Query: intersecting of two nonlinear lines python

Relevant code:
def segment_intersection(start0, end0, start1, end1):
    r"""Determine the intersection of two line segments.

    Assumes each line is parametric

    .. math::

       \begin{alignat*}{2}
        L_0(s) &= S_0 (1 - s) + E_0 s &&= S_0 + s \Delta_0 \\
        L_1(t) &= S_1 (1 - t) + E_1 t &&= S_1 + t \Delta_1.
       \end{alignat*}

    To solve :math:`S_0 + s \Delta_0 = S_1 + t \Delta_1`, we use the
    cross product:

    .. math::

       \left(S_0 + s \Delta_0\right) \times \Delta_1 =
           \left(S_1 + t \Delta_1\right) \times \Delta_1 \Longrightarrow
       s \left(\Delta_0 \times \Delta_1\right) =
           \left(S_1 - S_0\right) \times \Delta_1.

    Similarly

    .. math::

       \Delta_0 \times \left(S_0 + s \Delta_0\right) =
           \Delta_0 \times \left(S_1 + t \Delta_1\right) \Longrightarrow
       \left(S_1 - S_0\right) \times \Delta_0 =
           \Delta_0 \times \left(S_0 - S_1\right) =
           t \left(\Delta_0 \times \Delta_1\right).

    .. note::

       Since our points are in :math:`\mathbf{R}^2`, the "traditional"
       cross product in :math:`\mathbf{R}^3` will always point in the
       :math:`z` direction, so in the above we mean the :math:`z`
       component of the cross product, rather than the entire vector.

    For example, the diagonal lines

    .. math::

       \begin{align*}
        L_0(s) &= \left[\begin{array}{c} 0 \\ 0 \end{array}\right] (1 - s) +
                  \left[\begin{array}{c} 2 \\ 2 \end{array}\right] s \\
        L_1(t) &= \left[\begin{array}{c} -1 \\ 2 \end{array}\right] (1 - t) +
                  \left[\begin{array}{c} 1 \\ 0 \end{array}\right] t
       \end{align*}

    intersect at :math:`L_0\left(\frac{1}{4}\right) =
    L_1\left(\frac{3}{4}\right) =
    \frac{1}{2} \left[\begin{array}{c} 1 \\ 1 \end{array}\right]`.

    .. image:: ../images/segment_intersection1.png
       :align: center

    .. testsetup:: segment-intersection1, segment-intersection2

       import numpy as np
       from bezier._geometric_intersection import segment_intersection

    .. doctest:: segment-intersection1
       :options: +NORMALIZE_WHITESPACE

       >>> start0 = np.asfortranarray([0.0, 0.0])
       >>> end0 = np.asfortranarray([2.0, 2.0])
       >>> start1 = np.asfortranarray([-1.0, 2.0])
       >>> end1 = np.asfortranarray([1.0, 0.0])
       >>> s, t, _ = segment_intersection(start0, end0, start1, end1)
       >>> s
       0.25
       >>> t
       0.75

    .. testcleanup:: segment-intersection1

       import make_images
       make_images.segment_intersection1(start0, end0, start1, end1, s)

    Taking the parallel (but different) lines

    .. math::

       \begin{align*}
        L_0(s) &= \left[\begin{array}{c} 1 \\ 0 \end{array}\right] (1 - s) +
                  \left[\begin{array}{c} 0 \\ 1 \end{array}\right] s \\
        L_1(t) &= \left[\begin{array}{c} -1 \\ 3 \end{array}\right] (1 - t) +
                  \left[\begin{array}{c} 3 \\ -1 \end{array}\right] t
       \end{align*}

    we should be able to determine that the lines don't intersect, but
    this function is not meant for that check:

    .. image:: ../images/segment_intersection2.png
       :align: center

    .. doctest:: segment-intersection2
       :options: +NORMALIZE_WHITESPACE

       >>> start0 = np.asfortranarray([1.0, 0.0])
       >>> end0 = np.asfortranarray([0.0, 1.0])
       >>> start1 = np.asfortranarray([-1.0, 3.0])
       >>> end1 = np.asfortranarray([3.0, -1.0])
       >>> _, _, success = segment_intersection(start0, end0, start1, end1)
       >>> success
       False

    .. testcleanup:: segment-intersection2

       import make_images
       make_images.segment_intersection2(start0, end0, start1, end1)

    Instead, we use :func:`parallel_lines_parameters`:

    .. testsetup:: segment-intersection2-continued

       import numpy as np
       from bezier._geometric_intersection import parallel_lines_parameters

       start0 = np.asfortranarray([1.0, 0.0])
       end0 = np.asfortranarray([0.0, 1.0])
       start1 = np.asfortranarray([-1.0, 3.0])
       end1 = np.asfortranarray([3.0, -1.0])

    .. doctest:: segment-intersection2-continued

       >>> disjoint, _ = parallel_lines_parameters(start0, end0, start1, end1)
       >>> disjoint
       True

    .. note::

       There is also a Fortran implementation of this function, which
       will be used if it can be built.

    Args:
        start0 (numpy.ndarray): A 1D NumPy ``2``-array that is the start
            vector :math:`S_0` of the parametric line :math:`L_0(s)`.
        end0 (numpy.ndarray): A 1D NumPy ``2``-array that is the end
            vector :math:`E_0` of the parametric line :math:`L_0(s)`.
        start1 (numpy.ndarray): A 1D NumPy ``2``-array that is the start
            vector :math:`S_1` of the parametric line :math:`L_1(s)`.
        end1 (numpy.ndarray): A 1D NumPy ``2``-array that is the end
            vector :math:`E_1` of the parametric line :math:`L_1(s)`.

    Returns:
        Tuple[float, float, bool]: Pair of :math:`s_{\ast}` and
        :math:`t_{\ast}` such that the lines intersect:
        :math:`L_0\left(s_{\ast}\right) = L_1\left(t_{\ast}\right)` and then
        a boolean indicating if an intersection was found (i.e. if the lines
        aren't parallel).
    """
    delta0 = end0 - start0
    delta1 = end1 - start1
    cross_d0_d1 = _helpers.cross_product(delta0, delta1)
    if cross_d0_d1 == 0.0:
        return None, None, False

    else:
        start_delta = start1 - start0
        s = _helpers.cross_product(start_delta, delta1) / cross_d0_d1
        t = _helpers.cross_product(start_delta, delta0) / cross_d0_d1
        return s, t, True

Similar but not relevant code:
def boxes_intersect(box1, box2):
    """Determines if two rectangles, each input as a tuple
        (xmin, xmax, ymin, ymax), intersect."""
    xmin1, xmax1, ymin1, ymax1 = box1
    xmin2, xmax2, ymin2, ymax2 = box2
    if interval_intersection_width(xmin1, xmax1, xmin2, xmax2) and \
            interval_intersection_width(ymin1, ymax1, ymin2, ymax2):
        return True
    else:
        return False

Similar but not relevant code:
def is_intersection(g, n):
    """
    Determine if a node is an intersection

    graph: 1 -->-- 2 -->-- 3

    >>> is_intersection(g, 2)
    False

    graph:
     1 -- 2 -- 3
          |
          4

    >>> is_intersection(g, 2)
    True

    Parameters
    ----------
    g : networkx DiGraph
    n : node id

    Returns
    -------
    bool

    """
    return len(set(g.predecessors(n) + g.successors(n))) > 2

Similar but not relevant code:
def _linear_interpolation(x, X, Y):
    """Given two data points [X,Y], linearly interpolate those at x.
    """
    return (Y[1] * (x - X[0]) + Y[0] * (X[1] - x)) / (X[1] - X[0])