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Json Decoded-Result Reference For The Hdf5 Hierarchy And Its Normalisation Invariant

JSON decoded-result reference for the HDF5 hierarchy and its normalisation invariant, kept beside the binary source for semantic rather than byte-level comparison. Stable P8 artifact p8-convert-hdf5-expected.

Conversion testingScientific dataSchema validation
Preview, first 41 linesjson
{
  "datasets": [
    {
      "path": "/raw/detector_counts",
      "shape": [
        32,
        8
      ],
      "units": "counts"
    },
    {
      "path": "/raw/timestamp_s",
      "shape": [
        32
      ],
      "units": "s"
    },
    {
      "path": "/processed/normalised",
      "shape": [
        32,
        8
      ],
      "units": "1"
    },
    {
      "path": "/metadata/channel_index",
      "shape": [
        8
      ],
      "units": "1"
    }
  ],
  "groups": [
    "/raw",
    "/processed",
    "/metadata"
  ],
  "normalisation": "processed/normalised = raw/detector_counts / 1000"
}

Specifications

Source Format
h5
Delivery Mode
download-only
Provider
converter-v2
Provenance
Synthetic deterministic P8 fixture generated by generation/p8_content.py; seed namespace 2026082300
Fixture Reserve
convert-v2

Testing contract

Reference control
Scenario
Validate the decoded HDF5 result against this JSON artifact.
Expected result
Three named groups, four datasets, and the divide-by-1000 normalisation rule remain explicit and machine-checkable.

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

“Json Decoded-Result Reference For The Hdf5 Hierarchy And Its Normalisation Invariant” is a deterministic Testaroo fixture for Conversion testing, Scientific data, Schema validation. The same content exported across many formats and linked as a group, so you can convert one and diff against the expected twin.

Documented properties for this file: JSON · 614 bytes. 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.

Scientific fixtures are small, valid, and fully synthetic, no real organism, patient, sample, or observation. Point your parser or loader at the file and check it reads the documented records, variables, or headers; binary formats ship a readable twin or metadata listing for comparison.

Code examples

import json

with open("experiment-hierarchy-expected.json") as f:
    data = json.load(f)
print(type(data), len(data))

Generated by generation/p8_content.py. Free for any use, no attribution required, license.

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