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#
#  This program is free software; you can redistribute it and/or
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#  as published by the Free Software Foundation; either version 2
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#  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
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import bpy
import numpy as np
from bpy.props import EnumProperty
from mathutils import Vector

from sverchok.node_tree import SverchCustomTreeNode
from sverchok.data_structure import (updateNode)
from sverchok.utils.geom import circle_approximation

class SvCircleApproxNode(SverchCustomTreeNode, bpy.types.Node):
    """
    Triggers: Circle Fit
    Tooltip: Fit a circle through vertices
    """
    bl_idname = 'SvCircleApproxNode'
    bl_label = 'Circle Fit'
    bl_icon = 'OUTLINER_OB_EMPTY'
    sv_icon = 'SV_CIRCLE_FIT'

    def sv_init(self, context):
        self.inputs.new('SvVerticesSocket', "Vertices")
        self.outputs.new('SvVerticesSocket', "Center")
        self.outputs.new('SvStringsSocket', "Radius")
        self.outputs.new('SvVerticesSocket', "Normal")
        self.outputs.new('SvMatrixSocket', "Matrix")
        self.outputs.new('SvVerticesSocket', "Projections")
        self.outputs.new('SvVerticesSocket', "Diffs")
        self.outputs.new('SvStringsSocket', "Distances")

    def process(self):
        if not any(output.is_linked for output in self.outputs):
            return

        vertices_s = self.inputs['Vertices'].sv_get()

        centers_out = []
        radiuses_out = []
        normals_out = []
        matrices_out = []
        projections_out = []
        diffs_out = []
        distances_out = []

        for vertices in vertices_s:
            approx = circle_approximation(vertices)
            centers_out.append(approx.center.tolist())
            radiuses_out.append(approx.radius)
            normals_out.append(approx.normal)
            matrices_out.append(approx.get_matrix())
            if self.outputs['Projections'].is_linked or self.outputs['Diffs'].is_linked or self.outputs['Distances'].is_linked:
                projections = approx.get_projections(vertices)
                diffs = projections - np.array(vertices)
                distances = np.linalg.norm(diffs, axis=1)
                projections_out.append(projections.tolist())
                diffs_out.append(diffs.tolist())
                distances_out.append(distances.tolist())

        self.outputs['Center'].sv_set([centers_out])
        self.outputs['Radius'].sv_set([radiuses_out])
        self.outputs['Normal'].sv_set([normals_out])
        self.outputs['Matrix'].sv_set(matrices_out)
        self.outputs['Projections'].sv_set(projections_out)
        self.outputs['Diffs'].sv_set(diffs_out)
        self.outputs['Distances'].sv_set(distances_out)

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

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

