Conditional threshold logic implementation

Conditional threshold logic implementation

Summary

Apply data-driven threshold logic to automatically adjust visualization parameters (e.g., histogram bin width) based on computed statistics of numeric columns. This skill enables adaptive rendering that scales visualization granularity to the statistical properties of the input data.

When to use

When visualizing numeric columns with unknown or highly variable ranges, and the visualization quality depends on parameter selection tied to data statistics. Specifically: after loading a numeric column (e.g., H/C ratio, O/C ratio) and computing its span (max – min), use this skill to decide whether to apply fine-grained (0.1) or coarse-grained (1.0) bin widths for histogram rendering.

When NOT to use

• Input column contains categorical or nominal data (e.g., formulas, sample IDs) — threshold logic applies only to numeric columns.
• Bin width has been manually specified by the user — conditional logic is unnecessary when explicit parameters override adaptive rules.
• Data span is already known to fall consistently in one regime — pre-selected static bin widths are more efficient.

Inputs

• numeric column vector (extracted from CSV via column recognition)
• threshold value (e.g., 3.0 for span comparison)

Outputs

• histogram bin-width parameter (0.1 or 1.0)
• rendered histogram visualization with adaptive spacing

How to apply

Load a numeric column from the uploaded CSV file using Punc'data's column recognition system (which infers column type from keywords such as m/z, intensity, formula). Calculate the span as max – min of the column values. Compare the span against a threshold (e.g., 3.0): if span ≤ 3.0, set histogram bin width to 0.1 (fine granularity); if span > 3.0, set bin width to 1.0 (coarse granularity). Render the histogram in the Canvas tab using the selected bin width, then validate that bar spacing and bin boundaries align with the threshold rule.

Related tools

• Punc'data (Platform for loading numeric columns, computing statistics, storing threshold parameters, and rendering adaptive histograms in the Canvas tab based on conditional bin-width logic.) — https://wtvoe.github.io/puncdata/

Evaluation signals

• Verify that columns with span ≤ 3.0 render histograms with bin width 0.1 and fine-grained bar boundaries.
• Verify that columns with span > 3.0 render histograms with bin width 1.0 and coarse-grained bar boundaries.
• Confirm that the threshold comparison is applied consistently: max – min is correctly computed and compared against 3.0.
• Inspect the Canvas tab histogram rendering to ensure bar spacing and bin alignment match the selected parameter.
• Test boundary cases: span exactly equal to 3.0 should apply bin width 0.1 (≤ threshold applies fine granularity).

Limitations

• No evidence in the provided documentation describes the specific rationale (statistical or perceptual) for the 3.0 threshold or the choice of 0.1 vs. 1.0 bin widths.
• The threshold rule is inferred from the task description; it is not explicitly documented in the Punc'data README or source material.
• Punc'data recognizes column types by keyword matching — if a numeric column lacks keywords (e.g., 'm/z', 'intensity', 'ratio'), manual column-type assignment via the 'parameters' tab may be required before threshold logic can apply.
• No validation benchmarks or performance data are provided for this adaptive rule against standard datasets.

Evidence

• [readme] Punc'data recognizes which column corresponds to which information based on keywords.: "Punc'data recognizes which column corresponds to which information based on keywords."
• [readme] Canvas A/B tab allows interactive chart production: ""Canvas A,B" and "Stats" [allow] charts be produced. On Canvas A,B and Stats, charts are interactive."
• [other] Threshold rule for histogram bin width based on range span: "if span ≤ 3.0, set histogram bin width to 0.1; otherwise, set bin width to 1.0"