ixxi-dante/nw2vec

View on GitHub
julia/scratchpad.ipynb

Summary

Maintainability
Test Coverage
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next steps:\n",
    "- profiling for 10000 nodes (seems like most of the time is out of BLAS)\n",
    "- resume next nw2vec changes + think interactions to explore them"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "include(\"utils.jl\")\n",
    "include(\"layers.jl\")\n",
    "include(\"generative.jl\")\n",
    "using .Utils\n",
    "using .Layers\n",
    "using .Generative\n",
    "\n",
    "using Flux\n",
    "using LightGraphs\n",
    "using GraphPlot\n",
    "using Makie\n",
    "using Colors\n",
    "using ProgressMeter\n",
    "using Statistics\n",
    "using Distributions\n",
    "using Random"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define the graph and features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n",
       "<svg xmlns=\"http://www.w3.org/2000/svg\"\n",
       "     xmlns:xlink=\"http://www.w3.org/1999/xlink\"\n",
       "     version=\"1.2\"\n",
       "     width=\"141.42mm\" height=\"100mm\" viewBox=\"0 0 141.42 100\"\n",
       "     stroke=\"none\"\n",
       "     fill=\"#000000\"\n",
       "     stroke-width=\"0.3\"\n",
       "     font-size=\"3.88\"\n",
       ">\n",
       "<defs>\n",
       "  <marker id=\"arrow\" markerWidth=\"15\" markerHeight=\"7\" refX=\"5\" refY=\"3.5\" orient=\"auto\" markerUnits=\"strokeWidth\">\n",
       "    <path d=\"M0,0 L15,3.5 L0,7 z\" stroke=\"context-stroke\" fill=\"context-stroke\"/>\n",
       "  </marker>\n",
       "</defs>\n",
       "<g stroke-width=\"0.3\" fill=\"#000000\" fill-opacity=\"0.000\" stroke=\"#D3D3D3\" id=\"img-5678eed9-1\">\n",
       "  <g transform=\"translate(72.39,25.38)\">\n",
       "    <path fill=\"none\" d=\"M-5.9,-3.92 L 5.9 3.92\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(60.92,18.82)\">\n",
       "    <path fill=\"none\" d=\"M3.24,1.38 L -3.24 -1.38\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(63.05,21.13)\">\n",
       "    <path fill=\"none\" d=\"M0.93,-0.15 L -0.93 0.15\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(59.51,23.77)\">\n",
       "    <path fill=\"none\" d=\"M4.71,-2.41 L -4.71 2.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(60.26,14.93)\">\n",
       "    <path fill=\"none\" d=\"M-0.35,-5.55 L 0.35 5.55\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(73.3,26.49)\">\n",
       "    <path fill=\"none\" d=\"M4.92,2.85 L -4.92 -2.85\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(78.85,36.92)\">\n",
       "    <path fill=\"none\" d=\"M0.44,-5.87 L -0.44 5.87\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(88.38,34.95)\">\n",
       "    <path fill=\"none\" d=\"M-7.85,-4.3 L 7.85 4.3\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(52.38,35.28)\">\n",
       "    <path fill=\"none\" d=\"M-1.08,7.45 L 1.08 -7.45\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(52.35,53.25)\">\n",
       "    <path fill=\"none\" d=\"M-1.06,-8.44 L 1.06 8.44\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(56.01,20.46)\">\n",
       "    <path fill=\"none\" d=\"M-0.29,2.52 L 0.29 -2.52\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(58.59,27.57)\">\n",
       "    <path fill=\"none\" d=\"M-2.23,-2.66 L 2.23 2.66\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(58.14,22.77)\">\n",
       "    <path fill=\"none\" d=\"M-1.33,0.65 L 1.33 -0.65\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(54.61,25.41)\">\n",
       "    <path fill=\"none\" d=\"M0.33,-0.46 L -0.33 0.46\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(54.43,29.64)\">\n",
       "    <path fill=\"none\" d=\"M0.96,-4.59 L -0.96 4.59\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(63.96,22.25)\">\n",
       "    <path fill=\"none\" d=\"M1.88,0.42 L -1.88 -0.42\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(60.42,24.89)\">\n",
       "    <path fill=\"none\" d=\"M5.45,-1.52 L -5.45 1.52\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(55.01,21.85)\">\n",
       "    <path fill=\"none\" d=\"M1.12,-3.93 L -1.12 3.93\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(69.95,37.48)\">\n",
       "    <path fill=\"none\" d=\"M-7.38,-5.59 L 7.38 5.59\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(53.12,28.67)\">\n",
       "    <path fill=\"none\" d=\"M7.09,2.06 L -7.09 -2.06\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(57.14,24.16)\">\n",
       "    <path fill=\"none\" d=\"M2.52,-1.88 L -2.52 1.88\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(62.38,29.8)\">\n",
       "    <path fill=\"none\" d=\"M-1.49,-7.25 L 1.49 7.25\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(44.49,28.91)\">\n",
       "    <path fill=\"none\" d=\"M7.72,-1.79 L -7.72 1.79\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(77.78,78.99)\">\n",
       "    <path fill=\"none\" d=\"M0.4,4.44 L -0.4 -4.44\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(74.9,81.7)\">\n",
       "    <path fill=\"none\" d=\"M2.42,1.98 L -2.42 -1.98\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(81.79,84.18)\">\n",
       "    <path fill=\"none\" d=\"M-2.05,0.17 L 2.05 -0.17\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(80.39,66.14)\">\n",
       "    <path fill=\"none\" d=\"M-2.17,-0.21 L 2.17 0.21\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(74.14,72.36)\">\n",
       "    <path fill=\"none\" d=\"M2.22,-5.56 L -2.22 5.56\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(79.61,68.67)\">\n",
       "    <path fill=\"none\" d=\"M-2.01,-2.03 L 2.01 2.03\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(75.51,56.76)\">\n",
       "    <path fill=\"none\" d=\"M1.11,8 L -1.11 -8\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(80.65,70.01)\">\n",
       "    <path fill=\"none\" d=\"M-2.54,2.65 L 2.54 -2.65\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(82.81,78.21)\">\n",
       "    <path fill=\"none\" d=\"M-4.58,-3.9 L 4.58 3.9\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(81.3,78.71)\">\n",
       "    <path fill=\"none\" d=\"M-3.3,-4.28 L 3.3 4.28\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(83.64,67.89)\">\n",
       "    <path fill=\"none\" d=\"M-5.44,4.81 L 5.44 -4.81\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(65.41,68.12)\">\n",
       "    <path fill=\"none\" d=\"M10.63,4.83 L -10.63 -4.83\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(86.34,69.9)\">\n",
       "    <path fill=\"none\" d=\"M-1.68,-2.46 L 1.68 2.46\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(83.61,59.11)\">\n",
       "    <path fill=\"none\" d=\"M0.35,6.35 L -0.35 -6.35\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(89.98,87.29)\">\n",
       "    <path fill=\"none\" d=\"M-1.28,-3.37 L 1.28 3.37\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(88.49,78.11)\">\n",
       "    <path fill=\"none\" d=\"M-0.13,3.77 L 0.13 -3.77\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(65.25,77.51)\">\n",
       "    <path fill=\"none\" d=\"M4.88,1.1 L -4.88 -1.1\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(88.47,87.78)\">\n",
       "    <path fill=\"none\" d=\"M2.43,2.98 L -2.43 -2.98\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(85.56,72.43)\">\n",
       "    <path fill=\"none\" d=\"M-1.73,-0.49 L 1.73 0.49\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(79.2,66.79)\">\n",
       "    <path fill=\"none\" d=\"M2.62,3.82 L -2.62 -3.82\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(86.98,78.6)\">\n",
       "    <path fill=\"none\" d=\"M-1.35,4.28 L 1.35 -4.28\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(88.38,65.3)\">\n",
       "    <path fill=\"none\" d=\"M0.24,6.96 L -0.24 -6.96\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(106.59,36.33)\">\n",
       "    <path fill=\"none\" d=\"M-5.37,3.02 L 5.37 -3.02\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(108.47,38.95)\">\n",
       "    <path fill=\"none\" d=\"M-6.94,0.87 L 6.94 -0.87\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(87.46,44.8)\">\n",
       "    <path fill=\"none\" d=\"M11.32,-4.31 L -11.32 4.31\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(121.58,39.59)\">\n",
       "    <path fill=\"none\" d=\"M3.87,-1.41 L -3.87 1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(121.81,37.8)\">\n",
       "    <path fill=\"none\" d=\"M3.48,-0.06 L -3.48 0.06\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(119.4,37.18)\">\n",
       "    <path fill=\"none\" d=\"M-2.34,3.36 L 2.34 -3.36\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(107.61,44.87)\">\n",
       "    <path fill=\"none\" d=\"M7.5,-2.9 L -7.5 2.9\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(108.35,35.12)\">\n",
       "    <path fill=\"none\" d=\"M3.57,-1.85 L -3.57 1.85\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(117.75,32.77)\">\n",
       "    <path fill=\"none\" d=\"M-3.16,-0.07 L 3.16 0.07\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(117.93,29.53)\">\n",
       "    <path fill=\"none\" d=\"M-3.73,2.42 L 3.73 -2.42\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(110.23,37.74)\">\n",
       "    <path fill=\"none\" d=\"M5.16,0.12 L -5.16 -0.12\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(119.63,35.39)\">\n",
       "    <path fill=\"none\" d=\"M-1.86,1.69 L 1.86 -1.69\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(123.25,40.19)\">\n",
       "    <path fill=\"none\" d=\"M-5.07,-1.82 L 5.07 1.82\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(109.32,32.71)\">\n",
       "    <path fill=\"none\" d=\"M-4.79,4.07 L 4.79 -4.07\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(91.68,43.68)\">\n",
       "    <path fill=\"none\" d=\"M10.72,-5.48 L -10.72 5.48\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(122.57,29.64)\">\n",
       "    <path fill=\"none\" d=\"M-0.12,2.2 L 0.12 -2.2\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(118.9,27.12)\">\n",
       "    <path fill=\"none\" d=\"M-2.42,0.45 L 2.42 -0.45\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(127.01,45.75)\">\n",
       "    <path fill=\"none\" d=\"M1.9,-2.35 L -1.9 2.35\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(81.49,50.74)\">\n",
       "    <path fill=\"none\" d=\"M-0.57,-0.32 L 0.57 0.32\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(73.34,49.45)\">\n",
       "    <path fill=\"none\" d=\"M4.96,0.25 L -4.96 -0.25\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(75.39,46.33)\">\n",
       "    <path fill=\"none\" d=\"M3.4,2.67 L -3.4 -2.67\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(79.58,47.81)\">\n",
       "    <path fill=\"none\" d=\"M0.09,0.92 L -0.09 -0.92\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(77.01,48.75)\">\n",
       "    <path fill=\"none\" d=\"M1.45,0.54 L -1.45 -0.54\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(77.31,49.68)\">\n",
       "    <path fill=\"none\" d=\"M0.99,0.03 L -0.99 -0.03\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(72.64,58.61)\">\n",
       "    <path fill=\"none\" d=\"M6.4,-7.94 L -6.4 7.94\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(67.54,40.49)\">\n",
       "    <path fill=\"none\" d=\"M-2.41,-1.67 L 2.41 1.67\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(66,39.6)\">\n",
       "    <path fill=\"none\" d=\"M-0.94,-0.74 L 0.94 0.74\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(69.46,43.84)\">\n",
       "    <path fill=\"none\" d=\"M-4.57,-4.89 L 4.57 4.89\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(58.8,43.33)\">\n",
       "    <path fill=\"none\" d=\"M4.42,-4.39 L -4.42 4.39\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(77.11,47.3)\">\n",
       "    <path fill=\"none\" d=\"M5.07,3.67 L -5.07 -3.67\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(81.3,48.78)\">\n",
       "    <path fill=\"none\" d=\"M1.29,1.98 L -1.29 -1.98\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(79.03,50.66)\">\n",
       "    <path fill=\"none\" d=\"M2.79,0.71 L -2.79 -0.71\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(91.01,49.99)\">\n",
       "    <path fill=\"none\" d=\"M-6.4,1.41 L 6.4 -1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(65.17,47.37)\">\n",
       "    <path fill=\"none\" d=\"M0.89,0.9 L -0.89 -0.9\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(70.88,49.36)\">\n",
       "    <path fill=\"none\" d=\"M-2.5,-0.15 L 2.5 0.15\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(60.22,48.85)\">\n",
       "    <path fill=\"none\" d=\"M5.22,0.22 L -5.22 -0.22\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(75.2,44.37)\">\n",
       "    <path fill=\"none\" d=\"M-2.87,-1.02 L 2.87 1.02\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(72.63,45.31)\">\n",
       "    <path fill=\"none\" d=\"M-0.99,-1.48 L 0.99 1.48\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(62.13,39.07)\">\n",
       "    <path fill=\"none\" d=\"M7.62,3.27 L -7.62 -3.27\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(65.68,43.36)\">\n",
       "    <path fill=\"none\" d=\"M1.4,-1.4 L -1.4 1.4\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(71.38,45.35)\">\n",
       "    <path fill=\"none\" d=\"M-2.72,-3.34 L 2.72 3.34\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(77.11,47.73)\">\n",
       "    <path fill=\"none\" d=\"M1.31,-1.08 L -1.31 1.08\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(68.85,46.67)\">\n",
       "    <path fill=\"none\" d=\"M3.99,0.78 L -3.99 -0.78\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(74.55,48.66)\">\n",
       "    <path fill=\"none\" d=\"M0.03,0.08 L -0.03 -0.08\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(86.54,48)\">\n",
       "    <path fill=\"none\" d=\"M-10.81,-0.24 L 10.81 0.24\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(63.75,41.49)\">\n",
       "    <path fill=\"none\" d=\"M9.38,5.57 L -9.38 -5.57\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(58.48,47.1)\">\n",
       "    <path fill=\"none\" d=\"M3.6,-1.07 L -3.6 1.07\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(81.39,49.15)\">\n",
       "    <path fill=\"none\" d=\"M-5.08,0.35 L 5.08 -0.35\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(63.91,58.44)\">\n",
       "    <path fill=\"none\" d=\"M9.96,-8.06 L -9.96 8.06\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(83.22,50.57)\">\n",
       "    <path fill=\"none\" d=\"M4.21,5.81 L -4.21 -5.81\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(88.02,53)\">\n",
       "    <path fill=\"none\" d=\"M0.06,3.25 L -0.06 -3.25\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(88.5,54.82)\">\n",
       "    <path fill=\"none\" d=\"M-0.23,1.44 L 0.23 -1.44\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(92.13,55.87)\">\n",
       "    <path fill=\"none\" d=\"M-2.72,0.96 L 2.72 -0.96\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(81.48,38.21)\">\n",
       "    <path fill=\"none\" d=\"M2.61,-4.65 L -2.61 4.65\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(91.13,50.24)\">\n",
       "    <path fill=\"none\" d=\"M1.97,0.94 L -1.97 -0.94\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(91.61,52.06)\">\n",
       "    <path fill=\"none\" d=\"M1.26,-0.13 L -1.26 0.13\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(92.16,57.01)\">\n",
       "    <path fill=\"none\" d=\"M1.75,-4.25 L -1.75 4.25\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(94.51,47.1)\">\n",
       "    <path fill=\"none\" d=\"M-0.15,3.62 L 0.15 -3.62\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(83.14,46.27)\">\n",
       "    <path fill=\"none\" d=\"M-3.61,-1.83 L 3.61 1.83\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(84.42,45.01)\">\n",
       "    <path fill=\"none\" d=\"M-4.66,-0.91 L 4.66 0.91\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(83.62,48.09)\">\n",
       "    <path fill=\"none\" d=\"M-4.31,-3.47 L 4.31 3.47\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(71.87,52.54)\">\n",
       "    <path fill=\"none\" d=\"M5.78,-7.78 L -5.78 7.78\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(92.06,51.58)\">\n",
       "    <path fill=\"none\" d=\"M-3.07,-2.14 L 3.07 2.14\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(91.33,45.57)\">\n",
       "    <path fill=\"none\" d=\"M-2.49,2.31 L 2.49 -2.31\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(89.7,49.27)\">\n",
       "    <path fill=\"none\" d=\"M0.53,-2.05 L -0.53 2.05\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(93.34,50.32)\">\n",
       "    <path fill=\"none\" d=\"M-2.19,-3.19 L 2.19 3.19\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(89.45,57.31)\">\n",
       "    <path fill=\"none\" d=\"M-0.43,-3.92 L 0.43 3.92\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(93.08,58.36)\">\n",
       "    <path fill=\"none\" d=\"M-2.37,3 L 2.37 -3\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(101.62,53.36)\">\n",
       "    <path fill=\"none\" d=\"M-4.03,0.81 L 4.03 -0.81\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(39.45,57.59)\">\n",
       "    <path fill=\"none\" d=\"M4.38,1.72 L -4.38 -1.72\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(49.32,61.27)\">\n",
       "    <path fill=\"none\" d=\"M-2.89,-1 L 2.89 1\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(42.25,54.96)\">\n",
       "    <path fill=\"none\" d=\"M2.29,3.89 L -2.29 -3.89\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(48.95,58.89)\">\n",
       "    <path fill=\"none\" d=\"M-2.45,0.59 L 2.45 -0.59\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(44.05,57.15)\">\n",
       "    <path fill=\"none\" d=\"M0.66,1.66 L -0.66 -1.66\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(50.9,64.31)\">\n",
       "    <path fill=\"none\" d=\"M-4.79,-3.73 L 4.79 3.73\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(28.34,45.91)\">\n",
       "    <path fill=\"none\" d=\"M0.67,-3.61 L -0.67 3.61\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(34.3,45.68)\">\n",
       "    <path fill=\"none\" d=\"M-4.16,-3.62 L 4.16 3.62\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(30.34,34.82)\">\n",
       "    <path fill=\"none\" d=\"M-0.95,5.4 L 0.95 -5.4\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(30.63,52.96)\">\n",
       "    <path fill=\"none\" d=\"M-2.15,-1.64 L 2.15 1.64\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(31.56,54.14)\">\n",
       "    <path fill=\"none\" d=\"M-3.16,-2.78 L 3.16 2.78\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(46.46,49.34)\">\n",
       "    <path fill=\"none\" d=\"M5.61,-0.61 L -5.61 0.61\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(53.16,53.27)\">\n",
       "    <path fill=\"none\" d=\"M0.29,-3.65 L -0.29 3.65\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(48.26,51.53)\">\n",
       "    <path fill=\"none\" d=\"M4.12,-2.31 L -4.12 2.31\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(34.71,56.55)\">\n",
       "    <path fill=\"none\" d=\"M-0.21,-0.27 L 0.21 0.27\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(38.39,54.93)\">\n",
       "    <path fill=\"none\" d=\"M-3.14,0.3 L 3.14 -0.3\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(37.52,53.92)\">\n",
       "    <path fill=\"none\" d=\"M-1.39,2.83 L 1.39 -2.83\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(44.22,57.84)\">\n",
       "    <path fill=\"none\" d=\"M-7.1,-0.09 L 7.1 0.09\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(39.31,56.11)\">\n",
       "    <path fill=\"none\" d=\"M-2.42,1.07 L 2.42 -1.07\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(29.71,61.46)\">\n",
       "    <path fill=\"none\" d=\"M4.83,-3.04 L -4.83 3.04\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(53.17,60.34)\">\n",
       "    <path fill=\"none\" d=\"M0.21,1.35 L -0.21 -1.35\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(43.66,65.17)\">\n",
       "    <path fill=\"none\" d=\"M8.49,-2.1 L -8.49 2.1\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(41.19,52.3)\">\n",
       "    <path fill=\"none\" d=\"M-1.06,-1.29 L 1.06 1.29\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(47.89,56.22)\">\n",
       "    <path fill=\"none\" d=\"M3.58,1.27 L -3.58 -1.27\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(64.37,59.99)\">\n",
       "    <path fill=\"none\" d=\"M-10.15,-1.79 L 10.15 1.79\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(20.91,65.8)\">\n",
       "    <path fill=\"none\" d=\"M-4.02,-4.65 L 4.02 4.65\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(17.34,53.7)\">\n",
       "    <path fill=\"none\" d=\"M-1.03,5.54 L 1.03 -5.54\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(100.61,54.85)\">\n",
       "    <path fill=\"none\" d=\"M-3.13,2.71 L 3.13 -2.71\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(86.24,60.2)\">\n",
       "    <path fill=\"none\" d=\"M8.87,-1.58 L -8.87 1.58\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(102.66,53.98)\">\n",
       "    <path fill=\"none\" d=\"M-5.08,3.64 L 5.08 -3.64\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(101.04,45.61)\">\n",
       "    <path fill=\"none\" d=\"M-3.03,-4.77 L 3.03 4.77\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(103.09,44.74)\">\n",
       "    <path fill=\"none\" d=\"M-4.75,-4.05 L 4.75 4.05\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(105,50.49)\">\n",
       "    <path fill=\"none\" d=\"M4.87,1.75 L -4.87 -1.75\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(107.93,52.03)\">\n",
       "    <path fill=\"none\" d=\"M1.85,0.39 L -1.85 -0.39\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(109.98,51.16)\">\n",
       "    <path fill=\"none\" d=\"M0.5,0.64 L -0.5 -0.64\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(109.12,52.49)\">\n",
       "    <path fill=\"none\" d=\"M0.61,0.07 L -0.61 -0.07\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(103.8,48.93)\">\n",
       "    <path fill=\"none\" d=\"M-3.54,-0.47 L 3.54 0.47\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(102.94,50.27)\">\n",
       "    <path fill=\"none\" d=\"M-2.91,-1.41 L 2.91 1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(106.73,50.47)\">\n",
       "    <path fill=\"none\" d=\"M-0.79,0.33 L 0.79 -0.33\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(116.58,49.3)\">\n",
       "    <path fill=\"none\" d=\"M-6.33,0.24 L 6.33 -0.24\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(16.39,79.03)\">\n",
       "    <path fill=\"none\" d=\"M1.82,2.39 L -1.82 -2.39\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(25.28,79.87)\">\n",
       "    <path fill=\"none\" d=\"M-5.07,1.96 L 5.07 -1.96\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(13.6,61.1)\">\n",
       "    <path fill=\"none\" d=\"M1.48,-4.55 L -1.48 4.55\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(18.8,62.17)\">\n",
       "    <path fill=\"none\" d=\"M-2.89,-5.66 L 2.89 5.66\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(19.04,47.15)\">\n",
       "    <path fill=\"none\" d=\"M-3.2,7.38 L 3.2 -7.38\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(29.74,69.48)\">\n",
       "    <path fill=\"none\" d=\"M2.81,-1.29 L -2.81 1.29\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(27.99,68.21)\">\n",
       "    <path fill=\"none\" d=\"M4.34,-0.45 L -4.34 0.45\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(28.79,66.41)\">\n",
       "    <path fill=\"none\" d=\"M3.6,0.87 L -3.6 -0.87\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(12.83,71.19)\">\n",
       "    <path fill=\"none\" d=\"M-0.81,-3.5 L 0.81 3.5\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(17.79,65.93)\">\n",
       "    <path fill=\"none\" d=\"M-4.55,0.56 L 4.55 -0.56\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(28.05,82.76)\">\n",
       "    <path fill=\"none\" d=\"M-2.96,4.41 L 2.96 -4.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(19.79,73.53)\">\n",
       "    <path fill=\"none\" d=\"M-4.61,1.71 L 4.61 -1.71\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(23.95,70.07)\">\n",
       "    <path fill=\"none\" d=\"M0.73,0.52 L -0.73 -0.52\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(24.75,68.27)\">\n",
       "    <path fill=\"none\" d=\"M0.64,2.06 L -0.64 -2.06\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(35.27,73.57)\">\n",
       "    <path fill=\"none\" d=\"M-8.17,-1.9 L 8.17 1.9\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(26.92,73.11)\">\n",
       "    <path fill=\"none\" d=\"M-3.83,-3.48 L 3.83 3.48\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(42.32,72.35)\">\n",
       "    <path fill=\"none\" d=\"M-9.44,4.48 L 9.44 -4.48\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(37.39,22)\">\n",
       "    <path fill=\"none\" d=\"M-0.54,-3.1 L 0.54 3.1\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(40.67,22.04)\">\n",
       "    <path fill=\"none\" d=\"M-3.17,-3.31 L 3.17 3.31\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(42.91,36.48)\">\n",
       "    <path fill=\"none\" d=\"M8.88,-1.05 L -8.88 1.05\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(45.68,30.69)\">\n",
       "    <path fill=\"none\" d=\"M6.45,3.89 L -6.45 -3.89\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(48.96,30.73)\">\n",
       "    <path fill=\"none\" d=\"M3.47,3.66 L -3.47 -3.66\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(33.97,34.37)\">\n",
       "    <path fill=\"none\" d=\"M-0.98,2.34 L 0.98 -2.34\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(25.57,42.41)\">\n",
       "    <path fill=\"none\" d=\"M5.94,-3.99 L -5.94 3.99\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(27.62,38.24)\">\n",
       "    <path fill=\"none\" d=\"M3.5,-0.38 L -3.5 0.38\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(31.49,26.89)\">\n",
       "    <path fill=\"none\" d=\"M5.17,-0.59 L -5.17 0.59\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(34.79,27.26)\">\n",
       "    <path fill=\"none\" d=\"M1.99,-0.68 L -1.99 0.68\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(41.39,26.17)\">\n",
       "    <path fill=\"none\" d=\"M-1.81,-0.02 L 1.81 0.02\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(29.02,34.9)\">\n",
       "    <path fill=\"none\" d=\"M5.24,-3.19 L -5.24 3.19\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(28.17,28.02)\">\n",
       "    <path fill=\"none\" d=\"M-1.85,-0.21 L 1.85 0.21\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(23.76,33.21)\">\n",
       "    <path fill=\"none\" d=\"M0.9,-4.53 L -0.9 4.53\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(38.07,27.3)\">\n",
       "    <path fill=\"none\" d=\"M-5.16,0.85 L 5.16 -0.85\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(57.83,72.45)\">\n",
       "    <path fill=\"none\" d=\"M0.82,2.62 L -0.82 -2.62\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(59.03,77.35)\">\n",
       "    <path fill=\"none\" d=\"M-0.01,-0.22 L 0.01 0.22\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(51.9,75.94)\">\n",
       "    <path fill=\"none\" d=\"M5.59,0.12 L -5.59 -0.12\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(62.24,71.77)\">\n",
       "    <path fill=\"none\" d=\"M-2.58,3.4 L 2.58 -3.4\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(56.62,74.97)\">\n",
       "    <path fill=\"none\" d=\"M1.13,0.54 L -1.13 -0.54\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(54.83,68.05)\">\n",
       "    <path fill=\"none\" d=\"M0.57,0.24 L -0.57 -0.24\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(57.89,73.71)\">\n",
       "    <path fill=\"none\" d=\"M-0.95,-3.87 L 0.95 3.87\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(61.05,65.03)\">\n",
       "    <path fill=\"none\" d=\"M-3.43,2.98 L 3.43 -2.98\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(61.1,68.13)\">\n",
       "    <path fill=\"none\" d=\"M-2.97,0.46 L 2.97 -0.46\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(48.91,71.54)\">\n",
       "    <path fill=\"none\" d=\"M3.25,-3.39 L -3.25 3.39\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(54.28,71.96)\">\n",
       "    <path fill=\"none\" d=\"M-1.01,-3.65 L 1.01 3.65\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(55.12,80.18)\">\n",
       "    <path fill=\"none\" d=\"M2.68,-1.06 L -2.68 1.06\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(62.3,73.03)\">\n",
       "    <path fill=\"none\" d=\"M-2.66,4.62 L 2.66 -4.62\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(47.99,78.77)\">\n",
       "    <path fill=\"none\" d=\"M2.27,2.15 L -2.27 -2.15\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(53.36,79.19)\">\n",
       "    <path fill=\"none\" d=\"M-1.44,1.68 L 1.44 -1.68\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(52.71,77.8)\">\n",
       "    <path fill=\"none\" d=\"M-1.17,2.95 L 1.17 -2.95\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(49.56,74.82)\">\n",
       "    <path fill=\"none\" d=\"M-3.31,0.68 L 3.31 -0.68\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(60.54,72.04)\">\n",
       "    <path fill=\"none\" d=\"M-4.07,3.76 L 4.07 -3.76\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(65.46,64.35)\">\n",
       "    <path fill=\"none\" d=\"M-0.03,-2.06 L 0.03 2.06\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(59.84,67.54)\">\n",
       "    <path fill=\"none\" d=\"M4.79,-5.42 L -4.79 5.42\" class=\"primitive\"/>\n",
       "  </g>\n",
       "</g>\n",
       "<g stroke-width=\"0.3\" stroke=\"#D3D3D3\" id=\"img-5678eed9-2\">\n",
       "</g>\n",
       "<g font-size=\"4\" stroke=\"#000000\" stroke-opacity=\"0.000\" fill=\"#000000\" id=\"img-5678eed9-3\">\n",
       "</g>\n",
       "<g stroke-width=\"0\" stroke=\"#000000\" stroke-opacity=\"0.000\" id=\"img-5678eed9-4\">\n",
       "  <g transform=\"translate(65.42,20.75)\" fill=\"#000000\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(59.84,8.33)\" fill=\"#000000\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(79.37,30)\" fill=\"#008029\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(51.15,43.77)\" fill=\"#000000\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(55.61,24.02)\" fill=\"#000000\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(67.24,22.98)\" fill=\"#005FD5\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(56.42,16.9)\" fill=\"#000000\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(61.57,31.12)\" fill=\"#A78600\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(60.68,21.52)\" fill=\"#005FD5\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(53.61,26.8)\" fill=\"#930068\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(78.28,84.47)\" fill=\"#000000\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(76.76,65.79)\" fill=\"#FFFF62\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(77.29,73.52)\" fill=\"#FFFF62\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(84.01,66.49)\" fill=\"#FFFF62\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(88.33,82.91)\" fill=\"#FFFF62\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(71.53,78.92)\" fill=\"#FFFF62\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(91.64,91.67)\" fill=\"#FFFF62\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(82.47,71.56)\" fill=\"#FFFF62\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(85.31,83.9)\" fill=\"#00D6FF\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(88.66,73.3)\" fill=\"#00D6FF\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(100.07,40)\" fill=\"#FF9FFF\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(126.76,37.71)\" fill=\"#FF9FFF\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(116.4,41.47)\" fill=\"#FF9FFF\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(113.12,32.66)\" fill=\"#FF9FFF\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(116.86,37.9)\" fill=\"#FF9FFF\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(103.59,37.59)\" fill=\"#FF9FFF\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(122.39,32.88)\" fill=\"#FF9FFF\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(115.05,27.83)\" fill=\"#FF9FFF\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(129.64,42.49)\" fill=\"#000000\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(122.74,26.4)\" fill=\"#FF9FFF\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(79.77,49.77)\" fill=\"#FFFF62\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(64.08,38.09)\" fill=\"#FFFF62\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(83.21,51.72)\" fill=\"#00D6FF\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(66.91,49.13)\" fill=\"#00D6FF\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(71.01,42.88)\" fill=\"#00D6FF\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(67.92,41.1)\" fill=\"#00D6FF\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(79.39,45.85)\" fill=\"#00D6FF\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(74.26,47.73)\" fill=\"#00D6FF\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(63.43,45.62)\" fill=\"#00D6FF\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(74.84,49.6)\" fill=\"#00D6FF\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(88.1,57.3)\" fill=\"#D74400\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(84.63,32.59)\" fill=\"#D74400\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(94.32,51.76)\" fill=\"#D74400\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(78.33,43.83)\" fill=\"#D74400\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(87.94,48.71)\" fill=\"#D74400\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(90.5,46.2)\" fill=\"#D74400\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(88.9,52.35)\" fill=\"#D74400\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(90,62.27)\" fill=\"#D74400\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(96.17,54.45)\" fill=\"#D74400\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(94.71,42.44)\" fill=\"#D74400\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(45.11,59.81)\" fill=\"#008029\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(29.21,41.26)\" fill=\"#008029\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(27.48,50.55)\" fill=\"#008029\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(53.53,48.57)\" fill=\"#008029\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(33.78,55.37)\" fill=\"#008029\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(35.64,57.73)\" fill=\"#008029\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(53.54,62.72)\" fill=\"#000000\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(39.39,50.11)\" fill=\"#FFFF62\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(52.79,57.96)\" fill=\"#008029\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(42.99,54.49)\" fill=\"#008029\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(16.12,60.27)\" fill=\"#005FD5\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(96.54,58.37)\" fill=\"#005FD5\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(97.4,39.89)\" fill=\"#005FD5\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(111.19,52.72)\" fill=\"#005FD5\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(98.82,48.26)\" fill=\"#005FD5\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(104.67,51.33)\" fill=\"#000000\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(75.94,62.03)\" fill=\"#005FD5\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(108.78,49.6)\" fill=\"#005FD5\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(107.06,52.27)\" fill=\"#005FD5\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(124.38,49)\" fill=\"#008029\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(18.91,82.33)\" fill=\"#930068\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(15.41,55.53)\" fill=\"#930068\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(33.78,67.61)\" fill=\"#930068\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(11.79,66.66)\" fill=\"#930068\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(24.45,88.11)\" fill=\"#FF9FFF\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(13.88,75.73)\" fill=\"#930068\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(25.71,71.34)\" fill=\"#930068\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(22.19,68.81)\" fill=\"#930068\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(31.65,77.41)\" fill=\"#930068\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(23.79,65.2)\" fill=\"#930068\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(36.67,17.86)\" fill=\"#FFCBB5\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(53.25,35.25)\" fill=\"#FFCBB5\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(32.57,37.7)\" fill=\"#FFCBB5\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(38.11,26.13)\" fill=\"#FFCBB5\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(35.38,31.03)\" fill=\"#FFCBB5\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(24.87,27.65)\" fill=\"#FFCBB5\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(31.47,28.39)\" fill=\"#FFCBB5\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(44.67,26.21)\" fill=\"#FFCBB5\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(18.56,47.13)\" fill=\"#FFCBB5\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(22.66,38.78)\" fill=\"#FFCBB5\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(58.97,76.09)\" fill=\"#A78600\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(56.68,68.81)\" fill=\"#A78600\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(52.98,67.29)\" fill=\"#A78600\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(59.09,78.61)\" fill=\"#A78600\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(51.15,81.76)\" fill=\"#A78600\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(44.84,75.79)\" fill=\"#000000\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(55.57,76.63)\" fill=\"#A78600\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(65.41,61.24)\" fill=\"#A78600\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(65.51,67.45)\" fill=\"#A78600\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(54.28,73.84)\" fill=\"#A78600\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "</g>\n",
       "<g font-size=\"4\" stroke=\"#000000\" stroke-opacity=\"0.000\" fill=\"#000000\" id=\"img-5678eed9-5\">\n",
       "</g>\n",
       "</svg>\n"
      ],
      "text/html": [
       "<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n",
       "<svg xmlns=\"http://www.w3.org/2000/svg\"\n",
       "     xmlns:xlink=\"http://www.w3.org/1999/xlink\"\n",
       "     version=\"1.2\"\n",
       "     width=\"141.42mm\" height=\"100mm\" viewBox=\"0 0 141.42 100\"\n",
       "     stroke=\"none\"\n",
       "     fill=\"#000000\"\n",
       "     stroke-width=\"0.3\"\n",
       "     font-size=\"3.88\"\n",
       "\n",
       "     id=\"img-9a2eaaa4\">\n",
       "<defs>\n",
       "  <marker id=\"arrow\" markerWidth=\"15\" markerHeight=\"7\" refX=\"5\" refY=\"3.5\" orient=\"auto\" markerUnits=\"strokeWidth\">\n",
       "    <path d=\"M0,0 L15,3.5 L0,7 z\" stroke=\"context-stroke\" fill=\"context-stroke\"/>\n",
       "  </marker>\n",
       "</defs>\n",
       "<g stroke-width=\"0.3\" fill=\"#000000\" fill-opacity=\"0.000\" stroke=\"#D3D3D3\" id=\"img-9a2eaaa4-1\">\n",
       "  <g transform=\"translate(72.39,25.38)\">\n",
       "    <path fill=\"none\" d=\"M-5.9,-3.92 L 5.9 3.92\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(60.92,18.82)\">\n",
       "    <path fill=\"none\" d=\"M3.24,1.38 L -3.24 -1.38\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(63.05,21.13)\">\n",
       "    <path fill=\"none\" d=\"M0.93,-0.15 L -0.93 0.15\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(59.51,23.77)\">\n",
       "    <path fill=\"none\" d=\"M4.71,-2.41 L -4.71 2.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(60.26,14.93)\">\n",
       "    <path fill=\"none\" d=\"M-0.35,-5.55 L 0.35 5.55\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(73.3,26.49)\">\n",
       "    <path fill=\"none\" d=\"M4.92,2.85 L -4.92 -2.85\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(78.85,36.92)\">\n",
       "    <path fill=\"none\" d=\"M0.44,-5.87 L -0.44 5.87\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(88.38,34.95)\">\n",
       "    <path fill=\"none\" d=\"M-7.85,-4.3 L 7.85 4.3\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(52.38,35.28)\">\n",
       "    <path fill=\"none\" d=\"M-1.08,7.45 L 1.08 -7.45\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(52.35,53.25)\">\n",
       "    <path fill=\"none\" d=\"M-1.06,-8.44 L 1.06 8.44\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(56.01,20.46)\">\n",
       "    <path fill=\"none\" d=\"M-0.29,2.52 L 0.29 -2.52\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(58.59,27.57)\">\n",
       "    <path fill=\"none\" d=\"M-2.23,-2.66 L 2.23 2.66\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(58.14,22.77)\">\n",
       "    <path fill=\"none\" d=\"M-1.33,0.65 L 1.33 -0.65\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(54.61,25.41)\">\n",
       "    <path fill=\"none\" d=\"M0.33,-0.46 L -0.33 0.46\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(54.43,29.64)\">\n",
       "    <path fill=\"none\" d=\"M0.96,-4.59 L -0.96 4.59\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(63.96,22.25)\">\n",
       "    <path fill=\"none\" d=\"M1.88,0.42 L -1.88 -0.42\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(60.42,24.89)\">\n",
       "    <path fill=\"none\" d=\"M5.45,-1.52 L -5.45 1.52\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(55.01,21.85)\">\n",
       "    <path fill=\"none\" d=\"M1.12,-3.93 L -1.12 3.93\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(69.95,37.48)\">\n",
       "    <path fill=\"none\" d=\"M-7.38,-5.59 L 7.38 5.59\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(53.12,28.67)\">\n",
       "    <path fill=\"none\" d=\"M7.09,2.06 L -7.09 -2.06\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(57.14,24.16)\">\n",
       "    <path fill=\"none\" d=\"M2.52,-1.88 L -2.52 1.88\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(62.38,29.8)\">\n",
       "    <path fill=\"none\" d=\"M-1.49,-7.25 L 1.49 7.25\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(44.49,28.91)\">\n",
       "    <path fill=\"none\" d=\"M7.72,-1.79 L -7.72 1.79\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(77.78,78.99)\">\n",
       "    <path fill=\"none\" d=\"M0.4,4.44 L -0.4 -4.44\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(74.9,81.7)\">\n",
       "    <path fill=\"none\" d=\"M2.42,1.98 L -2.42 -1.98\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(81.79,84.18)\">\n",
       "    <path fill=\"none\" d=\"M-2.05,0.17 L 2.05 -0.17\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(80.39,66.14)\">\n",
       "    <path fill=\"none\" d=\"M-2.17,-0.21 L 2.17 0.21\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(74.14,72.36)\">\n",
       "    <path fill=\"none\" d=\"M2.22,-5.56 L -2.22 5.56\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(79.61,68.67)\">\n",
       "    <path fill=\"none\" d=\"M-2.01,-2.03 L 2.01 2.03\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(75.51,56.76)\">\n",
       "    <path fill=\"none\" d=\"M1.11,8 L -1.11 -8\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(80.65,70.01)\">\n",
       "    <path fill=\"none\" d=\"M-2.54,2.65 L 2.54 -2.65\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(82.81,78.21)\">\n",
       "    <path fill=\"none\" d=\"M-4.58,-3.9 L 4.58 3.9\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(81.3,78.71)\">\n",
       "    <path fill=\"none\" d=\"M-3.3,-4.28 L 3.3 4.28\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(83.64,67.89)\">\n",
       "    <path fill=\"none\" d=\"M-5.44,4.81 L 5.44 -4.81\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(65.41,68.12)\">\n",
       "    <path fill=\"none\" d=\"M10.63,4.83 L -10.63 -4.83\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(86.34,69.9)\">\n",
       "    <path fill=\"none\" d=\"M-1.68,-2.46 L 1.68 2.46\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(83.61,59.11)\">\n",
       "    <path fill=\"none\" d=\"M0.35,6.35 L -0.35 -6.35\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(89.98,87.29)\">\n",
       "    <path fill=\"none\" d=\"M-1.28,-3.37 L 1.28 3.37\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(88.49,78.11)\">\n",
       "    <path fill=\"none\" d=\"M-0.13,3.77 L 0.13 -3.77\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(65.25,77.51)\">\n",
       "    <path fill=\"none\" d=\"M4.88,1.1 L -4.88 -1.1\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(88.47,87.78)\">\n",
       "    <path fill=\"none\" d=\"M2.43,2.98 L -2.43 -2.98\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(85.56,72.43)\">\n",
       "    <path fill=\"none\" d=\"M-1.73,-0.49 L 1.73 0.49\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(79.2,66.79)\">\n",
       "    <path fill=\"none\" d=\"M2.62,3.82 L -2.62 -3.82\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(86.98,78.6)\">\n",
       "    <path fill=\"none\" d=\"M-1.35,4.28 L 1.35 -4.28\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(88.38,65.3)\">\n",
       "    <path fill=\"none\" d=\"M0.24,6.96 L -0.24 -6.96\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(106.59,36.33)\">\n",
       "    <path fill=\"none\" d=\"M-5.37,3.02 L 5.37 -3.02\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(108.47,38.95)\">\n",
       "    <path fill=\"none\" d=\"M-6.94,0.87 L 6.94 -0.87\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(87.46,44.8)\">\n",
       "    <path fill=\"none\" d=\"M11.32,-4.31 L -11.32 4.31\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(121.58,39.59)\">\n",
       "    <path fill=\"none\" d=\"M3.87,-1.41 L -3.87 1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(121.81,37.8)\">\n",
       "    <path fill=\"none\" d=\"M3.48,-0.06 L -3.48 0.06\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(119.4,37.18)\">\n",
       "    <path fill=\"none\" d=\"M-2.34,3.36 L 2.34 -3.36\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(107.61,44.87)\">\n",
       "    <path fill=\"none\" d=\"M7.5,-2.9 L -7.5 2.9\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(108.35,35.12)\">\n",
       "    <path fill=\"none\" d=\"M3.57,-1.85 L -3.57 1.85\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(117.75,32.77)\">\n",
       "    <path fill=\"none\" d=\"M-3.16,-0.07 L 3.16 0.07\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(117.93,29.53)\">\n",
       "    <path fill=\"none\" d=\"M-3.73,2.42 L 3.73 -2.42\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(110.23,37.74)\">\n",
       "    <path fill=\"none\" d=\"M5.16,0.12 L -5.16 -0.12\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(119.63,35.39)\">\n",
       "    <path fill=\"none\" d=\"M-1.86,1.69 L 1.86 -1.69\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(123.25,40.19)\">\n",
       "    <path fill=\"none\" d=\"M-5.07,-1.82 L 5.07 1.82\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(109.32,32.71)\">\n",
       "    <path fill=\"none\" d=\"M-4.79,4.07 L 4.79 -4.07\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(91.68,43.68)\">\n",
       "    <path fill=\"none\" d=\"M10.72,-5.48 L -10.72 5.48\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(122.57,29.64)\">\n",
       "    <path fill=\"none\" d=\"M-0.12,2.2 L 0.12 -2.2\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(118.9,27.12)\">\n",
       "    <path fill=\"none\" d=\"M-2.42,0.45 L 2.42 -0.45\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(127.01,45.75)\">\n",
       "    <path fill=\"none\" d=\"M1.9,-2.35 L -1.9 2.35\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(81.49,50.74)\">\n",
       "    <path fill=\"none\" d=\"M-0.57,-0.32 L 0.57 0.32\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(73.34,49.45)\">\n",
       "    <path fill=\"none\" d=\"M4.96,0.25 L -4.96 -0.25\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(75.39,46.33)\">\n",
       "    <path fill=\"none\" d=\"M3.4,2.67 L -3.4 -2.67\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(79.58,47.81)\">\n",
       "    <path fill=\"none\" d=\"M0.09,0.92 L -0.09 -0.92\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(77.01,48.75)\">\n",
       "    <path fill=\"none\" d=\"M1.45,0.54 L -1.45 -0.54\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(77.31,49.68)\">\n",
       "    <path fill=\"none\" d=\"M0.99,0.03 L -0.99 -0.03\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(72.64,58.61)\">\n",
       "    <path fill=\"none\" d=\"M6.4,-7.94 L -6.4 7.94\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(67.54,40.49)\">\n",
       "    <path fill=\"none\" d=\"M-2.41,-1.67 L 2.41 1.67\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(66,39.6)\">\n",
       "    <path fill=\"none\" d=\"M-0.94,-0.74 L 0.94 0.74\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(69.46,43.84)\">\n",
       "    <path fill=\"none\" d=\"M-4.57,-4.89 L 4.57 4.89\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(58.8,43.33)\">\n",
       "    <path fill=\"none\" d=\"M4.42,-4.39 L -4.42 4.39\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(77.11,47.3)\">\n",
       "    <path fill=\"none\" d=\"M5.07,3.67 L -5.07 -3.67\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(81.3,48.78)\">\n",
       "    <path fill=\"none\" d=\"M1.29,1.98 L -1.29 -1.98\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(79.03,50.66)\">\n",
       "    <path fill=\"none\" d=\"M2.79,0.71 L -2.79 -0.71\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(91.01,49.99)\">\n",
       "    <path fill=\"none\" d=\"M-6.4,1.41 L 6.4 -1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(65.17,47.37)\">\n",
       "    <path fill=\"none\" d=\"M0.89,0.9 L -0.89 -0.9\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(70.88,49.36)\">\n",
       "    <path fill=\"none\" d=\"M-2.5,-0.15 L 2.5 0.15\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(60.22,48.85)\">\n",
       "    <path fill=\"none\" d=\"M5.22,0.22 L -5.22 -0.22\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(75.2,44.37)\">\n",
       "    <path fill=\"none\" d=\"M-2.87,-1.02 L 2.87 1.02\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(72.63,45.31)\">\n",
       "    <path fill=\"none\" d=\"M-0.99,-1.48 L 0.99 1.48\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(62.13,39.07)\">\n",
       "    <path fill=\"none\" d=\"M7.62,3.27 L -7.62 -3.27\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(65.68,43.36)\">\n",
       "    <path fill=\"none\" d=\"M1.4,-1.4 L -1.4 1.4\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(71.38,45.35)\">\n",
       "    <path fill=\"none\" d=\"M-2.72,-3.34 L 2.72 3.34\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(77.11,47.73)\">\n",
       "    <path fill=\"none\" d=\"M1.31,-1.08 L -1.31 1.08\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(68.85,46.67)\">\n",
       "    <path fill=\"none\" d=\"M3.99,0.78 L -3.99 -0.78\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(74.55,48.66)\">\n",
       "    <path fill=\"none\" d=\"M0.03,0.08 L -0.03 -0.08\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(86.54,48)\">\n",
       "    <path fill=\"none\" d=\"M-10.81,-0.24 L 10.81 0.24\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(63.75,41.49)\">\n",
       "    <path fill=\"none\" d=\"M9.38,5.57 L -9.38 -5.57\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(58.48,47.1)\">\n",
       "    <path fill=\"none\" d=\"M3.6,-1.07 L -3.6 1.07\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(81.39,49.15)\">\n",
       "    <path fill=\"none\" d=\"M-5.08,0.35 L 5.08 -0.35\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(63.91,58.44)\">\n",
       "    <path fill=\"none\" d=\"M9.96,-8.06 L -9.96 8.06\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(83.22,50.57)\">\n",
       "    <path fill=\"none\" d=\"M4.21,5.81 L -4.21 -5.81\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(88.02,53)\">\n",
       "    <path fill=\"none\" d=\"M0.06,3.25 L -0.06 -3.25\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(88.5,54.82)\">\n",
       "    <path fill=\"none\" d=\"M-0.23,1.44 L 0.23 -1.44\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(92.13,55.87)\">\n",
       "    <path fill=\"none\" d=\"M-2.72,0.96 L 2.72 -0.96\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(81.48,38.21)\">\n",
       "    <path fill=\"none\" d=\"M2.61,-4.65 L -2.61 4.65\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(91.13,50.24)\">\n",
       "    <path fill=\"none\" d=\"M1.97,0.94 L -1.97 -0.94\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(91.61,52.06)\">\n",
       "    <path fill=\"none\" d=\"M1.26,-0.13 L -1.26 0.13\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(92.16,57.01)\">\n",
       "    <path fill=\"none\" d=\"M1.75,-4.25 L -1.75 4.25\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(94.51,47.1)\">\n",
       "    <path fill=\"none\" d=\"M-0.15,3.62 L 0.15 -3.62\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(83.14,46.27)\">\n",
       "    <path fill=\"none\" d=\"M-3.61,-1.83 L 3.61 1.83\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(84.42,45.01)\">\n",
       "    <path fill=\"none\" d=\"M-4.66,-0.91 L 4.66 0.91\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(83.62,48.09)\">\n",
       "    <path fill=\"none\" d=\"M-4.31,-3.47 L 4.31 3.47\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(71.87,52.54)\">\n",
       "    <path fill=\"none\" d=\"M5.78,-7.78 L -5.78 7.78\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(92.06,51.58)\">\n",
       "    <path fill=\"none\" d=\"M-3.07,-2.14 L 3.07 2.14\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(91.33,45.57)\">\n",
       "    <path fill=\"none\" d=\"M-2.49,2.31 L 2.49 -2.31\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(89.7,49.27)\">\n",
       "    <path fill=\"none\" d=\"M0.53,-2.05 L -0.53 2.05\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(93.34,50.32)\">\n",
       "    <path fill=\"none\" d=\"M-2.19,-3.19 L 2.19 3.19\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(89.45,57.31)\">\n",
       "    <path fill=\"none\" d=\"M-0.43,-3.92 L 0.43 3.92\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(93.08,58.36)\">\n",
       "    <path fill=\"none\" d=\"M-2.37,3 L 2.37 -3\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(101.62,53.36)\">\n",
       "    <path fill=\"none\" d=\"M-4.03,0.81 L 4.03 -0.81\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(39.45,57.59)\">\n",
       "    <path fill=\"none\" d=\"M4.38,1.72 L -4.38 -1.72\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(49.32,61.27)\">\n",
       "    <path fill=\"none\" d=\"M-2.89,-1 L 2.89 1\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(42.25,54.96)\">\n",
       "    <path fill=\"none\" d=\"M2.29,3.89 L -2.29 -3.89\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(48.95,58.89)\">\n",
       "    <path fill=\"none\" d=\"M-2.45,0.59 L 2.45 -0.59\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(44.05,57.15)\">\n",
       "    <path fill=\"none\" d=\"M0.66,1.66 L -0.66 -1.66\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(50.9,64.31)\">\n",
       "    <path fill=\"none\" d=\"M-4.79,-3.73 L 4.79 3.73\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(28.34,45.91)\">\n",
       "    <path fill=\"none\" d=\"M0.67,-3.61 L -0.67 3.61\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(34.3,45.68)\">\n",
       "    <path fill=\"none\" d=\"M-4.16,-3.62 L 4.16 3.62\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(30.34,34.82)\">\n",
       "    <path fill=\"none\" d=\"M-0.95,5.4 L 0.95 -5.4\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(30.63,52.96)\">\n",
       "    <path fill=\"none\" d=\"M-2.15,-1.64 L 2.15 1.64\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(31.56,54.14)\">\n",
       "    <path fill=\"none\" d=\"M-3.16,-2.78 L 3.16 2.78\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(46.46,49.34)\">\n",
       "    <path fill=\"none\" d=\"M5.61,-0.61 L -5.61 0.61\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(53.16,53.27)\">\n",
       "    <path fill=\"none\" d=\"M0.29,-3.65 L -0.29 3.65\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(48.26,51.53)\">\n",
       "    <path fill=\"none\" d=\"M4.12,-2.31 L -4.12 2.31\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(34.71,56.55)\">\n",
       "    <path fill=\"none\" d=\"M-0.21,-0.27 L 0.21 0.27\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(38.39,54.93)\">\n",
       "    <path fill=\"none\" d=\"M-3.14,0.3 L 3.14 -0.3\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(37.52,53.92)\">\n",
       "    <path fill=\"none\" d=\"M-1.39,2.83 L 1.39 -2.83\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(44.22,57.84)\">\n",
       "    <path fill=\"none\" d=\"M-7.1,-0.09 L 7.1 0.09\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(39.31,56.11)\">\n",
       "    <path fill=\"none\" d=\"M-2.42,1.07 L 2.42 -1.07\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(29.71,61.46)\">\n",
       "    <path fill=\"none\" d=\"M4.83,-3.04 L -4.83 3.04\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(53.17,60.34)\">\n",
       "    <path fill=\"none\" d=\"M0.21,1.35 L -0.21 -1.35\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(43.66,65.17)\">\n",
       "    <path fill=\"none\" d=\"M8.49,-2.1 L -8.49 2.1\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(41.19,52.3)\">\n",
       "    <path fill=\"none\" d=\"M-1.06,-1.29 L 1.06 1.29\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(47.89,56.22)\">\n",
       "    <path fill=\"none\" d=\"M3.58,1.27 L -3.58 -1.27\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(64.37,59.99)\">\n",
       "    <path fill=\"none\" d=\"M-10.15,-1.79 L 10.15 1.79\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(20.91,65.8)\">\n",
       "    <path fill=\"none\" d=\"M-4.02,-4.65 L 4.02 4.65\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(17.34,53.7)\">\n",
       "    <path fill=\"none\" d=\"M-1.03,5.54 L 1.03 -5.54\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(100.61,54.85)\">\n",
       "    <path fill=\"none\" d=\"M-3.13,2.71 L 3.13 -2.71\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(86.24,60.2)\">\n",
       "    <path fill=\"none\" d=\"M8.87,-1.58 L -8.87 1.58\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(102.66,53.98)\">\n",
       "    <path fill=\"none\" d=\"M-5.08,3.64 L 5.08 -3.64\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(101.04,45.61)\">\n",
       "    <path fill=\"none\" d=\"M-3.03,-4.77 L 3.03 4.77\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(103.09,44.74)\">\n",
       "    <path fill=\"none\" d=\"M-4.75,-4.05 L 4.75 4.05\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(105,50.49)\">\n",
       "    <path fill=\"none\" d=\"M4.87,1.75 L -4.87 -1.75\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(107.93,52.03)\">\n",
       "    <path fill=\"none\" d=\"M1.85,0.39 L -1.85 -0.39\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(109.98,51.16)\">\n",
       "    <path fill=\"none\" d=\"M0.5,0.64 L -0.5 -0.64\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(109.12,52.49)\">\n",
       "    <path fill=\"none\" d=\"M0.61,0.07 L -0.61 -0.07\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(103.8,48.93)\">\n",
       "    <path fill=\"none\" d=\"M-3.54,-0.47 L 3.54 0.47\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(102.94,50.27)\">\n",
       "    <path fill=\"none\" d=\"M-2.91,-1.41 L 2.91 1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(106.73,50.47)\">\n",
       "    <path fill=\"none\" d=\"M-0.79,0.33 L 0.79 -0.33\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(116.58,49.3)\">\n",
       "    <path fill=\"none\" d=\"M-6.33,0.24 L 6.33 -0.24\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(16.39,79.03)\">\n",
       "    <path fill=\"none\" d=\"M1.82,2.39 L -1.82 -2.39\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(25.28,79.87)\">\n",
       "    <path fill=\"none\" d=\"M-5.07,1.96 L 5.07 -1.96\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(13.6,61.1)\">\n",
       "    <path fill=\"none\" d=\"M1.48,-4.55 L -1.48 4.55\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(18.8,62.17)\">\n",
       "    <path fill=\"none\" d=\"M-2.89,-5.66 L 2.89 5.66\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(19.04,47.15)\">\n",
       "    <path fill=\"none\" d=\"M-3.2,7.38 L 3.2 -7.38\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(29.74,69.48)\">\n",
       "    <path fill=\"none\" d=\"M2.81,-1.29 L -2.81 1.29\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(27.99,68.21)\">\n",
       "    <path fill=\"none\" d=\"M4.34,-0.45 L -4.34 0.45\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(28.79,66.41)\">\n",
       "    <path fill=\"none\" d=\"M3.6,0.87 L -3.6 -0.87\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(12.83,71.19)\">\n",
       "    <path fill=\"none\" d=\"M-0.81,-3.5 L 0.81 3.5\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(17.79,65.93)\">\n",
       "    <path fill=\"none\" d=\"M-4.55,0.56 L 4.55 -0.56\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(28.05,82.76)\">\n",
       "    <path fill=\"none\" d=\"M-2.96,4.41 L 2.96 -4.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(19.79,73.53)\">\n",
       "    <path fill=\"none\" d=\"M-4.61,1.71 L 4.61 -1.71\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(23.95,70.07)\">\n",
       "    <path fill=\"none\" d=\"M0.73,0.52 L -0.73 -0.52\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(24.75,68.27)\">\n",
       "    <path fill=\"none\" d=\"M0.64,2.06 L -0.64 -2.06\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(35.27,73.57)\">\n",
       "    <path fill=\"none\" d=\"M-8.17,-1.9 L 8.17 1.9\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(26.92,73.11)\">\n",
       "    <path fill=\"none\" d=\"M-3.83,-3.48 L 3.83 3.48\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(42.32,72.35)\">\n",
       "    <path fill=\"none\" d=\"M-9.44,4.48 L 9.44 -4.48\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(37.39,22)\">\n",
       "    <path fill=\"none\" d=\"M-0.54,-3.1 L 0.54 3.1\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(40.67,22.04)\">\n",
       "    <path fill=\"none\" d=\"M-3.17,-3.31 L 3.17 3.31\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(42.91,36.48)\">\n",
       "    <path fill=\"none\" d=\"M8.88,-1.05 L -8.88 1.05\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(45.68,30.69)\">\n",
       "    <path fill=\"none\" d=\"M6.45,3.89 L -6.45 -3.89\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(48.96,30.73)\">\n",
       "    <path fill=\"none\" d=\"M3.47,3.66 L -3.47 -3.66\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(33.97,34.37)\">\n",
       "    <path fill=\"none\" d=\"M-0.98,2.34 L 0.98 -2.34\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(25.57,42.41)\">\n",
       "    <path fill=\"none\" d=\"M5.94,-3.99 L -5.94 3.99\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(27.62,38.24)\">\n",
       "    <path fill=\"none\" d=\"M3.5,-0.38 L -3.5 0.38\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(31.49,26.89)\">\n",
       "    <path fill=\"none\" d=\"M5.17,-0.59 L -5.17 0.59\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(34.79,27.26)\">\n",
       "    <path fill=\"none\" d=\"M1.99,-0.68 L -1.99 0.68\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(41.39,26.17)\">\n",
       "    <path fill=\"none\" d=\"M-1.81,-0.02 L 1.81 0.02\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(29.02,34.9)\">\n",
       "    <path fill=\"none\" d=\"M5.24,-3.19 L -5.24 3.19\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(28.17,28.02)\">\n",
       "    <path fill=\"none\" d=\"M-1.85,-0.21 L 1.85 0.21\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(23.76,33.21)\">\n",
       "    <path fill=\"none\" d=\"M0.9,-4.53 L -0.9 4.53\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(38.07,27.3)\">\n",
       "    <path fill=\"none\" d=\"M-5.16,0.85 L 5.16 -0.85\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(57.83,72.45)\">\n",
       "    <path fill=\"none\" d=\"M0.82,2.62 L -0.82 -2.62\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(59.03,77.35)\">\n",
       "    <path fill=\"none\" d=\"M-0.01,-0.22 L 0.01 0.22\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(51.9,75.94)\">\n",
       "    <path fill=\"none\" d=\"M5.59,0.12 L -5.59 -0.12\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(62.24,71.77)\">\n",
       "    <path fill=\"none\" d=\"M-2.58,3.4 L 2.58 -3.4\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(56.62,74.97)\">\n",
       "    <path fill=\"none\" d=\"M1.13,0.54 L -1.13 -0.54\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(54.83,68.05)\">\n",
       "    <path fill=\"none\" d=\"M0.57,0.24 L -0.57 -0.24\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(57.89,73.71)\">\n",
       "    <path fill=\"none\" d=\"M-0.95,-3.87 L 0.95 3.87\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(61.05,65.03)\">\n",
       "    <path fill=\"none\" d=\"M-3.43,2.98 L 3.43 -2.98\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(61.1,68.13)\">\n",
       "    <path fill=\"none\" d=\"M-2.97,0.46 L 2.97 -0.46\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(48.91,71.54)\">\n",
       "    <path fill=\"none\" d=\"M3.25,-3.39 L -3.25 3.39\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(54.28,71.96)\">\n",
       "    <path fill=\"none\" d=\"M-1.01,-3.65 L 1.01 3.65\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(55.12,80.18)\">\n",
       "    <path fill=\"none\" d=\"M2.68,-1.06 L -2.68 1.06\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(62.3,73.03)\">\n",
       "    <path fill=\"none\" d=\"M-2.66,4.62 L 2.66 -4.62\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(47.99,78.77)\">\n",
       "    <path fill=\"none\" d=\"M2.27,2.15 L -2.27 -2.15\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(53.36,79.19)\">\n",
       "    <path fill=\"none\" d=\"M-1.44,1.68 L 1.44 -1.68\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(52.71,77.8)\">\n",
       "    <path fill=\"none\" d=\"M-1.17,2.95 L 1.17 -2.95\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(49.56,74.82)\">\n",
       "    <path fill=\"none\" d=\"M-3.31,0.68 L 3.31 -0.68\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(60.54,72.04)\">\n",
       "    <path fill=\"none\" d=\"M-4.07,3.76 L 4.07 -3.76\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(65.46,64.35)\">\n",
       "    <path fill=\"none\" d=\"M-0.03,-2.06 L 0.03 2.06\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(59.84,67.54)\">\n",
       "    <path fill=\"none\" d=\"M4.79,-5.42 L -4.79 5.42\" class=\"primitive\"/>\n",
       "  </g>\n",
       "</g>\n",
       "<g stroke-width=\"0.3\" stroke=\"#D3D3D3\" id=\"img-9a2eaaa4-2\">\n",
       "</g>\n",
       "<g font-size=\"4\" stroke=\"#000000\" stroke-opacity=\"0.000\" fill=\"#000000\" id=\"img-9a2eaaa4-3\">\n",
       "</g>\n",
       "<g stroke-width=\"0\" stroke=\"#000000\" stroke-opacity=\"0.000\" id=\"img-9a2eaaa4-4\">\n",
       "  <g transform=\"translate(65.42,20.75)\" fill=\"#000000\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(59.84,8.33)\" fill=\"#000000\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(79.37,30)\" fill=\"#008029\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(51.15,43.77)\" fill=\"#000000\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(55.61,24.02)\" fill=\"#000000\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(67.24,22.98)\" fill=\"#005FD5\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(56.42,16.9)\" fill=\"#000000\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(61.57,31.12)\" fill=\"#A78600\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(60.68,21.52)\" fill=\"#005FD5\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(53.61,26.8)\" fill=\"#930068\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(78.28,84.47)\" fill=\"#000000\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(76.76,65.79)\" fill=\"#FFFF62\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(77.29,73.52)\" fill=\"#FFFF62\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(84.01,66.49)\" fill=\"#FFFF62\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(88.33,82.91)\" fill=\"#FFFF62\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(71.53,78.92)\" fill=\"#FFFF62\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(91.64,91.67)\" fill=\"#FFFF62\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(82.47,71.56)\" fill=\"#FFFF62\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(85.31,83.9)\" fill=\"#00D6FF\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(88.66,73.3)\" fill=\"#00D6FF\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(100.07,40)\" fill=\"#FF9FFF\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(126.76,37.71)\" fill=\"#FF9FFF\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(116.4,41.47)\" fill=\"#FF9FFF\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(113.12,32.66)\" fill=\"#FF9FFF\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(116.86,37.9)\" fill=\"#FF9FFF\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(103.59,37.59)\" fill=\"#FF9FFF\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(122.39,32.88)\" fill=\"#FF9FFF\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(115.05,27.83)\" fill=\"#FF9FFF\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(129.64,42.49)\" fill=\"#000000\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(122.74,26.4)\" fill=\"#FF9FFF\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(79.77,49.77)\" fill=\"#FFFF62\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(64.08,38.09)\" fill=\"#FFFF62\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(83.21,51.72)\" fill=\"#00D6FF\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(66.91,49.13)\" fill=\"#00D6FF\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(71.01,42.88)\" fill=\"#00D6FF\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(67.92,41.1)\" fill=\"#00D6FF\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(79.39,45.85)\" fill=\"#00D6FF\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(74.26,47.73)\" fill=\"#00D6FF\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(63.43,45.62)\" fill=\"#00D6FF\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(74.84,49.6)\" fill=\"#00D6FF\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(88.1,57.3)\" fill=\"#D74400\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(84.63,32.59)\" fill=\"#D74400\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(94.32,51.76)\" fill=\"#D74400\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(78.33,43.83)\" fill=\"#D74400\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(87.94,48.71)\" fill=\"#D74400\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(90.5,46.2)\" fill=\"#D74400\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(88.9,52.35)\" fill=\"#D74400\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(90,62.27)\" fill=\"#D74400\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(96.17,54.45)\" fill=\"#D74400\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(94.71,42.44)\" fill=\"#D74400\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(45.11,59.81)\" fill=\"#008029\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(29.21,41.26)\" fill=\"#008029\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(27.48,50.55)\" fill=\"#008029\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(53.53,48.57)\" fill=\"#008029\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(33.78,55.37)\" fill=\"#008029\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(35.64,57.73)\" fill=\"#008029\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(53.54,62.72)\" fill=\"#000000\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(39.39,50.11)\" fill=\"#FFFF62\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(52.79,57.96)\" fill=\"#008029\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(42.99,54.49)\" fill=\"#008029\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(16.12,60.27)\" fill=\"#005FD5\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(96.54,58.37)\" fill=\"#005FD5\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(97.4,39.89)\" fill=\"#005FD5\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(111.19,52.72)\" fill=\"#005FD5\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(98.82,48.26)\" fill=\"#005FD5\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(104.67,51.33)\" fill=\"#000000\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(75.94,62.03)\" fill=\"#005FD5\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(108.78,49.6)\" fill=\"#005FD5\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(107.06,52.27)\" fill=\"#005FD5\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(124.38,49)\" fill=\"#008029\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(18.91,82.33)\" fill=\"#930068\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(15.41,55.53)\" fill=\"#930068\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(33.78,67.61)\" fill=\"#930068\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(11.79,66.66)\" fill=\"#930068\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(24.45,88.11)\" fill=\"#FF9FFF\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(13.88,75.73)\" fill=\"#930068\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(25.71,71.34)\" fill=\"#930068\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(22.19,68.81)\" fill=\"#930068\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(31.65,77.41)\" fill=\"#930068\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(23.79,65.2)\" fill=\"#930068\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(36.67,17.86)\" fill=\"#FFCBB5\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(53.25,35.25)\" fill=\"#FFCBB5\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(32.57,37.7)\" fill=\"#FFCBB5\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(38.11,26.13)\" fill=\"#FFCBB5\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(35.38,31.03)\" fill=\"#FFCBB5\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(24.87,27.65)\" fill=\"#FFCBB5\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(31.47,28.39)\" fill=\"#FFCBB5\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(44.67,26.21)\" fill=\"#FFCBB5\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(18.56,47.13)\" fill=\"#FFCBB5\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(22.66,38.78)\" fill=\"#FFCBB5\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(58.97,76.09)\" fill=\"#A78600\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(56.68,68.81)\" fill=\"#A78600\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(52.98,67.29)\" fill=\"#A78600\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(59.09,78.61)\" fill=\"#A78600\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(51.15,81.76)\" fill=\"#A78600\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(44.84,75.79)\" fill=\"#000000\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(55.57,76.63)\" fill=\"#A78600\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(65.41,61.24)\" fill=\"#A78600\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(65.51,67.45)\" fill=\"#A78600\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "  <g transform=\"translate(54.28,73.84)\" fill=\"#A78600\">\n",
       "    <circle cx=\"0\" cy=\"0\" r=\"1.41\" class=\"primitive\"/>\n",
       "  </g>\n",
       "</g>\n",
       "<g font-size=\"4\" stroke=\"#000000\" stroke-opacity=\"0.000\" fill=\"#000000\" id=\"img-9a2eaaa4-5\">\n",
       "</g>\n",
       "<script> <![CDATA[\n",
       "(function(N){var k=/[\\.\\/]/,L=/\\s*,\\s*/,C=function(a,d){return a-d},a,v,y={n:{}},M=function(){for(var a=0,d=this.length;a<d;a++)if(\"undefined\"!=typeof this[a])return this[a]},A=function(){for(var a=this.length;--a;)if(\"undefined\"!=typeof this[a])return this[a]},w=function(k,d){k=String(k);var f=v,n=Array.prototype.slice.call(arguments,2),u=w.listeners(k),p=0,b,q=[],e={},l=[],r=a;l.firstDefined=M;l.lastDefined=A;a=k;for(var s=v=0,x=u.length;s<x;s++)\"zIndex\"in u[s]&&(q.push(u[s].zIndex),0>u[s].zIndex&&\n",
       "(e[u[s].zIndex]=u[s]));for(q.sort(C);0>q[p];)if(b=e[q[p++] ],l.push(b.apply(d,n)),v)return v=f,l;for(s=0;s<x;s++)if(b=u[s],\"zIndex\"in b)if(b.zIndex==q[p]){l.push(b.apply(d,n));if(v)break;do if(p++,(b=e[q[p] ])&&l.push(b.apply(d,n)),v)break;while(b)}else e[b.zIndex]=b;else if(l.push(b.apply(d,n)),v)break;v=f;a=r;return l};w._events=y;w.listeners=function(a){a=a.split(k);var d=y,f,n,u,p,b,q,e,l=[d],r=[];u=0;for(p=a.length;u<p;u++){e=[];b=0;for(q=l.length;b<q;b++)for(d=l[b].n,f=[d[a[u] ],d[\"*\"] ],n=2;n--;)if(d=\n",
       "f[n])e.push(d),r=r.concat(d.f||[]);l=e}return r};w.on=function(a,d){a=String(a);if(\"function\"!=typeof d)return function(){};for(var f=a.split(L),n=0,u=f.length;n<u;n++)(function(a){a=a.split(k);for(var b=y,f,e=0,l=a.length;e<l;e++)b=b.n,b=b.hasOwnProperty(a[e])&&b[a[e] ]||(b[a[e] ]={n:{}});b.f=b.f||[];e=0;for(l=b.f.length;e<l;e++)if(b.f[e]==d){f=!0;break}!f&&b.f.push(d)})(f[n]);return function(a){+a==+a&&(d.zIndex=+a)}};w.f=function(a){var d=[].slice.call(arguments,1);return function(){w.apply(null,\n",
       "[a,null].concat(d).concat([].slice.call(arguments,0)))}};w.stop=function(){v=1};w.nt=function(k){return k?(new RegExp(\"(?:\\\\.|\\\\/|^)\"+k+\"(?:\\\\.|\\\\/|$)\")).test(a):a};w.nts=function(){return a.split(k)};w.off=w.unbind=function(a,d){if(a){var f=a.split(L);if(1<f.length)for(var n=0,u=f.length;n<u;n++)w.off(f[n],d);else{for(var f=a.split(k),p,b,q,e,l=[y],n=0,u=f.length;n<u;n++)for(e=0;e<l.length;e+=q.length-2){q=[e,1];p=l[e].n;if(\"*\"!=f[n])p[f[n] ]&&q.push(p[f[n] ]);else for(b in p)p.hasOwnProperty(b)&&\n",
       "q.push(p[b]);l.splice.apply(l,q)}n=0;for(u=l.length;n<u;n++)for(p=l[n];p.n;){if(d){if(p.f){e=0;for(f=p.f.length;e<f;e++)if(p.f[e]==d){p.f.splice(e,1);break}!p.f.length&&delete p.f}for(b in p.n)if(p.n.hasOwnProperty(b)&&p.n[b].f){q=p.n[b].f;e=0;for(f=q.length;e<f;e++)if(q[e]==d){q.splice(e,1);break}!q.length&&delete p.n[b].f}}else for(b in delete p.f,p.n)p.n.hasOwnProperty(b)&&p.n[b].f&&delete p.n[b].f;p=p.n}}}else w._events=y={n:{}}};w.once=function(a,d){var f=function(){w.unbind(a,f);return d.apply(this,\n",
       "arguments)};return w.on(a,f)};w.version=\"0.4.2\";w.toString=function(){return\"You are running Eve 0.4.2\"};\"undefined\"!=typeof module&&module.exports?module.exports=w:\"function\"===typeof define&&define.amd?define(\"eve\",[],function(){return w}):N.eve=w})(this);\n",
       "(function(N,k){\"function\"===typeof define&&define.amd?define(\"Snap.svg\",[\"eve\"],function(L){return k(N,L)}):k(N,N.eve)})(this,function(N,k){var L=function(a){var k={},y=N.requestAnimationFrame||N.webkitRequestAnimationFrame||N.mozRequestAnimationFrame||N.oRequestAnimationFrame||N.msRequestAnimationFrame||function(a){setTimeout(a,16)},M=Array.isArray||function(a){return a instanceof Array||\"[object Array]\"==Object.prototype.toString.call(a)},A=0,w=\"M\"+(+new Date).toString(36),z=function(a){if(null==\n",
       "a)return this.s;var b=this.s-a;this.b+=this.dur*b;this.B+=this.dur*b;this.s=a},d=function(a){if(null==a)return this.spd;this.spd=a},f=function(a){if(null==a)return this.dur;this.s=this.s*a/this.dur;this.dur=a},n=function(){delete k[this.id];this.update();a(\"mina.stop.\"+this.id,this)},u=function(){this.pdif||(delete k[this.id],this.update(),this.pdif=this.get()-this.b)},p=function(){this.pdif&&(this.b=this.get()-this.pdif,delete this.pdif,k[this.id]=this)},b=function(){var a;if(M(this.start)){a=[];\n",
       "for(var b=0,e=this.start.length;b<e;b++)a[b]=+this.start[b]+(this.end[b]-this.start[b])*this.easing(this.s)}else a=+this.start+(this.end-this.start)*this.easing(this.s);this.set(a)},q=function(){var l=0,b;for(b in k)if(k.hasOwnProperty(b)){var e=k[b],f=e.get();l++;e.s=(f-e.b)/(e.dur/e.spd);1<=e.s&&(delete k[b],e.s=1,l--,function(b){setTimeout(function(){a(\"mina.finish.\"+b.id,b)})}(e));e.update()}l&&y(q)},e=function(a,r,s,x,G,h,J){a={id:w+(A++).toString(36),start:a,end:r,b:s,s:0,dur:x-s,spd:1,get:G,\n",
       "set:h,easing:J||e.linear,status:z,speed:d,duration:f,stop:n,pause:u,resume:p,update:b};k[a.id]=a;r=0;for(var K in k)if(k.hasOwnProperty(K)&&(r++,2==r))break;1==r&&y(q);return a};e.time=Date.now||function(){return+new Date};e.getById=function(a){return k[a]||null};e.linear=function(a){return a};e.easeout=function(a){return Math.pow(a,1.7)};e.easein=function(a){return Math.pow(a,0.48)};e.easeinout=function(a){if(1==a)return 1;if(0==a)return 0;var b=0.48-a/1.04,e=Math.sqrt(0.1734+b*b);a=e-b;a=Math.pow(Math.abs(a),\n",
       "1/3)*(0>a?-1:1);b=-e-b;b=Math.pow(Math.abs(b),1/3)*(0>b?-1:1);a=a+b+0.5;return 3*(1-a)*a*a+a*a*a};e.backin=function(a){return 1==a?1:a*a*(2.70158*a-1.70158)};e.backout=function(a){if(0==a)return 0;a-=1;return a*a*(2.70158*a+1.70158)+1};e.elastic=function(a){return a==!!a?a:Math.pow(2,-10*a)*Math.sin(2*(a-0.075)*Math.PI/0.3)+1};e.bounce=function(a){a<1/2.75?a*=7.5625*a:a<2/2.75?(a-=1.5/2.75,a=7.5625*a*a+0.75):a<2.5/2.75?(a-=2.25/2.75,a=7.5625*a*a+0.9375):(a-=2.625/2.75,a=7.5625*a*a+0.984375);return a};\n",
       "return N.mina=e}(\"undefined\"==typeof k?function(){}:k),C=function(){function a(c,t){if(c){if(c.tagName)return x(c);if(y(c,\"array\")&&a.set)return a.set.apply(a,c);if(c instanceof e)return c;if(null==t)return c=G.doc.querySelector(c),x(c)}return new s(null==c?\"100%\":c,null==t?\"100%\":t)}function v(c,a){if(a){\"#text\"==c&&(c=G.doc.createTextNode(a.text||\"\"));\"string\"==typeof c&&(c=v(c));if(\"string\"==typeof a)return\"xlink:\"==a.substring(0,6)?c.getAttributeNS(m,a.substring(6)):\"xml:\"==a.substring(0,4)?c.getAttributeNS(la,\n",
       "a.substring(4)):c.getAttribute(a);for(var da in a)if(a[h](da)){var b=J(a[da]);b?\"xlink:\"==da.substring(0,6)?c.setAttributeNS(m,da.substring(6),b):\"xml:\"==da.substring(0,4)?c.setAttributeNS(la,da.substring(4),b):c.setAttribute(da,b):c.removeAttribute(da)}}else c=G.doc.createElementNS(la,c);return c}function y(c,a){a=J.prototype.toLowerCase.call(a);return\"finite\"==a?isFinite(c):\"array\"==a&&(c instanceof Array||Array.isArray&&Array.isArray(c))?!0:\"null\"==a&&null===c||a==typeof c&&null!==c||\"object\"==\n",
       "a&&c===Object(c)||$.call(c).slice(8,-1).toLowerCase()==a}function M(c){if(\"function\"==typeof c||Object(c)!==c)return c;var a=new c.constructor,b;for(b in c)c[h](b)&&(a[b]=M(c[b]));return a}function A(c,a,b){function m(){var e=Array.prototype.slice.call(arguments,0),f=e.join(\"\\u2400\"),d=m.cache=m.cache||{},l=m.count=m.count||[];if(d[h](f)){a:for(var e=l,l=f,B=0,H=e.length;B<H;B++)if(e[B]===l){e.push(e.splice(B,1)[0]);break a}return b?b(d[f]):d[f]}1E3<=l.length&&delete d[l.shift()];l.push(f);d[f]=c.apply(a,\n",
       "e);return b?b(d[f]):d[f]}return m}function w(c,a,b,m,e,f){return null==e?(c-=b,a-=m,c||a?(180*I.atan2(-a,-c)/C+540)%360:0):w(c,a,e,f)-w(b,m,e,f)}function z(c){return c%360*C/180}function d(c){var a=[];c=c.replace(/(?:^|\\s)(\\w+)\\(([^)]+)\\)/g,function(c,b,m){m=m.split(/\\s*,\\s*|\\s+/);\"rotate\"==b&&1==m.length&&m.push(0,0);\"scale\"==b&&(2<m.length?m=m.slice(0,2):2==m.length&&m.push(0,0),1==m.length&&m.push(m[0],0,0));\"skewX\"==b?a.push([\"m\",1,0,I.tan(z(m[0])),1,0,0]):\"skewY\"==b?a.push([\"m\",1,I.tan(z(m[0])),\n",
       "0,1,0,0]):a.push([b.charAt(0)].concat(m));return c});return a}function f(c,t){var b=O(c),m=new a.Matrix;if(b)for(var e=0,f=b.length;e<f;e++){var h=b[e],d=h.length,B=J(h[0]).toLowerCase(),H=h[0]!=B,l=H?m.invert():0,E;\"t\"==B&&2==d?m.translate(h[1],0):\"t\"==B&&3==d?H?(d=l.x(0,0),B=l.y(0,0),H=l.x(h[1],h[2]),l=l.y(h[1],h[2]),m.translate(H-d,l-B)):m.translate(h[1],h[2]):\"r\"==B?2==d?(E=E||t,m.rotate(h[1],E.x+E.width/2,E.y+E.height/2)):4==d&&(H?(H=l.x(h[2],h[3]),l=l.y(h[2],h[3]),m.rotate(h[1],H,l)):m.rotate(h[1],\n",
       "h[2],h[3])):\"s\"==B?2==d||3==d?(E=E||t,m.scale(h[1],h[d-1],E.x+E.width/2,E.y+E.height/2)):4==d?H?(H=l.x(h[2],h[3]),l=l.y(h[2],h[3]),m.scale(h[1],h[1],H,l)):m.scale(h[1],h[1],h[2],h[3]):5==d&&(H?(H=l.x(h[3],h[4]),l=l.y(h[3],h[4]),m.scale(h[1],h[2],H,l)):m.scale(h[1],h[2],h[3],h[4])):\"m\"==B&&7==d&&m.add(h[1],h[2],h[3],h[4],h[5],h[6])}return m}function n(c,t){if(null==t){var m=!0;t=\"linearGradient\"==c.type||\"radialGradient\"==c.type?c.node.getAttribute(\"gradientTransform\"):\"pattern\"==c.type?c.node.getAttribute(\"patternTransform\"):\n",
       "c.node.getAttribute(\"transform\");if(!t)return new a.Matrix;t=d(t)}else t=a._.rgTransform.test(t)?J(t).replace(/\\.{3}|\\u2026/g,c._.transform||aa):d(t),y(t,\"array\")&&(t=a.path?a.path.toString.call(t):J(t)),c._.transform=t;var b=f(t,c.getBBox(1));if(m)return b;c.matrix=b}function u(c){c=c.node.ownerSVGElement&&x(c.node.ownerSVGElement)||c.node.parentNode&&x(c.node.parentNode)||a.select(\"svg\")||a(0,0);var t=c.select(\"defs\"),t=null==t?!1:t.node;t||(t=r(\"defs\",c.node).node);return t}function p(c){return c.node.ownerSVGElement&&\n",
       "x(c.node.ownerSVGElement)||a.select(\"svg\")}function b(c,a,m){function b(c){if(null==c)return aa;if(c==+c)return c;v(B,{width:c});try{return B.getBBox().width}catch(a){return 0}}function h(c){if(null==c)return aa;if(c==+c)return c;v(B,{height:c});try{return B.getBBox().height}catch(a){return 0}}function e(b,B){null==a?d[b]=B(c.attr(b)||0):b==a&&(d=B(null==m?c.attr(b)||0:m))}var f=p(c).node,d={},B=f.querySelector(\".svg---mgr\");B||(B=v(\"rect\"),v(B,{x:-9E9,y:-9E9,width:10,height:10,\"class\":\"svg---mgr\",\n",
       "fill:\"none\"}),f.appendChild(B));switch(c.type){case \"rect\":e(\"rx\",b),e(\"ry\",h);case \"image\":e(\"width\",b),e(\"height\",h);case \"text\":e(\"x\",b);e(\"y\",h);break;case \"circle\":e(\"cx\",b);e(\"cy\",h);e(\"r\",b);break;case \"ellipse\":e(\"cx\",b);e(\"cy\",h);e(\"rx\",b);e(\"ry\",h);break;case \"line\":e(\"x1\",b);e(\"x2\",b);e(\"y1\",h);e(\"y2\",h);break;case \"marker\":e(\"refX\",b);e(\"markerWidth\",b);e(\"refY\",h);e(\"markerHeight\",h);break;case \"radialGradient\":e(\"fx\",b);e(\"fy\",h);break;case \"tspan\":e(\"dx\",b);e(\"dy\",h);break;default:e(a,\n",
       "b)}f.removeChild(B);return d}function q(c){y(c,\"array\")||(c=Array.prototype.slice.call(arguments,0));for(var a=0,b=0,m=this.node;this[a];)delete this[a++];for(a=0;a<c.length;a++)\"set\"==c[a].type?c[a].forEach(function(c){m.appendChild(c.node)}):m.appendChild(c[a].node);for(var h=m.childNodes,a=0;a<h.length;a++)this[b++]=x(h[a]);return this}function e(c){if(c.snap in E)return E[c.snap];var a=this.id=V(),b;try{b=c.ownerSVGElement}catch(m){}this.node=c;b&&(this.paper=new s(b));this.type=c.tagName;this.anims=\n",
       "{};this._={transform:[]};c.snap=a;E[a]=this;\"g\"==this.type&&(this.add=q);if(this.type in{g:1,mask:1,pattern:1})for(var e in s.prototype)s.prototype[h](e)&&(this[e]=s.prototype[e])}function l(c){this.node=c}function r(c,a){var b=v(c);a.appendChild(b);return x(b)}function s(c,a){var b,m,f,d=s.prototype;if(c&&\"svg\"==c.tagName){if(c.snap in E)return E[c.snap];var l=c.ownerDocument;b=new e(c);m=c.getElementsByTagName(\"desc\")[0];f=c.getElementsByTagName(\"defs\")[0];m||(m=v(\"desc\"),m.appendChild(l.createTextNode(\"Created with Snap\")),\n",
       "b.node.appendChild(m));f||(f=v(\"defs\"),b.node.appendChild(f));b.defs=f;for(var ca in d)d[h](ca)&&(b[ca]=d[ca]);b.paper=b.root=b}else b=r(\"svg\",G.doc.body),v(b.node,{height:a,version:1.1,width:c,xmlns:la});return b}function x(c){return!c||c instanceof e||c instanceof l?c:c.tagName&&\"svg\"==c.tagName.toLowerCase()?new s(c):c.tagName&&\"object\"==c.tagName.toLowerCase()&&\"image/svg+xml\"==c.type?new s(c.contentDocument.getElementsByTagName(\"svg\")[0]):new e(c)}a.version=\"0.3.0\";a.toString=function(){return\"Snap v\"+\n",
       "this.version};a._={};var G={win:N,doc:N.document};a._.glob=G;var h=\"hasOwnProperty\",J=String,K=parseFloat,U=parseInt,I=Math,P=I.max,Q=I.min,Y=I.abs,C=I.PI,aa=\"\",$=Object.prototype.toString,F=/^\\s*((#[a-f\\d]{6})|(#[a-f\\d]{3})|rgba?\\(\\s*([\\d\\.]+%?\\s*,\\s*[\\d\\.]+%?\\s*,\\s*[\\d\\.]+%?(?:\\s*,\\s*[\\d\\.]+%?)?)\\s*\\)|hsba?\\(\\s*([\\d\\.]+(?:deg|\\xb0|%)?\\s*,\\s*[\\d\\.]+%?\\s*,\\s*[\\d\\.]+(?:%?\\s*,\\s*[\\d\\.]+)?%?)\\s*\\)|hsla?\\(\\s*([\\d\\.]+(?:deg|\\xb0|%)?\\s*,\\s*[\\d\\.]+%?\\s*,\\s*[\\d\\.]+(?:%?\\s*,\\s*[\\d\\.]+)?%?)\\s*\\))\\s*$/i;a._.separator=\n",
       "RegExp(\"[,\\t\\n\\x0B\\f\\r \\u00a0\\u1680\\u180e\\u2000\\u2001\\u2002\\u2003\\u2004\\u2005\\u2006\\u2007\\u2008\\u2009\\u200a\\u202f\\u205f\\u3000\\u2028\\u2029]+\");var S=RegExp(\"[\\t\\n\\x0B\\f\\r \\u00a0\\u1680\\u180e\\u2000\\u2001\\u2002\\u2003\\u2004\\u2005\\u2006\\u2007\\u2008\\u2009\\u200a\\u202f\\u205f\\u3000\\u2028\\u2029]*,[\\t\\n\\x0B\\f\\r \\u00a0\\u1680\\u180e\\u2000\\u2001\\u2002\\u2003\\u2004\\u2005\\u2006\\u2007\\u2008\\u2009\\u200a\\u202f\\u205f\\u3000\\u2028\\u2029]*\"),X={hs:1,rg:1},W=RegExp(\"([a-z])[\\t\\n\\x0B\\f\\r \\u00a0\\u1680\\u180e\\u2000\\u2001\\u2002\\u2003\\u2004\\u2005\\u2006\\u2007\\u2008\\u2009\\u200a\\u202f\\u205f\\u3000\\u2028\\u2029,]*((-?\\\\d*\\\\.?\\\\d*(?:e[\\\\-+]?\\\\d+)?[\\t\\n\\x0B\\f\\r \\u00a0\\u1680\\u180e\\u2000\\u2001\\u2002\\u2003\\u2004\\u2005\\u2006\\u2007\\u2008\\u2009\\u200a\\u202f\\u205f\\u3000\\u2028\\u2029]*,?[\\t\\n\\x0B\\f\\r \\u00a0\\u1680\\u180e\\u2000\\u2001\\u2002\\u2003\\u2004\\u2005\\u2006\\u2007\\u2008\\u2009\\u200a\\u202f\\u205f\\u3000\\u2028\\u2029]*)+)\",\n",
       "\"ig\"),ma=RegExp(\"([rstm])[\\t\\n\\x0B\\f\\r \\u00a0\\u1680\\u180e\\u2000\\u2001\\u2002\\u2003\\u2004\\u2005\\u2006\\u2007\\u2008\\u2009\\u200a\\u202f\\u205f\\u3000\\u2028\\u2029,]*((-?\\\\d*\\\\.?\\\\d*(?:e[\\\\-+]?\\\\d+)?[\\t\\n\\x0B\\f\\r \\u00a0\\u1680\\u180e\\u2000\\u2001\\u2002\\u2003\\u2004\\u2005\\u2006\\u2007\\u2008\\u2009\\u200a\\u202f\\u205f\\u3000\\u2028\\u2029]*,?[\\t\\n\\x0B\\f\\r \\u00a0\\u1680\\u180e\\u2000\\u2001\\u2002\\u2003\\u2004\\u2005\\u2006\\u2007\\u2008\\u2009\\u200a\\u202f\\u205f\\u3000\\u2028\\u2029]*)+)\",\"ig\"),Z=RegExp(\"(-?\\\\d*\\\\.?\\\\d*(?:e[\\\\-+]?\\\\d+)?)[\\t\\n\\x0B\\f\\r \\u00a0\\u1680\\u180e\\u2000\\u2001\\u2002\\u2003\\u2004\\u2005\\u2006\\u2007\\u2008\\u2009\\u200a\\u202f\\u205f\\u3000\\u2028\\u2029]*,?[\\t\\n\\x0B\\f\\r \\u00a0\\u1680\\u180e\\u2000\\u2001\\u2002\\u2003\\u2004\\u2005\\u2006\\u2007\\u2008\\u2009\\u200a\\u202f\\u205f\\u3000\\u2028\\u2029]*\",\n",
       "\"ig\"),na=0,ba=\"S\"+(+new Date).toString(36),V=function(){return ba+(na++).toString(36)},m=\"http://www.w3.org/1999/xlink\",la=\"http://www.w3.org/2000/svg\",E={},ca=a.url=function(c){return\"url('#\"+c+\"')\"};a._.$=v;a._.id=V;a.format=function(){var c=/\\{([^\\}]+)\\}/g,a=/(?:(?:^|\\.)(.+?)(?=\\[|\\.|$|\\()|\\[('|\")(.+?)\\2\\])(\\(\\))?/g,b=function(c,b,m){var h=m;b.replace(a,function(c,a,b,m,t){a=a||m;h&&(a in h&&(h=h[a]),\"function\"==typeof h&&t&&(h=h()))});return h=(null==h||h==m?c:h)+\"\"};return function(a,m){return J(a).replace(c,\n",
       "function(c,a){return b(c,a,m)})}}();a._.clone=M;a._.cacher=A;a.rad=z;a.deg=function(c){return 180*c/C%360};a.angle=w;a.is=y;a.snapTo=function(c,a,b){b=y(b,\"finite\")?b:10;if(y(c,\"array\"))for(var m=c.length;m--;){if(Y(c[m]-a)<=b)return c[m]}else{c=+c;m=a%c;if(m<b)return a-m;if(m>c-b)return a-m+c}return a};a.getRGB=A(function(c){if(!c||(c=J(c)).indexOf(\"-\")+1)return{r:-1,g:-1,b:-1,hex:\"none\",error:1,toString:ka};if(\"none\"==c)return{r:-1,g:-1,b:-1,hex:\"none\",toString:ka};!X[h](c.toLowerCase().substring(0,\n",
       "2))&&\"#\"!=c.charAt()&&(c=T(c));if(!c)return{r:-1,g:-1,b:-1,hex:\"none\",error:1,toString:ka};var b,m,e,f,d;if(c=c.match(F)){c[2]&&(e=U(c[2].substring(5),16),m=U(c[2].substring(3,5),16),b=U(c[2].substring(1,3),16));c[3]&&(e=U((d=c[3].charAt(3))+d,16),m=U((d=c[3].charAt(2))+d,16),b=U((d=c[3].charAt(1))+d,16));c[4]&&(d=c[4].split(S),b=K(d[0]),\"%\"==d[0].slice(-1)&&(b*=2.55),m=K(d[1]),\"%\"==d[1].slice(-1)&&(m*=2.55),e=K(d[2]),\"%\"==d[2].slice(-1)&&(e*=2.55),\"rgba\"==c[1].toLowerCase().slice(0,4)&&(f=K(d[3])),\n",
       "d[3]&&\"%\"==d[3].slice(-1)&&(f/=100));if(c[5])return d=c[5].split(S),b=K(d[0]),\"%\"==d[0].slice(-1)&&(b/=100),m=K(d[1]),\"%\"==d[1].slice(-1)&&(m/=100),e=K(d[2]),\"%\"==d[2].slice(-1)&&(e/=100),\"deg\"!=d[0].slice(-3)&&\"\\u00b0\"!=d[0].slice(-1)||(b/=360),\"hsba\"==c[1].toLowerCase().slice(0,4)&&(f=K(d[3])),d[3]&&\"%\"==d[3].slice(-1)&&(f/=100),a.hsb2rgb(b,m,e,f);if(c[6])return d=c[6].split(S),b=K(d[0]),\"%\"==d[0].slice(-1)&&(b/=100),m=K(d[1]),\"%\"==d[1].slice(-1)&&(m/=100),e=K(d[2]),\"%\"==d[2].slice(-1)&&(e/=100),\n",
       "\"deg\"!=d[0].slice(-3)&&\"\\u00b0\"!=d[0].slice(-1)||(b/=360),\"hsla\"==c[1].toLowerCase().slice(0,4)&&(f=K(d[3])),d[3]&&\"%\"==d[3].slice(-1)&&(f/=100),a.hsl2rgb(b,m,e,f);b=Q(I.round(b),255);m=Q(I.round(m),255);e=Q(I.round(e),255);f=Q(P(f,0),1);c={r:b,g:m,b:e,toString:ka};c.hex=\"#\"+(16777216|e|m<<8|b<<16).toString(16).slice(1);c.opacity=y(f,\"finite\")?f:1;return c}return{r:-1,g:-1,b:-1,hex:\"none\",error:1,toString:ka}},a);a.hsb=A(function(c,b,m){return a.hsb2rgb(c,b,m).hex});a.hsl=A(function(c,b,m){return a.hsl2rgb(c,\n",
       "b,m).hex});a.rgb=A(function(c,a,b,m){if(y(m,\"finite\")){var e=I.round;return\"rgba(\"+[e(c),e(a),e(b),+m.toFixed(2)]+\")\"}return\"#\"+(16777216|b|a<<8|c<<16).toString(16).slice(1)});var T=function(c){var a=G.doc.getElementsByTagName(\"head\")[0]||G.doc.getElementsByTagName(\"svg\")[0];T=A(function(c){if(\"red\"==c.toLowerCase())return\"rgb(255, 0, 0)\";a.style.color=\"rgb(255, 0, 0)\";a.style.color=c;c=G.doc.defaultView.getComputedStyle(a,aa).getPropertyValue(\"color\");return\"rgb(255, 0, 0)\"==c?null:c});return T(c)},\n",
       "qa=function(){return\"hsb(\"+[this.h,this.s,this.b]+\")\"},ra=function(){return\"hsl(\"+[this.h,this.s,this.l]+\")\"},ka=function(){return 1==this.opacity||null==this.opacity?this.hex:\"rgba(\"+[this.r,this.g,this.b,this.opacity]+\")\"},D=function(c,b,m){null==b&&y(c,\"object\")&&\"r\"in c&&\"g\"in c&&\"b\"in c&&(m=c.b,b=c.g,c=c.r);null==b&&y(c,string)&&(m=a.getRGB(c),c=m.r,b=m.g,m=m.b);if(1<c||1<b||1<m)c/=255,b/=255,m/=255;return[c,b,m]},oa=function(c,b,m,e){c=I.round(255*c);b=I.round(255*b);m=I.round(255*m);c={r:c,\n",
       "g:b,b:m,opacity:y(e,\"finite\")?e:1,hex:a.rgb(c,b,m),toString:ka};y(e,\"finite\")&&(c.opacity=e);return c};a.color=function(c){var b;y(c,\"object\")&&\"h\"in c&&\"s\"in c&&\"b\"in c?(b=a.hsb2rgb(c),c.r=b.r,c.g=b.g,c.b=b.b,c.opacity=1,c.hex=b.hex):y(c,\"object\")&&\"h\"in c&&\"s\"in c&&\"l\"in c?(b=a.hsl2rgb(c),c.r=b.r,c.g=b.g,c.b=b.b,c.opacity=1,c.hex=b.hex):(y(c,\"string\")&&(c=a.getRGB(c)),y(c,\"object\")&&\"r\"in c&&\"g\"in c&&\"b\"in c&&!(\"error\"in c)?(b=a.rgb2hsl(c),c.h=b.h,c.s=b.s,c.l=b.l,b=a.rgb2hsb(c),c.v=b.b):(c={hex:\"none\"},\n",
       "c.r=c.g=c.b=c.h=c.s=c.v=c.l=-1,c.error=1));c.toString=ka;return c};a.hsb2rgb=function(c,a,b,m){y(c,\"object\")&&\"h\"in c&&\"s\"in c&&\"b\"in c&&(b=c.b,a=c.s,c=c.h,m=c.o);var e,h,d;c=360*c%360/60;d=b*a;a=d*(1-Y(c%2-1));b=e=h=b-d;c=~~c;b+=[d,a,0,0,a,d][c];e+=[a,d,d,a,0,0][c];h+=[0,0,a,d,d,a][c];return oa(b,e,h,m)};a.hsl2rgb=function(c,a,b,m){y(c,\"object\")&&\"h\"in c&&\"s\"in c&&\"l\"in c&&(b=c.l,a=c.s,c=c.h);if(1<c||1<a||1<b)c/=360,a/=100,b/=100;var e,h,d;c=360*c%360/60;d=2*a*(0.5>b?b:1-b);a=d*(1-Y(c%2-1));b=e=\n",
       "h=b-d/2;c=~~c;b+=[d,a,0,0,a,d][c];e+=[a,d,d,a,0,0][c];h+=[0,0,a,d,d,a][c];return oa(b,e,h,m)};a.rgb2hsb=function(c,a,b){b=D(c,a,b);c=b[0];a=b[1];b=b[2];var m,e;m=P(c,a,b);e=m-Q(c,a,b);c=((0==e?0:m==c?(a-b)/e:m==a?(b-c)/e+2:(c-a)/e+4)+360)%6*60/360;return{h:c,s:0==e?0:e/m,b:m,toString:qa}};a.rgb2hsl=function(c,a,b){b=D(c,a,b);c=b[0];a=b[1];b=b[2];var m,e,h;m=P(c,a,b);e=Q(c,a,b);h=m-e;c=((0==h?0:m==c?(a-b)/h:m==a?(b-c)/h+2:(c-a)/h+4)+360)%6*60/360;m=(m+e)/2;return{h:c,s:0==h?0:0.5>m?h/(2*m):h/(2-2*\n",
       "m),l:m,toString:ra}};a.parsePathString=function(c){if(!c)return null;var b=a.path(c);if(b.arr)return a.path.clone(b.arr);var m={a:7,c:6,o:2,h:1,l:2,m:2,r:4,q:4,s:4,t:2,v:1,u:3,z:0},e=[];y(c,\"array\")&&y(c[0],\"array\")&&(e=a.path.clone(c));e.length||J(c).replace(W,function(c,a,b){var h=[];c=a.toLowerCase();b.replace(Z,function(c,a){a&&h.push(+a)});\"m\"==c&&2<h.length&&(e.push([a].concat(h.splice(0,2))),c=\"l\",a=\"m\"==a?\"l\":\"L\");\"o\"==c&&1==h.length&&e.push([a,h[0] ]);if(\"r\"==c)e.push([a].concat(h));else for(;h.length>=\n",
       "m[c]&&(e.push([a].concat(h.splice(0,m[c]))),m[c]););});e.toString=a.path.toString;b.arr=a.path.clone(e);return e};var O=a.parseTransformString=function(c){if(!c)return null;var b=[];y(c,\"array\")&&y(c[0],\"array\")&&(b=a.path.clone(c));b.length||J(c).replace(ma,function(c,a,m){var e=[];a.toLowerCase();m.replace(Z,function(c,a){a&&e.push(+a)});b.push([a].concat(e))});b.toString=a.path.toString;return b};a._.svgTransform2string=d;a._.rgTransform=RegExp(\"^[a-z][\\t\\n\\x0B\\f\\r \\u00a0\\u1680\\u180e\\u2000\\u2001\\u2002\\u2003\\u2004\\u2005\\u2006\\u2007\\u2008\\u2009\\u200a\\u202f\\u205f\\u3000\\u2028\\u2029]*-?\\\\.?\\\\d\",\n",
       "\"i\");a._.transform2matrix=f;a._unit2px=b;a._.getSomeDefs=u;a._.getSomeSVG=p;a.select=function(c){return x(G.doc.querySelector(c))};a.selectAll=function(c){c=G.doc.querySelectorAll(c);for(var b=(a.set||Array)(),m=0;m<c.length;m++)b.push(x(c[m]));return b};setInterval(function(){for(var c in E)if(E[h](c)){var a=E[c],b=a.node;(\"svg\"!=a.type&&!b.ownerSVGElement||\"svg\"==a.type&&(!b.parentNode||\"ownerSVGElement\"in b.parentNode&&!b.ownerSVGElement))&&delete E[c]}},1E4);(function(c){function m(c){function a(c,\n",
       "b){var m=v(c.node,b);(m=(m=m&&m.match(d))&&m[2])&&\"#\"==m.charAt()&&(m=m.substring(1))&&(f[m]=(f[m]||[]).concat(function(a){var m={};m[b]=ca(a);v(c.node,m)}))}function b(c){var a=v(c.node,\"xlink:href\");a&&\"#\"==a.charAt()&&(a=a.substring(1))&&(f[a]=(f[a]||[]).concat(function(a){c.attr(\"xlink:href\",\"#\"+a)}))}var e=c.selectAll(\"*\"),h,d=/^\\s*url\\((\"|'|)(.*)\\1\\)\\s*$/;c=[];for(var f={},l=0,E=e.length;l<E;l++){h=e[l];a(h,\"fill\");a(h,\"stroke\");a(h,\"filter\");a(h,\"mask\");a(h,\"clip-path\");b(h);var t=v(h.node,\n",
       "\"id\");t&&(v(h.node,{id:h.id}),c.push({old:t,id:h.id}))}l=0;for(E=c.length;l<E;l++)if(e=f[c[l].old])for(h=0,t=e.length;h<t;h++)e[h](c[l].id)}function e(c,a,b){return function(m){m=m.slice(c,a);1==m.length&&(m=m[0]);return b?b(m):m}}function d(c){return function(){var a=c?\"<\"+this.type:\"\",b=this.node.attributes,m=this.node.childNodes;if(c)for(var e=0,h=b.length;e<h;e++)a+=\" \"+b[e].name+'=\"'+b[e].value.replace(/\"/g,'\\\\\"')+'\"';if(m.length){c&&(a+=\">\");e=0;for(h=m.length;e<h;e++)3==m[e].nodeType?a+=m[e].nodeValue:\n",
       "1==m[e].nodeType&&(a+=x(m[e]).toString());c&&(a+=\"</\"+this.type+\">\")}else c&&(a+=\"/>\");return a}}c.attr=function(c,a){if(!c)return this;if(y(c,\"string\"))if(1<arguments.length){var b={};b[c]=a;c=b}else return k(\"snap.util.getattr.\"+c,this).firstDefined();for(var m in c)c[h](m)&&k(\"snap.util.attr.\"+m,this,c[m]);return this};c.getBBox=function(c){if(!a.Matrix||!a.path)return this.node.getBBox();var b=this,m=new a.Matrix;if(b.removed)return a._.box();for(;\"use\"==b.type;)if(c||(m=m.add(b.transform().localMatrix.translate(b.attr(\"x\")||\n",
       "0,b.attr(\"y\")||0))),b.original)b=b.original;else var e=b.attr(\"xlink:href\"),b=b.original=b.node.ownerDocument.getElementById(e.substring(e.indexOf(\"#\")+1));var e=b._,h=a.path.get[b.type]||a.path.get.deflt;try{if(c)return e.bboxwt=h?a.path.getBBox(b.realPath=h(b)):a._.box(b.node.getBBox()),a._.box(e.bboxwt);b.realPath=h(b);b.matrix=b.transform().localMatrix;e.bbox=a.path.getBBox(a.path.map(b.realPath,m.add(b.matrix)));return a._.box(e.bbox)}catch(d){return a._.box()}};var f=function(){return this.string};\n",
       "c.transform=function(c){var b=this._;if(null==c){var m=this;c=new a.Matrix(this.node.getCTM());for(var e=n(this),h=[e],d=new a.Matrix,l=e.toTransformString(),b=J(e)==J(this.matrix)?J(b.transform):l;\"svg\"!=m.type&&(m=m.parent());)h.push(n(m));for(m=h.length;m--;)d.add(h[m]);return{string:b,globalMatrix:c,totalMatrix:d,localMatrix:e,diffMatrix:c.clone().add(e.invert()),global:c.toTransformString(),total:d.toTransformString(),local:l,toString:f}}c instanceof a.Matrix?this.matrix=c:n(this,c);this.node&&\n",
       "(\"linearGradient\"==this.type||\"radialGradient\"==this.type?v(this.node,{gradientTransform:this.matrix}):\"pattern\"==this.type?v(this.node,{patternTransform:this.matrix}):v(this.node,{transform:this.matrix}));return this};c.parent=function(){return x(this.node.parentNode)};c.append=c.add=function(c){if(c){if(\"set\"==c.type){var a=this;c.forEach(function(c){a.add(c)});return this}c=x(c);this.node.appendChild(c.node);c.paper=this.paper}return this};c.appendTo=function(c){c&&(c=x(c),c.append(this));return this};\n",
       "c.prepend=function(c){if(c){if(\"set\"==c.type){var a=this,b;c.forEach(function(c){b?b.after(c):a.prepend(c);b=c});return this}c=x(c);var m=c.parent();this.node.insertBefore(c.node,this.node.firstChild);this.add&&this.add();c.paper=this.paper;this.parent()&&this.parent().add();m&&m.add()}return this};c.prependTo=function(c){c=x(c);c.prepend(this);return this};c.before=function(c){if(\"set\"==c.type){var a=this;c.forEach(function(c){var b=c.parent();a.node.parentNode.insertBefore(c.node,a.node);b&&b.add()});\n",
       "this.parent().add();return this}c=x(c);var b=c.parent();this.node.parentNode.insertBefore(c.node,this.node);this.parent()&&this.parent().add();b&&b.add();c.paper=this.paper;return this};c.after=function(c){c=x(c);var a=c.parent();this.node.nextSibling?this.node.parentNode.insertBefore(c.node,this.node.nextSibling):this.node.parentNode.appendChild(c.node);this.parent()&&this.parent().add();a&&a.add();c.paper=this.paper;return this};c.insertBefore=function(c){c=x(c);var a=this.parent();c.node.parentNode.insertBefore(this.node,\n",
       "c.node);this.paper=c.paper;a&&a.add();c.parent()&&c.parent().add();return this};c.insertAfter=function(c){c=x(c);var a=this.parent();c.node.parentNode.insertBefore(this.node,c.node.nextSibling);this.paper=c.paper;a&&a.add();c.parent()&&c.parent().add();return this};c.remove=function(){var c=this.parent();this.node.parentNode&&this.node.parentNode.removeChild(this.node);delete this.paper;this.removed=!0;c&&c.add();return this};c.select=function(c){return x(this.node.querySelector(c))};c.selectAll=\n",
       "function(c){c=this.node.querySelectorAll(c);for(var b=(a.set||Array)(),m=0;m<c.length;m++)b.push(x(c[m]));return b};c.asPX=function(c,a){null==a&&(a=this.attr(c));return+b(this,c,a)};c.use=function(){var c,a=this.node.id;a||(a=this.id,v(this.node,{id:a}));c=\"linearGradient\"==this.type||\"radialGradient\"==this.type||\"pattern\"==this.type?r(this.type,this.node.parentNode):r(\"use\",this.node.parentNode);v(c.node,{\"xlink:href\":\"#\"+a});c.original=this;return c};var l=/\\S+/g;c.addClass=function(c){var a=(c||\n",
       "\"\").match(l)||[];c=this.node;var b=c.className.baseVal,m=b.match(l)||[],e,h,d;if(a.length){for(e=0;d=a[e++];)h=m.indexOf(d),~h||m.push(d);a=m.join(\" \");b!=a&&(c.className.baseVal=a)}return this};c.removeClass=function(c){var a=(c||\"\").match(l)||[];c=this.node;var b=c.className.baseVal,m=b.match(l)||[],e,h;if(m.length){for(e=0;h=a[e++];)h=m.indexOf(h),~h&&m.splice(h,1);a=m.join(\" \");b!=a&&(c.className.baseVal=a)}return this};c.hasClass=function(c){return!!~(this.node.className.baseVal.match(l)||[]).indexOf(c)};\n",
       "c.toggleClass=function(c,a){if(null!=a)return a?this.addClass(c):this.removeClass(c);var b=(c||\"\").match(l)||[],m=this.node,e=m.className.baseVal,h=e.match(l)||[],d,f,E;for(d=0;E=b[d++];)f=h.indexOf(E),~f?h.splice(f,1):h.push(E);b=h.join(\" \");e!=b&&(m.className.baseVal=b);return this};c.clone=function(){var c=x(this.node.cloneNode(!0));v(c.node,\"id\")&&v(c.node,{id:c.id});m(c);c.insertAfter(this);return c};c.toDefs=function(){u(this).appendChild(this.node);return this};c.pattern=c.toPattern=function(c,\n",
       "a,b,m){var e=r(\"pattern\",u(this));null==c&&(c=this.getBBox());y(c,\"object\")&&\"x\"in c&&(a=c.y,b=c.width,m=c.height,c=c.x);v(e.node,{x:c,y:a,width:b,height:m,patternUnits:\"userSpaceOnUse\",id:e.id,viewBox:[c,a,b,m].join(\" \")});e.node.appendChild(this.node);return e};c.marker=function(c,a,b,m,e,h){var d=r(\"marker\",u(this));null==c&&(c=this.getBBox());y(c,\"object\")&&\"x\"in c&&(a=c.y,b=c.width,m=c.height,e=c.refX||c.cx,h=c.refY||c.cy,c=c.x);v(d.node,{viewBox:[c,a,b,m].join(\" \"),markerWidth:b,markerHeight:m,\n",
       "orient:\"auto\",refX:e||0,refY:h||0,id:d.id});d.node.appendChild(this.node);return d};var E=function(c,a,b,m){\"function\"!=typeof b||b.length||(m=b,b=L.linear);this.attr=c;this.dur=a;b&&(this.easing=b);m&&(this.callback=m)};a._.Animation=E;a.animation=function(c,a,b,m){return new E(c,a,b,m)};c.inAnim=function(){var c=[],a;for(a in this.anims)this.anims[h](a)&&function(a){c.push({anim:new E(a._attrs,a.dur,a.easing,a._callback),mina:a,curStatus:a.status(),status:function(c){return a.status(c)},stop:function(){a.stop()}})}(this.anims[a]);\n",
       "return c};a.animate=function(c,a,b,m,e,h){\"function\"!=typeof e||e.length||(h=e,e=L.linear);var d=L.time();c=L(c,a,d,d+m,L.time,b,e);h&&k.once(\"mina.finish.\"+c.id,h);return c};c.stop=function(){for(var c=this.inAnim(),a=0,b=c.length;a<b;a++)c[a].stop();return this};c.animate=function(c,a,b,m){\"function\"!=typeof b||b.length||(m=b,b=L.linear);c instanceof E&&(m=c.callback,b=c.easing,a=b.dur,c=c.attr);var d=[],f=[],l={},t,ca,n,T=this,q;for(q in c)if(c[h](q)){T.equal?(n=T.equal(q,J(c[q])),t=n.from,ca=\n",
       "n.to,n=n.f):(t=+T.attr(q),ca=+c[q]);var la=y(t,\"array\")?t.length:1;l[q]=e(d.length,d.length+la,n);d=d.concat(t);f=f.concat(ca)}t=L.time();var p=L(d,f,t,t+a,L.time,function(c){var a={},b;for(b in l)l[h](b)&&(a[b]=l[b](c));T.attr(a)},b);T.anims[p.id]=p;p._attrs=c;p._callback=m;k(\"snap.animcreated.\"+T.id,p);k.once(\"mina.finish.\"+p.id,function(){delete T.anims[p.id];m&&m.call(T)});k.once(\"mina.stop.\"+p.id,function(){delete T.anims[p.id]});return T};var T={};c.data=function(c,b){var m=T[this.id]=T[this.id]||\n",
       "{};if(0==arguments.length)return k(\"snap.data.get.\"+this.id,this,m,null),m;if(1==arguments.length){if(a.is(c,\"object\")){for(var e in c)c[h](e)&&this.data(e,c[e]);return this}k(\"snap.data.get.\"+this.id,this,m[c],c);return m[c]}m[c]=b;k(\"snap.data.set.\"+this.id,this,b,c);return this};c.removeData=function(c){null==c?T[this.id]={}:T[this.id]&&delete T[this.id][c];return this};c.outerSVG=c.toString=d(1);c.innerSVG=d()})(e.prototype);a.parse=function(c){var a=G.doc.createDocumentFragment(),b=!0,m=G.doc.createElement(\"div\");\n",
       "c=J(c);c.match(/^\\s*<\\s*svg(?:\\s|>)/)||(c=\"<svg>\"+c+\"</svg>\",b=!1);m.innerHTML=c;if(c=m.getElementsByTagName(\"svg\")[0])if(b)a=c;else for(;c.firstChild;)a.appendChild(c.firstChild);m.innerHTML=aa;return new l(a)};l.prototype.select=e.prototype.select;l.prototype.selectAll=e.prototype.selectAll;a.fragment=function(){for(var c=Array.prototype.slice.call(arguments,0),b=G.doc.createDocumentFragment(),m=0,e=c.length;m<e;m++){var h=c[m];h.node&&h.node.nodeType&&b.appendChild(h.node);h.nodeType&&b.appendChild(h);\n",
       "\"string\"==typeof h&&b.appendChild(a.parse(h).node)}return new l(b)};a._.make=r;a._.wrap=x;s.prototype.el=function(c,a){var b=r(c,this.node);a&&b.attr(a);return b};k.on(\"snap.util.getattr\",function(){var c=k.nt(),c=c.substring(c.lastIndexOf(\".\")+1),a=c.replace(/[A-Z]/g,function(c){return\"-\"+c.toLowerCase()});return pa[h](a)?this.node.ownerDocument.defaultView.getComputedStyle(this.node,null).getPropertyValue(a):v(this.node,c)});var pa={\"alignment-baseline\":0,\"baseline-shift\":0,clip:0,\"clip-path\":0,\n",
       "\"clip-rule\":0,color:0,\"color-interpolation\":0,\"color-interpolation-filters\":0,\"color-profile\":0,\"color-rendering\":0,cursor:0,direction:0,display:0,\"dominant-baseline\":0,\"enable-background\":0,fill:0,\"fill-opacity\":0,\"fill-rule\":0,filter:0,\"flood-color\":0,\"flood-opacity\":0,font:0,\"font-family\":0,\"font-size\":0,\"font-size-adjust\":0,\"font-stretch\":0,\"font-style\":0,\"font-variant\":0,\"font-weight\":0,\"glyph-orientation-horizontal\":0,\"glyph-orientation-vertical\":0,\"image-rendering\":0,kerning:0,\"letter-spacing\":0,\n",
       "\"lighting-color\":0,marker:0,\"marker-end\":0,\"marker-mid\":0,\"marker-start\":0,mask:0,opacity:0,overflow:0,\"pointer-events\":0,\"shape-rendering\":0,\"stop-color\":0,\"stop-opacity\":0,stroke:0,\"stroke-dasharray\":0,\"stroke-dashoffset\":0,\"stroke-linecap\":0,\"stroke-linejoin\":0,\"stroke-miterlimit\":0,\"stroke-opacity\":0,\"stroke-width\":0,\"text-anchor\":0,\"text-decoration\":0,\"text-rendering\":0,\"unicode-bidi\":0,visibility:0,\"word-spacing\":0,\"writing-mode\":0};k.on(\"snap.util.attr\",function(c){var a=k.nt(),b={},a=a.substring(a.lastIndexOf(\".\")+\n",
       "1);b[a]=c;var m=a.replace(/-(\\w)/gi,function(c,a){return a.toUpperCase()}),a=a.replace(/[A-Z]/g,function(c){return\"-\"+c.toLowerCase()});pa[h](a)?this.node.style[m]=null==c?aa:c:v(this.node,b)});a.ajax=function(c,a,b,m){var e=new XMLHttpRequest,h=V();if(e){if(y(a,\"function\"))m=b,b=a,a=null;else if(y(a,\"object\")){var d=[],f;for(f in a)a.hasOwnProperty(f)&&d.push(encodeURIComponent(f)+\"=\"+encodeURIComponent(a[f]));a=d.join(\"&\")}e.open(a?\"POST\":\"GET\",c,!0);a&&(e.setRequestHeader(\"X-Requested-With\",\"XMLHttpRequest\"),\n",
       "e.setRequestHeader(\"Content-type\",\"application/x-www-form-urlencoded\"));b&&(k.once(\"snap.ajax.\"+h+\".0\",b),k.once(\"snap.ajax.\"+h+\".200\",b),k.once(\"snap.ajax.\"+h+\".304\",b));e.onreadystatechange=function(){4==e.readyState&&k(\"snap.ajax.\"+h+\".\"+e.status,m,e)};if(4==e.readyState)return e;e.send(a);return e}};a.load=function(c,b,m){a.ajax(c,function(c){c=a.parse(c.responseText);m?b.call(m,c):b(c)})};a.getElementByPoint=function(c,a){var b,m,e=G.doc.elementFromPoint(c,a);if(G.win.opera&&\"svg\"==e.tagName){b=\n",
       "e;m=b.getBoundingClientRect();b=b.ownerDocument;var h=b.body,d=b.documentElement;b=m.top+(g.win.pageYOffset||d.scrollTop||h.scrollTop)-(d.clientTop||h.clientTop||0);m=m.left+(g.win.pageXOffset||d.scrollLeft||h.scrollLeft)-(d.clientLeft||h.clientLeft||0);h=e.createSVGRect();h.x=c-m;h.y=a-b;h.width=h.height=1;b=e.getIntersectionList(h,null);b.length&&(e=b[b.length-1])}return e?x(e):null};a.plugin=function(c){c(a,e,s,G,l)};return G.win.Snap=a}();C.plugin(function(a,k,y,M,A){function w(a,d,f,b,q,e){null==\n",
       "d&&\"[object SVGMatrix]\"==z.call(a)?(this.a=a.a,this.b=a.b,this.c=a.c,this.d=a.d,this.e=a.e,this.f=a.f):null!=a?(this.a=+a,this.b=+d,this.c=+f,this.d=+b,this.e=+q,this.f=+e):(this.a=1,this.c=this.b=0,this.d=1,this.f=this.e=0)}var z=Object.prototype.toString,d=String,f=Math;(function(n){function k(a){return a[0]*a[0]+a[1]*a[1]}function p(a){var d=f.sqrt(k(a));a[0]&&(a[0]/=d);a[1]&&(a[1]/=d)}n.add=function(a,d,e,f,n,p){var k=[[],[],[] ],u=[[this.a,this.c,this.e],[this.b,this.d,this.f],[0,0,1] ];d=[[a,\n",
       "e,n],[d,f,p],[0,0,1] ];a&&a instanceof w&&(d=[[a.a,a.c,a.e],[a.b,a.d,a.f],[0,0,1] ]);for(a=0;3>a;a++)for(e=0;3>e;e++){for(f=n=0;3>f;f++)n+=u[a][f]*d[f][e];k[a][e]=n}this.a=k[0][0];this.b=k[1][0];this.c=k[0][1];this.d=k[1][1];this.e=k[0][2];this.f=k[1][2];return this};n.invert=function(){var a=this.a*this.d-this.b*this.c;return new w(this.d/a,-this.b/a,-this.c/a,this.a/a,(this.c*this.f-this.d*this.e)/a,(this.b*this.e-this.a*this.f)/a)};n.clone=function(){return new w(this.a,this.b,this.c,this.d,this.e,\n",
       "this.f)};n.translate=function(a,d){return this.add(1,0,0,1,a,d)};n.scale=function(a,d,e,f){null==d&&(d=a);(e||f)&&this.add(1,0,0,1,e,f);this.add(a,0,0,d,0,0);(e||f)&&this.add(1,0,0,1,-e,-f);return this};n.rotate=function(b,d,e){b=a.rad(b);d=d||0;e=e||0;var l=+f.cos(b).toFixed(9);b=+f.sin(b).toFixed(9);this.add(l,b,-b,l,d,e);return this.add(1,0,0,1,-d,-e)};n.x=function(a,d){return a*this.a+d*this.c+this.e};n.y=function(a,d){return a*this.b+d*this.d+this.f};n.get=function(a){return+this[d.fromCharCode(97+\n",
       "a)].toFixed(4)};n.toString=function(){return\"matrix(\"+[this.get(0),this.get(1),this.get(2),this.get(3),this.get(4),this.get(5)].join()+\")\"};n.offset=function(){return[this.e.toFixed(4),this.f.toFixed(4)]};n.determinant=function(){return this.a*this.d-this.b*this.c};n.split=function(){var b={};b.dx=this.e;b.dy=this.f;var d=[[this.a,this.c],[this.b,this.d] ];b.scalex=f.sqrt(k(d[0]));p(d[0]);b.shear=d[0][0]*d[1][0]+d[0][1]*d[1][1];d[1]=[d[1][0]-d[0][0]*b.shear,d[1][1]-d[0][1]*b.shear];b.scaley=f.sqrt(k(d[1]));\n",
       "p(d[1]);b.shear/=b.scaley;0>this.determinant()&&(b.scalex=-b.scalex);var e=-d[0][1],d=d[1][1];0>d?(b.rotate=a.deg(f.acos(d)),0>e&&(b.rotate=360-b.rotate)):b.rotate=a.deg(f.asin(e));b.isSimple=!+b.shear.toFixed(9)&&(b.scalex.toFixed(9)==b.scaley.toFixed(9)||!b.rotate);b.isSuperSimple=!+b.shear.toFixed(9)&&b.scalex.toFixed(9)==b.scaley.toFixed(9)&&!b.rotate;b.noRotation=!+b.shear.toFixed(9)&&!b.rotate;return b};n.toTransformString=function(a){a=a||this.split();if(+a.shear.toFixed(9))return\"m\"+[this.get(0),\n",
       "this.get(1),this.get(2),this.get(3),this.get(4),this.get(5)];a.scalex=+a.scalex.toFixed(4);a.scaley=+a.scaley.toFixed(4);a.rotate=+a.rotate.toFixed(4);return(a.dx||a.dy?\"t\"+[+a.dx.toFixed(4),+a.dy.toFixed(4)]:\"\")+(1!=a.scalex||1!=a.scaley?\"s\"+[a.scalex,a.scaley,0,0]:\"\")+(a.rotate?\"r\"+[+a.rotate.toFixed(4),0,0]:\"\")}})(w.prototype);a.Matrix=w;a.matrix=function(a,d,f,b,k,e){return new w(a,d,f,b,k,e)}});C.plugin(function(a,v,y,M,A){function w(h){return function(d){k.stop();d instanceof A&&1==d.node.childNodes.length&&\n",
       "(\"radialGradient\"==d.node.firstChild.tagName||\"linearGradient\"==d.node.firstChild.tagName||\"pattern\"==d.node.firstChild.tagName)&&(d=d.node.firstChild,b(this).appendChild(d),d=u(d));if(d instanceof v)if(\"radialGradient\"==d.type||\"linearGradient\"==d.type||\"pattern\"==d.type){d.node.id||e(d.node,{id:d.id});var f=l(d.node.id)}else f=d.attr(h);else f=a.color(d),f.error?(f=a(b(this).ownerSVGElement).gradient(d))?(f.node.id||e(f.node,{id:f.id}),f=l(f.node.id)):f=d:f=r(f);d={};d[h]=f;e(this.node,d);this.node.style[h]=\n",
       "x}}function z(a){k.stop();a==+a&&(a+=\"px\");this.node.style.fontSize=a}function d(a){var b=[];a=a.childNodes;for(var e=0,f=a.length;e<f;e++){var l=a[e];3==l.nodeType&&b.push(l.nodeValue);\"tspan\"==l.tagName&&(1==l.childNodes.length&&3==l.firstChild.nodeType?b.push(l.firstChild.nodeValue):b.push(d(l)))}return b}function f(){k.stop();return this.node.style.fontSize}var n=a._.make,u=a._.wrap,p=a.is,b=a._.getSomeDefs,q=/^url\\(#?([^)]+)\\)$/,e=a._.$,l=a.url,r=String,s=a._.separator,x=\"\";k.on(\"snap.util.attr.mask\",\n",
       "function(a){if(a instanceof v||a instanceof A){k.stop();a instanceof A&&1==a.node.childNodes.length&&(a=a.node.firstChild,b(this).appendChild(a),a=u(a));if(\"mask\"==a.type)var d=a;else d=n(\"mask\",b(this)),d.node.appendChild(a.node);!d.node.id&&e(d.node,{id:d.id});e(this.node,{mask:l(d.id)})}});(function(a){k.on(\"snap.util.attr.clip\",a);k.on(\"snap.util.attr.clip-path\",a);k.on(\"snap.util.attr.clipPath\",a)})(function(a){if(a instanceof v||a instanceof A){k.stop();if(\"clipPath\"==a.type)var d=a;else d=\n",
       "n(\"clipPath\",b(this)),d.node.appendChild(a.node),!d.node.id&&e(d.node,{id:d.id});e(this.node,{\"clip-path\":l(d.id)})}});k.on(\"snap.util.attr.fill\",w(\"fill\"));k.on(\"snap.util.attr.stroke\",w(\"stroke\"));var G=/^([lr])(?:\\(([^)]*)\\))?(.*)$/i;k.on(\"snap.util.grad.parse\",function(a){a=r(a);var b=a.match(G);if(!b)return null;a=b[1];var e=b[2],b=b[3],e=e.split(/\\s*,\\s*/).map(function(a){return+a==a?+a:a});1==e.length&&0==e[0]&&(e=[]);b=b.split(\"-\");b=b.map(function(a){a=a.split(\":\");var b={color:a[0]};a[1]&&\n",
       "(b.offset=parseFloat(a[1]));return b});return{type:a,params:e,stops:b}});k.on(\"snap.util.attr.d\",function(b){k.stop();p(b,\"array\")&&p(b[0],\"array\")&&(b=a.path.toString.call(b));b=r(b);b.match(/[ruo]/i)&&(b=a.path.toAbsolute(b));e(this.node,{d:b})})(-1);k.on(\"snap.util.attr.#text\",function(a){k.stop();a=r(a);for(a=M.doc.createTextNode(a);this.node.firstChild;)this.node.removeChild(this.node.firstChild);this.node.appendChild(a)})(-1);k.on(\"snap.util.attr.path\",function(a){k.stop();this.attr({d:a})})(-1);\n",
       "k.on(\"snap.util.attr.class\",function(a){k.stop();this.node.className.baseVal=a})(-1);k.on(\"snap.util.attr.viewBox\",function(a){a=p(a,\"object\")&&\"x\"in a?[a.x,a.y,a.width,a.height].join(\" \"):p(a,\"array\")?a.join(\" \"):a;e(this.node,{viewBox:a});k.stop()})(-1);k.on(\"snap.util.attr.transform\",function(a){this.transform(a);k.stop()})(-1);k.on(\"snap.util.attr.r\",function(a){\"rect\"==this.type&&(k.stop(),e(this.node,{rx:a,ry:a}))})(-1);k.on(\"snap.util.attr.textpath\",function(a){k.stop();if(\"text\"==this.type){var d,\n",
       "f;if(!a&&this.textPath){for(a=this.textPath;a.node.firstChild;)this.node.appendChild(a.node.firstChild);a.remove();delete this.textPath}else if(p(a,\"string\")?(d=b(this),a=u(d.parentNode).path(a),d.appendChild(a.node),d=a.id,a.attr({id:d})):(a=u(a),a instanceof v&&(d=a.attr(\"id\"),d||(d=a.id,a.attr({id:d})))),d)if(a=this.textPath,f=this.node,a)a.attr({\"xlink:href\":\"#\"+d});else{for(a=e(\"textPath\",{\"xlink:href\":\"#\"+d});f.firstChild;)a.appendChild(f.firstChild);f.appendChild(a);this.textPath=u(a)}}})(-1);\n",
       "k.on(\"snap.util.attr.text\",function(a){if(\"text\"==this.type){for(var b=this.node,d=function(a){var b=e(\"tspan\");if(p(a,\"array\"))for(var f=0;f<a.length;f++)b.appendChild(d(a[f]));else b.appendChild(M.doc.createTextNode(a));b.normalize&&b.normalize();return b};b.firstChild;)b.removeChild(b.firstChild);for(a=d(a);a.firstChild;)b.appendChild(a.firstChild)}k.stop()})(-1);k.on(\"snap.util.attr.fontSize\",z)(-1);k.on(\"snap.util.attr.font-size\",z)(-1);k.on(\"snap.util.getattr.transform\",function(){k.stop();\n",
       "return this.transform()})(-1);k.on(\"snap.util.getattr.textpath\",function(){k.stop();return this.textPath})(-1);(function(){function b(d){return function(){k.stop();var b=M.doc.defaultView.getComputedStyle(this.node,null).getPropertyValue(\"marker-\"+d);return\"none\"==b?b:a(M.doc.getElementById(b.match(q)[1]))}}function d(a){return function(b){k.stop();var d=\"marker\"+a.charAt(0).toUpperCase()+a.substring(1);if(\"\"==b||!b)this.node.style[d]=\"none\";else if(\"marker\"==b.type){var f=b.node.id;f||e(b.node,{id:b.id});\n",
       "this.node.style[d]=l(f)}}}k.on(\"snap.util.getattr.marker-end\",b(\"end\"))(-1);k.on(\"snap.util.getattr.markerEnd\",b(\"end\"))(-1);k.on(\"snap.util.getattr.marker-start\",b(\"start\"))(-1);k.on(\"snap.util.getattr.markerStart\",b(\"start\"))(-1);k.on(\"snap.util.getattr.marker-mid\",b(\"mid\"))(-1);k.on(\"snap.util.getattr.markerMid\",b(\"mid\"))(-1);k.on(\"snap.util.attr.marker-end\",d(\"end\"))(-1);k.on(\"snap.util.attr.markerEnd\",d(\"end\"))(-1);k.on(\"snap.util.attr.marker-start\",d(\"start\"))(-1);k.on(\"snap.util.attr.markerStart\",\n",
       "d(\"start\"))(-1);k.on(\"snap.util.attr.marker-mid\",d(\"mid\"))(-1);k.on(\"snap.util.attr.markerMid\",d(\"mid\"))(-1)})();k.on(\"snap.util.getattr.r\",function(){if(\"rect\"==this.type&&e(this.node,\"rx\")==e(this.node,\"ry\"))return k.stop(),e(this.node,\"rx\")})(-1);k.on(\"snap.util.getattr.text\",function(){if(\"text\"==this.type||\"tspan\"==this.type){k.stop();var a=d(this.node);return 1==a.length?a[0]:a}})(-1);k.on(\"snap.util.getattr.#text\",function(){return this.node.textContent})(-1);k.on(\"snap.util.getattr.viewBox\",\n",
       "function(){k.stop();var b=e(this.node,\"viewBox\");if(b)return b=b.split(s),a._.box(+b[0],+b[1],+b[2],+b[3])})(-1);k.on(\"snap.util.getattr.points\",function(){var a=e(this.node,\"points\");k.stop();if(a)return a.split(s)})(-1);k.on(\"snap.util.getattr.path\",function(){var a=e(this.node,\"d\");k.stop();return a})(-1);k.on(\"snap.util.getattr.class\",function(){return this.node.className.baseVal})(-1);k.on(\"snap.util.getattr.fontSize\",f)(-1);k.on(\"snap.util.getattr.font-size\",f)(-1)});C.plugin(function(a,v,y,\n",
       "M,A){function w(a){return a}function z(a){return function(b){return+b.toFixed(3)+a}}var d={\"+\":function(a,b){return a+b},\"-\":function(a,b){return a-b},\"/\":function(a,b){return a/b},\"*\":function(a,b){return a*b}},f=String,n=/[a-z]+$/i,u=/^\\s*([+\\-\\/*])\\s*=\\s*([\\d.eE+\\-]+)\\s*([^\\d\\s]+)?\\s*$/;k.on(\"snap.util.attr\",function(a){if(a=f(a).match(u)){var b=k.nt(),b=b.substring(b.lastIndexOf(\".\")+1),q=this.attr(b),e={};k.stop();var l=a[3]||\"\",r=q.match(n),s=d[a[1] ];r&&r==l?a=s(parseFloat(q),+a[2]):(q=this.asPX(b),\n",
       "a=s(this.asPX(b),this.asPX(b,a[2]+l)));isNaN(q)||isNaN(a)||(e[b]=a,this.attr(e))}})(-10);k.on(\"snap.util.equal\",function(a,b){var q=f(this.attr(a)||\"\"),e=f(b).match(u);if(e){k.stop();var l=e[3]||\"\",r=q.match(n),s=d[e[1] ];if(r&&r==l)return{from:parseFloat(q),to:s(parseFloat(q),+e[2]),f:z(r)};q=this.asPX(a);return{from:q,to:s(q,this.asPX(a,e[2]+l)),f:w}}})(-10)});C.plugin(function(a,v,y,M,A){var w=y.prototype,z=a.is;w.rect=function(a,d,k,p,b,q){var e;null==q&&(q=b);z(a,\"object\")&&\"[object Object]\"==\n",
       "a?e=a:null!=a&&(e={x:a,y:d,width:k,height:p},null!=b&&(e.rx=b,e.ry=q));return this.el(\"rect\",e)};w.circle=function(a,d,k){var p;z(a,\"object\")&&\"[object Object]\"==a?p=a:null!=a&&(p={cx:a,cy:d,r:k});return this.el(\"circle\",p)};var d=function(){function a(){this.parentNode.removeChild(this)}return function(d,k){var p=M.doc.createElement(\"img\"),b=M.doc.body;p.style.cssText=\"position:absolute;left:-9999em;top:-9999em\";p.onload=function(){k.call(p);p.onload=p.onerror=null;b.removeChild(p)};p.onerror=a;\n",
       "b.appendChild(p);p.src=d}}();w.image=function(f,n,k,p,b){var q=this.el(\"image\");if(z(f,\"object\")&&\"src\"in f)q.attr(f);else if(null!=f){var e={\"xlink:href\":f,preserveAspectRatio:\"none\"};null!=n&&null!=k&&(e.x=n,e.y=k);null!=p&&null!=b?(e.width=p,e.height=b):d(f,function(){a._.$(q.node,{width:this.offsetWidth,height:this.offsetHeight})});a._.$(q.node,e)}return q};w.ellipse=function(a,d,k,p){var b;z(a,\"object\")&&\"[object Object]\"==a?b=a:null!=a&&(b={cx:a,cy:d,rx:k,ry:p});return this.el(\"ellipse\",b)};\n",
       "w.path=function(a){var d;z(a,\"object\")&&!z(a,\"array\")?d=a:a&&(d={d:a});return this.el(\"path\",d)};w.group=w.g=function(a){var d=this.el(\"g\");1==arguments.length&&a&&!a.type?d.attr(a):arguments.length&&d.add(Array.prototype.slice.call(arguments,0));return d};w.svg=function(a,d,k,p,b,q,e,l){var r={};z(a,\"object\")&&null==d?r=a:(null!=a&&(r.x=a),null!=d&&(r.y=d),null!=k&&(r.width=k),null!=p&&(r.height=p),null!=b&&null!=q&&null!=e&&null!=l&&(r.viewBox=[b,q,e,l]));return this.el(\"svg\",r)};w.mask=function(a){var d=\n",
       "this.el(\"mask\");1==arguments.length&&a&&!a.type?d.attr(a):arguments.length&&d.add(Array.prototype.slice.call(arguments,0));return d};w.ptrn=function(a,d,k,p,b,q,e,l){if(z(a,\"object\"))var r=a;else arguments.length?(r={},null!=a&&(r.x=a),null!=d&&(r.y=d),null!=k&&(r.width=k),null!=p&&(r.height=p),null!=b&&null!=q&&null!=e&&null!=l&&(r.viewBox=[b,q,e,l])):r={patternUnits:\"userSpaceOnUse\"};return this.el(\"pattern\",r)};w.use=function(a){return null!=a?(make(\"use\",this.node),a instanceof v&&(a.attr(\"id\")||\n",
       "a.attr({id:ID()}),a=a.attr(\"id\")),this.el(\"use\",{\"xlink:href\":a})):v.prototype.use.call(this)};w.text=function(a,d,k){var p={};z(a,\"object\")?p=a:null!=a&&(p={x:a,y:d,text:k||\"\"});return this.el(\"text\",p)};w.line=function(a,d,k,p){var b={};z(a,\"object\")?b=a:null!=a&&(b={x1:a,x2:k,y1:d,y2:p});return this.el(\"line\",b)};w.polyline=function(a){1<arguments.length&&(a=Array.prototype.slice.call(arguments,0));var d={};z(a,\"object\")&&!z(a,\"array\")?d=a:null!=a&&(d={points:a});return this.el(\"polyline\",d)};\n",
       "w.polygon=function(a){1<arguments.length&&(a=Array.prototype.slice.call(arguments,0));var d={};z(a,\"object\")&&!z(a,\"array\")?d=a:null!=a&&(d={points:a});return this.el(\"polygon\",d)};(function(){function d(){return this.selectAll(\"stop\")}function n(b,d){var f=e(\"stop\"),k={offset:+d+\"%\"};b=a.color(b);k[\"stop-color\"]=b.hex;1>b.opacity&&(k[\"stop-opacity\"]=b.opacity);e(f,k);this.node.appendChild(f);return this}function u(){if(\"linearGradient\"==this.type){var b=e(this.node,\"x1\")||0,d=e(this.node,\"x2\")||\n",
       "1,f=e(this.node,\"y1\")||0,k=e(this.node,\"y2\")||0;return a._.box(b,f,math.abs(d-b),math.abs(k-f))}b=this.node.r||0;return a._.box((this.node.cx||0.5)-b,(this.node.cy||0.5)-b,2*b,2*b)}function p(a,d){function f(a,b){for(var d=(b-u)/(a-w),e=w;e<a;e++)h[e].offset=+(+u+d*(e-w)).toFixed(2);w=a;u=b}var n=k(\"snap.util.grad.parse\",null,d).firstDefined(),p;if(!n)return null;n.params.unshift(a);p=\"l\"==n.type.toLowerCase()?b.apply(0,n.params):q.apply(0,n.params);n.type!=n.type.toLowerCase()&&e(p.node,{gradientUnits:\"userSpaceOnUse\"});\n",
       "var h=n.stops,n=h.length,u=0,w=0;n--;for(var v=0;v<n;v++)\"offset\"in h[v]&&f(v,h[v].offset);h[n].offset=h[n].offset||100;f(n,h[n].offset);for(v=0;v<=n;v++){var y=h[v];p.addStop(y.color,y.offset)}return p}function b(b,k,p,q,w){b=a._.make(\"linearGradient\",b);b.stops=d;b.addStop=n;b.getBBox=u;null!=k&&e(b.node,{x1:k,y1:p,x2:q,y2:w});return b}function q(b,k,p,q,w,h){b=a._.make(\"radialGradient\",b);b.stops=d;b.addStop=n;b.getBBox=u;null!=k&&e(b.node,{cx:k,cy:p,r:q});null!=w&&null!=h&&e(b.node,{fx:w,fy:h});\n",
       "return b}var e=a._.$;w.gradient=function(a){return p(this.defs,a)};w.gradientLinear=function(a,d,e,f){return b(this.defs,a,d,e,f)};w.gradientRadial=function(a,b,d,e,f){return q(this.defs,a,b,d,e,f)};w.toString=function(){var b=this.node.ownerDocument,d=b.createDocumentFragment(),b=b.createElement(\"div\"),e=this.node.cloneNode(!0);d.appendChild(b);b.appendChild(e);a._.$(e,{xmlns:\"http://www.w3.org/2000/svg\"});b=b.innerHTML;d.removeChild(d.firstChild);return b};w.clear=function(){for(var a=this.node.firstChild,\n",
       "b;a;)b=a.nextSibling,\"defs\"!=a.tagName?a.parentNode.removeChild(a):w.clear.call({node:a}),a=b}})()});C.plugin(function(a,k,y,M){function A(a){var b=A.ps=A.ps||{};b[a]?b[a].sleep=100:b[a]={sleep:100};setTimeout(function(){for(var d in b)b[L](d)&&d!=a&&(b[d].sleep--,!b[d].sleep&&delete b[d])});return b[a]}function w(a,b,d,e){null==a&&(a=b=d=e=0);null==b&&(b=a.y,d=a.width,e=a.height,a=a.x);return{x:a,y:b,width:d,w:d,height:e,h:e,x2:a+d,y2:b+e,cx:a+d/2,cy:b+e/2,r1:F.min(d,e)/2,r2:F.max(d,e)/2,r0:F.sqrt(d*\n",
       "d+e*e)/2,path:s(a,b,d,e),vb:[a,b,d,e].join(\" \")}}function z(){return this.join(\",\").replace(N,\"$1\")}function d(a){a=C(a);a.toString=z;return a}function f(a,b,d,h,f,k,l,n,p){if(null==p)return e(a,b,d,h,f,k,l,n);if(0>p||e(a,b,d,h,f,k,l,n)<p)p=void 0;else{var q=0.5,O=1-q,s;for(s=e(a,b,d,h,f,k,l,n,O);0.01<Z(s-p);)q/=2,O+=(s<p?1:-1)*q,s=e(a,b,d,h,f,k,l,n,O);p=O}return u(a,b,d,h,f,k,l,n,p)}function n(b,d){function e(a){return+(+a).toFixed(3)}return a._.cacher(function(a,h,l){a instanceof k&&(a=a.attr(\"d\"));\n",
       "a=I(a);for(var n,p,D,q,O=\"\",s={},c=0,t=0,r=a.length;t<r;t++){D=a[t];if(\"M\"==D[0])n=+D[1],p=+D[2];else{q=f(n,p,D[1],D[2],D[3],D[4],D[5],D[6]);if(c+q>h){if(d&&!s.start){n=f(n,p,D[1],D[2],D[3],D[4],D[5],D[6],h-c);O+=[\"C\"+e(n.start.x),e(n.start.y),e(n.m.x),e(n.m.y),e(n.x),e(n.y)];if(l)return O;s.start=O;O=[\"M\"+e(n.x),e(n.y)+\"C\"+e(n.n.x),e(n.n.y),e(n.end.x),e(n.end.y),e(D[5]),e(D[6])].join();c+=q;n=+D[5];p=+D[6];continue}if(!b&&!d)return n=f(n,p,D[1],D[2],D[3],D[4],D[5],D[6],h-c)}c+=q;n=+D[5];p=+D[6]}O+=\n",
       "D.shift()+D}s.end=O;return n=b?c:d?s:u(n,p,D[0],D[1],D[2],D[3],D[4],D[5],1)},null,a._.clone)}function u(a,b,d,e,h,f,k,l,n){var p=1-n,q=ma(p,3),s=ma(p,2),c=n*n,t=c*n,r=q*a+3*s*n*d+3*p*n*n*h+t*k,q=q*b+3*s*n*e+3*p*n*n*f+t*l,s=a+2*n*(d-a)+c*(h-2*d+a),t=b+2*n*(e-b)+c*(f-2*e+b),x=d+2*n*(h-d)+c*(k-2*h+d),c=e+2*n*(f-e)+c*(l-2*f+e);a=p*a+n*d;b=p*b+n*e;h=p*h+n*k;f=p*f+n*l;l=90-180*F.atan2(s-x,t-c)/S;return{x:r,y:q,m:{x:s,y:t},n:{x:x,y:c},start:{x:a,y:b},end:{x:h,y:f},alpha:l}}function p(b,d,e,h,f,n,k,l){a.is(b,\n",
       "\"array\")||(b=[b,d,e,h,f,n,k,l]);b=U.apply(null,b);return w(b.min.x,b.min.y,b.max.x-b.min.x,b.max.y-b.min.y)}function b(a,b,d){return b>=a.x&&b<=a.x+a.width&&d>=a.y&&d<=a.y+a.height}function q(a,d){a=w(a);d=w(d);return b(d,a.x,a.y)||b(d,a.x2,a.y)||b(d,a.x,a.y2)||b(d,a.x2,a.y2)||b(a,d.x,d.y)||b(a,d.x2,d.y)||b(a,d.x,d.y2)||b(a,d.x2,d.y2)||(a.x<d.x2&&a.x>d.x||d.x<a.x2&&d.x>a.x)&&(a.y<d.y2&&a.y>d.y||d.y<a.y2&&d.y>a.y)}function e(a,b,d,e,h,f,n,k,l){null==l&&(l=1);l=(1<l?1:0>l?0:l)/2;for(var p=[-0.1252,\n",
       "0.1252,-0.3678,0.3678,-0.5873,0.5873,-0.7699,0.7699,-0.9041,0.9041,-0.9816,0.9816],q=[0.2491,0.2491,0.2335,0.2335,0.2032,0.2032,0.1601,0.1601,0.1069,0.1069,0.0472,0.0472],s=0,c=0;12>c;c++)var t=l*p[c]+l,r=t*(t*(-3*a+9*d-9*h+3*n)+6*a-12*d+6*h)-3*a+3*d,t=t*(t*(-3*b+9*e-9*f+3*k)+6*b-12*e+6*f)-3*b+3*e,s=s+q[c]*F.sqrt(r*r+t*t);return l*s}function l(a,b,d){a=I(a);b=I(b);for(var h,f,l,n,k,s,r,O,x,c,t=d?0:[],w=0,v=a.length;w<v;w++)if(x=a[w],\"M\"==x[0])h=k=x[1],f=s=x[2];else{\"C\"==x[0]?(x=[h,f].concat(x.slice(1)),\n",
       "h=x[6],f=x[7]):(x=[h,f,h,f,k,s,k,s],h=k,f=s);for(var G=0,y=b.length;G<y;G++)if(c=b[G],\"M\"==c[0])l=r=c[1],n=O=c[2];else{\"C\"==c[0]?(c=[l,n].concat(c.slice(1)),l=c[6],n=c[7]):(c=[l,n,l,n,r,O,r,O],l=r,n=O);var z;var K=x,B=c;z=d;var H=p(K),J=p(B);if(q(H,J)){for(var H=e.apply(0,K),J=e.apply(0,B),H=~~(H/8),J=~~(J/8),U=[],A=[],F={},M=z?0:[],P=0;P<H+1;P++){var C=u.apply(0,K.concat(P/H));U.push({x:C.x,y:C.y,t:P/H})}for(P=0;P<J+1;P++)C=u.apply(0,B.concat(P/J)),A.push({x:C.x,y:C.y,t:P/J});for(P=0;P<H;P++)for(K=\n",
       "0;K<J;K++){var Q=U[P],L=U[P+1],B=A[K],C=A[K+1],N=0.001>Z(L.x-Q.x)?\"y\":\"x\",S=0.001>Z(C.x-B.x)?\"y\":\"x\",R;R=Q.x;var Y=Q.y,V=L.x,ea=L.y,fa=B.x,ga=B.y,ha=C.x,ia=C.y;if(W(R,V)<X(fa,ha)||X(R,V)>W(fa,ha)||W(Y,ea)<X(ga,ia)||X(Y,ea)>W(ga,ia))R=void 0;else{var $=(R*ea-Y*V)*(fa-ha)-(R-V)*(fa*ia-ga*ha),aa=(R*ea-Y*V)*(ga-ia)-(Y-ea)*(fa*ia-ga*ha),ja=(R-V)*(ga-ia)-(Y-ea)*(fa-ha);if(ja){var $=$/ja,aa=aa/ja,ja=+$.toFixed(2),ba=+aa.toFixed(2);R=ja<+X(R,V).toFixed(2)||ja>+W(R,V).toFixed(2)||ja<+X(fa,ha).toFixed(2)||\n",
       "ja>+W(fa,ha).toFixed(2)||ba<+X(Y,ea).toFixed(2)||ba>+W(Y,ea).toFixed(2)||ba<+X(ga,ia).toFixed(2)||ba>+W(ga,ia).toFixed(2)?void 0:{x:$,y:aa}}else R=void 0}R&&F[R.x.toFixed(4)]!=R.y.toFixed(4)&&(F[R.x.toFixed(4)]=R.y.toFixed(4),Q=Q.t+Z((R[N]-Q[N])/(L[N]-Q[N]))*(L.t-Q.t),B=B.t+Z((R[S]-B[S])/(C[S]-B[S]))*(C.t-B.t),0<=Q&&1>=Q&&0<=B&&1>=B&&(z?M++:M.push({x:R.x,y:R.y,t1:Q,t2:B})))}z=M}else z=z?0:[];if(d)t+=z;else{H=0;for(J=z.length;H<J;H++)z[H].segment1=w,z[H].segment2=G,z[H].bez1=x,z[H].bez2=c;t=t.concat(z)}}}return t}\n",
       "function r(a){var b=A(a);if(b.bbox)return C(b.bbox);if(!a)return w();a=I(a);for(var d=0,e=0,h=[],f=[],l,n=0,k=a.length;n<k;n++)l=a[n],\"M\"==l[0]?(d=l[1],e=l[2],h.push(d),f.push(e)):(d=U(d,e,l[1],l[2],l[3],l[4],l[5],l[6]),h=h.concat(d.min.x,d.max.x),f=f.concat(d.min.y,d.max.y),d=l[5],e=l[6]);a=X.apply(0,h);l=X.apply(0,f);h=W.apply(0,h);f=W.apply(0,f);f=w(a,l,h-a,f-l);b.bbox=C(f);return f}function s(a,b,d,e,h){if(h)return[[\"M\",+a+ +h,b],[\"l\",d-2*h,0],[\"a\",h,h,0,0,1,h,h],[\"l\",0,e-2*h],[\"a\",h,h,0,0,1,\n",
       "-h,h],[\"l\",2*h-d,0],[\"a\",h,h,0,0,1,-h,-h],[\"l\",0,2*h-e],[\"a\",h,h,0,0,1,h,-h],[\"z\"] ];a=[[\"M\",a,b],[\"l\",d,0],[\"l\",0,e],[\"l\",-d,0],[\"z\"] ];a.toString=z;return a}function x(a,b,d,e,h){null==h&&null==e&&(e=d);a=+a;b=+b;d=+d;e=+e;if(null!=h){var f=Math.PI/180,l=a+d*Math.cos(-e*f);a+=d*Math.cos(-h*f);var n=b+d*Math.sin(-e*f);b+=d*Math.sin(-h*f);d=[[\"M\",l,n],[\"A\",d,d,0,+(180<h-e),0,a,b] ]}else d=[[\"M\",a,b],[\"m\",0,-e],[\"a\",d,e,0,1,1,0,2*e],[\"a\",d,e,0,1,1,0,-2*e],[\"z\"] ];d.toString=z;return d}function G(b){var e=\n",
       "A(b);if(e.abs)return d(e.abs);Q(b,\"array\")&&Q(b&&b[0],\"array\")||(b=a.parsePathString(b));if(!b||!b.length)return[[\"M\",0,0] ];var h=[],f=0,l=0,n=0,k=0,p=0;\"M\"==b[0][0]&&(f=+b[0][1],l=+b[0][2],n=f,k=l,p++,h[0]=[\"M\",f,l]);for(var q=3==b.length&&\"M\"==b[0][0]&&\"R\"==b[1][0].toUpperCase()&&\"Z\"==b[2][0].toUpperCase(),s,r,w=p,c=b.length;w<c;w++){h.push(s=[]);r=b[w];p=r[0];if(p!=p.toUpperCase())switch(s[0]=p.toUpperCase(),s[0]){case \"A\":s[1]=r[1];s[2]=r[2];s[3]=r[3];s[4]=r[4];s[5]=r[5];s[6]=+r[6]+f;s[7]=+r[7]+\n",
       "l;break;case \"V\":s[1]=+r[1]+l;break;case \"H\":s[1]=+r[1]+f;break;case \"R\":for(var t=[f,l].concat(r.slice(1)),u=2,v=t.length;u<v;u++)t[u]=+t[u]+f,t[++u]=+t[u]+l;h.pop();h=h.concat(P(t,q));break;case \"O\":h.pop();t=x(f,l,r[1],r[2]);t.push(t[0]);h=h.concat(t);break;case \"U\":h.pop();h=h.concat(x(f,l,r[1],r[2],r[3]));s=[\"U\"].concat(h[h.length-1].slice(-2));break;case \"M\":n=+r[1]+f,k=+r[2]+l;default:for(u=1,v=r.length;u<v;u++)s[u]=+r[u]+(u%2?f:l)}else if(\"R\"==p)t=[f,l].concat(r.slice(1)),h.pop(),h=h.concat(P(t,\n",
       "q)),s=[\"R\"].concat(r.slice(-2));else if(\"O\"==p)h.pop(),t=x(f,l,r[1],r[2]),t.push(t[0]),h=h.concat(t);else if(\"U\"==p)h.pop(),h=h.concat(x(f,l,r[1],r[2],r[3])),s=[\"U\"].concat(h[h.length-1].slice(-2));else for(t=0,u=r.length;t<u;t++)s[t]=r[t];p=p.toUpperCase();if(\"O\"!=p)switch(s[0]){case \"Z\":f=+n;l=+k;break;case \"H\":f=s[1];break;case \"V\":l=s[1];break;case \"M\":n=s[s.length-2],k=s[s.length-1];default:f=s[s.length-2],l=s[s.length-1]}}h.toString=z;e.abs=d(h);return h}function h(a,b,d,e){return[a,b,d,e,d,\n",
       "e]}function J(a,b,d,e,h,f){var l=1/3,n=2/3;return[l*a+n*d,l*b+n*e,l*h+n*d,l*f+n*e,h,f]}function K(b,d,e,h,f,l,n,k,p,s){var r=120*S/180,q=S/180*(+f||0),c=[],t,x=a._.cacher(function(a,b,c){var d=a*F.cos(c)-b*F.sin(c);a=a*F.sin(c)+b*F.cos(c);return{x:d,y:a}});if(s)v=s[0],t=s[1],l=s[2],u=s[3];else{t=x(b,d,-q);b=t.x;d=t.y;t=x(k,p,-q);k=t.x;p=t.y;F.cos(S/180*f);F.sin(S/180*f);t=(b-k)/2;v=(d-p)/2;u=t*t/(e*e)+v*v/(h*h);1<u&&(u=F.sqrt(u),e*=u,h*=u);var u=e*e,w=h*h,u=(l==n?-1:1)*F.sqrt(Z((u*w-u*v*v-w*t*t)/\n",
       "(u*v*v+w*t*t)));l=u*e*v/h+(b+k)/2;var u=u*-h*t/e+(d+p)/2,v=F.asin(((d-u)/h).toFixed(9));t=F.asin(((p-u)/h).toFixed(9));v=b<l?S-v:v;t=k<l?S-t:t;0>v&&(v=2*S+v);0>t&&(t=2*S+t);n&&v>t&&(v-=2*S);!n&&t>v&&(t-=2*S)}if(Z(t-v)>r){var c=t,w=k,G=p;t=v+r*(n&&t>v?1:-1);k=l+e*F.cos(t);p=u+h*F.sin(t);c=K(k,p,e,h,f,0,n,w,G,[t,c,l,u])}l=t-v;f=F.cos(v);r=F.sin(v);n=F.cos(t);t=F.sin(t);l=F.tan(l/4);e=4/3*e*l;l*=4/3*h;h=[b,d];b=[b+e*r,d-l*f];d=[k+e*t,p-l*n];k=[k,p];b[0]=2*h[0]-b[0];b[1]=2*h[1]-b[1];if(s)return[b,d,k].concat(c);\n",
       "c=[b,d,k].concat(c).join().split(\",\");s=[];k=0;for(p=c.length;k<p;k++)s[k]=k%2?x(c[k-1],c[k],q).y:x(c[k],c[k+1],q).x;return s}function U(a,b,d,e,h,f,l,k){for(var n=[],p=[[],[] ],s,r,c,t,q=0;2>q;++q)0==q?(r=6*a-12*d+6*h,s=-3*a+9*d-9*h+3*l,c=3*d-3*a):(r=6*b-12*e+6*f,s=-3*b+9*e-9*f+3*k,c=3*e-3*b),1E-12>Z(s)?1E-12>Z(r)||(s=-c/r,0<s&&1>s&&n.push(s)):(t=r*r-4*c*s,c=F.sqrt(t),0>t||(t=(-r+c)/(2*s),0<t&&1>t&&n.push(t),s=(-r-c)/(2*s),0<s&&1>s&&n.push(s)));for(r=q=n.length;q--;)s=n[q],c=1-s,p[0][q]=c*c*c*a+3*\n",
       "c*c*s*d+3*c*s*s*h+s*s*s*l,p[1][q]=c*c*c*b+3*c*c*s*e+3*c*s*s*f+s*s*s*k;p[0][r]=a;p[1][r]=b;p[0][r+1]=l;p[1][r+1]=k;p[0].length=p[1].length=r+2;return{min:{x:X.apply(0,p[0]),y:X.apply(0,p[1])},max:{x:W.apply(0,p[0]),y:W.apply(0,p[1])}}}function I(a,b){var e=!b&&A(a);if(!b&&e.curve)return d(e.curve);var f=G(a),l=b&&G(b),n={x:0,y:0,bx:0,by:0,X:0,Y:0,qx:null,qy:null},k={x:0,y:0,bx:0,by:0,X:0,Y:0,qx:null,qy:null},p=function(a,b,c){if(!a)return[\"C\",b.x,b.y,b.x,b.y,b.x,b.y];a[0]in{T:1,Q:1}||(b.qx=b.qy=null);\n",
       "switch(a[0]){case \"M\":b.X=a[1];b.Y=a[2];break;case \"A\":a=[\"C\"].concat(K.apply(0,[b.x,b.y].concat(a.slice(1))));break;case \"S\":\"C\"==c||\"S\"==c?(c=2*b.x-b.bx,b=2*b.y-b.by):(c=b.x,b=b.y);a=[\"C\",c,b].concat(a.slice(1));break;case \"T\":\"Q\"==c||\"T\"==c?(b.qx=2*b.x-b.qx,b.qy=2*b.y-b.qy):(b.qx=b.x,b.qy=b.y);a=[\"C\"].concat(J(b.x,b.y,b.qx,b.qy,a[1],a[2]));break;case \"Q\":b.qx=a[1];b.qy=a[2];a=[\"C\"].concat(J(b.x,b.y,a[1],a[2],a[3],a[4]));break;case \"L\":a=[\"C\"].concat(h(b.x,b.y,a[1],a[2]));break;case \"H\":a=[\"C\"].concat(h(b.x,\n",
       "b.y,a[1],b.y));break;case \"V\":a=[\"C\"].concat(h(b.x,b.y,b.x,a[1]));break;case \"Z\":a=[\"C\"].concat(h(b.x,b.y,b.X,b.Y))}return a},s=function(a,b){if(7<a[b].length){a[b].shift();for(var c=a[b];c.length;)q[b]=\"A\",l&&(u[b]=\"A\"),a.splice(b++,0,[\"C\"].concat(c.splice(0,6)));a.splice(b,1);v=W(f.length,l&&l.length||0)}},r=function(a,b,c,d,e){a&&b&&\"M\"==a[e][0]&&\"M\"!=b[e][0]&&(b.splice(e,0,[\"M\",d.x,d.y]),c.bx=0,c.by=0,c.x=a[e][1],c.y=a[e][2],v=W(f.length,l&&l.length||0))},q=[],u=[],c=\"\",t=\"\",x=0,v=W(f.length,\n",
       "l&&l.length||0);for(;x<v;x++){f[x]&&(c=f[x][0]);\"C\"!=c&&(q[x]=c,x&&(t=q[x-1]));f[x]=p(f[x],n,t);\"A\"!=q[x]&&\"C\"==c&&(q[x]=\"C\");s(f,x);l&&(l[x]&&(c=l[x][0]),\"C\"!=c&&(u[x]=c,x&&(t=u[x-1])),l[x]=p(l[x],k,t),\"A\"!=u[x]&&\"C\"==c&&(u[x]=\"C\"),s(l,x));r(f,l,n,k,x);r(l,f,k,n,x);var w=f[x],z=l&&l[x],y=w.length,U=l&&z.length;n.x=w[y-2];n.y=w[y-1];n.bx=$(w[y-4])||n.x;n.by=$(w[y-3])||n.y;k.bx=l&&($(z[U-4])||k.x);k.by=l&&($(z[U-3])||k.y);k.x=l&&z[U-2];k.y=l&&z[U-1]}l||(e.curve=d(f));return l?[f,l]:f}function P(a,\n",
       "b){for(var d=[],e=0,h=a.length;h-2*!b>e;e+=2){var f=[{x:+a[e-2],y:+a[e-1]},{x:+a[e],y:+a[e+1]},{x:+a[e+2],y:+a[e+3]},{x:+a[e+4],y:+a[e+5]}];b?e?h-4==e?f[3]={x:+a[0],y:+a[1]}:h-2==e&&(f[2]={x:+a[0],y:+a[1]},f[3]={x:+a[2],y:+a[3]}):f[0]={x:+a[h-2],y:+a[h-1]}:h-4==e?f[3]=f[2]:e||(f[0]={x:+a[e],y:+a[e+1]});d.push([\"C\",(-f[0].x+6*f[1].x+f[2].x)/6,(-f[0].y+6*f[1].y+f[2].y)/6,(f[1].x+6*f[2].x-f[3].x)/6,(f[1].y+6*f[2].y-f[3].y)/6,f[2].x,f[2].y])}return d}y=k.prototype;var Q=a.is,C=a._.clone,L=\"hasOwnProperty\",\n",
       "N=/,?([a-z]),?/gi,$=parseFloat,F=Math,S=F.PI,X=F.min,W=F.max,ma=F.pow,Z=F.abs;M=n(1);var na=n(),ba=n(0,1),V=a._unit2px;a.path=A;a.path.getTotalLength=M;a.path.getPointAtLength=na;a.path.getSubpath=function(a,b,d){if(1E-6>this.getTotalLength(a)-d)return ba(a,b).end;a=ba(a,d,1);return b?ba(a,b).end:a};y.getTotalLength=function(){if(this.node.getTotalLength)return this.node.getTotalLength()};y.getPointAtLength=function(a){return na(this.attr(\"d\"),a)};y.getSubpath=function(b,d){return a.path.getSubpath(this.attr(\"d\"),\n",
       "b,d)};a._.box=w;a.path.findDotsAtSegment=u;a.path.bezierBBox=p;a.path.isPointInsideBBox=b;a.path.isBBoxIntersect=q;a.path.intersection=function(a,b){return l(a,b)};a.path.intersectionNumber=function(a,b){return l(a,b,1)};a.path.isPointInside=function(a,d,e){var h=r(a);return b(h,d,e)&&1==l(a,[[\"M\",d,e],[\"H\",h.x2+10] ],1)%2};a.path.getBBox=r;a.path.get={path:function(a){return a.attr(\"path\")},circle:function(a){a=V(a);return x(a.cx,a.cy,a.r)},ellipse:function(a){a=V(a);return x(a.cx||0,a.cy||0,a.rx,\n",
       "a.ry)},rect:function(a){a=V(a);return s(a.x||0,a.y||0,a.width,a.height,a.rx,a.ry)},image:function(a){a=V(a);return s(a.x||0,a.y||0,a.width,a.height)},line:function(a){return\"M\"+[a.attr(\"x1\")||0,a.attr(\"y1\")||0,a.attr(\"x2\"),a.attr(\"y2\")]},polyline:function(a){return\"M\"+a.attr(\"points\")},polygon:function(a){return\"M\"+a.attr(\"points\")+\"z\"},deflt:function(a){a=a.node.getBBox();return s(a.x,a.y,a.width,a.height)}};a.path.toRelative=function(b){var e=A(b),h=String.prototype.toLowerCase;if(e.rel)return d(e.rel);\n",
       "a.is(b,\"array\")&&a.is(b&&b[0],\"array\")||(b=a.parsePathString(b));var f=[],l=0,n=0,k=0,p=0,s=0;\"M\"==b[0][0]&&(l=b[0][1],n=b[0][2],k=l,p=n,s++,f.push([\"M\",l,n]));for(var r=b.length;s<r;s++){var q=f[s]=[],x=b[s];if(x[0]!=h.call(x[0]))switch(q[0]=h.call(x[0]),q[0]){case \"a\":q[1]=x[1];q[2]=x[2];q[3]=x[3];q[4]=x[4];q[5]=x[5];q[6]=+(x[6]-l).toFixed(3);q[7]=+(x[7]-n).toFixed(3);break;case \"v\":q[1]=+(x[1]-n).toFixed(3);break;case \"m\":k=x[1],p=x[2];default:for(var c=1,t=x.length;c<t;c++)q[c]=+(x[c]-(c%2?l:\n",
       "n)).toFixed(3)}else for(f[s]=[],\"m\"==x[0]&&(k=x[1]+l,p=x[2]+n),q=0,c=x.length;q<c;q++)f[s][q]=x[q];x=f[s].length;switch(f[s][0]){case \"z\":l=k;n=p;break;case \"h\":l+=+f[s][x-1];break;case \"v\":n+=+f[s][x-1];break;default:l+=+f[s][x-2],n+=+f[s][x-1]}}f.toString=z;e.rel=d(f);return f};a.path.toAbsolute=G;a.path.toCubic=I;a.path.map=function(a,b){if(!b)return a;var d,e,h,f,l,n,k;a=I(a);h=0;for(l=a.length;h<l;h++)for(k=a[h],f=1,n=k.length;f<n;f+=2)d=b.x(k[f],k[f+1]),e=b.y(k[f],k[f+1]),k[f]=d,k[f+1]=e;return a};\n",
       "a.path.toString=z;a.path.clone=d});C.plugin(function(a,v,y,C){var A=Math.max,w=Math.min,z=function(a){this.items=[];this.bindings={};this.length=0;this.type=\"set\";if(a)for(var f=0,n=a.length;f<n;f++)a[f]&&(this[this.items.length]=this.items[this.items.length]=a[f],this.length++)};v=z.prototype;v.push=function(){for(var a,f,n=0,k=arguments.length;n<k;n++)if(a=arguments[n])f=this.items.length,this[f]=this.items[f]=a,this.length++;return this};v.pop=function(){this.length&&delete this[this.length--];\n",
       "return this.items.pop()};v.forEach=function(a,f){for(var n=0,k=this.items.length;n<k&&!1!==a.call(f,this.items[n],n);n++);return this};v.animate=function(d,f,n,u){\"function\"!=typeof n||n.length||(u=n,n=L.linear);d instanceof a._.Animation&&(u=d.callback,n=d.easing,f=n.dur,d=d.attr);var p=arguments;if(a.is(d,\"array\")&&a.is(p[p.length-1],\"array\"))var b=!0;var q,e=function(){q?this.b=q:q=this.b},l=0,r=u&&function(){l++==this.length&&u.call(this)};return this.forEach(function(a,l){k.once(\"snap.animcreated.\"+\n",
       "a.id,e);b?p[l]&&a.animate.apply(a,p[l]):a.animate(d,f,n,r)})};v.remove=function(){for(;this.length;)this.pop().remove();return this};v.bind=function(a,f,k){var u={};if(\"function\"==typeof f)this.bindings[a]=f;else{var p=k||a;this.bindings[a]=function(a){u[p]=a;f.attr(u)}}return this};v.attr=function(a){var f={},k;for(k in a)if(this.bindings[k])this.bindings[k](a[k]);else f[k]=a[k];a=0;for(k=this.items.length;a<k;a++)this.items[a].attr(f);return this};v.clear=function(){for(;this.length;)this.pop()};\n",
       "v.splice=function(a,f,k){a=0>a?A(this.length+a,0):a;f=A(0,w(this.length-a,f));var u=[],p=[],b=[],q;for(q=2;q<arguments.length;q++)b.push(arguments[q]);for(q=0;q<f;q++)p.push(this[a+q]);for(;q<this.length-a;q++)u.push(this[a+q]);var e=b.length;for(q=0;q<e+u.length;q++)this.items[a+q]=this[a+q]=q<e?b[q]:u[q-e];for(q=this.items.length=this.length-=f-e;this[q];)delete this[q++];return new z(p)};v.exclude=function(a){for(var f=0,k=this.length;f<k;f++)if(this[f]==a)return this.splice(f,1),!0;return!1};\n",
       "v.insertAfter=function(a){for(var f=this.items.length;f--;)this.items[f].insertAfter(a);return this};v.getBBox=function(){for(var a=[],f=[],k=[],u=[],p=this.items.length;p--;)if(!this.items[p].removed){var b=this.items[p].getBBox();a.push(b.x);f.push(b.y);k.push(b.x+b.width);u.push(b.y+b.height)}a=w.apply(0,a);f=w.apply(0,f);k=A.apply(0,k);u=A.apply(0,u);return{x:a,y:f,x2:k,y2:u,width:k-a,height:u-f,cx:a+(k-a)/2,cy:f+(u-f)/2}};v.clone=function(a){a=new z;for(var f=0,k=this.items.length;f<k;f++)a.push(this.items[f].clone());\n",
       "return a};v.toString=function(){return\"Snap\\u2018s set\"};v.type=\"set\";a.set=function(){var a=new z;arguments.length&&a.push.apply(a,Array.prototype.slice.call(arguments,0));return a}});C.plugin(function(a,v,y,C){function A(a){var b=a[0];switch(b.toLowerCase()){case \"t\":return[b,0,0];case \"m\":return[b,1,0,0,1,0,0];case \"r\":return 4==a.length?[b,0,a[2],a[3] ]:[b,0];case \"s\":return 5==a.length?[b,1,1,a[3],a[4] ]:3==a.length?[b,1,1]:[b,1]}}function w(b,d,f){d=q(d).replace(/\\.{3}|\\u2026/g,b);b=a.parseTransformString(b)||\n",
       "[];d=a.parseTransformString(d)||[];for(var k=Math.max(b.length,d.length),p=[],v=[],h=0,w,z,y,I;h<k;h++){y=b[h]||A(d[h]);I=d[h]||A(y);if(y[0]!=I[0]||\"r\"==y[0].toLowerCase()&&(y[2]!=I[2]||y[3]!=I[3])||\"s\"==y[0].toLowerCase()&&(y[3]!=I[3]||y[4]!=I[4])){b=a._.transform2matrix(b,f());d=a._.transform2matrix(d,f());p=[[\"m\",b.a,b.b,b.c,b.d,b.e,b.f] ];v=[[\"m\",d.a,d.b,d.c,d.d,d.e,d.f] ];break}p[h]=[];v[h]=[];w=0;for(z=Math.max(y.length,I.length);w<z;w++)w in y&&(p[h][w]=y[w]),w in I&&(v[h][w]=I[w])}return{from:u(p),\n",
       "to:u(v),f:n(p)}}function z(a){return a}function d(a){return function(b){return+b.toFixed(3)+a}}function f(b){return a.rgb(b[0],b[1],b[2])}function n(a){var b=0,d,f,k,n,h,p,q=[];d=0;for(f=a.length;d<f;d++){h=\"[\";p=['\"'+a[d][0]+'\"'];k=1;for(n=a[d].length;k<n;k++)p[k]=\"val[\"+b++ +\"]\";h+=p+\"]\";q[d]=h}return Function(\"val\",\"return Snap.path.toString.call([\"+q+\"])\")}function u(a){for(var b=[],d=0,f=a.length;d<f;d++)for(var k=1,n=a[d].length;k<n;k++)b.push(a[d][k]);return b}var p={},b=/[a-z]+$/i,q=String;\n",
       "p.stroke=p.fill=\"colour\";v.prototype.equal=function(a,b){return k(\"snap.util.equal\",this,a,b).firstDefined()};k.on(\"snap.util.equal\",function(e,k){var r,s;r=q(this.attr(e)||\"\");var x=this;if(r==+r&&k==+k)return{from:+r,to:+k,f:z};if(\"colour\"==p[e])return r=a.color(r),s=a.color(k),{from:[r.r,r.g,r.b,r.opacity],to:[s.r,s.g,s.b,s.opacity],f:f};if(\"transform\"==e||\"gradientTransform\"==e||\"patternTransform\"==e)return k instanceof a.Matrix&&(k=k.toTransformString()),a._.rgTransform.test(k)||(k=a._.svgTransform2string(k)),\n",
       "w(r,k,function(){return x.getBBox(1)});if(\"d\"==e||\"path\"==e)return r=a.path.toCubic(r,k),{from:u(r[0]),to:u(r[1]),f:n(r[0])};if(\"points\"==e)return r=q(r).split(a._.separator),s=q(k).split(a._.separator),{from:r,to:s,f:function(a){return a}};aUnit=r.match(b);s=q(k).match(b);return aUnit&&aUnit==s?{from:parseFloat(r),to:parseFloat(k),f:d(aUnit)}:{from:this.asPX(e),to:this.asPX(e,k),f:z}})});C.plugin(function(a,v,y,C){var A=v.prototype,w=\"createTouch\"in C.doc;v=\"click dblclick mousedown mousemove mouseout mouseover mouseup touchstart touchmove touchend touchcancel\".split(\" \");\n",
       "var z={mousedown:\"touchstart\",mousemove:\"touchmove\",mouseup:\"touchend\"},d=function(a,b){var d=\"y\"==a?\"scrollTop\":\"scrollLeft\",e=b&&b.node?b.node.ownerDocument:C.doc;return e[d in e.documentElement?\"documentElement\":\"body\"][d]},f=function(){this.returnValue=!1},n=function(){return this.originalEvent.preventDefault()},u=function(){this.cancelBubble=!0},p=function(){return this.originalEvent.stopPropagation()},b=function(){if(C.doc.addEventListener)return function(a,b,e,f){var k=w&&z[b]?z[b]:b,l=function(k){var l=\n",
       "d(\"y\",f),q=d(\"x\",f);if(w&&z.hasOwnProperty(b))for(var r=0,u=k.targetTouches&&k.targetTouches.length;r<u;r++)if(k.targetTouches[r].target==a||a.contains(k.targetTouches[r].target)){u=k;k=k.targetTouches[r];k.originalEvent=u;k.preventDefault=n;k.stopPropagation=p;break}return e.call(f,k,k.clientX+q,k.clientY+l)};b!==k&&a.addEventListener(b,l,!1);a.addEventListener(k,l,!1);return function(){b!==k&&a.removeEventListener(b,l,!1);a.removeEventListener(k,l,!1);return!0}};if(C.doc.attachEvent)return function(a,\n",
       "b,e,h){var k=function(a){a=a||h.node.ownerDocument.window.event;var b=d(\"y\",h),k=d(\"x\",h),k=a.clientX+k,b=a.clientY+b;a.preventDefault=a.preventDefault||f;a.stopPropagation=a.stopPropagation||u;return e.call(h,a,k,b)};a.attachEvent(\"on\"+b,k);return function(){a.detachEvent(\"on\"+b,k);return!0}}}(),q=[],e=function(a){for(var b=a.clientX,e=a.clientY,f=d(\"y\"),l=d(\"x\"),n,p=q.length;p--;){n=q[p];if(w)for(var r=a.touches&&a.touches.length,u;r--;){if(u=a.touches[r],u.identifier==n.el._drag.id||n.el.node.contains(u.target)){b=\n",
       "u.clientX;e=u.clientY;(a.originalEvent?a.originalEvent:a).preventDefault();break}}else a.preventDefault();b+=l;e+=f;k(\"snap.drag.move.\"+n.el.id,n.move_scope||n.el,b-n.el._drag.x,e-n.el._drag.y,b,e,a)}},l=function(b){a.unmousemove(e).unmouseup(l);for(var d=q.length,f;d--;)f=q[d],f.el._drag={},k(\"snap.drag.end.\"+f.el.id,f.end_scope||f.start_scope||f.move_scope||f.el,b);q=[]};for(y=v.length;y--;)(function(d){a[d]=A[d]=function(e,f){a.is(e,\"function\")&&(this.events=this.events||[],this.events.push({name:d,\n",
       "f:e,unbind:b(this.node||document,d,e,f||this)}));return this};a[\"un\"+d]=A[\"un\"+d]=function(a){for(var b=this.events||[],e=b.length;e--;)if(b[e].name==d&&(b[e].f==a||!a)){b[e].unbind();b.splice(e,1);!b.length&&delete this.events;break}return this}})(v[y]);A.hover=function(a,b,d,e){return this.mouseover(a,d).mouseout(b,e||d)};A.unhover=function(a,b){return this.unmouseover(a).unmouseout(b)};var r=[];A.drag=function(b,d,f,h,n,p){function u(r,v,w){(r.originalEvent||r).preventDefault();this._drag.x=v;\n",
       "this._drag.y=w;this._drag.id=r.identifier;!q.length&&a.mousemove(e).mouseup(l);q.push({el:this,move_scope:h,start_scope:n,end_scope:p});d&&k.on(\"snap.drag.start.\"+this.id,d);b&&k.on(\"snap.drag.move.\"+this.id,b);f&&k.on(\"snap.drag.end.\"+this.id,f);k(\"snap.drag.start.\"+this.id,n||h||this,v,w,r)}if(!arguments.length){var v;return this.drag(function(a,b){this.attr({transform:v+(v?\"T\":\"t\")+[a,b]})},function(){v=this.transform().local})}this._drag={};r.push({el:this,start:u});this.mousedown(u);return this};\n",
       "A.undrag=function(){for(var b=r.length;b--;)r[b].el==this&&(this.unmousedown(r[b].start),r.splice(b,1),k.unbind(\"snap.drag.*.\"+this.id));!r.length&&a.unmousemove(e).unmouseup(l);return this}});C.plugin(function(a,v,y,C){y=y.prototype;var A=/^\\s*url\\((.+)\\)/,w=String,z=a._.$;a.filter={};y.filter=function(d){var f=this;\"svg\"!=f.type&&(f=f.paper);d=a.parse(w(d));var k=a._.id(),u=z(\"filter\");z(u,{id:k,filterUnits:\"userSpaceOnUse\"});u.appendChild(d.node);f.defs.appendChild(u);return new v(u)};k.on(\"snap.util.getattr.filter\",\n",
       "function(){k.stop();var d=z(this.node,\"filter\");if(d)return(d=w(d).match(A))&&a.select(d[1])});k.on(\"snap.util.attr.filter\",function(d){if(d instanceof v&&\"filter\"==d.type){k.stop();var f=d.node.id;f||(z(d.node,{id:d.id}),f=d.id);z(this.node,{filter:a.url(f)})}d&&\"none\"!=d||(k.stop(),this.node.removeAttribute(\"filter\"))});a.filter.blur=function(d,f){null==d&&(d=2);return a.format('<feGaussianBlur stdDeviation=\"{def}\"/>',{def:null==f?d:[d,f]})};a.filter.blur.toString=function(){return this()};a.filter.shadow=\n",
       "function(d,f,k,u,p){\"string\"==typeof k&&(p=u=k,k=4);\"string\"!=typeof u&&(p=u,u=\"#000\");null==k&&(k=4);null==p&&(p=1);null==d&&(d=0,f=2);null==f&&(f=d);u=a.color(u||\"#000\");return a.format('<feGaussianBlur in=\"SourceAlpha\" stdDeviation=\"{blur}\"/><feOffset dx=\"{dx}\" dy=\"{dy}\" result=\"offsetblur\"/><feFlood flood-color=\"{color}\"/><feComposite in2=\"offsetblur\" operator=\"in\"/><feComponentTransfer><feFuncA type=\"linear\" slope=\"{opacity}\"/></feComponentTransfer><feMerge><feMergeNode/><feMergeNode in=\"SourceGraphic\"/></feMerge>',\n",
       "{color:u,dx:d,dy:f,blur:k,opacity:p})};a.filter.shadow.toString=function(){return this()};a.filter.grayscale=function(d){null==d&&(d=1);return a.format('<feColorMatrix type=\"matrix\" values=\"{a} {b} {c} 0 0 {d} {e} {f} 0 0 {g} {b} {h} 0 0 0 0 0 1 0\"/>',{a:0.2126+0.7874*(1-d),b:0.7152-0.7152*(1-d),c:0.0722-0.0722*(1-d),d:0.2126-0.2126*(1-d),e:0.7152+0.2848*(1-d),f:0.0722-0.0722*(1-d),g:0.2126-0.2126*(1-d),h:0.0722+0.9278*(1-d)})};a.filter.grayscale.toString=function(){return this()};a.filter.sepia=\n",
       "function(d){null==d&&(d=1);return a.format('<feColorMatrix type=\"matrix\" values=\"{a} {b} {c} 0 0 {d} {e} {f} 0 0 {g} {h} {i} 0 0 0 0 0 1 0\"/>',{a:0.393+0.607*(1-d),b:0.769-0.769*(1-d),c:0.189-0.189*(1-d),d:0.349-0.349*(1-d),e:0.686+0.314*(1-d),f:0.168-0.168*(1-d),g:0.272-0.272*(1-d),h:0.534-0.534*(1-d),i:0.131+0.869*(1-d)})};a.filter.sepia.toString=function(){return this()};a.filter.saturate=function(d){null==d&&(d=1);return a.format('<feColorMatrix type=\"saturate\" values=\"{amount}\"/>',{amount:1-\n",
       "d})};a.filter.saturate.toString=function(){return this()};a.filter.hueRotate=function(d){return a.format('<feColorMatrix type=\"hueRotate\" values=\"{angle}\"/>',{angle:d||0})};a.filter.hueRotate.toString=function(){return this()};a.filter.invert=function(d){null==d&&(d=1);return a.format('<feComponentTransfer><feFuncR type=\"table\" tableValues=\"{amount} {amount2}\"/><feFuncG type=\"table\" tableValues=\"{amount} {amount2}\"/><feFuncB type=\"table\" tableValues=\"{amount} {amount2}\"/></feComponentTransfer>',{amount:d,\n",
       "amount2:1-d})};a.filter.invert.toString=function(){return this()};a.filter.brightness=function(d){null==d&&(d=1);return a.format('<feComponentTransfer><feFuncR type=\"linear\" slope=\"{amount}\"/><feFuncG type=\"linear\" slope=\"{amount}\"/><feFuncB type=\"linear\" slope=\"{amount}\"/></feComponentTransfer>',{amount:d})};a.filter.brightness.toString=function(){return this()};a.filter.contrast=function(d){null==d&&(d=1);return a.format('<feComponentTransfer><feFuncR type=\"linear\" slope=\"{amount}\" intercept=\"{amount2}\"/><feFuncG type=\"linear\" slope=\"{amount}\" intercept=\"{amount2}\"/><feFuncB type=\"linear\" slope=\"{amount}\" intercept=\"{amount2}\"/></feComponentTransfer>',\n",
       "{amount:d,amount2:0.5-d/2})};a.filter.contrast.toString=function(){return this()}});return C});\n",
       "\n",
       "]]> </script>\n",
       "</svg>\n"
      ],
      "text/plain": [
       "Compose.Context(Measures.BoundingBox{Tuple{Measures.Length{:w,Float64},Measures.Length{:h,Float64}},Tuple{Measures.Length{:w,Float64},Measures.Length{:h,Float64}}}((0.0w, 0.0h), (1.0w, 1.0h)), Compose.UnitBox{Float64,Float64,Float64,Float64}(-1.2, -1.2, 2.4, 2.4, 0.0mm, 0.0mm, 0.0mm, 0.0mm), nothing, nothing, List([Compose.Context(Measures.BoundingBox{Tuple{Measures.Length{:w,Float64},Measures.Length{:h,Float64}},Tuple{Measures.Length{:w,Float64},Measures.Length{:h,Float64}}}((0.0w, 0.0h), (1.0w, 1.0h)), nothing, nothing, nothing, List([]), List([Compose.Form{Compose.LinePrimitive}(Compose.LinePrimitive[LinePrimitive{Tuple{Measure,Measure}}(Tuple{Measures.Measure,Measures.Measure}[(-0.0716107cx, -0.68497cy), (0.128692cx, -0.497017cy)]), LinePrimitive{Tuple{Measure,Measure}}(Tuple{Measures.Measure,Measures.Measure}[(-0.111232cx, -0.715017cy), (-0.221113cx, -0.781485cy)]), LinePrimitive{Tuple{Measure,Measure}}(Tuple{Measures.Measure,Measures.Measure}[(-0.114204cx, -0.696466cy), (-0.145893cx, -0.689167cy)]), LinePrimitive{Tuple{Measure,Measure}}(Tuple{Measures.Measure,Measures.Measure}[(-0.110087cx, -0.68741cy), (-0.269991cx, -0.571561cy)]), LinePrimitive{Tuple{Measure,Measure}}(Tuple{Measures.Measure,Measures.Measure}[(-0.183309cx, -0.975025cy), (-0.171374cx, -0.708532cy)]), LinePrimitive{Tuple{Measure,Measure}}(Tuple{Measures.Measure,Measures.Measure}[(0.127582cx, -0.495751cy), (-0.0395465cx, -0.632628cy)]), LinePrimitive{Tuple{Measure,Measure}}(Tuple{Measures.Measure,Measures.Measure}[(0.145603cx, -0.454946cy), (0.130692cx, -0.173021cy)]), LinePrimitive{Tuple{Measure,Measure}}(Tuple{Measures.Measure,Measures.Measure}[(0.166681cx, -0.464593cy), (0.433177cx, -0.257997cy)]), LinePrimitive{Tuple{Measure,Measure}}(Tuple{Measures.Measure,Measures.Measure}[(-0.329361cx, -0.174336cy), (-0.29278cx, -0.532023cy)]), LinePrimitive{Tuple{Measure,Measure}}(Tuple{Measures.Measure,Measures.Measure}[(-0.329687cx, -0.124564cy), (-0.293624cx, 0.280425cy)])  …  LinePrimitive{Tuple{Measure,Measure}}(Tuple{Measures.Measure,Measures.Measure}[(-0.296011cx, 0.439467cy), (-0.261719cx, 0.614468cy)]), LinePrimitive{Tuple{Measure,Measure}}(Tuple{Measures.Measure,Measures.Measure}[(-0.219023cx, 0.698881cy), (-0.310118cx, 0.749947cy)]), LinePrimitive{Tuple{Measure,Measure}}(Tuple{Measures.Measure,Measures.Measure}[(-0.187797cx, 0.663499cy), (-0.0976817cx, 0.441945cy)]), LinePrimitive{Tuple{Measure,Measure}}(Tuple{Measures.Measure,Measures.Measure}[(-0.346904cx, 0.742155cy), (-0.424109cx, 0.638988cy)]), LinePrimitive{Tuple{Measure,Measure}}(Tuple{Measures.Measure,Measures.Measure}[(-0.318922cx, 0.740819cy), (-0.269915cx, 0.660353cy)]), LinePrimitive{Tuple{Measure,Measure}}(Tuple{Measures.Measure,Measures.Measure}[(-0.325206cx, 0.738091cy), (-0.285624cx, 0.596252cy)]), LinePrimitive{Tuple{Measure,Measure}}(Tuple{Measures.Measure,Measures.Measure}[(-0.415091cx, 0.611961cy), (-0.302901cx, 0.579183cy)]), LinePrimitive{Tuple{Measure,Measure}}(Tuple{Measures.Measure,Measures.Measure}[(-0.241711cx, 0.619153cy), (-0.103463cx, 0.438635cy)]), LinePrimitive{Tuple{Measure,Measure}}(Tuple{Measures.Measure,Measures.Measure}[(-0.0896685cx, 0.294849cy), (-0.0885461cx, 0.393789cy)]), LinePrimitive{Tuple{Measure,Measure}}(Tuple{Measures.Measure,Measures.Measure}[(-0.103202cx, 0.291051cy), (-0.265654cx, 0.550972cy)])], Symbol(\"\"))]), List([Compose.Property{Compose.LineWidthPrimitive}(Compose.LineWidthPrimitive[LineWidthPrimitive(0.3mm)]), Compose.Property{Compose.FillPrimitive}(Compose.FillPrimitive[FillPrimitive(RGBA{Float64}(0.0,0.0,0.0,0.0))]), Compose.Property{Compose.StrokePrimitive}(Compose.StrokePrimitive[StrokePrimitive(RGBA{Float64}(0.827451,0.827451,0.827451,1.0))])]), 0, false, false, false, false, nothing, nothing, 0.0, Symbol(\"\")), Compose.Context(Measures.BoundingBox{Tuple{Measures.Length{:w,Float64},Measures.Length{:h,Float64}},Tuple{Measures.Length{:w,Float64},Measures.Length{:h,Float64}}}((0.0w, 0.0h), (1.0w, 1.0h)), nothing, nothing, nothing, List([]), List([]), List([Compose.Property{Compose.LineWidthPrimitive}(Compose.LineWidthPrimitive[LineWidthPrimitive(0.3mm)]), Compose.Property{Compose.StrokePrimitive}(Compose.StrokePrimitive[StrokePrimitive(RGBA{Float64}(0.827451,0.827451,0.827451,1.0))])]), 0, false, false, false, false, nothing, nothing, 0.0, Symbol(\"\")), Compose.Context(Measures.BoundingBox{Tuple{Measures.Length{:w,Float64},Measures.Length{:h,Float64}},Tuple{Measures.Length{:w,Float64},Measures.Length{:h,Float64}}}((0.0w, 0.0h), (1.0w, 1.0h)), nothing, nothing, nothing, List([]), List([]), List([Compose.Property{Compose.FontSizePrimitive}(Compose.FontSizePrimitive[FontSizePrimitive(4.0mm)]), Compose.Property{Compose.StrokePrimitive}(Compose.StrokePrimitive[StrokePrimitive(RGBA{Float64}(0.0,0.0,0.0,0.0))]), Compose.Property{Compose.FillPrimitive}(Compose.FillPrimitive[FillPrimitive(RGBA{Float64}(0.0,0.0,0.0,1.0))])]), 0, false, false, false, false, nothing, nothing, 0.0, Symbol(\"\")), Compose.Context(Measures.BoundingBox{Tuple{Measures.Length{:w,Float64},Measures.Length{:h,Float64}},Tuple{Measures.Length{:w,Float64},Measures.Length{:h,Float64}}}((0.0w, 0.0h), (1.0w, 1.0h)), nothing, nothing, nothing, List([]), List([Compose.Form{Compose.CirclePrimitive{Tuple{Measures.Measure,Measures.Measure},Measures.Measure}}(Compose.CirclePrimitive{Tuple{Measures.Measure,Measures.Measure},Measures.Measure}[CirclePrimitive{Tuple{Measure,Measure},Measure}((-0.0898414cx, -0.702077cy), 0.01w), CirclePrimitive{Tuple{Measure,Measure},Measure}((-0.184428cx, -1.0cy), 0.01w), CirclePrimitive{Tuple{Measure,Measure},Measure}((0.146923cx, -0.479911cy), 0.01w), CirclePrimitive{Tuple{Measure,Measure},Measure}((-0.331904cx, -0.149465cy), 0.01w), CirclePrimitive{Tuple{Measure,Measure},Measure}((-0.256308cx, -0.623573cy), 0.01w), CirclePrimitive{Tuple{Measure,Measure},Measure}((-0.0588877cx, -0.648468cy), 0.01w), CirclePrimitive{Tuple{Measure,Measure},Measure}((-0.242504cx, -0.794425cy), 0.01w), CirclePrimitive{Tuple{Measure,Measure},Measure}((-0.15515cx, -0.453092cy), 0.01w), CirclePrimitive{Tuple{Measure,Measure},Measure}((-0.170256cx, -0.683557cy), 0.01w), CirclePrimitive{Tuple{Measure,Measure},Measure}((-0.290236cx, -0.556894cy), 0.01w)  …  CirclePrimitive{Tuple{Measure,Measure},Measure}((-0.199257cx, 0.626156cy), 0.01w), CirclePrimitive{Tuple{Measure,Measure},Measure}((-0.238078cx, 0.4514cy), 0.01w), CirclePrimitive{Tuple{Measure,Measure},Measure}((-0.300819cx, 0.414933cy), 0.01w), CirclePrimitive{Tuple{Measure,Measure},Measure}((-0.197216cx, 0.686657cy), 0.01w), CirclePrimitive{Tuple{Measure,Measure},Measure}((-0.331926cx, 0.762171cy), 0.01w), CirclePrimitive{Tuple{Measure,Measure},Measure}((-0.439088cx, 0.618972cy), 0.01w), CirclePrimitive{Tuple{Measure,Measure},Measure}((-0.256911cx, 0.639001cy), 0.01w), CirclePrimitive{Tuple{Measure,Measure},Measure}((-0.0899521cx, 0.269851cy), 0.01w), CirclePrimitive{Tuple{Measure,Measure},Measure}((-0.0882625cx, 0.418787cy), 0.01w), CirclePrimitive{Tuple{Measure,Measure},Measure}((-0.278904cx, 0.572172cy), 0.01w)], Symbol(\"\"))]), List([Compose.Property{Compose.LineWidthPrimitive}(Compose.LineWidthPrimitive[LineWidthPrimitive(0.0mm)]), Compose.Property{Compose.StrokePrimitive}(Compose.StrokePrimitive[StrokePrimitive(RGBA{Float64}(0.0,0.0,0.0,0.0))]), Compose.Property{Compose.FillPrimitive}(Compose.FillPrimitive[FillPrimitive(RGBA{Float64}(0.0,0.0,0.0,1.0)), FillPrimitive(RGBA{Float64}(0.0,0.0,0.0,1.0)), FillPrimitive(RGBA{Float64}(0.0,0.501961,0.160784,1.0)), FillPrimitive(RGBA{Float64}(0.0,0.0,0.0,1.0)), FillPrimitive(RGBA{Float64}(0.0,0.0,0.0,1.0)), FillPrimitive(RGBA{Float64}(0.0,0.372549,0.835294,1.0)), FillPrimitive(RGBA{Float64}(0.0,0.0,0.0,1.0)), FillPrimitive(RGBA{Float64}(0.654902,0.52549,0.0,1.0)), FillPrimitive(RGBA{Float64}(0.0,0.372549,0.835294,1.0)), FillPrimitive(RGBA{Float64}(0.576471,0.0,0.407843,1.0))  …  FillPrimitive(RGBA{Float64}(0.654902,0.52549,0.0,1.0)), FillPrimitive(RGBA{Float64}(0.654902,0.52549,0.0,1.0)), FillPrimitive(RGBA{Float64}(0.654902,0.52549,0.0,1.0)), FillPrimitive(RGBA{Float64}(0.654902,0.52549,0.0,1.0)), FillPrimitive(RGBA{Float64}(0.654902,0.52549,0.0,1.0)), FillPrimitive(RGBA{Float64}(0.0,0.0,0.0,1.0)), FillPrimitive(RGBA{Float64}(0.654902,0.52549,0.0,1.0)), FillPrimitive(RGBA{Float64}(0.654902,0.52549,0.0,1.0)), FillPrimitive(RGBA{Float64}(0.654902,0.52549,0.0,1.0)), FillPrimitive(RGBA{Float64}(0.654902,0.52549,0.0,1.0))])]), 0, false, false, false, false, nothing, nothing, 0.0, Symbol(\"\")), Compose.Context(Measures.BoundingBox{Tuple{Measures.Length{:w,Float64},Measures.Length{:h,Float64}},Tuple{Measures.Length{:w,Float64},Measures.Length{:h,Float64}}}((0.0w, 0.0h), (1.0w, 1.0h)), nothing, nothing, nothing, List([]), List([]), List([Compose.Property{Compose.FontSizePrimitive}(Compose.FontSizePrimitive[FontSizePrimitive(4.0mm)]), Compose.Property{Compose.StrokePrimitive}(Compose.StrokePrimitive[StrokePrimitive(RGBA{Float64}(0.0,0.0,0.0,0.0))]), Compose.Property{Compose.FillPrimitive}(Compose.FillPrimitive[FillPrimitive(RGBA{Float64}(0.0,0.0,0.0,1.0))])]), 0, false, false, false, false, nothing, nothing, 0.0, Symbol(\"\"))]), List([]), List([]), 0, false, false, false, false, nothing, nothing, 0.0, Symbol(\"\"))"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g, communities = Generative.make_sbm(10, 10, 0.4, 0.01)\n",
    "colors = Generative.make_colors(communities, 0.8)\n",
    "\n",
    "communitiesoh, colorsoh = Utils.onehotmaxbatch(communities), Utils.onehotmaxbatch(colors)\n",
    "features = scale_center(colorsoh)\n",
    "\n",
    "palette = distinguishable_colors(size(features, 1))\n",
    "colorsrgb = map(i -> getindex(palette, i), colors)\n",
    "gplot(g, nodefillc = colorsrgb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n",
       "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
       " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n",
       "<svg xmlns=\"http://www.w3.org/2000/svg\" version=\"1.1\"\n",
       "     width=\"180.0mm\" height=\"25.0mm\"\n",
       "     shape-rendering=\"crispEdges\">\n",
       "<rect x=\"0.0mm\" y=\"0.0mm\"\n",
       "      width=\"17.0mm\" height=\"24.0mm\"\n",
       "      fill=\"#000000\" stroke=\"none\" />\n",
       "<rect x=\"18.0mm\" y=\"0.0mm\"\n",
       "      width=\"17.0mm\" height=\"24.0mm\"\n",
       "      fill=\"#FFFF62\" stroke=\"none\" />\n",
       "<rect x=\"36.0mm\" y=\"0.0mm\"\n",
       "      width=\"17.0mm\" height=\"24.0mm\"\n",
       "      fill=\"#FF9FFF\" stroke=\"none\" />\n",
       "<rect x=\"54.0mm\" y=\"0.0mm\"\n",
       "      width=\"17.0mm\" height=\"24.0mm\"\n",
       "      fill=\"#00D6FF\" stroke=\"none\" />\n",
       "<rect x=\"72.0mm\" y=\"0.0mm\"\n",
       "      width=\"17.0mm\" height=\"24.0mm\"\n",
       "      fill=\"#D74400\" stroke=\"none\" />\n",
       "<rect x=\"90.0mm\" y=\"0.0mm\"\n",
       "      width=\"17.0mm\" height=\"24.0mm\"\n",
       "      fill=\"#008029\" stroke=\"none\" />\n",
       "<rect x=\"108.0mm\" y=\"0.0mm\"\n",
       "      width=\"17.0mm\" height=\"24.0mm\"\n",
       "      fill=\"#005FD5\" stroke=\"none\" />\n",
       "<rect x=\"126.0mm\" y=\"0.0mm\"\n",
       "      width=\"17.0mm\" height=\"24.0mm\"\n",
       "      fill=\"#930068\" stroke=\"none\" />\n",
       "<rect x=\"144.0mm\" y=\"0.0mm\"\n",
       "      width=\"17.0mm\" height=\"24.0mm\"\n",
       "      fill=\"#FFCBB5\" stroke=\"none\" />\n",
       "<rect x=\"162.0mm\" y=\"0.0mm\"\n",
       "      width=\"17.0mm\" height=\"24.0mm\"\n",
       "      fill=\"#A78600\" stroke=\"none\" />\n",
       "</svg>"
      ],
      "text/plain": [
       "10-element Array{RGB{N0f8},1} with eltype RGB{FixedPointNumbers.Normed{UInt8,8}}:\n",
       " RGB{N0f8}(0.0,0.0,0.0)    \n",
       " RGB{N0f8}(1.0,1.0,0.384)  \n",
       " RGB{N0f8}(1.0,0.624,1.0)  \n",
       " RGB{N0f8}(0.0,0.839,1.0)  \n",
       " RGB{N0f8}(0.843,0.267,0.0)\n",
       " RGB{N0f8}(0.0,0.502,0.161)\n",
       " RGB{N0f8}(0.0,0.373,0.835)\n",
       " RGB{N0f8}(0.576,0.0,0.408)\n",
       " RGB{N0f8}(1.0,0.796,0.71) \n",
       " RGB{N0f8}(0.655,0.525,0.0)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "palette"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "Scene (960px, 540px):\n",
       "events:\n",
       "    window_area: GeometryTypes.HyperRectangle{2,Int64}([0, 0], [0, 0])\n",
       "    window_dpi: 100.0\n",
       "    window_open: false\n",
       "    mousebuttons: Set(AbstractPlotting.Mouse.Button[])\n",
       "    mouseposition: (0.0, 0.0)\n",
       "    mousedrag: notpressed\n",
       "    scroll: (0.0, 0.0)\n",
       "    keyboardbuttons: Set(AbstractPlotting.Keyboard.Button[])\n",
       "    unicode_input: Char[]\n",
       "    dropped_files: String[]\n",
       "    hasfocus: false\n",
       "    entered_window: false\n",
       "plots:\n",
       "subscenes:\n",
       "   *scene(480px, 540px)\n",
       "   *scene(480px, 540px)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vbox(\n",
    "    heatmap(1:size(communitiesoh, 1), 1:size(communitiesoh, 2), communitiesoh),\n",
    "    heatmap(1:size(colorsoh, 1), 1:size(colorsoh, 2), softmax(convert(Array{Float64}, colorsoh)), colorrange = (0, 1)),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting model state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "plotweights (generic function with 1 method)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adims(a, dims) = [a[i, :] for i = dims]\n",
    "\n",
    "function plotstate(;enc, vae, x, refx, g, dims)\n",
    "    @assert length(dims) in [2, 3]\n",
    "    embμ, emblogσ = enc(x)\n",
    "    logitÂ, unormF̂ = vae(x)\n",
    "    hbox(\n",
    "        vbox(\n",
    "            Scene(),\n",
    "            heatmap(σ.(logitÂ).data, colorrange = (0, 1)),\n",
    "            heatmap(1:size(x, 1), 1:size(x, 2), softmax(unormF̂).data, colorrange = (0, 1)),\n",
    "            sizes = [.45, .45, .1]\n",
    "        ),\n",
    "        vbox(\n",
    "            scatter(adims(embμ, dims)..., color = colorsrgb, markersize = markersize(embμ)),\n",
    "            heatmap(Array(adjacency_matrix(g)), colorrange = (0, 1)),\n",
    "            heatmap(1:size(x, 1), 1:size(x, 2), refx, colorrange = (0, 1)),\n",
    "            sizes = [.45, .45, .1]\n",
    "        ),\n",
    "    )\n",
    "end\n",
    "\n",
    "function plotweights(layers...)\n",
    "    theme = Theme(align = (:left, :bottom), raw = true, camera = campixel!)\n",
    "    vbox([hbox(heatmap(l.W.data), text(theme, repr(l))) for l in layers]...)\n",
    "end"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "vae (generic function with 1 method)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dimξadj = 2\n",
    "dimξfeat = 2\n",
    "diml1 = Int64(round(sqrt(size(features, 1) * (dimξadj + dimξfeat))))\n",
    "overlap = 1\n",
    "\n",
    "# Encoder\n",
    "l1 = Layers.GC(g, size(features, 1), diml1, Flux.relu, initb = Layers.nobias)\n",
    "lμ = Layers.Apply(Layers.VOverlap(overlap),\n",
    "    Layers.GC(g, diml1, dimξadj, initb = Layers.nobias),\n",
    "    Layers.GC(g, diml1, dimξfeat, initb = Layers.nobias))\n",
    "llogσ = Layers.Apply(Layers.VOverlap(overlap),\n",
    "    Layers.GC(g, diml1, dimξadj, initb = Layers.nobias),\n",
    "    Layers.GC(g, diml1, dimξfeat, initb = Layers.nobias))\n",
    "enc(x) = (h = l1(x); (lμ(h), llogσ(h)))\n",
    "\n",
    "# Sampler\n",
    "sampleξ(μ, logσ) = μ .+ exp.(logσ) .* randn_like(μ)\n",
    "\n",
    "# Decoder\n",
    "decadj = Chain(\n",
    "    Dense(dimξadj, diml1, Flux.relu, initb = Layers.nobias),\n",
    "    Layers.Bilin(diml1)\n",
    ")\n",
    "decfeat = Chain(\n",
    "    Dense(dimξfeat, diml1, Flux.relu, initb = Layers.nobias),\n",
    "    Dense(diml1, size(features, 1), initb = Layers.nobias),\n",
    ")\n",
    "@views dec(ξ) = (decadj(ξ[1:dimξadj, :]), decfeat(ξ[end-dimξfeat+1:end, :]))\n",
    "\n",
    "# Final model\n",
    "vae(x) = dec(sampleξ(enc(x)...))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "Scene (960px, 540px):\n",
       "events:\n",
       "    window_area: GeometryTypes.HyperRectangle{2,Int64}([0, 0], [0, 0])\n",
       "    window_dpi: 100.0\n",
       "    window_open: false\n",
       "    mousebuttons: Set(AbstractPlotting.Mouse.Button[])\n",
       "    mouseposition: (0.0, 0.0)\n",
       "    mousedrag: notpressed\n",
       "    scroll: (0.0, 0.0)\n",
       "    keyboardbuttons: Set(AbstractPlotting.Keyboard.Button[])\n",
       "    unicode_input: Char[]\n",
       "    dropped_files: String[]\n",
       "    hasfocus: false\n",
       "    entered_window: false\n",
       "plots:\n",
       "subscenes:\n",
       "   *scene(960px, 270px)\n",
       "   *scene(960px, 270px)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plotstate(enc = enc, vae = vae, x = features, refx = colorsoh, g = g, dims = 1:3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "Scene (960px, 540px):\n",
       "events:\n",
       "    window_area: GeometryTypes.HyperRectangle{2,Int64}([0, 0], [0, 0])\n",
       "    window_dpi: 100.0\n",
       "    window_open: false\n",
       "    mousebuttons: Set(AbstractPlotting.Mouse.Button[])\n",
       "    mouseposition: (0.0, 0.0)\n",
       "    mousedrag: notpressed\n",
       "    scroll: (0.0, 0.0)\n",
       "    keyboardbuttons: Set(AbstractPlotting.Keyboard.Button[])\n",
       "    unicode_input: Char[]\n",
       "    dropped_files: String[]\n",
       "    hasfocus: false\n",
       "    entered_window: false\n",
       "plots:\n",
       "subscenes:\n",
       "   *scene(107px, 540px)\n",
       "   *scene(107px, 540px)\n",
       "   *scene(107px, 540px)\n",
       "   *scene(107px, 540px)\n",
       "   *scene(107px, 540px)\n",
       "   *scene(107px, 540px)\n",
       "   *scene(107px, 540px)\n",
       "   *scene(107px, 540px)\n",
       "   *scene(107px, 540px)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plotweights(l1, lμ.args..., llogσ.args..., decadj.layers..., decfeat.layers...)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define losses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "Adiag = adjacency_matrix_diag(g)\n",
    "densityA = mean(adjacency_matrix(g));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "loss (generic function with 1 method)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Decoder regularizer\n",
    "decregularizer(l = 0.01) = l * sum(x -> sum(x.^2), Flux.params(decadj, decfeat))\n",
    "\n",
    "# Kullback-Leibler divergence\n",
    "Lkl(μ, logσ) = 0.5 * sum(exp.(2 .* logσ) + μ.^2 .- 1 .- 2 .* logσ)\n",
    "κkl = size(g, 1) * (dimξadj - overlap + dimξfeat)\n",
    "\n",
    "# Adjacency loss\n",
    "Ladj(logitApred) = (\n",
    "    0.5 * sum(logitbinarycrossentropy.(logitApred, Adiag, pos_weight = (1 / densityA) - 1))\n",
    "    / (1 - densityA)\n",
    ")\n",
    "κadj = size(g, 1)^2 * log(2)\n",
    "\n",
    "# Features loss\n",
    "Lfeat(unormfeatpred) = - softmaxcategoricallogprob(unormfeatpred, colorsoh)\n",
    "κfeat = size(g, 1) * log(size(features, 1))\n",
    "\n",
    "# Total loss\n",
    "klscale = 1e-3\n",
    "regscale = 1e-3\n",
    "function losses(x)\n",
    "    μ, logσ = enc(x)\n",
    "    logitApred, unormfeatpred = dec(sampleξ(μ, logσ))\n",
    "    Dict(\"kl\" => klscale * Lkl(μ, logσ) / κkl,\n",
    "        \"adj\" => Ladj(logitApred) / κadj,\n",
    "        \"feat\" => Lfeat(unormfeatpred) / κfeat,\n",
    "        \"reg\" => regscale * decregularizer())\n",
    "end\n",
    "\n",
    "function loss(x)\n",
    "    sum(values(losses(x)))\n",
    "end"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "history = nothing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32mProgress: 100%|█████████████████████████████████████████| Time: 0:00:31\u001b[39m\n"
     ]
    }
   ],
   "source": [
    "nepochs = 1000\n",
    "if isdefined(Main, :history) && history !== nothing\n",
    "    for (name, values) in history\n",
    "        history[name] = cat(history[name], zeros(nepochs), dims = 1)\n",
    "    end\n",
    "    totalepochs += nepochs\n",
    "else\n",
    "    history = Dict(name => zeros(nepochs) for name in keys(losses(features)))\n",
    "    history[\"total\"] = zeros(nepochs)\n",
    "    totalepochs = nepochs\n",
    "end\n",
    "\n",
    "opt = ADAM(0.01)\n",
    "@showprogress for i = 1:nepochs\n",
    "    Flux.train!(loss, Flux.params(l1, lμ, llogσ, decadj, decfeat), [(features,)], opt)\n",
    "\n",
    "    lossparts = losses(features)\n",
    "    for (name, value) in lossparts\n",
    "        history[name][totalepochs-nepochs+i] = value.data\n",
    "    end\n",
    "    history[\"total\"][totalepochs-nepochs+i] = sum(values(lossparts)).data\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "Scene (960px, 540px):\n",
       "events:\n",
       "    window_area: GeometryTypes.HyperRectangle{2,Int64}([0, 0], [0, 0])\n",
       "    window_dpi: 100.0\n",
       "    window_open: false\n",
       "    mousebuttons: Set(AbstractPlotting.Mouse.Button[])\n",
       "    mouseposition: (0.0, 0.0)\n",
       "    mousedrag: notpressed\n",
       "    scroll: (0.0, 0.0)\n",
       "    keyboardbuttons: Set(AbstractPlotting.Keyboard.Button[])\n",
       "    unicode_input: Char[]\n",
       "    dropped_files: String[]\n",
       "    hasfocus: false\n",
       "    entered_window: false\n",
       "plots:\n",
       "subscenes:\n",
       "   *scene(192px, 540px)\n",
       "   *scene(192px, 540px)\n",
       "   *scene(192px, 540px)\n",
       "   *scene(192px, 540px)\n",
       "   *scene(192px, 540px)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "theme = Theme(align = (:left, :bottom), raw = true, camera = campixel!)\n",
    "vbox([hbox(lines(1:totalepochs, history[name], color = color), text(theme, name))\n",
    "        for (name, color) in [(\"total\", :blue), (\"kl\", :red), (\"reg\", :red), (\"adj\", :green), (\"feat\", :green)]]...)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "Scene (960px, 540px):\n",
       "events:\n",
       "    window_area: GeometryTypes.HyperRectangle{2,Int64}([0, 0], [0, 0])\n",
       "    window_dpi: 100.0\n",
       "    window_open: false\n",
       "    mousebuttons: Set(AbstractPlotting.Mouse.Button[])\n",
       "    mouseposition: (0.0, 0.0)\n",
       "    mousedrag: notpressed\n",
       "    scroll: (0.0, 0.0)\n",
       "    keyboardbuttons: Set(AbstractPlotting.Keyboard.Button[])\n",
       "    unicode_input: Char[]\n",
       "    dropped_files: String[]\n",
       "    hasfocus: false\n",
       "    entered_window: false\n",
       "plots:\n",
       "subscenes:\n",
       "   *scene(960px, 270px)\n",
       "   *scene(960px, 270px)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plotstate(enc = enc, vae = vae, x = features, refx = colorsoh, g = g, dims = 1:3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "Scene (960px, 540px):\n",
       "events:\n",
       "    window_area: GeometryTypes.HyperRectangle{2,Int64}([0, 0], [0, 0])\n",
       "    window_dpi: 100.0\n",
       "    window_open: false\n",
       "    mousebuttons: Set(AbstractPlotting.Mouse.Button[])\n",
       "    mouseposition: (0.0, 0.0)\n",
       "    mousedrag: notpressed\n",
       "    scroll: (0.0, 0.0)\n",
       "    keyboardbuttons: Set(AbstractPlotting.Keyboard.Button[])\n",
       "    unicode_input: Char[]\n",
       "    dropped_files: String[]\n",
       "    hasfocus: false\n",
       "    entered_window: false\n",
       "plots:\n",
       "subscenes:\n",
       "   *scene(107px, 540px)\n",
       "   *scene(107px, 540px)\n",
       "   *scene(107px, 540px)\n",
       "   *scene(107px, 540px)\n",
       "   *scene(107px, 540px)\n",
       "   *scene(107px, 540px)\n",
       "   *scene(107px, 540px)\n",
       "   *scene(107px, 540px)\n",
       "   *scene(107px, 540px)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plotweights(l1, lμ.args..., llogσ.args..., decadj.layers..., decfeat.layers...)"
   ]
  }
 ],
 "metadata": {
  "@webio": {
   "lastCommId": "4f0134a45c7b4210aac552416bd865e8",
   "lastKernelId": "92e340f4-0e98-44d1-a0d5-e8cb265cc633"
  },
  "kernelspec": {
   "display_name": "Julia 1.1.0",
   "language": "julia",
   "name": "julia-1.1"
  },
  "language_info": {
   "file_extension": ".jl",
   "mimetype": "application/julia",
   "name": "julia",
   "version": "1.1.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}