random

Generating and picking random values.

• Managing Seed Values
  • seed(number) - specifies the seed that all following random calls will use
• Generating Random Numbers
  • randomInteger(min, max) - inclusive integer between min and max values
  • randomInteger(min, max) - inclusive float between min and max values
• Working with Arrays
  • pickRandom(array) - picks a value at random from the list
  • randomArray(size, fn) - creates an array of size length, with each value generated from fn
• Simplex Noise
  • simplexGenerator(seed) - Number generator between -1 and 1 given an x/y/z coordinate

If leveraging

While generating a simple number between two values is common, it is very important - and useful in generating fake data.

const firstNames = ['jane', 'john', 'paul', 'ringo'];
const lastNames = ['do', 'doe', 'dough', 'doh'];
fakeName = `${utils.random.pickRandom(firstNames)} ${utils.random.pickRandom(lastNames)}`;
// 'john dough'

Additionally, there are so many different ways of generating visualizations based on simplex noise.

From straight (red - negative / green - positive)

To indicators with length, and rotation (negative ccw / positive cw)

The possibilities are endless.

This library is meant to provide simple use cases. Please use Standard libraries

• like d3-random for additional alternatives

see format.mapArrayDomain - as it projects a value from between a range a value, and picks the corresponding value from an array.

For example:

require('esm-hook');
d3 = require('d3');
utils = require('jupyter-ijavascript-utils');

//-- create a number generator using Normal / Gaussian distribution
randomGenerator = d3.randomNormal(
 0.5, // mu - or centerline
 0.1 // sigma - or spread of values
);

randomValue = randomGenerator();
// randomValue - 0.4

randomDataset = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j'];

numPicks = 3; // increase to a larger number (ex: 1000) to better see distributions

//-- create an array of 3 items, each with the results from randomGenerator
results = utils.array.size(numPicks, () => randomGenerator());
// [ 0.6235937672428706, 0.4991359903898883, 0.4279365561645624 ]

//-- map those values to the randomDataset
resultPicks = results.map(val => ({ pick: utils.format.mapArrayDomain(val, randomDataset) }));
// [ { pick: 'g' }, { pick: 'e' }, { pick: 'e' } ]

//-- group them by the pick field
//-- then add a new property called count - using the # of records with the same value
groupedResults = utils.group.by(resultPicks, 'pick')
    .reduce((list) => ({ count: list.length }));
// [ { pick: 'g', count: 1 }, { pick: 'e', count: 2 } ]

//-- make a bar chart (only with 10k results)
utils.vega.embed((vl) => {
    return vl
      .markBar()
      .title('Distribution')
      .data(groupedResults)
      .encode(
        vl.x().fieldN('pick'),
        vl.y().fieldQ('count').scale({type: 'log'})
      );
});