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Water Monitoring Equal-Width Histogram
x402
Water Monitoring Equal-Width Histogram is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-19).
Computes an equal-width histogram over water monitoring records (litres or cost) with a configurable number of bins, returning bin boundaries and counts.
Price / call$0.01
Success—
StatusUnknown
How this service works
Water monitoring - Equal-width histogram: Equal-width histogram for Water monitoring. Up to 200 supplied typed records. Local numeric and data-quality processing; no external enrichment or forecasting. One of 25 profiles or calculations across 20 shared operations in this sector. 0.01 USDC includes one bounded equal-width histogram deliverable.
Output
Returns a bounded equal-width histogram with up to 30 bins over the selected numeric field (litres or cost). Each bin includes its lower and upper boundary and the count of records falling within that range, derived purely from the supplied records with no external enrichment.
Example use cases
Water usage distribution audit
“I have 50 water meter readings across my sites for last month — can you bin the litres field into 10 equal-width buckets so I can see how consumption is distributed?”
Cost outlier detection via histogram
“Take these 80 water billing records and build an equal-width histogram of the cost column using 8 bins — I want to see if there are any unusual spikes in spending.”
Comparing site consumption profiles
“Here are 120 daily water monitoring records from two sites, site-north and site-south. Run an equal-width histogram on litres with 6 bins so I can compare their usage patterns.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-sector-analytics-water-monitoring-histogram?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a fast, local equal-width histogram computation over water monitoring records (up to 200) with no external data dependencies. Ideal for distribution analysis, outlier detection, or summarizing water consumption or cost data into frequency buckets. Prefer this over general-purpose analytics tools when working specifically with water monitoring data structured around site, date, litres, and cost fields.
Side effects
- Fewer than 1 or more than 200 rows supplied returns a validation error
- Invalid date format (non-YYYY-MM-DD) causes record rejection
- Bins value outside 1–30 range returns a schema validation error
- All values null in selected field may produce empty or degenerate histogram
- Missing required fields (row_id, site, date, litres, cost) causes record-level rejection
- Invalid field enum value (not 'litres' or 'cost') returns a validation error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| bins | integer | |
| rows | array | |
| field | string |
Tags
x402
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