Na dnešní lekci si do virtuálního prostředí nainstalujte následující balíčky:
$ python -m pip install --upgrade pip
$ python -m pip install notebook numpy scipy matplotlib pillow
Mezitím, co se to bude instalovat, si stáhněte do adresáře static tyto soubory:
A až bude nainstalováno, spusťte si nový Notebook. (Viz lekce o Notebooku.)
NumPy
NumPy je základní knihovna pro vědce a analytiky, kteří pracují s Pythonem. Definuje typ pro n-rozměrné homogenní pole (nejčastěji čísel) a API pro práci s takovým polem.
Téměř všechny knihovny, kde se objevují větší matice či tabulky, jsou buď postavené na NumPy, nebo podporují numpy.array: od pandas pro datovou analýzu a matplotlib pro grafy, přes scipy, kde najdete základní algoritmy pro interpolaci, integraci aj., astrofyzikální astropy, librosa pro analýzu hudby, až po integraci v knihovnách jako Pillow nebo Tensorflow.
Podobně jako „Djangonauti” kolem webového frameworku Django tvoří vědci a datoví analytici podskupinu pythonní komunity s vlastními konferencemi (PyData), organizacemi (NumFocus, Continuum Analytics) a knihovnami jako NumPy, Pandas, SciPy, Matplotlib či Astropy. Potřeby této komunity se samozřejmě odrážejí i v Pythonu samotném (např. ... a @, které si ukážeme dále, byly do jazyka přidány pro ulehčení výpočtů) a naopak (na rozdíl od specializovaných jazyků jako R nebo Matlab se tu stále indexuje od nuly). Většina těchto knihoven ale má jednu zvláštnost, kterou ve zbytku pythonního světa tolik nevidíme: důraz na použití v interaktivním režimu.
Nejednoznačnost a zkratky
Čísla můžeme buď prozkoumávat, hrát si s nimi, zjišťovat zajímavé souvislosti; anebo můžeme připravovat programy, které nějaké výpočty provedou automaticky.
Na obojí se používají podobné nástroje.
Automaticky pouštěné skripty musí být samozřejmě robustní. Nástroje ke zkoumání dat ale bývají přívětivé k vědcům, často na úkor robustnosti nebo „dobrých programátorských mravů”. Například některé funkce tak trochu „hádají”, co uživatel chtěl, a v tutoriálech se setkáte se zkratkami jako import numpy as np či dokonce from numpy import *.
Toto je kurz programovací, kde nám záleží více na znovupoužitelném kódu než na jednom konkrétním výsledku. Budeme proto preferovat explicitní a jednoznačné operace. Ty jsou v použitých knihovnách vždy vedle zkratek k dispozici a popsány v dokumentaci.
Jak s polem pracovat? Nejprve si ho vytvoříme, nejlépe ze seznamu čísel:
In [1]:
import numpy
array = numpy.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
array
Out[1]:
Každé pole má dvě základní, neměnné vlastnosti: tvar (shape), neboli velikost, a datový typ (dtype).
In [2]:
array.shape
Out[2]:
In [3]:
array.dtype
Out[3]:
Tvar je n-tice, kde n je počet dimenzí pole; shape=(3, 3) dtype='int64' znamená pole 3×3 celých čísel.
Dimenzí může být libovolně mnoho, např. trojrozměrnou matici můžeme vytvořit z trojnásobně vnořených seznamů a NumPy ji „rozumně” vypíše:
In [4]:
cube = numpy.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]])
print(cube.shape)
cube
Out[4]:
Základní operace
Základní vlastnost NumPy polí je to, že aritmetické operace se skalárními hodnotami (např. čísly) fungují po prvcích.
In [5]:
array
Out[5]:
In [6]:
array - 1
Out[6]:
In [7]:
array / 2
Out[7]:
In [8]:
-(array % 4)
Out[8]:
Kromě aritmetických operací takto funguje i porovnávání. Následujícím výrazem dostanete pravdivostní tabulku, která má True na místech, kde pro příslušný prvek platí podmínka:
In [9]:
array > 5
Out[9]:
Protože Python neumožňuje předefinovat chování operátorů and a or, logické spojení operací se tradičně dělá přes bitové operátory & (a) a | (nebo). Ty mají ale neintuitivní prioritu, proto se jednotlivé výrazy hodí uzavřít do závorek:
In [10]:
(array > 3) & (array < 7)
Out[10]:
Operace s jinými poli pracují po prvcích. Obě pole musí být stejně velké.
In [11]:
array + array
Out[11]:
In [12]:
array * numpy.array([[0, 1, 0], [1, 0, 1], [0, 1, 0]])
Out[12]:
Sekvence (jako seznamy) jsou před operací převedeny na pole.
In [13]:
array * [[0, 1, 0], [1, 0, 1], [0, 1, 0]]
Out[13]:
Indexování
NumPy pole jde různými způsoby indexovat. Základní způsoby známe už ze samotného Pythonu – pole se chová jako sekvence menších polí:
In [14]:
array
Out[14]:
In [15]:
array[0]
Out[15]:
In [16]:
array[0:-1]
Out[16]:
In [17]:
array[0][1]
Out[17]:
Na rozdíl od Pythonu se ale dá vícerozměrné pole indexovat přímo n-ticí. Toto je dokonce preferovaný způsob – přehlednější a mnohem rychlejší:
In [18]:
array[0, 1]
Out[18]:
In [19]:
array[0:-1, 1:]
Out[19]:
Chceme-li vybrat určitý sloupec, řekneme si „kompletním intervalem“ (:) o všechny řádky:
In [20]:
array[:, 2]
Out[20]:
Další způsob specifický pro NumPy je indexování pravdivostní tabulkou.
Když potřebujete z matice vybrat prvky, pro které platí nějaká podmínka, napřed si vytvořte pole hodnot True/False:
In [21]:
array > 4
Out[21]:
To pak použijte jako index:
In [22]:
array[array > 4]
Out[22]:
Výsledek je jednorozměrný – informace o původních pozicích prvků se ztratí.
Pro úplnost uvedu ještě dva způsoby indexování. První je seznamem indexů, kterým můžete vybírat, přehazovat nebo i duplikovat konkrétní řádky:
In [23]:
array[[0, 2, 1, 1]] # Řádky 0, 2, 1 a 1
Out[23]:
In [24]:
array[:, [2, 2, 0, 0]] # Podobně pro sloupce
Out[24]:
Druhý je indexování pomocí n-tice „řádkových souřadnic“ a n-tice odpovídajících „sloupcových souřadnic“:
In [25]:
array[(0, 1, 2), (1, 2, 0)] # Vybere prvky (0, 1), (1, 2), (2, 0)
Out[25]:
Trochu specifické je indexování vícerozměrných polí. U nich se často využije „kompletní interval“ (:):
In [26]:
cube
Out[26]:
In [27]:
cube[0, :, :] # První „vrstva“ - to samé jako cube[0]
Out[27]:
In [28]:
cube[:, 0, :] # První řádky - to samé jako cube[:, 0]
Out[28]:
In [29]:
cube[:, :, 0] # První sloupce
Out[29]:
Má-li pole hodně rozměrů, je psaní spousty :, zdlouhavé a nepřehledné. Existuje proto speciální hodnota ... (Ellipsis), která doplní tolik „kompletních intervalů“ (:), aby souhlasil počet dimenzí:
In [30]:
cube[..., 0] # První sloupce – ekvivalent [:, :, 3]
Out[30]:
Broadcasting a změny
Už jsme si ukázali, že aritmetické operace se skalárními hodnotami se provede pro všechny prvky, zatímco operace mezi dvěma stejně velkými poli se provede po prvcích:
In [31]:
array
Out[31]:
In [32]:
array * 3
Out[32]:
In [33]:
array * [[0, 1, 0], [1, 0, 1], [0, 1, 0]]
Out[33]:
Jak je to ale s různě velkými poli?
Nemá-li sekvence, se kterou pracujeme, dost dimenzí, poslední dimenze se „rozšíří“, jako bychom pracovali v každém sloupci se skalární hodnotou. Tomuto „rozšiřování” se obecně říká broadcasting.
In [34]:
array
Out[34]:
In [35]:
array * [0, 1, 10] # vynásobí 1. sloupec nulou, 2. jedničkou, 3. deseti
Out[35]:
Podobné rozšiřování nastane, má-li některá dimenze velikost 1:
In [36]:
array * [[0], [1], [10]] # vynásobí 1. *řádek* nulou, atd.
Out[36]:
Jednotlivé hodnoty v poli lze měnit:
In [37]:
array[0, 0] = 123
array
Out[37]:
...a i na měnění se vztahuje broadcasting:
In [38]:
array[0] = 123
array
Out[38]:
In [39]:
array[:] = 123
array
Out[39]:
Obecně platí, že lze-li něčím vybírat prvky, lze tím i pole měnit:
In [40]:
array[(1, 2, 0), (0, 2, 1)] = 0
array
Out[40]:
Další způsob, jak pole měnit, je rozšířeným přiřazením.
In [41]:
array *= 2
array
Out[41]:
Tato operace není totéž co array = array * 2, ačkoli má stejný výsledek.
array *= 2 změní existující pole, zatímco array = array * 2 vytvoří nové pole, které pak přiřadí do původní proměnné.
Pozor na to, že není možné měnit typ pole:
In [42]:
try:
array /= 2
except Exception as e:
print("Chyba!!", type(e), e)
Tvoření matic, část 2
Časté druhy matic se dají vytvořit pomocí pomocných funkcí. Výsledky se dají použít přímo nebo naplnit vypočítanými daty:
In [43]:
numpy.zeros((4, 4))
Out[43]:
In [44]:
numpy.ones((4, 4))
Out[44]:
In [45]:
numpy.full((4, 4), 12.34)
Out[45]:
In [46]:
numpy.eye(4) # Jednotková matice (je čtvercová – n×n)
Out[46]:
In [47]:
numpy.diag([1, 2, 3, 4]) # Diagonální matice
Out[47]:
U těchto funkcí lze obecně použít argument dtype, kterým specifikujeme datový typ:
In [48]:
int_zeros = numpy.zeros((4, 4), dtype='int8')
print(int_zeros.dtype)
int_zeros
Out[48]:
Další funkce tvoří jednorozměrné matice. Základní je arange, která bere stejné argumenty jako range v Pythonu:
In [49]:
numpy.arange(50) # Celočíselné – argumenty jako range() v Pythonu
Out[49]:
Navíc umí pracovat s reálnými čísly (float). Pozor ale na to, že reálná čísla jsou nepřesná! arange k začátku sekvence postupně přičítá „krok”, takže chyba narůstá celkem rychle:
In [50]:
numpy.arange(0.0, 50.0, 0.3)[-1]
Out[50]:
V krajních případech takto dokonce můžeme dostat pole jiné velikosti, než jsme zamýšleli. Proto arange používejte jen pro celá čísla; pro reálná je tu linspace, která bere začátek a konec intervalu, plus počet prvků:
In [51]:
numpy.linspace(0, 50, num=11) # vždy 11 prvků
Out[51]:
Reshape
Ačkoli indexování polí v NumPy je dost mocné, v paměti jsou jednotlivé hodnoty reprezentovány jako (metadata a) jednorozměrné pole, známé z jazyka C (ačkoli samotné rozmístění prvků může být jiné než po řádcích, jak jsme zvyklí u C).
Je jednoduché změnit tvar pole, nezmění-li se tím celkový počet prvků:
In [52]:
array = numpy.arange(12)
array
Out[52]:
In [53]:
reshaped = array.reshape((3, 4))
reshaped
Out[53]:
Pozor na to, že reshape sice vrací nový objekt, ale může (ale nemusí!) to být jen nový pohled na existující data. Změny v pohledu se projeví i v původním poli:
In [54]:
reshaped[2, 2] = -99
reshaped
Out[54]:
In [55]:
array
Out[55]:
Podobně tvoří pohledy i jednoduché indexování:
In [56]:
a_slice = array[2:4]
a_slice[:] = -99, -99
array
Out[56]:
Potřebujete-li kopii dat, použijte metodu copy:
In [57]:
array.reshape((3, 4)).copy()
Out[57]:
Podobně jako reshape funguje transpozice, což je tak častá operace, že má jednopísmennou zkratku – atribut T. (Tohle hodně napomáhá tomu, že zápis maticových výpočtů v NumPy se podobá odpovídajícím matematickým vzorcům.)
In [58]:
reshaped.T
Out[58]:
Až budete NumPy zkoušet, doporučuji si u nových funkcí najít, zda tvoří nová pole, vracejí pohled nebo modifikují původní pole. U některých funkcí najdete pojmenovaný argument inplace (modifikuje původní pole), případně out, („naplní“ jiné, existující pole).
Datové typy
Jak už bylo řečeno, matice v NumPy mají určené datové typy. Ty jdou nastavit ve většině funkcí, které matice tvoří, argumentem dtype:
In [59]:
numpy.zeros(4, dtype=int)
Out[59]:
In [60]:
numpy.zeros(4, dtype=float)
Out[60]:
In [61]:
numpy.zeros(4, dtype=bool)
Out[61]:
Nejobecnější typ je object (jehož použitím ale přicházíme o většinu výhod NumPy).
In [62]:
numpy.array([0, 1.3, "foobar"], dtype=object)
Out[62]:
Kromě pythonních typů bere dtype i řetězcové specifikace typu:
In [63]:
numpy.array([1, 8, 500], dtype='int8') # 8-bitové číslo
Out[63]:
Znáte-li modul array ze standardní knihovny, můžete jeho specifikaci použít i tady:
In [64]:
numpy.zeros(4, dtype='<I')
Out[64]:
Navíc dtype umí řetězcové a bytestring typy. Tyto mají danou maximální velikost a nesmí obsahovat \0 (resp. znakem '\0' jsou ukončeny):
In [65]:
numpy.full(4, 'abcdef', dtype=('U', 10)) # Unicode
Out[65]:
In [66]:
numpy.full(4, 'abcdef', dtype=('a', 3)) # "ASCII"
Out[66]:
Typy v NumPy můžou být poměrně složité; např. existují i složené datové typy (records). Ty nebudeme používat, ale je dobré o nich aspoň tušit:
In [67]:
record_type = numpy.dtype([('a', int), ('b', float), ('c', ('U', 3))])
numpy.array([(1, 2, 'abc')] * 4, record_type)
Out[67]:
Maticové násobení
Kromě základních aritmetických operací se u vícerozměrných polí často setkáme s maticovým násobením. Předpokládám, že jako bakaláři jste se s ním už setkali a tušíte co dělá – jestli ne, tuto sekci ignorujte.
V Pythonu 3.5 byl na výzvu vědecké komunity do jazyka přidán operátor @ (mATrix multiplication), který je vyhrazen pro maticové násobení. V samotném Pythonu ani ve standardní knihovně není typ, který ho podporuje, ale matice v NumPy tuto operaci samozřejmě umí.
In [68]:
array1 = numpy.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
array2 = numpy.array([[1, 0, 0], [0, 2, 0], [0, 0, 3]])
array1 @ array2
Out[68]:
Ve starších verzích Pythonu je potřeba používat metodu nebo funkci dot.
Důvod přidání operátoru @ byl prostý – zjednodušení zápisu maticových operací. Jako příklad uvedený v návrhu je uveden tento vzorec pro testování hypotéz v lineárním regresním modelu:
$ S=(H\beta-r)^T(HVH^T)^{-1} (H\beta-r) $
V NumPy se dá přepsat jako:
from numpy import dot
from numpy.linalg import inv, solve
Pomocí funkce dot:
S = dot((dot(H, beta) - r).T,
dot(inv(dot(dot(H, V), H.T)), dot(H, beta) - r))
Pomocí metoody dot:
S = (H.dot(beta) - r).T.dot(inv(H.dot(V).dot(H.T))).dot(H.dot(beta) - r)
Pomocí operátoru @:
S = (H @ beta - r).T @ inv(H @ V @ H.T) @ (H @ beta - r)
Poslední varianta nápadně připomíná původní vzorec; u prvních dvou se člověk snadno ztratí ve změti závorek.
Booleovské hodnoty polí
Použijeme-li pole v příkazu if, NumPy nám vynadá. Standardní pythonní seznam je „pravdivý“ pokud obsahuje nějaké prvky, ale u pole, které má fixní velikost, je tahle informace téměř vždy zbytečná.
In [69]:
try:
if numpy.eye(3):
pass
except ValueError as e:
print("Chyba!", type(e), e)
Musíme říct přesně, co chceme.
In [70]:
if numpy.eye(3).any():
print('Alespoň jeden prvek je nenulový')
if numpy.eye(3).all():
print('Všechny prvky jsou nenulové')
if numpy.eye(3).size:
print('Pole obsahuje nějaké prvky')
Z historických důvodů existují dvě výjimky: pole s právě jedním prvkem má pravdivostní hodnotu podle daného prvku a prázdné pole je „nepravdivé“:
In [71]:
if numpy.ones((1, 1, 1, 1)):
print('Ano')
if numpy.zeros((1, 1, 1, 1)):
print('Ne')
In [72]:
if numpy.ones((0, 0)):
print('Ano')
Další operace
Modul numpy obsahuje spoustu základních funkcí, které pracují s maticemi; mimo jiné většinu funkcií z pythonního modulu math. Oproti math zvládají funkce z NumPy broadcasting.
In [73]:
array = numpy.linspace(0, numpy.pi, num=1000)
array[:10]
Out[73]:
In [74]:
sine = numpy.sin(array)
sine[:10]
Out[74]:
Další operace doporučuji hledat buď v Notebooku přes tab, v dokumentaci, nebo obecně na Internetu (kde najdete i případné knihovny, které implementují operace, které v NumPy nejsou).
Příklady použití
Dost teorie. Tahle n-rozměrná pole se používají na spoustu zajímavých věcí. Podívejme se na některé příklady.
Matematika a grafy
Jak se používají matice, jistě znáte z matematiky a cílem tohoto kurzu není vás to naučit. Ukážu ale pár ochutnávek.
Použijeme knihovnu Matplotlib, která vykresluje grafy. Jak ji použít dohledáte v dokumentaci nebo – často efektivněji – v galerii příkladů.
Matplotlib nemá automatickou integraci s Jupyter Notebookem, proto ji je potřeba po importu zapnout:
In [75]:
from matplotlib import pyplot
# Zapnutí integrace s notebookem – `%` je "magický" příkaz IPythonu, podobně jako `!` pro shell
%matplotlib inline
A teď můžeme nakreslit třeba graf funkce:
In [76]:
x = numpy.linspace(0, numpy.pi * 4) # definiční obor
y = numpy.sin(x) # odpovídající hodnoty funkce
pyplot.plot(x, y)
Out[76]:
In [77]:
s = numpy.linspace(-10, 10, num=100)
# meshgrid vrátí dvě 100×100 matice:
# - jednu, kde v každém řádku jsou čísla od -10 do 10,
# - druhou, kde v každém sloupci jsou čísla od -10 do 10.
x, y = numpy.meshgrid(s, s)
# vyhodnotíme (x**2 + y**2) pro každý prvek
z = x ** 2 + y ** 2
# výsledek vykreslíme jako obrázek
pyplot.imshow(z)
# přidáme legendu
pyplot.colorbar()
Out[77]:
In [78]:
# Ta samá data můžeme vykreslit i ve 3D
from mpl_toolkits.mplot3d import Axes3D
fig = pyplot.figure()
axes = fig.gca(projection='3d')
surf = axes.plot_surface(x, y, z, cmap='viridis')
Obrázky
Typický barevný obrázek není nic než matice $m \times n \times 3$ čísel: $m \times n$ pixelů na šířku a výšku a 3 kanály pro červenou, zelenou a modrou barvu.
Knihovna pillow (nástupce knihovny PIL, který se stále importuje jako PIL) obsahuje nástroje na práci s obrázky, např. „nakresli čáru“ nebo „převeď na černobílý obrázek“ nebo „načti PNG“. Není postavena přímo na NumPy, ale umí obrázky převádět z a na NumPy pole, pokud máme NumPy nainstalované.
V knihovně scipy.ndimage existuje spousta nástrojů na analýzu obrazových dat jako 2D signálů, např. konvoluce nebo Sobelův filtr. Jako celé SciPy je postavená přímo na NumPy.
Nás bude na začátku zajímat funkce scipy.ndimage.imread, která pomocí Pillow/PIL načte obrázek jako 3D matici 8-bitových čísel. Já načtu obrázek hada, vy najděte na internetu jakýkoli barevný obrázek a načtěte si ten.
Použitý obrázek je stažený z Wikimedia Commons a je pod licencí CC-BY-SA 3.0. Autor je uživatel Mokele na anglické Wikipedii.
In [79]:
import scipy.ndimage
img = scipy.ndimage.imread('static/python.jpg', mode='RGB')
img
Out[79]:
Pomocí nám už známé knihovny matplotlib takovou matici můžeme zobrazit:
In [80]:
pyplot.imshow(img)
Out[80]:
Podívejme se teď na strukturu matice:
In [81]:
img.shape
Out[81]:
První rozměr jsou řádky (y); můj obrázek je 887 pixelů vysoký. Druhý jsou sloupce (x); tento obrázek má na šířku 1037 px. Třetí rozměr jsou barevné kanály.
Pomocí indexování se můžeme na jednotlivé barevné kanály dostat: je to poslední index, takže řádky a sloupce nahradíme buď dvěma kompletními intervaly (:, :) nebo vynechávkou (...). Červený kanál tedy bude [..., 1], modrý [..., -1].
Zobrazení chceme černobílé; na to má matplotlib pojmenovaný argument cmap. Výchozí způsob obarvování je vhodný spíše pro grafy funkcí.
In [82]:
blue_channel = img[..., -1]
pyplot.imshow(blue_channel, cmap='gray')
Out[82]:
Zajímavé využití obrázku jako matice je steganografie: ukrytí informace v obrazových datech.
Načteme jiný obrázek stejné velikosti, tentokrát černobílý (s módem L). Informace v něm schováme do posledního bitu modrého kanálu.
In [83]:
secret = scipy.ndimage.imread('static/secret.png', mode='L')
img[..., -1] = (img[..., -1] & 0b11111110) + (secret.astype(bool))
Obrázek vypadá na první pohled stejně...
In [84]:
pyplot.imshow(img)
Out[84]:
... ale v posledím modrém bitu se skrývá tajná informace.
In [85]:
pyplot.imshow(img[..., -1] & 1, cmap='gray')
Out[85]:
Výsledek je dobré uložit v bezztrátovém formátu (PNG), aby se informace neztratila:
In [86]:
scipy.misc.imsave('python.png', img)
Zvuk
Jako pole lze reprezentovat i zvukový záznam. Mám záznam, na kterém zkouším zpívat; pomocí funkce scipy.io.wavfile ho můžu načíst jako NumPy pole:
In [87]:
import scipy.io.wavfile
sample_rate, sound = scipy.io.wavfile.read('static/sample.wav')
print(sample_rate)
sound
Out[87]:
Zvuk je stereo, má dvě stopy; jednu z nich si vykreslím:
In [88]:
channel = sound[..., 1]
pyplot.plot(channel)
Out[88]:
Případně můžu využít možností Jupyter Notebooku a HTML a zvuk si přehrát:
In [89]:
from IPython.display import Audio
Audio(data=channel, rate=sample_rate)
print('(Zkuste si to sami; tento print vymažte)')
Podívám se na detail první „noty”, kde je patrná vlna s nějakou frekvencí:
In [90]:
segment = channel[50000:55000]
pyplot.plot(segment)
Out[90]:
In [91]:
from IPython.display import Audio
Audio(data=segment, rate=sample_rate)
Out[91]:
Jaká to je frekvence? Znáte-li analýzu signálů, tušíte, že na podobné otázky odpovídá Fourierova transformace.
NumPy obsahuje diskrétní Fourierovu transformaci v modulu numpy.fft spolu s funkcí, která spočítá odpovídající frekvence v Hz:
In [92]:
spectrum = numpy.fft.fft(segment)
freqs = numpy.fft.fftfreq(len(spectrum), 1/sample_rate)
pyplot.xlabel('Frekvence (Hz)')
pyplot.plot(freqs, numpy.abs(spectrum))
Out[92]:
V tomto grafu hledám maximum. Můžu se zaměřit na prvních pár hodnot spektra:
In [93]:
pyplot.xlabel('Frekvence (Hz)')
pyplot.plot(freqs[:100], numpy.abs(spectrum[:100]))
Out[93]:
Maximum je někde kolem 120 Hz; abych to zjistil přesně, použiji funkci argmax:
In [94]:
amax = numpy.argmax(abs(spectrum))
amax
Out[94]:
... a najdu odpovídající frekvenci:
In [95]:
freqs[amax]
Out[95]:
Což je podle seznamu not skoro H$_2$ (123,5 Hz).
{
"data": {
"sessionMaterial": {
"id": "session-material:2019/mipyt-zima:numpy:1",
"title": "NumPy",
"html": "\n \n \n\n <div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>Na dnešní lekci si do virtuálního prostředí nainstalujte následující balíčky:</p>\n<div class=\"highlight\"><pre><span></span><span class=\"gp\">$ </span>python -m pip install --upgrade pip\n<span class=\"gp\">$ </span>python -m pip install notebook numpy scipy matplotlib pillow\n</pre></div>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>Mezitím, co se to bude instalovat, si stáhněte do adresáře <code>static</code> tyto soubory:</p>\n<ul>\n<li><a href=\"/2019/mipyt-zima/intro/numpy/static/python.jpg\">python.jpg</a></li>\n<li><a href=\"/2019/mipyt-zima/intro/numpy/static/sample.wav\">sample.wav</a></li>\n<li><a href=\"/2019/mipyt-zima/intro/numpy/static/secret.png\">secret.png</a></li>\n</ul>\n<p>A až bude nainstalováno, spusťte si nový Notebook. (Viz <a href=\"/2019/mipyt-zima/intro/notebook/\">lekce o Notebooku</a>.)</p>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<hr>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<h1>NumPy</h1>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>NumPy je základní knihovna pro vědce a analytiky, kteří pracují s Pythonem. Definuje typ pro <em>n</em>-rozměrné homogenní pole (nejčastěji čísel) a API pro práci s takovým polem.</p>\n<p>Téměř všechny knihovny, kde se objevují větší matice či tabulky, jsou buď postavené na NumPy, nebo podporují <code>numpy.array</code>: od <code>pandas</code> pro datovou analýzu a <code>matplotlib</code> pro grafy, přes <code>scipy</code>, kde najdete základní algoritmy pro interpolaci, integraci aj., astrofyzikální <code>astropy</code>, <code>librosa</code> pro analýzu hudby, až po integraci v knihovnách jako <code>Pillow</code> nebo <code>Tensorflow</code>.</p>\n<p>Podobně jako „Djangonauti” kolem webového frameworku Django tvoří vědci a datoví analytici podskupinu pythonní komunity s vlastními konferencemi (PyData), organizacemi (NumFocus, Continuum Analytics) a knihovnami jako NumPy, Pandas, SciPy, Matplotlib či Astropy. Potřeby této komunity se samozřejmě odrážejí i v Pythonu samotném (např. <code>...</code> a <code>@</code>, které si ukážeme dále, byly do jazyka přidány pro ulehčení výpočtů) a naopak (na rozdíl od specializovaných jazyků jako R nebo Matlab se tu stále indexuje od nuly). Většina těchto knihoven ale má jednu zvláštnost, kterou ve zbytku pythonního světa tolik nevidíme: důraz na použití v interaktivním režimu.</p>\n<h2>Nejednoznačnost a zkratky</h2>\n<p>Čísla můžeme buď prozkoumávat, hrát si s nimi, zjišťovat zajímavé souvislosti; anebo můžeme připravovat programy, které nějaké výpočty provedou automaticky.\nNa obojí se používají podobné nástroje.\nAutomaticky pouštěné skripty musí být samozřejmě robustní. Nástroje ke zkoumání dat ale bývají přívětivé k vědcům, často na úkor robustnosti nebo „dobrých programátorských mravů”. Například některé funkce tak trochu „hádají”, co uživatel chtěl, a v tutoriálech se setkáte se zkratkami jako <code>import numpy as np</code> či dokonce <code>from numpy import *</code>.</p>\n<p>Toto je kurz programovací, kde nám záleží více na znovupoužitelném kódu než na jednom konkrétním výsledku. Budeme proto preferovat explicitní a jednoznačné operace. Ty jsou v použitých knihovnách vždy vedle zkratek k dispozici a popsány v dokumentaci.</p>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>Jak s polem pracovat? Nejprve si ho vytvoříme, nejlépe ze seznamu čísel:</p>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In [1]:</div>\n<div class=\"inner_cell\">\n <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"kn\">import</span> <span class=\"nn\">numpy</span>\n<span class=\"n\">array</span> <span class=\"o\">=</span> <span class=\"n\">numpy</span><span class=\"o\">.</span><span class=\"n\">array</span><span class=\"p\">([[</span><span class=\"mi\">1</span><span class=\"p\">,</span> <span class=\"mi\">2</span><span class=\"p\">,</span> <span class=\"mi\">3</span><span class=\"p\">],</span> <span class=\"p\">[</span><span class=\"mi\">4</span><span class=\"p\">,</span> <span class=\"mi\">5</span><span class=\"p\">,</span> <span class=\"mi\">6</span><span class=\"p\">],</span> <span class=\"p\">[</span><span class=\"mi\">7</span><span class=\"p\">,</span> <span class=\"mi\">8</span><span class=\"p\">,</span> <span class=\"mi\">9</span><span class=\"p\">]])</span>\n<span class=\"n\">array</span>\n</pre></div>\n\n </div>\n</div>\n</div>\n\n<div class=\"output_wrapper\">\n<div class=\"output\">\n\n\n<div class=\"output_area\">\n\n <div class=\"prompt output_prompt\">Out[1]:</div>\n\n\n\n\n<div class=\"output_text output_subarea output_execute_result\">\n<pre>array([[1, 2, 3],\n [4, 5, 6],\n [7, 8, 9]])</pre>\n</div>\n\n</div>\n\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>Každé pole má dvě základní, neměnné vlastnosti: <em>tvar</em> (<code>shape</code>), neboli velikost, a <em>datový typ</em> (<code>dtype</code>).</p>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In [2]:</div>\n<div class=\"inner_cell\">\n <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"n\">array</span><span class=\"o\">.</span><span class=\"n\">shape</span>\n</pre></div>\n\n </div>\n</div>\n</div>\n\n<div class=\"output_wrapper\">\n<div class=\"output\">\n\n\n<div class=\"output_area\">\n\n <div class=\"prompt output_prompt\">Out[2]:</div>\n\n\n\n\n<div class=\"output_text output_subarea output_execute_result\">\n<pre>(3, 3)</pre>\n</div>\n\n</div>\n\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In [3]:</div>\n<div class=\"inner_cell\">\n <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"n\">array</span><span class=\"o\">.</span><span class=\"n\">dtype</span>\n</pre></div>\n\n </div>\n</div>\n</div>\n\n<div class=\"output_wrapper\">\n<div class=\"output\">\n\n\n<div class=\"output_area\">\n\n <div class=\"prompt output_prompt\">Out[3]:</div>\n\n\n\n\n<div class=\"output_text output_subarea output_execute_result\">\n<pre>dtype('int64')</pre>\n</div>\n\n</div>\n\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>Tvar je <em>n</em>-tice, kde <em>n</em> je počet dimenzí pole; <code>shape=(3, 3) dtype='int64'</code> znamená pole 3×3 celých čísel.\nDimenzí může být libovolně mnoho, např. trojrozměrnou matici můžeme vytvořit z trojnásobně vnořených seznamů a NumPy ji „rozumně” vypíše:</p>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In [4]:</div>\n<div class=\"inner_cell\">\n <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"n\">cube</span> <span class=\"o\">=</span> <span class=\"n\">numpy</span><span class=\"o\">.</span><span class=\"n\">array</span><span class=\"p\">([[[</span><span class=\"mi\">1</span><span class=\"p\">,</span> <span class=\"mi\">2</span><span class=\"p\">],</span> <span class=\"p\">[</span><span class=\"mi\">3</span><span class=\"p\">,</span> <span class=\"mi\">4</span><span class=\"p\">]],</span> <span class=\"p\">[[</span><span class=\"mi\">5</span><span class=\"p\">,</span> <span class=\"mi\">6</span><span class=\"p\">],</span> <span class=\"p\">[</span><span class=\"mi\">7</span><span class=\"p\">,</span> <span class=\"mi\">8</span><span class=\"p\">]]])</span>\n<span class=\"nb\">print</span><span class=\"p\">(</span><span class=\"n\">cube</span><span class=\"o\">.</span><span class=\"n\">shape</span><span class=\"p\">)</span>\n<span class=\"n\">cube</span>\n</pre></div>\n\n </div>\n</div>\n</div>\n\n<div class=\"output_wrapper\">\n<div class=\"output\">\n\n\n<div class=\"output_area\">\n\n <div class=\"prompt\"></div>\n\n\n<div class=\"output_subarea output_stream output_stdout output_text\">\n<pre>(2, 2, 2)\n</pre>\n</div>\n</div>\n\n<div class=\"output_area\">\n\n <div class=\"prompt output_prompt\">Out[4]:</div>\n\n\n\n\n<div class=\"output_text output_subarea output_execute_result\">\n<pre>array([[[1, 2],\n [3, 4]],\n\n [[5, 6],\n [7, 8]]])</pre>\n</div>\n\n</div>\n\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<h2>Základní operace</h2>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>Základní vlastnost NumPy polí je to, že aritmetické operace se skalárními hodnotami (např. čísly) fungují po prvcích.</p>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In [5]:</div>\n<div class=\"inner_cell\">\n <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"n\">array</span>\n</pre></div>\n\n </div>\n</div>\n</div>\n\n<div class=\"output_wrapper\">\n<div class=\"output\">\n\n\n<div class=\"output_area\">\n\n <div class=\"prompt output_prompt\">Out[5]:</div>\n\n\n\n\n<div class=\"output_text output_subarea output_execute_result\">\n<pre>array([[1, 2, 3],\n [4, 5, 6],\n [7, 8, 9]])</pre>\n</div>\n\n</div>\n\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In [6]:</div>\n<div class=\"inner_cell\">\n <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"n\">array</span> <span class=\"o\">-</span> <span class=\"mi\">1</span>\n</pre></div>\n\n </div>\n</div>\n</div>\n\n<div class=\"output_wrapper\">\n<div class=\"output\">\n\n\n<div class=\"output_area\">\n\n <div class=\"prompt output_prompt\">Out[6]:</div>\n\n\n\n\n<div class=\"output_text output_subarea output_execute_result\">\n<pre>array([[0, 1, 2],\n [3, 4, 5],\n [6, 7, 8]])</pre>\n</div>\n\n</div>\n\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In [7]:</div>\n<div class=\"inner_cell\">\n <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"n\">array</span> <span class=\"o\">/</span> <span class=\"mi\">2</span>\n</pre></div>\n\n </div>\n</div>\n</div>\n\n<div class=\"output_wrapper\">\n<div class=\"output\">\n\n\n<div class=\"output_area\">\n\n <div class=\"prompt output_prompt\">Out[7]:</div>\n\n\n\n\n<div class=\"output_text output_subarea output_execute_result\">\n<pre>array([[ 0.5, 1. , 1.5],\n [ 2. , 2.5, 3. ],\n [ 3.5, 4. , 4.5]])</pre>\n</div>\n\n</div>\n\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In [8]:</div>\n<div class=\"inner_cell\">\n <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"o\">-</span><span class=\"p\">(</span><span class=\"n\">array</span> <span class=\"o\">%</span> <span class=\"mi\">4</span><span class=\"p\">)</span>\n</pre></div>\n\n </div>\n</div>\n</div>\n\n<div class=\"output_wrapper\">\n<div class=\"output\">\n\n\n<div class=\"output_area\">\n\n <div class=\"prompt output_prompt\">Out[8]:</div>\n\n\n\n\n<div class=\"output_text output_subarea output_execute_result\">\n<pre>array([[-1, -2, -3],\n [ 0, -1, -2],\n [-3, 0, -1]])</pre>\n</div>\n\n</div>\n\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>Kromě aritmetických operací takto funguje i porovnávání. Následujícím výrazem dostanete <em>pravdivostní tabulku</em>, která má <code>True</code> na místech, kde pro příslušný prvek platí podmínka:</p>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In [9]:</div>\n<div class=\"inner_cell\">\n <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"n\">array</span> <span class=\"o\">></span> <span class=\"mi\">5</span>\n</pre></div>\n\n </div>\n</div>\n</div>\n\n<div class=\"output_wrapper\">\n<div class=\"output\">\n\n\n<div class=\"output_area\">\n\n <div class=\"prompt output_prompt\">Out[9]:</div>\n\n\n\n\n<div class=\"output_text output_subarea output_execute_result\">\n<pre>array([[False, False, False],\n [False, False, True],\n [ True, True, True]], dtype=bool)</pre>\n</div>\n\n</div>\n\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>Protože Python neumožňuje předefinovat chování operátorů <code>and</code> a <code>or</code>, logické spojení operací se tradičně dělá přes bitové operátory <code>&</code> (a) a <code>|</code> (nebo). Ty mají ale neintuitivní prioritu, proto se jednotlivé výrazy hodí uzavřít do závorek:</p>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In [10]:</div>\n<div class=\"inner_cell\">\n <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"p\">(</span><span class=\"n\">array</span> <span class=\"o\">></span> <span class=\"mi\">3</span><span class=\"p\">)</span> <span class=\"o\">&</span> <span class=\"p\">(</span><span class=\"n\">array</span> <span class=\"o\"><</span> <span class=\"mi\">7</span><span class=\"p\">)</span>\n</pre></div>\n\n </div>\n</div>\n</div>\n\n<div class=\"output_wrapper\">\n<div class=\"output\">\n\n\n<div class=\"output_area\">\n\n <div class=\"prompt output_prompt\">Out[10]:</div>\n\n\n\n\n<div class=\"output_text output_subarea output_execute_result\">\n<pre>array([[False, False, False],\n [ True, True, True],\n [False, False, False]], dtype=bool)</pre>\n</div>\n\n</div>\n\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>Operace s jinými poli pracují po prvcích. Obě pole musí být stejně velké.</p>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In [11]:</div>\n<div class=\"inner_cell\">\n <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"n\">array</span> <span class=\"o\">+</span> <span class=\"n\">array</span>\n</pre></div>\n\n </div>\n</div>\n</div>\n\n<div class=\"output_wrapper\">\n<div class=\"output\">\n\n\n<div class=\"output_area\">\n\n <div class=\"prompt output_prompt\">Out[11]:</div>\n\n\n\n\n<div class=\"output_text output_subarea output_execute_result\">\n<pre>array([[ 2, 4, 6],\n [ 8, 10, 12],\n [14, 16, 18]])</pre>\n</div>\n\n</div>\n\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In [12]:</div>\n<div class=\"inner_cell\">\n <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"n\">array</span> <span class=\"o\">*</span> <span class=\"n\">numpy</span><span class=\"o\">.</span><span class=\"n\">array</span><span class=\"p\">([[</span><span class=\"mi\">0</span><span class=\"p\">,</span> <span class=\"mi\">1</span><span class=\"p\">,</span> <span class=\"mi\">0</span><span class=\"p\">],</span> <span class=\"p\">[</span><span class=\"mi\">1</span><span class=\"p\">,</span> <span class=\"mi\">0</span><span class=\"p\">,</span> <span class=\"mi\">1</span><span class=\"p\">],</span> <span class=\"p\">[</span><span class=\"mi\">0</span><span class=\"p\">,</span> <span class=\"mi\">1</span><span class=\"p\">,</span> <span class=\"mi\">0</span><span class=\"p\">]])</span>\n</pre></div>\n\n </div>\n</div>\n</div>\n\n<div class=\"output_wrapper\">\n<div class=\"output\">\n\n\n<div class=\"output_area\">\n\n <div class=\"prompt output_prompt\">Out[12]:</div>\n\n\n\n\n<div class=\"output_text output_subarea output_execute_result\">\n<pre>array([[0, 2, 0],\n [4, 0, 6],\n [0, 8, 0]])</pre>\n</div>\n\n</div>\n\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>Sekvence (jako seznamy) jsou před operací převedeny na pole.</p>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In [13]:</div>\n<div class=\"inner_cell\">\n <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"n\">array</span> <span class=\"o\">*</span> <span class=\"p\">[[</span><span class=\"mi\">0</span><span class=\"p\">,</span> <span class=\"mi\">1</span><span class=\"p\">,</span> <span class=\"mi\">0</span><span class=\"p\">],</span> <span class=\"p\">[</span><span class=\"mi\">1</span><span class=\"p\">,</span> <span class=\"mi\">0</span><span class=\"p\">,</span> <span class=\"mi\">1</span><span class=\"p\">],</span> <span class=\"p\">[</span><span class=\"mi\">0</span><span class=\"p\">,</span> <span class=\"mi\">1</span><span class=\"p\">,</span> <span class=\"mi\">0</span><span class=\"p\">]]</span>\n</pre></div>\n\n </div>\n</div>\n</div>\n\n<div class=\"output_wrapper\">\n<div class=\"output\">\n\n\n<div class=\"output_area\">\n\n <div class=\"prompt output_prompt\">Out[13]:</div>\n\n\n\n\n<div class=\"output_text output_subarea output_execute_result\">\n<pre>array([[0, 2, 0],\n [4, 0, 6],\n [0, 8, 0]])</pre>\n</div>\n\n</div>\n\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<h2>Indexování</h2>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>NumPy pole jde různými způsoby indexovat. Základní způsoby známe už ze samotného Pythonu – pole se chová jako sekvence menších polí:</p>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In [14]:</div>\n<div class=\"inner_cell\">\n <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"n\">array</span>\n</pre></div>\n\n </div>\n</div>\n</div>\n\n<div class=\"output_wrapper\">\n<div class=\"output\">\n\n\n<div class=\"output_area\">\n\n <div class=\"prompt output_prompt\">Out[14]:</div>\n\n\n\n\n<div class=\"output_text output_subarea output_execute_result\">\n<pre>array([[1, 2, 3],\n [4, 5, 6],\n [7, 8, 9]])</pre>\n</div>\n\n</div>\n\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In [15]:</div>\n<div class=\"inner_cell\">\n <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"n\">array</span><span class=\"p\">[</span><span class=\"mi\">0</span><span class=\"p\">]</span>\n</pre></div>\n\n </div>\n</div>\n</div>\n\n<div class=\"output_wrapper\">\n<div class=\"output\">\n\n\n<div class=\"output_area\">\n\n <div class=\"prompt output_prompt\">Out[15]:</div>\n\n\n\n\n<div class=\"output_text output_subarea output_execute_result\">\n<pre>array([1, 2, 3])</pre>\n</div>\n\n</div>\n\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In [16]:</div>\n<div class=\"inner_cell\">\n <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"n\">array</span><span class=\"p\">[</span><span class=\"mi\">0</span><span class=\"p\">:</span><span class=\"o\">-</span><span class=\"mi\">1</span><span class=\"p\">]</span>\n</pre></div>\n\n </div>\n</div>\n</div>\n\n<div class=\"output_wrapper\">\n<div class=\"output\">\n\n\n<div class=\"output_area\">\n\n <div class=\"prompt output_prompt\">Out[16]:</div>\n\n\n\n\n<div class=\"output_text output_subarea output_execute_result\">\n<pre>array([[1, 2, 3],\n [4, 5, 6]])</pre>\n</div>\n\n</div>\n\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In [17]:</div>\n<div class=\"inner_cell\">\n <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"n\">array</span><span class=\"p\">[</span><span class=\"mi\">0</span><span class=\"p\">][</span><span class=\"mi\">1</span><span class=\"p\">]</span>\n</pre></div>\n\n </div>\n</div>\n</div>\n\n<div class=\"output_wrapper\">\n<div class=\"output\">\n\n\n<div class=\"output_area\">\n\n <div class=\"prompt output_prompt\">Out[17]:</div>\n\n\n\n\n<div class=\"output_text output_subarea output_execute_result\">\n<pre>2</pre>\n</div>\n\n</div>\n\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>Na rozdíl od Pythonu se ale dá vícerozměrné pole indexovat přímo <em>n</em>-ticí. Toto je dokonce preferovaný způsob – přehlednější a mnohem rychlejší:</p>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In [18]:</div>\n<div class=\"inner_cell\">\n <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"n\">array</span><span class=\"p\">[</span><span class=\"mi\">0</span><span class=\"p\">,</span> <span class=\"mi\">1</span><span class=\"p\">]</span>\n</pre></div>\n\n </div>\n</div>\n</div>\n\n<div class=\"output_wrapper\">\n<div class=\"output\">\n\n\n<div class=\"output_area\">\n\n <div class=\"prompt output_prompt\">Out[18]:</div>\n\n\n\n\n<div class=\"output_text output_subarea output_execute_result\">\n<pre>2</pre>\n</div>\n\n</div>\n\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In [19]:</div>\n<div class=\"inner_cell\">\n <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"n\">array</span><span class=\"p\">[</span><span class=\"mi\">0</span><span class=\"p\">:</span><span class=\"o\">-</span><span class=\"mi\">1</span><span class=\"p\">,</span> <span class=\"mi\">1</span><span class=\"p\">:]</span>\n</pre></div>\n\n </div>\n</div>\n</div>\n\n<div class=\"output_wrapper\">\n<div class=\"output\">\n\n\n<div class=\"output_area\">\n\n <div class=\"prompt output_prompt\">Out[19]:</div>\n\n\n\n\n<div class=\"output_text output_subarea output_execute_result\">\n<pre>array([[2, 3],\n [5, 6]])</pre>\n</div>\n\n</div>\n\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>Chceme-li vybrat určitý sloupec, řekneme si „kompletním intervalem“ (<code>:</code>) o všechny řádky:</p>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In [20]:</div>\n<div class=\"inner_cell\">\n <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"n\">array</span><span class=\"p\">[:,</span> <span class=\"mi\">2</span><span class=\"p\">]</span>\n</pre></div>\n\n </div>\n</div>\n</div>\n\n<div class=\"output_wrapper\">\n<div class=\"output\">\n\n\n<div class=\"output_area\">\n\n <div class=\"prompt output_prompt\">Out[20]:</div>\n\n\n\n\n<div class=\"output_text output_subarea output_execute_result\">\n<pre>array([3, 6, 9])</pre>\n</div>\n\n</div>\n\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>Další způsob specifický pro NumPy je indexování pravdivostní tabulkou.</p>\n<p>Když potřebujete z matice vybrat prvky, pro které platí nějaká podmínka, napřed si vytvořte pole hodnot <code>True</code>/<code>False</code>:</p>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In [21]:</div>\n<div class=\"inner_cell\">\n <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"n\">array</span> <span class=\"o\">></span> <span class=\"mi\">4</span>\n</pre></div>\n\n </div>\n</div>\n</div>\n\n<div class=\"output_wrapper\">\n<div class=\"output\">\n\n\n<div class=\"output_area\">\n\n <div class=\"prompt output_prompt\">Out[21]:</div>\n\n\n\n\n<div class=\"output_text output_subarea output_execute_result\">\n<pre>array([[False, False, False],\n [False, True, True],\n [ True, True, True]], dtype=bool)</pre>\n</div>\n\n</div>\n\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>To pak použijte jako index:</p>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In [22]:</div>\n<div class=\"inner_cell\">\n <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"n\">array</span><span class=\"p\">[</span><span class=\"n\">array</span> <span class=\"o\">></span> <span class=\"mi\">4</span><span class=\"p\">]</span>\n</pre></div>\n\n </div>\n</div>\n</div>\n\n<div class=\"output_wrapper\">\n<div class=\"output\">\n\n\n<div class=\"output_area\">\n\n <div class=\"prompt output_prompt\">Out[22]:</div>\n\n\n\n\n<div class=\"output_text output_subarea output_execute_result\">\n<pre>array([5, 6, 7, 8, 9])</pre>\n</div>\n\n</div>\n\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>Výsledek je jednorozměrný – informace o původních pozicích prvků se ztratí.</p>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>Pro úplnost uvedu ještě dva způsoby indexování. První je seznamem indexů, kterým můžete vybírat, přehazovat nebo i duplikovat konkrétní řádky:</p>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In [23]:</div>\n<div class=\"inner_cell\">\n <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"n\">array</span><span class=\"p\">[[</span><span class=\"mi\">0</span><span class=\"p\">,</span> <span class=\"mi\">2</span><span class=\"p\">,</span> <span class=\"mi\">1</span><span class=\"p\">,</span> <span class=\"mi\">1</span><span class=\"p\">]]</span> <span class=\"c1\"># Řádky 0, 2, 1 a 1</span>\n</pre></div>\n\n </div>\n</div>\n</div>\n\n<div class=\"output_wrapper\">\n<div class=\"output\">\n\n\n<div class=\"output_area\">\n\n <div class=\"prompt output_prompt\">Out[23]:</div>\n\n\n\n\n<div class=\"output_text output_subarea output_execute_result\">\n<pre>array([[1, 2, 3],\n [7, 8, 9],\n [4, 5, 6],\n [4, 5, 6]])</pre>\n</div>\n\n</div>\n\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In [24]:</div>\n<div class=\"inner_cell\">\n <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"n\">array</span><span class=\"p\">[:,</span> <span class=\"p\">[</span><span class=\"mi\">2</span><span class=\"p\">,</span> <span class=\"mi\">2</span><span class=\"p\">,</span> <span class=\"mi\">0</span><span class=\"p\">,</span> <span class=\"mi\">0</span><span class=\"p\">]]</span> <span class=\"c1\"># Podobně pro sloupce</span>\n</pre></div>\n\n </div>\n</div>\n</div>\n\n<div class=\"output_wrapper\">\n<div class=\"output\">\n\n\n<div class=\"output_area\">\n\n <div class=\"prompt output_prompt\">Out[24]:</div>\n\n\n\n\n<div class=\"output_text output_subarea output_execute_result\">\n<pre>array([[3, 3, 1, 1],\n [6, 6, 4, 4],\n [9, 9, 7, 7]])</pre>\n</div>\n\n</div>\n\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>Druhý je indexování pomocí <em>n</em>-tice „řádkových souřadnic“ a <em>n</em>-tice odpovídajících „sloupcových souřadnic“:</p>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In [25]:</div>\n<div class=\"inner_cell\">\n <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"n\">array</span><span class=\"p\">[(</span><span class=\"mi\">0</span><span class=\"p\">,</span> <span class=\"mi\">1</span><span class=\"p\">,</span> <span class=\"mi\">2</span><span class=\"p\">),</span> <span class=\"p\">(</span><span class=\"mi\">1</span><span class=\"p\">,</span> <span class=\"mi\">2</span><span class=\"p\">,</span> <span class=\"mi\">0</span><span class=\"p\">)]</span> <span class=\"c1\"># Vybere prvky (0, 1), (1, 2), (2, 0)</span>\n</pre></div>\n\n </div>\n</div>\n</div>\n\n<div class=\"output_wrapper\">\n<div class=\"output\">\n\n\n<div class=\"output_area\">\n\n <div class=\"prompt output_prompt\">Out[25]:</div>\n\n\n\n\n<div class=\"output_text output_subarea output_execute_result\">\n<pre>array([2, 6, 7])</pre>\n</div>\n\n</div>\n\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>Trochu specifické je indexování vícerozměrných polí. U nich se často využije „kompletní interval“ (<code>:</code>):</p>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In [26]:</div>\n<div class=\"inner_cell\">\n <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"n\">cube</span>\n</pre></div>\n\n </div>\n</div>\n</div>\n\n<div class=\"output_wrapper\">\n<div class=\"output\">\n\n\n<div class=\"output_area\">\n\n <div class=\"prompt output_prompt\">Out[26]:</div>\n\n\n\n\n<div class=\"output_text output_subarea output_execute_result\">\n<pre>array([[[1, 2],\n [3, 4]],\n\n [[5, 6],\n [7, 8]]])</pre>\n</div>\n\n</div>\n\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In [27]:</div>\n<div class=\"inner_cell\">\n <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"n\">cube</span><span class=\"p\">[</span><span class=\"mi\">0</span><span class=\"p\">,</span> <span class=\"p\">:,</span> <span class=\"p\">:]</span> <span class=\"c1\"># První „vrstva“ - to samé jako cube[0]</span>\n</pre></div>\n\n </div>\n</div>\n</div>\n\n<div class=\"output_wrapper\">\n<div class=\"output\">\n\n\n<div class=\"output_area\">\n\n <div class=\"prompt output_prompt\">Out[27]:</div>\n\n\n\n\n<div class=\"output_text output_subarea output_execute_result\">\n<pre>array([[1, 2],\n [3, 4]])</pre>\n</div>\n\n</div>\n\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In [28]:</div>\n<div class=\"inner_cell\">\n <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"n\">cube</span><span class=\"p\">[:,</span> <span class=\"mi\">0</span><span class=\"p\">,</span> <span class=\"p\">:]</span> <span class=\"c1\"># První řádky - to samé jako cube[:, 0]</span>\n</pre></div>\n\n </div>\n</div>\n</div>\n\n<div class=\"output_wrapper\">\n<div class=\"output\">\n\n\n<div class=\"output_area\">\n\n <div class=\"prompt output_prompt\">Out[28]:</div>\n\n\n\n\n<div class=\"output_text output_subarea output_execute_result\">\n<pre>array([[1, 2],\n [5, 6]])</pre>\n</div>\n\n</div>\n\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In [29]:</div>\n<div class=\"inner_cell\">\n <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"n\">cube</span><span class=\"p\">[:,</span> <span class=\"p\">:,</span> <span class=\"mi\">0</span><span class=\"p\">]</span> <span class=\"c1\"># První sloupce</span>\n</pre></div>\n\n </div>\n</div>\n</div>\n\n<div class=\"output_wrapper\">\n<div class=\"output\">\n\n\n<div class=\"output_area\">\n\n <div class=\"prompt output_prompt\">Out[29]:</div>\n\n\n\n\n<div class=\"output_text output_subarea output_execute_result\">\n<pre>array([[1, 3],\n [5, 7]])</pre>\n</div>\n\n</div>\n\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>Má-li pole hodně rozměrů, je psaní spousty <code>:,</code> zdlouhavé a nepřehledné. Existuje proto speciální hodnota <code>...</code> (<code>Ellipsis</code>), která doplní tolik „kompletních intervalů“ (<code>:</code>), aby souhlasil počet dimenzí:</p>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In [30]:</div>\n<div class=\"inner_cell\">\n <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"n\">cube</span><span class=\"p\">[</span><span class=\"o\">...</span><span class=\"p\">,</span> <span class=\"mi\">0</span><span class=\"p\">]</span> <span class=\"c1\"># První sloupce – ekvivalent [:, :, 3]</span>\n</pre></div>\n\n </div>\n</div>\n</div>\n\n<div class=\"output_wrapper\">\n<div class=\"output\">\n\n\n<div class=\"output_area\">\n\n <div class=\"prompt output_prompt\">Out[30]:</div>\n\n\n\n\n<div class=\"output_text output_subarea output_execute_result\">\n<pre>array([[1, 3],\n [5, 7]])</pre>\n</div>\n\n</div>\n\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<h2>Broadcasting a změny</h2>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>Už jsme si ukázali, že aritmetické operace se skalárními hodnotami se provede pro všechny prvky, zatímco operace mezi dvěma stejně velkými poli se provede po prvcích:</p>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In [31]:</div>\n<div class=\"inner_cell\">\n <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"n\">array</span>\n</pre></div>\n\n </div>\n</div>\n</div>\n\n<div class=\"output_wrapper\">\n<div class=\"output\">\n\n\n<div class=\"output_area\">\n\n <div class=\"prompt output_prompt\">Out[31]:</div>\n\n\n\n\n<div class=\"output_text output_subarea output_execute_result\">\n<pre>array([[1, 2, 3],\n [4, 5, 6],\n [7, 8, 9]])</pre>\n</div>\n\n</div>\n\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In [32]:</div>\n<div class=\"inner_cell\">\n <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"n\">array</span> <span class=\"o\">*</span> <span class=\"mi\">3</span>\n</pre></div>\n\n </div>\n</div>\n</div>\n\n<div class=\"output_wrapper\">\n<div class=\"output\">\n\n\n<div class=\"output_area\">\n\n <div class=\"prompt output_prompt\">Out[32]:</div>\n\n\n\n\n<div class=\"output_text output_subarea output_execute_result\">\n<pre>array([[ 3, 6, 9],\n [12, 15, 18],\n [21, 24, 27]])</pre>\n</div>\n\n</div>\n\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In [33]:</div>\n<div class=\"inner_cell\">\n <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"n\">array</span> <span class=\"o\">*</span> <span class=\"p\">[[</span><span class=\"mi\">0</span><span class=\"p\">,</span> <span class=\"mi\">1</span><span class=\"p\">,</span> <span class=\"mi\">0</span><span class=\"p\">],</span> <span class=\"p\">[</span><span class=\"mi\">1</span><span class=\"p\">,</span> <span class=\"mi\">0</span><span class=\"p\">,</span> <span class=\"mi\">1</span><span class=\"p\">],</span> <span class=\"p\">[</span><span class=\"mi\">0</span><span class=\"p\">,</span> <span class=\"mi\">1</span><span class=\"p\">,</span> <span class=\"mi\">0</span><span class=\"p\">]]</span>\n</pre></div>\n\n </div>\n</div>\n</div>\n\n<div class=\"output_wrapper\">\n<div class=\"output\">\n\n\n<div class=\"output_area\">\n\n <div class=\"prompt output_prompt\">Out[33]:</div>\n\n\n\n\n<div class=\"output_text output_subarea output_execute_result\">\n<pre>array([[0, 2, 0],\n [4, 0, 6],\n [0, 8, 0]])</pre>\n</div>\n\n</div>\n\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>Jak je to ale s různě velkými poli?</p>\n<p>Nemá-li sekvence, se kterou pracujeme, dost dimenzí, poslední dimenze se „rozšíří“, jako bychom pracovali v každém sloupci se skalární hodnotou. Tomuto „rozšiřování” se obecně říká <em>broadcasting</em>.</p>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In [34]:</div>\n<div class=\"inner_cell\">\n <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"n\">array</span>\n</pre></div>\n\n </div>\n</div>\n</div>\n\n<div class=\"output_wrapper\">\n<div class=\"output\">\n\n\n<div class=\"output_area\">\n\n <div class=\"prompt output_prompt\">Out[34]:</div>\n\n\n\n\n<div class=\"output_text output_subarea output_execute_result\">\n<pre>array([[1, 2, 3],\n [4, 5, 6],\n [7, 8, 9]])</pre>\n</div>\n\n</div>\n\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In [35]:</div>\n<div class=\"inner_cell\">\n <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"n\">array</span> <span class=\"o\">*</span> <span class=\"p\">[</span><span class=\"mi\">0</span><span class=\"p\">,</span> <span class=\"mi\">1</span><span class=\"p\">,</span> <span class=\"mi\">10</span><span class=\"p\">]</span> <span class=\"c1\"># vynásobí 1. sloupec nulou, 2. jedničkou, 3. deseti</span>\n</pre></div>\n\n </div>\n</div>\n</div>\n\n<div class=\"output_wrapper\">\n<div class=\"output\">\n\n\n<div class=\"output_area\">\n\n <div class=\"prompt output_prompt\">Out[35]:</div>\n\n\n\n\n<div class=\"output_text output_subarea output_execute_result\">\n<pre>array([[ 0, 2, 30],\n [ 0, 5, 60],\n [ 0, 8, 90]])</pre>\n</div>\n\n</div>\n\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>Podobné rozšiřování nastane, má-li některá dimenze velikost 1:</p>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In [36]:</div>\n<div class=\"inner_cell\">\n <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"n\">array</span> <span class=\"o\">*</span> <span class=\"p\">[[</span><span class=\"mi\">0</span><span class=\"p\">],</span> <span class=\"p\">[</span><span class=\"mi\">1</span><span class=\"p\">],</span> <span class=\"p\">[</span><span class=\"mi\">10</span><span class=\"p\">]]</span> <span class=\"c1\"># vynásobí 1. *řádek* nulou, atd.</span>\n</pre></div>\n\n </div>\n</div>\n</div>\n\n<div class=\"output_wrapper\">\n<div class=\"output\">\n\n\n<div class=\"output_area\">\n\n <div class=\"prompt output_prompt\">Out[36]:</div>\n\n\n\n\n<div class=\"output_text output_subarea output_execute_result\">\n<pre>array([[ 0, 0, 0],\n [ 4, 5, 6],\n [70, 80, 90]])</pre>\n</div>\n\n</div>\n\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>Jednotlivé hodnoty v poli lze měnit:</p>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In [37]:</div>\n<div class=\"inner_cell\">\n <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"n\">array</span><span class=\"p\">[</span><span class=\"mi\">0</span><span class=\"p\">,</span> <span class=\"mi\">0</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"mi\">123</span>\n<span class=\"n\">array</span>\n</pre></div>\n\n </div>\n</div>\n</div>\n\n<div class=\"output_wrapper\">\n<div class=\"output\">\n\n\n<div class=\"output_area\">\n\n <div class=\"prompt output_prompt\">Out[37]:</div>\n\n\n\n\n<div class=\"output_text output_subarea output_execute_result\">\n<pre>array([[123, 2, 3],\n [ 4, 5, 6],\n [ 7, 8, 9]])</pre>\n</div>\n\n</div>\n\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>...a i na měnění se vztahuje <em>broadcasting</em>:</p>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In [38]:</div>\n<div class=\"inner_cell\">\n <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"n\">array</span><span class=\"p\">[</span><span class=\"mi\">0</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"mi\">123</span>\n<span class=\"n\">array</span>\n</pre></div>\n\n </div>\n</div>\n</div>\n\n<div class=\"output_wrapper\">\n<div class=\"output\">\n\n\n<div class=\"output_area\">\n\n <div class=\"prompt output_prompt\">Out[38]:</div>\n\n\n\n\n<div class=\"output_text output_subarea output_execute_result\">\n<pre>array([[123, 123, 123],\n [ 4, 5, 6],\n [ 7, 8, 9]])</pre>\n</div>\n\n</div>\n\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In [39]:</div>\n<div class=\"inner_cell\">\n <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"n\">array</span><span class=\"p\">[:]</span> <span class=\"o\">=</span> <span class=\"mi\">123</span>\n<span class=\"n\">array</span>\n</pre></div>\n\n </div>\n</div>\n</div>\n\n<div class=\"output_wrapper\">\n<div class=\"output\">\n\n\n<div class=\"output_area\">\n\n <div class=\"prompt output_prompt\">Out[39]:</div>\n\n\n\n\n<div class=\"output_text output_subarea output_execute_result\">\n<pre>array([[123, 123, 123],\n [123, 123, 123],\n [123, 123, 123]])</pre>\n</div>\n\n</div>\n\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>Obecně platí, že lze-li něčím vybírat prvky, lze tím i pole měnit:</p>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In [40]:</div>\n<div class=\"inner_cell\">\n <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"n\">array</span><span class=\"p\">[(</span><span class=\"mi\">1</span><span class=\"p\">,</span> <span class=\"mi\">2</span><span class=\"p\">,</span> <span class=\"mi\">0</span><span class=\"p\">),</span> <span class=\"p\">(</span><span class=\"mi\">0</span><span class=\"p\">,</span> <span class=\"mi\">2</span><span class=\"p\">,</span> <span class=\"mi\">1</span><span class=\"p\">)]</span> <span class=\"o\">=</span> <span class=\"mi\">0</span>\n<span class=\"n\">array</span>\n</pre></div>\n\n </div>\n</div>\n</div>\n\n<div class=\"output_wrapper\">\n<div class=\"output\">\n\n\n<div class=\"output_area\">\n\n <div class=\"prompt output_prompt\">Out[40]:</div>\n\n\n\n\n<div class=\"output_text output_subarea output_execute_result\">\n<pre>array([[123, 0, 123],\n [ 0, 123, 123],\n [123, 123, 0]])</pre>\n</div>\n\n</div>\n\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>Další způsob, jak pole měnit, je rozšířeným přiřazením.</p>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In [41]:</div>\n<div class=\"inner_cell\">\n <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"n\">array</span> <span class=\"o\">*=</span> <span class=\"mi\">2</span>\n<span class=\"n\">array</span>\n</pre></div>\n\n </div>\n</div>\n</div>\n\n<div class=\"output_wrapper\">\n<div class=\"output\">\n\n\n<div class=\"output_area\">\n\n <div class=\"prompt output_prompt\">Out[41]:</div>\n\n\n\n\n<div class=\"output_text output_subarea output_execute_result\">\n<pre>array([[246, 0, 246],\n [ 0, 246, 246],\n [246, 246, 0]])</pre>\n</div>\n\n</div>\n\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>Tato operace není totéž co <code>array = array * 2</code>, ačkoli má stejný výsledek.</p>\n<p><code>array *= 2</code> změní existující pole, zatímco <code>array = array * 2</code> vytvoří <em>nové</em> pole, které pak přiřadí do původní proměnné.</p>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>Pozor na to, že není možné měnit typ pole:</p>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In [42]:</div>\n<div class=\"inner_cell\">\n <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"k\">try</span><span class=\"p\">:</span>\n <span class=\"n\">array</span> <span class=\"o\">/=</span> <span class=\"mi\">2</span>\n<span class=\"k\">except</span> <span class=\"ne\">Exception</span> <span class=\"k\">as</span> <span class=\"n\">e</span><span class=\"p\">:</span>\n <span class=\"nb\">print</span><span class=\"p\">(</span><span class=\"s2\">"Chyba!!"</span><span class=\"p\">,</span> <span class=\"nb\">type</span><span class=\"p\">(</span><span class=\"n\">e</span><span class=\"p\">),</span> <span class=\"n\">e</span><span class=\"p\">)</span>\n</pre></div>\n\n </div>\n</div>\n</div>\n\n<div class=\"output_wrapper\">\n<div class=\"output\">\n\n\n<div class=\"output_area\">\n\n <div class=\"prompt\"></div>\n\n\n<div class=\"output_subarea output_stream output_stdout output_text\">\n<pre>Chyba!! <class 'TypeError'> ufunc 'true_divide' output (typecode 'd') could not be coerced to provided output parameter (typecode 'l') according to the casting rule ''same_kind''\n</pre>\n</div>\n</div>\n\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<h2>Tvoření matic, část 2</h2>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>Časté druhy matic se dají vytvořit pomocí pomocných funkcí. Výsledky se dají použít přímo nebo naplnit vypočítanými daty:</p>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In [43]:</div>\n<div class=\"inner_cell\">\n <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"n\">numpy</span><span class=\"o\">.</span><span class=\"n\">zeros</span><span class=\"p\">((</span><span class=\"mi\">4</span><span class=\"p\">,</span> <span class=\"mi\">4</span><span class=\"p\">))</span>\n</pre></div>\n\n </div>\n</div>\n</div>\n\n<div class=\"output_wrapper\">\n<div class=\"output\">\n\n\n<div class=\"output_area\">\n\n <div class=\"prompt output_prompt\">Out[43]:</div>\n\n\n\n\n<div class=\"output_text output_subarea output_execute_result\">\n<pre>array([[ 0., 0., 0., 0.],\n [ 0., 0., 0., 0.],\n [ 0., 0., 0., 0.],\n [ 0., 0., 0., 0.]])</pre>\n</div>\n\n</div>\n\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In [44]:</div>\n<div class=\"inner_cell\">\n <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"n\">numpy</span><span class=\"o\">.</span><span class=\"n\">ones</span><span class=\"p\">((</span><span class=\"mi\">4</span><span class=\"p\">,</span> <span class=\"mi\">4</span><span class=\"p\">))</span>\n</pre></div>\n\n </div>\n</div>\n</div>\n\n<div class=\"output_wrapper\">\n<div class=\"output\">\n\n\n<div class=\"output_area\">\n\n <div class=\"prompt output_prompt\">Out[44]:</div>\n\n\n\n\n<div class=\"output_text output_subarea output_execute_result\">\n<pre>array([[ 1., 1., 1., 1.],\n [ 1., 1., 1., 1.],\n [ 1., 1., 1., 1.],\n [ 1., 1., 1., 1.]])</pre>\n</div>\n\n</div>\n\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In [45]:</div>\n<div class=\"inner_cell\">\n <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"n\">numpy</span><span class=\"o\">.</span><span class=\"n\">full</span><span class=\"p\">((</span><span class=\"mi\">4</span><span class=\"p\">,</span> <span class=\"mi\">4</span><span class=\"p\">),</span> <span class=\"mf\">12.34</span><span class=\"p\">)</span>\n</pre></div>\n\n </div>\n</div>\n</div>\n\n<div class=\"output_wrapper\">\n<div class=\"output\">\n\n\n<div class=\"output_area\">\n\n <div class=\"prompt output_prompt\">Out[45]:</div>\n\n\n\n\n<div class=\"output_text output_subarea output_execute_result\">\n<pre>array([[ 12.34, 12.34, 12.34, 12.34],\n [ 12.34, 12.34, 12.34, 12.34],\n [ 12.34, 12.34, 12.34, 12.34],\n [ 12.34, 12.34, 12.34, 12.34]])</pre>\n</div>\n\n</div>\n\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In [46]:</div>\n<div class=\"inner_cell\">\n <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"n\">numpy</span><span class=\"o\">.</span><span class=\"n\">eye</span><span class=\"p\">(</span><span class=\"mi\">4</span><span class=\"p\">)</span> <span class=\"c1\"># Jednotková matice (je čtvercová – n×n)</span>\n</pre></div>\n\n </div>\n</div>\n</div>\n\n<div class=\"output_wrapper\">\n<div class=\"output\">\n\n\n<div class=\"output_area\">\n\n <div class=\"prompt output_prompt\">Out[46]:</div>\n\n\n\n\n<div class=\"output_text output_subarea output_execute_result\">\n<pre>array([[ 1., 0., 0., 0.],\n [ 0., 1., 0., 0.],\n [ 0., 0., 1., 0.],\n [ 0., 0., 0., 1.]])</pre>\n</div>\n\n</div>\n\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In [47]:</div>\n<div class=\"inner_cell\">\n <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"n\">numpy</span><span class=\"o\">.</span><span class=\"n\">diag</span><span class=\"p\">([</span><span class=\"mi\">1</span><span class=\"p\">,</span> <span class=\"mi\">2</span><span class=\"p\">,</span> <span class=\"mi\">3</span><span class=\"p\">,</span> <span class=\"mi\">4</span><span class=\"p\">])</span> <span class=\"c1\"># Diagonální matice</span>\n</pre></div>\n\n </div>\n</div>\n</div>\n\n<div class=\"output_wrapper\">\n<div class=\"output\">\n\n\n<div class=\"output_area\">\n\n <div class=\"prompt output_prompt\">Out[47]:</div>\n\n\n\n\n<div class=\"output_text output_subarea output_execute_result\">\n<pre>array([[1, 0, 0, 0],\n [0, 2, 0, 0],\n [0, 0, 3, 0],\n [0, 0, 0, 4]])</pre>\n</div>\n\n</div>\n\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>U těchto funkcí lze obecně použít argument <code>dtype</code>, kterým specifikujeme datový typ:</p>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In [48]:</div>\n<div class=\"inner_cell\">\n <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"n\">int_zeros</span> <span class=\"o\">=</span> <span class=\"n\">numpy</span><span class=\"o\">.</span><span class=\"n\">zeros</span><span class=\"p\">((</span><span class=\"mi\">4</span><span class=\"p\">,</span> <span class=\"mi\">4</span><span class=\"p\">),</span> <span class=\"n\">dtype</span><span class=\"o\">=</span><span class=\"s1\">'int8'</span><span class=\"p\">)</span>\n<span class=\"nb\">print</span><span class=\"p\">(</span><span class=\"n\">int_zeros</span><span class=\"o\">.</span><span class=\"n\">dtype</span><span class=\"p\">)</span>\n<span class=\"n\">int_zeros</span>\n</pre></div>\n\n </div>\n</div>\n</div>\n\n<div class=\"output_wrapper\">\n<div class=\"output\">\n\n\n<div class=\"output_area\">\n\n <div class=\"prompt\"></div>\n\n\n<div class=\"output_subarea output_stream output_stdout output_text\">\n<pre>int8\n</pre>\n</div>\n</div>\n\n<div class=\"output_area\">\n\n <div class=\"prompt output_prompt\">Out[48]:</div>\n\n\n\n\n<div class=\"output_text output_subarea output_execute_result\">\n<pre>array([[0, 0, 0, 0],\n [0, 0, 0, 0],\n [0, 0, 0, 0],\n [0, 0, 0, 0]], dtype=int8)</pre>\n</div>\n\n</div>\n\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>Další funkce tvoří jednorozměrné matice. Základní je <code>arange</code>, která bere stejné argumenty jako <code>range</code> v Pythonu:</p>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In [49]:</div>\n<div class=\"inner_cell\">\n <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"n\">numpy</span><span class=\"o\">.</span><span class=\"n\">arange</span><span class=\"p\">(</span><span class=\"mi\">50</span><span class=\"p\">)</span> <span class=\"c1\"># Celočíselné – argumenty jako range() v Pythonu</span>\n</pre></div>\n\n </div>\n</div>\n</div>\n\n<div class=\"output_wrapper\">\n<div class=\"output\">\n\n\n<div class=\"output_area\">\n\n <div class=\"prompt output_prompt\">Out[49]:</div>\n\n\n\n\n<div class=\"output_text output_subarea output_execute_result\">\n<pre>array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,\n 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49])</pre>\n</div>\n\n</div>\n\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>Navíc umí pracovat s reálnými čísly (<code>float</code>). Pozor ale na to, že reálná čísla jsou <em>nepřesná</em>! <code>arange</code> k začátku sekvence postupně přičítá „krok”, takže chyba narůstá celkem rychle:</p>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In [50]:</div>\n<div class=\"inner_cell\">\n <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"n\">numpy</span><span class=\"o\">.</span><span class=\"n\">arange</span><span class=\"p\">(</span><span class=\"mf\">0.0</span><span class=\"p\">,</span> <span class=\"mf\">50.0</span><span class=\"p\">,</span> <span class=\"mf\">0.3</span><span class=\"p\">)[</span><span class=\"o\">-</span><span class=\"mi\">1</span><span class=\"p\">]</span>\n</pre></div>\n\n </div>\n</div>\n</div>\n\n<div class=\"output_wrapper\">\n<div class=\"output\">\n\n\n<div class=\"output_area\">\n\n <div class=\"prompt output_prompt\">Out[50]:</div>\n\n\n\n\n<div class=\"output_text output_subarea output_execute_result\">\n<pre>49.799999999999997</pre>\n</div>\n\n</div>\n\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>V krajních případech takto dokonce můžeme dostat pole jiné <em>velikosti</em>, než jsme zamýšleli. Proto <code>arange</code> používejte jen pro celá čísla; pro reálná je tu <code>linspace</code>, která bere začátek a konec intervalu, plus počet prvků:</p>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In [51]:</div>\n<div class=\"inner_cell\">\n <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"n\">numpy</span><span class=\"o\">.</span><span class=\"n\">linspace</span><span class=\"p\">(</span><span class=\"mi\">0</span><span class=\"p\">,</span> <span class=\"mi\">50</span><span class=\"p\">,</span> <span class=\"n\">num</span><span class=\"o\">=</span><span class=\"mi\">11</span><span class=\"p\">)</span> <span class=\"c1\"># vždy 11 prvků</span>\n</pre></div>\n\n </div>\n</div>\n</div>\n\n<div class=\"output_wrapper\">\n<div class=\"output\">\n\n\n<div class=\"output_area\">\n\n <div class=\"prompt output_prompt\">Out[51]:</div>\n\n\n\n\n<div class=\"output_text output_subarea output_execute_result\">\n<pre>array([ 0., 5., 10., 15., 20., 25., 30., 35., 40., 45., 50.])</pre>\n</div>\n\n</div>\n\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<h2>Reshape</h2>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>Ačkoli indexování polí v NumPy je dost mocné, v paměti jsou jednotlivé hodnoty reprezentovány jako (metadata a) jednorozměrné pole, známé z jazyka C (ačkoli samotné rozmístění prvků může být jiné než po řádcích, jak jsme zvyklí u C).</p>\n<p>Je jednoduché změnit tvar pole, nezmění-li se tím celkový počet prvků:</p>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In [52]:</div>\n<div class=\"inner_cell\">\n <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"n\">array</span> <span class=\"o\">=</span> <span class=\"n\">numpy</span><span class=\"o\">.</span><span class=\"n\">arange</span><span class=\"p\">(</span><span class=\"mi\">12</span><span class=\"p\">)</span>\n<span class=\"n\">array</span>\n</pre></div>\n\n </div>\n</div>\n</div>\n\n<div class=\"output_wrapper\">\n<div class=\"output\">\n\n\n<div class=\"output_area\">\n\n <div class=\"prompt output_prompt\">Out[52]:</div>\n\n\n\n\n<div class=\"output_text output_subarea output_execute_result\">\n<pre>array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11])</pre>\n</div>\n\n</div>\n\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In [53]:</div>\n<div class=\"inner_cell\">\n <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"n\">reshaped</span> <span class=\"o\">=</span> <span class=\"n\">array</span><span class=\"o\">.</span><span class=\"n\">reshape</span><span class=\"p\">((</span><span class=\"mi\">3</span><span class=\"p\">,</span> <span class=\"mi\">4</span><span class=\"p\">))</span>\n<span class=\"n\">reshaped</span>\n</pre></div>\n\n </div>\n</div>\n</div>\n\n<div class=\"output_wrapper\">\n<div class=\"output\">\n\n\n<div class=\"output_area\">\n\n <div class=\"prompt output_prompt\">Out[53]:</div>\n\n\n\n\n<div class=\"output_text output_subarea output_execute_result\">\n<pre>array([[ 0, 1, 2, 3],\n [ 4, 5, 6, 7],\n [ 8, 9, 10, 11]])</pre>\n</div>\n\n</div>\n\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>Pozor na to, že <code>reshape</code> sice vrací nový objekt, ale může (ale nemusí!) to být jen nový <em>pohled</em> na existující data. Změny v pohledu se projeví i v původním poli:</p>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In [54]:</div>\n<div class=\"inner_cell\">\n <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"n\">reshaped</span><span class=\"p\">[</span><span class=\"mi\">2</span><span class=\"p\">,</span> <span class=\"mi\">2</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"o\">-</span><span class=\"mi\">99</span>\n<span class=\"n\">reshaped</span>\n</pre></div>\n\n </div>\n</div>\n</div>\n\n<div class=\"output_wrapper\">\n<div class=\"output\">\n\n\n<div class=\"output_area\">\n\n <div class=\"prompt output_prompt\">Out[54]:</div>\n\n\n\n\n<div class=\"output_text output_subarea output_execute_result\">\n<pre>array([[ 0, 1, 2, 3],\n [ 4, 5, 6, 7],\n [ 8, 9, -99, 11]])</pre>\n</div>\n\n</div>\n\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In [55]:</div>\n<div class=\"inner_cell\">\n <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"n\">array</span>\n</pre></div>\n\n </div>\n</div>\n</div>\n\n<div class=\"output_wrapper\">\n<div class=\"output\">\n\n\n<div class=\"output_area\">\n\n <div class=\"prompt output_prompt\">Out[55]:</div>\n\n\n\n\n<div class=\"output_text output_subarea output_execute_result\">\n<pre>array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, -99, 11])</pre>\n</div>\n\n</div>\n\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>Podobně tvoří pohledy i jednoduché indexování:</p>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In [56]:</div>\n<div class=\"inner_cell\">\n <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"n\">a_slice</span> <span class=\"o\">=</span> <span class=\"n\">array</span><span class=\"p\">[</span><span class=\"mi\">2</span><span class=\"p\">:</span><span class=\"mi\">4</span><span class=\"p\">]</span>\n<span class=\"n\">a_slice</span><span class=\"p\">[:]</span> <span class=\"o\">=</span> <span class=\"o\">-</span><span class=\"mi\">99</span><span class=\"p\">,</span> <span class=\"o\">-</span><span class=\"mi\">99</span>\n\n<span class=\"n\">array</span>\n</pre></div>\n\n </div>\n</div>\n</div>\n\n<div class=\"output_wrapper\">\n<div class=\"output\">\n\n\n<div class=\"output_area\">\n\n <div class=\"prompt output_prompt\">Out[56]:</div>\n\n\n\n\n<div class=\"output_text output_subarea output_execute_result\">\n<pre>array([ 0, 1, -99, -99, 4, 5, 6, 7, 8, 9, -99, 11])</pre>\n</div>\n\n</div>\n\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>Potřebujete-li kopii dat, použijte metodu <code>copy</code>:</p>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In [57]:</div>\n<div class=\"inner_cell\">\n <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"n\">array</span><span class=\"o\">.</span><span class=\"n\">reshape</span><span class=\"p\">((</span><span class=\"mi\">3</span><span class=\"p\">,</span> <span class=\"mi\">4</span><span class=\"p\">))</span><span class=\"o\">.</span><span class=\"n\">copy</span><span class=\"p\">()</span>\n</pre></div>\n\n </div>\n</div>\n</div>\n\n<div class=\"output_wrapper\">\n<div class=\"output\">\n\n\n<div class=\"output_area\">\n\n <div class=\"prompt output_prompt\">Out[57]:</div>\n\n\n\n\n<div class=\"output_text output_subarea output_execute_result\">\n<pre>array([[ 0, 1, -99, -99],\n [ 4, 5, 6, 7],\n [ 8, 9, -99, 11]])</pre>\n</div>\n\n</div>\n\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>Podobně jako <code>reshape</code> funguje transpozice, což je tak častá operace, že má jednopísmennou zkratku – atribut <code>T</code>. (Tohle hodně napomáhá tomu, že zápis maticových výpočtů v NumPy se podobá odpovídajícím matematickým vzorcům.)</p>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In [58]:</div>\n<div class=\"inner_cell\">\n <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"n\">reshaped</span><span class=\"o\">.</span><span class=\"n\">T</span>\n</pre></div>\n\n </div>\n</div>\n</div>\n\n<div class=\"output_wrapper\">\n<div class=\"output\">\n\n\n<div class=\"output_area\">\n\n <div class=\"prompt output_prompt\">Out[58]:</div>\n\n\n\n\n<div class=\"output_text output_subarea output_execute_result\">\n<pre>array([[ 0, 4, 8],\n [ 1, 5, 9],\n [-99, 6, -99],\n [-99, 7, 11]])</pre>\n</div>\n\n</div>\n\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>Až budete NumPy zkoušet, doporučuji si u nových funkcí najít, zda tvoří nová pole, vracejí pohled nebo modifikují původní pole. U některých funkcí najdete pojmenovaný argument <code>inplace</code> (modifikuje původní pole), případně <code>out</code>, („naplní“ jiné, existující pole).</p>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<h2>Datové typy</h2>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>Jak už bylo řečeno, matice v NumPy mají určené datové typy. Ty jdou nastavit ve většině funkcí, které matice tvoří, argumentem <code>dtype</code>:</p>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In [59]:</div>\n<div class=\"inner_cell\">\n <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"n\">numpy</span><span class=\"o\">.</span><span class=\"n\">zeros</span><span class=\"p\">(</span><span class=\"mi\">4</span><span class=\"p\">,</span> <span class=\"n\">dtype</span><span class=\"o\">=</span><span class=\"nb\">int</span><span class=\"p\">)</span>\n</pre></div>\n\n </div>\n</div>\n</div>\n\n<div class=\"output_wrapper\">\n<div class=\"output\">\n\n\n<div class=\"output_area\">\n\n <div class=\"prompt output_prompt\">Out[59]:</div>\n\n\n\n\n<div class=\"output_text output_subarea output_execute_result\">\n<pre>array([0, 0, 0, 0])</pre>\n</div>\n\n</div>\n\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In [60]:</div>\n<div class=\"inner_cell\">\n <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"n\">numpy</span><span class=\"o\">.</span><span class=\"n\">zeros</span><span class=\"p\">(</span><span class=\"mi\">4</span><span class=\"p\">,</span> <span class=\"n\">dtype</span><span class=\"o\">=</span><span class=\"nb\">float</span><span class=\"p\">)</span>\n</pre></div>\n\n </div>\n</div>\n</div>\n\n<div class=\"output_wrapper\">\n<div class=\"output\">\n\n\n<div class=\"output_area\">\n\n <div class=\"prompt output_prompt\">Out[60]:</div>\n\n\n\n\n<div class=\"output_text output_subarea output_execute_result\">\n<pre>array([ 0., 0., 0., 0.])</pre>\n</div>\n\n</div>\n\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In [61]:</div>\n<div class=\"inner_cell\">\n <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"n\">numpy</span><span class=\"o\">.</span><span class=\"n\">zeros</span><span class=\"p\">(</span><span class=\"mi\">4</span><span class=\"p\">,</span> <span class=\"n\">dtype</span><span class=\"o\">=</span><span class=\"nb\">bool</span><span class=\"p\">)</span>\n</pre></div>\n\n </div>\n</div>\n</div>\n\n<div class=\"output_wrapper\">\n<div class=\"output\">\n\n\n<div class=\"output_area\">\n\n <div class=\"prompt output_prompt\">Out[61]:</div>\n\n\n\n\n<div class=\"output_text output_subarea output_execute_result\">\n<pre>array([False, False, False, False], dtype=bool)</pre>\n</div>\n\n</div>\n\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>Nejobecnější typ je <code>object</code> (jehož použitím ale přicházíme o většinu výhod NumPy).</p>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In [62]:</div>\n<div class=\"inner_cell\">\n <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"n\">numpy</span><span class=\"o\">.</span><span class=\"n\">array</span><span class=\"p\">([</span><span class=\"mi\">0</span><span class=\"p\">,</span> <span class=\"mf\">1.3</span><span class=\"p\">,</span> <span class=\"s2\">"foobar"</span><span class=\"p\">],</span> <span class=\"n\">dtype</span><span class=\"o\">=</span><span class=\"nb\">object</span><span class=\"p\">)</span>\n</pre></div>\n\n </div>\n</div>\n</div>\n\n<div class=\"output_wrapper\">\n<div class=\"output\">\n\n\n<div class=\"output_area\">\n\n <div class=\"prompt output_prompt\">Out[62]:</div>\n\n\n\n\n<div class=\"output_text output_subarea output_execute_result\">\n<pre>array([0, 1.3, 'foobar'], dtype=object)</pre>\n</div>\n\n</div>\n\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>Kromě pythonních typů bere <code>dtype</code> i řetězcové specifikace typu:</p>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In [63]:</div>\n<div class=\"inner_cell\">\n <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"n\">numpy</span><span class=\"o\">.</span><span class=\"n\">array</span><span class=\"p\">([</span><span class=\"mi\">1</span><span class=\"p\">,</span> <span class=\"mi\">8</span><span class=\"p\">,</span> <span class=\"mi\">500</span><span class=\"p\">],</span> <span class=\"n\">dtype</span><span class=\"o\">=</span><span class=\"s1\">'int8'</span><span class=\"p\">)</span> <span class=\"c1\"># 8-bitové číslo</span>\n</pre></div>\n\n </div>\n</div>\n</div>\n\n<div class=\"output_wrapper\">\n<div class=\"output\">\n\n\n<div class=\"output_area\">\n\n <div class=\"prompt output_prompt\">Out[63]:</div>\n\n\n\n\n<div class=\"output_text output_subarea output_execute_result\">\n<pre>array([ 1, 8, -12], dtype=int8)</pre>\n</div>\n\n</div>\n\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>Znáte-li modul <a href=\"https://docs.python.org/3/library/array.html\"><code>array</code></a> ze standardní knihovny, můžete jeho specifikaci použít i tady:</p>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In [64]:</div>\n<div class=\"inner_cell\">\n <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"n\">numpy</span><span class=\"o\">.</span><span class=\"n\">zeros</span><span class=\"p\">(</span><span class=\"mi\">4</span><span class=\"p\">,</span> <span class=\"n\">dtype</span><span class=\"o\">=</span><span class=\"s1\">'<I'</span><span class=\"p\">)</span>\n</pre></div>\n\n </div>\n</div>\n</div>\n\n<div class=\"output_wrapper\">\n<div class=\"output\">\n\n\n<div class=\"output_area\">\n\n <div class=\"prompt output_prompt\">Out[64]:</div>\n\n\n\n\n<div class=\"output_text output_subarea output_execute_result\">\n<pre>array([0, 0, 0, 0], dtype=uint32)</pre>\n</div>\n\n</div>\n\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>Navíc <code>dtype</code> umí řetězcové a <em>bytestring</em> typy. Tyto mají danou maximální velikost a nesmí obsahovat <code>\\0</code> (resp. znakem <code>'\\0'</code> jsou ukončeny):</p>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In [65]:</div>\n<div class=\"inner_cell\">\n <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"n\">numpy</span><span class=\"o\">.</span><span class=\"n\">full</span><span class=\"p\">(</span><span class=\"mi\">4</span><span class=\"p\">,</span> <span class=\"s1\">'abcdef'</span><span class=\"p\">,</span> <span class=\"n\">dtype</span><span class=\"o\">=</span><span class=\"p\">(</span><span class=\"s1\">'U'</span><span class=\"p\">,</span> <span class=\"mi\">10</span><span class=\"p\">))</span> <span class=\"c1\"># Unicode</span>\n</pre></div>\n\n </div>\n</div>\n</div>\n\n<div class=\"output_wrapper\">\n<div class=\"output\">\n\n\n<div class=\"output_area\">\n\n <div class=\"prompt output_prompt\">Out[65]:</div>\n\n\n\n\n<div class=\"output_text output_subarea output_execute_result\">\n<pre>array(['abcdef', 'abcdef', 'abcdef', 'abcdef'], \n dtype='<U10')</pre>\n</div>\n\n</div>\n\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In [66]:</div>\n<div class=\"inner_cell\">\n <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"n\">numpy</span><span class=\"o\">.</span><span class=\"n\">full</span><span class=\"p\">(</span><span class=\"mi\">4</span><span class=\"p\">,</span> <span class=\"s1\">'abcdef'</span><span class=\"p\">,</span> <span class=\"n\">dtype</span><span class=\"o\">=</span><span class=\"p\">(</span><span class=\"s1\">'a'</span><span class=\"p\">,</span> <span class=\"mi\">3</span><span class=\"p\">))</span> <span class=\"c1\"># "ASCII"</span>\n</pre></div>\n\n </div>\n</div>\n</div>\n\n<div class=\"output_wrapper\">\n<div class=\"output\">\n\n\n<div class=\"output_area\">\n\n <div class=\"prompt output_prompt\">Out[66]:</div>\n\n\n\n\n<div class=\"output_text output_subarea output_execute_result\">\n<pre>array([b'abc', b'abc', b'abc', b'abc'], \n dtype='|S3')</pre>\n</div>\n\n</div>\n\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>Typy v NumPy můžou být poměrně složité; např. existují i složené datové typy (<code>records</code>). Ty nebudeme používat, ale je dobré o nich aspoň tušit:</p>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In [67]:</div>\n<div class=\"inner_cell\">\n <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"n\">record_type</span> <span class=\"o\">=</span> <span class=\"n\">numpy</span><span class=\"o\">.</span><span class=\"n\">dtype</span><span class=\"p\">([(</span><span class=\"s1\">'a'</span><span class=\"p\">,</span> <span class=\"nb\">int</span><span class=\"p\">),</span> <span class=\"p\">(</span><span class=\"s1\">'b'</span><span class=\"p\">,</span> <span class=\"nb\">float</span><span class=\"p\">),</span> <span class=\"p\">(</span><span class=\"s1\">'c'</span><span class=\"p\">,</span> <span class=\"p\">(</span><span class=\"s1\">'U'</span><span class=\"p\">,</span> <span class=\"mi\">3</span><span class=\"p\">))])</span>\n<span class=\"n\">numpy</span><span class=\"o\">.</span><span class=\"n\">array</span><span class=\"p\">([(</span><span class=\"mi\">1</span><span class=\"p\">,</span> <span class=\"mi\">2</span><span class=\"p\">,</span> <span class=\"s1\">'abc'</span><span class=\"p\">)]</span> <span class=\"o\">*</span> <span class=\"mi\">4</span><span class=\"p\">,</span> <span class=\"n\">record_type</span><span class=\"p\">)</span>\n</pre></div>\n\n </div>\n</div>\n</div>\n\n<div class=\"output_wrapper\">\n<div class=\"output\">\n\n\n<div class=\"output_area\">\n\n <div class=\"prompt output_prompt\">Out[67]:</div>\n\n\n\n\n<div class=\"output_text output_subarea output_execute_result\">\n<pre>array([(1, 2., 'abc'), (1, 2., 'abc'), (1, 2., 'abc'), (1, 2., 'abc')], \n dtype=[('a', '<i8'), ('b', '<f8'), ('c', '<U3')])</pre>\n</div>\n\n</div>\n\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<h2>Maticové násobení</h2>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>Kromě základních aritmetických operací se u vícerozměrných polí často setkáme s maticovým násobením. Předpokládám, že jako bakaláři jste se s ním už setkali a tušíte co dělá – jestli ne, tuto sekci ignorujte.</p>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>V Pythonu 3.5 byl na <a href=\"http://legacy.python.org/dev/peps/pep-0465/\">výzvu vědecké komunity</a> do jazyka přidán operátor <code>@</code> (mATrix multiplication), který je vyhrazen pro maticové násobení. V samotném Pythonu ani ve standardní knihovně není typ, který ho podporuje, ale matice v NumPy tuto operaci samozřejmě umí.</p>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In [68]:</div>\n<div class=\"inner_cell\">\n <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"n\">array1</span> <span class=\"o\">=</span> <span class=\"n\">numpy</span><span class=\"o\">.</span><span class=\"n\">array</span><span class=\"p\">([[</span><span class=\"mi\">1</span><span class=\"p\">,</span> <span class=\"mi\">2</span><span class=\"p\">,</span> <span class=\"mi\">3</span><span class=\"p\">],</span> <span class=\"p\">[</span><span class=\"mi\">4</span><span class=\"p\">,</span> <span class=\"mi\">5</span><span class=\"p\">,</span> <span class=\"mi\">6</span><span class=\"p\">],</span> <span class=\"p\">[</span><span class=\"mi\">7</span><span class=\"p\">,</span> <span class=\"mi\">8</span><span class=\"p\">,</span> <span class=\"mi\">9</span><span class=\"p\">]])</span>\n<span class=\"n\">array2</span> <span class=\"o\">=</span> <span class=\"n\">numpy</span><span class=\"o\">.</span><span class=\"n\">array</span><span class=\"p\">([[</span><span class=\"mi\">1</span><span class=\"p\">,</span> <span class=\"mi\">0</span><span class=\"p\">,</span> <span class=\"mi\">0</span><span class=\"p\">],</span> <span class=\"p\">[</span><span class=\"mi\">0</span><span class=\"p\">,</span> <span class=\"mi\">2</span><span class=\"p\">,</span> <span class=\"mi\">0</span><span class=\"p\">],</span> <span class=\"p\">[</span><span class=\"mi\">0</span><span class=\"p\">,</span> <span class=\"mi\">0</span><span class=\"p\">,</span> <span class=\"mi\">3</span><span class=\"p\">]])</span>\n<span class=\"n\">array1</span> <span class=\"o\">@</span> <span class=\"n\">array2</span>\n</pre></div>\n\n </div>\n</div>\n</div>\n\n<div class=\"output_wrapper\">\n<div class=\"output\">\n\n\n<div class=\"output_area\">\n\n <div class=\"prompt output_prompt\">Out[68]:</div>\n\n\n\n\n<div class=\"output_text output_subarea output_execute_result\">\n<pre>array([[ 1, 4, 9],\n [ 4, 10, 18],\n [ 7, 16, 27]])</pre>\n</div>\n\n</div>\n\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>Ve starších verzích Pythonu je potřeba používat metodu nebo funkci <code>dot</code>.</p>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>Důvod přidání operátoru <code>@</code> byl prostý – zjednodušení zápisu maticových operací. Jako příklad uvedený v návrhu je uveden tento vzorec pro testování hypotéz v lineárním regresním modelu:</p>\n<p>$ S=(H\\beta-r)^T(HVH^T)^{-1} (H\\beta-r) $</p>\n<p>V NumPy se dá přepsat jako:</p>\n<div class=\"highlight\"><pre><span></span><span class=\"kn\">from</span> <span class=\"nn\">numpy</span> <span class=\"kn\">import</span> <span class=\"n\">dot</span>\n<span class=\"kn\">from</span> <span class=\"nn\">numpy.linalg</span> <span class=\"kn\">import</span> <span class=\"n\">inv</span><span class=\"p\">,</span> <span class=\"n\">solve</span>\n</pre></div><p>Pomocí funkce <code>dot</code>:</p>\n<div class=\"highlight\"><pre><span></span><span class=\"n\">S</span> <span class=\"o\">=</span> <span class=\"n\">dot</span><span class=\"p\">((</span><span class=\"n\">dot</span><span class=\"p\">(</span><span class=\"n\">H</span><span class=\"p\">,</span> <span class=\"n\">beta</span><span class=\"p\">)</span> <span class=\"o\">-</span> <span class=\"n\">r</span><span class=\"p\">)</span><span class=\"o\">.</span><span class=\"n\">T</span><span class=\"p\">,</span>\n <span class=\"n\">dot</span><span class=\"p\">(</span><span class=\"n\">inv</span><span class=\"p\">(</span><span class=\"n\">dot</span><span class=\"p\">(</span><span class=\"n\">dot</span><span class=\"p\">(</span><span class=\"n\">H</span><span class=\"p\">,</span> <span class=\"n\">V</span><span class=\"p\">),</span> <span class=\"n\">H</span><span class=\"o\">.</span><span class=\"n\">T</span><span class=\"p\">)),</span> <span class=\"n\">dot</span><span class=\"p\">(</span><span class=\"n\">H</span><span class=\"p\">,</span> <span class=\"n\">beta</span><span class=\"p\">)</span> <span class=\"o\">-</span> <span class=\"n\">r</span><span class=\"p\">))</span>\n</pre></div><p>Pomocí metoody <code>dot</code>:</p>\n<div class=\"highlight\"><pre><span></span><span class=\"n\">S</span> <span class=\"o\">=</span> <span class=\"p\">(</span><span class=\"n\">H</span><span class=\"o\">.</span><span class=\"n\">dot</span><span class=\"p\">(</span><span class=\"n\">beta</span><span class=\"p\">)</span> <span class=\"o\">-</span> <span class=\"n\">r</span><span class=\"p\">)</span><span class=\"o\">.</span><span class=\"n\">T</span><span class=\"o\">.</span><span class=\"n\">dot</span><span class=\"p\">(</span><span class=\"n\">inv</span><span class=\"p\">(</span><span class=\"n\">H</span><span class=\"o\">.</span><span class=\"n\">dot</span><span class=\"p\">(</span><span class=\"n\">V</span><span class=\"p\">)</span><span class=\"o\">.</span><span class=\"n\">dot</span><span class=\"p\">(</span><span class=\"n\">H</span><span class=\"o\">.</span><span class=\"n\">T</span><span class=\"p\">)))</span><span class=\"o\">.</span><span class=\"n\">dot</span><span class=\"p\">(</span><span class=\"n\">H</span><span class=\"o\">.</span><span class=\"n\">dot</span><span class=\"p\">(</span><span class=\"n\">beta</span><span class=\"p\">)</span> <span class=\"o\">-</span> <span class=\"n\">r</span><span class=\"p\">)</span>\n</pre></div><p>Pomocí operátoru <code>@</code>:</p>\n<div class=\"highlight\"><pre><span></span><span class=\"n\">S</span> <span class=\"o\">=</span> <span class=\"p\">(</span><span class=\"n\">H</span> <span class=\"o\">@</span> <span class=\"n\">beta</span> <span class=\"o\">-</span> <span class=\"n\">r</span><span class=\"p\">)</span><span class=\"o\">.</span><span class=\"n\">T</span> <span class=\"o\">@</span> <span class=\"n\">inv</span><span class=\"p\">(</span><span class=\"n\">H</span> <span class=\"o\">@</span> <span class=\"n\">V</span> <span class=\"o\">@</span> <span class=\"n\">H</span><span class=\"o\">.</span><span class=\"n\">T</span><span class=\"p\">)</span> <span class=\"o\">@</span> <span class=\"p\">(</span><span class=\"n\">H</span> <span class=\"o\">@</span> <span class=\"n\">beta</span> <span class=\"o\">-</span> <span class=\"n\">r</span><span class=\"p\">)</span>\n</pre></div><p>Poslední varianta nápadně připomíná původní vzorec; u prvních dvou se člověk snadno ztratí ve změti závorek.</p>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<h2>Booleovské hodnoty polí</h2>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>Použijeme-li pole v příkazu <em>if</em>, NumPy nám vynadá. Standardní pythonní seznam je „pravdivý“ pokud obsahuje nějaké prvky, ale u pole, které má fixní velikost, je tahle informace téměř vždy zbytečná.</p>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In [69]:</div>\n<div class=\"inner_cell\">\n <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"k\">try</span><span class=\"p\">:</span>\n <span class=\"k\">if</span> <span class=\"n\">numpy</span><span class=\"o\">.</span><span class=\"n\">eye</span><span class=\"p\">(</span><span class=\"mi\">3</span><span class=\"p\">):</span>\n <span class=\"k\">pass</span>\n<span class=\"k\">except</span> <span class=\"ne\">ValueError</span> <span class=\"k\">as</span> <span class=\"n\">e</span><span class=\"p\">:</span>\n <span class=\"nb\">print</span><span class=\"p\">(</span><span class=\"s2\">"Chyba!"</span><span class=\"p\">,</span> <span class=\"nb\">type</span><span class=\"p\">(</span><span class=\"n\">e</span><span class=\"p\">),</span> <span class=\"n\">e</span><span class=\"p\">)</span>\n</pre></div>\n\n </div>\n</div>\n</div>\n\n<div class=\"output_wrapper\">\n<div class=\"output\">\n\n\n<div class=\"output_area\">\n\n <div class=\"prompt\"></div>\n\n\n<div class=\"output_subarea output_stream output_stdout output_text\">\n<pre>Chyba! <class 'ValueError'> The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()\n</pre>\n</div>\n</div>\n\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>Musíme říct přesně, co chceme.</p>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In [70]:</div>\n<div class=\"inner_cell\">\n <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"k\">if</span> <span class=\"n\">numpy</span><span class=\"o\">.</span><span class=\"n\">eye</span><span class=\"p\">(</span><span class=\"mi\">3</span><span class=\"p\">)</span><span class=\"o\">.</span><span class=\"n\">any</span><span class=\"p\">():</span>\n <span class=\"nb\">print</span><span class=\"p\">(</span><span class=\"s1\">'Alespoň jeden prvek je nenulový'</span><span class=\"p\">)</span>\n<span class=\"k\">if</span> <span class=\"n\">numpy</span><span class=\"o\">.</span><span class=\"n\">eye</span><span class=\"p\">(</span><span class=\"mi\">3</span><span class=\"p\">)</span><span class=\"o\">.</span><span class=\"n\">all</span><span class=\"p\">():</span>\n <span class=\"nb\">print</span><span class=\"p\">(</span><span class=\"s1\">'Všechny prvky jsou nenulové'</span><span class=\"p\">)</span>\n<span class=\"k\">if</span> <span class=\"n\">numpy</span><span class=\"o\">.</span><span class=\"n\">eye</span><span class=\"p\">(</span><span class=\"mi\">3</span><span class=\"p\">)</span><span class=\"o\">.</span><span class=\"n\">size</span><span class=\"p\">:</span>\n <span class=\"nb\">print</span><span class=\"p\">(</span><span class=\"s1\">'Pole obsahuje nějaké prvky'</span><span class=\"p\">)</span>\n</pre></div>\n\n </div>\n</div>\n</div>\n\n<div class=\"output_wrapper\">\n<div class=\"output\">\n\n\n<div class=\"output_area\">\n\n <div class=\"prompt\"></div>\n\n\n<div class=\"output_subarea output_stream output_stdout output_text\">\n<pre>Alespoň jeden prvek je nenulový\nPole obsahuje nějaké prvky\n</pre>\n</div>\n</div>\n\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>Z historických důvodů existují dvě výjimky: pole s právě jedním prvkem má pravdivostní hodnotu podle daného prvku a prázdné pole je „nepravdivé“:</p>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In [71]:</div>\n<div class=\"inner_cell\">\n <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"k\">if</span> <span class=\"n\">numpy</span><span class=\"o\">.</span><span class=\"n\">ones</span><span class=\"p\">((</span><span class=\"mi\">1</span><span class=\"p\">,</span> <span class=\"mi\">1</span><span class=\"p\">,</span> <span class=\"mi\">1</span><span class=\"p\">,</span> <span class=\"mi\">1</span><span class=\"p\">)):</span>\n <span class=\"nb\">print</span><span class=\"p\">(</span><span class=\"s1\">'Ano'</span><span class=\"p\">)</span>\n<span class=\"k\">if</span> <span class=\"n\">numpy</span><span class=\"o\">.</span><span class=\"n\">zeros</span><span class=\"p\">((</span><span class=\"mi\">1</span><span class=\"p\">,</span> <span class=\"mi\">1</span><span class=\"p\">,</span> <span class=\"mi\">1</span><span class=\"p\">,</span> <span class=\"mi\">1</span><span class=\"p\">)):</span>\n <span class=\"nb\">print</span><span class=\"p\">(</span><span class=\"s1\">'Ne'</span><span class=\"p\">)</span>\n</pre></div>\n\n </div>\n</div>\n</div>\n\n<div class=\"output_wrapper\">\n<div class=\"output\">\n\n\n<div class=\"output_area\">\n\n <div class=\"prompt\"></div>\n\n\n<div class=\"output_subarea output_stream output_stdout output_text\">\n<pre>Ano\n</pre>\n</div>\n</div>\n\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In [72]:</div>\n<div class=\"inner_cell\">\n <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"k\">if</span> <span class=\"n\">numpy</span><span class=\"o\">.</span><span class=\"n\">ones</span><span class=\"p\">((</span><span class=\"mi\">0</span><span class=\"p\">,</span> <span class=\"mi\">0</span><span class=\"p\">)):</span>\n <span class=\"nb\">print</span><span class=\"p\">(</span><span class=\"s1\">'Ano'</span><span class=\"p\">)</span>\n</pre></div>\n\n </div>\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<h2>Další operace</h2>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>Modul <code>numpy</code> obsahuje spoustu základních funkcí, které pracují s maticemi; mimo jiné většinu funkcií z pythonního modulu <code>math</code>. Oproti <code>math</code> zvládají funkce z NumPy <em>broadcasting</em>.</p>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In [73]:</div>\n<div class=\"inner_cell\">\n <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"n\">array</span> <span class=\"o\">=</span> <span class=\"n\">numpy</span><span class=\"o\">.</span><span class=\"n\">linspace</span><span class=\"p\">(</span><span class=\"mi\">0</span><span class=\"p\">,</span> <span class=\"n\">numpy</span><span class=\"o\">.</span><span class=\"n\">pi</span><span class=\"p\">,</span> <span class=\"n\">num</span><span class=\"o\">=</span><span class=\"mi\">1000</span><span class=\"p\">)</span>\n<span class=\"n\">array</span><span class=\"p\">[:</span><span class=\"mi\">10</span><span class=\"p\">]</span>\n</pre></div>\n\n </div>\n</div>\n</div>\n\n<div class=\"output_wrapper\">\n<div class=\"output\">\n\n\n<div class=\"output_area\">\n\n <div class=\"prompt output_prompt\">Out[73]:</div>\n\n\n\n\n<div class=\"output_text output_subarea output_execute_result\">\n<pre>array([ 0. , 0.00314474, 0.00628947, 0.00943421, 0.01257895,\n 0.01572369, 0.01886842, 0.02201316, 0.0251579 , 0.02830264])</pre>\n</div>\n\n</div>\n\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In [74]:</div>\n<div class=\"inner_cell\">\n <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"n\">sine</span> <span class=\"o\">=</span> <span class=\"n\">numpy</span><span class=\"o\">.</span><span class=\"n\">sin</span><span class=\"p\">(</span><span class=\"n\">array</span><span class=\"p\">)</span>\n<span class=\"n\">sine</span><span class=\"p\">[:</span><span class=\"mi\">10</span><span class=\"p\">]</span>\n</pre></div>\n\n </div>\n</div>\n</div>\n\n<div class=\"output_wrapper\">\n<div class=\"output\">\n\n\n<div class=\"output_area\">\n\n <div class=\"prompt output_prompt\">Out[74]:</div>\n\n\n\n\n<div class=\"output_text output_subarea output_execute_result\">\n<pre>array([ 0. , 0.00314473, 0.00628943, 0.00943407, 0.01257862,\n 0.01572304, 0.0188673 , 0.02201138, 0.02515525, 0.02829886])</pre>\n</div>\n\n</div>\n\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>Další operace doporučuji hledat buď v Notebooku přes <kbd>tab</kbd>, v dokumentaci, nebo obecně na Internetu (kde najdete i případné knihovny, které implementují operace, které v NumPy nejsou).</p>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<h1>Příklady použití</h1>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>Dost teorie. Tahle <em>n</em>-rozměrná pole se používají na spoustu zajímavých věcí. Podívejme se na některé příklady.</p>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<h2>Matematika a grafy</h2>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>Jak se používají matice, jistě znáte z matematiky a cílem tohoto kurzu není vás to naučit. Ukážu ale pár ochutnávek.</p>\n<p>Použijeme knihovnu <a href=\"https://matplotlib.org/\">Matplotlib</a>, která vykresluje grafy. Jak ji použít dohledáte v <a href=\"https://matplotlib.org/contents.html\">dokumentaci</a> nebo – často efektivněji – v <a href=\"https://matplotlib.org/gallery/index.html\">galerii příkladů</a>.</p>\n<p>Matplotlib nemá automatickou integraci s Jupyter Notebookem, proto ji je potřeba po importu zapnout:</p>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In [75]:</div>\n<div class=\"inner_cell\">\n <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"kn\">from</span> <span class=\"nn\">matplotlib</span> <span class=\"k\">import</span> <span class=\"n\">pyplot</span>\n\n<span class=\"c1\"># Zapnutí integrace s notebookem – `%` je "magický" příkaz IPythonu, podobně jako `!` pro shell</span>\n<span class=\"o\">%</span><span class=\"n\">matplotlib</span> <span class=\"n\">inline</span>\n</pre></div>\n\n </div>\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>A teď můžeme nakreslit třeba graf funkce:</p>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In [76]:</div>\n<div class=\"inner_cell\">\n <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"n\">x</span> <span class=\"o\">=</span> <span class=\"n\">numpy</span><span class=\"o\">.</span><span class=\"n\">linspace</span><span class=\"p\">(</span><span class=\"mi\">0</span><span class=\"p\">,</span> <span class=\"n\">numpy</span><span class=\"o\">.</span><span class=\"n\">pi</span> <span class=\"o\">*</span> <span class=\"mi\">4</span><span class=\"p\">)</span> <span class=\"c1\"># definiční obor</span>\n<span class=\"n\">y</span> <span class=\"o\">=</span> <span class=\"n\">numpy</span><span class=\"o\">.</span><span class=\"n\">sin</span><span class=\"p\">(</span><span class=\"n\">x</span><span class=\"p\">)</span> <span class=\"c1\"># odpovídající hodnoty funkce</span>\n\n<span class=\"n\">pyplot</span><span class=\"o\">.</span><span class=\"n\">plot</span><span class=\"p\">(</span><span class=\"n\">x</span><span class=\"p\">,</span> <span class=\"n\">y</span><span class=\"p\">)</span>\n</pre></div>\n\n </div>\n</div>\n</div>\n\n<div class=\"output_wrapper\">\n<div class=\"output\">\n\n\n<div class=\"output_area\">\n\n <div class=\"prompt output_prompt\">Out[76]:</div>\n\n\n\n\n<div class=\"output_text output_subarea output_execute_result\">\n<pre>[<matplotlib.lines.Line2D at 0x7fefa7f4f6a0>]</pre>\n</div>\n\n</div>\n\n<div class=\"output_area\">\n\n <div class=\"prompt\"></div>\n\n\n\n\n<div class=\"output_png output_subarea \">\n<img 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wx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%0ApUbRxKCUUmoUTQxKKaVG+f/n+EQN/NCuPwAAAABJRU5ErkJggg==%0A\">\n</div>\n\n</div>\n\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In [77]:</div>\n<div class=\"inner_cell\">\n <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"n\">s</span> <span class=\"o\">=</span> <span class=\"n\">numpy</span><span class=\"o\">.</span><span class=\"n\">linspace</span><span class=\"p\">(</span><span class=\"o\">-</span><span class=\"mi\">10</span><span class=\"p\">,</span> <span class=\"mi\">10</span><span class=\"p\">,</span> <span class=\"n\">num</span><span class=\"o\">=</span><span class=\"mi\">100</span><span class=\"p\">)</span>\n\n<span class=\"c1\"># meshgrid vrátí dvě 100×100 matice:</span>\n<span class=\"c1\"># - jednu, kde v každém řádku jsou čísla od -10 do 10,</span>\n<span class=\"c1\"># - druhou, kde v každém sloupci jsou čísla od -10 do 10.</span>\n<span class=\"n\">x</span><span class=\"p\">,</span> <span class=\"n\">y</span> <span class=\"o\">=</span> <span class=\"n\">numpy</span><span class=\"o\">.</span><span class=\"n\">meshgrid</span><span class=\"p\">(</span><span class=\"n\">s</span><span class=\"p\">,</span> <span class=\"n\">s</span><span class=\"p\">)</span>\n\n<span class=\"c1\"># vyhodnotíme (x**2 + y**2) pro každý prvek</span>\n<span class=\"n\">z</span> <span class=\"o\">=</span> <span class=\"n\">x</span> <span class=\"o\">**</span> <span class=\"mi\">2</span> <span class=\"o\">+</span> <span class=\"n\">y</span> <span class=\"o\">**</span> <span class=\"mi\">2</span>\n\n<span class=\"c1\"># výsledek vykreslíme jako obrázek</span>\n<span class=\"n\">pyplot</span><span class=\"o\">.</span><span class=\"n\">imshow</span><span class=\"p\">(</span><span class=\"n\">z</span><span class=\"p\">)</span>\n\n<span class=\"c1\"># přidáme legendu</span>\n<span class=\"n\">pyplot</span><span class=\"o\">.</span><span class=\"n\">colorbar</span><span class=\"p\">()</span>\n</pre></div>\n\n </div>\n</div>\n</div>\n\n<div class=\"output_wrapper\">\n<div class=\"output\">\n\n\n<div class=\"output_area\">\n\n <div class=\"prompt output_prompt\">Out[77]:</div>\n\n\n\n\n<div class=\"output_text output_subarea output_execute_result\">\n<pre><matplotlib.colorbar.Colorbar at 0x7fefa7f74b70></pre>\n</div>\n\n</div>\n\n<div class=\"output_area\">\n\n <div class=\"prompt\"></div>\n\n\n\n\n<div class=\"output_png output_subarea \">\n<img 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h7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%0A\">\n</div>\n\n</div>\n\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In [78]:</div>\n<div class=\"inner_cell\">\n <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"c1\"># Ta samá data můžeme vykreslit i ve 3D</span>\n<span class=\"kn\">from</span> <span class=\"nn\">mpl_toolkits.mplot3d</span> <span class=\"k\">import</span> <span class=\"n\">Axes3D</span>\n\n<span class=\"n\">fig</span> <span class=\"o\">=</span> <span class=\"n\">pyplot</span><span class=\"o\">.</span><span class=\"n\">figure</span><span class=\"p\">()</span>\n<span class=\"n\">axes</span> <span class=\"o\">=</span> <span class=\"n\">fig</span><span class=\"o\">.</span><span class=\"n\">gca</span><span class=\"p\">(</span><span class=\"n\">projection</span><span class=\"o\">=</span><span class=\"s1\">'3d'</span><span class=\"p\">)</span>\n\n<span class=\"n\">surf</span> <span class=\"o\">=</span> <span class=\"n\">axes</span><span class=\"o\">.</span><span class=\"n\">plot_surface</span><span class=\"p\">(</span><span class=\"n\">x</span><span class=\"p\">,</span> <span class=\"n\">y</span><span class=\"p\">,</span> <span class=\"n\">z</span><span class=\"p\">,</span> <span class=\"n\">cmap</span><span class=\"o\">=</span><span class=\"s1\">'viridis'</span><span class=\"p\">)</span>\n</pre></div>\n\n </div>\n</div>\n</div>\n\n<div class=\"output_wrapper\">\n<div class=\"output\">\n\n\n<div class=\"output_area\">\n\n <div class=\"prompt\"></div>\n\n\n\n\n<div class=\"output_png output_subarea \">\n<img 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04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%0AXqN2vn5dOyMAAAAASUVORK5CYII=%0A\">\n</div>\n\n</div>\n\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<h2>Obrázky</h2>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>Typický barevný obrázek není nic než matice $m \\times n \\times 3$ čísel: $m \\times n$ pixelů na šířku a výšku a 3 kanály pro červenou, zelenou a modrou barvu.</p>\n<p>Knihovna <code>pillow</code> (nástupce knihovny PIL, který se stále importuje jako PIL) obsahuje nástroje na práci s obrázky, např. „nakresli čáru“ nebo „převeď na černobílý obrázek“ nebo „načti PNG“. Není postavena přímo na NumPy, ale umí obrázky převádět z a na NumPy pole, pokud máme NumPy nainstalované.</p>\n<p>V knihovně <code>scipy.ndimage</code> existuje spousta nástrojů na analýzu obrazových dat jako 2D signálů, např. konvoluce nebo Sobelův filtr. Jako celé SciPy je postavená přímo na NumPy.</p>\n<p>Nás bude na začátku zajímat funkce <code>scipy.ndimage.imread</code>, která pomocí Pillow/PIL načte obrázek jako 3D matici 8-bitových čísel. Já načtu obrázek hada, vy najděte na internetu jakýkoli barevný obrázek a načtěte si ten.</p>\n<p><em>Použitý obrázek je stažený <a href=\"https://commons.wikimedia.org/wiki/File:Ball_python_lucy.JPG\">z Wikimedia Commons</a> a je pod licencí <a href=\"https://creativecommons.org/licenses/by-sa/3.0/deed.en\">CC-BY-SA 3.0</a>. Autor je uživatel <a href=\"https://en.wikipedia.org/wiki/User:HCA\">Mokele</a> na <a href=\"https://en.wikipedia.org/wiki/\">anglické Wikipedii</a>.</em></p>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In [79]:</div>\n<div class=\"inner_cell\">\n <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"kn\">import</span> <span class=\"nn\">scipy.ndimage</span>\n<span class=\"n\">img</span> <span class=\"o\">=</span> <span class=\"n\">scipy</span><span class=\"o\">.</span><span class=\"n\">ndimage</span><span class=\"o\">.</span><span class=\"n\">imread</span><span class=\"p\">(</span><span class=\"s1\">'static/python.jpg'</span><span class=\"p\">,</span> <span class=\"n\">mode</span><span class=\"o\">=</span><span class=\"s1\">'RGB'</span><span class=\"p\">)</span>\n<span class=\"n\">img</span>\n</pre></div>\n\n </div>\n</div>\n</div>\n\n<div class=\"output_wrapper\">\n<div class=\"output\">\n\n\n<div class=\"output_area\">\n\n <div class=\"prompt output_prompt\">Out[79]:</div>\n\n\n\n\n<div class=\"output_text output_subarea output_execute_result\">\n<pre>array([[[172, 173, 165],\n [173, 174, 166],\n [173, 174, 168],\n ..., \n [172, 171, 167],\n [173, 172, 168],\n [173, 172, 168]],\n\n [[175, 176, 168],\n [174, 175, 167],\n [172, 173, 167],\n ..., \n [172, 171, 167],\n [173, 172, 168],\n [174, 173, 169]],\n\n [[176, 177, 169],\n [174, 175, 167],\n [171, 172, 166],\n ..., \n [173, 172, 168],\n [173, 172, 168],\n [172, 171, 167]],\n\n ..., \n [[209, 211, 206],\n [210, 212, 207],\n [211, 213, 208],\n ..., \n [202, 203, 197],\n [200, 201, 195],\n [200, 201, 195]],\n\n [[208, 210, 205],\n [209, 211, 206],\n [209, 211, 206],\n ..., \n [202, 203, 197],\n [202, 203, 197],\n [203, 204, 198]],\n\n [[207, 209, 204],\n [209, 211, 206],\n [210, 212, 207],\n ..., \n [201, 202, 196],\n [201, 202, 196],\n [201, 202, 196]]], dtype=uint8)</pre>\n</div>\n\n</div>\n\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>Pomocí nám už známé knihovny <code>matplotlib</code> takovou matici můžeme zobrazit:</p>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In [80]:</div>\n<div class=\"inner_cell\">\n <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"n\">pyplot</span><span class=\"o\">.</span><span class=\"n\">imshow</span><span class=\"p\">(</span><span class=\"n\">img</span><span class=\"p\">)</span>\n</pre></div>\n\n </div>\n</div>\n</div>\n\n<div class=\"output_wrapper\">\n<div class=\"output\">\n\n\n<div class=\"output_area\">\n\n <div class=\"prompt output_prompt\">Out[80]:</div>\n\n\n\n\n<div class=\"output_text output_subarea output_execute_result\">\n<pre><matplotlib.image.AxesImage at 0x7fefcc3d65c0></pre>\n</div>\n\n</div>\n\n<div class=\"output_area\">\n\n <div class=\"prompt\"></div>\n\n\n\n\n<div class=\"output_png output_subarea \">\n<img 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88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%0A\">\n</div>\n\n</div>\n\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>Podívejme se teď na strukturu matice:</p>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In [81]:</div>\n<div class=\"inner_cell\">\n <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"n\">img</span><span class=\"o\">.</span><span class=\"n\">shape</span>\n</pre></div>\n\n </div>\n</div>\n</div>\n\n<div class=\"output_wrapper\">\n<div class=\"output\">\n\n\n<div class=\"output_area\">\n\n <div class=\"prompt output_prompt\">Out[81]:</div>\n\n\n\n\n<div class=\"output_text output_subarea output_execute_result\">\n<pre>(887, 1037, 3)</pre>\n</div>\n\n</div>\n\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>První rozměr jsou řádky (y); můj obrázek je 887 pixelů vysoký. Druhý jsou sloupce (x); tento obrázek má na šířku 1037 px.\nTřetí rozměr jsou barevné kanály.</p>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>Pomocí indexování se můžeme na jednotlivé barevné kanály dostat: je to poslední index, takže řádky a sloupce nahradíme buď dvěma kompletními intervaly (<code>:, :</code>) nebo vynechávkou (<code>...</code>). Červený kanál tedy bude <code>[..., 1]</code>, modrý <code>[..., -1]</code>.</p>\n<p>Zobrazení chceme černobílé; na to má matplotlib pojmenovaný argument <code>cmap</code>. Výchozí způsob obarvování je vhodný spíše pro grafy funkcí.</p>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In [82]:</div>\n<div class=\"inner_cell\">\n <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"n\">blue_channel</span> <span class=\"o\">=</span> <span class=\"n\">img</span><span class=\"p\">[</span><span class=\"o\">...</span><span class=\"p\">,</span> <span class=\"o\">-</span><span class=\"mi\">1</span><span class=\"p\">]</span>\n<span class=\"n\">pyplot</span><span class=\"o\">.</span><span class=\"n\">imshow</span><span class=\"p\">(</span><span class=\"n\">blue_channel</span><span class=\"p\">,</span> <span class=\"n\">cmap</span><span class=\"o\">=</span><span class=\"s1\">'gray'</span><span class=\"p\">)</span>\n</pre></div>\n\n </div>\n</div>\n</div>\n\n<div class=\"output_wrapper\">\n<div class=\"output\">\n\n\n<div class=\"output_area\">\n\n <div class=\"prompt output_prompt\">Out[82]:</div>\n\n\n\n\n<div class=\"output_text output_subarea output_execute_result\">\n<pre><matplotlib.image.AxesImage at 0x7fefcc2ee828></pre>\n</div>\n\n</div>\n\n<div class=\"output_area\">\n\n <div class=\"prompt\"></div>\n\n\n\n\n<div class=\"output_png output_subarea \">\n<img 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0p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%0ABpWk9F77r+59bH8u1gAAAABJRU5ErkJggg==%0A\">\n</div>\n\n</div>\n\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>Zajímavé využití obrázku jako matice je steganografie: ukrytí informace v obrazových datech.</p>\n<p>Načteme jiný obrázek stejné velikosti, tentokrát černobílý (s módem <code>L</code>). Informace v něm schováme do posledního bitu modrého kanálu.</p>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In [83]:</div>\n<div class=\"inner_cell\">\n <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"n\">secret</span> <span class=\"o\">=</span> <span class=\"n\">scipy</span><span class=\"o\">.</span><span class=\"n\">ndimage</span><span class=\"o\">.</span><span class=\"n\">imread</span><span class=\"p\">(</span><span class=\"s1\">'static/secret.png'</span><span class=\"p\">,</span> <span class=\"n\">mode</span><span class=\"o\">=</span><span class=\"s1\">'L'</span><span class=\"p\">)</span>\n\n<span class=\"n\">img</span><span class=\"p\">[</span><span class=\"o\">...</span><span class=\"p\">,</span> <span class=\"o\">-</span><span class=\"mi\">1</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"p\">(</span><span class=\"n\">img</span><span class=\"p\">[</span><span class=\"o\">...</span><span class=\"p\">,</span> <span class=\"o\">-</span><span class=\"mi\">1</span><span class=\"p\">]</span> <span class=\"o\">&</span> <span class=\"mb\">0b11111110</span><span class=\"p\">)</span> <span class=\"o\">+</span> <span class=\"p\">(</span><span class=\"n\">secret</span><span class=\"o\">.</span><span class=\"n\">astype</span><span class=\"p\">(</span><span class=\"nb\">bool</span><span class=\"p\">))</span>\n</pre></div>\n\n </div>\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>Obrázek vypadá na první pohled stejně...</p>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In [84]:</div>\n<div class=\"inner_cell\">\n <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"n\">pyplot</span><span class=\"o\">.</span><span class=\"n\">imshow</span><span class=\"p\">(</span><span class=\"n\">img</span><span class=\"p\">)</span>\n</pre></div>\n\n </div>\n</div>\n</div>\n\n<div class=\"output_wrapper\">\n<div class=\"output\">\n\n\n<div class=\"output_area\">\n\n <div class=\"prompt output_prompt\">Out[84]:</div>\n\n\n\n\n<div class=\"output_text output_subarea output_execute_result\">\n<pre><matplotlib.image.AxesImage at 0x7fefa41d7470></pre>\n</div>\n\n</div>\n\n<div class=\"output_area\">\n\n <div class=\"prompt\"></div>\n\n\n\n\n<div class=\"output_png output_subarea \">\n<img 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6p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%0ANHDhRAAAAABJRU5ErkJggg==%0A\">\n</div>\n\n</div>\n\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>... ale v posledím modrém bitu se skrývá tajná informace.</p>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In [85]:</div>\n<div class=\"inner_cell\">\n <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"n\">pyplot</span><span class=\"o\">.</span><span class=\"n\">imshow</span><span class=\"p\">(</span><span class=\"n\">img</span><span class=\"p\">[</span><span class=\"o\">...</span><span class=\"p\">,</span> <span class=\"o\">-</span><span class=\"mi\">1</span><span class=\"p\">]</span> <span class=\"o\">&</span> <span class=\"mi\">1</span><span class=\"p\">,</span> <span class=\"n\">cmap</span><span class=\"o\">=</span><span class=\"s1\">'gray'</span><span class=\"p\">)</span>\n</pre></div>\n\n </div>\n</div>\n</div>\n\n<div class=\"output_wrapper\">\n<div class=\"output\">\n\n\n<div class=\"output_area\">\n\n <div class=\"prompt output_prompt\">Out[85]:</div>\n\n\n\n\n<div class=\"output_text output_subarea output_execute_result\">\n<pre><matplotlib.image.AxesImage at 0x7fefa40f1438></pre>\n</div>\n\n</div>\n\n<div class=\"output_area\">\n\n <div class=\"prompt\"></div>\n\n\n\n\n<div class=\"output_png output_subarea \">\n<img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAASoAAAD8CAYAAADAKumpAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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%0AeA4qMyueg8rMiuegMrPiOajMrHj/DwnA83L0Z3HhAAAAAElFTkSuQmCC%0A\">\n</div>\n\n</div>\n\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>Výsledek je dobré uložit v bezztrátovém formátu (PNG), aby se informace neztratila:</p>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In [86]:</div>\n<div class=\"inner_cell\">\n <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"n\">scipy</span><span class=\"o\">.</span><span class=\"n\">misc</span><span class=\"o\">.</span><span class=\"n\">imsave</span><span class=\"p\">(</span><span class=\"s1\">'python.png'</span><span class=\"p\">,</span> <span class=\"n\">img</span><span class=\"p\">)</span>\n</pre></div>\n\n </div>\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<h2>Zvuk</h2>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>Jako pole lze reprezentovat i zvukový záznam. Mám záznam, na kterém zkouším zpívat; pomocí funkce <code>scipy.io.wavfile</code> ho můžu načíst jako NumPy pole:</p>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In [87]:</div>\n<div class=\"inner_cell\">\n <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"kn\">import</span> <span class=\"nn\">scipy.io.wavfile</span>\n<span class=\"n\">sample_rate</span><span class=\"p\">,</span> <span class=\"n\">sound</span> <span class=\"o\">=</span> <span class=\"n\">scipy</span><span class=\"o\">.</span><span class=\"n\">io</span><span class=\"o\">.</span><span class=\"n\">wavfile</span><span class=\"o\">.</span><span class=\"n\">read</span><span class=\"p\">(</span><span class=\"s1\">'static/sample.wav'</span><span class=\"p\">)</span>\n<span class=\"nb\">print</span><span class=\"p\">(</span><span class=\"n\">sample_rate</span><span class=\"p\">)</span>\n<span class=\"n\">sound</span>\n</pre></div>\n\n </div>\n</div>\n</div>\n\n<div class=\"output_wrapper\">\n<div class=\"output\">\n\n\n<div class=\"output_area\">\n\n <div class=\"prompt\"></div>\n\n\n<div class=\"output_subarea output_stream output_stdout output_text\">\n<pre>48000\n</pre>\n</div>\n</div>\n\n<div class=\"output_area\">\n\n <div class=\"prompt output_prompt\">Out[87]:</div>\n\n\n\n\n<div class=\"output_text output_subarea output_execute_result\">\n<pre>array([[ -58, -88],\n [ -65, -49],\n [ 56, -18],\n ..., \n [ 1, 231],\n [ -85, 234],\n [-118, 212]], dtype=int16)</pre>\n</div>\n\n</div>\n\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>Zvuk je stereo, má dvě stopy; jednu z nich si vykreslím:</p>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In [88]:</div>\n<div class=\"inner_cell\">\n <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"n\">channel</span> <span class=\"o\">=</span> <span class=\"n\">sound</span><span class=\"p\">[</span><span class=\"o\">...</span><span class=\"p\">,</span> <span class=\"mi\">1</span><span class=\"p\">]</span>\n<span class=\"n\">pyplot</span><span class=\"o\">.</span><span class=\"n\">plot</span><span class=\"p\">(</span><span class=\"n\">channel</span><span class=\"p\">)</span>\n</pre></div>\n\n </div>\n</div>\n</div>\n\n<div class=\"output_wrapper\">\n<div class=\"output\">\n\n\n<div class=\"output_area\">\n\n <div class=\"prompt output_prompt\">Out[88]:</div>\n\n\n\n\n<div class=\"output_text output_subarea output_execute_result\">\n<pre>[<matplotlib.lines.Line2D at 0x7fef9c13fdd8>]</pre>\n</div>\n\n</div>\n\n<div class=\"output_area\">\n\n <div class=\"prompt\"></div>\n\n\n\n\n<div class=\"output_png output_subarea \">\n<img 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3k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%0A/xsnmCBKOUlOAAAAAElFTkSuQmCC%0A\">\n</div>\n\n</div>\n\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>Případně můžu využít možností Jupyter Notebooku a HTML a zvuk si přehrát:</p>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In [89]:</div>\n<div class=\"inner_cell\">\n <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"kn\">from</span> <span class=\"nn\">IPython.display</span> <span class=\"k\">import</span> <span class=\"n\">Audio</span>\n<span class=\"n\">Audio</span><span class=\"p\">(</span><span class=\"n\">data</span><span class=\"o\">=</span><span class=\"n\">channel</span><span class=\"p\">,</span> <span class=\"n\">rate</span><span class=\"o\">=</span><span class=\"n\">sample_rate</span><span class=\"p\">)</span>\n<span class=\"nb\">print</span><span class=\"p\">(</span><span class=\"s1\">'(Zkuste si to sami; tento print vymažte)'</span><span class=\"p\">)</span>\n</pre></div>\n\n </div>\n</div>\n</div>\n\n<div class=\"output_wrapper\">\n<div class=\"output\">\n\n\n<div class=\"output_area\">\n\n <div class=\"prompt\"></div>\n\n\n<div class=\"output_subarea output_stream output_stdout output_text\">\n<pre>(Zkuste si to sami; tento print vymažte)\n</pre>\n</div>\n</div>\n\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>Podívám se na detail první „noty”, kde je patrná vlna s nějakou frekvencí:</p>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In [90]:</div>\n<div class=\"inner_cell\">\n <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"n\">segment</span> <span class=\"o\">=</span> <span class=\"n\">channel</span><span class=\"p\">[</span><span class=\"mi\">50000</span><span class=\"p\">:</span><span class=\"mi\">55000</span><span class=\"p\">]</span>\n<span class=\"n\">pyplot</span><span class=\"o\">.</span><span class=\"n\">plot</span><span class=\"p\">(</span><span class=\"n\">segment</span><span class=\"p\">)</span>\n</pre></div>\n\n </div>\n</div>\n</div>\n\n<div class=\"output_wrapper\">\n<div class=\"output\">\n\n\n<div class=\"output_area\">\n\n <div class=\"prompt output_prompt\">Out[90]:</div>\n\n\n\n\n<div class=\"output_text output_subarea output_execute_result\">\n<pre>[<matplotlib.lines.Line2D at 0x7fef9c0e4160>]</pre>\n</div>\n\n</div>\n\n<div class=\"output_area\">\n\n <div class=\"prompt\"></div>\n\n\n\n\n<div class=\"output_png output_subarea \">\n<img 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kA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%0AAAAASUVORK5CYII=%0A\">\n</div>\n\n</div>\n\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In [91]:</div>\n<div class=\"inner_cell\">\n <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"kn\">from</span> <span class=\"nn\">IPython.display</span> <span class=\"k\">import</span> <span class=\"n\">Audio</span>\n<span class=\"n\">Audio</span><span class=\"p\">(</span><span class=\"n\">data</span><span class=\"o\">=</span><span class=\"n\">segment</span><span class=\"p\">,</span> <span class=\"n\">rate</span><span class=\"o\">=</span><span class=\"n\">sample_rate</span><span class=\"p\">)</span>\n</pre></div>\n\n </div>\n</div>\n</div>\n\n<div class=\"output_wrapper\">\n<div class=\"output\">\n\n\n<div class=\"output_area\">\n\n <div class=\"prompt output_prompt\">Out[91]:</div>\n\n\n\n<div class=\"output_html rendered_html output_subarea output_execute_result\">\n\n <audio controls=\"controls\">\n <source 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\" type=\"audio/wav\">\n Your browser does not support the audio element.\n </audio>\n \n</div>\n\n</div>\n\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>Jaká to je frekvence? Znáte-li analýzu signálů, tušíte, že na podobné otázky odpovídá Fourierova transformace.\nNumPy obsahuje diskrétní Fourierovu transformaci v modulu <code>numpy.fft</code> spolu s funkcí, která spočítá odpovídající frekvence v Hz:</p>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In [92]:</div>\n<div class=\"inner_cell\">\n <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"n\">spectrum</span> <span class=\"o\">=</span> <span class=\"n\">numpy</span><span class=\"o\">.</span><span class=\"n\">fft</span><span class=\"o\">.</span><span class=\"n\">fft</span><span class=\"p\">(</span><span class=\"n\">segment</span><span class=\"p\">)</span>\n<span class=\"n\">freqs</span> <span class=\"o\">=</span> <span class=\"n\">numpy</span><span class=\"o\">.</span><span class=\"n\">fft</span><span class=\"o\">.</span><span class=\"n\">fftfreq</span><span class=\"p\">(</span><span class=\"nb\">len</span><span class=\"p\">(</span><span class=\"n\">spectrum</span><span class=\"p\">),</span> <span class=\"mi\">1</span><span class=\"o\">/</span><span class=\"n\">sample_rate</span><span class=\"p\">)</span>\n<span class=\"n\">pyplot</span><span class=\"o\">.</span><span class=\"n\">xlabel</span><span class=\"p\">(</span><span class=\"s1\">'Frekvence (Hz)'</span><span class=\"p\">)</span>\n<span class=\"n\">pyplot</span><span class=\"o\">.</span><span class=\"n\">plot</span><span class=\"p\">(</span><span class=\"n\">freqs</span><span class=\"p\">,</span> <span class=\"n\">numpy</span><span class=\"o\">.</span><span class=\"n\">abs</span><span class=\"p\">(</span><span class=\"n\">spectrum</span><span class=\"p\">))</span>\n</pre></div>\n\n </div>\n</div>\n</div>\n\n<div class=\"output_wrapper\">\n<div class=\"output\">\n\n\n<div class=\"output_area\">\n\n <div class=\"prompt output_prompt\">Out[92]:</div>\n\n\n\n\n<div class=\"output_text output_subarea output_execute_result\">\n<pre>[<matplotlib.lines.Line2D at 0x7fef9558b2b0>]</pre>\n</div>\n\n</div>\n\n<div class=\"output_area\">\n\n <div class=\"prompt\"></div>\n\n\n\n\n<div class=\"output_png output_subarea \">\n<img 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CO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%0AhTlMzMysMIeJmZkV5jAxM7PC/j+FWeYv64YrqAAAAABJRU5ErkJggg==%0A\">\n</div>\n\n</div>\n\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>V tomto grafu hledám maximum. Můžu se zaměřit na prvních pár hodnot spektra:</p>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In [93]:</div>\n<div class=\"inner_cell\">\n <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"n\">pyplot</span><span class=\"o\">.</span><span class=\"n\">xlabel</span><span class=\"p\">(</span><span class=\"s1\">'Frekvence (Hz)'</span><span class=\"p\">)</span>\n<span class=\"n\">pyplot</span><span class=\"o\">.</span><span class=\"n\">plot</span><span class=\"p\">(</span><span class=\"n\">freqs</span><span class=\"p\">[:</span><span class=\"mi\">100</span><span class=\"p\">],</span> <span class=\"n\">numpy</span><span class=\"o\">.</span><span class=\"n\">abs</span><span class=\"p\">(</span><span class=\"n\">spectrum</span><span class=\"p\">[:</span><span class=\"mi\">100</span><span class=\"p\">]))</span>\n</pre></div>\n\n </div>\n</div>\n</div>\n\n<div class=\"output_wrapper\">\n<div class=\"output\">\n\n\n<div class=\"output_area\">\n\n <div class=\"prompt output_prompt\">Out[93]:</div>\n\n\n\n\n<div class=\"output_text output_subarea output_execute_result\">\n<pre>[<matplotlib.lines.Line2D at 0x7fef9550cc50>]</pre>\n</div>\n\n</div>\n\n<div class=\"output_area\">\n\n <div class=\"prompt\"></div>\n\n\n\n\n<div class=\"output_png output_subarea \">\n<img 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kD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%0AjBkyCybGGGOG7P8DiXlYVzaan1kAAAAASUVORK5CYII=%0A\">\n</div>\n\n</div>\n\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>Maximum je někde kolem 120 Hz; abych to zjistil přesně, použiji funkci <code>argmax</code>:</p>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In [94]:</div>\n<div class=\"inner_cell\">\n <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"n\">amax</span> <span class=\"o\">=</span> <span class=\"n\">numpy</span><span class=\"o\">.</span><span class=\"n\">argmax</span><span class=\"p\">(</span><span class=\"nb\">abs</span><span class=\"p\">(</span><span class=\"n\">spectrum</span><span class=\"p\">))</span>\n<span class=\"n\">amax</span>\n</pre></div>\n\n </div>\n</div>\n</div>\n\n<div class=\"output_wrapper\">\n<div class=\"output\">\n\n\n<div class=\"output_area\">\n\n <div class=\"prompt output_prompt\">Out[94]:</div>\n\n\n\n\n<div class=\"output_text output_subarea output_execute_result\">\n<pre>13</pre>\n</div>\n\n</div>\n\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>... a najdu odpovídající frekvenci:</p>\n</div>\n</div>\n</div>\n<div class=\"cell border-box-sizing code_cell rendered\">\n<div class=\"input\">\n<div class=\"prompt input_prompt\">In [95]:</div>\n<div class=\"inner_cell\">\n <div class=\"input_area\">\n<div class=\" highlight hl-ipython3\"><pre><span></span><span class=\"n\">freqs</span><span class=\"p\">[</span><span class=\"n\">amax</span><span class=\"p\">]</span>\n</pre></div>\n\n </div>\n</div>\n</div>\n\n<div class=\"output_wrapper\">\n<div class=\"output\">\n\n\n<div class=\"output_area\">\n\n <div class=\"prompt output_prompt\">Out[95]:</div>\n\n\n\n\n<div class=\"output_text output_subarea output_execute_result\">\n<pre>124.80000000000001</pre>\n</div>\n\n</div>\n\n</div>\n</div>\n\n</div>\n<div class=\"cell border-box-sizing text_cell rendered\"><div class=\"prompt input_prompt\">\n</div><div class=\"inner_cell\">\n<div class=\"text_cell_render border-box-sizing rendered_html\">\n<p>Což je podle <a href=\"https://en.wikipedia.org/wiki/Piano_key_frequencies\">seznamu not</a> skoro H$_2$ (123,5 Hz).</p>\n</div>\n</div>\n</div>\n \n\n\n\n\n "
}
}
}