dPCA 1.0.5

🚀 Quickstart

pip install dpca

🎉 Use dPCA

Simple example code for surrogate data can be found in [dpca_demo.ipynb](http://nbviewer.ipython.org/github/wielandbrendel/dPCA/blob/master/python/dPCA_demo.ipynb) and dpca_demo.m.

API of dPCA is similar to sklearn. To use dPCA, you should first import dPCA and initialize it before callling the fitting function,

from dpca import dPCA
dpca = dPCA(labels, n_components, regularizer)
Z = dpca.fit_transform(X)

The required initialization parameters are:

• X: A multidimensional array containing the trial-averaged data. E.g. X[n,t,s,d] could correspond to the mean response of the n-th neuron at time t in trials with stimulus s and decision d. The observable (e.g. neuron index) needs to come first.

• labels: Optional; list of characters with which to describe the parameter axes, e.g. ‘tsd’ to denote time, stimulus and decision axis. All marginalizations (e.g. time-stimulus) are refered to by subsets of those characters (e.g. ‘ts’).

• n_components: Dictionary or integer; if integer use the same number of components in each marginalization, otherwise every (key,value) pair refers to the number of components (value) in a marginalization (key).

More detailed documentation, and additional options, can be found in dpca.py.