{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1b1c1dc2",
   "metadata": {},
   "source": [
    "\\textbf{Copyright:} Wojciech Kowalewski WMiI UAM\n",
    "\n",
    "\\textbf{Kurs}: Modelowanie geometryczne 2021/22\n",
    "\n",
    "---\n",
    "---\n",
    "#  Modelowanie geometryczne\n",
    "---\n",
    "---\n",
    "#  I. Modelowanie parametryczne"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e2b5e255",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import math\n",
    "import ipywidgets as widgets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b306a0ae",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [],
   "source": [
    "def dot(a,b):\n",
    "    \"\"\"\n",
    "    a,b: wektory\n",
    "    \"\"\"\n",
    "    assert len(a)==len(b), \"Nierówne długosci wektrów\"\n",
    "    res = 0\n",
    "    for i in range(len(a)):\n",
    "        res += a[i]*b[i]\n",
    "    return res"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ed3ebee",
   "metadata": {},
   "source": [
    "## 1. Krzywe Hermite'a stopnia 3\n",
    "======================================"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7502ecf",
   "metadata": {},
   "source": [
    "### 1.1. Macierz funkcji bazowych"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9a0525f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[2.0, -3.0, 0.0, 1.0],\n",
       " [-2.0, 3.0, 0.0, 0.0],\n",
       " [1.0, -2.0, 1.0, 0.0],\n",
       " [1.0, -1.0, 0.0, 0.0]]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hermite_basis_matrix_2D = [\n",
    "        [2.0,-3.0,0.0,1.0],\n",
    "        [-2.0,3.0,0.0,0.0],\n",
    "        [1.0,-2.0,1.0,0.0],\n",
    "        [1.0,-1.0,0.0,0.0]\n",
    "]\n",
    "hermite_basis_matrix_2D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "27713848",
   "metadata": {},
   "outputs": [],
   "source": [
    "hermite_basis_function_2D = []\n",
    "hermite_basis_function_2D.append(\n",
    "    lambda t: hermite_basis_matrix_2D[0][0]*t**3 + hermite_basis_matrix_2D[0][1]*t**2 + hermite_basis_matrix_2D[0][2]*t + hermite_basis_matrix_2D[0][3],\n",
    ")    \n",
    "hermite_basis_function_2D.append(\n",
    "lambda t: hermite_basis_matrix_2D[1][0]*t**3 + hermite_basis_matrix_2D[1][1]*t**2 + hermite_basis_matrix_2D[1][2]*t + hermite_basis_matrix_2D[1][3]\n",
    ")\n",
    "hermite_basis_function_2D.append(\n",
    "    lambda t: hermite_basis_matrix_2D[2][0]*t**3 + hermite_basis_matrix_2D[2][1]*t**2 + hermite_basis_matrix_2D[2][2]*t + hermite_basis_matrix_2D[2][3]\n",
    ")\n",
    "hermite_basis_function_2D.append(\n",
    "lambda t: hermite_basis_matrix_2D[3][0]*t**3 + hermite_basis_matrix_2D[3][1]*t**2 + hermite_basis_matrix_2D[3][2]*t + hermite_basis_matrix_2D[3][3]\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19dd701d",
   "metadata": {},
   "source": [
    "### 1.2. Wykresy funkcji bazowych"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7b1508e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "t = np.arange(0.0, 1.01, 0.01)\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "#ustawienie rozmiaru obrazka na 10x10 cali\n",
    "fig.set_size_inches(25.4/2.54, 25.4/2.54, forward=True)\n",
    "plt.title(\"Funkcje bazowe Hermite\")\n",
    "\n",
    "for n in range(4):\n",
    "    ax.plot(t,hermite_basis_function_2D[n](t) , label=\"h_\"+str(n)+\"(t)\")\n",
    "\n",
    "leg = ax.legend(loc='upper center', ncol=2, shadow='True')\n",
    "leg.get_frame().set_alpha(0.5)\n",
    "plt.grid();\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c411dee2",
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_ext(x,y, lp, lk):\n",
    "    t = np.arange(lp, lk, 0.001)\n",
    "    xmin = xmax = x(lp)\n",
    "    ymin = ymax = y(lp)\n",
    "    for p in t:\n",
    "        if x(p) < xmin:\n",
    "            xmin = x(p)\n",
    "        else:\n",
    "            if x(p) > xmax:\n",
    "                xmax = x(p)\n",
    "        if y(p) < ymin:\n",
    "            ymin = y(p)\n",
    "        else:\n",
    "            if y(p) > ymax:\n",
    "                ymax = y(p)\n",
    "    return [[xmin,xmax],[ymin,ymax]]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c446ac17",
   "metadata": {},
   "source": [
    "### 1.3. Przykłady\n",
    "\n",
    "#### 1.3.1. Przykład 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e1a529b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "t = np.arange(0.0, 1.01, 0.01)\n",
    "\n",
    "Pp = [0.0,0.0]\n",
    "Pk = [1.0,0.0]\n",
    "Tp = [4.0,4.0]\n",
    "Tk = [4.0,-4.0]\n",
    "\n",
    "lenTp = math.sqrt(Tp[0]**2+Tp[1]**2)/2.54\n",
    "lenTk = math.sqrt(Tk[0]**2+Tk[1]**2)/2.54 \n",
    "\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "#ustawienie rozmiaru obrazka na 10x10 cali\n",
    "fig.set_size_inches(25.4/2.54, 25.4/2.54, forward=True)\n",
    "plt.title(\"Segment krzywej Hermite'a\")\n",
    "\n",
    "\n",
    "def X(t):\n",
    "    return Pp[0]*hermite_basis_function_2D[0](t) + Pk[0]*hermite_basis_function_2D[1](t) + Tp[0]*hermite_basis_function_2D[2](t) + Tk[0]*hermite_basis_function_2D[3](t)\n",
    "\n",
    "def Y(t):\n",
    "    return Pp[1]*hermite_basis_function_2D[0](t) + Pk[1]*hermite_basis_function_2D[1](t) + Tp[1]*hermite_basis_function_2D[2](t) + Tk[1]*hermite_basis_function_2D[3](t)\n",
    "\n",
    "\n",
    "extr = find_ext(X,Y,0.0,1.01)\n",
    "\n",
    "borderXp = min(extr[0][0],Pp[0]+Tp[0],Pk[0]+Tk[0])-0.1\n",
    "borderXk = max(extr[0][1],Pp[0]+Tp[0],Pk[0]+Tk[0])+0.1\n",
    "\n",
    "borderYp = min(extr[1][0],Pp[1]+Tp[1],Pk[1]+Tk[1])-0.1\n",
    "borderYk = max(extr[1][1],Pp[1]+Tp[1],Pk[1]+Tk[1])+0.1\n",
    "\n",
    "\n",
    "plt.xlim(borderXp, borderXk)\n",
    "plt.ylim(borderYp, borderYk)\n",
    "\n",
    "x = X(t)\n",
    "y = Y(t)\n",
    "\n",
    "ax.set_aspect('equal')\n",
    "\n",
    "ax.plot(x,y, color='red')\n",
    "\n",
    "ax.plot(Pp[0], Pp[1],  color='blue', marker=\".\", markersize=20)\n",
    "ax.plot(Pk[0], Pk[1],  color='blue', marker=\".\", markersize=20)\n",
    "\n",
    "plt.quiver(Pp[0],Pp[1], Tp[0] , Tp[1],color='green', angles='xy', scale_units='xy', scale=1)\n",
    "plt.quiver(Pk[0],Pk[1], Tk[0] , Tk[1],color='green', angles='xy', scale_units='xy', scale=1)\n",
    "\n",
    "\n",
    "#eg = ax.legend(loc='upper center', ncol=2, shadow='True')\n",
    "leg.get_frame().set_alpha(0.5)\n",
    "plt.grid();\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b4e7777",
   "metadata": {},
   "source": [
    "#### 1.3.2. Przykład 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a91edba9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "t = np.arange(0.0, 1.01, 0.01)\n",
    "\n",
    "Pp = [1.0,0.5]\n",
    "Pk = [0.0,-0.5]\n",
    "Tp = [1.0,1.0]\n",
    "Tk = [1.0,-1.0]\n",
    "\n",
    "lenTp = math.sqrt(Tp[0]**2+Tp[1]**2)/2.54\n",
    "lenTk = math.sqrt(Tk[0]**2+Tk[1]**2)/2.54 \n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "#ustawienie rozmiaru obrazka na 10x10 cali\n",
    "fig.set_size_inches(25.4/2.54, 25.4/2.54, forward=True)\n",
    "plt.title(\"Segment krzywej Hermite'a\")\n",
    "\n",
    "\n",
    "def X(t):\n",
    "    return Pp[0]*hermite_basis_function_2D[0](t) + Pk[0]*hermite_basis_function_2D[1](t) + Tp[0]*hermite_basis_function_2D[2](t) + Tk[0]*hermite_basis_function_2D[3](t)\n",
    "\n",
    "def Y(t):\n",
    "    return Pp[1]*hermite_basis_function_2D[0](t) + Pk[1]*hermite_basis_function_2D[1](t) + Tp[1]*hermite_basis_function_2D[2](t) + Tk[1]*hermite_basis_function_2D[3](t)\n",
    "\n",
    "\n",
    "extr = find_ext(X,Y,0.0,1.01)\n",
    "\n",
    "borderXp = min(extr[0][0],Pp[0]+Tp[0],Pk[0]+Tk[0])-0.1\n",
    "borderXk = max(extr[0][1],Pp[0]+Tp[0],Pk[0]+Tk[0])+0.1\n",
    "\n",
    "borderYp = min(extr[1][0],Pp[1]+Tp[1],Pk[1]+Tk[1])-0.1\n",
    "borderYk = max(extr[1][1],Pp[1]+Tp[1],Pk[1]+Tk[1])+0.1\n",
    "\n",
    "\n",
    "plt.xlim(borderXp, borderXk)\n",
    "plt.ylim(borderYp, borderYk)\n",
    "\n",
    "x = X(t)\n",
    "y = Y(t)\n",
    "\n",
    "ax.set_aspect('equal')\n",
    "\n",
    "ax.plot(x,y, color='red')\n",
    "\n",
    "ax.plot(Pp[0], Pp[1],  color='blue', marker=\".\", markersize=20)\n",
    "ax.plot(Pk[0], Pk[1],  color='blue', marker=\".\", markersize=20)\n",
    "\n",
    "plt.quiver(Pp[0],Pp[1], Tp[0] , Tp[1],color='green', angles='xy', scale_units='xy', scale=1)\n",
    "plt.quiver(Pk[0],Pk[1], Tk[0] , Tk[1],color='green', angles='xy', scale_units='xy', scale=1)\n",
    "\n",
    "\n",
    "#eg = ax.legend(loc='upper center', ncol=2, shadow='True')\n",
    "leg.get_frame().set_alpha(0.5)\n",
    "plt.grid();\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b606bf77",
   "metadata": {},
   "source": [
    "#### 1.3.3. Wersja interaktywna"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "cfd6c277",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7bc108357870438d88015b015bcc558b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(VBox(children=(FloatSlider(value=0.0, description='PpX', layout=Layout(grid_area='widget001'), …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "54144e7d46cf4d5ea3103f2ff44a7525",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "\n",
    "def draw_Hermite_segment(PpX,PpY,PkX,PkY,TpX,TpY,TkX,TkY):\n",
    "    t = np.arange(0.0, 1.01, 0.01)\n",
    "    \n",
    "    lenTp = math.sqrt(TpX**2+TpY**2)/2.54\n",
    "    lenTk = math.sqrt(TkX**2+TkY**2)/2.54 \n",
    "\n",
    "    fig, ax = plt.subplots()\n",
    "    #ustawienie rozmiaru obrazka na 10x10 cali\n",
    "    fig.set_size_inches(25.4/2.54, 25.4/2.54, forward=True)\n",
    "    plt.title(\"Segment krzywej Hermite'a\")\n",
    "\n",
    "\n",
    "    def X(t):\n",
    "        return PpX*hermite_basis_function_2D[0](t) + PkX*hermite_basis_function_2D[1](t) + TpX*hermite_basis_function_2D[2](t) + TkX*hermite_basis_function_2D[3](t)\n",
    "\n",
    "    def Y(t):\n",
    "        return PpY*hermite_basis_function_2D[0](t) + PkY*hermite_basis_function_2D[1](t) + TpY*hermite_basis_function_2D[2](t) + TkY*hermite_basis_function_2D[3](t)\n",
    "\n",
    "\n",
    "    extr = find_ext(X,Y,0.0,1.01)\n",
    "\n",
    "    borderXp = min(extr[0][0],PpX+TpX,PkX+TkX)-0.1\n",
    "    borderXk = max(extr[0][1],PpX+TpX,PkX+TkX)+0.1\n",
    "\n",
    "    borderYp = min(extr[1][0],PpY+TpY,PkY+TkY)-0.1\n",
    "    borderYk = max(extr[1][1],PpY+TpY,PkY+TkY)+0.1\n",
    "\n",
    "\n",
    "    plt.xlim(borderXp, borderXk)\n",
    "    plt.ylim(borderYp, borderYk)\n",
    "\n",
    "    x = X(t)\n",
    "    y = Y(t)\n",
    "\n",
    "    ax.set_aspect('equal')\n",
    "\n",
    "    ax.plot(x,y, color='red')\n",
    "\n",
    "    ax.plot(PpX, PpY,  color='blue', marker=\".\", markersize=20)\n",
    "    ax.plot(PkX, PkY,  color='blue', marker=\".\", markersize=20)\n",
    "\n",
    "    plt.quiver(PpX,PpY, TpX , TpY,color='green', angles='xy', scale_units='xy', scale=1)\n",
    "    plt.quiver(PkX,PkY, TkX , TkY,color='green', angles='xy', scale_units='xy', scale=1)\n",
    "\n",
    "\n",
    "    #eg = ax.legend(loc='upper center', ncol=2, shadow='True')\n",
    "    #leg.get_frame().set_alpha(0.5)\n",
    "    plt.grid();\n",
    "\n",
    "    plt.show()\n",
    "\n",
    "Pp1 = [0.0,0.0]\n",
    "Pk1 = [1.0,0.0]\n",
    "Tp1 = [1.0,1.0]\n",
    "Tk1 = [1.0,-1.0]\n",
    "    \n",
    "#draw_Hermite_segment(Pp1[0],Pp1[1],Pk1[0],Pk1[1],Tp1[0],Tp1[1],Tk1[0],Tk1[1])\n",
    "grid = widgets.GridspecLayout(4, 2)\n",
    "grid[0, 0] = widgets.FloatSlider(min=-5, max=5, step=0.2,value=Pp1[0],description='PpX')\n",
    "grid[0, 1] = widgets.FloatSlider(min=-5, max=5, step=0.2,value=Pp1[1],description='PpY')\n",
    "grid[1, 0] = widgets.FloatSlider(min=-5, max=5, step=0.2,value=Pk1[0],description='PkX')\n",
    "grid[1, 1] = widgets.FloatSlider(min=-5, max=5, step=0.2,value=Pk1[1],description='PkY')\n",
    "grid[2, 0] = widgets.FloatSlider(min=-5, max=5, step=0.2,value=Tp1[0],description='TpX')\n",
    "grid[2, 1] = widgets.FloatSlider(min=-5, max=5, step=0.2,value=Tp1[1],description='TpY')\n",
    "grid[3, 0] = widgets.FloatSlider(min=-5, max=5, step=0.2,value=Tk1[0],description='TkX')\n",
    "grid[3, 1] = widgets.FloatSlider(min=-5, max=5, step=0.2,value=Tk1[1],description='TkY')\n",
    "\n",
    "k1 = widgets.VBox([grid[0,0],grid[1,0],grid[2,0],grid[3,0]])\n",
    "k2 = widgets.VBox([grid[0,1],grid[1,1],grid[2,1],grid[3,1]])\n",
    "ui = widgets.HBox([k1,k2])\n",
    "\n",
    "out = widgets.interactive_output(draw_Hermite_segment, {'PpX': grid[0,0], 'PpY': grid[0,1], \n",
    "                                                        'PkX': grid[1,0], 'PkY': grid[1,1],\n",
    "                                                        'TpX': grid[2,0], 'TpY': grid[2,1], \n",
    "                                                        'TkX': grid[3,0], 'TkY': grid[3,1]\n",
    "                                                       })\n",
    "\n",
    "display(ui, out)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5968b7a3",
   "metadata": {},
   "source": [
    "## 2. Krzywe Beziera stopnia 3\n",
    "===================================="
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d982705f",
   "metadata": {},
   "source": [
    "### 2.1. Macierz funkcji bazowych"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "90b8487d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[-1.0, 3.0, -3.0, 1.0],\n",
       " [3.0, -6.0, 3.0, 0.0],\n",
       " [-3.0, 3.0, 0.0, 0.0],\n",
       " [1.0, 0.0, 0.0, 0.0]]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bezier_basis_matrix_2D = [\n",
    "        [-1.0,3.0,-3.0,1.0],\n",
    "        [3.0,-6.0,3.0,0.0],\n",
    "        [-3.0,3.0,.0,0.0],\n",
    "        [1.0,0.0,0.0,0.0]\n",
    "]\n",
    "bezier_basis_matrix_2D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f0f7435c",
   "metadata": {},
   "outputs": [],
   "source": [
    "bezier_basis_function_2D = []\n",
    "bezier_basis_function_2D.append(\n",
    "    lambda t: bezier_basis_matrix_2D[0][0]*t**3 + bezier_basis_matrix_2D[0][1]*t**2 + bezier_basis_matrix_2D[0][2]*t + bezier_basis_matrix_2D[0][3],\n",
    ")    \n",
    "bezier_basis_function_2D.append(\n",
    "lambda t: bezier_basis_matrix_2D[1][0]*t**3 + bezier_basis_matrix_2D[1][1]*t**2 + bezier_basis_matrix_2D[1][2]*t + bezier_basis_matrix_2D[1][3]\n",
    ")\n",
    "bezier_basis_function_2D.append(\n",
    "    lambda t: bezier_basis_matrix_2D[2][0]*t**3 + bezier_basis_matrix_2D[2][1]*t**2 + bezier_basis_matrix_2D[2][2]*t + bezier_basis_matrix_2D[2][3]\n",
    ")\n",
    "bezier_basis_function_2D.append(\n",
    "lambda t: bezier_basis_matrix_2D[3][0]*t**3 + bezier_basis_matrix_2D[3][1]*t**2 + bezier_basis_matrix_2D[3][2]*t + bezier_basis_matrix_2D[3][3]\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7cb6a787",
   "metadata": {},
   "source": [
    "### 2.2. Wykresy funkcji bazowych"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "041f6aee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "t = np.arange(0.0, 1.01, 0.01)\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "#ustawienie rozmiaru obrazka na 10x10 cali\n",
    "fig.set_size_inches(25.4/2.54, 25.4/2.54, forward=True)\n",
    "plt.title(\"Funkcje bazowe Beziera stopnia 3\")\n",
    "\n",
    "for n in range(4):\n",
    "    ax.plot(t,bezier_basis_function_2D[n](t) , label=\"b_\"+str(n)+\"(t)\")\n",
    "\n",
    "leg = ax.legend(loc='upper center', ncol=2, shadow='True')\n",
    "leg.get_frame().set_alpha(0.5)\n",
    "plt.grid();\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8a0079a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_ext(x,y, lp, lk):\n",
    "    t = np.arange(lp, lk, 0.001)\n",
    "    xmin = xmax = x(lp)\n",
    "    ymin = ymax = y(lp)\n",
    "    for p in t:\n",
    "        if x(p) < xmin:\n",
    "            xmin = x(p)\n",
    "        else:\n",
    "            if x(p) > xmax:\n",
    "                xmax = x(p)\n",
    "        if y(p) < ymin:\n",
    "            ymin = y(p)\n",
    "        else:\n",
    "            if y(p) > ymax:\n",
    "                ymax = y(p)\n",
    "    return [[xmin,xmax],[ymin,ymax]]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0356ad14",
   "metadata": {},
   "source": [
    "### 2.3. Przykłady\n",
    "\n",
    "#### 2.3.1. Przykład 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "49b5119f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### %matplotlib inline\n",
    "\n",
    "t = np.arange(0.0, 1.01, 0.01)\n",
    "\n",
    "P0 = [0.0,0.0]\n",
    "P1 = [0.25,1.0]\n",
    "P2 = [0.75,1.0]\n",
    "P3 = [1.0,0.0]\n",
    "\n",
    "Tp= [3.0*(P1[0]-P0[0]),3.0*(P1[1]-P0[1])]\n",
    "Tk= [3.0*(P3[0]-P2[0]),3.0*(P3[1]-P2[1])]\n",
    "\n",
    "lenTp = math.sqrt(Tp[0]**2+Tp[1]**2)/2.54\n",
    "lenTk = math.sqrt(Tk[0]**2+Tk[1]**2)/2.54 \n",
    "\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "#ustawienie rozmiaru obrazka na 10x10 cali\n",
    "fig.set_size_inches(25.4/2.54, 25.4/2.54, forward=True)\n",
    "plt.title(\"Segment krzywej Beziera\")\n",
    "\n",
    "\n",
    "def X(t):\n",
    "    return P0[0]*bezier_basis_function_2D[0](t) + P1[0]*bezier_basis_function_2D[1](t) + P2[0]*bezier_basis_function_2D[2](t) + P3[0]*bezier_basis_function_2D[3](t)\n",
    "\n",
    "def Y(t):\n",
    "    return P0[1]*bezier_basis_function_2D[0](t) + P1[1]*bezier_basis_function_2D[1](t) + P2[1]*bezier_basis_function_2D[2](t) + P3[1]*bezier_basis_function_2D[3](t)\n",
    "\n",
    "\n",
    "extr = find_ext(X,Y,0.0,1.01)\n",
    "\n",
    "borderXp = min(extr[0][0],P0[0]+Tp[0],P3[0]+Tk[0],P0[0],P1[0],P2[0],P3[0])-0.1\n",
    "borderXk = max(extr[0][1],P0[0]+Tp[0],P3[0]+Tk[0],P0[0],P1[0],P2[0],P3[0])+0.1\n",
    "\n",
    "borderYp = min(extr[1][0],P0[1]+Tp[1],P3[1]+Tk[1],P0[1],P1[1],P2[1],P3[1])-0.1\n",
    "borderYk = max(extr[1][1],P0[1]+Tp[1],P3[1]+Tk[1],P0[1],P1[1],P2[1],P3[1])+0.1\n",
    "\n",
    "\n",
    "plt.xlim(borderXp, borderXk)\n",
    "plt.ylim(borderYp, borderYk)\n",
    "\n",
    "x = X(t)\n",
    "y = Y(t)\n",
    "\n",
    "ax.set_aspect('equal')\n",
    "\n",
    "ax.plot(x,y, color='red')\n",
    "\n",
    "ax.plot(P0[0], P0[1],  color='blue', marker=\".\", markersize=20)\n",
    "ax.plot(P1[0], P1[1],  color='blue', marker=\".\", markersize=20)\n",
    "ax.plot(P2[0], P2[1],  color='blue', marker=\".\", markersize=20)\n",
    "ax.plot(P3[0], P3[1],  color='blue', marker=\".\", markersize=20)\n",
    "\n",
    "\n",
    "\n",
    "plt.quiver(P0[0],P0[1], Tp[0] , Tp[1],color='green', angles='xy', scale_units='xy', scale=1)\n",
    "plt.quiver(P3[0],P3[1], Tk[0] , Tk[1],color='green', angles='xy', scale_units='xy', scale=1)\n",
    "\n",
    "\n",
    "#eg = ax.legend(loc='upper center', ncol=2, shadow='True')\n",
    "#eg.get_frame().set_alpha(0.5)\n",
    "plt.grid();\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4c94e72",
   "metadata": {},
   "source": [
    "#### 2.3.2. Przykład 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "1a029ca7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### %matplotlib inline\n",
    "\n",
    "t = np.arange(0.0, 1.01, 0.01)\n",
    "\n",
    "P0 = [0.0,0.0]\n",
    "P1 = [0.25,1.0]\n",
    "P2 = [1.0,0.0]\n",
    "P3 = [0.75,1.0]\n",
    "\n",
    "Tp= [3.0*(P1[0]-P0[0]),3.0*(P1[1]-P0[1])]\n",
    "Tk= [3.0*(P3[0]-P2[0]),3.0*(P3[1]-P2[1])]\n",
    "\n",
    "lenTp = math.sqrt(Tp[0]**2+Tp[1]**2)/2.54\n",
    "lenTk = math.sqrt(Tk[0]**2+Tk[1]**2)/2.54 \n",
    "\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "#ustawienie rozmiaru obrazka na 10x10 cali\n",
    "fig.set_size_inches(25.4/2.54, 25.4/2.54, forward=True)\n",
    "plt.title(\"Segment krzywej Beziera\")\n",
    "\n",
    "\n",
    "def X(t):\n",
    "    return P0[0]*bezier_basis_function_2D[0](t) + P1[0]*bezier_basis_function_2D[1](t) + P2[0]*bezier_basis_function_2D[2](t) + P3[0]*bezier_basis_function_2D[3](t)\n",
    "\n",
    "def Y(t):\n",
    "    return P0[1]*bezier_basis_function_2D[0](t) + P1[1]*bezier_basis_function_2D[1](t) + P2[1]*bezier_basis_function_2D[2](t) + P3[1]*bezier_basis_function_2D[3](t)\n",
    "\n",
    "\n",
    "extr = find_ext(X,Y,0.0,1.01)\n",
    "\n",
    "borderXp = min(extr[0][0],P0[0]+Tp[0],P3[0]+Tk[0],P0[0],P1[0],P2[0],P3[0])-0.1\n",
    "borderXk = max(extr[0][1],P0[0]+Tp[0],P3[0]+Tk[0],P0[0],P1[0],P2[0],P3[0])+0.1\n",
    "\n",
    "borderYp = min(extr[1][0],P0[1]+Tp[1],P3[1]+Tk[1],P0[1],P1[1],P2[1],P3[1])-0.1\n",
    "borderYk = max(extr[1][1],P0[1]+Tp[1],P3[1]+Tk[1],P0[1],P1[1],P2[1],P3[1])+0.1\n",
    "\n",
    "plt.xlim(borderXp, borderXk)\n",
    "plt.ylim(borderYp, borderYk)\n",
    "\n",
    "x = X(t)\n",
    "y = Y(t)\n",
    "\n",
    "ax.set_aspect('equal')\n",
    "\n",
    "ax.plot(x,y, color='red')\n",
    "\n",
    "ax.plot(P0[0], P0[1],  color='blue', marker=\".\", markersize=20)\n",
    "ax.plot(P1[0], P1[1],  color='blue', marker=\".\", markersize=20)\n",
    "ax.plot(P2[0], P2[1],  color='blue', marker=\".\", markersize=20)\n",
    "ax.plot(P3[0], P3[1],  color='blue', marker=\".\", markersize=20)\n",
    "\n",
    "\n",
    "\n",
    "plt.quiver(P0[0],P0[1], Tp[0] , Tp[1],color='green', angles='xy', scale_units='xy', scale=1)\n",
    "plt.quiver(P3[0],P3[1], Tk[0] , Tk[1],color='green', angles='xy', scale_units='xy', scale=1)\n",
    "\n",
    "\n",
    "#eg = ax.legend(loc='upper center', ncol=2, shadow='True')\n",
    "#leg.get_frame().set_alpha(0.5)\n",
    "plt.grid();\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ecf0af3",
   "metadata": {},
   "source": [
    "#### 2.3.3. Wersja interaktywna"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d4210ae6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ae1e5e758e194d3aa8b88313f8b3d7ed",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(VBox(children=(FloatSlider(value=0.0, description='P0X', layout=Layout(grid_area='widget001'), …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8106e842beb4475780a5b2d1627faf12",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "\n",
    "def draw_Bezier_segment(P0X,P0Y,P1X,P1Y,P2X,P2Y,P3X,P3Y):\n",
    "    t = np.arange(0.0, 1.01, 0.01)\n",
    "    \n",
    "    ### %matplotlib inline\n",
    "\n",
    "    t = np.arange(0.0, 1.01, 0.01)\n",
    "\n",
    "    Tp= [3.0*(P1X-P0X),3.0*(P1Y-P0Y)]\n",
    "    Tk= [3.0*(P3X-P2X),3.0*(P3Y-P2Y)]\n",
    "\n",
    "    lenTp = math.sqrt(Tp[0]**2+Tp[1]**2)/2.54\n",
    "    lenTk = math.sqrt(Tk[0]**2+Tk[1]**2)/2.54 \n",
    "\n",
    "\n",
    "    fig, ax = plt.subplots()\n",
    "    #ustawienie rozmiaru obrazka na 10x10 cali\n",
    "    fig.set_size_inches(25.4/2.54, 25.4/2.54, forward=True)\n",
    "    plt.title(\"Segment krzywej Beziera\")\n",
    "\n",
    "\n",
    "    def X(t):\n",
    "        return P0X*bezier_basis_function_2D[0](t) + P1X*bezier_basis_function_2D[1](t) + P2X*bezier_basis_function_2D[2](t) + P3X*bezier_basis_function_2D[3](t)\n",
    "\n",
    "    def Y(t):\n",
    "        return P0Y*bezier_basis_function_2D[0](t) + P1Y*bezier_basis_function_2D[1](t) + P2Y*bezier_basis_function_2D[2](t) + P3Y*bezier_basis_function_2D[3](t)\n",
    "\n",
    "\n",
    "    extr = find_ext(X,Y,0.0,1.01)\n",
    "\n",
    "    borderXp = min(extr[0][0],P0X+Tp[0],P3X+Tk[0],P0X,P1X,P2X,P3X)-0.2\n",
    "    borderXk = max(extr[0][1],P0X+Tp[0],P3X+Tk[0],P0X,P1X,P2X,P3X)+0.2\n",
    "\n",
    "    borderYp = min(extr[1][0],P0Y+Tp[1],P3Y+Tk[1],P0Y,P1Y,P2Y,P3Y)-0.2\n",
    "    borderYk = max(extr[1][1],P0Y+Tp[1],P3Y+Tk[1],P0Y,P1Y,P2Y,P3Y)+0.2\n",
    "\n",
    "    plt.xlim(borderXp, borderXk)\n",
    "    plt.ylim(borderYp, borderYk)\n",
    "\n",
    "    x = X(t)\n",
    "    y = Y(t)\n",
    "\n",
    "    ax.set_aspect('equal')\n",
    "\n",
    "    ax.plot(x,y, color='red')\n",
    "\n",
    "    ax.plot(P0X, P0Y,  color='blue', marker=\".\", markersize=20)\n",
    "    ax.plot(P1X, P1Y,  color='blue', marker=\".\", markersize=20)\n",
    "    ax.plot(P2X, P2Y,  color='blue', marker=\".\", markersize=20)\n",
    "    ax.plot(P3X, P3Y,  color='blue', marker=\".\", markersize=20)\n",
    "\n",
    "\n",
    "\n",
    "    plt.quiver(P0X,P0Y, Tp[0] , Tp[1],color='green', angles='xy', scale_units='xy', scale=1)\n",
    "    plt.quiver(P3X,P3Y, Tk[0] , Tk[1],color='green', angles='xy', scale_units='xy', scale=1)\n",
    "\n",
    "\n",
    "    #eg = ax.legend(loc='upper center', ncol=2, shadow='True')\n",
    "    #leg.get_frame().set_alpha(0.5)\n",
    "    plt.grid();\n",
    "\n",
    "    plt.show()\n",
    "\n",
    "p0 = [0.0,0.0]\n",
    "p1 = [0.25,1.0]\n",
    "p2 = [0.75,1.0]\n",
    "p3 = [1.0,0.0]\n",
    "    \n",
    "#draw_Hermite_segment(Pp1[0],Pp1[1],Pk1[0],Pk1[1],Tp1[0],Tp1[1],Tk1[0],Tk1[1])\n",
    "grid = widgets.GridspecLayout(4, 2)\n",
    "grid[0, 0] = widgets.FloatSlider(min=-5, max=5, step=0.2,value=p0[0],description='P0X')\n",
    "grid[0, 1] = widgets.FloatSlider(min=-5, max=5, step=0.2,value=p0[1],description='P0Y')\n",
    "grid[1, 0] = widgets.FloatSlider(min=-5, max=5, step=0.2,value=p1[0],description='P1X')\n",
    "grid[1, 1] = widgets.FloatSlider(min=-5, max=5, step=0.2,value=p1[1],description='P1Y')\n",
    "grid[2, 0] = widgets.FloatSlider(min=-5, max=5, step=0.2,value=p2[0],description='P2X')\n",
    "grid[2, 1] = widgets.FloatSlider(min=-5, max=5, step=0.2,value=p2[1],description='P2Y')\n",
    "grid[3, 0] = widgets.FloatSlider(min=-5, max=5, step=0.2,value=p3[0],description='P3X')\n",
    "grid[3, 1] = widgets.FloatSlider(min=-5, max=5, step=0.2,value=p3[1],description='P3Y')\n",
    "\n",
    "k1 = widgets.VBox([grid[0,0],grid[1,0],grid[2,0],grid[3,0]])\n",
    "k2 = widgets.VBox([grid[0,1],grid[1,1],grid[2,1],grid[3,1]])\n",
    "ui = widgets.HBox([k1,k2])\n",
    "\n",
    "out = widgets.interactive_output(draw_Bezier_segment, {'P0X': grid[0,0], 'P0Y': grid[0,1], \n",
    "                                                        'P1X': grid[1,0], 'P1Y': grid[1,1],\n",
    "                                                        'P2X': grid[2,0], 'P2Y': grid[2,1], \n",
    "                                                        'P3X': grid[3,0], 'P3Y': grid[3,1]\n",
    "                                                       })\n",
    "\n",
    "display(ui, out)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31c960d2",
   "metadata": {},
   "source": [
    "### 2.4. Krzywe Beziera dowolnego stopnia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c0e13970",
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy.special\n",
    "def bernstein(n,i,t):\n",
    "    return scipy.special.binom(n,i)*t**i*(1-t)**(n-i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ea3578d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "model_id": "cbcc840f1bd9484382925bfc740223af",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "GridspecLayout(children=(FloatText(value=0.0, description='pX', layout=Layout(grid_area='widget001', width='20…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "model_id": "c56363fef08f414a8db211f01116d59e",
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       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "model_id": "e351bb5029214249abd6e2d4218c8d07",
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       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(Checkbox(value=False, description='Show convex hull', indent=False), Output()), _dom_cla…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "from IPython.display import clear_output\n",
    "from scipy.spatial import ConvexHull, convex_hull_plot_2d\n",
    "from ipywidgets import Button, Layout\n",
    "from ipywidgets import interact\n",
    "\n",
    "out = widgets.Output()\n",
    "\n",
    "def onclick(event):\n",
    "    print('%s click: button=%d, x=%d, y=%d, xdata=%f, ydata=%f' %\n",
    "          ('double' if event.dblclick else 'single', event.button,\n",
    "           event.x, event.y, event.xdata, event.ydata))\n",
    "\n",
    "\n",
    "def draw_Bezier_segment_n(control_points,out, hull = False):\n",
    "    t = np.arange(0.0, 1.01, 0.01)\n",
    "    \n",
    "    ### %matplotlib inline\n",
    "\n",
    "    t = np.arange(0.0, 1.01, 0.01)\n",
    "    \n",
    "    n = len(control_points)-1\n",
    "\n",
    "    Tp= [3.0*(control_points[1][0]-control_points[0][0]),3.0*(control_points[1][1]-control_points[0][1])]\n",
    "    Tk= [3.0*(control_points[n][0]-control_points[n-1][0]),3.0*(control_points[n][1]-control_points[n-1][1])]\n",
    "\n",
    "    lenTp = math.sqrt(Tp[0]**2+Tp[1]**2)/2.54\n",
    "    lenTk = math.sqrt(Tk[0]**2+Tk[1]**2)/2.54 \n",
    "\n",
    "    \n",
    "    with out:\n",
    "        fig, ax = plt.subplots()\n",
    "    \n",
    "    cid = fig.canvas.mpl_connect('button_press_event', onclick)\n",
    "\n",
    "    #ustawienie rozmiaru obrazka na 10x10 cali\n",
    "    fig.set_size_inches(25.4/2.54, 25.4/2.54, forward=True)\n",
    "    plt.title(\"Segment krzywej Beziera stopnia \"+str(n))\n",
    "\n",
    "\n",
    "    def X(t):\n",
    "        res = 0\n",
    "        for i in range(n+1):\n",
    "            res += control_points[i][0]*bernstein(n,i,t)\n",
    "        return res\n",
    "    \n",
    "    \n",
    "    def Y(t):\n",
    "        res = 0\n",
    "        for i in range(n+1):\n",
    "            res += control_points[i][1]*bernstein(n,i,t)\n",
    "        return res\n",
    "    \n",
    "\n",
    "    extr = find_ext(X,Y,0.0,1.01)\n",
    "\n",
    "    \n",
    "    extr_points = [[control_points[0][0],control_points[0][0]],[control_points[0][1],control_points[0][1]]]\n",
    "    for i in range(1,n+1):\n",
    "        if control_points[i][0] <  extr_points[0][0]:\n",
    "            extr_points[0][0] = control_points[i][0]\n",
    "        else:\n",
    "            if control_points[i][0] >  extr_points[0][1]:\n",
    "                extr_points[0][1] = control_points[i][0]\n",
    "        if control_points[i][1] <  extr_points[1][0]:\n",
    "            extr_points[1][0] = control_points[i][1]\n",
    "        else:\n",
    "            if control_points[i][1] >  extr_points[1][1]:\n",
    "                extr_points[1][1] = control_points[i][1]\n",
    "\n",
    "    borderXp = min(extr[0][0],control_points[0][0]+Tp[0],control_points[n][0]+Tk[0],extr_points[0][0])-0.2\n",
    "    borderXk = max(extr[0][1],control_points[0][0]+Tp[0],control_points[n][0]+Tk[0],extr_points[0][1])+0.2\n",
    "    \n",
    "    borderYp = min(extr[1][0],control_points[0][1]+Tp[1],control_points[n][1]+Tk[1],extr_points[1][0])-0.2\n",
    "    borderYk = max(extr[1][1],control_points[0][1]+Tp[1],control_points[n][1]+Tk[1],extr_points[1][1])+0.2\n",
    "    \n",
    "    \n",
    "\n",
    "    plt.xlim(borderXp, borderXk)\n",
    "    plt.ylim(borderYp, borderYk)\n",
    "\n",
    "    x = X(t)\n",
    "    y = Y(t)\n",
    "\n",
    "    ax.set_aspect('equal')\n",
    "\n",
    "    with out:\n",
    "        clear_output(True)\n",
    "        \n",
    "        ax.plot(x,y, color='red')\n",
    "\n",
    "        for i in range(n+1):\n",
    "            ax.plot(control_points[i][0], control_points[i][1],  color='blue', marker=\".\", markersize=20)\n",
    "\n",
    "\n",
    "\n",
    "        plt.quiver(control_points[0][0], control_points[0][1], Tp[0] , Tp[1],color='green', angles='xy', scale_units='xy', scale=1)\n",
    "        plt.quiver(control_points[n][0], control_points[n][1], Tk[0] , Tk[1],color='green', angles='xy', scale_units='xy', scale=1)\n",
    "\n",
    "\n",
    "        #eg = ax.legend(loc='upper center', ncol=2, shadow='True')\n",
    "        #leg.get_frame().set_alpha(0.5)\n",
    "        plt.grid();\n",
    "\n",
    "        if hull and len(points)>2:\n",
    "            Hull = ConvexHull(points)\n",
    "            #print(Hull.vertices.tolist())\n",
    "            a=[]\n",
    "            b=[]\n",
    "            for i in Hull.vertices.tolist():\n",
    "                #ax.plot(points[i][0], points[i][1],  color='orange', marker=\"*\", markersize=8)\n",
    "                a.append(points[i][0])\n",
    "                b.append(points[i][1])\n",
    "            a.append(a[0])\n",
    "            b.append(b[0])\n",
    "            #print(a)\n",
    "            #print(b)\n",
    "            for i in Hull.vertices.tolist():\n",
    "                #print(points[Hull.vertices.tolist()[i]])\n",
    "                #ax.plot(points[i][0], points[i][1], 'r--', color='black')\n",
    "                ax.plot(a, b, 'r--', color='black')\n",
    "            for i in Hull.vertices.tolist():\n",
    "                ax.plot(points[i][0], points[i][1],  color='orange', marker=\"*\", markersize=8)\n",
    "            \n",
    "            #_ = convex_hull_plot_2d(Hull)\n",
    "            \n",
    "        plt.show()\n",
    "\n",
    "\n",
    "points = [[0.0,0.0], [1.0,0.0],[2.0,2.0]]\n",
    "\n",
    "\n",
    "pointX = widgets.FloatText(\n",
    "    value=0.0,\n",
    "    description='pX',\n",
    "    disabled=False,\n",
    "    layout=Layout(width='200px')\n",
    ")\n",
    "\n",
    "pointY = widgets.FloatText(\n",
    "    value=0.0,\n",
    "    description='pY',\n",
    "    disabled=False,\n",
    "    layout=Layout(width='200px')\n",
    ")\n",
    "\n",
    "\n",
    "addIdx = widgets.IntText(\n",
    "    value=0,\n",
    "    description='Add Idx',\n",
    "    disabled=False,\n",
    "    layout=Layout(width='200px')\n",
    ")\n",
    "\n",
    "delIdx = widgets.IntText(\n",
    "    value=0,\n",
    "    description='Del Idx',\n",
    "    disabled=False,\n",
    "    layout=Layout(width='200px')\n",
    ")\n",
    "\n",
    "show_hull = widgets.Checkbox(\n",
    "    value=False,\n",
    "    description='Show convex hull',\n",
    "    disabled=False,\n",
    "    indent=False\n",
    ")\n",
    "\n",
    "def hull_fun(val):\n",
    "    draw_Bezier_segment_n(points,out,val)\n",
    "\n",
    "def update_plot(b):\n",
    "    if b.description == 'Add Point':\n",
    "        points.insert(addIdx.value,[pointX.value,pointY.value])\n",
    "    if b.description == 'Del Point':\n",
    "        del points[delIdx.value]\n",
    "    draw_Bezier_segment_n(points,out,show_hull.value)\n",
    "    \n",
    "add_button = widgets.Button(description='Add Point',layout=Layout(width='200px'),merge=False)\n",
    "del_button = widgets.Button(description='Del Point',layout=Layout(width='200px'))\n",
    "add_button.on_click(update_plot)\n",
    "del_button.on_click(update_plot)\n",
    "\n",
    "grid = widgets.GridspecLayout(4, 2,width='460px')\n",
    "grid[0, 0] = pointX\n",
    "grid[1, 0] = pointY\n",
    "grid[2, 0] = addIdx\n",
    "grid[3, 0] = add_button\n",
    "grid[0, 1] = delIdx\n",
    "grid[1, 1] = del_button\n",
    "\n",
    "\n",
    "\n",
    "display(grid,out)\n",
    "interact(hull_fun,val=show_hull)\n",
    "draw_Bezier_segment_n(points,out,show_hull.value)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dac98b97",
   "metadata": {},
   "source": [
    "## Zadania "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2ba0722",
   "metadata": {},
   "source": [
    "\n",
    "   \\begin{enumerate}\n",
    "   \\item Do skryptu w punkcie 2.4. dodać możliwość modyfikacji dowolnego punktu kontrolnego (nieobowiązkowe)\n",
    "   \\item Napisać skrypt demonstrujący łączenie krzywych Beziera 3-go stopnia, z ciągłością parametryczną w punkcie łączenia.\n",
    "   \\item Napisać skrypt implementujący rysowanie krzywych Beziera 3-go stopnia algorytmem de Casteljau.\n",
    "   \\end{enumerate}\n",
    "    Alternatywnie napisać własny program realizujący te zadania.<p></p>\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9a635bc7",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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 },
 "nbformat": 4,
 "nbformat_minor": 5
}