How rootfig relates to uproot, Awkward, hist, mplhep and matplotlib

rootfig is a thin, opinionated layer over the Scientific Python HEP stack. It owns no file format, array type, histogram type or drawing primitive of its own; every object you get back belongs to one of the libraries below.

uproot

uproot reads and writes ROOT files in pure Python. rootfig uses it for all I/O: opening files, detecting the tree, listing branches, and reading the branches an expression needs from one or many files (TTree and RNTuple). uproot also has its own expression language for TTree.arrays(expressions, cut=...); rootfig implements a similar one so that it can validate names before reading, give precise errors, support in-memory data, and define the per-object/per-event rules that arrays(cut=...) leaves to the caller.

Awkward Array

Awkward Array is the array library for jagged data. Everything rootfig computes is an Awkward array; the selection layer is a small set of rules (broadcast, mask, flatten) expressed with ak.num, ak.broadcast_arrays, boolean indexing and ak.flatten. rf.load returns an Awkward record array, and rf.evaluate runs one expression on arrays you already have.

hist and boost-histogram

hist provides the histogram objects. rootfig fills hist.Hist with Weight storage (sum of weights and sum of squared weights per bin), including under/overflow bins, and returns them unchanged: rf.histogram gives you the hist.Hist, Plot.hists lists them. Rebinning, projecting, slicing, saving to ROOT files with uproot: all of that is hist functionality and works directly on the returned objects.

mplhep

mplhep draws histograms with matplotlib and ships the style sheets and label helpers of the LHC experiments. rootfig draws every histogram through mplhep.histplot/hist2dplot and uses mplhep's per-experiment label functions when a Style(experiment=...) is given. The experiment-neutral default style and the ratio panel are rootfig's.

matplotlib

Every figure is a plain matplotlib.figure.Figure with plain Axes. rootfig applies its style only inside a plt.style.context while drawing, so it does not change global rcParams (unless you call rf.use_style). Anything you would do to a matplotlib figure, you can do to Plot.fig and Plot.ax.

When to use something else

• You already have flat NumPy columns or boost-histogram objects and want rich comparison panels: plothist.
• You need a full columnar analysis framework with lazy, distributed processing: coffea. rootfig reads every branch a plot needs from every file into memory at once (only the required branches, optionally an entry range), which suits quick looks and moderate ntuples, not multi-terabyte datasets.
• You want to build fit templates and workspaces: cabinetry.
• You want a quick terminal look at a branch: histoprint.