blosc

Blosc data compressor

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Project description

python-blosc: a Python wrapper for the extremely fast Blosc compression library

Author:: The Blosc development team
Contact:: blosc@blosc.org
Github:: https://github.com/Blosc/python-blosc
URL:: http://python-blosc.blosc.org
PyPi:
Anaconda:
Gitter:
Code of Conduct:

What it is

Blosc (http://blosc.org) is a high performance compressor optimized for binary data. It has been designed to transmit data to the processor cache faster than the traditional, non-compressed, direct memory fetch approach via a memcpy() OS call.

Blosc works well for compressing numerical arrays that contains data with relatively low entropy, like sparse data, time series, grids with regular-spaced values, etc.

python-blosc a Python package that wraps Blosc. python-blosc supports Python 3.7 or higher versions.

Installing

Blosc is now offering Python wheels for the main OS (Win, Mac and Linux) and platforms. You can install binary packages from PyPi using pip:

$ pip install blosc

Documentation

The Sphinx based documentation is here:

http://python-blosc.blosc.org

Also, some examples are available on python-blosc wiki page:

http://github.com/blosc/python-blosc/wiki

Lastly, here is the recording and the slides from the talk “Compress me stupid” at the EuroPython 2014.

Building

If you need more control, there are different ways to compile python-blosc, depending if you want to link with an already installed Blosc library or not.

Installing via setuptools

python-blosc comes with the Blosc sources with it and can be built with:

$ python -m pip install -r requirements-dev.txt
$ python setup.py build_ext --inplace

Any codec can be enabled (=1) or disabled (=0) on this build-path with the appropriate OS environment variables INCLUDE_LZ4, INCLUDE_SNAPPY, INCLUDE_ZLIB, and INCLUDE_ZLIB. By default all the codecs in Blosc are enabled except Snappy (due to some issues with C++ with the gcc toolchain).

Compiler specific optimisations are automatically enabled by inspecting the CPU flags building Blosc. They can be manually disabled by setting the following environmental variables: DISABLE_BLOSC_SSE2 and DISABLE_BLOSC_AVX2.

setuptools is limited to using the compiler specified in the environment variable CC which on posix systems is usually gcc. This often causes trouble with the Snappy codec, which is written in C++, and as a result Snappy is no longer compiled by default. This problem is not known to affect MSVC or clang. Snappy is considered optional in Blosc as its compression performance is below that of the other codecs.

That’s all. You can proceed with testing section now.

Compiling with an installed Blosc library

This approach uses pre-built, fully optimized versions of Blosc built via CMake.

Go to https://github.com/Blosc/c-blosc/releases and download and install the C-Blosc library. Then, you can tell python-blosc where is the C-Blosc library in a couple of ways:

Using an environment variable:

$ BLOSC_DIR=/usr/local     (or "set BLOSC_DIR=\blosc" on Win)
$ export BLOSC_DIR         (not needed on Win)
$ python setup.py build_clib
$ python setup.py build_ext --inplace

Using a flag:

$ python setup.py build_clib
$ python setup.py build_ext --inplace --blosc=/usr/local

Testing

After compiling, you can quickly check that the package is sane by running the doctests in blosc/test.py:

$ PYTHONPATH=.   (or "set PYTHONPATH=." on Win)
$ export PYTHONPATH=.  (not needed on Win)
$ python blosc/test.py  (add -v for verbose mode)

Or alternatively, you can use the third-party nosetests script:

$ nosetests --with-doctest (add -v for verbose mode)

Once installed, you can re-run the tests at any time with:

$ python -c "import blosc; blosc.test()"

Benchmarking

If curious, you may want to run a small benchmark that compares a plain NumPy array copy against compression through different compressors in your Blosc build:

$ PYTHONPATH=. python bench/compress_ptr.py

Just to whet your appetite, here are the results for an Intel Xeon E5-2695 v3 @ 2.30GHz, running Python 3.5, CentOS 7, but YMMV (and will vary!):

-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
python-blosc version: 1.5.1.dev0
Blosc version: 1.11.2 ($Date:: 2017-01-27 #$)
Compressors available: ['blosclz', 'lz4', 'lz4hc', 'snappy', 'zlib', 'zstd']
Compressor library versions:
  BloscLZ: 1.0.5
  LZ4: 1.7.5
  Snappy: 1.1.1
  Zlib: 1.2.7
  Zstd: 1.1.2
Python version: 3.5.2 |Continuum Analytics, Inc.| (default, Jul  2 2016, 17:53:06)
[GCC 4.4.7 20120313 (Red Hat 4.4.7-1)]
Platform: Linux-3.10.0-327.18.2.el7.x86_64-x86_64 (#1 SMP Thu May 12 11:03:55 UTC 2016)
Linux dist: CentOS Linux 7.2.1511
Processor: x86_64
Byte-ordering: little
Detected cores: 56
Number of threads to use by default: 4
  -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
Creating NumPy arrays with 10**8 int64/float64 elements:
  *** ctypes.memmove() *** Time for memcpy(): 0.276 s (2.70 GB/s)

Times for compressing/decompressing with clevel=5 and 24 threads

*** the arange linear distribution ***
  *** blosclz , noshuffle  ***  0.382 s (1.95 GB/s) / 0.300 s (2.48 GB/s)     Compr. ratio:   1.0x
  *** blosclz , shuffle    ***  0.042 s (17.77 GB/s) / 0.027 s (27.18 GB/s)   Compr. ratio:  57.1x
  *** blosclz , bitshuffle ***  0.094 s (7.94 GB/s) / 0.041 s (18.28 GB/s)    Compr. ratio:  74.0x
  *** lz4     , noshuffle  ***  0.156 s (4.79 GB/s) / 0.052 s (14.30 GB/s)    Compr. ratio:   2.0x
  *** lz4     , shuffle    ***  0.033 s (22.58 GB/s) / 0.034 s (22.03 GB/s)   Compr. ratio:  68.6x
  *** lz4     , bitshuffle ***  0.059 s (12.63 GB/s) / 0.053 s (14.18 GB/s)   Compr. ratio:  33.1x
  *** lz4hc   , noshuffle  ***  0.443 s (1.68 GB/s) / 0.070 s (10.62 GB/s)    Compr. ratio:   2.0x
  *** lz4hc   , shuffle    ***  0.102 s (7.31 GB/s) / 0.029 s (25.42 GB/s)    Compr. ratio:  97.5x
  *** lz4hc   , bitshuffle ***  0.206 s (3.62 GB/s) / 0.038 s (19.85 GB/s)    Compr. ratio: 180.5x
  *** snappy  , noshuffle  ***  0.154 s (4.84 GB/s) / 0.056 s (13.28 GB/s)    Compr. ratio:   2.0x
  *** snappy  , shuffle    ***  0.044 s (16.89 GB/s) / 0.047 s (15.95 GB/s)   Compr. ratio:  17.4x
  *** snappy  , bitshuffle ***  0.064 s (11.58 GB/s) / 0.061 s (12.26 GB/s)   Compr. ratio:  18.2x
  *** zlib    , noshuffle  ***  1.172 s (0.64 GB/s) / 0.135 s (5.50 GB/s)     Compr. ratio:   5.3x
  *** zlib    , shuffle    ***  0.260 s (2.86 GB/s) / 0.086 s (8.67 GB/s)     Compr. ratio: 120.8x
  *** zlib    , bitshuffle ***  0.262 s (2.84 GB/s) / 0.094 s (7.96 GB/s)     Compr. ratio: 260.1x
  *** zstd    , noshuffle  ***  0.973 s (0.77 GB/s) / 0.093 s (8.00 GB/s)     Compr. ratio:   7.8x
  *** zstd    , shuffle    ***  0.093 s (7.97 GB/s) / 0.023 s (32.71 GB/s)    Compr. ratio: 156.7x
  *** zstd    , bitshuffle ***  0.115 s (6.46 GB/s) / 0.029 s (25.60 GB/s)    Compr. ratio: 320.6x

*** the linspace linear distribution ***
  *** blosclz , noshuffle  ***  0.341 s (2.19 GB/s) / 0.291 s (2.56 GB/s)     Compr. ratio:   1.0x
  *** blosclz , shuffle    ***  0.132 s (5.65 GB/s) / 0.023 s (33.10 GB/s)    Compr. ratio:   2.0x
  *** blosclz , bitshuffle ***  0.166 s (4.50 GB/s) / 0.036 s (20.89 GB/s)    Compr. ratio:   2.8x
  *** lz4     , noshuffle  ***  0.142 s (5.26 GB/s) / 0.028 s (27.07 GB/s)    Compr. ratio:   1.0x
  *** lz4     , shuffle    ***  0.093 s (8.01 GB/s) / 0.030 s (24.87 GB/s)    Compr. ratio:   3.4x
  *** lz4     , bitshuffle ***  0.102 s (7.31 GB/s) / 0.039 s (19.13 GB/s)    Compr. ratio:   5.3x
  *** lz4hc   , noshuffle  ***  0.700 s (1.06 GB/s) / 0.044 s (16.77 GB/s)    Compr. ratio:   1.1x
  *** lz4hc   , shuffle    ***  0.203 s (3.67 GB/s) / 0.021 s (36.22 GB/s)    Compr. ratio:   8.6x
  *** lz4hc   , bitshuffle ***  0.342 s (2.18 GB/s) / 0.028 s (26.50 GB/s)    Compr. ratio:  14.2x
  *** snappy  , noshuffle  ***  0.271 s (2.75 GB/s) / 0.274 s (2.72 GB/s)     Compr. ratio:   1.0x
  *** snappy  , shuffle    ***  0.099 s (7.54 GB/s) / 0.042 s (17.55 GB/s)    Compr. ratio:   4.2x
  *** snappy  , bitshuffle ***  0.127 s (5.86 GB/s) / 0.043 s (17.20 GB/s)    Compr. ratio:   6.1x
  *** zlib    , noshuffle  ***  1.525 s (0.49 GB/s) / 0.158 s (4.70 GB/s)     Compr. ratio:   1.6x
  *** zlib    , shuffle    ***  0.346 s (2.15 GB/s) / 0.098 s (7.59 GB/s)     Compr. ratio:  10.7x
  *** zlib    , bitshuffle ***  0.420 s (1.78 GB/s) / 0.104 s (7.20 GB/s)     Compr. ratio:  18.0x
  *** zstd    , noshuffle  ***  1.061 s (0.70 GB/s) / 0.096 s (7.79 GB/s)     Compr. ratio:   1.9x
  *** zstd    , shuffle    ***  0.203 s (3.68 GB/s) / 0.052 s (14.21 GB/s)    Compr. ratio:  14.2x
  *** zstd    , bitshuffle ***  0.251 s (2.97 GB/s) / 0.047 s (15.84 GB/s)    Compr. ratio:  22.2x

*** the random distribution ***
  *** blosclz , noshuffle  ***  0.340 s (2.19 GB/s) / 0.285 s (2.61 GB/s)     Compr. ratio:   1.0x
  *** blosclz , shuffle    ***  0.091 s (8.21 GB/s) / 0.017 s (44.29 GB/s)    Compr. ratio:   3.9x
  *** blosclz , bitshuffle ***  0.080 s (9.27 GB/s) / 0.029 s (26.12 GB/s)    Compr. ratio:   6.1x
  *** lz4     , noshuffle  ***  0.150 s (4.95 GB/s) / 0.027 s (28.05 GB/s)    Compr. ratio:   2.4x
  *** lz4     , shuffle    ***  0.068 s (11.02 GB/s) / 0.029 s (26.03 GB/s)   Compr. ratio:   4.5x
  *** lz4     , bitshuffle ***  0.063 s (11.87 GB/s) / 0.054 s (13.70 GB/s)   Compr. ratio:   6.2x
  *** lz4hc   , noshuffle  ***  0.645 s (1.15 GB/s) / 0.019 s (39.22 GB/s)    Compr. ratio:   3.5x
  *** lz4hc   , shuffle    ***  0.257 s (2.90 GB/s) / 0.022 s (34.62 GB/s)    Compr. ratio:   5.1x
  *** lz4hc   , bitshuffle ***  0.128 s (5.80 GB/s) / 0.029 s (25.52 GB/s)    Compr. ratio:   6.2x
  *** snappy  , noshuffle  ***  0.164 s (4.54 GB/s) / 0.048 s (15.46 GB/s)    Compr. ratio:   2.2x
  *** snappy  , shuffle    ***  0.082 s (9.09 GB/s) / 0.043 s (17.39 GB/s)    Compr. ratio:   4.3x
  *** snappy  , bitshuffle ***  0.071 s (10.48 GB/s) / 0.046 s (16.08 GB/s)   Compr. ratio:   5.0x
  *** zlib    , noshuffle  ***  1.223 s (0.61 GB/s) / 0.093 s (7.97 GB/s)     Compr. ratio:   4.0x
  *** zlib    , shuffle    ***  0.636 s (1.17 GB/s) / 0.126 s (5.89 GB/s)     Compr. ratio:   5.5x
  *** zlib    , bitshuffle ***  0.327 s (2.28 GB/s) / 0.109 s (6.81 GB/s)     Compr. ratio:   6.2x
  *** zstd    , noshuffle  ***  1.432 s (0.52 GB/s) / 0.103 s (7.27 GB/s)     Compr. ratio:   4.2x
  *** zstd    , shuffle    ***  0.388 s (1.92 GB/s) / 0.031 s (23.71 GB/s)    Compr. ratio:   5.9x
  *** zstd    , bitshuffle ***  0.127 s (5.86 GB/s) / 0.033 s (22.77 GB/s)    Compr. ratio:   6.4x

Also, Blosc works quite well on ARM processors (even without NEON support yet):

-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
python-blosc version: 1.4.4
Blosc version: 1.11.2 ($Date:: 2017-01-27 #$)
Compressors available: ['blosclz', 'lz4', 'lz4hc', 'snappy', 'zlib', 'zstd']
Compressor library versions:
  BloscLZ: 1.0.5
  LZ4: 1.7.5
  Snappy: 1.1.1
  Zlib: 1.2.8
  Zstd: 1.1.2
Python version: 3.6.0 (default, Dec 31 2016, 21:20:16)
[GCC 4.9.2]
Platform: Linux-3.4.113-sun8i-armv7l (#50 SMP PREEMPT Mon Nov 14 08:41:55 CET 2016)
Linux dist: debian 9.0
Processor: not recognized
Byte-ordering: little
Detected cores: 4
Number of threads to use by default: 4
-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
  *** ctypes.memmove() *** Time for memcpy():   0.015 s (93.57 MB/s)

Times for compressing/decompressing with clevel=5 and 4 threads

*** user input ***
  *** blosclz , noshuffle  ***  0.015 s (89.93 MB/s) / 0.010 s (138.32 MB/s)    Compr. ratio:   2.7x
  *** blosclz , shuffle    ***  0.023 s (60.25 MB/s) / 0.012 s (112.71 MB/s)    Compr. ratio:   2.3x
  *** blosclz , bitshuffle ***  0.018 s (77.63 MB/s) / 0.021 s (66.76 MB/s)     Compr. ratio:   7.3x
  *** lz4     , noshuffle  ***  0.008 s (177.14 MB/s) / 0.009 s (159.00 MB/s)   Compr. ratio:   3.6x
  *** lz4     , shuffle    ***  0.010 s (131.29 MB/s) / 0.012 s (117.69 MB/s)   Compr. ratio:   3.5x
  *** lz4     , bitshuffle ***  0.015 s (89.97 MB/s) / 0.022 s (63.62 MB/s)     Compr. ratio:   8.4x
  *** lz4hc   , noshuffle  ***  0.071 s (19.30 MB/s) / 0.007 s (186.64 MB/s)    Compr. ratio:   8.6x
  *** lz4hc   , shuffle    ***  0.079 s (17.30 MB/s) / 0.014 s (95.99 MB/s)     Compr. ratio:   6.2x
  *** lz4hc   , bitshuffle ***  0.062 s (22.23 MB/s) / 0.027 s (51.53 MB/s)     Compr. ratio:   9.7x
  *** snappy  , noshuffle  ***  0.008 s (173.87 MB/s) / 0.009 s (148.77 MB/s)   Compr. ratio:   4.4x
  *** snappy  , shuffle    ***  0.011 s (123.22 MB/s) / 0.016 s (85.16 MB/s)    Compr. ratio:   4.4x
  *** snappy  , bitshuffle ***  0.015 s (89.02 MB/s) / 0.021 s (64.87 MB/s)     Compr. ratio:   6.2x
  *** zlib    , noshuffle  ***  0.047 s (29.26 MB/s) / 0.011 s (121.83 MB/s)    Compr. ratio:  14.7x
  *** zlib    , shuffle    ***  0.080 s (17.20 MB/s) / 0.022 s (63.61 MB/s)     Compr. ratio:   9.4x
  *** zlib    , bitshuffle ***  0.059 s (23.50 MB/s) / 0.033 s (41.10 MB/s)     Compr. ratio:  10.5x
  *** zstd    , noshuffle  ***  0.113 s (12.21 MB/s) / 0.011 s (124.64 MB/s)    Compr. ratio:  15.6x
  *** zstd    , shuffle    ***  0.154 s (8.92 MB/s) / 0.026 s (52.56 MB/s)      Compr. ratio:   9.9x
  *** zstd    , bitshuffle ***  0.116 s (11.86 MB/s) / 0.036 s (38.40 MB/s)     Compr. ratio:  11.4x

For details on the ARM benchmark see: https://github.com/Blosc/python-blosc/issues/105

In case you find your own results interesting, please report them back to the authors!

License

The software is licenses under a 3-Clause BSD licsense. A copy of the python-blosc license can be found in LICENSE.txt. A copy of all licenses can be found in LICENSES/.

Mailing list

Discussion about this module is welcome in the Blosc list:

blosc@googlegroups.com

http://groups.google.es/group/blosc

Enjoy data!

Project details

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1.11.2

Jun 24, 2024

1.11.1

Dec 15, 2022

1.11.0

Dec 7, 2022

1.10.6

Oct 7, 2021

1.10.5

Oct 6, 2021

This version

1.10.4

Jun 1, 2021

1.10.2

Jan 19, 2021

1.10.1

Dec 24, 2020

1.10.1.dev0 pre-release yanked

Dec 24, 2020

1.10.0

Dec 23, 2020

1.10.0rc0 pre-release

Dec 23, 2020

1.9.3.dev0 pre-release yanked

Dec 10, 2020

1.9.2

Sep 9, 2020

1.9.1

Apr 11, 2020

1.9.0

Mar 29, 2020

1.8.3

Dec 14, 2019

1.8.2

Dec 13, 2019

1.8.1

Mar 4, 2019

1.8.1.dev0 pre-release

Mar 4, 2019

1.8.0

Mar 4, 2019

1.7.0

Dec 27, 2018

1.6.2

Nov 5, 2018

1.6.1

Oct 21, 2018

1.5.1

Mar 10, 2017

1.5.0

Feb 8, 2017

1.4.4

Sep 3, 2016

1.4.3

Sep 2, 2016

1.4.1

Jul 27, 2016

1.4.0

Jul 22, 2016

1.3.3

Jul 12, 2016

1.3.2

Apr 7, 2016

1.3.1

Apr 7, 2016

1.3.0

Mar 31, 2016

1.2.8

Sep 16, 2015

1.2.8.dev0 pre-release

Sep 16, 2015

1.2.7

May 6, 2015

1.2.5

Apr 15, 2015

1.2.4

Jul 7, 2014

1.2.3

Mar 22, 2014

1.2.2

Mar 20, 2014

1.2.1

Feb 15, 2014

1.2.0

Jan 19, 2014

1.1

May 24, 2013

1.0.6

Sep 21, 2012

1.0.5

Sep 16, 2012

1.0.4

Sep 14, 2012

1.0.3

Nov 17, 2010

1.0.1

Oct 1, 2010

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Algorithm	Hash digest
SHA256	`e43c14914ae1267c63550500c64878a4ad3a696138b8c7f92ba14ee8425fefb7`
MD5	`f753e966f53743c0dc5d3c8db15ec297`
BLAKE2b-256	`bbba8135a21d4d23e0f027d7d3a04db0ef63c2a4b667031260c7f6a904a3ce00`

Algorithm	Hash digest
SHA256	`6977fb3ffc825958420c5e63f49bcdb7195c82942e12dbc1481e63119a2bbe5e`
MD5	`0c55659e221991dfa41311e0d64af26b`
BLAKE2b-256	`8bc26829533658d6b42b2ad8519aaec15c0abf3f418ed016f10db6350b2ce504`

Algorithm	Hash digest
SHA256	`140bda212993809ca2144949a4b2cdc0bdc5f898f39beb2681eb1a12094f1112`
MD5	`facb02fa7071877f95fc496494f64242`
BLAKE2b-256	`dc757fe3e63d819f929cc14b786b95b2e952e5080cacbd0d47d8f9ec3159072b`

Algorithm	Hash digest
SHA256	`9495ba6e330fad6a0ea843087f8d33a48073cfc78637e7203f64fedeb2bedade`
MD5	`f4f8faec885e97e9375af65db87d9d2c`
BLAKE2b-256	`2c9dd1027ab15d4d9d189195468f78262d9a6f72dabe8d79af504fc666dced6c`

Algorithm	Hash digest
SHA256	`29969e02098598609fa8067af7a6700b54b5601b8281bf4e80a37d5ae8f9f94c`
MD5	`e04bb5dcab7e90af571c3846bb34c314`
BLAKE2b-256	`96b2b4202784adb78e699676b1d25a55079f830203f845c7df7f8091070bf785`

Algorithm	Hash digest
SHA256	`68228d871b8dabf63cdcb2c600da1a4c56d32988d0d179ec625f51dbffee1291`
MD5	`5b27b7929a21c6d566b440adab4fab6a`
BLAKE2b-256	`88e4d2147539bf27697728a9e23aaa9c92a481f05dc1018aa0b94ff02993a627`

Algorithm	Hash digest
SHA256	`aa66e0b4fd68ce18e9f2bee47dbaf3828c47b2cdf140a19d4e2c9487b9e97258`
MD5	`3c4c30cd9659300ff2ecff69d6782b60`
BLAKE2b-256	`3541aac1057aed796c4fadbf1b935822e60cfdda5a0dcea7f87f38a12e9aec19`

Algorithm	Hash digest
SHA256	`6baaea68c4ec727fa1fa0611ece6ae3120fc6cfd5b4d1a686db55cc244927f9e`
MD5	`5b55046541da8ff8c641c44844a42487`
BLAKE2b-256	`7213d98361ad34396e1d7553f6361fab52dc4bdf030a8a7723f291b80ab98309`

Algorithm	Hash digest
SHA256	`3714142c54e0d44f57d337db43eac4bbd7e930cae9caf526ca0ab6e898c39e69`
MD5	`a60de3f67c2c893d599e8242c64fb41c`
BLAKE2b-256	`3a0974612047711f3860f97f956d97d8a86d2b9956a845dd891f472407d668d5`

Algorithm	Hash digest
SHA256	`afbef8d37089c27a40e2cc1d391b7f940e6f921a17e09d356f4b77357539ee37`
MD5	`7725907f4765193f04d40d7949696ef1`
BLAKE2b-256	`70ebd8abbd92b0926b27bb06b64fe7aaa72019b44345a757ef1160b57d525326`

Algorithm	Hash digest
SHA256	`5c6540d39058ea180575aa773ee67f7e5b959032f59c4274e670e5a66dc4126a`
MD5	`d1cbb023e1326c7134a480ef50c790c5`
BLAKE2b-256	`016e813801364df0e7bbe76ce1705bcef163f663da1506415899563599e178d1`

Algorithm	Hash digest
SHA256	`4c6c904550c32436b8e3401aebcf5bc52064db785f8729ee84c9bfa3c5dad75e`
MD5	`73887783f89d726d927e93410b31468b`
BLAKE2b-256	`7bfddc4d2ddb87aafc8cb3c71bc0e5e19c0b95fdd5271bf814ec1f594bcf25d8`

Algorithm	Hash digest
SHA256	`7f08b0e002d548fb3ab23ec506fe11615eca922ea18a7ef25cdb6b4cc9c4676e`
MD5	`8566a36390fa93cb685de55dd2d72e75`
BLAKE2b-256	`69eb34ae69bf65b6b2ecaed401e0be9788689423bc88c2800f9f1d88e05f4d9a`

Algorithm	Hash digest
SHA256	`84b8a20be69295a40b72be0f7fbb297792535f713f5f1f44f1cd6d4372c84cc9`
MD5	`b737073be76409a290d2f4b672c42e13`
BLAKE2b-256	`024262bf703b8f344beefd555bf8ed80b945ac925488c845ff1c1bed231ee21e`

Algorithm	Hash digest
SHA256	`188ce4bcbef976ad58947885b10fe906fc1f3edc83e2bae633e4cbe4d7422379`
MD5	`bfc89768b127c12928f512a2339d339f`
BLAKE2b-256	`c244eaa74161aa5ec300411d5aa6daafcf12a61665c36e8d0f92ad4ce515c455`

Algorithm	Hash digest
SHA256	`a1567b6fe7aa0ee9cff21d915fe89570ad62977595c91f1845edb888fd07c885`
MD5	`152d1395c025fe60a6093caf05053ffc`
BLAKE2b-256	`31c1f11ddf79b49550cc13d4b46ef2bdf16febd554a16a044769d7dcaaf604c0`

Algorithm	Hash digest
SHA256	`1ac01f03da32dff8800fddb530b5fcba50ac6e822e113b1dadc9561431316e48`
MD5	`4314ae92ac9309de8f8b5d62448415a7`
BLAKE2b-256	`983cc693ff6cca0b9c2f91a67a498cb2e152bebd67697f2ffdefd66248155317`

Algorithm	Hash digest
SHA256	`a8bd5d1f80d9246ec52d2bc538b834170c030522ea1785477fbada335da5d9e3`
MD5	`72ecb6089f52be774c846e503bb3d957`
BLAKE2b-256	`bc41fba004e0dddd303c63adeed2394514c694e9c3a5add5fc17e440cf52e283`

blosc 1.10.4

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python-blosc: a Python wrapper for the extremely fast Blosc compression library

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Algorithm	Hash digest
SHA256	`c5264df46eb1a3643ca4950bb1047f0ed8f2ea053255d802f35d2ec7a9ac71aa`
MD5	`5d1f27cfce656a02184d5d80f42c5326`
BLAKE2b-256	`3a9c7fbf19a8adc7d7f092dce1041cdba365d40e2e3a03a1f88b93fdcad18e7f`