histogram — Create histogram objects with bin-edge semantics, normalization controls, and MATLAB histogram workflows.
histogram is the object-style histogram builtin in RunMat. Unlike legacy hist, it uses bin-edge semantics, supports normalization workflows through the histogram evaluation path, and returns a histogram handle that works with get and set as part of the plotting object model.
How histogram works in RunMat
histogram(data)creates a histogram object handle.- When you pass explicit bins through histogram-style arguments, they are treated as bin edges rather than bin centers.
- Normalization modes such as count, probability, percentage, density-style variants, and cumulative forms are part of the evaluation path.
- The returned histogram handle exposes properties like bin edges, counts, and normalization through
getand accepts updates throughsetwhere supported. - This is the preferred histogram API for new code; use
histonly when you intentionally want legacy center-based MATLAB semantics.
Examples
Create a histogram object from data
data = randn(1, 1000);
h = histogram(data);Expected output:
% h is a histogram object handleUse explicit bin edges and inspect the handle
data = randn(1, 500);
edges = -3:0.25:3;
h = histogram(data, 'BinEdges', edges);
get(h, 'BinEdges');Apply normalization through histogram semantics
data = randn(1, 500);
h = histogram(data, 'Normalization', 'probability');
get(h, 'Normalization')Expected output:
ans =
'probability'Related functions to explore
These functions work well alongside histogram. Each page has runnable examples you can try in the browser.
Open-source implementation
Unlike proprietary runtimes, every RunMat function is open-source. Read exactly how histogram works, line by line, in Rust.
- View histogram.rs on GitHub
- Learn how the runtime works
- Found a bug? Open an issue with a minimal reproduction.
About RunMat
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