A package for CSEMRI with C++ accelerated components.

Navigation

Verified details

These details have been verified by PyPI
Maintainers
Avatar for dtamada from gravatar.com dtamada

Unverified details

These details have not been verified by PyPI
Project links
Meta
Report project as malware

Project description

PyCSEMRI: Python Water-Fat Separation Package for CSE-MRI

Overview

PyWaterFat is a Python package for water-fat separation in Chemical Shift Encoding Magnetic Resonance Imaging (CSE-MRI). This package is based on Diego Hernando's MATLAB fat-water toolbox, with modifications to improve performance and compatibility with Python ecosystems. The confidence map function will be integrated in the near future.

Key Features:

  • Implements complex, mixed, and magnitude fitting algorithms for water-fat separation
  • No GSL dependency
  • Utilizes Eigen C++ library for fast and efficient computations
  • Highly portable and easy to install in various environments, including MRI scanners

Background

This package builds upon the work presented at the ISMRM Workshop on Fat-Water Separation: ISMRM Fat-Water Separation Workshop

Dependencies and Licensing

This project utilizes the following third-party libraries for core functionalities:

  • Eigen: We use the Eigen library for efficient matrix and vector calculations.

    • Availability: Eigen is an open-source C++ template library for linear algebra, available at https://eigen.tuxfamily.org/.

    • License: Eigen is licensed under the Mozilla Public License, Version 2.0 (MPL 2.0).

  • Boost: The Boost C++ Libraries are used in this project specifically for the graph cut algorithm.

    • Availability: The Boost C++ Libraries are a collection of peer-reviewed, portable C++ source libraries, available at https://www.boost.org/.

    • License: Boost is licensed under The Boost Software License.

Please refer to the respective project websites and their associated license files for full details on their terms and conditions.

Installation

This package contains C++ components and requires a compiler for installation from source. However, pre-compiled wheels are provided for common platforms (macOS, Linux), making installation easy via pip.

From PyPI (Recommended for Users)

If you just want to use the package, you can install it directly from the Python Package Index (PyPI). This method will automatically download the correct pre-compiled version for your system.

pip install PyCSEMRI

Usage

A sample code is provided in Example_ChanComb_h5.py.

Project details

Verified details

These details have been verified by PyPI
Maintainers
Avatar for dtamada from gravatar.com dtamada

Unverified details

These details have not been verified by PyPI
Project links
Meta

Release history Release notifications | RSS feed

0.2.4

0.2.2

This version

0.1.9

0.1.7

0.1.6

0.1.4

0.1.2

0.1.0

Download files

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

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distributions

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

pycsemri-0.1.9-cp312-cp312-musllinux_1_2_x86_64.whl (1.2 MB view details)

Uploaded CPython 3.12musllinux: musl 1.2+ x86-64

pycsemri-0.1.9-cp312-cp312-musllinux_1_2_i686.whl (1.3 MB view details)

Uploaded CPython 3.12musllinux: musl 1.2+ i686

pycsemri-0.1.9-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (231.9 kB view details)

Uploaded CPython 3.12manylinux: glibc 2.17+ x86-64

pycsemri-0.1.9-cp312-cp312-manylinux_2_17_i686.manylinux2014_i686.whl (239.9 kB view details)

Uploaded CPython 3.12manylinux: glibc 2.17+ i686

pycsemri-0.1.9-cp312-cp312-macosx_11_0_arm64.whl (189.8 kB view details)

Uploaded CPython 3.12macOS 11.0+ ARM64

pycsemri-0.1.9-cp311-cp311-musllinux_1_2_x86_64.whl (1.2 MB view details)

Uploaded CPython 3.11musllinux: musl 1.2+ x86-64

pycsemri-0.1.9-cp311-cp311-musllinux_1_2_i686.whl (1.3 MB view details)

Uploaded CPython 3.11musllinux: musl 1.2+ i686

pycsemri-0.1.9-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (231.9 kB view details)

Uploaded CPython 3.11manylinux: glibc 2.17+ x86-64

pycsemri-0.1.9-cp311-cp311-manylinux_2_17_i686.manylinux2014_i686.whl (239.9 kB view details)

Uploaded CPython 3.11manylinux: glibc 2.17+ i686

pycsemri-0.1.9-cp311-cp311-macosx_11_0_arm64.whl (189.8 kB view details)

Uploaded CPython 3.11macOS 11.0+ ARM64

pycsemri-0.1.9-cp310-cp310-musllinux_1_2_x86_64.whl (1.2 MB view details)

Uploaded CPython 3.10musllinux: musl 1.2+ x86-64

pycsemri-0.1.9-cp310-cp310-musllinux_1_2_i686.whl (1.3 MB view details)

Uploaded CPython 3.10musllinux: musl 1.2+ i686

pycsemri-0.1.9-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (231.9 kB view details)

Uploaded CPython 3.10manylinux: glibc 2.17+ x86-64

pycsemri-0.1.9-cp310-cp310-manylinux_2_17_i686.manylinux2014_i686.whl (239.9 kB view details)

Uploaded CPython 3.10manylinux: glibc 2.17+ i686

pycsemri-0.1.9-cp310-cp310-macosx_11_0_arm64.whl (189.8 kB view details)

Uploaded CPython 3.10macOS 11.0+ ARM64

pycsemri-0.1.9-cp39-cp39-musllinux_1_2_x86_64.whl (1.2 MB view details)

Uploaded CPython 3.9musllinux: musl 1.2+ x86-64

pycsemri-0.1.9-cp39-cp39-musllinux_1_2_i686.whl (1.3 MB view details)

Uploaded CPython 3.9musllinux: musl 1.2+ i686

pycsemri-0.1.9-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (231.9 kB view details)

Uploaded CPython 3.9manylinux: glibc 2.17+ x86-64

pycsemri-0.1.9-cp39-cp39-manylinux_2_17_i686.manylinux2014_i686.whl (239.9 kB view details)

Uploaded CPython 3.9manylinux: glibc 2.17+ i686

pycsemri-0.1.9-cp39-cp39-macosx_11_0_arm64.whl (189.8 kB view details)

Uploaded CPython 3.9macOS 11.0+ ARM64

pycsemri-0.1.9-cp38-cp38-musllinux_1_2_x86_64.whl (1.2 MB view details)

Uploaded CPython 3.8musllinux: musl 1.2+ x86-64

pycsemri-0.1.9-cp38-cp38-musllinux_1_2_i686.whl (1.3 MB view details)

Uploaded CPython 3.8musllinux: musl 1.2+ i686

pycsemri-0.1.9-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (231.7 kB view details)

Uploaded CPython 3.8manylinux: glibc 2.17+ x86-64

pycsemri-0.1.9-cp38-cp38-manylinux_2_17_i686.manylinux2014_i686.whl (239.7 kB view details)

Uploaded CPython 3.8manylinux: glibc 2.17+ i686

pycsemri-0.1.9-cp38-cp38-macosx_11_0_arm64.whl (189.6 kB view details)

Uploaded CPython 3.8macOS 11.0+ ARM64

pycsemri-0.1.9-cp36-cp36m-musllinux_1_2_x86_64.whl (1.2 MB view details)

Uploaded CPython 3.6mmusllinux: musl 1.2+ x86-64

pycsemri-0.1.9-cp36-cp36m-musllinux_1_2_i686.whl (1.3 MB view details)

Uploaded CPython 3.6mmusllinux: musl 1.2+ i686

pycsemri-0.1.9-cp36-cp36m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (231.7 kB view details)

Uploaded CPython 3.6mmanylinux: glibc 2.17+ x86-64

pycsemri-0.1.9-cp36-cp36m-manylinux_2_17_i686.manylinux2014_i686.whl (239.7 kB view details)

Uploaded CPython 3.6mmanylinux: glibc 2.17+ i686

File details

Details for the file pycsemri-0.1.9-cp312-cp312-musllinux_1_2_x86_64.whl.

File metadata

File hashes

Hashes for pycsemri-0.1.9-cp312-cp312-musllinux_1_2_x86_64.whl
Algorithm Hash digest
SHA256 257718b43e2644128f40853a8ee9eb0583565fddf7b58f369ec3d7b2cc4343f5
MD5 55813fac80f1b5e5e74df874d25f6cb6
BLAKE2b-256 04389f74f732f7d3424293b2ef16a89a12a56cb2d14ea83c977a4dba061c8cff

See more details on using hashes here.

File details

Details for the file pycsemri-0.1.9-cp312-cp312-musllinux_1_2_i686.whl.

File metadata

File hashes

Hashes for pycsemri-0.1.9-cp312-cp312-musllinux_1_2_i686.whl
Algorithm Hash digest
SHA256 98ab564c3feb8ef11d19e6f1fa879b579f65432263fb6b25058216d4de56c6cb
MD5 28ca1e52c5c88313fc4134d883505cb3
BLAKE2b-256 9becb0a49aa7482892990b4e2712bb1ca464dacad525daa5715115ccb7600638

See more details on using hashes here.

File details

Details for the file pycsemri-0.1.9-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pycsemri-0.1.9-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 faf78b6094a5f946ca14622b6bff72b9f806fe7c980f3eb39089be33d01cbcdf
MD5 44ab88f22fcba58daf7c5248a3659f57
BLAKE2b-256 ed0cd3eaa58938e3831566e6c9722d2109b461855c45ac9423a7dc096e0f4c59

See more details on using hashes here.

File details

Details for the file pycsemri-0.1.9-cp312-cp312-manylinux_2_17_i686.manylinux2014_i686.whl.

File metadata

File hashes

Hashes for pycsemri-0.1.9-cp312-cp312-manylinux_2_17_i686.manylinux2014_i686.whl
Algorithm Hash digest
SHA256 568081c9453bb79cc21e7e2acec530797b3350ae4906d6a31f72a055b6f0bfca
MD5 6b37d5fb60de6b734f2ef1e5ba6844d9
BLAKE2b-256 c278234f7e4bc0d08e834a32f716d0770c633717d3fff834d25c5039ddc79a16

See more details on using hashes here.

File details

Details for the file pycsemri-0.1.9-cp312-cp312-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pycsemri-0.1.9-cp312-cp312-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 f663aba2ee92488c8c7de4e107c8ba5f1edfd3a70efdf2bc67b6e1a0f7fcb6f3
MD5 25948fcda1740572d5a0f9b21f27bcbc
BLAKE2b-256 801aab07481551c957b07075ea106c82cb16d690fed3ecd7c1088e08d5acb6b4

See more details on using hashes here.

File details

Details for the file pycsemri-0.1.9-cp311-cp311-musllinux_1_2_x86_64.whl.

File metadata

File hashes

Hashes for pycsemri-0.1.9-cp311-cp311-musllinux_1_2_x86_64.whl
Algorithm Hash digest
SHA256 b94ae1790ffdde850af8472fa628eb76f6be5fdad76f8dff31065141df3d593b
MD5 c70e1021ead0341547b0f2406ee0e2c6
BLAKE2b-256 5654448d74d5abc52cdcda6d5e323185094dcc8977d1b21733dec4c100b57789

See more details on using hashes here.

File details

Details for the file pycsemri-0.1.9-cp311-cp311-musllinux_1_2_i686.whl.

File metadata

File hashes

Hashes for pycsemri-0.1.9-cp311-cp311-musllinux_1_2_i686.whl
Algorithm Hash digest
SHA256 0bbd85dd2f342d49c1cffeb12e5ec58e77f3719ce63068646a43773aaecc27d7
MD5 c19ee194597ad6349c2fa4b65cfba0f7
BLAKE2b-256 e0cd1ad3bfec5c6e346dc62f70bbfc9fec0cab3700deb7df341d7795a6b10b96

See more details on using hashes here.

File details

Details for the file pycsemri-0.1.9-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pycsemri-0.1.9-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 2dcfd8c4cb29fcf14c0853bf21ed62607d2f11dfadcd901c70f4952d881e31c2
MD5 ea99b4b8fe971f04d7fa00a074c1a14b
BLAKE2b-256 a8ec09dc4b9cd624226e4dd262a16fa29c63fc46915608d39123bbf817e14ecf

See more details on using hashes here.

File details

Details for the file pycsemri-0.1.9-cp311-cp311-manylinux_2_17_i686.manylinux2014_i686.whl.

File metadata

File hashes

Hashes for pycsemri-0.1.9-cp311-cp311-manylinux_2_17_i686.manylinux2014_i686.whl
Algorithm Hash digest
SHA256 1d6e740aff61eee689a208c2df1c997bdec6b0286c313bc4caca6429e8c287cc
MD5 a585e78073ca79e5f3df68813cdb62ec
BLAKE2b-256 b0fdd19f27b80a39ba769ec8fd308c281449d1525d589784fc87488d122a7e46

See more details on using hashes here.

File details

Details for the file pycsemri-0.1.9-cp311-cp311-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pycsemri-0.1.9-cp311-cp311-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 886ef9d633977e9cc5664701011aa58221e6836965f14dd7ca11c61c0f21566b
MD5 dfd9b605e2d468712c0fbdabc9d2e4a7
BLAKE2b-256 687ae78c23427803b4a18d7e680e3772b615e75a871c24d3ad3142b54af6fc98

See more details on using hashes here.

File details

Details for the file pycsemri-0.1.9-cp310-cp310-musllinux_1_2_x86_64.whl.

File metadata

File hashes

Hashes for pycsemri-0.1.9-cp310-cp310-musllinux_1_2_x86_64.whl
Algorithm Hash digest
SHA256 846466fd346dbb3a6e17f199f1acd22e30f92a7a50092e0c8d7123ee3d4b3543
MD5 3a53734fb9a330460358b46adf4e9caa
BLAKE2b-256 c0eb729afc8e6d79393a5925e5b0ba1f861b0cee8ecf73340e1bb7702c857abc

See more details on using hashes here.

File details

Details for the file pycsemri-0.1.9-cp310-cp310-musllinux_1_2_i686.whl.

File metadata

File hashes

Hashes for pycsemri-0.1.9-cp310-cp310-musllinux_1_2_i686.whl
Algorithm Hash digest
SHA256 7c4d225d826651e7587c7d7a2d6a1dc525e5a52ec989b654d50a871938c975d3
MD5 75eb2c9a71bc55363baf2e91ddc51351
BLAKE2b-256 a37cfd8f20b97169d6ff1b402419b8d640dd10c50a850661572bab0f76919778

See more details on using hashes here.

File details

Details for the file pycsemri-0.1.9-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pycsemri-0.1.9-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 09acd4e3982ad3fa015dc912264f2b569786bbd0bd1ff0c2113ebd9d5bb1b148
MD5 00684461f83043287a72cdc733c601e7
BLAKE2b-256 b582e6c214965d9e2e1cb697d2b8ab5a5c3734da9c00f3ca53db02c3dbe4ee58

See more details on using hashes here.

File details

Details for the file pycsemri-0.1.9-cp310-cp310-manylinux_2_17_i686.manylinux2014_i686.whl.

File metadata

File hashes

Hashes for pycsemri-0.1.9-cp310-cp310-manylinux_2_17_i686.manylinux2014_i686.whl
Algorithm Hash digest
SHA256 a2124901022edb4371cf1a3f6e3e99d92727a3f657dc442b55602d2ee292a517
MD5 e381671584d3f6900f9a9beb1a23cf1a
BLAKE2b-256 2be48c8f5e52250880334b7b1201c1b16d664fffbbe139bf18b84ba1a4f2e6d0

See more details on using hashes here.

File details

Details for the file pycsemri-0.1.9-cp310-cp310-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pycsemri-0.1.9-cp310-cp310-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 56c41663d8481250d73bc6edb7be13a4d672146754f24e60f9ef8449f9867520
MD5 1f455ddb28ae8382693c4655a22c4fb7
BLAKE2b-256 f2bb4c928b8765c7dd46389b82dcab1a016e46ec313a2e3087377c5200895038

See more details on using hashes here.

File details

Details for the file pycsemri-0.1.9-cp39-cp39-musllinux_1_2_x86_64.whl.

File metadata

File hashes

Hashes for pycsemri-0.1.9-cp39-cp39-musllinux_1_2_x86_64.whl
Algorithm Hash digest
SHA256 5580c273edc927c4a70e049833b2beba85ad3947a2bc7ceb4e0f45d92a50422d
MD5 c956301fea782554f4bc862ef988ab4c
BLAKE2b-256 ce161d779f33dd4c04ac9d232e1883eeba0f1e9f67ad9228281f1dfaaa0f8dd4

See more details on using hashes here.

File details

Details for the file pycsemri-0.1.9-cp39-cp39-musllinux_1_2_i686.whl.

File metadata

File hashes

Hashes for pycsemri-0.1.9-cp39-cp39-musllinux_1_2_i686.whl
Algorithm Hash digest
SHA256 f3817d40694c9d405713968ac177f3aaf3b0c383921ca75ef603c5bfc174ab8a
MD5 119f946ab3267e6b801281cb1eb4d3f6
BLAKE2b-256 cc506b1ad178b237287f3c6b961795520ee99f9114de7b3fe37dcafdc3871829

See more details on using hashes here.

File details

Details for the file pycsemri-0.1.9-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pycsemri-0.1.9-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 a7f980c123bbc0da3e25b6fe1bb5c53cfa0e56553d62980714b7e33597cc64a9
MD5 55a02fa23698d0ae0e746965995819f0
BLAKE2b-256 9ee351607fdafe1490483cfcc1d3790c3e494d6a2cda338177b5ba55af028e4c

See more details on using hashes here.

File details

Details for the file pycsemri-0.1.9-cp39-cp39-manylinux_2_17_i686.manylinux2014_i686.whl.

File metadata

File hashes

Hashes for pycsemri-0.1.9-cp39-cp39-manylinux_2_17_i686.manylinux2014_i686.whl
Algorithm Hash digest
SHA256 9acdf75d2fcb5678b06ebabb9cbf233eae6e0bb635a328cc65626b058fe0df7a
MD5 b081de98bd304b6432490bd5c43ef052
BLAKE2b-256 61749798c6ca48e80983058e3aa570be4d9b9c98e738d6d040af131f5a975673

See more details on using hashes here.

File details

Details for the file pycsemri-0.1.9-cp39-cp39-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pycsemri-0.1.9-cp39-cp39-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 3461e10970da32ffa9e010c885d397df944e23129bd7ee28800718d9d9d93d03
MD5 fe2cf6f9d14006f009029076feb0e4ad
BLAKE2b-256 9aaa5071ea2d43c7d6921959e1f58f47ce359cfda4383ee2fec2aae36ec7c6da

See more details on using hashes here.

File details

Details for the file pycsemri-0.1.9-cp38-cp38-musllinux_1_2_x86_64.whl.

File metadata

File hashes

Hashes for pycsemri-0.1.9-cp38-cp38-musllinux_1_2_x86_64.whl
Algorithm Hash digest
SHA256 7c2f3dc2bf34741881535a54ce202e0c47be5940fae6a5f87b24b6b18e3122ae
MD5 019db04dcae85c2a4d8bba629fc0e102
BLAKE2b-256 1004d55162ccbe0e51998dab7aa4089ba2c2c5b3a9ced227c745b9f1a610cd76

See more details on using hashes here.

File details

Details for the file pycsemri-0.1.9-cp38-cp38-musllinux_1_2_i686.whl.

File metadata

File hashes

Hashes for pycsemri-0.1.9-cp38-cp38-musllinux_1_2_i686.whl
Algorithm Hash digest
SHA256 8872dbca9945ab7eb8e6c01ecb38aae293ad94de08d4039afd25918e17dfbc4f
MD5 0d99aac030febf03cc0255d5596b2648
BLAKE2b-256 4620f09b48602ee9da3d4dac20f3936efef557724d7c4a97b5102b7fb117032e

See more details on using hashes here.

File details

Details for the file pycsemri-0.1.9-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pycsemri-0.1.9-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 028ac34d1d2ad60cf5a18bdf20caff97244d64cf43b595132a12d041dac1df25
MD5 2dd88d2b5a68d53835e80173f9615bd6
BLAKE2b-256 1e0e7f80ecc82e850611338a89ca1f54862cadbc4241da8df99d1dcd8ac017ec

See more details on using hashes here.

File details

Details for the file pycsemri-0.1.9-cp38-cp38-manylinux_2_17_i686.manylinux2014_i686.whl.

File metadata

File hashes

Hashes for pycsemri-0.1.9-cp38-cp38-manylinux_2_17_i686.manylinux2014_i686.whl
Algorithm Hash digest
SHA256 ce4ea1d686527869bfec3bc1ee179ac0d75b932d440b11f1ede6d513c7174e88
MD5 4bc73395f5f5b62d11a26b2e7e4e01d0
BLAKE2b-256 367fe20fdc155994650127380d69ca2d3ccbeb4b31939c8f40a1dde2aa7afcbe

See more details on using hashes here.

File details

Details for the file pycsemri-0.1.9-cp38-cp38-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pycsemri-0.1.9-cp38-cp38-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 ffea6cbfbb78fee6130d81cb2af1655390ce8e57e189ecc08a25390596c5ba88
MD5 144a4cd5c0962309f76645f21133c13a
BLAKE2b-256 94d68f5245b02485558b2e1037e9a6f58d55db323b5e1bd15e72721a88eb401c

See more details on using hashes here.

File details

Details for the file pycsemri-0.1.9-cp36-cp36m-musllinux_1_2_x86_64.whl.

File metadata

File hashes

Hashes for pycsemri-0.1.9-cp36-cp36m-musllinux_1_2_x86_64.whl
Algorithm Hash digest
SHA256 4b0bc5b246247b13bd7dfceafadf2f1ad8883179451970e3bf40c5b1bc1012b9
MD5 f908e9d78b7d692f5986a0ceb747c59d
BLAKE2b-256 7a8c1bf101e2dd017c4e9fe7344b978bb3c6195f7f16ad262d920be403f18772

See more details on using hashes here.

File details

Details for the file pycsemri-0.1.9-cp36-cp36m-musllinux_1_2_i686.whl.

File metadata

File hashes

Hashes for pycsemri-0.1.9-cp36-cp36m-musllinux_1_2_i686.whl
Algorithm Hash digest
SHA256 b7ddb8c17dcf014a54524c78fd8eb10a220dcb5c2deba1b9fbe5e6d7cbd57467
MD5 a4e0cb315758a2af57c7ee0d19880565
BLAKE2b-256 92c9bf1368803879d5710379f4b16aeefd7df7ec124439a96efa2afe519435f3

See more details on using hashes here.

File details

Details for the file pycsemri-0.1.9-cp36-cp36m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pycsemri-0.1.9-cp36-cp36m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 d2c4dea1ddfe1e318554e634fd9a9d48fe32c9279ea6bfe859e5f75854b90cdf
MD5 a3c265ef95b50665952c754a385adcb8
BLAKE2b-256 399bed19a23882ee62d3ced734f7a2db8fc02be55b97ac2d965584fa7dfd76fc

See more details on using hashes here.

File details

Details for the file pycsemri-0.1.9-cp36-cp36m-manylinux_2_17_i686.manylinux2014_i686.whl.

File metadata

File hashes

Hashes for pycsemri-0.1.9-cp36-cp36m-manylinux_2_17_i686.manylinux2014_i686.whl
Algorithm Hash digest
SHA256 5e167d57f5409c44dbe09d2a2b498ed8f8398ded94e5797f93eb7b682943aecd
MD5 d415aa1783c0de85eeada81def7ccfaa
BLAKE2b-256 bbb149658855c24ac369de21ac2f761caf600f02856941571bab34641892c7e2

See more details on using hashes here.

Supported by

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