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import bpy
from mathutils import Matrix

from sverchok.node_tree import SverchCustomTreeNode
from sverchok.data_structure import (updateNode, fullList)


# Matrix are assumed to be in format
# [M1 M2 Mn ...] per Matrix
# Instead of empty matrix input identity matrix is used.
# So only one matrix input is needed for useful result
# Factor a list of value float values between 0.0 and 1.0,


class MatrixInterpolationNode(bpy.types.Node, SverchCustomTreeNode):
    ''' Interpolate between two matrices '''
    bl_idname = 'MatrixInterpolationNode'
    bl_label = 'Matrix Interpolation'
    bl_icon = 'OUTLINER_OB_EMPTY'
    sv_icon = 'SV_MATRIX_INTERPOLATION'

    factor_: bpy.props.FloatProperty(
        name='Factor', description='Interpolation', default=0.5, min=0.0, max=1.0, update=updateNode)

    def sv_init(self, context):
        self.inputs.new('SvStringsSocket', "Factor").prop_name = 'factor_'
        self.inputs.new('SvMatrixSocket', "A")
        self.inputs.new('SvMatrixSocket', "B")
        self.outputs.new('SvMatrixSocket', "C")

    def process(self):
        # inputs
        if not self.outputs['C'].is_linked:
            return
        id_mat = [Matrix.Identity(4)]
        A = self.inputs['A'].sv_get(default=id_mat)
        B = self.inputs['B'].sv_get(default=id_mat)
        factor = self.inputs['Factor'].sv_get()


        matrixes_ = []
        # match inputs, first matrix A and B using fullList
        # then extend the factor list if necessary,
        # A and B should control length of list, not interpolation lists
        max_l = max(len(A), len(B))
        fullList(A, max_l)
        fullList(B, max_l)
        if len(factor) < max_l:
            fullList(factor, max_l)
        for i in range(max_l):
            for k in range(len(factor[i])):
                matrixes_.append(A[i].lerp(B[i], factor[i][k]))

        self.outputs['C'].sv_set(matrixes_)


def register():
    bpy.utils.register_class(MatrixInterpolationNode)


def unregister():
    bpy.utils.unregister_class(MatrixInterpolationNode)
