{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Úvod do jazyka Python" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Tato přednáška přestavuje stručný úvod do programovacího jazyka Python. Probrány jsou jak základní tak pokročilejší nástroje, které budou používány po zbytek kurzu. Pro zájemce o hlubší porozumění Pythonu doporučuji kurz [Vědecké programování v Pythonu](https://fjfi.pythonic.eu/) (12PYTH), ze kterého tato přednáška čerpá.\n", "\n", "Další užitečný kurz v češtině je například od [Pyladies](https://naucse.python.cz/course/pyladies/)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "(install-all)=\n", "## Instalace Pythonu a prostředí Jupyter notebook" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "V druhé části kurzu Numerických metod budeme používat moderní prostědí [Jupyter notebook](https://jupyter.org/) (soubory s koncovkou `.ipynb`), které jsou velmi vhodné jak na výuku tak na rychlé otestování a prototipování kódu.\n", "\n", "Máte volbu mezi dvěmi možnostmi, jak sledovat přednášku a mít možnost spouštět kód:\n", "1. Pracovat v *online* interaktivním prostředí - *Binder*, ***JupiterHub FJFI***, *Google Colab*\n", "2. Pracovat *lokálně* na svém počítači" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Postup instalace\n", "\n", "**Online interaktivní prostředí** (doporučuji)\n", "\n", "Pro spuštení interaktivní verze této stránky klikněte ve vrchním menu na a zvolte:\n", "* *Binder* - ve kterém lze Jupyter notebook (JP) upravovat a následně uložit na disk\n", "* *JupyterHub* - místní prostědí běžící u nás na FJFI, umožňuje **uložit** ve vlastním účtu\n", "* *Google Colab* - na vlastní nebezpečí\n", "\n", "
\n", "
Pozor
\n", "
\n", "

\n", "\n", "Spuštění prostředí *Binder* může trvat několik desítek sekund. Jupyter notebook je v tomto prostředí uložen pod dočasným URL. Při zavření okna se postup ztratí! Je nutné tedy upravený soubor před zavřením okna stáhnout. \n", "

\n", "
\n", "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Lokální prostření**\n", "\n", "Pro programování na svém počítači je třeba:\n", "1. Nainstalovat [Python](https://www.python.org/downloads/) (často již nainstalován s OS)\n", "2. Nainstalovat [Jupyter notebook](https://jupyter.org/install)\n", "4. Stáhnout repozitář z GitHubu (ve vrchním menu tlačítko )\n", "5. Pak příkazem `> jupyter notebook` se spustí prostědí v prohlížeči.\n", "\n", "Jako alternativu doporučuji použít IDE (Integrated Development Environment) [Visual Studio Code](https://code.visualstudio.com/), ve kterém je možné přidat rozšíření pro [Python](https://marketplace.visualstudio.com/items?itemName=ms-python.python) a [Jupyter notebook](https://marketplace.visualstudio.com/items?itemName=ms-toolsai.jupyter).\n", "Také je možné přímo stáhnout GitHub repozitáře přes [odpovídající rozšíření](https://marketplace.visualstudio.com/items?itemName=GitHub.vscode-pull-request-github).\n", "\n", "\n", "\n", "
\n", "
Tip
\n", "
\n", "

\n", "\n", "Další možnosti instalace nástrojů jsou popsány [zde](https://fjfi.pythonic.eu/posts/nastroje.html#Doporu%C4%8Den%C3%A1-sada-n%C3%A1stroj%C5%AF).\n", "\n", "Pro pokročilé možnosti *virtuálních prostředí* postupujte podle postupu [zde](https://fjfi.pythonic.eu/posts/prvni-krucky.html#Virtu%C3%A1ln%C3%AD-prost%C5%99ed%C3%AD) nebo [zde](https://valenpe7.github.io/numerical_methods/README.html#run-the-notebooks-offline).\n", "

\n", "
\n", "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Jupyter notebook\n", "\n", "Jupyter notebook je interaktivní dokument obsahující buňky se spustitelným kódem, textem, obrázky, vzorci a apod. Je to velmi vhodný nástroj pro výuku a proto všechen materiál k tomuto cvičení je ve formě těchto notebooků.\n", "\n", "V této kapitolce se krátce seznámíme, jak se s Jupyter notebooky pracuje. Podrobný popis všech vychytávek si můžete přečíst [zde](https://realpython.com/jupyter-notebook-introduction/)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Hello world!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "
Úkol
\n", "
\n", "

\n", "\n", "Napište `print(\"Hello world!\")` a stiskněte `Shift` `+` `Enter` (nebo `Ctrl` `+` `Enter`)\n", "

\n", "
\n", "
" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "## DOPLŇTE ##" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pomocí klávesy `b` vložíte další řádek do JP.\n", "* Stiskněte `b`." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Odstranění řádku: vyberte řádek v JP a stiskněte `d` `+` `d`\n", "\n", "Klávesou `a` vložíte nový řádek nad právě vybraný.\n", "\n", "Přidání buňky s textem:\n", "1. Stiskněte `a`. \n", "2. Vyberte tento nový řádek a stiskněte `m`. Nyní lze do řádku místo kódu zapisovat text.\n", "\n", "Pokud chcete řádek převést na kód, stiskněte `y`.\n", "\n", "\n", "\n", "
\n", "
Tip
\n", "
\n", "

\n", "\n", "Pro nápovědu možných funkcí stiskněte `Tab`.\n", "\n", "Pro zobrazení dokumentace k vybrané funkci stiskněte `Shift`+`Tab`.\n", "

\n", "
\n", "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Jazyk Python\n", "Python je univerzální programovací jazyk, hojně používaný vědeckou komunitou. Existuje spousta knihoven řešící celou řadou úloh za vás. Některé nejpoužívanější knihovny si v tomto kurzu ukážeme.\n", "\n", "Základní vlastnosti Pythonu (vs C++):\n", "* *Dynamicky typovaný* jazyk - typ proměnné se určuje až při běhu programu\n", "* *Silně typovaný* (strongly typed) jazyk - vše má jasně definovaný typed\n", "* *Interpretovaný* jazyk - kód v Pythonu není kompilován, až při běhu dochází k jeho interpretaci\n", "* Obsahuje rozsáhlou *vestavěnou knihovnu modulů* a funkcí\n", "* Přehledná, jednoduchá a čitelná *syntaxe*\n", "* Objekově orientovaný + funkcionální programování" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Komentáře" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Jednořádkový komentář se zadává za znak `#`, více řádků lze zakomentovat obklopením `\"\"\"`:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Hello world!\n" ] } ], "source": [ "# toto je komentar\n", "\"\"\"\n", "Komentar\n", "na vice\n", "radku...\n", "\"\"\"\n", "print(\"Hello world!\") \n", "#print(\"Hello world not printed!\") " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Základní aritmetické operace" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Inicializace proměnných `a`, `b`, základní aritmetické operace a výpis výsledku pomocí funkce `print()`:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Součet 48.0\n", "Rozdíl 38.0\n", "Součin 215.0\n", "Podíl 8.6\n", "Podíl beze zbytku 8.0\n", "Mocnina 147008443.0\n", "Zbytek po dělení 3.0\n" ] } ], "source": [ "a = 43\n", "b = 5\n", "\n", "soucet = a + b\n", "rozdil = a - b\n", "soucin = a * b\n", "podil = a / b\n", "podil_beze_zbytku = a // b\n", "\n", "mocnina = a**b # v C++ pow()\n", "zbytek_po_deleni = a % b\n", "\n", "# vypis\n", "print('Součet ', soucet)\n", "print('Rozdíl ', rozdil)\n", "print('Součin ', soucin)\n", "print('Podíl ', podil)\n", "print('Podíl beze zbytku ', podil_beze_zbytku)\n", "\n", "print('Mocnina ', mocnina)\n", "print('Zbytek po dělení ', zbytek_po_deleni)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Podmínky - if, else a elif" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Za klíčovými slovy `if` a `else` musíme psát `:`." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "musi to byt nula\n", "Tento text se vypise vzdy.\n" ] } ], "source": [ "cislo = 0\n", "\n", "if cislo > 0:\n", " print(cislo, \"je kladne.\")\n", "elif (cislo < 0): # logický výraz může být uzavřen závorky\n", " print(cislo, \"je zaporne\")\n", "else:\n", " print(\"musi to byt nula\")\n", "\n", "print(\"Tento text se vypise vzdy.\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "
Pozor
\n", "
\n", "

\n", "\n", "Vnitřní bloky kódu se v Pythonu **odsazují** o `tab` nebo **čtyři mezery**! (V C++ je block uzavřen závorky `{` a `}`, a odsazení může být libovolné.)\n", "

\n", "
\n", "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Logické hodnoty jsou `True` a `False`, operátory pro složitější logické výrazy jsou `or`, `and` a `not`." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "False True\n", "True\n" ] } ], "source": [ "x = 4\n", "print(x > 1 and x < 3, x == 3 or x == 4)\n", "\n", "print(True == (not False))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Základní datové struktury" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**String** (řetězec)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Abc3Abc3Abc3!\n" ] } ], "source": [ "a = 3 * (\"Abc%i\" % 3) + \"!\" # string lze vytvořit například takto\n", "print(a)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "cislo = 37\n", "cislo = 37\n", "cislo = 37, cislo^2 = 1369\n", "cislo = 37, cislo^2 = 1369\n" ] } ], "source": [ "# různé způsoby tisku čísla a textu\n", "cislo = 37\n", "\n", "print(\"cislo =\", cislo)\n", "print(\"cislo = \" + str(cislo))\n", "print(\"cislo = %i\" % cislo)\n", "print(f\"cislo = {cislo}, cislo^2 = {cislo**2}\")\n", "print(\"cislo = {0}, cislo^2 = {1}\".format(cislo, cislo**2))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Konverze typů**\n", "\n", "Každá proměnná má v Pythonu jasně definovaný typ." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "4 \n", "text \n" ] } ], "source": [ "x = 4\n", "print(x, type(x))\n", "\n", "s = \"text\"\n", "print(s, type(s)) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Mezi datovými typy je možné přecházet pomocí speciálních funkcí:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'3.14'" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "int(\"3\") # string -> int\n", "float(\"3.14\") # string -> float\n", "str(3.14) # float -> string" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Kontejnery\n", "\n", "Kontejnery jsou datové struktury obsahující větší počet prvků.\n", "Mezi základní patří:\n", "* `tuple` - *neměnitelný* (immutable) seznam\n", "* `list` - *měnitelný* (mutable) seznam\n", "* `dics` - *asociativní* pole\n", "* `set` - množina prvků\n", "\n", "Dále uvidíme, že:\n", "* Prvky nemusejí být stejného typu!\n", "* Pro indexování (přístup k jednotlivým prvkům) se používají závorky `[]` a indexuje se od **0**." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Tuple**\n", "\n", "Tuple je n-tice prvků, které po vytvoření nelze měnit!" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "tuple1=(1, 'a', 5)\n", "tuple2=(1, 'a')\n", "tuple3=('a', 'b')\n", "tuple4=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9)\n", "tuple5=()\n", "tuple6=('single',)\n", "tuple7=(0, '1', (0, 1, 2))\n" ] } ], "source": [ "tuple1 = (1, 'a', 5) # Základní syntax vytváření tuple (kulaté závorky)\n", "tuple2 = 1, 'a' # Závorky nejsou povinné, ale... !\n", "tuple3 = tuple([\"a\", \"b\"]) # Pokročilé: Vytvoření tuple z jiného kontejneru\n", "tuple4 = tuple(range(0, 10)) # Pokročilé: Vytvoření tuple z iterátoru / generátoru\n", "tuple5 = () # Prázdný tuple\n", "tuple6 = (\"single\", ) # Tuple s jedním prvkem\n", "tuple7 = 0, \"1\", (0, 1, 2) # Tuple může pochopitelně obsahovat další tuple\n", "\n", "print(f\"tuple1={tuple1}\")\n", "print(f\"tuple2={tuple2}\")\n", "print(f\"tuple3={tuple3}\")\n", "print(f\"tuple4={tuple4}\")\n", "print(f\"tuple5={tuple5}\")\n", "print(f\"tuple6={tuple6}\")\n", "print(f\"tuple7={tuple7}\")" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0\n", "9\n", "8\n" ] } ], "source": [ "print(tuple4[0]) # První prvek\n", "print(tuple4[-1]) # Poslední prvek\n", "print(tuple4[-2]) # Předposlední prvek" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Vyzkoušejte, že skutečně není možné přepsat prvky `tuple`:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "#tuple1[0] = 58" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "V praxi se s tímto typem setkáte při volání funkcí, které vrací více hodnot. Odpovídající proměnné je pak možné *rozbalit* následujícím způsobem: " ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "37 58\n" ] } ], "source": [ "def dvecisla():\n", " return 37, 58\n", "\n", "a, b = dvecisla() # rozbalení tuplu\n", "print(a, b)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**List**\n", "\n", "List je opět n-tice prvků, ale oproti `tuple` je možné prvky měnit. Odpovídá polím (array) v C++. Navíc ale může obsahovat prvky různých typů!" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['a', 'b', 'c']\n", "[0, 0.0, '0.0']\n", "[1, 'a', 5]\n" ] } ], "source": [ "list(), [] # prázdný list\n", "list1 = [\"a\", \"b\", \"c\"] # list vytvoříme pomocí [...]\n", "list2 = [0, 0.0, \"0.0\"] # můžeme tam dát libovolné typy\n", "list3 = list(tuple1) # nebo list vytvořit z tuple\n", "\n", "print(list1)\n", "print(list2)\n", "print(list3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Jazyk Python poskytuje tyto základní operace pro práci se seznamy:" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'append, clear, copy, count, extend, index, insert, pop, remove, reverse, sort'" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# všechny dostupné funkce pro operace s polem\n", "\", \".join(item for item in dir(list) if not item.startswith(\"_\"))" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['a', 'b', 'c', 'd']\n", "['d', 'c', 'b', 'a']\n", "a\n", "['d', 'c', 'b']\n", "['c', 'b']\n" ] } ], "source": [ "list1.append(\"d\") # přidání prvku\n", "print(list1) # list1 se změnil!\n", "list1.sort(reverse=True)\n", "print(list1)\n", "print(list1.pop()) # vyjme poslední prvek\n", "print(list1)\n", "\n", "list1.remove(\"d\") # odstranění prvku(ů)\n", "print(list1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "U kontejnerů typu `tuple` a `list` lze provádět výběr více prvků najednou pomocí **řezů** podle pravidla:\n", "```\n", "[[dolní_mez] : [horní_mez] : [krok]]\n", "```" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\n", "[0, 1]\n", "[0, 1, 2, 3, 4]\n", "[1, 2, 3, 4, 5, 6]\n", "[5, 6, 7, 8, 9]\n", "[7, 8, 9]\n", "[0, 2, 4, 6, 8]\n" ] } ], "source": [ "l = list(range(10))\n", "\n", "print(l[:]) # vyber všechny prvky\n", "print(l[0:2]) # vyber první 2 prvky\n", "print(l[:5]) # vyber prvních 5 prvků\n", "print(l[1:7]) # vyber prvky 1-6\n", "print(l[5:]) # vyber všechny prvky od indexu 5 dál \n", "print(l[-3:]) # poslední tři prvky\n", "\n", "print(l[::2]) # sudé prvky" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Dict** a **Set**\n", "\n", "Tyto kontejnery používat typicky v kurzu nebudeme. Zájemci se o nich můžou vzdělat opět [zde](https://fjfi.pythonic.eu/posts/kontejnery.html)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Opakování - cykly" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**For**\n", "\n", "Zde využijeme funkci `range(min,max,krok)`, která vytvoří sekvenci celých čísel od `min` po `max` ***(prvek max není v sekvenci obsažen)*** s uvedeným krokem. Výchozí hodnoty jsou `min = 0` a `krok = 1`.\n", "Tuto funkci lze použít několika různými způsoby:" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\n", "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\n", "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\n" ] } ], "source": [ "print(list(range(10)))\n", "print(list(range(0, 10)))\n", "print(list(range(0, 10, 1)))" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0\n", "1\n", "2\n", "3\n", "4\n", "5\n", "6\n", "7\n", "8\n", "9\n" ] } ], "source": [ "for i in range(10):\n", " print(i)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1\n", "2\n", "4\n", "8\n", "16\n" ] } ], "source": [ "pole = [1,2,4,8,16] # procházení pole verze 1\n", "for i in range(len(pole)):\n", " print(pole[i])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Foreach**\n", "\n", "Další způsob procházení pole je pomocí *foreach* konstrukce, která v Pythonu má stejný zápis:" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1\n", "2\n", "4\n", "8\n", "16\n" ] } ], "source": [ "pole = [1,2,4,8,16] # procházení pole verze 2\n", "for x in pole:\n", " print(x)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**While**\n", "\n", "Dalším typem cyklu je `while` cyklus, který běží dokud je splněna určitá podmínka." ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0\n", "1\n", "2\n", "3\n", "4\n", "5\n", "6\n", "7\n", "8\n", "9\n" ] } ], "source": [ "x = 0\n", "while x < 10:\n", " print(x)\n", " x = x + 1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "
Tip
\n", "
\n", "

\n", "\n", "`x = x + 1` je možné napsat zkratkou pomocí `x += 1` (ovšem C++ operátor `x++` v Pythonu použít nelze!).\n", "

\n", "
\n", "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Continue**\n", "\n", "Pomocí příkazu `continue` lze přeskočit zbytek iterace v cyklu a skočit na další:" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "a = 1\n", "a = 3\n", "a = 5\n", "Konec, a = 1\n" ] } ], "source": [ "a = 0\n", "while a < 5:\n", " a += 1\n", " if a % 2 == 0: # sudá čísla nebudeme vypisovat\n", " continue\n", " print(\"a = %i\" % a)\n", "else:\n", " a = 1\n", "print(\"Konec, a = %i\" % a)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Break**\n", "\n", "Pomocí příkazu `break` lze předčasně ukončit cyklus:" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Výsledek je: 988\n" ] } ], "source": [ "# pomocí break najdeme největší trojciferené číslo dělitelné 19ti\n", "a = 999\n", "while a > 0:\n", " if a % 19 == 0:\n", " print(\"Výsledek je: %i\" % a)\n", " break\n", " a -= 1\n", "else:\n", " # break nespustí else část\n", " print(\"Nic jsme nenašli\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "
Poznámka
\n", "
\n", "

\n", "\n", "V Pythonu je možné psát `else` blok za `while` cyklus, který se spustí ve chvíli, kdy je podmínka `False`.\n", "

\n", "
\n", "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Definice vlastní funkce\n", "\n", "Vlastní funkce se definují pomocí hesla `def`. Ukážeme si to na výpočtu faktoriálu pomocí rekurze:" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "3628800" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def factorial(n):\n", " if (n < 2):\n", " return n\n", " return n*factorial(n-1)\n", "\n", "factorial(10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Další základní funkce\n", "Python obsahuje několik základních funkcí, které se běžně při psaní kódu používají.\n", "Celý jejich seznam můžete nalézt [zde](https://docs.python.org/3.12/library/functions.html).\n", "Tady je výtah těch nejběžnějších, které se nám budou hodit:\n", "* `dir()` - vrací seznam dostupných funkcí\n", "* `print()` - výpis na standartní výstup\n", "* `len()` - vrací délku řetězce\n", "* `range()` - vrací sekvenci/pole po sobě jdoucích čísel\n", "* `input()` - čtení standartního vstupu\n", "* `str()` - převod proměnné (čísla) na řetězec (string)\n", "* `int()` - převod řetezce na celé číslo\n", "* `float()` - převod řetezce na desetinné číslo\n", "* `type()` - vrací typ proměnné\n", "* `open()` - otevření souboru\n", "* `close()` - zavření souboru\n", "\n", "\n", "
\n", "
Úkol
\n", "
\n", "

\n", "\n", "Vyžádejte si od uživatele číslo pomocí funkce `input()` a vytiskněte jeho druhou mocninu.\n", "

\n", "
\n", "
Tip
\n", "
\n", "

\n", "\n", "Budete potřebovat také funkci `int()` nebo `float()`.\n", "

\n", "
\n", "
\n", "
\n", "
" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "### DOPLŇTE ###" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Import knihoven\n", "Pro využití určité knihovny v kódu je třeba danou knihovnu importovat.\n", "Například knihovnu pro různé matematické funkce přidáme pomocí `import math` (v C++ ekvivalentní `#include `)." ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.7071067811865476\n" ] } ], "source": [ "import math # chceme načíst vestavěný modul math\n", "\n", "print(math.sin(math.pi / 4)) # použijeme funkci sin a konstantu pi" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Využívání knihovny je možné usnadnit specifikováním, které funkce (proměnné) chceme importovat:" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.7071067811865476\n" ] } ], "source": [ "from math import sin, pi # import pouze některých položek\n", "\n", "print(sin(pi / 4))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nebo můžeme místo jména knihovny zadefinovat alias: " ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.7071067811865476\n" ] } ], "source": [ "import math as m\n", "\n", "print(m.sin(m.pi / 4))" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "3628800" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "m.factorial(10) # volání funkce faktorial z knihovny math" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Knihovna numpy\n", "\n", "[NumPy](http://www.numpy.org/) je základní Python knihovna pro práci s *numerickými daty*, konkrétně s 1- až n-rozměrnými maticemi. Implementace je z velké části napsána v C a Fortranu a používá BLAS knihovny. Numpy tak umožňuje pracovat s numerickými daty ve stylu Python kontejnerů (existují samozřejmě rozdíly) a zároveň zachovat rychlost kompilovaných jazyků.\n", "\n", "
\n", "
Poznámka
\n", "
\n", "

\n", "\n", "Numerické knihovny implementovány v rychlém jazyce (C, Fortran) pomocí optimalizovaných a odladěných algoritmů. Proto používání již implementovaných funkcí není jen pohodlnější, ale vede na významně rychlejší kód!\n", "\n", "Je dobrá praxe snažit se vyhnout používání cyklů tam, kde lze využít některou z knihovních funkcí (jednoduchý příklad - násobení dvou polí po prvcích).\n", "

\n", "
\n", "
\n", "\n", "V této kapitolce se načíte:\n", "* Jak pracovat s poli a maticemi v knihovně `numpy`.\n", "* Jak provádět různé algebraické operace.\n", "* Jak efektivně pracovat s daty pomocí různých triků." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np # import knihovny numpy" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "
Tip
\n", "
\n", "

\n", "\n", "Pro nápovědu možných funkcí v knihovně `numpy` stiskněte `Tab` po napsaní `np.`.\n", "\n", "Pro zobrazení dokumentace k vybrané funkci klikněte na funkci a stiskněte `Ctrl` nebo `Shift`+`Tab` (`Ctrl`+`Space` vs VS Code).\n", "

\n", "
\n", "
\n", "\n", "
\n", "
Úkol
\n", "
\n", "

\n", "\n", "Vyzkoušejte si používání nápovědy a dokumentace. Najděte funkci, která vrátí diagonální matici s posloupoností čísel (1, 2, 3) na diagonále.\n", "\n", "Co dělá funkce `np.diagonal()`?\n", "

\n", "
\n", "
" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "### DOPLŇTE ###" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Numpy array\n", "\n", "Vytváření pole (obdoba `list`) je v knihovně `numpy` možné několika způsoby:\n", "* Z již existujícího kontejneru (`list` nebo `tuple`).\n", "* Pomocí knihovní funkce, která pole vygeneruje podle daného pravidla (`arange()`, `ndarray()`, `zeros`, ...).\n", "* Načtením ze souboru." ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([1, 2, 3, 4])" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "vector = np.array([1, 2, 3, 4])\n", "vector" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[1, 2],\n", " [3, 4]])" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "matrix = np.array([[1, 2], [3, 4]])\n", "matrix" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Proměnné `vector` a `matrix` jsou stejného typu (`ndarray`), ale liší se rozměrem - `shape`." ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " \n", "(4,) (2, 2)\n" ] } ], "source": [ "print(type(vector), type(matrix))\n", "\n", "print(vector.shape, matrix.shape)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Celkový počet prvků lze získat pomocí `size`, zatímco dimenze pole pomocí `ndim`:" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "size: 4 dim: 1\n", "size: 4 dim: 2\n" ] } ], "source": [ "print(\"size: %i\" % vector.size, \"dim: %i\" % vector.ndim)\n", "\n", "print(f\"size: {matrix.size}\", f\"dim: {matrix.ndim}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Numpy array vs list**\n", "\n", "Pole v knihovně `numpy` mají několik zásadních výhod pro numerické výpočty:\n", "* Python seznamy (`list`) jsou příliž obecné, dynamicky typované a nepodporují matematické funkce.\n", "* V `numpy` jsou pole staticky typované a homogenní - při vytvoření je určen typ, který je jednotný pro všechny prvky.\n", "* Efektivní uložení v paměti, implementace matematických operací v rychlém jazyce (C, Fortran)." ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "dtype('int32')" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "matrix.dtype" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[1.+0.j, 2.+0.j],\n", " [3.+0.j, 4.+0.j]])" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "matrix_c = np.array([[1, 2], [3, 4]], dtype=complex)\n", "matrix_c" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Změnit typ lze vytvořením nové kopie pole nebo přes speciální funkce knihovny Numpy (`np.int32()`, `np.float32()`, ...):" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "5\n" ] }, { "data": { "text/plain": [ "5.5" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "matrix[1,1] = 5.5\n", "print(matrix[1,1])\n", "\n", "matrix = np.array(matrix, dtype=float)\n", "matrix[1,1] = 5.5\n", "matrix[1,1]" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[1. , 2. ],\n", " [3. , 5.5]], dtype=float32)" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "matrix = np.float32(matrix)\n", "matrix" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Pomocné generátory polí**\n", "\n", "V Numpy existuje množství pomocných funkcí, které výrazně zesnadňují vytváření polí:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`arange()` vygeneruje posloupnost, syntaxe je stejná jako `range()`:" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.arange(0, 10, 1) # argumenty: start, stop, step" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([-1. , -0.9, -0.8, -0.7, -0.6, -0.5, -0.4, -0.3, -0.2, -0.1])" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.arange(-1, 0, 0.1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`linspace()` a `logspace()` vytváří posloupnosti s daným počtem prvků (budou se hodit obzvlášť při různých vizualizacích dat):" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 0. , 2.5, 5. , 7.5, 10. ])" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.linspace(0, 10, 5) # argumenty: start, stop, počet" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([1.e+00, 1.e+01, 1.e+02, 1.e+03, 1.e+04, 1.e+05, 1.e+06, 1.e+07,\n", " 1.e+08, 1.e+09, 1.e+10])" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.logspace(0, 10, 11, base=10) # argumenty: start, stop, počet, základ logaritmu" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`ones()` a `zeros()` vytvoří pole ze samých nul nebo jedniček:" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([1., 1., 1.])" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.ones(3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pokud chceme 2 a více rozměrů, musíme zadat rozměr jako `tuple` nebo `list`:" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(array([[0., 0., 0.],\n", " [0., 0., 0.]]),\n", " array([[0, 0, 0],\n", " [0, 0, 0]]))" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.zeros([2, 3]), np.zeros((2, 3), dtype=int)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Indexování**\n", "\n", "Přístup k prvkům pole je analogický jako u Python seznamů. Pole jde stejným způsobem *řezat*." ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 1, 2, 3, 4],\n", " [ 5, 6, 7, 8],\n", " [ 9, 10, 11, 12],\n", " [13, 14, 15, 16]])" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "matrix = np.array([[1, 2, 3, 4],\n", " [5, 6, 7, 8],\n", " [9, 10, 11, 12],\n", " [13, 14, 15, 16]], dtype=int)\n", "matrix" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "6" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "matrix[1, 1] # prvek na indexu (1,1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Řez vybraným řádkem nebo sloupcem:" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([5, 6, 7, 8])" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "matrix[1, :] # řez druhým řádkem" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 2, 6, 10, 14])" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "matrix[:, 1] # řez druhým sloupcem" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Řezy jsou **mutable**: pokud do nich něco přiřadíme, projeví se to na původním objektu:" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 0, 2, 3, 4],\n", " [ 0, 6, 7, 8],\n", " [ 0, 10, 11, 12],\n", " [ 0, 14, 15, 16]])" ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ "matrix[:, 0] = 0\n", "matrix" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 0, 2, 3, 4],\n", " [ 1, 6, 7, 8],\n", " [ 2, 10, 11, 12],\n", " [ 3, 14, 15, 16]])" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "matrix[:, 0] = np.arange(4)\n", "matrix" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "
Úkol
\n", "
\n", "

\n", "\n", "Vyberte libovolnou submatici matice `matrix` o velikosti 2x2, transponujte a uložte ji do původní matice na stejné místo.\n", "

\n", "
\n", "
" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [], "source": [ "### DOPLŇTE ###" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Vyřezávání pomocí pole indexů**\n", "\n", "V Numpy je navíc možné řezat pomocí pole indexů:" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 1, 6, 7, 8],\n", " [ 3, 14, 15, 16]])" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ "matrix[[1,3], :] # výběr 2. a 4. řádku " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Výběr pomocí masky:" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 0, 4, 3, 16])" ] }, "execution_count": 54, "metadata": {}, "output_type": "execute_result" } ], "source": [ "maska = [[True,False,False,True], \n", " [False,False,False,False], \n", " [False,False,False,False], \n", " [True,False,False,True]]\n", "print(type(maska))\n", "\n", "matrix[maska] # výběr rohových prvků" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 0, 3, 6, 12, 3, 15])" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" } ], "source": [ "matrix[matrix % 3 == 0] # výběr prvků dělitelných 3" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Append** a **Insert**\n", "\n", "Podobně jako u Python polí, Numpy poskytuje funkce na přidání prvku nakonec `append()` a na daný index `insert()`:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 0, 1, 2, 3, 4, -1])" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "array = np.arange(5)\n", "\n", "array1 = np.append(array, -1)\n", "array1" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 0, 1, -1, 2, 3, 4])" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "array2 = np.insert(array, 2, -1)\n", "array2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Maticové operace" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Součet/rozdíl matice a skaláru:" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[2., 2., 2.],\n", " [2., 2., 2.],\n", " [2., 2., 2.]])" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.ones((3, 3)) + 1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Násobení/dělení skalárem:" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([0, 2, 4, 6, 8])" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.arange(0, 5) * 2" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[0.25, 0.25, 0.25],\n", " [0.25, 0.25, 0.25],\n", " [0.25, 0.25, 0.25]])" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.ones((3, 3)) / 4" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Násobení matic a vektorů se provádí pomocí funkce `dot()` nebo operátoru `@`:" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[1 2 3]\n", " [4 5 6]] (2, 3) \n", "\n", "[[1 2]\n", " [3 4]\n", " [5 6]] (3, 2) \n", "\n" ] } ], "source": [ "# matice 2x3\n", "A = np.array([[1,2,3],[4,5,6]])\n", "print(A, A.shape, \"\\n\")\n", "\n", "# matice 3x2\n", "B = np.array([[1,2],[3,4],[5,6]])\n", "print(B, B.shape, \"\\n\")" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(array([[22, 28],\n", " [49, 64]]),\n", " (2, 2))" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "C = np.dot(A,B) # součin matic\n", "C, C.shape" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[22, 28],\n", " [49, 64]])" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "C = A @ B # součin matic\n", "C" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Skalární součin dvou vektorů:" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "10" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.array([1, 2, 3]) @ np.array([3, 2, 1])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "
Pozor
\n", "
\n", "

\n", "\n", "Operace `C`*`C` násobí matice po prvcích (není to maticové násobení)!\n", "

\n", "
\n", "
" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[1856 2408]\n", " [4214 5468]] \n", "\n", "[[ 484 784]\n", " [2401 4096]]\n" ] } ], "source": [ "# maticove nasobeni\n", "print(np.dot(C,C), '\\n')\n", "\n", "# nasobeni po prvcich\n", "print(C*C)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Reshape**\n", "\n", "Funkce `reshape()` umožňuje změnit tvar pole/matice podle požadovaných rozměrů. Je třeba si ovšem dát pozor na *indexové pořadí*, podle kterého se prvky přerovnávají." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]),\n", " array([[ 1, 5, 9, 13],\n", " [ 2, 6, 10, 14],\n", " [ 3, 7, 11, 15],\n", " [ 4, 8, 12, 16]]))" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.arange(1,17), np.reshape(np.arange(1,17), [4,4], order='F') # pořadí indexů 'C' nebo 'F'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Matematické funkce" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`numpy` obsahuje často používané funkce a konstanty (napr. `sqrt()`, `log()`, `log10()`, `sin()`, `abs()`, `e`, `pi`, ...):" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2.23606797749979\n", "1.6094379124341003\n", "0.6989700043360189\n", "-0.9589242746631385\n", "3\n", "2.718281828459045\n", "3.141592653589793\n" ] } ], "source": [ "print(np.sqrt(5))\n", "print(np.log(5))\n", "print(np.log10(5))\n", "print(np.sin(5))\n", "print(np.abs(-3))\n", "print(np.e)\n", "print(np.pi)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Součet prvků v poli je dán funkcí `sum()`:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1683" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.sum(np.arange(0, 100, 3)) # součet čísel dělitelných třemi od 0 do 100 " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Minimální a maximální hodnotu v poli určíme funkcí `min()` a `max()`:" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(1, 6)" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.min(A), np.max(A)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Polohu (index) minimální a maximální hodnoty v poli získáme pomocí funkce `argmin()` a `argmax()`:" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(0, 5)" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.argmin(A), np.argmax(A)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Další užitečné funkce" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Náhoda**\n", "\n", "Pole náhodných čísel v rozmezí 0 az 1 se vygeneruje pomocí funkce `np.random.rand()`:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[0.85196594, 0.66928364],\n", " [0.17104743, 0.51678847],\n", " [0.8152541 , 0.17603683]])" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "np.random.rand(3,2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Statistické funkce**" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Funkce `average()` vrací průměrnou hodnotu; `std()` je směrodatná odchylka a `var()` je rozptyl:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "3.5" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "np.average(A) # průměrná hodnota" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1.707825127659933" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "np.std(A) # směrodaná odchylka (standard deviation)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2.9166666666666665" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "np.var(A) # rozptyl (variance)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Práce se soubory** - můžete se dozvědět [zde](https://fjfi.pythonic.eu/posts/zaklady-numpy.html#Pr%C3%A1ce-se-soubory)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "
Tip
\n", "
\n", "

\n", "\n", "Více se můžete dozvědet v [dokumentaci](https://numpy.org/doc/stable/reference/) nebo opět v kurzu [12PYTH](https://fjfi.pythonic.eu/posts/zaklady-numpy.html).\n", "

\n", "
\n", "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Vizualizace dat - matplotlib.pyplot\n", "\n", "Pro vizualizaci dat v Pythonu existuje obsáhlá knihovna `matplotlib.pyplot`." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1D plot" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Možnost 1 - vykreslování ala Matlab**" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Vygenerujeme $x$ a $y$ hodnoty pro funkci `sin()`:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "x = np.linspace(0,4*np.pi,100)\n", "y = np.sin(x)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nejdříve je potřeba vytvořit obrázek pomocí `figure()`. Vykreslení dat pak provedeme příkazem `plot()`.\n", "Přidáme popisky os pomocí `xlabel()`, `ylabel()` a název grafu pomocí `title()`:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(0, figsize=(5,3))\n", "\n", "plt.plot(x,y) # vykreslení 1D grafu/funkce\n", "\n", "plt.title('six(x)')\n", "plt.xlabel('x')\n", "plt.ylabel('y');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Knihovna matplotlib nabízí základní rozložení více grafů v jednom obrázku pomocí funkcí `subplot()` nebo `subplots()`:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": 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FSkxM1Jw5c9SrVy/NnDlTAwYM0M6dO9W8eXN16NBBDz/8sK6++mpFRESod+/e7q0EAACoafQ4+ph+/frJMAw1bNjQ6igAUCl/Xxxn4MCBevbZZzVkyJByr8+YMUOjRo3SiBEj1KFDB82ZM0ehoaGlpg088MADSk1N1ZdffqnAwMBy9/UtUVBQoJycnFIPwFPs27dPM2fO1HXXXaeYmBgFBQUpKipKt9xyi9avX291PACicAQAWOBkkVMFxeYG2/7a41iZwsJCpaSkKD4+3n0uICBA8fHxWrdunfvcwYMHJUk7d+7Uhg0bNGDAgAqfMykpSREREe5HyRZPgCd4+eWXNW7cOP3000+67rrr9Oijj+ryyy/XRx99pMsuu0yLFi2yOiLg93x+qOrvLfiCusffCYCSYar2AJsaOHy+Kaq2w4cPy+l0KjIystT5yMhI7dixw3180003KTs7W/Xr19ebb76pevUqfi0nTJhQajGznJwcikd4jJ49e2rlypW66qqrSp1fs2aNrrnmGj300EMaPHiwHA6HRQkB+GyPY2Cg+Q52fn6+xUnwW4WF5oIYzMUB/FfJwjgRIYGy2WwWp/Fe69at0/bt27Vx40Z169at0nsdDofCw8NLPQBPcfPNN5cpGiXpiiuuUP/+/XXs2DFt3bq11LU+ffrIZrOV6oWXzDdF4uLi5HA4tGzZslrNDfgTn32b1263q2HDhu5hPKGhofxy4gFcLpcOHTqk0NDQSt8ZB+DbSrbiaOin8xt/T9OmTWW325WZmVnqfGZmpqKioixKBVijpDPgt783TJ8+XVdddZX+9re/KTk5WZL55vSQIUP07bffauHChbr22mvrPC/gq3z6N/eSxrWkeIRnCAgIUExMDIU84MeyShbGYX5juYKCgtStWzclJydr8ODBksw33pKTkzVmzBhrwwF1aO/evVq+fLlatGihTp06lbp25ZVX6oYbbtCSJUvcw1zvuecerVixQjNnzmRPU6CG+XThaLPZ1KJFCzVv3lxFRUVWx8EpQUFBCgjw2VHSAKqAHkcpNzdXu3fvdh/v2bNHaWlpaty4sWJiYpSYmKiEhAR1795dPXv21MyZM5WXl6cRI0ZYmBqoO0VFRRo+fLgKCgo0ffr0cqe4JCUl6fPPP9fEiRPVs2dPvfvuu5owYYLGjh1rQWLAt/l04VjCbrcznw4APMiZcxz91aZNm9S/f3/3ccnCNQkJCZo3b55uv/12HTp0SJMmTVJGRobi4uK0dOnSMgvmAL7I5XLpnnvu0erVqzVq1CgNHz683Ps6deqku+66S/Pnz9dXX32lkSNHaurUqXWcFvAPflE4AgA8S8mqqg1DgyxOYp2SfXcrM2bMGIamwu+4XC6NHDlSCxcu1F133aU5c+ZUen+zZs0kSWFhYZo1a1ZdRAT8EuMFAQB1LrtkjqMf9zgCv6eoqEhRUVGy2Wz6wx/+8Lv3b9u2TTabTTabTc8991wdJCxrx44devTRRxUXF6eGDRvK4XDo/PPP13333aedO3f+7ue7XC6NGDFCb731lu68807Nmzev0uktr7zyiv7xj38oMjJSx48f11tvvVWT3w7gmQ4elP70J+mnn+r0y1I4AgDqXMniOA1ZHAeoUGBgoO6++25J0rJly7Rv375K7587d64kc/XRks+rK4WFhXr44Yd16aWXasaMGdqyZYuys7NVWFion3/+WW+88Ya6dOmijz76qMLnKCka58+fr9tvv10LFiyodKrR+++/r7Fjx6p///7avHmzIiIiNGXKFLZig2/76COpY0fp3Xel+++v0y9N4QgAqHPuxXEoHIFK3XfffZLMomr+/PkV3ldUVKS3335bkjRw4MA63bbF6XTq5ptv1ssvvyyXy6UbbrhB8+fP19q1a7Vs2TJNnjxZ4eHhOnHihP70pz9p7969ZZ6jZHjq/PnzNXToUL399tuVFo3JyckaPny4OnXqpMWLF6tFixYaN26cDhw4oBdffLE2v13AGjk50siR0uDB0qFDUqdO0owZdRqBOY4AgDrH4jhA1bRv315XXHGF1qxZo3nz5mnChAnl3vfpp5/q0KFDkqSRI0eWuV4TW2C9+eabuueee8qc/+tf/6olS5aoQYMG+vDDDxUfH1/qenx8vP74xz/qsssuU35+vv71r39p2rRppe55+umn9dZbb6lBgwZq3769nn322TJfZ/DgwYqLi1NqaqqGDBmi6Ohoff755woPD5ckjRs3Ti+99JKmT5+uBx54QI0bNz7n7xnwCCtXSvfcI/3yi2SzSY89Jj39tORw1GkMCkcAQJ0rWRwnIsR/F8cBquq+++7TmjVr9MMPP+jrr79W3759y9zz5ptvSpKaN2+uG2+8sc6ybdq0yd3D99FHH+nqq68u976uXbuqX79++uKLL7R27doy13/++WdJ5jY1f//738t9jjZt2igsLEzXX3+9goKCtHTpUrVo0cJ9PTw8XI8//rgef/xxJSUl6fnnnz/H7w7wAJ99Jt14o2QY0vnnS2+9JV1xhSVRKBwBAHUumzmO/iMvr+JrdrsUHFy1ewMCpJCQs7s3P9/8pas8NpsUGnp29544Ufrr1JKhQ4fq4YcfVnZ2tt58880yhWNmZqY+//xzSdLw4cNVr17ZX++2bt16zjlatmxZ5tyECRNkGIZuvfXWCovGEjExMZKkrKysMtfmzZunefPmVSlHRkZGhdf+53/+R//zP/9TpecBvMI110idO0s9e0r/+IcUFmZZFApHAECdKna6dPxksSSpIUNVfV+DBhVfu/56acmS08fNm5uFW3muusocrlWiTRvp8OHy7+3eXdq48fRxhw7mEK/ydOggfffd6eMePaTt28u/t3Vr6VTPmCTpyitLf51aEhISoj/96U+aPXu23n//fb300ksKPaOAXbBggYqLzf9T5Q1TlaSOHTvWeK79+/dr+fLlkqQPPvigysNhGzZsWONZAJ9RXCzNmyclJEiBgeZw1K+/lurXtzoZi+MAAOpWzqmiUZLCKRyBKilZJOf48eP64IMPSl0rGabaq1cvdejQoc4yLTmz6K+GCy64oIaTAD5i927zTbJRo6SpU0+f94CiUaLHEQBQx7LyzYVxGjjqKdDO+5c+Lze34mu/XTXz4MGK7/3tXn5n9vz93r3bt1c+/PRMGzdW/d7VqyvOUMO6du2qLl26aPPmzXrzzTfd222sX79e20/1kFbU2yiZezyeq5YtW5bqLSx5zlatWumzzz6r8vM0a9bsnLMAPsUwpFdflR591Bx1ER4ueeAbLBSOAIA6VTK/kRVV/UR13imvrXvPnJdYk/fWwfzGM913330aPXq0Vq1apT179uj888939zaGhobqjjvuqPBzO3XqdM5f/7erqmZmZkqSoqKiamUoLOAX9u+X7r1XWrrUPO7f3xyqempOsCfhrV4AQJ3KYmEc4KwMGzZMISEhMgxD8+bN04kTJ/Tee+9Jkm655Rb3thR15cSJE5KkI0eO1OnXBXzGsmXmfoxLl5pzGf/5T2n5co8sGiV6HAEAdSw7n8IROBsRERG69dZbtWDBAr311lu68MILlZ2dLanyYaqSZFQ0/PYcnHfeeZKkn376Sbt27VK7du1q/GsAPi0mxlyduWtXacECc7EuD0aPIwCgTpXMcWSoKlB9JYvk/PLLL+5tJy644AJdddVVdZ4lPj7e/fGDDz6okydPVnhvbm6uZs+eXRexAM/200+nP77oIunLL6VvvvH4olGicAQA1LEs9xzHIIuTAN7nyiuvVPv27SWd3s/wnnvuqfJWGDXppptuUmxsrCRpxYoV6tKli2bNmqVvvvlGmzdv1sqVKzVnzhzdddddatGihRYuXFjnGQGPkZ8v/eUvUvv20ldfnT7fq5e57YYXYKgqAKBOZTPHETgn9957rx5//HFJUkBAQKkFa+qS3W7Xxx9/rBtuuEHbtm3Tjh07NGbMmArv79GjRx2mAzzIxo3S8OHSzp3m8apV0uWXW5vpLNDjCACoU+45jgxVBc7K8OHD3R9fe+21atmypWVZYmJitGnTJr366qsaMGCAIiMjFRgYKIfDoejoaF111VWaMGGCvv76a82YMcOynIAlioqkp56S+vQxi8boaOmLL6Qnn7Q62VmhxxEAUKdYVRU4N2fuyfh7i+LUBYfDofvvv1/333+/1VEAz7Fjh9nLuGmTeXzHHdKsWVLjxtbmOgcUjgCAOsXiOMC5mTt3riSpSZMmuummmyxOA6BcX39tFo2NGkn/+pdZOHo5CkcAQJ3KZnEc4Kz9+OOP+uCDDyRJI0aMkMPhsDgRADeXSwo4NRNw5Ehp/37zz1Nb13g75jgCAOoUi+MA1bNv3z7t2rVLX3zxhW655RYVFxcrODhY48aNszoaAEkyDOntt6W4OCkryzxns0kTJ/pM0SjR4wgAqEOGYSgrn8IRqI5hw4Zp1apVpc4988wzio6OtigRALfDh6WHHpJOjQTQSy9JkyZZm6mWUDgCAOpMXqFTxS5DktSQoapAtYSGhqp9+/Z65JFHlJCQYHUcAJ99Jt17r5SRIdWrJ02eLI0fb3WqWkPhCACoMyUL4wTZAxQcyGwJoCpWrlxpdQQAZ8rNlR59VHrtNfP4kkukBQukbt2szVXLPKbVnjZtmmw2mx555BGrowAAaol7YZzQQNlsNovTAABwFp588nTR+MgjUkqKzxeNkof0OG7cuFGvvvqqOnfubHUUAEAtyi6Z38hWHAAAbzVpkrRhgzR1qtS/v9Vp6ozlPY65ubkaNmyYXn/9dTVq1MjqOACAWpTFiqoAAG+zdav0xBPm6qmS1KSJtHatXxWNkgcUjqNHj9YNN9yg+Pj43723oKBAOTk5pR4AAO9RsqIqezgCADye0yk9/7zUvbuUlCQtXHj6mh9Ot7B0qOp7772n1NRUbdy4sUr3JyUlacqUKbWcCgBQW9xzHBmqCgDwZHv2SAkJ0po15vGNN0rXXGNtJotZ1uOYnp6usWPH6p133lFwcHCVPmfChAnKzs52P9LT02s5JQCgJmWdMFdVZagqAMAjGYY0d67UubNZNDZoIL3+uvTxx1JUlNXpLGVZj2NKSooOHjyorl27us85nU6tXr1ar7zyigoKCmS320t9jsPhkMPhqOuoAIAacjTXLBwbUTgCADzRmDHSv/5lfnz55dJbb0lt21qbyUNY1uN4zTXXaOvWrUpLS3M/unfvrmHDhiktLa1M0QgA8H5H88zCsWkD3gT0dkbJIhGAD+HfNXTrrZLDIU2fLq1cSdF4Bst6HMPCwtSxY8dS5+rXr68mTZqUOQ8A8A2HTxWOjeuzOI63Cggw33N2uVwWJwFqXsm/65J/5/AD2dnS5s1Sv37mcf/+0i+/SJGRlsbyRPyvAADUmSO5BZKkJvQ4eq3AwEDZ7Xbl5eVZHQWocSdPnlRAQIDq1fOIrc5R21auNOcy3nij9OOPp89TNJbLo/5XrFy50uoIAIBadCS3ZKgqPY7eymazKSwsTDk5OWrWrJlsfrgkPXxXbm6uQkND6XH0dSdPmvsy/vOf5nHbtlJWlqWRvAH/KwAAdSK/sFgnipyS6HH0dhERESoqKtL+/fuZEwafcezYMeXn5ys8PNzqKKhNqalSt26ni8ZRo6QtW8xzqJRH9TgCAHxXSW9jUL0A1Q9iATRvFhoaqpYtW+rXX3/ViRMnFB4ertDQUNntdnog4VUMw1BxcbGys7N1/PhxNWrUSBEREVbHQm2ZNk2aOFEqLjaHo/773+YwVVQJhSMAoE4cKVlRtX4QxYUPCAsLU+vWrZWdna2srCwdOXLE6kjAWXM4HIqMjFSjRo2sjoLalJNjFo033yzNmSM1a2Z1Iq9C4QgAqBMsjON7QkNDFRoaqqioKBUVFbHSKryS3W5XvXr1eEPLFxmGdOyY1LixefzUU1KPHtLgwRJ/39VG4QgAqBMlPY5NWBjH59hsNgUF8fcKwIPs3y/de690+LC0dq0UGCgFBUlDhlidzGuxOA4AoE6UzHFkD0cAQK16/32pUydp6VJp2zYpJcXqRD6BwhEAUCdKhqo2ZagqAKA2HDsm/elP0u23S0ePmiulpqZKvXtbncwnUDgCAOqEe6gqPY4AgJq2bJnZy/juu5Ldbq6eum6ddMklVifzGRSOAIA6cXqOIz2ONWHnzp2Ki4tzP0JCQrR48WKrYwFA3XO5pEmTpH37pPbtpa+/lp5+2pzXiBrD4jgAgDrhXlWVHscacdFFFyktLU2SlJubqzZt2ujaa6+1NhQAWCEgQHrrLelf/5L+/nepfn2rE/kkCkcAQJ0oWRyHVVVr3scff6xrrrlG9fllCYA/KCqSnn1WcjrNPyWzp3HmTEtj+TqGqgIAap1hGDqSxz6OZ1q9erUGDRqk6Oho2Wy2coeZzpo1S23atFFwcLB69eqlDRs2lPtc77//vm6//fZaTgwAHuD776U+fcyhqFOnSjt2WJ3Ib1A4AgBq3fGCYhU5DUkMVS2Rl5en2NhYzZo1q9zrixYtUmJioiZPnqzU1FTFxsZqwIABOnjwYKn7cnJytHbtWl1//fV1ERsArOFySS++KHXtam6v0aiRuRDOxRdbncxvMFQVAFDrSoapNnDUU3Cg3eI0nmHgwIEaOHBghddnzJihUaNGacSIEZKkOXPmaMmSJZo7d67Gjx/vvu+jjz7Sddddp+Dg4Eq/XkFBgQoKCtzHOTk55/gdAEAd2btXGjFCWrHCPB4wQJo7V4qOtjaXn6HHEQBQ60oWxmlMb2OVFBYWKiUlRfHx8e5zAQEBio+P17p160rdW9VhqklJSYqIiHA/WrVqVeO5AaDGFRZKffuaRWNoqLkAzuefUzRagMIRAFDrDrMwTrUcPnxYTqdTkZGRpc5HRkYqIyPDfZydna0NGzZowIABv/ucEyZMUHZ2tvuRnp5e47kBoMYFBUlPPSX17i2lpUkPPSTZbFan8ksMVQUA1LqjJXs41mdhnJoUERGhzMzMKt3rcDjkcPD6A/ACS5aYW2r062cejxwpJSRI9ShdrESPIwCg1pUMVW1Kj2OVNG3aVHa7vUxRmJmZqaioKItSAUAty82V7r9fuvFGafhwKSvLPG+zUTR6AApHAECtO3Kqx5E5jlUTFBSkbt26KTk52X3O5XIpOTlZffr0sTAZANSSr76SYmOl1183C8XbbpMYJeFRKN0BALXucC57OP5Wbm6udu/e7T7es2eP0tLS1LhxY8XExCgxMVEJCQnq3r27evbsqZkzZyovL8+9yioA+ISCAmnyZOm55yTDkGJipHnzpP79rU6G36BwBADUupI5jgxVPW3Tpk3qf8YvRomJiZKkhIQEzZs3T7fffrsOHTqkSZMmKSMjQ3FxcVq6dGmZBXMAwGtlZ0tXXil9+615nJBg7tUYEWFtLpSLwhEAUOtK9nFkcZzT+vXrJ8MwKr1nzJgxGjNmTB0lAoA6Fh4uXXyxtH+/9Npr0pAhVidCJSgcAQC17kge+zgCACTt2SOFhUlNm5pzGWfPloqKJEZTeDwWxwEA1CqXy2CoKgD4O8OQ3nhD6txZeuAB81iSGjemaPQS9DgCAGpV1okiuU79ftCIHkcA8D+ZmdKoUdInn5jHhw5JeXlSgwbW5kK10OMIAKhVJXs4NgwNVKCdZgcA/Mp//yt17GgWjUFB0vPPS19+SdHohehxBADUqsO57OEIAH4nJ0f6y1+k+fPN49hYacECqVMna3PhrPHWLwCgVpUsjNOUFVUBwH8YhrRypRQQII0fL61fT9Ho5ehxBADUqpKFcZqwMA4A+LaCAnM4qs1m7sW4cKF5vm9fa3OhRtDjCACoVSVDVSkcAcCHpaZKXbtKr79++lzfvhSNPoTCEQBQq0oWx2nMUFUA8D3FxdKzz0q9eknbt0vPPWfuywifQ+EIAKhVR3LZwxEAfNKuXdIVV0gTJ5oF5C23SN98IwUGWp0MtYDCEQBQq9xzHOlxBADfYBjSv/4lxcWZhWJEhLli6n/+IzVtanU61BIWxwEA1KrDp1ZVZY4jAPiI774zt9pwuaSrr5bmzZNatbI6FWoZhSMAoFaVDFVtwj6OAOAbOnaUnnpKCg83C8gABjH6A/6WAQC1psjpUvYJc5GEJg0YqgoAXunoUSkhwVz8psTEidLYsRSNfoQeRwBArSmZ3xhgkxqGsFgCAHid//s/acQIaf9+aedOad06c59G+B3eIgAA1JqDOeb8xmZhDgUE8IsGAHiNvDxpzBhpwACzaGzfXnrpJYpGP0aPIwCg1mTknJQkRYYHW5wEAFBl69dLw4eb221I0ujR5v6MoaHW5oKlKBwBALWGwhEAvMyqVdI110hOp3TeedLcudJ111mdCh6AwhEAUGsOugtHFsYBAK/Qt6/Uo4fUtq30yitSo0ZWJ4KHoHAEANSajGyzcIyixxEAPJPLZe7D+Kc/ScHBUr160rJlUoMGVieDh2FxHABArWGoKgB4sL17pfh46d57pUmTTp+naEQ5KBwBALWmZFVVCkcA8CCGIS1YIHXuLH35pbnoTdu2VqeCh2OoKgCg1pT0OEZFUDgCgEc4fFh64AHpv/81j3v3lubPl9q1szYXPB49jgCAWnGyyKnsE0WS6HEEAI/w1VdSx45m0VivnvTss9KaNRSNqBJ6HAEAtSLzVG9jcGCAwoNpbgDAci1bSvn5UocO0ttvS126WJ0IXsTSHsfZs2erc+fOCg8PV3h4uPr06aPPP//cykgAgBpy5oqqNpvN4jQA4Kf27Dn9cZs25oqpKSkUjag2SwvHli1batq0aUpJSdGmTZt09dVX66abbtJ3331nZSwAQA1gRVUAsFBBgfT44+Yw1GXLTp/v1cvcdgOoJksLx0GDBun6669Xu3bt1L59e/39739XgwYN9M0331gZCwBQA1hRFQAs8u23Us+e0nPPSU6nlJxsdSL4AI+ZdOJ0OvWf//xHeXl56tOnT7n3FBQUqKCgwH2ck5NTV/EAANXEiqoAUMecTumFF6SJE6WiIqlZM+m116TBg61OBh9geeG4detW9enTRydPnlSDBg304YcfqkOHDuXem5SUpClTptRxQgDA2WCoKgDUoZ9+khISzJVTJemPf5Ref11q3tzaXPAZlm/HcdFFFyktLU3r16/XQw89pISEBG3fvr3ceydMmKDs7Gz3Iz09vY7TAgCq6qC7cHRYnAQA/MCGDWbRGBYmzZ0rLV5M0YgaZXmPY1BQkC688EJJUrdu3bRx40a9+OKLevXVV8vc63A45HDwCwgAeAP3UFV6HAGgdrhcUsCpfqA77jB7He+8Uzr/fGtzwSdZ3uP4Wy6Xq9Q8RgCA9zEMQ5ksjgMAtee//5U6d5YOHTp97oknKBpRayztcZwwYYIGDhyomJgYHT9+XAsXLtTKlSv1xRdfWBkLAHCOsvKLVFjskiQ1Z6gqANSc7GzpL3+RFiwwj6dPl55/XmK/XNQySwvHgwcP6u6779aBAwcUERGhzp0764svvtC1115rZSwAwDkqGabauH6QHPXsFqcBAB+xYoV0zz1Sero5RPXxx6XJkykaUScsLRzfeOMNK788AKCWsKIqANSgEyfMYagzZ5rHbdtK8+dLfftaGgv+xePmOAIAvB8rqgJADXr22dNF4/33S1u2UDSizlE4AgBqXEa2uTAOK6oCQA14/HGpd29pyRLp1VelBg2sTgQ/ROEIAKhxDFUFgHPwww9msWgY5nF4uLR2rXT99dbmgl+zfB9HAIDvOUjhCADVZxjSv/4lPfaYOa/xggvMoakSC+DAchSOAIAaV9LjGBXBHEcAqJJ9+6SRI6X/+z/z+JprpIEDrc0EnIGhqgCAGpdJjyMAVN1770kdO5pFY3Cw9NJL5setWlmdDHCjxxEAUKOKnC4dzi2UROFY29q0aaPw8HAFBASoUaNG+vLLL62OBKC6Hn9ceu458+Pu3aUFC6SLL7Y2E1AOCkcAQI06eNxcUTXQblPj0CCL0/i+tWvXqgErLALea8gQ6cUXpfHjpSeflAIDrU4ElIvCEQBQozKyzWGqzcOCFRDAYg4AUEpenrR+vXT11eZx797Snj1SixbW5gJ+B3McAQA16vSKqiyMU5nVq1dr0KBBio6Ols1m0+LFi8vcM2vWLLVp00bBwcHq1auXNmzYUOq6zWbTVVddpR49euidd96po+QAzto330hdupjbamzffvo8RSO8AIUjAKBGnV5RlfmNlcnLy1NsbKxmzZpV7vVFixYpMTFRkydPVmpqqmJjYzVgwAAdPHjQfc9XX32llJQUffzxx5o6daq+/fbbuooPoDqKiqRJk6S+faVdu6RmzaSsLKtTAdVC4QgAqFGZOeYcx+ZhFI6VGThwoJ599lkNGTKk3OszZszQqFGjNGLECHXo0EFz5sxRaGio5s6d677nvPPOkyS1aNFC119/vVJTUyv8egUFBcrJySn1AFAHtm83h6M+84zkcknDhklbt0qXXWZ1MqBaKBwBADUqkx7Hc1ZYWKiUlBTFx8e7zwUEBCg+Pl7r1q2TZPZYHj9+XJKUm5urFStW6NJLL63wOZOSkhQREeF+tGKZf6D2vfKK1LWrlJoqNW4sLVokvf221LCh1cmAaqNwBADUqH3HTkiSWlA4nrXDhw/L6XQqMjKy1PnIyEhlZGRIkjIzM3X55ZcrNjZWvXv31t13360ePXpU+JwTJkxQdna2+5Genl6r3wMASdnZUkGBNHCgtG2bdNttVicCzhqrqgIAatSvx/IlSa0ah1qcxLe1bdtWW7ZsqfL9DodDDgcLFgG1yjCkY8fM3kXJ3KOxfXvp1lslG6tMw7vR4wgAqDGFxS4dODVUtWWjEIvTeK+mTZvKbrcrMzOz1PnMzExFRUVZlApApQ4dkm65RbrySunkSbOItNuloUMpGuETKBwBADXmQPYJGYYUHBigZg3o3TpbQUFB6tatm5KTk93nXC6XkpOT1adPHwuTASjXJ59IHTtKH34o/fCD9PXX5nkKRvgQhqoCAGpM+lFzfmPLRqGy8QtTpXJzc7V792738Z49e5SWlqbGjRsrJiZGiYmJSkhIUPfu3dWzZ0/NnDlTeXl5GjFihIWpAZRy/Lg0bpz0xhvm8aWXmovfxMVZGguoDRSOAIAak35qfiPDVH/fpk2b1L9/f/dxYmKiJCkhIUHz5s3T7bffrkOHDmnSpEnKyMhQXFycli5dWmbBHAAWWbNGSkiQ9uwxexYffdTcciOYhcHgmygcAQA1xr0wTiMWxvk9/fr1k2EYld4zZswYjRkzpo4SAagyw5CmTDGLxtatpbfekq66yupUQK1ijiMAoMacHqpKjyMAH2azmcNTH3pI+vZbikb4BQpHAECNYSsOAD7J6ZSmTzfnM5Zo3Vr617+k8HDrcgF1iKGqAIAak37M7HFkqCoAn/Hjj+ZcxpKVUocNk7p3tzYTYIFq9zgmJCRo9erVtZEFAODFThY5deh4gSTvHapKGwfAzTCk116TYmPNojEsTHrzTalbN6uTAZaoduGYnZ2t+Ph4tWvXTlOnTtW+fftqIxcAwMv8eqq3sYGjnhqGBlqc5uzQxgGQJGVkSIMGSQ88IOXlmXMYv/1Wuuce9maE36p24bh48WLt27dPDz30kBYtWqQ2bdpo4MCB+uCDD1RUVFQbGQEAXuDMrTi8dQ9H2jgAcjrNQnHJEikoSHrhBWnFCqlNG6uTAZY6q8VxmjVrpsTERG3ZskXr16/XhRdeqOHDhys6Olrjxo3Trl27ajonAMDDlfQ4tvTy+Y20cYCfs9vN/Rjj4qSUFHN/xgDWkwTO6X/BgQMHtGzZMi1btkx2u13XX3+9tm7dqg4dOuif//xnTWUEAHiBX4+WrKjqnfMbf4s2DvAjycnSF1+cPr7tNmnjRqljR+syAR6m2oVjUVGR/vd//1c33nijWrdurf/85z965JFHtH//fr311ltavny53n//fT399NO1kRcA4KFOD1X13h5H2jjAz5w4IT3yiBQfL919t3To0Olr9dh8ADhTtf9HtGjRQi6XS3feeac2bNiguLi4Mvf0799fDRs2rIF4AABv8at7Kw7v7XGkjQP8SEqKNHy49P335vGQIVKI9/78AmpbtQvHf/7znxo6dKiCg4MrvKdhw4bas2fPOQUDAHiXdPdQVe/tcaSNA/xAcbE0dao5j7G4WIqKkubOlQYOtDoZ4NGqXTgOHz68NnIAALxYbkGxjuWbq4566x6OEm0c4PNOnJD695fWrzePhw6VZs+WmjSxNhfgBVgiCgBwzn49Nb+xYWigwoK9cw9HAH4gJES69FKpYUPpnXekRYsoGoEqonAEAJyz9KMlW3F4b28jAB/166/SgQOnj2fOlL79VvrTnyQv3XMWsAKFIwDgnJX0OLby4hVVAfgYw5AWLpQ6dZJGjDCPJSksTGrVytpsgBeicAQAnLOSHkdvXhgHgA85ckS64w5p2DApK0s6elQ6dszqVIBXo3AEAJyzX917ODJUFYDFPv/c7GV8/33JbpemTJHWrpUaN7Y6GeDV2NkUAHDO0t17ONLjCMAi+fnSo49Kc+aYxxdfLC1YIHXvbm0uwEfQ4wgAOCeGYejXo/Q4ArCYYUjJyebHY8dKqakUjUANoscRAHBOck4U63hBsSSpJT2OAOpSYaFUr54UECDVr29usZGTI11zjdXJAJ9DjyMA4Jykn5rf2LSBQyFBdovTAPAb330n9e4tvfji6XM9elA0ArWEwhEAcE72HM6TJLVuQm8jgDrgckkzZkjdukmbN0v/+Id04oTVqQCfR+EIADgnPx7KlSRd0Ky+xUkA+LxffjF7FB99VCookK6/Xtq4UQphfjVQ2ygcAQDn5MdDZo/jBc0aWJwEgM8yDGnePHObjZUrzfmMr74qffqp1KKF1ekAv8DiOACAc/LjwZIeRwpHALXkp5+k+++Xioqkyy6T5s+XLrjA6lSAX6FwBACcNZfL0E+HTxWOzSkcAdSSCy6Qpk6Vioulxx6T7CzEBdQ1S4eqJiUlqUePHgoLC1Pz5s01ePBg7dy508pIAIBq2J99QieLXAq029SKPRwB1JScHLOHcfPm0+f++ldp/HiKRsAilhaOq1at0ujRo/XNN99o2bJlKioq0nXXXae8vDwrYwEAqqhkfmObJvVVz860eQA1YM0aKTZWev11KSFBcjqtTgRAFg9VXbp0aanjefPmqXnz5kpJSdGVV15pUSoAQFX9dIj5jQBqSEGBNHGi9MIL5mI4rVtLr7xCDyPgITxqjmN2drYkqXHjxuVeLygoUEFBgfs4JyenTnIBAMrn3oqjOVtxADgHW7ZId90lbdtmHo8YIc2cKYWHWxoLwGkeM67I5XLpkUceUd++fdWxY8dy70lKSlJERIT70apVqzpOCQA4048H2YoDwDnatEnq0cMsGps1kxYvlubOpWgEPIzHFI6jR4/Wtm3b9N5771V4z4QJE5Sdne1+pKen12FCAMBv/chQVQDnqmtX6fLLpcGDzeLxppusTgSgHB4xVHXMmDH69NNPtXr1arVs2bLC+xwOhxwORx0mAwBUJOdkkQ4eN6cPtG3GUFUAVWQY0jvvSEOGSPXrSwEB0scfmx/bbFanA1ABS3scDcPQmDFj9OGHH2rFihU6//zzrYwDAKiGn06tqNo8zKGw4ECL0wDwChkZ0qBB0vDh5n6MJRo0oGgEPJylPY6jR4/WwoUL9dFHHyksLEwZGRmSpIiICIWEsB8YAHiyHw8yTBVANXzwgfTgg9KRI5LDIbVrZ3UiANVgaY/j7NmzlZ2drX79+qlFixbux6JFi6yMBQCoAlZUBVAlWVlmD+PQoWbR2KWLlJIijRtndTIA1WBpj6NhGFZ+eQDAOSgZqkqPI4AKbdok3XyzlJ5uzmWcMEGaNEkKCrI6GYBq8ojFcQAA3ocVVQH8rvPOk/LypAsvlObPl/r0sToRgLNE4QgAqLZip0s/HznV49icwhHAGX7+WWrTxvy4RQtp6VKpQwdz1VQAXstj9nEEAHiP9GMnVOQ0FBJoV4vwYKvjAPAERUXSlCnmojeLF58+36MHRSPgAygcAQDVVrKiattm9RUQwBL6gN/bsUPq21d66impuFhatszqRABqGIUjAKDamN8IQJLkckkvv2yulLpxo9SwobRwoTRrltXJANQwCkcAQLWVFI5tmzH8zCpZWVnq3r274uLi1LFjR73++utWR4K/+fVXacAA6eGHpZMnpeuuk7Ztk+680+pkAGoBi+MAAKptt3uoKj2OVgkLC9Pq1asVGhqqvLw8dezYUTfffLOaNGlidTT4i7Q0aflyKSREev556c9/lmwMXQd8FYUjAKBaXC5DOzOOS5IuiQqzOI3/stvtCg0NlSQVFBTIMAz2R0btc7nM4tBmk268UXruOemPf5QuusjqZABqGUNVAQDV8vORPOUVOhUcGECP4zlYvXq1Bg0apOjoaNlsNi0+cxXKU2bNmqU2bdooODhYvXr10oYNG0pdz8rKUmxsrFq2bKnHHntMTZs2raP08Euffy517izt33/63GOPUTQCfoLCEQBQLd/tz5EkXRwVLjsrqp61vLw8xcbGalYFi4gsWrRIiYmJmjx5slJTUxUbG6sBAwbo4MGD7nsaNmyoLVu2aM+ePVq4cKEyMzPrKj78SW6u9NBD0vXXS999Jz3zjNWJAFiAwhEAUC0lhWOH6HCLk3i3gQMH6tlnn9WQIUPKvT5jxgyNGjVKI0aMUIcOHTRnzhyFhoZq7ty5Ze6NjIxUbGys1qxZU+HXKygoUE5OTqkH8LvWrZPi4qQ5c8zjsWOlf/7T0kgArEHhCAColu0HzILjUgrHWlNYWKiUlBTFx8e7zwUEBCg+Pl7r1q2TJGVmZur4cXOuaXZ2tlavXq2LKhkymJSUpIiICPejVatWtftNwLsVFkpPPildfrn0449Sy5bmQjgzZ5qL4QDwOxSOAIAqMwxD2/dnS5IujY6wOI3vOnz4sJxOpyIjI0udj4yMVEZGhiTpl19+0RVXXKHY2FhdccUV+stf/qJOnTpV+JwTJkxQdna2+5Genl6r3wO83IwZ0tSp5mI4w4dLW7dK11xjdSoAFmJVVQBAlR08XqDDuYWyB9h0MSuqWqpnz55KS0ur8v0Oh0MOh6P2AsG3PPyw9Omn0rhx0i23WJ0GgAegxxEAUGXfneptvKBZfQUH2i1O47uaNm0qu91eZrGbzMxMRUVFWZQKPu3nn80VUl0u8zg0VFqzhqIRgBuFIwCgyr7bVzK/kWGqtSkoKEjdunVTcnKy+5zL5VJycrL69OljYTL4HMOQ3nzT3GbjhRekF188fc3GqskATmOoKgCgykpWVGVhnHOXm5ur3bt3u4/37NmjtLQ0NW7cWDExMUpMTFRCQoK6d++unj17aubMmcrLy9OIESMsTA2fcvCgdP/90kcfmcd9+0p//KO1mQB4LApHAECVlayo2qEFheO52rRpk/r37+8+TkxMlCQlJCRo3rx5uv3223Xo0CFNmjRJGRkZiouL09KlS8ssmAOclY8+kkaNkg4dkgIDzb0Z//pXyc4QdADlo3AEAFRJzski7T2aL4k9HGtCv379ZBhGpfeMGTNGY8aMqaNE8BvPPitNnGh+3KmTtGCBFBtrbSYAHo85jgCAKtl+apjqeQ1D1DA0yOI0AM7aoEFScLC5GM7GjRSNAKqEHkcAQJUwvxHwUidPSl9/fXofxthY6aefpBYtrM0FwKvQ4wgAqJKSrThYURXwImlpUo8e0h/+IKWmnj5P0QigmigcAQBVsp0eR8B7OJ1SUpLUs6e0bZvUuLF07JjVqQB4MYaqAgB+18kip3YdzJXEwjiAx9u9W0pIkNauNY+HDJFefVVq1szaXAC8Gj2OAIDftSszV06XoUahgWoREWx1HAAVmTtXioszi8bwcGnePOl//5eiEcA5o8cRAPC7tvyaJUnqeF6EbDabtWEAVCwnR8rLk/r3l958U2rd2upEAHwEhSMA4Hel/GLOjeoa08jiJADKOHZManTq/+bDD5sL3wwdKgUwsAxAzeEnCgDgd238+agkqUebxhYnAeB27Jh0113mAjh5eea5gADp9tspGgHUOH6qAAAqdSD7hH49dkIBNikupqHVcQBI0vLlUqdO0jvvmHsyrlhhdSIAPo7CEQBQqU0/m8NUO0SHq4GDGQ6ApfLzzeGo114r7dsntWsnff21NGiQ1ckA+Dh+AwAAVKpkfmP31gxTBSy1caM0fLi0c6d5/Oc/S889J9Wvb20uAH6BwhEAUCnmNwIe4tlnzaKxRQtzxdQBA6xOBMCPUDgCACp0/GSRvj+QI0nq3oYVVQFLzZkjRUZK06ZJjXkjB0DdYo4jAKBCm/dmyWVIrRqHKDI82Oo4gP9wuaSXXzaHo5Zo0UJ67TWKRgCWoMcRAFChTSXDVJnfCNSd9HRpxAgpOdk8vv126aqrrM0EwO/R4wgAqNCmUwvjdGOYKlD7DEN6+21zm43kZCkkRJo1S7rySquTAQA9jgCA8hU5Xdq8N0sSC+MAte7IEenBB6UPPjCPe/WS5s+X2re3NhcAnELhCAAo1/b9OTpR5FRESKAubNbA6jiA7zIMKT5eSkuT6tWTJk2SJkwwPwYAD8FQVQBAuUq24ejeupECAmwWpwF8mM0mPfOM1KGD9M030sSJFI0APA4/lQAA5dr0M/MbgVqzdq05PHXQIPP4xhvNfRkDA63NBQAVoMcRAFCGy2Wc0ePI/EagxhQWSk88IV1xhXT33dKvv56+RtEIwIPR4wgAKGPb/mwdyStU/SC74lo1tDoO4Bu2bZPuukvassU8HjRIasD8YQDegR5HAEAZK3cekiT1vbCpgurRVADnxOmUXnhB6tbNLBqbNDFXT50/X2rY0Op0AFAl9DgCAMpYufOgJKnfRc0tTgJ4uaIi6dprpVWrzOMbb5Ref12KirI2FwBUE28jAwBKycovVFp6liSp30XNrA0DeLvAQKlzZ6l+fbNg/PhjikYAXonCEQBQyupdh+UypPaRDRTdMMTqOID3OXhQSk8/fTxtmvTtt9J995lbbwCAF6JwBACUwjBV4BwsXix17CgNG2bObZSk0FCpbVtLYwHAubK0cFy9erUGDRqk6Oho2Ww2LV682Mo4AOD3XC5Dq04tjNOvPcNUgSrLzpZGjJCGDJEOHZKysqTDh61OBQA1xtLCMS8vT7GxsZo1a5aVMQAAp5y5DUf3NuzfCFTJypXmPMZ588yhqI8/Lm3cKEVGWp0MAGqMpauqDhw4UAMHDrQyAgDgDGzDAVRDQYH0xBPSP/8pGYZ0/vnmFhuXX251MgCocV61HUdBQYEKCgrcxzk5ORamAQDfw/xGoJqWLTOLxlGjpH/8QwoLszoRANQKryock5KSNGXKFKtjAIBPYhsOoAqKi80/69WTHA7p7belvXvN/RkBwId51TikCRMmKDs72/1IP3OpawDAOWEbDuB37N4tXXmllJR0+lznzhSNAPyCV/U4OhwOORwOq2MAgE/67NsDkqSrL2ZBD6AUw5BefVV69FEpP1/64Qdp7FgpPNzqZABQZ7yqxxEAUDtyThZpxan5jX+MjbY4DeBBDhyQbrhBeughs2js319KTaVoBOB3LO1xzM3N1e7du93He/bsUVpamho3bqyYmBgLkwGAf/liW4YKi126sHkDXdKCxT0ASdL775sF49Gj5nzGadOkhx+WAnjfHYD/sbRw3LRpk/r37+8+TkxMlCQlJCRo3rx5FqUCAP/z8Zb9kqSbYqNls9ksTgN4gP37pYQE6eRJqWtXacECqUMHq1MBgGUsLRz79esnwzCsjAAAfu/Q8QJ9vfuwJGkQw1QBU3S09MILUkaGNHGiFBRkdSIAsJRXLY4DAKh5S77dL5chxbZqqDZN61sdB7BGfr40frx0xx3SZZeZ50aPtjYTAHgQCkcA8HNnDlMF/NLGjdLw4dLOndLnn0vbt0uBgVanAgCPwuxuAPBj6Ufzlbo3SwE26cbOLayOA9StoiLpqaekPn3MojE6Wpo1i6IRAMpB4QgAfqykt7HPBU3UPDzY4jSoriFDhqhRo0a69dZbrY7ifXbsMIekTpkiOZ3mENWtW6XrrrM6GQB4JApHAPBThmHok1OFI3s3eqexY8dq/vz5VsfwPt99J3XpIm3aJDVqJL37rvlo3NjqZADgsSgcAcBPbU7P0o6M4wqqF6A/XMowVW/Ur18/hYWx72a1deggXXON2bu4davZ2wgAqBSFIwD4qXlf/yzJ7G2MCGVOV11bvXq1Bg0apOhoc+/MxYsXl7ln1qxZatOmjYKDg9WrVy9t2LCh7oP6AsOQ3n9fys42j2026b33pKVLpfPOszYbAHgJCkcA8EOZOSf12dYDkqR7LmtjbRg/lZeXp9jYWM2aNavc64sWLVJiYqImT56s1NRUxcbGasCAATp48GAdJ/Vyhw9Lt90m3X679PDDp883aGAWkACAKmE7DgDwQ++s36til6HurRup43kRVsfxSwMHDtTAgQMrvD5jxgyNGjVKI0aMkCTNmTNHS5Ys0dy5czV+/Phqf72CggIVFBS4j3Nycqof2tt89pl0771SRoZUr57Uvr3Z+0jBCADVRo8jAPiZgmKnFq7fK0m6p28ba8OgXIWFhUpJSVF8fLz7XEBAgOLj47Vu3bqzes6kpCRFRES4H61ataqpuJ4nN1d64AHphhvMorFDB2n9eunJJykaAeAsUTgCgJ/5bOsBHc4tUFR4sAZcGmV1HJTj8OHDcjqdioyMLHU+MjJSGRkZ7uP4+HgNHTpUn332mVq2bFlpUTlhwgRlZ2e7H+np6bWW31LbtkmxsdJrr5nHjzxirp7ataulsQDA2zFUFQD8zLy1v0iS7uodo0A77x96s+XLl1f5XofDIYfDUYtpPERUlJSXJ7VqJb31ltS/v9WJAMAnUDgCgB/ZvPeYtqRnKcgeoDt6xlgdBxVo2rSp7Ha7MjMzS53PzMxUVBS9xGX88osUE2MOQ23aVFqyRLrwQimC+bsAUFN4qxkA/Mirq36SJA2KjVbTBn7Q++SlgoKC1K1bNyUnJ7vPuVwuJScnq0+fPhYm8zBOp/T88+aiNwsXnj7frRtFIwDUMHocAcBPbEnP0tLvMmSzSQ9c1dbqOH4vNzdXu3fvdh/v2bNHaWlpaty4sWJiYpSYmKiEhAR1795dPXv21MyZM5WXl+deZdXv7dkjJSRIa9aYx198IQ0bZm0mAPBhFI4A4Cde+L+dkqQhXc5T+8gwi9Ng06ZN6n/G/LvExERJUkJCgubNm6fbb79dhw4d0qRJk5SRkaG4uDgtXbq0zII5fscwpLlzzUVvcnPN/RhnzpRGjrQ6GQD4NApHAPAD6348ojW7DivQbtO4+PZWx4Gkfv36yTCMSu8ZM2aMxowZU0eJvEBmpnT//dLHH5vHl19uLoDTlh50AKhtzHEEAB9nGIae/2KHJOmOHjFq1TjU4kTAWfruO7NoDAqSnntOWrmSohEA6gg9jgDg41bsOKjUvVkKDgzQX66+0Oo4QPW4XFLAqfe5r75amjFDuuYaqXNna3MBgJ+hxxEAfJjLZej5L8y5jQmXtVHz8GCLEwHVsHKlWSD+9NPpc+PGUTQCgAUoHAHAh729/hftyDiuMEc9PXjlBVbHAarm5EkpMVHq398cnjppktWJAMDvMVQVAHzUvqwTmv65Obfxf/5wkRrVD7I4EVAFqanS8OHS9u3m8ahR0j/+YW0mAAA9jgDgiwzD0N8+3Kq8Qqe6tW6kYb1aWx0JqFxxsfT3v0u9eplFY2Sk9Mkn0muvSWFsHwMAVqNwBAAf9Mm3B/TlzkMKsgdo2s2dFBBgszoSULk5c6S//c0sIG+5Rdq2TbrxRqtTAQBOoXAEAB9zLK9QUz7+TpI0uv+FahdJbw28wKhR0lVXSQsWSP/5j9S0qdWJAABnoHAEAB9iGIb+9tE2HckrVPvIBnqoHwviwEPt328ugFNUZB47HNKXX0p33SXZ6CEHAE/D4jgA4EPmfv2zlnx7QPUCbJp+S2cF1eP9QXigRYukhx6Sjh2TGjY8vWoqBSMAeCx+owAAH7H+pyOa+tn3kqQnrr9EXWIaWZwI+I2jR6U//Um64w6zaOzWTRo61OpUAIAqoHAEAB+QmXNSoxdultNl6Ka4aI3o28bqSEBp//d/UqdO0rvvSna72cu4bp10ySVWJwMAVAFDVQHAyxUUO/Xnd1J1OLdAF0eFKenmTrIx5A+eZOZMadw48+P27c0FcHr2tDQSAKB66HEEAC9W5HTpLws3K+WXYwoLrqc5d3VTaBDvCcLDDBwohYZKo0dLmzdTNAKAF+K3CwDwUk6XocT3t+j/tmcqqF6A5tzVTW2a1rc6FmCulLpypXTttebxRRdJu3ZJ0dGWxgIAnD16HAHAC7lchh7/32/1yZb9CrTbNOeurup7IfvewQN8/73Up480YID01Venz1M0AoBXo3AEAC9T7HTpiQ+36oOUXxVgk166o4uuvjjS6ljwdy6X9OKLUteuUkqKuc1GVpbVqQAANYShqgDgRXJOFmn0O6las+uwbDbpH7fFamCnFlbHgr/bu1caMUJascI8/sMfpDfeoJcRAHwIhSMAeIn0o/kaOW+jdh3MVUigXTPviNOAS6OsjgV/99570gMPSDk55gI4L7wgPfigxMq+AOBTKBwBwAus2XVI4xal6XBuoSLDHXojoYc6nhdhdSxAysszi8bevaX586V27axOBACoBRSOAODBThQ6NX3pDs1b+7MkqUOLcL1xT3e1iAixNhhQYuRIqX596dZbpXr8WgEAvoqf8ADgobakZynx/TT9eChPknR3n9aaMPAShQTZLU4GnMFmk+64w+oUAIBaRuEIAB7mYM5JPf/FTn2Q+qsMQ2oe5tBzt3ZWv4uaWx0NAAD4KQpHAPAQeQXFevPrPfrXyh+VX+iUJA2Oi9bkQZeqUf0gi9MBAAB/RuEIABY7klugeWt/1vx1vyj7RJEkqUtMQ028sYO6xjSyOB0AAACFIwBYwjAMbfk1W+9vStf/pvyqgmKXJKlNk1CNu7a9/hgbLRvbGQAAAA9B4QgAdWh/1gkt+faA/pOSrh8yc93nO7eM0INXXaABl0bJHkDBCAAAPAuFIwDUIpfL0PYDOUr+/qCWfZ+hbfty3Ncc9QL0h45RuqNHjHq3bUwPIwAA8FgUjgBQgwqLXdqZcVwpvxzVup+OaP2eo8rKL3Jft9mk7q0baXCX83Rj52hFhARamBYAAKBqKBwB4CwdzSvUD5nHtSvzuHZmHtfWX7P1/YHjKnS6St0XGmTXZRc00XUdonT1Jc3VtIHDosQAAABnxyMKx1mzZun5559XRkaGYmNj9fLLL6tnz55WxwLgxwzDUM6JYmXknNT+7BM6kHVSB7JP6Jcj+frlaL5+OZJXqifxTBEhgYpt1VC9zm+sPhc0UafzIhRoD6jj7wAAAKDmWF44Llq0SImJiZozZ4569eqlmTNnasCAAdq5c6eaN2ezawBnxzAMFTpdOlnoUn5RsfIKnMorKFZeQbFyC4qVc7JYOSeKdPxksY7lFyorv1DH8ot0LL9Qh48X6HBuYZmew/K0bBSi9pFhahfZQJdGRyi2ZYRiGocyXxEAAPgUywvHGTNmaNSoURoxYoQkac6cOVqyZInmzp2r8ePH1/rXP3S8QCm/HK31rwP/Yxhn+Xln+ZzGbz7zzHt/+2nGGRdLPiz5fPex+7x5v3HqwJDxm2uSy33d/NPlOvWncepzT93jKrnXMOR0SU7DkMtlnP7zjI+LTx0XuwwVO10qOvVnsdNQkctQUbFLRU6XCp0uFRSV/OnUyeLTfzpdZ/mXcIaIkEC1iAhWdMMQRUUEK6ZxqFo3DlXrJvXVukmo6jss/zEKAABQ6yz9jaewsFApKSmaMGGC+1xAQIDi4+O1bt26MvcXFBSooKDAfZyTk1Pmnur6/kCOHnw79ZyfB4DnsgfYVD/IrgaOeqp/6hEeEqjwYPPPiJBANQoNVKPQIDUKDVLTMIeahTnUpH6QggPtVscHAACwnKWF4+HDh+V0OhUZGVnqfGRkpHbs2FHm/qSkJE2ZMqVGM4SHBKp760Y1+pyoWb4y4s+mc/hGKvnUMy/99rU682uWuWY7fU95r7HNdvqzbTad8bF53vwcmwJsJdfN5wk4dbNN5sfmdZv74wDbqfsCbLKXnDv1sT3ApoAAm+oFmB+bfwYo0H7qY3uAguw2BdoDVO/Ux0H1AhRktyuoXoCCAwPkqGdXcGCAggPtCg60KzTIzvxCAACAc+RVY6wmTJigxMRE93FOTo5atWp1Ts8Z16qhPnjosnONBgAAAAA+y9LCsWnTprLb7crMzCx1PjMzU1FRUWXudzgccjhYxh4AAAAA6pKl47eCgoLUrVs3JScnu8+5XC4lJyerT58+FiYDAAAAAJSwfKhqYmKiEhIS1L17d/Xs2VMzZ85UXl6ee5VVAAAAAIC1LC8cb7/9dh06dEiTJk1SRkaG4uLitHTp0jIL5gAAAAAArGF54ShJY8aM0ZgxY6yOAQAAAAAoB2vUAwAAAAAqReEIAICXGjJkiBo1aqRbb73V6igAAB9H4QgAgJcaO3as5s+fb3UMAIAfoHAEAMBL9evXT2FhYVbHAAD4AQpHAABqwerVqzVo0CBFR0fLZrNp8eLFZe6ZNWuW2rRpo+DgYPXq1UsbNmyo+6AAAFQBhSMAALUgLy9PsbGxmjVrVrnXFy1apMTERE2ePFmpqamKjY3VgAEDdPDgQfc9cXFx6tixY5nH/v376+rbAABAkodsx3G2DMOQJOXk5FicBABgpZJ2oKRd8AQDBw7UwIEDK7w+Y8YMjRo1SiNGjJAkzZkzR0uWLNHcuXM1fvx4SVJaWlqN5SkoKFBBQYH7ODs7WxJtKAD4u6q2oV5dOB4/flyS1KpVK4uTAAA8wfHjxxUREWF1jN9VWFiolJQUTZgwwX0uICBA8fHxWrduXa18zaSkJE2ZMqXMedpQAID0+22oVxeO0dHRSk9PV1hYmGw221k/T05Ojlq1aqX09HSFh4fXYELvxutSMV6b8vG6VIzXpnw19boYhqHjx48rOjq6BtPVnsOHD8vpdCoyMrLU+cjISO3YsaPKzxMfH68tW7YoLy9PLVu21H/+8x/16dOn3HsnTJigxMRE97HL5dLRo0fVpEkT2tBawOtSMV6b8vG6VIzXpnx13YZ6deEYEBCgli1b1tjzhYeH84+xHLwuFeO1KR+vS8V4bcpXE6+LN/Q01rTly5dX+V6HwyGHw1HqXMOGDWssC/+2y8frUjFem/LxulSM16Z8ddWGsjgOAAB1rGnTprLb7crMzCx1PjMzU1FRURalAgCgYhSOAADUsaCgIHXr1k3Jycnucy6XS8nJyRUONQUAwEpePVS1pjgcDk2ePLnMEB5/x+tSMV6b8vG6VIzXpny+/Lrk5uZq9+7d7uM9e/YoLS1NjRs3VkxMjBITE5WQkKDu3burZ8+emjlzpvLy8tyrrHoLX/47PBe8LhXjtSkfr0vFeG3KV9evi83wpLXLAQDwEStXrlT//v3LnE9ISNC8efMkSa+88oqef/55ZWRkKC4uTi+99JJ69epVx0kBAPh9FI4AAAAAgEoxxxEAAAAAUCkKRwAAAABApSgcAQAAAACV8vvCcdasWWrTpo2Cg4PVq1cvbdiwwepIlktKSlKPHj0UFham5s2ba/Dgwdq5c6fVsTzOtGnTZLPZ9Mgjj1gdxSPs27dPd911l5o0aaKQkBB16tRJmzZtsjqWpZxOpyZOnKjzzz9fISEhuuCCC/TMM8/IH6eWr169WoMGDVJ0dLRsNpsWL15c6rphGJo0aZJatGihkJAQxcfHa9euXdaERZXRhpZFG1o1tKGl0YaWRRt6mqe0oX5dOC5atEiJiYmaPHmyUlNTFRsbqwEDBujgwYNWR7PUqlWrNHr0aH3zzTdatmyZioqKdN111ykvL8/qaB5j48aNevXVV9W5c2ero3iEY8eOqW/fvgoMDNTnn3+u7du36x//+IcaNWpkdTRLTZ8+XbNnz9Yrr7yi77//XtOnT9dzzz2nl19+2epodS4vL0+xsbGaNWtWudefe+45vfTSS5ozZ47Wr1+v+vXra8CAATp58mQdJ0VV0YaWjzb099GGlkYbWj7a0NM8pg01/FjPnj2N0aNHu4+dTqcRHR1tJCUlWZjK8xw8eNCQZKxatcrqKB7h+PHjRrt27Yxly5YZV111lTF27FirI1nu8ccfNy6//HKrY3icG264wRg5cmSpczfffLMxbNgwixJ5BknGhx9+6D52uVxGVFSU8fzzz7vPZWVlGQ6Hw3j33XctSIiqoA2tGtrQ0mhDy6INLR9taPmsbEP9tsexsLBQKSkpio+Pd58LCAhQfHy81q1bZ2Eyz5OdnS1Jaty4scVJPMPo0aN1ww03lPq34+8+/vhjde/eXUOHDlXz5s3VpUsXvf7661bHstxll12m5ORk/fDDD5KkLVu26KuvvtLAgQMtTuZZ9uzZo4yMjFL/pyIiItSrVy9+Hnso2tCqow0tjTa0LNrQ8tGGVk1dtqH1avTZvMjhw4fldDoVGRlZ6nxkZKR27NhhUSrP43K59Mgjj6hv377q2LGj1XEs99577yk1NVUbN260OopH+emnnzR79mwlJibqiSee0MaNG/Xwww8rKChICQkJVsezzPjx45WTk6OLL75YdrtdTqdTf//73zVs2DCro3mUjIwMSSr353HJNXgW2tCqoQ0tjTa0fLSh5aMNrZq6bEP9tnBE1YwePVrbtm3TV199ZXUUy6Wnp2vs2LFatmyZgoODrY7jUVwul7p3766pU6dKkrp06aJt27Zpzpw5ft3ovf/++3rnnXe0cOFCXXrppUpLS9Mjjzyi6Ohov35dAH9BG3oabWjFaEPLRxvqefx2qGrTpk1lt9uVmZlZ6nxmZqaioqIsSuVZxowZo08//VRffvmlWrZsaXUcy6WkpOjgwYPq2rWr6tWrp3r16mnVqlV66aWXVK9ePTmdTqsjWqZFixbq0KFDqXOXXHKJ9u7da1Eiz/DYY49p/PjxuuOOO9SpUycNHz5c48aNU1JSktXRPErJz1x+HnsP2tDfRxtaGm1oxWhDy0cbWjV12Yb6beEYFBSkbt26KTk52X3O5XIpOTlZffr0sTCZ9QzD0JgxY/Thhx9qxYoVOv/8862O5BGuueYabd26VWlpae5H9+7dNWzYMKWlpclut1sd0TJ9+/Yts9z8Dz/8oNatW1uUyDPk5+crIKD0j1m73S6Xy2VRIs90/vnnKyoqqtTP45ycHK1fv97vfx57KtrQitGGlo82tGK0oeWjDa2aumxD/XqoamJiohISEtS9e3f17NlTM2fOVF5enkaMGGF1NEuNHj1aCxcu1EcffaSwsDD3+OiIiAiFhIRYnM46YWFhZeao1K9fX02aNPH7uSvjxo3TZZddpqlTp+q2227Thg0b9Nprr+m1116zOpqlBg0apL///e+KiYnRpZdeqs2bN2vGjBkaOXKk1dHqXG5urnbv3u0+3rNnj9LS0tS4cWPFxMTokUce0bPPPqt27drp/PPP18SJExUdHa3BgwdbFxqVog0tH21o+WhDK0YbWj7a0NM8pg2t0TVavdDLL79sxMTEGEFBQUbPnj2Nb775xupIlpNU7uPNN9+0OprHYSnx0z755BOjY8eOhsPhMC6++GLjtddeszqS5XJycoyxY8caMTExRnBwsNG2bVvjySefNAoKCqyOVue+/PLLcn+uJCQkGIZhLic+ceJEIzIy0nA4HMY111xj7Ny509rQ+F20oWXRhlYdbehptKFl0Yae5iltqM0wDKNmS1EAAAAAgC/x2zmOAAAAAICqoXAEAAAAAFSKwhEAAAAAUCkKRwAAAABApSgcAQAAAACVonAEAAAAAFSKwhEAAAAAUCkKRwAAAABApSgcAQAAAACVonAEAAAAAFSKwhHwQocOHVJUVJSmTp3qPrd27VoFBQUpOTnZwmQAAHg22lDg7NgMwzCsDgGg+j777DMNHjxYa9eu1UUXXaS4uDjddNNNmjFjhtXRAADwaLShQPVROAJebPTo0Vq+fLm6d++urVu3auPGjXI4HFbHAgDA49GGAtVD4Qh4sRMnTqhjx45KT09XSkqKOnXqZHUkAAC8Am0oUD3McQS82I8//qj9+/fL5XLp559/tjoOAABegzYUqB56HAEvVVhYqJ49eyouLk4XXXSRZs6cqa1bt6p58+ZWRwMAwKPRhgLVR+EIeKnHHntMH3zwgbZs2aIGDRroqquuUkREhD799FOrowEA4NFoQ4HqY6gq4IVWrlypmTNnasGCBQoPD1dAQIAWLFigNWvWaPbs2VbHAwDAY9GGAmeHHkcAAAAAQKXocQQAAAAAVIrCEQAAAABQKQpHAAAAAEClKBwBAAAAAJWicAQAAAAAVIrCEQAAAABQKQpHAAAAAEClKBwBAAAAAJWicAQAAAAAVIrCEQAAAABQKQpHAAAAAEClKBwBAAAAAJX6fy+824VnO4F7AAAAAElFTkSuQmCC", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x = np.linspace(0,10,100)\n", "y = np.exp(2*x)\n", "\n", "plt.figure(0, figsize=(11,4))\n", "\n", "plt.subplot(121)\n", "plt.plot(x,y, label='exp(2x)')\n", "\n", "plt.xlabel('x')\n", "plt.ylabel('y')\n", "plt.legend()\n", "\n", "plt.subplot(122)\n", "plt.plot(x,y, 'r--')\n", "\n", "plt.xlabel('x')\n", "plt.ylabel('y')\n", "plt.legend(['y=$e^{2x}$'], fontsize=20, loc='upper left') # LaTex výraz\n", "\n", "plt.yscale('log') # škála osy y\n", "plt.ylim([0.1, 1e10]);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Možnost 2 - pokročilejší možnosti vykreslování**" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "x = np.linspace(0,4*np.pi,100)\n", "y = np.sin(x)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, axs = plt.subplots(2,2) # 4 grafy v základním rozložení 2x2\n", "fig.set_size_inches(10, 7)\n", "fig.tight_layout()\n", "\n", "axs[0,0].plot(x,y, '*')\n", "axs[0,1].plot(x,y**2, 'g:')\n", "axs[1,0].plot(x,y**3, 'k', linestyle='-.',)\n", "axs[1,1].plot(x,y**4, color='red', linestyle='dashed', linewidth=2, label='sin(x)', alpha=0.5)\n", "\n", "axs[0,0].set_xlabel('x')\n", "axs[0,0].set_ylabel('sin(x)')\n", "axs[0,0].set_title('Graf funkce sin(x)')\n", "axs[0,0].legend(['sin(x)'])\n", "\n", "axs[1,1].legend();" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Uložení obrázku**\n", "\n", "Na závěr obrázek uložíme příkazem `savefig()`:" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [], "source": [ "fig.savefig(\"obrazek.png\", dpi=300)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2D plot\n", "\n", "Mějme funkci $z(x,y)$, která závisí na dvou proměnných:\n", "$\n", "\\begin{equation}\n", "z(x,y)=\\exp(-\\sqrt{x^2+y^2})\\cos(2x)\\sin(2y),\n", "\\end{equation}\n", "$\n", "a vykreslíme její závislost v 2D grafu.\n", "\n", "Vytvoříme mřížku $x\\times y$ pomocí funkce `meshgrid()`:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "osa_x = np.linspace(-2, 2, 50)\n", "osa_y = np.linspace(-2, 2, 50)\n", "(x,y) = np.meshgrid(osa_x, osa_y);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Spočítáme hodnoty funkce $z(x,y)$:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "z = np.exp(-np.sqrt(x**2 + y**2)) * np.cos(2*x) * np.sin(2*y)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.pcolormesh(x,y,z,shading='auto')\n", "plt.xlabel('x')\n", "plt.ylabel('y')\n", "plt.title('$\\exp(-\\sqrt{x^2+y^2})\\cos(2x)\\sin(2y)$')\n", "plt.colorbar();" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Kontury získáme použitím funkce `contour()`:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.contour(x,y,z, levels=10);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plot obrázku pomocí funkce `imshow()`:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.imshow(z)\n", "plt.colorbar();" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Další typy grafů\n", "\n", "Knihovna obsahuje řadu dalších nástrojů a typů grafů, které je možné vykreslit. Obsáhlejší, přesto stále stručný, popis knihovny najdete opět [zde](https://fjfi.pythonic.eu/posts/matplotlib.html).\n", "\n", "Seznam všech dostupných typů grafů najdete v [dokumentaci](https://matplotlib.org/stable/plot_types/index.html) knihovny.\n", "\n", "Tady jen zmíníme některé z často používaných typů grafů:\n", "* [`scatter()`](https://matplotlib.org/stable/plot_types/basic/scatter_plot.html#sphx-glr-plot-types-basic-scatter-plot-py) - bodový graf\n", "* [`bar()`](https://matplotlib.org/stable/plot_types/basic/bar.html#sphx-glr-plot-types-basic-bar-py) - schodový graf\n", "* [`hist()`](https://matplotlib.org/stable/plot_types/stats/hist_plot.html#sphx-glr-plot-types-stats-hist-plot-py), [`hist2d()`](https://matplotlib.org/stable/plot_types/stats/hist2d.html#sphx-glr-plot-types-stats-hist2d-py) - histogram 1D a 2D dat\n", "* [`contour()`](https://matplotlib.org/stable/plot_types/arrays/contour.html#sphx-glr-plot-types-arrays-contour-py) - kontury 2D funkce\n", "* [`streamplot()`](https://matplotlib.org/stable/plot_types/arrays/streamplot.html#sphx-glr-plot-types-arrays-streamplot-py) - vektorové pole\n", "* [`plot_surface()`](https://matplotlib.org/stable/plot_types/3D/surface3d_simple.html#sphx-glr-plot-types-3d-surface3d-simple-py) - 3D plot, vykreslení funkce dvou proměnných\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Knihovna Scipy\n", "\n", "[SciPy](http://scipy.org/scipylib/index.html) je základní referenční knihovnou, obsahující nástroje pro vědecké výpočty. Najdeme v ní např. speciální funkce, interpolace, Fourierovu transformaci, numerické integrátory a mnohé další. Naším cílem bude ukázat některé z funkcí SciPy." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Knihovna `scipy` obsahuje ředu užitečných modulů:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "c:\\Users\\jiral\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\scipy\\__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.26.2\n", " warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n" ] } ], "source": [ "import scipy.linalg # lineární algebra\n", "import scipy.constants # důležité fyzikální a další konstanty\n", "import scipy.interpolate # interpolace funkcí\n", "import scipy.optimize # optimalizace, hledaní extrémů\n", "import scipy.integrate # numerická integrace a řešení ODR" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "
Poznámka
\n", "
\n", "

\n", "\n", "V Numpy také existuje modul `np.linalg`, ale oproti modulu knihovny `scipy` není zdaleka tak rozsáhlý.\n", "

\n", "
\n", "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Příklad využití modulu konstant:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from scipy.constants import pi, golden_ratio, c\n", "print(pi, golden_ratio, c)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Dále si ukážeme příklady využití funkcí z různých modulů na úlohách, se kterými jste se setkali v první části tohoto kurzu." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Řešení soustav rovnic, vlastní čísla matice" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Aproximace funkcí, interpolace" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Třídění" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Další funkce si představíme v následujících cvičeních." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Více se opět můžete dozvědět v oficiální [dokumentaci]( https://docs.scipy.org/doc/scipy/reference/) knihovny." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Bonus\n", "\n", "
\n", "
Úkol - bonus 1
\n", "
\n", "

\n", "\n", "Doplňte chybějící části označené `???` a opravte chyby, aby fungoval program na hru Oko bere (Black jack):\n", "

\n", "
\n", "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```\n", "import random\n", "\n", "soucet = 0\n", "while ???:\n", " print('Máš', soucet, 'bodů')\n", " odpoved = input('Otočit kartu? ')\n", " if odpoved == 'ano':\n", " ??? = random.randrange(2, 11)\n", " print('Otočil/a jsi', karta)\n", " soucet = ???\n", " elif ???:\n", " break\n", " else:\n", " ???\n", "\n", "if soucet == 21:\n", " print('Gratuluji! Vyhrál/a jsi!')\n", "???\n", " print('Smůla!', soucet, 'bodů je moc!')\n", "else:\n", " print('Chybělo jen', 21 - soucet, 'bodů!')\n", "``` " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "
Úkol - bonus 2
\n", "
\n", "

\n", "\n", "1. Implementujte třídící metodu [Selection sort](https://www.geeksforgeeks.org/selection-sort/).\n", "2. Implementujte [Quicksort](https://www.geeksforgeeks.org/quick-sort/).\n", "\n", "Pro nápovědu se můžete podívat do materiálu jiných cvičení [zde](https://mjirka.github.io/nme/Cviceni06vyplnene.html#razeni-vyberem-selection-sort).\n", "

\n", "
\n", "
Tip
\n", "
\n", "

\n", "\n", "V Pythonu lze prohodit hodnotu dvou proměnných pomocí výrazu `a,b = b,a` (v C++ funkce `swap(a,b)`).\n", "

\n", "
\n", "
\n", "
\n", "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "1. Selection sort" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "### DOPLŇTE ###" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "2. Quicksort" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "### DOPLŇTE ###" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.7" } }, "nbformat": 4, "nbformat_minor": 2 }