def generatePlot(outputs, origData):
PLOT_PRECISION = <NUM_LIT:100><EOL>distribMatrix = np.zeros((PLOT_PRECISION+<NUM_LIT:1>,PLOT_PRECISION+<NUM_LIT:1>))<EOL>outputSize = len(outputs)<EOL>for i in range(<NUM_LIT:0>,outputSize):<EOL><INDENT>for j in range(i+<NUM_LIT:1>,outputSize):<EOL><INDENT>in1 = outputs[i]<EOL>in2 = outputs[j]<EOL>dist = (abs(in1-in2) > <NUM_LIT:0.1>)<EOL>intDist = int(dist.sum()/<NUM_LIT:2>+<NUM_LIT:0.1>)<EOL>orig1 = origData[i]<EOL>orig2 = origData[j]<EOL>origDist = (abs(orig1-orig2) > <NUM_LIT:0.1>)<EOL>intOrigDist = int(origDist.sum()/<NUM_LIT:2>+<NUM_LIT:0.1>)<EOL>if intDist < <NUM_LIT:2> and intOrigDist > <NUM_LIT:10>:<EOL><INDENT>print('<STR_LIT>' % (i, j, intDist))<EOL>print('<STR_LIT>' % intOrigDist)<EOL><DEDENT>x = int(PLOT_PRECISION*intDist/<NUM_LIT>)<EOL>y = int(PLOT_PRECISION*intOrigDist/<NUM_LIT>)<EOL>if distribMatrix[x, y] < <NUM_LIT:0.1>:<EOL><INDENT>distribMatrix[x, y] = <NUM_LIT:3><EOL><DEDENT>else:<EOL><INDENT>if distribMatrix[x, y] < <NUM_LIT:10>:<EOL><INDENT>distribMatrix[x, y] += <NUM_LIT:1><EOL><DEDENT><DEDENT><DEDENT><DEDENT>distribMatrix[<NUM_LIT:4>, <NUM_LIT:50>] = <NUM_LIT:3><EOL>distribMatrix[<NUM_LIT:4>, <NUM_LIT>] = <NUM_LIT:4><EOL>distribMatrix[<NUM_LIT:4>, <NUM_LIT>] = <NUM_LIT:5><EOL>distribMatrix[<NUM_LIT:4>, <NUM_LIT>] = <NUM_LIT:6><EOL>distribMatrix[<NUM_LIT:4>, <NUM_LIT>] = <NUM_LIT:7><EOL>distribMatrix[<NUM_LIT:4>, <NUM_LIT>] = <NUM_LIT:8><EOL>distribMatrix[<NUM_LIT:4>, <NUM_LIT>] = <NUM_LIT:9><EOL>distribMatrix[<NUM_LIT:4>, <NUM_LIT:64>] = <NUM_LIT:10><EOL>return distribMatrix<EOL>
Generates a table where each cell represent a frequency of pairs
    as described below.
    x coordinate is the % difference between input records (origData list),
    y coordinate is the % difference between corresponding output records.