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