Skip to main content

Python implementation of fast azimuthal integration

Project description

Main development website: https://github.com/silx-kit/pyFAI

Github Actions Appveyor Status myBinder Launcher Zenodo DOI RTD docs

PyFAI is an azimuthal integration library that tries to be fast (as fast as C and even more using OpenCL and GPU). It is based on histogramming of the 2theta/Q positions of each (center of) pixel weighted by the intensity of each pixel, but parallel version uses a SparseMatrix-DenseVector multiplication. Neighboring output bins get also a contribution of pixels next to the border thanks to pixel splitting. Finally pyFAI provides also tools to calibrate the experimental setup using Debye-Scherrer rings of a reference compound.

References

Installation

With PIP

As most Python packages, pyFAI is available via PIP:

pip install pyFAI[gui]

It is advised to run this in a vitural environment . Provide the –user option to perform an installation local to your user-space (not recommended). Under UNIX, you may have to run the command via sudo to gain root access and perform a system wide installation (which is neither recommended).

With conda

pyFAI is also available via conda:

conda install pyfai -c conda-forge

To install conda please see either conda or Anaconda.

From source code

The current development version of pyFAI can be downloaded from Github. Presently the source code has been distributed as a zip package. Download it one and unpack it:

unzip pyFAI-main.zip

All files are unpacked into the directory pyFAI-main:

cd pyFAI-main

Install dependencies:

pip install -r requirements.txt

Build it & test it:

python3 run_tests.py

For its tests, pyFAI downloads test images from the internet. Depending on your network connection and your local network configuration, you may have to setup a proxy configuration like this (not needed at ESRF):

export http_proxy=http://proxy.site.org:3128

Finally, install pyFAI in the virtualenv after testing it:

pip install .

The newest development version can also be obtained by checking out from the git repository:

git clone https://github.com/silx-kit/pyFAI.git
cd pyFAI
pip install .

If you want pyFAI to make use of your graphic card, please install pyopencl

Documentation

Documentation can be build using this command and Sphinx (installed on your computer):

python3 build-doc.py

Dependencies

Python 3.9, … 3.13 are well tested and officially supported. For full functionality of pyFAI the following modules need to be installed.

Those dependencies can simply be installed by:

pip install -r requirements.txt

Ubuntu and Debian-like Linux distributions

To use pyFAI on Ubuntu/Debian the needed python modules can be installed either through the Synaptic Package Manager (found in System -> Administration) or using apt-get on from the command line in a terminal:

sudo apt-get install pyfai

The extra Ubuntu packages needed are:

  • python3-numpy

  • python3-scipy

  • python3-matplotlib

  • python3-dev

  • python3-fabio

  • python3-pyopencl

  • python3-pyqt5

  • python3-silx

  • python3-numexpr

using apt-get these can be installed as:

sudo apt-get build-dep pyfai

MacOSX

One needs to manually install a recent version of Python (>=3.8) prior to installing pyFAI. Apple provides only an outdated version of Python 2.7 which is now incomatible. If you want to build pyFAI from sources, you will also need Xcode which is available from the Apple store. The compiled extension will use only one core due to the limitation of the compiler. OpenCL is hence greately adviced on Apple systems. Then install the missing dependencies with pip:

pip install -r requirements.txt

Windows

Under Windows, one needs to install Python (>=3.8) prior to pyFAI. The Visual Studio C++ compiler is also needed when building from sources. Then install the missing dependencies with pip:

pip install  -r requirements.txt

Getting help

A mailing-list, pyfai@esrf.fr, is available to get help on the program and how to use it. One needs to subscribe by sending an email to sympa@esrf.fr with a subject “subscribe pyfai”.

Maintainers

  • Jérôme Kieffer (ESRF)

  • Edgar Gutierrez Fernandez (ESRF)

  • Loïc Huder (ESRF)

Contributors

  • Valentin Valls (ESRF)

  • Frédéric-Emmanuel Picca (Soleil)

  • Thomas Vincent (ESRF)

  • Dimitris Karkoulis (Formerly ESRF)

  • Aurore Deschildre (Formerly ESRF)

  • Giannis Ashiotis (Formerly ESRF)

  • Zubair Nawaz (Formerly Sesame)

  • Jon Wright (ESRF)

  • Amund Hov (Formerly ESRF)

  • Dodogerstlin @github

  • Gunthard Benecke (Desy)

  • Gero Flucke (Desy)

  • Maciej Jankowski (ESRF)

Indirect contributors (ideas…)

  • Peter Boesecke

  • Manuel Sánchez del Río

  • Vicente Armando Solé

  • Brian Pauw

  • Veijo Honkimaki

Project details


Download files

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

Source Distribution

pyfai-2025.3.0.tar.gz (67.1 MB view details)

Uploaded Source

Built Distributions

If you're not sure about the file name format, learn more about wheel file names.

pyfai-2025.3.0-cp314-cp314-win_amd64.whl (5.2 MB view details)

Uploaded CPython 3.14Windows x86-64

pyfai-2025.3.0-cp313-cp313-win_amd64.whl (5.4 MB view details)

Uploaded CPython 3.13Windows x86-64

pyfai-2025.3.0-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (6.9 MB view details)

Uploaded CPython 3.13manylinux: glibc 2.17+ x86-64

pyfai-2025.3.0-cp313-cp313-macosx_11_0_x86_64.whl (5.7 MB view details)

Uploaded CPython 3.13macOS 11.0+ x86-64

pyfai-2025.3.0-cp313-cp313-macosx_11_0_arm64.whl (5.4 MB view details)

Uploaded CPython 3.13macOS 11.0+ ARM64

pyfai-2025.3.0-cp312-cp312-win_amd64.whl (5.4 MB view details)

Uploaded CPython 3.12Windows x86-64

pyfai-2025.3.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (7.0 MB view details)

Uploaded CPython 3.12manylinux: glibc 2.17+ x86-64

pyfai-2025.3.0-cp312-cp312-macosx_11_0_x86_64.whl (5.8 MB view details)

Uploaded CPython 3.12macOS 11.0+ x86-64

pyfai-2025.3.0-cp312-cp312-macosx_11_0_arm64.whl (5.5 MB view details)

Uploaded CPython 3.12macOS 11.0+ ARM64

pyfai-2025.3.0-cp311-cp311-win_amd64.whl (5.5 MB view details)

Uploaded CPython 3.11Windows x86-64

pyfai-2025.3.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (7.2 MB view details)

Uploaded CPython 3.11manylinux: glibc 2.17+ x86-64

pyfai-2025.3.0-cp311-cp311-macosx_11_0_x86_64.whl (5.8 MB view details)

Uploaded CPython 3.11macOS 11.0+ x86-64

pyfai-2025.3.0-cp311-cp311-macosx_11_0_arm64.whl (5.5 MB view details)

Uploaded CPython 3.11macOS 11.0+ ARM64

pyfai-2025.3.0-cp310-cp310-win_amd64.whl (5.5 MB view details)

Uploaded CPython 3.10Windows x86-64

pyfai-2025.3.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (7.3 MB view details)

Uploaded CPython 3.10manylinux: glibc 2.17+ x86-64

pyfai-2025.3.0-cp310-cp310-macosx_11_0_x86_64.whl (5.8 MB view details)

Uploaded CPython 3.10macOS 11.0+ x86-64

pyfai-2025.3.0-cp310-cp310-macosx_11_0_arm64.whl (5.5 MB view details)

Uploaded CPython 3.10macOS 11.0+ ARM64

pyfai-2025.3.0-cp39-cp39-win_amd64.whl (5.5 MB view details)

Uploaded CPython 3.9Windows x86-64

pyfai-2025.3.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (7.3 MB view details)

Uploaded CPython 3.9manylinux: glibc 2.17+ x86-64

pyfai-2025.3.0-cp39-cp39-macosx_11_0_x86_64.whl (5.8 MB view details)

Uploaded CPython 3.9macOS 11.0+ x86-64

pyfai-2025.3.0-cp39-cp39-macosx_11_0_arm64.whl (5.5 MB view details)

Uploaded CPython 3.9macOS 11.0+ ARM64

pyfai-2025.3.0-cp38-cp38-win_amd64.whl (5.5 MB view details)

Uploaded CPython 3.8Windows x86-64

pyfai-2025.3.0-cp38-cp38-macosx_11_0_arm64.whl (5.5 MB view details)

Uploaded CPython 3.8macOS 11.0+ ARM64

File details

Details for the file pyfai-2025.3.0.tar.gz.

File metadata

  • Download URL: pyfai-2025.3.0.tar.gz
  • Upload date:
  • Size: 67.1 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.11.0

File hashes

Hashes for pyfai-2025.3.0.tar.gz
Algorithm Hash digest
SHA256 67b3bb7625170c63c33039c738e8aff02f0f1156f68a5eae444d56b5cdd06275
MD5 032cb56e453dbdc11cf5a6b30634d6b1
BLAKE2b-256 2583f3d2cf65770e162c9c72ab2a02fbb6e87b09a556e441b42588055bd23b68

See more details on using hashes here.

File details

Details for the file pyfai-2025.3.0-cp314-cp314-win_amd64.whl.

File metadata

  • Download URL: pyfai-2025.3.0-cp314-cp314-win_amd64.whl
  • Upload date:
  • Size: 5.2 MB
  • Tags: CPython 3.14, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.13.1

File hashes

Hashes for pyfai-2025.3.0-cp314-cp314-win_amd64.whl
Algorithm Hash digest
SHA256 066d2d7842bcb540075b7384b951a12f3f63a2e351b558c5b6b3f0b0f45216df
MD5 6229cbfed8c4fae85719c9558e3afa7e
BLAKE2b-256 52d1ec4f26935e740cbca7b3ae77d561b76385da304f0a750c83296616014b2f

See more details on using hashes here.

File details

Details for the file pyfai-2025.3.0-cp313-cp313-win_amd64.whl.

File metadata

  • Download URL: pyfai-2025.3.0-cp313-cp313-win_amd64.whl
  • Upload date:
  • Size: 5.4 MB
  • Tags: CPython 3.13, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.11.0

File hashes

Hashes for pyfai-2025.3.0-cp313-cp313-win_amd64.whl
Algorithm Hash digest
SHA256 595f67a43697a3502c5b06454446f8e899425f0a641205f6e1f8e4400d365744
MD5 f3bc644708108651086749c78cd35c2b
BLAKE2b-256 8bdaa7551af143e5a94adf0cabd6f220df472b86a0fbf8a1dad42f1ba8f7261a

See more details on using hashes here.

File details

Details for the file pyfai-2025.3.0-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pyfai-2025.3.0-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 ceeeca0ef2600b6d593cf7be9dd6cc79f08face10e6ccfb1071217d651bf1afa
MD5 5ca360bef4f47502316f9d2e2220dccf
BLAKE2b-256 e8709546d830fd7a28ba887583aa5658fb66827f97a62baae159f47f9607f0e8

See more details on using hashes here.

File details

Details for the file pyfai-2025.3.0-cp313-cp313-macosx_11_0_x86_64.whl.

File metadata

File hashes

Hashes for pyfai-2025.3.0-cp313-cp313-macosx_11_0_x86_64.whl
Algorithm Hash digest
SHA256 7567b8e578f0920c9000af63de37205a7e91d94dd613065c6e01278a45c9522e
MD5 78d16c90088bc048d9979d0a4835a60f
BLAKE2b-256 2522ca162cfc38ba4c9e876aedbe7640f20c89614f5e9fcf0ae6f7abd7717dbb

See more details on using hashes here.

File details

Details for the file pyfai-2025.3.0-cp313-cp313-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pyfai-2025.3.0-cp313-cp313-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 760e275381a0db8e5bd89aa51e524dd312e3ba493e4be613c3eebf2e215b12b7
MD5 cb874fc061b992f63a1d96de7fd4d7ab
BLAKE2b-256 1d32f10f70061da91758b0ffb09661f984f427e73a033fdde2962bf6bc4e4935

See more details on using hashes here.

File details

Details for the file pyfai-2025.3.0-cp312-cp312-win_amd64.whl.

File metadata

  • Download URL: pyfai-2025.3.0-cp312-cp312-win_amd64.whl
  • Upload date:
  • Size: 5.4 MB
  • Tags: CPython 3.12, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.11.0

File hashes

Hashes for pyfai-2025.3.0-cp312-cp312-win_amd64.whl
Algorithm Hash digest
SHA256 9ebea99aee6978fabeb6b4dd27ac6dc7814156286d3d8ae08117cd680b3b7a1f
MD5 931a0648edfbd98d647fa967e6e863b3
BLAKE2b-256 75583fc8b3ec9d3f8830f454c61d6da23362c5ba021d689221f868f9284f36b2

See more details on using hashes here.

File details

Details for the file pyfai-2025.3.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pyfai-2025.3.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 dbf53cb1e70487e0d75af2d30f728b5947a3062f20dd9478e19563ca4e4a8d40
MD5 a1462f5540b2449f6978fd193db7fbad
BLAKE2b-256 f163ec2b78bdf59e3f91ff088aa37ea9378633ab9592a46309fac164b9bf2583

See more details on using hashes here.

File details

Details for the file pyfai-2025.3.0-cp312-cp312-macosx_11_0_x86_64.whl.

File metadata

File hashes

Hashes for pyfai-2025.3.0-cp312-cp312-macosx_11_0_x86_64.whl
Algorithm Hash digest
SHA256 4a2a8ac572555413e1ed157180a4a7c8dbc43a59b7aa8058e1ff00a120f79298
MD5 60f684b83167fde321b435c3e0ef97f9
BLAKE2b-256 e1783431d93f9db3093e62a43d226f49da9f638e4ca9140bfde4bd1c3c15ca2c

See more details on using hashes here.

File details

Details for the file pyfai-2025.3.0-cp312-cp312-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pyfai-2025.3.0-cp312-cp312-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 fc747992df98866009f5e0b3b3909e1fd0c177417a972483d307c2f6a567177b
MD5 c1f94b21ad17716f37a7a47bdaf91505
BLAKE2b-256 9ce386e9224551ae9cc304eee368ca549c68f9911c5d6e377c2b27ad54cb9534

See more details on using hashes here.

File details

Details for the file pyfai-2025.3.0-cp311-cp311-win_amd64.whl.

File metadata

  • Download URL: pyfai-2025.3.0-cp311-cp311-win_amd64.whl
  • Upload date:
  • Size: 5.5 MB
  • Tags: CPython 3.11, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.11.0

File hashes

Hashes for pyfai-2025.3.0-cp311-cp311-win_amd64.whl
Algorithm Hash digest
SHA256 3631179ad0cd8cda406560bde5b3b407c321959ccc32650d850a34253512c276
MD5 f44a8d158206b46499b44c5f8767ca69
BLAKE2b-256 aa4a47bc64f008dd43439292cfda9a017f0a0a48de5e6275f4ec667d10a6ccd8

See more details on using hashes here.

File details

Details for the file pyfai-2025.3.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pyfai-2025.3.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 f102157978f39af049132fd05b7f9f8264eea0468ed65855729f860461f31ae5
MD5 8955678fd2999e98c24d514091b4144b
BLAKE2b-256 092dcb95032c097283dfdeab5a4c3d0564d79bd3e45a66dd4a9179f4d8f7cd94

See more details on using hashes here.

File details

Details for the file pyfai-2025.3.0-cp311-cp311-macosx_11_0_x86_64.whl.

File metadata

File hashes

Hashes for pyfai-2025.3.0-cp311-cp311-macosx_11_0_x86_64.whl
Algorithm Hash digest
SHA256 192bff8645fd2c01ccb0ae3b134a0ceb6b6aac8228020e6f07a8feb9399fafa4
MD5 c04348d262838ce2bbf2d7148116e117
BLAKE2b-256 fde9c6e6faf619d4874969969a87923e3e3a24d4bd7862e02cc5af85dd489c4e

See more details on using hashes here.

File details

Details for the file pyfai-2025.3.0-cp311-cp311-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pyfai-2025.3.0-cp311-cp311-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 5f8777f38d78c7db27c9bb834324d6ebf35d8471c0f856000b8b663e9ee96fbf
MD5 7e745f66baba92d953ad8df032e71e3d
BLAKE2b-256 fd8eb23c212f567f0dbeff64c009a377a17334db7eed1306b31bf8ef04fe5a8d

See more details on using hashes here.

File details

Details for the file pyfai-2025.3.0-cp310-cp310-win_amd64.whl.

File metadata

  • Download URL: pyfai-2025.3.0-cp310-cp310-win_amd64.whl
  • Upload date:
  • Size: 5.5 MB
  • Tags: CPython 3.10, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.11.0

File hashes

Hashes for pyfai-2025.3.0-cp310-cp310-win_amd64.whl
Algorithm Hash digest
SHA256 abfae14db5b03a13ffad9e66d5d2d1894c81d0704287d4adae9ca040997c870f
MD5 0d9f3c99b3e0f582cd0975df9d084fef
BLAKE2b-256 21ff2a09da33e444c6c0c50d4586f58e9873d4f46ac02c045673ba42434155c1

See more details on using hashes here.

File details

Details for the file pyfai-2025.3.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pyfai-2025.3.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 3de5969d84f5c1fb44b6f768c0e891ec27b5573514f346bc27ddfbec2edd3a29
MD5 2684005c496ce4f30fb463b8ed9f29ca
BLAKE2b-256 2f7709ef4f3b96c18f4b2190e9476d82417fcfcf18062e02f1731bdff2075e59

See more details on using hashes here.

File details

Details for the file pyfai-2025.3.0-cp310-cp310-macosx_11_0_x86_64.whl.

File metadata

File hashes

Hashes for pyfai-2025.3.0-cp310-cp310-macosx_11_0_x86_64.whl
Algorithm Hash digest
SHA256 272698d43fc361746c4507a16ea3c735c72c6b6c4443541320d0a80194a2b565
MD5 180f3a963073f3c564fbad433bd65363
BLAKE2b-256 fb87c9e6213237182f7741330b25333ef5827014040d936d2af3de55173bb78f

See more details on using hashes here.

File details

Details for the file pyfai-2025.3.0-cp310-cp310-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pyfai-2025.3.0-cp310-cp310-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 fa0e261c02ba34ea6ba1386d81b68c495ac04451eddaae051fff175fd76e39ed
MD5 de412720118c864592466fe096893c86
BLAKE2b-256 4220f6e080c27366f4a33b16acd349498fce510512b9755999808594dbd31b49

See more details on using hashes here.

File details

Details for the file pyfai-2025.3.0-cp39-cp39-win_amd64.whl.

File metadata

  • Download URL: pyfai-2025.3.0-cp39-cp39-win_amd64.whl
  • Upload date:
  • Size: 5.5 MB
  • Tags: CPython 3.9, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.11.0

File hashes

Hashes for pyfai-2025.3.0-cp39-cp39-win_amd64.whl
Algorithm Hash digest
SHA256 01f158bf57dbf995a4c07a6e466a01b9e527da823103e9cc97a7f5a3dea7440a
MD5 002addcc7c4bb5886aeea3b32fbf9bd8
BLAKE2b-256 8c0a7a399fef01bbad8d5279f94874591ebca079e31efff366e2c2d97fd49c02

See more details on using hashes here.

File details

Details for the file pyfai-2025.3.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pyfai-2025.3.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 5b1aebcc10124b6b116df71d721606ada8f546de5a925a303fe3d28956273c20
MD5 e3e00320850ee4db235bfa2394b19ac0
BLAKE2b-256 d16eb0691a45ea1465204f5d504a5359a60d8ed33ee46004c3d514f13fac0f7b

See more details on using hashes here.

File details

Details for the file pyfai-2025.3.0-cp39-cp39-macosx_11_0_x86_64.whl.

File metadata

File hashes

Hashes for pyfai-2025.3.0-cp39-cp39-macosx_11_0_x86_64.whl
Algorithm Hash digest
SHA256 f03264a7b34730555d77f7308f12a3bb8d35591645918ed102482355576c7a43
MD5 ff986b09208a43159b39b30015dc757b
BLAKE2b-256 688f42b52ab4886445f2b6a155727b2ea0229b4f2ebd0112e4417615e68e518d

See more details on using hashes here.

File details

Details for the file pyfai-2025.3.0-cp39-cp39-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pyfai-2025.3.0-cp39-cp39-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 a2dea0a14f9c27b2279ce7c25626f9b90b03e7a24e2c7434f8b1d7a1eb200a20
MD5 b917d84ddcdbdc3aa419418db776d24c
BLAKE2b-256 a48774074b65c38375c0457713e1bb839d4d5fb459a9d4a88ef4162b7405d5e3

See more details on using hashes here.

File details

Details for the file pyfai-2025.3.0-cp38-cp38-win_amd64.whl.

File metadata

  • Download URL: pyfai-2025.3.0-cp38-cp38-win_amd64.whl
  • Upload date:
  • Size: 5.5 MB
  • Tags: CPython 3.8, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.11.0

File hashes

Hashes for pyfai-2025.3.0-cp38-cp38-win_amd64.whl
Algorithm Hash digest
SHA256 b3e3ecb03695c15c58034e6444bb541d277164c097de671095ce42c89ea0c52c
MD5 7a43e268a8f03b78059906d4f4829a9e
BLAKE2b-256 380b6bc90ff94feace3266d8e892f25cfd314b7e5e65cff6c845b7cc4817888b

See more details on using hashes here.

File details

Details for the file pyfai-2025.3.0-cp38-cp38-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pyfai-2025.3.0-cp38-cp38-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 7a8cdd6c60c32f144319c622c70444a5b542e508811dcca58942ac3580ac4d50
MD5 175700cb58ad9cf097028008cd29819e
BLAKE2b-256 29a1dcbec625084f45253eac5a095e0b591f108b10a7c494cb571e8c782260d2

See more details on using hashes here.

Supported by

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