{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "IPython.notebook.execute_cells_below()"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Javascript\n",
    "Javascript('IPython.notebook.execute_cells_below()')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "def next_alive(k):\n",
    "    k = k % N\n",
    "    while dead[k]: k = (k + 1) % N\n",
    "    return k\n",
    "\n",
    "N = 6\n",
    "dead = [False for _ in range(N)]\n",
    "alive = 0\n",
    "for i in range(N- 1):\n",
    "    alive = next_alive(next_alive(alive) + 1)\n",
    "    dead[alive] = True\n",
    "\n",
    "dead"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline \n",
    "\n",
    "from math import sin\n",
    "import graphviz\n",
    "from ipywidgets import interactive\n",
    "from IPython.display import Image\n",
    "import matplotlib.pylab as plt\n",
    "\n",
    "soldati = lambda n: list(range(1, n + 1))\n",
    "\n",
    "def gioca(soldati):\n",
    "    V = list(soldati)\n",
    "    M = []\n",
    "    m = 1\n",
    "    while len(V) > 1:\n",
    "        M.append(V[m])\n",
    "        del V[m]\n",
    "        m = m + 1\n",
    "        if m >= len(V): m = m - len(V)\n",
    "    return M\n",
    "\n",
    "def giuseppe(N, size = 400):\n",
    "    \n",
    "    G = graphviz.Digraph(engine = 'neato', format = 'png', graph_attr = {'size': '8!', 'ratio': 'compress', 'dpi': '75'})\n",
    "\n",
    "    for i in range(N):\n",
    "        G.node(str(i), label=str(i+1))\n",
    "        G.edge(str(i), str((i +1)%N))\n",
    "\n",
    "    seq = list(gioca(soldati(N)))\n",
    "    images = [Image(G.pipe(), width = size)]\n",
    "    for i in seq: \n",
    "        G.node(str(i-1), color = 'red', style = 'filled')\n",
    "        images.append(Image(G.pipe(), width = size))\n",
    "\n",
    "    def red(s = 0):\n",
    "        display(images[s])\n",
    "    \n",
    "    iw = interactive(red, s = (0, len(images) - 1))\n",
    "    iw.children[-1].layout.height = f'{size + 60}px'\n",
    "\n",
    "    return iw\n",
    "\n",
    "def disegna(N):\n",
    "    x = soldati(N)\n",
    "    y = [sopravvissuto(n) for n in x]\n",
    "    plt.subplots(figsize=(16, 8))\n",
    "    i = 1\n",
    "    while i <= x[-1]:\n",
    "        plt.axvline(i, color = 'r', lw = .5)\n",
    "        i *= 2\n",
    "    plt.plot(x,y, '.-')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "DEBUG = False\n",
    "\n",
    "from IPython.core.ultratb import AutoFormattedTB\n",
    "\n",
    "itb = AutoFormattedTB(mode = 'Plain', tb_offset = 1)\n",
    "\n",
    "def custom_exc(shell, etype, evalue, tb, tb_offset=None):\n",
    "\n",
    "    if DEBUG: shell.showtraceback((etype, evalue, tb), tb_offset=tb_offset)\n",
    "    stb = itb.structured_traceback(etype, evalue, tb)\n",
    "    print(itb.stb2text(stb[-1]))\n",
    "\n",
    "get_ipython().set_custom_exc((Exception,), custom_exc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<h1 class=\"text-center\">Il metodo scientifico e la</h1>\n",
    "<h1 class=\"text-center\">scrittura di programmi informatici</h1>\n",
    "\n",
    "&nbsp;\n",
    "\n",
    "&nbsp;\n",
    "\n",
    "<div style=\"text-align: right\">Massimo Santini</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<center><img src=\"img/copertine/Galileo.jpg\"></center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<h1 class=\"text-center\">metodo scientifico ➡ programmazione</h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<h1 class=\"text-center\">programmazione ➡ metodo scientifico</h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "scrolled": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<center><img src=\"img/copertine/Popper.jpg\"></center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<center><img src=\"img/pirates.jpg\"></center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<center><img src=\"img/copertine/Kuhn.png\"></center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<center><img src=\"img/copertine/Feyerabend-Lakatos.jpg\"></center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<h1 class=\"text-center\">Programmare…</h1>\n",
    "\n",
    "&nbsp;\n",
    "\n",
    "<center><img src=\"img/Python.jpg\"></center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## … in dieci minuti\n",
    "\n",
    "* Linguaggio *formale* (**sintassi**)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "* Esecuzione *automatica* (**semantica**)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Espressioni (atomi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "3 + 4 * 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "4 < 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "5 == 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.1411200080598672"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sin(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5\n"
     ]
    }
   ],
   "source": [
    "print(3 + 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": [
     "raises-exception"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1;31mNameError\u001b[0m\u001b[1;31m:\u001b[0m name 'x' is not defined\n",
      "\n"
     ]
    }
   ],
   "source": [
    "x + 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "x = 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x + 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "x = x + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[10, 30, 60, 50]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v = [10, 30, 60, 50]\n",
    "v"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "$\\vec{\\mathbf{v}} = (v_0, v_1, \\ldots, v_{n-1})$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "60"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "del v[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[10, 30, 60]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Strutture di controllo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = 3\n",
    "y = 2\n",
    "x + y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "$$\n",
    "x = \n",
    "\\begin{cases}\n",
    "7 \\quad\\text{se $y = 2$}\\\\\n",
    "3 \\quad\\text{altrimenti.}\n",
    "\\end{cases}\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "if y == 2:\n",
    "    x = 7\n",
    "else:\n",
    "    x = 3\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n",
      "6\n",
      "9\n",
      "12\n",
      "15\n",
      "18\n",
      "21\n",
      "24\n",
      "27\n",
      "30\n"
     ]
    }
   ],
   "source": [
    "i = 1\n",
    "while i <= 10:\n",
    "    print(3 * i)\n",
    "    i = i + 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<h1 class=\"text-center\">E il metodo scientifico?</h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<center><img src=\"img/metodo/1.png\"></center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<center><img src=\"img/metodo/2.png\"></center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<center><img src=\"img/metodo/3.png\"></center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<center><img src=\"img/metodo/4.png\"></center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<center><img src=\"img/metodo/5.png\"></center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<center><img src=\"img/metodo/6.png\"></center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Che c'entra la programmazione?\n",
    "\n",
    "Citando Frederic Brooks, in *The Mythical Man-Month* (1975)…"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<center><img src=\"img/joy1.png\"></center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<center><img src=\"img/joy2.png\"></center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<h1 class=\"text-center\">Il problema di Yosef ben Matityahu</h1>\n",
    "\n",
    "&nbsp;\n",
    "\n",
    "<center><img src=\"img/Giuseppe.jpg\"></center>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "deb01829db754d8db74d05100adeed53",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntSlider(value=0, description='s', max=10), Output(layout=Layout(height='460px'))), _do…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "giuseppe(11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "593169fdfa4d42c684d3c5c75b7bb4da",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntSlider(value=0, description='s', max=40), Output(layout=Layout(height='660px'))), _do…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "giuseppe(41, 600)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Come salvarsi la vita?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3, 4, 5]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vivi = soldati(5)\n",
    "vivi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 3, 4, 5]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "del vivi[1]\n",
    "vivi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "vivi = soldati(5)\n",
    "i = 1\n",
    "while i < 5:\n",
    "    del vivi[i]\n",
    "    i = i + 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 3, 4]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vivi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<h1 class=\"text-center\">And Now for Something Completely Different</h1>\n",
    "\n",
    "&nbsp;\n",
    "\n",
    "<center><img src=\"img/bug.jpg\"></center>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": [
     "raises-exception"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1;31mIndexError\u001b[0m\u001b[1;31m:\u001b[0m list assignment index out of range\n",
      "\n"
     ]
    }
   ],
   "source": [
    "vivi = soldati(6)\n",
    "i = 1\n",
    "while i < 6:\n",
    "    del vivi[i]\n",
    "    i = i + 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": [
     "raises-exception"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 [1, 2, 3, 4, 5, 6]\n",
      "3 [1, 3, 4, 5, 6]\n",
      "5 [1, 3, 4, 6]\n",
      "\u001b[1;31mIndexError\u001b[0m\u001b[1;31m:\u001b[0m list assignment index out of range\n",
      "\n"
     ]
    }
   ],
   "source": [
    "N = 6\n",
    "vivi = soldati(N)\n",
    "i = 1\n",
    "while i < N:\n",
    "    print(i, vivi)\n",
    "    del vivi[i]\n",
    "    i = i + 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": [
     "raises-exception"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1;31mIndexError\u001b[0m\u001b[1;31m:\u001b[0m list assignment index out of range\n",
      "\n"
     ]
    }
   ],
   "source": [
    "vivi = soldati(N)\n",
    "i = 1\n",
    "while i < N:\n",
    "    del vivi[i]\n",
    "    i = i + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 3, 5]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vivi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "vivi = soldati(N)\n",
    "i = 1\n",
    "while i < len(vivi):\n",
    "    del vivi[i]\n",
    "    i = i + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 3, 5]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vivi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "i"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 3, 5, 1, 3, 5]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vivi + vivi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "vivi = soldati(N)\n",
    "i = 1\n",
    "while i < len(vivi):\n",
    "    del vivi[i]\n",
    "    i = i + 1\n",
    "    if i >= len(vivi): i = i - len(vivi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vivi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "vivi = soldati(N)\n",
    "i = 1\n",
    "while len(vivi) > 1:\n",
    "    del vivi[i]\n",
    "    i = i + 1\n",
    "    if i >= len(vivi): i = i - len(vivi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[5]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vivi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Una nuova funzione…"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "def sopravvissuto(N):\n",
    "    vivi = soldati(N)\n",
    "    i = 1\n",
    "    while len(vivi) > 1:\n",
    "        del vivi[i]\n",
    "        i = i + 1\n",
    "        if i >= len(vivi): i = i - len(vivi)\n",
    "    return vivi[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sopravvissuto(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 1\n",
      "2 1\n",
      "3 3\n",
      "4 1\n",
      "5 3\n",
      "6 5\n",
      "7 7\n",
      "8 1\n",
      "9 3\n",
      "10 5\n",
      "11 7\n"
     ]
    }
   ],
   "source": [
    "N = 1\n",
    "while N < 12:\n",
    "    print(N, sopravvissuto(N))\n",
    "    N = N + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6402a7fb0b2748af884d2709d519a1f7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntSlider(value=0, description='s', max=15), Output(layout=Layout(height='460px'))), _do…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "giuseppe(16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n"
     ]
    }
   ],
   "source": [
    "N = 1\n",
    "while N <= 1024:\n",
    "    print(sopravvissuto(N))\n",
    "    N = N * 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "disegna(11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "disegna(130)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Una dipendenza lineare?\n",
    "\n",
    "Se $n$ è il più grande intero tale che\n",
    "\n",
    "$$N = 2^n + \\ell$$\n",
    "\n",
    "(ossia $\\ell < 2^n$), dal grafico formuliamo l'**ipotesi** che\n",
    "\n",
    "$$\\text{sopravvisuto}(N) = m \\ell + q$$\n",
    "\n",
    "restano da determinare $m$ e $q$…"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 1\n",
      "0 1\n",
      "1 3\n",
      "0 1\n",
      "1 3\n",
      "2 5\n",
      "3 7\n",
      "0 1\n",
      "1 3\n",
      "2 5\n",
      "3 7\n",
      "4 9\n",
      "5 11\n",
      "6 13\n",
      "7 15\n",
      "0 1\n"
     ]
    }
   ],
   "source": [
    "N = 1\n",
    "due_alla_n = 1\n",
    "while N <= 16:\n",
    "    l = N - due_alla_n\n",
    "    print(l, sopravvissuto(N))\n",
    "    N = N + 1\n",
    "    if 2 * due_alla_n <= N: due_alla_n = due_alla_n * 2\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Giuseppe, il diciannovesimo soldato\n",
    "\n",
    "Possiamo concludere che $$\\text{sopravvisuto}(2^n + \\ell)= 2 \\ell + 1.$$\n",
    "\n",
    "Quando $N = 41 = 32 + 9 = 2^6 + 9$, per sopravvivere bisogna quindi essere al posto \n",
    "\n",
    "$$2 \\times 9 + 1 = 19$$\n",
    "    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<center><img src=\"img/MontyPython.jpg\"></center>"
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Slideshow",
  "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": "3.7.4"
  },
  "rise": {
   "auto_select": "none",
   "autolaunch": true,
   "theme": "simple",
   "transition": "fade"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}