Animace.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "┌ Info: Precompiling Makie [ee78f7c6-11fb-53f2-987a-cfe4a2b5a57a]\n",
      "└ @ Base loading.jl:1187\n"
     ]
    }
   ],
   "source": [
    "using Makie\n",
    "#using Plots\n",
    "using QuadGK\n",
    "using Dierckx\n",
    "#using ColorSchemes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "┌ Warning: `linspace(start, stop, length::Integer)` is deprecated, use `range(start, stop=stop, length=length)` instead.\n",
      "│   caller = top-level scope at In[2]:10\n",
      "└ @ Core In[2]:10\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "\"D:\\\\Documents\\\\Diplomka\\\\string0.mp4\""
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scene = Scene(resolution = (500, 500))\n",
    "\n",
    "#f(t, s, r0) = (r0*(cos(t*r0+s)+cos(t*r0-s))/2*cos(s), r0*(cos(t*r0+s)+cos(t*r0-s))/2*sin(s))\n",
    "n = 0\n",
    "f(t, s, r0) = (r0*cos(t*r0)*cos(n*s)*cos(s), r0*cos(t*r0)*cos(n*s)*sin(s))\n",
    "f(t,s) = f(t, s, 1)\n",
    "time = Node(0.0)\n",
    "p1 = lines!(scene, lift(t-> f.(t, range(0, stop = 2pi, length = 200)), time), limits = FRect(-1.2, -1.2, 2.4, 2.4))[end]\n",
    "N = 150\n",
    "record(scene, \"D:\\\\Documents\\\\Diplomka\\\\string\"*\"$n\"*\".mp4\", linspace(0, 10, N)) do i\n",
    "    push!(time, i)\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "des_integral (generic function with 1 method)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "function des_integral(R,E,sgn)\n",
    "    sqrt(E^2*R^2 + sgn*2*E*R*sqrt(E^2 - R^2 + R^4) + E^2 - R^4 + R^6)/(R*sqrt(E^2*R^2 + sgn*2*E*R*sqrt(E^2 - R^2 + R^4) + E^2 - R^4 + R^6) - E*R - sgn*sqrt(E^2 - R^2 + R^4))\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "energy2 (generic function with 1 method)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "energy1(R0) = R0*sqrt(1-R0^2)\n",
    "energy2(R0) = R0*(1+R0^2)/sqrt(1-R0^2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7071067811865475"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "R0 = 1/sqrt(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "E = energy1(R0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "des_integralm1 (generic function with 1 method)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "des_integral1(x) = des_integral(x,E, 1)\n",
    "des_integralm1(x) = des_integral(x,E, -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "step = 0.001\n",
    "R = R0-step:-step:-R0+step;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "result1 = [quadgk(des_integral1, 0, R[i]) for i = 1:size(R,1)]\n",
    "resultm1 = [quadgk(des_integralm1, 0, R[i]) for i = 1:size(R,1)];"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t0 = maximum([(result1[i])[1] for i = 1:size(result1, 1)]) - minimum([(resultm1[i])[1] for i = 1:size(resultm1, 1)]) = 8.226319576571832\n"
     ]
    }
   ],
   "source": [
    "@show t0 = maximum([result1[i][1] for i = 1:size(result1,1)]) - minimum([resultm1[i][1] for i = 1:size(resultm1,1)])\n",
    "aa = [[[result1[i][1] for i = 1:size(result1,1)][1:end-1]\n",
    "        [t0+resultm1[i][1] for i = 1:size(resultm1,1)][end:-1:1]\n",
    "    [2*t0+result1[i][1] for i = 1:size(result1,1)][1:end-1]\n",
    "    [3*t0+resultm1[i][1] for i = 1:size(resultm1,1)][end:-1:1]] [R[1:end-1]\n",
    "        R[end:-1:1]\n",
    "        R[1:end-1]\n",
    "R[end:-1:1]]];"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "sorta = sortperm(aa[:,1]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Spline1D(knots=[-4.59487,-3.81821 … 27.8124,28.5835] (5648 elements), k=3, extrapolation=\"nearest\", residual=0.0)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spl = Spline1D(aa[:,1][sorta], aa[:,2][sorta])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-4.594870920890465:0.1:28.505129079109537"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "minimum(aa[:,1]):0.1:maximum(aa[:,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "┌ Info: Precompiling ImageMagick [6218d12a-5da1-5696-b52f-db25d2ecc6d1]\n",
      "└ @ Base loading.jl:1187\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
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\">"
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\">"
      ],
      "text/plain": [
       "Scene(Events(434: \"window_area\" = GeometryTypes.HyperRectangle{2,Int64}([0, 0], [1, 1]) GeometryTypes.HyperRectangle{2,Int64} , 435: \"window_dpi\" = 100.0 Float64 , 436: \"window_open\" = false Bool , 437: \"mousebuttons\" = Set(AbstractPlotting.Mouse.Button[]) Set{AbstractPlotting.Mouse.Button} , 438: \"mouseposition\" = (0.0, 0.0) Tuple{Float64,Float64} , 439: \"mousedrag\" = notpressed AbstractPlotting.Mouse.DragEnum , 440: \"scroll\" = (0.0, 0.0) Tuple{Float64,Float64} , 441: \"keyboardbuttons\" = Set(AbstractPlotting.Keyboard.Button[]) Set{AbstractPlotting.Keyboard.Button} , 442: \"unicode_input\" = Char[] Array{Char,1} , 443: \"dropped_files\" = String[] Array{String,1} , 444: \"hasfocus\" = false Bool , 445: \"entered_window\" = false Bool ), 446: \"foldp(window_area)-2\" = GeometryTypes.HyperRectangle{2,Int64}([0, 0], [960, 540]) GeometryTypes.HyperRectangle{2,Int64} , Camera(447: \"input-80\" = Float32[1.0 0.0 0.0 -9.84257; 0.0 1.0 0.0 1.79621; 0.0 0.0 1.0 0.0; 0.0 0.0 0.0 1.0] StaticArrays.SArray{Tuple{4,4},Float32,2,16} , 448: \"input-81\" = Float32[0.0444631 0.0 0.0 -0.0; 0.0 0.0790456 0.0 -0.0; 0.0 0.0 -0.0001 -0.0; 0.0 0.0 0.0 1.0] StaticArrays.SArray{Tuple{4,4},Float32,2,16} , 449: \"input-82\" = Float32[0.0444631 0.0 0.0 -0.437631; 0.0 0.0790456 0.0 0.141983; 0.0 0.0 -0.0001 0.0; 0.0 0.0 0.0 1.0] StaticArrays.SArray{Tuple{4,4},Float32,2,16} , 450: \"map(foldp(window_area)-2)\" = Float32[960.0, 540.0] Vec{2,Float32} , 451: \"input-83\" = Float32[1.0, 1.0, 1.0] Vec{3,Float32} , Reactive.Signal[710: \"map(scroll)-2\" = nothing Nothing , 714: \"map(input-103, input-104, input-105, mousedrag)\" = nothing Nothing , 715: \"map(foldp(window_area)-2)-2\" = nothing Nothing , 780: \"map(mousedrag, map(selectionbutton)-2)\" = GeometryTypes.HyperRectangle{2,Float32}(Float32[0.0, 0.0], Float32[0.0, 0.0]) GeometryTypes.HyperRectangle{2,Float32} ]), Base.RefValue{Any}(Camera2D(705: \"node\" = GeometryTypes.HyperRectangle{2,Float32}(Float32[-12.648, -14.4471], Float32[44.9811, 25.3019]) GeometryTypes.HyperRectangle{2,Float32} , 706: \"node\" = 0.1 Float32 , 707: \"node\" = nothing Union{Nothing, Button, Button} , 708: \"node\" = right Union{Nothing, Button, Button} , 709: \"node\" = 0.001 Float32 )), 452: \"scene_limits\" = GeometryTypes.HyperRectangle{3,Float32}(Float32[-7.91187, -0.847116, 0.0], Float32[39.804, 1.69468, 0.0]) GeometryTypes.HyperRectangle{3,Float32} , Transformation(456: \"translation\" = Float32[0.0, 0.0, 0.0] Vec{3,Float32} , 455: \"map(flip, scale)-9\" = Float32[1.0, 13.2118, 1.0] Vec{3,Float32} , 457: \"rotation\" = 1.0 + 0.0im + 0.0jm + 0.0km Quaternion{Float32} , 459: \"map(map(flip, scale)-9, translation, rotation, align)\" = Float32[1.0 0.0 0.0 0.0; 0.0 13.2118 0.0 0.0; 0.0 0.0 1.0 0.0; 0.0 0.0 0.0 1.0] StaticArrays.SArray{Tuple{4,4},Float32,2,16} , 453: \"flip\" = (false, false, false) Tuple{Bool,Bool,Bool} , 458: \"align\" = Float32[0.0, 0.0] Vec{2,Float32} , 460: \"node\" = identity Any ), AbstractPlot[Axis2D{...}(Scene(#= circular reference @-3 =#), Transformation(562: \"translation\" = Float32[0.0, 0.0, 0.0] Vec{3,Float32} , 561: \"map(flip, scale)-11\" = Float32[1.0, 1.0, 1.0] Vec{3,Float32} , 563: \"rotation\" = 1.0 + 0.0im + 0.0jm + 0.0km Quaternion{Float32} , 565: \"map(map(flip, scale)-11, translation, rotation, align, map(map(flip, scale)-9, translation, rotation, align))\" = Float32[1.0 0.0 0.0 0.0; 0.0 13.2118 0.0 0.0; 0.0 0.0 1.0 0.0; 0.0 0.0 0.0 1.0] StaticArrays.SArray{Tuple{4,4},Float32,2,16} , 559: \"flip\" = (false, false, false) Tuple{Bool,Bool,Bool} , 564: \"align\" = Float32[0.0, 0.0] Vec{2,Float32} , 567: \"node\" = identity Any ), Attributes(Dict{Symbol,Reactive.Signal}(:grid=>555: \"grid\" = Attributes(Dict{Symbol,Reactive.Signal}(:linestyle=>535: \"linestyle\" = (nothing, nothing) Any ,:linewidth=>533: \"linewidth\" = (0.5, 0.5) Any ,:linecolor=>534: \"linecolor\" = ((:black, 0.3), (:black, 0.3)) Any )) Any ,:ticks=>556: \"ticks\" = Attributes(Dict{Symbol,Reactive.Signal}(:ranges=>569: \"map(arg1, input-89)\" = ([0.0, 10.0, 20.0, 30.0], [-0.6, -0.3, 0.0, 0.3, 0.6]) Tuple{Array{Float64,1},Array{Float64,1}} ,:linecolor=>526: \"linecolor\" = ((:black, 0.4), (:black, 0.4)) Any ,:linestyle=>527: \"linestyle\" = (nothing, nothing) Any ,:labels=>570: \"map(map(arg1, input-89), formatter)\" = ([\"0\", \"10\", \"20\", \"30\"], [\"-0.6\", \"-0.3\", \"0.0\", \"0.3\", \"0.6\"]) Tuple{Array{String,1},Array{String,1}} ,:font=>532: \"font\" = (\"Dejavu Sans\", \"Dejavu Sans\") Any ,:formatter=>522: \"formatter\" = AbstractPlotting.Formatters.plain Any ,:align=>531: \"align\" = ((:center, :top), (:right, :center)) Any ,:textsize=>529: \"textsize\" = (5, 5) Any ,:rotation=>530: \"rotation\" = (0.0, 0.0) Any ,:textcolor=>528: \"textcolor\" = (RGBA{Float32}(0.0f0,0.0f0,0.0f0,0.4f0), RGBA{Float32}(0.0f0,0.0f0,0.0f0,0.4f0)) Any ,:gap=>523: \"gap\" = 3 Any ,:title_gap=>524: \"title_gap\" = 3 Any ,:linewidth=>525: \"linewidth\" = (1, 1) Any )) Any ,:frame=>557: \"frame\" = Attributes(Dict{Symbol,Reactive.Signal}(:axis_position=>539: \"axis_position\" = origin Any ,:linestyle=>538: \"linestyle\" = nothing Any ,:linewidth=>536: \"linewidth\" = 1.0 Any ,:arrow_size=>541: \"arrow_size\" = 2.5 Any ,:axis_arrow=>540: \"axis_arrow\" = false Any ,:linecolor=>537: \"linecolor\" = black Any ,:frames=>542: \"frames\" = ((false, false), (false, false)) Any )) Any ,:names=>558: \"names\" = Attributes(Dict{Symbol,Reactive.Signal}(:axisnames=>544: \"axisnames\" = (\"X Axis\", \"Y Axis\") Any ,:rotation=>547: \"rotation\" = (0.0, -4.71238898038469) Any ,:font=>549: \"font\" = (\"Dejavu Sans\", \"Dejavu Sans\") Any ,:textcolor=>545: \"textcolor\" = (:black, :black) Any ,:align=>548: \"align\" = ((:center, :top), (:center, :bottom)) Any ,:textsize=>546: \"textsize\" = (6, 6) Any )) Any ,:model=>565: \"map(map(flip, scale)-11, translation, rotation, align, map(map(flip, scale)-9, translation, rotation, align))\" = Float32[1.0 0.0 0.0 0.0; 0.0 13.2118 0.0 0.0; 0.0 0.0 1.0 0.0; 0.0 0.0 0.0 1.0] StaticArrays.SArray{Tuple{4,4},Float32,2,16} )), (516: \"input 1\" = GeometryTypes.HyperRectangle{3,Float32}(Float32[-7.91187, -0.847116, 0.0], Float32[39.804, 1.69468, 0.0]) GeometryTypes.HyperRectangle{3,Float32} ,), (518: \"arg1\" = ((-7.911871f0, 31.892126f0), (-0.84711635f0, 0.8475628f0)) Tuple{Tuple{Float32,Float32},Tuple{Float32,Float32}} ,), AbstractPlot[Annotations{...}(Axis2D{...}(#= circular reference @-3 =#), Transformation(606: \"translation\" = Float32[0.0, 0.0, 0.0] Vec{3,Float32} , 605: \"map(flip, scale)-12\" = Float32[1.0, 1.0, 1.0] Vec{3,Float32} , 607: \"rotation\" = 1.0 + 0.0im + 0.0jm + 0.0km Quaternion{Float32} , 609: \"map(map(flip, scale)-12, translation, rotation, align, map(map(flip, scale)-11, translation, rotation, align, map(map(flip, scale)-9, translation, rotation, align)))\" = Float32[1.0 0.0 0.0 0.0; 0.0 13.2118 0.0 0.0; 0.0 0.0 1.0 0.0; 0.0 0.0 0.0 1.0] StaticArrays.SArray{Tuple{4,4},Float32,2,16} , 603: \"flip\" = (false, false, false) Tuple{Bool,Bool,Bool} , 608: \"align\" = Float32[0.0, 0.0] Vec{2,Float32} , 611: \"node\" = identity Any ), Attributes(Dict{Symbol,Reactive.Signal}(:alpha=>595: \"map(alpha)-7\" = 1.0 Any ,:strokecolor=>597: \"map(strokecolor)-3\" = (:black, 0.0) Any ,:position=>598: \"map(position)-2\" = Float32[0.0, 0.0] Any ,:visible=>599: \"map(visible)-6\" = true Any ,:font=>574: \"input-93\" = Array{Ptr{FreeType.FT_FaceRec},1}[[Ptr{FT_FaceRec} @0x0000000039c41680], [Ptr{FT_FaceRec} @0x0000000039c41680], [Ptr{FT_FaceRec} @0x0000000039c41680], [Ptr{FT_FaceRec} @0x0000000039c41680], [Ptr{FT_FaceRec} @0x0000000039c41680], [Ptr{FT_FaceRec} @0x0000000039c41680], [Ptr{FT_FaceRec} @0x0000000039c41680], [Ptr{FT_FaceRec} @0x0000000039c41680], [Ptr{FT_FaceRec} @0x0000000039c41680], [Ptr{FT_FaceRec} @0x0000000039c41680], [Ptr{FT_FaceRec} @0x0000000039c41680]] Array{Array{Ptr{FreeType.FT_FaceRec},1},1} ,:align=>575: \"input-94\" = Vec{2,Float32}[[0.5, 1.0], [0.5, 1.0], [0.5, 1.0], [0.5, 1.0], [1.0, 0.5], [1.0, 0.5], [1.0, 0.5], [1.0, 0.5], [1.0, 0.5], [0.5, 1.0], [0.5, 0.0]] Array{Vec{2,Float32},1} ,:textsize=>573: \"input-92\" = Float32[1.03747, 1.03747, 1.03747, 1.03747, 1.03747, 1.03747, 1.03747, 1.03747, 1.03747, 1.24496, 1.24496] Array{Float32,1} ,:strokewidth=>594: \"map(strokewidth)-3\" = 0 Any ,:light=>596: \"map(light)-7\" = Vec{3,Float32}[[1.0, 1.0, 1.0], [0.1, 0.1, 0.1], [0.9, 0.9, 0.9], [20.0, 20.0, 20.0]] Any ,:rotation=>571: \"input-90\" = Quaternion{Float32}[1.0 + 0.0im + 0.0jm + 0.0km, 1.0 + 0.0im + 0.0jm + 0.0km, 1.0 + 0.0im + 0.0jm + 0.0km, 1.0 + 0.0im + 0.0jm + 0.0km, 1.0 + 0.0im + 0.0jm + 0.0km, 1.0 + 0.0im + 0.0jm + 0.0km, 1.0 + 0.0im + 0.0jm + 0.0km, 1.0 + 0.0im + 0.0jm + 0.0km, 1.0 + 0.0im + 0.0jm + 0.0km, 1.0 + 0.0im + 0.0jm + 0.0km, -0.70710677 + -0.0im + -0.0jm - 0.70710677km] Array{Quaternion{Float32},1} …)), (577: \"input 1\" = [\"0\", \"10\", \"20\", \"30\", \"-0.6\", \"-0.3\", \"0.0\", \"0.3\", \"0.6\", \"X Axis\", \"Y Axis\"] Array{String,1} , 578: \"input 2\" = Point{2,Float32}[[0.0, -0.894232], [10.0, -0.894232], [20.0, -0.894232], [30.0, -0.894232], [-8.53435, -0.6], [-8.53435, -0.3], [-8.53435, 0.0], [-8.53435, 0.3], [-8.53435, 0.6], [11.9901, -1.00417], [-11.4102, 0.000223219]] Array{Point{2,Float32},1} ), (580: \"text\" = [\"0\", \"10\", \"20\", \"30\", \"-0.6\", \"-0.3\", \"0.0\", \"0.3\", \"0.6\", \"X Axis\", \"Y Axis\"] Array{String,1} , 581: \"position\" = Point{2,Float32}[[0.0, -0.894232], [10.0, -0.894232], [20.0, -0.894232], [30.0, -0.894232], [-8.53435, -0.6], [-8.53435, -0.3], [-8.53435, 0.0], [-8.53435, 0.3], [-8.53435, 0.6], [11.9901, -1.00417], [-11.4102, 0.000223219]] Array{Point{2,Float32},1} ), AbstractPlot[Text{...}(Annotations{...}(#= circular reference @-3 =#), Transformation(647: \"translation\" = Float32[0.0, 0.0, 0.0] Vec{3,Float32} , 646: \"map(flip, scale)-13\" = Float32[1.0, 1.0, 1.0] Vec{3,Float32} , 648: \"rotation\" = 1.0 + 0.0im + 0.0jm + 0.0km Quaternion{Float32} , 650: \"map(map(flip, scale)-13, translation, rotation, align, map(map(flip, scale)-12, translation, rotation, align, map(map(flip, scale)-11, translation, rotation, align, map(map(flip, scale)-9, translation, rotation, align))))\" = Float32[1.0 0.0 0.0 0.0; 0.0 13.2118 0.0 0.0; 0.0 0.0 1.0 0.0; 0.0 0.0 0.0 1.0] StaticArrays.SArray{Tuple{4,4},Float32,2,16} , 644: \"flip\" = (false, false, false) Tuple{Bool,Bool,Bool} , 649: \"align\" = Float32[0.0, 0.0] Vec{2,Float32} , 652: \"node\" = identity Any ), Attributes(Dict{Symbol,Reactive.Signal}(:alpha=>638: \"map(alpha)-8\" = 1.0 Any ,:strokecolor=>640: \"map(strokecolor)-4\" = (:black, 0.0) Any ,:position=>613: \"map(map(map(map(flip, scale)-12, translation, rotation, align, map(map(flip, scale)-11, translation, rotation, align, map(map(flip, scale)-9, translation, rotation, align))), input-93, text, position, input-91, input-92, input-94, input-90))\" = Point{2,Float32}[[-0.346514, -12.6112], [9.29245, -12.6112], [10.0145, -12.6112], [19.2924, -12.6112], [20.0145, -12.6112], [29.2924, -12.6112], [30.0145, -12.6112], [-10.7753, -8.32546], [-10.3437, -8.32546], [-9.62162, -8.32546], [-9.22738, -8.32546], [-10.7753, -4.36192], [-10.3437, -4.36192], [-9.62162, -4.36192], [-9.22738, -4.36192], [-10.3437, -0.398387], [-9.62162, -0.398387], [-9.22738, -0.398387], [-10.3437, 3.56515], [-9.62162, 3.56515], [-9.22738, 3.56515], [-10.3437, 7.52869], [-9.62162, 7.52869], [-9.22738, 7.52869], [9.78157, -14.223], [10.7427, -14.223], [11.1759, -14.223], [12.137, -14.223], [12.9388, -14.223], [13.3671, -14.223], [-11.4102, -2.17573], [-11.4102, -1.27438], [-11.4102, -0.841134], [-11.4102, 0.119975], [-11.4102, 0.92173], [-11.4102, 1.35]] Array{Point{2,Float32},1} ,:visible=>641: \"map(visible)-7\" = true Any ,:font=>616: \"map(map(map(map(flip, scale)-12, translation, rotation, align, map(map(flip, scale)-11, translation, rotation, align, map(map(flip, scale)-9, translation, rotation, align))), input-93, text, position, input-91, input-92, input-94, input-90))-4\" = Array{Ptr{FreeType.FT_FaceRec},1}[[Ptr{FT_FaceRec} @0x0000000039c41680], [Ptr{FT_FaceRec} @0x0000000039c41680], [Ptr{FT_FaceRec} @0x0000000039c41680], [Ptr{FT_FaceRec} @0x0000000039c41680], [Ptr{FT_FaceRec} @0x0000000039c41680], [Ptr{FT_FaceRec} @0x0000000039c41680], [Ptr{FT_FaceRec} @0x0000000039c41680], [Ptr{FT_FaceRec} @0x0000000039c41680], [Ptr{FT_FaceRec} @0x0000000039c41680], [Ptr{FT_FaceRec} @0x0000000039c41680], [Ptr{FT_FaceRec} @0x0000000039c41680], [Ptr{FT_FaceRec} @0x0000000039c41680], [Ptr{FT_FaceRec} @0x0000000039c41680], [Ptr{FT_FaceRec} @0x0000000039c41680], [Ptr{FT_FaceRec} @0x0000000039c41680], [Ptr{FT_FaceRec} @0x0000000039c41680], [Ptr{FT_FaceRec} @0x0000000039c41680], [Ptr{FT_FaceRec} @0x0000000039c41680], [Ptr{FT_FaceRec} @0x0000000039c41680], [Ptr{FT_FaceRec} @0x0000000039c41680], [Ptr{FT_FaceRec} @0x0000000039c41680], [Ptr{FT_FaceRec} @0x0000000039c41680], [Ptr{FT_FaceRec} @0x0000000039c41680], [Ptr{FT_FaceRec} @0x0000000039c41680], [Ptr{FT_FaceRec} @0x0000000039c41680], [Ptr{FT_FaceRec} @0x0000000039c41680], [Ptr{FT_FaceRec} @0x0000000039c41680], [Ptr{FT_FaceRec} @0x0000000039c41680], [Ptr{FT_FaceRec} @0x0000000039c41680], [Ptr{FT_FaceRec} @0x0000000039c41680], [Ptr{FT_FaceRec} @0x0000000039c41680], [Ptr{FT_FaceRec} @0x0000000039c41680], [Ptr{FT_FaceRec} @0x0000000039c41680], [Ptr{FT_FaceRec} @0x0000000039c41680], [Ptr{FT_FaceRec} @0x0000000039c41680], [Ptr{FT_FaceRec} @0x0000000039c41680]] Array{Array{Ptr{FreeType.FT_FaceRec},1},1} ,:align=>618: \"align\" = Float32[0.0, 0.0] Any ,:textsize=>615: \"map(map(map(map(flip, scale)-12, translation, rotation, align, map(map(flip, scale)-11, translation, rotation, align, map(map(flip, scale)-9, translation, rotation, align))), input-93, text, position, input-91, input-92, input-94, input-90))-3\" = Vec{2,Float32}[[0.693028, 0.829974], [0.601731, 0.796775], [0.693028, 0.829974], [0.705477, 0.813374], [0.693028, 0.829974], [0.705477, 0.829974], [0.693028, 0.829974], [0.390088, 0.190894], [0.693028, 0.829974], [0.265592, 0.236542], [0.680578, 0.829974], [0.390088, 0.190894], [0.693028, 0.829974], [0.265592, 0.236542], [0.705477, 0.829974], [0.693028, 0.829974], [0.265592, 0.236542], [0.693028, 0.829974], [0.693028, 0.829974], [0.265592, 0.236542], [0.705477, 0.829974], [0.693028, 0.829974], [0.265592, 0.236542], [0.680578, 0.829974], [1.12544, 0.95613], [0.0497984, 0.0497984], [0.995968, 0.95613], [0.94119, 0.732037], [0.458145, 0.995968], [0.741996, 0.766896], [0.986009, 0.95613], [0.0497984, 0.0497984], [0.995968, 0.95613], [0.94119, 0.732037], [0.458145, 0.995968], [0.741996, 0.766896]] Array{Vec{2,Float32},1} ,:strokewidth=>637: \"map(strokewidth)-4\" = 0 Any ,:light=>639: \"map(light)-8\" = Vec{3,Float32}[[1.0, 1.0, 1.0], [0.1, 0.1, 0.1], [0.9, 0.9, 0.9], [20.0, 20.0, 20.0]] Any ,:rotation=>617: \"map(map(map(map(flip, scale)-12, translation, rotation, align, map(map(flip, scale)-11, translation, rotation, align, map(map(flip, scale)-9, translation, rotation, align))), input-93, text, position, input-91, input-92, input-94, input-90))-5\" = Quaternion{Float32}[1.0 + 0.0im + 0.0jm + 0.0km, 1.0 + 0.0im + 0.0jm + 0.0km, 1.0 + 0.0im + 0.0jm + 0.0km, 1.0 + 0.0im + 0.0jm + 0.0km, 1.0 + 0.0im + 0.0jm + 0.0km, 1.0 + 0.0im + 0.0jm + 0.0km, 1.0 + 0.0im + 0.0jm + 0.0km, 1.0 + 0.0im + 0.0jm + 0.0km, 1.0 + 0.0im + 0.0jm + 0.0km, 1.0 + 0.0im + 0.0jm + 0.0km, 1.0 + 0.0im + 0.0jm + 0.0km, 1.0 + 0.0im + 0.0jm + 0.0km, 1.0 + 0.0im + 0.0jm + 0.0km, 1.0 + 0.0im + 0.0jm + 0.0km, 1.0 + 0.0im + 0.0jm + 0.0km, 1.0 + 0.0im + 0.0jm + 0.0km, 1.0 + 0.0im + 0.0jm + 0.0km, 1.0 + 0.0im + 0.0jm + 0.0km, 1.0 + 0.0im + 0.0jm + 0.0km, 1.0 + 0.0im + 0.0jm + 0.0km, 1.0 + 0.0im + 0.0jm + 0.0km, 1.0 + 0.0im + 0.0jm + 0.0km, 1.0 + 0.0im + 0.0jm + 0.0km, 1.0 + 0.0im + 0.0jm + 0.0km, 1.0 + 0.0im + 0.0jm + 0.0km, 1.0 + 0.0im + 0.0jm + 0.0km, 1.0 + 0.0im + 0.0jm + 0.0km, 1.0 + 0.0im + 0.0jm + 0.0km, 1.0 + 0.0im + 0.0jm + 0.0km, 1.0 + 0.0im + 0.0jm + 0.0km, -0.70710677 + -0.0im + -0.0jm - 0.70710677km, -0.70710677 + -0.0im + -0.0jm - 0.70710677km, -0.70710677 + -0.0im + -0.0jm - 0.70710677km, -0.70710677 + -0.0im + -0.0jm - 0.70710677km, -0.70710677 + -0.0im + -0.0jm - 0.70710677km, -0.70710677 + -0.0im + -0.0jm - 0.70710677km] Array{Quaternion{Float32},1} …)), (622: \"input 1\" = 0102030-0.6-0.30.00.30.6X AxisY Axis String ,), (624: \"arg1\" = 0102030-0.6-0.30.00.30.6X AxisY Axis String ,))]), LineSegments{...}(Axis2D{...}(#= circular reference @-3 =#), Transformation(681: \"translation\" = Float32[0.0, 0.0, 0.0] Vec{3,Float32} , 680: \"map(flip, scale)-14\" = Float32[1.0, 1.0, 1.0] Vec{3,Float32} , 682: \"rotation\" = 1.0 + 0.0im + 0.0jm + 0.0km Quaternion{Float32} , 684: \"map(map(flip, scale)-14, translation, rotation, align, map(map(flip, scale)-11, translation, rotation, align, map(map(flip, scale)-9, translation, rotation, align)))\" = Float32[1.0 0.0 0.0 0.0; 0.0 13.2118 0.0 0.0; 0.0 0.0 1.0 0.0; 0.0 0.0 0.0 1.0] StaticArrays.SArray{Tuple{4,4},Float32,2,16} , 678: \"flip\" = (false, false, false) Tuple{Bool,Bool,Bool} , 683: \"align\" = Float32[0.0, 0.0] Vec{2,Float32} , 686: \"node\" = identity Any ), Attributes(Dict{Symbol,Reactive.Signal}(:alpha=>669: \"map(alpha)-9\" = 1.0 Any ,:colormap=>670: \"map(colormap)-5\" = viridis Any ,:visible=>671: \"map(visible)-8\" = true Any ,:linestyle=>672: \"map(linestyle)-4\" = nothing Any ,:light=>673: \"map(light)-9\" = Vec{3,Float32}[[1.0, 1.0, 1.0], [0.1, 0.1, 0.1], [0.9, 0.9, 0.9], [20.0, 20.0, 20.0]] Any ,:transformation=>674: \"map(transformation)-9\" = AbstractPlotting.Automatic() Any ,:model=>684: \"map(map(flip, scale)-14, translation, rotation, align, map(map(flip, scale)-11, translation, rotation, align, map(map(flip, scale)-9, translation, rotation, align)))\" = Float32[1.0 0.0 0.0 0.0; 0.0 13.2118 0.0 0.0; 0.0 0.0 1.0 0.0; 0.0 0.0 0.0 1.0] StaticArrays.SArray{Tuple{4,4},Float32,2,16} ,:color=>653: \"input-98\" = ColorTypes.RGBA{Float32}[RGBA{Float32}(0.0,0.0,0.0,0.3), RGBA{Float32}(0.0,0.0,0.0,0.3), RGBA{Float32}(0.0,0.0,0.0,0.3), RGBA{Float32}(0.0,0.0,0.0,0.3), RGBA{Float32}(0.0,0.0,0.0,0.3), RGBA{Float32}(0.0,0.0,0.0,0.3), RGBA{Float32}(0.0,0.0,0.0,0.3), RGBA{Float32}(0.0,0.0,0.0,0.3), RGBA{Float32}(0.0,0.0,0.0,0.3), RGBA{Float32}(0.0,0.0,0.0,0.3), RGBA{Float32}(0.0,0.0,0.0,0.3), RGBA{Float32}(0.0,0.0,0.0,0.3), RGBA{Float32}(0.0,0.0,0.0,0.3), RGBA{Float32}(0.0,0.0,0.0,0.3), RGBA{Float32}(0.0,0.0,0.0,0.3), RGBA{Float32}(0.0,0.0,0.0,0.3), RGBA{Float32}(0.0,0.0,0.0,0.3), RGBA{Float32}(0.0,0.0,0.0,0.3), RGBA{Float32}(0.0,0.0,0.0,1.0), RGBA{Float32}(0.0,0.0,0.0,1.0), RGBA{Float32}(0.0,0.0,0.0,1.0), RGBA{Float32}(0.0,0.0,0.0,1.0), RGBA{Float32}(0.0,0.0,0.0,1.0), RGBA{Float32}(0.0,0.0,0.0,1.0), RGBA{Float32}(0.0,0.0,0.0,1.0), RGBA{Float32}(0.0,0.0,0.0,1.0), RGBA{Float32}(0.0,0.0,0.0,1.0), RGBA{Float32}(0.0,0.0,0.0,1.0), RGBA{Float32}(0.0,0.0,0.0,1.0), RGBA{Float32}(0.0,0.0,0.0,1.0), RGBA{Float32}(0.0,0.0,0.0,1.0), RGBA{Float32}(0.0,0.0,0.0,1.0), RGBA{Float32}(0.0,0.0,0.0,1.0), RGBA{Float32}(0.0,0.0,0.0,1.0), RGBA{Float32}(0.0,0.0,0.0,1.0), RGBA{Float32}(0.0,0.0,0.0,1.0), RGBA{Float32}(0.0,0.0,0.0,1.0), RGBA{Float32}(0.0,0.0,0.0,1.0)] Array{ColorTypes.RGBA{Float32},1} ,:linewidth=>654: \"input-99\" = Float32[0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0] Array{Float32,1} ,:fxaa=>677: \"map(fxaa)-5\" = false Any …)), (656: \"input 1\" = Point{2,Float32}[[0.0, -0.847116], [0.0, 0.847563], [10.0, -0.847116], [10.0, 0.847563], [20.0, -0.847116], [20.0, 0.847563], [30.0, -0.847116], [30.0, 0.847563], [-7.91187, -0.6], [31.8921, -0.6], [-7.91187, -0.3], [31.8921, -0.3], [-7.91187, 0.0], [31.8921, 0.0], [-7.91187, 0.3], [31.8921, 0.3], [-7.91187, 0.6], [31.8921, 0.6], [-7.91187, -0.0], [31.8921, 0.0], [-0.0, -0.847116], [0.0, 0.847563], [-7.91187, -0.847116], [-7.91187, -0.847116], [-7.91187, -0.847116], [-7.91187, -0.847116], [-7.91187, -0.847116], [31.8921, -0.847116], [-7.91187, -0.847116], [-7.91187, 0.847563], [31.8921, 0.847563], [-7.91187, 0.847563], [31.8921, 0.847563], [31.8921, -0.847116], [31.8921, 0.847563], [31.8921, 0.847563], [31.8921, 0.847563], [31.8921, 0.847563]] Array{Point{2,Float32},1} ,), (658: \"arg1\" = Point{2,Float32}[[0.0, -0.847116], [0.0, 0.847563], [10.0, -0.847116], [10.0, 0.847563], [20.0, -0.847116], [20.0, 0.847563], [30.0, -0.847116], [30.0, 0.847563], [-7.91187, -0.6], [31.8921, -0.6], [-7.91187, -0.3], [31.8921, -0.3], [-7.91187, 0.0], [31.8921, 0.0], [-7.91187, 0.3], [31.8921, 0.3], [-7.91187, 0.6], [31.8921, 0.6], [-7.91187, -0.0], [31.8921, 0.0], [-0.0, -0.847116], [0.0, 0.847563], [-7.91187, -0.847116], [-7.91187, -0.847116], [-7.91187, -0.847116], [-7.91187, -0.847116], [-7.91187, -0.847116], [31.8921, -0.847116], [-7.91187, -0.847116], [-7.91187, 0.847563], [31.8921, 0.847563], [-7.91187, 0.847563], [31.8921, 0.847563], [31.8921, -0.847116], [31.8921, 0.847563], [31.8921, 0.847563], [31.8921, 0.847563], [31.8921, 0.847563]] Array{Point{2,Float32},1} ,))]), Lines{...}(Scene(#= circular reference @-3 =#), Transformation(486: \"translation\" = Float32[0.0, 0.0, 0.0] Vec{3,Float32} , 485: \"map(flip, scale)-10\" = Float32[1.0, 1.0, 1.0] Vec{3,Float32} , 487: \"rotation\" = 1.0 + 0.0im + 0.0jm + 0.0km Quaternion{Float32} , 489: \"map(map(flip, scale)-10, translation, rotation, align, map(map(flip, scale)-9, translation, rotation, align))\" = Float32[1.0 0.0 0.0 0.0; 0.0 13.2118 0.0 0.0; 0.0 0.0 1.0 0.0; 0.0 0.0 0.0 1.0] StaticArrays.SArray{Tuple{4,4},Float32,2,16} , 483: \"flip\" = (false, false, false) Tuple{Bool,Bool,Bool} , 488: \"align\" = Float32[0.0, 0.0] Vec{2,Float32} , 490: \"node\" = identity Any ), Attributes(Dict{Symbol,Reactive.Signal}(:alpha=>474: \"map(alpha)-6\" = 1.0 Any ,:colormap=>475: \"map(colormap)-4\" = viridis Any ,:visible=>476: \"map(visible)-5\" = true Any ,:linestyle=>477: \"map(linestyle)-3\" = nothing Any ,:light=>478: \"map(light)-6\" = Vec{3,Float32}[[1.0, 1.0, 1.0], [0.1, 0.1, 0.1], [0.9, 0.9, 0.9], [20.0, 20.0, 20.0]] Any ,:transformation=>479: \"map(transformation)-6\" = AbstractPlotting.Automatic() Any ,:model=>489: \"map(map(flip, scale)-10, translation, rotation, align, map(map(flip, scale)-9, translation, rotation, align))\" = Float32[1.0 0.0 0.0 0.0; 0.0 13.2118 0.0 0.0; 0.0 0.0 1.0 0.0; 0.0 0.0 0.0 1.0] StaticArrays.SArray{Tuple{4,4},Float32,2,16} ,:color=>480: \"map(color)-2\" = black Any ,:linewidth=>481: \"map(linewidth)-4\" = 1.0 Any ,:fxaa=>482: \"map(fxaa)-4\" = false Any …)), (461: \"input 1\" = -4.594870920890465:0.01:28.575129079109537 StepRangeLen{Float64,Base.TwicePrecision{Float64},Base.TwicePrecision{Float64}} , 462: \"input 2\" = [0.706107, 0.706092, 0.706076, 0.706061, 0.706045, 0.70603, 0.706014, 0.705998, 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0.694513], [26.8351, 0.694699], [26.8451, 0.694882], [26.8551, 0.695062], [26.8651, 0.69524], [26.8751, 0.695414], [26.8851, 0.695586], [26.8951, 0.695756], [26.9051, 0.695922], [26.9151, 0.696086], [26.9251, 0.696248], [26.9351, 0.696407], [26.9451, 0.696564], [26.9551, 0.696719], [26.9651, 0.696871], [26.9751, 0.69702], [26.9851, 0.697168], [26.9951, 0.697313], [27.0051, 0.697456], [27.0151, 0.697597], [27.0251, 0.697735], [27.0351, 0.697872], [27.0451, 0.698007], [27.0551, 0.698139], [27.0651, 0.69827], [27.0751, 0.698398], [27.0851, 0.698525], [27.0951, 0.69865], [27.1051, 0.698772], [27.1151, 0.698893], [27.1251, 0.699013], [27.1351, 0.69913], [27.1451, 0.699246], [27.1551, 0.699359], [27.1651, 0.699472], [27.1751, 0.699582], [27.1851, 0.699691], [27.1951, 0.699798], [27.2051, 0.699904], [27.2151, 0.700008], [27.2251, 0.700111], [27.2351, 0.700212], [27.2451, 0.700311], [27.2551, 0.700409], [27.2651, 0.700506], [27.2751, 0.700601], [27.2851, 0.700695], [27.2951, 0.700787], [27.3051, 0.700878], [27.3151, 0.700968], [27.3251, 0.701056], [27.3351, 0.701143], [27.3451, 0.701229], [27.3551, 0.701314], [27.3651, 0.701397], [27.3751, 0.701479], [27.3851, 0.70156], [27.3951, 0.701639], [27.4051, 0.701718], [27.4151, 0.701795], [27.4251, 0.701872], [27.4351, 0.701947], [27.4451, 0.702021], [27.4551, 0.702093], [27.4651, 0.702165], [27.4751, 0.702236], [27.4851, 0.702306], [27.4951, 0.702375], [27.5051, 0.702442], [27.5151, 0.702509], [27.5251, 0.702575], [27.5351, 0.70264], [27.5451, 0.702704], [27.5551, 0.702766], [27.5651, 0.702828], [27.5751, 0.70289], [27.5851, 0.70295], [27.5951, 0.703009], [27.6051, 0.703068], [27.6151, 0.703125], [27.6251, 0.703182], [27.6351, 0.703238], [27.6451, 0.703293], [27.6551, 0.703348], [27.6651, 0.703401], [27.6751, 0.703454], [27.6851, 0.703506], [27.6951, 0.703557], [27.7051, 0.703608], [27.7151, 0.703658], [27.7251, 0.703707], [27.7351, 0.703755], [27.7451, 0.703803], [27.7551, 0.70385], [27.7651, 0.703896], [27.7751, 0.703942], [27.7851, 0.703987], [27.7951, 0.704031], [27.8051, 0.704075], [27.8151, 0.704118], [27.8251, 0.704161], [27.8351, 0.704203], [27.8451, 0.704244], [27.8551, 0.704285], [27.8651, 0.704325], [27.8751, 0.704365], [27.8851, 0.704404], [27.8951, 0.704442], [27.9051, 0.70448], [27.9151, 0.704517], [27.9251, 0.704554], [27.9351, 0.70459], [27.9451, 0.704626], [27.9551, 0.704661], [27.9651, 0.704696], [27.9751, 0.70473], [27.9851, 0.704764], [27.9951, 0.704797], [28.0051, 0.70483], [28.0151, 0.704862], [28.0251, 0.704894], [28.0351, 0.704925], [28.0451, 0.704956], [28.0551, 0.704987], [28.0651, 0.705016], [28.0751, 0.705046], [28.0851, 0.705075], [28.0951, 0.705104], [28.1051, 0.705132], [28.1151, 0.705159], [28.1251, 0.705187], [28.1351, 0.705214], [28.1451, 0.70524], [28.1551, 0.705266], [28.1651, 0.705292], [28.1751, 0.705317], [28.1851, 0.705343], [28.1951, 0.705367], [28.2051, 0.705391], [28.2151, 0.705415], [28.2251, 0.705439], [28.2351, 0.705462], [28.2451, 0.705485], [28.2551, 0.705508], [28.2651, 0.70553], [28.2751, 0.705552], [28.2851, 0.705573], [28.2951, 0.705595], [28.3051, 0.705616], [28.3151, 0.705636], [28.3251, 0.705657], [28.3351, 0.705677], [28.3451, 0.705697], [28.3551, 0.705716], [28.3651, 0.705736], [28.3751, 0.705755], [28.3851, 0.705774], [28.3951, 0.705792], [28.4051, 0.705811], [28.4151, 0.705829], [28.4251, 0.705847], [28.4351, 0.705865], [28.4451, 0.705882], [28.4551, 0.705899], [28.4651, 0.705917], [28.4751, 0.705933], [28.4851, 0.70595], [28.4951, 0.705967], [28.5051, 0.705983], [28.5151, 0.705999], [28.5251, 0.706016], [28.5351, 0.706032], [28.5451, 0.706047], [28.5551, 0.706063], [28.5651, 0.706079], [28.5751, 0.706094]] Array{Point{2,Float32},1} ,))], Attributes(Dict{Symbol,Reactive.Signal}(:colormap=>4: \"colormap\" = viridis Any ,:visible=>6: \"visible\" = true Any ,:text=>636: \"text\" = Attributes(Dict{Symbol,Reactive.Signal}(:alpha=>629: \"alpha\" = 1.0 Any ,:strokecolor=>630: \"strokecolor\" = (:black, 0.0) Any ,:position=>635: \"position\" = Float32[0.0, 0.0] Any ,:visible=>6: \"visible\" = true Any ,:font=>1: \"font\" = Dejavu Sans Any ,:align=>632: \"align\" = (:left, :bottom) Any ,:textsize=>634: \"textsize\" = 20 Any ,:strokewidth=>631: \"strokewidth\" = 0 Any ,:light=>626: \"light\" = Vec{3,Float32}[[1.0, 1.0, 1.0], [0.1, 0.1, 0.1], [0.9, 0.9, 0.9], [20.0, 20.0, 20.0]] Any ,:rotation=>633: \"rotation\" = 0.0 Any ,:transformation=>627: \"transformation\" = AbstractPlotting.Automatic() Any ,:model=>628: \"model\" = AbstractPlotting.Automatic() Any ,:color=>3: \"color\" = black Any ,:linewidth=>625: \"linewidth\" = 1 Any )) Any ,:axis2d=>554: \"axis2d\" = Attributes(Dict{Symbol,Reactive.Signal}(:grid=>551: \"grid\" = Attributes(Dict{Symbol,Reactive.Signal}(:linestyle=>535: \"linestyle\" = (nothing, nothing) Any ,:linewidth=>533: \"linewidth\" = (0.5, 0.5) Any ,:linecolor=>534: \"linecolor\" = ((:black, 0.3), (:black, 0.3)) Any )) Any ,:ticks=>550: \"ticks\" = Attributes(Dict{Symbol,Reactive.Signal}(:ranges=>569: \"map(arg1, input-89)\" = ([0.0, 10.0, 20.0, 30.0], [-0.6, -0.3, 0.0, 0.3, 0.6]) Tuple{Array{Float64,1},Array{Float64,1}} ,:linecolor=>526: \"linecolor\" = ((:black, 0.4), (:black, 0.4)) Any ,:linestyle=>527: \"linestyle\" = (nothing, nothing) Any ,:labels=>570: \"map(map(arg1, input-89), formatter)\" = ([\"0\", \"10\", \"20\", \"30\"], [\"-0.6\", \"-0.3\", \"0.0\", \"0.3\", \"0.6\"]) Tuple{Array{String,1},Array{String,1}} ,:font=>532: \"font\" = (\"Dejavu Sans\", \"Dejavu Sans\") Any ,:formatter=>522: \"formatter\" = AbstractPlotting.Formatters.plain Any ,:align=>531: \"align\" = ((:center, :top), (:right, :center)) Any ,:textsize=>529: \"textsize\" = (5, 5) Any ,:rotation=>530: \"rotation\" = (0.0, 0.0) Any ,:textcolor=>528: \"textcolor\" = (RGBA{Float32}(0.0f0,0.0f0,0.0f0,0.4f0), RGBA{Float32}(0.0f0,0.0f0,0.0f0,0.4f0)) Any ,:gap=>523: \"gap\" = 3 Any ,:title_gap=>524: \"title_gap\" = 3 Any ,:linewidth=>525: \"linewidth\" = (1, 1) Any )) Any ,:frame=>552: \"frame\" = Attributes(Dict{Symbol,Reactive.Signal}(:axis_position=>539: \"axis_position\" = origin Any ,:linestyle=>538: \"linestyle\" = nothing Any ,:linewidth=>536: \"linewidth\" = 1.0 Any ,:arrow_size=>541: \"arrow_size\" = 2.5 Any ,:axis_arrow=>540: \"axis_arrow\" = false Any ,:linecolor=>537: \"linecolor\" = black Any ,:frames=>542: \"frames\" = ((false, false), (false, false)) Any )) Any ,:names=>553: \"names\" = Attributes(Dict{Symbol,Reactive.Signal}(:axisnames=>544: \"axisnames\" = (\"X Axis\", \"Y Axis\") Any ,:rotation=>547: \"rotation\" = (0.0, -4.71238898038469) Any ,:font=>549: \"font\" = (\"Dejavu Sans\", \"Dejavu Sans\") Any ,:textcolor=>545: \"textcolor\" = (:black, :black) Any ,:align=>548: \"align\" = ((:center, :top), (:center, :bottom)) Any ,:textsize=>546: \"textsize\" = (6, 6) Any )) Any )) Any ,:linesegments=>668: \"linesegments\" = Attributes(Dict{Symbol,Reactive.Signal}(:alpha=>663: \"alpha\" = 1.0 Any ,:colormap=>4: \"colormap\" = viridis Any ,:visible=>6: \"visible\" = true Any ,:linestyle=>666: \"linestyle\" = nothing Any ,:light=>660: \"light\" = Vec{3,Float32}[[1.0, 1.0, 1.0], [0.1, 0.1, 0.1], [0.9, 0.9, 0.9], [20.0, 20.0, 20.0]] Any ,:transformation=>661: \"transformation\" = AbstractPlotting.Automatic() Any ,:model=>662: \"model\" = AbstractPlotting.Automatic() Any ,:colorrange=>665: \"colorrange\" = AbstractPlotting.Automatic() Any ,:color=>3: \"color\" = black Any ,:linewidth=>664: \"linewidth\" = 1.0 Any ,:fxaa=>667: \"fxaa\" = false Any )) Any ,:cam2d=>698: \"cam2d\" = Attributes(Dict{Symbol,Reactive.Signal}(:padding=>697: \"padding\" = 0.001 Any ,:selectionbutton=>696: \"selectionbutton\" = (space::Button = 32, left::Button = 0) Any ,:zoomspeed=>693: \"zoomspeed\" = 0.1 Any ,:panbutton=>695: \"panbutton\" = right Any ,:area=>692: \"area\" = GeometryTypes.HyperRectangle{2,Float32}(Float32[0.0, 0.0], Float32[1.0, 1.0]) Any ,:zoombutton=>694: \"zoombutton\" = nothing Any )) Any ,:font=>1: \"font\" = Dejavu Sans Any ,:annotations=>593: \"annotations\" = Attributes(Dict{Symbol,Reactive.Signal}(:alpha=>586: \"alpha\" = 1.0 Any ,:strokecolor=>587: \"strokecolor\" = (:black, 0.0) Any ,:position=>592: \"position\" = Float32[0.0, 0.0] Any ,:visible=>6: \"visible\" = true Any ,:font=>1: \"font\" = Dejavu Sans Any ,:align=>589: \"align\" = (:left, :bottom) Any ,:textsize=>591: \"textsize\" = 20 Any ,:strokewidth=>588: \"strokewidth\" = 0 Any ,:light=>583: \"light\" = Vec{3,Float32}[[1.0, 1.0, 1.0], [0.1, 0.1, 0.1], [0.9, 0.9, 0.9], [20.0, 20.0, 20.0]] Any ,:rotation=>590: \"rotation\" = 0.0 Any ,:transformation=>584: \"transformation\" = AbstractPlotting.Automatic() Any ,:model=>585: \"model\" = AbstractPlotting.Automatic() Any ,:color=>3: \"color\" = black Any ,:linewidth=>582: \"linewidth\" = 1 Any )) Any ,:resolution=>5: \"resolution\" = (960, 540) Any ,:plot=>501: \"plot\" = Attributes(Dict{Symbol,Reactive.Signal}(:show_legend=>492: \"show_legend\" = false Any ,:camera=>497: \"camera\" = AbstractPlotting.Automatic() Any ,:padding=>499: \"padding\" = Float32[0.1, 0.1, 0.1] Any ,:legend=>496: \"legend\" = Attributes(Dict{Symbol,Reactive.Signal}()) Any ,:show_axis=>491: \"show_axis\" = true Any ,:scale_plot=>493: \"scale_plot\" = true Any ,:axis=>495: \"axis\" = Attributes(Dict{Symbol,Reactive.Signal}()) Any ,:center=>494: \"center\" = true Any ,:raw=>500: \"raw\" = false Any ,:limits=>498: \"limits\" = AbstractPlotting.Automatic() Any )) Any …)), Scene[Scene(Events(434: \"window_area\" = GeometryTypes.HyperRectangle{2,Int64}([0, 0], [1, 1]) GeometryTypes.HyperRectangle{2,Int64} , 435: \"window_dpi\" = 100.0 Float64 , 436: \"window_open\" = false Bool , 437: \"mousebuttons\" = Set(AbstractPlotting.Mouse.Button[]) Set{AbstractPlotting.Mouse.Button} , 438: \"mouseposition\" = (0.0, 0.0) Tuple{Float64,Float64} , 439: \"mousedrag\" = notpressed AbstractPlotting.Mouse.DragEnum , 440: \"scroll\" = (0.0, 0.0) Tuple{Float64,Float64} , 441: \"keyboardbuttons\" = Set(AbstractPlotting.Keyboard.Button[]) Set{AbstractPlotting.Keyboard.Button} , 442: \"unicode_input\" = Char[] Array{Char,1} , 443: \"dropped_files\" = String[] Array{String,1} , 444: \"hasfocus\" = false Bool , 445: \"entered_window\" = false Bool ), 446: \"foldp(window_area)-2\" = GeometryTypes.HyperRectangle{2,Int64}([0, 0], [960, 540]) GeometryTypes.HyperRectangle{2,Int64} , Camera(447: \"input-80\" = Float32[1.0 0.0 0.0 -9.84257; 0.0 1.0 0.0 1.79621; 0.0 0.0 1.0 0.0; 0.0 0.0 0.0 1.0] StaticArrays.SArray{Tuple{4,4},Float32,2,16} , 448: \"input-81\" = Float32[0.0444631 0.0 0.0 -0.0; 0.0 0.0790456 0.0 -0.0; 0.0 0.0 -0.0001 -0.0; 0.0 0.0 0.0 1.0] StaticArrays.SArray{Tuple{4,4},Float32,2,16} , 449: \"input-82\" = Float32[0.0444631 0.0 0.0 -0.437631; 0.0 0.0790456 0.0 0.141983; 0.0 0.0 -0.0001 0.0; 0.0 0.0 0.0 1.0] StaticArrays.SArray{Tuple{4,4},Float32,2,16} , 450: \"map(foldp(window_area)-2)\" = Float32[960.0, 540.0] Vec{2,Float32} , 451: \"input-83\" = Float32[1.0, 1.0, 1.0] Vec{3,Float32} , Reactive.Signal[710: \"map(scroll)-2\" = nothing Nothing , 714: \"map(input-103, input-104, input-105, mousedrag)\" = nothing Nothing , 715: \"map(foldp(window_area)-2)-2\" = nothing Nothing , 780: \"map(mousedrag, map(selectionbutton)-2)\" = GeometryTypes.HyperRectangle{2,Float32}(Float32[0.0, 0.0], Float32[0.0, 0.0]) GeometryTypes.HyperRectangle{2,Float32} ]), RefValue{Any}(Camera2D(705: \"node\" = GeometryTypes.HyperRectangle{2,Float32}(Float32[-12.648, -14.4471], Float32[44.9811, 25.3019]) GeometryTypes.HyperRectangle{2,Float32} , 706: \"node\" = 0.1 Float32 , 707: \"node\" = nothing Union{Nothing, Button, Button} , 708: \"node\" = right Union{Nothing, Button, Button} , 709: \"node\" = 0.001 Float32 )), 725: \"input-107\" = GeometryTypes.HyperRectangle{3,Float32}(Float32[0.0, 0.0, 0.0], Float32[1.0, 1.0, 1.0]) GeometryTypes.HyperRectangle{3,Float32} , Transformation(719: \"translation\" = Float32[0.0, 0.0, 0.0] Vec{3,Float32} , 718: \"map(flip, scale)-15\" = Float32[1.0, 1.0, 1.0] Vec{3,Float32} , 720: \"rotation\" = 1.0 + 0.0im + 0.0jm + 0.0km Quaternion{Float32} , 722: \"map(map(flip, scale)-15, translation, rotation, align)\" = Float32[1.0 0.0 0.0 0.0; 0.0 1.0 0.0 0.0; 0.0 0.0 1.0 0.0; 0.0 0.0 0.0 1.0] StaticArrays.SArray{Tuple{4,4},Float32,2,16} , 716: \"flip\" = (false, false, false) Tuple{Bool,Bool,Bool} , 721: \"align\" = Float32[0.0, 0.0] Vec{2,Float32} , 724: \"node\" = identity Any ), AbstractPlot[Lines{...}(Scene(#= circular reference @-3 =#), Transformation(754: \"translation\" = Float32[0.0, 0.0, 0.0] Vec{3,Float32} , 753: \"map(flip, scale)-16\" = Float32[1.0, 1.0, 1.0] Vec{3,Float32} , 755: \"rotation\" = 1.0 + 0.0im + 0.0jm + 0.0km Quaternion{Float32} , 757: \"map(map(flip, scale)-16, translation, rotation, align, map(map(flip, scale)-15, translation, rotation, align))\" = Float32[1.0 0.0 0.0 0.0; 0.0 1.0 0.0 0.0; 0.0 0.0 1.0 0.0; 0.0 0.0 0.0 1.0] StaticArrays.SArray{Tuple{4,4},Float32,2,16} , 751: \"flip\" = (false, false, false) Tuple{Bool,Bool,Bool} , 756: \"align\" = Float32[0.0, 0.0] Vec{2,Float32} , 759: \"node\" = identity Any ), Attributes(Dict{Symbol,Reactive.Signal}(:alpha=>744: \"map(alpha)-10\" = 1.0 Any ,:colormap=>745: \"map(colormap)-6\" = viridis Any ,:visible=>729: \"input-111\" = false Bool ,:linestyle=>726: \"input-108\" = dot Symbol ,:light=>746: \"map(light)-10\" = Vec{3,Float32}[[1.0, 1.0, 1.0], [0.1, 0.1, 0.1], [0.9, 0.9, 0.9], [20.0, 20.0, 20.0]] Any ,:transformation=>747: \"map(transformation)-10\" = AbstractPlotting.Automatic() Any ,:model=>757: \"map(map(flip, scale)-16, translation, rotation, align, map(map(flip, scale)-15, translation, rotation, align))\" = Float32[1.0 0.0 0.0 0.0; 0.0 1.0 0.0 0.0; 0.0 0.0 1.0 0.0; 0.0 0.0 0.0 1.0] StaticArrays.SArray{Tuple{4,4},Float32,2,16} ,:color=>728: \"input-110\" = (:black, 0.4) Tuple{Symbol,Float64} ,:linewidth=>727: \"input-109\" = 2.0 Float32 ,:fxaa=>750: \"map(fxaa)-6\" = false Any …)), (731: \"input 1\" = GeometryTypes.HyperRectangle{2,Float32}(Float32[0.0, 0.0], Float32[0.0, 0.0]) GeometryTypes.HyperRectangle{2,Float32} ,), (733: \"arg1\" = Point{2,Float32}[[0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0]] Array{Point{2,Float32},1} ,))], Attributes(Dict{Symbol,Reactive.Signal}(:color=>3: \"color\" = black Any ,:font=>1: \"font\" = Dejavu Sans Any ,:backgroundcolor=>2: \"backgroundcolor\" = RGBA{Float32}(1.0f0,1.0f0,1.0f0,1.0f0) Any ,:colormap=>4: \"colormap\" = viridis Any ,:resolution=>5: \"resolution\" = (960, 540) Any ,:visible=>6: \"visible\" = true Any ,:plot=>770: \"plot\" = Attributes(Dict{Symbol,Reactive.Signal}(:show_legend=>761: \"show_legend\" = false Any ,:camera=>766: \"camera\" = AbstractPlotting.Automatic() Any ,:padding=>768: \"padding\" = Float32[0.1, 0.1, 0.1] Any ,:legend=>765: \"legend\" = Attributes(Dict{Symbol,Reactive.Signal}()) Any ,:show_axis=>760: \"show_axis\" = true Any ,:scale_plot=>762: \"scale_plot\" = true Any ,:axis=>764: \"axis\" = Attributes(Dict{Symbol,Reactive.Signal}()) Any ,:center=>763: \"center\" = true Any ,:raw=>769: \"raw\" = false Any ,:limits=>767: \"limits\" = AbstractPlotting.Automatic() Any )) Any ,:lines=>743: \"lines\" = Attributes(Dict{Symbol,Reactive.Signal}(:alpha=>738: \"alpha\" = 1.0 Any ,:colormap=>4: \"colormap\" = viridis Any ,:visible=>6: \"visible\" = true Any ,:linestyle=>741: \"linestyle\" = nothing Any ,:light=>735: \"light\" = Vec{3,Float32}[[1.0, 1.0, 1.0], [0.1, 0.1, 0.1], [0.9, 0.9, 0.9], [20.0, 20.0, 20.0]] Any ,:transformation=>736: \"transformation\" = AbstractPlotting.Automatic() Any ,:model=>737: \"model\" = AbstractPlotting.Automatic() Any ,:colorrange=>740: \"colorrange\" = AbstractPlotting.Automatic() Any ,:color=>3: \"color\" = black Any ,:linewidth=>739: \"linewidth\" = 1.0 Any ,:fxaa=>742: \"fxaa\" = false Any )) Any )), Scene[], AbstractScreen[])], AbstractScreen[])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lines(minimum(aa[:,1]):0.01:maximum(aa[:,1]), spl(minimum(aa[:,1]):0.01:maximum(aa[:,1])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "scene = Scene(resolution = (1000, 1000))\n",
    "\n",
    "ff(t, s) = (sin(t)*cos(s), sin(t)*sin(s))\n",
    "time = Node(minimum(aa[:,1]))\n",
    "p1 = lines!(scene, lift(t-> ff.(t, range(0, stop = 2pi, length = 300)), time), color = :blue, linewidth = 3)\n",
    "N = 300\n",
    "time_arr = range(minimum(aa[:,1]), stop = maximum(aa[:,1]), length = N)\n",
    "for i = 1:size(time_arr,1)\n",
    "    \n",
    "Makie.save(\"D:\\\\Documents\\\\Diplomka\\\\Animations\\\\string_flat\\\\string_flat_$(i).png\",scene)\n",
    "    push!(time, time_arr[i])\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "scene = Scene(resolution = (1000, 1000))\n",
    "\n",
    "ff(t, s) = (spl(t)*cos(s), spl(t)*sin(s))\n",
    "time = Node(minimum(aa[:,1]))\n",
    "p1 = lines!(scene, lift(t-> ff.(t, range(0, stop = 2pi, length = 300)), time), color = :blue, linewidth = 3)\n",
    "N = 300\n",
    "time_arr = range(minimum(aa[:,1]), stop = maximum(aa[:,1]), length = N)\n",
    "for i = 1:size(time_arr,1)\n",
    "    \n",
    "Makie.save(\"D:\\\\Documents\\\\Diplomka\\\\Animations\\\\string_desitter\\\\string_desitter_$(i).png\",scene)\n",
    "    push!(time, time_arr[i])\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "scene = Scene(resolution = (1000, 1000))\n",
    "\n",
    "ff(t, s) = (sin(t)*cos(s), sin(t)*sin(s))\n",
    "time = Node(minimum(aa[:,1]))\n",
    "p1 = lines!(scene, lift(t-> ff.(t, range(0, stop = 2pi, length = 300)), time), color = :blue, linewidth = 3)\n",
    "N = 300\n",
    "record(scene, \"D:\\\\Documents\\\\Diplomka\\\\Animations\\\\string_flat.gif\", range(minimum(aa[:,1]), stop = maximum(aa[:,1]), length = N)) do i\n",
    "    push!(time, i)\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"D:\\\\Documents\\\\Diplomka\\\\Animations\\\\string_desitter.gif\""
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scene = Scene(resolution = (1000, 1000))\n",
    "\n",
    "ff(t, s) = (spl(t)*cos(s), spl(t)*sin(s))\n",
    "time = Node(minimum(aa[:,1]))\n",
    "p1 = lines!(scene, lift(t-> ff.(t, range(0, stop = 2pi, length = 300)), time), color = :blue, linewidth = 3)\n",
    "N = 1000\n",
    "record(scene, \"D:\\\\Documents\\\\Diplomka\\\\Animations\\\\string_desitter.gif\", range(minimum(aa[:,1]), stop = maximum(aa[:,1]), length = N)) do i\n",
    "    push!(time, i)\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "┌ Warning: `linspace(start, stop, length::Integer)` is deprecated, use `range(start, stop=stop, length=length)` instead.\n",
      "│   caller = (::getfield(Main, Symbol(\"##27#28\")))(::Float64) at In[16]:6\n",
      "└ @ Main .\\In[16]:6\n",
      "┌ Warning: `linspace(start, stop, length::Integer)` is deprecated, use `range(start, stop=stop, length=length)` instead.\n",
      "│   caller = (::getfield(Main, Symbol(\"##29#30\")))(::Float64) at In[16]:7\n",
      "└ @ Main .\\In[16]:7\n"
     ]
    },
    {
     "ename": "ErrorException",
     "evalue": "cannot assign variable AbstractPlotting.lines from module Main",
     "output_type": "error",
     "traceback": [
      "cannot assign variable AbstractPlotting.lines from module Main",
      "",
      "Stacktrace:",
      " [1] top-level scope at In[16]:8"
     ]
    }
   ],
   "source": [
    "scene = Scene(resolution = (1000, 1000))\n",
    "\n",
    "f(t, v, s) = (sin(v + t) * s, cos(v + t) * s, (cos(v + t) + sin(v)) * s)\n",
    "t = Node(Base.time()) # create a life signal\n",
    "limits = FRect3D(Vec3f0(-1.5, -1.5, -3), Vec3f0(3, 3, 6))\n",
    "p1 = meshscatter!(scene, lift(t-> f.(t, linspace(0, 2pi, 50), 1), t), markersize = 0.05)[end]\n",
    "p2 = meshscatter!(scene, lift(t-> f.(t * 2.0, linspace(0, 2pi, 50), 1.5), t), markersize = 0.05)[end]\n",
    "\n",
    "lines = lift(p1[1], p2[1]) do pos1, pos2\n",
    "    map((a, b)-> (a, b), pos1, pos2)\n",
    "end\n",
    "linesegments!(scene, lines, linestyle = :dot, limits = limits)\n",
    "# record a video\n",
    "N = 150\n",
    "record(scene, \"D:\\\\Documents\\\\Diplomka\\\\Animations\\\\test.mp4\", 1:N) do i\n",
    "    push!(t, Base.time())\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "t00 = 0.0\n",
    "t_fin = 3*pi\n",
    "r0 = 1.0\n",
    "n = 0\n",
    "N = 100\n",
    "k = 0\n",
    "\n",
    "scene = Scene(resolution = (1000, 1000))\n",
    "\n",
    "f(t, s, r0, n) = (r0*cos(t/r0)*cos(n*s+k*pi/2)*cos(s), r0*cos(t/r0)*cos(n*s+k*pi/2)*sin(s))\n",
    "\n",
    "\n",
    "function surf_func(t0, t_fin, N, n, r0)\n",
    "    t_range = range(t0, stop = t_fin+t0, length = N)\n",
    "    s_range = range(0, stop = 2pi, length = N)\n",
    "    srf = [f(t, s, r0, n) for t in t_range, s in s_range]\n",
    "    x = [srf[i,j][1] for i = 1:N, j = 1:N]\n",
    "    y = [srf[i,j][2] for i = 1:N, j = 1:N]\n",
    "    z = [t-t_fin for t in t_range, s in s_range]\n",
    "    return x, y, z\n",
    "end\n",
    "\n",
    "\n",
    "\n",
    "x, y, z = surf_func(t00, t_fin, N, n, r0)\n",
    "\n",
    "surf = surface!(scene, z, x, y, colormap = :Blues)[end]\n",
    "str = lines!(scene, z[end,:], x[end,:], y[end,:], color = :red, linewidth = 2, limits = FRect3D(Vec3f0(-10, -1.1, -1.1), Vec3f0(20, 2.2, 2.2)))[end]\n",
    "update_cam!(scene, Vec3f0(4, 8, 8), Vec3f0(-3, 0, 0))\n",
    "\n",
    "record(scene, \"D:\\\\Documents\\\\Diplomka\\\\Animations\\\\string_surf\"*\"$n\"*\".mp4\", range(t00, stop = t_fin, length = 300)) do i\n",
    "    x, y, z = surf_func(i, t_fin, N, n, r0)\n",
    "    surf[1] = z\n",
    "    surf[2] = x\n",
    "    surf[3] = y\n",
    "    str[1] = z[end,:]\n",
    "    str[2] = x[end,:]\n",
    "    str[3] = y[end,:]\n",
    "    update_cam!(scene, Vec3f0(4+i, 8, 8), Vec3f0(-3+i, 0, 0))\n",
    "end\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "N = 100\n",
    "\n",
    "scene = Scene(resolution = (1000, 1000))\n",
    "\n",
    "\n",
    "f(t, s) = (spl(t)*cos(s), spl(t)*sin(s))\n",
    "\n",
    "function surf_func(t, N)\n",
    "    t_fin = maximum(t)\n",
    "    s_range = range(0, stop = 2pi, length = N)\n",
    "    srf = [f(t[i], s) for i = 1:size(t,1) for s in s_range]\n",
    "    x = [srf[i,j][1] for i = 1:N, j = 1:N]\n",
    "    y = [srf[i,j][2] for i = 1:N, j = 1:N]\n",
    "    z = [t[i]-t_fin for i = 1:size(t,1), s in s_range]\n",
    "    return x, y, z\n",
    "end\n",
    "\n",
    "x, y, z = surf_func(aa[:,1], N)\n",
    "\n",
    "surf = surface!(scene, z, x, y, colormap = :Blues)[end]\n",
    "str = lines!(scene, z[end,:], x[end,:], y[end,:], color = :red, linewidth = 2, limits = FRect3D(Vec3f0(-10, -1.1, -1.1), Vec3f0(20, 2.2, 2.2)))[end]\n",
    "update_cam!(scene, Vec3f0(4, 8, 8), Vec3f0(-3, 0, 0))\n",
    "\n",
    "record(scene, \"D:\\\\Documents\\\\Diplomka\\\\Animations\\\\string_surf_desitter\"*\"$n\"*\".mp4\", range(t00, stop = t_fin, length = 300)) do i\n",
    "    x, y, z = surf_func(i, t_fin, N, n, r0)\n",
    "    surf[1] = z\n",
    "    surf[2] = x\n",
    "    surf[3] = y\n",
    "    str[1] = z[end,:]\n",
    "    str[2] = x[end,:]\n",
    "    str[3] = y[end,:]\n",
    "    update_cam!(scene, Vec3f0(4+i, 8, 8), Vec3f0(-3+i, 0, 0))\n",
    "end\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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