wyklady/js-python_w01.ipynb

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    "# Języki skryptowe - Python\n",
    "## Wykład 1"
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    "## Dane kontaktowe\n",
    "\n",
    "---\n",
    "\n",
    "* Tomasz Golan\n",
    "* pokój: 438\n",
    "* email: <tomasz.golan@uwr.edu.pl>\n",
    "* konsultacje: poniedziałek 10:00-11:30; piątek 15:00-16:30"
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    "## Program\n",
    "\n",
    "---\n",
    "\n",
    "* wprowadzenie\n",
    "* podstawowe typy danych i operatory\n",
    "* wyrażenia warunkowe i pętle\n",
    "* funkcje i moduły\n",
    "* struktury danych\n",
    "* klasy i obiekty\n",
    "* operacje wejścia/wyjścia\n",
    "* obsługa wyjątków\n",
    "\n",
    "---\n",
    "\n",
    "* obliczenia numeryczne\n",
    "* debugowanie i profilowanie\n",
    "* tworzenie graficznego interfejsu użytkownika\n",
    "* dekoratory, funkcje lambda, generatory ..."
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    "## Materiały do wykładów\n",
    "\n",
    "---\n",
    "\n",
    "* wykłady: https://github.com/TomaszGolan/js-python\n",
    "* wykłady archiwalne: https://tomaszgolan.github.io/js-python/\n",
    "* dokumentacja: https://www.python.org/\n",
    "\n",
    "---\n",
    "\n",
    "* A. B. Downey, J. Elkner, C. Meyers, “Think Python. How to Think Like a Computer Scientist”: http://www.greenteapress.com/thinkpython/\n",
    "* M. Pilgrim, “Dive into Python”: http://www.diveintopython.net/\n",
    "* Swaroop CH \"A Byte of Python\": https://python.swaroopch.com/ (tłumaczenie: http://python.edu.pl/byteofpython/)"
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    "## Zaliczenie\n",
    "\n",
    "---\n",
    "\n",
    "* ćwiczenia: listy zadań\n",
    "\n",
    "    * 4 listy zadań (październik, listopad, grudzień, styczeń)\n",
    "    * termin oddania: koniec miesiąca w nazwie listy\n",
    "    * spóźnienie: -n oceny (n = [miesiąc oddania] - [termin oddania])\n",
    "    * za oddanie zadania, którego studentka/student nie potrafi wyjaśnić: punkty karne z wagą 10\n",
    "    \n",
    "\n",
    "* wykład: egzamin praktyczny"
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    "## Język kompilowany\n",
    "\n",
    "---\n",
    "\n",
    "* przykłady: C / C++, Pascal\n",
    "* zalety: wydajność, błędy na etapie kompilacji, utrzymanie i rozwijanie kodu\n",
    "* wady: czas pisania, kompilacja dużych projektów"
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    "## Język interpretowany\n",
    "\n",
    "---\n",
    "\n",
    "* przykłady: Python, Perl\n",
    "* zalety: prosty, szybki i przyjemny\n",
    "* wady: wydajność, debugowanie (brak kompilacji)"
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    "## Python\n",
    "\n",
    "---\n",
    "\n",
    "> Python is powerful... and fast;\n",
    "> plays well with others;\n",
    "> runs everywhere;\n",
    "> is friendly & easy to learn;\n",
    "> is Open.\n",
    "\n",
    "---\n",
    "\n",
    "* otwarte oprogramowanie (*open source*)\n",
    "* przenośny\n",
    "    * Linux, Mac OS - prawdopodobnie już jest\n",
    "    * Windows - instalator ze strony https://www.python.org\n",
    "    * Anaconda - https://www.continuum.io/\n",
    "* zwięzły i elegancki (prawie jak pseudokod)\n",
    "* automatyczne zarządzanie pamięcią (*garbage collection*)\n",
    "* bogata bilbioteka standardowa i niezliczona ilość bibliotek zewnętrznych\n",
    "* struktury wysokiego poziomu"
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      "The Zen of Python, by Tim Peters\n",
      "\n",
      "Beautiful is better than ugly.\n",
      "Explicit is better than implicit.\n",
      "Simple is better than complex.\n",
      "Complex is better than complicated.\n",
      "Flat is better than nested.\n",
      "Sparse is better than dense.\n",
      "Readability counts.\n",
      "Special cases aren't special enough to break the rules.\n",
      "Although practicality beats purity.\n",
      "Errors should never pass silently.\n",
      "Unless explicitly silenced.\n",
      "In the face of ambiguity, refuse the temptation to guess.\n",
      "There should be one-- and preferably only one --obvious way to do it.\n",
      "Although that way may not be obvious at first unless you're Dutch.\n",
      "Now is better than never.\n",
      "Although never is often better than *right* now.\n",
      "If the implementation is hard to explain, it's a bad idea.\n",
      "If the implementation is easy to explain, it may be a good idea.\n",
      "Namespaces are one honking great idea -- let's do more of those!\n"
     ]
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   "source": [
    "import this"
   ]
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    "## Hello World!\n",
    "\n",
    "---\n",
    "\n",
    "```py\n",
    "# Python\n",
    "print(\"Hello World!\")\n",
    "```\n",
    "\n",
    "---\n",
    "\n",
    "```cpp\n",
    "// C++\n",
    "#include <iostream>\n",
    "\n",
    "int main()\n",
    "{\n",
    "    std::cout << \"Hello World!\";\n",
    "}\n",
    "```\n",
    "\n",
    "---\n",
    "\n",
    "\n",
    "```java\n",
    "// Java\n",
    "public class Hello {\n",
    "    public static void main(String []args) {\n",
    "        System.out.println(\"Hello World!\");\n",
    "    }\n",
    "}\n",
    "```"
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    "## Zastosowanie\n",
    "\n",
    "---\n",
    "\n",
    "* internet: Yahoo, Google, ...\n",
    "* gry: Battlefield 2, Civilization 4, ...\n",
    "* grafika: Walt Disney, Blender 3D, ...\n",
    "* finanse: Altis Investment Management, Bellco Credit Union, ...\n",
    "* nauka: NASA, Los Alamos National Laboratory, ...\n",
    "* interfejs programistyczny aplikacji (*Application Programming Interface = API*): tensorflow (Google's machine learning), Amazon, Facebook, Youtube, ..."
   ]
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   "source": [
    "## Python 2 vs Python 3\n",
    "\n",
    "---\n",
    "\n",
    "* *Short version: Python 2.x is legacy, Python 3.x is the present and future of the language* (https://wiki.python.org/moin/Python2orPython3)\n",
    "* Python 3 jest gotowy, jednak nie wszystkie zewnętrzne biblioteki są już z nim kompatybilne"
   ]
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   "source": [
    "## Python 2 vs Python 3\n",
    "\n",
    "---\n",
    "\n",
    "* Niektóre zmiany są kosmetyczne\n",
    "\n",
    "```py\n",
    "# Python 2\n",
    "\n",
    "print \"Hello World!\"\n",
    "\n",
    "# Python 3\n",
    "\n",
    "print(\"Hello World!\")\n",
    "```"
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   "source": [
    "## Python 2 vs Python 3\n",
    "\n",
    "---\n",
    "\n",
    "* Niektóre niebezpieczne\n",
    "\n",
    "```py\n",
    "# Python 2\n",
    "\n",
    "3 / 2.0 # = 1.5\n",
    "3 / 2   # = 1\n",
    "\n",
    "# Python 3\n",
    "\n",
    "3 / 2.0 # = 1.5\n",
    "3 / 2   # = 1.5\n",
    "3 // 2  # = 1\n",
    "```"
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   "source": [
    "## Python 2 vs Python 3\n",
    "\n",
    "---\n",
    "\n",
    "* A niektóre gruntowne: iteratory\n",
    "* Niektóre elementy Pythona 3 można zaimportować w Pythonie 2\n",
    "\n",
    "```bash\n",
    "Python 2.7.12 (default, Jul  1 2016, 15:12:24) \n",
    "[GCC 5.4.0 20160609] on linux2\n",
    "Type \"help\", \"copyright\", \"credits\" or \"license\" for more information.\n",
    ">>> 3 / 2\n",
    "1\n",
    ">>> from __future__ import division\n",
    ">>> 3 / 2\n",
    "1.5\n",
    ">>> \n",
    "```\n",
    "\n",
    "* Więcej informacji: https://wiki.python.org/moin/Python2orPython3\n",
    "\n",
    "* Na wykładzie omawiany będzie Python 3"
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   "source": [
    "## Tryb interaktywny\n",
    "\n",
    "---\n",
    "\n",
    "```bash\n",
    "$ python\n",
    "Python 3.5.2 |Anaconda custom (64-bit)| (default, Jul  2 2016, 17:53:06) \n",
    "[GCC 4.4.7 20120313 (Red Hat 4.4.7-1)] on linux\n",
    "Type \"help\", \"copyright\", \"credits\" or \"license\" for more information.\n",
    ">>> oceny_uwr = {2.0: \"niedostateczny\", 3.0: \"dostateczny\",\n",
    "...              3.5: \"dostateczny plus\", 4.0: \"dobry\",\n",
    "...              4.5: \"dobry plus\", 5.0: \"bardzo dobry\"}\n",
    ">>> for ocena in sorted(oceny_uwr, reverse=True):\n",
    "...     print(ocena, oceny_uwr[ocena])\n",
    "... \n",
    "5.0 bardzo dobry\n",
    "4.5 dobry plus\n",
    "4.0 dobry\n",
    "3.5 dostateczny plus\n",
    "3.0 dostateczny\n",
    "2.0 niedostateczny\n",
    ">>> \n",
    "```"
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   "source": [
    "## Tryb skryptowy\n",
    "\n",
    "---\n",
    "\n",
    "```bash\n",
    "$ cat skala_ocen.py \n",
    "#!/usr/bin/env python\n",
    "\n",
    "oceny_uwr = {2.0: \"niedostateczny\", 3.0: \"dostateczny\",\n",
    "             3.5: \"dostateczny plus\", 4.0: \"dobry\",\n",
    "             4.5: \"dobry plus\", 5.0: \"bardzo dobry\"}\n",
    "\n",
    "for ocena in sorted(oceny_uwr, reverse=True):\n",
    "    print(ocena, oceny_uwr[ocena])\n",
    "    \n",
    "$ python skala_ocen.py \n",
    "5.0 bardzo dobry\n",
    "4.5 dobry plus\n",
    "4.0 dobry\n",
    "3.5 dostateczny plus\n",
    "3.0 dostateczny\n",
    "2.0 niedostateczny\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "a7edd500-6a72-4478-b5b2-8a320893104d"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Tryb skryptowy\n",
    "\n",
    "---\n",
    "\n",
    "```bash\n",
    "$ ./skala_ocen.py\n",
    "bash: ./skala_ocen.py: Permission denied\n",
    "\n",
    "$ chmod +x skala_ocen.py \n",
    "\n",
    "$ ./skala_ocen.py \n",
    "5.0 bardzo dobry\n",
    "4.5 dobry plus\n",
    "4.0 dobry\n",
    "3.5 dostateczny plus\n",
    "3.0 dostateczny\n",
    "2.0 niedostateczny\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Jupyter\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "nbpresent": {
     "id": "6bf5ab4c-bf38-4b2a-83ed-2c4326ee151f"
    },
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.0 bardzo dobry\n",
      "4.5 dobry plus\n",
      "4.0 dobry\n",
      "3.5 dostateczny plus\n",
      "3.0 dostateczny\n",
      "2.0 niedostateczny\n"
     ]
    }
   ],
   "source": [
    "oceny_uwr = {2.0: \"niedostateczny\", 3.0: \"dostateczny\",\n",
    "             3.5: \"dostateczny plus\", 4.0: \"dobry\",\n",
    "             4.5: \"dobry plus\", 5.0: \"bardzo dobry\"}\n",
    "\n",
    "for ocena in sorted(oceny_uwr, reverse=True):\n",
    "    print(ocena, oceny_uwr[ocena])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "6704c308-4b74-4c21-861c-d60cf3e276d4"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Czytelność kodu\n",
    "\n",
    "---\n",
    "\n",
    "* przejrzysty kod\n",
    "* zrozumiałe nazwy zmiennych\n",
    "* komentarzy nigdy za wiele\n",
    "* dzielenie linii nie boli\n",
    "* konsekwencja w konwencji\n",
    "* najlepiej trzymać się standardu PEP 8: https://www.python.org/dev/peps/pep-0008/\n",
    "* ale bez przesady - przede wszystkim kod ma być czytelny"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Czytelność kodu\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "nbpresent": {
     "id": "039b7d59-5ae9-4350-a2c3-36ce2f7175b6"
    },
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Kasia  ->  dostateczny plus\n",
      "Basia  ->  dobry\n",
      "Józek  ->  dostateczny\n",
      "Marek  ->  dostateczny\n"
     ]
    }
   ],
   "source": [
    "import random\n",
    "\n",
    "# skala ocen obowiązująca na Uniwersytecie Wrocławskim\n",
    "oceny_uwr = {2.0: \"niedostateczny\", 3.0: \"dostateczny\",\n",
    "             3.5: \"dostateczny plus\", 4.0: \"dobry\",\n",
    "             4.5: \"dobry plus\", 5.0: \"bardzo dobry\"}\n",
    "\n",
    "# lista studentów uczęszczających na zajęcia z Pythona\n",
    "lista_studentow = [\"Kasia\", \"Basia\", \"Józek\", \"Marek\"]\n",
    "\n",
    "# przez lenistwo prowadzącego oceny wystawiane losowo\n",
    "for student in lista_studentow:\n",
    "    # losowa ocena\n",
    "    ocena = random.choice(list(oceny_uwr))\n",
    "    # przypisanie oceny\n",
    "    print(student, \" -> \", oceny_uwr[ocena])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Czytelność kodu\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "nbpresent": {
     "id": "e009a058-b362-4d1f-8744-ecacfdeece38"
    },
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Kasia  ->  dostateczny\n",
      "Basia  ->  niedostateczny\n",
      "Józek  ->  niedostateczny\n",
      "Marek  ->  dobry\n"
     ]
    }
   ],
   "source": [
    "import random\n",
    "o = {2.0: \"niedostateczny\", 3.0: \"dostateczny\", 3.5: \"dostateczny plus\", 4.0: \"dobry\", 4.5: \"dobry plus\", 5.0: \"bardzo dobry\"}\n",
    "s = [\"Kasia\", \\\n",
    "     \"Basia\", \"Józek\",\n",
    "     \"Marek\"]\n",
    "for i in range(len(s)): x = random.randint(2, len(o) - 1); print(s[i], \" -> \", o[x])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "b9b188ad-5ba4-4464-9bff-2f534c82618d"
    },
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Podstawowe typy danych \n",
    "\n",
    "---\n",
    "\n",
    "* typ całkowity (*int*)\n",
    "* typ zmiennoprzecinkowy (*float*)\n",
    "* typ zespolony (*complex*)\n",
    "* typ tekstowy (*str*)\n",
    "* zmienne są typowane dynamicznie"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "nbpresent": {
     "id": "56a633a5-1be6-429e-bb7e-5b28d2aa99c9"
    },
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'int'>, <class 'float'>, <class 'complex'>\n"
     ]
    }
   ],
   "source": [
    "x = 1   # [zmienna] [operator przypisania] [wartość]\n",
    "y = 1.0\n",
    "z = 1j\n",
    "\n",
    "# drukuj typy zmiennych x, y i z (oddzielone przecinkiem)\n",
    "print(type(x), type(y), type(z), sep=\", \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "nbpresent": {
     "id": "69fa5041-5f32-4332-b807-68d4324ee6bf"
    },
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "float"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = y # przypisz zmiennej x wartość zmiennej y\n",
    "\n",
    "type(x) # a czemu nie <class 'float'>?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "8d5f99ab-c8a2-4a9d-a42c-eb660dda2b10"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Operacje na liczbach\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "nbpresent": {
     "id": "65a6ba86-2ed8-4426-a3ee-d340e8f63896"
    },
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2 + 2 # suma dwóch liczb całkowitych -> liczba całkowita"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "nbpresent": {
     "id": "8384c78d-f549-4375-a9e0-e9211abd6481"
    },
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2 + 2.0 # wynikiem tej sumy jest liczba zmiennoprzecinkowa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "nbpresent": {
     "id": "f2913760-c7f5-458d-a1c7-64eb02f5a32e"
    },
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "5 - 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "nbpresent": {
     "id": "be71fbaf-645d-4b7b-8cb2-cb78667dd9a1"
    },
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-10"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "-5 * 2 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "nbpresent": {
     "id": "b84c6d91-d154-4d83-8329-abab3ff87a81"
    },
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.5"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "5 / 2 # w Pythonie 3 dzielenie zawsze zwraca liczbę zmiennoprzecinkową"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "nbpresent": {
     "id": "00ea453f-4913-4a90-8661-a68b5a4574b2"
    },
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "6 / 2 # nawet jeśli jest \"całkowita\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "nbpresent": {
     "id": "d2cf357c-bbb0-4e8b-b0f6-e203dfa9dcc0"
    },
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "5 // 2 # chyba że użyjemy //"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Operacje na liczbach\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "nbpresent": {
     "id": "80756bc1-7a0e-4479-9125-832c3a8f1a19"
    },
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "5 % 2 # reszta z dzielenia (operator modulo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "nbpresent": {
     "id": "e5988748-0449-4520-b98c-43765e71ac61"
    },
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "int(2.5) # jawne rzutowanie na int"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "nbpresent": {
     "id": "c1f10933-6724-42d1-bd5d-eb7ab1b549ea"
    },
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "float(1) # jawne rzutowanie na float"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "nbpresent": {
     "id": "053891c8-3473-4ac9-b14d-2cedd09e595c"
    },
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1+1j)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "complex(1,1) # liczby zespolone"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "nbpresent": {
     "id": "9425fde2-889e-4b7d-baa7-a2cf307da56c"
    },
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1+1j)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1+1j # można wprowadzać na dwa sposoby"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "nbpresent": {
     "id": "8f72ac2b-9056-4564-a98d-208561c46ec9"
    },
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(1+2j).real # część rzeczywista"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "nbpresent": {
     "id": "4f5d7e2e-f33c-4afc-bc9d-91324a5c211e"
    },
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(1+2j).imag # część urojona"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Operacje na liczbach\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "nbpresent": {
     "id": "2cc5396d-c854-4706-8ab1-bdd224118add"
    },
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "abs(-10) # moduł"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "nbpresent": {
     "id": "ca2dd409-2de2-4d06-a4b5-c6e35b76c3ec"
    },
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.5"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "abs(-2.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "nbpresent": {
     "id": "f719ef75-168e-4c8b-a082-8c8b3a572266"
    },
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.4142135623730951"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "abs(1+1j) # moduł liczby zespolonej"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "nbpresent": {
     "id": "03f201f2-23f0-4966-965c-05db4412542f"
    },
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "can't convert complex to int",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-27-dd7895e870c6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1j\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# rzutowania bez sensu zwrócą błąd\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m: can't convert complex to int"
     ]
    }
   ],
   "source": [
    "int(1+1j) # rzutowania bez sensu zwrócą błąd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "nbpresent": {
     "id": "d1b50ea1-9fd0-4878-96ca-a961b1e9a9e8"
    },
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1-1j)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(1+1j).conjugate() # sprzężenie liczby zespolonej"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Operacje na liczbach\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "nbpresent": {
     "id": "be052e12-679f-4f2d-8ea7-8a006e14fd56"
    },
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 1)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "divmod(10, 3) # para (10 // 3 i 10 % 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "nbpresent": {
     "id": "a39bd788-8677-4933-8373-173d80050edd"
    },
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "can't take floor or mod of complex number.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-30-0f920bac9923>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdivmod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1j\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# no bo jak...\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m: can't take floor or mod of complex number."
     ]
    }
   ],
   "source": [
    "divmod(10, 3+1j) # no bo jak..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "nbpresent": {
     "id": "8db31111-50de-4c13-95c4-e7a9c9557a56"
    },
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pow(2, 3) # pow(a, b) = a^b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "nbpresent": {
     "id": "e074efaf-afc5-4a62-bb55-77ffa854ec88"
    },
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8.0"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pow(2, 3.0) # zwróć uwagę na typy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "nbpresent": {
     "id": "3c784469-a6d6-48f8-bfe9-8e7184f6b534"
    },
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2 ** 3 # inna wersja a^b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "nbpresent": {
     "id": "ac8bad1e-f19f-4bb2-85ac-cb05a3449339"
    },
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-1+0j)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1j ** 2 # dla zespolonych też działa"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "5a3db116-371a-42d6-ae5f-c640827e9792"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Moduł math\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "nbpresent": {
     "id": "fd9af1ea-8ca6-4c87-ae2c-96fb85713d29"
    },
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['__doc__',\n",
       " '__file__',\n",
       " '__loader__',\n",
       " '__name__',\n",
       " '__package__',\n",
       " '__spec__',\n",
       " 'acos',\n",
       " 'acosh',\n",
       " 'asin',\n",
       " 'asinh',\n",
       " 'atan',\n",
       " 'atan2',\n",
       " 'atanh',\n",
       " 'ceil',\n",
       " 'copysign',\n",
       " 'cos',\n",
       " 'cosh',\n",
       " 'degrees',\n",
       " 'e',\n",
       " 'erf',\n",
       " 'erfc',\n",
       " 'exp',\n",
       " 'expm1',\n",
       " 'fabs',\n",
       " 'factorial',\n",
       " 'floor',\n",
       " 'fmod',\n",
       " 'frexp',\n",
       " 'fsum',\n",
       " 'gamma',\n",
       " 'gcd',\n",
       " 'hypot',\n",
       " 'inf',\n",
       " 'isclose',\n",
       " 'isfinite',\n",
       " 'isinf',\n",
       " 'isnan',\n",
       " 'ldexp',\n",
       " 'lgamma',\n",
       " 'log',\n",
       " 'log10',\n",
       " 'log1p',\n",
       " 'log2',\n",
       " 'modf',\n",
       " 'nan',\n",
       " 'pi',\n",
       " 'pow',\n",
       " 'radians',\n",
       " 'sin',\n",
       " 'sinh',\n",
       " 'sqrt',\n",
       " 'tan',\n",
       " 'tanh',\n",
       " 'tau',\n",
       " 'trunc']"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import math # import [moduł]\n",
    "\n",
    "dir (math) # lista dostępnych funkcji"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Moduł math\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "nbpresent": {
     "id": "393c29ee-e3a7-4dec-9b7a-1c1862e3216f"
    },
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on built-in function floor in module math:\n",
      "\n",
      "floor(...)\n",
      "    floor(x)\n",
      "    \n",
      "    Return the floor of x as an Integral.\n",
      "    This is the largest integer <= x.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(math.floor) # dokumentacja funkcji floor z math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "nbpresent": {
     "id": "51464f15-ec1e-4f9b-b16b-39a0e63ac18e"
    },
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on built-in function ceil in module math:\n",
      "\n",
      "ceil(...)\n",
      "    ceil(x)\n",
      "    \n",
      "    Return the ceiling of x as an Integral.\n",
      "    This is the smallest integer >= x.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(math.ceil) # zwróć uwagę na: moduł.funkcja"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Moduł math\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "nbpresent": {
     "id": "b64bde8a-0766-459a-adb3-496998937270"
    },
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "int(2.8) # rzutowanie float na int \"ucina wszystko po kropce\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "nbpresent": {
     "id": "cf431f59-5051-4338-ba4d-36d1567edea3"
    },
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "math.floor(2.8) # tak jak funkcja math.floor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "nbpresent": {
     "id": "014732d4-e815-4ea3-ab8d-e4a3d975466c"
    },
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "math.ceil(2.8) # funkcja math.ceil zaokrągla w górę"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "nbpresent": {
     "id": "c603b8c8-9666-4566-8099-b3ec10409940"
    },
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "math.ceil(2.1) # zawsze w górę (patrz definicja)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Moduł math\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "nbpresent": {
     "id": "9dcd6dca-e868-41d8-b32c-bf4405cbbf8f"
    },
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['__doc__',\n",
       " '__file__',\n",
       " '__loader__',\n",
       " '__name__',\n",
       " '__package__',\n",
       " '__spec__',\n",
       " 'acos',\n",
       " 'acosh',\n",
       " 'asin',\n",
       " 'asinh',\n",
       " 'atan',\n",
       " 'atanh',\n",
       " 'cos',\n",
       " 'cosh',\n",
       " 'e',\n",
       " 'exp',\n",
       " 'inf',\n",
       " 'infj',\n",
       " 'isclose',\n",
       " 'isfinite',\n",
       " 'isinf',\n",
       " 'isnan',\n",
       " 'log',\n",
       " 'log10',\n",
       " 'nan',\n",
       " 'nanj',\n",
       " 'phase',\n",
       " 'pi',\n",
       " 'polar',\n",
       " 'rect',\n",
       " 'sin',\n",
       " 'sinh',\n",
       " 'sqrt',\n",
       " 'tan',\n",
       " 'tanh',\n",
       " 'tau']"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import cmath # biblioteka standardowa dla liczb zespolonych\n",
    "\n",
    "dir(cmath) # porównaj z dir(math), jasne czemu moduł.funkcja?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Moduł math\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "nbpresent": {
     "id": "eff2592c-45c3-4f35-afa0-b696b0ebb5d1"
    },
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1j"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cmath.sqrt(-1) # cmath potrafi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "nbpresent": {
     "id": "237e0b19-8298-4e7b-8b4e-99bad2b5e130"
    },
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "math domain error",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-44-9baf3c39fc4a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqrt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# ale math już nie\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m: math domain error"
     ]
    }
   ],
   "source": [
    "math.sqrt(-1) # ale math już nie"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Typ tekstowy\n",
    "\n",
    "---\n",
    "\n",
    "* Python 3 używa domyślnie kodowania *unicode* dla typu *str*\n",
    "* Dzięki czemu mamy polskie znaki od ręki\n",
    "\n",
    "```py\n",
    "Python 3.5.2 |Anaconda 4.2.0 (64-bit)| (default, Jul  2 2016, 17:53:06) \n",
    "[GCC 4.4.7 20120313 (Red Hat 4.4.7-1)] on linux\n",
    "Type \"help\", \"copyright\", \"credits\" or \"license\" for more information.\n",
    ">>> s = \"zażółć gęślą jaźń\"\n",
    ">>> s\n",
    "'zażółć gęślą jaźń'\n",
    ">>> \n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Typ tekstowy bez *unicode*\n",
    "\n",
    "---\n",
    "\n",
    "```py\n",
    "Python 2.7.12 (default, Jul  1 2016, 15:12:24) \n",
    "[GCC 5.4.0 20160609] on linux2\n",
    "Type \"help\", \"copyright\", \"credits\" or \"license\" for more information.\n",
    ">>> s = \"zażółć gęślą jaźń\"\n",
    ">>> s\n",
    "'za\\xc5\\xbc\\xc3\\xb3\\xc5\\x82\\xc4\\x87 g\\xc4\\x99\\xc5\\x9bl\\xc4\\x85 ja\\xc5\\xba\\xc5\\x84'\n",
    ">>> \n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "c561fd19-4de2-4ec7-bad3-cf8ce43223b0"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Typ tekstowy\n",
    "\n",
    "---\n",
    "\n",
    "* Literał łańcuchowy można wprowadzić przez:\n",
    "    * pojedynczy cudzysłów\n",
    "    ```py\n",
    "    s = 'zażółć gęślą jaźń'\n",
    "    ```\n",
    "    * podwójny cudzysłów\n",
    "    ```py\n",
    "    s = \"zażółć gęślą jaźń\"\n",
    "    ```\n",
    "    * potrójny cudzysłów\n",
    "    ```py\n",
    "    s = '''zażółć gęślą jaźń'''\n",
    "    s = \"\"\"zażółć gęślą jaźń\"\"\"\n",
    "    ```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "5ed05482-343a-4c6b-9180-602cd21c35d5"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Znak ucieczki\n",
    "\n",
    "---\n",
    "\n",
    "* zmienia interpretację znaku\n",
    "* w Pythonie (i prawie zawsze) jest to \"\\\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "nbpresent": {
     "id": "50524b13-3e11-4278-a01a-dc9dedf4f5df"
    },
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-45-2bf66ab2182a>, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-45-2bf66ab2182a>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    print(\"Ala powiedziała: \"Mam kota.\" ...\") # cudzysłów kończy literał\u001b[0m\n\u001b[0m                               ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "print(\"Ala powiedziała: \"Mam kota.\" ...\") # cudzysłów kończy literał"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "nbpresent": {
     "id": "23d0cd53-c23b-448c-8509-4398144a2305"
    },
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ala powiedziała: \"Mam kota.\" ...\n"
     ]
    }
   ],
   "source": [
    "print(\"Ala powiedziała: \\\"Mam kota.\\\" ...\") # chyba że \"uciekniemy\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "nbpresent": {
     "id": "4bdca58d-9beb-4acb-9690-6d9e688cc824"
    },
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ala powiedziała: \"Mam kota.\" ...\n"
     ]
    }
   ],
   "source": [
    "print('Ala powiedziała: \"Mam kota.\" ...') # kombinacja ' i \""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "nbpresent": {
     "id": "78187419-0fd3-4472-9f98-52ef73f4b0f6"
    },
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ala powiedziała: 'Mam kota.' ...\n"
     ]
    }
   ],
   "source": [
    "print(\"Ala powiedziała: 'Mam kota.' ...\") # działa w obie strony"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "e180dd3b-3dfe-4cfc-88ff-3d949fce3188"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Wybrane sekewncje specjalne\n",
    "\n",
    "---\n",
    "\n",
    "* `\\n` - nowa linia\n",
    "* `\\t` - tabulacja\n",
    "* `\\r` - powrót kursora\n",
    "* `\\v` - pionowa tabulacja"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "nbpresent": {
     "id": "885fbec7-8f4d-42ec-9118-cb1c6e9b78d0"
    },
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\n wstawia nową linię.\n",
      "Nowa linia.\n"
     ]
    }
   ],
   "source": [
    "print(\"\\\\n wstawia nową linię.\\nNowa linia.\") # zwróć uwagę na \\\\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "nbpresent": {
     "id": "d97b06d1-3c24-4e92-a1a6-03b4693aba6c"
    },
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\tTabulacja też się przydaje.\n"
     ]
    }
   ],
   "source": [
    "print(\"\\tTabulacja też się przydaje.\") # brak spacji po \\t"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "d2826524-8484-48dd-ad3a-c3d82558371b"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## *Carriage return*\n",
    "\n",
    "---\n",
    "\n",
    "*(Jupyter nie rozumie `\\r`)* \n",
    "\n",
    "```py\n",
    "# nowa forma importowania: from [moduł] import [funkcja]\n",
    "from time import sleep\n",
    "# alternatywa: import time\n",
    "\n",
    "# drukujemy liczby od 0 do 100\n",
    "for i in range(100):\n",
    "    print(i, end=\"%\\r\") # kończąc każdą linię % i carriage return\n",
    "    sleep(1) # i czekamy sekundę na następną liczbę\n",
    "    # alternatywa: time.sleep(1)\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "7a05a25b-d4c0-4010-9181-98a8602c189d"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Długie łancuchy\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "nbpresent": {
     "id": "b8912f59-09de-4394-959f-99f009a49ee2"
    },
    "scrolled": true,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lorem Ipsum jest tekstem stosowanym jako przykładowy wypełniacz w przemyśle poligraficznym. Został po raz pierwszy użyty w XV w. przez nieznanego drukarza do wypełnienia tekstem próbnej książki. Pięć wieków później zaczął być używany przemyśle elektronicznym, pozostając praktycznie niezmienionym. Spopularyzował się w latach 60. XX w. wraz z publikacją arkuszy Letrasetu, zawierających fragmenty Lorem Ipsum, a ostatnio z zawierającym różne wersje Lorem Ipsum oprogramowaniem przeznaczonym do realizacji druków na komputerach osobistych, jak Aldus PageMaker\n"
     ]
    }
   ],
   "source": [
    "tekst = \"Lorem Ipsum jest tekstem stosowanym jako przykładowy wypełniacz w przemyśle poligraficznym. Został po raz pierwszy użyty w XV w. przez nieznanego drukarza do wypełnienia tekstem próbnej książki. Pięć wieków później zaczął być używany przemyśle elektronicznym, pozostając praktycznie niezmienionym. Spopularyzował się w latach 60. XX w. wraz z publikacją arkuszy Letrasetu, zawierających fragmenty Lorem Ipsum, a ostatnio z zawierającym różne wersje Lorem Ipsum oprogramowaniem przeznaczonym do realizacji druków na komputerach osobistych, jak Aldus PageMaker\"\n",
    "\n",
    "print(tekst)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Długie łancuchy\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "nbpresent": {
     "id": "078d8915-79e6-48e0-ad14-2c053d37f867"
    },
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lorem Ipsum jest tekstem stosowanym jako przykładowy         wypełniacz w przemyśle poligraficznym. Został po raz         pierwszy użyty w XV w. przez nieznanego drukarza do         wypełnienia tekstem próbnej książki. Pięć wieków później         zaczął być używany przemyśle elektronicznym,         pozostając praktycznie niezmienionym. Spopularyzował się         w latach 60. XX w. wraz z publikacją arkuszy Letrasetu,         zawierających fragmenty Lorem Ipsum, a ostatnio z zawierającym         różne wersje Lorem Ipsum oprogramowaniem przeznaczonym do         realizacji druków na komputerach osobistych, jak Aldus PageMaker\n"
     ]
    }
   ],
   "source": [
    "tekst = \"Lorem Ipsum jest tekstem stosowanym jako przykładowy \\\n",
    "        wypełniacz w przemyśle poligraficznym. Został po raz \\\n",
    "        pierwszy użyty w XV w. przez nieznanego drukarza do \\\n",
    "        wypełnienia tekstem próbnej książki. Pięć wieków później \\\n",
    "        zaczął być używany przemyśle elektronicznym, \\\n",
    "        pozostając praktycznie niezmienionym. Spopularyzował się \\\n",
    "        w latach 60. XX w. wraz z publikacją arkuszy Letrasetu, \\\n",
    "        zawierających fragmenty Lorem Ipsum, a ostatnio z zawierającym \\\n",
    "        różne wersje Lorem Ipsum oprogramowaniem przeznaczonym do \\\n",
    "        realizacji druków na komputerach osobistych, jak Aldus PageMaker\"\n",
    "\n",
    "print(tekst)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Długie łancuchy\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "nbpresent": {
     "id": "62548a5a-6081-4a86-9f09-6b456a1f5287"
    },
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lorem Ipsum jest tekstem stosowanym jako przykładowy wypełniacz w przemyśle poligraficznym. Został po raz pierwszy użyty w XV w. przez nieznanego drukarza do wypełnienia tekstem próbnej książki. Pięć wieków później zaczął być używany przemyśle elektronicznym, pozostając praktycznie niezmienionym. Spopularyzował się w latach 60. XX w. wraz z publikacją arkuszy Letrasetu, zawierających fragmenty Lorem Ipsum, a ostatnio z zawierającym różne wersje Lorem Ipsum oprogramowaniem przeznaczonym do realizacji druków na komputerach osobistych, jak Aldus PageMaker \n"
     ]
    }
   ],
   "source": [
    "tekst = \"Lorem Ipsum jest tekstem stosowanym jako przykładowy \" \\\n",
    "        \"wypełniacz w przemyśle poligraficznym. Został po raz \" \\\n",
    "        \"pierwszy użyty w XV w. przez nieznanego drukarza do \" \\\n",
    "        \"wypełnienia tekstem próbnej książki. Pięć wieków później \" \\\n",
    "        \"zaczął być używany przemyśle elektronicznym, \" \\\n",
    "        \"pozostając praktycznie niezmienionym. Spopularyzował się \" \\\n",
    "        \"w latach 60. XX w. wraz z publikacją arkuszy Letrasetu, \" \\\n",
    "        \"zawierających fragmenty Lorem Ipsum, a ostatnio z zawierającym \" \\\n",
    "        \"różne wersje Lorem Ipsum oprogramowaniem przeznaczonym do \" \\\n",
    "        \"realizacji druków na komputerach osobistych, jak Aldus PageMaker \"\n",
    "\n",
    "print(tekst) # \"slowo\" \"slowo\" = \"slowo\" + \"slowo\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Długie łancuchy\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "nbpresent": {
     "id": "ebf68a71-7774-4238-88cb-af2bb01de4de"
    },
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lorem Ipsum jest tekstem stosowanym jako przykładowy\n",
      "wypełniacz w przemyśle poligraficznym. Został po raz\n",
      "pierwszy użyty w XV w. przez nieznanego drukarza do\n",
      "wypełnienia tekstem próbnej książki. Pięć wieków później\n",
      "zaczął być używany przemyśle elektronicznym,\n",
      "pozostając praktycznie niezmienionym. Spopularyzował się\n",
      "w latach 60. XX w. wraz z publikacją arkuszy Letrasetu,\n",
      "zawierających fragmenty Lorem Ipsum, a ostatnio z zawierającym\n",
      "różne wersje Lorem Ipsum oprogramowaniem przeznaczonym do\n",
      "realizacji druków na komputerach osobistych, jak Aldus PageMaker\n",
      "\n"
     ]
    }
   ],
   "source": [
    "tekst = \"Lorem Ipsum jest tekstem stosowanym jako przykładowy\\n\" \\\n",
    "        \"wypełniacz w przemyśle poligraficznym. Został po raz\\n\" \\\n",
    "        \"pierwszy użyty w XV w. przez nieznanego drukarza do\\n\" \\\n",
    "        \"wypełnienia tekstem próbnej książki. Pięć wieków później\\n\" \\\n",
    "        \"zaczął być używany przemyśle elektronicznym,\\n\" \\\n",
    "        \"pozostając praktycznie niezmienionym. Spopularyzował się\\n\" \\\n",
    "        \"w latach 60. XX w. wraz z publikacją arkuszy Letrasetu,\\n\" \\\n",
    "        \"zawierających fragmenty Lorem Ipsum, a ostatnio z zawierającym\\n\" \\\n",
    "        \"różne wersje Lorem Ipsum oprogramowaniem przeznaczonym do\\n\" \\\n",
    "        \"realizacji druków na komputerach osobistych, jak Aldus PageMaker\\n\"\n",
    "\n",
    "print(tekst)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Długie łancuchy\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "nbpresent": {
     "id": "fd768496-5fac-4871-a6d3-b34379222d33"
    },
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lorem Ipsum jest tekstem stosowanym jako przykładowy\n",
      "wypełniacz w przemyśle poligraficznym. Został po raz\n",
      "pierwszy użyty w XV w. przez nieznanego drukarza do\n",
      "wypełnienia tekstem próbnej książki. Pięć wieków później\n",
      "zaczął być używany przemyśle elektronicznym,\n",
      "pozostając praktycznie niezmienionym. Spopularyzował się\n",
      "w latach 60. XX w. wraz z publikacją arkuszy Letrasetu,\n",
      "zawierających fragmenty Lorem Ipsum, a ostatnio z zawierającym\n",
      "różne wersje Lorem Ipsum oprogramowaniem przeznaczonym do\n",
      "realizacji druków na komputerach osobistych, jak Aldus PageMaker\n",
      "\n"
     ]
    }
   ],
   "source": [
    "tekst = \"\"\"Lorem Ipsum jest tekstem stosowanym jako przykładowy\n",
    "wypełniacz w przemyśle poligraficznym. Został po raz\n",
    "pierwszy użyty w XV w. przez nieznanego drukarza do\n",
    "wypełnienia tekstem próbnej książki. Pięć wieków później\n",
    "zaczął być używany przemyśle elektronicznym,\n",
    "pozostając praktycznie niezmienionym. Spopularyzował się\n",
    "w latach 60. XX w. wraz z publikacją arkuszy Letrasetu,\n",
    "zawierających fragmenty Lorem Ipsum, a ostatnio z zawierającym\n",
    "różne wersje Lorem Ipsum oprogramowaniem przeznaczonym do\n",
    "realizacji druków na komputerach osobistych, jak Aldus PageMaker\n",
    "\"\"\"\n",
    "\n",
    "print(tekst)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "68e73f56-cbf7-4e22-aa34-197ca6ee5779"
    },
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Przykłady prostych programów"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "# źródło: http://jakevdp.github.io/blog/2013/05/28/a-simple-animation-the-magic-triangle/\n",
    "\n",
    "from JSAnimation.IPython_display import display_animation\n",
    "from matplotlib import animation\n",
    "# importowanie jeszcze inaczej: import [moduł].[funkcja] as [moja nazwa]\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# Set up the axes, making sure the axis ratio is equal\n",
    "fig = plt.figure(figsize=(6.5, 2.5))\n",
    "ax = fig.add_axes([0, 0, 1, 1], xlim=(-0.02, 13.02), ylim=(-0.02, 5.02),\n",
    "                  xticks=range(14), yticks=range(6), aspect='equal', frameon=False)\n",
    "ax.grid(True)\n",
    "\n",
    "# Define the shapes of the polygons\n",
    "P1 = np.array([[0, 0], [5, 0], [5, 2], [0, 0]])\n",
    "P2 = np.array([[0, 0], [8, 0], [8, 3], [0, 0]])\n",
    "P3 = np.array([[0, 0], [5, 0], [5, 1], [3, 1], [3, 2], [0, 2], [0, 0]])\n",
    "P4 = np.array([[0, 1], [3, 1], [3, 0], [5, 0], [5, 2], [0, 2], [0, 1]])\n",
    "\n",
    "# Draw the empty polygons for the animation\n",
    "patches = [ax.add_patch(plt.Polygon(P1, fc='g', ec='k', alpha=0.5)),\n",
    "           ax.add_patch(plt.Polygon(P2, fc='b', ec='k', alpha=0.5)),\n",
    "           ax.add_patch(plt.Polygon(P3, fc='y', ec='k', alpha=0.5)),\n",
    "           ax.add_patch(plt.Polygon(P4, fc='r', ec='k', alpha=0.5))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "nbpresent": {
     "id": "09636484-8db0-49c8-a4e6-de77e2c76730"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<script language=\"javascript\">\n",
       "  /* Define the Animation class */\n",
       "  function Animation(frames, img_id, slider_id, interval, loop_select_id){\n",
       "    this.img_id = img_id;\n",
       "    this.slider_id = slider_id;\n",
       "    this.loop_select_id = loop_select_id;\n",
       "    this.interval = interval;\n",
       "    this.current_frame = 0;\n",
       "    this.direction = 0;\n",
       "    this.timer = null;\n",
       "    this.frames = new Array(frames.length);\n",
       "\n",
       "    for (var i=0; i<frames.length; i++)\n",
       "    {\n",
       "     this.frames[i] = new Image();\n",
       "     this.frames[i].src = frames[i];\n",
       "    }\n",
       "    document.getElementById(this.slider_id).max = this.frames.length - 1;\n",
       "    this.set_frame(this.current_frame);\n",
       "  }\n",
       "\n",
       "  Animation.prototype.get_loop_state = function(){\n",
       "    var button_group = document[this.loop_select_id].state;\n",
       "    for (var i = 0; i < button_group.length; i++) {\n",
       "        var button = button_group[i];\n",
       "        if (button.checked) {\n",
       "            return button.value;\n",
       "        }\n",
       "    }\n",
       "    return undefined;\n",
       "  }\n",
       "\n",
       "  Animation.prototype.set_frame = function(frame){\n",
       "    this.current_frame = frame;\n",
       "    document.getElementById(this.img_id).src = this.frames[this.current_frame].src;\n",
       "    document.getElementById(this.slider_id).value = this.current_frame;\n",
       "  }\n",
       "\n",
       "  Animation.prototype.next_frame = function()\n",
       "  {\n",
       "    this.set_frame(Math.min(this.frames.length - 1, this.current_frame + 1));\n",
       "  }\n",
       "\n",
       "  Animation.prototype.previous_frame = function()\n",
       "  {\n",
       "    this.set_frame(Math.max(0, this.current_frame - 1));\n",
       "  }\n",
       "\n",
       "  Animation.prototype.first_frame = function()\n",
       "  {\n",
       "    this.set_frame(0);\n",
       "  }\n",
       "\n",
       "  Animation.prototype.last_frame = function()\n",
       "  {\n",
       "    this.set_frame(this.frames.length - 1);\n",
       "  }\n",
       "\n",
       "  Animation.prototype.slower = function()\n",
       "  {\n",
       "    this.interval /= 0.7;\n",
       "    if(this.direction > 0){this.play_animation();}\n",
       "    else if(this.direction < 0){this.reverse_animation();}\n",
       "  }\n",
       "\n",
       "  Animation.prototype.faster = function()\n",
       "  {\n",
       "    this.interval *= 0.7;\n",
       "    if(this.direction > 0){this.play_animation();}\n",
       "    else if(this.direction < 0){this.reverse_animation();}\n",
       "  }\n",
       "\n",
       "  Animation.prototype.anim_step_forward = function()\n",
       "  {\n",
       "    this.current_frame += 1;\n",
       "    if(this.current_frame < this.frames.length){\n",
       "      this.set_frame(this.current_frame);\n",
       "    }else{\n",
       "      var loop_state = this.get_loop_state();\n",
       "      if(loop_state == \"loop\"){\n",
       "        this.first_frame();\n",
       "      }else if(loop_state == \"reflect\"){\n",
       "        this.last_frame();\n",
       "        this.reverse_animation();\n",
       "      }else{\n",
       "        this.pause_animation();\n",
       "        this.last_frame();\n",
       "      }\n",
       "    }\n",
       "  }\n",
       "\n",
       "  Animation.prototype.anim_step_reverse = function()\n",
       "  {\n",
       "    this.current_frame -= 1;\n",
       "    if(this.current_frame >= 0){\n",
       "      this.set_frame(this.current_frame);\n",
       "    }else{\n",
       "      var loop_state = this.get_loop_state();\n",
       "      if(loop_state == \"loop\"){\n",
       "        this.last_frame();\n",
       "      }else if(loop_state == \"reflect\"){\n",
       "        this.first_frame();\n",
       "        this.play_animation();\n",
       "      }else{\n",
       "        this.pause_animation();\n",
       "        this.first_frame();\n",
       "      }\n",
       "    }\n",
       "  }\n",
       "\n",
       "  Animation.prototype.pause_animation = function()\n",
       "  {\n",
       "    this.direction = 0;\n",
       "    if (this.timer){\n",
       "      clearInterval(this.timer);\n",
       "      this.timer = null;\n",
       "    }\n",
       "  }\n",
       "\n",
       "  Animation.prototype.play_animation = function()\n",
       "  {\n",
       "    this.pause_animation();\n",
       "    this.direction = 1;\n",
       "    var t = this;\n",
       "    if (!this.timer) this.timer = setInterval(function(){t.anim_step_forward();}, this.interval);\n",
       "  }\n",
       "\n",
       "  Animation.prototype.reverse_animation = function()\n",
       "  {\n",
       "    this.pause_animation();\n",
       "    this.direction = -1;\n",
       "    var t = this;\n",
       "    if (!this.timer) this.timer = setInterval(function(){t.anim_step_reverse();}, this.interval);\n",
       "  }\n",
       "</script>\n",
       "\n",
       "<div class=\"animation\" align=\"center\">\n",
       "    <img id=\"_anim_imgGDAOFPEXRUHUWEBE\">\n",
       "    <br>\n",
       "    <input id=\"_anim_sliderGDAOFPEXRUHUWEBE\" type=\"range\" style=\"width:350px\" name=\"points\" min=\"0\" max=\"1\" step=\"1\" value=\"0\" onchange=\"animGDAOFPEXRUHUWEBE.set_frame(parseInt(this.value));\"></input>\n",
       "    <br>\n",
       "    <button onclick=\"animGDAOFPEXRUHUWEBE.slower()\">&#8211;</button>\n",
       "    <button onclick=\"animGDAOFPEXRUHUWEBE.first_frame()\"><img class=\"anim_icon\" src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABQAAAAUCAQAAAAngNWGAAAAAXNSR0IArs4c6QAAAAJiS0dEAP+Hj8y/AAAACXBIWXMAAAsTAAALEwEAmpwYAAAAB3RJTUUH3QURCAgaeZk4EQAAASlJREFUKM/dkj9LQnEUhp9zr3bpj1uBcKGiJWxzLWivKAIRjIhcCqcgqJbKRagPICiVSVEuNTu0tLYGUg4tkRGUdxLJ0u79Ndxr5FfwTO/L+xzO4XCgO+v2T70AFU+/A/Dhmlzg6Pr0DKAMwOH4zQxAAbAkv2xNeF2RoQUVc1ytgttXUbWVdN1dOPE8pz4j4APQsdFtKA0WY6vpKjqvVciHnvZTS6Ja4HgggJLs7MHxl9nCh8NYcO+iGG0agiaC4h9oa6Vsw2yiK+QHSZT934YoEQABNBcTNDszsrhm1m1B+bFS86PT6QFppx6oeSaeOwlMXRp1h4aK13Y2kuHhUo9ykPboPvFjeEvsrhTMt3ylHyB0r8KZyYdCrbfj4OveoHMANjuyx+76rV+/blxKMZUnLgAAAABJRU5ErkJggg==\"></button>\n",
       "    <button onclick=\"animGDAOFPEXRUHUWEBE.previous_frame()\"><img class=\"anim_icon\" src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABQAAAAUCAQAAAAngNWGAAAAAXNSR0IArs4c6QAAAAJiS0dEAP+Hj8y/AAAACXBIWXMAAAsTAAALEwEAmpwYAAAAB3RJTUUH3QURCAgyTCyQ6wAAANRJREFUKM9jYBjO4AiUfgzFGGAp4+yayUvX6jMwMDCsYmBgOCS4OAOrSYmMgcc8/pd5Q3irC+Neh/1AlmeBMVgZmP8yMLD8/c/cqv9r90whzv/MX7Eq/MfAwMDIwCuZdfSV8U8WDgZGRmYGrAoZGRgY/jO8b3sj/J2F6T8j4z80pzEhmIwMjAxsSbqqlkeZGP//Z8SlkJnhPwMjwx/Guoe1NhmRwk+YGH5jV8jOwMPHzcDBysAwh8FrxQwtPU99HrwBXsnAwMDAsJiBgYGBoZ1xmKYqALHhMpn1o7igAAAAAElFTkSuQmCC\"></button>\n",
       "    <button onclick=\"animGDAOFPEXRUHUWEBE.reverse_animation()\"><img class=\"anim_icon\" src=\"data:image/png;base64,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\"></button>\n",
       "    <button onclick=\"animGDAOFPEXRUHUWEBE.pause_animation()\"><img class=\"anim_icon\" src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABQAAAAUCAQAAAAngNWGAAAAAXNSR0IArs4c6QAAAAJiS0dEAP+Hj8y/AAAACXBIWXMAAAsTAAALEwEAmpwYAAAAB3RJTUUH3QURCAkR91DQ2AAAAKtJREFUKM9jYCANTEVib2K4jcRbzQihGWEC00JuNjN8Z2Q0Zo3VYWA4lL005venH9+c3ZK5IfIsMIXMBtc12Bj+MMgxMDAwMPzWe2TBzPCf4SLcZCYY4/9/RgZGBiaYFf8gljFhKiQERhUOeoX/Gf8y/GX4y/APmlj+Mfxj+MfwH64Qnnq0zr9fyfLrPzP3eQYGBobvk5x4GX4xMIij23gdib0cRWYHiVmAAQDK5ircshCbHQAAAABJRU5ErkJggg==\"></button>\n",
       "    <button onclick=\"animGDAOFPEXRUHUWEBE.play_animation()\"><img class=\"anim_icon\" src=\"data:image/png;base64,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\"></button>\n",
       "    <button onclick=\"animGDAOFPEXRUHUWEBE.next_frame()\"><img class=\"anim_icon\" src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABQAAAAUCAQAAAAngNWGAAAAAXNSR0IArs4c6QAAAAJiS0dEAP+Hj8y/AAAACXBIWXMAAAsTAAALEwEAmpwYAAAAB3RJTUUH3QURCAkd/uac8wAAAMhJREFUKM9jYBie4DEUQ8B+fEq3+3UrMzAwMFxjYGBgYJizYubaOUxYFUaXh/6vWfRfEMIL/+//P5gZJoei4/f/7wxnY1PeNUXdE2RgYGZgYoCrY2BBVsjKwMDAwvCS4f3SG/dXxm5gYESSQ1HIwvCPgZmB8f8Pxv+Kxxb/YfiPJIdi9T8GJgaG/38ZFd4Fx0xUYsZt4h8GBgb2D2bLy7KnMTAwMEIxFoVCXIYr1IoDnkF4XAysqNIwUMDAwMDAsADKS2NkGL4AAIARMlfNIfZMAAAAAElFTkSuQmCC\"></button>\n",
       "    <button onclick=\"animGDAOFPEXRUHUWEBE.last_frame()\"><img class=\"anim_icon\" src=\"data:image/png;base64,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\"></button>\n",
       "    <button onclick=\"animGDAOFPEXRUHUWEBE.faster()\">+</button>\n",
       "  <form action=\"#n\" name=\"_anim_loop_selectGDAOFPEXRUHUWEBE\" class=\"anim_control\">\n",
       "    <input type=\"radio\" name=\"state\" value=\"once\" checked> Once </input>\n",
       "    <input type=\"radio\" name=\"state\" value=\"loop\" > Loop </input>\n",
       "    <input type=\"radio\" name=\"state\" value=\"reflect\" > Reflect </input>\n",
       "  </form>\n",
       "</div>\n",
       "\n",
       "\n",
       "<script language=\"javascript\">\n",
       "  /* Instantiate the Animation class. */\n",
       "  /* The IDs given should match those used in the template above. */\n",
       "  (function() {\n",
       "    var img_id = \"_anim_imgGDAOFPEXRUHUWEBE\";\n",
       "    var slider_id = \"_anim_sliderGDAOFPEXRUHUWEBE\";\n",
       "    var loop_select_id = \"_anim_loop_selectGDAOFPEXRUHUWEBE\";\n",
       "    var frames = new Array(0);\n",
       "    \n",
       "  frames[0] = 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OCNP+OxZLoyN2R1jx3/W1ACafOWIW1lZ4do1k8bGAebn8/F6awkGf4zP55wrTUSOBhWqHDmxWIybN2/S0mIyNLQIvE15+S8wjBN6pSsiB0aFKkeCZVlMT0+zvLzK3//9vxOLGRQX/17mla7b7ngikgNUqJLVlpeXuX59gMuXTRYWCrh4sZpA4O/w+YrtjiYiOUaFKlknFosxPDxMS4vJ8PBD4G0CgV9SU3MCny+sMhURW6hQJSukUimmp6fp6DBpbx8nFqvB7/8BVVVvaNIVEUdQoYqjPXz4kGvX0l+8sLRUlPnihY90hYqI46hQxXG2trYYHh6mudnk5s1lXK53CAR+iWGcsDuaiMhTqVDFEVKpFFNTU3R0mHR0TLC9/RrFxe9RXf11TboikhVUqGKrpaWlnUn34UM/Hs85QqGf4vUW2R1NROSFqFDl0G1tbTE0NERzs8no6Cou1zuUl/85hlFpdzQRkZemQpVDkUqluH379s6kG4+/jt//I6qqXtekKyJHggpVDtTi4iL9/elJd2WlJDPp/oEmXRE5clSosu82Nze5cWOIlpYBRkdXycs7RyDw3zGMkN3RREQOjApV9kUqleLWrVu0t5t0dk6SSHwdv/99DON1XK48u+OJiBw4Faq8ksXFRdbXN/inf/p3VlZK8HprCYX+CK+30O5oIiKHSoUqL+zxpNvcbDI2ts7Fi2/idv8KwwjaHU1ExDYqVHkuqVSKyclJ2ttNurpuEY+/QUnJRQzja/h8rRQXq0xFJLepUGVPCwsL9PWZNDUNsrpampl0/1iTrojIl6hQ5Sui0Sg3bgzR1GQyMREhL+8cFRV/gWFU2B1NRMSxVKgCQDKZ3Jl0u7unMpNuPYbxml7piog8BxVqjpufn89MujdYWyvD662lsvJneDwFdkcTEckqKtQcFI1GGRy8QVOTyeTko8yk+z8wjIDd0UREspYKNUc8nnTb2tKTbjL5JiUlH2IYNZp0RUT2gQr1iJubm6O316S5+Qbr6wF8vlpOnNCkKyKy31SoR9CjR492Jt1btzYzk+5fatIVETlAKtQjIplMMjExQVubSU/PNMnkWUpKPspMui6744mIHHkq1CxmWdbOpNvSMsT6ekVm0v0Yjyff7ngiIjlFhZqFUqkUHR1dNDWZTE/HcLnOEQz+FWVl5XZHExHJWSrULJFMJhkfH6e11aSgwEU4XEBJye9TXa1JV0TECVSoDmZZFg8ePMi80h0iEgmRn1/Lj360gmFctDueiIg8QYXqQJFIBNMcpKnJ5M6deOaV7q8JBMoAcLnC9gYUEZGvUKE6RCKR2Jl0+/rukkp9k+PH/4DqakOTrohIFlCh2siyLGZnZ+ntHaClZYhIpJL8/FpOnfoMt9tndzwREXkBrrq6Oqu+vp7z58/j9/vtzrMjEok4Js9+Z0mlUkSjm6yvR9netnC5ivB4CsnLcz/XP5+fHyEWc8bvDTgrj5OygPI8i9e7SElJwu4YAGxt5VNQELM7xg6n5Vlf97C6MG93DABKg5WsLjojC0BZ6ASffvYZnv7+fgDC4TAXLlywN9UTnJRnP7IkEgnGxsa4etXk2rUZkslvUlr6Oxw/Xv3Ck+7Zs2HGxl4tz35yUh4nZQHleZaTJ/+DuroFu2MAMDFxljfeGLM7xg4n5qmrW7I7BgATEwHq3nVGFoDOzvQfUjX5HqDHk25PT/qVbjR6koKCWk6d+rkmXRGRI0aFegDW19cxzUEaG01mZlK43bUEg39DMHjc7mgiInJAVKj7JJFIMDo6mpl075FKfYuysj/BMKr0SldEJAeoUF+BZVncv3+fnh6TlpZhotFTFBSc4/TpX+B2e+2OJyIih0iF+hLW19e5fn2ApiaTe/fQpCsiIirU5xWPxxkdHSUcNvn881ks61uUlv4phnFGk66IiKhQ92JZFvfu3WNlZY3f/Ob/EI2epqCgltOn/0yTroiI/Bcq1F2sra3x+efpV7r377u4eLGGkpK/JRgssTuaiIg4lAo1Ix6PMzIyQjhsYpoPsKxvU1b2MYZxGp/vKvn5KlMREXm6nC5Uy7KYmZmhu9skHB4hGj1NUdF3OXPmG+Tl5fRvjYiIvKCcbI21tTWuXx+gsdFkdtaNx1NLMPg/CYWO2R1NRESyVM4U6vb2NiMjI1y5YjI4OJ+ZdD/FME7pla6IiLyyI12olmVx9+5durpMrl4dYXOziqKidzlz5qwmXRER2VdHslVWV1e5fn2Ay5dN5uY8eDzfIRj8gMpKTboiInIwjkyhbm9vc/PmTcLhAQYG5oG3KC//OYZxUpOuiIgcuKwuVMuyuHPnTmbSHWVrq5ri4u9RVfWmJl0RETlUWdk6KysrO6905+Z8eL3fIRj8MT6f3+5oIiKSo7KmUB9Pui0tJjduLOByvU1Z2S8wjBOadEVExHaOLlTLspienqary6S1dYytLYPi4t+lquoNTboiIuIojmyl5eVlNjY2+Od//r/Mz+fj9dYSDH6oSVdERBzLMYUai8V2Jt2hoSXef/8syeSfUVNzwu5oIiIiz2RroVqWxdTUFJ2dJm1t42xv11Bc/H2qqt4gP78Nv19lKiIi2cGWQl1eXqa/36SpaYDFxULc7lpCoY/w+YrtiCMiIvLKDq1Qt7a2uHnzJs3NJsPDD3G53iEQ+CWGoStURESy34EWaiqVYmpqio4Ok46OCba3X6O4+IdUV3+dvDz3Qf7SIiIih+pACvXhw4f095s0Ng7w8GExHk8todBP8XqLDuKXExERsd2+FerW1hbDw8M0N5uMjKzgcr1DefmfYxiV+/VLiIiIONYrFWoqleL27dt0dg7Q3j5BPP41/P7zVFW9rklXRERyyksV6tLSUuaV7iAPH/o16YqISM577kLd3NxkaGiYlhaT0dHVzCvd/4ZhhA4yn4iISFbYs1AfT7rpV7qTxOOv4/e/T3X167hceYeVUURExPF2LdTFxUX6+tKT7spKSea7dP8Qr7fwsPOJiIhkhZ1CtSyL3t4+mptNxsfXM5PurzCMoJ35REREsoKrrq7Oqq+v5/vf/wHr61FSKbsjpR07ls/GRszuGICzsoDy7MVJWUB5nsXv9xKLReyOAUBh4TE2NzfsjrFDeZ7OSVkAiopK+PjjT/j/eBIsBSnMTIEAAAAASUVORK5CYII=\"\n",
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\"\n",
       "  frames[2] = 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\"\n",
       "  frames[3] = 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\"\n",
       "  frames[4] = 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\"\n",
       "  frames[5] = 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\"\n",
       "  frames[6] = 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\"\n",
       "  frames[7] = 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Q4uJke5HkKQqjnSJfg8LlYh+hxACCwsLGBiQ4fGMY3f3dGKk+2OYTBat4xHpFguViADsj3SHh0fQ1iZjYQEc6RI9JRYqURaLRqOYnJyExyPj1q2lxEj3Mke6RM+AhUqUZYQQmJ+fT4x0J7C3V4mcnAuorPyMI12i58BCJcoSW1tb2N5W8Fd/9fdYWjLCZKqDy/XncLnytY5GlBFYqEQZLBqNYmJiAp2dMkZGHuLtt19CKPQJJKmSI12iE8ZCJcowj0a6/f37d+mGQtXIyalHVVUtbLYe5OdXaR2RKCOxUIkyRCAQSNylO4KlJRPM5jo4nf8JNtspraMRZQUWKlEai0Qij410lwF8E0VFn0KSTnOkS5RiLFSiNCOEwIMHDxIj3QmEwzXIzX0L1dUvwmjkS5pIK3z1EaWJzc1NDA+PoLV1BMvLlsRI9x2OdIl0goVKpGORSATj4+Po7JQxOroK4JsoLv4RJKmCI10inWGhEumMEAJzc3Po75dx/fokwmEJubnf4kiXSOf46iTSic3NTQwNyWhtHcHKig0WSx2czndhtTq0jkZEx8BCJdJQOBzG+Pg4Ojpk3LmzBuA1FBf/GJJUzpEuUZphoRKlmBACPp8PfX0yurqmEA5LyMv7g8RI16R1PCJ6Rob6+nrR1NSEhoYGOBz6GS0piqKbPHrKAjDPYfSUBfjdPPF4HLu7uwgG9xCNGmAw5MJszoHRaExZHptNQTh89PXZ2VmCEJGk5ykqyINxdS3p6xyHscwJdeXJWYSIwWpN3R5ELXHC6D/62hiNJuQ5kv88ZsVmgyMcTvo6x6GnLACwYbXik08/hdnr9QIAPB4PGhsbtU31GD3l0VMWgHkOo6csANDZ2Yn8/Hx0dMgYG/MDeA0lJW/B4SjXJE9trQdTU41H/r2BgS/w2WfupOfZ3nThQts/Jn2d43j4w5+g4pAsoZAPtbXulOUZfvMneGP86Gvj9QbQ+N7lpOfx1NaicWoq6esch56yAMBv3G4AHPkSnThVVeHz+dDbK0OICL78cgUOx3dRXX2OI12iDMZCJTohfr8fQ0P7D15YX8+F2VyHxsYduN3vaB2NiFKAhUr0HEKhEMbGxtDeLmN8fAMGw3mUlHwOSdof6RqNHm0DElHKsFCJnpKqqpidnUVvr4ze3mlEImeQl/c91NR8gyNdoizGQiU6pvX19YORrt/vgNn8OlyuH8BiydU6GhHpAAuV6BChUAh37txBe7uMyckADIbzKC7+Y0hSmdbRiEhnWKhEv0dVVczMzByMdKPRF+BwvI3q6hc40j1hBrMJv1oPaB0DANAUi+F/H5IlEgmheiF1WS+cj+FvjrHe1JaCXp8v6XlekaSkr5HuWKhECWtra/B690e6m5v5iZHuH3Gkm0SnCvPx7n9M/mcoj+PUqaJDswQCHnz0UWPK8kxPF+EvfnX0tfn1r3345d9eSXqe31xJ/hrpjoVKWW1vbw+3b99BR8cIJicDMBpfR0nJTyFJLq2jEVGaYaFS1lFVFffv30dPj4y+vnuIxb4Bh+P7kKQXYDCk7jGARJRZWKiUNdbW1jA4KKOtbRSbm/mwWOrgcl2CxZKjdTQiygAsVMpoj0a67e0ypqaCiZHun0CSnFpHI6IMw0KljKOqKu7du4eeHhn9/fcRjZ5Dfv5FSNJZjnSJKGlYqJQxVldXsbUVxF/+5X9DIFCYGOn+kCNdIkoJFiqltd3dXdy+fQdtbTKmpxVcvHgOFssXkKRSraMRUZZhoVLaicfjByPdgYHZxEi3CZJ0BlZrF3JzWaZElHosVEobKysribt0b2NrqwgWSx3Kyj6C2WzXOhoREQuV9G13dxejo7fR1ibj3r0dGI2vo7T030GSSrSORkT0O1iopDuPRrrd3fsj3Xj8ReTnvwdJcvMuXSLSLRYq6cby8jJu3pTR3n4bwWAJrNY6lJdzpEtE6YGFSpra2dk5GOnev7+XGOn+e450iSjtsFAp5eLxOKanp9HdLePGDR/i8Vrk53+QGOkatI5HRPRMWKiUEkKIg5FuR8cdBIOliZHuxzCbbVrHIyJ6bixUSipFUTAysj/S9fnCMBheh9P5H1BUVKx1NCKiE8VCpRMXj8dx9+5ddHXJuHlzDqr6EvLzP0RNDUe6RJS5WKh0IoQQePjwYeIu3TtQFBdstjpUVHzCkS4RZQUWKj0XRVGgKDv467/+n5ibiybu0v1TlJQUaR2NiCilWKj01GKx2MFId3DwARoaXkYg8EeoqZE40iWirMVCpWMRQmBpaQk3b46go+MOFKUMNlsdTp/+N7DZ+lBY6NY6IhGRpliodKjt7W3I8ija2mTMzcVgMtXB6fwzlJQUah2NiEhXWKj0L8RiMUxNTeH6dRlDQ/OIx19GYeElSFINR7pERE/AQiUAX410b9zYv0t3d7cCdnsdTp/+EUwmq9bxiIh0j4Wa5YLBIGR5FK2tMubn1cRI92dwOgu0jkZElFZYqFkoFothcnIyMdJdgKq+gqKifw1JquZIl4joGRnq6+tFU1MTGhoa4HA4tM5zQFEU3eTRUxbg2fNEo1Hs7OwiGNyDqlphNObAbLY/d4nabArCYX1cHz1lAdI3j6L4UFyc/AdymEw2xOPhpK9zHEdlice3UVh4KmV5QiEb7Pajr43fH4KqJv8HYZs1D7aNtaSvcxxqiRNG/9FZjEYT8hz5Sc+zYbXik08/hdnr9QIAPB4PGhsbk77wcekpj56yAE+XJxgMYnh4BG1tMhYWkBjpnofdfnIj3dpaD6amjpcn2fSUBUjfPAMDX+Czz9xJz3PqVC22t6eSvs5xHJUlEPDgrbcaU5ZneroW584dfW3OnUtBGAC9HS40jf9jahY7wvCbP8Ebx8ji9QbQ+N7lpOf5jdsNgCPfjBSNRjE5OQmPR8atW0sQ4hUUFl6GJFVxpEtElCQs1AwhhMDCwgIGBmR4POPY3a2E3V6HysrPYDJZtI5HRJTxWKhpbmtrC7du7d+lu7hogNlcB6fzz+F0Jv/3BkRE9BUWahoSQmB0dBQejwxZfgghXkVR0ceQpEqOdImINMJCTRNCCMzPz2NgQEYotIsvv1xEbu4bqKp6CUYjv4xERFrjO7HObW1tYXh4BK2tMpaWTDCb63DxogNu9ztaRyMiosewUHUoEolgYmICnZ0yRkdXEiPdTyFJp2EwGGA0erSOSERZxmg14W8WAlrHAABcOB87VpapLQW9Pl/S87zy4osAWKi6IYTAgwcP0N8v4/r1CeztVSM3901UVdVypEtEmisty8df/Cr5n+k8junpomNl+fWvffjl315Jeh6PxwOAhaq5QCCA4eERXLsmY3nZDLP5ApzOd1BWlronshAR0fNjoWogEolgfHwcHs8IRkZWAHwTxcU/giRV8C5dIqI0xUJNESEE5ubmEiPdSYRCNcjLewvV1S9ypEtElAH4Tp5km5ubB3fpLi9bYbFcgNP5LqxW/TwwnYiInh8LNQkejXQ7OmTcvr0Kg+E1FBX9GJJUzpEuEVGGYqGeECEEfD4f+vtldHVNIRSSkJf3bVRXn+NIl4goC/Cd/jltbGwkRrojWFmxwWKpg9P5Hke6RERZhoX6DMLh8MFI986ddQCvoaTkM7jd5VpHIyIijbBQj0kIgdnZWfT1yejuvotIxI28vO8kRromreMREZHGWKhH2NjYQDC4jV/84r9jbS0HJlMdXK4PYLXmaR2NiIh0hIX6NUKhEMbHx9HeLmNszI/Gxlqo6ueQJI50iYjo67FQE1RVxezsLHp7ZfT2TiMSOYO8vD9ETc03YLN1w+FgmRIR0ZNlfaH6/X54vTJaW0fg9+fBbK6Dy/UDWCy5WkcjIqI0kpWFGgqFMDY2hvZ2GRMTmzAYzqO4+I8hSWVaRyMiojSVNYWqqipmZmbQ1zeCnp5pRKNn4XA0oLr6Bd6lS0REzy3jC3V9fR1er4y2tlH4/Q6OdImIKCkyslD39vZw584YOjpkTE4GYDCcR0nJv4UkubSORkREGSpjCvXRSHf/Lt17iEZfgMPxfdTUvACDwah1PCIiynBpX6hra2sYHNwf6W5u5ieepfuvYLHkaB2NiIiySFoW6t7eHm7fvoP2dhl37wYTI90/gSQ5tY5GRERZKi0L9erVdrS0eBEK7f+3EL1YW+vF2lpy1nO7a+Hz/TI5//gzYJ4n01MWIH3zKIqMQMCX9Dy5uacRCHiSvs5xHJWloMCeujCUltKyUD/++BI+/vhSytbzeDy4cuXzlK13FOZ5Mj1lAdI3zy9+4cNHH7mTnmd6+hTeeqsx6esch56yUHri3TpEREQngIVKRER0AlioREREJ8BQX18vmpqa0NDQAIfDoXWeA4qi6CaPnrIAzHMYPWUB0jfPysoSYrFI0vPk5JzC3t520tc5Dj1lAZjnMMfNYjZbUVZ2Oul5FEXBpUuX8P8Btg9zOkqTaNMAAAAASUVORK5CYII=\"\n",
       "  frames[8] = 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\"\n",
       "  frames[9] = 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\"\n",
       "  frames[10] = 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\"\n",
       "  frames[11] = 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\"\n",
       "  frames[12] = 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\"\n",
       "  frames[13] = 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\"\n",
       "  frames[14] = 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\"\n",
       "  frames[15] = 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\"\n",
       "  frames[16] = 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\"\n",
       "  frames[17] = 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\"\n",
       "  frames[18] = 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\"\n",
       "  frames[19] = 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\"\n",
       "  frames[20] = 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\"\n",
       "  frames[21] = 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\"\n",
       "  frames[22] = 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\"\n",
       "  frames[23] = 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\"\n",
       "  frames[24] = 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\"\n",
       "  frames[25] = 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ZXi+/EztGACAsl/VaiYLwDyHOW4WWfbg88+8Z5bn1fffB8BCpTQTiUTw8OFD3G27i8nVSRhKDXDdcMF1wQUA0Mmp/xxSCIHttW0sjj2B17OLePQyTJYfIM9ZDoNpF5YL1pRnShuJKP57QYXaKQAAstGimSwA8xzmuFn88OLDihf//uP6Irp3WykWKmne08PfO7o70NLXgrA1jLzLeZCuS9Dp1dvIE5bDWJlcx0x/APJWAfSGHyCn4GUYTTmqZSIi9bBQSbPC4TCGh4fR0NaAmY0ZGEuNcP3YBXO2endQeTrSnX3gx9pjHYDXkZ1bifwiB3fpEp1zLFTSlKeHHLR3t8Pd70Y0L7q3Gv2epFphPR3pLow+wdzQLuLRl2DK+iHynWXQ6dW/6wwRaYOuurpa1NXVoaamBlardj7rkWVZM3m0lAXIzDxCCIRCIQTkACLxCHRmHYwWI/T64/3vLpaEBRFD5EQZdnwBiETimUyASCSRiAsIoQNghE6nB45R7E6HDRubvq+eMyFgNqm3wrbZcuHzBVW7/jfZ8i9Av75xgkcaYDYrewBGwm6BYetk752zwDzPd9ws8XgQBbbcM8uzZTbj408+gbG/vx8A4Ha7UVtbe2YXPC4t5dFSFiBz8gghMD8/j7buNrQOtiKaH0XBSwXIL8k/8Wq0Uq7EhHXiRI/t/tcv8bNf5iG0HYJvJYwdHwC4YLQUwmDMPlEmW7IW67sdB1+Hn4Rx7ZVrJ8qnBJ2uEiaTV7Xrf1PQ58KbTf9y7MeNjPhhv/SholnkP66E9Z9P9t45C8zzfMfN4ve7Ufth7Znl+WJ/wxNHvpRyoVAIgw8G0dDWgIXgAkylJrhuumDKUufcWiEE/Kt+bC0HMN0bRTJhg954CZYLeXsrUiKiF8BCpZQQQsDr9aK1qxXtnnbE7XEUXC6A5FLvs9HdwC5WptYx0x/Ejs+O6JYTRvMr0PNAeiI6ARYqnSlZljEwOICG9gYsh5b3VqN1Lpgs6pRWPBrf26U7uI11rx46vIHsvEoUFNvhl+dZpkR0YixUUpwQAjMzM2jtbkXncCcSjgRsV2yQCtVZjQoh4F/x7+3SHY4gEX8Z5qy3ke8s5S5dIlIMC5UUEwwG0T/Qj/q2eqxH12G6ZELxu8UwmtV5m+0GdrE0sYbHAzJCfgf0hh/hQsFLMJiyVclDRJmNhUqnkkwmEYlE8Ns7v0XPWA+ShUnYv2OHVKjOTbtFUmBlcuVgpAvdG8jZH+kSEZ0lFiqdyPb29t5qtL0er73yGgaiAyh5rwQGU+pHqCIp4FvxYWFkE0Uvl6Pn32wwZ7+NfBdHukSUOixUemHJZBKTk5No6WxB/6N+CJeA43UHLGYLLpZcTHme0HYIyxPreyPd7ULojW9Df7UQBcUvpTwLERELlY7k8/nQ19+H+o56+IQPljILSm+VwmDcX/3JqcsSj8axPruOx4Pb2Jw3QuAN5ORdPRjp6nQpDENE9AwWKn2rRCKBiYkJNHc048HUA4gigcLrhZBsqf9s9NmR7vzDCBLxKzBn1yDPWcKRLhFpBguVvmZraws9fT1o6GjAtn4bWWVZuHTrEvSG1J8YFNoOYWliDbP9OwgFnNAb397bpWvMSnkWIqKjsFAJ8Xgc4+PjaGpvwtDjIeiKdCisLoSUn/rVaCwSw/rs3sELT+aNz+zStaU8CxHRcbBQz7EnT56gu7cbjV2NCBgDyC7PRtmtspSvRp+OdOdHNrHwMLo30s35XeQXlfAsXSJKGyzUcyYWi+HRo0dobG/EiHcEuos6ON9ywpaX+hXgjn8Hy+PreDy4g92AC3rTjf2RriXlWYiITouFek6sr6+jq7cLTV1NCFqCuCBdUGU1GovEsPZ4Hd7BAJ4smqDDd5GdfxUFxQUpzUFEpDQWagaLxWIYHR3F3ba7GFsYg75ED9fvuOCwOlTLNNM7h+k+H+JRE4AIBDoR8ncipNDzx2M/gX/1/53osdHdMYRlr0JJ9iQTbyEsd391jUgUfv+qotc4jpycEvj9btWu/015WR8jHPYe+3HRaFj5f0dcWz8b5jnEMbPk56dmIyMLNQOtrq6is6cTTd1NCOWEYJWskF6ToNOrc5u0Z12reQXXas7u+W1yDj78H1Unemz3HS+u3VB2pZwlm3DtRvHB1/5xPz64VavoNY5jaioXb72l3vW/qaPFiMrKimM/Lhj04733ahXN4rblnulNqI+LeZ5PS1mexULNEJFIBCMjI7jbfhcTyxMwlBrguuGC64JL7WhEROcCCzWNCSGwsrKCju4OtPS1YPfCLnIrciG9oY3VKBHRecJCTUNCCPT29uJux11Mr03vrUZrXCjKKVI7GhHRucVCTRNCCCwtLaG9ux3RSBQtsy3Iu5wHqUqdm3YTEdHXsVA1bnd3F0NDQ6hvq4d3ywtjqRE3y2+i4kaF2tGIiOgZLFQNEkJgfn4e7T3tuD9wH7GCGPIq8iB9f281qpd5ehARkdawUDUkFArhgecB6lvrsRBYgOmSCa6bLpiyTGpHIyKiI7BQVSaEgNfrRVt3G9oetCFuj6PgcgEkFz8bJSJKJyxUlezs7GDwwSDq2+qxJC/BVGqCq84Fk4WrUSKidMRCTSEhBB4/foz7XffROdyJuCMO+yt2SIVcjRIRpTsWagoEg0EMDA6gvq0eq+FVmC+ZUfxuMYxm/viJiDIF/6KfkWQyiZmZGbg73ege6UayMAn7NTskB1ejRESZiIWqsEAggP6BftS31WMjvgFTqQkX37vI1SjRt9CbDfifi/5jP25iW0aH16tolsqKCnyu8HOehtbyvCpJakfQPP6VV0AymcTU1BTudd5D71gvhEvA8boDkp1vQKLDFBbl4S//94fHftxvfuPF539/R9Esbrcbn95R9jlPQ2t5vtBQFq1ioZ6C3+9Hb9/embqbYhOWMgtK3y+FwWRQOxoREaUYC/WYEokEJicn0dLRgoHJAQiXQOH1Qkg2rkaJiM4zFuoL2traQk9fD+523IVf70dWWdbeatTI1SgREQG66upqUVdXh5qaGlitVrXzHJBlWRN5wuEwdnd3EQgFIIwCRotR9RK1JCyIGCKqZniWlvKcJov8xAd7obL/jWlI2JEwbB18ndiNoyDfpug1jiMctiArSxuvFXDyPJubYSSTyu6Wz87Oxe5uUNHnPI3T5DEazSgqKlE0z9bmJuzRqKLPeVKyxQJrRDvv4y2zGR9/8gmM/f39APY+AK+trVU31TPUzLO5uYmu3i40djYiYAzg5us3MVM8A71BG4fSV8qVmLBOqB3jgJbynCZL979+iV98VqBonlz5jxG0/vPB1/5FP976/vE34ShlaqoSV65o47UCTp7nypWzyfLmm17ln/iETpPnN7/x4u8V3rT1xZ07qNXIrmN3ZSVqJ7TzPv6iogIAR74H4vE4xsbG0NzRjOHZYegu6lD4/UJI+RJMskkzZUpERNp07gt1fX0d3X3daOxshGyRkVOeg7JbZSxQIiI6lnNZqLFYDKOjo2hsa8TY4hh0F3Vw/tAJR65D7WhERJSmzlWhrq6uoqu3C03dTdjJ3oFVsqL8tXLo9DwKkIiITifjCzUajWJkZAQNbQ2YWJ6AocQA54+ccFqdakcjIqIMkrGFury8jK7eLjT3NCOUE0JuRS6kNySuRomI6ExkVKFGIhEMDw+job0B02vTMJQY4LrhguuCS+1oRESU4dK+UIUQWFpaQnt3O9z9bkRyI3ur0SreJo2IiFInbQs1HA5jaGgI9W31mN2chbHECNePXTBnm9WORkRE51BaFaoQAgsLC2jvbsf9gfuI5EeQfzkfUjVXo0REpK60KNRQKATPkAcNbQ2Y883BeMkIVy1Xo0REpB1pUagNjQ2ob61HJLF3GHJ8Ko7lqeWUXb+iugLeJm/KrncU5nm+02SR52X4x5XNk+OIw7/oP/g6/0K+shcgIs1Ii0L96IOP8NEHH6l2fbfbjTu/vKPa9b+JeZ7vNFl+/evP8MGtCkXzTE3ZVD0Mn4hShwfWEhERKYCFSkREpAAWKhERkQJYqERERApgoRIRESmAhUpERKQAFioREZECWKhEREQKYKESEREpgIVKRESkABYqERGRAlioRERECmChEhERKYCFSkREpAAWKhERkQLS4n6oRESkLoPZjM+9XrVjAAAqKyo0kwUAXr16FQALlYiIXoCrpAQ/u3NH7RgAALfbjU81kgXYywNw5EtERKQIFioREZECWKhEREQK0FVXV4u6ujrU1NTAarWqneeALMuayaOlLADzHOY0WdbWlhGPRxXNk52di93doKLPeRrM83xaygKcLo/RaEZRUYmieTLl9/wsyLKM27dv4z8Ax+Z9SL5HOTQAAAAASUVORK5CYII=\"\n",
       "  frames[26] = 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\"\n",
       "  frames[27] = 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\"\n",
       "  frames[28] = 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\"\n",
       "  frames[29] = 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\"\n",
       "\n",
       "\n",
       "    /* set a timeout to make sure all the above elements are created before\n",
       "       the object is initialized. */\n",
       "    setTimeout(function() {\n",
       "        animGDAOFPEXRUHUWEBE = new Animation(frames, img_id, slider_id, 50, loop_select_id);\n",
       "    }, 0);\n",
       "  })()\n",
       "</script>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This function moves the polygons as a function of the frame i\n",
    "Nframes = 30\n",
    "def animate(nframe):\n",
    "    f = nframe / (Nframes - 1.0)\n",
    "    patches[0].set_xy(P1 + (8 - 8 * f, 3 - 3 * f + 0.5 * np.sin(f * np.pi)))\n",
    "    patches[1].set_xy(P2 + (5 * f, 2 * f - 0.5 * np.sin(f * np.pi)))\n",
    "    patches[2].set_xy(P3 + (8 - 3 * f, 0))\n",
    "    patches[3].set_xy(P4 + (8, 1 - f))\n",
    "    return patches\n",
    "\n",
    "anim = animation.FuncAnimation(fig, animate, frames=Nframes, interval=50)\n",
    "display_animation(anim, default_mode='once')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "nbpresent": {
     "id": "b41a18b7-326c-448a-b86e-419779d64494"
    },
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "import turtle # kto zna AC Logo?\n",
    "\n",
    "pen = turtle.Turtle() # o tym na kolejnych wykładach\n",
    "\n",
    "n_steps = 90 # w ilu krokach robimy 360 stopni\n",
    "\n",
    "for i in range(n_steps):\n",
    "    \n",
    "    pen.forward(100) # rysuj 100 do przodu\n",
    "    pen.right(45)    # obróć się w prawo o 45 stopni\n",
    "    pen.forward(50)  # rysuj 50 do przodu\n",
    "    pen.left(90)     # obróć się w lewo o 90 stopni\n",
    "    pen.forward(100) # rysuj do przodu o 100\n",
    "    pen.right(45)    # obróź się w prawo o 45 stopni\n",
    "    \n",
    "    pen.penup() # podnieś długopis (nie rysuj przy przemieszczeniu)\n",
    "    pen.setposition(0, 0) # wróć na środek\n",
    "    pen.pendown() # rysujemy dalej\n",
    "    \n",
    "    pen.right(360/n_steps) # obróć się w prawo o n_stepną część 360"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'scroll': True}"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from notebook.services.config import ConfigManager\n",
    "cm = ConfigManager()\n",
    "cm.update('livereveal', {\n",
    "              'scroll': True,\n",
    "})"
   ]
  }
 ],
 "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"
  },
  "nbpresent": {
   "slides": {},
   "themes": {
    "default": "17151fe0-c7c4-47fa-b621-0e0506358ba7",
    "theme": {}
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}