| Name | gsoup JSON |
| Version |
0.2.4
JSON |
| download |
| home_page | None |
| Summary | A geoemtry & graphics library with focus on clarity rather than performance. |
| upload_time | 2024-07-25 11:23:21 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.7 |
| license | MIT License Copyright (c) 2022 Yotam Erel Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. |
| keywords |
geometry
graphics
autograd
vision
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# gsoup
## In a nutshell
A python library implementing various geometry / graphics algorithms, with focus on clarity rather than performance.
## Long version
This library is the result of me getting tired of replicating pieces of utility code to do similar work across different projects. I decided to push any fundamental piece of code into this repository. The focus is on clarity and concisness for all implementations, but here and there some immediate/easy performance gains are used.
The majority of the code uses numpy, but some effort has been made to also be compatible with pytorch for GPU computations, as many of my interests rely on a strong auto-differentiaion (and GPU enabled) package.
All the code is used for self-educational purposes and to facilitate faster research of concepts in graphics, and specifically for applications in AR.
## Installation
`pip install gsoup`
## Usage
`import gsoup`
## Developers
`git clone https://github.com/yoterel/gsoup.git`
`cd gsoup`
`pip install -e .[dev]`
Feel free to submit pull requests (run the tests first).
## Build
`bumpver update --no-fetch --patch`
`python -m build`
`twine check dist/*`
`twine upload dist/*`
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