| Name | ibm2ieee JSON |
| Version |
1.3.3
JSON |
| download |
| home_page | |
| Summary | Convert IBM hexadecimal floating-point to IEEE 754 floating-point |
| upload_time | 2023-10-08 08:53:24 |
| maintainer | |
| docs_url | None |
| author | |
| requires_python | >=3.7 |
| license | BSD 3-Clause License (C) Copyright 2018-2023 Enthought, Inc., Austin, TX All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. |
| keywords |
ibm
hfp
ieee754
hexadecimal
floating-point
ufunc
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
The **ibm2ieee** package provides NumPy universal functions ("ufuncs") for
converting IBM single-precision and double-precision hexadecimal floats
to the IEEE 754-format floats used by Python and NumPy on almost all
current platforms.
Features
--------
- Fast: 200-400 million values converted per second on a typical modern
machine, assuming normal inputs.
- Correct: converted results are correctly rounded, according to the default
IEEE 754 round-ties-to-even rounding mode. Corner cases (overflow, underflow,
subnormal results, signed zeros, non-normalised input) are all handled
correctly. Where the rounded converted value is out of range for the target
type, an appropriately-signed infinity is returned.
- Handles both single-precision and double-precision input and output formats.
Portability note: the conversion functions provided in this module assume that
``numpy.float32`` and ``numpy.float64`` are based on the standard IEEE 754
binary32 and binary64 floating-point formats. This is true on the overwhelming
majority of current platforms, but is not guaranteed by the relevant language
standards.
Usage
-----
The package provides two functions:
- ``ibm2float32`` converts IBM single- or double-precision data to
IEEE 754 single-precision values, in ``numpy.float32`` format.
- ``ibm2float64`` converts IBM single- or double-precision data to
IEEE 754 double-precision values, in ``numpy.float64`` format.
For both functions, IBM single-precision input data must be represented
using the ``numpy.uint32`` dtype, while IBM double-precision inputs must
be represented using ``numpy.uint64``.
Both functions assume that the IBM data have been converted to NumPy integer
format in such a way that the most significant bits of the floating-point
number become the most significant bits of the integer values. So when decoding
byte data representing IBM hexadecimal floating-point numbers, it's important
to take the endianness of the byte data into account. See the Examples section
below for an example of converting big-endian byte data.
Examples
--------
>>> import numpy
>>> from ibm2ieee import ibm2float32, ibm2float64
>>> ibm2float32(numpy.uint32(0xc1180000))
-1.5
>>> type(ibm2float32(numpy.uint32(0xc1180000)))
<class 'numpy.float32'>
>>> ibm2float32(numpy.uint64(0x413243f6a8885a31))
3.1415927
>>> ibm2float32(numpy.uint32(0x61100000))
inf
>>> ibm2float64(numpy.uint32(0xc1180000))
-1.5
>>> ibm2float64(numpy.uint64(0x413243f6a8885a31))
3.141592653589793
>>> ibm2float64(numpy.uint32(0x61100000))
3.402823669209385e+38
>>> input_array = numpy.arange(
0x40fffffe, 0x41000002, dtype=numpy.uint32).reshape(2, 2)
>>> input_array
array([[1090519038, 1090519039],
[1090519040, 1090519041]], dtype=uint32)
>>> ibm2float64(input_array)
array([[9.99999881e-01, 9.99999940e-01],
[0.00000000e+00, 9.53674316e-07]])
When converting byte data read from a file, it's important to know the
endianness of that data (which is frequently big-endian in historical data
files using IBM hex floating-point). Here's an example of converting IBM
single-precision data stored in big-endian form to ``numpy.float32``. Note the
use of the ``'>u4'`` dtype when converting the bytestring to a NumPy ``uint32``
array. For little-endian input data, you would use ``'<u4'`` instead.
>>> input_data = b'\xc12C\xf7\xc1\x19!\xfb\x00\x00\x00\x00A\x19!\xfbA2C\xf7'
>>> input_as_uint32 = numpy.frombuffer(input_data, dtype='>u4')
>>> input_as_uint32
array([3241296887, 3239649787, 0, 1092166139, 1093813239],
dtype=uint32)
>>> ibm2float32(input_as_uint32)
array([-3.141593, -1.570796, 0. , 1.570796, 3.141593],
dtype=float32)
Notes on the formats
--------------------
The IBM single-precision format has a precision of 6 hexadecimal digits, which
in practice translates to a precision of 21-24 bits, depending on the binade
that the relevant value belongs to. IEEE 754 single-precision has a precision
of 24 bits. So all not-too-small, not-too-large IBM single-precision values can
be translated to IEEE 754 single-precision values with no loss of precision.
However, the IBM single precision range is larger than the corresponding IEEE
754 range, so extreme IBM single-precision values may overflow to infinity,
underflow to zero, or be rounded to a subnormal value when converted to IEEE
754 single-precision.
For double-precision conversions, the tradeoff works the other way: the IBM
double-precision format has an effective precision of 53-56 bits, while IEEE
754 double-precision has 53-bit precision. So most IBM values will be rounded
when converted to IEEE 754. However, the IEEE 754 double-precision range is
larger than that of IBM double-precision, so there's no danger of overflow,
underflow, or reduced-precision subnormal results when converting IBM
double-precision to IEEE 754 double-precision.
Every IBM single-precision value can be exactly represented in IEEE 754
double-precision, so if you want a lossless representation of IBM
single-precision data, use ``ibm2float64``.
Note that the IBM formats do not allow representations of special values like
infinities and NaNs. However, signed zeros are representable, and the sign of a
zero is preserved under all conversions.
Installation
------------
The latest release of ibm2ieee is available from the Python Package Index, at
https://pypi.org/project/ibm2ieee. It can be installed with ``pip`` in the
usual way::
pip install ibm2ieee
Wheels are provided for common platforms and Python versions. If installing
from source, note that ibm2ieee includes a C extension, so you'll need the
appropriate compiler on your system to be able to install.
ibm2ieee requires Python >= 3.7.
License
-------
(C) Copyright 2018-2023 Enthought, Inc., Austin, TX
All rights reserved.
This software is provided without warranty under the terms of the BSD
license included in LICENSE.txt and may be redistributed only under
the conditions described in the aforementioned license. The license
is also available online at http://www.enthought.com/licenses/BSD.txt
Thanks for using Enthought open source!
Raw data
{
"_id": null,
"home_page": "",
"name": "ibm2ieee",
"maintainer": "",
"docs_url": null,
"requires_python": ">=3.7",
"maintainer_email": "",
"keywords": "ibm,hfp,ieee754,hexadecimal,floating-point,ufunc",
"author": "",
"author_email": "Enthought <info@enthought.com>",
"download_url": "https://files.pythonhosted.org/packages/52/36/a08ca13a725fabcea975dce99de27f435a8dcb1715765b0ca2f29a4ce269/ibm2ieee-1.3.3.tar.gz",
"platform": null,
"description": "The **ibm2ieee** package provides NumPy universal functions (\"ufuncs\") for\nconverting IBM single-precision and double-precision hexadecimal floats\nto the IEEE 754-format floats used by Python and NumPy on almost all\ncurrent platforms.\n\n\nFeatures\n--------\n\n- Fast: 200-400 million values converted per second on a typical modern\n machine, assuming normal inputs.\n- Correct: converted results are correctly rounded, according to the default\n IEEE 754 round-ties-to-even rounding mode. Corner cases (overflow, underflow,\n subnormal results, signed zeros, non-normalised input) are all handled\n correctly. Where the rounded converted value is out of range for the target\n type, an appropriately-signed infinity is returned.\n- Handles both single-precision and double-precision input and output formats.\n\nPortability note: the conversion functions provided in this module assume that\n``numpy.float32`` and ``numpy.float64`` are based on the standard IEEE 754\nbinary32 and binary64 floating-point formats. This is true on the overwhelming\nmajority of current platforms, but is not guaranteed by the relevant language\nstandards.\n\n\nUsage\n-----\n\nThe package provides two functions:\n\n- ``ibm2float32`` converts IBM single- or double-precision data to\n IEEE 754 single-precision values, in ``numpy.float32`` format.\n\n- ``ibm2float64`` converts IBM single- or double-precision data to\n IEEE 754 double-precision values, in ``numpy.float64`` format.\n\nFor both functions, IBM single-precision input data must be represented\nusing the ``numpy.uint32`` dtype, while IBM double-precision inputs must\nbe represented using ``numpy.uint64``.\n\nBoth functions assume that the IBM data have been converted to NumPy integer\nformat in such a way that the most significant bits of the floating-point\nnumber become the most significant bits of the integer values. So when decoding\nbyte data representing IBM hexadecimal floating-point numbers, it's important\nto take the endianness of the byte data into account. See the Examples section\nbelow for an example of converting big-endian byte data.\n\n\nExamples\n--------\n\n>>> import numpy\n>>> from ibm2ieee import ibm2float32, ibm2float64\n>>> ibm2float32(numpy.uint32(0xc1180000))\n-1.5\n>>> type(ibm2float32(numpy.uint32(0xc1180000)))\n<class 'numpy.float32'>\n>>> ibm2float32(numpy.uint64(0x413243f6a8885a31))\n3.1415927\n>>> ibm2float32(numpy.uint32(0x61100000))\ninf\n>>> ibm2float64(numpy.uint32(0xc1180000))\n-1.5\n>>> ibm2float64(numpy.uint64(0x413243f6a8885a31))\n3.141592653589793\n>>> ibm2float64(numpy.uint32(0x61100000))\n3.402823669209385e+38\n>>> input_array = numpy.arange(\n 0x40fffffe, 0x41000002, dtype=numpy.uint32).reshape(2, 2)\n>>> input_array\narray([[1090519038, 1090519039],\n [1090519040, 1090519041]], dtype=uint32)\n>>> ibm2float64(input_array)\narray([[9.99999881e-01, 9.99999940e-01],\n [0.00000000e+00, 9.53674316e-07]])\n\nWhen converting byte data read from a file, it's important to know the\nendianness of that data (which is frequently big-endian in historical data\nfiles using IBM hex floating-point). Here's an example of converting IBM\nsingle-precision data stored in big-endian form to ``numpy.float32``. Note the\nuse of the ``'>u4'`` dtype when converting the bytestring to a NumPy ``uint32``\narray. For little-endian input data, you would use ``'<u4'`` instead.\n\n>>> input_data = b'\\xc12C\\xf7\\xc1\\x19!\\xfb\\x00\\x00\\x00\\x00A\\x19!\\xfbA2C\\xf7'\n>>> input_as_uint32 = numpy.frombuffer(input_data, dtype='>u4')\n>>> input_as_uint32\narray([3241296887, 3239649787, 0, 1092166139, 1093813239],\n dtype=uint32)\n>>> ibm2float32(input_as_uint32)\narray([-3.141593, -1.570796, 0. , 1.570796, 3.141593],\n dtype=float32)\n\n\nNotes on the formats\n--------------------\n\nThe IBM single-precision format has a precision of 6 hexadecimal digits, which\nin practice translates to a precision of 21-24 bits, depending on the binade\nthat the relevant value belongs to. IEEE 754 single-precision has a precision\nof 24 bits. So all not-too-small, not-too-large IBM single-precision values can\nbe translated to IEEE 754 single-precision values with no loss of precision.\nHowever, the IBM single precision range is larger than the corresponding IEEE\n754 range, so extreme IBM single-precision values may overflow to infinity,\nunderflow to zero, or be rounded to a subnormal value when converted to IEEE\n754 single-precision.\n\nFor double-precision conversions, the tradeoff works the other way: the IBM\ndouble-precision format has an effective precision of 53-56 bits, while IEEE\n754 double-precision has 53-bit precision. So most IBM values will be rounded\nwhen converted to IEEE 754. However, the IEEE 754 double-precision range is\nlarger than that of IBM double-precision, so there's no danger of overflow,\nunderflow, or reduced-precision subnormal results when converting IBM\ndouble-precision to IEEE 754 double-precision.\n\nEvery IBM single-precision value can be exactly represented in IEEE 754\ndouble-precision, so if you want a lossless representation of IBM\nsingle-precision data, use ``ibm2float64``.\n\nNote that the IBM formats do not allow representations of special values like\ninfinities and NaNs. However, signed zeros are representable, and the sign of a\nzero is preserved under all conversions.\n\n\nInstallation\n------------\n\nThe latest release of ibm2ieee is available from the Python Package Index, at\nhttps://pypi.org/project/ibm2ieee. It can be installed with ``pip`` in the\nusual way::\n\n pip install ibm2ieee\n\nWheels are provided for common platforms and Python versions. If installing\nfrom source, note that ibm2ieee includes a C extension, so you'll need the\nappropriate compiler on your system to be able to install.\n\nibm2ieee requires Python >= 3.7.\n\n\nLicense\n-------\n\n(C) Copyright 2018-2023 Enthought, Inc., Austin, TX\nAll rights reserved.\n\nThis software is provided without warranty under the terms of the BSD\nlicense included in LICENSE.txt and may be redistributed only under\nthe conditions described in the aforementioned license. The license\nis also available online at http://www.enthought.com/licenses/BSD.txt\n\nThanks for using Enthought open source!\n",
"bugtrack_url": null,
"license": "BSD 3-Clause License (C) Copyright 2018-2023 Enthought, Inc., Austin, TX All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. ",
"summary": "Convert IBM hexadecimal floating-point to IEEE 754 floating-point",
"version": "1.3.3",
"project_urls": {
"issues": "https://github.com/enthought/ibm2ieee/issues",
"readme": "https://github.com/enthought/ibm2ieee/blob/main/README.rst",
"repository": "https://github.com/enthought/ibm2ieee"
},
"split_keywords": [
"ibm",
"hfp",
"ieee754",
"hexadecimal",
"floating-point",
"ufunc"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "9d39a7927800c673c04259e955d5e7bcac5a4b4dd9092a9cfeba2b9f6deb1ee0",
"md5": "29a729c7852d362ee3fd21857e5d73a7",
"sha256": "5340c49aabb04e9e11d34b86d6a988db4dc763d30df54c1ef538ccd2c493b46e"
},
"downloads": -1,
"filename": "ibm2ieee-1.3.3-cp310-cp310-macosx_10_9_universal2.whl",
"has_sig": false,
"md5_digest": "29a729c7852d362ee3fd21857e5d73a7",
"packagetype": "bdist_wheel",
"python_version": "cp310",
"requires_python": ">=3.7",
"size": 21865,
"upload_time": "2023-10-08T09:03:46",
"upload_time_iso_8601": "2023-10-08T09:03:46.325491Z",
"url": "https://files.pythonhosted.org/packages/9d/39/a7927800c673c04259e955d5e7bcac5a4b4dd9092a9cfeba2b9f6deb1ee0/ibm2ieee-1.3.3-cp310-cp310-macosx_10_9_universal2.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "15d31b589ff2553d9840e143ef5728a204b994009e022f2b9035844567419498",
"md5": "31bea4410d9a5db457ec4a76ee460535",
"sha256": "55b32b617f32771d406dd9448902734a0c3dc5e8002be0ed88c92629563f9064"
},
"downloads": -1,
"filename": "ibm2ieee-1.3.3-cp310-cp310-macosx_10_9_x86_64.whl",
"has_sig": false,
"md5_digest": "31bea4410d9a5db457ec4a76ee460535",
"packagetype": "bdist_wheel",
"python_version": "cp310",
"requires_python": ">=3.7",
"size": 18230,
"upload_time": "2023-10-08T09:03:47",
"upload_time_iso_8601": "2023-10-08T09:03:47.738742Z",
"url": "https://files.pythonhosted.org/packages/15/d3/1b589ff2553d9840e143ef5728a204b994009e022f2b9035844567419498/ibm2ieee-1.3.3-cp310-cp310-macosx_10_9_x86_64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "13fb4b88903393e8a61069342306f5970c270579bc8e56eddac5145956e23b25",
"md5": "b932a62d55eace09c3a8f670bcded85e",
"sha256": "e4b133f603d4c84b5bfd3c7c39b2312c98b8f1363acd41b2d5094e76d3bbf802"
},
"downloads": -1,
"filename": "ibm2ieee-1.3.3-cp310-cp310-win32.whl",
"has_sig": false,
"md5_digest": "b932a62d55eace09c3a8f670bcded85e",
"packagetype": "bdist_wheel",
"python_version": "cp310",
"requires_python": ">=3.7",
"size": 20942,
"upload_time": "2023-10-08T09:06:40",
"upload_time_iso_8601": "2023-10-08T09:06:40.970815Z",
"url": "https://files.pythonhosted.org/packages/13/fb/4b88903393e8a61069342306f5970c270579bc8e56eddac5145956e23b25/ibm2ieee-1.3.3-cp310-cp310-win32.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "a7b974f8d3b5bbe3a226659d3837d301d3159c9c73009d5f4271303d0b951382",
"md5": "6e6336011a0ae13771a0f8f04ff56e4b",
"sha256": "0fd98b23047f172791268c5aecda4e3b2779ff20c6ce4e81153c44bf03623e52"
},
"downloads": -1,
"filename": "ibm2ieee-1.3.3-cp310-cp310-win_amd64.whl",
"has_sig": false,
"md5_digest": "6e6336011a0ae13771a0f8f04ff56e4b",
"packagetype": "bdist_wheel",
"python_version": "cp310",
"requires_python": ">=3.7",
"size": 21309,
"upload_time": "2023-10-08T09:06:41",
"upload_time_iso_8601": "2023-10-08T09:06:41.963401Z",
"url": "https://files.pythonhosted.org/packages/a7/b9/74f8d3b5bbe3a226659d3837d301d3159c9c73009d5f4271303d0b951382/ibm2ieee-1.3.3-cp310-cp310-win_amd64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "2cb166766b47203acbb018eb802543264f3d2f77f78eeddfcfaf8ea24edd30b2",
"md5": "85f1d8c168e66f84238afb6a99ee2913",
"sha256": "b26a1ae2d2b53227469e68e735f1b0a4fa9fb21c510b25f55dd8810433230003"
},
"downloads": -1,
"filename": "ibm2ieee-1.3.3-cp311-cp311-macosx_10_9_universal2.whl",
"has_sig": false,
"md5_digest": "85f1d8c168e66f84238afb6a99ee2913",
"packagetype": "bdist_wheel",
"python_version": "cp311",
"requires_python": ">=3.7",
"size": 21867,
"upload_time": "2023-10-08T09:03:49",
"upload_time_iso_8601": "2023-10-08T09:03:49.148830Z",
"url": "https://files.pythonhosted.org/packages/2c/b1/66766b47203acbb018eb802543264f3d2f77f78eeddfcfaf8ea24edd30b2/ibm2ieee-1.3.3-cp311-cp311-macosx_10_9_universal2.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "c163e87fa737627db23b3837ff528ab75abb2d63101894bcc9672ddba47c3dd2",
"md5": "228ab6dab75c4b3ab44f5579342fa4ff",
"sha256": "eec915bc08020c599e195a5b84b248fbe13319bb223708885eeaeb747b3f13dd"
},
"downloads": -1,
"filename": "ibm2ieee-1.3.3-cp311-cp311-macosx_10_9_x86_64.whl",
"has_sig": false,
"md5_digest": "228ab6dab75c4b3ab44f5579342fa4ff",
"packagetype": "bdist_wheel",
"python_version": "cp311",
"requires_python": ">=3.7",
"size": 18228,
"upload_time": "2023-10-08T09:03:51",
"upload_time_iso_8601": "2023-10-08T09:03:51.041810Z",
"url": "https://files.pythonhosted.org/packages/c1/63/e87fa737627db23b3837ff528ab75abb2d63101894bcc9672ddba47c3dd2/ibm2ieee-1.3.3-cp311-cp311-macosx_10_9_x86_64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "d16e92ced26d1a8423cec1fb853c95b7a0d067d8ba973279bcf87cb7a087d01e",
"md5": "32a10074712efa5f30c6ef1370846ac0",
"sha256": "421d34251718f9438cdb31af8a950e56fb26517de439cd29d99f350fdaa640e0"
},
"downloads": -1,
"filename": "ibm2ieee-1.3.3-cp311-cp311-win32.whl",
"has_sig": false,
"md5_digest": "32a10074712efa5f30c6ef1370846ac0",
"packagetype": "bdist_wheel",
"python_version": "cp311",
"requires_python": ">=3.7",
"size": 20937,
"upload_time": "2023-10-08T09:06:43",
"upload_time_iso_8601": "2023-10-08T09:06:43.467515Z",
"url": "https://files.pythonhosted.org/packages/d1/6e/92ced26d1a8423cec1fb853c95b7a0d067d8ba973279bcf87cb7a087d01e/ibm2ieee-1.3.3-cp311-cp311-win32.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "df5efcff91cbd3c02c2c49e471f0eb1830e7ec93eacc8d65ae1e7a5b7d0e08d7",
"md5": "fd184400cec5b530410a375cec5c4e11",
"sha256": "4ac75199641bcdce9a53685b912cd4fb087919db72c90c5cd62a81a4301af251"
},
"downloads": -1,
"filename": "ibm2ieee-1.3.3-cp311-cp311-win_amd64.whl",
"has_sig": false,
"md5_digest": "fd184400cec5b530410a375cec5c4e11",
"packagetype": "bdist_wheel",
"python_version": "cp311",
"requires_python": ">=3.7",
"size": 21303,
"upload_time": "2023-10-08T09:06:45",
"upload_time_iso_8601": "2023-10-08T09:06:45.777303Z",
"url": "https://files.pythonhosted.org/packages/df/5e/fcff91cbd3c02c2c49e471f0eb1830e7ec93eacc8d65ae1e7a5b7d0e08d7/ibm2ieee-1.3.3-cp311-cp311-win_amd64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "e423dae6e26bc749b07d3a11f2509006b2d9e2fb4ffdf30eeb9566b416f645fc",
"md5": "e4f09e110b625d2b251d188ae5c85541",
"sha256": "0f78585170c1ff3c9a0be658b71c0ce1ddf5a005376ee5cacf2184031575a509"
},
"downloads": -1,
"filename": "ibm2ieee-1.3.3-cp312-cp312-macosx_10_9_universal2.whl",
"has_sig": false,
"md5_digest": "e4f09e110b625d2b251d188ae5c85541",
"packagetype": "bdist_wheel",
"python_version": "cp312",
"requires_python": ">=3.7",
"size": 22167,
"upload_time": "2023-10-08T09:03:52",
"upload_time_iso_8601": "2023-10-08T09:03:52.037522Z",
"url": "https://files.pythonhosted.org/packages/e4/23/dae6e26bc749b07d3a11f2509006b2d9e2fb4ffdf30eeb9566b416f645fc/ibm2ieee-1.3.3-cp312-cp312-macosx_10_9_universal2.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "02809984268a888e9afe2a3a1a91f53d9d372c89a768643554d702dd1293bef4",
"md5": "556f7183e0fea7f028c4f9dda4fe0948",
"sha256": "26efbebc1dd7395b131f98b6381ebf01c5c802275f457dbc59d8c467eaea291f"
},
"downloads": -1,
"filename": "ibm2ieee-1.3.3-cp312-cp312-macosx_10_9_x86_64.whl",
"has_sig": false,
"md5_digest": "556f7183e0fea7f028c4f9dda4fe0948",
"packagetype": "bdist_wheel",
"python_version": "cp312",
"requires_python": ">=3.7",
"size": 18378,
"upload_time": "2023-10-08T09:03:53",
"upload_time_iso_8601": "2023-10-08T09:03:53.437071Z",
"url": "https://files.pythonhosted.org/packages/02/80/9984268a888e9afe2a3a1a91f53d9d372c89a768643554d702dd1293bef4/ibm2ieee-1.3.3-cp312-cp312-macosx_10_9_x86_64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "d4336d686bf7791662f6aeae96a0153d5f5e2bea8a9b31b56269bf86cc92bda7",
"md5": "206ca919f27169264f0f33b1175c03fa",
"sha256": "03042c6e1b25c8f1e6255544ddc831049df047b8832c5bf1397643de82050021"
},
"downloads": -1,
"filename": "ibm2ieee-1.3.3-cp312-cp312-win32.whl",
"has_sig": false,
"md5_digest": "206ca919f27169264f0f33b1175c03fa",
"packagetype": "bdist_wheel",
"python_version": "cp312",
"requires_python": ">=3.7",
"size": 21094,
"upload_time": "2023-10-08T09:06:47",
"upload_time_iso_8601": "2023-10-08T09:06:47.244640Z",
"url": "https://files.pythonhosted.org/packages/d4/33/6d686bf7791662f6aeae96a0153d5f5e2bea8a9b31b56269bf86cc92bda7/ibm2ieee-1.3.3-cp312-cp312-win32.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "5ae3f422836427704cb108d974e30edb18d8e25f9bc3589e382259e53f9926d7",
"md5": "4c60348f50a6eaceccc9da57ccc029aa",
"sha256": "5f8fdf3b5689f840eee06d502bef129e72383dcef42aba47a14bbe6b8808540c"
},
"downloads": -1,
"filename": "ibm2ieee-1.3.3-cp312-cp312-win_amd64.whl",
"has_sig": false,
"md5_digest": "4c60348f50a6eaceccc9da57ccc029aa",
"packagetype": "bdist_wheel",
"python_version": "cp312",
"requires_python": ">=3.7",
"size": 21442,
"upload_time": "2023-10-08T09:06:48",
"upload_time_iso_8601": "2023-10-08T09:06:48.182384Z",
"url": "https://files.pythonhosted.org/packages/5a/e3/f422836427704cb108d974e30edb18d8e25f9bc3589e382259e53f9926d7/ibm2ieee-1.3.3-cp312-cp312-win_amd64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "0211ec263ed82d22d330dacc92013f45e4bb1fad963d4dc9c8500317e8e955f3",
"md5": "7ff30cdc0686d3c4d6e094b075cf02b5",
"sha256": "09435bf063ccca4fa30aa79dbf340529e37ba931e76274cd98f73ce9615821d4"
},
"downloads": -1,
"filename": "ibm2ieee-1.3.3-cp37-cp37m-macosx_10_9_x86_64.whl",
"has_sig": false,
"md5_digest": "7ff30cdc0686d3c4d6e094b075cf02b5",
"packagetype": "bdist_wheel",
"python_version": "cp37",
"requires_python": ">=3.7",
"size": 18140,
"upload_time": "2023-10-08T09:03:54",
"upload_time_iso_8601": "2023-10-08T09:03:54.829462Z",
"url": "https://files.pythonhosted.org/packages/02/11/ec263ed82d22d330dacc92013f45e4bb1fad963d4dc9c8500317e8e955f3/ibm2ieee-1.3.3-cp37-cp37m-macosx_10_9_x86_64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "d9f2d74243f48c621d9f6eddcf14cc46798530da21385fa488268418344b693a",
"md5": "d1c757ce042291b7c53f6843f314bc61",
"sha256": "1404305922aabe8df170a0e647182ab674703b326a99c8624d9b498efbd058fb"
},
"downloads": -1,
"filename": "ibm2ieee-1.3.3-cp37-cp37m-win32.whl",
"has_sig": false,
"md5_digest": "d1c757ce042291b7c53f6843f314bc61",
"packagetype": "bdist_wheel",
"python_version": "cp37",
"requires_python": ">=3.7",
"size": 20916,
"upload_time": "2023-10-08T09:06:49",
"upload_time_iso_8601": "2023-10-08T09:06:49.513881Z",
"url": "https://files.pythonhosted.org/packages/d9/f2/d74243f48c621d9f6eddcf14cc46798530da21385fa488268418344b693a/ibm2ieee-1.3.3-cp37-cp37m-win32.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "6359427b39252cf45325490f07e46e76cbe249ca974a2f0ae8b1113227cf3616",
"md5": "155993ff7418beb346f42eec3f8a68f1",
"sha256": "f545f2da5992c637730bc5e84c3913f04aae716abe7dbd8e5b1ef5ca64febac8"
},
"downloads": -1,
"filename": "ibm2ieee-1.3.3-cp37-cp37m-win_amd64.whl",
"has_sig": false,
"md5_digest": "155993ff7418beb346f42eec3f8a68f1",
"packagetype": "bdist_wheel",
"python_version": "cp37",
"requires_python": ">=3.7",
"size": 21279,
"upload_time": "2023-10-08T09:06:50",
"upload_time_iso_8601": "2023-10-08T09:06:50.910535Z",
"url": "https://files.pythonhosted.org/packages/63/59/427b39252cf45325490f07e46e76cbe249ca974a2f0ae8b1113227cf3616/ibm2ieee-1.3.3-cp37-cp37m-win_amd64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "cd727d22ce91464dfc44f3b27bf81cde5f3ebcb1c6bd89ba575bccbb9669c6b5",
"md5": "9f1d5eb2ef41fbed4c048ba018872a1a",
"sha256": "48c49dbc50fcb16936f65b93edbbc0c484c6342ff5458eb4b63adf8d7cb1bd95"
},
"downloads": -1,
"filename": "ibm2ieee-1.3.3-cp38-cp38-macosx_10_9_universal2.whl",
"has_sig": false,
"md5_digest": "9f1d5eb2ef41fbed4c048ba018872a1a",
"packagetype": "bdist_wheel",
"python_version": "cp38",
"requires_python": ">=3.7",
"size": 21870,
"upload_time": "2023-10-08T09:03:55",
"upload_time_iso_8601": "2023-10-08T09:03:55.805823Z",
"url": "https://files.pythonhosted.org/packages/cd/72/7d22ce91464dfc44f3b27bf81cde5f3ebcb1c6bd89ba575bccbb9669c6b5/ibm2ieee-1.3.3-cp38-cp38-macosx_10_9_universal2.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "01406c54fb90f9168d6e5e185e0a36dd34b859f927a834f641a31f1375e65406",
"md5": "e1e9877ef40b16bffc70d3a10364de35",
"sha256": "4851503b186edf461e7fdcf04ccbafae2fab2f9a6c9fdad6a4188ab0c8d2a044"
},
"downloads": -1,
"filename": "ibm2ieee-1.3.3-cp38-cp38-macosx_10_9_x86_64.whl",
"has_sig": false,
"md5_digest": "e1e9877ef40b16bffc70d3a10364de35",
"packagetype": "bdist_wheel",
"python_version": "cp38",
"requires_python": ">=3.7",
"size": 18227,
"upload_time": "2023-10-08T09:03:57",
"upload_time_iso_8601": "2023-10-08T09:03:57.254344Z",
"url": "https://files.pythonhosted.org/packages/01/40/6c54fb90f9168d6e5e185e0a36dd34b859f927a834f641a31f1375e65406/ibm2ieee-1.3.3-cp38-cp38-macosx_10_9_x86_64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "420cc4de8422de69d62035d8fb7898df8e2ffe57da24de034b94cccbf6c7c43e",
"md5": "d3b99d7a0247781dd2138e29ab79e9de",
"sha256": "5e596927c89cbcecd1d79a7192d33ef1d12d7134d5387e28ad0a0db0f9ac60b5"
},
"downloads": -1,
"filename": "ibm2ieee-1.3.3-cp38-cp38-win32.whl",
"has_sig": false,
"md5_digest": "d3b99d7a0247781dd2138e29ab79e9de",
"packagetype": "bdist_wheel",
"python_version": "cp38",
"requires_python": ">=3.7",
"size": 20937,
"upload_time": "2023-10-08T09:06:51",
"upload_time_iso_8601": "2023-10-08T09:06:51.821317Z",
"url": "https://files.pythonhosted.org/packages/42/0c/c4de8422de69d62035d8fb7898df8e2ffe57da24de034b94cccbf6c7c43e/ibm2ieee-1.3.3-cp38-cp38-win32.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "b8b0e35461fcada70b011f4fb2de363e797ed1c2628227b628020c96f7012537",
"md5": "3563428fbe74c47a9603afe645cfde49",
"sha256": "cdb2bd1c71c63da72fb2b7ca6fc028cec51435778e63a502da03059e38a6b715"
},
"downloads": -1,
"filename": "ibm2ieee-1.3.3-cp38-cp38-win_amd64.whl",
"has_sig": false,
"md5_digest": "3563428fbe74c47a9603afe645cfde49",
"packagetype": "bdist_wheel",
"python_version": "cp38",
"requires_python": ">=3.7",
"size": 21300,
"upload_time": "2023-10-08T09:06:52",
"upload_time_iso_8601": "2023-10-08T09:06:52.906045Z",
"url": "https://files.pythonhosted.org/packages/b8/b0/e35461fcada70b011f4fb2de363e797ed1c2628227b628020c96f7012537/ibm2ieee-1.3.3-cp38-cp38-win_amd64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "aac57a3709f0e43cc74970dd12773f5e0e5f9957c148e54c22a36d76271a4c70",
"md5": "0aea1c6e4ab100e34756461b0e3649de",
"sha256": "c0ca1e8739e8a1bfe6bf8c5e103eb19fa876cf2f16dce1c88c04d09eaf3ec3a9"
},
"downloads": -1,
"filename": "ibm2ieee-1.3.3-cp39-cp39-macosx_10_9_universal2.whl",
"has_sig": false,
"md5_digest": "0aea1c6e4ab100e34756461b0e3649de",
"packagetype": "bdist_wheel",
"python_version": "cp39",
"requires_python": ">=3.7",
"size": 21868,
"upload_time": "2023-10-08T09:03:58",
"upload_time_iso_8601": "2023-10-08T09:03:58.665711Z",
"url": "https://files.pythonhosted.org/packages/aa/c5/7a3709f0e43cc74970dd12773f5e0e5f9957c148e54c22a36d76271a4c70/ibm2ieee-1.3.3-cp39-cp39-macosx_10_9_universal2.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "7e24e8b8628b3665252d5fec348dd56ac4a5b77ba2c88d2547a766f3a1b5bb74",
"md5": "b2516c0fe0c72482fee71c4aa1d3b845",
"sha256": "76a93726dc9f476a61ab52b60ab7b9b6bf57d1642d852fc3f37e292151c14cbe"
},
"downloads": -1,
"filename": "ibm2ieee-1.3.3-cp39-cp39-macosx_10_9_x86_64.whl",
"has_sig": false,
"md5_digest": "b2516c0fe0c72482fee71c4aa1d3b845",
"packagetype": "bdist_wheel",
"python_version": "cp39",
"requires_python": ">=3.7",
"size": 18226,
"upload_time": "2023-10-08T09:03:59",
"upload_time_iso_8601": "2023-10-08T09:03:59.483715Z",
"url": "https://files.pythonhosted.org/packages/7e/24/e8b8628b3665252d5fec348dd56ac4a5b77ba2c88d2547a766f3a1b5bb74/ibm2ieee-1.3.3-cp39-cp39-macosx_10_9_x86_64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "0abd8d7e441fb265b56ecbf8a2e8e9b61c9b34c090bf4132fbaddb681bb520de",
"md5": "08a38ccbc750c7a9bdfcb71ee0e44242",
"sha256": "626b505b92702a556cc1e9c63c5b134edaf1bce77a57d9590e04fcd82f159ab3"
},
"downloads": -1,
"filename": "ibm2ieee-1.3.3-cp39-cp39-win32.whl",
"has_sig": false,
"md5_digest": "08a38ccbc750c7a9bdfcb71ee0e44242",
"packagetype": "bdist_wheel",
"python_version": "cp39",
"requires_python": ">=3.7",
"size": 20933,
"upload_time": "2023-10-08T09:06:54",
"upload_time_iso_8601": "2023-10-08T09:06:54.374455Z",
"url": "https://files.pythonhosted.org/packages/0a/bd/8d7e441fb265b56ecbf8a2e8e9b61c9b34c090bf4132fbaddb681bb520de/ibm2ieee-1.3.3-cp39-cp39-win32.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "dab9be1ec735c02f40128893f79628364ce67b50bff8ceb55d78b3c91df8447f",
"md5": "e00d8aefef675c9c1d09c486bb456b8e",
"sha256": "fbe6351cd79093ed87d4dd85bc147ec16baf01f98fce906310039756efc2fe1a"
},
"downloads": -1,
"filename": "ibm2ieee-1.3.3-cp39-cp39-win_amd64.whl",
"has_sig": false,
"md5_digest": "e00d8aefef675c9c1d09c486bb456b8e",
"packagetype": "bdist_wheel",
"python_version": "cp39",
"requires_python": ">=3.7",
"size": 21303,
"upload_time": "2023-10-08T09:06:55",
"upload_time_iso_8601": "2023-10-08T09:06:55.733396Z",
"url": "https://files.pythonhosted.org/packages/da/b9/be1ec735c02f40128893f79628364ce67b50bff8ceb55d78b3c91df8447f/ibm2ieee-1.3.3-cp39-cp39-win_amd64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "5236a08ca13a725fabcea975dce99de27f435a8dcb1715765b0ca2f29a4ce269",
"md5": "2bd40208bbc09b5b689acc06de0de577",
"sha256": "c913cce4315502badd335738e89632a60e0e4253a01bffa5a6d21d6d9172334f"
},
"downloads": -1,
"filename": "ibm2ieee-1.3.3.tar.gz",
"has_sig": false,
"md5_digest": "2bd40208bbc09b5b689acc06de0de577",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.7",
"size": 17088,
"upload_time": "2023-10-08T08:53:24",
"upload_time_iso_8601": "2023-10-08T08:53:24.473118Z",
"url": "https://files.pythonhosted.org/packages/52/36/a08ca13a725fabcea975dce99de27f435a8dcb1715765b0ca2f29a4ce269/ibm2ieee-1.3.3.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2023-10-08 08:53:24",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "enthought",
"github_project": "ibm2ieee",
"travis_ci": false,
"coveralls": false,
"github_actions": true,
"lcname": "ibm2ieee"
}
