Hist

Hist is an analyst-friendly front-end for boost-histogram, designed for Python 3.7+ (3.6 users get version 2.4). See what's new.

Installation

You can install this library from PyPI with pip:

python3 -m pip install "hist[plot]"

If you do not need the plotting features, you can skip the [plot] extra.

Features

Hist currently provides everything boost-histogram provides, and the following enhancements:

Hist augments axes with names:
- name= is a unique label describing each axis.
- label= is an optional string that is used in plotting (defaults to name if not provided).
- Indexing, projection, and more support named axes.
- Experimental NamedHist is a Hist that disables most forms of positional access, forcing users to use only names.
The Hist class augments bh.Histogram with simpler construction:
- flow=False is a fast way to turn off flow for the axes on construction.
- Storages can be given by string.
- storage= can be omitted, strings and storages can be positional.
- data= can initialize a histogram with existing data.
- Hist.from_columns can be used to initialize with a DataFrame or dict.
- You can cast back and forth with boost-histogram (or any other extensions).
Hist support QuickConstruct, an import-free construction system that does not require extra imports:
- Use Hist.new.<axis>().<axis>().<storage>().
- Axes names can be full (Regular) or short (Reg).
- Histogram arguments (like data=) can go in the storage.
Extended Histogram features:
- Direct support for .name and .label, like axes.
- .density() computes the density as an array.
- .profile(remove_ax) can convert a ND COUNT histogram into a (N-1)D MEAN histogram.
- .sort(axis) supports sorting a histogram by a categorical axis. Optionally takes a function to sort by.
Hist implements UHI+; an extension to the UHI (Unified Histogram Indexing) system designed for import-free interactivity:
- Uses j suffix to switch to data coordinates in access or slices.
- Uses j suffix on slices to rebin.
- Strings can be used directly to index into string category axes.
Quick plotting routines encourage exploration:
- .plot() provides 1D and 2D plots (or use plot1d(), plot2d())
- .plot2d_full() shows 1D projects around a 2D plot.
- .plot_ratio(...) make a ratio plot between the histogram and another histogram or callable.
- .plot_pull(...) performs a pull plot.
- .plot_pie() makes a pie plot.
- .show() provides a nice str printout using Histoprint.
Stacks: work with groups of histograms with identical axes
- Stacks can be created with h.stack(axis), using index or name of an axis (StrCategory axes ideal).
- You can also create with hist.stacks.Stack(h1, h2, ...), or use from_iter or from_dict.
- You can index a stack, and set an entry with a matching histogram.
- Stacks support .plot() and .show(), with names (plot labels default to original axes info).
- Stacks pass through .project, *, +, and -.
New modules
- intervals supports frequentist coverage intervals.
Notebook ready: Hist has gorgeous in-notebook representation.
- No dependencies required

Usage

from hist import Hist

# Quick construction, no other imports needed:
h = (
    Hist.new.Reg(10, 0, 1, name="x", label="x-axis")
    .Var(range(10), name="y", label="y-axis")
    .Int64()
)

# Filling by names is allowed:
h.fill(y=[1, 4, 6], x=[3, 5, 2])

# Names can be used to manipulate the histogram:
h.project("x")
h[{"y": 0.5j + 3, "x": 5j}]

# You can access data coordinates or rebin with a `j` suffix:
h[0.3j:, ::2j]  # x from .3 to the end, y is rebinned by 2

# Elegant plotting functions:
h.plot()
h.plot2d_full()
h.plot_pull(Callable)

Development

From a git checkout, either use nox, or run:

python -m pip install -e .[dev]

See Contributing guidelines for information on setting up a development environment.

Contributors

We would like to acknowledge the contributors that made this project possible (emoji key):

_{Henry Schreiner} 🚧 💻 📖	_{Nino Lau} 🚧 💻 📖	_{Chris Burr} 💻	_{Nick Amin} 💻	_{Eduardo Rodrigues} 💻	_{Andrzej Novak} 💻	_{Matthew Feickert} 💻
_{Kyle Cranmer} 📖	_{Daniel Antrim} 💻	_{Nicholas Smith} 💻	_{Michael Eliachevitch} 💻	_{Jonas Eschle} 📖

This project follows the all-contributors specification.

Talks

Acknowledgements

This library was primarily developed by Henry Schreiner and Nino Lau.

Support for this work was provided by the National Science Foundation cooperative agreement OAC-1836650 (IRIS-HEP) and OAC-1450377 (DIANA/HEP). Any opinions, findings, conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.

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