wyklady/js-python_w09.ipynb

{
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   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
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   "source": [
    "# Języki skryptowe - Python\n",
    "## Wykład 9\n",
    "\n",
    "---\n",
    "\n",
    "* programowania obiektowego ciąg dalszy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Na ostatnim wykładzie\n",
    "\n",
    "---\n",
    "\n",
    "* klasy pozwalają na definiowanie własnych typów\n",
    "* funkcje specjalne - definiowanie działania operatorów lub wywołań funkcji wbudowanych (*print*, *len*...)\n",
    "* nie ma \"pełnej prywatności\"\n",
    "* atrybuty klasowe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Konwencja *self*\n",
    "\n",
    "---\n",
    "\n",
    "* pierwszym argumentem każdej metody jest wskaźnik na obiekt\n",
    "* przyjęto nazywać ten argument *self* (choć nic nie stoi na przeszkodzie, żeby używać innej nazwy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## *self* test\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true,
    "slideshow": {
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   "outputs": [],
   "source": [
    "class Foo:\n",
    "    \n",
    "    def moja_metoda(): # błąd\n",
    "        pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "moja_metoda() takes 0 positional arguments but 1 was given",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-2-d5d408a69202>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mFoo\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmoja_metoda\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[1;31m# bo x jest pierwszym argumentem pozycyjnym!\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: moja_metoda() takes 0 positional arguments but 1 was given"
     ]
    }
   ],
   "source": [
    "x = Foo()\n",
    "x.moja_metoda()\n",
    "# bo x jest pierwszym argumentem pozycyjnym!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Funkcja globalna\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "-"
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   "outputs": [],
   "source": [
    "class Foo:\n",
    "    \n",
    "    def __init__(self): # \"konstruktor\"\n",
    "        self.bar = False\n",
    "        \n",
    "    def __str__(self): # print\n",
    "        return \"Foobar = {}\".format(self.bar)\n",
    "    \n",
    "def funkcja(obiekt):  # funkcja globalna\n",
    "    obiekt.bar = True # ustaw flagę bar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Foobar = False\n",
      "Foobar = True\n"
     ]
    }
   ],
   "source": [
    "x = Foo() # stwórz obiekt typu Foo\n",
    "\n",
    "print(x)\n",
    "\n",
    "funkcja(x) # globalna na x\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Metoda z funkcji globalnej\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true,
    "slideshow": {
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   "outputs": [],
   "source": [
    "class Foo:\n",
    "    \n",
    "    def __init__(self): # \"konstruktor\"\n",
    "        self.bar = False\n",
    "        \n",
    "    def __str__(self): # print\n",
    "        return \"Foobar = {}\".format(self.bar)\n",
    "    \n",
    "    def funkcja(obiekt):  # metoda\n",
    "        obiekt.bar = True # ustaw flagę bar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Foobar = False\n",
      "Foobar = True\n"
     ]
    }
   ],
   "source": [
    "x = Foo() # stwórz obiekt typu Foo\n",
    "\n",
    "print(x)\n",
    "\n",
    "x.funkcja() # metoda (x jest arg pozycyjnym)\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Punkt\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "class Punkt:\n",
    "    \n",
    "    def __init__(self, x=0.0, y=0.0):\n",
    "        self.x = x # odcięta \n",
    "        self.y = y # rzędna\n",
    "        \n",
    "    def __str__(self): # (x, y)\n",
    "        return \"({}, {})\".format(self.x, self.y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1.0, 2.0)\n"
     ]
    }
   ],
   "source": [
    "x = Punkt(1.0, 2.0) \n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Odcinek\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "from math import sqrt\n",
    "\n",
    "class Odcinek:\n",
    "    \n",
    "    def __init__(self, start, end):\n",
    "        self.poczatek = start # Punkt\n",
    "        self.koniec = end     # Punkt\n",
    "        \n",
    "    def dlugosc(self):\n",
    "        # Odcinek.Punkt.Wspolrzedna\n",
    "        x = self.koniec.x - self.poczatek.x\n",
    "        y = self.koniec.y - self.poczatek.y\n",
    "        return sqrt(x**2 + y**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = Punkt()  # (0, 0)\n",
    "b = Punkt(2) # (2, 0)\n",
    "o = Odcinek(a, b)\n",
    "\n",
    "o.dlugosc()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Enkapsulacja\n",
    "\n",
    "---\n",
    "\n",
    "* enkapsulacja - ukrywanie implementacji\n",
    "* brak bezpośredniego dostępu do danych\n",
    "* tylko własne metody moga zmienić stan\n",
    "* w Pythonie jest to umowne, bo nie ma zmiennych prywatnych"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Przykład - cząstka\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "class Particle:\n",
    "    \n",
    "    def __init__(self, mass, velocity=0.0):\n",
    "        self.m = mass     # masa\n",
    "        self.v = velocity # prędkość\n",
    "        self.ped()        # ustala pęd\n",
    "\n",
    "    def __str__(self):\n",
    "        return \"m = {m}, v = {v}, p = {p}\"\\\n",
    "                .format(m=self.m, v=self.v, p=self.p)\n",
    "        \n",
    "    def ped(self): # liczy pęd cząstki\n",
    "        self.p = self.m*self.v"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Cząstka - test\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "m = 10, v = 10, p = 100\n"
     ]
    }
   ],
   "source": [
    "x = Particle(10, 10) # masa = 10, prędkość = 10\n",
    "\n",
    "print(x) # pęd = masa * prędkość"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "m = 10, v = 0, p = 100\n"
     ]
    }
   ],
   "source": [
    "x.v = 0  # zmień prędkość\n",
    "\n",
    "print(x) # pęd nie został uaktulaniony"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## *Gettery* i *settery*\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "class Particle:\n",
    "    \n",
    "    def __init__(self, mass, velocity=0.0):\n",
    "        self.__m = mass     # masa\n",
    "        self.__v = velocity # prędkość\n",
    "        self.ped()          # ustala pęd\n",
    "        \n",
    "    def __str__(self):\n",
    "        return \"m = {m}, v = {v}, p = {p}\"\\\n",
    "                .format(m=self.__m, v=self.__v, p=self.__p)\n",
    "        \n",
    "    def ped(self): # liczy pęd cząstki\n",
    "        self.__p = self.__m*self.__v\n",
    "        \n",
    "    def get_v(self): # pobierz prędkośc\n",
    "        return self.__v\n",
    "    \n",
    "    def set_v(self, v): # ustaw prędkość\n",
    "        self.__v = v\n",
    "        self.ped() # aktualizuj pęd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## *get*, *set* - test\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "m = 10, v = 10, p = 100\n"
     ]
    }
   ],
   "source": [
    "x = Particle(10, 10) # masa = 10, prędkość = 10\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "m = 10, v = 0, p = 0\n"
     ]
    }
   ],
   "source": [
    "x.set_v(0) # zmień prędkość\n",
    "\n",
    "print(x) # pęd został uaktulaniony"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.get_v() # pobierz prędkość"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Dziedziczenie\n",
    "\n",
    "---\n",
    "\n",
    "* umożliwia ponowne wykorzystanie funkcjonalności *klas bazowych* w *klasach pochodnych*\n",
    "* przykład:\n",
    "    * klasa pojazd: jedz, hamuj...\n",
    "    * klasa samochód: to co pojazd + otwórz bagażnik...\n",
    "    * klasa motor: to co pojazd + jedz na jednym kole..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Przykład\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "class Baza:\n",
    "    \n",
    "    def whoami(self):\n",
    "        print(\"Jestem obiektem klasy Baza.\")\n",
    "        \n",
    "class Pochodna(Baza): # dziedziczy z Baza\n",
    "    \n",
    "    def sayhi(self):\n",
    "        print(\"Hello!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello!\n"
     ]
    }
   ],
   "source": [
    "x = Pochodna()\n",
    "\n",
    "x.sayhi() # zdefiniowane w Pochodna"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Jestem obiektem klasy Baza.\n"
     ]
    }
   ],
   "source": [
    "x.whoami() # zdefiinowane w Baza"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Konstruktor klasy bazowej\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "class Foo:\n",
    "    \n",
    "    def __init__(self):\n",
    "        print(\"Tworzę Foo\")\n",
    "        \n",
    "class Bar(Foo):\n",
    "    \n",
    "    def __init__(self):\n",
    "        print(\"Tworzę Bar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tworzę Bar\n"
     ]
    }
   ],
   "source": [
    "x = Bar() # __init__ z Foo nie jest wywołany"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Wywołanie konstruktora bazy\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "class Foo:\n",
    "    \n",
    "    def __init__(self):\n",
    "        print(\"Tworzę Foo\")\n",
    "        \n",
    "class Bar(Foo):\n",
    "    \n",
    "    def __init__(self):\n",
    "        super().__init__() # wywołaj konstruktor klasy nadrzędnej\n",
    "        print(\"Tworzę Bar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tworzę Foo\n",
      "Tworzę Bar\n"
     ]
    }
   ],
   "source": [
    "x = Bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Przykład - Student\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "class Student:\n",
    "    \n",
    "    last_index = 1234 # atrybut klasy\n",
    "    \n",
    "    def __init__(self, name):\n",
    "        # aktualizuj numer indeksu\n",
    "        self.__class__.last_index += 1\n",
    "        # przypisz studentowi imię i numer indeksu\n",
    "        self.name = name\n",
    "        self.index = self.__class__.last_index\n",
    "        \n",
    "    def __str__(self): # Student [imię] (nr indeksu)\n",
    "        return \"Student {} (nr {})\".format(self.name, self.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Student Anna (nr 1235)\n"
     ]
    }
   ],
   "source": [
    "student = Student(\"Anna\")\n",
    "\n",
    "print(student)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Przykład - StudentISSP\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "class StudentISSP(Student): # też Student\n",
    "    \n",
    "    def __init__(self, name, przedmioty):\n",
    "        \n",
    "        super().__init__(name) # wywołaj Student.__init__\n",
    "        \n",
    "        self.przedmioty = przedmioty\n",
    "        \n",
    "    def __str__(self):\n",
    "        return super().__str__() + \\\n",
    "                \": \" + \", \".join(self.przedmioty)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Student Jan (nr 1236): programowanie, fizyka\n"
     ]
    }
   ],
   "source": [
    "studentISSP = StudentISSP(\"Jan\", [\"programowanie\", \"fizyka\"])\n",
    "\n",
    "print(studentISSP)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Polimorfizm\n",
    "\n",
    "---\n",
    "\n",
    "* współdzielenie *interfejsu* przez różne typy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "class Kot:\n",
    "    def glos(self):\n",
    "        print(\"Miau\")\n",
    "\n",
    "class Pies:\n",
    "    def glos(self):\n",
    "        print(\"Hau\")\n",
    "\n",
    "class Krowa:\n",
    "    def glos(self):\n",
    "        print(\"Muu\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Miau\n",
      "Hau\n",
      "Muu\n"
     ]
    }
   ],
   "source": [
    "for zwierze in [Kot(), Pies(), Krowa()]:\n",
    "    zwierze.glos() # za każdym razem inny typ"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Ryby głosu nie mają\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "class Ryba:\n",
    "    pass # brak definicji glos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Miau\n",
      "Hau\n",
      "Muu\n"
     ]
    },
    {
     "ename": "AttributeError",
     "evalue": "'Ryba' object has no attribute 'glos'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-32-6d74614a5413>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mzwierze\u001b[0m \u001b[1;32min\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mKot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mPies\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mKrowa\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mRyba\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m     \u001b[0mzwierze\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mglos\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# Ryba nie ma zdefiniowanej metody glos\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m: 'Ryba' object has no attribute 'glos'"
     ]
    }
   ],
   "source": [
    "for zwierze in [Kot(), Pies(), Krowa(), Ryba()]:\n",
    "    zwierze.glos() # Ryba nie ma zdefiniowanej metody glos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Wymuszanie interfejsu\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "class Zwierze:\n",
    "    def glos(self):\n",
    "        pass\n",
    "    \n",
    "class Kot(Zwierze):\n",
    "    def glos(self): # nadpisuje Zwierze.glos\n",
    "        print(\"Miau\")\n",
    "        \n",
    "class Ryba(Zwierze):\n",
    "    pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Miau\n"
     ]
    }
   ],
   "source": [
    "for zwierze in [Kot(), Ryba()]:\n",
    "    zwierze.glos() # Ryba siedzi cicho"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Większe wymuszanie interfejsu\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "class Zwierze:\n",
    "    def glos(self):\n",
    "        raise NotImplementedError(\"Każde zwiesze musi mieć głos.\")\n",
    "    \n",
    "class Kot(Zwierze):\n",
    "    def glos(self): # nadpisuje Zwierze.glos\n",
    "        print(\"Miau\")\n",
    "        \n",
    "class Ryba(Zwierze):\n",
    "    pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Miau\n"
     ]
    },
    {
     "ename": "NotImplementedError",
     "evalue": "Każde zwiesze musi mieć głos.",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNotImplementedError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-36-8b317967163a>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mzwierze\u001b[0m \u001b[1;32min\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mKot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mRyba\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m     \u001b[0mzwierze\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mglos\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# Ryba zwróci błąd\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-35-e744aa713040>\u001b[0m in \u001b[0;36mglos\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mclass\u001b[0m \u001b[0mZwierze\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mglos\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m         \u001b[1;32mraise\u001b[0m \u001b[0mNotImplementedError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Każde zwiesze musi mieć głos.\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;32mclass\u001b[0m \u001b[0mKot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mZwierze\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNotImplementedError\u001b[0m: Każde zwiesze musi mieć głos."
     ]
    }
   ],
   "source": [
    "for zwierze in [Kot(), Ryba()]:\n",
    "    zwierze.glos() # Ryba zwróci błąd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Przykład - Wielokąt\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "class Wielokat:\n",
    "    \n",
    "    def __init__(self, *boki):\n",
    "        self.boki = boki # krotka\n",
    "        \n",
    "    def obwod(self):          # suma długości\n",
    "        return sum(self.boki) # boków\n",
    "        \n",
    "    def pole(self): raise NotImplementedError"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w = Wielokat(1, 2, 3, 4, 5) # pięciokąt\n",
    "\n",
    "w.obwod()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Przykład - Trójkąt\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "class Trojkat(Wielokat):\n",
    "    \n",
    "    def __init__(self, *boki):\n",
    "        super().__init__(*boki) # utwórz wielokąt\n",
    "        \n",
    "    def pole(self):\n",
    "        pole = p = self.obwod()/2 # obwod zdefiniowane w Wielokat\n",
    "        \n",
    "        for bok in self.boki: # wzór Herona\n",
    "            pole *= (p - bok) # p(p - a)(p - b)(p - c)\n",
    "            \n",
    "        return pole**0.5 # pierwiastek"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t = Trojkat(3, 4, 5)\n",
    "\n",
    "t.obwod() # z Wielokat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.0"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t.pole() # z Pole"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Przykład - Trójkąt Równoboczny\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "class TrojkatR(Trojkat):\n",
    "    \n",
    "    def __init__(self, bok):\n",
    "        super().__init__(bok, bok, bok) # Trójkąt -> Wielokąt\n",
    "        \n",
    "    def pole(self):\n",
    "        return (self.boki[0]**2)*(3**0.5)/4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.8971143170299736"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t = TrojkatR(3) # (3, 3, 3)\n",
    "\n",
    "t.pole() # TrojkatR.pole()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.897114317029974"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t = Trojkat(3, 3, 3)\n",
    "\n",
    "t.pole() # Trojkat.pole() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Wielokrotne dziedziczenie\n",
    "\n",
    "---\n",
    "\n",
    "* klasa może posiadać kilka klas bazowych\n",
    "* posiada wtedy wszystkie dane i metody\n",
    "* a co jeśli się powtarzają?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Przykład - amifibia\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "class Samochod:\n",
    "    \n",
    "    def jedz(self):\n",
    "        print(\"Jadę...\")\n",
    "        \n",
    "class Lodz:\n",
    "    \n",
    "    def plyn(self):\n",
    "        print(\"Płynę...\")\n",
    "        \n",
    "class Amfibia(Samochod, Lodz): pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Jadę...\n",
      "Płynę...\n"
     ]
    }
   ],
   "source": [
    "amfibia = Amfibia()\n",
    "\n",
    "amfibia.jedz() # Samochod\n",
    "amfibia.plyn() # Lodz"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Przykład - amifibia v2\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "class Samochod:\n",
    "    def run(self):\n",
    "        print(\"Jadę...\")\n",
    "        \n",
    "class Lodz:\n",
    "    def run(self):\n",
    "        print(\"Płynę...\")\n",
    "        \n",
    "class Amfibia1(Samochod, Lodz): pass\n",
    "class Amfibia2(Lodz, Samochod): pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Jadę...\n"
     ]
    }
   ],
   "source": [
    "amifibia1 = Amfibia1()\n",
    "amifibia1.run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Płynę...\n"
     ]
    }
   ],
   "source": [
    "amifibia2 = Amfibia2()\n",
    "amifibia2.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Klasa Screen\n",
    "\n",
    "---\n",
    "\n",
    "* napiszemy klasę, która będzie naszym wirtualnym ekranem\n",
    "* do *rysowania* użyjemy modułu *matplotlib*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## *matshow*\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1, 0, 0, 0, 0], [2, 0, 0, 0, 0], [3, 0, 0, 0, 0], [4, 0, 0, 0, 0], [5, 0, 0, 0, 0]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f2c74549978>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPcAAAD7CAYAAAC2TgIoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngaSIG0iKuIGk/gcBFL491jJ4NwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2c76a29588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tablica = [[0]*5 for _ in range(5)] # tablica 5x5\n",
    "\n",
    "for i in range(5):\n",
    "    tablica[i][0] = i + 1 # pierwsza kolumna\n",
    "\n",
    "print(tablica)\n",
    "\n",
    "plt.matshow(tablica) # \"rysuj\" tablicę"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Screen\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "class Screen:\n",
    "    \"\"\"Ekran na bazie matplotlib.pyplot.matshow\"\"\"\n",
    "    \n",
    "    def __init__(self, width, height): # ustal wymiary ekrany\n",
    "        self.w = width  # zapamiętaj szerokość\n",
    "        self.h = height # zapamiętaj wysokość\n",
    "        self.pixels = [[0]*width for _ in range(height)]\n",
    "        \n",
    "    def show(self, cmap=plt.cm.gray_r):\n",
    "        plt.matshow(self.pixels, origin='lower', cmap=cmap)\n",
    "        \n",
    "    def set(self, y, x, value): # ustaw wartość piksela\n",
    "        if y < self.w and x < self.h: # x <-> y\n",
    "            self.pixels[x][y] = value # dla naszej wygody"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Screen test\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkYAAAD7CAYAAACPFlvuAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nCCMAgCGMAACGMAIAGP8PfpC9xWGeQ5IAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2c76a4f198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "s = Screen(10, 4)\n",
    "\n",
    "s.set(5, 1, 10) # [5,1] = 10\n",
    "s.set(1, 2, 50) # [1,2] = 50\n",
    "\n",
    "s.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Screen test II\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2c7403b828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "s = Screen(200, 20)\n",
    "\n",
    "for x in range(200):\n",
    "    s.set(x, int(x**0.5), x)\n",
    "    \n",
    "s.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Zbiór Mandelbrota\n",
    "\n",
    "---\n",
    "\n",
    "* punkty $p$ (zespolone), dla których ciąg\n",
    "\n",
    "$$z_{n+1} = z_n^2 + p$$\n",
    "\n",
    "(gdzie $z_0 = 0$) nie dąży do nieskończoności lub\n",
    "\n",
    "$$|z_n| < 2$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Implementacja\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "def mandelbrot(p, limit=100):\n",
    "    \"\"\"Zwraca n, dla którego |zn| < 2\"\"\"\n",
    "    zn = p # bo z0 = 0\n",
    "    \n",
    "    for i in range(limit): # sprawdzamy do pewnego momentu\n",
    "        if abs(zn) > 2: # zwróć miejsce, w którym\n",
    "            return i    # warunek nie jest już spełniony\n",
    "        \n",
    "        zn = zn**2 + p # następny wyraz ciągu\n",
    "        \n",
    "    return limit # warunek spełniony do limitu"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Przygotowanie zbioru\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "size = 1000\n",
    "\n",
    "s = Screen(size, size)\n",
    "\n",
    "# podwójna pętla po wszystkich pikselach\n",
    "for x in range(size):\n",
    "    for y in range(size):\n",
    "        xs = -2.0 + 2.5 * x / size   # [-2.0, 0.5]\n",
    "        ys = -1.25 + 2.5 * y / size # [-1.25, 1.25]\n",
    "        s.set(x, y, mandelbrot(complex(xs, ys)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Najsłynniejszy fraktal\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "matplotlib.rcParams['figure.figsize'] = (10, 8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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g0IxEYc+EQtZTJmVMVj79mru609quXbvw+/2MGjWK+vp67HY74XCYXbt2cd55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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2c74041780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "s.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## IFS (*iterated function system*)\n",
    "\n",
    "---\n",
    "\n",
    "* geometria fraktalna\n",
    "* przykład - paproć Barnsleya:\n",
    "\n",
    "$$f_1(x, y) = (0.85x + 0.04y, -0.04x + 0.85y + 1.6)$$\n",
    "$$f_2(x, y) = (-0.15x + 0.28y, 0.26x + 0.24y + 0.44)$$\n",
    "$$f_3(x, y) = (0.20x - 0.26y, 0.23x + 0.22y + 1.6)$$\n",
    "$$f_4(x, y) = (0, 0.16y)$$\n",
    "\n",
    "* w proporcjach 85:7:71"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Paproć Barnsleya\n",
    "\n",
    "---\n",
    "\n",
    "![](https://upload.wikimedia.org/wikipedia/commons/9/9c/Fractal_fern-Barnsley_animation.gif)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## IFS - przykład\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "# źródło: https://scratch.mit.edu/projects/92421819/\n",
    "\n",
    "def f1(x, y): return 0.03*x, 0.1*y\n",
    "\n",
    "def f2(x, y): return 0.85*x, 0.85*y + 1.5\n",
    "\n",
    "def f3(x, y): return 0.8*x, 0.8*y + 1.5\n",
    "\n",
    "def f4(x, y): return 0.2*x - 0.08*y, 0.15*x + 0.22*y + 0.85\n",
    "\n",
    "def f5(x, y): return -0.2*x + 0.08*y, 0.15*x + 0.22*y + 0.85\n",
    "\n",
    "def f6(x, y): return 0.25*x-0.1*y, 0.12*x + 0.25*y + 0.3\n",
    "\n",
    "def f7(x, y): return -0.2*x + 0.1*y, 0.12*x + 0.2*y + 0.4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Losowanie\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "from random import randint\n",
    "\n",
    "def random_f():\n",
    "    # proporcje 2:60:10:7:7:7:7\n",
    "    x = randint(1,100)\n",
    "    if x <= 2: return f1\n",
    "    if x <= 62: return f2\n",
    "    if x <= 72: return f3\n",
    "    if x <= 79: return f4\n",
    "    if x <= 86: return f5\n",
    "    if x <= 93: return f6\n",
    "    return f7"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Przygotowanie danych\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "n_iter = 100000\n",
    "size = 1000\n",
    "\n",
    "s = Screen(size, size)\n",
    "\n",
    "x, y = 0, 0 # punkt początkowy\n",
    "\n",
    "for _ in range(n_iter):\n",
    "    f = random_f() # losowa funkcja\n",
    "    x, y = f(x, y) # nowe współrzędne\n",
    "    xs = (x + 1.0) * size / 2\n",
    "    ys = y * size / 10.0\n",
    "    s.set(int(xs), int(ys), 500 - abs(xs - 500))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Wesołych Świąt\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
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Wr2ngPaPGMcG+0+Cd820Bm26pcTneM3SGMzTqef96KW15T+au7o/f0QTBmk/l10ZDnfny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pTDQ2VLfm2jOUgN60/UmbuqVRl+eizm1J4q/77o7+1+R7Hfma2W70f4bflCBJNa/uC3+9D8WXNF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\nDBfcHQ6Hw+HIYbjg7nA4HA5HDuP/A/TL3Qbqyc9RAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2c73f63e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "s.show(plt.cm.Greens)"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "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": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}