##
##  Java Information Dynamics Toolkit (JIDT)
##  Copyright (C) 2012, Joseph T. Lizier
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##  GNU General Public License for more details.
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##

# = Example 7 - Ensemble method with transfer entropy on continuous data using Kraskov estimators =

#  Calculation of transfer entropy (TE) by supplying an ensemble of samples from multiple time series.
# We use continuous-valued data using the Kraskov-estimator TE calculator here.

from jpype import *
import random
import math

# Change location of jar to match yours:
jarLocation = "../../infodynamics.jar"
# Start the JVM (add the "-Xmx" option with say 1024M if you get crashes due to not enough memory space)
startJVM(getDefaultJVMPath(), "-ea", "-Djava.class.path=" + jarLocation)

# Generate some random normalised data.
numObservations = 1000
covariance=0.4
numTrials=10
kHistoryLength=1

# Create a TE calculator and run it:
teCalcClass = JPackage("infodynamics.measures.continuous.kraskov").TransferEntropyCalculatorKraskov
teCalc = teCalcClass()
teCalc.setProperty("k", "4") # Use Kraskov parameter K=4 for 4 nearest points
teCalc.initialise(kHistoryLength) # Use target history length of kHistoryLength (Schreiber k)
teCalc.startAddObservations()

for trial in range(0,numTrials):
	# Create a new trial, with destArray correlated to
	#  previous value of sourceArray:
	sourceArray = [random.normalvariate(0,1) for r in range(numObservations)]
	destArray = [0] + [sum(pair) for pair in zip([covariance*y for y in sourceArray[0:numObservations-1]], \
		[(1-covariance)*y for y in [random.normalvariate(0,1) for r in range(numObservations-1)]] ) ]
	
	# Add observations for this trial:
	print("Adding samples from trial %d ..." % trial)
	teCalc.addObservations(JArray(JDouble, 1)(sourceArray), JArray(JDouble, 1)(destArray))

# We've finished adding trials:
print("Finished adding trials")
teCalc.finaliseAddObservations()

# Compute the result:
print("Computing TE ...")
result = teCalc.computeAverageLocalOfObservations()
# Note that the calculation is a random variable (because the generated
#  data is a set of random variables) - the result will be of the order
#  of what we expect, but not exactly equal to it; in fact, there will
#  be some variance around it (smaller than example 4 since we have more samples).
print("TE result %.4f nats; expected to be close to %.4f nats for these correlated Gaussians " % \
	(result, math.log(1.0/(1-math.pow(covariance,2)))))

# And here's how to pull the local TEs out corresponding to each input time series.
# Normally you would need to track how to split these up yourself -- here
# it's easy because our input time series are all of the same length
localTEs=teCalc.computeLocalOfPreviousObservations()
localValuesPerTrial = int(len(localTEs)/numTrials)  # Need to convert to int for indices later
for trial in range(0,numTrials):
	startIndex = localValuesPerTrial*trial
	endIndex = localValuesPerTrial*(trial+1)-1
	print("Local TEs for trial %d go from array index %d to %d" % (trial, startIndex, endIndex))
	print("  corresponding to time points %d:%d (indexed from 0) of that trial" % (kHistoryLength, numObservations-1))
	# Access the local TEs for this trial as:
	localTEForThisTrial = localTEs[startIndex:endIndex]

