mdapy

A simple, fast and cross-platform python library to handle the data generated from molecular dynamics simulations.

These details have not been verified by PyPI

Project links

GitHub Statistics

Project description

mdapy : Molecular Dynamics Analysis with Python

Overview

The mdapy python library provides an array of powerful, flexible, and straightforward tools to analyze atomic trajectories generated from Molecular Dynamics (MD) simulations. The library is fully cross-platform, making it accessible to users in Windows, Linux, and Mac OS. Benefited by the TaiChi project, we can effectively accelerate the pure python code, bringing it closer to the speed of code written in C++. Furthermore, mdapy is highly parallelized, allowing users to leverage the resources of both multicore CPU and GPU. mdapy can directly handle the DUMP and DATA formats in LAMMPS, POSCAR format in VASP, universal XYZ format and CIF format. Besides, all data in mdapy is stored in NDARRAY format in NumPy, which enables easy integration with the scientific ecosystem in python and facilitates collaboration with other post-progressing tools such as OVITO and freud.

Resources

Homepage: https://github.com/mushroomfire/mdapy
Documentation: https://mdapy.readthedocs.io/
Issue Tracker: https://github.com/mushroomfire/mdapy/issues

Project details

These details have not been verified by PyPI

Project links

GitHub Statistics

Release history Release notifications | RSS feed

0.11.0

Jun 22, 2024

0.10.9

Jun 20, 2024

0.10.8

May 17, 2024

0.10.7

Apr 25, 2024

0.10.6

Apr 14, 2024

0.10.5

Apr 12, 2024

This version

0.10.4

Mar 12, 2024

0.10.3

Jan 30, 2024

0.10.2

Jan 8, 2024

0.10.1

Dec 18, 2023

0.10.0

Nov 28, 2023

0.9.9

Nov 21, 2023

0.9.8

Nov 13, 2023

0.9.7

Nov 5, 2023

0.9.6

Nov 2, 2023

0.9.5

Oct 24, 2023

0.9.4

Oct 20, 2023

0.9.3

Oct 19, 2023

0.9.2

Oct 12, 2023

0.9.1

Oct 10, 2023

0.9.0

Sep 23, 2023

0.8.9

Sep 5, 2023

0.8.8

Aug 24, 2023

0.8.7

May 25, 2023

0.8.6

Apr 22, 2023

0.8.5

Apr 9, 2023

0.8.4

Mar 30, 2023

0.8.3

Mar 20, 2023

0.8.2

Mar 13, 2023

0.8.1

Mar 12, 2023

0.8.0

Feb 28, 2023

0.7.9

Feb 13, 2023

0.7.8

Jan 20, 2023

0.7.7

Jan 18, 2023

0.7.6

Jan 6, 2023

Download files

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

Source Distribution

mdapy-0.10.4.tar.gz (4.2 MB view hashes)

Uploaded Mar 12, 2024 Source

Built Distributions

mdapy-0.10.4-cp311-cp311-win_amd64.whl (1.1 MB view hashes)

Uploaded Mar 12, 2024 CPython 3.11 Windows x86-64

mdapy-0.10.4-cp311-cp311-manylinux_2_35_x86_64.whl (1.3 MB view hashes)

Uploaded Mar 12, 2024 CPython 3.11 manylinux: glibc 2.35+ x86-64

mdapy-0.10.4-cp311-cp311-manylinux1_x86_64.whl (732.8 kB view hashes)

Uploaded Mar 12, 2024 CPython 3.11

mdapy-0.10.4-cp311-cp311-macosx_11_0_arm64.whl (1.2 MB view hashes)

Uploaded Mar 12, 2024 CPython 3.11 macOS 11.0+ ARM64

mdapy-0.10.4-cp310-cp310-win_amd64.whl (1.1 MB view hashes)

Uploaded Mar 12, 2024 CPython 3.10 Windows x86-64

mdapy-0.10.4-cp310-cp310-manylinux_2_35_x86_64.whl (1.3 MB view hashes)

Uploaded Mar 12, 2024 CPython 3.10 manylinux: glibc 2.35+ x86-64

mdapy-0.10.4-cp310-cp310-manylinux1_x86_64.whl (727.7 kB view hashes)

Uploaded Mar 12, 2024 CPython 3.10

mdapy-0.10.4-cp310-cp310-macosx_11_0_arm64.whl (1.2 MB view hashes)

Uploaded Mar 12, 2024 CPython 3.10 macOS 11.0+ ARM64

mdapy-0.10.4-cp39-cp39-win_amd64.whl (1.1 MB view hashes)

Uploaded Mar 12, 2024 CPython 3.9 Windows x86-64

mdapy-0.10.4-cp39-cp39-manylinux_2_35_x86_64.whl (1.3 MB view hashes)

Uploaded Mar 12, 2024 CPython 3.9 manylinux: glibc 2.35+ x86-64

mdapy-0.10.4-cp39-cp39-manylinux1_x86_64.whl (729.7 kB view hashes)

Uploaded Mar 12, 2024 CPython 3.9

mdapy-0.10.4-cp39-cp39-macosx_11_0_arm64.whl (1.2 MB view hashes)

Uploaded Mar 12, 2024 CPython 3.9 macOS 11.0+ ARM64

mdapy-0.10.4-cp38-cp38-win_amd64.whl (1.1 MB view hashes)

Uploaded Mar 12, 2024 CPython 3.8 Windows x86-64

mdapy-0.10.4-cp38-cp38-manylinux_2_35_x86_64.whl (1.2 MB view hashes)

Uploaded Mar 12, 2024 CPython 3.8 manylinux: glibc 2.35+ x86-64

mdapy-0.10.4-cp38-cp38-manylinux1_x86_64.whl (724.5 kB view hashes)

Uploaded Mar 12, 2024 CPython 3.8

mdapy-0.10.4-cp38-cp38-macosx_11_0_arm64.whl (1.2 MB view hashes)

Uploaded Mar 12, 2024 CPython 3.8 macOS 11.0+ ARM64

Hashes for mdapy-0.10.4.tar.gz

Hashes for mdapy-0.10.4.tar.gz
Algorithm	Hash digest
SHA256	`f9e4b38d623e25e4c5bca4c1b6cd7127478a10ebf6105762d79786ae09ae5c40`
MD5	`d8d7a6561ca406d2924fe9e45ac6808a`
BLAKE2b-256	`2c51e56433536e85c26b4985f12adfd68c7a39dc24027914df82d7a01a16fda1`

Hashes for mdapy-0.10.4-cp311-cp311-win_amd64.whl

Hashes for mdapy-0.10.4-cp311-cp311-win_amd64.whl
Algorithm	Hash digest
SHA256	`9bf817a9604859b512e8ee60322ae6dc38668ce96f884fa29710f5f13e393c5a`
MD5	`8fc175c4e03e7795b8407c407b6ae147`
BLAKE2b-256	`be7e6fb5fa4801cd148308eb9692eaea33bda92dcf17e07cb86aba364c3a75dd`

Hashes for mdapy-0.10.4-cp311-cp311-manylinux_2_35_x86_64.whl

Hashes for mdapy-0.10.4-cp311-cp311-manylinux_2_35_x86_64.whl
Algorithm	Hash digest
SHA256	`cd5af94932f86f65bdde026d3a8539f35dfccea001410fe2405a816ee4462171`
MD5	`c206e9c0ff40b337255a39e900148b2b`
BLAKE2b-256	`7cb7f3407ca768ecbef70a955d98786f35cb38d5591739ac69b120a2d86e9a54`

Hashes for mdapy-0.10.4-cp311-cp311-manylinux1_x86_64.whl

Hashes for mdapy-0.10.4-cp311-cp311-manylinux1_x86_64.whl
Algorithm	Hash digest
SHA256	`403c05a988194fdc6027c4cdcb17162317a37ef6e01ecbfc66dee27ede6a28f8`
MD5	`d6c0d595e0cd7d5493d1aa249a663880`
BLAKE2b-256	`e4df1cd34bb3c54b64d48c71b226d2b3d9b5cbfb3a688986c3377125767e482b`

Hashes for mdapy-0.10.4-cp311-cp311-macosx_11_0_arm64.whl

Hashes for mdapy-0.10.4-cp311-cp311-macosx_11_0_arm64.whl
Algorithm	Hash digest
SHA256	`37764098b792efdbcc069abc3b4236f4704fbc99662ce692b98eb8e286907105`
MD5	`ad801327c8f1a43c28956d7033dd958d`
BLAKE2b-256	`01629cc591d5e8570e45d67eadd51d60c3ceb24eb05e792290233b81d19b4ea8`

Hashes for mdapy-0.10.4-cp310-cp310-win_amd64.whl

Hashes for mdapy-0.10.4-cp310-cp310-win_amd64.whl
Algorithm	Hash digest
SHA256	`3f7c852460a24dbacac7d512073e73fd1cf46d6b84fcf509d6bef7a7595d9a39`
MD5	`0733f712f2bfae646f32eb3b2dee0d33`
BLAKE2b-256	`cd9daaec0055ddd4ac68afdbb2f15c9d5ebf3f0e7abc87b54c9d2c21e5597986`

Hashes for mdapy-0.10.4-cp310-cp310-manylinux_2_35_x86_64.whl

Hashes for mdapy-0.10.4-cp310-cp310-manylinux_2_35_x86_64.whl
Algorithm	Hash digest
SHA256	`cc4bed04a35c99027c6d3c8f97c894270d4f0434e1b54b82e68835d716aba07d`
MD5	`686bc2c97a1288acb3af325cef3cc8f4`
BLAKE2b-256	`080b6c3aa6eb409e5e8657d8613d7497c05b346d037390dc7080bdae5a0933f6`

Hashes for mdapy-0.10.4-cp310-cp310-manylinux1_x86_64.whl

Hashes for mdapy-0.10.4-cp310-cp310-manylinux1_x86_64.whl
Algorithm	Hash digest
SHA256	`10c9e1caa1762ec902ed39e32544da187118693cd37738f8fbe3f932c179f3a7`
MD5	`165adb850444018d01e30187d05e46e8`
BLAKE2b-256	`b01b072b87e4dcb704c46c71de8775151a9aebc896c081daf2c6eca9f2a9c5d6`

Hashes for mdapy-0.10.4-cp310-cp310-macosx_11_0_arm64.whl

Hashes for mdapy-0.10.4-cp310-cp310-macosx_11_0_arm64.whl
Algorithm	Hash digest
SHA256	`ed705ec331c2cb76971c2a5db4e95897011734d460a5d9ec7fce7b225f85d45b`
MD5	`1fd822b1aecd289c4b9a87de913ff522`
BLAKE2b-256	`432f5a24e2b372660de2e7e2d137bf6723a7c1699269697a95c22de4872a0f33`

Hashes for mdapy-0.10.4-cp39-cp39-win_amd64.whl

Hashes for mdapy-0.10.4-cp39-cp39-win_amd64.whl
Algorithm	Hash digest
SHA256	`3777b07604fef19b32037800682103814d945f5acc4a4e2d3498ac5ef61ed1f4`
MD5	`50bb8092fa2b8610a274d1f62a6aba21`
BLAKE2b-256	`e54c5aff21a99c96a8cb0c8d27aa704d125c08f486cdbc327a9af6295e10b554`

Hashes for mdapy-0.10.4-cp39-cp39-manylinux_2_35_x86_64.whl

Hashes for mdapy-0.10.4-cp39-cp39-manylinux_2_35_x86_64.whl
Algorithm	Hash digest
SHA256	`980c231e7678ddf81a9a89db36f9762919d90906112839b230eec4ea9d820047`
MD5	`2f6f8da9cc1f1ab59fc86c123c85d76b`
BLAKE2b-256	`2a6b4080ea45fcbeb5e1c926ae86fda10e41a79c6f4472306c291b335f4ae0f8`

Hashes for mdapy-0.10.4-cp39-cp39-manylinux1_x86_64.whl

Hashes for mdapy-0.10.4-cp39-cp39-manylinux1_x86_64.whl
Algorithm	Hash digest
SHA256	`43f2b82966d9c2379fed32d4f59e28f6d0f5a7b3e11a7cb461453d56bd159e53`
MD5	`d753fba34c4ab01cebff720528fe53a3`
BLAKE2b-256	`f40d799864e549690489335702ca96e161a37dafa9883efeb6021b1594d4e762`

Hashes for mdapy-0.10.4-cp39-cp39-macosx_11_0_arm64.whl

Hashes for mdapy-0.10.4-cp39-cp39-macosx_11_0_arm64.whl
Algorithm	Hash digest
SHA256	`6fe528fffd67e91a33b59e0f8c572f5c27c982c9b84f2b35e180a243281defcb`
MD5	`81ba445f98f8a5b40b8028901328c35c`
BLAKE2b-256	`60c4a2b0c0775d2f651f4afda0d03ce6b296408cd8415160f8509c0d038b7cfa`

Hashes for mdapy-0.10.4-cp38-cp38-win_amd64.whl

Hashes for mdapy-0.10.4-cp38-cp38-win_amd64.whl
Algorithm	Hash digest
SHA256	`9bfb3ac9dc9f334313000402f5bd487c3306d563c0b52b180edff020432ce80b`
MD5	`d4c99b09b7248596b93c345a75aaa8b9`
BLAKE2b-256	`11acb2b3f43c2f3125dd57145436f255e9e656892f1acdbc0d6379b96c811480`

Hashes for mdapy-0.10.4-cp38-cp38-manylinux_2_35_x86_64.whl

Hashes for mdapy-0.10.4-cp38-cp38-manylinux_2_35_x86_64.whl
Algorithm	Hash digest
SHA256	`f6538ee79ec7f8d6c4e8ea1b0baec0e96a462fce8f31aa2e09a7e77402ac8461`
MD5	`5bffbe8f5e531d75f687aa1db5cc31f5`
BLAKE2b-256	`127c938c6cb2e851882d30184b60dfb0a351a9fdf31b9ca5e7aeb1f8c7b63922`

Hashes for mdapy-0.10.4-cp38-cp38-manylinux1_x86_64.whl

Hashes for mdapy-0.10.4-cp38-cp38-manylinux1_x86_64.whl
Algorithm	Hash digest
SHA256	`e4e428b9b94bd4b1e3e86af84c23c9b12822b6030a01a8907a795a2243f93789`
MD5	`ba8ea6225c6a83912671dcb405d43d86`
BLAKE2b-256	`f5e3efec85411f82c3b4c7da0bcedfdf3cfde9f8fd3d8fc1df9bbd4ab22f7ee6`

Hashes for mdapy-0.10.4-cp38-cp38-macosx_11_0_arm64.whl

Hashes for mdapy-0.10.4-cp38-cp38-macosx_11_0_arm64.whl
Algorithm	Hash digest
SHA256	`3568ac593810470fbb79c3801bf15a1b57cfa5d4d57ebfa6d8ee999c25867dd3`
MD5	`97153984ed4bebac070b9e3c66d757c0`
BLAKE2b-256	`5280ee146ec39cf5306612ff5c9a21b19ede77e95cc387a0ed627ee568b0882b`