GitLocker: The Coding Marketplace

Description:

boosthistogram 1.5.0

boost-histogram for Python

Python bindings for Boost::Histogram (source), a C++14 library. This is one of the fastest libraries for
histogramming, while still providing the power of a full histogram object. See
what's new.
Other members of the boost-histogram family include:

Hist: The first-party analyst-friendly histogram library that extends
boost-histogram with named axes, many new shortcuts including UHI+, plotting
shortcuts, and more.
UHI: Specification for Histogram library interop, especially for plotting.
mplhep: Plotting extension for matplotlib with support for UHI histograms.
histoprint: Histogram display library for the command line with support for UHI.
dask-histogram: Dask support for boost-histogram.

Usage

Text intro (click to expand)
import boost_histogram as bh

# Compose axis however you like; this is a 2D histogram
hist = bh.Histogram(
bh.axis.Regular(2, 0, 1),
bh.axis.Regular(4, 0.0, 1.0),
)

# Filling can be done with arrays, one per dimension
hist.fill(
[0.3, 0.5, 0.2],
[0.1, 0.4, 0.9],
)

# NumPy array view into histogram counts, no overflow bins
values = hist.values()

# Make a new histogram with just the second axis, summing over the first, and
# rebinning the second into larger bins:
h2 = hist[::sum, :: bh.rebin(2)]

We support the uhi PlottableHistogram protocol, so boost-histogram/Hist
histograms can be plotted via any compatible library, such as mplhep.
Cheatsheet
Simplified list of features (click to expand)

Many axis types (all support metadata=...)

bh.axis.Regular(n, start, stop, ...): Make a regular axis. Options listed below.

overflow=False: Turn off overflow bin
underflow=False: Turn off underflow bin
growth=True: Turn on growing axis, bins added when out-of-range items added
circular=True: Turn on wrapping, so that out-of-range values wrap around into the axis
transform=bh.axis.transform.Log: Log spacing
transform=bh.axis.transform.Sqrt: Square root spacing
transform=bh.axis.transform.Pow(v): Power spacing
See also the flexible Function transform

bh.axis.Integer(start, stop, *, underflow=True, overflow=True, growth=False, circular=False): Special high-speed version of regular for evenly spaced bins of width 1
bh.axis.Variable([start, edge1, edge2, ..., stop], *, underflow=True, overflow=True, circular=False): Uneven bin spacing
bh.axis.IntCategory([...], *, growth=False): Integer categories
bh.axis.StrCategory([...], *, growth=False): String categories
bh.axis.Boolean(): A True/False axis

Axis features:

.index(value): The index at a point (or points) on the axis
.value(index): The value for a fractional bin (or bins) in the axis
.bin(i): The bin edges (continuous axis) or a bin value (discrete axis)
.centers: The N bin centers (if continuous)
.edges: The N+1 bin edges (if continuous)
.extent: The number of bins (including under/overflow)
.metadata: Anything a user wants to store
.traits: The options set on the axis
.size: The number of bins (not including under/overflow)
.widths: The N bin widths

Many storage types

bh.storage.Double(): Doubles for weighted values (default)
bh.storage.Int64(): 64-bit unsigned integers
bh.storage.Unlimited(): Starts small, but can go up to unlimited precision ints or doubles.
bh.storage.AtomicInt64(): Threadsafe filling, experimental. Does not support growing axis in threads.
bh.storage.Weight(): Stores a weight and sum of weights squared.
bh.storage.Mean(): Accepts a sample and computes the mean of the samples (profile).
bh.storage.WeightedMean(): Accepts a sample and a weight. It computes the weighted mean of the samples.

Accumulators

bh.accumulator.Sum: High accuracy sum (Neumaier) - used by the sum method when summing a numerical histogram
bh.accumulator.WeightedSum: Tracks a weighted sum and variance
bh.accumulator.Mean: Running count, mean, and variance (Welfords's incremental algorithm)
bh.accumulator.WeightedMean: Tracks a weighted sum, mean, and variance (West's incremental algorithm)

Histogram operations

h.ndim: The number of dimensions
h.size or len(h): The number of bins
+: Add two histograms (storages must match types currently)
*=: Multiply by a scaler (not all storages) (hist * scalar and scalar * hist supported too)
/=: Divide by a scaler (not all storages) (hist / scalar supported too)
.kind: Either bh.Kind.COUNT or bh.Kind.MEAN, depending on storage
.storage_type: Fetch the histogram storage type
.sum(flow=False): The total count of all bins
.project(ax1, ax2, ...): Project down to listed axis (numbers). Can also reorder axes.
.to_numpy(flow=False, view=False): Convert to a NumPy style tuple (with or without under/overflow bins)
.view(flow=False): Get a view on the bin contents (with or without under/overflow bins)
.values(flow=False): Get a view on the values (counts or means, depending on storage)
.variances(flow=False): Get the variances if available
.counts(flow=False): Get the effective counts for all storage types
.reset(): Set counters to 0 (growing axis remain the same size)
.empty(flow=False): Check to see if the histogram is empty (can check flow bins too if asked)
.copy(deep=False): Make a copy of a histogram
.axes: Get the axes as a tuple-like (all properties of axes are available too)

.axes[0]: Get the 0th axis
.axes.edges: The lower values as a broadcasting-ready array
.axes.centers: The centers of the bins broadcasting-ready array
.axes.widths: The bin widths as a broadcasting-ready array
.axes.metadata: A tuple of the axes metadata
.axes.size: A tuple of the axes sizes (size without flow)
.axes.extent: A tuple of the axes extents (size with flow)
.axes.bin(*args): Returns the bin edges as a tuple of pairs (continuous axis) or values (describe)
.axes.index(*args): Returns the bin index at a value for each axis
.axes.value(*args): Returns the bin value at an index for each axis

Indexing - Supports UHI Indexing

Bin content access / setting

v = h[b]: Access bin content by index number
v = h[{0:b}]: All actions can be represented by axis:item dictionary instead of by position (mostly useful for slicing)

Slicing to get histogram or set array of values

h2 = h[a:b]: Access a slice of a histogram, cut portions go to flow bins if present
h2 = h[:, ...]: Using : and ... supported just like NumPy
h2 = h[::sum]: Third item in slice is the "action"
h[...] = array: Set the bin contents, either include or omit flow bins

Special accessors

bh.loc(v): Supply value in axis coordinates instead of bin number
bh.underflow: The underflow bin (use empty beginning on slice for slicing instead)
bh.overflow: The overflow bin (use empty end on slice for slicing instead)

Special actions (third item in slice)

sum: Remove axes via projection; if limits are given, use those
bh.rebin(n): Rebin an axis

NumPy compatibility

bh.numpy provides faster drop in replacements for NumPy histogram functions
Histograms follow the buffer interface, and provide .view()
Histograms can be converted to NumPy style output tuple with .to_numpy()

Details

All objects support copy/deepcopy/pickle
Fully statically typed, tested with MyPy.

Installation
You can install this library from PyPI with pip:
python3 -m pip install boost-histogram

All the normal best-practices for Python apply; Pip should not be very old (Pip
9 is very old), you should be in a virtual environment, etc. Python 3.8+ is
required; for older versions of Python (3.5 and 2.7), 0.13 will be installed
instead, which is API equivalent to 1.0, but will not be gaining new features.
1.3.x was the last series to support Python 3.6. 1.4.x was the last series to
support Python 3.7.
Binaries available:
The easiest way to get boost-histogram is to use a binary wheel, which happens
when you run the above command on a supported platform. Wheels are produced using
cibuildwheel; all common
platforms have wheels provided in boost-histogram:

System
Arch
Python versions
PyPy versions

manylinux2014
64-bit
3.8, 3.9, 3.10, 3.11, 3.12, 3.13, 3.13t
3.9, 3.10

manylinux2014
ARM64
3.8, 3.9, 3.10, 3.11, 3.12, 3.13, 3.13t
3.9, 3.10

musllinux_1_1
64-bit
3.8, 3.9, 3.10, 3.11, 3.12, 3.13, 3.13t

macOS 10.9+ Intel
64-bit
3.8, 3.9, 3.10, 3.11, 3.12, 3.13, 3.13t
3.9, 3.10

macOS 11+ AS
Arm64
3.8, 3.9, 3.10, 3.11, 3.12, 3.13, 3.13t
3.9, 3.10

Windows
32-bit
3.8, 3.9, 3.10, 3.11, 3.12, 3.13, 3.13t

Windows
64-bit
3.8, 3.9, 3.10, 3.11, 3.12, 3.13, 3.13t
3.9, 3.10

PowerPC or IBM-Z wheels are not provided but are available on request.
If you are on a Linux system that is not part of the "many" in manylinux or musl in musllinux, such as ClearLinux, building from source is usually fine, since the compilers on those systems are often quite new. It will just take longer to install when it is using the sdist instead of a wheel. All dependencies are header-only and included.
Conda-Forge
The boost-histogram package is available on conda-forge, as well. All supported variants are available.
conda install -c conda-forge boost-histogram

Source builds
For a source build, for example from an "SDist" package, the only requirements are a C++14 compatible compiler. The compiler requirements are dictated by Boost.Histogram's C++ requirements: gcc >= 5.5, clang >= 3.8, or msvc >= 14.1.
Boost is not required or needed (this only depends on included header-only dependencies). You can install directly from GitHub if you would like.
python -m pip install git+https://github.com/scikit-hep/boost-histogram.git@develop

Developing
See CONTRIBUTING.md for details on how to set up a development environment.
Contributors
We would like to acknowledge the contributors that made this project possible (emoji key):

Henry Schreiner🚧 💻 📖
Hans Dembinski🚧 💻
N!no⚠️ 📖
Jim Pivarski🤔
Nicholas Smith🐛
physicscitizen🐛
Chanchal Kumar Maji📖

Doug Davis🐛
Pierre Grimaud📖
Beojan Stanislaus🐛
Popinaodude🐛
Congqiao Li🐛
alexander-held🐛
Chris Burr📖

Konstantin Gizdov📦 🐛
Kyle Cranmer📖
Aman Goel📖 💻
Jay Gohil📖

This project follows the all-contributors specification.
Talks and other documentation/tutorial sources
The official documentation is here, and includes a quickstart.

2019-4-15 IRIS-HEP Topical meeting
2019-10-17 PyHEP Histogram session - repo with talks and workbook
2019-11-7 CHEP
2020-07-07 SciPy
2020-07-17 PyHEP

Acknowledgements
This library was primarily developed by Henry Schreiner and Hans Dembinski.
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.