The Boost::Histogram Python wrapper.

Navigation

Verified details

These details have been verified by PyPI
Maintainers
Avatar for hdembins from gravatar.com hdembins Avatar for henryiii from gravatar.com henryiii Avatar for scikit-hep from gravatar.com scikit-hep

Unverified details

These details have not been verified by PyPI
Project links
Meta
Classifiers

Project description

boost-histogram for Python

Gitter Build Status Documentation Status DOI Code style: black

Python bindings for Boost::Histogram (source), a C++14 library. This should become one of the fastest libraries for histogramming, while still providing the power of a full histogram object.

0.5.1: Public beta

Please feel free to try out boost-histogram and give feedback. Join the discussion on gitter or open an issue!

Installation

You can install this library from PyPI with pip:

python -m pip install boost-histogram

This library is under active development; you can install directly from GitHub if you would like. You need a C++14 compiler and Python 2.7--3.8. Boost 1.71 is not required or needed (this only depends on included header-only dependencies).

All the normal best-practices for Python apply; you should be in a virtual environment, otherwise add --user, etc.

python -m pip install git+https://github.com/scikit-hep/boost-histogram.git@develop

For the moment, you need to uninstall and reinstall to ensure you have the latest version - pip will not rebuild if it thinks the version number has not changed. In the future, this may be addressed differently in boost-histogram.

Conda support is planned.

Usage

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([.3, .5, .2],
          [.1, .4, .9])

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

Features

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

    • bh.axis.regular(n, start, stop, underflow=True, overflow=True, growth=False): shortcut to make the types below. flow=False is also supported.
    • bh.axis.circular(n, start, stop): Value outside the range wrap into the range
    • bh.axis.regular_log(n, start, stop): Regularly spaced values in log 10 scale
    • bh.axis.regular_sqrt(n, start, stop): Regularly spaced value in sqrt scale
    • bh.axis.regular_pow(n, start, stop, power): Regularly spaced value to some power
    • bh.axis.integer(start, stop, underflow=True, overflow=True, growth=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): Uneven bin spacing
    • bh.axis.category([...], growth=False): Integer or string categories

  • Axis features:

    • .index(values): The index at a point (or points) on the axis
    • .value(indexes): The value for a fractional bin in the axis
    • .bin(i): The bin edges or a bin value (categories)
    • .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
    • .options: The options set on the axis (bh.axis.options)
    • .size: The number of bins (not including under/overflow)
    • .widths: The N bin widths

  • Many storage types

    • bh.storage.int: 64 bit unsigned integers for high performance and useful view access
    • bh.storage.double: Doubles for weighted values (default)
    • bh.storage.unlimited: Starts small, but can go up to unlimited precision ints or doubles.
    • bh.storage.atomic_int: Threadsafe filling, for higher performance on multhreaded backends. 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.
    • bh.storage.weighted_mean: Accepts a sample and a weight. It computes the weighted mean of the samples.

  • Accumulators

    • bh.accumulator.weighted_sum: Tracks a weighted sum and variance
    • bh.accumulator.weighted_mean: Tracks a weighted sum, mean, and variance (West's incremental algorithm)
    • bh.accumulator.sum: High accuracy sum (Neumaier)
    • bh.accumulator.mean: Running count, mean, and variance (Welfords's incremental algorithm)

  • Histogram operations

    • h.fill(arr, ..., weight=...) Fill with N arrays or single values
    • h.rank: The number of dimensions
    • h.size or len(h): The number of bins
    • .reset(): Set counters to 0
    • +: Add two histograms
    • *=: 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)
    • .to_numpy(flow=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)
    • .axes: Get the axes
      • .axes[0]: Get the 0th axis
      • .axes.edges: The lower values as a broadcasting-ready array
      • All other properties of axes available here, too
    • .sum(flow=False): The total count of all bins
    • .project(ax1, ax2, ...): Project down to listed axis (numbers)
    • .reduce(ax, reduce_option, ...): shrink, rebin, or slice, or any combination

  • Indexing - Supports the Unified Histogram Indexing (UHI) proposal

  • Details

    • Use bh.histogram(..., storage=...) to make a histogram (there are several different types)

Supported platforms

Binaries available:

The easiest way to get boost-histogram is to use a binary wheel. These are the supported platforms for which wheels are produced:

System Arch Python versions
ManyLinux1 (custom GCC 8.3) 64 & 32-bit 2.7, 3.5, 3.6, 3.7
ManyLinux2010 64-bit 2.7, 3.5, 3.6, 3.7, 3.8
macOS 10.9+ 64-bit 2.7, 3.6, 3.7, 3.8
Windows 64 & 32-bit 2.7, 3.6, 3.7
  • Linux: I'm not supporting 3.4 because I have to build the Numpy wheels to do so.
  • manylinux1: Using a custom docker container with GCC 9.2; should work but can't be called directly other compiled extensions unless they do the same thing (think that's the main caveat). Supporting 32 bits because it's there. Numpy does not build correctly with Python 3.8, GCC 9.2, and manylinux1, so Python 3.8 is not supported; use manylinux2010 instead.
  • manylinux2010: Requires pip 10+ and a version of Linux newer than 2010. This is very new technology.
  • MacOS: Uses the dedicated 64 bit 10.9+ Python.org builds. We are not supporting 3.5 because those no longer provide binaries (could add a 32+64 fat 10.6+ that really was 10.9+, but not worth it unless there is a need for it).
  • Windows: PyBind11 requires compilation with a newer copy of Visual Studio than Python 2.7's Visual Studio 2008; you need to have the Visual Studio 2015 distributable installed (the dll is included in 2017 and 2019, as well). Wheels are not provided for 3.8, waiting on support from Azure.

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 a little longer to install when it's using the sdist instead of a wheel.

Source builds

For a source build, for example from an "sdist" package, the only requirements are a C++14 compatible compiler. If you are using Python 2.7 on Windows, you will need to use a recent version of Visual studio and force distutils to use it, or just upgrade to Python 3.6 or newer. Check the PyBind11 documentation for more help. On some Linux systems, you may need to use a newer compiler than the one your distribution ships with.

Developing

See CONTRIBUTING.md for details on how to set up a development environment.

Talks and other documentation/tutorial sources

Acknowledgements

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

Verified details

These details have been verified by PyPI
Maintainers
Avatar for hdembins from gravatar.com hdembins Avatar for henryiii from gravatar.com henryiii Avatar for scikit-hep from gravatar.com scikit-hep

Unverified details

These details have not been verified by PyPI
Project links
Meta
Classifiers

Release history Release notifications | RSS feed

1.5.0

1.4.1

1.4.0

1.3.2

1.3.1

1.3.0

1.2.1

1.2.0

1.1.0

1.0.2

1.0.1

1.0.0

0.13.2

0.13.1

0.13.0

0.12.0

0.11.1

0.11.0

0.10.2

0.10.1

0.10.0

0.9.0

0.8.0

0.7.0

0.6.2

0.6.1

0.6.0

0.5.2

This version

0.5.1

0.5.0

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

boost-histogram-0.5.1.tar.gz (422.0 kB view details)

Uploaded Source

Built Distributions

boost_histogram-0.5.1-cp38-cp38-manylinux2010_x86_64.whl (25.7 MB view details)

Uploaded CPython 3.8 manylinux: glibc 2.12+ x86-64

boost_histogram-0.5.1-cp38-cp38-macosx_10_9_x86_64.whl (1.5 MB view details)

Uploaded CPython 3.8 macOS 10.9+ x86-64

boost_histogram-0.5.1-cp37-cp37m-win_amd64.whl (577.4 kB view details)

Uploaded CPython 3.7m Windows x86-64

boost_histogram-0.5.1-cp37-cp37m-win32.whl (442.9 kB view details)

Uploaded CPython 3.7m Windows x86

boost_histogram-0.5.1-cp37-cp37m-manylinux2010_x86_64.whl (26.3 MB view details)

Uploaded CPython 3.7m manylinux: glibc 2.12+ x86-64

boost_histogram-0.5.1-cp37-cp37m-manylinux1_x86_64.whl (22.5 MB view details)

Uploaded CPython 3.7m

boost_histogram-0.5.1-cp37-cp37m-manylinux1_i686.whl (21.8 MB view details)

Uploaded CPython 3.7m

boost_histogram-0.5.1-cp37-cp37m-macosx_10_9_x86_64.whl (1.5 MB view details)

Uploaded CPython 3.7m macOS 10.9+ x86-64

boost_histogram-0.5.1-cp36-cp36m-win_amd64.whl (577.4 kB view details)

Uploaded CPython 3.6m Windows x86-64

boost_histogram-0.5.1-cp36-cp36m-win32.whl (442.9 kB view details)

Uploaded CPython 3.6m Windows x86

boost_histogram-0.5.1-cp36-cp36m-manylinux2010_x86_64.whl (26.3 MB view details)

Uploaded CPython 3.6m manylinux: glibc 2.12+ x86-64

boost_histogram-0.5.1-cp36-cp36m-manylinux1_x86_64.whl (22.5 MB view details)

Uploaded CPython 3.6m

boost_histogram-0.5.1-cp36-cp36m-manylinux1_i686.whl (21.8 MB view details)

Uploaded CPython 3.6m

boost_histogram-0.5.1-cp36-cp36m-macosx_10_9_x86_64.whl (1.5 MB view details)

Uploaded CPython 3.6m macOS 10.9+ x86-64

boost_histogram-0.5.1-cp35-cp35m-manylinux2010_x86_64.whl (26.3 MB view details)

Uploaded CPython 3.5m manylinux: glibc 2.12+ x86-64

boost_histogram-0.5.1-cp35-cp35m-manylinux1_x86_64.whl (22.5 MB view details)

Uploaded CPython 3.5m

boost_histogram-0.5.1-cp35-cp35m-manylinux1_i686.whl (21.8 MB view details)

Uploaded CPython 3.5m

boost_histogram-0.5.1-cp27-cp27mu-manylinux2010_x86_64.whl (25.7 MB view details)

Uploaded CPython 2.7mu manylinux: glibc 2.12+ x86-64

boost_histogram-0.5.1-cp27-cp27mu-manylinux1_x86_64.whl (21.9 MB view details)

Uploaded CPython 2.7mu

boost_histogram-0.5.1-cp27-cp27mu-manylinux1_i686.whl (21.2 MB view details)

Uploaded CPython 2.7mu

boost_histogram-0.5.1-cp27-cp27m-win_amd64.whl (625.9 kB view details)

Uploaded CPython 2.7m Windows x86-64

boost_histogram-0.5.1-cp27-cp27m-win32.whl (464.3 kB view details)

Uploaded CPython 2.7m Windows x86

boost_histogram-0.5.1-cp27-cp27m-manylinux2010_x86_64.whl (25.7 MB view details)

Uploaded CPython 2.7m manylinux: glibc 2.12+ x86-64

boost_histogram-0.5.1-cp27-cp27m-manylinux1_x86_64.whl (21.9 MB view details)

Uploaded CPython 2.7m

boost_histogram-0.5.1-cp27-cp27m-manylinux1_i686.whl (21.2 MB view details)

Uploaded CPython 2.7m

boost_histogram-0.5.1-cp27-cp27m-macosx_10_9_x86_64.whl (1.5 MB view details)

Uploaded CPython 2.7m macOS 10.9+ x86-64

File details

Details for the file boost-histogram-0.5.1.tar.gz.

File metadata

  • Download URL: boost-histogram-0.5.1.tar.gz
  • Upload date:
  • Size: 422.0 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/2.0.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/40.6.2 requests-toolbelt/0.9.1 tqdm/4.36.1 CPython/3.6.8

File hashes

Hashes for boost-histogram-0.5.1.tar.gz
Algorithm Hash digest
SHA256 43cc8ed3d8493f94a04d675ce6015f19d91c50bbc85eff65b21c8cc9d328407d
MD5 a4922465e2bc386f3b51a1c8d52b1d49
BLAKE2b-256 f982bc8263134c445b98dc788c40fc3d7ae55831aca5708c4f7d1506b0b3b571

See more details on using hashes here.

File details

Details for the file boost_histogram-0.5.1-cp38-cp38-manylinux2010_x86_64.whl.

File metadata

  • Download URL: boost_histogram-0.5.1-cp38-cp38-manylinux2010_x86_64.whl
  • Upload date:
  • Size: 25.7 MB
  • Tags: CPython 3.8, manylinux: glibc 2.12+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/2.0.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/40.6.2 requests-toolbelt/0.9.1 tqdm/4.36.1 CPython/3.6.8

File hashes

Hashes for boost_histogram-0.5.1-cp38-cp38-manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 858edd99d52a1319a6a60e360d55b663df083dcc00ad67bd3563a929ba2b4971
MD5 b4ebbb298a3208f33bf27e478e5b55ba
BLAKE2b-256 61b4ace07924436e993be0b4566a4d0f4f82c76cf40149e726ee38c79bdc29aa

See more details on using hashes here.

File details

Details for the file boost_histogram-0.5.1-cp38-cp38-macosx_10_9_x86_64.whl.

File metadata

  • Download URL: boost_histogram-0.5.1-cp38-cp38-macosx_10_9_x86_64.whl
  • Upload date:
  • Size: 1.5 MB
  • Tags: CPython 3.8, macOS 10.9+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/2.0.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/40.6.2 requests-toolbelt/0.9.1 tqdm/4.36.1 CPython/3.6.8

File hashes

Hashes for boost_histogram-0.5.1-cp38-cp38-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 cf6f024e8aea5aad03b18bcfcfb373231e7bfa87193a33ada6a7e5e1d75ea4fc
MD5 8b7df0db37bd3feb4d122148d938250b
BLAKE2b-256 9c6b0b0a2fe8c9f2eaa06a3507f969777e1d33b41ab56a9657feff252c20ea22

See more details on using hashes here.

File details

Details for the file boost_histogram-0.5.1-cp37-cp37m-win_amd64.whl.

File metadata

  • Download URL: boost_histogram-0.5.1-cp37-cp37m-win_amd64.whl
  • Upload date:
  • Size: 577.4 kB
  • Tags: CPython 3.7m, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/2.0.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/40.6.2 requests-toolbelt/0.9.1 tqdm/4.36.1 CPython/3.6.8

File hashes

Hashes for boost_histogram-0.5.1-cp37-cp37m-win_amd64.whl
Algorithm Hash digest
SHA256 641f7e1f9ad3738c48669a01a0bf6bc462702fab19915e9bf4ac38c29bd5db89
MD5 fd828d670c320465efe35586819e4b2c
BLAKE2b-256 dd25a5346fec9ba845ebefa49d9940fba2675cd4f8e0b1ec86c26b0e3ec6a08c

See more details on using hashes here.

File details

Details for the file boost_histogram-0.5.1-cp37-cp37m-win32.whl.

File metadata

  • Download URL: boost_histogram-0.5.1-cp37-cp37m-win32.whl
  • Upload date:
  • Size: 442.9 kB
  • Tags: CPython 3.7m, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/2.0.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/40.6.2 requests-toolbelt/0.9.1 tqdm/4.36.1 CPython/3.6.8

File hashes

Hashes for boost_histogram-0.5.1-cp37-cp37m-win32.whl
Algorithm Hash digest
SHA256 de7d789c744eb81ef96d1ac179ce901076d38c9bb7ebaeee0497d216e69ef2fb
MD5 f4e23ca9dfe3c34d1688590d87429690
BLAKE2b-256 c7215463f02e8ae30287687fa322db114d129993de0415da489410f8e0025f13

See more details on using hashes here.

File details

Details for the file boost_histogram-0.5.1-cp37-cp37m-manylinux2010_x86_64.whl.

File metadata

  • Download URL: boost_histogram-0.5.1-cp37-cp37m-manylinux2010_x86_64.whl
  • Upload date:
  • Size: 26.3 MB
  • Tags: CPython 3.7m, manylinux: glibc 2.12+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/2.0.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/40.6.2 requests-toolbelt/0.9.1 tqdm/4.36.1 CPython/3.6.8

File hashes

Hashes for boost_histogram-0.5.1-cp37-cp37m-manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 8ffb284dd95face5786c55289d17afd5e2b98aba456875d6a7f6174e8b1b7c43
MD5 356d5ce2d883185d8783310d1dabdc14
BLAKE2b-256 fc67bc9fdf77ac91b8b5a5a74ec9a154fc2d5cfa20f27b2e639c0b9a656bc5bc

See more details on using hashes here.

File details

Details for the file boost_histogram-0.5.1-cp37-cp37m-manylinux1_x86_64.whl.

File metadata

  • Download URL: boost_histogram-0.5.1-cp37-cp37m-manylinux1_x86_64.whl
  • Upload date:
  • Size: 22.5 MB
  • Tags: CPython 3.7m
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/2.0.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/40.6.2 requests-toolbelt/0.9.1 tqdm/4.36.1 CPython/3.6.8

File hashes

Hashes for boost_histogram-0.5.1-cp37-cp37m-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 a84732984c92253b8d749463c967c21d2bf9133d69538c9c29d969a8a504f654
MD5 58709960b37d0b4b2e5c5c9f88301b6a
BLAKE2b-256 4ccaba8039ad7121d1fec1b803f35527ed6929c3f8c6e8cbc5cf587428f349a1

See more details on using hashes here.

File details

Details for the file boost_histogram-0.5.1-cp37-cp37m-manylinux1_i686.whl.

File metadata

  • Download URL: boost_histogram-0.5.1-cp37-cp37m-manylinux1_i686.whl
  • Upload date:
  • Size: 21.8 MB
  • Tags: CPython 3.7m
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/2.0.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/40.6.2 requests-toolbelt/0.9.1 tqdm/4.36.1 CPython/3.6.8

File hashes

Hashes for boost_histogram-0.5.1-cp37-cp37m-manylinux1_i686.whl
Algorithm Hash digest
SHA256 c603ad94925841021f8885ec5ab8232910a2f9a853ac6d6562e75a410df9fb70
MD5 702fd167a4ea4cc10ca2021eed1a07a0
BLAKE2b-256 d07d1a9566a8f78bed0a393516b1a501bdf42ad79003ccf884c2470f46688729

See more details on using hashes here.

File details

Details for the file boost_histogram-0.5.1-cp37-cp37m-macosx_10_9_x86_64.whl.

File metadata

  • Download URL: boost_histogram-0.5.1-cp37-cp37m-macosx_10_9_x86_64.whl
  • Upload date:
  • Size: 1.5 MB
  • Tags: CPython 3.7m, macOS 10.9+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/2.0.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/40.6.2 requests-toolbelt/0.9.1 tqdm/4.36.1 CPython/3.6.8

File hashes

Hashes for boost_histogram-0.5.1-cp37-cp37m-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 1c39a744e590129d9d768e3781569bd1c3d1eb98f9ec5ee364411bc9053761c3
MD5 e50da42f5d5c90250f366aa2f4c2e224
BLAKE2b-256 cc24d9410a49f96315b95161d5afa4ea78c98b82cad57447a3f0ee7da95e28b8

See more details on using hashes here.

File details

Details for the file boost_histogram-0.5.1-cp36-cp36m-win_amd64.whl.

File metadata

  • Download URL: boost_histogram-0.5.1-cp36-cp36m-win_amd64.whl
  • Upload date:
  • Size: 577.4 kB
  • Tags: CPython 3.6m, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/2.0.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/40.6.2 requests-toolbelt/0.9.1 tqdm/4.36.1 CPython/3.6.8

File hashes

Hashes for boost_histogram-0.5.1-cp36-cp36m-win_amd64.whl
Algorithm Hash digest
SHA256 92d3b4a06233eca1dc09613cfae07993ebbcc61acb9eefcd842183f63d1caa0a
MD5 18b246a0086aa57b194b094ddf2ddaab
BLAKE2b-256 2ef17dea71d18ed215545454228784ed9cc6f8926ebf862e28e9d7d5863356af

See more details on using hashes here.

File details

Details for the file boost_histogram-0.5.1-cp36-cp36m-win32.whl.

File metadata

  • Download URL: boost_histogram-0.5.1-cp36-cp36m-win32.whl
  • Upload date:
  • Size: 442.9 kB
  • Tags: CPython 3.6m, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/2.0.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/40.6.2 requests-toolbelt/0.9.1 tqdm/4.36.1 CPython/3.6.8

File hashes

Hashes for boost_histogram-0.5.1-cp36-cp36m-win32.whl
Algorithm Hash digest
SHA256 084e3a80d8edacf4c427ef75b488a11ac0dc6697c2270816177131f00c7db057
MD5 19879f3083ed61a17f7bda26a3334a1e
BLAKE2b-256 6fbfc51ecfd70e45e3fb74d4f2f5ea8e3961b4d977db6ecad4f696f811a631a9

See more details on using hashes here.

File details

Details for the file boost_histogram-0.5.1-cp36-cp36m-manylinux2010_x86_64.whl.

File metadata

  • Download URL: boost_histogram-0.5.1-cp36-cp36m-manylinux2010_x86_64.whl
  • Upload date:
  • Size: 26.3 MB
  • Tags: CPython 3.6m, manylinux: glibc 2.12+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/2.0.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/40.6.2 requests-toolbelt/0.9.1 tqdm/4.36.1 CPython/3.6.8

File hashes

Hashes for boost_histogram-0.5.1-cp36-cp36m-manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 156ae7b2be451d6d6d43fd65fd6fa362680f74d4df74a274af731623e959dd38
MD5 9c9995e106f47e1d920e4671492ce8ea
BLAKE2b-256 4a8d99b75346d4661624d48b1c0252fea3d85a45d7bc27303ad031f3fdd53422

See more details on using hashes here.

File details

Details for the file boost_histogram-0.5.1-cp36-cp36m-manylinux1_x86_64.whl.

File metadata

  • Download URL: boost_histogram-0.5.1-cp36-cp36m-manylinux1_x86_64.whl
  • Upload date:
  • Size: 22.5 MB
  • Tags: CPython 3.6m
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/2.0.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/40.6.2 requests-toolbelt/0.9.1 tqdm/4.36.1 CPython/3.6.8

File hashes

Hashes for boost_histogram-0.5.1-cp36-cp36m-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 11f6782b47bbb6ed9f05b1d5b83d4a830d66edc115fc9803ae177859cb9b6ba6
MD5 e906dbb18f501f7d54474bd84bb7f897
BLAKE2b-256 dbc3f818436cb3530db828696cb9fd9910c9be799b004b30724628d07ec75bec

See more details on using hashes here.

File details

Details for the file boost_histogram-0.5.1-cp36-cp36m-manylinux1_i686.whl.

File metadata

  • Download URL: boost_histogram-0.5.1-cp36-cp36m-manylinux1_i686.whl
  • Upload date:
  • Size: 21.8 MB
  • Tags: CPython 3.6m
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/2.0.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/40.6.2 requests-toolbelt/0.9.1 tqdm/4.36.1 CPython/3.6.8

File hashes

Hashes for boost_histogram-0.5.1-cp36-cp36m-manylinux1_i686.whl
Algorithm Hash digest
SHA256 97b34d7d77e7246e17c22a7e57f564fe4ca577f8117038590a56a44557ed09a9
MD5 47b69e9395d1a1b81e65c4c04032cb7d
BLAKE2b-256 7de1af000086caec83a5c4d0e09c1fc115f72deb8a691d9bff6b05cc3a7e2a4f

See more details on using hashes here.

File details

Details for the file boost_histogram-0.5.1-cp36-cp36m-macosx_10_9_x86_64.whl.

File metadata

  • Download URL: boost_histogram-0.5.1-cp36-cp36m-macosx_10_9_x86_64.whl
  • Upload date:
  • Size: 1.5 MB
  • Tags: CPython 3.6m, macOS 10.9+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/2.0.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/40.6.2 requests-toolbelt/0.9.1 tqdm/4.36.1 CPython/3.6.8

File hashes

Hashes for boost_histogram-0.5.1-cp36-cp36m-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 c6543b4d4f0703589f95806a6d25434c42fa46befca73a6f1d58c538870044a3
MD5 655f7981c9f3d5e0b5651d2e7b83f345
BLAKE2b-256 8a1a1bdf999bb0fb048bc9de7d4d6aa12c0aaf5a3eb1cc0b6620a09505662b52

See more details on using hashes here.

File details

Details for the file boost_histogram-0.5.1-cp35-cp35m-manylinux2010_x86_64.whl.

File metadata

  • Download URL: boost_histogram-0.5.1-cp35-cp35m-manylinux2010_x86_64.whl
  • Upload date:
  • Size: 26.3 MB
  • Tags: CPython 3.5m, manylinux: glibc 2.12+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/2.0.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/40.6.2 requests-toolbelt/0.9.1 tqdm/4.36.1 CPython/3.6.8

File hashes

Hashes for boost_histogram-0.5.1-cp35-cp35m-manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 cd04bb78447a8b64f8014de186bb42db4dc267f0ebaf676e8f26fed9901f8d3b
MD5 81b99ae4ada58a00111211b71659f443
BLAKE2b-256 574c23139be245594232c4495c6d8b16aea3dd39a913a2acabbc5d3cd5a258b5

See more details on using hashes here.

File details

Details for the file boost_histogram-0.5.1-cp35-cp35m-manylinux1_x86_64.whl.

File metadata

  • Download URL: boost_histogram-0.5.1-cp35-cp35m-manylinux1_x86_64.whl
  • Upload date:
  • Size: 22.5 MB
  • Tags: CPython 3.5m
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/2.0.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/40.6.2 requests-toolbelt/0.9.1 tqdm/4.36.1 CPython/3.6.8

File hashes

Hashes for boost_histogram-0.5.1-cp35-cp35m-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 67558eba2607bfdbb7bec3680a1317f69c821ad48b0bfe36560becad7d61339f
MD5 d5dc8a76b58bd8bfe20f65d3ed6acdc1
BLAKE2b-256 fd1dc4585cb1295402a8eb6f1737942d8be96a988eab6f2525f495115c291e9c

See more details on using hashes here.

File details

Details for the file boost_histogram-0.5.1-cp35-cp35m-manylinux1_i686.whl.

File metadata

  • Download URL: boost_histogram-0.5.1-cp35-cp35m-manylinux1_i686.whl
  • Upload date:
  • Size: 21.8 MB
  • Tags: CPython 3.5m
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/2.0.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/40.6.2 requests-toolbelt/0.9.1 tqdm/4.36.1 CPython/3.6.8

File hashes

Hashes for boost_histogram-0.5.1-cp35-cp35m-manylinux1_i686.whl
Algorithm Hash digest
SHA256 2bf971560122666d6e64196d22449457757e5f603b6c884513392f819394ef9d
MD5 151db7453cd0a87d3c0711296e57d903
BLAKE2b-256 7cc3a6fbae4db9d23d886d582c3b6e58f913589d1c35657c8d58b5c84d6fa892

See more details on using hashes here.

File details

Details for the file boost_histogram-0.5.1-cp27-cp27mu-manylinux2010_x86_64.whl.

File metadata

  • Download URL: boost_histogram-0.5.1-cp27-cp27mu-manylinux2010_x86_64.whl
  • Upload date:
  • Size: 25.7 MB
  • Tags: CPython 2.7mu, manylinux: glibc 2.12+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/2.0.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/40.6.2 requests-toolbelt/0.9.1 tqdm/4.36.1 CPython/3.6.8

File hashes

Hashes for boost_histogram-0.5.1-cp27-cp27mu-manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 ccac7c6a67fb8950490ead0010bdee95fae7a85647fffb30cbb26916498b010c
MD5 78a85bf2ed74957e4b5a27c12b073fbe
BLAKE2b-256 5f431bdba32e5b8613db5b21d4ce5191fc107d8af29c7ca8f3aaaa12c3aa767b

See more details on using hashes here.

File details

Details for the file boost_histogram-0.5.1-cp27-cp27mu-manylinux1_x86_64.whl.

File metadata

  • Download URL: boost_histogram-0.5.1-cp27-cp27mu-manylinux1_x86_64.whl
  • Upload date:
  • Size: 21.9 MB
  • Tags: CPython 2.7mu
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/2.0.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/40.6.2 requests-toolbelt/0.9.1 tqdm/4.36.1 CPython/3.6.8

File hashes

Hashes for boost_histogram-0.5.1-cp27-cp27mu-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 03528f936680869612880c28954779f4a4153bb8ca7877e519ff34627421f916
MD5 d27baaf47c713b79fc35f16d0a444fba
BLAKE2b-256 bce728867035004f2d27e437eb318e59b051c50947429281134aa409cb59b5eb

See more details on using hashes here.

File details

Details for the file boost_histogram-0.5.1-cp27-cp27mu-manylinux1_i686.whl.

File metadata

  • Download URL: boost_histogram-0.5.1-cp27-cp27mu-manylinux1_i686.whl
  • Upload date:
  • Size: 21.2 MB
  • Tags: CPython 2.7mu
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/2.0.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/40.6.2 requests-toolbelt/0.9.1 tqdm/4.36.1 CPython/3.6.8

File hashes

Hashes for boost_histogram-0.5.1-cp27-cp27mu-manylinux1_i686.whl
Algorithm Hash digest
SHA256 d1a67e6857627e1c598e69a218b90e39b06a9854ab4d58da01e98e9da0b25d85
MD5 25e0071b345f2604cd115b826a84a42d
BLAKE2b-256 ae35097247267ca59bd8df74c569db304c4011c6356695df09b13f5e7d2964c1

See more details on using hashes here.

File details

Details for the file boost_histogram-0.5.1-cp27-cp27m-win_amd64.whl.

File metadata

  • Download URL: boost_histogram-0.5.1-cp27-cp27m-win_amd64.whl
  • Upload date:
  • Size: 625.9 kB
  • Tags: CPython 2.7m, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/2.0.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/40.6.2 requests-toolbelt/0.9.1 tqdm/4.36.1 CPython/3.6.8

File hashes

Hashes for boost_histogram-0.5.1-cp27-cp27m-win_amd64.whl
Algorithm Hash digest
SHA256 0e484fcee16b35dea429ce92ecd233bc2670ecf1a5a9b564153bd84abd3a50a3
MD5 fb72bf0a92083d6352a493d5ebc18bf8
BLAKE2b-256 980646377c9122eea995c33356829586f84f2f2afdd24802bca4899c63570139

See more details on using hashes here.

File details

Details for the file boost_histogram-0.5.1-cp27-cp27m-win32.whl.

File metadata

  • Download URL: boost_histogram-0.5.1-cp27-cp27m-win32.whl
  • Upload date:
  • Size: 464.3 kB
  • Tags: CPython 2.7m, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/2.0.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/40.6.2 requests-toolbelt/0.9.1 tqdm/4.36.1 CPython/3.6.8

File hashes

Hashes for boost_histogram-0.5.1-cp27-cp27m-win32.whl
Algorithm Hash digest
SHA256 30203c666dc741a6bd53ba0244347e9838e94fe7af706da0078472f18f875d02
MD5 7bf1ee58453c1bb3c920d5905b07952d
BLAKE2b-256 3c2c447188f2822cb031861304be1d80efd0dcaa6cd0bf09f7e2871281589572

See more details on using hashes here.

File details

Details for the file boost_histogram-0.5.1-cp27-cp27m-manylinux2010_x86_64.whl.

File metadata

  • Download URL: boost_histogram-0.5.1-cp27-cp27m-manylinux2010_x86_64.whl
  • Upload date:
  • Size: 25.7 MB
  • Tags: CPython 2.7m, manylinux: glibc 2.12+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/2.0.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/40.6.2 requests-toolbelt/0.9.1 tqdm/4.36.1 CPython/3.6.8

File hashes

Hashes for boost_histogram-0.5.1-cp27-cp27m-manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 c6eb5f41a7664baf7a55f34a2e60dcbd4ec0bacb31840a66694f4321de1633b4
MD5 4e3c212e0fc32c9a7e1f10a8d6a6963b
BLAKE2b-256 64c10974efd0d7857ebd2f99c4d1f3ec7faa8e26656172c539aeca62f045fe61

See more details on using hashes here.

File details

Details for the file boost_histogram-0.5.1-cp27-cp27m-manylinux1_x86_64.whl.

File metadata

  • Download URL: boost_histogram-0.5.1-cp27-cp27m-manylinux1_x86_64.whl
  • Upload date:
  • Size: 21.9 MB
  • Tags: CPython 2.7m
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/2.0.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/40.6.2 requests-toolbelt/0.9.1 tqdm/4.36.1 CPython/3.6.8

File hashes

Hashes for boost_histogram-0.5.1-cp27-cp27m-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 c7c63af6a04ccc618e0082e4452397479cff97d3510410309c8a0849799d76ed
MD5 bfb7eca4ba4cb721f696f79fecf71c8b
BLAKE2b-256 cc07a3086b4db7dcf72c4add68f562dd915327e5ba431b88da30477551e03c8b

See more details on using hashes here.

File details

Details for the file boost_histogram-0.5.1-cp27-cp27m-manylinux1_i686.whl.

File metadata

  • Download URL: boost_histogram-0.5.1-cp27-cp27m-manylinux1_i686.whl
  • Upload date:
  • Size: 21.2 MB
  • Tags: CPython 2.7m
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/2.0.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/40.6.2 requests-toolbelt/0.9.1 tqdm/4.36.1 CPython/3.6.8

File hashes

Hashes for boost_histogram-0.5.1-cp27-cp27m-manylinux1_i686.whl
Algorithm Hash digest
SHA256 ae64d62ee5bd6d3f1dac1e1c790c3aad29981105eb258943b19e18b41069a42c
MD5 05628eaf3cb0456c42d755f5f96e42bd
BLAKE2b-256 6f4082e5dd9d6ed01b8358ddbbf397edaf7f9658b718298d74c09769d141fc86

See more details on using hashes here.

File details

Details for the file boost_histogram-0.5.1-cp27-cp27m-macosx_10_9_x86_64.whl.

File metadata

  • Download URL: boost_histogram-0.5.1-cp27-cp27m-macosx_10_9_x86_64.whl
  • Upload date:
  • Size: 1.5 MB
  • Tags: CPython 2.7m, macOS 10.9+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/2.0.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/40.6.2 requests-toolbelt/0.9.1 tqdm/4.36.1 CPython/3.6.8

File hashes

Hashes for boost_histogram-0.5.1-cp27-cp27m-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 048ed645c329c1735558eb7f059cf8762e45d6d8b52a1201f3314db904ff0445
MD5 159ba94ade5bd998ac648fad1825855e
BLAKE2b-256 d16f82d558f33c43efa4b7d30863af78ce164993bae827a66badba8c32d534a1

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page