# -*- coding: utf-8 -*-

# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is
# holder of all proprietary rights on this computer program.
# You can only use this computer program if you have closed
# a license agreement with MPG or you get the right to use the computer
# program from someone who is authorized to grant you that right.
# Any use of the computer program without a valid license is prohibited and
# liable to prosecution.
#
# Copyright©2020 Max-Planck-Gesellschaft zur Förderung
# der Wissenschaften e.V. (MPG). acting on behalf of its Max Planck Institute
# for Intelligent Systems. All rights reserved.
#
# Code from Chumpy and OpenDR. Placed here to avoid chumpy dependency
# The original code can be found in https://github.com/MPI-IS/mesh
import numpy as np
import scipy.sparse as sp


def row(A):
    return A.reshape((1, -1))


def col(A):
    return A.reshape((-1, 1))


def get_vert_connectivity(mesh_v, mesh_f):
    """Returns a sparse matrix (of size #verts x #verts) where each nonzero
    element indicates a neighborhood relation. For example, if there is a
    nonzero element in position (15,12), that means vertex 15 is connected
    by an edge to vertex 12."""

    vpv = sp.csc_matrix((len(mesh_v), len(mesh_v)))

    # for each column in the faces...
    for i in range(3):
        IS = mesh_f[:, i]
        JS = mesh_f[:, (i + 1) % 3]
        data = np.ones(len(IS))
        ij = np.vstack((row(IS.flatten()), row(JS.flatten())))
        mtx = sp.csc_matrix((data, ij), shape=vpv.shape)
        vpv = vpv + mtx + mtx.T

    return vpv


def get_vertices_per_edge(mesh_v, mesh_f):
    """Returns an Ex2 array of adjacencies between vertices, where
    each element in the array is a vertex index. Each edge is included
    only once. If output of get_faces_per_edge is provided, this is used to
    avoid call to get_vert_connectivity()"""

    vc = sp.coo_matrix(get_vert_connectivity(mesh_v, mesh_f))
    result = np.hstack((col(vc.row), col(vc.col)))
    result = result[result[:, 0] < result[:, 1]]  # for uniqueness

    return result