Examples
Examples
Try the starting example
- JSON Check-in/out records [member ID,start hour,end hour]
- [["A",0,2],["B",1,3],["C",3,5]]
- Increasing bucket boundary hours
- 0,2,4,6
- Bucket [start,end,peak,time-weighted average]
- [[0,2,2,1.5],[2,4,1,1],[4,6,1,0.5]]
- Overall peak occupancy
- 2
- Overall time-weighted occupancy
- 1
Try another scenario
- JSON Check-in/out records [member ID,start hour,end hour]
- [["A",0,4],["B",1,3],["C",3,5]]
- Increasing bucket boundary hours
- 0,2,4,6
- Bucket [start,end,peak,time-weighted average]
- [[0,2,2,1.5],[2,4,2,2],[4,6,1,0.5]]
- Overall peak occupancy
- 2
- Overall time-weighted occupancy
- 1.333333
Calculation method
Calculation method
Bucket [start,end,peak,time-weighted average]
Clip each interval to bucket; occupied member-hours divided by bucket width; endpoint sweep for peak.
Overall peak occupancy
Maximum bucket peak.
Overall time-weighted occupancy
Total clipped member-hours / full reporting span.
Symbols and units
- a
- JSON Check-in/out records [member ID,start hour,end hour]
- b
- Increasing bucket boundary hours
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
- Actual attendance trace with explicit buckets; half-open intervals. Same-member records must not overlap; no forecast. Supported limits: 1–1000 intervals, 2–101 bucket boundaries.
Are these inputs private?
These calculations run in your browser. A shared link contains the values you choose to share. Only questions submitted to the separate AI finder are sent to OpenAI.