AC207 Final Project - AutoDiffpy
Team03 Members
| Member | |
|---|---|
| Jinglun Gao | jgao1@g.harvard.edu |
| Chuqin Zhao | chuqingzhao@g.harvard.edu |
| Chao Wang | chaowang@g.harvard.edu |
| Jiaping Lin | jiapinglin@g.harvard.edu |
Introduction
The AutoDiffpy Python package is the project topic of the Harvard AC207 Final Project in Fall 2022, which we writed a python automatic differentiation library. AD is a very broad area spanning computer science and mathematics with applications in fields across science and engineering. Thus, we have build this package to support getting derivatives of large scalar functions and vector functions. Particularly, AutoDiffpy could perform forward automatic differentiation that takes dual numbers to compute derivatives sequentially, and could also perform reverse automatic differentiation that computes accurate derivatives to solve problems.
Simple Install
To install the AutoDiffpy, user could run the following command in the terminal.
!pip install AutoDiffpy
Documentation
The documentation for the AutoDiffpy package can be found here.
1 maintainer
Metadata
1 maintainer
Release files for AutoDiffpyyy 1.0.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | |
|---|---|---|---|---|
| AutoDiffpyyy-1.0.0.tar.gz | 10.1 kB | Without provenance | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| AutoDiffpyyy-1.0.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 20.6 kB
Release files / AutoDiffpyyy-1.0.0.tar.gz
| Download URL | AutoDiffpyyy-1.0.0.tar.gz |
|---|---|
| Size | 10.1 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
2750e23ec57457dbffd9d442726e0b9c177666260fb46c14682f71f1204585dd
|
|
BLAKE2b-256 checksum How to use checksums |
7af5b14a37215f572c83b667d177d9f354d2c8ea5a1f6b79ff9f908d59d6066a
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/4.0.1 CPython/3.9.12
|
Release files / AutoDiffpyyy-1.0.0-py3-none-any.whl
| Download URL | AutoDiffpyyy-1.0.0-py3-none-any.whl |
|---|---|
| Size | 10.5 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
a2cd0f3a9b2265c1908ca609a66e734a0239271c2d510bcfde03da3c95626ce9
|
|
BLAKE2b-256 checksum How to use checksums |
af7c2ae6552dd3c6b10cd20932acf8ba41694baf86b5a4c435598750dae60b6d
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/4.0.1 CPython/3.9.12
|
Security for AutoDiffpyyy 1.0.0
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
No provenance found
None of the files in this release were uploaded with provenance metadata.
A note on package security
PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.
