Examples
Examples
Try the starting example
- Samples JSON [[timestamp minutes,concentration µg/m³],...]
- [[0,10],[10,20],[20,30],[40,15]]
- Rolling window duration (minutes)
- 30
- Expected sampling interval (minutes)
- 10
- Minimum completeness fraction
- 0.75
- Rolling [end minute,mean concentration,completeness,meets threshold] table
- [[0,10,0.333333333333,false],[10,15,0.666666666667,false],[20,20,1,true],[40,22.5,0.666666666667,false]]
Try another scenario
- Samples JSON [[timestamp minutes,concentration µg/m³],...]
- [[1,10],[10,20],[20,30],[40,15]]
- Rolling window duration (minutes)
- 30
- Expected sampling interval (minutes)
- 10
- Minimum completeness fraction
- 0.75
- Rolling [end minute,mean concentration,completeness,meets threshold] table
- [[1,10,0.333333333333,false],[10,15,0.666666666667,false],[20,20,1,true],[40,22.5,0.666666666667,false]]
Calculation method
Calculation method
Rolling [end minute,mean concentration,completeness,meets threshold] table
At each sample time use samples in (end-window,end], expected count=ceil(window/interval); report sample mean and completeness capped1.
Symbols and units
- a
- Samples JSON [[timestamp minutes,concentration µg/m³],...]
- b
- Rolling window duration (minutes)
- c
- Expected sampling interval (minutes)
- d
- Minimum completeness fraction
Use the units shown. A list keeps the order in which you enter its values. The symbol ^ means “raised to the power of”.
Assumptions & limits
Assumptions & limits
- Equally scheduled point-sample rolling mean, not duration-weighted irregular sampling or regulatory AQI compliance. Supported limits: All required fields must be finite values of the exact declared type and within displayed bounds. Lists contain1–1000 finite numbers. JSON tables are numeric rectangular rows, at most100 rows and30 columns; individual tools have stricter shape/domain constraints. Equally scheduled point-sample rolling mean, not duration-weighted irregular sampling or regulatory AQI compliance.
Are these inputs private?
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