## ----include = FALSE----------------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

## ----setup--------------------------------------------------------------------
library(musclesyneRgies)

## ----eval = FALSE-------------------------------------------------------------
# # Load the built-in example data set
# data("FILT_EMG")
# 
# # Create cluster for parallel computing if not already done
# clusters <- objects()
# 
# if (sum(grepl("^cl$", clusters)) == 0) {
#   # Decide how many processor threads have to be excluded from the cluster
#   # It is a good idea to leave at least one free, so that the machine can be
#   # used during computation
#   cl <- parallel::makeCluster(max(1, parallel::detectCores() - 1))
# }
# # Extract synergies in parallel (will speed up computation only for larger data sets)
# # with a useful progress bar from `pbapply`
# SYNS <- pbapply::pblapply(FILT_EMG, musclesyneRgies::synsNMF, cl = cl)
# 
# parallel::stopCluster(cl)

## -----------------------------------------------------------------------------
# Thirty-cycle activation pattern from Santuz & Akay (2020)
data(act_pattern)

# HFD with k_max = 10 to consider only the most linear part of the log-log plot
# (it's the default value for this function anyway)
Higuchi_fd <- HFD(act_pattern$signal, k_max = 10)$Higuchi
message("Higuchi's fractal dimension: ", round(Higuchi_fd, 3))

# H with min_win = 200 points, which is the length of each cycle
Hurst_exp <- Hurst(act_pattern$signal, min_win = max(act_pattern$time))$Hurst
message("Hurst exponent: ", round(Hurst_exp, 3))