Unsupervised Systematics Removal
These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.
The package comes with two classes:
PCA and SYSREM
The first one is the Principal Component Analisys, while the second is SYSREM (Tamuz et al. 2005):
sys = SYSREM(pre_processing=True, norm_method='normalize', after_processing=False)
pca = PCA(pre_processing=True, norm_method='normalize', after_processing=False)
result_sys = sys.fit_transform(matrx, components=1)
result_pca = pca.fit_transform(matrx, components=1, comp_end=(-1))
The components argument is the number of components with higher variance to be substracted.
The comp_end argument is the number of components with lower variance to be subtracted.
The after_processing divide each column of the result matrix by its standard deviation.
1.0.0
- Mario Damiano - Github: MDamiano - Twitter: @MarioDami - e-mail: mdamiano91@gmail.com
This project is distributed under the MIT License - see the LICENSE file for details