# Path: thejoker/thejoker.py
# class TheJoker:
#     def __init__(self, prior, pool=None, random_state=None,
#                  tempfile_path=None):
#     def tempfile_path(self):
#     def _make_joker_helper(self, data):
#     def marginal_ln_likelihood(self, data, prior_samples, n_batches=None,
#                                in_memory=False):
#     def rejection_sample(self, data, prior_samples,
#                          n_prior_samples=None,
#                          max_posterior_samples=None,
#                          n_linear_samples=1,
#                          return_logprobs=False,
#                          return_all_logprobs=False,
#                          n_batches=None,
#                          randomize_prior_order=False,
#                          in_memory=False):
#     def iterative_rejection_sample(self, data, prior_samples,
#                                    n_requested_samples,
#                                    max_prior_samples=None,
#                                    n_linear_samples=1,
#                                    return_logprobs=False,
#                                    n_batches=None,
#                                    randomize_prior_order=False,
#                                    init_batch_size=None,
#                                    growth_factor=128,
#                                    in_memory=False):
#     def setup_mcmc(self, data, joker_samples, model=None, custom_func=None):
#     def trace_to_samples(self, trace, data, names=None):
#             N = prior_samples
#         M = get_trend_design_matrix(data, ids, self.prior.poly_trend)


import os
import warnings
        import h5py
    import exoplanet.units as xu
    import pymc3 as pm
    import exoplanet.units as xu
from astropy.table.meta import get_header_from_yaml, get_yaml_from_table
from astropy.io.misc.hdf5 import _encode_mixins, meta_path
from astropy.utils.exceptions import AstropyUserWarning
from astropy.utils import metadata
from thejoker.thejoker import validate_prepare_data
    from astropy.table import meta
    from thejoker.samples import JokerSamples
    from thejoker.samples import JokerSamples
                "object's table datatype do not match. "
                f"{existing_header['datatype']} vs. {this_header['datatype']}")

        # If we got here, we can now try to append:
        current_size = len(output_group[name])
        output_group[name].resize((current_size + len(table), ))
        output_group[name][current_size:] = table.as_array()


def inferencedata_to_samples(joker_prior, inferencedata, data,
                             prune_divergences=True):
    """
    Create a ``JokerSamples`` instance from an arviz object.

    Parameters
    ----------
    joker_prior : `thejoker.JokerPrior`
    inferencedata : `arviz.InferenceData`
    data : `thejoker.RVData`
    prune_divergences : bool (optional)

    """

    if hasattr(inferencedata, 'posterior'):
        posterior = inferencedata.posterior

    else:
        posterior = inferencedata
        inferencedata = None


# Path: thejoker/thejoker.py
# class TheJoker:
#     def __init__(self, prior, pool=None, random_state=None,
#                  tempfile_path=None):
#     def tempfile_path(self):
#     def _make_joker_helper(self, data):
#     def marginal_ln_likelihood(self, data, prior_samples, n_batches=None,
#                                in_memory=False):
#     def rejection_sample(self, data, prior_samples,
#                          n_prior_samples=None,
#                          max_posterior_samples=None,
#                          n_linear_samples=1,
#                          return_logprobs=False,
#                          return_all_logprobs=False,
#                          n_batches=None,
#                          randomize_prior_order=False,
#                          in_memory=False):
#     def iterative_rejection_sample(self, data, prior_samples,
#                                    n_requested_samples,
#                                    max_prior_samples=None,
#                                    n_linear_samples=1,
#                                    return_logprobs=False,
#                                    n_batches=None,
#                                    randomize_prior_order=False,
#                                    init_batch_size=None,
#                                    growth_factor=128,
#                                    in_memory=False):
#     def setup_mcmc(self, data, joker_samples, model=None, custom_func=None):
#     def trace_to_samples(self, trace, data, names=None):
#             N = prior_samples
#         M = get_trend_design_matrix(data, ids, self.prior.poly_trend)


# Path: thejoker/samples_helpers.py
import os
import warnings
        import h5py
    import exoplanet.units as xu
    import pymc3 as pm
    import exoplanet.units as xu
from astropy.table.meta import get_header_from_yaml, get_yaml_from_table
from astropy.io.misc.hdf5 import _encode_mixins, meta_path
from astropy.utils.exceptions import AstropyUserWarning
from astropy.utils import metadata
from thejoker.thejoker import validate_prepare_data
    from astropy.table import meta
    from thejoker.samples import JokerSamples
    from thejoker.samples import JokerSamples

                "object's table datatype do not match. "
                f"{existing_header['datatype']} vs. {this_header['datatype']}")

        # If we got here, we can now try to append:
        current_size = len(output_group[name])
        output_group[name].resize((current_size + len(table), ))
        output_group[name][current_size:] = table.as_array()


def inferencedata_to_samples(joker_prior, inferencedata, data,
                             prune_divergences=True):
    """
    Create a ``JokerSamples`` instance from an arviz object.

    Parameters
    ----------
    joker_prior : `thejoker.JokerPrior`
    inferencedata : `arviz.InferenceData`
    data : `thejoker.RVData`
    prune_divergences : bool (optional)

    """

    if hasattr(inferencedata, 'posterior'):
        posterior = inferencedata.posterior

    else:
        posterior = inferencedata
        inferencedata = None


    data, *_ = validate_prepare_data(data,