While most numerical functions needed by OPS are provided by libraries such as numpy, there are a few specialized tools we have implemented. These are in the numerics subpackage.


Tools for getting errors on pandas DataFrames.

ResamplingStatistics(function, inputs)

Contains and organizes resampled statistics.

BlockResampling(all_samples[, n_blocks, ...])

Select samples according to block resampling.

Lookup Functions

Interpolation tools that turn tables into functions.

LookupFunction(ordinate, abscissa)

Interpolation between datapoints.

LookupFunctionGroup(functions[, use_x])

Simple mean and std for a group of LookupFunctions.

VoxelLookupFunction(left_bin_edges, ...)

Turn sparse histogram into a lookup function.


Histogram([n_bins, bin_width, bin_range])

Wrapper for numpy.histogram with additional conveniences.

SparseHistogram(bin_widths, left_bin_edges)

Base class for sparse-based histograms.

HistogramPlotter2D(histogram[, normed, ...])

Convenience tool for plotting 2D histograms and plotting data atop them.

histograms_to_pandas_dataframe(hists[, fcn, ...])

Converts histograms in hists to a pandas data frame

Histogrammer(f[, f_args, hist_args])

Basically a dictionary to track what each histogram should be making.

Histogram Combiners

WHAM([tol, max_iter, cutoff, interfaces])

Weighted Histogram Analysis Method