from .PyMCObjects import Stochastic
from .Container import Container
from .InstantiationDecorators import stochastic
from .flib import mod_to_circle
from .distributions import rvon_mises, von_mises_like
import numpy as np
from .distributions import valuewrapper

__all__ = ['CircularStochastic', 'CircVonMises']


class CircularStochastic(Stochastic):

    """
    C = CircularStochastic(lo, hi, *args, **kwargs)

    Takes two special parents, lo and hi; any incoming value
    will be mapped into the interval [lo,hi).

    args and kwargs will be passed to Stochastic.__init__.

    :Attributes:
      lo : float
        Lower bound
      hi : float
        Upper bound

    :SeeAlso: Stochastic
    """
    def __init__(self, lo, hi, *args, **kwargs):
        self.interval_parents = Container([hi, lo])
        Stochastic.__init__(self, *args, **kwargs)

    def set_value(self, value):
        modded_value = mod_to_circle(
            value,
            *
            self.interval_parents.value).reshape(
                np.shape(
                    value))
        Stochastic.set_value(self, modded_value)
    value = property(Stochastic.get_value, set_value)


class CircVonMises(CircularStochastic):

    """
    V = CircVonMises(name, mu, kappa, value=None, observed=False, size=1, trace=True, rseed=True, doc=None)

    Stochastic variable with Von Mises distribution.
    Parents are: mu, kappa.

    Docstring of log-probability function:
    """ + von_mises_like.__doc__
    def __init__(self,
                 name,
                 mu, kappa,
                 value=None,
                 observed=False,
                 size=1,
                 trace=True,
                 cache_depth=2,
                 rseed=True,
                 plot=None,
                 verbose=-1):

        if value is None:
            arg_eval = Container([mu, kappa]).value
            value = rvon_mises(arg_eval[0], arg_eval[1], size=size)

        parents = {'mu': mu, 'kappa': kappa}
        logp = valuewrapper(von_mises_like)
        random = lambda mu, kappa, size=size: rvon_mises(mu, kappa, size=size)
        CircularStochastic.__init__(
            self,
            -np.pi,
            np.pi,
            logp,
            'A Von Mises-distributed variable',
            name,
            parents,
            random,
            trace,
            value,
            np.dtype('float'),
            rseed,
            observed,
            cache_depth,
            plot,
            verbose)
