diff --git a/demos/octave/DetectingInteractionLags/transferWithSourceMemory.m b/demos/octave/DetectingInteractionLags/transferWithSourceMemory.m
index cd250ea..233e659 100755
--- a/demos/octave/DetectingInteractionLags/transferWithSourceMemory.m
+++ b/demos/octave/DetectingInteractionLags/transferWithSourceMemory.m
@@ -77,10 +77,10 @@ function transferWithSourceMemory(savePlot)
 		% Compute TEs
 		teCalc = javaObject('infodynamics.measures.discrete.TransferEntropyCalculator', 4, 1);
 		teCalc.initialise();
-		teCalc.addObservations(y, x);
+		teCalc.addObservations(x, y);
 		teXnToYnplus1(deltaIndex) = teCalc.computeAverageLocalOfObservations();
 		teCalc.initialise();
-		teCalc.addObservations(y(2:length(y)), x(1:length(x)-1));
+		teCalc.addObservations(x(1:length(x)-1), y(2:length(y)));
 		teXnminus1ToYnplus1(deltaIndex) = teCalc.computeAverageLocalOfObservations();
 		
 		% Compute MITs
diff --git a/demos/octave/example1TeBinaryData.m b/demos/octave/example1TeBinaryData.m
index 7eb3c8c..1f87a57 100755
--- a/demos/octave/example1TeBinaryData.m
+++ b/demos/octave/example1TeBinaryData.m
@@ -15,11 +15,11 @@ sourceArray2=(rand(100,1)>0.5)*1;
 teCalc=javaObject('infodynamics.measures.discrete.TransferEntropyCalculator', 2, 1);
 teCalc.initialise();
 % Since we have simple arrays of ints, we can directly pass these in:
-teCalc.addObservations(destArray, sourceArray);
+teCalc.addObservations(sourceArray, destArray);
 fprintf('For copied source, result should be close to 1 bit : ');
 result = teCalc.computeAverageLocalOfObservations()
 teCalc.initialise();
-teCalc.addObservations(destArray, sourceArray2);
+teCalc.addObservations(sourceArray2, destArray);
 fprintf('For random source, result should be close to 0 bits: ');
 result2 = teCalc.computeAverageLocalOfObservations()
 
diff --git a/demos/octave/example5TeBinaryMultivarTransfer.m b/demos/octave/example5TeBinaryMultivarTransfer.m
index bd1d3d8..ac79a11 100755
--- a/demos/octave/example5TeBinaryMultivarTransfer.m
+++ b/demos/octave/example5TeBinaryMultivarTransfer.m
@@ -20,13 +20,13 @@ teCalc.initialise();
 % We need to construct the joint values of the dest and source before we pass them in,
 %  and need to use the matrix conversion routine when calling from Matlab/Octave:
 mUtils= javaObject('infodynamics.utils.MatrixUtils');
-teCalc.addObservations(mUtils.computeCombinedValues(octaveToJavaIntMatrix(destArray), 2), ...
-		mUtils.computeCombinedValues(octaveToJavaIntMatrix(sourceArray), 2));
+teCalc.addObservations(mUtils.computeCombinedValues(octaveToJavaIntMatrix(sourceArray), 2), ...
+		mUtils.computeCombinedValues(octaveToJavaIntMatrix(destArray), 2));
 fprintf('For source which the 2 bits are determined from, result should be close to 2 bits : ');
 result = teCalc.computeAverageLocalOfObservations()
 teCalc.initialise();
-teCalc.addObservations(mUtils.computeCombinedValues(octaveToJavaIntMatrix(destArray), 2), ...
-		mUtils.computeCombinedValues(octaveToJavaIntMatrix(sourceArray2), 2));
+teCalc.addObservations(mUtils.computeCombinedValues(octaveToJavaIntMatrix(sourceArray2), 2), ...
+		mUtils.computeCombinedValues(octaveToJavaIntMatrix(destArray), 2));
 fprintf('For random source, result should be close to 0 bits in theory: ');
 result2 = teCalc.computeAverageLocalOfObservations()
 fprintf('\nThe result for random source is inflated towards 0.3 due to finite observation length (%d).\nOne can verify that the answer is consistent with that from a\nrandom source by checking: teCalc.computeSignificance(1000); ans.pValue\n', teCalc.getNumObservations());
diff --git a/demos/python/example1TeBinaryData.py b/demos/python/example1TeBinaryData.py
index 4823b46..f2401a0 100755
--- a/demos/python/example1TeBinaryData.py
+++ b/demos/python/example1TeBinaryData.py
@@ -20,10 +20,10 @@ teCalcClass = JPackage("infodynamics.measures.discrete").TransferEntropyCalculat
 teCalc = teCalcClass(2,1)
 teCalc.initialise()
 # Since we have simple arrays of ints, we can directly pass these in:
-teCalc.addObservations(destArray, sourceArray)
+teCalc.addObservations(sourceArray, destArray)
 print("For copied source, result should be close to 1 bit : %.4f" % teCalc.computeAverageLocalOfObservations())
 teCalc.initialise()
-teCalc.addObservations(destArray, sourceArray2)
+teCalc.addObservations(sourceArray2, destArray)
 print("For random source, result should be close to 0 bits: %.4f" % teCalc.computeAverageLocalOfObservations())
 
 shutdownJVM() 
diff --git a/demos/python/example5TeBinaryMultivarTransfer.py b/demos/python/example5TeBinaryMultivarTransfer.py
index 89a1f37..1c66e0d 100755
--- a/demos/python/example5TeBinaryMultivarTransfer.py
+++ b/demos/python/example5TeBinaryMultivarTransfer.py
@@ -28,13 +28,13 @@ teCalc.initialise()
 # We need to construct the joint values of the dest and source before we pass them in,
 #  and need to use the matrix conversion routine when calling from Matlab/Octave:
 mUtils= JPackage('infodynamics.utils').MatrixUtils
-teCalc.addObservations(mUtils.computeCombinedValues(destArray, 2), \
-		mUtils.computeCombinedValues(sourceArray, 2))
+teCalc.addObservations(mUtils.computeCombinedValues(sourceArray, 2), \
+		mUtils.computeCombinedValues(destArray, 2))
 result = teCalc.computeAverageLocalOfObservations()
 print('For source which the 2 bits are determined from, result should be close to 2 bits : %.3f' % result)
 teCalc.initialise()
-teCalc.addObservations(mUtils.computeCombinedValues(destArray, 2), \
-		mUtils.computeCombinedValues(sourceArray2, 2))
+teCalc.addObservations(mUtils.computeCombinedValues(sourceArray2, 2), \
+		mUtils.computeCombinedValues(destArray, 2))
 result2 = teCalc.computeAverageLocalOfObservations()
 print('For random source, result should be close to 0 bits in theory: %.3f' % result2)
 print('The result for random source is inflated towards 0.3 due to finite observation length (%d). One can verify that the answer is consistent with that from a random source by checking: teCalc.computeSignificance(1000); ans.pValue\n' % teCalc.getNumObservations())