.. sails documentation master file, created by sphinx-quickstart on Tue Jun 27 22:33:00 2017. You can adapt this file completely to your liking, but it should at least contain the root `toctree` directive. .. image:: _static/sails_montage.png SAILS is a python package for modelling frequency domain power and connectivity in time-series and networks. It provides implementations of a range of autoregressive model fitting, validation and selection routines to describe the linear dependencies in time-series. The spectral content of the fitted models can be explored with within and between channel frequency metrics. Features ======== SAILS currently provides: * Multivariate autoregressive (MVAR) model fits using OLS or Vieira-Morf * A range of MVAR model diagnostics (Stablility-index, R-square, Durbin-Watson, Percent-Consistency) * Model order selection via AIC or BIC * Decomposition of models into oscillatory modes * Estimation of power spectra using Fourier or Modal transforms * Wide range of connectivity metrics - Transfer Function, Spectral Matrix - Coherency, Magnitude Squared Coherence, Phase Coherence - Directed Transfer Function and variants - Partial Directed Coherence - Isolated Effective Coherence Quick Start =========== SAILS can be installed from `PyPI `_ using pip:: pip install sails and used to model and describe frequency content in networks of time-series:: import sails import numpy as np # Create a simulated signal sample_rate = 100 siggen = sails.Baccala2001_fig2() X = siggen.generate_signal(sample_rate=sample_rate,num_samples=1000) # Fit an autoregressive model with order 3 model = sails.OLSLinearModel.fit_model(X,np.arange(4)) # Compute power spectra and connectivity freq_vect = np.linspace(0,sample_rate/2) metrics = sails.FourierMvarMetrics.initialise(model,sample_rate,freq_vect) Tutorials ========= Please see our tutorials and example gallery for help getting started with datawoi analysis in SAILS. .. toctree:: :maxdepth: 2 tutorials gallery api references cite