The Boost::Histogram Python wrapper.

Project description

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.

For end users interested in analysis, see Hist, a first-party analyst-friendly histogram library that extends boost-histogram with named axes, many new shortcuts including UHI+, plotting shortcuts, and more.

Usage

Slideshow of features. See expandable text below if the image is not readable.

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
- .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
- .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 3.6+ 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.

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
ManyLinux1 (custom GCC 9.2)	32 & 64-bit	3.6, 3.7, 3.8
ManyLinux2010	32 & 64-bit	3.6, 3.7, 3.8, 3.9	7.3: 3.6, 3.7
ManyLinux2014	ARM64	3.6, 3.7, 3.8, 3.9
macOS 10.9+	64-bit	3.6, 3.7, 3.8, 3.9	7.3: 3.6, 3.7
macOS Universal2	Arm64	3.9
Windows	32 & 64-bit	3.6, 3.7, 3.8, 3.9	(32 bit) 7.3: 3.6, 3.7

manylinux1: Using a custom docker container with GCC 9 to produce. Anything running Python 3.9 should be compatible with manylinux2010, so manylinux1 not provided for Python 3.9 (like NumPy).
manylinux2010: Requires pip 10+.
PyPy 7.3.x: Supported for both pypy3.6 and pypy3.7 variants on all platforms.
ARM on Linux is supported for newer Python versions via manylinux2014. PowerPC or IBM-Z available on request, or manylinux_2_24.
macOS Universal2 wheels for Apple Silicon and Intel provided for Python 3.9 (requires Pip 21.0.1).

If you are on a Linux system that is not part of the "many" in manylinux, such as Alpine or 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. You should have a version of pip less than 2-3 years old (10+).

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} 📖

This project follows the all-contributors specification.

Talks and other documentation/tutorial sources

The official documentation is here, and includes a quickstart.

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.

Project details

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1.5.0

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Oct 18, 2019

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SHA256	`1011a125fdc4b41e038459ea447785846a37943ee034c6755ac3d034df526a6a`
MD5	`9490784dc142aaed7cd07927592f82f3`
BLAKE2b-256	`0bb4d44159c653e02cd28e001b17f7d5cba6da0ed6bcd9a0608b86f39e782a8b`

Algorithm	Hash digest
SHA256	`492d6ed304598c5e3399065fa6e9389cd93a3567185eda058619dd9353711714`
MD5	`8e65d74770049661db5d5d3b3e7a0d68`
BLAKE2b-256	`6d346a2a955074b50c2a3b293c05b59e9346eecfc34bf85514f99a6374f27623`

Algorithm	Hash digest
SHA256	`6cb898ee68bced1ec9ec4d50a2968611770c2f406304254eda41623cdffc0cbb`
MD5	`5e8f1f7c8c83b15a7728d040e8db7420`
BLAKE2b-256	`df621f9c56ede0cf0994abc92f3447288fab7ab831617bb029dd7352ed5ca857`

Algorithm	Hash digest
SHA256	`d2b46ae0bbbea0b03d08614a516ff235f2482b6eb397f3447a5fe7e7867edf82`
MD5	`9390b95af6f856b475038f2ccfa3af1f`
BLAKE2b-256	`b3675912de777aa6b4f4aaabf836ff0601320c432ab41c38698d923d52e5c4d6`

Algorithm	Hash digest
SHA256	`d783c827afc1f8396dab3b827399a24c08e35d5237b4c3cb7d9f15d5e5a55835`
MD5	`99551cfa51bdfd3069b68b24214d4901`
BLAKE2b-256	`f3a8096fe1b01cd1fd8591a279dc0adb5b89ac587ccb9e2108e43687c83ed6c8`

Algorithm	Hash digest
SHA256	`00c2d85b980bc29cfe27f308c07f3c840ed8db3267d13d6664b822fb6fe70d95`
MD5	`d514bee2d02e111f8983da8924590ad3`
BLAKE2b-256	`dae0d49c34bfbc3f0bd2d74a6ebc2baf4490f900047862d2fea592a50f322c6f`

boost-histogram 1.1.0

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boost-histogram for Python

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Algorithm	Hash digest
SHA256	`cb9da1c9397c9fcbc97902a4beba8ef9b9de1e7b46e14092e67eb6e377cee6e5`
MD5	`ee58c4dd2e758a6ef3dcfda7daca3b7a`
BLAKE2b-256	`2d8c10144bc631a17fb0bdeb22f71fd532f989ee38d6d703c06e88b0bc69d14d`

Algorithm	Hash digest
SHA256	`b0dafec665543f60d1e6de66ab4594ba4e1327bca6a8260a0fc401bd4872e65d`
MD5	`8c48df6ece079eb590976ef626a65c61`
BLAKE2b-256	`e42c24a97e761b89466a636107cfec72f77861768c38161d963ad282bf1e4566`

Algorithm	Hash digest
SHA256	`c1320eff578eb5923f1464cf693f11b64be73590f135720d072959e7badf3724`
MD5	`d317f48e4b456bd43a29c446939454ab`
BLAKE2b-256	`36e36049687c11f168031b747c9273e5c2447e16440153cb6683d1028ce5ce4f`

Algorithm	Hash digest
SHA256	`fece60abbab6e7292280475bb292faf67b296a5e47342a3d565ac28d9fbb7c86`
MD5	`b93b3c07d8781acd09a1fb67f9861ffa`
BLAKE2b-256	`09909188944a96289e41832a1a2b2b582cdcd1bf6bf86b7b30569557a782e0b5`

Algorithm	Hash digest
SHA256	`0020a58eb13526ae9b284a875bfcbc1d233cabdd28497dcdffdd33c0e49825df`
MD5	`45400fe8dbd346e9abe6333ae7284140`
BLAKE2b-256	`75e6d8f4f58d023d07b385dc17af67a1c6086d613d58f7b8fc2e0c8db2965cc9`

Algorithm	Hash digest
SHA256	`a9bd63ea042bf31734e6abd36f4397e59c40618de4d6586e593e8645b7ec0f8d`
MD5	`0cc6a1f5e53f7572552b0a8a442abcee`
BLAKE2b-256	`60e8f2d6ffc9b3cb56f15647eb11505bf46b1aefd4db88e8433fc7ad5ec64e1a`

Algorithm	Hash digest
SHA256	`61531814f97e3874a535ad79ffc2be8638255030c4f530bd548d1c2c02e5eb55`
MD5	`8f9ffdb4636d8888933bba493b6cec94`
BLAKE2b-256	`6525460b0187a5e708b3d0ff13a765d2fffb11e79e74ac5f7ba96571af62607f`

Algorithm	Hash digest
SHA256	`1aff07ccbb266c51ffa6c94e33c9e5fb14cc728a8915b8e8f99381eb3947ef3f`
MD5	`5ef151694d3411fa03ea376db0e6f4e3`
BLAKE2b-256	`4a5c067b749893d622bccd272082ce3aec552078d90d61aca9405b7828c6036d`

Algorithm	Hash digest
SHA256	`6002bdc285c5a2277721ced35acd07c797b58851e6736823768aa040a7faa671`
MD5	`2d7b1cb5697aa97aaed7ce20a03e6ce3`
BLAKE2b-256	`eac31f774aea7de4278969d3f7eff6c79dbc27592e3035b373e84856ab51278c`

Algorithm	Hash digest
SHA256	`69371793816bb6d7a422a65d9360b86b15cd2fe5ca41719c2ae4ab9fe444102c`
MD5	`98f9dd9e8cc1360da988f9e47ba3c805`
BLAKE2b-256	`d7d6ef645cf0d266515af7071e7468a1470c27bd1184e5c0606afdecd58e9c90`