%global _empty_manifest_terminate_build 0 Name: python-mmh3 Version: 3.1.0 Release: 1 Summary: Python wrapper for MurmurHash (MurmurHash3), a set of fast and robust hash functions. License: License :: CC0 1.0 Universal (CC0 1.0) Public Domain Dedication URL: https://github.com/hajimes/mmh3 Source0: https://mirrors.nju.edu.cn/pypi/web/packages/73/65/33dce4b13a77ed6aeb1f41994240cc4d3c49fb79b3acdac9a502ae6e254d/mmh3-3.1.0.tar.gz %description # mmh3 [![GitHub Super-Linter](https://github.com/hajimes/mmh3/workflows/Super-Linter/badge.svg?branch=master)](https://github.com/hajimes/mmh3/actions?query=workflow%3ASuper-Linter+branch%3Amaster) [![Build](https://github.com/hajimes/mmh3/actions/workflows/build.yml/badge.svg?branch=master)](https://github.com/hajimes/mmh3/actions/workflows/build.yml?branch=master) [![PyPi Version](https://img.shields.io/pypi/v/mmh3.svg?style=flat-square&logo=pypi&logoColor=white)](https://pypi.org/project/mmh3/) [![Python Versions](https://img.shields.io/pypi/pyversions/mmh3.svg)](https://pypi.org/project/mmh3/) [![License: CC0-1.0](https://img.shields.io/badge/License-CC0%201.0-lightgrey.svg)](http://creativecommons.org/publicdomain/zero/1.0/) [![Total Downloads](https://pepy.tech/badge/mmh3)](https://pepy.tech/project/mmh3) [![Recent Downloads](https://pepy.tech/badge/mmh3/month)](https://pepy.tech/project/mmh3) [![Conda Version](https://img.shields.io/conda/vn/conda-forge/mmh3.svg?style=flat-square&logo=conda-forge&logoColor=white)](https://anaconda.org/conda-forge/mmh3) mmh3 is a Python wrapper for [MurmurHash (MurmurHash3)](https://en.wikipedia.org/wiki/MurmurHash), a set of fast and robust non-cryptographic hash functions invented by Austin Appleby. Combined with probabilistic techniques like a [Bloom filter](https://en.wikipedia.org/wiki/Bloom_filter), [MinHash](https://en.wikipedia.org/wiki/MinHash), and [feature hashing](https://en.wikipedia.org/wiki/Feature_hashing), mmh3 allows you to develop high-performance systems in fields such as data mining, machine learning, and natural language processing. Another common use of mmh3 is to [calculate favicon hashes](https://gist.github.com/yehgdotnet/b9dfc618108d2f05845c4d8e28c5fc6a) used by [Shodan](https://www.shodan.io), the world's first IoT search engine. ## How to use Install: ```shell pip install mmh3 # for macOS, use "pip3 install mmh3" and python3 ``` Quickstart: ```shell >>> import mmh3 >>> mmh3.hash("foo") # returns a 32-bit signed int -156908512 >>> mmh3.hash("foo", 42) # uses 42 as a seed -1322301282 >>> mmh3.hash("foo", signed=False) # returns a 32-bit unsigned int 4138058784 ``` Other functions: ```shell >>> mmh3.hash64("foo") # two 64 bit signed ints (by using the 128-bit algorithm as its backend) (-2129773440516405919, 9128664383759220103) >>> mmh3.hash64("foo", signed=False) # two 64 bit unsigned ints (16316970633193145697, 9128664383759220103) >>> mmh3.hash128("foo", 42) # 128 bit unsigned int 215966891540331383248189432718888555506 >>> mmh3.hash128("foo", 42, signed=True) # 128 bit signed int -124315475380607080215185174712879655950 >>> mmh3.hash_bytes("foo") # 128 bit value as bytes 'aE\xf5\x01W\x86q\xe2\x87}\xba+\xe4\x87\xaf~' >>> import numpy as np >>> a = np.zeros(2 ** 32, dtype=np.int8) >>> mmh3.hash_bytes(a) b'V\x8f}\xad\x8eNM\xa84\x07FU\x9c\xc4\xcc\x8e' ``` Beware that `hash64` returns **two** values, because it uses the 128-bit version of MurmurHash3 as its backend. `hash_from_buffer` hashes byte-likes without memory copying. The method is suitable when you hash a large memory-view such as `numpy.ndarray`. ```shell >>> mmh3.hash_from_buffer(numpy.random.rand(100)) -2137204694 >>> mmh3.hash_from_buffer(numpy.random.rand(100), signed=False) 3812874078 ``` `hash64`, `hash128`, and `hash_bytes` have the third argument for architecture optimization. Use True for x64 and False for x86 (default: True): ```shell >>> mmh3.hash64("foo", 42, True) (-840311307571801102, -6739155424061121879) ``` ## Changelog ### 3.1.0 (2023-03-24) * Add support for Python 3.10 and 3.11. Thanks [wouter bolsterlee](https://github.com/wbolster) and [Dušan Nikolić](https://github.com/n-dusan)! * Drop support for Python 3.6; remove legacy code for Python 2.x at the source code level. * Add support for 32-bit architectures such as `i686` and `armv7l`. From now on, `hash` and `hash_from_buffer` on these architectures will generate the same hash values as those on other environments. Thanks [Danil Shein](https://github.com/dshein-alt)! * In relation to the above, `manylinux2014_i686` wheels are now available. * Support for hashing huge data (>16GB). Thanks [arieleizenberg](https://github.com/arieleizenberg)! ### 3.0.0 (2021-02-23) * Python wheels are now available, thanks to the power of [cibuildwheel](https://github.com/joerick/cibuildwheel). * Supported platforms are `manylinux1_x86_64`, `manylinux2010_x86_64`, `manylinux2014_aarch64`, `win32`, `win_amd64`, `macosx_10_9_x86_64`, and `macosx_11_0_arm64` (Apple Silicon). * Add support for newer macOS environments. Thanks [Matthew Honnibal](https://github.com/honnibal)! * Drop support for Python 2.7, 3.3, 3.4, and 3.5. * Add support for Python 3.7, 3.8, and 3.9. * Migrate Travis CI and AppVeyor to GitHub Actions. ### 2.5.1 (2017-10-31) * Bugfix for `hash_bytes`. Thanks [doozr](https://github.com/doozr)! See [CHANGELOG.md](./CHANGELOG.md) for the complete changelog. ## License [CC0-1.0](./LICENSE). ## Known Issues ### Getting different results from other MurmurHash3-based libraries By default, mmh3 returns **signed** values for 32-bit and 64-bit versions and **unsigned** values for `hash128`, due to historical reasons. Please use the keyword argument `signed` to obtain a desired result. For compatibility with Google Guava (Java), see ### Unexpected results when given non 32-bit seeds Version 2.4 changed the type of seeds from signed 32-bit int to unsigned 32-bit int. The resulting values with signed seeds still remain the same as before, as long as they are 32-bit. ```shell >>> mmh3.hash("aaaa", -1756908916) # signed representation for 0x9747b28c 1519878282 >>> mmh3.hash("aaaa", 2538058380) # unsigned representation for 0x9747b28c 1519878282 ``` Be careful so that these seeds do not exceed 32-bit. Unexpected results may happen with invalid values. ```shell >>> mmh3.hash("foo", 2 ** 33) -156908512 >>> mmh3.hash("foo", 2 ** 34) -156908512 ``` ## Authors MurmurHash3 was originally developed by Austin Appleby and distributed under public domain. * Ported and modified for Python by Hajime Senuma. * * ## See also ### Tutorials (High-Performance Computing) The following textbooks and tutorials are great sources to learn how to use mmh3 (and other hash algorithms in general) for high-performance computing. * Chapter 11: *Using Less Ram* in Micha Gorelick and Ian Ozsvald. 2014. *High Performance Python: Practical Performant Programming for Humans*. O'Reilly Media. [ISBN: 978-1-4493-6159-4](https://www.amazon.com/dp/1449361595). * 2nd edition of the above (2020). [ISBN: 978-1492055020](https://www.amazon.com/dp/1492055026). * Max Burstein. February 2, 2013. *[Creating a Simple Bloom Filter](http://www.maxburstein.com/blog/creating-a-simple-bloom-filter/)*. * Duke University. April 14, 2016. *[Efficient storage of data in memory](http://people.duke.edu/~ccc14/sta-663-2016/20B_Big_Data_Structures.html)*. * Bugra Akyildiz. August 24, 2016. *[A Gentle Introduction to Bloom Filter](https://www.kdnuggets.com/2016/08/gentle-introduction-bloom-filter.html)*. KDnuggets. ### Tutorials (Internet of Things) [Shodan](https://www.shodan.io), the world's first [IoT](https://en.wikipedia.org/wiki/Internet_of_things) search engine, uses MurmurHash3 hash values for [favicons](https://en.wikipedia.org/wiki/Favicon) (icons associated with web pages). [ZoomEye](https://www.zoomeye.org) follows Shodan's convention. [Calculating these values with mmh3](https://gist.github.com/yehgdotnet/b9dfc618108d2f05845c4d8e28c5fc6a) is useful for OSINT and cybersecurity activities. * Jan Kopriva. April 19, 2021. *[Hunting phishing websites with favicon hashes](https://isc.sans.edu/diary/Hunting+phishing+websites+with+favicon+hashes/27326)*. SANS Internet Storm Center. * Nikhil Panwar. May 2, 2022. *[Using Favicons to Discover Phishing & Brand Impersonation Websites](https://bolster.ai/blog/how-to-use-favicons-to-find-phishing-websites)*. Bolster. * Faradaysec. July 25, 2022. *[Understanding Spring4Shell: How used is it?](https://faradaysec.com/understanding-spring4shell/)*. Faraday Security. * Debjeet. August 2, 2022. *[How To Find Assets Using Favicon Hashes](https://payatu.com/blog/favicon-hash/)*. Payatu. ### Similar libraries * : mmh3 in pure python (Fredrik Kihlander and Swapnil Gusani) * : Python bindings for CityHash (Eugene Scherba) * : Python bindigs for FarmHash (Veelion Chong) * : Python bindings for MetroHash (Eugene Scherba) * : Python bindings for xxHash (Yue Du) %package -n python3-mmh3 Summary: Python wrapper for MurmurHash (MurmurHash3), a set of fast and robust hash functions. Provides: python-mmh3 BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip BuildRequires: python3-cffi BuildRequires: gcc BuildRequires: gdb %description -n python3-mmh3 # mmh3 [![GitHub Super-Linter](https://github.com/hajimes/mmh3/workflows/Super-Linter/badge.svg?branch=master)](https://github.com/hajimes/mmh3/actions?query=workflow%3ASuper-Linter+branch%3Amaster) [![Build](https://github.com/hajimes/mmh3/actions/workflows/build.yml/badge.svg?branch=master)](https://github.com/hajimes/mmh3/actions/workflows/build.yml?branch=master) [![PyPi Version](https://img.shields.io/pypi/v/mmh3.svg?style=flat-square&logo=pypi&logoColor=white)](https://pypi.org/project/mmh3/) [![Python Versions](https://img.shields.io/pypi/pyversions/mmh3.svg)](https://pypi.org/project/mmh3/) [![License: CC0-1.0](https://img.shields.io/badge/License-CC0%201.0-lightgrey.svg)](http://creativecommons.org/publicdomain/zero/1.0/) [![Total Downloads](https://pepy.tech/badge/mmh3)](https://pepy.tech/project/mmh3) [![Recent Downloads](https://pepy.tech/badge/mmh3/month)](https://pepy.tech/project/mmh3) [![Conda Version](https://img.shields.io/conda/vn/conda-forge/mmh3.svg?style=flat-square&logo=conda-forge&logoColor=white)](https://anaconda.org/conda-forge/mmh3) mmh3 is a Python wrapper for [MurmurHash (MurmurHash3)](https://en.wikipedia.org/wiki/MurmurHash), a set of fast and robust non-cryptographic hash functions invented by Austin Appleby. Combined with probabilistic techniques like a [Bloom filter](https://en.wikipedia.org/wiki/Bloom_filter), [MinHash](https://en.wikipedia.org/wiki/MinHash), and [feature hashing](https://en.wikipedia.org/wiki/Feature_hashing), mmh3 allows you to develop high-performance systems in fields such as data mining, machine learning, and natural language processing. Another common use of mmh3 is to [calculate favicon hashes](https://gist.github.com/yehgdotnet/b9dfc618108d2f05845c4d8e28c5fc6a) used by [Shodan](https://www.shodan.io), the world's first IoT search engine. ## How to use Install: ```shell pip install mmh3 # for macOS, use "pip3 install mmh3" and python3 ``` Quickstart: ```shell >>> import mmh3 >>> mmh3.hash("foo") # returns a 32-bit signed int -156908512 >>> mmh3.hash("foo", 42) # uses 42 as a seed -1322301282 >>> mmh3.hash("foo", signed=False) # returns a 32-bit unsigned int 4138058784 ``` Other functions: ```shell >>> mmh3.hash64("foo") # two 64 bit signed ints (by using the 128-bit algorithm as its backend) (-2129773440516405919, 9128664383759220103) >>> mmh3.hash64("foo", signed=False) # two 64 bit unsigned ints (16316970633193145697, 9128664383759220103) >>> mmh3.hash128("foo", 42) # 128 bit unsigned int 215966891540331383248189432718888555506 >>> mmh3.hash128("foo", 42, signed=True) # 128 bit signed int -124315475380607080215185174712879655950 >>> mmh3.hash_bytes("foo") # 128 bit value as bytes 'aE\xf5\x01W\x86q\xe2\x87}\xba+\xe4\x87\xaf~' >>> import numpy as np >>> a = np.zeros(2 ** 32, dtype=np.int8) >>> mmh3.hash_bytes(a) b'V\x8f}\xad\x8eNM\xa84\x07FU\x9c\xc4\xcc\x8e' ``` Beware that `hash64` returns **two** values, because it uses the 128-bit version of MurmurHash3 as its backend. `hash_from_buffer` hashes byte-likes without memory copying. The method is suitable when you hash a large memory-view such as `numpy.ndarray`. ```shell >>> mmh3.hash_from_buffer(numpy.random.rand(100)) -2137204694 >>> mmh3.hash_from_buffer(numpy.random.rand(100), signed=False) 3812874078 ``` `hash64`, `hash128`, and `hash_bytes` have the third argument for architecture optimization. Use True for x64 and False for x86 (default: True): ```shell >>> mmh3.hash64("foo", 42, True) (-840311307571801102, -6739155424061121879) ``` ## Changelog ### 3.1.0 (2023-03-24) * Add support for Python 3.10 and 3.11. Thanks [wouter bolsterlee](https://github.com/wbolster) and [Dušan Nikolić](https://github.com/n-dusan)! * Drop support for Python 3.6; remove legacy code for Python 2.x at the source code level. * Add support for 32-bit architectures such as `i686` and `armv7l`. From now on, `hash` and `hash_from_buffer` on these architectures will generate the same hash values as those on other environments. Thanks [Danil Shein](https://github.com/dshein-alt)! * In relation to the above, `manylinux2014_i686` wheels are now available. * Support for hashing huge data (>16GB). Thanks [arieleizenberg](https://github.com/arieleizenberg)! ### 3.0.0 (2021-02-23) * Python wheels are now available, thanks to the power of [cibuildwheel](https://github.com/joerick/cibuildwheel). * Supported platforms are `manylinux1_x86_64`, `manylinux2010_x86_64`, `manylinux2014_aarch64`, `win32`, `win_amd64`, `macosx_10_9_x86_64`, and `macosx_11_0_arm64` (Apple Silicon). * Add support for newer macOS environments. Thanks [Matthew Honnibal](https://github.com/honnibal)! * Drop support for Python 2.7, 3.3, 3.4, and 3.5. * Add support for Python 3.7, 3.8, and 3.9. * Migrate Travis CI and AppVeyor to GitHub Actions. ### 2.5.1 (2017-10-31) * Bugfix for `hash_bytes`. Thanks [doozr](https://github.com/doozr)! See [CHANGELOG.md](./CHANGELOG.md) for the complete changelog. ## License [CC0-1.0](./LICENSE). ## Known Issues ### Getting different results from other MurmurHash3-based libraries By default, mmh3 returns **signed** values for 32-bit and 64-bit versions and **unsigned** values for `hash128`, due to historical reasons. Please use the keyword argument `signed` to obtain a desired result. For compatibility with Google Guava (Java), see ### Unexpected results when given non 32-bit seeds Version 2.4 changed the type of seeds from signed 32-bit int to unsigned 32-bit int. The resulting values with signed seeds still remain the same as before, as long as they are 32-bit. ```shell >>> mmh3.hash("aaaa", -1756908916) # signed representation for 0x9747b28c 1519878282 >>> mmh3.hash("aaaa", 2538058380) # unsigned representation for 0x9747b28c 1519878282 ``` Be careful so that these seeds do not exceed 32-bit. Unexpected results may happen with invalid values. ```shell >>> mmh3.hash("foo", 2 ** 33) -156908512 >>> mmh3.hash("foo", 2 ** 34) -156908512 ``` ## Authors MurmurHash3 was originally developed by Austin Appleby and distributed under public domain. * Ported and modified for Python by Hajime Senuma. * * ## See also ### Tutorials (High-Performance Computing) The following textbooks and tutorials are great sources to learn how to use mmh3 (and other hash algorithms in general) for high-performance computing. * Chapter 11: *Using Less Ram* in Micha Gorelick and Ian Ozsvald. 2014. *High Performance Python: Practical Performant Programming for Humans*. O'Reilly Media. [ISBN: 978-1-4493-6159-4](https://www.amazon.com/dp/1449361595). * 2nd edition of the above (2020). [ISBN: 978-1492055020](https://www.amazon.com/dp/1492055026). * Max Burstein. February 2, 2013. *[Creating a Simple Bloom Filter](http://www.maxburstein.com/blog/creating-a-simple-bloom-filter/)*. * Duke University. April 14, 2016. *[Efficient storage of data in memory](http://people.duke.edu/~ccc14/sta-663-2016/20B_Big_Data_Structures.html)*. * Bugra Akyildiz. August 24, 2016. *[A Gentle Introduction to Bloom Filter](https://www.kdnuggets.com/2016/08/gentle-introduction-bloom-filter.html)*. KDnuggets. ### Tutorials (Internet of Things) [Shodan](https://www.shodan.io), the world's first [IoT](https://en.wikipedia.org/wiki/Internet_of_things) search engine, uses MurmurHash3 hash values for [favicons](https://en.wikipedia.org/wiki/Favicon) (icons associated with web pages). [ZoomEye](https://www.zoomeye.org) follows Shodan's convention. [Calculating these values with mmh3](https://gist.github.com/yehgdotnet/b9dfc618108d2f05845c4d8e28c5fc6a) is useful for OSINT and cybersecurity activities. * Jan Kopriva. April 19, 2021. *[Hunting phishing websites with favicon hashes](https://isc.sans.edu/diary/Hunting+phishing+websites+with+favicon+hashes/27326)*. SANS Internet Storm Center. * Nikhil Panwar. May 2, 2022. *[Using Favicons to Discover Phishing & Brand Impersonation Websites](https://bolster.ai/blog/how-to-use-favicons-to-find-phishing-websites)*. Bolster. * Faradaysec. July 25, 2022. *[Understanding Spring4Shell: How used is it?](https://faradaysec.com/understanding-spring4shell/)*. Faraday Security. * Debjeet. August 2, 2022. *[How To Find Assets Using Favicon Hashes](https://payatu.com/blog/favicon-hash/)*. Payatu. ### Similar libraries * : mmh3 in pure python (Fredrik Kihlander and Swapnil Gusani) * : Python bindings for CityHash (Eugene Scherba) * : Python bindigs for FarmHash (Veelion Chong) * : Python bindings for MetroHash (Eugene Scherba) * : Python bindings for xxHash (Yue Du) %package help Summary: Development documents and examples for mmh3 Provides: python3-mmh3-doc %description help # mmh3 [![GitHub Super-Linter](https://github.com/hajimes/mmh3/workflows/Super-Linter/badge.svg?branch=master)](https://github.com/hajimes/mmh3/actions?query=workflow%3ASuper-Linter+branch%3Amaster) [![Build](https://github.com/hajimes/mmh3/actions/workflows/build.yml/badge.svg?branch=master)](https://github.com/hajimes/mmh3/actions/workflows/build.yml?branch=master) [![PyPi Version](https://img.shields.io/pypi/v/mmh3.svg?style=flat-square&logo=pypi&logoColor=white)](https://pypi.org/project/mmh3/) [![Python Versions](https://img.shields.io/pypi/pyversions/mmh3.svg)](https://pypi.org/project/mmh3/) [![License: CC0-1.0](https://img.shields.io/badge/License-CC0%201.0-lightgrey.svg)](http://creativecommons.org/publicdomain/zero/1.0/) [![Total Downloads](https://pepy.tech/badge/mmh3)](https://pepy.tech/project/mmh3) [![Recent Downloads](https://pepy.tech/badge/mmh3/month)](https://pepy.tech/project/mmh3) [![Conda Version](https://img.shields.io/conda/vn/conda-forge/mmh3.svg?style=flat-square&logo=conda-forge&logoColor=white)](https://anaconda.org/conda-forge/mmh3) mmh3 is a Python wrapper for [MurmurHash (MurmurHash3)](https://en.wikipedia.org/wiki/MurmurHash), a set of fast and robust non-cryptographic hash functions invented by Austin Appleby. Combined with probabilistic techniques like a [Bloom filter](https://en.wikipedia.org/wiki/Bloom_filter), [MinHash](https://en.wikipedia.org/wiki/MinHash), and [feature hashing](https://en.wikipedia.org/wiki/Feature_hashing), mmh3 allows you to develop high-performance systems in fields such as data mining, machine learning, and natural language processing. Another common use of mmh3 is to [calculate favicon hashes](https://gist.github.com/yehgdotnet/b9dfc618108d2f05845c4d8e28c5fc6a) used by [Shodan](https://www.shodan.io), the world's first IoT search engine. ## How to use Install: ```shell pip install mmh3 # for macOS, use "pip3 install mmh3" and python3 ``` Quickstart: ```shell >>> import mmh3 >>> mmh3.hash("foo") # returns a 32-bit signed int -156908512 >>> mmh3.hash("foo", 42) # uses 42 as a seed -1322301282 >>> mmh3.hash("foo", signed=False) # returns a 32-bit unsigned int 4138058784 ``` Other functions: ```shell >>> mmh3.hash64("foo") # two 64 bit signed ints (by using the 128-bit algorithm as its backend) (-2129773440516405919, 9128664383759220103) >>> mmh3.hash64("foo", signed=False) # two 64 bit unsigned ints (16316970633193145697, 9128664383759220103) >>> mmh3.hash128("foo", 42) # 128 bit unsigned int 215966891540331383248189432718888555506 >>> mmh3.hash128("foo", 42, signed=True) # 128 bit signed int -124315475380607080215185174712879655950 >>> mmh3.hash_bytes("foo") # 128 bit value as bytes 'aE\xf5\x01W\x86q\xe2\x87}\xba+\xe4\x87\xaf~' >>> import numpy as np >>> a = np.zeros(2 ** 32, dtype=np.int8) >>> mmh3.hash_bytes(a) b'V\x8f}\xad\x8eNM\xa84\x07FU\x9c\xc4\xcc\x8e' ``` Beware that `hash64` returns **two** values, because it uses the 128-bit version of MurmurHash3 as its backend. `hash_from_buffer` hashes byte-likes without memory copying. The method is suitable when you hash a large memory-view such as `numpy.ndarray`. ```shell >>> mmh3.hash_from_buffer(numpy.random.rand(100)) -2137204694 >>> mmh3.hash_from_buffer(numpy.random.rand(100), signed=False) 3812874078 ``` `hash64`, `hash128`, and `hash_bytes` have the third argument for architecture optimization. Use True for x64 and False for x86 (default: True): ```shell >>> mmh3.hash64("foo", 42, True) (-840311307571801102, -6739155424061121879) ``` ## Changelog ### 3.1.0 (2023-03-24) * Add support for Python 3.10 and 3.11. Thanks [wouter bolsterlee](https://github.com/wbolster) and [Dušan Nikolić](https://github.com/n-dusan)! * Drop support for Python 3.6; remove legacy code for Python 2.x at the source code level. * Add support for 32-bit architectures such as `i686` and `armv7l`. From now on, `hash` and `hash_from_buffer` on these architectures will generate the same hash values as those on other environments. Thanks [Danil Shein](https://github.com/dshein-alt)! * In relation to the above, `manylinux2014_i686` wheels are now available. * Support for hashing huge data (>16GB). Thanks [arieleizenberg](https://github.com/arieleizenberg)! ### 3.0.0 (2021-02-23) * Python wheels are now available, thanks to the power of [cibuildwheel](https://github.com/joerick/cibuildwheel). * Supported platforms are `manylinux1_x86_64`, `manylinux2010_x86_64`, `manylinux2014_aarch64`, `win32`, `win_amd64`, `macosx_10_9_x86_64`, and `macosx_11_0_arm64` (Apple Silicon). * Add support for newer macOS environments. Thanks [Matthew Honnibal](https://github.com/honnibal)! * Drop support for Python 2.7, 3.3, 3.4, and 3.5. * Add support for Python 3.7, 3.8, and 3.9. * Migrate Travis CI and AppVeyor to GitHub Actions. ### 2.5.1 (2017-10-31) * Bugfix for `hash_bytes`. Thanks [doozr](https://github.com/doozr)! See [CHANGELOG.md](./CHANGELOG.md) for the complete changelog. ## License [CC0-1.0](./LICENSE). ## Known Issues ### Getting different results from other MurmurHash3-based libraries By default, mmh3 returns **signed** values for 32-bit and 64-bit versions and **unsigned** values for `hash128`, due to historical reasons. Please use the keyword argument `signed` to obtain a desired result. For compatibility with Google Guava (Java), see ### Unexpected results when given non 32-bit seeds Version 2.4 changed the type of seeds from signed 32-bit int to unsigned 32-bit int. The resulting values with signed seeds still remain the same as before, as long as they are 32-bit. ```shell >>> mmh3.hash("aaaa", -1756908916) # signed representation for 0x9747b28c 1519878282 >>> mmh3.hash("aaaa", 2538058380) # unsigned representation for 0x9747b28c 1519878282 ``` Be careful so that these seeds do not exceed 32-bit. Unexpected results may happen with invalid values. ```shell >>> mmh3.hash("foo", 2 ** 33) -156908512 >>> mmh3.hash("foo", 2 ** 34) -156908512 ``` ## Authors MurmurHash3 was originally developed by Austin Appleby and distributed under public domain. * Ported and modified for Python by Hajime Senuma. * * ## See also ### Tutorials (High-Performance Computing) The following textbooks and tutorials are great sources to learn how to use mmh3 (and other hash algorithms in general) for high-performance computing. * Chapter 11: *Using Less Ram* in Micha Gorelick and Ian Ozsvald. 2014. *High Performance Python: Practical Performant Programming for Humans*. O'Reilly Media. [ISBN: 978-1-4493-6159-4](https://www.amazon.com/dp/1449361595). * 2nd edition of the above (2020). [ISBN: 978-1492055020](https://www.amazon.com/dp/1492055026). * Max Burstein. February 2, 2013. *[Creating a Simple Bloom Filter](http://www.maxburstein.com/blog/creating-a-simple-bloom-filter/)*. * Duke University. April 14, 2016. *[Efficient storage of data in memory](http://people.duke.edu/~ccc14/sta-663-2016/20B_Big_Data_Structures.html)*. * Bugra Akyildiz. August 24, 2016. *[A Gentle Introduction to Bloom Filter](https://www.kdnuggets.com/2016/08/gentle-introduction-bloom-filter.html)*. KDnuggets. ### Tutorials (Internet of Things) [Shodan](https://www.shodan.io), the world's first [IoT](https://en.wikipedia.org/wiki/Internet_of_things) search engine, uses MurmurHash3 hash values for [favicons](https://en.wikipedia.org/wiki/Favicon) (icons associated with web pages). [ZoomEye](https://www.zoomeye.org) follows Shodan's convention. [Calculating these values with mmh3](https://gist.github.com/yehgdotnet/b9dfc618108d2f05845c4d8e28c5fc6a) is useful for OSINT and cybersecurity activities. * Jan Kopriva. April 19, 2021. *[Hunting phishing websites with favicon hashes](https://isc.sans.edu/diary/Hunting+phishing+websites+with+favicon+hashes/27326)*. SANS Internet Storm Center. * Nikhil Panwar. May 2, 2022. *[Using Favicons to Discover Phishing & Brand Impersonation Websites](https://bolster.ai/blog/how-to-use-favicons-to-find-phishing-websites)*. Bolster. * Faradaysec. July 25, 2022. *[Understanding Spring4Shell: How used is it?](https://faradaysec.com/understanding-spring4shell/)*. Faraday Security. * Debjeet. August 2, 2022. *[How To Find Assets Using Favicon Hashes](https://payatu.com/blog/favicon-hash/)*. Payatu. ### Similar libraries * : mmh3 in pure python (Fredrik Kihlander and Swapnil Gusani) * : Python bindings for CityHash (Eugene Scherba) * : Python bindigs for FarmHash (Veelion Chong) * : Python bindings for MetroHash (Eugene Scherba) * : Python bindings for xxHash (Yue Du) %prep %autosetup -n mmh3-3.1.0 %build %py3_build %install %py3_install install -d -m755 %{buildroot}/%{_pkgdocdir} if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi pushd %{buildroot} if [ -d usr/lib ]; then find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/lib64 ]; then find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/bin ]; then find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/sbin ]; then find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst fi touch doclist.lst if [ -d usr/share/man ]; then find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst fi popd mv %{buildroot}/filelist.lst . mv %{buildroot}/doclist.lst . %files -n python3-mmh3 -f filelist.lst %dir %{python3_sitearch}/* %files help -f doclist.lst %{_docdir}/* %changelog * Mon Apr 10 2023 Python_Bot - 3.1.0-1 - Package Spec generated