Testing

Generate deterministic CGM series and scenario fixtures with @glucoseiq/testing.

@glucoseiq/testing produces repeatable CGM data for unit tests, Storybook stories, product demos, screenshots, and documentation. Give it the same options and seed, and it returns the same chronological readings every time.

npm install --save-dev @glucoseiq/testing

Generate a series

import { generateCGMSeries } from '@glucoseiq/testing'

const readings = generateCGMSeries({
  days: 14,
  seed: 7,
  start: '2025-01-01T00:00:00Z',
})

readings[0]
// {
//   value: 96,
//   unit: 'mg/dL',
//   timestamp: '2025-01-01T00:00:00.000Z'
// }

The generator combines a circadian baseline, three default meal responses, seeded noise, and optional nocturnal hypoglycemia. Values are clamped to 40–400 mg/dL before output conversion.

Option	Default	Purpose
`days`	`1`	Number of days to generate
`intervalMin`	`5`	Minutes between readings
`seed`	`42`	Deterministic random seed
`start`	`2024-01-01T00:00:00Z`	ISO timestamp for the first reading
`basal`	`110`	Baseline in mg/dL
`mealTimes`	`07:00`, `13:00`, `19:00`	Meal times as minutes from midnight
`mealAmplitude`	`70`	Peak meal-excursion amplitude in mg/dL
`noise`	`8`	Noise amplitude in mg/dL
`nocturnalHypoDays`	`[]`	Zero-based day indexes with a 02:00–04:00 dip
`unit`	`mg/dL`	Output as `mg/dL` or `mmol/L`

Test an analysis

Generated readings use the GlucoseReading contract from @glucoseiq/core, so they can go directly into any analytic:

import { analyzeGlucose } from '@glucoseiq/core'
import { generateCGMSeries } from '@glucoseiq/testing'

const readings = generateCGMSeries({ days: 14, seed: 19 })
const report = analyzeGlucose(readings)

expect(report.valid).toBe(true)
expect(report.dataSufficiency.totalReadings).toBe(14 * 288)
expect(report.agpProfile?.bins).toHaveLength(288)

Use a fixed start in tests that group by calendar date or local time. The default starts at midnight UTC.

Start from a scenario

The scenarios collection provides four named, deterministic fixtures:

import { scenarios } from '@glucoseiq/testing'

const steady = scenarios.steadyDay()
const hypo = scenarios.hypoNight()
const volatile = scenarios.rollercoaster()
const withDropout = scenarios.gappyTrace()

Scenario	Shape
`steadyDay()`	A calm, mostly in-range day with smaller meals and less noise
`hypoNight()`	A day with a 02:00–04:00 nocturnal dip
`rollercoaster()`	Larger post-meal excursions and more noise
`gappyTrace()`	A day with readings removed from 15:00 through 16:30 UTC

Scenarios return fresh arrays, so a test can safely derive a variant without changing the next fixture:

const sparseHypo = scenarios.hypoNight().filter((_, index) => index % 2 === 0)

Produce mmol/L fixtures

import { generateCGMSeries } from '@glucoseiq/testing'

const readings = generateCGMSeries({
  unit: 'mmol/L',
  days: 2,
  seed: 23,
})

readings[0].unit // 'mmol/L'

The underlying curve is generated in mg/dL and converted to one decimal place for mmol/L output.

Synthetic data is not clinical validation

These fixtures are designed for deterministic software testing and visual demos. They do not model a specific person, sensor, therapy, or clinical outcome, and they are not a substitute for validated reference datasets.

Generate a series

Test an analysis

Start from a scenario

Produce mmol/L fixtures

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