Description
Plan logarithmically spaced positive histogram edges with explicit endpoints and a supplied bin count.
Histogram edges divide a numeric coordinate axis into ordered bins, while each bin mass represents a count or nonnegative weight. Probability mass is a fraction of the total; probability density additionally divides by bin width and therefore has inverse coordinate units. An edge plan alone contains no observations.
Plan logarithmically spaced positive histogram edges with explicit endpoints and a supplied bin count.
When to use this tool
Use a logarithmic edge plan for positive coordinates whose useful resolution follows multiplicative scale.
How to use the tool and how it works
- Enter minimum, maximum, bins using the definitions below.
- Run the operation. Resolve input-format errors before interpreting an output.
- Read result.edges, using the stated boundary, ordering, sign and absence conventions.
Set the first and last edges to minimum and maximum. For each interior index j, calculate exp(log(minimum)+(log(maximum)-log(minimum))*j/bins), then verify that all resulting edges strictly increase.1
| Input field | Meaning and units | Accepted input |
|---|---|---|
| minimum | Lower coordinate bound, at most maximum; logarithmic plans additionally require minimum>0 and maximum>minimum. | Finite numeric value satisfying the stated comparisons. |
| maximum | Upper coordinate bound on the same scale as minimum; strictly greater for a logarithmic plan. | Finite numeric value satisfying the stated comparisons. |
| bins | Number of positive-width logarithmic bins, integer 1–127. | Finite safe integer satisfying the stated bounds. |
| Output field | Interpretation |
|---|---|
| result | Object containing the fields below. |
| result.edges | Resulting increasing coordinate boundaries; no observations are implicitly added. |
{
"input": {
"minimum": 1,
"maximum": 100,
"bins": 2
},
"output": {
"result": {
"edges": [
1,
10.000000000000002,
100
]
}
}
}Limitations and assumptions
- minimum must be positive, maximum greater than minimum, and bins an integer 1–127. Floating-point interiors can differ slightly from exact powers; unrepresentably close edges are rejected.
- This operation returns a coordinate edge plan only. It does not select a statistically optimal bin count, count observations or assign sample weights. Floating-point representability remains part of the plan’s validation.
- Every declared outer field is required and unknown outer fields are rejected. Strings and JSON text have size limits, lists are bounded to 128 entries, and JSON nesting/node counts are bounded. Tighter operation-specific bounds stated above take precedence.
- Numeric outputs must remain finite and within safe-integer magnitude. Original record indexes and cell coordinates are zero-based; stated half-open ranges exclude their end.
Alternative or Complementary methods
Keep edge definitions with mass arrays. Use common-boundary coarsening and whole-bin merging for aligned comparisons; use observation-level data when a calculation requires locations within bins.
References
Similar or alternative tools
- Histogram Integer Centered Edge Plan
Plan unit-width histogram bins centered on every integer from first through last inclusive.
- Histogram Sample Provenance Groups
Group original observation indexes into explicit histogram bins and retain outside-domain indexes.