Variable names used in IDTxl - pwollstadt/IDTxl GitHub Wiki

We try to stick to our variable naming conventions to produce consistent code.

Where applicable, IDTxl uses SI units for all inputs
Internally, all time-series data are handled in samples not some time interval, i.e. user input should be translated into sample
In general, we speak of arrays when we want to denote any non-scalar variable

Measures

Var. name	Explanation
ent	entropy
mi	mutual information
cmi	conditional mutual information
multi	multiinformation (not yet implemented)
lais	local active information storage
ais	active information storage
lte	local transfer entropy
te	transfer entropy
syn	synergistic information
unq	unique information
shd	shared information

Variable Types

Var. name	Explanation
variable	generic random variable (RV)
realisations	realisations of a single RV in space or time
sample	single realisation of a variable
process	indexed series of variables (e.g., indexed by timestamps)
replication	copy (e.g., physical or in time) of a process
source(_set)	process(es) that have information about another process
target	process that receives information from a single or multiple sources
current_value	current sample in time in the target process that is predicted from the sources' past
past/history	past variables in source and target (relative to the current value)
conditional	variable to be conditioned on (e.g. in CMI estimation)

Estimator Names

Estimator class names are composed of the backend/compute platform, estimator type, and measure estimated, e.g. JidtKraskovCMI.

Name	Estimator type
kraskov	Kraskov estimator
kl	Kozachenko-Leonenko estimator
gaussian	Gaussian estimator
kernel	Kernel estimator

Algorithm

Var. name	Explanation
idx_*(_set)	index (or set of indices) of single variable
candidate(_set)	potential variable(s) for (non-)uniform embedding
selected_vars_*	candidate variables currently included in the conditioning set, * may be either 'full', 'sources', or 'target' to indicate all variables and sub-sets of variables coming from source or target processes respectively
max_lag	maximum lag for variables entering the candidate set
min_lag	minimum lag for variables entering the candidate set
theiler_k	n.o. samples to be excluded in neighbour searches, Theiler correction
kraskov_k	n.o. nearest neighbours for the Kraskov estimator