# 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/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):


import numpy as np
from ..constants import res_path as res
from ..constants import tol_path as tol
    from scipy.interpolate import splev
        from scipy.special import binom
    assert len(result) >= 2

    return result


def discretize_bspline(control, knots, count=None, scale=1.0):
    '''
    Given a B-Splines control points and knot vector, return
    a sampled version of the curve.

    Arguments
    ----------
    control:  (o,d) list of control points of the b- spline.
    knots:    (j) list of knots
    count:    int, number of sections to discretize the spline in to.
              If not specified, RES_LENGTH will be used to inform this.

    Returns
    ----------
    discrete: (count,d) list of points, a polyline of the B-spline.
    '''

    # evaluate the b-spline using scipy/fitpack
    # (n, d) control points where d is the dimension of vertices
    control = np.array(control)
    degree = len(knots) - len(control) - 1
    if count is None:
        norm = np.linalg.norm(np.diff(control, axis=0), axis=1).sum()
        count = int(np.clip(norm / (res.seg_frac * scale),
                            res.min_sections * len(control),

# 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/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/curve.py
import numpy as np
from ..constants import res_path as res
from ..constants import tol_path as tol
    from scipy.interpolate import splev
        from scipy.special import binom

    assert len(result) >= 2

    return result


def discretize_bspline(control, knots, count=None, scale=1.0):
    '''
    Given a B-Splines control points and knot vector, return
    a sampled version of the curve.

    Arguments
    ----------
    control:  (o,d) list of control points of the b- spline.
    knots:    (j) list of knots
    count:    int, number of sections to discretize the spline in to.
              If not specified, RES_LENGTH will be used to inform this.

    Returns
    ----------
    discrete: (count,d) list of points, a polyline of the B-spline.
    '''

    # evaluate the b-spline using scipy/fitpack
    # (n, d) control points where d is the dimension of vertices
    control = np.array(control)
    degree = len(knots) - len(control) - 1
    if count is None:
        norm = np.linalg.norm(np.diff(control, axis=0), axis=1).sum()
        count = int(np.clip(norm / (res.seg_frac * scale),
                            res.min_sections * len(control),

                            res.max_sections * len(control)))