amplpy

Python API for AMPL

These details have not been verified by PyPI

Project links

Project description

AMPL API is an interface that allows developers to access the features of the AMPL interpreter from within a programming language. All model generation and solver interaction is handled directly by AMPL, which leads to great stability and speed; the library just acts as an intermediary, and the added overhead (in terms of memory and CPU usage) depends mostly on how much data is read back from AMPL, the size of the model as such is irrelevant. Functions for directly assigning data to AMPL parameters and sets are provided, which can be used instead of the normal AMPL data reading procedures. AMPL API has been written with usability in mind, and it is easy to access its functionalities from C++, Java, C#, MATLAB, R and Python.

The AMPL API can function as an add-on to any existing AMPL installation. If you do not yet have an AMPL installation on the computer where you will be working with the API, see our demo page or trial page to download a working version that can be installed quickly.

Documentation:

Repositories:

GitHub Repository: https://github.com/ampl/amplpy
PyPI Repository: https://pypi.python.org/pypi/amplpy

Project details

These details have not been verified by PyPI

Project links

Release history Release notifications | RSS feed

0.16.0

Dec 8, 2025

0.16.0b1 pre-release

Oct 28, 2025

0.16.0b0 pre-release

Oct 24, 2025

0.15.2

Jul 9, 2025

0.15.1

Jun 20, 2025

0.15.0

Jun 18, 2025

0.15.0b9 pre-release

Jun 16, 2025

0.15.0b8 pre-release

May 14, 2025

0.15.0b7 pre-release

May 12, 2025

0.15.0b6 pre-release

May 10, 2025

0.15.0b5 pre-release

May 9, 2025

0.15.0b4 pre-release

Apr 25, 2025

0.15.0b3 pre-release

Apr 1, 2025

0.15.0b2 pre-release

Mar 25, 2025

0.15.0b1 pre-release

Mar 24, 2025

0.15.0b0 pre-release

Nov 26, 2024

0.14.1b0 pre-release

May 21, 2024

0.14.0

May 21, 2024

0.14.0b0 pre-release

May 21, 2024

0.13.4b0 pre-release

May 20, 2024

0.13.3

Feb 20, 2024

0.13.3b0 pre-release

Feb 20, 2024

0.13.2

Jan 5, 2024

0.13.2b1 pre-release

Feb 20, 2024

0.13.2b0 pre-release

Jan 5, 2024

0.13.1

Dec 29, 2023

0.13.1b0 pre-release

Dec 29, 2023

0.13.0

Dec 26, 2023

0.13.0b0 pre-release

Dec 26, 2023

0.12.2

Dec 11, 2023

0.12.1

Sep 21, 2023

0.12.0

Aug 29, 2023

0.11.2

Jul 11, 2023

0.11.1

Jul 4, 2023

0.11.0

Jun 21, 2023

0.10.0

May 22, 2023

0.9.3

Apr 21, 2023

0.9.2

Apr 5, 2023

0.9.1

Mar 22, 2023

0.9.0

Mar 14, 2023

0.8.6

Jan 23, 2023

0.8.5

Aug 24, 2022

0.8.4

Aug 2, 2022

0.8.3

Jun 23, 2022

0.8.2

May 27, 2022

0.8.1

Jan 21, 2022

0.8.0

Dec 24, 2021

0.7.2

Dec 1, 2021

0.7.1

Jul 26, 2021

0.7.0

Jan 25, 2021

0.6.11

Feb 28, 2020

0.6.10

Nov 17, 2019

0.6.7

Jun 22, 2019

0.6.6

May 11, 2019

0.6.5

Apr 14, 2019

0.6.4

Feb 13, 2019

0.6.3

Dec 4, 2018

0.6.2

Nov 20, 2018

0.6.1

Nov 13, 2018

0.6.0

Nov 4, 2018

0.5.0

May 6, 2018

This version

0.4.1

Apr 12, 2018

0.4.0

Apr 9, 2018

0.3.4

Feb 24, 2018

0.3.3

Feb 12, 2018

0.3.2

Dec 14, 2017

0.3.1

Nov 17, 2017

0.3.0

Oct 13, 2017

0.2.0

Aug 4, 2017

0.1.2

Aug 4, 2017

0.1.1

Jul 30, 2017

0.1.0

Jul 22, 2017

Download files

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

Source Distribution

amplpy-0.4.1.tar.gz (8.6 MB view details)

Uploaded Apr 12, 2018 Source

Built Distributions

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

The dropdown lists show the available interpreters, ABIs, and platforms. Enable javascript to be able to filter the list of wheel files.

amplpy-0.4.1-cp36-cp36m-win_amd64.whl (9.1 MB view details)

Uploaded Apr 12, 2018 CPython 3.6mWindows x86-64

amplpy-0.4.1-cp36-cp36m-win32.whl (9.0 MB view details)

Uploaded Apr 12, 2018 CPython 3.6mWindows x86

amplpy-0.4.1-cp36-cp36m-manylinux1_x86_64.whl (11.3 MB view details)

Uploaded Apr 12, 2018 CPython 3.6m

amplpy-0.4.1-cp36-cp36m-manylinux1_i686.whl (11.4 MB view details)

Uploaded Apr 12, 2018 CPython 3.6m

amplpy-0.4.1-cp36-cp36m-macosx_10_6_intel.whl (9.8 MB view details)

Uploaded Apr 12, 2018 CPython 3.6mmacOS 10.6+ Intel (x86-64, i386)

amplpy-0.4.1-cp35-cp35m-win_amd64.whl (9.1 MB view details)

Uploaded Apr 12, 2018 CPython 3.5mWindows x86-64

amplpy-0.4.1-cp35-cp35m-win32.whl (9.0 MB view details)

Uploaded Apr 12, 2018 CPython 3.5mWindows x86

amplpy-0.4.1-cp35-cp35m-manylinux1_x86_64.whl (11.3 MB view details)

Uploaded Apr 12, 2018 CPython 3.5m

amplpy-0.4.1-cp35-cp35m-manylinux1_i686.whl (11.4 MB view details)

Uploaded Apr 12, 2018 CPython 3.5m

amplpy-0.4.1-cp35-cp35m-macosx_10_6_intel.whl (9.8 MB view details)

Uploaded Apr 12, 2018 CPython 3.5mmacOS 10.6+ Intel (x86-64, i386)

amplpy-0.4.1-cp34-cp34m-win_amd64.whl (9.1 MB view details)

Uploaded Apr 12, 2018 CPython 3.4mWindows x86-64

amplpy-0.4.1-cp34-cp34m-win32.whl (9.0 MB view details)

Uploaded Apr 12, 2018 CPython 3.4mWindows x86

amplpy-0.4.1-cp34-cp34m-manylinux1_x86_64.whl (11.3 MB view details)

Uploaded Apr 12, 2018 CPython 3.4m

amplpy-0.4.1-cp34-cp34m-manylinux1_i686.whl (11.4 MB view details)

Uploaded Apr 12, 2018 CPython 3.4m

amplpy-0.4.1-cp34-cp34m-macosx_10_6_intel.whl (9.8 MB view details)

Uploaded Apr 12, 2018 CPython 3.4mmacOS 10.6+ Intel (x86-64, i386)

amplpy-0.4.1-cp33-cp33m-win_amd64.whl (9.1 MB view details)

Uploaded Apr 12, 2018 CPython 3.3mWindows x86-64

amplpy-0.4.1-cp33-cp33m-win32.whl (9.0 MB view details)

Uploaded Apr 12, 2018 CPython 3.3mWindows x86

amplpy-0.4.1-cp33-cp33m-manylinux1_x86_64.whl (11.3 MB view details)

Uploaded Apr 12, 2018 CPython 3.3m

amplpy-0.4.1-cp33-cp33m-manylinux1_i686.whl (11.4 MB view details)

Uploaded Apr 12, 2018 CPython 3.3m

amplpy-0.4.1-cp27-cp27mu-manylinux1_x86_64.whl (11.3 MB view details)

Uploaded Apr 12, 2018 CPython 2.7mu

amplpy-0.4.1-cp27-cp27mu-manylinux1_i686.whl (11.4 MB view details)

Uploaded Apr 12, 2018 CPython 2.7mu

amplpy-0.4.1-cp27-cp27m-win_amd64.whl (9.1 MB view details)

Uploaded Apr 12, 2018 CPython 2.7mWindows x86-64

amplpy-0.4.1-cp27-cp27m-win32.whl (9.0 MB view details)

Uploaded Apr 12, 2018 CPython 2.7mWindows x86

amplpy-0.4.1-cp27-cp27m-manylinux1_x86_64.whl (11.3 MB view details)

Uploaded Apr 12, 2018 CPython 2.7m

amplpy-0.4.1-cp27-cp27m-manylinux1_i686.whl (11.4 MB view details)

Uploaded Apr 12, 2018 CPython 2.7m

amplpy-0.4.1-cp27-cp27m-macosx_10_6_intel.whl (9.8 MB view details)

Uploaded Apr 12, 2018 CPython 2.7mmacOS 10.6+ Intel (x86-64, i386)

File details

Details for the file amplpy-0.4.1.tar.gz.

File metadata

Download URL: amplpy-0.4.1.tar.gz
Upload date: Apr 12, 2018
Size: 8.6 MB
Tags: Source
Uploaded using Trusted Publishing? No

File hashes

Hashes for amplpy-0.4.1.tar.gz
Algorithm	Hash digest
SHA256	`3fd964aa989003716da657a779e0ad73bf105e6b4cc86160b8d0320003f941cf`
MD5	`0b42f115faeed55353e2731109225222`
BLAKE2b-256	`526ac653eff298bc171034faa6f57b71afdb1e59dbc7bbcd9e24d3c028700e82`

Algorithm	Hash digest
SHA256	`dd37c9aa07eb1708aaed3bf767095d7e9e362671352f89eccea02887679f17f2`
MD5	`74498abaa321e46abc7fecc5ec8b0504`
BLAKE2b-256	`fac2eeeed297e9df4b11d3f1c4ae0cec6630e6ad644c5ea74912ba1d641fbb68`

Algorithm	Hash digest
SHA256	`0283da3a80aae3d18cba43419e50bc382c6068d16aecb23e2a711847f7d0495c`
MD5	`b9328dc6eaf9f5450f09a298cbcbb98b`
BLAKE2b-256	`6ec6d7c8a0d5c93c338e2d68f0958a4b1d9a0592d4c14a65eb047152220686f1`

Algorithm	Hash digest
SHA256	`bae717eaeb66054f29c84ba74e58aad6720fa50b45bbd4f196bd527caa501781`
MD5	`c17b7026954f8e0fcb21b3910e617f00`
BLAKE2b-256	`408e41f5a0aaeb3bba0e83b0c6c8fe241e85dcf27391be5e9459c31bd9334bc3`

Algorithm	Hash digest
SHA256	`7c4d7cf848e99562b2adab363423cfa93742d3deaeea741ebf67b1e68f8a172b`
MD5	`aac1678b23ec4f4a45cd038fd3428257`
BLAKE2b-256	`5e71cdb61f45545165778e16a58537645c48c4825225f6b093d230894fa9f355`

Algorithm	Hash digest
SHA256	`2f5d443719ab2a602015bc2f050200e07b253d1c68c1c3b579fe0ff0ab43c4da`
MD5	`dde670dc839ff507a39ce344549ca362`
BLAKE2b-256	`31a8583ab374dc6fb69f794692ca47770d33beff273ef32ef14ede69fabdb0d1`

Algorithm	Hash digest
SHA256	`7f5dd64f6f30d9dcb4fbb2f8de59577dccc57f999872235944c74d13cda45379`
MD5	`bcee8d389a3ae284823dd586b52153ae`
BLAKE2b-256	`7adf89d1e5b7a34c2e5a377ce22d868efb31af0700a65ab5717174b2ab781999`

Algorithm	Hash digest
SHA256	`785ddaa45ba3e099a70806fc6a73d004c12c5a1921cd74948cfb2a62b6dbe359`
MD5	`777c62a6b172e5fcd47d12e86c974788`
BLAKE2b-256	`821c4d9838914d85addec48f17ac7dbd343112baefbbd30ff63998dc9c15764f`

Algorithm	Hash digest
SHA256	`b088a3b4378a9dea026e30dc35e4516e8507181c6ee3b32207c13430c0763c84`
MD5	`eb67b961a7b2ec1f1a9fdecf0876491b`
BLAKE2b-256	`2c660c8fe9c8f355ff3eb3c049b4f35c05bc7266ac54ec9f6ec1a8e01a037efa`

Algorithm	Hash digest
SHA256	`7dcaaec1402f39e0a39c2def49c97b351f312bb0282e0560a7c03682b059b3d4`
MD5	`03c6d899cf109ce983bdeb478c19d45e`
BLAKE2b-256	`39c26f135a4fba454fd0773cfdd0168ecd9c706ff025ce9375e289bab6910571`

Algorithm	Hash digest
SHA256	`6dbb24ac0f5b0429aab3ecd38516bbad352aa26cf39bdee90279b2e6be2374b3`
MD5	`6c90761f14efdb3416fe031092b0b11a`
BLAKE2b-256	`d552ae2a7371c9dc3c9da1c7cbd20bd4c601e9bca30cc1f4fd1b16380d99c528`

Algorithm	Hash digest
SHA256	`1d02369f2436b07c28b4d522e3a678135b9234e35fc7d51df9e483c691cad7a3`
MD5	`26acd487c2ed5e79247b5f2d40329a10`
BLAKE2b-256	`e2249b1e838dd8463e668de884b790c638e831075acc294fc9ea8d1bb61c6f6f`

Algorithm	Hash digest
SHA256	`0cf2736465c0a88a43eb5c88ccdc8ef28daaa8e7c4749168b874cab472ea4af8`
MD5	`a06cac0450419830e5597c0c3c241bb1`
BLAKE2b-256	`46d0548fda37a76bc3ec5741bd6afd9c37a422a21a6f9278ea0b94f40108dde4`

Algorithm	Hash digest
SHA256	`5dc719ce6b848d9e51080ed6fe5e11405607048164bb76cacf2fdf1dc44ec7cb`
MD5	`2be84f1a4132f280713f556a6c8e3725`
BLAKE2b-256	`51e0a676f776e37c7da901a3b516a91b0fcfd0957898e0765fcc22285a5979c8`

Algorithm	Hash digest
SHA256	`18aced91b02d3e6bade966735fc916c7c151d4b222c23a6becd59ef0b2a44892`
MD5	`0e1380335e6d99e209c43b3471e9a11c`
BLAKE2b-256	`bfa07e6ec09273bf14dbb0208e75a5ed758e799357f44d8065de1d8a4cb28fe3`

Algorithm	Hash digest
SHA256	`3f095577d69c070a9ba913d5483ee0c149c32da88dc349384ce10efd534c79b7`
MD5	`e705508fc8844e34d343aa0f62e9f900`
BLAKE2b-256	`25d8a337de969c4862636cf8c513bed2b8cf8e234a0eb9fc6a14a98307d578c5`

Algorithm	Hash digest
SHA256	`7e5a286394f9a109cb8b72842ceef59c3cf64804525dae2cfad84f41e9c43433`
MD5	`a5cd4e72903289eef296fab73f5f782c`
BLAKE2b-256	`205c001612bd2d117a986746195b6eb2d3e3b3aaee9709f733f1bc7f944b33dd`

Algorithm	Hash digest
SHA256	`148fdb0c62e4f5d972db99554552a32d7828a863f92306e3bde9f933a816583a`
MD5	`0c900ca94be2d94e9c90c3130cc74e1b`
BLAKE2b-256	`764da6598645dbd2d86e621f53799f78d5e49eaded76dfd56b74efdd07db7539`

Algorithm	Hash digest
SHA256	`0e7550800762c068129f89bbaf55e9ecdc2d13253c729eb18bf5cd78b739f958`
MD5	`780769cf7ad2d3ac13fba21d9dccd26f`
BLAKE2b-256	`86979f4c0a37e3b38bbe90db6280c5227e6eaa112fe9e179e46e1393aca0d6d7`

Algorithm	Hash digest
SHA256	`617c43c8282e1555ff5fd01d0511e4d1238fc3f1b6dd667216d7b3a0c78c4c09`
MD5	`5bd6b7d25c00f349c246aaed8fa9da31`
BLAKE2b-256	`728ac00ba443965a40cf14fde90d39f88527cbc2c252f7fe203c77f4bf6e7c2f`

Algorithm	Hash digest
SHA256	`d44dcac1bce7e4a4944feddd85ab145b7c38371e366559bfeeb980bbec7fa321`
MD5	`2f5712c6f233a7e474337ef8db604dcc`
BLAKE2b-256	`490c5c550261a307a52fdb541384d076a10709d6f3b1effcaab2c9099a554d89`

amplpy 0.4.1

Navigation

Verified details

Maintainers

Unverified details

Project links

Meta

Classifiers

Project description

Documentation:

Repositories:

Project details

Verified details

Maintainers

Unverified details

Project links

Meta

Classifiers

Release history Release notifications | RSS feed

Download files

Source Distribution

Built Distributions

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

File metadata

File hashes

File details

Algorithm	Hash digest
SHA256	`a548e3455c4bfd7b6d0995e87dda15e1045743a13d7746b62ff41aeb31a4da8c`
MD5	`6431df0c962f80239deeee6f8bb4a984`
BLAKE2b-256	`3e7040c2da6c9d5bbf7db0b3836a7d31c80a78003e39982ffa0f98701cfcf230`

Algorithm	Hash digest
SHA256	`604a0a5e8e2550439f221e28b01053cadbeade963013717ad6cc100f2d55a68f`
MD5	`b75b754397813a29995b01309769a337`
BLAKE2b-256	`60020d8129543e034f76f3a67b690d3f887dc99101ad20cec00fefac2b3a3fba`

Algorithm	Hash digest
SHA256	`290af315c3455ef0973008eacbd3d777ea8025d4ddb01bfce2ea0a4920864e87`
MD5	`11a11d3bcebdf0a9561dfc2151a569a4`
BLAKE2b-256	`09fb7e3fb87e69047e6ee9a855e5949fe0ca641cc10e252f5042da56541a0474`

Algorithm	Hash digest
SHA256	`1fbb022405a8292602c98872b9fb6ef849cf21c65978b195ae99edba4d36527b`
MD5	`56ad3b76e0905c0a35e166625ef55557`
BLAKE2b-256	`2b1ddc9eea47f08a3499ecffe748bb281c695cb748a6a88d87ae925dbd1028b9`

Algorithm	Hash digest
SHA256	`0c8fda9f0535734a556bc40ae97e59108cf93ecc2871ce781d6d4a0494d3c2ba`
MD5	`dead9c5575f2ad825983d730143f6c0c`
BLAKE2b-256	`9d9c5a983c975649c9d4d9431cad84011e05023653ef91531e01c250477d531d`

Algorithm	Hash digest
SHA256	`1b8d8b52c9185708e94e87851ea82f6ff0d9af6917700832cdc3c8e992bc22a1`
MD5	`89571adf0f7f465191ac31e7aba56e50`
BLAKE2b-256	`e38c9b128cc2fe57ec8fcbe6aa517f9008d5b06457152241b4b3e85149085442`