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Time-series — Irregular Intervals (JSON)

JSON twin of irregular-interval sensor readings for charting library tests.

Time-series dataData importCSV parsing
Preview — first 43 linesjson
[
  {
    "ts": "2026-03-08T00:00:00Z",
    "value": 10.0,
    "sensor": "SAMPLE-A"
  },
  {
    "ts": "2026-03-08T00:05:00Z",
    "value": 11.7,
    "sensor": "SAMPLE-A"
  },
  {
    "ts": "2026-03-08T00:07:00Z",
    "value": 13.4,
    "sensor": "SAMPLE-A"
  },
  {
    "ts": "2026-03-08T00:20:00Z",
    "value": 15.1,
    "sensor": "SAMPLE-A"
  },
  {
    "ts": "2026-03-08T00:21:00Z",
    "value": 16.8,
    "sensor": "SAMPLE-A"
  },
  {
    "ts": "2026-03-08T00:45:00Z",
    "value": 18.5,
    "sensor": "SAMPLE-A"
  },
  {
    "ts": "2026-03-08T01:30:00Z",
    "value": 20.2,
    "sensor": "SAMPLE-A"
  },
  {
    "ts": "2026-03-08T02:00:00Z",
    "value": 21.9,
    "sensor": "SAMPLE-A"
  }
]

Specifications

Records
8
Edge
irregular timestamps

What is a .json file?

JSON (JavaScript Object Notation) is a lightweight, text-based data-interchange format representing objects, arrays, strings, numbers, booleans, and null. It is language-independent, human-readable, and the dominant format for web APIs and configuration. It requires a single well-formed root value.

How to use this file

Use an example JSON file to test parsers and serializers, schema validation, Unicode and number-precision handling, and API request or response processing.

How to use this file for testing

“Time-series — Irregular Intervals (JSON)” is a deterministic Novus Examples fixture for Time-series data, Data import, CSV parsing. Irregular timestamps, DST gaps, and duplicate keys for time-series importers and charting libraries.

Documented properties for this file: 8 records. Compare results against paired or grouped companions on this page when present (clean↔damaged, searchable↔scanned, or format twins) so scores stay reproducible across runs.

Download the file once, keep the path stable in CI or local scripts, and treat the spec table as the contract: dimensions, seeds, field lists, and roles are intentional. Corrupt or invalid samples are labelled as such — expect parsers to fail loudly rather than silently accept them.

Data fixtures document their exact quirks — delimiters, encodings, null handling, schema, and row counts — in the spec table. Point your parser or importer at the file and assert it handles the documented edge cases; clean and deliberately-messy siblings make before/after diffs straightforward.

Valid and intentionally invalid siblings are labelled in title and description. Assert parsers accept the valid twin and fail loudly on the invalid one; for time series, check DST gaps and duplicate keys against the spec table.

Code examples

import json

with open("irregular-intervals.json") as f:
    data = json.load(f)
print(type(data), len(data))

Related files

Generated by generation/data_wave_f.py. Free for any use, no attribution required — license.