# Path: gptwosample/data/data_base.py
# def get_training_data_structure(x1, x2, y1, y2):
# def get_model_structure(individual=None, common=None):
# def has_model_structure(structure):
#     def __init__(self, *args, **kwargs):
# class DataStructureError(TypeError):


from gptwosample.data.data_base import input_id, output_id, individual_id, \
    common_id, has_model_structure, get_model_structure, DataStructureError
from pygp.gp.gp_base import GP
from pygp.gp.composite import GroupGP
from pygp.optimize.optimize_base import opt_hyper
from pygp.plot.gpr_plot import plot_sausage, plot_training_data
from copy import deepcopy
from matplotlib import cm
from pygp.likelihood.likelihood_base import GaussLikISO
import scipy
import numpy
        import pylab
        # self.covar = covar
        # set models for this TwoSampleBase Test
        self._models = {individual_id:GroupGP([gpr1, gpr2]),
                        common_id:GP(deepcopy(covar), likelihood=GaussLikISO())}

class TwoSampleSeparate(TwoSampleBase):
    """
    This class provides comparison of two Timeline Groups to one another, inlcuding timeshifts in replicates, respectively.

    see :py:class:`gptwosample.twosample.twosample_base.TwoSampleBase` for detailed description of provided methods.

    Note that this model will need one covariance function for each model, respectively!
    """
    def __init__(self, covar_individual_1, covar_individual_2, covar_common, **kwargs):
        """
        see :py:class:`gptwosample.twosample.twosample_base.TwoSampleBase`
        """
        if not kwargs.has_key('initial_hyperparameters'):
            kwargs['initial_hyperparameters'] = \
                get_model_structure(individual={'covar':numpy.zeros(covar_individual_1.get_number_of_parameters())},
                                    common={'covar':numpy.zeros(covar_common.get_number_of_parameters())})
        super(TwoSampleSeparate, self).__init__(**kwargs)
        gpr1 = GP(deepcopy(covar_individual_1), likelihood=GaussLikISO())
        gpr2 = GP(deepcopy(covar_individual_2), likelihood=GaussLikISO())
        # self.covar_individual_1 = covar_individual_1
        # self.covar_individual_2 = covar_individual_2
        # self.covar_common = covar_common
        # set models for this TwoSampleBase Test



# Path: gptwosample/data/data_base.py
# def get_training_data_structure(x1, x2, y1, y2):
# def get_model_structure(individual=None, common=None):
# def has_model_structure(structure):
#     def __init__(self, *args, **kwargs):
# class DataStructureError(TypeError):


# Path: gptwosample/twosample/twosample_base.py
from gptwosample.data.data_base import input_id, output_id, individual_id, \
    common_id, has_model_structure, get_model_structure, DataStructureError
from pygp.gp.gp_base import GP
from pygp.gp.composite import GroupGP
from pygp.optimize.optimize_base import opt_hyper
from pygp.plot.gpr_plot import plot_sausage, plot_training_data
from copy import deepcopy
from matplotlib import cm
from pygp.likelihood.likelihood_base import GaussLikISO
import scipy
import numpy
        import pylab

        # self.covar = covar
        # set models for this TwoSampleBase Test
        self._models = {individual_id:GroupGP([gpr1, gpr2]),
                        common_id:GP(deepcopy(covar), likelihood=GaussLikISO())}

class TwoSampleSeparate(TwoSampleBase):
    """
    This class provides comparison of two Timeline Groups to one another, inlcuding timeshifts in replicates, respectively.

    see :py:class:`gptwosample.twosample.twosample_base.TwoSampleBase` for detailed description of provided methods.

    Note that this model will need one covariance function for each model, respectively!
    """
    def __init__(self, covar_individual_1, covar_individual_2, covar_common, **kwargs):
        """
        see :py:class:`gptwosample.twosample.twosample_base.TwoSampleBase`
        """
        if not kwargs.has_key('initial_hyperparameters'):
            kwargs['initial_hyperparameters'] = \
                get_model_structure(individual={'covar':numpy.zeros(covar_individual_1.get_number_of_parameters())},
                                    common={'covar':numpy.zeros(covar_common.get_number_of_parameters())})
        super(TwoSampleSeparate, self).__init__(**kwargs)
        gpr1 = GP(deepcopy(covar_individual_1), likelihood=GaussLikISO())
        gpr2 = GP(deepcopy(covar_individual_2), likelihood=GaussLikISO())
        # self.covar_individual_1 = covar_individual_1
        # self.covar_individual_2 = covar_individual_2
        # self.covar_common = covar_common
        # set models for this TwoSampleBase Test



        self._models = {individual_id:GroupGP([gpr1, gpr2]), common_id:GP(deepcopy(covar_common), likelihood=GaussLikISO())}