Predavanje 1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Matematički softver - prvo predavanje"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Metapodaci"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Materijali"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Materijali će se nalaziti na Githubu, u repozitoriju kolegija (https://github.com/vedgar/ms). Kako ih budemo obrađivali na nastavi, bit će _linkani_ s Merlina. Svi studenti koji planiraju slušati i/ili položiti ovaj kolegij, moraju upisati e-kolegij \"Matematički softver\" na Merlinu."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Potreban softver"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Na računalo na kojem ćete pratiti predavanja, pisati zadaće i slično, instalirajte `Anaconda` distribuciju (full (**ne** \n",
    "Miniconda!), Python 3.7, 64bit ako imate 64bitno računalo, ne trebate instalirati Visual Studio Code niti bilo kakav dodatni softver). Instalacija i pokretanje traje dosta dugo. Pokrenite Anaconda Navigator (iz Start izbornika ili iz komandne linije) i odaberite Jupyter Notebook (launch). Odaberite folder gdje ćete držati datoteke vezane uz kolegij, i napravite novu bilježnicu (u browseru, `New`... `Python 3`). Odaberite `Help`... `User Interface Tour` da biste se upoznali sa sučeljem."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Na računalima u Pr2 (pod Linuxom!) već je instalirano sve potrebno, samo da biste pokrenuli Anaconda Navigator, morate prvo u terminalu izvršiti naredbu\n",
    "```bash\n",
    "export PATH=/opt/anaconda3/bin:$PATH\n",
    "anaconda-navigator\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sadržaj kolegija"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* IPython / Jupyter\n",
    "* Scipy stack: Numpy, Sympy, Pandas, Matplotlib (, Scikit)\n",
    "* Markdown\n",
    "* LaTeX"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cilj kolegija"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Razviti sposobnost korištenja Pythona kao moćnog alata za znanstvenike\n",
    "* Osposobiti vas za produkciju visokokvalitetnih publikacija"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Polaganje"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ocjena se formira iz dva kolokvija i domaćih zadaća. Kolokviji su _open-book_ tipa i nose 80 bodova. Domaće zadaće su u obliku eseja/projekta sa zadanim elementima.\n",
    "\n",
    "Predaja svih domaćih zadaća nužan je uvjet za polaganje kolegija. Domaća zadaća je uspješno predana samo ako ima _sve_ tražene elemente. Domaće zadaće nose 20 bodova, koji se dodjeljuju za _dodatne_ elemente.\n",
    "\n",
    "Zbog kontinuiranog praćenja, popravni ispit ne postoji. U slučaju opravdane spriječenosti prisustvovanja kolokviju, javite se što prije da dogovorimo alternativne metode ocjenjivanja."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Literatura"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* [Lectures on scientific computing with Python](https://github.com/jrjohansson/scientific-python-lectures) - kolegij po uzoru na koji je ovaj nastao (davno)\n",
    "\n",
    "* [Astro 599 Course](http://nbviewer.jupyter.org/github/jakevdp/2013_fall_ASTR599/tree/master/notebooks/) - još jedan sličan kolegij, nažalost u Pythonu 2 ali s puno korisnih informacija\n",
    "\n",
    "* [Reproducible data analysis in Jupyter](http://jakevdp.github.io/blog/2017/03/03/reproducible-data-analysis-in-jupyter/) - serija videâ koji detaljno objašnjavaju _workflow_ potreban za \"idealnu\" prvu zadaću\n",
    "\n",
    "* [xkcd 1313: Regex Golf](http://nbviewer.jupyter.org/url/norvig.com/ipython/xkcd1313.ipynb) - duhovit ali i poučan primjer netrivijalnog rezultata dobivenog pomoću Jupytera\n",
    "\n",
    "* [A gallery of interesting Jupyter Notebooks](https://github.com/jupyter/jupyter/wiki/A-gallery-of-interesting-Jupyter-Notebooks) - ogromni repozitorij raznovrsnih bilježnica, dobro za upoznavanje s mogućnostima"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Primjeri"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Slaganje bilježnica"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Možemo uključivati slike, zvukove, videe, matematičke zapise, JSON objekte,... Za detalje pogledati [dokumentaciju](http://ipython.readthedocs.io/en/stable/api/generated/IPython.display.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.display import Image, YouTubeVideo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/gif": "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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image('http://python.org/images/python-logo.gif')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"400\"\n",
       "            height=\"300\"\n",
       "            src=\"https://www.youtube.com/embed/T4WXs6niloU\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.YouTubeVideo at 0x1cf83b13fd0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "YouTubeVideo('T4WXs6niloU')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Možemo i izvršavati sistemske naredbe (počinju uskličnikom `!`), ali time naša bilježnica postaje izvršiva samo na određenom OSu. Zato je dobro to izbjegavati."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Volume in drive C is Acer\n",
      " Volume Serial Number is 569A-7002\n",
      "\n",
      " Directory of C:\\Users\\Veky\\Documents\\Nastava\\MS\n",
      "\n",
      "01.03.2019.  20:58    <DIR>          .\n",
      "01.03.2019.  20:58    <DIR>          ..\n",
      "21.04.2018.  05:41    <DIR>          .ipynb_checkpoints\n",
      "24.03.2018.  19:08             9.560 6 grafova.pdf\n",
      "16.01.2018.  16:42           537.592 amshelp.pdf\n",
      "07.03.2018.  22:04           424.520 astro.png\n",
      "16.03.2018.  21:02             1.863 bootcamp1.tsv\n",
      "16.03.2018.  21:13             1.896 bootcamp2.tsv\n",
      "16.03.2018.  21:16             1.896 bootcamp3.tsv\n",
      "16.03.2018.  21:22             1.917 bootcamp4.tsv\n",
      "09.06.2018.  13:56    <DIR>          Druga zadaća\n",
      "17.03.2018.  10:04         1.512.378 HNB tečaj EUR.json\n",
      "17.03.2018.  12:28           239.466 HNB tečaj mjesečno.json\n",
      "17.03.2018.  10:01             3.091 HNB tečaj.json\n",
      "03.04.2018.  05:59               565 Ideje za kolokvij.txt\n",
      "02.04.2018.  14:43            15.058 izraz.svg\n",
      "13.11.2017.  10:17         2.612.144 KOMA-Script guide.pdf\n",
      "24.03.2018.  16:26         2.163.250 latex-novices-report.pdf\n",
      "06.01.2019.  19:19       430.340.768 MAMP_MAMP_PRO_4.1.exe\n",
      "11.03.2018.  14:05                28 matrica.csv\n",
      "11.03.2018.  14:08               160 matrica.npy\n",
      "16.03.2018.  21:31            14.471 Pandas bootcamp.ipynb\n",
      "19.03.2018.  11:35           174.137 Penn World Table analiza.ipynb\n",
      "01.03.2019.  20:58           412.634 Predavanje 1.ipynb\n",
      "11.03.2018.  16:08            53.145 Predavanje 2.ipynb\n",
      "19.03.2018.  17:37           472.421 Predavanje 3.ipynb\n",
      "24.03.2018.  21:24           845.470 Predavanje 4.ipynb\n",
      "02.04.2018.  21:40           155.382 Predavanje 5.ipynb\n",
      "16.04.2018.  12:46    <DIR>          Prva zadaća\n",
      "01.05.2018.  19:31    <DIR>          Prvi kolokvij\n",
      "19.03.2018.  09:00         3.131.446 pwt90.dta\n",
      "03.05.2018.  02:16           540.508 short math guide.pdf\n",
      "11.03.2018.  15:21         2.864.946 Stockholm.dat\n",
      "31.03.2018.  09:41    <DIR>          Sympy excercises\n",
      "11.03.2018.  14:04                 0 tenzor.csv\n",
      "07.03.2018.  22:09            21.467 thumb.png\n",
      "11.03.2018.  13:19             2.106 tpt-world.csv\n",
      "17.03.2018.  20:30             2.096 Ubrzavanje.ipynb\n",
      "21.04.2018.  05:51             3.490 Untitled.ipynb\n",
      "              32 File(s)    446.559.871 bytes\n",
      "               7 Dir(s)  326.090.203.136 bytes free\n"
     ]
    }
   ],
   "source": [
    "!dir"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Magične naredbe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\"Magične\" naredbe počinju znakom `%`. Linijske (počinju jednim znakom `%`) primaju argumente do kraja linije, ćelijske (počinju s `%%`) primaju argumente do kraja ćelije."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/json": {
       "cell": {
        "!": "OSMagics",
        "HTML": "Other",
        "SVG": "Other",
        "bash": "Other",
        "capture": "ExecutionMagics",
        "cmd": "Other",
        "debug": "ExecutionMagics",
        "file": "Other",
        "html": "DisplayMagics",
        "javascript": "DisplayMagics",
        "js": "DisplayMagics",
        "latex": "DisplayMagics",
        "markdown": "DisplayMagics",
        "perl": "Other",
        "prun": "ExecutionMagics",
        "pypy": "Other",
        "python": "Other",
        "python2": "Other",
        "python3": "Other",
        "ruby": "Other",
        "script": "ScriptMagics",
        "sh": "Other",
        "svg": "DisplayMagics",
        "sx": "OSMagics",
        "system": "OSMagics",
        "time": "ExecutionMagics",
        "timeit": "ExecutionMagics",
        "writefile": "OSMagics"
       },
       "line": {
        "alias": "OSMagics",
        "alias_magic": "BasicMagics",
        "autoawait": "AsyncMagics",
        "autocall": "AutoMagics",
        "automagic": "AutoMagics",
        "autosave": "KernelMagics",
        "bookmark": "OSMagics",
        "cd": "OSMagics",
        "clear": "KernelMagics",
        "cls": "KernelMagics",
        "colors": "BasicMagics",
        "config": "ConfigMagics",
        "connect_info": "KernelMagics",
        "copy": "Other",
        "ddir": "Other",
        "debug": "ExecutionMagics",
        "dhist": "OSMagics",
        "dirs": "OSMagics",
        "doctest_mode": "BasicMagics",
        "echo": "Other",
        "ed": "Other",
        "edit": "KernelMagics",
        "env": "OSMagics",
        "gui": "BasicMagics",
        "hist": "Other",
        "history": "HistoryMagics",
        "killbgscripts": "ScriptMagics",
        "ldir": "Other",
        "less": "KernelMagics",
        "load": "CodeMagics",
        "load_ext": "ExtensionMagics",
        "loadpy": "CodeMagics",
        "logoff": "LoggingMagics",
        "logon": "LoggingMagics",
        "logstart": "LoggingMagics",
        "logstate": "LoggingMagics",
        "logstop": "LoggingMagics",
        "ls": "Other",
        "lsmagic": "BasicMagics",
        "macro": "ExecutionMagics",
        "magic": "BasicMagics",
        "matplotlib": "PylabMagics",
        "mkdir": "Other",
        "more": "KernelMagics",
        "notebook": "BasicMagics",
        "page": "BasicMagics",
        "pastebin": "CodeMagics",
        "pdb": "ExecutionMagics",
        "pdef": "NamespaceMagics",
        "pdoc": "NamespaceMagics",
        "pfile": "NamespaceMagics",
        "pinfo": "NamespaceMagics",
        "pinfo2": "NamespaceMagics",
        "pip": "BasicMagics",
        "popd": "OSMagics",
        "pprint": "BasicMagics",
        "precision": "BasicMagics",
        "prun": "ExecutionMagics",
        "psearch": "NamespaceMagics",
        "psource": "NamespaceMagics",
        "pushd": "OSMagics",
        "pwd": "OSMagics",
        "pycat": "OSMagics",
        "pylab": "PylabMagics",
        "qtconsole": "KernelMagics",
        "quickref": "BasicMagics",
        "recall": "HistoryMagics",
        "rehashx": "OSMagics",
        "reload_ext": "ExtensionMagics",
        "ren": "Other",
        "rep": "Other",
        "rerun": "HistoryMagics",
        "reset": "NamespaceMagics",
        "reset_selective": "NamespaceMagics",
        "rmdir": "Other",
        "run": "ExecutionMagics",
        "save": "CodeMagics",
        "sc": "OSMagics",
        "set_env": "OSMagics",
        "store": "StoreMagics",
        "sx": "OSMagics",
        "system": "OSMagics",
        "tb": "ExecutionMagics",
        "time": "ExecutionMagics",
        "timeit": "ExecutionMagics",
        "unalias": "OSMagics",
        "unload_ext": "ExtensionMagics",
        "who": "NamespaceMagics",
        "who_ls": "NamespaceMagics",
        "whos": "NamespaceMagics",
        "xdel": "NamespaceMagics",
        "xmode": "BasicMagics"
       }
      },
      "text/plain": [
       "Available line magics:\n",
       "%alias  %alias_magic  %autoawait  %autocall  %automagic  %autosave  %bookmark  %cd  %clear  %cls  %colors  %config  %connect_info  %copy  %ddir  %debug  %dhist  %dirs  %doctest_mode  %echo  %ed  %edit  %env  %gui  %hist  %history  %killbgscripts  %ldir  %less  %load  %load_ext  %loadpy  %logoff  %logon  %logstart  %logstate  %logstop  %ls  %lsmagic  %macro  %magic  %matplotlib  %mkdir  %more  %notebook  %page  %pastebin  %pdb  %pdef  %pdoc  %pfile  %pinfo  %pinfo2  %popd  %pprint  %precision  %prun  %psearch  %psource  %pushd  %pwd  %pycat  %pylab  %qtconsole  %quickref  %recall  %rehashx  %reload_ext  %ren  %rep  %rerun  %reset  %reset_selective  %rmdir  %run  %save  %sc  %set_env  %store  %sx  %system  %tb  %time  %timeit  %unalias  %unload_ext  %who  %who_ls  %whos  %xdel  %xmode\n",
       "\n",
       "Available cell magics:\n",
       "%%!  %%HTML  %%SVG  %%bash  %%capture  %%cmd  %%debug  %%file  %%html  %%javascript  %%js  %%latex  %%markdown  %%perl  %%prun  %%pypy  %%python  %%python2  %%python3  %%ruby  %%script  %%sh  %%svg  %%sx  %%system  %%time  %%timeit  %%writefile\n",
       "\n",
       "Automagic is ON, % prefix IS NOT needed for line magics."
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%lsmagic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image\t YouTubeVideo\t \n"
     ]
    }
   ],
   "source": [
    "%who"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Exception reporting mode: Minimal\n"
     ]
    }
   ],
   "source": [
    "%xmode minimal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "ename": "ZeroDivisionError",
     "evalue": "division by zero",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mZeroDivisionError\u001b[0m\u001b[1;31m:\u001b[0m division by zero\n"
     ]
    }
   ],
   "source": [
    "1/0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<h5>Neki naslov</h5>\n",
       "<p>Jedan jednostavan <em>primjer</em>.</p>\n",
       "<p>Sastoji se od dva odlomka teksta.</p>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%HTML\n",
    "<h5>Neki naslov</h5>\n",
    "<p>Jedan jednostavan <em>primjer</em>.</p>\n",
    "<p>Sastoji se od dva odlomka teksta.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "499999500000\n",
      "Wall time: 604 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "zbroj = 0\n",
    "for broj in range(1_000_000):\n",
    "    zbroj += broj\n",
    "print(zbroj)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "console.log('hello')\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%js\n",
    "console.log('hello')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Traženje pomoći"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dodavanjem `?` (ili `??` za više detalja) na praktički bilo koje ime možemo zatražiti pomoć u vezi objekta imenovanog tim imenom. Pomoć se prikazuje u zasebnom prozoru. `help` je standardni Pythonov help."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "YouTubeVideo?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on built-in function chr in module builtins:\n",
      "\n",
      "chr(i, /)\n",
      "    Return a Unicode string of one character with ordinal i; 0 <= i <= 0x10ffff.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(chr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['__add__',\n",
       " '__class__',\n",
       " '__contains__',\n",
       " '__delattr__',\n",
       " '__delitem__',\n",
       " '__dir__',\n",
       " '__doc__',\n",
       " '__eq__',\n",
       " '__format__',\n",
       " '__ge__',\n",
       " '__getattribute__',\n",
       " '__getitem__',\n",
       " '__gt__',\n",
       " '__hash__',\n",
       " '__iadd__',\n",
       " '__imul__',\n",
       " '__init__',\n",
       " '__init_subclass__',\n",
       " '__iter__',\n",
       " '__le__',\n",
       " '__len__',\n",
       " '__lt__',\n",
       " '__mul__',\n",
       " '__ne__',\n",
       " '__new__',\n",
       " '__reduce__',\n",
       " '__reduce_ex__',\n",
       " '__repr__',\n",
       " '__reversed__',\n",
       " '__rmul__',\n",
       " '__setattr__',\n",
       " '__setitem__',\n",
       " '__sizeof__',\n",
       " '__str__',\n",
       " '__subclasshook__',\n",
       " 'append',\n",
       " 'clear',\n",
       " 'copy',\n",
       " 'count',\n",
       " 'extend',\n",
       " 'index',\n",
       " 'insert',\n",
       " 'pop',\n",
       " 'remove',\n",
       " 'reverse',\n",
       " 'sort']"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Kompleksniji primjer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from skimage import data\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1cf86fc3b00>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "coins = data.coins()\n",
    "plt.imshow(coins, cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(303, 384)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coins.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "coins_zoom = coins[10:80, 300:370]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "from skimage import restoration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "tv_coins = restoration.denoise_tv_chambolle(coins_zoom, weight=.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1cf86984470>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.imshow(coins_zoom)\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.imshow(tv_coins)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Osnove Pythona"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Objekti i njihova imena"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Osnovna razlika Pythona od jezika poput C-a je: U C-u, memorijske lokacije su primarni objekti kojima baratamo, vrijednosti zapisane u njima su sekundarne. Varijable su zato uvijek imena za \"kutije\": nizove od nekoliko (fiksno i konačno mnogo) uzastopnih memorijskih lokacija, unutar kojih se nalazi vrijednost objekta. Čak i kod dinamički alocirane memorije, deklarirane varijable uvijek imaju fiksnu i ograničenu veličinu (`sizeof`), dok o našoj interpretaciji njihovog sadržaja kao pointera ovisi naša sposobnost da spremimo potencijalno više podataka u memoriju nego što smo statički alocirali.\n",
    "\n",
    "U Pythonu, pogled na svijet je bitno drugačiji: primarne \"vrijednosti\" kojima baratamo su upravo objekti, memorijske lokacije su potpuno irelevantne, a da bismo došli do objekata odnosno da bismo ih mogli spomenuti u kodu, dajemo im _imena_. `x = 382` ne znači (kao u C-u) \"stvori kutiju imena `x` i u nju stavi bit-pattern `0...0101111110`\", već \"stvori objekt `382` (tipa `int`), i daj mu ime `x`\".\n",
    "\n",
    "Direktna posljedica: tip više nije svojstvo varijable (imena), nego objekta. `x = 3; x = 'tri'` je sasvim legalno. Naravno, hoće li `x.islower()` dignuti `AttributeError` ili vratiti `True`, ovisi o konkretnoj vrijednosti od `x` u trenutku poziva -- baš kao da smo napisali `3 .islower()` odnosno `'tri'.islower()`. Još jedna posljedica: isti objekt može imati više imena. `x = y` jednostavno uzme objekt imena `y`, i dade mu još jedno ime `x`. _Ništa se nikamo ne kopira._ Na primjer kod poziva funkcije, objekti koje smo naveli kao funkcijske argumente jednostavno dobiju još neka _lokalna_ imena, koja zovemo parametri. Opet, ništa se nikamo ne kopira."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Da bismo ustanovili referiraju li imena `x` i `y` na isti objekt, možemo koristiti `x is y`. Napomena: to nema veze s jednakošću, koja se testira sa `x == y`. Implementacija jednakosti, kao i mnogih drugih operatora/metoda, ovisi o tipu od `x` (i možda od `y`): npr. razlomci bitno drugačije definiraju jednakost nego IP-adrese. Čak i da dva objekta imaju potpuno istu reprezentaciju u memoriji (tada će vjerojatno biti `x == y`), to i dalje mogu biti dva objekta (`x is not y`), i promjena jednog neće utjecati na drugi objekt. Druga implikacija (`x is y` povlači `x == y`, odnosno zapravo `x == x`) vrijedi puno češće, i jedini standardni kontraprimjer je NaN (`math.nan`)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prostori imena (_namespaces_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Kako su imena očito vrlo važna i zapravo jedini način da u kodu govorimo o objektima, Python posvećuje posebnu pažnju njihovom razvrstavanju. Svako ime postoji u točno jednom prostoru, te se prostori imena dijele na implicitne (čija imena se obično ne navode) i eksplicitne (čija imena se moraju navesti).\n",
    "\n",
    "Implicitnih prostora imena ima četiri vrste, i uvijek su ugniježđeni na isti način. Vanjski je `builtins`, u kojem žive preddefinirana imena za Pythonove ugrađene funkcije i ostale objekte (npr. `print`). Obično se ne mijenja, iako Python dopušta i njegovu promjenu.\n",
    "\n",
    "Sljedeći prema unutra je `globals`, u kojem žive ili varijable koje smo definirali unutar interaktivnog rada (npr. pisanja u Jupyterovoj bilježnici), ili pak \"globalne\" varijable pojedinog _modula_ (ugrubo, datoteke s nastavkom `.py`) koje nisu ni u kojem unutrašnjem (klasnom ili lokalnom) prostoru. U njega možemo slobodno dodavati imena, a možemo i mijenjati njihove vrijednosti ali to se ne smatra dobrom praksom iz svih uobičajenih razloga protiv globalnih varijabli (globalne _konstante_, imena čije se vrijednosti ne mijenjaju -- npr. funkcije i klase koje modul definira -- su sasvim u redu).\n",
    "\n",
    "Unutar `globals` postoje dvije vrste prostora imena, koji se oba zovu `locals` ali ne vide jedan drugog bez obzira na sintaksnu ugniježđenost: klasni i funkcijski. U klasnom prostoru žive atributi (uključujući metode i svojstva) klase unutar koje se nalazimo (ako se uopće nalazimo unutar `class ...:` bloka). Točna priroda ovog bloka, pa tako i mogućnost mijenjanja, ovisi o tipu trenutne klase (tzv. \"metaklasi\"), no često se u njemu nalaze samo metode.\n",
    "\n",
    "U funkcijskom lokalnom prostoru žive \"prave\" lokalne varijable (uključivo parametri) funkcije unutar koje se nalazimo (ako se uopće nalazimo unutar `def ...:` bloka). U ovom prostoru imena su fiksna (samo ona koja se sintaksno pojavljuju u kodu funkcije) i nije moguće dodavati nova dinamički, iako je naravno moguće mijenjati njihove vrijednosti. Ovaj prostor imena iznova se stvara svakim pozivom funkcije, i prestaje postojati povratkom iz funkcije; zato je posebno optimiziran. Funkcijski lokalni prostori mogu biti i međusobno ugniježđeni, ako imamo jednu funkciju definiranu unutar druge.\n",
    "\n",
    "Za implicitne prostore imena vrijedi jednostavno pravilo: _čitanje_ vrijednosti imena (npr. korištenje imena u nekom izrazu) obavlja se prvo u trenutnom prostoru, te ako ga tamo nema, u prvom iznad, te ako ga ni tamo nema, u sljedećem iznad njega... i tako dalje u hijerarhiji. Ako se ime ne nađe ni u jednom implicitnom prostoru imena (čak niti u `builtins`), prijavljuje se greška `NameError`. S druge strane, _postavljanje_ vrijednosti imena (npr. pridruživanje tog imena nekom objektu), kao i _brisanje_ vrijednosti imena (npr. naredbom `del`), _uvijek_ se obavlja u trenutnom prostoru imena (osim ako smo to promijenili `global` ili `nonlocal` direktivom)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Eksplicitni prostori imena su puno jednostavniji, i ima ih dva tipa: atributni (oni čija imena se navode kao `prostor.ime`) i spremnički (oni čija imena se navode kao `prostor[ime]`). Atributni su vrlo slični implicitnima, samo su vezani uz _objekte_ (instance) pojedinih klasa. Gotovo svaki objekt u Pythonu ima svoj atributni prostor imena. Važan specijalni slučaj: `import nekimodul` stvara objekt imena `nekimodul`, čiji atributni prostor je upravo globalni prostor modula `nekimodul.py`. Na taj način možemo jednostavno koristiti globalna imena iz jednog modula u drugom. Atributni prostori su često dinamički (možemo dodavati i brisati imena), iako pomoću `__slots__` definicije možemo fiksirati skup imena, vrlo slično funkcijskom lokalnom prostoru.\n",
    "\n",
    "Spremnički prostor imena imaju samo posebni tipovi _spremnici_, kao što su npr. liste i rječnici. Njihova posebnost je u tome da \"imena\" u njima mogu biti proizvoljni objekti (ipak, najčešće se zahtijeva da budu nepromjenjivi) -- u svim ostalim prostorima imena su morala biti validni identifikatori: nizovi znakova koji sadrže slova, znamenke i znak `_`, te ne počinju znamenkom. U slučaju listi (i raznih drugih sekvenci kao što su slogovi, stringovi, polja,...), validna imena su cijeli brojevi i zovu se _indeksi_. U slučaju rječnika, validna imena su bilo kakvi _hashabilni_ objekti i zovu se _ključevi_."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Primjena: uvoz modula"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Recimo da imamo modul `m.py`, unutar kojeg se nalazi kod\n",
    "```py\n",
    "x = y = 2\n",
    "z = 3\n",
    "```\n",
    "\n",
    "Naredbom `import m` dobit ćemo (u trenutnom prostoru imena) ime `m` koje će se odnositi na objekt tipa `module`, čiji atributni prostor imena će imati tri imena: `m.x` i `m.y` će biti imena za jedan te isti broj `2`, dok će `m.z` biti ime za broj `3`. Naredbom `import m as n` dobit ćemo ime `n` koje će se odnositi na isti objekt opisan gore. Tako će `n.x` i `n.y` biti imena za `2`, dok će `n.z` biti ime za `3`. Ovo najčešće koristimo kad želimo skratiti ime modula, posebno kad je modul u paketu te je time njegovo ime zapravo cijela \"staza\": `import matplotlib.pyplot as plt`.\n",
    "\n",
    "Naredbom `from m import x` dobit ćemo (u trenutnom prostoru imena) ime `x` koje će se odnositi na broj `2`. Naredbom `from m import x as z` dobit ćemo ime `z` koje će se odnositi na broj `2`. Naredbom `from m import x, z` dobit ćemo imena `x` i `z` koja će se odnositi redom na `2` i `3`. Ovo koristimo kad imamo module koji sadrže sve i svašta, i treba nam samo uzak dio njihovih funkcionalnosti: `from functools import partial`. Također je korisno kad je glavnina funkcionalnosti modula u jednoj funkciji/klasi koja se zove jednako kao modul: `from datetime import datetime`.\n",
    "\n",
    "Naredbom `from m import *` dobit ćemo imena `x`, `y` i `z`, takva da će se prva dva odnositi na broj `2`, a treće na broj `3`. Primijetite da se ovakav oblik naredbe ne može naći unutar funkcijske definicije, jer se time imena `x`, `y` i `z` ne bi nalazila sintaksno u tekstu funkcijske definicije, te Python ne bi mogao konstruirati lokalni funkcijski prostor imena (ne zna koja će se imena pojaviti u lokalnom prostoru sve do trenutka poziva funkcije). Također, loša strana ovog je teže razumijevanje koda: posebno pri _debuggiranju_, izuzetno je važno za svako ime znati iz kojeg prostora dolazi. Ako imamo\n",
    "\n",
    "```py\n",
    "from a import *\n",
    "from b import *\n",
    "from c import *\n",
    "...\n",
    "... z ...\n",
    "```\n",
    "\n",
    "nemamo nikakav način da saznamo iz kojeg modula je `z`, te najčešće moramo istraživati jedan po jedan. Ipak, u interaktivnom _quick&dirty_ kodiranju često je vrlo praktično koristiti ovaj oblik."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Primjena: \"prijenos\" argumenata u funkciju"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8\n",
      "3\n"
     ]
    }
   ],
   "source": [
    "def f(x):\n",
    "    x = 8\n",
    "    print(x)\n",
    "x = 3\n",
    "f(x)\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ime `x` u prve tri linije različito je od imena `x` u druge tri linije: prvo živi u lokalnom funkcijskom prostoru funkcije `f`, dok drugo živi u globalnom prostoru ove Jupyter bilježnice.\n",
    "\n",
    "Prvo se definira funkcija `f`, s jednim lokalnim imenom `x`, koje je (jednom kad se funkcija pozove) prvo ime za argument funkcije, nakon toga ime za broj `8`, i nakon toga se njegova vrijednost (dakle `8`) prenese u funkciju `print` (precizno, funkciju čije ime u `builtins` prostoru je `print`).\n",
    "\n",
    "Nakon toga stvorimo broj `3`, damo mu _globalno_ ime `x`, te s njime pozovemo funkciju `f` (precizno, funkciju čije ime u `globals` prostoru je `f`). Tada se stvori lokalni funkcijski prostor imena, u njemu ime `x` za broj `3`, zatim se to isto (lokalno) ime pridruži broju `8`, te se ispiše `8`. Time funkcija dolazi do kraja, njen lokalni prostor imena nestaje, te se izvršava sljedeća naredba nakon funkcijskog poziva, a to je ovaj `print(x)` u zadnjoj liniji. U tom trenutku, postoji samo jedan `x`, globalno ime `x` za objekt `3`, te se ispisuje `3`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 5, 8]\n",
      "[1, 5, 8]\n"
     ]
    }
   ],
   "source": [
    "def g(x):\n",
    "    x[2] = 8\n",
    "    print(x)\n",
    "x = [1, 5, 3]\n",
    "g(x)\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Velika razlika od prethodnog primjera: također postoje dva imena, lokalni i globalni `x`, no ovaj put se oba cijelo vrijeme odnose na isti objekt: jednu jedinu listu u gornjem kodu. Naredba pridruživanja `x[2] = 8` ne mijenja lokalni funkcijski prostor funkcije `g` (u kojem živi `x`), već `x`ov spremnički prostor imena. Zapravo, to nije pridruživanje ničega imenu `x`, već je to pridruživanje (broja `8`) \"imenu\" `2` unutar prostora imena koji odgovara objektu -- spremniku `x`. To se najbolje može vidjeti po tome da u ovoj drugoj naredbi \"x\" može biti izraz: `(x if 2 < 3 else y)[2] = 8`, dok je naravno pridruživanje `(x if 2 < 3 else y) = 8` besmisleno."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Za detaljniji prikaz ..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Izuzetno koristan alat za vizualizaciju Pythonove memorije: http://pythontutor.com/visualize.html#mode=edit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe width=\"800\" height=\"500\" frameborder=\"0\" src=\"http://pythontutor.com/iframe-embed.html#code=def%20f%28x%29%3A%0A%20%20%20%20x%20%3D%208%0A%20%20%20%20print%28x%29%0Ax%20%3D%203%0Af%28x%29%0Aprint%28x%29%0A%0Adef%20g%28x%29%3A%0A%20%20%20%20x%5B2%5D%20%3D%208%0A%20%20%20%20print%28x%29%0Ax%20%3D%20%5B1,%205,%203%5D%0Ag%28x%29%0Aprint%28x%29&codeDivHeight=400&codeDivWidth=350&cumulative=false&curInstr=0&heapPrimitives=true&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false\"> </iframe>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%HTML\n",
    "<iframe width=\"800\" height=\"500\" frameborder=\"0\" src=\"http://pythontutor.com/iframe-embed.html#code=def%20f%28x%29%3A%0A%20%20%20%20x%20%3D%208%0A%20%20%20%20print%28x%29%0Ax%20%3D%203%0Af%28x%29%0Aprint%28x%29%0A%0Adef%20g%28x%29%3A%0A%20%20%20%20x%5B2%5D%20%3D%208%0A%20%20%20%20print%28x%29%0Ax%20%3D%20%5B1,%205,%203%5D%0Ag%28x%29%0Aprint%28x%29&codeDivHeight=400&codeDivWidth=350&cumulative=false&curInstr=0&heapPrimitives=true&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false\"> </iframe>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Matematičke operacije"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(25, 11, 126, 2.5714285714285716)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "18 + 7, 18 - 7, 18 * 7, 18 / 7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 4, (2, 4))"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "18 // 7, 18 % 7, divmod(18, 7) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(612220032, 612220032, 32)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "18 ** 7, pow(18, 7), pow(18, 7, 1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Rad s razlomcima, decimalnim brojevima proizvoljne preciznosti, kompleksnim brojevima, ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "from fractions import Fraction as F\n",
    "from decimal import Decimal as D, getcontext\n",
    "import cmath"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Fraction(30, 49)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "F(18, 7)**2 % 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('30/49', 30, 49)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "str(_), _.numerator, _.denominator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Decimal('2.571428571428571618895375650026835501194000244140625'),\n",
       " Decimal('2.57142857142857142857142857143'))"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "getcontext().prec = 30\n",
    "D(18/7), D(18)/D(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Decimal('1.4142135623730950488016887242096980785696718753769480731766797379907324784621070388503875343276415727350138462309122970249248360558507372126441214970999358314132226659275055927557999505011527820605715')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "getcontext().prec = 200\n",
    "D(2).sqrt()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Decimal('1E-199')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "_ ** 2 - 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2417851639229258349412352, 4096, 2417851639229258349412352)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2 ** 3 ** 4, (2 ** 3) ** 4, 2 ** (3 ** 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1.7099759466766968, 2.6457513110645907, (6.123233995736766e-17+1j))"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "5 ** 3**-1, 7 ** .5, (-1) ** .5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, True)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1j.real, 1j.imag, cmath.isclose(_[2], 1j)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((122-597j), (16+0j))"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(2 + 3j) ** 5, (1 + 1j) ** 8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cmath.isclose(cmath.e ** (1j * cmath.pi) + 1, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on built-in function isclose in module cmath:\n",
      "\n",
      "isclose(a, b, *, rel_tol=1e-09, abs_tol=0.0)\n",
      "    Determine whether two complex numbers are close in value.\n",
      "    \n",
      "      rel_tol\n",
      "        maximum difference for being considered \"close\", relative to the\n",
      "        magnitude of the input values\n",
      "      abs_tol\n",
      "        maximum difference for being considered \"close\", regardless of the\n",
      "        magnitude of the input values\n",
      "    \n",
      "    Return True if a is close in value to b, and False otherwise.\n",
      "    \n",
      "    For the values to be considered close, the difference between them must be\n",
      "    smaller than at least one of the tolerances.\n",
      "    \n",
      "    -inf, inf and NaN behave similarly to the IEEE 754 Standard. That is, NaN is\n",
      "    not close to anything, even itself. inf and -inf are only close to themselves.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(cmath.isclose)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cmath.isclose(cmath.e ** (1j * cmath.pi) + 1, 0, abs_tol=1e-9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2.3.imag"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "nan"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cmath.inf * 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "_ == _"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cmath.isclose(cmath.atan(cmath.inf), cmath.pi / 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Spremnici"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[3, -2, 5.8, 2j, 'bla', [3, 5], {8}, <function print>, ZeroDivisionError]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lista = [3, -2, 5.8, 2j, 'bla', [3, 5], {8}, print, ZeroDivisionError]\n",
    "lista"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Duljina, indeksiranje s početka i s kraja"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9, 2j, True, {8})"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(lista), lista[3], lista[-2] is lista[len(lista)-2], lista[~2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Uzastopno indeksiranje"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 'a')"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lista[5][0], lista[4][2][0][0][0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Rezanje liste od ili do nekog mjesta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([3, -2, 5.8],\n",
       " [2j, 'bla', [3, 5], {8}, <function print>, ZeroDivisionError],\n",
       " [{8}, <function print>, ZeroDivisionError],\n",
       " [3, -2, 5.8, 2j, 'bla', [3, 5]])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lista[:3], lista[3:], lista[-3:], lista[:-3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Rezanje liste između mjesta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([5.8, 2j, 'bla', [3, 5]],\n",
       " [-2, 5.8, 2j, 'bla', [3, 5], {8}, <function print>],\n",
       " [],\n",
       " [2j, 'bla', [3, 5], {8}, <function print>, ZeroDivisionError])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lista[2:6], lista[1:-1], lista[7:7], lista[3:99]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Preskakanje elemenata (u ovom slučaju, uzimanje svakog trećeg elementa)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([3, -2, 5.8, 2j, 'bla', [3, 5], {8}, <function print>, ZeroDivisionError],\n",
       " [3, 2j, {8}],\n",
       " [-2, 'bla', <function print>])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lista, lista[::3], lista[1::3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pretvaranje spremnika: string -> lista -> skup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['M', 'a', 't', 'S', 'o', 'f', 't']"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list('MatSoft')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'M', 'S', 'a', 'f', 'o', 't'}"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Rad sa skupovima"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "mat = set('matematički')\n",
    "samoglasnici = set('aeiou')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Presjek, razlika"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "({'a', 'e', 'i'}, {'k', 'm', 't', 'č'})"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mat & samoglasnici, mat - samoglasnici"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Unija, simetrična razlika"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "({'a', 'e', 'i', 'k', 'm', 'o', 't', 'u', 'č'}, {'k', 'm', 'o', 't', 'u', 'č'})"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mat | samoglasnici, mat ^ samoglasnici"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Formula uključivanja-isključivanja"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(samoglasnici) + len(mat) == len(mat | samoglasnici) + len(mat & samoglasnici)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Podskup, pravi podskup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(False, True)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "samoglasnici <= mat, samoglasnici & mat < mat"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vraćamo se listama: dodavanje na kraj,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[3, -2, 5.8, 2j, 'bla', [3, 5], {8}, <function print>, ZeroDivisionError, 23]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lista.append(23)\n",
    "lista"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "micanje po vrijednosti,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[3, -2, 5.8, 'bla', [3, 5], <function print>, ZeroDivisionError, 23]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lista.remove({8})\n",
    "lista.remove(2j)\n",
    "lista"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "micanje po indeksu,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[3, -2, 5.8, 'bla', [3, 5], {8}]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "del lista[3]\n",
    "del lista[-2:]\n",
    "lista"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Provjera nalazi li se nešto u spremniku"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(True, False, False, True)"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "-2 in lista, 3 not in lista, 17 in lista, 117 not in lista"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ažuriranje skupova"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'a', 'e', 'i', 'k', 'm', 't', 'č'}"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'0', '1', '2', '3', '4', 'a', 'e', 'i', 'k', 'm', 't', 'z'}"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mat |= set('01234')\n",
    "mat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'0', '1', '2', '3', '4', 'a', 'e', 'i', 'k', 'm', 't', 'z'}"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mat.add('z')\n",
    "mat.remove('č')\n",
    "mat.discard('ž')\n",
    "mat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mat.isdisjoint(samoglasnici)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Rječnici"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boje = {'jabuka': 'crveno', 'kruška': 'žuto', 'limun': 'žuto'}\n",
    "boje['grožđe'] = 'plavo'\n",
    "len(boje)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "jabuka je boje: crveno\n",
      "kruška je boje: žuto\n",
      "limun je boje: žuto\n",
      "grožđe je boje: plavo\n"
     ]
    }
   ],
   "source": [
    "for voće, boja in boje.items(): print(f'{voće} je boje: {boja}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [],
   "source": [
    "del boje['limun']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [],
   "source": [
    "for voće in boje:\n",
    "    if voće != 'grožđe':\n",
    "        boje[voće] = 'zeleno'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'grožđe': 'plavo', 'jabuka': 'zeleno', 'kruška': 'zeleno'}"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boje"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'jabuka', 'kruška'}"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "{voće for voće in boje if boje[voće] == 'zeleno'}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Funkcije"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def potencije(x): return x ** 2, x ** 3, x ** 4\n",
    "kvadrat, kub, četvrta = potencije(3)\n",
    "kvadrat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "from skimage import io, transform\n",
    "def thumbnail(slika, širina=100, ime='thumb.png'):\n",
    "    \"\"\"Proizvodi thumbnail za sliku, zadanog imena i širine (visina se određuje proporcionalno).\"\"\"\n",
    "    izvorna_širina, izvorna_visina, *_ = slika.shape\n",
    "    visina = izvorna_visina * širina // izvorna_širina\n",
    "    io.imsave(ime, transform.resize(slika, (širina, visina), mode='constant'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(512, 512, 3)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "astro = data.astronaut()\n",
    "astro.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1cf87092978>"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(astro)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "io.imsave('astro.png', astro)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Veky\\Anaconda3\\lib\\site-packages\\skimage\\transform\\_warps.py:110: UserWarning: Anti-aliasing will be enabled by default in skimage 0.15 to avoid aliasing artifacts when down-sampling images.\n",
      "  warn(\"Anti-aliasing will be enabled by default in skimage 0.15 to \"\n",
      "C:\\Users\\Veky\\Anaconda3\\lib\\site-packages\\skimage\\util\\dtype.py:141: UserWarning: Possible precision loss when converting from float64 to uint8\n",
      "  .format(dtypeobj_in, dtypeobj_out))\n"
     ]
    }
   ],
   "source": [
    "astro_s_diska = io.imread('astro.png')\n",
    "thumbnail(astro_s_diska)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image('thumb.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Funkcije višeg reda"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "def linearna(a, b):\n",
    "    def funkcija(x): return a*x + b\n",
    "    return funkcija"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "f = linearna(a=1/2, b=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<function __main__.linearna.<locals>.funkcija(x)>,\n",
       " ('a', 'b'),\n",
       " ('x',),\n",
       " [0.5, 3])"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f, f.__code__.co_freevars, f.__code__.co_varnames, [c.cell_contents for c in f.__closure__]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13.0"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "def komponiraj(*funkcije):\n",
    "    def kompozicija(x):\n",
    "        for f in reversed(funkcije):\n",
    "            x = f(x)\n",
    "        return x\n",
    "    return kompozicija"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "f = komponiraj(lambda x: x+1, lambda x: x*2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "from functools import partial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "dvana = partial(pow, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4096"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dvana(12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function __main__.komponiraj.<locals>.kompozicija(x)>"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "komponiraj(dvana, dvana, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "256"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "_(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Grananja i petlje"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sve ok\n"
     ]
    }
   ],
   "source": [
    "if 2 < 1: print('Nešto je čudno')\n",
    "elif 2 == 1: print('Još čudnije')\n",
    "else: print('Sve ok')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 nije između 2 i 5\n"
     ]
    }
   ],
   "source": [
    "x = 1\n",
    "if 2 <= x < 5: print(f'{x} je između 2 i 5')\n",
    "else: print(f'{x} nije između 2 i 5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "23\n",
      "30\n",
      "37\n",
      "44\n",
      "51\n",
      "58\n",
      "65\n",
      "72\n",
      "79\n",
      "86\n",
      "93\n"
     ]
    }
   ],
   "source": [
    "for i in range(23, 99, 7): print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Znanstvenici\n",
      "vole\n",
      "koristiti\n",
      "Python\n"
     ]
    }
   ],
   "source": [
    "for riječ in 'Znanstvenici', 'vole', 'koristiti', 'Python': print(riječ)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " p1 =  1.00\n",
      " p2 =  2.88\n",
      "p31 = 38.00\n"
     ]
    }
   ],
   "source": [
    "params = dict(p1=1, p2=2.879, p31=38)\n",
    "for parametar, vrijednost in params.items():\n",
    "    print(f'{parametar:>3} = {vrijednost:5.2f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{16: 4, 9: 3, 4: -2, 1: -1, 0: 0}"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "{x**2: x for x in range(4, -3, -1)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[8, 3, 5, 2, 1, 3]"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "listalisti = [[], [8, 3, 5], [2, 1], [3]]\n",
    "[element for lista in listalisti for element in lista]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "82\t41\t124\t62\t31\t94\t47\t142\t71\t214\t107\t322\t161\t484\t242\t121\t364\t182\t91\t274\t137\t412\t206\t103\t310\t155\t466\t233\t700\t350\t175\t526\t263\t790\t395\t1186\t593\t1780\t890\t445\t1336\t668\t334\t167\t502\t251\t754\t377\t1132\t566\t283\t850\t425\t1276\t638\t319\t958\t479\t1438\t719\t2158\t1079\t3238\t1619\t4858\t2429\t7288\t3644\t1822\t911\t2734\t1367\t4102\t2051\t6154\t3077\t9232\t4616\t2308\t1154\t577\t1732\t866\t433\t1300\t650\t325\t976\t488\t244\t122\t61\t184\t92\t46\t23\t70\t35\t106\t53\t160\t80\t40\t20\t10\t5\t16\t8\t4\t2\t1\t"
     ]
    }
   ],
   "source": [
    "broj = 27\n",
    "while broj > 1:\n",
    "    if broj % 2: broj = broj*3 + 1\n",
    "    else: broj //= 2\n",
    "    print(broj, end='\\t')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Interaktivnost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from ipywidgets import interact\n",
    "import networkx as nx\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_random_graph(n, m, p, generator):\n",
    "    nx.draw(generator(n, m, p))\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7dd63524218549efb32f8fc0643c2c3c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntSlider(value=16, description='n', max=30, min=2), IntSlider(value=5, description='m',…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "interact(plot_random_graph, n=(2, 30), m=(1, 10), p=(0, 1, 1e-3), generator={\n",
    "                   'lobster': lambda n, m, p: nx.random_lobster             (n,    p, p / m),\n",
    "                 'power law': lambda n, m, p: nx.powerlaw_cluster_graph     (n, m, p),\n",
    "     'Newman-Watts-Strogatz': lambda n, m, p: nx.newman_watts_strogatz_graph(n, m, p),\n",
    "               'Erdős-Rényi': lambda n, m, p: nx.erdos_renyi_graph          (n,    p),\n",
    "});"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Zadaci za vježbu"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Napišite funkciju koja prima $n$, a vraća listu neparnih brojeva od $1$ do $n$.\n",
    "2. Napišite funkciju koja rješava kvadratnu jednadžbu.\n",
    "3. Napišite funkciju `trapezint(f, n, a, b)` koja numerički računa integral funkcije $f$ na intervalu $[a,b]$, koristeći trapeznu formulu $$\\int_a^b f(x)\\,dx\\approx\\frac{h}{2}\\sum_{i=1}^n{\\bigl(f(x_{i-1})+f(x_i)\\bigr)}.$$\n",
    "4. Napišite funkciju za numeričko deriviranje oblika `diff(f, x, h=1e-6)`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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