# Path: pomp/klampt/cspace.py
# class CSpace:
#     """Used alongside MotionPlan to define a configuration space for
#     motion planning.
# 
#     To define a custom CSpace, subclasses will need to override:
#         
#     - feasible(x): returns true if the vector x is in the feasible space
#     - *sample(): returns a new vector x from a superset of the feasible space
#     - *sampleneighborhood(c,r): returns a new vector x from a neighborhood of c
#       with radius r
#     - *visible(a,b): returns true if the path between a and b is feasible
#     - *distance(a,b): return a distance between a and b
#     - *interpolate(a,b,u): interpolate between a, b with parameter u
#     (* indicates an optional override.)
# 
#     To avoid memory leaks, CSpace.close() or motionplanning.destroy() must
#     be called when you are done.  (The latter deallocates all previously
#     created cspaces and planners)
# 
#     If sample() is not defined, then subclasses should set self.bound to be a
#     list of pairs defining an axis-aligned bounding box.  The setBounds method
#     is a convenient way of defining this.
# 
#     If visible() is not defined, then paths are checked by subdivision, with
#     the collision tolerance self.eps.
# 
#     To help planners know a bit more about the CSpace, you can set the
#     self.properties member to a map from strings to values.  Useful values
#     are
#     
#     - euclidean (0 or 1): indicates a euclidean space
#     - geodesic (0 or 1): indicates whether the interpolation is along
#       geodesics.
#     - volume (real): a size of the space
#     - minimum, maximum (real array): bounds on the space
#     - metric (string): the metric used by distance, can be "euclidean",
#       "weighted euclidean", "manhattan", "weighted manhattan", "Linf", etc.
#     - metricWeights (real array): weights used by weighted metrics
#     
#     volume is necessary for Lazy PRM* and Lazy RRG* to work.
#     
#     metricWeights is necessary for KD-tree point location structures to work,
#     for FMM methods to work, etc.
# 
#     minimum/maximum can be used by grid-based methods (optional for FMM, FMM*).
#     """
#     def __init__(self):
#         self.cspace = None
#         self.eps = 1e-3
#         self.bound = [(0,1)]
#         self.properties = {}
# 
#     def setBounds(self,bounds):
#         """Convenience function: sets the sampling bound and the
#         space properties in one line."""
#         self.bounds = bounds
#         self.properties["minimum"] = [b[0] for b in bounds]
#         self.properties["maximum"] = [b[1] for b in bounds]
# 
#     def close(self):
#         """This method must be called to free the memory associated with the
#         planner.  Alternatively, motionplanning.destroy() can be called to
#         free all previously constructed CSpace and MotionPlan objects."""
#         if self.cspace != None:
#             self.cspace.destroy()
#             self.cspace = None
#     
#     def setup(self):
#         """Called internally by the MotionPlan class to set up planning
#         hooks."""
#         assert self.cspace == None
#         self.cspace = motionplanning.CSpaceInterface()
#         if hasattr(self,'feasible'):
#             self.cspace.setFeasibility(getattr(self,'feasible'))
#         else:
#             raise 'Need feasible method'
#         if hasattr(self,'visible'):
#             self.cspace.setVisibility(getattr(self,'visible'))
#         else:
#             self.cspace.setVisibilityEpsilon(self.eps)
#         if hasattr(self,'sample'):
#             self.cspace.setSampler(getattr(self,'sample'))
#         else:
#             raise 'Need sample method'
#         if hasattr(self,'sampleneighborhood'):
#             self.cspace.setNeighborhoodSampler(getattr(self,'sampleneighborhood'))
#         if hasattr(self,'distance'):
#             self.cspace.setDistance(getattr(self,'distance'))
#         if hasattr(self,'interpolate'):
#             self.cspace.setInterpolate(getattr(self,'interpolate'))
#         for (k,v) in self.properties:
#             if isinstance(v,(list,tuple)):
#                 self.cspace.setPropety(k," ".join([str(item) for item in v]))
#             else:
#                 self.cspace.setProperty(k,str(v))
# 
#     def sample(self):
#         """Overload this to define a nonuniform sampler.
#         By default, it will sample from the axis-aligned bounding box
#         defined by self.bound. To define a different domain, set self.bound
#         to the desired bound.
#         """
#         return [random.uniform(*b) for b in self.bound]
# 
#     def feasible(self,x):
#         """Overload this to define your new feasibility test"""
#         return all(a<=xi<=b for (xi,(a,b)) in zip(x,self.bound))


from six import iteritems
from builtins import range
from .cspace import CSpace
import math
import time

    def add_test(self,f):
        self.tests.append(f)
        self.reset_history(self.tests[-1])

    def update_order(self):
        thelist = [(f._sum_cost/f._num_fail,f) for f in self.tests]
        self.tests = [t for (ec,t) in sorted(thelist)]

    def testmax(self,*args):
        """Tests all tests, returning the max absolute deviation from 0."""
        vmax = 0.0
        for f in self.tests:
            t1 = time.time()
            res = f(*args)
            t2 = time.time()
            self.update_stats(f,t2-t1,res)
            vmax = max(vmax,abs(res))
        self.update_order()
        return vmax

    def test(self,*args):
        """Tests whether *args passes all tests.  Updates the stats and
        internal order"""
        for f in self.tests:
            t1 = time.time()
            res = f(*args)
            t2 = time.time()
            self.update_stats(f,t2-t1,res)
            if res != 0:

# Path: pomp/klampt/cspace.py
# class CSpace:
#     """Used alongside MotionPlan to define a configuration space for
#     motion planning.
# 
#     To define a custom CSpace, subclasses will need to override:
#         
#     - feasible(x): returns true if the vector x is in the feasible space
#     - *sample(): returns a new vector x from a superset of the feasible space
#     - *sampleneighborhood(c,r): returns a new vector x from a neighborhood of c
#       with radius r
#     - *visible(a,b): returns true if the path between a and b is feasible
#     - *distance(a,b): return a distance between a and b
#     - *interpolate(a,b,u): interpolate between a, b with parameter u
#     (* indicates an optional override.)
# 
#     To avoid memory leaks, CSpace.close() or motionplanning.destroy() must
#     be called when you are done.  (The latter deallocates all previously
#     created cspaces and planners)
# 
#     If sample() is not defined, then subclasses should set self.bound to be a
#     list of pairs defining an axis-aligned bounding box.  The setBounds method
#     is a convenient way of defining this.
# 
#     If visible() is not defined, then paths are checked by subdivision, with
#     the collision tolerance self.eps.
# 
#     To help planners know a bit more about the CSpace, you can set the
#     self.properties member to a map from strings to values.  Useful values
#     are
#     
#     - euclidean (0 or 1): indicates a euclidean space
#     - geodesic (0 or 1): indicates whether the interpolation is along
#       geodesics.
#     - volume (real): a size of the space
#     - minimum, maximum (real array): bounds on the space
#     - metric (string): the metric used by distance, can be "euclidean",
#       "weighted euclidean", "manhattan", "weighted manhattan", "Linf", etc.
#     - metricWeights (real array): weights used by weighted metrics
#     
#     volume is necessary for Lazy PRM* and Lazy RRG* to work.
#     
#     metricWeights is necessary for KD-tree point location structures to work,
#     for FMM methods to work, etc.
# 
#     minimum/maximum can be used by grid-based methods (optional for FMM, FMM*).
#     """
#     def __init__(self):
#         self.cspace = None
#         self.eps = 1e-3
#         self.bound = [(0,1)]
#         self.properties = {}
# 
#     def setBounds(self,bounds):
#         """Convenience function: sets the sampling bound and the
#         space properties in one line."""
#         self.bounds = bounds
#         self.properties["minimum"] = [b[0] for b in bounds]
#         self.properties["maximum"] = [b[1] for b in bounds]
# 
#     def close(self):
#         """This method must be called to free the memory associated with the
#         planner.  Alternatively, motionplanning.destroy() can be called to
#         free all previously constructed CSpace and MotionPlan objects."""
#         if self.cspace != None:
#             self.cspace.destroy()
#             self.cspace = None
#     
#     def setup(self):
#         """Called internally by the MotionPlan class to set up planning
#         hooks."""
#         assert self.cspace == None
#         self.cspace = motionplanning.CSpaceInterface()
#         if hasattr(self,'feasible'):
#             self.cspace.setFeasibility(getattr(self,'feasible'))
#         else:
#             raise 'Need feasible method'
#         if hasattr(self,'visible'):
#             self.cspace.setVisibility(getattr(self,'visible'))
#         else:
#             self.cspace.setVisibilityEpsilon(self.eps)
#         if hasattr(self,'sample'):
#             self.cspace.setSampler(getattr(self,'sample'))
#         else:
#             raise 'Need sample method'
#         if hasattr(self,'sampleneighborhood'):
#             self.cspace.setNeighborhoodSampler(getattr(self,'sampleneighborhood'))
#         if hasattr(self,'distance'):
#             self.cspace.setDistance(getattr(self,'distance'))
#         if hasattr(self,'interpolate'):
#             self.cspace.setInterpolate(getattr(self,'interpolate'))
#         for (k,v) in self.properties:
#             if isinstance(v,(list,tuple)):
#                 self.cspace.setPropety(k," ".join([str(item) for item in v]))
#             else:
#                 self.cspace.setProperty(k,str(v))
# 
#     def sample(self):
#         """Overload this to define a nonuniform sampler.
#         By default, it will sample from the axis-aligned bounding box
#         defined by self.bound. To define a different domain, set self.bound
#         to the desired bound.
#         """
#         return [random.uniform(*b) for b in self.bound]
# 
#     def feasible(self,x):
#         """Overload this to define your new feasibility test"""
#         return all(a<=xi<=b for (xi,(a,b)) in zip(x,self.bound))


# Path: pomp/klampt/cspaceutils.py
from six import iteritems
from builtins import range
from .cspace import CSpace
import math
import time


    def add_test(self,f):
        self.tests.append(f)
        self.reset_history(self.tests[-1])

    def update_order(self):
        thelist = [(f._sum_cost/f._num_fail,f) for f in self.tests]
        self.tests = [t for (ec,t) in sorted(thelist)]

    def testmax(self,*args):
        """Tests all tests, returning the max absolute deviation from 0."""
        vmax = 0.0
        for f in self.tests:
            t1 = time.time()
            res = f(*args)
            t2 = time.time()
            self.update_stats(f,t2-t1,res)
            vmax = max(vmax,abs(res))
        self.update_order()
        return vmax

    def test(self,*args):
        """Tests whether *args passes all tests.  Updates the stats and
        internal order"""
        for f in self.tests:
            t1 = time.time()
            res = f(*args)
            t2 = time.time()
            self.update_stats(f,t2-t1,res)
            if res != 0:

                self.update_order()