Description
Sum explicit nonnegative observation weights in supplied histogram bins while retaining total outside weight; not probability normalization or automatic edge selection.
A histogram uses ordered edges to partition numeric values into bins. This family assigns an interior boundary to the bin on its right and includes the final right edge in the last bin. Raw counts, sums of weights and probability density are different quantities; edges and observations must use consistent units.
Sum explicit nonnegative observation weights in supplied histogram bins while retaining total outside weight; not probability normalization or automatic edge selection.
When to use this tool
Use this tool to sum explicit nonnegative observation weights in supplied histogram bins while retaining total outside weight; not probability normalization or automatic edge selection.
How to use the tool and how it works
- Enter values, weights, edges using the field definitions below.
- Run the operation. Correct input errors before interpreting an output.
- Read result.weights, result.outsideWeight. Use the ordering, boundary rules and absence conditions described below.
Validate strictly increasing edges and aligned nonnegative weights, assign each value to its bin and sum weights per bin plus outsideWeight.1
| Input field | Meaning and units | Accepted input |
|---|---|---|
| values | Finite observations in the same numeric units as edges. | Dense list of at most 128 finite numbers; integer/shape constraints above also apply. |
| weights | Nonnegative weights aligned one-to-one with values. | Dense list of at most 128 finite numbers; integer/shape constraints above also apply. |
| edges | Finite strictly increasing bin edges; at least two for bin operations. | Dense list of at most 128 finite numbers; integer/shape constraints above also apply. |
| Output field | Interpretation |
|---|---|
| result | Object containing the named fields below. |
| result.weights | Raw weight sum for every bin, in edge order. |
| result.outsideWeight | Weight sum of observations outside the complete edge interval. |
{
"input": {
"values": [
-1,
0,
1,
2,
3
],
"edges": [
0,
1,
2
],
"weights": [
1,
2,
3,
4,
5
]
},
"output": {
"result": {
"weights": [
2,
7
],
"outsideWeight": 6
}
}
}Limitations and assumptions
- Bins are left-inclusive/right-exclusive except the final right edge is included; weights are not normalized.
- Bin operations require at least two finite strictly increasing edges. Every bin is left-inclusive; right ends are exclusive except for the final right edge. The monotonicity audit reports violations instead of requiring valid ordering in advance.
- Every declared top-level input field is required and unexpected fields are rejected. Ordinary strings are limited to 8,192 UTF-16 code units and lists to 128 entries, with tighter operation-specific bounds where stated. JSON text has bounded nesting and node counts.
- Numeric outputs must be finite and no larger in absolute magnitude than the safe-integer limit; operations returning exact large integers use decimal text. Positions are zero-based and spans end exclusively unless the operation explicitly states a different convention.
Alternative or Complementary methods
Choose bin edges deliberately, then distinguish membership, count, weight and density calculations. Preserve underflow and overflow information instead of silently discarding observations outside the supplied range.
References
Similar or alternative tools
- Histogram Explicit Edge Count Table
Count explicit observations in supplied ordered histogram bins with final edge included, reporting outside count without clamping under/overflow into endpoint bins.
- Histogram Explicit Edge Membership Indexes
Assign explicit finite observations to strictly increasing finite bins, left-closed/right-open except final upper edge included; outside values retain null/index provenance.