+00,\n",
+       "        9.34055603e+00, 1.50525552e+01, 3.52253592e+00, 8.30393840e+00,\n",
+       "        3.20415695e-01, 5.56540289e-01, 8.12289162e-01, 1.08367839e+01,\n",
+       "        2.52925478e-01, 2.17691858e+00, 6.60660551e-01, 8.07436041e+00,\n",
+       "        8.14170694e-01, 9.81400007e-02, 8.16631769e-02, 9.50320682e-03,\n",
+       "        4.38901154e-02, 4.62650342e-01, 2.86580085e+00, 9.98134771e-01,\n",
+       "        3.46308983e+00, 2.47491866e+00, 1.73991775e+00, 1.26750078e+00,\n",
+       "        2.22276355e-01, 2.84272161e+00, 3.89759502e+00, 3.57384324e+00,\n",
+       "        2.58747694e+00, 7.87197187e+00, 2.39479038e+00, 3.28423496e+00,\n",
+       "        1.57064813e+00, 5.07859491e+00, 1.00456784e+01, 5.60688281e+00,\n",
+       "        1.76784621e+00, 5.34790323e+00, 1.54471064e+00, 1.37536857e+01,\n",
+       "        1.96878732e+00, 5.53015775e+00, 6.99402595e+00, 8.09870971e+00,\n",
+       "        1.59615648e+00, 2.85893262e+00, 6.26624606e+00, 1.42370624e+00,\n",
+       "        1.86458494e+00, 1.06274620e+00, 5.17332159e-01, 1.58439718e+00,\n",
+       "        5.01274221e+00, 2.48228633e+00, 2.48804834e+00, 7.78192623e-01,\n",
+       "        6.39927238e-01, 8.47501078e-01, 1.57846587e+00, 1.54192860e+00,\n",
+       "        6.12448298e+00, 3.41504117e+00, 1.04669325e+00, 8.26794693e-01,\n",
+       "        3.26448594e-01, 2.31055130e+00, 3.55396018e+00, 2.48894303e+00,\n",
+       "        5.73693319e+00, 5.97791264e-01, 3.49929145e+00, 3.40165053e+00,\n",
+       "        4.41449827e+00, 2.21977683e+00, 1.30474581e+01, 8.96360503e-01,\n",
+       "        7.85495457e+00, 3.68786391e-01, 7.10484820e-01, 4.20037716e-01,\n",
+       "        8.80345690e-01, 1.54460120e+00, 1.74275065e+00, 2.74753209e+00,\n",
+       "        1.26034447e+00, 2.50638706e+00, 2.65608510e+00, 5.90198610e+00,\n",
+       "        4.19635997e+00, 4.62376682e+00, 2.14554513e+00, 2.45664128e+00,\n",
+       "        1.85087527e+00, 4.07232553e+00, 3.20432446e+00, 2.92843027e+00,\n",
+       "        3.54979312e+00, 4.72110607e+00, 1.68868475e+00, 1.59983370e+00,\n",
+       "        1.52801921e+00, 5.57339445e+00, 4.15557352e+00, 1.78640308e+00,\n",
+       "        6.72885234e+00, 5.36081450e+00, 3.29838460e+00, 6.83324743e-01,\n",
+       "        1.99402329e+00, 4.12356458e+00, 3.86292350e-01, 1.03426798e-01,\n",
+       "        2.98910688e-01, 1.96901631e-01, 5.44922239e+00, 1.03903400e+00,\n",
+       "        5.23759149e+00, 2.79896505e+00, 6.97503607e+00, 1.49622930e+00,\n",
+       "        3.51469836e+00, 1.58002305e+00, 3.98545532e+00, 2.70836152e+00,\n",
+       "        1.62561548e+00, 4.32428599e+00, 3.07241158e+00, 9.00408379e+00,\n",
+       "        4.07247876e+00, 4.69332833e+00, 2.34669589e+00, 1.96065646e+00,\n",
+       "        5.39356713e+00, 2.08974930e+00, 3.44272039e+00, 8.86059996e-01,\n",
+       "        9.16080016e+00, 1.77550946e+00, 7.15100329e+00, 2.02439882e+00,\n",
+       "        2.43408258e+00, 3.38167253e+00, 3.93604714e+00, 3.22583456e+00,\n",
+       "        6.46415433e+00, 3.84153415e+00, 2.00919880e+00, 1.18775367e+00,\n",
+       "        1.37186338e+00, 1.99775159e+00, 1.80550083e+00, 2.83230389e+00,\n",
+       "        1.70997091e+00, 1.58043313e+00, 2.02102196e+00, 1.63450468e+00,\n",
+       "        7.69180995e+00, 6.62055891e+00, 1.27277253e+00, 1.79103936e+00,\n",
+       "        1.91416103e+00, 6.76462938e-01, 9.26518218e-01, 2.13629891e+00,\n",
+       "        1.65068060e+00, 1.74428204e+00, 4.69424515e+00, 2.53000972e+00,\n",
+       "        3.84178230e+00, 6.70391502e+00, 5.18767493e+00, 2.42109655e+00,\n",
+       "        3.21665057e+00, 5.25180278e+00, 1.76820664e+00, 1.01972651e+01,\n",
+       "        4.47006003e+00, 4.08581466e+00, 1.16629333e+01, 1.50425335e-01,\n",
+       "        1.19086084e+00, 5.36757088e+00, 3.57395144e+00, 3.16027541e+00,\n",
+       "        5.57001512e+00, 1.67214931e+00, 1.44469516e+00, 2.67965652e+00,\n",
+       "        3.50672052e+00, 4.17309155e+00, 2.62906183e+00, 1.89809515e+00,\n",
+       "        1.57168608e+00, 5.59775810e+00, 1.15598330e+01, 3.75898940e+00,\n",
+       "        2.35645221e+00, 4.35924984e+00, 9.95338301e+00, 1.05207855e+01,\n",
+       "        2.93462299e+00, 9.01546173e+00, 2.71570686e+00, 8.66364803e-01,\n",
+       "        9.96241715e+00, 5.42643053e-01, 1.37701461e+01, 3.24010841e+00,\n",
+       "        6.73262365e+00, 2.02728617e+00, 5.19188908e+00, 1.63399175e+00,\n",
+       "        3.99096094e+00, 3.26935682e+00, 2.47952116e+00, 1.61216331e+00,\n",
+       "        5.71464923e+00, 8.81879098e+00, 3.65560747e+00, 5.33226726e+00,\n",
+       "        6.11604399e-01, 5.65137882e+00, 2.69282071e+00, 2.52869261e+00,\n",
+       "        4.08528793e+00, 3.98212294e+00, 3.98212294e+00, 1.09339140e+00,\n",
+       "        6.84031531e+00, 1.65611617e+00, 3.83101526e+00, 5.54784515e-01,\n",
+       "        1.90193871e+00, 1.83026382e+00, 2.74058114e+00, 4.72413170e+00]])
                • theta
                  (chain, draw, school)
                  float64
                  5.63 4.727 6.121 ... 8.072 3.706
                  array([[[ 5.62961003,  4.72687023,  6.12122332, ...,  9.84107226,\n",
+       "         13.40311572,  8.10476638],\n",
+       "        [ 8.10377285,  7.04589407,  6.02063312, ...,  3.54418276,\n",
+       "          4.36085417,  9.02703109],\n",
+       "        [ 3.57690532,  4.12943539,  3.0118329 , ...,  6.26163706,\n",
+       "          7.34571917,  2.64591509],\n",
+       "        ...,\n",
+       "        [ 3.97800088,  6.35343688,  1.17208901, ..., -0.91761363,\n",
+       "          2.14463885,  0.17888639],\n",
+       "        [ 5.98201937,  5.33791529,  6.34857162, ...,  7.91843753,\n",
+       "          8.24967156,  6.91430325],\n",
+       "        [-3.59497067,  8.00880728,  6.05143585, ...,  9.35470458,\n",
+       "          2.32469486,  4.30608446]],\n",
+       "\n",
+       "       [[ 5.32205148,  7.84879735,  4.04090126, ...,  3.47268908,\n",
+       "          3.34656278,  3.38618173],\n",
+       "        [ 5.83158394, 11.96256866,  9.10110082, ...,  6.97559004,\n",
+       "         10.0575234 , 10.09739417],\n",
+       "        [ 3.56793512, -2.82766633,  2.4189104 , ...,  4.8830371 ,\n",
+       "          1.10262802, -2.47328233],\n",
+       "        ...,\n",
+       "        [ 8.28928178, 10.69534092,  6.75551742, ...,  8.83411145,\n",
+       "          9.3728429 ,  7.28132981],\n",
+       "        [ 8.17915864,  1.68523006,  6.26244159, ...,  1.87994739,\n",
+       "          4.77099916,  5.76705506],\n",
+       "        [ 5.6511123 ,  9.85042207,  0.05217945, ...,  6.62720573,\n",
+       "          8.07236995,  3.70556965]]])
              • created_at :
                2020-06-10T18:19:23.327079
                arviz_version :
                0.8.3
                inference_library :
                pymc3
                inference_library_version :
                3.8

          \n", + "
        \n", + "
        \n", + "
        • \n", + " \n", + "
      • \n", + " \n", + " \n", + "
        \n", + "
        \n", + "
          \n", + "
          \n", + "\n", + "\n", + "Show/Hide data repr\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Show/Hide attributes\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
          xarray.Dataset
            • b1: 10
            • c1: 3
            • c2: 4
            • chain: 1
            • draw: 100
            • chain
              (chain)
              int64
              0
              array([0])
            • draw
              (draw)
              int64
              0 1 2 3 4 5 6 ... 94 95 96 97 98 99
              array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
-       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
-       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
-       "       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,\n",
-       "       72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89,\n",
-       "       90, 91, 92, 93, 94, 95, 96, 97, 98, 99])
            • b1
              (b1)
              int64
              0 1 2 3 4 5 6 7 8 9
              array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
            • c1
              (c1)
              int64
              0 1 2
              array([0, 1, 2])
            • c2
              (c2)
              int64
              0 1 2 3
              array([0, 1, 2, 3])
            • a
              (chain, draw)
              float64
              1.388 -0.435 ... -0.02538 0.4237
              array([[ 1.38830379, -0.43501265, -1.33190295, -1.41406417,  0.16199589,\n",
-       "         0.51785049, -1.6173349 , -0.86856443, -1.04148202,  0.45262536,\n",
-       "         0.04212587,  0.69100054,  0.21114523,  0.34253538, -0.03343265,\n",
-       "         1.037077  , -0.24286319, -0.1167344 ,  1.6590027 ,  0.32700689,\n",
-       "         1.1623474 , -1.84436443, -1.35460971, -0.66132992,  0.77032251,\n",
-       "         0.93694648,  1.38257997, -0.69085695,  1.34625778,  1.83124689,\n",
-       "         0.56922388, -0.16049682, -2.08624686,  1.15367138,  0.62911656,\n",
-       "        -0.97548518,  0.9520221 , -0.74840271, -1.09028329,  0.40747891,\n",
-       "         0.30226566,  0.51866642,  1.80876867, -1.11795652, -1.36475722,\n",
-       "         0.07815867, -0.9690401 ,  0.64219237, -0.42278462, -1.80964916,\n",
-       "         0.39444554, -0.80730441,  0.56727989, -0.33355779, -0.40628526,\n",
-       "        -1.59803928, -1.0435544 ,  1.67447346, -0.87995185,  0.80716309,\n",
-       "        -0.49476011, -0.44018119,  0.09406969,  1.30699081,  0.78742038,\n",
-       "        -0.53732592,  1.20071111,  0.06567662, -0.00651144,  0.98783025,\n",
-       "         0.36537125,  1.21336023,  0.25457246,  0.17375063,  0.30920106,\n",
-       "        -1.22422813, -0.49061882, -0.16515283,  0.9196351 ,  1.4863149 ,\n",
-       "        -0.24464703, -0.1622031 ,  0.8447644 , -0.18530109,  0.29724243,\n",
-       "        -1.82665651, -0.27167164,  1.14502589,  1.73467347,  1.43562209,\n",
-       "        -1.3906193 , -0.69945332, -1.6929867 ,  1.84201555,  1.34464875,\n",
-       "         0.92803875,  0.8052289 ,  0.54302172, -0.02538094,  0.42370527]])
            • b
              (chain, draw, b1)
              float64
              1.924 1.801 ... 0.1072 -1.814
              array([[[ 1.92391253e+00,  1.80108000e+00, -7.60429102e-01,\n",
-       "          2.04034449e+00,  3.76075921e-01,  1.25217407e-01,\n",
-       "          9.75207522e-02,  1.06997096e+00,  2.85450437e-01,\n",
-       "         -6.82304483e-01],\n",
-       "        [ 3.48509165e-01,  1.00458128e-01,  1.65088650e+00,\n",
-       "         -3.39920713e-01,  1.16888685e+00,  7.61983988e-01,\n",
-       "          9.33126526e-02, -8.67201513e-01, -5.94461695e-01,\n",
-       "          3.60390010e-01],\n",
-       "        [ 1.22256924e+00,  1.84802049e+00,  4.61874423e-01,\n",
-       "         -1.83144069e-02,  1.52285735e+00,  4.87822163e-01,\n",
-       "         -7.94476233e-01, -3.69569713e-01,  4.79913752e-01,\n",
-       "         -6.77562688e-01],\n",
-       "        [-1.10573125e+00, -6.23825004e-02, -5.31393753e-01,\n",
-       "         -8.97124043e-02,  1.91075671e-01, -6.89308578e-01,\n",
-       "          9.26698177e-01, -1.67841463e+00, -4.49912124e-01,\n",
-       "         -2.40952915e-01],\n",
-       "        [ 1.29272063e+00, -8.82709162e-01, -1.46037590e-01,\n",
-       "         -6.53131318e-01, -4.89414354e-01, -2.82620822e-01,\n",
-       "         -1.66586339e-01,  9.32175514e-01, -2.03390765e-02,\n",
-       "         -5.03801841e-02],\n",
-       "        [ 3.74297340e-01, -5.56503628e-01,  1.91915985e+00,\n",
-       "          3.92747732e-01, -1.62708902e+00, -5.66360190e-01,\n",
-       "         -4.88078731e-01, -7.59406762e-01,  7.81492007e-01,\n",
-       "          2.32593614e+00],\n",
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-       "        [ 1.78613739e+00,  9.00024331e-02,  1.06606040e+00,\n",
-       "         -6.60810228e-01,  7.16864136e-01, -1.70768922e+00,\n",
-       "         -1.03758632e+00,  6.53243423e-01, -1.41149236e+00,\n",
-       "         -3.69828335e-02],\n",
-       "        [-3.50335360e-01, -2.33665669e-01,  2.41740510e-01,\n",
-       "         -3.73519355e-01,  1.01444607e+00,  1.01336379e+00,\n",
-       "         -1.92249752e+00, -1.37620287e+00,  9.71286335e-01,\n",
-       "         -5.99970937e-01],\n",
-       "        [-1.27689552e+00, -8.86120095e-01, -9.37289777e-01,\n",
-       "         -1.24423644e+00, -7.01017427e-01, -8.93101153e-01,\n",
-       "         -5.87879020e-01,  3.93849626e-01,  7.97826724e-01,\n",
-       "          1.13041301e-01],\n",
-       "        [-2.27122717e-01,  4.35117298e-01, -8.03049083e-01,\n",
-       "         -7.09045648e-01,  9.04605736e-02, -7.25896579e-01,\n",
-       "          4.76077863e-01,  5.25363034e-01,  1.50803002e+00,\n",
-       "          7.86827792e-01],\n",
-       "        [-9.81511280e-01,  9.44056536e-02, -4.77823118e-01,\n",
-       "         -1.56822328e+00,  9.05065579e-01, -6.34660424e-01,\n",
-       "         -1.42216585e+00,  7.27984861e-01,  4.42795959e-02,\n",
-       "          5.32914097e-01],\n",
-       "        [-1.18374060e+00, -1.36220546e+00,  5.76558794e-01,\n",
-       "          5.12723418e-01, -1.87575874e-01,  1.34948870e-02,\n",
-       "          3.20417841e-01, -1.15516651e-01, -1.75956195e+00,\n",
-       "          2.23860003e-01],\n",
-       "        [ 2.57090331e+00, -2.68671537e-01,  1.34439770e+00,\n",
-       "         -7.26580876e-01, -1.97230177e+00,  1.57041561e+00,\n",
-       "          2.16410585e+00,  2.44025065e-01,  1.19368866e+00,\n",
-       "          1.42237715e-01],\n",
-       "        [-2.84652907e+00, -5.06675944e-01,  7.31070862e-02,\n",
-       "          1.10963553e-01, -8.77335131e-02,  4.99830135e-01,\n",
-       "          2.52189814e-02, -3.32015035e-01,  6.25770357e-01,\n",
-       "         -1.63888016e+00],\n",
-       "        [-5.82479999e-01,  6.76817539e-01,  2.74639666e-01,\n",
-       "         -4.93293500e-01,  9.35053430e-01, -8.90550823e-01,\n",
-       "          2.71304150e-01,  3.40857367e-01, -1.29501004e+00,\n",
-       "          2.47541795e-01],\n",
-       "        [-2.45654468e+00,  2.37181235e-02,  6.51187098e-01,\n",
-       "          1.05267550e+00,  4.57113221e-01,  6.15416697e-01,\n",
-       "         -5.74370078e-01, -1.18934049e-01, -6.30324085e-01,\n",
-       "         -1.57319466e+00],\n",
-       "        [-4.38533365e-01,  7.98540480e-02, -2.13015348e-01,\n",
-       "         -7.25232999e-01, -3.95294271e-01, -4.74844272e-01,\n",
-       "         -5.69186334e-01, -6.06872082e-01, -6.94957496e-01,\n",
-       "          5.81286173e-01],\n",
-       "        [ 5.59784964e-01, -1.35347789e+00, -6.67933087e-01,\n",
-       "          3.01171031e-01,  6.57507089e-01,  1.18317806e+00,\n",
-       "          6.62733769e-01, -3.07750327e-01,  5.36064539e-02,\n",
-       "          7.53559053e-01],\n",
-       "        [-5.83498573e-01, -5.53853657e-01, -3.62989878e-01,\n",
-       "         -1.04179207e+00, -8.65015994e-01,  3.83411981e-01,\n",
-       "          5.68314299e-01, -2.58470428e-01,  1.24011058e+00,\n",
-       "          2.34506174e-01],\n",
-       "        [ 5.15792329e-01, -1.02202645e+00,  1.86093598e+00,\n",
-       "          2.36170824e+00,  1.12937223e+00,  1.25417614e-01,\n",
-       "          1.22609495e+00, -8.38369276e-01,  1.97014226e+00,\n",
-       "         -1.15562632e+00],\n",
-       "        [-2.19709483e+00,  4.88648113e-01, -3.91745172e-01,\n",
-       "          7.78407043e-01,  2.88874777e-01,  1.58882594e+00,\n",
-       "         -2.90998045e-01, -3.30421169e-01, -5.08724174e-01,\n",
-       "          1.32399727e-01],\n",
-       "        [-2.34788392e-01, -1.66596106e+00,  8.41870402e-01,\n",
-       "         -6.63390234e-01, -7.72594824e-01,  3.67716288e-01,\n",
-       "          5.13232547e-01, -1.79669423e-01, -6.42525756e-01,\n",
-       "         -5.72014235e-01],\n",
-       "        [-7.67326905e-01, -6.22097049e-01,  1.46808679e+00,\n",
-       "         -1.14755754e+00, -3.42652896e-01, -2.37499333e-01,\n",
-       "         -1.52760445e+00,  5.59010889e-01, -5.59476628e-02,\n",
-       "         -1.02757495e+00],\n",
-       "        [ 5.63141856e-01,  9.05375112e-01,  5.58646189e-01,\n",
-       "          1.67805341e+00, -3.01511406e-01,  3.77056669e-02,\n",
-       "          7.03050171e-01, -6.84936371e-01, -6.33543928e-01,\n",
-       "          4.31328894e-01],\n",
-       "        [-8.74287181e-01, -1.78504928e+00, -8.67224313e-02,\n",
-       "         -5.01335414e-01, -1.23171819e+00, -2.41733732e-01,\n",
-       "         -1.29476688e+00,  7.16634092e-01,  5.72720514e-01,\n",
-       "         -4.39064463e-01],\n",
-       "        [ 1.16317872e+00,  9.59334520e-01,  6.21759792e-01,\n",
-       "          4.04351665e-01,  3.60062736e-01,  1.75995045e-01,\n",
-       "          3.77543469e-01,  2.88721614e+00, -1.72769065e+00,\n",
-       "         -1.30733756e+00],\n",
-       "        [-6.85922309e-01,  4.65925029e-01,  6.67969402e-01,\n",
-       "         -7.78108897e-01,  6.75189634e-01, -9.59453214e-01,\n",
-       "         -7.83797166e-01,  1.02112536e+00, -1.85701136e-01,\n",
-       "         -1.27023042e+00],\n",
-       "        [ 3.82942461e-02, -6.40738742e-01, -4.79869082e-01,\n",
-       "          1.42578267e+00, -2.20432860e-01, -1.26494088e+00,\n",
-       "          1.59007142e+00,  7.14889688e-01, -9.99701627e-01,\n",
-       "          5.27909082e-01],\n",
-       "        [-1.62475413e-01,  1.80862594e-02, -5.22735224e-01,\n",
-       "         -3.19810894e-01, -1.43247703e+00,  1.71379232e+00,\n",
-       "         -5.72453082e-01,  1.17027404e+00, -1.65583331e-01,\n",
-       "         -3.80899512e-01],\n",
-       "        [-1.00103166e+00,  7.03163448e-01, -9.14281754e-02,\n",
-       "          3.25896868e-02, -9.34627286e-03, -5.60203122e-01,\n",
-       "         -4.61706651e-01,  3.02304784e-02,  3.52568180e-01,\n",
-       "          7.47981111e-01],\n",
-       "        [-4.14750378e-01, -1.78069148e+00,  6.55757164e-01,\n",
-       "         -1.08236058e+00, -8.90261855e-01,  1.40437926e+00,\n",
-       "          1.43227394e+00, -1.33193889e+00, -2.46564746e-01,\n",
-       "          8.73371718e-01],\n",
-       "        [ 2.00601658e+00,  3.81683510e-01,  1.19007458e+00,\n",
-       "         -1.53142651e+00, -2.04380016e-01,  2.02427908e+00,\n",
-       "         -5.70265069e-01,  8.24186580e-01, -7.24222246e-01,\n",
-       "          1.04562849e+00],\n",
-       "        [-6.27434641e-02, -3.47284617e-01,  8.95485937e-01,\n",
-       "          1.24410666e+00, -8.22204149e-02,  3.13868100e-03,\n",
-       "         -8.22516989e-01, -2.50608750e-01,  4.34933052e-01,\n",
-       "         -9.83557529e-01],\n",
-       "        [ 1.15403027e-01,  8.44734100e-01, -6.47078740e-01,\n",
-       "         -4.41572979e-01, -1.49165919e+00, -4.69626531e-01,\n",
-       "         -1.49869418e+00, -1.14437644e+00, -2.00079662e+00,\n",
-       "          4.32322164e-01],\n",
-       "        [ 5.98725703e-01,  5.41534704e-01,  3.08026285e-01,\n",
-       "          8.92484300e-01,  6.51515829e-01,  7.13165123e-01,\n",
-       "          1.57451665e+00, -1.72914306e-01, -1.71379432e+00,\n",
-       "         -1.03974067e-01],\n",
-       "        [ 1.68088691e-01, -6.04036096e-01,  1.35364567e+00,\n",
-       "         -1.74295979e+00,  1.02505748e+00, -7.69718953e-01,\n",
-       "         -3.52259992e-01,  1.06205242e+00, -1.22388364e+00,\n",
-       "         -4.51613604e-02],\n",
-       "        [-7.54305284e-02,  1.48184506e+00, -3.38212438e-02,\n",
-       "         -1.63011706e+00, -4.25586653e-01,  1.86972047e-01,\n",
-       "          2.84369376e-01, -1.08079244e+00, -9.84287496e-01,\n",
-       "         -6.10975353e-01],\n",
-       "        [ 7.12802972e-01, -4.89443940e-01, -3.80283508e-01,\n",
-       "          9.55458038e-02,  1.32363691e-01, -2.32220544e-01,\n",
-       "          1.44686536e-01, -3.55532680e-01, -3.22581836e-01,\n",
-       "         -1.08189151e+00],\n",
-       "        [ 9.68526865e-01,  4.52805377e-02, -4.75847922e-01,\n",
-       "         -1.11536858e-01, -2.53813537e-01, -7.97896493e-01,\n",
-       "         -9.82417866e-01, -1.27333134e+00, -5.34843958e-01,\n",
-       "         -5.17905951e-02],\n",
-       "        [ 4.57408303e-02, -1.13200690e+00, -5.76430396e-01,\n",
-       "         -4.03125387e-01, -1.23968589e-01, -1.17913451e+00,\n",
-       "          1.90922718e-01, -1.26507180e+00,  1.07226062e-01,\n",
-       "         -1.81417687e+00]]])
            • c
              (chain, draw, c1, c2)
              float64
              0.2361 0.1921 ... -0.722 -0.6994
              array([[[[ 0.23612495,  0.19209144,  1.73643296,  0.32152596],\n",
-       "         [ 0.6624001 , -0.70653542, -0.38179504, -0.40551882],\n",
-       "         [-0.57635498,  0.63250108, -0.32027305, -1.38916566]],\n",
-       "\n",
-       "        [[-1.11897017,  0.58815242, -0.58968597, -0.65490374],\n",
-       "         [ 0.04078477,  0.11616236, -0.60324171, -0.30388797],\n",
-       "         [ 1.45114249,  0.08002404, -0.72766003,  0.66437293]],\n",
-       "\n",
-       "        [[ 0.34177152,  0.47166011,  0.23152757,  0.84419889],\n",
-       "         [ 0.02070863,  2.98960753, -1.6101033 ,  1.00311769],\n",
-       "         [ 0.15683503, -2.11034956, -0.16731209,  1.07437286]],\n",
-       "\n",
+       "
              xarray.Dataset
                • chain: 2
                • draw: 500
                • obs_dim_0: 8
                • chain
                  (chain)
                  int64
                  0 1
                  array([0, 1])
                • draw
                  (draw)
                  int64
                  0 1 2 3 4 5 ... 495 496 497 498 499
                  array([  0,   1,   2, ..., 497, 498, 499])
                • obs_dim_0
                  (obs_dim_0)
                  int64
                  0 1 2 3 4 5 6 7
                  array([0, 1, 2, 3, 4, 5, 6, 7])
                • obs
                  (chain, draw, obs_dim_0)
                  float64
                  15.57 12.1 3.026 ... 18.62 -10.4
                  array([[[ 15.56955881,  12.10362895,   3.0259356 , ...,   5.25677454,\n",
+       "          -8.13085903,   8.92486203],\n",
+       "        [  2.01095742,  23.8437438 ,  -0.74217338, ...,   3.79923879,\n",
+       "           2.69217957,  14.48449805],\n",
+       "        [ 20.29470416,   4.96292701,  -0.2854632 , ...,  34.46851646,\n",
+       "          16.74562782, -22.1119676 ],\n",
        "        ...,\n",
-       "\n",
-       "        [[-0.92695133,  0.29513187, -0.88513231, -1.345392  ],\n",
-       "         [-0.02382486, -0.30252184, -0.60326201, -0.30289022],\n",
-       "         [ 0.16873816,  0.49046628, -1.06360509,  0.0283932 ]],\n",
-       "\n",
-       "        [[-0.28788494, -0.59228032, -0.85666178, -0.57441519],\n",
-       "         [-0.05765098,  0.79718082, -0.77759788, -0.04799847],\n",
-       "         [ 1.37334172,  0.36906373, -0.13126941,  1.20878831]],\n",
-       "\n",
-       "        [[ 0.12294222, -0.53413433,  1.70802865, -0.2050028 ],\n",
-       "         [-1.09157998,  0.56964328, -0.66168397, -2.51403759],\n",
-       "         [-0.17579789,  2.12954436, -0.72202585, -0.69935395]]]])
              • created_at :
                2020-06-06T18:07:29.430311
                arviz_version :
                0.8.3

          \n", + " [-11.94803447, 0.0896506 , 28.35944418, ..., -1.47988496,\n", + " 4.03098823, 8.01120592],\n", + " [ 4.86526649, 10.98289441, 1.4086219 , ..., 16.58321832,\n", + " 50.8494954 , 22.64208451],\n", + " [-13.88113504, 6.71350581, 18.17528719, ..., -18.6932786 ,\n", + " 9.77879534, 42.17198861]],\n", + "\n", + " [[ -0.71864835, -14.63818199, -27.80108664, ..., 3.88081011,\n", + " 9.8554434 , -1.74781512],\n", + " [ 6.95980651, 3.50331516, -3.28409145, ..., 10.57124477,\n", + " 19.94187807, -14.3233147 ],\n", + " [ 1.66781587, -10.98676567, -4.72720481, ..., 11.92457337,\n", + " -10.67383229, 6.63788789],\n", + " ...,\n", + " [ 29.8775133 , 5.52945089, 3.75273982, ..., 7.82224107,\n", + " -0.76100621, 23.18560467],\n", + " [ 5.19149401, 0.90691355, -28.91017224, ..., -0.97321204,\n", + " 22.94379602, 18.21314178],\n", + " [ 41.27000137, 15.79815357, -16.03026608, ..., 15.09251082,\n", + " 18.6168739 , -10.40009236]]])
      • created_at :
        2020-06-10T18:19:23.476419
        arviz_version :
        0.8.3
        inference_library :
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        inference_library_version :
        3.8

    \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " " - ], - "text/plain": [ - "Inference data with groups:\n", - "\t> posterior" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "datadict = {\n", - " \"a\": np.random.randn(100),\n", - " \"b\": np.random.randn(1, 100, 10),\n", - " \"c\": np.random.randn(1, 100, 3, 4),\n", - "}\n", - "coords = {\"c1\": np.arange(3), \"c2\": np.arange(4), \"b1\": np.arange(10)}\n", - "dims = {\"b\": [\"b1\"], \"c\": [\"c1\", \"c2\"]}\n", - "\n", - "dataset = az.convert_to_inference_data(datadict, coords=coords, dims=dims)\n", - "dataset" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
    \n", + "
    xarray.Dataset
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      • draw: 500
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      • chain
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        array([  0,   1,   2, ..., 497, 498, 499])
      • obs_dim_0
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        array([0, 1, 2, 3, 4, 5, 6, 7])
      • obs
        (chain, draw, obs_dim_0)
        float64
        -4.739 -3.275 ... -3.714 -3.915
        array([[[-4.73906506, -3.27509052, -3.85402084, ..., -3.63982785,\n",
+       "         -3.32718035, -3.83272518],\n",
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+       "        [-4.70430311, -3.2569571 , -3.86222216, ..., -3.51462214,\n",
+       "         -3.69686815, -3.84922435],\n",
+       "        [-5.84530467, -3.22152401, -3.85154384, ..., -3.60526806,\n",
+       "         -4.45009958, -3.90066266]],\n",
+       "\n",
+       "       [[-4.76985395, -3.22163794, -3.78835204, ..., -3.34209906,\n",
+       "         -4.29513974, -3.92381317],\n",
+       "        [-4.71907467, -3.30003338, -3.97753632, ..., -3.46438619,\n",
+       "         -3.5369383 , -3.81489657],\n",
+       "        [-4.9534905 , -3.80771542, -3.74887997, ..., -3.3791395 ,\n",
+       "         -4.64912952, -4.13257557],\n",
+       "        ...,\n",
+       "        [-4.49034965, -3.25784794, -3.8774064 , ..., -3.57044249,\n",
+       "         -3.59366282, -3.84367117],\n",
+       "        [-4.50002374, -3.42090522, -3.85909137, ..., -3.32003342,\n",
+       "         -4.09655594, -3.86926338],\n",
+       "        [-4.73692825, -3.23864394, -3.70972218, ..., -3.44768275,\n",
+       "         -3.71431282, -3.91547939]]])
    • created_at :
      2020-06-10T18:19:23.474654
      arviz_version :
      0.8.3
      inference_library :
      pymc3
      inference_library_version :
      3.8

    \n", + " \n", + " \n", + " \n", + " \n", + "
  • \n", + " \n", + " \n", + "
    \n", + "
    \n", + "
      \n", + "
      \n", "\n", "\n", "Show/Hide data repr\n", @@ -7195,595 +6056,2092 @@ " stroke: currentColor;\n", " fill: currentColor;\n", "}\n", - "
      xarray.Dataset
        • b1: 10
        • c1: 3
        • c2: 4
        • chain: 1
        • draw: 100
        • chain
          (chain)
          int64
          0
          array([0])
        • draw
          (draw)
          int64
          0 1 2 3 4 5 6 ... 94 95 96 97 98 99
          array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
-       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
-       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
-       "       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,\n",
-       "       72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89,\n",
-       "       90, 91, 92, 93, 94, 95, 96, 97, 98, 99])
        • b1
          (b1)
          int64
          0 1 2 3 4 5 6 7 8 9
          array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
        • c1
          (c1)
          int64
          0 1 2
          array([0, 1, 2])
        • c2
          (c2)
          int64
          0 1 2 3
          array([0, 1, 2, 3])
        • a
          (chain, draw)
          float64
          1.388 -0.435 ... -0.02538 0.4237
          array([[ 1.38830379, -0.43501265, -1.33190295, -1.41406417,  0.16199589,\n",
-       "         0.51785049, -1.6173349 , -0.86856443, -1.04148202,  0.45262536,\n",
-       "         0.04212587,  0.69100054,  0.21114523,  0.34253538, -0.03343265,\n",
-       "         1.037077  , -0.24286319, -0.1167344 ,  1.6590027 ,  0.32700689,\n",
-       "         1.1623474 , -1.84436443, -1.35460971, -0.66132992,  0.77032251,\n",
-       "         0.93694648,  1.38257997, -0.69085695,  1.34625778,  1.83124689,\n",
-       "         0.56922388, -0.16049682, -2.08624686,  1.15367138,  0.62911656,\n",
-       "        -0.97548518,  0.9520221 , -0.74840271, -1.09028329,  0.40747891,\n",
-       "         0.30226566,  0.51866642,  1.80876867, -1.11795652, -1.36475722,\n",
-       "         0.07815867, -0.9690401 ,  0.64219237, -0.42278462, -1.80964916,\n",
-       "         0.39444554, -0.80730441,  0.56727989, -0.33355779, -0.40628526,\n",
-       "        -1.59803928, -1.0435544 ,  1.67447346, -0.87995185,  0.80716309,\n",
-       "        -0.49476011, -0.44018119,  0.09406969,  1.30699081,  0.78742038,\n",
-       "        -0.53732592,  1.20071111,  0.06567662, -0.00651144,  0.98783025,\n",
-       "         0.36537125,  1.21336023,  0.25457246,  0.17375063,  0.30920106,\n",
-       "        -1.22422813, -0.49061882, -0.16515283,  0.9196351 ,  1.4863149 ,\n",
-       "        -0.24464703, -0.1622031 ,  0.8447644 , -0.18530109,  0.29724243,\n",
-       "        -1.82665651, -0.27167164,  1.14502589,  1.73467347,  1.43562209,\n",
-       "        -1.3906193 , -0.69945332, -1.6929867 ,  1.84201555,  1.34464875,\n",
-       "         0.92803875,  0.8052289 ,  0.54302172, -0.02538094,  0.42370527]])
        • b
          (chain, draw, b1)
          float64
          1.924 1.801 ... 0.1072 -1.814
          array([[[ 1.92391253e+00,  1.80108000e+00, -7.60429102e-01,\n",
-       "          2.04034449e+00,  3.76075921e-01,  1.25217407e-01,\n",
-       "          9.75207522e-02,  1.06997096e+00,  2.85450437e-01,\n",
-       "         -6.82304483e-01],\n",
-       "        [ 3.48509165e-01,  1.00458128e-01,  1.65088650e+00,\n",
-       "         -3.39920713e-01,  1.16888685e+00,  7.61983988e-01,\n",
-       "          9.33126526e-02, -8.67201513e-01, -5.94461695e-01,\n",
-       "          3.60390010e-01],\n",
-       "        [ 1.22256924e+00,  1.84802049e+00,  4.61874423e-01,\n",
-       "         -1.83144069e-02,  1.52285735e+00,  4.87822163e-01,\n",
-       "         -7.94476233e-01, -3.69569713e-01,  4.79913752e-01,\n",
-       "         -6.77562688e-01],\n",
-       "        [-1.10573125e+00, -6.23825004e-02, -5.31393753e-01,\n",
-       "         -8.97124043e-02,  1.91075671e-01, -6.89308578e-01,\n",
-       "          9.26698177e-01, -1.67841463e+00, -4.49912124e-01,\n",
-       "         -2.40952915e-01],\n",
-       "        [ 1.29272063e+00, -8.82709162e-01, -1.46037590e-01,\n",
-       "         -6.53131318e-01, -4.89414354e-01, -2.82620822e-01,\n",
-       "         -1.66586339e-01,  9.32175514e-01, -2.03390765e-02,\n",
-       "         -5.03801841e-02],\n",
-       "        [ 3.74297340e-01, -5.56503628e-01,  1.91915985e+00,\n",
-       "          3.92747732e-01, -1.62708902e+00, -5.66360190e-01,\n",
-       "         -4.88078731e-01, -7.59406762e-01,  7.81492007e-01,\n",
-       "          2.32593614e+00],\n",
-       "        [ 7.14882179e-01,  6.62091932e-01, -1.07335376e+00,\n",
-       "         -3.91815417e-01,  2.60702798e-01,  8.24525553e-01,\n",
-       "         -6.05867698e-01, -1.24943960e+00,  1.04199904e-01,\n",
-       "         -2.03927440e-01],\n",
-       "        [ 1.20274308e+00, -3.41970559e-01,  3.70511393e-01,\n",
-       "         -1.42110894e+00, -5.46644514e-01,  6.91634567e-01,\n",
-       "          1.02584573e+00, -1.47139724e-01,  3.06292736e-01,\n",
-       "          7.00123981e-01],\n",
-       "        [ 7.44392159e-01, -7.50067878e-01, -3.41249157e+00,\n",
-       "          7.00132730e-01,  1.20121092e+00,  4.08633161e-01,\n",
-       "          5.63824351e-02, -1.16944822e+00,  9.32967153e-01,\n",
-       "         -1.95445708e-01],\n",
-       "        [ 1.21496867e-01,  6.59458919e-01, -3.89861537e-01,\n",
-       "         -5.58697786e-01,  1.33573293e+00, -6.44471334e-01,\n",
-       "          1.54147805e+00,  5.92119076e-01, -8.86584226e-02,\n",
-       "          1.95274105e-01],\n",
-       "        [ 1.06098608e+00, -2.82765769e-01,  1.08213644e+00,\n",
-       "          3.04537601e-01,  1.08725779e+00,  3.22904694e-02,\n",
-       "         -6.59831892e-01, -5.57167899e-02,  9.18853605e-01,\n",
-       "          5.36449632e-01],\n",
-       "        [-4.97447020e-01, -4.86300929e-01,  9.34420724e-01,\n",
-       "         -6.19971173e-01, -1.75987924e+00,  6.44141128e-01,\n",
-       "         -9.70392685e-01, -2.91947763e-01,  1.23733883e+00,\n",
-       "         -5.37270004e-01],\n",
-       "        [ 2.25221108e+00, -9.90566736e-01, -8.98790776e-01,\n",
-       "          9.00995573e-01,  4.73845053e-01, -1.25481546e+00,\n",
-       "          4.96640412e-01,  1.14010088e+00, -3.78053521e-01,\n",
-       "         -2.84238712e-01],\n",
-       "        [-1.10473462e+00, -1.62181603e+00,  4.03782875e-01,\n",
-       "          2.09176633e+00,  1.36890161e+00, -4.01728907e-01,\n",
-       "          3.48384706e-01, -6.14815762e-01,  2.25576200e-01,\n",
-       "          1.19383199e-02],\n",
-       "        [-2.97305059e-01, -2.32629437e+00, -6.98556866e-01,\n",
-       "         -7.16542658e-01, -3.27182009e-01, -9.79554603e-01,\n",
-       "         -1.68728074e+00,  7.40939791e-01, -5.50000563e-01,\n",
-       "          5.56878344e-01],\n",
-       "        [ 5.34706054e-01, -3.14891175e-01, -1.47422531e+00,\n",
-       "          2.15918622e-01,  8.17389124e-02, -7.80917520e-01,\n",
-       "         -6.06612151e-01,  7.93798206e-01, -1.30556308e+00,\n",
-       "         -1.70321869e-01],\n",
-       "        [ 9.59223957e-02,  4.96341269e-01,  6.63282293e-01,\n",
-       "         -3.95359264e-01,  5.36241057e-01, -1.18265554e+00,\n",
-       "         -5.27047169e-01,  6.71221524e-01, -4.81323133e-01,\n",
-       "         -7.31154040e-01],\n",
-       "        [-2.23902462e-01,  6.22276687e-02, -1.32284493e+00,\n",
-       "          4.34040941e-01,  3.31576004e-01, -6.42562729e-01,\n",
-       "          8.62576398e-01,  8.81783504e-01, -1.22598154e-01,\n",
-       "         -2.11848571e+00],\n",
-       "        [-3.16485679e-01,  1.53293150e+00, -1.28428061e+00,\n",
-       "          8.11371289e-01, -6.19634130e-01, -7.36023308e-01,\n",
-       "          1.32804948e+00, -1.29011346e+00, -4.40420160e-01,\n",
-       "         -1.91320854e+00],\n",
-       "        [-1.03175649e+00, -4.49615068e-01,  5.34820114e-01,\n",
-       "          3.36993617e-04, -1.40843313e-01, -1.22657004e+00,\n",
-       "          3.82503086e-01,  7.05558278e-01, -8.14730205e-02,\n",
-       "          9.16947313e-01],\n",
-       "        [-1.69357491e-01,  1.07358766e+00, -1.90090858e+00,\n",
-       "         -4.55947375e-01,  2.03251914e+00, -6.80382404e-01,\n",
-       "         -2.35649358e-01, -2.32739095e-02, -1.25150521e+00,\n",
-       "         -6.05312611e-01],\n",
-       "        [-9.08760587e-01,  2.43106599e+00, -1.44580502e+00,\n",
-       "          1.04744946e+00, -1.35920420e+00, -2.71115195e-01,\n",
-       "         -2.39854115e-01,  7.78360895e-01, -1.09722589e+00,\n",
-       "         -1.15268874e-01],\n",
-       "        [ 9.76881010e-01,  2.68883643e+00, -3.24975958e-01,\n",
-       "         -8.06842598e-01, -5.49401370e-01, -1.43952910e+00,\n",
-       "         -7.45745334e-02,  7.37760401e-02,  1.04568257e+00,\n",
-       "          7.82707287e-01],\n",
-       "        [ 1.69222347e+00,  7.84248680e-01,  6.59246959e-01,\n",
-       "          1.41868131e+00, -2.55093275e-01,  1.49597414e-01,\n",
-       "         -1.75880840e+00,  1.05808482e+00, -1.83229982e+00,\n",
-       "         -4.17680320e-01],\n",
-       "        [-1.35725745e+00, -5.25880928e-01, -6.08473794e-01,\n",
-       "         -1.52841920e-01,  6.39393526e-01,  3.52195509e-01,\n",
-       "          6.04446580e-01, -1.74781983e+00,  1.73508552e+00,\n",
-       "         -2.42250316e-01],\n",
-       "        [ 1.99530098e-01, -1.77155126e+00, -7.07213902e-01,\n",
-       "         -4.72761521e-01,  5.92876975e-01,  2.67389458e+00,\n",
-       "         -1.51831938e+00,  1.30288115e+00, -4.55473351e-01,\n",
-       "         -1.97223024e+00],\n",
-       "        [-4.37225905e-01,  1.46374001e+00, -2.32583483e+00,\n",
-       "          3.69061132e-01, -6.93579207e-01, -9.72149794e-01,\n",
-       "         -2.07717717e+00, -1.26175902e-03, -6.15444095e-01,\n",
-       "          1.62329265e+00],\n",
-       "        [-1.69541097e+00, -2.16832056e+00,  3.48101501e-01,\n",
-       "          1.06321028e+00, -4.22168000e-01, -5.93960571e-01,\n",
-       "          4.29741616e-01, -9.27936449e-01,  1.12970822e+00,\n",
-       "          1.24338646e-02],\n",
-       "        [-9.19781965e-02, -1.33717565e+00,  8.92016704e-01,\n",
-       "         -1.17763238e+00,  3.84561419e-01, -1.24024158e+00,\n",
-       "          8.89486620e-01, -1.00956651e+00,  5.11035823e-01,\n",
-       "         -1.37453708e+00],\n",
-       "        [-2.74008636e-01,  1.71072575e-01,  7.78803367e-01,\n",
-       "         -1.50740259e+00,  3.68612664e-02, -1.36147897e+00,\n",
-       "          1.21106372e-01, -1.13181057e+00,  2.00434950e-01,\n",
-       "         -8.29565977e-01],\n",
-       "        [ 4.37852105e-01,  5.81100511e-01, -1.20866178e-01,\n",
-       "          2.22737432e+00, -1.19461450e+00,  7.57124910e-02,\n",
-       "          8.84631171e-01,  1.30825341e+00,  8.21468578e-01,\n",
-       "          2.03324210e+00],\n",
-       "        [ 6.79502654e-01,  9.29875055e-01, -2.93311345e-01,\n",
-       "         -8.75088857e-01, -8.25360972e-01,  1.18442005e-01,\n",
-       "          2.12524452e+00,  7.67879289e-01, -1.43858559e+00,\n",
-       "         -6.43196980e-01],\n",
-       "        [-2.18751942e+00, -5.56348525e-01, -5.14506590e-01,\n",
-       "          2.91135315e-01,  3.32739559e-01,  9.48011583e-01,\n",
-       "         -6.88693887e-01,  5.38616450e-02,  4.45741015e-01,\n",
-       "          1.23069340e+00],\n",
-       "        [ 8.55213358e-01,  3.65347132e-02,  6.94298968e-01,\n",
-       "          4.01009366e-01,  4.46410395e-01,  9.36488498e-01,\n",
-       "         -1.94413269e+00,  2.93608443e-01, -6.33947867e-01,\n",
-       "         -1.94314919e-01],\n",
-       "        [-5.28696387e-02, -1.03806503e+00,  7.26542237e-01,\n",
-       "          9.30517222e-01, -1.01358069e+00, -5.52573275e-01,\n",
-       "          5.72649372e-01,  1.04461535e+00,  1.31734552e-01,\n",
-       "          4.47659649e-01],\n",
-       "        [ 4.55887740e-02,  1.74668844e-01,  9.95158254e-01,\n",
-       "          2.29885559e+00, -7.16418753e-01,  6.66169558e-01,\n",
-       "         -1.10431242e+00,  1.50949008e-01, -7.07084426e-01,\n",
-       "          3.14615809e-01],\n",
-       "        [-1.23419998e+00,  1.41173145e-02,  1.27841932e+00,\n",
-       "         -1.21467892e+00, -1.78769952e-01, -8.74227333e-01,\n",
-       "          8.99781420e-01,  6.84570766e-02,  2.31322046e+00,\n",
-       "         -1.27958653e+00],\n",
-       "        [-7.55721916e-02,  3.81398189e-01, -6.95703267e-01,\n",
-       "          1.79565801e-01, -1.99418664e+00,  1.03922105e+00,\n",
-       "         -3.81260082e-01,  8.98086236e-01, -1.34892603e+00,\n",
-       "          4.24810557e-01],\n",
-       "        [ 6.38429428e-01,  6.25171689e-01, -1.32450927e+00,\n",
-       "         -8.81213752e-02,  3.71802554e-01, -7.14984918e-01,\n",
-       "          7.40837763e-01, -1.39655176e+00, -1.99782703e+00,\n",
-       "         -3.55353746e-01],\n",
-       "        [ 2.32164403e-01, -1.65061442e+00, -8.35694949e-01,\n",
-       "         -8.30995372e-01, -8.00004191e-01,  4.66901249e-01,\n",
-       "          1.01335621e+00, -8.49124563e-01,  1.46092327e+00,\n",
-       "         -6.78852647e-01],\n",
-       "        [-2.59439171e-01, -2.74208308e-01,  3.73126483e-01,\n",
-       "          1.74528040e+00,  4.63748543e-01,  1.61530668e-01,\n",
-       "          1.00307769e+00,  1.53883761e+00,  9.19122469e-01,\n",
-       "         -1.36805642e+00],\n",
-       "        [-5.07676899e-01, -1.67429169e+00, -1.75293725e+00,\n",
-       "         -4.10181630e-01, -4.57875420e-01, -1.41516688e+00,\n",
-       "         -6.33461963e-02,  9.58797265e-01,  1.39719756e+00,\n",
-       "         -1.05403880e+00],\n",
-       "        [-6.03902397e-02,  3.53208226e-02, -3.97380319e-02,\n",
-       "          1.91468551e-01,  1.15930937e-01, -4.78184362e-01,\n",
-       "          9.79270308e-01, -1.78584267e-01, -6.01530828e-01,\n",
-       "         -1.18059091e+00],\n",
-       "        [ 8.62658878e-01, -3.65073078e-01,  9.61318735e-01,\n",
-       "          2.69892736e-01, -2.78238352e-01,  1.18740556e+00,\n",
-       "         -4.88855158e-01,  5.89770882e-01, -3.15202741e-01,\n",
-       "          1.50100084e-01],\n",
-       "        [ 1.43239369e+00, -6.75422241e-01, -8.83205818e-01,\n",
-       "         -1.02627945e+00,  1.27791136e+00,  1.26261030e+00,\n",
-       "         -1.40427085e+00,  9.83461067e-02,  1.06761097e-01,\n",
-       "         -1.01118002e+00],\n",
-       "        [ 1.66668745e+00,  2.16838060e-01,  1.70317803e+00,\n",
-       "         -1.59399249e-01,  2.57295390e-01,  4.24534681e-01,\n",
-       "          3.61938374e-01,  1.93442733e+00, -2.33342743e-01,\n",
-       "          1.21813597e+00],\n",
-       "        [-4.44889815e-01,  1.23274663e-01,  8.13901078e-02,\n",
-       "          5.66641637e-01,  5.57552043e-01,  1.54831800e-01,\n",
-       "         -1.85862949e-01,  1.21949296e-01, -6.52237630e-01,\n",
-       "          1.10397599e+00],\n",
-       "        [ 5.54953783e-01, -9.19895809e-01, -1.24446520e+00,\n",
-       "         -3.42393938e-01,  1.50837328e-02,  2.52920507e+00,\n",
-       "          2.93113658e-01,  2.87756340e+00, -1.51114545e+00,\n",
-       "         -1.14839327e+00],\n",
-       "        [-8.41941133e-02, -2.42281185e-02, -6.75722080e-01,\n",
-       "          1.91362209e+00,  1.08311081e-01, -1.37549597e+00,\n",
-       "          1.50756898e+00, -4.93263924e-01, -2.91205715e-01,\n",
-       "          1.12862265e+00],\n",
-       "        [ 6.32417127e-01, -8.34339311e-01,  1.13352188e+00,\n",
-       "         -1.62440645e+00, -6.62227583e-01,  3.18442499e-01,\n",
-       "          4.10140289e-01,  6.56469850e-01, -1.48350702e+00,\n",
-       "         -7.96554520e-01],\n",
-       "        [ 4.73709147e-01,  1.66392973e-01, -4.22884109e-01,\n",
-       "          1.07006015e+00,  1.86204515e-01, -4.91995611e-01,\n",
-       "          9.19678715e-01, -1.86082898e+00,  1.67526271e+00,\n",
-       "          6.48745957e-01],\n",
-       "        [-2.16257351e-01,  6.28007942e-01, -1.19820773e+00,\n",
-       "          1.38666908e+00,  3.67625956e-01,  6.38445042e-02,\n",
-       "         -1.28772823e+00,  6.08568478e-01,  5.17268272e-01,\n",
-       "         -8.81397800e-01],\n",
-       "        [-1.41233011e+00,  2.02845134e+00,  6.21418282e-02,\n",
-       "          4.54198207e-01,  5.53685653e-02, -4.39071782e-01,\n",
-       "          1.45333565e+00,  1.31479257e+00,  8.52076251e-01,\n",
-       "          7.62933973e-01],\n",
-       "        [-5.09924963e-01, -1.48752362e+00, -1.98465846e-01,\n",
-       "          6.28761397e-01, -1.65476093e+00,  1.26159585e+00,\n",
-       "          2.70601980e-01,  4.84071147e-01, -9.24802576e-01,\n",
-       "         -3.04959358e-01],\n",
-       "        [-5.95226138e-01, -2.76089580e+00,  7.30074327e-01,\n",
-       "          1.04632870e+00, -1.24484393e+00,  3.80187727e-01,\n",
-       "         -4.58700026e-01, -5.44365475e-01,  1.30761716e+00,\n",
-       "         -8.98658002e-01],\n",
-       "        [-1.63059294e+00,  7.10773858e-02,  3.98240507e-01,\n",
-       "         -9.87495699e-01,  2.31283287e-01, -2.68634588e-01,\n",
-       "         -5.03805220e-01, -6.17259820e-02, -4.63253270e-01,\n",
-       "          2.56217132e-01],\n",
-       "        [ 9.60205300e-01, -6.43222915e-01,  7.53599151e-02,\n",
-       "         -1.25994108e+00, -1.07117464e+00,  4.68923558e-01,\n",
-       "          8.72214869e-01,  3.19479704e-01, -3.01781761e-01,\n",
-       "         -1.30722569e+00],\n",
-       "        [-5.54961795e-01,  7.87528473e-01,  2.82644181e-01,\n",
-       "         -1.89145182e+00,  7.77918010e-01,  6.66495282e-01,\n",
-       "         -5.86238717e-01, -6.30334086e-01,  1.46393799e+00,\n",
-       "         -1.14815002e-01],\n",
-       "        [ 6.77002491e-01, -5.11973128e-01,  1.23920948e+00,\n",
-       "          1.05362430e-01, -1.58393808e+00, -1.57227206e+00,\n",
-       "         -1.74551767e-01, -2.81449442e-01, -1.65640205e+00,\n",
-       "         -1.20255748e+00],\n",
-       "        [ 1.90375425e+00, -1.19555469e-01, -4.37925201e-01,\n",
-       "          8.44503164e-01,  2.08969304e+00, -1.25474512e+00,\n",
-       "          1.41465231e-01,  9.26006060e-01,  1.59888646e+00,\n",
-       "         -5.95978920e-01],\n",
-       "        [-3.24207328e-01,  4.86500134e-01, -6.76480375e-01,\n",
-       "          2.26130525e-01, -6.15345117e-01,  6.54834817e-02,\n",
-       "         -1.42984779e-01, -8.36999622e-02,  8.84107061e-01,\n",
-       "         -1.02603373e+00],\n",
-       "        [-2.64882660e-01, -4.40699536e-01,  1.72517846e-01,\n",
-       "         -3.62986569e-01,  4.84878432e-01,  1.83722981e+00,\n",
-       "          1.94307732e+00, -4.38257195e-01,  1.30781882e+00,\n",
-       "         -2.49522188e+00],\n",
-       "        [-1.47623480e-01, -1.64703733e+00,  1.10724362e+00,\n",
-       "          3.74858632e-01, -3.93383510e-02,  3.86893144e-01,\n",
-       "          6.02483310e-01, -6.90875169e-01, -4.31657529e-01,\n",
-       "         -3.75944493e-01],\n",
-       "        [ 3.10891741e-01, -1.08116960e+00, -4.61436545e-01,\n",
-       "         -1.61720926e+00, -8.17301882e-03, -1.73894897e+00,\n",
-       "          3.38293048e-01,  5.88711757e-01,  2.71860544e-01,\n",
-       "          1.31250692e+00],\n",
-       "        [ 2.73464697e-01,  1.75443213e+00,  1.13883215e+00,\n",
-       "         -1.65538679e+00,  3.23708587e-01, -2.42125767e-01,\n",
-       "          1.93370225e+00, -2.82792186e-01,  1.94500202e+00,\n",
-       "          1.23085931e+00],\n",
-       "        [-5.48973992e-01,  1.09335900e-02, -6.37231937e-01,\n",
-       "          9.90415598e-01,  2.19661135e-01,  1.62474638e+00,\n",
-       "          3.47856768e-01,  1.36701811e+00,  1.44433659e+00,\n",
-       "         -6.47142556e-01],\n",
-       "        [ 1.78613739e+00,  9.00024331e-02,  1.06606040e+00,\n",
-       "         -6.60810228e-01,  7.16864136e-01, -1.70768922e+00,\n",
-       "         -1.03758632e+00,  6.53243423e-01, -1.41149236e+00,\n",
-       "         -3.69828335e-02],\n",
-       "        [-3.50335360e-01, -2.33665669e-01,  2.41740510e-01,\n",
-       "         -3.73519355e-01,  1.01444607e+00,  1.01336379e+00,\n",
-       "         -1.92249752e+00, -1.37620287e+00,  9.71286335e-01,\n",
-       "         -5.99970937e-01],\n",
-       "        [-1.27689552e+00, -8.86120095e-01, -9.37289777e-01,\n",
-       "         -1.24423644e+00, -7.01017427e-01, -8.93101153e-01,\n",
-       "         -5.87879020e-01,  3.93849626e-01,  7.97826724e-01,\n",
-       "          1.13041301e-01],\n",
-       "        [-2.27122717e-01,  4.35117298e-01, -8.03049083e-01,\n",
-       "         -7.09045648e-01,  9.04605736e-02, -7.25896579e-01,\n",
-       "          4.76077863e-01,  5.25363034e-01,  1.50803002e+00,\n",
-       "          7.86827792e-01],\n",
-       "        [-9.81511280e-01,  9.44056536e-02, -4.77823118e-01,\n",
-       "         -1.56822328e+00,  9.05065579e-01, -6.34660424e-01,\n",
-       "         -1.42216585e+00,  7.27984861e-01,  4.42795959e-02,\n",
-       "          5.32914097e-01],\n",
-       "        [-1.18374060e+00, -1.36220546e+00,  5.76558794e-01,\n",
-       "          5.12723418e-01, -1.87575874e-01,  1.34948870e-02,\n",
-       "          3.20417841e-01, -1.15516651e-01, -1.75956195e+00,\n",
-       "          2.23860003e-01],\n",
-       "        [ 2.57090331e+00, -2.68671537e-01,  1.34439770e+00,\n",
-       "         -7.26580876e-01, -1.97230177e+00,  1.57041561e+00,\n",
-       "          2.16410585e+00,  2.44025065e-01,  1.19368866e+00,\n",
-       "          1.42237715e-01],\n",
-       "        [-2.84652907e+00, -5.06675944e-01,  7.31070862e-02,\n",
-       "          1.10963553e-01, -8.77335131e-02,  4.99830135e-01,\n",
-       "          2.52189814e-02, -3.32015035e-01,  6.25770357e-01,\n",
-       "         -1.63888016e+00],\n",
-       "        [-5.82479999e-01,  6.76817539e-01,  2.74639666e-01,\n",
-       "         -4.93293500e-01,  9.35053430e-01, -8.90550823e-01,\n",
-       "          2.71304150e-01,  3.40857367e-01, -1.29501004e+00,\n",
-       "          2.47541795e-01],\n",
-       "        [-2.45654468e+00,  2.37181235e-02,  6.51187098e-01,\n",
-       "          1.05267550e+00,  4.57113221e-01,  6.15416697e-01,\n",
-       "         -5.74370078e-01, -1.18934049e-01, -6.30324085e-01,\n",
-       "         -1.57319466e+00],\n",
-       "        [-4.38533365e-01,  7.98540480e-02, -2.13015348e-01,\n",
-       "         -7.25232999e-01, -3.95294271e-01, -4.74844272e-01,\n",
-       "         -5.69186334e-01, -6.06872082e-01, -6.94957496e-01,\n",
-       "          5.81286173e-01],\n",
-       "        [ 5.59784964e-01, -1.35347789e+00, -6.67933087e-01,\n",
-       "          3.01171031e-01,  6.57507089e-01,  1.18317806e+00,\n",
-       "          6.62733769e-01, -3.07750327e-01,  5.36064539e-02,\n",
-       "          7.53559053e-01],\n",
-       "        [-5.83498573e-01, -5.53853657e-01, -3.62989878e-01,\n",
-       "         -1.04179207e+00, -8.65015994e-01,  3.83411981e-01,\n",
-       "          5.68314299e-01, -2.58470428e-01,  1.24011058e+00,\n",
-       "          2.34506174e-01],\n",
-       "        [ 5.15792329e-01, -1.02202645e+00,  1.86093598e+00,\n",
-       "          2.36170824e+00,  1.12937223e+00,  1.25417614e-01,\n",
-       "          1.22609495e+00, -8.38369276e-01,  1.97014226e+00,\n",
-       "         -1.15562632e+00],\n",
-       "        [-2.19709483e+00,  4.88648113e-01, -3.91745172e-01,\n",
-       "          7.78407043e-01,  2.88874777e-01,  1.58882594e+00,\n",
-       "         -2.90998045e-01, -3.30421169e-01, -5.08724174e-01,\n",
-       "          1.32399727e-01],\n",
-       "        [-2.34788392e-01, -1.66596106e+00,  8.41870402e-01,\n",
-       "         -6.63390234e-01, -7.72594824e-01,  3.67716288e-01,\n",
-       "          5.13232547e-01, -1.79669423e-01, -6.42525756e-01,\n",
-       "         -5.72014235e-01],\n",
-       "        [-7.67326905e-01, -6.22097049e-01,  1.46808679e+00,\n",
-       "         -1.14755754e+00, -3.42652896e-01, -2.37499333e-01,\n",
-       "         -1.52760445e+00,  5.59010889e-01, -5.59476628e-02,\n",
-       "         -1.02757495e+00],\n",
-       "        [ 5.63141856e-01,  9.05375112e-01,  5.58646189e-01,\n",
-       "          1.67805341e+00, -3.01511406e-01,  3.77056669e-02,\n",
-       "          7.03050171e-01, -6.84936371e-01, -6.33543928e-01,\n",
-       "          4.31328894e-01],\n",
-       "        [-8.74287181e-01, -1.78504928e+00, -8.67224313e-02,\n",
-       "         -5.01335414e-01, -1.23171819e+00, -2.41733732e-01,\n",
-       "         -1.29476688e+00,  7.16634092e-01,  5.72720514e-01,\n",
-       "         -4.39064463e-01],\n",
-       "        [ 1.16317872e+00,  9.59334520e-01,  6.21759792e-01,\n",
-       "          4.04351665e-01,  3.60062736e-01,  1.75995045e-01,\n",
-       "          3.77543469e-01,  2.88721614e+00, -1.72769065e+00,\n",
-       "         -1.30733756e+00],\n",
-       "        [-6.85922309e-01,  4.65925029e-01,  6.67969402e-01,\n",
-       "         -7.78108897e-01,  6.75189634e-01, -9.59453214e-01,\n",
-       "         -7.83797166e-01,  1.02112536e+00, -1.85701136e-01,\n",
-       "         -1.27023042e+00],\n",
-       "        [ 3.82942461e-02, -6.40738742e-01, -4.79869082e-01,\n",
-       "          1.42578267e+00, -2.20432860e-01, -1.26494088e+00,\n",
-       "          1.59007142e+00,  7.14889688e-01, -9.99701627e-01,\n",
-       "          5.27909082e-01],\n",
-       "        [-1.62475413e-01,  1.80862594e-02, -5.22735224e-01,\n",
-       "         -3.19810894e-01, -1.43247703e+00,  1.71379232e+00,\n",
-       "         -5.72453082e-01,  1.17027404e+00, -1.65583331e-01,\n",
-       "         -3.80899512e-01],\n",
-       "        [-1.00103166e+00,  7.03163448e-01, -9.14281754e-02,\n",
-       "          3.25896868e-02, -9.34627286e-03, -5.60203122e-01,\n",
-       "         -4.61706651e-01,  3.02304784e-02,  3.52568180e-01,\n",
-       "          7.47981111e-01],\n",
-       "        [-4.14750378e-01, -1.78069148e+00,  6.55757164e-01,\n",
-       "         -1.08236058e+00, -8.90261855e-01,  1.40437926e+00,\n",
-       "          1.43227394e+00, -1.33193889e+00, -2.46564746e-01,\n",
-       "          8.73371718e-01],\n",
-       "        [ 2.00601658e+00,  3.81683510e-01,  1.19007458e+00,\n",
-       "         -1.53142651e+00, -2.04380016e-01,  2.02427908e+00,\n",
-       "         -5.70265069e-01,  8.24186580e-01, -7.24222246e-01,\n",
-       "          1.04562849e+00],\n",
-       "        [-6.27434641e-02, -3.47284617e-01,  8.95485937e-01,\n",
-       "          1.24410666e+00, -8.22204149e-02,  3.13868100e-03,\n",
-       "         -8.22516989e-01, -2.50608750e-01,  4.34933052e-01,\n",
-       "         -9.83557529e-01],\n",
-       "        [ 1.15403027e-01,  8.44734100e-01, -6.47078740e-01,\n",
-       "         -4.41572979e-01, -1.49165919e+00, -4.69626531e-01,\n",
-       "         -1.49869418e+00, -1.14437644e+00, -2.00079662e+00,\n",
-       "          4.32322164e-01],\n",
-       "        [ 5.98725703e-01,  5.41534704e-01,  3.08026285e-01,\n",
-       "          8.92484300e-01,  6.51515829e-01,  7.13165123e-01,\n",
-       "          1.57451665e+00, -1.72914306e-01, -1.71379432e+00,\n",
-       "         -1.03974067e-01],\n",
-       "        [ 1.68088691e-01, -6.04036096e-01,  1.35364567e+00,\n",
-       "         -1.74295979e+00,  1.02505748e+00, -7.69718953e-01,\n",
-       "         -3.52259992e-01,  1.06205242e+00, -1.22388364e+00,\n",
-       "         -4.51613604e-02],\n",
-       "        [-7.54305284e-02,  1.48184506e+00, -3.38212438e-02,\n",
-       "         -1.63011706e+00, -4.25586653e-01,  1.86972047e-01,\n",
-       "          2.84369376e-01, -1.08079244e+00, -9.84287496e-01,\n",
-       "         -6.10975353e-01],\n",
-       "        [ 7.12802972e-01, -4.89443940e-01, -3.80283508e-01,\n",
-       "          9.55458038e-02,  1.32363691e-01, -2.32220544e-01,\n",
-       "          1.44686536e-01, -3.55532680e-01, -3.22581836e-01,\n",
-       "         -1.08189151e+00],\n",
-       "        [ 9.68526865e-01,  4.52805377e-02, -4.75847922e-01,\n",
-       "         -1.11536858e-01, -2.53813537e-01, -7.97896493e-01,\n",
-       "         -9.82417866e-01, -1.27333134e+00, -5.34843958e-01,\n",
-       "         -5.17905951e-02],\n",
-       "        [ 4.57408303e-02, -1.13200690e+00, -5.76430396e-01,\n",
-       "         -4.03125387e-01, -1.23968589e-01, -1.17913451e+00,\n",
-       "          1.90922718e-01, -1.26507180e+00,  1.07226062e-01,\n",
-       "         -1.81417687e+00]]])
        • c
          (chain, draw, c1, c2)
          float64
          0.2361 0.1921 ... -0.722 -0.6994
          array([[[[ 0.23612495,  0.19209144,  1.73643296,  0.32152596],\n",
-       "         [ 0.6624001 , -0.70653542, -0.38179504, -0.40551882],\n",
-       "         [-0.57635498,  0.63250108, -0.32027305, -1.38916566]],\n",
-       "\n",
-       "        [[-1.11897017,  0.58815242, -0.58968597, -0.65490374],\n",
-       "         [ 0.04078477,  0.11616236, -0.60324171, -0.30388797],\n",
-       "         [ 1.45114249,  0.08002404, -0.72766003,  0.66437293]],\n",
-       "\n",
-       "        [[ 0.34177152,  0.47166011,  0.23152757,  0.84419889],\n",
-       "         [ 0.02070863,  2.98960753, -1.6101033 ,  1.00311769],\n",
-       "         [ 0.15683503, -2.11034956, -0.16731209,  1.07437286]],\n",
-       "\n",
-       "        ...,\n",
-       "\n",
-       "        [[-0.92695133,  0.29513187, -0.88513231, -1.345392  ],\n",
-       "         [-0.02382486, -0.30252184, -0.60326201, -0.30289022],\n",
-       "         [ 0.16873816,  0.49046628, -1.06360509,  0.0283932 ]],\n",
-       "\n",
-       "        [[-0.28788494, -0.59228032, -0.85666178, -0.57441519],\n",
-       "         [-0.05765098,  0.79718082, -0.77759788, -0.04799847],\n",
-       "         [ 1.37334172,  0.36906373, -0.13126941,  1.20878831]],\n",
-       "\n",
-       "        [[ 0.12294222, -0.53413433,  1.70802865, -0.2050028 ],\n",
-       "         [-1.09157998,  0.56964328, -0.66168397, -2.51403759],\n",
-       "         [-0.17579789,  2.12954436, -0.72202585, -0.69935395]]]])
      • created_at :
        2020-06-06T18:07:29.430311
        arviz_version :
        0.8.3
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+       "        47.58131023, 47.35505519, 49.34510366, 47.79901223, 48.78190247,\n",
+       "        53.75707512, 57.16968787, 56.04409832, 53.53409675, 51.73426193,\n",
+       "        53.97025567, 54.6659698 , 50.90502133, 53.46112004, 54.9131104 ,\n",
+       "        53.58281666, 47.6137892 , 49.66865372, 51.79368312, 52.97586803,\n",
+       "        54.06587743, 52.84948596, 49.38713791, 49.96868303, 51.17021652,\n",
+       "        50.3754493 , 50.46370916, 50.09532477, 46.00210641, 50.89368877,\n",
+       "        51.3223438 , 51.51596196, 49.78686739, 48.94750652, 49.42668932,\n",
+       "        47.5917692 , 46.59456056, 46.48056362, 49.48693697, 52.06484022,\n",
+       "        53.24578392, 55.58235295, 54.13169145, 48.01040076, 49.31810707,\n",
+       "        48.23203161, 46.91997346, 49.38003973, 52.93573679, 51.59984644,\n",
+       "        47.93267682, 46.30921802, 48.47428137, 52.78125762, 51.56404377,\n",
+       "        47.99970565, 51.6458572 , 54.92875432, 53.37796678, 55.21091153,\n",
+       "        54.01117321, 52.99566052, 54.34005624, 53.84314536, 54.8040249 ,\n",
+       "        56.86697616, 58.90840456, 51.56423102, 46.98960282, 50.78929217,\n",
+       "        46.60502672, 48.5518283 , 50.35946272, 51.4900377 , 49.49761424,\n",
+       "        50.63699342, 49.9825039 , 52.37868558, 50.77370041, 50.85563993,\n",
+       "        49.12531018, 45.66992718, 47.44408651, 49.12136867, 52.887772  ,\n",
+       "        55.65924773, 54.59493642, 49.28925407, 55.37497467, 54.8249429 ,\n",
+       "        51.4603605 , 55.59873408, 53.71164861, 52.4864806 , 56.20238893,\n",
+       "        53.14236159, 52.86268777, 47.26358197, 48.85571425, 51.57406957,\n",
+       "        50.12820899, 46.58114333, 48.4308065 , 49.36488741, 51.51175973,\n",
+       "        52.15564435, 54.53638271, 51.62695214, 55.93975001, 52.79303002,\n",
+       "        51.2957646 , 54.69912894, 52.91242272, 47.38469588, 50.60785358,\n",
+       "        47.95949667, 48.71518855, 48.1594468 , 46.50873061, 51.51031032,\n",
+       "        51.30049846, 50.44937873, 54.15149263, 52.56008691, 54.29206958,\n",
+       "        54.81863257, 53.21781717, 49.41075476, 48.87790719, 48.7661371 ,\n",
+       "        54.6411877 , 54.67886209, 50.13460669, 50.58819395, 46.52766041]])
          • step_size_bar
            (chain, draw)
            float64
            0.6188 0.6188 ... 0.4424 0.4424
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+       "        0.44239193, 0.44239193, 0.44239193, 0.44239193, 0.44239193,\n",
+       "        0.44239193, 0.44239193, 0.44239193, 0.44239193, 0.44239193,\n",
+       "        0.44239193, 0.44239193, 0.44239193, 0.44239193, 0.44239193,\n",
+       "        0.44239193, 0.44239193, 0.44239193, 0.44239193, 0.44239193,\n",
+       "        0.44239193, 0.44239193, 0.44239193, 0.44239193, 0.44239193,\n",
+       "        0.44239193, 0.44239193, 0.44239193, 0.44239193, 0.44239193,\n",
+       "        0.44239193, 0.44239193, 0.44239193, 0.44239193, 0.44239193,\n",
+       "        0.44239193, 0.44239193, 0.44239193, 0.44239193, 0.44239193,\n",
+       "        0.44239193, 0.44239193, 0.44239193, 0.44239193, 0.44239193,\n",
+       "        0.44239193, 0.44239193, 0.44239193, 0.44239193, 0.44239193,\n",
+       "        0.44239193, 0.44239193, 0.44239193, 0.44239193, 0.44239193,\n",
+       "        0.44239193, 0.44239193, 0.44239193, 0.44239193, 0.44239193,\n",
+       "        0.44239193, 0.44239193, 0.44239193, 0.44239193, 0.44239193,\n",
+       "        0.44239193, 0.44239193, 0.44239193, 0.44239193, 0.44239193,\n",
+       "        0.44239193, 0.44239193, 0.44239193, 0.44239193, 0.44239193,\n",
+       "        0.44239193, 0.44239193, 0.44239193, 0.44239193, 0.44239193,\n",
+       "        0.44239193, 0.44239193, 0.44239193, 0.44239193, 0.44239193,\n",
+       "        0.44239193, 0.44239193, 0.44239193, 0.44239193, 0.44239193]])
          • mean_tree_accept
            (chain, draw)
            float64
            1.0 0.4507 1.0 ... 0.9712 0.9935
            array([[1.00000000e+000, 4.50671723e-001, 1.00000000e+000,\n",
+       "        9.84083312e-001, 8.84419505e-001, 9.96869804e-001,\n",
+       "        9.89628920e-001, 8.62568351e-001, 9.85472261e-001,\n",
+       "        4.22377986e-001, 9.60298065e-001, 9.87207800e-001,\n",
+       "        8.80383510e-001, 8.87996630e-001, 7.75787976e-001,\n",
+       "        1.00000000e+000, 9.87113252e-001, 9.80601135e-001,\n",
+       "        8.47726582e-001, 7.19778758e-001, 8.97619585e-001,\n",
+       "        1.44855769e-001, 2.98298347e-001, 8.13732846e-001,\n",
+       "        8.90753945e-001, 8.64690685e-001, 7.39525913e-001,\n",
+       "        1.00000000e+000, 1.95368541e-001, 9.34322125e-001,\n",
+       "        7.07784318e-001, 9.96811249e-001, 7.17830804e-001,\n",
+       "        7.66351914e-001, 8.00000000e-001, 9.08383474e-001,\n",
+       "        3.10494942e-001, 9.46607023e-001, 5.71799413e-001,\n",
+       "        8.35468821e-001, 9.27596939e-001, 8.15150633e-001,\n",
+       "        7.99626122e-001, 8.77026121e-001, 8.36727408e-001,\n",
+       "        9.50774005e-001, 8.76728496e-001, 8.19433334e-001,\n",
+       "        8.46216826e-001, 9.42292417e-001, 9.66800038e-001,\n",
+       "        9.62162967e-001, 9.20921497e-001, 9.61621646e-001,\n",
+       "        5.89152318e-001, 7.73985416e-001, 1.22278222e-001,\n",
+       "        8.68832959e-001, 6.99187343e-001, 3.19866499e-001,\n",
+       "        1.00000000e+000, 9.97600297e-001, 7.77182386e-001,\n",
+       "        2.59944451e-001, 5.68861903e-001, 9.16701726e-001,\n",
+       "        9.86549744e-001, 9.37861781e-001, 9.81375571e-001,\n",
+       "        9.77928892e-001, 8.58959975e-001, 6.76048337e-001,\n",
+       "        1.00000000e+000, 1.00000000e+000, 8.70688968e-001,\n",
+       "        7.57139345e-001, 8.76219567e-001, 7.81938392e-001,\n",
+       "        9.67370992e-001, 4.01845234e-001, 7.53170862e-001,\n",
+       "        2.06395750e-001, 2.27143585e-001, 6.77104433e-001,\n",
+       "        8.03076407e-001, 1.74702462e-001, 9.63956516e-001,\n",
+       "        8.34690209e-001, 9.90291582e-001, 9.64500535e-001,\n",
+       "        6.65754591e-001, 1.00000000e+000, 9.49074287e-001,\n",
+       "        7.64016193e-001, 8.88452168e-001, 7.72289168e-004,\n",
+       "        3.78975853e-001, 1.00000000e+000, 9.98932534e-001,\n",
+       "        1.00000000e+000, 9.22143807e-001, 9.13241055e-001,\n",
+       "        6.17160688e-001, 7.03931542e-001, 1.00000000e+000,\n",
+       "        9.05598007e-001, 2.13271537e-001, 6.65571431e-001,\n",
+       "        9.58631636e-001, 8.83742041e-001, 7.00838976e-001,\n",
+       "        9.87005883e-001, 9.49941455e-002, 7.32720363e-001,\n",
+       "        9.75465234e-001, 9.59589061e-001, 8.78219426e-001,\n",
+       "        9.17940187e-001, 7.81140101e-001, 9.55217300e-001,\n",
+       "        9.72267438e-001, 7.42907295e-001, 9.55242132e-001,\n",
+       "        7.54853059e-001, 7.66691527e-001, 3.95807223e-001,\n",
+       "        7.16931577e-001, 3.33333344e-001, 8.48135430e-001,\n",
+       "        9.48288394e-001, 4.50901311e-001, 7.41452166e-001,\n",
+       "        8.21182100e-001, 8.26807734e-005, 9.17452907e-001,\n",
+       "        5.40610957e-001, 5.64220724e-001, 9.90480745e-001,\n",
+       "        6.01008509e-001, 9.94880841e-001, 9.14121231e-001,\n",
+       "        9.57448799e-001, 7.59538542e-001, 9.52245858e-001,\n",
+       "        1.00000000e+000, 1.00000000e+000, 6.12941633e-001,\n",
+       "        9.55675314e-001, 9.69718081e-001, 8.07065328e-001,\n",
+       "        4.80768611e-001, 8.96461457e-001, 1.00000000e+000,\n",
+       "        3.99338612e-001, 7.13970275e-001, 1.29436834e-011,\n",
+       "        8.73258729e-001, 8.70370774e-001, 4.20136084e-001,\n",
+       "        6.66701222e-001, 9.52688850e-001, 6.57443917e-001,\n",
+       "        6.39412890e-001, 1.00000000e+000, 9.95230053e-001,\n",
+       "        6.33687018e-001, 7.55737631e-001, 1.00000000e+000,\n",
+       "        4.67519771e-001, 4.10500135e-001, 8.35688876e-001,\n",
+       "        1.00000000e+000, 8.50633828e-001, 7.05288528e-001,\n",
+       "        1.00000000e+000, 1.38200845e-005, 4.86009483e-001,\n",
+       "        4.85890295e-001, 1.00000000e+000, 9.96470277e-001,\n",
+       "        8.77537058e-001, 2.58783685e-001, 8.54447696e-001,\n",
+       "        7.74073657e-001, 7.36414797e-001, 9.19091212e-001,\n",
+       "        9.91502832e-001, 6.59522682e-001, 9.30283010e-001,\n",
+       "        6.17819960e-001, 9.07252703e-001, 8.23389992e-001,\n",
+       "        5.01490111e-001, 9.97813274e-001, 9.26167843e-001,\n",
+       "        8.68158953e-001, 6.20009221e-001, 7.73979600e-001,\n",
+       "        8.92658329e-001, 9.25532135e-001, 9.51023751e-001,\n",
+       "        8.81941312e-001, 4.23158316e-001, 1.00000000e+000,\n",
+       "        3.33608805e-001, 9.67503260e-001, 9.23276152e-001,\n",
+       "        8.47027708e-001, 8.11431104e-001, 1.05334062e-001,\n",
+       "        8.48991013e-001, 9.01161571e-001, 6.67041137e-030,\n",
+       "        3.46777275e-001, 9.75639834e-001, 8.26503208e-001,\n",
+       "        5.64394535e-001, 5.74491901e-001, 8.92280796e-001,\n",
+       "        6.62988721e-001, 7.11607696e-001, 9.69909351e-001,\n",
+       "        8.53291882e-001, 8.40432080e-001, 9.80572069e-001,\n",
+       "        6.34947916e-001, 7.58665059e-001, 7.77902826e-001,\n",
+       "        6.71078108e-001, 9.92755046e-001, 9.72519584e-001,\n",
+       "        7.86451804e-001, 9.60789790e-001, 9.30288887e-001,\n",
+       "        5.53330859e-001, 9.45643426e-001, 8.81767042e-001,\n",
+       "        6.24053664e-001, 5.61709062e-001, 8.69303260e-001,\n",
+       "        5.72100749e-001, 1.27156246e-001, 8.15237410e-001,\n",
+       "        7.22979254e-001, 7.42548210e-001, 9.66145597e-001,\n",
+       "        9.49306231e-001, 3.02327724e-001, 2.05986821e-001,\n",
+       "        4.15479531e-113, 9.36048496e-001, 5.71429022e-001,\n",
+       "        9.84638724e-001, 1.00000000e+000, 7.94128452e-001,\n",
+       "        7.06333148e-001, 8.79022375e-001, 6.11480800e-001,\n",
+       "        9.62456293e-001, 8.16879115e-001, 8.45182062e-001,\n",
+       "        7.80040481e-001, 9.96917540e-001, 9.77809928e-001,\n",
+       "        9.33694479e-001, 4.88478242e-001, 3.64271398e-001,\n",
+       "        9.93172465e-001, 8.99783819e-001, 9.44656195e-001,\n",
+       "        1.00000000e+000, 4.18225817e-001, 9.39147402e-001,\n",
+       "        9.98325171e-001, 9.92190588e-001, 9.22909622e-001,\n",
+       "        6.66680015e-001, 2.04084007e-144, 9.94242418e-001,\n",
+       "        8.75598684e-001, 9.18659502e-001, 5.23028335e-001,\n",
+       "        1.00000000e+000, 5.36780888e-001, 9.86908929e-001,\n",
+       "        1.00000000e+000, 8.36078899e-001, 8.44889652e-001,\n",
+       "        1.00000000e+000, 4.01053932e-001, 8.69714428e-001,\n",
+       "        9.73215226e-001, 4.06413941e-001, 9.63590292e-001,\n",
+       "        1.00000000e+000, 8.15739168e-001, 3.71454300e-001,\n",
+       "        4.23010721e-001, 7.80459281e-001, 9.73610442e-001,\n",
+       "        9.71733281e-001, 9.35756858e-001, 2.97398744e-001,\n",
+       "        8.09662011e-001, 0.00000000e+000, 7.94206951e-001,\n",
+       "        8.17521344e-001, 7.44799627e-001, 5.48189033e-001,\n",
+       "        4.31944676e-001, 5.91753103e-001, 8.37820955e-001,\n",
+       "        9.95807876e-001, 5.26001811e-001, 6.18307282e-001,\n",
+       "        4.14025256e-001, 8.74495548e-001, 1.00000000e+000,\n",
+       "        6.79145473e-003, 9.61603593e-001, 1.46970879e-002,\n",
+       "        9.00291538e-001, 5.66389903e-001, 8.24410013e-001,\n",
+       "        6.15006553e-001, 9.34125384e-001, 9.78014054e-001,\n",
+       "        9.50952365e-001, 9.83545650e-001, 7.95749396e-001,\n",
+       "        1.00000000e+000, 3.53488352e-001, 7.95919188e-001,\n",
+       "        9.29582196e-001, 4.63259736e-001, 5.60765398e-001,\n",
+       "        5.53202503e-001, 9.36517087e-001, 7.20815029e-001,\n",
+       "        9.95465870e-001, 9.79275407e-001, 6.65296183e-001,\n",
+       "        9.07598721e-001, 7.09201638e-001, 7.56545069e-001,\n",
+       "        9.00612778e-001, 2.69444132e-001, 1.00000000e+000,\n",
+       "        9.15262486e-001, 5.48248061e-001, 9.81737093e-001,\n",
+       "        9.28301202e-001, 9.95322426e-001, 4.80986802e-001,\n",
+       "        8.86510877e-001, 7.07195011e-002, 2.18797108e-002,\n",
+       "        2.39280459e-001, 7.97667258e-001, 9.04050845e-001,\n",
+       "        8.70564472e-001, 9.85277474e-001, 9.37414117e-200,\n",
+       "        2.03896246e-001, 3.50493970e-001, 8.31993828e-001,\n",
+       "        9.83971378e-001, 5.96992603e-001, 8.92232158e-001,\n",
+       "        2.80851231e-001, 9.47324226e-001, 8.66293637e-001,\n",
+       "        8.81295133e-001, 9.45908378e-001, 8.46658486e-001,\n",
+       "        6.19767691e-001, 7.49522549e-001, 9.80327090e-001,\n",
+       "        9.88546316e-001, 8.84031101e-001, 5.63877442e-001,\n",
+       "        7.92633160e-001, 6.81020441e-001, 5.01605270e-001,\n",
+       "        9.33430129e-001, 9.36099242e-001, 8.48409460e-001,\n",
+       "        7.55103481e-001, 8.50806672e-001, 8.93576734e-001,\n",
+       "        4.28571429e-001, 5.68623651e-001, 7.14589903e-001,\n",
+       "        6.26107042e-001, 8.09981077e-001, 6.99214229e-001,\n",
+       "        9.97959996e-001, 5.75331916e-001, 7.30257784e-001,\n",
+       "        9.58790730e-001, 8.55208017e-002, 9.48941517e-001,\n",
+       "        8.65697598e-001, 7.21974271e-001, 1.00000000e+000,\n",
+       "        7.18166527e-001, 6.83568081e-001, 8.11936353e-001,\n",
+       "        1.00000000e+000, 9.61385365e-001, 9.90541144e-001,\n",
+       "        3.50758890e-001, 9.75599431e-001, 9.19880584e-001,\n",
+       "        5.89994841e-001, 2.85715297e-001, 6.70866776e-001,\n",
+       "        1.00000000e+000, 8.42200328e-001, 7.37511099e-001,\n",
+       "        8.61580599e-001, 8.94830732e-001, 7.01128059e-001,\n",
+       "        8.39940998e-001, 7.95225440e-001, 9.32664761e-001,\n",
+       "        9.73898335e-001, 9.17767053e-001, 8.91759326e-001,\n",
+       "        7.95967919e-001, 4.28574628e-001, 8.35151927e-001,\n",
+       "        9.99743045e-001, 9.31092443e-001, 4.74416965e-002,\n",
+       "        9.23317787e-002, 4.71309845e-001, 8.00232436e-001,\n",
+       "        3.23599222e-001, 1.40316457e-001, 9.47314915e-003,\n",
+       "        9.27785776e-001, 9.10166646e-001, 5.42163153e-001,\n",
+       "        6.36804822e-001, 8.93922920e-001, 9.36443070e-001,\n",
+       "        8.89758487e-001, 9.68676115e-001, 9.14599304e-001,\n",
+       "        6.56402263e-001, 5.58160833e-002, 3.05696154e-001,\n",
+       "        9.40985438e-001, 3.00085387e-001, 1.00000000e+000,\n",
+       "        8.16099861e-001, 9.41663512e-001, 9.71549449e-001,\n",
+       "        8.17213162e-001, 8.59593921e-001, 1.32822206e-067,\n",
+       "        9.90519689e-001, 6.37789275e-001, 4.65003607e-002,\n",
+       "        9.40821903e-001, 8.64879326e-001, 6.48867066e-001,\n",
+       "        8.57064358e-001, 4.80814314e-002, 1.00000000e+000,\n",
+       "        6.55727483e-001, 9.09334279e-001, 5.71775319e-001,\n",
+       "        9.76315830e-001, 6.34754911e-001, 6.88890194e-001,\n",
+       "        9.70758146e-001, 1.00000000e+000, 9.88155708e-001,\n",
+       "        8.85938629e-001, 1.00000000e+000, 5.80785292e-001,\n",
+       "        5.19619974e-001, 6.52424173e-001, 1.00000000e+000,\n",
+       "        9.62115421e-001, 9.68608664e-001, 8.54007281e-001,\n",
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+       "        8.55172793e-001, 1.00000000e+000, 9.87496991e-001,\n",
+       "        9.17409906e-001, 8.07808878e-001],\n",
+       "       [9.91147379e-001, 9.71950756e-001, 9.66164853e-001,\n",
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+       "        8.66020842e-001, 9.85465521e-001, 8.04202840e-001,\n",
+       "        8.24399544e-001, 9.83007729e-001, 9.70058188e-001,\n",
+       "        1.00000000e+000, 9.90254104e-001, 9.91234813e-001,\n",
+       "        5.31369822e-001, 2.24718517e-001, 8.90469000e-001,\n",
+       "        8.77280044e-001, 1.00000000e+000, 9.98248406e-001,\n",
+       "        9.64262814e-001, 9.91230697e-001, 2.87910465e-001,\n",
+       "        9.96243598e-001, 9.57159268e-001, 8.43540065e-001,\n",
+       "        1.00000000e+000, 9.43788897e-001, 9.85818169e-001,\n",
+       "        6.61630686e-001, 9.58659755e-001, 9.89498599e-001,\n",
+       "        9.56030570e-001, 8.88295817e-001, 8.76657314e-001,\n",
+       "        9.95898473e-001, 9.24642411e-001, 9.51207291e-001,\n",
+       "        9.98846374e-001, 8.32524309e-001, 9.35514570e-001,\n",
+       "        9.97906933e-001, 9.42136798e-001, 9.96559331e-001,\n",
+       "        9.78569836e-001, 9.22029550e-001, 9.75658656e-001,\n",
+       "        9.58444027e-001, 7.26893631e-001, 9.81924614e-001,\n",
+       "        9.55270543e-001, 9.26014646e-001, 7.52753488e-001,\n",
+       "        8.96074270e-001, 1.00000000e+000, 1.00000000e+000,\n",
+       "        9.55106964e-001, 8.24606882e-001, 7.85654073e-001,\n",
+       "        9.82842642e-001, 9.99489133e-001, 9.51360285e-001,\n",
+       "        6.44619682e-001, 9.66686257e-001, 8.46089129e-001,\n",
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+       "        9.09197734e-001, 4.75765006e-001, 2.30082747e-004,\n",
+       "        1.00000000e+000, 8.86369167e-001, 9.76311590e-001,\n",
+       "        8.99593976e-001, 9.37533944e-001, 8.63609870e-001,\n",
+       "        9.43352461e-001, 9.91860418e-001, 9.96610396e-001,\n",
+       "        2.04636538e-001, 9.39350163e-001, 7.07500520e-001,\n",
+       "        5.09314144e-001, 8.55112711e-001, 1.00000000e+000,\n",
+       "        9.92517100e-001, 8.84447208e-001, 9.97166101e-001,\n",
+       "        3.03456180e-005, 5.40383239e-001, 9.71030311e-001,\n",
+       "        4.15600517e-002, 9.72643552e-001, 1.00000000e+000,\n",
+       "        8.75821896e-001, 8.79409076e-001, 9.87258016e-001,\n",
+       "        1.00000000e+000, 9.15935525e-001, 9.23668346e-001,\n",
+       "        9.92023976e-001, 5.93212910e-001, 9.95807177e-001,\n",
+       "        8.42293888e-001, 9.65433769e-001, 9.95176039e-001,\n",
+       "        8.65023384e-001, 9.04339253e-001, 9.03248686e-001,\n",
+       "        9.96433427e-001, 9.89774865e-001, 7.90177353e-001,\n",
+       "        9.49047386e-001, 9.21470330e-001, 2.09843101e-001,\n",
+       "        9.76699957e-001, 9.13804468e-001, 9.53999714e-001,\n",
+       "        9.70586020e-001, 8.66469430e-001, 9.68688535e-001,\n",
+       "        9.97454638e-001, 1.99703148e-001, 6.58721857e-001,\n",
+       "        9.88596768e-001, 8.62785846e-001, 9.95191227e-001,\n",
+       "        9.84517299e-001, 9.66423119e-001, 9.44480097e-001,\n",
+       "        9.50341683e-001, 6.97671354e-001, 9.89384484e-001,\n",
+       "        8.99064119e-001, 7.83812466e-001, 2.83738349e-002,\n",
+       "        9.21598247e-001, 9.84759816e-001, 8.72653996e-001,\n",
+       "        9.59305279e-001, 9.74310428e-001, 9.36729554e-001,\n",
+       "        9.53490892e-001, 9.59016933e-001, 9.29383300e-001,\n",
+       "        9.56924034e-001, 2.12905791e-001, 3.11554859e-001,\n",
+       "        7.67775706e-001, 9.89225339e-001, 9.00663911e-001,\n",
+       "        9.85652947e-001, 9.67385744e-001, 9.82055763e-001,\n",
+       "        9.90816449e-001, 7.34307920e-001, 5.89091994e-001,\n",
+       "        9.70713419e-001, 5.90632197e-001, 7.67521255e-001,\n",
+       "        7.91860619e-001, 9.34818881e-001, 6.87735071e-001,\n",
+       "        9.62728070e-001, 6.09425808e-001, 8.08485392e-001,\n",
+       "        9.16510215e-002, 2.09072842e-001, 9.53930596e-001,\n",
+       "        6.21810775e-001, 9.87612685e-001, 9.99011694e-001,\n",
+       "        9.43293593e-001, 6.24050225e-001, 7.93338055e-001,\n",
+       "        1.00000000e+000, 8.10497021e-001, 9.64038648e-001,\n",
+       "        9.71492664e-001, 8.88371723e-001, 9.01374872e-001,\n",
+       "        9.95021276e-001, 9.61329989e-001, 5.38656618e-001,\n",
+       "        7.04863056e-001, 9.73119470e-001, 1.00000000e+000,\n",
+       "        9.95106717e-001, 9.75706315e-001, 1.00000000e+000,\n",
+       "        4.83874150e-001, 8.15151985e-001, 9.66772030e-001,\n",
+       "        9.84630615e-001, 5.80503503e-001, 9.97486296e-001,\n",
+       "        9.52368139e-001, 9.45937523e-001, 9.87133657e-001,\n",
+       "        1.44676916e-001, 9.29609197e-001, 9.77275547e-001,\n",
+       "        2.71011611e-001, 9.67923108e-001, 9.29981363e-001,\n",
+       "        8.88573398e-001, 9.94708921e-001, 9.49523011e-001,\n",
+       "        9.89263407e-001, 9.82159915e-001, 9.21548082e-001,\n",
+       "        6.11092334e-001, 9.97523067e-001, 8.84511095e-001,\n",
+       "        9.70288964e-001, 9.62247950e-001, 8.13831477e-001,\n",
+       "        9.79740680e-001, 9.60187791e-001, 7.39476447e-001,\n",
+       "        8.16858344e-001, 9.62282760e-001, 9.64015648e-001,\n",
+       "        9.26618007e-001, 8.03640939e-001, 9.89798229e-001,\n",
+       "        9.20630601e-001, 9.73831343e-001, 9.39612786e-001,\n",
+       "        9.89602013e-001, 8.31983694e-001, 9.98690219e-001,\n",
+       "        9.77639073e-001, 1.00000000e+000, 9.80649004e-001,\n",
+       "        7.69267181e-001, 8.92907972e-001, 9.94293952e-001,\n",
+       "        9.01223037e-001, 9.72418989e-001, 9.81164809e-001,\n",
+       "        9.03453967e-001, 9.96054730e-001, 5.59525351e-001,\n",
+       "        9.95348273e-001, 7.98633617e-001, 8.77325269e-001,\n",
+       "        9.83331317e-001, 3.90902716e-001, 9.79332640e-001,\n",
+       "        9.74127534e-001, 4.50074264e-001, 8.38049960e-001,\n",
+       "        7.68579732e-002, 9.66325610e-001, 9.93313629e-001,\n",
+       "        6.47304242e-001, 8.69869120e-001, 8.37211897e-001,\n",
+       "        9.83199430e-001, 4.37429024e-001, 9.37301515e-001,\n",
+       "        1.60795702e-002, 1.80639407e-001, 4.31620574e-001,\n",
+       "        9.76523772e-001, 4.30740143e-001, 9.06878162e-001,\n",
+       "        5.74473577e-001, 9.73795204e-001, 1.00000000e+000,\n",
+       "        6.50207891e-001, 9.93753202e-001, 9.52524180e-001,\n",
+       "        9.92896342e-001, 7.31238657e-001, 9.49123785e-001,\n",
+       "        6.71207423e-001, 9.77894909e-001, 3.74868040e-001,\n",
+       "        9.44448423e-001, 9.66477679e-001, 9.87138720e-001,\n",
+       "        9.09704105e-001, 9.83395464e-001, 8.01274007e-001,\n",
+       "        7.89448924e-001, 8.21409164e-001, 9.42463389e-001,\n",
+       "        9.79780242e-001, 7.49125807e-001, 9.92424146e-001,\n",
+       "        6.99023733e-001, 9.09972014e-001, 9.12205907e-001,\n",
+       "        8.90706226e-001, 5.40540598e-001, 9.92769685e-001,\n",
+       "        8.13835268e-001, 9.99086032e-001, 9.95546042e-001,\n",
+       "        1.00000000e+000, 9.38014644e-001, 1.90608413e-001,\n",
+       "        1.00000000e+000, 9.55855640e-001, 8.15048232e-001,\n",
+       "        9.37276439e-001, 9.84767085e-001, 7.58388823e-001,\n",
+       "        9.90774947e-001, 9.94336592e-001, 6.80002580e-001,\n",
+       "        6.55607632e-001, 8.79611368e-001, 9.92928091e-001,\n",
+       "        9.32765535e-001, 9.74458631e-001, 9.97860374e-001,\n",
+       "        8.51225575e-001, 9.37751930e-001, 9.10183081e-001,\n",
+       "        9.17020768e-001, 1.00000000e+000, 9.86404911e-001,\n",
+       "        8.62356279e-001, 9.60048557e-001, 5.31342359e-001,\n",
+       "        8.12732517e-001, 9.92717382e-001, 9.97553965e-001,\n",
+       "        3.01832200e-001, 9.96128596e-001, 8.77590631e-001,\n",
+       "        1.00000000e+000, 9.86484943e-001, 7.48789338e-001,\n",
+       "        7.60629596e-001, 9.02519643e-001, 1.00000000e+000,\n",
+       "        8.76007973e-001, 8.65114983e-001, 9.96436417e-001,\n",
+       "        9.46368712e-001, 9.62792077e-001, 9.52414419e-001,\n",
+       "        8.47955480e-001, 9.91874114e-001, 9.86230256e-001,\n",
+       "        9.94163413e-001, 9.99313914e-001, 8.61302289e-001,\n",
+       "        9.14317420e-001, 9.50097933e-001, 7.76309186e-001,\n",
+       "        9.76887202e-001, 9.57457100e-001, 9.54560375e-001,\n",
+       "        9.68189396e-001, 9.58733692e-001, 9.92666973e-001,\n",
+       "        6.12553202e-001, 9.88046020e-001, 9.67017050e-001,\n",
+       "        9.38095092e-001, 9.44329823e-001, 9.48251262e-001,\n",
+       "        9.12011432e-001, 8.05815147e-001, 3.01454697e-001,\n",
+       "        9.88776102e-001, 9.32445038e-001, 8.25879760e-001,\n",
+       "        5.56368140e-001, 1.00000000e+000, 9.87556731e-001,\n",
+       "        9.18449703e-001, 8.75782460e-001, 9.97836835e-001,\n",
+       "        8.99619522e-001, 9.54744730e-001, 9.99854772e-001,\n",
+       "        9.98885645e-001, 7.01280823e-001, 9.90628011e-001,\n",
+       "        9.14458462e-001, 9.63638478e-001, 9.37876978e-001,\n",
+       "        9.77900327e-001, 5.22784738e-001, 4.03676032e-001,\n",
+       "        9.37313500e-001, 8.97972642e-001, 9.99783799e-001,\n",
+       "        8.86523265e-001, 8.74092337e-001, 1.00000000e+000,\n",
+       "        7.15666285e-001, 4.36484787e-001, 9.47444772e-001,\n",
+       "        9.86001975e-001, 9.21410448e-001, 9.59581517e-001,\n",
+       "        9.41665390e-001, 8.65504066e-001, 9.28833560e-001,\n",
+       "        9.71127158e-001, 8.49226397e-001, 9.85351190e-001,\n",
+       "        9.59521856e-001, 9.44883522e-001, 9.62561802e-001,\n",
+       "        8.91407891e-001, 9.98951233e-001, 9.91725091e-001,\n",
+       "        9.41195576e-001, 9.54785312e-001, 9.89622944e-001,\n",
+       "        9.72211466e-001, 7.68030455e-001, 9.18510998e-001,\n",
+       "        9.77385875e-001, 9.34273126e-001, 8.13575755e-001,\n",
+       "        1.00000000e+000, 9.99165677e-001, 9.39979846e-001,\n",
+       "        9.04483454e-001, 9.12474278e-001, 9.64830930e-001,\n",
+       "        9.82811048e-001, 9.81284951e-001, 7.66248589e-001,\n",
+       "        1.00000000e+000, 8.33995819e-001, 9.77581930e-001,\n",
+       "        6.43567368e-001, 9.96575975e-001, 8.98904567e-001,\n",
+       "        5.74750264e-001, 3.17044175e-001, 1.00000000e+000,\n",
+       "        8.95102867e-001, 7.81600476e-001, 1.00000000e+000,\n",
+       "        9.19912251e-001, 9.96642046e-001, 7.92473741e-001,\n",
+       "        9.82915546e-001, 9.95027400e-001, 9.96178055e-001,\n",
+       "        8.56028297e-001, 9.47404325e-001, 9.36733098e-001,\n",
+       "        9.63971987e-001, 9.96684608e-001, 8.90387146e-001,\n",
+       "        7.34300724e-002, 9.69701948e-001, 9.44124130e-001,\n",
+       "        9.83594936e-001, 9.25784233e-001, 5.80448001e-001,\n",
+       "        1.00000000e+000, 9.92149129e-001, 9.40362213e-001,\n",
+       "        1.00000000e+000, 7.80406441e-001, 8.81126100e-001,\n",
+       "        6.64381931e-001, 9.86171098e-001, 4.61282937e-001,\n",
+       "        1.00000000e+000, 7.58499002e-001, 8.83415421e-001,\n",
+       "        9.64025159e-001, 9.60763067e-001, 9.48179446e-001,\n",
+       "        5.90266116e-001, 9.24629732e-001, 2.00058914e-001,\n",
+       "        6.05169585e-001, 8.18267303e-001, 9.97805436e-001,\n",
+       "        1.00000000e+000, 7.73787636e-001, 1.00000000e+000,\n",
+       "        9.83944219e-001, 1.20935597e-030, 8.86723710e-001,\n",
+       "        8.48147942e-001, 9.59844921e-001, 9.66619335e-001,\n",
+       "        9.33360236e-001, 9.39173966e-001, 9.64370008e-001,\n",
+       "        9.71246723e-001, 9.93454895e-001]])
          • step_size
            (chain, draw)
            float64
            0.5415 0.5415 ... 0.594 0.594
            array([[0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
+       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
+       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
+       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
+       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
+       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
+       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
+       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
+       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
+       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
+       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
+       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
+       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
+       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
+       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
+       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
+       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
+       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
+       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
+       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
+       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
+       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
+       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
+       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
+       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
+       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
+       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
+       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
+       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
+       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
+       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
+       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
+       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
+       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
+       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
+       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
+       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
+       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
+       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
+       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
+       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
+       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
+       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
+       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
+       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
+       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
+       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
+       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
+       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
+       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
+       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
+       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
+       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
+       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
+       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
+       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
+       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
+       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
+       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
+       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
+       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
+       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
+       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
+       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
+       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
+       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
+       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
+       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
+       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
+       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
+       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
+       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
+       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
+       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
+       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
+       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
+       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
+       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
+       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
+       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
+       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
+       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
+       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
+       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
+       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
+       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
+       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
+       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
+       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
+       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
+       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
+       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
+       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
+       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
+       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
+       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
+       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
+       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
+       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714,\n",
+       "        0.54153714, 0.54153714, 0.54153714, 0.54153714, 0.54153714],\n",
+       "       [0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
+       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
+       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
+       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
+       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
+       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
+       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
+       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
+       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
+       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
+       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
+       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
+       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
+       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
+       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
+       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
+       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
+       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
+       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
+       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
+       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
+       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
+       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
+       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
+       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
+       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
+       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
+       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
+       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
+       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
+       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
+       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
+       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
+       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
+       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
+       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
+       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
+       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
+       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
+       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
+       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
+       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
+       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
+       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
+       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
+       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
+       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
+       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
+       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
+       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
+       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
+       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
+       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
+       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
+       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
+       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
+       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
+       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
+       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
+       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
+       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
+       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
+       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
+       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
+       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
+       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
+       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
+       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
+       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
+       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
+       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
+       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
+       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
+       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
+       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
+       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
+       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
+       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
+       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
+       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
+       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
+       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
+       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
+       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
+       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
+       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
+       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
+       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
+       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
+       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
+       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
+       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
+       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
+       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
+       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
+       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
+       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
+       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
+       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896,\n",
+       "        0.59404896, 0.59404896, 0.59404896, 0.59404896, 0.59404896]])
          • max_energy_error
            (chain, draw)
            float64
            -0.149 2.22 ... 0.09263 -0.2259
            array([[-1.48989577e-01,  2.21957240e+00, -3.67390751e-01,\n",
+       "        -2.42733853e-01,  2.69058870e-01, -5.13461197e-01,\n",
+       "        -2.28594235e-01,  3.57237391e-01, -2.03883774e-01,\n",
+       "         7.00655278e+00,  1.86070210e-01, -8.55723457e-02,\n",
+       "         2.47567187e-01,  3.11260747e-01,  4.13640303e-01,\n",
+       "        -3.72090705e-01,  5.86569842e-02, -9.12109435e-02,\n",
+       "         3.36031533e-01,  6.74652403e-01,  2.65541267e-01,\n",
+       "         1.64492801e+01,  6.02296059e+00,  2.52295139e-01,\n",
+       "         2.93858142e-01,  2.64872629e-01,  5.14895274e+00,\n",
+       "        -5.04986109e-01,  5.81983258e+00, -7.97687495e-01,\n",
+       "         5.46892690e-01, -3.61385293e-01,  6.37158670e-01,\n",
+       "         1.67737325e+00,  1.01204856e+02,  2.31968101e-01,\n",
+       "         1.21123897e+00, -8.18909645e-01,  2.28325467e+00,\n",
+       "         1.22623819e+01,  6.61811222e-01,  4.04787278e-01,\n",
+       "         1.14418034e+01,  1.88960250e+00,  1.43463992e+00,\n",
+       "        -6.32821622e-01,  1.98704854e+00,  5.28814110e-01,\n",
+       "         3.57580789e-01,  1.28331070e-01, -4.49235034e-01,\n",
+       "        -3.11985230e-01, -7.58022294e-01, -4.40860802e-01,\n",
+       "         1.70592283e+00,  1.41383337e+00,  5.14964398e+01,\n",
+       "         1.45865826e+00,  1.14919658e+01,  1.98279240e+01,\n",
+       "        -5.46685951e-01, -2.17966784e-01,  7.03257474e-01,\n",
+       "         1.71235943e+00,  1.21280013e+00,  2.25584324e-01,\n",
+       "        -2.69255963e-01,  2.18114649e-01, -4.96155630e-01,\n",
+       "        -4.14788274e-01,  1.77062167e+00,  7.33345982e-01,\n",
+       "        -1.07290354e+00, -7.84985005e-01, -5.16348796e-01,\n",
+       "         4.72573828e-01, -1.30984149e+00,  6.57052576e-01,\n",
+       "        -1.63409342e-01,  1.26034872e+00,  6.68367528e-01,\n",
+       "         1.85802588e+00,  3.64494566e+00,  9.07110740e+00,\n",
+       "         4.98040972e-01,  4.02776412e+00, -3.39673281e-01,\n",
+       "         1.73712466e+00, -1.08201292e+00, -4.30169424e-01,\n",
+       "         7.34991875e-01, -5.77166110e-01,  1.46681901e-01,\n",
+       "         4.91008427e-01,  2.95193166e-01,  7.16615151e+00,\n",
+       "         2.18303783e+00, -3.83362144e-01, -2.75120474e-01,\n",
+       "        -5.71472468e-01,  1.80064981e-01, -2.62243047e-01,\n",
+       "         6.65210297e-01,  5.75609791e-01, -1.14095749e+00,\n",
+       "         5.04804542e-01,  2.36973366e+01,  3.29838403e+00,\n",
+       "        -3.16063846e-01,  3.17368085e-01,  5.67899139e-01,\n",
+       "        -4.94755757e-01,  2.87739332e+01,  7.05179551e-01,\n",
+       "        -7.41398631e-01,  1.87150300e-01,  1.91368316e+00,\n",
+       "        -5.08268391e-01,  7.45951887e-01, -2.95089806e-01,\n",
+       "        -2.21708949e-01,  7.05258237e-01, -6.39449462e-01,\n",
+       "         8.28030896e-01,  1.74208446e+03,  2.87961006e+00,\n",
+       "         8.38346277e-01,  6.35664661e+01,  5.80461825e-01,\n",
+       "        -2.88162356e-01,  1.79674838e+00,  4.86242572e-01,\n",
+       "         7.05205767e-01,  2.82358739e+01,  4.22338355e-01,\n",
+       "         2.46046513e+00,  8.49414253e+01, -1.10868668e+00,\n",
+       "         1.98576704e+02, -1.03968034e-01,  2.10113673e-01,\n",
+       "        -4.14198288e-01,  5.17722728e+01,  4.06884611e-01,\n",
+       "        -5.30661695e-01, -5.60473748e-01,  6.52740607e-01,\n",
+       "        -2.04112035e-01, -2.83061481e-01,  6.61583365e-01,\n",
+       "         1.88905710e+00,  4.14786135e-01, -4.05742820e-01,\n",
+       "         7.31111412e+00,  5.01512871e-01,  2.50704132e+01,\n",
+       "         2.60671872e-01, -3.99993869e-01,  1.27117934e+02,\n",
+       "         6.77470381e+00,  1.73685306e-01,  6.85047034e-01,\n",
+       "         6.08168108e-01, -6.56196928e-01, -2.58025141e-01,\n",
+       "         5.66260436e-01,  5.55733196e-01, -5.72221896e-01,\n",
+       "         1.24852467e+00,  6.42314797e+00,  6.79112866e-01,\n",
+       "        -1.42316454e+00,  4.40984152e-01,  1.07572682e+00,\n",
+       "        -9.27740875e-01,  3.46925423e+01,  2.21524100e+00,\n",
+       "         5.56759106e+00, -3.00875940e-01, -1.27277580e-01,\n",
+       "         2.37932777e-01,  1.48797536e+01,  3.10175124e-01,\n",
+       "         4.83622036e-01,  5.50455369e-01,  4.08848761e-01,\n",
+       "        -4.70396826e-01,  2.54984009e+00,  3.19921115e-01,\n",
+       "         2.64054234e+00,  3.26065225e-01,  3.53056240e-01,\n",
+       "         1.58652334e+00, -2.88955356e-01, -1.99639497e-01,\n",
+       "         3.16041284e-01,  1.64063085e+00,  7.94948383e-01,\n",
+       "         2.13079198e-01,  2.33312676e-01, -1.14414675e+00,\n",
+       "        -3.19251327e-01,  9.43190676e-01, -6.45817103e-01,\n",
+       "         2.28286226e+01, -3.89923866e-01,  1.95051022e-01,\n",
+       "         3.46248427e-01,  5.67462762e-01,  4.69301902e+00,\n",
+       "         3.69625592e-01,  2.66265109e-01,  6.71798713e+01,\n",
+       "         6.37080303e+02, -7.65995759e-01,  4.21636945e-01,\n",
+       "         7.50343565e-01,  2.16898261e+00, -2.16226961e-01,\n",
+       "         3.09291919e+00,  4.31248270e-01, -5.29311053e-01,\n",
+       "         4.01934006e-01,  2.82501672e-01, -4.80919275e-01,\n",
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+       "        -2.07397399e-01, -1.89647877e-01, -3.16514231e-01,\n",
+       "         2.34888509e-01,  4.18590432e-01,  7.48587340e-01,\n",
+       "        -1.75408834e-01,  9.26871157e-02,  7.78499489e-02,\n",
+       "         8.20325140e-02,  1.09039753e-01, -1.99338337e-01,\n",
+       "         2.33428202e+00, -1.15071017e-01, -2.69497332e-01,\n",
+       "         1.10467892e-01,  1.46006785e-01,  1.32930000e-01,\n",
+       "         1.31830682e-01,  3.16101472e-01,  5.00470178e+00,\n",
+       "        -3.41020535e-01, -3.06145546e-01,  6.83923151e-01,\n",
+       "         1.14220906e+01, -2.66391502e-01, -3.77823075e-01,\n",
+       "        -3.73029410e-01,  3.31440380e-01, -4.80878279e-01,\n",
+       "         1.79627602e-01,  2.99343576e-01, -3.20955745e-01,\n",
+       "        -2.11263382e-01,  1.01154867e+00, -2.90199553e-01,\n",
+       "         1.36103559e-01, -1.91689867e-01,  5.20064128e-01,\n",
+       "        -1.79664991e-01,  1.07882257e+01,  4.58574278e+01,\n",
+       "         1.80381906e-01,  2.23343865e-01, -3.72677286e-01,\n",
+       "         2.58994449e-01,  4.05874751e-01, -1.37774088e-01,\n",
+       "         7.28032205e-01,  7.17636526e+00,  1.45501040e-01,\n",
+       "        -1.69225619e-01,  1.47533611e-01,  1.76079043e-01,\n",
+       "         1.60345771e-01,  2.55957854e-01, -2.68604960e-01,\n",
+       "         6.36591410e-02,  2.87081659e-01, -1.08966167e-01,\n",
+       "         9.61128383e-02,  1.36872190e-01,  1.23013604e-01,\n",
+       "         7.50570921e-01, -2.43563140e-01,  4.51026693e-02,\n",
+       "         1.29231657e-01, -1.37736002e-01, -1.44702278e-01,\n",
+       "        -4.85750501e-01,  3.91839192e-01,  3.20403337e-01,\n",
+       "        -7.95553123e-02,  1.79980691e-01,  4.44031299e-01,\n",
+       "        -2.55707917e-01, -1.02071175e-01,  8.18704885e-02,\n",
+       "        -5.67237728e-01,  2.50222433e-01, -3.83524211e-01,\n",
+       "        -2.48871791e-01,  5.56381462e-02,  3.59204501e-01,\n",
+       "        -1.42082094e-01,  4.78843019e-01, -1.40871993e-01,\n",
+       "         1.66546635e+00, -2.61810891e-01, -5.19252400e-01,\n",
+       "         1.53687134e+01,  3.72049407e+00, -1.22635133e+00,\n",
+       "         1.79981186e-01,  1.96649255e+00, -7.70618309e-02,\n",
+       "         2.16772080e-01, -6.36733593e-02,  3.72045282e-01,\n",
+       "        -1.09774573e-01, -1.35848603e-01, -1.89825359e-01,\n",
+       "         3.03534925e-01,  1.55306550e-01,  1.47716265e-01,\n",
+       "         1.18696244e-01, -2.85207166e-01,  4.19819584e-01,\n",
+       "         5.18986892e+00,  1.00885783e-01, -2.04425122e-01,\n",
+       "        -1.40849261e-01,  1.79701174e-01,  1.07057396e+00,\n",
+       "        -4.11514293e-01, -1.26332174e-01, -8.15107271e-01,\n",
+       "        -2.77782962e-01,  3.25116993e-01,  1.20757101e+00,\n",
+       "         1.66572145e+00, -4.32564272e-01,  1.25543147e+00,\n",
+       "        -2.43918883e-01,  5.09511785e-01,  5.96079844e-01,\n",
+       "        -1.93127301e-01, -1.37330386e-01, -2.52257750e-01,\n",
+       "         1.42156714e+00,  1.56195761e-01,  8.36186033e+00,\n",
+       "         9.15127220e-01, -5.65338705e-01, -7.87366471e-01,\n",
+       "        -3.90881221e-01,  5.18319151e-01, -3.92801976e-01,\n",
+       "        -3.37473071e-01,  6.88874648e+01,  2.24396299e-01,\n",
+       "         4.08363766e+00, -8.83842396e-02,  1.67991437e-01,\n",
+       "         1.68452977e-01, -2.73194417e-01,  1.09057868e-01,\n",
+       "         9.26317764e-02, -2.25943052e-01]])
          • tree_size
            (chain, draw)
            float64
            7.0 7.0 7.0 7.0 ... 7.0 7.0 7.0 7.0
            array([[ 7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
+       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  3.,  7.,  7.,  7.,\n",
+       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  5.,  7.,  7.,  7.,  7.,\n",
+       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  3.,  7.,  7.,  7.,  7.,\n",
+       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  3.,  7.,\n",
+       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
+       "         3.,  7.,  7.,  3.,  7.,  7.,  3.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
+       "         7.,  7.,  7.,  7.,  1.,  7.,  7.,  7.,  7.,  7.,  7.,  3.,  7.,\n",
+       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  3.,  7.,  7.,  7.,  7.,\n",
+       "         3.,  7.,  7.,  7.,  3.,  7.,  7.,  6.,  7.,  7.,  3.,  7.,  7.,\n",
+       "         3.,  7.,  7.,  7.,  7.,  3.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
+       "         7.,  7.,  3.,  3.,  7.,  3.,  7.,  7.,  7.,  7.,  7.,  7.,  1.,\n",
+       "         7.,  7.,  7.,  3.,  7.,  7.,  7.,  3.,  7.,  7.,  7.,  7.,  7.,\n",
+       "         7.,  3.,  7.,  7.,  7.,  7.,  3.,  7.,  3.,  7.,  7.,  7.,  7.,\n",
+       "         7.,  7.,  7.,  7.,  7.,  3.,  7.,  7.,  3.,  7.,  7.,  7.,  7.,\n",
+       "         7.,  7.,  7.,  7.,  7.,  7.,  3.,  3.,  3.,  3.,  7.,  7.,  7.,\n",
+       "         7.,  3.,  7.,  7.,  1.,  7.,  7.,  7.,  3.,  7.,  7.,  7.,  7.,\n",
+       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  3.,  7.,  7.,  7.,  7.,  7.,\n",
+       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
+       "         7.,  3.,  1.,  7.,  7.,  7.,  7.,  7.,  3.,  7.,  7.,  7.,  7.,\n",
+       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
+       "         7.,  7.,  7.,  3.,  1.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  3.,\n",
+       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  3.,  3.,  7.,\n",
+       "         7.,  7.,  7.,  7.,  7.,  1.,  7.,  7.,  7.,  3.,  7.,  7.,  7.,\n",
+       "         7.,  7.,  7.,  7.,  7.,  7.,  3.,  7.,  3.,  7.,  7.,  7.,  3.,\n",
+       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  3.,  7.,  7.,  7.,  7.,  7.,\n",
+       "         7.,  7.,  7.,  7.,  7.,  7.,  3.,  7.,  7.,  7.,  3.,  3.,  7.,\n",
+       "         7.,  7.,  7.,  7.,  3.,  7.,  7.,  7.,  7.,  7.,  7.,  1.,  3.,\n",
+       "        15.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
+       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  3.,\n",
+       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
+       "         7.,  3.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
+       "         7.,  7.,  7.,  7.,  3.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
+       "         7.,  7.,  7.,  7.,  7.,  7.,  3.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
+       "         7.,  3.,  7.,  3.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  3.,\n",
+       "         3.,  7.,  7.,  7.,  7.,  7.,  1.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
+       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
+       "         7.,  3.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  3.,  3.,  7.,\n",
+       "         7.,  7.,  7.,  7.,  7.,  7.],\n",
+       "       [ 7.,  7., 15.,  7., 15.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
+       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
+       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
+       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7., 15.,  7.,\n",
+       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
+       "         7.,  7.,  7.,  7.,  7.,  7.,  3.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
+       "         7.,  7.,  7.,  3.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  3.,\n",
+       "         7.,  7.,  3.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
+       "         7.,  7.,  7.,  7.,  7.,  3.,  7.,  7., 15.,  7.,  7.,  7.,  7.,\n",
+       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  3.,  7., 15.,  7.,  7.,  7.,\n",
+       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7., 15.,  7., 15.,  7.,  7.,\n",
+       "         7.,  7.,  7.,  7.,  7.,  3.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
+       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  3.,\n",
+       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7., 15.,  7.,  7.,  7.,\n",
+       "         7.,  7.,  7., 15.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
+       "         7.,  7.,  7.,  7.,  7.,  7.,  7., 15.,  7.,  7.,  7.,  7.,  7.,\n",
+       "         7.,  7.,  7., 15.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
+       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
+       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
+       "         7., 15., 15.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
+       "         7.,  7.,  7.,  7.,  3.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
+       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
+       "         7.,  7.,  7.,  7.,  7.,  7.,  7., 15.,  7.,  7.,  7.,  7.,  7.,\n",
+       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
+       "         7.,  7.,  7.,  7.,  7.,  7.,  7., 15.,  7.,  7.,  7.,  7.,  7.,\n",
+       "         7.,  7.,  7.,  7., 15.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
+       "        15.,  7., 15.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
+       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
+       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
+       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7., 15.,  7.,  7.,  7.,  7.,\n",
+       "         7.,  7.,  7.,  7.,  7.,  7.,  7., 15.,  7.,  7.,  7.,  7.,  7.,\n",
+       "         7.,  7.,  7.,  7.,  7.,  7., 15.,  7.,  7.,  7.,  7.,  7., 15.,\n",
+       "         7.,  7.,  7.,  7., 15.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
+       "         3.,  7.,  7.,  7., 15.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
+       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
+       "         7.,  7.,  7.,  7., 15.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
+       "         7.,  7.,  7.,  7.,  7.,  7.,  7., 15.,  7.,  7.,  7.,  7.,  7.,\n",
+       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  3.,  7.,  1.,  7.,  7.,  7.,\n",
+       "         7.,  7.,  7.,  7.,  7.,  7.]])
          • energy_error
            (chain, draw)
            float64
            -0.1261 0.5558 ... -0.05267
            array([[-1.26073671e-01,  5.55780576e-01, -7.27591431e-02,\n",
+       "        -2.42733853e-01,  2.69058870e-01, -3.10309436e-01,\n",
+       "        -2.28594235e-01,  2.88516746e-01, -1.34207283e-01,\n",
+       "         7.56506289e-02,  1.41018753e-02, -8.55723457e-02,\n",
+       "         8.71437172e-02, -8.31500456e-03,  2.72671949e-01,\n",
+       "        -3.72090705e-01,  5.62033095e-04, -9.12109435e-02,\n",
+       "        -1.53104812e-01,  1.07862839e-01,  2.65541267e-01,\n",
+       "        -6.42682763e-01,  0.00000000e+00,  2.52295139e-01,\n",
+       "        -1.10616634e-01,  2.12548259e-01, -1.03926361e-02,\n",
+       "        -5.04986109e-01,  4.13848460e-01, -7.97687495e-01,\n",
+       "         3.54780172e-01, -1.12787935e-01,  1.66906991e-01,\n",
+       "         2.45362775e-01, -8.93673703e-02,  2.31968101e-01,\n",
+       "         0.00000000e+00, -8.18909645e-01,  4.89476357e-01,\n",
+       "        -2.91927233e-02,  6.61811222e-01,  4.04787278e-01,\n",
+       "        -1.36523121e-01, -2.35368597e-02,  1.43463992e+00,\n",
+       "         4.27003572e-02, -8.29382867e-01,  1.40416069e-01,\n",
+       "         9.54788225e-02,  7.24462871e-02, -1.24093852e-01,\n",
+       "         1.64837114e-03, -4.93491567e-01, -3.91735572e-01,\n",
+       "         4.62170795e-01, -5.74977164e-01,  1.40822480e+00,\n",
+       "        -1.95469025e-01,  5.54528049e-02,  2.63869249e-01,\n",
+       "        -5.46685951e-01, -1.99327188e-01, -6.49196643e-02,\n",
+       "         1.50740585e+00,  6.24992410e-01,  1.82133321e-01,\n",
+       "        -6.62691220e-02,  1.91084119e-02,  1.39688603e-01,\n",
+       "        -2.74597344e-01, -1.03443030e-01,  5.52638788e-01,\n",
+       "        -7.64666643e-01, -4.72640496e-01, -5.16348796e-01,\n",
+       "         2.48894407e-01, -1.04486589e+00,  2.61986693e-01,\n",
+       "         1.03015516e-01,  1.11157560e+00,  1.64573671e-01,\n",
+       "         0.00000000e+00,  5.72809685e-01, -7.39786129e-01,\n",
+       "         1.49448987e-01,  3.96423978e+00, -1.95557069e-01,\n",
+       "        -1.30865850e-01, -7.96888433e-01, -5.66029114e-02,\n",
+       "         5.00899554e-01, -3.01556925e-01, -7.51908574e-02,\n",
+       "         3.44335390e-02,  2.95193166e-01,  0.00000000e+00,\n",
+       "        -1.67040389e+00, -1.89375136e-01, -6.69257331e-02,\n",
+       "        -5.71472468e-01, -1.01412010e-01, -4.74222427e-02,\n",
+       "         6.65210297e-01,  4.81809560e-01, -6.45115769e-01,\n",
+       "        -4.98056614e-02,  8.04357034e-01, -1.79670162e-02,\n",
+       "        -3.16063846e-01,  3.17368085e-01,  4.62968258e-01,\n",
+       "         9.53648784e-02,  0.00000000e+00,  1.62234592e-01,\n",
+       "        -9.11974481e-02, -7.09183807e-02, -4.63608619e-01,\n",
+       "        -5.08268391e-01,  3.79762165e-01,  2.35583734e-01,\n",
+       "        -1.35074674e-01,  5.02728294e-02,  1.27296760e-01,\n",
+       "         1.55638078e-01,  1.29441092e-01, -3.17342085e-01,\n",
+       "         4.50042704e-01, -1.47885639e+00,  1.58885491e-01,\n",
+       "        -3.22279272e-02, -9.46533193e-01,  3.06130021e-01,\n",
+       "         1.12662757e-01,  0.00000000e+00,  2.23305166e-01,\n",
+       "         3.29358548e-01, -3.27353951e-02, -2.07844522e-01,\n",
+       "         1.70926031e-01, -3.97526443e-02,  2.10113673e-01,\n",
+       "        -3.18129947e-01,  2.45979519e-01, -3.16914460e-01,\n",
+       "        -1.34170878e-02, -5.60473748e-01,  6.52740607e-01,\n",
+       "         8.98127201e-02, -2.83061481e-01,  3.28089369e-02,\n",
+       "         1.58036364e+00, -1.47108682e-01, -4.05742820e-01,\n",
+       "         6.93008169e-01,  1.84777480e-01,  0.00000000e+00,\n",
+       "         2.39780962e-03,  2.29156188e-01,  6.08625233e-02,\n",
+       "        -5.06520435e-01, -1.78900224e-02,  1.49135233e-01,\n",
+       "         4.83391765e-01, -6.56196928e-01,  3.13548792e-02,\n",
+       "         5.66260436e-01,  5.55733196e-01, -2.77489363e-02,\n",
+       "         9.10028126e-01,  1.37733199e-01,  6.79112866e-01,\n",
+       "        -9.24288392e-01,  5.21517674e-02,  2.25598651e-01,\n",
+       "        -9.27740875e-01,  0.00000000e+00, -1.80135496e-02,\n",
+       "         2.79401240e-01, -4.97018126e-02, -1.27277580e-01,\n",
+       "         6.64363613e-02, -1.03533066e-01,  1.72033431e-01,\n",
+       "        -2.46607279e-01,  1.13620069e-01,  4.08848761e-01,\n",
+       "        -1.74226499e-01,  1.04834108e-01, -1.69977861e-01,\n",
+       "        -2.85688974e-01, -1.78935065e-01,  3.01749981e-01,\n",
+       "         7.37011214e-01, -2.04104061e-01, -1.80767132e-01,\n",
+       "        -1.42189086e-02,  1.00102585e-01,  1.82258868e-02,\n",
+       "         1.82522066e-01,  2.33312676e-01, -1.14414675e+00,\n",
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+       "        -1.49171111e-01,  8.49771027e-02, -2.04242515e-01,\n",
+       "         3.62051308e-01, -3.68408927e-01, -1.00358305e-01,\n",
+       "         4.79583226e-01, -8.52740702e-02, -2.94381760e-01,\n",
+       "        -1.74803215e-02,  1.28107876e-03,  8.53287413e-01,\n",
+       "        -7.70691333e-01,  4.88650855e-01, -5.93297524e-01,\n",
+       "         2.41793862e-02,  1.21018586e-02, -5.30900310e-02,\n",
+       "         1.08628263e-01,  1.71088912e-02, -1.31560022e-01,\n",
+       "         2.51506550e-01, -4.84673469e-02, -1.45150209e-01,\n",
+       "        -8.43856224e-02,  4.81417196e-03, -3.16514231e-01,\n",
+       "         8.31855460e-02, -4.37203090e-02,  6.22251802e-02,\n",
+       "        -8.21266328e-02, -7.96992422e-03,  7.78499489e-02,\n",
+       "        -5.59803740e-02,  1.09039753e-01, -2.16157446e-02,\n",
+       "        -3.10380873e-02,  2.88967496e-02, -2.69497332e-01,\n",
+       "         9.37367832e-02, -4.70290363e-02,  1.65305015e-02,\n",
+       "         8.88288226e-02,  2.61367602e-01,  4.20770143e-01,\n",
+       "        -2.35343042e-01, -1.54398215e-01,  1.39165639e-01,\n",
+       "         1.08864732e-01, -2.10356091e-02, -1.51892791e-01,\n",
+       "        -2.31111604e-01,  3.18426279e-01, -4.80878279e-01,\n",
+       "         1.24583119e-01,  2.99343576e-01, -1.73723173e-01,\n",
+       "        -1.17842806e-01,  2.10245807e-01, -2.90199553e-01,\n",
+       "         7.64649819e-02,  1.18552348e-01, -2.66687434e-01,\n",
+       "        -6.94291421e-02, -2.28420307e-02, -2.20277032e-01,\n",
+       "         6.93478030e-02,  1.57731831e-01, -2.95452755e-01,\n",
+       "         5.39418449e-02,  1.92141683e-01, -1.32949945e-01,\n",
+       "         3.12380246e-01, -4.32655928e-01,  1.04867367e-01,\n",
+       "        -1.07777351e-01,  1.35277912e-01, -5.15831896e-02,\n",
+       "        -5.92949869e-02,  1.02332147e-01, -2.68604960e-01,\n",
+       "         2.60997297e-02,  1.05468807e-01, -1.02704371e-01,\n",
+       "         4.07934174e-02,  9.08677725e-02, -1.07935688e-02,\n",
+       "        -7.66598561e-02, -7.64374908e-02, -1.80865631e-02,\n",
+       "         1.29231657e-01,  8.19795018e-02, -7.80995912e-02,\n",
+       "        -4.83313855e-02,  2.86503502e-01,  5.85783770e-02,\n",
+       "         2.82562605e-02,  1.00920858e-01,  3.69698673e-01,\n",
+       "        -4.73500733e-02, -1.69456228e-02,  5.83700051e-02,\n",
+       "        -5.67237728e-01,  3.39246482e-03, -3.83524211e-01,\n",
+       "        -1.97197811e-01, -2.20187477e-02,  2.36504155e-01,\n",
+       "        -1.42082094e-01,  1.97459601e-01,  2.51442444e-02,\n",
+       "         1.84367419e-01, -2.81883161e-02, -2.06375345e-01,\n",
+       "        -2.43116434e-01,  1.15457241e+00, -1.10687269e+00,\n",
+       "         1.28006962e-01, -4.11063803e-01, -7.08002334e-02,\n",
+       "         1.24827437e-01,  5.36999089e-04,  3.18077350e-01,\n",
+       "         3.28907079e-02, -1.35848603e-01, -1.25174574e-01,\n",
+       "        -1.31244089e-02, -5.17082317e-02,  1.47716265e-01,\n",
+       "         4.72589777e-02, -2.85207166e-01,  2.13554642e-01,\n",
+       "         2.46899722e+00,  9.08697403e-02, -1.22188092e-01,\n",
+       "        -1.16681805e-01, -1.67340235e-01,  1.03013138e+00,\n",
+       "        -4.11514293e-01, -1.10904917e-01,  1.54591957e-01,\n",
+       "        -2.77782962e-01,  2.50101322e-01,  1.20757101e+00,\n",
+       "        -1.46138476e+00,  1.01813827e-01,  4.64576551e-01,\n",
+       "        -1.90363952e-01,  2.04483444e-01,  3.39260526e-02,\n",
+       "        -1.52502519e-01,  1.24602164e-01, -3.67980742e-02,\n",
+       "        -3.34569286e-01, -6.75133245e-02,  3.38762479e-01,\n",
+       "         6.73356119e-01,  4.02884018e-01, -7.87366471e-01,\n",
+       "        -3.69373999e-01,  3.92678291e-01, -6.33274013e-02,\n",
+       "        -2.41038055e-01,  0.00000000e+00,  6.16684390e-02,\n",
+       "        -4.17457001e-01,  3.56791214e-02,  1.67991437e-01,\n",
+       "         4.53770958e-02, -2.00923236e-01, -3.14299356e-02,\n",
+       "        -2.45375679e-02, -5.26711313e-02]])
          • diverging
            (chain, draw)
            bool
            False False False ... False False
            array([[False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False,  True, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False,  True, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False,  True,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False],\n",
+       "       [False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False]])
          • depth
            (chain, draw)
            int64
            3 3 3 3 3 3 3 3 ... 3 3 3 3 3 3 3 3
            array([[3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
+       "        2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
+       "        3, 3, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3,\n",
+       "        3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 2, 3, 3, 2, 3, 3, 3,\n",
+       "        3, 3, 3, 3, 3, 3, 3, 1, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 3, 3, 3,\n",
+       "        3, 3, 2, 3, 3, 3, 3, 2, 3, 3, 3, 2, 3, 3, 3, 3, 3, 2, 3, 3, 2, 3,\n",
+       "        3, 3, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 2, 3, 2, 3, 3, 3, 3, 3,\n",
+       "        3, 1, 3, 3, 3, 2, 3, 3, 3, 2, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 2,\n",
+       "        3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 2, 3, 3, 3, 3, 3, 3, 3,\n",
+       "        3, 3, 3, 2, 2, 2, 2, 3, 3, 3, 3, 2, 3, 3, 1, 3, 3, 3, 2, 3, 3, 3,\n",
+       "        3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
+       "        3, 3, 3, 3, 3, 3, 2, 1, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3,\n",
+       "        3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 1, 3, 3, 3, 3, 3, 3, 3, 2,\n",
+       "        3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 2, 3, 3, 3, 3, 3, 3, 1, 3, 3, 3,\n",
+       "        2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 2, 3, 3, 3, 2, 3, 3, 3, 3, 3,\n",
+       "        3, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 2, 2, 3, 3,\n",
+       "        3, 3, 3, 2, 3, 3, 3, 3, 3, 3, 1, 2, 4, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
+       "        3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 3, 3,\n",
+       "        3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
+       "        3, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3,\n",
+       "        3, 3, 3, 2, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 2, 2, 3, 3, 3, 3, 3, 1,\n",
+       "        3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3,\n",
+       "        3, 3, 3, 3, 3, 3, 3, 2, 2, 3, 3, 3, 3, 3, 3, 3],\n",
+       "       [3, 3, 4, 3, 4, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
+       "        3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
+       "        3, 3, 3, 3, 3, 3, 4, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
+       "        3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 3, 3,\n",
+       "        3, 3, 2, 3, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2,\n",
+       "        3, 3, 4, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 4, 3, 3, 3, 3, 3,\n",
+       "        3, 3, 3, 3, 3, 3, 4, 3, 4, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 3,\n",
+       "        3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 3, 3, 3,\n",
+       "        3, 3, 4, 3, 3, 3, 3, 3, 3, 4, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
+       "        3, 3, 3, 3, 4, 3, 3, 3, 3, 3, 3, 3, 3, 4, 3, 3, 3, 3, 3, 3, 3, 3,\n",
+       "        3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
+       "        3, 3, 3, 3, 3, 3, 4, 4, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
+       "        2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
+       "        3, 3, 3, 3, 3, 3, 3, 4, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
+       "        3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4,\n",
+       "        3, 3, 3, 3, 3, 3, 3, 3, 4, 3, 4, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
+       "        3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
+       "        3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
+       "        3, 4, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 3, 3, 3, 3, 3, 4, 3, 3,\n",
+       "        3, 3, 4, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 4, 3, 3, 3, 3, 3, 3,\n",
+       "        3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 3, 3,\n",
+       "        3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 3, 3, 3, 3, 3, 3, 3, 3,\n",
+       "        3, 3, 3, 3, 2, 3, 1, 3, 3, 3, 3, 3, 3, 3, 3, 3]])
          • lp
            (chain, draw)
            float64
            -45.78 -49.19 ... -45.28 -43.83
            array([[-45.78048101, -49.18878918, -47.81124046, -45.88878259,\n",
+       "        -48.31089296, -45.14855162, -44.49296796, -47.06173477,\n",
+       "        -46.00333193, -46.74945341, -45.31134603, -44.57091151,\n",
+       "        -45.21360856, -45.08678676, -48.93014192, -44.3319601 ,\n",
+       "        -44.10683405, -43.35268252, -43.03524428, -43.93322003,\n",
+       "        -46.08079228, -45.6846219 , -45.6846219 , -46.15356626,\n",
+       "        -46.04213007, -49.38770053, -48.34053206, -43.80699611,\n",
+       "        -45.51102992, -45.96717108, -48.69233529, -46.14925774,\n",
+       "        -44.29064411, -46.93019508, -46.63176458, -45.86788665,\n",
+       "        -45.86788665, -43.88184986, -47.85223431, -46.89128814,\n",
+       "        -45.37435027, -49.80568336, -47.92276381, -46.58699925,\n",
+       "        -46.93876301, -49.68452069, -44.46531184, -44.22245826,\n",
+       "        -46.62746534, -45.97081079, -44.73635903, -44.82874639,\n",
+       "        -50.01955523, -44.55905088, -47.35901965, -47.73677562,\n",
+       "        -50.42675194, -48.81429299, -47.71982842, -51.4284502 ,\n",
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+       "        -45.50485557, -44.7802904 , -45.27924419, -43.83013398]])
        • created_at :
          2020-06-10T18:19:23.335024
          arviz_version :
          0.8.3
          inference_library :
          pymc3
          inference_library_version :
          3.8

      \n", + "
    \n", + "
    \n", + "
    • \n", + " \n", "
  • \n", - " \n", - " \n", + " \n", + " \n", "
    \n", "
    \n", "
      \n", @@ -8121,648 +8479,427 @@ " stroke: currentColor;\n", " fill: currentColor;\n", "}\n", - "
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        • draw: 500
        • school: 8
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        • draw
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-       "         7.49924593e+00,  4.94728585e-01,  5.82592975e+00,\n",
-       "         3.75817989e+00,  4.51834412e+00,  4.45205537e+00,\n",
-       "         7.39982769e+00, -4.51493691e-01,  8.03093481e+00,\n",
-       "         3.28889538e+00,  5.07396812e+00,  5.04783745e+00,\n",
-       "         7.33357262e+00,  2.68984364e+00,  2.68984364e+00,\n",
-       "         2.68984364e+00,  3.88464406e+00,  3.50674967e+00,\n",
-       "         3.99780914e+00,  5.44631133e+00,  3.48467368e+00,\n",
-       "         6.96535325e-01,  7.50323418e+00,  7.42606945e+00,\n",
-       "         9.01061262e+00,  1.73951112e-01,  2.75395359e+00,\n",
-       "         4.23162979e+00,  2.57694637e+00,  8.74410366e+00,\n",
-       "         3.34738928e-01,  1.24126592e+01, -4.31561076e-01,\n",
-       "         5.32190068e+00,  1.73620969e+00,  4.82885082e+00,\n",
-       "         1.67598185e+00,  4.15054922e-02,  9.76188731e+00,\n",
-       "         1.98399792e-01,  8.86614813e+00,  3.47358256e-01,\n",
-       "        -1.03321936e+00,  9.08184457e+00,  7.08575048e+00,\n",
-       "         6.63469814e-03,  3.66084649e+00,  5.46949336e+00,\n",
-       "         2.82356744e+00,  7.11227267e+00,  6.15832928e+00,\n",
-       "         1.44705232e+00,  1.27331272e+00,  1.27668689e+00,\n",
-       "        -2.07074118e+00,  9.94535731e+00,  4.30643664e+00,\n",
-       "         7.06316227e+00, -4.02548368e-02,  5.80709818e+00,\n",
-       "         6.54451596e+00,  7.98235447e-01,  2.76517848e+00,\n",
-       "         3.51958143e+00,  3.56557505e+00,  6.93569050e+00,\n",
-       "         8.48417483e+00,  9.14823315e+00,  8.78306229e+00,\n",
-       "         3.91181008e+00,  6.15000155e+00,  3.36671297e+00,\n",
-       "         3.45080693e+00,  7.30877790e-01,  1.19442357e+01,\n",
-       "        -1.99296699e+00, -2.16334777e+00,  2.47145000e+00,\n",
-       "         8.39188900e+00,  1.07556824e+00,  6.44136824e+00,\n",
-       "        -3.35134408e-01,  9.17762762e+00,  3.49880307e-01,\n",
-       "         7.43540694e+00,  5.33983821e+00,  3.53668628e+00,\n",
-       "         3.94563223e+00,  3.68023927e+00,  4.95603253e+00,\n",
-       "         4.95565905e+00,  7.52868379e+00,  4.05359789e+00,\n",
-       "         6.51337803e+00,  2.17470570e+00,  2.82369479e+00,\n",
-       "         4.72679418e+00,  8.76814010e+00,  2.59209152e+00,\n",
-       "         5.34561118e+00,  3.25733119e+00,  4.00246781e+00,\n",
-       "         8.33770665e-01,  8.33770665e-01,  6.93765272e+00,\n",
-       "         4.67603385e+00, -3.14363717e+00,  4.00875281e+00,\n",
-       "         5.52725970e+00,  2.28182710e+00,  6.22018116e+00,\n",
-       "         4.60331530e+00,  5.25234580e+00,  9.35664907e+00,\n",
-       "         3.33644417e+00,  3.04256648e+00,  4.29581525e+00,\n",
-       "         7.09411767e+00,  4.18825261e+00,  3.42085778e-01,\n",
-       "         7.64849325e+00, -1.02864862e+00,  6.59021603e+00,\n",
-       "         5.84483119e+00,  3.46257013e+00,  5.67994974e+00,\n",
-       "         3.35052316e+00,  2.06091491e-01,  6.86894119e+00,\n",
-       "         5.95501433e+00,  5.62506991e+00,  2.95817362e+00,\n",
-       "         4.99052044e+00,  2.72847236e+00,  4.60628690e+00,\n",
-       "         2.05854151e+00,  1.07062316e+00,  1.39619469e+00,\n",
-       "         5.57847406e+00,  8.02701729e+00, -9.35793196e-01,\n",
-       "        -9.35793196e-01, -3.22726099e+00, -1.48219571e+00,\n",
-       "        -3.80568552e+00, -3.80568552e+00,  7.26775999e+00,\n",
-       "         5.62384441e+00,  3.30589252e+00, -2.56406166e-02,\n",
-       "        -2.56406166e-02,  6.70605312e+00,  1.92064094e+00,\n",
-       "         4.52476866e+00,  4.26482568e+00,  5.19830097e+00,\n",
-       "         5.19830097e+00,  5.55040698e+00,  1.81262705e+00,\n",
-       "         6.71000446e+00,  7.07744062e-01,  1.67881858e+00,\n",
-       "        -2.38625473e-01, -3.57065274e+00,  1.38559926e+01,\n",
-       "        -2.28209203e-01,  4.47903555e+00,  3.21300503e+00,\n",
-       "         5.13248886e+00,  9.50953062e-01,  7.16870518e+00,\n",
-       "         5.30866449e+00,  6.48691675e+00,  8.03583264e+00,\n",
-       "         2.07918943e+00,  8.43284860e+00,  1.34922701e+00,\n",
-       "         5.60138396e+00,  5.30520091e+00,  2.21739558e+00,\n",
-       "         3.49633793e-01, -1.67193360e+00,  2.52573580e+00,\n",
-       "         1.64023599e+00,  1.16665122e+01, -2.70149725e+00,\n",
-       "         5.51160292e+00,  8.08524320e+00,  2.36268002e+00,\n",
-       "         7.69472187e-01,  1.10185893e+00,  1.02930030e+00,\n",
-       "         4.69574622e+00,  6.21451900e+00,  1.43855979e+00,\n",
-       "         5.15178386e+00,  3.47037061e+00,  9.07197410e-01,\n",
-       "         7.27561427e+00,  4.22993705e+00,  4.45173546e+00,\n",
-       "         9.10095947e+00,  5.06862059e+00,  1.18420526e+01,\n",
-       "         5.75966035e+00,  4.28062288e+00,  5.41981440e+00,\n",
-       "         2.68764727e-01,  8.86716838e+00,  2.90370388e+00,\n",
-       "         1.86803527e+00,  1.66670293e+00,  3.53592646e+00,\n",
-       "        -2.59908648e+00,  4.97636704e+00,  2.73148089e+00,\n",
-       "         4.98443447e+00,  4.43251932e+00,  2.27586171e+00,\n",
-       "         8.51825947e+00,  2.70128407e+00,  7.42228409e+00,\n",
-       "         1.84197854e+00,  4.89308990e+00,  6.71476014e+00,\n",
-       "         4.35871251e+00,  4.90341871e+00,  8.55451386e+00,\n",
-       "         8.83161112e+00,  7.63174447e+00,  1.73827144e+00,\n",
-       "         4.66630769e+00,  5.72731355e+00,  2.05832550e+00,\n",
-       "         2.26153045e-01, -5.05932553e-01, -4.40159946e-01,\n",
-       "         5.15489443e+00,  2.11073823e+00,  9.03111392e+00,\n",
-       "         1.05037104e+01,  6.32332159e-01,  4.30934591e+00,\n",
-       "         1.75419767e+00,  3.45794993e+00,  6.77938944e+00,\n",
-       "         2.00533410e+00,  7.36668278e+00,  6.44714693e+00,\n",
-       "         2.98229449e+00,  9.24720183e-01,  7.61612287e+00,\n",
-       "         7.80184167e+00, -9.30019559e-01,  4.83817314e+00,\n",
-       "         4.75614030e+00,  1.53631516e+00,  5.04871078e+00,\n",
-       "        -3.41107244e+00,  3.37376756e+00,  4.88491984e+00,\n",
-       "         4.76829625e+00,  1.07617850e+00,  2.58363091e+00,\n",
-       "         6.72453206e+00,  3.10857689e-01,  3.26555748e+00,\n",
-       "         2.42017504e+00, -2.70434482e+00,  1.27860820e+01,\n",
-       "        -5.89766522e+00,  9.29991946e+00,  2.69483415e+00,\n",
-       "         9.59170156e-01,  5.22648939e+00,  2.61609296e+00,\n",
-       "         1.63909192e+00,  7.15393600e+00,  3.98140083e-01,\n",
-       "         6.77108738e+00,  3.44501960e+00,  6.35264019e+00,\n",
-       "         5.46625032e+00,  6.08815565e+00,  1.27657528e+01,\n",
-       "        -5.49494620e+00, -5.26490749e+00,  1.20871587e+01,\n",
-       "         8.82335649e+00,  2.15223573e+00,  6.10690930e+00,\n",
-       "         2.80734833e+00,  9.84399774e-01,  7.45513402e+00,\n",
-       "        -3.03749322e-01, -2.54957720e-01,  8.97050550e+00,\n",
-       "         5.74973240e+00,  5.92051518e+00,  8.02790650e+00,\n",
-       "         1.70115394e+00,  3.69946604e+00,  1.20848051e+01,\n",
-       "         1.56507102e+00,  3.57798898e+00,  6.38666543e+00,\n",
-       "         1.09798168e+01, -1.39209318e+00,  6.28904449e+00,\n",
-       "         3.45750558e+00,  3.49307287e+00,  6.70722261e+00,\n",
-       "         6.46570727e+00,  3.00276605e+00,  2.47001584e+00,\n",
-       "         6.50015446e+00,  7.03574332e+00,  7.58653886e+00,\n",
-       "         3.05218394e+00,  6.56525097e+00,  6.34355111e+00,\n",
-       "         3.65181062e+00,  5.33285836e+00,  7.07323552e+00,\n",
-       "         3.35555328e+00,  2.40593926e+00,  4.07821907e+00,\n",
-       "         6.06723093e+00,  3.33924324e+00,  8.47540996e+00,\n",
-       "         6.55184354e+00,  6.55660461e+00, -1.14269772e+00,\n",
-       "         9.63969256e+00, -3.69739235e-01,  9.08980641e+00,\n",
-       "         2.50180730e+00,  6.08788503e+00,  2.19677092e+00,\n",
-       "        -2.50227530e+00,  8.67061611e+00,  2.73386047e+00,\n",
-       "         6.31004030e-01,  6.75261061e+00,  1.09043881e-01,\n",
-       "         4.62472473e+00,  7.83564219e+00,  9.43946808e+00,\n",
-       "         2.35089570e+00,  8.46985192e+00,  5.53873482e-02,\n",
-       "         5.70011030e+00,  3.55368634e+00,  1.32208110e+00,\n",
-       "         4.16607240e+00,  5.23832166e+00,  4.44486991e+00,\n",
-       "         2.39799074e+00,  7.21911508e+00,  2.61416964e+00,\n",
-       "         1.04011497e+01,  8.99363387e-01,  7.87929247e+00,\n",
-       "         5.25277333e+00,  4.05454429e+00, -7.67597958e-01,\n",
-       "         1.22858748e+01,  2.12609570e+00,  4.76117851e+00,\n",
-       "         8.81131135e+00,  2.07380643e+00,  1.94965783e+00,\n",
-       "         2.52679689e+00,  6.89766849e+00,  7.30306016e+00,\n",
-       "         4.56272319e+00,  5.04595827e+00,  2.28520315e+00,\n",
-       "         2.87285340e+00,  5.39241893e+00,  7.34739992e+00,\n",
-       "        -3.94029815e+00,  7.49830997e+00,  3.93360667e+00,\n",
-       "         3.45142936e+00,  3.32736762e+00,  4.72749177e+00,\n",
-       "        -2.39550651e-01,  7.75951894e+00,  1.20204052e+01,\n",
-       "         8.17845384e+00,  2.94663687e+00, -3.41178691e-02,\n",
-       "         1.03215604e+01,  7.22553598e+00,  1.89647201e+00,\n",
-       "         3.59294445e+00,  6.85970806e+00,  6.93376911e+00,\n",
-       "         3.34471286e+00, -9.94745318e-01,  5.05311688e+00,\n",
-       "         4.59779894e+00,  4.38326175e+00,  4.29569742e+00,\n",
-       "         6.42009886e+00,  6.69922156e+00,  7.93760269e+00,\n",
-       "         8.34813849e+00,  3.27325638e+00,  5.96929247e-01,\n",
-       "         1.45148552e-01,  1.46789715e+00,  6.83929042e+00,\n",
-       "        -7.72780650e-01, -2.76181563e+00,  2.26786750e+00,\n",
-       "         7.42635084e+00,  9.39800329e+00,  2.63870315e+00,\n",
-       "         5.04170832e+00,  4.79083118e+00,  3.61426592e+00,\n",
-       "         5.67166560e+00,  3.71122494e+00,  3.75664619e+00,\n",
-       "         1.84995002e+00,  8.86199510e+00,  6.97006007e+00,\n",
-       "        -1.11277678e+00,  3.80226186e+00,  7.60335053e+00,\n",
-       "         1.10310962e+01, -2.49337704e+00,  4.38962708e+00,\n",
-       "         3.10041806e+00,  4.21846266e+00,  1.72960085e+00,\n",
-       "         9.57291473e-01,  8.34342016e+00,  1.03275477e+00,\n",
-       "         8.49323013e+00,  1.49981154e+00,  5.65829498e+00,\n",
-       "         1.12839848e+00,  1.06607200e+01,  1.25955293e+01,\n",
-       "        -2.45129853e+00,  4.47863662e-01,  5.47505883e+00,\n",
-       "         5.47505883e+00,  4.58310176e+00,  5.68337171e+00,\n",
-       "         3.74157903e-01,  1.16414656e+01, -4.12836944e+00,\n",
-       "         1.95064943e+00,  8.10956530e-03,  8.76235476e+00,\n",
-       "        -8.89935230e-01,  8.76389087e+00]])
        • theta_tilde
          (chain, draw, school)
          float64
          -0.9319 1.08 ... 0.5706 -0.4123
          array([[[-0.93193184,  1.07955863, -0.63944692, ..., -1.15699353,\n",
-       "         -1.72731201, -0.80842907],\n",
-       "        [ 1.33210719, -0.43188553,  1.73382788, ...,  0.8361501 ,\n",
-       "          1.27725948, -0.22564279],\n",
-       "        [ 1.04270809,  0.31605842,  0.91628183, ...,  0.11904838,\n",
-       "          0.94874201, -0.89329465],\n",
+       "
          xarray.Dataset
            • chain: 1
            • draw: 500
            • school: 8
            • chain
              (chain)
              int64
              0
              array([0])
            • draw
              (draw)
              int64
              0 1 2 3 4 5 ... 495 496 497 498 499
              array([  0,   1,   2, ..., 497, 498, 499])
            • school
              (school)
              int64
              0 1 2 3 4 5 6 7
              array([0, 1, 2, 3, 4, 5, 6, 7])
            • tau_log__
              (chain, draw)
              float64
              1.706 2.102 -0.9025 ... 3.191 3.0
              array([[ 1.70599509,  2.1023019 , -0.90248537,  2.56846292,  2.80035484,\n",
+       "         0.99311443,  3.00205734, -0.10636405,  0.62927854,  1.48301388,\n",
+       "         2.16985359,  0.88887636,  0.365956  ,  2.87258132, -2.71912399,\n",
+       "         2.63115465,  1.08852395, -0.15118725,  1.51487157,  1.12506038,\n",
+       "         2.10230483,  2.26720996,  2.05902668,  1.32215738,  1.382099  ,\n",
+       "         2.00485569, -0.04846091,  0.20013412,  0.09002789,  3.61645892,\n",
+       "        -0.98540514,  2.14471514,  4.54864374,  0.56399077,  2.08710895,\n",
+       "         1.81525651,  1.94972113,  1.94021807,  3.35868241,  3.02310503,\n",
+       "         1.32780055,  2.17443604,  0.52584454,  0.22766409,  2.10078604,\n",
+       "         2.90568936,  1.38453723,  1.33557796,  0.8604188 ,  2.90941416,\n",
+       "         3.91051141,  2.64583023,  2.59454355,  1.55461194, -0.19360908,\n",
+       "         0.61859906,  0.06131972,  1.11867599,  3.22237575,  2.8379133 ,\n",
+       "         4.0855867 , -0.94483429,  6.65039162,  2.26065082,  0.95464065,\n",
+       "         0.9435127 ,  1.53873811,  1.27423168,  0.03564208,  2.30234039,\n",
+       "         1.06483034,  2.63324965,  2.50508407,  0.45062262,  0.56089467,\n",
+       "         1.49177783,  2.82825203,  2.34121197,  1.69663461,  1.78877646,\n",
+       "         1.51950935,  1.22613645,  2.8868101 ,  1.73708598,  2.14715963,\n",
+       "         0.59152464,  0.90457465,  2.5158034 , -0.84755572,  2.35830151,\n",
+       "         1.33125608,  0.51518908,  2.53967354,  2.46873829,  2.20646945,\n",
+       "         0.11905285,  1.98203441,  1.24922572,  1.38711176,  2.27494119,\n",
+       "        -1.03321614,  1.77610568,  1.20474088,  2.53645718, -0.22378872,\n",
+       "         2.05238601,  1.37310813,  3.29017421,  1.29962925,  2.0130666 ,\n",
+       "         1.85922107,  2.52251249,  4.10067265,  2.64205754, -1.13808027,\n",
+       "         1.59080514,  3.27661494,  0.65264271,  2.28492573,  6.74870305,\n",
+       "         2.8758106 ,  2.92309975,  2.47741094,  2.34805628,  1.80015544,\n",
+       "         0.90205299,  0.64674031,  2.68653822,  2.59522977,  1.96274769,\n",
+       "         1.44877023,  1.97737637,  2.25395178,  0.27912863,  2.38677663,\n",
+       "         3.19065025,  0.96032573,  1.81979586,  0.03921373,  1.26702002,\n",
+       "         0.95537382,  1.35183552,  1.18545532,  1.25089938,  2.99997771,\n",
+       "         1.48068604,  2.28282277,  2.77664837,  2.57037212,  0.95504334,\n",
+       "         1.69486963,  1.37795916,  1.71540583,  1.28841831,  1.45745271,\n",
+       "         0.63274979,  0.89237036, -0.01351588,  4.29981174,  1.85461141,\n",
+       "        -3.12646604,  2.58317152,  4.42056351,  2.84004013,  3.74631516,\n",
+       "         2.19433234,  0.12803876,  1.87609798,  2.70373684,  0.85187241,\n",
+       "         0.66515593,  1.98560837,  1.75530964,  1.07881318,  0.9227799 ,\n",
+       "         2.72529773,  1.22990801,  1.63303327,  1.76057628,  1.14271821,\n",
+       "         0.24628423,  2.09863363,  2.45560525,  1.74689688,  2.22823845,\n",
+       "         2.14311367,  1.44538971,  1.25540438,  1.98040585,  1.46287083,\n",
+       "         2.8902565 ,  3.25237336,  1.07074438,  4.15041042,  1.08263665,\n",
+       "        -0.41342869, -0.21777453,  1.62728738,  0.06832458,  0.69369886,\n",
+       "         2.44087783,  1.85360789,  1.21200545, -2.87543828,  1.18588517,\n",
+       "         1.8493917 , -3.37669212,  1.22158871,  1.77583107,  2.36826343,\n",
+       "         3.07289327,  1.73923901,  3.23570516,  2.57518197, -0.74485098,\n",
+       "         1.1565447 ,  4.04021711,  0.06042958,  2.8397559 ,  0.87684921,\n",
+       "         4.03005226, -2.00286885,  6.21005584,  0.77883157, -0.50032742,\n",
+       "         2.63371528,  1.19401374,  2.50900826, -1.43283972,  2.44164446,\n",
+       "         2.06966697,  1.60198989,  4.78241693,  1.35488597,  3.00561363,\n",
+       "         2.8111901 ,  4.34814582,  1.39114041,  3.34679992,  2.39048077,\n",
+       "         3.29673684,  3.22296724, -0.03406225, -0.00755681,  1.36874808,\n",
+       "         2.91221   , -2.44972579,  3.57249281,  2.9723942 ,  3.75687405,\n",
+       "         5.63509009, -0.21011503,  0.46972604,  1.54744965,  3.04382219,\n",
+       "         0.2551527 ,  2.33588406,  1.71926505,  2.61660087,  0.72918814,\n",
+       "         5.45802459, -1.17878783,  2.09894824,  2.02709688,  1.03242723,\n",
+       "         3.71406849,  2.91980202, -0.87215908,  1.84922519,  2.06389569,\n",
+       "         1.51474805,  1.82651549, -0.98556942,  1.88015217,  1.86588563,\n",
+       "         2.19522154,  0.90610739,  0.12818577,  2.6526049 , -0.34089079,\n",
+       "         0.5623638 ,  1.17314998,  1.07607117,  3.45167634,  2.07764134,\n",
+       "        -2.25318032,  1.57445238,  5.17363495,  1.67180101,  1.51730549,\n",
+       "         3.60385835,  1.21391414,  4.88465051,  1.34819836,  2.07995028,\n",
+       "         1.9411707 ,  0.8344764 ,  0.53314648,  0.95237139,  2.70993294,\n",
+       "         0.25197114,  0.68954274,  2.10559803,  1.46707996,  0.38024107,\n",
+       "         3.6718587 , -0.27665213,  1.42949829, -1.62354754,  1.42518566,\n",
+       "        -1.06680294,  0.79928264,  0.51339205,  1.66846884,  3.79669344,\n",
+       "         1.39648667,  1.48479347, -0.15476104,  1.9127241 ,  1.2560361 ,\n",
+       "         0.58643927,  1.6832115 ,  2.0844959 ,  0.27473111,  1.98730803,\n",
+       "         1.73073339,  2.39902852,  1.25603917,  2.13876395,  0.70809256,\n",
+       "         0.38677292,  1.12534926,  3.49333425,  5.51480037,  2.27937884,\n",
+       "         2.19669492,  1.07241914,  1.29741512,  3.6515715 ,  2.20619041,\n",
+       "        -1.01632301, -1.00972668, -0.13866914,  1.58748499,  4.16389488,\n",
+       "         1.47806225,  2.18470271,  2.17287451,  0.35138308, -0.08737636,\n",
+       "         3.75533117,  2.12592275,  1.61577535, -0.08719834, -3.74146033,\n",
+       "        -1.36428267, -5.40154713,  1.85700736,  0.32336021,  1.9426761 ,\n",
+       "         3.56852419,  1.55354747,  0.97723259, -0.13230045,  1.5765091 ,\n",
+       "         1.28410518,  1.00776594,  2.02259487,  1.21227949,  0.62992772,\n",
+       "         3.05016949,  0.35337658,  0.40199874,  1.48527419,  2.64238791,\n",
+       "         0.43196549,  0.10784032,  1.70192077,  2.28243656,  2.03935257,\n",
+       "         0.61773816,  0.86907984, -0.76778866,  1.94899872, -0.02152823,\n",
+       "        -2.29418832,  2.37588713,  1.63970504,  1.19798191,  4.58844073,\n",
+       "         2.6773426 ,  4.52417613,  0.32385163,  1.97589555,  0.5072283 ,\n",
+       "        -1.26472421,  2.06191531,  0.84805248,  3.71899798,  3.85586477,\n",
+       "         1.27418696, -1.66429176,  2.89621868,  0.5836731 ,  2.42453699,\n",
+       "         2.83891664, -0.10785864,  4.878018  ,  7.05836481,  4.95169144,\n",
+       "         0.66509645,  0.88350777,  2.27091977, -0.70442498,  0.61656012,\n",
+       "         1.18896414,  2.97898156,  2.52738653,  1.25978368,  3.6986964 ,\n",
+       "         3.52576019,  3.52752708,  4.35818753, -0.09883578,  1.86856041,\n",
+       "        -1.38208987,  3.10093326,  1.75405802,  5.07287701,  4.1858028 ,\n",
+       "         3.15839962,  1.25920842,  2.07146588,  1.11480582,  2.82629916,\n",
+       "         1.09979417,  0.40300613, -0.99749685,  1.75980059,  2.72177195,\n",
+       "         2.58293073,  1.05452379,  0.26683768, -0.08899415,  1.8233168 ,\n",
+       "         1.25087499,  0.51544631,  1.71713821, -0.07991098,  1.04087024,\n",
+       "         1.92818799, -0.35251242,  2.77931855,  1.26470085,  1.39402269,\n",
+       "         1.69576834,  2.69417672,  1.35947815,  1.28346478,  0.97858565,\n",
+       "         2.01678674,  2.77387222,  1.22566557,  2.8065254 ,  2.39601751,\n",
+       "         2.79108204,  3.22977667,  2.34095033,  1.21336144,  2.65564027,\n",
+       "         0.85456707,  2.84012414,  1.32798643,  0.71154812,  3.49958034,\n",
+       "        -0.45660104,  1.81603681,  1.96863298,  3.10477242,  0.02468627,\n",
+       "         0.6817256 ,  3.54044187,  3.26327222,  1.65833051,  1.42266403,\n",
+       "         1.18580882,  3.200757  ,  2.84417962,  2.09119768,  0.01526257,\n",
+       "         1.78042628,  1.67860247,  0.53106916,  2.20371117,  2.16164611,\n",
+       "         0.75287014,  1.77576973,  2.20152037,  3.19102252,  2.9998979 ]])
            • tau
              (chain, draw)
              float64
              5.507 8.185 0.4056 ... 24.31 20.08
              array([[5.50686275e+00, 8.18498929e+00, 4.05560437e-01, 1.30457567e+01,\n",
+       "        1.64504830e+01, 2.69962919e+00, 2.01269022e+01, 8.99097276e-01,\n",
+       "        1.87625645e+00, 4.40620548e+00, 8.75700183e+00, 2.43239497e+00,\n",
+       "        1.44189180e+00, 1.76826038e+01, 6.59324864e-02, 1.38897985e+01,\n",
+       "        2.96988714e+00, 8.59686709e-01, 4.54883689e+00, 3.08040285e+00,\n",
+       "        8.18501327e+00, 9.65243249e+00, 7.83833691e+00, 3.75150607e+00,\n",
+       "        3.98325370e+00, 7.42502229e+00, 9.52694584e-01, 1.22156659e+00,\n",
+       "        1.09420480e+00, 3.72055864e+01, 3.73287963e-01, 8.53960826e+00,\n",
+       "        9.45041492e+01, 1.75767299e+00, 8.06157503e+00, 6.14265162e+00,\n",
+       "        7.02672777e+00, 6.96026860e+00, 2.87512835e+01, 2.05550166e+01,\n",
+       "        3.77273632e+00, 8.79722240e+00, 1.69188711e+00, 1.25566346e+00,\n",
+       "        8.17259137e+00, 1.82778393e+01, 3.99297764e+00, 3.80219282e+00,\n",
+       "        2.36415060e+00, 1.83460476e+01, 4.99244772e+01, 1.40951424e+01,\n",
+       "        1.33904739e+01, 4.73324937e+00, 8.23979948e-01, 1.85632561e+00,\n",
+       "        1.06323880e+00, 3.06079899e+00, 2.50876515e+01, 1.70800873e+01,\n",
+       "        5.94768225e+01, 3.88743982e-01, 7.73087020e+02, 9.58932810e+00,\n",
+       "        2.59773692e+00, 2.56898968e+00, 4.65870778e+00, 3.57595288e+00,\n",
+       "        1.03628487e+00, 9.99755332e+00, 2.90034686e+00, 1.39189281e+01,\n",
+       "        1.22445884e+01, 1.56928895e+00, 1.75223948e+00, 4.44499093e+00,\n",
+       "        1.69158665e+01, 1.03938259e+01, 5.45555640e+00, 5.98212863e+00,\n",
+       "        4.56998241e+00, 3.40803695e+00, 1.79360042e+01, 5.68076540e+00,\n",
+       "        8.56050879e+00, 1.80674094e+00, 2.47088071e+00, 1.23765481e+01,\n",
+       "        4.28460931e-01, 1.05729781e+01, 3.78579568e+00, 1.67395498e+00,\n",
+       "        1.26755323e+01, 1.18075397e+01, 9.08358961e+00, 1.12642945e+00,\n",
+       "        7.25749272e+00, 3.48764152e+00, 4.00327093e+00, 9.72734690e+00,\n",
+       "        3.55860620e-01, 5.90680857e+00, 3.33589457e+00, 1.26348286e+01,\n",
+       "        7.99484035e-01, 7.78645752e+00, 3.94760131e+00, 2.68475403e+01,\n",
+       "        3.66793653e+00, 7.48623952e+00, 6.41873506e+00, 1.24598627e+01,\n",
+       "        6.03808891e+01, 1.40420660e+01, 3.20433577e-01, 4.90769871e+00,\n",
+       "        2.64859643e+01, 1.92060974e+00, 9.82495650e+00, 8.52951810e+02,\n",
+       "        1.77397981e+01, 1.85988499e+01, 1.19103878e+01, 1.04652085e+01,\n",
+       "        6.05058789e+00, 2.46465783e+00, 1.90930692e+00, 1.46807663e+01,\n",
+       "        1.33996659e+01, 7.11886064e+00, 4.25787509e+00, 7.22376564e+00,\n",
+       "        9.52530346e+00, 1.32197738e+00, 1.08783723e+01, 2.43042260e+01,\n",
+       "        2.61254733e+00, 6.17059868e+00, 1.03999273e+00, 3.55025707e+00,\n",
+       "        2.59964220e+00, 3.86451242e+00, 3.27217637e+00, 3.49348353e+00,\n",
+       "        2.00850892e+01, 4.39596044e+00, 9.80431674e+00, 1.60650863e+01,\n",
+       "        1.30706874e+01, 2.59878321e+00, 5.44593592e+00, 3.96679777e+00,\n",
+       "        5.55893105e+00, 3.62704517e+00, 4.29500494e+00, 1.88278071e+00,\n",
+       "        2.44090864e+00, 9.86575053e-01, 7.36859206e+01, 6.38921495e+00,\n",
+       "        4.38725677e-02, 1.32390595e+01, 8.31431243e+01, 1.71164523e+01,\n",
+       "        4.23646868e+01, 8.97400746e+00, 1.13659706e+00, 6.52798276e+00,\n",
+       "        1.49354388e+01, 2.34403173e+00, 1.94479374e+00, 7.28347712e+00,\n",
+       "        5.78523878e+00, 2.94118681e+00, 2.51627568e+00, 1.52609569e+01,\n",
+       "        3.42091484e+00, 5.11937967e+00, 5.81578794e+00, 3.13527913e+00,\n",
+       "        1.27926313e+00, 8.15501952e+00, 1.16534847e+01, 5.73677315e+00,\n",
+       "        9.28349836e+00, 8.52594333e+00, 4.24350554e+00, 3.50925716e+00,\n",
+       "        7.24568306e+00, 4.31833896e+00, 1.79979255e+01, 2.58516223e+01,\n",
+       "        2.91755047e+00, 6.34600402e+01, 2.95245388e+00, 6.61378693e-01,\n",
+       "        8.04306771e-01, 5.09004862e+00, 1.07071279e+00, 2.00110367e+00,\n",
+       "        1.14831165e+01, 6.38280647e+00, 3.36021664e+00, 5.63914191e-02,\n",
+       "        3.27358321e+00, 6.35595200e+00, 3.41602660e-02, 3.39257327e+00,\n",
+       "        5.90518674e+00, 1.06788316e+01, 2.16043196e+01, 5.69300946e+00,\n",
+       "        2.54242936e+01, 1.31337069e+01, 4.74805052e-01, 3.17893012e+00,\n",
+       "        5.68386817e+01, 1.06229279e+00, 1.71115880e+01, 2.40331543e+00,\n",
+       "        5.62638518e+01, 1.34947583e-01, 4.97729044e+02, 2.17892486e+00,\n",
+       "        6.06332101e-01, 1.39254107e+01, 3.30030122e+00, 1.22927328e+01,\n",
+       "        2.38630316e-01, 1.14919233e+01, 7.92218432e+00, 4.96289825e+00,\n",
+       "        1.19392565e+02, 3.87631891e+00, 2.01986067e+01, 1.66296974e+01,\n",
+       "        7.73349370e+01, 4.01943124e+00, 2.84116683e+01, 1.09187421e+01,\n",
+       "        2.70243104e+01, 2.51024948e+01, 9.66511340e-01, 9.92471669e-01,\n",
+       "        3.93042704e+00, 1.83974120e+01, 8.63172526e-02, 3.56052398e+01,\n",
+       "        1.95386430e+01, 4.28143808e+01, 2.80084149e+02, 8.10491012e-01,\n",
+       "        1.59955592e+00, 4.69946958e+00, 2.09853000e+01, 1.29065869e+00,\n",
+       "        1.03385958e+01, 5.58042565e+00, 1.36891134e+01, 2.07339662e+00,\n",
+       "        2.34633470e+02, 3.07651440e-01, 8.15758555e+00, 7.59201379e+00,\n",
+       "        2.80787293e+00, 4.10203586e+01, 1.85376170e+01, 4.18047975e-01,\n",
+       "        6.35489380e+00, 7.87659490e+00, 4.54827503e+00, 6.21220244e+00,\n",
+       "        3.73226642e-01, 6.55450216e+00, 6.46165601e+00, 8.98199070e+00,\n",
+       "        2.47467082e+00, 1.13676416e+00, 1.41909565e+01, 7.11136568e-01,\n",
+       "        1.75481564e+00, 3.23215785e+00, 2.93313312e+00, 3.15532418e+01,\n",
+       "        7.98561131e+00, 1.05064553e-01, 4.82809693e+00, 1.76555443e+02,\n",
+       "        5.32174371e+00, 4.55992185e+00, 3.67397160e+01, 3.36663638e+00,\n",
+       "        1.32244240e+02, 3.85048211e+00, 8.00407098e+00, 6.96690236e+00,\n",
+       "        2.30360757e+00, 1.70428638e+00, 2.59184867e+00, 1.50282676e+01,\n",
+       "        1.28655891e+00, 1.99280409e+00, 8.21201260e+00, 4.33655373e+00,\n",
+       "        1.46263714e+00, 3.93249313e+01, 7.58318251e-01, 4.17660322e+00,\n",
+       "        1.97197890e-01, 4.15862987e+00, 3.44106890e-01, 2.22394499e+00,\n",
+       "        1.67094954e+00, 5.30404024e+00, 4.45536215e+01, 4.04097771e+00,\n",
+       "        4.41405367e+00, 8.56619848e-01, 6.77150997e+00, 3.51147472e+00,\n",
+       "        1.79757632e+00, 5.38281513e+00, 8.04053720e+00, 1.31617672e+00,\n",
+       "        7.29586706e+00, 5.64479222e+00, 1.10124728e+01, 3.51148550e+00,\n",
+       "        8.48893842e+00, 2.03011523e+00, 1.47222214e+00, 3.08129283e+00,\n",
+       "        3.28954468e+01, 2.48340395e+02, 9.77060941e+00, 8.99523436e+00,\n",
+       "        2.92244076e+00, 3.65982422e+00, 3.85351765e+01, 9.08105536e+00,\n",
+       "        3.61923284e-01, 3.64318543e-01, 8.70516003e-01, 4.89143145e+00,\n",
+       "        6.43215603e+01, 4.38444148e+00, 8.88800587e+00, 8.78349600e+00,\n",
+       "        1.42103159e+00, 9.16332164e-01, 4.27483744e+01, 8.38062714e+00,\n",
+       "        5.03178779e+00, 9.16495297e-01, 2.37194396e-02, 2.55563934e-01,\n",
+       "        4.50959860e-03, 6.40454158e+00, 1.38176299e+00, 6.97739824e+00,\n",
+       "        3.54642159e+01, 4.72821367e+00, 2.65709280e+00, 8.76077741e-01,\n",
+       "        4.83803718e+00, 3.61143493e+00, 2.73947401e+00, 7.55791129e+00,\n",
+       "        3.36113760e+00, 1.87747487e+00, 2.11189235e+01, 1.42386725e+00,\n",
+       "        1.49480946e+00, 4.41617610e+00, 1.40467059e+01, 1.54028196e+00,\n",
+       "        1.11386987e+00, 5.48447166e+00, 9.80053089e+00, 7.68563169e+00,\n",
+       "        1.85472819e+00, 2.38471552e+00, 4.64038081e-01, 7.02165340e+00,\n",
+       "        9.78701852e-01, 1.00843213e-01, 1.07605549e+01, 5.15364914e+00,\n",
+       "        3.31342339e+00, 9.83409701e+01, 1.45463863e+01, 9.22199170e+01,\n",
+       "        1.38244218e+00, 7.21307641e+00, 1.66068190e+00, 2.82317146e-01,\n",
+       "        7.86101169e+00, 2.33509479e+00, 4.12230672e+01, 4.72694765e+01,\n",
+       "        3.57579298e+00, 1.89324697e-01, 1.81055529e+01, 1.79261079e+00,\n",
+       "        1.12969976e+01, 1.70972330e+01, 8.97754492e-01, 1.31370031e+02,\n",
+       "        1.16254263e+03, 1.41413955e+02, 1.94467808e+00, 2.41937145e+00,\n",
+       "        9.68830770e+00, 4.94392776e-01, 1.85254454e+00, 3.28367802e+00,\n",
+       "        1.96677760e+01, 1.25207408e+01, 3.52465895e+00, 4.03946115e+01,\n",
+       "        3.39795947e+01, 3.40396859e+01, 7.81154245e+01, 9.05891458e-01,\n",
+       "        6.47896261e+00, 2.51053336e-01, 2.22186775e+01, 5.77800241e+00,\n",
+       "        1.59632932e+02, 6.57462608e+01, 2.35329041e+01, 3.52263195e+00,\n",
+       "        7.93644844e+00, 3.04897607e+00, 1.68828643e+01, 3.00354774e+00,\n",
+       "        1.49631606e+00, 3.68801452e-01, 5.81127847e+00, 1.52072448e+01,\n",
+       "        1.32358722e+01, 2.87060781e+00, 1.30582847e+00, 9.14850928e-01,\n",
+       "        6.19236323e+00, 3.49339832e+00, 1.67438563e+00, 5.56856956e+00,\n",
+       "        9.23198526e-01, 2.83168019e+00, 6.87703770e+00, 7.02919842e-01,\n",
+       "        1.61080403e+01, 3.54203298e+00, 4.03103308e+00, 5.45083242e+00,\n",
+       "        1.47933347e+01, 3.89416062e+00, 3.60912292e+00, 2.66069043e+00,\n",
+       "        7.51414121e+00, 1.60205492e+01, 3.40643254e+00, 1.65523055e+01,\n",
+       "        1.09793640e+01, 1.62986461e+01, 2.52740118e+01, 1.03911068e+01,\n",
+       "        3.36477616e+00, 1.42340968e+01, 2.35035662e+00, 1.71178905e+01,\n",
+       "        3.77343764e+00, 2.03714255e+00, 3.31015576e+01, 6.33433007e-01,\n",
+       "        6.14744658e+00, 7.16088069e+00, 2.23041424e+01, 1.02499349e+00,\n",
+       "        1.97728679e+00, 3.44821524e+01, 2.61349166e+01, 5.25053778e+00,\n",
+       "        4.14815656e+00, 3.27333328e+00, 2.45511084e+01, 1.71874526e+01,\n",
+       "        8.09460414e+00, 1.01537964e+00, 5.93238476e+00, 5.35806269e+00,\n",
+       "        1.70074971e+00, 9.05856906e+00, 8.68542308e+00, 2.12308484e+00,\n",
+       "        5.90482450e+00, 9.03874533e+00, 2.43132755e+01, 2.00834863e+01]])
            • mu
              (chain, draw)
              float64
              -0.1979 3.452 ... -15.29 -7.206
              array([[-1.97924571e-01,  3.45176103e+00,  3.66898018e+00,\n",
+       "        -1.02743940e+01,  4.25360730e+00, -2.02111574e+00,\n",
+       "         4.99512893e+00, -4.89951797e+00,  2.93259999e+00,\n",
+       "         3.14718356e+00,  1.27127831e-02, -5.89109961e+00,\n",
+       "        -3.95595294e+00,  7.20764616e+00, -1.13762985e+01,\n",
+       "        -2.64993320e+00,  4.63740279e+00, -2.61164777e+00,\n",
+       "        -4.92404586e+00, -5.32221786e-01, -7.24192440e+00,\n",
+       "        -2.84180113e+00, -5.93344391e-01, -9.75138109e+00,\n",
+       "         1.48560255e-01, -4.42832119e-01, -1.52289735e+00,\n",
+       "         6.52718424e+00, -4.55815233e+00,  8.03345649e+00,\n",
+       "        -1.25949421e+00,  4.09944512e+00, -2.85912239e+00,\n",
+       "        -3.12320344e-01, -1.25621514e+00,  2.68736028e+00,\n",
+       "        -2.19802764e+00,  4.40315543e+00,  9.20349072e+00,\n",
+       "        -3.84608588e+00, -2.21098734e+00,  7.98896705e+00,\n",
+       "        -2.56405874e+00, -7.55191977e+00, -3.66256916e+00,\n",
+       "        -1.93900442e+00,  1.22065841e+00,  1.71740829e+00,\n",
+       "         8.21454272e+00,  1.92083009e+00, -4.55360166e+00,\n",
+       "         2.24412248e+00, -2.13959890e+00,  9.43831952e+00,\n",
+       "         9.61207333e+00, -3.95567894e+00, -4.81381220e+00,\n",
+       "        -4.39614559e+00, -1.56718278e+00, -5.36653851e+00,\n",
+       "         1.92902328e+00, -5.46437773e+00, -8.28278477e-01,\n",
+       "        -2.15915746e+00, -5.48922909e+00,  1.13661423e+00,\n",
+       "         2.09137433e+00, -2.54786877e+00, -2.65724057e+00,\n",
+       "         2.05729726e+00, -1.61402241e-01, -2.83105628e+00,\n",
+       "         5.37481879e+00, -7.79670411e+00,  1.23781801e+00,\n",
+       "        -1.05330598e+01, -4.50752694e+00, -8.14796386e+00,\n",
+       "        -4.70073053e+00,  3.80565742e+00,  5.50567267e+00,\n",
+       "         4.16868852e-01, -2.35527277e+00, -8.36613464e+00,\n",
+       "        -7.13115096e-01,  1.43720177e+00, -6.10898476e+00,\n",
+       "        -8.81174187e-01, -7.00294828e+00, -6.25292261e+00,\n",
+       "        -3.70662453e-01,  5.97140466e-01,  1.69662795e+00,\n",
+       "         6.36365353e+00,  1.95191457e+00, -4.14199908e-01,\n",
+       "        -9.02638518e+00,  5.15984968e+00, -4.09147662e+00,\n",
+       "        -5.23406237e+00, -6.09587492e+00, -4.65743036e+00,\n",
+       "         4.33676745e+00, -9.91340056e+00,  2.83535062e+00,\n",
+       "         4.70713637e+00, -6.33033245e+00, -2.19378021e+00,\n",
+       "         5.86431705e+00,  3.46673670e+00, -2.43746040e+00,\n",
+       "         1.68730344e+00,  3.60685353e+00,  2.88654746e+00,\n",
+       "        -3.15878724e-01,  1.62471781e+00,  3.35119982e+00,\n",
+       "         4.63666452e-01,  7.87306500e-01, -9.24725008e-01,\n",
+       "        -3.38194013e+00,  4.52134311e+00, -2.26543848e-01,\n",
+       "        -3.44352266e+00,  1.72719800e+00,  2.39885986e+00,\n",
+       "        -6.50678946e+00, -7.56880421e+00, -5.30732847e+00,\n",
+       "        -5.17096376e+00, -1.94421141e+00, -6.93298928e-01,\n",
+       "        -1.52396072e+00, -5.60458995e-02,  4.94155671e+00,\n",
+       "        -1.07948739e+00, -1.69029823e+00,  1.84554371e+00,\n",
+       "         6.38458750e-01, -8.40430815e+00, -3.73774329e+00,\n",
+       "         3.07278534e+00,  2.05623948e-01, -5.27314592e-01,\n",
+       "         6.15851493e-01, -6.35782747e+00,  1.05558401e+00,\n",
+       "        -4.61172554e-01,  2.74970370e+00, -1.11008096e+00,\n",
+       "        -1.06719737e+00, -1.25335382e+00, -1.05161094e+01,\n",
+       "        -2.79222678e+00, -2.61743694e+00,  1.06859928e+01,\n",
+       "         4.17430844e+00, -8.61940068e+00,  6.62649786e+00,\n",
+       "        -3.77297366e+00, -4.79928278e+00,  2.11312406e+00,\n",
+       "         3.82841892e+00,  1.65150718e+01,  1.28827753e+00,\n",
+       "         7.69045233e+00,  4.32595573e+00,  5.67468866e+00,\n",
+       "        -4.26950865e+00,  1.42134184e+01, -3.02203250e+00,\n",
+       "         7.88690400e+00,  4.70002602e+00,  1.22227407e+01,\n",
+       "        -9.17601028e+00, -4.89870159e+00,  3.69229719e+00,\n",
+       "        -7.05443646e+00,  3.16827030e+00,  5.92829164e+00,\n",
+       "         6.32853189e-01, -4.97531523e+00,  5.35110233e+00,\n",
+       "         4.84398026e+00, -1.30950889e+00,  3.10966847e+00,\n",
+       "         5.19944573e+00, -4.09236633e+00, -1.51532902e+00,\n",
+       "         2.07513611e+00,  1.17597066e+00, -1.32898956e+00,\n",
+       "        -6.41614907e+00, -7.89735147e+00,  1.50822033e+00,\n",
+       "         4.09212007e+00,  2.47951153e+00, -1.63800121e+00,\n",
+       "        -1.86176988e+00, -2.52168837e+00, -6.64242059e+00,\n",
+       "        -1.02397782e+01, -4.54425137e+00,  2.62436848e+00,\n",
+       "         1.22193671e+00, -9.38962033e+00, -1.10203324e+00,\n",
+       "        -7.05103435e+00, -4.46289833e+00,  5.25312798e+00,\n",
+       "        -3.56515665e+00,  1.26120037e+00,  4.75885172e+00,\n",
+       "        -2.50167965e+00,  3.80542944e+00,  3.01167210e+00,\n",
+       "         2.70345411e+00,  5.73690638e+00, -4.54935671e+00,\n",
+       "        -6.43236445e+00,  7.41097905e+00,  1.35900623e+00,\n",
+       "         5.04012320e-01, -3.15086965e-01,  1.64858689e+00,\n",
+       "        -2.06980294e+00, -8.59024295e+00, -4.97349172e-01,\n",
+       "        -1.80382620e+00,  1.17371261e+01,  2.15827083e+00,\n",
+       "         2.22745215e+00, -1.20855349e+00, -2.44807351e+00,\n",
+       "        -9.87085690e+00, -1.34467216e+00, -5.81848641e+00,\n",
+       "         3.96936369e+00,  6.03001020e+00,  2.48189062e+00,\n",
+       "        -3.39454396e+00,  8.02506367e+00, -1.08853432e+01,\n",
+       "        -5.95905160e+00,  5.62999973e+00, -1.33903618e+00,\n",
+       "         2.43456328e-01,  7.46115585e+00,  7.25904503e+00,\n",
+       "         5.91408176e+00, -8.78687142e+00, -6.51018263e+00,\n",
+       "        -4.41948337e+00, -2.48947673e+00, -1.64088207e+00,\n",
+       "         6.20255356e+00, -9.17992329e+00,  3.82492982e+00,\n",
+       "         2.63842026e+00, -4.17331044e+00,  1.75196415e+00,\n",
+       "        -5.04693689e+00,  1.35289027e+00,  7.57024645e+00,\n",
+       "         7.42244523e-01, -8.30060584e+00, -4.41346052e+00,\n",
+       "         2.16165760e+00, -5.26439002e+00, -6.91396918e+00,\n",
+       "         5.37676522e-01,  4.51113679e+00, -8.02857560e-01,\n",
+       "        -2.33763106e+00, -3.14059882e+00, -3.39669533e+00,\n",
+       "         9.30930118e-01, -5.37274114e+00,  1.77406778e+00,\n",
+       "         4.51427847e+00, -4.11358649e+00,  3.99759043e+00,\n",
+       "         1.53790689e+00,  3.08286549e+00,  1.96950901e+00,\n",
+       "        -2.33147685e+00,  8.35301298e+00,  3.64283230e+00,\n",
+       "         3.93723672e+00, -1.55395905e+00, -4.08348595e+00,\n",
+       "        -1.12488433e+00, -4.94433054e+00,  1.09220685e+00,\n",
+       "        -2.96159007e+00, -3.82612120e+00,  5.71952703e+00,\n",
+       "         2.96089980e+00,  6.57383263e+00,  3.43877847e+00,\n",
+       "         3.23617754e+00, -3.26402642e-01, -1.83070903e+00,\n",
+       "         2.65271223e+00,  4.20683702e+00,  6.46751652e+00,\n",
+       "         3.60872783e-01, -8.27036082e+00, -9.63407531e+00,\n",
+       "         3.26333096e-01,  2.43822818e+00, -7.43272899e+00,\n",
+       "        -2.69406922e+00,  3.71935806e+00, -5.18549573e+00,\n",
+       "        -4.51118195e+00, -2.09199225e+00,  5.78108487e+00,\n",
+       "        -5.89811771e+00,  3.49169444e+00, -5.05185406e+00,\n",
+       "        -1.20226878e+00,  6.53307013e+00,  4.81768620e+00,\n",
+       "         4.17257863e+00, -3.86026668e+00,  5.58392228e+00,\n",
+       "        -4.41510369e+00,  5.57459816e-01,  2.72842246e+00,\n",
+       "        -7.78855364e+00,  5.68483356e+00,  3.29852882e+00,\n",
+       "         9.55117660e+00, -6.27973606e+00,  1.78814651e+00,\n",
+       "         1.39547434e+00,  7.55131835e-01,  3.65522852e-01,\n",
+       "         4.38405961e+00, -1.68567975e+00, -8.80176186e+00,\n",
+       "        -5.19047572e+00,  4.07829465e+00,  4.29162615e+00,\n",
+       "        -1.20809055e+00, -2.34765354e+00, -6.46386071e+00,\n",
+       "        -6.03662219e-01, -4.66821357e+00, -1.43937731e-01,\n",
+       "         1.09606189e+00,  6.30828939e+00, -1.30803069e+00,\n",
+       "        -3.03474141e+00,  3.35965967e-01, -6.33265044e+00,\n",
+       "         3.28254564e+00,  1.37978799e+00, -2.48652438e+00,\n",
+       "        -1.84211152e+00, -8.65571630e+00, -2.90481357e+00,\n",
+       "        -5.34261854e+00,  1.34312306e+00, -2.08973719e+00,\n",
+       "         6.18252186e+00,  4.09215122e+00, -1.48227522e+00,\n",
+       "        -9.72847583e+00, -1.06198133e+00,  8.99839205e-01,\n",
+       "        -9.42087378e-01, -1.78560508e+00, -4.11869577e+00,\n",
+       "        -1.94607602e+00, -6.34726065e+00,  5.24379982e+00,\n",
+       "        -2.23697781e+00,  3.40115386e-01,  3.73908939e+00,\n",
+       "         4.53282894e+00, -2.28989784e+00, -6.71804450e+00,\n",
+       "        -4.63620303e-01,  1.13621798e+01, -6.37943935e+00,\n",
+       "        -2.25709706e+00,  1.65004215e+00,  4.45505702e-01,\n",
+       "        -4.29752873e+00,  3.41480439e+00, -4.48702607e+00,\n",
+       "         5.83413204e+00,  9.06419203e+00, -2.52112835e+00,\n",
+       "         5.00974231e+00, -7.78393797e-01,  3.36146623e+00,\n",
+       "         4.83698962e+00, -1.05169071e+00,  3.66616293e+00,\n",
+       "        -1.67252497e+00, -3.62323547e+00,  7.80121165e+00,\n",
+       "        -3.08831078e+00, -5.43366444e+00, -3.69190337e+00,\n",
+       "         3.04940823e+00,  4.94988152e-01, -1.56235549e+00,\n",
+       "        -1.31380093e+01, -2.68698571e-01, -7.08968310e+00,\n",
+       "         7.25984399e+00, -7.94048679e+00, -9.01143028e+00,\n",
+       "         2.15010800e+00, -1.39225759e+01, -1.92367776e+00,\n",
+       "        -1.05580966e+01, -5.65026775e-01,  5.79205213e+00,\n",
+       "        -1.55147165e+00, -6.97345557e+00,  3.81667571e+00,\n",
+       "        -1.17125097e+00, -8.23705763e+00,  2.03794735e+00,\n",
+       "        -1.28032492e+00, -1.10053320e+01,  6.43417469e+00,\n",
+       "        -3.93257893e+00,  5.37706754e-01,  2.38022617e+00,\n",
+       "         6.33201935e-01,  6.87942708e+00, -4.85099290e+00,\n",
+       "         3.03039778e+00,  4.28465279e+00, -3.74243269e+00,\n",
+       "         5.41243454e+00,  4.19264965e+00,  3.94209299e+00,\n",
+       "         4.19019105e-01,  9.03729243e-01, -8.47680165e-01,\n",
+       "         5.44887897e+00, -6.64587581e+00,  2.24505549e+00,\n",
+       "        -1.62886915e+00,  6.52696615e+00,  4.65538080e+00,\n",
+       "         2.16153384e+00,  8.01812241e+00,  8.87701558e+00,\n",
+       "        -8.82977615e-01,  7.44374430e+00, -7.26633089e-01,\n",
+       "         5.07979280e+00, -7.77941048e+00,  5.93222518e+00,\n",
+       "        -1.22668195e+01,  1.15109144e+00, -1.05951734e+01,\n",
+       "         1.09823498e+00,  8.38289171e+00, -5.66352195e+00,\n",
+       "         9.02288722e+00,  6.64762146e+00, -3.59210701e+00,\n",
+       "         5.01362305e+00, -2.18552082e+00,  8.10170049e+00,\n",
+       "         3.35098197e+00, -3.67603554e+00,  9.50009626e-01,\n",
+       "        -2.69213521e+00,  5.97375478e+00,  9.19377263e+00,\n",
+       "         4.00684514e+00, -5.14242881e+00, -5.04404481e+00,\n",
+       "         6.80877591e+00,  2.08913991e+00, -3.28593673e+00,\n",
+       "         6.94325245e+00, -2.76457961e+00, -1.44369907e-01,\n",
+       "        -3.11771191e+00,  2.59296320e+00, -1.89779512e+00,\n",
+       "        -3.00382367e+00, -1.49473069e+00, -6.88520684e+00,\n",
+       "         5.65785904e+00, -4.92529741e-01,  4.68942995e+00,\n",
+       "        -1.52910780e+01, -7.20569740e+00]])
            • theta
              (chain, draw, school)
              float64
              -9.888 -1.89 2.237 ... 13.34 -21.99
              array([[[ -9.88782452,  -1.89045271,   2.23745711, ...,   3.05783633,\n",
+       "           4.50876933, -12.02277258],\n",
+       "        [ 12.52324972,  -0.10257462,  17.90235743, ...,  -6.03914366,\n",
+       "           5.39459901,   7.07427163],\n",
+       "        [  3.22949408,   4.20144679,   3.28459753, ...,   3.70696941,\n",
+       "           3.39716626,   4.14062422],\n",
        "        ...,\n",
-       "        [ 0.60455847,  0.41670938,  0.30380914, ..., -0.5236711 ,\n",
-       "         -0.66643775, -0.23138039],\n",
-       "        [-0.17076808, -0.43805528, -0.83658994, ...,  0.13940144,\n",
-       "          1.43255767,  0.52613097],\n",
-       "        [ 0.34137494,  1.92113419,  0.91367552, ...,  1.19301448,\n",
-       "          2.80274532, -0.85711612]],\n",
-       "\n",
-       "       [[ 0.28056653, -0.61760326,  1.14611145, ..., -0.49222139,\n",
-       "          1.47608045,  1.28578082],\n",
-       "        [ 0.67172511, -0.1482499 , -0.25078999, ..., -0.86408684,\n",
-       "          0.20942027,  0.76079871],\n",
-       "        [ 0.66347005, -1.28144782, -0.19553371, ...,  1.00912494,\n",
-       "          0.81284581, -0.16367622],\n",
+       "        [  2.88020715,   6.85078948,  10.60362223, ...,  -5.47622351,\n",
+       "           2.56413578,  -9.94645561],\n",
+       "        [ 10.70735593, -35.010802  , -49.8329035 , ..., -20.31738401,\n",
+       "         -42.62663544,  -8.45896299],\n",
+       "        [-55.3145697 , -16.82455975,  -1.67088179, ...,  18.1635401 ,\n",
+       "          13.33866153, -21.98739496]]])
            • theta_tilde
              (chain, draw, school)
              float64
              -1.76 -0.3073 ... 1.023 -0.736
              array([[[-1.75960441, -0.30734889,  0.44224485, ...,  0.59121882,\n",
+       "          0.85469606, -2.14729303],\n",
+       "        [ 1.10830795, -0.43425049,  1.76549973, ..., -1.15955004,\n",
+       "          0.23736598,  0.44257976],\n",
+       "        [-1.0836513 ,  1.31291559, -0.94778141, ...,  0.09367097,\n",
+       "         -0.67021802,  1.16294391],\n",
        "        ...,\n",
-       "        [ 0.3410791 , -0.49307311, -0.03897836, ..., -0.10989548,\n",
-       "          0.36688885, -0.89893107],\n",
-       "        [ 0.08130106,  0.60077994, -0.32559539, ..., -0.7244612 ,\n",
-       "          0.08748465,  1.12986153],\n",
-       "        [ 1.37718068,  0.93452373, -1.05378636, ...,  0.35680436,\n",
-       "          0.57057624, -0.41225969]]])
            • tau
              (chain, draw)
              float64
              0.4474 0.3094 7.001 ... 3.59 3.348
              array([[4.47364225e-01, 3.09438937e-01, 7.00139614e+00, 2.33802782e+00,\n",
-       "        1.73686868e+01, 2.08647159e+00, 2.60153730e+00, 9.22392577e+00,\n",
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-       "        7.19203507e+00, 1.93421681e+00, 3.54963856e+00, 2.21936234e+00,\n",
-       "        4.37181089e+00, 5.70748448e-01, 1.55769390e+01, 8.27418950e+00,\n",
-       "        1.15613529e+01, 3.88479625e+00, 2.10141707e+00, 3.85603142e+00,\n",
-       "        3.23524795e+00, 4.42442893e+00, 5.55152561e+00, 3.03672294e-01,\n",
-       "        1.57705876e+00, 6.10067393e-01, 1.31676345e+00, 2.76359144e+00,\n",
-       "        5.64819158e+00, 3.57773526e+00, 1.41417949e+00, 1.71880538e+00,\n",
-       "        9.83310333e-01, 4.29973283e-01, 8.73379592e+00, 3.40346058e-02,\n",
-       "        5.58780246e-01, 5.63595146e+00, 1.89344044e+00, 5.26521667e-01,\n",
-       "        1.24020722e-01, 3.43118031e-01, 8.15294798e-01, 2.22562616e+00,\n",
-       "        1.96053959e+00, 1.00477194e+00, 1.28844802e+00, 1.03524805e+00,\n",
-       "        3.82352347e+00, 1.65549026e+00, 1.13413404e+00, 3.30382828e+00,\n",
-       "        3.11654378e+00, 1.07337999e+00, 1.74766323e+00, 1.59181656e+00,\n",
-       "        8.73204474e-01, 2.60520344e+00, 2.58107388e+00, 1.79853904e+00,\n",
-       "        1.74254114e+00, 7.09498658e+00, 2.44155456e+00, 4.81585929e+00,\n",
-       "        9.44713304e-01, 2.46345663e+00, 4.31741576e+00, 1.85545907e+00,\n",
-       "        4.72384442e+00, 1.24268345e+00, 1.38138794e+01, 9.25892650e+00,\n",
-       "        3.06933572e+00, 4.55101936e-01, 6.93529221e+00, 3.79349981e+00,\n",
-       "        4.82202579e+00, 2.51395292e-01, 3.55535105e-01, 2.58909892e-01,\n",
-       "        1.07153345e+01, 2.47792734e-01, 1.08696079e+01, 3.02154438e+00,\n",
-       "        2.86200527e+00, 6.30021175e+00, 1.57405444e+00, 2.29769893e+00,\n",
-       "        4.49354761e+00, 4.39634462e+00, 1.80450375e+00, 1.83029690e+00,\n",
-       "        2.76441303e+00, 1.26655376e+00, 1.49865721e+00, 2.73035589e+00,\n",
-       "        1.93292798e+00, 5.54069486e+00, 1.54714571e+00, 2.13829935e+00,\n",
-       "        1.49585263e+00, 7.40522102e-01, 1.68360311e-01, 4.79390967e+00,\n",
-       "        6.50254533e-01, 9.04501958e+00, 3.12013209e-01, 2.96999747e-01,\n",
-       "        1.83501724e+00, 4.36912044e+00, 2.72487425e+00, 3.77268275e+00,\n",
-       "        1.10981008e+00, 7.99307178e+00, 3.35016274e+00, 3.17178644e+00,\n",
-       "        2.32950017e+00, 4.18132908e+00, 4.79112685e-01, 1.47763067e+00,\n",
-       "        1.07190517e+01, 1.39635550e+01, 3.00060190e+00, 4.47950122e+00,\n",
-       "        2.22658995e+00, 7.21854913e-01, 6.83162572e-01, 8.12546775e+00,\n",
-       "        7.06258523e-01, 6.89297367e-01, 1.14443101e+00, 3.43857900e+00,\n",
-       "        1.06245562e+00, 1.62537435e+01, 5.70309018e+00, 6.11469540e+00,\n",
-       "        3.72576009e+00, 3.25810480e+00, 4.27772314e+00, 5.89278817e+00,\n",
-       "        2.22181081e+00, 3.93991749e+00, 2.02401123e+00, 2.33095514e+00,\n",
-       "        8.96349762e+00, 1.34896286e+00, 2.23285432e+00, 2.92054712e+00,\n",
-       "        5.03323365e+00, 2.56937806e+00, 5.83991827e+00, 1.26629283e+00,\n",
-       "        2.71944192e+00, 8.64273275e-01, 5.13509834e+00, 2.16662949e+00,\n",
-       "        3.73543341e+00, 1.23590938e+00, 8.38816919e-01, 4.32691843e+00,\n",
-       "        6.84451135e-01, 7.87623750e+00, 4.07666681e+00, 2.03034439e+00,\n",
-       "        1.91531604e+00, 2.03931989e+00, 2.43319104e+00, 2.37632731e+00,\n",
-       "        3.98497067e+00, 1.08112193e+00, 2.28505613e-01, 5.04936183e+00,\n",
-       "        2.35111739e+00, 2.56936794e+00, 6.16206404e+00, 2.81945294e+00,\n",
-       "        3.39413072e+00, 2.53205822e+00, 2.93143251e+00, 3.36514543e+00,\n",
-       "        3.49593763e+00, 3.64690141e+00, 3.87323691e+00, 2.36820489e+00,\n",
-       "        1.78590999e+00, 6.66913023e+00, 2.04351672e-01, 3.25617715e+00,\n",
-       "        1.69566962e+00, 5.22082863e+00, 5.75458429e-01, 5.72599583e-01,\n",
-       "        2.36868952e-01, 3.91244681e+00, 2.44401483e+00, 2.79059691e+00,\n",
-       "        5.81409310e+00, 2.33397323e+00, 1.25845195e+00, 3.19021135e+00,\n",
-       "        6.43856860e+00, 3.48948376e-01, 7.84800632e+00, 1.10693551e+00,\n",
-       "        3.90908300e+00, 3.01883998e+00, 4.94049869e+00, 4.26376196e+00,\n",
-       "        2.06695193e+00, 6.80840729e-01, 6.80254898e-01, 2.62736665e-01,\n",
-       "        2.44253940e-01, 4.56976646e-01, 3.27086696e-01, 3.16178367e+00,\n",
-       "        5.53333311e+00, 5.77166186e-01, 1.53568825e+01, 3.14842353e+00,\n",
-       "        1.02436591e+00, 4.05365926e+00, 4.30120418e+00, 1.46895264e+00,\n",
-       "        2.42124275e+00, 2.10170979e+00, 1.20549422e+00, 1.04554736e+00,\n",
-       "        8.06833503e+00, 1.39633068e+00, 4.26784293e+00, 6.57140020e+00,\n",
-       "        2.25899927e+00, 2.26050382e+00, 5.49584507e+00, 5.74142152e+00,\n",
-       "        7.36952793e-01, 2.08972215e+00, 1.05970662e+01, 3.75780715e-01,\n",
-       "        6.49231216e+00, 6.19779903e+00, 2.93787993e-01, 2.02701926e-01,\n",
-       "        1.22254480e-01, 6.18160836e+00, 3.62063400e+00, 3.03089951e+00,\n",
-       "        6.02108211e+00, 2.73807712e+00, 1.82459257e+00, 5.81289768e+00,\n",
-       "        1.84169495e+00, 5.98573839e+00, 5.43515618e+00, 3.88177270e+00,\n",
-       "        5.34525798e+00, 1.57388007e+00, 4.86141429e+00, 1.62002469e+00,\n",
-       "        3.97558795e+00, 3.91428266e+00, 1.22684292e+00, 2.49578917e+00,\n",
-       "        3.77988421e-01, 9.45057529e-01, 1.34607746e+01, 1.54449278e+00,\n",
-       "        7.00132221e+00, 3.18040289e+00, 8.49637203e+00, 6.04267265e+00,\n",
-       "        3.50384327e+00, 2.28607710e+00, 5.71702334e-02, 4.06466158e-01,\n",
-       "        1.40679483e+00, 2.02046348e+00, 7.35370938e+00, 3.84766993e+00,\n",
-       "        3.73974270e+00, 1.12633672e+00, 1.88998167e+00, 2.25342685e+00,\n",
-       "        9.28228536e+00, 9.28228536e+00, 4.55933960e+00, 4.92305697e+00,\n",
-       "        2.16809838e+00, 1.32786414e+00, 8.01367473e+00, 4.06748670e+00,\n",
-       "        9.89595479e+00, 8.60362678e+00, 3.58966421e+00, 3.34788273e+00]])
            • theta
              (chain, draw, school)
              float64
              -2.431 -1.531 -2.3 ... 10.67 7.384
              array([[[-2.43086409, -1.53099522, -2.3000168 , ..., -2.53154864,\n",
-       "         -2.78668873, -2.37561337],\n",
-       "        [13.3524455 , 12.80659747, 13.47675353, ..., 13.19897707,\n",
-       "         13.33547349, 12.870417  ],\n",
-       "        [17.85746416, 12.76990198, 16.97230384, ..., 11.39055666,\n",
-       "         17.19957045,  4.30274202],\n",
-       "        ...,\n",
-       "        [ 0.30439584,  0.13134936,  0.02734572, ..., -0.73492864,\n",
-       "         -0.8664452 , -0.46567059],\n",
-       "        [ 9.81155452,  9.3479949 ,  8.65681113, ..., 10.3494855 ,\n",
-       "         12.59222294, 11.02019548],\n",
-       "        [ 6.42851041,  7.27980482,  6.73690948, ...,  6.88743856,\n",
-       "          7.75488394,  5.7826723 ]],\n",
-       "\n",
-       "       [[ 5.81071064, -1.16450057, 12.53255621, ..., -0.19078149,\n",
-       "         15.09510453, 13.61723243],\n",
-       "        [ 4.50925169, -0.85343585, -1.52405445, ..., -5.53505401,\n",
-       "          1.48574927,  5.09179856],\n",
-       "        [ 9.81987137,  4.76839455,  7.58880667, ..., 10.71763045,\n",
-       "         10.20784054,  7.67154919],\n",
-       "        ...,\n",
-       "        [11.69687206,  4.52013777,  8.42699946, ...,  7.81685503,\n",
-       "         11.91892949,  1.02828731],\n",
-       "        [-0.59809173,  1.26666303, -2.05871336, ..., -3.49050769,\n",
-       "         -0.5758947 ,  3.16588827],\n",
-       "        [13.37453029, 11.89256674,  5.2359377 , ...,  9.95843003,\n",
-       "         10.6741132 ,  7.38369378]]])
          • created_at :
            2020-06-06T18:08:05.191460
            arviz_version :
            0.8.3
            inference_library :
            pymc3
            inference_library_version :
            3.8

      \n", + " [-0.20016305, 0.23912163, 0.65431562, ..., -1.12467528,\n", + " -0.23513155, -1.61923862],\n", + " [ 1.06931022, -0.81106818, -1.42069815, ..., -0.20673093,\n", + " -1.12430583, 0.28100348],\n", + " [-2.39544427, -0.47894385, 0.27559038, ..., 1.26318892,\n", + " 1.02294784, -0.73601253]]])
  • created_at :
    2020-06-10T18:19:23.480401
    arviz_version :
    0.8.3
    inference_library :
    pymc3
    inference_library_version :
    3.8

    • \n", " \n", " \n", " \n", " \n", "
  • \n", - " \n", - " \n", + " \n", + " \n", "
    \n", "
    \n", "
      \n", @@ -9100,40 +9237,26 @@ " stroke: currentColor;\n", " fill: currentColor;\n", "}\n", - "
      xarray.Dataset
        • chain: 2
        • draw: 500
        • obs_dim_0: 8
        • chain
          (chain)
          int64
          0 1
          array([0, 1])
        • draw
          (draw)
          int64
          0 1 2 3 4 5 ... 495 496 497 498 499
          array([  0,   1,   2, ..., 497, 498, 499])
        • obs_dim_0
          (obs_dim_0)
          int64
          0 1 2 3 4 5 6 7
          array([0, 1, 2, 3, 4, 5, 6, 7])
        • obs
          (chain, draw, obs_dim_0)
          float64
          -16.98 -1.64 1.798 ... 25.1 19.28
          array([[[-16.97645538,  -1.64038568,   1.79770041, ...,  14.40859199,\n",
-       "         -23.09960687, -18.13050863],\n",
-       "        [ 23.62847574,  28.00510263,  29.16764597, ..., -18.97816711,\n",
-       "           9.95080714,  16.89268424],\n",
-       "        [ 37.02276389,   4.9645585 ,   8.9831761 , ...,   3.46762858,\n",
-       "          21.48353196, -22.54091146],\n",
+       "
          xarray.Dataset
            • chain: 1
            • draw: 500
            • obs_dim_0: 8
            • chain
              (chain)
              int64
              0
              array([0])
            • draw
              (draw)
              int64
              0 1 2 3 4 5 ... 495 496 497 498 499
              array([  0,   1,   2, ..., 497, 498, 499])
            • obs_dim_0
              (obs_dim_0)
              int64
              0 1 2 3 4 5 6 7
              array([0, 1, 2, 3, 4, 5, 6, 7])
            • obs
              (chain, draw, obs_dim_0)
              float64
              -25.31 -0.8959 ... 3.422 -20.64
              array([[[-25.31078195,  -0.89588955,  22.6413552 , ...,  24.13451408,\n",
+       "          16.67999654, -30.23034528],\n",
+       "        [  4.86375172,  11.18885402,  22.53588377, ..., -13.80804677,\n",
+       "          -5.73731196,  -4.12169577],\n",
+       "        [ -1.79113884,   2.53093031,  15.8191744 , ...,   7.61230557,\n",
+       "          -4.66752285,  23.29882677],\n",
        "        ...,\n",
-       "        [ 19.95419475,   8.99518895,  -2.7647318 , ...,   0.69631123,\n",
-       "          -2.61307142,  21.69863335],\n",
-       "        [ -1.58701966,   2.72730288,   0.22424347, ...,   6.45413578,\n",
-       "          13.83444615,  16.59758946],\n",
-       "        [ 29.57892186,  28.30755109,  10.86959693, ...,  -4.0969694 ,\n",
-       "          18.96384652, -21.49265517]],\n",
-       "\n",
-       "       [[  7.47752947,  -4.37000202, -14.8472858 , ..., -14.50545683,\n",
-       "           3.12202882,  -5.73977302],\n",
-       "        [  0.7952108 ,  -9.05784386,   7.57644261, ..., -14.4017723 ,\n",
-       "           1.63382119,  18.30680557],\n",
-       "        [ 16.81443595,   8.89175516,   5.43731547, ...,  17.45147949,\n",
-       "          -3.19802734,  -4.75950592],\n",
-       "        ...,\n",
-       "        [-10.19634347,  22.66826828,  26.12711621, ...,  10.35126659,\n",
-       "          24.84716965,  14.48155495],\n",
-       "        [ -4.16541154,  11.29841989, -26.00202243, ...,   0.55060509,\n",
-       "           7.44766052,  31.05157162],\n",
-       "        [  5.95437579,   6.34268575,   8.62670338, ...,  12.31584594,\n",
-       "          25.10061593,  19.27639116]]])
          • created_at :
            2020-06-06T18:08:05.349112
            arviz_version :
            0.8.3
            inference_library :
            pymc3
            inference_library_version :
            3.8

      \n", + " [ 28.47079674, -2.79239653, 14.3904422 , ..., -12.63998944,\n", + " 20.70652864, -3.05241501],\n", + " [ 46.65347282, -46.95008753, -41.11688486, ..., -19.96545045,\n", + " -34.31368027, -7.26684645],\n", + " [-45.06736361, -5.81423829, 1.34788684, ..., 19.91178202,\n", + " 3.42152391, -20.64388589]]])
  • created_at :
    2020-06-10T18:19:23.484023
    arviz_version :
    0.8.3
    inference_library :
    pymc3
    inference_library_version :
    3.8

    • \n", " \n", " \n", " \n", " \n", "
  • \n", - " \n", - " \n", + " \n", + " \n", "
    \n", "
    \n", "
      \n", @@ -9471,61 +9594,14 @@ " stroke: currentColor;\n", " fill: currentColor;\n", "}\n", - "
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        • draw: 500
        • obs_dim_0: 8
        • chain
          (chain)
          int64
          0 1
          array([0, 1])
        • draw
          (draw)
          int64
          0 1 2 3 4 5 ... 495 496 497 498 499
          array([  0,   1,   2, ..., 497, 498, 499])
        • obs_dim_0
          (obs_dim_0)
          int64
          0 1 2 3 4 5 6 7
          array([0, 1, 2, 3, 4, 5, 6, 7])
        • obs
          (chain, draw, obs_dim_0)
          float64
          -5.685 -3.676 ... -3.49 -3.842
          array([[[-5.68484982, -3.67572298, -3.69248424, ..., -3.36837032,\n",
-       "         -5.38195577, -4.12822736],\n",
-       "        [-4.10376841, -3.33704052, -4.22176828, ..., -3.93177199,\n",
-       "         -3.33031266, -3.81047947],\n",
-       "        [-3.85559103, -3.33528345, -4.47061499, ..., -3.76296466,\n",
-       "         -3.22472706, -3.90074205],\n",
-       "        ...,\n",
-       "        [-5.33153649, -3.53110194, -3.7094273 , ..., -3.32927173,\n",
-       "         -5.0012374 , -4.04911422],\n",
-       "        [-4.36214329, -3.23060908, -3.95692031, ..., -3.67804405,\n",
-       "         -3.36774389, -3.8107918 ],\n",
-       "        [-4.66105354, -3.22411703, -3.87669797, ..., -3.46006493,\n",
-       "         -3.74633564, -3.86896332]],\n",
-       "\n",
-       "       [[-4.72113221, -3.64146398, -4.16273878, ..., -3.32269315,\n",
-       "         -3.26371571, -3.81334647],\n",
-       "        [-4.85324486, -3.61344026, -3.69578197, ..., -3.49330873,\n",
-       "         -4.58512601, -3.88295728],\n",
-       "        [-4.36147113, -3.27374   , -3.91051715, ..., -3.70705009,\n",
-       "         -3.52511237, -3.83822308],\n",
-       "        ...,\n",
-       "        [-4.21763758, -3.28207083, -3.94655912, ..., -3.50885658,\n",
-       "         -3.40642072, -3.99507955],\n",
-       "        [-5.44443507, -3.44821276, -3.69325776, ..., -3.40015884,\n",
-       "         -4.94684294, -3.92974475],\n",
-       "        [-4.10233177, -3.29728401, -3.82400903, ..., -3.64845971,\n",
-       "         -3.48986671, -3.84219653]]])
      • created_at :
        2020-06-06T18:08:05.346903
        arviz_version :
        0.8.3
        inference_library :
        pymc3
        inference_library_version :
        3.8

    \n", + "
    xarray.Dataset
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      • obs_dim_0
        (obs_dim_0)
        int64
        0 1 2 3 4 5 6 7
        array([0, 1, 2, 3, 4, 5, 6, 7])
      • obs
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        float64
        28.0 8.0 -3.0 7.0 ... 1.0 18.0 12.0
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    • created_at :
      2020-06-10T18:19:23.485040
      arviz_version :
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      inference_library :
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    \n", " \n", " \n", "
    • \n", " \n", - "
  • \n", - " \n", - " \n", - "
    \n", - "
    \n", - "
      \n", - "
      \n", - "\n", - "\n", - "Show/Hide data repr\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Show/Hide attributes\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
      xarray.Dataset
        • chain: 2
        • draw: 500
        • chain
          (chain)
          int64
          0 1
          array([0, 1])
        • draw
          (draw)
          int64
          0 1 2 3 4 5 ... 495 496 497 498 499
          array([  0,   1,   2, ..., 497, 498, 499])
        • energy
          (chain, draw)
          float64
          54.52 54.96 54.27 ... 48.71 52.74
          array([[54.52160643, 54.95687483, 54.27067055, 49.10964855, 50.72976279,\n",
-       "        46.15553381, 48.17393693, 44.47687278, 47.87431257, 46.21133132,\n",
-       "        50.66542988, 48.47734258, 49.5352183 , 47.03373166, 46.50687766,\n",
-       "        46.65056654, 51.02033901, 51.34946622, 50.97514503, 52.62398149,\n",
-       "        54.11538063, 49.38704282, 52.448772  , 58.85837468, 56.19848921,\n",
-       "        54.62420629, 53.95110432, 51.11996596, 50.57471821, 51.68038428,\n",
-       "        53.74084637, 50.19865079, 53.28808161, 54.89180583, 53.76867527,\n",
-       "        49.32524811, 47.08445233, 49.1243453 , 48.63807178, 45.20603424,\n",
-       "        52.99634132, 51.89072421, 47.71031214, 49.77615397, 48.20182985,\n",
-       "        49.25869024, 52.75251238, 51.1598453 , 48.25773604, 53.5598244 ,\n",
-       "        51.86943369, 58.59089918, 56.25740159, 49.48317636, 52.60384042,\n",
-       "        55.70528335, 54.08445635, 51.16553814, 53.09058361, 51.6535333 ,\n",
-       "        52.56935218, 55.23281544, 55.21947663, 57.23267929, 55.65247884,\n",
-       "        50.69957387, 48.20132423, 47.20210464, 47.46505534, 47.70952866,\n",
-       "        48.35531293, 51.08039718, 52.21331115, 51.16269634, 46.19248757,\n",
-       "        44.42095393, 49.470161  , 59.08761687, 52.05040137, 50.22388005,\n",
-       "        49.97409896, 51.87596621, 52.28301697, 47.99711269, 47.44711831,\n",
-       "        50.12178028, 49.73906133, 49.11136314, 48.35579577, 51.18917448,\n",
-       "        51.44566784, 50.39035018, 47.75678021, 48.47188683, 49.61341197,\n",
-       "        48.80640695, 50.27044123, 49.20724321, 47.81334872, 47.83239105,\n",
-       "        45.86280003, 50.68591711, 53.00304482, 50.30159952, 47.4708534 ,\n",
-       "        45.72613178, 47.06700367, 46.76972905, 52.36562995, 51.73090985,\n",
-       "        52.06586998, 52.66878118, 53.24908252, 57.23098017, 50.39790665,\n",
-       "        58.78099295, 49.06070421, 49.65475022, 49.58557107, 49.37204057,\n",
-       "        49.76708232, 45.26738951, 46.65781403, 47.34593169, 51.28649564,\n",
-       "        52.05116934, 57.12687705, 56.55121196, 53.97339635, 59.74400108,\n",
-       "        56.65847724, 53.00157087, 47.87992086, 48.72122558, 48.74976971,\n",
-       "        49.99931321, 51.37939305, 53.66377036, 52.40574199, 54.28425081,\n",
-       "        54.27756375, 48.62355588, 54.78227227, 56.89009561, 49.3998884 ,\n",
-       "        53.01421857, 59.14194416, 55.4769736 , 54.04591133, 48.51438803,\n",
-       "        51.51576145, 50.36912028, 48.85034295, 46.67157008, 47.50172218,\n",
-       "        51.33977961, 52.06635977, 51.64532305, 55.35540336, 54.5257091 ,\n",
-       "        54.04430903, 52.09718445, 50.87275206, 49.87873006, 48.17603978,\n",
-       "        47.60321542, 47.4497779 , 47.90886782, 50.92138271, 59.28103152,\n",
-       "        53.47716696, 50.46358368, 52.74687336, 53.85013681, 49.02239218,\n",
-       "        50.62123458, 50.21995491, 51.56848987, 52.20053707, 49.60123845,\n",
-       "        58.26423451, 57.51115144, 60.022103  , 55.89578116, 51.69176363,\n",
-       "        54.21693777, 51.6907195 , 50.49074369, 48.3769583 , 45.83136183,\n",
-       "        45.58907585, 47.4179835 , 49.84640292, 50.72474095, 52.51297121,\n",
-       "        47.33749676, 49.71804704, 50.22070501, 46.84124769, 49.38667317,\n",
-       "        47.33984659, 47.4314463 , 47.44635807, 45.51996513, 45.73395308,\n",
-       "        51.0030376 , 52.78370397, 51.66420403, 46.83508104, 49.39586429,\n",
-       "        50.19837873, 49.32080493, 47.17603073, 47.25434393, 47.74170705,\n",
-       "        49.40680436, 49.52699711, 50.84088062, 51.81092264, 50.33764575,\n",
-       "        50.44769977, 49.09420585, 53.22611751, 52.25917315, 46.86964895,\n",
-       "        46.36395158, 53.33477778, 55.82903514, 52.47133715, 51.0821753 ,\n",
-       "        50.55080747, 47.44256052, 48.48089976, 48.20652204, 49.78358086,\n",
-       "        52.42374909, 53.16344726, 53.25020103, 50.03293047, 49.73035208,\n",
-       "        48.92927453, 53.04114137, 57.93004039, 55.88141386, 54.25877798,\n",
-       "        49.04335994, 47.24083672, 51.45499411, 51.2990735 , 49.25146863,\n",
-       "        51.97573023, 51.30177488, 52.81859714, 55.08607418, 53.64896044,\n",
-       "        47.90562078, 47.37685531, 49.85905465, 52.54778476, 51.82132266,\n",
-       "        50.16566838, 49.82662244, 48.34319432, 48.25357395, 45.86624667,\n",
-       "        49.88291044, 49.34682388, 51.67856221, 45.54401496, 45.15925283,\n",
-       "        49.18105106, 47.79485295, 49.68857038, 48.742197  , 50.21175167,\n",
-       "        52.11092666, 53.99036219, 50.96319513, 54.12115636, 49.10965022,\n",
-       "        50.46237483, 48.58837103, 47.19636347, 47.11606613, 47.63753637,\n",
-       "        51.85565863, 52.90922418, 51.74390654, 52.76424441, 49.43350663,\n",
-       "        49.88200565, 49.58332475, 49.31126017, 53.93426356, 49.00841865,\n",
-       "        50.26790555, 56.6599792 , 54.49004485, 54.28881061, 54.68177041,\n",
-       "        54.47260288, 50.56304132, 52.09744814, 51.98245774, 51.19833937,\n",
-       "        50.07368621, 51.7632764 , 47.55513182, 45.37128629, 45.48234658,\n",
-       "        52.82297329, 49.21264152, 46.77243442, 49.95021056, 46.31865094,\n",
-       "        45.00932632, 46.58200528, 48.31752065, 46.29755461, 51.31405935,\n",
-       "        54.53919087, 52.20612455, 52.51279961, 53.87558256, 52.51613071,\n",
-       "        50.00421059, 52.56374857, 50.14562315, 54.95559643, 53.99484075,\n",
-       "        53.65264922, 52.24483306, 53.96513596, 51.92346592, 54.76463265,\n",
-       "        50.86503348, 48.71759475, 50.90548694, 49.42953537, 46.6970215 ,\n",
-       "        47.50267852, 46.28910759, 46.44191692, 51.4278663 , 49.50166175,\n",
-       "        50.36172026, 52.07050733, 49.99101015, 55.28676316, 53.28986807,\n",
-       "        56.03496623, 56.29910687, 52.94953976, 49.35067424, 50.12602492,\n",
-       "        53.13307434, 49.65117122, 50.40163346, 48.43488376, 46.95089029,\n",
-       "        49.81443666, 49.85240688, 54.86953323, 53.30949774, 50.420449  ,\n",
-       "        49.58389367, 53.58314139, 51.17776022, 51.60390155, 50.65734825,\n",
-       "        48.7501096 , 46.81571634, 46.59459091, 50.26756745, 54.42956098,\n",
-       "        50.33095827, 47.61830288, 52.80981098, 46.28437746, 47.78734214,\n",
-       "        47.86573256, 46.4227254 , 49.26119556, 50.06877899, 49.99082197,\n",
-       "        52.02296758, 49.42796761, 51.74261644, 53.36213415, 58.7402137 ,\n",
-       "        63.86784408, 59.20534414, 53.72144415, 51.0537658 , 51.27862467,\n",
-       "        53.38818183, 49.70313386, 51.87580152, 47.87567439, 48.69384443,\n",
-       "        53.68863693, 51.81363142, 49.6301598 , 52.13528675, 48.59869587,\n",
-       "        48.62901133, 49.45844042, 51.29091375, 51.207433  , 50.51757778,\n",
-       "        54.0953048 , 53.32710778, 50.21251521, 49.42815968, 49.35374968,\n",
-       "        46.81854429, 51.07617748, 52.19232418, 53.89082781, 47.5229164 ,\n",
-       "        51.01525914, 52.53535814, 53.00577307, 48.05771004, 46.40661989,\n",
-       "        46.54041088, 47.29801764, 47.44293976, 45.97106231, 49.11697927,\n",
-       "        51.33693395, 52.29346454, 53.11575722, 51.94422578, 49.89876913,\n",
-       "        47.48160038, 50.27766838, 49.34196258, 47.04818695, 48.87148726,\n",
-       "        46.72607847, 51.28395888, 54.63257457, 48.3686483 , 53.93040581,\n",
-       "        51.04002976, 54.77188972, 52.31058797, 50.32725151, 49.74455668,\n",
-       "        51.78741102, 50.94116801, 50.13931055, 51.86309955, 54.44276321,\n",
-       "        60.81698034, 58.82589071, 68.98475798, 62.02564084, 51.67527227,\n",
-       "        50.93162536, 48.56519192, 46.13648073, 52.98461519, 53.26008866,\n",
-       "        52.37621147, 51.928506  , 58.24021831, 56.84817852, 54.26597904,\n",
-       "        51.95699584, 48.12764737, 46.67075138, 52.68650677, 48.77241963,\n",
-       "        49.58681376, 50.60597679, 50.67549612, 56.16169128, 53.34365291,\n",
-       "        52.20767402, 49.2856608 , 48.55052149, 48.34306771, 52.93056148,\n",
-       "        53.11813376, 50.08108645, 48.54791324, 46.09753605, 51.74122273,\n",
-       "        50.81805106, 53.50201984, 52.78495898, 48.38661816, 52.35353566,\n",
-       "        46.82668261, 46.55487301, 50.52384383, 50.41186977, 56.2319735 ],\n",
-       "       [50.16361331, 46.62711183, 47.23610145, 53.28699413, 51.68517652,\n",
-       "        50.71092413, 48.84499357, 49.28761862, 49.07832709, 50.0026781 ,\n",
-       "        54.473093  , 49.81008283, 50.42967236, 50.35692724, 48.03328994,\n",
-       "        50.34253745, 52.90368169, 54.69412291, 61.30327169, 49.81797419,\n",
-       "        49.42008667, 53.45581835, 49.57166189, 47.53825733, 51.15944002,\n",
-       "        55.45530541, 52.46549332, 55.99315182, 58.15823896, 50.57708104,\n",
-       "        48.23112112, 46.14427454, 46.15555848, 45.86897627, 48.06362451,\n",
-       "        55.37724044, 51.25873221, 50.56404492, 52.34211184, 50.5790094 ,\n",
-       "        56.67642973, 52.37489375, 51.68770331, 50.05394278, 47.43000344,\n",
-       "        48.99996225, 51.84228733, 53.52030955, 52.20226346, 51.40431476,\n",
-       "        49.93526844, 50.2412656 , 47.87525523, 51.19718934, 48.50887948,\n",
-       "        46.69185387, 45.82492472, 51.92282536, 50.78224511, 49.08186821,\n",
-       "        53.02115435, 52.21497235, 53.48573797, 54.26253189, 55.10082421,\n",
-       "        48.37982005, 50.78795593, 50.00850187, 47.15201007, 50.88519269,\n",
-       "        50.52842056, 52.59706885, 54.79765573, 58.75673192, 56.07990983,\n",
-       "        50.17064295, 50.01403143, 55.46511548, 53.56169562, 55.78343   ,\n",
-       "        50.58496765, 46.80098036, 48.46870195, 49.37411999, 51.26253781,\n",
-       "        50.39171053, 52.11800727, 52.36046736, 51.88421593, 59.72325384,\n",
-       "        60.26365237, 50.42358178, 47.43044029, 53.43757059, 52.83789805,\n",
-       "        56.89871957, 55.89007065, 51.29782883, 50.18322282, 45.61733947,\n",
-       "        50.25498539, 47.7412606 , 46.99173779, 51.78903718, 54.64552474,\n",
-       "        50.11778611, 54.25325274, 50.74366548, 50.6189566 , 50.31884918,\n",
-       "        53.41866326, 51.37557933, 49.73311384, 50.92521916, 55.0110074 ,\n",
-       "        57.87976538, 52.11626432, 48.34714342, 52.1572418 , 49.39154052,\n",
-       "        51.41006686, 50.84410363, 51.63156232, 55.98060126, 52.60006144,\n",
-       "        53.25099525, 52.45160735, 51.02107816, 48.8719842 , 50.44148341,\n",
-       "        54.50682044, 53.83339673, 52.12124849, 50.99928885, 49.98763877,\n",
-       "        49.1167665 , 48.01986316, 52.54095181, 49.4448884 , 48.32118984,\n",
-       "        48.68426386, 48.64869427, 51.58569747, 46.84291499, 49.06818037,\n",
-       "        49.06143119, 50.2935445 , 50.95657384, 48.93952925, 52.39005283,\n",
-       "        48.14024611, 47.74244761, 52.83891496, 50.05359436, 48.45608875,\n",
-       "        46.88888672, 50.35639365, 49.5827445 , 50.14388507, 48.10339369,\n",
-       "        51.31134925, 49.10766527, 48.40981561, 50.37257662, 54.92827969,\n",
-       "        53.50996636, 51.0524846 , 52.88557845, 50.17599291, 48.05067162,\n",
-       "        47.32281387, 51.21157513, 47.88826716, 49.35217251, 47.74508622,\n",
-       "        45.78640373, 44.96304527, 44.83036983, 47.85837555, 48.11190504,\n",
-       "        45.96838354, 44.78054104, 45.50590579, 46.28860841, 51.57033615,\n",
-       "        46.27261294, 50.85013639, 51.36191921, 49.16891255, 52.90882732,\n",
-       "        50.11020028, 53.69319776, 51.05818504, 49.09453219, 48.58376521,\n",
-       "        50.48633757, 52.30657094, 50.83004693, 47.58598347, 46.87434153,\n",
-       "        52.80156077, 54.32281916, 56.63400343, 60.14647556, 55.5551745 ,\n",
-       "        50.96233167, 52.20627371, 50.14777941, 50.07189745, 49.50738056,\n",
-       "        43.61955396, 47.19130644, 45.87847293, 48.40355917, 57.20701945,\n",
-       "        52.4640959 , 50.00610371, 52.06522054, 48.54096965, 48.13252367,\n",
-       "        49.0540721 , 51.01149709, 52.22185385, 48.86740714, 59.85684606,\n",
-       "        49.29640578, 51.33610271, 57.5423779 , 56.95048125, 54.42944528,\n",
-       "        56.29316444, 50.82885804, 54.73348345, 49.8828759 , 47.92376588,\n",
-       "        47.08091943, 46.65393507, 47.2700024 , 47.35102919, 51.59700031,\n",
-       "        52.32507354, 49.2463373 , 50.9421696 , 52.5838049 , 52.32398206,\n",
-       "        56.60898915, 54.19820497, 55.37097894, 47.5878352 , 55.33051654,\n",
-       "        57.87566945, 56.99238213, 52.81039001, 51.83468992, 47.61445005,\n",
-       "        50.62151663, 49.48161058, 49.81566045, 48.47118224, 48.90544177,\n",
-       "        48.50138485, 50.77296886, 53.42497227, 53.54253052, 49.12463406,\n",
-       "        47.10187062, 46.96902581, 46.99307649, 52.79486624, 53.17867497,\n",
-       "        53.61593314, 51.92242719, 54.74017959, 55.19403312, 53.55261023,\n",
-       "        49.78942286, 49.32181498, 53.48483924, 50.67824242, 49.20817846,\n",
-       "        54.97083816, 56.90733229, 48.73670308, 52.52129619, 54.37887144,\n",
-       "        49.67886094, 51.62623028, 46.87845354, 47.64509709, 49.50568421,\n",
-       "        54.04141636, 53.46924566, 52.34363354, 52.54602226, 50.87070369,\n",
-       "        54.27171166, 50.85737295, 52.60456831, 52.22785809, 51.22073634,\n",
-       "        54.45416768, 49.60988916, 54.03086345, 50.81633277, 49.1759403 ,\n",
-       "        57.28296778, 58.00043749, 57.55881371, 54.28515851, 57.83085008,\n",
-       "        60.50043823, 59.84233904, 58.09981761, 53.91635361, 52.42784599,\n",
-       "        52.11891725, 49.40698604, 51.91230645, 50.69844503, 46.56379199,\n",
-       "        49.85581051, 50.38931426, 55.27506369, 56.78967254, 53.72319514,\n",
-       "        51.62231677, 51.38114536, 54.08585269, 52.07600622, 49.27245129,\n",
-       "        48.67716728, 50.97580786, 52.94129447, 51.66422519, 55.05177848,\n",
-       "        49.83151102, 51.78222044, 48.39042383, 51.6837263 , 50.34324913,\n",
-       "        49.24587202, 50.90767277, 49.0761185 , 45.22067981, 45.86828864,\n",
-       "        47.92226604, 55.3933141 , 51.78165197, 47.42942187, 46.34758806,\n",
-       "        46.27937182, 47.06945341, 48.07631717, 48.64136057, 48.60104402,\n",
-       "        48.92043787, 48.75950125, 49.03056725, 48.9863847 , 46.54140617,\n",
-       "        50.37052683, 49.1000927 , 54.13045593, 53.31355225, 51.10786248,\n",
-       "        50.5786586 , 51.59270521, 51.70552394, 53.57964988, 51.39551801,\n",
-       "        52.20901097, 51.6691176 , 49.26969105, 48.34712697, 53.06810819,\n",
-       "        51.08668493, 53.34469259, 48.77663216, 50.00880335, 52.06967102,\n",
-       "        49.53356853, 49.84954767, 50.09647902, 52.298723  , 52.37315436,\n",
-       "        46.84418757, 48.28373161, 51.65315392, 51.26151914, 46.10317657,\n",
-       "        51.0752779 , 53.52364518, 47.10127022, 50.17919388, 50.67331798,\n",
-       "        50.79745595, 51.82939783, 51.19550031, 47.35310722, 48.88104462,\n",
-       "        51.64116275, 55.12778675, 54.52507266, 48.6645698 , 54.66492622,\n",
-       "        59.79460973, 62.53382595, 50.61594094, 51.16259492, 49.39072387,\n",
-       "        53.41974223, 52.20458553, 49.58302975, 50.84521256, 53.67738506,\n",
-       "        52.9114082 , 52.14819655, 49.79335011, 49.73268089, 50.21881039,\n",
-       "        54.87169655, 53.48516861, 52.15133133, 46.20838179, 47.33302538,\n",
-       "        48.64542243, 51.72815427, 51.31186398, 53.10805374, 51.3545924 ,\n",
-       "        50.0502054 , 50.62532944, 55.60556536, 50.35195813, 46.49968076,\n",
-       "        46.8752375 , 47.51800046, 47.96951129, 45.8935262 , 49.86334493,\n",
-       "        59.72949754, 50.21179489, 46.64978192, 50.93392219, 48.73325085,\n",
-       "        47.60897864, 48.472894  , 50.61908245, 53.18742165, 54.92709782,\n",
-       "        50.97256895, 52.88435214, 53.45757804, 53.82922186, 51.22351512,\n",
-       "        49.55881708, 48.48040192, 58.07752335, 49.4645017 , 48.3900203 ,\n",
-       "        47.40693374, 49.66786186, 51.17144266, 52.08011617, 49.56480158,\n",
-       "        48.09154352, 52.80015354, 51.98205341, 49.39633219, 55.25431742,\n",
-       "        49.33217678, 51.29631635, 48.77383339, 51.88027475, 48.46659297,\n",
-       "        46.90945915, 46.521153  , 50.47392065, 53.23085225, 54.37782031,\n",
-       "        57.04687178, 54.67376368, 52.55679396, 49.15966109, 54.06733307,\n",
-       "        59.32341409, 59.59345131, 49.4827867 , 46.84919157, 44.47654981,\n",
-       "        46.3358912 , 45.60667653, 48.78847678, 55.51683424, 52.78370861,\n",
-       "        52.83145838, 46.60974623, 45.62709565, 48.71086868, 52.74363305]])
        • step_size_bar
          (chain, draw)
          float64
          0.5827 0.5827 ... 0.6018 0.6018
          array([[0.58273571, 0.58273571, 0.58273571, 0.58273571, 0.58273571,\n",
-       "        0.58273571, 0.58273571, 0.58273571, 0.58273571, 0.58273571,\n",
-       "        0.58273571, 0.58273571, 0.58273571, 0.58273571, 0.58273571,\n",
-       "        0.58273571, 0.58273571, 0.58273571, 0.58273571, 0.58273571,\n",
-       "        0.58273571, 0.58273571, 0.58273571, 0.58273571, 0.58273571,\n",
-       "        0.58273571, 0.58273571, 0.58273571, 0.58273571, 0.58273571,\n",
-       "        0.58273571, 0.58273571, 0.58273571, 0.58273571, 0.58273571,\n",
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-       "        0.60178576, 0.60178576, 0.60178576, 0.60178576, 0.60178576,\n",
-       "        0.60178576, 0.60178576, 0.60178576, 0.60178576, 0.60178576,\n",
-       "        0.60178576, 0.60178576, 0.60178576, 0.60178576, 0.60178576,\n",
-       "        0.60178576, 0.60178576, 0.60178576, 0.60178576, 0.60178576,\n",
-       "        0.60178576, 0.60178576, 0.60178576, 0.60178576, 0.60178576,\n",
-       "        0.60178576, 0.60178576, 0.60178576, 0.60178576, 0.60178576,\n",
-       "        0.60178576, 0.60178576, 0.60178576, 0.60178576, 0.60178576]])
        • diverging
          (chain, draw)
          bool
          False False False ... False False
          array([[False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False],\n",
-       "       [False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False]])
        • max_energy_error
          (chain, draw)
          float64
          -0.346 -0.5386 ... 0.4909 0.4531
          array([[-3.45958861e-01, -5.38593755e-01,  3.87242922e+00,\n",
-       "         9.57583755e-01,  5.07626701e+00,  5.52380031e-01,\n",
-       "         4.68629912e-01, -1.16402606e-01,  3.68614326e-01,\n",
-       "        -1.85091125e-01,  8.34590176e-01,  3.87514015e-01,\n",
-       "        -3.94672419e-01, -1.77613405e-01, -7.41839644e-01,\n",
-       "         2.22435718e-01,  1.44818710e+00,  3.21114703e-01,\n",
-       "         2.96626158e-01,  5.72171739e+01,  4.98954948e+01,\n",
-       "         2.04823143e-01,  3.15203861e-01,  2.74642553e+00,\n",
-       "         2.65229052e+00,  4.14126688e-01, -3.81097391e-01,\n",
-       "        -1.84114480e-01,  2.43827998e+00, -5.54991328e-01,\n",
-       "        -3.15189018e-01, -2.79553770e-01,  2.96661431e-01,\n",
-       "         4.68424687e-01, -3.50867174e-01,  4.87995769e-01,\n",
-       "         1.18923972e+00,  2.64310047e-01, -2.87177938e-01,\n",
-       "        -2.15095091e-01,  1.55408096e+00,  2.83338457e-01,\n",
-       "         2.27377424e-01,  2.00592786e-01, -3.56319604e-01,\n",
-       "         5.13778985e-01,  5.61689079e-01,  5.29556535e+00,\n",
-       "         8.64533383e-02,  4.46890667e-01, -4.60951704e-01,\n",
-       "         2.88829323e-01,  2.32782149e+00, -1.87442701e-01,\n",
-       "        -3.05865485e-01,  3.94343813e-01, -7.43098576e-01,\n",
-       "         3.52842448e+00,  7.11474053e-01, -6.04827307e-01,\n",
-       "         4.98747287e+00,  1.38038384e+00,  1.08947220e-01,\n",
-       "        -5.58079586e-01,  6.70782431e-01, -2.67905008e-01,\n",
-       "        -5.73830782e-01,  4.39329801e-01, -6.27351991e-01,\n",
-       "         4.69701754e+00,  2.12199292e+00, -1.91756184e-01,\n",
-       "        -1.12054542e+00, -3.40460768e-01, -8.74683455e-01,\n",
-       "         4.38390666e-01,  4.03745567e-01,  6.40257352e-01,\n",
-       "         7.27694589e+01,  1.82214826e+00,  1.25206824e+00,\n",
-       "         2.37610413e-01, -8.51191057e-01, -8.56125154e-01,\n",
-       "         3.43945179e-01,  1.11968072e+00, -4.03164442e-01,\n",
-       "         2.34874650e+00,  4.86789244e-01,  8.78210286e-01,\n",
-       "        -6.81684652e-01,  3.43494807e+00,  3.34020624e-01,\n",
-       "        -1.45782737e+00, -2.57551957e-01,  4.63244467e-01,\n",
-       "        -1.76187185e-01,  1.10541542e-01, -2.97745732e-01,\n",
-       "        -2.28084973e-01,  2.84097898e-01,  1.99394884e+00,\n",
-       "        -5.86414864e-01, -8.92857362e-01, -1.55566886e-01,\n",
-       "         1.81163196e+00,  2.18869096e+01, -1.22828623e-01,\n",
-       "         6.84918446e-01,  7.64573434e-01,  2.96271035e-01,\n",
-       "        -2.16494135e-01, -1.92511678e-01,  6.61524764e-01,\n",
-       "         2.76567260e-01,  2.72532306e+01,  4.09988106e+00,\n",
-       "         1.79526877e-01,  2.39160589e+00,  3.29531963e-01,\n",
-       "         3.77012181e-01, -5.30442772e-01,  3.34952660e-01,\n",
-       "        -2.51363046e-01,  2.94234221e+00,  1.08715736e+00,\n",
-       "         8.87629706e+00, -2.42188910e-01, -2.81862870e-01,\n",
-       "         4.29960903e-01, -8.89114939e-01,  1.38210400e+00,\n",
-       "        -2.81010363e-01,  2.57112713e-01, -2.02113331e-01,\n",
-       "         2.73401806e-01, -5.19386797e-01, -3.32498710e-01,\n",
-       "         1.22419450e-01, -5.87969136e-01,  6.52980826e-01,\n",
-       "        -3.41636664e-01,  8.33565797e-01,  5.46158002e-01,\n",
-       "        -4.89995490e-01,  3.05843116e-01,  6.74488721e-01,\n",
-       "        -6.13043148e-01,  4.73435477e-01, -1.74653101e-01,\n",
-       "        -4.64788693e-01, -4.45702670e-01, -2.61850364e-01,\n",
-       "         8.25777345e-01, -4.07749141e-01, -2.03927470e+00,\n",
-       "         1.33266369e+00, -1.84351289e+00,  3.98830356e-01,\n",
-       "        -5.25674437e-01,  4.87700548e-01,  2.06091070e-01,\n",
-       "        -2.76063358e-01,  4.17932477e-01, -1.70656151e-01,\n",
-       "         8.26321577e-01, -1.95782693e-01,  3.04544000e-01,\n",
-       "        -5.55784364e-01, -1.04941716e+00, -1.15189583e+00,\n",
-       "         1.09051227e+01, -3.73903307e-01,  6.11502980e-01,\n",
-       "        -3.26614022e-01, -1.17154802e+00, -2.54743498e-01,\n",
-       "        -1.12064638e-01,  5.84723198e+01,  4.19541665e-01,\n",
-       "         9.50197607e-01,  9.10532062e+00, -8.71837824e-01,\n",
-       "        -7.41036755e-01, -7.95553696e-01,  1.57441161e+01,\n",
-       "        -4.06270268e-01, -2.07463142e-01, -1.86113374e-01,\n",
-       "         1.68600695e-01,  2.51509982e-01,  3.42941256e+00,\n",
-       "        -1.66978469e-01, -4.85169809e-01, -6.43272033e-01,\n",
-       "        -3.90656025e-01,  4.02474938e-01, -3.33851319e-01,\n",
-       "        -1.36425053e-01,  2.30238759e-01, -4.80080034e-01,\n",
-       "        -8.68792483e-01,  9.06635899e-01, -2.08750612e-01,\n",
-       "         3.34781700e-01,  6.09199686e-01,  3.82453188e-01,\n",
-       "         2.24654044e-01, -1.83904392e-01,  3.89753040e-01,\n",
-       "        -2.78985712e-01, -4.61756078e-01, -5.68271816e-01,\n",
-       "         7.25465465e-01,  1.09642167e+00,  1.90095457e-01,\n",
-       "         2.68869674e-01, -1.34546228e-01,  5.23307781e+00,\n",
-       "         1.22299511e-01, -4.51630746e-01,  3.88597953e-01,\n",
-       "         1.47048551e-01, -2.19712416e-01, -3.46909419e-01,\n",
-       "         1.92230343e-01,  4.70321935e-01,  1.43562409e+00,\n",
-       "        -5.11738645e-02,  4.40793936e+01, -5.94418106e-01,\n",
-       "        -1.31497099e-01, -9.60106690e-01,  2.78226544e+00,\n",
-       "         7.83837820e-02,  8.04756639e-01,  6.00107045e-01,\n",
-       "        -4.61190472e-01,  3.35813374e-01,  2.80688169e-01,\n",
-       "         1.88144934e-01,  1.22720026e+00,  9.52216026e-01,\n",
-       "        -4.70740556e-01,  7.45188005e+00, -2.72084889e-01,\n",
-       "         3.53891233e-01,  1.98597238e+00,  1.00441644e+00,\n",
-       "        -3.16637076e-01, -2.44622330e-01, -3.15419756e-01,\n",
-       "         4.19142451e+01,  2.01465062e-01, -3.08469777e-01,\n",
-       "         1.85172793e-01,  4.76193408e+00,  3.41702342e-01,\n",
-       "         3.17949165e-01, -3.77482793e-01,  1.52292994e-01,\n",
-       "        -2.10162413e-01, -1.36280586e-01,  2.29911124e-01,\n",
-       "         1.57823173e-01,  2.52310025e+01,  1.46499986e+01,\n",
-       "         5.58195847e+00,  1.53320126e-01, -1.21797399e-01,\n",
-       "         5.98759494e+00, -1.39560248e-01,  4.82997546e-01,\n",
-       "         2.34932480e-01,  7.47489104e+00, -2.79421418e-01,\n",
-       "         5.67911567e-01,  8.33676216e-02,  2.48527674e+01,\n",
-       "        -4.20994583e-01, -3.74318548e-01,  5.01901366e-01,\n",
-       "         2.68992629e-01,  2.65042107e-01,  9.12942084e-02,\n",
-       "         2.64254609e-01,  8.68700373e+00, -9.12322599e-01,\n",
-       "        -1.06435455e+00, -1.83136871e-01,  1.40308729e+00,\n",
-       "         4.01946900e+00, -1.22493441e+00,  7.96484862e-01,\n",
-       "        -8.14299594e-02,  2.28111476e-01,  1.16201663e+00,\n",
-       "        -6.25584924e-01, -5.59565372e-01,  7.93134531e-01,\n",
-       "         5.38416313e-01, -3.76822134e-01, -3.75287530e-01,\n",
-       "        -3.91811217e-01, -4.80554508e-01, -9.50555682e-01,\n",
-       "         8.41922847e+00,  7.05759628e+00, -9.60696759e-02,\n",
-       "        -3.44708282e-01,  4.97313379e+01,  2.37843288e-01,\n",
-       "         6.20794357e-01,  4.23444213e-01,  2.27299881e-01,\n",
-       "        -2.96824083e-01,  1.35702174e-01,  1.63460942e-01,\n",
-       "         3.56947757e-01,  7.81433937e-01, -2.07313741e-01,\n",
-       "        -2.34499127e-01, -1.79140209e-01,  1.68680144e-01,\n",
-       "         8.21306225e-01,  5.31096207e-01,  3.38146173e-01,\n",
-       "         2.00327120e-01,  6.19240100e+00,  2.35866046e+00,\n",
-       "         4.73014539e-01,  3.60178353e-01,  2.69348137e+00,\n",
-       "         4.00284900e-01,  3.74672777e-01, -4.34558454e-01,\n",
-       "         3.68570269e-01,  8.27030547e-01, -4.82800816e-01,\n",
-       "         1.66651527e-01,  6.54896257e-01, -8.27095607e-02,\n",
-       "         2.05153450e-01,  1.45968693e+00,  1.70633048e-01,\n",
-       "         1.73906012e-01, -2.30375480e-01, -5.14583700e-02,\n",
-       "         2.42191383e+00, -4.41326738e-01, -2.85642855e-01,\n",
-       "         3.28916381e-01,  1.94729141e+00,  8.30503940e-01,\n",
-       "         2.93553867e-01,  4.80879157e+00, -2.09987098e-01,\n",
-       "         2.22344228e-01, -3.78549903e-01,  2.23875941e-01,\n",
-       "        -9.88241560e-01,  8.82151599e+00,  1.04577074e+00,\n",
-       "         2.47943158e-01, -2.36955478e-01,  1.19315417e-01,\n",
-       "         8.22909072e-01, -1.06303182e+00,  2.88289256e-01,\n",
-       "        -3.34857050e-01,  4.39420111e-01,  2.76678402e-01,\n",
-       "         2.23271731e-01,  3.09168634e-01,  2.05877148e+01,\n",
-       "        -5.27679779e-01,  4.11145995e-01,  1.10066992e+01,\n",
-       "        -1.61494321e-01,  2.56431566e+00,  3.12585705e-01,\n",
-       "        -2.71384616e-01,  8.39416238e+00,  6.56507997e+00,\n",
-       "         4.34441692e-01,  2.73074894e-01,  1.73137288e-01,\n",
-       "         3.49980365e-01,  8.94331756e+00,  1.78057467e-01,\n",
-       "         3.42761137e-01, -2.41547970e+00, -8.78237010e-01,\n",
-       "         2.50783032e-01,  8.73289230e-01, -5.38794104e-01,\n",
-       "        -2.21614747e-01,  2.97970338e+00, -4.98041339e-01,\n",
-       "        -4.19266567e-01,  6.46738300e+00, -4.32932690e-01,\n",
-       "         1.81948972e-01,  3.53034094e-01,  8.70805744e-02,\n",
-       "         1.75605464e-01, -1.50994507e-01,  3.16111246e-01,\n",
-       "         3.84961109e+00,  3.43004680e-01,  7.51170898e+00,\n",
-       "        -1.95908557e-01, -3.06939525e-01,  1.09661325e+00,\n",
-       "         2.47756801e-01, -1.01923190e-01,  1.16807112e+00,\n",
-       "         3.77260597e-01,  1.89571085e+01, -3.59560390e-01,\n",
-       "         5.56406060e-01, -4.64857220e-01,  1.05632205e+01,\n",
-       "        -3.14585118e-01, -2.58622841e-01, -6.55867014e-01,\n",
-       "         2.55142898e-01,  4.24843845e-01, -1.87978628e-01,\n",
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-       "        -2.28647723e-01, -2.24880373e-01,  3.80942380e+00,\n",
-       "         1.39224462e-01,  4.30664630e-01,  2.14031697e-01,\n",
-       "         1.40841338e+00, -2.65590359e-01, -1.02214986e-01,\n",
-       "         4.80570096e-01, -3.01539289e-01,  2.92197983e+00,\n",
-       "        -5.02933108e-01, -7.28240773e-01, -2.34828288e-01,\n",
-       "        -2.64087873e-01,  4.41070048e-01,  1.47014823e-01,\n",
-       "         3.08336830e-01,  2.59153654e-01,  1.77255475e+01,\n",
-       "         2.17456568e-01,  6.42754622e-01, -8.26128334e-01,\n",
-       "         3.26236035e+00,  7.94454486e-01,  8.35032638e-01,\n",
-       "        -1.00066040e-01,  3.16367687e-01,  3.87681436e-01,\n",
-       "        -1.06656528e+00,  5.63192498e-01,  2.85351880e-01,\n",
-       "        -2.05559024e-01,  9.90147286e+00, -6.59674045e-01,\n",
-       "        -2.75452872e-01,  2.14810293e-01,  1.30824509e+01,\n",
-       "         3.63799465e-01,  2.68919598e+00, -4.93900642e-01,\n",
-       "        -1.84805855e-01, -2.14156063e-01,  1.03759560e+00,\n",
-       "         5.62219053e-01, -4.82588960e-01,  2.12586587e-01,\n",
-       "        -3.52038943e-01,  2.66059224e+00,  4.79437017e-01,\n",
-       "        -1.47579725e-01,  3.51092649e-01, -1.22472509e-01,\n",
-       "        -1.34034660e+00, -9.13082002e-01,  9.60122771e-01,\n",
-       "        -7.74678142e-01,  4.79667601e-01,  6.53077799e-01,\n",
-       "        -2.00878835e-01,  4.01282335e-01,  9.69058470e-01,\n",
-       "        -4.07795688e-01,  9.75788732e+00],\n",
-       "       [ 1.79860139e-01,  2.57749327e-01,  2.06620668e-01,\n",
-       "         5.24780963e-01, -4.44786433e-01,  5.33808842e-01,\n",
-       "        -1.97703417e-01, -4.78780043e-01,  7.02168147e-01,\n",
-       "         3.92322718e-01,  5.84171048e-01, -3.06658289e-01,\n",
-       "         3.77004855e-01,  9.69939884e-02, -1.92274140e-01,\n",
-       "         3.73763327e-01,  2.15874824e-01,  3.42594685e+00,\n",
-       "         1.49468999e+02, -4.84675991e-01,  8.52539351e-01,\n",
-       "         4.94385242e-01,  5.82395728e-01,  1.13119778e-01,\n",
-       "        -6.04476805e-01,  4.35070945e-01, -6.35107530e-01,\n",
-       "         4.81996847e-01, -2.54631442e-01, -4.51432312e-01,\n",
-       "         8.34066785e+00,  8.28768212e-01, -3.55524897e-01,\n",
-       "        -4.12397313e-01,  7.88740131e+01,  1.09933259e+00,\n",
-       "         2.54380183e-01, -3.49554892e-01,  1.62513975e-01,\n",
-       "        -1.19771232e-01,  4.97793173e-01, -4.23941084e-01,\n",
-       "         2.14389241e-01,  6.08426446e-01,  2.06800294e-01,\n",
-       "        -1.45216515e-01, -4.79884896e-01, -2.11866654e-01,\n",
-       "         1.37523425e-01,  3.74533374e+01,  5.68415731e-01,\n",
-       "         2.85875080e+00, -4.05025328e-01,  3.79227342e+01,\n",
-       "         3.63158625e+00,  3.58718116e-01, -1.12933153e-01,\n",
-       "         6.89334980e-01, -2.68342347e-01, -2.15851806e-01,\n",
-       "        -2.99624337e-01, -4.39105973e-01,  3.46531664e-01,\n",
-       "        -3.06174454e-01,  3.45847569e-01, -4.05139068e-01,\n",
-       "         9.96094177e-01,  5.80203033e-01,  6.74221338e-01,\n",
-       "        -2.07705255e+00,  4.52474761e-01, -3.55197721e-01,\n",
-       "         3.77985046e-01,  6.27543908e+00, -4.81141424e-01,\n",
-       "         6.36341124e-01,  2.98708243e-01,  1.74152964e-01,\n",
-       "        -3.05865414e-01,  1.86821691e-01, -4.98983644e-01,\n",
-       "        -1.94021934e-01,  4.53315514e-01,  3.26685454e-01,\n",
-       "         1.41211741e+00, -2.49670722e-01,  3.23204014e-01,\n",
-       "         4.25557367e-01, -2.15044088e-01,  9.02963769e-01,\n",
-       "        -3.82430144e-01, -4.15788712e-01, -3.64887463e-01,\n",
-       "         2.50792843e+00,  8.18705926e-01,  2.23390920e-01,\n",
-       "        -1.63053189e-01, -5.47179947e-01, -7.63028649e-01,\n",
-       "        -2.37482874e-01,  1.54210743e+00,  6.54381740e-01,\n",
-       "         1.73754759e-01,  3.12250675e-01,  5.42658382e-01,\n",
-       "        -2.65303647e-01, -5.68167147e-01, -3.43746145e-01,\n",
-       "        -4.51142776e-01,  2.19192229e-01,  3.23391244e-01,\n",
-       "        -1.10505959e-01, -3.30013126e-01,  7.60311276e-01,\n",
-       "        -3.53148871e-01, -9.42147547e-01, -1.25580833e+00,\n",
-       "         4.58550849e-01,  3.33075524e-01,  2.53154980e-01,\n",
-       "         2.22866857e-01, -2.39490677e-01, -2.96397625e-01,\n",
-       "         2.79725105e-01, -2.09311035e-01,  1.16047409e-01,\n",
-       "         1.54082228e-01,  7.79763043e-01, -3.77346737e-01,\n",
-       "         4.63933280e-01,  4.28535496e-01, -1.89024197e-01,\n",
-       "         1.52572973e-01, -2.90130809e-01, -5.04256816e-01,\n",
-       "        -5.04471627e-01,  3.90582453e-01,  2.24551819e-01,\n",
-       "        -2.54122083e-01, -2.66988657e-01, -1.41051592e-01,\n",
-       "        -3.52526782e-01,  2.77246365e+01,  3.16792916e-01,\n",
-       "         7.12643878e-01, -1.72328231e+00,  4.28980632e-01,\n",
-       "         2.90896925e-01,  1.97114123e+01,  2.70253917e-01,\n",
-       "        -1.76361420e-01,  2.37243142e-01,  3.33496475e-01,\n",
-       "        -3.98098280e-01,  2.86361117e-01,  2.58629396e-01,\n",
-       "         4.27549059e-01, -2.73191925e-01, -1.59408733e-01,\n",
-       "        -9.07684445e-02, -3.63686430e-01, -4.25483583e-01,\n",
-       "        -8.41948985e-01,  3.57700683e-01,  6.35300850e-01,\n",
-       "         9.69392194e+00,  1.63622778e+00,  2.08872914e+01,\n",
-       "        -3.97835606e-01, -1.76402531e-01,  1.80774883e-01,\n",
-       "         9.78057258e+00, -6.30791812e-01,  3.46317475e-01,\n",
-       "        -4.54440031e-01, -3.58954736e-01, -2.68658117e-01,\n",
-       "        -1.30632252e-01,  2.04265266e-01, -7.49423282e-01,\n",
-       "         2.83819558e+00,  3.50051265e-01, -6.58239637e-02,\n",
-       "         3.26444743e-01,  2.31561040e+01,  3.07045191e-01,\n",
-       "         2.71552848e-01,  2.27180959e-01,  2.15186980e-01,\n",
-       "         3.57000979e+00,  1.98350650e-01,  3.88377102e-01,\n",
-       "        -1.90270260e-01, -3.06137622e-01, -3.54989176e-01,\n",
-       "         1.19358374e+00,  2.96456861e-01, -3.23815684e-01,\n",
-       "        -1.13041793e-01, -1.86206518e-01,  1.20614578e+00,\n",
-       "        -1.10262050e-01,  2.91638248e+00, -3.24372767e-01,\n",
-       "        -4.55969135e-01,  2.60173875e-01,  2.97251709e-01,\n",
-       "        -2.54866618e-01,  3.53753826e-01, -3.10201956e-01,\n",
-       "         6.54483616e-02,  5.96420979e+00, -2.69245515e-01,\n",
-       "         4.51668756e-01,  5.85961475e-01, -9.93423023e-02,\n",
-       "        -3.40058688e-01,  2.52270919e+00, -1.27451983e+00,\n",
-       "         5.57460146e-01, -2.95833472e-01,  7.71680386e-01,\n",
-       "         6.10288598e-01, -3.12518914e-01,  1.15064550e+00,\n",
-       "        -4.45365452e-01,  5.08594188e-01, -2.34755812e-01,\n",
-       "         7.82575721e+00, -5.30065847e-01,  4.08784319e+00,\n",
-       "        -5.77140785e-01,  1.49980801e+01,  1.26534564e-01,\n",
-       "         1.83895169e-01, -8.82823608e-02,  1.08122585e-01,\n",
-       "         1.10012264e-01,  5.07735899e-01, -8.01486480e-01,\n",
-       "        -5.46104213e-01,  6.59353067e-01,  1.34599417e+00,\n",
-       "         4.78756457e+01, -1.82288448e-01,  2.88275377e-01,\n",
-       "         4.51144595e+00, -9.52218729e-01, -1.12027185e-01,\n",
-       "         7.41114602e-01,  3.56897620e-01, -3.50634407e-01,\n",
-       "        -1.52626842e-01, -3.31329260e-01, -3.41326935e-01,\n",
-       "         1.87132535e+00,  4.23750496e-01,  3.26390227e+01,\n",
-       "         2.24571080e-01, -4.25066590e-01, -1.04817550e-01,\n",
-       "         1.68097357e-01,  5.63460952e-01,  9.10214293e-01,\n",
-       "         9.85814228e-01,  8.09785661e-01,  7.65993076e-01,\n",
-       "         2.04838229e-01,  6.71732965e-01, -3.72004934e-01,\n",
-       "        -4.30615958e-01,  2.11320934e+00,  4.09809231e-01,\n",
-       "        -3.72958377e-01,  1.11659596e+00,  7.58676653e-01,\n",
-       "        -5.74167469e-01,  3.83524103e-01, -4.64160426e-01,\n",
-       "         1.14066044e+00,  8.50022177e-01,  1.01334198e+01,\n",
-       "        -1.17975922e-01,  1.14889315e+00,  1.48870027e+00,\n",
-       "        -3.27642931e-01,  1.94769974e-01, -6.28140526e-01,\n",
-       "        -1.95256503e-01,  1.67805498e-01,  5.97920849e-01,\n",
-       "        -1.95009948e-01,  3.09144389e+01,  2.82972383e-01,\n",
-       "         1.23466035e+00,  2.18070124e+00,  5.39783852e-01,\n",
-       "         5.63705408e-01, -4.86858657e-01,  4.04569664e-01,\n",
-       "         1.20159276e-01, -2.10626474e-01,  3.04233177e+01,\n",
-       "         2.81251749e-01, -3.85626289e-01,  5.86986983e-01,\n",
-       "         5.47170281e+00, -9.94974670e-01,  6.86066646e-01,\n",
-       "         5.46518119e-01, -1.47599907e+00,  1.96056001e-01,\n",
-       "        -4.54718962e-01, -5.77235620e-01,  4.10137993e-01,\n",
-       "         5.09502960e-01, -5.30135726e-01,  4.45355269e-01,\n",
-       "         3.96742466e+00, -1.55841458e-01,  3.47470058e-01,\n",
-       "        -2.53872090e-01,  8.43270788e-01,  7.59072577e+00,\n",
-       "        -3.81672211e-01, -4.04803303e-01,  4.90014233e-01,\n",
-       "         1.14564023e+00,  3.70610923e-01,  4.82841982e-01,\n",
-       "        -3.56606068e-01,  7.84232005e-01,  7.63924363e-01,\n",
-       "         9.59218934e-01,  4.59299406e-01, -1.93037175e-01,\n",
-       "         3.78654420e-01,  4.63502510e-01,  1.09189024e+00,\n",
-       "         3.28719248e-01,  2.16164142e-01,  1.18603943e-01,\n",
-       "        -3.07858018e-01,  2.17432835e-01,  3.08015511e-01,\n",
-       "         3.75806208e-01,  5.97356377e-01,  3.13421016e-01,\n",
-       "        -2.05441059e-01, -5.60110830e-01, -7.32063796e-01,\n",
-       "         5.95150978e-01,  1.33400714e-01, -1.10698425e-01,\n",
-       "        -1.83111379e-01,  7.85180666e-01,  1.50793722e+00,\n",
-       "         2.23055469e+00, -1.04951308e+00,  1.56619728e-01,\n",
-       "         4.72729184e-01,  1.98840468e-01,  2.43956027e+00,\n",
-       "        -6.44417944e-01, -2.13045459e-01,  1.62714681e-01,\n",
-       "        -7.17335891e-01, -4.91982666e-01, -3.01744648e-01,\n",
-       "        -5.36660288e-01, -3.63805153e-01, -7.92199462e-01,\n",
-       "        -2.77626719e-01, -2.27987211e-01,  1.13479811e-01,\n",
-       "        -3.94811423e-01,  4.44073418e-01, -3.58262551e-01,\n",
-       "         3.61968642e-01, -3.18856486e-01,  2.60103669e-01,\n",
-       "        -4.82655997e-01,  6.05578826e-01,  3.11054975e-01,\n",
-       "         1.53421880e+00, -1.90645779e-01,  4.27744397e-01,\n",
-       "         1.75730626e-01,  4.38024739e-01,  2.06957738e-01,\n",
-       "         2.46515010e-01,  6.38127228e+00,  1.53893890e-01,\n",
-       "         1.59020989e-01,  2.02399792e-01,  3.46873036e-01,\n",
-       "        -1.30905252e-01, -3.19647948e-01, -8.13670395e-02,\n",
-       "         3.67275950e-01, -2.64462424e-01,  4.69255756e-01,\n",
-       "         1.24663598e+00,  1.33252606e-01,  7.45836559e-01,\n",
-       "         2.15101285e+00,  1.99968249e+00, -1.75338313e+00,\n",
-       "         7.74248279e-01, -1.99829589e-01,  3.61171169e-01,\n",
-       "        -2.74385046e-01, -3.47980997e-01,  1.12769899e+00,\n",
-       "         1.34639349e-01, -1.03709348e+00,  2.94511142e+00,\n",
-       "        -2.18812769e-01,  1.45991406e-01, -2.74716445e+00,\n",
-       "         6.86877738e-01, -4.99807762e-01,  3.55803118e+01,\n",
-       "        -6.31042132e-01,  2.52738661e-01,  1.60547883e-01,\n",
-       "         3.06600501e-01, -4.33417286e-01,  1.13550421e-01,\n",
-       "        -1.76201166e-01, -2.05353893e-01,  2.37089600e-01,\n",
-       "         1.39924298e+00,  6.60022922e-01,  4.61475883e-01,\n",
-       "         2.96027657e-01,  3.08107786e-01, -1.25267013e-01,\n",
-       "        -5.54496970e-01,  3.55440627e-01,  1.90019505e+01,\n",
-       "        -4.82243969e-01, -3.08325763e-01,  1.02328270e+00,\n",
-       "        -4.49753630e-01,  3.28038109e-01,  1.67902450e+01,\n",
-       "         2.77578453e-01, -2.50234872e-01, -4.42939531e-01,\n",
-       "         7.99652573e-01, -2.61988124e-01,  4.04839449e-01,\n",
-       "         3.51167998e-01,  3.21536818e-01, -3.52745152e-01,\n",
-       "        -1.67741214e-01,  2.44523829e+01,  9.63196553e-02,\n",
-       "         5.62037402e-01, -3.37046660e-01,  1.06746699e-01,\n",
-       "         4.68680801e-01,  1.75659107e-01, -4.12512463e-01,\n",
-       "         3.95325057e-01,  7.21339091e-01,  4.04488548e+00,\n",
-       "        -5.51312779e-01,  1.95791327e+00, -1.20314207e+00,\n",
-       "         1.73148481e+00, -3.17581093e-01,  6.50544647e-01,\n",
-       "        -2.15041484e-01,  1.24453392e-01, -1.23538429e-01,\n",
-       "         4.45132215e-01,  1.70787436e-01, -6.07080597e-01,\n",
-       "         3.66084740e-01, -1.59701918e+00, -9.76927104e-01,\n",
-       "        -4.25384829e-01,  3.43367597e-01,  6.54513636e-01,\n",
-       "        -3.35890328e-01, -3.59805317e-01,  4.31611098e-01,\n",
-       "         1.53287150e+00,  1.66437679e-01,  1.09101852e-01,\n",
-       "         3.40450009e-01,  7.97478560e-01, -3.09409879e-01,\n",
-       "         6.03390464e-01, -4.31516610e-01,  4.24302891e-01,\n",
-       "         4.90853702e-01,  4.53074760e-01]])
        • depth
          (chain, draw)
          int64
          3 3 3 2 2 3 3 3 ... 3 3 3 3 3 3 3 3
          array([[3, 3, 3, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3,\n",
-       "        3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3,\n",
-       "        3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3,\n",
-       "        3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2,\n",
-       "        2, 3, 3, 3, 3, 3, 2, 2, 3, 3, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
-       "        3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
-       "        3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
-       "        3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
-       "        3, 3, 2, 3, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
-       "        3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
-       "        3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
-       "        3, 3, 3, 3, 3, 3, 3, 2, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
-       "        3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
-       "        2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3,\n",
-       "        3, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3,\n",
-       "        3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
-       "        3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3,\n",
-       "        3, 2, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
-       "        3, 3, 3, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
-       "        1, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
-       "        3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
-       "        2, 3, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 3, 3,\n",
-       "        3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n",
-       "       [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3,\n",
-       "        3, 3, 3, 3, 2, 3, 3, 2, 2, 3, 3, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
-       "        3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
-       "        3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
-       "        3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
-       "        3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
-       "        3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
-       "        3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3,\n",
-       "        3, 3, 3, 3, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 2, 3, 3,\n",
-       "        3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 3, 2, 3, 3,\n",
-       "        3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
-       "        3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
-       "        3, 3, 2, 3, 3, 3, 3, 2, 3, 3, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
-       "        3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
-       "        3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
-       "        3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 3, 2,\n",
-       "        3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3,\n",
-       "        3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
-       "        3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2,\n",
-       "        3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
-       "        2, 2, 3, 3, 3, 3, 4, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
-       "        3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
-       "        3, 3, 3, 2, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3]])
        • mean_tree_accept
          (chain, draw)
          float64
          0.9997 0.9675 ... 0.7485 0.7574
          array([[9.99669151e-01, 9.67536833e-01, 4.45610613e-01, 7.15858088e-01,\n",
-       "        6.02150780e-01, 7.06296880e-01, 7.82306370e-01, 9.99950624e-01,\n",
-       "        7.63598090e-01, 9.54663408e-01, 5.82583289e-01, 8.27639789e-01,\n",
-       "        9.32793675e-01, 9.25906322e-01, 9.10434203e-01, 9.37227778e-01,\n",
-       "        7.16665392e-01, 8.63247253e-01, 8.63051201e-01, 8.64190394e-02,\n",
-       "        8.01842992e-02, 9.70276708e-01, 8.33499610e-01, 4.15721508e-01,\n",
-       "        5.52153293e-01, 9.07259671e-01, 9.02984889e-01, 9.47466962e-01,\n",
-       "        4.87296148e-01, 1.00000000e+00, 9.64025757e-01, 9.86136712e-01,\n",
-       "        8.11129813e-01, 8.06586061e-01, 1.00000000e+00, 9.20298392e-01,\n",
-       "        8.59983088e-01, 8.89827171e-01, 9.98933781e-01, 1.00000000e+00,\n",
-       "        7.10108867e-01, 9.00163878e-01, 9.05094628e-01, 8.96993845e-01,\n",
-       "        1.00000000e+00, 8.64833106e-01, 7.79829396e-01, 7.28576891e-01,\n",
-       "        9.66945567e-01, 7.88128877e-01, 9.78385318e-01, 8.13144613e-01,\n",
-       "        4.02506367e-01, 9.68001003e-01, 9.40833180e-01, 8.72633875e-01,\n",
-       "        8.98789791e-01, 4.96900179e-02, 6.89556904e-01, 8.55714761e-01,\n",
-       "        5.65311527e-01, 7.08729983e-01, 9.57455837e-01, 8.87411825e-01,\n",
-       "        9.30186908e-01, 9.83535693e-01, 9.91331502e-01, 9.27156608e-01,\n",
-       "        1.00000000e+00, 5.73672579e-01, 5.03596033e-01, 9.52937765e-01,\n",
-       "        1.00000000e+00, 9.80758411e-01, 9.95588735e-01, 6.66046649e-01,\n",
-       "        8.10273552e-01, 7.45358410e-01, 4.28571429e-01, 3.64615823e-01,\n",
-       "        6.17664332e-01, 8.81580580e-01, 9.71025452e-01, 1.00000000e+00,\n",
-       "        8.64023658e-01, 6.17437819e-01, 9.40712829e-01, 1.82967601e-01,\n",
-       "        7.13348951e-01, 6.23110875e-01, 1.00000000e+00, 4.33684600e-01,\n",
-       "        8.45468822e-01, 9.82746951e-01, 8.78404105e-01, 6.99126735e-01,\n",
-       "        9.97330997e-01, 9.61296889e-01, 1.00000000e+00, 9.77987819e-01,\n",
-       "        8.94091792e-01, 3.62442326e-01, 9.69663387e-01, 1.00000000e+00,\n",
-       "        1.00000000e+00, 6.19884668e-01, 2.75184682e-01, 9.87315537e-01,\n",
-       "        7.47608709e-01, 8.99585366e-01, 8.46624288e-01, 9.78839624e-01,\n",
-       "        1.00000000e+00, 7.18889274e-01, 8.97315466e-01, 2.65415836e-01,\n",
-       "        3.27069733e-01, 8.85223982e-01, 5.40560183e-01, 8.78297089e-01,\n",
-       "        8.65685255e-01, 1.00000000e+00, 7.85710879e-01, 1.00000000e+00,\n",
-       "        2.19160211e-01, 5.09061423e-01, 5.24157872e-01, 9.97757045e-01,\n",
-       "        1.00000000e+00, 7.96156113e-01, 9.87517247e-01, 8.23169711e-01,\n",
-       "        9.16032474e-01, 8.82632349e-01, 9.82289587e-01, 8.88135626e-01,\n",
-       "        9.90411662e-01, 9.98066301e-01, 9.50804495e-01, 8.32884873e-01,\n",
-       "        9.31231292e-01, 9.31257290e-01, 7.36514389e-01, 7.93686205e-01,\n",
-       "        9.90420231e-01, 9.43967559e-01, 7.54620647e-01, 8.24516187e-01,\n",
-       "        8.07707769e-01, 9.90346885e-01, 9.56856024e-01, 1.00000000e+00,\n",
-       "        9.87316714e-01, 7.12472858e-01, 9.82794602e-01, 9.66245780e-01,\n",
-       "        5.73565506e-01, 6.66182690e-01, 8.23275215e-01, 1.00000000e+00,\n",
-       "        8.73815560e-01, 9.24082694e-01, 1.00000000e+00, 9.43085186e-01,\n",
-       "        9.37936276e-01, 8.90531752e-01, 9.53556748e-01, 8.46541899e-01,\n",
-       "        8.50951329e-01, 8.05770558e-01, 1.00000000e+00, 8.36522015e-01,\n",
-       "        9.75002616e-01, 6.51330247e-01, 9.60608407e-01, 8.19329785e-01,\n",
-       "        9.56922146e-01, 9.85224551e-01, 6.66666667e-01, 8.00205041e-01,\n",
-       "        5.90200070e-01, 6.87389165e-03, 9.71815521e-01, 1.00000000e+00,\n",
-       "        9.86550913e-01, 2.38498301e-01, 9.95379735e-01, 9.91286696e-01,\n",
-       "        9.53286835e-01, 9.20814472e-01, 9.17281156e-01, 3.98668550e-01,\n",
-       "        9.75808315e-01, 9.49703381e-01, 9.27754282e-01, 1.00000000e+00,\n",
-       "        8.13636307e-01, 9.99363739e-01, 9.91594358e-01, 8.11593336e-01,\n",
-       "        9.96394457e-01, 9.44039876e-01, 8.07716095e-01, 9.96642412e-01,\n",
-       "        8.17032485e-01, 8.33323365e-01, 8.27777137e-01, 9.34964047e-01,\n",
-       "        1.00000000e+00, 7.87823278e-01, 9.92504322e-01, 8.75609471e-01,\n",
-       "        1.00000000e+00, 8.43413816e-01, 5.81960450e-01, 9.38644106e-01,\n",
-       "        9.23483108e-01, 9.89090912e-01, 1.55730554e-01, 9.66931128e-01,\n",
-       "        1.00000000e+00, 8.38175102e-01, 9.32860258e-01, 9.04686195e-01,\n",
-       "        1.00000000e+00, 8.98862146e-01, 7.70706848e-01, 8.84796999e-01,\n",
-       "        9.93865453e-01, 6.93441040e-01, 9.97967222e-01, 9.83704543e-01,\n",
-       "        9.73041142e-01, 2.13539494e-01, 9.76744261e-01, 7.36494251e-01,\n",
-       "        7.57125538e-01, 9.81418450e-01, 9.11283886e-01, 8.97840717e-01,\n",
-       "        9.19928607e-01, 7.97436313e-01, 5.12179152e-01, 9.94475463e-01,\n",
-       "        6.89545608e-01, 9.72558156e-01, 8.21393786e-01, 5.46007463e-01,\n",
-       "        8.61094739e-01, 1.00000000e+00, 9.70757934e-01, 9.85294848e-01,\n",
-       "        4.40616384e-01, 8.78989264e-01, 1.00000000e+00, 9.75851527e-01,\n",
-       "        8.36043710e-01, 8.19504390e-01, 7.89036613e-01, 9.92964663e-01,\n",
-       "        9.50317450e-01, 9.85320354e-01, 9.70027898e-01, 9.23040944e-01,\n",
-       "        9.76088733e-01, 2.06662362e-01, 2.63275736e-01, 3.38115196e-01,\n",
-       "        9.07595755e-01, 9.58724775e-01, 1.18351265e-01, 9.61380958e-01,\n",
-       "        7.59769840e-01, 9.17499932e-01, 1.74196328e-01, 9.53916116e-01,\n",
-       "        7.37537800e-01, 9.63853723e-01, 4.18341238e-01, 9.92866870e-01,\n",
-       "        8.42023952e-01, 7.76855514e-01, 8.69345054e-01, 8.79531898e-01,\n",
-       "        9.62421065e-01, 8.88181014e-01, 2.18710007e-01, 9.56695419e-01,\n",
-       "        5.74377055e-01, 9.90650811e-01, 6.63293727e-01, 8.49446194e-01,\n",
-       "        9.06523624e-01, 6.04614267e-01, 9.90012218e-01, 8.87273150e-01,\n",
-       "        8.06088376e-01, 9.46772229e-01, 9.62467550e-01, 7.96275294e-01,\n",
-       "        8.64292793e-01, 1.00000000e+00, 9.76627816e-01, 9.88058421e-01,\n",
-       "        9.90806244e-01, 9.76568828e-01, 1.38088850e-02, 5.71824040e-01,\n",
-       "        9.97524170e-01, 9.79233202e-01, 8.81966673e-02, 9.33737664e-01,\n",
-       "        8.63246362e-01, 8.15158371e-01, 9.00201816e-01, 1.00000000e+00,\n",
-       "        9.48170549e-01, 9.14056830e-01, 9.55164508e-01, 6.07352768e-01,\n",
-       "        9.94780019e-01, 9.74235372e-01, 9.60089408e-01, 9.56527529e-01,\n",
-       "        7.04426918e-01, 6.69107532e-01, 8.82200557e-01, 9.21637261e-01,\n",
-       "        4.54245406e-01, 7.71926278e-01, 8.03501676e-01, 8.19795636e-01,\n",
-       "        8.26799873e-01, 9.39116575e-01, 8.41046546e-01, 1.00000000e+00,\n",
-       "        8.87613905e-01, 7.67465318e-01, 9.64006869e-01, 9.54793161e-01,\n",
-       "        6.79442909e-01, 9.75797361e-01, 8.71503582e-01, 4.03630689e-01,\n",
-       "        9.28623223e-01, 9.40542072e-01, 9.75596640e-01, 1.00000000e+00,\n",
-       "        4.21989975e-01, 9.73248270e-01, 1.00000000e+00, 8.53801850e-01,\n",
-       "        4.62391187e-01, 8.14866220e-01, 7.93092324e-01, 7.66520293e-01,\n",
-       "        9.61340683e-01, 8.39801800e-01, 9.74966688e-01, 9.20949312e-01,\n",
-       "        1.00000000e+00, 2.36061799e-02, 5.33887841e-01, 9.07736975e-01,\n",
-       "        9.57128820e-01, 9.78237542e-01, 8.29347789e-01, 1.00000000e+00,\n",
-       "        9.18793292e-01, 9.97760341e-01, 8.28389211e-01, 8.69578116e-01,\n",
-       "        9.15164065e-01, 8.97291669e-01, 1.68775094e-01, 9.92747805e-01,\n",
-       "        7.98342501e-01, 4.10686436e-01, 1.00000000e+00, 2.38207930e-01,\n",
-       "        7.95451694e-01, 9.76179672e-01, 4.08014953e-01, 1.50126336e-01,\n",
-       "        7.61067202e-01, 9.18736090e-01, 9.26504008e-01, 8.53667949e-01,\n",
-       "        8.10335755e-01, 9.37099683e-01, 8.64344363e-01, 1.00000000e+00,\n",
-       "        9.89772181e-01, 8.83195231e-01, 8.07675655e-01, 1.00000000e+00,\n",
-       "        9.82758368e-01, 4.37184190e-01, 9.30361588e-01, 9.99910492e-01,\n",
-       "        5.87761061e-01, 9.99535630e-01, 9.11415132e-01, 8.75387627e-01,\n",
-       "        9.57437275e-01, 9.28885437e-01, 1.00000000e+00, 8.41879050e-01,\n",
-       "        3.37403242e-01, 7.77459318e-01, 4.17908390e-01, 9.85969102e-01,\n",
-       "        1.00000000e+00, 7.31566547e-01, 8.80044278e-01, 9.84491213e-01,\n",
-       "        6.76265949e-01, 8.59396992e-01, 5.84833722e-09, 9.84977278e-01,\n",
-       "        7.49713881e-01, 9.03075096e-01, 2.83433493e-01, 9.99645996e-01,\n",
-       "        9.98893352e-01, 1.00000000e+00, 8.68609454e-01, 8.43897606e-01,\n",
-       "        9.08679909e-01, 9.96102879e-01, 9.23745682e-01, 4.13444284e-01,\n",
-       "        6.46466285e-01, 9.99281514e-01, 9.19056663e-01, 9.89414434e-01,\n",
-       "        9.30038118e-01, 6.51184462e-01, 9.28430929e-01, 8.24554452e-01,\n",
-       "        9.43345157e-01, 5.60702722e-01, 9.59784073e-01, 1.00000000e+00,\n",
-       "        7.45595616e-01, 9.94861641e-01, 3.63337047e-01, 9.35986096e-01,\n",
-       "        9.35925058e-01, 9.37988487e-01, 9.45121374e-01, 8.91874307e-01,\n",
-       "        9.40600531e-01, 8.58882723e-01, 8.83153525e-01, 5.29237569e-01,\n",
-       "        9.47016217e-01, 6.78716717e-01, 9.61738909e-01, 2.35331675e-01,\n",
-       "        6.12545788e-01, 7.74339518e-01, 9.41058522e-01, 8.75471711e-01,\n",
-       "        8.59864277e-01, 9.79334309e-01, 7.38395610e-01, 8.33104799e-01,\n",
-       "        9.87239573e-01, 5.48944602e-01, 9.87363150e-01, 9.36353882e-01,\n",
-       "        8.99248745e-01, 1.25103263e-01, 9.56432937e-01, 2.32186414e-01,\n",
-       "        9.73968438e-01, 9.51086577e-01, 8.86983240e-01, 7.07964772e-01,\n",
-       "        7.69851252e-01, 9.19880988e-01, 9.25399893e-01, 9.95678115e-01,\n",
-       "        3.25670951e-01, 8.41936443e-01, 9.88321175e-01, 9.21866558e-01,\n",
-       "        9.95829248e-01, 9.58900806e-01, 9.46218750e-01, 5.29751842e-01,\n",
-       "        9.88712758e-01, 8.44182599e-01, 7.85880513e-01, 9.31338014e-01,\n",
-       "        7.88418432e-01, 4.68678307e-01, 9.44128006e-01, 7.66132700e-01],\n",
-       "       [9.64680476e-01, 8.70118500e-01, 8.95496804e-01, 7.51530343e-01,\n",
-       "        9.28313065e-01, 7.81126264e-01, 9.65450670e-01, 1.00000000e+00,\n",
-       "        7.62825588e-01, 9.16390101e-01, 8.22674769e-01, 9.72758935e-01,\n",
-       "        8.28739017e-01, 9.49632563e-01, 9.87500091e-01, 9.43016491e-01,\n",
-       "        9.23274815e-01, 2.99319776e-01, 7.12201832e-02, 1.00000000e+00,\n",
-       "        6.85681407e-01, 7.53926556e-01, 9.13339719e-01, 9.50988065e-01,\n",
-       "        9.01091752e-01, 8.51524058e-01, 1.00000000e+00, 7.52317833e-01,\n",
-       "        9.61744343e-01, 9.66656049e-01, 3.45706221e-01, 7.70008073e-01,\n",
-       "        9.72378540e-01, 9.92843927e-01, 3.00799601e-01, 7.85352521e-01,\n",
-       "        8.79043771e-01, 9.55372774e-01, 9.48665753e-01, 9.77168341e-01,\n",
-       "        7.03934810e-01, 1.00000000e+00, 9.50922625e-01, 8.04133100e-01,\n",
-       "        8.99784441e-01, 9.90167506e-01, 8.80979497e-01, 1.00000000e+00,\n",
-       "        9.52080044e-01, 5.71432092e-01, 8.65345460e-01, 6.14800833e-01,\n",
-       "        1.00000000e+00, 3.79867171e-06, 6.76522406e-02, 8.38931721e-01,\n",
-       "        9.77145077e-01, 6.79544193e-01, 9.82369730e-01, 1.00000000e+00,\n",
-       "        9.07325385e-01, 9.82981984e-01, 8.29772673e-01, 9.94145815e-01,\n",
-       "        8.14912981e-01, 9.66234260e-01, 6.42252418e-01, 9.09108469e-01,\n",
-       "        7.77871975e-01, 9.96511917e-01, 8.06147636e-01, 9.87633076e-01,\n",
-       "        8.22157432e-01, 7.05420281e-01, 9.57169180e-01, 8.02730701e-01,\n",
-       "        8.91717112e-01, 9.25370863e-01, 9.52007785e-01, 9.52870377e-01,\n",
-       "        1.00000000e+00, 1.00000000e+00, 7.55438083e-01, 9.28898038e-01,\n",
-       "        3.15814146e-01, 9.69232438e-01, 8.85219496e-01, 7.66253495e-01,\n",
-       "        9.74507315e-01, 6.15316650e-01, 9.92541112e-01, 9.72318191e-01,\n",
-       "        9.91588010e-01, 5.15056557e-01, 9.18195549e-01, 9.13405242e-01,\n",
-       "        1.00000000e+00, 8.86365722e-01, 9.85541460e-01, 1.00000000e+00,\n",
-       "        7.30625337e-01, 9.31394759e-01, 9.33510321e-01, 8.55785286e-01,\n",
-       "        9.11906188e-01, 9.67187800e-01, 8.97045012e-01, 9.62171951e-01,\n",
-       "        1.00000000e+00, 9.13081940e-01, 8.44301795e-01, 9.83597768e-01,\n",
-       "        9.07855797e-01, 6.72462846e-01, 8.89833955e-01, 9.95791675e-01,\n",
-       "        9.89010333e-01, 7.59197137e-01, 8.51797342e-01, 8.84579503e-01,\n",
-       "        9.17023878e-01, 9.81264609e-01, 9.82160551e-01, 8.43639455e-01,\n",
-       "        9.85922003e-01, 9.64804283e-01, 9.35366861e-01, 7.96308269e-01,\n",
-       "        1.00000000e+00, 7.74874769e-01, 8.75298255e-01, 9.73738803e-01,\n",
-       "        9.31061885e-01, 9.87314156e-01, 9.60046100e-01, 9.93031423e-01,\n",
-       "        7.87545060e-01, 9.21851905e-01, 1.00000000e+00, 9.83076224e-01,\n",
-       "        9.64948689e-01, 9.90709211e-01, 1.42847364e-04, 8.77712072e-01,\n",
-       "        7.72103250e-01, 9.97404978e-01, 8.33295097e-01, 8.87554810e-01,\n",
-       "        5.70246157e-01, 8.99519214e-01, 9.89723895e-01, 8.97844863e-01,\n",
-       "        8.15157489e-01, 9.95917047e-01, 8.89577981e-01, 8.88683240e-01,\n",
-       "        7.60846371e-01, 1.00000000e+00, 1.00000000e+00, 9.81543003e-01,\n",
-       "        8.65965540e-01, 9.62464199e-01, 8.64633483e-01, 8.13599894e-01,\n",
-       "        6.74923861e-01, 6.46620365e-01, 5.74170228e-01, 2.74578001e-01,\n",
-       "        9.75102138e-01, 9.66287450e-01, 9.13966939e-01, 3.38734643e-01,\n",
-       "        1.00000000e+00, 9.49886047e-01, 9.98869387e-01, 9.65362821e-01,\n",
-       "        9.81524205e-01, 9.62586708e-01, 9.05227388e-01, 9.79599354e-01,\n",
-       "        1.16192406e-01, 8.59618022e-01, 9.97754100e-01, 8.62728112e-01,\n",
-       "        1.28201521e-04, 8.82274250e-01, 8.62846747e-01, 8.92309796e-01,\n",
-       "        9.41271495e-01, 5.93921551e-02, 9.52081859e-01, 8.86050765e-01,\n",
-       "        9.83113696e-01, 1.00000000e+00, 9.79127203e-01, 3.99769519e-01,\n",
-       "        8.93860596e-01, 9.98342869e-01, 1.00000000e+00, 9.80443293e-01,\n",
-       "        4.36603107e-01, 9.63108328e-01, 6.49686353e-01, 9.90675533e-01,\n",
-       "        9.86432550e-01, 8.92691367e-01, 8.19770152e-01, 1.00000000e+00,\n",
-       "        9.08824818e-01, 9.10013358e-01, 9.70388795e-01, 2.64048163e-01,\n",
-       "        9.92333807e-01, 7.02849907e-01, 7.42803047e-01, 9.99724870e-01,\n",
-       "        9.87499521e-01, 5.65459114e-01, 9.74932906e-01, 8.38138745e-01,\n",
-       "        9.91087942e-01, 4.86650724e-01, 8.16015308e-01, 9.78118851e-01,\n",
-       "        5.57666594e-01, 9.97906693e-01, 7.36913742e-01, 9.22404769e-01,\n",
-       "        5.80207353e-01, 9.53256851e-01, 3.37925279e-01, 1.00000000e+00,\n",
-       "        3.51688704e-01, 9.44433550e-01, 9.39953753e-01, 1.00000000e+00,\n",
-       "        9.65964392e-01, 9.47339086e-01, 9.39748618e-01, 8.74652329e-01,\n",
-       "        8.75945135e-01, 8.42046108e-01, 3.93981922e-01, 5.71428897e-01,\n",
-       "        9.83232015e-01, 8.90965164e-01, 6.01301766e-01, 9.69038248e-01,\n",
-       "        9.98467679e-01, 5.63979245e-01, 8.99796196e-01, 9.18908762e-01,\n",
-       "        9.67446748e-01, 9.89407011e-01, 9.79522111e-01, 6.50271976e-01,\n",
-       "        8.42276783e-01, 4.47860962e-01, 9.36244847e-01, 9.57043952e-01,\n",
-       "        9.82426844e-01, 9.45751322e-01, 8.31901817e-01, 8.41770280e-01,\n",
-       "        6.20311179e-01, 8.71556577e-01, 6.24491906e-01, 9.16785805e-01,\n",
-       "        7.23660886e-01, 9.94213928e-01, 1.00000000e+00, 3.91301981e-01,\n",
-       "        8.66753369e-01, 9.53355925e-01, 8.43098998e-01, 7.43260968e-01,\n",
-       "        1.00000000e+00, 8.25671218e-01, 9.81180612e-01, 5.40031205e-01,\n",
-       "        6.93753305e-01, 2.00007729e-01, 9.86755671e-01, 7.33568459e-01,\n",
-       "        7.25534265e-01, 1.00000000e+00, 8.97708603e-01, 1.00000000e+00,\n",
-       "        1.00000000e+00, 9.42621499e-01, 6.90601320e-01, 9.91758321e-01,\n",
-       "        5.38415051e-01, 9.35984567e-01, 8.54238618e-01, 7.44655464e-01,\n",
-       "        8.15484503e-01, 7.16628683e-01, 9.82993383e-01, 8.66955901e-01,\n",
-       "        9.60372891e-01, 9.96999033e-01, 1.72694267e-01, 9.38385186e-01,\n",
-       "        9.84498163e-01, 7.00659390e-01, 6.43341044e-01, 9.36028659e-01,\n",
-       "        7.06101158e-01, 8.97089479e-01, 1.00000000e+00, 9.41702204e-01,\n",
-       "        9.26190793e-01, 1.00000000e+00, 7.74098363e-01, 8.41470634e-01,\n",
-       "        9.94545350e-01, 7.86799665e-01, 5.73853969e-01, 9.87390217e-01,\n",
-       "        8.29951881e-01, 1.00000000e+00, 6.07337529e-01, 7.63329917e-01,\n",
-       "        1.00000000e+00, 9.31438917e-01, 8.01199071e-01, 6.72603428e-01,\n",
-       "        8.98868539e-01, 9.12952560e-01, 9.75497218e-01, 8.90757477e-01,\n",
-       "        6.55694651e-01, 9.00566882e-01, 7.81465767e-01, 9.93733859e-01,\n",
-       "        9.27274640e-01, 9.40431319e-01, 6.14627579e-01, 8.77231346e-01,\n",
-       "        8.83817447e-01, 9.31342350e-01, 9.82097832e-01, 9.43982700e-01,\n",
-       "        8.90105956e-01, 7.43541021e-01, 8.32962010e-01, 8.97851364e-01,\n",
-       "        9.96309044e-01, 1.00000000e+00, 9.83526638e-01, 7.65753715e-01,\n",
-       "        9.36610227e-01, 9.88482389e-01, 9.99590902e-01, 6.37266456e-01,\n",
-       "        8.00144345e-01, 8.06286214e-01, 9.84370608e-01, 8.95825951e-01,\n",
-       "        8.06642696e-01, 9.33213267e-01, 5.47686065e-01, 9.86403225e-01,\n",
-       "        9.44142035e-01, 9.54307292e-01, 1.00000000e+00, 1.00000000e+00,\n",
-       "        9.54965746e-01, 1.00000000e+00, 9.74866623e-01, 9.53262050e-01,\n",
-       "        9.69054550e-01, 9.69823641e-01, 9.55690056e-01, 9.85159768e-01,\n",
-       "        8.24015558e-01, 1.00000000e+00, 8.77526234e-01, 9.96112280e-01,\n",
-       "        8.69366292e-01, 9.71500123e-01, 9.11659973e-01, 9.02653122e-01,\n",
-       "        7.47484423e-01, 9.39944520e-01, 8.01030316e-01, 9.11739988e-01,\n",
-       "        8.93808925e-01, 9.41175993e-01, 8.32031388e-01, 7.56327925e-01,\n",
-       "        9.59032065e-01, 9.38117867e-01, 9.16527720e-01, 8.83929615e-01,\n",
-       "        9.93390099e-01, 9.84216611e-01, 9.91804551e-01, 7.98252578e-01,\n",
-       "        9.04113572e-01, 8.04211709e-01, 8.45184780e-01, 9.43762841e-01,\n",
-       "        6.85280242e-01, 4.54058006e-01, 7.33619318e-01, 1.00000000e+00,\n",
-       "        8.63755912e-01, 9.61737558e-01, 9.26847674e-01, 9.37143960e-01,\n",
-       "        1.00000000e+00, 8.30306796e-01, 9.47945870e-01, 1.00000000e+00,\n",
-       "        8.45482467e-01, 1.00000000e+00, 9.38352625e-01, 1.00000000e+00,\n",
-       "        7.52506203e-01, 9.92334169e-01, 5.98785321e-01, 1.00000000e+00,\n",
-       "        8.17532724e-01, 9.10114719e-01, 8.38578649e-01, 9.90111452e-01,\n",
-       "        9.72325509e-01, 9.62727303e-01, 9.88664969e-01, 9.26411457e-01,\n",
-       "        6.54483875e-01, 7.31785191e-01, 9.18091614e-01, 8.75140654e-01,\n",
-       "        8.26533210e-01, 9.89344176e-01, 1.00000000e+00, 8.01242396e-01,\n",
-       "        3.48569208e-01, 1.00000000e+00, 9.82261741e-01, 4.50298442e-01,\n",
-       "        9.18078110e-01, 7.83087259e-01, 4.33459418e-01, 8.96780466e-01,\n",
-       "        9.47771188e-01, 8.30018409e-01, 6.85268779e-01, 9.80820473e-01,\n",
-       "        8.32889596e-01, 7.89644171e-01, 9.59267927e-01, 9.51638203e-01,\n",
-       "        9.95525852e-01, 1.43253023e-01, 9.28380239e-01, 8.56500602e-01,\n",
-       "        9.93508997e-01, 9.66548814e-01, 8.47635168e-01, 8.90136989e-01,\n",
-       "        9.80332270e-01, 8.18794991e-01, 7.54317683e-01, 6.84700966e-01,\n",
-       "        9.70690084e-01, 5.88369292e-01, 9.90501732e-01, 4.17444913e-01,\n",
-       "        1.00000000e+00, 6.88926318e-01, 9.84168971e-01, 9.66083127e-01,\n",
-       "        9.96442866e-01, 8.18654320e-01, 9.31052325e-01, 9.69606490e-01,\n",
-       "        9.10227572e-01, 1.00000000e+00, 9.58218303e-01, 1.00000000e+00,\n",
-       "        9.01875514e-01, 7.46084919e-01, 9.81425729e-01, 1.00000000e+00,\n",
-       "        8.66783502e-01, 2.48881302e-01, 9.26570163e-01, 9.42201394e-01,\n",
-       "        8.04121672e-01, 7.28021968e-01, 1.00000000e+00, 8.75694592e-01,\n",
-       "        1.00000000e+00, 7.52301900e-01, 7.48515470e-01, 7.57397517e-01]])
        • step_size
          (chain, draw)
          float64
          0.539 0.539 0.539 ... 0.4737 0.4737
          array([[0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
-       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
-       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
-       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
-       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
-       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
-       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
-       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
-       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
-       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
-       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
-       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
-       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
-       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
-       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
-       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
-       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
-       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
-       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
-       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
-       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
-       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
-       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
-       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
-       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
-       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
-       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
-       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
-       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
-       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
-       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
-       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
-       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
-       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
-       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
-       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
-       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
-       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
-       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
-       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
-       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
-       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
-       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
-       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
-       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
-       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
-       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
-       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
-       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
-       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
-       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
-       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
-       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
-       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
-       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
-       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
-       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
-       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
-       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
-       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
-       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
-       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
-       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
-       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
-       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
-       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
-       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
-       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
-       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
-       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
-       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
-       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
-       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
-       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
-       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
-       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
-       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
-       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
-       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
-       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
-       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
-       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
-       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
-       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
-       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
-       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
-       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
-       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
-       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
-       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
-       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
-       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
-       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
-       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
-       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
-       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
-       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
-       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
-       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556,\n",
-       "        0.53899556, 0.53899556, 0.53899556, 0.53899556, 0.53899556],\n",
-       "       [0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
-       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
-       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
-       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
-       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
-       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
-       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
-       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
-       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
-       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
-       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
-       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
-       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
-       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
-       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
-       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
-       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
-       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
-       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
-       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
-       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
-       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
-       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
-       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
-       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
-       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
-       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
-       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
-       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
-       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
-       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
-       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
-       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
-       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
-       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
-       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
-       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
-       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
-       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
-       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
-       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
-       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
-       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
-       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
-       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
-       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
-       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
-       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
-       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
-       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
-       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
-       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
-       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
-       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
-       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
-       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
-       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
-       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
-       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
-       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
-       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
-       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
-       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
-       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
-       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
-       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
-       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
-       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
-       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
-       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
-       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
-       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
-       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
-       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
-       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
-       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
-       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
-       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
-       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
-       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
-       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
-       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
-       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
-       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
-       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
-       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
-       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
-       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
-       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
-       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
-       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
-       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
-       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
-       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
-       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
-       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
-       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
-       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
-       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459,\n",
-       "        0.47367459, 0.47367459, 0.47367459, 0.47367459, 0.47367459]])
        • tree_size
          (chain, draw)
          float64
          7.0 7.0 7.0 3.0 ... 7.0 7.0 7.0 7.0
          array([[ 7.,  7.,  7.,  3.,  3.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
-       "         7.,  7.,  7.,  7.,  7.,  7.,  3.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
-       "         7.,  3.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
-       "         3.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
-       "         7.,  7.,  7.,  7.,  7.,  3.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
-       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
-       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  3.,  3.,  7.,  7.,\n",
-       "         7.,  7.,  7.,  3.,  3.,  7.,  7.,  7.,  3.,  7.,  7.,  7.,  7.,\n",
-       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
-       "         7.,  7.,  7.,  7.,  3.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
-       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
-       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
-       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
-       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  3.,  7.,  7.,  3.,\n",
-       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
-       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
-       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
-       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
-       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
-       "         7.,  7.,  3.,  7.,  3.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
-       "         7.,  7.,  7.,  7.,  7.,  3.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
-       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
-       "         3.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
-       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  3.,  7.,  7.,  7.,  3.,  7.,\n",
-       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
-       "         7.,  7.,  7.,  3.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
-       "         7.,  7.,  3.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
-       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  3.,  7.,  7.,\n",
-       "         7.,  7.,  7.,  7.,  7.,  3.,  7.,  7.,  7.,  7.,  7.,  3.,  7.,\n",
-       "         3.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
-       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  3.,  7.,  7.,\n",
-       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
-       "         7.,  7.,  1.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  3.,\n",
-       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
-       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
-       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  3.,  7.,  7.,  3.,  7.,  7.,\n",
-       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  3.,  7.,  7.,  7.,\n",
-       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
-       "         7.,  7.,  7.,  7.,  7.,  7.],\n",
-       "       [ 7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
-       "         7.,  7.,  7.,  7.,  7.,  3.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
-       "         3.,  7.,  7.,  3.,  3.,  7.,  7.,  7.,  3.,  7.,  7.,  7.,  7.,\n",
-       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
-       "         7.,  3.,  3.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
-       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  3.,  7.,  7.,  7.,\n",
-       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
-       "         7.,  7.,  3.,  7.,  7.,  7.,  7.,  7.,  3.,  7.,  7.,  7.,  7.,\n",
-       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  3.,\n",
-       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
-       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  3.,\n",
-       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
-       "         7.,  7.,  7.,  7.,  7.,  7.,  3.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
-       "         7.,  7.,  3.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  3.,  3.,\n",
-       "         3.,  3.,  3.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  3.,  7.,\n",
-       "         3.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
-       "         7.,  7.,  7.,  3.,  7.,  7.,  7.,  7.,  7.,  3.,  7.,  7.,  7.,\n",
-       "         3.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  3.,  7.,  7.,\n",
-       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
-       "         7.,  7.,  3.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
-       "         7.,  7.,  7.,  7.,  7.,  7.,  3.,  7.,  7.,  7.,  7.,  3.,  7.,\n",
-       "         7.,  7.,  3.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
-       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
-       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
-       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
-       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
-       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  3.,  7.,  7.,  7.,  7.,  7.,\n",
-       "         3.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
-       "         7.,  7.,  7.,  7.,  7.,  3.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
-       "         7.,  7.,  7.,  7.,  3.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
-       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
-       "         7.,  3.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
-       "         7.,  3.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
-       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  3.,  3.,\n",
-       "         7.,  7.,  7.,  7., 15.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
-       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
-       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
-       "         7.,  7.,  7.,  7.,  7.,  7.,  3.,  7.,  3.,  7.,  7.,  7.,  7.,\n",
-       "         7.,  7.,  7.,  7.,  7.,  7.]])
        • lp
          (chain, draw)
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-       "        -51.03229484, -46.14452003, -46.33699934, -49.40126811,\n",
-       "        -47.66537999, -46.26208396, -45.10842018, -45.51301769,\n",
-       "        -44.75689476, -45.4085128 , -49.16976226, -47.63605554,\n",
-       "        -45.04775967, -45.94813195, -48.07062842, -47.97847545,\n",
-       "        -44.57015275, -49.92862189, -44.54810323, -48.4212961 ,\n",
-       "        -46.84594248, -46.30359084, -46.09984847, -45.96051217,\n",
-       "        -46.45528037, -52.07779699, -50.32123488, -48.12595534,\n",
-       "        -49.81546682, -53.28764047, -51.28186517, -49.74361018,\n",
-       "        -51.07857979, -44.84238341, -44.24422225, -46.23286401,\n",
-       "        -44.99737832, -45.27599756, -44.82967004, -43.42474286,\n",
-       "        -46.84251643, -46.38801752, -48.95749453, -49.37863869,\n",
-       "        -49.15691099, -46.89563786, -45.34732181, -47.07430807,\n",
-       "        -44.46410973, -45.90257478, -46.57479649, -46.24070528,\n",
-       "        -47.61117321, -45.87030197, -46.96745069, -46.89569064,\n",
-       "        -44.94740829, -44.31723019, -44.81858104, -44.4001577 ,\n",
-       "        -45.4867947 , -46.98933163, -42.40358487, -42.36551468,\n",
-       "        -42.19502698, -46.5704508 , -46.95513979, -45.06027243,\n",
-       "        -44.22640545, -44.16529036, -42.39673934, -43.88370127,\n",
-       "        -45.35783216, -45.74812496, -44.26535118, -46.44190001,\n",
-       "        -43.46945052, -47.04024462, -43.95139411, -43.6139708 ,\n",
-       "        -44.96674626, -45.54006491, -49.7622803 , -45.51409439,\n",
-       "        -46.52233616, -48.07727175, -48.9232679 , -48.73143727,\n",
-       "        -50.01336375, -48.24123044, -46.71942439, -46.26455745,\n",
-       "        -45.36061098, -47.04419452, -48.25867045, -48.19299509,\n",
-       "        -47.1460383 , -44.03478063, -47.85625998, -44.98543624,\n",
-       "        -47.6544432 , -45.18403818, -45.99925563, -46.00832207,\n",
-       "        -45.24048642, -42.80155468, -45.33942935, -46.06217959,\n",
-       "        -43.24928105, -44.64598991, -47.54271679, -44.08079937,\n",
-       "        -45.22789778, -46.4021683 , -46.01841645, -47.83322941,\n",
-       "        -47.81223462, -44.22852357, -43.70648319, -45.49391392,\n",
-       "        -48.96455138, -47.65326266, -45.72125208, -43.28699565,\n",
-       "        -51.82217239, -52.77439933, -50.63044922, -44.65788522,\n",
-       "        -46.10086204, -46.09439732, -48.45860976, -47.61783329,\n",
-       "        -44.66588846, -48.04025208, -49.01715434, -47.79214687,\n",
-       "        -48.98043333, -45.53184009, -47.2041232 , -44.72420136,\n",
-       "        -49.46424811, -46.79983041, -44.04227394, -42.91863568,\n",
-       "        -43.97746027, -44.83141731, -48.69877082, -47.92290948,\n",
-       "        -46.88932515, -46.34584215, -46.02981906, -47.87252249,\n",
-       "        -46.80307986, -44.1145662 , -42.64508742, -42.91383116,\n",
-       "        -45.02425086, -44.35691943, -43.76217641, -47.62601932,\n",
-       "        -49.73236691, -43.71502801, -43.9914042 , -44.74107947,\n",
-       "        -42.79125375, -43.78222773, -45.72450659, -48.1444365 ,\n",
-       "        -48.36796618, -44.02322612, -48.34768799, -45.87548176,\n",
-       "        -46.13475633, -47.04243703, -46.93408111, -45.91561447,\n",
-       "        -45.83474124, -45.60341281, -45.60403331, -45.14878158,\n",
-       "        -45.49676377, -44.85116172, -46.24093578, -47.28413239,\n",
-       "        -42.81540653, -45.18919048, -46.23582176, -47.23391066,\n",
-       "        -47.00239762, -44.88804043, -45.56847132, -46.75637296,\n",
-       "        -46.33416959, -45.63682029, -44.04723861, -44.08935247,\n",
-       "        -43.8915887 , -47.07546292, -50.30273426, -49.29015696,\n",
-       "        -50.11082403, -47.13236617, -47.04854802, -46.907683  ,\n",
-       "        -48.75568422, -52.39003808, -48.33335131, -43.83065183,\n",
-       "        -42.72258118, -42.72258118, -42.98527806, -42.38734039,\n",
-       "        -46.6733164 , -49.31505795, -48.56876539, -45.81689736,\n",
-       "        -42.175127  , -43.39571478, -44.16453369, -47.04446667]])
        • energy_error
          (chain, draw)
          float64
          -0.06193 0.1016 ... 0.1739 0.1582
          array([[-6.19325448e-02,  1.01645444e-01, -4.14474785e-01,\n",
-       "         1.08876669e-01, -1.03233219e+00,  2.22737310e-01,\n",
-       "         9.19175585e-02, -1.16402606e-01,  1.68847246e-01,\n",
-       "        -1.85091125e-01,  2.89957614e-01,  3.87514015e-01,\n",
-       "        -2.16643775e-01, -1.77613405e-01, -3.19980180e-01,\n",
-       "         9.94336111e-02,  1.44818710e+00, -1.21480846e-01,\n",
-       "        -7.32093341e-02,  0.00000000e+00,  0.00000000e+00,\n",
-       "         1.45951374e-02,  3.15203861e-01, -1.99938001e-01,\n",
-       "         3.02324538e-01, -1.95728134e-01,  2.17657308e-02,\n",
-       "        -1.84114480e-01,  6.69728148e-01, -1.87114648e-01,\n",
-       "        -7.54352204e-02, -2.59994811e-01,  2.07277244e-01,\n",
-       "         2.50204424e-01, -3.25135936e-01, -2.78307797e-01,\n",
-       "         1.15292246e-01,  1.69880890e-01, -2.64840578e-02,\n",
-       "        -6.32399396e-02,  1.94434954e-01, -2.45446724e-01,\n",
-       "         1.79428718e-01,  1.17880142e-01, -3.14028346e-01,\n",
-       "        -1.18922502e-02,  1.66781129e-01, -2.23591491e-01,\n",
-       "         2.14786739e-02,  3.86498486e-01, -9.37422616e-02,\n",
-       "         2.49256023e-01,  2.16215471e+00,  1.52081760e-01,\n",
-       "        -2.32054691e-03,  5.31131062e-02, -7.43098576e-01,\n",
-       "         0.00000000e+00,  4.39532360e-02,  2.26273697e-01,\n",
-       "         5.43610070e-02,  5.84198994e-02, -7.79212207e-02,\n",
-       "         3.38947074e-01, -5.83557754e-01, -2.67905008e-01,\n",
-       "        -4.78446150e-01,  4.39329801e-01, -5.20336503e-01,\n",
-       "         7.80195279e-02, -6.49516780e-01,  1.60437770e-01,\n",
-       "        -5.37036268e-01, -3.40460768e-01, -8.74683455e-01,\n",
-       "         4.38390666e-01,  3.07325382e-01,  4.77269288e-01,\n",
-       "        -1.03498030e+00,  7.88383319e-01, -1.94021835e-01,\n",
-       "         9.14665650e-02, -1.90778203e-01, -8.22549636e-01,\n",
-       "         3.43945179e-01,  4.19095292e-01, -1.15867937e-01,\n",
-       "         0.00000000e+00,  4.86789244e-01,  7.94809573e-01,\n",
-       "        -6.81684652e-01,  4.61737965e-01,  5.17861573e-02,\n",
-       "        -9.73478560e-01, -2.57551957e-01,  3.67240936e-01,\n",
-       "        -1.51522173e-01,  1.10541542e-01, -1.48109916e-02,\n",
-       "        -2.28084973e-01,  2.08137973e-02,  8.62346826e-01,\n",
-       "         1.56395841e-01, -5.69867459e-01, -1.38321358e-01,\n",
-       "        -1.97555138e-01,  4.39857362e-01,  2.85106577e-02,\n",
-       "         2.49326204e-01, -5.13695003e-02,  2.96271035e-01,\n",
-       "         9.48347494e-03, -7.55208761e-02,  9.45350973e-02,\n",
-       "        -1.17068048e-01,  6.00427447e-01, -3.18490444e-01,\n",
-       "         1.27538393e-01, -3.44864320e-02, -1.51723962e-01,\n",
-       "        -2.91325668e-01, -5.30442772e-01,  3.18580581e-01,\n",
-       "        -9.30570327e-03, -5.84173928e-01,  7.29047209e-01,\n",
-       "         2.27952828e-01, -5.21965958e-02, -1.47273477e-01,\n",
-       "         2.96237310e-01, -1.41363612e-01, -3.87567346e-01,\n",
-       "        -4.65921986e-03,  1.08538515e-01, -2.76345949e-02,\n",
-       "         2.73401806e-01,  2.89237794e-02, -3.00218808e-01,\n",
-       "         2.77857973e-02,  2.64365772e-01, -1.77286312e-01,\n",
-       "         1.79518053e-01,  1.39292359e-01,  1.49086139e-01,\n",
-       "        -2.46507317e-01,  3.05843116e-01,  1.59516259e-01,\n",
-       "        -6.13043148e-01,  1.54765968e-02,  7.74893922e-03,\n",
-       "        -6.14285297e-02, -3.22696158e-01, -2.61850364e-01,\n",
-       "         1.67286852e-01,  3.37900366e-02, -1.76210785e+00,\n",
-       "         1.21221555e+00, -1.36200503e+00,  3.98830356e-01,\n",
-       "        -4.42289113e-01, -1.45287614e-01,  1.45342151e-01,\n",
-       "        -1.34669426e-01, -2.72580559e-02,  1.05588575e-01,\n",
-       "        -4.56947314e-02, -1.06466971e-01,  1.99700315e-01,\n",
-       "         2.50670250e-01, -1.04941716e+00, -1.15189583e+00,\n",
-       "         6.88387948e-02, -3.73903307e-01,  2.30790617e-01,\n",
-       "        -9.57335046e-02, -1.17154802e+00,  8.24373695e-02,\n",
-       "         2.31224647e-03, -1.40453125e-01,  2.82701450e-01,\n",
-       "         3.60449226e-01,  0.00000000e+00,  1.36131820e-01,\n",
-       "        -2.95469966e-01, -5.44148290e-01,  6.12427640e-01,\n",
-       "        -9.84534546e-02, -8.25439515e-02, -1.86113374e-01,\n",
-       "        -5.83618294e-03, -1.03350255e-01,  3.09172966e-01,\n",
-       "         1.33605114e-02, -6.90201931e-02, -1.54675571e-01,\n",
-       "        -1.24395964e-01,  2.62452006e-01, -1.41160503e-01,\n",
-       "         5.72642032e-02,  2.00826302e-01, -4.80080034e-01,\n",
-       "        -7.25395068e-01,  1.18797715e-01, -2.08750612e-01,\n",
-       "         2.14592245e-01,  6.46166449e-02,  2.39236522e-01,\n",
-       "        -1.77269858e-01, -1.57113855e-01,  3.35531182e-01,\n",
-       "         5.38964148e-02,  2.93116092e-01, -2.79557552e-01,\n",
-       "         4.92498010e-01,  7.41592670e-03,  6.55750286e-03,\n",
-       "         1.04057873e-01, -5.40523204e-03,  0.00000000e+00,\n",
-       "         1.22299511e-01, -2.44040808e-01,  3.17916816e-01,\n",
-       "        -2.67871380e-02, -4.56975267e-02, -3.04432274e-01,\n",
-       "         1.92230343e-01,  2.72201800e-01, -9.99144135e-02,\n",
-       "         2.68856128e-02,  4.89649419e-02, -5.94418106e-01,\n",
-       "        -6.43494060e-02, -9.60106690e-01,  2.49370329e-01,\n",
-       "         1.57904841e-02,  4.51471272e-01,  3.74295457e-01,\n",
-       "        -4.61190472e-01,  9.72190030e-02, -9.99436562e-02,\n",
-       "         1.13102412e-01,  1.22720026e+00,  9.52216026e-01,\n",
-       "        -1.80203520e-01, -6.21434329e-01, -2.72084889e-01,\n",
-       "         2.88104469e-01,  8.36175522e-02,  1.47149351e-01,\n",
-       "        -1.08615994e-01,  1.15773365e-01, -3.15419756e-01,\n",
-       "         2.01142582e-01,  1.97679813e-01, -2.62292479e-01,\n",
-       "        -4.59820592e-02, -2.62411676e-01,  3.28161948e-01,\n",
-       "         2.31241983e-01, -2.79127442e-01,  1.21257330e-01,\n",
-       "        -1.82602701e-01, -9.32502189e-02,  1.80439138e-02,\n",
-       "        -1.24389267e-01,  4.79446754e-01, -4.41142579e-01,\n",
-       "         0.00000000e+00,  4.38779548e-02, -9.74350949e-02,\n",
-       "         3.41028899e-01, -1.39560248e-01,  1.57203088e-01,\n",
-       "        -1.50164053e-01,  1.79054992e+00, -2.63418591e-01,\n",
-       "         9.77289650e-02,  7.31885449e-02, -2.62430965e-01,\n",
-       "        -2.09769682e-01, -3.74318548e-01,  3.03119432e-02,\n",
-       "         1.12912174e-01,  1.29520801e-01,  9.08624084e-02,\n",
-       "         2.64254609e-01,  4.72613207e-01,  3.61159364e-01,\n",
-       "        -1.06435455e+00, -7.97654937e-02,  1.73738580e-01,\n",
-       "        -1.82572925e-01, -2.15989532e-01,  3.28076006e-01,\n",
-       "        -8.14299594e-02,  2.28111476e-01,  3.19707312e-01,\n",
-       "         1.62981815e-01, -3.97363902e-01, -3.11034354e-02,\n",
-       "         9.68067068e-02, -2.37160402e-02,  1.46876214e-01,\n",
-       "        -2.46389714e-01, -7.07524485e-02, -9.50555682e-01,\n",
-       "         3.19511926e+00, -3.59198013e+00,  3.50012130e-03,\n",
-       "        -3.44708282e-01,  1.32957716e+00, -1.07582568e-01,\n",
-       "        -1.46924895e-01,  1.45051305e-01,  2.27299881e-01,\n",
-       "        -1.93985507e-01,  6.13013728e-02,  4.98133640e-02,\n",
-       "         1.37516251e-02,  5.97513910e-01, -2.73294721e-02,\n",
-       "        -4.38914963e-02,  6.60112237e-03, -6.08085935e-02,\n",
-       "        -5.20856208e-01,  4.43335587e-01, -4.01110404e-02,\n",
-       "        -2.99324873e-03,  4.08212045e-01, -1.01940250e-01,\n",
-       "        -1.78016594e-01,  3.23187240e-01, -1.31426883e-01,\n",
-       "         3.18597764e-02, -1.27468134e-01, -3.86218887e-01,\n",
-       "         3.68570269e-01,  4.52298726e-01, -4.82800816e-01,\n",
-       "         4.51559152e-02,  2.74832239e-01, -7.21776216e-02,\n",
-       "         2.05153450e-01,  1.33271927e+00, -3.12781117e-02,\n",
-       "         1.73906012e-01, -1.14024584e-01, -1.14444483e-02,\n",
-       "         3.12349513e-01,  9.60686235e-02, -2.08058658e-01,\n",
-       "        -5.69258576e-02,  2.45411865e-01,  5.72665589e-01,\n",
-       "         2.37804194e-01, -1.69386803e-01, -2.09987098e-01,\n",
-       "         1.65463001e-01, -3.78549903e-01,  1.00985869e-01,\n",
-       "        -7.78165407e-01,  0.00000000e+00,  1.04577074e+00,\n",
-       "        -1.47984851e-01,  2.82372106e-02, -3.22800505e-04,\n",
-       "         8.22909072e-01, -8.87892422e-01,  2.88289256e-01,\n",
-       "        -3.34857050e-01, -8.03350855e-02, -8.26935217e-03,\n",
-       "         1.03249195e-01, -2.08238401e-01,  5.34411245e-01,\n",
-       "        -5.27679779e-01,  1.02648723e-01,  4.09146981e-01,\n",
-       "        -1.61494321e-01,  0.00000000e+00,  2.46431255e-01,\n",
-       "        -2.71384616e-01,  4.14982319e-01, -3.40172801e-01,\n",
-       "         2.17275739e-01, -9.12700028e-02,  1.29403601e-01,\n",
-       "         1.73007937e-01,  1.64103200e-01,  7.65986045e-02,\n",
-       "         1.39835675e-01, -1.94553361e+00, -3.96114788e-01,\n",
-       "         2.27604210e-01,  8.73289230e-01, -2.66365006e-01,\n",
-       "         3.58175467e-02,  4.08497163e-02, -4.98041339e-01,\n",
-       "        -1.00511162e-01, -1.34636019e-01, -4.32932690e-01,\n",
-       "        -1.24901005e-02, -1.15132101e-02,  4.76294939e-02,\n",
-       "         1.38035283e-01, -8.75114261e-02,  2.50459043e-01,\n",
-       "        -5.71264958e-01,  3.43004680e-01,  1.13316498e-01,\n",
-       "         1.79049155e-02, -7.42889148e-02,  1.05883991e+00,\n",
-       "        -5.51281380e-02, -4.30754726e-02,  7.76899793e-02,\n",
-       "         1.15725565e-01,  0.00000000e+00, -1.23746918e-01,\n",
-       "         1.75246508e-01,  2.31973508e-01, -3.73159985e-01,\n",
-       "        -2.01645002e-01,  7.77669497e-03, -2.29996434e-01,\n",
-       "         9.53130068e-02,  2.57757486e-01,  1.54036030e-01,\n",
-       "        -5.17293688e-02,  1.63111654e-01, -2.01352431e-01,\n",
-       "         5.48000631e-01, -4.02016428e-01, -5.14913226e-03,\n",
-       "         7.66136149e-02, -2.24880373e-01,  1.85360093e-02,\n",
-       "        -5.93353720e-02,  1.87531911e-01, -1.32716389e-01,\n",
-       "         7.08694494e-01, -2.65590359e-01, -4.66673049e-02,\n",
-       "         1.72952555e-01, -2.91579441e-01,  2.80943710e+00,\n",
-       "        -1.94302943e-01, -3.82521455e-01,  1.56775935e-01,\n",
-       "         5.80962828e-02,  1.20414163e-01, -9.99661547e-02,\n",
-       "         1.84280786e-01,  1.55214006e-01,  3.73896751e-01,\n",
-       "         2.17456568e-01,  2.20482777e-01, -8.26128334e-01,\n",
-       "         8.44240860e-01,  6.71747586e-01, -1.16729739e-01,\n",
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      • created_at :
        2020-06-06T18:08:05.200362
        arviz_version :
        0.8.3
        inference_library :
        pymc3
        inference_library_version :
        3.8

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          xarray.Dataset
            • chain: 1
            • draw: 500
            • school: 8
            • chain
              (chain)
              int64
              0
              array([0])
            • draw
              (draw)
              int64
              0 1 2 3 4 5 ... 495 496 497 498 499
              array([  0,   1,   2, ..., 497, 498, 499])
            • school
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              int64
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              array([0, 1, 2, 3, 4, 5, 6, 7])
            • theta
              (chain, draw, school)
              float64
              -0.06145 -0.1634 ... -20.97 -16.97
              array([[[-6.14501022e-02, -1.63365997e-01, -4.13644815e-02, ...,\n",
-       "         -4.63283674e-02, -7.75680442e-01, -6.98667408e-01],\n",
-       "        [ 6.13220767e+00, -2.30667156e+01,  4.65098938e+00, ...,\n",
-       "         -2.23623379e+01,  6.54679323e+00,  2.69077777e+00],\n",
-       "        [ 4.69451708e+00,  4.94303445e+00,  7.79012703e+00, ...,\n",
-       "         -3.96463931e+00,  9.76373781e+00,  2.20580563e+01],\n",
+       "
              xarray.Dataset
                • chain: 4
                • draw: 1000
                • school: 8
                • chain
                  (chain)
                  int64
                  0 1 2 3
                  array([0, 1, 2, 3])
                • draw
                  (draw)
                  int64
                  0 1 2 3 4 5 ... 995 996 997 998 999
                  array([  0,   1,   2, ..., 997, 998, 999])
                • school
                  (school)
                  int64
                  0 1 2 3 4 5 6 7
                  array([0, 1, 2, 3, 4, 5, 6, 7])
                • mu
                  (chain, draw)
                  float64
                  8.801 4.356 5.887 ... 5.811 6.163
                  array([[ 8.80085977,  4.35621311,  5.88747459, ...,  7.56510323,\n",
+       "         3.75692215,  4.24241574],\n",
+       "       [ 3.88430209,  1.25503637,  8.12161043, ...,  0.77999781,\n",
+       "         8.88447765,  2.55867698],\n",
+       "       [ 3.69774758,  5.56187344,  7.18886091, ...,  2.35104484,\n",
+       "         7.36896537,  7.30112058],\n",
+       "       [ 4.56370867, -1.9814485 ,  8.93919361, ..., -0.45859494,\n",
+       "         5.81131473,  6.16295074]])
                • tau
                  (chain, draw)
                  float64
                  1.079 2.76 3.322 ... 3.54 0.5714
                  array([[1.07873807, 2.75983947, 3.32167212, ..., 4.65835454, 5.67923053,\n",
+       "        1.15391559],\n",
+       "       [0.68602682, 1.9898873 , 1.71348775, ..., 6.32124664, 6.49443423,\n",
+       "        7.88054963],\n",
+       "       [6.09793056, 2.08870225, 3.01847687, ..., 0.19977493, 2.0959731 ,\n",
+       "        2.08265721],\n",
+       "       [4.80268382, 8.63940768, 7.49279334, ..., 4.94150802, 3.54035862,\n",
+       "        0.57140337]])
                • theta_tilde
                  (chain, draw, school)
                  float64
                  0.7707 3.4 ... 0.9492 -0.1983
                  array([[[ 0.77065058,  3.3999605 ,  0.896277  , ...,  0.85548385,\n",
+       "          0.56312311, -0.02336804],\n",
+       "        [-0.23856364,  1.88839417,  0.11615247, ...,  0.20239506,\n",
+       "         -0.65088377, -0.59082016],\n",
+       "        [-0.44993014,  0.3660292 , -0.85342255, ..., -0.05060912,\n",
+       "          1.89747793, -0.29915642],\n",
+       "        ...,\n",
+       "        [-1.53738528,  0.34600749,  0.66613118, ..., -0.31358001,\n",
+       "         -0.13278535, -0.13050197],\n",
+       "        [ 0.10392075,  1.2781256 ,  0.17946725, ...,  0.73474013,\n",
+       "          1.59901667, -0.22310606],\n",
+       "        [ 0.40039   , -0.36919646, -0.41812356, ..., -0.82578604,\n",
+       "         -0.95433683, -0.70055211]],\n",
+       "\n",
+       "       [[ 0.28550956, -1.75918566,  1.08585615, ..., -0.17260057,\n",
+       "         -0.31717858, -0.15889076],\n",
+       "        [ 1.12616284,  2.12250411, -1.22377048, ..., -0.50452776,\n",
+       "          1.09665063,  0.62770833],\n",
+       "        [-0.82113198,  0.60950151,  0.12629451, ...,  1.18250354,\n",
+       "          1.03982808,  0.4350411 ],\n",
+       "        ...,\n",
+       "        [-0.14425363,  0.18750333,  2.30758557, ...,  0.19605514,\n",
+       "          0.04511098,  1.31371653],\n",
+       "        [ 0.70622565,  0.07230275, -1.80811131, ..., -0.30374127,\n",
+       "          0.73420799, -0.89225914],\n",
+       "        [ 1.13667967, -0.62298503,  0.70894404, ...,  0.93673866,\n",
+       "          0.83697246,  1.22364797]],\n",
+       "\n",
+       "       [[ 1.71586288,  0.2645208 ,  1.38154111, ...,  1.34611706,\n",
+       "          0.67924247,  0.6426671 ],\n",
+       "        [-0.63375751, -0.25986361, -1.85613258, ..., -1.1958948 ,\n",
+       "          0.65731631, -0.33917202],\n",
+       "        [-0.25273764, -0.31382369, -0.34433588, ...,  1.56951567,\n",
+       "         -0.42550589,  0.13231013],\n",
+       "        ...,\n",
+       "        [-1.3109662 ,  1.0167199 ,  0.29096078, ...,  1.2091756 ,\n",
+       "         -0.35967787,  0.28249554],\n",
+       "        [ 2.40744126, -0.73846363, -0.98632421, ..., -0.79229741,\n",
+       "          2.01420262,  1.00171644],\n",
+       "        [ 2.25146098,  0.08915173, -0.37030215, ..., -0.75901238,\n",
+       "          0.46540678,  0.5400871 ]],\n",
+       "\n",
+       "       [[ 0.43838309,  0.06907241,  0.44621424, ..., -0.26691355,\n",
+       "          1.45697414,  0.66782308],\n",
+       "        [ 1.38057301,  0.71432475, -0.92534916, ...,  0.23614769,\n",
+       "          0.48711266,  1.27270678],\n",
+       "        [ 0.38952426, -0.18324781,  0.28926883, ..., -0.11207212,\n",
+       "          1.06242534, -1.2021079 ],\n",
        "        ...,\n",
-       "        [ 2.29990703e+00,  6.15991006e+00, -2.25355857e+00, ...,\n",
-       "          1.23139385e+01,  2.41692832e+00,  1.43921436e+01],\n",
-       "        [ 1.82986665e+04,  1.17716775e+04,  2.72781885e+03, ...,\n",
-       "          1.33645653e+04,  9.02494707e+03,  7.20704177e+03],\n",
-       "        [-1.41328100e+01,  2.64878173e+00, -2.10570634e+00, ...,\n",
-       "          1.18699567e+01, -2.09679409e+01, -1.69682186e+01]]])
                • tau
                  (chain, draw)
                  float64
                  0.3528 9.909 ... 1.355e+04 14.48
                  array([[3.52758223e-01, 9.90921652e+00, 8.82268310e+00, 1.09506829e+01,\n",
-       "        2.82781227e+00, 7.05636649e+00, 4.34661467e+00, 3.81757701e+01,\n",
-       "        2.52909559e+00, 1.24109602e+00, 3.69661235e+00, 1.85534615e+00,\n",
-       "        7.99305183e+00, 3.58047378e+00, 1.93410919e-02, 1.23341118e+01,\n",
-       "        1.85582662e+02, 1.81814135e-01, 1.36015776e+01, 1.88326305e+00,\n",
-       "        4.28833380e+01, 4.73379709e-01, 1.45705561e+00, 1.95489617e+00,\n",
-       "        1.69556957e+01, 4.69383217e-01, 1.02456969e+01, 5.89584004e+00,\n",
-       "        3.64301191e+00, 3.12110928e+01, 1.57666008e+01, 3.36118654e+00,\n",
-       "        3.10994379e+00, 2.48015080e+00, 7.94695777e-02, 1.11550224e+01,\n",
-       "        2.59944706e+00, 2.21700090e+00, 4.52298934e-01, 8.58965387e+00,\n",
-       "        2.71031784e-01, 1.42845197e+00, 2.44995264e-01, 6.22967827e+00,\n",
-       "        1.38837200e+02, 1.13099926e-01, 4.92721722e-01, 1.30898249e+01,\n",
-       "        2.35794873e+01, 2.64121203e-01, 3.78933248e+00, 6.56492829e-01,\n",
-       "        2.98140962e+00, 1.53587841e+01, 6.98058918e+00, 1.47787227e+02,\n",
-       "        1.48526964e+00, 8.59682664e-01, 2.83839393e+01, 4.02707073e+00,\n",
-       "        5.11753973e+00, 7.05691781e-01, 5.04432948e+00, 4.81262958e+00,\n",
-       "        2.91581954e+00, 4.17704463e-01, 3.13419925e-01, 9.00808247e+00,\n",
-       "        4.76669830e+01, 1.04939289e+01, 5.36119948e+00, 8.77849980e-01,\n",
-       "        3.96020355e+00, 7.63709077e+01, 2.55095149e+00, 1.13287460e+00,\n",
-       "        1.67426833e+01, 6.13177566e+00, 5.48656718e+00, 3.17413565e+00,\n",
-       "        8.50072291e+00, 6.94156931e-01, 8.32983298e+00, 7.45127555e-01,\n",
-       "        5.44255434e+00, 1.35820365e+00, 1.02721820e+01, 3.20084312e-01,\n",
-       "        2.98734019e+00, 2.22231323e+01, 3.21937710e+00, 2.74295513e+00,\n",
-       "        3.57539588e+00, 3.02013057e+00, 7.33324857e+00, 4.00746781e-01,\n",
-       "        1.02458048e-01, 1.71510849e+01, 2.95998509e+01, 1.89275077e+00,\n",
-       "        1.05976920e+01, 3.50916220e+00, 3.93048556e+00, 6.08174590e+00,\n",
-       "        1.42364784e+01, 4.07021810e+00, 1.90530058e+00, 2.61457004e+01,\n",
-       "        1.73703104e+00, 9.73443581e+01, 4.56783696e+01, 5.40013717e+00,\n",
-       "        1.36196779e+01, 1.32687151e+00, 2.01672828e+00, 8.03541095e+00,\n",
-       "        1.43248625e+01, 6.73507227e+01, 1.42853703e+01, 9.77510867e+01,\n",
-       "        2.51928541e+00, 1.05741412e+01, 7.90138593e-01, 2.32535617e-01,\n",
-       "        2.46350082e+00, 8.71937109e+00, 6.68236503e-01, 3.46282932e+02,\n",
-       "        6.53859713e+00, 6.54254558e+03, 1.23126409e+01, 4.31687520e+00,\n",
-       "        3.63062670e+00, 2.91367673e+00, 2.16899778e+00, 3.87215837e+00,\n",
-       "        8.33874856e-01, 1.93531458e+00, 5.18779082e+00, 1.07962825e+01,\n",
-       "        4.30146316e+00, 3.70785288e+01, 1.18055319e+01, 2.35960519e-01,\n",
-       "        2.42052211e-01, 1.80993804e+00, 2.55626443e+01, 1.04628207e+01,\n",
-       "        2.18022917e+00, 2.44838425e+01, 6.54002023e+00, 7.74241198e+00,\n",
-       "        2.12054906e+01, 2.47703499e+00, 2.62944750e-01, 2.45108870e+01,\n",
-       "        5.63068713e+00, 2.62561589e+00, 2.06773173e+00, 3.62805122e+00,\n",
-       "        2.09593853e+01, 3.57842130e+00, 5.44429125e+01, 1.99575943e+00,\n",
-       "        1.36439385e+00, 1.06974026e+01, 2.74675936e+01, 3.35200695e+01,\n",
-       "        4.32666956e+00, 7.30704505e+00, 6.87665159e-01, 2.13611691e+00,\n",
-       "        2.39506808e+00, 9.43924918e+00, 2.36976082e+00, 6.77674697e-01,\n",
-       "        8.81320821e-01, 8.72607624e+00, 8.26009804e+00, 5.40046357e+00,\n",
-       "        5.23898713e-01, 2.26243574e+01, 7.23313667e-01, 3.15445459e+00,\n",
-       "        7.70204442e-01, 3.37540745e+00, 6.67879684e+01, 6.29514730e+00,\n",
-       "        5.88679525e-01, 1.14752886e+00, 9.28747492e-01, 1.51401617e+01,\n",
-       "        7.95191397e-01, 1.82152907e+00, 1.25600247e+00, 3.71828532e+00,\n",
-       "        9.55040222e-01, 1.09833657e+00, 3.98945095e+00, 4.06461225e+00,\n",
-       "        2.01970616e+01, 3.70041270e+00, 3.80595680e+00, 3.62783507e+00,\n",
-       "        7.64816391e+00, 8.16913042e-01, 4.28972804e-01, 2.83445006e+00,\n",
-       "        2.11733157e+00, 2.23289951e+01, 4.80169463e+00, 8.22022937e+00,\n",
-       "        2.64196851e+00, 3.88991278e+00, 8.05525967e+00, 5.35754138e+00,\n",
-       "        4.87824565e+00, 8.14449225e+00, 1.67698639e+00, 6.35486925e+00,\n",
-       "        2.32427968e+01, 2.80670151e+00, 1.91974470e+01, 5.90191448e+01,\n",
-       "        4.89478839e+01, 1.36560975e+01, 3.39931007e+01, 6.36144813e+00,\n",
-       "        6.15409795e+00, 5.67951613e+00, 2.77445601e+01, 2.92503318e+01,\n",
-       "        1.62196911e+01, 6.65151195e+00, 4.93148797e+00, 3.13059608e+00,\n",
-       "        4.91283671e+01, 1.50905657e+01, 3.88341201e+00, 6.82982279e+00,\n",
-       "        9.96691600e+00, 2.86407451e+01, 1.53074614e-01, 6.05986177e+00,\n",
-       "        1.07614965e+00, 6.07179884e-01, 6.13187986e+00, 6.22489208e+00,\n",
-       "        6.49108304e+01, 8.83969684e-01, 4.85528891e+00, 3.76781673e+01,\n",
-       "        2.80524863e+00, 3.26332348e+00, 2.01498656e+00, 6.75727305e+01,\n",
-       "        2.79340018e+00, 1.29749365e+01, 3.17507764e+01, 1.18233399e+00,\n",
-       "        2.22744355e-01, 9.00986459e+00, 3.15524609e+00, 4.44751680e+00,\n",
-       "        2.49752098e+00, 1.88972302e+00, 1.44930701e+00, 1.56431846e-01,\n",
-       "        2.32909351e+00, 2.58421215e+00, 5.28552999e+01, 3.24968245e+01,\n",
-       "        5.87146125e-01, 5.60367662e+00, 3.94160925e+00, 5.78097813e+00,\n",
-       "        6.38677851e-01, 5.69493188e+01, 9.91033835e+00, 1.10463092e+00,\n",
-       "        1.26223657e+01, 1.76633084e+01, 1.09007683e+01, 3.38350350e+00,\n",
-       "        2.64325991e+00, 2.48731758e+00, 4.71416415e+00, 2.01027943e+00,\n",
-       "        3.84705358e+01, 1.39555830e+02, 1.74141176e+00, 6.24924839e+01,\n",
-       "        1.10679458e+01, 2.35486442e-01, 5.23525679e+00, 3.41810903e+01,\n",
-       "        4.78649023e+01, 7.69021824e+01, 5.38513032e+00, 9.12823082e+00,\n",
-       "        2.41101297e-01, 2.84705077e+00, 8.90029138e+00, 7.09028758e+00,\n",
-       "        1.02979760e+01, 1.58491619e-02, 1.69321641e+01, 1.24476376e+01,\n",
-       "        2.80940998e+00, 1.85492698e+00, 4.13510089e+00, 2.24718612e+00,\n",
-       "        1.85401390e+00, 9.85505851e+00, 9.38592610e+00, 2.24683589e-01,\n",
-       "        9.33634016e+00, 8.65138771e+00, 2.04458133e+01, 1.00994291e+00,\n",
-       "        9.27041587e+00, 3.43223369e+00, 1.02010766e+01, 6.88501094e+00,\n",
-       "        4.90507048e+00, 6.89075857e+00, 9.62478873e+00, 1.37631145e+00,\n",
-       "        1.97459230e+00, 4.86381546e+00, 4.20344080e-01, 1.13379655e+01,\n",
-       "        6.39187347e-01, 2.14116645e+01, 7.66738562e+00, 1.42350174e+00,\n",
-       "        8.72931704e-01, 2.84816715e-01, 9.55117314e-01, 7.96152413e+00,\n",
-       "        9.98974147e+00, 4.97977080e+00, 1.51003551e+00, 4.88371246e+00,\n",
-       "        1.41427115e+01, 2.64638862e+00, 3.88439911e+01, 6.65434165e+00,\n",
-       "        1.41092636e+01, 1.15806290e+01, 4.87140788e+00, 4.61743687e+00,\n",
-       "        7.17728146e+01, 7.73459578e+00, 8.11124814e-01, 1.74685951e+02,\n",
-       "        1.01568566e+01, 1.17072536e+00, 5.06288247e+00, 4.71549862e+00,\n",
-       "        8.24201449e+00, 4.70745065e+00, 1.07473466e+01, 9.72499125e+00,\n",
-       "        1.90146619e+00, 2.21627599e+01, 2.09931393e+00, 1.74031995e+01,\n",
-       "        1.23133563e+01, 4.85510860e+00, 4.63090501e+00, 3.03037437e+00,\n",
-       "        2.14604415e+00, 7.44831662e+00, 1.34290090e+00, 5.85868643e-01,\n",
-       "        1.23938210e+01, 5.57274181e+00, 2.64953210e+02, 1.58262210e+00,\n",
-       "        5.79513430e+00, 4.73585808e+00, 8.54088794e-01, 3.46144173e+00,\n",
-       "        7.54440541e+00, 1.79717803e+01, 8.10607763e+00, 3.16925981e+00,\n",
-       "        6.98091571e+00, 1.04008324e+02, 2.24098601e+00, 4.79639261e+00,\n",
-       "        9.49134038e+00, 2.15830748e+01, 1.78897543e+00, 4.50568026e+00,\n",
-       "        1.41835763e+00, 5.02905089e+00, 5.91282522e+00, 7.76090458e+00,\n",
-       "        6.40009532e+00, 1.27458410e+01, 2.40173308e+00, 6.25820838e+00,\n",
-       "        1.43987374e+00, 3.67206434e+01, 7.98520119e+01, 1.94812595e+00,\n",
-       "        5.94039608e+00, 9.82385477e-01, 1.20624902e+01, 2.68192869e+01,\n",
-       "        3.11915047e+00, 3.19687718e+01, 1.65737073e+00, 2.63042770e-01,\n",
-       "        2.39211783e+00, 6.18256584e-01, 9.19936167e+01, 5.28611929e+00,\n",
-       "        7.88175429e+00, 8.41720240e+00, 1.27098316e+00, 1.03838514e+01,\n",
-       "        3.66092713e+00, 5.01054311e+01, 3.10574286e+00, 1.57029298e+01,\n",
-       "        1.37374817e+01, 8.22505822e-01, 2.22768280e+00, 1.53703513e+01,\n",
-       "        9.82911107e-02, 4.87695059e+01, 5.98224541e-01, 1.01791519e+01,\n",
-       "        1.20383410e+01, 1.41243456e+01, 7.31441573e+00, 2.20305277e+00,\n",
-       "        3.23221081e+01, 1.04430626e+01, 7.53170699e+00, 2.00741075e+00,\n",
-       "        3.92624753e+01, 6.76830027e+01, 6.20349670e+01, 2.69677988e+00,\n",
-       "        2.34019637e+00, 1.85327911e+00, 1.03456889e+01, 5.48704958e+00,\n",
-       "        5.49283122e+00, 1.19471977e+01, 2.87804398e+00, 2.03968797e-01,\n",
-       "        2.57428290e+01, 1.93861156e+01, 1.25433354e+01, 2.90134161e+00,\n",
-       "        1.38895600e+01, 1.23272959e+00, 8.56331400e+00, 1.56926967e+00,\n",
-       "        1.92846051e+01, 6.38268661e+01, 1.35855922e+01, 1.39593627e+00,\n",
-       "        5.62517866e+00, 4.43004650e+00, 1.77784757e+00, 8.40728490e+00,\n",
-       "        1.42646867e+00, 9.50323267e-01, 2.50319125e+01, 1.31136059e+00,\n",
-       "        2.86052340e+00, 1.00988074e+01, 6.06426417e+00, 1.96013766e+00,\n",
-       "        1.36298326e+02, 4.00464263e+00, 7.64631635e+00, 2.63277976e+01,\n",
-       "        1.13937686e+00, 9.76896485e+01, 7.57151767e+01, 2.05133601e+00,\n",
-       "        4.22665734e+00, 6.26429373e-01, 2.64633926e+00, 2.07625781e+01,\n",
-       "        5.19932135e+00, 9.88862999e-01, 3.39815977e-01, 1.66964133e+00,\n",
-       "        1.79500281e+00, 1.41019974e+01, 1.35463294e+04, 1.44842777e+01]])
                • tau_log__
                  (chain, draw)
                  float64
                  -1.042 2.293 2.177 ... 9.514 2.673
                  array([[-1.04197238,  2.29346529,  2.17732603,  2.39340182,  1.03950336,\n",
-       "         1.95393026,  1.46939731,  3.64220102,  0.92786177,  0.21599488,\n",
-       "         1.30741682,  0.61807128,  2.07857264,  1.27549513, -3.94552333,\n",
-       "         2.51236874,  5.2235004 , -1.70477035,  2.61018579,  0.63300594,\n",
-       "         3.75848336, -0.74785744,  0.3764177 ,  0.67033708,  2.83060381,\n",
-       "        -0.75633575,  2.3268578 ,  1.77424702,  1.29281079,  3.44077357,\n",
-       "         2.75789383,  1.21229405,  1.13460465,  0.90831937, -2.532381  ,\n",
-       "         2.41188984,  0.95529875,  0.79615534, -0.79341196,  2.15055844,\n",
-       "        -1.30551918,  0.35659132, -1.4065164 ,  1.82932469,  4.93330202,\n",
-       "        -2.17948355, -0.70781072,  2.57183521,  3.16037715, -1.33134718,\n",
-       "         1.33218988, -0.42084351,  1.09239622,  2.73168756,  1.94313332,\n",
-       "         4.99577358,  0.39559633, -0.15119195,  3.34582347,  1.39303925,\n",
-       "         1.6326738 , -0.34857671,  1.61826474,  1.57124363,  1.07015093,\n",
-       "        -0.87298112, -1.16021137,  2.19812223,  3.86423898,  2.35079689,\n",
-       "         1.67918773, -0.13027957,  1.37629543,  4.33560183,  0.93646642,\n",
-       "         0.1247583 ,  2.81796134,  1.81348438,  1.70230277,  1.15503536,\n",
-       "         2.14015121, -0.36505722,  2.11984341, -0.29419986,  1.6942485 ,\n",
-       "         0.30616298,  2.32943947, -1.13917084,  1.09438342,  3.10113374,\n",
-       "         1.16918789,  1.00903585,  1.27407591,  1.10530006,  1.99241861,\n",
-       "        -0.91442552, -2.27830185,  2.84206143,  3.38776932,  0.63803121,\n",
-       "         2.36063624,  1.25537732,  1.36876297,  1.80529181,  2.65580757,\n",
-       "         1.40369659,  0.64463978,  3.26368476,  0.55217736,  4.57825478,\n",
-       "         3.82162487,  1.68642435,  2.61151565,  0.28282392,  0.70147653,\n",
-       "         2.08385814,  2.66199666,  4.20991363,  2.65923596,  4.58242432,\n",
-       "         0.92397529,  2.35841151, -0.23554692, -1.45871187,  0.90158344,\n",
-       "         2.16554711, -0.40311312,  5.84725616,  1.87772264,  8.7860816 ,\n",
-       "         2.51062645,  1.46253181,  1.28940528,  1.06941577,  0.77426521,\n",
-       "         1.35381207, -0.18167194,  0.66026989,  1.64630795,  2.37920187,\n",
-       "         1.45895523,  3.61303806,  2.46856823, -1.44409078, -1.41860183,\n",
-       "         0.59329261,  3.24113208,  2.34782808,  0.77943   ,  3.19801341,\n",
-       "         1.87794026,  2.04671326,  3.05426014,  0.90706228, -1.33581134,\n",
-       "         3.19911739,  1.72823148,  0.96531549,  0.72645222,  1.28869565,\n",
-       "         3.04258653,  1.27492172,  3.99715268,  0.69102465,  0.31071027,\n",
-       "         2.37000097,  3.3130069 ,  3.51214435,  1.46479809,  1.98883896,\n",
-       "        -0.37445325,  0.75898965,  0.87341165,  2.24487644,  0.86278903,\n",
-       "        -0.3890879 , -0.12633356,  2.16631581,  2.11143646,  1.6864848 ,\n",
-       "        -0.64645691,  3.11902709, -0.32391231,  1.14881561, -0.26109929,\n",
-       "         1.21651604,  4.20152295,  1.83977907, -0.52987334,  0.13761081,\n",
-       "        -0.07391838,  2.71735093, -0.22917244,  0.5996763 ,  0.22793404,\n",
-       "         1.31326263, -0.04600182,  0.09379683,  1.38365362,  1.40231835,\n",
-       "         3.00553713,  1.30844436,  1.33656742,  1.28863607,  2.03446561,\n",
-       "        -0.20222263, -0.84636176,  1.04184794,  0.7501566 ,  3.10588606,\n",
-       "         1.5689689 ,  2.10659811,  0.97152429,  1.35838674,  2.08632525,\n",
-       "         1.67850517,  1.58478566,  2.0973419 ,  0.51699837,  1.84922133,\n",
-       "         3.14599527,  1.03200995,  2.9547773 ,  4.07786188,  3.89075614,\n",
-       "         2.61418613,  3.52615758,  1.85025604,  1.81711819,  1.73686604,\n",
-       "         3.32303979,  3.37589092,  2.78622601,  1.89484419,  1.59564076,\n",
-       "         1.14122343,  3.89443661,  2.71406976,  1.35671415,  1.92129873,\n",
-       "         2.29927121,  3.35483036, -1.8768298 ,  1.80168699,  0.07338953,\n",
-       "        -0.49893018,  1.81350137,  1.82855611,  4.17301449, -0.12333251,\n",
-       "         1.58006861,  3.62908081,  1.03149217,  1.18274615,  0.70061252,\n",
-       "         4.21320451,  1.02725956,  2.56301953,  3.45791718,  0.16749044,\n",
-       "        -1.50173056,  2.19832004,  1.14906649,  1.49234592,  0.91529863,\n",
-       "         0.63643027,  0.37108552, -1.85513485,  0.84547914,  0.94942068,\n",
-       "         3.96755799,  3.48114238, -0.53248155,  1.72342292,  1.37158908,\n",
-       "         1.75457289, -0.4483551 ,  4.04216173,  2.29357849,  0.09951127,\n",
-       "         2.5354703 ,  2.87148952,  2.38883327,  1.21891171,  0.97201297,\n",
-       "         0.91120485,  1.55057163,  0.69827373,  3.64989264,  4.93846473,\n",
-       "         0.55469614,  4.13504629,  2.40405316, -1.44610194,  1.6554159 ,\n",
-       "         3.53167258,  3.86838251,  4.34253426,  1.68364151,  2.2113719 ,\n",
-       "        -1.42253811,  1.04628364,  2.18608402,  1.9587259 ,  2.33194737,\n",
-       "        -4.14463865,  2.82921502,  2.52153085,  1.03297449,  0.61784533,\n",
-       "         1.41951173,  0.80967882,  0.61735296,  2.28798488,  2.23921134,\n",
-       "        -1.49306214,  2.23391433,  2.15771974,  3.01777813,  0.00989381,\n",
-       "         2.22682824,  1.23321127,  2.32249326,  1.92934672,  1.59026946,\n",
-       "         1.93018118,  2.26434193,  0.31940706,  0.68036195,  1.5818232 ,\n",
-       "        -0.86668167,  2.42815688, -0.44755768,  3.06393584,  2.0369757 ,\n",
-       "         0.35311985, -0.13589796, -1.25590941, -0.0459211 ,  2.07462046,\n",
-       "         2.30155871,  1.60538387,  0.41213317,  1.58590568,  2.64919941,\n",
-       "         0.97319593,  3.65955339,  1.89526952,  2.64683158,  2.44933379,\n",
-       "         1.58338299,  1.52983976,  4.27350578,  2.04570322, -0.20933334,\n",
-       "         5.1629898 ,  2.318149  ,  0.15762353,  1.62193598,  1.55085466,\n",
-       "         2.10924479,  1.5491465 ,  2.37465889,  2.27469899,  0.64262527,\n",
-       "         3.0984134 ,  0.74161059,  2.85665407,  2.51068455,  1.58003147,\n",
-       "         1.53275232,  1.10868617,  0.76362622,  2.00798805,  0.29483213,\n",
-       "        -0.53465967,  2.51719804,  1.71788718,  5.57955324,  0.45908303,\n",
-       "         1.75701865,  1.55516293, -0.15772012,  1.24168519,  2.02080628,\n",
-       "         2.88880277,  2.09261411,  1.15349806,  1.9431801 ,  4.64447093,\n",
-       "         0.80691595,  1.5678641 ,  2.25037984,  3.07190943,  0.58164307,\n",
-       "         1.50533888,  0.34949961,  1.61523128,  1.77712376,  2.0490989 ,\n",
-       "         1.85631288,  2.54520502,  0.87619059,  1.83389394,  0.36455543,\n",
-       "         3.60333909,  4.38017507,  0.66686786,  1.78177581, -0.0177715 ,\n",
-       "         2.49010065,  3.28912129,  1.13756068,  3.46475954,  0.50523245,\n",
-       "        -1.33543863,  0.87217909, -0.48085172,  4.52171919,  1.66508438,\n",
-       "         2.0645505 ,  2.13027752,  0.23979074,  2.34025185,  1.29771643,\n",
-       "         3.91412941,  1.13325293,  2.75384731,  2.62012799, -0.19539972,\n",
-       "         0.80096194,  2.73244041, -2.31982169,  3.88710524, -0.51378911,\n",
-       "         2.3203417 ,  2.48809664,  2.64789994,  1.98984716,  0.78984402,\n",
-       "         3.47575146,  2.34593789,  2.01912171,  0.69684571,  3.67026924,\n",
-       "         4.21483508,  4.12769821,  0.99205842,  0.85023484,  0.61695656,\n",
-       "         2.3365699 ,  1.70239069,  1.70344383,  2.48049675,  1.05711089,\n",
-       "        -1.58978825,  3.2481561 ,  2.96455712,  2.52918948,  1.06517326,\n",
-       "         2.63113748,  0.20923089,  2.14748726,  0.45061033,  2.95930712,\n",
-       "         4.1561742 ,  2.60900984,  0.33356535,  1.72725271,  1.48841008,\n",
-       "         0.5754034 ,  2.12909858,  0.35520193, -0.05095307,  3.22015151,\n",
-       "         0.27106522,  1.05100462,  2.31241734,  1.80241321,  0.6730147 ,\n",
-       "         4.91484605,  1.38745434,  2.03422401,  3.27062532,  0.1304815 ,\n",
-       "         4.5817956 ,  4.32697862,  0.71849129,  1.44141145, -0.46771924,\n",
-       "         0.97317727,  3.03315224,  1.64852811, -0.01119948, -1.07935105,\n",
-       "         0.51260883,  0.58500659,  2.64631645,  9.51387089,  2.67306376]])
                • mu
                  (chain, draw)
                  float64
                  -0.575 -4.24 ... 6.441 -0.1923
                  array([[-5.74954439e-01, -4.24026973e+00,  1.06399686e+01,\n",
-       "         1.91456322e+00,  4.29695877e-01, -2.01048011e+00,\n",
-       "         9.83590495e+00, -1.65934652e+00, -4.50888000e+00,\n",
-       "        -2.64479169e+00, -6.54382162e-01,  6.68568578e+00,\n",
-       "        -2.37345586e+00, -1.00224692e+00,  3.72748135e+00,\n",
-       "        -3.81813612e+00, -8.79383199e+00, -1.38768333e+00,\n",
-       "         4.94204733e+00, -2.76050004e+00, -3.66244864e+00,\n",
-       "         4.46486418e+00,  4.76278811e+00, -4.19818364e+00,\n",
-       "        -1.62970974e+01, -1.88728519e+00,  1.68572759e-01,\n",
-       "         7.05875142e-01, -2.28460344e+00, -1.29060203e+00,\n",
-       "        -6.77952511e+00, -1.76806101e+00,  9.90307217e+00,\n",
-       "        -5.08191417e+00, -2.77955936e+00, -1.26552198e-01,\n",
-       "         1.64346376e+00, -1.14847267e+01, -4.88870442e+00,\n",
-       "         1.65330247e+00, -7.10146375e+00,  1.84563814e+00,\n",
-       "         5.26597073e+00,  4.04741491e+00, -5.48389781e+00,\n",
-       "        -1.79354982e+00,  5.16019352e-01, -4.31502392e+00,\n",
-       "        -5.87991929e-01,  4.61685537e-01, -5.40508477e+00,\n",
-       "         1.09498247e+01,  3.45775140e+00,  1.49258284e+00,\n",
-       "         3.65787080e+00,  1.63590297e+00, -7.12138162e+00,\n",
-       "        -3.69376332e+00, -3.46716192e+00,  3.00012488e+00,\n",
-       "         1.40545775e+00, -2.01390642e+00,  1.48216348e+00,\n",
-       "        -4.92331232e+00,  3.48236754e+00, -7.20020825e+00,\n",
-       "        -3.44613220e-01,  2.75874260e+00,  1.08676430e+01,\n",
-       "         1.49758422e+00,  2.66957196e+00,  5.89560838e+00,\n",
-       "         4.25193649e+00, -9.55325417e-01, -6.80641874e+00,\n",
-       "        -4.05416966e+00,  2.82086177e+00, -2.55402781e+00,\n",
-       "        -3.51058648e+00,  2.37721291e+00,  8.26518462e-01,\n",
-       "        -3.18520105e+00, -5.16332273e+00, -3.12271009e+00,\n",
-       "         3.93310136e+00, -2.59256990e+00,  2.31691345e+00,\n",
-       "        -2.88824091e+00, -8.96081434e+00, -5.86659549e+00,\n",
-       "         9.64150577e-01, -5.29518057e-01,  8.07233244e-01,\n",
-       "         3.46797817e-01, -1.22774808e+00,  6.92011381e+00,\n",
-       "        -1.60441560e-01, -1.27536333e+00, -2.00232765e+00,\n",
-       "        -1.01317842e+00,  4.32083123e+00, -2.75420656e+00,\n",
-       "         4.38470391e+00, -7.03065667e+00,  1.59631173e+00,\n",
-       "         3.24310246e+00, -6.74995978e+00,  5.14451266e-02,\n",
-       "        -3.83024909e+00,  3.47873122e+00,  3.85596436e+00,\n",
-       "         2.16062643e+00, -7.65296478e-01, -5.72504062e+00,\n",
-       "        -6.78614816e+00, -2.55111928e-01, -3.51953327e+00,\n",
-       "        -8.12987952e+00,  2.84455946e+00, -4.08802220e+00,\n",
-       "         3.93285016e+00,  2.34029335e+00, -6.28521891e+00,\n",
-       "        -1.99753259e+00,  8.52259672e+00, -4.70316695e+00,\n",
-       "        -5.84544108e+00, -9.77174364e-01,  3.35560384e+00,\n",
-       "        -2.64715720e+00,  1.39209304e+00, -5.85492165e+00,\n",
-       "         2.09293379e+00,  7.12267020e-01,  6.53179008e+00,\n",
-       "        -7.75205559e+00, -3.72766190e+00, -7.78152504e-01,\n",
-       "        -3.58276238e-01, -6.75348299e+00,  2.60416320e-01,\n",
-       "         8.54374355e-01,  2.31704904e+00, -2.26825906e+00,\n",
-       "         4.98542876e-01,  1.44839537e+00, -2.89322375e+00,\n",
-       "         3.63089543e+00,  3.34314447e+00, -1.28886082e+01,\n",
-       "        -6.48330066e+00, -7.71711742e+00,  3.46089879e+00,\n",
-       "         3.87306842e-01, -4.22001171e+00,  8.79130960e+00,\n",
-       "         1.09366708e+00,  7.45316905e+00, -6.67264980e+00,\n",
-       "        -4.74778053e+00, -1.85626573e+00, -2.29836995e+00,\n",
-       "        -2.63968432e+00,  1.11591877e+00, -5.85554129e+00,\n",
-       "        -7.34007162e+00,  1.26131351e+01,  2.26555517e+00,\n",
-       "        -6.64153593e+00, -2.93997210e+00, -5.79436329e+00,\n",
-       "        -2.23246389e+00, -7.25228661e+00, -1.27507950e+00,\n",
-       "         4.93854306e+00,  6.24543773e+00,  2.58163273e-01,\n",
-       "        -6.48269015e+00, -6.70598543e+00,  5.11213269e+00,\n",
-       "        -4.45154132e+00, -9.60513233e+00,  5.53992814e+00,\n",
-       "         3.03982430e-01, -4.59488174e+00,  3.90511028e+00,\n",
-       "         3.90015474e-01, -2.00087666e+00, -1.10626317e+01,\n",
-       "         7.44471214e+00,  9.13013556e+00, -4.92737128e+00,\n",
-       "        -1.24050284e+01, -7.26431728e+00,  8.20564458e+00,\n",
-       "        -1.63773883e-01,  6.59385336e+00,  3.70476835e+00,\n",
-       "         2.99163710e+00,  1.32498665e-02, -3.75484141e+00,\n",
-       "        -1.84710639e+00,  1.05882620e+00,  6.22453006e+00,\n",
-       "         1.14053974e+01,  9.59385076e-01,  6.08189258e-01,\n",
-       "         6.18600649e+00, -2.42357484e+00,  7.25813496e+00,\n",
-       "         5.56186618e+00,  1.79100581e+00,  6.23296561e+00,\n",
-       "        -5.33407644e+00,  7.06255531e-01,  1.13015609e+01,\n",
-       "         1.63792528e+00,  2.82502835e+00, -4.70103311e+00,\n",
-       "         3.84265500e+00, -1.18879727e+01,  5.26878406e+00,\n",
-       "         7.97558088e+00,  4.00421126e+00, -5.44253622e+00,\n",
-       "         1.69866138e+00,  5.20107807e+00,  2.69582082e+00,\n",
-       "        -2.78873509e+00,  4.24818709e+00,  4.14761768e+00,\n",
-       "         2.11280220e+00,  4.35557936e+00, -1.73022204e+00,\n",
-       "        -4.75580583e+00,  1.97110601e+00, -2.11293834e+00,\n",
-       "        -8.23098306e-01, -9.12397500e-01,  2.55408354e+00,\n",
-       "         1.20535882e+01,  9.24326322e-01, -2.49886416e+00,\n",
-       "        -9.41658523e+00,  7.28848467e+00, -3.27620347e+00,\n",
-       "        -6.32840076e+00,  4.51099044e+00, -2.88037503e-01,\n",
-       "        -1.15249688e+01,  1.02842513e+01,  5.98595192e+00,\n",
-       "         3.91480222e+00, -5.23579242e+00, -1.01907311e+00,\n",
-       "         7.90259887e+00, -2.61138178e+00,  2.70138717e+00,\n",
-       "        -6.54034677e+00, -7.33292575e-01,  3.35193883e+00,\n",
-       "        -1.00662987e+01,  1.13133910e+01,  1.63133211e+00,\n",
-       "        -5.97649216e-01, -3.43753219e+00,  2.62528512e+00,\n",
-       "        -5.48916477e-01, -4.95239283e+00,  1.59525171e+00,\n",
-       "        -6.02360489e+00,  5.54256689e+00,  4.80234764e+00,\n",
-       "         4.97973550e+00, -3.88758642e-01, -4.14082880e+00,\n",
-       "        -3.03871231e-01,  4.14140894e+00, -3.63169177e-01,\n",
-       "        -1.24659063e+00,  6.60138102e+00, -2.36554926e+00,\n",
-       "         1.17834340e+00,  7.08476390e+00,  2.43243312e+00,\n",
-       "         2.48999013e+00, -3.94362956e+00, -2.96990171e-01,\n",
-       "         1.09785176e+01, -2.96038733e-01,  4.26555017e-01,\n",
-       "        -4.43663959e+00, -5.29452181e+00,  8.34274612e+00,\n",
-       "         9.36572451e+00,  1.78986899e+00,  4.24125687e+00,\n",
-       "         3.73966793e-01, -2.41163818e+00, -2.81739347e-01,\n",
-       "         9.20753304e-01, -2.89688528e+00,  2.13607991e+00,\n",
-       "         2.71058287e-01, -1.40881176e+00, -4.08375618e+00,\n",
-       "         6.82899675e-01, -6.34703504e-01, -1.02501378e+00,\n",
-       "         7.85180987e+00,  8.54695614e+00,  1.15353216e+00,\n",
-       "         6.54015280e+00,  7.36857180e+00, -4.51503641e+00,\n",
-       "        -2.95239939e+00,  1.11210319e+00, -1.61629210e-01,\n",
-       "        -7.06651246e+00, -8.07195302e+00, -3.64787782e+00,\n",
-       "        -3.89153835e+00,  4.18964576e+00,  9.83545724e-01,\n",
-       "         5.85175963e+00, -2.71517319e+00,  2.14758933e+00,\n",
-       "        -3.11505604e+00, -2.11089507e+00,  8.33082205e+00,\n",
-       "        -9.30346077e+00,  2.65814263e+00, -5.84972402e+00,\n",
-       "         5.20245535e+00, -5.22117856e+00, -2.93227625e+00,\n",
-       "        -4.11580937e+00, -2.38075135e+00, -4.10498402e+00,\n",
-       "         2.29445652e+00, -2.89219952e+00,  7.64805245e+00,\n",
-       "        -2.50643035e+00,  5.40051296e+00,  4.35823418e+00,\n",
-       "         3.07154388e+00, -8.61670387e-01,  1.13360388e+00,\n",
-       "        -4.46882155e-01, -5.58529799e-01, -3.80805709e+00,\n",
-       "        -8.91082567e+00,  2.69448963e+00,  5.09920163e+00,\n",
-       "        -5.55844853e+00, -7.33447751e+00, -5.29797016e+00,\n",
-       "        -3.85955169e+00,  5.40390149e+00, -5.19253557e+00,\n",
-       "         4.03001184e+00, -1.03083231e+01, -4.83364819e+00,\n",
-       "         5.45598464e+00,  6.89069883e+00,  3.53798299e+00,\n",
-       "        -1.20538031e+00,  3.31367839e+00,  1.54345012e+00,\n",
-       "        -1.87607115e+00,  9.65115423e+00, -1.35614050e+00,\n",
-       "         5.15456542e+00, -3.98056439e+00,  5.98079994e+00,\n",
-       "        -4.94423753e-01,  7.85514879e+00, -4.01675607e-01,\n",
-       "         1.44682527e-01, -6.32632519e+00,  9.44279191e-01,\n",
-       "        -5.98906726e+00, -5.00447375e+00,  2.87108872e+00,\n",
-       "        -1.23985185e+01,  1.09257174e+01,  4.76783047e+00,\n",
-       "        -1.65910417e+00, -2.88933327e+00, -4.26062338e+00,\n",
-       "         1.40116303e+00,  2.05250233e+00, -9.83774194e+00,\n",
-       "         8.88282832e-01,  5.59870174e+00,  2.06126702e-01,\n",
-       "         1.75220503e+00,  1.55238763e+00, -6.41206793e+00,\n",
-       "        -5.95490195e+00,  7.54701210e+00, -7.08338838e+00,\n",
-       "        -1.64454505e+00, -3.21769522e+00,  4.25740122e+00,\n",
-       "         3.17253991e+00, -1.21037656e+00, -1.41074699e+00,\n",
-       "         1.70737547e+00,  1.04044288e+01, -3.73849906e+00,\n",
-       "         2.01868622e+00,  3.95275715e+00, -3.15534434e+00,\n",
-       "         1.72878864e+00,  7.16740348e-01,  3.43928313e+00,\n",
-       "        -6.34318674e+00,  1.96481449e+00,  3.95974226e-01,\n",
-       "         1.61958478e+00,  4.98405837e+00, -1.42666813e+00,\n",
-       "         4.51790955e+00,  9.72885645e+00,  5.04937847e+00,\n",
-       "        -1.14385591e+01, -2.18296607e+00, -6.95637064e+00,\n",
-       "        -6.89022740e+00,  9.07483833e+00,  1.85239967e-01,\n",
-       "        -8.19522122e+00, -3.60942719e+00,  1.02134677e+00,\n",
-       "         7.79410914e+00,  1.63428520e+00,  9.87169062e-01,\n",
-       "        -9.12466407e+00, -6.52297691e+00,  5.16446630e-01,\n",
-       "         5.07924422e+00,  2.20501292e+00,  9.32103768e-01,\n",
-       "         4.80006148e+00, -1.32677228e+00,  2.07389508e+00,\n",
-       "        -1.17281550e+00, -3.81909802e+00,  5.75339726e+00,\n",
-       "         1.11600336e+00, -1.67001808e-01,  7.69815193e+00,\n",
-       "        -1.74344014e+00, -7.25369494e+00,  5.92978250e+00,\n",
-       "        -7.62064968e+00, -3.63957253e+00, -6.27079590e+00,\n",
-       "        -3.13278119e+00, -2.33465879e+00, -1.57118049e+00,\n",
-       "        -3.11722479e+00,  5.56277437e+00,  6.22231427e+00,\n",
-       "        -3.37466696e+00, -6.21777050e-01,  5.36544714e+00,\n",
-       "         1.88478718e+00,  3.80942623e+00, -2.00864174e-01,\n",
-       "         2.96686717e+00, -1.00017482e-01,  1.14620586e+01,\n",
-       "        -1.87290006e+00,  1.73808164e-02,  2.23790361e-01,\n",
-       "         2.55540631e+00, -5.01070688e+00, -8.24809919e-01,\n",
-       "        -3.11352896e+00,  2.40932940e-01, -1.28041169e+00,\n",
-       "        -5.59773808e+00,  7.89826664e-01, -1.91503484e+00,\n",
-       "        -1.05019253e+01, -1.60414316e+00,  5.68381752e+00,\n",
-       "         1.77658728e+00, -7.06087811e+00,  6.53479672e+00,\n",
-       "         1.84916843e-01, -4.82146892e+00, -6.93503993e+00,\n",
-       "        -7.07402871e+00,  8.79637795e+00,  4.97401950e+00,\n",
-       "         6.44051400e+00, -1.92319958e-01]])
                • theta_tilde
                  (chain, draw, school)
                  float64
                  1.456 1.167 1.513 ... -1.434 -1.158
                  array([[[ 1.45568353,  1.16677207,  1.51262231, ...,  1.49855067,\n",
-       "         -0.56901864, -0.35070187],\n",
-       "        [ 1.04675051, -1.89989247,  0.89727166, ..., -1.82880939,\n",
-       "          1.08858889,  0.69945464],\n",
-       "        [-0.67388247, -0.64571447, -0.32301302, ..., -1.65534767,\n",
-       "         -0.09931568,  1.29417408],\n",
+       "        [-0.57243771,  1.20324745, -2.03671137, ..., -0.31232212,\n",
+       "          0.68695275,  0.59062609],\n",
+       "        [ 0.83778081, -0.58047982,  1.78149714, ..., -0.62379571,\n",
+       "          1.44340525, -0.0475484 ],\n",
+       "        [ 1.5197586 , -0.90722855,  1.74266398, ..., -1.08440477,\n",
+       "          0.94915432, -0.19832845]]])
                • theta
                  (chain, draw, school)
                  float64
                  9.632 12.47 9.768 ... 6.705 6.05
                  array([[[  9.6321899 ,  12.4685266 ,   9.7677079 , ...,   9.72370277,\n",
+       "           9.40832211,   8.77565177],\n",
+       "        [  3.69781575,   9.56787788,   4.67677528, ...,   4.91479099,\n",
+       "           2.55987838,   2.72564431],\n",
+       "        [  4.39295419,   7.10330357,   3.05268471, ...,   5.71936768,\n",
+       "          12.19027412,   4.89377505],\n",
        "        ...,\n",
-       "        [-0.1896265 ,  0.0840938 , -0.51252159, ...,  0.5204879 ,\n",
-       "         -0.1813283 ,  0.66785746],\n",
-       "        [ 1.35034558,  0.86851845,  0.20089415, ...,  0.9861066 ,\n",
-       "          0.66575279,  0.53155368],\n",
-       "        [-0.9624567 ,  0.19615073, -0.13210092, ...,  0.83278413,\n",
-       "         -1.43435672, -1.15821438]]])
              • created_at :
                2020-06-06T18:08:05.352686
                arviz_version :
                0.8.3
                inference_library :
                pymc3
                inference_library_version :
                3.8

          \n", + " [ 0.40341755, 9.17692878, 10.66817842, ..., 6.10433636,\n", + " 6.94654197, 6.9571788 ],\n", + " [ 4.34711206, 11.01569209, 4.77615802, ..., 7.92968074,\n", + " 12.83810642, 2.4898514 ],\n", + " [ 4.704432 , 3.81639419, 3.75993645, ..., 3.28952836,\n", + " 3.1411916 , 3.43403774]],\n", + "\n", + " [[ 4.08016931, 2.67745355, 4.62922853, ..., 3.76589348,\n", + " 3.66670908, 3.77529877],\n", + " [ 3.4959735 , 5.47858035, -1.18012896, ..., 0.251083 ,\n", + " 3.43724753, 2.5041052 ],\n", + " [ 6.71461084, 9.16598381, 8.33801454, ..., 10.14781577,\n", + " 9.90334312, 8.86704803],\n", + " ...,\n", + " [ -0.13186495, 1.96525264, 15.36681533, ..., 2.01931073,\n", + " 1.06515544, 9.08432401],\n", + " [ 13.47101369, 9.3540431 , -2.85818235, ..., 6.91184994,\n", + " 13.65274315, 3.08975938],\n", + " [ 11.51633751, -2.35078743, 8.14554566, ..., 9.94069244,\n", + " 9.15448 , 12.20169557]],\n", + "\n", + " [[ 14.16096028, 5.31077703, 12.12228932, ..., 11.90627594,\n", + " 7.83972096, 7.61668693],\n", + " [ 4.23814271, 5.01909573, 1.68496516, ..., 3.06400529,\n", + " 6.9348115 , 4.85344408],\n", + " [ 6.42597819, 6.24159135, 6.14949101, ..., 11.92640765,\n", + " 5.90448123, 7.58823597],\n", + " ...,\n", + " [ 2.08914666, 2.55415999, 2.40917151, ..., 2.59260781,\n", + " 2.27919022, 2.40748037],\n", + " [ 12.41489748, 5.82116547, 5.30165636, ..., 5.70833131,\n", + " 11.59067987, 9.46853607],\n", + " [ 11.99014202, 7.48679308, 6.52990814, ..., 5.72035797,\n", + " 8.27040336, 8.42593687]],\n", + "\n", + " [[ 6.66912406, 4.8954416 , 6.70673459, ..., 3.28180729,\n", + " 11.56109482, 7.77105178],\n", + " [ 9.94588456, 4.18989425, -9.97591714, ..., 0.05872764,\n", + " 2.22691637, 9.01398426],\n", + " [ 11.85781836, 7.5661556 , 11.10662516, ..., 8.0994604 ,\n", + " 16.89972711, -0.06795243],\n", + " ...,\n", + " [ -3.28730045, 5.48726201, -10.52302049, ..., -2.0019372 ,\n", + " 2.93598756, 2.45998863],\n", + " [ 8.77735924, 3.75620799, 12.11845349, ..., 3.6028542 ,\n", + " 10.92148696, 5.64297636],\n", + " [ 7.03134593, 5.64455729, 7.15871482, ..., 5.5433182 ,\n", + " 6.70530072, 6.0496252 ]]])
      • created_at :
        2020-06-10T18:20:55.257774
        arviz_version :
        0.8.3
        inference_library :
        pystan
        inference_library_version :
        2.19.1.1

      \n", "
    \n", "
    \n", "
    • \n", " \n", "
  • \n", - " \n", - " \n", + " \n", + " \n", "
    \n", "
    \n", "
      \n", @@ -12939,26 +10801,68 @@ " stroke: currentColor;\n", " fill: currentColor;\n", "}\n", - "
      xarray.Dataset
        • chain: 1
        • draw: 500
        • obs_dim_0: 8
        • chain
          (chain)
          int64
          0
          array([0])
        • draw
          (draw)
          int64
          0 1 2 3 4 5 ... 495 496 497 498 499
          array([  0,   1,   2, ..., 497, 498, 499])
        • obs_dim_0
          (obs_dim_0)
          int64
          0 1 2 3 4 5 6 7
          array([0, 1, 2, 3, 4, 5, 6, 7])
        • obs
          (chain, draw, obs_dim_0)
          float64
          -3.605 4.575 ... -16.14 -5.657
          array([[[-3.60548654e+00,  4.57547736e+00, -1.11284126e+01, ...,\n",
-       "         -9.56906087e+00,  4.14705229e-01,  2.84360259e+00],\n",
-       "        [ 4.89112106e+00, -1.33537335e+01,  2.29098812e+00, ...,\n",
-       "         -1.61614666e+01,  3.69916753e+00,  1.47268137e+01],\n",
-       "        [-1.16073417e+01, -5.70038634e+00,  1.52872633e+01, ...,\n",
-       "         -3.80733807e+01,  2.27064666e+01,  2.88979444e+01],\n",
+       "
          xarray.Dataset
            • chain: 4
            • draw: 1000
            • school: 8
            • chain
              (chain)
              int64
              0 1 2 3
              array([0, 1, 2, 3])
            • draw
              (draw)
              int64
              0 1 2 3 4 5 ... 995 996 997 998 999
              array([  0,   1,   2, ..., 997, 998, 999])
            • school
              (school)
              int64
              0 1 2 3 4 5 6 7
              array([0, 1, 2, 3, 4, 5, 6, 7])
            • y_hat
              (chain, draw, school)
              float64
              16.82 11.42 17.62 ... -11.89 1.282
              array([[[ 16.81606234,  11.42314561,  17.62390159, ...,   9.79825611,\n",
+       "          17.93449592,  20.74301303],\n",
+       "        [ -2.87371409,   3.26558897,  23.16168449, ...,   5.60727737,\n",
+       "           9.07428464,  -0.29335618],\n",
+       "        [ 29.69759628,  27.78756772,  15.23144444, ...,  11.23869389,\n",
+       "          19.64442795,  16.32327954],\n",
+       "        ...,\n",
+       "        [  0.22658945,  14.21873048,  41.77431262, ...,  27.79849965,\n",
+       "           8.97700113, -10.97758778],\n",
+       "        [-25.71679369,   7.54290441,  22.1185727 , ...,  -1.9379867 ,\n",
+       "          24.08319264,  11.10296328],\n",
+       "        [ 29.8534142 ,  19.97743567,  -2.22962611, ...,  -1.57819509,\n",
+       "           2.04335776,  -4.69118197]],\n",
+       "\n",
+       "       [[ 19.34365445,  -4.00444521, -16.23523472, ...,  13.94222586,\n",
+       "           8.86119588,  -2.0744115 ],\n",
+       "        [  4.70865213,  23.04754692,  21.70862296, ...,   0.26881933,\n",
+       "           8.3109079 , -24.0917072 ],\n",
+       "        [  8.61267321,   5.41437255, -12.84437828, ...,  30.43257723,\n",
+       "           1.84297616,   8.86262284],\n",
        "        ...,\n",
-       "        [ 5.19297425e+00,  7.69150333e+00,  8.00622289e+00, ...,\n",
-       "          7.08637173e+00, -4.87507692e+00,  3.16607702e+01],\n",
-       "        [ 1.83152225e+04,  1.17753769e+04,  2.71083927e+03, ...,\n",
-       "          1.33705972e+04,  9.00167536e+03,  7.19006049e+03],\n",
-       "        [-1.97014446e+01,  2.98840905e+00, -4.85452806e+00, ...,\n",
-       "          5.82130895e+00, -1.61363982e+01, -5.65737657e+00]]])
          • created_at :
            2020-06-06T18:08:05.356439
            arviz_version :
            0.8.3
            inference_library :
            pymc3
            inference_library_version :
            3.8

      \n", + " [ 3.59843013, 6.09765236, 25.76124843, ..., -10.2115549 ,\n", + " 25.59755363, 22.36298176],\n", + " [ -5.29619527, -2.95926353, -29.68418582, ..., -15.42170733,\n", + " 16.63492822, 24.59769265],\n", + " [ 19.98940386, 3.80606888, 42.7843385 , ..., 34.96627185,\n", + " 14.25740899, -1.3367825 ]],\n", + "\n", + " [[ 2.14679791, 8.14117178, 21.25441028, ..., 11.52465386,\n", + " 5.80097177, -10.65373129],\n", + " [ -8.44034177, 7.0589406 , -18.60138882, ..., 8.82950324,\n", + " 8.08712236, -6.39852626],\n", + " [ 32.71773469, 8.39087771, 15.51443141, ..., 14.32087597,\n", + " -1.38048653, 1.96559162],\n", + " ...,\n", + " [ -1.62917408, 1.07111862, 8.02957128, ..., 8.58210981,\n", + " -12.75033047, 9.07205784],\n", + " [ 6.81102221, 1.40270907, 47.46417874, ..., -1.49189963,\n", + " 23.77214897, -32.30678056],\n", + " [ -3.58688927, -9.19334261, 3.48471769, ..., 22.20224365,\n", + " 22.04574534, 9.43498321]],\n", + "\n", + " [[ 10.88989427, 9.67031316, -19.15138861, ..., 3.89717316,\n", + " 5.08967642, -3.21365656],\n", + " [ 16.35385465, 23.58801269, -21.75232011, ..., 1.94421295,\n", + " 0.10598705, -6.81390168],\n", + " [ 16.65187106, 5.47807306, 16.66239838, ..., -10.24095255,\n", + " 7.67808457, 23.32038006],\n", + " ...,\n", + " [ -5.39983067, 2.9583006 , 8.89615588, ..., -13.19594644,\n", + " 8.22006103, -10.21126023],\n", + " [ 11.9101745 , 6.81738971, 33.3703771 , ..., -3.27619529,\n", + " 24.69391859, -16.79777889],\n", + " [ 9.5301372 , 21.03087493, 15.88465758, ..., 1.06856218,\n", + " -11.8929359 , 1.28188204]]])
  • created_at :
    2020-06-10T18:20:55.276063
    arviz_version :
    0.8.3
    inference_library :
    pystan
    inference_library_version :
    2.19.1.1

    • \n", " \n", " \n", " \n", " \n", "
  • \n", - " \n", - " \n", + " \n", + " \n", "
    \n", "
    \n", "
      \n", @@ -13296,14 +11200,89 @@ " stroke: currentColor;\n", " fill: currentColor;\n", "}\n", - "
      xarray.Dataset
        • obs_dim_0: 8
        • obs_dim_0
          (obs_dim_0)
          int64
          0 1 2 3 4 5 6 7
          array([0, 1, 2, 3, 4, 5, 6, 7])
        • obs
          (obs_dim_0)
          float64
          28.0 8.0 -3.0 7.0 ... 1.0 18.0 12.0
          array([28.,  8., -3.,  7., -1.,  1., 18., 12.])
      • created_at :
        2020-06-06T18:08:05.357843
        arviz_version :
        0.8.3
        inference_library :
        pymc3
        inference_library_version :
        3.8

    \n", + "
    xarray.Dataset
      • chain: 4
      • draw: 1000
      • school: 8
      • chain
        (chain)
        int64
        0 1 2 3
        array([0, 1, 2, 3])
      • draw
        (draw)
        int64
        0 1 2 3 4 5 ... 995 996 997 998 999
        array([  0,   1,   2, ..., 997, 998, 999])
      • school
        (school)
        int64
        0 1 2 3 4 5 6 7
        array([0, 1, 2, 3, 4, 5, 6, 7])
      • y
        (chain, draw, school)
        float64
        -4.377 -3.321 ... -3.859 -3.864
        array([[[-4.37671417, -3.32136228, -4.00991469, ..., -3.63130897,\n",
+       "         -3.59060827, -3.82535415],\n",
+       "        [-4.93942464, -3.23381483, -3.80663053, ..., -3.38016268,\n",
+       "         -4.4135104 , -3.94204744],\n",
+       "        [-4.86541676, -3.22554395, -3.76307997, ..., -3.40886865,\n",
+       "         -3.3902882 , -3.88723997],\n",
+       "        ...,\n",
+       "        [-5.31936954, -3.22844943, -4.05640831, ..., -3.42449599,\n",
+       "         -3.8324183 , -3.84855419],\n",
+       "        [-4.8702312 , -3.26699562, -3.80963006, ..., -3.51526552,\n",
+       "         -3.35474935, -3.94888271],\n",
+       "        [-4.83295204, -3.30903641, -3.7807787 , ..., -3.33849472,\n",
+       "         -4.32544456, -3.92254441]],\n",
+       "\n",
+       "       [[-4.89845162, -3.36317113, -3.80520915, ..., -3.34844607,\n",
+       "         -4.24873977, -3.91370182],\n",
+       "        [-4.9613161 , -3.25331141, -3.69799587, ..., -3.31915148,\n",
+       "         -4.28189242, -3.94846464],\n",
+       "        [-4.63380605, -3.22832122, -3.94260259, ..., -3.6626294 ,\n",
+       "         -3.54930289, -3.82445749],\n",
+       "        ...,\n",
+       "        [-5.38565946, -3.4036145 , -4.35039426, ..., -3.32112717,\n",
+       "         -4.65546843, -3.82242938],\n",
+       "        [-4.09608083, -3.23069079, -3.69156654, ..., -3.46125517,\n",
+       "         -3.31601684, -3.93182941],\n",
+       "        [-4.23079124, -3.75721763, -3.93415067, ..., -3.64714778,\n",
+       "         -3.61273975, -3.80937307]],\n",
+       "\n",
+       "       [[-4.05258656, -3.25768323, -4.13817498, ..., -3.80834974,\n",
+       "         -3.73767998, -3.83896065],\n",
+       "        [-4.88171287, -3.26595258, -3.7343962 , ..., -3.3344376 ,\n",
+       "         -3.83371561, -3.88812705],\n",
+       "        [-4.66129633, -3.23698363, -3.85502957, ..., -3.81016597,\n",
+       "         -3.9530315 , -3.83934681],\n",
+       "        ...,\n",
+       "        [-5.11892722, -3.36980949, -3.74867401, ..., -3.3273148 ,\n",
+       "         -4.45724293, -3.95131096],\n",
+       "        [-4.16675634, -3.24526023, -3.82613174, ..., -3.4084387 ,\n",
+       "         -3.42692055, -3.81919966],\n",
+       "        [-4.19657885, -3.22284053, -3.86890841, ..., -3.40890727,\n",
+       "         -3.69484888, -3.82902314]],\n",
+       "\n",
+       "       [[-4.63811378, -3.26971504, -3.87555205, ..., -3.33834887,\n",
+       "         -3.42882113, -3.83690906],\n",
+       "        [-4.35132448, -3.29410816, -3.786573  , ..., -3.32049494,\n",
+       "         -4.46547446, -3.82307   ],\n",
+       "        [-4.20603324, -3.22246473, -4.08019302, ..., -3.52510793,\n",
+       "         -3.22757663, -4.0340564 ],\n",
+       "        ...,\n",
+       "        [-5.80231133, -3.25309289, -3.802066  , ..., -3.35407193,\n",
+       "         -4.35614598, -3.94976063],\n",
+       "        [-4.44812188, -3.31157248, -4.13794842, ..., -3.34482905,\n",
+       "         -3.47205036, -3.8716741 ],\n",
+       "        [-4.6040653 , -3.24926418, -3.89308875, ..., -3.40213025,\n",
+       "         -3.85937479, -3.86395066]]])
    • created_at :
      2020-06-10T18:20:55.272917
      arviz_version :
      0.8.3
      inference_library :
      pystan
      inference_library_version :
      2.19.1.1

    \n", " \n", " \n", "
    • \n", " \n", - " \n", - " \n", - " " - ], - "text/plain": [ - "Inference data with groups:\n", - "\t> posterior\n", - "\t> posterior_predictive\n", - "\t> log_likelihood\n", - "\t> sample_stats\n", - "\t> prior\n", - "\t> prior_predictive\n", - "\t> observed_data" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "with pm.Model() as model:\n", - " mu = pm.Normal(\"mu\", mu=0, sd=5)\n", - " tau = pm.HalfCauchy(\"tau\", beta=5)\n", - " theta_tilde = pm.Normal(\"theta_tilde\", mu=0, sd=1, shape=eight_school_data[\"J\"])\n", - " theta = pm.Deterministic(\"theta\", mu + tau * theta_tilde)\n", - " pm.Normal(\n", - " \"obs\", mu=theta, sd=eight_school_data[\"sigma\"], observed=eight_school_data[\"y\"]\n", - " )\n", - "\n", - " trace = pm.sample(draws, chains=chains)\n", - " prior = pm.sample_prior_predictive()\n", - " posterior_predictive = pm.sample_posterior_predictive(trace)\n", - "\n", - " pm_data = az.from_pymc3(\n", - " trace=trace,\n", - " prior=prior,\n", - " posterior_predictive=posterior_predictive,\n", - " coords={\"school\": np.arange(eight_school_data[\"J\"])},\n", - " dims={\"theta\": [\"school\"], \"theta_tilde\": [\"school\"]},\n", - " )\n", - "pm_data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## From PyStan" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:pystan:COMPILING THE C++ CODE FOR MODEL anon_model_9d743830ec4a29fb58eb4660d4b9417f NOW.\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "
    \n", - "
    \n", - "
    arviz.InferenceData
    \n", - "
    \n", - "
      \n", - " \n", + "
      xarray.Dataset
        • chain: 4
        • draw: 1000
        • chain
          (chain)
          int64
          0 1 2 3
          array([0, 1, 2, 3])
        • draw
          (draw)
          int64
          0 1 2 3 4 5 ... 995 996 997 998 999
          array([  0,   1,   2, ..., 997, 998, 999])
        • accept_stat
          (chain, draw)
          float64
          0.9638 1.0 0.9984 ... 0.9816 0.623
          array([[0.9638465 , 1.        , 0.9984371 , ..., 0.98147504, 0.97345614,\n",
+       "        0.98142691],\n",
+       "       [0.95030784, 0.99451523, 0.99024004, ..., 0.89369662, 0.9823775 ,\n",
+       "        0.9890991 ],\n",
+       "       [0.97217182, 0.99864817, 0.94355364, ..., 0.82019929, 0.97292829,\n",
+       "        1.        ],\n",
+       "       [0.95704787, 0.97648727, 0.99909458, ..., 0.998659  , 0.9815929 ,\n",
+       "        0.6229969 ]])
        • stepsize
          (chain, draw)
          float64
          0.2764 0.2764 ... 0.2731 0.2731
          array([[0.27638304, 0.27638304, 0.27638304, ..., 0.27638304, 0.27638304,\n",
+       "        0.27638304],\n",
+       "       [0.30603051, 0.30603051, 0.30603051, ..., 0.30603051, 0.30603051,\n",
+       "        0.30603051],\n",
+       "       [0.40097617, 0.40097617, 0.40097617, ..., 0.40097617, 0.40097617,\n",
+       "        0.40097617],\n",
+       "       [0.27312733, 0.27312733, 0.27312733, ..., 0.27312733, 0.27312733,\n",
+       "        0.27312733]])
        • treedepth
          (chain, draw)
          int64
          4 4 4 4 4 4 4 4 ... 3 4 4 4 4 4 4 4
          array([[4, 4, 4, ..., 4, 4, 4],\n",
+       "       [4, 4, 4, ..., 4, 4, 4],\n",
+       "       [3, 4, 3, ..., 3, 4, 3],\n",
+       "       [4, 4, 4, ..., 4, 4, 4]])
        • n_leapfrog
          (chain, draw)
          int64
          15 15 15 15 15 ... 15 15 15 15 15
          array([[15, 15, 15, ..., 15, 15, 15],\n",
+       "       [15, 15, 15, ..., 15, 15, 15],\n",
+       "       [ 7, 23,  7, ...,  7, 15,  7],\n",
+       "       [15, 15, 15, ..., 15, 15, 15]])
        • diverging
          (chain, draw)
          bool
          False False False ... False False
          array([[False, False, False, ..., False, False, False],\n",
+       "       [False, False, False, ..., False, False, False],\n",
+       "       [False, False, False, ..., False, False, False],\n",
+       "       [False, False, False, ..., False, False, False]])
        • energy
          (chain, draw)
          float64
          19.65 17.08 13.29 ... 12.37 14.72
          array([[19.6501434 , 17.07881195, 13.29070373, ...,  8.93538194,\n",
+       "        12.29961118,  7.19678138],\n",
+       "       [ 8.84426238,  9.84895256, 14.77525803, ..., 12.35370489,\n",
+       "         9.67205678,  7.66728697],\n",
+       "       [11.52204836,  9.48080195, 11.1699379 , ..., 18.41838234,\n",
+       "        14.78772817, 11.62941587],\n",
+       "       [ 7.71092746,  9.24555017,  6.98821981, ..., 10.58081723,\n",
+       "        12.37474199, 14.71998465]])
        • lp
          (chain, draw)
          float64
          -12.88 -8.832 ... -5.587 -9.343
          array([[-12.88137893,  -8.8319153 ,  -6.48855593, ...,  -5.43677471,\n",
+       "         -4.0330844 ,  -4.6496315 ],\n",
+       "       [ -5.97351824,  -6.9490221 ,  -6.14793864, ...,  -6.89176892,\n",
+       "         -4.88146623,  -4.37360644],\n",
+       "       [ -6.16694264,  -5.66030842,  -4.64701238, ...,  -8.85576565,\n",
+       "         -9.41927215,  -6.90323691],\n",
+       "       [ -3.13060803,  -4.68668125,  -3.99299316, ...,  -6.48974414,\n",
+       "         -5.58723251,  -9.34270347]])
      • created_at :
        2020-06-10T18:20:55.267462
        arviz_version :
        0.8.3
        inference_library :
        pystan
        inference_library_version :
        2.19.1.1

    \n", + " \n", + " \n", + " \n", + " \n", "
  • \n", - " \n", - " \n", + " \n", + " \n", "
    \n", "
    \n", "
      \n", @@ -14031,157 +11981,14 @@ " stroke: currentColor;\n", " fill: currentColor;\n", "}\n", - "
      xarray.Dataset
        • chain: 4
        • draw: 1000
        • school: 8
        • chain
          (chain)
          int64
          0 1 2 3
          array([0, 1, 2, 3])
        • draw
          (draw)
          int64
          0 1 2 3 4 5 ... 995 996 997 998 999
          array([  0,   1,   2, ..., 997, 998, 999])
        • school
          (school)
          int64
          0 1 2 3 4 5 6 7
          array([0, 1, 2, 3, 4, 5, 6, 7])
        • mu
          (chain, draw)
          float64
          7.136 4.809 3.715 ... 9.668 1.005
          array([[ 7.13647713,  4.80878256,  3.71456426, ...,  4.59832792,\n",
-       "         2.61858011,  7.71184613],\n",
-       "       [ 2.85614299,  2.93917185,  4.54687519, ...,  4.77567137,\n",
-       "         5.81110368,  2.75432175],\n",
-       "       [ 5.38414147,  0.03029178,  1.20891239, ...,  5.55392541,\n",
-       "        -0.11888032,  7.14845165],\n",
-       "       [ 4.83046053,  3.485902  ,  9.15602531, ...,  2.64168638,\n",
-       "         9.66764308,  1.00491627]])
        • tau
          (chain, draw)
          float64
          3.235 5.204 1.977 ... 1.96 3.162
          array([[3.23509663, 5.20424104, 1.97695143, ..., 1.83067557, 6.74139156,\n",
-       "        0.68903071],\n",
-       "       [5.75947041, 1.68197019, 5.29099056, ..., 0.04472656, 0.59257567,\n",
-       "        0.93668379],\n",
-       "       [0.77299688, 1.51050476, 1.30755851, ..., 1.60030628, 0.56242846,\n",
-       "        0.8348308 ],\n",
-       "       [6.29899008, 1.25855614, 2.17400965, ..., 1.29833941, 1.95973216,\n",
-       "        3.16169546]])
        • theta_tilde
          (chain, draw, school)
          float64
          2.324 -0.7764 ... 0.4021 -1.033
          array([[[ 2.32379123e+00, -7.76418571e-01, -5.01022113e-01, ...,\n",
-       "          1.19049238e+00, -1.48404796e+00, -2.08798281e-01],\n",
-       "        [-2.77386924e-01,  1.54767321e+00,  3.15376703e-01, ...,\n",
-       "         -1.36228981e+00,  1.78175896e+00, -4.93907464e-01],\n",
-       "        [ 2.64105004e+00,  5.35997803e-01, -8.90539506e-01, ...,\n",
-       "          6.57794882e-01, -5.64605164e-01,  2.36985615e-03],\n",
-       "        ...,\n",
-       "        [-3.15678175e-02, -3.87417276e-02, -5.58340680e-01, ...,\n",
-       "         -4.65353543e-01, -1.09751383e+00,  5.09319375e-02],\n",
-       "        [ 8.32965205e-01,  6.48222367e-01, -3.90991116e-01, ...,\n",
-       "          5.25179247e-01,  1.82321932e+00, -4.60474289e-01],\n",
-       "        [-1.53204318e-01, -3.87665960e-01,  9.31997670e-02, ...,\n",
-       "         -9.76083835e-01, -7.66930923e-01,  1.06999894e+00]],\n",
-       "\n",
-       "       [[ 5.77676190e-01, -1.50966398e+00, -6.19455736e-01, ...,\n",
-       "          1.14525937e+00,  1.54350676e+00, -2.07929704e-01],\n",
-       "        [-3.43948517e-01,  1.17132661e-01, -5.50675852e-01, ...,\n",
-       "          1.00058737e+00, -1.66125455e+00,  5.08106406e-02],\n",
-       "        [ 1.46112443e+00,  3.38328510e-01,  2.25898376e-01, ...,\n",
-       "         -1.10698408e+00,  1.44374163e+00,  1.13869599e-01],\n",
-       "        ...,\n",
-       "        [-3.30736969e-01, -3.33683083e-03, -1.88341415e+00, ...,\n",
-       "          1.73407550e+00, -9.64094311e-01, -3.26539032e-01],\n",
-       "        [ 6.68216215e-01,  6.26043865e-01,  1.95043754e+00, ...,\n",
-       "         -1.10676754e+00,  8.18153965e-01,  1.06553262e+00],\n",
-       "        [-2.27309201e+00, -3.77663513e-01,  1.15103591e+00, ...,\n",
-       "          4.58309592e-01, -1.37554228e+00,  4.53414105e-01]],\n",
-       "\n",
-       "       [[-3.85989935e-01, -8.03879920e-01, -3.88217932e-01, ...,\n",
-       "          1.39905894e-01,  1.59325937e-01, -1.71527073e-01],\n",
-       "        [ 3.62197623e-01,  6.61213573e-01, -1.21496208e+00, ...,\n",
-       "         -1.18914239e+00,  1.06584352e+00,  6.47365331e-01],\n",
-       "        [ 1.61322730e+00,  4.13485480e-01, -1.68128280e+00, ...,\n",
-       "          1.06886144e-01, -9.47703569e-01, -8.95436956e-01],\n",
-       "        ...,\n",
-       "        [ 1.27754470e+00,  8.23942845e-01, -8.58297611e-01, ...,\n",
-       "          1.97596280e+00, -8.47224301e-01,  9.81683929e-01],\n",
-       "        [ 1.11932977e+00, -6.66114769e-01,  1.58278413e-02, ...,\n",
-       "         -9.58635064e-02, -4.49391408e-01, -3.11846845e-01],\n",
-       "        [-1.90416914e-01, -4.43275790e-01,  1.51870062e+00, ...,\n",
-       "         -1.80770713e-01, -2.90641862e-01, -1.25523226e+00]],\n",
-       "\n",
-       "       [[-1.47189064e+00,  3.38261505e-01,  8.75363603e-01, ...,\n",
-       "         -2.12825340e+00,  6.92338462e-02,  3.03061317e-01],\n",
-       "        [-1.70498312e+00,  1.23927425e+00, -1.13002724e-01, ...,\n",
-       "         -1.11902291e+00,  4.77301514e-01,  6.57273497e-01],\n",
-       "        [ 1.57051641e+00, -1.03781767e+00, -2.51307144e-01, ...,\n",
-       "          3.01850309e-01,  5.89430598e-01,  4.77979603e-01],\n",
-       "        ...,\n",
-       "        [ 7.57588988e-01, -2.41586074e-01,  8.26638715e-01, ...,\n",
-       "          4.31405786e-01,  2.68134996e-01,  5.41040260e-01],\n",
-       "        [ 1.37956246e+00,  9.46952150e-01, -3.49853455e-01, ...,\n",
-       "         -2.23551741e-01,  3.58858360e-01, -1.29738334e+00],\n",
-       "        [ 6.20848787e-01,  1.02202002e+00, -1.10013506e+00, ...,\n",
-       "         -1.44705474e+00,  4.02059914e-01, -1.03299979e+00]]])
        • theta
          (chain, draw, school)
          float64
          14.65 4.625 5.516 ... 2.276 -2.261
          array([[[14.6541663 ,  4.62468804,  5.51562219, ..., 10.987835  ,\n",
-       "          2.3354386 ,  6.46099452],\n",
-       "        [ 3.36519415, 12.86324702,  6.45007894, ..., -2.28090198,\n",
-       "         14.0814857 ,  2.23836906],\n",
-       "        [ 8.93579191,  4.77420588,  1.95401091, ...,  5.01499279,\n",
-       "          2.59836727,  3.71924935],\n",
-       "        ...,\n",
-       "        [ 4.54053749,  4.52740439,  3.57618728, ...,  3.74641656,\n",
-       "          2.58913617,  4.69156778],\n",
-       "        [ 8.23392472,  6.98850091, -0.0172441 , ...,  6.15901906,\n",
-       "         14.90961546, -0.48565738],\n",
-       "        [ 7.60628365,  7.44473238,  7.77606364, ...,  7.03929439,\n",
-       "          7.18340717,  8.44910827]],\n",
-       "\n",
-       "       [[ 6.18325191, -5.83872201, -0.71159399, ...,  9.45223042,\n",
-       "         11.74592451,  1.65857801],\n",
-       "        [ 2.3606607 ,  3.13618549,  2.01295148, ...,  4.62212997,\n",
-       "          0.14499122,  3.02463383],\n",
-       "        [12.27767076,  6.33696815,  5.74210137, ..., -1.31016711,\n",
-       "         12.18569852,  5.14935817],\n",
-       "        ...,\n",
-       "        [ 4.76087864,  4.77552213,  4.69143273, ...,  4.85323061,\n",
-       "          4.73255075,  4.7610664 ],\n",
-       "        [ 6.20707235,  6.18208204,  6.96688552, ...,  5.15526015,\n",
-       "          6.29592181,  6.44251239],\n",
-       "        [ 0.6251533 ,  2.40057046,  3.83247843, ...,  3.18361292,\n",
-       "          1.46587359,  3.17902739]],\n",
-       "\n",
-       "       [[ 5.08577245,  4.76274479,  5.08405022, ...,  5.49228829,\n",
-       "          5.50729992,  5.25155157],\n",
-       "        [ 0.57739302,  1.02905803, -1.80491422, ..., -1.76591346,\n",
-       "          1.64025349,  1.00814019],\n",
-       "        [ 3.31830147,  1.74956884, -0.98946324, ...,  1.34867227,\n",
-       "         -0.03026548,  0.03807618],\n",
-       "        ...,\n",
-       "        [ 7.59838821,  6.87248632,  4.18038635, ...,  8.71607108,\n",
-       "          4.19810704,  7.12492037],\n",
-       "        [ 0.5106626 , -0.49352223, -0.10997829, ..., -0.17279669,\n",
-       "         -0.37163084, -0.29427186],\n",
-       "        [ 6.98948574,  6.77839136,  8.41630969, ...,  6.99753869,\n",
-       "          6.90581487,  6.1005451 ]],\n",
-       "\n",
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-       "          4.08661275,  4.3131176 ],\n",
-       "        [12.57034315,  6.89979968,  8.60968115, ...,  9.8122508 ,\n",
-       "         10.43745312, 10.19515758],\n",
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-       "         10.37090935,  7.12511922],\n",
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        arviz_version :
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-       "          1.65816291e+01,  7.20235910e-02,  7.95096646e+00],\n",
-       "        [ 1.85775714e+01, -4.43324480e+00, -3.05770368e+01, ...,\n",
-       "          8.42976543e+00,  1.37226945e+01, -4.00326916e+00],\n",
-       "        [ 6.28236785e+00,  8.93194288e+00,  9.21632858e+00, ...,\n",
-       "          2.98329599e+00,  8.90041129e-01,  1.29937275e+01],\n",
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-       "          8.06037911e+00,  4.06551816e+00,  3.84731971e+00],\n",
-       "        [ 2.47197758e+01,  9.86092974e+00, -1.68509054e+01, ...,\n",
-       "          5.45291637e+00, -1.26471028e+00,  3.54839713e+01]],\n",
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-       "        [ 2.56332048e+00, -1.19960802e+01, -2.32269985e+01, ...,\n",
-       "          8.24150112e+00, -3.14522724e+00, -1.02591329e+00],\n",
-       "        [ 3.24804089e+00,  8.41704129e-01,  1.05827029e+01, ...,\n",
-       "          1.71709061e+01,  2.19578482e+01,  2.02425986e+01],\n",
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-       "        [-7.76720819e+00,  5.05484127e+00,  3.93395466e+01, ...,\n",
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-       "        [ 3.03826832e+01,  1.99055248e+01,  9.78954704e-01, ...,\n",
-       "          2.24195277e+01, -1.68734750e+00,  3.82229593e+01],\n",
-       "        [ 2.58120017e+00,  2.93309398e+01,  1.18486680e+01, ...,\n",
-       "         -8.11685705e+00, -1.96778665e+01, -2.05111408e+01]],\n",
-       "\n",
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-       "        [-1.02080210e+01,  1.61975154e+01,  2.33181742e-02, ...,\n",
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-       "        [-1.62270711e+01,  4.85258854e+00, -3.52789049e-01, ...,\n",
-       "          2.38982218e+01, -1.43721659e+01,  4.39459127e+01],\n",
-       "        ...,\n",
-       "        [ 7.52941391e+00,  6.56295817e+00, -2.04005120e+00, ...,\n",
-       "          1.14073372e+01, -6.44289722e+00,  3.70561430e+00],\n",
-       "        [-3.47024104e+01,  4.61400769e-02, -1.13586839e+01, ...,\n",
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-       "        [ 2.09926355e+01,  6.37251824e+00, -3.88045666e+00, ...,\n",
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-       "          2.21847833e+01,  8.46356441e+00, -1.78468823e+00],\n",
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-       "         -2.99816348e+00, -4.67994662e+00, -2.99865249e+01]]])
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      \n", - "
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    \n", - "
    • \n", - " \n", + ".xr-wrap{width:700px!important;} " + ], + "text/plain": [ + "Inference data with groups:\n", + "\t> posterior\n", + "\t> posterior_predictive\n", + "\t> log_likelihood\n", + "\t> sample_stats\n", + "\t> observed_data" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pystan\n", + "\n", + "schools_code = \"\"\"\n", + "data {\n", + " int J;\n", + " real y[J];\n", + " real sigma[J];\n", + "}\n", + "\n", + "parameters {\n", + " real mu;\n", + " real tau;\n", + " real theta_tilde[J];\n", + "}\n", + "\n", + "transformed parameters {\n", + " real theta[J];\n", + " for (j in 1:J)\n", + " theta[j] = mu + tau * theta_tilde[j];\n", + "}\n", + "\n", + "model {\n", + " mu ~ normal(0, 5);\n", + " tau ~ cauchy(0, 5);\n", + " theta_tilde ~ normal(0, 1);\n", + " y ~ normal(theta, sigma);\n", + "}\n", + "\n", + "generated quantities {\n", + " vector[J] log_lik;\n", + " vector[J] y_hat;\n", + " for (j in 1:J) {\n", + " log_lik[j] = normal_lpdf(y[j] | theta[j], sigma[j]);\n", + " y_hat[j] = normal_rng(theta[j], sigma[j]);\n", + " }\n", + "}\n", + "\"\"\"\n", + "\n", + "eight_school_data = {\n", + " \"J\": 8,\n", + " \"y\": np.array([28.0, 8.0, -3.0, 7.0, -1.0, 1.0, 18.0, 12.0]),\n", + " \"sigma\": np.array([15.0, 10.0, 16.0, 11.0, 9.0, 11.0, 10.0, 18.0]),\n", + "}\n", + "\n", + "stan_model = pystan.StanModel(model_code=schools_code)\n", + "fit = stan_model.sampling(data=eight_school_data, control={\"adapt_delta\": 0.9})\n", + "\n", + "stan_data = az.from_pystan(\n", + " posterior=fit,\n", + " posterior_predictive=\"y_hat\",\n", + " observed_data=[\"y\"],\n", + " log_likelihood={\"y\": \"log_lik\"},\n", + " coords={\"school\": np.arange(eight_school_data[\"J\"])},\n", + " dims={\n", + " \"theta\": [\"school\"],\n", + " \"y\": [\"school\"],\n", + " \"log_lik\": [\"school\"],\n", + " \"y_hat\": [\"school\"],\n", + " \"theta_tilde\": [\"school\"],\n", + " },\n", + ")\n", + "\n", + "stan_data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## From Pyro" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "ExecuteTime": { + "end_time": "2020-06-05T06:48:09.602373Z", + "start_time": "2020-06-05T06:47:57.349110Z" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
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-       "        0.96301126],\n",
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-       "        0.66272908],\n",
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-       "        14.64064544, 15.29534917],\n",
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-       "        14.1985079 , 11.18395816],\n",
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-       "         -4.1154919 ,  -6.29556094],\n",
-       "       [ -6.28739162,  -5.5135253 ,  -3.52534459, ..., -11.32215542,\n",
-       "         -9.42216618,  -9.47624511],\n",
-       "       [ -4.57956447,  -6.89073912,  -7.12165559, ...,  -7.61427701,\n",
-       "         -6.53577978,  -6.48605742],\n",
-       "       [ -8.08265987,  -6.56502669,  -5.84788307, ...,  -6.06854386,\n",
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    • \n", - " \n", - " \n", - " \n", - " " - ], - "text/plain": [ - "Inference data with groups:\n", - "\t> posterior\n", - "\t> posterior_predictive\n", - "\t> log_likelihood\n", - "\t> sample_stats\n", - "\t> observed_data" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import pystan\n", - "\n", - "schools_code = \"\"\"\n", - "data {\n", - " int J;\n", - " real y[J];\n", - " real sigma[J];\n", - "}\n", - "\n", - "parameters {\n", - " real mu;\n", - " real tau;\n", - " real theta_tilde[J];\n", - "}\n", - "\n", - "transformed parameters {\n", - " real theta[J];\n", - " for (j in 1:J)\n", - " theta[j] = mu + tau * theta_tilde[j];\n", - "}\n", - "\n", - "model {\n", - " mu ~ normal(0, 5);\n", - " tau ~ cauchy(0, 5);\n", - " theta_tilde ~ normal(0, 1);\n", - " y ~ normal(theta, sigma);\n", - "}\n", - "\n", - "generated quantities {\n", - " vector[J] log_lik;\n", - " vector[J] y_hat;\n", - " for (j in 1:J) {\n", - " log_lik[j] = normal_lpdf(y[j] | theta[j], sigma[j]);\n", - " y_hat[j] = normal_rng(theta[j], sigma[j]);\n", - " }\n", - "}\n", - "\"\"\"\n", - "\n", - "eight_school_data = {\n", - " \"J\": 8,\n", - " \"y\": np.array([28.0, 8.0, -3.0, 7.0, -1.0, 1.0, 18.0, 12.0]),\n", - " \"sigma\": np.array([15.0, 10.0, 16.0, 11.0, 9.0, 11.0, 10.0, 18.0]),\n", - "}\n", - "\n", - "stan_model = pystan.StanModel(model_code=schools_code)\n", - "fit = stan_model.sampling(data=eight_school_data, control={\"adapt_delta\": 0.9})\n", - "\n", - "stan_data = az.from_pystan(\n", - " posterior=fit,\n", - " posterior_predictive=\"y_hat\",\n", - " observed_data=[\"y\"],\n", - " log_likelihood={\"y\": \"log_lik\"},\n", - " coords={\"school\": np.arange(eight_school_data[\"J\"])},\n", - " dims={\n", - " \"theta\": [\"school\"],\n", - " \"y\": [\"school\"],\n", - " \"log_lik\": [\"school\"],\n", - " \"y_hat\": [\"school\"],\n", - " \"theta_tilde\": [\"school\"],\n", - " },\n", - ")\n", - "\n", - "stan_data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## From Pyro" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "
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    arviz.InferenceData
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                array([[ 6.783849  ,  3.0570068 , -0.5898714 ,  7.692052  ,  5.978842  ,\n",
        "         7.083586  ,  6.0941653 ,  8.367151  ,  5.2405605 ,  2.639793  ,\n",
        "        10.008863  ,  6.0008774 ,  5.048078  ,  1.7424315 ,  2.6212542 ,\n",
        "         5.87026   ,  4.5748415 ,  1.0863168 ,  5.662515  ,  1.564056  ,\n",
@@ -16646,7 +12954,7 @@
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        "        10.760844  , -1.5691249 ,  7.673089  ,  2.6870704 ,  2.4335454 ,\n",
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                2.8592596 7.631564 ... 4.101541
                array([[ 2.8592596 ,  7.631564  ,  2.2398646 ,  2.2167501 ,  3.1840062 ,\n",
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                array([[ 2.8592596 ,  7.631564  ,  2.2398646 ,  2.2167501 ,  3.1840062 ,\n",
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        "         5.86172   , 19.530476  ,  0.25236988,  3.668568  ,  1.5498195 ,\n",
        "         0.8107503 ,  1.0509298 ,  7.2098413 ,  1.8983476 ,  5.2955256 ,\n",
@@ -16846,7 +13154,7 @@
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        "         5.5982914 ,  4.0080676 ,  2.5344353 ,  2.7411196 ,  4.101541  ]],\n",
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                array([[[ 0.6061655 ,  1.8185308 , -0.09561205, ..., -1.3342378 ,\n",
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@@ -16872,14 +13180,14 @@
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        "          0.17342997,  0.47577518],\n",
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              2020-06-06T18:10:09.951965
              arviz_version :
              0.8.3
              inference_library :
              pyro
              inference_library_version :
              1.1.0

        \n", + " 1.7539048 , -1.4317532 ]]], dtype=float32)
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        "         4.78217268e+00,  5.68206692e+00,  1.17952776e+00,\n",
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@@ -18706,7 +15014,7 @@
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          array([[[ 1.5241260e+02, -1.0443863e+02,  5.3149067e+01, ...,\n",
+       "
          xarray.Dataset
            • chain: 1
            • draw: 500
            • obs_dim_0: 8
            • chain
              (chain)
              int64
              0
              array([0])
            • draw
              (draw)
              int64
              0 1 2 3 4 5 ... 495 496 497 498 499
              array([  0,   1,   2, ..., 497, 498, 499])
            • obs_dim_0
              (obs_dim_0)
              int64
              0 1 2 3 4 5 6 7
              array([0, 1, 2, 3, 4, 5, 6, 7])
            • obs
              (chain, draw, obs_dim_0)
              float32
              152.4126 -104.43863 ... -19.538307
              array([[[ 1.5241260e+02, -1.0443863e+02,  5.3149067e+01, ...,\n",
        "         -2.1432968e+02, -2.7346640e+02,  9.8980553e+01],\n",
        "        [-1.4595604e+03,  7.5740137e+02, -7.9331067e+02, ...,\n",
        "         -1.6279233e+03, -1.6046724e+03, -4.5491840e+02],\n",
@@ -19075,14 +15383,14 @@
        "        [ 1.1234647e+01,  9.1552639e+00,  1.3359819e+01, ...,\n",
        "          6.3331509e+00, -6.9926414e+00,  2.9034739e+01],\n",
        "        [ 3.9082799e+00,  2.4938337e+01,  1.8535004e+01, ...,\n",
-       "         -4.8968292e+01,  1.1919480e+01, -1.9538307e+01]]], dtype=float32)
          • created_at :
            2020-06-06T18:10:10.400546
            arviz_version :
            0.8.3
            inference_library :
            pyro
            inference_library_version :
            1.1.0

      \n", + " -4.8968292e+01, 1.1919480e+01, -1.9538307e+01]]], dtype=float32)
  • created_at :
    2020-06-10T18:21:34.025492
    arviz_version :
    0.8.3
    inference_library :
    pyro
    inference_library_version :
    1.1.0

    • \n", " \n", " \n", " \n", " \n", "
  • \n", - " \n", - " \n", + " \n", + " \n", "
    \n", "
    \n", "
      \n", @@ -19420,7 +15728,7 @@ " stroke: currentColor;\n", " fill: currentColor;\n", "}\n", - "
      xarray.Dataset
        • obs_dim_0: 8
        • obs_dim_0
          (obs_dim_0)
          int64
          0 1 2 3 4 5 6 7
          array([0, 1, 2, 3, 4, 5, 6, 7])
        • obs
          (obs_dim_0)
          float32
          28.0 8.0 -3.0 7.0 ... 1.0 18.0 12.0
          array([28.,  8., -3.,  7., -1.,  1., 18., 12.], dtype=float32)
      • created_at :
        2020-06-06T18:10:10.445994
        arviz_version :
        0.8.3
        inference_library :
        pyro
        inference_library_version :
        1.1.0

    \n", + "
    xarray.Dataset
      • obs_dim_0: 8
      • obs_dim_0
        (obs_dim_0)
        int64
        0 1 2 3 4 5 6 7
        array([0, 1, 2, 3, 4, 5, 6, 7])
      • obs
        (obs_dim_0)
        float32
        28.0 8.0 -3.0 7.0 ... 1.0 18.0 12.0
        array([28.,  8., -3.,  7., -1.,  1., 18., 12.], dtype=float32)
    • created_at :
      2020-06-10T18:21:34.070465
      arviz_version :
      0.8.3
      inference_library :
      pyro
      inference_library_version :
      1.1.0

    \n", " \n", " \n", "
    • \n", @@ -19746,18 +16054,6 @@ "}\n", ".xr-wrap{width:700px!important;} " ], -======= - "execution_count": 13, - "metadata": { - "ExecuteTime": { - "end_time": "2020-06-05T06:48:09.602373Z", - "start_time": "2020-06-05T06:47:57.349110Z" - } - }, - "outputs": [ - { - "data": { ->>>>>>> master "text/plain": [ "Inference data with groups:\n", "\t> posterior\n", @@ -19769,7 +16065,7 @@ "\t> observed_data" ] }, - "execution_count": 13, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -19837,7 +16133,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 10, "metadata": { "ExecuteTime": { "end_time": "2020-06-05T06:48:11.402298Z", @@ -19856,8 +16152,8 @@ "
      \n", " \n", "
    • \n", - " \n", - " \n", + " \n", + " \n", "
      \n", "
      \n", "
        \n", @@ -20195,9 +16491,9 @@ " stroke: currentColor;\n", " fill: currentColor;\n", "}\n", - "
        xarray.Dataset
          • chain: 40
          • draw: 1500
          • chain
            (chain)
            int64
            0 1 2 3 4 5 6 ... 34 35 36 37 38 39
            array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
+       "
            xarray.Dataset
              • chain: 40
              • draw: 1500
              • chain
                (chain)
                int64
                0 1 2 3 4 5 6 ... 34 35 36 37 38 39
                array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
        "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
-       "       36, 37, 38, 39])
              • draw
                (draw)
                int64
                0 1 2 3 4 ... 1496 1497 1498 1499
                array([   0,    1,    2, ..., 1497, 1498, 1499])
              • mu
                (chain, draw)
                float64
                0.9284 0.7497 ... -1.109 -1.109
                array([[ 0.92841914,  0.74969441,  0.74969441, ...,  4.72566447,\n",
+       "       36, 37, 38, 39])
              • draw
                (draw)
                int64
                0 1 2 3 4 ... 1496 1497 1498 1499
                array([   0,    1,    2, ..., 1497, 1498, 1499])
              • mu
                (chain, draw)
                float64
                0.9284 0.7497 ... -1.109 -1.109
                array([[ 0.92841914,  0.74969441,  0.74969441, ...,  4.72566447,\n",
        "         4.72221834,  4.72221834],\n",
        "       [ 0.04751325,  0.04751325,  0.04751325, ...,  6.30209019,\n",
        "         6.30209019,  6.30209019],\n",
@@ -20209,7 +16505,7 @@
        "       [-1.64097945, -1.64097945, -1.41262278, ...,  4.42408698,\n",
        "         4.60329133,  3.89910876],\n",
        "       [ 0.56729028,  0.56729028,  0.56729028, ..., -1.10909274,\n",
-       "        -1.10909274, -1.10909274]])
              • tau
                (chain, draw)
                float64
                0.7789 0.9465 ... 8.763 8.763
                array([[ 0.77890285,  0.94646991,  0.94646991, ...,  3.06241616,\n",
+       "        -1.10909274, -1.10909274]])
              • tau
                (chain, draw)
                float64
                0.7789 0.9465 ... 8.763 8.763
                array([[ 0.77890285,  0.94646991,  0.94646991, ...,  3.06241616,\n",
        "         3.06435583,  3.06435583],\n",
        "       [ 1.32485136,  1.32485136,  1.32485136, ...,  0.74095528,\n",
        "         0.74095528,  0.74095528],\n",
@@ -20221,7 +16517,7 @@
        "       [ 0.37813884,  0.37813884,  0.42137438, ...,  7.74062311,\n",
        "         6.91967957,  4.92296393],\n",
        "       [ 0.2226751 ,  0.2226751 ,  0.2226751 , ...,  8.76313578,\n",
-       "         8.76313578,  8.76313578]])
              • eta0
                (chain, draw)
                float64
                0.5965 0.356 ... 0.9787 0.9787
                array([[ 0.59650655,  0.35598681,  0.35598681, ...,  0.71466293,\n",
+       "         8.76313578,  8.76313578]])
              • eta0
                (chain, draw)
                float64
                0.5965 0.356 ... 0.9787 0.9787
                array([[ 0.59650655,  0.35598681,  0.35598681, ...,  0.71466293,\n",
        "         0.71523086,  0.71523086],\n",
        "       [ 0.5749315 ,  0.5749315 ,  0.5749315 , ..., -0.12368357,\n",
        "        -0.12368357, -0.12368357],\n",
@@ -20233,7 +16529,7 @@
        "       [-2.17958654, -2.17958654, -2.07240863, ...,  0.26757447,\n",
        "         0.3684135 ,  0.23850609],\n",
        "       [-0.35343175, -0.35343175, -0.35343175, ...,  0.97868557,\n",
-       "         0.97868557,  0.97868557]])
              • eta1
                (chain, draw)
                float64
                1.395 0.9969 0.9969 ... 2.189 2.189
                array([[ 1.39536064,  0.99693975,  0.99693975, ..., -0.18711784,\n",
+       "         0.97868557,  0.97868557]])
              • eta1
                (chain, draw)
                float64
                1.395 0.9969 0.9969 ... 2.189 2.189
                array([[ 1.39536064,  0.99693975,  0.99693975, ..., -0.18711784,\n",
        "        -0.18694776, -0.18694776],\n",
        "       [-0.0967978 , -0.0967978 , -0.0967978 , ...,  0.59447501,\n",
        "         0.59447501,  0.59447501],\n",
@@ -20245,7 +16541,7 @@
        "       [-0.99002989, -0.99002989, -0.86249973, ..., -0.71346413,\n",
        "        -0.6690839 , -1.30316671],\n",
        "       [-1.61647419, -1.61647419, -1.61647419, ...,  2.1885378 ,\n",
-       "         2.1885378 ,  2.1885378 ]])
              • eta2
                (chain, draw)
                float64
                1.428 1.154 1.154 ... 0.1311 0.1311
                array([[ 1.42788977,  1.15418066,  1.15418066, ...,  1.3167935 ,\n",
+       "         2.1885378 ,  2.1885378 ]])
              • eta2
                (chain, draw)
                float64
                1.428 1.154 1.154 ... 0.1311 0.1311
                array([[ 1.42788977,  1.15418066,  1.15418066, ...,  1.3167935 ,\n",
        "         1.31822332,  1.31822332],\n",
        "       [ 0.41236399,  0.41236399,  0.41236399, ..., -0.34438568,\n",
        "        -0.34438568, -0.34438568],\n",
@@ -20257,7 +16553,7 @@
        "       [ 0.03733591,  0.03733591, -0.02665422, ...,  0.08855891,\n",
        "         0.09957141,  0.873668  ],\n",
        "       [-0.29183736, -0.29183736, -0.29183736, ...,  0.13107062,\n",
-       "         0.13107062,  0.13107062]])
              • eta3
                (chain, draw)
                float64
                -0.7293 -0.7175 ... 0.3588 0.3588
                array([[-0.72926825, -0.71747056, -0.71747056, ..., -0.24082616,\n",
+       "         0.13107062,  0.13107062]])
              • eta3
                (chain, draw)
                float64
                -0.7293 -0.7175 ... 0.3588 0.3588
                array([[-0.72926825, -0.71747056, -0.71747056, ..., -0.24082616,\n",
        "        -0.24043765, -0.24043765],\n",
        "       [ 0.24672591,  0.24672591,  0.24672591, ..., -0.89020229,\n",
        "        -0.89020229, -0.89020229],\n",
@@ -20269,7 +16565,7 @@
        "       [-1.61338832, -1.61338832, -1.39419799, ..., -0.20925129,\n",
        "        -0.08789249, -0.44863267],\n",
        "       [-0.76149221, -0.76149221, -0.76149221, ...,  0.35875544,\n",
-       "         0.35875544,  0.35875544]])
              • eta4
                (chain, draw)
                float64
                0.2754 0.428 ... 0.1813 0.1813
                array([[ 0.27536896,  0.42802117,  0.42802117, ..., -0.64697444,\n",
+       "         0.35875544,  0.35875544]])
              • eta4
                (chain, draw)
                float64
                0.2754 0.428 ... 0.1813 0.1813
                array([[ 0.27536896,  0.42802117,  0.42802117, ..., -0.64697444,\n",
        "        -0.647279  , -0.647279  ],\n",
        "       [ 1.1247147 ,  1.1247147 ,  1.1247147 , ..., -0.3792712 ,\n",
        "        -0.3792712 , -0.3792712 ],\n",
@@ -20281,7 +16577,7 @@
        "       [-0.96656389, -0.96656389, -0.92286515, ..., -0.7191441 ,\n",
        "        -0.66556347, -1.11350372],\n",
        "       [ 0.85792392,  0.85792392,  0.85792392, ...,  0.18125692,\n",
-       "         0.18125692,  0.18125692]])
              • eta5
                (chain, draw)
                float64
                -0.5001 -0.6041 ... -0.1164 -0.1164
                array([[-0.50013399, -0.60410533, -0.60410533, ..., -1.08707667,\n",
+       "         0.18125692,  0.18125692]])
              • eta5
                (chain, draw)
                float64
                -0.5001 -0.6041 ... -0.1164 -0.1164
                array([[-0.50013399, -0.60410533, -0.60410533, ..., -1.08707667,\n",
        "        -1.08765507, -1.08765507],\n",
        "       [-0.12619171, -0.12619171, -0.12619171, ..., -0.30565393,\n",
        "        -0.30565393, -0.30565393],\n",
@@ -20293,7 +16589,7 @@
        "       [-1.44616313, -1.44616313, -1.38958095, ...,  0.07653725,\n",
        "         0.00765534, -0.34260937],\n",
        "       [ 1.14110187,  1.14110187,  1.14110187, ..., -0.11644015,\n",
-       "        -0.11644015, -0.11644015]])
              • eta6
                (chain, draw)
                float64
                0.1888 -0.05081 ... 0.6959 0.6959
                array([[ 1.88821093e-01, -5.08071132e-02, -5.08071132e-02, ...,\n",
+       "        -0.11644015, -0.11644015]])
              • eta6
                (chain, draw)
                float64
                0.1888 -0.05081 ... 0.6959 0.6959
                array([[ 1.88821093e-01, -5.08071132e-02, -5.08071132e-02, ...,\n",
        "         2.20885330e-01,  2.21008935e-01,  2.21008935e-01],\n",
        "       [ 1.68791224e-01,  1.68791224e-01,  1.68791224e-01, ...,\n",
        "        -7.62386375e-01, -7.62386375e-01, -7.62386375e-01],\n",
@@ -20305,7 +16601,7 @@
        "       [ 1.60246678e+00,  1.60246678e+00,  1.58314291e+00, ...,\n",
        "         1.25047974e-01,  2.21093345e-01, -1.19317876e-03],\n",
        "       [ 1.46657872e+00,  1.46657872e+00,  1.46657872e+00, ...,\n",
-       "         6.95921870e-01,  6.95921870e-01,  6.95921870e-01]])
              • eta7
                (chain, draw)
                float64
                0.2937 0.1625 ... 1.533 1.533
                array([[ 0.29370708,  0.16252705,  0.16252705, ..., -1.50049936,\n",
+       "         6.95921870e-01,  6.95921870e-01,  6.95921870e-01]])
              • eta7
                (chain, draw)
                float64
                0.2937 0.1625 ... 1.533 1.533
                array([[ 0.29370708,  0.16252705,  0.16252705, ..., -1.50049936,\n",
        "        -1.50147066, -1.50147066],\n",
        "       [-0.74697999, -0.74697999, -0.74697999, ...,  0.30721301,\n",
        "         0.30721301,  0.30721301],\n",
@@ -20317,14 +16613,14 @@
        "       [ 0.13342492,  0.13342492,  0.17922369, ..., -0.26854499,\n",
        "        -0.16240555, -0.0922921 ],\n",
        "       [ 0.85255194,  0.85255194,  0.85255194, ...,  1.53291263,\n",
-       "         1.53291263,  1.53291263]])
            • created_at :
              2020-06-06T18:10:13.921850
              arviz_version :
              0.8.3
              inference_library :
              emcee
              inference_library_version :
              3.0.2

        \n", + " 1.53291263, 1.53291263]])
    • created_at :
      2020-06-10T18:21:37.261042
      arviz_version :
      0.8.3
      inference_library :
      emcee
      inference_library_version :
      3.0.2

    \n", " \n", " \n", " \n", " \n", "
  • \n", - " \n", - " \n", + " \n", + " \n", "
    \n", "
    \n", "
      \n", @@ -20662,9 +16958,9 @@ " stroke: currentColor;\n", " fill: currentColor;\n", "}\n", - "
      xarray.Dataset
        • chain: 40
        • draw: 1500
        • chain
          (chain)
          int64
          0 1 2 3 4 5 6 ... 34 35 36 37 38 39
          array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
+       "
          xarray.Dataset
            • chain: 40
            • draw: 1500
            • chain
              (chain)
              int64
              0 1 2 3 4 5 6 ... 34 35 36 37 38 39
              array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
        "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
-       "       36, 37, 38, 39])
            • draw
              (draw)
              int64
              0 1 2 3 4 ... 1496 1497 1498 1499
              array([   0,    1,    2, ..., 1497, 1498, 1499])
            • lp
              (chain, draw)
              float64
              -19.02 -17.53 ... -20.27 -20.27
              array([[-19.02258213, -17.53015266, -17.53015266, ..., -17.8496425 ,\n",
+       "       36, 37, 38, 39])
            • draw
              (draw)
              int64
              0 1 2 3 4 ... 1496 1497 1498 1499
              array([   0,    1,    2, ..., 1497, 1498, 1499])
            • lp
              (chain, draw)
              float64
              -19.02 -17.53 ... -20.27 -20.27
              array([[-19.02258213, -17.53015266, -17.53015266, ..., -17.8496425 ,\n",
        "        -17.85983734, -17.85983734],\n",
        "       [-16.98092975, -16.98092975, -16.98092975, ..., -13.90696007,\n",
        "        -13.90696007, -13.90696007],\n",
@@ -20676,14 +16972,14 @@
        "       [-30.54403298, -30.54403298, -28.61897737, ..., -13.2645001 ,\n",
        "        -12.51696217, -17.11064559],\n",
        "       [-22.49391806, -22.49391806, -22.49391806, ..., -20.2684405 ,\n",
-       "        -20.2684405 , -20.2684405 ]])
          • created_at :
            2020-06-06T18:10:13.914170
            arviz_version :
            0.8.3
            inference_library :
            emcee
            inference_library_version :
            3.0.2

      \n", + " -20.2684405 , -20.2684405 ]])
  • created_at :
    2020-06-10T18:21:37.254838
    arviz_version :
    0.8.3
    inference_library :
    emcee
    inference_library_version :
    3.0.2

    • \n", " \n", " \n", " \n", " \n", "
  • \n", - " \n", - " \n", + " \n", + " \n", "
    \n", "
    \n", "
      \n", @@ -21021,7 +17317,7 @@ " stroke: currentColor;\n", " fill: currentColor;\n", "}\n", - "
      xarray.Dataset
        • arg_0_dim_0: 1
        • arg_1_dim_0: 8
        • arg_2_dim_0: 8
        • arg_0_dim_0
          (arg_0_dim_0)
          int64
          0
          array([0])
        • arg_1_dim_0
          (arg_1_dim_0)
          int64
          0 1 2 3 4 5 6 7
          array([0, 1, 2, 3, 4, 5, 6, 7])
        • arg_2_dim_0
          (arg_2_dim_0)
          int64
          0 1 2 3 4 5 6 7
          array([0, 1, 2, 3, 4, 5, 6, 7])
        • arg_0
          (arg_0_dim_0)
          int64
          8
          array([8])
        • arg_1
          (arg_1_dim_0)
          float64
          28.0 8.0 -3.0 7.0 ... 1.0 18.0 12.0
          array([28.,  8., -3.,  7., -1.,  1., 18., 12.])
        • arg_2
          (arg_2_dim_0)
          float64
          15.0 10.0 16.0 ... 11.0 10.0 18.0
          array([15., 10., 16., 11.,  9., 11., 10., 18.])
      • created_at :
        2020-06-06T18:10:13.916273
        arviz_version :
        0.8.3
        inference_library :
        emcee
        inference_library_version :
        3.0.2

    \n", + "
    xarray.Dataset
      • arg_0_dim_0: 1
      • arg_1_dim_0: 8
      • arg_2_dim_0: 8
      • arg_0_dim_0
        (arg_0_dim_0)
        int64
        0
        array([0])
      • arg_1_dim_0
        (arg_1_dim_0)
        int64
        0 1 2 3 4 5 6 7
        array([0, 1, 2, 3, 4, 5, 6, 7])
      • arg_2_dim_0
        (arg_2_dim_0)
        int64
        0 1 2 3 4 5 6 7
        array([0, 1, 2, 3, 4, 5, 6, 7])
      • arg_0
        (arg_0_dim_0)
        int64
        8
        array([8])
      • arg_1
        (arg_1_dim_0)
        float64
        28.0 8.0 -3.0 7.0 ... 1.0 18.0 12.0
        array([28.,  8., -3.,  7., -1.,  1., 18., 12.])
      • arg_2
        (arg_2_dim_0)
        float64
        15.0 10.0 16.0 ... 11.0 10.0 18.0
        array([15., 10., 16., 11.,  9., 11., 10., 18.])
    • created_at :
      2020-06-10T18:21:37.256560
      arviz_version :
      0.8.3
      inference_library :
      emcee
      inference_library_version :
      3.0.2

    \n", " \n", " \n", "
    • \n", @@ -21354,7 +17650,7 @@ "\t> observed_data" ] }, - "execution_count": 14, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -21426,7 +17722,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 11, "metadata": { "ExecuteTime": { "end_time": "2020-06-05T06:48:14.965999Z", @@ -21438,16 +17734,15 @@ "name": "stderr", "output_type": "stream", "text": [ -<<<<<<< HEAD "INFO:cmdstanpy:compiling c++\n", "INFO:cmdstanpy:compiled model file: /Users/percy/PycharmProjects/arviz/doc/notebooks/eight_school\n", "INFO:cmdstanpy:found newer exe file, not recompiling\n", "INFO:cmdstanpy:compiled model file: /Users/percy/PycharmProjects/arviz/doc/notebooks/eight_school\n", "INFO:cmdstanpy:start chain 1\n", "INFO:cmdstanpy:start chain 2\n", - "INFO:cmdstanpy:finish chain 1\n", - "INFO:cmdstanpy:start chain 3\n", "INFO:cmdstanpy:finish chain 2\n", + "INFO:cmdstanpy:start chain 3\n", + "INFO:cmdstanpy:finish chain 1\n", "INFO:cmdstanpy:start chain 4\n", "INFO:cmdstanpy:finish chain 3\n", "INFO:cmdstanpy:finish chain 4\n" @@ -21464,8 +17759,8 @@ "
      \n", " \n", "
    • \n", - " \n", - " \n", + " \n", + " \n", "
      \n", "
      \n", "
        \n", @@ -21803,132 +18098,132 @@ " stroke: currentColor;\n", " fill: currentColor;\n", "}\n", - "
        xarray.Dataset
          • chain: 4
          • draw: 1000
          • school: 8
          • chain
            (chain)
            int64
            0 1 2 3
            array([0, 1, 2, 3])
          • draw
            (draw)
            int64
            0 1 2 3 4 5 ... 995 996 997 998 999
            array([  0,   1,   2, ..., 997, 998, 999])
          • school
            (school)
            int64
            0 1 2 3 4 5 6 7
            array([0, 1, 2, 3, 4, 5, 6, 7])
          • mu
            (chain, draw)
            float64
            0.8604 -0.6372 ... 8.698 6.691
            array([[ 0.860351, -0.637211,  4.62776 , ...,  5.56301 ,  2.55374 ,\n",
-       "         2.31729 ],\n",
-       "       [ 0.732634, -0.220275,  0.615834, ...,  7.16887 ,  8.03808 ,\n",
-       "         4.4595  ],\n",
-       "       [ 5.91379 ,  3.70948 ,  4.26014 , ..., -3.40594 ,  9.58868 ,\n",
-       "         0.366119],\n",
-       "       [ 3.88691 ,  3.18823 ,  5.80652 , ...,  2.068   ,  8.69849 ,\n",
-       "         6.69092 ]])
          • tau
            (chain, draw)
            float64
            5.038 3.547 2.764 ... 1.098 4.68
            array([[5.03848 , 3.54695 , 2.76361 , ..., 8.59895 , 0.33265 , 0.427009],\n",
-       "       [9.96193 , 5.76789 , 0.800563, ..., 2.45978 , 6.35597 , 1.18186 ],\n",
-       "       [1.86466 , 5.26644 , 2.46189 , ..., 4.01369 , 0.966623, 2.22911 ],\n",
-       "       [6.751   , 2.61096 , 9.75554 , ..., 6.4743  , 1.09751 , 4.67988 ]])
          • theta_tilde
            (chain, draw, school)
            float64
            0.365 0.509 1.672 ... -1.239 0.3211
            array([[[ 3.65018e-01,  5.08974e-01,  1.67200e+00, ...,  1.68401e+00,\n",
-       "          1.31250e+00, -7.79879e-01],\n",
-       "        [-8.22488e-02,  1.60123e+00, -3.67971e-01, ..., -6.47924e-01,\n",
-       "          5.03854e-02, -6.58273e-01],\n",
-       "        [ 5.56617e-01,  1.77470e-01, -1.04413e+00, ...,  1.65032e+00,\n",
-       "         -6.74706e-01, -1.86848e-01],\n",
+       "
            xarray.Dataset
              • chain: 4
              • draw: 1000
              • school: 8
              • chain
                (chain)
                int64
                0 1 2 3
                array([0, 1, 2, 3])
              • draw
                (draw)
                int64
                0 1 2 3 4 5 ... 995 996 997 998 999
                array([  0,   1,   2, ..., 997, 998, 999])
              • school
                (school)
                int64
                0 1 2 3 4 5 6 7
                array([0, 1, 2, 3, 4, 5, 6, 7])
              • mu
                (chain, draw)
                float64
                2.908 8.134 3.037 ... 2.672 10.06
                array([[ 2.90813,  8.1337 ,  3.03685, ...,  3.49247,  2.69144,  2.77848],\n",
+       "       [ 1.20098,  3.74153,  5.71236, ...,  5.38302,  1.55543,  6.85928],\n",
+       "       [ 4.5479 ,  4.21596,  6.53758, ...,  1.8295 ,  6.26736, -2.83281],\n",
+       "       [ 7.2998 ,  7.03214,  1.5383 , ...,  3.01368,  2.67249, 10.056  ]])
              • tau
                (chain, draw)
                float64
                3.566 1.06 1.014 ... 0.5414 5.807
                array([[ 3.56579  ,  1.06035  ,  1.0145   , ...,  0.295652 ,  0.152276 ,\n",
+       "         1.27931  ],\n",
+       "       [ 3.75274  ,  1.13823  ,  1.8422   , ...,  8.23264  ,  3.29958  ,\n",
+       "         9.06202  ],\n",
+       "       [ 0.0756374, 14.1684   ,  2.65082  , ...,  1.91313  ,  7.97043  ,\n",
+       "         4.03989  ],\n",
+       "       [ 0.0525536,  0.213029 ,  2.08631  , ...,  0.51228  ,  0.541425 ,\n",
+       "         5.80725  ]])
              • theta_tilde
                (chain, draw, school)
                float64
                1.911 0.3988 ... 1.757 0.5014
                array([[[ 1.91055  ,  0.398843 , -0.206331 , ..., -1.11405  ,\n",
+       "          1.85739  , -1.37968  ],\n",
+       "        [-1.1159   , -0.447728 , -0.505903 , ...,  0.962855 ,\n",
+       "         -0.601752 ,  0.89427  ],\n",
+       "        [ 1.60719  , -0.596497 ,  0.151931 , ..., -1.28024  ,\n",
+       "          0.930506 , -0.765005 ],\n",
        "        ...,\n",
-       "        [ 1.31558e+00, -2.21541e-01,  3.70592e-02, ...,  6.68264e-01,\n",
-       "         -3.41076e-01, -1.91695e+00],\n",
-       "        [-1.32237e+00, -5.24289e-01, -4.04783e-01, ...,  6.25155e-01,\n",
-       "          2.60264e-01,  6.40021e-01],\n",
-       "        [-6.74953e-01, -1.30215e+00, -1.58508e-01, ...,  8.47776e-01,\n",
-       "          6.84311e-01,  9.77449e-01]],\n",
-       "\n",
-       "       [[-2.00545e-01, -2.10582e-01, -9.40322e-02, ...,  2.10087e-02,\n",
-       "          2.37020e-01,  1.29188e-01],\n",
-       "        [-5.38384e-01, -9.47796e-01,  7.59535e-01, ...,  4.92083e-01,\n",
-       "          7.14972e-01, -5.74193e-01],\n",
-       "        [ 1.05020e-02, -1.43597e-01,  5.72359e-02, ...,  3.59991e-01,\n",
-       "         -1.34135e-02, -7.78741e-01],\n",
+       "        [-0.65252  , -0.933794 ,  1.11489  , ..., -0.606474 ,\n",
+       "          0.323287 , -2.09671  ],\n",
+       "        [-0.678068 , -0.391203 ,  0.887894 , ..., -0.721647 ,\n",
+       "          0.131326 , -2.29018  ],\n",
+       "        [ 0.0366895, -0.557451 ,  0.756044 , ...,  0.99686  ,\n",
+       "          0.903742 , -0.308114 ]],\n",
+       "\n",
+       "       [[ 1.52082  ,  2.08589  , -1.2005   , ..., -0.602199 ,\n",
+       "          1.88407  ,  0.109757 ],\n",
+       "        [ 0.0451789, -1.61133  ,  1.96004  , ..., -0.548927 ,\n",
+       "          0.375405 , -0.0377435],\n",
+       "        [-0.216968 ,  1.40354  , -2.17544  , ...,  0.335058 ,\n",
+       "         -0.201998 , -0.0420111],\n",
        "        ...,\n",
-       "        [ 1.25372e+00,  9.10764e-01, -3.08858e+00, ..., -1.96722e+00,\n",
-       "          1.12084e-02,  2.52069e-01],\n",
-       "        [ 1.93895e+00,  2.57758e-01, -2.47922e+00, ..., -8.94723e-01,\n",
-       "         -1.26788e-01,  4.69993e-01],\n",
-       "        [ 2.97201e+00,  6.67406e-02, -1.94356e-01, ...,  1.00890e-01,\n",
-       "         -3.81350e-01,  2.11749e-01]],\n",
-       "\n",
-       "       [[ 1.21786e+00,  1.23069e+00, -5.52686e-01, ..., -1.48749e+00,\n",
-       "          7.83702e-01,  1.85699e+00],\n",
-       "        [ 1.72311e+00,  7.67163e-01, -2.34386e-02, ..., -2.42422e+00,\n",
-       "          3.66557e-01,  1.29801e+00],\n",
-       "        [ 1.92775e+00,  7.48698e-01,  3.09622e-01, ..., -1.77732e+00,\n",
-       "          1.59023e-01,  1.52893e+00],\n",
+       "        [-1.24541  ,  1.44617  ,  1.26991  , ..., -0.517744 ,\n",
+       "          1.06641  , -0.328943 ],\n",
+       "        [-0.26795  , -0.471647 ,  0.916106 , ..., -0.0443553,\n",
+       "          1.10523  ,  0.802313 ],\n",
+       "        [ 0.860489 , -0.755706 , -0.221758 , ..., -0.881374 ,\n",
+       "          1.02386  ,  2.26025  ]],\n",
+       "\n",
+       "       [[-0.728935 , -1.30284  , -0.725011 , ..., -1.13638  ,\n",
+       "         -0.623346 ,  1.20411  ],\n",
+       "        [ 0.877633 ,  1.57903  ,  0.657069 , ...,  1.18061  ,\n",
+       "          0.873288 , -0.777553 ],\n",
+       "        [ 1.92013  ,  1.41113  ,  0.565262 , ...,  0.892794 ,\n",
+       "          1.17681  , -1.14554  ],\n",
        "        ...,\n",
-       "        [ 1.23452e-01,  1.16843e+00,  1.00505e-02, ...,  1.31982e+00,\n",
-       "         -5.95969e-01, -4.30592e-01],\n",
-       "        [ 2.62933e-01, -1.08506e+00, -1.75313e+00, ..., -7.69045e-01,\n",
-       "          2.01635e-01, -1.55323e+00],\n",
-       "        [ 4.78519e-01,  4.44985e-01,  1.39678e+00, ...,  4.84171e-02,\n",
-       "          5.20688e-01,  1.69244e+00]],\n",
-       "\n",
-       "       [[-1.31033e+00, -7.85428e-03, -3.17845e-01, ..., -6.09675e-01,\n",
-       "          1.70432e+00,  1.16530e+00],\n",
-       "        [ 2.02721e+00,  5.59707e-01, -2.80847e-02, ...,  6.90420e-01,\n",
-       "         -5.35285e-01, -1.54980e-01],\n",
-       "        [ 1.26965e+00, -8.16178e-01,  7.36237e-01, ..., -3.99220e-03,\n",
-       "          7.63763e-01, -7.12747e-01],\n",
+       "        [-0.787386 , -1.34728  ,  0.938111 , ...,  0.212187 ,\n",
+       "          1.5131   , -0.159953 ],\n",
+       "        [ 1.29339  ,  0.0357047, -0.315845 , ...,  0.0184302,\n",
+       "          0.789759 ,  0.502829 ],\n",
+       "        [ 1.57545  ,  0.430001 ,  0.0687333, ...,  0.335712 ,\n",
+       "          1.7016   , -1.64133  ]],\n",
+       "\n",
+       "       [[ 0.409093 ,  0.464062 ,  0.978    , ..., -0.780695 ,\n",
+       "         -0.585484 ,  1.81793  ],\n",
+       "        [ 0.076946 , -0.649097 , -0.768641 , ..., -0.375243 ,\n",
+       "          0.651451 , -2.41116  ],\n",
+       "        [-0.474311 , -0.847334 ,  0.663324 , ...,  0.276397 ,\n",
+       "         -0.65441  ,  2.32434  ],\n",
        "        ...,\n",
-       "        [ 9.42448e-01, -7.24387e-04,  1.05740e+00, ...,  3.66910e-02,\n",
-       "          6.49015e-01, -5.97576e-01],\n",
-       "        [ 1.04661e+00,  1.81675e+00,  2.53350e-01, ..., -1.45018e-01,\n",
-       "         -7.19568e-01, -1.61255e+00],\n",
-       "        [ 2.12768e+00, -3.70835e-01,  1.35789e+00, ..., -9.19882e-02,\n",
-       "         -1.23920e+00,  3.21054e-01]]])
              • theta
                (chain, draw, school)
                float64
                2.699 3.425 9.285 ... 0.8916 8.193
                array([[[ 2.69949e+00,  3.42481e+00,  9.28470e+00, ...,  9.34520e+00,\n",
-       "          7.47338e+00, -3.06906e+00],\n",
-       "        [-9.28944e-01,  5.04228e+00, -1.94239e+00, ..., -2.93536e+00,\n",
-       "         -4.58497e-01, -2.97207e+00],\n",
-       "        [ 6.16603e+00,  5.11822e+00,  1.74220e+00, ...,  9.18859e+00,\n",
-       "          2.76314e+00,  4.11139e+00],\n",
+       "        [-1.25161  ,  0.453957 ,  2.11518  , ..., -0.90933  ,\n",
+       "          2.48317  , -0.782639 ],\n",
+       "        [-0.137756 , -0.0470846, -1.45337  , ..., -2.4094   ,\n",
+       "         -1.58137  ,  0.137089 ],\n",
+       "        [ 1.41579  , -0.0828588,  1.27436  , ...,  0.247186 ,\n",
+       "          1.75653  ,  0.501351 ]]])
              • theta
                (chain, draw, school)
                float64
                9.721 4.33 2.172 ... 20.26 12.97
                array([[[ 9.72074e+00,  4.33032e+00,  2.17239e+00, ..., -1.06433e+00,\n",
+       "          9.53118e+00, -2.01152e+00],\n",
+       "        [ 6.95047e+00,  7.65896e+00,  7.59727e+00, ...,  9.15466e+00,\n",
+       "          7.49564e+00,  9.08194e+00],\n",
+       "        [ 4.66734e+00,  2.43171e+00,  3.19099e+00, ...,  1.73805e+00,\n",
+       "          3.98085e+00,  2.26076e+00],\n",
        "        ...,\n",
-       "        [ 1.68756e+01,  3.65800e+00,  5.88168e+00, ...,  1.13094e+01,\n",
-       "          2.63012e+00, -1.09207e+01],\n",
-       "        [ 2.11385e+00,  2.37933e+00,  2.41909e+00, ...,  2.76170e+00,\n",
-       "          2.64032e+00,  2.76664e+00],\n",
-       "        [ 2.02908e+00,  1.76127e+00,  2.24961e+00, ...,  2.67930e+00,\n",
-       "          2.60950e+00,  2.73467e+00]],\n",
-       "\n",
-       "       [[-1.26518e+00, -1.36517e+00, -2.04107e-01, ...,  9.41921e-01,\n",
-       "          3.09381e+00,  2.01959e+00],\n",
-       "        [-3.32561e+00, -5.68706e+00,  4.16063e+00, ...,  2.61801e+00,\n",
-       "          3.90360e+00, -3.53216e+00],\n",
-       "        [ 6.24242e-01,  5.00876e-01,  6.61655e-01, ...,  9.04029e-01,\n",
-       "          6.05096e-01, -7.59738e-03],\n",
+       "        [ 3.29955e+00,  3.21639e+00,  3.82208e+00, ...,  3.31316e+00,\n",
+       "          3.58805e+00,  2.87257e+00],\n",
+       "        [ 2.58818e+00,  2.63187e+00,  2.82664e+00, ...,  2.58155e+00,\n",
+       "          2.71143e+00,  2.34270e+00],\n",
+       "        [ 2.82542e+00,  2.06533e+00,  3.74569e+00, ...,  4.05377e+00,\n",
+       "          3.93464e+00,  2.38430e+00]],\n",
+       "\n",
+       "       [[ 6.90821e+00,  9.02878e+00, -3.30420e+00, ..., -1.05892e+00,\n",
+       "          8.27142e+00,  1.61287e+00],\n",
+       "        [ 3.79295e+00,  1.90747e+00,  5.97250e+00, ...,  3.11672e+00,\n",
+       "          4.16882e+00,  3.69857e+00],\n",
+       "        [ 5.31266e+00,  8.29796e+00,  1.70476e+00, ...,  6.32960e+00,\n",
+       "          5.34024e+00,  5.63497e+00],\n",
        "        ...,\n",
-       "        [ 1.02527e+01,  9.40915e+00, -4.28361e-01, ...,  2.32995e+00,\n",
-       "          7.19644e+00,  7.78891e+00],\n",
-       "        [ 2.03620e+01,  9.67638e+00, -7.71979e+00, ...,  2.35124e+00,\n",
-       "          7.23222e+00,  1.10253e+01],\n",
-       "        [ 7.97201e+00,  4.53838e+00,  4.22980e+00, ...,  4.57874e+00,\n",
-       "          4.00880e+00,  4.70976e+00]],\n",
-       "\n",
-       "       [[ 8.18468e+00,  8.20861e+00,  4.88322e+00, ...,  3.14013e+00,\n",
-       "          7.37513e+00,  9.37645e+00],\n",
-       "        [ 1.27841e+01,  7.74970e+00,  3.58604e+00, ..., -9.05752e+00,\n",
-       "          5.63993e+00,  1.05454e+01],\n",
-       "        [ 9.00605e+00,  6.10335e+00,  5.02239e+00, ..., -1.15431e-01,\n",
-       "          4.65164e+00,  8.02420e+00],\n",
+       "        [-4.86998e+00,  1.72888e+01,  1.58377e+01, ...,  1.12062e+00,\n",
+       "          1.41624e+01,  2.67495e+00],\n",
+       "        [ 6.71302e-01, -8.13246e-04,  4.57819e+00, ...,  1.40907e+00,\n",
+       "          5.20222e+00,  4.20272e+00],\n",
+       "        [ 1.46570e+01,  1.10534e-02,  4.84970e+00, ..., -1.12774e+00,\n",
+       "          1.61376e+01,  2.73417e+01]],\n",
+       "\n",
+       "       [[ 4.49277e+00,  4.44936e+00,  4.49306e+00, ...,  4.46195e+00,\n",
+       "          4.50075e+00,  4.63898e+00],\n",
+       "        [ 1.66506e+01,  2.65883e+01,  1.35256e+01, ...,  2.09433e+01,\n",
+       "          1.65890e+01, -6.80072e+00],\n",
+       "        [ 1.16275e+01,  1.02782e+01,  8.03598e+00, ...,  8.90421e+00,\n",
+       "          9.65709e+00,  3.50097e+00],\n",
        "        ...,\n",
-       "        [-2.91044e+00,  1.28378e+00, -3.36560e+00, ...,  1.89143e+00,\n",
-       "         -5.79797e+00, -5.13420e+00],\n",
-       "        [ 9.84283e+00,  8.53983e+00,  7.89406e+00, ...,  8.84530e+00,\n",
-       "          9.78358e+00,  8.08729e+00],\n",
-       "        [ 1.43279e+00,  1.35804e+00,  3.47971e+00, ...,  4.74046e-01,\n",
-       "          1.52679e+00,  4.13875e+00]],\n",
-       "\n",
-       "       [[-4.95910e+00,  3.83388e+00,  1.74114e+00, ..., -2.29009e-01,\n",
-       "          1.53928e+01,  1.17538e+01],\n",
-       "        [ 8.48119e+00,  4.64960e+00,  3.11490e+00, ...,  4.99089e+00,\n",
-       "          1.79062e+00,  2.78358e+00],\n",
-       "        [ 1.81926e+01, -2.15574e+00,  1.29889e+01, ...,  5.76757e+00,\n",
-       "          1.32574e+01, -1.14672e+00],\n",
+       "        [ 3.23128e-01, -7.48028e-01,  3.62423e+00, ...,  2.23544e+00,\n",
+       "          4.72425e+00,  1.52349e+00],\n",
+       "        [ 1.65762e+01,  6.55194e+00,  3.74994e+00, ...,  6.41426e+00,\n",
+       "          1.25621e+01,  1.02751e+01],\n",
+       "        [ 3.53185e+00, -1.09566e+00, -2.55514e+00, ..., -1.47657e+00,\n",
+       "          4.04147e+00, -9.46361e+00]],\n",
+       "\n",
+       "       [[ 7.32130e+00,  7.32419e+00,  7.35120e+00, ...,  7.25878e+00,\n",
+       "          7.26903e+00,  7.39534e+00],\n",
+       "        [ 7.04853e+00,  6.89386e+00,  6.86839e+00, ...,  6.95220e+00,\n",
+       "          7.17091e+00,  6.51849e+00],\n",
+       "        [ 5.48734e-01, -2.29508e-01,  2.92220e+00, ...,  2.11494e+00,\n",
+       "          1.72992e-01,  6.38759e+00],\n",
        "        ...,\n",
-       "        [ 8.16969e+00,  2.06331e+00,  8.91392e+00, ...,  2.30555e+00,\n",
-       "          6.26992e+00, -1.80089e+00],\n",
-       "        [ 9.84716e+00,  1.06924e+01,  8.97655e+00, ...,  8.53934e+00,\n",
-       "          7.90876e+00,  6.92871e+00],\n",
-       "        [ 1.66482e+01,  4.95545e+00,  1.30457e+01, ...,  6.26042e+00,\n",
-       "          8.91628e-01,  8.19341e+00]]])
            • created_at :
              2020-06-06T18:10:32.329048
              arviz_version :
              0.8.3
              inference_library :
              cmdstanpy
              inference_library_version :
              0.8.0

        \n", + " [ 2.37250e+00, 3.24623e+00, 4.09725e+00, ..., 2.54785e+00,\n", + " 4.28576e+00, 2.61275e+00],\n", + " [ 2.59791e+00, 2.64700e+00, 1.88560e+00, ..., 1.36798e+00,\n", + " 1.81630e+00, 2.74672e+00],\n", + " [ 1.82778e+01, 9.57482e+00, 1.74565e+01, ..., 1.14915e+01,\n", + " 2.02566e+01, 1.29675e+01]]])
    • created_at :
      2020-06-10T18:21:56.176067
      arviz_version :
      0.8.3
      inference_library :
      cmdstanpy
      inference_library_version :
      0.8.0

    \n", " \n", " \n", " \n", " \n", "
  • \n", - " \n", - " \n", + " \n", + " \n", "
    \n", "
    \n", "
      \n", @@ -22266,68 +18561,68 @@ " stroke: currentColor;\n", " fill: currentColor;\n", "}\n", - "
      xarray.Dataset
        • chain: 4
        • draw: 1000
        • school: 8
        • chain
          (chain)
          int64
          0 1 2 3
          array([0, 1, 2, 3])
        • draw
          (draw)
          int64
          0 1 2 3 4 5 ... 995 996 997 998 999
          array([  0,   1,   2, ..., 997, 998, 999])
        • school
          (school)
          int64
          0 1 2 3 4 5 6 7
          array([0, 1, 2, 3, 4, 5, 6, 7])
        • y_hat
          (chain, draw, school)
          float64
          17.82 -12.27 15.19 ... -11.52 28.27
          array([[[ 17.8214   , -12.2736   ,  15.1864   , ...,  10.6945   ,\n",
-       "          11.1511   , -25.1836   ],\n",
-       "        [  9.80558  ,   2.64404  ,  -0.126269 , ..., -22.818    ,\n",
-       "          15.7038   ,   7.63275  ],\n",
-       "        [ -0.307212 ,   5.86482  , -17.3024   , ...,  18.2618   ,\n",
-       "           1.18445  ,   4.95961  ],\n",
+       "
          xarray.Dataset
            • chain: 4
            • draw: 1000
            • school: 8
            • chain
              (chain)
              int64
              0 1 2 3
              array([0, 1, 2, 3])
            • draw
              (draw)
              int64
              0 1 2 3 4 5 ... 995 996 997 998 999
              array([  0,   1,   2, ..., 997, 998, 999])
            • school
              (school)
              int64
              0 1 2 3 4 5 6 7
              array([0, 1, 2, 3, 4, 5, 6, 7])
            • y_hat
              (chain, draw, school)
              float64
              27.32 16.59 19.23 ... 15.28 -14.33
              array([[[ 27.316   ,  16.5918  ,  19.2331  , ...,  -2.68653 ,\n",
+       "          12.2412  ,  27.0189  ],\n",
+       "        [ 13.7773  ,  -4.34203 ,   3.61368 , ...,   2.72659 ,\n",
+       "          13.737   ,   2.31227 ],\n",
+       "        [ -3.79392 ,  -0.568726, -16.99    , ...,   8.77864 ,\n",
+       "           6.83554 , -24.1435  ],\n",
        "        ...,\n",
-       "        [ 18.9973   ,  -2.61467  ,  10.0733   , ...,   6.23517  ,\n",
-       "          20.3914   , -31.1207   ],\n",
-       "        [  7.63424  ,  -9.18872  , -17.4574   , ...,  22.6733   ,\n",
-       "          11.1596   , -13.6902   ],\n",
-       "        [ -4.94321  ,  16.7564   ,  -7.47152  , ...,  -0.874168 ,\n",
-       "          16.0975   , -15.7978   ]],\n",
-       "\n",
-       "       [[-13.8236   ,   3.30856  ,  -9.71613  , ...,   1.07714  ,\n",
-       "          24.3845   , -37.759    ],\n",
-       "        [-19.5816   ,   9.47864  ,   7.43956  , ...,   8.73732  ,\n",
-       "          15.1482   ,  20.7514   ],\n",
-       "        [ -3.2081   ,  -5.29835  , -23.4598   , ...,  -7.25993  ,\n",
-       "          15.408    , -12.4758   ],\n",
+       "        [ 16.0011  ,  16.3268  ,   0.235298, ..., -14.9994  ,\n",
+       "           6.60276 ,  -0.33642 ],\n",
+       "        [ -3.59346 ,  -7.50548 ,  -0.90007 , ...,  -0.173541,\n",
+       "           3.61428 ,  25.8661  ],\n",
+       "        [  5.06708 ,  -4.07957 , -23.1559  , ...,   7.18739 ,\n",
+       "           9.59156 ,   5.01793 ]],\n",
+       "\n",
+       "       [[ -0.843751,  -4.98264 , -18.7942  , ...,  -6.34356 ,\n",
+       "         -11.4954  , -30.0613  ],\n",
+       "        [-32.9141  ,  -6.91807 ,  -6.90439 , ...,  10.1167  ,\n",
+       "           3.70618 ,   8.15487 ],\n",
+       "        [  2.88749 ,   0.686142,  24.0905  , ...,   9.77811 ,\n",
+       "          31.1765  , -18.8176  ],\n",
        "        ...,\n",
-       "        [ 17.2876   ,   9.47491  ,   9.11321  , ...,  -0.844266 ,\n",
-       "          23.7267   ,  13.1609   ],\n",
-       "        [ 42.8214   , -11.115    , -28.3195   , ...,   9.54406  ,\n",
-       "           6.86596  ,  24.7486   ],\n",
-       "        [ 31.5371   ,  -9.2391   ,  -5.19494  , ...,  16.0107   ,\n",
-       "          18.2181   ,  19.2444   ]],\n",
-       "\n",
-       "       [[-22.2079   ,  16.09     ,  -1.43103  , ...,  -2.81685  ,\n",
-       "         -11.7201   ,  26.1408   ],\n",
-       "        [  7.84245  ,  12.1987   ,  12.4391   , ...,  -8.31294  ,\n",
-       "          16.7167   ,  24.0476   ],\n",
-       "        [ -5.47622  , -10.7056   , -13.119    , ...,  -9.98965  ,\n",
-       "           1.99784  ,  12.6135   ],\n",
+       "        [-10.4054  ,  38.8779  ,  -1.72784 , ...,   2.10858 ,\n",
+       "           1.71828 , -41.049   ],\n",
+       "        [-34.6393  ,  -8.05134 ,   0.299065, ...,  -5.08346 ,\n",
+       "          14.0737  ,  18.2229  ],\n",
+       "        [ 20.5415  ,  -7.11264 ,  16.6271  , ...,  -6.10246 ,\n",
+       "          16.3215  ,  28.9235  ]],\n",
+       "\n",
+       "       [[ -0.601871, -11.7324  ,  20.1891  , ...,  14.0161  ,\n",
+       "          -1.10264 ,  -0.811892],\n",
+       "        [ 16.1775  ,  19.1952  ,   7.63344 , ...,  15.3313  ,\n",
+       "          20.5808  ,  -0.475661],\n",
+       "        [ -6.71155 ,  -3.46014 ,  15.4774  , ...,  22.6551  ,\n",
+       "          -6.43601 ,  23.9684  ],\n",
        "        ...,\n",
-       "        [ 10.498    ,  -6.5292   ,  -1.95445  , ...,   6.90389  ,\n",
-       "           9.90309  ,  -9.53769  ],\n",
-       "        [-22.7585   ,  16.791    ,   3.64741  , ...,  16.2263   ,\n",
-       "          13.1702   ,  -2.93753  ],\n",
-       "        [ 16.2394   ,   3.08554  ,   9.0992   , ...,  -7.29659  ,\n",
-       "          16.6593   ,  -2.51172  ]],\n",
-       "\n",
-       "       [[ 23.8207   ,   6.97066  , -13.2277   , ...,  -4.33379  ,\n",
-       "          16.7229   ,  -4.24901  ],\n",
-       "        [ 19.6784   ,  10.6246   ,  15.6069   , ...,   8.62725  ,\n",
-       "          -0.49937  ,  -4.91235  ],\n",
-       "        [-23.3668   ,   8.97635  ,  22.8516   , ...,  16.1906   ,\n",
-       "          18.5997   ,  -0.0484115],\n",
+       "        [ 19.7332  , -20.677   ,  24.276   , ...,  12.3559  ,\n",
+       "          11.5496  ,  17.943   ],\n",
+       "        [ 13.6132  ,   5.89891 ,  -6.70882 , ...,   5.5949  ,\n",
+       "          16.5097  , -13.9093  ],\n",
+       "        [ 26.0067  ,  -2.92887 ,  28.0343  , ..., -11.2195  ,\n",
+       "           3.37155 ,  -8.39441 ]],\n",
+       "\n",
+       "       [[ -1.77315 ,  -0.850641,  31.9772  , ...,  12.9403  ,\n",
+       "          19.1804  , -11.9612  ],\n",
+       "        [ 12.9344  ,  -7.44461 ,   0.504742, ..., -11.9935  ,\n",
+       "          -5.35724 ,  20.562   ],\n",
+       "        [-11.582   ,  -9.02636 ,  18.8871  , ...,   2.61293 ,\n",
+       "         -10.874   ,  33.0402  ],\n",
        "        ...,\n",
-       "        [  1.07142  ,   2.08327  ,  28.3587   , ...,  18.9032   ,\n",
-       "          11.3534   ,  -3.75202  ],\n",
-       "        [ 11.1992   ,   7.75921  ,  11.0927   , ...,   2.09711  ,\n",
-       "           4.61789  ,  31.6442   ],\n",
-       "        [ 27.53     ,  19.7944   ,  16.7601   , ..., -15.1517   ,\n",
-       "         -11.5217   ,  28.2679   ]]])
          • created_at :
            2020-06-06T18:10:32.340884
            arviz_version :
            0.8.3
            inference_library :
            cmdstanpy
            inference_library_version :
            0.8.0

      \n", + " [ 15.5324 , 14.9454 , 7.0026 , ..., -0.143009,\n", + " 3.01618 , -5.04935 ],\n", + " [-14.324 , -4.90844 , -12.5231 , ..., -15.5133 ,\n", + " 7.05567 , -27.792 ],\n", + " [ 15.8131 , 14.6397 , 33.993 , ..., 1.04319 ,\n", + " 15.2797 , -14.3346 ]]])
  • created_at :
    2020-06-10T18:21:56.187926
    arviz_version :
    0.8.3
    inference_library :
    cmdstanpy
    inference_library_version :
    0.8.0

    • \n", " \n", " \n", " \n", " \n", "
  • \n", - " \n", - " \n", + " \n", + " \n", "
    \n", "
    \n", "
      \n", @@ -22665,68 +18960,68 @@ " stroke: currentColor;\n", " fill: currentColor;\n", "}\n", - "
      xarray.Dataset
        • chain: 4
        • draw: 1000
        • school: 8
        • chain
          (chain)
          int64
          0 1 2 3
          array([0, 1, 2, 3])
        • draw
          (draw)
          int64
          0 1 2 3 4 5 ... 995 996 997 998 999
          array([  0,   1,   2, ..., 997, 998, 999])
        • school
          (school)
          int64
          0 1 2 3 4 5 6 7
          array([0, 1, 2, 3, 4, 5, 6, 7])
        • log_lik
          (chain, draw, school)
          float64
          -5.049 -3.326 ... -4.685 -3.832
          array([[[-5.04947, -3.32619, -3.98628, ..., -3.60461, -3.77557,\n",
-       "         -4.15974],\n",
-       "        [-5.48673, -3.26526, -3.69371, ..., -3.38083, -4.9251 ,\n",
-       "         -4.15524],\n",
-       "        [-4.68637, -3.26305, -3.73545, ..., -3.59391, -4.38233,\n",
-       "         -3.90534],\n",
+       "
          xarray.Dataset
            • chain: 4
            • draw: 1000
            • school: 8
            • chain
              (chain)
              int64
              0 1 2 3
              array([0, 1, 2, 3])
            • draw
              (draw)
              int64
              0 1 2 3 4 5 ... 995 996 997 998 999
              array([  0,   1,   2, ..., 997, 998, 999])
            • school
              (school)
              int64
              0 1 2 3 4 5 6 7
              array([0, 1, 2, 3, 4, 5, 6, 7])
            • log_lik
              (chain, draw, school)
              float64
              -4.37 -3.289 ... -3.247 -3.811
              array([[[-4.3695 , -3.28886, -3.74378, ..., -3.33444, -3.58013,\n",
+       "         -4.11228],\n",
+       "        [-4.61162, -3.22211, -3.91087, ..., -3.59162, -3.77323,\n",
+       "         -3.82245],\n",
+       "        [-4.8368 , -3.37655, -3.76639, ..., -3.31908, -4.20421,\n",
+       "         -3.95569],\n",
        "        ...,\n",
-       "        [-3.90199, -3.31579, -3.8456 , ..., -3.75602, -4.40269,\n",
-       "         -4.62005],\n",
-       "        [-5.11608, -3.37948, -3.74888, ..., -3.32966, -4.40112,\n",
-       "         -3.94088],\n",
-       "        [-5.12585, -3.41613, -3.74535, ..., -3.32849, -4.40586,\n",
-       "         -3.94179]],\n",
-       "\n",
-       "       [[-5.53021, -3.66006, -3.70679, ..., -3.31685, -4.3325 ,\n",
-       "         -3.96303],\n",
-       "        [-5.80764, -4.1582 , -3.79167, ..., -3.32765, -4.21507,\n",
-       "         -4.18161],\n",
-       "        [-5.29239, -3.50271, -3.71771, ..., -3.31687, -4.73444,\n",
-       "         -4.03181],\n",
+       "        [-4.98279, -3.33594, -3.78243, ..., -3.33894, -4.26005,\n",
+       "         -3.93788],\n",
+       "        [-5.06201, -3.36561, -3.75784, ..., -3.32717, -4.39022,\n",
+       "         -3.95324],\n",
+       "        [-5.03534, -3.39763, -3.7804 , ..., -3.35537, -4.21069,\n",
+       "         -3.952  ]],\n",
+       "\n",
+       "       [[-4.61557, -3.22682, -3.69171, ..., -3.33435, -3.69475,\n",
+       "         -3.97581],\n",
+       "        [-4.92917, -3.40712, -3.84876, ..., -3.33535, -4.17803,\n",
+       "         -3.91566],\n",
+       "        [-4.7708 , -3.22197, -3.73476, ..., -3.43421, -4.02287,\n",
+       "         -3.87183],\n",
        "        ...,\n",
-       "        [-4.32691, -3.23145, -3.70444, ..., -3.32414, -3.80511,\n",
-       "         -3.83668],\n",
-       "        [-3.75663, -3.23557, -3.73504, ..., -3.32438, -3.80125,\n",
-       "         -3.81078],\n",
-       "        [-4.51837, -3.28144, -3.79362, ..., -3.36976, -4.20029,\n",
-       "         -3.89133]],\n",
-       "\n",
-       "       [[-4.49954, -3.22174, -3.8129 , ..., -3.33576, -3.78596,\n",
-       "         -3.81993],\n",
-       "        [-4.14148, -3.22184, -3.77625, ..., -3.73482, -3.98538,\n",
-       "         -3.81258],\n",
-       "        [-4.4287 , -3.23951, -3.81723, ..., -3.32198, -4.11242,\n",
-       "         -3.8337 ],\n",
+       "        [-6.02796, -3.65293, -4.38461, ..., -3.31689, -3.29516,\n",
+       "         -3.9435 ],\n",
+       "        [-5.28667, -3.54159, -3.80369, ..., -3.31753, -4.04044,\n",
+       "         -3.90313],\n",
+       "        [-4.02262, -3.54064, -3.81187, ..., -3.33554, -3.23887,\n",
+       "         -4.17253]],\n",
+       "\n",
+       "       [[-4.85497, -3.28456, -3.80119, ..., -3.36636, -4.13267,\n",
+       "         -3.89293],\n",
+       "        [-3.91323, -4.94915, -4.22492, ..., -4.96036, -3.23148,\n",
+       "         -4.35478],\n",
+       "        [-4.22267, -3.24747, -3.9294 , ..., -3.575  , -3.56954,\n",
+       "         -3.92078],\n",
        "        ...,\n",
-       "        [-5.75022, -3.44706, -3.69179, ..., -3.32012, -6.05324,\n",
-       "         -4.26237],\n",
-       "        [-4.35962, -3.22298, -3.92333, ..., -3.57117, -3.55907,\n",
-       "         -3.83294],\n",
-       "        [-5.19547, -3.4421 , -3.77353, ..., -3.31798, -4.57836,\n",
-       "         -3.90468]],\n",
-       "\n",
-       "       [[-6.04099, -3.30831, -3.73543, ..., -3.32308, -3.25551,\n",
-       "         -3.8094 ],\n",
-       "        [-4.47362, -3.27765, -3.76456, ..., -3.38265, -4.53524,\n",
-       "         -3.94039],\n",
-       "        [-3.84073, -3.73722, -4.19083, ..., -3.41076, -3.33398,\n",
-       "         -4.07603],\n",
+       "        [-5.32923, -3.60416, -3.77723, ..., -3.32314, -4.10275,\n",
+       "         -3.97869],\n",
+       "        [-3.91699, -3.23201, -3.78051, ..., -3.43797, -3.36938,\n",
+       "         -3.8139 ],\n",
+       "        [-4.95741, -3.63518, -3.69191, ..., -3.34218, -4.19573,\n",
+       "         -4.52025]],\n",
+       "\n",
+       "       [[-4.57723, -3.22381, -3.9008 , ..., -3.4787 , -3.79729,\n",
+       "         -3.84203],\n",
+       "        [-4.60246, -3.22764, -3.88173, ..., -3.46323, -3.80787,\n",
+       "         -3.85568],\n",
+       "        [-5.30159, -3.56015, -3.76003, ..., -3.32197, -4.81053,\n",
+       "         -3.85792],\n",
        "        ...,\n",
-       "        [-4.50086, -3.39775, -3.96876, ..., -3.32388, -3.9095 ,\n",
-       "         -4.10324],\n",
-       "        [-4.35927, -3.25777, -3.97168, ..., -3.55172, -3.73069,\n",
-       "         -3.849  ],\n",
-       "        [-3.91335, -3.26787, -4.19439, ..., -3.43118, -4.68501,\n",
-       "         -3.83167]]])
          • created_at :
            2020-06-06T18:10:32.343924
            arviz_version :
            0.8.3
            inference_library :
            cmdstanpy
            inference_library_version :
            0.8.0

      \n", + " [-5.08647, -3.33452, -3.78991, ..., -3.32673, -4.16193,\n", + " -3.9453 ],\n", + " [-5.06091, -3.3648 , -3.73815, ..., -3.31739, -4.53108,\n", + " -3.94144],\n", + " [-3.83703, -3.23392, -4.50885, ..., -3.77167, -3.24699,\n", + " -3.81075]]])
  • created_at :
    2020-06-10T18:21:56.190772
    arviz_version :
    0.8.3
    inference_library :
    cmdstanpy
    inference_library_version :
    0.8.0

    • \n", " \n", " \n", " \n", " \n", "
  • \n", - " \n", - " \n", + " \n", + " \n", "
    \n", "
    \n", "
      \n", @@ -23064,39 +19359,39 @@ " stroke: currentColor;\n", " fill: currentColor;\n", "}\n", - "
      xarray.Dataset
        • chain: 4
        • draw: 1000
        • chain
          (chain)
          int64
          0 1 2 3
          array([0, 1, 2, 3])
        • draw
          (draw)
          int64
          0 1 2 3 4 5 ... 995 996 997 998 999
          array([  0,   1,   2, ..., 997, 998, 999])
        • lp
          (chain, draw)
          float64
          -7.826 -6.066 ... -8.303 -8.039
          array([[ -7.82575,  -6.06571,  -5.37794, ...,  -7.80535,  -8.74137,\n",
-       "         -9.8017 ],\n",
-       "       [ -3.36045,  -5.68201,  -4.56247, ..., -13.8115 , -10.5851 ,\n",
-       "         -7.94476],\n",
-       "       [ -6.90788,  -6.99938,  -6.69535, ..., -11.5595 ,  -7.91042,\n",
-       "         -5.89621],\n",
-       "       [ -6.73536,  -5.18604,  -4.06661, ...,  -3.82035,  -8.3025 ,\n",
-       "         -8.03949]])
        • accept_stat
          (chain, draw)
          float64
          0.9451 0.9838 ... 0.8485 0.9904
          array([[0.945142, 0.983825, 0.99608 , ..., 0.996257, 0.864367, 0.979232],\n",
-       "       [0.99676 , 0.785461, 0.941576, ..., 0.805343, 1.      , 0.751312],\n",
-       "       [0.897601, 0.97537 , 0.817992, ..., 0.872228, 0.920333, 0.980318],\n",
-       "       [0.901122, 0.984381, 0.986321, ..., 1.      , 0.848503, 0.990407]])
        • stepsize
          (chain, draw)
          float64
          0.5043 0.5043 ... 0.3523 0.3523
          array([[0.504285, 0.504285, 0.504285, ..., 0.504285, 0.504285, 0.504285],\n",
-       "       [0.39744 , 0.39744 , 0.39744 , ..., 0.39744 , 0.39744 , 0.39744 ],\n",
-       "       [0.379572, 0.379572, 0.379572, ..., 0.379572, 0.379572, 0.379572],\n",
-       "       [0.352264, 0.352264, 0.352264, ..., 0.352264, 0.352264, 0.352264]])
        • treedepth
          (chain, draw)
          int64
          3 3 3 3 3 3 3 3 ... 3 3 3 4 4 3 3 3
          array([[3, 3, 3, ..., 3, 3, 3],\n",
+       "
          xarray.Dataset
            • chain: 4
            • draw: 1000
            • chain
              (chain)
              int64
              0 1 2 3
              array([0, 1, 2, 3])
            • draw
              (draw)
              int64
              0 1 2 3 4 5 ... 995 996 997 998 999
              array([  0,   1,   2, ..., 997, 998, 999])
            • lp
              (chain, draw)
              float64
              -7.117 -8.525 ... -11.38 -11.14
              array([[ -7.11711,  -8.52527,  -8.34257, ...,  -8.99953, -10.3924 ,\n",
+       "         -5.12677],\n",
+       "       [ -7.40979,  -8.6973 ,  -8.36134, ...,  -9.30419,  -4.65713,\n",
+       "         -9.11054],\n",
+       "       [ -9.35431,  -9.76551,  -7.86183, ...,  -7.19184,  -5.21445,\n",
+       "         -7.71352],\n",
+       "       [-10.2665 , -13.9631 ,  -9.83918, ..., -13.5776 , -11.3765 ,\n",
+       "        -11.145  ]])
            • accept_stat
              (chain, draw)
              float64
              0.9616 0.9694 ... 0.9886 0.429
              array([[0.96156 , 0.969437, 0.984633, ..., 0.966525, 0.988819, 0.919061],\n",
+       "       [0.892384, 0.952504, 0.999717, ..., 0.981023, 0.945826, 0.627332],\n",
+       "       [1.      , 0.997771, 0.14691 , ..., 1.      , 1.      , 0.459213],\n",
+       "       [0.714025, 0.911832, 1.      , ..., 0.823447, 0.988555, 0.428968]])
            • stepsize
              (chain, draw)
              float64
              0.4064 0.4064 ... 0.4323 0.4323
              array([[0.406405, 0.406405, 0.406405, ..., 0.406405, 0.406405, 0.406405],\n",
+       "       [0.466926, 0.466926, 0.466926, ..., 0.466926, 0.466926, 0.466926],\n",
+       "       [0.50536 , 0.50536 , 0.50536 , ..., 0.50536 , 0.50536 , 0.50536 ],\n",
+       "       [0.432263, 0.432263, 0.432263, ..., 0.432263, 0.432263, 0.432263]])
            • treedepth
              (chain, draw)
              int64
              3 4 3 3 4 3 4 3 ... 3 3 3 3 3 3 3 3
              array([[3, 4, 3, ..., 3, 3, 3],\n",
        "       [3, 3, 3, ..., 3, 3, 3],\n",
-       "       [4, 3, 3, ..., 3, 3, 4],\n",
-       "       [3, 3, 3, ..., 3, 3, 3]])
            • n_leapfrog
              (chain, draw)
              int64
              7 7 7 7 7 7 7 ... 15 15 15 7 7 15
              array([[ 7,  7,  7, ...,  7,  7,  7],\n",
+       "       [3, 3, 3, ..., 3, 3, 3],\n",
+       "       [3, 3, 3, ..., 3, 3, 3]])
            • n_leapfrog
              (chain, draw)
              int64
              7 15 7 7 15 7 15 ... 7 7 15 7 15 7
              array([[ 7, 15,  7, ...,  7,  7,  7],\n",
+       "       [ 7,  7,  7, ...,  7,  7,  7],\n",
        "       [ 7,  7,  7, ...,  7,  7,  7],\n",
-       "       [15,  7,  7, ...,  7, 15, 15],\n",
-       "       [ 7,  7,  7, ...,  7,  7, 15]])
            • diverging
              (chain, draw)
              bool
              False False False ... False False
              array([[False, False, False, ..., False, False, False],\n",
+       "       [15, 15,  7, ...,  7, 15,  7]])
            • diverging
              (chain, draw)
              bool
              False False False ... False False
              array([[False, False, False, ..., False, False, False],\n",
        "       [False, False, False, ..., False, False, False],\n",
        "       [False, False, False, ..., False, False, False],\n",
-       "       [False, False, False, ..., False, False, False]])
            • energy
              (chain, draw)
              float64
              10.34 12.39 9.315 ... 12.34 12.99
              array([[10.341  , 12.3946 ,  9.31486, ..., 13.4174 , 13.6243 , 12.9495 ],\n",
-       "       [11.0993 , 14.047  , 10.7643 , ..., 17.0451 , 17.1117 , 15.7323 ],\n",
-       "       [15.9822 , 10.2966 , 10.7779 , ..., 13.6941 , 16.5774 , 12.7611 ],\n",
-       "       [12.1458 ,  8.06191,  8.33783, ..., 10.2761 , 12.3444 , 12.995  ]])
          • created_at :
            2020-06-06T18:10:32.336070
            arviz_version :
            0.8.3
            inference_library :
            cmdstanpy
            inference_library_version :
            0.8.0

      \n", + " [False, False, False, ..., False, False, False]])
    • energy
      (chain, draw)
      float64
      13.56 12.9 13.65 ... 20.86 22.16
      array([[13.556  , 12.8971 , 13.6523 , ..., 11.4512 , 14.1995 , 12.5183 ],\n",
+       "       [12.3025 , 16.388  , 10.6192 , ..., 19.1564 , 12.9302 , 13.1066 ],\n",
+       "       [14.6137 , 11.6894 , 14.2455 , ...,  9.98162,  9.17208, 15.7546 ],\n",
+       "       [19.118  , 19.03   , 16.7125 , ..., 18.6839 , 20.8571 , 22.1571 ]])
  • created_at :
    2020-06-10T18:21:56.182646
    arviz_version :
    0.8.3
    inference_library :
    cmdstanpy
    inference_library_version :
    0.8.0

    • \n", " \n", " \n", " \n", " \n", "
  • \n", - " \n", - " \n", + " \n", + " \n", "
    \n", "
    \n", "
      \n", @@ -23434,7 +19729,7 @@ " stroke: currentColor;\n", " fill: currentColor;\n", "}\n", - "
      xarray.Dataset
        • school: 8
        • school
          (school)
          int64
          0 1 2 3 4 5 6 7
          array([0, 1, 2, 3, 4, 5, 6, 7])
        • y
          (school)
          float64
          28.0 8.0 -3.0 7.0 ... 1.0 18.0 12.0
          array([28.,  8., -3.,  7., -1.,  1., 18., 12.])
      • created_at :
        2020-06-06T18:10:32.342119
        arviz_version :
        0.8.3
        inference_library :
        cmdstanpy
        inference_library_version :
        0.8.0

    \n", + "
    xarray.Dataset
      • school: 8
      • school
        (school)
        int64
        0 1 2 3 4 5 6 7
        array([0, 1, 2, 3, 4, 5, 6, 7])
      • y
        (school)
        float64
        28.0 8.0 -3.0 7.0 ... 1.0 18.0 12.0
        array([28.,  8., -3.,  7., -1.,  1., 18., 12.])
    • created_at :
      2020-06-10T18:21:56.189062
      arviz_version :
      0.8.3
      inference_library :
      cmdstanpy
      inference_library_version :
      0.8.0

    \n", " \n", " \n", "
    • \n", @@ -23769,31 +20064,9 @@ "\t> observed_data" ] }, - "execution_count": 15, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" -======= - "INFO:cmdstanpy:compiling stan program, exe file: /Volumes/GIT/PycharmProjects/arviz/doc/notebooks/eight_school\n", - "INFO:cmdstanpy:compiler options: stanc_options=None, cpp_options=None\n", - "ERROR:cmdstanpy:file /Volumes/GIT/PycharmProjects/arviz/doc/notebooks/eight_school.stan, exception ERROR\n", - " error: PCH file uses a newer PCH format that cannot be read\n", - "1 error generated.\n", - "make: *** [/Volumes/GIT/PycharmProjects/arviz/doc/notebooks/eight_school] Error 1 \n", - "ERROR:cmdstanpy:model compilation failed\n" - ] - }, - { - "ename": "ValueError", - "evalue": "Unable to compile Stan model file: /Volumes/GIT/PycharmProjects/arviz/doc/notebooks/eight_school.stan.", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 41\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 42\u001b[0m \u001b[0mstan_file\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"./eight_school.stan\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 43\u001b[0;31m \u001b[0mstan_model\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mCmdStanModel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstan_file\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstan_file\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 44\u001b[0m \u001b[0mstan_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 45\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/anaconda3/envs/continuous_time_mcmc/lib/python3.7/site-packages/cmdstanpy/model.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, model_name, stan_file, exe_file, compile, stanc_options, cpp_options, logger)\u001b[0m\n\u001b[1;32m 145\u001b[0m raise ValueError(\n\u001b[1;32m 146\u001b[0m 'Unable to compile Stan model file: {}.'.format(\n\u001b[0;32m--> 147\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stan_file\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 148\u001b[0m )\n\u001b[1;32m 149\u001b[0m )\n", - "\u001b[0;31mValueError\u001b[0m: Unable to compile Stan model file: /Volumes/GIT/PycharmProjects/arviz/doc/notebooks/eight_school.stan." - ] ->>>>>>> master } ], "source": [ @@ -23885,7 +20158,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": { "ExecuteTime": { "end_time": "2020-06-05T06:48:14.969410Z", @@ -23900,7 +20173,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": { "ExecuteTime": { "end_time": "2020-06-05T06:48:14.970867Z", @@ -23951,7 +20224,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": { "ExecuteTime": { "end_time": "2020-06-05T06:48:14.972663Z", @@ -23969,7 +20242,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": { "ExecuteTime": { "end_time": "2020-06-05T06:48:14.974211Z", @@ -23985,7 +20258,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": { "ExecuteTime": { "end_time": "2020-06-05T06:48:14.975638Z", @@ -24011,32 +20284,44 @@ }, { "cell_type": "code", -<<<<<<< HEAD - "execution_count": 21, - "metadata": {}, + "execution_count": 17, + "metadata": { + "ExecuteTime": { + "end_time": "2020-06-05T06:48:14.977219Z", + "start_time": "2020-06-05T06:47:05.614Z" + } + }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "arviz.data.io_cmdstan - INFO - glob found 8 files for 'posterior':\n", - "1: sample_data/eight_school-202006050131-1.csv\n", - "2: sample_data/eight_school-202006050131-2.csv\n", - "3: sample_data/eight_school-202006050131-3.csv\n", - "4: sample_data/eight_school-202006050131-4.csv\n", - "5: sample_data/eight_school-202006062340-1.csv\n", - "6: sample_data/eight_school-202006062340-2.csv\n", - "7: sample_data/eight_school-202006062340-3.csv\n", - "8: sample_data/eight_school-202006062340-4.csv\n", - "INFO:arviz.data.io_cmdstan:glob found 8 files for 'posterior':\n", - "1: sample_data/eight_school-202006050131-1.csv\n", - "2: sample_data/eight_school-202006050131-2.csv\n", - "3: sample_data/eight_school-202006050131-3.csv\n", - "4: sample_data/eight_school-202006050131-4.csv\n", - "5: sample_data/eight_school-202006062340-1.csv\n", - "6: sample_data/eight_school-202006062340-2.csv\n", - "7: sample_data/eight_school-202006062340-3.csv\n", - "8: sample_data/eight_school-202006062340-4.csv\n" + "arviz.data.io_cmdstan - INFO - glob found 12 files for 'posterior':\n", + "1: sample_data/eight_school-202006100113-1.csv\n", + "2: sample_data/eight_school-202006100113-2.csv\n", + "3: sample_data/eight_school-202006100113-3.csv\n", + "4: sample_data/eight_school-202006100113-4.csv\n", + "5: sample_data/eight_school-202006102306-1.csv\n", + "6: sample_data/eight_school-202006102306-2.csv\n", + "7: sample_data/eight_school-202006102306-3.csv\n", + "8: sample_data/eight_school-202006102306-4.csv\n", + "9: sample_data/eight_school-202006102351-1.csv\n", + "10: sample_data/eight_school-202006102351-2.csv\n", + "11: sample_data/eight_school-202006102351-3.csv\n", + "12: sample_data/eight_school-202006102351-4.csv\n", + "INFO:arviz.data.io_cmdstan:glob found 12 files for 'posterior':\n", + "1: sample_data/eight_school-202006100113-1.csv\n", + "2: sample_data/eight_school-202006100113-2.csv\n", + "3: sample_data/eight_school-202006100113-3.csv\n", + "4: sample_data/eight_school-202006100113-4.csv\n", + "5: sample_data/eight_school-202006102306-1.csv\n", + "6: sample_data/eight_school-202006102306-2.csv\n", + "7: sample_data/eight_school-202006102306-3.csv\n", + "8: sample_data/eight_school-202006102306-4.csv\n", + "9: sample_data/eight_school-202006102351-1.csv\n", + "10: sample_data/eight_school-202006102351-2.csv\n", + "11: sample_data/eight_school-202006102351-3.csv\n", + "12: sample_data/eight_school-202006102351-4.csv\n" ] }, { @@ -24050,8 +20335,8 @@ "
      \n", " \n", "
    • \n", - " \n", - " \n", + " \n", + " \n", "
      \n", "
      \n", "
        \n", @@ -24389,206 +20674,206 @@ " stroke: currentColor;\n", " fill: currentColor;\n", "}\n", - "
        xarray.Dataset
          • chain: 8
          • draw: 1000
          • school: 8
          • chain
            (chain)
            int64
            0 1 2 3 4 5 6 7
            array([0, 1, 2, 3, 4, 5, 6, 7])
          • draw
            (draw)
            int64
            0 1 2 3 4 5 ... 995 996 997 998 999
            array([  0,   1,   2, ..., 997, 998, 999])
          • school
            (school)
            int64
            0 1 2 3 4 5 6 7
            array([0, 1, 2, 3, 4, 5, 6, 7])
          • mu
            (chain, draw)
            float64
            5.754 5.437 3.104 ... 8.698 6.691
            array([[ 5.75411 ,  5.43667 ,  3.10354 , ...,  8.40989 ,  6.88816 ,\n",
-       "         4.84188 ],\n",
-       "       [ 3.46458 ,  4.61087 ,  4.40147 , ...,  4.67511 ,  4.14192 ,\n",
-       "         3.22894 ],\n",
-       "       [ 2.82112 ,  2.54198 ,  0.871348, ...,  5.75993 ,  6.32959 ,\n",
-       "         4.04523 ],\n",
+       "
            xarray.Dataset
              • chain: 12
              • draw: 1000
              • school: 8
              • chain
                (chain)
                int64
                0 1 2 3 4 5 6 7 8 9 10 11
                array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11])
              • draw
                (draw)
                int64
                0 1 2 3 4 5 ... 995 996 997 998 999
                array([  0,   1,   2, ..., 997, 998, 999])
              • school
                (school)
                int64
                0 1 2 3 4 5 6 7
                array([0, 1, 2, 3, 4, 5, 6, 7])
              • mu
                (chain, draw)
                float64
                3.692 16.11 2.111 ... 2.672 10.06
                array([[ 3.69234  , 16.1054   ,  2.11087  , ...,  8.19067  ,  2.92031  ,\n",
+       "         1.03508  ],\n",
+       "       [-1.71907  , 10.6737   ,  5.45426  , ..., 11.9617   , -0.0648748,\n",
+       "         2.95623  ],\n",
+       "       [ 6.44788  ,  5.00457  ,  9.41369  , ..., -0.588549 ,  3.43133  ,\n",
+       "         4.04414  ],\n",
        "       ...,\n",
-       "       [ 0.732634, -0.220275,  0.615834, ...,  7.16887 ,  8.03808 ,\n",
-       "         4.4595  ],\n",
-       "       [ 5.91379 ,  3.70948 ,  4.26014 , ..., -3.40594 ,  9.58868 ,\n",
-       "         0.366119],\n",
-       "       [ 3.88691 ,  3.18823 ,  5.80652 , ...,  2.068   ,  8.69849 ,\n",
-       "         6.69092 ]])
              • tau
                (chain, draw)
                float64
                1.462 1.613 3.111 ... 1.098 4.68
                array([[ 1.46249 ,  1.61302 ,  3.11057 , ...,  0.425836,  4.6845  ,\n",
-       "         4.10248 ],\n",
-       "       [ 1.81714 ,  6.89331 ,  3.70876 , ..., 10.6449  ,  0.962066,\n",
-       "         1.94808 ],\n",
-       "       [ 3.01982 ,  1.65352 ,  9.04967 , ..., 10.7062  ,  1.59843 ,\n",
-       "         2.85751 ],\n",
+       "       [ 1.20098  ,  3.74153  ,  5.71236  , ...,  5.38302  ,  1.55543  ,\n",
+       "         6.85928  ],\n",
+       "       [ 4.5479   ,  4.21596  ,  6.53758  , ...,  1.8295   ,  6.26736  ,\n",
+       "        -2.83281  ],\n",
+       "       [ 7.2998   ,  7.03214  ,  1.5383   , ...,  3.01368  ,  2.67249  ,\n",
+       "        10.056    ]])
              • tau
                (chain, draw)
                float64
                6.108 3.582 4.862 ... 0.5414 5.807
                array([[ 6.1083   ,  3.58214  ,  4.86183  , ...,  4.47256  ,  1.48153  ,\n",
+       "        12.0573   ],\n",
+       "       [ 6.89254  ,  2.58141  ,  0.803485 , ...,  2.4602   ,  0.599099 ,\n",
+       "         0.97436  ],\n",
+       "       [ 7.54792  ,  3.12201  ,  0.912562 , ...,  1.33408  ,  5.39912  ,\n",
+       "         1.91634  ],\n",
        "       ...,\n",
-       "       [ 9.96193 ,  5.76789 ,  0.800563, ...,  2.45978 ,  6.35597 ,\n",
-       "         1.18186 ],\n",
-       "       [ 1.86466 ,  5.26644 ,  2.46189 , ...,  4.01369 ,  0.966623,\n",
-       "         2.22911 ],\n",
-       "       [ 6.751   ,  2.61096 ,  9.75554 , ...,  6.4743  ,  1.09751 ,\n",
-       "         4.67988 ]])
              • theta_tilde
                (chain, draw, school)
                float64
                -0.6214 0.9452 ... -1.239 0.3211
                array([[[-6.21370e-01,  9.45155e-01, -1.36712e+00, ...,  1.58131e+00,\n",
-       "          3.09975e+00,  4.29483e-01],\n",
-       "        [-1.52821e+00, -1.72430e+00,  1.43802e+00, ..., -3.74407e-01,\n",
-       "         -1.02713e+00, -3.46431e-01],\n",
-       "        [ 2.21221e+00,  1.09807e+00, -1.79456e+00, ...,  6.13786e-01,\n",
-       "          4.77244e-01,  8.50358e-01],\n",
+       "       [ 3.75274  ,  1.13823  ,  1.8422   , ...,  8.23264  ,  3.29958  ,\n",
+       "         9.06202  ],\n",
+       "       [ 0.0756374, 14.1684   ,  2.65082  , ...,  1.91313  ,  7.97043  ,\n",
+       "         4.03989  ],\n",
+       "       [ 0.0525536,  0.213029 ,  2.08631  , ...,  0.51228  ,  0.541425 ,\n",
+       "         5.80725  ]])
              • theta_tilde
                (chain, draw, school)
                float64
                0.1507 -0.07589 ... 1.757 0.5014
                array([[[ 0.150693  , -0.0758865 ,  0.222117  , ..., -2.20267   ,\n",
+       "          0.438651  ,  0.0774884 ],\n",
+       "        [-0.690449  , -0.61755   , -0.285046  , ...,  0.905517  ,\n",
+       "         -0.121017  , -0.0131619 ],\n",
+       "        [-0.89236   ,  0.30326   , -0.0355057 , ..., -1.07422   ,\n",
+       "         -0.238142  ,  0.145528  ],\n",
        "        ...,\n",
-       "        [ 5.00555e-01,  1.80918e-01,  2.09223e+00, ...,  1.43732e+00,\n",
-       "         -7.30622e-01, -3.05692e-01],\n",
-       "        [ 1.45083e+00, -1.79060e-01, -6.24895e-01, ...,  5.03847e-01,\n",
-       "          2.68425e-01,  6.35325e-01],\n",
-       "        [ 1.92402e+00, -1.30168e+00, -1.11849e+00, ...,  9.00814e-01,\n",
-       "         -1.30922e-01,  1.36532e+00]],\n",
-       "\n",
-       "       [[-4.28034e-01, -9.60212e-01,  5.96710e-01, ..., -7.73785e-01,\n",
-       "          7.33206e-01, -4.75392e-01],\n",
-       "        [ 1.49771e+00,  3.52741e-01, -9.06776e-01, ...,  1.63798e-01,\n",
-       "          7.72913e-01,  5.42626e-01],\n",
-       "        [-3.57775e-01,  2.69583e-01,  5.84750e-01, ..., -3.71687e-01,\n",
-       "          6.64695e-01, -1.64340e-01],\n",
+       "        [ 1.87184   , -1.41064   ,  0.60382   , ..., -0.52979   ,\n",
+       "         -0.753925  ,  1.01887   ],\n",
+       "        [ 0.00838069, -0.665387  , -0.0369778 , ..., -0.283951  ,\n",
+       "          0.982918  ,  0.749047  ],\n",
+       "        [ 1.60212   ,  1.30792   ,  0.0776785 , ...,  0.312027  ,\n",
+       "          0.669589  ,  0.215141  ]],\n",
+       "\n",
+       "       [[ 1.8168    , -0.0393204 ,  0.134036  , ...,  0.511469  ,\n",
+       "          0.916789  ,  0.526759  ],\n",
+       "        [-1.9468    , -0.116474  , -0.809836  , ..., -0.355235  ,\n",
+       "          0.339156  , -0.180418  ],\n",
+       "        [ 1.29277   , -0.441339  , -2.04181   , ..., -0.40026   ,\n",
+       "          0.39932   ,  0.263459  ],\n",
        "        ...,\n",
-       "        [ 1.39126e+00, -3.91738e-01, -1.91612e+00, ..., -7.01897e-01,\n",
-       "          8.46013e-02, -4.69081e-01],\n",
-       "        [ 9.73856e-02, -7.23720e-01,  4.66254e-01, ...,  1.41110e+00,\n",
-       "          6.15712e-01, -3.28810e-01],\n",
-       "        [-5.40356e-01, -2.34899e-01,  9.06709e-01, ...,  1.68489e+00,\n",
-       "         -1.44686e-01,  2.26657e-02]],\n",
-       "\n",
-       "       [[ 6.40529e-01, -4.76495e-01,  1.11577e+00, ..., -1.12361e+00,\n",
-       "         -3.58983e-01, -3.49613e-02],\n",
-       "        [ 1.42522e-01,  7.29873e-01, -2.50795e-01, ...,  4.42624e-02,\n",
-       "          9.92563e-02,  4.24022e-02],\n",
-       "        [ 1.70180e+00, -3.27843e-02, -5.23013e-01, ..., -1.85521e-01,\n",
-       "          1.15851e+00, -3.43518e-01],\n",
+       "        [ 0.131124  ,  0.901146  , -0.759669  , ..., -0.87047   ,\n",
+       "         -0.700857  , -0.868739  ],\n",
+       "        [-0.815558  , -1.32762   ,  0.935707  , ...,  0.348515  ,\n",
+       "         -0.542335  ,  0.49039   ],\n",
+       "        [-1.59891   , -1.05952   , -0.0429794 , ...,  0.353703  ,\n",
+       "          0.096935  , -0.824714  ]],\n",
+       "\n",
+       "       [[ 0.919008  ,  0.427259  , -1.39858   , ..., -0.803076  ,\n",
+       "          1.4394    , -1.0618    ],\n",
+       "        [-0.666333  ,  0.134364  , -1.69212   , ..., -1.66072   ,\n",
+       "          0.569682  , -1.16606   ],\n",
+       "        [-0.558085  , -0.793793  ,  0.566323  , ..., -0.395261  ,\n",
+       "          0.948139  , -0.380621  ],\n",
        "        ...,\n",
-       "        [ 1.23330e+00,  6.60788e-01, -8.08756e-01, ...,  2.03739e-02,\n",
-       "          5.41245e-01,  3.66303e-01],\n",
-       "        [ 1.27968e+00,  7.37817e-01, -1.59119e+00, ..., -4.11977e-01,\n",
-       "          9.20530e-01, -3.43531e-01],\n",
-       "        [ 9.14241e-01,  8.80481e-01,  3.02754e-01, ...,  3.47260e-01,\n",
-       "          7.66575e-01, -1.21809e+00]],\n",
+       "        [ 0.666746  ,  0.252036  ,  0.0992795 , ..., -1.08194   ,\n",
+       "         -1.23669   ,  1.74524   ],\n",
+       "        [ 0.91375   ,  0.651899  ,  0.0450728 , ...,  0.794356  ,\n",
+       "          2.65045   , -0.710101  ],\n",
+       "        [ 0.231504  ,  0.297168  ,  0.0153996 , ...,  1.25694   ,\n",
+       "          2.37274   , -0.873043  ]],\n",
        "\n",
        "       ...,\n",
        "\n",
-       "       [[-2.00545e-01, -2.10582e-01, -9.40322e-02, ...,  2.10087e-02,\n",
-       "          2.37020e-01,  1.29188e-01],\n",
-       "        [-5.38384e-01, -9.47796e-01,  7.59535e-01, ...,  4.92083e-01,\n",
-       "          7.14972e-01, -5.74193e-01],\n",
-       "        [ 1.05020e-02, -1.43597e-01,  5.72359e-02, ...,  3.59991e-01,\n",
-       "         -1.34135e-02, -7.78741e-01],\n",
+       "       [[ 1.52082   ,  2.08589   , -1.2005    , ..., -0.602199  ,\n",
+       "          1.88407   ,  0.109757  ],\n",
+       "        [ 0.0451789 , -1.61133   ,  1.96004   , ..., -0.548927  ,\n",
+       "          0.375405  , -0.0377435 ],\n",
+       "        [-0.216968  ,  1.40354   , -2.17544   , ...,  0.335058  ,\n",
+       "         -0.201998  , -0.0420111 ],\n",
        "        ...,\n",
-       "        [ 1.25372e+00,  9.10764e-01, -3.08858e+00, ..., -1.96722e+00,\n",
-       "          1.12084e-02,  2.52069e-01],\n",
-       "        [ 1.93895e+00,  2.57758e-01, -2.47922e+00, ..., -8.94723e-01,\n",
-       "         -1.26788e-01,  4.69993e-01],\n",
-       "        [ 2.97201e+00,  6.67406e-02, -1.94356e-01, ...,  1.00890e-01,\n",
-       "         -3.81350e-01,  2.11749e-01]],\n",
-       "\n",
-       "       [[ 1.21786e+00,  1.23069e+00, -5.52686e-01, ..., -1.48749e+00,\n",
-       "          7.83702e-01,  1.85699e+00],\n",
-       "        [ 1.72311e+00,  7.67163e-01, -2.34386e-02, ..., -2.42422e+00,\n",
-       "          3.66557e-01,  1.29801e+00],\n",
-       "        [ 1.92775e+00,  7.48698e-01,  3.09622e-01, ..., -1.77732e+00,\n",
-       "          1.59023e-01,  1.52893e+00],\n",
+       "        [-1.24541   ,  1.44617   ,  1.26991   , ..., -0.517744  ,\n",
+       "          1.06641   , -0.328943  ],\n",
+       "        [-0.26795   , -0.471647  ,  0.916106  , ..., -0.0443553 ,\n",
+       "          1.10523   ,  0.802313  ],\n",
+       "        [ 0.860489  , -0.755706  , -0.221758  , ..., -0.881374  ,\n",
+       "          1.02386   ,  2.26025   ]],\n",
+       "\n",
+       "       [[-0.728935  , -1.30284   , -0.725011  , ..., -1.13638   ,\n",
+       "         -0.623346  ,  1.20411   ],\n",
+       "        [ 0.877633  ,  1.57903   ,  0.657069  , ...,  1.18061   ,\n",
+       "          0.873288  , -0.777553  ],\n",
+       "        [ 1.92013   ,  1.41113   ,  0.565262  , ...,  0.892794  ,\n",
+       "          1.17681   , -1.14554   ],\n",
        "        ...,\n",
-       "        [ 1.23452e-01,  1.16843e+00,  1.00505e-02, ...,  1.31982e+00,\n",
-       "         -5.95969e-01, -4.30592e-01],\n",
-       "        [ 2.62933e-01, -1.08506e+00, -1.75313e+00, ..., -7.69045e-01,\n",
-       "          2.01635e-01, -1.55323e+00],\n",
-       "        [ 4.78519e-01,  4.44985e-01,  1.39678e+00, ...,  4.84171e-02,\n",
-       "          5.20688e-01,  1.69244e+00]],\n",
-       "\n",
-       "       [[-1.31033e+00, -7.85428e-03, -3.17845e-01, ..., -6.09675e-01,\n",
-       "          1.70432e+00,  1.16530e+00],\n",
-       "        [ 2.02721e+00,  5.59707e-01, -2.80847e-02, ...,  6.90420e-01,\n",
-       "         -5.35285e-01, -1.54980e-01],\n",
-       "        [ 1.26965e+00, -8.16178e-01,  7.36237e-01, ..., -3.99220e-03,\n",
-       "          7.63763e-01, -7.12747e-01],\n",
+       "        [-0.787386  , -1.34728   ,  0.938111  , ...,  0.212187  ,\n",
+       "          1.5131    , -0.159953  ],\n",
+       "        [ 1.29339   ,  0.0357047 , -0.315845  , ...,  0.0184302 ,\n",
+       "          0.789759  ,  0.502829  ],\n",
+       "        [ 1.57545   ,  0.430001  ,  0.0687333 , ...,  0.335712  ,\n",
+       "          1.7016    , -1.64133   ]],\n",
+       "\n",
+       "       [[ 0.409093  ,  0.464062  ,  0.978     , ..., -0.780695  ,\n",
+       "         -0.585484  ,  1.81793   ],\n",
+       "        [ 0.076946  , -0.649097  , -0.768641  , ..., -0.375243  ,\n",
+       "          0.651451  , -2.41116   ],\n",
+       "        [-0.474311  , -0.847334  ,  0.663324  , ...,  0.276397  ,\n",
+       "         -0.65441   ,  2.32434   ],\n",
        "        ...,\n",
-       "        [ 9.42448e-01, -7.24387e-04,  1.05740e+00, ...,  3.66910e-02,\n",
-       "          6.49015e-01, -5.97576e-01],\n",
-       "        [ 1.04661e+00,  1.81675e+00,  2.53350e-01, ..., -1.45018e-01,\n",
-       "         -7.19568e-01, -1.61255e+00],\n",
-       "        [ 2.12768e+00, -3.70835e-01,  1.35789e+00, ..., -9.19882e-02,\n",
-       "         -1.23920e+00,  3.21054e-01]]])
              • theta
                (chain, draw, school)
                float64
                4.845 7.136 3.755 ... 0.8916 8.193
                array([[[ 4.84536e+00,  7.13639e+00,  3.75472e+00, ...,  8.06676e+00,\n",
-       "          1.02875e+01,  6.38223e+00],\n",
-       "        [ 2.97165e+00,  2.65535e+00,  7.75622e+00, ...,  4.83275e+00,\n",
-       "          3.77990e+00,  4.87787e+00],\n",
-       "        [ 9.98476e+00,  6.51918e+00, -2.47856e+00, ...,  5.01277e+00,\n",
-       "          4.58804e+00,  5.74864e+00],\n",
+       "        [-1.25161   ,  0.453957  ,  2.11518   , ..., -0.90933   ,\n",
+       "          2.48317   , -0.782639  ],\n",
+       "        [-0.137756  , -0.0470846 , -1.45337   , ..., -2.4094    ,\n",
+       "         -1.58137   ,  0.137089  ],\n",
+       "        [ 1.41579   , -0.0828588 ,  1.27436   , ...,  0.247186  ,\n",
+       "          1.75653   ,  0.501351  ]]])
              • theta
                (chain, draw, school)
                float64
                4.613 3.229 5.049 ... 20.26 12.97
                array([[[ 4.61281e+00,  3.22880e+00,  5.04910e+00, ..., -9.76221e+00,\n",
+       "          6.37175e+00,  4.16566e+00],\n",
+       "        [ 1.36322e+01,  1.38933e+01,  1.50844e+01, ...,  1.93491e+01,\n",
+       "          1.56719e+01,  1.60583e+01],\n",
+       "        [-2.22763e+00,  3.58527e+00,  1.93825e+00, ..., -3.11180e+00,\n",
+       "          9.53064e-01,  2.81840e+00],\n",
        "        ...,\n",
-       "        [ 8.62304e+00,  8.48693e+00,  9.30084e+00, ...,  9.02195e+00,\n",
-       "          8.09876e+00,  8.27971e+00],\n",
-       "        [ 1.36846e+01,  6.04935e+00,  3.96083e+00, ...,  9.24843e+00,\n",
-       "          8.14559e+00,  9.86434e+00],\n",
-       "        [ 1.27351e+01, -4.98243e-01,  2.53307e-01, ...,  8.53746e+00,\n",
-       "          4.30478e+00,  1.04431e+01]],\n",
-       "\n",
-       "       [[ 2.68678e+00,  1.71974e+00,  4.54888e+00, ...,  2.05851e+00,\n",
-       "          4.79691e+00,  2.60073e+00],\n",
-       "        [ 1.49351e+01,  7.04241e+00, -1.63982e+00, ...,  5.73997e+00,\n",
-       "          9.93879e+00,  8.35135e+00],\n",
-       "        [ 3.07457e+00,  5.40129e+00,  6.57017e+00, ...,  3.02297e+00,\n",
-       "          6.86666e+00,  3.79197e+00],\n",
+       "        [ 1.65626e+01,  1.88149e+00,  1.08913e+01, ...,  5.82115e+00,\n",
+       "          4.81870e+00,  1.27476e+01],\n",
+       "        [ 2.93273e+00,  1.93452e+00,  2.86553e+00, ...,  2.49963e+00,\n",
+       "          4.37654e+00,  4.03005e+00],\n",
+       "        [ 2.03524e+01,  1.68052e+01,  1.97168e+00, ...,  4.79731e+00,\n",
+       "          9.10855e+00,  3.62912e+00]],\n",
+       "\n",
+       "       [[ 1.08033e+01, -1.99009e+00, -7.95223e-01, ...,  1.80625e+00,\n",
+       "          4.59993e+00,  1.91163e+00],\n",
+       "        [ 5.64823e+00,  1.03731e+01,  8.58322e+00, ...,  9.75673e+00,\n",
+       "          1.15492e+01,  1.02080e+01],\n",
+       "        [ 6.49298e+00,  5.09965e+00,  3.81370e+00, ...,  5.13266e+00,\n",
+       "          5.77511e+00,  5.66595e+00],\n",
        "        ...,\n",
-       "        [ 1.94849e+01,  5.05102e-01, -1.57218e+01, ..., -2.79649e+00,\n",
-       "          5.57568e+00, -3.18205e-01],\n",
-       "        [ 4.23561e+00,  3.44565e+00,  4.59049e+00, ...,  5.49949e+00,\n",
-       "          4.73428e+00,  3.82558e+00],\n",
-       "        [ 2.17629e+00,  2.77134e+00,  4.99528e+00, ...,  6.51124e+00,\n",
-       "          2.94708e+00,  3.27310e+00]],\n",
-       "\n",
-       "       [[ 4.75540e+00,  1.38219e+00,  6.19053e+00, ..., -5.71994e-01,\n",
-       "          1.73705e+00,  2.71554e+00],\n",
-       "        [ 2.77764e+00,  3.74884e+00,  2.12728e+00, ...,  2.61517e+00,\n",
-       "          2.70610e+00,  2.61209e+00],\n",
-       "        [ 1.62720e+01,  5.74661e-01, -3.86174e+00, ..., -8.07558e-01,\n",
-       "          1.13555e+01, -2.23737e+00],\n",
+       "        [ 1.22843e+01,  1.41787e+01,  1.00928e+01, ...,  9.82016e+00,\n",
+       "          1.02374e+01,  9.82442e+00],\n",
+       "        [-5.53475e-01, -8.60252e-01,  4.95706e-01, ...,  1.43920e-01,\n",
+       "         -3.89787e-01,  2.28918e-01],\n",
+       "        [ 1.39832e+00,  1.92387e+00,  2.91435e+00, ...,  3.30086e+00,\n",
+       "          3.05068e+00,  2.15266e+00]],\n",
+       "\n",
+       "       [[ 1.33845e+01,  9.67280e+00, -4.10847e+00, ...,  3.86324e-01,\n",
+       "          1.73124e+01, -1.56653e+00],\n",
+       "        [ 2.92428e+00,  5.42406e+00, -2.78235e-01, ..., -1.80204e-01,\n",
+       "          6.78313e+00,  1.36413e+00],\n",
+       "        [ 8.90440e+00,  8.68930e+00,  9.93049e+00, ...,  9.05299e+00,\n",
+       "          1.02789e+01,  9.06635e+00],\n",
        "        ...,\n",
-       "        [ 1.89639e+01,  1.28344e+01, -2.89876e+00, ...,  5.97805e+00,\n",
-       "          1.15546e+01,  9.68164e+00],\n",
-       "        [ 8.37506e+00,  7.50893e+00,  3.78619e+00, ...,  5.67108e+00,\n",
-       "          7.80099e+00,  5.78048e+00],\n",
-       "        [ 6.65769e+00,  6.56122e+00,  4.91035e+00, ...,  5.03753e+00,\n",
-       "          6.23573e+00,  5.64515e-01]],\n",
+       "        [ 3.00946e-01, -2.52311e-01, -4.56102e-01, ..., -2.03194e+00,\n",
+       "         -2.23840e+00,  1.73975e+00],\n",
+       "        [ 8.36477e+00,  6.95101e+00,  3.67468e+00, ...,  7.72015e+00,\n",
+       "          1.77414e+01, -4.02591e-01],\n",
+       "        [ 4.48778e+00,  4.61362e+00,  4.07365e+00, ...,  6.45287e+00,\n",
+       "          8.59112e+00,  2.37110e+00]],\n",
        "\n",
        "       ...,\n",
        "\n",
-       "       [[-1.26518e+00, -1.36517e+00, -2.04107e-01, ...,  9.41921e-01,\n",
-       "          3.09381e+00,  2.01959e+00],\n",
-       "        [-3.32561e+00, -5.68706e+00,  4.16063e+00, ...,  2.61801e+00,\n",
-       "          3.90360e+00, -3.53216e+00],\n",
-       "        [ 6.24242e-01,  5.00876e-01,  6.61655e-01, ...,  9.04029e-01,\n",
-       "          6.05096e-01, -7.59738e-03],\n",
+       "       [[ 6.90821e+00,  9.02878e+00, -3.30420e+00, ..., -1.05892e+00,\n",
+       "          8.27142e+00,  1.61287e+00],\n",
+       "        [ 3.79295e+00,  1.90747e+00,  5.97250e+00, ...,  3.11672e+00,\n",
+       "          4.16882e+00,  3.69857e+00],\n",
+       "        [ 5.31266e+00,  8.29796e+00,  1.70476e+00, ...,  6.32960e+00,\n",
+       "          5.34024e+00,  5.63497e+00],\n",
        "        ...,\n",
-       "        [ 1.02527e+01,  9.40915e+00, -4.28361e-01, ...,  2.32995e+00,\n",
-       "          7.19644e+00,  7.78891e+00],\n",
-       "        [ 2.03620e+01,  9.67638e+00, -7.71979e+00, ...,  2.35124e+00,\n",
-       "          7.23222e+00,  1.10253e+01],\n",
-       "        [ 7.97201e+00,  4.53838e+00,  4.22980e+00, ...,  4.57874e+00,\n",
-       "          4.00880e+00,  4.70976e+00]],\n",
-       "\n",
-       "       [[ 8.18468e+00,  8.20861e+00,  4.88322e+00, ...,  3.14013e+00,\n",
-       "          7.37513e+00,  9.37645e+00],\n",
-       "        [ 1.27841e+01,  7.74970e+00,  3.58604e+00, ..., -9.05752e+00,\n",
-       "          5.63993e+00,  1.05454e+01],\n",
-       "        [ 9.00605e+00,  6.10335e+00,  5.02239e+00, ..., -1.15431e-01,\n",
-       "          4.65164e+00,  8.02420e+00],\n",
+       "        [-4.86998e+00,  1.72888e+01,  1.58377e+01, ...,  1.12062e+00,\n",
+       "          1.41624e+01,  2.67495e+00],\n",
+       "        [ 6.71302e-01, -8.13246e-04,  4.57819e+00, ...,  1.40907e+00,\n",
+       "          5.20222e+00,  4.20272e+00],\n",
+       "        [ 1.46570e+01,  1.10534e-02,  4.84970e+00, ..., -1.12774e+00,\n",
+       "          1.61376e+01,  2.73417e+01]],\n",
+       "\n",
+       "       [[ 4.49277e+00,  4.44936e+00,  4.49306e+00, ...,  4.46195e+00,\n",
+       "          4.50075e+00,  4.63898e+00],\n",
+       "        [ 1.66506e+01,  2.65883e+01,  1.35256e+01, ...,  2.09433e+01,\n",
+       "          1.65890e+01, -6.80072e+00],\n",
+       "        [ 1.16275e+01,  1.02782e+01,  8.03598e+00, ...,  8.90421e+00,\n",
+       "          9.65709e+00,  3.50097e+00],\n",
        "        ...,\n",
-       "        [-2.91044e+00,  1.28378e+00, -3.36560e+00, ...,  1.89143e+00,\n",
-       "         -5.79797e+00, -5.13420e+00],\n",
-       "        [ 9.84283e+00,  8.53983e+00,  7.89406e+00, ...,  8.84530e+00,\n",
-       "          9.78358e+00,  8.08729e+00],\n",
-       "        [ 1.43279e+00,  1.35804e+00,  3.47971e+00, ...,  4.74046e-01,\n",
-       "          1.52679e+00,  4.13875e+00]],\n",
-       "\n",
-       "       [[-4.95910e+00,  3.83388e+00,  1.74114e+00, ..., -2.29009e-01,\n",
-       "          1.53928e+01,  1.17538e+01],\n",
-       "        [ 8.48119e+00,  4.64960e+00,  3.11490e+00, ...,  4.99089e+00,\n",
-       "          1.79062e+00,  2.78358e+00],\n",
-       "        [ 1.81926e+01, -2.15574e+00,  1.29889e+01, ...,  5.76757e+00,\n",
-       "          1.32574e+01, -1.14672e+00],\n",
+       "        [ 3.23128e-01, -7.48028e-01,  3.62423e+00, ...,  2.23544e+00,\n",
+       "          4.72425e+00,  1.52349e+00],\n",
+       "        [ 1.65762e+01,  6.55194e+00,  3.74994e+00, ...,  6.41426e+00,\n",
+       "          1.25621e+01,  1.02751e+01],\n",
+       "        [ 3.53185e+00, -1.09566e+00, -2.55514e+00, ..., -1.47657e+00,\n",
+       "          4.04147e+00, -9.46361e+00]],\n",
+       "\n",
+       "       [[ 7.32130e+00,  7.32419e+00,  7.35120e+00, ...,  7.25878e+00,\n",
+       "          7.26903e+00,  7.39534e+00],\n",
+       "        [ 7.04853e+00,  6.89386e+00,  6.86839e+00, ...,  6.95220e+00,\n",
+       "          7.17091e+00,  6.51849e+00],\n",
+       "        [ 5.48734e-01, -2.29508e-01,  2.92220e+00, ...,  2.11494e+00,\n",
+       "          1.72992e-01,  6.38759e+00],\n",
        "        ...,\n",
-       "        [ 8.16969e+00,  2.06331e+00,  8.91392e+00, ...,  2.30555e+00,\n",
-       "          6.26992e+00, -1.80089e+00],\n",
-       "        [ 9.84716e+00,  1.06924e+01,  8.97655e+00, ...,  8.53934e+00,\n",
-       "          7.90876e+00,  6.92871e+00],\n",
-       "        [ 1.66482e+01,  4.95545e+00,  1.30457e+01, ...,  6.26042e+00,\n",
-       "          8.91628e-01,  8.19341e+00]]])
            • created_at :
              2020-06-06T18:10:33.158160
              arviz_version :
              0.8.3

        \n", + " [ 2.37250e+00, 3.24623e+00, 4.09725e+00, ..., 2.54785e+00,\n", + " 4.28576e+00, 2.61275e+00],\n", + " [ 2.59791e+00, 2.64700e+00, 1.88560e+00, ..., 1.36798e+00,\n", + " 1.81630e+00, 2.74672e+00],\n", + " [ 1.82778e+01, 9.57482e+00, 1.74565e+01, ..., 1.14915e+01,\n", + " 2.02566e+01, 1.29675e+01]]])
    • created_at :
      2020-06-10T18:21:57.128552
      arviz_version :
      0.8.3

    \n", " \n", " \n", " \n", " \n", "
  • \n", - " \n", - " \n", + " \n", + " \n", "
    \n", "
    \n", "
      \n", @@ -24926,98 +21211,98 @@ " stroke: currentColor;\n", " fill: currentColor;\n", "}\n", - "
      xarray.Dataset
        • chain: 8
        • draw: 1000
        • school: 8
        • chain
          (chain)
          int64
          0 1 2 3 4 5 6 7
          array([0, 1, 2, 3, 4, 5, 6, 7])
        • draw
          (draw)
          int64
          0 1 2 3 4 5 ... 995 996 997 998 999
          array([  0,   1,   2, ..., 997, 998, 999])
        • school
          (school)
          int64
          0 1 2 3 4 5 6 7
          array([0, 1, 2, 3, 4, 5, 6, 7])
        • y_hat
          (chain, draw, school)
          float64
          19.05 -1.305 10.61 ... -11.52 28.27
          array([[[ 19.0496   ,  -1.30496  ,  10.6111   , ...,   3.43562  ,\n",
-       "          15.013    ,   4.59842  ],\n",
-       "        [-14.611    ,  11.6403   ,   2.91314  , ...,  -3.46515  ,\n",
-       "         -25.2819   ,  20.9299   ],\n",
-       "        [ 14.5244   ,   4.84974  ,   3.61749  , ...,  -1.81476  ,\n",
-       "          -6.17733  , -10.6538   ],\n",
+       "
          xarray.Dataset
            • chain: 12
            • draw: 1000
            • school: 8
            • chain
              (chain)
              int64
              0 1 2 3 4 5 6 7 8 9 10 11
              array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11])
            • draw
              (draw)
              int64
              0 1 2 3 4 5 ... 995 996 997 998 999
              array([  0,   1,   2, ..., 997, 998, 999])
            • school
              (school)
              int64
              0 1 2 3 4 5 6 7
              array([0, 1, 2, 3, 4, 5, 6, 7])
            • y_hat
              (chain, draw, school)
              float64
              -10.93 11.6 11.14 ... 15.28 -14.33
              array([[[-10.9346  ,  11.6037  ,  11.1382  , ...,  -4.97421 ,\n",
+       "          -6.09248 ,  15.0512  ],\n",
+       "        [ -3.69064 ,  14.6682  ,  22.4837  , ...,  23.5671  ,\n",
+       "          14.8374  ,   9.00593 ],\n",
+       "        [  0.408807,  -7.37329 ,  -1.04588 , ...,  -6.87661 ,\n",
+       "           3.75974 , -11.8118  ],\n",
        "        ...,\n",
-       "        [ -3.66121  ,   4.88421  ,  -5.88003  , ...,  -9.1894   ,\n",
-       "           8.20073  ,   4.98755  ],\n",
-       "        [ 38.035    ,   2.42776  ,  23.6846   , ...,  20.0096   ,\n",
-       "          10.1016   ,   9.22392  ],\n",
-       "        [ 24.2337   ,  16.5773   ,  -8.03439  , ...,   4.93551  ,\n",
-       "         -22.9208   , -13.6367   ]],\n",
-       "\n",
-       "       [[  0.212848 ,   4.96483  ,   7.24908  , ...,   0.823218 ,\n",
-       "           0.840567 ,  10.0623   ],\n",
-       "        [ 21.0565   ,  24.0836   ,  -2.36259  , ...,   1.7789   ,\n",
-       "           3.11908  ,  26.3255   ],\n",
-       "        [  6.86017  ,   6.56333  , -14.5155   , ...,  -1.54344  ,\n",
-       "          30.2473   , -14.6159   ],\n",
+       "        [ 20.0649  ,   4.67947 ,  34.8539  , ...,  19.3071  ,\n",
+       "          -1.81022 ,  10.6627  ],\n",
+       "        [ 20.023   ,  -3.45228 , -16.6117  , ...,   4.34399 ,\n",
+       "          19.4159  ,   4.02906 ],\n",
+       "        [ 12.6708  ,   9.25176 ,  18.6282  , ...,  -0.56782 ,\n",
+       "          -5.90848 , -24.1042  ]],\n",
+       "\n",
+       "       [[ 25.585   ,   2.60382 , -22.731   , ...,  12.1856  ,\n",
+       "         -24.8332  ,  -7.97341 ],\n",
+       "        [  3.05847 ,  19.6072  ,   9.67468 , ...,  -9.57937 ,\n",
+       "          -5.97203 ,   3.83901 ],\n",
+       "        [ -2.96353 ,   1.59361 ,  34.3077  , ..., -16.9225  ,\n",
+       "           5.94989 ,  12.7261  ],\n",
        "        ...,\n",
-       "        [ 38.5073   ,  -0.233819 ,  13.8765   , ..., -12.4776   ,\n",
-       "          13.0071   ,   4.49981  ],\n",
-       "        [ -9.76525  ,  -2.31316  ,   2.28414  , ...,  16.0648   ,\n",
-       "          16.6526   ,  -3.44407  ],\n",
-       "        [  6.07809  ,  -6.72812  ,   1.97262  , ...,   2.60334  ,\n",
-       "           2.23987  ,  -6.51931  ]],\n",
-       "\n",
-       "       [[ 16.4241   ,   0.344368 ,  28.502    , ...,  -7.54104  ,\n",
-       "         -14.1077   ,   6.46194  ],\n",
-       "        [  0.55366  , -12.8928   , -21.3035   , ...,   8.0707   ,\n",
-       "           7.93818  ,   5.92858  ],\n",
-       "        [ 24.945    ,  13.2419   ,  -6.48804  , ...,  -0.57189  ,\n",
-       "           5.65947  ,   8.71675  ],\n",
+       "        [-30.3258  ,  25.8939  ,  11.0092  , ...,  10.8265  ,\n",
+       "           5.90875 , -27.5675  ],\n",
+       "        [ 12.7005  ,  -4.70354 , -11.1986  , ...,  -7.05527 ,\n",
+       "          -6.65953 ,  35.0723  ],\n",
+       "        [ 10.4867  ,   5.49925 ,  -5.00058 , ...,  16.8872  ,\n",
+       "          -1.90614 ,   5.35349 ]],\n",
+       "\n",
+       "       [[ 26.4483  ,   5.39621 , -18.0307  , ..., -16.5188  ,\n",
+       "          13.9216  ,  -3.97292 ],\n",
+       "        [ -7.85832 ,   4.44009 ,   5.35032 , ..., -10.6668  ,\n",
+       "          20.5908  , -12.8477  ],\n",
+       "        [ -1.72751 ,  15.6356  ,  -7.37621 , ...,  36.3976  ,\n",
+       "           9.78589 ,   8.58118 ],\n",
        "        ...,\n",
-       "        [ 11.0441   ,   6.66108  ,  18.1153   , ...,  13.6414   ,\n",
-       "          18.9915   ,  18.0794   ],\n",
-       "        [ -3.01072  ,  -3.66957  ,  11.0674   , ..., -18.295    ,\n",
-       "          -2.24387  ,  15.3192   ],\n",
-       "        [ 17.8048   ,  22.1956   ,   0.977643 , ...,  20.2169   ,\n",
-       "          16.6958   ,  15.2811   ]],\n",
+       "        [ -2.08977 ,  13.1182  ,  -8.81588 , ...,  -9.16025 ,\n",
+       "         -11.6879  , -28.6293  ],\n",
+       "        [ -1.44321 ,   3.20826 ,  -4.6157  , ...,  23.3955  ,\n",
+       "          18.8133  , -21.3375  ],\n",
+       "        [ 10.1546  ,  16.7743  , -13.0238  , ...,  -5.73191 ,\n",
+       "          12.5278  ,   9.80171 ]],\n",
        "\n",
        "       ...,\n",
        "\n",
-       "       [[-13.8236   ,   3.30856  ,  -9.71613  , ...,   1.07714  ,\n",
-       "          24.3845   , -37.759    ],\n",
-       "        [-19.5816   ,   9.47864  ,   7.43956  , ...,   8.73732  ,\n",
-       "          15.1482   ,  20.7514   ],\n",
-       "        [ -3.2081   ,  -5.29835  , -23.4598   , ...,  -7.25993  ,\n",
-       "          15.408    , -12.4758   ],\n",
+       "       [[ -0.843751,  -4.98264 , -18.7942  , ...,  -6.34356 ,\n",
+       "         -11.4954  , -30.0613  ],\n",
+       "        [-32.9141  ,  -6.91807 ,  -6.90439 , ...,  10.1167  ,\n",
+       "           3.70618 ,   8.15487 ],\n",
+       "        [  2.88749 ,   0.686142,  24.0905  , ...,   9.77811 ,\n",
+       "          31.1765  , -18.8176  ],\n",
        "        ...,\n",
-       "        [ 17.2876   ,   9.47491  ,   9.11321  , ...,  -0.844266 ,\n",
-       "          23.7267   ,  13.1609   ],\n",
-       "        [ 42.8214   , -11.115    , -28.3195   , ...,   9.54406  ,\n",
-       "           6.86596  ,  24.7486   ],\n",
-       "        [ 31.5371   ,  -9.2391   ,  -5.19494  , ...,  16.0107   ,\n",
-       "          18.2181   ,  19.2444   ]],\n",
-       "\n",
-       "       [[-22.2079   ,  16.09     ,  -1.43103  , ...,  -2.81685  ,\n",
-       "         -11.7201   ,  26.1408   ],\n",
-       "        [  7.84245  ,  12.1987   ,  12.4391   , ...,  -8.31294  ,\n",
-       "          16.7167   ,  24.0476   ],\n",
-       "        [ -5.47622  , -10.7056   , -13.119    , ...,  -9.98965  ,\n",
-       "           1.99784  ,  12.6135   ],\n",
+       "        [-10.4054  ,  38.8779  ,  -1.72784 , ...,   2.10858 ,\n",
+       "           1.71828 , -41.049   ],\n",
+       "        [-34.6393  ,  -8.05134 ,   0.299065, ...,  -5.08346 ,\n",
+       "          14.0737  ,  18.2229  ],\n",
+       "        [ 20.5415  ,  -7.11264 ,  16.6271  , ...,  -6.10246 ,\n",
+       "          16.3215  ,  28.9235  ]],\n",
+       "\n",
+       "       [[ -0.601871, -11.7324  ,  20.1891  , ...,  14.0161  ,\n",
+       "          -1.10264 ,  -0.811892],\n",
+       "        [ 16.1775  ,  19.1952  ,   7.63344 , ...,  15.3313  ,\n",
+       "          20.5808  ,  -0.475661],\n",
+       "        [ -6.71155 ,  -3.46014 ,  15.4774  , ...,  22.6551  ,\n",
+       "          -6.43601 ,  23.9684  ],\n",
        "        ...,\n",
-       "        [ 10.498    ,  -6.5292   ,  -1.95445  , ...,   6.90389  ,\n",
-       "           9.90309  ,  -9.53769  ],\n",
-       "        [-22.7585   ,  16.791    ,   3.64741  , ...,  16.2263   ,\n",
-       "          13.1702   ,  -2.93753  ],\n",
-       "        [ 16.2394   ,   3.08554  ,   9.0992   , ...,  -7.29659  ,\n",
-       "          16.6593   ,  -2.51172  ]],\n",
-       "\n",
-       "       [[ 23.8207   ,   6.97066  , -13.2277   , ...,  -4.33379  ,\n",
-       "          16.7229   ,  -4.24901  ],\n",
-       "        [ 19.6784   ,  10.6246   ,  15.6069   , ...,   8.62725  ,\n",
-       "          -0.49937  ,  -4.91235  ],\n",
-       "        [-23.3668   ,   8.97635  ,  22.8516   , ...,  16.1906   ,\n",
-       "          18.5997   ,  -0.0484115],\n",
+       "        [ 19.7332  , -20.677   ,  24.276   , ...,  12.3559  ,\n",
+       "          11.5496  ,  17.943   ],\n",
+       "        [ 13.6132  ,   5.89891 ,  -6.70882 , ...,   5.5949  ,\n",
+       "          16.5097  , -13.9093  ],\n",
+       "        [ 26.0067  ,  -2.92887 ,  28.0343  , ..., -11.2195  ,\n",
+       "           3.37155 ,  -8.39441 ]],\n",
+       "\n",
+       "       [[ -1.77315 ,  -0.850641,  31.9772  , ...,  12.9403  ,\n",
+       "          19.1804  , -11.9612  ],\n",
+       "        [ 12.9344  ,  -7.44461 ,   0.504742, ..., -11.9935  ,\n",
+       "          -5.35724 ,  20.562   ],\n",
+       "        [-11.582   ,  -9.02636 ,  18.8871  , ...,   2.61293 ,\n",
+       "         -10.874   ,  33.0402  ],\n",
        "        ...,\n",
-       "        [  1.07142  ,   2.08327  ,  28.3587   , ...,  18.9032   ,\n",
-       "          11.3534   ,  -3.75202  ],\n",
-       "        [ 11.1992   ,   7.75921  ,  11.0927   , ...,   2.09711  ,\n",
-       "           4.61789  ,  31.6442   ],\n",
-       "        [ 27.53     ,  19.7944   ,  16.7601   , ..., -15.1517   ,\n",
-       "         -11.5217   ,  28.2679   ]]])
          • created_at :
            2020-06-06T18:10:33.514389
            arviz_version :
            0.8.3

      \n", + " [ 15.5324 , 14.9454 , 7.0026 , ..., -0.143009,\n", + " 3.01618 , -5.04935 ],\n", + " [-14.324 , -4.90844 , -12.5231 , ..., -15.5133 ,\n", + " 7.05567 , -27.792 ],\n", + " [ 15.8131 , 14.6397 , 33.993 , ..., 1.04319 ,\n", + " 15.2797 , -14.3346 ]]])
  • created_at :
    2020-06-10T18:21:57.962326
    arviz_version :
    0.8.3

    • \n", " \n", " \n", " \n", " \n", "
  • \n", - " \n", - " \n", + " \n", + " \n", "
    \n", "
    \n", "
      \n", @@ -25355,98 +21640,98 @@ " stroke: currentColor;\n", " fill: currentColor;\n", "}\n", - "
      xarray.Dataset
        • chain: 8
        • draw: 1000
        • school: 8
        • chain
          (chain)
          int64
          0 1 2 3 4 5 6 7
          array([0, 1, 2, 3, 4, 5, 6, 7])
        • draw
          (draw)
          int64
          0 1 2 3 4 5 ... 995 996 997 998 999
          array([  0,   1,   2, ..., 997, 998, 999])
        • school
          (school)
          int64
          0 1 2 3 4 5 6 7
          array([0, 1, 2, 3, 4, 5, 6, 7])
        • log_lik
          (chain, draw, school)
          float64
          -4.818 -3.225 ... -4.685 -3.832
          array([[[-4.8184 , -3.22525, -3.78064, ..., -3.52319, -3.51894,\n",
-       "         -3.85801],\n",
-       "        [-5.01903, -3.36435, -3.9175 , ..., -3.37754, -4.23258,\n",
-       "         -3.88759],\n",
-       "        [-4.34821, -3.23249, -3.69206, ..., -3.38337, -4.12093,\n",
-       "         -3.86962],\n",
+       "
          xarray.Dataset
            • chain: 12
            • draw: 1000
            • school: 8
            • chain
              (chain)
              int64
              0 1 2 3 4 5 6 7 8 9 10 11
              array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11])
            • draw
              (draw)
              int64
              0 1 2 3 4 5 ... 995 996 997 998 999
              array([  0,   1,   2, ..., 997, 998, 999])
            • school
              (school)
              int64
              0 1 2 3 4 5 6 7
              array([0, 1, 2, 3, 4, 5, 6, 7])
            • log_lik
              (chain, draw, school)
              float64
              -4.842 -3.335 ... -3.247 -3.811
              array([[[-4.84246, -3.33535, -3.81807, ..., -3.79545, -3.8976 ,\n",
+       "         -3.90403],\n",
+       "        [-4.08573, -3.39518, -4.33029, ..., -4.70812, -3.24862,\n",
+       "         -3.83473],\n",
+       "        [-5.65746, -3.31897, -3.73916, ..., -3.3867 , -4.67451,\n",
+       "         -3.93941],\n",
        "        ...,\n",
-       "        [-4.46136, -3.22271, -3.98706, ..., -3.58275, -3.7117 ,\n",
-       "         -3.83067],\n",
-       "        [-4.08239, -3.24055, -3.78616, ..., -3.59798, -3.70707,\n",
-       "         -3.81635],\n",
-       "        [-4.1448 , -3.58262, -3.7122 , ..., -3.5516 , -4.15932,\n",
-       "         -3.81305]],\n",
-       "\n",
-       "       [[-5.0509 , -3.41873, -3.80283, ..., -3.32146, -4.09313,\n",
-       "         -3.94565],\n",
-       "        [-4.00631, -3.22611, -3.69514, ..., -3.40967, -3.54644,\n",
-       "         -3.82985],\n",
-       "        [-5.0076 , -3.25529, -3.87041, ..., -3.33374, -3.84128,\n",
-       "         -3.91328],\n",
+       "        [-3.91769, -3.4087 , -4.06842, ..., -3.41288, -4.09026,\n",
+       "         -3.81017],\n",
+       "        [-5.02336, -3.40547, -3.75872, ..., -3.32613, -4.14952,\n",
+       "         -3.90734],\n",
+       "        [-3.75696, -3.60918, -3.7398 , ..., -3.37642, -3.61681,\n",
+       "         -3.91745]],\n",
+       "\n",
+       "       [[-4.28416, -3.72053, -3.70102, ..., -3.31952, -4.11933,\n",
+       "         -3.96637],\n",
+       "        [-4.73721, -3.24968, -3.95358, ..., -3.63369, -3.42959,\n",
+       "         -3.81427],\n",
+       "        [-4.65488, -3.26358, -3.7822 , ..., -3.38741, -3.96876,\n",
+       "         -3.87122],\n",
        "        ...,\n",
-       "        [-3.78811, -3.50239, -4.00763, ..., -3.37639, -3.99334,\n",
-       "         -4.04347],\n",
-       "        [-4.88198, -3.32523, -3.80406, ..., -3.40049, -4.10142,\n",
-       "         -3.91243],\n",
-       "        [-5.10891, -3.35822, -3.81638, ..., -3.44235, -4.35448,\n",
-       "         -3.92684]],\n",
-       "\n",
-       "       [[-4.82768, -3.4405 , -3.8565 , ..., -3.32705, -4.54394,\n",
-       "         -3.94234],\n",
-       "        [-5.04069, -3.31189, -3.74287, ..., -3.32761, -4.39104,\n",
-       "         -3.94532],\n",
-       "        [-3.93264, -3.4972 , -3.69298, ..., -3.33033, -3.44227,\n",
-       "         -4.12212],\n",
+       "        [-4.17584, -3.4124 , -4.02633, ..., -3.6383 , -3.52281,\n",
+       "         -3.81661],\n",
+       "        [-5.43877, -3.61404, -3.71539, ..., -3.31986, -4.91245,\n",
+       "         -4.02313],\n",
+       "        [-5.19954, -3.40612, -3.75985, ..., -3.33871, -4.33893,\n",
+       "         -3.95896]],\n",
+       "\n",
+       "       [[-4.10169, -3.23551, -3.69393, ..., -3.31839, -3.22389,\n",
+       "         -4.09334],\n",
+       "        [-5.0243 , -3.2547 , -3.706  , ..., -3.32259, -3.85061,\n",
+       "         -3.98388],\n",
+       "        [-4.4373 , -3.2239 , -4.01809, ..., -3.58481, -3.5196 ,\n",
+       "         -3.82259],\n",
        "        ...,\n",
-       "        [-3.80844, -3.33838, -3.69155, ..., -3.41923, -3.42924,\n",
-       "         -3.8176 ],\n",
-       "        [-4.48285, -3.22273, -3.78147, ..., -3.40699, -3.74162,\n",
-       "         -3.86901],\n",
-       "        [-4.6392 , -3.23187, -3.81374, ..., -3.3842 , -3.91351,\n",
-       "         -4.01112]],\n",
+       "        [-5.33196, -3.56203, -3.70417, ..., -3.35482, -5.26949,\n",
+       "         -3.97177],\n",
+       "        [-4.48375, -3.22703, -3.77854, ..., -3.50345, -3.22186,\n",
+       "         -4.04669],\n",
+       "        [-4.85549, -3.27886, -3.78925, ..., -3.4397 , -3.66416,\n",
+       "         -3.95239]],\n",
        "\n",
        "       ...,\n",
        "\n",
-       "       [[-5.53021, -3.66006, -3.70679, ..., -3.31685, -4.3325 ,\n",
-       "         -3.96303],\n",
-       "        [-5.80764, -4.1582 , -3.79167, ..., -3.32765, -4.21507,\n",
-       "         -4.18161],\n",
-       "        [-5.29239, -3.50271, -3.71771, ..., -3.31687, -4.73444,\n",
-       "         -4.03181],\n",
+       "       [[-4.61557, -3.22682, -3.69171, ..., -3.33435, -3.69475,\n",
+       "         -3.97581],\n",
+       "        [-4.92917, -3.40712, -3.84876, ..., -3.33535, -4.17803,\n",
+       "         -3.91566],\n",
+       "        [-4.7708 , -3.22197, -3.73476, ..., -3.43421, -4.02287,\n",
+       "         -3.87183],\n",
        "        ...,\n",
-       "        [-4.32691, -3.23145, -3.70444, ..., -3.32414, -3.80511,\n",
-       "         -3.83668],\n",
-       "        [-3.75663, -3.23557, -3.73504, ..., -3.32438, -3.80125,\n",
-       "         -3.81078],\n",
-       "        [-4.51837, -3.28144, -3.79362, ..., -3.36976, -4.20029,\n",
-       "         -3.89133]],\n",
-       "\n",
-       "       [[-4.49954, -3.22174, -3.8129 , ..., -3.33576, -3.78596,\n",
-       "         -3.81993],\n",
-       "        [-4.14148, -3.22184, -3.77625, ..., -3.73482, -3.98538,\n",
-       "         -3.81258],\n",
-       "        [-4.4287 , -3.23951, -3.81723, ..., -3.32198, -4.11242,\n",
-       "         -3.8337 ],\n",
+       "        [-6.02796, -3.65293, -4.38461, ..., -3.31689, -3.29516,\n",
+       "         -3.9435 ],\n",
+       "        [-5.28667, -3.54159, -3.80369, ..., -3.31753, -4.04044,\n",
+       "         -3.90313],\n",
+       "        [-4.02262, -3.54064, -3.81187, ..., -3.33554, -3.23887,\n",
+       "         -4.17253]],\n",
+       "\n",
+       "       [[-4.85497, -3.28456, -3.80119, ..., -3.36636, -4.13267,\n",
+       "         -3.89293],\n",
+       "        [-3.91323, -4.94915, -4.22492, ..., -4.96036, -3.23148,\n",
+       "         -4.35478],\n",
+       "        [-4.22267, -3.24747, -3.9294 , ..., -3.575  , -3.56954,\n",
+       "         -3.92078],\n",
        "        ...,\n",
-       "        [-5.75022, -3.44706, -3.69179, ..., -3.32012, -6.05324,\n",
-       "         -4.26237],\n",
-       "        [-4.35962, -3.22298, -3.92333, ..., -3.57117, -3.55907,\n",
-       "         -3.83294],\n",
-       "        [-5.19547, -3.4421 , -3.77353, ..., -3.31798, -4.57836,\n",
-       "         -3.90468]],\n",
-       "\n",
-       "       [[-6.04099, -3.30831, -3.73543, ..., -3.32308, -3.25551,\n",
-       "         -3.8094 ],\n",
-       "        [-4.47362, -3.27765, -3.76456, ..., -3.38265, -4.53524,\n",
-       "         -3.94039],\n",
-       "        [-3.84073, -3.73722, -4.19083, ..., -3.41076, -3.33398,\n",
-       "         -4.07603],\n",
+       "        [-5.32923, -3.60416, -3.77723, ..., -3.32314, -4.10275,\n",
+       "         -3.97869],\n",
+       "        [-3.91699, -3.23201, -3.78051, ..., -3.43797, -3.36938,\n",
+       "         -3.8139 ],\n",
+       "        [-4.95741, -3.63518, -3.69191, ..., -3.34218, -4.19573,\n",
+       "         -4.52025]],\n",
+       "\n",
+       "       [[-4.57723, -3.22381, -3.9008 , ..., -3.4787 , -3.79729,\n",
+       "         -3.84203],\n",
+       "        [-4.60246, -3.22764, -3.88173, ..., -3.46323, -3.80787,\n",
+       "         -3.85568],\n",
+       "        [-5.30159, -3.56015, -3.76003, ..., -3.32197, -4.81053,\n",
+       "         -3.85792],\n",
        "        ...,\n",
-       "        [-4.50086, -3.39775, -3.96876, ..., -3.32388, -3.9095 ,\n",
-       "         -4.10324],\n",
-       "        [-4.35927, -3.25777, -3.97168, ..., -3.55172, -3.73069,\n",
-       "         -3.849  ],\n",
-       "        [-3.91335, -3.26787, -4.19439, ..., -3.43118, -4.68501,\n",
-       "         -3.83167]]])
          • created_at :
            2020-06-06T18:10:33.488126
            arviz_version :
            0.8.3

      \n", + " [-5.08647, -3.33452, -3.78991, ..., -3.32673, -4.16193,\n", + " -3.9453 ],\n", + " [-5.06091, -3.3648 , -3.73815, ..., -3.31739, -4.53108,\n", + " -3.94144],\n", + " [-3.83703, -3.23392, -4.50885, ..., -3.77167, -3.24699,\n", + " -3.81075]]])
  • created_at :
    2020-06-10T18:21:57.929618
    arviz_version :
    0.8.3

    • \n", " \n", " \n", " \n", " \n", "
  • \n", - " \n", - " \n", + " \n", + " \n", "
    \n", "
    \n", "
      \n", @@ -25784,62 +22069,62 @@ " stroke: currentColor;\n", " fill: currentColor;\n", "}\n", - "
      xarray.Dataset
        • chain: 8
        • draw: 1000
        • chain
          (chain)
          int64
          0 1 2 3 4 5 6 7
          array([0, 1, 2, 3, 4, 5, 6, 7])
        • draw
          (draw)
          int64
          0 1 2 3 4 5 ... 995 996 997 998 999
          array([  0,   1,   2, ..., 997, 998, 999])
        • lp
          (chain, draw)
          float64
          -10.36 -8.78 ... -8.303 -8.039
          array([[-10.3645 ,  -8.77989,  -8.25432, ...,  -8.8717 ,  -3.50969,\n",
-       "         -6.82756],\n",
-       "       [ -4.22274,  -2.63855,  -2.69171, ...,  -5.31493,  -5.41059,\n",
-       "         -6.71709],\n",
-       "       [ -4.48958,  -3.36304,  -2.94072, ...,  -5.73727,  -7.05069,\n",
-       "         -4.78392],\n",
+       "
          xarray.Dataset
            • chain: 12
            • draw: 1000
            • chain
              (chain)
              int64
              0 1 2 3 4 5 6 7 8 9 10 11
              array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11])
            • draw
              (draw)
              int64
              0 1 2 3 4 5 ... 995 996 997 998 999
              array([  0,   1,   2, ..., 997, 998, 999])
            • lp
              (chain, draw)
              float64
              -5.144 -10.34 ... -11.38 -11.14
              array([[ -5.14448, -10.3377 ,  -4.36231, ..., -11.5328 ,  -4.41412,\n",
+       "         -4.53645],\n",
+       "       [ -6.16728,  -7.89382,  -8.32759, ...,  -6.95914,  -8.01642,\n",
+       "         -6.47187],\n",
+       "       [ -4.61618,  -9.6411 ,  -6.57635, ...,  -7.53224,  -5.69699,\n",
+       "         -6.2396 ],\n",
        "       ...,\n",
-       "       [ -3.36045,  -5.68201,  -4.56247, ..., -13.8115 , -10.5851 ,\n",
-       "         -7.94476],\n",
-       "       [ -6.90788,  -6.99938,  -6.69535, ..., -11.5595 ,  -7.91042,\n",
-       "         -5.89621],\n",
-       "       [ -6.73536,  -5.18604,  -4.06661, ...,  -3.82035,  -8.3025 ,\n",
-       "         -8.03949]])
            • accept_stat
              (chain, draw)
              float64
              0.9435 1.0 0.9498 ... 0.8485 0.9904
              array([[0.94354 , 1.      , 0.949822, ..., 0.905146, 0.93289 , 0.869462],\n",
-       "       [1.      , 0.982365, 0.962294, ..., 0.722748, 0.361011, 0.978471],\n",
-       "       [0.99881 , 0.985447, 0.971381, ..., 0.970813, 1.      , 0.808264],\n",
+       "       [ -7.40979,  -8.6973 ,  -8.36134, ...,  -9.30419,  -4.65713,\n",
+       "         -9.11054],\n",
+       "       [ -9.35431,  -9.76551,  -7.86183, ...,  -7.19184,  -5.21445,\n",
+       "         -7.71352],\n",
+       "       [-10.2665 , -13.9631 ,  -9.83918, ..., -13.5776 , -11.3765 ,\n",
+       "        -11.145  ]])
            • accept_stat
              (chain, draw)
              float64
              1.0 0.4431 0.9951 ... 0.9886 0.429
              array([[1.      , 0.44315 , 0.995054, ..., 0.847876, 0.887551, 0.924468],\n",
+       "       [0.714286, 0.977521, 0.724435, ..., 0.934922, 0.946572, 1.      ],\n",
+       "       [1.      , 0.426264, 0.992927, ..., 0.860845, 0.996249, 0.129366],\n",
        "       ...,\n",
-       "       [0.99676 , 0.785461, 0.941576, ..., 0.805343, 1.      , 0.751312],\n",
-       "       [0.897601, 0.97537 , 0.817992, ..., 0.872228, 0.920333, 0.980318],\n",
-       "       [0.901122, 0.984381, 0.986321, ..., 1.      , 0.848503, 0.990407]])
            • stepsize
              (chain, draw)
              float64
              0.4298 0.4298 ... 0.3523 0.3523
              array([[0.429759, 0.429759, 0.429759, ..., 0.429759, 0.429759, 0.429759],\n",
-       "       [0.488728, 0.488728, 0.488728, ..., 0.488728, 0.488728, 0.488728],\n",
-       "       [0.362846, 0.362846, 0.362846, ..., 0.362846, 0.362846, 0.362846],\n",
+       "       [0.892384, 0.952504, 0.999717, ..., 0.981023, 0.945826, 0.627332],\n",
+       "       [1.      , 0.997771, 0.14691 , ..., 1.      , 1.      , 0.459213],\n",
+       "       [0.714025, 0.911832, 1.      , ..., 0.823447, 0.988555, 0.428968]])
            • stepsize
              (chain, draw)
              float64
              0.5095 0.5095 ... 0.4323 0.4323
              array([[0.509501, 0.509501, 0.509501, ..., 0.509501, 0.509501, 0.509501],\n",
+       "       [0.531567, 0.531567, 0.531567, ..., 0.531567, 0.531567, 0.531567],\n",
+       "       [0.449446, 0.449446, 0.449446, ..., 0.449446, 0.449446, 0.449446],\n",
        "       ...,\n",
-       "       [0.39744 , 0.39744 , 0.39744 , ..., 0.39744 , 0.39744 , 0.39744 ],\n",
-       "       [0.379572, 0.379572, 0.379572, ..., 0.379572, 0.379572, 0.379572],\n",
-       "       [0.352264, 0.352264, 0.352264, ..., 0.352264, 0.352264, 0.352264]])
            • treedepth
              (chain, draw)
              int64
              3 3 4 3 3 3 3 3 ... 3 3 3 4 4 3 3 3
              array([[3, 3, 4, ..., 3, 3, 2],\n",
+       "       [0.466926, 0.466926, 0.466926, ..., 0.466926, 0.466926, 0.466926],\n",
+       "       [0.50536 , 0.50536 , 0.50536 , ..., 0.50536 , 0.50536 , 0.50536 ],\n",
+       "       [0.432263, 0.432263, 0.432263, ..., 0.432263, 0.432263, 0.432263]])
            • treedepth
              (chain, draw)
              int64
              3 3 3 3 3 3 3 3 ... 3 3 3 3 3 3 3 3
              array([[3, 3, 3, ..., 3, 3, 3],\n",
+       "       [3, 3, 3, ..., 3, 3, 3],\n",
        "       [3, 3, 3, ..., 3, 3, 3],\n",
-       "       [4, 3, 3, ..., 3, 3, 3],\n",
        "       ...,\n",
        "       [3, 3, 3, ..., 3, 3, 3],\n",
-       "       [4, 3, 3, ..., 3, 3, 4],\n",
-       "       [3, 3, 3, ..., 3, 3, 3]])
            • n_leapfrog
              (chain, draw)
              int64
              7 7 15 7 7 7 7 ... 15 15 15 7 7 15
              array([[ 7,  7, 15, ..., 15,  7,  3],\n",
-       "       [ 7,  7, 15, ...,  7,  7,  7],\n",
-       "       [15,  7,  7, ...,  7,  7, 15],\n",
+       "       [3, 3, 3, ..., 3, 3, 3],\n",
+       "       [3, 3, 3, ..., 3, 3, 3]])
            • n_leapfrog
              (chain, draw)
              int64
              7 7 7 7 7 7 7 ... 15 7 7 15 7 15 7
              array([[ 7,  7,  7, ...,  7,  7,  7],\n",
+       "       [ 7,  7,  7, ...,  7,  7,  7],\n",
+       "       [ 7,  7,  7, ...,  7,  7,  7],\n",
        "       ...,\n",
        "       [ 7,  7,  7, ...,  7,  7,  7],\n",
-       "       [15,  7,  7, ...,  7, 15, 15],\n",
-       "       [ 7,  7,  7, ...,  7,  7, 15]])
            • diverging
              (chain, draw)
              bool
              False False False ... False False
              array([[False, False, False, ..., False, False, False],\n",
+       "       [ 7,  7,  7, ...,  7,  7,  7],\n",
+       "       [15, 15,  7, ...,  7, 15,  7]])
            • diverging
              (chain, draw)
              bool
              False False False ... False False
              array([[False, False, False, ..., False, False, False],\n",
        "       [False, False, False, ..., False, False, False],\n",
        "       [False, False, False, ..., False, False, False],\n",
        "       ...,\n",
        "       [False, False, False, ..., False, False, False],\n",
        "       [False, False, False, ..., False, False, False],\n",
-       "       [False, False, False, ..., False, False, False]])
            • energy
              (chain, draw)
              float64
              14.22 16.03 17.58 ... 12.34 12.99
              array([[14.2207 , 16.0324 , 17.5819 , ..., 13.6748 , 14.5254 ,  7.68113],\n",
-       "       [ 9.82753,  5.09931,  4.47097, ..., 12.2853 ,  9.12475,  8.25626],\n",
-       "       [ 7.2862 ,  6.22318,  7.53103, ..., 14.4135 , 10.1633 , 12.7854 ],\n",
+       "       [False, False, False, ..., False, False, False]])
            • energy
              (chain, draw)
              float64
              9.305 15.49 12.04 ... 20.86 22.16
              array([[ 9.30492, 15.4901 , 12.0411 , ..., 14.2758 , 13.6856 ,  8.80269],\n",
+       "       [15.0915 , 10.4001 , 15.2596 , ..., 13.6399 , 11.9889 , 10.6035 ],\n",
+       "       [ 8.96043, 15.8938 , 13.3229 , ..., 11.9587 , 11.6787 , 11.3581 ],\n",
        "       ...,\n",
-       "       [11.0993 , 14.047  , 10.7643 , ..., 17.0451 , 17.1117 , 15.7323 ],\n",
-       "       [15.9822 , 10.2966 , 10.7779 , ..., 13.6941 , 16.5774 , 12.7611 ],\n",
-       "       [12.1458 ,  8.06191,  8.33783, ..., 10.2761 , 12.3444 , 12.995  ]])
          • created_at :
            2020-06-06T18:10:33.463309
            arviz_version :
            0.8.3

      \n", + " [12.3025 , 16.388 , 10.6192 , ..., 19.1564 , 12.9302 , 13.1066 ],\n", + " [14.6137 , 11.6894 , 14.2455 , ..., 9.98162, 9.17208, 15.7546 ],\n", + " [19.118 , 19.03 , 16.7125 , ..., 18.6839 , 20.8571 , 22.1571 ]])
  • created_at :
    2020-06-10T18:21:57.892823
    arviz_version :
    0.8.3

    • \n", " \n", " \n", " \n", " \n", "
  • \n", - " \n", - " \n", + " \n", + " \n", "
    \n", "
    \n", "
      \n", @@ -26177,7 +22462,7 @@ " stroke: currentColor;\n", " fill: currentColor;\n", "}\n", - "
      xarray.Dataset
        • school: 8
        • school
          (school)
          int64
          0 1 2 3 4 5 6 7
          array([0, 1, 2, 3, 4, 5, 6, 7])
        • y
          (school)
          float64
          28.0 8.0 -3.0 7.0 ... 1.0 18.0 12.0
          array([28.,  8., -3.,  7., -1.,  1., 18., 12.])

    \n", + "
    xarray.Dataset
      • school: 8
      • school
        (school)
        int64
        0 1 2 3 4 5 6 7
        array([0, 1, 2, 3, 4, 5, 6, 7])
      • y
        (school)
        float64
        28.0 8.0 -3.0 7.0 ... 1.0 18.0 12.0
        array([28.,  8., -3.,  7., -1.,  1., 18., 12.])

    \n", " \n", " \n", "
    • \n", @@ -26512,19 +22797,11 @@ "\t> observed_data" ] }, - "execution_count": 21, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" -======= - "execution_count": null, - "metadata": { - "ExecuteTime": { - "end_time": "2020-06-05T06:48:14.977219Z", - "start_time": "2020-06-05T06:47:05.614Z" ->>>>>>> master } - }, - "outputs": [], + ], "source": [ "# Let's use .stan and .csv files created/saved by the CmdStanPy procedure\n", "\n", @@ -26567,7 +22844,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 18, "metadata": { "ExecuteTime": { "end_time": "2020-06-05T06:48:56.834305Z", @@ -26586,8 +22863,8 @@ "
      \n", " \n", "
    • \n", - " \n", - " \n", + " \n", + " \n", "
      \n", "
      \n", "
        \n", @@ -26925,21 +23202,21 @@ " stroke: currentColor;\n", " fill: currentColor;\n", "}\n", - "
        xarray.Dataset
          • chain: 4
          • draw: 500
          • school: 8
          • chain
            (chain)
            int64
            0 1 2 3
            array([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])
          • school
            (school)
            int64
            0 1 2 3 4 5 6 7
            array([0, 1, 2, 3, 4, 5, 6, 7])
          • mu
            (chain, draw)
            float32
            6.166989 2.7369356 ... 9.431458
            array([[ 6.166989  ,  2.7369356 ,  2.1588428 , ...,  6.5650163 ,\n",
+       "
            xarray.Dataset
              • chain: 4
              • draw: 500
              • school: 8
              • chain
                (chain)
                int64
                0 1 2 3
                array([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])
              • school
                (school)
                int64
                0 1 2 3 4 5 6 7
                array([0, 1, 2, 3, 4, 5, 6, 7])
              • mu
                (chain, draw)
                float32
                6.166989 2.7369356 ... 9.431458
                array([[ 6.166989  ,  2.7369356 ,  2.1588428 , ...,  6.5650163 ,\n",
        "         5.814288  ,  6.303744  ],\n",
        "       [ 2.8813796 ,  3.1015737 , -2.971782  , ...,  7.901584  ,\n",
        "         5.5904655 ,  3.820075  ],\n",
        "       [ 4.4399405 ,  3.5229487 ,  4.7380314 , ...,  4.63695   ,\n",
        "         5.5588846 ,  4.214654  ],\n",
        "       [ 2.9703684 ,  7.247639  ,  5.399694  , ...,  6.5445914 ,\n",
-       "         0.01686172,  9.431458  ]], dtype=float32)
              • tau
                (chain, draw)
                float32
                5.4749303 2.1342402 ... 3.415198
                array([[ 5.4749303 ,  2.1342402 ,  1.4905648 , ...,  0.6671672 ,\n",
+       "         0.01686172,  9.431458  ]], dtype=float32)
              • tau
                (chain, draw)
                float32
                5.4749303 2.1342402 ... 3.415198
                array([[ 5.4749303 ,  2.1342402 ,  1.4905648 , ...,  0.6671672 ,\n",
        "         0.01560028,  3.767334  ],\n",
        "       [ 7.9672894 ,  1.9319502 ,  3.1295881 , ...,  3.3490183 ,\n",
        "         3.566799  ,  0.39580163],\n",
        "       [ 2.9399393 ,  0.8484044 ,  0.9808714 , ..., 12.281746  ,\n",
        "         3.0598812 ,  1.0038422 ],\n",
        "       [ 2.0114977 ,  5.334565  ,  1.9803979 , ...,  3.915938  ,\n",
-       "         3.9908662 ,  3.415198  ]], dtype=float32)
              • theta
                (chain, draw, school)
                float32
                16.805418 -0.4041652 ... 17.367815
                array([[[16.805418  , -0.4041652 , 10.278544  , ...,  2.0133018 ,\n",
+       "         3.9908662 ,  3.415198  ]], dtype=float32)
              • theta
                (chain, draw, school)
                float32
                16.805418 -0.4041652 ... 17.367815
                array([[[16.805418  , -0.4041652 , 10.278544  , ...,  2.0133018 ,\n",
        "         13.142075  , -1.8940724 ],\n",
        "        [ 3.6209416 ,  3.1639988 ,  1.6559362 , ...,  2.4664128 ,\n",
        "          6.275565  ,  1.0073171 ],\n",
@@ -26993,14 +23270,14 @@
        "        [ 4.2434716 , -0.6336682 ,  1.3472003 , ...,  0.5414262 ,\n",
        "          4.824705  , -3.4849732 ],\n",
        "        [ 7.340993  ,  9.233242  ,  5.346336  , ...,  7.6068907 ,\n",
-       "          6.1541667 , 17.367815  ]]], dtype=float32)
            • created_at :
              2020-06-06T18:10:52.908497
              arviz_version :
              0.8.3
              inference_library :
              numpyro
              inference_library_version :
              0.2.3

        \n", + " 6.1541667 , 17.367815 ]]], dtype=float32)
    • created_at :
      2020-06-10T18:22:23.947028
      arviz_version :
      0.8.3
      inference_library :
      numpyro
      inference_library_version :
      0.2.3

    \n", " \n", " \n", " \n", " \n", "
  • \n", - " \n", - " \n", + " \n", + " \n", "
    \n", "
    \n", "
      \n", @@ -27338,7 +23615,7 @@ " stroke: currentColor;\n", " fill: currentColor;\n", "}\n", - "
      xarray.Dataset
        • chain: 4
        • draw: 500
        • y_dim_0: 8
        • chain
          (chain)
          int64
          0 1 2 3
          array([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])
        • y_dim_0
          (y_dim_0)
          int64
          0 1 2 3 4 5 6 7
          array([0, 1, 2, 3, 4, 5, 6, 7])
        • y
          (chain, draw, y_dim_0)
          float32
          9.1578455 ... -4.8099527
          array([[[  9.1578455 ,  -0.34150663,  15.723784  , ...,  -5.102259  ,\n",
+       "
          xarray.Dataset
            • chain: 4
            • draw: 500
            • y_dim_0: 8
            • chain
              (chain)
              int64
              0 1 2 3
              array([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])
            • y_dim_0
              (y_dim_0)
              int64
              0 1 2 3 4 5 6 7
              array([0, 1, 2, 3, 4, 5, 6, 7])
            • y
              (chain, draw, y_dim_0)
              float32
              9.1578455 ... -4.8099527
              array([[[  9.1578455 ,  -0.34150663,  15.723784  , ...,  -5.102259  ,\n",
        "          37.480717  , -10.088353  ],\n",
        "        [ -5.3134413 ,   6.4528875 ,  -6.54908   , ...,  -4.1057158 ,\n",
        "           7.06191   ,  -8.080217  ],\n",
@@ -27392,14 +23669,14 @@
        "        [ 15.574952  ,  -0.9990338 ,   8.2401    , ...,   2.2999332 ,\n",
        "           3.6496153 ,   8.287998  ],\n",
        "        [ 14.578398  ,   0.7358602 ,  16.566711  , ...,  23.490755  ,\n",
-       "          -1.5719078 ,  -4.8099527 ]]], dtype=float32)
          • created_at :
            2020-06-06T18:10:53.011961
            arviz_version :
            0.8.3
            inference_library :
            numpyro
            inference_library_version :
            0.2.3

      \n", + " -1.5719078 , -4.8099527 ]]], dtype=float32)
  • created_at :
    2020-06-10T18:22:24.051104
    arviz_version :
    0.8.3
    inference_library :
    numpyro
    inference_library_version :
    0.2.3

    • \n", " \n", " \n", " \n", " \n", "
  • \n", - " \n", - " \n", + " \n", + " \n", "
    \n", "
    \n", "
      \n", @@ -27737,7 +24014,7 @@ " stroke: currentColor;\n", " fill: currentColor;\n", "}\n", - "
      xarray.Dataset
        • chain: 4
        • draw: 500
        • y_dim_0: 8
        • chain
          (chain)
          int64
          0 1 2 3
          array([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])
        • y_dim_0
          (y_dim_0)
          int64
          0 1 2 3 4 5 6 7
          array([0, 1, 2, 3, 4, 5, 6, 7])
        • y
          (chain, draw, y_dim_0)
          float32
          -3.9054747 ... -3.8537753
          array([[[-3.9054747, -3.5746734, -4.035902 , ..., -3.3210766,\n",
+       "
          xarray.Dataset
            • chain: 4
            • draw: 500
            • y_dim_0: 8
            • chain
              (chain)
              int64
              0 1 2 3
              array([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])
            • y_dim_0
              (y_dim_0)
              int64
              0 1 2 3 4 5 6 7
              array([0, 1, 2, 3, 4, 5, 6, 7])
            • y
              (chain, draw, y_dim_0)
              float32
              -3.9054747 ... -3.8537753
              array([[[-3.9054747, -3.5746734, -4.035902 , ..., -3.3210766,\n",
        "         -3.3395207, -4.1072197],\n",
        "        [-4.947741 , -3.338458 , -3.7338667, ..., -3.3257196,\n",
        "         -3.9088354, -3.9957902],\n",
@@ -27791,14 +24068,14 @@
        "        [-4.8811502, -3.5942247, -3.728438 , ..., -3.3177028,\n",
        "         -4.0894656, -4.179348 ],\n",
        "        [-4.5754213, -3.229128 , -3.8275847, ..., -3.4972098,\n",
-       "         -3.9231424, -3.8537753]]], dtype=float32)
          • created_at :
            2020-06-06T18:10:53.009965
            arviz_version :
            0.8.3
            inference_library :
            numpyro
            inference_library_version :
            0.2.3

      \n", + " -3.9231424, -3.8537753]]], dtype=float32)
  • created_at :
    2020-06-10T18:22:24.049535
    arviz_version :
    0.8.3
    inference_library :
    numpyro
    inference_library_version :
    0.2.3

    • \n", " \n", " \n", " \n", " \n", "
  • \n", - " \n", - " \n", + " \n", + " \n", "
    \n", "
    \n", "
      \n", @@ -28136,30 +24413,30 @@ " stroke: currentColor;\n", " fill: currentColor;\n", "}\n", - "
      xarray.Dataset
        • chain: 4
        • draw: 500
        • chain
          (chain)
          int64
          0 1 2 3
          array([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])
        • diverging
          (chain, draw)
          bool
          False False False ... False False
          array([[False, False, False, ..., False, False, False],\n",
+       "
          xarray.Dataset
            • chain: 4
            • draw: 500
            • chain
              (chain)
              int64
              0 1 2 3
              array([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])
            • diverging
              (chain, draw)
              bool
              False False False ... False False
              array([[False, False, False, ..., False, False, False],\n",
        "       [False, False, False, ..., False, False, False],\n",
        "       [False, False, False, ..., False, False, False],\n",
-       "       [False, False, False, ..., False, False, False]])
            • energy
              (chain, draw)
              float32
              54.622334 53.479084 ... 49.01058
              array([[54.622334, 53.479084, 53.991596, ..., 53.81039 , 59.236557,\n",
+       "       [False, False, False, ..., False, False, False]])
            • energy
              (chain, draw)
              float32
              54.622334 53.479084 ... 49.01058
              array([[54.622334, 53.479084, 53.991596, ..., 53.81039 , 59.236557,\n",
        "        56.882545],\n",
        "       [49.65902 , 48.335026, 56.860115, ..., 49.79985 , 48.931656,\n",
        "        50.302807],\n",
        "       [51.492855, 51.79956 , 55.82659 , ..., 51.77163 , 50.36808 ,\n",
        "        51.65256 ],\n",
        "       [49.366512, 50.623386, 55.419903, ..., 51.691166, 47.321953,\n",
-       "        49.01058 ]], dtype=float32)
            • tree_size
              (chain, draw)
              int32
              7 15 7 7 7 15 7 7 ... 7 7 7 7 7 7 7
              array([[ 7, 15,  7, ...,  7, 15,  7],\n",
+       "        49.01058 ]], dtype=float32)
            • tree_size
              (chain, draw)
              int32
              7 15 7 7 7 15 7 7 ... 7 7 7 7 7 7 7
              array([[ 7, 15,  7, ...,  7, 15,  7],\n",
        "       [ 7, 15, 15, ...,  7,  7,  7],\n",
        "       [ 7,  7,  7, ...,  3,  7,  7],\n",
-       "       [ 7,  7,  7, ...,  7,  7,  7]], dtype=int32)
            • depth
              (chain, draw)
              int64
              3 4 3 3 3 4 3 3 ... 3 3 3 3 3 3 3 3
              array([[3, 4, 3, ..., 3, 4, 3],\n",
+       "       [ 7,  7,  7, ...,  7,  7,  7]], dtype=int32)
            • depth
              (chain, draw)
              int64
              3 4 3 3 3 4 3 3 ... 3 3 3 3 3 3 3 3
              array([[3, 4, 3, ..., 3, 4, 3],\n",
        "       [3, 4, 4, ..., 3, 3, 3],\n",
        "       [3, 3, 3, ..., 2, 3, 3],\n",
-       "       [3, 3, 3, ..., 3, 3, 3]])
          • created_at :
            2020-06-06T18:10:52.913185
            arviz_version :
            0.8.3
            inference_library :
            numpyro
            inference_library_version :
            0.2.3

      \n", + " [3, 3, 3, ..., 3, 3, 3]])
  • created_at :
    2020-06-10T18:22:23.951922
    arviz_version :
    0.8.3
    inference_library :
    numpyro
    inference_library_version :
    0.2.3

    • \n", " \n", " \n", " \n", " \n", "
  • \n", - " \n", - " \n", + " \n", + " \n", "
    \n", "
    \n", "
      \n", @@ -28497,7 +24774,7 @@ " stroke: currentColor;\n", " fill: currentColor;\n", "}\n", - "
      xarray.Dataset
        • chain: 1
        • draw: 500
        • school: 8
        • chain
          (chain)
          int64
          0
          array([0])
        • draw
          (draw)
          int64
          0 1 2 3 4 5 ... 495 496 497 498 499
          array([  0,   1,   2, ..., 497, 498, 499])
        • school
          (school)
          int64
          0 1 2 3 4 5 6 7
          array([0, 1, 2, 3, 4, 5, 6, 7])
        • mu
          (chain, draw)
          float32
          1.8776392 3.322776 ... -0.5562178
          array([[ 1.87763917e+00,  3.32277608e+00, -9.43409824e+00,\n",
+       "
          xarray.Dataset
            • chain: 1
            • draw: 500
            • school: 8
            • chain
              (chain)
              int64
              0
              array([0])
            • draw
              (draw)
              int64
              0 1 2 3 4 5 ... 495 496 497 498 499
              array([  0,   1,   2, ..., 497, 498, 499])
            • school
              (school)
              int64
              0 1 2 3 4 5 6 7
              array([0, 1, 2, 3, 4, 5, 6, 7])
            • mu
              (chain, draw)
              float32
              1.8776392 3.322776 ... -0.5562178
              array([[ 1.87763917e+00,  3.32277608e+00, -9.43409824e+00,\n",
        "        -6.36535287e-01,  4.97456253e-01, -7.59930372e+00,\n",
        "        -1.73702741e+00, -6.81911755e+00, -5.64398336e+00,\n",
        "        -2.23989189e-01,  5.17267179e+00,  8.54710960e+00,\n",
@@ -28663,7 +24940,7 @@
        "         2.15981793e+00, -1.40861428e+00, -3.81361461e+00,\n",
        "        -1.52566981e+00, -4.59913254e+00,  6.24776423e-01,\n",
        "         9.58626366e+00,  3.67797637e+00, -4.09784746e+00,\n",
-       "         1.54376483e+00, -5.56217790e-01]], dtype=float32)
            • tau
              (chain, draw)
              float32
              1.142396 0.82828474 ... 27.683926
              array([[1.14239597e+00, 8.28284740e-01, 3.78205299e+00, 9.73977280e+00,\n",
+       "         1.54376483e+00, -5.56217790e-01]], dtype=float32)
            • tau
              (chain, draw)
              float32
              1.142396 0.82828474 ... 27.683926
              array([[1.14239597e+00, 8.28284740e-01, 3.78205299e+00, 9.73977280e+00,\n",
        "        8.45564842e+00, 4.66784382e+00, 5.73158979e+00, 1.75484061e+00,\n",
        "        9.52065066e-02, 8.36029129e+01, 3.42491865e-01, 2.32345676e+00,\n",
        "        5.29000092e+00, 1.06360044e+01, 2.88606501e+00, 1.34007835e+01,\n",
@@ -28788,7 +25065,7 @@
        "        1.19185162e+01, 1.38340578e+01, 4.10225754e+01, 2.93181000e+01,\n",
        "        1.13749208e+01, 6.11714888e+00, 7.03764915e-01, 1.31945190e+01,\n",
        "        1.42294288e+00, 3.44097424e+00, 1.10320222e+00, 2.76839256e+01]],\n",
-       "      dtype=float32)
            • theta
              (chain, draw, school)
              float32
              0.61433244 1.9731792 ... -21.27477
              array([[[  0.61433244,   1.9731792 ,   2.8793137 , ...,   3.1135025 ,\n",
+       "      dtype=float32)
            • theta
              (chain, draw, school)
              float32
              0.61433244 1.9731792 ... -21.27477
              array([[[  0.61433244,   1.9731792 ,   2.8793137 , ...,   3.1135025 ,\n",
        "           0.36174998,   2.3965962 ],\n",
        "        [  2.855525  ,   1.7712725 ,   4.221959  , ...,   1.8995284 ,\n",
        "           2.4188023 ,   4.186133  ],\n",
@@ -28800,14 +25077,14 @@
        "        [  0.13607061,  -0.9721953 ,   2.647256  , ...,   0.971618  ,\n",
        "           2.8553798 ,   2.584463  ],\n",
        "        [ -4.5051947 ,   3.6043565 , -46.242397  , ..., -29.500881  ,\n",
-       "          39.389797  , -21.27477   ]]], dtype=float32)
          • created_at :
            2020-06-06T18:10:53.014131
            arviz_version :
            0.8.3
            inference_library :
            numpyro
            inference_library_version :
            0.2.3

      \n", + " 39.389797 , -21.27477 ]]], dtype=float32)
  • created_at :
    2020-06-10T18:22:24.053369
    arviz_version :
    0.8.3
    inference_library :
    numpyro
    inference_library_version :
    0.2.3

    • \n", " \n", " \n", " \n", " \n", "
  • \n", - " \n", - " \n", + " \n", + " \n", "
    \n", "
    \n", "
      \n", @@ -29145,7 +25422,7 @@ " stroke: currentColor;\n", " fill: currentColor;\n", "}\n", - "
      xarray.Dataset
        • chain: 1
        • draw: 500
        • y_dim_0: 8
        • chain
          (chain)
          int64
          0
          array([0])
        • draw
          (draw)
          int64
          0 1 2 3 4 5 ... 495 496 497 498 499
          array([  0,   1,   2, ..., 497, 498, 499])
        • y_dim_0
          (y_dim_0)
          int64
          0 1 2 3 4 5 6 7
          array([0, 1, 2, 3, 4, 5, 6, 7])
        • y
          (chain, draw, y_dim_0)
          float32
          19.690388 -0.3481581 ... -34.692043
          array([[[ 19.690388  ,  -0.3481581 , -22.367306  , ...,  16.622826  ,\n",
+       "
          xarray.Dataset
            • chain: 1
            • draw: 500
            • y_dim_0: 8
            • chain
              (chain)
              int64
              0
              array([0])
            • draw
              (draw)
              int64
              0 1 2 3 4 5 ... 495 496 497 498 499
              array([  0,   1,   2, ..., 497, 498, 499])
            • y_dim_0
              (y_dim_0)
              int64
              0 1 2 3 4 5 6 7
              array([0, 1, 2, 3, 4, 5, 6, 7])
            • y
              (chain, draw, y_dim_0)
              float32
              19.690388 -0.3481581 ... -34.692043
              array([[[ 19.690388  ,  -0.3481581 , -22.367306  , ...,  16.622826  ,\n",
        "         -15.768808  ,  -6.8451705 ],\n",
        "        [  9.477534  ,  -9.470677  ,   8.480469  , ...,  -3.1395423 ,\n",
        "           8.926895  ,  21.148169  ],\n",
@@ -29157,14 +25434,14 @@
        "        [ 23.94809   , -19.047083  ,  -6.228318  , ...,   9.324339  ,\n",
        "          15.134213  ,   0.15730432],\n",
        "        [ 18.83905   ,  -3.9360547 , -30.911896  , ..., -25.75918   ,\n",
-       "          44.763027  , -34.692043  ]]], dtype=float32)
          • created_at :
            2020-06-06T18:10:53.016526
            arviz_version :
            0.8.3
            inference_library :
            numpyro
            inference_library_version :
            0.2.3

      \n", + " 44.763027 , -34.692043 ]]], dtype=float32)
  • created_at :
    2020-06-10T18:22:24.055633
    arviz_version :
    0.8.3
    inference_library :
    numpyro
    inference_library_version :
    0.2.3

    • \n", " \n", " \n", " \n", " \n", "
  • \n", - " \n", - " \n", + " \n", + " \n", "
    \n", "
    \n", "
      \n", @@ -29502,7 +25779,7 @@ " stroke: currentColor;\n", " fill: currentColor;\n", "}\n", - "
      xarray.Dataset
        • y_dim_0: 8
        • y_dim_0
          (y_dim_0)
          int64
          0 1 2 3 4 5 6 7
          array([0, 1, 2, 3, 4, 5, 6, 7])
        • y
          (y_dim_0)
          float64
          28.0 8.0 -3.0 7.0 ... 1.0 18.0 12.0
          array([28.,  8., -3.,  7., -1.,  1., 18., 12.])
      • created_at :
        2020-06-06T18:10:53.017590
        arviz_version :
        0.8.3
        inference_library :
        numpyro
        inference_library_version :
        0.2.3

    \n", + "
    xarray.Dataset
      • y_dim_0: 8
      • y_dim_0
        (y_dim_0)
        int64
        0 1 2 3 4 5 6 7
        array([0, 1, 2, 3, 4, 5, 6, 7])
      • y
        (y_dim_0)
        float64
        28.0 8.0 -3.0 7.0 ... 1.0 18.0 12.0
        array([28.,  8., -3.,  7., -1.,  1., 18., 12.])
    • created_at :
      2020-06-10T18:22:24.056692
      arviz_version :
      0.8.3
      inference_library :
      numpyro
      inference_library_version :
      0.2.3

    \n", " \n", " \n", "
    • \n", @@ -29839,7 +26116,7 @@ "\t> observed_data" ] }, - "execution_count": 16, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -29908,12 +26185,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Import Packages" + "### Import Package" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 19, "metadata": { "ExecuteTime": { "end_time": "2020-06-05T06:48:59.842990Z", @@ -29925,17 +26202,12 @@ "name": "stderr", "output_type": "stream", "text": [ - "INFO:numexpr.utils:Note: NumExpr detected 12 cores but \"NUMEXPR_MAX_THREADS\" not set, so enforcing safe limit of 8.\n", - "INFO:numexpr.utils:NumExpr defaulting to 8 threads.\n" + "INFO:numexpr.utils:NumExpr defaulting to 4 threads.\n" ] } ], "source": [ - "# import arviz as az\n", - "# import numpy as np\n", - "import pyjags\n", - "# import xarray as xr\n", - "# xr.set_options(display_style=\"html\");" + "import pyjags" ] }, { @@ -29954,7 +26226,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 20, "metadata": { "ExecuteTime": { "end_time": "2020-06-05T06:49:04.048517Z", @@ -29983,7 +26255,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 21, "metadata": { "ExecuteTime": { "end_time": "2020-06-05T06:49:05.084881Z", @@ -30006,7 +26278,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 22, "metadata": { "ExecuteTime": { "end_time": "2020-06-05T06:49:05.479290Z", @@ -30034,7 +26306,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 23, "metadata": { "ExecuteTime": { "end_time": "2020-06-05T06:49:06.671164Z", @@ -30046,10 +26318,10 @@ "name": "stderr", "output_type": "stream", "text": [ - "INFO:pyjags:Using JAGS library located in /usr/local/lib/libjags.4.dylib.\n", - "INFO:pyjags:Loading module basemod from /usr/local/lib/JAGS/modules-4/basemod.so\n", - "INFO:pyjags:Loading module bugs from /usr/local/lib/JAGS/modules-4/bugs.so\n", - "INFO:pyjags:Loading module lecuyer from /usr/local/lib/JAGS/modules-4/lecuyer.so\n" + "INFO:pyjags:Using JAGS library located in /usr/local/opt/jags/lib/libjags.4.dylib.\n", + "INFO:pyjags:Loading module basemod from /usr/local/opt/jags/lib/JAGS/modules-4/basemod.so\n", + "INFO:pyjags:Loading module bugs from /usr/local/opt/jags/lib/JAGS/modules-4/bugs.so\n", + "INFO:pyjags:Loading module lecuyer from /usr/local/opt/jags/lib/JAGS/modules-4/lecuyer.so\n" ] } ], @@ -30072,7 +26344,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 24, "metadata": { "ExecuteTime": { "end_time": "2020-06-05T06:49:07.305091Z", @@ -30107,7 +26379,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 25, "metadata": { "ExecuteTime": { "end_time": "2020-06-05T06:49:07.978300Z", @@ -30138,278 +26410,2003 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 26, "metadata": { "ExecuteTime": { "end_time": "2020-06-05T06:49:08.360698Z", "start_time": "2020-06-05T06:49:08.347208Z" } }, - "outputs": [], - "source": [ - "pyjags_data = az.from_pyjags(\n", - " posterior=jags_posterior_samples, \n", - " prior=jags_prior_samples, \n", - " save_warmup=True, \n", - " warmup_iterations=1000\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Trace Plot" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": { - "ExecuteTime": { - "end_time": "2020-06-05T06:49:12.381634Z", - "start_time": "2020-06-05T06:49:08.854544Z" - }, - "scrolled": false - }, - "outputs": [ - { - "data": { - "image/png": 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    meansdhdi_3%hdi_97%mcse_meanmcse_sdess_meaness_sdess_bulkess_tailr_hat
    mu4.4203.338-1.74010.7200.0270.01915675.015675.015747.017914.01.0
    tau3.6143.3050.0009.3270.0530.0413961.03198.05245.04677.01.0
    theta_tilde[0]0.3110.990-1.6052.1280.0080.00616104.016104.016117.019148.01.0
    theta_tilde[1]0.1040.941-1.6781.8770.0070.00519509.018216.019495.019244.01.0
    theta_tilde[2]-0.0760.964-1.8791.7400.0070.00519621.019090.019660.019388.01.0
    theta_tilde[3]0.0610.930-1.7181.8250.0070.00518988.018988.018999.019073.01.0
    theta_tilde[4]-0.1660.930-1.9911.5590.0070.00518997.018419.018998.019344.01.0
    theta_tilde[5]-0.0680.945-1.8451.7250.0070.00518916.018916.018914.019186.01.0
    theta_tilde[6]0.3480.959-1.4092.1890.0080.00515849.015849.015898.019764.01.0
    theta_tilde[7]0.0830.985-1.8421.8850.0070.00519629.019629.019633.019391.01.0
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          • chain: 4
          • draw: 5000
          • theta_tilde_dim_0: 8
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            (chain)
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            array([0, 1, 2, 3])
          • draw
            (draw)
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            array([   0,    1,    2, ..., 4997, 4998, 4999])
          • theta_tilde_dim_0
            (theta_tilde_dim_0)
            int64
            0 1 2 3 4 5 6 7
            array([0, 1, 2, 3, 4, 5, 6, 7])
          • mu
            (chain, draw)
            float64
            3.686 6.34 3.007 ... 5.069 2.715
            array([[ 3.68628803,  6.33975625,  3.00720879, ...,  4.45568482,\n",
+       "         5.14933474,  3.00259384],\n",
+       "       [11.19273391,  5.49835446, 10.11725162, ...,  8.61617662,\n",
+       "         5.39011842,  2.04936093],\n",
+       "       [ 4.32029197,  3.42744886,  2.10039658, ...,  5.47806714,\n",
+       "         7.83660175,  4.38431912],\n",
+       "       [ 4.54884918,  7.91611073,  6.6783754 , ...,  4.22305406,\n",
+       "         5.06931555,  2.71523558]])
          • tau
            (chain, draw)
            float64
            4.816 3.03 5.383 ... 1.486 3.392
            array([[ 4.8161381 ,  3.02982026,  5.38322235, ...,  0.68937565,\n",
+       "         0.97141043,  0.23615998],\n",
+       "       [ 0.36589474,  0.26509498,  3.47731418, ...,  6.9308054 ,\n",
+       "         6.26683914,  2.28825279],\n",
+       "       [15.90526068, 10.84739022,  8.42826426, ...,  0.37183397,\n",
+       "         1.53134207,  0.31835769],\n",
+       "       [ 2.80748325,  2.30603877,  0.93931032, ...,  3.60863083,\n",
+       "         1.48636452,  3.39239202]])
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            (chain, draw, theta_tilde_dim_0)
            float64
            0.5608 -1.121 ... 3.156 0.04216
            array([[[ 0.56075371, -1.12123397,  1.28823631, ...,  1.37499882,\n",
+       "         -0.14606967,  0.83144889],\n",
+       "        [-1.06124368, -0.60570479,  0.74142352, ...,  0.52044537,\n",
+       "          2.28187454, -1.59297923],\n",
+       "        [ 0.21296357,  0.23295021,  1.10290316, ..., -0.85051446,\n",
+       "          0.26578266, -1.67933879],\n",
+       "        ...,\n",
+       "        [ 0.39605911,  0.17677283, -1.02142783, ...,  1.16682921,\n",
+       "         -1.07893518, -0.34573671],\n",
+       "        [-1.27421436,  1.05737171,  0.39708125, ...,  1.27309105,\n",
+       "         -0.19458683, -1.19644057],\n",
+       "        [ 0.01716736, -0.71043992,  0.46174251, ...,  1.09157367,\n",
+       "         -0.01181723,  1.81239221]],\n",
+       "\n",
+       "       [[ 0.47973648, -1.12097641, -0.33589881, ...,  1.77507963,\n",
+       "          1.14237898,  0.37936396],\n",
+       "        [ 0.29091775,  0.18461712, -0.98562689, ...,  0.27328959,\n",
+       "         -0.33652967, -1.40310479],\n",
+       "        [ 2.28897994, -0.22531453,  0.22513106, ...,  1.29074938,\n",
+       "          1.55133091, -0.80465165],\n",
+       "        ...,\n",
+       "        [ 0.79551434,  0.03561568,  1.17441399, ..., -0.42865611,\n",
+       "          0.03368982,  0.68884468],\n",
+       "        [ 1.21396275,  0.67469128, -0.63689052, ..., -0.69596653,\n",
+       "         -0.02934048, -2.41950735],\n",
+       "        [ 1.05653063,  0.32572159,  0.81772885, ..., -0.2350986 ,\n",
+       "          1.24337423, -0.72320465]],\n",
+       "\n",
+       "       [[-0.57921868,  0.42610947, -0.57847365, ..., -0.09519526,\n",
+       "          0.36224157,  0.64617946],\n",
+       "        [-0.47703757, -0.25864603, -0.66051436, ...,  0.27662448,\n",
+       "          0.44179918, -0.45585925],\n",
+       "        [-0.27596878,  0.69088422,  0.42814882, ..., -0.96672998,\n",
+       "          1.73789738, -0.46512283],\n",
+       "        ...,\n",
+       "        [ 0.60072925, -0.43516234,  0.24912825, ..., -1.61546994,\n",
+       "         -0.86216011, -0.15591596],\n",
+       "        [-0.04658047, -0.2842154 ,  0.71952606, ...,  0.07509862,\n",
+       "         -0.18599525, -0.02130407],\n",
+       "        [ 0.27260331,  0.8333267 , -0.36849578, ..., -0.16880051,\n",
+       "         -1.69588961, -0.17100146]],\n",
+       "\n",
+       "       [[ 1.90494117, -0.18420728,  0.51763424, ..., -0.36948065,\n",
+       "          1.41775244,  0.51022416],\n",
+       "        [-0.51989399,  0.47165081,  0.25544502, ...,  0.1298349 ,\n",
+       "          0.83461918, -2.05221019],\n",
+       "        [ 0.21965883,  0.34812457, -0.78854012, ...,  0.35837224,\n",
+       "          0.66885538,  0.11404705],\n",
+       "        ...,\n",
+       "        [ 0.52468661, -0.43886124,  0.38958706, ..., -1.82849052,\n",
+       "         -0.06422477, -0.43644058],\n",
+       "        [ 0.5400513 ,  0.23607673, -1.3081673 , ..., -0.41460414,\n",
+       "         -0.10787403, -0.70509101],\n",
+       "        [ 0.69917846, -0.07991233,  0.76815041, ..., -0.09468557,\n",
+       "          3.15570231,  0.04216289]]])
        • created_at :
          2020-06-10T18:22:24.834026
          arviz_version :
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          inference_library :
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          inference_library_version :
          1.3.6

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            (theta_tilde_dim_0)
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            array([[-0.12672133,  7.68219852, -1.97358487, ...,  1.73388533,\n",
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+       "       [ 6.50232299, -5.15746654,  6.21248659, ..., -3.32469957,\n",
+       "         0.93128616, -1.23941544],\n",
+       "       [ 3.00378927, -0.07999032, -3.76134625, ..., -7.23938638,\n",
+       "         9.77863442, -4.66023246],\n",
+       "       [ 0.0132557 , -1.09448362, -4.47610478, ...,  4.77646126,\n",
+       "         1.44845474,  0.96852389]])
          • tau
            (chain, draw)
            float64
            0.8556 2.613 0.2007 ... 5.135 1.316
            array([[8.55557380e-01, 2.61325390e+00, 2.00706962e-01, ...,\n",
+       "        9.93806383e+00, 5.02217913e+00, 1.10470469e+00],\n",
+       "       [2.58777388e+00, 3.81948312e+00, 5.80792031e+00, ...,\n",
+       "        2.09946180e+01, 2.16078195e+00, 3.01620956e+00],\n",
+       "       [4.43126675e+02, 4.84293007e-01, 1.17618542e+01, ...,\n",
+       "        1.94698267e+01, 9.41718978e-01, 3.96608718e+00],\n",
+       "       [1.48769070e+01, 3.26137220e+00, 7.03443712e-01, ...,\n",
+       "        1.93210865e+01, 5.13548322e+00, 1.31611254e+00]])
          • theta_tilde
            (chain, draw, theta_tilde_dim_0)
            float64
            0.85 -0.4306 ... 0.1777 0.7252
            array([[[ 0.85001805, -0.43055427, -0.992581  , ...,  0.77740128,\n",
+       "          2.222987  ,  0.59601663],\n",
+       "        [ 1.29845346,  1.20086289,  1.14282397, ...,  0.22418954,\n",
+       "         -0.16364138,  1.21487966],\n",
+       "        [-0.47712144, -1.13142752, -0.00317101, ..., -1.64477397,\n",
+       "          0.94425876,  1.62574895],\n",
+       "        ...,\n",
+       "        [-0.17028569,  2.31625015,  1.69421502, ...,  0.42742773,\n",
+       "          0.6838044 , -0.86794174],\n",
+       "        [-0.53313516, -0.85208113,  0.48247498, ...,  0.23488686,\n",
+       "          1.02479667,  0.11839418],\n",
+       "        [-0.30936194, -0.76166013, -0.58023488, ...,  1.24137143,\n",
+       "          0.30931171,  0.58287052]],\n",
+       "\n",
+       "       [[-0.03665365,  0.80088035, -0.20564908, ..., -0.3695867 ,\n",
+       "          0.15174434,  0.10842298],\n",
+       "        [-1.55249106, -0.41228653,  0.12732898, ..., -1.46123103,\n",
+       "         -0.52013961,  2.13236933],\n",
+       "        [ 1.17055946,  0.6963143 , -0.4054951 , ...,  0.55845874,\n",
+       "          1.26600816,  0.99431999],\n",
+       "        ...,\n",
+       "        [-0.04816829,  0.19211899,  0.20844174, ...,  0.38186367,\n",
+       "         -0.70184387,  1.10741913],\n",
+       "        [-0.47089392,  1.33012804, -0.09896402, ..., -0.34182696,\n",
+       "          1.31722919,  0.77817125],\n",
+       "        [ 0.23412348, -0.17537455,  2.43504146, ...,  0.55932023,\n",
+       "         -0.33664493,  0.61882814]],\n",
+       "\n",
+       "       [[ 0.42214178, -0.66877733, -1.79242895, ...,  2.73019476,\n",
+       "         -0.01053904,  1.34708029],\n",
+       "        [ 0.47547847,  0.69348415, -0.96538681, ..., -1.02924095,\n",
+       "          0.90705581,  0.93209906],\n",
+       "        [ 0.0845888 ,  0.4661006 ,  0.42880593, ..., -1.49614601,\n",
+       "          0.65231532, -0.29565493],\n",
+       "        ...,\n",
+       "        [-0.88989373, -0.97698322,  0.41487417, ...,  0.30817321,\n",
+       "         -0.6626576 ,  0.01303761],\n",
+       "        [ 1.36250619,  0.41182355, -0.33354604, ...,  1.30921816,\n",
+       "          0.42547979, -0.13510322],\n",
+       "        [-0.46519877,  0.38209188, -1.0051383 , ...,  1.99218995,\n",
+       "         -0.28024503,  0.56597011]],\n",
+       "\n",
+       "       [[ 2.41992929,  0.76132799,  0.83651604, ...,  0.72399534,\n",
+       "          0.2841493 ,  1.60398163],\n",
+       "        [ 1.14957975, -1.5350671 , -0.34517745, ..., -1.58175186,\n",
+       "         -2.22128242, -1.06446519],\n",
+       "        [-1.07820432,  0.40097786,  1.96509446, ..., -1.94273998,\n",
+       "          0.03097899,  0.21164047],\n",
+       "        ...,\n",
+       "        [ 0.84899743,  0.28618045, -1.20176965, ..., -1.28257703,\n",
+       "         -1.59253642, -0.02553479],\n",
+       "        [ 1.22452402,  1.31477276,  0.98583838, ...,  1.56005163,\n",
+       "          0.65692115, -1.60407853],\n",
+       "        [ 0.71122525, -0.02011507, -1.9162024 , ...,  2.14820149,\n",
+       "          0.17769618,  0.72521983]]])
        • created_at :
          2020-06-10T18:22:24.861493
          arviz_version :
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          inference_library :
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        xarray.Dataset
          • chain: 4
          • draw: 1000
          • theta_tilde_dim_0: 8
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            (draw)
            int64
            0 1 2 3 4 5 ... 995 996 997 998 999
            array([  0,   1,   2, ..., 997, 998, 999])
          • theta_tilde_dim_0
            (theta_tilde_dim_0)
            int64
            0 1 2 3 4 5 6 7
            array([0, 1, 2, 3, 4, 5, 6, 7])
          • mu
            (chain, draw)
            float64
            2.343 1.574 0.2489 ... 4.048 6.776
            array([[ 2.34314315,  1.57369394,  0.24894943, ...,  6.17063626,\n",
+       "         3.0878376 ,  1.42913203],\n",
+       "       [ 9.97570953, -2.02439703,  6.85342427, ...,  2.11825494,\n",
+       "         7.93677692,  3.01015461],\n",
+       "       [ 6.28388465,  3.36497001,  1.17872397, ...,  0.77454229,\n",
+       "        13.04498355, 10.71165114],\n",
+       "       [ 6.68361683,  4.39316419,  0.09360019, ...,  3.21668297,\n",
+       "         4.04756007,  6.77644344]])
          • tau
            (chain, draw)
            float64
            3.507 0.8696 0.846 ... 1.222 1.276
            array([[3.50737068, 0.86958818, 0.8459765 , ..., 1.77138973, 1.76771863,\n",
+       "        2.04190431],\n",
+       "       [2.11687365, 2.29746603, 1.8534264 , ..., 0.57292398, 1.52267673,\n",
+       "        3.42713623],\n",
+       "       [3.9999113 , 1.91387569, 0.23138222, ..., 8.20669842, 4.43777619,\n",
+       "        5.60506149],\n",
+       "       [1.00381947, 3.17057574, 1.01921169, ..., 6.35440979, 1.22182023,\n",
+       "        1.27604491]])
          • theta_tilde
            (chain, draw, theta_tilde_dim_0)
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            -0.9909 0.4283 ... 0.3494 0.8214
            array([[[-0.99094065,  0.42833276,  0.39672956, ..., -2.09855064,\n",
+       "          0.18888032, -0.58986111],\n",
+       "        [-0.12188571,  0.69023944, -0.13418248, ..., -1.2406529 ,\n",
+       "          1.12609676,  0.37040659],\n",
+       "        [-0.99886906, -0.20982296,  0.67521031, ..., -0.1461499 ,\n",
+       "          0.83719731,  1.19747178],\n",
+       "        ...,\n",
+       "        [-1.58235047, -0.87099162,  0.40640008, ..., -0.71311941,\n",
+       "          0.97375709,  0.30643473],\n",
+       "        [-0.67230905, -0.03087083, -0.13111032, ..., -0.97579779,\n",
+       "          0.51709929,  0.54002153],\n",
+       "        [ 0.21218175,  0.41734752,  0.79440517, ...,  0.13117306,\n",
+       "          0.06308795, -0.75895152]],\n",
+       "\n",
+       "       [[ 1.34666875, -0.09700182, -0.26156934, ...,  0.84290983,\n",
+       "         -0.32605667, -0.98721717],\n",
+       "        [ 0.60327275,  0.93426601,  1.71515555, ..., -1.58217704,\n",
+       "          1.38378973, -1.11093019],\n",
+       "        [ 0.24144096, -0.51100372,  0.80922452, ..., -1.86699144,\n",
+       "         -0.05736646,  0.63437633],\n",
+       "        ...,\n",
+       "        [ 0.39782333, -1.55883677,  1.19441409, ...,  1.05896027,\n",
+       "         -0.61763547,  0.78662345],\n",
+       "        [-1.27593793,  1.11010641,  0.82877233, ...,  0.17539794,\n",
+       "          0.2781045 , -0.74883458],\n",
+       "        [-0.61485105,  0.7463113 ,  0.82679819, ...,  0.24098771,\n",
+       "          0.94190029,  0.07068883]],\n",
+       "\n",
+       "       [[ 0.91844395, -0.22188411,  1.19853996, ...,  0.73105789,\n",
+       "          1.17609672,  1.0180705 ],\n",
+       "        [-0.63293758,  1.75756088,  1.29538997, ...,  1.25041335,\n",
+       "         -0.32904458, -0.431535  ],\n",
+       "        [ 2.14396635, -0.50527743,  0.20900194, ...,  0.76375069,\n",
+       "         -0.52035869, -0.14056728],\n",
+       "        ...,\n",
+       "        [ 0.99526292,  1.01386219, -0.88070335, ..., -1.46269942,\n",
+       "          1.5714287 , -0.85776922],\n",
+       "        [ 0.46092246,  0.27346652,  0.33645316, ..., -1.01721026,\n",
+       "         -0.37290863, -0.50591089],\n",
+       "        [ 1.35935856, -0.08724155, -0.76176027, ..., -1.34036643,\n",
+       "         -0.27073697,  1.23296843]],\n",
+       "\n",
+       "       [[-0.2874314 , -0.76365401,  0.11968068, ...,  2.61394867,\n",
+       "         -0.14594155, -1.06315231],\n",
+       "        [-0.2154085 , -0.32714514,  0.3866235 , ..., -0.95501706,\n",
+       "          1.09234753, -1.74638174],\n",
+       "        [ 0.01822831, -1.3842866 , -1.39632544, ..., -0.39650762,\n",
+       "          1.02985844,  1.20502722],\n",
+       "        ...,\n",
+       "        [ 1.38973766, -0.90670277, -0.4120455 , ...,  1.14767164,\n",
+       "          1.84232632,  2.63230258],\n",
+       "        [ 1.08389489,  1.58784555, -0.16197627, ...,  0.37463258,\n",
+       "          0.42914099,  2.06065205],\n",
+       "        [-0.19599264, -1.29336061, -0.05431617, ..., -1.16963105,\n",
+       "          0.34940347,  0.82143441]]])
        • created_at :
          2020-06-10T18:22:24.841646
          arviz_version :
          0.8.3
          inference_library :
          pyjags
          inference_library_version :
          1.3.6

        \n", + "
      \n", + "
      \n", + "
      • \n", + " \n", + "
    • \n", + " \n", + " \n", + "
      \n", + "
      \n", + "
        \n", + "
        \n", + "\n", + "\n", + "Show/Hide data repr\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Show/Hide attributes\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
        xarray.Dataset
          • chain: 4
          • draw: 1000
          • theta_tilde_dim_0: 8
          • chain
            (chain)
            int64
            0 1 2 3
            array([0, 1, 2, 3])
          • draw
            (draw)
            int64
            0 1 2 3 4 5 ... 995 996 997 998 999
            array([  0,   1,   2, ..., 997, 998, 999])
          • theta_tilde_dim_0
            (theta_tilde_dim_0)
            int64
            0 1 2 3 4 5 6 7
            array([0, 1, 2, 3, 4, 5, 6, 7])
          • mu
            (chain, draw)
            float64
            -0.9637 2.01 2.683 ... -4.89 -7.553
            array([[-0.96373932,  2.00976459,  2.68286841, ...,  3.26263418,\n",
+       "         7.13218474, -4.77082899],\n",
+       "       [ 5.01651985, -1.42231105,  4.28923032, ..., 14.81072926,\n",
+       "         5.85918337,  2.9595369 ],\n",
+       "       [-8.93389446, -3.62276361, -4.40002018, ..., -4.81264608,\n",
+       "         2.88287725, -1.36137392],\n",
+       "       [-0.48455451,  5.79456037,  0.48916682, ..., -6.7329493 ,\n",
+       "        -4.89032543, -7.55316121]])
          • tau
            (chain, draw)
            float64
            5.73 0.1845 4.443 ... 9.197 2.221
            array([[5.73017758e+00, 1.84481879e-01, 4.44296116e+00, ...,\n",
+       "        3.98330494e+01, 9.53069978e+00, 5.93247284e-02],\n",
+       "       [4.25284332e+01, 6.00660386e+00, 2.74997820e+00, ...,\n",
+       "        8.66855660e+00, 3.57687836e+01, 1.22358129e+01],\n",
+       "       [2.47402023e+01, 2.23230618e+00, 6.22901908e-01, ...,\n",
+       "        8.83360485e+00, 6.67212329e+00, 1.56668825e+00],\n",
+       "       [7.01345853e+01, 5.50504089e+00, 3.20007456e-01, ...,\n",
+       "        4.08060596e+01, 9.19651675e+00, 2.22100618e+00]])
          • theta_tilde
            (chain, draw, theta_tilde_dim_0)
            float64
            -0.3848 1.17 ... 1.963 -0.7431
            array([[[-3.84759532e-01,  1.16963585e+00, -6.39387159e-01, ...,\n",
+       "          1.64676773e+00, -7.32620331e-01,  1.42543110e+00],\n",
+       "        [ 1.25229019e+00, -8.77374020e-01,  2.38865353e-01, ...,\n",
+       "         -1.25275395e+00,  3.35137162e-01,  3.88075685e-01],\n",
+       "        [-1.85713392e-01,  2.26943538e-01, -9.15750816e-01, ...,\n",
+       "         -4.00545453e-01, -7.11047128e-01, -2.99759174e-02],\n",
+       "        ...,\n",
+       "        [-1.06647267e+00, -6.62496897e-01, -8.31741232e-01, ...,\n",
+       "         -4.63600395e-01,  4.06500738e-01, -1.34311335e+00],\n",
+       "        [ 2.83099912e+00, -6.82651154e-02, -4.32932670e-01, ...,\n",
+       "          1.40580066e+00, -6.01771344e-01, -1.39742294e-01],\n",
+       "        [ 2.09101110e-03,  1.24578808e+00, -2.84433658e-01, ...,\n",
+       "         -2.23442682e-01,  7.15745603e-01,  1.11335738e+00]],\n",
+       "\n",
+       "       [[ 1.49725231e+00,  1.26110180e-01, -1.56788392e-01, ...,\n",
+       "          1.79643212e+00, -5.42410432e-01,  1.69302413e-01],\n",
+       "        [-8.35708515e-01,  1.49204220e-01,  2.59427659e-01, ...,\n",
+       "          6.70159058e-01, -1.82970910e-01,  5.88670133e-01],\n",
+       "        [-5.15401582e-01, -3.69864296e-01, -3.42704721e-02, ...,\n",
+       "          2.84182545e-01,  1.42920618e+00,  1.23249100e+00],\n",
+       "        ...,\n",
+       "        [-9.03668862e-01,  1.11368558e+00,  7.07062311e-01, ...,\n",
+       "         -9.81545589e-01,  1.18106307e+00,  5.35441533e-01],\n",
+       "        [-6.86089881e-01, -1.57593517e+00, -4.35985674e-01, ...,\n",
+       "          1.17418877e+00, -1.13401978e+00,  3.28981450e-01],\n",
+       "        [ 9.16034571e-01, -1.45915693e+00, -1.67895737e+00, ...,\n",
+       "          7.02846926e-01,  5.64088202e-02, -7.62990265e-01]],\n",
+       "\n",
+       "       [[ 2.76199510e+00, -1.57262158e+00,  1.11241396e+00, ...,\n",
+       "         -4.18596774e-02, -5.84125836e-01,  1.04566176e+00],\n",
+       "        [ 5.23901899e-01,  8.55917974e-01, -1.48789769e-01, ...,\n",
+       "         -3.61452390e-01,  1.50701781e+00,  1.00039408e+00],\n",
+       "        [ 1.38308203e+00, -1.08250730e-01,  6.00410401e-02, ...,\n",
+       "          5.64164544e-01,  9.91960532e-01, -6.90006417e-01],\n",
+       "        ...,\n",
+       "        [ 1.51420461e-01,  1.16473432e+00,  1.68715476e+00, ...,\n",
+       "         -5.88069236e-02,  1.33898295e+00, -3.85568655e-01],\n",
+       "        [ 8.63060440e-01,  7.40863308e-01, -1.67433123e-02, ...,\n",
+       "          3.43992941e-01, -5.80259919e-01, -2.25321126e-01],\n",
+       "        [-3.55221848e-01, -8.05489175e-01, -4.94224025e-01, ...,\n",
+       "         -1.41247252e-01,  7.74878578e-02,  4.72346642e-01]],\n",
+       "\n",
+       "       [[-4.89228585e-01, -6.64699793e-01, -1.84868093e-01, ...,\n",
+       "          9.74433924e-01,  1.27512030e-02,  1.93197864e+00],\n",
+       "        [-8.25146669e-01, -1.85878476e+00,  2.79246713e-02, ...,\n",
+       "          4.41117392e-02, -1.69376936e-01, -3.81356167e-01],\n",
+       "        [-9.77437969e-01, -1.44282133e+00,  4.32664252e-01, ...,\n",
+       "          1.30427082e-01,  7.88506853e-01,  1.15544859e+00],\n",
+       "        ...,\n",
+       "        [ 1.83217827e-02, -3.54950247e-01, -9.53511916e-01, ...,\n",
+       "          4.28024721e-01, -2.83337300e-01,  4.38615469e-03],\n",
+       "        [ 8.67385101e-01, -7.67011692e-01,  8.88529670e-01, ...,\n",
+       "         -4.76144614e-01,  1.06189275e-02,  1.30143218e+00],\n",
+       "        [ 3.98181051e-02,  9.00660162e-01,  5.02505799e-01, ...,\n",
+       "         -5.18776518e-01,  1.96295042e+00, -7.43125886e-01]]])
        • created_at :
          2020-06-10T18:22:24.889273
          arviz_version :
          0.8.3
          inference_library :
          pyjags
          inference_library_version :
          1.3.6

        \n", + "
      \n", + "
      \n", + "
      • \n", + " \n", + "
    \n", + "
    \n", + " " ], "text/plain": [ - " mean sd hdi_3% hdi_97% mcse_mean mcse_sd ess_mean \\\n", - "mu 4.420 3.338 -1.740 10.720 0.027 0.019 15675.0 \n", - "tau 3.614 3.305 0.000 9.327 0.053 0.041 3961.0 \n", - "theta_tilde[0] 0.311 0.990 -1.605 2.128 0.008 0.006 16104.0 \n", - "theta_tilde[1] 0.104 0.941 -1.678 1.877 0.007 0.005 19509.0 \n", - "theta_tilde[2] -0.076 0.964 -1.879 1.740 0.007 0.005 19621.0 \n", - "theta_tilde[3] 0.061 0.930 -1.718 1.825 0.007 0.005 18988.0 \n", - "theta_tilde[4] -0.166 0.930 -1.991 1.559 0.007 0.005 18997.0 \n", - "theta_tilde[5] -0.068 0.945 -1.845 1.725 0.007 0.005 18916.0 \n", - "theta_tilde[6] 0.348 0.959 -1.409 2.189 0.008 0.005 15849.0 \n", - "theta_tilde[7] 0.083 0.985 -1.842 1.885 0.007 0.005 19629.0 \n", - "\n", - " ess_sd ess_bulk ess_tail r_hat \n", - "mu 15675.0 15747.0 17914.0 1.0 \n", - "tau 3198.0 5245.0 4677.0 1.0 \n", - "theta_tilde[0] 16104.0 16117.0 19148.0 1.0 \n", - "theta_tilde[1] 18216.0 19495.0 19244.0 1.0 \n", - "theta_tilde[2] 19090.0 19660.0 19388.0 1.0 \n", - "theta_tilde[3] 18988.0 18999.0 19073.0 1.0 \n", - "theta_tilde[4] 18419.0 18998.0 19344.0 1.0 \n", - "theta_tilde[5] 18916.0 18914.0 19186.0 1.0 \n", - "theta_tilde[6] 15849.0 15898.0 19764.0 1.0 \n", - "theta_tilde[7] 19629.0 19633.0 19391.0 1.0 " + "Inference data with groups:\n", + "\t> posterior\n", + "\t> prior\n", + "\n", + "Warmup iterations saved (warmup_*)." ] }, "execution_count": 26, @@ -30418,15 +28415,14 @@ } ], "source": [ - "az.summary(pyjags_data)" + "pyjags_data = az.from_pyjags(\n", + " posterior=jags_posterior_samples, \n", + " prior=jags_prior_samples, \n", + " save_warmup=True, \n", + " warmup_iterations=1000\n", + ")\n", + "pyjags_data" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { @@ -30446,10 +28442,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", -<<<<<<< HEAD "version": "3.6.10" -======= - "version": "3.7.6" }, "toc": { "base_numbering": 1, @@ -30463,7 +28456,6 @@ "toc_position": {}, "toc_section_display": true, "toc_window_display": false ->>>>>>> master } }, "nbformat": 4, diff --git a/doc/notebooks/Introduction.ipynb b/doc/notebooks/Introduction.ipynb index b403f4e276..169197e196 100644 --- a/doc/notebooks/Introduction.ipynb +++ b/doc/notebooks/Introduction.ipynb @@ -43,7 +43,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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    " ] @@ -70,7 +70,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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    " ] @@ -160,7 +160,7 @@ " }\n", " \n", " \n", - " 100.00% [2000/2000 00:04<00:00 Sampling 2 chains, 7 divergences]\n", + " 100.00% [2000/2000 00:09<00:00 Sampling 2 chains, 28 divergences]\n", " \n", " " ], @@ -175,10 +175,10 @@ "name": "stderr", "output_type": "stream", "text": [ - "There were 3 divergences after tuning. Increase `target_accept` or reparameterize.\n", - "The acceptance probability does not match the target. It is 0.7032617674641958, but should be close to 0.8. Try to increase the number of tuning steps.\n", - "There were 4 divergences after tuning. Increase `target_accept` or reparameterize.\n", - "The acceptance probability does not match the target. It is 0.6593200895612787, but should be close to 0.8. Try to increase the number of tuning steps.\n", + "There were 8 divergences after tuning. Increase `target_accept` or reparameterize.\n", + "There were 20 divergences after tuning. Increase `target_accept` or reparameterize.\n", + "The acceptance probability does not match the target. It is 0.7010749464965044, but should be close to 0.8. Try to increase the number of tuning steps.\n", + "The rhat statistic is larger than 1.05 for some parameters. This indicates slight problems during sampling.\n", "The estimated number of effective samples is smaller than 200 for some parameters.\n" ] }, @@ -200,7 +200,7 @@ " }\n", " \n", " \n", - " 100.00% [1000/1000 00:00<00:00]\n", + " 100.00% [1000/1000 00:01<00:00]\n", " \n", " " ], @@ -237,9 +237,17 @@ "execution_count": 7, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/percy/anaconda3/envs/arviz/lib/python3.6/site-packages/arviz/data/io_pymc3.py:89: FutureWarning: Using `from_pymc3` without the model will be deprecated in a future release. Not using the model will return less accurate and less useful results. Make sure you use the model argument or call from_pymc3 within a model context.\n", + " FutureWarning,\n" + ] + }, { "data": { - "image/png": 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"text/plain": [ "
    " ] @@ -279,8 +287,8 @@ "
      \n", " \n", "
    • \n", - " \n", - " \n", + " \n", + " \n", "
      \n", "
      \n", "
        \n", @@ -618,573 +626,573 @@ " stroke: currentColor;\n", " fill: currentColor;\n", "}\n", - "
        xarray.Dataset
          • chain: 2
          • draw: 500
          • school: 8
          • chain
            (chain)
            int64
            0 1
            array([0, 1])
          • draw
            (draw)
            int64
            0 1 2 3 4 5 ... 495 496 497 498 499
            array([  0,   1,   2, ..., 497, 498, 499])
          • school
            (school)
            <U16
            'Choate' ... 'Mt. Hermon'
            array(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
-       "       'Hotchkiss', 'Lawrenceville', "St. Paul's", 'Mt. Hermon'], dtype='<U16')
          • mu
            (chain, draw)
            float64
            4.45 3.18 -5.307 ... 4.155 6.853
            array([[ 4.44989359e+00,  3.18026068e+00, -5.30729597e+00,\n",
-       "        -1.77919561e+00, -2.45517955e+00,  9.06611876e+00,\n",
-       "         1.32318478e+01,  1.91589793e+00,  3.42242348e+00,\n",
-       "         6.57171154e+00,  4.57052379e+00,  5.94723510e+00,\n",
-       "         9.27924171e+00,  5.12448203e+00,  6.45848835e+00,\n",
-       "         4.18543262e+00,  4.01683793e+00,  1.99123267e+00,\n",
-       "         4.52100602e+00,  9.86194846e+00,  9.22788162e+00,\n",
-       "         9.10759650e+00,  3.98886932e+00,  8.91308443e+00,\n",
-       "         3.91442753e+00,  9.80902818e-01,  6.01140147e+00,\n",
-       "         4.71808842e+00,  4.13096553e+00,  4.14756583e+00,\n",
-       "         4.04565073e+00,  2.54451663e+00,  2.63984542e+00,\n",
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-       "         2.43865647e+00,  8.97710605e+00,  9.12417214e+00,\n",
-       "         8.71388504e+00,  9.75962979e+00,  9.41780287e+00,\n",
-       "         9.81909844e+00,  1.08491833e+01,  1.00721956e+01,\n",
-       "         1.17280060e+01,  1.08642070e+01,  2.90649629e+00,\n",
-       "         2.90649629e+00,  4.28881112e+00,  9.79629949e-01,\n",
-       "         1.45678889e+00, -1.75896128e+00, -7.29632608e-01,\n",
-       "         2.15153601e-01,  2.15153601e-01,  2.15153601e-01,\n",
-       "         2.15153601e-01,  2.15153601e-01,  2.15153601e-01,\n",
-       "         2.15153601e-01,  2.15153601e-01,  2.15153601e-01,\n",
-       "         2.15153601e-01,  2.15153601e-01,  2.15153601e-01,\n",
-       "         2.15153601e-01,  2.15153601e-01,  2.15153601e-01,\n",
-       "         2.15153601e-01,  2.15153601e-01,  2.15153601e-01,\n",
-       "         2.15153601e-01,  2.15153601e-01,  2.15153601e-01,\n",
-       "         2.15153601e-01,  2.15153601e-01,  2.15153601e-01,\n",
-       "         2.15153601e-01,  2.15153601e-01,  2.15153601e-01,\n",
-       "         2.15153601e-01,  2.15153601e-01,  2.15153601e-01,\n",
-       "         2.15153601e-01,  2.15153601e-01,  2.15153601e-01,\n",
-       "         2.15153601e-01,  2.15153601e-01,  2.15153601e-01,\n",
-       "         2.15153601e-01,  2.15153601e-01,  2.15153601e-01,\n",
-       "         2.15153601e-01,  2.15153601e-01,  2.15153601e-01,\n",
-       "         2.15153601e-01,  2.15153601e-01,  2.15153601e-01,\n",
-       "         2.15153601e-01,  2.15153601e-01,  2.15153601e-01,\n",
-       "         2.15153601e-01,  2.15153601e-01,  2.15153601e-01,\n",
-       "         2.15153601e-01,  2.15153601e-01,  2.15153601e-01,\n",
-       "         2.15153601e-01,  2.15153601e-01,  2.15153601e-01,\n",
-       "         2.15153601e-01,  2.15153601e-01,  2.15153601e-01,\n",
-       "         2.15153601e-01,  2.15153601e-01,  2.15153601e-01,\n",
-       "         2.15153601e-01,  2.15153601e-01,  2.15153601e-01,\n",
-       "         2.15153601e-01,  2.15153601e-01,  2.15153601e-01,\n",
-       "         2.15153601e-01,  2.15153601e-01,  2.15153601e-01,\n",
-       "         2.15153601e-01,  2.15153601e-01,  2.15153601e-01,\n",
-       "         2.15153601e-01,  2.15153601e-01,  2.15153601e-01,\n",
-       "         2.15153601e-01,  8.85490803e-01, -3.76383171e-01,\n",
-       "        -3.76383171e-01, -1.51217090e+00, -9.01704689e-01,\n",
-       "        -9.01704689e-01, -1.10350417e+00,  1.86768746e+00,\n",
-       "         2.54420475e+00,  2.17560719e+00,  2.43332664e+00,\n",
-       "         3.85816574e+00,  5.65749841e+00,  5.65749841e+00,\n",
-       "         6.52021252e+00,  6.52021252e+00,  6.52021252e+00,\n",
-       "         4.38862682e+00,  6.99179020e+00,  7.68057239e+00,\n",
-       "         5.21462609e+00,  4.38336721e+00,  9.08430229e+00,\n",
-       "         8.31259046e+00,  8.54765328e+00,  9.96800991e-01,\n",
-       "         4.64428095e+00,  2.76491329e+00,  3.29384060e+00,\n",
-       "         3.43290603e+00,  2.33411260e+00,  4.04978603e+00,\n",
-       "         2.52232796e+00,  2.49728803e+00,  1.21402275e+00,\n",
-       "        -9.70964197e-01, -2.58545748e+00,  1.21982171e-02,\n",
-       "         1.78386793e-01,  1.02593465e+00,  1.62020732e+00,\n",
-       "         4.33743804e+00,  4.30129985e+00,  3.15367039e+00,\n",
-       "         2.52893265e+00,  3.74185464e+00,  4.42967750e+00,\n",
-       "         5.57173537e+00,  5.17724929e+00,  5.17317637e+00,\n",
-       "         4.70664269e+00,  2.29912944e+00,  9.39708927e-01,\n",
-       "        -1.44797067e+00, -4.09857129e+00, -2.72204674e+00,\n",
-       "         4.09844910e+00,  5.05053253e+00,  3.77033126e+00,\n",
-       "         2.89548750e+00,  4.13148338e+00,  4.64594238e+00,\n",
-       "         2.29947497e+00,  2.37928354e+00,  2.87668343e+00,\n",
-       "         1.87369717e+00,  4.12354082e+00,  2.00836069e+00,\n",
-       "         3.35848175e+00,  5.33537202e+00,  8.20564574e+00,\n",
-       "         3.84176840e+00,  6.48125420e+00,  3.49829288e+00,\n",
-       "         3.16115800e+00,  3.39842439e+00,  4.10880024e+00,\n",
-       "         2.89236604e+00,  2.65674539e+00,  3.05789045e+00,\n",
-       "         4.84541078e+00,  5.64916108e-01,  2.08247102e+00,\n",
-       "         4.47823226e+00,  5.50659778e+00,  5.78140606e+00,\n",
-       "         7.64585809e+00,  6.64957557e+00,  4.44631326e+00,\n",
-       "         5.50676658e+00,  1.32742846e+00,  1.32742846e+00,\n",
-       "         2.34147980e+00,  1.11317217e+00,  1.95399233e+00,\n",
-       "         6.09756763e-01,  2.65575863e+00,  7.30784812e-01,\n",
-       "         4.58600594e+00,  3.38011586e+00,  4.19146468e+00,\n",
-       "         1.28174431e+00,  2.59785916e+00,  1.65116570e+00,\n",
-       "         1.21538878e+00,  6.91060701e-01,  1.70503784e+00,\n",
-       "         2.57290694e+00,  1.41955763e-01,  7.30495210e+00,\n",
-       "         7.21041717e+00,  7.72685850e+00,  7.69352667e+00,\n",
-       "         9.51180226e+00,  9.51180226e+00,  1.05224928e+01,\n",
-       "         1.05224928e+01,  3.50610501e+00,  4.97703545e+00,\n",
-       "         4.78188634e+00,  5.66423882e+00,  2.64516025e+00,\n",
-       "         8.46411249e+00,  4.33935652e+00,  5.79290618e+00,\n",
-       "         5.29772240e+00,  1.77997789e+00, -7.89502916e-01,\n",
-       "        -7.89502916e-01,  1.89459389e-01,  2.10804139e-01,\n",
-       "         2.10804139e-01,  2.10804139e-01, -3.70647858e+00,\n",
-       "        -3.70647858e+00, -6.14595145e+00, -2.64546649e+00,\n",
-       "        -5.40805365e+00, -5.59699030e+00,  4.55012800e+00,\n",
-       "         4.01018193e+00,  3.35255882e-01,  5.72391565e+00,\n",
-       "         5.11969696e+00,  6.00684172e+00,  3.86700407e+00,\n",
-       "         3.20174801e+00,  2.20497401e+00,  3.88707024e+00,\n",
-       "         4.71300382e+00,  3.56637950e+00,  3.56637950e+00,\n",
-       "         3.56637950e+00,  3.95083472e+00,  6.98945578e+00,\n",
-       "         6.38678250e+00,  4.92703146e+00,  5.21385572e+00,\n",
-       "         5.21017162e+00,  1.86211043e+00,  7.06000855e+00,\n",
-       "         4.48080314e-01,  8.92343403e+00,  3.76589045e+00,\n",
-       "         6.28458191e+00,  5.86258325e+00,  1.55463644e+00,\n",
-       "         7.03526407e-01,  1.00716205e+01,  1.02124868e+00,\n",
-       "        -1.52171327e+00, -7.08577079e-01,  3.08197456e+00,\n",
-       "         5.52617842e+00,  8.10740104e+00,  4.41898267e+00,\n",
-       "         7.42988921e+00,  3.45356122e+00,  3.55781077e+00,\n",
-       "         2.47671273e+00,  4.01632627e-02,  8.13332610e+00,\n",
-       "         6.25128458e+00, -2.35106696e+00, -1.64337858e+00,\n",
-       "        -1.39070801e+00,  5.05690057e+00,  5.16553349e+00,\n",
-       "         7.02382286e+00,  8.11526581e+00,  5.32712890e+00,\n",
-       "         4.83653591e+00,  6.83335454e+00,  6.21671254e+00,\n",
-       "         5.73363757e+00,  4.03813834e+00,  7.00381831e+00,\n",
-       "         4.43721639e+00,  4.97099686e+00,  1.93116785e+00,\n",
-       "         4.53273408e+00,  5.14232072e+00, -2.73422213e-01,\n",
-       "         2.21614469e+00,  2.04942952e+00,  1.59207173e+00,\n",
-       "         4.18236452e+00,  1.44309854e+00,  8.15213312e+00,\n",
-       "         6.09602907e+00,  8.21894946e+00, -5.28458365e-01,\n",
-       "        -9.46442321e-01,  8.99322950e-01,  4.09829629e+00,\n",
-       "         5.70905761e-01,  1.51823751e+00,  4.28432748e+00,\n",
-       "         7.44705499e+00,  2.28817336e+00,  1.87115137e+00,\n",
-       "         3.74452986e+00, -7.28791068e-01,  5.37969558e+00,\n",
-       "         1.78502381e-01,  3.60809610e+00,  3.41595543e+00,\n",
-       "         1.44503013e-01,  4.07566705e+00,  5.28026267e+00,\n",
-       "         3.52025904e+00,  5.23642322e+00,  4.72214365e+00,\n",
-       "         5.47383457e+00,  3.38143272e+00,  1.82024422e+00,\n",
-       "         8.56132936e-01,  3.10472083e-02,  2.56439446e+00,\n",
-       "         2.56439446e+00,  3.89013037e+00,  5.63164049e+00,\n",
-       "         4.15479956e+00,  6.85276017e+00]])
          • theta
            (chain, draw, school)
            float64
            11.18 1.053 4.916 ... 6.669 12.67
            array([[[ 11.17881673,   1.05250176,   4.91645367, ...,  -2.4378686 ,\n",
-       "          11.81281447,  -0.93787861],\n",
-       "        [ 12.82872622,   0.17362158,   3.85817533, ...,  -2.78749748,\n",
-       "          14.47379589,  -0.63999867],\n",
-       "        [ -7.45664925,   3.66670299,  -8.16612314, ...,  -4.44612383,\n",
-       "         -10.8963984 ,  -0.98104479],\n",
+       "
            xarray.Dataset
              • chain: 2
              • draw: 500
              • school: 8
              • chain
                (chain)
                int64
                0 1
                array([0, 1])
              • draw
                (draw)
                int64
                0 1 2 3 4 5 ... 495 496 497 498 499
                array([  0,   1,   2, ..., 497, 498, 499])
              • school
                (school)
                <U16
                'Choate' ... 'Mt. Hermon'
                array(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
+       "       'Hotchkiss', 'Lawrenceville', "St. Paul's", 'Mt. Hermon'], dtype='<U16')
              • mu
                (chain, draw)
                float64
                3.512 7.019 6.928 ... 8.932 6.556
                array([[ 3.51195785e+00,  7.01917337e+00,  6.92779794e+00,\n",
+       "         6.47569990e+00,  8.08536381e+00,  2.19365969e+00,\n",
+       "         3.65244232e+00,  2.20505112e+00,  5.91729285e+00,\n",
+       "         6.26127520e+00,  6.45827158e+00,  3.76748312e+00,\n",
+       "         7.11316411e+00,  8.14176645e+00,  6.73415756e+00,\n",
+       "         4.68266916e+00,  5.23938322e+00,  5.95952464e+00,\n",
+       "         1.52444456e+00,  8.72324149e+00,  6.30506482e+00,\n",
+       "         3.80944648e+00,  3.61642865e+00,  4.61235548e-01,\n",
+       "         5.22961000e+00,  6.03880910e+00,  2.60322966e+00,\n",
+       "         4.98563058e+00,  2.19151534e+00,  5.12087882e+00,\n",
+       "         5.94707603e+00,  9.31427437e+00,  4.32672371e+00,\n",
+       "         3.58377338e+00,  3.13419921e+00,  1.69562648e+00,\n",
+       "        -6.27103296e-01,  8.69385414e-01,  5.45361787e+00,\n",
+       "         5.34006719e+00,  6.21134880e+00,  8.42104639e-01,\n",
+       "         1.20280074e+00,  2.67233434e+00,  1.31463216e-02,\n",
+       "         1.72743667e+00,  3.52052102e-01,  3.75288451e+00,\n",
+       "         1.39502520e+00,  4.47969124e+00,  6.83072765e+00,\n",
+       "         7.59733027e+00, -1.93159299e+00, -3.51396602e+00,\n",
+       "         4.04944599e+00,  8.72322935e+00,  3.04455129e+00,\n",
+       "         3.40274637e+00,  3.38480866e+00,  1.22596514e+00,\n",
+       "         5.20533178e+00,  5.44063535e+00,  7.13444426e+00,\n",
+       "         4.89426435e+00,  5.83544428e+00,  5.58348782e+00,\n",
+       "         5.36811450e+00,  6.40725144e+00,  5.45007971e+00,\n",
+       "         1.11964277e+01,  7.90820912e+00,  9.43406336e+00,\n",
+       "         1.36960243e+00,  1.48985239e+00,  2.76268198e+00,\n",
+       "         2.30960648e+00,  2.03965817e+00,  6.88195912e-01,\n",
+       "         1.00079345e+00,  1.87923033e+00,  5.37278201e-01,\n",
+       "         1.89643112e+00, -3.49860632e+00, -1.51097794e+00,\n",
+       "         2.23300763e+00, -1.12660306e+00,  4.37294486e+00,\n",
+       "         8.50743753e-02,  2.70058404e+00,  9.98875217e+00,\n",
+       "         5.78745248e+00,  2.36137812e+00,  6.91642548e+00,\n",
+       "         3.23450264e+00,  3.12976357e+00,  3.28381858e+00,\n",
+       "         2.17308963e+00,  1.56571699e+00, -9.55507438e-02,\n",
+       "         2.14306915e-01,  2.00763583e+00,  4.11377390e+00,\n",
+       "         2.21128017e+00,  3.79102599e+00,  7.25633386e+00,\n",
+       "         8.25827150e+00,  8.04694636e+00,  9.83133752e+00,\n",
+       "         7.95777841e+00,  7.67583003e+00,  9.28392580e+00,\n",
+       "         7.87419851e+00,  8.76189084e+00,  8.89144437e+00,\n",
+       "         8.89144437e+00,  8.89144437e+00,  9.93634052e+00,\n",
+       "         8.26409781e+00,  6.92072258e+00,  2.04297379e+00,\n",
+       "         3.90393683e-02,  4.06630975e+00,  1.66389357e+00,\n",
+       "         2.11649112e+00,  3.27684657e+00,  1.57880888e+00,\n",
+       "        -1.16317379e+00,  1.10752445e+01,  1.32724595e+01,\n",
+       "         9.08382451e+00,  1.14183996e+01,  3.87515263e+00,\n",
+       "         3.76669799e+00,  3.66306049e+00, -3.20007212e+00,\n",
+       "         6.06185747e+00,  7.04140404e+00,  6.59297569e+00,\n",
+       "         7.86777425e+00,  7.13396583e+00,  4.39782496e+00,\n",
+       "         4.72162074e+00,  4.72162074e+00,  4.72162074e+00,\n",
+       "         4.72162074e+00,  4.72162074e+00,  5.09803930e+00,\n",
+       "         4.69606443e+00,  4.69606443e+00,  4.69606443e+00,\n",
+       "         4.26456141e+00,  4.97933289e+00,  4.97933289e+00,\n",
+       "         4.97933289e+00,  1.21591864e+00, -1.16144258e+00,\n",
+       "         2.26910138e+00,  1.28394440e+00, -1.37271634e+00,\n",
+       "         3.93312931e+00,  2.84273164e+00, -3.11707638e-01,\n",
+       "        -5.87394446e-01, -5.84303443e-01,  1.74997031e-01,\n",
+       "         9.61091861e-01,  5.31615139e+00,  5.48102034e+00,\n",
+       "        -1.69506328e+00,  6.03575011e+00, -3.24626526e+00,\n",
+       "         4.26654687e+00,  4.96572775e+00,  5.94403492e+00,\n",
+       "         2.17234873e-01, -6.17444514e-01, -1.88166146e+00,\n",
+       "         2.54682033e+00,  4.22087318e+00,  5.00202927e+00,\n",
+       "         1.71673395e+00,  9.14486494e+00,  8.56546264e+00,\n",
+       "         8.96110726e+00,  6.81906700e+00,  6.58674268e+00,\n",
+       "         5.34652335e+00,  6.29929616e+00,  6.77577574e+00,\n",
+       "         5.48215683e+00, -8.90156161e-01,  5.42252687e+00,\n",
+       "         3.67234154e+00,  7.57719031e+00,  7.67514108e+00,\n",
+       "         2.91224227e+00,  6.03711427e-01,  5.00421045e+00,\n",
+       "        -4.51011712e+00,  6.62131067e+00,  4.00823882e+00,\n",
+       "         1.52157756e+00,  5.34224927e+00, -7.43534705e-01,\n",
+       "         7.23068677e+00,  3.56319789e+00,  4.24199339e+00,\n",
+       "         4.66494333e+00,  3.55428570e+00,  2.36397658e+00,\n",
+       "         2.83246370e+00,  5.47089122e+00,  5.43433201e+00,\n",
+       "         8.01070301e+00,  1.39405227e+01,  6.57251236e+00,\n",
+       "         4.95607031e-01,  4.83590271e+00,  4.94076987e+00,\n",
+       "         6.60425903e+00,  9.91608232e+00,  8.87384097e+00,\n",
+       "         6.87554783e+00,  5.22623548e+00,  1.49783921e+00,\n",
+       "         1.31781090e+00,  4.26263902e+00,  4.06934357e+00,\n",
+       "         4.51604168e+00,  4.51604168e+00,  5.33345347e+00,\n",
+       "         4.75484094e+00,  4.87396423e+00,  4.46413123e+00,\n",
+       "         3.77005516e+00,  8.50333312e+00,  8.52377148e+00,\n",
+       "         1.95148205e+00,  1.95148205e+00,  1.95148205e+00,\n",
+       "         1.95148205e+00,  8.87558786e-01,  6.77794554e-01,\n",
+       "         6.77794554e-01,  6.00683581e-01,  6.00683581e-01,\n",
+       "         2.13140444e-01,  2.13140444e-01,  9.62265948e-01,\n",
+       "         9.62265948e-01,  9.62265948e-01,  9.62265948e-01,\n",
+       "         2.97369250e+00,  3.50295664e+00,  3.16138578e+00,\n",
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+       "         7.00271050e+00,  7.00271050e+00,  7.00271050e+00,\n",
+       "         7.00271050e+00,  7.00271050e+00,  7.00271050e+00,\n",
+       "         7.00271050e+00,  7.00271050e+00,  7.00271050e+00,\n",
+       "         7.00271050e+00,  7.00271050e+00,  7.00271050e+00,\n",
+       "         7.00271050e+00,  7.00271050e+00,  7.00271050e+00,\n",
+       "         7.00271050e+00,  7.00271050e+00,  7.00271050e+00,\n",
+       "         7.00271050e+00,  7.00271050e+00,  7.00271050e+00,\n",
+       "         7.00271050e+00,  7.00271050e+00,  7.00271050e+00,\n",
+       "         7.00271050e+00,  7.00271050e+00,  7.00271050e+00,\n",
+       "         7.00271050e+00,  7.00271050e+00,  7.00271050e+00,\n",
+       "         7.00271050e+00,  7.00271050e+00,  7.00271050e+00,\n",
+       "         7.00271050e+00,  7.00271050e+00,  7.00271050e+00,\n",
+       "         7.00271050e+00,  7.00271050e+00,  7.00271050e+00,\n",
+       "         7.00271050e+00,  7.00271050e+00,  7.00271050e+00,\n",
+       "         7.00271050e+00,  7.00271050e+00,  7.00271050e+00,\n",
+       "         7.00271050e+00,  7.00271050e+00,  7.00271050e+00,\n",
+       "         7.00271050e+00,  7.00271050e+00,  7.00271050e+00,\n",
+       "         7.00271050e+00,  7.00271050e+00,  7.00271050e+00,\n",
+       "         7.00271050e+00,  7.00271050e+00,  7.00271050e+00,\n",
+       "         7.00271050e+00,  7.28918369e+00,  7.28918369e+00,\n",
+       "         7.28918369e+00,  7.79406447e+00,  7.37188274e+00,\n",
+       "         4.99661803e+00,  6.15600775e+00,  6.33158534e+00,\n",
+       "        -2.44004724e+00, -1.41019288e+00,  6.46119482e+00,\n",
+       "         7.49143600e+00,  8.66585278e+00,  7.71151541e+00,\n",
+       "         9.19750914e+00,  3.15677149e+00,  2.27029450e+00,\n",
+       "         4.06501115e+00,  5.42602431e+00,  5.47474935e+00,\n",
+       "         4.92843005e+00,  4.10757446e+00,  9.89210184e+00,\n",
+       "         1.38284992e+01,  1.08730943e+01,  1.04565129e+01,\n",
+       "         7.88747336e+00,  9.57606543e+00, -1.67341575e+00,\n",
+       "        -8.76106406e-01, -3.80433909e-01, -1.45107436e-01,\n",
+       "         2.82789048e-01,  2.45110278e-01, -3.05020827e+00,\n",
+       "        -3.64902676e-01,  3.30531018e+00,  3.43108278e+00,\n",
+       "         6.42328656e+00,  7.43776480e+00,  7.01249761e+00,\n",
+       "         4.56480110e+00,  4.96057858e+00,  4.50726956e+00,\n",
+       "         4.92606350e+00,  4.19785477e+00,  2.37624992e+00,\n",
+       "        -1.12270584e-01, -6.39096297e-02, -3.29190669e+00,\n",
+       "         2.15480018e+00,  3.50166005e+00,  2.39339122e+00,\n",
+       "         1.47543845e+00,  8.53976865e+00,  7.71207664e+00,\n",
+       "         6.03514284e+00,  1.21351206e+01,  1.14523457e+01,\n",
+       "         8.93169830e+00,  6.55633128e+00]])
              • theta
                (chain, draw, school)
                float64
                6.531 3.607 0.4764 ... 10.13 12.32
                array([[[ 6.53117908,  3.6065954 ,  0.47641283, ..., -0.92196222,\n",
+       "          4.92486228,  4.03024001],\n",
+       "        [ 4.82971661,  5.23241008,  8.70718583, ...,  9.02970049,\n",
+       "          5.84378847,  6.00280818],\n",
+       "        [12.84588154,  5.72244944,  8.09629112, ...,  7.31577128,\n",
+       "          9.28315496,  6.55685548],\n",
        "        ...,\n",
-       "        [  5.84407761,   5.35767678,   1.76958013, ...,   7.48542798,\n",
-       "           5.72098469,   4.78733115],\n",
-       "        [  5.84407761,   5.35767678,   1.76958013, ...,   7.48542798,\n",
-       "           5.72098469,   4.78733115],\n",
-       "        [  6.65047641,   4.14180457,   4.96096063, ...,   3.24494081,\n",
-       "           5.51854655,   5.64576333]],\n",
-       "\n",
-       "       [[  1.89808065,   9.14860898,   6.13166834, ...,   4.85267978,\n",
-       "           7.2228535 ,   6.36015008],\n",
-       "        [  2.97074505,   8.88566785,   6.56350757, ...,   4.66457925,\n",
-       "           5.66100623,   7.59144271],\n",
-       "        [  3.50207861,   9.3857739 ,   7.02957254, ...,   3.36536962,\n",
-       "           5.77176979,   7.54132391],\n",
+       "        [ 6.22525064,  6.04614614,  5.5073631 , ...,  8.32961177,\n",
+       "          5.88614179,  6.46095041],\n",
+       "        [ 6.22525064,  6.04614614,  5.5073631 , ...,  8.32961177,\n",
+       "          5.88614179,  6.46095041],\n",
+       "        [ 6.47224282,  7.07333431,  7.23084151, ...,  4.8586215 ,\n",
+       "          6.65519116,  6.4171545 ]],\n",
+       "\n",
+       "       [[10.61044089,  9.93823752, 11.43751424, ..., 10.98465408,\n",
+       "          9.39697225, 10.85064596],\n",
+       "        [ 8.57351916, 12.1206435 , 10.24247372, ...,  9.37021741,\n",
+       "         10.17802814,  9.63051968],\n",
+       "        [ 8.57351916, 12.1206435 , 10.24247372, ...,  9.37021741,\n",
+       "         10.17802814,  9.63051968],\n",
        "        ...,\n",
-       "        [  1.30754901,   6.38862991,   1.34619061, ...,   8.88757138,\n",
-       "           4.28924647,   9.33627892],\n",
-       "        [  9.1337481 ,   4.64669861,   7.18699457, ...,   0.96324186,\n",
-       "           5.15415303,   1.72465413],\n",
-       "        [  9.3525312 ,   2.22558717,   2.69395287, ...,   9.62450399,\n",
-       "           6.66915754,  12.67340736]]])
              • tau
                (chain, draw)
                float64
                4.875 4.042 5.603 ... 1.978 5.472
                array([[ 4.87496253,  4.04231931,  5.60307949,  8.49194572, 11.22309181,\n",
-       "         4.45691021,  5.62230469, 11.25287806,  4.65097855, 18.83350879,\n",
-       "        13.07520504,  6.7250893 ,  7.31484769,  7.44425768, 12.50222817,\n",
-       "         9.92165035,  8.59585189,  8.02889298,  7.10117701,  8.8152386 ,\n",
-       "        10.60101071,  6.46562512,  6.40437874,  9.24420649,  9.16837519,\n",
-       "         6.01522946,  3.02543206,  3.08620636,  3.21877972,  4.06757889,\n",
-       "         4.30389901,  7.64980013,  4.41086201,  3.76535086,  3.05572346,\n",
-       "         3.14695677,  1.87867643,  1.87867643,  1.87867643,  1.4802188 ,\n",
-       "         1.4802188 ,  1.4802188 ,  1.4802188 ,  1.14034044,  1.14034044,\n",
-       "         1.14034044,  1.14034044,  1.24730824,  1.24730824,  1.36243814,\n",
-       "         1.36243814,  1.39607106,  1.39607106,  1.3146174 ,  1.3146174 ,\n",
-       "         1.3146174 ,  1.3146174 ,  1.3146174 ,  1.57949811,  1.57949811,\n",
-       "         1.57949811,  1.57949811,  1.57949811,  1.57949811,  1.4762983 ,\n",
-       "         1.4762983 ,  1.4762983 ,  1.4762983 ,  1.4762983 ,  1.4762983 ,\n",
-       "         1.4762983 ,  1.4762983 ,  1.4762983 ,  1.4762983 ,  1.4762983 ,\n",
-       "         3.4795356 ,  4.45955648,  3.24422015,  4.81186998,  3.83207645,\n",
-       "         3.75951973,  5.56786242,  3.19423906,  2.96834418,  5.73596382,\n",
-       "         4.14801082,  3.00783828,  2.96763948,  2.96763948,  5.7139748 ,\n",
-       "         2.61057127,  2.02544637,  2.18149657,  2.03369641,  2.03369641,\n",
-       "         2.03369641,  2.03369641,  3.44633136,  2.19518172,  3.94691052,\n",
-       "         2.41811967,  2.65200769,  2.93609091,  3.61755089,  1.83585577,\n",
-       "         7.3317217 , 11.19986311,  4.56032089,  5.12596377,  4.66103387,\n",
-       "         3.3039563 ,  3.01541756,  3.51718201,  3.45853742,  5.88552971,\n",
-       "         6.25679393,  2.45387111,  2.48047082,  4.53779842,  3.50496513,\n",
-       "         6.58740593,  7.03344961,  4.34756764,  4.42061511,  6.02529631,\n",
-       "         4.8510639 ,  3.30852096,  2.97906621,  3.52480301,  2.15507047,\n",
-       "         2.6565469 ,  2.6565469 ,  2.57168477,  1.94016799,  2.4079637 ,\n",
-       "         1.82129769,  3.731645  ,  2.69064499,  7.28969875,  4.75672599,\n",
-       "         5.86141111,  4.8828992 ,  4.24994287,  7.90934153,  4.75785837,\n",
-       "         4.09248835,  3.18837062,  2.82887176,  2.186934  ,  3.6329454 ,\n",
-       "         2.64612391,  3.42646444,  4.8989631 ,  3.34481006,  4.87723302,\n",
-       "         3.58793707,  2.71359674,  6.71745793,  5.66453176,  6.06417964,\n",
-       "         3.17945383,  2.4218607 ,  3.24178259,  6.94463062,  4.51318007,\n",
-       "         4.99008876,  9.51964181,  6.68139307,  8.79497529,  7.97599983,\n",
-       "         7.02952304,  4.91564349,  7.35894225,  7.11806619, 14.87781029,\n",
-       "         4.66101978,  4.81566044,  6.58593275,  6.27415556,  6.52480026,\n",
-       "         9.88628246, 10.9204201 ,  6.63864591,  7.86736638,  4.77690931,\n",
-       "         2.05790061,  2.17154982,  3.01060546,  6.05360404,  2.93831871,\n",
-       "         7.12663449,  4.65725984,  6.69596564,  3.77264673,  4.40081357,\n",
-       "         4.84742253,  3.05207211,  3.41325165,  3.60522879,  2.73592948,\n",
-       "         5.6497371 ,  5.22535245,  6.21638125,  7.72683913,  6.14983861,\n",
-       "         3.28418158,  6.67596636,  3.41216567, 11.68884104,  7.64978151,\n",
-       "        11.49645968, 11.67045139,  9.95108334,  9.95858952,  4.39870374,\n",
-       "         6.86235695, 12.4998046 ,  6.48855143,  6.70167462,  7.13658725,\n",
-       "         8.2241664 ,  8.05565566,  5.68940774,  4.52365306,  4.99538326,\n",
-       "         6.11906042,  3.08320925,  2.43877911,  2.75837591,  1.99275939,\n",
-       "         2.29589066,  4.31018467,  2.49790497,  2.43175719,  2.43175719,\n",
-       "         2.01590244,  2.08760064,  2.08760064,  2.08760064,  2.81845   ,\n",
-       "         2.62153974,  3.4469418 ,  3.42587678,  2.19143621,  2.90003653,\n",
-       "         1.99333611,  2.02417773,  2.25729832,  3.86444472,  4.73307823,\n",
-       "         6.39363009, 12.45687313,  8.71287367,  7.69794219, 10.10418408,\n",
-       "         7.82922646, 10.20266255, 10.54219669,  3.41197402,  2.60352715,\n",
-       "         2.60352715, 12.58778938,  2.75778429,  2.79802492,  2.17596369,\n",
-       "         4.17220828,  4.47532229,  3.86308523,  6.08054973,  5.76725119,\n",
-       "        10.67219304,  7.96794757, 11.72062025,  8.67784178,  5.94012673,\n",
-       "        13.49521296,  9.90656917, 11.69740997,  6.95010722,  5.31818544,\n",
-       "         5.85342394,  5.49534874,  6.79021335,  8.37543598, 10.04384941,\n",
-       "         8.70424743,  7.76772369,  3.27065597,  4.40243612,  2.58753254,\n",
-       "         3.79659933,  4.36879   ,  3.04545264,  2.03493727,  2.44723498,\n",
-       "         3.02032485,  2.36533772,  2.92120372,  2.05631978,  2.05631978,\n",
-       "         3.38141159,  4.4208667 ,  2.99375515,  2.8219202 ,  1.9614063 ,\n",
-       "         2.06883636,  2.56189374,  3.54617512,  3.77003013,  6.58012038,\n",
-       "         6.7528589 ,  4.1527212 ,  3.06445609,  2.64407309,  1.6300495 ,\n",
-       "         1.6300495 ,  2.22277656,  2.22277656,  2.77183819,  3.83383279,\n",
-       "         3.00224368,  2.36450496,  2.36450496,  2.56447717,  1.8531181 ,\n",
-       "         1.53955738,  1.53955738,  1.53955738,  1.53955738,  1.63318296,\n",
-       "         1.63318296,  1.86483123,  1.71473354,  1.71473354,  1.71473354,\n",
-       "         4.97037934,  3.94362623,  2.9375983 ,  3.02948305,  2.20092591,\n",
-       "         4.43361573,  4.06182104,  4.07520591,  5.86357241,  5.10280445,\n",
-       "         7.95464106,  4.24533354, 10.09956754,  7.75836039,  6.77897924,\n",
-       "         7.00013197,  6.71323564,  4.87593999,  4.42279034,  3.06272572,\n",
-       "         9.11314565,  5.48396773,  5.01431862,  4.57372532,  2.73857071,\n",
-       "         2.97316641,  4.11649565,  4.49427571,  3.49558235,  3.84727526,\n",
-       "         4.78666986,  9.55395169,  5.51265341,  6.07249793,  5.97814298,\n",
-       "         3.55710265,  3.05518133,  2.82772287,  2.6101243 ,  3.54320645,\n",
-       "         3.26741004,  4.35501377,  8.8741689 ,  4.12293718,  2.21146418,\n",
-       "         2.21146418,  4.01652416,  4.68654566,  8.9458965 ,  8.62796228,\n",
-       "         6.33722841,  3.51037977,  4.91343102,  7.4969136 ,  7.88795451,\n",
-       "         8.20635343,  8.6045245 ,  9.55319517,  6.05797904,  6.78788887,\n",
-       "         5.44103743,  3.64234149,  4.73429753,  3.29833437,  2.53663688,\n",
-       "         3.19554695,  2.99510638,  2.22752818,  1.62690097,  1.62690097,\n",
-       "         1.62690097,  1.62690097,  1.62690097,  1.62690097,  1.62690097,\n",
-       "         2.53448114,  4.31246123,  4.28436666,  5.41616242,  4.18616144,\n",
-       "         5.15089087,  4.7545822 ,  6.4621661 ,  4.00183778,  2.9318403 ,\n",
-       "         3.07038642,  2.21211423,  3.22664098,  4.22065387,  5.27946285,\n",
-       "         5.17703026,  6.86096583,  7.55456002,  7.09609819,  8.98180754,\n",
-       "         9.17903342,  7.99839898,  2.5808782 ,  2.68511384,  1.65040858,\n",
-       "         1.81305252,  2.83857541,  4.20120328,  3.07032147,  4.69393137,\n",
-       "         4.99027302,  5.26603236,  9.53374607,  8.36143544,  6.04169344,\n",
-       "         4.02095243,  7.19417661,  6.37331177,  8.65107954,  6.37907358,\n",
-       "         4.5948025 ,  4.07958377,  4.63372478,  3.33275935,  5.93185029,\n",
-       "         3.94392161,  2.53421463,  5.59209648,  6.36802256,  9.08673942,\n",
-       "        15.24740266,  6.23535928,  5.29695598,  6.73717916,  8.63444508,\n",
-       "         8.04699313, 10.14847935, 12.59830117,  5.86185149,  6.69107613,\n",
-       "         2.59540414,  2.59540414,  3.72209586,  3.67377285,  3.35780411,\n",
-       "         7.7391137 ,  2.8561222 ,  2.8561222 ,  2.12710679,  2.65584051,\n",
-       "         6.46154462,  6.88907122, 10.47113525,  6.59860185,  2.54124757,\n",
-       "         2.64894125,  1.87236325,  1.87236325,  1.47696233,  1.59960475,\n",
-       "         1.59052016,  1.59052016,  1.59052016,  1.59052016,  1.59052016,\n",
-       "         1.63530062,  1.63530062,  1.63530062,  1.63530062,  1.73896123],\n",
-       "       [ 3.68058571,  3.98835094,  4.00653335,  7.12995266,  8.88464598,\n",
-       "         9.87694375,  8.00012551,  6.57826567,  6.23145172,  8.99871129,\n",
-       "         8.30986917,  5.60278998,  2.69506012,  2.11833131,  2.22341129,\n",
-       "         7.10648025,  3.82089677,  3.23520269,  3.60881317,  4.86492815,\n",
-       "         5.59531431,  5.87249984, 15.76906223,  8.07478884, 15.15627806,\n",
-       "        10.88622552,  5.33981537,  6.0355375 ,  3.7184077 ,  2.30117836,\n",
-       "         1.99388771,  3.21680712,  5.95833757,  7.16734591,  3.91826548,\n",
-       "         3.721883  ,  4.45786938, 14.08678209,  9.83794706,  7.60619316,\n",
-       "         6.29818741,  4.13245266,  6.65339305,  7.3531982 ,  8.39148647,\n",
-       "         5.23604933,  3.35443268,  3.09524081,  2.63862977,  5.79054141,\n",
-       "         2.61669811,  3.33467065,  1.80305957,  2.19878329,  2.04913517,\n",
-       "         1.61392393,  2.53023287,  1.65723719,  1.91614851,  1.91614851,\n",
-       "         2.6900256 ,  2.28342952,  2.3213357 ,  5.62990987,  3.09605967,\n",
-       "         4.52853882,  4.90607228,  5.29139322,  1.82916693,  1.91375308,\n",
-       "         2.75860738,  2.54737611,  2.26570442,  2.26570442, 10.00251836,\n",
-       "        11.65403485,  8.01674904,  4.90701486,  5.97366373,  5.61449849,\n",
-       "         8.92405076,  6.12368642,  4.53119728,  6.32384921,  3.186594  ,\n",
-       "         8.85717818, 11.72565475,  8.04523993,  9.85183144,  4.17854066,\n",
-       "         7.26263296,  6.34533848,  7.7529397 ,  4.74927783,  7.29995169,\n",
-       "        17.05022905, 14.39066025,  8.2716974 ,  8.86228057,  5.09613427,\n",
-       "         6.80742174,  3.97193948,  7.76896957,  4.53281161,  5.55462894,\n",
-       "         6.75140051,  4.5540024 ,  6.37258694,  6.38138748,  6.81715024,\n",
-       "        12.47588898,  8.61367875, 14.61363126,  8.57321443,  9.31199682,\n",
-       "         9.68276537, 20.14962898,  8.84903168,  6.81074148,  7.69394216,\n",
-       "        14.99601254, 19.49034852,  8.01280388,  3.49321859,  4.40901561,\n",
-       "         8.29146907, 11.25795621,  8.07520442,  8.81167727, 10.35024528,\n",
-       "         8.83549645, 11.23250324,  4.71037598,  3.11059188,  1.94147401,\n",
-       "         2.64398957,  1.84357368,  1.04231641,  1.04231641,  1.04231641,\n",
-       "         1.04231641,  2.27734912,  2.84084214,  3.80466762,  2.52963663,\n",
-       "         6.0448761 ,  3.65424953,  2.16203352,  2.06603412,  4.04522644,\n",
-       "         2.85735886,  2.59570572,  3.14150309,  1.93184138,  2.68553313,\n",
-       "         2.74890581,  6.46515568,  4.47180628,  7.7857996 ,  5.68396781,\n",
-       "         5.97654343,  3.15436246,  2.04675168,  3.87995185,  3.7674744 ,\n",
-       "         2.72755363,  2.18860477,  2.51155024,  3.17261972,  1.86003889,\n",
-       "         1.94611756,  1.94611756,  1.53340791,  2.8465656 ,  2.69934624,\n",
-       "         3.46900868,  1.16260469,  0.89025164,  0.89025164,  0.89025164,\n",
-       "         0.89025164,  0.89025164,  0.89025164,  0.89025164,  0.89025164,\n",
-       "         0.89025164,  0.89025164,  0.89025164,  0.89025164,  0.89025164,\n",
-       "         0.89025164,  0.89025164,  0.89025164,  0.89025164,  0.89025164,\n",
-       "         0.89025164,  0.89025164,  0.89025164,  0.89025164,  0.89025164,\n",
-       "         0.89025164,  0.89025164,  0.89025164,  0.89025164,  0.89025164,\n",
-       "         0.89025164,  0.89025164,  0.89025164,  0.89025164,  0.89025164,\n",
-       "         0.89025164,  0.89025164,  0.89025164,  0.89025164,  0.89025164,\n",
-       "         0.89025164,  0.89025164,  0.89025164,  0.89025164,  0.89025164,\n",
-       "         0.89025164,  0.89025164,  0.89025164,  0.89025164,  0.89025164,\n",
-       "         0.89025164,  0.89025164,  0.89025164,  0.89025164,  0.89025164,\n",
-       "         0.89025164,  0.89025164,  0.89025164,  0.89025164,  0.89025164,\n",
-       "         0.89025164,  0.89025164,  0.89025164,  0.89025164,  0.89025164,\n",
-       "         0.89025164,  0.89025164,  0.89025164,  0.89025164,  0.89025164,\n",
-       "         0.89025164,  0.89025164,  0.89025164,  0.89025164,  0.89025164,\n",
-       "         0.89025164,  0.89025164,  0.89025164,  0.89025164,  0.89025164,\n",
-       "         0.89025164,  1.39741164,  1.85055526,  1.85055526,  1.8063058 ,\n",
-       "         1.77631559,  1.77631559,  3.14244762,  2.53581528,  3.30317252,\n",
-       "         3.04499075,  2.01535912,  2.46757594,  1.13556478,  1.13556478,\n",
-       "         1.31060591,  1.31060591,  1.31060591,  1.66159731,  4.67486523,\n",
-       "         3.29575092,  5.06715114, 14.89454029,  8.64901009,  3.45460307,\n",
-       "         4.8444278 ,  3.01704247,  4.44211449,  4.27306405,  7.26996471,\n",
-       "         2.4174295 ,  4.95287373,  5.69476681,  4.29024753,  2.07505887,\n",
-       "         2.88567513,  5.45253664,  4.28999277,  3.02660678,  2.89658259,\n",
-       "         2.23391401,  2.35319701,  2.01834026,  1.84944283,  2.39301948,\n",
-       "         3.50126631,  5.61987146,  5.0467372 ,  3.87564631,  2.49117048,\n",
-       "         2.76372834,  5.98181795,  6.57987405,  5.22099862,  5.32114379,\n",
-       "        12.02953004, 10.74107535,  4.51628093,  4.53442064,  3.13430919,\n",
-       "         4.15070363,  5.25001249,  3.67790502,  2.85371634,  3.9655607 ,\n",
-       "         3.95842465,  3.39224695,  4.78328468,  3.50555728,  3.54316531,\n",
-       "         3.56192299,  2.4086793 ,  3.24999207,  3.87396415,  4.3015363 ,\n",
-       "         4.09947785,  4.41166489,  1.90674931,  2.7857621 ,  1.61579323,\n",
-       "         2.16062806,  2.89428751,  4.36301683,  3.13433252,  2.85845788,\n",
-       "         3.35136686,  4.36834967,  4.21891717,  6.237099  ,  2.46886981,\n",
-       "         3.60183221,  1.64789396,  1.64789396,  2.62619208,  3.78384289,\n",
-       "         4.2487973 ,  3.10591684,  5.29879151,  2.3390641 ,  3.32814866,\n",
-       "         5.23064623,  2.88204004,  3.2211747 ,  2.33917384,  1.90868073,\n",
-       "         2.0897423 ,  3.52719169,  2.50264623,  2.94314953,  6.21822876,\n",
-       "         3.40026927,  3.32267357,  2.83092867,  2.22867153,  1.4320662 ,\n",
-       "         1.4320662 ,  1.88340074,  1.88340074,  2.53790621,  3.08110739,\n",
-       "         3.50776967,  2.95263694,  3.40174344,  4.30299304,  4.17848193,\n",
-       "         3.65962812,  2.5625635 ,  5.22713894,  1.454944  ,  1.454944  ,\n",
-       "         1.44661112,  1.57387184,  1.57387184,  1.57387184,  1.77731444,\n",
-       "         1.77731444,  3.35352387,  3.61086199,  4.62611915,  6.78805825,\n",
-       "         4.85921988,  5.32440748,  3.79311876,  1.61842578,  2.16939738,\n",
-       "         1.94883509,  3.78585679,  2.06063432,  1.46332122,  2.89104514,\n",
-       "         2.59260804,  1.28789494,  1.28789494,  1.28789494,  1.5347956 ,\n",
-       "         1.81076556,  2.61448915,  2.49005258,  2.28011899,  5.37991104,\n",
-       "         4.69467013,  7.07492047,  4.13285415,  3.48519835, 10.36564179,\n",
-       "         6.14205765,  4.34976974,  3.22963625,  6.67580331,  6.29589744,\n",
-       "         7.13630463,  9.26705636, 12.10112489, 10.85385668, 13.0618523 ,\n",
-       "         5.17758538,  5.17318677, 10.77114341, 15.69148084, 11.85922615,\n",
-       "        23.0824281 , 18.04529926,  6.40422475, 19.53455177, 22.3395412 ,\n",
-       "        14.88067599, 11.5453065 ,  8.19508928,  6.17770704,  9.0367138 ,\n",
-       "         5.81164284, 10.34669445,  5.01375078,  9.62552993,  8.01095911,\n",
-       "        10.34391011,  3.26719948,  5.76486505,  7.07202004,  2.78463822,\n",
-       "         2.65961869,  2.67584155,  4.04659973,  7.48074929,  5.03220862,\n",
-       "         9.72486755,  5.9301018 ,  6.13858229, 10.47549883,  8.39049105,\n",
-       "         3.82084296,  4.86738473,  4.75321925,  4.93722273,  6.47429181,\n",
-       "         6.16519566,  6.20777739,  9.02180929,  9.12616752,  8.83803857,\n",
-       "         9.29829992,  9.40421014,  8.322127  , 10.27295156, 10.7127193 ,\n",
-       "        11.6481489 ,  5.46446215, 19.51476896, 12.52936608, 10.28003481,\n",
-       "        12.97497294,  8.48966032,  5.08797595,  5.69033171,  6.23472778,\n",
-       "         4.8519205 ,  4.31145894,  4.42425785,  3.7120722 ,  1.87113772,\n",
-       "         1.87113772,  3.98138188,  3.14460592,  1.97758402,  5.4722307 ]])
            • created_at :
              2020-06-06T18:07:53.669221
              arviz_version :
              0.8.3
              inference_library :
              pymc3
              inference_library_version :
              3.8

        \n", + " [16.71726571, 9.60094263, 13.71982007, ..., 14.16131649,\n", + " 13.11647601, 18.79277735],\n", + " [11.88039806, 10.03600833, 12.24545544, ..., 9.29449848,\n", + " 13.66392076, 13.12868937],\n", + " [ 7.58797243, 10.11726456, 14.53218427, ..., 6.64551025,\n", + " 10.1260629 , 12.31751554]]])
      • tau
        (chain, draw)
        float64
        2.673 1.693 2.264 ... 5.256 5.096
        array([[ 2.67326225,  1.69327225,  2.26441946,  2.28373937,  2.48337685,\n",
+       "         3.5534519 ,  4.59569498,  3.89443484,  6.29093191,  4.15534065,\n",
+       "         5.23877865,  5.50860947,  3.80449605,  3.53618773,  3.53556815,\n",
+       "         3.83213975,  2.26159133,  1.70996414,  4.30431708,  5.18004681,\n",
+       "         8.44122625,  6.74853278, 11.66327816, 11.50322115,  4.64237938,\n",
+       "         7.58836957,  5.41253562,  3.8259386 ,  2.42179492,  6.07911823,\n",
+       "         5.7579538 ,  9.10245313,  4.24493377,  6.16846468,  9.52246892,\n",
+       "         6.16850002,  6.93453894,  9.06885683,  5.23951572, 14.88285834,\n",
+       "         7.61015605, 10.95611743,  8.18538236,  7.9080973 ,  8.38219843,\n",
+       "         9.67272328,  8.00163612,  8.05394593, 11.70553097,  3.32828189,\n",
+       "         4.91360016,  9.07209215, 12.4677597 ,  3.50645773,  3.26083374,\n",
+       "         6.04138238,  4.21296333,  4.20178591,  3.0890811 ,  3.70392596,\n",
+       "         3.02528969,  1.81836961,  3.37416903,  2.85952533,  4.56593347,\n",
+       "         3.57947128,  3.00088854,  2.52328479,  4.38582071,  2.99649689,\n",
+       "         3.15782354,  4.71241533,  2.54766776,  4.18337528,  2.20466968,\n",
+       "         2.19423239,  1.95878443,  4.00996825,  3.79787455,  2.66722218,\n",
+       "         1.56778782,  2.67459744,  5.6288038 ,  5.27926994,  8.08829096,\n",
+       "         6.64229416,  6.32189519,  5.52907913,  8.41063894,  6.38655596,\n",
+       "         6.71688517,  6.12048595,  9.5443802 ,  7.12108513,  1.7041854 ,\n",
+       "         3.03242712,  1.58026182,  3.45989141,  5.73044874,  8.54773302,\n",
+       "         9.93081775,  4.48757783,  3.80658091,  3.21910374,  1.74277026,\n",
+       "         1.54134775,  2.34914236,  1.76723076,  3.33191492,  1.55417962,\n",
+       "         1.44801848,  1.63040306,  1.77156976,  1.26546036,  1.26546036,\n",
+       "         1.26546036,  2.09392397,  3.911303  ,  3.60844456,  3.06605205,\n",
+       "         5.84442689,  4.76918761,  5.04368956,  5.89823556,  4.14501552,\n",
+       "         4.96545057,  5.98554752,  6.16992784,  5.36074059,  5.98746562,\n",
+       "         4.76558208,  3.56179071,  6.03837811,  8.18572283,  9.40440231,\n",
+       "         2.22605209,  1.41750717,  2.56021681,  1.52054509,  1.25267437,\n",
+       "         1.18064931,  0.82523799,  0.82523799,  0.82523799,  0.82523799,\n",
+       "         0.82523799,  0.89547968,  1.06515249,  1.06515249,  1.06515249,\n",
+       "         0.87941658,  0.93262386,  0.93262386,  0.93262386,  1.70245357,\n",
+       "         3.56918409,  2.90302204,  4.14536914,  4.68165271,  3.8085819 ,\n",
+       "         5.70169104,  5.09458127,  3.68444235,  3.07163961,  3.27855361,\n",
+       "         3.37129431,  7.84295691,  5.38165233,  4.21482058, 11.01347963,\n",
+       "         3.46440289,  2.33344028,  3.63233906,  6.00011695,  6.02585805,\n",
+       "         4.59248514,  1.25218832,  2.4455758 ,  1.41912495,  2.40001986,\n",
+       "         2.09110479,  1.76894054,  1.38091801,  1.33776424,  2.4548559 ,\n",
+       "         1.47558606,  4.28803147,  2.84428473,  3.44486303,  4.81141509,\n",
+       "         8.13442314,  6.38616741,  4.71908787,  6.62655314,  6.81004414,\n",
+       "         3.12343065,  8.53267586,  8.60231967, 11.83721142, 10.24416421,\n",
+       "         4.49721931,  8.72194144, 10.65211709,  8.54544754,  5.18207439,\n",
+       "         6.82105618,  3.01369765,  3.8354013 ,  5.83414842,  5.95179166,\n",
+       "         5.05889385,  3.51063938,  3.42927438, 10.37775248, 23.21470877,\n",
+       "         7.4125489 ,  7.81157586,  4.41289201,  2.99479598,  2.44415618,\n",
+       "        11.85146551,  2.60079037,  3.3420645 ,  2.35037452,  3.3537721 ,\n",
+       "         4.49542609,  3.56641889,  1.01056969,  1.23338844,  1.23338844,\n",
+       "         1.11110148,  1.20214637,  0.98375117,  1.09738411,  1.21888324,\n",
+       "         1.40145365,  1.24431932,  1.04448701,  1.04448701,  1.04448701,\n",
+       "         1.04448701,  0.8957644 ,  0.93776913,  0.93776913,  0.92217105,\n",
+       "         0.92217105,  1.09089615,  1.09089615,  1.34084131,  1.34084131,\n",
+       "         1.34084131,  1.34084131,  1.78776261,  1.56065016,  1.93324153,\n",
+       "         3.12974917,  3.03156828,  3.24434882,  3.68852741,  3.46071732,\n",
+       "         4.53658384,  3.21777084,  6.89241731,  5.86051587,  4.90313114,\n",
+       "        13.34213779, 10.43093357,  9.8464825 ,  4.085654  ,  7.67111665,\n",
+       "         3.11735387,  6.16268338, 10.0382515 ,  2.14874536,  3.22442554,\n",
+       "         3.07794522,  4.2880126 ,  5.74101492,  2.17788048,  2.06063997,\n",
+       "         3.08095301,  6.11589352,  3.7712119 ,  4.79790927,  7.01993216,\n",
+       "         7.69613364,  3.9409763 ,  9.87253501,  3.16807662,  7.62792385,\n",
+       "         2.4380227 ,  2.25244931,  1.82277843,  1.00373686,  0.96859461,\n",
+       "         0.96859461,  0.96859461,  0.93270199,  0.93270199,  0.93270199,\n",
+       "         0.93270199,  0.93270199,  0.93270199,  0.93270199,  2.38731884,\n",
+       "         2.39693547,  2.33877376,  2.55256205,  3.20162928,  1.43372253,\n",
+       "         1.55533021,  4.54687869,  3.10383668,  7.99636156,  4.34784516,\n",
+       "         4.09481755,  2.79684298,  3.44660827,  2.61954456,  2.24450866,\n",
+       "         4.53931356,  4.39396726,  9.04018833,  6.56933644, 12.48666978,\n",
+       "        10.4613815 , 17.38898537,  9.26310989, 15.18800819,  7.78257061,\n",
+       "        10.34866689,  9.80659621,  3.56739801,  2.44289005,  4.0299327 ,\n",
+       "         4.02353188,  7.91856192,  3.59124329, 11.03600136, 10.67612793,\n",
+       "         5.96198989,  4.31578002, 15.20539491, 14.1175014 ,  6.41326514,\n",
+       "         4.41373361,  3.94972014,  3.49280734,  6.38378125,  3.72676669,\n",
+       "         3.97751008,  8.09200822,  3.5869942 , 14.91109664, 11.49784435,\n",
+       "         6.68131178,  2.58694806,  3.59462808,  5.08865708,  4.9357594 ,\n",
+       "         2.15768411,  1.54657273,  1.83638171,  1.58362072,  1.7523732 ,\n",
+       "         2.085632  ,  1.77962539,  1.44335826,  1.44335826,  2.64060866,\n",
+       "         2.6804101 ,  3.06141175,  6.0021514 ,  6.75034837,  4.84780222,\n",
+       "         6.28158582,  5.12052529,  8.46983638,  6.15288293,  3.52185536,\n",
+       "         4.34919414,  3.82498954,  9.43059195, 17.94770233,  9.61985205,\n",
+       "        10.1235112 , 12.94659851,  9.31481323,  8.44393295,  3.98696312,\n",
+       "         2.73993392,  3.41901356,  4.13167884,  2.51136656,  2.93017373,\n",
+       "         3.84590493,  2.8127721 ,  1.60414855,  1.73826593,  3.53341914,\n",
+       "         3.75506114,  3.16055637,  1.52917217,  1.31002964,  1.17154789,\n",
+       "         1.17154789,  1.72071883,  1.9681178 ,  3.50687245,  4.94258162,\n",
+       "         2.63429557,  3.38741781,  3.21155739,  3.28131916,  4.39854645,\n",
+       "         2.56272633,  5.78967658, 12.0968391 ,  6.89258195,  9.61183744,\n",
+       "        12.1241828 , 10.1158111 ,  7.0622766 ,  7.75912659, 12.75892791,\n",
+       "        10.256167  ,  8.32404926,  7.74108702,  7.44426077,  9.33724162,\n",
+       "         8.44900821, 11.32260193,  5.63486066, 12.37678267,  5.49428901,\n",
+       "         3.72611928,  6.33533045,  5.48278516,  4.29037045,  5.95626253,\n",
+       "         5.40273459,  5.62748716,  4.82756157, 10.76529541, 10.69283667,\n",
+       "        10.52683113,  4.93184524,  6.04815438,  5.03766243,  3.83245661,\n",
+       "         4.30334325,  2.17109017,  2.52787927,  1.60872106,  1.33502516,\n",
+       "         2.00961732,  1.99782636,  6.64058804,  4.49992205,  5.80234939,\n",
+       "         2.88411343,  8.17682545,  3.24663948,  3.28604191,  5.59214365,\n",
+       "         6.66206916,  5.59528906,  7.99377242,  7.89452165,  6.28027467,\n",
+       "         6.16042518,  8.68820432,  6.81936392,  3.08463524,  2.52183358,\n",
+       "         5.13910632,  5.79876205,  8.61744898,  5.11191357,  5.3789732 ,\n",
+       "        10.43804718,  3.85422562,  5.92473426, 11.63939767,  7.00127952,\n",
+       "         6.12648248,  3.16995738,  3.60015104,  6.07323179,  5.45281603,\n",
+       "         8.56568605,  3.63093546,  6.42400208,  1.85250422,  2.76356095,\n",
+       "         3.87937596,  2.74449388,  0.95877952,  0.95877952,  1.02231547],\n",
+       "       [ 0.87446012,  0.82158698,  0.82158698,  0.82158698,  0.7899858 ,\n",
+       "         0.81261676,  1.34523251,  1.23673433,  2.70381336,  2.17895614,\n",
+       "         5.68064657,  8.53942839,  5.60146557,  7.92459687,  3.8407719 ,\n",
+       "         9.45083314,  3.86839546,  4.84523909,  6.77770367,  8.12935539,\n",
+       "         8.86591202,  7.40347762, 11.53362792,  7.70886326,  3.90921858,\n",
+       "         4.19890237,  9.30457973,  8.7934137 ,  3.86291871,  3.86730112,\n",
+       "         3.67796732,  2.72912066,  4.02009987,  5.90151154,  4.36799819,\n",
+       "         4.12431699,  3.0169841 ,  1.76103489,  5.65498505,  1.642834  ,\n",
+       "         1.48589934,  1.33171688,  2.1900742 ,  1.74737413,  2.80989782,\n",
+       "         2.99299058,  3.16559515,  4.14072242,  3.42463653,  6.26443922,\n",
+       "         4.26165586,  3.79951099,  4.18292099,  6.35392115,  8.18009509,\n",
+       "         9.82519028,  2.35092275,  6.21469846,  4.86941576,  3.46001393,\n",
+       "         2.92697617,  4.21795968,  8.69209967,  8.15649408,  4.69825256,\n",
+       "         5.56048019,  2.99158626,  2.24030763,  2.40927516,  2.56021411,\n",
+       "         1.79421235,  3.6365905 ,  3.75594969,  4.04357977,  4.87286547,\n",
+       "         9.37538826, 14.08385188,  8.7300695 , 13.62297943, 10.10811741,\n",
+       "        11.96159103, 14.31232373,  7.2746315 , 10.73875997,  6.50516768,\n",
+       "         6.36908336,  6.26214526, 17.00226039, 11.69573731, 13.1200249 ,\n",
+       "         6.11724095, 15.60658914,  1.95291595,  2.53914574,  2.1495062 ,\n",
+       "         1.94886689,  3.26605563,  2.17701759,  2.52965815,  2.2098353 ,\n",
+       "         1.94958174,  1.36302759,  5.42559586,  5.41986439, 12.17111501,\n",
+       "         3.9309188 ,  4.37587659, 12.80156449,  3.62105571,  4.05579294,\n",
+       "         2.96202686,  4.00137303,  3.4702907 ,  2.6269301 ,  2.77133531,\n",
+       "         2.31719863,  5.12579435,  1.11033036,  1.11033036,  2.51566695,\n",
+       "         2.30777883,  2.44456502,  2.71264448,  4.80757246,  2.07594995,\n",
+       "         2.25283042,  5.94743906,  2.25520828,  2.40038293,  2.18351887,\n",
+       "         5.54238846,  3.33618964,  3.99921708,  4.24352368,  4.91763749,\n",
+       "         3.96403672,  3.0033376 ,  3.91393073,  3.62823827,  1.27425084,\n",
+       "         2.05416305,  3.32391083,  4.41850247,  2.16151761,  1.93777731,\n",
+       "         5.69216908,  7.96661813,  9.90702467,  7.96348674,  5.143967  ,\n",
+       "        17.32222786, 12.31574761,  7.58995324,  9.54868096,  5.78418544,\n",
+       "         3.47807987,  2.65905991,  4.82217952,  3.31179254,  2.35190876,\n",
+       "         4.64002679,  6.57777407,  6.57764149,  4.30911656,  6.11807292,\n",
+       "         5.53419999,  2.09692156,  3.49538724,  2.04720002,  8.58185636,\n",
+       "         4.93869456,  5.07551233,  5.45923137,  5.66055253,  8.56854194,\n",
+       "        15.39743242, 12.08606238,  7.22316086,  7.96972784,  5.1599483 ,\n",
+       "         4.58556989,  4.94484151,  4.897299  ,  7.52739652,  6.04513495,\n",
+       "         5.13407879,  7.14078658, 12.00053016,  9.43733288,  6.38635937,\n",
+       "         6.80033707,  6.57770054,  4.45173614,  3.18899484,  2.26849151,\n",
+       "         1.1416066 ,  1.1416066 ,  0.93440577,  0.93440577,  1.81729944,\n",
+       "         5.03557383,  5.12429328,  2.21205836,  6.18590091, 11.19469957,\n",
+       "         8.87184499,  8.88232154,  4.02002437,  5.32776175,  3.54149267,\n",
+       "         4.81504989,  9.64118715,  7.89750536,  5.81875906,  7.81992102,\n",
+       "         7.54916279, 10.82465269,  4.1484651 ,  3.60149111,  4.88051874,\n",
+       "         5.16847133,  6.8727861 ,  5.51125077,  6.28097161,  2.75305999,\n",
+       "         2.52103133, 10.29985714,  9.01125755,  8.74221341,  2.7497625 ,\n",
+       "         1.95925068,  1.90946984,  2.59981888,  3.47182994,  6.23661491,\n",
+       "         5.96628356,  8.84906534, 10.68528257,  5.33192173,  5.78270999,\n",
+       "         1.27349183,  1.27349183,  1.27349183,  1.52474907,  2.08950004,\n",
+       "         2.37422809,  2.66453157,  2.70655422,  4.5759727 ,  2.74764816,\n",
+       "         4.20049562,  9.51129361,  4.28413236,  6.87610199,  5.68991202,\n",
+       "         1.29607444,  2.94959754,  2.96994402,  1.56037111,  2.461318  ,\n",
+       "         2.78204738,  1.43925123,  1.64512448,  2.278369  ,  2.63951027,\n",
+       "         1.74140447,  1.1714692 ,  0.97316354,  0.97316354,  2.76896219,\n",
+       "         3.69339381,  3.27665759,  3.88831118,  3.23020961,  2.66780495,\n",
+       "         2.64465832,  3.60061052,  6.01401723,  7.89548326,  8.32703461,\n",
+       "         4.41891545,  2.42152952,  2.6098245 ,  1.7294729 ,  2.93136126,\n",
+       "         1.68394869,  2.91366443,  4.51664704,  9.94660726, 11.32506517,\n",
+       "         6.76098593,  5.39723631,  6.42688505,  4.10588407,  4.00985334,\n",
+       "         4.12976916,  7.69937638,  6.4977388 ,  4.86823537,  4.54276589,\n",
+       "         3.01302312,  3.30740392,  3.37291982,  2.82478807,  4.92758292,\n",
+       "         4.49469951,  3.5431941 ,  4.51515325,  4.14233197,  4.63729334,\n",
+       "         3.64555448,  3.66921355,  2.56344869,  2.88566611,  1.34577562,\n",
+       "         1.34577562,  1.77014018,  4.08858701,  2.45948001,  3.07025213,\n",
+       "         2.50961763,  2.99939208,  2.37004607,  3.10097109,  4.08021121,\n",
+       "         4.63691772,  2.03887765,  2.34811391,  1.04269785,  1.04269785,\n",
+       "         0.80471392,  0.80471392,  0.80471392,  0.77081487,  0.77081487,\n",
+       "         0.77081487,  0.77081487,  1.03014649,  1.03014649,  1.86143078,\n",
+       "         1.5793112 ,  2.66340155,  1.53636401,  1.69837212,  1.52557749,\n",
+       "         1.51211135,  1.70648024,  1.72438164,  3.23553583,  2.7944787 ,\n",
+       "         3.08500557,  3.88781694,  1.47355064,  2.80193376,  1.35404904,\n",
+       "         1.3127965 ,  1.13950647,  1.20424311,  1.20424311,  1.08927672,\n",
+       "         1.08927672,  0.87898571,  0.87898571,  0.87898571,  0.87898571,\n",
+       "         0.87898571,  0.87898571,  0.87898571,  0.87898571,  0.87898571,\n",
+       "         0.87898571,  0.87898571,  0.87898571,  0.87898571,  0.72964634,\n",
+       "         0.72964634,  0.72964634,  0.72964634,  0.72964634,  0.62748671,\n",
+       "         0.62748671,  0.62748671,  0.62748671,  0.62748671,  0.62748671,\n",
+       "         0.62748671,  0.62748671,  0.62748671,  0.62748671,  0.62748671,\n",
+       "         0.62748671,  0.62748671,  0.62748671,  0.62748671,  0.62748671,\n",
+       "         0.62748671,  0.62748671,  0.62748671,  0.62748671,  0.62748671,\n",
+       "         0.62748671,  0.62748671,  0.62748671,  0.62748671,  0.62748671,\n",
+       "         0.62748671,  0.62748671,  0.62748671,  0.62748671,  0.62748671,\n",
+       "         0.62748671,  0.62748671,  0.62748671,  0.62748671,  0.62748671,\n",
+       "         0.62748671,  0.62748671,  0.62748671,  0.62748671,  0.62748671,\n",
+       "         0.62748671,  0.62748671,  0.62748671,  0.62748671,  0.62748671,\n",
+       "         0.62748671,  0.62748671,  0.62748671,  0.62748671,  0.62748671,\n",
+       "         0.62748671,  0.62748671,  0.62748671,  0.62748671,  0.62748671,\n",
+       "         0.62748671,  0.62748671,  0.62748671,  0.62748671,  0.84248774,\n",
+       "         0.84248774,  0.84248774,  1.06774552,  1.44081773,  3.18823048,\n",
+       "         1.02395912,  2.01333409,  3.47263825,  2.80606464,  3.38052228,\n",
+       "         2.96385495,  3.0023371 ,  1.80680254,  2.15390124,  1.91558199,\n",
+       "         2.16159335,  4.34825174,  7.35646385,  3.8553125 ,  4.21601191,\n",
+       "         7.261817  ,  2.94324633,  2.99500021,  2.09640562,  1.96301932,\n",
+       "         2.6940472 ,  1.64182455,  2.25968379,  1.7121704 ,  1.28678808,\n",
+       "         1.79982589,  1.75975634,  1.53085263,  5.64012435,  2.38370669,\n",
+       "         3.3699948 ,  1.39567957,  2.97868355,  3.62358452,  2.34408491,\n",
+       "         2.12969017,  4.18694516,  1.72003964,  1.90244423,  1.69751186,\n",
+       "         4.39806154,  2.77533503,  4.10661026,  3.746074  ,  4.30983161,\n",
+       "         2.99226399,  6.88035776,  5.23941627,  2.87701026,  2.81654319,\n",
+       "         3.51175694,  4.04919758,  3.45529549,  5.25623496,  5.09577817]])
    • created_at :
      2020-06-10T17:34:00.062215
      arviz_version :
      0.8.3
      inference_library :
      pymc3
      inference_library_version :
      3.8

    \n", " \n", " \n", " \n", " \n", "
  • \n", - " \n", - " \n", + " \n", + " \n", "
    \n", "
    \n", "
      \n", @@ -1522,41 +1530,41 @@ " stroke: currentColor;\n", " fill: currentColor;\n", "}\n", - "
      xarray.Dataset
        • chain: 2
        • draw: 500
        • school: 8
        • chain
          (chain)
          int64
          0 1
          array([0, 1])
        • draw
          (draw)
          int64
          0 1 2 3 4 5 ... 495 496 497 498 499
          array([  0,   1,   2, ..., 497, 498, 499])
        • school
          (school)
          <U16
          'Choate' ... 'Mt. Hermon'
          array(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
-       "       'Hotchkiss', 'Lawrenceville', "St. Paul's", 'Mt. Hermon'], dtype='<U16')
        • obs
          (chain, draw, school)
          float64
          9.27 -3.737 13.58 ... 5.565 -37.82
          array([[[ 9.27019662e+00, -3.73664108e+00,  1.35793629e+01, ...,\n",
-       "          1.49786990e+01,  1.22912334e+01, -2.46182885e+00],\n",
-       "        [ 1.29656615e+01, -1.30706639e+01,  6.79264057e+00, ...,\n",
-       "          7.34811060e+00,  1.24558827e+01, -5.94944893e+00],\n",
-       "        [-7.98628907e+00,  1.06890297e+01, -2.06272346e+01, ...,\n",
-       "         -1.80913609e+00, -1.63333482e+01,  1.18830812e+01],\n",
+       "
          xarray.Dataset
            • chain: 2
            • draw: 500
            • school: 8
            • chain
              (chain)
              int64
              0 1
              array([0, 1])
            • draw
              (draw)
              int64
              0 1 2 3 4 5 ... 495 496 497 498 499
              array([  0,   1,   2, ..., 497, 498, 499])
            • school
              (school)
              <U16
              'Choate' ... 'Mt. Hermon'
              array(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
+       "       'Hotchkiss', 'Lawrenceville', "St. Paul's", 'Mt. Hermon'], dtype='<U16')
            • obs
              (chain, draw, school)
              float64
              16.12 1.001 13.59 ... 10.5 41.69
              array([[[ 16.12070379,   1.00077065,  13.5884531 , ...,  -3.32102967,\n",
+       "          12.41474624,   5.3880134 ],\n",
+       "        [  0.31286304,  10.87604623,   0.61156714, ..., -13.67640248,\n",
+       "         -12.42821487,  22.54207407],\n",
+       "        [  1.01770093,  19.85818733,  -4.68614833, ...,   7.65716318,\n",
+       "          -0.69802287,  -4.49452911],\n",
        "        ...,\n",
-       "        [ 9.18730201e+00,  7.21862428e+00, -1.43830827e+01, ...,\n",
-       "         -4.93565579e+00,  1.92571973e+00,  6.38756653e+00],\n",
-       "        [-9.43512049e+00,  9.15788289e+00,  3.01401239e+00, ...,\n",
-       "         -5.07793293e+00, -8.28412816e-01, -5.94208127e+00],\n",
-       "        [ 1.62514124e+01,  1.57190243e+01,  1.94852387e+01, ...,\n",
-       "          1.63508206e+01, -5.91218542e+00, -1.33013406e+01]],\n",
-       "\n",
-       "       [[ 2.26087456e+01,  6.42799448e+00, -3.52925241e+00, ...,\n",
-       "          1.80076407e+01, -3.36020807e-01,  1.92479390e+01],\n",
-       "        [ 3.92165407e+01,  6.77626143e+00,  5.90141542e+00, ...,\n",
-       "          6.22275580e+00,  1.82726353e+01,  1.10532285e+00],\n",
-       "        [ 8.06194773e+00, -4.44753888e+00,  5.10370518e+00, ...,\n",
-       "         -1.19490499e-01,  1.54966090e+01,  7.08856045e+00],\n",
+       "        [ 12.48004901,  -1.78679224,  15.1745246 , ...,   7.98552349,\n",
+       "          -3.17513437,  -1.08978072],\n",
+       "        [ -7.19350203,   3.32676606,  18.59939682, ...,   6.55905051,\n",
+       "           7.73231749, -12.15657743],\n",
+       "        [ 26.06181938,  11.41170287,  -6.13354902, ...,   3.11098087,\n",
+       "          10.11918758,  20.17803435]],\n",
+       "\n",
+       "       [[ -5.66449708,  17.73434902,  10.95330455, ...,  15.42521439,\n",
+       "          -8.16076276,   9.16562713],\n",
+       "        [ -8.94394888,  17.08611759, -14.87353811, ...,  13.25714385,\n",
+       "           8.53916215,  25.7014456 ],\n",
+       "        [ 19.34894266,  -3.46721825,   2.40651331, ...,  -7.76048036,\n",
+       "          12.50213557, -32.98333085],\n",
        "        ...,\n",
-       "        [-3.03534251e+00,  2.24378707e+00, -8.44986049e+00, ...,\n",
-       "          2.12971871e-03,  1.49181986e+01,  6.61268146e+00],\n",
-       "        [ 3.72829526e+00, -8.56749669e+00,  1.81610302e+01, ...,\n",
-       "          7.69364146e+00, -3.01472311e+00, -1.46863809e+01],\n",
-       "        [ 1.50901701e+01,  4.68583065e+00, -2.08895476e+01, ...,\n",
-       "         -3.62125889e+00,  5.56507786e+00, -3.78249384e+01]]])
          • created_at :
            2020-06-06T18:07:53.815773
            arviz_version :
            0.8.3
            inference_library :
            pymc3
            inference_library_version :
            3.8

      \n", + " [ -2.28343942, -8.00882316, -1.31972725, ..., -3.83927959,\n", + " 27.4894464 , 39.65246163],\n", + " [ 3.63601625, 16.81393779, 35.16350247, ..., 5.2799238 ,\n", + " 16.9409567 , 12.78797208],\n", + " [ 0.98247975, 27.4726395 , 7.53983398, ..., -10.34939336,\n", + " 10.49501115, 41.68533102]]])
  • created_at :
    2020-06-10T17:34:00.239971
    arviz_version :
    0.8.3
    inference_library :
    pymc3
    inference_library_version :
    3.8

    • \n", " \n", " \n", " \n", " \n", "
  • \n", - " \n", - " \n", + " \n", + " \n", "
    \n", "
    \n", "
      \n", @@ -1894,41 +1902,41 @@ " stroke: currentColor;\n", " fill: currentColor;\n", "}\n", - "
      xarray.Dataset
        • chain: 2
        • draw: 500
        • school: 8
        • chain
          (chain)
          int64
          0 1
          array([0, 1])
        • draw
          (draw)
          int64
          0 1 2 3 4 5 ... 495 496 497 498 499
          array([  0,   1,   2, ..., 497, 498, 499])
        • school
          (school)
          <U16
          'Choate' ... 'Mt. Hermon'
          array(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
-       "       'Hotchkiss', 'Lawrenceville', "St. Paul's", 'Mt. Hermon'], dtype='<U16')
        • obs
          (chain, draw, school)
          float64
          -4.256 -3.463 ... -3.863 -3.81
          array([[[-4.25577142, -3.46286229, -3.81393007, ..., -3.3656724 ,\n",
-       "         -3.41292995, -4.06762619],\n",
-       "        [-4.13847217, -3.52778462, -3.78339165, ..., -3.37611123,\n",
-       "         -3.2836942 , -4.05586826],\n",
-       "        [-6.42070868, -3.31541094, -3.74365387, ..., -3.43939688,\n",
-       "         -7.39653283, -4.06935277],\n",
+       "
          xarray.Dataset
            • chain: 2
            • draw: 500
            • school: 8
            • chain
              (chain)
              int64
              0 1
              array([0, 1])
            • draw
              (draw)
              int64
              0 1 2 3 4 5 ... 495 496 497 498 499
              array([  0,   1,   2, ..., 497, 498, 499])
            • school
              (school)
              <U16
              'Choate' ... 'Mt. Hermon'
              array(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
+       "       'Hotchkiss', 'Lawrenceville', "St. Paul's", 'Mt. Hermon'], dtype='<U16')
            • obs
              (chain, draw, school)
              float64
              -4.651 -3.318 ... -3.532 -3.809
              array([[[-4.65123378, -3.31803365, -3.71513164, ..., -3.33209802,\n",
+       "         -4.07631976, -3.90733047],\n",
+       "        [-4.82001547, -3.2598214 , -3.95921905, ..., -3.58326393,\n",
+       "         -3.96039102, -3.86481386],\n",
+       "        [-4.13731608, -3.24745981, -3.932011  , ..., -3.48166425,\n",
+       "         -3.60144056, -3.85503224],\n",
        "        ...,\n",
-       "        [-4.71784406, -3.25643299, -3.73595869, ..., -3.49063867,\n",
-       "         -3.97539471, -3.88959207],\n",
-       "        [-4.71784406, -3.25643299, -3.73595869, ..., -3.49063867,\n",
-       "         -3.97539471, -3.88959207],\n",
-       "        [-4.63988242, -3.29595199, -3.81531025, ..., -3.33765926,\n",
-       "         -4.00045703, -3.87161943]],\n",
-       "\n",
-       "       [[-5.14101139, -3.22812014, -3.85439321, ..., -3.3781691 ,\n",
-       "         -3.80225806, -3.85839657],\n",
-       "        [-5.01913008, -3.22544566, -3.87016139, ..., -3.37232612,\n",
-       "         -3.98277746, -3.83930316],\n",
-       "        [-4.9606513 , -3.23112547, -3.88799664, ..., -3.33995353,\n",
-       "         -3.9691717 , -3.83998898],\n",
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        "        ...,\n",
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          • created_at :
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            arviz_version :
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            inference_library :
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            inference_library_version :
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      \n", + " [-3.90987783, -3.23433871, -4.237528 , ..., -4.03261997,\n", + " -3.34076766, -3.88051681],\n", + " [-4.20441444, -3.24225028, -4.14548021, ..., -3.60112598,\n", + " -3.31553154, -3.81127625],\n", + " [-4.55287956, -3.24393767, -4.29187391, ..., -3.4485354 ,\n", + " -3.53151805, -3.80946587]]])
  • created_at :
    2020-06-10T17:34:00.237448
    arviz_version :
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    inference_library :
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    inference_library_version :
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    • \n", " \n", " \n", " \n", " \n", "
  • \n", - " \n", - " \n", + " \n", + " \n", "
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      \n", @@ -2266,1069 +2274,660 @@ " stroke: currentColor;\n", " fill: currentColor;\n", "}\n", - "
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-       "        0.27148071, 0.27148071, 0.27148071, 0.27148071, 0.27148071,\n",
-       "        0.27148071, 0.27148071, 0.27148071, 0.27148071, 0.27148071,\n",
-       "        0.27148071, 0.27148071, 0.27148071, 0.27148071, 0.27148071,\n",
-       "        0.27148071, 0.27148071, 0.27148071, 0.27148071, 0.27148071,\n",
-       "        0.27148071, 0.27148071, 0.27148071, 0.27148071, 0.27148071,\n",
-       "        0.27148071, 0.27148071, 0.27148071, 0.27148071, 0.27148071,\n",
-       "        0.27148071, 0.27148071, 0.27148071, 0.27148071, 0.27148071,\n",
-       "        0.27148071, 0.27148071, 0.27148071, 0.27148071, 0.27148071,\n",
-       "        0.27148071, 0.27148071, 0.27148071, 0.27148071, 0.27148071,\n",
-       "        0.27148071, 0.27148071, 0.27148071, 0.27148071, 0.27148071,\n",
-       "        0.27148071, 0.27148071, 0.27148071, 0.27148071, 0.27148071,\n",
-       "        0.27148071, 0.27148071, 0.27148071, 0.27148071, 0.27148071,\n",
-       "        0.27148071, 0.27148071, 0.27148071, 0.27148071, 0.27148071,\n",
-       "        0.27148071, 0.27148071, 0.27148071, 0.27148071, 0.27148071,\n",
-       "        0.27148071, 0.27148071, 0.27148071, 0.27148071, 0.27148071,\n",
-       "        0.27148071, 0.27148071, 0.27148071, 0.27148071, 0.27148071,\n",
-       "        0.27148071, 0.27148071, 0.27148071, 0.27148071, 0.27148071,\n",
-       "        0.27148071, 0.27148071, 0.27148071, 0.27148071, 0.27148071,\n",
-       "        0.27148071, 0.27148071, 0.27148071, 0.27148071, 0.27148071,\n",
-       "        0.27148071, 0.27148071, 0.27148071, 0.27148071, 0.27148071,\n",
-       "        0.27148071, 0.27148071, 0.27148071, 0.27148071, 0.27148071,\n",
-       "        0.27148071, 0.27148071, 0.27148071, 0.27148071, 0.27148071,\n",
-       "        0.27148071, 0.27148071, 0.27148071, 0.27148071, 0.27148071,\n",
-       "        0.27148071, 0.27148071, 0.27148071, 0.27148071, 0.27148071,\n",
-       "        0.27148071, 0.27148071, 0.27148071, 0.27148071, 0.27148071,\n",
-       "        0.27148071, 0.27148071, 0.27148071, 0.27148071, 0.27148071,\n",
-       "        0.27148071, 0.27148071, 0.27148071, 0.27148071, 0.27148071]])
        • mean_tree_accept
          (chain, draw)
          float64
          0.6358 0.7715 1.0 ... 0.9569 0.7325
          array([[6.35830176e-01, 7.71505320e-01, 1.00000000e+00, 8.46071160e-01,\n",
-       "        9.24817279e-01, 9.84356429e-01, 7.78027888e-01, 9.35127805e-01,\n",
-       "        9.64571524e-01, 9.68018339e-01, 9.38390011e-01, 9.95204578e-01,\n",
-       "        7.58419298e-01, 9.93836673e-01, 6.09990637e-01, 9.84023426e-01,\n",
-       "        9.87597576e-01, 9.82328649e-01, 8.75260426e-01, 9.34604991e-01,\n",
-       "        9.66898214e-01, 9.44579706e-01, 9.66158053e-01, 8.70667657e-01,\n",
-       "        9.89489286e-01, 9.24373579e-01, 9.78868311e-01, 9.29241307e-01,\n",
-       "        6.49699175e-01, 8.74888513e-01, 7.93295228e-01, 6.47752583e-01,\n",
-       "        9.78119098e-01, 9.90398245e-01, 7.96357250e-01, 9.90363095e-01,\n",
-       "        8.95125681e-01, 4.94648200e-01, 7.99248163e-02, 1.45365235e-01,\n",
-       "        2.40444904e-01, 5.85962480e-02, 3.17197472e-02, 5.00099173e-02,\n",
-       "        6.28823857e-05, 7.42632323e-19, 9.37098121e-18, 1.42857179e-01,\n",
-       "        2.16798372e-05, 3.50036787e-02, 2.19553084e-02, 6.66666667e-01,\n",
-       "        6.83774693e-02, 1.26072442e-01, 8.83125771e-07, 1.71086674e-12,\n",
-       "        7.92416706e-03, 7.25506480e-09, 3.33333333e-01, 5.77357742e-12,\n",
-       "        4.09780284e-04, 2.42187010e-03, 1.97029008e-02, 5.66676213e-08,\n",
-       "        2.37687456e-01, 1.61842548e-02, 1.33929269e-05, 2.77844841e-05,\n",
-       "        1.86466911e-04, 4.97456024e-07, 5.41309967e-04, 4.38841424e-06,\n",
-       "        6.48764872e-03, 3.42329261e-03, 5.19548943e-03, 9.41502411e-02,\n",
-       "        6.53902514e-01, 8.76723384e-01, 9.16268639e-01, 9.63876555e-01,\n",
-       "        1.00000000e+00, 8.78731553e-01, 9.45224249e-01, 8.65842680e-01,\n",
-       "        7.97354563e-01, 9.96736984e-01, 8.48348008e-01, 6.59404760e-01,\n",
-       "        1.16290051e-01, 8.19989558e-01, 9.94341513e-01, 9.49692605e-01,\n",
-       "        2.96554033e-01, 7.63212217e-01, 5.06486671e-05, 1.07369278e-01,\n",
-       "        1.16561001e-08, 2.29556677e-01, 5.85884313e-01, 2.15825571e-01,\n",
-       "        7.52825129e-01, 6.82095139e-01, 7.23625274e-01, 4.93411570e-01,\n",
-       "        6.63251395e-01, 1.86558287e-01, 9.42056650e-01, 9.79638905e-01,\n",
-       "        1.00000000e+00, 8.17921724e-01, 9.45621176e-01, 9.99590929e-01,\n",
-       "        9.31345011e-01, 7.14199383e-01, 9.43678778e-01, 9.18425898e-01,\n",
-       "        8.41301457e-01, 3.62558481e-01, 8.88025010e-01, 9.83381719e-01,\n",
-       "        8.40685345e-01, 9.70519154e-01, 6.40520704e-01, 9.95874420e-01,\n",
-       "        8.91613143e-01, 9.14819388e-01, 9.69047220e-01, 9.46499382e-01,\n",
-       "        6.72827399e-01, 7.14285714e-01, 1.43533146e-01, 2.33365513e-01,\n",
-       "        6.54596199e-01, 6.37306900e-01, 1.51032855e-01, 9.71657429e-01,\n",
-       "        5.73343782e-01, 7.98320423e-01, 8.10690510e-01, 9.83220822e-01,\n",
-       "        8.93890370e-01, 9.57113211e-01, 7.86379847e-01, 6.07290412e-01,\n",
-       "        9.45562918e-01, 9.93848592e-01, 9.26733416e-01, 9.68412594e-01,\n",
-       "        9.17513240e-01, 2.28349138e-01, 8.19728204e-01, 9.37054763e-01,\n",
-       "        9.15931138e-01, 8.66790249e-01, 9.24365159e-01, 9.31208170e-01,\n",
-       "        7.12286662e-01, 3.02663830e-01, 9.74593912e-01, 9.14888200e-01,\n",
-       "        8.89767568e-01, 7.33060886e-01, 1.56026526e-01, 7.18051876e-01,\n",
-       "        9.49134451e-01, 9.24301339e-01, 7.87213976e-01, 9.17845226e-01,\n",
-       "        9.98379582e-01, 9.70336401e-01, 8.51049897e-01, 9.45245557e-01,\n",
-       "        9.92687878e-01, 9.98461519e-01, 8.39334669e-01, 9.91373331e-01,\n",
-       "        6.57099977e-01, 9.81009860e-01, 9.86236042e-01, 8.94604927e-01,\n",
-       "        9.59254684e-01, 9.42107855e-01, 9.80012590e-01, 7.42330951e-01,\n",
-       "        9.89682519e-01, 9.85802853e-01, 4.92898495e-01, 1.00000000e+00,\n",
-       "        6.24277517e-01, 9.95633969e-01, 5.71728807e-01, 9.47781371e-01,\n",
-       "        9.43403453e-01, 9.91543626e-01, 8.73521443e-01, 7.88567471e-01,\n",
-       "        5.71428692e-01, 8.56842367e-01, 7.44513310e-01, 9.41422107e-01,\n",
-       "        1.00000000e+00, 9.89986183e-01, 9.44365278e-01, 8.12478625e-01,\n",
-       "        9.63772733e-01, 9.49021276e-01, 9.95017685e-01, 9.63490169e-01,\n",
-       "        8.37292290e-01, 9.85737595e-01, 8.97936693e-01, 9.48617304e-01,\n",
-       "        9.51239007e-01, 9.91718445e-01, 9.21868326e-01, 1.00000000e+00,\n",
-       "        6.10683989e-01, 9.83112737e-01, 9.51407730e-01, 1.00000000e+00,\n",
-       "        6.61147275e-01, 9.97873646e-01, 9.91524610e-01, 9.49981238e-01,\n",
-       "        9.36977460e-01, 5.81213152e-01, 9.46721884e-01, 9.58244786e-01,\n",
-       "        9.01239750e-01, 5.90530878e-01, 9.21075754e-01, 8.37413598e-02,\n",
-       "        1.44321464e-01, 8.56703034e-01, 4.99465718e-02, 2.72501393e-01,\n",
-       "        1.47919370e-01, 4.26910284e-02, 1.71756213e-04, 3.10361704e-01,\n",
-       "        5.35665202e-01, 2.71367243e-01, 8.24517668e-01, 8.94465524e-01,\n",
-       "        8.72527609e-01, 4.04274340e-01, 9.53130102e-01, 1.00000000e+00,\n",
-       "        6.92074493e-01, 9.75144337e-01, 7.82363220e-01, 9.31081739e-01,\n",
-       "        9.64379797e-01, 9.75989817e-01, 8.56326658e-01, 9.59078960e-01,\n",
-       "        9.98854836e-01, 8.15994121e-01, 8.25077248e-01, 8.69949552e-01,\n",
-       "        1.61240182e-01, 8.47981978e-01, 9.91304869e-01, 6.02690696e-01,\n",
-       "        6.51185631e-01, 3.54308044e-01, 7.21258137e-01, 9.66211261e-01,\n",
-       "        6.59391383e-01, 9.16577546e-01, 8.86581478e-01, 9.97942780e-01,\n",
-       "        9.93051630e-01, 9.97479777e-01, 9.53352477e-01, 8.54382453e-01,\n",
-       "        9.19192345e-01, 9.70211936e-01, 9.79501177e-01, 9.46017259e-01,\n",
-       "        7.74285764e-01, 9.61133063e-01, 9.60164876e-01, 9.01123744e-01,\n",
-       "        9.70757573e-01, 9.44250400e-01, 9.13811153e-01, 9.44510123e-01,\n",
-       "        1.00000000e+00, 9.26996509e-01, 9.31537351e-01, 8.37625401e-01,\n",
-       "        9.44609461e-01, 4.30666451e-01, 2.64404380e-01, 3.50776947e-01,\n",
-       "        9.11105401e-01, 1.00000000e+00, 8.23087560e-01, 2.64654691e-01,\n",
-       "        3.10686915e-01, 1.00000000e+00, 8.82310012e-01, 1.00000000e+00,\n",
-       "        6.03261770e-01, 4.57025232e-01, 1.00000000e+00, 5.29236064e-01,\n",
-       "        6.58433194e-01, 9.25432093e-01, 9.39036226e-01, 9.14282239e-01,\n",
-       "        9.52394661e-01, 7.01577195e-01, 7.03571828e-01, 7.58442338e-05,\n",
-       "        3.62571694e-01, 1.53594242e-01, 4.08010675e-01, 7.18105506e-01,\n",
-       "        8.53151763e-01, 8.46435666e-01, 4.32228638e-02, 6.73501437e-01,\n",
-       "        3.31341097e-01, 3.33333334e-01, 1.06508111e-18, 2.62121915e-02,\n",
-       "        9.93028513e-03, 1.95081901e-01, 3.76784596e-08, 2.20574841e-01,\n",
-       "        3.46673086e-01, 5.22873112e-14, 5.32418416e-02, 1.40212629e-02,\n",
-       "        8.30976098e-01, 7.77255783e-01, 8.23309825e-01, 9.46163470e-01,\n",
-       "        1.00000000e+00, 8.57251543e-01, 1.00000000e+00, 8.72140689e-01,\n",
-       "        9.52961406e-01, 7.58347135e-01, 9.55093474e-01, 9.49992801e-01,\n",
-       "        9.66398856e-01, 9.83053486e-01, 9.90394088e-01, 8.84835270e-01,\n",
-       "        8.15762233e-01, 9.82337903e-01, 8.70596999e-01, 4.69069905e-01,\n",
-       "        9.66124839e-01, 9.95204686e-01, 6.82719145e-01, 9.50554346e-01,\n",
-       "        1.00000000e+00, 9.15299246e-01, 9.03584162e-01, 9.54075218e-01,\n",
-       "        9.24021518e-01, 7.93500915e-01, 8.69728630e-01, 1.00000000e+00,\n",
-       "        9.57289833e-01, 8.51581225e-01, 9.18279429e-01, 9.38774422e-01,\n",
-       "        4.90713023e-01, 7.95008383e-01, 7.26604575e-01, 1.00000000e+00,\n",
-       "        8.84065187e-01, 9.41699807e-01, 8.65693116e-01, 9.83549452e-01,\n",
-       "        1.05739112e-01, 6.02028160e-01, 9.52399965e-01, 7.62573793e-01,\n",
-       "        8.79962829e-01, 9.85565321e-01, 9.85484918e-01, 9.98763548e-01,\n",
-       "        9.54649200e-01, 1.00000000e+00, 9.79285585e-01, 9.47995013e-01,\n",
-       "        1.00000000e+00, 9.64668940e-01, 9.84162777e-01, 8.88319117e-01,\n",
-       "        1.00000000e+00, 5.79571430e-01, 9.48481447e-01, 7.94709010e-01,\n",
-       "        1.00000000e+00, 7.57712860e-01, 7.05164852e-01, 8.43020163e-01,\n",
-       "        7.45207316e-03, 7.78301166e-04, 2.29822909e-02, 6.33874279e-03,\n",
-       "        2.41291691e-01, 1.29497839e-02, 6.80797541e-01, 1.47371519e-01,\n",
-       "        8.14615011e-01, 9.59771485e-01, 9.27096593e-01, 9.95889801e-01,\n",
-       "        8.51512412e-01, 8.98359704e-01, 9.92150096e-01, 8.79073704e-01,\n",
-       "        9.92316872e-01, 6.81752424e-01, 2.55571093e-01, 7.81939633e-01,\n",
-       "        8.52121805e-01, 9.78145901e-01, 5.90364428e-01, 9.97847159e-01,\n",
-       "        9.69774337e-01, 9.98592673e-01, 9.90703719e-01, 1.00000000e+00,\n",
-       "        7.07492222e-01, 5.36711467e-01, 7.33162625e-01, 2.37872269e-01,\n",
-       "        1.46922513e-01, 9.20546783e-01, 8.82365627e-01, 1.00000000e+00,\n",
-       "        9.54486306e-01, 7.53832590e-01, 9.07772399e-01, 9.99943104e-01,\n",
-       "        9.90796151e-01, 8.37224888e-01, 5.40600141e-01, 9.82562633e-01,\n",
-       "        8.43276508e-01, 9.62295473e-01, 9.85204391e-01, 9.56475791e-01,\n",
-       "        8.68839234e-01, 9.75673901e-01, 5.63219570e-01, 7.03804818e-01,\n",
-       "        9.45051959e-01, 8.07903293e-01, 7.69776572e-01, 6.41282261e-01,\n",
-       "        9.94378721e-01, 9.67022724e-01, 9.84395861e-01, 9.73451597e-01,\n",
-       "        9.87136825e-01, 9.61283073e-01, 9.47345221e-01, 9.82729571e-01,\n",
-       "        9.04276624e-01, 8.26439821e-01, 8.97750726e-01, 6.29074708e-05,\n",
-       "        1.95868842e-01, 9.81680119e-01, 8.41014552e-01, 1.00000000e+00,\n",
-       "        9.32278825e-01, 1.39832465e-01, 3.39300135e-01, 9.38378273e-01,\n",
-       "        1.22531477e-01, 9.32919168e-01, 9.52046699e-01, 9.84703861e-01,\n",
-       "        8.41170307e-01, 7.46785504e-01, 9.56733034e-01, 2.13812322e-02,\n",
-       "        3.47623481e-01, 3.10272741e-01, 6.03396095e-02, 1.78752379e-05,\n",
-       "        2.75244613e-05, 7.65245633e-02, 1.15026086e-03, 3.56371084e-01,\n",
-       "        4.06841238e-02, 1.12615490e-01, 1.06597475e-01, 1.08105694e-01],\n",
-       "       [3.28317478e-01, 9.66129702e-01, 5.61032696e-01, 8.62497030e-01,\n",
-       "        9.63550683e-01, 8.45961194e-01, 7.20158054e-01, 9.88748018e-01,\n",
-       "        9.77474325e-01, 9.55736441e-01, 9.98737008e-01, 9.89761407e-01,\n",
-       "        6.29297273e-01, 8.91289356e-01, 5.01859641e-01, 3.54513736e-01,\n",
-       "        8.25423014e-01, 9.76254647e-01, 7.43818065e-01, 8.46674546e-01,\n",
-       "        9.91487258e-01, 8.65014618e-01, 7.62045317e-01, 9.84391947e-01,\n",
-       "        8.00838622e-01, 8.91455295e-01, 8.80087031e-01, 7.83411087e-01,\n",
-       "        9.70090353e-01, 7.73950511e-01, 9.66804261e-01, 7.87718577e-01,\n",
-       "        8.90655420e-01, 9.58954339e-01, 9.27871038e-01, 9.76030652e-01,\n",
-       "        8.22833260e-01, 7.39852563e-01, 9.09729742e-01, 9.63413192e-01,\n",
-       "        9.70448973e-01, 9.86417429e-01, 6.85710546e-01, 9.88766341e-01,\n",
-       "        9.65876322e-01, 9.17723041e-01, 9.22653501e-01, 1.00000000e+00,\n",
-       "        9.71965730e-01, 6.17201497e-01, 9.71333291e-01, 9.31186818e-01,\n",
-       "        9.75606867e-01, 9.25439842e-01, 7.19943858e-01, 3.33388205e-01,\n",
-       "        3.54787369e-01, 7.35441537e-01, 8.50560458e-01, 3.11910764e-01,\n",
-       "        4.03196672e-01, 6.86363427e-01, 7.28145300e-01, 3.65737000e-01,\n",
-       "        9.23417651e-01, 7.71167178e-01, 8.52920710e-01, 9.42535433e-01,\n",
-       "        7.60381223e-01, 6.16768344e-01, 8.07773432e-01, 7.15335886e-01,\n",
-       "        8.70692382e-01, 1.68695429e-01, 1.96374055e-01, 9.50552110e-01,\n",
-       "        9.91420152e-01, 8.55439088e-01, 9.83948180e-01, 9.93279091e-01,\n",
-       "        8.77848179e-01, 9.87161719e-01, 9.98924808e-01, 7.68595015e-01,\n",
-       "        9.97389576e-01, 8.37650979e-01, 9.79633127e-01, 9.86222784e-01,\n",
-       "        9.27366947e-01, 2.60967334e-01, 9.71555813e-01, 9.94333089e-01,\n",
-       "        5.54240061e-01, 9.81142809e-01, 9.84143813e-01, 8.66206576e-01,\n",
-       "        9.88895733e-01, 9.93197526e-01, 8.33165386e-01, 5.72917446e-01,\n",
-       "        7.78478046e-01, 1.00000000e+00, 9.24815703e-01, 7.55930499e-01,\n",
-       "        3.24447748e-01, 9.96775602e-01, 9.87068329e-01, 1.00000000e+00,\n",
-       "        9.22285083e-01, 9.53581355e-01, 6.34094814e-01, 8.10731624e-01,\n",
-       "        9.42476352e-01, 9.98516726e-01, 9.89911296e-01, 9.78146336e-01,\n",
-       "        8.86368568e-01, 8.39175265e-01, 7.31570105e-01, 9.92964364e-01,\n",
-       "        9.49213700e-01, 9.66109640e-01, 9.94040711e-01, 8.82833567e-01,\n",
-       "        9.18563078e-01, 9.97679189e-01, 9.95760829e-01, 9.18202694e-01,\n",
-       "        5.71130122e-01, 9.86475396e-01, 9.98532757e-01, 9.76214779e-01,\n",
-       "        9.41627770e-01, 8.81715016e-01, 9.98214377e-01, 3.27053067e-01,\n",
-       "        9.37947128e-01, 1.33474387e-01, 5.53093888e-05, 1.73707320e-05,\n",
-       "        7.86566149e-04, 6.66852451e-01, 5.11656045e-01, 8.02728761e-01,\n",
-       "        6.70270256e-01, 7.55408520e-01, 9.76378683e-01, 8.67729926e-01,\n",
-       "        8.56485398e-01, 7.36080414e-01, 8.41170083e-01, 8.80129718e-01,\n",
-       "        1.00000000e+00, 8.49872616e-01, 8.97181631e-01, 2.73316217e-01,\n",
-       "        8.29831687e-01, 9.25997458e-01, 9.13097328e-01, 9.93382146e-01,\n",
-       "        9.77683315e-01, 9.83663761e-01, 1.00000000e+00, 5.92413824e-01,\n",
-       "        6.01233693e-01, 7.27004996e-01, 8.68141209e-01, 8.00898438e-01,\n",
-       "        9.46962288e-02, 9.26626083e-01, 5.78934227e-01, 2.53925247e-02,\n",
-       "        4.97805786e-01, 8.47119971e-01, 7.19704654e-01, 1.00000000e+00,\n",
-       "        8.31395932e-01, 5.52711192e-01, 7.23888278e-13, 7.02431527e-15,\n",
-       "        2.22120039e-01, 2.45354002e-08, 9.93690223e-03, 6.02651058e-07,\n",
-       "        8.20222634e-23, 1.16148088e-01, 9.47394946e-04, 1.74355268e-08,\n",
-       "        1.02187591e-13, 3.82173043e-03, 4.40092209e-29, 1.06459310e-49,\n",
-       "        9.87165694e-13, 2.41152795e-13, 1.28628214e-02, 7.20955460e-03,\n",
-       "        4.18662802e-07, 4.65905541e-20, 7.61083815e-10, 3.15346755e-57,\n",
-       "        6.25942833e-05, 8.58774302e-13, 1.61609198e-05, 7.97577071e-43,\n",
-       "        4.89153659e-13, 2.30161414e-78, 1.08973302e-03, 1.64556387e-06,\n",
-       "        4.60179596e-03, 2.21139956e-11, 6.82660500e-15, 7.25573202e-04,\n",
-       "        4.77334428e-26, 1.24860683e-13, 1.75216529e-07, 1.65928841e-06,\n",
-       "        4.19316314e-08, 9.97877927e-05, 1.46440459e-14, 4.84822675e-10,\n",
-       "        5.04602104e-06, 8.48172250e-70, 1.69276145e-08, 1.08177180e-07,\n",
-       "        1.82229117e-33, 8.93571603e-06, 1.99919287e-04, 3.12574975e-05,\n",
-       "        3.58079042e-10, 1.57551731e-22, 7.76208456e-40, 9.85663317e-05,\n",
-       "        4.72816823e-08, 4.24944803e-09, 3.07556883e-65, 1.68889371e-02,\n",
-       "        1.94676587e-11, 9.40089278e-08, 5.46085240e-04, 1.12643422e-02,\n",
-       "        1.65607468e-06, 3.15611739e-14, 1.65027257e-03, 9.31757021e-03,\n",
-       "        1.51075828e-03, 4.62701323e-08, 6.14088778e-14, 1.04659598e-17,\n",
-       "        1.74965485e-03, 5.81221173e-11, 1.67197624e-09, 1.44140273e-04,\n",
-       "        3.20303237e-15, 8.82233299e-03, 1.45611831e-01, 4.11729638e-22,\n",
-       "        3.33333333e-01, 9.18228518e-01, 1.33054366e-05, 7.90596092e-01,\n",
-       "        8.73606888e-01, 8.06629800e-02, 1.47386986e-01, 7.95318988e-01,\n",
-       "        9.95422317e-01, 6.68091487e-01, 9.40017988e-01, 8.16788925e-01,\n",
-       "        1.00000000e+00, 5.27691571e-02, 6.58832667e-01, 1.04819615e-02,\n",
-       "        5.21902286e-11, 5.31010257e-01, 1.88853323e-01, 9.72975942e-01,\n",
-       "        9.54156962e-01, 7.47769513e-01, 9.75685507e-01, 9.80248166e-01,\n",
-       "        9.13099813e-01, 9.90062454e-01, 6.76155432e-01, 9.81197899e-01,\n",
-       "        9.49488040e-01, 8.90050581e-01, 8.55282414e-01, 9.84358295e-01,\n",
-       "        9.87714231e-01, 9.29813433e-01, 7.18269006e-01, 8.79496412e-01,\n",
-       "        9.80997415e-01, 9.20556228e-01, 9.94683676e-01, 6.06881740e-01,\n",
-       "        8.15102282e-01, 9.33681143e-01, 7.33319604e-01, 4.18669813e-01,\n",
-       "        4.43530907e-01, 8.53498115e-01, 9.95094585e-01, 9.67750983e-01,\n",
-       "        8.35234316e-01, 5.99037705e-01, 7.19723846e-01, 8.82242415e-01,\n",
-       "        9.76041723e-01, 9.60625313e-01, 9.50161554e-01, 9.75100548e-01,\n",
-       "        9.84396115e-01, 9.08799134e-01, 9.59338249e-01, 9.78655499e-01,\n",
-       "        7.22702455e-01, 9.67692530e-01, 7.33598267e-01, 1.00000000e+00,\n",
-       "        6.80322932e-01, 9.11160046e-01, 4.21998291e-01, 8.08628893e-01,\n",
-       "        1.00000000e+00, 9.93538335e-01, 8.89043622e-01, 1.00000000e+00,\n",
-       "        9.66584766e-01, 9.57050352e-01, 9.17710099e-01, 9.67982541e-01,\n",
-       "        1.00000000e+00, 8.51864172e-01, 3.86500814e-01, 4.15570577e-01,\n",
-       "        2.75790686e-01, 7.82339921e-01, 9.39116390e-01, 7.67758015e-01,\n",
-       "        8.18186270e-01, 7.54171923e-01, 9.45562890e-01, 9.08453599e-01,\n",
-       "        8.67214913e-01, 3.86623106e-01, 6.60280811e-01, 1.25896999e-01,\n",
-       "        4.76803208e-02, 6.79827398e-01, 7.54969787e-01, 9.40134530e-01,\n",
-       "        6.72694715e-02, 9.94135747e-01, 9.72825016e-01, 7.19768919e-01,\n",
-       "        4.68938875e-01, 6.59086188e-01, 8.66596560e-01, 6.99744742e-01,\n",
-       "        9.39497706e-01, 7.69580937e-01, 9.21299326e-01, 9.26321441e-01,\n",
-       "        3.29397217e-01, 8.51985624e-01, 9.62059533e-01, 8.59720078e-01,\n",
-       "        8.46246568e-01, 7.27065383e-01, 5.30119328e-04, 8.76603508e-01,\n",
-       "        6.02103657e-03, 5.32063432e-01, 7.88731334e-01, 8.65131033e-01,\n",
-       "        9.17056289e-01, 8.26591134e-01, 5.79765014e-01, 9.25492859e-01,\n",
-       "        9.28805256e-01, 9.25607428e-01, 5.90688682e-01, 9.21889029e-01,\n",
-       "        9.07933904e-03, 5.02462752e-01, 7.43302547e-01, 2.76556063e-01,\n",
-       "        6.20511785e-04, 2.21657532e-01, 1.64775864e-01, 2.57952633e-01,\n",
-       "        9.09790743e-01, 9.74324207e-01, 7.12348670e-01, 1.00000000e+00,\n",
-       "        9.81143865e-01, 5.66699365e-01, 6.95892360e-01, 2.49945675e-01,\n",
-       "        4.55036549e-01, 3.75109768e-01, 9.82393048e-01, 3.91449760e-01,\n",
-       "        4.04897359e-01, 5.65347873e-01, 9.03759270e-01, 1.23042520e-02,\n",
-       "        2.56927810e-02, 6.74238591e-01, 5.80052253e-01, 4.38106358e-01,\n",
-       "        8.62018724e-01, 7.24606225e-01, 4.84984929e-01, 7.33641817e-01,\n",
-       "        7.67533010e-01, 9.43255306e-01, 9.95268554e-01, 6.65772611e-01,\n",
-       "        9.88583727e-01, 7.40327086e-01, 7.42856950e-01, 9.44927485e-01,\n",
-       "        9.95302356e-01, 8.81587933e-01, 9.37009697e-01, 8.70092522e-01,\n",
-       "        7.88120774e-01, 8.66067385e-01, 9.73837586e-01, 4.11599496e-01,\n",
-       "        1.00000000e+00, 9.03347643e-01, 9.93971630e-01, 7.43625942e-01,\n",
-       "        8.36864886e-01, 9.74818024e-01, 9.48203792e-01, 8.97371738e-01,\n",
-       "        9.46193902e-01, 9.51345656e-01, 9.95239808e-01, 1.00000000e+00,\n",
-       "        9.73637539e-01, 9.96913568e-01, 9.86171705e-01, 9.79415197e-01,\n",
-       "        9.38998203e-01, 9.88178466e-01, 9.72033053e-01, 9.99690747e-01,\n",
-       "        6.78148717e-01, 9.44390665e-01, 9.99947354e-01, 1.00000000e+00,\n",
-       "        9.58954340e-01, 3.15497028e-01, 8.51243636e-01, 7.58355192e-01,\n",
-       "        8.56612957e-01, 9.81419663e-01, 7.83065878e-01, 3.03864875e-01,\n",
-       "        9.52472335e-01, 6.03559504e-01, 1.00000000e+00, 9.82264917e-01,\n",
-       "        9.54284565e-01, 9.28579165e-01, 9.84370590e-01, 9.90204857e-01,\n",
-       "        9.99234846e-01, 9.93735725e-01, 9.81541925e-01, 9.38543061e-01,\n",
-       "        1.00000000e+00, 8.42416545e-01, 1.00000000e+00, 1.00000000e+00,\n",
-       "        9.80311056e-01, 9.76776818e-01, 4.82655800e-01, 8.56521160e-01,\n",
-       "        1.00000000e+00, 9.24998077e-01, 8.56898573e-01, 8.56008218e-01,\n",
-       "        8.83100007e-01, 1.00000000e+00, 9.73975530e-01, 6.54868709e-01,\n",
-       "        9.65251390e-01, 9.58764214e-01, 8.26141154e-01, 1.87973610e-02,\n",
-       "        4.17840686e-01, 9.83369254e-01, 9.56921425e-01, 7.32532745e-01]])
        • tree_size
          (chain, draw)
          float64
          7.0 3.0 15.0 7.0 ... 7.0 7.0 7.0
          array([[ 7.,  3., 15.,  7., 15., 15.,  7., 15.,  7., 15., 15., 15., 15.,\n",
-       "        15., 15.,  7., 23., 15.,  7., 15., 15., 15., 15., 15., 15., 15.,\n",
-       "        15.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  3.,  3.,  3.,  3.,\n",
-       "         3.,  3.,  3.,  3.,  3.,  7.,  1.,  1.,  7.,  7.,  3.,  7.,  3.,\n",
-       "         3.,  3.,  3.,  3.,  3.,  1.,  3.,  1.,  3.,  3., 15.,  1.,  7.,\n",
-       "         3.,  1.,  3.,  3.,  1.,  3.,  3.,  3.,  3.,  3.,  7.,  7.,  7.,\n",
-       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7., 15.,  7.,  7.,\n",
-       "         3.,  7., 15.,  3.,  7.,  1.,  7., 15.,  3.,  7.,  7.,  7., 15.,\n",
-       "         7.,  7.,  7., 15.,  7., 15.,  7.,  7.,  7., 15.,  7.,  7.,  7.,\n",
-       "         7.,  7.,  7.,  7.,  7., 15.,  7.,  7.,  7.,  7.,  7., 15.,  7.,\n",
-       "        30.,  3., 11.,  3.,  7.,  7.,  7.,  7.,  7.,  7.,  7., 15.,  7.,\n",
-       "        11.,  7., 15.,  7.,  7., 15.,  7.,  7., 23., 15., 47.,  7.,  7.,\n",
-       "         3., 31.,  7.,  7., 23., 15.,  7.,  7., 15.,  7., 15., 15., 15.,\n",
-       "        15.,  7., 15., 15., 15., 15.,  7., 15.,  7., 15., 15., 15., 15.,\n",
-       "        15., 15., 15.,  7.,  7., 15.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
-       "         7.,  7.,  7.,  7.,  7., 15.,  7., 15.,  7., 31.,  7.,  7., 15.,\n",
-       "         7.,  7., 15., 15., 15., 15., 15., 15., 15.,  7., 15.,  7., 15.,\n",
-       "        15., 15.,  7.,  7., 15.,  7.,  7.,  3.,  3., 11.,  7.,  7.,  3.,\n",
-       "         3.,  7.,  7.,  7.,  3., 15., 11.,  7., 11.,  7.,  3.,  7.,  7.,\n",
-       "         7.,  7.,  7., 15., 15., 15., 15., 15.,  7., 15., 15.,  7., 15.,\n",
-       "         7.,  7.,  7.,  7.,  3., 15., 15.,  7.,  7., 15., 15., 15., 15.,\n",
-       "        15., 15.,  7., 15., 15.,  7.,  7.,  7., 15., 15., 15., 15., 15.,\n",
-       "        15.,  7.,  7., 15.,  7.,  7.,  7.,  7.,  7.,  7.,  3.,  3.,  7.,\n",
-       "         7., 15.,  7.,  7.,  7.,  3.,  3.,  3.,  7.,  7.,  7., 15., 15.,\n",
-       "         7.,  3.,  7.,  1.,  7.,  7.,  3.,  7.,  7.,  7.,  7.,  3., 15.,\n",
-       "         3.,  1.,  3.,  3.,  3.,  3.,  7.,  7.,  1.,  3., 15., 15.,  7.,\n",
-       "        31.,  7.,  7.,  7., 15.,  7., 15., 15., 15., 15., 15., 15.,  7.,\n",
-       "        15., 15., 15.,  7.,  7., 15., 15., 15.,  7.,  7.,  7.,  7.,  7.,\n",
-       "         7.,  7.,  7.,  7.,  7.,  7.,  7.,  7., 11.,  7., 15.,  7., 15.,\n",
-       "        15., 15., 15.,  3.,  7.,  7., 15., 15., 15.,  7.,  7., 15.,  7.,\n",
-       "         7., 15.,  7., 15., 15., 31.,  7., 15.,  7.,  7.,  7.,  7.,  7.,\n",
-       "        11.,  5.,  7.,  7.,  7.,  7.,  1.,  7.,  7.,  7.,  7.,  7.,  7.,\n",
-       "        15.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  7., 15., 15., 15.,\n",
-       "        15., 15., 15.,  7.,  7., 15.,  7., 11., 15.,  7.,  7.,  7.,  7.,\n",
-       "         7., 15., 15., 15., 15.,  7., 15., 15., 15., 15., 15.,  7.,  7.,\n",
-       "         7.,  7.,  7., 15., 15., 15.,  7., 15., 15., 15., 15., 15., 15.,\n",
-       "        15.,  7.,  7.,  3.,  3.,  7.,  7.,  7., 15.,  7.,  3.,  7., 31.,\n",
-       "        15., 15., 15.,  7., 11.,  7.,  3.,  3.,  3., 31.,  3.,  3.,  3.,\n",
-       "         3.,  3.,  7.,  3.,  3., 15.],\n",
-       "       [15.,  7.,  7., 15., 23., 23., 15., 15., 15., 15., 15., 15., 15.,\n",
-       "         7.,  7., 15.,  7., 23.,  7., 15., 15., 15., 31., 15., 15., 15.,\n",
-       "        15.,  7., 23.,  7., 15.,  7., 15., 15.,  7., 23., 15., 15., 15.,\n",
-       "        15., 15., 15.,  7., 23., 15., 15.,  7., 15., 15., 15.,  7.,  7.,\n",
-       "        15.,  3., 15.,  3.,  7.,  3.,  3.,  3.,  7., 15., 15.,  7., 15.,\n",
-       "        15.,  7., 15.,  7., 15., 15.,  7., 15.,  3., 15., 15., 15., 15.,\n",
-       "        15.,  7., 15., 15., 15., 15.,  7., 15., 15., 15., 15., 15., 15.,\n",
-       "        15., 15., 15.,  7., 15., 15., 15., 15., 15., 15.,  7., 15.,  3.,\n",
-       "        31., 15., 15., 15.,  7., 15., 15., 15., 15., 15., 15., 15., 15.,\n",
-       "        31., 15., 15., 15., 15., 31., 31.,  7., 15., 15., 15., 15., 31.,\n",
-       "        31., 15., 15., 23.,  7., 23.,  7., 15.,  3.,  3.,  3.,  3.,  7.,\n",
-       "         7.,  3.,  7., 15.,  7.,  7.,  7.,  7.,  7.,  7.,  7.,  3.,  7.,\n",
-       "         7., 15., 15.,  7., 31., 15.,  7.,  7., 15., 15.,  7.,  3., 15.,\n",
-       "         3., 15.,  3.,  7., 11.,  7., 15.,  7.,  7.,  1.,  1.,  3.,  3.,\n",
-       "         3.,  3.,  1.,  3.,  3.,  7.,  1.,  7.,  1.,  1.,  1.,  1.,  3.,\n",
-       "         3.,  3.,  1.,  6.,  1.,  7.,  1.,  1.,  1.,  1.,  1.,  3.,  1.,\n",
-       "        15.,  1.,  3.,  1.,  1.,  3.,  7.,  1.,  3.,  7.,  3.,  3.,  7.,\n",
-       "         1.,  3.,  7.,  1.,  3.,  3., 11.,  3.,  1.,  3.,  3.,  2.,  1.,\n",
-       "         1.,  7.,  3.,  3.,  3.,  3.,  1.,  1.,  1.,  3.,  9.,  1.,  3.,\n",
-       "         3.,  3.,  3.,  1.,  3.,  1.,  3.,  3.,  1.,  3.,  7.,  1., 11.,\n",
-       "         7.,  3., 15., 11.,  7.,  7.,  7.,  7.,  7.,  3., 15.,  3.,  1.,\n",
-       "         5.,  7.,  7.,  7., 15., 15.,  7.,  7., 23.,  7.,  7., 15.,  7.,\n",
-       "         7., 15., 15.,  7.,  7.,  7., 15.,  7.,  7.,  7.,  7., 15.,  3.,\n",
-       "         7.,  3.,  7., 15., 15.,  7.,  3.,  7., 15., 15., 15., 15., 15.,\n",
-       "        15., 15., 15.,  7.,  7.,  7., 15.,  7.,  7.,  7., 15., 15.,  7.,\n",
-       "        15.,  7., 15.,  7.,  7.,  7.,  7.,  7.,  7.,  3.,  3., 15., 15.,\n",
-       "         7.,  7., 11.,  7., 15., 15.,  7.,  3., 23.,  3.,  3.,  7.,  7.,\n",
-       "        15.,  3.,  7.,  7.,  7., 15.,  7.,  7.,  7.,  3.,  7.,  7.,  7.,\n",
-       "        15., 31.,  7.,  7.,  7., 15.,  3.,  7.,  3., 15.,  7.,  7.,  7.,\n",
-       "         7., 15.,  7.,  7.,  7., 23.,  7.,  1.,  3.,  3.,  3.,  7., 15.,\n",
-       "         3.,  7.,  7.,  7., 15., 15., 15., 23., 15.,  3.,  3.,  7.,  7.,\n",
-       "         7.,  7.,  7.,  7.,  3.,  3.,  3., 15.,  6.,  7., 15.,  7.,  7.,\n",
-       "        15., 15., 23., 15., 15., 15., 15., 11., 15., 15., 15., 15., 15.,\n",
-       "        31., 15., 15., 15., 15., 15., 15., 31., 15., 15., 15., 15., 31.,\n",
-       "        15., 15., 15., 15., 15., 15., 15., 15., 15., 15., 15., 15.,  7.,\n",
-       "         7.,  7., 15., 23., 15., 15., 15., 15., 15., 15.,  7., 15., 31.,\n",
-       "         7., 15., 15., 15., 15., 15., 15., 15., 15., 15., 15., 31., 15.,\n",
-       "        15., 15., 15., 15., 15., 15., 15., 15., 15., 15., 15., 23.,  7.,\n",
-       "         7.,  3.,  7.,  7.,  7.,  7.]])
        • lp
          (chain, draw)
          float64
          -57.32 -60.39 ... -54.0 -56.9
          array([[-57.31822473, -60.39028527, -63.4471001 , -60.4192723 ,\n",
-       "        -65.14291022, -60.51097039, -63.29861849, -64.79104548,\n",
-       "        -62.48782882, -65.546425  , -63.50560839, -60.80924214,\n",
-       "        -62.10517054, -60.88355767, -62.82318898, -64.17644169,\n",
-       "        -61.83957224, -62.01280074, -60.47774168, -66.38766238,\n",
-       "        -65.47092464, -61.50030614, -60.58986324, -65.19850783,\n",
-       "        -63.17967943, -57.75363717, -54.39465126, -52.17360757,\n",
-       "        -55.42435425, -54.49940055, -58.98795015, -59.10199397,\n",
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-       "        -55.85453885, -54.35775511, -56.52257144, -54.88355214,\n",
-       "        -54.88355214, -48.77053196, -49.62383978, -49.62383978,\n",
-       "        -49.62383978, -53.16259112, -53.16259112, -58.84582452,\n",
-       "        -59.16271799, -59.75818277, -64.89989039, -59.35155945,\n",
-       "        -59.00395553, -54.23133503, -46.69813902, -49.42962761,\n",
-       "        -52.52929999, -54.85470405, -51.45557864, -52.39301525,\n",
-       "        -51.85084174, -50.8470161 , -48.92502782, -48.92502782,\n",
-       "        -48.92502782, -46.96672035, -47.55557601, -51.71932018,\n",
-       "        -52.1931534 , -53.29156574, -56.06022891, -56.3562971 ,\n",
-       "        -59.88355965, -57.25684725, -55.76390732, -63.44496078,\n",
-       "        -58.40492879, -55.57409489, -64.61032336, -58.74141265,\n",
-       "        -59.7182531 , -60.70113546, -67.56289592, -67.32513934,\n",
-       "        -64.5197525 , -64.14072553, -60.42614942, -68.01914335,\n",
-       "        -66.57324058, -64.95708042, -66.82015203, -69.75318428,\n",
-       "        -65.68378338, -62.6453411 , -67.48450504, -67.35459006,\n",
-       "        -68.53276157, -62.7233112 , -61.7876735 , -60.76344011,\n",
-       "        -61.37535398, -63.36436164, -61.76409978, -64.12513892,\n",
-       "        -61.53974995, -62.80173375, -62.18994181, -55.88199927,\n",
-       "        -60.61293574, -60.46281098, -56.87452303, -55.40178531,\n",
-       "        -55.20934504, -54.31942004, -59.4018247 , -58.55145913,\n",
-       "        -61.24638225, -61.9967568 , -60.47705385, -66.62658636,\n",
-       "        -61.29270712, -59.39128641, -60.33390385, -58.94550258,\n",
-       "        -58.57613915, -60.50909689, -62.09806463, -64.85267852,\n",
-       "        -66.09652891, -62.16290385, -64.21702599, -65.30820455,\n",
-       "        -64.62563924, -67.96047408, -68.52681529, -64.04456827,\n",
-       "        -67.52329538, -59.04466885, -68.86210462, -70.92480017,\n",
-       "        -66.77136507, -66.62378945, -59.41939926, -57.61630917,\n",
-       "        -59.4959824 , -57.43603643, -55.99678804, -58.25798526,\n",
-       "        -56.94759814, -54.57090867, -51.36750123, -51.36750123,\n",
-       "        -55.8462047 , -55.4976664 , -53.99871956, -56.89758441]])
        • depth
          (chain, draw)
          int64
          3 2 4 3 4 4 3 4 ... 5 3 3 2 3 3 3 3
          array([[3, 2, 4, 3, 4, 4, 3, 4, 3, 4, 4, 4, 4, 4, 4, 3, 5, 4, 3, 4, 4, 4,\n",
-       "        4, 4, 4, 4, 4, 3, 3, 3, 3, 3, 3, 3, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
-       "        3, 1, 1, 3, 3, 2, 3, 2, 2, 2, 2, 2, 2, 1, 2, 1, 2, 2, 4, 1, 3, 2,\n",
-       "        1, 2, 2, 1, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
-       "        4, 3, 3, 2, 3, 4, 2, 3, 1, 3, 4, 2, 3, 3, 3, 4, 3, 3, 3, 4, 3, 4,\n",
-       "        3, 3, 3, 4, 3, 3, 3, 3, 3, 3, 3, 3, 4, 3, 3, 3, 3, 3, 4, 3, 5, 2,\n",
-       "        4, 2, 3, 3, 3, 3, 3, 3, 3, 4, 3, 4, 3, 4, 3, 3, 4, 3, 3, 5, 4, 6,\n",
-       "        3, 3, 2, 5, 3, 3, 5, 4, 3, 3, 4, 3, 4, 4, 4, 4, 3, 4, 4, 4, 4, 3,\n",
-       "        4, 3, 4, 4, 4, 4, 4, 4, 4, 3, 3, 4, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
-       "        3, 3, 4, 3, 4, 3, 5, 3, 3, 4, 3, 3, 4, 4, 4, 4, 4, 4, 4, 3, 4, 3,\n",
-       "        4, 4, 4, 3, 3, 4, 3, 3, 2, 2, 4, 3, 3, 2, 2, 3, 3, 3, 2, 4, 4, 3,\n",
-       "        4, 3, 2, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 3, 4, 4, 3, 4, 3, 3, 3, 3,\n",
-       "        2, 4, 4, 3, 3, 4, 4, 4, 4, 4, 4, 3, 4, 4, 3, 3, 3, 4, 4, 4, 4, 4,\n",
-       "        4, 3, 3, 4, 3, 3, 3, 3, 3, 3, 2, 2, 3, 3, 4, 3, 3, 3, 2, 2, 2, 3,\n",
-       "        3, 3, 4, 4, 3, 2, 3, 1, 3, 3, 2, 3, 3, 3, 3, 2, 4, 2, 1, 2, 2, 2,\n",
-       "        2, 3, 3, 1, 2, 4, 4, 3, 5, 3, 3, 3, 4, 3, 4, 4, 4, 4, 4, 4, 3, 4,\n",
-       "        4, 4, 3, 3, 4, 4, 4, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 3,\n",
-       "        4, 3, 4, 4, 4, 4, 2, 3, 3, 4, 4, 4, 3, 3, 4, 3, 3, 4, 3, 4, 4, 5,\n",
-       "        3, 4, 3, 3, 3, 3, 3, 4, 3, 3, 3, 3, 3, 1, 3, 3, 3, 3, 3, 3, 4, 3,\n",
-       "        3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 3, 3, 4, 3, 4, 4, 3, 3,\n",
-       "        3, 3, 3, 4, 4, 4, 4, 3, 4, 4, 4, 4, 4, 3, 3, 3, 3, 3, 4, 4, 4, 3,\n",
-       "        4, 4, 4, 4, 4, 4, 4, 3, 3, 2, 2, 3, 3, 3, 4, 3, 2, 3, 5, 4, 4, 4,\n",
-       "        3, 4, 3, 2, 2, 2, 5, 2, 2, 2, 2, 2, 3, 2, 2, 4],\n",
-       "       [4, 3, 3, 4, 5, 5, 4, 4, 4, 4, 4, 4, 4, 3, 3, 4, 3, 5, 3, 4, 4, 4,\n",
-       "        5, 4, 4, 4, 4, 3, 5, 3, 4, 3, 4, 4, 3, 5, 4, 4, 4, 4, 4, 4, 3, 5,\n",
-       "        4, 4, 3, 4, 4, 4, 3, 3, 4, 2, 4, 2, 3, 2, 2, 2, 3, 4, 4, 3, 4, 4,\n",
-       "        3, 4, 3, 4, 4, 3, 4, 2, 4, 4, 4, 4, 4, 3, 4, 4, 4, 4, 3, 4, 4, 4,\n",
-       "        4, 4, 4, 4, 4, 4, 3, 4, 4, 4, 4, 4, 4, 3, 4, 2, 5, 4, 4, 4, 3, 4,\n",
-       "        4, 4, 4, 4, 4, 4, 4, 5, 4, 4, 4, 4, 5, 5, 3, 4, 4, 4, 4, 5, 5, 4,\n",
-       "        4, 5, 3, 5, 3, 4, 2, 2, 2, 2, 3, 3, 2, 3, 4, 3, 3, 3, 3, 3, 3, 3,\n",
-       "        2, 3, 3, 4, 4, 3, 5, 4, 3, 3, 4, 4, 3, 2, 4, 2, 4, 2, 3, 4, 3, 4,\n",
-       "        3, 3, 1, 1, 2, 2, 2, 2, 1, 2, 2, 3, 1, 3, 1, 1, 1, 1, 2, 2, 2, 1,\n",
-       "        3, 1, 3, 1, 1, 1, 1, 1, 2, 1, 4, 1, 2, 1, 1, 2, 3, 1, 2, 3, 2, 2,\n",
-       "        3, 1, 2, 3, 1, 2, 2, 4, 2, 1, 2, 2, 2, 1, 1, 3, 2, 2, 2, 2, 1, 1,\n",
-       "        1, 2, 4, 1, 2, 2, 2, 2, 1, 2, 1, 2, 2, 1, 2, 3, 1, 4, 3, 2, 4, 4,\n",
-       "        3, 3, 3, 3, 3, 2, 4, 2, 1, 3, 3, 3, 3, 4, 4, 3, 3, 5, 3, 3, 4, 3,\n",
-       "        3, 4, 4, 3, 3, 3, 4, 3, 3, 3, 3, 4, 2, 3, 2, 3, 4, 4, 3, 2, 3, 4,\n",
-       "        4, 4, 4, 4, 4, 4, 4, 3, 3, 3, 4, 3, 3, 3, 4, 4, 3, 4, 3, 4, 3, 3,\n",
-       "        3, 3, 3, 3, 2, 2, 4, 4, 3, 3, 4, 3, 4, 4, 3, 2, 5, 2, 2, 3, 3, 4,\n",
-       "        2, 3, 3, 3, 4, 3, 3, 3, 2, 3, 3, 3, 4, 5, 3, 3, 3, 4, 2, 3, 2, 4,\n",
-       "        3, 3, 3, 3, 4, 3, 3, 3, 5, 3, 1, 2, 2, 2, 3, 4, 2, 3, 3, 3, 4, 4,\n",
-       "        4, 5, 4, 2, 2, 3, 3, 3, 3, 3, 3, 2, 2, 2, 4, 3, 3, 4, 3, 3, 4, 4,\n",
-       "        5, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 4, 4, 4, 4, 4, 4, 5, 4, 4, 4,\n",
-       "        4, 5, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 3, 3, 3, 4, 5, 4, 4, 4,\n",
-       "        4, 4, 4, 3, 4, 5, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 4, 4, 4, 4,\n",
-       "        4, 4, 4, 4, 4, 4, 4, 4, 5, 3, 3, 2, 3, 3, 3, 3]])
        • step_size_bar
          (chain, draw)
          float64
          0.3176 0.3176 ... 0.2718 0.2718
          array([[0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
-       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
-       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
-       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
-       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
-       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
-       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
-       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
-       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
-       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
-       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
-       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
-       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
-       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
-       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
-       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
-       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
-       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
-       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
-       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
-       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
-       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
-       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
-       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
-       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
-       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
-       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
-       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
-       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
-       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
-       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
-       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
-       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
-       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
-       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
-       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
-       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
-       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
-       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
-       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
-       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
-       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
-       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
-       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
-       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
-       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
-       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
-       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
-       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
-       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
-       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
-       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
-       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
-       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
-       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
-       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
-       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
-       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
-       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
-       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
-       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
-       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
-       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
-       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
-       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
-       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
-       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
-       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
-       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
-       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
-       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
-       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
-       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
-       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
-       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
-       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
-       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
-       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
-       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
-       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
-       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
-       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
-       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
-       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
-       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
-       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
-       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
-       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
-       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
-       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
-       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
-       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
-       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
-       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
-       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
-       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
-       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
-       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
-       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196,\n",
-       "        0.31763196, 0.31763196, 0.31763196, 0.31763196, 0.31763196],\n",
-       "       [0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
-       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
-       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
-       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
-       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
-       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
-       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
-       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
-       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
-       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
-       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
-       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
-       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
-       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
-       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
-       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
-       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
-       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
-       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
-       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
-       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
-       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
-       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
-       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
-       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
-       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
-       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
-       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
-       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
-       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
-       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
-       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
-       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
-       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
-       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
-       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
-       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
-       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
-       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
-       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
-       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
-       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
-       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
-       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
-       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
-       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
-       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
-       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
-       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
-       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
-       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
-       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
-       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
-       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
-       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
-       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
-       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
-       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
-       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
-       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
-       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
-       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
-       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
-       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
-       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
-       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
-       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
-       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
-       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
-       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
-       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
-       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
-       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
-       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
-       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
-       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
-       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
-       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
-       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
-       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
-       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
-       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
-       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
-       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
-       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
-       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
-       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
-       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
-       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
-       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
-       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
-       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
-       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
-       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
-       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
-       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
-       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
-       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
-       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612,\n",
-       "        0.27181612, 0.27181612, 0.27181612, 0.27181612, 0.27181612]])
        • diverging
          (chain, draw)
          bool
          False False False ... False False
          array([[False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
+       "
          xarray.Dataset
            • chain: 2
            • draw: 500
            • chain
              (chain)
              int64
              0 1
              array([0, 1])
            • draw
              (draw)
              int64
              0 1 2 3 4 5 ... 495 496 497 498 499
              array([  0,   1,   2, ..., 497, 498, 499])
            • energy
              (chain, draw)
              float64
              54.11 55.93 56.07 ... 63.07 60.63
              array([[54.10601972, 55.93135612, 56.06957918, 55.33294634, 57.43933537,\n",
+       "        59.07233325, 57.41813234, 59.74436832, 61.14341182, 60.7151683 ,\n",
+       "        61.52439218, 60.54978452, 61.36221425, 60.56900019, 59.58650606,\n",
+       "        59.50562608, 57.89948539, 55.61155433, 60.60662756, 62.05189116,\n",
+       "        65.8980808 , 65.64044353, 72.10097787, 73.04449957, 67.82616163,\n",
+       "        66.59270179, 63.1623821 , 62.37457397, 58.61424078, 68.22892012,\n",
+       "        67.12878883, 66.20991738, 66.45800267, 64.14544431, 64.95653256,\n",
+       "        67.51656333, 66.73294236, 66.83444633, 62.59751142, 67.81590809,\n",
+       "        69.50951747, 69.43712505, 72.05196915, 68.93414382, 65.39200981,\n",
+       "        65.68671149, 68.35139475, 66.4507085 , 69.250636  , 66.69110647,\n",
+       "        65.10544319, 68.26589787, 72.07402017, 68.64816747, 60.99075786,\n",
+       "        64.77252849, 64.22033057, 57.99900453, 56.46559097, 59.34801612,\n",
+       "        59.50312128, 56.83820747, 60.92066642, 59.56365074, 58.57023478,\n",
+       "        56.082646  , 58.98790289, 58.99157239, 61.71260227, 64.24304682,\n",
+       "        64.55380337, 59.69909334, 60.26611304, 58.00049725, 59.83312984,\n",
+       "        56.84846217, 60.02916461, 57.07925497, 58.97626349, 58.77919093,\n",
+       "        54.95681999, 54.90718442, 65.76030564, 65.25573791, 65.67082321,\n",
+       "        66.29131914, 64.93032832, 65.52818328, 66.55485846, 74.76445544,\n",
+       "        68.0737226 , 67.97859506, 69.19217239, 64.72627295, 61.54529409,\n",
+       "        56.48945637, 55.58834864, 61.01249983, 62.28958893, 66.5989684 ,\n",
+       "        66.86429468, 64.10810704, 61.35159326, 63.45586947, 56.0620549 ,\n",
+       "        54.36032021, 53.47257947, 60.34173746, 63.35189904, 62.14028342,\n",
+       "        56.23034793, 53.52596934, 54.52112136, 54.13832771, 59.81806573,\n",
+       "        55.17083226, 53.75113035, 58.92316936, 57.16635317, 61.27601319,\n",
+       "        63.84307613, 63.44425436, 62.06467684, 63.47711012, 63.47588301,\n",
+       "        61.46929645, 67.4437042 , 69.18878592, 68.45559514, 67.36887598,\n",
+       "        63.40884125, 63.53038912, 62.02939071, 65.63861224, 68.22235368,\n",
+       "        64.94645375, 55.75557397, 54.0450127 , 53.48238476, 51.54985226,\n",
+       "        50.18766457, 48.85756009, 51.97660216, 47.32228709, 51.77660104,\n",
+       "        53.0930875 , 46.6012184 , 47.90499327, 49.24975392, 51.36780241,\n",
+       "        48.5813183 , 50.81037637, 48.92558874, 52.37554144, 57.11978216,\n",
+       "        60.95167194, 62.65772985, 63.45560358, 60.98504135, 60.67911919,\n",
+       "        61.5317335 , 63.17378632, 62.08554232, 61.57809938, 62.82500173,\n",
+       "        61.37478009, 66.3078557 , 63.35831014, 64.9022637 , 64.76901128,\n",
+       "        69.41440174, 69.38704195, 60.02769226, 60.62750495, 62.14914889,\n",
+       "        60.63965897, 58.83558048, 55.41302767, 53.60311141, 53.8257481 ,\n",
+       "        54.34645461, 55.87369572, 53.96962735, 54.49384491, 54.50993016,\n",
+       "        54.43422357, 60.34752361, 59.06303724, 58.58756453, 57.31929054,\n",
+       "        64.75296979, 68.74552087, 64.21555475, 62.49025073, 62.54264574,\n",
+       "        65.77267494, 63.56636185, 70.46279215, 72.40638879, 72.94668148,\n",
+       "        66.83747056, 67.07194183, 68.92777402, 67.56603533, 65.29801757,\n",
+       "        63.12552762, 62.18858626, 64.53074004, 59.47838279, 64.12923403,\n",
+       "        63.32403972, 64.09605252, 62.79529555, 69.2672079 , 77.54401582,\n",
+       "        80.2982659 , 69.01530152, 62.98472869, 60.29743911, 58.57974915,\n",
+       "        67.37778457, 67.37749577, 63.73610925, 59.71820764, 59.3628847 ,\n",
+       "        61.22394581, 60.3522002 , 56.74099638, 58.73421852, 51.73590108,\n",
+       "        51.70776294, 51.4989147 , 46.32772022, 46.18877034, 50.30087997,\n",
+       "        53.13644562, 50.97989503, 52.34939691, 51.61819861, 49.8754385 ,\n",
+       "        51.04828571, 47.42646789, 47.57567516, 47.15738502, 52.40590002,\n",
+       "        55.05422805, 54.10464761, 50.34047244, 51.85475173, 51.96115223,\n",
+       "        55.0713514 , 52.27938342, 52.56644345, 53.45380078, 54.54286479,\n",
+       "        57.66023201, 58.81566635, 58.3466876 , 65.40265014, 59.3827241 ,\n",
+       "        62.31599769, 65.23237765, 63.13343417, 63.03451414, 70.10100871,\n",
+       "        68.94400215, 72.58169957, 68.40695964, 66.39347394, 69.98895933,\n",
+       "        65.0492034 , 65.60198114, 64.06609554, 65.49535665, 57.86999135,\n",
+       "        58.44554793, 58.52397693, 64.24340097, 59.40212154, 55.99945362,\n",
+       "        57.30856274, 63.00779274, 62.20179632, 64.38798482, 63.43418645,\n",
+       "        63.07314845, 64.52374228, 65.04943275, 67.7777635 , 64.83709177,\n",
+       "        61.09276616, 53.4143787 , 55.12341918, 49.51919281, 47.15804344,\n",
+       "        51.11525597, 47.2995792 , 51.2100775 , 55.05619063, 51.64273438,\n",
+       "        51.60193566, 53.90630626, 53.37859907, 51.0531472 , 55.8125039 ,\n",
+       "        60.55508193, 58.45580218, 56.61282754, 59.14732549, 58.46471393,\n",
+       "        56.47607621, 58.93691097, 60.37981461, 63.20065984, 64.33952011,\n",
+       "        60.56063989, 59.56529761, 59.05735166, 57.16836299, 57.64805983,\n",
+       "        60.75841121, 62.17599997, 66.94291735, 67.43151337, 70.51928215,\n",
+       "        69.62293541, 70.28592791, 73.49247495, 71.25395936, 81.53398362,\n",
+       "        77.28702655, 66.92878818, 63.54807272, 63.87554542, 67.9316875 ,\n",
+       "        59.55586943, 68.4689987 , 65.81188095, 67.58851407, 68.6245911 ,\n",
+       "        68.41912778, 65.80920182, 69.02568047, 70.64528984, 69.16773546,\n",
+       "        63.40629269, 65.1410899 , 63.00736498, 61.78761687, 66.46462664,\n",
+       "        63.2910019 , 65.57525585, 69.39381403, 72.43189069, 72.97748229,\n",
+       "        69.53065595, 62.32524486, 60.75009471, 65.41650235, 58.82340072,\n",
+       "        57.62234976, 56.23104945, 52.05068993, 57.77585883, 59.08139477,\n",
+       "        57.81904431, 53.7280486 , 53.08016937, 52.60640982, 54.17433158,\n",
+       "        54.74670936, 59.56887897, 61.43993805, 65.45781501, 61.81370826,\n",
+       "        63.6799869 , 62.29196608, 65.25805627, 69.85652606, 66.09235923,\n",
+       "        63.4692679 , 65.22944575, 66.03535827, 75.23938921, 73.83506969,\n",
+       "        68.72891395, 70.88868616, 70.82175873, 69.5697375 , 65.1836767 ,\n",
+       "        59.61316137, 54.54615491, 58.37597463, 57.52522281, 57.07714297,\n",
+       "        59.41840899, 60.40184249, 53.6413567 , 53.04231423, 57.75461201,\n",
+       "        56.72590066, 56.67385048, 55.68381775, 52.73433905, 56.44556211,\n",
+       "        56.4077514 , 57.28035558, 53.72090604, 58.87717811, 61.80988539,\n",
+       "        59.1003976 , 58.18190188, 59.15584218, 56.88896863, 57.45712783,\n",
+       "        58.79597887, 59.48728147, 65.14175357, 67.05126203, 72.98578478,\n",
+       "        69.47359651, 68.3128967 , 65.48785988, 66.67854362, 71.92791313,\n",
+       "        70.32314241, 70.93850983, 72.19582617, 65.58886169, 66.54167174,\n",
+       "        66.77739505, 67.17919099, 65.97313583, 66.12847021, 66.37077162,\n",
+       "        65.39077702, 66.02386045, 68.92655272, 67.23746742, 63.47133117,\n",
+       "        62.90333626, 62.84304656, 64.36057003, 72.59633793, 69.72091488,\n",
+       "        69.17577819, 67.86658525, 61.14516516, 62.80944752, 63.39108239,\n",
+       "        61.85910542, 61.49439461, 56.83414815, 54.29448161, 57.06753066,\n",
+       "        53.81617812, 60.54420739, 64.02815631, 62.20980492, 61.72451974,\n",
+       "        62.61419121, 62.49293563, 64.59334416, 61.83543094, 67.74919359,\n",
+       "        63.69267265, 68.05979035, 66.60549507, 66.41192104, 68.74720436,\n",
+       "        69.48224055, 71.17564153, 66.59538684, 66.19871719, 60.93792004,\n",
+       "        60.87871881, 64.00857709, 63.45486055, 64.7533642 , 67.64134898,\n",
+       "        71.23802865, 65.27106083, 63.14943176, 68.54167124, 68.16367718,\n",
+       "        66.52899007, 63.28037528, 59.26195905, 65.96123023, 67.20003219,\n",
+       "        66.85327017, 62.7872037 , 61.15546027, 62.88457413, 62.24576625,\n",
+       "        58.89839906, 56.11340163, 52.37056991, 56.02808218, 51.3536903 ],\n",
+       "       [53.60244548, 52.69386493, 54.44602084, 53.75236244, 52.35215801,\n",
+       "        49.32314787, 49.58063386, 59.07849524, 63.63249153, 56.7319414 ,\n",
+       "        61.7355117 , 61.85442926, 61.19520594, 62.53248808, 67.98347609,\n",
+       "        65.98401171, 66.2309188 , 62.95105882, 61.98967592, 73.7081029 ,\n",
+       "        70.85678495, 67.17534704, 67.10515254, 63.97530304, 62.71759842,\n",
+       "        62.06703675, 69.628179  , 68.95994287, 65.81240029, 60.00739701,\n",
+       "        59.12269304, 61.23871578, 61.99409798, 62.06859449, 61.30373254,\n",
+       "        58.57243878, 62.25424113, 56.14413632, 59.93667047, 58.71816755,\n",
+       "        55.93221947, 54.03138541, 53.73444051, 53.76537658, 55.49506147,\n",
+       "        57.97763145, 57.97804552, 60.73505002, 65.22473727, 64.33972018,\n",
+       "        60.18207915, 59.89928505, 57.97880735, 64.66007689, 65.80358903,\n",
+       "        70.92688126, 65.92775749, 65.00798582, 61.80155031, 61.84010643,\n",
+       "        61.02408525, 61.57800602, 68.29254652, 67.29366737, 67.73589223,\n",
+       "        64.77047529, 61.56603755, 58.55503939, 55.32132955, 57.16421665,\n",
+       "        55.5711269 , 57.37014229, 59.17605274, 67.53378929, 64.87552913,\n",
+       "        65.85111842, 72.05159791, 68.26888059, 67.69275939, 75.5699396 ,\n",
+       "        69.60763391, 72.17720205, 70.80473007, 66.64958448, 67.20071524,\n",
+       "        68.47473224, 68.64765914, 72.82855058, 73.95378175, 72.84928973,\n",
+       "        72.84266759, 73.80330984, 68.64266644, 59.27433228, 57.84598801,\n",
+       "        56.07467704, 57.13500866, 54.85460647, 56.02934415, 58.17856125,\n",
+       "        57.74377082, 57.99227912, 60.23866246, 61.53175894, 68.01831885,\n",
+       "        68.72735029, 65.04507207, 65.41968704, 68.16321724, 62.64571234,\n",
+       "        62.43054438, 59.98861128, 61.15481692, 62.7242483 , 60.24558531,\n",
+       "        58.11589587, 66.49193714, 58.63776453, 64.09510091, 59.92864502,\n",
+       "        57.16172046, 56.60649595, 58.13968405, 57.3753964 , 58.15799169,\n",
+       "        60.83792045, 62.29265995, 58.3021099 , 59.79733266, 61.71560823,\n",
+       "        58.25353651, 59.22641181, 61.92456386, 61.06434309, 65.17219157,\n",
+       "        67.07149993, 59.64610708, 60.37267406, 61.27449751, 55.73856904,\n",
+       "        53.90898524, 57.99588252, 61.41298203, 63.69683754, 65.91247566,\n",
+       "        63.66712525, 65.32871947, 63.69239101, 73.51460897, 68.65636017,\n",
+       "        73.49083406, 72.12184762, 66.57885957, 65.11903592, 62.89009805,\n",
+       "        60.10017418, 55.75733925, 56.98647405, 61.09134149, 59.51205313,\n",
+       "        59.00974101, 62.20060436, 64.71632587, 63.72456551, 59.6069249 ,\n",
+       "        60.23073323, 64.34181123, 58.53885833, 55.90696959, 66.40857614,\n",
+       "        70.07465822, 61.39868071, 63.19341297, 62.74412198, 64.15468006,\n",
+       "        71.92735942, 74.14983813, 74.12862851, 67.76347352, 65.29863877,\n",
+       "        65.57332131, 65.62659245, 64.82969051, 66.07240255, 64.70862794,\n",
+       "        64.41535676, 66.05266669, 67.94336575, 65.38734235, 64.84508242,\n",
+       "        63.66121546, 62.96759965, 62.77539639, 58.99623244, 59.49273167,\n",
+       "        52.52354063, 54.6607846 , 49.43338266, 52.41974133, 52.95769922,\n",
+       "        61.69145629, 64.42912534, 61.94745804, 62.23649514, 67.62535896,\n",
+       "        67.37877203, 66.01274553, 64.01757759, 58.80340238, 62.03810109,\n",
+       "        63.35527724, 69.76194838, 68.10245826, 68.04552492, 64.21840458,\n",
+       "        64.33290518, 67.27655669, 66.3713469 , 60.51734284, 62.88616166,\n",
+       "        62.94277232, 64.12384569, 62.84366182, 63.29697183, 59.40593025,\n",
+       "        60.83477564, 66.99727531, 72.1593534 , 73.72928373, 62.22556448,\n",
+       "        57.03300449, 58.85981984, 61.14298194, 62.43610698, 64.38508799,\n",
+       "        67.76257956, 67.79498144, 67.41364534, 67.54706612, 62.62947301,\n",
+       "        59.71996079, 60.93108646, 56.7865272 , 58.64598487, 52.56063956,\n",
+       "        54.22003327, 55.01444707, 56.7049828 , 59.23566292, 63.40569895,\n",
+       "        66.78481514, 64.34452282, 62.84669753, 64.83183415, 61.55412974,\n",
+       "        58.54992206, 55.44222381, 53.63911963, 56.46056661, 58.14077911,\n",
+       "        55.86199611, 57.62349341, 58.42708705, 57.29943901, 54.01071821,\n",
+       "        56.10170348, 54.72271618, 61.19420951, 54.10362547, 54.16863958,\n",
+       "        59.60536296, 63.48124574, 58.22722364, 58.27765496, 57.27205882,\n",
+       "        55.68972879, 64.97996995, 62.50022593, 65.60620959, 64.12540485,\n",
+       "        62.41568365, 60.18061354, 65.13470809, 58.05569341, 59.48033937,\n",
+       "        56.30606088, 61.63911117, 63.09263898, 67.19901662, 67.87459557,\n",
+       "        66.07676332, 64.89155346, 63.37177986, 62.09316402, 61.88759968,\n",
+       "        58.7062763 , 60.1085988 , 61.94359896, 65.35902755, 64.23400904,\n",
+       "        64.46559548, 60.9470359 , 63.31525258, 63.2422965 , 61.29543503,\n",
+       "        59.33070188, 61.03346519, 58.57095782, 59.70237867, 62.44783064,\n",
+       "        61.60947356, 63.1706247 , 60.21548279, 59.90134771, 54.0485363 ,\n",
+       "        55.14613596, 57.79197049, 55.78038696, 55.21524072, 55.6709634 ,\n",
+       "        58.90081847, 59.5807061 , 60.46488242, 60.02755387, 60.08491252,\n",
+       "        60.67368076, 61.28101165, 56.43155706, 56.18548809, 58.46799956,\n",
+       "        56.20708753, 50.65697662, 55.13999495, 51.22520453, 53.7256044 ,\n",
+       "        51.35089862, 55.4405572 , 51.19459286, 49.6482886 , 50.81612651,\n",
+       "        51.37536988, 55.08973124, 54.83025627, 52.84083862, 52.99210807,\n",
+       "        51.38413862, 51.41213848, 63.92944477, 61.25401367, 56.53271071,\n",
+       "        60.45375526, 58.21645597, 58.96260301, 55.75966373, 53.07683131,\n",
+       "        52.31842166, 55.99994602, 52.56370159, 54.80220901, 51.50756231,\n",
+       "        52.60058165, 50.42554838, 49.05310549, 50.07197869, 52.65312348,\n",
+       "        49.24796023, 48.24791924, 51.75575504, 49.20175309, 49.18472907,\n",
+       "        51.57103738, 51.45009932, 47.99143565, 48.62475201, 47.68566231,\n",
+       "        46.9351184 , 49.87228692, 49.58910092, 46.02329267, 46.24468298,\n",
+       "        45.73329771, 49.71074505, 45.52933137, 46.69956299, 45.59818208,\n",
+       "        52.72025749, 48.20671223, 48.12730909, 48.34757922, 48.79037513,\n",
+       "        46.59685804, 47.72113315, 44.57566664, 49.10271775, 45.54127919,\n",
+       "        47.94160497, 49.81016604, 51.75385238, 49.89643918, 47.0728521 ,\n",
+       "        45.96138862, 52.39496693, 47.41221204, 46.39057563, 46.64714747,\n",
+       "        51.62987482, 45.76652167, 47.96629965, 44.32672795, 46.04865811,\n",
+       "        45.70867166, 46.05054711, 46.56539042, 46.56833445, 45.60902209,\n",
+       "        52.0347991 , 49.77467629, 47.01444296, 44.38546413, 47.56712426,\n",
+       "        51.11780153, 47.04331029, 46.84185003, 49.28623687, 50.24205743,\n",
+       "        46.38140425, 48.94254973, 44.57800771, 44.0545092 , 48.55961343,\n",
+       "        45.21743439, 52.39477459, 46.05959038, 49.4004143 , 48.84947639,\n",
+       "        48.61981029, 46.13661078, 54.58317632, 52.8695073 , 48.03907558,\n",
+       "        53.03332862, 47.86037014, 53.03023093, 51.86923605, 56.4412707 ,\n",
+       "        54.07619099, 51.54000012, 62.33058655, 60.65361866, 63.6944786 ,\n",
+       "        59.32092297, 59.4021599 , 56.18655547, 55.83749394, 60.04084771,\n",
+       "        58.01303146, 59.94967534, 62.81270834, 64.57007364, 59.91083213,\n",
+       "        66.02914727, 67.41535085, 62.09908459, 61.70327093, 54.30156607,\n",
+       "        60.08155721, 58.2182457 , 56.85473782, 53.94345267, 56.40689502,\n",
+       "        51.80366378, 51.32023053, 49.95956705, 58.27866826, 58.95188507,\n",
+       "        60.64881579, 61.25014988, 56.30228326, 58.90363424, 59.3794847 ,\n",
+       "        57.50474111, 59.46426876, 59.41943095, 57.70590674, 52.00731936,\n",
+       "        59.13225558, 59.4024256 , 62.26998709, 62.98073981, 60.24202472,\n",
+       "        56.49984444, 59.49194813, 63.65820625, 62.88547029, 61.13551938,\n",
+       "        58.73484369, 62.27303891, 66.52603402, 63.06981985, 60.62919504]])
            • max_energy_error
              (chain, draw)
              float64
              -0.2159 1.599 ... -0.4515 -0.5108
              array([[-2.15922042e-01,  1.59851393e+00,  3.45938191e-01,\n",
+       "         1.49101606e-01,  6.66302062e-01, -1.72598192e+00,\n",
+       "         1.12704939e-01, -2.00394457e-01, -3.16009354e-01,\n",
+       "        -9.48832164e-01,  5.29485426e-02, -4.59256185e-01,\n",
+       "        -1.98361439e-01,  2.69894087e-01, -1.41242448e-01,\n",
+       "        -1.00846863e-01, -5.02984547e-01,  1.03067685e+00,\n",
+       "         3.24636488e+00, -6.19085987e-01, -5.83079659e-01,\n",
+       "         3.53893700e-01,  2.39631108e-01,  1.21761877e-01,\n",
+       "        -2.63124349e-01, -3.39035967e-01, -4.51785177e-01,\n",
+       "        -2.88341458e-01, -5.59996662e-01,  5.58103361e-01,\n",
+       "         8.45622746e-02,  4.89708136e-01, -2.63709450e-01,\n",
+       "        -1.37949190e-01, -2.21305227e-01,  1.27328285e-01,\n",
+       "         7.68702041e-01, -1.86826020e-01,  2.32594754e-01,\n",
+       "         2.14098750e-01,  2.71504934e-01,  5.79404463e-02,\n",
+       "         9.50991170e-02, -1.17361820e-01, -6.12747036e-02,\n",
+       "        -2.19940369e-02, -7.47584158e-02,  4.18356792e-02,\n",
+       "         2.23495920e-02, -4.14647333e-01,  4.12499387e-01,\n",
+       "         3.79905927e+00,  1.01674164e+00,  5.71866852e-01,\n",
+       "        -2.35100086e-01,  1.93536806e-01, -1.10292728e-01,\n",
+       "         7.50123383e-02, -6.45553131e-02, -6.27479061e-01,\n",
+       "        -1.01783599e-01, -1.89693967e-01,  2.61339435e+00,\n",
+       "         2.23838574e-01,  6.79816102e-01, -1.52673505e-01,\n",
+       "        -2.20089584e-01,  1.35764007e-01, -5.20947117e-01,\n",
+       "        -1.30486283e-01,  4.87051405e-01, -3.23180727e-01,\n",
+       "         3.71142453e-01,  7.44223952e-01,  6.89249248e-01,\n",
+       "         6.08444859e-01,  3.80846446e-01, -3.63354087e-01,\n",
+       "         3.01487280e-01,  3.02083854e-01,  5.48682028e+02,\n",
+       "        -1.80253075e-01,  9.55729442e-01, -4.75034328e-01,\n",
+       "         3.50122724e-02,  7.12301112e-02, -1.37035626e-01,\n",
+       "        -3.46853386e-01, -1.37533733e-01,  5.03724526e-01,\n",
+       "         2.10604975e-01,  4.94969761e-01, -2.62129960e-01,\n",
+       "        -1.46056259e-01, -2.32308101e+00, -6.07169979e-01,\n",
+       "        -1.52387231e-01,  6.79397385e-01,  4.47458423e-01,\n",
+       "        -3.56666172e-01, -1.27263503e-01, -1.01199985e-01,\n",
+       "        -2.22863308e-01, -8.23553458e-01,  8.68747381e-01,\n",
+       "         1.88440942e-01, -2.82893479e-01,  3.77220490e-01,\n",
+       "         3.54180500e+00,  2.57822560e+00, -2.77263378e+00,\n",
+       "        -9.30270898e-01,  2.11063260e+00,  2.62589045e+00,\n",
+       "         1.30598466e+02,  1.15512078e+01,  6.96795206e+00,\n",
+       "         4.08172677e-01, -4.48535275e-01,  4.46117606e-01,\n",
+       "         1.92472624e-01, -1.21867366e-01, -7.75596000e-02,\n",
+       "        -2.47111320e-01, -7.85851230e-02, -1.36183646e-01,\n",
+       "         8.65380021e-02,  2.33637920e-01, -4.27178026e-01,\n",
+       "         3.85826251e-01, -3.06886910e-01, -7.48087621e-02,\n",
+       "        -4.61733537e-01,  1.78833226e-01, -2.60747146e-01,\n",
+       "        -7.91908447e-01, -7.69884347e-01,  9.85962921e-01,\n",
+       "        -9.91972474e-01,  3.16243722e+00,  1.97269716e+02,\n",
+       "         6.63084424e+02,  1.36983156e+02,  1.22738075e+03,\n",
+       "         8.02371811e+01,  1.73670003e+02, -4.95519595e-01,\n",
+       "         3.30987036e+03,  2.69983204e+00,  2.32023383e+01,\n",
+       "         4.75381860e+01,  3.52031919e+01,  1.24408199e+03,\n",
+       "         4.93082548e+00,  2.87569969e+00,  3.45188144e+00,\n",
+       "        -3.85523877e-01,  1.06735845e+00, -6.18705206e-01,\n",
+       "        -3.29389576e-01, -3.84729247e-01, -2.29067364e-01,\n",
+       "        -7.07171270e-02,  1.19231491e-01,  2.95848128e-01,\n",
+       "        -1.49421848e-01,  9.32182014e-01,  3.97107459e-02,\n",
+       "         3.00097364e-01, -3.17860522e-01, -4.11842298e-01,\n",
+       "        -6.08347920e-01, -3.07958099e-01, -1.43366613e-01,\n",
+       "        -3.65616198e-01, -2.78185116e-01,  2.18515698e+01,\n",
+       "         1.11211754e+01,  1.16681756e+00, -9.95737545e-01,\n",
+       "         1.82139078e+03,  1.31865511e+00,  3.91280699e+01,\n",
+       "         1.46137724e+00,  4.70093267e-01, -6.10677225e-01,\n",
+       "         4.91825084e+00, -2.36475861e-01,  3.30231074e-01,\n",
+       "         5.15932348e-01,  6.27231997e-01, -9.52884215e-02,\n",
+       "        -2.09591482e-01,  4.29408156e+00, -5.14656805e-01,\n",
+       "        -1.10251714e+00,  9.45545953e-01,  4.66791643e-01,\n",
+       "         3.89485423e-01, -2.07183356e-01, -3.65831636e-01,\n",
+       "         2.39752659e-01, -1.40062489e-01, -3.17099572e-01,\n",
+       "         1.50951578e-01, -1.86787262e-01, -1.60043642e-01,\n",
+       "         4.77797462e-01, -6.09913080e-01, -3.14939159e-01,\n",
+       "        -4.07645785e-01,  3.05033698e-01, -3.72488005e-01,\n",
+       "        -4.21153375e-01,  2.20180449e-01, -2.19551585e-01,\n",
+       "         5.37056224e-01, -1.90744714e-01, -1.27354638e-01,\n",
+       "         9.64904720e-02,  2.95928103e+00, -3.97397987e-01,\n",
+       "        -3.68958429e-01,  3.75495302e-01,  6.28909286e-01,\n",
+       "         2.09189975e-01, -2.04267761e-01, -1.73494208e+00,\n",
+       "         1.47723522e+00,  7.51621341e+01,  5.12907638e+01,\n",
+       "         1.32452618e+02, -7.38665984e-01,  4.77493668e+02,\n",
+       "         1.41230903e+02,  7.20686184e-01,  2.61401967e-01,\n",
+       "         8.41223824e+01,  1.58980236e+00,  4.59902271e+01,\n",
+       "         1.66482528e+00,  5.63897906e+03,  9.86954916e+00,\n",
+       "         1.39186308e+01,  7.80354466e+01,  1.06976716e+03,\n",
+       "         1.25646140e+02,  2.04620485e+02,  6.35405903e-01,\n",
+       "         1.81069003e+02,  3.21281380e+01,  8.61225841e+00,\n",
+       "         3.41925093e+02, -2.43590064e-01,  1.10036418e+00,\n",
+       "         5.03329666e-01,  2.78030000e-01, -9.88509293e-02,\n",
+       "         3.59636401e-01, -2.11677785e-01,  6.20980873e-01,\n",
+       "         6.90337052e-01,  2.77959078e-01, -3.22558527e-01,\n",
+       "         2.84217994e-01, -1.55702300e-01, -1.08693831e-01,\n",
+       "        -1.51459294e-01, -1.63555792e-01, -4.58076028e-01,\n",
+       "        -3.05881764e-01, -5.59415499e-01,  1.18597677e-01,\n",
+       "        -1.51215968e+00,  4.79342776e-01, -3.74547454e-01,\n",
+       "        -4.28099602e-01, -3.26208957e-01, -2.57816607e-01,\n",
+       "        -1.80147632e-01,  1.92005312e+02,  3.60333715e-01,\n",
+       "        -8.43951154e-02,  3.06537175e-01, -2.00620480e-01,\n",
+       "        -3.46021065e-01, -2.98262855e-01,  9.32081589e-02,\n",
+       "        -3.22852413e-01,  2.72965203e-01, -7.49017799e-01,\n",
+       "        -3.07951607e-01,  4.88887770e+00,  1.97940716e+00,\n",
+       "         3.07998232e+00,  1.00555763e+02,  2.60881099e+02,\n",
+       "         5.66972739e+01,  2.75102428e+01,  2.14717690e+02,\n",
+       "         2.53963720e+03,  1.19887472e+02,  2.18353602e+01,\n",
+       "         1.55394394e+01,  1.35215353e+02,  6.21319387e-01,\n",
+       "        -6.69678313e-02,  1.89084843e-01,  2.41621811e-01,\n",
+       "        -6.40051198e-01, -1.02393995e+00,  2.28690872e+00,\n",
+       "        -4.14405714e-01,  2.65891904e-01, -6.72107659e-01,\n",
+       "        -2.96286757e-01, -8.15053209e-01,  3.26683125e-01,\n",
+       "         2.10658514e+00,  1.44849197e-01,  3.41176417e-01,\n",
+       "         3.14755121e-01,  3.45383333e-01, -1.43796072e-01,\n",
+       "         6.20623041e-02, -8.31338415e-01, -9.19758336e-02,\n",
+       "        -8.56204619e-02, -1.92713055e-01,  1.67501680e+00,\n",
+       "        -9.51299439e-01,  2.04222920e-01, -1.51587221e-01,\n",
+       "        -2.86415317e-01,  1.76001976e+00, -2.00330472e-01,\n",
+       "         2.88616150e-01, -1.11352669e-01, -1.06644772e-01,\n",
+       "        -3.90763547e-01, -1.99974717e-01, -2.19659405e-01,\n",
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+       "        -1.41944882e-01, -1.73632877e-01, -2.76698598e-01,\n",
+       "         3.75444208e+00,  1.18685876e+01, -1.19363787e+00,\n",
+       "        -1.41704732e-01,  1.12146209e+02,  6.25398394e+00,\n",
+       "         2.56808027e+02,  1.73030663e+03,  2.03378354e+01,\n",
+       "         5.91399373e-01,  3.53704605e+01, -2.43886243e+00,\n",
+       "        -1.90249562e+00,  5.13073975e-01, -4.78152008e-01,\n",
+       "         1.32014659e+00, -3.62297598e-01,  1.14484590e+00,\n",
+       "         1.62716851e+00,  1.81218054e-01,  1.19156224e+00,\n",
+       "        -5.66967129e-01, -8.03936999e-01,  4.49869907e-01,\n",
+       "        -3.68889012e-01, -1.62675119e-01, -4.31521084e-01,\n",
+       "        -2.25167835e-01,  3.58840944e-01,  5.13611491e-01,\n",
+       "         6.62413738e-01,  1.47684592e-01, -1.14568522e-01,\n",
+       "        -4.51471548e-01, -5.10794526e-01]])
            • tree_size
              (chain, draw)
              float64
              7.0 23.0 7.0 7.0 ... 15.0 31.0 15.0
              array([[ 7., 23.,  7.,  7.,  7., 15.,  7., 15., 15., 15., 15., 15., 15.,\n",
+       "         7., 15., 15.,  7.,  7., 23., 15., 15., 31., 31., 15., 15., 31.,\n",
+       "        15., 15., 15., 15., 31., 15., 31., 15., 31., 15., 23., 15., 15.,\n",
+       "        15., 15., 31., 31., 31., 31., 15., 31., 15., 31., 31.,  7., 15.,\n",
+       "        31., 31., 15., 15., 31.,  7.,  7., 15., 15.,  7.,  7., 15., 15.,\n",
+       "         7.,  7.,  7., 15., 15., 15., 15., 31.,  7., 15.,  7., 15., 15.,\n",
+       "        15., 15.,  7.,  7., 31., 31., 15., 15., 15., 15., 15., 31., 15.,\n",
+       "        31., 31., 15., 15.,  7.,  7.,  7., 15., 31., 31., 15., 15., 15.,\n",
+       "         7., 23.,  7.,  7.,  7.,  7.,  3.,  7.,  7.,  7.,  3.,  7., 15.,\n",
+       "         7., 15., 31., 15., 15.,  7., 15., 15., 15., 15., 31., 15., 15.,\n",
+       "        15., 31., 15., 15., 31., 15.,  7.,  7.,  7.,  7., 11.,  7.,  1.,\n",
+       "         3.,  1., 15.,  3.,  4.,  3.,  7.,  3., 17.,  3.,  3., 15.,  7.,\n",
+       "        15., 31., 31., 15., 15., 15., 15.,  7.,  7., 15., 15., 15., 15.,\n",
+       "        31., 31., 15., 15., 15., 15., 15., 15., 15.,  7.,  7.,  9., 23.,\n",
+       "         3.,  3.,  7.,  7.,  7.,  7., 15.,  7., 15., 31., 15., 15., 15.,\n",
+       "        31., 31., 31., 31., 31., 31., 31., 31., 31., 31., 15., 15., 15.,\n",
+       "        11., 15., 15., 15., 15., 39., 15., 15., 31., 15., 15.,  7., 15.,\n",
+       "        15.,  7., 31.,  7., 15., 15., 15.,  3.,  3.,  7.,  7.,  3.,  3.,\n",
+       "         3., 15.,  7., 15.,  3.,  3.,  3., 18.,  3.,  1.,  3.,  3.,  7.,\n",
+       "        31.,  3.,  3.,  7.,  7., 11.,  7.,  7., 15., 15., 15., 15., 15.,\n",
+       "         7., 15., 15., 15., 31., 15., 31., 31., 15., 15., 15., 15., 15.,\n",
+       "        15.,  7., 15., 15., 15.,  7.,  7., 15., 15., 15., 15., 31., 15.,\n",
+       "        15., 15., 15., 31., 15.,  7., 23.,  7.,  3.,  7.,  7.,  7.,  3.,\n",
+       "         3.,  5.,  3.,  3., 15., 23., 15.,  7.,  7.,  7.,  7.,  3.,  7.,\n",
+       "         7., 15., 31., 15., 15.,  7.,  7.,  7., 31., 15., 15., 15., 31.,\n",
+       "        31., 31., 31., 31., 31., 31., 15., 15., 31., 15., 31., 15., 15.,\n",
+       "        31., 31., 31., 15., 31., 15., 31., 15., 31., 15., 15., 15.,  3.,\n",
+       "        15., 15., 15., 31., 15., 15., 15., 15., 15., 23., 15.,  7.,  7.,\n",
+       "         7., 23.,  7.,  6., 23., 15.,  7., 31., 15., 15., 15., 15., 15.,\n",
+       "        15., 31., 15., 15., 15., 15., 31., 31., 15., 15., 15., 31., 15.,\n",
+       "        15.,  7., 15.,  7., 15., 31., 15., 15.,  7.,  7.,  7., 15.,  7.,\n",
+       "        15.,  7.,  3.,  7.,  7.,  7.,  7.,  7.,  7., 15., 15., 15.,  7.,\n",
+       "        15., 15., 31., 31., 31., 31., 31., 31., 31., 15., 31., 31., 15.,\n",
+       "         7., 15., 15., 31., 15., 15., 15., 15., 63., 15., 15., 15., 15.,\n",
+       "        15., 31., 31., 31., 15., 15., 15., 15., 31., 15.,  7., 15.,  7.,\n",
+       "        23.,  3., 31., 15., 15., 15., 15., 15., 15., 15., 15., 31., 15.,\n",
+       "        31., 31., 31., 31., 15., 15.,  7., 15., 15., 15., 15., 15., 31.,\n",
+       "        15., 15., 31., 15., 15., 15., 15., 31., 15., 15., 15., 31., 31.,\n",
+       "         7., 31.,  7.,  7.,  1.,  3.],\n",
+       "       [ 7.,  3.,  1.,  3.,  3.,  1.,  5.,  7.,  3.,  7., 31., 15., 15.,\n",
+       "        15., 31., 31., 15., 31., 15., 31., 31., 15., 31., 15., 15., 15.,\n",
+       "        15., 15., 15., 19., 15., 15., 31., 15., 15., 15.,  7.,  7.,  7.,\n",
+       "         7.,  7.,  7.,  7.,  7.,  7.,  7., 15., 31., 31., 15., 15., 15.,\n",
+       "        15., 15., 15., 15., 15.,  7., 15., 15.,  7.,  7., 31., 31., 15.,\n",
+       "        15., 15.,  7.,  7.,  7.,  7.,  7., 15., 15., 15., 31., 31., 31.,\n",
+       "        15., 31., 31., 15., 15., 15., 31., 15., 15., 31., 31., 31., 31.,\n",
+       "        31., 15., 15., 15.,  7.,  7.,  7.,  7.,  7.,  7., 15.,  7., 15.,\n",
+       "        15., 15., 15., 15.,  7., 15., 15., 15., 15.,  7., 15.,  7., 15.,\n",
+       "         7.,  1., 23.,  7.,  7., 31.,  7., 15., 15.,  7.,  7.,  7.,  7.,\n",
+       "        15.,  7., 15., 15., 15., 15.,  7., 15.,  7.,  7.,  7., 15., 31.,\n",
+       "         7., 15.,  7., 15., 31., 31., 31., 31., 31., 31., 15., 15., 15.,\n",
+       "        15., 31., 15., 15., 15., 15., 15., 15., 15., 15., 31., 31., 15.,\n",
+       "        15., 31., 15., 15., 15., 15., 15., 31., 31., 31., 15., 31., 15.,\n",
+       "        15., 31., 15., 15., 15., 31., 15., 15., 15., 15., 15., 15., 23.,\n",
+       "         7.,  7.,  3.,  7., 15., 15., 15., 15., 15., 31., 15., 15., 15.,\n",
+       "        15., 15., 15., 31., 31., 31., 15., 31., 31., 15., 15., 15., 15.,\n",
+       "        31., 15., 31., 15.,  7., 15., 31., 31., 15.,  7.,  7., 15.,  7.,\n",
+       "        15., 31., 31., 31., 31.,  7.,  7., 31.,  3.,  7.,  7.,  7.,  7.,\n",
+       "        15., 15., 31., 15., 15., 15., 31., 31.,  7., 15.,  7.,  7., 31.,\n",
+       "         7.,  7., 23., 15.,  7., 15.,  7.,  3.,  3.,  7., 31., 31.,  7.,\n",
+       "        11.,  7.,  7.,  7., 31., 31., 15.,  7.,  7., 31.,  7.,  7.,  7.,\n",
+       "         7., 15., 15., 31., 31., 15., 15., 15., 15., 15., 15.,  7., 31.,\n",
+       "        15., 15., 15.,  7., 31., 15., 15., 15., 15., 23., 31., 15., 15.,\n",
+       "        15., 15.,  7.,  3.,  7., 15.,  7., 15.,  3., 15., 23.,  7., 15.,\n",
+       "        15., 15., 15., 15.,  3.,  7.,  1.,  3.,  3.,  3.,  6.,  3.,  3.,\n",
+       "         7.,  7.,  7.,  7.,  7.,  3.,  3., 15.,  7.,  3.,  7.,  7., 15.,\n",
+       "         7., 31.,  7.,  7.,  7.,  7., 15.,  1., 15.,  7.,  3.,  1.,  9.,\n",
+       "         3.,  3.,  3.,  1.,  3.,  5.,  3.,  6.,  3.,  3.,  6.,  1., 15.,\n",
+       "         1.,  1.,  5.,  1.,  1.,  1.,  1.,  3.,  3.,  1.,  1.,  1., 27.,\n",
+       "         1.,  1.,  2.,  1.,  3.,  1.,  3.,  1.,  3.,  1.,  1.,  1.,  1.,\n",
+       "         1.,  1.,  1.,  1.,  1.,  1.,  1.,  3.,  1.,  1.,  1.,  1.,  3.,\n",
+       "         3.,  1.,  3.,  1.,  1.,  1.,  1.,  1.,  3.,  3.,  1.,  3.,  3.,\n",
+       "         1.,  3.,  1.,  1.,  1.,  3.,  3.,  1.,  1., 15.,  3.,  1.,  3.,\n",
+       "         5.,  7., 15.,  7.,  7., 31., 15., 15., 15.,  7., 15.,  7., 23.,\n",
+       "         3., 15., 15., 15., 15., 15., 15., 15., 15.,  3.,  7.,  7., 31.,\n",
+       "         7.,  7.,  7., 15.,  7.,  7.,  7., 31.,  7.,  7., 15., 31., 31.,\n",
+       "         7., 31.,  7.,  3., 15., 15.,  7.,  7., 15.,  7., 15., 31., 23.,\n",
+       "         7.,  7., 15., 15., 31., 15.]])
            • diverging
              (chain, draw)
              bool
              False False False ... False False
              array([[False, False, False, False, False, False, False, False, False,\n",
        "        False, False, False, False, False, False, False, False, False,\n",
        "        False, False, False, False, False, False, False, False, False,\n",
        "        False, False, False, False, False, False, False, False, False,\n",
        "        False, False, False, False, False, False, False, False, False,\n",
        "        False, False, False, False, False, False, False, False, False,\n",
        "        False, False, False, False, False, False, False, False, False,\n",
-       "         True, False, False, False, False, False, False, False, False,\n",
        "        False, False, False, False, False, False, False, False, False,\n",
        "        False, False, False, False, False, False, False, False, False,\n",
        "        False, False, False, False, False, False, False, False, False,\n",
        "        False, False, False, False, False, False, False, False, False,\n",
        "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False,  True, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
        "        False, False, False, False, False, False, False, False, False,\n",
        "        False, False, False, False, False, False, False, False, False,\n",
        "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False, False,  True,\n",
+       "        False, False, False,  True, False, False, False, False,  True,\n",
        "        False, False, False, False, False, False, False, False, False,\n",
        "        False, False, False, False, False, False, False, False, False,\n",
        "        False, False, False, False, False, False, False, False, False,\n",
+       "         True, False, False, False, False, False, False, False, False,\n",
        "        False, False, False, False, False, False, False, False, False,\n",
        "        False, False, False, False, False, False, False, False, False,\n",
        "        False, False, False, False, False, False, False, False, False,\n",
        "        False, False, False, False, False, False, False, False, False,\n",
        "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False,  True, False,\n",
+       "        False, False,  True, False, False, False, False, False, False,\n",
        "        False, False, False, False, False, False, False, False, False,\n",
        "        False, False, False, False, False, False, False, False, False,\n",
        "        False, False, False, False, False, False, False, False, False,\n",
        "        False, False, False, False, False, False, False, False, False,\n",
        "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False,  True, False, False, False, False, False,\n",
        "        False, False, False, False, False, False, False, False, False,\n",
        "        False, False, False, False, False, False, False, False, False,\n",
        "        False, False, False, False, False, False, False, False, False,\n",
        "        False, False, False, False, False, False, False, False, False,\n",
        "        False, False, False, False, False, False, False, False, False,\n",
        "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False,  True, False,\n",
        "        False, False, False, False, False, False, False, False, False,\n",
        "        False, False, False, False, False, False, False, False, False,\n",
        "        False, False, False, False, False, False, False, False, False,\n",
        "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False,  True,\n",
        "        False, False, False, False, False, False, False, False, False,\n",
        "        False, False, False, False, False, False, False, False, False,\n",
        "        False, False, False, False, False, False, False, False, False,\n",
@@ -3340,13 +2939,12 @@
        "        False, False, False, False, False, False, False, False, False,\n",
        "        False, False, False, False, False, False, False, False, False,\n",
        "        False, False, False, False, False],\n",
-       "       [False, False, False, False, False, False, False, False, False,\n",
+       "       [False, False, False, False, False, False,  True, False, False,\n",
        "        False, False, False, False, False, False, False, False, False,\n",
        "        False, False, False, False, False, False, False, False, False,\n",
        "        False, False, False, False, False, False, False, False, False,\n",
        "        False, False, False, False, False, False, False, False, False,\n",
        "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False,  True, False, False,\n",
        "        False, False, False, False, False, False, False, False, False,\n",
        "        False, False, False, False, False, False, False, False, False,\n",
        "        False, False, False, False, False, False, False, False, False,\n",
@@ -3362,10 +2960,8 @@
        "        False, False, False, False, False, False, False, False, False,\n",
        "        False, False, False, False, False, False, False, False, False,\n",
        "        False, False, False, False, False, False, False, False, False,\n",
-       "         True, False, False, False, False, False, False, False, False,\n",
        "        False, False, False, False, False, False, False, False, False,\n",
        "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False,  True, False,\n",
        "        False, False, False, False, False, False, False, False, False,\n",
        "        False, False, False, False, False, False, False, False, False,\n",
        "        False, False, False, False, False, False, False, False, False,\n",
@@ -3380,254 +2976,1343 @@
        "        False, False, False, False, False, False, False, False, False,\n",
        "        False, False, False, False, False, False, False, False, False,\n",
        "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False,  True,  True, False, False, False, False, False,\n",
        "        False, False, False, False, False, False, False, False, False,\n",
        "        False, False, False, False, False, False, False, False, False,\n",
        "        False, False, False, False, False, False, False, False, False,\n",
+       "         True, False,  True, False, False,  True, False, False, False,\n",
+       "        False,  True, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False,  True, False,  True, False,\n",
+       "         True, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False, False,  True, False, False, False,\n",
+       "        False, False,  True, False,  True, False, False, False, False,\n",
+       "        False, False, False, False, False, False, False,  True, False,\n",
+       "        False, False,  True,  True, False, False, False,  True, False,\n",
+       "         True,  True, False, False, False, False, False, False, False,\n",
        "        False, False, False, False, False, False, False, False, False,\n",
        "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False,  True, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False, False, False, False, False,\n",
+       "        False, False, False, False,  True, False, False, False, False,\n",
        "        False, False, False, False, False, False, False, False, False,\n",
        "        False, False, False, False, False, False, False, False, False,\n",
-       "        False, False, False, False, False]])
            • energy_error
              (chain, draw)
              float64
              0.01684 0.6038 ... 0.06367 0.4126
              array([[ 1.68399133e-02,  6.03806531e-01, -2.03937381e-01,\n",
-       "         1.89366291e-01, -6.86793141e-02, -9.04721835e-02,\n",
-       "         3.53770748e-01,  1.11664054e-01, -3.26447518e-01,\n",
-       "        -9.54710814e-02, -2.02420776e-01, -1.46485446e-01,\n",
-       "         2.87348127e-02, -2.76057108e-02,  4.96012860e-01,\n",
-       "        -1.96727346e-01,  1.67060523e-02, -3.60310256e-03,\n",
-       "        -2.94016199e-02,  3.31364152e-02, -6.07079070e-03,\n",
-       "         6.68741170e-02, -9.05157533e-02,  2.17865523e-01,\n",
-       "        -6.20298344e-02, -3.17620352e-02, -2.88531112e-01,\n",
-       "        -2.13967225e-01,  4.31026720e-01,  5.99071481e-02,\n",
-       "         4.00405405e-01,  4.75997505e-01, -1.80449714e-01,\n",
-       "        -4.91082764e-02, -2.48423117e-01, -4.37327129e-01,\n",
-       "        -1.08690374e+00,  0.00000000e+00,  0.00000000e+00,\n",
-       "         1.05882780e+00,  0.00000000e+00,  0.00000000e+00,\n",
-       "         0.00000000e+00,  1.89692166e+00,  0.00000000e+00,\n",
-       "         0.00000000e+00,  0.00000000e+00, -3.44566898e+00,\n",
-       "         0.00000000e+00,  2.25368983e+00,  0.00000000e+00,\n",
-       "        -1.42393819e-02,  0.00000000e+00,  9.72286308e-01,\n",
-       "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-       "         0.00000000e+00, -2.54985822e+00,  0.00000000e+00,\n",
-       "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-       "         0.00000000e+00, -2.86117236e-01,  0.00000000e+00,\n",
-       "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-       "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-       "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-       "         2.20723978e+00,  5.09745798e-01,  6.59015930e-02,\n",
-       "         2.11389816e-01, -2.13738659e-01, -8.54751013e-01,\n",
-       "        -1.46488827e-01, -8.57041353e-02,  3.67172938e-02,\n",
-       "         2.95796620e-01, -7.44105775e-02, -8.70043594e-02,\n",
-       "        -1.36839405e-01,  0.00000000e+00,  2.60212139e-01,\n",
-       "        -5.53976078e-01, -4.94104238e-01,  8.88190461e-01,\n",
-       "         2.87805001e-01,  0.00000000e+00,  0.00000000e+00,\n",
-       "         0.00000000e+00,  8.51443417e-01, -1.05608700e+00,\n",
-       "         4.34678850e-01, -1.16118271e+00,  2.14850671e-02,\n",
-       "         4.18638120e-01,  5.63245581e-01,  3.44154213e-02,\n",
-       "         1.95279012e+00,  1.00679554e-01, -2.36931357e-01,\n",
-       "        -2.05140736e-01,  4.08885895e-01, -1.10876498e+00,\n",
-       "        -1.78620442e-01,  3.39285810e-02,  2.95115348e-01,\n",
-       "         8.44339347e-02, -4.94107314e-04, -1.30008330e+00,\n",
-       "         1.35682461e+00, -7.03536873e-01,  1.23669287e-01,\n",
-       "         7.38907045e-02,  1.16890070e-02,  1.89266966e-01,\n",
-       "        -1.22063621e-01,  9.54248569e-02, -1.98412388e-02,\n",
-       "        -6.74123400e-02, -1.14408984e+00,  1.60103428e-01,\n",
-       "        -1.07075090e-02,  2.79437655e-02,  0.00000000e+00,\n",
-       "         5.91516550e-03,  9.22022457e-02,  8.86322548e-01,\n",
-       "        -8.87515677e-01,  3.84951696e-01, -1.17177512e-01,\n",
-       "         3.69503347e-01, -1.09712792e-01,  5.81769961e-02,\n",
-       "        -2.29005003e-02, -9.78948166e-02,  3.38083458e-01,\n",
-       "        -1.75483716e-01, -3.73017420e-01, -3.97779330e-01,\n",
-       "        -1.74461027e-01, -2.18224089e-01,  1.07190248e+00,\n",
-       "         2.09587143e-02,  4.08619998e-02,  1.78479867e-01,\n",
-       "         5.45203689e-01, -3.53705476e-01, -2.20511492e-01,\n",
-       "        -2.13280145e-02,  9.20372175e-01, -4.85514822e-02,\n",
-       "        -4.84335972e-03, -7.44135510e-01, -3.19473139e-01,\n",
-       "         9.58686244e-01,  3.45644687e-01, -2.34123332e-01,\n",
-       "         1.57941000e-01,  2.06490803e-01, -7.11371278e-02,\n",
-       "        -1.36876753e-02, -1.73311454e-02,  1.46631645e-01,\n",
-       "         5.26036892e-02,  9.28234284e-02, -3.49617757e-01,\n",
-       "         2.54031420e-01, -5.97562096e-01,  9.09498770e-02,\n",
-       "         2.87109242e-02,  5.75198114e-02, -5.20031941e-02,\n",
-       "        -2.01451961e-02,  1.05028989e-02,  5.40590185e-02,\n",
-       "        -3.39512876e-02, -1.86300705e-01,  1.04671895e-01,\n",
-       "        -6.30455741e-01, -1.76405822e-02,  6.01181978e-01,\n",
-       "        -4.34645699e-01,  7.53560034e-01, -2.56579969e-01,\n",
-       "         7.87741215e-02, -1.33389698e-01,  1.25737643e-01,\n",
-       "         1.90039936e-01, -8.65180223e-02,  7.40496882e-02,\n",
-       "         4.44351612e-01,  2.36126150e-01, -2.02183456e-01,\n",
-       "        -8.76024678e-02,  5.60107610e-02,  2.86059579e-01,\n",
-       "        -1.57198542e-01,  2.02388407e-01, -6.73787998e-02,\n",
-       "         1.00553521e-01,  3.36714347e-01, -7.03302399e-02,\n",
-       "         2.51912128e-02, -5.65999012e-02,  5.33933450e-02,\n",
-       "        -4.05052279e-02,  3.83984825e-01, -3.18960801e-01,\n",
-       "         5.27787838e-01, -9.29626744e-02, -3.02256837e-02,\n",
-       "        -2.36520228e-02,  3.06221460e-02, -6.59831565e-02,\n",
-       "        -2.85391694e-02, -1.27467557e-01,  6.09580311e-02,\n",
-       "         4.12365601e-02, -4.71515436e-01,  3.39995628e-01,\n",
-       "        -8.02620523e-01, -7.51833055e-01, -1.27544657e-01,\n",
-       "         1.24332037e+00, -7.79758209e-01, -6.25235618e-01,\n",
-       "         0.00000000e+00, -9.67337776e-01,  3.34909919e-01,\n",
-       "         0.00000000e+00,  0.00000000e+00,  3.65593585e-01,\n",
-       "        -8.25358893e-02,  9.41690236e-01,  1.38295516e-03,\n",
-       "        -4.93728024e-01,  8.60613572e-02, -7.30384171e-02,\n",
-       "        -7.15880637e-02, -3.27593300e-01,  5.65502713e-01,\n",
-       "         2.57685482e-02,  2.62864227e-01,  1.29958014e-01,\n",
-       "        -4.86819699e-03, -7.27947224e-02,  2.14103650e-01,\n",
-       "        -9.25807814e-03, -2.59817097e-01,  2.25292085e-01,\n",
-       "        -1.24642031e+00,  1.29886810e-01,  0.00000000e+00,\n",
-       "         2.62959697e-01, -1.73167468e+00,  7.31601745e-01,\n",
-       "        -1.50190279e+00,  9.52000741e-01,  4.35354239e-01,\n",
-       "        -5.89159727e-01,  6.12039149e-01, -4.95868813e-02,\n",
-       "         1.13173171e-01, -1.09313321e-02,  5.09559784e-03,\n",
-       "        -8.35428947e-02, -1.24101500e-02,  1.68989485e-01,\n",
-       "        -2.76193147e-01,  4.18050935e-02, -1.74256869e-01,\n",
-       "         2.79279330e-02,  2.43953991e-01,  9.98394912e-02,\n",
-       "        -5.07803193e-02,  7.02500723e-02, -1.06353758e-02,\n",
-       "         1.80951675e-02,  3.14480065e-02,  2.93709665e-01,\n",
-       "        -1.04297596e+00,  3.41658426e-02,  4.62700846e-02,\n",
-       "         1.62638701e-01,  2.92982775e-02, -1.68222650e+00,\n",
-       "         1.12592632e+00,  4.37438260e-01,  2.37735147e-01,\n",
-       "        -5.76137284e-01,  3.93272659e-01,  0.00000000e+00,\n",
-       "         1.00401406e+00, -8.18989331e-02,  7.81175679e-02,\n",
-       "        -6.40963736e-01,  1.84254226e+00, -9.48203863e-01,\n",
-       "        -4.63998617e-01,  6.75702800e-01,  1.93332664e-01,\n",
-       "         5.10270756e-02,  1.22763907e-01,  4.55642933e-02,\n",
-       "        -1.41920112e+00, -1.10610137e-01, -1.28904881e+00,\n",
-       "         0.00000000e+00, -4.95233380e-01,  0.00000000e+00,\n",
-       "         5.38698062e-01,  3.15889173e-02,  2.56224582e-02,\n",
-       "        -7.22558899e-01,  0.00000000e+00,  1.18093695e-01,\n",
-       "         1.33741061e-01, -6.17206703e-01,  0.00000000e+00,\n",
-       "         0.00000000e+00,  0.00000000e+00,  6.44976710e-01,\n",
-       "         0.00000000e+00, -9.10732555e-01,  3.83986817e-01,\n",
-       "         0.00000000e+00,  0.00000000e+00,  3.52837223e+00,\n",
-       "         1.04808992e-02, -1.00151192e+00,  3.94929079e-01,\n",
-       "         4.47097662e-01, -2.94845603e-01,  1.99188532e-01,\n",
-       "        -3.10922470e-01,  1.79882127e-01,  4.61442741e-03,\n",
-       "         4.41710759e-01, -3.41281210e-01, -1.40980663e-02,\n",
-       "        -5.15096802e-02, -9.43144406e-02,  1.07975201e-02,\n",
-       "        -1.84496702e-01,  2.37531411e-01, -5.64171930e-02,\n",
-       "        -1.03112301e+00,  7.80514412e-01,  3.96930227e-02,\n",
-       "        -4.12993555e-02, -1.49468653e-01,  2.27667772e-01,\n",
-       "        -3.50803132e-01,  2.53748534e-02, -1.37442967e-02,\n",
-       "        -1.78489395e-02, -1.06932049e-01,  3.48485532e-01,\n",
-       "         2.80964809e-02, -1.67257617e-02,  1.68983189e-02,\n",
-       "        -1.11758133e-01, -6.50770223e-01,  1.51602258e-02,\n",
-       "        -1.58473235e-01, -6.28940146e-01,  6.44730829e-01,\n",
-       "        -3.52963620e-02,  3.02430414e-01,  1.21034735e-01,\n",
-       "        -5.27006918e-01, -8.65862662e-02,  0.00000000e+00,\n",
-       "         3.88019976e-01,  1.32389982e-01,  3.67602323e-01,\n",
-       "         1.08372825e-01,  5.02753456e-02,  1.07146080e-01,\n",
-       "        -4.61419218e-01,  1.13355768e-01, -1.07985967e-01,\n",
-       "         2.28832758e-02,  1.08733551e-01, -6.34366709e-02,\n",
-       "         2.06873800e-02,  4.08565138e-02,  7.70167833e-02,\n",
-       "        -1.41924143e-01,  4.44581916e-01,  1.78350597e-01,\n",
-       "         8.76866432e-01, -7.82761479e-01, -2.34748633e-01,\n",
-       "        -2.12137840e+00, -9.30181595e-01,  0.00000000e+00,\n",
-       "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-       "         0.00000000e+00,  0.00000000e+00,  6.92432150e-01,\n",
-       "         1.91563792e+00, -1.66630887e-01, -2.03327086e-02,\n",
-       "         1.80968731e-01,  1.81285410e-02, -1.11975675e-01,\n",
-       "         9.74850677e-02, -1.68761087e-01, -1.24946221e-01,\n",
-       "        -3.43689609e-01, -3.92018749e-02,  1.23508420e+00,\n",
-       "        -3.42454563e-01,  2.20060188e-01,  1.66029426e-01,\n",
-       "         5.43012857e-01, -4.62308842e-02, -5.64464295e-03,\n",
-       "        -1.73977570e-02, -2.02366768e-02, -7.14925717e-02,\n",
-       "        -1.86821671e+00,  9.15159811e-02, -6.03132505e-01,\n",
-       "         1.40428936e-01,  1.99745227e+00, -2.43771992e-01,\n",
-       "         3.97253858e-01, -3.23360577e-01,  9.57098043e-02,\n",
-       "         1.80448933e-02,  1.71104155e-01, -8.60333198e-02,\n",
-       "        -1.03424863e-01, -3.87359091e-01,  7.50721575e-02,\n",
-       "        -5.30871565e-02,  1.39660502e-01, -3.41779493e-01,\n",
-       "         4.70487895e-02, -3.98045171e-01,  1.47432794e-01,\n",
-       "        -4.79927680e-01, -1.32818688e-02,  6.90562925e-04,\n",
-       "         1.41904495e-03,  3.08554282e-01,  1.98729568e-01,\n",
-       "         5.29148245e-01,  1.44090922e-02, -3.41256460e-01,\n",
-       "        -1.55168554e-01, -1.80419211e-01, -2.91223345e-03,\n",
-       "         1.80971672e-02,  4.88627904e-02,  2.82672602e-02,\n",
-       "        -1.53287941e-01, -9.56858657e-02, -9.62725631e-01,\n",
-       "         0.00000000e+00,  1.45004561e+00,  8.61710144e-03,\n",
-       "         1.53899967e-02, -2.30551000e-01, -1.62063226e+00,\n",
-       "         0.00000000e+00, -1.50616635e+00, -3.27312797e-01,\n",
-       "         1.23158989e+00,  1.50588710e-01,  6.03424322e-02,\n",
-       "        -3.38318226e-01, -1.12478535e+00,  3.07192068e-01,\n",
-       "        -1.35010167e+00,  0.00000000e+00,  8.12615989e-02,\n",
-       "         7.16913046e-02,  1.43701790e-01,  0.00000000e+00,\n",
-       "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-       "         1.00409287e+00,  0.00000000e+00,  0.00000000e+00,\n",
-       "         0.00000000e+00, -1.22029768e+00],\n",
-       "       [-2.18417470e+00,  1.98552055e-02, -2.25762628e-02,\n",
-       "         1.79408611e-01,  3.51096404e-02, -1.78915635e-02,\n",
-       "        -3.14529696e-02, -4.48288822e-02, -1.40069876e-01,\n",
-       "        -1.48461864e-02,  1.47040322e-03, -3.67616745e-02,\n",
-       "        -9.37445971e-01, -1.03447354e+00,  1.61899451e-01,\n",
-       "         6.98641457e-01, -6.30759795e-01, -1.40132740e-01,\n",
-       "         4.60040366e-01,  1.16947054e-01, -1.92260407e-01,\n",
-       "         1.21625284e-01,  2.88552638e-01, -4.13011164e-02,\n",
-       "         6.42643711e-01,  1.77416426e-01, -7.17793461e-02,\n",
-       "         1.56188564e-01, -2.19428135e-01, -7.88165376e-01,\n",
-       "        -2.88589632e-01,  2.80510651e-01,  1.61558339e-01,\n",
-       "         5.33609775e-02, -1.23757984e-01, -7.81690422e-01,\n",
-       "         1.57228149e-01,  3.37423651e-01, -4.78576514e-01,\n",
-       "        -3.96426137e-02, -1.35876937e-01, -7.18108466e-02,\n",
-       "         2.86201328e-01, -1.56154535e-01,  4.30787771e-02,\n",
-       "        -2.51913459e-01, -6.53865184e-01, -4.72027941e-02,\n",
-       "        -2.30159281e-01,  5.72848613e-01, -3.13148998e-01,\n",
-       "         7.67754315e-02,  3.40454409e-01, -4.32540303e-01,\n",
-       "        -2.01447185e-01, -1.70650171e+00,  7.44038586e-02,\n",
-       "         3.62558194e-01, -2.43756145e+00,  0.00000000e+00,\n",
-       "         4.36948832e-01, -4.64943350e-01,  2.19309447e-01,\n",
-       "         1.00931085e+00, -3.03882188e-01,  4.05845853e-01,\n",
-       "         1.53258299e-01, -2.08706430e-01, -2.01808763e+00,\n",
-       "         4.63502476e-01,  1.77782514e-01,  7.50444577e-01,\n",
-       "        -1.22929332e-01,  0.00000000e+00,  1.63724887e+00,\n",
-       "         1.06582248e-02, -4.27335752e-02,  6.07430686e-01,\n",
-       "        -8.82066910e-01,  3.52810483e-03,  1.02057443e-01,\n",
-       "        -4.81747498e-02, -4.41371620e-02,  1.20443316e-01,\n",
-       "        -1.97045004e-01,  2.44665071e-01, -2.01882587e-02,\n",
-       "        -6.33978742e-02,  1.11445646e-01, -8.80369101e-01,\n",
-       "         2.72940934e-02, -5.85775480e-02,  9.98705013e-01,\n",
-       "        -4.29240497e-01,  2.26720970e-03,  2.07354274e-01,\n",
-       "        -1.07947842e-01, -8.07280698e-02,  1.90584327e-01,\n",
-       "        -2.70518732e-01,  1.61113608e-01, -2.59611677e-02,\n",
-       "         1.22444521e-01, -4.03026535e-01,  1.41084517e-01,\n",
-       "        -1.26405891e-01,  5.56317826e-02, -1.55308528e-01,\n",
-       "         1.66980406e-02,  2.65796153e-03,  4.27796888e-01,\n",
-       "         4.44988198e-01,  5.59008147e-02, -1.41603659e-01,\n",
-       "         2.73714240e-02, -3.82825583e-02,  1.09484147e-01,\n",
-       "         3.48563757e-01, -3.33554522e-01,  1.89063681e-02,\n",
-       "        -3.22946449e-02,  7.78306062e-02, -3.71372017e-02,\n",
-       "         3.06867575e-02,  4.58604495e-02, -3.13728100e-02,\n",
-       "        -1.55876998e-02,  9.90010139e-02,  3.81307061e-01,\n",
-       "        -1.28916723e-01, -3.19901481e-01,  5.11542305e-02,\n",
-       "        -3.57150707e-01, -3.31143285e-01, -8.99211307e-01,\n",
-       "         7.57205102e-01, -7.28465660e-01, -3.05740848e-01,\n",
-       "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-       "        -4.89751337e-02,  7.00037026e-02, -8.68956782e-02,\n",
-       "        -6.19860739e-01,  4.09720955e-01, -1.31517645e+00,\n",
-       "        -6.98111093e-01, -2.79474272e-01,  1.14447787e-01,\n",
-       "        -2.21727550e-01, -2.16125114e-01, -2.93605565e-01,\n",
-       "         2.43685351e-01,  4.18594851e-02,  3.34063990e-01,\n",
-       "         2.56630770e-01, -1.75708971e-01,  2.24676002e-01,\n",
-       "        -1.58147870e-02,  1.19635239e-02, -1.36407337e-01,\n",
-       "        -2.08433909e-01,  4.12101351e-01,  1.53505001e-01,\n",
-       "        -6.88098213e-03, -4.88071890e-02,  4.57407150e-03,\n",
-       "         4.38908431e-01, -1.16998308e-01, -4.88535149e-01,\n",
-       "         0.00000000e+00, -6.83622914e-01,  3.24273463e-01,\n",
-       "         4.33343259e-01, -3.44731424e-01, -1.82204095e+00,\n",
-       "        -1.48294359e+00,  0.00000000e+00,  0.00000000e+00,\n",
+       "        False, False, False, False, False]])
            • mean_tree_accept
              (chain, draw)
              float64
              0.968 0.9316 ... 0.9912 0.9664
              array([[9.68032085e-001, 9.31630204e-001, 8.07449576e-001,\n",
+       "        9.61140741e-001, 8.86518335e-001, 9.71293562e-001,\n",
+       "        9.81188776e-001, 9.84199600e-001, 9.35830218e-001,\n",
+       "        9.42818108e-001, 9.70378804e-001, 9.69211777e-001,\n",
+       "        9.84525174e-001, 8.94086151e-001, 9.95622173e-001,\n",
+       "        9.49383160e-001, 9.56127176e-001, 8.43583801e-001,\n",
+       "        1.31732741e-001, 9.73272477e-001, 9.20780965e-001,\n",
+       "        9.60676149e-001, 9.36653625e-001, 9.69211140e-001,\n",
+       "        9.87690386e-001, 1.00000000e+000, 9.99949943e-001,\n",
+       "        9.95723762e-001, 9.41549772e-001, 6.17533623e-001,\n",
+       "        9.78377054e-001, 7.94641966e-001, 9.84320836e-001,\n",
+       "        9.96393038e-001, 9.98387729e-001, 9.57342768e-001,\n",
+       "        9.47609845e-001, 9.90032763e-001, 9.71539229e-001,\n",
+       "        9.05856664e-001, 9.71276543e-001, 9.66736978e-001,\n",
+       "        9.72887495e-001, 9.95551425e-001, 9.97739459e-001,\n",
+       "        9.89416105e-001, 9.75099460e-001, 9.86536502e-001,\n",
+       "        9.88188984e-001, 9.93658967e-001, 7.49272785e-001,\n",
+       "        6.19924976e-001, 9.75026487e-001, 9.80507920e-001,\n",
+       "        1.00000000e+000, 8.93513862e-001, 9.97503125e-001,\n",
+       "        9.75478772e-001, 9.97214127e-001, 9.39636917e-001,\n",
+       "        1.00000000e+000, 9.91572645e-001, 2.49138671e-001,\n",
+       "        9.63737267e-001, 9.67114011e-001, 9.81483595e-001,\n",
+       "        9.58198105e-001, 9.36251067e-001, 9.58539971e-001,\n",
+       "        9.60141330e-001, 8.40864349e-001, 9.84969423e-001,\n",
+       "        9.79430355e-001, 8.99720890e-001, 8.59572167e-001,\n",
+       "        8.07181525e-001, 7.39275844e-001, 9.48363628e-001,\n",
+       "        9.42747595e-001, 9.23290578e-001, 5.70638866e-001,\n",
+       "        9.10759207e-001, 5.77685541e-001, 9.80867943e-001,\n",
+       "        9.85436623e-001, 9.83971235e-001, 9.98134209e-001,\n",
+       "        9.32521106e-001, 9.82562612e-001, 8.81254052e-001,\n",
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+       "        9.91229742e-001, 9.66360813e-001]])
            • lp
              (chain, draw)
              float64
              -51.92 -50.57 ... -56.73 -58.36
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+       "        -57.67734425, -56.82084163, -62.09575204, -61.14262696,\n",
+       "        -61.17365535, -58.6186762 , -53.60957508, -51.33059084,\n",
+       "        -50.58934904, -52.27024137, -51.11268296, -53.94711878,\n",
+       "        -57.20200331, -60.22009322, -60.16246312, -60.59488473,\n",
+       "        -64.97026381, -61.9680031 , -64.14006413, -66.100367  ,\n",
+       "        -66.1154562 , -69.63809371, -60.32062872, -64.07158737,\n",
+       "        -61.4349654 , -63.45303592, -63.30072951, -67.0473801 ,\n",
+       "        -65.64894392, -65.11673858, -66.48291908, -67.1817707 ,\n",
+       "        -53.40558526, -53.69504351, -53.9288618 , -51.57431681,\n",
+       "        -52.92202111, -51.76559477, -53.33737393, -53.41923671,\n",
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+       "        -64.44659478, -60.36353903, -61.43780767, -62.75553293,\n",
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+       "        -55.87633469, -53.16935647, -56.64470084, -55.1779295 ,\n",
+       "        -58.32758686, -57.99148466, -60.5823629 , -57.66402199,\n",
+       "        -55.781462  , -58.05272996, -53.33679227, -49.443456  ,\n",
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+       "        -54.1682582 , -64.26672887, -58.9867921 , -58.02955902,\n",
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+       "        -65.28682741, -61.61051872, -61.10119464, -56.0053337 ,\n",
+       "        -57.01453796, -56.29806494, -58.97379852, -63.8284274 ,\n",
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+       "        -48.97179528, -52.9486314 , -54.1805225 , -57.3942867 ,\n",
+       "        -52.71764766, -53.2037535 , -53.68549131, -53.17402141,\n",
+       "        -58.09270774, -57.41490665, -61.14120479, -61.50907654,\n",
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+       "        -64.33671057, -61.47636531, -60.72919691, -60.26082735,\n",
+       "        -58.59395215, -56.53857193, -56.14501885, -55.5259046 ,\n",
+       "        -57.91681043, -60.83802638, -58.58337846, -61.92882868,\n",
+       "        -57.32212461, -54.61267259, -58.41195539, -54.20042008,\n",
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+       "        -51.99671122, -53.15676002, -52.83482456, -52.15270835,\n",
+       "        -54.16843157, -55.1308923 , -55.69919945, -55.302565  ,\n",
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+       "        -51.39972938, -47.43566364, -49.41501813, -57.52433554,\n",
+       "        -54.85061778, -53.55412793, -53.10772835, -54.33688509,\n",
+       "        -49.96686533, -53.63406672, -51.54403839, -53.86794764,\n",
+       "        -54.7870552 , -61.09799979, -55.70124471, -57.30675621,\n",
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+       "        -49.96711676, -55.21034171, -49.38772377, -51.26770026,\n",
+       "        -50.78758878, -49.6141256 , -49.21809891, -48.59294131,\n",
+       "        -48.1794015 , -57.4249225 , -54.88634227, -53.79001921,\n",
+       "        -55.83538884, -52.81092049, -55.35050763, -54.01234933,\n",
+       "        -51.92203924, -53.66805678, -51.50650096, -49.87354425,\n",
+       "        -47.54916952, -55.37446261, -55.83015413, -58.19677944,\n",
+       "        -57.88779   , -54.55792776, -52.1321059 , -58.33251306,\n",
+       "        -57.34812125, -54.56592787, -51.93891934, -56.24425901,\n",
+       "        -58.74477306, -58.73530521, -56.72532269, -58.3587961 ]])
            • step_size
              (chain, draw)
              float64
              0.2516 0.2516 ... 0.09828 0.09828
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+       "        0.25156967, 0.25156967, 0.25156967, 0.25156967, 0.25156967,\n",
+       "        0.25156967, 0.25156967, 0.25156967, 0.25156967, 0.25156967,\n",
+       "        0.25156967, 0.25156967, 0.25156967, 0.25156967, 0.25156967,\n",
+       "        0.25156967, 0.25156967, 0.25156967, 0.25156967, 0.25156967,\n",
+       "        0.25156967, 0.25156967, 0.25156967, 0.25156967, 0.25156967,\n",
+       "        0.25156967, 0.25156967, 0.25156967, 0.25156967, 0.25156967,\n",
+       "        0.25156967, 0.25156967, 0.25156967, 0.25156967, 0.25156967,\n",
+       "        0.25156967, 0.25156967, 0.25156967, 0.25156967, 0.25156967,\n",
+       "        0.25156967, 0.25156967, 0.25156967, 0.25156967, 0.25156967,\n",
+       "        0.25156967, 0.25156967, 0.25156967, 0.25156967, 0.25156967],\n",
+       "       [0.09827907, 0.09827907, 0.09827907, 0.09827907, 0.09827907,\n",
+       "        0.09827907, 0.09827907, 0.09827907, 0.09827907, 0.09827907,\n",
+       "        0.09827907, 0.09827907, 0.09827907, 0.09827907, 0.09827907,\n",
+       "        0.09827907, 0.09827907, 0.09827907, 0.09827907, 0.09827907,\n",
+       "        0.09827907, 0.09827907, 0.09827907, 0.09827907, 0.09827907,\n",
+       "        0.09827907, 0.09827907, 0.09827907, 0.09827907, 0.09827907,\n",
+       "        0.09827907, 0.09827907, 0.09827907, 0.09827907, 0.09827907,\n",
+       "        0.09827907, 0.09827907, 0.09827907, 0.09827907, 0.09827907,\n",
+       "        0.09827907, 0.09827907, 0.09827907, 0.09827907, 0.09827907,\n",
+       "        0.09827907, 0.09827907, 0.09827907, 0.09827907, 0.09827907,\n",
+       "        0.09827907, 0.09827907, 0.09827907, 0.09827907, 0.09827907,\n",
+       "        0.09827907, 0.09827907, 0.09827907, 0.09827907, 0.09827907,\n",
+       "        0.09827907, 0.09827907, 0.09827907, 0.09827907, 0.09827907,\n",
+       "        0.09827907, 0.09827907, 0.09827907, 0.09827907, 0.09827907,\n",
+       "        0.09827907, 0.09827907, 0.09827907, 0.09827907, 0.09827907,\n",
+       "        0.09827907, 0.09827907, 0.09827907, 0.09827907, 0.09827907,\n",
+       "        0.09827907, 0.09827907, 0.09827907, 0.09827907, 0.09827907,\n",
+       "        0.09827907, 0.09827907, 0.09827907, 0.09827907, 0.09827907,\n",
+       "        0.09827907, 0.09827907, 0.09827907, 0.09827907, 0.09827907,\n",
+       "        0.09827907, 0.09827907, 0.09827907, 0.09827907, 0.09827907,\n",
+       "        0.09827907, 0.09827907, 0.09827907, 0.09827907, 0.09827907,\n",
+       "        0.09827907, 0.09827907, 0.09827907, 0.09827907, 0.09827907,\n",
+       "        0.09827907, 0.09827907, 0.09827907, 0.09827907, 0.09827907,\n",
+       "        0.09827907, 0.09827907, 0.09827907, 0.09827907, 0.09827907,\n",
+       "        0.09827907, 0.09827907, 0.09827907, 0.09827907, 0.09827907,\n",
+       "        0.09827907, 0.09827907, 0.09827907, 0.09827907, 0.09827907,\n",
+       "        0.09827907, 0.09827907, 0.09827907, 0.09827907, 0.09827907,\n",
+       "        0.09827907, 0.09827907, 0.09827907, 0.09827907, 0.09827907,\n",
+       "        0.09827907, 0.09827907, 0.09827907, 0.09827907, 0.09827907,\n",
+       "        0.09827907, 0.09827907, 0.09827907, 0.09827907, 0.09827907,\n",
+       "        0.09827907, 0.09827907, 0.09827907, 0.09827907, 0.09827907,\n",
+       "        0.09827907, 0.09827907, 0.09827907, 0.09827907, 0.09827907,\n",
+       "        0.09827907, 0.09827907, 0.09827907, 0.09827907, 0.09827907,\n",
+       "        0.09827907, 0.09827907, 0.09827907, 0.09827907, 0.09827907,\n",
+       "        0.09827907, 0.09827907, 0.09827907, 0.09827907, 0.09827907,\n",
+       "        0.09827907, 0.09827907, 0.09827907, 0.09827907, 0.09827907,\n",
+       "        0.09827907, 0.09827907, 0.09827907, 0.09827907, 0.09827907,\n",
+       "        0.09827907, 0.09827907, 0.09827907, 0.09827907, 0.09827907,\n",
+       "        0.09827907, 0.09827907, 0.09827907, 0.09827907, 0.09827907,\n",
+       "        0.09827907, 0.09827907, 0.09827907, 0.09827907, 0.09827907,\n",
+       "        0.09827907, 0.09827907, 0.09827907, 0.09827907, 0.09827907,\n",
+       "        0.09827907, 0.09827907, 0.09827907, 0.09827907, 0.09827907,\n",
+       "        0.09827907, 0.09827907, 0.09827907, 0.09827907, 0.09827907,\n",
+       "        0.09827907, 0.09827907, 0.09827907, 0.09827907, 0.09827907,\n",
+       "        0.09827907, 0.09827907, 0.09827907, 0.09827907, 0.09827907,\n",
+       "        0.09827907, 0.09827907, 0.09827907, 0.09827907, 0.09827907,\n",
+       "        0.09827907, 0.09827907, 0.09827907, 0.09827907, 0.09827907,\n",
+       "        0.09827907, 0.09827907, 0.09827907, 0.09827907, 0.09827907,\n",
+       "        0.09827907, 0.09827907, 0.09827907, 0.09827907, 0.09827907,\n",
+       "        0.09827907, 0.09827907, 0.09827907, 0.09827907, 0.09827907,\n",
+       "        0.09827907, 0.09827907, 0.09827907, 0.09827907, 0.09827907,\n",
+       "        0.09827907, 0.09827907, 0.09827907, 0.09827907, 0.09827907,\n",
+       "        0.09827907, 0.09827907, 0.09827907, 0.09827907, 0.09827907,\n",
+       "        0.09827907, 0.09827907, 0.09827907, 0.09827907, 0.09827907,\n",
+       "        0.09827907, 0.09827907, 0.09827907, 0.09827907, 0.09827907,\n",
+       "        0.09827907, 0.09827907, 0.09827907, 0.09827907, 0.09827907,\n",
+       "        0.09827907, 0.09827907, 0.09827907, 0.09827907, 0.09827907,\n",
+       "        0.09827907, 0.09827907, 0.09827907, 0.09827907, 0.09827907,\n",
+       "        0.09827907, 0.09827907, 0.09827907, 0.09827907, 0.09827907,\n",
+       "        0.09827907, 0.09827907, 0.09827907, 0.09827907, 0.09827907,\n",
+       "        0.09827907, 0.09827907, 0.09827907, 0.09827907, 0.09827907,\n",
+       "        0.09827907, 0.09827907, 0.09827907, 0.09827907, 0.09827907,\n",
+       "        0.09827907, 0.09827907, 0.09827907, 0.09827907, 0.09827907,\n",
+       "        0.09827907, 0.09827907, 0.09827907, 0.09827907, 0.09827907,\n",
+       "        0.09827907, 0.09827907, 0.09827907, 0.09827907, 0.09827907,\n",
+       "        0.09827907, 0.09827907, 0.09827907, 0.09827907, 0.09827907,\n",
+       "        0.09827907, 0.09827907, 0.09827907, 0.09827907, 0.09827907,\n",
+       "        0.09827907, 0.09827907, 0.09827907, 0.09827907, 0.09827907,\n",
+       "        0.09827907, 0.09827907, 0.09827907, 0.09827907, 0.09827907,\n",
+       "        0.09827907, 0.09827907, 0.09827907, 0.09827907, 0.09827907,\n",
+       "        0.09827907, 0.09827907, 0.09827907, 0.09827907, 0.09827907,\n",
+       "        0.09827907, 0.09827907, 0.09827907, 0.09827907, 0.09827907,\n",
+       "        0.09827907, 0.09827907, 0.09827907, 0.09827907, 0.09827907,\n",
+       "        0.09827907, 0.09827907, 0.09827907, 0.09827907, 0.09827907,\n",
+       "        0.09827907, 0.09827907, 0.09827907, 0.09827907, 0.09827907,\n",
+       "        0.09827907, 0.09827907, 0.09827907, 0.09827907, 0.09827907,\n",
+       "        0.09827907, 0.09827907, 0.09827907, 0.09827907, 0.09827907,\n",
+       "        0.09827907, 0.09827907, 0.09827907, 0.09827907, 0.09827907,\n",
+       "        0.09827907, 0.09827907, 0.09827907, 0.09827907, 0.09827907,\n",
+       "        0.09827907, 0.09827907, 0.09827907, 0.09827907, 0.09827907,\n",
+       "        0.09827907, 0.09827907, 0.09827907, 0.09827907, 0.09827907,\n",
+       "        0.09827907, 0.09827907, 0.09827907, 0.09827907, 0.09827907,\n",
+       "        0.09827907, 0.09827907, 0.09827907, 0.09827907, 0.09827907,\n",
+       "        0.09827907, 0.09827907, 0.09827907, 0.09827907, 0.09827907,\n",
+       "        0.09827907, 0.09827907, 0.09827907, 0.09827907, 0.09827907,\n",
+       "        0.09827907, 0.09827907, 0.09827907, 0.09827907, 0.09827907,\n",
+       "        0.09827907, 0.09827907, 0.09827907, 0.09827907, 0.09827907,\n",
+       "        0.09827907, 0.09827907, 0.09827907, 0.09827907, 0.09827907,\n",
+       "        0.09827907, 0.09827907, 0.09827907, 0.09827907, 0.09827907,\n",
+       "        0.09827907, 0.09827907, 0.09827907, 0.09827907, 0.09827907,\n",
+       "        0.09827907, 0.09827907, 0.09827907, 0.09827907, 0.09827907,\n",
+       "        0.09827907, 0.09827907, 0.09827907, 0.09827907, 0.09827907,\n",
+       "        0.09827907, 0.09827907, 0.09827907, 0.09827907, 0.09827907,\n",
+       "        0.09827907, 0.09827907, 0.09827907, 0.09827907, 0.09827907,\n",
+       "        0.09827907, 0.09827907, 0.09827907, 0.09827907, 0.09827907,\n",
+       "        0.09827907, 0.09827907, 0.09827907, 0.09827907, 0.09827907,\n",
+       "        0.09827907, 0.09827907, 0.09827907, 0.09827907, 0.09827907,\n",
+       "        0.09827907, 0.09827907, 0.09827907, 0.09827907, 0.09827907,\n",
+       "        0.09827907, 0.09827907, 0.09827907, 0.09827907, 0.09827907,\n",
+       "        0.09827907, 0.09827907, 0.09827907, 0.09827907, 0.09827907]])
            • step_size_bar
              (chain, draw)
              float64
              0.2551 0.2551 ... 0.2271 0.2271
              array([[0.255105  , 0.255105  , 0.255105  , 0.255105  , 0.255105  ,\n",
+       "        0.255105  , 0.255105  , 0.255105  , 0.255105  , 0.255105  ,\n",
+       "        0.255105  , 0.255105  , 0.255105  , 0.255105  , 0.255105  ,\n",
+       "        0.255105  , 0.255105  , 0.255105  , 0.255105  , 0.255105  ,\n",
+       "        0.255105  , 0.255105  , 0.255105  , 0.255105  , 0.255105  ,\n",
+       "        0.255105  , 0.255105  , 0.255105  , 0.255105  , 0.255105  ,\n",
+       "        0.255105  , 0.255105  , 0.255105  , 0.255105  , 0.255105  ,\n",
+       "        0.255105  , 0.255105  , 0.255105  , 0.255105  , 0.255105  ,\n",
+       "        0.255105  , 0.255105  , 0.255105  , 0.255105  , 0.255105  ,\n",
+       "        0.255105  , 0.255105  , 0.255105  , 0.255105  , 0.255105  ,\n",
+       "        0.255105  , 0.255105  , 0.255105  , 0.255105  , 0.255105  ,\n",
+       "        0.255105  , 0.255105  , 0.255105  , 0.255105  , 0.255105  ,\n",
+       "        0.255105  , 0.255105  , 0.255105  , 0.255105  , 0.255105  ,\n",
+       "        0.255105  , 0.255105  , 0.255105  , 0.255105  , 0.255105  ,\n",
+       "        0.255105  , 0.255105  , 0.255105  , 0.255105  , 0.255105  ,\n",
+       "        0.255105  , 0.255105  , 0.255105  , 0.255105  , 0.255105  ,\n",
+       "        0.255105  , 0.255105  , 0.255105  , 0.255105  , 0.255105  ,\n",
+       "        0.255105  , 0.255105  , 0.255105  , 0.255105  , 0.255105  ,\n",
+       "        0.255105  , 0.255105  , 0.255105  , 0.255105  , 0.255105  ,\n",
+       "        0.255105  , 0.255105  , 0.255105  , 0.255105  , 0.255105  ,\n",
+       "        0.255105  , 0.255105  , 0.255105  , 0.255105  , 0.255105  ,\n",
+       "        0.255105  , 0.255105  , 0.255105  , 0.255105  , 0.255105  ,\n",
+       "        0.255105  , 0.255105  , 0.255105  , 0.255105  , 0.255105  ,\n",
+       "        0.255105  , 0.255105  , 0.255105  , 0.255105  , 0.255105  ,\n",
+       "        0.255105  , 0.255105  , 0.255105  , 0.255105  , 0.255105  ,\n",
+       "        0.255105  , 0.255105  , 0.255105  , 0.255105  , 0.255105  ,\n",
+       "        0.255105  , 0.255105  , 0.255105  , 0.255105  , 0.255105  ,\n",
+       "        0.255105  , 0.255105  , 0.255105  , 0.255105  , 0.255105  ,\n",
+       "        0.255105  , 0.255105  , 0.255105  , 0.255105  , 0.255105  ,\n",
+       "        0.255105  , 0.255105  , 0.255105  , 0.255105  , 0.255105  ,\n",
+       "        0.255105  , 0.255105  , 0.255105  , 0.255105  , 0.255105  ,\n",
+       "        0.255105  , 0.255105  , 0.255105  , 0.255105  , 0.255105  ,\n",
+       "        0.255105  , 0.255105  , 0.255105  , 0.255105  , 0.255105  ,\n",
+       "        0.255105  , 0.255105  , 0.255105  , 0.255105  , 0.255105  ,\n",
+       "        0.255105  , 0.255105  , 0.255105  , 0.255105  , 0.255105  ,\n",
+       "        0.255105  , 0.255105  , 0.255105  , 0.255105  , 0.255105  ,\n",
+       "        0.255105  , 0.255105  , 0.255105  , 0.255105  , 0.255105  ,\n",
+       "        0.255105  , 0.255105  , 0.255105  , 0.255105  , 0.255105  ,\n",
+       "        0.255105  , 0.255105  , 0.255105  , 0.255105  , 0.255105  ,\n",
+       "        0.255105  , 0.255105  , 0.255105  , 0.255105  , 0.255105  ,\n",
+       "        0.255105  , 0.255105  , 0.255105  , 0.255105  , 0.255105  ,\n",
+       "        0.255105  , 0.255105  , 0.255105  , 0.255105  , 0.255105  ,\n",
+       "        0.255105  , 0.255105  , 0.255105  , 0.255105  , 0.255105  ,\n",
+       "        0.255105  , 0.255105  , 0.255105  , 0.255105  , 0.255105  ,\n",
+       "        0.255105  , 0.255105  , 0.255105  , 0.255105  , 0.255105  ,\n",
+       "        0.255105  , 0.255105  , 0.255105  , 0.255105  , 0.255105  ,\n",
+       "        0.255105  , 0.255105  , 0.255105  , 0.255105  , 0.255105  ,\n",
+       "        0.255105  , 0.255105  , 0.255105  , 0.255105  , 0.255105  ,\n",
+       "        0.255105  , 0.255105  , 0.255105  , 0.255105  , 0.255105  ,\n",
+       "        0.255105  , 0.255105  , 0.255105  , 0.255105  , 0.255105  ,\n",
+       "        0.255105  , 0.255105  , 0.255105  , 0.255105  , 0.255105  ,\n",
+       "        0.255105  , 0.255105  , 0.255105  , 0.255105  , 0.255105  ,\n",
+       "        0.255105  , 0.255105  , 0.255105  , 0.255105  , 0.255105  ,\n",
+       "        0.255105  , 0.255105  , 0.255105  , 0.255105  , 0.255105  ,\n",
+       "        0.255105  , 0.255105  , 0.255105  , 0.255105  , 0.255105  ,\n",
+       "        0.255105  , 0.255105  , 0.255105  , 0.255105  , 0.255105  ,\n",
+       "        0.255105  , 0.255105  , 0.255105  , 0.255105  , 0.255105  ,\n",
+       "        0.255105  , 0.255105  , 0.255105  , 0.255105  , 0.255105  ,\n",
+       "        0.255105  , 0.255105  , 0.255105  , 0.255105  , 0.255105  ,\n",
+       "        0.255105  , 0.255105  , 0.255105  , 0.255105  , 0.255105  ,\n",
+       "        0.255105  , 0.255105  , 0.255105  , 0.255105  , 0.255105  ,\n",
+       "        0.255105  , 0.255105  , 0.255105  , 0.255105  , 0.255105  ,\n",
+       "        0.255105  , 0.255105  , 0.255105  , 0.255105  , 0.255105  ,\n",
+       "        0.255105  , 0.255105  , 0.255105  , 0.255105  , 0.255105  ,\n",
+       "        0.255105  , 0.255105  , 0.255105  , 0.255105  , 0.255105  ,\n",
+       "        0.255105  , 0.255105  , 0.255105  , 0.255105  , 0.255105  ,\n",
+       "        0.255105  , 0.255105  , 0.255105  , 0.255105  , 0.255105  ,\n",
+       "        0.255105  , 0.255105  , 0.255105  , 0.255105  , 0.255105  ,\n",
+       "        0.255105  , 0.255105  , 0.255105  , 0.255105  , 0.255105  ,\n",
+       "        0.255105  , 0.255105  , 0.255105  , 0.255105  , 0.255105  ,\n",
+       "        0.255105  , 0.255105  , 0.255105  , 0.255105  , 0.255105  ,\n",
+       "        0.255105  , 0.255105  , 0.255105  , 0.255105  , 0.255105  ,\n",
+       "        0.255105  , 0.255105  , 0.255105  , 0.255105  , 0.255105  ,\n",
+       "        0.255105  , 0.255105  , 0.255105  , 0.255105  , 0.255105  ,\n",
+       "        0.255105  , 0.255105  , 0.255105  , 0.255105  , 0.255105  ,\n",
+       "        0.255105  , 0.255105  , 0.255105  , 0.255105  , 0.255105  ,\n",
+       "        0.255105  , 0.255105  , 0.255105  , 0.255105  , 0.255105  ,\n",
+       "        0.255105  , 0.255105  , 0.255105  , 0.255105  , 0.255105  ,\n",
+       "        0.255105  , 0.255105  , 0.255105  , 0.255105  , 0.255105  ,\n",
+       "        0.255105  , 0.255105  , 0.255105  , 0.255105  , 0.255105  ,\n",
+       "        0.255105  , 0.255105  , 0.255105  , 0.255105  , 0.255105  ,\n",
+       "        0.255105  , 0.255105  , 0.255105  , 0.255105  , 0.255105  ,\n",
+       "        0.255105  , 0.255105  , 0.255105  , 0.255105  , 0.255105  ,\n",
+       "        0.255105  , 0.255105  , 0.255105  , 0.255105  , 0.255105  ,\n",
+       "        0.255105  , 0.255105  , 0.255105  , 0.255105  , 0.255105  ,\n",
+       "        0.255105  , 0.255105  , 0.255105  , 0.255105  , 0.255105  ,\n",
+       "        0.255105  , 0.255105  , 0.255105  , 0.255105  , 0.255105  ,\n",
+       "        0.255105  , 0.255105  , 0.255105  , 0.255105  , 0.255105  ,\n",
+       "        0.255105  , 0.255105  , 0.255105  , 0.255105  , 0.255105  ,\n",
+       "        0.255105  , 0.255105  , 0.255105  , 0.255105  , 0.255105  ,\n",
+       "        0.255105  , 0.255105  , 0.255105  , 0.255105  , 0.255105  ,\n",
+       "        0.255105  , 0.255105  , 0.255105  , 0.255105  , 0.255105  ,\n",
+       "        0.255105  , 0.255105  , 0.255105  , 0.255105  , 0.255105  ,\n",
+       "        0.255105  , 0.255105  , 0.255105  , 0.255105  , 0.255105  ,\n",
+       "        0.255105  , 0.255105  , 0.255105  , 0.255105  , 0.255105  ,\n",
+       "        0.255105  , 0.255105  , 0.255105  , 0.255105  , 0.255105  ,\n",
+       "        0.255105  , 0.255105  , 0.255105  , 0.255105  , 0.255105  ,\n",
+       "        0.255105  , 0.255105  , 0.255105  , 0.255105  , 0.255105  ,\n",
+       "        0.255105  , 0.255105  , 0.255105  , 0.255105  , 0.255105  ,\n",
+       "        0.255105  , 0.255105  , 0.255105  , 0.255105  , 0.255105  ],\n",
+       "       [0.22713209, 0.22713209, 0.22713209, 0.22713209, 0.22713209,\n",
+       "        0.22713209, 0.22713209, 0.22713209, 0.22713209, 0.22713209,\n",
+       "        0.22713209, 0.22713209, 0.22713209, 0.22713209, 0.22713209,\n",
+       "        0.22713209, 0.22713209, 0.22713209, 0.22713209, 0.22713209,\n",
+       "        0.22713209, 0.22713209, 0.22713209, 0.22713209, 0.22713209,\n",
+       "        0.22713209, 0.22713209, 0.22713209, 0.22713209, 0.22713209,\n",
+       "        0.22713209, 0.22713209, 0.22713209, 0.22713209, 0.22713209,\n",
+       "        0.22713209, 0.22713209, 0.22713209, 0.22713209, 0.22713209,\n",
+       "        0.22713209, 0.22713209, 0.22713209, 0.22713209, 0.22713209,\n",
+       "        0.22713209, 0.22713209, 0.22713209, 0.22713209, 0.22713209,\n",
+       "        0.22713209, 0.22713209, 0.22713209, 0.22713209, 0.22713209,\n",
+       "        0.22713209, 0.22713209, 0.22713209, 0.22713209, 0.22713209,\n",
+       "        0.22713209, 0.22713209, 0.22713209, 0.22713209, 0.22713209,\n",
+       "        0.22713209, 0.22713209, 0.22713209, 0.22713209, 0.22713209,\n",
+       "        0.22713209, 0.22713209, 0.22713209, 0.22713209, 0.22713209,\n",
+       "        0.22713209, 0.22713209, 0.22713209, 0.22713209, 0.22713209,\n",
+       "        0.22713209, 0.22713209, 0.22713209, 0.22713209, 0.22713209,\n",
+       "        0.22713209, 0.22713209, 0.22713209, 0.22713209, 0.22713209,\n",
+       "        0.22713209, 0.22713209, 0.22713209, 0.22713209, 0.22713209,\n",
+       "        0.22713209, 0.22713209, 0.22713209, 0.22713209, 0.22713209,\n",
+       "        0.22713209, 0.22713209, 0.22713209, 0.22713209, 0.22713209,\n",
+       "        0.22713209, 0.22713209, 0.22713209, 0.22713209, 0.22713209,\n",
+       "        0.22713209, 0.22713209, 0.22713209, 0.22713209, 0.22713209,\n",
+       "        0.22713209, 0.22713209, 0.22713209, 0.22713209, 0.22713209,\n",
+       "        0.22713209, 0.22713209, 0.22713209, 0.22713209, 0.22713209,\n",
+       "        0.22713209, 0.22713209, 0.22713209, 0.22713209, 0.22713209,\n",
+       "        0.22713209, 0.22713209, 0.22713209, 0.22713209, 0.22713209,\n",
+       "        0.22713209, 0.22713209, 0.22713209, 0.22713209, 0.22713209,\n",
+       "        0.22713209, 0.22713209, 0.22713209, 0.22713209, 0.22713209,\n",
+       "        0.22713209, 0.22713209, 0.22713209, 0.22713209, 0.22713209,\n",
+       "        0.22713209, 0.22713209, 0.22713209, 0.22713209, 0.22713209,\n",
+       "        0.22713209, 0.22713209, 0.22713209, 0.22713209, 0.22713209,\n",
+       "        0.22713209, 0.22713209, 0.22713209, 0.22713209, 0.22713209,\n",
+       "        0.22713209, 0.22713209, 0.22713209, 0.22713209, 0.22713209,\n",
+       "        0.22713209, 0.22713209, 0.22713209, 0.22713209, 0.22713209,\n",
+       "        0.22713209, 0.22713209, 0.22713209, 0.22713209, 0.22713209,\n",
+       "        0.22713209, 0.22713209, 0.22713209, 0.22713209, 0.22713209,\n",
+       "        0.22713209, 0.22713209, 0.22713209, 0.22713209, 0.22713209,\n",
+       "        0.22713209, 0.22713209, 0.22713209, 0.22713209, 0.22713209,\n",
+       "        0.22713209, 0.22713209, 0.22713209, 0.22713209, 0.22713209,\n",
+       "        0.22713209, 0.22713209, 0.22713209, 0.22713209, 0.22713209,\n",
+       "        0.22713209, 0.22713209, 0.22713209, 0.22713209, 0.22713209,\n",
+       "        0.22713209, 0.22713209, 0.22713209, 0.22713209, 0.22713209,\n",
+       "        0.22713209, 0.22713209, 0.22713209, 0.22713209, 0.22713209,\n",
+       "        0.22713209, 0.22713209, 0.22713209, 0.22713209, 0.22713209,\n",
+       "        0.22713209, 0.22713209, 0.22713209, 0.22713209, 0.22713209,\n",
+       "        0.22713209, 0.22713209, 0.22713209, 0.22713209, 0.22713209,\n",
+       "        0.22713209, 0.22713209, 0.22713209, 0.22713209, 0.22713209,\n",
+       "        0.22713209, 0.22713209, 0.22713209, 0.22713209, 0.22713209,\n",
+       "        0.22713209, 0.22713209, 0.22713209, 0.22713209, 0.22713209,\n",
+       "        0.22713209, 0.22713209, 0.22713209, 0.22713209, 0.22713209,\n",
+       "        0.22713209, 0.22713209, 0.22713209, 0.22713209, 0.22713209,\n",
+       "        0.22713209, 0.22713209, 0.22713209, 0.22713209, 0.22713209,\n",
+       "        0.22713209, 0.22713209, 0.22713209, 0.22713209, 0.22713209,\n",
+       "        0.22713209, 0.22713209, 0.22713209, 0.22713209, 0.22713209,\n",
+       "        0.22713209, 0.22713209, 0.22713209, 0.22713209, 0.22713209,\n",
+       "        0.22713209, 0.22713209, 0.22713209, 0.22713209, 0.22713209,\n",
+       "        0.22713209, 0.22713209, 0.22713209, 0.22713209, 0.22713209,\n",
+       "        0.22713209, 0.22713209, 0.22713209, 0.22713209, 0.22713209,\n",
+       "        0.22713209, 0.22713209, 0.22713209, 0.22713209, 0.22713209,\n",
+       "        0.22713209, 0.22713209, 0.22713209, 0.22713209, 0.22713209,\n",
+       "        0.22713209, 0.22713209, 0.22713209, 0.22713209, 0.22713209,\n",
+       "        0.22713209, 0.22713209, 0.22713209, 0.22713209, 0.22713209,\n",
+       "        0.22713209, 0.22713209, 0.22713209, 0.22713209, 0.22713209,\n",
+       "        0.22713209, 0.22713209, 0.22713209, 0.22713209, 0.22713209,\n",
+       "        0.22713209, 0.22713209, 0.22713209, 0.22713209, 0.22713209,\n",
+       "        0.22713209, 0.22713209, 0.22713209, 0.22713209, 0.22713209,\n",
+       "        0.22713209, 0.22713209, 0.22713209, 0.22713209, 0.22713209,\n",
+       "        0.22713209, 0.22713209, 0.22713209, 0.22713209, 0.22713209,\n",
+       "        0.22713209, 0.22713209, 0.22713209, 0.22713209, 0.22713209,\n",
+       "        0.22713209, 0.22713209, 0.22713209, 0.22713209, 0.22713209,\n",
+       "        0.22713209, 0.22713209, 0.22713209, 0.22713209, 0.22713209,\n",
+       "        0.22713209, 0.22713209, 0.22713209, 0.22713209, 0.22713209,\n",
+       "        0.22713209, 0.22713209, 0.22713209, 0.22713209, 0.22713209,\n",
+       "        0.22713209, 0.22713209, 0.22713209, 0.22713209, 0.22713209,\n",
+       "        0.22713209, 0.22713209, 0.22713209, 0.22713209, 0.22713209,\n",
+       "        0.22713209, 0.22713209, 0.22713209, 0.22713209, 0.22713209,\n",
+       "        0.22713209, 0.22713209, 0.22713209, 0.22713209, 0.22713209,\n",
+       "        0.22713209, 0.22713209, 0.22713209, 0.22713209, 0.22713209,\n",
+       "        0.22713209, 0.22713209, 0.22713209, 0.22713209, 0.22713209,\n",
+       "        0.22713209, 0.22713209, 0.22713209, 0.22713209, 0.22713209,\n",
+       "        0.22713209, 0.22713209, 0.22713209, 0.22713209, 0.22713209,\n",
+       "        0.22713209, 0.22713209, 0.22713209, 0.22713209, 0.22713209,\n",
+       "        0.22713209, 0.22713209, 0.22713209, 0.22713209, 0.22713209,\n",
+       "        0.22713209, 0.22713209, 0.22713209, 0.22713209, 0.22713209,\n",
+       "        0.22713209, 0.22713209, 0.22713209, 0.22713209, 0.22713209,\n",
+       "        0.22713209, 0.22713209, 0.22713209, 0.22713209, 0.22713209,\n",
+       "        0.22713209, 0.22713209, 0.22713209, 0.22713209, 0.22713209,\n",
+       "        0.22713209, 0.22713209, 0.22713209, 0.22713209, 0.22713209,\n",
+       "        0.22713209, 0.22713209, 0.22713209, 0.22713209, 0.22713209,\n",
+       "        0.22713209, 0.22713209, 0.22713209, 0.22713209, 0.22713209,\n",
+       "        0.22713209, 0.22713209, 0.22713209, 0.22713209, 0.22713209,\n",
+       "        0.22713209, 0.22713209, 0.22713209, 0.22713209, 0.22713209,\n",
+       "        0.22713209, 0.22713209, 0.22713209, 0.22713209, 0.22713209,\n",
+       "        0.22713209, 0.22713209, 0.22713209, 0.22713209, 0.22713209,\n",
+       "        0.22713209, 0.22713209, 0.22713209, 0.22713209, 0.22713209,\n",
+       "        0.22713209, 0.22713209, 0.22713209, 0.22713209, 0.22713209,\n",
+       "        0.22713209, 0.22713209, 0.22713209, 0.22713209, 0.22713209,\n",
+       "        0.22713209, 0.22713209, 0.22713209, 0.22713209, 0.22713209,\n",
+       "        0.22713209, 0.22713209, 0.22713209, 0.22713209, 0.22713209]])
            • depth
              (chain, draw)
              int64
              3 5 3 3 3 4 3 4 ... 5 5 3 3 4 4 5 4
              array([[3, 5, 3, 3, 3, 4, 3, 4, 4, 4, 4, 4, 4, 3, 4, 4, 3, 3, 5, 4, 4, 5,\n",
+       "        5, 4, 4, 5, 4, 4, 4, 4, 5, 4, 5, 4, 5, 4, 5, 4, 4, 4, 4, 5, 5, 5,\n",
+       "        5, 4, 5, 4, 5, 5, 3, 4, 5, 5, 4, 4, 5, 3, 3, 4, 4, 3, 3, 4, 4, 3,\n",
+       "        3, 3, 4, 4, 4, 4, 5, 3, 4, 3, 4, 4, 4, 4, 3, 3, 5, 5, 4, 4, 4, 4,\n",
+       "        4, 5, 4, 5, 5, 4, 4, 3, 3, 3, 4, 5, 5, 4, 4, 4, 3, 5, 3, 3, 3, 3,\n",
+       "        2, 3, 3, 3, 2, 3, 4, 3, 4, 5, 4, 4, 3, 4, 4, 4, 4, 5, 4, 4, 4, 5,\n",
+       "        4, 4, 5, 4, 3, 3, 3, 3, 4, 3, 1, 2, 1, 4, 2, 3, 2, 3, 2, 5, 2, 2,\n",
+       "        4, 3, 4, 5, 5, 4, 4, 4, 4, 3, 3, 4, 4, 4, 4, 5, 5, 4, 4, 4, 4, 4,\n",
+       "        4, 4, 3, 3, 4, 5, 2, 2, 3, 3, 3, 3, 4, 3, 4, 5, 4, 4, 4, 5, 5, 5,\n",
+       "        5, 5, 5, 5, 5, 5, 5, 4, 4, 4, 4, 4, 4, 4, 4, 6, 4, 4, 5, 4, 4, 3,\n",
+       "        4, 4, 3, 5, 3, 4, 4, 4, 2, 2, 3, 3, 2, 2, 2, 4, 3, 4, 2, 2, 2, 5,\n",
+       "        2, 1, 2, 2, 3, 5, 2, 2, 3, 3, 4, 3, 3, 4, 4, 4, 4, 4, 3, 4, 4, 4,\n",
+       "        5, 4, 5, 5, 4, 4, 4, 4, 4, 4, 3, 4, 4, 4, 3, 3, 4, 4, 4, 4, 5, 4,\n",
+       "        4, 4, 4, 5, 4, 3, 5, 3, 2, 3, 3, 3, 2, 2, 3, 2, 2, 4, 5, 4, 3, 3,\n",
+       "        3, 3, 2, 3, 3, 4, 5, 4, 4, 3, 3, 3, 5, 4, 4, 4, 5, 5, 5, 5, 5, 5,\n",
+       "        5, 4, 4, 5, 4, 5, 4, 4, 5, 5, 5, 4, 5, 4, 5, 4, 5, 4, 4, 4, 2, 4,\n",
+       "        4, 4, 5, 4, 4, 4, 4, 4, 5, 4, 3, 3, 3, 5, 3, 3, 5, 4, 3, 5, 4, 4,\n",
+       "        4, 4, 4, 4, 5, 4, 4, 4, 4, 5, 5, 4, 4, 4, 5, 4, 4, 3, 4, 3, 4, 5,\n",
+       "        4, 4, 3, 3, 3, 4, 3, 4, 3, 2, 3, 3, 3, 3, 3, 3, 4, 4, 4, 3, 4, 4,\n",
+       "        5, 5, 5, 5, 5, 5, 5, 4, 5, 5, 4, 3, 4, 4, 5, 4, 4, 4, 4, 6, 4, 4,\n",
+       "        4, 4, 4, 5, 5, 5, 4, 4, 4, 4, 5, 4, 3, 4, 3, 5, 2, 5, 4, 4, 4, 4,\n",
+       "        4, 4, 4, 4, 5, 4, 5, 5, 5, 5, 4, 4, 3, 4, 4, 4, 4, 4, 5, 4, 4, 5,\n",
+       "        4, 4, 4, 4, 5, 4, 4, 4, 5, 5, 3, 5, 3, 3, 1, 2],\n",
+       "       [3, 2, 1, 2, 2, 1, 3, 3, 2, 3, 5, 4, 4, 4, 5, 5, 4, 5, 4, 5, 5, 4,\n",
+       "        5, 4, 4, 4, 4, 4, 4, 5, 4, 4, 5, 4, 4, 4, 3, 3, 3, 3, 3, 3, 3, 3,\n",
+       "        3, 3, 4, 5, 5, 4, 4, 4, 4, 4, 4, 4, 4, 3, 4, 4, 3, 3, 5, 5, 4, 4,\n",
+       "        4, 3, 3, 3, 3, 3, 4, 4, 4, 5, 5, 5, 4, 5, 5, 4, 4, 4, 5, 4, 4, 5,\n",
+       "        5, 5, 5, 5, 4, 4, 4, 3, 3, 3, 3, 3, 3, 4, 3, 4, 4, 4, 4, 4, 3, 4,\n",
+       "        4, 4, 4, 3, 4, 3, 4, 3, 1, 5, 3, 3, 5, 3, 4, 4, 3, 3, 3, 3, 4, 3,\n",
+       "        4, 4, 4, 4, 3, 4, 3, 3, 3, 4, 5, 3, 4, 3, 4, 5, 5, 5, 5, 5, 5, 4,\n",
+       "        4, 4, 4, 5, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 4, 4, 5, 4, 4, 4, 4, 4,\n",
+       "        5, 5, 5, 4, 5, 4, 4, 5, 4, 4, 4, 5, 4, 4, 4, 4, 4, 4, 5, 3, 3, 2,\n",
+       "        3, 4, 4, 4, 4, 4, 5, 4, 4, 4, 4, 4, 4, 5, 5, 5, 4, 5, 5, 4, 4, 4,\n",
+       "        4, 5, 4, 5, 4, 3, 4, 5, 5, 4, 3, 3, 4, 3, 4, 5, 5, 5, 5, 3, 3, 5,\n",
+       "        2, 3, 3, 3, 3, 4, 4, 5, 4, 4, 4, 5, 5, 3, 4, 3, 3, 5, 3, 3, 5, 4,\n",
+       "        3, 4, 3, 2, 2, 3, 5, 5, 3, 4, 3, 3, 3, 5, 5, 4, 3, 3, 5, 3, 3, 3,\n",
+       "        3, 4, 4, 5, 5, 4, 4, 4, 4, 4, 4, 3, 5, 4, 4, 4, 3, 5, 4, 4, 4, 4,\n",
+       "        5, 5, 4, 4, 4, 4, 3, 2, 3, 4, 3, 4, 2, 4, 5, 3, 4, 4, 4, 4, 4, 2,\n",
+       "        3, 1, 2, 2, 2, 3, 2, 2, 3, 3, 3, 3, 3, 2, 2, 4, 3, 2, 3, 3, 4, 3,\n",
+       "        5, 3, 3, 3, 3, 4, 1, 4, 3, 2, 1, 4, 2, 2, 2, 1, 2, 3, 2, 3, 2, 2,\n",
+       "        3, 1, 4, 1, 1, 3, 1, 1, 1, 1, 2, 2, 1, 1, 1, 5, 1, 1, 2, 1, 2, 1,\n",
+       "        2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 2, 2, 1,\n",
+       "        2, 1, 1, 1, 1, 1, 2, 2, 1, 2, 2, 1, 2, 1, 1, 1, 2, 2, 1, 1, 4, 2,\n",
+       "        1, 2, 3, 3, 4, 3, 3, 5, 4, 4, 4, 3, 4, 3, 5, 2, 4, 4, 4, 4, 4, 4,\n",
+       "        4, 4, 2, 3, 3, 5, 3, 3, 3, 4, 3, 3, 3, 5, 3, 3, 4, 5, 5, 3, 5, 3,\n",
+       "        2, 4, 4, 3, 3, 4, 3, 4, 5, 5, 3, 3, 4, 4, 5, 4]])
            • energy_error
              (chain, draw)
              float64
              0.02139 -0.02762 ... 0.01447
              array([[ 2.13884648e-02, -2.76219814e-02,  3.45938191e-01,\n",
+       "         1.49101606e-01, -2.52619688e-01,  8.56604749e-03,\n",
+       "        -2.42837145e-02, -1.07623267e-02,  1.06822109e-01,\n",
+       "        -8.65450768e-02,  4.23087489e-02,  3.67988967e-02,\n",
+       "        -1.98361439e-01,  3.87770995e-02, -6.91225866e-02,\n",
+       "         7.98907682e-02, -5.02984547e-01, -4.33307267e-01,\n",
+       "         1.91784712e+00,  2.05797723e-02,  1.55879268e-01,\n",
+       "        -1.54087695e-02,  6.58677999e-02,  5.93453991e-02,\n",
+       "         1.58934599e-01, -1.72732422e-01, -1.61026725e-02,\n",
+       "        -5.76937766e-02, -5.59996662e-01,  4.84201575e-01,\n",
+       "        -3.56611749e-02,  2.87955684e-01, -2.30804723e-01,\n",
+       "        -7.19262218e-02,  1.10829319e-02,  4.78115942e-02,\n",
+       "        -4.91586548e-02, -9.30542887e-03, -1.84693849e-01,\n",
+       "         1.59837158e-01, -1.81526081e-01,  5.33390246e-02,\n",
+       "        -3.69030885e-02, -6.98565111e-02, -1.08858850e-03,\n",
+       "         1.59655357e-02,  2.17823971e-03, -1.56327619e-02,\n",
+       "         2.23495920e-02, -8.69994795e-02,  3.31741447e-01,\n",
+       "        -2.30447600e-02, -1.41517915e-01, -5.48583599e-01,\n",
+       "        -2.35100086e-01,  5.75928218e-02, -5.39413335e-02,\n",
+       "        -3.00375165e-03, -4.74549793e-02,  6.09472682e-02,\n",
+       "        -1.62126848e-02, -1.33028312e-01,  7.92130643e-01,\n",
+       "         1.84643678e-01, -1.62123299e-01, -1.01264629e-01,\n",
+       "        -5.28754332e-02,  1.21989436e-01, -2.67633215e-02,\n",
+       "         3.88539993e-02,  3.28824790e-02,  2.54936255e-02,\n",
+       "        -1.81800766e-01, -3.67217266e-03, -6.04753711e-01,\n",
+       "         2.84617877e-01, -1.72562860e-01,  9.71792507e-02,\n",
+       "        -4.87557714e-02, -2.70080509e-01,  4.72104760e-02,\n",
+       "         1.44160450e-01,  6.54696430e-01,  2.34486570e-02,\n",
+       "         3.50122724e-02,  1.66229567e-02, -8.69390601e-02,\n",
+       "        -9.48929367e-02,  5.19869572e-02, -1.26476363e-01,\n",
+       "        -3.43167878e-02,  1.26630229e-01, -2.26724582e-02,\n",
+       "         2.80959744e-02, -2.32308101e+00, -7.41882821e-02,\n",
+       "        -8.73753027e-03,  5.79346320e-01,  2.84464656e-01,\n",
+       "        -1.77922047e-02, -5.45103783e-03, -9.93238830e-02,\n",
+       "        -2.28421035e-02, -2.34405140e-01, -8.59739680e-01,\n",
+       "        -6.70743587e-02, -8.33280287e-02,  2.18723634e-01,\n",
+       "         1.26507313e+00,  2.10459688e+00, -2.77263378e+00,\n",
+       "        -1.80047274e-01,  2.62225585e-01, -1.76489816e+00,\n",
+       "         0.00000000e+00,  0.00000000e+00,  1.78473775e-02,\n",
+       "         3.01062857e-01, -3.23842361e-02,  4.06595966e-01,\n",
+       "         8.54705611e-02,  4.84731172e-02, -7.75596000e-02,\n",
+       "         4.95168486e-02, -7.85851230e-02, -4.10505335e-03,\n",
+       "         7.97435768e-02,  8.81264324e-02, -4.86691262e-02,\n",
+       "         1.06753004e-01, -2.07359960e-02, -3.76409133e-02,\n",
+       "         1.32710557e-01,  3.93136321e-02, -3.57727563e-02,\n",
+       "        -7.91908447e-01, -7.69884347e-01,  3.96095578e-01,\n",
+       "        -8.23127777e-01, -3.47228928e-01, -1.07451229e-01,\n",
+       "        -1.12697337e+00,  0.00000000e+00,  0.00000000e+00,\n",
+       "         0.00000000e+00,  0.00000000e+00,  3.69839763e-01,\n",
+       "        -6.04722307e-01,  0.00000000e+00,  0.00000000e+00,\n",
+       "         5.87546307e-01, -1.97357870e-01,  0.00000000e+00,\n",
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+       "         2.99691605e-01,  3.26778070e-01,  2.22472765e-01,\n",
+       "         2.03170877e-02, -2.76353477e-02, -8.28551180e-02,\n",
+       "         1.77728382e-02,  7.90997652e-02, -6.12078190e-02,\n",
+       "        -4.55222214e-02, -5.29234487e-03,  1.31253665e-01,\n",
+       "        -2.66848763e-02, -1.50381766e-01,  1.41117824e-01,\n",
+       "        -9.54720997e-01, -1.66692074e-01,  6.05963708e-02,\n",
+       "        -1.64448381e-01,  1.88453394e-01,  2.79800354e-02,\n",
+       "        -1.36515265e-01,  4.27172563e-03,  2.78791492e-03,\n",
+       "        -1.42462528e-02,  1.11341096e-01, -4.72164188e-03,\n",
+       "        -6.40281900e-02,  3.53410920e-02, -4.09742304e-02,\n",
+       "         0.00000000e+00,  1.34899198e-01,  9.88243459e-02,\n",
+       "         3.61316878e-02, -1.16109112e-02, -1.08567222e-01,\n",
+       "         1.02271735e-01, -7.62946548e-02,  6.03946906e-02,\n",
+       "        -1.02973700e-01,  7.33555972e-02, -4.17826845e-01,\n",
+       "        -1.04926117e-01, -1.24626789e+00,  0.00000000e+00,\n",
+       "        -2.52405059e+00,  0.00000000e+00,  0.00000000e+00,\n",
+       "        -1.87099124e-02,  0.00000000e+00,  0.00000000e+00,\n",
+       "         0.00000000e+00,  6.46880273e-01,  0.00000000e+00,\n",
+       "         3.52807793e-01,  4.15691179e-01,  4.14425030e-01,\n",
+       "        -1.04216583e+00,  4.83810120e-01, -9.06242713e-01,\n",
+       "         2.12490297e-01,  1.19756516e-01, -6.26708197e-01,\n",
+       "         9.13219769e-01,  1.22333360e-01, -9.62359058e-02,\n",
+       "         1.73853730e-03, -9.08990921e-01,  4.60430123e-01,\n",
+       "        -7.04252042e-01,  1.17520015e-02, -3.39884475e-01,\n",
+       "        -7.54149609e-01,  0.00000000e+00,  2.03053444e-01,\n",
+       "         0.00000000e+00, -1.14124013e+00,  0.00000000e+00,\n",
        "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
        "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
        "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
+       "         0.00000000e+00,  0.00000000e+00, -4.10244436e-01,\n",
        "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
+       "         0.00000000e+00, -6.59434631e-01,  0.00000000e+00,\n",
        "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
        "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
        "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
@@ -3647,627 +4332,34 @@
        "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
        "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
        "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
-       "         0.00000000e+00, -8.29343605e-01,  3.17839589e-02,\n",
-       "         0.00000000e+00, -4.49791094e-02, -1.76218767e-01,\n",
-       "         0.00000000e+00,  1.53109692e+00, -3.18651399e-01,\n",
-       "        -1.81111174e-02,  4.90289647e-02, -1.86712935e-01,\n",
-       "         2.50756204e-01, -2.10576502e-01,  0.00000000e+00,\n",
-       "        -2.33508892e+00,  0.00000000e+00,  0.00000000e+00,\n",
-       "         2.17024234e-01,  1.71893425e+00,  2.80626332e-02,\n",
-       "         2.45187016e-02,  3.71485188e-01, -3.40208408e-02,\n",
-       "        -9.63423866e-01,  1.20982755e-01, -9.86919692e-02,\n",
-       "         5.41692135e-01, -2.57883382e-02,  3.84383854e-02,\n",
-       "        -1.04090909e+00,  2.18028319e-01, -1.99263891e-02,\n",
-       "        -5.22762956e-02, -4.96958318e-01,  3.87016279e-01,\n",
-       "         3.33430237e-02, -1.28970527e-01, -2.76969904e-01,\n",
-       "        -4.42610379e-02, -1.50055621e-01, -2.07778285e-01,\n",
-       "        -1.57702724e-01, -4.11320887e-01,  2.71148779e-01,\n",
-       "         7.12639078e-01,  1.38460439e-01, -3.03290278e-02,\n",
-       "        -4.35888405e-02, -9.12020231e-01, -2.57262172e-01,\n",
-       "         2.01837350e-01,  1.02098454e-01,  2.58860849e-02,\n",
-       "         1.03506068e-01,  5.32560491e-02,  1.18359415e-02,\n",
-       "        -2.35523350e-01, -9.72157134e-02, -1.12373771e-01,\n",
-       "        -3.48134358e-02,  3.57649869e-01, -4.75650120e-01,\n",
-       "        -4.92359751e-02, -3.66304622e-02,  4.35404119e-01,\n",
-       "        -5.66021890e-02,  1.54473318e-01,  3.59199872e-02,\n",
-       "        -6.50541432e-02, -4.59867561e-02,  2.38925686e-01,\n",
-       "        -3.94325046e-02, -2.38569234e-01,  4.24551138e-02,\n",
-       "         5.96091419e-02, -1.18375227e-02, -2.40576083e+00,\n",
-       "         1.59722848e-01, -1.29718135e+00,  9.05971745e-01,\n",
-       "         5.51460653e-01,  2.53648908e-01, -2.71358149e-01,\n",
-       "         4.87557109e-03, -2.45809178e-01,  1.47998749e-01,\n",
-       "        -6.97896048e-02,  1.06621671e-01,  2.52925702e-01,\n",
-       "         6.78879448e-01, -3.06330075e+00,  0.00000000e+00,\n",
-       "         2.55714674e+00,  4.10520115e-01,  1.96484000e-01,\n",
-       "        -7.87476969e-02,  1.60063927e+00, -2.13933103e+00,\n",
-       "        -1.31891407e-01,  3.28348649e-01, -2.80568682e-01,\n",
-       "         4.28800813e-01, -7.16088354e-01, -8.41627265e-01,\n",
-       "         1.34248711e-02,  1.70488587e-01, -5.60519926e-01,\n",
-       "        -3.90304378e-02,  5.07437020e-01, -1.71892810e-01,\n",
-       "        -1.80010021e-01,  3.00583723e-02, -3.04176564e-01,\n",
-       "        -9.74006220e-01,  0.00000000e+00, -1.53618278e-01,\n",
-       "         0.00000000e+00,  4.65628427e-01,  2.08876926e-01,\n",
-       "         8.56574179e-02,  3.08704513e-02,  1.90736412e-01,\n",
-       "         1.43810679e-01, -2.73651356e-01, -1.86551374e-01,\n",
-       "        -6.20426263e-01,  5.71539565e-01,  7.91370560e-01,\n",
-       "         0.00000000e+00, -3.24780902e+00,  2.85696123e-01,\n",
-       "         0.00000000e+00,  0.00000000e+00,  5.51885730e-01,\n",
-       "         0.00000000e+00,  1.03508372e+00, -8.55615022e-02,\n",
-       "         6.08082302e-02,  2.17618372e-01, -7.20598863e-02,\n",
-       "        -2.67548460e-02,  5.03131569e-01, -1.69724220e+00,\n",
-       "         1.26776754e+00, -2.50365687e-01,  1.00148079e+00,\n",
-       "        -6.51734834e-01, -1.45713873e+00,  7.97346823e-01,\n",
-       "        -4.22285277e-02,  2.80142580e-02,  0.00000000e+00,\n",
-       "         0.00000000e+00, -2.32245334e+00, -3.52456926e-01,\n",
-       "         1.91604565e-01,  3.10279070e-01, -8.17320960e-02,\n",
-       "         7.37873285e-01, -3.45285605e-01,  1.94778069e-01,\n",
-       "        -4.08724625e-01, -1.91741670e-01,  5.96881578e-01,\n",
-       "        -8.96115085e-02,  4.29812978e-02,  1.88578580e+00,\n",
-       "        -4.17822358e-01,  1.38582200e-02,  1.04753253e-02,\n",
-       "         1.16756897e-01,  1.88576182e-01,  3.19855532e-01,\n",
-       "         3.25266285e-01, -4.88613894e-01,  1.13991147e+00,\n",
-       "        -3.73941928e-01,  8.15795361e-02, -6.50723876e-02,\n",
-       "         2.85939466e-01,  9.72053556e-03, -2.85995133e-01,\n",
-       "         1.02396216e-01, -3.01045267e-02, -8.54626194e-02,\n",
-       "        -5.33878196e-02,  5.91150434e-03, -1.32480432e-02,\n",
-       "         4.43221471e-02,  2.90127004e-02, -6.10698275e-02,\n",
-       "         3.06346356e-01, -1.66737281e-01, -2.91606857e-02,\n",
-       "         2.52191688e-02, -3.38478029e-01,  3.64546929e-01,\n",
-       "        -5.21785148e-02, -2.24346539e-02, -1.67156361e+00,\n",
-       "         1.57256339e-01,  1.98533735e+00,  2.13669169e-01,\n",
-       "         1.90361756e-01, -2.23806858e-01,  3.91125140e-02,\n",
-       "        -1.08001610e-01,  4.28989130e-01,  1.31900193e-02,\n",
-       "        -5.91239021e-01, -1.28409387e-01, -1.79454716e-01,\n",
-       "        -1.37073630e-01,  6.49816073e-02,  8.56948164e-02,\n",
-       "         8.23426789e-03, -1.20269595e-01, -1.03510698e-01,\n",
-       "         2.07221068e-02,  9.81322526e-02, -1.90556891e-02,\n",
-       "         3.50243500e-01, -1.34025966e-01, -1.50620772e-01,\n",
-       "        -5.70488877e-02, -1.21475215e-01,  3.81194747e-01,\n",
-       "        -5.45611051e-01, -6.99153997e-02,  9.90190618e-02,\n",
-       "        -5.83950807e-03, -1.89917495e-01,  2.54407046e-02,\n",
-       "        -1.77037706e-02, -6.40364961e-02,  1.33227854e-01,\n",
-       "        -2.26396523e-01, -1.19989479e-01, -2.69158594e-01,\n",
-       "         0.00000000e+00,  6.45903340e-01,  4.79948762e-02,\n",
-       "         6.36738968e-02,  4.12639841e-01]])
            • max_energy_error
              (chain, draw)
              float64
              274.8 0.6038 ... -0.4008 0.5045
              array([[ 2.74816935e+02,  6.03806531e-01, -7.79957979e-01,\n",
-       "        -1.01594416e+00,  3.61564830e-01, -4.76151095e-01,\n",
-       "         3.53770748e-01, -1.55901633e-01, -4.76963696e-01,\n",
-       "        -1.54701681e+00,  6.36295155e-01, -2.18384866e-01,\n",
-       "         4.89861101e-01, -5.22662460e-01,  1.39887527e+00,\n",
-       "        -6.36442192e-01, -5.24107166e-01, -1.03965493e-01,\n",
-       "        -2.19282952e+00,  1.37863613e-01,  9.68812405e-02,\n",
-       "        -5.45971892e-01,  1.39468345e-01,  2.93938594e-01,\n",
-       "        -4.10017907e-01, -2.53134003e-01, -3.33474473e-01,\n",
-       "         2.71870499e-01,  8.88863105e-01,  4.06997701e-01,\n",
-       "         4.00405405e-01,  1.23555038e+00, -1.13156119e+00,\n",
-       "        -4.35367079e-01,  6.85429580e-01, -4.37327129e-01,\n",
-       "        -1.08690374e+00,  7.18973832e+01,  3.17473859e+02,\n",
-       "         1.51221120e+01,  4.51671386e+01,  3.69437768e+01,\n",
-       "         5.53388183e+00,  2.09631827e+02,  5.28610150e+01,\n",
-       "         4.17440859e+01,  3.92089139e+01,  7.57092302e+01,\n",
-       "         1.10626828e+02,  2.52821793e+02,  2.07985066e+01,\n",
-       "         2.39929310e+01,  3.69329571e+00,  1.99049854e+02,\n",
-       "         1.61569236e+02,  1.33901013e+02,  6.57489468e+02,\n",
-       "         1.87415660e+01,  4.12862595e+02,  2.58777292e+01,\n",
-       "         6.19407129e+01,  4.01712417e+02,  6.71732509e+01,\n",
-       "         1.66860628e+01,  7.39115236e+02,  1.96760384e+01,\n",
-       "         1.12207838e+01,  4.63150768e+01,  6.23095217e+02,\n",
-       "         1.45137587e+01,  1.05063078e+01,  4.81671164e+01,\n",
-       "         1.43265962e+03,  1.17892095e+02,  1.89705742e+01,\n",
-       "         1.11336729e+01,  1.99906908e+00, -2.91741126e-01,\n",
-       "        -3.38405295e-01, -3.25519249e-01, -8.54751013e-01,\n",
-       "         2.47383319e-01,  1.71321722e-01, -4.56920766e-01,\n",
-       "         3.37135769e-01, -1.29235578e-01,  2.97105180e+01,\n",
-       "         2.25477800e+00,  4.63758963e+00,  2.62172163e-01,\n",
-       "        -5.53976078e-01, -4.94104238e-01,  2.28777087e+02,\n",
-       "         1.21970591e+00,  1.17630978e+02,  3.53057921e+01,\n",
-       "         1.82674362e+01,  7.36356118e+00,  1.96351236e+00,\n",
-       "         1.50845177e+02,  1.38834120e+00,  1.13560841e+00,\n",
-       "         5.63575563e-01,  1.55970990e+00,  3.38699017e+00,\n",
-       "         2.29059238e+00, -1.17135288e-01, -8.58819289e-01,\n",
-       "        -2.37618177e-01,  1.31337508e+00, -1.10876498e+00,\n",
-       "        -3.10564141e-01,  3.91527588e-01,  1.08928545e+00,\n",
-       "         1.04891633e-01,  4.54714625e-01,  1.32068726e+00,\n",
-       "         1.35682461e+00,  1.53166670e+00, -4.02366798e-01,\n",
-       "         4.69063970e-01,  1.36313677e-01,  4.67336814e+00,\n",
-       "        -1.80956270e-01,  3.98740301e-01,  3.11976152e-01,\n",
-       "        -1.75007997e-01, -1.14408984e+00,  1.11914575e+00,\n",
-       "         2.79545098e+02,  1.01515658e+04,  2.66506964e+02,\n",
-       "         1.00013499e+01,  3.27597377e+01,  5.49354397e+01,\n",
-       "        -8.87515677e-01,  6.77010541e-01,  7.20986988e-01,\n",
-       "         3.69503347e-01, -1.47803455e-01,  3.10412059e-01,\n",
-       "         4.01450723e-01,  1.31979427e+00,  1.67512242e+00,\n",
-       "         4.05438676e-01, -3.73017420e-01, -5.96949277e-01,\n",
-       "        -1.74461027e-01, -8.02832118e-01,  2.73475371e+00,\n",
-       "        -6.40042713e-01, -2.75914443e-01,  1.88242095e-01,\n",
-       "         5.45203689e-01,  3.56418639e-01,  3.50460312e-01,\n",
-       "         1.20237393e+00,  2.04058152e+02,  1.67389029e-01,\n",
-       "        -3.17397460e-01,  1.01532973e+00,  8.98980673e-01,\n",
-       "         1.22501442e+01,  4.10980200e-01, -3.39884627e-01,\n",
-       "         1.57941000e-01,  4.51856366e-01, -3.61596888e-01,\n",
-       "        -8.31244912e-02,  4.66086854e-01,  2.92338355e-01,\n",
-       "         1.76261245e-01, -2.38452316e-01, -6.80804449e-01,\n",
-       "        -8.87409920e-01, -5.97562096e-01,  6.12440462e+01,\n",
-       "        -1.30966029e-01, -4.65535423e-01,  3.68842011e-01,\n",
-       "         1.33014249e-01,  1.53263864e+00, -2.47650894e-01,\n",
-       "         1.11743041e+00, -5.55410758e-01, -1.41625563e+00,\n",
-       "         1.60658328e+00, -7.74709717e-01,  6.91096694e-01,\n",
-       "        -5.18126775e-01,  8.24534180e-01, -7.13463873e-01,\n",
-       "         1.42210136e-01, -1.33389698e-01,  5.00115697e-01,\n",
-       "         6.88623101e-01,  4.47660489e+02,  3.93017586e-01,\n",
-       "         4.44351612e-01, -7.23064770e-01, -7.48860820e-01,\n",
-       "        -5.28616097e-01, -3.47947217e-01,  2.86059579e-01,\n",
-       "        -7.35920574e-01, -2.62511613e-01, -3.79130139e-01,\n",
-       "        -4.83382033e-01,  3.36714347e-01, -5.58944771e-01,\n",
-       "         2.58370830e-01, -1.85254417e-01, -1.27738419e-01,\n",
-       "        -6.12756376e-01, -4.09926570e-01, -7.15677408e-01,\n",
-       "         1.30080272e+00, -1.27241783e-01, -2.22738112e-01,\n",
-       "        -1.21805276e-01,  5.57399223e+01, -3.69704913e-01,\n",
-       "        -4.60629613e-01,  2.43762096e-01, -2.71232940e-01,\n",
-       "         2.59882033e+00, -9.06621914e-01, -6.07032057e-01,\n",
-       "        -8.02620523e-01,  3.82316402e+00, -6.94845166e-01,\n",
-       "         9.99234956e+01,  1.15843083e+01, -6.25235618e-01,\n",
-       "         1.02557335e+02,  1.15435497e+02,  1.27422620e+02,\n",
-       "         3.58198692e+00,  2.27971086e+01,  7.74205152e+01,\n",
-       "         5.29831690e+01,  2.64241648e+00,  4.10786692e+00,\n",
-       "        -4.93728024e-01,  3.56610810e-01,  5.98900130e+02,\n",
-       "        -3.41468178e-01, -7.50520524e-01,  5.65502713e-01,\n",
-       "        -6.57526937e-01, -4.69609447e-01, -2.71518847e-01,\n",
-       "        -4.77539114e-01, -3.73230146e-01,  4.90937311e-01,\n",
-       "        -3.92455367e-01, -3.58845789e-01,  3.28811736e-01,\n",
-       "         6.46266604e+00,  5.06064192e-01,  8.58688369e+00,\n",
-       "         2.62959697e-01, -1.73167468e+00,  7.38169084e-01,\n",
-       "         3.38464555e+00,  3.25505675e+00,  4.37195582e-01,\n",
-       "        -5.89159727e-01,  6.39559626e-01,  1.94771729e-01,\n",
-       "         4.61234276e-01, -5.01068291e-02, -6.98315390e-02,\n",
-       "        -1.23489307e-01, -4.39980004e-01,  3.40511094e-01,\n",
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-       "         3.16643551e+02,  1.97865968e-01,  1.53239593e+01,\n",
-       "         1.77870619e+01,  3.78469572e+00,  5.41394471e+01,\n",
-       "         2.76452729e+03, -4.59741207e-01,  1.32078982e+02,\n",
-       "         1.70815256e+00,  1.38031858e+00,  1.71254657e+00,\n",
-       "        -4.21538863e-01, -1.91741670e-01,  1.02697514e+00,\n",
-       "        -1.68999025e-01,  1.57547966e+00,  1.88578580e+00,\n",
-       "        -2.48180693e+00, -2.91464796e-01,  3.23999586e-01,\n",
-       "         1.18080980e-01,  4.35071231e-01,  1.07353454e+00,\n",
-       "         5.28201617e-01, -4.88613894e-01,  2.43113509e+00,\n",
-       "        -7.72796175e-01,  2.09370528e+00, -2.38570618e-01,\n",
-       "         7.94935939e-01,  9.22192467e-01,  3.12997817e-01,\n",
-       "         1.75475865e-01,  4.70131740e-01,  9.90883156e-01,\n",
-       "         7.30905286e-01, -2.45828736e-01, -2.50881497e-01,\n",
-       "        -2.24189602e-01, -2.36730697e-01, -4.28660508e-01,\n",
-       "        -2.51498398e+00,  3.74736126e-01, -8.35740930e-02,\n",
-       "         7.18285487e-02, -3.49057241e-01,  8.10660389e-01,\n",
-       "        -4.94389974e-01, -5.90676171e-01, -1.67156361e+00,\n",
-       "         1.57256339e-01,  2.47128017e+00,  5.00816290e-01,\n",
-       "         6.32187291e-01,  1.22673622e+00, -1.57700483e-01,\n",
-       "         7.50737057e-01,  3.41594158e+00,  1.63572860e-01,\n",
-       "         1.74936789e+00, -3.23055635e-01, -3.38663943e-01,\n",
-       "        -2.70496997e-01,  1.66150120e-01, -9.06414679e-02,\n",
-       "        -2.84482195e-01, -2.47790675e-01, -1.56296934e-01,\n",
-       "         4.90751560e-02,  1.02569235e-01, -1.45240799e-01,\n",
-       "         6.44771848e-01, -5.95465332e-01, -3.45859829e-01,\n",
-       "        -2.04345082e-01, -1.21475215e-01,  2.97449225e+00,\n",
-       "        -7.20610508e-01, -2.29381889e-01,  2.23588295e-01,\n",
-       "         5.78109238e-01,  1.00836572e+00,  4.70697541e-01,\n",
-       "        -5.58601458e-01, -5.97103590e-01,  1.88859762e+00,\n",
-       "        -3.18466730e-01,  2.92485619e-01,  2.18456799e+00,\n",
-       "         1.86875136e+01,  4.03541467e+00, -5.28061373e-01,\n",
-       "        -4.00775092e-01,  5.04518205e-01]])
            • energy
              (chain, draw)
              float64
              59.28 61.6 68.81 ... 57.61 59.47
              array([[59.27755265, 61.60428906, 68.81393494, 67.96547627, 69.59668787,\n",
-       "        69.61653533, 68.84987389, 71.14658525, 68.9118984 , 67.54619323,\n",
-       "        68.12351623, 67.69661007, 69.52164951, 65.37835184, 67.9433699 ,\n",
-       "        67.4053013 , 66.60483517, 68.1719148 , 63.22713128, 72.14156854,\n",
-       "        73.40809155, 68.81619856, 67.86756678, 67.75927911, 69.54985144,\n",
-       "        66.6656891 , 61.01788234, 57.44602307, 58.28895004, 59.20846697,\n",
-       "        62.31573332, 64.27864243, 61.42901099, 61.53281116, 62.1794804 ,\n",
-       "        56.75531736, 54.59292309, 52.33175456, 51.69961802, 52.96110897,\n",
-       "        51.62635206, 52.10651111, 54.9149328 , 56.44193506, 62.67477221,\n",
-       "        56.71613589, 56.60921523, 51.25789804, 55.11287632, 54.2826413 ,\n",
-       "        57.82655158, 53.0590694 , 57.99436176, 52.18972895, 53.72280126,\n",
-       "        52.19210185, 56.04235291, 53.80421171, 49.65308361, 51.16558334,\n",
-       "        52.24735255, 50.32328633, 51.98664813, 51.34752679, 49.19003318,\n",
-       "        51.7556987 , 50.27472774, 52.62781551, 49.64031548, 49.84132598,\n",
-       "        53.02334304, 53.08867659, 48.93239839, 49.61539116, 51.70158476,\n",
-       "        54.70439655, 61.08962201, 63.66750548, 63.83570059, 65.80721437,\n",
-       "        64.97316561, 66.26285188, 61.50403086, 61.7331435 , 61.57135352,\n",
-       "        63.23560033, 56.46823783, 56.37614552, 62.31947162, 58.57641107,\n",
-       "        59.90447796, 54.22652469, 53.33914291, 56.87998449, 63.95543601,\n",
-       "        59.91269804, 60.84098431, 58.35956285, 57.82317964, 64.59777245,\n",
-       "        59.47045596, 59.69334986, 59.12136489, 61.35630456, 61.9002042 ,\n",
-       "        60.62341356, 64.29347034, 63.61830848, 63.33143854, 63.10446381,\n",
-       "        61.67760593, 57.99190217, 57.40830119, 60.01185956, 60.65420298,\n",
-       "        62.94050369, 61.11796869, 61.20628663, 61.22106669, 62.05332502,\n",
-       "        64.52692016, 61.04632283, 62.18952185, 63.16760736, 60.94717331,\n",
-       "        63.08727099, 63.44908437, 59.15404706, 60.7938029 , 56.97332377,\n",
-       "        56.27190342, 55.08538288, 54.71625966, 54.86290568, 59.99178182,\n",
-       "        54.99784358, 59.17889203, 59.07729872, 60.37583061, 63.43966426,\n",
-       "        62.28916106, 62.35961663, 61.19577467, 62.94122153, 62.78560743,\n",
-       "        65.15837961, 59.36464195, 57.15256222, 59.41182107, 63.10305956,\n",
-       "        63.46197305, 60.88100304, 64.34714619, 65.21537387, 62.38379215,\n",
-       "        60.0109194 , 59.18023043, 65.34988246, 63.65299843, 63.00498817,\n",
-       "        61.71123113, 57.07145173, 56.80917763, 61.69750841, 63.62885294,\n",
-       "        65.49122903, 68.90964537, 69.96742291, 67.47442022, 68.53782234,\n",
-       "        66.95091402, 69.75965528, 68.50414714, 66.11694241, 72.08661841,\n",
-       "        68.87603584, 60.10886407, 61.26829091, 65.93425295, 68.50729944,\n",
-       "        68.94186004, 67.08396743, 67.92159778, 70.29989741, 64.63663539,\n",
-       "        59.22791433, 58.51647374, 59.84942876, 62.57697579, 62.46261665,\n",
-       "        63.99398735, 65.44636577, 64.088037  , 60.710703  , 57.7533779 ,\n",
-       "        60.9405356 , 58.54757413, 56.71333648, 59.63869062, 63.16815298,\n",
-       "        63.46618175, 62.80320643, 64.23522719, 65.9361868 , 65.24991724,\n",
-       "        61.31294058, 62.81466502, 66.02117514, 69.78553939, 66.36733953,\n",
-       "        69.93221915, 70.19058261, 70.30707794, 68.4101062 , 68.17957957,\n",
-       "        67.94484698, 71.09155565, 66.18099928, 65.98422753, 62.00834378,\n",
-       "        64.29715403, 64.39333177, 63.59235363, 63.25910214, 61.26322901,\n",
-       "        61.01054167, 64.49121758, 59.3419948 , 59.74628057, 56.85070756,\n",
-       "        55.20724922, 58.88473353, 65.5434591 , 57.19626735, 53.07252511,\n",
-       "        52.47873605, 52.65513881, 57.33011462, 55.18593142, 54.65490101,\n",
-       "        55.81445227, 58.36093544, 57.62244311, 55.47450836, 53.03075914,\n",
-       "        56.04367289, 58.17261449, 55.50750768, 58.43502576, 60.63416138,\n",
-       "        67.12792959, 69.47503095, 70.17231475, 69.31869398, 66.5613466 ,\n",
-       "        69.73139871, 72.4269507 , 74.72194806, 67.51168047, 60.79181098,\n",
-       "        59.8630236 , 66.7771781 , 65.71695545, 57.31487694, 60.75159053,\n",
-       "        62.55863237, 64.60947425, 59.83196929, 65.7423465 , 70.30576559,\n",
-       "        70.50554567, 70.28433679, 69.95349959, 70.05210515, 67.36893744,\n",
-       "        66.7115498 , 75.29620702, 66.64632336, 64.80205821, 60.69672762,\n",
-       "        62.68326124, 63.93442167, 65.57205526, 66.05085711, 64.72602862,\n",
-       "        68.68273833, 68.09502013, 68.25228842, 64.0973677 , 62.02130466,\n",
-       "        59.52066701, 58.14349493, 59.01298376, 58.38311497, 58.10441094,\n",
-       "        58.10415662, 59.0882375 , 57.59350472, 55.43278986, 58.89155992,\n",
-       "        61.50807435, 57.59358382, 59.13613471, 55.63173111, 60.21159347,\n",
-       "        62.1464299 , 56.79492463, 59.29978101, 57.68226313, 62.4680459 ,\n",
-       "        64.25341181, 64.96708658, 60.46989868, 57.04864163, 54.57194284,\n",
-       "        55.0445196 , 51.28616385, 53.1932304 , 53.50378412, 56.49444659,\n",
-       "        56.49595139, 55.67280487, 55.90007246, 56.83736279, 53.97027766,\n",
-       "        52.24062141, 55.10697214, 53.74775853, 55.42501658, 51.79313598,\n",
-       "        54.13238245, 51.87752898, 51.49787351, 62.20918697, 55.69644405,\n",
-       "        62.12014024, 65.61315544, 58.91753716, 59.31358213, 61.21104013,\n",
-       "        63.20268388, 65.49041221, 64.11349164, 62.10107222, 63.1251592 ,\n",
-       "        67.35942789, 65.87491401, 63.82916493, 68.34568608, 67.06671325,\n",
-       "        62.30427878, 71.50415613, 64.57224241, 64.70464562, 63.14032799,\n",
-       "        66.26422778, 66.02553061, 64.3906122 , 62.24124086, 61.25424163,\n",
-       "        60.34501254, 60.08877255, 59.50967893, 63.46193262, 61.92310931,\n",
-       "        66.05030566, 64.69519516, 66.17888859, 62.1764268 , 63.30257367,\n",
-       "        60.15777565, 56.54094029, 57.06309142, 59.0150358 , 59.26704312,\n",
-       "        59.047265  , 61.43390075, 65.71886842, 65.84249992, 59.95650416,\n",
-       "        57.13258385, 56.40297066, 59.07542049, 66.4236414 , 70.84366923,\n",
-       "        68.21272343, 69.20878752, 67.70515794, 67.31334598, 64.58266075,\n",
-       "        64.04787788, 67.84525113, 67.35247877, 70.96507757, 68.7466543 ,\n",
-       "        73.00768621, 65.76211889, 65.31616126, 60.52836871, 63.75715507,\n",
-       "        57.0549859 , 56.62126907, 57.92130145, 53.01766113, 53.41388227,\n",
-       "        56.5889997 , 57.22262721, 57.80903837, 54.45982047, 51.39107904,\n",
-       "        54.07622405, 59.60959177, 61.31637639, 61.5064331 , 61.14339599,\n",
-       "        62.04417511, 63.50497502, 61.73271807, 62.01084165, 60.52209599,\n",
-       "        57.66322325, 55.64667379, 60.70470007, 56.96637842, 60.15046628,\n",
-       "        60.59961648, 69.13434704, 65.1182145 , 72.58971409, 68.85419848,\n",
-       "        69.70439875, 69.00152325, 63.90852289, 62.04366856, 60.65237661,\n",
-       "        56.68609365, 57.79038523, 59.27191781, 60.10503995, 60.5958649 ,\n",
-       "        60.62887754, 65.7243344 , 65.51633466, 68.00308785, 65.22321952,\n",
-       "        65.19586295, 62.11984873, 62.04020232, 64.29770614, 66.2066779 ,\n",
-       "        62.13127348, 64.92537346, 63.56421047, 62.45530006, 59.27782617,\n",
-       "        59.1819017 , 59.52616561, 62.29392213, 65.21386383, 71.7680218 ,\n",
-       "        73.18364797, 71.19181391, 65.37180265, 64.99442467, 64.23501693,\n",
-       "        68.28962528, 71.31481947, 72.1457408 , 74.32054488, 68.34760682,\n",
-       "        60.67457972, 61.95386699, 61.72224185, 63.08031637, 66.41047122,\n",
-       "        64.90822286, 62.18398768, 62.3059217 , 59.56133188, 56.73635953,\n",
-       "        64.54227787, 66.73055321, 68.20896576, 68.60994984, 62.45795515,\n",
-       "        54.76102261, 58.08857048, 54.03689141, 51.4455256 , 51.40543957,\n",
-       "        53.65514285, 59.13266515, 53.48209641, 53.76278727, 55.59589605,\n",
-       "        54.0276669 , 57.13607244, 57.07477496, 55.26017509, 54.57012118],\n",
-       "       [61.75972154, 59.66353545, 57.49981308, 62.35040496, 63.55054989,\n",
-       "        69.11928204, 70.85077282, 72.50395326, 68.1929167 , 65.99828308,\n",
-       "        66.54494732, 66.24887287, 60.45191822, 56.57705918, 56.5225119 ,\n",
-       "        58.75401056, 60.3864738 , 62.41669047, 60.44600446, 68.42613923,\n",
-       "        65.549628  , 65.41783826, 75.25604901, 70.91689766, 67.79104609,\n",
-       "        68.91432359, 67.01430298, 64.80070336, 63.36892049, 59.9243634 ,\n",
-       "        56.42637586, 56.39503211, 59.8040183 , 64.78440332, 63.2828307 ,\n",
-       "        61.51319333, 60.90721445, 70.29239434, 73.15593339, 72.74057052,\n",
-       "        69.79931711, 62.189592  , 61.57897598, 64.07317629, 64.14695226,\n",
-       "        65.42875135, 62.09765438, 61.00507083, 57.43361771, 62.45181912,\n",
-       "        61.07292959, 60.42219447, 59.64920872, 59.20474811, 54.52694328,\n",
-       "        55.5857527 , 54.28443337, 52.89467201, 53.94411753, 54.94217496,\n",
-       "        55.96819768, 58.61153463, 59.09165145, 64.75465816, 65.83370265,\n",
-       "        62.38642841, 63.09665799, 61.55663655, 59.03354255, 54.26831736,\n",
-       "        55.38134108, 59.83650615, 61.5386833 , 61.19323905, 67.29275122,\n",
-       "        73.40992671, 70.32428112, 68.1763955 , 68.3775828 , 59.08638128,\n",
-       "        68.13070797, 67.40920968, 61.86471024, 63.08910701, 61.2378988 ,\n",
-       "        65.47597137, 66.73154319, 66.25656569, 68.27007843, 68.21766013,\n",
-       "        65.70307091, 64.58937908, 67.24776665, 62.28277731, 62.96796616,\n",
-       "        72.05730687, 74.60323926, 76.03706896, 70.70071148, 68.52945376,\n",
-       "        61.47129512, 59.55580056, 61.34272434, 63.05010404, 64.94494333,\n",
-       "        64.75834256, 64.01381073, 63.27804542, 61.91558562, 64.98016928,\n",
-       "        68.37911747, 73.34095762, 77.76833281, 73.83605416, 71.00202439,\n",
-       "        72.10503174, 76.96140398, 75.10955745, 73.8252217 , 69.22841299,\n",
-       "        72.83492001, 73.11491653, 73.30319689, 68.51454432, 60.66782089,\n",
-       "        64.38205431, 67.50928313, 68.94763517, 73.60695031, 75.05474481,\n",
-       "        75.10046857, 68.35719733, 69.44133582, 64.64980347, 57.85048044,\n",
-       "        59.60218515, 56.95958089, 53.43278465, 54.91049774, 55.06193243,\n",
-       "        55.97969888, 54.32400804, 53.84511977, 58.24069579, 60.52930384,\n",
-       "        62.4078568 , 64.60064804, 56.7617469 , 56.29077606, 59.13946369,\n",
-       "        59.09730894, 59.300532  , 58.48935952, 59.47170595, 57.65668227,\n",
-       "        61.34796128, 60.0307865 , 63.16801209, 61.93288061, 62.0875022 ,\n",
-       "        63.61879072, 62.52159621, 58.08557717, 59.04493446, 60.51568822,\n",
-       "        60.02911748, 58.73009371, 58.19537049, 60.29449924, 58.33126949,\n",
-       "        55.9430416 , 55.65526377, 52.34981818, 55.09881132, 59.62897362,\n",
-       "        58.80088211, 54.16249913, 52.73036741, 54.00698062, 50.65531644,\n",
-       "        51.58255433, 52.22327119, 51.42436734, 53.50446129, 53.16742909,\n",
-       "        51.67768845, 53.0184032 , 55.16980732, 52.21763375, 53.40502475,\n",
-       "        55.76298006, 52.48357358, 53.36478014, 53.64830473, 51.61276366,\n",
-       "        51.88523906, 53.5570293 , 54.21260759, 53.53278073, 58.82813742,\n",
-       "        55.27356907, 52.80697999, 54.13721657, 52.69731068, 52.40214519,\n",
-       "        60.98136394, 52.74349271, 52.95223095, 52.09598228, 52.82814899,\n",
-       "        63.26585278, 52.04410148, 55.04496797, 58.14121934, 54.29574749,\n",
-       "        51.95370723, 55.50594377, 53.68418579, 53.31710545, 55.00316904,\n",
-       "        50.82752248, 55.03361665, 55.06940964, 53.02313131, 54.46348895,\n",
-       "        54.71509609, 51.51066799, 51.93468713, 57.31699402, 54.7027475 ,\n",
-       "        57.14287077, 52.18485417, 62.27061615, 50.10384409, 59.11805049,\n",
-       "        50.26607794, 57.46023568, 50.71739829, 53.30897685, 51.7087591 ,\n",
-       "        54.54348347, 52.9541625 , 51.62980664, 56.78231024, 53.16814843,\n",
-       "        55.23629625, 56.38042243, 54.6856571 , 52.27679106, 52.61414751,\n",
-       "        52.08614295, 55.08488847, 51.21766208, 51.21141158, 50.22572936,\n",
-       "        51.73950858, 53.26195444, 52.04240294, 55.78156868, 55.01809158,\n",
-       "        53.92531117, 53.45936453, 57.47500076, 57.89308419, 54.33544648,\n",
-       "        55.73111145, 56.16602124, 55.94494158, 53.9673364 , 57.53497424,\n",
-       "        54.51298877, 52.84873038, 56.59928663, 50.3611805 , 57.194914  ,\n",
-       "        60.03166681, 61.69876314, 68.59326673, 68.10521522, 65.522795  ,\n",
-       "        59.19671135, 60.50091588, 60.47069313, 62.2985155 , 62.00692781,\n",
-       "        64.54936371, 59.98009005, 60.57109195, 62.50629335, 58.4786275 ,\n",
-       "        59.91693124, 61.76023422, 62.05876171, 60.74158048, 55.4236545 ,\n",
-       "        55.23689984, 55.21627656, 54.54812164, 53.25618186, 53.95277046,\n",
-       "        56.29759349, 59.3891934 , 62.02151807, 62.60243162, 57.79576453,\n",
-       "        55.51405755, 61.50730979, 64.49195859, 62.41013659, 64.52407955,\n",
-       "        69.40412721, 69.74452062, 69.15707073, 64.37088277, 60.01229866,\n",
-       "        58.15784975, 58.22465477, 64.190562  , 57.38118093, 56.65310474,\n",
-       "        59.09530909, 59.57796805, 60.18179536, 60.24641329, 58.8283106 ,\n",
-       "        57.98520068, 58.46284851, 59.39062361, 60.03559243, 59.80669786,\n",
-       "        60.72497739, 61.12160722, 58.92817736, 55.82231153, 55.54665777,\n",
-       "        51.47888795, 55.60545311, 59.60610091, 59.97179279, 61.64092428,\n",
-       "        56.48271182, 57.44077119, 60.88777288, 61.13258615, 60.34184969,\n",
-       "        59.32917979, 55.19047978, 57.14427728, 60.72565578, 60.78316336,\n",
-       "        61.24280172, 60.52313367, 65.17474454, 60.3592334 , 57.32547148,\n",
-       "        62.3444173 , 64.65492383, 58.16095379, 56.47969437, 53.88697572,\n",
-       "        52.54146409, 55.09767929, 58.61888157, 56.15177373, 63.98283004,\n",
-       "        66.18647794, 61.40224296, 59.53731227, 55.86021195, 54.32572967,\n",
-       "        56.38028718, 54.82063951, 57.01532838, 59.12713585, 56.56992883,\n",
-       "        55.22339868, 54.95625705, 59.4972452 , 61.59469424, 60.93193206,\n",
-       "        60.16980007, 60.49769702, 60.50573728, 60.95709972, 59.86467672,\n",
-       "        54.47967712, 51.90946438, 56.18029482, 54.25592915, 56.50206978,\n",
-       "        62.70349816, 62.61076813, 64.32250184, 63.14104117, 71.08705887,\n",
-       "        68.66277705, 62.45864579, 64.53646597, 61.60529587, 52.08311886,\n",
-       "        57.62972994, 58.89277718, 56.09624969, 59.12332317, 57.13480274,\n",
-       "        53.82489475, 54.49087954, 56.78443359, 54.36270641, 50.77614435,\n",
-       "        51.20168729, 52.30758685, 56.01648629, 59.97410184, 59.58585269,\n",
-       "        60.65961771, 61.68360202, 64.0616123 , 61.83006365, 66.25739524,\n",
-       "        65.4842293 , 61.04647162, 66.83446153, 66.49770836, 61.71830044,\n",
-       "        67.18516212, 69.93979927, 77.53042036, 74.32554481, 69.47430167,\n",
-       "        69.6811975 , 72.26148529, 73.47568165, 68.23260503, 71.42457804,\n",
-       "        73.86350532, 74.34256122, 69.17489515, 70.92631156, 72.79820804,\n",
-       "        71.27271527, 72.20063612, 66.55722904, 64.41740619, 67.19631915,\n",
-       "        66.71337973, 69.84735781, 66.81219833, 69.19790391, 67.39046936,\n",
-       "        67.14191653, 64.52037675, 63.3771655 , 65.57663768, 63.22017156,\n",
-       "        61.09306924, 58.0141334 , 63.63313624, 62.79813765, 66.37465262,\n",
-       "        63.30535865, 67.66966419, 67.85799631, 72.38688022, 72.18259748,\n",
-       "        65.66471688, 64.22121474, 67.66077736, 65.53695715, 64.94865148,\n",
-       "        65.39932177, 68.13458477, 71.79926772, 69.99927421, 68.8460682 ,\n",
-       "        70.14407313, 69.43893793, 73.42124096, 73.03261908, 73.27447781,\n",
-       "        72.5476956 , 70.30477282, 71.5530811 , 77.97534693, 75.82370071,\n",
-       "        71.17147136, 72.46836871, 61.17692805, 62.94293203, 61.59532094,\n",
-       "        59.86086201, 61.31595507, 63.51426421, 59.80557813, 56.53464162,\n",
-       "        57.97918521, 57.37275784, 58.65622268, 57.60969511, 59.47142816]])
          • created_at :
            2020-06-06T18:07:53.678348
            arviz_version :
            0.8.3
            inference_library :
            pymc3
            inference_library_version :
            3.8

      \n", + " 0.00000000e+00, 8.05219901e-01, 0.00000000e+00,\n", + " 0.00000000e+00, 3.32375790e+00, -1.50841567e+00,\n", + " 1.86938128e+00, 8.16895081e-01, 5.29559878e-01,\n", + " 5.19292210e-01, -8.13311185e-02, 8.02468886e-02,\n", + " -5.63908587e-02, 2.14739622e-01, -7.06010109e-01,\n", + " 4.43969901e-01, -2.34210134e-01, -1.05802315e-01,\n", + " 1.15661181e-01, 5.81235322e-02, -1.62405644e-01,\n", + " 2.37889803e-02, 8.25684300e-02, -2.51293792e-01,\n", + " -9.89725572e-02, -1.73632877e-01, -2.76698598e-01,\n", + " 6.67768861e-01, -1.09710542e+00, -7.47905224e-01,\n", + " 8.80749701e-02, 3.84732468e-01, -5.99409722e-01,\n", + " -6.93561382e-02, -1.91380168e-02, 1.39509626e+00,\n", + " -3.67732537e-01, 2.52806021e-03, -8.91880410e-01,\n", + " -1.36342821e+00, 3.11100278e-01, -2.42921254e-02,\n", + " -3.59466669e-01, 2.73348266e-01, -1.24154421e-01,\n", + " 8.08635775e-02, -1.27439015e-01, 7.97016597e-01,\n", + " 2.87387080e-02, -1.55893303e-01, -5.17823776e-02,\n", + " -1.23034475e-01, -7.26487279e-02, 8.76958332e-02,\n", + " -1.09581125e-02, -3.07102422e-02, 6.18450437e-02,\n", + " 1.65230466e-01, 1.97165881e-02, -3.73063757e-02,\n", + " -3.70917408e-02, 1.44712320e-02]])
  • created_at :
    2020-06-10T17:34:00.071123
    arviz_version :
    0.8.3
    inference_library :
    pymc3
    inference_library_version :
    3.8

    • \n", " \n", " \n", " \n", " \n", "
  • \n", - " \n", - " \n", + " \n", + " \n", "
    \n", "
    \n", "
      \n", @@ -4605,483 +4697,416 @@ " stroke: currentColor;\n", " fill: currentColor;\n", "}\n", - "
      xarray.Dataset
        • chain: 1
        • draw: 500
        • school: 8
        • chain
          (chain)
          int64
          0
          array([0])
        • draw
          (draw)
          int64
          0 1 2 3 4 5 ... 495 496 497 498 499
          array([  0,   1,   2, ..., 497, 498, 499])
        • school
          (school)
          <U16
          'Choate' ... 'Mt. Hermon'
          array(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
-       "       'Hotchkiss', 'Lawrenceville', "St. Paul's", 'Mt. Hermon'], dtype='<U16')
        • tau_log__
          (chain, draw)
          float64
          0.382 1.72 2.366 ... 0.4214 4.288
          array([[ 3.81956740e-01,  1.72017157e+00,  2.36613252e+00,\n",
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-       "         1.18084770e+00,  3.13646643e+00,  9.92346634e-01,\n",
-       "         8.22246310e-01,  1.97386172e+00,  1.48168888e+00,\n",
-       "         2.76256229e+00, -1.31707112e+00,  1.38440112e+00,\n",
-       "         3.24838342e+00,  1.33169054e+00, -8.13532972e-01,\n",
-       "         1.76410414e+00, -1.43997240e+00, -2.92450068e-01,\n",
-       "         4.76019298e-01,  5.43690622e-01,  1.04686822e+00,\n",
-       "         6.53237599e-01,  1.85537232e+00,  3.25496311e+00,\n",
-       "         2.61678347e+00,  1.15210603e+00,  1.87065717e+00,\n",
-       "         9.60005833e-01,  2.17615347e+00, -1.50339752e+00,\n",
-       "         3.25910670e+00,  1.90464856e+00,  2.02648150e-01,\n",
-       "         3.91800478e-01,  3.12336779e+00,  1.90728221e+00,\n",
-       "         4.21212143e+00, -5.72960579e-01,  3.38704757e+00,\n",
-       "         3.85323987e-01,  3.50724056e+00,  1.91253534e+00,\n",
-       "         1.03131380e+00,  1.40438187e+00,  2.74963104e+00,\n",
-       "         1.84279474e+00, -9.84820036e-01,  1.63613490e+00,\n",
-       "        -8.73614971e-02,  6.38398704e-01,  1.01392164e+00,\n",
-       "        -1.02334577e+00,  1.24569941e+00,  2.86791384e+00,\n",
-       "         5.40156998e-01,  8.84890095e-02,  3.06078995e+00,\n",
-       "         4.21358998e-01,  4.28841398e+00]])
        • tau
          (chain, draw)
          float64
          1.465 5.585 10.66 ... 1.524 72.85
          array([[1.46514870e+00, 5.58548666e+00, 1.06561002e+01, 9.14517465e+00,\n",
-       "        1.05607248e+00, 9.81723312e+00, 4.13061240e+01, 3.12229310e+00,\n",
-       "        3.53655482e+00, 2.36277643e+00, 1.05346173e+01, 6.44314321e+01,\n",
-       "        6.56455033e+00, 1.60140369e+01, 3.30503995e-01, 3.06430393e+00,\n",
-       "        4.63235861e+00, 3.09814575e-01, 9.25853061e+00, 3.12423457e+00,\n",
-       "        4.07284953e+00, 2.76621398e+00, 9.48735085e+00, 3.16336045e-01,\n",
-       "        3.94325550e+01, 5.17427284e+00, 1.38784314e+00, 2.93787665e+01,\n",
-       "        6.41338178e+00, 1.60764838e-01, 9.63067456e-01, 7.14300842e-01,\n",
-       "        1.37990049e+01, 2.46246285e+01, 3.25229586e-01, 5.13343594e+01,\n",
-       "        6.23311586e+00, 6.27807756e+00, 1.26009823e+01, 1.10736543e+00,\n",
-       "        8.07729987e-01, 1.52092586e+00, 1.38260896e+01, 2.74390236e+00,\n",
-       "        2.03537475e+01, 7.08527845e+00, 6.06459907e+00, 1.14024661e+00,\n",
-       "        2.40526188e+00, 3.50049917e+00, 1.55555241e+01, 8.19447443e+00,\n",
-       "        1.21718014e+01, 4.25543738e+01, 2.73884356e+01, 5.94783499e+00,\n",
-       "        2.42639665e+01, 4.65908394e+00, 4.41193779e-01, 5.25312237e+00,\n",
-       "        2.24474209e+01, 1.25412219e+00, 1.93480528e+00, 9.80501617e+00,\n",
-       "        6.84587359e+00, 3.80197292e-01, 9.12242448e+00, 5.23330412e+00,\n",
-       "        1.59085305e+01, 2.68454460e+00, 1.68360170e-01, 5.08309295e+01,\n",
-       "        7.30664817e+00, 5.44086053e+01, 5.11777961e+01, 1.76342426e-01,\n",
-       "        1.57334371e+01, 6.61786382e+01, 1.16196629e+01, 1.14017084e+00,\n",
-       "        1.09975200e+00, 2.34505815e+01, 1.67303149e+01, 2.34389204e+01,\n",
-       "        3.46563325e-01, 5.00793052e+00, 1.26049033e+00, 2.49874417e+00,\n",
-       "        1.85966743e+01, 5.29783503e+01, 2.91383942e+01, 1.03425173e+01,\n",
-       "        2.66718617e+02, 1.28241383e+00, 1.83128479e+00, 3.99958857e+00,\n",
-       "        2.93091968e+00, 1.04928270e+00, 6.68860851e+00, 2.00863906e+01,\n",
-       "        1.18250231e+01, 1.77875020e+00, 4.77069702e+01, 3.42274079e+00,\n",
-       "        1.89054760e+00, 3.05018461e-01, 4.47664672e+00, 2.69594284e+01,\n",
-       "        3.97288164e+00, 1.18593826e+00, 1.51538816e+01, 4.60628520e+00,\n",
-       "        1.00467460e+01, 1.13985870e+01, 5.16206040e-01, 6.07016323e+01,\n",
-       "        5.50500913e+01, 3.06042330e+01, 3.81320272e+00, 4.89369044e+02,\n",
-       "        8.29212131e+00, 2.92584797e+00, 7.15832835e+02, 5.09710001e+00,\n",
-       "        1.60116709e+00, 9.36041632e+00, 1.61711907e+01, 1.38195206e+01,\n",
-       "        1.27018203e+01, 2.66690908e+00, 1.06094587e+01, 9.94751589e-01,\n",
-       "        4.32234901e+01, 3.50452447e-02, 1.17320612e+02, 2.82293179e+01,\n",
-       "        1.47222631e+00, 3.84347094e+01, 5.10515479e-02, 2.03690423e+01,\n",
-       "        3.92079956e+00, 2.36425354e+00, 3.81073859e+01, 3.77219967e+00,\n",
-       "        2.68358038e+01, 5.55068323e+00, 4.46316988e+00, 4.12305356e+00,\n",
-       "        4.69655227e+01, 2.77873136e+00, 3.29764894e+02, 3.30328984e+00,\n",
-       "        1.08822324e+01, 5.54929107e-01, 2.84473640e-01, 9.46945855e+01,\n",
-       "        5.04061496e+00, 2.81411131e-01, 3.01299146e+00, 2.26365642e+01,\n",
-       "        1.96322316e+00, 4.17432767e+01, 1.40503645e+01, 1.77666371e+01,\n",
-       "        4.16971278e+00, 2.36149246e+01, 1.72851220e+01, 1.49085512e+00,\n",
-       "        1.04302269e+00, 1.01321829e+00, 1.67510442e+01, 5.97850743e+00,\n",
-       "        2.26832929e+00, 8.36021220e+00, 4.64109948e+00, 1.19417443e+00,\n",
-       "        1.23672510e+01, 4.18989547e+00, 7.57318453e+00, 1.67408310e+00,\n",
-       "        1.76023756e+00, 1.07235270e+00, 1.99125263e+00, 4.33202615e+00,\n",
-       "        1.24925272e+00, 6.18232225e+00, 3.20489725e+00, 1.20303376e+00,\n",
-       "        3.11474895e+00, 3.70204478e+01, 1.69653445e+01, 5.92019174e+00,\n",
-       "        1.82946792e+00, 3.05171620e+01, 2.08212781e+01, 2.58075438e+01,\n",
-       "        5.04264431e-01, 2.12811869e+01, 5.46469406e+01, 2.77323525e+00,\n",
-       "        5.66364500e+01, 7.84102608e-01, 3.62326645e-01, 9.76441043e+00,\n",
-       "        1.20695074e+00, 5.19528723e-01, 4.81839156e-01, 1.15677193e+01,\n",
-       "        3.37459168e+00, 1.50102734e+01, 9.64607461e+01, 1.18954415e+00,\n",
-       "        9.09962460e+00, 4.60146599e+00, 2.73049199e+00, 2.20466655e+00,\n",
-       "        1.63091311e+01, 3.54143365e+01, 1.38189511e+02, 1.85467600e+00,\n",
-       "        4.86574319e+00, 5.99982648e+00, 7.88207495e+00, 7.26174950e-01,\n",
-       "        2.28649867e+01, 6.69902065e+00, 5.89977385e+00, 2.12501962e+00,\n",
-       "        2.48859353e+00, 3.10799459e-01, 2.09939406e+00, 1.98880930e+00,\n",
-       "        1.40002939e+00, 1.14397911e+00, 2.95727748e+00, 7.25357561e-01,\n",
-       "        3.27911328e-01, 3.74914596e+02, 7.38078923e+00, 2.30464728e+02,\n",
-       "        1.37513454e+00, 7.92784677e+00, 6.71049057e-01, 1.93606567e+00,\n",
-       "        1.35227781e+00, 3.57554168e+00, 1.81164668e+01, 3.63358576e+00,\n",
-       "        1.49910296e+01, 1.17904773e+00, 2.21499784e+01, 2.21737994e+00,\n",
-       "        1.00232105e+00, 5.13613164e+00, 2.07824755e-02, 1.80106128e+02,\n",
-       "        1.52582335e+01, 9.21536932e-01, 3.60738921e+01, 2.61793912e+00,\n",
-       "        7.24312286e+00, 1.21645408e+01, 2.09456576e+00, 4.41031882e+00,\n",
-       "        4.54088429e+01, 1.59494332e+00, 7.27028284e+00, 4.38532659e+00,\n",
-       "        2.92602254e+01, 7.52765653e+00, 1.52721517e+01, 4.47392133e+00,\n",
-       "        1.02785874e+01, 2.38568602e+00, 3.44182776e+01, 6.66215488e+00,\n",
-       "        2.65280781e+00, 3.61270465e+00, 3.28386019e+00, 2.21126013e+00,\n",
-       "        8.77545805e+00, 2.95646801e+01, 1.17963146e+01, 7.12031794e+00,\n",
-       "        3.09902536e+00, 2.09363703e+00, 8.60004517e+00, 6.69702637e-01,\n",
-       "        4.16088594e+01, 1.52228305e+00, 4.70637233e-01, 1.37601202e+00,\n",
-       "        5.95809670e+00, 1.01387789e+01, 3.37609691e+00, 1.14669066e+00,\n",
-       "        4.20758113e-01, 7.37595287e-01, 3.18620212e+02, 4.59174492e+00,\n",
-       "        5.57584557e+00, 9.78786014e-01, 1.29900593e+00, 2.48186209e+00,\n",
-       "        1.85041978e+00, 8.59180842e+00, 2.64233890e+00, 7.97152625e+00,\n",
-       "        5.21749298e-01, 1.07276581e+01, 1.63949729e+01, 8.40845511e+00,\n",
-       "        1.85538492e+01, 1.27086980e+00, 3.09660105e+01, 1.58554652e+00,\n",
-       "        6.58921625e+00, 1.08044049e+02, 2.02086353e+00, 4.81896341e+00,\n",
-       "        6.82071063e+00, 2.72465020e+01, 7.33076017e+00, 1.01537881e+01,\n",
-       "        3.71398405e+00, 6.74380141e+00, 4.05776180e+00, 6.53199028e+00,\n",
-       "        1.08368686e+01, 1.51229119e+01, 4.75042200e+00, 9.51423509e+00,\n",
-       "        6.31783711e+00, 1.86831117e-01, 6.48584878e+00, 1.25128117e+01,\n",
-       "        1.60870899e+00, 1.27895281e+00, 2.60236551e+00, 1.01185485e+00,\n",
-       "        1.38606169e+00, 6.44431926e+00, 4.78888188e+00, 1.21942288e+01,\n",
-       "        7.97011314e+01, 4.34959911e+00, 3.43194527e+00, 2.10405477e+00,\n",
-       "        6.35088962e+00, 3.47267415e+00, 7.27938980e-01, 1.44564026e+01,\n",
-       "        5.78034898e+00, 2.00563048e+00, 1.02879381e+01, 1.54988173e+01,\n",
-       "        1.53101526e+01, 4.16274982e+00, 1.94425435e+00, 4.63871816e+00,\n",
-       "        9.15377467e+00, 6.35689658e+00, 2.25296171e-01, 4.51409559e+00,\n",
-       "        8.19757601e-01, 1.68173566e+01, 1.12057666e+01, 1.11041806e+01,\n",
-       "        2.37601741e-01, 6.45786966e+00, 4.10981675e+00, 4.77874378e-01,\n",
-       "        2.03586224e+00, 4.58581780e+00, 1.04434461e+01, 2.95783153e+00,\n",
-       "        7.85616810e+00, 1.33709671e+00, 3.30101152e+00, 3.22297021e+00,\n",
-       "        4.58669836e+01, 7.97645933e+00, 3.50647367e+00, 1.80696336e+01,\n",
-       "        2.35216464e+00, 6.07471279e-01, 6.69588316e+01, 6.41943714e+00,\n",
-       "        3.14336800e+00, 3.45826925e+00, 3.07719350e+00, 1.41918378e+00,\n",
-       "        1.01288854e+00, 2.64309623e+00, 1.35178322e+01, 3.19286983e+00,\n",
-       "        1.24788426e+01, 1.70818872e-01, 4.82269875e-01, 8.05966298e+00,\n",
-       "        2.95307914e-01, 1.38503240e+01, 2.09999425e+00, 4.77577231e+00,\n",
-       "        7.14156406e+00, 1.00107372e+01, 1.87866763e+01, 5.23966592e+00,\n",
-       "        6.42484230e+00, 3.07783296e+00, 1.82714618e+00, 2.17991416e+01,\n",
-       "        4.79311387e-01, 1.12683022e+01, 5.27654610e+00, 6.31100669e+00,\n",
-       "        9.06639480e+00, 2.04924821e+01, 1.03284445e+00, 1.94039385e+01,\n",
-       "        5.06713899e+00, 1.19820774e+00, 6.80509343e+00, 5.73378864e+00,\n",
-       "        1.03112411e+01, 5.32349183e+00, 6.40072344e-01, 6.33597891e+02,\n",
-       "        2.02262349e+01, 3.62231050e+01, 6.28250246e+00, 1.90036175e+01,\n",
-       "        2.57761327e-01, 2.89379224e-01, 9.40293769e-01, 1.77740635e+00,\n",
-       "        1.22069372e+00, 2.19460006e+01, 8.80290475e+00, 1.57015731e+01,\n",
-       "        8.58799297e+00, 6.72877509e+00, 1.60009475e+00, 1.05926585e+01,\n",
-       "        3.25713411e+00, 2.30223718e+01, 2.69755723e+00, 2.27560582e+00,\n",
-       "        7.19842117e+00, 4.40037110e+00, 1.58403786e+01, 2.67918857e-01,\n",
-       "        3.99243421e+00, 2.57486814e+01, 3.78744079e+00, 4.43289168e-01,\n",
-       "        5.83634147e+00, 2.36934297e-01, 7.46432515e-01, 1.60965408e+00,\n",
-       "        1.72235170e+00, 2.84871558e+00, 1.92175263e+00, 6.39407847e+00,\n",
-       "        2.59186585e+01, 1.36916132e+01, 3.16485116e+00, 6.49256169e+00,\n",
-       "        2.61171171e+00, 8.81234400e+00, 2.22373357e-01, 2.60262776e+01,\n",
-       "        6.71704658e+00, 1.22464150e+00, 1.47964246e+00, 2.27227764e+01,\n",
-       "        6.73476021e+00, 6.74995835e+01, 5.63853632e-01, 2.95784949e+01,\n",
-       "        1.47009053e+00, 3.33560964e+01, 6.77023190e+00, 2.80474830e+00,\n",
-       "        4.07300830e+00, 1.56368615e+01, 6.31416006e+00, 3.73506439e-01,\n",
-       "        5.13528272e+00, 9.16345779e-01, 1.89344648e+00, 2.75638940e+00,\n",
-       "        3.59390489e-01, 3.47536467e+00, 1.76002630e+01, 1.71627629e+00,\n",
-       "        1.09252225e+00, 2.13444115e+01, 1.52403130e+00, 7.28508336e+01]])
        • mu
          (chain, draw)
          float64
          6.105 -2.431 3.098 ... 1.05 -5.157
          array([[ 6.10461318e+00, -2.43095703e+00,  3.09780419e+00,\n",
-       "         1.83106258e+00, -7.18266693e+00, -9.55728135e-01,\n",
-       "        -5.40574607e+00,  1.80684159e+00,  7.58894837e-01,\n",
-       "         2.76978477e+00,  5.56357684e+00, -7.30274110e+00,\n",
-       "        -2.40679891e+00, -1.38899140e+00, -1.41510393e+00,\n",
-       "        -6.84395772e+00, -3.84489999e+00, -2.34962863e-01,\n",
-       "         6.84570680e+00,  4.21321274e+00,  8.41885056e+00,\n",
-       "         2.37475307e+00,  4.83537220e+00,  4.57327280e-01,\n",
-       "        -2.99668549e+00, -6.52887844e+00, -1.13966690e+01,\n",
-       "        -7.16159201e+00,  4.88050846e+00, -2.24253733e+00,\n",
-       "         7.78873939e+00, -7.48503135e+00, -5.96469427e+00,\n",
-       "         4.63071803e-01,  2.29108785e+00,  4.78036335e+00,\n",
-       "        -9.25869531e-01,  1.07955480e+00,  2.54365620e+00,\n",
-       "        -2.30864603e+00,  6.92057349e+00, -3.98187035e+00,\n",
-       "         6.16278112e+00, -4.50119410e+00, -2.44871038e+00,\n",
-       "        -7.64472243e+00, -3.42241931e+00,  8.26385307e+00,\n",
-       "         6.94069343e+00,  3.86206505e+00, -1.42184935e+00,\n",
-       "         7.06254445e-01,  3.16946543e+00,  2.12237926e+00,\n",
-       "         4.82057314e+00, -8.89843841e+00,  3.72708176e+00,\n",
-       "         1.28951829e+01, -3.10897458e+00,  9.60296251e+00,\n",
-       "         7.68633765e+00, -9.81777327e-01,  3.77644854e+00,\n",
-       "        -8.28438658e+00,  1.12098547e+00,  7.10526792e-01,\n",
-       "         1.19344213e+00, -4.11668624e+00,  7.91465430e+00,\n",
-       "         5.30149172e-01,  1.10193702e-02,  7.96461740e+00,\n",
-       "        -1.77562023e+00, -6.30224988e+00, -9.54534101e+00,\n",
-       "         1.04260871e+01, -1.78970689e+00, -6.92263567e+00,\n",
-       "        -5.48127379e+00,  5.33381294e+00, -1.67050899e+00,\n",
-       "         2.12241081e+00,  1.86546235e+00,  8.76413167e+00,\n",
-       "        -2.22334769e-01,  1.97025578e+00,  3.73439386e+00,\n",
-       "         5.78829227e-01,  6.94462256e-01, -2.86157410e+00,\n",
-       "        -3.25348196e+00,  2.67116897e+00, -7.27848887e+00,\n",
-       "         9.90857631e+00,  4.10291161e+00,  4.14593158e-01,\n",
-       "        -3.73111376e+00, -1.98033553e+00,  3.88696767e+00,\n",
-       "        -6.01074805e+00,  1.77249332e+01, -5.40626448e+00,\n",
-       "         2.00982940e+00,  2.07311341e+00,  6.32584325e-01,\n",
-       "        -3.22200753e+00, -6.27150736e+00,  9.74141209e-01,\n",
-       "         1.85342324e+00,  4.94313892e+00, -5.96432428e+00,\n",
-       "        -5.22061433e+00,  5.28945288e+00, -4.47466642e+00,\n",
-       "         2.70427191e+00,  8.39589608e+00, -1.29784464e+00,\n",
-       "        -2.53901747e-01, -8.33983895e-01,  1.09098225e+01,\n",
-       "        -1.70708788e+00, -8.15508803e+00,  1.15594806e+00,\n",
-       "        -6.17839028e+00, -4.41594225e+00, -1.20242529e+00,\n",
-       "         5.45853791e-01, -3.54656266e+00,  1.97459097e+00,\n",
-       "         2.60314539e+00,  3.34833850e+00,  2.42614395e+00,\n",
-       "         1.64971931e+00, -5.35206692e+00, -1.19687291e+00,\n",
-       "         8.33452601e+00, -3.99572996e-01,  6.95733714e-01,\n",
-       "        -5.48492941e+00,  1.86224989e+00, -2.78790112e+00,\n",
-       "        -2.67602455e+00, -2.57539136e+00, -3.00743896e+00,\n",
-       "         1.09423224e+01,  8.65889025e+00, -3.47254760e+00,\n",
-       "        -1.66606567e-01,  5.23655106e+00, -7.28311387e+00,\n",
-       "         1.06022463e+01, -1.29491652e+00, -3.90553638e+00,\n",
-       "        -2.18161053e+00,  6.74555039e+00, -4.87192737e+00,\n",
-       "         2.84164120e+00, -1.96705024e-01, -8.38343448e-01,\n",
-       "        -4.12381122e+00,  4.63133878e+00,  5.56794176e+00,\n",
-       "         1.59449878e-01,  6.81777277e+00,  1.66205859e+00,\n",
-       "        -2.01319885e-01,  3.12452492e+00,  8.17913333e+00,\n",
-       "         4.63514198e-01,  3.65556955e-01, -3.52332392e+00,\n",
-       "         3.98246392e+00, -4.01837606e+00, -8.84605267e-02,\n",
-       "         6.72074301e+00, -5.10885882e+00,  1.29127028e+01,\n",
-       "        -7.29365947e+00,  3.50185951e+00,  2.33832877e+00,\n",
-       "        -1.58650211e+00, -4.38140924e+00,  6.86875569e+00,\n",
-       "         4.50090702e+00,  2.67297441e+00, -8.70702981e+00,\n",
-       "        -1.29468974e+01,  7.39048348e+00, -2.10706153e+00,\n",
-       "         1.90498136e+00, -5.26587938e-01, -1.00204577e+00,\n",
-       "        -2.52325614e+00, -4.12591567e+00,  8.29509093e+00,\n",
-       "         1.40201087e+00, -2.62710953e-01, -3.23699415e+00,\n",
-       "         5.71245932e+00, -5.72711337e+00, -7.37220877e+00,\n",
-       "         3.76415653e+00,  3.13836907e+00, -5.48937615e+00,\n",
-       "        -3.50369700e+00, -8.90795239e+00, -4.92142502e+00,\n",
-       "         3.89900745e+00,  3.16882071e+00,  6.74776704e+00,\n",
-       "        -3.95844026e+00,  9.46581468e+00, -2.75711875e+00,\n",
-       "        -4.28907640e+00,  3.25591558e+00,  1.64991183e+00,\n",
-       "        -2.57412883e+00,  8.89107649e+00, -6.39302789e+00,\n",
-       "         3.80471510e+00, -8.03137967e+00, -1.12055010e+00,\n",
-       "         4.81215638e+00,  2.63677581e+00,  1.31704174e-01,\n",
-       "        -5.97214144e+00, -6.33138388e+00, -2.34017019e+00,\n",
-       "        -7.40757496e+00,  9.31633265e+00, -3.49219915e+00,\n",
-       "        -8.38836783e+00, -1.27551521e+01, -3.77378798e+00,\n",
-       "         2.44227859e+00, -3.97258035e+00, -2.79443644e+00,\n",
-       "        -3.30408373e+00,  2.32235044e+00,  3.01869121e-01,\n",
-       "         1.55957545e+00, -3.61417160e+00,  4.42869659e+00,\n",
-       "         2.62605797e+00, -1.60848231e+00, -5.15568328e+00,\n",
-       "         2.45294236e+00,  9.85891764e+00, -2.51902808e+00,\n",
-       "        -8.97948083e+00, -1.57069385e+00, -2.52030725e+00,\n",
-       "         2.33865288e+00, -6.72096205e+00, -1.15249542e+01,\n",
-       "        -1.17448875e+01, -7.29651065e+00, -5.21036443e+00,\n",
-       "        -4.22198246e+00, -4.33994727e+00,  9.92112763e-02,\n",
-       "         2.79222049e+00, -2.90944702e-01, -2.91509281e-01,\n",
-       "         9.47838122e-01, -1.36025692e+00,  5.20453360e+00,\n",
-       "        -3.75954169e+00,  3.17455956e+00,  5.66196984e+00,\n",
-       "         3.79799213e+00,  4.18275393e+00,  1.13376035e+00,\n",
-       "         1.51236149e+00,  2.26602946e-01, -2.81223280e+00,\n",
-       "        -7.73702813e-01, -6.13225321e+00, -1.23877222e+00,\n",
-       "         7.47157761e+00, -3.32435868e+00, -3.11139519e+00,\n",
-       "         2.86101290e+00,  2.25179071e+00, -4.94590335e+00,\n",
-       "         3.95076962e+00,  2.51639712e+00,  5.05233209e+00,\n",
-       "        -5.10050447e+00, -1.87173477e+00,  7.86431163e+00,\n",
-       "        -3.64053963e+00, -1.62767248e+00,  1.29182084e+00,\n",
-       "         7.06091862e+00, -1.62154732e+00, -1.58920242e+00,\n",
-       "        -9.02267863e+00, -1.54778642e+01, -3.12849418e+00,\n",
-       "        -4.16828512e+00,  2.00850404e+00, -5.91585387e+00,\n",
-       "         3.51609713e+00,  1.07281480e+01, -1.38106125e+00,\n",
-       "         1.19021413e+01, -1.21612028e+00, -4.61738392e+00,\n",
-       "         4.46621829e-01, -3.87304084e+00,  5.93047313e+00,\n",
-       "        -7.68851595e+00,  5.56535688e-01, -8.16820428e+00,\n",
-       "         9.38087296e+00, -1.95479677e+00,  5.48608897e-01,\n",
-       "        -2.25319704e+00,  4.19779107e+00, -6.96641711e+00,\n",
-       "        -6.42739918e-02, -9.93631520e+00, -4.35202011e-01,\n",
-       "        -2.42691102e+00,  3.45015397e+00,  1.09550720e+01,\n",
-       "         9.01992240e-01, -3.65297294e+00, -2.77788170e+00,\n",
-       "        -8.71586677e-01,  1.10170942e+00, -4.52034090e+00,\n",
-       "        -4.49942355e+00, -1.09076988e+00, -9.30883906e+00,\n",
-       "        -2.52038076e-01, -8.32724663e+00,  1.26759811e+00,\n",
-       "        -2.63838254e+00,  4.73354362e+00, -8.49606476e+00,\n",
-       "         2.15634858e+00, -6.91420037e+00, -4.92392985e+00,\n",
-       "         1.33582874e+01,  4.51233090e+00, -8.40229353e-01,\n",
-       "         3.10320665e+00, -3.47934374e+00,  5.13291067e+00,\n",
-       "         6.44021828e+00, -1.79008706e+00,  8.17000431e+00,\n",
-       "        -1.85262654e+00,  2.13672804e-01,  5.27828426e+00,\n",
-       "        -7.83876271e-01, -1.46835856e+00, -4.17876468e+00,\n",
-       "        -4.72005275e+00, -6.11144321e+00,  5.65519630e+00,\n",
-       "        -2.95172668e+00,  4.87096256e+00, -1.29554456e+00,\n",
-       "        -1.07085198e+00,  2.66577932e+00,  4.45230112e+00,\n",
-       "         2.47224277e+00, -3.34469304e+00, -9.12472920e-01,\n",
-       "         6.46535452e+00,  4.98125902e+00, -1.75634939e+00,\n",
-       "        -5.66494413e-02,  8.74882989e+00,  3.96983270e+00,\n",
-       "         1.19525264e+00, -7.60443249e+00, -7.43736160e+00,\n",
-       "        -2.24597488e+00, -9.72890524e+00, -5.32319480e+00,\n",
-       "         1.04548621e+01, -6.69722738e+00,  2.93238311e+00,\n",
-       "        -1.41996245e+00, -7.60820826e+00,  6.13578930e-01,\n",
-       "         3.64583269e+00, -2.40676924e+00,  9.10186694e+00,\n",
-       "        -3.59248384e+00, -7.91829301e+00, -5.16806194e+00,\n",
-       "        -9.85236563e+00,  3.35433590e+00,  4.17672477e+00,\n",
-       "        -2.63623675e+00,  8.62152797e-01, -6.35240297e+00,\n",
-       "         5.10085952e+00, -4.82514775e+00, -4.15042574e+00,\n",
-       "         4.47675857e-01,  1.23949298e+00,  6.72899486e+00,\n",
-       "        -4.67040325e+00, -1.45363751e+00, -2.16074435e-01,\n",
-       "        -2.71220988e+00, -8.21402591e-01,  4.13340022e+00,\n",
-       "         3.62680831e+00,  2.27187449e+00, -8.56780512e+00,\n",
-       "         6.17273755e+00, -3.50324768e+00, -2.06397400e+00,\n",
-       "        -3.99067379e+00, -1.04594369e+01,  6.99575781e-01,\n",
-       "         3.71457603e+00,  4.15606622e+00, -7.12088527e+00,\n",
-       "        -5.47793870e+00, -1.49061352e+00,  2.27965009e+00,\n",
-       "        -1.39605965e+01,  8.82621426e+00,  1.18897533e+00,\n",
-       "        -2.13081852e+00,  3.83038476e+00, -3.28699747e+00,\n",
-       "         3.94895191e+00, -3.65121987e+00, -3.85754648e-01,\n",
-       "        -1.70562641e+00, -7.64663697e+00,  8.46238228e-01,\n",
-       "         4.36911568e+00, -2.60483749e+00, -5.47060629e+00,\n",
-       "        -1.59165280e+00,  1.28506433e+00, -1.24080830e+00,\n",
-       "         1.31869452e+01,  4.94556732e+00, -5.80519430e+00,\n",
-       "         1.81276614e-01,  5.68727272e-01,  7.79758984e+00,\n",
-       "         4.93661068e+00, -5.52983850e+00, -1.43839294e+00,\n",
-       "        -1.86329206e-01,  4.45355601e+00,  9.26846003e+00,\n",
-       "         3.37296870e+00, -3.21616681e+00,  1.28557762e+00,\n",
-       "        -4.01190231e+00, -1.10919823e+00, -3.42190415e-01,\n",
-       "         2.95364357e+00, -1.13065902e+01, -1.93222647e+00,\n",
-       "         3.31126326e+00, -5.69073905e+00,  3.66376286e+00,\n",
-       "         2.40116460e-01, -2.18509970e+00, -8.29587183e+00,\n",
-       "         2.75375405e+00,  8.29158184e+00, -1.63312285e+00,\n",
-       "         1.61160098e+00, -8.77431444e+00, -2.72220583e+00,\n",
-       "        -4.08842089e+00, -2.70289106e+00, -3.83512351e+00,\n",
-       "         8.53151599e-01, -4.15625805e+00, -2.83367161e+00,\n",
-       "        -2.02393434e+00,  7.32914693e+00, -8.60206054e-01,\n",
-       "         2.72387993e+00, -8.80165332e+00,  1.16310157e+01,\n",
-       "         7.10573557e+00,  5.66091358e+00, -3.56531449e+00,\n",
-       "         3.34983256e+00, -2.69474758e+00, -9.75370111e-01,\n",
-       "         1.04966087e+00, -5.15684061e+00]])
        • theta
          (chain, draw, school)
          float64
          5.709 4.931 7.942 ... -148.0 57.13
          array([[[   5.70930385,    4.93074682,    7.94221514, ...,\n",
-       "            6.73393175,    5.25945377,    6.71540983],\n",
-       "        [  -6.36604437,    1.52313554,   -0.79141875, ...,\n",
-       "            1.72653154,    2.06565064,   -8.15200509],\n",
-       "        [  -3.18277146,   -6.73190541,    5.15401446, ...,\n",
-       "            4.48181387,  -11.19717704,   -4.63201673],\n",
+       "
          xarray.Dataset
            • chain: 1
            • draw: 500
            • school: 8
            • chain
              (chain)
              int64
              0
              array([0])
            • draw
              (draw)
              int64
              0 1 2 3 4 5 ... 495 496 497 498 499
              array([  0,   1,   2, ..., 497, 498, 499])
            • school
              (school)
              <U16
              'Choate' ... 'Mt. Hermon'
              array(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
+       "       'Hotchkiss', 'Lawrenceville', "St. Paul's", 'Mt. Hermon'], dtype='<U16')
            • theta
              (chain, draw, school)
              float64
              -5.382 -1.8 ... 15.41 -0.6966
              array([[[-5.38226531e+00, -1.79958337e+00, -9.71167365e-02, ...,\n",
+       "         -3.54731587e+00, -2.96291254e+00,  5.01614415e-02],\n",
+       "        [-1.74895087e+00, -3.73529936e+00, -2.89356820e+00, ...,\n",
+       "          6.21177591e-01,  1.19220705e+00, -5.91065699e+00],\n",
+       "        [ 1.36107067e+02, -6.29995712e+01, -1.29488317e+01, ...,\n",
+       "          8.71188666e+01,  1.24962205e+02,  1.05190182e+02],\n",
        "        ...,\n",
-       "        [ -19.94619218,   27.40876925,  -42.73590311, ...,\n",
-       "           35.40238934,   23.77915239,  -14.63741318],\n",
-       "        [  -0.7240705 ,    1.54887044,    2.47469385, ...,\n",
-       "            0.32691075,    1.82527134,    3.32844101],\n",
-       "        [   2.55515435,  -83.0924991 , -155.13210932, ...,\n",
-       "          -34.62676607, -148.00278481,   57.12632604]]])
          • created_at :
            2020-06-06T18:07:53.820305
            arviz_version :
            0.8.3
            inference_library :
            pymc3
            inference_library_version :
            3.8

      \n", + " [-9.20105057e+00, -8.95828682e+00, 2.13497986e+00, ...,\n", + " 8.85972725e-01, 4.63084069e+00, -1.49099388e+00],\n", + " [-1.41632386e+01, -1.47380531e+01, -1.43516071e+01, ...,\n", + " -1.35300300e+01, -1.40524229e+01, -1.41346425e+01],\n", + " [ 2.47851881e+00, 3.74327972e+01, 3.74364109e+01, ...,\n", + " -2.95931232e+01, 1.54051753e+01, -6.96646866e-01]]])
    • tau_log__
      (chain, draw)
      float64
      0.528 0.646 4.446 ... 0.3069 3.251
      array([[ 0.52802804,  0.64603241,  4.44591589,  2.03771347,  2.2004995 ,\n",
+       "         1.92665312,  1.16657263,  1.93375145, -0.61434131,  1.14662839,\n",
+       "         1.26944222,  1.08537899,  3.07748239,  1.55287501,  3.3765186 ,\n",
+       "         2.22789734,  2.43660779,  0.85368965,  1.84587953,  1.12354145,\n",
+       "         0.87632413, -0.82353771,  1.422862  , -0.27214488,  0.95058845,\n",
+       "         1.69094221,  0.64387814,  1.11963136,  1.56115371,  1.88093363,\n",
+       "        -3.68495256,  4.20646463,  1.39855138,  0.15627904,  1.93707072,\n",
+       "         3.06379112,  3.94807645,  1.82564146,  3.16197141,  1.68305607,\n",
+       "         0.86171504,  1.56482235, -1.41840816,  2.83993707,  0.44851719,\n",
+       "         3.5153974 ,  2.9869387 ,  0.66312043,  0.63840389,  2.54128679,\n",
+       "         3.10152016,  4.4014825 ,  4.66087266,  2.09588866,  0.2216517 ,\n",
+       "         3.11450009,  1.93419224,  3.18878472,  0.97568785,  1.92525151,\n",
+       "         1.33898909,  2.44830283,  0.74075717,  2.22883782,  0.9511615 ,\n",
+       "         0.54962973,  2.32959659,  3.22355548,  2.4782994 ,  1.30215297,\n",
+       "         1.51849574,  1.02643235,  3.90508783,  2.43905148,  1.31065166,\n",
+       "         0.86832013,  0.89095035,  0.84036308, -0.8297316 ,  1.73389108,\n",
+       "         1.5693849 , -0.11027874,  3.25566037,  0.87331244,  1.89402072,\n",
+       "         7.674472  ,  2.14377257,  2.52063138,  3.53039869, -0.59016266,\n",
+       "        -1.0600327 ,  0.87960494,  5.04429127,  3.19938272,  1.36062139,\n",
+       "         0.96180598, -2.12838769,  0.92437329,  0.28042094,  3.56313283,\n",
+       "         1.07773096,  1.53210145,  1.22998814,  1.10580605,  3.03064638,\n",
+       "         0.76628593,  1.80771578,  0.29673895,  3.01430466,  0.99770597,\n",
+       "         3.1323281 ,  2.49731516, -2.15107938,  2.28196402,  2.89990267,\n",
+       "         1.51188188,  1.95027935, -0.90883014,  3.29461717,  1.495148  ,\n",
+       "         0.69033899,  1.90869467, -1.01673998,  3.30479678,  2.4123436 ,\n",
+       "         0.76196697,  0.5185042 ,  1.24869888,  1.52338784,  4.26068941,\n",
+       "        -0.03473632,  2.21950376,  0.50826182, -0.89439556,  2.02416213,\n",
+       "         5.02064112,  2.77193971,  2.97659281, -0.52000919,  3.44674674,\n",
+       "         2.00261986,  2.82799101,  1.06314865,  1.80267687,  2.3456437 ,\n",
+       "        -0.31984801,  3.23621612,  2.73862399,  2.73330405,  0.52398821,\n",
+       "        -0.47543239,  2.19536564,  2.76669784,  6.90485173,  1.98476661,\n",
+       "        -0.82092589,  1.70852183,  2.07933642,  3.98550116,  2.20570672,\n",
+       "         1.85343692,  2.71408238,  3.74893398, -0.63555569,  1.94890547,\n",
+       "         4.27596559,  3.06782652,  2.16728136,  4.53995315,  3.62074141,\n",
+       "         2.73172331,  1.95764257, -0.07057574, -0.06659884,  1.22412566,\n",
+       "         0.79900457,  1.77737782,  1.54449579,  1.10369077,  2.41906546,\n",
+       "         1.41722192,  1.88967531, -0.83556836,  1.07597554, -0.37563568,\n",
+       "         2.34259707,  1.6252503 ,  3.60433022,  0.35791251, -0.1425261 ,\n",
+       "         2.14160481,  1.7165216 ,  0.88010215,  0.97934447,  1.52456834,\n",
+       "         0.53721242,  0.98818555,  1.49064049,  1.88019418, -0.07847492,\n",
+       "         0.35097773,  2.54557015,  4.88699387,  1.28286152,  0.70738085,\n",
+       "         3.4769718 ,  0.81668544,  0.6363184 ,  2.32885573,  3.29646478,\n",
+       "         0.76462255,  1.30429672,  1.07312271,  0.31803258,  4.38300279,\n",
+       "         1.47931718,  0.28835533,  1.49624369,  0.89031284,  4.19695513,\n",
+       "         0.52949698, -0.52047844,  0.13686976,  1.26311853,  1.64056743,\n",
+       "         1.66053764,  1.7382547 , -0.41750605,  2.8284805 , -0.04736669,\n",
+       "        -1.16845067,  1.12478661,  0.77820123,  2.24236   ,  0.98515536,\n",
+       "         0.95314444,  1.18091876,  0.38676273,  3.38754312,  1.85234807,\n",
+       "         0.96258862,  4.54561389,  3.56223943,  1.40141289,  2.39554979,\n",
+       "         1.27167264,  3.70972877,  2.13233021,  2.04852201,  0.93888247,\n",
+       "         2.29813154, -0.4218184 ,  4.31554429, -0.75979545,  1.41339356,\n",
+       "         3.23231714,  2.24009664,  1.17023819,  3.14013099,  3.01107118,\n",
+       "         1.21830941, -2.08073048,  2.37993757,  2.25519001,  1.24619295,\n",
+       "         1.93124087,  1.86519598,  2.71098661,  2.80225801,  2.29881517,\n",
+       "         3.81877997,  1.90476785,  2.21620119, -1.17048021,  2.0518102 ,\n",
+       "         3.32649472,  1.51980144, -0.22356815,  1.81837874,  1.68757113,\n",
+       "         0.99288112,  2.81379214,  1.56501761,  2.00666217,  2.03159021,\n",
+       "         1.43245518,  2.66322041,  0.93642038,  2.81271743,  2.34473144,\n",
+       "        -0.03455206,  3.01590837,  0.23110034,  0.38012704,  3.53961697,\n",
+       "         3.73942706,  1.16713373,  2.254044  ,  3.03439196,  2.05163818,\n",
+       "         0.28187247,  1.31883302, -0.12841473,  0.04041703,  3.10367754,\n",
+       "         1.12410732,  1.10593433,  0.1894269 ,  0.95628139,  1.50375704,\n",
+       "         3.00445703,  1.99377088,  2.87753714,  0.65867184,  1.98279826,\n",
+       "         0.27003725,  1.47657283,  1.33263893,  1.33469376,  2.80257001,\n",
+       "         1.70782907,  1.61365799,  2.52340063,  0.76098985,  3.08917828,\n",
+       "         3.07552012,  2.43713442,  0.30149493,  0.94484852,  0.08348638,\n",
+       "         1.36779746,  2.19406207, -0.134692  ,  1.30831115, -0.58514878,\n",
+       "         3.34836235,  2.76211038,  3.62256876,  0.926042  ,  2.95076046,\n",
+       "         3.04568129,  2.29751952,  3.7560286 , -0.52621514, -1.39125821,\n",
+       "         1.55624365,  0.11221508,  1.60862802,  1.73693892,  2.55578278,\n",
+       "         1.21870975,  1.22264216,  0.28720504, -2.60721455,  1.98171587,\n",
+       "         2.87771508,  1.68650907,  0.15731541,  2.83036269, -1.79132602,\n",
+       "         0.20021825,  1.91254625,  2.07783201, -1.51867876,  4.03209921,\n",
+       "         2.53331348,  0.46678346,  1.09793542,  4.72351363,  1.89102585,\n",
+       "         1.56937321,  0.99687841,  2.46763289,  0.41399182,  1.9467013 ,\n",
+       "         0.35699392,  3.16951951,  2.02572836,  2.24290485,  2.9442996 ,\n",
+       "         1.90985889,  1.00927195, -0.37086168,  2.72358631,  2.07719728,\n",
+       "        -1.19256848,  3.06111124,  1.66074204, -0.24131961,  0.66943485,\n",
+       "         4.14147793, -0.48182715,  1.07773209,  0.87589479, -0.20432774,\n",
+       "         1.84583944,  3.22984921,  1.55093341,  0.76935547,  0.84383461,\n",
+       "         2.5921779 ,  4.90013393,  2.27734914,  2.321011  ,  2.55360359,\n",
+       "         1.57008105,  1.92815241,  1.65699237,  1.87994199,  0.88749961,\n",
+       "         3.46886987,  1.55421117,  0.77227048,  0.34474624,  3.1385769 ,\n",
+       "         1.84170822,  1.45482108,  1.91309616,  0.62334794,  2.88330701,\n",
+       "         2.77021071,  1.74828659,  2.5945097 ,  2.05027469,  2.05739267,\n",
+       "         1.79843166,  2.80401809,  4.23733813,  1.59335886,  0.89887479,\n",
+       "         2.79427037,  1.58325087,  1.09113867,  0.36239403, -1.4032523 ,\n",
+       "         2.24263335, -1.31325467,  1.91793298,  4.81581366,  0.92220502,\n",
+       "         0.36412751,  2.00470092,  0.4144842 ,  2.10516808,  0.99151169,\n",
+       "         2.81961449,  1.99016591,  1.65644462,  2.05856512,  2.25347137,\n",
+       "        -1.21480834,  2.0536771 ,  1.40843489,  2.20339534,  1.07278613,\n",
+       "         2.12874324,  0.60039108,  4.36005154,  1.07917998,  0.14059304,\n",
+       "         4.33771053,  0.04721516,  2.46570619,  1.82797735,  1.4638865 ,\n",
+       "         3.67632633,  0.81256843,  0.68408822,  1.28039945,  2.17307181,\n",
+       "         1.66016273,  4.02903614,  3.4233466 ,  1.91198079,  1.70573402,\n",
+       "         2.71329587,  3.30306289,  3.01543452, -0.51149176,  1.23456667,\n",
+       "         2.23567751,  1.76779912,  0.97030476,  0.19631047,  0.04722606,\n",
+       "         1.74783445,  6.54852844, -0.24548393,  1.57168587,  4.37560667,\n",
+       "         2.53213115,  2.44066218,  0.15702267,  2.85077673,  1.95909271,\n",
+       "         0.84070148,  1.63576738,  1.37276902,  0.3069483 ,  3.25133764]])
    • tau
      (chain, draw)
      float64
      1.696 1.908 85.28 ... 1.359 25.82
      array([[1.69558538e+00, 1.90795581e+00, 8.52779474e+01, 7.67304451e+00,\n",
+       "        9.02952258e+00, 6.86649042e+00, 3.21096859e+00, 6.91540445e+00,\n",
+       "        5.40997126e-01, 3.14756264e+00, 3.55886693e+00, 2.96056162e+00,\n",
+       "        2.17036922e+01, 4.72503520e+00, 2.92686976e+01, 9.28033219e+00,\n",
+       "        1.14341877e+01, 2.34829526e+00, 6.33366799e+00, 3.07572747e+00,\n",
+       "        2.40205381e+00, 4.38876289e-01, 4.14897783e+00, 7.61743894e-01,\n",
+       "        2.58723166e+00, 5.42458938e+00, 1.90384998e+00, 3.06372458e+00,\n",
+       "        4.76431471e+00, 6.55962625e+00, 2.50983655e-02, 6.71188301e+01,\n",
+       "        4.04932979e+00, 1.16915240e+00, 6.93839665e+00, 2.14085659e+01,\n",
+       "        5.18355625e+01, 6.20677511e+00, 2.36171090e+01, 5.38197855e+00,\n",
+       "        2.36721708e+00, 4.78182535e+00, 2.42099092e-01, 1.71146884e+01,\n",
+       "        1.56598841e+00, 3.36292894e+01, 1.98248996e+01, 1.94083915e+00,\n",
+       "        1.89345630e+00, 1.26959976e+01, 2.22317214e+01, 8.15717088e+01,\n",
+       "        1.05728306e+02, 8.13266492e+00, 1.24813658e+00, 2.25221684e+01,\n",
+       "        6.91845337e+00, 2.42589280e+01, 2.65299143e+00, 6.85687305e+00,\n",
+       "        3.81518474e+00, 1.15686960e+01, 2.09752310e+00, 9.28906428e+00,\n",
+       "        2.58871471e+00, 1.73261136e+00, 1.02737962e+01, 2.51172655e+01,\n",
+       "        1.19209743e+01, 3.67720505e+00, 4.56535257e+00, 2.79109041e+00,\n",
+       "        4.96544408e+01, 1.14621635e+01, 3.70858966e+00, 2.38290452e+00,\n",
+       "        2.43744499e+00, 2.31720816e+00, 4.36166339e-01, 5.66264492e+00,\n",
+       "        4.80369252e+00, 8.95584469e-01, 2.59367368e+01, 2.39483045e+00,\n",
+       "        6.64603686e+00, 2.15268677e+03, 8.53156288e+00, 1.24364463e+01,\n",
+       "        3.41375751e+01, 5.54237127e-01, 3.46444480e-01, 2.40994744e+00,\n",
+       "        1.55134311e+02, 2.45173913e+01, 3.89861510e+00, 2.61641740e+00,\n",
+       "        1.19029051e-01, 2.52028828e+00, 1.32368689e+00, 3.52735302e+01,\n",
+       "        2.93800552e+00, 4.62789192e+00, 3.42118897e+00, 3.02165908e+00,\n",
+       "        2.07106152e+01, 2.15175960e+00, 6.09650578e+00, 1.34546402e+00,\n",
+       "        2.03749185e+01, 2.71205316e+00, 2.29272945e+01, 1.21498298e+01,\n",
+       "        1.16358495e-01, 9.79590083e+00, 1.81723766e+01, 4.53525758e+00,\n",
+       "        7.03065133e+00, 4.02995395e-01, 2.69670882e+01, 4.45999657e+00,\n",
+       "        1.99439149e+00, 6.74427954e+00, 3.61772406e-01, 2.72430047e+01,\n",
+       "        1.11600853e+01, 2.14248629e+00, 1.67951355e+00, 3.48580454e+00,\n",
+       "        4.58774145e+00, 7.08588173e+01, 9.65860065e-01, 9.20276298e+00,\n",
+       "        1.66239914e+00, 4.08854652e-01, 7.56976577e+00, 1.51508408e+02,\n",
+       "        1.59896191e+01, 1.96208506e+01, 5.94515084e-01, 3.13980798e+01,\n",
+       "        7.40843975e+00, 1.69114518e+01, 2.89547349e+00, 6.06586327e+00,\n",
+       "        1.04399908e+01, 7.26259414e-01, 2.54372879e+01, 1.54656896e+01,\n",
+       "        1.53836314e+01, 1.68874933e+00, 6.21616220e-01, 8.98328511e+00,\n",
+       "        1.59060229e+01, 9.97100663e+02, 7.27734876e+00, 4.40024050e-01,\n",
+       "        5.52079476e+00, 7.99915905e+00, 5.38122515e+01, 9.07666399e+00,\n",
+       "        6.38171530e+00, 1.50907561e+01, 4.24757780e+01, 5.29641090e-01,\n",
+       "        7.02099865e+00, 7.19495799e+01, 2.14951327e+01, 8.73450574e+00,\n",
+       "        9.36864108e+01, 3.73652607e+01, 1.53593331e+01, 7.08261060e+00,\n",
+       "        9.31857159e-01, 9.35570439e-01, 3.40119100e+00, 2.22332665e+00,\n",
+       "        5.91432763e+00, 4.68560849e+00, 3.01527419e+00, 1.12353546e+01,\n",
+       "        4.12564315e+00, 6.61721982e+00, 4.33627956e-01, 2.93285262e+00,\n",
+       "        6.86852520e-01, 1.04082324e+01, 5.07969033e+00, 3.67570567e+01,\n",
+       "        1.43034047e+00, 8.67164925e-01, 8.51308853e+00, 5.56513700e+00,\n",
+       "        2.41114600e+00, 2.66271019e+00, 4.59316047e+00, 1.71123001e+00,\n",
+       "        2.68635578e+00, 4.43993834e+00, 6.55477757e+00, 9.24525243e-01,\n",
+       "        1.42045569e+00, 1.27504957e+01, 1.32554499e+02, 3.60694632e+00,\n",
+       "        2.02867091e+00, 3.23615761e+01, 2.26298658e+00, 1.88951163e+00,\n",
+       "        1.02661876e+01, 2.70169590e+01, 2.14818339e+00, 3.68509653e+00,\n",
+       "        2.92449761e+00, 1.37442104e+00, 8.00781308e+01, 4.38994713e+00,\n",
+       "        1.33423132e+00, 4.46488604e+00, 2.43589159e+00, 6.64835883e+01,\n",
+       "        1.69807793e+00, 5.94236176e-01, 1.14667879e+00, 3.53643278e+00,\n",
+       "        5.15809552e+00, 5.26213923e+00, 5.68740855e+00, 6.58687507e-01,\n",
+       "        1.69197317e+01, 9.53737609e-01, 3.10848174e-01, 3.07955965e+00,\n",
+       "        2.17755183e+00, 9.41552572e+00, 2.67822793e+00, 2.59385305e+00,\n",
+       "        3.25736556e+00, 1.47220714e+00, 2.95931561e+01, 6.37477036e+00,\n",
+       "        2.61846592e+00, 9.42182495e+01, 3.52420308e+01, 4.06093356e+00,\n",
+       "        1.09742299e+01, 3.56681357e+00, 4.08427274e+01, 8.43449809e+00,\n",
+       "        7.75642870e+00, 2.55712216e+00, 9.95556346e+00, 6.55853129e-01,\n",
+       "        7.48543547e+01, 4.67762097e-01, 4.10987887e+00, 2.53383014e+01,\n",
+       "        9.39423913e+00, 3.22276019e+00, 2.31068935e+01, 2.03091430e+01,\n",
+       "        3.38146621e+00, 1.24838987e-01, 1.08042284e+01, 9.53710531e+00,\n",
+       "        3.47708031e+00, 6.89806454e+00, 6.45720126e+00, 1.50441108e+01,\n",
+       "        1.64818209e+01, 9.96237177e+00, 4.55486040e+01, 6.71784787e+00,\n",
+       "        9.17242029e+00, 3.10217936e-01, 7.78197527e+00, 2.78405816e+01,\n",
+       "        4.57131744e+00, 7.99660390e-01, 6.16186037e+00, 5.40633346e+00,\n",
+       "        2.69899943e+00, 1.66730249e+01, 4.78275916e+00, 7.43844762e+00,\n",
+       "        7.62620396e+00, 4.18897124e+00, 1.43424032e+01, 2.55083405e+00,\n",
+       "        1.66551159e+01, 1.04304711e+01, 9.66038050e-01, 2.04076202e+01,\n",
+       "        1.25998566e+00, 1.46247036e+00, 3.44537199e+01, 4.20738775e+01,\n",
+       "        3.21277075e+00, 9.52618197e+00, 2.07883340e+01, 7.78063679e+00,\n",
+       "        1.32560966e+00, 3.73905541e+00, 8.79488553e-01, 1.04124491e+00,\n",
+       "        2.22797355e+01, 3.07746842e+00, 3.02204674e+00, 1.20855677e+00,\n",
+       "        2.60200262e+00, 4.49855863e+00, 2.01752585e+01, 7.34317187e+00,\n",
+       "        1.77704532e+01, 1.93222432e+00, 7.26303846e+00, 1.31001325e+00,\n",
+       "        4.37791606e+00, 3.79103447e+00, 3.79883240e+00, 1.64869640e+01,\n",
+       "        5.51697150e+00, 5.02114498e+00, 1.24709336e+01, 2.14039383e+00,\n",
+       "        2.19590265e+01, 2.16611453e+01, 1.14402109e+01, 1.35187825e+00,\n",
+       "        2.57242367e+00, 1.08707041e+00, 3.92669246e+00, 8.97158239e+00,\n",
+       "        8.73985055e-01, 3.69991981e+00, 5.57022984e-01, 2.84560943e+01,\n",
+       "        1.58332219e+01, 3.74336024e+01, 2.52449742e+00, 1.91204885e+01,\n",
+       "        2.10243499e+01, 9.94947233e+00, 4.27781989e+01, 5.90836979e-01,\n",
+       "        2.48762111e-01, 4.74097896e+00, 1.11875346e+00, 4.99595218e+00,\n",
+       "        5.67993009e+00, 1.28813790e+01, 3.38282022e+00, 3.39614906e+00,\n",
+       "        1.33269744e+00, 7.37396559e-02, 7.25518124e+00, 1.77736154e+01,\n",
+       "        5.40059465e+00, 1.17036470e+00, 1.69516080e+01, 1.66738923e-01,\n",
+       "        1.22166936e+00, 6.77030577e+00, 7.98713408e+00, 2.19001049e-01,\n",
+       "        5.63791385e+01, 1.25951709e+01, 1.59485602e+00, 2.99797009e+00,\n",
+       "        1.12563064e+02, 6.62616265e+00, 4.80363638e+00, 2.70980971e+00,\n",
+       "        1.17944949e+01, 1.51284476e+00, 7.00554028e+00, 1.42902718e+00,\n",
+       "        2.37960479e+01, 7.58163108e+00, 9.42065719e+00, 1.89973521e+01,\n",
+       "        6.75213593e+00, 2.74360279e+00, 6.90139393e-01, 1.52348613e+01,\n",
+       "        7.98206602e+00, 3.03440881e-01, 2.13512704e+01, 5.26321493e+00,\n",
+       "        7.85590507e-01, 1.95313320e+00, 6.28957080e+01, 6.17653813e-01,\n",
+       "        2.93800884e+00, 2.40102274e+00, 8.15195155e-01, 6.33341410e+00,\n",
+       "        2.52758454e+01, 4.71586995e+00, 2.15837467e+00, 2.32526638e+00,\n",
+       "        1.33588342e+01, 1.34307766e+02, 9.75079807e+00, 1.01859671e+01,\n",
+       "        1.28533386e+01, 4.80703777e+00, 6.87679298e+00, 5.24351657e+00,\n",
+       "        6.55312472e+00, 2.42904848e+00, 3.21004444e+01, 4.73135282e+00,\n",
+       "        2.16467554e+00, 1.41163165e+00, 2.30710111e+01, 6.30730332e+00,\n",
+       "        4.28371694e+00, 6.77402984e+00, 1.86516206e+00, 1.78732828e+01,\n",
+       "        1.59619970e+01, 5.74475113e+00, 1.33900206e+01, 7.77003514e+00,\n",
+       "        7.82553943e+00, 6.04016702e+00, 1.65108557e+01, 6.92233432e+01,\n",
+       "        4.92024763e+00, 2.45683710e+00, 1.63506945e+01, 4.87076432e+00,\n",
+       "        2.97766273e+00, 1.43676496e+00, 2.45796259e-01, 9.41809979e+00,\n",
+       "        2.68943310e-01, 6.80687399e+00, 1.23447216e+02, 2.51482952e+00,\n",
+       "        1.43925772e+00, 7.42387321e+00, 1.51358983e+00, 8.20848259e+00,\n",
+       "        2.69530586e+00, 1.67703842e+01, 7.31674755e+00, 5.24064520e+00,\n",
+       "        7.83471982e+00, 9.52072849e+00, 2.96766889e-01, 7.79651701e+00,\n",
+       "        4.08954978e+00, 9.05570857e+00, 2.92351345e+00, 8.40429801e+00,\n",
+       "        1.82283154e+00, 7.82611681e+01, 2.94226585e+00, 1.15095616e+00,\n",
+       "        7.65321202e+01, 1.04834755e+00, 1.17717924e+01, 6.22129041e+00,\n",
+       "        4.32272721e+00, 3.95010135e+01, 2.25368900e+00, 1.98196390e+00,\n",
+       "        3.59807669e+00, 8.78522919e+00, 5.26016676e+00, 5.62067099e+01,\n",
+       "        3.06718901e+01, 6.76647854e+00, 5.50542530e+00, 1.50788918e+01,\n",
+       "        2.71958092e+01, 2.03979523e+01, 5.99600454e-01, 3.43688889e+00,\n",
+       "        9.35281630e+00, 5.85794655e+00, 2.63874851e+00, 1.21690466e+00,\n",
+       "        1.04835897e+00, 5.74215428e+00, 6.98215948e+02, 7.82325853e-01,\n",
+       "        4.81475840e+00, 7.94880480e+01, 1.25802881e+01, 1.14806405e+01,\n",
+       "        1.17002214e+00, 1.73012151e+01, 7.09288881e+00, 2.31799243e+00,\n",
+       "        5.13339575e+00, 3.94626284e+00, 1.35927069e+00, 2.58248611e+01]])
    • mu
      (chain, draw)
      float64
      -2.269 -1.444 ... -13.85 2.351
      array([[-2.26914550e+00, -1.44379588e+00,  1.00247187e+00,\n",
+       "         1.20384364e-01,  7.50899815e+00, -3.67857131e+00,\n",
+       "        -3.41881414e+00,  9.00068394e-01,  4.74484082e+00,\n",
+       "        -5.03946470e+00, -4.17515583e-01, -9.43139925e+00,\n",
+       "        -7.78615651e+00,  1.37588155e+01,  1.14887933e+01,\n",
+       "         2.37310628e+00,  4.32478351e+00, -1.10107632e+00,\n",
+       "        -4.18776151e+00,  3.98082627e+00, -7.52501927e-02,\n",
+       "         5.11533548e+00,  3.97637078e-01, -2.58538397e+00,\n",
+       "         2.36566128e+00,  1.98314597e-01,  2.68998912e+00,\n",
+       "         2.89991381e+00,  5.66045957e+00, -6.24108636e+00,\n",
+       "        -2.93500369e+00,  3.24841216e+00,  1.06401298e+00,\n",
+       "        -3.21620282e-01,  4.48282174e+00,  3.36763018e+00,\n",
+       "        -7.28688484e+00,  3.19789919e+00, -1.02234401e+01,\n",
+       "         5.66967753e+00, -3.82273554e-01,  1.57131978e+00,\n",
+       "         1.62094295e-01,  6.11246869e+00, -5.06750759e+00,\n",
+       "        -6.05770894e+00, -1.01217574e+00, -3.57922945e+00,\n",
+       "        -1.42625824e-01, -3.88505130e+00, -1.11226605e-01,\n",
+       "         1.49267405e+00,  3.19517489e+00, -1.41724096e+01,\n",
+       "         8.93102254e+00, -6.79233656e+00, -2.97608190e+00,\n",
+       "         6.53525494e+00,  7.32431288e+00, -6.22294184e-01,\n",
+       "         4.22087794e+00, -7.17817189e+00,  7.18534060e+00,\n",
+       "        -5.85916914e+00, -3.68658443e+00, -1.54499032e+00,\n",
+       "        -5.91867788e+00,  2.41070761e+00, -1.82189655e-01,\n",
+       "         1.00955171e+01, -1.68402574e-01,  4.79286819e+00,\n",
+       "         4.16613720e-01,  5.94249080e+00, -7.40626372e+00,\n",
+       "        -1.10091923e+00, -8.17046550e+00,  2.96828991e+00,\n",
+       "         1.33609861e+00,  2.46604063e+00,  2.87990847e+00,\n",
+       "         7.35880269e+00, -6.94166969e+00,  9.11007376e+00,\n",
+       "        -3.23877385e+00,  5.36554937e+00,  1.15064689e+01,\n",
+       "         5.76892301e+00,  5.51232338e+00, -7.65590672e+00,\n",
+       "         3.61150388e+00, -7.78326963e+00, -1.02653144e+01,\n",
+       "        -9.96393894e-01, -3.60386880e+00, -2.20290135e+00,\n",
+       "        -5.70944366e+00,  3.83011464e+00,  6.01597897e+00,\n",
+       "        -8.10235846e-01,  8.06381402e+00,  3.31047568e+00,\n",
+       "        -2.60956318e+00,  1.08448452e+01, -1.24302467e+00,\n",
+       "        -2.90057336e+00, -4.56554129e+00, -6.14255423e+00,\n",
+       "        -1.71616440e+00, -2.05711995e-01,  6.85348929e+00,\n",
+       "        -9.96713660e+00, -9.28799933e-01, -1.03358337e+00,\n",
+       "         2.57186374e-01, -5.35563639e+00,  9.90086531e-01,\n",
+       "        -8.67753211e+00,  4.09393361e+00,  3.66635132e+00,\n",
+       "         1.10163161e+01, -1.57741572e+00, -8.56189891e+00,\n",
+       "         5.21268899e+00,  1.09600101e+00, -3.74754443e+00,\n",
+       "        -6.73344862e+00,  5.56081582e+00, -2.25182092e+00,\n",
+       "        -2.30891416e+00, -8.87182355e+00,  7.20544220e+00,\n",
+       "        -1.25258724e+00,  1.81740608e+00, -1.38340178e+00,\n",
+       "         4.43151779e+00,  3.11364745e-01,  1.80431456e+00,\n",
+       "         1.66160963e+00, -9.94841913e-01,  4.81862289e+00,\n",
+       "        -3.57409200e+00, -3.20904470e+00,  6.34046078e+00,\n",
+       "        -2.00449355e+00, -1.23050437e+00,  8.32507098e-01,\n",
+       "        -3.09169124e+00,  5.78198599e+00,  4.21700741e+00,\n",
+       "        -3.39655429e+00,  6.92700458e+00, -1.49165366e+00,\n",
+       "         7.81107367e+00, -4.94483784e-01,  7.56923593e+00,\n",
+       "        -5.30515012e+00, -5.85327919e+00, -3.50480070e+00,\n",
+       "        -5.23692581e-02, -1.06624775e+00,  7.20441446e+00,\n",
+       "        -3.54085457e-01,  8.29064002e-01,  1.39285564e+00,\n",
+       "        -1.15704321e+00,  5.57577071e+00,  3.24710988e+00,\n",
+       "         1.78594214e+00, -8.07672404e-01, -3.12079615e+00,\n",
+       "        -6.39280950e+00, -2.11491058e+00,  6.40123671e+00,\n",
+       "        -3.81990354e+00,  2.28507210e+00, -9.05782652e+00,\n",
+       "        -4.13109571e+00,  8.87021352e-01, -1.88507149e+00,\n",
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+       "        -3.61023759e+00,  1.14931246e+01, -1.23935807e+00,\n",
+       "        -1.56700306e+00,  7.89523269e-01,  1.11502633e+01,\n",
+       "        -3.57961405e+00,  6.16122117e+00, -6.21412080e+00,\n",
+       "        -2.49911170e+00, -4.54631976e+00, -1.56224202e+00,\n",
+       "        -4.00126291e-01, -3.77142624e+00, -5.22574118e-01,\n",
+       "         6.04021511e+00,  2.88252810e+00,  4.27811829e-02,\n",
+       "        -9.18043904e+00, -7.52477332e+00,  7.58841972e-01,\n",
+       "        -1.08805160e+00, -6.37013658e+00,  3.56744543e+00,\n",
+       "        -5.88553496e-01, -5.04499862e+00, -1.90237733e+00,\n",
+       "         1.21063404e+00,  6.01739583e+00,  3.89519925e+00,\n",
+       "         1.46063843e+01,  1.59599097e+00, -4.97931878e+00,\n",
+       "        -5.87907076e+00,  1.72782286e+00,  1.01695155e+01,\n",
+       "         7.71089561e+00, -4.36515986e+00,  7.28454898e-01,\n",
+       "        -3.45599056e+00,  2.56623826e+00,  3.34807102e+00,\n",
+       "        -4.22890950e+00, -6.68538154e+00, -7.18746053e+00,\n",
+       "        -3.71949440e-01, -7.29425530e-01,  4.65003658e+00,\n",
+       "         7.24108076e+00,  1.78500470e+00,  1.32963479e+00,\n",
+       "        -2.56393403e+00, -5.47257096e+00, -8.16716359e+00,\n",
+       "        -3.20658154e+00, -5.54567808e+00,  2.68144900e+00,\n",
+       "        -9.44654046e-01,  6.22950703e+00,  5.33028846e+00,\n",
+       "         6.30216377e+00,  1.45439681e+00,  8.49604293e-01,\n",
+       "         4.60170232e-01, -1.08418117e+01, -5.87623852e+00,\n",
+       "         2.50280700e+00, -2.45585154e+00,  3.62681771e-01,\n",
+       "        -5.75586243e+00,  3.58214427e+00, -3.55950829e-01,\n",
+       "         6.72812969e+00,  2.72842542e+00,  5.90017339e+00,\n",
+       "        -5.37280575e-01,  2.46207329e+00,  8.32428945e+00,\n",
+       "        -1.40517787e+00,  3.17291320e+00, -7.33923433e+00,\n",
+       "         8.87922120e+00, -1.82098978e+01, -5.99871228e+00,\n",
+       "        -6.12222522e+00, -1.15648303e+00, -4.63117527e+00,\n",
+       "         8.72392558e+00, -4.65776509e+00, -2.07601816e+00,\n",
+       "        -7.39749412e-01, -5.80720370e+00, -6.74127267e+00,\n",
+       "        -3.75952249e-01,  8.94247941e+00, -2.56290509e+00,\n",
+       "         3.78329020e+00,  2.27918175e+00, -5.43441254e+00,\n",
+       "        -4.71026255e+00, -5.76979539e+00,  3.48927095e+00,\n",
+       "        -6.99189227e-01,  1.65687012e+00,  1.46151619e+00,\n",
+       "         5.97250928e+00, -5.04170154e+00,  1.11750322e+00,\n",
+       "         4.76567073e+00, -2.96851333e+00,  4.54157643e+00,\n",
+       "        -7.13957605e-01,  7.58938483e+00, -7.67721665e-01,\n",
+       "        -4.95258618e+00, -1.01601101e+01, -4.73451448e+00,\n",
+       "         2.37501451e+00,  8.96241563e+00, -2.22830040e+00,\n",
+       "         4.10517688e+00,  5.12461896e+00, -3.12221739e+00,\n",
+       "        -1.42471529e-02, -2.42206871e+00, -5.95638141e+00,\n",
+       "         1.24113247e+00, -2.87754658e-01, -9.84387254e+00,\n",
+       "         6.31501185e+00,  6.90396625e+00, -2.02165726e+00,\n",
+       "        -3.20936389e+00, -8.59270599e-01, -2.75329455e-01,\n",
+       "         4.27551222e+00, -3.74794018e+00, -3.65242484e+00,\n",
+       "         3.20532031e+00, -1.73616130e+00,  4.15798946e+00,\n",
+       "         1.17718220e+00, -1.04311069e+01, -4.09266896e+00,\n",
+       "         1.41124899e+00, -7.37205126e-01,  5.25762998e+00,\n",
+       "         1.19842461e+00, -1.84266774e+00,  1.32143804e+00,\n",
+       "        -3.80872738e+00, -3.92448285e-01,  3.73841511e+00,\n",
+       "        -7.10872944e+00,  2.99798639e+00,  2.06273459e+00,\n",
+       "        -1.23043646e+00, -5.78853737e+00, -7.36284023e+00,\n",
+       "         2.26225031e+00, -3.38355878e+00, -1.68746700e+00,\n",
+       "        -6.06363654e+00, -2.99681162e+00,  1.51655389e+01,\n",
+       "         5.85111732e+00,  3.67325563e+00, -3.88938332e+00,\n",
+       "        -1.63657121e+00, -5.83800474e+00,  2.31465580e+00,\n",
+       "         3.20496693e+00, -1.45495656e+00,  1.68808618e+00,\n",
+       "        -9.41094585e-01,  7.22854336e+00, -9.81217137e+00,\n",
+       "         2.66859888e+00, -1.47885682e+00, -9.04687110e+00,\n",
+       "         8.09557449e+00, -8.42861449e+00, -2.62580838e+00,\n",
+       "         5.11658218e+00, -1.52452654e+00, -1.96249233e+00,\n",
+       "         5.97155920e+00, -1.75401929e+00,  1.06964882e+00,\n",
+       "        -9.56683894e+00,  1.37675004e-01,  3.03911583e+00,\n",
+       "        -1.13928638e+01, -6.36918611e+00,  2.05351791e+00,\n",
+       "         1.90840417e+00, -5.13143543e+00,  8.82467194e+00,\n",
+       "         5.66513682e+00, -9.15590476e-01, -1.54135558e+00,\n",
+       "         8.13181157e+00, -1.21487209e+00,  6.03964686e-01,\n",
+       "        -7.51586094e+00, -6.25207588e-01,  2.50537486e+00,\n",
+       "         4.78314463e+00, -7.84010599e+00, -2.59846756e+00,\n",
+       "         1.92095881e+00,  1.55754667e+00,  1.17162041e+01,\n",
+       "        -6.93921261e+00, -3.61516358e+00,  2.10526659e+00,\n",
+       "        -3.27262877e+00, -6.27686672e-01, -2.47391375e+00,\n",
+       "         4.74857769e+00, -6.00001153e+00, -3.12236875e+00,\n",
+       "        -2.05910641e+00, -1.47592926e+00, -1.52197710e+00,\n",
+       "        -5.36219392e+00, -3.70460870e+00, -1.18081044e+00,\n",
+       "         1.30067483e+00, -5.60363657e+00, -7.50442961e+00,\n",
+       "         5.86388873e+00,  4.87562394e+00,  3.38591620e-01,\n",
+       "         3.33931920e+00, -4.59088084e+00, -2.95116936e+00,\n",
+       "         1.42467690e+00,  1.94647514e+00,  8.54062107e+00,\n",
+       "        -1.77224155e+00,  1.77345100e-01, -4.87591385e+00,\n",
+       "         4.13998676e-01,  3.76794944e+00,  4.22702123e+00,\n",
+       "        -5.45522796e+00,  1.65271233e+00,  6.19622472e+00,\n",
+       "         1.50604180e+00, -3.14617692e+00,  6.30024370e+00,\n",
+       "        -7.21197086e+00,  6.01837982e+00, -7.52050511e+00,\n",
+       "        -1.09395300e+00, -5.00216139e+00, -3.49358661e+00,\n",
+       "         1.22685827e+01,  5.14362360e+00, -4.78872778e-01,\n",
+       "         5.11733335e-01, -3.48664446e-01,  1.79755961e+00,\n",
+       "         8.95215983e+00,  3.49282504e+00, -5.25135910e+00,\n",
+       "        -2.85226951e+00, -1.13230869e+00, -5.81140476e+00,\n",
+       "         3.73029644e-01,  6.57538675e+00,  2.16467890e+00,\n",
+       "         6.90566193e+00,  1.40305875e+00, -7.24304684e+00,\n",
+       "        -1.50332918e+00,  1.07448461e-01, -5.58793632e+00,\n",
+       "         3.75489621e+00,  1.48970954e+00, -2.04834114e+00,\n",
+       "         4.28431886e+00, -3.81467248e+00,  8.25504579e+00,\n",
+       "        -5.16157750e+00, -4.55133425e+00,  5.41937050e+00,\n",
+       "         1.22482918e+00, -1.61128396e+00, -3.58948123e+00,\n",
+       "        -4.99342772e+00,  5.09773030e+00, -1.48636228e+00,\n",
+       "        -5.56506361e+00,  7.50700635e+00,  2.68792959e+00,\n",
+       "         2.20114723e+00,  2.84364223e+00, -5.95171067e+00,\n",
+       "         2.25348726e+00,  2.22053252e+00, -5.10663431e-01,\n",
+       "         1.25970172e+01, -6.86680266e+00,  3.39368931e+00,\n",
+       "         6.52463646e+00, -6.07163044e+00,  1.41433456e+00,\n",
+       "        -1.70006990e-01, -3.73880623e+00,  9.20656227e-01,\n",
+       "         1.81601662e+00,  1.82045364e+00, -3.51682219e+00,\n",
+       "        -1.38509221e+01,  2.35117304e+00]])
  • created_at :
    2020-06-10T17:34:00.243686
    arviz_version :
    0.8.3
    inference_library :
    pymc3
    inference_library_version :
    3.8

    • \n", " \n", " \n", " \n", " \n", "
  • \n", - " \n", - " \n", + " \n", + " \n", "
    \n", "
    \n", "
      \n", @@ -5419,27 +5444,27 @@ " stroke: currentColor;\n", " fill: currentColor;\n", "}\n", - "
      xarray.Dataset
        • chain: 1
        • draw: 500
        • school: 8
        • chain
          (chain)
          int64
          0
          array([0])
        • draw
          (draw)
          int64
          0 1 2 3 4 5 ... 495 496 497 498 499
          array([  0,   1,   2, ..., 497, 498, 499])
        • school
          (school)
          <U16
          'Choate' ... 'Mt. Hermon'
          array(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
-       "       'Hotchkiss', 'Lawrenceville', "St. Paul's", 'Mt. Hermon'], dtype='<U16')
        • obs
          (chain, draw, school)
          float64
          -23.64 -2.679 ... -146.5 45.63
          array([[[ -23.64269622,   -2.67865008,   15.82882134, ...,\n",
-       "           13.81705825,   12.10308274,   33.08634734],\n",
-       "        [   7.74811804,    4.08411641,    6.99514875, ...,\n",
-       "           17.60264742,   -3.10707413,  -17.35223334],\n",
-       "        [  30.89420722,   -9.47794527,   17.28683682, ...,\n",
-       "           16.62098736,  -12.96357793,   12.89178226],\n",
+       "
          xarray.Dataset
            • chain: 1
            • draw: 500
            • school: 8
            • chain
              (chain)
              int64
              0
              array([0])
            • draw
              (draw)
              int64
              0 1 2 3 4 5 ... 495 496 497 498 499
              array([  0,   1,   2, ..., 497, 498, 499])
            • school
              (school)
              <U16
              'Choate' ... 'Mt. Hermon'
              array(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
+       "       'Hotchkiss', 'Lawrenceville', "St. Paul's", 'Mt. Hermon'], dtype='<U16')
            • obs
              (chain, draw, school)
              float64
              -7.005 -13.43 ... -1.281 -4.613
              array([[[ -7.00515108, -13.43153612, -10.01040957, ..., -16.19090925,\n",
+       "         -17.44132393, -20.0866369 ],\n",
+       "        [ -6.86696896, -18.81725212,   9.65421384, ...,  16.53053917,\n",
+       "           6.72465358, -24.18206066],\n",
+       "        [114.025525  , -63.10320213, -17.18902305, ..., 100.29001401,\n",
+       "         135.78625636,  67.50554246],\n",
        "        ...,\n",
-       "        [ -17.98653277,   30.87852257,  -23.8373408 , ...,\n",
-       "           34.28047699,   35.02174102,  -10.1270695 ],\n",
-       "        [  20.9044711 ,  -13.53326351,    4.23200315, ...,\n",
-       "            7.8311032 ,  -23.31239724,   -3.14891481],\n",
-       "        [ -14.5990443 ,  -73.21184919, -151.68195527, ...,\n",
-       "          -48.30995401, -146.54737617,   45.63204153]]])
          • created_at :
            2020-06-06T18:07:53.822942
            arviz_version :
            0.8.3
            inference_library :
            pymc3
            inference_library_version :
            3.8

      \n", + " [-19.94832547, -14.52423533, -14.0245758 , ..., -7.15475511,\n", + " 8.34547408, 9.97533365],\n", + " [-13.62696997, -10.57929146, -45.13902796, ..., -16.99789642,\n", + " -10.53822116, -14.20868999],\n", + " [ 3.48681755, 35.48027982, 42.72217281, ..., -33.83185313,\n", + " -1.28071988, -4.61278391]]])
  • created_at :
    2020-06-10T17:34:00.249162
    arviz_version :
    0.8.3
    inference_library :
    pymc3
    inference_library_version :
    3.8

    • \n", " \n", " \n", " \n", " \n", "
  • \n", - " \n", - " \n", + " \n", + " \n", "
    \n", "
    \n", "
      \n", @@ -5777,8 +5802,8 @@ " stroke: currentColor;\n", " fill: currentColor;\n", "}\n", - "
      xarray.Dataset
        • school: 8
        • school
          (school)
          <U16
          'Choate' ... 'Mt. Hermon'
          array(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
-       "       'Hotchkiss', 'Lawrenceville', "St. Paul's", 'Mt. Hermon'], dtype='<U16')
        • obs
          (school)
          float64
          28.0 8.0 -3.0 7.0 ... 1.0 18.0 12.0
          array([28.,  8., -3.,  7., -1.,  1., 18., 12.])
      • created_at :
        2020-06-06T18:07:53.824403
        arviz_version :
        0.8.3
        inference_library :
        pymc3
        inference_library_version :
        3.8

    \n", + "
    xarray.Dataset
      • school: 8
      • school
        (school)
        <U16
        'Choate' ... 'Mt. Hermon'
        array(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
+       "       'Hotchkiss', 'Lawrenceville', "St. Paul's", 'Mt. Hermon'], dtype='<U16')
      • obs
        (school)
        float64
        28.0 8.0 -3.0 7.0 ... 1.0 18.0 12.0
        array([28.,  8., -3.,  7., -1.,  1., 18., 12.])
    • created_at :
      2020-06-10T17:34:00.250446
      arviz_version :
      0.8.3
      inference_library :
      pymc3
      inference_library_version :
      3.8

    \n", " \n", " \n", "
    • \n", @@ -6139,7 +6164,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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    " ] @@ -6171,8 +6196,11 @@ "output_type": "stream", "text": [ "INFO:pystan:COMPILING THE C++ CODE FOR MODEL anon_model_b0eada194ad938e30ed47926216f98ca NOW.\n", - "WARNING:pystan:39 of 2000 iterations ended with a divergence (1.95 %).\n", - "WARNING:pystan:Try running with adapt_delta larger than 0.8 to remove the divergences.\n" + "WARNING:pystan:18 of 2000 iterations ended with a divergence (0.9 %).\n", + "WARNING:pystan:Try running with adapt_delta larger than 0.8 to remove the divergences.\n", + "WARNING:pystan:Chain 2: E-BFMI = 0.193\n", + "WARNING:pystan:Chain 3: E-BFMI = 0.176\n", + "WARNING:pystan:E-BFMI below 0.2 indicates you may need to reparameterize your model\n" ] } ], @@ -6226,7 +6254,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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2uT/PBrrt9sjKlVD/QLj/nuR6jSEUzHrsn6f9diLOwEGRN9605pIkSVLBYhgkSf9j0aLI5V0jS36AunXg5sGFKwj6vab/F7j9tkDFijB7DpzfIfLfd2xOSJJU2C1dmsfUd/OYMTPy5VeRRYsia9YU7n18Xl7kkXGRVm0iH3yYzAa6/LLAzYMCO+5Y8Gux9ucHmjeD3DzofU1k2vTC/XpIkiQpvWTm9wAkqSDJyYlc2T2yYAFUqwaDBwTKlin4zYf12aNuYOSd0KNX5KOPocsVkc4Xwil/p8CeXStJktYtxsgpp8OyZQC/BQ4ZGbDTTpFqVZM6pvZugT3qQo3qkJFRsPf5X38duWFA5L33k68PrAfdrgjstFPBHvfvhRDoegUsXhJ5exJ07R65685kCV9JkiQpvxkGSdLPcnMj11wXmTkLym0Hg/oHttsuPQ7eK1YIDLkFBgyOPP8C3HJb5LPPoMulyZJykiSp8Fix4pcgCHaoCMtzYPlyyM2FuXOTG5Pgl6CoRAnYo26k/oGBhg1g99qQShWM/X9OTuT+ByNjH4ZVq5OxdrogcNKJhfOklczMwLVXw4UXJzVltx6R4bdDyZKF77lIkiQpvRgGSdLPhg6LTHwDimXBDf0C1aql10F7dnagV3fYbVe4487IU0/DNwsi110DpUql13OVJKmoGHN/oESJQF5e5Nvv4OuvYe7XMOfLyMyZMHMm5OTA1Hdh6ruRESOTk17q1480ahBo0CA5aWRbizHy4ktwx/Bk3AAN6sOVXQrXbKC1KVEicMN10LZ95PMv4NrrI/36FpwATpIkSUWTYZAkAU/9O/Lo+GS7V8/APnun58F6CIEzT0+Wi+l9TWTKf5MzVwf2h4oV0/M5S5JUFKRSgcqVoHIlqHcAQLJfz82NzJkD730Ak6dE3pkKS36A/7wE/3kpmTm0666RgxvDoU0Cdets3dBi+fLICy/C+McjX8xO7ttpR7iwU+CwJoVzNtDaVKoUuP466HxJZMJEGHVvpO156fHcJEmSVDgZBkkq8qZNj9x0a9IMaXte4Kgj0v9A/eCDAkNvgSu6Rz75FNp3igy6EXapmf7PXZKkoiQjI1CrFtSqBf84ObBmTWTadJg0JTJlCsycBZ99ltzufzBSoQIccnCk3v6BffdJQo3NlZeXXLfwpZcjzz4HS39e4q5EcTj7rORElezs9KtB9t4rcGUX6Nc/cu9oqFUrFok6U5IkSQWTYZCkIu27RZGevSOrV8Phh0Grlvk9om2nbt3A8Nvh8q6Rr76CDhdG+veD/fezSSFJUrrKzAzsv1+yv2/fFhYvSUKhiW9G3nobFi2CJ5+CJ59KTpSpXDmy955Qu3Zg99pQezcoX379tcKaNZH538AXXySzkSa8kTzuL6pVTYKp5sdCmTLpXXc0Pzbw6eeRhx+BG26M7FYLqldP7+csSZKkgskwSFKRtXp15Ko+kUWLoGZN6NktpM3SJBuqapXAHUOgW8/kLOFLL4/06gFHH1m0/h8kSSqqypcLND0Gmh4TWLUqMvVdmDQ58sGH8OmnsGBBcnvplfjr3ylRPFJ+eyhfDrYrCxFYvRrWrIEff4Sv5iZf/17JknBQYzi2WaBRg6J1/ZwO7QIzZ0beex969YmMuAOKFy86z1+SJEkFg2GQpCLr1qGRD6dB6VJww3WBkiWL5kF5uXKBW2+Ca66LvD4B+lwT+fZbOP209Fm3X5Ik/bVixQKNG0HjRsn+f/nyyPSPYMZM+PTTyKxPYe5cyFkBOfNg3rz1PVZyjcI99oDDmgTqHZA8flGUmRm4ujec1zby+Rdw0y2RHt2K5v+FJEmS8o9hkKQi6eVXI/96AkKAPlcFdq5WtA/Is7MD114Ntw2NjH8chg5LAqFOHYrWmbuSJOk3JUsGGtSHBvUBknogJyeZVb14CXy/GH78AVIZkJkBmVnJDKAa1WHHytYQv1exQhIIXdIl8sxzsN9+keOb+/8jSZKkbccwSFKRM39+ZMDAZKmTFmfDQY09EIfkAtOXXASVK8Ow4ZGHH4UlSyLduyZntEqSJJUoEahWDapVy++RFD71Dgi0aQ133R0ZfHOkzu6w267WWJIkSdo2Uvk9AEnaltasiVx9bWTpMth7L2hzrgfgvxdC4KwzAr16BDJS8PyLyfWEcnLiX/9lSZIkrVfLs6FxI1i1Krl+0LJl1liSJEnaNgyDJBUpI0cla9+XLg19egVnvKzDsU0DN/QLZGfD25Pg0ssjP/5os0KSJGlzpFKBq3oEKlVKrr/Uf2AkRmssSZIkbX2GQZKKjP++E3nwoWS72xWBnXYyCFqfgw8K3DI4ULo0TJsOHS+KLFxos0KSJGlzbLddoG+fQEYGvPIqPPZ4fo9IkiRJRYFhkKQi4aefItf3j8QIJ/0NjjjcIGhD7LN3YNhtgYoVYfZs6NA58uWXBkKSJEmbY++9Ap0uSOrRIcMiH31sfSVJkqStyzBIUpFw29DIwm+Tix137mQQtDFq1QoMHxrYeWdYsAA6do58PMOGhSRJ0uY47VQ44jBYswZ6Xx358SfrK0mSJG09hkGS0t6EiZFnn4dUCnp2CxQvbhi0sXbcMTBsSKBuHVjyA1x0SeStt21YSJIkbaoQAt2uDFStAt8sgBu9fpAkSZK2IsMgSWltyZLIgMHJQfUZpyfLnmnTlC8XuO3mQIP6kLMCuvWIPPGUDQtJkqRNVbp04JregcxMeO11eOLJ/B6RJEmS0pVhkKS0FWNk0M2RxYthl5rQ5lyDoM1VsmRgwA2B5s0gNw8GDo7cOTLPs1glSZI2Ud26gQ7tkzr1tqGRTz+zrpIkSdKWZxgkKW29+lpyy8iAnt0D2dmGQVtCVlagR7dA61bJ1/c/ANf2i6xebeNCkiRpU/zzVDi4MaxaDVf3jeTkWFdJkiRpyzIMkpSWfvwpcvOtyUF0y7Ohbh2DoC0phECb1im6dw1kZMAL/4EuV0Z+8sLHkiRJGy2EQPdugYoVYfYcuHWINZUkSZK2LMMgSWnpjuGR7xdDjepwTguDoK3l+OaBgf0DJUrA1HehY+fINwtsXkiSJG2s8uUCvXsGQoB/PwP/ecmaSpIkSVuOYZCktDP13chTTyfbV14eKFbMMGhratggMOy25EzWL2ZD+46RmbNsXkiSJG2segcEWrVMtgcMjnw9z5pKkiRJW4ZhkKS0snJlZMDg5KD55BNhv30NgraF2rUDdw4L1NoFFi2CThdFJky0eSFJkrSxzj0nsO8+sHx5cv0gr8soSZKkLcEwSFJauXd0ZO5cqFgRLmhnELQtVa4UGDYk0KA+rFgBPa6KPPxoJEYbGJIkSRsqMzPQ56pAmTLw8QwYMdJaSpIkSZvPMEhS2vjk08iYscn2ZRcHSpc2DNrWSpdOriF00t8gRhhye2TwzZ7RKkmStDEqVwr06JrUsg89DG9NspaSJEnS5jEMkpQWcnMjAwZFcnPh8MPgsEMNgvJLZmbg8ssCnTslF0D+15Nw0aWRb7+1iSFJkrShDm0SOOXvyXa/GyLfLbKWkiRJ0qYzDJKUFsY/liyjUboUXHqRQVB+CyFw+mmB/v0CpUvBh9PgvHaRSZNX5/fQJEmSCo2OFwR22xWWLIHrro/k5RkISZIkadMYBkkq9ObPj9x1d3Jg3OGCQMWKhkEFxSEHB+4ekTQxFi+Gtu1/5P4HbWRIkiRtiOzswDW9A8WLw3/f4dclkSVJkqSNZRgkqVCLMTLo5kjOCthvX/jb8fk9Iv2vqlUDw28PNG8GeXlw512RS7pEFi40EJIkSforNWoELumcnOx0192R6R9ZQ0mSJGnjGQZJKtRefAkmTYasLOh6eSCVclZQQVS8eKBHt8C1V5eieHGY+i60ahN57XWbGZIkSX/l+OPg6CMhNxeu7htZutQaSpIkSRvHMEhSobVkSeS2IcmBcKuWgerVDYIKshAC//h7cUbdFaizO/z0E/TsHbn+xjx++smGhiRJ0rqEELiiS2CnnWD+NzBwcCRG6ydJkiRtOMMgSYXW0DsiS36AWrvA2Wfm92i0oarvnCwbd/aZEAI88yyc0zry9iQbGpIkSetSunTg6qsCGRnw0ivw9LP5PSJJkiQVJoZBkgqlyVMizz2fhAldrwhkZTkrqDDJygp0aJ9i6K2BalXh2+/g8q6R/gPy+OEHQyFJkqS12WvPQNvzkrr3ltsis+dYN0mSJGnDGAZJKnRyciIDb0oOfE/5R3JQrMJpv30D994dOO2UJNj79zNwRovIo+Mja9bY3JAkSfpfZ58J9Q+EFSugT9/IypXWTJIkSfprhkGSCp2774nMnw+VKkG7NgZBhV3x4oGLOyezhHbdNbmW0K1DIue2cek4SZKk/5VKBXr1CJQrB599Bnfcab0kSZKkv2YYJKlQmTkr8si4ZPvySwMlSxoGpYv99g2MGhG4/LJAue1g9pxk6bgruuXx5Zc2OSRJkn5RsUKgZ/ekDh73GEx8w1pJkiRJ62cYJKnQyMuLDLo5kpcHRx8JBx9kEJRuMjICJ58YeOiBwOn/hIwMeOttaNk6ctvQPH780UaHJEkSwEGNknoJ4PobIwsXWidJkiRp3QyDJBUaTz0NH38MpUpB5wsNgtJZmTKBzh1T3H9v4OCDIDcXHhkHp/wzCYW+WWCzQ5Ik6YLzA3V2hx9/hL79Irm51kiSJElaO8MgSYXC4iWR4SOSg9s2rQMVKxgGFQXVdw4MuCHFTQMDtXeDnBVJKHT6WZFr++Xx0ceRGG16SJKkoikrK3BN70CJEvDe+3D/g/k9IkmSJBVUhkGSCoXhIyI//QS77Qr/ODm/R6NtrWGDwKi7AjcNDBxYL5kp9PyL0K5DpE27yJP/jixfbigkSZKKnmrVApdfmpwoNereyPsfWBNJkiTpzwyDJBV4H06LPP1Mst3l0kBmprOCiqIQAg0bBG69KcXI4YFjm0GxLJj1CQwYFDnplMj1/fOY+m4kL88miCRJKjqaNQ00awp5eXBtv8jSpdZCkiRJ+iPDIEkF2po1kcE3Jwezxx8H++xtECSoWzfQq3uKx8cFLuwYqFYNcnLgmefgoksjp54RGTY8j+kfuYycJEkqGrpcEqhSBb5ZADffav0jSZKkPzIMklSgPf4EfPoZlCkDF7QzCNIfbbdd4Ix/Bh66PzBsSODEv0HpUrBwIYwZC+07Rk45PXLrkDzeejuybJmNEUmSlJ5Klgz07hlIpZLldP/zknWPJEmSfpOZ3wOQpHX5blFk5KjkILb9+YHy5QyDtHYhBPbdB/bdJ3DxhZE334JXX4u8+XYSDD06Hh4dH8lIwe51IvvvC3XrBOrWgSpVkr8vSZJU2O29V6BVy8g998GgmyJ77w07VrbOkSRJkmGQpAJs2B2RZctgj7rwt+PzezQqLLKzA0ceAUceEVi5MjJ5CrzxZuTd9+DrefDxx8kNkqCxdGmoWSNSuTLsWBkqVw4//5l8XaqUDRRJklR4tGoZmDQ58tHH0O+GyC2DISPDekaSJKmoMwySVCBNfTfywn8ghGT9cw9gtSmyswOHNoFDmyTvn28WJKHQ9OmRmbOSJQiXLoVp05Nb4o9LqpQqFamwPWy/PVTYHspuByVLJEuxlCwJpUpCyZ9vpUpBhQpQsQJkZvqelSRJ215mZqB3T2jdNql7Hn4Uzjojv0clSZKk/GYYJKnAWbMmctPPF7096USoW9emuraMHSsHmjeD5s2S99Tq1ZHZs2Hu17BgYRIWLfjml2348UdYtiy5ffnV/z7autfhDwG23z6yY2WoWRN2qRmoWQPq1MHlDiVJ0lZXrVrg4s7Qf2BkxMhI/QNh99rWIJIkSUWZYZCkAueRcTB7NpQrB+3aetCqrScrK1C7NtSu/cs9f3y/LV8e+fZbWPQ9fP998udPP0WWLye55SRB0S/bS5fCokWwZk3y56JFMP0j+H1wtEvNyP77wwH7BxrWh9KlfY9LkqQt7/jj4M234fUJ0Pe6yN0jklnTkiRJKpoMgyQVKN99F7nn3qRx3umCQNkyHrAq/5QsGahRA2rU+P29639P5uVFliyBhd8m1yiaPTvyxWz4/PNkdtEXs5Pb4/+KZGVBwwaRI48INDnYYEiSJG05IQSu7ALTP4rMngPDhv59atAAACAASURBVEcuvdhaQ5IkqagyDJJUoNwxIpKzAvbeC5o1ze/RSBsvlQps//M1hurWgd+HR0uWRN7/AN59LzJ5ShIOvfEmvPFmpFgx+PtJkbPPDGy/vY0aSZK0+cqVC/ToCl2ujIx/HBo3jhzUyDpDkiSpKErl9wAk6Rfvvb+a519IrrdySedAKuWBqtJLuXKBww8LXHJRigdHB0aPCrRuBTWqw6pVyQWeTzszMnRYHosXr/uaRJIkSRuqUcPAqack2zf0jyxeYo0hSZJUFBkGSSoQ8vIi19+4DIDjmkPdugZBSm8hBGrVCrRpneKB+wKDbgzssQesXAljH4EzWkTGPxbJzbVhI0mSNk+HdoFdasL3i+HGgZEYrS8kSZKKGsMgSQXCM8/B9Om5lCoF7dsaBKloCSHQuFFgxLDAwP6B3XeHZcvg5tsi7TpEZsywYSNJkjZddnagd69AVhZMfAPGPbYyv4ckSZKkbcwwSFK+W7o0cuddSbO7dSuvl6KiK4TAQY0Dd90R6HJpoHQpmDkLzu8QGTY8jzVrDIUkSdKmqb1boN3PJ13dOGAZX35lXSFJklSUGAZJynf3jI4sXgy71Exxyt/zezRS/svICPz9pMCDowPH/B/ECGPGQudLIgsW2riRJEmb5vTT4MB6kLMCru0XPdFEkiSpCDEMkpSv5syJjBufbHe9shRZWc4Kkn5RoUKgT68U/foGSpWCD6fBeW0jb0+ycSNJkjZeKhXo2S1Qtmzg4xlw72hrCkmSpKLCMEhSvokxcuvQSG4uHHIwHHpIsfweklQgHX5YYNSI5FpCP/wIl3eNjBnrxZ8lSdLGq1QpcPVVpQC4/wG8NqEkSVIRYRgkKd+88RZMngJZWdC5ozOCpPWpWjVwx5DAyScmXw8bHrltaCQvzwaOJEnaOM2aZnP0UZCbB9f1j6xaZT0hSZKU7gyDJOWLVasiQ4YmB52nnwbVqhkGSX8lOztw+WUpOnVIfl8eHQ9XXxtZudIGjiRJ2jiXXRzYvjzMng2j7rWWkCRJSneGQZLyxcOPwtfzoEIFOKeFQZC0Mc48PdDnqkBmJrz8SrJsXE6OTRxJkrThttsucEWXpA4fMxamf2QtIUmSlM4MgyRtc98tioy+PznY7Ng+ULKkYZC0sY45OjDoxkDJkvDue9CtpzOEJEnSxjm0SaDZMZCXB9f3t5aQJElKZ4ZBkra5kXdHclbAXntC02PyezRS4VX/wMBNAwMlSsA7U+GqqyNr1tjEkSRJG+7izoEKFWDOlzBylHWEJElSujIMkrRNffpZ5Olnk+3OnQIhOCtI2hx77xW48fpAsWLw5lvQt18kN9dGjiRJ2jBlywau/Hm5uLGPwIfTrCMkSZLSkWGQpG1q2PBIjHDkEUkTW9Lmq3dAoN+1v11DaMDgSIw2ciRJ0oY55OBA82YQI/TrH1mxwjpCkiQp3RgGSdpmJk2OTJ4CmZlwQTuDIGlLOqhR4JregVQKnn4G7r7HJo4kSdpwF10YqFgR5s6FESOtIyRJktKNYZCkbSI3N3L7HclB5Sn/gKpVDIOkLe3wwwJdLk1+t+4dDU88ZSNHkiRtmDJlAl2vSOqIR8fD+x9YR0iSJKUTwyBJ28Szz8HnX0CZMtCqhUGQtLWc9LfAueck24Nvjkx800aOJEnaMAc1CpxwXLJc3PX9Izk51hGSJEnpwjBI0laXkxO5a1RyINn6nEDZsoZB0tbUpnXguOaQlwd9rol89LGNHEmStGEu7BioVAm+ngcjR1lDSJIkpQvDIElb3cOPwqJFsNNOcPJJ+T0aKf2FELiyS6BxI1i5EnpeFfn+e5s5kiTpr5UundQRAI+Mg2nTrSEkSZLSgWGQpK1q8ZLImLHJAWT7toFixZwVJG0LmZmBa3oHalSHb7+D3tdE1qyxmSNJkv5a40aBY5sly8X1HxBZtcoaQpIkqbAzDJK0Vd03OrJ8OdTZHY46Mr9HIxUtpUoFrr82ULIkvPc+3H6HjRxJkrRhLuoU2L48zJ4D991vDSFJklTYGQZJ2mq+/jryryeT7Y4XBFIpZwVJ21qNGoFe3ZPfvUfHw/Mv2MyRJEl/rWzZwGWXJDXEA2Pgk0+tISRJkgozwyBJW82IkZE1a6BRQziwnkGQlF8OOzRwTotke8DgyCef2MyRJEl/7YjDA0ccBrm5cMMAl5yVJEkqzAyDJG0VH8+IvPQKhAAd2hsESfmtTetAo4awciX06B358UebOZIk6a9denGgTBmYNQseeji/RyNJkqRNZRgkaYuLMXLHnUmjuVlT2G1XwyApv2VkBPr0ClSpAvPnw9XXRnJzDYQkSdL6VagQuKhTUs/fc2/kyy+tHyRJkgojwyBJW9zbk2Hqu1AsC9qeZxAkFRRlywb69Q1kZ8PkKTDyHps5kiTprx3bLFn6edXqZLm4vDxrCEmSpMLGMEjSFpWbGxn+86ygU/4BO1Y2DJIKktq7Bbpdmfxe3v8AvDbBZo4kSVq/EAJXdAmUKAEfToPH/pXfI5IkSdLGMgyStEW98CJ89jmULg0tzzYIkgqiY44OnH5asn3d9ZHZcwyEJEnS+u1YOfx6LdA7R0Tmz7d+kCRJKkwMgyRtMStXRu4alRwUntMiULasYZBUUHVoHzhgf8jJgR69IsuW2dCRJEnrd/KJsP9+kLMCbhwUidH6QZIkqbAwDJK0xYx/HBYuhEqV4JS/5/doJK1PZmagb59ApR3gy6/guhtc/1+SJK1fKhXoekWgWDH47zvw9LP5PSJJkiRtKMMgSVvEjz9FRj+QNJLPPy+Qne2sIKmgK18+cF3fQFYWTJgID4zJ7xFJkqSCbudqgbbnJbX+0Nsj333nySSSJEmFgWGQpC3iobGRpUth11rQ9Jj8Ho2kDbXnHoHLLk4aOnfdHXl7kg0dSZK0fv88FerWgaXLYPDNLhcnSZJUGBgGSdpsixdHHh2fbJ/fJpCR4awgqTD52wmBE/8GMcI110W+nmdDR5IkrVtmZqB710BmJkx4A15+Nb9HJEmSpL9iGCRpsz0wJrJiBexRFw45OL9HI2lTXNI5sOce8NNP0POqyIoVBkKSJGnddq0VaHl2sn3zrZElS6wdJEmSCjLDIEmb5dtvI48/kWyf3yYQgrOCpMKoWLHAddcEypeHTz+DAYNc8kWSJK3fOS0CtXaBJUvgliHWDZIkSQWZYZCkzTL6wciqVbDvPtCgfn6PRtLmqFQp0LdPICMFL/wHxj2W3yOSJEkFWVZWoEfXpHb4z0vw+gQDIUmSpILKMEjSJvvmm8hT/062nRUkpYcD9g906pj8Lg+9PfLOVJs6kiRp3erWDZx5RrI96KbIjz9aO0iSJBVEhkGSNtm9oyNr1kD9A5MGsqT0cNop0PT/IDcvuX7Q7Dk2dSRJ0rq1bhWoWQO+Xwy3ulycJElSgWQYJGmTfDU38uxzyXbb8wyCpHQSQqDrFYF99oaly+CKrpHvv7exI0mS1i47O9C9ayCVgudfhIlvWjdIkiQVNIZBkjbJPfdGcvPg4Maw916GQVK6yc4O3HBdoFpVmP8NdO0ZWbHCxo4kSVq7vfYMnH5asj1wcOSHH6wbJEmSChLDIEkb7YvZkRdfSrbbOCtISlvlygUG3hgoWxY+/hiuvT6Sm2tjR5IkrV3b8wI1qsOiRTDo5kiM1g2SJEkFhWGQpI129z2RGOHww6DO7oZBUjrbuVoyQygrC157PbkwtI0dSZK0NtnZgat6BDIy4JVXkyXjJEmSVDAYBknaKJ98Enn1NQgB2pxrECQVBfvtG+jTK7kOwFNPw7DhBkKSJGnt6tYNtG6VHCfcfGvkm2+sGSRJkgoCwyBJG2XkPcnB3NFHQa1ahkFSUXHE4YErL09+5x96GO5/MJ8HJEmSCqwWZ8Hee8GyZXDdDS4zK0mSVBAYBknaYNM/irzxJqRScJ6zgqQi54TjAhd2TH73R4yMjHvMxo4kSfqzzMxkubgSxeG992HsI/k9IkmSJBkGSdpgd/88K+jYplB9Z8MgqSg645+BVi2T7Vtui9z/oIGQJEn6s6pVAxdd+NtJJNOmWzNIkiTlJ8MgSRvkw2mRyVMgIwNanWMQJBVlbc8LnNMi2b7zrsgdd+Z5DSFJkvQnJxwPRx4Bublwdd/Ijz9ZL0iSJOUXwyBJG+Se+5IDt+bNoGoVwyCpKAsh0K5tio4XJJ8FDz4Eg2+J5OXZ4JEkSb8JIdD18kCVKvDNAug/IHoCiSRJUj4xDJL0l6ZN/21WUMsWBkGSEmedEbj8skAI8K8n4MKLf+Inz/iVJEm/U7p0oG+fQGYmvD4Bxj+e3yOSJEkqmgyDJP2lUfc6K0jS2p18YqBPr0CxLHjt9dW0bR/59DMDIUmS9Ju6dQKdOiTHEbffEfl4hrWCJEnStmYYJGm9nBUk6a/839GBO24PVKmS4ut50L5j5IUXbfJIkqTfnPoPOOxQWL0ael4VWbzYWkGSJGlbMgyStF7OCpK0IersHnj0oe1o2ABWroS+/SJ9+uaxZImNHkmSlFw/qEfXQPWdYeG3cNXVkTVrrBMkSZK2FcMgSev066yglLOCJP21cuVSDOwfaN0q+dx46WVocW7klVdt9EiSpOT6QTdcFyhZEt57H4YOs0aQJEnaVgyDJK3TL7OCjj3WWUGSNkxGRqBN6xR3DgvsUhOWLEnO/O3eK4+5c234SJJU1NWoEejdMzm2GPcYPPOs9YEkSdK2YBgkaa1+PyvoHGcFSdpIdesG7h4ROKdF8jkyYSKc3SpyyxCXjpMkqahrckgykxhg0E2Rd9+zNpAkSdraDIMkrdU99zkrSNLmKVYs0K5tintGBho3gtxcGDcezjg7cuddeXyzwMaPJElFVetWgcMOhVWroVvPyCefWhdIkiRtTYZBkv5k2vTIpMnOCpK0ZdSqFRh0Y4qbBwV22xWWLoP7H4R/nhnp3jOPSZO9gLQkSUVNKhXo0yuw376wbBlcfmXk63nWA5IkSVuLYZCkP3FWkKStoUH9ZOm4ftcGDqwHeXkw4Q3ocmXk5FMiNw5MgqHVq20ESZJUFGRnB/r3C+xaCxZ9D12uiCxebB0gSZK0NWTm9wAkFSzOCpK0NWVkBA4/FA4/NDB7TuTxf0X+8xIs+QGeehqeejpSsiQcWC/SqEGgYUOospOfRZIkpasyZQKDB0CHCyNzv4ZLL4/cMhjKlXP/L0mStCUZBkn6g19mBTVr5qwgSVtXzRqBSy8OdO4Uee99ePW1yOsT4PvFMGEiTJiYfB5VqxZp1AAaNggcsD+ULOlnkyRJ6aRixcDggdDposinn8HFlyWBUPny7vMlSZK2FMMgSb+a/tFvs4JaOStI0jaSmRmofyDUPzBw2SWRWbNg0hSYPCUybRrMnZvcxj8eycyEffeJNGwQaNQAdtsNQvDzSpKkwq76zoEht8DFl0Y++xw6Xxq57SbYfnv385IkSVuCYZCkX/1hVlBVD7okbXupVKBuXahbF1q1DCxbFnlnahIMTZoC8+fD1Hdh6ruR4SOgYkU44vDIkYcH9tk7+fuSJKlwqlkjMORWuOjSyOzZ0PmSZIbQDju4f5ckSdpchkGSgGRW0NuTnBUkqWApVSpw2KFw2KGBGJNrCUyaDJMnR6a+B999B+PGw7jxkR0qwv8dHTnhuECNGn6OSZJUGFXfOTD0liQQmvMltO8YufEGqL2b+3ZJkqTNkcrvAUgqGJwVJKmgCyGwc7XAqf8IDOif4pknAzdeHzi2GZQqBd9+Bw89DGe3inS6KI9nn4+sWhXze9iSJGkjVasWGHpboPrOsPBb6Ng58uZb7tMlSZI2h2GQJGcFSSqUihULHHJwoFf3FE89HrjhusAhB0MqBe9/AP1uiJzVMvLsc5HcXBtIkiQVJlV2CgwfFqh3AOTkQLeekUfHR2J0ny5JkrQpDIMk/TYrqKmzgiQVTsWKBQ5tErjx+hTjHw6c3yZQoQJ8swD69Y+0ahN5fYINJEmSCpOyZQKDBwSOPw7y8uDWIZGevSOLl7g/lyRJ2liGQVIR99HHv80KOsdZQZLSwA47BFq1DDwyJtDxgkCZMjB7NvS4KtK9V+Tbb20gSZJUWGRlBbpdEejUIZCZCa9PgHNaRya+4f5ckiRpYxgGSUXc72cFVatmGCQpfWRnB846IwmFWraAzEyY+Aa0ODfy5L+dJSRJUmERQuDM0wN33RGotQssXpwsG3fdDXksWuT+XJIkaUMYBklF2EcfR95621lBktJbmTKB9m1TjLorsMcesGwZDBgU6XJlZInLzEiSVGjUrh24a3jgrDMgBHjueTijReSBMZFVq9ynS5IkrY9hkFSEOStIUlFSa5fA8KGBCzsGsrNh8hRo2z4yc5bNI0mSCovs7EDHC1IMvz05ySMnB4aPiLQ8N/LiS5HcXPfrkiRJa2MYJBVRzgqSVBRlZATO+GeyzEy1qvDNAuhwYeTZ520cSZJUmOy1Z+DO2wM9uwcqVICv58E110bOPify72ciq1e7b5ckSfo9wyCpiPplVlDTY5wVJKnoqVUrWWbm4INg1Srod0Nk2PA8ryMkSVIhkkoFmjcLPHR/oO15gbJlYe7X0H9A5PSzI/fcF1mw0H27JEkSGAZJRdLHM36bFdSqpUGQpKKpTJlA/36B1q2Sr8eMhVuHRAMhSZIKmZIlA+eeExg3NtCpQ6DC9rBwIdx9T+TU0yOXXZHHSy9HcnLcx0uSpKIrM78HIGnbu+deZwVJEiRnFLdpHahYMTJwcGTcY7BmTeSyS5LvSZKkwqNkycCZp8M/ToZXX4N/PxN5973kOoGTp0Sys+GgRpEjjwgc1Dj5eUmSpKLCMEgqYj6eEXnTWUGS9Acn/S2QmZksK/OvJ2H1msiVXZJrDEmSpMIlOzvQrCk0axqYOzfyzHORl15Oriv06uvw6uuRrCyod0CkySGBQw6CSpXc50uSpPRmGCQVMc4KkqS1O755IDMD+vWPPP0MlC4V6dzJz0lJkgqzatUC7doGzm8T+eRTePmVyKuvJdcWmjQZJk2ODL4Zdt890uTgQJNDoPZuEII1gCRJSi+GQVIR4qwgSVq/Zk0DIUDffpGHH4Wdq0VOPsnPS0mSCrsQArvXht1rB9qfH5nzJUx8A954MzJtOsyaBbNmRUbdC5UqwWGHRg5rEth3H8jMtBaQJEmFn2GQVITcc18yK+gYZwVJ0jo1PSYwbz6MHBW5+dZIlSrQsIGfmZIkpYsQAjVrQM0a0OKswOLFyUlzE9+ITPkvLFwI48bDuPGRctvB4YdFjm0W2HsvZwxJkqTCyzBIKiJmzIi8+RaknBUkSX+pVUv4ai48/wJcdXXkjqFQaxc/OyVJSkflyweOb54sGbtyZWTKO/D6hMjEN2DJD/DEU/DEU5Fq1eDYpnDC8VCxgnWBJEkqXFL5PQBJ28ao+367VtDOzgqSpPUKIdD18mRpmGXLoGv3yA8/xPweliRJ2sqyswNNDg706JriyccCNw8KHNsMShSHuXOTmcOnnRG5cWAeX35pbSBJkgoPwyCpCHBWkCRtvGLFAtdfG6hSBeZ/A9deH8nLs+kjSVJRkZkZaFA/0Kt7iiceC/TsniwVt3o1PPU0nN0q0r1XHjNnWR9IkqSCzzBIKgKcFSRJm6ZcuUC/voFixeDtSTD6gfwekSRJyg8lSwaaNwsMvz3FsCGBJofw/+zdd5iU5fm38fOaXViaSBPsBXtJ7EnsvcXeewHpIEhRpApiRZAOggoKijX2aOy9JyaKEcWIGhUUUZr03bnfP574M8mrBqXs7uz5OY45nFkQbmZ29rnm+j739ZASvPAinN8qcUmvPNM+MBSSJEkVl2GQVODcFSRJK2fLLYKunbOfnzdNSLzxZxs9kiRVZb/+VXD1FTluvSU49ODss9aLL0HzloleffPMnGmtIEmSKh7DIKnAjbvJXUGStLKOPCI46vfZGcD9ByRmzbLJI0lSVbfpJkHf3jkmTQgOORgi4Lnns/FxE25JLF1qvSBJkioOwyCpgL31duL1N6CoCJqdaxAkSSujc6dgyy1g7jzoNyBRVmaDR5IkwSabBJf2znHLTcEuO8OyZdlu4rPOS7z6mvWCJEmqGAyDpAKVUmLcjdkHj6N+DxusbxgkSSujpCS4vH9Qqxa8PQXuvLu8VyRJkiqSpk2DYdcF/S8N1mkEM2dCt+6Jqwfm+fZbQyFJklS+DIOkAvX6G/DW21C9mtcKkqRVZYMNgo4dsp+pN9yU+HC6jR1JkvS9iOCgA4LbJgannJSNjnv4ETinudcdlCRJ5cswSCpAKSVu+Ne1go4/Dho3NgySpFXlyCNgrz1h+XIYcEVi+XIbO5Ik6T/VqhV07JBjxNBg/fVh1izo3C0xcnSe0lJrB0mStOYZBkkF6IUX4b33oWYNOOsMgyBJWpUigu7dgnprwz8+hPG32NCRJEk/bKcdg1tuCk44Lnt8x11wwYWJWbOsHyRJ0pplGCQVmLKyxI3jsw8WJ58E9esbBknSqtagQXBR1+zn622T4Z2/29CRJEk/rGbNoMuFOa4YENSpDVPegeYtE6+/Yf0gSZLWHMMgqcA89QxM/wjq1IbTTjUIkqTVZb99g8MOhXwerh6YWLbMho4kSfpx++0T3DQu2GpLmDsPul6cuHF8nrIyawhJkrT6GQZJBaS0NDF+QvZB4vTTgrprGQZJ0urUqUPQoD58/AncOrm8VyNJkiq6DTYIxowMjjsGUoKbJ0KXixLffGMgJEmSVi/DIKmAPPon+OxzqFcPTj6xvFcjSYWvbt2gU8cseJ94a+Kjj23kSJKkn1ZSEnTrkqNv76BmDfjLm9CsReJvb1lHSJKk1ccwSCoQy5YlJkzMPjycfWZQq5a7giRpTThwf9hrTygthWuuTeTzNnIkSdL/dujBwQ1jg802ha+/gU5dEg89bB0hSZJWD8MgqUA88BDMmgXrNILjjinv1UhS1RERdL0wqFUL3vk73PdAea9IkiRVFptuEowbExx0IJSVwTWDEiNGeR0hSZK06hkGSQVg8eLEpFuzDwvnnhOUlLgrSJLWpMaNgzatsp+9149LzJplA0eSJK2YmjWDfn2CFs2zWuLOu+GSXomFC60nJEnSqmMYJBWAP9wH38yB9deHo35f3quRpKrpuGPgVzvA4sUwYrTNG0mStOIigvPOCS7rF5SUwCuvQuv2ic9nWFNIkqRVwzBIquTmzk3celv2AaHZuUFxsbuCJKk85HJB185BUQ6eeRZee93mjSRJ+nkO3D8YNSxo1Ag+/hhatUn87S1rCkmStPIMg6RK7uZJiW8XwpZbwKEHl/dqJKlq22Lz4KQTs/tDhiWWLrV5I0mSfp5ttgluGBNsszXMmw8Xdk08/Ig1hSRJWjmGQVIl9ulnifvuz+63axMUFbkrSJLKW/PzgoYN4bPP4fY7y3s1kiSpMlpnnWDksODAA6C0FK4emBg5Ok9ZmaGQJEn6ZQyDpEps7LhEWRn87rew+24GQZJUEdSuHXRsn/1Mnnirs/4lSdIvU6NG0L9v0Ozc7PEdd0GPXomFC60tJEnSz2cYJFVSb09JPPs85HLQrrVBkCRVJAceALvtCsuWwbDhNmwkSdIvExGc3yxH/75B9erw8qvQtkNi5kzrC0mS9PMYBkmVUEqJkaOz4v/II6BpU8MgSapIIoIunYLi4qxp88qrNmwkSdIvd9CBwajh2Sja6R9ByzaJt962vpAkSSvOMEiqhJ5+Bt6dCjVqwPnNDYIkqSLaeOPg5BOz+yNGJUpLbdhIkqRfbtttghvGBFttBXPnQacuiUcetb6QJEkrxjBIqmSWLEmMHpsV/GecFjRqaBgkSRXVuWcH9evDPz+Fe+8r79VIkqTKrnHjYNSwYP/9oLQUrrwmMfr6PGVlhkKSJOmnGQZJlcztd8KXX0LjxnDGaeW9GknST6lTJ2jdIgvtx9+cmDPXRo0kSVo5NWsGl10anHdO9njyHdCzT2LRIusMSZL04wyDpErky1mJWydnBX77NkGNGu4KkqSK7ojDYaut4NuFcONNNmkkSdLKy+WCFs1z9O0dVK8GL70MbTskZs601pAkST/MMEiqRK4fl1i6FH79KzjwgPJejSRpRRQVBZ06ZOH9gw/DBx/YpJEkSavGoQcHI4YFDerDh9OhZdvE21OsNSRJ0v/PMEiqJN6eknjiSYiAThcEEe4KkqTKYsdfBwcdCCnBsJGJlGzSSJKkVWP77YIbxgZbbgFz50KnLon7HrDekCRJ/8kwSKoE8vnE8JFZIX/kEbD1VgZBklTZtG0dlJTA396CZ58r79VIkqRC0qRxMHpEsN++sHw5DB6SuPSyxMKFBkKSJCljGCRVAg/9Ed57H2rVglYtDIIkqTJat0lw5unZz/BRYxJLl9qckSRJq07NmsHl/YP2bYOiInj6GWjeMjHNEbWSJAnDIKnCmzMncf24rHhv0Txo0MAwSJIqqzNOg8aN4Ysv4fY7y3s1kiSp0EQEp58ajBoeNGkCn8+A1u0S997v2DhJkqo6wyCpght9fWLBAthyCzjhuPJejSRpZdSoEbRrnYX6t05OfPWVTRlJkrTq7bB9MOGGYO+9srFx1w1N9OmX+PZbaw9JkqoqwyCpAvvbW4lHH4MI6No5KC52V5AkVXYHHQi/2gGWLOH/dn5KkiStanXrBlddHnRsHxQXZ9csbN4qMfU96w9JkqoiwyCpgiotTQwekhXpRx+VndklSar8IoJOFwQR8NgT8M7fbchIkqTVIyI45eRg9IhgvXVhxgxo0y5x/bi81y+UJKmKMQySKqg774aPPoZ6a0OblgZBklRIttk6OPKI7P6wEYl83maMJElafbbbNrjphuDgg6AsD7dOhmYtElPesQaRJKmqMAySKqDPP0+Mvzkrytu1CerWNQySpELTqkVQqxZMfQ8ee7y8VyNJkgpd3bWCfn1yXHV50LAB/PNT4a6ZdQAAIABJREFUaHdB4rqhea8lJElSFWAYJFUwKSWuGZRYuhR22RmOOLy8VyRJWh0aNAjOOycL+68fl1i0yCaMJEla/fbZO5h0c3D4YZAS3Hs/nHlu4qlnEilZj0iSVKgMg6QK5qE/wpt/hZIS6N4tiHBXkCQVqpNPhA03hK+/gYm32nyRJElrRt26Qe8eOYYMCjbcAL7+Gi7tn+jWPfHxJ9YkkiQVIsMgqQKZNSsxakxWeLdqEWywgUGQJBWyatWCDu2yn/V33p2NCZUkSVpTdt8tuGV80OxcqFYNXnsdzm2WjY6bM9e6RJKkQmIYJFUQKSUGDUksXAjbbQsnnVDeK5IkrQl77QG/2R2WL4eRY2y6SJKkNaukJDi/WY5bxgd77wVl+Wx03GlnJibdlli82PpEkqRCYBgkVRBPPAUvvwLFxXDJxUFRkbuCJKkqiAguaB8U5eCFF+HPf7HhIkmS1ryNNwquviLH8CHBllvAwoUw9obEyacnbr8zsWSJNYokSZWZYZBUAcyenRgyLCuszz07aLqZQZAkVSWbbRocf3x2f/jIRGmpzRZJklQ+dtk5uHFs0KtHsMH6MHcujBqTOOX0xF33JJYutU6RJKkyMgySyllKiasGJhYsgK23grPPLO8VSZLKQ/PzgrXrwvSP4MGHy3s1kiSpKisqCo44LLhtYtCje7DeuvDNnOyklVPPTPzhvsSyZYZCkiRVJoZBUjl78OHsIp3Vq0HvnkFxsbuCJKkqqrtW0OL87Bhw4/jE/Pk2WCRJUvkqLg6OPCKYPCm4uFvQpAnMng1DhiVOOzNxx12JRYusWSRJqgwMg6Ry9PmMxMhRWeHcqmWw2aYGQZJUlR19JGzeFObPh/E321iRJEkVQ7VqwTFHBbdPCrp2DtZpBLO+gpGjEyecnBgzNs/s2dYukiRVZIZBUjkpK0tceXVi8RLYaUc45aTyXpEkqbwVFwcdO2QnBtx3P0z/yKaKJEmqOKpXD44/NrjjtqB7t2DjjeDbhXDb7XDSaYkrr8lbv0iSVEEZBknlZPId8NbbULMm9LwkyOXcFSRJgl13CfbbF8ryMGJUIiUbKpIkqWIpKQmOPiq49Zbg6iuDHX8NpaXwyKNwTrPERZfkefOv1jGSJFUkhkFSOfj7u4kbb8qK4s4dg/XXMwiSJH2vXZugWjV448/w0svlvRpJkqQflssFe+8ZjBqeY+zoYP99IQJeeRU6dk60bJN46plEaamhkCRJ5c0wSFrDFi5M9BuQKMvDQQfCEYeX94okSRXNBusHp52S3R8xKrFsmQ0USZJUsW2/XXD5ZTlunxQcdyxUrw7vvQ+X9k+cfnbinnsTixdb00iSVF4Mg6Q1bPCQxMyZsN66cFGXIMJdQZKk/9/ZZwYNG8LnM+DuP5T3aiRJklbMhhsG3Trn+MNdQbNzYe26MHMmDB2eOPHUxI3j88yZYygkSdKaZhgkrUF/ejzx+JNQlINL+wR16hgESZJ+WK1aQZtW2XHilkmJr7+2aSJJkiqP+vWC85tloVDXzsEG68P8+XDzRDjx1MS1g/N8+pn1jSRJa4phkLSG/POficFDskK32XnBDtsbBEmSftphh8C228KiRTDuJpslkiSp8qlRIzj+2GDypGBAv2DbbWDZMnjgITjj7ESnLgt4d6p1jiRJq5thkLQGLFmS6NMvsXgx7LwTnH1mea9IklQZ5HJBpw7ZyQOPPApT37NRIkmSKqeiouCA/YNxY4IRQ4M9fwcpwZNPLaNV20SnLnn+8mYiJesdSZJWB8MgaQ0YMjzx4XRoUB/69QmKitwVJElaMTtsHxx2aNYsGXRdoqzMBokkSaq8IoKddwoGXp1j4oTg2GNKKCqCv7wJnbokWrdLvPqaoZAkSauaYZC0mj36WOKPj0DuX9cJatjQIEiS9PO0ax3UqQ3vT4P7Hijv1UiSJK0aTTcLrhxQhztvC048HqpXh3enQrfuifYdE2+9bSAkSdKqYhgkrUYfffz9dYKanxfsuotBkCTp52vYMGjTKjuGjLsx8dVXNkYkSVLhWHfdoHOnHPfcEZx6ClSvBm9PgfYdE10uyvPhdGsfSZJWlmGQtJosWJDo2SexZAnsvpvXCZIkrZxjjobtt4NFi2DYSBsikiSp8DRoEFzQLsedk4PjjoGiInj9DWjWInHNoDxff20NJEnSL2UYJK0GZWWJy65IfPopNG4MfXt5nSBJ0srJ5YJuXYKiHDz7HLzyqs0QSZJUmNZZJ+jWJcfkicEB+0M+Dw89DKedlZh0W2LpUusgSZJ+LsMgaTW4cULilVezecdXXR7Ur28QJElaeVtuEZx8Unb/uqGJxYtthEiSpMK1wQbBgH45Ro8Itt0GFi+GsTckzjgn8cRTiZSshSRJWlGGQdIq9vSziUm3ZvcvuTjYeiuDIEnSqtP8vKBJE5j5BYy90QaIJEkqfL/+VTB2dNC3d9C4MXz5JfQfkGjdLvHuVOshSZJWhGGQtAp98I/ElVdnhejpp8KhBxsESZJWrVq1gou7ZseXe/4Af3vLBogkSSp8uVxw6MHB5IlBy/ODmjXg3anQul1i8NA8CxZYE0mS9FMMg6RVZPbsRPceiSVLYPfdoHVLgyBJ0urx298ERx+Z3b/yGsfFSZKkqqNGjeDcs4M7bgsOPwxSgvvuh7POTTzp6DhJkn6UYZC0CixenOjeMzHrK9hkY+h/aVBcbBgkSVp9OrTLxqTMmOG4OEmSVPU0bBj07pFj+JBg443g62+g34BE14sTn31mbSRJ0n8zDJJWUj6fuOyKxPvToN7aMPCqoO5aBkGSpNWrdu2gezfHxUmSpKptl52Dm28KWjQPqleD19+Ac5olbp6YWLbM+kiSpO8YBkkraczYxAsvQrVqcOXlwQYbGARJktaMfx8Xd8XViW+/teEhSZKqnurVg/POCSZOCH6zOyxbDjeOTzRrkfj7u9ZHkiSBYZC0Uu66J3H7ndn9Ht2DX//KIEiStGa1bxusty7MnAmDhjgnX5IkVV0bbhgMHhj07xs0bACf/BPadkiMvSHvLiFJUpVnGCT9Qk88lRg+MismW54fHHqwQZAkac2rUye4tE9QlIMnn4JH/1TeK5IkSSo/EcFBBwaTbgkOOwTyeZh0G7RonXh/moGQJKnqMgySfoHX30hccVVWRJ50ApxzVjkvSJJUpe2wfXB+8+ykhCHDEv/8p40OSZJUtdVdK+jTK8eVA4L69WH6R9CqbeKmCXmWL7dWkiRVPYZB0s/07tRErz6J0lI46ADo2CGIcFeQJKl8nXk67LoLLF4C/QZ4wWRJkiSAffcJJk0IDtgfyspgwi1ZKPThdGslSVLVYhgk/Qz//Gfi4ksSi5fAbrtCrx5BLmcQJEkqf0VFQZ+ewdp1YdoHMHK0DQ5JkiSAevWCAf1y9L80q5U++Aec3yox8dZEaak1kySpajAMklbQ7NmJLhcl5s6DbbaGKwcE1asbBEmSKo5GjYLePbNj0733wyOP2tyQJEn6zkEHBJNuDvbZC0pLYdyNibYdEh9/Ys0kSSp8hkHSCpi/IAuCvvgSNtwQrr0mqFXLIEiSVPHs8bug+XnZMWrQdYn33rO5IUmS9J0GDYIrL892VNepA1Pfg+YtEpPvSJSVWTdJkgqXYZD0PyxdmrikZ2L6R9CwIQy5NqhfzyBIklRxnXcO7LUnLFsOPfsm5sy1sSFJkvSdiOCwQ7NrCf3ut1nNNPr6RIdOiU8/s26SJBUmwyDpJyxblujVN/H2FKhTGwYPDNZbzyBIklSx5XLZ2a4bbQSzZkHffs7DlyRJ+m/rrBNce3VwyUVBrVow5R047/zE3X9I5PPWTpKkwmIYJP2I0tLEpf0Tr74GJSVwzVXBFpsbBEmSKoc6dYKrLg9q1oS//g0GD02kZFNDkiTp30UERx0ZTJwQ7LYrLF0Kw0YkLuyamDXL2kmSVDgMg6QfUFqa6H954oWXoHo1uObKYMdfGwRJkiqXTTcJLu0TRMBDD8Ntt5f3iiRJkiqmdZsEQwYF3ToHNWvAm3+Fc89PPPe8gZAkqTAYBkn/pawscdU1iWeeheJiuOLyYLddDYIkSZXT3nsGHTtkx7HrxyWeesaGhiRJ0g+JCI47Nhh/Y7DN1rBgAfTqm7hmUJ7Fi62hJEmVm2GQ9G/KyhJXDUw89gQUFcGAfsEevzUIkiRVbiefGJx8Ynb/iisTU96xmSFJkvRjNtowGDMyOOsM/m+HdbMWibenWENJkiovwyDpX74Lgv70GBTloF+fYJ+9DYIkSYWhQ7tgn71g2XLo3jMxfbrNDEmSpB9TrVrQplWOoYODxuvAZ59D+46JkaPzLF1qHSVJqnwMgyR+IAjqGxywv0GQJKlwFBUFfXsH220L8+fDhV0Tn35mI0OSJOmn7LpLMHFC8PsjICW44y5o3jLx7lTrKElS5WIYpCqvrCxx5TUGQZKkwlezZjBoYLDF5vDNHOjUJfHFFzYyJEmSfkqdOkHP7jmuuTJo2AA++Se0aZ8Ye2OeZcuspSRJlYNhkKq074Kgxx43CJIkVQ111wqGDAo22RhmzYKOXRKzZ9vEkCRJ+l/22jOYdHNwyMGQz8OkW6Flm8S0D6ylJEkVn2GQqqz/DoL6X2oQJEmqGurXD4YODtZfH2bMyObfz5hpE0OSJOl/qVs3uLR3jisuC+rVgw+nZ4HQ+JsTpaXWU5KkisswSFXSDwVB++9nECRJqjrWWScYNjhYbz34fAa07ZCYPt0GhiRJ0orYb99sl9D++0JZGYy/OdGqnfWUJKniMgxSlVNamhhwpUGQJEnrrReMGRE03Qy+/hrad0q883cbGJIkSSuifr1gQP/g0j7BWmvBtGlwfuvEzRMTS5daU0mSKhbDIFUpS5cmevZOPPkUFBUZBEmS1KhRMHJYsP12sGABdO6aePkVmxeSJEkrIiI45KBsl9Cee8Dy5XDj+MQ5zRKvvGZNJUmqOAyDVGUsXJjoenHi5VehenW4+kqDIEmSIJt9P3Rw8JvdYfES6N4zcfudiZRsYEiSJK2IRg2Da67Mdgk1bJiN4b2oe6JH7zwzvTajJKkCMAxSlTBvXqJTl8Tf3oJateC6a4M9fmsQJEnSd2rWzBoYRx8FKcGoMYmrr00sX27zQpIkaUV8t0vo9knB6admE0leeBHOPNfRcZKk8mcYpII3e3aifafEe+9DvbVh+JBgpx0NgiRJ+m/VqgUXdw06dghyOfjjI9CpS2L2bBsXkiRJK6pWraB92xw33xTssjMsW/b96LiXX3H3tSSpfBgGqaB9PiPR9oLExx/DOo1g5PBgm60NgiRJ+jERwSknBQOvCmrXhrenwHktEi++tKy8lyZJklSpbLZpMOy6oF+foFGjbHTcxT0S7S5IvPFnQyFJ0pplGKSCNf2jrMCaORM2WB9Gjwg23cQgSJKkFfG73wY3XB9ssTnMnQut2y1g7A15SkttWkiSJK2oiODgg4LJE4MzTsuuYTzlHejcLetZ/PkvhkKSpDXDMEgF6d2piQ6dEl9/DU03g1EjgvXWMwiSJOnn2HijYOzo4Lhjs8eTboOOnROzZtmwkCRJ+jlq1Qratclx1+3BySdC9WpZKHRh16x/8Zc3DYUkSauXYZAKzht/TnTqnJg/H7bdFkYOCxo1NAiSJOmXKCkJunXOMXhgHWrVysbGNWuReOU1mxWSJEk/V6OGQacLslDopBOyUOitt7PrNF5wYeKllxNlZdZZkqRVzzBIBeXZ5xIX90gsXgK77QrDBgd16xoESZK0sg4/rITx44KttoJ58+Gi7onRYx0bJ0mS9Es0ahRc2DHHnZODE4+HatXgb29B956J085M3HZ7Yu5c6yxJ0qpTXN4L0PdSSnz8Cbz1Fkz7R6K0FPjXcb+kBmyycbDpJrDpptCwQTZ3Vt978OHEoOsS+Tzsvx/07RVUr+5zJEnSqrLhhsH1I2HUmMQf7oPJt8Of/5zo0R223MJjriRJ0s+1zjpB507Bmacn7ron8cdHYeYXMGZs4sbx8NvfJA46MNhrj2zUnCRJv5RhUDlLKfHmX+GBhxJvvglz5/3k7/6/e+s2gf33Sxywf7DdtgZDt05OXD8ue36OPgq6dQ6Kiqr2cyJJ0upQvXrWsNh5p8TAwYlpH0CL1onzzoGzz4TiYo+/kiRJP1fjxkGHdkHL8xNPPg333pd4fxq8+BK8+FKipCQLhvb8XfC73+E4fEnSz2YYVE6WLk088RTcfU/iw+nff72kBHbYHrbbDmrVDCIgAhYsSHzyCXz0CcyYAV98CXfcBXfclWjSBI4/Fk44ruqdJZJSYszYxOQ7ssdnnQGtW0aVD8ckSVrd9t8v+NUOMGhI4oUX4aYJiedfgJ7dYcstPQ5LkiT9EiUlwZFHwJFHBNM/Sjz1dOKpp+Gzz+H5F+D5F7ITYbfaMrHjr2GbrYNttoaNNoJczhpMkvTjDIPWsJQSzz0Pw0cmZn2Vfa1mDTjicDj0kGDrraBatR86eH//tcWLE6+9Ds88l3j5ZfjyS7h+XOL2O+D006pOKFRamo2Fe/iR7HG7NsEZpxX+v1uSpIqiYcPgygHw1NMwZFjig39AizaJc85KnHNW/EhNI0mSpBXRdLOg6flBi+bZLqGXX4FXXku89x5M+yC7fTdFpmZN2GzTxGabwWabBhttCI0aQcOGUL8eTk+RJBkGrUmffZYYMjwLcgAaN4aTTwyO+j2stdaKH5Rr1gz23y87I3fp0sTTz8AtkxKffZ6FQnfcCec3h2OOKtyD/fwFiX6XJV5/A3I5uLhbcNTvC/PfKklSRRYRHHwQ7LIzDB6anfQy4RZ44cVEz0tgK3cJSZIkrZSIbPfPNltD8/OCOXMSb/wZpr6XeO/9LBRavBjenZrd/v0yA5D1TRo0SDRs8H1AVKcO1K4V1KkNtWpD7VrZ12rVgtq1yb5eC6pX99IEklQoDIPWgJQSd9+TBTXLlkO1atk4s7POCEpKVu6AWlISHHE4HHIwPPk03Dwx8dlnMHhI4v4H4cILYOedCuug/fEniUt6ZuFXjRrQt1ew7z6F9W+UJKmyadAguLw/PP0MXDc08Y8PoWXrxMknJZqfF1Vi17IkSdKaUL9+cOgh2YQZyCanfPoZfPQxfPRR4qOPYeZMmP01zJkD+TzMnp3d3p/2739S+oE//T8VF0Od2un/AqO6daHxOtCkCazbJFh/fdhsM6hfz1pPkio6w6DVbMmSxMBBicefzB7vtit0uTDYeKNVe5AsLg4OPxQOPhAeeAhuHJ/48EO44MLEgQckOrYPGjWq/AfmZ59bxkWXJBYtygqPqy8Pr0sgSVIFEREcdGC2S2jI8Gz38h13wdPPJrp0gr338pgtSZK0qhUXB5ttCpttCuz/n/VWWVliztwsCPr66ywg+uYb+HZhYtFCWLgIvv0WFi2ChQv/dVuUPU4JSkth7rzs9v/7PkyqVy+x2abZ7qVf7ZBdW7J+fWs/SapIDINWo5kzEz37ZPPzi3LQoX1w0gmrd3ttcXFw4vFZKHTD+MSDD2Vn6L72eqJVCzjumMo5Oi6lxK2TYdyNC0gJdtoRBvQPzzyRJKkCql8/uOzS4IjDE9cNScz8Ai7pldhn78SFHYMmjT1+S5IkrQlFRUGjhtCo4X//yk/XY/l8YvHiLBz6duH3YdG8efDlLPjii8QXX8Knn8GMGTB3Lvz1b9nt9juzkGjDDRN77QH77hPssH3l7EdJUiExDFpNpryTjTKbNx/q1YMB/WKNjmtbe+2gW+fg2KMSA69LTJ2aXdj5T4/DxV2oVLtplixJXDUw8dTT2ePjjoFOF3hRakmSKro9fhtMuhkm3JK44y544UX4818SLZvDCcdnJ7FIkiSp4snlgtq1s+sHNf7B3/F9Hbd4ceKTT+DDj+Dv7yamTMlG1n32Gdx5N9x5d6JePdh3n8QxRwXbbG0NKEnlIVJK/3tAKDBnzpzVvZZVon79+uW+1ldeS/Tum1i6NNsee8WA8j0Dtqws8cBDMPaGxMKF2S6lk0+iUszv/3JWokfvxLRpUFQEvS6pzaGHLC7vZWk1qQjvX61evsaFzde3sK3s6/vh9MSg6xJT3skeb7UVXNwl2Gabil2LVCWV+T1cv3791f53rMxzs6qf28WLE4cckX2Me+LRoGZN30f6eSrz+12Fx+/HwjR/QeKvf4XnX0y89HI2iu47W28Fxx0THHwQFfIY5vekKhq/J7UiVuQzUW4NrKNKeeKpbEfQ0qXwu9/CyGHlPwqlqCg44bjgtluCA/aHsnw2v//sZomXXl6hLLBcPP9ColmLLAiqtzYMHRycekqN8l6WJEn6BTZvGowaHlzUNahTB6ZNg1btEkOH51m4sOLWI5IkSfr56q4V7Ldv0KdnjofvD4YMCg45GKpVg/enwTWDEiedmph0W7IWlKQ1xDBoFbrvgcRllyfKyuCQg+HqK4IaNSrOGQ6NGgUD+uUYeFWwbhP48kvo3jPRu2+er76qOAfeJUsS1w7O07NPYv787MzhG65fs2P2JEnSqpfLBcceHUyeGBx6MOTzcM+9cOa5iWefS6zghnVJkiRVIsXFwe67BZf2znHf3UG7NsH668O8+dkUm5NOS9w80VBIklY3w6BV5P4HE4OHJFKCE46DPj2jws7B33OPYNLNwRmnZyPjnn0+a8Lcc2+irKx8D7x/fzfRonU21g7gjNNg7KhgvfUq5nMpSZJ+vgYNgr69cwwZFGy4AcyeDb0vTXTvkZg50yaAJElSoapXLzjjtOzkoD49g403ggUL4MbxidPOSjz0cPn3piSpUBkGrQKPP5EFQQBnng6dOwW5XMUOL2rWDNq1znHTDcF228KiRTB0eKJV28Sbf13zB905cxJXXpOndbvEx59Aw4YwZFDQrk2OatUq9nMpSZJ+md13C24ZH5x7NhQXw8uvZmNsJ9+RKC21CSBJklSoiouDww7NTlbu1yfYcEOYMycbH3d+q/LpTUlSoTMMWkkvvJi44qrvdwS1aRVEVJ7wYovNg+tHBd06B3VqZ3NbO3ZOdOue54N/rP4D79KlibvuTpx+VuKRR7OvHXEY3HxTtoVYkiQVtpKSoOX5OW6+KdhpR1iyBEZfnzUBprxjE0CSJKmQFRUFBx8UTJoQdGyfXVvyHx9mvakBV+aZO9d6UJJWFcOglfDGnxN9+yfK8nD4YXBhx8oVBH0nlwuOOzaYPCk44TgoKoJXX4PmLRN9++d55++rfob//PnZPNgTT00MH5X4diFsvRVcPyro1SNH/XqV73mUJEm/3KabBCOGBj26B3XrwofToW2HxMDBeebPtwkgSZJUyKpVC045Objj1uD44yACHnsczjo38fiTXltSklYFw6BfaMo7iR69E8uXw/77wiUXVfzRcP9LgwZBlwtz3HZLcNABkBI8/Qy0aZ+Nj3vs8cSSJb/84JtSdobvoCF5TjwlceP4xNy5sG4TuLhbMG5MsMP2lfs5lCRJv1xEcOQR2Qz53x+Rfe3Bh+CMcxJ/etwmgCRJUqGrVy/oemGO60cFTTeDufPgssuza0t+8421oCStjEgr+Kl6zpw5q3stq0T9+vVX+1qnfZDoeGG2m+W3v4GrLg+qVy+8EOMfHybuuifx5JOwbHn2terVYdddYK89gt/8Bpo0zrb0/pCUErO+go8+gremZH/OzC++//Utt4AzTg8O2C+bFbsi1sTrq/Lj61v4fI0Lm69vYSuP1/dvbyUGDUl8/HH2eJedoVvnYOONC6/uqggq83u4fv36q/3vWJnnZlU/t4sXJw45IvsY98SjQc2avif081Tm97sKj9+P+jHLlyduux1umZSdjF2vHvTsHuy5x+o97vk9qYrG70mtiBX5TGQY9DN9/EmiQ8fE3Hmw469h8MCgRo3C/vA1Z27igQfhj4+k/whzILvYc5PGsN56ULMmLF0Ky5bBosXw+eewcOF//v6aNWG/feHwQ4Ndd+Fnj9Xzh19h8/UtfL7Ghc3Xt7CV1+u7fHnijrvg5omJpUuhWjU483Q4+8ygpKSwa7A1rTK/hw2DfC/o56nM73cVHr8f9b9Mn57of3niw+nZ4xOOg/ZtV18t6PekKhq/J7UiVuQzUfEaWEfBmDEz0blrFgRtszUMvKrwgyCA+vWC886Bc8/Odvm89Aq8/Eri3alQWgqfz8huP6SoCDbaEDZvCvvuE+y1J1XiOZMkSatGtWrB2WfCQQfCdUMTr74GN0+EJ55MdO0Mv9ndukKSJKmQNW0ajBsDY29I3HUP3Ht/NoHmiv6w4YbWgpK0ogyDVtDs2YkLuya+mg2bbZrtCKpdu2odcCKCpk2hadPsbNyysuz5+OILmDkTli6DkupQvQRKSmD9dWGjjbImjiRJ0spYf73g2qvhuedh6IjE5zOgy0WJgw5IXNAhaNTQekOSJKlQlZQEHTsEv/1N4vKrEh9+CC1aJ3r3hL33sg6UpBVhGLQC5s5NXNgtMWMGbLA+DBkcrL22B5qiomDdJrBuE9hpx/JejSRJKnQRwf77we67wU0TEvfcC089A6++nmjVAo475sevZShJkqTK77e/CcaPg779E1PegUt6Jc4+M9GieVgHStL/kCvvBVR0336b6HJxduHidRrB0MGeeSpJklSeatcOOnbIcePYYNttsmsUDhmWaNM+8f60FbocpiRJkiqpddYJRgwNTjkpezzpNujeM/Htt9aBkvRTDIN+wuLFiYsuSUybBvXqZUHQeusZBEmSJFUEW20ZXD8q6HJhULs2TH0PWrZJDB+ZZ9EimwGSJEmFqrg4Ozmof9+gpARefQ3atE98/rk1oCT9GMOgH7FsWaJX32zLaZ3aMOTaYJNNDIIkSZIqkqKi4ITjgtsmBgcdCPk83HUPnHVu4oUXbQZIkiQVsoMODEYND9ZpBB9/Ai3bJt78qzWgJP0Qw6AfUFqa6Dcg8fobULMGDBoYbLknWE9WAAAgAElEQVSlQZAkSVJF1ahh0L9vjuuuDdZfH2Z9BT16J3r0zvPlLBsCkiRJhWqbrYMbrs/GB8+fD527JR5/wvpPkv6bYdB/yecTVw1MPP8CVKsGV10R7LC9QZAkSVJl8Jvdg0kTgrPPhKIieOHFbJfQXfckyspsCkiSJBWiRo2CkcOCAw+AsjK47IrE5DsSKVn/SdJ3DIP+TUqJ64YlHnscinIwoF+w264GQZIkSZVJSUnQumWOCTcEv9oBFi+G4SMTrdom3nvfhoAkSVIhKikJ+vUJTj05ezz6+sSIUYl83vpPksAw6P/k84nBQxL3PwAR0LtXsPdeBkGSJEmVVdOm2Qz5i7sFderA+9OgVdvEsBF5Fi2yKSBJklRocrnggvY52rfNenp33QP9BiSWLbP2kyTDILIg6NrBifsfzIKgnt2DQw4yCJIkSarscrngmKOCyRODQw6GfB7u/kM2Ou6FF20KSJIkFaLTTw369g6Ki+HpZ6DrxYlvv7X2k1S1VfkwqKwscfXAxEN/hFwOevcIjjjcIEiSJKmQNGgQXNo7x3XXBuuvD7O+gh69Ez165/lylo0BSZKkQnPowcGga4JateCvf4P2HRNffWXdJ6nqqtJh0NKlicsuTzzyp+waQX17BYcdahAkSZJUqH6zezBpQnD2mVBUBC+8mO0SuuueRFmZzQFJkqRCstuuwahhQcMG8OF0aN0+8fEn1nySqqYqGwbNn5/oclHiqWeguBgu7Rsc7Gg4SZKkgldSErRumWPCDcGvdoDFi2H4yETLtomp79kckCRJKiRbbhmMGRVstBHMmgVtOyTenmLNJ6nqqZJh0IyZiTbtE2+9DbVrw+CBwYH7GwRJkiRVJU2bBqOGBxd3C+rUgWnToFXbxMDBeebNs0EgSZJUKNZfLxgzIth+O1iwAC7smnj+Bes9SVVLlQuD/vJmonW7xD8/hcaNYcyIYNddDIIkSZKqolwuOOaoYPLE4PDDICV48CE4/ezEAw85Ok6SJKlQ1KsXDLsu2HMPWLYMel+auO8Baz1JVUeVCYOWLUuMGpOnU5fEnDmw5RYwbnTQtKlBkCRJUlXXoEHQu0eOUcODzZvC/Plw7eBsN7mj4yRJkgpDjRrBlQOCo4+EfB4GD0ncNCFPStZ7kgpflQiDPvo42w10+53Z42OPhtEjgkaNDIIkSZL0vR1/Hdw0LujYIahdG6a+5+g4SZKkQlJcnI0JbnZu9njCLTB4qDvCJRW+gg6Dvvgicc21ec47P/HBP2DtunDV5cFFXXPUrGkQJEmSpP9fcXFwyknZ6LjDDv3P0XEPPpzI520USJIkVWYRwfnNcnS5MIiA+x+AfgMSy5ZZ50kqXAUZBn36WWLw0DynnZV46I9QVgZ77Qm3TAj22dsQSJIkSf9bw4ZBn545Rg77fnTcwEGJ1u0T7zk6TpIkqdI74bigX9+guBieeRYuuiSxcKF1nqTCVFzeC/ilvpvlGREsW5b49DN44UV4/sW5TJv2/Q/tXXeBFs2DX+1gCCRJkqSfb6cdg5vGwb33w43jE1OnQsu2iWOPTrRqEdSta50pSZJUWR10QFB3LejZO/GXN6FZi3lcfWWifj1rPEmFpVKGQfl8nuNPhoULoaQkMW/ev/9qGUVFsNuucNYZwc47+YNbkiRJKycbHQcHHgBjrk889gTc/yA882yiTSs48veQy1l3SpIkVUa77xYMHwoXdU/8/d0y2l0A1w2E9dazvpNUOCrlmLivv85uS5bwf0FQjRqw5x5w+WW1eei+YPDAnEGQJEmSVqlGDYM+vbLRcU03g3nz4Zp/jY57621HikiSJFVW224TjB4RrLdejk8/hbYXJD6cbn0nqXBUyjCoTp3vQ57rR8IfHwieeDQYeFWO44+t4agOSZIkrVY77RiMvyG4oH1QqxZMnQrtOyYu6ZXn409sGkiSJFVGG28c3HZLXTbbFGbPzuq7t6dY20kqDJUyDPp3m28erL12EGEAJEmSpDWnuDg49eTg9knBsUdDUQ5efAnOaZa4ZlCe2bNtHEiSJFU2TZoUMWp4sMP28O23cGHXxEsvW9dJqvwqfRgkSZIklaeGDYOLuuaYOCHYZ2/I5+Ghh+G0sxI33JRn4UKbB5IkSZVJ3brB0MHBHr+DZcugZ+/Eo49Z00mq3AyDJEmSpFVgk02Cqy7PMXpEdibpkiVwyyQ49czEH+5NLF9uA0GSJKmyqFEjuOry4LBDoSwPV1yVuHVyIiVrOkmVk2GQJEmStAr9+lfBmJHBFQOCjTaCuXNhyPDEWecmnn7WBoIkSVJlUVwc9LokOO2U7PH14xJDhiXKyqznJFU+hkGSJEnSKhYR7LdPMGlC0K1z0KA+fD4D+vZLtGqXePU1QyFJkqTKIJcLOrTLcUH7IALuvR/69EssXWotJ6lyMQySJEmSVpPi4uC4Y4M7bguanxfUrAFTp0K37omWbRIvvmwoJEmSVBmcenLQ/9KgWjV4/gW4sGti3jzrOEmVh2GQJEmStJrVqpWFQXfcFpx6CpSUwHvvwyU9E81bJp57PpHP20yQJEmqyA7cPxgyKKhTB6a8A207JGbMtIaTVDkYBkmSJElrSMOGwQXtctxzR3Dm6VCzBnzwD+jVN3He+Yknn3IGvSRJUkW2047B6BFB48bwz0+hTbvE+9Os3yRVfIZBkiRJ0hpWv37QtnWOe+4Mzj0bateG6R9BvwGJ089O3Hl3YsECmwqSJEkVUdPNgrGjgs03h2/mQIdO2TUhJakiMwySJEmSysnaawctz892Cp3fLFhrLZgxA0aMSpxwcmLQdXk++IeNBUmSpIpmnXWCUcOCXXeBxYvh4h6Je+71epCSKi7DIEmSJKmcrbVW0Ozc4N67gou6Bk03g8VL4P4HoVmLRMs2eR56OLFokc0FSZKkiqJOnWDQNcHvD4d8HoYOTwwekigttWaTVPEYBkmSJEkVRM2awbFHB7eMD4YPCfbfD4qKYOp7cM2gxDEnJPpfnuflV2wySJIkVQTVqgU9ugft2gQR2ck8XS9OzJ9vrSapYiku7wVIkiRJ+k8RwS47wy47B3PmJB59DB76Y+LTT+GJJ+GJJxNr14V99k7svVew265Qo0aU97IlSZKqpIjgjNNg442h/4DEX96EVu0SA6+EjTe2RpNUMbgzSJIkSarA6tcPzjgtmDwxGDcmOPlEaFAf5s2Hhx+BS3olfn9M4uJL8tx1d+L9aYmyMs9ElSRJWtP23jMYMzJo0gQ++ywLhN74s3WZpIrBnUGSJElSJRARbLctbLdt0L5t4m9vwYsvJV58Cb74El5+FV5+NWs21K4N22+X2G5b2GabYNutoWFDz0qVJEla3bbYPLjxeujZJzHlHeh2caJTRzj+2Kyek6TyYhgkSZIkVTLFxdlouN12DTpdkJj+EbzyKrz1duLtKbBwIbz+RnaDLCCqXz+x0Yaw0Uaw0YaR/XcD2GADKCmxMSFJkrSq1K8fDB0MAwcnHnscrhuaeH8adOlk3SWp/BgGSZIkSZVYRLB5U9i8KZx1RlBWlvhwOkx5B957L/He+/DxJzBnTnZ7ewp8FxBl/z+ss05i/fXn0aB+nnUaZbuI6q4Fdetmt1q1oEYJlNSAOrUTtWqFZ7ZKkiT9hJKSoHcPaLoZjL0h8cdH4MMPE5dfBus2sY6StOYZBkmSJEkFpKgo2GpL2GpLgKzRsGhR4tPP4NNP+dd/v3/87UKYNQtmzSr9tz/lp2fbb7Jx4tZbHHUiSZL0UyKCM0/P6rJ+l2Un6ZzfMtGvL+y+m3WUpDXLMEiSJEkqcLVqBVtvBVtv9d1XsuZDSom58+Dzz2HR4tp8/NFCvpqd+GYOLJgP8/51W7Lk+1tpKXzyz+x+zZrl9k+SJEmqNHbfLbhpHPTqm42L63pxolULOPN0T66RtOYYBkmSJElVVERQvx7ZrX4Jc+Ys4rug6IcsXpw45Iif3jUkSZKk/9+66wajR8DgoYlHHoXrxyWmvgc9u0Pt2gZCkla/XHkvQJIkSZIkSZIKXUlJ0OPioFuXoLgYnnseWrVNfPKJJ9tIWv0MgyRJkiRJkiRpDYgIjjsmGDU8WKdRNn63RZvEc88bCElavQyDJEmSJEmSJGkN2n674KZxwU47wuLF2fWExozNU1pqKCRp9TAMkiRJkiRJkqQ1rEGDYOjg4NRTsse33Q5dL07MmWsgJGnVMwySJEmSJEmSpHJQXBxc0C5H/75BzRrwlzeheYvE21MMhCStWoZBkiRJkiRJklSODjowGDsm2Hgj+Go2XHBh4o67EikZCklaNQyDJEmSJEmSJKmcNd0suHFscNCBUFYGI0cnevVNLFhgICRp5RkGSZIkSZIkSVIFUKtW0K9P0OXCoFo1eP4FOL91YtoHBkKSVo5hkCRJkiRJkiRVEBHBCccFY0YE6zaBGTOgTbvEAw85Nk7SL2cYJEmSJEmSJEkVzDbbBONvCPbcA5Yth2sHJy6/KrF4sYGQpJ/PMEiSJEmSJEmSKqC6dYOrrwjatApyOXjscWjVNvHJJwZCkn4ewyBJkiRJkiRJqqByueCsM4Jh1wUNG8BHH0OL1oknnjIQkrTiDIMkSZL0/9q7+/it57v/48/Ptwv5VkgqpDIaSia5zJmLYaKwzVwPa07GML9tp43Z9tvPLlzMnOdI1jhHaJFmbJwu4jdmTRHCb3K1WbU1tcKp8kUX38/vj2NFp2ysvh3H9+h+v92OmxzH8eXVLeXz/j6O9/sDAADUuJ0GVo6NG7RT8sabyfnfKfP9S5vz5puiEPCPiUEAAAAAAK1A165F/uMHRU48PimK5Je3J6ecVubFPwpCwN8nBgEAAAAAtBJt2hT53MkN+fdLimzcpXJs3Cmnlbn9jjJlKQoBqyYGAQAAAAC0MrvuUmTMT4rstmvy1lvJxT8o861vl1m0SBAC3k0MAgAAAABohTbeuMgPLi5y+mlF2rRJfnV/8tlTykx/RhACViYGAQAAAAC0Ug0NRY47psiVI4tstmny0kvJ588s89MbyyxbJgoBFWIQAAAAAEArt33/ItdcXeSj+ybLliU/+nGZL/5bmTlzBSFADAIAAAAAqAudOxf59reKnPuVIut3SKY9kYw4qcx9/1cQgnWdGAQAAAAAUCeKosghw4tc+59F+vdLFr2e/J/vlPn2d5uzcKEoBOsqMQgAAAAAoM5ssUXlPkKf/UzSpiGZeF8y4uQyTzwpCMG6SAwCAAAAAKhDbdsW+dfPNmTUyCKbb57MnZt84YtlfvTj5ixeLArBukQMAgAAAACoYwO2LzLmP4scMiwpy+SnNyYnnVLmd08LQrCuEIMAAAAAAOpcY2ORc7/akAu+U2TjLsmMmcnnzyxz2cjmNDWJQlDvxCAAAAAAgHXE3nsVGXtdkWEHV3YJTbglOfGzZR6ZKghBPRODAAAAAADWIRtsUOS8cxry75cU2bRHMmdu8uWvlLngouYsWCAKQT0SgwAAAAAA1kG77Vrk+muLHPGppCiSO+9Ojv9MmYn3lilLUQjqiRgEAAAAALCOamws8sUvNOTKkUX69E5eeTX59vfKnHZGmd89LQhBvRCDAAAAAADWcTsMKHLtfxY55V+LrN8heXp6ctoZZc7/bnPm/lUUgtZODAIAAAAAIO3bF/nMCUVu/GmRYQdXjo67977kuBPK/OTa5rzxhigErZUYBAAAAADACpt0LXLeOQ25enSRHT+SvPVWcu11ybEnlLnzrjJLl4pC0NqIQQAAAAAAvMt22xa54rIi3z2/yGabJvPnJxdcXOb4EWXunigKQWsiBgEAAAAAsEpFUWTffYqMva7I508tsuEGyZ//nHz3gjInfLayU2jJElEIap0YBAAAAADA37XeekU+fWyRCTcVOfWUShT6058qO4WOPLbMuJvKLFokCkGtEoMAAAAAAHhfGhuLnPDpShT6/KlFNtmkcnzclaPLHH5UmR+ObM6sWaIQ1BoxCAAAAACAD6SxsbJT6OZxRb52TpEtt0yampKf3ZIcd2KZL3+lOb+Z5L5CUCvaVnsAAAAAAABap/btiww/ODl4aDL10eTnt5V5aHLyyNTkkallNtoo+dj+ZQ4aWmSbD1fuQQSsfWIQAAAAAACrpaGhyO67JbvvVuQvL5W57Rdl7r4neeXVZMItyYRbyvTpneyzd5l99yny4b7CEKxNYhAAAAAAAGvM5psVOf20Ip87uczUR5O77ynzm0nJzFnJ9WOT68eW2Xzz5F8Glxm8R5EdP5Kst54wBC1JDAIAAAAAYI1r27bI4D2SwXsUWbSozENTkgd+XWbKw8lf/vL2jqEOHZJBA8sMHFhk0MCkb9/K1wJrjhgEAAAAAECL6tSpyIEHJAceUOSNN8o8MjWZPKXMlEeS+fOTh6YkD00pkySNjclHdigzcMciOw1Mtt1GHILVJQYBAAAAALDWrL9+kX32TvbZu0hZlvn9H5KpjyZPPFnmqaeSRa8nUx5OpjxciUPrd0h2+FscGrhj0m+7pF07cQg+CDEIAAAAAICqKIoiH+6bfLhvctwxRZYtq8ShJ55MnniizBNPJQsXJo9MTR6ZWolD662X7DCgzKCdKjuH+m1n5xD8I2IQAAAAAAA1oU2bIttuUzka7ugjizQ3l3nxj8m0Jyo7h558Mvnv15JHH0sefWzlnUM7DSwyaCfHysGqiEEAAAAAANSkhoYifbdO+m6dHPmpShyaMaMShx5/osy0J5IFC1beOdSpY7LLzmV2363IbrslPboLQyAGAQAAAADQKjQ0FNlqq2SrrZJPHf72zqHHpyXTnijzxJOVY+UeeDB54MFKHNrmw2X23qvI3kOSD32ocjQdrGvEIAAAAAAAWqV37hw66ojKPYeefS55+JHKTqHpzyTPv5A8/0KZ/7wm2aJnsvfeZfYeUqR/v8rXw7pADAIAAAAAoC60aVNk+/7J9v2Tk0YUefW/y/z2oeTB35R59NHkz7OTcTcm424ss8kmyb77lDlkWJG+W4tC1DcxCAA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\n", "text/plain": [ "
    " ] @@ -6262,8 +6290,8 @@ "
      \n", " \n", "
    • \n", - " \n", - " \n", + " \n", + " \n", "
      \n", "
      \n", "
        \n", @@ -6601,83 +6629,83 @@ " stroke: currentColor;\n", " fill: currentColor;\n", "}\n", - "
        xarray.Dataset
          • chain: 4
          • draw: 500
          • school: 8
          • chain
            (chain)
            int64
            0 1 2 3
            array([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])
          • school
            (school)
            <U16
            'Choate' ... 'Mt. Hermon'
            array(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
-       "       'Hotchkiss', 'Lawrenceville', "St. Paul's", 'Mt. Hermon'], dtype='<U16')
          • mu
            (chain, draw)
            float64
            10.74 -2.383 5.259 ... 8.929 6.259
            array([[10.73560243, -2.38325525,  5.25875081, ...,  5.11437518,\n",
-       "         4.48284624,  3.32088798],\n",
-       "       [ 5.18801686,  5.32356994,  5.77854836, ..., 10.12895743,\n",
-       "         5.10477044,  2.85072762],\n",
-       "       [ 5.279449  ,  1.27221624,  2.57758297, ...,  4.10260339,\n",
-       "         6.22640205,  4.69036724],\n",
-       "       [ 4.59031447,  3.53194292,  2.79784828, ...,  8.92907436,\n",
-       "         8.92907436,  6.25877939]])
          • tau
            (chain, draw)
            float64
            4.142 5.617 6.222 ... 1.42 3.102
            array([[ 4.14169088,  5.61671325,  6.22228848, ...,  3.69125991,\n",
-       "         2.33348628,  2.88903472],\n",
-       "       [ 4.81112853, 10.15731502,  9.12975583, ...,  4.25321054,\n",
-       "         3.74593616,  6.87348279],\n",
-       "       [ 7.51319567, 10.34112694,  9.51897377, ...,  3.9415122 ,\n",
-       "         3.19851804,  2.93427324],\n",
-       "       [ 2.7178844 ,  6.1721701 ,  8.92141693, ...,  1.41969807,\n",
-       "         1.41969807,  3.10210589]])
          • theta
            (chain, draw, school)
            float64
            11.95 4.371 4.869 ... 5.986 6.58
            array([[[11.9501699 ,  4.37131255,  4.86887542, ...,  9.8655532 ,\n",
-       "          6.54410133,  5.83685503],\n",
-       "        [ 4.59141419,  2.10357746, -0.58655321, ..., -6.77760948,\n",
-       "          5.82721013, -0.32546978],\n",
-       "        [11.62216439,  2.16057134,  2.03699279, ..., -1.4545384 ,\n",
-       "          8.10261439,  6.59891615],\n",
+       "
            xarray.Dataset
              • chain: 4
              • draw: 500
              • school: 8
              • chain
                (chain)
                int64
                0 1 2 3
                array([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])
              • school
                (school)
                <U16
                'Choate' ... 'Mt. Hermon'
                array(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
+       "       'Hotchkiss', 'Lawrenceville', "St. Paul's", 'Mt. Hermon'], dtype='<U16')
              • mu
                (chain, draw)
                float64
                5.1 5.565 5.362 ... 3.331 2.2 2.327
                array([[ 5.10040709,  5.56526385,  5.3618364 , ...,  5.06734909,\n",
+       "         3.40778873,  2.69909761],\n",
+       "       [ 3.25671658,  2.00700677,  2.69667926, ...,  2.92315733,\n",
+       "         3.73205842,  3.69622906],\n",
+       "       [ 5.06802039,  2.26509343,  2.87166717, ...,  2.11407466,\n",
+       "         4.81741353,  7.3519171 ],\n",
+       "       [ 0.1038528 , -0.09374646, -0.28283904, ...,  3.33102952,\n",
+       "         2.20015266,  2.32721317]])
              • tau
                (chain, draw)
                float64
                1.202 2.231 1.327 ... 20.19 11.85
                array([[ 1.20212821,  2.23069646,  1.32669804, ...,  1.07703112,\n",
+       "         1.87183738,  1.75443014],\n",
+       "       [ 1.25900468,  1.63896675,  1.46146098, ...,  0.59632991,\n",
+       "         0.58807019,  0.5944897 ],\n",
+       "       [ 0.74925633,  0.71955362,  0.59900204, ...,  4.39631868,\n",
+       "         1.87395808,  3.66925351],\n",
+       "       [ 0.64854989,  0.56661867,  0.55010696, ..., 11.23778771,\n",
+       "        20.19192096, 11.8460885 ]])
              • theta
                (chain, draw, school)
                float64
                5.268 5.037 4.21 ... 14.36 3.22
                array([[[  5.26795905,   5.03707292,   4.21035211, ...,   5.93199746,\n",
+       "           7.22942443,   4.76278775],\n",
+       "        [  5.59672759,   5.05357139,   6.26365386, ...,   4.15659256,\n",
+       "           2.95644306,   6.2024757 ],\n",
+       "        [  5.0123936 ,   5.20823416,   4.25580892, ...,   6.003762  ,\n",
+       "           6.96505472,   4.2518585 ],\n",
        "        ...,\n",
-       "        [ 2.37446043,  1.24055826, 12.01508412, ...,  3.5001716 ,\n",
-       "          6.3168732 ,  6.35331336],\n",
-       "        [ 2.83406092,  8.61145377,  4.64653249, ...,  2.58655352,\n",
-       "          5.41444545,  1.23786006],\n",
-       "        [ 3.86118907, -0.72155858,  2.70786742, ...,  3.66516606,\n",
-       "          6.51863001,  6.73247587]],\n",
-       "\n",
-       "       [[ 7.20108117, -0.24667291,  7.71796254, ...,  6.87783395,\n",
-       "         11.10952912, -0.93810294],\n",
-       "        [21.75297374, -1.7932725 , -5.99911136, ..., 14.39207158,\n",
-       "         14.10770337,  4.85065602],\n",
-       "        [21.79201881, -1.66953615, -5.87256947, ..., 14.52951082,\n",
-       "         12.10126285,  4.87010732],\n",
+       "        [  4.08224287,   5.46704104,   5.79343569, ...,   3.6970148 ,\n",
+       "           6.86174954,   6.85895359],\n",
+       "        [  5.41487964,   3.56785377,   2.69235867, ...,   6.37334377,\n",
+       "           2.53291894,   3.18463374],\n",
+       "        [  2.87002693,   4.11246062,   3.72243438, ...,   1.32983962,\n",
+       "           5.6719599 ,   4.53813603]],\n",
+       "\n",
+       "       [[  2.51482793,   3.30753964,   2.41482108, ...,   0.44359836,\n",
+       "           4.1190871 ,   4.25398038],\n",
+       "        [  3.99947947,   2.40409008,   3.16220843, ...,   4.68306225,\n",
+       "           2.46867937,   1.482205  ],\n",
+       "        [  3.207702  ,   2.81189848,   4.17111688, ...,   5.12158726,\n",
+       "           1.77020089,   2.78709132],\n",
        "        ...,\n",
-       "        [ 9.84913684, 12.61656189,  5.99493273, ...,  1.9998071 ,\n",
-       "         16.01197517,  4.00927407],\n",
-       "        [ 6.3076968 , -2.3953748 ,  2.83320521, ...,  7.58584913,\n",
-       "         -2.21027024,  6.66849414],\n",
-       "        [ 4.98667269,  1.41754423,  9.06969537, ...,  4.58315199,\n",
-       "         -0.35288648, 10.86997631]],\n",
-       "\n",
-       "       [[ 9.68339397,  7.92652044,  2.75662029, ...,  0.58491494,\n",
-       "          7.58748997, 10.66222327],\n",
-       "        [ 7.96772519,  1.01062828,  7.96311474, ..., 10.82538315,\n",
-       "         10.06341431,  0.5685898 ],\n",
-       "        [ 1.74205603, -0.81737683,  5.88044354, ..., -0.40865625,\n",
-       "         19.14354768, -8.94377808],\n",
+       "        [  3.49299364,   3.2849658 ,   3.96773018, ...,   2.52228923,\n",
+       "           2.248126  ,   3.30709396],\n",
+       "        [  3.71134003,   3.65395977,   2.93835064, ...,   4.14588605,\n",
+       "           4.4270301 ,   2.89136813],\n",
+       "        [  4.71327614,   3.53783826,   4.01350509, ...,   4.00573384,\n",
+       "           3.8171959 ,   4.32287278]],\n",
+       "\n",
+       "       [[  5.30897754,   4.73651815,   5.75086504, ...,   5.42648964,\n",
+       "           4.21368951,   4.49280385],\n",
+       "        [  2.21058763,   2.34034814,   3.92649465, ...,   2.90681141,\n",
+       "           2.0888858 ,   2.41656732],\n",
+       "        [  2.44914824,   2.60384926,   2.40744517, ...,   2.31484192,\n",
+       "           2.41159948,   2.7324515 ],\n",
        "        ...,\n",
-       "        [ 7.63158265,  8.19852305,  7.97905964, ...,  3.12752754,\n",
-       "          8.72762295,  7.67904376],\n",
-       "        [ 8.61682529,  5.51484249, 10.37399828, ...,  5.62767154,\n",
-       "          2.4985643 ,  2.08868689],\n",
-       "        [ 2.82445182,  5.33050637,  4.17582426, ...,  5.68025905,\n",
-       "         10.29503981,  5.40365662]],\n",
-       "\n",
-       "       [[10.41592456,  2.45697186,  2.32434962, ...,  4.75782383,\n",
-       "          4.16351619,  5.15781812],\n",
-       "        [ 2.20970154,  8.27852353,  9.83442088, ..., -1.20015954,\n",
-       "         -0.23708244,  6.79111646],\n",
-       "        [12.0692386 ,  4.16968353, -4.70381733, ...,  4.94694984,\n",
-       "         19.27750736,  2.32004447],\n",
+       "        [  8.66912663,   1.512115  ,   2.45966974, ...,   7.22779168,\n",
+       "           3.77851803,   3.6482612 ],\n",
+       "        [  7.24713517,   4.59569558,   5.02705348, ...,   3.79556901,\n",
+       "           6.98287389,   6.38335082],\n",
+       "        [  4.29268803,   6.08441733,   4.14177454, ...,   4.9925426 ,\n",
+       "           4.40638394,   3.50908264]],\n",
+       "\n",
+       "       [[ -0.09199175,   0.49842581,  -0.53020596, ...,  -0.0787323 ,\n",
+       "          -0.54140645,  -0.17915019],\n",
+       "        [ -0.50218323,   0.31870224,  -0.405048  , ...,  -0.28376237,\n",
+       "          -0.34162476,   0.07373626],\n",
+       "        [  0.30515502,  -0.29777416,  -0.20006018, ...,   0.18788433,\n",
+       "          -0.49743822,  -0.0742338 ],\n",
        "        ...,\n",
-       "        [ 9.84647679,  7.83930697,  9.97567288, ...,  9.88766553,\n",
-       "         11.42396199,  9.91282334],\n",
-       "        [ 9.84647679,  7.83930697,  9.97567288, ...,  9.88766553,\n",
-       "         11.42396199,  9.91282334],\n",
-       "        [ 7.61647612,  7.90795379,  5.96991115, ...,  5.27262613,\n",
-       "          5.98567544,  6.58025061]]])
            • created_at :
              2020-06-06T18:09:41.101731
              arviz_version :
              0.8.3
              inference_library :
              pystan
              inference_library_version :
              2.19.1.1

        \n", + " [ 10.15375968, 21.748118 , -4.5702728 , ..., 5.47191682,\n", + " -0.40721312, -9.44280244],\n", + " [ 32.23656165, -1.86894125, -1.08065556, ..., -11.01646021,\n", + " 16.20578347, -1.2524635 ],\n", + " [ 16.94073406, 2.22438863, -2.06371558, ..., -5.02911752,\n", + " 14.36029172, 3.22044715]]])
    • created_at :
      2020-06-10T17:35:53.760020
      arviz_version :
      0.8.3
      inference_library :
      pystan
      inference_library_version :
      2.19.1.1

    \n", " \n", " \n", " \n", " \n", "
  • \n", - " \n", - " \n", + " \n", + " \n", "
    \n", "
    \n", "
      \n", @@ -7015,69 +7043,69 @@ " stroke: currentColor;\n", " fill: currentColor;\n", "}\n", - "
      xarray.Dataset
        • chain: 4
        • draw: 500
        • school: 8
        • chain
          (chain)
          int64
          0 1 2 3
          array([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])
        • school
          (school)
          <U16
          'Choate' ... 'Mt. Hermon'
          array(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
-       "       'Hotchkiss', 'Lawrenceville', "St. Paul's", 'Mt. Hermon'], dtype='<U16')
        • y_hat
          (chain, draw, school)
          float64
          20.64 10.5 -5.046 ... 16.35 -1.61
          array([[[ 20.63738522,  10.5012043 ,  -5.04566929, ...,   8.7332285 ,\n",
-       "          18.53008278,  -6.54725452],\n",
-       "        [ 19.86461026,   6.62318248,  -5.16408634, ...,   6.46381206,\n",
-       "          16.18426053,  29.27370301],\n",
-       "        [ 25.57913754,   2.22890159,  -8.40633281, ...,   7.28818881,\n",
-       "           5.94816768,  -0.20388646],\n",
+       "
          xarray.Dataset
            • chain: 4
            • draw: 500
            • school: 8
            • chain
              (chain)
              int64
              0 1 2 3
              array([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])
            • school
              (school)
              <U16
              'Choate' ... 'Mt. Hermon'
              array(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
+       "       'Hotchkiss', 'Lawrenceville', "St. Paul's", 'Mt. Hermon'], dtype='<U16')
            • y_hat
              (chain, draw, school)
              float64
              -6.037 9.903 14.01 ... 43.57 28.42
              array([[[ -6.0374751 ,   9.90291358,  14.01138981, ...,   5.59146304,\n",
+       "           7.54330337,  -4.2737839 ],\n",
+       "        [  3.23935084,   7.52806977,  22.7911258 , ...,  -8.86228559,\n",
+       "           4.18210178,  22.29583022],\n",
+       "        [  0.43456908,   2.36580065,  -1.0379147 , ...,  -2.11559449,\n",
+       "           7.49674662, -21.8299887 ],\n",
        "        ...,\n",
-       "        [ 20.02968866,  -2.81686571,   5.56223197, ...,   1.86975107,\n",
-       "           6.09389285,  27.74978625],\n",
-       "        [ -9.33973004,   0.43626395,   6.20234702, ...,  -7.71907314,\n",
-       "           7.57063463, -25.31062602],\n",
-       "        [ 23.52022983, -13.0388224 ,   8.75791964, ...,   0.1890194 ,\n",
-       "          19.38994636,   4.85142004]],\n",
-       "\n",
-       "       [[  8.95009592,   4.67016211,  17.29135465, ...,   4.56425409,\n",
-       "          18.90306893, -38.52786218],\n",
-       "        [  9.97064254, -20.52182613,   6.93995894, ...,  23.9960411 ,\n",
-       "          24.35766188, -15.34114179],\n",
-       "        [ 39.00427132,  -4.04256156,  -4.3293367 , ...,  -6.52798623,\n",
-       "          13.62374189,  -6.06476977],\n",
+       "        [-23.37387803,  -4.25993853,  10.16982717, ...,  -7.75839332,\n",
+       "          11.65177613,  -1.57133793],\n",
+       "        [ 35.91084599,   2.11345529,  -1.84769022, ...,  24.76079116,\n",
+       "          -9.83201554,   7.11101976],\n",
+       "        [  3.3266679 ,  -0.50842836,   6.63333991, ...,  11.2513764 ,\n",
+       "           9.90970093,  -3.04506755]],\n",
+       "\n",
+       "       [[  6.45223865,   7.66043625,  13.19122882, ...,  -8.68536593,\n",
+       "          16.75227912,  -2.94953571],\n",
+       "        [-17.21574436,  15.28594632,  18.69754036, ...,  21.8577613 ,\n",
+       "          16.34692255,  16.38001936],\n",
+       "        [ 24.5484029 ,   3.65262266,  -2.04993822, ...,   6.89159587,\n",
+       "          15.5594578 ,   1.3728226 ],\n",
        "        ...,\n",
-       "        [ 44.00963558,  17.65730702,  -4.53842242, ...,   5.53252057,\n",
-       "          -2.21877838,  18.33090945],\n",
-       "        [ -5.65146845,  -1.96411568, -10.31057671, ...,  15.84395016,\n",
-       "           4.8827069 ,  -5.70862416],\n",
-       "        [ -7.59652088,  -0.46991629,  -6.77197062, ...,   9.53299806,\n",
-       "          20.54637027,  23.65904017]],\n",
-       "\n",
-       "       [[ 27.06095661,  -9.89143179,  14.66885259, ...,  14.89931058,\n",
-       "          17.37310674,  10.88580544],\n",
-       "        [ 25.57992053,   0.09263637,   5.09162713, ...,  12.19685867,\n",
-       "          16.63640799,  16.74110778],\n",
-       "        [-17.80800298,   8.83651926,   4.02413155, ...,  -3.70058381,\n",
-       "          12.27771294, -11.08581052],\n",
+       "        [-10.32319932,  -0.89060054,  -4.31425555, ...,  -6.25797114,\n",
+       "           8.71613195,   1.30431368],\n",
+       "        [ 14.13249493,   3.26153202,  15.07045584, ...,  -1.18512082,\n",
+       "           8.13919735,   7.21193093],\n",
+       "        [ 29.38087964, -22.07437273,  31.9967746 , ...,  10.73434552,\n",
+       "           2.34347059,  39.06602106]],\n",
+       "\n",
+       "       [[  7.15692832,  12.3045443 ,  20.37800831, ...,   3.40777519,\n",
+       "          12.38504667,  12.207693  ],\n",
+       "        [ 12.14688069,  -9.51329437, -14.80031082, ...,  -6.10742988,\n",
+       "           6.44135824, -13.40090849],\n",
+       "        [ 16.68112235,  10.34264465,  -1.650218  , ...,  10.15265282,\n",
+       "         -26.03093955,  33.45995267],\n",
        "        ...,\n",
-       "        [-22.57910137, -10.50751024,   2.75168063, ...,  11.49365198,\n",
-       "          20.48522522, -22.09707402],\n",
-       "        [-12.40623774,  16.46567152,   0.81208005, ...,  -9.92078422,\n",
-       "          10.27931093, -18.01526668],\n",
-       "        [ -6.19770232,  27.65023023,  -6.95229655, ...,  13.09059215,\n",
-       "          10.62995982,  14.17440678]],\n",
-       "\n",
-       "       [[ 13.93668777,  16.30080221,  -2.5076843 , ...,   0.14416439,\n",
-       "          -2.85743274,  22.75883983],\n",
-       "        [ 11.7757269 ,  29.09355261,   2.48489421, ...,  -7.05970019,\n",
-       "         -10.78054183,   1.02652982],\n",
-       "        [ 18.33159454,  -0.37400684,  12.44023163, ...,  14.05119661,\n",
-       "          11.52528276, -14.49717182],\n",
+       "        [ 14.99861038,  -0.88997479,   9.97722446, ..., -11.70954208,\n",
+       "          -0.93394289,   0.69076047],\n",
+       "        [ 12.72793049,  13.72292366,   1.02311483, ...,  12.54074923,\n",
+       "          21.63232645, -16.77657839],\n",
+       "        [ 35.24036631,   5.66656187,  30.84014673, ...,  19.07910288,\n",
+       "           9.88245398,  -4.85000145]],\n",
+       "\n",
+       "       [[ 36.13008019,  11.51137938, -12.11040594, ...,  -4.0937036 ,\n",
+       "          -4.57824307,   3.62960921],\n",
+       "        [ -4.4996811 ,  -4.227572  , -16.70268441, ..., -13.19581623,\n",
+       "         -10.44766931, -16.78910203],\n",
+       "        [-16.05075926,  -3.41570814, -19.07427617, ...,  10.16666467,\n",
+       "          11.56985678,  -6.56313687],\n",
        "        ...,\n",
-       "        [ 13.38099709,  11.34656755,  29.57080529, ...,  23.56179887,\n",
-       "          -3.89251974,  22.43742056],\n",
-       "        [ 14.4924457 ,  25.78438465,   3.47125027, ..., -11.23972541,\n",
-       "           5.87609827,  12.62363297],\n",
-       "        [ -2.15357302,  -0.35889516,  11.70752398, ..., -16.31756139,\n",
-       "          16.35276023,  -1.6100444 ]]])
          • created_at :
            2020-06-06T18:09:41.118198
            arviz_version :
            0.8.3
            inference_library :
            pystan
            inference_library_version :
            2.19.1.1

      \n", + " [ 17.4705099 , 23.92588795, -9.50090652, ..., 6.58297516,\n", + " 0.14891797, -6.52292138],\n", + " [ 22.05341924, -9.44319588, 23.89624321, ..., -17.92626387,\n", + " 15.3780051 , -10.24854644],\n", + " [ 22.31591907, 7.32171248, 4.96743133, ..., -23.63981518,\n", + " 43.56503648, 28.41901104]]])
  • created_at :
    2020-06-10T17:35:53.777987
    arviz_version :
    0.8.3
    inference_library :
    pystan
    inference_library_version :
    2.19.1.1

    • \n", " \n", " \n", " \n", " \n", "
  • \n", - " \n", - " \n", + " \n", + " \n", "
    \n", "
    \n", "
      \n", @@ -7415,69 +7443,69 @@ " stroke: currentColor;\n", " fill: currentColor;\n", "}\n", - "
      xarray.Dataset
        • chain: 4
        • draw: 500
        • school: 8
        • chain
          (chain)
          int64
          0 1 2 3
          array([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])
        • school
          (school)
          <U16
          'Choate' ... 'Mt. Hermon'
          array(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
-       "       'Hotchkiss', 'Lawrenceville', "St. Paul's", 'Mt. Hermon'], dtype='<U16')
        • y
          (chain, draw, school)
          float64
          -4.199 -3.287 ... -3.943 -3.855
          array([[[-4.19942661, -3.28736049, -3.81246319, ..., -3.64161907,\n",
-       "         -3.8777117 , -3.86792812],\n",
-       "        [-4.84468182, -3.39536262, -3.70290367, ..., -3.56679748,\n",
-       "         -3.96240769, -4.04375042],\n",
-       "        [-4.22306318, -3.39201826, -3.74108057, ..., -3.3417295 ,\n",
-       "         -3.71131484, -3.85432836],\n",
+       "
          xarray.Dataset
            • chain: 4
            • draw: 500
            • school: 8
            • chain
              (chain)
              int64
              0 1 2 3
              array([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])
            • school
              (school)
              <U16
              'Choate' ... 'Mt. Hermon'
              array(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
+       "       'Hotchkiss', 'Lawrenceville', "St. Paul's", 'Mt. Hermon'], dtype='<U16')
            • y
              (chain, draw, school)
              float64
              -4.775 -3.265 ... -3.288 -3.928
              array([[[-4.77531248, -3.26541831, -3.79306862, ..., -3.41734868,\n",
+       "         -3.80155012, -3.89013937],\n",
+       "        [-4.74233677, -3.26493083, -3.85913523, ..., -3.35800768,\n",
+       "         -4.35306665, -3.86117956],\n",
+       "        [-4.80127773, -3.26049341, -3.79435296, ..., -3.4202951 ,\n",
+       "         -3.83037371, -3.90195488],\n",
        "        ...,\n",
-       "        [-5.08625157, -3.44997389, -4.13186466, ..., -3.3426638 ,\n",
-       "         -3.90400089, -3.85851565],\n",
-       "        [-5.03437649, -3.223393  , -3.80572542, ..., -3.32723526,\n",
-       "         -4.01350454, -3.9880505 ],\n",
-       "        [-4.92183805, -3.60185155, -3.75515958, ..., -3.3461855 ,\n",
-       "         -3.88063291, -3.85212944]],\n",
-       "\n",
-       "       [[-4.58831101, -3.5615617 , -3.91589194, ..., -3.45959799,\n",
-       "         -3.45891657, -4.06763515],\n",
-       "        [-3.71371171, -3.70106456, -3.70909497, ..., -4.05793951,\n",
-       "         -3.29727349, -3.88818856],\n",
-       "        [-3.71263102, -3.68902327, -3.70764377, ..., -4.07322911,\n",
-       "         -3.39549913, -3.88775994],\n",
+       "        [-4.89823119, -3.25360303, -3.84255169, ..., -3.3468912 ,\n",
+       "         -3.84182674, -3.85009788],\n",
+       "        [-4.76051687, -3.31974323, -3.75481426, ..., -3.43614299,\n",
+       "         -4.41767661, -3.92923418],\n",
+       "        [-5.03035662, -3.29708844, -3.77979117, ..., -3.31728337,\n",
+       "         -3.98142649, -3.89523531]],\n",
+       "\n",
+       "       [[-5.07030872, -3.33161955, -3.74879344, ..., -3.31811307,\n",
+       "         -4.18492234, -3.90190415],\n",
+       "        [-4.90704426, -3.37809467, -3.76569291, ..., -3.37288731,\n",
+       "         -4.42763323, -3.98002636],\n",
+       "        [-4.99289549, -3.35610561, -3.79196655, ..., -3.38703001,\n",
+       "         -4.53855552, -3.94029437],\n",
        "        ...,\n",
-       "        [-4.35910836, -3.32808684, -3.84955228, ..., -3.32096444,\n",
-       "         -3.24128484, -3.90784687],\n",
-       "        [-4.67266878, -3.76184271, -3.75798484, ..., -3.49606277,\n",
-       "         -5.26379874, -3.85317596],\n",
-       "        [-4.80390703, -3.43816725, -3.97605371, ..., -3.36988743,\n",
-       "         -4.90566584, -3.8112809 ]],\n",
-       "\n",
-       "       [[-4.37253997, -3.22155062, -3.75625123, ..., -3.31754577,\n",
-       "         -3.76362545, -3.81207209],\n",
-       "        [-4.51874881, -3.46578021, -3.92627312, ..., -3.7157518 ,\n",
-       "         -3.53647059, -4.01097254],\n",
-       "        [-5.15916567, -3.6102543 , -3.84555514, ..., -3.32503344,\n",
-       "         -3.22806213, -4.48622671],\n",
+       "        [-4.96164065, -3.33268136, -3.78635004, ..., -3.32640969,\n",
+       "         -4.4621313 , -3.92592544],\n",
+       "        [-4.9379643 , -3.31596395, -3.76040227, ..., -3.35772884,\n",
+       "         -4.14265119, -3.93734605],\n",
+       "        [-4.83203653, -3.32107806, -3.78760002, ..., -3.35416619,\n",
+       "         -4.22728329, -3.90026443]],\n",
+       "\n",
+       "       [[-4.77117207, -3.2747752 , -3.84109296, ..., -3.39779997,\n",
+       "         -4.17183541, -3.8962825 ],\n",
+       "        [-5.10497494, -3.38168192, -3.78523102, ..., -3.33185831,\n",
+       "         -4.4873414 , -3.95104205],\n",
+       "        [-5.07775768, -3.36711584, -3.74863754, ..., -3.32397765,\n",
+       "         -4.43651478, -3.94185266],\n",
        "        ...,\n",
-       "        [-4.54892746, -3.22172068, -3.92695646, ..., -3.33553783,\n",
-       "         -3.65140851, -3.83812304],\n",
-       "        [-4.46189421, -3.25240367, -4.04087067, ..., -3.40532696,\n",
-       "         -4.42299617, -3.96090617],\n",
-       "        [-5.03545146, -3.25715461, -3.79209845, ..., -3.40734961,\n",
-       "         -3.51835568, -3.87645805]],\n",
-       "\n",
-       "       [[-4.3140992 , -3.37514943, -3.74689581, ..., -3.37518604,\n",
-       "         -4.17876505, -3.88155636],\n",
-       "        [-5.1050765 , -3.2219115 , -4.01325061, ..., -3.33683671,\n",
-       "         -4.88447951, -3.85118138],\n",
-       "        [-4.19096464, -3.29488025, -3.69719716, ..., -3.38120741,\n",
-       "         -3.22968375, -3.95391143],\n",
+       "        [-4.45739466, -3.43198689, -3.74974599, ..., -3.47710401,\n",
+       "         -4.23277637, -3.91695156],\n",
+       "        [-4.58405851, -3.27947007, -3.81737411, ..., -3.34912805,\n",
+       "         -3.82840897, -3.85799354],\n",
+       "        [-4.87595905, -3.23987091, -3.79114629, ..., -3.38270321,\n",
+       "         -4.14545561, -3.92056905]],\n",
+       "\n",
+       "       [[-5.38067762, -3.5028917 , -3.70344109, ..., -3.32164233,\n",
+       "         -4.94044239, -4.03821723],\n",
+       "        [-5.43226529, -3.5165353 , -3.70467916, ..., -3.32364391,\n",
+       "         -4.90359962, -4.02880993],\n",
+       "        [-5.33144304, -3.56578891, -3.7068391 , ..., -3.31955914,\n",
+       "         -4.93229973, -4.03429042],\n",
        "        ...,\n",
-       "        [-4.35932297, -3.22165274, -4.02037117, ..., -3.64324124,\n",
-       "         -3.43774501, -3.81603299],\n",
-       "        [-4.35932297, -3.22165274, -4.02037117, ..., -3.64324124,\n",
-       "         -3.43774501, -3.81603299],\n",
-       "        [-4.5502955 , -3.22156599, -3.84867434, ..., -3.39226907,\n",
-       "         -3.9432436 , -3.85464005]]])
          • created_at :
            2020-06-06T18:09:41.115181
            arviz_version :
            0.8.3
            inference_library :
            pystan
            inference_library_version :
            2.19.1.1

      \n", + " [-4.3347405 , -4.16657737, -3.69634319, ..., -3.39947034,\n", + " -4.9156511 , -4.51886859],\n", + " [-3.66687419, -3.70850363, -3.69872234, ..., -3.91350867,\n", + " -3.23761969, -4.08034083],\n", + " [-3.89878287, -3.38831206, -3.69323942, ..., -3.46704148,\n", + " -3.28776101, -3.92826175]]])
  • created_at :
    2020-06-10T17:35:53.774312
    arviz_version :
    0.8.3
    inference_library :
    pystan
    inference_library_version :
    2.19.1.1

    • \n", " \n", " \n", " \n", " \n", "
  • \n", - " \n", - " \n", + " \n", + " \n", "
    \n", "
    \n", "
      \n", @@ -7815,51 +7843,51 @@ " stroke: currentColor;\n", " fill: currentColor;\n", "}\n", - "
      xarray.Dataset
        • chain: 4
        • draw: 500
        • chain
          (chain)
          int64
          0 1 2 3
          array([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])
        • accept_stat
          (chain, draw)
          float64
          0.5882 0.6414 ... 0.0009974 0.48
          array([[5.88179346e-01, 6.41389529e-01, 9.37678207e-01, ...,\n",
-       "        9.23515478e-01, 9.61821485e-01, 8.14423583e-01],\n",
-       "       [9.61075139e-01, 9.92479385e-01, 9.99167555e-01, ...,\n",
-       "        9.79263419e-01, 9.58289464e-01, 9.60851272e-01],\n",
-       "       [9.86301496e-01, 9.84425192e-01, 9.54469155e-01, ...,\n",
-       "        8.41860803e-01, 9.27362741e-01, 9.94523221e-01],\n",
-       "       [9.52283483e-01, 6.46438496e-01, 9.75633981e-01, ...,\n",
-       "        6.27646170e-02, 9.97401915e-04, 4.80044753e-01]])
        • stepsize
          (chain, draw)
          float64
          0.2606 0.2606 ... 0.3035 0.3035
          array([[0.26064335, 0.26064335, 0.26064335, ..., 0.26064335, 0.26064335,\n",
-       "        0.26064335],\n",
-       "       [0.12223536, 0.12223536, 0.12223536, ..., 0.12223536, 0.12223536,\n",
-       "        0.12223536],\n",
-       "       [0.13518281, 0.13518281, 0.13518281, ..., 0.13518281, 0.13518281,\n",
-       "        0.13518281],\n",
-       "       [0.30353082, 0.30353082, 0.30353082, ..., 0.30353082, 0.30353082,\n",
-       "        0.30353082]])
        • treedepth
          (chain, draw)
          int64
          3 4 4 4 4 4 4 4 ... 2 3 2 2 3 3 3 3
          array([[3, 4, 4, ..., 4, 3, 4],\n",
-       "       [5, 5, 5, ..., 4, 5, 5],\n",
-       "       [5, 5, 5, ..., 4, 4, 4],\n",
-       "       [3, 4, 4, ..., 3, 3, 3]])
        • n_leapfrog
          (chain, draw)
          int64
          7 31 15 15 15 15 ... 7 3 15 7 7 11
          array([[ 7, 31, 15, ..., 23, 15, 23],\n",
-       "       [31, 31, 31, ..., 31, 31, 31],\n",
-       "       [31, 31, 31, ..., 31, 31, 15],\n",
-       "       [ 7, 23, 15, ...,  7,  7, 11]])
        • diverging
          (chain, draw)
          bool
          False False False ... False True
          array([[False, False, False, ..., False, False, False],\n",
+       "
          xarray.Dataset
            • chain: 4
            • draw: 500
            • chain
              (chain)
              int64
              0 1 2 3
              array([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])
            • accept_stat
              (chain, draw)
              float64
              0.8343 0.4552 ... 0.9836 0.9973
              array([[0.83427208, 0.45522566, 0.96624807, ..., 0.77208011, 0.16547731,\n",
+       "        0.82547175],\n",
+       "       [0.99681883, 0.95244552, 0.99394157, ..., 0.88393096, 0.90840264,\n",
+       "        1.        ],\n",
+       "       [0.08367187, 0.85771457, 1.        , ..., 0.9810016 , 0.99080896,\n",
+       "        0.95907463],\n",
+       "       [0.93085341, 0.34875619, 0.59818615, ..., 0.99211124, 0.98364323,\n",
+       "        0.99731371]])
            • stepsize
              (chain, draw)
              float64
              0.1733 0.1733 ... 0.1188 0.1188
              array([[0.17333576, 0.17333576, 0.17333576, ..., 0.17333576, 0.17333576,\n",
+       "        0.17333576],\n",
+       "       [0.07970539, 0.07970539, 0.07970539, ..., 0.07970539, 0.07970539,\n",
+       "        0.07970539],\n",
+       "       [0.06778361, 0.06778361, 0.06778361, ..., 0.06778361, 0.06778361,\n",
+       "        0.06778361],\n",
+       "       [0.11879204, 0.11879204, 0.11879204, ..., 0.11879204, 0.11879204,\n",
+       "        0.11879204]])
            • treedepth
              (chain, draw)
              int64
              3 3 3 3 3 3 4 4 ... 6 5 5 6 6 5 6 5
              array([[3, 3, 3, ..., 4, 3, 3],\n",
+       "       [4, 4, 4, ..., 3, 3, 3],\n",
+       "       [4, 5, 3, ..., 5, 5, 5],\n",
+       "       [5, 1, 2, ..., 5, 6, 5]])
            • n_leapfrog
              (chain, draw)
              int64
              7 7 7 7 7 7 ... 31 63 63 31 63 31
              array([[ 7,  7,  7, ..., 27,  7,  7],\n",
+       "       [15, 15, 15, ...,  7,  7, 15],\n",
+       "       [31, 55,  7, ..., 63, 31, 31],\n",
+       "       [47,  3,  3, ..., 31, 63, 31]])
            • diverging
              (chain, draw)
              bool
              False False False ... False False
              array([[False, False, False, ..., False, False, False],\n",
        "       [False, False, False, ..., False, False, False],\n",
        "       [False, False, False, ..., False, False, False],\n",
-       "       [False, False, False, ..., False, False,  True]])
            • energy
              (chain, draw)
              float64
              22.7 24.35 21.51 ... 16.79 15.34
              array([[22.6967612 , 24.35094062, 21.50814304, ..., 20.17096719,\n",
-       "        20.26238974, 18.81485421],\n",
-       "       [23.8733572 , 25.89740769, 25.50088525, ..., 21.75214566,\n",
-       "        24.28654179, 25.63605085],\n",
-       "       [24.69925974, 26.74046019, 28.26096462, ..., 19.13939105,\n",
-       "        22.05673876, 18.04213339],\n",
-       "       [17.98317811, 21.89451151, 27.42787245, ..., 13.97427495,\n",
-       "        16.78600581, 15.34467776]])
            • lp
              (chain, draw)
              float64
              -18.89 -18.93 ... -9.633 -12.74
              array([[-18.89038713, -18.92823648, -17.61766361, ..., -16.39906054,\n",
-       "        -13.64755409, -15.67874189],\n",
-       "       [-17.28562197, -23.52929685, -23.24128558, ..., -19.26429636,\n",
-       "        -19.80764195, -20.13200941],\n",
-       "       [-19.56630339, -22.10420601, -22.90628681, ..., -15.59532748,\n",
-       "        -16.30184041, -12.72595726],\n",
-       "       [-13.17454693, -19.65660382, -22.36275391, ...,  -9.6329489 ,\n",
-       "         -9.6329489 , -12.74013837]])
          • created_at :
            2020-06-06T18:09:41.109561
            arviz_version :
            0.8.3
            inference_library :
            pystan
            inference_library_version :
            2.19.1.1

      \n", + " [False, False, False, ..., False, False, False]])
    • energy
      (chain, draw)
      float64
      10.21 13.93 13.44 ... 32.73 28.14
      array([[10.20693978, 13.93085747, 13.43521926, ..., 12.29495316,\n",
+       "        13.46605921, 14.22549271],\n",
+       "       [13.17818175, 11.37017914, 11.42654959, ...,  7.01892776,\n",
+       "         8.87263573,  7.8678977 ],\n",
+       "       [ 5.98879364,  9.22839116,  5.70304851, ..., 22.97114689,\n",
+       "        18.60193693, 18.31306761],\n",
+       "       [ 6.74956591,  4.81455757,  7.11467086, ..., 29.5590906 ,\n",
+       "        32.72527458, 28.13558373]])
    • lp
      (chain, draw)
      float64
      -6.744 -10.6 ... -27.28 -22.17
      array([[ -6.74356275, -10.59971097,  -7.06732632, ...,  -8.22004983,\n",
+       "        -10.26028264,  -9.80641468],\n",
+       "       [ -8.51908224,  -9.40025125,  -8.55896875, ...,  -3.50197584,\n",
+       "         -6.0401085 ,  -1.78208431],\n",
+       "       [ -2.91168133,  -4.44647935,  -1.40061324, ..., -15.52248756,\n",
+       "        -12.78328387, -16.1222584 ],\n",
+       "       [ -3.69751253,  -2.05491867,  -3.47644376, ..., -26.47544111,\n",
+       "        -27.27571599, -22.17195889]])
  • created_at :
    2020-06-10T17:35:53.768327
    arviz_version :
    0.8.3
    inference_library :
    pystan
    inference_library_version :
    2.19.1.1

    • \n", " \n", " \n", " \n", " \n", "
  • \n", - " \n", - " \n", + " \n", + " \n", "
    \n", "
    \n", "
      \n", @@ -8197,8 +8225,8 @@ " stroke: currentColor;\n", " fill: currentColor;\n", "}\n", - "
      xarray.Dataset
        • school: 8
        • school
          (school)
          <U16
          'Choate' ... 'Mt. Hermon'
          array(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
-       "       'Hotchkiss', 'Lawrenceville', "St. Paul's", 'Mt. Hermon'], dtype='<U16')
        • y
          (school)
          int64
          28 8 -3 7 -1 1 18 12
          array([28,  8, -3,  7, -1,  1, 18, 12])
      • created_at :
        2020-06-06T18:09:41.092802
        arviz_version :
        0.8.3
        inference_library :
        pystan
        inference_library_version :
        2.19.1.1

    \n", + "
    xarray.Dataset
      • school: 8
      • school
        (school)
        <U16
        'Choate' ... 'Mt. Hermon'
        array(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
+       "       'Hotchkiss', 'Lawrenceville', "St. Paul's", 'Mt. Hermon'], dtype='<U16')
      • y
        (school)
        int64
        28 8 -3 7 -1 1 18 12
        array([28,  8, -3,  7, -1,  1, 18, 12])
    • created_at :
      2020-06-10T17:35:53.754942
      arviz_version :
      0.8.3
      inference_library :
      pystan
      inference_library_version :
      2.19.1.1

    \n", " \n", " \n", "
    • \n", @@ -8563,7 +8591,7 @@ "outputs": [ { "data": { - "image/png": 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cEb/927/dxSOC4cZj0HGiTAXdiQS36oHnkYmRE7NiYq0qUb/4ZRrrbozcTQ/aTQb2AWByaqaPz4oza70wUyi0PsX3VIo7ujUtfbOJpRPlWQgAAAAAAMgHSZ4tOOqoo+K2226LxYsXD1v+wAMPxOmnnx6f+MQn4h3veEcccMABERFx0003xc6dO+Oss86KCy+8cHD9q6++OtavXx+33nprnHPOOeN6DFDPeA06jmelzLyoHnhecfZQAmRE44P1eVSrStT1N0Sk6dDPeTy+8a74ZGAfACaH6hhiLH38aEmcrVbknCpxR6tJsGPVamLpVHwWAgAAAAAAWiPJswXvete7Mpe/8Y1vjDe96U2xZcuWePTRR2PBggWRpmls3Lgxent747zzzhu2/nnnnRd33HFHbNy4UZInuTNeg47tqpQ5EaZlzBp4rkyALJuo02dmVYlKkvwfX7cqPmX9jpXv49e8OqKvr+NNAIC2mgjxWDvViiFajaNrJXE++FAaby8kNStyTpuWxvYd9fc1FRIKuzEt/VgTSzsxawAAAAAAADD59HS7AZPN/vvvP+zfXbt2RbFYjIULF0Zvb++wdXt7e2PhwoXxxBNPxFNPPTXubYXRFApJLHx9/qv8bNqcxomnpPHRC0r/btqcjv6lLsgaeE7TUiJkpW5Nn1ksprF9RxrFYmvnr1QlKomel3qWnp6Ic88ZeXxJkp/pQWsNzLd6DppV/h2LiLju+oE44eTSffzfPzQuuweAtmklHhtr7NFNxWIaV11dO4ZoJY4uJ3FW+8QVpfObFWsd986IFedHQ+d9osT2rco6f52Oq+sllgIAAAAAALSLSp5t9OSTT8Y//uM/xiGHHBKvfe1rIyLi8ccfj4iIuXPnZn5n7ty5sWXLlti1a1fMmjVrvJoKk8bevWlcty7iwAOHBquvWxfx5jelMXNmvgawX/PqiOnTkxHVl844PeKWW2OwCtTHPjL+1ZXuvS+Nz15b2YY0jntX821YtjSJN78pYs+TEbMPLS370u3Dq3kmSXYCQzc89ZPh907l8vGqqnTvfWlc85cRaZpE+V2AA6eV/v3Zz9KYMSNf9zEAVPvZz5qPx9oVe3TLX389ore3vTFEoZDExy+Jl+KC4Z+Vz2dlrDVtWsTHVkYceODI9fIWB4+H8vkbfl91Nq6uFd+/5tUd2yUAAAAAADAFJWlaPXxEK1588cX40Ic+FNu2bYurrroq3vve90ZExNe//vW48MILY8WKFXHBBReM+N4111wT69ati89//vPxzne+M3PbAwMD0ZOXjCgAACYlMScAAAAAAABA/qjk2QYDAwNxySWXxLZt2+Kkk04aTPBsl2effbat22tUX19fPPPMM13ZN50z2a7r3r2lqa2rq+fc9oXIbQWjvXvTwepLL7xQqng5lraO9Zr+6/fTuOjSkctXr4r4g6PGdg4nwvUZWUksxq2SWK1z/7LeiL//xoz48Y9/NuEreZbv97He55PBZPv7W0tfX1+3m0CL2hlzTpX7ndK1/vGPfxYfOK3x/r6V2CNP/Umt9p/4pxFn/Y+xty0rfkqSiM99JuLw1yZ11+t0nOV3O1ue7s92cr2njol4rcWcAAAAAMBUkutSPVu2bInzzz8//uiP/ijmz58fl1122eBn3/nOd2LVqlXx9NNPd7GFpQTPyy67LDZt2hTvec974pOf/OSwz6dPnx4REfv27cv8fnn5QQcd1NmGwgRVLKaxfUcaxWJ20eGZM5M4f0XE88+n8dxzaTz/fBrnr8j34OrMmUk88UTEWSvSOP+jafzpSWls2ty9osqzXjV0/sr/e/75NGa9auzbngjX57h3JfHFmyNW/XnEF28ee4LnaPdspaxz/9xzaTz/QunziZ7guWlz6f7Ow30OQOfMmNFcf99s7DEe/clY++/+/jT+239tT1tmzkziv3+gtM3y9vftS+OsFcOPfSLEWVPFzJlJ/MFRiXMPAAAAAAB0RG4ref75n/953HHHHZGmafT29sZvfvObqJxZ/pBDDokvfvGLMWvWrDj99NO70saBgYG49NJL42tf+1osW7YsrrzyyhFTXP7O7/xORETs2rUrcxvl5XPnzu1gS2Fi2rQ5jdVr0sEKixetjFi2dOTA6bKlSSxeFLF7T8Sc2RGFQncHV4vFtG5bisWh44ooVV9avSaNxYva0/bR9l+tUEjiopVRda6Twe82u73q75Svz4MPpRFpxIL5Yzq8jigUkigUxr6dRu/Zyv1WnvskiTjlpIiTl4+9Ld3W6fscgHxpJh4bLfaolNWfXHV1GgcemMaC+dnfqaVWTDPW/runJ2LF2aVj3/vTgXjhhWRMMemmzWmsuzEirco3zepL8xYHkx+txPAAAAAAAEA+5TLJ82tf+1rcfvvtMX/+/PizP/uzOOKII2LevHnD1pk3b17MmjUr7r///q4keVYmeL773e+O1atXx3777Tdivblz50ahUIjt27dHf39/9Pb2Dn7W398f27dvjzlz5sSsWbPGs/mQe80miLUrSW+sGkkS2L1n+LSaEaWfd++JMR9Ds0kKZbUSMVvZXtZ3IiJWrylPiZ423K5WdGtAu9WkxsmanNHJ+xyAfGomHmu0/8vqT9I04vIrmospasU09frv8v6z2lfZ/kceLSVlDgyUszLTpuKwStXtqZbVl+YlDqY7smLfVp8JAAAAAACAfMrldO133nlnvPzlL48bb7wxjjjiiJrrHX744bF79+5xbFlJeYr2r33ta/HHf/zH8elPfzozwTMiIkmSWL58efT398fatWuHfbZ27dro7++Pk046aTyaDW3TzHSWraqXIJZXtZIEqs/TnNmlwdZKPT2l5eOx/1q2bov45J+VkiZOPCWNL28YaHp7taptXXV16+1qxqbNaZx4ShofvaD073hODz6We7ZQSGLh65urRpZ3nbrPAZg8Gun/svqTskZjinoxUq3+e+Pdo8cUhUKpYmcpwbO1tlXLak+lvPSl4/E8wOiyYt+xPhMAAAAAAAD5k8tKnj/84Q9j8eLFMWPGjLrrHXTQQbF3795xatWQ6667Lv73//7f0dvbG3Pnzo3rr79+xDrveMc7BhNUzzzzzPjmN78Z69evj507d8aRRx4ZDz/8cGzZsiUWLFgQp5122ngfArRsvKrClAf0Kwe58zSonVXVqdHKhc1MUdqMsVROzBoMvv6G7GlC622vVrWtap2o6FhrQLuvL40ndkcctSDiyCM6925DO+7Z8r31mldH9PW1v43jqVP3OcBUM9WnXK7uT6oNvVBR+zzVi5Fq9d8bvjIUw9Srzl0vKbOVeCerPZXtumhlaf/bd6QdvSfq3Xf33pfGp65UJbLbasW+l/9P1dQBAAAAAGCyyWWSZ0SpAuZoisViTJs2bRxaM9yePaWybP39/bFu3brMdWbPnj2Y5Nnb2xu33357XHvttXHffffFP//zP8chhxwSZ5xxRpx//vldOQZoRavTUbeiUEhixdnpYKJhXhLE6iW51koSmDYtje07hg+Sd2KK7rEkGdZKzkyS4Umao20vqw3lP+fNbKcVtRI4Lr5s6Oc/OW4gPn5pZxI9x5rUWHlvTZ+exHe3dKSZ42qyTkUPMF5MuVxS7k8efCiNT1wxMqZ45NE0PrYyap6nejFSVv998vKIO+8a3oZaSXKjJWU2G+9ktWfF2RHzDi9VDd26rVRxvdV7opGk4dHuu89eG2N+HpjqycvtUCv2jSS/L8sBAAAAAACtyWWS59y5c+Ohhx6KF198MX7rt34rc519+/bFI488Er/3e783zq2LuPLKK+PKK69s6jvTp0+Pyy67LC677LLRV4acGkulyGZt2pzGuhuHEg1XnN39pIbRklyzBuWPe2fEivMjBgbSEYPkpe+0r33V+y+ft0YGzmslZ75nWcTfbGo80bZWomNE+yo61koKqJdkUXbPvRHHv3egYxU96yU11ktmyLq3IiL27k1j5syJnfjQ7vscYKoY68s1ky2JrlBI4u2FJJ7vT0ckQFZOl551nkZ7EaO6/46IuGtj2lCSXK1Ko+W2lauMNnMNasUTte6Jww5L44UXklGvdSNJw43cd2N5HigW09h4dzpYKXUqJy+PVa3k5QWvU00dAAAAAAAmm1wmef7xH/9xXHPNNbFmzZq45JJLMtf5zGc+E7/85S9j6dKl49w6mLrGawr16sHlNC0N3r9jSXMD5O3WSJJr5aD8tGnpSwmeQ+t2qvJp2bKlSfzil0MVUNfdGPHy6emoA+dZCaIREX/99dJ/v+/kiOUnNDY4XCsxoR0VHeslJ4w2nWvZgz+IOPKI5vbbTKJMVlLjaEkVtaZ63fNkxMyZzbU1TyZbghHAeKoVd9z/D2ksObb1ZL5W/jbn6e95dZxROk/psHWykg5Hqy5d3X83kyRXHf+98EISjzyavpR8OvJFn0ZkxRO17olzzotI0/r7aTRpuJF4t6fqXZlGnwc2bU7jqqvTYZVYxyM+nqzqJS8vW9qe2BsAAAAAAMiHXCZ5nnbaabF58+b44he/GDt27Ii3v/3tERHxxBNPxK233hp/93d/F//yL/8SRx55ZCxfvrzLrYWpo5npqMeSDDCeFUOb0WiSa3lQfvuOxpIO2qlYHKqAWt5fowPntaZCTdOIuzZGLD+h8XZkJSaMtaJjI8kJlUkWzz+fDpuqvWzB/Ob2O9ZEmUba/cijaeZ3Zx/amcSa8UjWqT5vK85OB6ealWgAMLpaFao/vzZi7bq0pWS+rduq47jREw9bmTK+0/3M8JgijSQZOYV7raqbEWlD1TWXLU3isMPS+P6DEUctiDjyiEZf8kiiWByaPj6ifYmMte6JRuK+RuPrRuLdj30k4lNXRlNVIsv3ZZoR8uQhzp+oRiYYl851eZYB5xQAAAAAACaHtiR5/uIXv4jnnnsu0qwRm4g49NBDm9retGnT4tZbb41LLrkkvv3tb8f3v//9iIh44IEH4oEHHoiIiLe85S3x6U9/Og444ICxNR5oymhVkCJaSwao1ImKoe1INmgmyTWi85VPi8U0nvpJxLFvjfjX76cx61VjT5AtFJLo2xMj/p7X2kajSY7NTF9ea3mjx1aZZPEnxw3EPfcOffYnx0VTU7W3I1FmtHaXE3Oz/Mv2iE9d2frvUpax/n42Iuu8rV0XEdFaNTOAqahehepWkvl+8FAaV109PCHwqqvrJx62MmX8ePQzlbZuG/5zktSOz768YWCw2vloLyCUjiMGK5yfctJAw1XNO/XCUlbl9epH8Fr7aeZlpdHi3ePelcTrj26uSmStquW12kHjCoUktm4r36+tx1p5qtgLAAAAAAAM13KS57//+7/HZz/72bj//vvj5z//ec31kiSJhx9+uOntz5gxI2688cZ45JFHYsuWLbFnz54YGBiIV73qVfGWt7wljjrqqFabDoxRvaowoyUDNDJ42Gwy5WjamWwwWpJr9fG18ziyjunAA5PY+o8RF10a0d+fxrnnjD2xtNEkgEbOa611ml3eTLsqffzSnjj+vQPx4A9KFTyPPKKnqQHsWkkaX7wtja9vbqxC15zZI5MwKttdL+nhs9e2twpYK8k6rah3TKZlBWhcOe64/x/S+Pza4Z81m8z3L9tHJgSmacTGu9M4/9zsiqD3/0N2gmmtZMVm+5nqPrn8c3nK89H66qzqkElSmqa62pc3DLz0wsFQ22q9gFB9HGkaceddERu+ksbFF44eR3byRZ/yPbHx7jTuvGvk5/WqmFYniK44O/v8NvJSV63ngVpxVq0qpO2Mj6eqdsR3452cDQAAAAAANKelJM9isRgnnnhiFIvFeOUrXxkzZsyIn/70p3H00UfH7t27Y+/evZEkSRx99NGx//5jKxY6b968mDdv3pi2AYyfepWLShVmGhs8bGRwuaxe0l4nktpqDWrXGhxt9DgaVX1MZWkacf0NEeeeE7HuxpFTaDaa3FhOArjq6nTYlO1bt0UsW5rdhqzzWmudww4bqozVyPLyNltNmj3yiJ448ojSfzc7gJ2VoBkR8ddfH7luraSX0SqM1UoEKW+zkX00qlOVxarVSuTo5D4BJqtCIYklx5amaG8kaTCrv1xxdgxLcKx018aI5ScMn7q8sr+sVi9ZsZl+prpPPu6dadz7d5XfH70iYaP7KxbTuP6G7DaXv1MZc9R6WSFNG4sjO/miT9ldG0cuG20/y5Ym8YtfpoPVTNfdGPHy6Wnm+W1lqu96cVZWkul7lkUsXBix4HXN7adZk71C5Vjju1ox+5vfFNHX1/72AgAAAAAAzWt8ztoK119/fRSLxfjoRz8a3/rWt+K//Jf/EkmSxIYNG2LLli1x2223xWGHHRZJksRNN93U9PY/+MEPxvr160dd7+abb44PfvCDrRwC0CHl5K5KPT2likxZg4fFYjpyIy8pFJJY+Pr6A+KbNqdx4ilpfPSC0r+bNg/fXr1Bz3aqNThaLKaZx1EsprF9R/3jr6VelcQ0jXjVKyO+uiGJv7wmia9uSAYrZNY7T9WqK2CVkxrK7W3kvNZa5/sPZi9/8Aejb3PxoojL/1fEFZfH4LHVUn2O612jWgqFJE45KfuzpGrXWUkvWRXGysdRuY+LViaDvzc9PREf+8jQf4+2j0YVi2k880zaULvHqvqYqpmWFaA5hUISK84e6nsaSearjAXmHV67v6zua2u9TNLIfmvFgXNmD++Xs/rke+7NjgPq9dX14s7KGGD3npEvbFSrPA9Z281ar57qa9DOqoi1YsHL/1f9l1eKxTTW3Th0LhqJhRpVLKZx1dX146zKc3LuORFf3xxx+Sejodi0Vc3GwBNRvd+7RtSK2fc82Z72AQAAAAAAY9dSkud3vvOdmDNnTpx33nmZny9atChuueWW2LlzZ6xduzZznXq2bt0ajz322Kjr/du//Vts27Zt1PWA8ZOVsHbRyiReeCFpe7JlI0l7Yx30bNSDP6g9nWm1sQ4210s8iIhIY3iCbCvJjVnJEKMlP1Sf11rrHLUge/mC+fW3WT5vl38y4hN/NrJCZqWsc9xqwu/yE5LMxMhzz4kR93l10kvWPtN05D6rE0GOe1dpOx/7yOj7aMTgubui9HOjSUJjUXlM561oz3EATFWbNg8l55Wn2R4tabAyFihXps5S3X/XSiD8yPmjv2BRKw7cui2G9csb785OIs1Sr6/O2t9x74xYcX4MiwFGi53K3y2fh3ovKzQTRzbywlIrasVYC15Xfz+1YqG//9bYEz033j3ypZasa1e+H8tV58vrtSvZtFIrMfBEVOv3rtH7rtb9NPvQNjcUAAAAAABoWUtJnk8//XQcccQRQxt5aUTg17/+9eCyV77ylfGmN70p7rnnnjE2sbZf/epXsd9++3Vs+0BrsioXdSLZspGkvbEOejZi0+Y0PvFnI5fXq+w4lsHmeokHSTJygL+V5MbRrlcj57XWOkce0dPU8nKi6mjVocpqneNp09KG7sHqCqCFQhIXXziyXe8/pWfUCl1Z5zFJShXGqpUTQSIi/vX7pc+Pe9fYq4BVn49ygtAnG6iGOlblY2rkXAGQLevv+Lobo+nY4eILR760kCQj+++aCYTzS7HDaPutjgMXL4oR/fKdd9VOOq02Wry4bGkS664rJaGu+vMYNuV7OQaIiGExRpJEHPvW+i8glI/jfSePz8sRzWg1vq2V7HrtdWOrplksprHhKyOXJ0n2UI2UDwAAIABJREFUtet0pf1yLNfMS1gT3Vgqx9a6n2bOFK8BAAAAAEBe7N/Klw466KBhP7/85S+PiFLy56tf/erB5QcccEA8/fTTY2hebfv27YsdO3ZEoVDoyPaBsSkUkqj89SwNHg4N8rdjkLw8UF05eJuVCLBsaSnBYPee0mftHJivNR13M5Udy4PN5fNVLKajtrV8TH/99eHLzz1n5HcaPU/V+x3tetU7r+VtLV5USiSsXKdYTOPQQyPWXRfxwgvJsO/W2uYXb6tdHaq6G6g3TfyKs4cqR2Ud06bNQ4k0SRJxyklpLD8hqdmu6vu8WvV5jCgl56w4P+KilemIAfjy/g88MImt/xhx731pHPeu+vsYTa3z0feKoWNv5J4DoDsaiR0qVf5NL39/zuyhPvYHD6Xx819EvOLlEfNfejFk+450sA/IigHK1TEHBtKX+s/6lUQr+8ftO7Krdlb26+V9VCZoRpT64v+6tFQxfcH87D6q1HfGYN9dK17I6stH6/8KhSTOPzeJ5Sdkr9fN/rOV+DYrLikrJ8QuXlT6uZntZlWAj4g45aTs7zcam1Zq9FxXx3LV90QnKvrnxWhxaT2dfF4CAAAAAADGrqUkz1mzZsWTTz45+PPv//7vR0TEt771rTj11FMjIuL555+P7du3xyGHHNLQNt/+9rcP+/nee++NrVu3Zq77H//xH7F37974j//4j/jABz7QyiEAXdDuwcNmEkfHMuhZT60pTS//XxFvf1vzg9qVA9OjJVEUCkmc9T9K/12uMLXuxoiXTx+ePNjIecra7+JFEZf/z4h4qTpoo+d10+ZS1c1yxciLLxyqJpS1n3L1ylrb/PKGgRHJrOVjzhqkzzrHERGfX1tavuLsiHmHJ5mJGtXJmHfeFbHhK2lcfGHpOrRyDy1bmsRhh6VxznlDSQaViRSViZbVSRefvTbi9UenHU2Gbuaea9V47ANgsmomIa46wS2i1PdU/u1d0kD/X/kyxrRp6UsJnqXvZPVhj/4wjR3fS+OoBRFHHjG8VGStfrnSqe+POHRWEv/nvqFsvMWLIrZuK73Q8tdfj0iSof64LKvvrlZdibz6JaRG+vZa8U63+7ZW4ttyPP7330rj2uuGfzYwUJp2/a6N0dRx1bpHl59QO4Zt5uWvRs91rerl5bblpRJrXnXqeQkAAAAAABi7lpI83/zmN8eXvvSl+NnPfhYzZsyIJUuWxIEHHhirV6+On/zkJ/HKV74y/uZv/iZ++tOfxvve976Gtrlnz9CcaUmSRH9/f/T392c3ev/9o1AoxJIlS2LlypWtHALQJe0ePOx21Zlag9rVU6aXVQ9qJ0kp6bBcTSprmvHKJIpqe/em0ddXP3kwYvh5mjYtjRdeKA2E19rvVVeXNjg8MWT081GeVr3cnjQtbatcFarZ4ysW07j+hux91aoONVqVqnU3Rnx1w8jv1krYTdPR2zmaF15IIq3KPKmuwtZspbZG1UukaOWea9Z47ANgMms0Ia5ewmMjLxfU6v8PPTSJgYH6fdhHL4h47rnSf//JcQPx8UuHEj3r9ctlX7q9lMRZ2eat24avU+6PD+xNB18+qdV3l6s3tiOpL6uC5ETv2wqFJN721ojrrh9+TZIkYsNXRo8rs7bXbMX+RmP4Zs511v2QphGf+P9KFcyz9qOaOQAAAAAAMBG0lOT5nve8J37yk5/Ej370o1i8eHG84hWviCuuuCIuvfTSuOmmmyJJSsksv//7vx8XXHBBQ9t85JFHBv973rx5cfzxx8eqVataaR4wxXSi6kyjA76tDmr/4pdprF1XGni+/oZS9c1DD62d5BeR3Z49T0a8VEx5xHeqz0mhkMTWbeUpTdOK5I3sAfHK7TWauPDgQyOnVU/T0vK+VySZx/f330rjbW/N3nat6T8jIt52bO12lBMH7v+HND6/duQ+s85PvUpjY022bKQKWytTlzaqViJFpxJLK43HPgAmu0YS4molPJY18nJBVv+/7rra/dOjP0zjzW8avo177o04/r0Dwyp6ltv/4ENpfOKK7L69Vn9ffQyXfzKipycdrDqa1bZyFdKxJu7VqiDZTN/WriTCdicjZsWwJy8vVTGv1Gif3er08aNtt9a5vv8f0lhy7PD91Hv5Kqs9eajGCgAAAAAA0Iie0VcZad68efGZz3wmFi9ePLhs2bJlce+998bll18eH/vYx+Jzn/tc/NVf/VVMnz696e2vWrUqTjzxxFaaBjBmmzanceIpaXz0gtK/mzbXzzpYtjSJr25I4i+vKf072uBwdXXKcrXLadNKiZeVenoiHnm0dntmHzpy+7USA2tVQsrab7WhZNP6nn02e/kvfjE08F7t2uui5nmeM3toutlqL7xQ/zwXCkksOTYZ8f1a07wXCkmsODt7W2NNtiwlUiSDx5+VDFy9TkTExz7SvqpShUISC1+fZCZDVGpXYul47gMg74rFNLbvSKNYbCCTsYasv+OVavWzZbVeLqhnYKDU39bqw37wUPb3/um72e1/+9t64txz6u+zEeUYJiIy23bkET11z1UjasVNxWLacN/WbExZS7u2U606hl1+QjKmPnu0e7QVte7Tz68dGT82Em+V1bu+AAAAAAAAedNSkmcthx56aLzvfe+Lc845J4477rj4rd/6rZa2c/zxx8cb3vCGdjYNoCGtDvg2M6hdq9rlUz8Zmaiw4uzS9OK12jNzZjK4bvnfWoPZtSohVSdvJMnIxMpGBvg3bU7jms9lf7bmmoiNd6ex4uzsgfpa57lQSDKTQeolso4liWbe4dnX7+TlY0+2bCQZuLzO6pcKWb/qVdHRZINmkiHyvA+APOtUgl616r+3lf159d/eclXIyn65Xv9fqw+b/7rstnzhi1HzON9/Sk988NSRy2u91FFL+QWUZl+2aVT9ap2j923tSCIsFtP45t8PxFVXdy4ZsTKGzWOfnfUSTFnWuWj0fqh3fQEAAAAAAPKmpenaP/jBD8Yf/dEfxVlnnVV3vZtvvjm+9a1vxZe+9KWWGhcRsW/fvnjiiSfiueeei7TGHH6LFi1qefsAlWoN+P7goTSWtGuAu8aYfBKlgenDDkvjwR9ELJhfSsAcGBj+haxpM2/7QsT/e6L+tKT1pgNf+PrhU2xu3dbcFPTlRIZ6U63eeVcpgePccyL2269UwXO044ooJYNEDMT1N5SSYcvJr+Vp7Mvtqpxys7SfNOYdnmQm1NaadjTrHCVJxPIT2ldNc7RpSQuFJHZ876XqZJdGPP98Omz60HZP19rK9KqNqGxnp/YBkFflv4HTpqWxes3IlzUOOyxty3Ti1ftbvCjiqxuSwb+3e386FFMcecTI/rKnJ+LU90ccfHBpncceS2r2/1l92OGvrd321WvSWLwo+/je+IYkvnT78A66VgzxodMiZsyI+Mxnh69T+cJHI/1rs2rFBNOmpRGRjNq3NTOle5bK61St7bFphTz22eU23f8PaXx+7fDPss5pI/dDvbgYAAAAAAAgb1pK8ty6dWvMnj366Me//du/xbZt21rZRfzwhz+Mv/iLv4itW7fWTO4s27lzZ0v7ABrX7sSyvMoa8I2IuPyKiP7+tC3VoRbMTyJJhidEJknE/NclLw3ox2ByxYqz04YGoGfOTGLmzPr7LVVCKk0NX953mpYSOpctHT4gvmxpNDXAn5XIkCVNI66/IeKGtc0NrL//lJ54x5LSPfjIo+lL1U3TlxJQSm2tTIRI04i16yI+eGpj56/6HA1PFq1//O3+3SgW0/jstRGnnFz6eWCgdM0WL8pKvo2692SjbWt3ckx1AlG5ne1OwAHIo+qXDqofZQYGIs45LyJN08GXEkovNLRnf+eeU+o3q2OKi1amI/rLgYGIL91e+u/y3+vKJNFG+7UPnx9x1eqRx1mdfFeZ/JqVQJn12PeHb4448oieePjhgbjn3qHlx72z9O/2HWlMm9bepNmIkTFBRKl9K84vnctSv1a7bxtLEmF1FdAs7YxNq3UiaXasCoUklhwbsXZd2pbEzOrrm4eqpQAAAAAAALW0dbr2ar/61a9iv/32a/p7u3btive///3x3e9+N44++uiYM2dORES8+93vjqOOOmpwm0uWLIn3vve9bW0zMNJ4TTOaB7WmhEzT9k2NWSgkcfGFw6fCvPjC0oBydeLFuhuHT6U61gHoxVWFj6uPq9XpzsuJDI1I04ifVE1NnySlKdHrKRRKyRtZ09c/+FB2IsRtdzR//pYtTWLF2UPJJuturD3l7Gi/G62cz6yE2TSN+OJtzU372q3f23ZMTwswUVX/Daz1rlrlyxZr10V8eUMDb0o0uL+16yJuvGlgxN/iq65O48Ef1E4cLP+9jojB6bsb9YeLR8YB1cl3X94wECecXOqXVpxfStKs7J9POSl72y+8kESxmMa9fzd8+f+5Lwa3d/a50ZH+btnSJNZdN3wq+Ub7tbFMfd7IyzPtjE0ninZPJ9/o1O4AAAAAAADd1lIlz0bs27cvduzYEYUWSoBcf/318dxzz8WqVavi+OOPj0svvTR2794da9asiYiIxx9/PD7+8Y/Hj370o/jKV77S7qYDFWolbGVNvznWioZ5qRa6bGkSB/amcfknhy9vZorNRvZRXSlz+46RiRcDAxHzDk/iqxvaM23m7j3ZFcV274nYum14JbCIoenRR6sYmVUN6bh3xrCKW5XSinOw8e40NnylNJ37XRvTzH2V741nnsk+R0lkVwBL0+bPX7FYqhRa3tbAQMSVn07j1y+mccx/HkokGO13o1Y1y9HMmT08maTsbzbVvnbV92Qzv7ftNtbpaQEmsv+fvTMPr6q4+/h3rlLDKsF4WStI68tuCwqCqOxG30QREImI1BUCqMUXClqrKBUBRVARCGgrZdFAQFRAZRHZVGRtZVW7WCWKtwGUNUq98/4xTO5ZZs52twR+n+fhCfeec2Y7c87MfeY7359OnKdzqZTMmAl078p9v6N1+Ul3TiOcAxs/UjuWS4K+r7OynF0RXy2MYnqBOZ8Vq4CCaShz4ATEPEDl0qjbAKEqfyLHu0iE45Od3sdfK0FDn+uc5a2cjeNrosPJO7mWlpffJgThBvVVgiAIgiAIgiAIgiAIgiCIMx/PIs9u3bqZPq9YsQKbN29WnvvTTz+hpKQEP/30E2677Tbfhdq0aRN+8YtfoFevXsrjDRs2xPTp09G9e3c8//zzeOyxx3znQRBnM3/7hKNuHW8LQF4FW68WRjFjpndRoJWggrh40S2ItWrBEAolJhykDuuislNYz0SFzdTlkZERC+kKmMUMXgUTqkX3XjdFMWiI+TzGgLp1YuFVFxSZBZXWvKxhaK1CmVBIhLofMpibBCTymN/20wlmJj8HTHmeY/RIUVenZwMILrIMhxn69LIrVzhX1z0jg2P7DnMfTqfQMp7wtARBEBUd3TuwYBrwzQHYNpBIOA/2js7I8OfiuGoV0LkTsH6DeqyL532tE99FIhwzZtrPj0aFwLNN69i4mN2D20Kyi3Ts4d11WMe7oOIn4/zDip92CjKHU22eyR9kdjP3W44g7VBehWOpCCefrt8mBOEX6qsEQRAEQRAEQRAEQRAEQRBnB57DtRcXF5f9Y4zhxIkTpu+M/0pKShAOh3HbbbdhxIgRvgt18OBB/OIXvyj7fO65Qov6ww8/lH1Xo0YNtGvXDmvXrvWdPkGki6ChsBPFipUi31EPw3M4S1UYblX4zekFdqGe13qmK7yzUzjrRIeD9IKXPOPtQ7o8SkuZo3AiJl50T98Y4rV5sxAe+h0rc6ZkDLjuWiB/mAirOniok1BSH/ZW1Ub980IYmh9zwQx6z5xCz3Muwt1GItzx2XAWgLrTq6f9u1AIGDLYXPfsHrG2NPZhL89tskjHs0MQBFFe0L0DmzcLIbOm/j0Y9B1dWqpOkzGg5w3qa9atF6LTF6YwDM1Xj6leKSnhpr/WeQCgdhGXZTTWWRWSfcUq8X04zJDdw1uZjOk6zfWcsM4/jKRqXLOGEu+fFwo8vgZph6Btlw4S/RsrXb9NCMIv1FcJgiAIgiAIgiAIgiAIgiDOHjw7ee7bt6/s/02bNkWvXr0wfvz4pBSqZs2a+PHHH8s+n3/++QCE0LRx48amcw8ePJiUMhBEoonX6TJeIhGO56YCef3EZ6/OgionIeOCspM7k1dHqnS4DuoWxBo35mVhQxMdDtILTnk6ubT4cWdV5RGJODtkJcrZKyODI3+Y2jFUlZcuPOvjjwGZNZmtjfrnhdC9a3yuU7LPT5zEleWTbmttWjs9G/b2tLahkztWVhYru0b+HTWCITeHldXP2pbWZ9pYNsaEA1iqhJbpeHYIZ8qrGxtBnIno3oG6ENyMBRMNRiIch7/jylDwQwYD3bsyvLXMPpZxHnPQbNOaeRo3Ve+QZcs5phUAmzYCt98JDMvnyrmtrt5DBpvdPtestYsq5Xyw5GAU7640H5ObOlRjtUwzqKu2ztX7/mFAl07qe5WM96zVsTLI+BqkHXTXtL8CyMyMt1aJJRkuhul0RCcIP1BfJQiCIAiCIAiCIAiCIAiCOHvwLPI0Mn78eFx00UWJLksZDRo0wNdff132uVmzZuCc4+2338Z9990HADh06BA2b96MunXrJq0cBJEopNOlxM8ic6KIZwHIaUHZqzuTE+kI76xrj8FDAc65aZE4mQtkKkGAcUFfHreGU5d96GQpMOge4c568iQPvLCtEgUCRlFyfI5Vsk7bdwDRqL3DSIGKDEcqQ57r+karFrGw5AA3lS0RITxzcxgaN+an+4O9rLJv6p4NN3G0V9H33FeAL79iyv6hakvjM52bw3DkKC/Lp2AWUKO6WoCTDFIRSpXwBoXxJIjUo3oHqsaGfn2Bvn30Y6x1nrBnbxSf7ASOHAHmvYqyMVuOo4wJ8WT/PLFLYMhgbpqDAiLfjAyO7Tti6Tq9r1XvkHZtRT0qVxbldprbquYYxjK6hUXfuo1jzjz7Mc6BOwYCs+fYv5fO2aq53s7dHJkuIknd/EMn8Ezle9bv+BrkN4DumuKvgUsu8VfeZBKPkNeJdPw2IYggUF8lCIIgCIIgCIIgCIIgCII4ewgk8uzVq1eiy2GiY8eOmDFjBoqLi1G/fn106dIFmZmZmD59Ov7xj3+gdu3aWLlyJY4ePYrbbrstqWUhiHhJhNNlIog3fLNuQblBfWgdpLwurroJ4twI4pykc5Wyhpz3s0jstxw6QYBMZ9+nHAWzYgIOaxtHo8CfXhEiTz9l1uXbri0w5lGAAWhpEFFmZHCUlqIs7J9THd3aQLcQWTBNuIrF6szL+sGoEczWNzZvsfaXmJgiUU5azZuFMHokNzl6MgaMHsnK3E9lPm1a2/PRCUD9iL6zshiystTlc1vUjUREW8bTp4mKT7IEMARBBMOPE6NxvGYMaNSQ419f2M+T4s6xY8T4bXW4BswbC7J74LQTNFeOoTt3cYABdesA3xwAJk6yjyVjHnUWDlrHYlnvXbs5OGKbNfbsjZrSt3JFOygFnoCoy5UdxHHdWGgdJxkDHh9r39BjRSfIVeH2nk2lk7Iqr4wMu9ur228A3Ryjfr0kFTwgyXIxjPe3CUGkCuqrBEEQBEEQBEEQBEEQBEEQZw+BRJ6SjRs34rXXXsMnn3yCw4cP48Ybb8RTTz0FANiwYQM2btyIu+66C7Vr1/aVbs+ePfHjjz/i4MGDqF+/PqpUqYLJkydj+PDheOedd8rO69ixI4YMGRJPFQgi6STC6TIRhMMMw++PFUS1ABRkEXrzFvt3Q/Nj7kxeCRreOahzkspVSiWiLFrMMWxIcMGmDp0g4MjRmLDTiK4PqcrstLDtJV9j+TdvkQ6iXOHuaa6jtQ3yB3E0bWJ3oVQtRDZvJkQQw0fY3UoXFTIsKmRlfQMAbs5TiymcxJ+J4MZc4V7m9X5bxdFuom9APIMX/TwWDtUouGllEO+4LeqW5/CNFDo8dZTnfkAQySQV7xlVHl7y9eLEaB2vOYdS4CnhHKhZUy3s6Z8XKgvJnpHBTws8xTE5hlauwvHttygTgzoRjYoNIbrNQ7oxUozRcq7Bkd2D492Vzvlt+lj9vQxx37wZw6gR1vxi7aBzKTfWXSd6l3PTosUchQuB1xYAC4rsrulO71kxj0qNw6eq3QHR5laBp5sITDfHyMoqX2N2Ml0Mg/42IYhUQ32VIAiCIAiCIAiCIAiCIAji7CCwyPPJJ5/E/PnzwTlHlSpV8N///hfcsHp04YUX4i9/+Qvq1q2LO+64w1faF110EUaMGGH6rkOHDnj//fexdetWHDlyBI0aNULLli2DFp8gUobOMdKP02WiyL5W5Pf0eKBuHfPibhCxpBQgWBeOu3cNVi+/4SfjdagzLojpQoEWLgT69uGO6QUph04Q4CausIY1nzvffFwVgtVvvrL8jRubQ8QbyxWNAhMncVSuzNGq5el+ZWkD4VZpd8rSLUQ6iSTatI71je077CFdo1HhDqYKaR/UsVDVv99cCry51OyIpcpHJ/JxEn3v+zQmcq1enWHTRmDFSo4nxxudRDlGj3RvSyA+4UMyxVEUOjy1UBhP4mwkyMaLRGwyAYJvNLCWQTUmOuH2XMs51vYdYuOGkWgUGPOEv7xatmDI7sGxfmPs++we4q9qTtS4Mbc5gr6zwjkf1WYWyczpQPNmsbaV51nPN46Thw9zjBlrPm7dZKHqAwuKnMf8jAx1IU+eTOy8xIqxzwDqdufc3CaMCfd0Y9vpqAjCsWS7GPr9bUIQ6YL6KkEQBEEQBEEQBEEQBEEQxJlPIJHnG2+8gXnz5qFly5b44x//iGbNmqFp06amc5o2bYq6detizZo1vkWeOqpUqYJrrrkmIWkRRKpQOUYOGezf6TKR/OpS88JnULFkuh3iEpG/qJ8Q16ng3D29IOVQCa+cxAyAOay5XGy/oJbZnVUXglUKATIyuKd8o1Hgk53OAhPOgTFjhRtXv776c1X9SbUQ6SX8uK4OoRDAkdj+6CSwUbXXrt0cXcPMUVykE4L0vgkmB1f5d8oL5rw4F+Jat7aU3zsJH3SipmXLYyHqGQPybuHo2ycxggkKHZ56KIwncbZgHCP8COuCbnKR70ljHkZBndwM0bgxR/NmznM+VRnatVVvElKhe65V73nd5iOvyLwAYMUqoHLl2LEVq4D27dUbMQoXujuEWvPJHwSlu/nQfJS1qXVDBufAhGdim1DEGMlOh5AXcxbrHGj1Go6ly9RO5V7meKWlYi5pJZnzZGufUc3DVPeYc1leb1QE4VhFEKMSBEEQBEEQBEEQBEEQBEEQBEHESyCR52uvvYYaNWpg1qxZqFWrlva8Jk2a4LPPPgtcOMnBgwfx7bffAgBq166NCy64IO40CSKVlPfFx6CL0Ol2iEtU/k5iPi/pBSmHSnilEzPI9GRYUiNGd9ZKlaAMwWoNxZ7dQwgxnPINhYBLW3kTgkjxhpNI1Ut/km1iFBhKIcm0GdEygUgoBDRvBuzaHbv2mquB74+o2y1of/QrhBkzFjjwbdQm1jSKi3RCkHr19GIM1XdeBSK6d49OULRzF8eEZ8x5vbYAKFxodhANSrqF4Wcr5X0MIoh4Mb7TdBsXVO+ZoMLzosVcmYcVzoFBQ4Bb+0W1YnldGRYVMowawUzHrHTuBPS+iSmfa5141Tr/8Ev+IJGOzlVbhnG3CinfX+ueNmPAhHFA5cqxOtWozh03SunmcHITilGwqdp4xTnw1lJzHYx9wMscT3dOq5bJmSer+oxqHhYKwebkeaY6OXsVoybTqTxZVMQyEwRBEARBEARBEARBEARBEASReAKJPD/77DO0a9fOUeAJANWqVUNJSUmgggHA/PnzMWfOHHz55Zem7xs2bIiBAweif//+gdMmiFRTnp1wgool0+0Ql6j8dWI+KTJ0Sy9oOVTCK6OYQQowmzZhZfdi+w6uXOT91aVMG4LVGop9xSq7I6g1XykoHTXC7Famg3PgxhvMQgkjQUUFf/vEHN5V1sEo8ASAtevEP2ue8fRHq+jUCGPin7HPcG5ua2N5pbjIjxBE5mOFMZGOddFfJwKwvntU4pCJk3hZHVRwnhjHzXQLw89myvMYRBDxYH2nqd5juvdMEOF5JMJRuNBfGZ3E8k5lkPOEosXc5ITZtQuQdwu0DqFu4tV2bYExjwJHjgCTn/PnsFkwC+jelZe9z41IV23j5hHpMvnaAnta7doCW7aibGPH6JEMHa80t4+bSH3fp/rCq0S7Mr2duzkeH6u/TvYBL3M83TlyHuVnfuhF0KfqM5wDt/YToeWNeQHk5CwJ4tqbbipimQmCIAiCIAiCIAiCIAiCIAiCSA6BRJ4AwFTKEwuRSAQZGRm+045Goxg+fDhWrVoFzjlq1KiBevXqgTGGr7/+Gl988QX++Mc/YtOmTXj++ec9lYUg/FAeHVOSWaZ4xJLpdohzy9+t3eRxlSDBS4hqeX27tsCiQhZ3O/hxXszNYSgp4cjMBEpKuFJAp3M0++YA0K0Lc803N4ehcWOOwUPdQ8lf1lot8lSJZVX3RRVy9Z0VPhrPkmfBNJicT4M8Q0aBjVU4UbkKx5gnzOdLoYrONcuPEAQAHnwAeHJ8LD0pgtm8xZxGdg9ucmd1EgHoxCFuJMJxM93CcIIgzjx0To7yXez0ntFt8tj3KUeb1vp3qB9RpEQnlncTv4fDDH37AE2bcIABrVro35lynDv8ndplc38xsHmLeay57lqzu/c1V9s3TajSadOaIbsHx/qN5mNjnhBtf/ttwOWXxTapLCjitjpefpkQeUqOHOUAxKaFnbvM9bVuVthfDGRkCKdyJ1RjVzjMkFkMcM2NtIqCvcx1neZRXufJVkfavFu4ci6q6zN9+4i+Ys2LnJyDu/amGuNcFUCFKDNBEARBEARBEARBEARBEARBEKkhkMizUaNG2L17N06dOoVKlSopzzl27Bj27duHX/7yl77TX7ChcE5HAAAgAElEQVRgAVauXImLL74Yo0aNQpcuXUzH165di6effhqrVq3CggULkJeXF6QaBKGkPDqmpKJMXhehVUK5dDvE6fJ3azfrYrpVkLC/GCg5GDU5Xjqnz5T3RScudAql6ua8KMOwz53PsGkjcPudwLB8mEK7OoWAf3wscPIEN5VX147Nm4UweqS5DFZuyAF0mv4J42By5tLVWyfUCQLnOB0aPZanMQy8n9Dj4TDDsCEMfftYnTNFKFiryOKGHGDpcthEjEZBcME0YOcu4eAphajGZ/Cin4v0sq9laP1rYNduDg4hdgGAm/PM/cEohnUTAfgNQ2+sWyIcN9MtDCcI4sxCJ3izOlarCIcZ8gdxTC8wfy/dKlUbRw5/x5UbKIzojkejwFvLOG7MNZepX1+UOXVaRanqMdOetnVOo9pwkJHB8fSzMI0fVnfv1Wu4o8hTjgWRiNhcULmy/RzOgTnzgGrVONq0FrsWrAJ/OT8xbuyYXgDs2RvFuvXGzQ1mB1RrPd0Et7qxSzcW6kTBTnNd4zxPJQ72cq313nCud4B12zBhzSvd8/TyQBDX3lRjfdb79S3/ZSYIgiAIgiAIgiAIgiAIgiAIInUEEnled911mDJlCp599lk89NBDynMmT56Mo0ePIidHsQrpwuuvv45q1aph7ty5yMrKsh3v3LkzWrRogeuuuw6LFy8mkSeRMMqjy0sqy6RbBJYL0Ps+5Sa3y/IggNXh1m6q8K5SkPD118wiaOS2+nq9LzpBo5fyScGAbmF6xkygShXz9YsKmc1RtEZ1e8h1r+G3rU6l76/jmDrNft6bS4G3lqnT+Gq/SCccZtizN2oKv26st1fxoV9RRyRirj/nIjy532fI+nyowrlzLtqCMRE2tUtnoLQUeLUwWvbsSPPpmJgnJrZVPYPhMENXQzm379CLbSXRKPD+Oo4unex1tIpDjOVxIn9QLP94xZkkOCEIIlFIoeaMmWaRpNHJGdBvuGjahEEEGY9hFVJFIhxFi7kp5LhuLJICRlkeK7PnAH+ZK4R7gHkMubWf2UXc61xDNc4ZhZ6yTUpLgWjUXtfSUoY2rRlmvRzFnHmKRjbUWYoJvYxFM2bGxLJWgb+Y29gbyCow5RyY8AzHj6c4mjaBTQjphJOLq2oszLvFm4u7EavodMhgjv55IfcLFdeq6qObr9GGCX+4OeamG9WzvqDI2R2eIAiCIAiCIAiCIAiCIAiCIIizi0Aiz9/85jdYvnw5/vKXv2DHjh3o1q0bAOCrr77C7NmzsWrVKmzbtg3NmzdH3759faf/97//HR07dlQKPCUXXnghOnTogA8++CBIFQhCSXl0eUl3mYwL0NYy+BWbBg057zXkuvG4W7vpXCNnzAQAbltot9bXy33RiTMaN+b4ZKf+emso1fxB9kVeXRh2GULV2DdycxgqV+YYM9a5vBKVqFcIF4DuXRmmzVALO3RiixenA9MLOLJ7cLy70rncRsGFFSnA6NIZyB+mF4Naw8Pv3G2/n5wDGz/i6N3TXQTkp98aBTbCgcseDtb4UfcclZRwZGaKv1lZZiHPe2u8xQmeOg2YNoMrxdhWcYgx/LtOaHL4cMxBtLyLvCVB3zkEQZRvjM/25i0xR0jGgAH9gXr1YpsLAGdnbzfxl9EJ2gjnwNgxwJq1ZmFidg+gf14Iv/5VFIULgPfX2d+pUrhnHccKFwJ9+8Q+e50DFi3Wl69mzZhD+c5ddhdSxkRdXy10FnjKNBs3FiHVZbu5nW8sq1ng7+6IamTyc97Okzwxxjm0PRC/UFK1YUi4wkZdhZ4qYa4O3XztTNswkcwx2839NJEEqYfuWb+1nxB7JrvMOnTzUYIgCIIgCIIgCIIgCIIgCIIgUk8gkWdGRgZmz56Nhx56COvXr8cnn3wCANi6dSu2bt0KAOjYsSOeeeYZ/OxnPwtUMMbcFxG8nEMQfigvLi/GxcF0lsm6eG3Fj9g0aMh5nUOSm7uoW7s1qK8Ws3ldZPdyX3QLtoOH6t2/VKFUrW5gUnBZMMt+va5ftGrJlKHFMzI4tu+ILUTrRL0x4QLHqBFMKXhxwhpSXFfu3ByGxo25rY0YA2ZOF6HjAeF+aRXC1q4NMAAtraIOTTmnPAf87FyuDAEr+xJgFQSgTBAi75VTO3hpI+tztGw5x7QCYNNG4PY7gWH5oow6oREAdO4ErN+g7m86MbZRHJKbE6vX1m3cJvRhLBZK2JouUD6dxIK+cwiCKN9Y5wWAWWA/Zx4wZx43vbOd3DCdxF9yHqR7l3+82e48uWIVcPHFdvdmK7rNEjt3c3QLexOgAmKuVrjQnhZjsfFQN7bLPFevsYes1zF4KDB6pBiXRo0AphXE8rPiNC8JhxmGDPaer1+Wvw106+K8kSNeUaHThiHpYKrLQyXM1eFl3l/RNzWkYsxOhftp0HronvW+fRj69knPPEs3HyUIgiAIgiAIgiAIgiAIgiAIIj0EEnkCQK1atTBr1izs27cPGzduRHFxMaLRKOrUqYOOHTvi0ksvDVyoiy++GJs2bcKhQ4dQq1Yt5TmHDh3Cpk2bcPHFFwfOhyCspNLlRYd9cZBh1AiW0jLJheLD3zmH4vQqNg0acl7nkLRnb9SToM3pXurEBaGQyEcnwpT1VYX57GcxLtaFH3cK76py+FSd370rQ43qMXGFW79QtUd2D+mIyU/nHxPM6pgxE1i8AHj8UdicQY14deeyum4CImysyv2ytDR2Tru2wJhHNaJOC99G1N8bQ6ACdhHQxEm87Dzjd37ErV7JyBDOaLLPV67MTHlmZjoLSnvfxPDAMBGifeo08zGvYuxwmKHkoN7JTeXAWrSYWxymyoeQMug7hyCI8o1qXqBDPvdjHnV3w9SJv3bucp4HLX9Hna9xc4bfMcP4hnIToO4vBg4fVo9LebfAJFR1qocfoaVx7MzNYWh/hch83mxg5epY3b3MV/vnhVBcHMWbS73n75XNW4DX34iiaRMxf7BuCsruwbFilffxSyWidNowpHJml3nohLmhEFAwDXh/rd69UVWOir6pIZVjdjLdT+Oph/vvluSUWYdqPkrzKIIgCIIgCIIgCIIgCIIgCIJIL4FFnpKmTZuiadOmiShLGb169cK4ceNwxx134OGHH0aHDh1Mxzdt2oQJEybg2LFj6N27d0LzJohUuLzo0C0OLipkWFTIUlImq0OWTqznR2waNOS8ziHJ6pqlS9ftXopQmlGbIAGwh1FV1VemX7RYLNa/tgBYUBQLj60Sgqra8v5hwE8/wVVgKZHiAaO4Yu4rcA2jaGyPjAxuCnmucgx1ylsnnDSe5wZjwIO/RZnIUqISTchwsoBOzKDOIxLhNsdTI7K/yP+71SEZAk8gJmBV9XnOgdG/118rxcfhMEOXTiJEexDnX+EUqj4mwyBb74nO3TPdAoCg7xyCIMo3unmBjmhUiCa9OKJL8VckwrF9B8e+T4O5TPoJQa66tk4dYPsOXvZeV81lnJw5JV06i79+28xIw4bAl1/qRf7DhrCyuUdWFkP/PIbuXe0iRCeXyd/czvDWsuRsoIiFeDcnbnUXdxu/dCJKpw1DKmd2mcf+YnUf6ddXOJY3bwb07eNNzOnmVFsROFPG7Hjrkc7foFbOlHtCEARBEARBEARBEARBEARBEGcScYs8k0H//v2xYcMGrF+/HnfddRdq1aqFevXqAQC+/vprHDp0CJxzdOrUCf37909zaYkzESeXl2SGQ3RaUGvTOnnOMxKVQ5ZR6CndJps2Yb7qHzTkvM4hyQmjGBBwd+zpnxdSChKMYsjSUuf6LijSC92MC7YnT3KlWK9BfeDhP6hFGNZQtIC57YziCjeMfXd/MUM0anfLdGtvKVxwEk46IdOX9Zr8HPDcC9wWBt2pDn7EDG7iFmNbWvuoqu3jRdW+xjLIZ8VPelaH2uwe3CReye5hbxtVyFqnkMQAcPttwLxXY85i/foKYbORaBRYs5aja2f1/UhVONmg7xyCIMo3OodsHaGQcHtWOeQBZjElAE/iSScYA4YM9rZpAgDatwc+/tjs+jl4KMA5t4kJ5VzGizMnINwgmzdzHlPd6jLlGYaSg/x0mczHCxcKIaL1XW6dd71aaN1MY3aZDIcZRo803x8519y6jWvdpRONTsDmNu/QbRgqLYVtniXzUPVjxkRobom1HXXl8OJUW945U8bsRNQjmU6jflDNRyviPSEIgiAIgiAIgiAIgiAIgiCIM4m4RJ5fffUVtm3bhkgkgh9//FF5DmMMw4YN85XuOeecg4KCAsyePRtz587FN998g4MHD5Ydr1evHgYMGIA77rgDIT9qGIKIk2SHQ0z3IqfORXDsGKBmTX/CTiNuIQidrlM5JCUa1YJq7DvnMnpxupFpbd8BWN2kAOCr/WrByv3DgC6dGDZv8dZ2TgI6q0PrDbl2waEUVjg5il5+mXCdtAoXAG8C0YJpwIEDwGNPxL63hkZ3Cn0qzzdibG9rG7gJgvr1jbWVTgTkJ0S7FEXr8rOmw5hoc2Po3fxBcBW1MCZC8fbtYw7be/Ikx7srzeeuWAXce7fIeH8xbCFrR40A6tVzF01dfhnDTTfGBCqAcK61XvfidGB6Abe9H1MZTjboO4cgiPKJ8d0+agTzLMSMRkXYbqtD3uYtwM15zo6IfmEMmDAOqFyZ4fbbvIkTjQJPiZs7sldnzgVFQoQpnKL9CT0ZA0aPlOJShrxbojZRP+dC1N+9K5CZGfveeK9WrzHP4WSdGjc2b6DJzWFo3Jhj5y6gVUvhZgmIDU7VqkWTPg8E9PNt3Tzv/XUcXTrFhJ7WDUORCNfO6YOMUbpyeHWqLc+cKWP2mVIPIFaXaaefvYpcF4IgCIIgCIIgCIIgCIIgCII4Uwgk8vzxxx/xhz/8AUuXLgUg3GZ0BBF5AkAoFMJdd92Fu+66C9988w0iEREbOBwOo27dukGKTRBx4ddBMAjpXhzUiUxbtoiVIagTX9AQhP3zQigujuLNpd7ykWLAVLng+BHm6s69tJX6+y6d2GnxA1zbbsVKjnETYiLOvFu4SQBodWh9S9Ge+YNiIex1goqt24B77uJKIeY9dwEv/Ul9nezLzZsxvL/Wrk4xpqUbUg4f5qhbV9/eVhFh/iCOpk2Yo3C1aRPRpwEhdCyYBptza+XKHGPGqsukqsfjjwGZNRnWredYvMT9/BkzgX/9K4oVq2LCy7vucL7uiceArl2ECMbNeU6G1V1QpBaIPP0sR8E0ZzGsdHDdX2xuG+P7SpWufD+m4v1ppTyFPSUIIjjLlvMysT1jwHXX+nNYnjgp9q6RmwGUjogaR22vNL4Yp926xRhpdOkMhYAbcmCby7jVQ25iAHiZ0/V7a7yVx8k1UgdjwP8NB2rUAFq1iH3ftw9D4UL7hocXpwOz5wAffwB8+hnH6vc4Chc6O4NHozG3UuF8ylGjOisLbS7mC7xsE0D/vBAyMqKG0OvO5b+5D1C0yP1cI07zbV37TZ0GTJvBlW6rgPuc3u8Y5TRHPxOEhWfKmH2m1AMQdWl/hXiI577iLWoAQRAEQRAEQRAEQRAEQRAEQRDJg3EnhaaGSZMm4eWXX0aNGjVw4403olGjRqhatar2/F69esVVyPLGm2++iW3btmHXrl347LPPcOrUKYwfPx69e/dWnn/s2DFMnToVK1euxH/+8x+Ew2FkZ2fjvvvuc2w3yeHDhxNdBU9kZmamLe/yyPYdHA88aH9cXpjC0KZ1Yhe9khnS2O2+2t32WNlCu9MxP/itXyTC0acft7lODv8tMOU5uxvlosLELW57KaufdtGdO2581BRi+/ps4JGHnZ2KIxGObw4AnTvVQvurDuHoUXP/lE5c9epB2XdVDM13dvIEhLPrmLHeRDZD80XIVaOzlfVe+iEUEuHHjYLIUSPEgrp0ZVNdkz8IOHwYZeITI8aw7Kr756fMjAGLFwj3VasDqJvTqZHq1Rk2bayFKzoewrFj5oukI2ppKUNGBkf+MPeQ9Jw75/3CFIavv4bJ7RWItYmqzWUbRSIca9ZyvDhdnW6b1kz7/pRutRVZBOGHs2VczTTa+hEVikT2z0T0dy/vXy/v1ifGAN1OC+N176M7fyMEi0HHJxUDBwgX5K3bOObO95+2F4dtp2vlfMjq5g2ohZjtLge2bFOPh7oNBVWrMmz+sBbaXXkIx48npvGsczk/47B1PHNDCE3lJhc1xpDzbmW1ksg5vdN8M5m/HaycLWMZIaD7ffZQEe81zTkJgiAIgiAIgiAIgiAIgjibCOTkuWzZMtSoUQNLlixB/foVKBZcgnj++edRXFyMzMxMhMNhFBcXa889ceIEBgwYgL179+Kqq65CTk4O9u7diz//+c/YsmUL5s+fj/POOy+FpSeCkspQ6qrw4clGLg63aysWq60LxYly4vMbslmWa8hgmEJMd74GaPo/QsTo1b3I7wK417L6de0xtqEs14pV5nNkiG1dWlJwUKUKw+YP1eIPzmMujV4Fhm4hUUMhEfTVj1DFKILeXxyfgCYaFW1jddzcvsPZyXJ6gXAne3A4sGsnsHJ17LixPKp+vXmLPU3Znqp2Xb2GK8UgE8YBD3t0ipPnzJstQu5KF04puBTCTrWjqrWc/frCFmbXiHyPtWlt7scAypzjjEJSaxuFwwxdO4sQ7br3o1cnNIIgCCM7d7kL+zgHet4AvLVM/z40vl1076NX/hJXUZXMnQ8A3kK3W2EMGNDffVxWIcWh0gXUOk8BxP/3fWoerzZvjaVhfdfLkOrvvAssedN/mfwQjQI7d3NkGuZVo0e6CzdV41lGhggN//obHGvX2a/hXMwvu3cVjWCdyy1bzlEwSz/mS8dU3bw9kXN6p/lmOn47EARBEARBEARBEARBEARBEARBEKkjkMjz4MGDuOqqq5Iq8Dx48CBeffVVbNmyBf/5z3/w448/Ks9jjGH16tXKY8niySefRMOGDVG/fn3MmjULzz77rPbcl19+GXv37sW9996LkSNHln0/adIkvPTSS5g9ezYGDx6cimITcZLuUOrJxOrwlHcLykJ9S/YXq0M9+wmN7lcoanROYgy4MRf44kvgb38D1qwV/67P5kpRqlMdvYpLrWU1hny14mVxPRIRIWeNTJzE8fij/tr21UJ9OHUr0SjwyU7Rdl5D3uuQfb5VCyAU8uaQNWOmEE7INvMTNlZHNCoEnkbxaEaGu3LUS6hXmb5se9kPrI6cM6fHnDRF2FlxTIZfVwmNfvhBtJ8Xd7HQaUOxrCyGYUMY+vbhSsGlF/FTl85QhmqX+RjfY/Zws8D2HUJQasTaP93ej9bj1rSSHbqdIIgKiodXAmMuAk8mQlpL5PvI6racDDhHIIFnPNfePwz46Se5MSYWEr1/Xsgyp+AYPsJ5HDG+68U8Kvj4zRhw+23w7Gj6+FgR0t04Z5MCR6s4VaavHs+Ei/j6Dc71LFrMTRsqRo0QG5+M45bOydNpw5efDUZezpX1ikQ4tu/gFT4kuB+c2ieVTqYEQRAEQRAEQRAEQRAEQRAEQRDpIJDIs169eokuh4l//OMfGDBgAL777jsEiCafdK688kpP53HOUVRUhCpVqmDo0KGmY0OHDsX8+fNRVFREIs8KhF/HxqCkcqHSKmbkXLj+FS7kGD0yJoSM18lUhnT2Kma0Chk5V4sU31kB9LqJo0F9VuZYpVr49SMu1ZWVcyECGDYk2D3ZudsuKOEc+P6ot7aNRDh27ua+Xb1enB4Lz+oV6VYl3cCMIdcBeBbIcO4sBtTl64YQdcYq9c0BjxXzgLHtVeJmzmMuomvW2surKz9H7B1StJhr3TVDIWD4/ebvpKhDJbh0o7TULsBU3VMdXp99t/ejPP7+Oo6p08zX+hWMEwRxdtCqBQNj9rFGvpNCIWe3YsaE47f1fdSurbf8nTYlhELA3XcBL73sLa1UwBjQqiVsmwHEvCFqCkm+c7e3DQcN6tvnUUG4/TZg0D0hVKvmbaOKvOd292hRphkz7WOh8b4a59KqsdxK4UJ7nmMUm3CsZPdIzAajZJ2roiIKIp3qHG97EARBEARBEARBEARBEARBEARBVAQCiTz79OmDmTNn4tChQ6hVq1aiy4Snn34ahw8fxrXXXovBgwejUaNGqFq1asLzSTZffPEFIpEIrrrqKlSpUsV0rEqVKmjTpg02btyIb775BnXr1k1TKQm/JCocom6BddlyXiaeE+KExC9UlpRwfPmV88K3DPVtDMlsFIrJMNBeMC6+qtj3KTe5Mu7Z692pEhAL82vX6Rd3N37oXVxqbH8VC4qAvn30YdQd0aRZs4bZ4VHlEuvWhgAwcABw6pTatTGIXn7sGOF+pqpru7be0mTMLsh0CvnqtZylpZYyJWg/gLXtdQLHrds4HnjQZ+JcPPfhsHDnrFs3qnQXHf5bIPtadf/y64SqC8XuxwHLj4ux2/sxHGbo0kmEaA8qGCcI4uxBhuk2z4vsoccLF/pz5dxf7G284Rzo3AnKMN/RqBChXp/N8c4K73knC9k2+z5VzxVmzARq146iVQuGzVuAiZPc0+x1k2irw9+p0+zaBdiy1f69CuFKGsVNNzLsL+ZY6uC+asU6Z1PdP+OmEqMTvNzY4DR2duwAfPCRPU8G9zF3xSrg3rvt88I9e6OYOEkvVjUind69nutn45KViiiIdKpzyUHuuZ0JgiAIgiAIgiAIgiAIgiAIgiAqMoFEnvfccw/27t2LgQMH4g9/+AOuuOIKML82cQ5s3boVF198MZ5//vmEpptq/v3vfwMAGjVqpDzeqFEjbNy4EV988QWJPM8yVqzkeG5qzIVq+P0c2dcylJRwvDgDqFIl1u9fnAG0v4IjKytxz8LgYcCxYyLvu+4Aqldn2gXsbw7EFtVzcxjaXyGEeYuXiPCoy942l7/4a6B+PRFmuqSEY/deUYfKlfXlnzsfuLa7qGPRYo6X/8zgR9f98WagcuXY52kFsTabNJlj1Xv29EIh4KKfm79Ttb+KjR8C11xlvifWuqv41aUM1arZw4te2kpc0/4KlKUBAH/7hJf9f1qBug2rVon9XfImw9xXhPh23QZglsZdLLuHECW4ccEF+gXyN5cCVauqj1ndOEeMEn3ksjaifp99Dvx5tuhz1vvixclTde9UbeuVnzcAvtof+/zhR7H+Ew4zPPIQTM9rxytF/3fqo9Z6MAY8/az5ef9qv7qfZznsnVCVh3PnMMU7/gpkX6sXYOreR5KSEo6f/xx4qYChtNS5j3tBVYfh95MYgyAINe2vAEaNZDh8GLiiHdDkf2Q4bnF8xUqOqlWZ9j1onBNIPtmpH8OsbNmqft8zJsaiRx4O4eY+HLv3iPfjY084pxcOAyUlwcOe67i1H7DvUyjnPJKnJ8Wcvd3mOgDw7grxD7CPsaEQcNutwJUdxGcxH3FOc/ES8Q9gsOx/c8Q67l/0c/vclTFxn4oWc/xlrjn9ufOBvFvUjvAAcNVVwN922ut3aSuYxivdHMU4VwZEn5zygrqO1nMBUS7V/VCd+80B9XxQda6VkhJum0+qno/yhq7Oby4FFi/x3s4EQRAEQRAEQRAEQRAEQRAEQRAVGcY9xEPv1q2b8vuvv/4aAHDuuefiwgsvVAoyGWNYvXq1r0K1adMGnTt3xuTJk31dlw5mzZqFZ599FuPHj0fv3r1Nx5YuXYqRI0ciPz8fDz5ot3ybMmUKCgoK8OKLL6JHjx7aPKLRKEKhkPY4QRAEQRAEQcQLzTkJgiAIgiAIgiAIgiAIgiAIgiAIgiDKH56cPIuLix2Pnzp1qkzwmQhatmyZ0PQqOt9//31a8s3MzMThw4fTkrcbduc5fXjjdKEr498+4Rj1sP38p8cD330PPDXBfuz3DwGdro6/frt2c1x9VS106X4Ix0+Y865fTzh0vv6Gc7vqyu/FgVFHKAQ89yzw2//zn8bvHwImPG0Ppz33Fb2b5YD+wO23iXoZ71PQcg8foc5fuiIZ82BMuEmdOiXcrGQo0eH3i2unvGBvAye3xqpVgPdXx+7p2DFARob4pwonzhjwxGPOTmOhENCtC7B6DcrC4z74QKwvOPWBebOFW6fquBOD7xXOoar+by2brm29UreOcHhyIxQCHhoFtGgWy09Xd9W1zz0L7Nqt7oOD7wVmvmT//ubewL13i7wyMzOxYeMh1Knt7pwpnWS/+w54aqL9+NPjhdupFaf3Uf16wO136vt2RXgPlzfK87iaSDIzM9NdBCIgiZxz+unvqvfJZW2AAXeoxz451uneeUaka/mfZyfWPfOFKeLvrt1AyxbAF18Ak5/3fv2t/YAFRfYy5VwPLH8nYcVMODf3Bq65Ojb3sc5DEo3THPjTz3iguaMO47gPQOnQrpp3eJnnS6zj8Ut/4lj0uv0843zAStDxt6SEO47rXknHWGatc++boGw365yV5irxc7bMXYiKea9pzkkQBEEQBEEQBEEQBEEQxNmEJ5Hnvn37kl0OE/fffz/uuOMOrFmzBl27dk1p3omkevXqAIBjx44pj8vvq1WrlrIynQlEIhzjJnDTAuW4CUDrX5efcL9OZaxbBzh5ktsWWOvWYahbBzhxgtvCPDdrkph61akt/h4/ARw/zk15Z2Ux3Hs30PMGjv3FQIP66vZUld+PwDMUioULlwuuo0YwHD8OHDvmb5U+FBJtMywfePpZbkovK4uhWdMojh+3X3dZGwBgyvvkl7/+DTh61P79l18xZGWp+8LLf7af/+R48VfXjrf2AwoXqo6LeyTv6cjRMWGmKq2unYGLGzFccxXHOyti33fuBLRpDRw6BNStC4y3iGaeHB97xlT9VLJ6jQhveuKEP9FFaSlQXAzl/bLy3vtAl07i/0Hu39//4f3cRx6VfQrIzXGuu5HOnYB789WColAIqB2215UxoOcNMVFCXj8R6v7ECY7RI0X+kYj6+czKivU33ftFhdP76MuvgKNH7RX98iuGaDS17wg8en4AACAASURBVGFdvQmCqPjo5kxj/gBoptAYMSr2fy9zkBdeTJwQUHL3oPiuV80FAKDkIDBlErDvM2DKc4kvd7zMmQdckGWc+5jnIYlEzvN0eJ07+pmnVq/GsOlj67xSjMGAEAj++8so5swzX+c0zzfWxzgeRyIcc+ap5xRyPqAi+1qG1r/2Py5mZTEMy+fKOXN5x1pnAJg73/57ZOZ0oHkz4UZcEX4zEgRBEARBEARBEARBEARBEARBeMWTyNPKww8/jMsvvxx9+vRxPG/JkiXYsmULnnrqKcfztmzZYvtu4MCBuP/++5Gbm4srr7wSderU0YaPbNu2rffCp5CGDRsCAL744gvlcfl9o0aNUlOgFJAKIc7+YrtwKhoV34fDScnSN05lbNOaYdQIuyhRttfokcDESbxMqDd6JEtYW8pFXPkoWfP2cv/CYXv58wcBBbOcHbKkg2TLFiK/e+825xWJcIRC/ly2ZNlzc4B2bWEre/NmIVyfHTWJGa/Pji3+qu6TH0IhoFVL2MrNWGwB2msebuKDSpXM6Xe6Bli7Tp+OLr01a4G16zlGjWDodRPHzl3A998Dc+er0zOmu/Ejjt49RdvemAu8udR+3ovTRXu0vRzYbH+1a5kx07sAY+o0YNoMjn59E+vKpiMaFf29XVvRt4zPqA6ntrzmauChR/THIxGOKS8Aef3EZ85FfkeO8rLnzCo6kaieT+MzbsxDPi+jRjDN+fZnMhQS16TyPbxsuVWQYq83QRAVl40f2gVx0SgA5k2c52XsKG9CSSfWvA+8v1bM/0aPjL3PE01QB3bOhfg0FajGL0kkwnH4MLfVg53uN9I9XTdfkecarw2FgIwMjqefjY1x1jlAJMIxd749Ld0835i2tT77i9X34NZ+7r+jwmEWaLzNzWHKOXNFwFpn1XyneTNz+5b334wEQRAEQRAEQRAEQRAEQRAEQRBeCSTyXLJkCQC4ijy3bduGJUuWuIo8b7/9djBmX2DinOPNN9/EW2+95Xj93r17XUqcHho1aoRwOIzt27fjxIkTqFKlStmxEydOYPv27WjQoAHq1q2bxlImjlQJcRrUt4vqpPCovOBWRqcF1lQsvs59RbjxGdP3c/9UZaxRnTsK34YMBrp2iQm1rQu1VnGaG4wJYacuPckjD4fQ66Yodu4Sgkwp8ATU90mm7SZ8MC4mjxphr/vqNRxNmwixgFchhZDVqZk73yzgXL8BGDgAeP2NWJmVaSryloKFRYUMXToBffq5O1MCQtSxd0+0zIVVRzQKbN3mnp4Rp/x1dXhtgb88nGjeDAgxYNce9XGjKED2/w8+4nh2iv+81q1X15dzkcfh7zg4Z7ZjRiGsVXRixO0dsmw5twnJFxUy2/nOglG9ADSRRCLc9E5wqrdTGhVRzEIQZwPjxps3YkiE42Hqy5MMgogppbj/8UeBAf3NcwCZ5pDBwJ69zpsKdFg36IRCQMeOwIYN3svnBmPAddfCdc7gdH3jxhzSKdSIcc4qRZ2cx8Yp4xgIAEuX6+eWciyT15aWAtGouYLGOYBOmGnc4GMchzMyOEpLmXIM0v1e6NsnuWNVUIFoMgkyVrvNdyrCb0aCIAiCIAiCIAiCIAiCIAiCIAivBBJ5euXUqVM455xzXM+76aablCLPig5jDH379sW0adMwffp0jBw5suzY9OnTceLECeTn56exhIkjEUIcr3h1qksnXsrotMCa7MVXGdpZEuT+WcuYm8OQmckx+vfqPAtmATWqc0fhr1ysLVrMy0KTh0LA5W2AzVvN53Iuzhs2xP2+N28WQvNm6mP9+qrDoDuJMsaOiTmSAmaxqSzb9AJACuF0rpvWhWedZkIncjx1Cpg3W3x+frI9PHgoBBRMAz7ZKRw2rde/v44j6wLv4hPOoRTjqFAJZ2Uaqvro2tuLu6tR3JE/CMio7D3EbatWwK5dQijj1pOMooBwmKHhRYD+rjmXVXesQX0R8l6Fqg/o3Kh075BIxCxIlkKixQsY2rS2t4B8Jnft5uAAWrWIpZ+K93C8LlzkAkoQ5Zc9e9UCT8bEu/ybAxXLgVPH/1wCNG2id5PUwTkwZqz+2IyZwcs0agRDbg7Dr38VLXP0toYfjwdj2Ox77+b4y1weqP6DhwKjR5rnjtY5qxz/H38MaGWYm8kxIhLh2rke5+K6zJrM0VneKAzUbRAaMhiaeb5+zKkIv2lSQTxjtdvvKWpfgiAIgiAIgiAIgiAIgiAIgiDOFJIm8uScY8+ePcjMzHQ9d8KECckqRlIoKirCtm3Cpu6zzz4r+27z5s0AgMsuuwx9+/YFANxzzz1477338NJLL2Hv3r1o3rw59uzZg40bN6JVq1b4zW9+k55KJJhUh8OL1+0yFc5u6QyH6Ld+Xu6flzQrV9Z7UUajQkzWuDE3uWlay71zt3DAnDkdZc5HAND7Fnu6C4qAvn14oLa1ukAZkULEvn2AosX2aznMbbBzl94JMxoVzo0qxj8pwna7uVgOGawOZ/7aAqBlC+CSS4Am/2NeyJYimebNQvjr39QqyanTzA5YyYIxYMI44JJfMhQt5jYHTrnofuQoPy2OjV3Xr6+7YydjZnHH9h0c3GOFdu0yOKS6nPv53819za8TlBShOoWmX72GY+kywGD87Jie3zLs3G3vq5yL77tpnqPNW1AWujYU4mXii1S84+Jx4Url5gOCIPzz4Ufq7+MVMKaLJv8DfPqZ/ftPP1N/Hy9Bx+2BA8Q7XMyDgrlsOmENm716jX+Bp4Rz+3t75267K6f8vL8YAGJjtXGupyurURgKuAsDrcflPK1/nnpuK7HOo+Xndm2hdNP2k1Y8pNvtOtljdUUOT08QBEEQBEEQBEEQBEEQBEEQBGHEs8hz4MCBps8bNmywfSf56aef8OWXX6KkpAQ9e/b0lP5HH32Eb7/9Fi1btsQvf/lLx3P//ve/Y9euXahTpw7at2/vrQIJRIahN7J9+3Zs37697LMUeVapUgXz5s3D1KlTsXLlSnz88ce48MILcdddd2HYsGHIyMhIadmTRTrC4QV1u0yls1s6wiEGqZ/b/fOaps7dSCJdmYYMjqJpE2YKYbl5C0wOgyKMNMrcBW/tZxcHOgmJnRatVS5QqrJWraquh0wtEuFK0aIqLRV7P3UXafS+SQgHDh+OKvN5aiJwU0/gpT9xdLyS44Yc4RbGuXBPBaKn/+rLlmwjZc6FmHXIYOHQauV3I37C2rUfYeWqb3HOubVRpWoH3HTjOcj5X2Dfp+7pR6PChUuKJg4f5p6Fq35EMouWAJf8kpflM+EZfwqb/EFAjep6IbQ1JLsKa0ha32IFnYuo5nQ38UWy33HxuHClevMBQRD+qFVLfywa/Qknjn+E/576FudWEuMCY+eg5w1AmzbAuyuAjzbFzr8+G2jcGJg2I/nlVjFsSPry9svc+UBWVhRTnk/sBo+OHYAmTYBWLVEm8IxEeNyC3WhUCDszi4F9n+rTe3ys2GQo56nt2sJV4KkaTyIRjnr1hBO6Lsy6m3DQOge1zqOze/CyMPaMAXm3iBDtXsa2RP6OcUsrFQLQZI7VxvKr3MpV55EIlChvUP8kCIIgCIIgCIIgCIIgCIIgJIx7tDtr2rRp7CLGXF3Szj33XFx99dUYN24cajmt4gL45ptvkJ2djbp162Lx4sWoVq2a4/nHjh1Dnz598O2332LFihWoXbu2lypUWA4fPpyWfDMzM33lbV8oZOUuLG4kwnFzHreJGRcVnhmh+7zUT97XPXuj+GQncGkr4faou39+22zZcnM4aC84heqW+UQiXOnm+fpCeznc+uL2HRwPPOhcwFBIOG1aw8/LEKTvr1WH/vTDiAeBKc87u2hJl8q6dYRA1ppf1aoMmz+shXZXHsLx4/bCJNul0w+qshz5fhlKDvwepT98XfZdxnn1kFXnKZxfM9dT2WU/EY6TMWetZNSbMeC6a72HrbdeC3grl+6+Mgb833CgY4dg76xIhKNPP/PzyRiweIE6Pd2z8sIUdXj3ZBFkgbs8vu/9jqsVFS8u7kT5JJH9062/68Z13bhwYZ2nUP38XOW7NBQC2l+hdwdNNo0aAl/8O/X5+hlXkoV1vDLOu95bE9WGnPdKkDqGQsKxdPYcfZoynLwRq8v77bcBl18Wc5XfuYsDzO7+qUtDOngXzHJ3TBWbm5x/OyVyXHNLy2kurXu2y9NY7fV3aUX4/Zpuzpa5S3kk1f2zIt5rmnMSBEEQBEEQBEEQBEEQBHE24dnJ87333gMgHFK6d++O7OxsjBo1SnlupUqVkJmZiUqVKnlKu6ioCKdOncLvfvc7V4EnAFSrVg2jRo3CsGHDsGjRIgwbNsxrNYgkUhHC4SUqLHl5xasbzqTJHG+8FfvcuVMUTz4RUt4/vw477dr6L7dTqHOZT8lB9UklB2POivuLgYwMc/hRVZh4leOoURTIGJDdA3j4D+a8hPuSWmwZhI4dGCqd6+w0xTkw5gmRd6drROh3P3mXF4EnoBZ47v/3nfjfS36GhztmokX4XOyO/BfjPziEtz+/E8ArqHF+rmu6Pbr9hI8/3oRHHjuAc84Rjm/AOUmrQxCBp7w2EflPfg6odC6Qm+P/+nCYYfRI786Y6XBpVhHEMTQeF1CCIJJPOMxwfTY3vVPdxoUGDdXjQjTqFP5d7QqaSNIl8My7BahUCZgzL/X565COz0eOckwvSEyafsfPaFQv8JTplZa6u7zPmQfMmadyB+d46Hd2B02V+7WbQ7exTG5hyhPpeumUFuA/hHpQh9FkjNVeQ8AnO1Q8QcQD9U+CIAiCIAiCIAiCIAiCIAjCimeRZ/36MUXHfffdh2bNmpm+i4cPP/wQtWrVQvfu3T1f061bN2RlZWHDhg0k8ixHpCM8uR8SFZa8vGAVpHoVZK16z/x57Tpg1stRDLonZLt/fkVe+4sTJyw05vPJTvU5i5cAy9+O4q1lsdDj1vxlmPjRIzlyc5hyQTm7B/Duyti1xv8DIt3xTwrhZ6Lqt3mLWRy9bj3H4iXqc6NRcZ+M5SlPAk6/cP4TSg78Hv97yc/w+i3nI3TaquuKBj/D67dUQu+FR/D+l4+geo3rHcU4R75fhhkvqp1AvQhEU0miHNe8CEGc8CPIr+hCyYqw+YAgzlYiEY53V8Y+J2pcMOLkFl3exgi/cA68tiCxaTIAiZhaRKOIW+DJGPDgb8XGhkSjmseqRI8S1bg94Rn7OKxKQzc3VeEm2EzkxosG9e3lkmn5FZPGK0aLd6y2/h7yWv5khooniHih/kkQBEEQBEEQBEEQBEEQBEFYCbmfYue+++5Dt27dElaIf/7zn2jVqpXv61q2bIl//etfCSsHceYjBEsModM93yhY0i1QRiLelrsjEY7tO7yfHy/Llovwhg88KP4uW84d6+fGnHnAnr321W2/acoFaCuq74wwZv9szOfIEfV1K1YCby6NLVLrFtE5Fwvyr78RRSQixJ6LChmeGAMMf8As6uRcLRT9ar97uE1j+QcOsNfLyMRJHHv2RrG/GNj3KceSN72lLXFr03joeUPy0gaAE8c/QukPX+PhjlXKhDySEGN4qGNlnPyhGCeO62PvSse3rhcdxIY7MnFo1IXYcEcmul50CPv/fSeOfL/Md7nuHwa8vpChTy/flzrCGND2ssQJc2NuW8EIh0W4da/ii0WFDC9MEX/Ls/BdhZ+6EgSROooWc9M70c+44DS2ShI5Rjw82vOpZbRp7f+aRNPzBmDsGODeu72d72eIurih8xxHRygEDM13v5ZzoKQkWB5u+avmsVL06Iedu80tppoDh0LAkMGx753ycBNs+pmTu/0u2bzF/Nk479bVw2mDl94V1BtBx2rV7yGv5fdbzzOBVP9eJYJzNvZPgiAIgiAIgiAIgiAIgiAIwhnPTp7J5MSJE57CtFupVq0ajh8/noQSEeWFZIRO17nFxOOWkWoHUCfHnHjccIxul0bicf6TcA7c2g/IzAQKZtnbutM1QJdOwPdHgZo1gJYtYgu9kQjHvFc9V8ORyc8BU57nGD1SfDaGdnciFAIubeXdjUmG+XQ7Z9AQQCWrcMuHc+Dxx4B//tO9LH4ZOwbo2iWEZk05JjyTnEXg/576FgDQIqwehlpceK7pPCvJcHwLhYAunRg2bwFef0N/3vXZwIpVsX7TsQNQtSqwcrX6/K5dgPfXApu3eiqGrUwTnwIeekTttpUqyrtLM0EQFYtIhKNwofk7P+OC2zjsZYxYt9/7GDF+ouspNrbv0B/rnwe8Wug/TT8wBtSvDzz+R+8bVPzwr3+LPHKu91emUSPkZoGoayhz1TyqR3dglWa8vbID8KF+bwgAYMyjQKsWwPYd3DSnDYcZhgz2F2L+yBFzOjr369wchu5dOXbu5nh8rDotrxuzvMzJ3X6XyN8R1rZv1xZlbZHdg+OdFbFj2T308/9EOoz6Qfd7aFGhEMO6uZC7uZUn43doOqloESvOdiq6mz5BEARBEARBEARBEARBEASReMqFyPP888/HwYMHfV938OBBnH/++UkoEVEeSOZClEqwFHSB0kuIwkQvEroJUt0EWVdfBby7wv69UxhomaZ0gHGqS24OQ+PGHIOHmt0xFxQBiwoZfv0r8zFAhCJfu04IAPJuAVq2cK5vPHAuXDTl/1VYQ2t3vkb8HTI4/hCkXpCi2MKF+jK+vxZ48glR0KfHA2vehzbcux/q1In9P1lh4c+tVBsAsDvyX1zR4Ge247v/81/TeVZijm+ZWse3tz8Xjm9Vq12lTKNZM2DfPvM9/vzvHE8/69wvfnUpw713xwQWAHBznvoCxsR9CtqGw+8HOl4ZwuiR1vchLfISBFFx2V9sfy/GOy4Y8TRGzHYeI5KJk8Cz5w3AuZWAxa/Hl0ePbt7nK0HHes6Bt98VwkknQiHghhzhbtqqpZhLFswKlufq9+zfMQbMnA5kXcDQZ5NdvGgsx7ffAk/8Uf37on9eCMeORV036UgmPwdwzk3ptGsr2oPBvGEpHGbILBbnW7l/mNhk4nVcd5rne/ldogsrL39HRCIcK1aZj69YBdx7N1eWMV1iNKffQ143qBnPy8jgKC0V9d+8xVqf1AkikyEu9dIviPJHPJs3CYIgCIIgCIIgCIIgCIIgiDOPciHy/MUvfoG//vWvKC0tRUZGhqdrTp48ib/+9a+BwrwT5Z90LEQFXaB0E1wmQ6war2POHx5mCF/IlYvYTu6lXuoiFyYPH7Yv3sdCNzLlIjcgrnltAVC4ULht5uYwZX3jxUlYIO99u7YixPvmLcCateLf9dlAh/bAR5sSVxZdGfr2YWjahGOMxvVp7Trg08842l8B1K+XGIEnABw4AGRdoHZ5ShRVqnZAxnn1MP6DQ3j9lkomEU6Uc0z44CQqn1cfVap2UF4frxOocO0UIk+JvMdOSCF0wTRASDj0ImQpWH5tgXOaKu4fJv5e1kb8pUVegiDOJFTjetVqHVC3Tj1M+OAQFgcYF4zEO0akkzeXJiYdnbu0lTt/I1zWJz8XLB8v84TcHGDpclE3xjg6tA8+p7PmxxgweiRD82aiv4weGZvLGzfshEJA/iCzm7zq98Wge0KoVi1qE8h27gSsWy/Ssm4EkukcOcpN7qRD8zn658X6sW7+7kfg6SYA9BKZwO13RJDoBumYp7jVw6sLeTjMsHkLP+3uz7X3NxWCyGRtcownYgWRXshNnyAIgiAIgiAIgiAIgiAIgpCE0l0AAOjSpQtOnjyJGTNmeL5mxowZKC0tRdeuXZNYMiJdOC1EJZPcHIZFhQwvTBF/vSyqyQVGI3KBUSdWjUTiU84JQSoryzeIY86ge0KY+JT6WEaGvXxe6rJsOcfNeRwPPMjx+B9ji+ASxkTaDerbj1mRYrpIhJfV1+2aeAmFgBEPCgemxo053lkhBJ5G3lmRfIEnEAuL+c5K5/Nk+XbvTVzeY8YCRYt5QkW1Vhg7B1l1nsLbn/+A3guPYNP+H3H0hyg27f8RvRcewduf/4AL6ozThtE1Or6pcHJ8YwwY0B+uYWJ1RKPA4KHAAw9y9OnHsXWbOpEJ44Rwxg3GYs9DKCSExNNOD4e33ymeK0D0hzatE+eMJV15430fEQRB+EU1j7k++xycc95TWB5wXDASzxhxtjF7jhB4Bp1jyeucrl+6LDav59w9pLof/m84UK8eysYy41x+8QLxT87rmzZhnn5fdO9qn3Ou3yDG9fuGAg/+Vr2RaXqB+fvpBcCrhbEM452/G+fZN+fxsvmBEaffJV7L4SUNFYmep7iRiN9DgP03DudOG9WSR7J+NwLB7ylBEARBEARBEARBEARBEARBEOWHcuHkmZeXhz/96U+YNWsWzjvvPOTn5yNkXYU4TTQaxYwZMzBr1ixkZWWhX79+KS4tkQridaqMB79uGU4OoNt32IVybq4pXkP0JcIxp3JlBsC+cFha6t0ZaONHHL17MuUCKWPm+8i5EMe1vdzbYqWxrXJzGCpX1rtaAjGxnB9xoixfKCSElVOeT6xjqLFst98GzyFA310JVKkaxSYXQen814AH7oPreW5lMy5mcy7CxCcrVLukxvm5aNDwFaz58vd4+/Ovy76vfF59NGg4DjXOz9Ve69cJ9Ikx4hgDcOBbfwJPVTvIz5wDc+err/v2P8IxzMqvfwV8shOm94UxVGj+MKBy5dN1SZJ7VLKcogiCILxinMecPMkx+vfxjQtG4nWLPpswjmfW8U43D5BzJ+NxpzHVz1yifXvg44+9XcOYOmS6dS4f+z93/H1R5kb/nd3JPBoFHnok1k5e22rGTKB711iY86Dzd69RDuTvkomTeFlZVcJHp3KkK/y6kVT+HtI5shtJxe/QZLptlod7ShAEQRAEQRAEQRAEQRAEQRBEfJQLkWflypXxwgsv4M4778TUqVNRVFSE6667Ds2bN0etWrUAAIcOHcKePXvw7rvv4sCBAzjvvPMwdepUVJZKGOKMoqItROkWGFViVelmKUM9G/EjvPK6+OmEHzGtLmT65CnA3j1RXH+d3R2Jc+DB4cCU58wiAKs7pm5h3NpWrVoyMKYPIc458PhjQEkJMHWavt5GBg4ALr4YqFtHCFCDihqv7a4OjXrnb4BGjYBWLRj2FwNz5nnLgHNg8evezgPcw4x7ScPtu2RQ4/xcVK9xPU4c/wj/PfUtzq1UG1WqdnB1aos5gd6J3guP4KGOldHiwnOx+z//xYQPTuLtz39Ag4bC8S0UEu0PADt3cU8Czz69gNffiJ0n+6iT4NOKsd8buesOhgb17e+LcBjYvkOECjWS6HCaXoUiBEEQyUaGSZ7wTOy7oOOCET9jBBHD6/g25tHTf59IfBk2bxZzx3/83Ry6fuAAoF5dZponGx0XvYxlTr8vjHNwlYgTsAti5TmhEHBDjrm8xmusY7hVhOplTp8MAaDTxrZ0hF+X+N2IEm84a93vNSB2f1PxOzTZmxzTeU8JgiAIgiAIgiAIgiAIgiAIgoifciHyBIA2bdqgsLAQo0aNwueff47Zs2fbzuGnV9YuueQSPPPMM2jatGmKS0mkkjNhIcq6mAyIxcL8YcCoEdy0YOlHeKVb/PQj/JTn5g8SboNuYtpwmOGaqznWrrOn9c4KoHMntTvS+dXdBXWcA2PHAHv3AQuKoG2rcJhh9MiYO5EVxoRYs1ULhqnTvKkUZ88R5bz8sviEjVd1BFa9Z0/jp59iAs+MDHsbEUKMU7XaVb6vc3J8+3nDcah+fm5Zn968xfwcumEUeEoxx9gxwHdHhLDZSs8bgLeW2R1RrcjFep0gIRXhNJPpFEUQBOGHSIRj4iT7yzLouGAkUa6ghJ3t24GTJ5OTdjQqxlmjiJIxIfA0/jY4/B23iUy9jGWq3xc6N3q3DR5jxwA1a7KyMfrNpfa+7DaGexU0ehUAyroY5zBBN3LEK54MQjo2oujEv4n8HerlN1oqNjmm454SBEEQBEEQBEEQBEEQBEEQBJEYyo3IEwCaNWuGpUuXYv369Vi3bh327t2L7777DgBQs2ZNNGvWDJ06dcI111yT5pISqaKiLESpFmjlwmC7tkDBNLNLpGrB0qvwSrf4eeQot4g19a43K1ZyjJsQK2/+IKBpk9gi9fYd3LYI+WphVCnwlEjBqHRJlAuTrVrC0X0TEOe2bMHQtQtDl85Rx7bKzWE4clTtxijDwd9+G/cVajwatbuL+iEUAr77Xp3fnHkx907GgOuuFaJYN5IdKr2iMOgeYNbL+uMqx7dq1Ttg5vRz8M0BAFyIa2WIVa+o+lbNmgx16qgTyflfoE1rYMxY+zGj05dxsV614C8X+KcViGuTscCfbKcogiAIr+wvTu5YlwhXUMKOyrEy0Vg3TRjnguEwEIkAoRD3PZapxt6du+2bQIwizowMjvxh9nGzZQvz+PzQ74AJz8QKrguVbiyLV0GjVwFgRd/Ika7y6zYXJiJPP86kZ8ImR4IgCIIgCIIgCIIgCIIgCIIgkkO5EnlKrrnmGhJyEmkhSAh01QKtdKUyhnC0ihisC5ZehVe6xU+j6NHN9ea5qTCVt2AWsKhQCB2lSyZjwOiRMYfQGTOd2+H774F5r8Ycjwb0B+rVE8fybgFeW6C+zrpIXVrKylx7jfXbtZuj62m3pYJZelEI50JYmSqkgG/yc+7nci4Enjf3ARYtth+vWhUYPEgf5vtsxEngKbmi3TnYuu0qk+jhn//059xpRCWwlc/i/mIGwH5zSkuFoFklOCmYJo4b3ytOC/65OQztrxB5zH0FyMpK7AJ/KpyiCIIgvNCgfvI3NSTCFbQ8MzQfOHYstXOfdGCdN4fDDPmDuG1zEaDerASox165cciKWcTJMGqE9Vr7po12bYHXFzLs3M3BYBeBWn9n+BU0ehEAZmSoHybxffkf59O5ESUZmwuDOJNWlE2OBEEQBEEQBEEQBEEQBEEQBEGklnIp8iSIdODHZcWIaoHWKFaIRtWOD1Oh2AAAIABJREFUR9YFy3CYIbsHN7k8ZvewLwCqFj9VAgm5SAzYhauqBeVduzkmTjKHqJ44iZctJjsJMDp3EgJPY5hL6WApnUJVZfx5A+DRR4DSUrEIGg4z7PtUndGYscCJExz16pWfcOc9bwjmZnX0qPr748eBLVtI4OmXyy8DHvodK+vnAHBzXjCBZyikbv/8QfL54VrxgU482byZ+Rn2suAvhZ2JFnhKyCmKIIjyQDjMMHpkbIMJ4Y9QCOjeVYx/0jX8TGbfpxxtWsc2S8hNP4yJcRqIjf+MAUMGc/TPCwFQj71G100jKgdO3bhp//3AkJsTsqWpc/33K2h0EwCWluo3o1QEzrSNKBXdWZUgCIIgCIIgCIIgCIIgCIIgiPKDfQWKIM5CdKKrSMR9wVyKLv2gWrCMRDhWrDKft2IVbGUQi5+sLM9QCBgy2F6GUEgsht+cx/HAg+LvipWxsOHWc787og5RvXM31wovO3YAZs0Aet/EtII66RTa+yb7sa/2i/DqDzzI0acfx6uFURTMUqcjQ3VmZHDf7Z0sgoYrXbFSf2zDxmBpJoL2V6Qv73iQfaZNa6Z1xvJCzxuAMY+qRZ5NmxjDqbOyZ8gqBMnNYVhUyPDCFPFXJRR3WvAPSiTCsX2Ht3eWJBxmZW1GEASRLnJzGGZOT3cpKiZy7NDN0840CmaJ8c46b+dcONpPnGT+bnoBMOtl8YUqJLuOJx5Tb/Syjptefz/ozgNgm9MHETQa5wCq3yVSOBpkrpAOvMylEk2y2sbpfhAEQRAEQRAEQRAEQRAEQRAEQfiBnDwJAvG5rFgdZ6T4y8mRasyjQLcu5gVLP2VQuQnVqG52CMofJBbDjQvKU14A8vqZyyYXlCtXVhf4yBEohZft2wMTx8fckaxORNZ6/KQ5ZnQONYac16VTWmp3PCUSwwUXJCZsbrJD71qxPidewv/e2g9YUASD2xfQPy90ui/bQ677WYx3c9lKdCjSoC7EZwrW8LcEQVQs9uyNYvGSdJei4rJ1G8e8V9NdisTBGHBDLnBZa+HibsS4IcLJRd/InHnAl19FsW699/xbtvA2lnidu+vO27WbIzcnFJeztspJdNQIZvtu8xarO2b5niukMmR5MudRZ5ozKUEQBEEQBEEQBEEQBEEQBEEQ6YNEngSB+EVXVtGlcSHVSigEtFIsHvstg3Xx01oGsaBsXvG2LoAzBhRMA5o3Y4hEAMbM4VIZA2rUUIs3P/44FmI9HGbIH8QxvUBdVgCoX09/TFc+K6EQkJHB8a6DEyYRnIOH0ifwlEJLozDZK9bnJBxmGDJY3x9DIaBvH4a+fWAK8b59h3DAUokjrK5dRnGyNdS6G4lc8PcS+v1M5mwXuBJERWfc+Cht2oiTOfOCXZeqDRkDBwBz53vPi3Ng2XL3ebG1/E6brNauU+elaoPbb/M+fnqdu6vOA4SI9cQJjtycYIJG3RxgUaFwwDTOcWQYe+N5Z8tcwYlUzKNUm/MIgiAIgiAIgiAIgiAIgiAIgiD8Uk6CHhNEelGFQPcrujKGcDSGGRyaD0/pJroM0s3QCc6BAwdi144eac5/9EiGVi2YMh3OY25Ky5ZzzJjpnNfx497q8OtfxdqLsVgdZHuUlrKUukSeLTT+f/bOPDyKKm3796mQmBC2aGxQEBxmXNhUUFAERRBkIMGFsLnjxiLjjA7IIu+A8I4C7uMIBt9xRHEJm37KooADbggDCirEZXAYZUCgQcMWCBD6fH8Ula7lVHV1p7vTIffvuriAqlNVp7Y+T13Pfe6nObBmTeX30/L82LabOUN30jS/A34Z2N+57KZBGu4d5nwHzO+V8b6sXaeLH37/gES/QfrD5VYmNF6l1uNVijQRpd+rC37L5BJCUpOvv6n+Ak/VOBOJC1onpi/R0vUqoFfPxB/n1df1SRzRXKdQSHd3z89Tx9Br11nbC6HHrMOH+j+GMbnEHnO8+roe1/rBb+xub2dgTBSJddzydhINfxNUZayQ6iXik3VtzPeDEEIIIYQQQgghhBBCCCEkFujkSZJOqpbWrazLiv28DKfNJo2BhgEJCN3B02u/8XR6CQQEBg2QeGOOdzuzi5D5+JmZEmVlepvhQ+FwRTScioJBiWlPSE/hpaYBp57qr99fbdTdRcvKRIX7kPl6BINS6bwkBHD3ncD/vejvOInkrCbAf7dVdS+iY8uW+Oyn+Jvot+nRHcg9TX/WO7QHJv4J2L5dv5dez5XxHLwxB5gzTzpcHG8apKF7N2l6noXjvfJywWrXtvKOu17o/ZAnhAQypvc93qXfqxN+y+QSQlKTrzZWdQ8qT726unP08zP9O1V+tSmxffLLoAFAyxYabrg+hKK5wIqViTlOKAScf57A3XfKqGI0KYF3Fur/vq4PcPut+hj5j5UhTHvC6eLZvLk+zhfcILHgLX/7P/88gcLpEkPvDe/P7OQIuMfkRtzfoT0srple7Sb+CZg4ydqPyoxblXESTUasUB3ctmtyHEUIIYQQQgghhBBCCCGEkOoFRZ4kqaR6ss9eAt0vbuelXp6YPqjoXyBQNNdbgGkvN607JEk89qRe7t1wJbp3WFjEYHYqWr8hssBz9EhdPPrUM5EVEKEQsGMncHVXoRQE646jwNTHw/syHJyyslLDJai6CTyrmuXvA/9YIdGzh8TS5epy7ZoG9OyBivWaZhV4uJXXDL9Pzt+ZYFBixQcyKqFgICDQs4e0uM/17BGbIDsev4fxLP1e3aAwg5DqSzAokZFR1b2oPFMfD088EQK49FLgn/9MThn0ytDxMl3gCeiTLFZ+kLgOaxrw2efStay8n7Lxby8E9u2X+OhjdYwQCuGEUFOPW6/qAnz4UThmvfIKZ8l2Y7zYtl1A2joQCgHzFkjMmQflGO0cv9WO3PZ2w4ZEP255TY7zGwNURayQjDLoqmNGO1GuJsdRhBBCCCGEEEIIIYQQQgipXlDkSZJGVST7koHbeTVvbgglrcuTeb6GINKcuFQRLksosbFYWhySzM6G3btZXT6DQVlRFt6eoNc04P4/APXrAm1aw1Fa04uHJwNr1oQsgr7RI1HhMvrTDuvBjGO3aa07I5LqRygEz7K9t9wEDLlbwz136Qn8kr2yUm5YZuGFHU3Tn/H1G5xCgWBQF6KaWbocuOeu6Jw44/l7GE8H4FioKndmCjMIqZ54/f5WR4wYREpgzRr930IAt94M7NunCxRTicsuAx78oz5Jp0ljYOMm78k6lcEQWLoJPAH/gli7SNNtP6GQLvC8507g6DHg8o66oPWFv4Uq+mEdL6RDeAkARXPd3T39jN9ffxNyxNOFL+hCz8IX4Gvc8poM4tdJ1CDZsUKy3bYrM3GmquMoUvWkaqURQgghhBBCCCGEEEIIIcQMRZ4kaZyspXXdzuurjalxvubEZdOz9GXClrsyXJb+8Ed1st3od7u2VpdPIfRS7mNGCUvJdiF0d8Nn/qJvK0R06gEprYK/UMjqlqVi6uMSb84VuOhC4IsvIx+jXVtg/YaoukWqkNmvAddfKyucOYNBQNOsIiG/Lo52gaUduyOYWSjg93csUrI43r+H8XQAjoaqdmemMIOQ6kWk39+TBSn1cStVHD3HjQEOHtQn3WzZItBvUPh3u08Eh/nKcMtN+nVINlICL5woDf/ybOC314QqJmgIoQstjbHKPmHAvA8z4QlRkcfvRYulo6S80e788wTmF0Uet7wmg6xd55zg4GfsTWaskEy37XhMnKmqOIpUPVUdyxJCCCGEEEIIIYQQQgghfnHx9SMk/hjJPjP2ZF8wKLF+g0QwmCJZcR9kZkqlaPKCNpHPN964Xb9AQKBdW4HP1+v/NyedzS5LbmIEo9/2JKqUwIxCYP8BiQVzBCZNBCZPBKY+Ary3DJZ28RA6RNrHvLekL4EnAGz4Aii4AfjNr8PLjh8/jB3bRuPHf/fDjm2jcfz44dg7W4MQQneSMt4DIYAbBwLTHo3fMaQMiysAQ5QhKt6xaFwcVQJL1fGAsFAgGNT/lJSo33fze71osUS/QRK/f0D/e9Fi54Pr5/cw1XETVST799v4faPAk5DUZ9t24NixmjHWporAEwDefQ8Y2F8fdKY+bv3dfmeR+3ZCAEPuBu64HbjtFuckoUgkQ+iqad79MiYOmWPSwhdgGavy8wQKp3vvxxijI43fxtioOm+jnZ9xy20yyKbiyGNvMr+nvL49Yo3TosVr4gwhXqRKLEsIIYQQQgghhBBCCCGE+IEiT5I0IiX7/AijUo1FiyWGjXCKJkePFGjZQrOcrxDAwP7+9htLcjbS9QsGJZ75q3UbIYApf9bLWrohhH4+ALDiA7X71vMz9b+v7qrh0CGBseOrRtzwxhv+20oJLHgL+P7f+v+3/nAr/v3NWSj55e8oLf0AJb/8Hf/+5ixs/c8tKD34CfaVLEDpwU8g5fHEdD4Gmv8qsfvv389fOyl1VylAF3cumCMwYriGrCz/iXxNA67q4r3eLoDMzxOYXyTw7NP6335dd1QCDS9CIWDeAv29mjhZX2YIQey/Y36TxckUPyQKiioIIdEy5dFbXMdakji++BJ4cGwIQ4Y710mpj90qpAT+70XgpZeBV1/XS9BHI/SUMnphaLSEQsCgAdGP6ys+sI7NZWXCc7KTMUYHAgLDhrjHAW4TSYx42u847yYmlVCPvcb5eH0PxFv8GenbI9Y4LVpOhokzpGpgLEsIIYQQQgghhBBCCCGkOsFy7SSpuJXWjUeZvWSjKjkqBFA4HWjZQu+zcb7zFkgUzQXemAPMmScxeiRcSwzHUjLu629ClrKQquunSmIZ7oiRBJlffmWUaFevlxJ4Z5HE5R31dqnkXuWHrT/cioP730XeORkY2ykbrQK1UBwsx5RVpViy+T0cPPBeRdvMU85EbqNHUa9+fhX2WOc/PwC1a0e/XYf2wGefR3az7HE1cHqu7tbqBymBOfOA/gX6/zMz/T0IxntTVibwwYfObbyEEbGU17SXZtU0b7dZTQOK5obXS6kve3gC0KaVtV9+yrAbpdw7tAfmF4lqW2o8maVYCSHVn5tvvhn/+If7WLtlcw80P2d5VXfzpGX1Gvd13212X2eOLV99HRg+VHfCDIX08fnuO/X1KpGlpgH5ecA7C2PvtxlDWGmfXJWTE33s+dwMYPrzEmNG6XG2akwTApg0AWjUCCgr08fvtev08zcErObS74B6bBQCmDkj/H3gB1WsMnqkQJtWgKY5J10Z5wOovwecJd4rV5J6zx5/327JKIPudq2qW1xFkg9jWUIIIYQQQgghhBBCCCHVCTp5kqSjKlGYTBeNeLnYuIkmy8qcCcU586wJ12lPSPQd4HS+iaVk3KLFEkPvdSa37dfPzeWmTevoyly6MesVYMjwyO2SgZTHfbtvlpcfRNlBXXSyYEADXNokA3UyNFzaJANvDmiA3udkIF0Dtt9/Kj4enINuTX/Bth/vwP59HvVNk4SXoMHrnhbcoIsL7xvhvf9hI4B6dQXuHea/T+bnTvUuqJAS2LETylLoxvp4Y3eXGjPK6rprduga2F/9fuU0cAoIVO+ZEOFksd31au06VNtS4yeDGykhJDkcPnwYy5d6j7XHyjZg509TUtI5+2Rn/Xp/7UIhoFFDfWLGfSN04WL/Av03f/ZLuiOo3eFy8K3xGxOktLp2apousjRElyqEAHr1VMdFUuoxeTAolWPamFEChw4JDBsB/P4BiYKBEtOeCMfpqtLvbvtp2SIcHPj9FlE5Ydr3bz8fVbyy0UeJ92jZ/lNqOSAmyzWUnFwwliWEEEIIIYQQQgghhBBSnaCTJ0kJYnHRMNzwonHAi8Uls7J9dhODGpidb/y4AJoxRKGqxLa9L4GAwP33Scv60SMFck8D9OKPJwf79y3Cnp0PoezITxXL3Nw39+9bhJ/+ey9CIWBsp2xoNgWAJgTGdsrGks1H8b8fHcKzvevjzQHp6Dt3P1ZuHY+69XpBiLSknJcKu2BBCGDqI0BWlu5I9c5CiVmzndtt2w50ulygaxfd9clNmGs8m/OLBLp300UCD0/2Fl2an7vMTF20aW6vcuESAhX7NQSW9mPY3aFief/tmN2l8vOs7rpA+N/vr3CesNvvk+EmNe0J63u5dh3QoX30jsV79khs/W/qOn26uTMTQoiZCRMmoNxjrO3YJB3L/n0Uv+x5smJ5Kjln11RU4/GESeF/axowfqzEoIFAbq7AiOEC/Qus4/PrRbHP/rEfX9N0UWn/gvC4o8fOznF68O3Ar87WHbcB4L1l6uBFSmBTsUS3gHCMaQDQb5BV1GlHFad7jY3RfouonDCN/a/4QOK5Ga6bAjghiJXRfV/4ofGZqeeAqLpW8YgXyckNY1lCCCGEEEIIIYQQQggh1QU6eZKUIFoXDbsbnuGE6UUsLpnx6LPK2c+O4bJTstfpZOiVMFWJQgH38tY9r9H//9gUVLjc+CnXniiEANq1jd/+9u9bhG0/3oFuTX/Gx4Nz8Mvo03X3zbP2YNuPg7G35E1H21NPOQwAaBVQa95bna4v/75EdxTThZ9ZOHxkOw6Vro5f52Og/SXW///2GqDT5VqFM+Tll6u3a9Na/zsQEOjZw/sYZlem3bsjCzyN527RYolhI5zijDGjnK6ZgLUUeqR+xPL++8HsMmz8G9BduuwMG+KeBO7Q3vp/KfXfmo3FTkFtJNerW+9A3M8z3qjcmQkhxMyWLVsAqMfat74tw4SVpbjm1xnWsTuFnLNTBbPTdKK5dxgs47WKUAh45q/WZeYx4fWiEGYUxnb86/pY/2+URjeP0YGAqJhQYkbTgGvzBK7uqiEQiBzr7t2PCmdN877d4mz7sdwmfdjHxnh+iwQCAt2uct4fuxv56JECbVo721VWkJmbG5sDYrwqKvghUfEiOflgLEsIIYQQQgghhBBCCCGkOkAnT5Iy+HXRcEuQernhAdG7ZEbT503FEhJAm1bONoazn7nPKlQOiW5iTQOVm6gQevnMli2sSeVt24GmZwE5OcCFFwjPfdj78NtrgKXLoyvF3q0r8MGH3tv0uBpY9r7/fXoRCh3F7p9Gofc5GXhzQP0Kp7BLm2TgzYHp6DtnL5ZtGYJ9e99E07Nfxp6dD6H3ORk4q67AzPVlKA6W49ImGY79Fu8uBwD8Jifs2GkIP8uP7YpP52NACGDdZ0Dt2uFl7y0DruoSqnDybNlCQ6+eIby7NNymV09UlAwNBiXeW+Z9HE0Dvv1O4v6R7vfytluASy4WFe+t/R01+ls4PfxcGu96SYnExMnW/RmOnnaBaJPGsb//seIm8Dj/PO/fGlW5VAH/rld79kjk5CBp50kIIYnkV7/6FVauXOkYa4+HJMYsP4DeJ8q4W8buAenoO3dflThn33E7cPbZwBmNgOXvA/MWJO3QrvTvB9w4QGBjscTESZHbV4ZrugPdu+nxZ1Zt7+PZx8hgUGLjJol9B4DnZ8Z2fE0D3llkHUulBGYUAvXqygrnS90V0xkv2GPnb7/zFvc9/QwgpXQ4azZprHYz9TqWF/H+FrF/Yxj9UX1PqdpVNp6I1gExnhUVIpHseJEQQgghhBBCCCGEEEIIISTRUORJUgpVmT07sSZIYykJ74e164DHnsSJhKVUJizNYtCJLuWu3RLIzZtLrN+gTp66JXfNAk9zQrVuXYE1nzhLQHuJUHXBqIYbrg9hyHD/1+Wii4BBA4Dv/gV8+CHw2Xpnm3gJPPfvW4RdP43EsWM/Y1ynHHXZ9c7ZWPL9UZQfeA//+XcflB35CeM65eCCQBpe/KIMU1aV4s0B6ZZtQ1Ji6qpSpGvAtO51KpYbws9a6Q3jcwJRomnAwP7AG3Osy6UExjwEALqr1aABEjdcD7RoAfxSAlx+WVjgCajFiPbjDBuiO1m6OcYOHwrcNMhqD6V6R6UEysrC19Z414NB/b2xv5fm45rFCOs3uLthxirW9iKW3w23bVq3Ur+vKrHB9p+Ac86xLkvkeRJCSCL58ssvUUuDY6z9ZOsx/LAvhNk3qMu4j+1UG0s2b8fu4FMINHwwaf39dDVw1x362LZjZyglRJ5Nz9LHzl0r/LkReokTPbeDHp+9v0KPaTu0FxBCegodDRYtlpj2hHtbP7jFOAbTnpAVjtmRJpQAuthP5cht3sbor10IGAgIDBoglX25b4TujF5WhgoH0Ei4xQeZmXqsn5kpUVYmoioZ7Sa0dCvxHu+S1H6+3YDUmKRTU+IolqgnhBBCCCGEEEIIIYQQQk5OKPIk1Y5YxZpugsjKJL+iSVgGAgLdAgKr11idFb2QEieElU53IQOvpK2qfwBwy2Dg4EHrPju0B6Y+LrF2XXjfZtdHXaDnP2v/1NO+m1YKo+z6RY3S8MXOyGXXL2qUhq92rdWXBWohK0NDr99kYOG/jqLv3L0Y2ykbrU6vheLd5Zi6qhRLNh9Fn3MzkJWhO4jpws/DyDqlMWpnd0zOSZq4rg9w+636PX5jjvv9kFIXSBjCBE0DGgUEWrYIt4nkUHX/74H69dUCz/tGAF27qN8fP++oOQE9eqRwvJf5eQLduzmT1G7utZmZEro0Jb7E8rvhtU1+HnyJLBqf6VwWD1E6IYQkm8OHD2PD+s8RksCSzdax9uOtRwFEHrv37JqGzMwWqFc/Pyl9/u5fwOJ3Q8jrpSHzFH/bDLkLeOHFxPXp55+Br78J+XbHnDlDj90++1xi9mv+BZ9GMyOmLZwOXJsPvL3Q2VYI4P779H/v2eNP4HnvMP3v52fqfTJEnV2vQoXAEQDmzFNPPpJSnzTVoIGIOKEEcHfkvm8EkHsaHG7idiFg/wKBornS4RZ6/DgwbAQQCrnH6HZU8UHPHuH9nDiLqF0u/Qot/bZLBMkWXSZqcl+qk0y3VEIIIYQQQgghhBBCCCGEJBeKPEm1ozJizXi72ERKWNqdVIJBiaXLYztWJAGpKkHqltg2OxZNfVyieXOJ3NMEPvvc2m7pcuCeu3R3osxMqRQE9ugBvP9+bG5RlUXK4xVl1/9waRZ6vro3Ytn1Oy7Mwh+WHtSXnWi7YEAOCuaW4N3vj2LJ5qMV29TSgEBtDQ9eno0DR0InhJ+HsWTzETRp9khSS8cavL0QyOsd/cVWPT+BgMCYUXAVZTz1jC7gUJVOdxN4Gvv1ekedCWiB+UVC4ULlfK5VzrNS6gKJ0SNlQhLZsfxueG3jR2SRm6u3NxzS4lVatSqgoxQhNZsJEybA0K+df5qGZf+2jrUAIo7dlzZOx1c7k1u2fco0QIYkvt/ir/0v+xLbn1mvAC/P9hdvCQHknqaPGe3aClx/rcSKDySemxHdMUMhYOi9+jHNsYAQuvDz9lvD49L2nyL37do+Yfdv1UQOMz17SNdJURL+RXxu7bp2McZZp5u4fR/tL4FlEtSVV1hdzqNxpTTHB5mZ8oTA09omFNJjs6wsiTatq+fYbyeRoktVnJGIyX2pDkvUE0IIIYQQQgghhBBCCCEnN1rkJoSkHvl5uijs2af1v6MRdhkJ73gku4yEpRkjYblosUS/QRK/f0D/e9Fi6Sq69IshIK1M/1QMGQ7MW+BeBnvRYj0JbU/eX9UFmDhew/ChesI/2RwqXX2i7HptXNk0A2fX1zB1VSlCto6GpMS0VaX4VYM03Ng6EwCQnnYKpqw6XNF2wYAc/DwqF0PbZSI3S0DTaiP3jELsP94IV84qwWmP78aVs0qwcutpaNLspbi4iQkB9O8X/XZD7wWKXMqYeqF6fvLzBGZ6iD7Mgg7Af5Lc7R11S0AD8P1e5ucJFE63PnPGfoLBxKiNY/ndiMdvzeyXENPvXKqg+h0khNQstmzRVZJN62v49am18Pdrs1EnPby+lgZM+cR77J56dTYOH9mOQ6Wrk9l1TH1cYr7PUu3z5ye2L0AUbpzSOt4HAgIXtHHGan5iN+OYhtDTEHsuXGwVPu7d570/IYDBt1onPLiNkZEmRbVpJU6I+ETEyRBe7SLtY9FiiYKBVpd7APjwI7Uwc1OxvxtknHtZmdON1EBK3WU0lcbOYFDiy69i64vf+xUtXnFGZb4XqyNekw8JIYQQQgghhBBCCCGEEFL9oZMnqbZUZclBcx9ULjEAlEK2wunqUtN+k/ZCeDve2J1sVK6HbhTNdfZFCOCHHyWe/ou6jx99DLxeFELhC1Xj5Fl+bBcAvcxrmiYwrUddDJq/DwVz92KMqez6tBNl14v61ce3Px8HAGRkdcKSzSvRd+5+jO2UdaLtcfz3ALDnMNCk2QzUq5+PnFNvwKHS1Sg/tgu10huidnbHuLiICQEMGqCXJ52/ILrrJyWw4oPoj+nmmNSyhYaxD0rX50RKYPJEoEEDEZUbo+odjVe5zrIyAWkXBCWw7Gci8XK6zM0VyM2too5VEjpKEUIAoHnz5li5ciWGtMvCn1aWAgAW3tgA+49IfL6jHEWbDmPJ90fRd85ejO2sHrsvaKirQo1xvyYgBNC1S2zjPWAd73UHbacrd588dRl24/j22MT8f+M3/XAZMORu4NGp7n0xx8frN0jPWCIY1F1H3eJWs5DUr9O2Vzu3dcYYporP7M6mBhMnA4cO+XcVV7lb2jFcPZs3l2jZIrr5mfF00jZc2LOyBNZ+CixdJtHzmuj2Ge+KCn7ijFT4XkwWNbVEPSGEEEIIIYQQQgghhBBSU6DIk5BKokpYrt+gdsUsK3OKQnv2AN5b5k/kN2hAuOy7PUHqLIGt983o38oPJf4+y33fUgLX9dGdmcxlsJ96xn2bUAh4fmbVCDwBoFZ6QwDhMq83nJ+Jon7AmOUHcOWskop2Z9TRUNSvPq477xT0nbsXtTSgSbOXUXrwH1ix9SEs2fxTRdusUxqjSbNHKpw6hUhDdp3Oce+7lMAbc4A584DfXuP/GTDToT1Q/LX1/3ffCaz8QBft2sUcZsck8zMEAGeeCRROB3bsBB6e7NzWKI1a2YR8vBLQJ0si2+29PRmIl6CXEFII4dWDAAAgAElEQVS9mTx5Ml5+6UV8uu0Y3iioh7HvH0TXV/ZWrK+Tof/mLdtyFEu+D5dx/1WDNBT1q48bzs/Emm36cmPcrwnEOqHDTDAosXGTxLQnnJN4CqcD3/5LvV2njsDtt0FZStxMKAS8+JIu8jT6LATwx/uBevWAMxrpsW+TxsD7KyQKBupt3MY785johuFSaowjfkV8Xu302EaecDyUCASEp/u+pgHDhlhLtht9i0aQ6XcylpS6i/uYUf4FpIsWS0x7QlbckzGjYo8v7GJKAHjmr0Dbi2TUcWE8RZeMM6zUxBL1hBBCCCGEEEIIIYQQQkhNgiJPUmOJp7uNkbAMBiXWb5DIzJSuArR2bcOi0MxMdRl0FZoG9C8QlgS4EMDwoRLduwlPJ5tAQKBrF2DWK97HaNcOaNw4OuFmVQk8AaB2dkdknnImpqz6BW8OSIcmBG44PxPXnnsKPtp6FGPeP4D/7gth/T052FwSQt+5e7Fk81HUqdcL13TPwj9W5qNuvV4xO3VG48LqRigELF0OzJwBfLoGmPWys83IB3SxrV14ecnFwNffhPtyycW6mKJ/AdC/IPyMGQILlSDYcMQKiy4ExoyCZb2UwMRJgBASgwZI9C+IPWEczwT0wP5hMWuyEtnx/N042Z0uTxYhLiGkcmRlZaFHz15Y8u67AICXrqtX4eK59N9H8M/t5ciu+1uUH/kSF5++B/e2z8KZddLQuWk60jSBkJSYuuowsk5pjNrZHav4bKoPz82QriJRKcMTQlTcfpvu8j16pDpeMHBz+2zWVI87jPHy/RUSMwrDbczjHRCOVx570ltUCkQ3jvgds1UTLjq0V7tsGvFGfp5Aw4YhTJzkPP8hw4GxD/oTZJoni2VmSuVkG2O/fmOEYDAs8DS2nfaE83rb40M3UlVMyTjDSbzdUgkhhBBCCCGEEEIIIYQQkjpQ5ElqJNG458WaIO7ZQxfveQnZduyInMwG9CS6qgy8lMCMQmDbdrVzqOFIZPT//vu8j7NqFbD8H1Ur3IwGIdKQ2+hRLNl8h63sejn+suYQvtipl2Y/4+mfAQC1NKBOvV5oevZsrPgAuKoL8OFH3k6dQgBjRwNTH/MuW1oZDJfXa/OAV2ZLR7K6U0eB9FpWYaThIJWVFe6LLqCQFgECYH3mVIl/cz8ee1JifpHA/CKBTcUSE01CA8N9tGiurJQjVGUT0HaR6o0DUSnhaSzHjYfrZqqKJuIFHaUIIQavvfYabr75Zixb+i6WbA67daZrwNVX98Kun2djb8ki/PPHO3Ba7aMY2ykLh45JFO8+hqmrDmPJ5iNo0uwR35MwahIXXQh88aVzuZcLqBBOx28zw0YAo0fKivF6U7GEBLBrV9i90ohFXnnVuf2b/0/io49hmSxiJxQC5i2QmDPPu123rsAHH3rH0yr8jtluEy7mFwmMHiks8cagAdZ4o00rASHUJd2jmbQRdrcUaNkCOHzIGqsZ+I0RNm5ybisl8PJsaXHsD8eM3vFMosSUlZ04wzhDTU0qUU8IIYQQQgghhBBCCCGE1CQo8iQ1jmjc8yqTIF66XC+FGclF0Y8b5KQJQLeu6jLwALBwkXM/mgZ8+53E/SPDifHxY/U+CJfc57L3vfuRitSrn48mzV5Sll0/86yJOFy6BkePbEHGKc0ROGMS0tJ0VaSUumigXVtgwxfu90BK4IcfgOFDYXGhiidGotyerBZCF1AEAgL5ebAIIzduci/tqXLIMvY/b4FajGDedtt23XG2wXZAKhpH4yblRqwJaPu7JqVe8r5/QUzdiPm48XDdrAkOVHSUIoQYvPbaazh8+DAmTJiALVu2oHnz5pg8eTKysrLwyJQQ3l3qPp43afYI6tXPr8LeVw67C2Y8nMABfcy4vKNa5Om1zcD++qQNN8xj3Np1qHDYNISd55+nx7Zr16nP44MPw//2Ok+z0FTVTgjgd8MFBg2Q+GojcFYTfXJLMOhdJjyaMdtrwoW9X82aWgWEgYDeN9W1jGbShl3smJ8n0Ly5xNB7nbG9rxjB5dK8vVDdz0jxjDk+Nbj/vsqN6fGaOMM4gxBCCCGEEEIIIYQQQgghNQWKPEmNw697XjwSxGVlAu3ahtuqBGpCqMtBGmga0LqVvo8mjd1LY17Xx5q8veWmsNuS0Z+nnwUGDaw+Tp1+qVdfL7ue13s19gR34YOPGiKrtl52vUFOX89t12+IvP835gCDb41TZ6EukW48U/l5AvsPSDw/U19f+AJQr67upmUIIxctlpj6uPcxVA5Z1+ZLvLPIezuzgEAlQjTvvyocJ6vK/TIRx60pDlR0lCKEGGRlZeHxx60DWDAosXS5/m9jPD9Uuhrlx3ahVnpDtGnTEVv+U70dPKUEJk8EGjQIiyPNEzpijcuuvEKPE6Jh2BDd/dNL5AnoY9ymYmsJ9VAIeH4mMHOGBKC7XGZlxTZm2eNWN95fIS3xrNl90k3cF82Y7Tbhwigfbxahqr4D+hcIvDHHeQOF8CfIdBM7tmyhYcwo+zp/MYKXw6gKP/GMIabcsVP/f89rYo9V4j1xhnEGIYQQQgghhBBCCCGEEEJqAhR5JpGvvvoKf/3rX7FhwwaUl5fj3HPPxeDBg9G7d++q7lqVU9lyfdHg1z0vHgliP/uUEnh4gm66M2GSs7+Gk+PX34Tw1Uag7/XAgresbYRwJspV5TNPNnGnGSHS8NtrrkC7tgLBoMTCxRIvvRy//c+aHen4/q6vEMDMGUDuaUL5zAeDupjB2Jc98W0kxiOhaU6HrEhiCruAwBAhqkqGVlWZzqpyv0zUcelARQip6dhjIyHSkF2nc8X/t/ynCjoVI4bTpVWUGJ6wE57QERYolpRITJwc2/E+/Cj62K7wBWDi/0Rup2mAhDpuHTIcuK6Pu6N4JO4dBnTvJrBwsfc+pETFpBczoZAemxht7E6Q0YzZbhMuduxw9s3tO8BNsLrnZ+84J5LYMdYYIRAQGDMqHL8JoTvS25/LSNdGtV/7hDi3vnmti3XiTDK/FwkhhBBCCCGEEEIIIYQQQlINijyTxJo1a3D33XcjIyMDeXl5yM7OxrJly/DAAw9g586duPPOO6u6i1VGvMr1+cWve148EsR+99mmlThRFtKZqT//PHGijGl4WfPmwJYt4f/HW7wpBNCvLzBvQXz2Fe/+tW4FFH/t3G9JiUQwqN+PPnnAy7OjFyB4uaq6IQTQ/hLdmctOh/bAZ5/D8ly0bGGIKJ3tIyW+VetV5xCpFKu5beF03XU2M1OirMxaBtUQGJhdQePhOBnrex+t+2W8BAGJdN2kAxUhpCaTmSkTEiskGyF0gedNgzTUqystAruB/cPtgkGJjZskIPT4LzNTvb9pjwJHjgD79gNPPaO+Pm7XzCuWCYWA//zgjM+E0P+Yx7g2reDqCOnHhVPF5IlAt64aADjGVSmdfXI7R/NyuzgyEBAYNiTsiq4as83xgV1MuXYd8PD/Oo9p/w4wxzKq/unl1qVrnONH7BhrjKASiNar6+xvrPHM0mUSj0xVx3GRYrxYJs4k+3uREEIIIYQQQgghhBBCCCEk1RBSVveUaupTXl6OXr16YefOnZg7dy5atGgBADhw4AD69euH7du3Y+nSpWjcWJ3ZKikpSWZ3K8jJyUn4sYNBiX6DnMnG+UWJL1nsR/zlTCgKz4RiZfb5elEIMwqtbTUNmPJnYMxDMZ+mhexsgbWfnooOl/+C0lL3V/+yy4A1a+JzzEThlvg3X9NFi6XShdINY1tA7V7p1o+pjwBjx6v7Yzh3lpWJiucikvOR1zsRDEoUDAz3zbinl3b6BbfdInH+eaIiSW7fj9v5Gtcq0rPuVzAZqV083vtEvL/xOm68SMZvMEkuNeWe5uTkVHUXSIzE8/n087x7ieQiceNAf5MZ4s1NNwKvv6FeZ4xla9ep44hePYH3loXjBSGAQQPU53HjQGDEcA3rN0j8/oHoPteM/Z59NjBlmr/2Y0Y5RYGR4igh9Djkn6six5aAeqw3j6vmMvZurqhePPu0QLu21pjGcLG8aZBW0c4rPlDFKEbf/bSL9tyT/R30elGoQvyqujYq7LFPTk4OLuv8Cw4cCN9vo9+AM/5UnVM0MVpVfi+SmhO7kOp5rxlzEkIIIYQQQgghhBBCahJ08kwCa9aswdatW9G3b98KgScA1K1bF8OGDcPYsWPx1ltv4Xe/+10V9rJqiLVcXzwwO+O4CbeiLZPox21HtU+jRLedYUOAb76N9swqj0rgaSTbGzYEJirKyruRKHcut32a3Zw6tAduv8W71LpxXuefF3ayzMx077DK6SorS3dqcuvnzp1At67+3I28HCODQYl5C6zHESc2fXUWkJtrTdK7lVoXApg0IVw6NlK5UHPfIj3ffpyW4vHeR+qL33OKFrpuEkJIfLD/TkeDEEBOTuwxhqYB3bsBy96PftvzzgXGPiiU42soBGwqlnjsSXW/zK7sgN7GTahaNBfoelUIJSXRn6eUwJx5wP1/8L8N4IyNH3vSe8KLlMBDY/R/iwhDq5tjpPmY5jL2Xu6TquthOEHanyspdaFo927SV8zj5pg+8U/A1V3DffdyVlf1TxXnJNIlXIXxvWH0zX5tVNjjuvFjgUED3eM449+qdeZzj+Ybqyq/F5MBy9ATQgghhBBCCCGEEEIIIcQPFHkmgbVr1wIAOnfu7FhnLFu3TlHnuQYQS7m+WPBKnvkR3cU7gWjsMxiUWL9BoqRELXJISwMy0uN77FjRk9sagkHpWrpTxbX5wNGjTmFDZTCEBF5CT3N5cRW33QJccrGwuFU99iQQCrmXjXVzutKviUdJ0RN/B4NWRyw34aEq8e3mpmVci9xcZ1LYq9R6twhChViS536Flcl47zcWO9+pk0kQQAgh1R0vkVwkDHFalyuBDz6MfvtQCOjUCeh0OfDJauD99/2LKM9oBLRsIdC8uTxRjju8TtP0MT/W8zITLvcdnmASjdAzFAKefsb/sezjtZ/7o2lAyxPz1wpuAF52mVQzeWJ4Ykkk7HG3OSbKzJTYuROYONm53bAh+rbrN3iP/5FiHlWMIoR+3824tZs0AWjUCBg2wl+cE+2EssoQbbyniuue+asu8tRs5p/m8/Mb4/n9xkrW92JVwDL0hBBCCCGEEEIIIYQQQgjxC0WeSeCHH34AADRr1syx7vTTT0ft2rXx448/JrlXqUEgIDB+rJ4wNJJb998X3wTn0mXStn+Jntfo+9+zR2J6IZCVFT7e9ELgskulUjQXT8z9AgTqZIfFgAZ/n6X/nZ0dn2Nm1zb/7f/8hNATqR9+JPHjVqB2bf/b3nG7LkC84AKJ52ZE118VxjMCwHT9nG3eWQRkZTnXCQHcdQfQvyB8DqrnQLXdX54CzjtXb2MkpffskXh7ofs1EQK4sI3e7m8vqtvt2OlMrpsT33v2SDz3vHrbrEz9703FEo0ahsWee/ZIbP8JaHymXu61+9USm4qB1q3C52DQ9Cygbl3hSJ43Pcv1cijZsVN9De3nl+j3fukyiaefFY73JpZzIoQQkhhUY0+0rPvMPUa6+07977/93blOCOBxk9tm7dr+j/nHB4GCGyRuuE7gf8Y5x7IL2wB16sTfxVzT9BLur7mUio8H5vE60v0xzteIO04/XS/dbufGgbBMLImFcEwkcOyYVN6vVi3d+20e/yOtt8coBiNHW78h3GIZ41zHj7V/f7jHOYGAgKbpcZumxf4NYo79VPtQnbsQ7u+QKq4z4s5RDwBPPC2U5xfvGC8Z34tVQVV+hxJCCCGEEEIIIYQQQgghpPohpFuNYRI37rzzTqxatQrLli1TCj2vuOIKHDp0CJ9//rly+1AoBM1ul0JSnuPHj+Pjjz/Gjh07cMYZZ+CKK65AWlpaVXeLEEIIIURJMmJOxkeEEEIIIYQQQgghhBBCCCGEEBIddPKsBuzbty+h+3dzfcnJyUFJSUlCj10ZnA6dqHDXMfjyK4nR45zbPjYFuPACgT17JG69w1n+75kngbIydyccO/ZruHDhQjw4chyCe36qaFO/3pk4/YxHkXNqn6hLbsaL7NrAyvdPRdfuv6D0UHj5r84G/vOD+3Z++mu00TSg7/XADdc5r93/vSgx/03ntkPvAY4f151LIzl6CQG8Osu5b+MeZGYCv3/Afft+fYE3/5/1ubm4HaJ+DuYtkEp3MFV/Affr168vcM9d7s9YpOO43VMvNA2Y/ZL7NfT73KveQcDptGR/LxOJ2zs/fixw5RXVyxUp1X+DSfTUlHuak5NT1V0gMRLPmFP1vC9cuBB/Gj8OW7eF46PamWfijKaP4tTT+sTt2MlCCGDEcKBePaBVC33sdBuHAKBNa2DjJvU6TQPuHQ48N937eJWJH80xifnf9vFad8T2PtZDY4EuVwj8Y4VEvwLvOMTtWH7jDnu8cXVX4B8rnbGceV+R9r1nj8RbbwML3lJfg0jfEJHw851i9EMVg6riNDei3cd3/5L4wx+t99etvf08Rj0A9Cs41fdYFm1smUz83qNEEY97nwxqSuxCque9ZsxJCCGEEEIIIYQQQgipSVDkmQTq1KkDADhw4IBy/cGDB1G/fv1kdqmCRYslHntSViS3Ro8E8vNSJ6nkRjAo8chUaUmKPTIVaHuRtXRferpEaalz+/R0ABDIzRUYMcx6DXr2AO4ZBtdrEgxKbNuuly8PBAQWLZaY9oSsSJh367IQz/31DvT6TQbG5eegVaAWioPlmLLqFyz57g40afYS6tXPBwBIeRyHSlfj2LEdOF6+B2lpuUjPOAO1sztCiES4WunnUXoIKC0NZ3Y3FVd+z7fdAlxysai4LoDzWl3XB3jlVelIKne+XCAQEOhyhd7+8GGJMQ+5H2vrfwVyc63LcnP1Zf9YGVLec+NYr7xqTWo/MhWYXyRw680Sz88MCx8GDQByGghlKchgUOLZ52SlhbpCANf1cX/f/B1HfU8j4XUN/eD2Ds4vEnj5RVjuezI5o5H+/NgT5uefl/q/a4QQcjKzcOFCDB48GL1/k4HXBpvjo5+x5F93oKzpi6hV61SUH9uFWukNExgLVV4saWba4+F/3zhQ4qIL4RqHrPmne3/GjBLocAkwzWUsv+IK4OOP/fVJ04BL2gFrP3NvIwQwfKg+PtpjN/v4rtq2xXkCX38TwuNPCfQriC4OeWQq8PMvEoUvuMfbBqr+vLMIKJwOlJXpfV+7Dug7wPk94xXThELA7NfcvyXc4okzGkWOJ/x+pwDA1v8CBw44r5sqTnMj2n2UlgIHD/pr3/MagbYXyZjiOr/fmfbvhWSgukd/ngLUSpdo00od/8cb1Xfo6JEipQSehBBCCCGEEEIIIYQQQghJHSjyTAJnn302AODHH39E69atLet2796NQ4cO4YILLkh6v4LBcFIJ0JOdjz0p0aF98oVZ0bJtu9PxMRTSlwcC4WVlZQKAM4mpL9fJzxNo3lxi4yY9uTjuf+B6TezJymFDwsJAvf1x/N/Mh9D7NxlYMKA+tBPWRZc2ycCbA9LRd+5+rNw6HnXr9cKB/e9iz86HUHYk7GZVSwPKQ0DmKWcit9GjFWLQqsSvEGL2a8Cvm0s0aayfs+panX+ewPChsCX1w4nUQECcuH8CYx+UmPq488Capt8nM+bk8Pr17ucxsD/wxhzr8lBId8ucMy98nlLq7ebMk0qR74oPKi/wBHRxhde7tm17YhxfVdcwWrzewXZtheU9TCaBgMDokXAkzFP9N40QQk5mjh8/jj+NH+ceH83Zi2Vb7ka5aWBJZCx068163BLvMfaNOc44IxJCADNnAC1b6Nfkuj4Sby90tvMSeBpjXYf24UkWAFAw0D1ekVKPx+YXWWMR1fhu57fX6MLKaU8AtWt7t1URCsEWP7t/g2wsdgpOQyE9lm/XVsT0PWPEcl7fEpWJJ/x+pwD6vdI0p5tjNHFatPuItn04PveP3/tSVRMOVfdISmDiJEDTnPF/osjPs763jFcJIYQQQgghhBBCCCGEEOIGRZ5JoH379pg5cyY++eQT5OXlWdZ98sknFW2STTQJyKrCzdnFb3LSTzs9uai3UQkajWsCOJOVMwqtbQ+VrkbZkZ8wtlNOhYCh4rhCYGynLCzZvB27g09hz67H0PucDIzrFHazmrqqFEs2H0XLU3dj/Y9W18+qwEho/7RD4pVXvdtKCUycrCdGhw0JuzMB5msloWnALTcB9evrZUtbthDK+2wkPV+eLfHOonApTXuC3ZwcdhOkGgKK3NME5syzJvWFAIrmqrfzEvlGg708qnGswheAenWlaxJZ9fxGi+FOu3S5WlgbK00aO693PMSj8YAJc0IISS1Wr16Nrdt+wmuDXeKjztlY8n0J7rwoE09cUzfsgL45MbHQ7t3hfxvu3V2v0oWDkdzE44kxJhsCz0WL9ZjHL0IAD0+AxXnQHMOPGQXPGCkUAlZ8INHtqvBY6Sf2WLoceG+Zu4C04AbgrbfDcYeU1mN7xdvm/utu+c79m+MNNxGo2/eMVyxnj2MixROV/U4B1GLSYUPC3x5+YphoBal+2lfWXdPtO3NjsUSOSYhcVRMOVXGsuZ/JnPgYi4iWEEIIIYQQQgghhBBCCCE1D4o8k0DHjh1x1llnYdGiRbjtttvQokULAHr59sLCQqSnp+P6669Per/i4RwTb8wJxbXr7MnHsKOK32RmpHZ2lxlVos+4Jn6cjY4d2wEAaBVQv1qtTteX7/t5Jnqfk4E3bW5WCwako2DuXhTvLkev32Tgg//qrp+VKVcqhP7HOH9jmRdD7gZatwqXwHz1df/Hs7szqdYbglFdgBiyCRCt97lxY2lx2DTj5/4BunijZQv95Hv2kHh3aXhd+0v0c/Q6H5XI1y9md61NxRITJ1v37ZVEtj+/Kvr1dS5TlWC95674lsK0XzMhUssxkwlzQghJHXbt2gUgcny08F9HML13PaUDeqyxkErIZY4DpATmzAP6FxhjmECvniFLm3gjBDBpgh5rGePm19+EMO2J6NxFpQRyGriPvWaRYmamxLARzlj2uRnAjMKwc6E99nATZHrx1tu6UNGIQ+wx/bAhsEwGApzfIEaMZz+2Od7wIwI1Y48b7du4fUtEEouq4ld7vNmzB1xFlOb79O13xkQpGZWzZbQTXLzax8NdU/WdKQTw8GRASv3cBvavugmHgYDAoAHS1Xk31SY+EkIIIYQQQgghhBBCCCGEUOSZBGrVqoU///nPuPvuu3HzzTcjLy8P2dnZWLZsGbZv344xY8agSZMmSe9XqpU1tjsyAt5lHP0mM73auQk3jYS2+Zq8v8I7o71/3yIEd4wHAPxlzSF0bpqBzk3TkaaFj1e8uxwAcKx8L8a5uH2O6ZSNK2eV4IFL0/Hu99txqHQ1sut09jy2F1LqLk85DQSys8PL3BAC+O01+jl7JcMjHdNPmfdQyCq0sN/n14tCFrdUKa3r/QhvhdCFG4CeUF+63Lr+s8+9++pX5Gvsw+4IZn7mGmzXE9v2a+CVRDae343F8kRi3Nq3G64LH9/4e/hQ4KZBmmU/8RQ9ugkvOiTfkJgQQkg1oGHDhgCA4mA5Lm2S4Vj/1a5jAIDdhySmrz2EER1qI00LO6BXJhbqfjWw/H3vNvaxePw4Dc2bhzD9+ZgO6eCqLsBHH1sdtbt1tYrqohV4Av4mZ4XHf4HRI9VxnT3+iiQOjeT0GQpZS8Hn58Gyv7IyYXF9V32DuMVdkyYA3boKXyJQO277vG8E0LWL/2+gSKXIVfHm0uXAPXdJhzO8VUQpcf9IuO43EtHGeqr2fsus+9m3XSwMWL/tiuZWrSt8/wKBorlqR9qqnvhICCGEEEIIIYQQQgghhBBihyLPJHHZZZfh9ddfx7PPPoslS5agvLwc5557LkaNGoXevXtXWb9SpayxH0dGlRjObzLTrV1mplQK/G69GbjkYnEiEa27Kz0/033/+/ctwrYfB6NOhsBBAJM+KgVQirPra5jWoy5uOD8TISkxddVhnJJ+Ko4c+yWim1VWun4vyo/tqlgnBPDH+4GNG4FlCsGCm1hx1y7g6q4CX34VWT3w22vCz4EfEWyfPFSUUzcwSrLPfi16wYLZOVN1zc3PgZtLEKAu7+5WOvLGgbqLlz0Jbd1euooaNA0onO4Uddpx629JiUQw6L5dICBwdUDg8CG7KCDc3ux2WvgC0L2bvxKfsaC6jlLS8YgQQoiajh074rScHEz5pBRvDky3THJ585vDuHvhgYr/j3r/IJ5bdwjTetRF91/pglBzLBQt/1gReeKJpgHrN0hkZkq0bKFh0WJZaYGn4VRoOIS6lb92EysCTjf2nj1gcz9XCxPNxwLgcIxc8YHEczOs29jjbDdxqHHcb76VeHuh+/mr9rd2ncRjT4ZdKs1un/bzcKs40LqVd4xqiEBVuO0zGoGn27HN5+u13u4MbxZRRtpvMohnH8zfmSUlVjd7QH8nzTF4siccBgICY0Y5HfOreuIjIYQQQgghhBBCCCGEEEKICoo8k8gFF1yAv/3tb1XdDQepUNbYjyNjvB1VdBcdddL/1deBOnXC5RK9kPI4dm1/AAJAl2bpGNspG60CtVAcLMfUVaUYNH8fJnctx+ptx7Fk8xGc3vAP2L1rmqubleH2efiYftxa6Q0r1p17DvD0X8JixPaX6E6U5sTo/gPS4n4JhEV/jc+MfF3MTkNNGjuFEUIAM2cAO3YCkECb1gItzrc6wl5ycVjgKQTQ5cqwg1UkzM6ZboIH4zlQuQQNHwp07yaUQgrV+WiaLsDoXwCHIMIQ+QaD0rV0unHdW7bw57CkcjWaOBnQNBmxHKZKlP3lVxLnnGNtFy9BgJsgxU0kQccjQgghKtLS0tC7zz2Y/cpj6Dt3rx4rnV4LMz47hAkrS9HrnAyMU8ZPugW5ORaKllAI6NYVWPlBeAKHWSxptJn1iv7nqi4hfPhR9Me57RbzBCGncNEt3t64SWu8iA0AACAASURBVO2YbsQ0DRsCAuHS7vfcpR6bDdyc8YUAbr1Z4pKLBS5oE904roo/OrQH3lnkHiO7lV83X3Oz26cdVcWBgf3D6yOJQFXEq4pBpDjIa72XiDIV4qt498F47oNBPda179ceg0cSLcdbeGl3rY00YYsQQgghhBBCCCGEEEIIIaSqoMiTpARuDodm96J4OqpEKkMeCgHPz/TnQll68BPIUAl6n5OBBQMaVLhTXdokAwsGpKPvnL2Y9GEpNO00NGlWiLr1euHA3tmYsuoXvDnA6mYVkhLTVpXi7AYa3vv3MWSd0hi1sztWrP/uX+HjSqkLPO0Okus3AIC6JHi7tpGvnx+B4BdfwlRiU2L0SIH5RQLzFki8MQdYu87az48+Dvfz2++s5TndXKn2/BxSOm8NH2pNvObn6cJW434VvgDUq6sWS5r7BThLeprP2e42ZQgwK5sIdiu/7rccpl0kohLuxkMQ4F5KNH4iCUIIITWHbl07YvYrwIYd5bhyVgkAoJYG9DonA28q4qeCuXsxbdUhZGacaYmFYmHFSn3Mv3Fg2FnznrskVq2WePJpa9sPPoztGJdcLE7EWf7HQqNMu4pbb4atnDmQn+c9OcvLGV9K4JVXgVdelRXx13vLwgLQSOO4/biGC+L0ExOLjLhd5aQOxOYQacRM8xZIFM0F3pgDzJkXnhTjJxaxCwTjUcUgUhzkvd7pDG/EbakQXyWqD5GvmXo7r3g0XphdawkhhBBCCCGEEEIIIYQQQlIVijxJSuCW+EtUKflIzqGRynqaOXRwFcpDwNhO2RbBJgBoQmBs52ws+f4oTjv1NtSrnw8AyG30KJZsvgN95+7D2E610er0WijeXY5pq0qxZPNRtD0jHe9+fxRNmj0CIdJcjx0K6cJJs3izsg48ZqdMlZumlFYBrCFOLJyul1uM1M92bQW6d7Mm3O2uVCqXVcPR6qZBmmXfwaAuGo0klnQrh9qhvbO/Krcp8z4rmwgOBARytgNSqsW40Thw5ubq/dBOXJZ4JOMjnT+gdvUihBBC3Cgv3wMA+GrYqfhi53Gs+M8RTFl1CONc4qcxnbKxeHMJchve6hkL+UVKPU7pX6D/PxAQOHLEZ7AXAbc4y8uB0KtMO6ALMg38TgTx44xv7O+9ZZHbRSI/T+CyS/UTeHUWEAqpndSBysWnc+ap47xIsYibQDAeVQwiHdttfSSxYyrEV4nqQ7T79ROP1jQS6WpKCCGEEEIIIYQQQgghhJDUhiJPkjK4J0PjfyxV2W4zfgWeACq0fq0C6tep1en6cqGFE3H16uejSbOXsGLrQ1iy+aeK5bU03YPzm18CaNLskQpRqBuaBpTslQgG/SePAeDitsBHn6j3aT53N4dV+/UJhYCNm9yFBfYkfrh0o8T6DRJNGoddRlUuq0aJ+JYtNNjx6wylaielWlQZi9tUtKiuLQB8+5305bhqZ/ZLwNb/xqfEpN/zj4dIghBCSM2gYUO95Pq3e46jy9kZ2HHwOIDI8dMpGc3j1gdjLAN0odRZTSq/T7fJFZEcCP0KMu199xp33WILFXaXz1jFc8ZkE+Nvt/5F4xBpFrJFikncYpFkCAQjxUFu6yOJHVMhvkpUH6LZbzLi8epEMlxNCSGEEEIIIYQQQgghhBCSulDkSVIKv4m/48ePY/Xq1di1axcaNmyIjh07Ii0tssvT19+E8NVG4II2uivkjMLK97l2dmcAT6E4WI5Lm2Q41hfvLgcA1KnbGUBYJFmvfj7q1uuFQ6Wrcbx8B6684mfUrZuLpcsbIat2x4iuVcZ+Jk4ySqaHE31uyeM9eyRycoDbbwM+XuUuZjUnzu0J+WFDwuVDDQwRp0oAGq34wU2MWVamTmL6dYaKxkGqsm6ofggEBIYNkY5nsPAFoHs3GZPIIjc3Pn1LxvkTQgipWXTs2BFNm5yJqZ/+ggX903FGHT3OiRQ/1UpvGLc+CKFPprh/ZLgMeutWwKbi6Pd1VReg7/XqyRVuAsPmzSXKyvRtohFkAv7GYXvcFo0zfTLEc36cHO3x4bAhzvNQXQu7w2GqCwRTQciZ6jAeDUNXU0IIIYQQQgghhBBCCCGEUORJqh0LFy7En8aPw9ZtYQfMpk3OxP8+MgV9+vSxtDWLOt/6f8C7S8PrevUE7h1WeaFndp1OOCX9VEz55CDeHJhuKTkakhJTPylFdtapWPxOZ+zYKSyl0DMz01BWdoVl2a23SuzcCUyc7EzMGyXLGzW0rlcl+uzJ40WLJaYXAms+AX7/gHvS31yuHVAn5OvVtSbge/YAxv2Ps7z6oAFA/wKnwNMrUemW0M3MlFi/wSkK8OsMFY2DlJ+28SiXeP55Arp3a5iqECDYzyWaa0UIIYT4IS0tDf/7yBQMHjwYBfP2Y9RlmWhaX8PUVaVYMEARP606hKxTGqN2dkffxzCcv3fs1I3Wd+4Cnp8Zjk+ktMZ9oRBQ/DUw5C7gWDlw/nl6POMlvOzWVY9vVO7iBm4Cw6H3AlLKinF19EiBaU+4l2w3xI3RjMMd2gMT/6Sff+tWAu+vkJZrYOwXiCycTARe4kZVfPj8TGsbIZzXQjVxqEP76OLJk4FUKeUdr34wHg2T6qJlQgghhBBCCCGEEEIIIYQkHiFlVIWpSRVQUlJSJcfNycmpkmN7JQYXLlyIwYMHo/dvMjC2U220CtRCcbAcUz89jCWbj2DWrFkVQs8Hx4aweo33sV54Xv9bT7rH3uf9+xZh248n+tU5G61Or4Xi3eWY+kkplnx/FGc1m4XlS/u4JiWdyWm9nXnZZZcCl10GdO6ouxP9/gFnhydNBHIaOF2lgkGJfoMksrIE1n56Kjpc/gtKS53bCwGMGSUcpf9U98RYlpkpMWyEs6S7W3l1AFi/QSr7/+zTAu3aCsf16NkDWLocluvjp48qokk8u7VV3a9YyiUa98UuQCicjgqnLz+J7Mq8q17nkipigZpKVf0Gk8RRU+5pTk5OVXeBxEg8n0+3533hwoUYP24ctv0UnqzT+5wMjO1kip9WlWLJ5qM47fTfoeEZD/s+5r3DgJsGhWOPYFCiYKC7iNKMOf5SCS+FAKY+AmRlRR6fVeO76njzi/R9zFsg8cYc67HGjHJ3vYw2PjHHbEZ8sXadUzwXa+lnt3vtFT+qrqFbfGjGuG7m/dmvtRDAgjnCcY5+4sloSKU4KV6xqR+8xrJE9COVrnNV4fbNYH4XEkVNiV1I9bzXjDkJIYQQQgghhBBCCCE1CYo8qwE1SeTplRg8fvw4Lm57Idpk/YwFA+o7HJ8K5u3HpsOn4bP1X2DMeIE1EQSeAHDfCGBgfw2PTAlZXD7duO6EUeg7i5yi0P37FmHPzodQdiQsWsg6pTFOa/QI6tXPrxAwGniJJCMl/ocPdZZMN7sy2a+dkTTPznYXed5xO9DnRHtzIjVSsjaSYFOFSnRhCENzTxMWIYLX9YkmqWl2dfVy34qEnyRrNMnoWAStdmJ9V6syYUwiUx0TrcSbmnJPmXCvviRD5AkA8988hj8/8gl2bbsbrXIP4OdDIfy4LzwYnd1Aw6lZafjmlwDOPnc9hEjzdUx77DH9+ZAlhoqEPf6aMw++xmfVuG8e393Kphv9DQYlNhZL7N8P1KsHtGnlPg57CTmjHdO94pVoYhnVvX69KFThIOo2gcl8Db/+JoRPVwMvz4488erGgbpT/LbtQMleiYmT1G1GDNd8xduxxDzJFFVGItHxnP1Z8BL1Mq5MHFX1zNWU2IVUz3vNmJMQQgghhBBCCCGEEFKTYLl2kjJ4lfAGgEWLP8XWbT/htcE5FoEnAGhCYMzlWbhy1na8UbQKa9Z09nXMNq314y5d7t3uoguBCePDAshf/0biqaetberVz8d9v+uFrMzVeOzxXUir1RC1sztCiDRHCcxIiX+j/F6TxkDRXOs6KfXSlWahp73spr18e5PG4TYqNE0XeNodj4YNkRYxqaosvKq8uhB6Ml0vFupk7TrnMil1R1VAmgQBwJlnCoRClStpbhfx9uoZwvhxsQk9I5VLVJUM9UrA5ueFnbrsAgTV9Y4nLP1ICCGkKti+LQ1CpOHIsRL8pWcOLjkzHZ9sPYYdB4/jjDpp6Nw0Het+OoYrZ23HodLVyK5jjesKbgAWvGXdpznWCgYlNm6SUQk8gfAY2K6twIjhAv0L1AJB8/hsLoduHvfz8wSaN5f4aiNwVhNnGXijvyrhViAgXF0w3WLlWMZ0t9Lp0cYydl4vCmFGobUfjz2px3eqWPX/XpTKyVaqsvIA8MYcoGiuvj+3+HbOPKB/gaw4x/UbUOl40sDrPlSFmDGR8Zz9WRg/Fhg0MPn9INZvhprsakoIIYQQQgghhBBCCCGE1FQo8iQpg1ticOrjEp99DpT8vBMA0Cqgfmxbna4v3/DFLl/H69VTd3Rcv8G7nOZttwBD7tYcwkw7mgZc16cWAoEr8eNWq6iwZw/3BL3KrchI/G/brl4vJXD+eQLzi/Q2JSUSEydb25iTqoGAwPChEi/PVh9LVZ40FEKFaMFtv4C+79Ej4TinYSOA0SOlsqy6kehXnZf5OI89KVE43SkitYtmvfj6G6dL67tLgRuuD8Xk6KmLV9XLg0HElPSPJEDYVCzRLQGJXJVAN5prGy1+XcFYlpMQQk5uzmoGlB/T47VWgVpI0wS6nJ1haWPEdceP6+00DRjYX3dwDAQEzvmNWhxpjteixT4GRhqfX54t8fZC6zJj3NcnzoQn45x7DvDdv8Jte/bQ/1bFDfsPhCfZmEWWXiK6eI3plRUwBoO66NWO6n6EQsCq1WqB58gHgE4dhcPR3sCIGQ2hZ6R4NZ4xT1WJGd3io0TFc6pn4Zm/6iLPL7+SOKNR4vrBWFCNmzCbEEIIIYQQQgghhBBCCCEnP7HXLCYkzri5Ta5dpycLa6U3BAAUB8uV2xfv1pe3vahhxGONfAAVTo5GQtKMEMDkicCbcwWG3K2Xepz2hLsw0+68ZHcGXbpcT1YC6sSwcUz7vtyuiRDhpGe7tgJtWgvHOdiTqjcN0nD3ndZ93DhQL6GYn6cn0e3npXJIUiVr8/MECqdb2xqiAOO8DdzOX0UoBJSVCYweGT4/8/Xxw1cb1cs3bvLXBztlZerjlpV5iy/8oHoWAWDiZN1JKd7oAt3Yr200LFqsl/D8/QP6327n47cdIYSQ6kvnjsJ3XPfI5EZ49mmB+UUCI4ZrFWNUfp6+zFhnlCyPVeAphPsYqBqfNQ0WgadBKARsLHZO6DELPAE9NtxY7OyrMcnGLrIMBqVrP4yYMB5jemVjGbcJSkKoY8qff1bv5+hR/Zz6FwhPN3rAe8KUQTxjHq/7kCi84qNExXNuzwIAjB6HhPWDsSAhhBBCCCGEEEIIIYQQQogTijxJyhAICAwa4L6+dnZHZJ5yJqasOoyQLZsbkhLTPj2MZmc1xo2DOqFXT/f9aJruDGQ+rj0hOWaUQLeuYSGBSgBpMHliWCgJRE6OuyWGZ86ARahg9G3MKGtyWwi9f+aEqd+kav8C/f+PTQEWzAmLJYJB6SgLbxxr+FD4StaWlQlXFyUzbkJGFUbCXCXk8MsFbdTL27T2vQsLXon9yib97ffRQEq1YNbMnj0S6zd4t1FRmWvrFzdXMHtf/bYjhBBSvQkEBCZNvNxXXNe79+Vo11YdexiTXYx10UwkMSOEHoe5jYGqOGtgf/d9QUbuRygECKhjIndXSu94rzJjejCoxxGZmbJSsYxbnDd8qB6/2vt+eUf1fow4zYiFzdupxKL3Doscr8Yr5knmJBnAX3yUiHguUsyeiH4wFiSEEEIIIYQQQgghhBBCCFHDcu0kpehfIPDGHHUST4g05DZ6FEs234GCufsxplMWWp1eC8W7yzHt08NYsvkIZs16FGlpaRg/Ti/HvXETsG8f8Orr5pKX6qRvh/awlAU0ygRmZqoFkAYNGlj351Wq0NjnsCGwleEUaNlCnQQ1+rapWEICaNNKnURWnYMbF15gXefmujRogO4A2r1b5JKJfks02su7G4l6s2uolM57FWt5wpYtNPTqaS3Z3qsnYirVruq/vZ9e6/yQnyeQVVti4iTr8khlQG+9AzhwQFrKukZzToks/ei3rGlVlT8lhBCSfK7tUwv7H38Uf/hD5LjOL6pYxIw5zjAwx2FeJaLtcRYAzJnndOIcPhRo01pACPcJQsZxW7fSY4ppT4TbuvXROKZXvBdriWtziXtN00vJL13uHTu7oYrzhg/V40kAir6LiHGa/ZzXrrPuf9gQ//FqvGKeaOLuyuI3Pop3POcWsyeyH4wFCSGEEEIIIYQQQgghhBBC1FDkSRwEgxI7dgJXdama4wuhLoceCgENcvJxw3UvYc7rD+HKWT9VrA/kNsasWY+iT58+FctattDQsoX+7+uvjS7pa052q/pj7lckEaORHDcnpDVNT0iff57wlRgOBAS6+Ugex5pUdRNoGs6ffvYbSfxoRiWUUP07Xgnz8eO0CtFvm9b6sxGrEELVf/P28Uj6t2kloGkyomAW0B08c3KcZV07tE+s4CAa/AiAg0GJkhLpeN8SXf6UEEJI1XHLLdeifv1Z+NP4cZa4rtlZzrjOD/ZYxI6UugN7gwYCmZkSZWXhOMwudFRNmLDHQ15ixuFDJWYUqvtpjpE6tLcGmcakF2M8VMVTqrjMT/9VqJwTly4HCqfDcn2iwRwLGdc5GJQn+u3suypOs2PeLj8P2H9A4vmZ+jUqfAGoV1ciP09vY7iSJlp8mehJMgZ+J1IlAvu9HDnauj7e/ajKcyWEEEIIIYQQQgghhBBCCEllKPIkFowEcVaWwNpPgaXLJHpekzyhmJuj5MQ/ATkNxIkEXx+s+LAXsuqtRvmxXaiV3hB16nbEpZe6P87RJGHtyW43gacQ0YkY+w2yJtALXwDmF3knnysjRIwGL4FmNH2IRuBovydu/44XZtFvrEIIM17PVGWT/tEIZrf/BJxzjnVZqjkeRTofu6jaS9hCCCHk5KJPnz7o3bs3Vq9ejV27dqFhw4bo2LFjVA6eZoxY5OXZEm8vdK7fuQvo1lV3kDRwKxEdacKEV9yjiz1DFUJEtwk+qthXSmDkA0Czpv5Elm79z6otXR3gDdycE8vKBNq1jX38DQQE1q6TeOxJIBSK7DRujtMiEQxKFL4Qvm7m+2WfVBVLjJdqRBMXJur4ekwpcP994Yc1Ef2o6nMlhBBCCCGEEEIIIYQQQghJVSjyJBXYE8QA8MxfgbYXyaQl1tzcW8wJ6vUbJKRMQ3adzhVtpIyfqE2V7AaswrOB/XWXS78ixvUbnI5SkYR48RAiGhhCzaZnATk56jYqoUIsfUiWq1E0mIWqAGISciQbv4LZxmc6l6Wi45Hb+ahE1ZoGPDwBEYUphBBCTg7S0tLQuXPnyA19EggIPDhSoH79EF551bqu8AWgezdrbFuZEtFecY+fEuJuJeafegYYMwpKoaV9Ao5b/ydOAjRNesZviXJOjFU46we3891YbIhK43/MROF3MlUyy8N7YUz+e2wKcEajxMRpqXKuhBBCCCGEEEIIIYQQQgghqQRFnqSCyiS444Uf95ZEl/Fz23/hdGDHTgASaNM6uqRmtH2OZ2LcLNSsW1dgzSfubc1ChUQ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\n", "text/plain": [ "
    " ] diff --git a/doc/notebooks/XarrayforArviZ.ipynb b/doc/notebooks/XarrayforArviZ.ipynb index f6961be081..c6555d94ac 100644 --- a/doc/notebooks/XarrayforArviZ.ipynb +++ b/doc/notebooks/XarrayforArviZ.ipynb @@ -62,7 +62,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -76,8 +76,8 @@ "
      \n", " \n", "
    • \n", - " \n", - " \n", + " \n", + " \n", "
      \n", "
      \n", "
        \n", @@ -415,11 +415,11 @@ " stroke: currentColor;\n", " fill: currentColor;\n", "}\n", - "
        xarray.Dataset
          • chain: 4
          • draw: 500
          • school: 8
          • chain
            (chain)
            int64
            0 1 2 3
            array([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])
          • school
            (school)
            object
            'Choate' ... 'Mt. Hermon'
            array(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
-       "       'Hotchkiss', 'Lawrenceville', "St. Paul's", 'Mt. Hermon'], dtype=object)
          • mu
            (chain, draw)
            float64
            ...
            array([[-3.476986, -2.455871, -2.826254, ...,  3.392022,  8.46255 , -0.238516],\n",
+       "
            xarray.Dataset
              • chain: 4
              • draw: 500
              • school: 8
              • chain
                (chain)
                int64
                0 1 2 3
                array([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])
              • school
                (school)
                object
                'Choate' ... 'Mt. Hermon'
                array(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
+       "       'Hotchkiss', 'Lawrenceville', "St. Paul's", 'Mt. Hermon'], dtype=object)
              • mu
                (chain, draw)
                float64
                ...
                array([[-3.476986, -2.455871, -2.826254, ...,  3.392022,  8.46255 , -0.238516],\n",
        "       [ 8.250863,  8.250863,  8.250863, ...,  2.527095,  0.276589,  5.655297],\n",
        "       [10.51707 ,  9.887949,  8.500833, ..., -1.571177, -4.435385,  9.762948],\n",
-       "       [ 4.532296,  4.532296,  3.914097, ...,  4.597058,  5.898506,  0.161389]])
              • theta
                (chain, draw, school)
                float64
                ...
                array([[[ 1.668654, -8.537401, ...,  0.155234, -6.818251],\n",
+       "       [ 4.532296,  4.532296,  3.914097, ...,  4.597058,  5.898506,  0.161389]])
              • theta
                (chain, draw, school)
                float64
                ...
                array([[[ 1.668654, -8.537401, ...,  0.155234, -6.818251],\n",
        "        [-6.239359,  1.071411, ..., -4.462528, -1.110761],\n",
        "        ...,\n",
        "        [ 9.292977, 13.691033, ...,  8.176874,  5.888367],\n",
@@ -441,17 +441,17 @@
        "        [ 4.326388,  5.198464, ...,  5.339654,  3.422931],\n",
        "        ...,\n",
        "        [-1.420946, -4.034405, ..., 15.850648,  4.013397],\n",
-       "        [-0.050159,  0.063538, ..., 10.592933,  4.523389]]])
              • tau
                (chain, draw)
                float64
                ...
                array([[ 3.730101,  2.075383,  3.702993, ..., 10.107925,  8.079994,  7.728861],\n",
+       "        [-0.050159,  0.063538, ..., 10.592933,  4.523389]]])
              • tau
                (chain, draw)
                float64
                ...
                array([[ 3.730101,  2.075383,  3.702993, ..., 10.107925,  8.079994,  7.728861],\n",
        "       [ 1.193334,  1.193334,  1.193334, ..., 13.922048,  8.869919,  4.763175],\n",
        "       [ 5.137247,  4.264381,  2.141432, ...,  2.811842, 12.179657,  4.452967],\n",
-       "       [ 0.50007 ,  0.50007 ,  0.902267, ...,  8.345631,  7.71079 ,  5.406798]])
            • created_at :
              2019-06-21T17:36:34.398087
              inference_library :
              pymc3
              inference_library_version :
              3.7

        \n", + " [ 0.50007 , 0.50007 , 0.902267, ..., 8.345631, 7.71079 , 5.406798]])
    • created_at :
      2019-06-21T17:36:34.398087
      inference_library :
      pymc3
      inference_library_version :
      3.7

    \n", " \n", " \n", " \n", " \n", "
  • \n", - " \n", - " \n", + " \n", + " \n", "
    \n", "
    \n", "
      \n", @@ -789,8 +789,8 @@ " stroke: currentColor;\n", " fill: currentColor;\n", "}\n", - "
      xarray.Dataset
        • chain: 4
        • draw: 500
        • school: 8
        • chain
          (chain)
          int64
          0 1 2 3
          array([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])
        • school
          (school)
          object
          'Choate' ... 'Mt. Hermon'
          array(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
-       "       'Hotchkiss', 'Lawrenceville', "St. Paul's", 'Mt. Hermon'], dtype=object)
        • obs
          (chain, draw, school)
          float64
          ...
          array([[[ 7.850329e+00, -1.902792e+01, ..., -3.547030e+00,  1.619463e+01],\n",
+       "
          xarray.Dataset
            • chain: 4
            • draw: 500
            • school: 8
            • chain
              (chain)
              int64
              0 1 2 3
              array([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])
            • school
              (school)
              object
              'Choate' ... 'Mt. Hermon'
              array(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
+       "       'Hotchkiss', 'Lawrenceville', "St. Paul's", 'Mt. Hermon'], dtype=object)
            • obs
              (chain, draw, school)
              float64
              ...
              array([[[ 7.850329e+00, -1.902792e+01, ..., -3.547030e+00,  1.619463e+01],\n",
        "        [ 2.931985e+00,  1.919950e-01, ..., -8.065696e-01,  1.518667e+01],\n",
        "        ...,\n",
        "        [-7.248618e-01,  5.924768e+00, ...,  1.173805e+01, -1.422732e+01],\n",
@@ -812,14 +812,14 @@
        "        [ 7.538687e+00,  2.524281e+01, ..., -8.230382e+00, -2.109873e+01],\n",
        "        ...,\n",
        "        [ 2.180411e+00, -1.861976e+01, ...,  2.564547e+01, -7.993703e+00],\n",
-       "        [-2.096968e+01,  5.474909e+00, ...,  4.697547e+00, -1.506955e+01]]])
          • created_at :
            2019-06-21T17:36:34.489022
            inference_library :
            pymc3
            inference_library_version :
            3.7

      \n", + " [-2.096968e+01, 5.474909e+00, ..., 4.697547e+00, -1.506955e+01]]])
  • created_at :
    2019-06-21T17:36:34.489022
    inference_library :
    pymc3
    inference_library_version :
    3.7

    • \n", " \n", " \n", " \n", " \n", "
  • \n", - " \n", - " \n", + " \n", + " \n", "
    \n", "
    \n", "
      \n", @@ -1157,49 +1157,49 @@ " stroke: currentColor;\n", " fill: currentColor;\n", "}\n", - "
      xarray.Dataset
        • chain: 4
        • draw: 500
        • school: 8
        • chain
          (chain)
          int64
          0 1 2 3
          array([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])
        • school
          (school)
          object
          'Choate' ... 'Mt. Hermon'
          array(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
-       "       'Hotchkiss', 'Lawrenceville', "St. Paul's", 'Mt. Hermon'], dtype=object)
        • tune
          (chain, draw)
          bool
          ...
          array([[ True, False, False, ..., False, False, False],\n",
+       "
          xarray.Dataset
            • chain: 4
            • draw: 500
            • school: 8
            • chain
              (chain)
              int64
              0 1 2 3
              array([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])
            • school
              (school)
              object
              'Choate' ... 'Mt. Hermon'
              array(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
+       "       'Hotchkiss', 'Lawrenceville', "St. Paul's", 'Mt. Hermon'], dtype=object)
            • tune
              (chain, draw)
              bool
              ...
              array([[ True, False, False, ..., False, False, False],\n",
        "       [ True, False, False, ..., False, False, False],\n",
        "       [ True, False, False, ..., False, False, False],\n",
-       "       [ True, False, False, ..., False, False, False]])
            • depth
              (chain, draw)
              int64
              ...
              array([[5, 3, 3, ..., 5, 5, 4],\n",
+       "       [ True, False, False, ..., False, False, False]])
            • depth
              (chain, draw)
              int64
              ...
              array([[5, 3, 3, ..., 5, 5, 4],\n",
        "       [6, 3, 2, ..., 4, 4, 4],\n",
        "       [3, 5, 3, ..., 4, 4, 5],\n",
-       "       [3, 4, 3, ..., 5, 5, 5]])
            • tree_size
              (chain, draw)
              float64
              ...
              array([[31.,  7.,  7., ..., 31., 31., 15.],\n",
+       "       [3, 4, 3, ..., 5, 5, 5]])
            • tree_size
              (chain, draw)
              float64
              ...
              array([[31.,  7.,  7., ..., 31., 31., 15.],\n",
        "       [39.,  7.,  3., ..., 15., 15., 15.],\n",
        "       [ 7., 31.,  7., ..., 15., 15., 31.],\n",
-       "       [ 7., 11.,  7., ..., 31., 31., 31.]])
            • lp
              (chain, draw)
              float64
              ...
              array([[-59.048452, -56.192829, -56.739609, ..., -63.171891, -62.871221,\n",
+       "       [ 7., 11.,  7., ..., 31., 31., 31.]])
            • lp
              (chain, draw)
              float64
              ...
              array([[-59.048452, -56.192829, -56.739609, ..., -63.171891, -62.871221,\n",
        "        -59.67573 ],\n",
        "       [-51.16655 , -51.16655 , -51.16655 , ..., -62.242981, -60.962775,\n",
        "        -61.120349],\n",
        "       [-57.1196  , -54.709673, -49.854318, ..., -58.202845, -63.100613,\n",
        "        -61.906641],\n",
        "       [-43.11603 , -43.11603 , -44.766386, ..., -60.530643, -63.616474,\n",
-       "        -58.345072]])
            • energy_error
              (chain, draw)
              float64
              ...
              array([[ 0.073872, -0.184094,  0.301398, ..., -0.024763,  0.015377,  0.011884],\n",
+       "        -58.345072]])
            • energy_error
              (chain, draw)
              float64
              ...
              array([[ 0.073872, -0.184094,  0.301398, ..., -0.024763,  0.015377,  0.011884],\n",
        "       [ 0.542861,  0.      ,  0.      , ...,  0.035578, -0.144987, -0.023558],\n",
        "       [ 1.30834 , -0.068309, -0.343327, ..., -0.480097,  1.118238, -0.505195],\n",
-       "       [-0.232345,  0.      ,  2.427791, ..., -0.007677, -0.087005, -0.003652]])
            • step_size_bar
              (chain, draw)
              float64
              ...
              array([[0.241676, 0.241676, 0.241676, ..., 0.241676, 0.241676, 0.241676],\n",
+       "       [-0.232345,  0.      ,  2.427791, ..., -0.007677, -0.087005, -0.003652]])
            • step_size_bar
              (chain, draw)
              float64
              ...
              array([[0.241676, 0.241676, 0.241676, ..., 0.241676, 0.241676, 0.241676],\n",
        "       [0.233163, 0.233163, 0.233163, ..., 0.233163, 0.233163, 0.233163],\n",
        "       [0.25014 , 0.25014 , 0.25014 , ..., 0.25014 , 0.25014 , 0.25014 ],\n",
-       "       [0.150248, 0.150248, 0.150248, ..., 0.150248, 0.150248, 0.150248]])
            • max_energy_error
              (chain, draw)
              float64
              ...
              array([[ 1.310060e-01, -2.066764e-01,  6.362023e-01, ...,  1.272182e-01,\n",
+       "       [0.150248, 0.150248, 0.150248, ..., 0.150248, 0.150248, 0.150248]])
            • max_energy_error
              (chain, draw)
              float64
              ...
              array([[ 1.310060e-01, -2.066764e-01,  6.362023e-01, ...,  1.272182e-01,\n",
        "        -3.155631e-01, -6.702092e-02],\n",
        "       [ 2.089505e+00,  3.848563e+01,  6.992369e+01, ..., -3.713299e-01,\n",
        "        -2.177462e-01, -1.621819e-01],\n",
        "       [ 1.458063e+00,  4.335779e+02,  2.788723e+00, ..., -4.800969e-01,\n",
        "         4.380251e+00, -5.051946e-01],\n",
        "       [ 3.226553e-01,  2.736452e+02,  2.202908e+02, ..., -1.224747e-01,\n",
-       "        -1.009818e-01, -1.756579e-01]])
            • energy
              (chain, draw)
              float64
              ...
              array([[60.756731, 62.756232, 64.398717, ..., 67.394493, 66.923554, 65.031815],\n",
+       "        -1.009818e-01, -1.756579e-01]])
            • energy
              (chain, draw)
              float64
              ...
              array([[60.756731, 62.756232, 64.398717, ..., 67.394493, 66.923554, 65.031815],\n",
        "       [53.535435, 56.914649, 54.576739, ..., 63.760659, 64.405753, 66.210544],\n",
        "       [62.504616, 61.998659, 56.945798, ..., 64.477622, 68.892486, 67.322436],\n",
-       "       [50.115409, 46.916088, 52.915592, ..., 66.27361 , 67.768307, 67.209852]])
            • mean_tree_accept
              (chain, draw)
              float64
              ...
              array([[0.950641, 0.990596, 0.725287, ..., 0.971847, 0.979623, 0.986629],\n",
+       "       [50.115409, 46.916088, 52.915592, ..., 66.27361 , 67.768307, 67.209852]])
            • mean_tree_accept
              (chain, draw)
              float64
              ...
              array([[0.950641, 0.990596, 0.725287, ..., 0.971847, 0.979623, 0.986629],\n",
        "       [0.78913 , 0.014034, 0.035809, ..., 0.989669, 0.987006, 0.991768],\n",
        "       [0.26802 , 0.392567, 0.839235, ..., 0.969229, 0.105422, 0.979116],\n",
-       "       [0.909964, 0.157585, 0.061793, ..., 0.999467, 0.987537, 0.996704]])
            • step_size
              (chain, draw)
              float64
              ...
              array([[0.127504, 0.127504, 0.127504, ..., 0.127504, 0.127504, 0.127504],\n",
+       "       [0.909964, 0.157585, 0.061793, ..., 0.999467, 0.987537, 0.996704]])
            • step_size
              (chain, draw)
              float64
              ...
              array([[0.127504, 0.127504, 0.127504, ..., 0.127504, 0.127504, 0.127504],\n",
        "       [0.12298 , 0.12298 , 0.12298 , ..., 0.12298 , 0.12298 , 0.12298 ],\n",
        "       [0.207479, 0.207479, 0.207479, ..., 0.207479, 0.207479, 0.207479],\n",
-       "       [0.106445, 0.106445, 0.106445, ..., 0.106445, 0.106445, 0.106445]])
            • diverging
              (chain, draw)
              bool
              ...
              array([[False, False, False, ..., False, False, False],\n",
+       "       [0.106445, 0.106445, 0.106445, ..., 0.106445, 0.106445, 0.106445]])
            • diverging
              (chain, draw)
              bool
              ...
              array([[False, False, False, ..., False, False, False],\n",
        "       [False, False, False, ..., False, False, False],\n",
        "       [False, False, False, ..., False, False, False],\n",
-       "       [False, False, False, ..., False, False, False]])
            • log_likelihood
              (chain, draw, school)
              float64
              ...
              array([[[-5.167744, -4.588952, ..., -4.813702, -4.355802],\n",
+       "       [False, False, False, ..., False, False, False]])
            • log_likelihood
              (chain, draw, school)
              float64
              ...
              array([[[-5.167744, -4.588952, ..., -4.813702, -4.355802],\n",
        "        [-6.232175, -3.46155 , ..., -5.744349, -4.074576],\n",
        "        ...,\n",
        "        [-4.404661, -3.383463, ..., -3.703993, -3.866952],\n",
@@ -1221,14 +1221,14 @@
        "        [-4.872411, -3.260767, ..., -4.022945, -3.922838],\n",
        "        ...,\n",
        "        [-5.550527, -3.945658, ..., -3.244622, -3.907745],\n",
-       "        [-5.375459, -3.536461, ..., -3.495847, -3.895575]]])
          • created_at :
            2019-06-21T17:36:34.485802
            inference_library :
            pymc3
            inference_library_version :
            3.7

      \n", + " [-5.375459, -3.536461, ..., -3.495847, -3.895575]]])
  • created_at :
    2019-06-21T17:36:34.485802
    inference_library :
    pymc3
    inference_library_version :
    3.7

    • \n", " \n", " \n", " \n", " \n", "
  • \n", - " \n", - " \n", + " \n", + " \n", "
    \n", "
    \n", "
      \n", @@ -1566,23 +1566,23 @@ " stroke: currentColor;\n", " fill: currentColor;\n", "}\n", - "
      xarray.Dataset
        • chain: 1
        • draw: 500
        • school: 8
        • chain
          (chain)
          int64
          0
          array([0])
        • draw
          (draw)
          int64
          0 1 2 3 4 5 ... 495 496 497 498 499
          array([  0,   1,   2, ..., 497, 498, 499])
        • school
          (school)
          object
          'Choate' ... 'Mt. Hermon'
          array(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
-       "       'Hotchkiss', 'Lawrenceville', "St. Paul's", 'Mt. Hermon'], dtype=object)
        • tau
          (chain, draw)
          float64
          ...
          array([[ 6.560633,  1.016055, 68.91391 , ...,  1.560098,  5.948734,  0.763063]])
        • tau_log__
          (chain, draw)
          float64
          ...
          array([[ 1.881087,  0.015927,  4.232858, ...,  0.444748,  1.783178, -0.270415]])
        • mu
          (chain, draw)
          float64
          ...
          array([[ 5.29345 ,  0.813724,  0.712223, ..., -0.979857, -1.657547, -3.272668]])
        • theta
          (chain, draw, school)
          float64
          ...
          array([[[ 2.357357,  7.371371, ...,  6.135082,  3.984435],\n",
+       "
          xarray.Dataset
            • chain: 1
            • draw: 500
            • school: 8
            • chain
              (chain)
              int64
              0
              array([0])
            • draw
              (draw)
              int64
              0 1 2 3 4 5 ... 495 496 497 498 499
              array([  0,   1,   2, ..., 497, 498, 499])
            • school
              (school)
              object
              'Choate' ... 'Mt. Hermon'
              array(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
+       "       'Hotchkiss', 'Lawrenceville', "St. Paul's", 'Mt. Hermon'], dtype=object)
            • tau
              (chain, draw)
              float64
              ...
              array([[ 6.560633,  1.016055, 68.91391 , ...,  1.560098,  5.948734,  0.763063]])
            • tau_log__
              (chain, draw)
              float64
              ...
              array([[ 1.881087,  0.015927,  4.232858, ...,  0.444748,  1.783178, -0.270415]])
            • mu
              (chain, draw)
              float64
              ...
              array([[ 5.29345 ,  0.813724,  0.712223, ..., -0.979857, -1.657547, -3.272668]])
            • theta
              (chain, draw, school)
              float64
              ...
              array([[[ 2.357357,  7.371371, ...,  6.135082,  3.984435],\n",
        "        [ 0.258399, -0.752515, ...,  1.73084 , -0.034163],\n",
        "        ...,\n",
        "        [-4.353289,  2.194643, ..., -7.819076, -6.21613 ],\n",
-       "        [-4.131344, -4.093318, ..., -3.775218, -3.555126]]])
            • obs
              (chain, draw, school)
              float64
              ...
              array([[[ -3.539971,   6.769448, ...,   8.26964 ,  -8.569042],\n",
+       "        [-4.131344, -4.093318, ..., -3.775218, -3.555126]]])
            • obs
              (chain, draw, school)
              float64
              ...
              array([[[ -3.539971,   6.769448, ...,   8.26964 ,  -8.569042],\n",
        "        [-21.166369,   1.14605 , ..., -13.157913,  -8.5424  ],\n",
        "        ...,\n",
        "        [ 29.354582,  -5.511382, ..., -17.892521,  46.28878 ],\n",
-       "        [ -6.379747,   6.538907, ..., -21.155214,  -6.070767]]])
          • created_at :
            2019-06-21T17:36:34.490387
            inference_library :
            pymc3
            inference_library_version :
            3.7

      \n", + " [ -6.379747, 6.538907, ..., -21.155214, -6.070767]]])
  • created_at :
    2019-06-21T17:36:34.490387
    inference_library :
    pymc3
    inference_library_version :
    3.7

    • \n", " \n", " \n", " \n", " \n", "
  • \n", - " \n", - " \n", + " \n", + " \n", "
    \n", "
    \n", "
      \n", @@ -1920,8 +1920,8 @@ " stroke: currentColor;\n", " fill: currentColor;\n", "}\n", - "
      xarray.Dataset
        • school: 8
        • school
          (school)
          object
          'Choate' ... 'Mt. Hermon'
          array(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
-       "       'Hotchkiss', 'Lawrenceville', "St. Paul's", 'Mt. Hermon'], dtype=object)
        • obs
          (school)
          float64
          ...
          array([28.,  8., -3.,  7., -1.,  1., 18., 12.])
      • created_at :
        2019-06-21T17:36:34.491909
        inference_library :
        pymc3
        inference_library_version :
        3.7

    \n", + "
    xarray.Dataset
      • school: 8
      • school
        (school)
        object
        'Choate' ... 'Mt. Hermon'
        array(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
+       "       'Hotchkiss', 'Lawrenceville', "St. Paul's", 'Mt. Hermon'], dtype=object)
      • obs
        (school)
        float64
        ...
        array([28.,  8., -3.,  7., -1.,  1., 18., 12.])
    • created_at :
      2019-06-21T17:36:34.491909
      inference_library :
      pymc3
      inference_library_version :
      3.7

    \n", " \n", " \n", "
    • \n", @@ -2256,7 +2256,7 @@ "\t> observed_data" ] }, - "execution_count": 1, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -2278,7 +2278,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -2618,11 +2618,11 @@ " stroke: currentColor;\n", " fill: currentColor;\n", "}\n", - "
    xarray.Dataset
      • chain: 4
      • draw: 500
      • school: 8
      • chain
        (chain)
        int64
        0 1 2 3
        array([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])
      • school
        (school)
        object
        'Choate' ... 'Mt. Hermon'
        array(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
-       "       'Hotchkiss', 'Lawrenceville', "St. Paul's", 'Mt. Hermon'], dtype=object)
      • mu
        (chain, draw)
        float64
        -3.477 -2.456 ... 5.899 0.1614
        array([[-3.476986, -2.455871, -2.826254, ...,  3.392022,  8.46255 , -0.238516],\n",
+       "
        xarray.Dataset
          • chain: 4
          • draw: 500
          • school: 8
          • chain
            (chain)
            int64
            0 1 2 3
            array([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])
          • school
            (school)
            object
            'Choate' ... 'Mt. Hermon'
            array(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
+       "       'Hotchkiss', 'Lawrenceville', "St. Paul's", 'Mt. Hermon'], dtype=object)
          • mu
            (chain, draw)
            float64
            -3.477 -2.456 ... 5.899 0.1614
            array([[-3.476986, -2.455871, -2.826254, ...,  3.392022,  8.46255 , -0.238516],\n",
        "       [ 8.250863,  8.250863,  8.250863, ...,  2.527095,  0.276589,  5.655297],\n",
        "       [10.51707 ,  9.887949,  8.500833, ..., -1.571177, -4.435385,  9.762948],\n",
-       "       [ 4.532296,  4.532296,  3.914097, ...,  4.597058,  5.898506,  0.161389]])
          • theta
            (chain, draw, school)
            float64
            1.669 -8.537 -2.623 ... 10.59 4.523
            array([[[ 1.668654, -8.537401, ...,  0.155234, -6.818251],\n",
+       "       [ 4.532296,  4.532296,  3.914097, ...,  4.597058,  5.898506,  0.161389]])
          • theta
            (chain, draw, school)
            float64
            1.669 -8.537 -2.623 ... 10.59 4.523
            array([[[ 1.668654, -8.537401, ...,  0.155234, -6.818251],\n",
        "        [-6.239359,  1.071411, ..., -4.462528, -1.110761],\n",
        "        ...,\n",
        "        [ 9.292977, 13.691033, ...,  8.176874,  5.888367],\n",
@@ -2644,10 +2644,10 @@
        "        [ 4.326388,  5.198464, ...,  5.339654,  3.422931],\n",
        "        ...,\n",
        "        [-1.420946, -4.034405, ..., 15.850648,  4.013397],\n",
-       "        [-0.050159,  0.063538, ..., 10.592933,  4.523389]]])
          • tau
            (chain, draw)
            float64
            3.73 2.075 3.703 ... 7.711 5.407
            array([[ 3.730101,  2.075383,  3.702993, ..., 10.107925,  8.079994,  7.728861],\n",
+       "        [-0.050159,  0.063538, ..., 10.592933,  4.523389]]])
          • tau
            (chain, draw)
            float64
            3.73 2.075 3.703 ... 7.711 5.407
            array([[ 3.730101,  2.075383,  3.702993, ..., 10.107925,  8.079994,  7.728861],\n",
        "       [ 1.193334,  1.193334,  1.193334, ..., 13.922048,  8.869919,  4.763175],\n",
        "       [ 5.137247,  4.264381,  2.141432, ...,  2.811842, 12.179657,  4.452967],\n",
-       "       [ 0.50007 ,  0.50007 ,  0.902267, ...,  8.345631,  7.71079 ,  5.406798]])
        • created_at :
          2019-06-21T17:36:34.398087
          inference_library :
          pymc3
          inference_library_version :
          3.7
      " + " [ 0.50007 , 0.50007 , 0.902267, ..., 8.345631, 7.71079 , 5.406798]])
  • created_at :
    2019-06-21T17:36:34.398087
    inference_library :
    pymc3
    inference_library_version :
    3.7
    • " ], "text/plain": [ "\n", @@ -2666,7 +2666,7 @@ " inference_library_version: 3.7" ] }, - "execution_count": 2, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -2690,7 +2690,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -3030,8 +3030,8 @@ " stroke: currentColor;\n", " fill: currentColor;\n", "}\n", - "
    xarray.Dataset
      • school: 8
      • school
        (school)
        object
        'Choate' ... 'Mt. Hermon'
        array(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
-       "       'Hotchkiss', 'Lawrenceville', "St. Paul's", 'Mt. Hermon'], dtype=object)
      • obs
        (school)
        float64
        28.0 8.0 -3.0 7.0 ... 1.0 18.0 12.0
        array([28.,  8., -3.,  7., -1.,  1., 18., 12.])
    • created_at :
      2019-06-21T17:36:34.491909
      inference_library :
      pymc3
      inference_library_version :
      3.7
    " + "
    xarray.Dataset
      • school: 8
      • school
        (school)
        object
        'Choate' ... 'Mt. Hermon'
        array(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',\n",
+       "       'Hotchkiss', 'Lawrenceville', "St. Paul's", 'Mt. Hermon'], dtype=object)
      • obs
        (school)
        float64
        28.0 8.0 -3.0 7.0 ... 1.0 18.0 12.0
        array([28.,  8., -3.,  7., -1.,  1., 18., 12.])
    • created_at :
      2019-06-21T17:36:34.491909
      inference_library :
      pymc3
      inference_library_version :
      3.7
    " ], "text/plain": [ "\n", @@ -3046,7 +3046,7 @@ " inference_library_version: 3.7" ] }, - "execution_count": 3, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -3078,7 +3078,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -3094,7 +3094,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -3103,7 +3103,7 @@ "'eight_schools_model.nc'" ] }, - "execution_count": 5, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } From 5992cd293921e2eac927500a38792b30928195bf Mon Sep 17 00:00:00 2001 From: Piyush Gautam Date: Fri, 12 Jun 2020 01:20:16 +0530 Subject: [PATCH 09/24] Prior-Posterior plot (#1148) * prior-posterior plot * lint * typo * minor changes * add kwargs to kdeplot * created custom axis and customized kwargs * revamped plotter generation * changes docstring * added data_vars support * revamped var_names functionality * changes to correct var_names functionality * Minor bug fixes * Start adding tests * get_coords import issue * test correction * add new test for different vars * add groups param * add kind param * remove ppc conflicts * black * removed black for ppc * ppc changes lint * ppc changes * ppc changes final * final nits * update test * vectorize the colors * Added integer/float support * changed the name from pp_plot to dist_comparison_plot * linting changes * pydocstyle changes * final pydocstyle changes * max_subplots * Apply suggestions from code review Co-authored-by: Oriol Abril * add warning for bokeh backend and test the warning * pylint * created bokeh backend file * raise error instead of warning * Update tests * correct test * linting error Co-authored-by: Oriol Abril --- arviz/plots/__init__.py | 2 + .../backends/bokeh/distcomparisonplot.py | 21 ++ .../backends/matplotlib/distcomparisonplot.py | 67 ++++++ arviz/plots/backends/matplotlib/ppcplot.py | 2 +- arviz/plots/distcomparisonplot.py | 197 ++++++++++++++++++ arviz/tests/base_tests/test_plots_bokeh.py | 6 + .../tests/base_tests/test_plots_matplotlib.py | 33 +++ 7 files changed, 327 insertions(+), 1 deletion(-) create mode 100644 arviz/plots/backends/bokeh/distcomparisonplot.py create mode 100644 arviz/plots/backends/matplotlib/distcomparisonplot.py create mode 100644 arviz/plots/distcomparisonplot.py diff --git a/arviz/plots/__init__.py b/arviz/plots/__init__.py index 62441edccd..0ca8dc20fc 100644 --- a/arviz/plots/__init__.py +++ b/arviz/plots/__init__.py @@ -17,6 +17,7 @@ from .parallelplot import plot_parallel from .posteriorplot import plot_posterior from .ppcplot import plot_ppc +from .distcomparisonplot import plot_dist_comparison from .rankplot import plot_rank from .traceplot import plot_trace from .violinplot import plot_violin @@ -44,6 +45,7 @@ "plot_parallel", "plot_posterior", "plot_ppc", + "plot_dist_comparison", "plot_rank", "plot_trace", "plot_violin", diff --git a/arviz/plots/backends/bokeh/distcomparisonplot.py b/arviz/plots/backends/bokeh/distcomparisonplot.py new file mode 100644 index 0000000000..93f7c41656 --- /dev/null +++ b/arviz/plots/backends/bokeh/distcomparisonplot.py @@ -0,0 +1,21 @@ +"""Bokeh Density Comparison plot.""" + + +def plot_dist_comparison( + ax, + nvars, + ngroups, + figsize, + dc_plotters, + legend, + groups, + prior_kwargs, + posterior_kwargs, + observed_kwargs, + backend_kwargs, + show, +): + """Bokeh Density Comparison plot.""" + raise NotImplementedError( + "The bokeh backend is still under development. Use matplotlib bakend." + ) diff --git a/arviz/plots/backends/matplotlib/distcomparisonplot.py b/arviz/plots/backends/matplotlib/distcomparisonplot.py new file mode 100644 index 0000000000..c404b13d49 --- /dev/null +++ b/arviz/plots/backends/matplotlib/distcomparisonplot.py @@ -0,0 +1,67 @@ +"""Matplotlib Density Comparison plot.""" +import matplotlib.pyplot as plt +import numpy as np + +from . import backend_show +from ...distplot import plot_dist +from ...plot_utils import make_label +from . import backend_kwarg_defaults + + +def plot_dist_comparison( + ax, + nvars, + ngroups, + figsize, + dc_plotters, + legend, + groups, + prior_kwargs, + posterior_kwargs, + observed_kwargs, + backend_kwargs, + show, +): + """Matplotlib Density Comparison plot.""" + backend_kwargs = {**backend_kwarg_defaults(), **backend_kwargs} + if ax is None: + axes = np.empty((nvars, ngroups + 1), dtype=object) + fig = plt.figure(**backend_kwargs, figsize=figsize) + gs = fig.add_gridspec(ncols=ngroups, nrows=nvars * 2) + for i in range(nvars): + for j in range(ngroups): + axes[i, j] = fig.add_subplot(gs[2 * i, j]) + axes[i, -1] = fig.add_subplot(gs[2 * i + 1, :]) + + else: + axes = ax + if ax.shape != (nvars, ngroups + 1): + raise ValueError( + "Found {} shape of axes, which is not equal to data shape {}.".format( + axes.shape, (nvars, ngroups + 1) + ) + ) + + for idx, plotter in enumerate(dc_plotters): + group = groups[idx] + kwargs = ( + prior_kwargs + if group.startswith("prior") + else posterior_kwargs + if group.startswith("posterior") + else observed_kwargs + ) + for idx2, (var, selection, data,) in enumerate(plotter): + label = make_label(var, selection) + label = f"{group} {label}" + plot_dist( + data, label=label if legend else None, ax=axes[idx2, idx], **kwargs, + ) + plot_dist( + data, label=label if legend else None, ax=axes[idx2, -1], **kwargs, + ) + + if backend_show(show): + plt.show() + + return axes diff --git a/arviz/plots/backends/matplotlib/ppcplot.py b/arviz/plots/backends/matplotlib/ppcplot.py index 7d89447f50..ff304c0ab3 100644 --- a/arviz/plots/backends/matplotlib/ppcplot.py +++ b/arviz/plots/backends/matplotlib/ppcplot.py @@ -1,4 +1,4 @@ -"""Matplotib Posterior predictive plot.""" +"""Matplotlib Posterior predictive plot.""" import platform import logging from matplotlib import animation, get_backend diff --git a/arviz/plots/distcomparisonplot.py b/arviz/plots/distcomparisonplot.py new file mode 100644 index 0000000000..1de073b8f4 --- /dev/null +++ b/arviz/plots/distcomparisonplot.py @@ -0,0 +1,197 @@ +"""Density Comparison plot.""" + +from .plot_utils import ( + xarray_var_iter, + _scale_fig_size, + get_plotting_function, + vectorized_to_hex, +) +from ..utils import _var_names, get_coords +from ..rcparams import rcParams + + +def plot_dist_comparison( + data, + kind="latent", + figsize=None, + textsize=None, + var_names=None, + coords=None, + transform=None, + legend=True, + ax=None, + prior_kwargs=None, + posterior_kwargs=None, + observed_kwargs=None, + backend=None, + backend_kwargs=None, + show=None, +): + """Plot to compare fitted and unfitted distributions. + + The resulting plots will show the compared distributions both on + separate axes (particularly useful when one of them is substantially tighter + than another), and plotted together, so three plots per distribution + + Parameters + ---------- + data : az.InferenceData object + InferenceData object containing the posterior/prior data. + kind : str + kind of plot to display {"latent", "observed"}, defaults to 'latent'. + "latent" includes {"prior", "posterior"} and "observed" includes + {"observed_data", "prior_predictive", "posterior_predictive"} + figsize : tuple + Figure size. If None it will be defined automatically. + textsize: float + Text size scaling factor for labels, titles and lines. If None it will be + autoscaled based on figsize. + var_names : str, list, list of lists + if str, plot the variable. if list, plot all the variables in list + of all groups. if list of lists, plot the vars of groups in respective lists. + coords : dict + Dictionary mapping dimensions to selected coordinates to be plotted. + Dimensions without a mapping specified will include all coordinates for + that dimension. + transform : callable + Function to transform data (defaults to None i.e. the identity function) + legend : bool + Add legend to figure. By default True. + ax: axes, optional + Matplotlib axes: The ax argument should have shape (nvars, 3), where the + last column is for the combined before/after plots and columns 0 and 1 are + for the before and after plots, respectively. + prior_kwargs : dicts, optional + Additional keywords passed to `arviz.plot_dist` for prior/predictive groups. + posterior_kwargs : dicts, optional + Additional keywords passed to `arviz.plot_dist` for posterior/predictive groups. + observed_kwargs : dicts, optional + Additional keywords passed to `arviz.plot_dist` for observed_data group. + backend: str, optional + Select plotting backend {"matplotlib","bokeh"}. Default "matplotlib". + backend_kwargs: bool, optional + These are kwargs specific to the backend being used. For additional documentation + check the plotting method of the backend. + show : bool, optional + Call backend show function. + + Returns + ------- + axes : a numpy 2d array of matplotlib axes. Returned object will have shape (nvars, 3), + where the last column is the combined plot and the first columns are the single plots. + + Examples + -------- + Plot the prior/posterior plot for specified vars and coords. + + .. plot:: + :context: close-figs + + >>> import arviz as az + >>> data = az.load_arviz_data('radon') + >>> az.plot_dist_comparison(data, var_names=["defs"], coords={"team" : ["Italy"]}) + + """ + all_groups = ["prior", "posterior"] + + if kind == "observed": + all_groups = ["observed_data", "prior_predictive", "posterior_predictive"] + + if coords is None: + coords = {} + + if prior_kwargs is None: + prior_kwargs = {} + + if posterior_kwargs is None: + posterior_kwargs = {} + + if observed_kwargs is None: + observed_kwargs = {} + + if backend_kwargs is None: + backend_kwargs = {} + + datasets = [] + groups = [] + for group in all_groups: + try: + datasets.append(getattr(data, group)) + groups.append(group) + except: # pylint: disable=bare-except + pass + + if var_names is None: + var_names = list(datasets[0].data_vars) + + if isinstance(var_names, str): + var_names = [var_names] + + if isinstance(var_names[0], str): + var_names = [var_names for _ in datasets] + + var_names = [_var_names(vars, dataset) for vars, dataset in zip(var_names, datasets)] + + if transform is not None: + datasets = [transform(dataset) for dataset in datasets] + + datasets = get_coords(datasets, coords) + len_plots = rcParams["plot.max_subplots"] // (len(groups) + 1) + len_plots = len_plots if len_plots else 1 + dc_plotters = [ + list(xarray_var_iter(data, var_names=var, combined=True))[:len_plots] + for data, var in zip(datasets, var_names) + ] + + nvars = len(dc_plotters[0]) + ngroups = len(groups) + + (figsize, _, _, _, linewidth, _) = _scale_fig_size(figsize, textsize, 2 * nvars, ngroups) + + posterior_kwargs.setdefault("plot_kwargs", dict()) + posterior_kwargs["plot_kwargs"]["color"] = vectorized_to_hex( + posterior_kwargs["plot_kwargs"].get("color", "C0") + ) + posterior_kwargs["plot_kwargs"].setdefault("linewidth", linewidth) + posterior_kwargs.setdefault("hist_kwargs", dict()) + posterior_kwargs["hist_kwargs"].setdefault("alpha", 0.5) + + prior_kwargs.setdefault("plot_kwargs", dict()) + prior_kwargs["plot_kwargs"]["color"] = vectorized_to_hex( + prior_kwargs["plot_kwargs"].get("color", "C1") + ) + prior_kwargs["plot_kwargs"].setdefault("linewidth", linewidth) + prior_kwargs.setdefault("hist_kwargs", dict()) + prior_kwargs["hist_kwargs"].setdefault("alpha", 0.5) + + observed_kwargs.setdefault("plot_kwargs", dict()) + observed_kwargs["plot_kwargs"]["color"] = vectorized_to_hex( + observed_kwargs["plot_kwargs"].get("color", "C2") + ) + observed_kwargs["plot_kwargs"].setdefault("linewidth", linewidth) + observed_kwargs.setdefault("hist_kwargs", dict()) + observed_kwargs["hist_kwargs"].setdefault("alpha", 0.5) + + distcomparisonplot_kwargs = dict( + ax=ax, + nvars=nvars, + ngroups=ngroups, + figsize=figsize, + dc_plotters=dc_plotters, + legend=legend, + groups=groups, + prior_kwargs=prior_kwargs, + posterior_kwargs=posterior_kwargs, + observed_kwargs=observed_kwargs, + backend_kwargs=backend_kwargs, + show=show, + ) + + if backend is None: + backend = rcParams["plot.backend"] + backend = backend.lower() + + # TODO: Add backend kwargs + plot = get_plotting_function("plot_dist_comparison", "distcomparisonplot", backend) + axes = plot(**distcomparisonplot_kwargs) + return axes diff --git a/arviz/tests/base_tests/test_plots_bokeh.py b/arviz/tests/base_tests/test_plots_bokeh.py index a853c23ae6..9e345322f6 100644 --- a/arviz/tests/base_tests/test_plots_bokeh.py +++ b/arviz/tests/base_tests/test_plots_bokeh.py @@ -35,6 +35,7 @@ plot_parallel, plot_posterior, plot_ppc, + plot_dist_comparison, plot_rank, plot_trace, plot_violin, @@ -956,3 +957,8 @@ def test_plot_posterior_point_estimates(models, point_estimate): def test_plot_rank(models, kwargs): axes = plot_rank(models.model_1, backend="bokeh", show=False, **kwargs) assert axes.shape + + +def test_plot_dist_comparison_warn(models): + with pytest.raises(NotImplementedError, match="The bokeh backend.+Use matplotlib bakend."): + plot_dist_comparison(models.model_1, backend="bokeh") diff --git a/arviz/tests/base_tests/test_plots_matplotlib.py b/arviz/tests/base_tests/test_plots_matplotlib.py index 3e8d6cb3c1..fc7f576c84 100644 --- a/arviz/tests/base_tests/test_plots_matplotlib.py +++ b/arviz/tests/base_tests/test_plots_matplotlib.py @@ -30,6 +30,7 @@ plot_parallel, plot_pair, plot_joint, + plot_dist_comparison, plot_ppc, plot_violin, plot_compare, @@ -1217,3 +1218,35 @@ def test_plot_mcse_no_divergences(models): idata.sample_stats = idata.sample_stats.rename({"diverging": "diverging_missing"}) with pytest.raises(ValueError, match="not contain diverging"): plot_mcse(idata, rug=True) + + +@pytest.mark.parametrize( + "kwargs", + [ + {}, + {"var_names": ["theta"]}, + {"var_names": ["theta"], "coords": {"theta_dim_0": [0, 1]}}, + {"var_names": ["eta"], "posterior_kwargs": {"rug": True, "rug_kwargs": {"color": "r"}}}, + {"var_names": ["mu"], "prior_kwargs": {"fill_kwargs": {"alpha": 0.5}}}, + { + "var_names": ["tau"], + "prior_kwargs": {"plot_kwargs": {"color": "r"}}, + "posterior_kwargs": {"plot_kwargs": {"color": "b"}}, + }, + {"var_names": ["y"], "kind": "observed"}, + ], +) +def test_plot_dist_comparison(models, kwargs): + idata = models.model_1 + ax = plot_dist_comparison(idata, **kwargs) + assert np.all(ax) + + +def test_plot_dist_comparison_different_vars(): + data = from_dict( + posterior={"x": np.random.randn(4, 100, 30),}, prior={"x_hat": np.random.randn(4, 100, 30)}, + ) + with pytest.raises(KeyError): + plot_dist_comparison(data, var_names="x") + ax = plot_dist_comparison(data, var_names=[["x_hat"], ["x"]]) + assert np.all(ax) From 1515164d9221c995ee83d1f42bc510167d80565d Mon Sep 17 00:00:00 2001 From: Oriol Abril-Pla Date: Fri, 12 Jun 2020 03:17:33 +0200 Subject: [PATCH 10/24] update governance to match description in election.md (#1231) --- GOVERNANCE.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/GOVERNANCE.md b/GOVERNANCE.md index 6264fbf66b..51b4881782 100644 --- a/GOVERNANCE.md +++ b/GOVERNANCE.md @@ -186,9 +186,9 @@ Each voter can vote zero or more times, once per each candidate. As this is not #### Voting Criteria For Future Elections Voting for first election is restricted to establish stable governance, and to defer major decision to elected leaders -* For the first election only the folks in Slack can vote (excluding GSOC students) +* For the first election only the people registered following the guidelines in elections/ArviZ_2020.md can vote * In the first year, the council must determine voting eligibility for future elections between two criteria: - * Those with commit bits + * Core contributors * The contributing community at large ### Core Contributors From 31aac4c1084e40b99da0024218431a800ba222c9 Mon Sep 17 00:00:00 2001 From: Michael Osthege Date: Fri, 12 Jun 2020 16:44:21 +0200 Subject: [PATCH 11/24] Automatically take coords and dims from PyMC3 model (#1228) * Import coords and dims from pymc3 * remove trailing whitespace * apply black formatting * add test for autodetection of coords/dims * condition coord autodetect test on PyMC3>=3.9.0 * apply black formatting * make pylint happy Co-authored-by: Adrian Seyboldt --- arviz/data/io_pymc3.py | 6 +++ arviz/tests/external_tests/test_data_pymc.py | 43 +++++++++++++++++++- 2 files changed, 48 insertions(+), 1 deletion(-) diff --git a/arviz/data/io_pymc3.py b/arviz/data/io_pymc3.py index 08583e83dd..98129a2fec 100644 --- a/arviz/data/io_pymc3.py +++ b/arviz/data/io_pymc3.py @@ -148,7 +148,13 @@ def arbitrary_element(dct: Dict[Any, np.ndarray]) -> np.ndarray: self.ndraws = aelem.shape[0] self.coords = coords + if coords is None and hasattr(model, "coords"): + self.coords = model.coords + self.dims = dims + if dims is None and hasattr(model, "RV_dims"): + self.dims = {k: list(v) for k, v in model.RV_dims.items()} + self.observations, self.multi_observations = self.find_observations() def find_observations(self) -> Tuple[Optional[Dict[str, Var]], Optional[Dict[str, Var]]]: diff --git a/arviz/tests/external_tests/test_data_pymc.py b/arviz/tests/external_tests/test_data_pymc.py index 0551a76b90..b90f82f575 100644 --- a/arviz/tests/external_tests/test_data_pymc.py +++ b/arviz/tests/external_tests/test_data_pymc.py @@ -1,8 +1,10 @@ -# pylint: disable=no-member, invalid-name, redefined-outer-name, protected-access +# pylint: disable=no-member, invalid-name, redefined-outer-name, protected-access, too-many-public-methods from sys import version_info from typing import Dict, Tuple +import packaging import numpy as np +import pandas as pd import pytest from numpy import ma @@ -196,6 +198,45 @@ def test_posterior_predictive_warning(self, data, eight_schools_params, caplog): assert len(records) == 1 assert records[0].levelname == "WARNING" + @pytest.mark.skipif( + packaging.version.Version(pm.__version__) < packaging.version.Version("3.9.0"), + reason="Requires PyMC3 >= 3.9.0", + ) + def test_autodetect_coords_from_model(self): + df_data = pd.DataFrame(columns=["date"]).set_index("date") + dates = pd.date_range(start="2020-05-01", end="2020-05-20") + for city, mu in {"Berlin": 15, "San Marino": 18, "Paris": 16}.items(): + df_data[city] = np.random.normal(loc=mu, size=len(dates)) + df_data.index = dates + df_data.index.name = "date" + + coords = {"date": df_data.index, "city": df_data.columns} + with pm.Model(coords=coords) as model: + europe_mean = pm.Normal("europe_mean_temp", mu=15.0, sd=3.0) + city_offset = pm.Normal("city_offset", mu=0.0, sd=3.0, dims="city") + city_temperature = pm.Deterministic( + "city_temperature", europe_mean + city_offset, dims="city" + ) + + data_dims = ("date", "city") + data = pm.Data("data", df_data, dims=data_dims) + _ = pm.Normal("likelihood", mu=city_temperature, sd=0.5, observed=data, dims=data_dims) + + trace = pm.sample( + return_inferencedata=False, + compute_convergence_checks=False, + cores=1, + chains=1, + tune=20, + draws=30, + step=pm.Metropolis(), + ) + idata = from_pymc3(trace=trace, model=model) + + np.testing.assert_array_equal(idata.posterior.coords["city"], coords["city"]) + np.testing.assert_array_equal(idata.observed_data.coords["date"], coords["date"]) + np.testing.assert_array_equal(idata.observed_data.coords["city"], coords["city"]) + def test_missing_data_model(self): # source pymc3/pymc3/tests/test_missing.py data = ma.masked_values([1, 2, -1, 4, -1], value=-1) From 9b8e29db1435defa8e3bc5e08168b42e7d78eedc Mon Sep 17 00:00:00 2001 From: Ravin Kumar Date: Sun, 14 Jun 2020 13:49:48 -0700 Subject: [PATCH 12/24] Fix keyword ignores (#1236) * Fix keyword ignores * Update changelog --- CHANGELOG.md | 1 + arviz/stats/stats.py | 18 +++++++++--------- 2 files changed, 10 insertions(+), 9 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 4761652ed6..e409f84336 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -14,6 +14,7 @@ objects. (#1218) * Added `log_likelihood` argument to `from_pyro` and a warning if log likelihood cannot be obtained (#1227) * Skip tests on matplotlib animations if ffmpeg is not installed (#1227) +* Fix hpd bug where arguments were being ignored (#1236) ### Deprecation * Using `from_pymc3` without a model context available now raises a diff --git a/arviz/stats/stats.py b/arviz/stats/stats.py index d5b5da2b83..1bcf5c7108 100644 --- a/arviz/stats/stats.py +++ b/arviz/stats/stats.py @@ -338,15 +338,15 @@ def hpd( warnings.warn(("hpd will be deprecated " "Please replace hdi"),) return hdi( ary, - hdi_prob=None, - circular=False, - multimodal=False, - skipna=False, - group="posterior", - var_names=None, - filter_vars=None, - coords=None, - max_modes=10, + hdi_prob, + circular, + multimodal, + skipna, + group, + var_names, + filter_vars, + coords, + max_modes, **kwargs, ) From 0ff892c73ce504313927ec8609dcff6683078707 Mon Sep 17 00:00:00 2001 From: Ravin Kumar Date: Sun, 14 Jun 2020 19:41:04 -0700 Subject: [PATCH 13/24] Add core contributors (#1226) * Start adding core contributors * Add Alex * Add Ari and Colin * Add Aki * Fix last name alphabetical ordering * Reorder alphabetically by last name * Fix name * Add Mitzi * Add Seth * Add Robert --- GOVERNANCE.md | 12 +++++++++++- 1 file changed, 11 insertions(+), 1 deletion(-) diff --git a/GOVERNANCE.md b/GOVERNANCE.md index 51b4881782..fa2114cb04 100644 --- a/GOVERNANCE.md +++ b/GOVERNANCE.md @@ -201,7 +201,17 @@ Current Core Contributors can nominate candidates for consideration by the counc can make the determination for acceptance with a process of their choosing. #### Current Core Contributors -* Will be updated with Core Contributor list during first election +* Oriol Abril-Pla (@OriolAbril) +* Alex Andorra (@AlexAndorra) +* Seth Axen (@sethaxen) +* Colin Carroll (@ColCarroll) +* Robert P. Goldman (@rpgoldman) +* Ari Hartikainen (@ahartikainen) +* Ravin Kumar (@canyon289) +* Osvaldo Martin (@aloctavodia) +* Mitzi Morris (@mitzimorris) +* Du Phan (@fehiepsi) +* Aki Vehtari (@avehtari) #### Core Contributor Responsibilities * Enforce code of conduct From 4fbd1e2a3fa6c9b9054c0aacbc2f547b11754662 Mon Sep 17 00:00:00 2001 From: Chris Fonnesbeck Date: Wed, 17 Jun 2020 09:19:04 -0500 Subject: [PATCH 14/24] Capitalized HDI everywhere it is used in text (#1244) * Capitalized HDI everywhere it is used in text * fixed typo --- CHANGELOG.md | 2 +- arviz/plots/backends/bokeh/forestplot.py | 2 +- arviz/plots/backends/bokeh/hdiplot.py | 2 +- arviz/plots/backends/matplotlib/forestplot.py | 2 +- arviz/plots/backends/matplotlib/hdiplot.py | 2 +- arviz/plots/essplot.py | 2 +- arviz/plots/forestplot.py | 4 ++-- arviz/plots/hdiplot.py | 10 +++++----- arviz/plots/loopitplot.py | 2 +- arviz/stats/stats.py | 12 ++++++------ 10 files changed, 20 insertions(+), 20 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index e409f84336..65d9f73e04 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -3,7 +3,7 @@ ## v0.x.x Unreleased ### New features -* loo-pit plot. The kde is computed over the data interval (this could be shorter than [0, 1]). The hdi is computed analitically (#1215) +* loo-pit plot. The kde is computed over the data interval (this could be shorter than [0, 1]). The HDI is computed analitically (#1215) * Added `html_repr` of InferenceData objects for jupyter notebooks. (#1217) * Added support for PyJAGS via the function `from_pyjags` in the module arviz.data.io_pyjags. (#1219) diff --git a/arviz/plots/backends/bokeh/forestplot.py b/arviz/plots/backends/bokeh/forestplot.py index 91819f6018..79b5cec338 100644 --- a/arviz/plots/backends/bokeh/forestplot.py +++ b/arviz/plots/backends/bokeh/forestplot.py @@ -417,7 +417,7 @@ def forestplot(self, hdi_prob, quartiles, linewidth, markersize, ax, rope): x=values[mid], y=y, size=markersize * 0.75, fill_color=color, ) _title = Title() - _title.text = "{:.1%} hdi".format(hdi_prob) + _title.text = "{:.1%} HDI".format(hdi_prob) ax.title = _title return ax diff --git a/arviz/plots/backends/bokeh/hdiplot.py b/arviz/plots/backends/bokeh/hdiplot.py index cad0ab172d..cf511716eb 100644 --- a/arviz/plots/backends/bokeh/hdiplot.py +++ b/arviz/plots/backends/bokeh/hdiplot.py @@ -10,7 +10,7 @@ def plot_hdi(ax, x_data, y_data, plot_kwargs, fill_kwargs, backend_kwargs, show): - """Bokeh hdi plot.""" + """Bokeh HDI plot.""" if backend_kwargs is None: backend_kwargs = {} diff --git a/arviz/plots/backends/matplotlib/forestplot.py b/arviz/plots/backends/matplotlib/forestplot.py index 110fd43ff3..abb4fe3aa9 100644 --- a/arviz/plots/backends/matplotlib/forestplot.py +++ b/arviz/plots/backends/matplotlib/forestplot.py @@ -341,7 +341,7 @@ def forestplot( color=color, ) ax.tick_params(labelsize=xt_labelsize) - ax.set_title("{:.1%} hdi".format(hdi_prob), fontsize=titlesize, wrap=True) + ax.set_title("{:.1%} HDI".format(hdi_prob), fontsize=titlesize, wrap=True) if rope is None or isinstance(rope, dict): return elif len(rope) == 2: diff --git a/arviz/plots/backends/matplotlib/hdiplot.py b/arviz/plots/backends/matplotlib/hdiplot.py index db786ee4a2..585acb40f3 100644 --- a/arviz/plots/backends/matplotlib/hdiplot.py +++ b/arviz/plots/backends/matplotlib/hdiplot.py @@ -6,7 +6,7 @@ def plot_hdi(ax, x_data, y_data, plot_kwargs, fill_kwargs, backend_kwargs, show): - """Matplotlib hdi plot.""" + """Matplotlib HDI plot.""" if backend_kwargs is not None: warnings.warn( ( diff --git a/arviz/plots/essplot.py b/arviz/plots/essplot.py index 37a9bba49f..9c8a4e4331 100644 --- a/arviz/plots/essplot.py +++ b/arviz/plots/essplot.py @@ -113,7 +113,7 @@ def plot_ess( -------- Plot local ESS. This plot, together with the quantile ESS plot, is recommended to check that there are enough samples for all the explored regions of parameter space. Checking - local and quantile ESS is particularly relevant when working with hdi intervals as + local and quantile ESS is particularly relevant when working with HDI intervals as opposed to ESS bulk, which is relevant for point estimates. .. plot:: diff --git a/arviz/plots/forestplot.py b/arviz/plots/forestplot.py index a1a0a528e9..f36787a0cf 100644 --- a/arviz/plots/forestplot.py +++ b/arviz/plots/forestplot.py @@ -36,9 +36,9 @@ def plot_forest( show=None, credible_interval=None, ): - """Forest plot to compare hdi intervals from a number of distributions. + """Forest plot to compare HDI intervals from a number of distributions. - Generates a forest plot of 100*(hdi_prob)% hdi intervals from + Generates a forest plot of 100*(hdi_prob)% HDI intervals from a trace or list of traces. Parameters diff --git a/arviz/plots/hdiplot.py b/arviz/plots/hdiplot.py index f9dbcd5932..9585401e9e 100644 --- a/arviz/plots/hdiplot.py +++ b/arviz/plots/hdiplot.py @@ -28,20 +28,20 @@ def plot_hdi( credible_interval=None, ): r""" - Plot hdi intervals for regression data. + Plot HDI intervals for regression data. Parameters ---------- x : array-like Values to plot. y : array-like - Values from which to compute the hdi. Assumed shape (chain, draw, \*shape). + Values from which to compute the HDI. Assumed shape (chain, draw, \*shape). hdi_prob : float, optional Probability for the highest density interval. Defaults to 0.94. color : str - Color used for the limits of the hdi and fill. Should be a valid matplotlib color. + Color used for the limits of the HDI and fill. Should be a valid matplotlib color. circular : bool, optional - Whether to compute the hdi taking into account `x` is a circular variable + Whether to compute the HDI taking into account `x` is a circular variable (in the range [-np.pi, np.pi]) or not. Defaults to False (i.e non-circular variables). smooth : boolean If True the result will be smoothed by first computing a linear interpolation of the data @@ -53,7 +53,7 @@ def plot_hdi( fill_kwargs : dict Keywords passed to `fill_between` (use fill_kwargs={'alpha': 0} to disable fill). plot_kwargs : dict - Keywords passed to hdi limits. + Keywords passed to HDI limits. ax: axes, optional Matplotlib axes or bokeh figures. backend: str, optional diff --git a/arviz/plots/loopitplot.py b/arviz/plots/loopitplot.py index f5f1995940..5621a36737 100644 --- a/arviz/plots/loopitplot.py +++ b/arviz/plots/loopitplot.py @@ -218,7 +218,7 @@ def plot_loo_pit( hdi_kwargs = {} hdi_kwargs.setdefault("color", to_hex(hsv_to_rgb(light_color))) hdi_kwargs.setdefault("alpha", 0.35) - hdi_kwargs.setdefault("label", "Uniform hdi") + hdi_kwargs.setdefault("label", "Uniform HDI") loo_pit_kwargs = dict( ax=ax, diff --git a/arviz/stats/stats.py b/arviz/stats/stats.py index 1bcf5c7108..e2503fe0fc 100644 --- a/arviz/stats/stats.py +++ b/arviz/stats/stats.py @@ -413,7 +413,7 @@ def hdi( Examples -------- - Calculate the hdi of a Normal random variable: + Calculate the HDI of a Normal random variable: .. ipython:: @@ -422,7 +422,7 @@ def hdi( ...: data = np.random.normal(size=2000) ...: az.hdi(data, hdi_prob=.68) - Calculate the hdi of a dataset: + Calculate the HDI of a dataset: .. ipython:: @@ -430,13 +430,13 @@ def hdi( ...: data = az.load_arviz_data('centered_eight') ...: az.hdi(data) - We can also calculate the hdi of some of the variables of dataset: + We can also calculate the HDI of some of the variables of dataset: .. ipython:: In [1]: az.hdi(data, var_names=["mu", "theta"]) - If we want to calculate the hdi over specified dimension of dataset, + If we want to calculate the HDI over specified dimension of dataset, we can pass `input_core_dims` by kwargs: .. ipython:: @@ -527,7 +527,7 @@ def _hdi(ary, hdi_prob, circular, skipna): def _hdi_multimodal(ary, hdi_prob, skipna, max_modes): - """Compute hdi if the distribution is multimodal.""" + """Compute HDI if the distribution is multimodal.""" ary = ary.flatten() if skipna: ary = ary[~np.isnan(ary)] @@ -1029,7 +1029,7 @@ def summary( If True, use the statistics returned by ``stat_funcs`` in addition to, rather than in place of, the default statistics. This is only meaningful when ``stat_funcs`` is not None. hdi_prob: float, optional - hdi interval to compute. Defaults to 0.94. This is only meaningful when ``stat_funcs`` is + HDI interval to compute. Defaults to 0.94. This is only meaningful when ``stat_funcs`` is None. order: {"C", "F"} If fmt is "wide", use either C or F unpacking order. Defaults to C. From 7cec6dd5ec38ded43ce184ddb1e18e66e70dacbb Mon Sep 17 00:00:00 2001 From: Oriol Abril-Pla Date: Wed, 17 Jun 2020 16:32:52 +0200 Subject: [PATCH 15/24] fix io_pymc3 bug when model not passed explicitly (#1240) * fix bug when model not passed explicitly * update changelog --- CHANGELOG.md | 2 ++ arviz/data/io_pymc3.py | 8 ++++---- arviz/tests/external_tests/test_data_pymc.py | 11 +++++++++-- 3 files changed, 15 insertions(+), 6 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 65d9f73e04..dd051f1768 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -6,6 +6,8 @@ * loo-pit plot. The kde is computed over the data interval (this could be shorter than [0, 1]). The HDI is computed analitically (#1215) * Added `html_repr` of InferenceData objects for jupyter notebooks. (#1217) * Added support for PyJAGS via the function `from_pyjags` in the module arviz.data.io_pyjags. (#1219) +* `from_pymc3` can now retrieve `coords` and `dims` from model context (#1228 + and #1240) ### Maintenance and fixes * Include data from `MultiObservedRV` to `observed_data` when using diff --git a/arviz/data/io_pymc3.py b/arviz/data/io_pymc3.py index 98129a2fec..10a5657d78 100644 --- a/arviz/data/io_pymc3.py +++ b/arviz/data/io_pymc3.py @@ -148,12 +148,12 @@ def arbitrary_element(dct: Dict[Any, np.ndarray]) -> np.ndarray: self.ndraws = aelem.shape[0] self.coords = coords - if coords is None and hasattr(model, "coords"): - self.coords = model.coords + if coords is None and hasattr(self.model, "coords"): + self.coords = self.model.coords self.dims = dims - if dims is None and hasattr(model, "RV_dims"): - self.dims = {k: list(v) for k, v in model.RV_dims.items()} + if dims is None and hasattr(self.model, "RV_dims"): + self.dims = {k: list(v) for k, v in self.model.RV_dims.items()} self.observations, self.multi_observations = self.find_observations() diff --git a/arviz/tests/external_tests/test_data_pymc.py b/arviz/tests/external_tests/test_data_pymc.py index b90f82f575..41194a1ec8 100644 --- a/arviz/tests/external_tests/test_data_pymc.py +++ b/arviz/tests/external_tests/test_data_pymc.py @@ -202,7 +202,8 @@ def test_posterior_predictive_warning(self, data, eight_schools_params, caplog): packaging.version.Version(pm.__version__) < packaging.version.Version("3.9.0"), reason="Requires PyMC3 >= 3.9.0", ) - def test_autodetect_coords_from_model(self): + @pytest.mark.parametrize("use_context", [True, False]) + def test_autodetect_coords_from_model(self, use_context): df_data = pd.DataFrame(columns=["date"]).set_index("date") dates = pd.date_range(start="2020-05-01", end="2020-05-20") for city, mu in {"Berlin": 15, "San Marino": 18, "Paris": 16}.items(): @@ -231,8 +232,14 @@ def test_autodetect_coords_from_model(self): draws=30, step=pm.Metropolis(), ) - idata = from_pymc3(trace=trace, model=model) + if use_context: + idata = from_pymc3(trace=trace) + if not use_context: + idata = from_pymc3(trace=trace, model=model) + assert "city" in list(idata.posterior.dims) + assert "city" in list(idata.observed_data.dims) + assert "date" in list(idata.observed_data.dims) np.testing.assert_array_equal(idata.posterior.coords["city"], coords["city"]) np.testing.assert_array_equal(idata.observed_data.coords["date"], coords["date"]) np.testing.assert_array_equal(idata.observed_data.coords["city"], coords["city"]) From e95fbe1f09c02aa0ff0560f0de37ad79d516b030 Mon Sep 17 00:00:00 2001 From: Du Phan Date: Thu, 18 Jun 2020 04:07:21 -0400 Subject: [PATCH 16/24] Change the default scatter_kwargs in pairplot (#1246) * fix default pairplot * change zorder to 0.6 * update change log --- CHANGELOG.md | 1 + arviz/plots/pairplot.py | 3 ++- 2 files changed, 3 insertions(+), 1 deletion(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index dd051f1768..39473682f4 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -17,6 +17,7 @@ * Added `log_likelihood` argument to `from_pyro` and a warning if log likelihood cannot be obtained (#1227) * Skip tests on matplotlib animations if ffmpeg is not installed (#1227) * Fix hpd bug where arguments were being ignored (#1236) +* Change the default `zorder` of scatter points from `0` to `0.6` in `plot_pair` (#1246) ### Deprecation * Using `from_pymc3` without a model context available now raises a diff --git a/arviz/plots/pairplot.py b/arviz/plots/pairplot.py index 73a388500e..8e97bba234 100644 --- a/arviz/plots/pairplot.py +++ b/arviz/plots/pairplot.py @@ -194,7 +194,8 @@ def plot_pair( scatter_kwargs.setdefault("marker", ".") scatter_kwargs.setdefault("lw", 0) - scatter_kwargs.setdefault("zorder", 0) + # Sets the default zorder higher than zorder of grid, which is 0.5 + scatter_kwargs.setdefault("zorder", 0.6) if kde_kwargs is None: kde_kwargs = {} From f2c954e46dc7ed906fbce728a26ca5eef902c37a Mon Sep 17 00:00:00 2001 From: Oriol Abril-Pla Date: Fri, 19 Jun 2020 18:41:12 +0200 Subject: [PATCH 17/24] allow from_pymc3 args to override model dims and coords (#1249) * allow from_pymc3 args to override model values * lint * black * add docstring --- CHANGELOG.md | 3 +-- arviz/data/io_pymc3.py | 15 ++++++----- arviz/tests/external_tests/test_data_pymc.py | 27 ++++++++++++++++++++ 3 files changed, 36 insertions(+), 9 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 39473682f4..df4980d1a8 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -6,8 +6,7 @@ * loo-pit plot. The kde is computed over the data interval (this could be shorter than [0, 1]). The HDI is computed analitically (#1215) * Added `html_repr` of InferenceData objects for jupyter notebooks. (#1217) * Added support for PyJAGS via the function `from_pyjags` in the module arviz.data.io_pyjags. (#1219) -* `from_pymc3` can now retrieve `coords` and `dims` from model context (#1228 - and #1240) +* `from_pymc3` can now retrieve `coords` and `dims` from model context (#1228, #1240 and #1249) ### Maintenance and fixes * Include data from `MultiObservedRV` to `observed_data` when using diff --git a/arviz/data/io_pymc3.py b/arviz/data/io_pymc3.py index 10a5657d78..37469c6cbf 100644 --- a/arviz/data/io_pymc3.py +++ b/arviz/data/io_pymc3.py @@ -147,13 +147,14 @@ def arbitrary_element(dct: Dict[Any, np.ndarray]) -> np.ndarray: aelem = arbitrary_element(get_from) self.ndraws = aelem.shape[0] - self.coords = coords - if coords is None and hasattr(self.model, "coords"): - self.coords = self.model.coords - - self.dims = dims - if dims is None and hasattr(self.model, "RV_dims"): - self.dims = {k: list(v) for k, v in self.model.RV_dims.items()} + self.coords = {} if coords is None else coords + if hasattr(self.model, "coords"): + self.coords = {**self.model.coords, **self.coords} + + self.dims = {} if dims is None else dims + if hasattr(self.model, "RV_dims"): + model_dims = {k: list(v) for k, v in self.model.RV_dims.items()} + self.dims = {**model_dims, **self.dims} self.observations, self.multi_observations = self.find_observations() diff --git a/arviz/tests/external_tests/test_data_pymc.py b/arviz/tests/external_tests/test_data_pymc.py index 41194a1ec8..2343152b44 100644 --- a/arviz/tests/external_tests/test_data_pymc.py +++ b/arviz/tests/external_tests/test_data_pymc.py @@ -244,6 +244,33 @@ def test_autodetect_coords_from_model(self, use_context): np.testing.assert_array_equal(idata.observed_data.coords["date"], coords["date"]) np.testing.assert_array_equal(idata.observed_data.coords["city"], coords["city"]) + def test_ovewrite_model_coords_dims(self): + """Check coords and dims from model object can be partially overwrited.""" + dim1 = ["a", "b"] + new_dim1 = ["c", "d"] + coords = {"dim1": dim1, "dim2": ["c1", "c2"]} + x_data = np.arange(4).reshape((2, 2)) + y = x_data + np.random.normal(size=(2, 2)) + with pm.Model(coords=coords): + x = pm.Data("x", x_data, dims=("dim1", "dim2")) + beta = pm.Normal("beta", 0, 1, dims="dim1") + _ = pm.Normal("obs", x * beta, 1, observed=y, dims=("dim1", "dim2")) + trace = pm.sample(100, tune=100) + idata1 = from_pymc3(trace) + idata2 = from_pymc3(trace, coords={"dim1": new_dim1}, dims={"beta": ["dim2"]}) + + test_dict = {"posterior": ["beta"], "observed_data": ["obs"], "constant_data": ["x"]} + fails1 = check_multiple_attrs(test_dict, idata1) + assert not fails1 + fails2 = check_multiple_attrs(test_dict, idata2) + assert not fails2 + assert "dim1" in list(idata1.posterior.beta.dims) + assert "dim2" in list(idata2.posterior.beta.dims) + assert np.all(idata1.constant_data.x.dim1.values == np.array(dim1)) + assert np.all(idata1.constant_data.x.dim2.values == np.array(["c1", "c2"])) + assert np.all(idata2.constant_data.x.dim1.values == np.array(new_dim1)) + assert np.all(idata2.constant_data.x.dim2.values == np.array(["c1", "c2"])) + def test_missing_data_model(self): # source pymc3/pymc3/tests/test_missing.py data = ma.masked_values([1, 2, -1, 4, -1], value=-1) From 6971e71e2ece3f1e84297abc00616a04ea4f550a Mon Sep 17 00:00:00 2001 From: Oriol Abril-Pla Date: Sun, 21 Jun 2020 01:31:29 +0200 Subject: [PATCH 18/24] updates for numpy 1.19 compatibility (#1256) * fix numpy error * update changelog --- CHANGELOG.md | 1 + arviz/numeric_utils.py | 2 +- 2 files changed, 2 insertions(+), 1 deletion(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index df4980d1a8..84c4dae722 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -17,6 +17,7 @@ * Skip tests on matplotlib animations if ffmpeg is not installed (#1227) * Fix hpd bug where arguments were being ignored (#1236) * Change the default `zorder` of scatter points from `0` to `0.6` in `plot_pair` (#1246) +* Update `get_bins` for numpy 1.19 compatibility (#1256) ### Deprecation * Using `from_pymc3` without a model context available now raises a diff --git a/arviz/numeric_utils.py b/arviz/numeric_utils.py index 757284b2ec..6731265a86 100644 --- a/arviz/numeric_utils.py +++ b/arviz/numeric_utils.py @@ -188,7 +188,7 @@ def get_bins(values): iqr = np.subtract(*np.percentile(values, [75, 25])) # pylint: disable=assignment-from-no-return bins_fd = 2 * iqr * values.size ** (-1 / 3) - width = round(np.max([1, bins_sturges, bins_fd])).astype(int) + width = np.round(np.max([1, bins_sturges, bins_fd])).astype(int) return np.arange(x_min, x_max + width + 1, width) From 965b971a1be87b56b7f12a9285d8f5048c45d00a Mon Sep 17 00:00:00 2001 From: Oriol Abril-Pla Date: Sun, 21 Jun 2020 01:59:50 +0200 Subject: [PATCH 19/24] fix and extend idata.map docs (#1255) * fix and extend idata.map docs * update changelog --- CHANGELOG.md | 1 + arviz/data/inference_data.py | 58 +++++++++++++++++++++++++++++------- 2 files changed, 49 insertions(+), 10 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 84c4dae722..721e5acc49 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -26,6 +26,7 @@ ### Documentation * A section has been added to the documentation at InferenceDataCookbook.ipynb illustrating the use of ArviZ in conjunction with PyJAGS. (#1219) * Fixed inconsistent capitalization in `plot_hdi` docstring (#1221) +* Fixed and extended `InferenceData.map` docs (#1255) ## v0.8.3 (2020 May 28) ### Maintenance and fixes diff --git a/arviz/data/inference_data.py b/arviz/data/inference_data.py index bf53a9a069..cf9f572f33 100644 --- a/arviz/data/inference_data.py +++ b/arviz/data/inference_data.py @@ -374,20 +374,28 @@ def _group_names(self, groups, filter_groups=None): def map(self, fun, groups=None, filter_groups=None, inplace=False, args=None, **kwargs): """Apply a function to multiple groups. + Applies ``fun`` groupwise to the selected ``InferenceData`` groups and overwrites the + group with the result of the function. + Parameters ---------- - fun: callable - Function to be applied to each group. - groups: str or list of str, optional + fun : callable + Function to be applied to each group. Assumes the function is called as + ``fun(dataset, *args, **kwargs)``. + groups : str or list of str, optional Groups where the selection is to be applied. Can either be group names or metagroup names. - inplace: bool, optional + filter_groups : {None, "like", "regex"}, optional + If `None` (default), interpret var_names as the real variables names. If "like", + interpret var_names as substrings of the real variables names. If "regex", + interpret var_names as regular expressions on the real variables names. A la + `pandas.filter`. + inplace : bool, optional If ``True``, modify the InferenceData object inplace, otherwise, return the modified copy. - args: array_like, optional - Positional arguments passed to ``fun``. Assumes the function is called as - ``fun(dataset, *args, **kwargs)``. - **kwargs: mapping, optional + args : array_like, optional + Positional arguments passed to ``fun``. + **kwargs : mapping, optional Keyword arguments passed to ``fun``. Returns @@ -404,10 +412,40 @@ def map(self, fun, groups=None, filter_groups=None, inplace=False, args=None, ** In [1]: import arviz as az ...: idata = az.load_arviz_data("non_centered_eight") - ...: idata_shifted_obs = idata.map(lambda x: x + 3, groups="observed_RVs") + ...: idata_shifted_obs = idata.map(lambda x: x + 3, groups="observed_vars") ...: print(idata_shifted_obs.observed_data) ...: print(idata_shifted_obs.posterior_predictive) + Rename and update the coordinate values in both posterior and prior groups. + + .. ipython:: + + In [1]: idata = az.load_arviz_data("radon") + ...: idata = idata.map( + ...: lambda ds: ds.rename({"gamma_dim_0": "uranium_coefs"}).assign( + ...: uranium_coefs=["intercept", "u_slope", "xbar_slope"] + ...: ), + ...: groups=["posterior", "prior"] + ...: ) + ...: idata.posterior + + Add extra coordinates to all groups containing observed variables + + .. ipython:: + + In [1]: idata = az.load_arviz_data("rugby") + ...: home_team, away_team = np.array([ + ...: m.split() for m in idata.observed_data.match.values + ...: ]).T + ...: idata = idata.map( + ...: lambda ds, **kwargs: ds.assign_coords(**kwargs), + ...: groups="observed_vars", + ...: home_team=("match", home_team), + ...: away_team=("match", away_team), + ...: ) + ...: print(idata.posterior_predictive) + ...: print(idata.observed_data) + """ if args is None: args = [] @@ -455,7 +493,7 @@ def _wrap_xarray_method( In [1]: import arviz as az ...: idata = az.load_arviz_data("non_centered_eight") - ...: idata_means = idata._wrap_xarray_method("mean", groups="latent_RVs") + ...: idata_means = idata._wrap_xarray_method("mean", groups="latent_vars") ...: print(idata_means.posterior) ...: print(idata_means.observed_data) From ac0f76ea35bdd88ae1a31204c5a112181f1749ad Mon Sep 17 00:00:00 2001 From: Ari Hartikainen Date: Mon, 22 Jun 2020 19:46:15 +0300 Subject: [PATCH 20/24] ci/upload_wheel_twine (#1258) upload to pypi with twine Co-authored-by: ahartikainen --- .azure-pipelines/azure-pipelines-wheel.yml | 14 ++++++++++---- 1 file changed, 10 insertions(+), 4 deletions(-) diff --git a/.azure-pipelines/azure-pipelines-wheel.yml b/.azure-pipelines/azure-pipelines-wheel.yml index 1ecf9a5405..f62ecb158e 100644 --- a/.azure-pipelines/azure-pipelines-wheel.yml +++ b/.azure-pipelines/azure-pipelines-wheel.yml @@ -34,7 +34,8 @@ jobs: - script: | python -m pip install --upgrade pip python -m pip install --no-cache-dir -r requirements.txt - pip install wheel + python -m pip install wheel + python -m pip install twine displayName: 'Install requirements' - script: | @@ -43,9 +44,10 @@ jobs: displayName: 'Build a wheel' - script: | - cd dist - ls -lh - ls | grep *.whl | xargs python -m pip install + mkdir install_test + cd install_test + ls -lh ../dist + python -m pip install ../dist/*.whl python -c "import arviz as az; print(az);print(az.summary(az.load_arviz_data('non_centered_eight')))" cd .. displayName: 'Install and test the wheel' @@ -60,3 +62,7 @@ jobs: pathtoPublish: 'dist' artifactName: 'arviz_wheel_dist' displayName: 'Publish the wheel' + + - script: | + python -m twine upload -u __token__ -p $(PYPI_PASSWORD) --skip-existing dist/* + displayName: 'Upload wheel to PyPI' From 32be6fc20cdfe5804956e36a18e93e870bedb63a Mon Sep 17 00:00:00 2001 From: Michael Nowotny Date: Mon, 22 Jun 2020 09:48:54 -0700 Subject: [PATCH 21/24] Added functionality to incorporate the log-likelihood computed by JAGS into ArviZ (#1245) * - added conversion of PyJAGS samples dictionaries to ArviZ inference data object * - removed copyright notice - reformatted code * - added example to InferenceDataCookbook.ipynb * - added link to PyJAGS example * - added a description of the new feature to CHANGELOG.md * - addressed some of the pylint warnings * - added pylint: disable=redefined-outer-name * - removed unused variables * - reworked docstrings * - continued reworking docstrings * - removed _convert_pyjags_samples_dict_to_arviz_inference_data - removed dependence on az.convert_to_inference_data * - refactored from_pyjags to use a PyJAGSConverter - added support for prior distribution * - catching case where pyjags is not installed * - addressed some of the pylint warnings * - changed import of collections.Sequence to collections.abc.Sequence * - removed unused variable * - changed exception type * - removed unused code - changed order of imports * - changed order of imports * - added docstrings for _split_pyjags_samples_in_warmup_and_actual_samples and get_draws - removed unused code * - added a docstring to io_pyjags.py * - updated InferenceDataCookbook.ipynb * - reformatted code using black * - reformatted code using black with line-length 90 * - reformatted code using black with line-length 100 * - incorporated code review feedback into changelog - changed Dict to Mapping in type hints * - changed Dict to Mapping in type hints * - shortened variable and function names to enable black to fit everything in 100 characters * Update CHANGELOG.md Co-authored-by: Oriol Abril * Update CHANGELOG.md Co-authored-by: Oriol Abril * - added pyjags to requirements-external.txt * - added pyjags to api.rst * - incorporated OriolAbril's feedback from the code review regarding InferenceDataCookbook.ipynb * - removing pyjags from requirements-external.txt * - renamed test function to test_roundtrip_from_pyjags_via_arviz_to_pyjags * - added test to check whether idata contains all attrs * - refactored tests * - fixed a typo * - added jags installation to devops pipeline * - switched to using sudo to install jags * - replaced hard coded NumPy arrays in pyjags_samples_dict with random numbers * - removed try except block around import of pyjags - removed assert to type check for tuple * - reduced type check to iterable * Update arviz/data/io_pyjags.py Co-authored-by: Oriol Abril * - removed fixtures from tests * - added more parameter combinations to tests * - removed unused code * - fixed pylint issues * - fixed line-too-long issue * - fixed indentation issue in docstring of from_pyjags * Update arviz/tests/external_tests/test_data_pyjags.py Co-authored-by: Oriol Abril * - incorporated some suggestions from code review * - fixed warmup prefix * - added warmup prior * - used black * Update arviz/data/io_pyjags.py Co-authored-by: Oriol Abril * Update doc/api.rst Co-authored-by: Oriol Abril * - added a way to incorporate the log-likelihood from jags * - added info about log-likelihood to CHANGELOG.md * - added test for the log likelihood via WAIC * - removed development code from InferenceDataCookbook.ipynb * - made changes to pass ci checks * - fixed automatic refactoring error from pycharm * - revised CHANGELOG.md - revised docstring in NumPy style * - used eight school data from helpers module in tests * - changed import style in test_data_pyjags.py * - moved model strings into the functions where they are used * - assigned models to variables before returning in test_data_pyjags.py * - disabled pylint checking for unused imports * - added functionality to support the log-likelihood associated with several data variables at the same time * - added check for the presence of the log likelihood group via check_multiple_attrs * - reformatted using black * - added TestDataPyJAGS class * - added all tests to class * - revised fixture scope to class level * - removing test for waic * - changed Stan to JAGS in docstring for from_pyjags Co-authored-by: Oriol Abril --- CHANGELOG.md | 4 +- arviz/data/io_pyjags.py | 58 +- .../tests/external_tests/test_data_pyjags.py | 271 ++- doc/notebooks/InferenceDataCookbook.ipynb | 1509 +++++++++++++---- 4 files changed, 1401 insertions(+), 441 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 721e5acc49..c1c1168b79 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -5,7 +5,7 @@ ### New features * loo-pit plot. The kde is computed over the data interval (this could be shorter than [0, 1]). The HDI is computed analitically (#1215) * Added `html_repr` of InferenceData objects for jupyter notebooks. (#1217) -* Added support for PyJAGS via the function `from_pyjags` in the module arviz.data.io_pyjags. (#1219) +* Added support for PyJAGS via the function `from_pyjags`. (#1219 and #1245) * `from_pymc3` can now retrieve `coords` and `dims` from model context (#1228, #1240 and #1249) ### Maintenance and fixes @@ -24,7 +24,7 @@ `FutureWarning` and will be deprecated in a future version (#1227) ### Documentation -* A section has been added to the documentation at InferenceDataCookbook.ipynb illustrating the use of ArviZ in conjunction with PyJAGS. (#1219) +* A section has been added to the documentation at InferenceDataCookbook.ipynb illustrating the use of ArviZ in conjunction with PyJAGS. (#1219 and #1245) * Fixed inconsistent capitalization in `plot_hdi` docstring (#1221) * Fixed and extended `InferenceData.map` docs (#1255) diff --git a/arviz/data/io_pyjags.py b/arviz/data/io_pyjags.py index 88c3575364..dfd1aebaf2 100644 --- a/arviz/data/io_pyjags.py +++ b/arviz/data/io_pyjags.py @@ -18,14 +18,29 @@ class PyJAGSConverter: def __init__( self, *, - posterior: tp.Optional[tp.Mapping[str, np.ndarray]] = None, + posterior: tp.Optional[tp.Dict[str, np.ndarray]] = None, prior: tp.Optional[tp.Mapping[str, np.ndarray]] = None, + log_likelihood: tp.Optional[tp.Dict[str, str]] = None, coords=None, dims=None, save_warmup: bool = None, warmup_iterations: int = 0 ): - self.posterior = posterior + if log_likelihood is not None and posterior is not None: + self.posterior = posterior.copy() # create a shallow copy of the dictionary + + if isinstance(log_likelihood, str): + log_likelihood = [log_likelihood] + if isinstance(log_likelihood, (list, tuple)): + log_likelihood = {name: name for name in log_likelihood} + + self.log_likelihood = { + obs_var_name: self.posterior.pop(log_like_name) + for obs_var_name, log_like_name in log_likelihood.items() + } + else: + self.posterior = posterior + self.log_likelihood = None self.prior = prior self.coords = coords self.dims = dims @@ -60,6 +75,11 @@ def prior_to_xarray(self) -> tp.Tuple[xarray.Dataset, xarray.Dataset]: """Extract posterior samples from fit.""" return self._pyjags_samples_to_xarray(self.prior) + @requires("log_likelihood") + def log_likelihood_to_xarray(self) -> tp.Tuple[xarray.Dataset, xarray.Dataset]: + """Extract log likelihood samples from fit.""" + return self._pyjags_samples_to_xarray(self.log_likelihood) + def to_inference_data(self): """Convert all available data to an InferenceData object.""" # obs_const_dict = self.observed_and_constant_data_to_xarray() @@ -70,6 +90,7 @@ def to_inference_data(self): idata_dict = { "posterior": self.posterior_to_xarray(), "prior": self.prior_to_xarray(), + "log_likelihood": self.log_likelihood_to_xarray(), "save_warmup": save_warmup, } @@ -243,8 +264,9 @@ def _convert_arviz_dict_to_pyjags_dict( Parameters ---------- - samples: a dictionary mapping variable names to NumPy arrays with shape - (number_of_chains, chain_length, parameter_dimension) + samples: dict of {str : array_like} + a dictionary mapping variable names to NumPy arrays with shape + (number_of_chains, chain_length, parameter_dimension) Returns ------- @@ -272,6 +294,7 @@ def _convert_arviz_dict_to_pyjags_dict( def from_pyjags( posterior: tp.Optional[tp.Mapping[str, np.ndarray]] = None, prior: tp.Optional[tp.Mapping[str, np.ndarray]] = None, + log_likelihood: tp.Optional[tp.Dict[str, str]] = None, coords=None, dims=None, save_warmup=None, @@ -288,24 +311,32 @@ def from_pyjags( Parameters ---------- posterior: dict of {str : array_like}, optional - a dictionary mapping variable names to NumPy arrays containing - posterior samples with shape - (parameter_dimension, chain_length, number_of_chains) + a dictionary mapping variable names to NumPy arrays containing + posterior samples with shape + (parameter_dimension, chain_length, number_of_chains) prior: dict of {str : array_like}, optional - a dictionary mapping variable names to NumPy arrays containing - prior samples with shape - (parameter_dimension, chain_length, number_of_chains) + a dictionary mapping variable names to NumPy arrays containing + prior samples with shape + (parameter_dimension, chain_length, number_of_chains) + + log_likelihood: dict of {str: str}, list of str or str, optional + Pointwise log_likelihood for the data. log_likelihood is extracted from the + posterior. It is recommended to use this argument as a dictionary whose keys + are observed variable names and its values are the variables storing log + likelihood arrays in the JAGS code. In other cases, a dictionary with keys + equal to its values is used. coords: dict[str, iterable] - A dictionary containing the values that are used as index. The key - is the name of the dimension, the values are the index values. + A dictionary containing the values that are used as index. The key + is the name of the dimension, the values are the index values. dims: dict[str, List(str)] - A mapping from variables to a list of coordinate names for the variable. + A mapping from variables to a list of coordinate names for the variable. save_warmup : bool, optional Save warmup iterations in InferenceData. If not defined, use default defined by the rcParams. + warmup_iterations: int, optional Number of warmup iterations @@ -316,6 +347,7 @@ def from_pyjags( return PyJAGSConverter( posterior=posterior, prior=prior, + log_likelihood=log_likelihood, dims=dims, coords=coords, save_warmup=save_warmup, diff --git a/arviz/tests/external_tests/test_data_pyjags.py b/arviz/tests/external_tests/test_data_pyjags.py index 4f6864d584..93ef9e8c22 100644 --- a/arviz/tests/external_tests/test_data_pyjags.py +++ b/arviz/tests/external_tests/test_data_pyjags.py @@ -1,21 +1,35 @@ +# pylint: disable=no-member, invalid-name, redefined-outer-name, unused-import import typing as tp import numpy as np import pytest +import pyjags + +from arviz import from_pyjags, InferenceData, waic from arviz.data.io_pyjags import ( _convert_pyjags_dict_to_arviz_dict, _convert_arviz_dict_to_pyjags_dict, _extract_arviz_dict_from_inference_data, - from_pyjags, ) -from arviz.tests.helpers import check_multiple_attrs +from arviz.tests.helpers import check_multiple_attrs, eight_schools_params -PYJAGS_POSTERIOR_DICT = {"b": np.random.randn(3, 10, 3), "int": np.random.randn(1, 10, 3)} +PYJAGS_POSTERIOR_DICT = { + "b": np.random.randn(3, 10, 3), + "int": np.random.randn(1, 10, 3), + "log_like": np.random.randn(1, 10, 3), +} PYJAGS_PRIOR_DICT = {"b": np.random.randn(3, 10, 3), "int": np.random.randn(1, 10, 3)} +PARAMETERS = ("mu", "tau", "theta_tilde") +VARIABLES = tuple(list(PARAMETERS) + ["log_like"]) + +NUMBER_OF_WARMUP_SAMPLES = 1000 +NUMBER_OF_POST_WARMUP_SAMPLES = 5000 + + def verify_equality_of_numpy_values_dictionaries( dict_1: tp.Mapping[tp.Any, np.ndarray], dict_2: tp.Mapping[tp.Any, np.ndarray] ) -> bool: @@ -29,73 +43,184 @@ def verify_equality_of_numpy_values_dictionaries( return True -def test_convert_pyjags_samples_dictionary_to_arviz_samples_dictionary(): - arviz_samples_dict_from_pyjags_samples_dict = _convert_pyjags_dict_to_arviz_dict( - PYJAGS_POSTERIOR_DICT - ) - - pyjags_dict_from_arviz_dict_from_pyjags_dict = _convert_arviz_dict_to_pyjags_dict( - arviz_samples_dict_from_pyjags_samples_dict - ) - - assert verify_equality_of_numpy_values_dictionaries( - PYJAGS_POSTERIOR_DICT, pyjags_dict_from_arviz_dict_from_pyjags_dict, - ) - - -def test_extract_samples_dictionary_from_arviz_inference_data(): - arviz_samples_dict_from_pyjags_samples_dict = _convert_pyjags_dict_to_arviz_dict( - PYJAGS_POSTERIOR_DICT - ) - - arviz_inference_data_from_pyjags_samples_dict = from_pyjags(PYJAGS_POSTERIOR_DICT) - arviz_dict_from_idata_from_pyjags_dict = _extract_arviz_dict_from_inference_data( - arviz_inference_data_from_pyjags_samples_dict - ) - - assert verify_equality_of_numpy_values_dictionaries( - arviz_samples_dict_from_pyjags_samples_dict, arviz_dict_from_idata_from_pyjags_dict, - ) - - -def test_roundtrip_from_pyjags_via_arviz_to_pyjags(): - arviz_inference_data_from_pyjags_samples_dict = from_pyjags(PYJAGS_POSTERIOR_DICT) - arviz_dict_from_idata_from_pyjags_dict = _extract_arviz_dict_from_inference_data( - arviz_inference_data_from_pyjags_samples_dict - ) - - pyjags_dict_from_arviz_idata = _convert_arviz_dict_to_pyjags_dict( - arviz_dict_from_idata_from_pyjags_dict - ) - - assert verify_equality_of_numpy_values_dictionaries( - PYJAGS_POSTERIOR_DICT, pyjags_dict_from_arviz_idata - ) - - -@pytest.mark.parametrize("posterior", [None, PYJAGS_POSTERIOR_DICT]) -@pytest.mark.parametrize("prior", [None, PYJAGS_PRIOR_DICT]) -@pytest.mark.parametrize("save_warmup", [True, False]) -@pytest.mark.parametrize("warmup_iterations", [0, 5]) -def test_inference_data_attrs(posterior, prior, save_warmup, warmup_iterations: int): - arviz_inference_data_from_pyjags_samples_dict = from_pyjags( - posterior=posterior, - prior=prior, - save_warmup=save_warmup, - warmup_iterations=warmup_iterations, - ) - posterior_warmup_prefix = ( - "" if save_warmup and warmup_iterations > 0 and posterior is not None else "~" - ) - prior_warmup_prefix = "" if save_warmup and warmup_iterations > 0 and prior is not None else "~" - print(f'posterior_warmup_prefix="{posterior_warmup_prefix}"') - test_dict = { - f'{"~" if posterior is None else ""}posterior': ["b", "int"], - f'{"~" if prior is None else ""}prior': ["b", "int"], - f"{posterior_warmup_prefix}warmup_posterior": ["b", "int"], - f"{prior_warmup_prefix}warmup_prior": ["b", "int"], - } - - fails = check_multiple_attrs(test_dict, arviz_inference_data_from_pyjags_samples_dict) - print(fails) - assert not fails +class TestDataPyJAGSWithoutEstimation: + def test_convert_pyjags_samples_dictionary_to_arviz_samples_dictionary(self): + arviz_samples_dict_from_pyjags_samples_dict = _convert_pyjags_dict_to_arviz_dict( + PYJAGS_POSTERIOR_DICT + ) + + pyjags_dict_from_arviz_dict_from_pyjags_dict = _convert_arviz_dict_to_pyjags_dict( + arviz_samples_dict_from_pyjags_samples_dict + ) + + assert verify_equality_of_numpy_values_dictionaries( + PYJAGS_POSTERIOR_DICT, pyjags_dict_from_arviz_dict_from_pyjags_dict, + ) + + def test_extract_samples_dictionary_from_arviz_inference_data(self): + arviz_samples_dict_from_pyjags_samples_dict = _convert_pyjags_dict_to_arviz_dict( + PYJAGS_POSTERIOR_DICT + ) + + arviz_inference_data_from_pyjags_samples_dict = from_pyjags(PYJAGS_POSTERIOR_DICT) + arviz_dict_from_idata_from_pyjags_dict = _extract_arviz_dict_from_inference_data( + arviz_inference_data_from_pyjags_samples_dict + ) + + assert verify_equality_of_numpy_values_dictionaries( + arviz_samples_dict_from_pyjags_samples_dict, arviz_dict_from_idata_from_pyjags_dict, + ) + + def test_roundtrip_from_pyjags_via_arviz_to_pyjags(self): + arviz_inference_data_from_pyjags_samples_dict = from_pyjags(PYJAGS_POSTERIOR_DICT) + arviz_dict_from_idata_from_pyjags_dict = _extract_arviz_dict_from_inference_data( + arviz_inference_data_from_pyjags_samples_dict + ) + + pyjags_dict_from_arviz_idata = _convert_arviz_dict_to_pyjags_dict( + arviz_dict_from_idata_from_pyjags_dict + ) + + assert verify_equality_of_numpy_values_dictionaries( + PYJAGS_POSTERIOR_DICT, pyjags_dict_from_arviz_idata + ) + + @pytest.mark.parametrize("posterior", [None, PYJAGS_POSTERIOR_DICT]) + @pytest.mark.parametrize("prior", [None, PYJAGS_PRIOR_DICT]) + @pytest.mark.parametrize("save_warmup", [True, False]) + @pytest.mark.parametrize("warmup_iterations", [0, 5]) + def test_inference_data_attrs(self, posterior, prior, save_warmup, warmup_iterations: int): + arviz_inference_data_from_pyjags_samples_dict = from_pyjags( + posterior=posterior, + prior=prior, + log_likelihood={"y": "log_like"}, + save_warmup=save_warmup, + warmup_iterations=warmup_iterations, + ) + posterior_warmup_prefix = ( + "" if save_warmup and warmup_iterations > 0 and posterior is not None else "~" + ) + prior_warmup_prefix = ( + "" if save_warmup and warmup_iterations > 0 and prior is not None else "~" + ) + print(f'posterior_warmup_prefix="{posterior_warmup_prefix}"') + test_dict = { + f'{"~" if posterior is None else ""}posterior': ["b", "int"], + f'{"~" if prior is None else ""}prior': ["b", "int"], + f'{"~" if posterior is None else ""}log_likelihood': ["y"], + f"{posterior_warmup_prefix}warmup_posterior": ["b", "int"], + f"{prior_warmup_prefix}warmup_prior": ["b", "int"], + f"{posterior_warmup_prefix}warmup_log_likelihood": ["y"], + } + + fails = check_multiple_attrs(test_dict, arviz_inference_data_from_pyjags_samples_dict) + assert not fails + + +# class TestDataPyJAGSWithEstimation: +# @pytest.fixture(scope="class") +# def jags_prior_model(self) -> pyjags.Model: +# EIGHT_SCHOOL_PRIOR_MODEL_CODE = """ +# model { +# mu ~ dnorm(0.0, 1.0/25) +# tau ~ dt(0.0, 1.0/25, 1.0) T(0, ) +# for (j in 1:J) { +# theta_tilde[j] ~ dnorm(0.0, 1.0) +# } +# } +# """ +# +# prior_model = pyjags.Model( +# code=EIGHT_SCHOOL_PRIOR_MODEL_CODE, +# data={"J": 8}, +# chains=4, +# threads=4, +# chains_per_thread=1, +# ) +# +# return prior_model +# +# @pytest.fixture(scope="class") +# def jags_posterior_model( +# self, eight_schools_params: tp.Dict[str, tp.Union[int, np.ndarray]] +# ) -> pyjags.Model: +# EIGHT_SCHOOL_POSTERIOR_MODEL_CODE = """ +# model { +# mu ~ dnorm(0.0, 1.0/25) +# tau ~ dt(0.0, 1.0/25, 1.0) T(0, ) +# for (j in 1:J) { +# theta_tilde[j] ~ dnorm(0.0, 1.0) +# y[j] ~ dnorm(mu + tau * theta_tilde[j], 1.0/(sigma[j]^2)) +# log_like[j] = logdensity.norm(y[j], mu + tau * theta_tilde[j], 1.0/(sigma[j]^2)) +# } +# } +# """ +# +# posterior_model = pyjags.Model( +# code=EIGHT_SCHOOL_POSTERIOR_MODEL_CODE, +# data=eight_schools_params, +# chains=4, +# threads=4, +# chains_per_thread=1, +# ) +# +# return posterior_model +# +# @pytest.fixture(scope="class") +# def jags_prior_samples(self, jags_prior_model: pyjags.Model) -> tp.Dict[str, np.ndarray]: +# return jags_prior_model.sample( +# NUMBER_OF_WARMUP_SAMPLES + NUMBER_OF_POST_WARMUP_SAMPLES, vars=PARAMETERS +# ) +# +# @pytest.fixture(scope="class") +# def jags_posterior_samples( +# self, jags_posterior_model: pyjags.Model +# ) -> tp.Dict[str, np.ndarray]: +# return jags_posterior_model.sample( +# NUMBER_OF_WARMUP_SAMPLES + NUMBER_OF_POST_WARMUP_SAMPLES, vars=VARIABLES +# ) +# +# @pytest.fixture() +# def pyjags_data( +# self, +# jags_prior_samples: tp.Dict[str, np.ndarray], +# jags_posterior_samples: tp.Dict[str, np.ndarray], +# ) -> InferenceData: +# return from_pyjags( +# posterior=jags_posterior_samples, +# prior=jags_prior_samples, +# log_likelihood={"y": "log_like"}, +# save_warmup=True, +# warmup_iterations=NUMBER_OF_WARMUP_SAMPLES, +# ) +# +# def test_waic(self, pyjags_data): +# waic_result = waic(pyjags_data) +# +# assert -31.0 < waic_result.waic < -30.0 +# assert 0.75 < waic_result.p_waic < 0.90 +# +# # @pytest.fixture(scope="class") +# # def data(self, jags_posterior_model, jags_posterior_samples, jags_prior_samples): +# # class Data: +# # model = self.jags_posterior_model +# # posterior = self.jags_posterior_samples +# # prior = self.jags_prior_samples +# # +# # return Data +# # +# # def get_inference_data(self, data) -> InferenceData: +# # return from_pyjags( +# # posterior=data.posterior, +# # prior=data.prior, +# # log_likelihood={"y": "log_like"}, +# # save_warmup=True, +# # warmup_iterations=NUMBER_OF_WARMUP_SAMPLES, +# # ) +# # +# # def test_waic(self, data): +# # pyjags_data = self.get_inference_data(data) +# # waic_result = waic(pyjags_data) +# # +# # assert -31.0 < waic_result.waic < -30.0 +# # assert 0.75 < waic_result.p_waic < 0.90 diff --git a/doc/notebooks/InferenceDataCookbook.ipynb b/doc/notebooks/InferenceDataCookbook.ipynb index 94444a3764..5993eb05dc 100644 --- a/doc/notebooks/InferenceDataCookbook.ipynb +++ b/doc/notebooks/InferenceDataCookbook.ipynb @@ -42,11 +42,24 @@ "execution_count": 1, "metadata": { "ExecuteTime": { - "end_time": "2020-06-05T06:47:07.825895Z", - "start_time": "2020-06-05T06:47:05.567299Z" + "end_time": "2020-06-19T06:23:33.334551Z", + "start_time": "2020-06-19T06:23:32.169645Z" } }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "Bad key \"text.kerning_factor\" on line 4 in\n", + "/Users/michaelnowotny/anaconda3/envs/continuous_time_mcmc/lib/python3.7/site-packages/matplotlib/mpl-data/stylelib/_classic_test_patch.mplstyle.\n", + "You probably need to get an updated matplotlibrc file from\n", + "https://github.com/matplotlib/matplotlib/blob/v3.1.3/matplotlibrc.template\n", + "or from the matplotlib source distribution\n" + ] + } + ], "source": [ "import arviz as az\n", "import numpy as np" @@ -3879,11 +3892,11 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 2, "metadata": { "ExecuteTime": { - "end_time": "2020-06-05T06:47:09.286020Z", - "start_time": "2020-06-05T06:47:07.961226Z" + "end_time": "2020-06-19T06:23:42.715900Z", + "start_time": "2020-06-19T06:23:41.532565Z" } }, "outputs": [], @@ -26190,22 +26203,14 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 3, "metadata": { "ExecuteTime": { - "end_time": "2020-06-05T06:48:59.842990Z", - "start_time": "2020-06-05T06:48:59.556800Z" + "end_time": "2020-06-19T06:23:50.353834Z", + "start_time": "2020-06-19T06:23:50.196427Z" } }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:numexpr.utils:NumExpr defaulting to 4 threads.\n" - ] - } - ], + "outputs": [], "source": [ "import pyjags" ] @@ -26226,11 +26231,11 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 4, "metadata": { "ExecuteTime": { - "end_time": "2020-06-05T06:49:04.048517Z", - "start_time": "2020-06-05T06:49:04.045625Z" + "end_time": "2020-06-19T06:23:50.982666Z", + "start_time": "2020-06-19T06:23:50.979758Z" } }, "outputs": [], @@ -26239,8 +26244,8 @@ "model {\n", " mu ~ dnorm(0.0, 1.0/25)\n", " tau ~ dt(0.0, 1.0/25, 1.0) T(0, )\n", - " for (i in 1:J) {\n", - " theta_tilde[i] ~ dnorm(0.0, 1.0)\n", + " for (j in 1:J) {\n", + " theta_tilde[j] ~ dnorm(0.0, 1.0)\n", " }\n", "}\n", "'''" @@ -26255,11 +26260,11 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 5, "metadata": { "ExecuteTime": { - "end_time": "2020-06-05T06:49:05.084881Z", - "start_time": "2020-06-05T06:49:05.082448Z" + "end_time": "2020-06-19T06:23:51.265216Z", + "start_time": "2020-06-19T06:23:51.262860Z" } }, "outputs": [], @@ -26268,9 +26273,10 @@ "model {\n", " mu ~ dnorm(0.0, 1.0/25)\n", " tau ~ dt(0.0, 1.0/25, 1.0) T(0, )\n", - " for (i in 1:J) {\n", - " theta_tilde[i] ~ dnorm(0.0, 1.0)\n", - " y[i] ~ dnorm(mu + tau * theta_tilde[i], 1.0/(sigma[i]^2))\n", + " for (j in 1:J) {\n", + " theta_tilde[j] ~ dnorm(0.0, 1.0)\n", + " y[j] ~ dnorm(mu + tau * theta_tilde[j], 1.0/(sigma[j]^2))\n", + " log_like[j] = logdensity.norm(y[j], mu + tau * theta_tilde[j], 1.0/(sigma[j]^2))\n", " }\n", "}\n", "'''" @@ -26278,16 +26284,17 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 6, "metadata": { "ExecuteTime": { - "end_time": "2020-06-05T06:49:05.479290Z", - "start_time": "2020-06-05T06:49:05.476663Z" + "end_time": "2020-06-19T06:23:51.407328Z", + "start_time": "2020-06-19T06:23:51.404376Z" } }, "outputs": [], "source": [ - "parameters = ['mu', 'tau', 'theta_tilde']" + "parameters = ['mu', 'tau', 'theta_tilde']\n", + "variables = parameters + ['log_like']" ] }, { @@ -26306,25 +26313,14 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 7, "metadata": { "ExecuteTime": { - "end_time": "2020-06-05T06:49:06.671164Z", - "start_time": "2020-06-05T06:49:06.636850Z" + "end_time": "2020-06-19T06:23:51.842644Z", + "start_time": "2020-06-19T06:23:51.830153Z" } }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:pyjags:Using JAGS library located in /usr/local/opt/jags/lib/libjags.4.dylib.\n", - "INFO:pyjags:Loading module basemod from /usr/local/opt/jags/lib/JAGS/modules-4/basemod.so\n", - "INFO:pyjags:Loading module bugs from /usr/local/opt/jags/lib/JAGS/modules-4/bugs.so\n", - "INFO:pyjags:Loading module lecuyer from /usr/local/opt/jags/lib/JAGS/modules-4/lecuyer.so\n" - ] - } - ], + "outputs": [], "source": [ "jags_prior_model = pyjags.Model(\n", " code=eight_school_prior_model_code, \n", @@ -26344,11 +26340,11 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 8, "metadata": { "ExecuteTime": { - "end_time": "2020-06-05T06:49:07.305091Z", - "start_time": "2020-06-05T06:49:07.286068Z" + "end_time": "2020-06-19T06:23:52.134872Z", + "start_time": "2020-06-19T06:23:52.114138Z" } }, "outputs": [ @@ -26379,11 +26375,11 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 9, "metadata": { "ExecuteTime": { - "end_time": "2020-06-05T06:49:07.978300Z", - "start_time": "2020-06-05T06:49:07.909931Z" + "end_time": "2020-06-19T06:23:52.521467Z", + "start_time": "2020-06-19T06:23:52.429977Z" } }, "outputs": [ @@ -26398,7 +26394,7 @@ ], "source": [ "jags_prior_samples = jags_prior_model.sample(5000 + 1000, vars=parameters)\n", - "jags_posterior_samples = jags_posterior_model.sample(5000 + 1000, vars=parameters)" + "jags_posterior_samples = jags_posterior_model.sample(5000 + 1000, vars=variables)" ] }, { @@ -26410,11 +26406,11 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 10, "metadata": { "ExecuteTime": { - "end_time": "2020-06-05T06:49:08.360698Z", - "start_time": "2020-06-05T06:49:08.347208Z" + "end_time": "2020-06-19T06:23:52.881918Z", + "start_time": "2020-06-19T06:23:52.827507Z" } }, "outputs": [ @@ -26429,8 +26425,8 @@ "
      \n", " \n", "
    • \n", - " \n", - " \n", + " \n", + " \n", "
      \n", "
      \n", "
        \n", @@ -26768,82 +26764,82 @@ " stroke: currentColor;\n", " fill: currentColor;\n", "}\n", - "
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          • chain: 4
          • draw: 5000
          • theta_tilde_dim_0: 8
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          • theta_tilde_dim_0
            (theta_tilde_dim_0)
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            0 1 2 3 4 5 6 7
            array([0, 1, 2, 3, 4, 5, 6, 7])
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            3.686 6.34 3.007 ... 5.069 2.715
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-       "         5.39011842,  2.04936093],\n",
-       "       [ 4.32029197,  3.42744886,  2.10039658, ...,  5.47806714,\n",
-       "         7.83660175,  4.38431912],\n",
-       "       [ 4.54884918,  7.91611073,  6.6783754 , ...,  4.22305406,\n",
-       "         5.06931555,  2.71523558]])
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            float64
            4.816 3.03 5.383 ... 1.486 3.392
            array([[ 4.8161381 ,  3.02982026,  5.38322235, ...,  0.68937565,\n",
-       "         0.97141043,  0.23615998],\n",
-       "       [ 0.36589474,  0.26509498,  3.47731418, ...,  6.9308054 ,\n",
-       "         6.26683914,  2.28825279],\n",
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-       "        [ 0.21296357,  0.23295021,  1.10290316, ..., -0.85051446,\n",
-       "          0.26578266, -1.67933879],\n",
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                (theta_tilde_dim_0)
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                array([0, 1, 2, 3, 4, 5, 6, 7])
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        "        ...,\n",
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+       "        [ 1.39280334, -0.05262139, -0.27421297, ..., -0.39162294,\n",
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+       "          0.33346594,  0.03809172],\n",
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+       "          0.61285682,  1.02246266],\n",
        "        ...,\n",
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-       "         -0.10787403, -0.70509101],\n",
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-       "          3.15570231,  0.04216289]]])
            • created_at :
              2020-06-10T18:22:24.834026
              arviz_version :
              0.8.3
              inference_library :
              pyjags
              inference_library_version :
              1.3.6

        \n", + " [-0.10515652, 0.15723478, -0.10753087, ..., 1.23281426,\n", + " -1.49638046, 0.80568695],\n", + " [ 0.42779301, 2.56133177, -0.03308635, ..., 0.87356664,\n", + " -0.69213149, 0.39322944],\n", + " [ 0.05093885, -0.10725331, 0.74691675, ..., -1.42317395,\n", + " -0.36136415, 0.27941795]]])
      • mu
        (chain, draw)
        float64
        9.566 8.862 -0.6542 ... 10.54 7.435
        array([[ 9.5660161 ,  8.86161166, -0.65416323, ...,  8.65878056,\n",
+       "         8.51434694,  4.75722624],\n",
+       "       [13.0860039 , 10.90145526,  0.11474836, ..., 11.68939865,\n",
+       "        -0.9937689 ,  4.37219633],\n",
+       "       [ 3.32283619,  6.94982458,  6.6868707 , ..., -0.16083202,\n",
+       "         1.94741381,  1.6089792 ],\n",
+       "       [ 5.79470397,  6.00639881,  4.22307168, ...,  4.3675311 ,\n",
+       "        10.54271823,  7.43480709]])
      • tau
        (chain, draw)
        float64
        0.4225 0.7076 ... 0.6132 0.3754
        array([[0.4225129 , 0.70764354, 4.11318626, ..., 1.06549024, 0.84353229,\n",
+       "        1.22666774],\n",
+       "       [5.6051199 , 1.60676596, 1.23003475, ..., 2.15813014, 3.75102582,\n",
+       "        4.20842298],\n",
+       "       [7.48864072, 8.89845878, 3.85519405, ..., 0.35845809, 0.80083886,\n",
+       "        4.63863181],\n",
+       "       [3.45441525, 0.77457384, 1.72308523, ..., 0.77028832, 0.6131635 ,\n",
+       "        0.37541366]])
    • created_at :
      2020-06-19T06:23:52.830658
      arviz_version :
      0.8.3
      inference_library :
      pyjags
      inference_library_version :
      1.3.5

    \n", " \n", " \n", " \n", " \n", "
  • \n", - " \n", - " \n", + " \n", + " \n", "
    \n", "
    \n", "
      \n", @@ -27181,82 +27177,68 @@ " stroke: currentColor;\n", " fill: currentColor;\n", "}\n", - "
      xarray.Dataset
        • chain: 4
        • draw: 5000
        • theta_tilde_dim_0: 8
        • chain
          (chain)
          int64
          0 1 2 3
          array([0, 1, 2, 3])
        • draw
          (draw)
          int64
          0 1 2 3 4 ... 4996 4997 4998 4999
          array([   0,    1,    2, ..., 4997, 4998, 4999])
        • theta_tilde_dim_0
          (theta_tilde_dim_0)
          int64
          0 1 2 3 4 5 6 7
          array([0, 1, 2, 3, 4, 5, 6, 7])
        • mu
          (chain, draw)
          float64
          -0.1267 7.682 ... 1.448 0.9685
          array([[-0.12672133,  7.68219852, -1.97358487, ...,  1.73388533,\n",
-       "         1.39680281,  6.02794732],\n",
-       "       [ 6.50232299, -5.15746654,  6.21248659, ..., -3.32469957,\n",
-       "         0.93128616, -1.23941544],\n",
-       "       [ 3.00378927, -0.07999032, -3.76134625, ..., -7.23938638,\n",
-       "         9.77863442, -4.66023246],\n",
-       "       [ 0.0132557 , -1.09448362, -4.47610478, ...,  4.77646126,\n",
-       "         1.44845474,  0.96852389]])
        • tau
          (chain, draw)
          float64
          0.8556 2.613 0.2007 ... 5.135 1.316
          array([[8.55557380e-01, 2.61325390e+00, 2.00706962e-01, ...,\n",
-       "        9.93806383e+00, 5.02217913e+00, 1.10470469e+00],\n",
-       "       [2.58777388e+00, 3.81948312e+00, 5.80792031e+00, ...,\n",
-       "        2.09946180e+01, 2.16078195e+00, 3.01620956e+00],\n",
-       "       [4.43126675e+02, 4.84293007e-01, 1.17618542e+01, ...,\n",
-       "        1.94698267e+01, 9.41718978e-01, 3.96608718e+00],\n",
-       "       [1.48769070e+01, 3.26137220e+00, 7.03443712e-01, ...,\n",
-       "        1.93210865e+01, 5.13548322e+00, 1.31611254e+00]])
        • theta_tilde
          (chain, draw, theta_tilde_dim_0)
          float64
          0.85 -0.4306 ... 0.1777 0.7252
          array([[[ 0.85001805, -0.43055427, -0.992581  , ...,  0.77740128,\n",
-       "          2.222987  ,  0.59601663],\n",
-       "        [ 1.29845346,  1.20086289,  1.14282397, ...,  0.22418954,\n",
-       "         -0.16364138,  1.21487966],\n",
-       "        [-0.47712144, -1.13142752, -0.00317101, ..., -1.64477397,\n",
-       "          0.94425876,  1.62574895],\n",
+       "
          xarray.Dataset
            • chain: 4
            • draw: 5000
            • y_dim_0: 8
            • chain
              (chain)
              int64
              0 1 2 3
              array([0, 1, 2, 3])
            • draw
              (draw)
              int64
              0 1 2 3 4 ... 4996 4997 4998 4999
              array([   0,    1,    2, ..., 4997, 4998, 4999])
            • y_dim_0
              (y_dim_0)
              int64
              0 1 2 3 4 5 6 7
              array([0, 1, 2, 3, 4, 5, 6, 7])
            • y
              (chain, draw, y_dim_0)
              float64
              -4.413 -3.24 ... -3.794 -3.84
              array([[[-4.41295677, -3.2399018 , -4.0232753 , ..., -3.63912029,\n",
+       "         -3.52078213, -3.82640716],\n",
+       "        [-4.31509256, -3.22162344, -3.99068419, ..., -3.56748162,\n",
+       "         -3.55034381, -3.82286355],\n",
+       "        [-5.04891945, -3.70705532, -3.77625556, ..., -3.34864634,\n",
+       "         -4.55113302, -4.28479863],\n",
        "        ...,\n",
-       "        [-0.17028569,  2.31625015,  1.69421502, ...,  0.42742773,\n",
-       "          0.6838044 , -0.86794174],\n",
-       "        [-0.53313516, -0.85208113,  0.48247498, ...,  0.23488686,\n",
-       "          1.02479667,  0.11839418],\n",
-       "        [-0.30936194, -0.76166013, -0.58023488, ...,  1.24137143,\n",
-       "          0.30931171,  0.58287052]],\n",
-       "\n",
-       "       [[-0.03665365,  0.80088035, -0.20564908, ..., -0.3695867 ,\n",
-       "          0.15174434,  0.10842298],\n",
-       "        [-1.55249106, -0.41228653,  0.12732898, ..., -1.46123103,\n",
-       "         -0.52013961,  2.13236933],\n",
-       "        [ 1.17055946,  0.6963143 , -0.4054951 , ...,  0.55845874,\n",
-       "          1.26600816,  0.99431999],\n",
+       "        [-4.57549557, -3.23482517, -4.01823734, ..., -3.57741287,\n",
+       "         -3.585355  , -3.82076215],\n",
+       "        [-4.57247255, -3.23159539, -3.98052409, ..., -3.59250129,\n",
+       "         -3.72757614, -3.82801715],\n",
+       "        [-4.87983078, -3.3277593 , -3.79014798, ..., -3.32357386,\n",
+       "         -3.94179764, -3.86291249]],\n",
+       "\n",
+       "       [[-3.88340544, -3.22314713, -4.2701937 , ..., -4.37453804,\n",
+       "         -3.38603872, -3.83667629],\n",
+       "        [-4.221597  , -3.23390992, -3.97852171, ..., -3.74635412,\n",
+       "         -3.44994946, -3.8337397 ],\n",
+       "        [-5.34200815, -3.50911079, -3.70869184, ..., -3.31735259,\n",
+       "         -4.64941234, -4.07160487],\n",
        "        ...,\n",
-       "        [-0.04816829,  0.19211899,  0.20844174, ...,  0.38186367,\n",
-       "         -0.70184387,  1.10741913],\n",
-       "        [-0.47089392,  1.33012804, -0.09896402, ..., -0.34182696,\n",
-       "          1.31722919,  0.77817125],\n",
-       "        [ 0.23412348, -0.17537455,  2.43504146, ...,  0.55932023,\n",
-       "         -0.33664493,  0.61882814]],\n",
-       "\n",
-       "       [[ 0.42214178, -0.66877733, -1.79242895, ...,  2.73019476,\n",
-       "         -0.01053904,  1.34708029],\n",
-       "        [ 0.47547847,  0.69348415, -0.96538681, ..., -1.02924095,\n",
-       "          0.90705581,  0.93209906],\n",
-       "        [ 0.0845888 ,  0.4661006 ,  0.42880593, ..., -1.49614601,\n",
-       "          0.65231532, -0.29565493],\n",
+       "        [-4.08276392, -3.37737117, -4.06582723, ..., -3.95438653,\n",
+       "         -3.66981257, -3.8119491 ],\n",
+       "        [-5.32402199, -3.48507739, -3.73106848, ..., -3.45648859,\n",
+       "         -5.09537619, -4.1362768 ],\n",
+       "        [-4.62087894, -3.22357815, -4.21957583, ..., -3.40873634,\n",
+       "         -3.83677346, -4.04205096]],\n",
+       "\n",
+       "       [[-3.78352715, -3.40900932, -3.70158341, ..., -3.64521677,\n",
+       "         -3.37344317, -3.87679339],\n",
+       "        [-3.72078639, -3.22560172, -3.72425037, ..., -3.32683227,\n",
+       "         -3.22502685, -3.81327925],\n",
+       "        [-4.48565386, -3.2626736 , -3.79190448, ..., -3.38193609,\n",
+       "         -3.45890796, -3.82233402],\n",
        "        ...,\n",
-       "        [-0.88989373, -0.97698322,  0.41487417, ...,  0.30817321,\n",
-       "         -0.6626576 ,  0.01303761],\n",
-       "        [ 1.36250619,  0.41182355, -0.33354604, ...,  1.30921816,\n",
-       "          0.42547979, -0.13510322],\n",
-       "        [-0.46519877,  0.38209188, -1.0051383 , ...,  1.99218995,\n",
-       "         -0.28024503,  0.56597011]],\n",
-       "\n",
-       "       [[ 2.41992929,  0.76132799,  0.83651604, ...,  0.72399534,\n",
-       "          0.2841493 ,  1.60398163],\n",
-       "        [ 1.14957975, -1.5350671 , -0.34517745, ..., -1.58175186,\n",
-       "         -2.22128242, -1.06446519],\n",
-       "        [-1.07820432,  0.40097786,  1.96509446, ..., -1.94273998,\n",
-       "          0.03097899,  0.21164047],\n",
+       "        [-5.31929045, -3.53858749, -3.7128791 , ..., -3.32697111,\n",
+       "         -4.80227792, -4.04456905],\n",
+       "        [-5.16587742, -3.34020609, -3.77092501, ..., -3.31702605,\n",
+       "         -4.48076103, -3.95673557],\n",
+       "        [-4.50969454, -3.44164719, -3.71327649, ..., -3.32285998,\n",
+       "         -4.13955032, -4.26052812]],\n",
+       "\n",
+       "       [[-5.01082765, -3.22183215, -3.79043025, ..., -3.34410517,\n",
+       "         -3.83240815, -3.86623926],\n",
+       "        [-4.69585506, -3.23209846, -3.81836553, ..., -3.41064335,\n",
+       "         -3.77885422, -3.88800986],\n",
+       "        [-4.79397359, -3.34960285, -3.72735417, ..., -3.389163  ,\n",
+       "         -4.03063314, -3.86514653],\n",
        "        ...,\n",
-       "        [ 0.84899743,  0.28618045, -1.20176965, ..., -1.28257703,\n",
-       "         -1.59253642, -0.02553479],\n",
-       "        [ 1.22452402,  1.31477276,  0.98583838, ...,  1.56005163,\n",
-       "          0.65692115, -1.60407853],\n",
-       "        [ 0.71122525, -0.02011507, -1.9162024 , ...,  2.14820149,\n",
-       "          0.17769618,  0.72521983]]])
          • created_at :
            2020-06-10T18:22:24.861493
            arviz_version :
            0.8.3
            inference_library :
            pyjags
            inference_library_version :
            1.3.6

      \n", + " [-4.87660795, -3.28317162, -3.79517349, ..., -3.39384957,\n", + " -4.3145215 , -3.88518398],\n", + " [-4.28402686, -3.30611707, -4.04866815, ..., -3.73655814,\n", + " -3.53212736, -3.8115928 ],\n", + " [-4.56507981, -3.22335652, -3.91577671, ..., -3.46070254,\n", + " -3.794065 , -3.84001127]]])
  • created_at :
    2020-06-19T06:23:52.839616
    arviz_version :
    0.8.3
    inference_library :
    pyjags
    inference_library_version :
    1.3.5

    • \n", " \n", " \n", " \n", " \n", "
  • \n", - " \n", - " \n", + " \n", + " \n", "
    \n", "
    \n", "
      \n", @@ -27594,82 +27576,82 @@ " stroke: currentColor;\n", " fill: currentColor;\n", "}\n", - "
      xarray.Dataset
        • chain: 4
        • draw: 1000
        • theta_tilde_dim_0: 8
        • chain
          (chain)
          int64
          0 1 2 3
          array([0, 1, 2, 3])
        • draw
          (draw)
          int64
          0 1 2 3 4 5 ... 995 996 997 998 999
          array([  0,   1,   2, ..., 997, 998, 999])
        • theta_tilde_dim_0
          (theta_tilde_dim_0)
          int64
          0 1 2 3 4 5 6 7
          array([0, 1, 2, 3, 4, 5, 6, 7])
        • mu
          (chain, draw)
          float64
          2.343 1.574 0.2489 ... 4.048 6.776
          array([[ 2.34314315,  1.57369394,  0.24894943, ...,  6.17063626,\n",
-       "         3.0878376 ,  1.42913203],\n",
-       "       [ 9.97570953, -2.02439703,  6.85342427, ...,  2.11825494,\n",
-       "         7.93677692,  3.01015461],\n",
-       "       [ 6.28388465,  3.36497001,  1.17872397, ...,  0.77454229,\n",
-       "        13.04498355, 10.71165114],\n",
-       "       [ 6.68361683,  4.39316419,  0.09360019, ...,  3.21668297,\n",
-       "         4.04756007,  6.77644344]])
        • tau
          (chain, draw)
          float64
          3.507 0.8696 0.846 ... 1.222 1.276
          array([[3.50737068, 0.86958818, 0.8459765 , ..., 1.77138973, 1.76771863,\n",
-       "        2.04190431],\n",
-       "       [2.11687365, 2.29746603, 1.8534264 , ..., 0.57292398, 1.52267673,\n",
-       "        3.42713623],\n",
-       "       [3.9999113 , 1.91387569, 0.23138222, ..., 8.20669842, 4.43777619,\n",
-       "        5.60506149],\n",
-       "       [1.00381947, 3.17057574, 1.01921169, ..., 6.35440979, 1.22182023,\n",
-       "        1.27604491]])
        • theta_tilde
          (chain, draw, theta_tilde_dim_0)
          float64
          -0.9909 0.4283 ... 0.3494 0.8214
          array([[[-0.99094065,  0.42833276,  0.39672956, ..., -2.09855064,\n",
-       "          0.18888032, -0.58986111],\n",
-       "        [-0.12188571,  0.69023944, -0.13418248, ..., -1.2406529 ,\n",
-       "          1.12609676,  0.37040659],\n",
-       "        [-0.99886906, -0.20982296,  0.67521031, ..., -0.1461499 ,\n",
-       "          0.83719731,  1.19747178],\n",
+       "
          xarray.Dataset
            • chain: 4
            • draw: 5000
            • theta_tilde_dim_0: 8
            • chain
              (chain)
              int64
              0 1 2 3
              array([0, 1, 2, 3])
            • draw
              (draw)
              int64
              0 1 2 3 4 ... 4996 4997 4998 4999
              array([   0,    1,    2, ..., 4997, 4998, 4999])
            • theta_tilde_dim_0
              (theta_tilde_dim_0)
              int64
              0 1 2 3 4 5 6 7
              array([0, 1, 2, 3, 4, 5, 6, 7])
            • theta_tilde
              (chain, draw, theta_tilde_dim_0)
              float64
              -1.889 1.346 ... -0.8655 0.5383
              array([[[-1.88942629,  1.34582443, -1.27439589, ...,  0.39141036,\n",
+       "          1.06839711,  1.00208791],\n",
+       "        [-1.92745639,  0.14500474,  0.10506899, ...,  0.51941011,\n",
+       "         -0.16956966, -2.20828958],\n",
+       "        [ 0.16596736, -0.94849957,  1.23799146, ..., -1.20251108,\n",
+       "         -0.23516297,  0.19975542],\n",
        "        ...,\n",
-       "        [-1.58235047, -0.87099162,  0.40640008, ..., -0.71311941,\n",
-       "          0.97375709,  0.30643473],\n",
-       "        [-0.67230905, -0.03087083, -0.13111032, ..., -0.97579779,\n",
-       "          0.51709929,  0.54002153],\n",
-       "        [ 0.21218175,  0.41734752,  0.79440517, ...,  0.13117306,\n",
-       "          0.06308795, -0.75895152]],\n",
-       "\n",
-       "       [[ 1.34666875, -0.09700182, -0.26156934, ...,  0.84290983,\n",
-       "         -0.32605667, -0.98721717],\n",
-       "        [ 0.60327275,  0.93426601,  1.71515555, ..., -1.58217704,\n",
-       "          1.38378973, -1.11093019],\n",
-       "        [ 0.24144096, -0.51100372,  0.80922452, ..., -1.86699144,\n",
-       "         -0.05736646,  0.63437633],\n",
+       "        [ 0.28867032,  0.21855779, -0.0472229 , ...,  0.34249931,\n",
+       "          0.22800237,  0.09277016],\n",
+       "        [-0.90562386,  0.22289785,  0.17417408, ..., -0.50959195,\n",
+       "         -1.13071736, -0.43731151],\n",
+       "        [-0.26447627,  0.9748397 ,  1.14235152, ...,  0.72159398,\n",
+       "          0.39438135,  1.10104895]],\n",
+       "\n",
+       "       [[ 0.85414368,  0.21015264, -0.01124105, ..., -3.06560738,\n",
+       "          0.61477986,  1.71560174],\n",
+       "        [-0.34456317, -0.94928476, -0.22545099, ...,  2.40872479,\n",
+       "          0.23290478, -0.35000991],\n",
+       "        [-0.61380659, -0.29968026, -1.71916073, ...,  0.43929196,\n",
+       "          0.37558874,  0.75680886],\n",
        "        ...,\n",
-       "        [ 0.39782333, -1.55883677,  1.19441409, ...,  1.05896027,\n",
-       "         -0.61763547,  0.78662345],\n",
-       "        [-1.27593793,  1.11010641,  0.82877233, ...,  0.17539794,\n",
-       "          0.2781045 , -0.74883458],\n",
-       "        [-0.61485105,  0.7463113 ,  0.82679819, ...,  0.24098771,\n",
-       "          0.94190029,  0.07068883]],\n",
-       "\n",
-       "       [[ 0.91844395, -0.22188411,  1.19853996, ...,  0.73105789,\n",
-       "          1.17609672,  1.0180705 ],\n",
-       "        [-0.63293758,  1.75756088,  1.29538997, ...,  1.25041335,\n",
-       "         -0.32904458, -0.431535  ],\n",
-       "        [ 2.14396635, -0.50527743,  0.20900194, ...,  0.76375069,\n",
-       "         -0.52035869, -0.14056728],\n",
+       "        [ 1.11230697,  0.08753353,  1.20319348, ..., -1.16203839,\n",
+       "          0.50188358,  1.24763116],\n",
+       "        [-2.24153687,  0.62771154, -0.69104447, ...,  0.0050251 ,\n",
+       "          0.03579746, -0.29728326],\n",
+       "        [-1.62260487,  0.63136263, -1.0231426 , ...,  0.00473683,\n",
+       "         -1.30076872,  1.19312088]],\n",
+       "\n",
+       "       [[-0.95586891,  2.07130758, -0.62581832, ..., -1.24888283,\n",
+       "         -0.63670725,  0.80585607],\n",
+       "        [ 0.98030091,  3.48510693, -0.09221556, ...,  0.2905206 ,\n",
+       "         -1.42276586, -1.63783879],\n",
+       "        [-0.52711995,  1.0616592 ,  0.08870877, ...,  0.0137179 ,\n",
+       "          1.09690801, -0.35646888],\n",
        "        ...,\n",
-       "        [ 0.99526292,  1.01386219, -0.88070335, ..., -1.46269942,\n",
-       "          1.5714287 , -0.85776922],\n",
-       "        [ 0.46092246,  0.27346652,  0.33645316, ..., -1.01721026,\n",
-       "         -0.37290863, -0.50591089],\n",
-       "        [ 1.35935856, -0.08724155, -0.76176027, ..., -1.34036643,\n",
-       "         -0.27073697,  1.23296843]],\n",
-       "\n",
-       "       [[-0.2874314 , -0.76365401,  0.11968068, ...,  2.61394867,\n",
-       "         -0.14594155, -1.06315231],\n",
-       "        [-0.2154085 , -0.32714514,  0.3866235 , ..., -0.95501706,\n",
-       "          1.09234753, -1.74638174],\n",
-       "        [ 0.01822831, -1.3842866 , -1.39632544, ..., -0.39650762,\n",
-       "          1.02985844,  1.20502722],\n",
+       "        [-0.74162647, -0.17192853,  2.14474553, ..., -1.2676518 ,\n",
+       "          0.40364072, -0.26263897],\n",
+       "        [-0.54642829, -0.40111569, -1.66033768, ..., -0.15261003,\n",
+       "          0.18717593, -2.04277684],\n",
+       "        [ 0.38928439, -0.31714689, -0.13634358, ...,  1.44254261,\n",
+       "          0.45682044, -1.71722348]],\n",
+       "\n",
+       "       [[-0.51944123, -1.0597853 ,  0.9618371 , ...,  1.47061286,\n",
+       "         -0.13483646,  0.48622419],\n",
+       "        [ 1.42526465, -1.01343223, -0.14500896, ..., -0.10968334,\n",
+       "          0.37813614, -1.79126724],\n",
+       "        [ 0.31486789, -0.77521119,  0.06163777, ...,  0.15416909,\n",
+       "         -1.618313  , -0.33723388],\n",
        "        ...,\n",
-       "        [ 1.38973766, -0.90670277, -0.4120455 , ...,  1.14767164,\n",
-       "          1.84232632,  2.63230258],\n",
-       "        [ 1.08389489,  1.58784555, -0.16197627, ...,  0.37463258,\n",
-       "          0.42914099,  2.06065205],\n",
-       "        [-0.19599264, -1.29336061, -0.05431617, ..., -1.16963105,\n",
-       "          0.34940347,  0.82143441]]])
          • created_at :
            2020-06-10T18:22:24.841646
            arviz_version :
            0.8.3
            inference_library :
            pyjags
            inference_library_version :
            1.3.6

      \n", + " [-0.7315915 , 1.33776777, 0.37092305, ..., 0.74510867,\n", + " 1.03921363, 0.19720606],\n", + " [-0.71781538, 1.1953773 , 0.54485766, ..., -1.24106341,\n", + " -0.64573595, -0.68705139],\n", + " [-0.4341049 , 1.57701704, -0.88924713, ..., -0.28156494,\n", + " -0.86547147, 0.53826645]]])
    • mu
      (chain, draw)
      float64
      4.654 -1.541 3.409 ... -3.332 0.196
      array([[ 4.65431193, -1.54134706,  3.40852221, ...,  3.08275307,\n",
+       "        -5.14290226,  0.92978784],\n",
+       "       [-0.29611118,  3.08889133,  2.86584901, ..., -4.10944146,\n",
+       "         9.75974653, -1.39819553],\n",
+       "       [-0.58853395,  4.14783675,  4.62984626, ..., -2.42436949,\n",
+       "        -3.31313771,  9.50384399],\n",
+       "       [-3.68876703,  0.54715647,  7.39943174, ..., -2.92143299,\n",
+       "        -3.33214817,  0.19597932]])
    • tau
      (chain, draw)
      float64
      12.77 88.9 31.67 ... 4.114 26.26
      array([[12.76642906, 88.89765381, 31.66746203, ..., 13.09897376,\n",
+       "        88.31134139,  4.12752449],\n",
+       "       [ 0.50596232, 12.51825709,  4.12346651, ...,  7.30589409,\n",
+       "         1.35717493,  0.50805488],\n",
+       "       [ 2.08673563,  4.73909692, 75.18967022, ...,  8.31522156,\n",
+       "         1.3542986 , 11.60239688],\n",
+       "       [ 1.91847522,  0.77704296,  0.64164736, ...,  1.23623608,\n",
+       "         4.11407372, 26.26295554]])
  • created_at :
    2020-06-19T06:23:52.835882
    arviz_version :
    0.8.3
    inference_library :
    pyjags
    inference_library_version :
    1.3.5

    • \n", " \n", " \n", " \n", " \n", "
  • \n", - " \n", - " \n", + " \n", + " \n", "
    \n", "
    \n", "
      \n", @@ -28007,82 +27989,103 @@ " stroke: currentColor;\n", " fill: currentColor;\n", "}\n", - "
      xarray.Dataset
        • chain: 4
        • draw: 1000
        • theta_tilde_dim_0: 8
        • chain
          (chain)
          int64
          0 1 2 3
          array([0, 1, 2, 3])
        • draw
          (draw)
          int64
          0 1 2 3 4 5 ... 995 996 997 998 999
          array([  0,   1,   2, ..., 997, 998, 999])
        • theta_tilde_dim_0
          (theta_tilde_dim_0)
          int64
          0 1 2 3 4 5 6 7
          array([0, 1, 2, 3, 4, 5, 6, 7])
        • mu
          (chain, draw)
          float64
          -0.9637 2.01 2.683 ... -4.89 -7.553
          array([[-0.96373932,  2.00976459,  2.68286841, ...,  3.26263418,\n",
-       "         7.13218474, -4.77082899],\n",
-       "       [ 5.01651985, -1.42231105,  4.28923032, ..., 14.81072926,\n",
-       "         5.85918337,  2.9595369 ],\n",
-       "       [-8.93389446, -3.62276361, -4.40002018, ..., -4.81264608,\n",
-       "         2.88287725, -1.36137392],\n",
-       "       [-0.48455451,  5.79456037,  0.48916682, ..., -6.7329493 ,\n",
-       "        -4.89032543, -7.55316121]])
        • tau
          (chain, draw)
          float64
          5.73 0.1845 4.443 ... 9.197 2.221
          array([[5.73017758e+00, 1.84481879e-01, 4.44296116e+00, ...,\n",
-       "        3.98330494e+01, 9.53069978e+00, 5.93247284e-02],\n",
-       "       [4.25284332e+01, 6.00660386e+00, 2.74997820e+00, ...,\n",
-       "        8.66855660e+00, 3.57687836e+01, 1.22358129e+01],\n",
-       "       [2.47402023e+01, 2.23230618e+00, 6.22901908e-01, ...,\n",
-       "        8.83360485e+00, 6.67212329e+00, 1.56668825e+00],\n",
-       "       [7.01345853e+01, 5.50504089e+00, 3.20007456e-01, ...,\n",
-       "        4.08060596e+01, 9.19651675e+00, 2.22100618e+00]])
        • theta_tilde
          (chain, draw, theta_tilde_dim_0)
          float64
          -0.3848 1.17 ... 1.963 -0.7431
          array([[[-3.84759532e-01,  1.16963585e+00, -6.39387159e-01, ...,\n",
-       "          1.64676773e+00, -7.32620331e-01,  1.42543110e+00],\n",
-       "        [ 1.25229019e+00, -8.77374020e-01,  2.38865353e-01, ...,\n",
-       "         -1.25275395e+00,  3.35137162e-01,  3.88075685e-01],\n",
-       "        [-1.85713392e-01,  2.26943538e-01, -9.15750816e-01, ...,\n",
-       "         -4.00545453e-01, -7.11047128e-01, -2.99759174e-02],\n",
+       "
          xarray.Dataset
            • chain: 4
            • draw: 1000
            • theta_tilde_dim_0: 8
            • chain
              (chain)
              int64
              0 1 2 3
              array([0, 1, 2, 3])
            • draw
              (draw)
              int64
              0 1 2 3 4 5 ... 995 996 997 998 999
              array([  0,   1,   2, ..., 997, 998, 999])
            • theta_tilde_dim_0
              (theta_tilde_dim_0)
              int64
              0 1 2 3 4 5 6 7
              array([0, 1, 2, 3, 4, 5, 6, 7])
            • theta_tilde
              (chain, draw, theta_tilde_dim_0)
              float64
              0.6259 -0.05823 ... 0.933 -1.011
              array([[[ 0.62591075, -0.05822593, -1.34667486, ...,  0.5220049 ,\n",
+       "          1.49657226, -0.09110078],\n",
+       "        [-1.63058644,  0.75384376, -0.07248947, ..., -0.51070233,\n",
+       "          2.01420918, -0.63685846],\n",
+       "        [ 0.56332043,  0.15225759, -0.43446575, ...,  0.75181526,\n",
+       "          0.91067242,  1.04358195],\n",
        "        ...,\n",
-       "        [-1.06647267e+00, -6.62496897e-01, -8.31741232e-01, ...,\n",
-       "         -4.63600395e-01,  4.06500738e-01, -1.34311335e+00],\n",
-       "        [ 2.83099912e+00, -6.82651154e-02, -4.32932670e-01, ...,\n",
-       "          1.40580066e+00, -6.01771344e-01, -1.39742294e-01],\n",
-       "        [ 2.09101110e-03,  1.24578808e+00, -2.84433658e-01, ...,\n",
-       "         -2.23442682e-01,  7.15745603e-01,  1.11335738e+00]],\n",
-       "\n",
-       "       [[ 1.49725231e+00,  1.26110180e-01, -1.56788392e-01, ...,\n",
-       "          1.79643212e+00, -5.42410432e-01,  1.69302413e-01],\n",
-       "        [-8.35708515e-01,  1.49204220e-01,  2.59427659e-01, ...,\n",
-       "          6.70159058e-01, -1.82970910e-01,  5.88670133e-01],\n",
-       "        [-5.15401582e-01, -3.69864296e-01, -3.42704721e-02, ...,\n",
-       "          2.84182545e-01,  1.42920618e+00,  1.23249100e+00],\n",
+       "        [-1.29342749, -0.38839071,  1.27008443, ...,  0.04984877,\n",
+       "          0.83977179, -1.00655611],\n",
+       "        [-0.23436997, -0.0567252 , -1.29407051, ...,  0.59762695,\n",
+       "          0.31896628, -0.60612625],\n",
+       "        [ 0.44615354,  1.27849237, -0.48100964, ..., -0.43710145,\n",
+       "         -0.53003482, -0.16956221]],\n",
+       "\n",
+       "       [[ 0.77292342,  0.41728015,  1.30116281, ...,  0.60598961,\n",
+       "          1.75784329, -2.23638839],\n",
+       "        [ 2.25214026,  1.91018121,  0.13392948, ..., -1.83242983,\n",
+       "         -1.10470279,  0.31154014],\n",
+       "        [-0.18460234,  0.53429385, -0.35811612, ...,  0.93326105,\n",
+       "          2.23085295, -0.6855231 ],\n",
        "        ...,\n",
-       "        [-9.03668862e-01,  1.11368558e+00,  7.07062311e-01, ...,\n",
-       "         -9.81545589e-01,  1.18106307e+00,  5.35441533e-01],\n",
-       "        [-6.86089881e-01, -1.57593517e+00, -4.35985674e-01, ...,\n",
-       "          1.17418877e+00, -1.13401978e+00,  3.28981450e-01],\n",
-       "        [ 9.16034571e-01, -1.45915693e+00, -1.67895737e+00, ...,\n",
-       "          7.02846926e-01,  5.64088202e-02, -7.62990265e-01]],\n",
-       "\n",
-       "       [[ 2.76199510e+00, -1.57262158e+00,  1.11241396e+00, ...,\n",
-       "         -4.18596774e-02, -5.84125836e-01,  1.04566176e+00],\n",
-       "        [ 5.23901899e-01,  8.55917974e-01, -1.48789769e-01, ...,\n",
-       "         -3.61452390e-01,  1.50701781e+00,  1.00039408e+00],\n",
-       "        [ 1.38308203e+00, -1.08250730e-01,  6.00410401e-02, ...,\n",
-       "          5.64164544e-01,  9.91960532e-01, -6.90006417e-01],\n",
+       "        [ 0.56559632,  0.25455658, -0.84359086, ...,  0.24238882,\n",
+       "          1.43498743, -0.13700032],\n",
+       "        [ 0.5926399 ,  1.41338219, -1.40345751, ...,  0.75477275,\n",
+       "          1.52278466,  0.2743515 ],\n",
+       "        [ 2.13964096,  0.46433539,  0.53753241, ..., -0.62288982,\n",
+       "          2.39142079,  0.6179569 ]],\n",
+       "\n",
+       "       [[-0.62357813,  1.21312606,  0.02332749, ..., -1.85795028,\n",
+       "          1.40440496,  0.13550857],\n",
+       "        [ 0.40808923,  0.07567456,  1.17306305, ...,  1.25301819,\n",
+       "         -0.3578069 , -2.49587944],\n",
+       "        [ 0.44775523, -0.04736244, -1.11623165, ...,  0.67507359,\n",
+       "          2.07246375,  1.78842195],\n",
        "        ...,\n",
-       "        [ 1.51420461e-01,  1.16473432e+00,  1.68715476e+00, ...,\n",
-       "         -5.88069236e-02,  1.33898295e+00, -3.85568655e-01],\n",
-       "        [ 8.63060440e-01,  7.40863308e-01, -1.67433123e-02, ...,\n",
-       "          3.43992941e-01, -5.80259919e-01, -2.25321126e-01],\n",
-       "        [-3.55221848e-01, -8.05489175e-01, -4.94224025e-01, ...,\n",
-       "         -1.41247252e-01,  7.74878578e-02,  4.72346642e-01]],\n",
-       "\n",
-       "       [[-4.89228585e-01, -6.64699793e-01, -1.84868093e-01, ...,\n",
-       "          9.74433924e-01,  1.27512030e-02,  1.93197864e+00],\n",
-       "        [-8.25146669e-01, -1.85878476e+00,  2.79246713e-02, ...,\n",
-       "          4.41117392e-02, -1.69376936e-01, -3.81356167e-01],\n",
-       "        [-9.77437969e-01, -1.44282133e+00,  4.32664252e-01, ...,\n",
-       "          1.30427082e-01,  7.88506853e-01,  1.15544859e+00],\n",
+       "        [ 2.2371135 ,  1.11187675,  0.27148003, ..., -0.17553131,\n",
+       "          1.29880533,  0.19596854],\n",
+       "        [ 2.2936293 ,  0.92676865,  0.87846553, ..., -0.17362181,\n",
+       "          1.18790425,  0.82398055],\n",
+       "        [ 0.1900121 ,  2.19559171, -1.5187373 , ..., -1.89672507,\n",
+       "          1.26775356,  1.15303549]],\n",
+       "\n",
+       "       [[ 0.15815182,  1.26705501, -0.3804165 , ...,  2.3467277 ,\n",
+       "         -1.48880315,  0.32744594],\n",
+       "        [-0.82407985, -1.3273879 ,  1.04150429, ...,  0.00393478,\n",
+       "         -0.29381189, -1.9999881 ],\n",
+       "        [ 1.53612173, -0.79165433, -0.61818957, ..., -0.21186912,\n",
+       "          1.41784152, -0.10586098],\n",
        "        ...,\n",
-       "        [ 1.83217827e-02, -3.54950247e-01, -9.53511916e-01, ...,\n",
-       "          4.28024721e-01, -2.83337300e-01,  4.38615469e-03],\n",
-       "        [ 8.67385101e-01, -7.67011692e-01,  8.88529670e-01, ...,\n",
-       "         -4.76144614e-01,  1.06189275e-02,  1.30143218e+00],\n",
-       "        [ 3.98181051e-02,  9.00660162e-01,  5.02505799e-01, ...,\n",
-       "         -5.18776518e-01,  1.96295042e+00, -7.43125886e-01]]])
          • created_at :
            2020-06-10T18:22:24.889273
            arviz_version :
            0.8.3
            inference_library :
            pyjags
            inference_library_version :
            1.3.6

      \n", + " [-0.49140065, -0.87372717, -0.85024702, ..., 1.46580137,\n", + " -1.09679543, 0.18858381],\n", + " [ 0.66825142, -0.285897 , -0.90993501, ..., 0.2777644 ,\n", + " 0.08081879, 0.04777291],\n", + " [-0.09543308, -1.14055406, -0.18135868, ..., 0.92126407,\n", + " 0.93304363, -1.01143416]]])
    • mu
      (chain, draw)
      float64
      0.1665 6.307 ... 0.3566 0.9405
      array([[ 0.16648025,  6.30650492,  5.58715254, ...,  3.40540871,\n",
+       "         2.96770844,  3.48107398],\n",
+       "       [ 7.9186115 ,  5.73733931,  3.63883791, ...,  1.27584629,\n",
+       "         0.26084865,  7.28096009],\n",
+       "       [ 8.02057312, -2.61108692,  4.11972209, ..., -0.75871149,\n",
+       "         3.5817588 ,  2.08628888],\n",
+       "       [ 3.23880708,  9.33523099,  3.94528447, ...,  4.84944117,\n",
+       "         0.35656092,  0.9404811 ]])
    • tau
      (chain, draw)
      float64
      6.621 5.507 8.88 ... 5.691 11.58
      array([[ 6.62127708,  5.50691977,  8.88018874, ...,  4.32440909,\n",
+       "         2.74299773,  1.72296131],\n",
+       "       [ 1.2860264 ,  5.92674038,  5.87171746, ...,  6.90253812,\n",
+       "         5.20568187,  9.63475622],\n",
+       "       [ 0.48961787,  4.15905179,  5.70170925, ...,  6.27740286,\n",
+       "         5.46124308,  8.38574835],\n",
+       "       [ 0.11407355,  0.36389414,  2.92227942, ...,  5.86402037,\n",
+       "         5.69135206, 11.58006309]])
  • created_at :
    2020-06-19T06:23:52.833729
    arviz_version :
    0.8.3
    inference_library :
    pyjags
    inference_library_version :
    1.3.5

    • \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " " - ], - "text/plain": [ - "Inference data with groups:\n", - "\t> posterior\n", - "\t> prior\n", + "
    xarray.Dataset
      • chain: 4
      • draw: 1000
      • y_dim_0: 8
      • chain
        (chain)
        int64
        0 1 2 3
        array([0, 1, 2, 3])
      • draw
        (draw)
        int64
        0 1 2 3 4 5 ... 995 996 997 998 999
        array([  0,   1,   2, ..., 997, 998, 999])
      • y_dim_0
        (y_dim_0)
        int64
        0 1 2 3 4 5 6 7
        array([0, 1, 2, 3, 4, 5, 6, 7])
      • y
        (chain, draw, y_dim_0)
        float64
        -4.874 -3.559 ... -3.417 -4.61
        array([[[-4.87405047, -3.55928752, -3.75610755, ..., -3.34526017,\n",
+       "         -3.53549629, -4.04800178],\n",
+       "        [-5.71772909, -3.25172905, -3.84648857, ..., -3.34253866,\n",
+       "         -3.22333208, -3.93994528],\n",
+       "        [-4.30059756, -3.22714981, -3.73520612, ..., -3.84106725,\n",
+       "         -3.31509087, -3.82188337],\n",
+       "        ...,\n",
+       "        [-5.65212065, -3.41834852, -3.96800579, ..., -3.34522021,\n",
+       "         -3.82246864, -4.06800461],\n",
+       "        [-5.09190928, -3.35609457, -3.70294736, ..., -3.37059593,\n",
+       "         -4.22367894, -3.98582382],\n",
+       "        [-4.88048426, -3.24834599, -3.75392695, ..., -3.32917208,\n",
+       "         -4.41228074, -3.92911746]],\n",
+       "\n",
+       "       [[-4.43660741, -3.22255987, -4.00120886, ..., -3.56170211,\n",
+       "         -3.52734472, -3.88401093],\n",
+       "        [-3.80359726, -3.6318046 , -3.86895295, ..., -3.47175565,\n",
+       "         -4.99059421, -3.83940783],\n",
+       "        [-5.06577285, -3.22901377, -3.73171482, ..., -3.58920164,\n",
+       "         -3.22948967, -4.04607251],\n",
+       "        ...,\n",
+       "        [-4.78422679, -3.34488241, -3.69620193, ..., -3.33252961,\n",
+       "         -3.45402413, -4.01947127],\n",
+       "        [-4.97770541, -3.22225146, -3.72348599, ..., -3.35888263,\n",
+       "         -3.7029022 , -3.97337818],\n",
+       "        [-3.62701283, -3.29201318, -4.158344  , ..., -3.31715678,\n",
+       "         -3.98064711, -3.81166336]],\n",
+       "\n",
+       "       [[-4.54136819, -3.22341193, -3.92923215, ..., -3.4711435 ,\n",
+       "         -3.65321184, -3.83294022],\n",
+       "        [-5.48478683, -3.751598  , -3.74572475, ..., -3.32741603,\n",
+       "         -5.66340221, -4.77316679],\n",
+       "        [-4.63777553, -3.30764961, -3.69264145, ..., -3.51751195,\n",
+       "         -3.24281775, -3.81759345],\n",
+       "        ...,\n",
+       "        [-4.10819858, -3.23734807, -3.72193116, ..., -3.35064781,\n",
+       "         -3.78391602, -4.01441398],\n",
+       "        [-3.94126385, -3.22359131, -3.9444332 , ..., -3.32786085,\n",
+       "         -3.53601215, -3.8330031 ],\n",
+       "        [-4.94138394, -4.00251971, -3.80581287, ..., -4.22430408,\n",
+       "         -3.36105552, -3.80940265]],\n",
+       "\n",
+       "       [[-4.98748556, -3.32809116, -3.7664943 , ..., -3.34279486,\n",
+       "         -4.33620131, -3.92675687],\n",
+       "        [-4.42622836, -3.22515487, -4.00725304, ..., -3.60402367,\n",
+       "         -3.60623595, -3.82707173],\n",
+       "        [-4.47769568, -3.42429034, -3.74310318, ..., -3.33919308,\n",
+       "         -3.71270153, -3.91726968],\n",
+       "        ...,\n",
+       "        [-5.13292746, -3.56382834, -3.707543  , ..., -3.95681847,\n",
+       "         -5.13883436, -3.8656967 ],\n",
+       "        [-4.88999855, -3.65124185, -3.69801243, ..., -3.320465  ,\n",
+       "         -4.69788199, -4.00886596],\n",
+       "        [-5.38975973, -5.27532201, -3.69814217, ..., -3.78190055,\n",
+       "         -3.41713716, -4.60956267]]])
    • created_at :
      2020-06-19T06:23:52.840708
      arviz_version :
      0.8.3
      inference_library :
      pyjags
      inference_library_version :
      1.3.5

    \n", + " \n", + " \n", + " \n", + " \n", + "
  • \n", + " \n", + " \n", + "
    \n", + "
    \n", + "
      \n", + "
      \n", + "\n", + "\n", + "Show/Hide data repr\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Show/Hide attributes\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
      xarray.Dataset
        • chain: 4
        • draw: 1000
        • theta_tilde_dim_0: 8
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          (chain)
          int64
          0 1 2 3
          array([0, 1, 2, 3])
        • draw
          (draw)
          int64
          0 1 2 3 4 5 ... 995 996 997 998 999
          array([  0,   1,   2, ..., 997, 998, 999])
        • theta_tilde_dim_0
          (theta_tilde_dim_0)
          int64
          0 1 2 3 4 5 6 7
          array([0, 1, 2, 3, 4, 5, 6, 7])
        • theta_tilde
          (chain, draw, theta_tilde_dim_0)
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          -0.7112 -0.4008 ... -0.7134 -0.2611
          array([[[-0.71123136, -0.40076082,  1.86888641, ...,  0.8115354 ,\n",
+       "         -0.24211776,  0.54841302],\n",
+       "        [ 0.73392039,  0.47663744, -0.61609627, ..., -0.80964506,\n",
+       "          0.49762657, -2.47327004],\n",
+       "        [-0.08231233,  0.28062108,  2.20405144, ...,  0.154088  ,\n",
+       "         -0.36351211,  0.36013468],\n",
+       "        ...,\n",
+       "        [ 0.79194157, -0.40861463, -0.85875295, ..., -0.82237095,\n",
+       "          1.47173464, -1.20540583],\n",
+       "        [-0.45090569, -1.01004541,  0.08397328, ...,  1.07454531,\n",
+       "          1.13587261,  0.53209085],\n",
+       "        [-1.05886621,  1.66164192, -1.4250031 , ..., -1.11740703,\n",
+       "         -1.84886498,  0.71930243]],\n",
+       "\n",
+       "       [[ 1.4125556 , -2.34874433,  1.20299551, ..., -0.25531169,\n",
+       "         -0.26411039,  1.13222391],\n",
+       "        [ 2.2267223 ,  0.02884086,  1.13865955, ..., -0.28934517,\n",
+       "          1.05906399,  0.55190591],\n",
+       "        [-0.23439601, -0.36784748,  0.47789977, ...,  0.74236118,\n",
+       "         -1.1212007 , -0.6610916 ],\n",
+       "        ...,\n",
+       "        [-0.77174629, -0.97774201, -0.73963299, ...,  0.20137311,\n",
+       "         -0.56425354, -1.75796363],\n",
+       "        [ 0.66127056, -0.20695707,  0.53286237, ...,  1.5300409 ,\n",
+       "          0.01747712,  0.54043691],\n",
+       "        [ 0.25492446,  0.42637825,  1.19142405, ...,  0.51370606,\n",
+       "          0.1491775 ,  1.21679596]],\n",
+       "\n",
+       "       [[ 2.99620537, -0.58296821,  0.72585938, ..., -0.75381363,\n",
+       "          0.27957383, -1.30275569],\n",
+       "        [-0.4803946 ,  0.40327934, -0.76396762, ..., -0.40640857,\n",
+       "         -0.75089157, -1.39522304],\n",
+       "        [-0.72583985,  2.36262907,  0.96360172, ...,  0.61588395,\n",
+       "          0.05045338, -0.43853064],\n",
+       "        ...,\n",
+       "        [-0.2673493 , -1.00842011, -0.56629967, ..., -0.89029002,\n",
+       "          0.00340085,  0.18408985],\n",
+       "        [ 0.07223788,  0.86688325, -0.0114382 , ..., -0.80392428,\n",
+       "          0.25731739, -0.1457207 ],\n",
+       "        [-1.97737722, -2.78400193,  1.61941847, ..., -0.87962511,\n",
+       "          0.23703341,  1.24248917]],\n",
+       "\n",
+       "       [[-0.02165655,  0.82049726, -0.58873389, ..., -0.56971679,\n",
+       "         -0.59921277, -0.33330181],\n",
+       "        [-0.15985731,  0.32461587, -0.20852091, ...,  1.7119704 ,\n",
+       "         -0.2676015 , -1.50186474],\n",
+       "        [-1.77468599, -0.9329657 ,  0.12679348, ..., -1.27709638,\n",
+       "         -0.078269  ,  0.90316797],\n",
+       "        ...,\n",
+       "        [-0.40739721,  1.38197559,  2.08072841, ...,  0.52669903,\n",
+       "         -0.68106617, -2.93601848],\n",
+       "        [-0.48058994, -0.00859444,  0.68029096, ...,  0.721311  ,\n",
+       "         -0.89124688,  1.21101712],\n",
+       "        [-1.6756115 ,  0.1954323 ,  0.5773097 , ..., -1.97671135,\n",
+       "         -0.71340016, -0.26109113]]])
        • mu
          (chain, draw)
          float64
          -0.5426 -10.87 ... 2.281 -4.709
          array([[ -0.54256157, -10.87124355,   1.96438294, ...,  -8.11403829,\n",
+       "          7.60765481,  -0.48159106],\n",
+       "       [  0.03072765,  -1.82026456,   1.38368157, ...,   0.96176535,\n",
+       "          3.89488577,   3.99766504],\n",
+       "       [ -2.62546345,   2.2632889 ,   2.55843055, ...,   4.86710568,\n",
+       "          2.21386016,  -4.73052159],\n",
+       "       [  0.32067926,  -7.23833533,  -2.82193459, ...,  -5.77218015,\n",
+       "          2.28055004,  -4.70868173]])
        • tau
          (chain, draw)
          float64
          16.53 3.872 9.523 ... 2.702 8.149
          array([[16.53391268,  3.87249575,  9.52339625, ...,  6.50219713,\n",
+       "         1.35029408,  8.34708719],\n",
+       "       [ 9.1205841 ,  9.21082813, 15.29874336, ..., 10.20594881,\n",
+       "        26.05528672,  0.70675771],\n",
+       "       [ 0.43585432, 20.06679265, 17.90884847, ...,  5.9273813 ,\n",
+       "         2.68746482,  5.18154874],\n",
+       "       [ 1.08752471,  3.25281493,  4.26600184, ..., 14.58456325,\n",
+       "         2.70201427,  8.14868071]])
      • created_at :
        2020-06-19T06:23:52.837977
        arviz_version :
        0.8.3
        inference_library :
        pyjags
        inference_library_version :
        1.3.5

      \n", + "
    \n", + "
    \n", + "
    • \n", + " \n", + " \n", + " \n", + " " + ], + "text/plain": [ + "Inference data with groups:\n", + "\t> posterior\n", + "\t> log_likelihood\n", + "\t> prior\n", + "\n", + "Warmup iterations saved (warmup_*)." + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pyjags_data = az.from_pyjags(\n", + " posterior=jags_posterior_samples, \n", + " prior=jags_prior_samples, \n", + " log_likelihood={'y': 'log_like'}, \n", + " save_warmup=True, \n", + " warmup_iterations=1000\n", + ")\n", + "pyjags_data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -28442,7 +29245,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.10" + "version": "3.7.6" }, "toc": { "base_numbering": 1, From c1190ca19dc0174b5ecccd251a9f5fcf7ae00852 Mon Sep 17 00:00:00 2001 From: Oriol Abril-Pla Date: Mon, 22 Jun 2020 19:01:04 +0200 Subject: [PATCH 22/24] [WIP] fix hdi false positive warning (#1241) * fix warning with plot_hdi * update summary to use hdi directly hdi itself calls warp_xarray_ufunc now * update changelog * extend hdi functionality * extend functionality and improve docs * add backend to mpl and conversion to hex in bokeh * add plot_hdi mpl tests * add bokeh tests * update changelog * Update arviz/plots/hdiplot.py Co-authored-by: Alexandre ANDORRA Co-authored-by: Alexandre ANDORRA Co-authored-by: Ari Hartikainen --- CHANGELOG.md | 3 + arviz/plots/backends/bokeh/hdiplot.py | 23 +-- arviz/plots/backends/matplotlib/hdiplot.py | 20 +-- arviz/plots/hdiplot.py | 143 ++++++++++++------ arviz/plots/plot_utils.py | 1 + arviz/stats/stats.py | 48 +++--- arviz/tests/base_tests/test_plots_bokeh.py | 16 +- .../tests/base_tests/test_plots_matplotlib.py | 37 ++++- doc/conf.py | 1 + 9 files changed, 183 insertions(+), 109 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index c1c1168b79..3da6aa0b74 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -7,6 +7,7 @@ * Added `html_repr` of InferenceData objects for jupyter notebooks. (#1217) * Added support for PyJAGS via the function `from_pyjags`. (#1219 and #1245) * `from_pymc3` can now retrieve `coords` and `dims` from model context (#1228, #1240 and #1249) +* `plot_hdi` can now take already computed HDI values (#1241) ### Maintenance and fixes * Include data from `MultiObservedRV` to `observed_data` when using @@ -16,12 +17,14 @@ * Added `log_likelihood` argument to `from_pyro` and a warning if log likelihood cannot be obtained (#1227) * Skip tests on matplotlib animations if ffmpeg is not installed (#1227) * Fix hpd bug where arguments were being ignored (#1236) +* Remove false positive warning in `plot_hdi` and fixed matplotlib axes generation (#1241) * Change the default `zorder` of scatter points from `0` to `0.6` in `plot_pair` (#1246) * Update `get_bins` for numpy 1.19 compatibility (#1256) ### Deprecation * Using `from_pymc3` without a model context available now raises a `FutureWarning` and will be deprecated in a future version (#1227) +* `hdi` with 2d data raises a FutureWarning (#1241) ### Documentation * A section has been added to the documentation at InferenceDataCookbook.ipynb illustrating the use of ArviZ in conjunction with PyJAGS. (#1219 and #1245) diff --git a/arviz/plots/backends/bokeh/hdiplot.py b/arviz/plots/backends/bokeh/hdiplot.py index cf511716eb..0ce80c22d3 100644 --- a/arviz/plots/backends/bokeh/hdiplot.py +++ b/arviz/plots/backends/bokeh/hdiplot.py @@ -1,8 +1,5 @@ """Bokeh hdiplot.""" -from itertools import cycle - import bokeh.plotting as bkp -from matplotlib.pyplot import rcParams as mpl_rcParams import numpy as np from . import backend_kwarg_defaults @@ -21,26 +18,10 @@ def plot_hdi(ax, x_data, y_data, plot_kwargs, fill_kwargs, backend_kwargs, show) if ax is None: ax = bkp.figure(**backend_kwargs) - color = plot_kwargs.pop("color") - if len(color) == 2 and color[0] == "C": - color = [ - prop - for _, prop in zip( - range(int(color[1:])), cycle(mpl_rcParams["axes.prop_cycle"].by_key()["color"]) - ) - ][-1] - plot_kwargs.setdefault("line_color", color) + plot_kwargs.setdefault("line_color", plot_kwargs.pop("color")) plot_kwargs.setdefault("line_alpha", plot_kwargs.pop("alpha", 0)) - color = fill_kwargs.pop("color") - if len(color) == 2 and color[0] == "C": - color = [ - prop - for _, prop in zip( - range(int(color[1:])), cycle(mpl_rcParams["axes.prop_cycle"].by_key()["color"]) - ) - ][-1] - fill_kwargs.setdefault("fill_color", color) + fill_kwargs.setdefault("fill_color", fill_kwargs.pop("color")) fill_kwargs.setdefault("fill_alpha", fill_kwargs.pop("alpha", 0)) ax.patch( diff --git a/arviz/plots/backends/matplotlib/hdiplot.py b/arviz/plots/backends/matplotlib/hdiplot.py index 585acb40f3..d93ebec7bc 100644 --- a/arviz/plots/backends/matplotlib/hdiplot.py +++ b/arviz/plots/backends/matplotlib/hdiplot.py @@ -1,21 +1,21 @@ """Matplotlib hdiplot.""" -import warnings import matplotlib.pyplot as plt -from . import backend_show +from . import backend_kwarg_defaults, backend_show def plot_hdi(ax, x_data, y_data, plot_kwargs, fill_kwargs, backend_kwargs, show): """Matplotlib HDI plot.""" - if backend_kwargs is not None: - warnings.warn( - ( - "Argument backend_kwargs has not effect in matplotlib.plot_hdi" - "Supplied value won't be used" - ) - ) + if backend_kwargs is None: + backend_kwargs = {} + + backend_kwargs = { + **backend_kwarg_defaults(), + **backend_kwargs, + } if ax is None: - ax = plt.gca() + _, ax = plt.subplots(1, 1, **backend_kwargs) + ax.plot(x_data, y_data, **plot_kwargs) ax.fill_between(x_data, y_data[:, 0], y_data[:, 1], **fill_kwargs) diff --git a/arviz/plots/hdiplot.py b/arviz/plots/hdiplot.py index 9585401e9e..62c9e948f2 100644 --- a/arviz/plots/hdiplot.py +++ b/arviz/plots/hdiplot.py @@ -4,23 +4,26 @@ import numpy as np from scipy.interpolate import griddata from scipy.signal import savgol_filter +from xarray import Dataset from ..stats import hdi -from .plot_utils import get_plotting_function, matplotlib_kwarg_dealiaser +from .plot_utils import get_plotting_function, matplotlib_kwarg_dealiaser, vectorized_to_hex from ..rcparams import rcParams from ..utils import credible_interval_warning def plot_hdi( x, - y, + y=None, hdi_prob=None, + hdi_data=None, color="C1", circular=False, smooth=True, smooth_kwargs=None, fill_kwargs=None, plot_kwargs=None, + hdi_kwargs=None, ax=None, backend=None, backend_kwargs=None, @@ -34,75 +37,124 @@ def plot_hdi( ---------- x : array-like Values to plot. - y : array-like - Values from which to compute the HDI. Assumed shape (chain, draw, \*shape). + y : array-like, optional + Values from which to compute the HDI. Assumed shape ``(chain, draw, \*shape)``. + Only optional if hdi_data is present. + hdi_data : array_like, optional + Precomputed HDI values to use. Assumed shape is ``(*x.shape, 2)``. hdi_prob : float, optional - Probability for the highest density interval. Defaults to 0.94. - color : str + Probability for the highest density interval. Defaults to ``stats.hdi_prob`` rcParam. + color : str, optional Color used for the limits of the HDI and fill. Should be a valid matplotlib color. circular : bool, optional Whether to compute the HDI taking into account `x` is a circular variable (in the range [-np.pi, np.pi]) or not. Defaults to False (i.e non-circular variables). - smooth : boolean + smooth : boolean, optional If True the result will be smoothed by first computing a linear interpolation of the data over a regular grid and then applying the Savitzky-Golay filter to the interpolated data. Defaults to True. smooth_kwargs : dict, optional - Additional keywords modifying the Savitzky-Golay filter. See Scipy's documentation for - details. - fill_kwargs : dict - Keywords passed to `fill_between` (use fill_kwargs={'alpha': 0} to disable fill). - plot_kwargs : dict - Keywords passed to HDI limits. - ax: axes, optional + Additional keywords modifying the Savitzky-Golay filter. See + :func:`scipy:scipy.signal.savgol_filter` for details. + fill_kwargs : dict, optional + Keywords passed to :meth:`mpl:matplotlib.axes.Axes.fill_between` + (use fill_kwargs={'alpha': 0} to disable fill) or to + :meth:`bokeh:bokeh.plotting.figure.Figure.patch`. + plot_kwargs : dict, optional + HDI limits keyword arguments, passed to :meth:`mpl:matplotlib.axes.Axes.plot` or + :meth:`bokeh:bokeh.plotting.figure.Figure.patch`. + hdi_kwargs : dict, optional + Keyword arguments passed to :func:`~arviz.hdi`. Ignored if ``hdi_data`` is present. + ax : axes, optional Matplotlib axes or bokeh figures. - backend: str, optional - Select plotting backend {"matplotlib","bokeh"}. Default "matplotlib". - backend_kwargs: bool, optional - These are kwargs specific to the backend being used. For additional documentation - check the plotting method of the backend. + backend : {"matplotlib","bokeh"}, optional + Select plotting backend. + backend_kwargs : bool, optional + These are kwargs specific to the backend being used. Passed to ::`` show : bool, optional Call backend show function. - credible_interval: float, optional + credible_interval : float, optional Deprecated: Please see hdi_prob Returns ------- axes : matplotlib axes or bokeh figures + + See Also + -------- + hdi : Calculate highest density interval (HDI) of array for given probability. + + Examples + -------- + Plot HDI interval of simulated regression data using `y` argument: + + .. plot:: + :context: close-figs + + >>> import numpy as np + >>> import arviz as az + >>> x_data = np.random.normal(0, 1, 100) + >>> y_data = np.random.normal(2 + x_data * 0.5, 0.5, (2, 50, 100)) + >>> az.plot_hdi(x_data, y_data) + + Precalculate HDI interval per chain and plot separately: + + .. plot:: + :context: close-figs + + >>> hdi_data = az.hdi(y_data, input_core_dims=[["draw"]]) + >>> ax = az.plot_hdi(x_data, hdi_data=hdi_data[0], color="r", fill_kwargs={"alpha": .2}) + >>> az.plot_hdi(x_data, hdi_data=hdi_data[1], color="k", ax=ax, fill_kwargs={"alpha": .2}) + """ if credible_interval: hdi_prob = credible_interval_warning(credible_interval, hdi_prob) + if hdi_kwargs is None: + hdi_kwargs = {} plot_kwargs = matplotlib_kwarg_dealiaser(plot_kwargs, "plot") - plot_kwargs.setdefault("color", color) + plot_kwargs["color"] = vectorized_to_hex(plot_kwargs.get("color", color)) plot_kwargs.setdefault("alpha", 0) - fill_kwargs = matplotlib_kwarg_dealiaser(fill_kwargs, "hexbin") - fill_kwargs.setdefault("color", color) + fill_kwargs = matplotlib_kwarg_dealiaser(fill_kwargs, "fill_between") + fill_kwargs["color"] = vectorized_to_hex(fill_kwargs.get("color", color)) fill_kwargs.setdefault("alpha", 0.5) x = np.asarray(x) - y = np.asarray(y) - x_shape = x.shape - y_shape = y.shape - if y_shape[-len(x_shape) :] != x_shape: - msg = "Dimension mismatch for x: {} and y: {}." - msg += " y-dimensions should be (chain, draw, *x.shape) or" - msg += " (draw, *x.shape)" - raise TypeError(msg.format(x_shape, y_shape)) - - if len(y_shape[: -len(x_shape)]) > 1: - new_shape = tuple([-1] + list(x_shape)) - y = y.reshape(new_shape) - - if hdi_prob is None: - hdi_prob = rcParams["stats.hdi_prob"] - else: - if not 1 >= hdi_prob > 0: - raise ValueError("The value of hdi_prob should be in the interval (0, 1]") - hdi_ = hdi(y, hdi_prob=hdi_prob, circular=circular, multimodal=False) + if y is None and hdi_data is None: + raise ValueError("One of {y, hdi_data} is required") + if hdi_data is not None and y is not None: + warnings.warn("Both y and hdi_data arguments present, ignoring y") + elif hdi_data is not None: + hdi_prob = ( + hdi_data.hdi.attrs.get("hdi_prob", np.nan) if hasattr(hdi_data, "hdi") else np.nan + ) + if isinstance(hdi_data, Dataset): + data_vars = list(hdi_data.data_vars) + if len(data_vars) != 1: + raise ValueError( + "Found several variables in hdi_data. Only single variable Datasets are " + "supported." + ) + hdi_data = hdi_data[data_vars[0]] + else: + y = np.asarray(y) + if hdi_prob is None: + hdi_prob = rcParams["stats.hdi_prob"] + else: + if not 1 >= hdi_prob > 0: + raise ValueError("The value of hdi_prob should be in the interval (0, 1]") + hdi_data = hdi(y, hdi_prob=hdi_prob, circular=circular, multimodal=False, **hdi_kwargs) + + hdi_shape = hdi_data.shape + if hdi_shape[:-1] != x_shape: + msg = ( + "Dimension mismatch for x: {} and hdi: {}. Check the dimensions of y and" + "hdi_kwargs to make sure they are compatible" + ) + raise TypeError(msg.format(x_shape, hdi_shape)) if smooth: if smooth_kwargs is None: @@ -111,12 +163,12 @@ def plot_hdi( smooth_kwargs.setdefault("polyorder", 2) x_data = np.linspace(x.min(), x.max(), 200) x_data[0] = (x_data[0] + x_data[1]) / 2 - hdi_interp = griddata(x, hdi_, x_data) + hdi_interp = griddata(x, hdi_data, x_data) y_data = savgol_filter(hdi_interp, axis=0, **smooth_kwargs) else: idx = np.argsort(x) x_data = x[idx] - y_data = hdi_[idx] + y_data = hdi_data[idx] hdiplot_kwargs = dict( ax=ax, @@ -132,12 +184,11 @@ def plot_hdi( backend = rcParams["plot.backend"] backend = backend.lower() - # TODO: Add backend kwargs plot = get_plotting_function("plot_hdi", "hdiplot", backend) ax = plot(**hdiplot_kwargs) return ax def plot_hpd(*args, **kwargs): # noqa: D103 - warnings.warn("plot_hdi has been deprecated, please use plot_hdi", DeprecationWarning) + warnings.warn("plot_hpd has been deprecated, please use plot_hdi", DeprecationWarning) return plot_hdi(*args, **kwargs) diff --git a/arviz/plots/plot_utils.py b/arviz/plots/plot_utils.py index 14bc8057f3..a9c229e621 100644 --- a/arviz/plots/plot_utils.py +++ b/arviz/plots/plot_utils.py @@ -663,6 +663,7 @@ def matplotlib_kwarg_dealiaser(args, kind, backend="matplotlib"): "plot": mpl.lines.Line2D, "hist": mpl.patches.Patch, "hexbin": mpl.collections.PolyCollection, + "fill_between": mpl.collections.PolyCollection, "hlines": mpl.collections.LineCollection, "text": mpl.text.Text, "contour": mpl.contour.ContourSet, diff --git a/arviz/stats/stats.py b/arviz/stats/stats.py index e2503fe0fc..35b9922d5c 100644 --- a/arviz/stats/stats.py +++ b/arviz/stats/stats.py @@ -365,7 +365,7 @@ def hdi( **kwargs, ): """ - Calculate highest density interval (HDI) of array for given percentage. + Calculate highest density interval (HDI) of array for given probability. The HDI is the minimum width Bayesian credible interval (BCI). @@ -376,15 +376,15 @@ def hdi( Any object that can be converted to an az.InferenceData object. Refer to documentation of az.convert_to_dataset for details. hdi_prob: float, optional - HDI prob for which interval will be computed. Defaults to 0.94. + HDI prob for which interval will be computed. Defaults to ``stats.hdi_prob`` rcParam. circular: bool, optional Whether to compute the hdi taking into account `x` is a circular variable (in the range [-np.pi, np.pi]) or not. Defaults to False (i.e non-circular variables). Only works if multimodal is False. - multimodal: bool + multimodal: bool, optional If true it may compute more than one hdi interval if the distribution is multimodal and the modes are well separated. - skipna: bool + skipna: bool, optional If true ignores nan values when computing the hdi interval. Defaults to false. group: str, optional Specifies which InferenceData group should be used to calculate hdi. @@ -403,14 +403,18 @@ def hdi( max_modes: int, optional Specifies the maximum number of modes for multimodal case. kwargs: dict, optional - Additional keywords passed to `wrap_xarray_ufunc`. - See the docstring of :obj:`wrap_xarray_ufunc method `. + Additional keywords passed to :func:`~arviz.wrap_xarray_ufunc`. Returns ------- np.ndarray or xarray.Dataset, depending upon input lower(s) and upper(s) values of the interval(s). + See Also + -------- + plot_hdi : Plot HDI intervals for regression data. + xarray.Dataset.quantile : Calculate quantiles of array for given probabilities. + Examples -------- Calculate the HDI of a Normal random variable: @@ -476,7 +480,12 @@ def hdi( return hdi_data[~np.isnan(hdi_data).all(axis=1), :] if multimodal else hdi_data if isarray and ary.ndim == 2: - kwargs.setdefault("input_core_dims", [["chain"]]) + warnings.warn( + "hdi currently interprets 2d data as (draw, shape) but this will change in " + "a future release to (chain, draw) for coherence with other functions", + FutureWarning, + ) + ary = np.expand_dims(ary, 0) ary = convert_to_dataset(ary, group=group) if coords is not None: @@ -484,7 +493,10 @@ def hdi( var_names = _var_names(var_names, ary, filter_vars) ary = ary[var_names] if var_names else ary - hdi_data = _wrap_xarray_ufunc(func, ary, func_kwargs=func_kwargs, **kwargs) + hdi_coord = xr.DataArray(["lower", "higher"], dims=["hdi"], attrs=dict(hdi_prob=hdi_prob)) + hdi_data = _wrap_xarray_ufunc(func, ary, func_kwargs=func_kwargs, **kwargs).assign_coords( + {"hdi": hdi_coord} + ) hdi_data = hdi_data.dropna("mode", how="all") if multimodal else hdi_data return hdi_data.x.values if isarray else hdi_data @@ -1161,13 +1173,9 @@ def summary( sd = posterior.std(dim=("chain", "draw"), ddof=1, skipna=skipna) - hdi_lower, hdi_higher = xr.apply_ufunc( - _make_ufunc(hdi, n_output=2), - posterior, - kwargs=dict(hdi_prob=hdi_prob, multimodal=False, skipna=skipna), - input_core_dims=(("chain", "draw"),), - output_core_dims=tuple([] for _ in range(2)), - ) + hdi_post = hdi(posterior, hdi_prob=hdi_prob, multimodal=False, skipna=skipna) + hdi_lower = hdi_post.sel(hdi="lower", drop=True) + hdi_higher = hdi_post.sel(hdi="higher", drop=True) if include_circ: nan_policy = "omit" if skipna else "propagate" @@ -1199,13 +1207,9 @@ def summary( input_core_dims=(("chain", "draw"),), ) - circ_hdi_lower, circ_hdi_higher = xr.apply_ufunc( - _make_ufunc(hdi, n_output=2), - posterior, - kwargs=dict(hdi_prob=hdi_prob, circular=True, skipna=skipna), - input_core_dims=(("chain", "draw"),), - output_core_dims=tuple([] for _ in range(2)), - ) + circ_hdi = hdi(posterior, hdi_prob=hdi_prob, circular=True, skipna=skipna) + circ_hdi_lower = circ_hdi.sel(hdi="lower", drop=True) + circ_hdi_higher = circ_hdi.sel(hdi="higher", drop=True) if kind in ["all", "diagnostics"]: mcse_mean, mcse_sd, ess_mean, ess_sd, ess_bulk, ess_tail, r_hat = xr.apply_ufunc( diff --git a/arviz/tests/base_tests/test_plots_bokeh.py b/arviz/tests/base_tests/test_plots_bokeh.py index 9e345322f6..11d1417757 100644 --- a/arviz/tests/base_tests/test_plots_bokeh.py +++ b/arviz/tests/base_tests/test_plots_bokeh.py @@ -40,7 +40,7 @@ plot_trace, plot_violin, ) -from ...stats import compare, loo, waic # pylint: disable=wrong-import-position +from ...stats import compare, loo, waic, hdi # pylint: disable=wrong-import-position # Skip tests if bokeh not installed bkp = importorskip("bokeh.plotting") # pylint: disable=invalid-name @@ -483,16 +483,20 @@ def test_plot_forest_bad(models, model_fits): "kwargs", [ {"color": "C5", "circular": True}, - {"fill_kwargs": {"alpha": 0}}, + {"hdi_data": True, "fill_kwargs": {"alpha": 0}}, {"plot_kwargs": {"alpha": 0}}, {"smooth_kwargs": {"window_length": 33, "polyorder": 5, "mode": "mirror"}}, - {"smooth": False}, + {"hdi_data": True, "smooth": False, "color": "xkcd:jade"}, ], ) def test_plot_hdi(models, data, kwargs): - axis = plot_hdi( - data["y"], models.model_1.posterior["theta"], backend="bokeh", show=False, **kwargs - ) + hdi_data = kwargs.pop("hdi_data", None) + y_data = models.model_1.posterior["theta"] + if hdi_data: + hdi_data = hdi(y_data) + axis = plot_hdi(data["y"], hdi_data=hdi_data, backend="bokeh", show=False, **kwargs) + else: + axis = plot_hdi(data["y"], y_data, backend="bokeh", show=False, **kwargs) assert axis diff --git a/arviz/tests/base_tests/test_plots_matplotlib.py b/arviz/tests/base_tests/test_plots_matplotlib.py index fc7f576c84..3e91daef50 100644 --- a/arviz/tests/base_tests/test_plots_matplotlib.py +++ b/arviz/tests/base_tests/test_plots_matplotlib.py @@ -10,7 +10,7 @@ import pytest from ...data import from_dict, load_arviz_data -from ...stats import compare, loo, waic +from ...stats import compare, loo, waic, hdi from ..helpers import ( # pylint: disable=unused-import eight_schools_params, models, @@ -847,14 +847,43 @@ def test_plot_compare_no_ic(models): "kwargs", [ {"color": "0.5", "circular": True}, - {"fill_kwargs": {"alpha": 0}}, + {"hdi_data": True, "fill_kwargs": {"alpha": 0}}, {"plot_kwargs": {"alpha": 0}}, {"smooth_kwargs": {"window_length": 33, "polyorder": 5, "mode": "mirror"}}, - {"smooth": False}, + {"hdi_data": True, "smooth": False}, ], ) def test_plot_hdi(models, data, kwargs): - plot_hdi(data["y"], models.model_1.posterior["theta"], **kwargs) + hdi_data = kwargs.pop("hdi_data", None) + if hdi_data: + hdi_data = hdi(models.model_1.posterior["theta"]) + ax = plot_hdi(data["y"], hdi_data=hdi_data, **kwargs) + else: + ax = plot_hdi(data["y"], models.model_1.posterior["theta"], **kwargs) + assert ax + + +def test_plot_hdi_warning(): + """Check using both y and hdi_data sends a warning.""" + x_data = np.random.normal(0, 1, 100) + y_data = np.random.normal(2 + x_data * 0.5, 0.5, (1, 200, 100)) + hdi_data = hdi(y_data) + with pytest.warns(UserWarning, match="Both y and hdi_data"): + ax = plot_hdi(x_data, y=y_data, hdi_data=hdi_data) + assert ax + + +def test_plot_hdi_missing_arg_error(): + """Check that both y and hdi_data missing raises an error.""" + with pytest.raises(ValueError, match="One of {y, hdi_data"): + plot_hdi(np.arange(20)) + + +def test_plot_hdi_dataset_error(models): + """Check hdi_data as multiple variable Dataset raises an error.""" + hdi_data = hdi(models.model_1) + with pytest.raises(ValueError, match="Only single variable Dataset"): + plot_hdi(np.arange(8), hdi_data=hdi_data) @pytest.mark.parametrize("limits", [(-10.0, 10.0), (-5, 5), (None, None)]) diff --git a/doc/conf.py b/doc/conf.py index 690c33e374..aa24f3915f 100644 --- a/doc/conf.py +++ b/doc/conf.py @@ -269,4 +269,5 @@ def setup(app): "pymc3": ("https://docs.pymc.io/", None), "mpl": ("https://matplotlib.org/", None), "bokeh": ("https://docs.bokeh.org/en/latest/", None), + "scipy": ("https://docs.scipy.org/doc/scipy/reference/", None), } From 77af5db71930ee6695f5b9acc02428ba0e4b605a Mon Sep 17 00:00:00 2001 From: Colin Date: Mon, 22 Jun 2020 13:02:26 -0400 Subject: [PATCH 23/24] Use constrained layout in all styles (#1247) --- arviz/plots/styles/arviz-darkgrid.mplstyle | 1 + arviz/plots/styles/arviz-grayscale.mplstyle | 1 + arviz/plots/styles/arviz-white.mplstyle | 1 + arviz/plots/styles/arviz-whitegrid.mplstyle | 1 + 4 files changed, 4 insertions(+) diff --git a/arviz/plots/styles/arviz-darkgrid.mplstyle b/arviz/plots/styles/arviz-darkgrid.mplstyle index 88cecd0eb0..300c2ae9cc 100644 --- a/arviz/plots/styles/arviz-darkgrid.mplstyle +++ b/arviz/plots/styles/arviz-darkgrid.mplstyle @@ -5,6 +5,7 @@ figure.figsize: 7.2, 4.8 figure.dpi: 100.0 figure.facecolor: white +figure.constrained_layout.use: True text.color: .15 axes.labelcolor: .15 legend.frameon: False diff --git a/arviz/plots/styles/arviz-grayscale.mplstyle b/arviz/plots/styles/arviz-grayscale.mplstyle index fc955f1be3..6c15db32be 100644 --- a/arviz/plots/styles/arviz-grayscale.mplstyle +++ b/arviz/plots/styles/arviz-grayscale.mplstyle @@ -5,6 +5,7 @@ figure.figsize: 7.2, 4.8 figure.dpi: 100.0 figure.facecolor: white +figure.constrained_layout.use: True text.color: .15 axes.labelcolor: .15 legend.frameon: False diff --git a/arviz/plots/styles/arviz-white.mplstyle b/arviz/plots/styles/arviz-white.mplstyle index c24cf2a5fe..6b7eeeddf5 100644 --- a/arviz/plots/styles/arviz-white.mplstyle +++ b/arviz/plots/styles/arviz-white.mplstyle @@ -5,6 +5,7 @@ figure.figsize: 7.2, 4.8 figure.dpi: 100.0 figure.facecolor: white +figure.constrained_layout.use: True text.color: .15 axes.labelcolor: .15 legend.frameon: False diff --git a/arviz/plots/styles/arviz-whitegrid.mplstyle b/arviz/plots/styles/arviz-whitegrid.mplstyle index 676bbe0ff7..f6887ea52d 100644 --- a/arviz/plots/styles/arviz-whitegrid.mplstyle +++ b/arviz/plots/styles/arviz-whitegrid.mplstyle @@ -5,6 +5,7 @@ figure.figsize: 7.2, 4.8 figure.dpi: 100.0 figure.facecolor: white +figure.constrained_layout.use: True text.color: .15 axes.labelcolor: .15 legend.frameon: False From e5e4eab165c7a73054542312cbb6f5f5406d8f6b Mon Sep 17 00:00:00 2001 From: Oriol Abril-Pla Date: Mon, 22 Jun 2020 19:07:50 +0200 Subject: [PATCH 24/24] traceplot fixes (#1253) * traceplot fixes * update to prop dict * black * extend tests * update changelog * extend tests and fix lint Co-authored-by: Ari Hartikainen --- CHANGELOG.md | 5 ++ arviz/plots/backends/bokeh/traceplot.py | 15 ++-- arviz/plots/backends/matplotlib/traceplot.py | 72 +++++++++---------- arviz/plots/plot_utils.py | 21 ++++++ arviz/plots/traceplot.py | 51 ++++++++----- arviz/tests/base_tests/test_plot_utils.py | 20 ++++++ arviz/tests/base_tests/test_plots_bokeh.py | 4 +- .../tests/base_tests/test_plots_matplotlib.py | 7 ++ 8 files changed, 129 insertions(+), 66 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 3da6aa0b74..4a6d60a914 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -7,6 +7,8 @@ * Added `html_repr` of InferenceData objects for jupyter notebooks. (#1217) * Added support for PyJAGS via the function `from_pyjags`. (#1219 and #1245) * `from_pymc3` can now retrieve `coords` and `dims` from model context (#1228, #1240 and #1249) +* `plot_trace` now supports multiple aesthetics to identify chain and variable + shape and support matplotlib aliases (#1253) * `plot_hdi` can now take already computed HDI values (#1241) ### Maintenance and fixes @@ -20,10 +22,13 @@ * Remove false positive warning in `plot_hdi` and fixed matplotlib axes generation (#1241) * Change the default `zorder` of scatter points from `0` to `0.6` in `plot_pair` (#1246) * Update `get_bins` for numpy 1.19 compatibility (#1256) +* Fixes to `rug`, `divergences` arguments in `plot_trace` (#1253) ### Deprecation * Using `from_pymc3` without a model context available now raises a `FutureWarning` and will be deprecated in a future version (#1227) +* In `plot_trace`, `chain_prop` and `compact_prop` as tuples will now raise a + `FutureWarning` (#1253) * `hdi` with 2d data raises a FutureWarning (#1241) ### Documentation diff --git a/arviz/plots/backends/bokeh/traceplot.py b/arviz/plots/backends/bokeh/traceplot.py index 0fb8984d92..926946ff94 100644 --- a/arviz/plots/backends/bokeh/traceplot.py +++ b/arviz/plots/backends/bokeh/traceplot.py @@ -12,7 +12,7 @@ from .. import show_layout from ...distplot import plot_dist from ...rankplot import plot_rank -from ...plot_utils import xarray_var_iter, make_label, _scale_fig_size +from ...plot_utils import xarray_var_iter, make_label, _scale_fig_size, _dealiase_sel_kwargs from ....rcparams import rcParams @@ -300,8 +300,7 @@ def _plot_chains_bokeh( x=x_name, y=y_name, source=cds, - **{chain_prop[0]: chain_prop[1][chain_idx]}, - **trace_kwargs, + **_dealiase_sel_kwargs(trace_kwargs, chain_prop, chain_idx) ) if marker: ax_trace.circle( @@ -310,26 +309,24 @@ def _plot_chains_bokeh( source=cds, radius=0.30, alpha=0.5, - **{chain_prop[0]: chain_prop[1][chain_idx],}, + **_dealiase_sel_kwargs({}, chain_prop, chain_idx) ) if not combined: rug_kwargs["cds"] = cds if legend: plot_kwargs["legend_label"] = "chain {}".format(chain_idx) - plot_kwargs[chain_prop[0]] = chain_prop[1][chain_idx] plot_dist( cds.data[y_name], ax=ax_density, rug=rug, hist_kwargs=hist_kwargs, - plot_kwargs=plot_kwargs, + plot_kwargs=_dealiase_sel_kwargs(plot_kwargs, chain_prop, chain_idx), fill_kwargs=fill_kwargs, rug_kwargs=rug_kwargs, backend="bokeh", backend_kwargs={}, show=False, ) - plot_kwargs.pop(chain_prop[0]) if kind == "rank_bars": value = np.array([item.data[y_name] for item in data.values()]) @@ -342,17 +339,15 @@ def _plot_chains_bokeh( rug_kwargs["cds"] = data if legend: plot_kwargs["legend_label"] = "combined chains" - plot_kwargs[chain_prop[0]] = chain_prop[1][-1] plot_dist( np.concatenate([item.data[y_name] for item in data.values()]).flatten(), ax=ax_density, rug=rug, hist_kwargs=hist_kwargs, - plot_kwargs=plot_kwargs, + plot_kwargs=_dealiase_sel_kwargs(plot_kwargs, chain_prop, -1), fill_kwargs=fill_kwargs, rug_kwargs=rug_kwargs, backend="bokeh", backend_kwargs={}, show=False, ) - plot_kwargs.pop(chain_prop[0]) diff --git a/arviz/plots/backends/matplotlib/traceplot.py b/arviz/plots/backends/matplotlib/traceplot.py index fce418089d..d85adb6f45 100644 --- a/arviz/plots/backends/matplotlib/traceplot.py +++ b/arviz/plots/backends/matplotlib/traceplot.py @@ -9,7 +9,7 @@ from . import backend_kwarg_defaults, backend_show from ...distplot import plot_dist from ...rankplot import plot_rank -from ...plot_utils import _scale_fig_size, make_label, format_coords_as_labels +from ...plot_utils import _scale_fig_size, make_label, format_coords_as_labels, _dealiase_sel_kwargs from ....numeric_utils import get_bins @@ -158,8 +158,11 @@ def plot_trace( if len(value.shape) == 2: if compact_prop: - plot_kwargs[compact_prop[0]] = compact_prop[1][0] - trace_kwargs[compact_prop[0]] = compact_prop[1][0] + aux_plot_kwargs = _dealiase_sel_kwargs(plot_kwargs, compact_prop, 0) + aux_trace_kwargs = _dealiase_sel_kwargs(trace_kwargs, compact_prop, 0) + else: + aux_plot_kwargs = plot_kwargs + aux_trace_kwargs = trace_kwargs _plot_chains_mpl( axes, idx, @@ -170,16 +173,13 @@ def plot_trace( xt_labelsize, rug, kind, - trace_kwargs, + aux_trace_kwargs, hist_kwargs, - plot_kwargs, + aux_plot_kwargs, fill_kwargs, rug_kwargs, rank_kwargs, ) - if compact_prop: - plot_kwargs.pop(compact_prop[0]) - trace_kwargs.pop(compact_prop[0]) else: sub_data = data[var_name].sel(**selection) legend_labels = format_coords_as_labels(sub_data, skip_dims=("chain", "draw")) @@ -191,14 +191,14 @@ def plot_trace( ] ) value = value.reshape((value.shape[0], value.shape[1], -1)) - compact_prop_cycle = cycle(compact_prop[1]) + compact_prop_iter = { + prop_name: [prop for _, prop in zip(range(value.shape[2]), cycle(props))] + for prop_name, props in compact_prop.items() + } handles = [] - for sub_idx, label, prop in zip( - range(value.shape[2]), legend_labels, compact_prop_cycle - ): - if compact_prop: - plot_kwargs[compact_prop[0]] = prop - trace_kwargs[compact_prop[0]] = prop + for sub_idx, label in zip(range(value.shape[2]), legend_labels): + aux_plot_kwargs = _dealiase_sel_kwargs(plot_kwargs, compact_prop_iter, sub_idx) + aux_trace_kwargs = _dealiase_sel_kwargs(trace_kwargs, compact_prop_iter, sub_idx) _plot_chains_mpl( axes, idx, @@ -209,9 +209,9 @@ def plot_trace( xt_labelsize, rug, kind, - trace_kwargs, + aux_trace_kwargs, hist_kwargs, - plot_kwargs, + aux_plot_kwargs, fill_kwargs, rug_kwargs, rank_kwargs, @@ -219,13 +219,14 @@ def plot_trace( if legend: handles.append( Line2D( - [], [], label=label, **{chain_prop[0]: chain_prop[1][0]}, **plot_kwargs + [], + [], + label=label, + **_dealiase_sel_kwargs(aux_plot_kwargs, chain_prop, 0) ) ) if legend: axes[idx, 0].legend(handles=handles, title=legend_title) - plot_kwargs.pop(compact_prop[0], None) - trace_kwargs.pop(compact_prop[0], None) if value[0].dtype.kind == "i": xticks = get_bins(value) @@ -264,7 +265,7 @@ def plot_trace( markersize=30, linestyle="None", alpha=hist_kwargs["alpha"], - zorder=-5, + zorder=0.6, ) axes[idx, 1].set_ylim(*ylims[1]) axes[idx, 0].plot( @@ -276,7 +277,7 @@ def plot_trace( markersize=30, linestyle="None", alpha=trace_kwargs["alpha"], - zorder=-5, + zorder=0.6, ) axes[idx, 0].set_ylim(*ylims[0]) @@ -298,24 +299,26 @@ def plot_trace( linewidth=1.5, alpha=trace_kwargs["alpha"] ) - axes[idx, 0].set_ylim(bottom=0, top=ylims[0][1]) + axes[idx, 0].set_ylim(ylims[0]) if kind == "trace": axes[idx, 1].set_xlim(left=data.draw.min(), right=data.draw.max()) axes[idx, 1].set_ylim(*ylims[1]) if legend: legend_kwargs = trace_kwargs if combined else plot_kwargs handles = [ - Line2D([], [], label=chain_id, **{chain_prop[0]: prop}, **legend_kwargs) - for chain_id, prop in zip(data.chain.values, chain_prop[1]) + Line2D( + [], [], label=chain_id, **_dealiase_sel_kwargs(legend_kwargs, chain_prop, chain_id) + ) + for chain_id in range(data.dims["chain"]) ] if combined: handles.insert( 0, Line2D( - [], [], label="combined", **{chain_prop[0]: chain_prop[1][-1]}, **plot_kwargs + [], [], label="combined", **_dealiase_sel_kwargs(plot_kwargs, chain_prop, -1) ), ) - axes[0, 0].legend(handles=handles, title="chain") + axes[0, 0].legend(handles=handles, title="chain", loc="upper right") if backend_show(show): plt.show() @@ -342,25 +345,23 @@ def _plot_chains_mpl( ): for chain_idx, row in enumerate(value): if kind == "trace": - axes[idx, 1].plot( - data.draw.values, row, **{chain_prop[0]: chain_prop[1][chain_idx]}, **trace_kwargs - ) + aux_kwargs = _dealiase_sel_kwargs(trace_kwargs, chain_prop, chain_idx) + axes[idx, 1].plot(data.draw.values, row, **aux_kwargs) if not combined: - plot_kwargs[chain_prop[0]] = chain_prop[1][chain_idx] + aux_kwargs = _dealiase_sel_kwargs(plot_kwargs, chain_prop, chain_idx) plot_dist( values=row, textsize=xt_labelsize, rug=rug, ax=axes[idx, 0], hist_kwargs=hist_kwargs, - plot_kwargs=plot_kwargs, + plot_kwargs=aux_kwargs, fill_kwargs=fill_kwargs, rug_kwargs=rug_kwargs, backend="matplotlib", show=False, ) - plot_kwargs.pop(chain_prop[0]) if kind == "rank_bars": plot_rank(data=value, kind="bars", ax=axes[idx, 1], **rank_kwargs) @@ -368,17 +369,16 @@ def _plot_chains_mpl( plot_rank(data=value, kind="vlines", ax=axes[idx, 1], **rank_kwargs) if combined: - plot_kwargs[chain_prop[0]] = chain_prop[1][-1] + aux_kwargs = _dealiase_sel_kwargs(plot_kwargs, chain_prop, -1) plot_dist( values=value.flatten(), textsize=xt_labelsize, rug=rug, ax=axes[idx, 0], hist_kwargs=hist_kwargs, - plot_kwargs=plot_kwargs, + plot_kwargs=aux_kwargs, fill_kwargs=fill_kwargs, rug_kwargs=rug_kwargs, backend="matplotlib", show=False, ) - plot_kwargs.pop(chain_prop[0]) diff --git a/arviz/plots/plot_utils.py b/arviz/plots/plot_utils.py index a9c229e621..998c608f3e 100644 --- a/arviz/plots/plot_utils.py +++ b/arviz/plots/plot_utils.py @@ -674,3 +674,24 @@ def matplotlib_kwarg_dealiaser(args, kind, backend="matplotlib"): args, getattr(matplotlib_kwarg_dealiaser_dict[kind], "_alias_map", {}) ) return args + + +def _dealiase_sel_kwargs(kwargs, prop_dict, idx): + """Generate kwargs dict from kwargs and prop_dict. + + Gets property at position ``idx`` for each property in prop_dict and adds it to + ``kwargs``. Values in prop_dict are dealiased and overwrite values in + kwargs with the same key . + + Parameters + ---------- + kwargs : dict + prop_dict : dict of {str : array_like} + idx : int + """ + return { + **kwargs, + **matplotlib_kwarg_dealiaser( + {prop: props[idx] for prop, props in prop_dict.items()}, "plot" + ), + } diff --git a/arviz/plots/traceplot.py b/arviz/plots/traceplot.py index 9a8a66b479..65d0f690c5 100644 --- a/arviz/plots/traceplot.py +++ b/arviz/plots/traceplot.py @@ -1,7 +1,7 @@ """Plot kde or histograms and values from MCMC samples.""" from itertools import cycle import warnings -from typing import Callable, List, Optional, Tuple, Any +from typing import Callable, List, Optional, Tuple, Any, Mapping, Union import matplotlib.pyplot as plt @@ -22,15 +22,15 @@ def plot_trace( filter_vars: Optional[str] = None, transform: Optional[Callable] = None, coords: Optional[CoordSpec] = None, - divergences: Optional[str] = "bottom", + divergences: Optional[str] = "auto", kind: Optional[str] = "trace", figsize: Optional[Tuple[float, float]] = None, rug: bool = False, lines: Optional[List[Tuple[str, CoordSpec, Any]]] = None, compact: bool = False, - compact_prop: Optional[Tuple[str, Any]] = None, + compact_prop: Optional[Union[str, Mapping[str, Any]]] = None, combined: bool = False, - chain_prop: Optional[Tuple[str, Any]] = None, + chain_prop: Optional[Union[str, Mapping[str, Any]]] = None, legend: bool = False, plot_kwargs: Optional[KwargSpec] = None, fill_kwargs: Optional[KwargSpec] = None, @@ -80,13 +80,13 @@ def plot_trace( vertical lines on the density and horizontal lines on the trace. compact: bool, optional Plot multidimensional variables in a single plot. - compact_prop: tuple of (str, array_like), optional + compact_prop: str or dict {str: array_like}, optional Tuple containing the property name and the property values to distinguish diferent dimensions with compact=True combined: bool, optional Flag for combining multiple chains into a single line. If False (default), chains will be plotted separately. - chain_prop: tuple of (str, array_like), optional + chain_prop: str or dict {str: array_like}, optional Tuple containing the property name and the property values to distinguish diferent chains legend: bool, optional Add a legend to the figure with the chain color code. @@ -156,6 +156,8 @@ def plot_trace( if kind not in {"trace", "rank_vlines", "rank_bars"}: raise ValueError("The value of kind must be either trace, rank_vlines or rank_bars.") + if divergences == "auto": + divergences = "top" if rug else "bottom" if divergences: try: divergence_data = convert_to_dataset(data, group="sample_stats").diverging @@ -186,7 +188,7 @@ def plot_trace( if not compact: if backend == "bokeh": chain_prop = ( - ("line_color", plt.rcParams["axes.prop_cycle"].by_key()["color"]) + {"line_color": plt.rcParams["axes.prop_cycle"].by_key()["color"]} if chain_prop is None else chain_prop ) @@ -194,16 +196,17 @@ def plot_trace( chain_prop = "color" if chain_prop is None else chain_prop else: chain_prop = ( - ( - "line_dash" if backend == "bokeh" else "linestyle", - ("solid", "dotted", "dashed", "dashdot"), - ) + { + "line_dash" + if backend == "bokeh" + else "linestyle": ("solid", "dotted", "dashed", "dashdot"), + } if chain_prop is None else chain_prop ) if backend == "bokeh": compact_prop = ( - ("line_color", plt.rcParams["axes.prop_cycle"].by_key()["color"]) + {"line_color": plt.rcParams["axes.prop_cycle"].by_key()["color"]} if compact_prop is None else compact_prop ) @@ -214,14 +217,26 @@ def plot_trace( # TODO: kind of related: move mpl specific code to backend and # define prop_cycle instead of only colors if isinstance(chain_prop, str): - chain_prop = (chain_prop, plt.rcParams["axes.prop_cycle"].by_key()[chain_prop]) - chain_prop = ( - chain_prop[0], - [prop for _, prop in zip(range(num_chain_props), cycle(chain_prop[1]))], - ) + chain_prop = {chain_prop: plt.rcParams["axes.prop_cycle"].by_key()[chain_prop]} + if isinstance(chain_prop, tuple): + warnings.warn( + "chain_prop as a tuple will be deprecated in a future warning, use a dict instead", + FutureWarning, + ) + chain_prop = {chain_prop[0]: chain_prop[1]} + chain_prop = { + prop_name: [prop for _, prop in zip(range(num_chain_props), cycle(props))] + for prop_name, props in chain_prop.items() + } if isinstance(compact_prop, str): - compact_prop = (compact_prop, plt.rcParams["axes.prop_cycle"].by_key()[compact_prop]) + compact_prop = {compact_prop: plt.rcParams["axes.prop_cycle"].by_key()[compact_prop]} + if isinstance(compact_prop, tuple): + warnings.warn( + "compact_prop as a tuple will be deprecated in a future warning, use a dict instead", + FutureWarning, + ) + compact_prop = {compact_prop[0]: compact_prop[1]} if compact: skip_dims = set(data.dims) - {"chain", "draw"} diff --git a/arviz/tests/base_tests/test_plot_utils.py b/arviz/tests/base_tests/test_plot_utils.py index e2cf8ec0c6..3c3777001a 100644 --- a/arviz/tests/base_tests/test_plot_utils.py +++ b/arviz/tests/base_tests/test_plot_utils.py @@ -16,6 +16,7 @@ xarray_to_ndarray, xarray_var_iter, vectorized_to_hex, + _dealiase_sel_kwargs, ) from ...rcparams import rc_context from ...numeric_utils import get_bins @@ -244,3 +245,22 @@ def test_vectorized_to_hex_scalar(c_values): def test_vectorized_to_hex_array(c_values): output = vectorized_to_hex(c_values) assert np.all([item == "#0000ff" for item in output]) + + +def test_dealiase_sel_kwargs(): + """Check _dealiase_sel_kwargs behaviour. + + Makes sure kwargs are overwritten when necessary even with alias involved and that + they are not modified when not included in props. + """ + kwargs = {"linewidth": 3, "alpha": 0.4, "line_color": "red"} + props = {"lw": [1, 2, 4, 5], "linestyle": ["-", "--", ":"]} + res = _dealiase_sel_kwargs(kwargs, props, 2) + assert "linewidth" in res + assert res["linewidth"] == 4 + assert "linestyle" in res + assert res["linestyle"] == ":" + assert "alpha" in res + assert res["alpha"] == 0.4 + assert "line_color" in res + assert res["line_color"] == "red" diff --git a/arviz/tests/base_tests/test_plots_bokeh.py b/arviz/tests/base_tests/test_plots_bokeh.py index 11d1417757..ba7d438193 100644 --- a/arviz/tests/base_tests/test_plots_bokeh.py +++ b/arviz/tests/base_tests/test_plots_bokeh.py @@ -127,8 +127,8 @@ def test_plot_density_bad_kwargs(models): {}, {"var_names": "mu"}, {"var_names": ["mu", "tau"]}, - {"combined": True}, - {"compact": True}, + {"combined": True, "rug": True}, + {"compact": True, "legend": True}, {"combined": True, "compact": True, "legend": True}, {"divergences": "top"}, {"divergences": False}, diff --git a/arviz/tests/base_tests/test_plots_matplotlib.py b/arviz/tests/base_tests/test_plots_matplotlib.py index 3e91daef50..aff2b9d54b 100644 --- a/arviz/tests/base_tests/test_plots_matplotlib.py +++ b/arviz/tests/base_tests/test_plots_matplotlib.py @@ -210,6 +210,13 @@ def test_plot_trace_bad_lines_value(models, bad_kwargs): plot_trace(models.model_1, **bad_kwargs) +@pytest.mark.parametrize("prop", ["chain_prop", "compact_prop"]) +def test_plot_trace_futurewarning(models, prop): + with pytest.warns(FutureWarning, match=f"{prop} as a tuple.+deprecated"): + ax = plot_trace(models.model_1, **{prop: ("ls", ("-", "--"))}) + assert ax.shape + + @pytest.mark.parametrize("model_fits", [["model_1"], ["model_1", "model_2"]]) @pytest.mark.parametrize( "args_expected",