Slider

Sastāvdaļa, kas rāda savus bērnus slaidrādē vai karuseļa veidā.

Iespējas

• title | (string|node): slaidšova/karuseļa virsraksts, kas tiks parādīts tā augšpusē.. Default: none.
• dots | boolean: displeja punkti apakšā, lai ātri pārietu uz jebkuru slaidu.. Default: true.
• fade | boolean: nosaka, vai pārejai starp diapozitīviem tiek izmantota fedings.. Default: false.
• draggable | boolean: kontrolē, vai lietotāji var vilkt slaidus, lai pārvietotos starp tiem.. Default: false.
• pagination | string: vai lappušu numerācija tiks parādīta top, bottom vai bottom.. Default: 'bottom'.
• goto | number: Ja īpašība tiek mainīta, komponents pāriet uz slaidu ar izvēlēto indeksu.. Default: 0.
• infinite | boolean: kontrolē, vai slaidšovs apvij savu saturu.. Default: false.
• interval | number: nosaka laika intervālu automātiskai diapozitīvu maiņai.. Default: none.
• swipe | boolean: ļauj vilkt/slaucīt. Default: false.
• style | object: CSS iebūvētie stili. Default: {}.
• onClick | function: atsauces izsaukums, kas tiek izsaukts, noklikšķinot uz nākamās/priekšējās pogas.. Default: onClick() {}.

Piemēri

• Minimal
• Customize Pagination
• Slideshow
• Custom Styling

Tiešraides redaktors

<Slider title="Questions to explore:" > <p>Try generating new samples of the Normal data. You'll notice that the points don't always lie exactly on the line. This is typical variation. As you generate more random realizations of this plot you'll get better calibrated to the kind of deviation you can expect to see from this large a sample of Normal data.</p> <p>How do the normal qq plots change as you vary the df parameter for the t-distributed data?</p> <p>Can you tell just by looking at the t.data histograms that the data aren't normally distributed? Is it easier to tell from the QQ plots?</p> <p>How do the normal qq plots change as you vary the shape parameter in the gamma-distributed data?</p> </Slider>

<Slider title="Questions to explore:" >
    <p>Try generating new samples of the Normal data. You'll notice that the points don't always lie exactly on the line. This is typical variation. As you generate more random realizations of this plot you'll get better calibrated to the kind of deviation you can expect to see from this large a sample of Normal data.</p>
    <p>How do the normal qq plots change as you vary the df parameter for the t-distributed data?</p>
    <p>Can you tell just by looking at the t.data histograms that the data aren't normally distributed? Is it easier to tell from the QQ plots?</p>
    <p>How do the normal qq plots change as you vary the shape parameter in the gamma-distributed data?</p>
</Slider>

Rezultāts

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