We present Matlab/Octave functions to calculate measures of emergence, self-organization, and complexity of discrete and continuous data. The measures are based on Shannon's information and differential entropy, respectively. Examples from different datasets and probability distributions are used to illustrate the usage of the code.

This study was conducted to identify the sandfly fauna and the anthropophilic species in a coffee-growing area of Villanueva, Norte de Santander, Colombia, a focus of American cutaneous leishmaniasis, and to analyse the relationship between the most frequent species and rainfall, relative humidity and temperature, with the aim of contributing to epidemiological surveillance in the area. Sandfly collections were performed fortnightly between February 2006-September 2007 using automatic light traps, Shannon traps, protected human bait and aspiration in resting places. A total of 7,051 sandflies belonging to 12 species were captured. Pintomyia spinicrassa (95.7%) predominated. Pintomyia oresbia and Lutzomyia sp. of Pichinde were found in the state of Norte de Santander for the first time. Pi. spinicrassa, Pintomyia nuneztovari, Micropygomyia venezuelensis, Lutzomyia (Helcocyrtomyia) scorzai and Lu. (Helcocyrtomyia) sp. were captured on the protected human bait. A significant association between Pi. spinicrassa abundance and the total rainfall and the average temperature and humidity 10 days before the collection was observed. The dominance of Pi. spinicrassa, a recognised vector of Leishmania braziliensis, especially during the dry periods, indicates that the risk of parasite transmission may increase.

We extend previously proposed measures of complexity, emergence, and self-organization to continuous distributions using differential entropy. Given that the measures were based on Shannon’s information, the novel continuous complexity measures describe how a system’s predictability changes in terms of the probability distribution parameters. This allows us to calculate the complexity of phenomena for which distributions are known. We find that a broad range of common parameters found in Gaussian and scale-free distributions present high complexity values. We also explore the relationship between our measure of complexity and information adaptation.