# Path: trimesh/grouping.py
# def unique_ordered(data):
#     '''
#     Returns the same as np.unique, but ordered as per the
#     first occurance of the unique value in data.
# 
#     Example
#     ---------
#     In [1]: a = [0, 3, 3, 4, 1, 3, 0, 3, 2, 1]
# 
#     In [2]: np.unique(a)
#     Out[2]: array([0, 1, 2, 3, 4])
# 
#     In [3]: trimesh.grouping.unique_ordered(a)
#     Out[3]: array([0, 3, 4, 1, 2])
#     '''
#     data = np.asanyarray(data)
#     order = np.sort(np.unique(data, return_index=True)[1])
#     result = data[order]
#     return result
# 
# Path: trimesh/util.py
# def unitize(points, check_valid=False):
#     '''
#     Turn a list of vectors into a list of unit vectors.
# 
#     Arguments
#     ---------
#     points:       (n,m) or (j) input array of vectors.
#                   For 1D arrays, points is treated as a single vector
#                   For 2D arrays, each row is treated as a vector
#     check_valid:  boolean, if True enables valid output and checking
# 
#     Returns
#     ---------
#     unit_vectors: (n,m) or (j) length array of unit vectors
# 
#     valid:        (n) boolean array, output only if check_valid.
#                    True for all valid (nonzero length) vectors, thus m=sum(valid)
#     '''
#     points = np.asanyarray(points)
#     axis = len(points.shape) - 1
#     length = np.sum(points ** 2, axis=axis) ** .5
# 
#     if is_sequence(length):
#         length[np.isnan(length)] = 0.0
# 
#     if check_valid:
#         valid = np.greater(length, _TOL_ZERO)
#         if axis == 1:
#             unit_vectors = (points[valid].T / length[valid]).T
#         elif len(points.shape) == 1 and valid:
#             unit_vectors = points / length
#         else:
#             unit_vectors = np.array([])
#         return unit_vectors, valid
#     else:
#         unit_vectors = (points.T / length).T
#     return unit_vectors
# 
# Path: trimesh/constants.py
# class NumericalToleranceMesh(object):
# class NumericalResolutionMesh(object):
# class NumericalTolerancePath(object):
# class NumericalResolutionPath(object):
# class MeshError(Exception):
# class TransformError(Exception):
#     def __init__(self, **kwargs):
#     def __init__(self, **kwargs):
#     def __init__(self, **kwargs):
#     def __init__(self, **kwargs):
# def _log_time(method):
#     def timed(*args, **kwargs):
# 
# Path: trimesh/path/util.py
# def is_ccw(points):
#     '''
#     Given an (n,2) set of points, return True if they are counterclockwise
#     '''
#     xd = np.diff(points[:, 0])
#     yd = np.sum(np.column_stack((points[:, 1],
#                                  points[:, 1])).reshape(-1)[1:-1].reshape((-1, 2)), axis=1)
#     area = np.sum(xd * yd) * .5
#     ccw = area < 0
# 
#     return ccw


import numpy as np
import networkx as nx
from collections import deque
from ..grouping import unique_ordered
from ..util import unitize
from ..constants import tol_path as tol
from .util import is_ccw

    Arguments
    ----------
    points:   (n,d) sequence of points in space
    count:    number of points to sample to (aka np.linspace)
    step:     distance each step should take along the path (aka np.arange)

    Returns
    ----------
    resampled: (j,d) set of points on the path
    '''

    points = np.array(points, dtype=np.float)
    # generate samples along the perimeter from kwarg count or step
    if (count is not None) and (step is not None):
        raise ValueError('Only step OR count can be specified')
    if (count is None) and (step is None):
        raise ValueError('Either step or count must be specified')

    sampler = PathSample(points)
    if step is not None and step_round:
        count = int(np.ceil(sampler.length / step))
    if count is not None:
        samples = np.linspace(0, sampler.length, count)
    elif step is not None:
        samples = np.arange(0, sampler.length, step)

    resampled = sampler.sample(samples)

    check = np.linalg.norm(points[[0, -1]] - resampled[[0, -1]], axis=1)

# Path: trimesh/grouping.py
# def unique_ordered(data):
#     '''
#     Returns the same as np.unique, but ordered as per the
#     first occurance of the unique value in data.
# 
#     Example
#     ---------
#     In [1]: a = [0, 3, 3, 4, 1, 3, 0, 3, 2, 1]
# 
#     In [2]: np.unique(a)
#     Out[2]: array([0, 1, 2, 3, 4])
# 
#     In [3]: trimesh.grouping.unique_ordered(a)
#     Out[3]: array([0, 3, 4, 1, 2])
#     '''
#     data = np.asanyarray(data)
#     order = np.sort(np.unique(data, return_index=True)[1])
#     result = data[order]
#     return result
# 
# Path: trimesh/util.py
# def unitize(points, check_valid=False):
#     '''
#     Turn a list of vectors into a list of unit vectors.
# 
#     Arguments
#     ---------
#     points:       (n,m) or (j) input array of vectors.
#                   For 1D arrays, points is treated as a single vector
#                   For 2D arrays, each row is treated as a vector
#     check_valid:  boolean, if True enables valid output and checking
# 
#     Returns
#     ---------
#     unit_vectors: (n,m) or (j) length array of unit vectors
# 
#     valid:        (n) boolean array, output only if check_valid.
#                    True for all valid (nonzero length) vectors, thus m=sum(valid)
#     '''
#     points = np.asanyarray(points)
#     axis = len(points.shape) - 1
#     length = np.sum(points ** 2, axis=axis) ** .5
# 
#     if is_sequence(length):
#         length[np.isnan(length)] = 0.0
# 
#     if check_valid:
#         valid = np.greater(length, _TOL_ZERO)
#         if axis == 1:
#             unit_vectors = (points[valid].T / length[valid]).T
#         elif len(points.shape) == 1 and valid:
#             unit_vectors = points / length
#         else:
#             unit_vectors = np.array([])
#         return unit_vectors, valid
#     else:
#         unit_vectors = (points.T / length).T
#     return unit_vectors
# 
# Path: trimesh/constants.py
# class NumericalToleranceMesh(object):
# class NumericalResolutionMesh(object):
# class NumericalTolerancePath(object):
# class NumericalResolutionPath(object):
# class MeshError(Exception):
# class TransformError(Exception):
#     def __init__(self, **kwargs):
#     def __init__(self, **kwargs):
#     def __init__(self, **kwargs):
#     def __init__(self, **kwargs):
# def _log_time(method):
#     def timed(*args, **kwargs):
# 
# Path: trimesh/path/util.py
# def is_ccw(points):
#     '''
#     Given an (n,2) set of points, return True if they are counterclockwise
#     '''
#     xd = np.diff(points[:, 0])
#     yd = np.sum(np.column_stack((points[:, 1],
#                                  points[:, 1])).reshape(-1)[1:-1].reshape((-1, 2)), axis=1)
#     area = np.sum(xd * yd) * .5
#     ccw = area < 0
# 
#     return ccw


# Path: trimesh/path/traversal.py
import numpy as np
import networkx as nx
from collections import deque
from ..grouping import unique_ordered
from ..util import unitize
from ..constants import tol_path as tol
from .util import is_ccw


    Arguments
    ----------
    points:   (n,d) sequence of points in space
    count:    number of points to sample to (aka np.linspace)
    step:     distance each step should take along the path (aka np.arange)

    Returns
    ----------
    resampled: (j,d) set of points on the path
    '''

    points = np.array(points, dtype=np.float)
    # generate samples along the perimeter from kwarg count or step
    if (count is not None) and (step is not None):
        raise ValueError('Only step OR count can be specified')
    if (count is None) and (step is None):
        raise ValueError('Either step or count must be specified')

    sampler = PathSample(points)
    if step is not None and step_round:
        count = int(np.ceil(sampler.length / step))
    if count is not None:
        samples = np.linspace(0, sampler.length, count)
    elif step is not None:
        samples = np.arange(0, sampler.length, step)

    resampled = sampler.sample(samples)

    check = np.linalg.norm(points[[0, -1]] - resampled[[0, -1]], axis=1)

    assert check[0] < tol.merge