import numpy as np
from shapely import ops
from shapely.geometry import Polygon

from .. import bounds, geometry, graph, grouping
from ..constants import log
from ..constants import tol_path as tol
from ..transformations import transform_points
from ..typed import List, NDArray, Optional, float64
from .simplify import fit_circle_check
from .traversal import resample_path

try:
    import networkx as nx
except BaseException as E:
    # create a dummy module which will raise the ImportError
    # or other exception only when someone tries to use networkx
    from ..exceptions import ExceptionWrapper

    nx = ExceptionWrapper(E)
try:
    from rtree.index import Index
except BaseException as E:
    # create a dummy module which will raise the ImportError
    from ..exceptions import ExceptionWrapper

    Index = ExceptionWrapper(E)


def enclosure_tree(polygons: List[Polygon]):
    """
    Given a list of shapely polygons with only exteriors,
    find which curves represent the exterior shell or root curve
    and which represent holes which penetrate the exterior.

    This is done with an R-tree for rough overlap detection,
    and then exact polygon queries for a final result.

    Parameters
    -----------
    polygons : (n,) shapely.geometry.Polygon
       Polygons which only have exteriors and may overlap

    Returns
    -----------
    roots : (m,) int
        Index of polygons which are root
    contains : networkx.DiGraph
       Edges indicate a polygon is
       contained by another polygon
    """

    # get the bounds for every valid polygon
    bounds = {
        k: v
        for k, v in {
            i: getattr(polygon, "bounds", []) for i, polygon in enumerate(polygons)
        }.items()
        if len(v) == 4
    }

    # nodes are indexes in polygons
    contains = nx.DiGraph()
    # make sure we don't have orphaned polygon
    contains.add_nodes_from(bounds.keys())

    if len(bounds) > 0:
        # if there are no valid bounds tree creation will fail
        # and we won't be calling `tree.intersection` anywhere
        # we could return here but having multiple return paths
        # seems more dangerous than iterating through an empty graph
        tree = Index(zip(bounds.keys(), bounds.values(), [None] * len(bounds)))

    # loop through every polygon
    for i, b in bounds.items():
        # we first query for bounding box intersections from the R-tree
        for j in tree.intersection(b):
            # if we are checking a polygon against itself continue
            if i == j:
                continue
            # do a more accurate polygon in polygon test
            # for the enclosure tree information
            if polygons[i].contains(polygons[j]):
                contains.add_edge(i, j)
            elif polygons[j].contains(polygons[i]):
                contains.add_edge(j, i)

    # a root or exterior curve has an even number of parents
    # wrap in dict call to avoid networkx view
    degree = dict(contains.in_degree())
    # convert keys and values to numpy arrays
    indexes = np.array(list(degree.keys()))
    degrees = np.array(list(degree.values()))
    # roots are curves with an even inward degree (parent count)
    roots = indexes[(degrees % 2) == 0]
    # if there are multiple nested polygons split the graph
    # so the contains logic returns the individual polygons
    if len(degrees) > 0 and degrees.max() > 1:
        # collect new edges for graph
        edges = []
        # order the roots so they are sorted by degree
        roots = roots[np.argsort([degree[r] for r in roots])]
        # find edges of subgraph for each root and children
        for root in roots:
            children = indexes[degrees == degree[root] + 1]
            edges.extend(contains.subgraph(np.append(children, root)).edges())
        # stack edges into new directed graph
        contains = nx.from_edgelist(edges, nx.DiGraph())
        # if roots have no children add them anyway
        contains.add_nodes_from(roots)

    return roots, contains


def edges_to_polygons(edges, vertices):
    """
    Given an edge list of indices and associated vertices
    representing lines, generate a list of polygons.

    Parameters
    -----------
    edges : (n, 2) int
      Indexes of vertices which represent lines
    vertices : (m, 2) float
      Vertices in 2D space

    Returns
    ----------
    polygons : (p,) shapely.geometry.Polygon
      Polygon objects with interiors
    """

    assert isinstance(vertices, np.ndarray)

    # create closed polygon objects
    polygons = []
    # loop through a sequence of ordered traversals
    for dfs in graph.traversals(edges, mode="dfs"):
        try:
            # try to recover polygons before they are more complicated
            repaired = repair_invalid(Polygon(vertices[dfs]))
            # if it returned a multipolygon extend into a flat list
            if hasattr(repaired, "geoms"):
                polygons.extend(repaired.geoms)
            else:
                polygons.append(repaired)
        except ValueError:
            continue

    # if there is only one polygon, just return it
    if len(polygons) == 1:
        return polygons

    # find which polygons contain which other polygons
    roots, tree = enclosure_tree(polygons)

    # generate list of polygons with proper interiors
    complete = []
    for root in roots:
        interior = list(tree[root].keys())
        shell = polygons[root].exterior.coords
        holes = [polygons[i].exterior.coords for i in interior]
        complete.append(Polygon(shell=shell, holes=holes))
    return complete


def polygons_obb(polygons: List[Polygon]):
    """
    Find the OBBs for a list of shapely.geometry.Polygons
    """
    rectangles = [None] * len(polygons)
    transforms = [None] * len(polygons)
    for i, p in enumerate(polygons):
        transforms[i], rectangles[i] = polygon_obb(p)
    return np.array(transforms), np.array(rectangles)


def polygon_obb(polygon: Polygon):
    """
    Find the oriented bounding box of a Shapely polygon.

    The OBB is always aligned with an edge of the convex hull of the polygon.

    Parameters
    -------------
    polygons : shapely.geometry.Polygon
      Input geometry

    Returns
    -------------
    transform : (3, 3) float
      Transformation matrix
      which will move input polygon from its original position
      to the first quadrant where the AABB is the OBB
    extents : (2,) float
      Extents of transformed polygon
    """
    if hasattr(polygon, "exterior"):
        points = np.asanyarray(polygon.exterior.coords)
    elif isinstance(polygon, np.ndarray):
        points = polygon
    else:
        raise ValueError("polygon or points must be provided")

    transform, extents = bounds.oriented_bounds_2D(points)

    if tol.strict:
        moved = transform_points(points=points, matrix=transform)
        assert np.allclose(-extents / 2.0, moved.min(axis=0))
        assert np.allclose(extents / 2.0, moved.max(axis=0))

    return transform, extents


def transform_polygon(polygon, matrix):
    """
    Transform a polygon by a a 2D homogeneous transform.

    Parameters
    -------------
    polygon : shapely.geometry.Polygon
      2D polygon to be transformed.
    matrix  : (3, 3) float
      2D homogeneous transformation.

    Returns
    --------------
    result : shapely.geometry.Polygon
      Polygon transformed by matrix.
    """
    matrix = np.asanyarray(matrix, dtype=np.float64)

    if hasattr(polygon, "geoms"):
        result = [transform_polygon(p, t) for p, t in zip(polygon, matrix)]
        return result
    # transform the outer shell
    shell = transform_points(np.array(polygon.exterior.coords), matrix)[:, :2]
    # transform the interiors
    holes = [
        transform_points(np.array(i.coords), matrix)[:, :2] for i in polygon.interiors
    ]
    # create a new polygon with the result
    result = Polygon(shell=shell, holes=holes)
    return result


def polygon_bounds(polygon, matrix=None):
    """
    Get the transformed axis aligned bounding box of a
    shapely Polygon object.

    Parameters
    ------------
    polygon : shapely.geometry.Polygon
      Polygon pre-transform
    matrix : (3, 3) float or None.
      Homogeneous transform moving polygon in space

    Returns
    ------------
    bounds : (2, 2) float
      Axis aligned bounding box of transformed polygon.
    """
    if matrix is not None:
        assert matrix.shape == (3, 3)
        points = transform_points(points=np.array(polygon.exterior.coords), matrix=matrix)
    else:
        points = np.array(polygon.exterior.coords)

    bounds = np.array([points.min(axis=0), points.max(axis=0)])
    assert bounds.shape == (2, 2)
    return bounds


def plot(polygon=None, show=True, axes=None, **kwargs):
    """
    Plot a shapely polygon using matplotlib.

    Parameters
    ------------
    polygon : shapely.geometry.Polygon
      Polygon to be plotted
    show : bool
      If True will display immediately
    **kwargs
      Passed to plt.plot
    """
    import matplotlib.pyplot as plt

    def plot_single(single):
        axes.plot(*single.exterior.xy, **kwargs)
        for interior in single.interiors:
            axes.plot(*interior.xy, **kwargs)

    # make aspect ratio non-stupid
    if axes is None:
        axes = plt.axes()
    axes.set_aspect("equal", "datalim")

    if polygon.__class__.__name__ == "MultiPolygon":
        [plot_single(i) for i in polygon.geoms]
    elif hasattr(polygon, "__iter__"):
        [plot_single(i) for i in polygon]
    elif polygon is not None:
        plot_single(polygon)

    if show:
        plt.show()

    return axes


def resample_boundaries(polygon: Polygon, resolution: float, clip=None):
    """
    Return a version of a polygon with boundaries re-sampled
    to a specified resolution.

    Parameters
    -------------
    polygon : shapely.geometry.Polygon
      Source geometry
    resolution : float
      Desired distance between points on boundary
    clip : (2,) int
      Upper and lower bounds to clip
      number of samples to avoid exploding count

    Returns
    ------------
    kwargs : dict
     Keyword args for a Polygon constructor `Polygon(**kwargs)`
    """

    def resample_boundary(boundary):
        # add a polygon.exterior or polygon.interior to
        # the deque after resampling based on our resolution
        count = boundary.length / resolution
        count = int(np.clip(count, *clip))
        return resample_path(boundary.coords, count=count)

    if clip is None:
        clip = [8, 200]
    # create a sequence of [(n,2)] points
    kwargs = {"shell": resample_boundary(polygon.exterior), "holes": []}
    for interior in polygon.interiors:
        kwargs["holes"].append(resample_boundary(interior))

    return kwargs


def stack_boundaries(boundaries):
    """
    Stack the boundaries of a polygon into a single
    (n, 2) list of vertices.

    Parameters
    ------------
    boundaries : dict
      With keys 'shell', 'holes'

    Returns
    ------------
    stacked : (n, 2) float
      Stacked vertices
    """
    if len(boundaries["holes"]) == 0:
        return boundaries["shell"]
    result = np.vstack((boundaries["shell"], np.vstack(boundaries["holes"])))
    return result


def medial_axis(polygon: Polygon, resolution: Optional[float] = None, clip=None):
    """
    Given a shapely polygon, find the approximate medial axis
    using a voronoi diagram of evenly spaced points on the
    boundary of the polygon.

    Parameters
    ----------
    polygon : shapely.geometry.Polygon
      The source geometry
    resolution : float
      Distance between each sample on the polygon boundary
    clip : None, or (2,) int
      Clip sample count to min of clip[0] and max of clip[1]

    Returns
    ----------
    edges : (n, 2) int
      Vertex indices representing line segments
      on the polygon's medial axis
    vertices : (m, 2) float
      Vertex positions in space
    """
    # a circle will have a single point medial axis
    if len(polygon.interiors) == 0:
        # what is the approximate scale of the polygon
        scale = np.reshape(polygon.bounds, (2, 2)).ptp(axis=0).max()
        # a (center, radius, error) tuple
        fit = fit_circle_check(polygon.exterior.coords, scale=scale)
        # is this polygon in fact a circle
        if fit is not None:
            # return an edge that has the center as the midpoint
            epsilon = np.clip(fit["radius"] / 500, 1e-5, np.inf)
            vertices = np.array(
                [fit["center"] + [0, epsilon], fit["center"] - [0, epsilon]],
                dtype=np.float64,
            )
            # return a single edge to avoid consumers needing to special case
            edges = np.array([[0, 1]], dtype=np.int64)
            return edges, vertices

    from scipy.spatial import Voronoi
    from shapely import vectorized

    if resolution is None:
        resolution = np.reshape(polygon.bounds, (2, 2)).ptp(axis=0).max() / 100

    # get evenly spaced points on the polygons boundaries
    samples = resample_boundaries(polygon=polygon, resolution=resolution, clip=clip)
    # stack the boundary into a (m,2) float array
    samples = stack_boundaries(samples)
    # create the voronoi diagram on 2D points
    voronoi = Voronoi(samples)
    # which voronoi vertices are contained inside the polygon
    contains = vectorized.contains(polygon, *voronoi.vertices.T)
    # ridge vertices of -1 are outside, make sure they are False
    contains = np.append(contains, False)
    # make sure ridge vertices is numpy array
    ridge = np.asanyarray(voronoi.ridge_vertices, dtype=np.int64)
    # only take ridges where every vertex is contained
    edges = ridge[contains[ridge].all(axis=1)]

    # now we need to remove uncontained vertices
    contained = np.unique(edges)
    mask = np.zeros(len(voronoi.vertices), dtype=np.int64)
    mask[contained] = np.arange(len(contained))

    # mask voronoi vertices
    vertices = voronoi.vertices[contained]
    # re-index edges
    edges_final = mask[edges]

    if tol.strict:
        # make sure we didn't screw up indexes
        assert (vertices[edges_final] - voronoi.vertices[edges]).ptp() < 1e-5

    return edges_final, vertices


def identifier(polygon: Polygon) -> NDArray[float64]:
    """
    Return a vector containing values representative of
    a particular polygon.

    Parameters
    ---------
    polygon : shapely.geometry.Polygon
      Input geometry

    Returns
    ---------
    identifier : (8,) float
      Values which should be unique for this polygon.
    """
    result = [
        len(polygon.interiors),
        polygon.convex_hull.area,
        polygon.convex_hull.length,
        polygon.area,
        polygon.length,
        polygon.exterior.length,
    ]
    # include the principal second moments of inertia of the polygon
    # this is invariant to rotation and translation
    _, principal, _, _ = second_moments(polygon, return_centered=True)
    result.extend(principal)

    return np.array(result, dtype=np.float64)


def random_polygon(segments=8, radius=1.0):
    """
    Generate a random polygon with a maximum number of sides and approximate radius.

    Parameters
    ---------
    segments : int
      The maximum number of sides the random polygon will have
    radius : float
      The approximate radius of the polygon desired

    Returns
    ---------
    polygon : shapely.geometry.Polygon
      Geometry object with random exterior and no interiors.
    """
    angles = np.sort(np.cumsum(np.random.random(segments) * np.pi * 2) % (np.pi * 2))
    radii = np.random.random(segments) * radius

    points = np.column_stack((np.cos(angles), np.sin(angles))) * radii.reshape((-1, 1))
    points = np.vstack((points, points[0]))
    polygon = Polygon(points).buffer(0.0)
    if hasattr(polygon, "geoms"):
        return polygon.geoms[0]
    return polygon


def polygon_scale(polygon):
    """
    For a Polygon object return the diagonal length of the AABB.

    Parameters
    ------------
    polygon : shapely.geometry.Polygon
      Source geometry

    Returns
    ------------
    scale : float
      Length of AABB diagonal
    """
    extents = np.reshape(polygon.bounds, (2, 2)).ptp(axis=0)
    scale = (extents**2).sum() ** 0.5

    return scale


def paths_to_polygons(paths, scale=None):
    """
    Given a sequence of connected points turn them into
    valid shapely Polygon objects.

    Parameters
    -----------
    paths : (n,) sequence
      Of (m, 2) float closed paths
    scale : float
      Approximate scale of drawing for precision

    Returns
    -----------
    polys : (p,) list
      Filled with Polygon or None

    """
    polygons = [None] * len(paths)
    for i, path in enumerate(paths):
        if len(path) < 4:
            # since the first and last vertices are identical in
            # a closed loop a 4 vertex path is the minimum for
            # non-zero area
            continue
        try:
            polygons[i] = repair_invalid(Polygon(path), scale)
        except ValueError:
            # raised if a polygon is unrecoverable
            continue
        except BaseException:
            log.error("unrecoverable polygon", exc_info=True)
    polygons = np.array(polygons)
    return polygons


def sample(polygon, count, factor=1.5, max_iter=10):
    """
    Use rejection sampling to generate random points inside a
    polygon.

    Parameters
    -----------
    polygon : shapely.geometry.Polygon
      Polygon that will contain points
    count : int
      Number of points to return
    factor : float
      How many points to test per loop
    max_iter : int
      Maximum number of intersection checks is:
      > count * factor * max_iter

    Returns
    -----------
    hit : (n, 2) float
      Random points inside polygon
      where n <= count
    """
    # do batch point-in-polygon queries
    from shapely import vectorized

    # get size of bounding box
    bounds = np.reshape(polygon.bounds, (2, 2))
    extents = bounds.ptp(axis=0)

    # how many points to check per loop iteration
    per_loop = int(count * factor)

    # start with some rejection sampling
    points = bounds[0] + extents * np.random.random((per_loop, 2))
    # do the point in polygon test and append resulting hits
    mask = vectorized.contains(polygon, *points.T)
    hit = [points[mask]]
    hit_count = len(hit[0])
    # if our first non-looping check got enough samples exit
    if hit_count >= count:
        return hit[0][:count]

    # if we have to do iterations loop here slowly
    for _ in range(max_iter):
        # generate points inside polygons AABB
        points = np.random.random((per_loop, 2))
        points = (points * extents) + bounds[0]
        # do the point in polygon test and append resulting hits
        mask = vectorized.contains(polygon, *points.T)
        hit.append(points[mask])
        # keep track of how many points we've collected
        hit_count += len(hit[-1])
        # if we have enough points exit the loop
        if hit_count > count:
            break

    # stack the hits into an (n,2) array and truncate
    hit = np.vstack(hit)[:count]

    return hit


def repair_invalid(polygon, scale=None, rtol=0.5):
    """
    Given a shapely.geometry.Polygon, attempt to return a
    valid version of the polygon through buffering tricks.

    Parameters
    -----------
    polygon : shapely.geometry.Polygon
      Source geometry
    rtol : float
      How close does a perimeter have to be
    scale : float or None
      For numerical precision reference

    Returns
    ----------
    repaired : shapely.geometry.Polygon
      Repaired polygon

    Raises
    ----------
    ValueError
      If polygon can't be repaired
    """
    if hasattr(polygon, "is_valid") and polygon.is_valid:
        return polygon

    # basic repair involves buffering the polygon outwards
    # this will fix a subset of problems.
    basic = polygon.buffer(tol.zero)
    # if it returned multiple polygons check the largest
    if hasattr(basic, "geoms"):
        basic = basic.geoms[np.argmax([i.area for i in basic.geoms])]

    # check perimeter of result against original perimeter
    if basic.is_valid and np.isclose(basic.length, polygon.length, rtol=rtol):
        return basic

    if scale is None:
        distance = 0.002 * np.reshape(polygon.bounds, (2, 2)).ptp(axis=0).mean()
    else:
        distance = 0.002 * scale

    # if there are no interiors, we can work with just the exterior
    # ring, which is often more reliable
    if len(polygon.interiors) == 0:
        # try buffering the exterior of the polygon
        # the interior will be offset by -tol.buffer
        rings = polygon.exterior.buffer(distance).interiors
        if len(rings) == 1:
            # reconstruct a single polygon from the interior ring
            recon = Polygon(shell=rings[0]).buffer(distance)
            # check perimeter of result against original perimeter
            if recon.is_valid and np.isclose(recon.length, polygon.length, rtol=rtol):
                return recon

        # try de-deuplicating the outside ring
        points = np.array(polygon.exterior.coords)
        # remove any segments shorter than tol.merge
        # this is a little risky as if it was discretized more
        # finely than 1-e8 it may remove detail
        unique = np.append(True, (np.diff(points, axis=0) ** 2).sum(axis=1) ** 0.5 > 1e-8)
        # make a new polygon with result
        dedupe = Polygon(shell=points[unique])
        # check result
        if dedupe.is_valid and np.isclose(dedupe.length, polygon.length, rtol=rtol):
            return dedupe

    # buffer and unbuffer the whole polygon
    buffered = polygon.buffer(distance).buffer(-distance)
    # if it returned multiple polygons check the largest
    if hasattr(buffered, "geoms"):
        areas = np.array([b.area for b in buffered.geoms])
        return buffered.geoms[areas.argmax()]

    # check perimeter of result against original perimeter
    if buffered.is_valid and np.isclose(buffered.length, polygon.length, rtol=rtol):
        log.debug("Recovered invalid polygon through double buffering")
        return buffered

    raise ValueError("unable to recover polygon!")


def projected(
    mesh,
    normal,
    origin=None,
    ignore_sign=True,
    rpad=1e-5,
    apad=None,
    tol_dot=0.01,
    max_regions=200,
):
    """
    Project a mesh onto a plane and then extract the polygon
    that outlines the mesh projection on that plane.

    Note that this will ignore back-faces, which is only
    relevant if the source mesh isn't watertight.

    Also padding: this generates a result by unioning the
    polygons of multiple connected regions, which requires
    the polygons be padded by a distance so that a polygon
    union produces a single coherent result. This distance
    is calculated as: `apad + (rpad * scale)`

    Parameters
    ----------
    mesh : trimesh.Trimesh
      Source geometry
    check : bool
      If True make sure is flat
    normal : (3,) float
      Normal to extract flat pattern along
    origin : None or (3,) float
      Origin of plane to project mesh onto
    ignore_sign : bool
      Allow a projection from the normal vector in
      either direction: this provides a substantial speedup
      on watertight meshes where the direction is irrelevant
      but if you have a triangle soup and want to discard
      backfaces you should set this to False.
    rpad : float
      Proportion to pad polygons by before unioning
      and then de-padding result by to avoid zero-width gaps.
    apad : float
      Absolute padding to pad polygons by before unioning
      and then de-padding result by to avoid zero-width gaps.
    tol_dot : float
      Tolerance for discarding on-edge triangles.
    max_regions : int
      Raise an exception if the mesh has more than this
      number of disconnected regions to fail quickly before
      unioning.

    Returns
    ----------
    projected : shapely.geometry.Polygon or None
      Outline of source mesh

    Raises
    ---------
    ValueError
      If max_regions is exceeded
    """
    # make sure normal is a unitized copy
    normal = np.array(normal, dtype=np.float64)
    normal /= np.linalg.norm(normal)

    # the projection of each face normal onto facet normal
    dot_face = np.dot(normal, mesh.face_normals.T)
    if ignore_sign:
        # for watertight mesh speed up projection by handling side with less faces
        # check if face lies on front or back of normal
        front = dot_face > tol_dot
        back = dot_face < -tol_dot
        # divide the mesh into front facing section and back facing parts
        # and discard the faces perpendicular to the axis.
        # since we are doing a unary_union later we can use the front *or*
        # the back so we use which ever one has fewer triangles
        # we want the largest nonzero group
        count = np.array([front.sum(), back.sum()])
        if count.min() == 0:
            # if one of the sides has zero faces we need the other
            pick = count.argmax()
        else:
            # otherwise use the normal direction with the fewest faces
            pick = count.argmin()
        # use the picked side
        side = [front, back][pick]
    else:
        # if explicitly asked to care about the sign
        # only handle the front side of normal
        side = dot_face > tol_dot

    # subset the adjacency pairs to ones which have both faces included
    # on the side we are currently looking at
    adjacency_check = side[mesh.face_adjacency].all(axis=1)
    adjacency = mesh.face_adjacency[adjacency_check]

    # a sequence of face indexes that are connected
    face_groups = graph.connected_components(adjacency, nodes=np.nonzero(side)[0])

    # if something is goofy we may end up with thousands of
    # regions that do nothing except hang for an hour then segfault
    if len(face_groups) > max_regions:
        raise ValueError("too many disconnected groups!")

    # reshape edges into shape length of faces for indexing
    edges = mesh.edges_sorted.reshape((-1, 6))
    # transform from the mesh frame in 3D to the XY plane
    to_2D = geometry.plane_transform(origin=origin, normal=normal)
    # transform mesh vertices to 2D and clip the zero Z
    vertices_2D = transform_points(mesh.vertices, to_2D)[:, :2]

    polygons = []
    for faces in face_groups:
        # index edges by face then shape back to individual edges
        edge = edges[faces].reshape((-1, 2))
        # edges that occur only once are on the boundary
        group = grouping.group_rows(edge, require_count=1)
        # turn each region into polygons
        polygons.extend(edges_to_polygons(edges=edge[group], vertices=vertices_2D))

    padding = 0.0
    if apad is not None:
        # set padding by absolute value
        padding += float(apad)
    if rpad is not None:
        # get the 2D scale as the longest side of the AABB
        scale = vertices_2D.ptp(axis=0).max()
        # apply the scale-relative padding
        padding += float(rpad) * scale

    # some types of errors will lead to a bajillion disconnected
    # regions and the union will take forever to fail
    # so exit here early
    if len(polygons) > max_regions:
        raise ValueError("too many disconnected groups!")

    # if there is only one region we don't need to run a union
    elif len(polygons) == 1:
        return polygons[0]
    elif len(polygons) == 0:
        return None
    else:
        # inflate each polygon before unioning to remove zero-size
        # gaps then deflate the result after unioning by the same amount
        # note the following provides a 25% speedup but needs
        # more testing to see if it deflates to a decent looking
        # result:
        # polygon = ops.unary_union(
        #    [p.buffer(padding,
        #              join_style=2,
        #              mitre_limit=1.5)
        #     for p in polygons]).buffer(-padding)
        polygon = ops.unary_union([p.buffer(padding) for p in polygons]).buffer(-padding)
    return polygon


def second_moments(polygon: Polygon, return_centered=False):
    """
    Calculate the second moments of area of a polygon
    from the boundary.

    Parameters
    ------------
    polygon : shapely.geometry.Polygon
      Closed polygon.
    return_centered : bool
      Get second moments for a frame with origin at the centroid
      and perform a principal axis transformation.

    Returns
    ----------
    moments : (3,) float
      The values of `[Ixx, Iyy, Ixy]`
    principal_moments : (2,) float
      Principal second moments of inertia: `[Imax, Imin]`
      Only returned if `centered`.
    alpha : float
      Angle by which the polygon needs to be rotated, so the
      principal axis align with the X and Y axis.
      Only returned if `centered`.
    transform : (3, 3) float
      Transformation matrix which rotates the polygon by alpha.
      Only returned if `centered`.
    """

    transform = np.eye(3)
    if return_centered:
        # calculate centroid and move polygon
        transform[:2, 2] = -np.array(polygon.centroid.coords)
        polygon = transform_polygon(polygon, transform)

    # start with the exterior
    coords = np.array(polygon.exterior.coords)
    # shorthand the coordinates
    x1, y1 = np.vstack((coords[-1], coords[:-1])).T
    x2, y2 = coords.T
    # do vectorized operations
    v = x1 * y2 - x2 * y1
    Ixx = np.sum(v * (y1 * y1 + y1 * y2 + y2 * y2)) / 12.0
    Iyy = np.sum(v * (x1 * x1 + x1 * x2 + x2 * x2)) / 12.0
    Ixy = np.sum(v * (x1 * y2 + 2 * x1 * y1 + 2 * x2 * y2 + x2 * y1)) / 24.0

    for interior in polygon.interiors:
        coords = np.array(interior.coords)
        # shorthand the coordinates
        x1, y1 = np.vstack((coords[-1], coords[:-1])).T
        x2, y2 = coords.T
        # do vectorized operations
        v = x1 * y2 - x2 * y1
        Ixx -= np.sum(v * (y1 * y1 + y1 * y2 + y2 * y2)) / 12.0
        Iyy -= np.sum(v * (x1 * x1 + x1 * x2 + x2 * x2)) / 12.0
        Ixy -= np.sum(v * (x1 * y2 + 2 * x1 * y1 + 2 * x2 * y2 + x2 * y1)) / 24.0

    moments = [Ixx, Iyy, Ixy]

    if not return_centered:
        return moments

    # get the principal moments
    root = np.sqrt(((Iyy - Ixx) / 2.0) ** 2 + Ixy**2)
    Imax = (Ixx + Iyy) / 2.0 + root
    Imin = (Ixx + Iyy) / 2.0 - root
    principal_moments = [Imax, Imin]

    # do the principal axis transform
    if np.isclose(Ixy, 0.0, atol=1e-12):
        alpha = 0
    elif np.isclose(Ixx, Iyy):
        # prevent division by 0
        alpha = 0.25 * np.pi
    else:
        alpha = 0.5 * np.arctan(2.0 * Ixy / (Ixx - Iyy))

    # construct transformation matrix
    cos_alpha = np.cos(alpha)
    sin_alpha = np.sin(alpha)

    transform[0, 0] = cos_alpha
    transform[1, 1] = cos_alpha
    transform[0, 1] = -sin_alpha
    transform[1, 0] = sin_alpha

    return moments, principal_moments, alpha, transform
