# 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
        samples['ln_posterior'] = posterior.logp.values.ravel()

    for name in ['ln_likelihood', 'ln_prior']:
        if hasattr(posterior, name):
            samples[name] = getattr(posterior, name).values.ravel()

    if prune_divergences:
        if inferencedata is None:
            raise ValueError(
                "If you want to remove divergences, you must pass in the root "
                "level inferencedata object (instead of, e.g., inferencedata. "
                "posterior")

        divergences = inferencedata.sample_stats.diverging.values.ravel()
        samples = samples[~divergences]

    return samples


def trace_to_samples(self, trace, data, names=None):
    """
    Create a ``JokerSamples`` instance from a pymc3 trace object.

    Parameters
    ----------
    trace : `~pymc3.backends.base.MultiTrace`
    """

    df = pm.trace_to_dataframe(trace)


# 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

        samples['ln_posterior'] = posterior.logp.values.ravel()

    for name in ['ln_likelihood', 'ln_prior']:
        if hasattr(posterior, name):
            samples[name] = getattr(posterior, name).values.ravel()

    if prune_divergences:
        if inferencedata is None:
            raise ValueError(
                "If you want to remove divergences, you must pass in the root "
                "level inferencedata object (instead of, e.g., inferencedata. "
                "posterior")

        divergences = inferencedata.sample_stats.diverging.values.ravel()
        samples = samples[~divergences]

    return samples


def trace_to_samples(self, trace, data, names=None):
    """
    Create a ``JokerSamples`` instance from a pymc3 trace object.

    Parameters
    ----------
    trace : `~pymc3.backends.base.MultiTrace`
    """

    df = pm.trace_to_dataframe(trace)


    data, *_ = validate_prepare_data(data,