Matrix Optimization with Low-rank factorization for Automated analysis of SEC-SAXS
Project description
[!WARNING] This package is scheduled to be available in August, 2025.
This repository includes legacy codes of MOLASS.
To install this package, use pip as follows.
pip install -U molass_legacy
pip install -U molass
[!NOTE] The molass_legacy package currently depends mutually on molass, which is the package name of
Molass Libraryreferenced below. This mutual dependence is planned to be changed to one-way dependence from molass_legacy to molass.
The meaning of the planned change is as follows.
The molass package is a rewrite of molass_legacy and they have quite a lot in commmon, which will be rearranged and unified into the molass side.
For details, see also:
- Legacy Reference: https://freesemt.github.io/molass-legacy for legacy function reference
See also:
- Molass Library Repository: https://github.com/nshimizu0721/molass-library
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
File details
Details for the file molass_legacy-0.1.0.tar.gz.
File metadata
- Download URL: molass_legacy-0.1.0.tar.gz
- Upload date:
- Size: 3.3 MB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/6.1.0 CPython/3.12.9
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
426cd80b0c90e86677178a65481f9fd6145447263eed3030959c67d94688f71f
|
|
| MD5 |
32580efe710c6a661d7d6879f40643b2
|
|
| BLAKE2b-256 |
75ea33ec49339921dcc64de3017f2fbe553627e9c6120c991dcbfd85f0e1941e
|
File details
Details for the file molass_legacy-0.1.0-py3-none-any.whl.
File metadata
- Download URL: molass_legacy-0.1.0-py3-none-any.whl
- Upload date:
- Size: 4.2 MB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/6.1.0 CPython/3.12.9
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
0ff0b2da212fb976658e35923d98827355320b3c17294f664c9822e922c221d8
|
|
| MD5 |
98c6acf7411072738acf0ae14a1888b5
|
|
| BLAKE2b-256 |
e01f6e07912256782fe51adfd72c9d76ff373c927b8caed6d138799bee8416c7
|
