# Path: bmlingam/tests/test_pm3.py
# def _gen_data(i, gen_data_params):
# def _estimate_hparams(xs, infer_params):
# def _mainloop(n_trials, gen_data_params, infer_params, mcmc_params):
# def test_posterior_inference(
#     infer_params=None, mcmc_params=None, gen_data_params=None, 
#     n_trials=50, plot_result=False, plot_samples_all=False, show_result=False, 
#     show_result_all=False):


from copy import deepcopy
from bmlingam.tests.test_pm3 import test_posterior_inference, \
                                    infer_params_default, \
                                    mcmc_params_default, \
                                    gen_data_params_default
import six

            'mcmc_params_name': mcmc_params_name, 
            'gen_data_params_name': gen_data_params_name, 
            'infer_params': infer_params, 
            'mcmc_params': mcmc_params, 
            'gen_data_params': gen_data_params, 
        }
        for infer_params_name, infer_params in six.iteritems(infer_paramss)
        for mcmc_params_name, mcmc_params in six.iteritems(mcmc_paramss)
        for gen_data_params_name, gen_data_params in six.iteritems(gen_data_paramss)
    ]

    return test_conds

def _get_infer_paramss():
    infer_params_mp4 = deepcopy(infer_params_default)
    infer_params_mp4.sampling_mode = 'cache_mp4'

    infer_params_standardize = deepcopy(infer_params_mp4)
    infer_params_standardize.standardize = True

    infer_params_keepratio = deepcopy(infer_params_mp4)
    infer_params_keepratio.standardize = 'keepratio'

    infer_params_scaling = deepcopy(infer_params_mp4)
    infer_params_scaling.standardize = 'scaling'

    infer_params_commonscaling = deepcopy(infer_params_mp4)
    infer_params_commonscaling.standardize = 'commonscaling'

    return {

# Path: bmlingam/tests/test_pm3.py
# def _gen_data(i, gen_data_params):
# def _estimate_hparams(xs, infer_params):
# def _mainloop(n_trials, gen_data_params, infer_params, mcmc_params):
# def test_posterior_inference(
#     infer_params=None, mcmc_params=None, gen_data_params=None, 
#     n_trials=50, plot_result=False, plot_samples_all=False, show_result=False, 
#     show_result_all=False):


# Path: bmlingam/tests/test_bmlingam.py
from copy import deepcopy
from bmlingam.tests.test_pm3 import test_posterior_inference, \
                                    infer_params_default, \
                                    mcmc_params_default, \
                                    gen_data_params_default
import six

            'mcmc_params_name': mcmc_params_name, 
            'gen_data_params_name': gen_data_params_name, 
            'infer_params': infer_params, 
            'mcmc_params': mcmc_params, 
            'gen_data_params': gen_data_params, 
        }
        for infer_params_name, infer_params in six.iteritems(infer_paramss)
        for mcmc_params_name, mcmc_params in six.iteritems(mcmc_paramss)
        for gen_data_params_name, gen_data_params in six.iteritems(gen_data_paramss)
    ]

    return test_conds

def _get_infer_paramss():
    infer_params_mp4 = deepcopy(infer_params_default)
    infer_params_mp4.sampling_mode = 'cache_mp4'

    infer_params_standardize = deepcopy(infer_params_mp4)
    infer_params_standardize.standardize = True

    infer_params_keepratio = deepcopy(infer_params_mp4)
    infer_params_keepratio.standardize = 'keepratio'

    infer_params_scaling = deepcopy(infer_params_mp4)
    infer_params_scaling.standardize = 'scaling'

    infer_params_commonscaling = deepcopy(infer_params_mp4)
    infer_params_commonscaling.standardize = 'commonscaling'

    return {

        'default': infer_params_default, 
