fixedbin

Fixedbin is a term used in data analysis and signal processing to describe a binning scheme in which data values are mapped into a set of bins with fixed width. In a fixed-bin histogram, the range of interest is divided into equal-sized intervals, and each value is assigned to the corresponding bin. The bin index i for a value x is typically computed as i = floor((x - min) / width), where min is the lower bound and width is the bin width. Values that fall outside the predefined range can be clipped to the first or last bin, or mapped to overflow or underflow bins if supported.

Fixed-bin binning contrasts with variable-bin or adaptive histograms, where bin widths vary to reflect data density. The fixed-width approach offers simplicity and predictable behavior, but requires careful choice of the minimum, maximum, and bin width to balance resolution and coverage.

Applications of fixed-bin binning include creating histograms for statistical summaries, discretizing continuous signals in digital signal processing, and intensity or color quantization in image processing. In machine learning and data preprocessing, fixed-bin discretization can convert continuous features into categorical bins, supporting certain algorithms or enhancing robustness to outliers.

Advantages of fixed-bin binning include straightforward implementation, low memory footprint, and deterministic binning results. Limitations involve sensitivity to the chosen range and bin width, potential boundary artifacts, and the need to handle values outside the target interval, which may require clipping or overflow bins.

Implementation notes often involve predefining the number of bins, selecting min and width, applying floor division to map values to bin indices, and addressing NaN or missing data as a separate bin or by exclusion. See also histogram, binning, and quantization.