diff options
| author | CoprDistGit <infra@openeuler.org> | 2023-04-10 10:02:20 +0000 |
|---|---|---|
| committer | CoprDistGit <infra@openeuler.org> | 2023-04-10 10:02:20 +0000 |
| commit | 83ad79c46a014f351678804de95e2021ce9bc806 (patch) | |
| tree | b0c72d13fdb0a398df6872102f733447b1f95184 | |
| parent | c57400c31a1c45910790206d768962c19b92f04b (diff) | |
automatic import of python-mmh3
| -rw-r--r-- | .gitignore | 1 | ||||
| -rw-r--r-- | python-mmh3.spec | 533 | ||||
| -rw-r--r-- | sources | 1 |
3 files changed, 535 insertions, 0 deletions
@@ -0,0 +1 @@ +/mmh3-3.1.0.tar.gz diff --git a/python-mmh3.spec b/python-mmh3.spec new file mode 100644 index 0000000..a5d2047 --- /dev/null +++ b/python-mmh3.spec @@ -0,0 +1,533 @@ +%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 +[](https://github.com/hajimes/mmh3/actions?query=workflow%3ASuper-Linter+branch%3Amaster) +[](https://github.com/hajimes/mmh3/actions/workflows/build.yml?branch=master) +[](https://pypi.org/project/mmh3/) +[](https://pypi.org/project/mmh3/) +[](http://creativecommons.org/publicdomain/zero/1.0/) +[](https://pepy.tech/project/mmh3) +[](https://pepy.tech/project/mmh3) +[](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 <https://stackoverflow.com/questions/29932956/murmur3-hash-different-result-between-python-and-java-implementation> + +### 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. + +* <https://github.com/aappleby/smhasher> + +Ported and modified for Python by Hajime Senuma. + +* <http://pypi.python.org/pypi/mmh3> +* <http://github.com/hajimes/mmh3> + +## 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 +* <https://github.com/wc-duck/pymmh3>: mmh3 in pure python (Fredrik Kihlander and Swapnil Gusani) +* <https://github.com/escherba/python-cityhash>: Python bindings for CityHash (Eugene Scherba) +* <https://github.com/veelion/python-farmhash>: Python bindigs for FarmHash (Veelion Chong) +* <https://github.com/escherba/python-metrohash>: Python bindings for MetroHash (Eugene Scherba) +* <https://github.com/ifduyue/python-xxhash>: 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 +[](https://github.com/hajimes/mmh3/actions?query=workflow%3ASuper-Linter+branch%3Amaster) +[](https://github.com/hajimes/mmh3/actions/workflows/build.yml?branch=master) +[](https://pypi.org/project/mmh3/) +[](https://pypi.org/project/mmh3/) +[](http://creativecommons.org/publicdomain/zero/1.0/) +[](https://pepy.tech/project/mmh3) +[](https://pepy.tech/project/mmh3) +[](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 <https://stackoverflow.com/questions/29932956/murmur3-hash-different-result-between-python-and-java-implementation> + +### 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. + +* <https://github.com/aappleby/smhasher> + +Ported and modified for Python by Hajime Senuma. + +* <http://pypi.python.org/pypi/mmh3> +* <http://github.com/hajimes/mmh3> + +## 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 +* <https://github.com/wc-duck/pymmh3>: mmh3 in pure python (Fredrik Kihlander and Swapnil Gusani) +* <https://github.com/escherba/python-cityhash>: Python bindings for CityHash (Eugene Scherba) +* <https://github.com/veelion/python-farmhash>: Python bindigs for FarmHash (Veelion Chong) +* <https://github.com/escherba/python-metrohash>: Python bindings for MetroHash (Eugene Scherba) +* <https://github.com/ifduyue/python-xxhash>: Python bindings for xxHash (Yue Du) + + +%package help +Summary: Development documents and examples for mmh3 +Provides: python3-mmh3-doc +%description help +# mmh3 +[](https://github.com/hajimes/mmh3/actions?query=workflow%3ASuper-Linter+branch%3Amaster) +[](https://github.com/hajimes/mmh3/actions/workflows/build.yml?branch=master) +[](https://pypi.org/project/mmh3/) +[](https://pypi.org/project/mmh3/) +[](http://creativecommons.org/publicdomain/zero/1.0/) +[](https://pepy.tech/project/mmh3) +[](https://pepy.tech/project/mmh3) +[](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 <https://stackoverflow.com/questions/29932956/murmur3-hash-different-result-between-python-and-java-implementation> + +### 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. + +* <https://github.com/aappleby/smhasher> + +Ported and modified for Python by Hajime Senuma. + +* <http://pypi.python.org/pypi/mmh3> +* <http://github.com/hajimes/mmh3> + +## 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 +* <https://github.com/wc-duck/pymmh3>: mmh3 in pure python (Fredrik Kihlander and Swapnil Gusani) +* <https://github.com/escherba/python-cityhash>: Python bindings for CityHash (Eugene Scherba) +* <https://github.com/veelion/python-farmhash>: Python bindigs for FarmHash (Veelion Chong) +* <https://github.com/escherba/python-metrohash>: Python bindings for MetroHash (Eugene Scherba) +* <https://github.com/ifduyue/python-xxhash>: 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 <Python_Bot@openeuler.org> - 3.1.0-1 +- Package Spec generated @@ -0,0 +1 @@ +fa071dbe4ba5e39e0aa4716dea02c6e2 mmh3-3.1.0.tar.gz |