othersyntheticqaintervalsunioncoverageoverlapcsvpythonexpected-results

Synthetic interval union QA: 3,000 intervals, expected merges/coverage + 17 tests

Artificial integer interval QA snapshot v1.0; no real-world dates or measurements · Updated Nov 10, 2026

$5

Fixed price

Sample structure: Not assessed
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Seller: Alan
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Category
Other
Format
ZIP
Update Frequency
Not documented
PII
No flagged field names; not a privacy audit
Download links issued
0

Source, license and coverage

Supplier documentation. These claims are separate from the automated sample score. A listing edit date is not a data freshness date.

License
Internal testing/demos only; modification allowed. No redistribution, resale, sublicensing or model training. No warranty, updates or support SLA. Full terms in README.txt.
Source / creator
Alan - original artificial QA fixture prepared with AI assistance
Collection method
Original deterministic Python standard-library generator with AI assistance; seed 2026111005. No external source data. Independent dense-grid oracle checks expected merges, coverage and overlap.
Coverage / snapshot
Artificial integer interval QA snapshot v1.0; no real-world dates or measurements
Data last updated
Not documented
Update schedule
Fixed v1.0 snapshot. No future updates or support promised.

Half-open integer offsets 0..1000, not dates. Zero-length allowed; touching merges; duplicate coordinates count for overlap. Arbitrary fixture, no statistical/production/ML benchmark claim. No personal records.

Sample structure score: Not assessed

This automated check describes the inspected sample, not factual accuracy, legal rights, representativeness, or the quality of the entire dataset. It is not a customer rating.

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About this data

Test interval merging, covered-length calculations and peak overlap against supplied expected results. AI-assisted artificial QA fixture, not real schedules or measurements. ZIP includes intervals.csv (3,000 rows across 200 cases), expected_union.csv (1,378 merged segments), expected_stats.csv (200 summaries), schema, deterministic Python standard-library generator, independent grid-oracle validator and 17 integrity/corruption tests. Clean ZIP passes tests; rebuilding reproduces identical CSV bytes. Semantics: half-open integer intervals [start,end), coordinates 0..1000 in arbitrary fictional units. Zero-length intervals cover nothing; adjacent intervals merge; duplicate pairs count separately for peak overlap. Includes 217 zero-length intervals and 201 duplicate coordinate pairs beyond their first appearance per case. Unique interval IDs. Deliberate nesting, adjacency and duplicates, plus seeded artificial intervals. Suggested checks: import/group by case, merge intervals, compare output segments and covered units, then compare maximum overlap. Shuffle inputs to test order independence. Generator uses sorting/merging and endpoint sweep; validator independently counts coverage on a dense integer grid. Limits: not dates, calendars, floating-point/continuous interval arithmetic, resource scheduling, statistical benchmark or model training data. No observed business or personal records, names, contacts or external data. No algorithm speed/quality claim. Internal lawful personal/commercial testing and demos only; modification allowed. No redistribution, resale, sublicensing or model training. No warranty, future updates, support SLA or production compatibility. Full terms in README.txt. Automatic public file samples disabled; full files are paid.

Retrieve with your agent or Python

Create an account and configure DATABAZAAR_API_KEY. This example retrieves free or already purchased data; it never makes a purchase. For a multi-file dataset, choose a file index from the manifest.

Download the Python example
python3 retrieve-dataset.py f4faccd7-3485-47a4-9719-0ce1048a2a67 --output dataset.bin
Full supplier documentation
Test interval merging, covered-length calculations and peak overlap against supplied expected results. AI-assisted artificial QA fixture, not real schedules or measurements. ZIP includes intervals.csv (3,000 rows across 200 cases), expected_union.csv (1,378 merged segments), expected_stats.csv (200 summaries), schema, deterministic Python standard-library generator, independent grid-oracle validator and 17 integrity/corruption tests. Clean ZIP passes tests; rebuilding reproduces identical CSV bytes. Semantics: half-open integer intervals [start,end), coordinates 0..1000 in arbitrary fictional units. Zero-length intervals cover nothing; adjacent intervals merge; duplicate pairs count separately for peak overlap. Includes 217 zero-length intervals and 201 duplicate coordinate pairs beyond their first appearance per case. Unique interval IDs. Deliberate nesting, adjacency and duplicates, plus seeded artificial intervals. Suggested checks: import/group by case, merge intervals, compare output segments and covered units, then compare maximum overlap. Shuffle inputs to test order independence. Generator uses sorting/merging and endpoint sweep; validator independently counts coverage on a dense integer grid. Limits: not dates, calendars, floating-point/continuous interval arithmetic, resource scheduling, statistical benchmark or model training data. No observed business or personal records, names, contacts or external data. No algorithm speed/quality claim. Internal lawful personal/commercial testing and demos only; modification allowed. No redistribution, resale, sublicensing or model training. No warranty, future updates, support SLA or production compatibility. Full terms in README.txt. Automatic public file samples disabled; full files are paid.

Schema

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Sample Data

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For AI Agents

Via MCP Server
# 1. Add to your agent's MCP config (claude_desktop_config.json or similar):
{
  "mcpServers": {
    "databazaar": { "command": "npx", "args": ["databazaar-mcp"] }
  }
}

# 2. Your agent can then call:
search_datasets({ query: "Synthetic interval union QA: 3" })
// Found: f4faccd7-3485-47a4-9719-0ce1048a2a67
buy_now({ dataset_id: "f4faccd7-3485-47a4-9719-0ce1048a2a67" })  // uses your saved card
Via REST API
# Authenticate with your API key:
curl https://api.databazaar.io/datasets/f4faccd7-3485-47a4-9719-0ce1048a2a67 \
  -H "Authorization: Bearer YOUR_API_KEY"

# Purchase:
curl -X POST https://api.databazaar.io/datasets/f4faccd7-3485-47a4-9719-0ce1048a2a67/buy \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"dataset_id": "f4faccd7-3485-47a4-9719-0ce1048a2a67"}'