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Automate inputs

import Tabs from '@theme/Tabs'; import TabItem from '@theme/TabItem'; import BrowserWindow from '@site/src/components/browser-window';

Autocomplete values​

Use parameter suggestions to help users enter a correct value. For instance, when entering a product name, it might make sense to dynamically query a database for values starting with the already entered text, and suggest to auto-complete the value.

Suggestions are functions that take one string argument and return a list of strings to be suggested to the user. Suggestions work only for string parameters.

The following example helps the user enter a country name by dynamically retrieving a list of names from a web service:

script-param-suggestions
Run this code once
grok.functions.register({
  signature: 'List<String> jsSuggestCountryName(String text)',
  isAsync: true,
  run: async function(text) {
    let response = await fetch('https://restcountries.eu/rest/v2/name/' + text);
    return response.status === 200 ? (await response.json()).map(country => country['name']) : [];
  }
});
https://public.datagrok.ai/scripts
#name: Sales by country
#language: python
#input: string country = uk {suggestions: jsSuggestCountryName}

The same concept could be used for SQL queries:

auto-complete-sql

Precalculate inputs​

Use parameter editor to set the output of another function to the parameter value.

//input: dataframe table {editor: Package:DataQuery}
//input: dataframe table {editor: Package:DataQuery(1, "France")}

Here, Datagrok will execute the Package:DataQuery function right before your script and pass the output table to the script as an input parameter.

//input: dataframe table {editor: PowerPack:DataQuery; editor-button: Outliers...}

Specify the editor-button parameter to add a button that executes your subfunction separately and allows the user to check the output before starting the script.

Suggest choices​

Use choices to provide the editor a list of values to choose from. When choices are provided, the editor becomes a combo box. Choices can be either a fixed list, or a function that returns a list.

A choice provider is a function with no parameters that returns a list of strings.

The following example demonstrates two ways of defining choices:

script-param-choices
Run this code once
grok.functions.register({
    signature: 'List<String> jsVeggies()',
    run: () => ["Cucumber", "Cauliflower"]});
https://public.datagrok.ai/scripts
#input: string fruit {choices: ["apple", "banana"]}
#input: string vegetable {choices: jsveggies}

Validate inputs​

Validators check whether the value falls in the expected range and provide a visual cue if it does not. To add a validator to a parameter, provide a comma-separated list of functions that are invoked each time a value changes. A null indicates that the value is valid. Anything else indicates an error, which gets shown to the user.

A validator is a function that accepts one parameter of any type and returns a string. Choice providers are applicable only to string parameters.

script-param-validators
Run this code once
grok.functions.register({
    signature: 'List<String> jsVal1(int input)',
    run: (input) => input < 11 ? null : "Error val1" });

grok.functions.register({
    signature: 'List<String> jsVal2(int input)',
    run: (input) => input > 9 ? null : "Error val2" });
https://public.datagrok.ai/scripts
#name: Numbers
#language: python title="Use registered validators"
#input: int count1 {validators: ["jsval1", "jsval2"]} [Number of cells in table]
#input: int count2 {validators: ["jsval1"]} [Number of cells in table]
#input: int count3 {validators: ["jsval2"]} [Number of cells in table]