commit 31e7c6e66c4c022441497c7742938f88ed63ec6c
Author: fraktal.wk <fraktal@amu.edu.pl>
Date:   Sat Nov 18 22:31:43 2023 +0100

    pierwszy

diff --git a/jupyter/.ipynb_checkpoints/parametric_curves_1-checkpoint.ipynb b/jupyter/.ipynb_checkpoints/parametric_curves_1-checkpoint.ipynb
new file mode 100644
index 0000000..6f28023
--- /dev/null
+++ b/jupyter/.ipynb_checkpoints/parametric_curves_1-checkpoint.ipynb
@@ -0,0 +1,1303 @@
+{
+ "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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RAMSTTz5Z5pgWLlwoAIjHH3/cZXqfPn0EAPHZZ5+Vus3Vq1fF3/72N9GiRQsREBAgwsPDRdu2bcWkSZNEZmamc77Y2FgxZsyYMpddcr6S61GtWjXRsGFDMWLECHHw4MFS83///ffigQceEHXq1BFWq1XUrVtX9OrVS7z99ttCiFu/K2V9bN++3eXxLPmyvxBCpKWliYEDB4patWoJq9Uq6tevLwYOHOiy3craRkJIr1gOHz5c1KxZU1SvXl3069dP/Pjjjx6/6ugrFiH4riO+cOXKFTRv3hzDhg3zeHebSlu0aBEmTJiAH3/8EX/4wx8UX36tWrUwduxY5ykhpCzDP2WTQ2ZmJubMmYOePXsiIiICZ8+exYIFC5CXl+d8CkjeOXjwIE6fPo0XX3wRQ4cOVTyM/ve//+Hzzz/Hb7/9hvj4eEWXTSUoti9mINnZ2WLQoEEiKipKWK1WER4eLpKSksTevXvVHppuxcbGCpvNJu65555SBxYqITExUdStW1dMnTrV5chtUhafshGRZhj+SG0i0g8GEhFpBgOJiDRD06+yFRcX48KFC6hevXqZpyQQkbYJIZCXl4fo6OgKDxzWdCBduHABMTExag+DiHzg3LlzaNCgQbnzaDqQqlevDkBaEU+vc2y327F161b07du3zLO7jYjrzfXWqtzcXMTExDj/nsuj6UByPE0LCwvzKpCCg4MRFham+Q3lS1xvrrfWeVK7sNQmIs1gIBGRZjCQiEgzGEhEpBkMJCLSDFkDacmSJWjXrp3zVbL4+Hh88cUXci6SiHRM1kBq0KABXnnlFezfvx/79+9Hr169MHToUPz0009yLpaIdErW45Buf4+wOXPmYMmSJdi7d68qVwMkIm1T7MDIoqIirFmzBvn5+bwiHxG5JXsg/fDDD4iPj8f169cRGhqK9evXo3Xr1m7nLSwsRGFhofP73NxcANJRqXa73aPlOebzdH6jUHO9C28WwuZf9ptHyonbW/vr7c0YZb9i5I0bN5Ceno4rV65g7dq1WLp0KdLS0tyG0uzZs92+rc/KlSsRHBws5zCJSCYFBQUYOXIkcnJyKjwFTPFL2N5zzz1o2rRpqbdIBtzvIcXExCArK8urc9lSUlLQp08f3Zzj4wtqrfeWE1vwbMqz+N9T/1NsmSVxe2t/vXNzcxEZGelRICl+cq0QwiV0SrLZbG7fN95qtXr9oFfmNkag5Hpfv3kdE1MmIrcwV/XHmttbu7wZn6yB9Pzzz6N///6IiYlBXl4ePv74Y6SmpmLz5s1yLpYU8saeN3Dyt5MI8g9SeyhkELIG0sWLFzFq1ChkZGQgPDwc7dq1w+bNm53vx076lZ6Tjr/v+DsA4NrNaygqLkI1v2oqj4r0TtZAWrZsmZx3TyqaunUqrt285vw+356PMJtnPR9RWXguG3lt26ltWHN4jcu0qzeuqjQaMhIGEnlFCIFNxzfhiQ5PAACqWaSnaQwk8gUGEnnFYrFgQb8F6B7bHQAwtctUTImfwkAin9D0NbVJu1LPpAIAejfujT5N+8BepP0jhkn7uIdElZJ6JhX+fv7oEtMFAGCtpu1jYUgfGEjktfO55/Fz9s/oWL8jQgJC1B4OGQgDibyWdjYNAJAYm6juQMhwGEjkNUd/lNgoUdVxkPEwkMhrt/dHRL7CQCKvsD8iOTGQyCvsj0hODCTyCvsjkhMDibzC/ojkxEAij7E/IrkxkMhj7I9Ibgwk8hj7I5IbA4k8xv6I5MZAIo+wPyIlMJDII+yPSAkMJPII+yNSAgOJPML+iJTAQKIKsT8ipTCQqELsj0gpDCSqEPsjUgoDiSrE/oiUwkCicrE/IiUxkKhc7I9ISQwkKhf7I1ISA4nKxf6IlMRAojKxPyKlMZCoTOyPSGkMJCoT+yNSGgOJysT+iJTGQCK32B+RGhhI5Bb7I1IDA4ncYn9EamAgkVvsj0gNDCQqhf0RqYWBRKWwPyK1MJCoFPZHpBYGEpXC/ojUwkAiF+yPSE0MJHLB/ojUxEAiF+yPSE0MJHLB/ojUxEAiJ/ZHpDYGEjmxPyK1MZDIif0RqY2BRE7sj0htDCQCwP6ItIGBRADYH5E2MJAIAPsj0gYGEgFgf0TawEAi9kekGQwkYn9EmsFAIvZHpBkMJGJ/RJrBQDI59kekJQwkk2N/RFrCQDI59kekJQwkk2N/RFrCQDIx9kekNQwkE2N/RFrDQDIx9kekNQwkE2N/RFrDQDIp9kekRQwkk2J/RFrEQDIp9kekRQwkk2J/RFrEQDIh9kekVQwkE2J/RFrFQDIh9kekVQwkE2J/RFrFQDIZ9kekZQwkk2F/RFrGQDIZ9kekZQwkk2F/RFrGQDIR9kekdQwkE2F/RFrHQDIR9kekdQwkE2F/RFrHQDIJ9kekBwwkk2B/RHrAQDIJ9kekBwwkk2B/RHrAQDIB9kekFwwkE2B/RHrBQDIB9kekFwwkE2B/RHrBQDI49kekJwwkg2N/RHrCQDI49kekJwwkg2N/RHrCQDIw9kekNwwkA2N/RHrDQDIw9kekNwwkA2N/RHrDQDIo9kekRwwkg2J/RHrEQDIo9kekRwwkg2J/RHrEQDIg9kekVwwkA2J/RHrFQDIg9kekVwwkA2J/RHrFQDKYjLwM9kekWwwkg9l1bhcA9kekTwwkg9l19vdAYn9EOsRAMpid6TvZH5FuMZAM5uRvJ9kfkW4xkAyI/RHpFQPJgNgfkV4xkAyG/RHpmayBNHfuXNx9992oXr066tSpg2HDhuHYsWNyLtK0MvIyAAAdojuwPyLdkjWQ0tLSMG7cOOzduxcpKSm4efMm+vbti/z8fDkXa0qO44+6xXRTeSRElecv551v3rzZ5fvly5ejTp06OHDgALp37y7nok1n19ldSEISEhomqD0UokpTtEPKyckBANSqVUvJxZrCzvSdAIBO9TupPBKiypN1D6kkIQQmT56Mbt26oU2bNm7nKSwsRGFhofP73NxcAIDdbofdbvdoOY75PJ3fCDLyMnAh5wIQA1gtVlOtuxm3N6Cv9fZmjBYhhJBxLE7jxo3Dpk2bsGvXLjRo0MDtPLNnz0ZycnKp6StXrkRwcLDcQyQiGRQUFGDkyJHIyclBWFhYufMqEkjjx4/Hhg0bsGPHDjRu3LjM+dztIcXExCArK6vCFXGw2+1ISUlBnz59YLVaqzx2PXjmi2ew6odVeK/Ne6Zab8Cc2xvQ13rn5uYiMjLSo0CS9SmbEALjx4/H+vXrkZqaWm4YAYDNZoPNZis13Wq1ev2gV+Y2erXt7DbYIe0Wm2m9S+J6a5c345M1kMaNG4eVK1fi008/RfXq1ZGZmQkACA8PR1BQkJyLNg3H9bMTGyaqPRSiKpP1VbYlS5YgJycHiYmJqFevnvNj1apVci7WVBzXz+bxR2QEsj9lI3k5rp+d0DAB145cU3cwRFXEc9l0znH9bB5/REbAQNKxku+/FhzAwyJI/xhIOsb3XyOjYSDpGN9/jYyGgaRjfP81MhoGkk6V7I94/SMyCgaSTrE/IiNiIOkU+yMyIgaSTrE/IiNiIOkQ+yMyKgaSDrE/IqNiIOkQ+yMyKgaSDrE/IqNiIOkM+yMyMgaSzrA/IiNjIOkM+yMyMgaSzrA/IiNjIOkI+yMyOgaSjrA/IqNjIOkI+yMyOgaSjrA/IqNjIOkE+yMyAwaSTrA/IjNgIOkE+yMyAwaSTrA/IjNgIOkA+yMyCwaSDrA/IrNgIOkA+yMyCwaSDrA/IrNgIGkc+yMyEwaSxrE/IjNhIGkc+yMyEwaSxrE/IjNhIGkY+yMyGwaShrE/IrNhIGkY+yMyGwaShrE/IrNhIGkU+yMyIwaSRrE/IjNiIGkU+yMyIwaSRrE/IjNiIGkQ+yMyKwaSBrE/IrNiIGkQ+yMyKwaSBrE/IrNiIGkM+yMyMwaSxrA/IjNjIGkM+yMyMwaSxrA/IjNjIGkI+yMyOwaShrA/IrNjIGkI+yMyOwaShrA/IrNjIGkE+yMiBpJmsD8iYiBpBvsjIgaSZrA/ImIgaQL7IyIJA0kD2B8RSRhIGsD+iEjCQNIA9kdEEgaSytgfEd3CQFIZ+yOiWxhIKmN/RHQLA0ll7I+IbmEgqYj9EZErBpKK2B8RuWIgqYj9EZErBpKK2B8RuWIgqYT9EVFpDCSVsD8iKo2BpBL2R0SlMZBUwv6IqDQGkgrYHxG5x0BSAfsjIvcYSCpgf0TkHgNJBeyPiNxjICmM/RFR2RhICmN/RFQ2BpLC2B8RlY2BpDD2R0RlYyApiP0RUfkYSApif0RUPgaSgtgfEZWPgaQg9kdE5WMgKYT9EVHFGEgKYX9EVDEGkkLYHxFVjIGkEPZHRBVjICmA/RGRZxhICmB/ROQZBpIC2B8ReYaBpAD2R0SeYSDJjP0RkecYSDJjf0TkOQaSzNgfEXmOgSQz9kdEnmMgyYj9EZF3GEgyYn9E5B0GkozYHxF5h4EkI/ZHRN5hIMmE/RGR9xhIMmF/ROQ9BpJM2B8ReY+BJBP2R0TeYyDJgP0RUeUwkGTA/oiochhIMmB/RFQ5DCQZsD8iqhwGko+xPyKqPH+1B2AUQgCXLwOf7N8P5EegR8NEtYekCMd6X70KhIYCERGAxaL2qEivuIdURVeuAAsXAs2aAbVrAxP7DwVey8KKx2Zg4ULp50Z0+3o3bix9btYMhl5vkhcDqQq2bAEaNAAmTQJOnXL9Wea5IEyaJP18yxZ1xieX8tb71CkYdr1JfgykStqyBRg4ELh2TXraIoTrz4WwQAjp5wMHGuePs+L1hiHXm5QhayDt2LEDgwcPRnR0NCwWCzZs2CDn4hRz5QowfLj0h1dcXP68xcXSfMOH6/9pjFnXm5QjayDl5+ejffv2WLx4sZyLUdz77wMFBRX/UToUF0vz//vf8o5LbmZdb1KOrIHUv39//P3vf8d9990n52IUJQSwaFHlbvuPf5R+iqMXZl1vUpamXvYvLCxEYWGh8/vc3FwAgN1uh91u9+g+HPN5Or+3srKAkyetXt9OCODkSeDiRTsiInw/Lq63POutVXpab2/GqKlAmjt3LpKTk0tN37p1K4KDg726r5SUFF8Ny8XFi0EA+lb69p99th1RUdd8N6DbcL3NRQ/rXVBQ4PG8FiGU2Zm2WCxYv349hg0bVuY87vaQYmJikJWVhbCwMI+WY7fbkZKSgj59+sBq9f4/ekWysoDo6Mrfb0aGfHsKXG/fr7dW6Wm9c3NzERkZiZycnAr/jjW1h2Sz2WCz2UpNt1qtXj/olbmNJ+rWBZo2lY638SbKLRagSRMgKsoq65HMXG9z0cN6ezM+HofkJYsFGD++credMEG/p1WYdb1JWbIG0tWrV3Ho0CEcOnQIAHD69GkcOnQI6enpci5WdqNHA8HBgJ+Hj56fnzT/o4/KOy65mXW9STmyBtL+/fsRFxeHuLg4AMDkyZMRFxeHmTNnyrlY2dWoAaxdK/3Xr+iP089Pmm/dOul2embW9SblyBpIiYmJEEKU+lixYoWci1VEUhKwaRMQFAQAxb9/3GKxSB9BQcDnnwN9K/8ClaaUXG/HOpZk1PUmZbBDqoKkJODbwxeAfhNhq33B5WdNmgBvvgmcP2+8P8qkJOCXX6T1a9LE9WdGXm+Sn6ZeZdOjQzmpQOdFmDylOqbEzUFeHlC9OlCrlrGL3Bo1pLJ6/HggOxumWW+SFwOpihzXz+7ZOBEREZDlWBsts1hgyvUmefApWxXx+tlEvsNAqgJeP5vItxhIVcD3XyPyLQZSFfD914h8i4FUBeyPiHyLgVRJ7I+IfI+BVEnsj4h8j4FUSeyPiHyPgVRJ7I+IfI+BVAnsj4jkwUCqBPZHRPJgIFUC+yMieTCQKoH9EZE8GEheYn9EJB8GkpfYHxHJh4HkJfZHRPJhIHmJ/RGRfBhIXmB/RCQvBpIX2B8RyYuB5AX2R0Ty4kX+vaB6f1RUBPzwA/Dtt8D//gecOgVcuAD89hv8CwvR126Hf0QEUKcOEBsLtGwJ/PGPQJcuQM2a6oyZyAsMJA85+qMuMV2U7Y+uXZPemfGTT4CtW4HffnM7mwVAECC9J9HPPwNff13ihxagQwdg8GDgoYeA5s2VGDmR1xhIHlK8PzpxAvjHP4B//xvIybk1vXp1oHNn4M47pWCpXx+oVQv2atXw9Y4d6Na+Pfyzs6W9p59+kvamjh0D9u+XPmbNArp1A/7yF+D++wF//gqQdvC30UOK9UdHjgDJycDq1YAQ0rSGDaU9m6FDgY4d3YeI3Y6c8+chuncHrFbXn2VmSu9r7djL2rVL+nj+eWDGDGD06NK3IVIBS20Pyd4fZWVJey1t2gCrVklhNHAgsGULcPo08OqrUhdUmT2aunWBsWOlUEpPlwKvdm3gzBng8ceBdu2AL77w+SoReYuB5AFZjz8SAvjgA6mAXrIEKC6W9oQOHQI2bgT69gX8fLiZoqOBmTOBs2eBN94AIiOBo0eBAQOkp3CZmb5bFpGXGEgekK0/unQJuPde4NFHgcuXgbZtgbQ0YMMGoH173y7rdkFBwKRJUlc1ZQpQrZr0lK5NG2DtWnmXTVQGBpIHZOmPdu6UiulPP5X6mzlzgAMHgO7dfbcMT4SHA/PnS4X3nXdKwThihPT0sbBQ2bGQ6TGQPODT/kgIYNEioFcv6RiiVq2kMHj+eXWL5TvvBL75Bpg+Xfp+yRKgRw9pjEQKYSBVwKf90c2bwLhxwIQJ0tcjRwL79kmlshYEBABz50oFd82aUkB17Ah8/73aIyOTYCBVwGf90bVrwPDh0p6HxQK89hrw4YdAiAZP0u3XTwrKli2B8+eBhARg+3a1R0UmwECqgE/6o6tXpVexPvsMsNmk8njqVCmYtKppU2DPHiAxEcjLk0Jq40a1R0UGx0CqQJX7I8cfc2qqdJT1li3Afff5dIyyqVFDevo2bBhw44Y07k8/VXtUZGAMpHJUuT8qKJDOH/v6a+mPe9s2qSjWk8BA6ajxhx4C7HbpWCUeREkyYSCVo0r9kd0OPPCAdFxRWJh0ykbHjr4doFKsVungzfvvl9brvvukU0+IfIyBVI5K90dCAP/v/0ln6QcGSp/vvtvn41OUvz/w0UfS6SzXr0t7focPqz0qMhgGUjkq3R/9/e/AihXS0c9r1khn1xuB1So9fevSBbhyRSrqL15Ue1RkIAykMlS6P1qzRjpXDADeegsYNEieAaolOFgqtu+4Qzof7t57eUQ3+QwDqQyV6o++/x4YM0b6etIk4IknfD4uTYiMlJ6G1qghHRrwl7/culQKURUwkMrgdX905Yp04GNBgXSG/rx5cg1NG5o3ly6T4ucHvPcesHSp2iMiA2AglcGr/kgIac/o5EnpWtYrV5rjSox9+0p9GQA8/TTw3Xfqjod0j4Hkhtf90eLFUq8SECBduiMiQv5BasW0adIrbjduAA8+KB0ISlRJDCQ3vOqPfvhBOg0EkM5P69BBvoFpkZ8fsHw50KCBdG2lZ55Re0SkYwwkNzzuj65fBx55RNo7GDQIGD9e9rFpUkSE9DTVYpHCiRd4o0piILnhcX80e7a0h1SnDrBsmbZPlpVbQsKtayk98QSPT6JKYSDdxuP+aO9e6SkaALz7rhRKZjd7tnTp3cuXgaee4qEA5DUG0m086o9u3AAee0y6IP+f/iRdlJ+kUv/996VXGNevB9atU3tEpDMMpNt41B+98op0Hlft2sCbbyoxLP1o3/7WU7enny7znXaJ3GEg3abC/ujnn6UL8gPAwoXmeonfUzNmAC1aSG+pNGOG2qMhHWEglVBhfySEdE3sGzekgwIfekj5QepBYKB0qV4AePtt6e28iTzAQCqhwv5o/XogJUW6DO1bb5n7VbWK9OwJjBolhfjTT0t9G1EFGEgllNsfXbsGTJ4sff3Xv0pnu1P55s2TLtu7b590ORaiCjCQSii3P3rjDelyGzExt0pbKl/dusCsWdLXzz/P00qoQgyk35XbH2VmSu9XBgCvvipdE4g8M368tDd58aL02BGVg4H0u3L7o1mzgPx86ZrYLLK9ExBw61Isr78O/PKLuuMhTWMg/a7M/ujYMem0EED6g2KR7b1hw6RTS65fl47mJioDA+l3ZfZHf/sbUFQEDBlinGtjK81iufV0bfly4MgRdcdDmsVAQjn90XffSe8ya7EAL7+s3gCNID5e2lMqLuZeEpWJgYRy+qMXXpA+P/II8Ic/KDsoI3rxRSncV68GDh1SezSkQQwklNEfffst8Pnn0lsZOV66pqpp2/bWiwLJyeqOhTSJgYQy+iPHH8yoUTwI0pdeeEHaS9qwQXqXFqISTB9IbvujAwekvSM/P+mAPvKdVq2ktxgHgJdeUncspDmmDyS3/ZHjIMiHHwaaNVN+UEb3t79Jn9etA44eVXcspCmmD6RS/dHRo7cuLPbcc6qMyfDatJEuaicEj94mFwyk2/uj+fOlP5ShQ/nKmpwcYf/RR8D58+qOhTTD1IFUqj/KyAA++ED64V//qu7gjK5TJ6B7d8Buly50RwSTB1Kp/mjxYunia126SB8kr2eflT6/8w6Qm6vuWEgTTB1ILv1RQYF0dUPg1hs/krwGDABatpTCaPlytUdDGmD6QHL2Rx98AGRnA40bS+etkfz8/ICJE6WvFy6UzhkkUzNtILn0R9Zg4B//kH4wfrx0dDYpY9QooFYt4PRpYONGtUdDKjNtILn0R6mp0tsahYQAY8eqOi7TCQ6W3uMOkK5TTqZm2kBy6Y8WL5YmjhoFhIerNibTeuop6XSSlBQeKGlypg4kfz9/dPVrBHz6qTRx3DhVx2RajRsDgwZJX7/zjrpjIVWZMpBK9kfBH/xHKlMTEqQjiEkdTz0lfV6xQnqHFzIlUwaSoz/q2aA78K9/SROffFLFERH69gUaNQKuXJGul0SmZMpAcvRHw8+FShedr1ULuO8+dQdldtWqAY8/Ln29dKm6YyHVmDaQ/P380fa/30gTHn1UevtnUteYMdKxSbt2sdw2KdMFkqM/6lv9Tvhv+kKa6HjZmdQVHQ0MHCh97XinFzIV0wWSoz8adywcuHlTeq81ltna4TgO7MMPpe1DpmK6QEo9kwoIIOGrk9KE//s/VcdDtxk4EKhdW3q34C1b1B4NKcyUgXT3xWqofvwMYLMBDz6o9pCoJKsV+NOfpK9XrFB1KKQ8UwWSoz+acrKONGHIEKBmTXUHRaWNHi19/u9/pcMAyDRMFUhpZ9NQrQgYsP/3a++MGqXugMi9du2kXq+wUHqjTjINUwVS6plU9D4NVP8tH4iMBPr1U3tI5I7FcuufheMKnmQKpgukP/1okb554AGpryBtevhh6fPOndLBq2QKpgmk87nnce7iz7jv6O+r7PiFJ22KiQG6dZPecGHVKrVHQwoxTSClnU3DgJ+BkOtF0i87r5mtfY5/Gv/5j7rjIMWYJpBSz6TigZ9+/+bBB6VTFEjb7r9f2k4HDgAnT6o9GlKAaf4q9x7/CoOO//4Njz3Sh9q1gV69pK/XrFF3LKQIUwTS+dzzaP7NSYTYIV0MrEMHtYdEnnrgAekzL0liCqYIpLSzaRhx+PdvRoyQXlYmfbj3Xulp28GDwKlTao+GZGaKQNp1fBsG/vz7N8OHqzoW8lJkJNCjh/T1+vXqjoVkZ4pAKtqyGdVvAMUN6gN33632cMhbjn8ia9eqOw6SneED6XzueXTalwEA8Bt2L19d06Nhw6TPe/YAGRmqDoXkZfi/zh2ntt96de3ee1UdC1VS/frSdasA6YRbMizDB9K5LatRpwCwh4VK7yxC+jR0qPTZ8ZZVZEiGD6RaW3cCACwDB/LcNT1zPG3btg24elXVoZB8DB1I53PPo+v/rgAA/IfxXUV0rVUroEkT4MYNKZTIkAwdSAd2rkKrLKComh+QlKT2cKgqLBZg8GDpa/ZIhmXoQLq2Xjrd4ErHdkB4uMqjoSpzvN32pk1AcbG6YyFZGDqQ6u/6HwAgdBjPXTOE7t2B0FDg4kXpyG0yHMMGUmbmSdx9ogAAYBsyTN3BkG8EBAD33CN9/cUX6o6FZGHYQDq1filsRUB23RpAixZqD4d8ZcAA6fPnn6s7DpKFYQMJmzcDAPJ7J/BkWiPp31/6/M03QHa2umMhnzNsIDXeJ51NW/veP6k8EvKpBg2A1q2lUvvLL9UeDfmYIQMpOCMDDbNu4GY1CwL79ld7OORrjkM4+M62hmOoQBICyMoC7DvPIQsRONs6BqheXe1hka/9Hkhi8xZkXRK4eDEIWVnS9id9M0QgXbkCLFwINGsGREdbMXDlHNRGFnqlf4uFC/nmp0ZzpW0CFlabjGbntyO6fgCeeKIvoqOtaNYM3N46p/tA2rJFqhUmTSp9QcFzuXUwaZL0c+7dG8OWLUCD5sGYVPQaTqGJy89OnQK3t87pOpC2bAEGDgSuXZN212/fZRfCAiGknw8cyF9SvXPZ3vCDuO3X1/E7wO2tX4oE0j//+U80btwYgYGB6NChA3bu3Fnl+7xyRbqQoBAVn0VQXCzNN3w4d+f1itvbHGQPpFWrVmHixImYMWMGDh48iISEBPTv3x/p6elVut/33wcKCjw/pam4WJr/3/+u0mJJJdze5iB7IL3xxht47LHH8Oc//xmtWrXCm2++iZiYGCxZsqTS9ykEsGhR5W77j3/w1Ri94fY2D3857/zGjRs4cOAApk+f7jK9b9++2L17d6n5CwsLUVhY6Pw+NzcXAGC322G3253Ts7KAkye9v9iaENIboF68aEdEhNc31zTH41PycTIKbu/S9LS9vRmjrIGUlZWFoqIiREVFuUyPiopCZmZmqfnnzp2L5OTkUtO3bt2K4OBg5/cXLwYB6FvpcX322XZERV2r9O21LCUlRe0h+By3d9n0sL0LCgo8ntcihHw7tBcuXED9+vWxe/duxMfHO6fPmTMHH3zwAY4ePeoyv7s9pJiYGGRlZSEsLMw5PStLOt6osjIyjPkfMyUlBX369IHVYJfq5fYuTU/bOzc3F5GRkcjJyXH5O3ZH1j2kyMhIVKtWrdTe0K+//lpqrwkAbDYbbDZbqelWq9XlQa9bF2jaVDruxJs4tVikq6BGRVkNe77t7Y+VEXB7l00P29ub8claagcEBKBDhw6lditTUlLQpUuXSt+vxQKMH1+5206YwJP/9Ybb2zxkf5Vt8uTJWLp0Kd577z0cOXIEkyZNQnp6Op588skq3e/o0UBwsOfv++jnJ83/6KNVWiyphNvbHGR9ygYADz74IC5fvowXX3wRGRkZaNOmDT7//HPExsZW6X5r1JDeWXngQOmXr7zjU/z8pP+S69ZJtyP94fY2B0WO1P7LX/6CM2fOoLCwEAcOHED37t19cr9JSdL13oOCpF/A23fNHdOCgqQLDPat/As1pAEVb2/B7a1zuj6XDZB+SX/5BXjzTanALKlJE2n6+fP85TSK8rZ39bpZ3N46p/tAAqTd8gkTgJ9/ll7ifeedrcjIsOPnn6XpfAckY3G3vYOfa4iYv/Xi9tY5QwSSg8UCREQAUVHXEBHBV1eMruT2bt+4AX669CMu5V9Se1hUBYYKJDKvhIYJAIAdZ3eoPBKqCgYSGUK3ht0AAKlnUtUdCFUJA4kMIT4mHn4WP6SeTVV7KFQFDCQyhHBbOOLqxuHHX9kj6RkDiQwjsVEiAPZIesZAIsNwBBJ7JP1iIJFhdGvYjT2SzjGQyDBqBNZgj6RzDCQyFPZI+sZAIkNhj6RvDCQyFPZI+sZAIkNhj6RvDCQyHPZI+sVAIsNhj6RfDCQyHPZI+sVAIsNhj6RfDCQyJPZI+sRAIkNij6RPDCQyJPZI+sRAIkNij6RPDCQyLPZI+sNAIsNij6Q/DCQyLPZI+sNAIsNij6Q/DCQyNPZI+sJAIkNjj6QvDCQyNPZI+sJAIkNjj6QvDCQyPPZI+sFAIsNjj6QfDCQyPPZI+sFAIsNjj6QfDCQyBfZI+sBAIlNgj6QPDCQyBfZI+sBAIlNgj6QPDCQyDfZI2sdAItNgj6R9DCQyDfZI2sdAItNgj6R9DCQyFfZI2sZAIlNhj6RtDCQyFfZI2sZAIlNhj6RtDCQyHfZI2sVAItNhj6RdDCQyHfZI2sVAItNhj6RdDCQyJfZI2sRAIlNij6RNDCQyJfZI2sRAIlNij6RNDCQyLfZI2sNAItNij6Q9DCQyLfZI2sNAItNij6Q9DCQyNfZI2sJAIlNjj6QtDCQyNfZI2sJAIlNjj6QtDCQyPfZI2sFAItNjj6QdDCQyPfZI2sFAItNjj6QdDCQisEfSCgYSEdgjaQUDiQjskbSCgUQE9khawUAi+h17JPUxkIh+xx5JfQwkot+xR1IfA4nod+yR1MdAIiqBPZK6GEhEJbBHUhcDiagE9kjqYiARlcAeSV0MJKLbsEdSDwOJ6DbskdTDQCK6DXsk9TCQiG7DHkk9DCQiN9gjqYOBROQGeyR1MJCI3GCPpA4GEpEb7JHUwUAiKgN7JOUxkIjKwB5JeQwkojKwR1IeA4moDOyRlMdAIioHeyRlMZCIysEeSVkMJKJysEdSFgOJqBzskZTFQCKqAHsk5TCQiCrAHkk5DCSiCrBHUg4DiagC7JGUw0Ai8gB7JGUwkIg8wB5JGQwkIg+wR1IGA4nIA+yRlMFAIvIQeyT5MZCIPMQeSX4MJCIPsUeSHwOJyEPskeTHQCLyAnskeTGQiLzAHkleDCQiL7BHkhcDicgL7JHkxUAi8hJ7JPkwkIi8xB5JPrIG0pw5c9ClSxcEBwejRo0aci6KSDHskeQjayDduHED999/P5566ik5F0OkKPZI8pE1kJKTkzFp0iS0bdtWzsUQKY49kjz81R5ASYWFhSgsLHR+n5ubCwCw2+2w2+0e3YdjPk/nNwqut7Lr3SOmB/75zT+x8/RODGk2RNFlA/ra3t6M0SKEEDKOBQCwYsUKTJw4EVeuXCl3vtmzZyM5ObnU9JUrVyI4OFim0RGRnAoKCjBy5Ejk5OQgLCys3Hm93kMqKzRK2rdvH+666y5v7xrPPfccJk+e7Pw+NzcXMTEx6Nu3b4Ur4mC325GSkoI+ffrAarV6PQa94norv949VvTAocxDODnhJCKDIxVdtp62t+OZjie8DqSnn34aDz30ULnzNGrUyNu7BQDYbDbYbLZS061Wq9cPemVuYwRcb+V0ie2CPRf2YPf53Rjeeriiy3bQw/b2ZnxeB1JkZCQiI5X9b0CkRYmNEvH6nteReiZVtUAyGllL7fT0dGRnZyM9PR1FRUU4dOgQAOCOO+5AaGionIsmkh2PR/I9WQNp5syZeP/9953fx8XFAQC2b9+OxMREORdNJDvH8UgHMg7gUv4l1A6prfaQdE/W45BWrFgBIUSpD4YRGQWPR/ItnstGVAU8r823GEhEVcAeybcYSERVwPPafIuBRFRF7JF8h4FEVEXskXyHgURUReyRfIeBRFRF7JF8h4FE5APskXyDgUTkA+yRfIOBROQD7JF8g4FE5APskXyDgUTkI+yRqo6BROQj7JGqjoFE5CPskaqOgUTkI+yRqo6BRORD7JGqhoFE5EPskaqGgUTkQ+yRqoaBRORD7JGqhoFE5GPskSqPgUTkY+yRKo+BRORj7JEqj4FE5GPskSqPgUQkA/ZIlcNAIpIBe6TKYSARyYA9UuUwkIhkwB6pchhIRDJhj+Q9BhKRTNgjeY+BRCQT9kjeYyARyYQ9kvcYSEQyYo/kHQYSkYzYI3mHgUQkI/ZI3mEgEcmIPZJ3GEhEMmOP5DkGEpHM2CN5joFEJDP2SJ5jIBHJjD2S5xhIRApgj+QZBhKRAtgjeYaBRKQA9kieYSARKYA9kmcYSEQKYY9UMQYSkULYI1WMgUSkEPZIFWMgESmEPVLFGEhECmKPVD4GEpGC2COVj4FEpCD2SOVjIBEpiD1S+RhIRApjj1Q2BhKRwtgjlY2BRKQw9khlYyARKYw9UtkYSEQqYI/kHgOJSAXskdxjIBGpgD2SewwkIhWwR3KPgUSkEvZIpTGQiFTCHqk0BhKRStgjlcZAIlIJe6TSGEhEKmKP5IqBRKQi9kiuGEhEKmKP5IqBRKQi9kiuGEhEKmOPdAsDiUhl7JFuYSARqYw90i0MJCKVsUe6hYFEpAHskSQMJCINYI8kYSARaQB7JAkDiUgD2CNJGEhEGsEeiYFEpBnskRhIRJrBHomBRKQZ7JEYSESaYvYeiYFEpCFm75EYSEQaYvYeiYFEpCFm75EYSEQaY+YeiYFEpDFm7pEYSEQaY+YeiYFEpDFm7pEYSEQaZNYeiYFEpEFm7ZEYSEQaZNYeiYFEpEFm7ZEYSEQaZcYeiYFEpFFm7JEYSEQaZcYeiYFEpFFm7JEYSEQaZrYeiYFEpGFm65EYSEQaZrYeiYFEpGFm65EYSEQaZ6YeiYFEpHFm6pEYSEQaZ6YeiYFEpHFm6pEYSEQ6YJYeiYFEpANm6ZEYSEQ6YJYeiYFEpANm6ZEYSEQ6YYYeiYFEpBNm6JEYSEQ6YYYeiYFEpBMle6Ssgiy1hyMLBhKRjjietu1O363uQGTCQCLSEUcg7Uzfqe5AZCJbIJ05cwaPPfYYGjdujKCgIDRt2hSzZs3CjRs35FokkeE5eqRd53apPRRZ+Mt1x0ePHkVxcTHeeecd3HHHHfjxxx/x+OOPIz8/H/Pnz5drsUSG5uiRDl88DNRTezS+J1sg9evXD/369XN+36RJExw7dgxLlixhIBFVQWKjRCmQDEjRDiknJwe1atVScpFEhuPokYxItj2k2508eRKLFi3C66+/XuY8hYWFKCwsdH6fm5sLALDb7bDb7R4txzGfp/MbBdfbPOvdObozQqqFANDHenszRosQQnhz57Nnz0ZycnK58+zbtw933XWX8/sLFy6gR48e6NGjB5YuXer1fa9cuRLBwcHeDJOINKKgoAAjR45ETk4OwsLCyp3X60DKyspCVlb5B2U1atQIgYGBAKQw6tmzJzp16oQVK1bAz6/sZ4nu9pBiYmKQlZVV4Yo42O12pKSkoE+fPrBarR7dxgi43uZa71lfzkKnwk642egmhrQeovZwypWbm4vIyEiPAsnrp2yRkZGIjIz0aN7z58+jZ8+e6NChA5YvX15uGAGAzWaDzWYrNd1qtXr9y1aZ2xgB19sc4mPjUXy8GDt/2Ynh7YerPZxyebNdZCu1L1y4gMTERMTExGD+/Pm4dOkSMjMzkZmZKdciiUwjPiYeAAx3PJJspfbWrVtx4sQJnDhxAg0aNHD5mZfPEonoNuG2cADA4UuHcSn/EmqH1FZ5RL4h2x7SmDFjIIRw+0FEvmOk6yPxXDYinTPS9ZEYSEQ6ZrTrIzGQiHSsXVQ7Q11nm4FEpGMJDRMAGKdHYiAR6Vi3ht0AGKdHYiAR6Vh8TLyheiQGEpGOhdvCDfV+bQwkIp0z0vu1MZCIdM5I79fGQCLSOSO9XxsDiUjnSr5fm957JAYSkQEYpUdiIBEZgFF6JAYSkQEYpUdiIBEZgFF6JAYSkUEYoUdiIBEZhBF6JAYSkUEYoUdiIBEZhBF6JAYSkYHovUdiIBEZiN57JAYSkYHovUdiIBEZiN57JAYSkcHouUdiIBEZjJ57JAYSkcHouUdiIBEZjJ57JAYSkQHptUdiIBEZkF57JAYSkQHptUdiIBEZkF57JAYSkUHpsUdiIBEZlB57JAYSkUHpsUdiIBEZlB57JAYSkYHprUdiIBEZmN56JAYSkYHprUdiIBEZmN56JAYSkcHpqUdiIBEZnJ56JAYSkcHpqUdiIBEZnJ56JAYSkQnopUdiIBGZgF56JAYSkQnopUdiIBGZgF56JAYSkUnooUdiIBGZhB56JAYSkUnooUdiIBGZhB56JAYSkYlovUdiIBGZiNZ7JAYSkYlovUdiIBGZiNZ7JAYSkclouUdiIBGZjJZ7JAYSkclouUdiIBGZjJZ7JAYSkQlptUdiIBGZkFZ7JAYSkQlptUdiIBGZkFZ7JAYSkUlpsUdiIBGZlBZ7JAYSkUlpsUdiIBGZlBZ7JAYSkYlprUdiIBGZmNZ6JAYSkYlprUdiIBGZmNZ6JAYSkclpqUdiIBGZnJZ6JAYSkclpqUdiIBGZnJZ6JAYSEWmmR2IgEZFmeiQGEhFppkdiIBGRZnokBhIRAdBGj8RAIiIA2uiRGEhEBEAbPRIDiYgAaKNHYiARkZPaPRIDiYic1O6RGEhE5KR2j8RAIiIntXskBhIRuVCzR2IgEZELNXskBhIRuVCzR2IgEZELNXskBhIRlaJWj8RAIqJS1OqRGEhEVIpaPRIDiYhKUatHYiARkVtq9EgMJCJyS40eiYFERG6p0SMxkIjILTV6JAYSEZVJ6R6JgUREZVK6R2IgEVGZlO6RGEhEVCaleyQGEhGVS8keiYFEROVSskdiIBFRuZTskRhIRFQuJXskBhIRVUipHomBREQVKtkjFdgL8MPFH2RZDgOJiMp1/PJxdKrfCX4WP3xx4gskrkjEwcyDsiyLgURE5Uo9k4qYBTEAgJO/ncS+C/sQGhAqy7IYSERUrsfiHkObOm1QLIqd0xhIRKSKan7V8NaAt1ym6TKQhgwZgoYNGyIwMBD16tXDqFGjcOHCBTkXSUQy6NSgE8beOdb5fYg1RJblyBpIPXv2xOrVq3Hs2DGsXbsWJ0+exIgRI+RcJBHJZO49cxFuCweg0z2kSZMmoXPnzoiNjUWXLl0wffp07N27F3a7Xc7FEpEM6oTUwUs9XwKg00AqKTs7Gx999BG6dOkCq9Wq1GKJyIeeuvsptItqJ1sg+ctyryVMmzYNixcvRkFBATp37oyNGzeWOW9hYSEKCwud3+fm5gIA7Ha7x3tVjvnMthfG9eZ6K2VR0iL4w9/rv0lPWIQQwpvBzJ49G8nJyeXOs2/fPtx1110AgKysLGRnZ+Ps2bNITk5GeHg4Nm7cCIvF4vF9r1y5EsHBwd4Mk4g0oqCgACNHjkROTg7CwsLKndfrQMrKykJWVla58zRq1AiBgYGlpv/yyy+IiYnB7t27ER8fX+rn7vaQYmJikJWVVeGKONjtdqSkpKBPnz6memrI9eZ6a1Vubi4iIyM9CiSvn7JFRkYiMjKyUgNzZF/J0CnJZrPBZrOVmm61Wr1+0CtzGyPgepuLHtbbm/HJ1iF9++23+Pbbb9GtWzfUrFkTp06dwsyZM9G0aVO3e0dERLK9yhYUFIR169ahd+/eaNGiBcaOHYs2bdogLS3N7V4QEZFse0ht27bFV199JdfdE5EB8Vw2ItIMBhIRaQYDiYg0g4FERJrBQCIizZD9XLaqcBxI6TinzRN2ux0FBQXIzc3V/AFjvsT15nprlePv15OTQjQdSHl5eQCAmJgYlUdCRFWVl5eH8PDwcufx+lw2JRUXF+PChQuoXr2625Nx3XGc/3bu3DmPz38zAq4311urhBDIy8tDdHQ0/PzKb4k0vYfk5+eHBg0aVOq2YWFhmt9QcuB6m4te1ruiPSMHltpEpBkMJCLSDMMFks1mw6xZs0x3Ai/Xm+ttBJoutYnIXAy3h0RE+sVAIiLNYCARkWYwkIhIM3QXSP/85z/RuHFjBAYGokOHDti5c2e586elpaFDhw4IDAxEkyZN8Pbbbys0Ut/zZt1TU1NhsVhKfRw9elTBEVfNjh07MHjwYERHR8NisWDDhg0V3sYI29vb9TbCtnbQVSCtWrUKEydOxIwZM3Dw4EEkJCSgf//+SE9Pdzv/6dOnMWDAACQkJODgwYN4/vnnMWHCBKxdu1bhkVedt+vucOzYMWRkZDg/mjVrptCIqy4/Px/t27fH4sWLPZrfKNvb2/V20PO2dhI60rFjR/Hkk0+6TGvZsqWYPn262/n/+te/ipYtW7pMe+KJJ0Tnzp1lG6NcvF337du3CwDit99+U2B08gMg1q9fX+48RtreDp6st5G2tW72kG7cuIEDBw6gb9++LtP79u2L3bt3u73Nnj17Ss2flJSE/fv36+qtlyuz7g5xcXGoV68eevfuje3bt8s5TNUZZXtXlhG2tW4CKSsrC0VFRYiKinKZHhUVhczMTLe3yczMdDv/zZs3K3z3XS2pzLrXq1cP7777LtauXYt169ahRYsW6N27N3bs2KHEkFVhlO3tLSNta02f7e/O7ZchEUKUe2kSd/O7m64H3qx7ixYt0KJFC+f38fHxOHfuHObPn4/u3bvLOk41GWl7e8pI21o3e0iRkZGoVq1aqT2CX3/9tdR/RYe6deu6nd/f3x8RERGyjdXXKrPu7nTu3Bk///yzr4enGUbZ3r6g122tm0AKCAhAhw4dkJKS4jI9JSUFXbp0cXub+Pj4UvNv3boVd911l+Yv+1lSZdbdnYMHD6JevXq+Hp5mGGV7+4Jut7W6nbp3Pv74Y2G1WsWyZcvE4cOHxcSJE0VISIg4c+aMEEKI6dOni1GjRjnnP3XqlAgODhaTJk0Shw8fFsuWLRNWq1V88sknaq1CpXm77gsWLBDr168Xx48fFz/++KOYPn26ACDWrl2r1ip4LS8vTxw8eFAcPHhQABBvvPGGOHjwoDh79qwQwrjb29v1NsK2dtBVIAkhxFtvvSViY2NFQECA+OMf/yjS0tKcPxs9erTo0aOHy/ypqakiLi5OBAQEiEaNGoklS5YoPGLf8WbdX331VdG0aVMRGBgoatasKbp16yY2bdqkwqgrz/Fy9u0fo0ePFkIYd3t7u95G2NYOvPwIEWmGbjokIjI+BhIRaQYDiYg0g4FERJrBQCIizWAgEZFmMJCISDMYSESkGQwkItIMBhIRaQYDiYg0g4FERJrx/wFbd7L2ryX2SwAAAABJRU5ErkJggg==\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": {
+      "application/vnd.jupyter.widget-view+json": {
+       "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"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c56363fef08f414a8db211f01116d59e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e351bb5029214249abd6e2d4218c8d07",
+       "version_major": 2,
+       "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": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.13"
+  },
+  "latex_envs": {
+   "LaTeX_envs_menu_present": true,
+   "autoclose": false,
+   "autocomplete": true,
+   "bibliofile": "biblio.bib",
+   "cite_by": "apalike",
+   "current_citInitial": 1,
+   "eqLabelWithNumbers": true,
+   "eqNumInitial": 1,
+   "hotkeys": {
+    "equation": "Ctrl-E",
+    "itemize": "Ctrl-I"
+   },
+   "labels_anchors": false,
+   "latex_user_defs": false,
+   "report_style_numbering": false,
+   "user_envs_cfg": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/jupyter/buttons.ipynb b/jupyter/buttons.ipynb
new file mode 100644
index 0000000..d9a784c
--- /dev/null
+++ b/jupyter/buttons.ipynb
@@ -0,0 +1,132 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "# Buttons\n",
+    "\n",
+    "Constructing a simple button GUI to modify a sine wave.\n",
+    "\n",
+    "The ``next`` and ``previous`` button widget helps visualize the wave with\n",
+    "new frequencies.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib.widgets import Button\n",
+    "\n",
+    "freqs = np.arange(2, 20, 3)\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "plt.subplots_adjust(bottom=0.2)\n",
+    "t = np.arange(0.0, 1.0, 0.001)\n",
+    "s = np.sin(2*np.pi*freqs[0]*t)\n",
+    "l, = plt.plot(t, s, lw=2)\n",
+    "\n",
+    "\n",
+    "class Index:\n",
+    "    ind = 0\n",
+    "\n",
+    "    def next(self, event):\n",
+    "        self.ind += 1\n",
+    "        i = self.ind % len(freqs)\n",
+    "        ydata = np.sin(2*np.pi*freqs[i]*t)\n",
+    "        l.set_ydata(ydata)\n",
+    "        plt.draw()\n",
+    "\n",
+    "    def prev(self, event):\n",
+    "        self.ind -= 1\n",
+    "        i = self.ind % len(freqs)\n",
+    "        ydata = np.sin(2*np.pi*freqs[i]*t)\n",
+    "        l.set_ydata(ydata)\n",
+    "        plt.draw()\n",
+    "\n",
+    "callback = Index()\n",
+    "axprev = plt.axes([0.7, 0.05, 0.1, 0.075])\n",
+    "axnext = plt.axes([0.81, 0.05, 0.1, 0.075])\n",
+    "bnext = Button(axnext, 'Next')\n",
+    "bnext.on_clicked(callback.next)\n",
+    "bprev = Button(axprev, 'Previous')\n",
+    "bprev.on_clicked(callback.prev)\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.5"
+  },
+  "latex_envs": {
+   "LaTeX_envs_menu_present": true,
+   "autoclose": false,
+   "autocomplete": true,
+   "bibliofile": "biblio.bib",
+   "cite_by": "apalike",
+   "current_citInitial": 1,
+   "eqLabelWithNumbers": true,
+   "eqNumInitial": 1,
+   "hotkeys": {
+    "equation": "Ctrl-E",
+    "itemize": "Ctrl-I"
+   },
+   "labels_anchors": false,
+   "latex_user_defs": false,
+   "report_style_numbering": false,
+   "user_envs_cfg": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/jupyter/ipywidgets-tutorial-master/.gitignore b/jupyter/ipywidgets-tutorial-master/.gitignore
new file mode 100644
index 0000000..114a501
--- /dev/null
+++ b/jupyter/ipywidgets-tutorial-master/.gitignore
@@ -0,0 +1,6 @@
+.ipynb_checkpoints/
+.venv/
+
+*.pyc
+*.~
+*.swp
diff --git a/jupyter/ipywidgets-tutorial-master/README.md b/jupyter/ipywidgets-tutorial-master/README.md
new file mode 100644
index 0000000..7c089bc
--- /dev/null
+++ b/jupyter/ipywidgets-tutorial-master/README.md
@@ -0,0 +1,8 @@
+# Ipywidgets with matplotlib
+
+
+The notebook ipywidgets-tutorial.ipynb is the basis for the tutorial on
+[kapenikov.com](www.kapernikov.com)
+
+Please find a live version hosted by binder as well
+[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/Kapernikov/ipywidgets-tutorial/master?filepath=ipywidgets-tutorial.ipynb)
diff --git a/jupyter/ipywidgets-tutorial-master/ipywidgets-tutorial.ipynb b/jupyter/ipywidgets-tutorial-master/ipywidgets-tutorial.ipynb
new file mode 100644
index 0000000..fae91d1
--- /dev/null
+++ b/jupyter/ipywidgets-tutorial-master/ipywidgets-tutorial.ipynb
@@ -0,0 +1,1317 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Ipywidgets with matplotlib\n",
+    "\n",
+    "This tutorial gives a brief introduction into using ipywidgets in jupyter notebooks. Ipywidgets provide a set of building blocks for graphical user interfaces that are powerful, yet easy to use. \n",
+    "\n",
+    "A simple use case could be, adding some basic controls to a plot for interactive data exploration. On the other side of the spectrum, we can combine widgets together to build full-fledged graphical user interfaces.\n",
+    "\n",
+    "Here, we first introduce the `interact` function, which is a convenient way to quickly create suitable widgets to control functions. Second, we look into specific widgets, and stack them together to build a basic gui application. \n",
+    "\n",
+    "\n",
+    "## Requirements\n",
+    "\n",
+    "The notebook used for this tutorial is available on [github](https://github.com/Kapernikov/ipywidgets-tutorial), together with a link to a live version on [binder](https://mybinder.org/).\n",
+    "\n",
+    "To run the notebook locally, the very first requirement is a working jupyter environment. Setting up an installation lies outside the scope of the tutorial, but can be found in the official [docs](https://jupyter.readthedocs.io/en/latest/install.html). Note that for this tutorial all libraries were installed using pip, or the pacman package manager. Anaconda (at least on windows 10) currently has a matplotlib issue that gives some problems. Therefore, if you have problems displaying plots correctly, try using pip only, or linux. The examples were tested on Windows 10 and Arch Linux.\n",
+    "\n",
+    "The versions of packages explicitly used to create the examples are:\n",
+    " * python 3.8.1\n",
+    " * matplotlib 3.1.3\n",
+    " * numpy 1.18.1\n",
+    " * ipywidgets 7.5.1\n",
+    " * ipympl 0.4.1\n",
+    "\n",
+    "To get started we set the ipympl backend, which makes matplotlib plots interactive. We do this using a [magic command](https://ipython.readthedocs.io/en/stable/interactive/magics.html), starting with %.  We also import some libraries: [matplotlib](https://matplotlib.org/) for plotting, [numpy](https://numpy.org/) to generate data, and [ipywidgets](https://ipywidgets.readthedocs.io/en/latest/) for obvious reasons."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib widget\n",
+    "import ipywidgets as widgets\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Interact\n",
+    "Widgets can be created directly, or through the `interact` function. We explore `interact` first, as it is a convenient for quick use. `interact` takes a function as its first argument, followed by the function arguments with their possible values. This creates a widgets that allows selecting those values, calling the callback with the current value for every selection. This may sound a bit abstract, but an example will hopefully clear it up. \n",
+    "\n",
+    "The `say_my_name` function, below, prints the text 'my name is {name}'. We pass `say_my_name` into `interact`, together with a list of names. This creates a dropdown filled with the names in the list. Every time we pick a name, `say_my_name` is called with the currently selected name and the printed message gets updated."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1a4374c5d75342808239b087ad32f0d4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(Dropdown(description='name', options=('Jim', 'Emma', 'Bond'), value='Jim'), Output()), _…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def say_my_name(name):\n",
+    "    \"\"\"\n",
+    "    Print the current widget value in short sentence\n",
+    "    \"\"\"\n",
+    "    print(f'My name is {name}')\n",
+    "    \n",
+    "widgets.interact(say_my_name, name=[\"Jim\", \"Emma\", \"Bond\"]);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You might wonder how `interact` decided to create a dropdown list. It turns out this choice is based on the input options. In the example above these were given as a list of values, resulting in a dropdown list. \n",
+    "\n",
+    "We could also create a slider by passing a tuple of the form (start, stop, step), where the values are numerical. If all values are integers this creates an IntSlider, if floats are present (no surprise) a FloatSlider. Alternatively we could construct a checkbox by simply passing a boolean (i.e. True or False). \n",
+    "\n",
+    "The next cell shows this behaviour by reusing a single function with different input options to  create different kinds of widgets."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b0edfc109d5a411b9ccb5a5ebacf24e7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(Dropdown(description='x', options=(0, 1, 2, 3), value=0), Output()), _dom_classes=('widg…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1d66abae0ff14929a7f66b5e018996e9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(IntSlider(value=5, description='x', max=10), Output()), _dom_classes=('widget-interact',…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "26e2f70897ab4c8a8f65450a5ca1f56d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(FloatSlider(value=5.0, description='x', max=10.0, step=0.5), Output()), _dom_classes=('w…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "eec3a99fd96b4847ab20171572907a9d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(Checkbox(value=True, description='x'), Output()), _dom_classes=('widget-interact',))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def say_something(x):\n",
+    "    \"\"\"\n",
+    "    Print the current widget value in short sentence\n",
+    "    \"\"\"\n",
+    "    print(f'Widget says: {x}')\n",
+    "\n",
+    "widgets.interact(say_something, x=[0, 1, 2, 3])\n",
+    "widgets.interact(say_something, x=(0, 10, 1))\n",
+    "widgets.interact(say_something, x=(0, 10, .5))\n",
+    "_ = widgets.interact(say_something, x=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`interact` is not limited to single argument functions. We can pass multiple arguments to create multiple widgets, following the same rules as above. Below, we use a `three` function with three arguments to create three different widgets. Whenever one of the values is changed `three` is called with the current values of the three widgets as its arguments."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e3134e7181ca4c1c8e927052555daefb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(IntSlider(value=5, description='x', max=10), Checkbox(value=True, description='y'), Drop…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def three(x, y, z):\n",
+    "    return (x, y, z)\n",
+    "\n",
+    "_ = widgets.interact(\n",
+    "    three, \n",
+    "    x=(0, 10, 1), \n",
+    "    y=True, \n",
+    "    z=['a', 'b', 'c']\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Sometimes, not all arguments need to be linked to the widget. We can fix those using the `fixed` function. For example, fixing the `z` argument in `three` results in widgets for `x` and `y` only."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "41b214987c0d4c3cbfbc4b268f25c52d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(IntSlider(value=5, description='x', max=10), Checkbox(value=True, description='y'), Outp…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "_ = widgets.interact(\n",
+    "    three, \n",
+    "    x=(0, 10, 1), \n",
+    "    y=True, \n",
+    "    z=widgets.fixed('I am fixed')\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can also use decorator syntax to create widgets with `interact`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "81cd8b457650416a87bf17864c3d8037",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(IntSlider(value=5, description='x', max=10), Output()), _dom_classes=('widget-interact',…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "@widgets.interact(x=(0, 10, 1))\n",
+    "def foo(x):\n",
+    "    \"\"\"\n",
+    "    Print the current widget value in short sentence\n",
+    "    \"\"\"\n",
+    "    print(f'Slider says: {x}')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "## Putting widgets to use\n",
+    "Making widgets and printing stuff is all good and well, but let's do something 'slightly' more useful and create a perfectly fake oscilloscope. For this, we use matplotlib to create a plot with a fixed vertical scale and a grid. Next, we generate some `x` values between 0 and 2pi and define a function to return the sine of `x` for some frequency `w`, amplitude `amp` and phase angle `phi`. \n",
+    "\n",
+    "Finally, we define an update function that takes three arguments: `w`, `amp` and `phi`, corresponding with the parameters controlling our sine. This function clears all existing lines from the ax object (if any) and then plots a our sine wave. Adding the interact decorator completes our beautiful interactive plot."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7740bbb382cb48f38ac671af0624cb38",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(IntSlider(value=1, description='w', max=10), FloatSlider(value=1.0, description='amp', m…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "702aa719096e4b63a303884619d9c134",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "image/png": 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",
+      "text/html": [
+       "\n",
+       "            <div style=\"display: inline-block;\">\n",
+       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
+       "                    Figure\n",
+       "                </div>\n",
+       "                <img 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' width=600.0/>\n",
+       "            </div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# set up plot\n",
+    "fig, ax = plt.subplots(figsize=(6, 4))\n",
+    "ax.set_ylim([-4, 4])\n",
+    "ax.grid(True)\n",
+    "\n",
+    "# generate x values\n",
+    "x = np.linspace(0, 2 * np.pi, 100)\n",
+    "\n",
+    "\n",
+    "def my_sine(x, w, amp, phi):\n",
+    "    \"\"\"\n",
+    "    Return a sine for x with angular frequeny w and amplitude amp.\n",
+    "    \"\"\"\n",
+    "    return amp*np.sin(w * (x-phi))\n",
+    "\n",
+    "\n",
+    "@widgets.interact(w=(0, 10, 1), amp=(0, 4, .1), phi=(0, 2*np.pi+0.01, 0.01))\n",
+    "def update(w = 1.0, amp=1, phi=0):\n",
+    "    \"\"\"Remove old lines from plot and plot new one\"\"\"\n",
+    "    [l.remove() for l in ax.lines]\n",
+    "    ax.plot(x, my_sine(x, w, amp, phi), color='C0')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For more information on using `interact`, check the official [documentation](https://ipywidgets.readthedocs.io/en/latest/examples/Using%20Interact.html). In the next bit we'll use the widgets directly and stack them together to build larger apps."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Widgets\n",
+    "Often we will want to create widgets manually, for example to build larger interfaces with interconnected components. There are many widgets to choose from. The next cell shows a quick and dirty listing of all classes defined in the ipywidgets module. Not all of these are meant for everyday use, (e.g. DOMWidget and CoreWidgets), but most of them are immediately useful. The official list can be found [here](https://ipywidgets.readthedocs.io/en/latest/examples/Widget%20List.html)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "acfcb1b976ad4b1385ac032c57e2267b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Textarea(value='Accordion\\nAppLayout\\nAudio\\nBoundedFloatText\\nBoundedIntText\\nBox\\nButton\\nButtonStyle\\nCallb…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "widgets.Textarea(\n",
+    "    '\\n'.join([w for w in dir(widgets) if not w.islower()]),\n",
+    "    layout=widgets.Layout(height='200px')\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## A few widget examples\n",
+    "### Text\n",
+    "The `Label` widget shows the value text as uneditable text. If more formatting is required, one can use an `HTML` widget. As the name implies this widget renders an html string. For editable text, there are the `Text` and `Textarea` widgets."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ce2c6f915cf34db793e9d32ca74d9be8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "VBox(children=(Label(value='A label'), HTML(value='<b>Formatted</b> <font color=\"red\">html</font>'), Text(valu…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "label = widgets.Label(\n",
+    "    value='A label')\n",
+    "\n",
+    "html = widgets.HTML(\n",
+    "    value='<b>Formatted</b> <font color=\"red\">html</font>', \n",
+    "    description=''\n",
+    ")\n",
+    "\n",
+    "text = widgets.Text(\n",
+    "    value='A text field', \n",
+    "    description='text field'\n",
+    ")\n",
+    "\n",
+    "textarea = widgets.Textarea(\n",
+    "    value='A text area for longer texts', \n",
+    "    description='text area'\n",
+    ")\n",
+    "\n",
+    "# show the three together in a VBox (vertical box container)\n",
+    "widgets.VBox([label, html, text, textarea])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Selections\n",
+    "The cell below shows a few common selection widgets, some of which we met before. The IntRangeSlider is like an IntSlider but as the name implies it allows the selection of an lower and upper bound of a range. RadioButtons allow the selection of single value from a list of options, similar to the dropdown list. Checkboxes are displayed a little different with their description on the right, but still indented. The indent can be removed by passing the argument `indent`=False\n",
+    "\n",
+    "A personal favourite is the combobox at the end, which starts showing a list of matching possibilities as one starts typing."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "19792f9113dc459d9f689c40b3b190d8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "VBox(children=(IntSlider(value=5, description='slider', max=10), IntRangeSlider(value=(20, 40), description='r…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "int_slider = widgets.IntSlider(\n",
+    "    value=5, \n",
+    "    min=0, max=10, step=1, \n",
+    "    description='slider'\n",
+    ")\n",
+    "\n",
+    "int_range_slider = widgets.IntRangeSlider(\n",
+    "    value=(20, 40), \n",
+    "    min=0, max=100, step=2, \n",
+    "    description='range slider'\n",
+    ")\n",
+    "\n",
+    "dropdown = widgets.Dropdown(\n",
+    "    value='feb', \n",
+    "    options=['jan', 'feb', 'mar', 'apr'], \n",
+    "    description='dropdown'\n",
+    ")\n",
+    "\n",
+    "radiobuttons = widgets.RadioButtons(\n",
+    "    value='feb', \n",
+    "    options=['jan', 'feb', 'mar', 'apr'], \n",
+    "    description='radio buttons'\n",
+    ")\n",
+    "\n",
+    "combobox = widgets.Combobox(\n",
+    "    placeholder='start typing... (e.g. L or o)',\n",
+    "    options=['Amsterdam', 'Athens', 'Lisbon', 'London', 'Ljubljana'], \n",
+    "    description='combo box'\n",
+    ")\n",
+    "\n",
+    "checkbox = widgets.Checkbox(\n",
+    "    description='checkbox',\n",
+    "    value=True\n",
+    ")\n",
+    "\n",
+    "\n",
+    "# a VBox container to pack widgets vertically\n",
+    "widgets.VBox(\n",
+    "    [\n",
+    "        int_slider, \n",
+    "        int_range_slider, \n",
+    "        dropdown, \n",
+    "        radiobuttons,\n",
+    "        checkbox,\n",
+    "        combobox,\n",
+    "    ]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As before, making and displaying widgets is great but putting them to work is awesome. Below, an example is shown of a similar application as above, a sine and a slider (just the one this time). This example shows how we can use the `observe` method to connect a function to a widget trait. `Traits` are special properties that come from a parent class called `HasTraits`. To look more into this, check out the [`traitlets`](https://traitlets.readthedocs.io/en/stable/) library.\n",
+    "\n",
+    "The `value` property of a widget is such a `trait`, meaning we can use `observe` to connect a callback function, which will get called every time `value` changes. The next cell shows an example, where the frequency of a sine is connected to a slider. \n",
+    "Note the `continuous_update` option when creating the IntSlider. This option, also available in other widgets, makes sure the callback is only called when making changes is done (e.g. mousebutton release, enter), and not on every value traversed along the way."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4b34800f6f4f4a5bb4c2214823825956",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "IntSlider(value=1, continuous_update=False, description='$\\\\omega$', max=10)"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "faeaef0127f54180b47196521927f87a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "image/png": 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",
+      "text/html": [
+       "\n",
+       "            <div style=\"display: inline-block;\">\n",
+       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
+       "                    Figure\n",
+       "                </div>\n",
+       "                <img 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' width=640.0/>\n",
+       "            </div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x = np.linspace(0, 2 * np.pi, 100)\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "line, = ax.plot(x, np.sin(x))\n",
+    "ax.grid(True)\n",
+    "\n",
+    "def update(change):\n",
+    "    line.set_ydata(np.sin(change.new * x))\n",
+    "    fig.canvas.draw()\n",
+    "    \n",
+    "int_slider = widgets.IntSlider(\n",
+    "    value=1, \n",
+    "    min=0, max=10, step=1,\n",
+    "    description='$\\omega$',\n",
+    "    continuous_update=False\n",
+    ")\n",
+    "int_slider.observe(update, 'value')\n",
+    "int_slider"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "When the value of the slider changes, the callback function is called with a single argument, change. The next cell shows a slider with a callback that only prints its input argument. As you can see, change is a dictionary-like object with several items:  \n",
+    "    \n",
+    "    'name': the name of the trait  \n",
+    "    'old': the previous value of the trait  \n",
+    "    'new': the current value of the trait  \n",
+    "    'owner': the widget owning the trait (here, int_slider)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'name': 'value',\n",
+       " 'old': 7,\n",
+       " 'new': 6,\n",
+       " 'owner': IntSlider(value=6, max=10),\n",
+       " 'type': 'change'}"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def show_change(change):\n",
+    "    display(change)\n",
+    "\n",
+    "int_slider = widgets.IntSlider(value=7, min=0, max=10)\n",
+    "int_slider.observe(show_change, 'value')\n",
+    "int_slider.value = 6"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Linking\n",
+    "\n",
+    "Widgets can also be linked together using the `link` function. This function takes two tuples of the form (widget, trait) and links the given traits of the given widgets. In the example below, `value` on the first slider is connected to `min` on the second. In this way when we change the value of the first, the minimum value of the second is updated correspondingly. There are also a `dlink` and `jslink` function doing a similar thing. `dlink` works one direction only, i.e. updating slider 1 would update slider 2 two, but not the other way around. `jslink` only works in the front-end, in javascript, and does not need a live ipykernel to work. ([docs](https://ipywidgets.readthedocs.io/en/stable/examples/Widget%20Events.html#Linking-Widgets))\n",
+    "\n",
+    "Below the two sliders are initialised with the same `min` and `max` values. However, after linking them together, updating the `value` of the first to 5, automatically updates `min` for the second as well."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c0aef3c523d8416eaf3c273b49ceb12a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "VBox(children=(IntSlider(value=5, description='slider 1', max=10), IntSlider(value=5, description='slider 2', …"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sl1 = widgets.IntSlider(description='slider 1', min=0, max=10)\n",
+    "sl2 = widgets.IntSlider(description='slider 2', min=0, max=10)\n",
+    "\n",
+    "link = widgets.link(\n",
+    "    (sl1, 'value'), \n",
+    "    (sl2, 'min')\n",
+    ")\n",
+    "\n",
+    "sl1.value = 5\n",
+    "widgets.VBox([sl1, sl2])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "links can be removed using the unlink method on the link object, `link.unlink()`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Layout widgets\n",
+    "There are various ways organise widgets in an interface. We can use boxes, tabs, accordion, or a templated layout. Here, we will only look at boxes, to see the other options, please check [here](https://ipywidgets.readthedocs.io/en/latest/examples/Widget%20List.html#Container/Layout-widgets) and [here](https://ipywidgets.readthedocs.io/en/latest/examples/Layout%20Templates.html#). \n",
+    "\n",
+    "First, we create some buttons to play with."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "b1 = widgets.Button(description='button 1')\n",
+    "b2 = widgets.Button(description='button 2')\n",
+    "b3 = widgets.Button(description='button 3')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Using `HBox` and `VBox` widgets we can easily present our buttons in a row or column layout."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1484b094f42e4508aeacd8161af4c6f8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(Button(description='button 1', style=ButtonStyle()), Button(description='button 2', style=Butto…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "widgets.HBox([b1, b2, b3])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d2afbc2b45d24bc5b256f3b13a5fe2ab",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "VBox(children=(Button(description='button 1', style=ButtonStyle()), Button(description='button 2', style=Butto…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "widgets.VBox([b1, b2, b3])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "These boxes can also be nested to create more complicated layouts. Below, we create two VBoxes. One containing buttons and another containing a dropdown and some radiobuttons. Then we put the VBoxes themselves into an HBox to lay them out next to one another. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "61cafb80f92440659d79c9a0bbc98478",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(VBox(children=(Label(value='Left'), Button(description='button 1', style=ButtonStyle()), Button…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def make_boxes():\n",
+    "    vbox1 = widgets.VBox([widgets.Label('Left'), b1, b2])\n",
+    "    vbox2 = widgets.VBox([widgets.Label('Right'), dropdown, radiobuttons])\n",
+    "    return vbox1, vbox2\n",
+    "\n",
+    "vbox1, vbox2 = make_boxes()\n",
+    "\n",
+    "widgets.HBox([vbox1, vbox2])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To make the left and right boxes more visible we add some layout through the `Layout` widget. Note that the syntax for setting layout parameters resembles css. For our box layout we add a solid, 1px thick red border. In addition we add some space in te form of a margin (spacing to other widgets) and padding (spacing between border and widgets inside). Instead of having a margin-top, margin-left and so on, the margin and padding are given as a single string with the values in the order of top, right, bottom & left. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9a37dd5d302f48a6bb4fac541c61e180",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(VBox(children=(Label(value='Left'), Button(description='button 1', style=ButtonStyle()), Button…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "box_layout = widgets.Layout(\n",
+    "        border='solid 1px red',\n",
+    "        margin='0px 10px 10px 0px',\n",
+    "        padding='5px 5px 5px 5px')\n",
+    "\n",
+    "vbox1, vbox2 = make_boxes()\n",
+    "\n",
+    "vbox1.layout = box_layout\n",
+    "vbox2.layout = box_layout\n",
+    "\n",
+    "widgets.HBox([vbox1, vbox2])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This code can have some suprising behaviour. The `Layout` object is mutable and two boxes share a single instance. Hence, making changes to the layout of box1 will also be reflected in box2. For example, see what happens when we change the width and colour of vbox1. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bbc2ebdea1f84dbc9d6c957efe0bce93",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(VBox(children=(Label(value='Left'), Button(description='button 1', style=ButtonStyle()), Button…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "box_layout = widgets.Layout(\n",
+    "        border='solid 1px red',\n",
+    "        margin='0px 10px 10px 0px',\n",
+    "        padding='5px 5px 5px 5px')\n",
+    "\n",
+    "vbox1, vbox2 = make_boxes()\n",
+    "\n",
+    "vbox1.layout = box_layout\n",
+    "vbox2.layout = box_layout\n",
+    "\n",
+    "# change vbox1 only?\n",
+    "vbox1.layout.width = '400px'\n",
+    "vbox1.layout.border = 'solid 2px green'\n",
+    "\n",
+    "widgets.HBox([vbox1, vbox2])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A simple workaround is to put the layout in a function that returns a freshly created instance so that every widget gets its very own layout object. Then, when making changes to vbox1, vbox2 will not change. \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9f2a514fc586451cbaa988278ec215de",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(VBox(children=(Label(value='Left'), Button(description='button 1', style=ButtonStyle()), Button…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def make_box_layout():\n",
+    "     return widgets.Layout(\n",
+    "        border='solid 1px red',\n",
+    "        margin='0px 10px 10px 0px',\n",
+    "        padding='5px 5px 5px 5px'\n",
+    "     )\n",
+    "    \n",
+    "vbox1, vbox2 = make_boxes()\n",
+    "\n",
+    "vbox1.layout = make_box_layout()\n",
+    "vbox2.layout = make_box_layout()\n",
+    "\n",
+    "# really change vbox1 only\n",
+    "vbox1.layout.width = '400px'\n",
+    "vbox1.layout.border = 'solid 2px green'\n",
+    "\n",
+    "widgets.HBox([vbox1, vbox2])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As mentioned at the start of this section, there are other options to design more advanced applications. An interesting alternative is the `AppLayout` widget, which facilitates building a classic application layout using a column layout sandwiched between a header and footer. Head over to the offical ([docs](https://ipywidgets.readthedocs.io/en/latest/examples/Layout%20Templates.html#AppLayout)) for some examples.."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Putting widgets to use\n",
+    "In the next bit we put it all together and build a simple application. We will create a matplotlib figure again, but this time inside an `Output` widget. `Output` can take all kinds of input and display the notebook. By creating the figure inside the output context it will not be drawn until the output widget is used. We can also display the same figure in multiple places, which is sometimes useful in larger applications."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3bd51dc5368741769a2b3a129d3c8630",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "image/png": 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",
+      "text/html": [
+       "\n",
+       "            <div style=\"display: inline-block;\">\n",
+       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
+       "                    Figure\n",
+       "                </div>\n",
+       "                <img 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' width=600.0/>\n",
+       "            </div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "output = widgets.Output()\n",
+    "\n",
+    "# create some x data\n",
+    "x = np.linspace(0, 2 * np.pi, 100)\n",
+    "\n",
+    "# default line color\n",
+    "initial_color = '#FF00DD'\n",
+    "\n",
+    "with output:\n",
+    "    fig, ax = plt.subplots(constrained_layout=True, figsize=(6, 4))\n",
+    "    \n",
+    "# move the toolbar to the bottom\n",
+    "fig.canvas.toolbar_position = 'bottom'\n",
+    "ax.grid(True)    \n",
+    "line, = ax.plot(x, np.sin(x), initial_color)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "No figure is shown yet, until we use the output widget"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c34cba81729c47d18a85f6aa7cb7058c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "output"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note that we used the `constrained_layout`, when creating the figure. This is similar to the figure's `tight_layout` method, and makes space for the axis labels. However, `constrained_layout` is more convenient in combination with the widget matplotlib backend, as it can be applied before the figure is rendered. With tight_layout we would first have to show the figure and then call the method to make everything fit. It is an experimental feature though, so use with care ['constrained_layout'](https://matplotlib.org/3.1.1/tutorials/intermediate/constrainedlayout_guide.html)\n",
+    "\n",
+    "Next, we create control widgets with their callback functions and connect them. After getting the callbacks in place we set the default values for the labels through their corresponding widgets."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# create some control elements\n",
+    "int_slider = widgets.IntSlider(value=1, min=0, max=10, step=1, description='freq')\n",
+    "color_picker = widgets.ColorPicker(value=initial_color, description='pick a color')\n",
+    "text_xlabel = widgets.Text(value='', description='xlabel', continuous_update=False)\n",
+    "text_ylabel = widgets.Text(value='', description='ylabel', continuous_update=False)\n",
+    "\n",
+    "# callback functions\n",
+    "def update(change):\n",
+    "    \"\"\"redraw line (update plot)\"\"\"\n",
+    "    line.set_ydata(np.sin(change.new * x))\n",
+    "    fig.canvas.draw()\n",
+    "    \n",
+    "def line_color(change):\n",
+    "    \"\"\"set line color\"\"\"\n",
+    "    line.set_color(change.new)\n",
+    "    \n",
+    "def update_xlabel(change):\n",
+    "    ax.set_xlabel(change.new)\n",
+    "    \n",
+    "def update_ylabel(change):\n",
+    "    ax.set_ylabel(change.new)\n",
+    "\n",
+    "# connect callbacks and traits\n",
+    "int_slider.observe(update, 'value')\n",
+    "color_picker.observe(line_color, 'value')\n",
+    "text_xlabel.observe(update_xlabel, 'value')\n",
+    "text_ylabel.observe(update_ylabel, 'value')\n",
+    "\n",
+    "text_xlabel.value = 'x'\n",
+    "text_ylabel.value = 'y'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Finally, we box everything up and display evertyhing together."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "682365e9aa9d47e0871138e1c93155bc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(VBox(children=(IntSlider(value=1, description='freq', max=10), ColorPicker(value='#FF00DD', des…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "controls = widgets.VBox([int_slider, color_picker, text_xlabel, text_ylabel])\n",
+    "widgets.HBox([controls, output])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Packing components in a class\n",
+    "To create more high level components, we can also subclass a container and build up our gui from within. Containers have a `children` property to which we can assign a list of widgets that should be displayed. Although, we can assign a list, this is turned into a tuple and cannot be modified afterwards.\n",
+    "To remove or add a widget at runtime, the children tuple can be turned back into a list, followed by an insert or deletion and finalised by reassigning to the children property. Since it can be easy to make mistakes when going by index, I tend to add a placeholder box in which I only place the 'dynamic' widget.\n",
+    "\n",
+    "The example below, packs the entire oscilliscope 'dashboard' in a single component by subclassing `VBox`. All the required widgets are defined in the Sines class and added as its `children`. The callbacks are defined as instance methods. It may not be a masterpiece in object oriented programming, but hopefully it shows the idea of constructing larger reusable components. Note that, we need to call `super().__init__()` from `__init__` to properly initialise the parent class. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a38f245edb7040ecad588e93e83f08c2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Sines(children=(VBox(children=(IntSlider(value=1, description='freq', max=10), ColorPicker(value='#FF00DD', de…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ac5bf19e481e44a2abe56ceb191a83e0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "image/png": 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",
+      "text/html": [
+       "\n",
+       "            <div style=\"display: inline-block;\">\n",
+       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
+       "                    Figure\n",
+       "                </div>\n",
+       "                <img 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' width=500.0/>\n",
+       "            </div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def make_box_layout():\n",
+    "     return widgets.Layout(\n",
+    "        border='solid 1px black',\n",
+    "        margin='0px 10px 10px 0px',\n",
+    "        padding='5px 5px 5px 5px'\n",
+    "     )\n",
+    "\n",
+    "class Sines(widgets.HBox):\n",
+    "    \n",
+    "    def __init__(self):\n",
+    "        super().__init__()\n",
+    "        output = widgets.Output()\n",
+    "\n",
+    "        self.x = np.linspace(0, 2 * np.pi, 100)\n",
+    "        initial_color = '#FF00DD'\n",
+    "\n",
+    "        with output:\n",
+    "            self.fig, self.ax = plt.subplots(constrained_layout=True, figsize=(5, 3.5))\n",
+    "        self.line, = self.ax.plot(self.x, np.sin(self.x), initial_color)\n",
+    "        \n",
+    "        self.fig.canvas.toolbar_position = 'bottom'\n",
+    "        self.ax.grid(True)\n",
+    "\n",
+    "        # define widgets\n",
+    "        int_slider = widgets.IntSlider(\n",
+    "            value=1, \n",
+    "            min=0, \n",
+    "            max=10, \n",
+    "            step=1, \n",
+    "            description='freq'\n",
+    "        )\n",
+    "        color_picker = widgets.ColorPicker(\n",
+    "            value=initial_color, \n",
+    "            description='pick a color'\n",
+    "        )\n",
+    "        text_xlabel = widgets.Text(\n",
+    "            value='', \n",
+    "            description='xlabel', \n",
+    "            continuous_update=False\n",
+    "        )\n",
+    "        text_ylabel = widgets.Text(\n",
+    "            value='', \n",
+    "            description='ylabel', \n",
+    "            continuous_update=False\n",
+    "        )\n",
+    "\n",
+    "        controls = widgets.VBox([\n",
+    "            int_slider, \n",
+    "            color_picker, \n",
+    "            text_xlabel, \n",
+    "            text_ylabel\n",
+    "        ])\n",
+    "        controls.layout = make_box_layout()\n",
+    "        \n",
+    "        out_box = widgets.Box([output])\n",
+    "        output.layout = make_box_layout()\n",
+    "\n",
+    "        # observe stuff\n",
+    "        int_slider.observe(self.update, 'value')\n",
+    "        color_picker.observe(self.line_color, 'value')\n",
+    "        text_xlabel.observe(self.update_xlabel, 'value')\n",
+    "        text_ylabel.observe(self.update_ylabel, 'value')\n",
+    "        \n",
+    "        text_xlabel.value = 'x'\n",
+    "        text_ylabel.value = 'y'\n",
+    "        \n",
+    "\n",
+    "        # add to children\n",
+    "        self.children = [controls, output]\n",
+    "    \n",
+    "    def update(self, change):\n",
+    "        \"\"\"Draw line in plot\"\"\"\n",
+    "        self.line.set_ydata(np.sin(change.new * self.x))\n",
+    "        self.fig.canvas.draw()\n",
+    "\n",
+    "    def line_color(self, change):\n",
+    "        self.line.set_color(change.new)\n",
+    "\n",
+    "    def update_xlabel(self, change):\n",
+    "        self.ax.set_xlabel(change.new)\n",
+    "\n",
+    "    def update_ylabel(self, change):\n",
+    "        self.ax.set_ylabel(change.new)\n",
+    "        \n",
+    "        \n",
+    "Sines()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There is a lot more to ipywidgets than was presented here. A good first start are the official [ipywidgets](https://ipywidgets.readthedocs.io/en/stable/) and [traitlets](https://traitlets.readthedocs.io/en/stable/) docs. There is a lot of active development, so it is always interesting to check for updates. There is also a lot of ongoing work on `ipympl`, so staying up to date is a good idea when using it.\n",
+    "\n",
+    "Two other project that I would like to mention are Voila and ipyvuetify. The [Voila](https://github.com/voila-dashboards/voila) project makes it possible to present a notebook as an interactive web application with a live kernel. [Ipyvuetify](https://github.com/mariobuikhuizen/ipyvuetify) provides a great set of widgets based on vuetify (example in [binder](https://mybinder.org/v2/gh/mariobuikhuizen/ipyvuetify/master?filepath=examples%2FExamples%20template.ipynb))."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.13"
+  },
+  "latex_envs": {
+   "LaTeX_envs_menu_present": true,
+   "autoclose": false,
+   "autocomplete": true,
+   "bibliofile": "biblio.bib",
+   "cite_by": "apalike",
+   "current_citInitial": 1,
+   "eqLabelWithNumbers": true,
+   "eqNumInitial": 1,
+   "hotkeys": {
+    "equation": "Ctrl-E",
+    "itemize": "Ctrl-I"
+   },
+   "labels_anchors": false,
+   "latex_user_defs": false,
+   "report_style_numbering": false,
+   "user_envs_cfg": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/jupyter/ipywidgets-tutorial-master/requirements.txt b/jupyter/ipywidgets-tutorial-master/requirements.txt
new file mode 100644
index 0000000..1ccc931
--- /dev/null
+++ b/jupyter/ipywidgets-tutorial-master/requirements.txt
@@ -0,0 +1,4 @@
+ipympl==0.4.1
+ipywidgets==7.5.1
+matplotlib==3.1.3
+numpy==1.18.1
diff --git a/jupyter/ipywidgets-tutorial-master/runtime.txt b/jupyter/ipywidgets-tutorial-master/runtime.txt
new file mode 100644
index 0000000..1678965
--- /dev/null
+++ b/jupyter/ipywidgets-tutorial-master/runtime.txt
@@ -0,0 +1 @@
+python==3.8
diff --git a/jupyter/parametric_curves_1.ipynb b/jupyter/parametric_curves_1.ipynb
new file mode 100644
index 0000000..6f28023
--- /dev/null
+++ b/jupyter/parametric_curves_1.ipynb
@@ -0,0 +1,1303 @@
+{
+ "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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85S965ZVXNG3aNO3YsUMFBQVq0qSJevbsqQkTJpQcgClJEyZM0JlnnqkZM2bo3//+twoKCnTKKafod7/7nUaOHFmy3o033iifz6epU6fq8ssvV4sWLXTXXXfplVdeKTmQM1yCHVMoZs+erTvuuEOXXnqpCgoKNHTo0KBPlx0VFaXZs2erfv36uvbaa3XgwAFdf/31Fa7/1Vdf6ejRo1q1apXS0tLK3G6ccF6FwYMHa/z48RoyZIgSExPLfcyePXvqtddeK3XAZ2xsrM4//3zl5OSUWi5JdevW1XvvvacHH3xQTz31lDZv3qy4uDg1b95cPXv2LBNZZrxtceaZZ+rjjz/W/fffr7/85S/avXu3GjRooN/+9relDu60WkZGhlatWqWZM2dq0qRJMgxDmzdvVosWLfTkk0+qc+fOevLJJzVz5kwVFRWpadOm6tq1q37/+99X+JgXXXSRnn32WT300EPq37+/Tj31VN1www1q1KiRRowYYeHfDk7nM078PwTgYXv37lXr1q01YMAAPfXUU3YPx7Eef/xx3XLLLfrss8901llnWb79hg0bavjw4XrkkUcs3zaA0tijAc/auXOnJk+erB49eigpKUnff/+9pk2bpv3795e8FYPQrF+/Xps3b9akSZN02WWXWR4Zn3zyiZYuXapffvml3D0vAKzHHg141i+//KLrrrtOa9eu1c8//6z4+Hh17ty55LTLCF2LFi20c+dOdevWTS+++KJOOeUUS7ffo0cPffnll7r22ms1depU2z/1AYDQAAAAJuLMoAAAwDSEBgAAMA2hAQAATBPRnzopKirS9u3bVb9+fQ7qAgAgghiGof3796tp06aVnuQwokNj+/btSklJsXsYAACgAtu2bVOzZs0qvD2iQ6N+/fqSAn+JhISECtfz+/1avny5evXqVeGVHRF+zLv1mHN7MO/2YN7tEey85+fnKyUlpeR3dUUiOjSK3y5JSEioMjTi4+OVkJDAi9FCzLv1mHN7MO/2YN7tEeq8V3VoAweDAgAA0xAaAADANJaFxpQpU+Tz+TRmzBirNgkAAGxmSWisXbtWTz31lNq3b2/F5gAAQIQwPTR+/fVXXXPNNXr66ad10kknmb05AAAQQUwPjVGjRqlfv37q2bOn2ZsCAAARxtSPt86dO1cff/yx1q5dG9T6BQUFKigoKPk5Pz9fUuCjNn6/v8L7Fd9W2ToIP+bdesy5PZh3e+QfPPY7ANYJ9vUe7PNi2mXit23bpk6dOmn58uU6++yzJUndu3dXhw4dNH369HLvk5mZqaysrDLLs7OzFR8fb8YwAQBANRw8eFAZGRnat29fpee6Mi00Fi9erMsvv1zR0dElywoLC+Xz+RQVFaWCgoJSt0nl79FISUlRXl5elSfsysnJUXp6Oid1sRDzbj3m3B7MuzUMw9ArX72iSbmT9O0v3+qNq9/Q3s/3Mu8WC/b1np+fr+Tk5CpDw7S3Ti6++GJ9+umnpZb96U9/0hlnnKHx48eXiQxJio2NVWxsbJnlMTExQb3Igl0P4cW8W485twfzbp53N7+r8W+N19rtgbfa+7fury4tumjp50uZd5tUNe/BPiemhUb9+vXVtm3bUsvq1q2rpKSkMssBAN70353/1V1v36Vlm5aVLIvyRemvF//VxlEhnCL6WicAAHfa/Mtm3fvuvcr+NFuGSr+Df93Z16lto7YcBOoSlobGihUrrNwcACDC/HTgJ01+b7Jmrp0pf1HZkIiNjlVW97IfCoBzsUcDAGC6A0cOaNqaaZq6aqr2H9lf4Xo3//5mNU9sbuHIYDZCAwBgGn+hX898/IyycrO068CuStdNjE3UhG4TLBoZrEJoAABM8flPn+vyeZfr6z1fB7X+hPMnqGFcQ5NHBatxmXgAgCnOPPlMvX3d25ree7q6pHSpdN1T65+qW867xaKRwUqEBgDANM0SmunWzrfqrxf9VfExFZ/hObN7puJi4iwcGaxCaAAATJW7JVd9s/vqoP9gubefkXyGhnUYZu2gYBlCAwBgmvIio3HdxqXWmXLxFNWK4pBBtyI0AACmKC8yWjZoqQ9v+FCnJ50uSUprlqbLTr/MriHCAoQGACDsKoqMFcNWqHlic1115lWSpId6PiSfz2fXMGEB9lUBAMKqqsiQpEFnDdJ/d/1X3VK72TVMWIQ9GgCAsAkmMiSpbaO2mtlvph1DhMUIDQBAWAQbGZLk8/nULKGZ1UOEDQgNAECNhRIZ8BZCAwBQI0QGKkNoAACqjchAVQgNAEC1EBkIBqEBAAgZkYFgERoAgJAQGQgFoQEACBqRgVARGgCAoBAZqA5CAwBQJSID1UVoAAAqRWSgJggNAECFiAzUFKEBACgXkYFwIDQAAGUQGQgXQgMAUAqRgXAiNAAAJYgMhBuhAQCQRGTAHIQGAIDIgGkIDQDwOCIDZiI0AMDDiAyYjdAAAI8iMmAFQgMAPIjIgFUIDQDwGCIDViI0AMBDiAxYjdAAAI8gMmAHQgMAPIDIgF0IDQBwOSIDdiI0AMDFiAzYjdAAAJciMhAJCA0AcCEiA5GC0AAAlyEyEEkIDQBwESIDkYbQAACXIDIQiQgNAHABIgORitAAAIcjMhDJCA0AcDAiA5GO0AAAhyIy4ASEBgA4EJEBpyA0AMBhiAw4CaEBAA5CZMBpCA0AcAgiA05EaACAAxAZcCpCAwAiHJEBJyM0ACCCERlwOkIDACIUkQE3IDQAIAIRGXALQgMAIgyRATchNAAgghAZcBtCAwAiBJEBNyI0ACACEBlwK0IDAGxGZMDNCA0AsBGRAbcjNADAJkQGvIDQAAAbEBnwCkIDACxGZMBLCA0AsBCRAa8hNADAIkQGvIjQAAALEBnwKkIDAExGZMDLCA0AMBGRAa8jNADAJEQGQGgAgCmIDCCA0ACAMCMygGMIDQAIIyIDKI3QAIAwITKAsggNAAgDIgMoH6EBADVEZAAVIzQAoAaIDKByhAYAVBORAVSN0ACAaiAygOAQGgAQIiIDCB6hAQAhIDKA0JgaGrNmzVL79u2VkJCghIQEpaWl6Y033jBzkwBgmlVbVxEZQIhMDY1mzZrpwQcf1Lp167Ru3TpddNFFuuyyy7Rx40YzNwsAphj40kAiAwhRLTMfvH///qV+njx5smbNmqU1a9borLPOMnPTABA2q7aukiQiA6gGy47RKCws1Ny5c3XgwAGlpaVZtVkAqJHcLbka+NLAUsuIDCB4pu7RkKRPP/1UaWlpOnz4sOrVq6dFixbpzDPPLHfdgoICFRQUlPycn58vSfL7/fL7/RVuo/i2ytZB+DHv1mPOrbVq6yoNfGmgjEJDkhQXFafUxFQtvWapmsQ34XkwGa93ewQ778E+Lz7DMIwaj6oSR44c0datW7V3714tWLBAzzzzjHJzc8uNjczMTGVlZZVZnp2drfj4eDOHCQAAQnDw4EFlZGRo3759SkhIqHA900PjRD179lSrVq305JNPlrmtvD0aKSkpysvLq/Qv4ff7lZOTo/T0dMXExJgybpTFvFuPObdG8Z6M4mMy4qLi9GzbZ3VW57OU2jDV5tF5B693ewQ77/n5+UpOTq4yNEx/6+REhmGUionjxcbGKjY2tszymJiYoF5kwa6H8GLercecmyd3S676zetX6sDP1MRAXKQ2TGXebcDr3R5VzXuwz4mpoXH33XerT58+SklJ0f79+zV37lytWLFCy5YtM3OzAFAtFZ2Ma+k1S/XJ+5/YODLAuUwNjV27dmnIkCHasWOHEhMT1b59ey1btkzp6elmbhYAQlbZGT+bxDfRJyI0gOowNTRmz55t5sMDQFhUdVpxPvUAVB/XOgHgaVy7BDAXoQHAs4gMwHyEBgBPIjIAaxAaADyHyACsQ2gA8BQiA7AWoQHAM4gMwHqEBgBPIDIAexAaAFyPyADsQ2gAcDUiA7AXoQHAtYgMwH6EBgBXIjKAyEBoAHAdIgOIHIQGAFchMoDIQmgAcA0iA4g8hAYAVyAygMhEaABwPCIDiFyEBgBHIzKAyEZoAHAsIgOIfIQGAEciMgBnIDQAOA6RATgHoQHAUYgMwFkIDQCOQWQAzkNoAHAEIgNwJkIDQMQjMgDnIjQARDQiA3A2QgNAxCIyAOcjNABEJCIDcAdCA0DEITIA9yA0AEQUIgNwF0IDQMQgMgD3ITQARAQiA3AnQgOA7YgMwL0IDQC2IjIAdyM0ANiGyADcj9AAYAsiA/AGQgOA5YgMwDsIDQCWIjIAbyE0AFiGyAC8h9AAYAkiA/AmQgOA6YgMwLsIDQCmIjIAbyM0AJiGyABAaAAwBZEBQCI0AJiAyABQjNAAEFZEBoDjERoAwobIAHAiQgNAWBAZAMpDaACoMSIDQEUIDQA1QmQAqAyhAaDaiAwAVSE0AFQLkQEgGIQGgJARGQCCRWgACAmRASAUhAaAoBEZAEJFaAAICpEBoDoIDQBVIjIAVBehAaBSRAaAmiA0AFSIyABQU4QGgHIRGQDCgdAAUAaRASBcCA0ApRAZAMKJ0ABQgsgAEG6EBgBJRAYAcxAaAIgMAKYhNACPIzIAmInQADyMyABgNkID8CgiA4AVCA3Ag4gMAFYhNACPITIAWInQADyEyABgNUID8AgiA4AdCA3AA4gMAHYhNACXIzIA2InQAFyMyABgN0IDcCkiA0AkIDQAFyIyAEQKQgNwGSIDQCQhNAAXITIARBpCA3AJIgNAJCI0ABcgMgBEKkIDcDgiA0AkIzQAByMyAEQ6QgNwKCIDgBOYGhpTpkzR7373O9WvX1+NGjXSgAED9NVXX5m5ScATVm1dRWQAcARTQyM3N1ejRo3SmjVrlJOTo6NHj6pXr146cOCAmZsFXG/gSwOJDACOUMvMB1+2bFmpn+fMmaNGjRrpo48+0gUXXGDmpgFXWrV1lSQRGQAcw9TQONG+ffskSQ0bNiz39oKCAhUUFJT8nJ+fL0ny+/3y+/0VPm7xbZWtg/Bj3q21ausqXbvgWv399L8rLipOkpSamKql1yxVk/gmPA8m4rVuD+bdHsHOe7DPi88wDKPGowqCYRi67LLL9Msvv+i9994rd53MzExlZWWVWZ6dna34+HizhwgAAIJ08OBBZWRkaN++fUpISKhwPctCY9SoUXr99df1/vvvq1mzZuWuU94ejZSUFOXl5VX6l/D7/crJyVF6erpiYmLCPnaUj3m3xqqtq0qOyYiLitOzbZ/VxK0TtShjkZollP/fEsKL17o9mHd7BDvv+fn5Sk5OrjI0LHnr5Oabb9arr76qlStXVhgZkhQbG6vY2Ngyy2NiYoJ6kQW7HsKLeTdP7pZc9ZvXr9QxGZK0KGORWia1tGlU3sVr3R7Muz2qmvdgnxNTQ8MwDN18881atGiRVqxYoZYt+R8jEKzyzpORmpgqSezJAOAYpn68ddSoUfrnP/+p7Oxs1a9fXzt37tTOnTt16NAhMzcLOF5FJ+Naes1SG0cFAKEzNTRmzZqlffv2qXv37mrSpEnJn3nz5pm5WcDRKjvjJ3syADiN6W+dAAheVacV52N+AJyGa50AEYJrlwBwI0IDiABEBgC3IjQAmxEZANyM0ABsRGQAcDtCA7AJkQHACwgNwAZEBgCvIDQAixEZALyE0AAsRGQA8BpCA7AIkQHAiwgNwAJEBgCvIjQAkxEZALyM0ABMRGQA8DpCAzAJkQEAhAZgCiIDAAIIDSDMiAwAOIbQAMKIyACA0ggNIEyIDAAoi9AAwoDIAIDyERpADREZAFAxQgOoASIDACpHaADVRGQAQNUIDaAaiAwACA6hAYSIyACA4BEaQAiIDAAIDaEBBInIAIDQERpAEIgMAKgeQgOoApEBANVHaACVIDIAoGYIDaACRAYA1ByhAZSDyACA8CA0gBMQGQAQPoQGcBwiAwDCi9AA/ofIAIDwIzQAERkAYBZCA55HZACAeQgNeBqRAQDmIjTgWUQGAJiP0IAnERkAYA1CA55DZACAdQgNeAqRAQDWIjTgGUQGAFiP0IAnEBkAYA9CA65HZACAfQgNuBqRAQD2IjTgWkQGANiP0IArERkAEBkIDbgOkQEAkYPQgKsQGQAQWQgNuAaRAQCRh9CAKxAZABCZCA04HpEBAJGL0ICjERkAENkIDTgWkQEAkY/QgCMRGQDgDIQGHIfIAADnIDTgKEQGADgLoQHHIDIAwHkIDTgCkQEAzkRoIOIRGQDgXIQGIhqRAQDORmggYhEZAOB8hAYiEpEBAO5AaCDiEBkA4B6EBiIKkQEA7kJoIGIQGQDgPoQGIgKRAQDuRGjAdkQGALgXoQFbERkA4G6EBmxDZACA+xEasAWRAQDeQGjAckQGAHgHoQFLERkA4C2EBixDZACA9xAasASRAQDeRGjAdEQGAHgXoQFTERkA4G2EBkxDZAAACA2YgsgAAEiEBkxAZAAAihEaCCsiAwBwPEIDYUNkAABORGggLIgMAEB5atk9ADjfqq2r1G9ePyIDAFCGqXs0Vq5cqf79+6tp06by+XxavHixmZuDTQa+NJDIAACUy9TQOHDggM4++2w98cQTZm4GNlm1dZUkERkAgAqZ+tZJnz591KdPHzM3AZvkbsnVwJcGanab2SXLiAwAwIki6hiNgoICFRQUlPycn58vSfL7/fL7/RXer/i2ytaJOFu2KOq55+TbtElGs2YquvZaqW1bu0cVlFVbV2ngSwNlFBqSpLioOKUmpmrpNUvVJL6Js54Hh3Hka90FmHd7MO/2CHbeg31efIZhGDUeVTAb8vm0aNEiDRgwoMJ1MjMzlZWVVWZ5dna24uPjTRydtZq9+646zJyp6OOeJCMqSl9kZOibgQNtHBkAAME5ePCgMjIytG/fPiUkJFS4XkSFRnl7NFJSUpSXl1fpX8Lv9ysnJ0fp6emKiYkJ57DDzvfGG4oeMEA+w1DRhRfK6NtXvlWrFPXqq5KkwieeUNGNN9o8yvIV78koPiYjLipOz7Z9Vmd1PkupDVNtHp03OOm17ibMuz2Yd3sEO+/5+flKTk6uMjQi6q2T2NhYxcbGllkeExMT1Iss2PVsc+CAdNNNkmFIN9ygqCeflHy+wG2TJ0t/+Yui77hD0QMGSKeeautQT5S7JbfMR1hTEwNxkdowNbLn3YUi/rXuUsy7PZh3e1Q178E+J5ywy0rPPitt3y61bCn97W/HIkOSJkyQunaVDh2SHn3UvjGWo6KTcS29ZqmNowIAOIGpofHrr79qw4YN2rBhgyRp8+bN2rBhg7Zu3WrmZiPXnDmBr+PGSXXqlL4tKioQG5L04ovS0aPWjq0ClZ3xs1lCMxtHBgBwAlNDY926derYsaM6duwoSRo7dqw6duyo++67z8zNRqYdO6T16wN7MQYPLn+d3r2lk06S8vKkdeusHV85OK04AKCmTD1Go3v37rLoWNPIt2ZN4Gu7dlJycvnr1KolXXCB9Mor0gcfSJ07Wze+ExAZAIBw4BgNq3zxReDr2WdXvl7x7Z9/bu54KkFkAADChdCwSvFxKS1bVr5eixaBrz/8YOpwKkJkAADCidCwyi+/BL4mJVW+XvHtP/9s7njKQWQAAMKN0LDK4cOBryd+2uRExbcfd+IyKxAZAAAzEBpWifrfVFd1cGxRUeDr8efYMBmRAQAwC6FhleJrtRw4UPl6Bw+WXt9kRAYAwEyEhlUaNgx83bOn8vWKb6/qWI4wIDIAAGYjNKxyyimBrz/+WPl6xbc3bmzqcIgMAIAVCA2rFH9sdfPmytcrvj3VvKuhEhkAAKsQGlY5/fTA1y+/rHy94tvPOMOUYRAZAAArERpWadMm8EmS3bulnTvLX6ewUPrss8D3bduGfQhEBgDAaoSGVerWPbZX46OPyl/nyy8DnzqJj5d++9uwbp7IAADYgdCwUvFF0lavLv/24uW//70UHR22zRIZAAC7EBpWOv/8wNfc3PJvL17etWvYNklkAADsRGhY6aKLAl//8x9p//7StxmG9Pbbge8vvjgsmyMyAAB2IzSs1LKl1KqVdPTosagotmFD4CDR+HgpLa3GmyIyAACRgNCw2iWXBL6++mrp5a+9Fvianl71hdeqQGQAACIFoWG1yy4LfH3lFcnvP7Z8wYLA10svrdHDExkAgEhCaFitWzfp5JOln3+W3n03sOzrr6VPPpFq1ZIGDKj2QxMZAIBIQ2hYrVYtaeDAwPf/+lfg6z//Gfiann7s4mshIjIAAJGI0LDDtdcGvi5YIOXnHwuNIUOq9XBEBgAgUhEadkhLC5z588ABaeTIwIXUEhKOHb8RAiIDABDJCA07+HzSiBGB7//978DXa64JfLQ1BEQGACDSERp2GTas9M833hjS3YkMAIATEBp2ady49M8dOgR9VyIDAOAUhIZdDh0q/fP27UHdjcgAADgJoWGXF14o/fPjj1d5FyIDAOA0hIYdjh6Vpk4NfN+jR+DrzJnS3r0V3oXIAAA4EaFhh7lzpe++k5KTA9c8OeuswPk0/v73clcnMgAATkVoWK2wUJo8OfD9bbdJ9epJd98d+PmxxwLBcRwiAwDgZISG1bKzpS+/DJxqfPTowLLBg6XTTw9c/+RvfytZlcgAADgdoWGlI0ekrKzA93feGTgbqCRFR0uZmYHvH3lE+vlnIgMA4AqEhpWeflr69tvAOTSK92YUGzRIatdO2rdP2yaMIjIAAK5AaFhl/35p0qTA9xMnSnXrlr49Kkp68EFJ0snPzlXST0QGAMD5CA2rPPSQtHu39JvfSNdfX+4quW3itbJllOoclf76dmAZkQEAcDJCwwrffx849kIKnD8jJqbMKrlbctX33/10W3qRiiRd+6l02b6mRAYAwNEIDSuMHy8VFEgXXigNGFDm5uMP/Py4qfTC2YHl895vrOb1m1k7VgAAwojQMNu770rz5gWOwZg2LXCJ+OOU9+mSf1yRoqJ6dRW7dr304otWjxgAgLAhNMzk90s33xz4fuRIqWPHUjdX9BHW+be+r6j7JgYW3HlnpacmBwAgkhEaZpo2Tdq4MXCq8fvvL3VTlefJuPVW6YwzAgeQ3nOP1SMHACAsCA2zbNly7CRcDz8cOBPo/wR1Mq7atQMXWpOkWbOk//zHmnEDABBGhIYZDEMaNUo6dChwAOjQoSU3hXTGzx49Avc1DOnGGwNvxQAA4CCEhhmys6WlSwN7Jf7xj5IDQKt1WvFHHpGSkqRPPjl2aXkAAByC0Ai33bsDx1dI0n33BY6zUA0ukJacLM2YEfh+0iTpiy/MGjkAAGFHaIRT8Vsme/ZIZ58d+MSIwnAV1owMqW/fwEXZhg8PXGoeAAAHIDTCad486eWXA1djffZZKSYmPFdh9fkCb8EkJEhr1hw7yygAABGO0AiXnTsDezMk6S9/kc45J7yXek9JOfYWyn33SZ99FqaBAwBgHkIjHAxDGjFC+vnnwEm57rknvJFRbOhQ6ZJLAm+hDBkS+AoAQAQjNMLhH/8IfMokNlZ64QXl/rg6/JEhBd5CeeqpwKdQNmyQ7r235mMHAMBEhEZNffmlNG5c4PsHH1RuvT3mREaxJk2kZ54JfP/ww9KKFTV/TAAATEJo1ERBgfTHPwZOzJWertxLzzY3MooNGCBdf33gLZshQwKfcgEAIAIRGjVx552BtzCSk/XB/X9W37mXmB8ZxaZNk1q3ln74IXB8iGGEfxsAANQQoVFdr70m/e1vkqRPp96unm9fZ11kSFK9etLcuYGzj77yyrHrogAAEEEIjer4/vuS65f8MOIqdd4xydrIKNax47HTko8dK338sbnbAwAgRIRGqI4ckQYPln75Rfntz1C75kvsiYxit9wiXXZZYFxXXSXt3WvNdgEACAKhEarx46X//Ef+hHo6r9cW7TUOldxkeWRIgY+8zpkjtWghffdd4BTlHK8BAIgQhEYo5s+Xpk+XJGVcekRf1jtccpMtkVHspJOkl14KHK+xaJH06KPWjwEAgHIQGsH6/PPA3gJJj3WrpZd/c+ysnLZGRrFOnUoiSOPHS+++a99YAAD4H0IjGPn50hVXSAcOaMVpUbqz+9GSmyIiMoqNHCldd51UVBQ4jmTbNrtHBADwOEKjKkVFgZNiffWVfkj0adAVRSqMDtwUUZEhHbvKa4cO0k8/SVdeKR0+XOXdAAAwC6FRlaws6dVXdbiWdMVVhn6qF1gccZFRLC5OWrhQathQWrtW+vOfOTgUAGAbQqMyCxdKkyZJkv58ibS2WWBxxEZGsZYtAweuRkdLL7xw7PLyAABYjNCoyCefqHDINZKk6edJL3QILI74yCh28cXSI48Evh83Tlq+3N7xAAA8idAoz65dOtynl6IPHtZbLaXbewUWOyYyit16qzRsWOA4k0GDAleaBQDAQoTGiQ4f1r6+F6vO9l36Kkm6apBUGO3AyJCOHRzatau0b590ySVc6RUAYClC43iGoV1X91fixxv1Sx2p/x+lvXEOjYxisbGBk3i1aCF9+600cGDgdOUAAFiA0DjOltv+pMavvKWjvsBlQ75JdnhkFDv55MDVZuvXl1askG68kU+iAAAsQWj8z5eP3q0WM56XJN10ifR2K5dERrG2bY99EuX556XJk+0eEQDAAwgNSRvmTtdp46dIkh7sKj1zrssio9gf/iA98UTg+3vvlbKz7R0PAMD1PB8aa5c/p9Q/3abahdL8M6W7L3ZpZBQbOVK6/fbA98OGcU0UAICpPB0aH6x5WU0GDddJh6VVKdLQy6UWDV0cGcUeeihwUKjfLw0YIH36qd0jAgC4lGdD4/1PXlf9AYPUbJ+hL5OkS/8oNTnZA5EhSVFR0osvSt26BS4Y16cPF2ADAJjCk6Gx8qvlKrz8UrXdZWhHPekP10qJTT0SGcXq1JEWL5batJF+/DFw/MbPP9s9KgCAy3guNHK/fUd7ruyrC78rUn5tqe81UlRLj0VGsYYNpWXLpKZNpc8/l/r3lw4etHtUAAAX8VRo5G5eoW/+2FuXbyxUQbQ04GppXxuPRkax5s0DsdGggbR6deBU5X6/3aMCALiEZ0Ijd0uuVo1I1/Vrj6pI0jVXSFvO8XhkFGvXLnBCrzp1pNdfl66/PnB9FAAAasgToZG7JVdLR6Xr7nePSpL+r5/0cVcio5Tzzy99afmxYzl7KACgxlwfGrlbcjV3bC89tDTwdsBfekjL04mMcvXvLz37bOD7GTOkSZPsHQ8AwPFcHRq5W3L11N299MTiwEXEHu4iZV/agsiozHXXSY8/Hvg+M1OaPt3O0QAAHM7xoWEYUl6etGtXnPLyju3tz92Sq+kTe2vOvCOKNqSnz5FmXdVCK/6US2RUZfRo6YEHAt/fdps0e3aZVSqadwAAjufY0Ni7N7B3/7e/lZo2jdGf/9xLTZvG6Le/lW6euEkP/GWQsrMLVLtImneW9NC1REZI7r772KnKb7hB+te/JFU+7zNmBG4HAKBYLbsHUB1vvildeWX5p3z47jtDT0w6TXX1rVbqSh0+fbnuHd5C7wwnMkLi80lTp0oHDkizZklDh+rNL1N15bTzK5j3wM6Pe+6RFiyQeve2fsgAgMjjuNB4802pX7/Arvrydtcbhk+ST4cUp756XU27DdeqEQ8QGdXh8wWu9nr4sN6c86P6PdBZhs/43xyXVvxcHDoUeH5ef53YAABY9NbJzJkz1bJlS9WpU0fnnnuu3nvvvWo9zt69gT0ZhlH1aR6KFK0in08///s5JRhERrVFRWnvw0/rylqvyJBPReVExvGKigLPz5VX8jYKAMCC0Jg3b57GjBmje+65R+vXr1e3bt3Up08fbd26NeTHev75wNslQZ9LyojWoYNReuGFkDeF4zz/z2gdLIxVkaKDWr+oKPA8Me8AANND47HHHtOIESN0/fXXq02bNpo+fbpSUlI0a9askB7HMI596jJUf/sbn4qormPzXvmejPIw7wAAU4/ROHLkiD766CPdddddpZb36tVLq1evLrN+QUGBCgoKSn7Oz8+XJPn9fu3c6de338aEPAbDkL79Vtq1y6+kpJDv7nl5eWLeI4j/f9eh8XM9Gksx7/Zg3u0R7LwH+7yYGhp5eXkqLCxU48aNSy1v3Lixdu7cWWb9KVOmKCsrq8zy5cuXa//+JEm9qj2WV199V40bH6r2/b1q1644Me+RJycnx+4heBLzbg/m3R5VzfvBIK/27TMM83Zub9++XaeeeqpWr16ttLS0kuWTJ0/Wiy++qC+//LLU+uXt0UhJSVFeXp6OHElQ06ah/8u62I4d/Mu6OvLyxLxHEL/fr5ycHKWnpysmpvrPC0LDvNuDebdHsPOen5+v5ORk7du3TwkJCRWuZ+oejeTkZEVHR5fZe7F79+4yezkkKTY2VrGxsWWWx8TEqGHDGLVqFThfQyhp5PNJp50mNW4cI1/ohxl43imniHmPQDExMfyP1wbMuz2Yd3tUNe/BPiemHgxau3ZtnXvuuWV2v+Tk5KhLly4hPZbPJ918c/XGccst4pddNTHvAICaMP1TJ2PHjtUzzzyjZ599Vl988YVuu+02bd26VSNHjgz5sYYOleLjpaggRx0VFVj/uutC3hSOE+q8y1eo2nX8zDsAwPzQGDx4sKZPn65JkyapQ4cOWrlypZYuXarU1NSQH6tBg8DprX2+qn/pRUUF1lu4MHA/VF8o8y4VSjJ0ZGB/vfnDPPMHBwCIaJacGfT//u//tGXLFhUUFOijjz7SBRdcUO3H6t07cHrruLjAL74Td80XL4uLk5YulXpV/wMTOE7V825IKpJiDknX9JXR6k1lLMzQvM+IDQDwMkdevbV3b+mHH6Tp0wMHHB7vtNMCy3/8kcgIt8rn3acpjxxWh4cvkX4TOCanyCgiNgDA4xwZGlJgd/4tt0jffBP4COWTTy7Xjh1+ffNNYHliot0jdKfK5v2ucfF658ZF6tS0U8n6xAYAeJtjQ6OYzyclJUmNGx9SUhKfcrBKRfN+UtxJWn7tcmIDACDJBaGByENsAACKERowBbEBAJAIDZiI2AAAEBowFbEBAN5GaMB0xAYAeBehAUsQGwDgTYQGLENsAID3EBqwFLEBAN5CaMByxAYAeAehAVsQGwDgDYQGbENsAID7ERqwFbEBAO5GaMB2xAYAuBehgYhAbACAOxEaiBjEBgC4D6GBiEJsAIC7EBqIOMQGALgHoYGIRGwAgDsQGohYxAYAOB+hgYhGbACAsxEaiHjEBgA4F6EBRyA2AMCZCA04BrEBAM5DaMBRiA0AcBZCA45DbACAcxAacCRiAwCcgdCAYxEbABD5CA04GrEBAJGN0IDjERsAELkIDbgCsQEAkYnQgGsQGwAQeQgNuAqxAQCRhdCA6xAbABA5CA24ErEBAJGB0IBrERsAYD9CA65GbACAvQgNuB6xAQD2ITTgCcQGANiD0IBnEBsAYD1CA55CbACAtQgNeA6xAQDWITTgScQGAFiD0IBnERsAYD5CA55GbACAuQgNeB6xAQDmITQAERsAYBZCA/gfYgMAwo/QAI5DbABAeBEawAmIDQAIH0IDKAexAQDhQWgAFSA2AKDmCA2gEsQGANQMoQFUgdgAgOojNIAgEBsAUD2EBhAkYgMAQkdoACEgNgAgNIQGECJiAwCCR2gA1UBsAEBwCA2gmogNAKgaoQHUALEBAJUjNIAaIjYAoGKEBhAGxAYAlI/QAMKE2ACAsggNIIyIDQAojdAAwozYAIBjCA3ABMQGAAQQGoBJiA0AIDQAUxEbALyO0ABMRmwA8DJCA7AAsQHAqwgNwCLEBgAvIjQACxEbALyG0AAsRmwA8BJCA7ABsQHAKwgNwCbEBgAvIDQAGxEbANyO0ABsRmwAcDNCA4gAxAYAtyI0gAhBbABwI0IDiCDEBgC3MTU0Jk+erC5duig+Pl4NGjQwc1OAa1QWGwu/WGjjyAAgdKaGxpEjR3TVVVfppptuMnMzgOtUFBsjXh1h46gAIHSmhkZWVpZuu+02tWvXzszNAK5UUWxIYs8GAMeoZfcAjldQUKCCgoKSn/Pz8yVJfr9ffr+/wvsV31bZOgg/5t189WrV09Krl2rAvAFav2O94qLiJEmjl4yWJF3R5go7h+cZvNbtwbzbI9h5D/Z58RmGYdR4VFV47rnnNGbMGO3du7fS9TIzM5WVlVVmeXZ2tuLj400aHQAACNXBgweVkZGhffv2KSEhocL1Qt6jUVEMHG/t2rXq1KlTpeuUZ8KECRo7dmzJz/n5+UpJSVGvXr0q/Uv4/X7l5OQoPT1dMTExIW8X1cO8W2vv4b0aNH+QxiSP0fDPhutQ0SFF+aI0+9LZ7NkwGa91ezDv9gh23ovfdahKyKExevRoXX311ZWu06JFi1AfVpIUGxur2NjYMstjYmKCepEFux7Ci3m3xskxJ2v+oPla/c5qHSo6pENFhyRJGYszlB2VrcFtB9s8QvfjtW4P5t0eVc17sM9JyKGRnJys5OTkUO8GIAwa1GkgSerYpKNW/7ha0rGPvkoiNgBEHFM/dbJ161Zt2LBBW7duVWFhoTZs2KANGzbo119/NXOzgOstHryYk3oBcARTQ+O+++5Tx44dNXHiRP3666/q2LGjOnbsqHXr1pm5WcD1GtRpwBlEATiCqaHx3HPPyTCMMn+6d+9u5mYBT+B05QCcgGudAA5GbACIdIQG4HDEBoBIRmgALkBsAIhUhAbgEsQGgEhEaAAuQmwAiDSEBuAyxAaASEJoAC5EbACIFIQG4FLEBoBIQGgALkZsALAboQG4HLEBwE6EBuABxAYAuxAagEcQGwDsQGgAHkJsALAaoQF4DLEBwEqEBuBBxAYAqxAagEcRGwCsQGgAHkZsADAboQF4HLEBwEyEBgBiA4BpCA0AkogNAOYgNACUIDYAhBuhAaAUYgNAOBEaAMogNgCEC6EBoFzEBoBwIDQAVIjYAFBThAaAShEbAGqC0ABQJWIDQHURGgCCQmwAqA5CA0DQiA0AoSI0AISE2AAQCkIDQMiIDQDBIjQAVAuxASAYhAaAaiM2AFSF0ABQI8QGgMoQGgBqjNgAUBFCA0BYEBsAykNoAAgbYgPAiQgNAGFFbAA4HqEBIOyIDQDFCA0ApiA2AEiEBgATERsACA0ApiI2AG8jNACYjtgAvIvQAGAJYgPwJkIDgGWIDcB7CA0AliI2AG8hNABYjtgAvIPQAGALYgPwBkIDgG2IDcD9CA0AtiI2AHcjNADYjtgA3IvQABARiA3AnQgNABGD2ADch9AAEFGIDcBdCA0AEYfYANyD0AAQkYgNwB0IDQARi9gAnI/QABDRiA3A2QgNABGP2ACci9AA4AjEBuBMhAYAxyA2AOchNAA4CrEBOAuhAcBxiA3AOQgNAI5EbADOQGgAcCxiA4h8hAYARyM2gMhGaABwPGIDiFyEBgBXIDaAyERoAHANYgOIPIQGAFchNoDIQmgAcB1iA4gchAYAVyI2gMhAaABwLWIDsB+hAcDViA3AXoQGANcjNgD7EBoAPIHYAOxBaADwDGIDsB6hAcBTiA3AWoQGAM8hNgDrEBoAPInYAKxBaADwLGIDMB+hAcDTiA3AXIQGAM8jNgDzmBYaW7Zs0YgRI9SyZUvFxcWpVatWmjhxoo4cOWLWJgGg2iqLjYVfLLRxZICzmRYaX375pYqKivTkk09q48aNmjZtmv7xj3/o7rvvNmuTAFAjFcXGiFdH2DgqwNlMC40//OEPmjNnjnr16qXTTjtNl156qW6//XYtXMi/DABEropiQxJ7NoBqqGXlxvbt26eGDRtWeHtBQYEKCgpKfs7Pz5ck+f1++f3+Cu9XfFtl6yD8mHfrMefWqFernpZevVQD5g3Q+h3rFRcVJ0kavWS0JOmKNlfYOTzP4PVuj2DnPdjnxWcYhlHjUQXh22+/1TnnnKNHH31U119/fbnrZGZmKisrq8zy7OxsxcfHmz1EAAAQpIMHDyojI0P79u1TQkJCheuFHBoVxcDx1q5dq06dju123L59uy688EJdeOGFeuaZZyq8X3l7NFJSUpSXl1fpX8Lv9ysnJ0fp6emKiYkJ4W+DmmDercecW2/v4b0aNH+QxiSP0fDPhutQ0SFF+aI0+9LZ7NkwGa93ewQ77/n5+UpOTq4yNEJ+62T06NG6+uqrK12nRYsWJd9v375dPXr0UFpamp566qlK7xcbG6vY2Ngyy2NiYoJ6kQW7HsKLebcec26dk2NO1vxB87X6ndU6VHRIh4oOSZIyFmcoOypbg9sOtnmE7sfr3R5VzXuwz0nIoZGcnKzk5OSg1v3xxx/Vo0cPnXvuuZozZ46iojhtBwDnaVCngSSpY5OOWv3jaknHPvoqidgAKmHab/7t27ere/fuSklJ0SOPPKKffvpJO3fu1M6dO83aJACYavHgxZzUCwiRaZ86Wb58uTZt2qRNmzapWbNmpW6z6PhTAAirBnUaaPm1y9Xrn720bvs6SezZAKpi2h6NYcOGyTCMcv8AgFNxunIgNBw0AQAhIjaA4BEaAFANxAYQHEIDAKqJ2ACqRmgAQA0QG0DlCA0AqCFiA6gYoQEAYUBsAOUjNAAgTIgNoCxCAwDCiNgASiM0ACDMiA3gGEIDAExAbAABhAYAmITYAAgNADAVsQGvIzQAwGTEBryM0AAACxAb8CpCAwAsQmzAiwgNALAQsQGvITQAwGLEBryE0AAAGxAb8ApCAwBsQmzACwgNALARsQG3IzQAwGbEBtyM0ACACEBswK0IDQCIEMQG3IjQAIAIQmzAbQgNAIgwxAbchNAAgAhEbMAtCA0AiFDEBtyA0ACACEZswOkIDQCIcMQGnIzQAAAHIDbgVIQGADgEsQEnIjQAwEGIDTgNoQEADkNswEkIDQBwIGIDTkFoAIBDERtwAkIDAByM2ECkIzQAwOGIDUQyQgMAXIDYQKQiNADAJYgNRCJCAwBchNhApCE0AMBliA1EEkIDAFyI2ECkIDQAwKWIDUQCQgMAXIzYgN0IDQBwOWIDdiI0AMADiA3YhdAAAI8gNmAHQgMAPITYgNUIDQDwGGIDViI0AMCDiA1YhdAAAI8iNmAFQgMAPIzYgNkIDQDwOGIDZiI0AADEBkxDaAAAJBEbMAehAQAoQWwg3AgNAEApxAbCidAAAJRBbCBcCA0AQLmIDYQDoQEAqBCxgZoiNAAAlSI2UBOEBgCgSsQGqovQAAAEhdhAdRAaAICgERsIFaEBAAgJsYFQEBoAgJARGwgWoQEAqBZiA8EgNAAA1UZsoCqEBgCgRogNVIbQAADUGLGBihAaAICwIDZQHkIDABA2xAZORGgAAMKK2MDxCA0AQNgRGyhGaAAATEFsQCI0AAAmIjZAaAAATEVseBuhAQAwHbHhXYQGAMASxIY3ERoAAMsQG95DaAAALEVseAuhAQCwHLHhHYQGAMAWxIY3mBoal156qZo3b646deqoSZMmGjJkiLZv327mJgEADlJZbCz8YqGNI0O4mBoaPXr00Pz58/XVV19pwYIF+vbbbzVw4EAzNwkAcJiKYmPEqyNsHBXCxdTQuO2229S5c2elpqaqS5cuuuuuu7RmzRr5/X4zNwsAcJiKYkMSezYcrpZVG/r555/1r3/9S126dFFMTEy56xQUFKigoKDk5/z8fEmS3++vNE6KbyNgrMW8W485twfzbo16tepp6dVLNWDeAK3fsV5xUXGSpNFLRkuSrmhzhZ3D84xgX+/B/vfgMwzDqPGoKjF+/Hg98cQTOnjwoDp37qwlS5YoKSmp3HUzMzOVlZVVZnl2drbi4+PNHCYAAAjBwYMHlZGRoX379ikhIaHC9UIOjYpi4Hhr165Vp06B3V95eXn6+eef9f333ysrK0uJiYlasmSJfD5fmfuVt0cjJSVFeXl5lf4l/H6/cnJylJ6eXuHeEoQf82495twezLv19h7eq0HzB2lM8hgN/2y4DhUdUpQvSrMvnc2eDZMF+3rPz89XcnJylaER8lsno0eP1tVXX13pOi1atCj5Pjk5WcnJyWrdurXatGmjlJQUrVmzRmlpaWXuFxsbq9jY2DLLY2JigvqPO9j1EF7Mu/WYc3sw79Y5OeZkzR80X6vfWa1DRYd0qOiQJCljcYayo7I1uO1gm0foflW93oP9byHk0CgOh+oo3nly/F4LAADK06BOA0lSxyYdtfrH1ZKOffRVErHhEKZ96uTDDz/UE088oQ0bNuj777/Xu+++q4yMDLVq1arcvRkAAJRn8eDFnNTLwUwLjbi4OC1cuFAXX3yxTj/9dA0fPlxt27ZVbm5uuW+PAABQngZ1GnAGUQcz7eOt7dq10zvvvGPWwwMAPKT4PBu9/tlL67avk8TbKE7BtU4AAI7AtVGcidAAADgGseE8hAYAwFGIDWchNAAAjkNsOAehAQBwJGLDGQgNAIBjERuRj9AAADgasRHZCA0AgOMRG5GL0AAAuAKxEZkIDQCAaxAbkYfQAAC4CrERWQgNAIDrEBuRg9AAALgSsREZCA0AgGsRG/YjNAAArkZs2IvQAAC4HrFhH0IDAOAJxIY9CA0AgGcQG9YjNAAAnkJsWIvQAAB4DrFhHUIDAOBJxIY1CA0AgGcRG+YjNAAAnkZsmIvQAAB4HrFhHkIDAAARG2YhNAAA+B9iI/wIDQAAjkNshBehAQDACYiN8CE0AAAoB7ERHoQGAAAVIDZqjtAAAKASxEbNEBoAAFSB2Kg+QgMAgCAQG9VDaAAAECRiI3SEBgAAISA2QkNoAAAQImIjeIQGAADVQGwEh9AAAKCaiI2qERoAANQAsVE5QgMAgBoiNipGaAAAEAbERvkIDQAAwoTYKIvQAAAgjIiN0ggNAADCjNg4htAAAMAExEYAoQEAgEmIDUIDAABTeT02CA0AAEzm5dggNAAAsIBXY4PQAADAIl6MDUIDAAALeS02CA0AACzmpdggNAAAsIFXYoPQAADAJl6IDUIDAAAbuT02CA0AAGzm5tggNAAAiABujQ1CAwCACOHG2CA0AACIIG6LDUIDAIAI46bYIDQAAIhAbokNQgMAgAjlhtggNAAAiGBOjw1CAwCACOfk2CA0AABwAKfGBqEBAIBDODE2CA0AABzEabFBaAAA4DBOig1CAwAAB3JKbBAaAAA4lBNig9AAAMDBQomNHft3aMnXSywdH6EBAIDDBRsbC75YoPFvjVdhUaFlYyM0AABwgWBiY/7G+fr8p8/1wn9fsGxchAYAAC5RWWxMXzNd7299X5J034r7dMh/yJIxERoAALhIRbFx25u3yZAhSfoh/wf9fe3fLRkPoQEAgMuUFxsn+ut7f9Uvh34xfSyEBgAALpQQm6B7ut2jWlG1yr39l8O/6KFVD5k+DkIDAAAX+e/O/2rU66N06mOn6vJ5l+to0dEK153xnxn6If8HU8dDaAAA4CK/TfqtmiU006GjVR/sefjoYWWuyDR1PIQGAAAuEh8TrwndJui7W77T2M5jVTu6dqXrz9kwR1/89IVp4yE0AABwoaT4JD3a+1F9PfprDT17qHzylbtekVGku9+527RxEBoAALhYaoNUPTfgOf135H/V77f9yl1n8ZeLtXrbalO2b0loFBQUqEOHDvL5fNqwYYMVmwQAAMdp17idlmQs0YqhK3TeqeeVuX38W+NlGEbYt2tJaNx5551q2rSpFZsCAACVuLDFhfpgxAdaMGiBWie1Lln+/tb3Tbngmumh8cYbb2j58uV65JFHzN4UAAAIgs/n0xVtrtDG/9uopy55Sk3qNZEkTXh7QtgvuGZqaOzatUs33HCDXnzxRcXHx5u5KQAAEKJaUbV0w7k3aNMtm/TXi/6qbfnbNHfj3PBuI6yPdhzDMDRs2DCNHDlSnTp10pYtW6q8T0FBgQoKCkp+zs/PlyT5/X75/f4K71d8W2XrIPyYd+sx5/Zg3u3BvFsnRjG6vfPtGn72cM3/ZL5O0klVznuwz4vPCPHIj8zMTGVlZVW6ztq1a7V69WrNmzdPK1euVHR0tLZs2aKWLVtq/fr16tChQ0iPnZ2dzR4RAAAiyMGDB5WRkaF9+/YpISGhwvVCDo28vDzl5eVVuk6LFi109dVX67XXXpPPd+xzu4WFhYqOjtY111yj559/vsz9ytujkZKSory8vEr/En6/Xzk5OUpPT1dMTEwofx3UAPNuPebcHsy7PZh3ewQ77/n5+UpOTq4yNEJ+6yQ5OVnJyclVrve3v/1NDzzwQMnP27dvV+/evTVv3jydd17Zj9VIUmxsrGJjY8ssj4mJCepFFux6CC/m3XrMuT2Yd3sw7/aoat6DfU5MO0ajefPmpX6uV6+eJKlVq1Zq1qyZWZsFAAARhDODAgAA05i2R+NELVq0MOWMYwAAIHKxRwMAAJiG0AAAAKax7K2T6ih+q6X4xF0V8fv9OnjwoPLz8zky2ULMu/WYc3sw7/Zg3u0R7LwX/26u6rCIiA6N/fv3S5JSUlJsHgkAACjP/v37lZiYWOHtIZ+wy0pFRUXavn276tevX+rEXycqPrHXtm3bKj1pCMKLebcec24P5t0ezLs9gp13wzC0f/9+NW3aVFFRFR+JEdF7NKKiokI650ZCQgIvRhsw79Zjzu3BvNuDebdHMPNe2Z6MYhwMCgAATENoAAAA07giNGJjYzVx4sRyr5MC8zDv1mPO7cG824N5t0e45z2iDwYFAADO5oo9GgAAIDIRGgAAwDSEBgAAMA2hAQAATOP40Jg5c6ZatmypOnXq6Nxzz9V7771n95Bcb+XKlerfv7+aNm0qn8+nxYsX2z0k15syZYp+97vfqX79+mrUqJEGDBigr776yu5hud6sWbPUvn37khMXpaWl6Y033rB7WJ4yZcoU+Xw+jRkzxu6huF5mZqZ8Pl+pP6ecckqNH9fRoTFv3jyNGTNG99xzj9avX69u3bqpT58+2rp1q91Dc7UDBw7o7LPP1hNPPGH3UDwjNzdXo0aN0po1a5STk6OjR4+qV69eOnDggN1Dc7VmzZrpwQcf1Lp167Ru3TpddNFFuuyyy7Rx40a7h+YJa9eu1VNPPaX27dvbPRTPOOuss7Rjx46SP59++mmNH9PRH28977zzdM4552jWrFkly9q0aaMBAwZoypQpNo7MO3w+nxYtWqQBAwbYPRRP+emnn9SoUSPl5ubqggsusHs4ntKwYUM9/PDDGjFihN1DcbVff/1V55xzjmbOnKkHHnhAHTp00PTp0+0elqtlZmZq8eLF2rBhQ1gf17F7NI4cOaKPPvpIvXr1KrW8V69eWr16tU2jAqyxb98+SYFferBGYWGh5s6dqwMHDigtLc3u4bjeqFGj1K9fP/Xs2dPuoXjKN998o6ZNm6ply5a6+uqr9d1339X4MSP6omqVycvLU2FhoRo3blxqeePGjbVz506bRgWYzzAMjR07Vueff77atm1r93Bc79NPP1VaWpoOHz6sevXqadGiRTrzzDPtHparzZ07Vx9//LHWrl1r91A85bzzztMLL7yg1q1ba9euXXrggQfUpUsXbdy4UUlJSdV+XMeGRrETLx9vGEall5QHnG706NH65JNP9P7779s9FE84/fTTtWHDBu3du1cLFizQ0KFDlZubS2yYZNu2bbr11lu1fPly1alTx+7heEqfPn1Kvm/Xrp3S0tLUqlUrPf/88xo7dmy1H9exoZGcnKzo6Ogyey92795dZi8H4BY333yzXn31Va1cuVLNmjWzezieULt2bf3mN7+RJHXq1Elr167VjBkz9OSTT9o8Mnf66KOPtHv3bp177rklywoLC7Vy5Uo98cQTKigoUHR0tI0j9I66deuqXbt2+uabb2r0OI49RqN27do699xzlZOTU2p5Tk6OunTpYtOoAHMYhqHRo0dr4cKFeuedd9SyZUu7h+RZhmGooKDA7mG41sUXX6xPP/1UGzZsKPnTqVMnXXPNNdqwYQORYaGCggJ98cUXatKkSY0ex7F7NCRp7NixGjJkiDp16qS0tDQ99dRT2rp1q0aOHGn30Fzt119/1aZNm0p+3rx5szZs2KCGDRuqefPmNo7MvUaNGqXs7Gy98sorql+/fsmevMTERMXFxdk8Ove6++671adPH6WkpGj//v2aO3euVqxYoWXLltk9NNeqX79+mWOP6tatq6SkJI5JMtntt9+u/v37q3nz5tq9e7ceeOAB5efna+jQoTV6XEeHxuDBg7Vnzx5NmjRJO3bsUNu2bbV06VKlpqbaPTRXW7dunXr06FHyc/F7d0OHDtVzzz1n06jcrfgj3N27dy+1fM6cORo2bJj1A/KIXbt2aciQIdqxY4cSExPVvn17LVu2TOnp6XYPDQi7H374QX/84x+Vl5enk08+WZ07d9aaNWtq/DvV0efRAAAAkc2xx2gAAIDIR2gAAADTEBoAAMA0hAYAADANoQEAAExDaAAAANMQGgAAwDSEBgAAMA2hAQAATENoAAAA0xAaAADANIQGAAAwzf8D+INQb3VqlmAAAAAASUVORK5CYII=\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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RAMSTTz5Z5pgWLlwoAIjHH3/cZXqfPn0EAPHZZ5+Vus3Vq1fF3/72N9GiRQsREBAgwsPDRdu2bcWkSZNEZmamc77Y2FgxZsyYMpddcr6S61GtWjXRsGFDMWLECHHw4MFS83///ffigQceEHXq1BFWq1XUrVtX9OrVS7z99ttCiFu/K2V9bN++3eXxLPmyvxBCpKWliYEDB4patWoJq9Uq6tevLwYOHOiy3craRkJIr1gOHz5c1KxZU1SvXl3069dP/Pjjjx6/6ugrFiH4riO+cOXKFTRv3hzDhg3zeHebSlu0aBEmTJiAH3/8EX/4wx8UX36tWrUwduxY5ykhpCzDP2WTQ2ZmJubMmYOePXsiIiICZ8+exYIFC5CXl+d8CkjeOXjwIE6fPo0XX3wRQ4cOVTyM/ve//+Hzzz/Hb7/9hvj4eEWXTSUoti9mINnZ2WLQoEEiKipKWK1WER4eLpKSksTevXvVHppuxcbGCpvNJu65555SBxYqITExUdStW1dMnTrV5chtUhafshGRZhj+SG0i0g8GEhFpBgOJiDRD06+yFRcX48KFC6hevXqZpyQQkbYJIZCXl4fo6OgKDxzWdCBduHABMTExag+DiHzg3LlzaNCgQbnzaDqQqlevDkBaEU+vc2y327F161b07du3zLO7jYjrzfXWqtzcXMTExDj/nsuj6UByPE0LCwvzKpCCg4MRFham+Q3lS1xvrrfWeVK7sNQmIs1gIBGRZjCQiEgzGEhEpBkMJCLSDFkDacmSJWjXrp3zVbL4+Hh88cUXci6SiHRM1kBq0KABXnnlFezfvx/79+9Hr169MHToUPz0009yLpaIdErW45Buf4+wOXPmYMmSJdi7d68qVwMkIm1T7MDIoqIirFmzBvn5+bwiHxG5JXsg/fDDD4iPj8f169cRGhqK9evXo3Xr1m7nLSwsRGFhofP73NxcANJRqXa73aPlOebzdH6jUHO9C28WwuZf9ptHyonbW/vr7c0YZb9i5I0bN5Ceno4rV65g7dq1WLp0KdLS0tyG0uzZs92+rc/KlSsRHBws5zCJSCYFBQUYOXIkcnJyKjwFTPFL2N5zzz1o2rRpqbdIBtzvIcXExCArK8urc9lSUlLQp08f3Zzj4wtqrfeWE1vwbMqz+N9T/1NsmSVxe2t/vXNzcxEZGelRICl+cq0QwiV0SrLZbG7fN95qtXr9oFfmNkag5Hpfv3kdE1MmIrcwV/XHmttbu7wZn6yB9Pzzz6N///6IiYlBXl4ePv74Y6SmpmLz5s1yLpYU8saeN3Dyt5MI8g9SeyhkELIG0sWLFzFq1ChkZGQgPDwc7dq1w+bNm53vx076lZ6Tjr/v+DsA4NrNaygqLkI1v2oqj4r0TtZAWrZsmZx3TyqaunUqrt285vw+356PMJtnPR9RWXguG3lt26ltWHN4jcu0qzeuqjQaMhIGEnlFCIFNxzfhiQ5PAACqWaSnaQwk8gUGEnnFYrFgQb8F6B7bHQAwtctUTImfwkAin9D0NbVJu1LPpAIAejfujT5N+8BepP0jhkn7uIdElZJ6JhX+fv7oEtMFAGCtpu1jYUgfGEjktfO55/Fz9s/oWL8jQgJC1B4OGQgDibyWdjYNAJAYm6juQMhwGEjkNUd/lNgoUdVxkPEwkMhrt/dHRL7CQCKvsD8iOTGQyCvsj0hODCTyCvsjkhMDibzC/ojkxEAij7E/IrkxkMhj7I9Ibgwk8hj7I5IbA4k8xv6I5MZAIo+wPyIlMJDII+yPSAkMJPII+yNSAgOJPML+iJTAQKIKsT8ipTCQqELsj0gpDCSqEPsjUgoDiSrE/oiUwkCicrE/IiUxkKhc7I9ISQwkKhf7I1ISA4nKxf6IlMRAojKxPyKlMZCoTOyPSGkMJCoT+yNSGgOJysT+iJTGQCK32B+RGhhI5Bb7I1IDA4ncYn9EamAgkVvsj0gNDCQqhf0RqYWBRKWwPyK1MJCoFPZHpBYGEpXC/ojUwkAiF+yPSE0MJHLB/ojUxEAiF+yPSE0MJHLB/ojUxEAiJ/ZHpDYGEjmxPyK1MZDIif0RqY2BRE7sj0htDCQCwP6ItIGBRADYH5E2MJAIAPsj0gYGEgFgf0TawEAi9kekGQwkYn9EmsFAIvZHpBkMJGJ/RJrBQDI59kekJQwkk2N/RFrCQDI59kekJQwkk2N/RFrCQDIx9kekNQwkE2N/RFrDQDIx9kekNQwkE2N/RFrDQDIp9kekRQwkk2J/RFrEQDIp9kekRQwkk2J/RFrEQDIh9kekVQwkE2J/RFrFQDIh9kekVQwkE2J/RFrFQDIZ9kekZQwkk2F/RFrGQDIZ9kekZQwkk2F/RFrGQDIR9kekdQwkE2F/RFrHQDIR9kekdQwkE2F/RFrHQDIJ9kekBwwkk2B/RHrAQDIJ9kekBwwkk2B/RHrAQDIB9kekFwwkE2B/RHrBQDIB9kekFwwkE2B/RHrBQDI49kekJwwkg2N/RHrCQDI49kekJwwkg2N/RHrCQDIw9kekNwwkA2N/RHrDQDIw9kekNwwkA2N/RHrDQDIo9kekRwwkg2J/RHrEQDIo9kekRwwkg2J/RHrEQDIg9kekVwwkA2J/RHrFQDIg9kekVwwkA2J/RHrFQDKYjLwM9kekWwwkg9l1bhcA9kekTwwkg9l19vdAYn9EOsRAMpid6TvZH5FuMZAM5uRvJ9kfkW4xkAyI/RHpFQPJgNgfkV4xkAyG/RHpmayBNHfuXNx9992oXr066tSpg2HDhuHYsWNyLtK0MvIyAAAdojuwPyLdkjWQ0tLSMG7cOOzduxcpKSm4efMm+vbti/z8fDkXa0qO44+6xXRTeSRElecv551v3rzZ5fvly5ejTp06OHDgALp37y7nok1n19ldSEISEhomqD0UokpTtEPKyckBANSqVUvJxZrCzvSdAIBO9TupPBKiypN1D6kkIQQmT56Mbt26oU2bNm7nKSwsRGFhofP73NxcAIDdbofdbvdoOY75PJ3fCDLyMnAh5wIQA1gtVlOtuxm3N6Cv9fZmjBYhhJBxLE7jxo3Dpk2bsGvXLjRo0MDtPLNnz0ZycnKp6StXrkRwcLDcQyQiGRQUFGDkyJHIyclBWFhYufMqEkjjx4/Hhg0bsGPHDjRu3LjM+dztIcXExCArK6vCFXGw2+1ISUlBnz59YLVaqzx2PXjmi2ew6odVeK/Ne6Zab8Cc2xvQ13rn5uYiMjLSo0CS9SmbEALjx4/H+vXrkZqaWm4YAYDNZoPNZis13Wq1ev2gV+Y2erXt7DbYIe0Wm2m9S+J6a5c345M1kMaNG4eVK1fi008/RfXq1ZGZmQkACA8PR1BQkJyLNg3H9bMTGyaqPRSiKpP1VbYlS5YgJycHiYmJqFevnvNj1apVci7WVBzXz+bxR2QEsj9lI3k5rp+d0DAB145cU3cwRFXEc9l0znH9bB5/REbAQNKxku+/FhzAwyJI/xhIOsb3XyOjYSDpGN9/jYyGgaRjfP81MhoGkk6V7I94/SMyCgaSTrE/IiNiIOkU+yMyIgaSTrE/IiNiIOkQ+yMyKgaSDrE/IqNiIOkQ+yMyKgaSDrE/IqNiIOkM+yMyMgaSzrA/IiNjIOkM+yMyMgaSzrA/IiNjIOkI+yMyOgaSjrA/IqNjIOkI+yMyOgaSjrA/IqNjIOkE+yMyAwaSTrA/IjNgIOkE+yMyAwaSTrA/IjNgIOkA+yMyCwaSDrA/IrNgIOkA+yMyCwaSDrA/IrNgIGkc+yMyEwaSxrE/IjNhIGkc+yMyEwaSxrE/IjNhIGkY+yMyGwaShrE/IrNhIGkY+yMyGwaShrE/IrNhIGkU+yMyIwaSRrE/IjNiIGkU+yMyIwaSRrE/IjNiIGkQ+yMyKwaSBrE/IrNiIGkQ+yMyKwaSBrE/IrNiIGkM+yMyMwaSxrA/IjNjIGkM+yMyMwaSxrA/IjNjIGkI+yMyOwaShrA/IrNjIGkI+yMyOwaShrA/IrNjIGkE+yMiBpJmsD8iYiBpBvsjIgaSZrA/ImIgaQL7IyIJA0kD2B8RSRhIGsD+iEjCQNIA9kdEEgaSytgfEd3CQFIZ+yOiWxhIKmN/RHQLA0ll7I+IbmEgqYj9EZErBpKK2B8RuWIgqYj9EZErBpKK2B8RuWIgqYT9EVFpDCSVsD8iKo2BpBL2R0SlMZBUwv6IqDQGkgrYHxG5x0BSAfsjIvcYSCpgf0TkHgNJBeyPiNxjICmM/RFR2RhICmN/RFQ2BpLC2B8RlY2BpDD2R0RlYyApiP0RUfkYSApif0RUPgaSgtgfEZWPgaQg9kdE5WMgKYT9EVHFGEgKYX9EVDEGkkLYHxFVjIGkEPZHRBVjICmA/RGRZxhICmB/ROQZBpIC2B8ReYaBpAD2R0SeYSDJjP0RkecYSDJjf0TkOQaSzNgfEXmOgSQz9kdEnmMgyYj9EZF3GEgyYn9E5B0GkozYHxF5h4EkI/ZHRN5hIMmE/RGR9xhIMmF/ROQ9BpJM2B8ReY+BJBP2R0TeYyDJgP0RUeUwkGTA/oiochhIMmB/RFQ5DCQZsD8iqhwGko+xPyKqPH+1B2AUQgCXLwOf7N8P5EegR8NEtYekCMd6X70KhIYCERGAxaL2qEivuIdURVeuAAsXAs2aAbVrAxP7DwVey8KKx2Zg4ULp50Z0+3o3bix9btYMhl5vkhcDqQq2bAEaNAAmTQJOnXL9Wea5IEyaJP18yxZ1xieX8tb71CkYdr1JfgykStqyBRg4ELh2TXraIoTrz4WwQAjp5wMHGuePs+L1hiHXm5QhayDt2LEDgwcPRnR0NCwWCzZs2CDn4hRz5QowfLj0h1dcXP68xcXSfMOH6/9pjFnXm5QjayDl5+ejffv2WLx4sZyLUdz77wMFBRX/UToUF0vz//vf8o5LbmZdb1KOrIHUv39//P3vf8d9990n52IUJQSwaFHlbvuPf5R+iqMXZl1vUpamXvYvLCxEYWGh8/vc3FwAgN1uh91u9+g+HPN5Or+3srKAkyetXt9OCODkSeDiRTsiInw/Lq63POutVXpab2/GqKlAmjt3LpKTk0tN37p1K4KDg726r5SUFF8Ny8XFi0EA+lb69p99th1RUdd8N6DbcL3NRQ/rXVBQ4PG8FiGU2Zm2WCxYv349hg0bVuY87vaQYmJikJWVhbCwMI+WY7fbkZKSgj59+sBq9f4/ekWysoDo6Mrfb0aGfHsKXG/fr7dW6Wm9c3NzERkZiZycnAr/jjW1h2Sz2WCz2UpNt1qtXj/olbmNJ+rWBZo2lY638SbKLRagSRMgKsoq65HMXG9z0cN6ezM+HofkJYsFGD++credMEG/p1WYdb1JWbIG0tWrV3Ho0CEcOnQIAHD69GkcOnQI6enpci5WdqNHA8HBgJ+Hj56fnzT/o4/KOy65mXW9STmyBtL+/fsRFxeHuLg4AMDkyZMRFxeHmTNnyrlY2dWoAaxdK/3Xr+iP089Pmm/dOul2embW9SblyBpIiYmJEEKU+lixYoWci1VEUhKwaRMQFAQAxb9/3GKxSB9BQcDnnwN9K/8ClaaUXG/HOpZk1PUmZbBDqoKkJODbwxeAfhNhq33B5WdNmgBvvgmcP2+8P8qkJOCXX6T1a9LE9WdGXm+Sn6ZeZdOjQzmpQOdFmDylOqbEzUFeHlC9OlCrlrGL3Bo1pLJ6/HggOxumWW+SFwOpihzXz+7ZOBEREZDlWBsts1hgyvUmefApWxXx+tlEvsNAqgJeP5vItxhIVcD3XyPyLQZSFfD914h8i4FUBeyPiHyLgVRJ7I+IfI+BVEnsj4h8j4FUSeyPiHyPgVRJ7I+IfI+BVAnsj4jkwUCqBPZHRPJgIFUC+yMieTCQKoH9EZE8GEheYn9EJB8GkpfYHxHJh4HkJfZHRPJhIHmJ/RGRfBhIXmB/RCQvBpIX2B8RyYuB5AX2R0Ty4kX+vaB6f1RUBPzwA/Dtt8D//gecOgVcuAD89hv8CwvR126Hf0QEUKcOEBsLtGwJ/PGPQJcuQM2a6oyZyAsMJA85+qMuMV2U7Y+uXZPemfGTT4CtW4HffnM7mwVAECC9J9HPPwNff13ihxagQwdg8GDgoYeA5s2VGDmR1xhIHlK8PzpxAvjHP4B//xvIybk1vXp1oHNn4M47pWCpXx+oVQv2atXw9Y4d6Na+Pfyzs6W9p59+kvamjh0D9u+XPmbNArp1A/7yF+D++wF//gqQdvC30UOK9UdHjgDJycDq1YAQ0rSGDaU9m6FDgY4d3YeI3Y6c8+chuncHrFbXn2VmSu9r7djL2rVL+nj+eWDGDGD06NK3IVIBS20Pyd4fZWVJey1t2gCrVklhNHAgsGULcPo08OqrUhdUmT2aunWBsWOlUEpPlwKvdm3gzBng8ceBdu2AL77w+SoReYuB5AFZjz8SAvjgA6mAXrIEKC6W9oQOHQI2bgT69gX8fLiZoqOBmTOBs2eBN94AIiOBo0eBAQOkp3CZmb5bFpGXGEgekK0/unQJuPde4NFHgcuXgbZtgbQ0YMMGoH173y7rdkFBwKRJUlc1ZQpQrZr0lK5NG2DtWnmXTVQGBpIHZOmPdu6UiulPP5X6mzlzgAMHgO7dfbcMT4SHA/PnS4X3nXdKwThihPT0sbBQ2bGQ6TGQPODT/kgIYNEioFcv6RiiVq2kMHj+eXWL5TvvBL75Bpg+Xfp+yRKgRw9pjEQKYSBVwKf90c2bwLhxwIQJ0tcjRwL79kmlshYEBABz50oFd82aUkB17Ah8/73aIyOTYCBVwGf90bVrwPDh0p6HxQK89hrw4YdAiAZP0u3XTwrKli2B8+eBhARg+3a1R0UmwECqgE/6o6tXpVexPvsMsNmk8njqVCmYtKppU2DPHiAxEcjLk0Jq40a1R0UGx0CqQJX7I8cfc2qqdJT1li3Afff5dIyyqVFDevo2bBhw44Y07k8/VXtUZGAMpHJUuT8qKJDOH/v6a+mPe9s2qSjWk8BA6ajxhx4C7HbpWCUeREkyYSCVo0r9kd0OPPCAdFxRWJh0ykbHjr4doFKsVungzfvvl9brvvukU0+IfIyBVI5K90dCAP/v/0ln6QcGSp/vvtvn41OUvz/w0UfS6SzXr0t7focPqz0qMhgGUjkq3R/9/e/AihXS0c9r1khn1xuB1So9fevSBbhyRSrqL15Ue1RkIAykMlS6P1qzRjpXDADeegsYNEieAaolOFgqtu+4Qzof7t57eUQ3+QwDqQyV6o++/x4YM0b6etIk4IknfD4uTYiMlJ6G1qghHRrwl7/culQKURUwkMrgdX905Yp04GNBgXSG/rx5cg1NG5o3ly6T4ucHvPcesHSp2iMiA2AglcGr/kgIac/o5EnpWtYrV5rjSox9+0p9GQA8/TTw3Xfqjod0j4Hkhtf90eLFUq8SECBduiMiQv5BasW0adIrbjduAA8+KB0ISlRJDCQ3vOqPfvhBOg0EkM5P69BBvoFpkZ8fsHw50KCBdG2lZ55Re0SkYwwkNzzuj65fBx55RNo7GDQIGD9e9rFpUkSE9DTVYpHCiRd4o0piILnhcX80e7a0h1SnDrBsmbZPlpVbQsKtayk98QSPT6JKYSDdxuP+aO9e6SkaALz7rhRKZjd7tnTp3cuXgaee4qEA5DUG0m086o9u3AAee0y6IP+f/iRdlJ+kUv/996VXGNevB9atU3tEpDMMpNt41B+98op0Hlft2sCbbyoxLP1o3/7WU7enny7znXaJ3GEg3abC/ujnn6UL8gPAwoXmeonfUzNmAC1aSG+pNGOG2qMhHWEglVBhfySEdE3sGzekgwIfekj5QepBYKB0qV4AePtt6e28iTzAQCqhwv5o/XogJUW6DO1bb5n7VbWK9OwJjBolhfjTT0t9G1EFGEgllNsfXbsGTJ4sff3Xv0pnu1P55s2TLtu7b590ORaiCjCQSii3P3rjDelyGzExt0pbKl/dusCsWdLXzz/P00qoQgyk35XbH2VmSu9XBgCvvipdE4g8M368tDd58aL02BGVg4H0u3L7o1mzgPx86ZrYLLK9ExBw61Isr78O/PKLuuMhTWMg/a7M/ujYMem0EED6g2KR7b1hw6RTS65fl47mJioDA+l3ZfZHf/sbUFQEDBlinGtjK81iufV0bfly4MgRdcdDmsVAQjn90XffSe8ya7EAL7+s3gCNID5e2lMqLuZeEpWJgYRy+qMXXpA+P/II8Ic/KDsoI3rxRSncV68GDh1SezSkQQwklNEfffst8Pnn0lsZOV66pqpp2/bWiwLJyeqOhTSJgYQy+iPHH8yoUTwI0pdeeEHaS9qwQXqXFqISTB9IbvujAwekvSM/P+mAPvKdVq2ktxgHgJdeUncspDmmDyS3/ZHjIMiHHwaaNVN+UEb3t79Jn9etA44eVXcspCmmD6RS/dHRo7cuLPbcc6qMyfDatJEuaicEj94mFwyk2/uj+fOlP5ShQ/nKmpwcYf/RR8D58+qOhTTD1IFUqj/KyAA++ED64V//qu7gjK5TJ6B7d8Buly50RwSTB1Kp/mjxYunia126SB8kr2eflT6/8w6Qm6vuWEgTTB1ILv1RQYF0dUPg1hs/krwGDABatpTCaPlytUdDGmD6QHL2Rx98AGRnA40bS+etkfz8/ICJE6WvFy6UzhkkUzNtILn0R9Zg4B//kH4wfrx0dDYpY9QooFYt4PRpYONGtUdDKjNtILn0R6mp0tsahYQAY8eqOi7TCQ6W3uMOkK5TTqZm2kBy6Y8WL5YmjhoFhIerNibTeuop6XSSlBQeKGlypg4kfz9/dPVrBHz6qTRx3DhVx2RajRsDgwZJX7/zjrpjIVWZMpBK9kfBH/xHKlMTEqQjiEkdTz0lfV6xQnqHFzIlUwaSoz/q2aA78K9/SROffFLFERH69gUaNQKuXJGul0SmZMpAcvRHw8+FShedr1ULuO8+dQdldtWqAY8/Ln29dKm6YyHVmDaQ/P380fa/30gTHn1UevtnUteYMdKxSbt2sdw2KdMFkqM/6lv9Tvhv+kKa6HjZmdQVHQ0MHCh97XinFzIV0wWSoz8adywcuHlTeq81ltna4TgO7MMPpe1DpmK6QEo9kwoIIOGrk9KE//s/VcdDtxk4EKhdW3q34C1b1B4NKcyUgXT3xWqofvwMYLMBDz6o9pCoJKsV+NOfpK9XrFB1KKQ8UwWSoz+acrKONGHIEKBmTXUHRaWNHi19/u9/pcMAyDRMFUhpZ9NQrQgYsP/3a++MGqXugMi9du2kXq+wUHqjTjINUwVS6plU9D4NVP8tH4iMBPr1U3tI5I7FcuufheMKnmQKpgukP/1okb554AGpryBtevhh6fPOndLBq2QKpgmk87nnce7iz7jv6O+r7PiFJ22KiQG6dZPecGHVKrVHQwoxTSClnU3DgJ+BkOtF0i87r5mtfY5/Gv/5j7rjIMWYJpBSz6TigZ9+/+bBB6VTFEjb7r9f2k4HDgAnT6o9GlKAaf4q9x7/CoOO//4Njz3Sh9q1gV69pK/XrFF3LKQIUwTS+dzzaP7NSYTYIV0MrEMHtYdEnnrgAekzL0liCqYIpLSzaRhx+PdvRoyQXlYmfbj3Xulp28GDwKlTao+GZGaKQNp1fBsG/vz7N8OHqzoW8lJkJNCjh/T1+vXqjoVkZ4pAKtqyGdVvAMUN6gN33632cMhbjn8ia9eqOw6SneED6XzueXTalwEA8Bt2L19d06Nhw6TPe/YAGRmqDoXkZfi/zh2ntt96de3ee1UdC1VS/frSdasA6YRbMizDB9K5LatRpwCwh4VK7yxC+jR0qPTZ8ZZVZEiGD6RaW3cCACwDB/LcNT1zPG3btg24elXVoZB8DB1I53PPo+v/rgAA/IfxXUV0rVUroEkT4MYNKZTIkAwdSAd2rkKrLKComh+QlKT2cKgqLBZg8GDpa/ZIhmXoQLq2Xjrd4ErHdkB4uMqjoSpzvN32pk1AcbG6YyFZGDqQ6u/6HwAgdBjPXTOE7t2B0FDg4kXpyG0yHMMGUmbmSdx9ogAAYBsyTN3BkG8EBAD33CN9/cUX6o6FZGHYQDq1filsRUB23RpAixZqD4d8ZcAA6fPnn6s7DpKFYQMJmzcDAPJ7J/BkWiPp31/6/M03QHa2umMhnzNsIDXeJ51NW/veP6k8EvKpBg2A1q2lUvvLL9UeDfmYIQMpOCMDDbNu4GY1CwL79ld7OORrjkM4+M62hmOoQBICyMoC7DvPIQsRONs6BqheXe1hka/9Hkhi8xZkXRK4eDEIWVnS9id9M0QgXbkCLFwINGsGREdbMXDlHNRGFnqlf4uFC/nmp0ZzpW0CFlabjGbntyO6fgCeeKIvoqOtaNYM3N46p/tA2rJFqhUmTSp9QcFzuXUwaZL0c+7dG8OWLUCD5sGYVPQaTqGJy89OnQK3t87pOpC2bAEGDgSuXZN212/fZRfCAiGknw8cyF9SvXPZ3vCDuO3X1/E7wO2tX4oE0j//+U80btwYgYGB6NChA3bu3Fnl+7xyRbqQoBAVn0VQXCzNN3w4d+f1itvbHGQPpFWrVmHixImYMWMGDh48iISEBPTv3x/p6elVut/33wcKCjw/pam4WJr/3/+u0mJJJdze5iB7IL3xxht47LHH8Oc//xmtWrXCm2++iZiYGCxZsqTS9ykEsGhR5W77j3/w1Ri94fY2D3857/zGjRs4cOAApk+f7jK9b9++2L17d6n5CwsLUVhY6Pw+NzcXAGC322G3253Ts7KAkye9v9iaENIboF68aEdEhNc31zTH41PycTIKbu/S9LS9vRmjrIGUlZWFoqIiREVFuUyPiopCZmZmqfnnzp2L5OTkUtO3bt2K4OBg5/cXLwYB6FvpcX322XZERV2r9O21LCUlRe0h+By3d9n0sL0LCgo8ntcihHw7tBcuXED9+vWxe/duxMfHO6fPmTMHH3zwAY4ePeoyv7s9pJiYGGRlZSEsLMw5PStLOt6osjIyjPkfMyUlBX369IHVYJfq5fYuTU/bOzc3F5GRkcjJyXH5O3ZH1j2kyMhIVKtWrdTe0K+//lpqrwkAbDYbbDZbqelWq9XlQa9bF2jaVDruxJs4tVikq6BGRVkNe77t7Y+VEXB7l00P29ub8claagcEBKBDhw6lditTUlLQpUuXSt+vxQKMH1+5206YwJP/9Ybb2zxkf5Vt8uTJWLp0Kd577z0cOXIEkyZNQnp6Op588skq3e/o0UBwsOfv++jnJ83/6KNVWiyphNvbHGR9ygYADz74IC5fvowXX3wRGRkZaNOmDT7//HPExsZW6X5r1JDeWXngQOmXr7zjU/z8pP+S69ZJtyP94fY2B0WO1P7LX/6CM2fOoLCwEAcOHED37t19cr9JSdL13oOCpF/A23fNHdOCgqQLDPat/As1pAEVb2/B7a1zuj6XDZB+SX/5BXjzTanALKlJE2n6+fP85TSK8rZ39bpZ3N46p/tAAqTd8gkTgJ9/ll7ifeedrcjIsOPnn6XpfAckY3G3vYOfa4iYv/Xi9tY5QwSSg8UCREQAUVHXEBHBV1eMruT2bt+4AX669CMu5V9Se1hUBYYKJDKvhIYJAIAdZ3eoPBKqCgYSGUK3ht0AAKlnUtUdCFUJA4kMIT4mHn4WP6SeTVV7KFQFDCQyhHBbOOLqxuHHX9kj6RkDiQwjsVEiAPZIesZAIsNwBBJ7JP1iIJFhdGvYjT2SzjGQyDBqBNZgj6RzDCQyFPZI+sZAIkNhj6RvDCQyFPZI+sZAIkNhj6RvDCQyHPZI+sVAIsNhj6RfDCQyHPZI+sVAIsNhj6RfDCQyJPZI+sRAIkNij6RPDCQyJPZI+sRAIkNij6RPDCQyLPZI+sNAIsNij6Q/DCQyLPZI+sNAIsNij6Q/DCQyNPZI+sJAIkNjj6QvDCQyNPZI+sJAIkNjj6QvDCQyPPZI+sFAIsNjj6QfDCQyPPZI+sFAIsNjj6QfDCQyBfZI+sBAIlNgj6QPDCQyBfZI+sBAIlNgj6QPDCQyDfZI2sdAItNgj6R9DCQyDfZI2sdAItNgj6R9DCQyFfZI2sZAIlNhj6RtDCQyFfZI2sZAIlNhj6RtDCQyHfZI2sVAItNhj6RdDCQyHfZI2sVAItNhj6RdDCQyJfZI2sRAIlNij6RNDCQyJfZI2sRAIlNij6RNDCQyLfZI2sNAItNij6Q9DCQyLfZI2sNAItNij6Q9DCQyNfZI2sJAIlNjj6QtDCQyNfZI2sJAIlNjj6QtDCQyPfZI2sFAItNjj6QdDCQyPfZI2sFAItNjj6QdDCQisEfSCgYSEdgjaQUDiQjskbSCgUQE9khawUAi+h17JPUxkIh+xx5JfQwkot+xR1IfA4nod+yR1MdAIiqBPZK6GEhEJbBHUhcDiagE9kjqYiARlcAeSV0MJKLbsEdSDwOJ6DbskdTDQCK6DXsk9TCQiG7DHkk9DCQiN9gjqYOBROQGeyR1MJCI3GCPpA4GEpEb7JHUwUAiKgN7JOUxkIjKwB5JeQwkojKwR1IeA4moDOyRlMdAIioHeyRlMZCIysEeSVkMJKJysEdSFgOJqBzskZTFQCKqAHsk5TCQiCrAHkk5DCSiCrBHUg4DiagC7JGUw0Ai8gB7JGUwkIg8wB5JGQwkIg+wR1IGA4nIA+yRlMFAIvIQeyT5MZCIPMQeSX4MJCIPsUeSHwOJyEPskeTHQCLyAnskeTGQiLzAHkleDCQiL7BHkhcDicgL7JHkxUAi8hJ7JPkwkIi8xB5JPrIG0pw5c9ClSxcEBwejRo0aci6KSDHskeQjayDduHED999/P5566ik5F0OkKPZI8pE1kJKTkzFp0iS0bdtWzsUQKY49kjz81R5ASYWFhSgsLHR+n5ubCwCw2+2w2+0e3YdjPk/nNwqut7Lr3SOmB/75zT+x8/RODGk2RNFlA/ra3t6M0SKEEDKOBQCwYsUKTJw4EVeuXCl3vtmzZyM5ObnU9JUrVyI4OFim0RGRnAoKCjBy5Ejk5OQgLCys3Hm93kMqKzRK2rdvH+666y5v7xrPPfccJk+e7Pw+NzcXMTEx6Nu3b4Ur4mC325GSkoI+ffrAarV6PQa94norv949VvTAocxDODnhJCKDIxVdtp62t+OZjie8DqSnn34aDz30ULnzNGrUyNu7BQDYbDbYbLZS061Wq9cPemVuYwRcb+V0ie2CPRf2YPf53Rjeeriiy3bQw/b2ZnxeB1JkZCQiI5X9b0CkRYmNEvH6nteReiZVtUAyGllL7fT0dGRnZyM9PR1FRUU4dOgQAOCOO+5AaGionIsmkh2PR/I9WQNp5syZeP/9953fx8XFAQC2b9+OxMREORdNJDvH8UgHMg7gUv4l1A6prfaQdE/W45BWrFgBIUSpD4YRGQWPR/ItnstGVAU8r823GEhEVcAeybcYSERVwPPafIuBRFRF7JF8h4FEVEXskXyHgURUReyRfIeBRFRF7JF8h4FE5APskXyDgUTkA+yRfIOBROQD7JF8g4FE5APskXyDgUTkI+yRqo6BROQj7JGqjoFE5CPskaqOgUTkI+yRqo6BRORD7JGqhoFE5EPskaqGgUTkQ+yRqoaBRORD7JGqhoFE5GPskSqPgUTkY+yRKo+BRORj7JEqj4FE5GPskSqPgUQkA/ZIlcNAIpIBe6TKYSARyYA9UuUwkIhkwB6pchhIRDJhj+Q9BhKRTNgjeY+BRCQT9kjeYyARyYQ9kvcYSEQyYo/kHQYSkYzYI3mHgUQkI/ZI3mEgEcmIPZJ3GEhEMmOP5DkGEpHM2CN5joFEJDP2SJ5jIBHJjD2S5xhIRApgj+QZBhKRAtgjeYaBRKQA9kieYSARKYA9kmcYSEQKYY9UMQYSkULYI1WMgUSkEPZIFWMgESmEPVLFGEhECmKPVD4GEpGC2COVj4FEpCD2SOVjIBEpiD1S+RhIRApjj1Q2BhKRwtgjlY2BRKQw9khlYyARKYw9UtkYSEQqYI/kHgOJSAXskdxjIBGpgD2SewwkIhWwR3KPgUSkEvZIpTGQiFTCHqk0BhKRStgjlcZAIlIJe6TSGEhEKmKP5IqBRKQi9kiuGEhEKmKP5IqBRKQi9kiuGEhEKmOPdAsDiUhl7JFuYSARqYw90i0MJCKVsUe6hYFEpAHskSQMJCINYI8kYSARaQB7JAkDiUgD2CNJGEhEGsEeiYFEpBnskRhIRJrBHomBRKQZ7JEYSESaYvYeiYFEpCFm75EYSEQaYvYeiYFEpCFm75EYSEQaY+YeiYFEpDFm7pEYSEQaY+YeiYFEpDFm7pEYSEQaZNYeiYFEpEFm7ZEYSEQaZNYeiYFEpEFm7ZEYSEQaZcYeiYFEpFFm7JEYSEQaZcYeiYFEpFFm7JEYSEQaZrYeiYFEpGFm65EYSEQaZrYeiYFEpGFm65EYSEQaZ6YeiYFEpHFm6pEYSEQaZ6YeiYFEpHFm6pEYSEQ6YJYeiYFEpANm6ZEYSEQ6YJYeiYFEpANm6ZEYSEQ6YYYeiYFEpBNm6JEYSEQ6YYYeiYFEpBMle6Ssgiy1hyMLBhKRjjietu1O363uQGTCQCLSEUcg7Uzfqe5AZCJbIJ05cwaPPfYYGjdujKCgIDRt2hSzZs3CjRs35FokkeE5eqRd53apPRRZ+Mt1x0ePHkVxcTHeeecd3HHHHfjxxx/x+OOPIz8/H/Pnz5drsUSG5uiRDl88DNRTezS+J1sg9evXD/369XN+36RJExw7dgxLlixhIBFVQWKjRCmQDEjRDiknJwe1atVScpFEhuPokYxItj2k2508eRKLFi3C66+/XuY8hYWFKCwsdH6fm5sLALDb7bDb7R4txzGfp/MbBdfbPOvdObozQqqFANDHenszRosQQnhz57Nnz0ZycnK58+zbtw933XWX8/sLFy6gR48e6NGjB5YuXer1fa9cuRLBwcHeDJOINKKgoAAjR45ETk4OwsLCyp3X60DKyspCVlb5B2U1atQIgYGBAKQw6tmzJzp16oQVK1bAz6/sZ4nu9pBiYmKQlZVV4Yo42O12pKSkoE+fPrBarR7dxgi43uZa71lfzkKnwk642egmhrQeovZwypWbm4vIyEiPAsnrp2yRkZGIjIz0aN7z58+jZ8+e6NChA5YvX15uGAGAzWaDzWYrNd1qtXr9y1aZ2xgB19sc4mPjUXy8GDt/2Ynh7YerPZxyebNdZCu1L1y4gMTERMTExGD+/Pm4dOkSMjMzkZmZKdciiUwjPiYeAAx3PJJspfbWrVtx4sQJnDhxAg0aNHD5mZfPEonoNuG2cADA4UuHcSn/EmqH1FZ5RL4h2x7SmDFjIIRw+0FEvmOk6yPxXDYinTPS9ZEYSEQ6ZrTrIzGQiHSsXVQ7Q11nm4FEpGMJDRMAGKdHYiAR6Vi3ht0AGKdHYiAR6Vh8TLyheiQGEpGOhdvCDfV+bQwkIp0z0vu1MZCIdM5I79fGQCLSOSO9XxsDiUjnSr5fm957JAYSkQEYpUdiIBEZgFF6JAYSkQEYpUdiIBEZgFF6JAYSkUEYoUdiIBEZhBF6JAYSkUEYoUdiIBEZhBF6JAYSkYHovUdiIBEZiN57JAYSkYHovUdiIBEZiN57JAYSkcHouUdiIBEZjJ57JAYSkcHouUdiIBEZjJ57JAYSkQHptUdiIBEZkF57JAYSkQHptUdiIBEZkF57JAYSkUHpsUdiIBEZlB57JAYSkUHpsUdiIBEZlB57JAYSkYHprUdiIBEZmN56JAYSkYHprUdiIBEZmN56JAYSkcHpqUdiIBEZnJ56JAYSkcHpqUdiIBEZnJ56JAYSkQnopUdiIBGZgF56JAYSkQnopUdiIBGZgF56JAYSkUnooUdiIBGZhB56JAYSkUnooUdiIBGZhB56JAYSkYlovUdiIBGZiNZ7JAYSkYlovUdiIBGZiNZ7JAYSkclouUdiIBGZjJZ7JAYSkclouUdiIBGZjJZ7JAYSkQlptUdiIBGZkFZ7JAYSkQlptUdiIBGZkFZ7JAYSkUlpsUdiIBGZlBZ7JAYSkUlpsUdiIBGZlBZ7JAYSkYlprUdiIBGZmNZ6JAYSkYlprUdiIBGZmNZ6JAYSkclpqUdiIBGZnJZ6JAYSkclpqUdiIBGZnJZ6JAYSEWmmR2IgEZFmeiQGEhFppkdiIBGRZnokBhIRAdBGj8RAIiIA2uiRGEhEBEAbPRIDiYgAaKNHYiARkZPaPRIDiYic1O6RGEhE5KR2j8RAIiIntXskBhIRuVCzR2IgEZELNXskBhIRuVCzR2IgEZELNXskBhIRlaJWj8RAIqJS1OqRGEhEVIpaPRIDiYhKUatHYiARkVtq9EgMJCJyS40eiYFERG6p0SMxkIjILTV6JAYSEZVJ6R6JgUREZVK6R2IgEVGZlO6RGEhEVCaleyQGEhGVS8keiYFEROVSskdiIBFRuZTskRhIRFQuJXskBhIRVUipHomBREQVKtkjFdgL8MPFH2RZDgOJiMp1/PJxdKrfCX4WP3xx4gskrkjEwcyDsiyLgURE5Uo9k4qYBTEAgJO/ncS+C/sQGhAqy7IYSERUrsfiHkObOm1QLIqd0xhIRKSKan7V8NaAt1ym6TKQhgwZgoYNGyIwMBD16tXDqFGjcOHCBTkXSUQy6NSgE8beOdb5fYg1RJblyBpIPXv2xOrVq3Hs2DGsXbsWJ0+exIgRI+RcJBHJZO49cxFuCweg0z2kSZMmoXPnzoiNjUWXLl0wffp07N27F3a7Xc7FEpEM6oTUwUs9XwKg00AqKTs7Gx999BG6dOkCq9Wq1GKJyIeeuvsptItqJ1sg+ctyryVMmzYNixcvRkFBATp37oyNGzeWOW9hYSEKCwud3+fm5gIA7Ha7x3tVjvnMthfG9eZ6K2VR0iL4w9/rv0lPWIQQwpvBzJ49G8nJyeXOs2/fPtx1110AgKysLGRnZ+Ps2bNITk5GeHg4Nm7cCIvF4vF9r1y5EsHBwd4Mk4g0oqCgACNHjkROTg7CwsLKndfrQMrKykJWVla58zRq1AiBgYGlpv/yyy+IiYnB7t27ER8fX+rn7vaQYmJikJWVVeGKONjtdqSkpKBPnz6memrI9eZ6a1Vubi4iIyM9CiSvn7JFRkYiMjKyUgNzZF/J0CnJZrPBZrOVmm61Wr1+0CtzGyPgepuLHtbbm/HJ1iF9++23+Pbbb9GtWzfUrFkTp06dwsyZM9G0aVO3e0dERLK9yhYUFIR169ahd+/eaNGiBcaOHYs2bdogLS3N7V4QEZFse0ht27bFV199JdfdE5EB8Vw2ItIMBhIRaQYDiYg0g4FERJrBQCIizZD9XLaqcBxI6TinzRN2ux0FBQXIzc3V/AFjvsT15nprlePv15OTQjQdSHl5eQCAmJgYlUdCRFWVl5eH8PDwcufx+lw2JRUXF+PChQuoXr2625Nx3XGc/3bu3DmPz38zAq4311urhBDIy8tDdHQ0/PzKb4k0vYfk5+eHBg0aVOq2YWFhmt9QcuB6m4te1ruiPSMHltpEpBkMJCLSDMMFks1mw6xZs0x3Ai/Xm+ttBJoutYnIXAy3h0RE+sVAIiLNYCARkWYwkIhIM3QXSP/85z/RuHFjBAYGokOHDti5c2e586elpaFDhw4IDAxEkyZN8Pbbbys0Ut/zZt1TU1NhsVhKfRw9elTBEVfNjh07MHjwYERHR8NisWDDhg0V3sYI29vb9TbCtnbQVSCtWrUKEydOxIwZM3Dw4EEkJCSgf//+SE9Pdzv/6dOnMWDAACQkJODgwYN4/vnnMWHCBKxdu1bhkVedt+vucOzYMWRkZDg/mjVrptCIqy4/Px/t27fH4sWLPZrfKNvb2/V20PO2dhI60rFjR/Hkk0+6TGvZsqWYPn262/n/+te/ipYtW7pMe+KJJ0Tnzp1lG6NcvF337du3CwDit99+U2B08gMg1q9fX+48RtreDp6st5G2tW72kG7cuIEDBw6gb9++LtP79u2L3bt3u73Nnj17Ss2flJSE/fv36+qtlyuz7g5xcXGoV68eevfuje3bt8s5TNUZZXtXlhG2tW4CKSsrC0VFRYiKinKZHhUVhczMTLe3yczMdDv/zZs3K3z3XS2pzLrXq1cP7777LtauXYt169ahRYsW6N27N3bs2KHEkFVhlO3tLSNta02f7e/O7ZchEUKUe2kSd/O7m64H3qx7ixYt0KJFC+f38fHxOHfuHObPn4/u3bvLOk41GWl7e8pI21o3e0iRkZGoVq1aqT2CX3/9tdR/RYe6deu6nd/f3x8RERGyjdXXKrPu7nTu3Bk///yzr4enGUbZ3r6g122tm0AKCAhAhw4dkJKS4jI9JSUFXbp0cXub+Pj4UvNv3boVd911l+Yv+1lSZdbdnYMHD6JevXq+Hp5mGGV7+4Jut7W6nbp3Pv74Y2G1WsWyZcvE4cOHxcSJE0VISIg4c+aMEEKI6dOni1GjRjnnP3XqlAgODhaTJk0Shw8fFsuWLRNWq1V88sknaq1CpXm77gsWLBDr168Xx48fFz/++KOYPn26ACDWrl2r1ip4LS8vTxw8eFAcPHhQABBvvPGGOHjwoDh79qwQwrjb29v1NsK2dtBVIAkhxFtvvSViY2NFQECA+OMf/yjS0tKcPxs9erTo0aOHy/ypqakiLi5OBAQEiEaNGoklS5YoPGLf8WbdX331VdG0aVMRGBgoatasKbp16yY2bdqkwqgrz/Fy9u0fo0ePFkIYd3t7u95G2NYOvPwIEWmGbjokIjI+BhIRaQYDiYg0g4FERJrBQCIizWAgEZFmMJCISDMYSESkGQwkItIMBhIRaQYDiYg0g4FERJrx/wFbd7L2ryX2SwAAAABJRU5ErkJggg==\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": {
+      "application/vnd.jupyter.widget-view+json": {
+       "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"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c56363fef08f414a8db211f01116d59e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e351bb5029214249abd6e2d4218c8d07",
+       "version_major": 2,
+       "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": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.13"
+  },
+  "latex_envs": {
+   "LaTeX_envs_menu_present": true,
+   "autoclose": false,
+   "autocomplete": true,
+   "bibliofile": "biblio.bib",
+   "cite_by": "apalike",
+   "current_citInitial": 1,
+   "eqLabelWithNumbers": true,
+   "eqNumInitial": 1,
+   "hotkeys": {
+    "equation": "Ctrl-E",
+    "itemize": "Ctrl-I"
+   },
+   "labels_anchors": false,
+   "latex_user_defs": false,
+   "report_style_numbering": false,
+   "user_envs_cfg": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/jupyter/parametric_curves_2.ipynb b/jupyter/parametric_curves_2.ipynb
new file mode 100644
index 0000000..63a115f
--- /dev/null
+++ b/jupyter/parametric_curves_2.ipynb
@@ -0,0 +1,893 @@
+{
+ "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": 24,
+   "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": "markdown",
+   "id": "8a6112da",
+   "metadata": {},
+   "source": [
+    "## 3. Wymierne krzywe Beziera stopnia 3\n",
+    "================================================="
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b727c642",
+   "metadata": {},
+   "source": [
+    "### 3.1. Macierz funkcji bazowych"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "d1d7fad9",
+   "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": 25,
+     "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": 26,
+   "id": "c999b172",
+   "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": "f69f1721",
+   "metadata": {},
+   "source": [
+    "### 3.2. Wykresy funkcji bazowych"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "ee8f907c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "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": 28,
+   "id": "14de6217",
+   "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": "373fa553",
+   "metadata": {},
+   "source": [
+    "### 3.3. Przykłady"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "69663125",
+   "metadata": {},
+   "source": [
+    "#### 3.3.1. Przykład 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "acd666f8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "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",
+    "w = [0.5,5.0,10.0,1.0]\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 wymiernej krzywej Beziera\")\n",
+    "\n",
+    "\n",
+    "\n",
+    "def X(t):\n",
+    "    denominator = 1.0/(w[0]*bezier_basis_function_2D[0](t) + \n",
+    "                   w[1]*bezier_basis_function_2D[1](t) + \n",
+    "                   w[2]*bezier_basis_function_2D[2](t) + \n",
+    "                   w[3]*bezier_basis_function_2D[3](t)\n",
+    "                  )\n",
+    "    return denominator*(w[0]*P0[0]*bezier_basis_function_2D[0](t) + w[1]*P1[0]*bezier_basis_function_2D[1](t) + w[2]*P2[0]*bezier_basis_function_2D[2](t) + w[3]*P3[0]*bezier_basis_function_2D[3](t))\n",
+    "\n",
+    "def Y(t):\n",
+    "    denominator = 1.0/(w[0]*bezier_basis_function_2D[0](t) + \n",
+    "                   w[1]*bezier_basis_function_2D[1](t) + \n",
+    "                   w[2]*bezier_basis_function_2D[2](t) + \n",
+    "                   w[3]*bezier_basis_function_2D[3](t)\n",
+    "                  )\n",
+    "    return denominator*(w[0]*P0[1]*bezier_basis_function_2D[0](t) + w[1]*P1[1]*bezier_basis_function_2D[1](t) + w[2]*P2[1]*bezier_basis_function_2D[2](t) + w[3]*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",
+    "leg.get_frame().set_alpha(0.5)\n",
+    "plt.grid();\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "635f22cd",
+   "metadata": {},
+   "source": [
+    "#### 3.3.2.  Przykład 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "5cbd0130",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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HXbFeanxiIe2C9VLjEwtpF2w/XuMTC2kXbD9e4xMLaTeslxpfWEi7Yb3U+MJC2g3rpcYXFtJuWC81vrCQ9sB6qfGBhbQH1kuNDyykPbBeanxgIe2B9VLjAwtpL6yXmrRZSHthvdSkzULaC+ulJm0W0l5YLzVps5CWwHqpSZOFtATWS02aLKQlsF5q0mQhLYH1UpMmC2mJrJeatFhIS2S91KTFQloi66UmLRbSElkvNWmxkJbBeqlJg4W0DNZLTRospGWwXmrSYCEtg/VSkwYLaZmsl5qkWUjLZL3UJM1CWibrpSZpFtIyWS81SbOQVsB6qUmShbQC1ktNkiykFbBeapJkIa2A9VKTJAtphayXmqRYSCtkvdQkxUJaIeulJikW0gpZLzVJsZBWwXqpSYKFtArWS00SLKRVsF5qkmAhrYL1UpMEC2mVrJeauFlIq2S91MTNQlol66UmbhbSKlkvNXGzkEbAeqmJk4U0AtZLTZwspBGwXmriZCGNgPVSEycLaUSsl5q4WEgjYr3UxMVCGhHrpSYuFtKIWC81cYkkpCKyhYg8IiLzRGShiHy7i2lERC4XkUUiMl9ExkWxbJ9YLzVxiOqddC1wmKruA+wLTBGRCUXTTAV2DU7TgCsiWrY3rJeaOEQSUnU6g4t1wUmLJjsGuCaY9iFgsIgMi2L5vgh76fqN69MeSmpUlY26Me1h5IqoFmepwhmJ9AXmAu8Hfq6qXym6/lbgYlW9L7g8C/iKqs4pmm4a7p2WxsbGptbW1rLG0dnZSX19fcX3o1oLX1nI8AHDGdwwOLUxpPkYrFqzCtbBoIZBqSw/VMljMHny5LmqOj6mIVVOVSM9AYOBNmBM0d9vAyYWXJ4FNPU0r6amJi1XW1tb2beJ0vS/TNerb7461TGk9Ri8te4tHXXZKP3jHX9MZfmFKnkMgDkacR6iOEW+dVdVVwLtwJSiq5YBOxVcHgG8FPXy09YyqoWOtR1pDyMVP7j/ByxZuYRNuintoeRKVFt3txeRwcH5LYHDgaeKJrsFODnYyjsBWKWqy6NYvk+aRzWzduPamvu89Lk3nuPi+y4GYOMm66RRiuqddBjQJiLzgUeBu1T1VhGZLiLTg2luB54DFgG/As6IaNleGVo/lC36bVFzn5fe8ewd7DxoZ/r37W/vpBGLauvufFXdT1XHquoYVb0w+PtMVZ0ZnFdVPVNVR6vq3lq0wShPBvYfWHMfxZx5wJn07dOXXxz1CxoGNKQ9nFyxPY5iMHBA7YV0RecKnnr1KY4YfQRb1W2V9nByxUIag4H9B9bcfryzl8xml212YedBO6c9lNyxkMagX59+Nbcfb/uSdlpGtqQ9jFyykMak1vbjbX+hnZZRLWkPI5cspDGppf14wz7aPKo57aHkkoU0JrX0/VLro/GykMaklr5fan00XhbSGNVKL7U+Gi8LaYxqoZdaH42fhTRGtdBLrY/Gz0Iao1ropdZH42chjVnee6n10fhZSGOW515qfTQZFtKY5bmXWh9NhoU0ZnnupdZHk2EhTUBee6n10WRYSBOQx15qfTQ5FtIE5LGXWh9NjoU0AXnspdZHk2MhTUjeeqn10eRYSBOSp15qfTRZFtKE5KmXWh9NloU0IXnqpdZHk2UhTVBeeqn10WRZSBOUh15qfTR5FtIE5aGXWh9NnoU0QXnopdZHk2chTVjWe6n10eRZSBOW5V5qfTQdFtKEZbmXWh9Nh4U0YVnupdZH02EhTUFWe6n10XRYSFOQxV5qfTQ9FtIUZLGXWh9Nj4U0BVnspdZH02MhTUnWeqn10fRYSFOSpV5qfTRdFtKUZKmXWh9Nl4U0JVnqpdZH02UhTVFWeqn10XRZSFOUhV5qfTR9FtIUZaGXWh9Nn4U0RVnopdZH02chTZnvvdT6aPospCnzuZdaH/WDhTRlPvdS66N+sJCmzOdean3UDxZSD/jaS62P+sFC6gEfe6n1UX9YSD3gYy+1PuoPC6kHfOyl1kf9YSH1hG+91PqoPyyknvCpl1of9YuF1BM+9VLro36xkHrCp15qfdQvkYRURHYSkTYReVJEForI2V1M0yIiq0Tk8eB0QRTLzhNfeqn1Ub/0i2g+G4BzVfUfIjIQmCsid6nqP4umu1dVj45ombnTMqqFr9/99VTHYH3UP5G8k6rqclX9R3C+A3gSGB7FvGuJD73U+qh/RFWjnaHIKOAeYIyqri74ewtwE7AMeAn4kqou7OL204BpAI2NjU2tra1lLb+zs5P6+voKRx+Nasaw8JWFDKsfxrZbbpvK8peuWoqijBw0MpXlR6WSMUyePHmuqo6PaUiVU9XITkA9MBf4eBfXNQD1wfmjgGd7m19TU5OWq62trezbRK2aMUz/y3Q9/c+np7b8PWbsodc8fk1qy49KJWMA5miEeYjqFNnWXRGpw71TXq+qf+zixWC1qnYG528H6kRkSFTLz4s0Py+1PuqnqLbuCvBr4ElV/Uk30wwNpkNEDgiW/VoUy8+TNHup9VE/RfVOeghwEnBYwUcsR4nIdBGZHkxzHLBAROYBlwPHB6sYpkCan5fa56N+iuQjGFW9D5BeppkBzIhieXkXfl564tgTE11u+wvtfG3i1xJdpumd7XHkoTR6qfVRf1lIPZRGL7U+6i8LqYfS6KXWR/1lIfVU0vvx2v66/rKQeirJXmp91G8WUk8l2Uutj/rNQuqpJHup9VG/WUg9llQvtT7qNwupx5LopdZH/Wch9VgSvdT6qP8spB5LopdaH/WfhdRzcfdS66P+s5B6Ls5ean00Gyyknouzl1ofzQYLqefi7KXWR7PBQpoBcfVS66PZYCHNgDh6qfXR7LCQZkAcvdT6aHZYSDMgjl5qfTQ7LKQZEXUvtT6aHRbSjIiyl1ofzRYLaUZE2Uutj2aLhTQjouyl1kezxUKaIVH1Uuuj2WIhzZAoeqn10eyxkGZIFL3U+mj2WEgzJIpean00eyykGVNtL7U+mj0W0oypppdaH80mC2nGVNNLrY9mk4U0Y6rppdZHs8lCmkGV9lLro9lkIc2gSnqp9dHsspBmUCW91PpodllIM6iSXmp9NLsspBlVbi+1PppdFtKMKqeXWh/NNgtpRpXTS62PZpuFNKPK6aXWR7PNQpphpfZS66PZZiHNsFJ6qfXR7LOQZlgpvdT6aPZZSDOslF5qfTT7LKQZ11svtT6afRbSjOupl1ofzQcLacb11Eutj+aDhTTjeuql1kfzwUKaA931Uuuj+WAhzYGueqn10fywkOZAV73U+mh+WEhzoKtean00PyykOVHcS62P5oeFNCcKe+mGTRusj+aIhTQnCntpx7oO66M50i/tAZhoFPbSTWs3WR/NEXsnzZGwl3as67A+miORhFREdhKRNhF5UkQWisjZXUwjInK5iCwSkfkiMi6KZZt3tYxqYdbzs1izYY310RyJ6p10A3Cuqn4AmACcKSJ7Fk0zFdg1OE0Droho2SbQPKqZJSuX0L9vf+ujORJJSFV1uar+IzjfATwJDC+a7BjgGnUeAgaLyLAolm+cofVDGbzFYPr37Z/2UEyERFWjnaHIKOAeYIyqri74+63Axap6X3B5FvAVVZ1TdPtpuHdaGhsbm1pbW8tafmdnJ/X19VXdh2qlOYbHVzzOiC1GMGTwkFSWD9n9P5g8efJcVR0f05Aqp6qRnYB6YC7w8S6uuw2YWHB5FtDU0/yampq0XG1tbWXfJmppjWF5x3LlW+jlN1yeyvJDWf0/AOZohHmI6hTZ1l0RqQNuAq5X1T92MckyYKeCyyOAl6JavnH76+48aGfWbVxX0e+XGj9FtXVXgF8DT6rqT7qZ7Bbg5GAr7wRglaouj2L5xmlf0s7h7zucLfptUdHvlxo/RfVOeghwEnCYiDwenI4SkekiMj2Y5nbgOWAR8CvgjIiWbQLh/roD+w+s6PdLjZ8i2eNI3cYg6WUaBc6MYnlmc4XfH527fC7tz7SnPSQTEdvjKCcKvz86sP/Asn+/1PjLQpoThd8f7denX9m/X2r8ZSHNieLvj5b7+6XGXxbSHOjqeEbl/H6p8ZuFNAe6Op5ROb9favxmIc2Bro5nVM7vlxq/WUhzoLvjGVkvzQcLacb1dHxd66X5YCHNuJ6Or2u9NB8spBnX0/F1rZfmg4U043o7vq710uyzkGZYKb/3Yr00+yykGVbK771YL80+C2mGlfJ7L9ZLs89CmmGl/t6L9dJss5BmVDm/P2q9NNsspBlVzu+PWi/NNgtpRpXz+6PWS7PNQppR5f7+qPXS7LKQZlA5fTRkvTS7LKQZVE4fDVkvzS4LaQaV00dD1kuzy0KaQeX20ZD10myykGZMJX00ZL00myykGVNJHw1ZL80mC2nGVNJHQ9ZLs8lCmjGV9tGQ9dLssZBmSDV9NGS9NHsspBlSTR8NWS/NHgtphlTTR0PWS7PHQpoh1fbRkPXSbLGQZkQUfTRkvTRbLKQZEUUfDVkvzRYLaUZE0UdD1kuzxUKaEVH10ZD10uywkGZAlH00ZL00OyykGRBlHw1ZL80OC2kGRNlHQ9ZLs8NCmgFR99GQ9dJssJB6Lo4+GrJemg0WUs/F0UdD1kuzwULquTj6aMh6aTZYSD0XVx8NWS/1n4XUY3H20ZD1Uv9ZSD0WZx8NWS/1n4XUY3H20ZD1Uv9ZSD0Wdx8NWS/1m4XUU0n00ZD1Ur9ZSD2VRB8NWS/1m4XUU0n00ZD1Ur9ZSD2VVB8NWS/1l4XUQ0n20ZD1Un9ZSD2UZB8NWS/1l4XUQ0n20ZD1Un9ZSD2UdB8NWS/1k4XUM2n00ZD1Uj9FElIR+Y2IvCwiC7q5vkVEVonI48HpgiiWm0dp9NGQ9VI/RfVOejUwpZdp7lXVfYPThREtN3fS6KMh66V+iiSkqnoP8HoU86p1afXRkPVS/4iqRjMjkVHArao6povrWoCbgGXAS8CXVHVhN/OZBkwDaGxsbGptbS1rHJ2dndTX15d1m6hVOoYNmzYw79/z2HuHvenft3/iywd4Y80bvLj6RcbssNl/YyLLj0olY5g8efJcVR0f05Aqp6qRnIBRwIJurmsA6oPzRwHPljLPpqYmLVdbW1vZt4lapWNofaJVd/npLqktX1V1ecdy5Vvo0pVLU1l+VCoZAzBHI8pDlKdEtu6q6mpV7QzO3w7UiciQJJadJWn20ZD1Uv8kElIRGSoiEpw/IFjua0ksO0vS7qMh66V+6RfFTETk90ALMERElgHfBOoAVHUmcBzwXyKyAXgbOD5YvTCBND8fLdYyqoWv3/31tIdhApGEVFVP6OX6GcCMKJaVV2l+Plqs8PPSnQbtlPZwap7tceQJH/poyHqpXyyknvClj4asl/rDQuoBn/poyPbj9YeF1AM+9dGQ7cfrDwupB3zqoyHrpf6wkHrAtz4asl7qBwtpynzsoyHrpX6wkKbMxz4asl7qBwtpynzsoyHrpX6wkKbM1z4asl6aPgtpinzuoyHrpemzkKbI5z4asl6aPgtpinzuoyHrpemzkKbI9z4asl6aLgtpSrLQR0PWS9NlIU1JFvpoyHppuiykKclCHw1ZL02XhTQlWemjIeul6bGQpiBLfTRkvTQ9FtIUZKmPhqyXpsdCmoIs9dGQ9dL0WEhTkLU+GrJemg4LacKy2EdD1kvTYSFNWBb7aMh6aTospAnLYh8NWS9Nh4U0YVntoyHrpcmzkCYoy300ZL00eRbSBGW5j4aslybPQpqgLPfRkPXS5FlIE5T1PhqyXposC2lC8tBHQ9ZLk2UhTUge+mjIemmyLKQJyUMfDVkvTZaFNCF56aMh66XJsZAmIE99NGS9NDkW0gTkqY+GrJcmx0KagDz10ZD10uRYSBOQtz4asl6aDAtpzPLYR0PWS5NhIY1ZHvtoyHppMiykMctjHw1ZL02GhTRmee2jIeul8bOQxijPfTRkvTR+FtIY5bmPhqyXxs9CGqM899GQ9dL4WUhjlPc+GrJeGi8LaUxqoY+GrJfGy0Iak1rooyHrpfGykMakFvpoyHppvCykMamVPhqyXhofC2kMNmzaUDN9NGS9ND4W0hh0rOuomT4asl4aHwtpDDrWdtRMHw1ZL42PhTQiixfDGWdAQwOc8rGT+N/PXMEZZ7i/14LFi6HPbTM5bcInOOywZhoaqKn7HycLaQTuuAPGjoWrroKODkCFNW/256qr3N/vuCPtEcYrvP9P3zWR9W9viarQ0UHN3P+4RRJSEfmNiLwsIgu6uV5E5HIRWSQi80VkXBTL9cHixXDccfDWW7B+/XuvW7/e/f244/L7jlJ4/zdu6Pue62rh/ichqnfSq4EpPVw/Fdg1OE0Drohouam75JLNw1ls/Xq49NJkxpO0Wr//SYgkpKp6D/B6D5McA1yjzkPAYBEZFsWy03bddaU9Sa+9NpnxJK3W738S+iW0nOFA4bb5ZcHflhdPKCLTcO+2NDY20t7eXtaCOjs7y75NNTo7mwHpdbqODqW9PZktn0k+Bj7ef0j+eRArVY3kBIwCFnRz3W3AxILLs4Cm3ubZ1NSk5Wprayv7NtUYOFAVej81NCQ3piQfAx/vv2pljwEwRyPKQ5SnpLbuLgN2Krg8AngpoWXH6sQToa6u52nq6uCkk5IZT9Jq/f4nIamQ3gKcHGzlnQCsUtXNVnWz6NxzS3uSnnNOMuNJWq3f/yRE9RHM74EHgd1FZJmInC4i00VkejDJ7cBzwCLgV8AZUSzXB6NHw403wpZbbYI+695zXV0dbLWVu3706JQGGLPw/m+11eZhravT3N//JES1dfcEVR2mqnWqOkJVf62qM1V1ZnC9quqZqjpaVfdW1TlRLNcXU6fC92+8nYaDb6ChAUSUhgaYNg3mz3fX59nUqe5+TpvGu/efVUyb8q+auP9xsz2OIvLUxts47kvtrFoFd989m1WrYMaM2nkHGT3a3d9Vq2D2bX9lVd/tmLH3L2vm/sfJQhqRWvv+aE82brkljB8Ps21n+yhYSCNQS8czKllzMzzyiNsv0FTFQhqBWjqeUclaWtyuRg8+mPZIMs9CGoFaOp5RyQ45BPr0sVXeCFhII2B9tAsNDTBuHORl17wUWUirZH20By0t8PDD8PbbaY8k0yykVbI+2oPmZli3zgXVVMxCWiXroz2YONH1UlvlrYqFtErWR3sweDDsu69tPKqShbQK1kdL0NzsPoZZsybtkWSWhbQK1kdL0NICa9e6HRtMRSykVbA+WoJDDwURW+WtgoW0CtZHS7DNNu64nrbxqGIW0gpZHy1DS4vrpevW9Tqp2ZyFtELWR8vQ3Ox2aHj00bRHkkkW0gpZHy3DoYe6f22VtyIW0gpZHy3DkCGw99628ahCFtIKWB+tQHMz3H9/70fSNpuxkFbA+mgFmpvdF8Dn5OrwVomwkFbA+mgFJk1y/9oqb9kspBWwPlqBHXaAPfe0kFbAQlom66NVaGmB++6DDRvSHkmmWEjLZH20Cs3N0NkJ//hH2iPJFAtpmayPVqE5WPuwz0vLYiEtk/XRKjQ2wm67wb33pj2STLGQlsH6aAQmTXK9dNOmtEeSGRbSMlgfjcCkSbByJSxYkPZIMsNCWgbroxEI9+O95550x5EhFtIyWB+NwMiRsNNO1kvLYCEtkfXRiIi4d9N77gHVtEeTCRbSElkfjdCkSbBiBSxalPZIMsFCWiLroxEK9+O1Vd6SWEhLZH00Qnvs4b5jahuPSmIhLYH10YiFvdTeSUtiIS2B9dEYTJoEzz0Hy5alPRLvWUhLYH00BuHnpfZu2isLaQmsj8Zgn31g4EALaQkspL2wPhqTfv3g4INt41EJLKS9sD4ao0mTYOFCeO21tEfiNQtpL6yPxij8vPS++9Idh+cspL2wPhqj/feHAQNslbcXFtIeWB+N2YABcOCBFtJeWEh7YH00AYceCo895o59ZLpkIe2B9dEETJwIGzfCww+nPRJvWUh7YH00AQcd5HYTtI1H3bKQdsP6aEIGDXI/5nT//WmPxFsW0m5YH03QIYe4Hxm2g2Z3qV/aA/BVbH30zTfdwaEfewyeecZ9+fmNN9xRCvr2hW23fffQl3vuCePHQ0ND9OPwycSJcMUV8MQTsN9+aY/GOxbSbrS/0M7XJn4tmpl1dEBrK/zpTzBr1rs/Sz94MAwb5oLZpw+sWQNLl8JLL727tVPE7ec6ZQp85CMwYYKbNk8OOcT9e//9FtIuWEi7EFkfXbYMLrkEfvMbWL0adtkFzjwTPvhBGDfOBbQrqrB8uXtneeghaGuDH/8YLr7YHcTr05+Gz30O3v/+6sbni513hhEj3Majs85KezTeydlLcjSq7aN91qyBb3/brbLOmAFHH+0616JF8JOfwIc/3H1Awb177rgjHHkkfPOb7mcZXn0VrrsOxo51gd11V5g6Ff7+9+wf0EvEvZvaxqMuWUi7UFUfve8+Djj1VPjWt9zq6bPPwvXXu9VUkcoHNWgQfOYzcOut8MIL7kXgscfgiCPcvG+/PdthnTjRrXksXZr2SLxjIe1CRZ+PbtwIF14Izc1o375uV7cbboBRo6If4PDhcMEFsGQJXHklvPyye3eeNMm9Y2dR2Evt89LNWEiLVNRH33wTPvpRt2p6wgnMufLKd488EKcttoDPf95tJZ45ExYvhoMP5gPf+Q68+GL8y4/S3nu7L4HbKu9mLKRFyu6jr70Ghx8Of/2r+xjhuuvYuPXW8Q6yWF0d/Od/urB+4xsMue8+d0S+Sy917/BZ0K+fW223d9LNRBZSEZkiIk+LyCIR+WoX17eIyCoReTw4XRDVsqNUVh9dvvzdHcRvvBGmT491bL2qr4cLL+TR3/7Wrfp+8YvuiZ+VH0c65BC3RXv16rRH4pVIQioifYGfA1OBPYETRGTPLia9V1X3DU4XRrHsqJXcR1etcltXly5176If+1jsYyvVmh13dBuYWlvdRqZx4+B73/P/XfWgg9zGr0ceSXskXonqnfQAYJGqPqeq64BW4JiI5p2Ykvvo2rXw8Y+7Q3/cdBO0tCQyvrKIwKc+5cZ47LHw9a+7X9pesiTtkXXvwAPduB96KO2ReEU0gs32InIcMEVVPxdcPgk4UFXPKpimBbgJWAa8BHxJVRd2Ma9pwDSAxsbGptbW1rLG0tnZSX19fUX34401b/Di6hcZs8OY7ifatIk9L7qIHdraePL88/n3hz4U6RiisNnyVdnh739nt5/+FICnv/hFXjnssOSWX4b9Tz2VNUOH8sTFFyc+hsmTJ89V1fFVLTgOqlr1CfgkcFXB5ZOAnxVN0wDUB+ePAp7tbb5NTU1arra2trJvE5r+l+l62s2n9TzRj36kCqoXXxzLGKLQ7fKfe051wgQ3/unTVd9+O9nll+L001W33VZ106bExwDM0QjyEPUpqtXdZcBOBZdH4N4tC18MVqtqZ3D+dqBORIZEtPxI9NpHH3oIzj8fPvEJOO+8xMYVmfe9z31+++Uvu49sJk50ndUnBx0Er7/udgIxQHSd9FFgVxF5n4j0B44HbimcQESGirhdbkTkgGDZ3hzLsdc++sYbcPzxbt/Zq66qbu+hNNXVwQ9/CDff7HZTbGpyuxb6YsIE929Wd8qIQSQhVdUNwFnAncCTwB9UdaGITBeR8HOJ44AFIjIPuBw4PljF8EKPn4+qwmmnuW+n3HCD+/ZK1h1zDDz6KAwd6vYRvvRSP3Yr/MAH3C6QFtJ3RPYtmGAV9vaiv80sOD8DmBHV8qLW4+ejra3unefHP3aHocyLXXd1YTjlFPeZ6oIFboeM/v3TG1OfPm4rr23hfYftcRToto+uXAnnnOPC+YUvJDyqBAwc6HbE+MY33Ffqjjgi/SPKT5jgdmqwIwgCFlKglz56/vnwyivwy1+6IyfkUZ8+7ssB11/v3sEOOsj11bQccABs2uSOYGEspNBDH33oIRfOs8+ujSMGfPrTcPfdbuvqhAnp9cKwUjz6aDrL94yFlG766IYNbqf14cPdu0ytCA8KNngwHHaY6+JJ22EHGDnSdg8MWEjppo/+9rcwfz789Kdux/VaEm5QGjvWfSb8y18mP4b997d30kDNh7TLPhoe/uSgg7zacT5R22/vVn2nTHHf7rnwwmQ/otl/f3j+eXfYmBpX8yHtso9ecYX70vT3vpfdnRaisPXWbnX35JPdF9rPPttt0EnCAQe4f+3d1EK6WR/t6HDhPOIIP7/dkrS6Orfq/8Uvws9+5j5TXb8+/uU2NbkXyDlz4l+W5yykxX30ssvcKtZFF6U1JP/06eN25LjoInfEwk9+0lWCOA0c6A5Z+thj8S4nA2o6pJv10ddfd0/GY499d3XLOCLuO6kzZsCf/+yOhPjmm/Euc7/97LNSajykm/XRSy91q7vf+U66A/PZmWfC737nNiodeaQ7QkVcxo1z39J5/fX4lpEBNR3S9/TRt992G4yOOQbG9PClb+M2JP3hD+5zzMMPjy9E4Q4kjz8ez/wzorZDWthHr73W7bN6zjmpjikzPvEJ99s2TzwBkye7Y/9GLQxpjffSmg3pe/ropk1ug9G4cckcLzcvPvxh+Mtf3Be0W1rcL8RFafvt3VfpsnK0w5jUbEjf00fvvBOefNK9i9by56KVOOII9xMXS5e6A51FfVDuMWMspGkPIC3v6aOXXup+QOk//iPVMWVWS4t7oVu+HJqbGRDlqu+YMe6Ih0ntROGh2g1p2EcXLIC77nI/uZfml52z7pBD3OP4yivs+4UvRHfspDFj3Ea955+PZn4ZVJMhfU8fvewy2HJL940XU50DD4S//51+HR1u1TeKYH3gA+7fp56qfl4ZVZMhfaePbhro9qA56STYbru0h5UP++/PvEsucT8V0dzsfkSqGrvv7v595pnqx5ZRNRnSd/poa6s7Gn3av+GSM5277eZ2dnjrLRfUag7Pud12sO22FtJa804fvfpq953JffdNeUQ5tO++Lqjr1rmgVrO6+v73V/+OnGE1F9Kwj35w7Y5uj5lTTrGPXeIydiy0tbkts83NlX+UMnKkfwfxTlDNhTTsozv+8S53YLHPfCbtIeXbXnvB7Nnu90dbWirbe2jnnd3nsD4cFzgFNRfS9iXtTB4xye0GeNRR0NiY9pDyb/fd3c9bbL21O25SucfUHTbMfTWuRn+3tPZC+kI7J6wY4o5Gf8opaQ+ndowe7YK63XZup/y77y79tjvs4P6NY//gDKipkIZ9dMKsp90Ww6OPTntItWXkSLj3Xhg1yq3F/PnPpd0u/FkPeyfNv9lLZrPPgJFsfdvf3DFmBwxIe0i1Z9gw11H32cf9EPOvf937bbbc0v371lvxjs1TNRXS9iXtfHHpcPfZ6Kmnpj2c2rXddjBrlts5/3Ofcwc562mjUHhdjW6Fr62QvtDOlAdecVscx41Lezi1rb7efc3ts591hwv99Ke7f6cMfxNm662TG59HaiakKzpXsPq5p9hh3rNwwgk1+6rslbo6t7r7/e+7n5Q86KCu9yxautT9O3x4suPzRM2EdPaS2Zy+NNg/9+MfT3cw5l0i8NWvwm23ue+i7ref+9JD4WFD77rL/XhzuJW3xtRMSNuXtHP8M/3dZ3bhNyuMP6ZOhXnz3J5J55zj/p++8Q047zwX4M9+Nu0RpiayHxH23WP/nMUeC/8N552a9lBMd4YPd4G87Tb40Y/gu991G40+9Sn3E5Q1qiZCuqJzBbs9+Cx9NlK7v+2SFSLu8+ujj3YbkjZudAfKrmE1EdLZS2Zz0uKtYMS2MH582sMxpdpqq7RH4IWa6KQPPHUXzU+vde+itlXXZExNhFTv/Cv91220VV2TSbkP6YrOFRzw8Its3HYbO6auyaTch/TeZ2fx0UV96HvMse47jcZkTO5Duvwvv6fh7U22qmsyK/ch3eFv97N+ywFuZ25jMijXIV2x+iWa561k3ZGHwxZbpD0cYyqS65DO+9u1DOuErT9mPx9hsivXIX3z9pvdmcMPT3UcxlQj1yFtfHA+K0cPhx13THsoxlQstyFd8crzjFv0Fn0+NCXtoRhTldyG9Kk/XcWWG6DhaPvuqMm23IZ03Z23saFfH/f9RGMyLLchHfHIU7y67+41e1wckx+5DOnLzy9gz2Vr2WKqHVfXZF8uQ7r4/64EYPBHPpnySIypXi5Dqnf9jTfr+9thO00u5C+kquwyZzGvTtjH/WqaMRmXu5D2X/IcQ1duoN4+ejE5kYuQLl4MZ5wBDQ0w8bTTaGAl33j0v2r5x6FrUuHz4LDDmmlocJez/jzIfEjvuMP9oPRVV0FHByhCB4O46g+DGDvWXW/yb7PngQodHe5y1p8HkYVURKaIyNMiskhEvtrF9SIilwfXzxeRqrfqLF4Mxx3njvxYeMBzcJffestdn/VXUtOzvD8PIgmpiPQFfg5MBfYEThCRPYsmmwrsGpymAVdUu9xLLtn8P6XY+vVw6aXVLsn4LO/Pg6jeSQ8AFqnqc6q6DmgFjima5hjgGnUeAgaLyLBqFnrddaX951x7bTVLMb7L+/MgqiNzDQf+VXB5GXBgCdMMB5YXTiQi03DvtDQ2NtLe3t7tQjs7m4Hej6Pb0aG0t8/udbqodHZ29jhuW37Uy/PzeRCVqELa1SNU/KuwpUyDql4JXAkwfvx4bWlp6Xah9fVuI0FvBg4UeppP1Nrb2xNdXq0v39fnQVSiWt1dBuxUcHkE8FIF05TlxBPdT1z2pK4OTjqpmqUY3+X9eRBVSB8FdhWR94lIf+B44JaiaW4BTg628k4AVqnq8uIZlePcc0v7zznnnGqWYnyX9+dBJCFV1Q3AWcCdwJPAH1R1oYhMF5HpwWS3A88Bi4BfAWdUu9zRo+HGG93v+hT/J9XVub/feKObzuRX3p8HkX1Oqqq3q+puqjpaVb8b/G2mqs4Mzquqnhlcv7eqzoliuVOnwvz5MG2a29NERGlocJfnz3fXm/zL8/Mg83scgXuFnDEDVq2Cu++ezapV7nJWXzlNZfL6PMhFSI3JMwupMZ6zkBrjOQupMZ6zkBrjOQupMZ4T1c12n/WGiLwCvFDmzYYAr8YwnCyNodaXX+kYRqrq9nEMphpeh7QSIjJHVcfX8hhqffm+jCEqtrprjOcspMZ4Lo8hvTLtAZD+GGp9+eDHGCKRu05qTN7k8Z3UmFyxkBrjuUyFtJpj+/Z224iW/5lgufNF5AER2afguiUi8oSIPC4iFX2XtoTlt4jIqmAZj4vIBaXeNsIxfLlg+QtEZKOIbBtcF8Vj8BsReVlEFnRzfazPgVSoaiZOQF9gMbAL0B+YB+xZNM1RwB24g55NAB4u9bYRLf9gYJvg/NRw+cHlJcCQmO9/C3BrJbeNagxF038EuDuqxyCYxyRgHLCgm+tjew6kdcrSO2k1x/Yt5bZVL19VH1DVN4KLD+EOthaVau5DFPe/kvmcAPy+guV0S1XvAV7vYZI4nwOpyFJIuztubynTlHLbKJZf6HTcK3pIgb+JyNzg2MLlKnX5B4nIPBG5Q0T2KvO2UY0BEdkKmALcVPDnah+DasYY1WOQuKiOu5uEao7tW9IxfyNYvptQZDIupBML/nyIqr4kIjsAd4nIU8G7QpTL/wdu/9NOETkKuBn3sx5R3P9SxxD6CHC/qha+61X7GFQzxqgeg8Rl6Z20mmP7RnHM35LmISJjgauAY1T1tfDvqvpS8O/LwJ9wq1+RLl9VV6tqZ3D+dqBORIaUOvYoxlDgeIpWdSN4DKoZY+THfU5M2qW41BPuXf854H28W/z3Kprmw7x3o8Ejpd42ouXvjDtk6cFFf98aGFhw/gFgSgzLH8q7O6gcACwNHouq7385jyMwCNcbt47yMSiY1yi633AU23Mgted+2gMo8z/nKOAZ3Fa6rwd/mw5MD84L7tfdFgNPAON7um0My78KeAN4PDjNCf6+S/CkmAcsjHH5ZwXzn4fbcHVwT7eNYwzB5VOB1qLbRfUY/B73+0Hrce+Opyf5HEjjZLsFGuO5LHVSY2qShdQYz1lIjfGchdQYz1lIjfGchdQYz1lIjfHc/wc16CLVudN6UwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 720x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "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",
+    "w = [1.0,5.0,10.0,1.0]\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 wymiernej krzywej Beziera\")\n",
+    "\n",
+    "\n",
+    "\n",
+    "def X(t):\n",
+    "    denominator = 1.0/(w[0]*bezier_basis_function_2D[0](t) + \n",
+    "                   w[1]*bezier_basis_function_2D[1](t) + \n",
+    "                   w[2]*bezier_basis_function_2D[2](t) + \n",
+    "                   w[3]*bezier_basis_function_2D[3](t)\n",
+    "                  )\n",
+    "    return denominator*(w[0]*P0[0]*bezier_basis_function_2D[0](t) + w[1]*P1[0]*bezier_basis_function_2D[1](t) + w[2]*P2[0]*bezier_basis_function_2D[2](t) + w[3]*P3[0]*bezier_basis_function_2D[3](t))\n",
+    "\n",
+    "def Y(t):\n",
+    "    denominator = 1.0/(w[0]*bezier_basis_function_2D[0](t) + \n",
+    "                   w[1]*bezier_basis_function_2D[1](t) + \n",
+    "                   w[2]*bezier_basis_function_2D[2](t) + \n",
+    "                   w[3]*bezier_basis_function_2D[3](t)\n",
+    "                  )\n",
+    "    return denominator*(w[0]*P0[1]*bezier_basis_function_2D[0](t) + w[1]*P1[1]*bezier_basis_function_2D[1](t) + w[2]*P2[1]*bezier_basis_function_2D[2](t) + w[3]*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",
+    "leg.get_frame().set_alpha(0.5)\n",
+    "plt.grid();\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bbbf7f42",
+   "metadata": {},
+   "source": [
+    "#### 3.3.3. Wersja interaktywna"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "de29aa49",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a5a3cc4df7194feda51131cd6bb5daac",
+       "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": "58b872bec9cb40678924b267416a8a0d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "\n",
+    "\n",
+    "def draw_rational_Bezier_segment(P0X,P0Y,P1X,P1Y,P2X,P2Y,P3X,P3Y,w0,w1,w2,w3):\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 wymiernej krzywej Beziera\")\n",
+    "\n",
+    "\n",
+    "    def X(t):\n",
+    "        denominator = 1.0/(w0*bezier_basis_function_2D[0](t) + w1*bezier_basis_function_2D[1](t) + w2*bezier_basis_function_2D[2](t) + w3*bezier_basis_function_2D[3](t))\n",
+    "        return denominator*(w0*P0X*bezier_basis_function_2D[0](t) + w1*P1X*bezier_basis_function_2D[1](t) + w2*P2X*bezier_basis_function_2D[2](t) + w3*P3X*bezier_basis_function_2D[3](t))\n",
+    "\n",
+    "    def Y(t):\n",
+    "        denominator = 1.0/(w0*bezier_basis_function_2D[0](t) + w1*bezier_basis_function_2D[1](t) + w2*bezier_basis_function_2D[2](t) + w3*bezier_basis_function_2D[3](t))\n",
+    "        return denominator*(w0*P0Y*bezier_basis_function_2D[0](t) + w1*P1Y*bezier_basis_function_2D[1](t) + w2*P2Y*bezier_basis_function_2D[2](t) + w3*P3Y*bezier_basis_function_2D[3](t))\n",
+    "\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]\n",
+    "p2 = [0.75,1]\n",
+    "p3 = [1.0,0.0]\n",
+    "w = [1.0,2.0,0.5,1.0]\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, 3)\n",
+    "grid[0, 0] = widgets.FloatSlider(min=-5, max=5, step=0.1,value=p0[0],description='P0X')\n",
+    "grid[0, 1] = widgets.FloatSlider(min=-5, max=5, step=0.1,value=p0[1],description='P0Y')\n",
+    "grid[0, 2] = widgets.FloatSlider(min=-5, max=20, step=0.05,value=w[0],description='w0')\n",
+    "\n",
+    "grid[1, 0] = widgets.FloatSlider(min=-5, max=5, step=0.1,value=p1[0],description='P1X')\n",
+    "grid[1, 1] = widgets.FloatSlider(min=-5, max=5, step=0.1,value=p1[1],description='P1Y')\n",
+    "grid[1, 2] = widgets.FloatSlider(min=-5, max=20, step=0.05,value=w[1],description='w1')\n",
+    "\n",
+    "grid[2, 0] = widgets.FloatSlider(min=-5, max=5, step=0.1,value=p2[0],description='P2X')\n",
+    "grid[2, 1] = widgets.FloatSlider(min=-5, max=5, step=0.1,value=p2[1],description='P2Y')\n",
+    "grid[2, 2] = widgets.FloatSlider(min=-5, max=20, step=0.05,value=w[2],description='w2')\n",
+    "\n",
+    "grid[3, 0] = widgets.FloatSlider(min=-5, max=5, step=0.1,value=p3[0],description='P3X')\n",
+    "grid[3, 1] = widgets.FloatSlider(min=-5, max=5, step=0.1,value=p3[1],description='P3Y')\n",
+    "grid[3, 2] = widgets.FloatSlider(min=-5, max=20, step=0.05,value=w[3],description='w3')\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",
+    "k3 = widgets.VBox([grid[0,2],grid[1,2],grid[2,2],grid[3,2]])\n",
+    "ui = widgets.HBox([k1,k2,k3])\n",
+    "\n",
+    "out = widgets.interactive_output(draw_rational_Bezier_segment, {'P0X': grid[0,0], 'P0Y': grid[0,1], 'w0': grid[0,2],\n",
+    "                                                        'P1X': grid[1,0], 'P1Y': grid[1,1],'w1': grid[1,2],\n",
+    "                                                        'P2X': grid[2,0], 'P2Y': grid[2,1], 'w2': grid[2,2],\n",
+    "                                                        'P3X': grid[3,0], 'P3Y': grid[3,1], 'w3': grid[3,2]\n",
+    "                                                       })\n",
+    "\n",
+    "display(ui, out)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0520f4fe",
+   "metadata": {},
+   "source": [
+    "## 4. Krzywe B-spline\n",
+    "========================"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "70ff9eba",
+   "metadata": {},
+   "source": [
+    "### 4.1. Funkcje bazowe"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "cb73f70f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def N(t_,i_,p_,u_):\n",
+    "    \"\"\"\n",
+    "    t - wartość obliczeniowa parametru\n",
+    "    i - indeks  segmentu parametru\n",
+    "    u - wektor węzłów\n",
+    "    \"\"\"\n",
+    "    \n",
+    "    if p_ == 0:\n",
+    "        if u_[i_+1] > u_[i_] or i_ == 0:  \n",
+    "            if u_[i_] <= t_  and t_ < u_[i_+1]: \n",
+    "                return 1.0\n",
+    "            else:\n",
+    "                return 0.0\n",
+    "        if abs(u_[i_+1]- u_[i_])<eps:  \n",
+    "            if  abs(t_ - u_[i_+1])<eps: \n",
+    "                return 1.0\n",
+    "            else:\n",
+    "                return 0.0   \n",
+    "    else:\n",
+    "        \n",
+    "        if u_[i_+p_] == u_[i_]:\n",
+    "          c1 = 0.0\n",
+    "        else:\n",
+    "          c11 = (t_ - u_[i_])/(u_[i_+p_] - u_[i_])\n",
+    "          c12 =N(t_, i_,p_-1, u_)\n",
+    "          c1 =  c11 * c12 \n",
+    "        \n",
+    "        if u_[i_+p_+1] == u_[i_+1]:\n",
+    "          c2 = 0.0\n",
+    "        else:\n",
+    "          c21 = (u_[i_+p_+1] - t_)/(u_[i_+p_+1] - u_[i_+1])\n",
+    "          c22 = N(t_,i_+1, p_-1, u_)\n",
+    "          c2 =  c21 * c22  \n",
+    "        return c1 + c2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0cee63e8",
+   "metadata": {},
+   "source": [
+    "### 4.2. Wykresy funkcji bazowych"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "059ea3ca",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6a314a78238f4ac88efc4b7937924040",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "GridspecLayout(children=(HBox(children=(Label(value='Wektor węzłów (przecinek jako separator)', layout=Layout(…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b1e77d8685a24e3fb25e06fd628683b0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "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",
+    "\n",
+    "out = widgets.Output()\n",
+    "    \n",
+    "def draw_Bspline_basis(out):\n",
+    "    \n",
+    "    %matplotlib inline\n",
+    "\n",
+    "    if knots_input.value == \"\":\n",
+    "        return\n",
+    "    \n",
+    "    \n",
+    "    \n",
+    "    knots_txt = knots_input.value.split(',')\n",
+    "    p = fun_level.value\n",
+    "    i = fun_idx.value\n",
+    "    m = len(knots_txt) - 1\n",
+    "    \n",
+    "    \n",
+    "    if (m < 1):\n",
+    "        print(\"Wektor węzłów musi być listą dlugości co najmniej 2\")\n",
+    "        return\n",
+    "    if (p>=(m+1)):\n",
+    "        print(\"Stopień funkcji musi być mniejszy od  ilości węzłów \")\n",
+    "        return\n",
+    "    if (i>=m):\n",
+    "        print(\"Indeks funkcji musi być mniejszy od  ilości przedziałów wektora węzłów \")\n",
+    "        return\n",
+    "    \n",
+    "    knots = []\n",
+    "    for e in knots_txt:\n",
+    "        knots.append(float(e))\n",
+    "    \n",
+    "    t = np.arange(knots[0], knots[len(knots)-1], 0.001)\n",
+    "    with out:\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 B-spline \")\n",
+    "\n",
+    "    \n",
+    "    \n",
+    "    def Npi(t):\n",
+    "            return N(t,i,p,knots)\n",
+    "    \n",
+    "    with out:\n",
+    "        clear_output(True)\n",
+    "        \n",
+    "        y = list(map(Npi,t))\n",
+    "        ax.plot(t,y , label=\"N_\"+str(fun_idx.value)+\",\"+str(fun_level.value)+\"(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()\n",
+    "    \n",
+    "\n",
+    "knots_input = widgets.Text(\n",
+    "    value=\"\",\n",
+    "    description='',\n",
+    "    disabled=False,\n",
+    "    layout=Layout(width='400px')\n",
+    ")\n",
+    "\n",
+    "par_val = widgets.FloatText(\n",
+    "    value=0.0,\n",
+    "    description='',\n",
+    "    disabled=False,\n",
+    "    layout=Layout(width='150px')\n",
+    ")\n",
+    "\n",
+    "fun_level = widgets.IntText(\n",
+    "    value=0,\n",
+    "    description='',\n",
+    "    disabled=False,\n",
+    "    layout=Layout(width='150px')\n",
+    ")\n",
+    "\n",
+    "fun_idx = widgets.IntText(\n",
+    "    value=0,\n",
+    "    description='',\n",
+    "    disabled=False,\n",
+    "    layout=Layout(width='150px')\n",
+    ")\n",
+    "\n",
+    "\n",
+    "show_hull = widgets.Checkbox(\n",
+    "    value=False,\n",
+    "    description='Show convex hull',\n",
+    "    disabled=False,\n",
+    "    indent=False\n",
+    ")\n",
+    "\n",
+    "\n",
+    "def update_plot(b):\n",
+    "    if knots_input.value != \"\":\n",
+    "        draw_Bspline_basis(out)\n",
+    "    \n",
+    "draw_button = widgets.Button(description='Draw Bspline Basis',layout=Layout(width='400px'),merge=False)\n",
+    "draw_button.on_click(update_plot)\n",
+    "label1 = widgets.Label(value=\"Wektor węzłów (przecinek jako separator)\",  layout={'width': \"400px\"})\n",
+    "label2 = widgets.Label(value=\"Stopień funkcji bazowej(pusty == wszystkie)\",  layout={'width': \"400px\"})\n",
+    "label3 = widgets.Label(value=\"Indeks funkcji bazowej\",  layout={'width': \"400px\"})\n",
+    "\n",
+    "grid = widgets.GridspecLayout(4, 2)\n",
+    "grid[0,0] = widgets.HBox([label1,knots_input])\n",
+    "grid[0,1] = draw_button\n",
+    "#grid[1, 0] = widgets.HBox([label2,par_val])\n",
+    "grid[1, 0] = widgets.HBox([label2,fun_level])\n",
+    "grid[2, 0] = widgets.HBox([label3,fun_idx])\n",
+    "\n",
+    "display(grid,out)\n",
+    "#draw_Bspline(out\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4d014983",
+   "metadata": {},
+   "source": [
+    "## Zadania "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a2ba0722",
+   "metadata": {},
+   "source": [
+    "\n",
+    "\n",
+    "\n",
+    "<ol>\n",
+    "<li> <b>Wymierne krzywe  Beziera</b> \n",
+    "   <ol> \n",
+    "   <li> W skrypcie 3.3.3. dodaj możliwość wystąpienia wag ujemnych oraz zabezpiecz się na wagi powodujące dzielenie przez               zero. Przetestuj konstrukcje krzywych z niektórymi wagami równymi zero oraz (niezależnie) wagami ujemnymi\n",
+    "\n",
+    "   <li> Napisać wesję skryptu z punktu 2.4. dla wymiernych krzywych Beziera (<b>nieobowiązkowe</b>)\n",
+    "\n",
+    "   <li> Napisać program implementujący i wizualizujący krzywe Hermite'a oraz krzywe Beziera w 3D (w obu przypadkach 3                   stopnia) (zob. <a href=\"https://matplotlib.org/2.0.2/examples/mplot3d/lines3d_demo.html\">matplotlib demo</a>) \n",
+    "       oraz (nieobowiązkowo) ich łączenie.\n",
+    "\n",
+    "   \n",
+    "   <li>  Napisać program implementujący i wizualizujący wymierne krzywe Beziera stopnia 3 w 3D oraz  (nieobowiązkowo) ich łączenie. Przetestować rysowanie krzywych stożkowych.\n",
+    "\n",
+    "</ol>\n",
+    "    \n",
+    "<li> <b>Krzywe B-spline</b>\n",
+    "   \n",
+    "   <ol> \n",
+    "   <li> W skrypcie 4.2 wykonać następujące doświadzenia\n",
+    "   \n",
+    "   <ol>\n",
+    "\n",
+    "   <li> Ustalić wektor węzłów na 0,1,2,3, stopień funkcji bazowej na 2, indeks funkcji bazowej na 0.\n",
+    "\n",
+    "   Zaobserwować, że nośnik funkcji  bazowej, to 0-3 oraz  że najbardziej  wpływa ona w centrum tego zakresu, tzn. pomiędzy węzłami 1,2 \n",
+    "\n",
+    "   <li> Zmienić wektor węzłów na 0,1,1,3 - zwiększenie krotności węzła drugiego do 2,\n",
+    "\n",
+    "   Zaobserwować, że nośnik funkcji  bazowej pozostaje bez zmian, ale istotna wartość funkcji kumuluje się w otoczeniu węzła drugiego\n",
+    "\n",
+    "   <li> Zmienić  wektor węzłów na 0,1,1,1,3 - zwiększenie krotności węzła drugiego do 3\n",
+    "\n",
+    "   Zaobserwować, że nośnik funkcji  bazowej, to 0-1 (na prawo od drugiego węzła wpływ funkcji jest zerowy ) oraz, że funkcja najbardziej wpływa w węźle o krotności 3.\n",
+    "\n",
+    "<li> Zmienić  wektor węzłów na 0,1,3,3 - zwiększenie krotności węzła trzeciego do 2\n",
+    "\n",
+    "   Zaobserwować, że nośnik funkcji  bazowej, to nadal 0-3, ale  nastąpiło przesunięcie  maksymalnego wpływu funkcji w kierunku węzła 3\n",
+    "\n",
+    " <li> Zmienić  wektor węzłów na 0,3,3,3 - zwiększenie krotności węzła drugiego do 3\n",
+    "\n",
+    "   Zaobserwować, że nośnik funkcji  bazowej, to nadal 0-3, ale  nastąpiło kolejne przesunięcie  maksymalnego wpływu funkcji w kierunku węzła 3 (teraz maksimum jest w tym węźle)\n",
+    "\n",
+    "<li> Zmienić  wektor węzłów na 0,1,3,3,3 \n",
+    "\n",
+    "   Zaobserwować, że nośnik funkcji  bazowej, to nadal 0-3, ale ostatni węzeł nie ma wpływu na kształt funkcji. Następnie zmienić indeks funkcji na 1 i zaobserwowować, że tym razem węzeł o wartości 0 nie ma wpływu na wartość funkcji - wynika to z faktu, że dziedziną funkcji $N_{1,2}(t)$ jest przedział $[1,4)$ (natomiast funkcji $N_{0,2}(t)$ przedział $[0,3)$) \n",
+    "\n",
+    "   </ol>\n",
+    "   <li> Wykonać analogiczne doświadczenia dla funkcji bazowej stopnia 3 \n",
+    "   </ol> \n",
+    "    \n",
+    "</ol>\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ce32b4f7",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.5"
+  },
+  "latex_envs": {
+   "LaTeX_envs_menu_present": true,
+   "autoclose": false,
+   "autocomplete": true,
+   "bibliofile": "biblio.bib",
+   "cite_by": "apalike",
+   "current_citInitial": 1,
+   "eqLabelWithNumbers": true,
+   "eqNumInitial": 1,
+   "hotkeys": {
+    "equation": "Ctrl-E",
+    "itemize": "Ctrl-I"
+   },
+   "labels_anchors": false,
+   "latex_user_defs": false,
+   "report_style_numbering": false,
+   "user_envs_cfg": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/jupyter/slider_demo.ipynb b/jupyter/slider_demo.ipynb
new file mode 100644
index 0000000..1f88303
--- /dev/null
+++ b/jupyter/slider_demo.ipynb
@@ -0,0 +1,147 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "# Slider Demo\n",
+    "\n",
+    "\n",
+    "Using the slider widget to control visual properties of your plot.\n",
+    "\n",
+    "In this example, a slider is used to choose the frequency of a sine\n",
+    "wave. You can control many continuously-varying properties of your plot in\n",
+    "this way.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 5 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib.widgets import Slider, Button, RadioButtons\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "plt.subplots_adjust(left=0.25, bottom=0.25)\n",
+    "t = np.arange(0.0, 1.0, 0.001)\n",
+    "a0 = 5\n",
+    "f0 = 3\n",
+    "delta_f = 5.0\n",
+    "s = a0 * np.sin(2 * np.pi * f0 * t)\n",
+    "l, = plt.plot(t, s, lw=2)\n",
+    "ax.margins(x=0)\n",
+    "\n",
+    "axcolor = 'lightgoldenrodyellow'\n",
+    "axfreq = plt.axes([0.25, 0.1, 0.65, 0.03], facecolor=axcolor)\n",
+    "axamp = plt.axes([0.25, 0.15, 0.65, 0.03], facecolor=axcolor)\n",
+    "\n",
+    "sfreq = Slider(axfreq, 'Freq', 0.1, 30.0, valinit=f0, valstep=delta_f)\n",
+    "samp = Slider(axamp, 'Amp', 0.1, 10.0, valinit=a0)\n",
+    "\n",
+    "\n",
+    "def update(val):\n",
+    "    amp = samp.val\n",
+    "    freq = sfreq.val\n",
+    "    l.set_ydata(amp*np.sin(2*np.pi*freq*t))\n",
+    "    fig.canvas.draw_idle()\n",
+    "\n",
+    "\n",
+    "sfreq.on_changed(update)\n",
+    "samp.on_changed(update)\n",
+    "\n",
+    "resetax = plt.axes([0.8, 0.025, 0.1, 0.04])\n",
+    "button = Button(resetax, 'Reset', color=axcolor, hovercolor='0.975')\n",
+    "\n",
+    "\n",
+    "def reset(event):\n",
+    "    sfreq.reset()\n",
+    "    samp.reset()\n",
+    "button.on_clicked(reset)\n",
+    "\n",
+    "rax = plt.axes([0.025, 0.5, 0.15, 0.15], facecolor=axcolor)\n",
+    "radio = RadioButtons(rax, ('red', 'blue', 'green'), active=0)\n",
+    "\n",
+    "\n",
+    "def colorfunc(label):\n",
+    "    l.set_color(label)\n",
+    "    fig.canvas.draw_idle()\n",
+    "radio.on_clicked(colorfunc)\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.5"
+  },
+  "latex_envs": {
+   "LaTeX_envs_menu_present": true,
+   "autoclose": false,
+   "autocomplete": true,
+   "bibliofile": "biblio.bib",
+   "cite_by": "apalike",
+   "current_citInitial": 1,
+   "eqLabelWithNumbers": true,
+   "eqNumInitial": 1,
+   "hotkeys": {
+    "equation": "Ctrl-E",
+    "itemize": "Ctrl-I"
+   },
+   "labels_anchors": false,
+   "latex_user_defs": false,
+   "report_style_numbering": false,
+   "user_envs_cfg": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
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