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This repository contains a Python implementation of Principal Component Analysis (PCA) for dimensionality reduction and variance analysis. PCA is a powerful statistical technique used to identify patterns in data by transforming it into a set of orthogonal (uncorrelated) components, ranked by the amount of variance they explain.
A general purpose Snakemake workflow to perform unsupervised analyses (dimensionality reduction & cluster analysis) and visualizations of high-dimensional data.
Python library to handle Scanning Probe Microscopy Images. Can read nanoscan .xml data, Bruker AFM images, Nanonis SXM files as well as iontof images(ITA, ITM and ITS).
Economic analysis tool using tensor PCA modeling to interpolate GNP values, integrating tensor product, PCA, and linear regression for better interpolation.
Principal Component Analysis (PCA) Algorithm was implemented to determine the Functional Age of the Power Transformer using Return Voltage Measurement (RVM). [submitted]