Implementation of the Bin Hierarchy Method for restoring a smooth function from a sampled histogram

Abstract

We present $\text{BHM}$, a tool for restoring a smooth function from a sampled histogram using the bin hierarchy method. The theoretical background of the method is presented in [arXiv:1707.07625]. The code automatically generates a smooth polynomial spline with the minimal acceptable number of knots from the input data. It works universally for any sufficiently regular shaped distribution and any level of data quality, requiring almost no external parameter specification. It is particularly useful for large-scale numerical data analysis. This paper explains the details of the implementation and the use of the program.

Code is available at https://github.com/olgagoulko/BHM/tree/GPLv3

References in corpus (3)

• From Data to Probability Densities without Histograms
• Constructing smooth potentials of mean force, radial, distribution functions and probability densities from sampled data
• Restoring a smooth function from its noisy integrals