# Path: pomp/spaces/biassets.py
# class TimeBiasSet(BoxSet):
#     """A set for an integration time variable that samples more-or-less
#     uniformly from the *outcome* state, given a maximum integration duration
#     and a space of controls uspace.
# 
#     It assumes the next state is obtained by the integral
#        int[0,T] f(x(t),u) dt
#     and the function f(x,u) is not degenerate.  With this assumption, the
#     the volume of the reachable set grows proportionally to T^d where d
#     is the control dimension.  Hence, the sampler samples T from the range
#     [0,tmax] according to the distribution U(0,1)^(1/d)*tmax.  In practice,
#     this places more samples toward the tail end of the integration region. 
#     """
#     def __init__(self,tmax,uspace):
#         BoxSet.__init__(self,[0],[tmax])
#         self.tmax = tmax
#         self.controlDimension = len(uspace.sample())
#         #if self.controlDimension == 1:
#         #    self.controlDimension = 2
#     def sample(self):
#         #plain time sampling
#         #return [random.uniform(0,self.tmax)]
#         #sampling from upper half
#         #return [random.uniform(self.tmax*0.5,self.tmax)]
#         #sampling with a power law bias
#         return [math.pow(random.random(),1.0/self.controlDimension)*self.tmax]


from .configurationspace import *
from .timespace import *
from .interpolators import *
from .sets import *
from .biassets import TimeBiasSet
from . import differences
import numpy as np
            path.append(xnew)
            t = min(t+dt0,duration)
        return path
    def nextState(self,x,u):
        return self.trajectory(x,u)[-1]
    def interpolator(self,x,u):
        res = PiecewiseLinearInterpolator(self.trajectory(x,u))
        if hasattr(self.cspace,'geodesic'):
            res.geodesic = self.cspace.geodesic
        return res
    def connection(self,x,y):
        return None


class TimedKinodynamicSpace (ControlSpace):
    """Just like KinodynamicSpace except the state variable maintains the
    current time in the 0'th entry.

    State is x=(t,xbase), and control is u=(dt,ubase)."""
    def __init__(self,cspace,dspace,dt=0.01,dtmax=0.1):
        """Provide a ConfigurationSpace and a Dynamics and this class will
        merge them together"""
        self.cspace = cspace
        self.tcspace = TCSpace(cspace)
        self.dspace = dspace
        self.dt = dt
        self.dtmax = dtmax
    def configurationSpace(self):
        return self.tcspace
    def controlSet(self,x):

# Path: pomp/spaces/biassets.py
# class TimeBiasSet(BoxSet):
#     """A set for an integration time variable that samples more-or-less
#     uniformly from the *outcome* state, given a maximum integration duration
#     and a space of controls uspace.
# 
#     It assumes the next state is obtained by the integral
#        int[0,T] f(x(t),u) dt
#     and the function f(x,u) is not degenerate.  With this assumption, the
#     the volume of the reachable set grows proportionally to T^d where d
#     is the control dimension.  Hence, the sampler samples T from the range
#     [0,tmax] according to the distribution U(0,1)^(1/d)*tmax.  In practice,
#     this places more samples toward the tail end of the integration region. 
#     """
#     def __init__(self,tmax,uspace):
#         BoxSet.__init__(self,[0],[tmax])
#         self.tmax = tmax
#         self.controlDimension = len(uspace.sample())
#         #if self.controlDimension == 1:
#         #    self.controlDimension = 2
#     def sample(self):
#         #plain time sampling
#         #return [random.uniform(0,self.tmax)]
#         #sampling from upper half
#         #return [random.uniform(self.tmax*0.5,self.tmax)]
#         #sampling with a power law bias
#         return [math.pow(random.random(),1.0/self.controlDimension)*self.tmax]


# Path: pomp/spaces/controlspace.py
from .configurationspace import *
from .timespace import *
from .interpolators import *
from .sets import *
from .biassets import TimeBiasSet
from . import differences
import numpy as np

            path.append(xnew)
            t = min(t+dt0,duration)
        return path
    def nextState(self,x,u):
        return self.trajectory(x,u)[-1]
    def interpolator(self,x,u):
        res = PiecewiseLinearInterpolator(self.trajectory(x,u))
        if hasattr(self.cspace,'geodesic'):
            res.geodesic = self.cspace.geodesic
        return res
    def connection(self,x,y):
        return None


class TimedKinodynamicSpace (ControlSpace):
    """Just like KinodynamicSpace except the state variable maintains the
    current time in the 0'th entry.

    State is x=(t,xbase), and control is u=(dt,ubase)."""
    def __init__(self,cspace,dspace,dt=0.01,dtmax=0.1):
        """Provide a ConfigurationSpace and a Dynamics and this class will
        merge them together"""
        self.cspace = cspace
        self.tcspace = TCSpace(cspace)
        self.dspace = dspace
        self.dt = dt
        self.dtmax = dtmax
    def configurationSpace(self):
        return self.tcspace
    def controlSet(self,x):

        return MultiSet(TimeBiasSet(self.dtmax,self.dspace.controlSet(x)),self.dspace.controlSet(x))