scipy

Fundamental algorithms for scientific computing in Python

These details have been verified by PyPI

Maintainers

matthew.brett pv rgommers rkern teoliphant treddy

These details have not been verified by PyPI

Project links

Project description

https://img.shields.io/badge/powered%20by-NumFOCUS-orange.svg?style=flat&colorA=E1523D&colorB=007D8A

https://img.shields.io/pypi/dm/scipy.svg?label=Pypi%20downloads

https://img.shields.io/conda/dn/conda-forge/scipy.svg?label=Conda%20downloads

https://img.shields.io/badge/stackoverflow-Ask%20questions-blue.svg

https://img.shields.io/badge/DOI-10.1038%2Fs41592--019--0686--2-blue.svg

SciPy (pronounced “Sigh Pie”) is an open-source software for mathematics, science, and engineering. It includes modules for statistics, optimization, integration, linear algebra, Fourier transforms, signal and image processing, ODE solvers, and more.

Website: https://scipy.org
Documentation: https://docs.scipy.org/doc/scipy/
Development version of the documentation: https://scipy.github.io/devdocs
SciPy development forum: https://discuss.scientific-python.org/c/contributor/scipy
Source code: https://github.com/scipy/scipy
Contributing: https://scipy.github.io/devdocs/dev/index.html
Bug reports: https://github.com/scipy/scipy/issues
Code of Conduct: https://docs.scipy.org/doc/scipy/dev/conduct/code_of_conduct.html
Report a security vulnerability: https://tidelift.com/docs/security
Citing in your work: https://www.scipy.org/citing-scipy/

SciPy is built to work with NumPy arrays, and provides many user-friendly and efficient numerical routines, such as routines for numerical integration and optimization. Together, they run on all popular operating systems, are quick to install, and are free of charge. NumPy and SciPy are easy to use, but powerful enough to be depended upon by some of the world’s leading scientists and engineers. If you need to manipulate numbers on a computer and display or publish the results, give SciPy a try!

For the installation instructions, see our install guide.

Call for Contributions

We appreciate and welcome contributions. Small improvements or fixes are always appreciated; issues labeled as “good first issue” may be a good starting point. Have a look at our contributing guide.

Writing code isn’t the only way to contribute to SciPy. You can also:

review pull requests
triage issues
develop tutorials, presentations, and other educational materials
maintain and improve our website
develop graphic design for our brand assets and promotional materials
help with outreach and onboard new contributors
write grant proposals and help with other fundraising efforts

If you’re unsure where to start or how your skills fit in, reach out! You can ask on the forum or here, on GitHub, by leaving a comment on a relevant issue that is already open.

If you are new to contributing to open source, this guide helps explain why, what, and how to get involved.

Project details

These details have been verified by PyPI

Maintainers

matthew.brett pv rgommers rkern teoliphant treddy

These details have not been verified by PyPI

Project links

Release history Release notifications | RSS feed

This version

1.14.1

Aug 21, 2024

1.14.0

Jun 24, 2024

1.14.0rc2 pre-release

Jun 15, 2024

1.14.0rc1 pre-release yanked

May 30, 2024

Reason this release was yanked:

macOS wheels are broken

1.13.1

May 23, 2024

1.13.0

Apr 2, 2024

1.13.0rc1 pre-release

Mar 19, 2024

1.12.0

Jan 20, 2024

1.12.0rc2 pre-release

Jan 12, 2024

1.12.0rc1 pre-release

Dec 20, 2023

1.11.4

Nov 18, 2023

1.11.3

Sep 27, 2023

1.11.2

Aug 17, 2023

1.11.1

Jun 28, 2023

1.11.0 yanked

Jun 25, 2023

Reason this release was yanked:

License Violation

1.11.0rc2 pre-release

Jun 16, 2023

1.11.0rc1 pre-release

May 31, 2023

1.10.1

Feb 19, 2023

1.10.0

Jan 3, 2023

1.10.0rc2 pre-release

Dec 25, 2022

1.10.0rc1 pre-release

Dec 11, 2022

1.9.3

Oct 20, 2022

1.9.2

Oct 8, 2022

1.9.1

Aug 27, 2022

1.9.0

Jul 29, 2022

1.9.0rc3 pre-release

Jul 23, 2022

1.9.0rc2 pre-release

Jul 5, 2022

1.9.0rc1 pre-release

Jun 24, 2022

1.8.1

May 18, 2022

1.8.0

Feb 5, 2022

1.8.0rc4 pre-release

Jan 30, 2022

1.8.0rc3 pre-release

Jan 22, 2022

1.8.0rc2 pre-release

Dec 21, 2021

1.8.0rc1 pre-release

Dec 11, 2021

1.7.3

Nov 24, 2021

1.7.2

Nov 6, 2021

1.7.1

Aug 2, 2021

1.7.0

Jun 20, 2021

1.6.3

Apr 26, 2021

1.6.2

Mar 25, 2021

1.6.1

Feb 18, 2021

1.6.0

Dec 31, 2020

1.5.4

Nov 5, 2020

1.5.3

Oct 17, 2020

1.5.2

Jul 24, 2020

1.5.1

Jul 4, 2020

1.5.0

Jun 21, 2020

1.4.1

Dec 19, 2019

1.4.0

Dec 16, 2019

1.3.3

Nov 23, 2019

1.3.2

Nov 9, 2019

1.3.1

Aug 9, 2019

1.3.0

May 17, 2019

1.2.3

Jan 21, 2020

1.2.2

Jun 7, 2019

1.2.1

Feb 9, 2019

1.2.0

Dec 18, 2018

1.1.0

May 5, 2018

1.0.1

Mar 24, 2018

1.0.0

Oct 25, 2017

0.19.1

Jun 23, 2017

0.19.0

Mar 9, 2017

0.18.1

Sep 19, 2016

0.18.0

Jul 25, 2016

0.17.1

May 12, 2016

0.17.0

Jan 23, 2016

0.16.1

Oct 24, 2015

0.16.0

Jul 24, 2015

0.15.1

Jan 18, 2015

0.15.0

Jan 11, 2015

0.14.1

Dec 30, 2014

0.14.0

May 3, 2014

0.13.3

Feb 4, 2014

0.13.2

Dec 8, 2013

0.13.1

Nov 17, 2013

0.13.0

Oct 19, 2013

0.12.1

Oct 12, 2013

0.12.0

Apr 9, 2013

0.11.0

Sep 29, 2012

0.10.1

Feb 28, 2012

0.10.0

Nov 13, 2011

0.9.0

Feb 28, 2011

0.8.0

Jul 27, 2010

0.7.2

Apr 22, 2010

0.7.0

Feb 11, 2009

0.6.0

Oct 17, 2007

0.5.2

Dec 8, 2006

0.4.4

Jan 9, 2006

Download files

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

Source Distribution

scipy-1.14.1.tar.gz (58.6 MB view details)

Uploaded Aug 21, 2024 Source

Built Distributions

scipy-1.14.1-cp313-cp313-win_amd64.whl (44.5 MB view details)

Uploaded Aug 21, 2024 CPython 3.13 Windows x86-64

scipy-1.14.1-cp313-cp313-musllinux_1_2_x86_64.whl (42.5 MB view details)

Uploaded Aug 21, 2024 CPython 3.13 musllinux: musl 1.2+ x86-64

scipy-1.14.1-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (40.8 MB view details)

Uploaded Aug 21, 2024 CPython 3.13 manylinux: glibc 2.17+ x86-64

scipy-1.14.1-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (35.3 MB view details)

Uploaded Aug 21, 2024 CPython 3.13 manylinux: glibc 2.17+ ARM64

scipy-1.14.1-cp313-cp313-macosx_14_0_x86_64.whl (25.5 MB view details)

Uploaded Aug 21, 2024 CPython 3.13 macOS 14.0+ x86-64

scipy-1.14.1-cp313-cp313-macosx_14_0_arm64.whl (23.1 MB view details)

Uploaded Aug 21, 2024 CPython 3.13 macOS 14.0+ ARM64

scipy-1.14.1-cp313-cp313-macosx_12_0_arm64.whl (29.9 MB view details)

Uploaded Aug 21, 2024 CPython 3.13 macOS 12.0+ ARM64

scipy-1.14.1-cp313-cp313-macosx_10_13_x86_64.whl (39.1 MB view details)

Uploaded Aug 21, 2024 CPython 3.13 macOS 10.13+ x86-64

scipy-1.14.1-cp312-cp312-win_amd64.whl (44.5 MB view details)

Uploaded Aug 21, 2024 CPython 3.12 Windows x86-64

scipy-1.14.1-cp312-cp312-musllinux_1_2_x86_64.whl (42.5 MB view details)

Uploaded Aug 21, 2024 CPython 3.12 musllinux: musl 1.2+ x86-64

scipy-1.14.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (40.8 MB view details)

Uploaded Aug 21, 2024 CPython 3.12 manylinux: glibc 2.17+ x86-64

scipy-1.14.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (35.3 MB view details)

Uploaded Aug 21, 2024 CPython 3.12 manylinux: glibc 2.17+ ARM64

scipy-1.14.1-cp312-cp312-macosx_14_0_x86_64.whl (25.6 MB view details)

Uploaded Aug 21, 2024 CPython 3.12 macOS 14.0+ x86-64

scipy-1.14.1-cp312-cp312-macosx_14_0_arm64.whl (23.1 MB view details)

Uploaded Aug 21, 2024 CPython 3.12 macOS 14.0+ ARM64

scipy-1.14.1-cp312-cp312-macosx_12_0_arm64.whl (29.9 MB view details)

Uploaded Aug 21, 2024 CPython 3.12 macOS 12.0+ ARM64

scipy-1.14.1-cp312-cp312-macosx_10_13_x86_64.whl (39.1 MB view details)

Uploaded Aug 21, 2024 CPython 3.12 macOS 10.13+ x86-64

scipy-1.14.1-cp311-cp311-win_amd64.whl (44.8 MB view details)

Uploaded Aug 21, 2024 CPython 3.11 Windows x86-64

scipy-1.14.1-cp311-cp311-musllinux_1_2_x86_64.whl (42.8 MB view details)

Uploaded Aug 21, 2024 CPython 3.11 musllinux: musl 1.2+ x86-64

scipy-1.14.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (41.2 MB view details)

Uploaded Aug 21, 2024 CPython 3.11 manylinux: glibc 2.17+ x86-64

scipy-1.14.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (35.6 MB view details)

Uploaded Aug 21, 2024 CPython 3.11 manylinux: glibc 2.17+ ARM64

scipy-1.14.1-cp311-cp311-macosx_14_0_x86_64.whl (25.5 MB view details)

Uploaded Aug 21, 2024 CPython 3.11 macOS 14.0+ x86-64

scipy-1.14.1-cp311-cp311-macosx_14_0_arm64.whl (23.1 MB view details)

Uploaded Aug 21, 2024 CPython 3.11 macOS 14.0+ ARM64

scipy-1.14.1-cp311-cp311-macosx_12_0_arm64.whl (29.9 MB view details)

Uploaded Aug 21, 2024 CPython 3.11 macOS 12.0+ ARM64

scipy-1.14.1-cp311-cp311-macosx_10_13_x86_64.whl (39.1 MB view details)

Uploaded Aug 21, 2024 CPython 3.11 macOS 10.13+ x86-64

scipy-1.14.1-cp310-cp310-win_amd64.whl (44.8 MB view details)

Uploaded Aug 21, 2024 CPython 3.10 Windows x86-64

scipy-1.14.1-cp310-cp310-musllinux_1_2_x86_64.whl (42.8 MB view details)

Uploaded Aug 21, 2024 CPython 3.10 musllinux: musl 1.2+ x86-64

scipy-1.14.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (41.2 MB view details)

Uploaded Aug 21, 2024 CPython 3.10 manylinux: glibc 2.17+ x86-64

scipy-1.14.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (35.6 MB view details)

Uploaded Aug 21, 2024 CPython 3.10 manylinux: glibc 2.17+ ARM64

scipy-1.14.1-cp310-cp310-macosx_14_0_x86_64.whl (25.5 MB view details)

Uploaded Aug 21, 2024 CPython 3.10 macOS 14.0+ x86-64

scipy-1.14.1-cp310-cp310-macosx_14_0_arm64.whl (23.1 MB view details)

Uploaded Aug 21, 2024 CPython 3.10 macOS 14.0+ ARM64

scipy-1.14.1-cp310-cp310-macosx_12_0_arm64.whl (29.9 MB view details)

Uploaded Aug 21, 2024 CPython 3.10 macOS 12.0+ ARM64

scipy-1.14.1-cp310-cp310-macosx_10_13_x86_64.whl (39.1 MB view details)

Uploaded Aug 21, 2024 CPython 3.10 macOS 10.13+ x86-64

File details

Details for the file scipy-1.14.1.tar.gz.

File metadata

Download URL: scipy-1.14.1.tar.gz
Upload date: Aug 21, 2024
Size: 58.6 MB
Tags: Source
Uploaded using Trusted Publishing? No
Uploaded via: twine/5.1.0 CPython/3.10.13

File hashes

Hashes for scipy-1.14.1.tar.gz
Algorithm	Hash digest
SHA256	`5a275584e726026a5699459aa72f828a610821006228e841b94275c4a7c08417`
MD5	`efd579e4617ec0e539ad3d9f46ff9d5d`
BLAKE2b-256	`62114d44a1f274e002784e4dbdb81e0ea96d2de2d1045b2132d5af62cc31fd28`

Algorithm	Hash digest
SHA256	`baff393942b550823bfce952bb62270ee17504d02a1801d7fd0719534dfb9c84`
MD5	`dfc1a685cae137a72cd995051dbeaa75`
BLAKE2b-256	`f51b6ee032251bf4cdb0cc50059374e86a9f076308c1512b61c4e003e241efb7`

Algorithm	Hash digest
SHA256	`e4f5a7c49323533f9103d4dacf4e4f07078f360743dec7f7596949149efeec06`
MD5	`71d3e15ad38ad29fd5b8fd1925f70bc9`
BLAKE2b-256	`32cd9d86f7ed7f4497c9fd3e39f8918dd93d9f647ba80d7e34e4946c0c2d1a7c`

Algorithm	Hash digest
SHA256	`5149e3fd2d686e42144a093b206aef01932a0059c2a33ddfa67f5f035bdfe13e`
MD5	`f27148e0dc97ba0c832a540169d2de7b`
BLAKE2b-256	`56462449e6e51e0d7c3575f289f6acb7f828938eaab8874dbccfeb0cd2b71a27`

Algorithm	Hash digest
SHA256	`b99722ea48b7ea25e8e015e8341ae74624f72e5f21fc2abd45f3a93266de4c5d`
MD5	`522f2349e6cf0fe7722b91a20cf1c299`
BLAKE2b-256	`a72f6c142b352ac15967744d62b165537a965e95d557085db4beab2a11f7943b`

Algorithm	Hash digest
SHA256	`0c2f95de3b04e26f5f3ad5bb05e74ba7f68b837133a4492414b3afd79dfe540e`
MD5	`9b5c4201557171ded6eb2d9febe0f6da`
BLAKE2b-256	`3fd6b028e3f3e59fae61fb8c0f450db732c43dd1d836223a589a8be9f6377203`

Algorithm	Hash digest
SHA256	`e0cf28db0f24a38b2a0ca33a85a54852586e43cf6fd876365c86e0657cfe7d73`
MD5	`be71c62948aaa83006871b714a0fae31`
BLAKE2b-256	`3b2e35f549b7d231c1c9f9639f9ef49b815d816bf54dd050da5da1c11517a218`

Algorithm	Hash digest
SHA256	`4079b90df244709e675cdc8b93bfd8a395d59af40b72e339c2287c91860deb8e`
MD5	`b7d1ae56af30c0bcc614596edcccf5c6`
BLAKE2b-256	`b9cc70948fe9f393b911b4251e96b55bbdeaa8cca41f37c26fd1df0232933b9e`

Algorithm	Hash digest
SHA256	`1729560c906963fc8389f6aac023739ff3983e727b1a4d87696b7bf108316a79`
MD5	`4cb9f8410b4013443fb4a62c7b51b116`
BLAKE2b-256	`50efac98346db016ff18a6ad7626a35808f37074d25796fd0234c2bb0ed1e054`

Algorithm	Hash digest
SHA256	`2ff38e22128e6c03ff73b6bb0f85f897d2362f8c052e3b8ad00532198fbdae3f`
MD5	`84da76b7e8a67108c8b2f5169673a4d6`
BLAKE2b-256	`aa7d43ab67228ef98c6b5dd42ab386eae2d7877036970a0d7e3dd3eb47a0d530`

Algorithm	Hash digest
SHA256	`edaf02b82cd7639db00dbff629995ef185c8df4c3ffa71a5562a595765a06ce1`
MD5	`842ddeba253182525859b768b00049d2`
BLAKE2b-256	`a5cd06f72bc9187840f1c99e1a8750aad4216fc7dfdd7df46e6280add14b4822`

Algorithm	Hash digest
SHA256	`8f9ea80f2e65bdaa0b7627fb00cbeb2daf163caa015e59b7516395fe3bd1e066`
MD5	`ff9953962cd7ce154910282a841e4ba2`
BLAKE2b-256	`8eee8a26858ca517e9c64f84b4c7734b89bda8e63bec85c3d2f432d225bb1886`

Algorithm	Hash digest
SHA256	`30ac8812c1d2aab7131a79ba62933a2a76f582d5dbbc695192453dae67ad6310`
MD5	`53c7be5fd8e2baa5011ce7a97bd18f8d`
BLAKE2b-256	`f05aefa92a58dc3a2898705f1dc9dbaf390ca7d4fba26d6ab8cfffb0c72f656f`

Algorithm	Hash digest
SHA256	`eb58ca0abd96911932f688528977858681a59d61a7ce908ffd355957f7025cfc`
MD5	`bd8811fe39ea9af08f282758f72cbd1b`
BLAKE2b-256	`f6323a6dedd51d68eb7b8e7dc7947d5d841bcb699f1bf4463639554986f4d782`

Algorithm	Hash digest
SHA256	`2843f2d527d9eebec9a43e6b406fb7266f3af25a751aa91d62ff416f54170bc5`
MD5	`4897fbe2e5f3420f13d636881a02f5c7`
BLAKE2b-256	`66676ef192e0e4d77b20cc33a01e743b00bc9e68fb83b88e06e636d2619a8767`

Algorithm	Hash digest
SHA256	`af29a935803cc707ab2ed7791c44288a682f9c8107bc00f0eccc4f92c08d6e07`
MD5	`e443e2e8345f81e660fc866de18aa8d4`
BLAKE2b-256	`c85335b4d41f5fd42f5781dbd0dd6c05d35ba8aa75c84ecddc7d44756cd8da2e`

Algorithm	Hash digest
SHA256	`631f07b3734d34aced009aaf6fedfd0eb3498a97e581c3b1e5f14a04164a456d`
MD5	`abb6b8d2471dc5f3f78e8a46817aa10f`
BLAKE2b-256	`c0042bdacc8ac6387b15db6faa40295f8bd25eccf33f1f13e68a72dc3c60a99e`

Algorithm	Hash digest
SHA256	`716e389b694c4bb564b4fc0c51bc84d381735e0d39d3f26ec1af2556ec6aad94`
MD5	`e600f6eda5d772cb3b23859e1419670f`
BLAKE2b-256	`eac25ecadc5fcccefaece775feadcd795060adf5c3b29a883bff0e678cfe89af`

Algorithm	Hash digest
SHA256	`b05d43735bb2f07d689f56f7b474788a13ed8adc484a85aa65c0fd931cf9ccd2`
MD5	`1bb99c4029d85dfba7d08a47b6a0bf23`
BLAKE2b-256	`0657e6aa6f55729a8f245d8a6984f2855696c5992113a5dc789065020f8be753`

Algorithm	Hash digest
SHA256	`fef8c87f8abfb884dac04e97824b61299880c43f4ce675dd2cbeadd3c9b466d2`
MD5	`c15d2f7f2a791020270a881162d84741`
BLAKE2b-256	`936b701776d4bd6bdd9b629c387b5140f006185bd8ddea16788a44434376b98f`

Algorithm	Hash digest
SHA256	`278266012eb69f4a720827bdd2dc54b2271c97d84255b2faaa8f161a158c3b37`
MD5	`384b402a8d6f2dceb6fdf1b8e915cdb9`
BLAKE2b-256	`c24bb44bee3c2ddc316b0159b3d87a3d467ef8d7edfd525e6f7364a62cd87d90`

scipy 1.14.1

Navigation

Verified details

Maintainers

Unverified details

Project links

Meta

Classifiers

Project description

Call for Contributions

Project details

Verified details

Maintainers

Unverified details

Project links

Meta

Classifiers

Release history Release notifications | RSS feed

Download files

Source Distribution

Built Distributions

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

Algorithm	Hash digest
SHA256	`8475230e55549ab3f207bff11ebfc91c805dc3463ef62eda3ccf593254524ce8`
MD5	`6812bbd20fb191f2a95f32d18c334da4`
BLAKE2b-256	`5efc9f1413bef53171f379d786aabc104d4abeea48ee84c553a3e3d8c9f96a9c`

Algorithm	Hash digest
SHA256	`3a1b111fac6baec1c1d92f27e76511c9e7218f1695d61b59e05e0fe04dc59617`
MD5	`46b43e6cfe87702476ef173265230768`
BLAKE2b-256	`ed057f03e680cc5249c4f96c9e4e845acde08eb1aee5bc216eff8a089baa4ddb`

Algorithm	Hash digest
SHA256	`c0ee987efa6737242745f347835da2cc5bb9f1b42996a4d97d5c7ff7928cb6f2`
MD5	`3c0b2e69a7cab6249d3b2a5edaf52265`
BLAKE2b-256	`a7c502ac82f9bb8f70818099df7e86c3ad28dae64e1347b421d8e3adf26acab6`

Algorithm	Hash digest
SHA256	`2da0469a4ef0ecd3693761acbdc20f2fdeafb69e6819cc081308cc978153c675`
MD5	`8f30c48b36be83c33163dc06b587f24e`
BLAKE2b-256	`b2ab070ccfabe870d9f105b04aee1e2860520460ef7ca0213172abfe871463b9`

Algorithm	Hash digest
SHA256	`a49f6ed96f83966f576b33a44257d869756df6cf1ef4934f59dd58b25e0327e5`
MD5	`c4720b120cc3131d4e37d65a4ce01d29`
BLAKE2b-256	`e71c8daa6df17a945cb1a2a1e3bae3c49643f7b3b94017ff01a4787064f03f84`

Algorithm	Hash digest
SHA256	`8426251ad1e4ad903a4514712d2fa8fdd5382c978010d1c6f5f37ef286a713ad`
MD5	`b6a6987953c6289842b85b6a0e56047c`
BLAKE2b-256	`5daa994b45c34b897637b853ec04334afa55a85650a0d11dacfa67232260fb0a`

Algorithm	Hash digest
SHA256	`8e32dced201274bf96899e6491d9ba3e9a5f6b336708656466ad0522d8528f69`
MD5	`5381efcc3612359c4c6fbd4bec065f27`
BLAKE2b-256	`4778b0c2c23880dd1e99e938ad49ccfb011ae353758a2dc5ed7ee59baff684c3`

Algorithm	Hash digest
SHA256	`2ff0a7e01e422c15739ecd64432743cf7aae2b03f3084288f399affcefe5222d`
MD5	`4a7995dd43c6989a6f4cb1975c2514f0`
BLAKE2b-256	`d8dfcdb6be5274bc694c4c22862ac3438cb04f360ed9df0aecee02ce0b798380`

Algorithm	Hash digest
SHA256	`97c5dddd5932bd2a1a31c927ba5e1463a53b87ca96b5c9bdf5dfd6096e27efc3`
MD5	`5213b68a147735114a479307ca8f74dd`
BLAKE2b-256	`1547298ab6fef5ebf31b426560e978b8b8548421d4ed0bf99263e1eb44532306`

Algorithm	Hash digest
SHA256	`8bddf15838ba768bb5f5083c1ea012d64c9a444e16192762bd858f1e126196d0`
MD5	`667a13586320484898cc2f69cf84ca05`
BLAKE2b-256	`07420e0bea9666fcbf2cb6ea0205db42c81b1f34d7b729ba251010edf9c80ebd`