Added python example 2. Patched octave example 2 comments and python example 1 comments

demos/octave/example2TeMultidimBinaryData.m

@@ -7,7 +7,7 @@
 % Change location of jar to match yours:
 javaaddpath('../../infodynamics.jar');
 
-% Create many columns in a multidimensional array,
+% Create many columns in a multidimensional array (2 rows by 100 columns),
 %  where the next time step (row 2) copies the value of the column on the left
 %  from the previous time step (row 1):
 twoDTimeSeriesOctave       = (rand(1, 100)>0.5)*1;
@@ -25,8 +25,6 @@ twoDTimeSeriesJavaInt = octaveToJavaIntMatrix(twoDTimeSeriesOctave);
 teCalc=javaObject('infodynamics.measures.discrete.ApparentTransferEntropyCalculator', 2, 1);
 teCalc.initialise();
 % Add observations of transfer across one cell to the right per time step:
-%  (Note this only looks at column 1->2, we don't wrap around the columns unless
-%   we have previously called setPeriodicBoundaryConditions(true))
 teCalc.addObservations(twoDTimeSeriesJavaInt, 1);
 fprintf('The result should be close to 1 bit here, since we are executing copy operations of what is effectively a random bit to each cell here: ');
 result2D = teCalc.computeAverageLocalOfObservations()

demos/python/example1TeBinaryData.py

@@ -19,7 +19,7 @@ sourceArray2 = [random.randint(0,1) for r in xrange(100)]
 teCalcClass = JPackage("infodynamics.measures.discrete").ApparentTransferEntropyCalculator
 teCalc = teCalcClass(2,1)
 teCalc.initialise()
-# Since we have simple arrays of doubles, we can directly pass these in:
+# Since we have simple arrays of ints, we can directly pass these in:
 teCalc.addObservations(destArray, sourceArray)
 print("For copied source, result should be close to 1 bit : %.4f" % teCalc.computeAverageLocalOfObservations())
 teCalc.initialise()

demos/python/example2TeMultidimBinaryData.py

@@ -0,0 +1,36 @@
+# = Example 2 - Transfer entropy on multidimensional binary data =
+
+# Simple transfer entropy (TE) calculation on multidimensional binary data using the discrete TE calculator.
+
+# This example is important for Python users using JPype, because it shows how to handle multidimensional arrays from Python to Java.
+
+from jpype import *
+import random
+
+# 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)
+
+# Create many columns in a multidimensional array, e.g. for fully random values:
+# twoDTimeSeriesOctave = [[random.randint(0,1)]*2 for x in xrange(10)] # for 10 rows (time-steps) for 2 variables
+
+# However here we want 2 rows by 100 columns where the next time step (row 2) is to copy the
+# value of the column on the left from the previous time step (row 1):
+numObservations = 100
+row1 = [random.randint(0,1) for r in xrange(numObservations)]
+row2 = [row1[numObservations-1]] + row1[0:numObservations-1] # Copy the previous row, offset one column to the right
+twoDTimeSeriesPython = []
+twoDTimeSeriesPython.append(row1)
+twoDTimeSeriesPython.append(row2)
+twoDTimeSeriesJavaInt = JArray(JInt, 2)(twoDTimeSeriesPython);
+
+# Create a TE calculator and run it:
+teCalcClass = JPackage("infodynamics.measures.discrete").ApparentTransferEntropyCalculator
+teCalc = teCalcClass(2,1)
+teCalc.initialise()
+# Add observations of transfer across one cell to the right per time step:
+teCalc.addObservations(twoDTimeSeriesJavaInt, 1)
+result2D = teCalc.computeAverageLocalOfObservations()
+print('The result should be close to 1 bit here, since we are executing copy operations of what is effectively a random bit to each cell here: %.3f bits from %d observations' % (result2D, teCalc.getNumObservations()))
+