https://github.com/moldyn/python-packaging
An example python packaging
https://github.com/moldyn/python-packaging
Last synced: over 1 year ago
JSON representation
An example python packaging
- Host: GitHub
- URL: https://github.com/moldyn/python-packaging
- Owner: moldyn
- License: mit
- Created: 2021-12-01T08:04:48.000Z (over 4 years ago)
- Default Branch: main
- Last Pushed: 2021-12-02T14:43:56.000Z (over 4 years ago)
- Last Synced: 2025-02-12T19:45:42.276Z (over 1 year ago)
- Language: Python
- Size: 38.1 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE
Awesome Lists containing this project
README
# slowpca
Slowpca is a package to perform principal component analysis based on
correlation or covariance. This Repository is a only an example of
python packaging.
The method was published in:
> Authors, ..., *Title of publication goes here*, in preparation
We kindly ask you to cite these articles if you use this software package for
published works.
## Features
- performs PCA
- easy to install
- ...
## Installation
So far the package is only published to [PyPI](https://pypi.org). For installing
it to within a python environment simple call:
```bash
python3 -m pip install slowpca
```
or for the latest dev version
```bash
# via ssh key
python3 -m pip install git+ssh://git@github.com/moldyn/python-packaging.git
# , or via password-based login
python3 -m pip install git+https://github.com/moldyn/python-packaging.git
```
## Usage
### CI - Usage Directly from the Command Line
In general one can call the module directly by its entry point `$ slowpca`
or by calling the module `$ python -m slowpca`. The latter method is
preferred to ensure using the desired python environment.
For help simply call `$ python -m slowpca --help`.
### Module - Inside a Python Script
```python
from slowpca import estimate_pca
# Load file
# X is np.ndarray of shape (n_samples, n_features)
proj, evecs = estimate_pca(X)
...
```