Python bindings for ERFA

Navigation

Verified details

These details have been verified by PyPI
Maintainers
Avatar for antonio.valentino from gravatar.com antonio.valentino Avatar for robitaille from gravatar.com robitaille

Unverified details

These details have not been verified by PyPI
Project links
Meta
  • License: BSD License (BSD 3-Clause License)
  • Author: The PyERFA Developers
  • Tags astronomy, astrophysics, cosmology, space, science, coordinate
  • Requires: Python >=3.7
Classifiers

Project description

PyPI Status DOI 10.5281/zenodo.3940699 Travis CI Status Documentation Status

PyERFA is the Python wrapper for the ERFA library (Essential Routines for Fundamental Astronomy), a C library containing key algorithms for astronomy, which is based on the SOFA library published by the International Astronomical Union (IAU). All C routines are wrapped as Numpy universal functions, so that they can be called with scalar or array inputs.

The project is a split of astropy._erfa module, developed in the context of Astropy project, into a standalone package. It contains the ERFA C source code as a git submodule. The wrapping is done with help of the Jinja2 template engine.

If you use this package in your research, please cita it via DOI 10.5281/zenodo.3940699.

Installation instructions

The package can be installed from the package directory using a simple:

$ pip install .

and similarly a wheel can be created with:

$ pip wheel .

Testing

For testing, one can install the packages together with its testing dependencies and then test it with:

$ pip install .[test]
$ pytest

Alternatively, one can use tox, which will set up a separate testing environment for you, with:

$ tox -e test

Usage

The package can be imported as erfa which has all ERFA ufuncs wrapped with python code that tallies errors and warnings. Also exposed are the constants defined by ERFA in erfam.h, as well as numpy.dtype corresponding to structures used by ERFA. Examples:

>>> import erfa
>>> erfa.jd2cal(2460000., [0, 1, 2, 3])
(array([2023, 2023, 2023, 2023], dtype=int32),
 array([2, 2, 2, 2], dtype=int32),
 array([24, 25, 26, 27], dtype=int32),
 array([0.5, 0.5, 0.5, 0.5]))
>>> erfa.plan94(2460000., [0, 1, 2, 3], 1)
array([([ 0.09083713, -0.39041392, -0.21797389], [0.02192341, 0.00705449, 0.00149618]),
       ([ 0.11260694, -0.38275202, -0.21613731], [0.02160375, 0.00826891, 0.00217806]),
       ([ 0.13401992, -0.37387798, -0.21361622], [0.0212094 , 0.00947838, 0.00286503]),
       ([ 0.15500031, -0.36379788, -0.21040601], [0.02073822, 0.01068061, 0.0035561 ])],
      dtype={'names':['p','v'], 'formats':[('<f8', (3,)),('<f8', (3,))], 'offsets':[0,24], 'itemsize':48, 'aligned':True})
>>> erfa.dt_pv
dtype([('p', '<f8', (3,)), ('v', '<f8', (3,))], align=True)
>>> erfa.dt_eraLDBODY
dtype([('bm', '<f8'), ('dl', '<f8'), ('pv', [('p', '<f8', (3,)), ('v', '<f8', (3,))])], align=True)
>>> erfa.DAYSEC
86400.0

It is also possible to use the ufuncs directly, though then one has to deal with the warning and error states explicitly. For instance, compare:

>>> erfa.jd2cal(-600000., [0, 1, 2, 3])
Traceback (most recent call last):
...
ErfaError: ERFA function "jd2cal" yielded 4 of "unacceptable date (Note 1)"
>>> erfa.ufunc.jd2cal(-600000., [0, 1, 2, 3])
(array([-1, -1, -1, -1], dtype=int32),
 ...,
 array([-1, -1, -1, -1], dtype=int32))

License

PyERFA is licensed under a 3-clause BSD style license - see the LICENSE.rst file.

Project details

Verified details

These details have been verified by PyPI
Maintainers
Avatar for antonio.valentino from gravatar.com antonio.valentino Avatar for robitaille from gravatar.com robitaille

Unverified details

These details have not been verified by PyPI
Project links
Meta
  • License: BSD License (BSD 3-Clause License)
  • Author: The PyERFA Developers
  • Tags astronomy, astrophysics, cosmology, space, science, coordinate
  • Requires: Python >=3.7
Classifiers

Release history Release notifications | RSS feed

2.0.1.5

2.0.1.4

2.0.1.3

2.0.1.2 yanked

Reason this release was yanked:

Broken

2.0.1.1

2.0.1

2.0.0.3

2.0.0.2

This version

2.0.0.1

2.0.0

1.7.3

1.7.2

1.7.1.1

1.7.1

1.7.0

Download files

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

Source Distribution

pyerfa-2.0.0.1.tar.gz (808.5 kB view details)

Uploaded Source

Built Distributions

pyerfa-2.0.0.1-cp310-cp310-win_amd64.whl (366.4 kB view details)

Uploaded CPython 3.10 Windows x86-64

pyerfa-2.0.0.1-cp310-cp310-win32.whl (358.0 kB view details)

Uploaded CPython 3.10 Windows x86

pyerfa-2.0.0.1-cp310-cp310-musllinux_1_1_x86_64.whl (754.3 kB view details)

Uploaded CPython 3.10 musllinux: musl 1.1+ x86-64

pyerfa-2.0.0.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (696.0 kB view details)

Uploaded CPython 3.10 manylinux: glibc 2.17+ ARM64

pyerfa-2.0.0.1-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (743.2 kB view details)

Uploaded CPython 3.10 manylinux: glibc 2.12+ x86-64 manylinux: glibc 2.5+ x86-64

pyerfa-2.0.0.1-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_12_i686.manylinux2010_i686.whl (683.8 kB view details)

Uploaded CPython 3.10 manylinux: glibc 2.12+ i686 manylinux: glibc 2.5+ i686

pyerfa-2.0.0.1-cp310-cp310-macosx_10_9_x86_64.whl (340.4 kB view details)

Uploaded CPython 3.10 macOS 10.9+ x86-64

pyerfa-2.0.0.1-cp39-cp39-win_amd64.whl (366.4 kB view details)

Uploaded CPython 3.9 Windows x86-64

pyerfa-2.0.0.1-cp39-cp39-win32.whl (358.0 kB view details)

Uploaded CPython 3.9 Windows x86

pyerfa-2.0.0.1-cp39-cp39-musllinux_1_1_x86_64.whl (754.0 kB view details)

Uploaded CPython 3.9 musllinux: musl 1.1+ x86-64

pyerfa-2.0.0.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (695.7 kB view details)

Uploaded CPython 3.9 manylinux: glibc 2.17+ ARM64

pyerfa-2.0.0.1-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (742.9 kB view details)

Uploaded CPython 3.9 manylinux: glibc 2.12+ x86-64 manylinux: glibc 2.5+ x86-64

pyerfa-2.0.0.1-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_12_i686.manylinux2010_i686.whl (683.5 kB view details)

Uploaded CPython 3.9 manylinux: glibc 2.12+ i686 manylinux: glibc 2.5+ i686

pyerfa-2.0.0.1-cp39-cp39-macosx_10_9_x86_64.whl (340.4 kB view details)

Uploaded CPython 3.9 macOS 10.9+ x86-64

pyerfa-2.0.0.1-cp38-cp38-win_amd64.whl (366.4 kB view details)

Uploaded CPython 3.8 Windows x86-64

pyerfa-2.0.0.1-cp38-cp38-win32.whl (358.0 kB view details)

Uploaded CPython 3.8 Windows x86

pyerfa-2.0.0.1-cp38-cp38-musllinux_1_1_x86_64.whl (785.0 kB view details)

Uploaded CPython 3.8 musllinux: musl 1.1+ x86-64

pyerfa-2.0.0.1-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (711.1 kB view details)

Uploaded CPython 3.8 manylinux: glibc 2.17+ ARM64

pyerfa-2.0.0.1-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (775.7 kB view details)

Uploaded CPython 3.8 manylinux: glibc 2.12+ x86-64 manylinux: glibc 2.5+ x86-64

pyerfa-2.0.0.1-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.manylinux_2_12_i686.manylinux2010_i686.whl (708.8 kB view details)

Uploaded CPython 3.8 manylinux: glibc 2.12+ i686 manylinux: glibc 2.5+ i686

pyerfa-2.0.0.1-cp38-cp38-macosx_10_9_x86_64.whl (343.6 kB view details)

Uploaded CPython 3.8 macOS 10.9+ x86-64

pyerfa-2.0.0.1-cp37-cp37m-win_amd64.whl (365.1 kB view details)

Uploaded CPython 3.7m Windows x86-64

pyerfa-2.0.0.1-cp37-cp37m-win32.whl (357.9 kB view details)

Uploaded CPython 3.7m Windows x86

pyerfa-2.0.0.1-cp37-cp37m-musllinux_1_1_x86_64.whl (756.3 kB view details)

Uploaded CPython 3.7m musllinux: musl 1.1+ x86-64

pyerfa-2.0.0.1-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (696.3 kB view details)

Uploaded CPython 3.7m manylinux: glibc 2.17+ ARM64

pyerfa-2.0.0.1-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (746.4 kB view details)

Uploaded CPython 3.7m manylinux: glibc 2.12+ x86-64 manylinux: glibc 2.5+ x86-64

pyerfa-2.0.0.1-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.manylinux_2_12_i686.manylinux2010_i686.whl (684.0 kB view details)

Uploaded CPython 3.7m manylinux: glibc 2.12+ i686 manylinux: glibc 2.5+ i686

pyerfa-2.0.0.1-cp37-cp37m-macosx_10_9_x86_64.whl (342.9 kB view details)

Uploaded CPython 3.7m macOS 10.9+ x86-64

File details

Details for the file pyerfa-2.0.0.1.tar.gz.

File metadata

  • Download URL: pyerfa-2.0.0.1.tar.gz
  • Upload date:
  • Size: 808.5 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.2 importlib_metadata/4.8.1 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.9.7

File hashes

Hashes for pyerfa-2.0.0.1.tar.gz
Algorithm Hash digest
SHA256 2fd4637ffe2c1e6ede7482c13f583ba7c73119d78bef90175448ce506a0ede30
MD5 35b83b19d6cccf61f419c8da2f39df28
BLAKE2b-256 78fd0148f0e54f0c6f48a141409df65d74a5f1dae2e139f23d50a43c58c16098

See more details on using hashes here.

File details

Details for the file pyerfa-2.0.0.1-cp310-cp310-win_amd64.whl.

File metadata

  • Download URL: pyerfa-2.0.0.1-cp310-cp310-win_amd64.whl
  • Upload date:
  • Size: 366.4 kB
  • Tags: CPython 3.10, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.2 importlib_metadata/4.8.1 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.9.7

File hashes

Hashes for pyerfa-2.0.0.1-cp310-cp310-win_amd64.whl
Algorithm Hash digest
SHA256 f00dc4fc48a16eb39fd0121f2f06c03ee762b79a207cc5b0bc17d94191b51302
MD5 a80d794537def0edf9f10e946229748b
BLAKE2b-256 1a1bd75fad44e1c5f276041519e4448d953e25b4686c6ecd73496450e9f3538b

See more details on using hashes here.

File details

Details for the file pyerfa-2.0.0.1-cp310-cp310-win32.whl.

File metadata

  • Download URL: pyerfa-2.0.0.1-cp310-cp310-win32.whl
  • Upload date:
  • Size: 358.0 kB
  • Tags: CPython 3.10, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.2 importlib_metadata/4.8.1 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.9.7

File hashes

Hashes for pyerfa-2.0.0.1-cp310-cp310-win32.whl
Algorithm Hash digest
SHA256 f9e149bc3d423ae891f6587c1383fd471ae07744b88152e66b5e9f64a8bc9006
MD5 f2d99496c122ad8de76a41c934822789
BLAKE2b-256 b2cc59f9617830634ebd257fd1870db7f2e4db7fcc77b395ba14c9b9f2712ff8

See more details on using hashes here.

File details

Details for the file pyerfa-2.0.0.1-cp310-cp310-musllinux_1_1_x86_64.whl.

File metadata

  • Download URL: pyerfa-2.0.0.1-cp310-cp310-musllinux_1_1_x86_64.whl
  • Upload date:
  • Size: 754.3 kB
  • Tags: CPython 3.10, musllinux: musl 1.1+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.2 importlib_metadata/4.8.1 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.9.7

File hashes

Hashes for pyerfa-2.0.0.1-cp310-cp310-musllinux_1_1_x86_64.whl
Algorithm Hash digest
SHA256 041939a7554a98b72885904ffddd8882567191bee62358727679448480174c31
MD5 6f4b3164139d17ffbd43667ff8f85742
BLAKE2b-256 c054daa0a491c89a7bcdd4cde15e27cd1e8a375efcae867242864a74012d7d47

See more details on using hashes here.

File details

Details for the file pyerfa-2.0.0.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for pyerfa-2.0.0.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 629248cebc8626a52e80f69d4e2f30cc6e751f57803f5ba7ec99edd09785d181
MD5 def7f61fd8792a66dcf9e50f98715c72
BLAKE2b-256 fb26601d5717bab5e481b4f8085c66ddbc09c4fd1b134a109825a48987653c66

See more details on using hashes here.

File details

Details for the file pyerfa-2.0.0.1-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl.

File metadata

File hashes

Hashes for pyerfa-2.0.0.1-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 177f50f0e8354f1a7115c2d4784668b365f1cc2f2c7d1e2f4ddf354160559b32
MD5 e5fa6bf304a10cbcb615122b6b14ff46
BLAKE2b-256 f1713fc23d108826779bdca44b018b95730d0d6969a52978c1f797a59045904c

See more details on using hashes here.

File details

Details for the file pyerfa-2.0.0.1-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_12_i686.manylinux2010_i686.whl.

File metadata

File hashes

Hashes for pyerfa-2.0.0.1-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_12_i686.manylinux2010_i686.whl
Algorithm Hash digest
SHA256 3285d95dfe398a931a633da961f6f1c0b8690f2a3b1c510a4efe639f784cd9c7
MD5 88beb4a1a154897d8b058be196089d94
BLAKE2b-256 b3cfe04248d927d418eb1f9bc22bcea58bf5f22b4f84a4dfe38f8fccdbff4007

See more details on using hashes here.

File details

Details for the file pyerfa-2.0.0.1-cp310-cp310-macosx_10_9_x86_64.whl.

File metadata

  • Download URL: pyerfa-2.0.0.1-cp310-cp310-macosx_10_9_x86_64.whl
  • Upload date:
  • Size: 340.4 kB
  • Tags: CPython 3.10, macOS 10.9+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.2 importlib_metadata/4.8.1 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.9.7

File hashes

Hashes for pyerfa-2.0.0.1-cp310-cp310-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 278832de7803f2fb0ef4b14263200f98dfdb3eaa78dc63835d93796fd8fc42c6
MD5 87001ef4dc605f05517317ad6e2f3646
BLAKE2b-256 6bc78cca1135e4a8b9b8cc92c45c286a656d3f544c469a5b5c2d51adb0b38713

See more details on using hashes here.

File details

Details for the file pyerfa-2.0.0.1-cp39-cp39-win_amd64.whl.

File metadata

  • Download URL: pyerfa-2.0.0.1-cp39-cp39-win_amd64.whl
  • Upload date:
  • Size: 366.4 kB
  • Tags: CPython 3.9, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.2 importlib_metadata/4.8.1 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.9.7

File hashes

Hashes for pyerfa-2.0.0.1-cp39-cp39-win_amd64.whl
Algorithm Hash digest
SHA256 63a83c35cea8c5d50d53c18089f1e625c0ffc59a7a5b8d44e0f1b3ec5288f183
MD5 5c14bc74399ddf0eef8fb14624eee3ca
BLAKE2b-256 641ad2a09a3c140366e6f382adb6aea8c8b6fb3ec732f3d92c38d4a4bb8168f7

See more details on using hashes here.

File details

Details for the file pyerfa-2.0.0.1-cp39-cp39-win32.whl.

File metadata

  • Download URL: pyerfa-2.0.0.1-cp39-cp39-win32.whl
  • Upload date:
  • Size: 358.0 kB
  • Tags: CPython 3.9, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.2 importlib_metadata/4.8.1 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.9.7

File hashes

Hashes for pyerfa-2.0.0.1-cp39-cp39-win32.whl
Algorithm Hash digest
SHA256 7895b7e6f3bc36442d1969bf3bda5a4c3b661be7a5a468798369cbd5d81023d8
MD5 952f305957f04c4a1ba987da1df956df
BLAKE2b-256 0e43cab7256595bb6618ca8e703fceab5246de48362b52cf600b0402d713695b

See more details on using hashes here.

File details

Details for the file pyerfa-2.0.0.1-cp39-cp39-musllinux_1_1_x86_64.whl.

File metadata

  • Download URL: pyerfa-2.0.0.1-cp39-cp39-musllinux_1_1_x86_64.whl
  • Upload date:
  • Size: 754.0 kB
  • Tags: CPython 3.9, musllinux: musl 1.1+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.2 importlib_metadata/4.8.1 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.9.7

File hashes

Hashes for pyerfa-2.0.0.1-cp39-cp39-musllinux_1_1_x86_64.whl
Algorithm Hash digest
SHA256 da5ee24eaf5e5f841f36885ea16461800b7bea11df5b657bcff85d7a7f51d2d8
MD5 ed734d63df06935d27bbab2619e27d0a
BLAKE2b-256 be563bd7b00f9e3348bdb79f769edce2d56b0e26d3c1b7758aa613e6b636d207

See more details on using hashes here.

File details

Details for the file pyerfa-2.0.0.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for pyerfa-2.0.0.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 c7ca8c98842f1ae10c1fbcea0e03a41ddc13456da88da2dc9b8335a8c414d7a3
MD5 e636a7d328d34ab32aa8a28ed9c22dc6
BLAKE2b-256 49eca839dd71a05b735f0164248e54c1a9069ecfd8627db6efa109ca50260805

See more details on using hashes here.

File details

Details for the file pyerfa-2.0.0.1-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl.

File metadata

File hashes

Hashes for pyerfa-2.0.0.1-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 690116a6026ee84ce5fec794c9e21bdc8c0ac8345d6722323810181486745068
MD5 6267883384efde87476ecf735abbccff
BLAKE2b-256 e51f238cf7fddba34875cafd4e1e6a69404335fe93700079bac344d16f9701da

See more details on using hashes here.

File details

Details for the file pyerfa-2.0.0.1-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_12_i686.manylinux2010_i686.whl.

File metadata

File hashes

Hashes for pyerfa-2.0.0.1-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_12_i686.manylinux2010_i686.whl
Algorithm Hash digest
SHA256 d3e7dedce1d7e4e044f6f81d192b1f6b373c8ad6716aa8721ec6d3cf4d36f5f3
MD5 0c56f4945e8138a5ccd70eea822fa2da
BLAKE2b-256 c31011bc370ffbc50ad2ed2a25742f742e5fe8d43f6dd5fbe916a93d6a1e6d8d

See more details on using hashes here.

File details

Details for the file pyerfa-2.0.0.1-cp39-cp39-macosx_10_9_x86_64.whl.

File metadata

  • Download URL: pyerfa-2.0.0.1-cp39-cp39-macosx_10_9_x86_64.whl
  • Upload date:
  • Size: 340.4 kB
  • Tags: CPython 3.9, macOS 10.9+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.2 importlib_metadata/4.8.1 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.9.7

File hashes

Hashes for pyerfa-2.0.0.1-cp39-cp39-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 ef5590b2075c50395b958f102988e519e339d96509dfdca0360f26dde94c47e7
MD5 8e51ccf9163858f6943f1f89b61f1825
BLAKE2b-256 907f8ef8f3e494e39c45bf208f9f6ed9725eacf48683c24578d6dde1ebe304c9

See more details on using hashes here.

File details

Details for the file pyerfa-2.0.0.1-cp38-cp38-win_amd64.whl.

File metadata

  • Download URL: pyerfa-2.0.0.1-cp38-cp38-win_amd64.whl
  • Upload date:
  • Size: 366.4 kB
  • Tags: CPython 3.8, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.2 importlib_metadata/4.8.1 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.9.7

File hashes

Hashes for pyerfa-2.0.0.1-cp38-cp38-win_amd64.whl
Algorithm Hash digest
SHA256 d603f1e8123f98a0593433aa6dad4ba03f0b0ceef4cb3e96f9a69aa7ab8d5c61
MD5 5c3abb084eec889cb08f22dda21bcd52
BLAKE2b-256 1b9b7cd78b66e81b78ca393f3ea8a0f4805c412a67e152dbb80749612a5059e4

See more details on using hashes here.

File details

Details for the file pyerfa-2.0.0.1-cp38-cp38-win32.whl.

File metadata

  • Download URL: pyerfa-2.0.0.1-cp38-cp38-win32.whl
  • Upload date:
  • Size: 358.0 kB
  • Tags: CPython 3.8, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.2 importlib_metadata/4.8.1 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.9.7

File hashes

Hashes for pyerfa-2.0.0.1-cp38-cp38-win32.whl
Algorithm Hash digest
SHA256 486e672c52bf58eab61140968660ac7fb3b756116b53c26c334ae95dadd943ee
MD5 e89ef47c35e0f8da1f81ca337a75772d
BLAKE2b-256 8c10abc5d4858dc1b0eff55fa4934b48e291eae36f6a76d37809a164b7a0cd59

See more details on using hashes here.

File details

Details for the file pyerfa-2.0.0.1-cp38-cp38-musllinux_1_1_x86_64.whl.

File metadata

  • Download URL: pyerfa-2.0.0.1-cp38-cp38-musllinux_1_1_x86_64.whl
  • Upload date:
  • Size: 785.0 kB
  • Tags: CPython 3.8, musllinux: musl 1.1+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.2 importlib_metadata/4.8.1 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.9.7

File hashes

Hashes for pyerfa-2.0.0.1-cp38-cp38-musllinux_1_1_x86_64.whl
Algorithm Hash digest
SHA256 f76fb4b64a87da2af9d0b6b79cc25e1ecc5b4143b2b3c8c9f10b221748c5db4d
MD5 86daa250b761a04b62ce79ba32b5c3b0
BLAKE2b-256 605b1a8c9b73d15f90dde169cfbfff2faa327ac18b59b0ad411a2069191dc4b6

See more details on using hashes here.

File details

Details for the file pyerfa-2.0.0.1-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for pyerfa-2.0.0.1-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 67711a748821c5d91f7a8907b9125094dfc3e5ab6a6b7ad8e207fd6afbe6b37f
MD5 55262e824f3fbdd510f72db5671afe4b
BLAKE2b-256 14cb2d3a09fadaa02713e84fe71273614df326f922d7473f6b24b83722a63914

See more details on using hashes here.

File details

Details for the file pyerfa-2.0.0.1-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl.

File metadata

File hashes

Hashes for pyerfa-2.0.0.1-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 37249e1e2b378d1f56e9379e4bb8f2cf87645c160a8a3e92166a1b7bb7ad7ea6
MD5 b2064094ad18a4256dfd612030823b7b
BLAKE2b-256 bf29d2ac8f83c88ba2cf9313d0f7e5cd79ed77f0f11826e3d83db28e59f71e98

See more details on using hashes here.

File details

Details for the file pyerfa-2.0.0.1-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.manylinux_2_12_i686.manylinux2010_i686.whl.

File metadata

File hashes

Hashes for pyerfa-2.0.0.1-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.manylinux_2_12_i686.manylinux2010_i686.whl
Algorithm Hash digest
SHA256 d2c10838241aaf17279468dcc731cb2c09bfb7dd7b340c0f527fd70c7c9e53d1
MD5 7caff8c3144151a796d286962cd14cce
BLAKE2b-256 7a2f5ebf229c1e5e95382d4b0097c3a3ee8bfe1e1cd9d734b49d64a1457ef899

See more details on using hashes here.

File details

Details for the file pyerfa-2.0.0.1-cp38-cp38-macosx_10_9_x86_64.whl.

File metadata

  • Download URL: pyerfa-2.0.0.1-cp38-cp38-macosx_10_9_x86_64.whl
  • Upload date:
  • Size: 343.6 kB
  • Tags: CPython 3.8, macOS 10.9+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.2 importlib_metadata/4.8.1 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.9.7

File hashes

Hashes for pyerfa-2.0.0.1-cp38-cp38-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 b935fa9d10dfd7206760859236640c835aa652609c0ae8a6584593324eb6f318
MD5 b438671b6a8c9030f724b6d00d2406a0
BLAKE2b-256 5a9bdcd9f932362942422b1932f6a4ffa043b1fc74879ff44eb47889916603a6

See more details on using hashes here.

File details

Details for the file pyerfa-2.0.0.1-cp37-cp37m-win_amd64.whl.

File metadata

  • Download URL: pyerfa-2.0.0.1-cp37-cp37m-win_amd64.whl
  • Upload date:
  • Size: 365.1 kB
  • Tags: CPython 3.7m, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.2 importlib_metadata/4.8.1 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.9.7

File hashes

Hashes for pyerfa-2.0.0.1-cp37-cp37m-win_amd64.whl
Algorithm Hash digest
SHA256 e86c08c9c0b75e448818473c6d709e3887a439c05a1aa34042d26774251422b7
MD5 c5564b272797b8e7104095621a4fbf6c
BLAKE2b-256 af6d66abea123f1f31d96a00c6f10f2cf57a72986dda343b830b410f846e3516

See more details on using hashes here.

File details

Details for the file pyerfa-2.0.0.1-cp37-cp37m-win32.whl.

File metadata

  • Download URL: pyerfa-2.0.0.1-cp37-cp37m-win32.whl
  • Upload date:
  • Size: 357.9 kB
  • Tags: CPython 3.7m, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.2 importlib_metadata/4.8.1 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.9.7

File hashes

Hashes for pyerfa-2.0.0.1-cp37-cp37m-win32.whl
Algorithm Hash digest
SHA256 0833f8ebba9f84a19a04ee5ca5aa90be75729abfbb8328e7a6d89ed1b04e058c
MD5 da6b0552f985bae381a1a83e1f433de2
BLAKE2b-256 e4082d46d736b5cabaadb56e45ac6a03e355dd4889ffe36ca5133962a27f6627

See more details on using hashes here.

File details

Details for the file pyerfa-2.0.0.1-cp37-cp37m-musllinux_1_1_x86_64.whl.

File metadata

  • Download URL: pyerfa-2.0.0.1-cp37-cp37m-musllinux_1_1_x86_64.whl
  • Upload date:
  • Size: 756.3 kB
  • Tags: CPython 3.7m, musllinux: musl 1.1+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.2 importlib_metadata/4.8.1 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.9.7

File hashes

Hashes for pyerfa-2.0.0.1-cp37-cp37m-musllinux_1_1_x86_64.whl
Algorithm Hash digest
SHA256 5c077aed4ccd585c1fe2f96ada8edb66e9d27b4ae8ff13ea2783283b298ba0c6
MD5 5e2543abc8b75f5cf315af914d9590e6
BLAKE2b-256 c15adc6f615d7d331099bf2e96aa52c1c205cd4e8afa951f67d0f87cdcf48a17

See more details on using hashes here.

File details

Details for the file pyerfa-2.0.0.1-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for pyerfa-2.0.0.1-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 b8f08f6e6d75a261bb92b707bea19eba2e46a8fcbfb499b789f3eb0d0352ea00
MD5 b1a6dd35c0ca70b4e5c2568058606c98
BLAKE2b-256 8e2cd559173df84402f56db95e195a33a0ef99481e05ed377362f865fd94f9c9

See more details on using hashes here.

File details

Details for the file pyerfa-2.0.0.1-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl.

File metadata

File hashes

Hashes for pyerfa-2.0.0.1-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 36738ba75e7a69e0ea6a7e96a5d33a852816427e7e94e7089c188ef920b02669
MD5 bd8d063836b99ef2842fef424ed6f5a6
BLAKE2b-256 8f7668d862db9bd200776a3fa60d2c07fcee34285e5363adb88fdd8fbc3bce36

See more details on using hashes here.

File details

Details for the file pyerfa-2.0.0.1-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.manylinux_2_12_i686.manylinux2010_i686.whl.

File metadata

File hashes

Hashes for pyerfa-2.0.0.1-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.manylinux_2_12_i686.manylinux2010_i686.whl
Algorithm Hash digest
SHA256 da89304d6b25ac056e470f44f85770b04c9674eced07a7f93b5eb0ce1edaabd9
MD5 eecfe55f34d47dc5664a583fd5b0d071
BLAKE2b-256 208caee86d91df577f3ee805ebfca4bd9da22f42a9f1cf6714976c7b2cd1032f

See more details on using hashes here.

File details

Details for the file pyerfa-2.0.0.1-cp37-cp37m-macosx_10_9_x86_64.whl.

File metadata

  • Download URL: pyerfa-2.0.0.1-cp37-cp37m-macosx_10_9_x86_64.whl
  • Upload date:
  • Size: 342.9 kB
  • Tags: CPython 3.7m, macOS 10.9+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.2 importlib_metadata/4.8.1 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.9.7

File hashes

Hashes for pyerfa-2.0.0.1-cp37-cp37m-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 1ba3668e1e181a678ce788d23a4f8666aabd8518f77fdde5157ba4744bc73d4a
MD5 0b176fadf06ca45ecc07ab09a63fcaa0
BLAKE2b-256 d31a095209120b81c7fc3921bc7b7b49b0fea9b8e52c153b5ff6e6dbf24e8d9a

See more details on using hashes here.

Supported by

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