data-forgery

Version 1.2

Library is used for forging mocked data. It supports simple dependencies among the fields (for generating code-name values). It also supports post-generating operations, such as rounding, concatenating, etc.

Quick Start

Install package

npm install data-forgery
npm test

Create a javascript file (myGenerator.js)

"use strict"
var df = require("data-forgery");

var itemDefinition = {
    accountNumber: df.bank.accountNumber,                                       // Account Number
    sortCode: df.bank.sortCode,                                                 // Sort Code
    accountBalance: df.other.flt(1000, 9999),                                   // Balance between 1000 and 9999
    currency: df.other.ccy,                                                     // Currency
    exchangeRate: df.other.exchangeRate("{currency}"),                          // Exchange rate for that currency
    eligible: df.op.random(true, false)                                         // True or false randomly
}

var generated = df.forge(10, itemDefinition);                                   // Generate 10 rows
console.log(JSON.stringify(generated, null, 3));                                // Print out on screen

Save and run

node myGenerator.js

About

The engine consists of 3 types of operations

• Generators, which are responsible for value generation

• Operators, which are responsible for modification of generated value (such as formatting, concatenation, multiplication of numbers, etc.)

• Actions, which are responsible for performing actions on the "item" level (such as deleting given property after processing)

For example

To generate random float number between 0 and 5, do

var df = require("data-forgery");
var itemDefinition = {
    randomFloat: df.number.flt(0, 10)
}

If you want to multiply this number by 10 and then round it, do

var df = require("data-forgery");
var itemDefinition = {
    randomFloat: df.op.round(df.op.multiply(df.number.flt(0, 5), 10))
}

Argument types

There are 3 argument types which you can pass to the operators and generators

Raw value

Examples are 10, "abc", 0.111123, true, false, null

Referenced Values

var df = require("data-forgery");
var itemDefinition = {
    myProductCode: df.bank.productCode,
    productName: df.bank.productName("{myProductCode}"),
}

Generator

You can pass another generator as a parameter

var df = require("data-forgery");
var itemDefinition = {
    productName: df.bank.productName(df.bank.productCode),
}

Generators

Currently, the following generators are available: Generator is referenced by a full namespace, for example

df.date.randomDate

Date (df.date.*)

• randomDate(minDate, maxDate)
• randomTime(minTime, maxTime)

Number (df.number.*)

• flt(min, max)
• int(min, max)

String (df.string.*)

• firstName
• lastName
• postCode
• company
• street
• town
• address
• email
• random(length, [stringOfPossibleCharacters])

Other (df.other.*)

• ccy
• exchangeRate(targetCurrency, baseCurrency)

Product (df.product.*)

• productCode
• productName(productCode)
• productCategory(productCode)
• productType(productCode)
• productTypeDescription(productType)

Bank (df.bank.*)

• frn
• frnUk
• frnUlster
• bankName
• franchiseCode
• franchise
• accountNumber
• sortCode
• scvId
• customerType:
• customerTypeNonIndividual
• customerTypeDescription
• accountStatusCode

Operators / Formatters

All operators are stored in df.op namespace.

Currently, following formatters and post processing operations are available

• op.round(numericValue)
• op.dateFormat(dateRef, format)
• op.timeFormat(timeRef, format)
• op.join(args...)
• obsolete op.use(valueRef) - use op.ref instead
• op.ref(valueRef)
• op.random(args...)
• op.sequence(args...)
• op.sequenceSpread(values, spreadAcrossItemsCount)
• oneInN(desiredValue, inHowMany, ifNotDesiredThenWhatValue)
• when(value, compareAgainst, ifTrue, ifElse)
• dictValue(key, dictionary)
• genToArray(value, generatedArraySize)
• multiply(value1, value2)
• divide(value1, value2)
• sum(args...)
• addMinutes(date, numberOfMinutesToAdd)
• substr(string, start, length)

Actions

Actions don't generate or modify values. They perform some other action instead.

• action.delete(fieldName)

Example of action:

var df = require("data-forgery");
var itemDefinition = {
    myProductCode: df.bank.productCode,
    productName: df.bank.productName("{myProductCode}"),


    action1: df.op.delete("myProductCode")
}

which will result in

//...
{
  productName: "Current Account"
}
//...