Hat.exe vsn. 0.40: 2011.08.29.1120cdt JMS RunTime : 2011.08.29.1142.32 L Hat.exe is experimental. The .csv input data may have been incorrect. The .csv input data may have been mis-interpreted. USE THIS OUTPUT ONLY AT YOUR OWN RISK! Interpreting your `.csv` file using two passes: Pass 1: Reorder columns & export a revised .csv: /////////// Searching for: HatIn.csv 5823 bytes. Last modified 38 minute(s) ago. The first two lines of your .csv data are: HatIn.csv,2011.08.27,Jeff_Setterholm,_Description|Date|Analyst _____27,_____5,_____1,_____4,_nDatRows|nCols|nColIndex|MaxOrdblahblah nDatRowsIn = 27 nColsIn = 5 nColIndexIn = 1 MaxPolyOrder= 4 Parsing follows: Cell( -3: 5) truncated. !--- Pass 1 processing completed. @ 2011.08.29.1142.33 L Pass 2: Import reordered .csv data: //////////////////////// Searching for: HatOut.csv 1341 bytes. Last modified -1 seconds ago. The first two lines of your .csv data are: HatOut.csv,2011.08.27,Jeff_Setterholm,_Description|Date|Analyst _____27,_____5,_____1,_____4,_nDatRows|nCols|nColIndex|MaxOrd nDatRowsIn = 27 nColsIn = 5 nColIndexIn = 1 MaxPolyOrder= 4 Parsing follows: !--- Pass 2 processing completed. @ 2011.08.29.1142.33 L Converting matrix data to quad precision variables: Rows= 27 Columns= 4 ColNames X(1) X(2) X(3) Z Index 1: 2 2: 1 3: 1 4:-1 1 1.000000 0.200000 0.300000 1.100000 2 0.300000 1.000000 0.100000 2.200000 3 0.100000 0.200000 1.000000 -0.500000 4 -1.000000 0.300000 0.200000 -0.600000 5 0.500000 -1.000000 -0.300000 -1.200000 6 -1.000000 0.000000 2.000000 -3.000000 7 -1.000000 0.500000 -2.000000 2.000000 8 -1.000000 0.500000 0.000000 0.000000 9 -1.000000 0.500000 2.000000 -2.000000 11 0.000000 -0.500000 -2.000000 1.000000 10 0.000000 -0.500000 0.000000 -1.000000 12 0.000000 -0.500000 2.000000 -3.000000 13 0.000000 0.000000 -2.000000 2.000000 14 0.000000 0.000000 0.000000 0.000000 15 0.000000 0.000000 2.000000 -2.000000 16 0.000000 0.500000 -2.000000 3.000000 17 0.000000 0.500000 0.000000 1.000000 18 0.000000 0.500000 2.000000 -1.000000 19 1.000000 -0.500000 -2.000000 2.000000 20 1.000000 -0.500000 0.000000 0.000000 21 1.000000 -0.500000 2.000000 -2.000000 22 1.000000 0.000000 -2.000000 3.000000 23 1.000000 0.000000 0.000000 1.000000 24 1.000000 0.000000 2.000000 -1.000000 25 1.000000 0.500000 -2.000000 4.000000 26 1.000000 0.500000 0.000000 2.000000 27 1.000000 0.500000 2.000000 0.000000 .csv data has been imported. ////////////////////////////////////////////// Polynomial-based [C:Z] processing: Nominal number of polynomial coefficients= 12 Maximum possible order = 4 Maximum order to be used= 4 Pre-polynomialization GainBias1 scaling: Scaling the [B:Z] matrix columns into [-1.0,+1.0] using M1 and B1... as in Y=M1*X+B1: M1 : 1.000000 1.000000 0.500000 0.285714 B1 : 0.000000 0.000000 0.000000 -0.142857 Index X(1) X(2) X(3) Z 1 1.000000 0.200000 0.150000 0.171429 2 0.300000 1.000000 0.050000 0.485714 3 0.100000 0.200000 0.500000 -0.285714 4 -1.000000 0.300000 0.100000 -0.314286 5 0.500000 -1.000000 -0.150000 -0.485714 6 -1.000000 0.000000 1.000000 -1.000000 7 -1.000000 0.500000 -1.000000 0.428571 8 -1.000000 0.500000 0.000000 -0.142857 9 -1.000000 0.500000 1.000000 -0.714286 11 0.000000 -0.500000 -1.000000 0.142857 10 0.000000 -0.500000 0.000000 -0.428571 12 0.000000 -0.500000 1.000000 -1.000000 13 0.000000 0.000000 -1.000000 0.428571 14 0.000000 0.000000 0.000000 -0.142857 15 0.000000 0.000000 1.000000 -0.714286 16 0.000000 0.500000 -1.000000 0.714286 17 0.000000 0.500000 0.000000 0.142857 18 0.000000 0.500000 1.000000 -0.428571 19 1.000000 -0.500000 -1.000000 0.428571 20 1.000000 -0.500000 0.000000 -0.142857 21 1.000000 -0.500000 1.000000 -0.714286 22 1.000000 0.000000 -1.000000 0.714286 23 1.000000 0.000000 0.000000 0.142857 24 1.000000 0.000000 1.000000 -0.428571 25 1.000000 0.500000 -1.000000 1.000000 26 1.000000 0.500000 0.000000 0.428571 27 1.000000 0.500000 1.000000 -0.142857 [C:Z] (scaled by GB1) = Coeff#: 1 2 3 4 5 6 7 8 9 10 X(1) 0 1 2 0 1 2 0 1 2 0 X(2) 0 0 0 1 1 1 0 0 0 1 X(3) 0 0 0 0 0 0 1 1 1 1 1 1.000000 1.000000 1.000000 0.200000 0.200000 0.200000 0.150000 0.150000 0.150000 0.030000 2 1.000000 0.300000 0.090000 1.000000 0.300000 0.090000 0.050000 0.015000 0.004500 0.050000 3 1.000000 0.100000 0.010000 0.200000 0.020000 0.002000 0.500000 0.050000 0.005000 0.100000 4 1.000000 -1.000000 1.000000 0.300000 -0.300000 0.300000 0.100000 -0.100000 0.100000 0.030000 5 1.000000 0.500000 0.250000 -1.000000 -0.500000 -0.250000 -0.150000 -0.075000 -0.037500 0.150000 6 1.000000 -1.000000 1.000000 0.000000 0.000000 0.000000 1.000000 -1.000000 1.000000 0.000000 7 1.000000 -1.000000 1.000000 0.500000 -0.500000 0.500000 -1.000000 1.000000 -1.000000 -0.500000 8 1.000000 -1.000000 1.000000 0.500000 -0.500000 0.500000 0.000000 0.000000 0.000000 0.000000 9 1.000000 -1.000000 1.000000 0.500000 -0.500000 0.500000 1.000000 -1.000000 1.000000 0.500000 11 1.000000 0.000000 0.000000 -0.500000 0.000000 0.000000 -1.000000 0.000000 0.000000 0.500000 10 1.000000 0.000000 0.000000 -0.500000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 12 1.000000 0.000000 0.000000 -0.500000 0.000000 0.000000 1.000000 0.000000 0.000000 -0.500000 13 1.000000 0.000000 0.000000 0.000000 0.000000 0.000000 -1.000000 0.000000 0.000000 0.000000 14 1.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 15 1.000000 0.000000 0.000000 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 16 1.000000 0.000000 0.000000 0.500000 0.000000 0.000000 -1.000000 0.000000 0.000000 -0.500000 17 1.000000 0.000000 0.000000 0.500000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 18 1.000000 0.000000 0.000000 0.500000 0.000000 0.000000 1.000000 0.000000 0.000000 0.500000 19 1.000000 1.000000 1.000000 -0.500000 -0.500000 -0.500000 -1.000000 -1.000000 -1.000000 0.500000 20 1.000000 1.000000 1.000000 -0.500000 -0.500000 -0.500000 0.000000 0.000000 0.000000 0.000000 21 1.000000 1.000000 1.000000 -0.500000 -0.500000 -0.500000 1.000000 1.000000 1.000000 -0.500000 22 1.000000 1.000000 1.000000 0.000000 0.000000 0.000000 -1.000000 -1.000000 -1.000000 0.000000 23 1.000000 1.000000 1.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 24 1.000000 1.000000 1.000000 0.000000 0.000000 0.000000 1.000000 1.000000 1.000000 0.000000 25 1.000000 1.000000 1.000000 0.500000 0.500000 0.500000 -1.000000 -1.000000 -1.000000 -0.500000 26 1.000000 1.000000 1.000000 0.500000 0.500000 0.500000 0.000000 0.000000 0.000000 0.000000 27 1.000000 1.000000 1.000000 0.500000 0.500000 0.500000 1.000000 1.000000 1.000000 0.500000 Coeff#: 11 12 X(1) 1 2 X(2) 1 1 X(3) 1 1 1 0.030000 0.030000 0.171429 2 0.015000 0.004500 0.485714 3 0.010000 0.001000 -0.285714 4 -0.030000 0.030000 -0.314286 5 0.075000 0.037500 -0.485714 6 0.000000 0.000000 -1.000000 7 0.500000 -0.500000 0.428571 8 0.000000 0.000000 -0.142857 9 -0.500000 0.500000 -0.714286 11 0.000000 0.000000 0.142857 10 0.000000 0.000000 -0.428571 12 0.000000 0.000000 -1.000000 13 0.000000 0.000000 0.428571 14 0.000000 0.000000 -0.142857 15 0.000000 0.000000 -0.714286 16 0.000000 0.000000 0.714286 17 0.000000 0.000000 0.142857 18 0.000000 0.000000 -0.428571 19 0.500000 0.500000 0.428571 20 0.000000 0.000000 -0.142857 21 -0.500000 -0.500000 -0.714286 22 0.000000 0.000000 0.714286 23 0.000000 0.000000 0.142857 24 0.000000 0.000000 -0.428571 25 -0.500000 -0.500000 1.000000 26 0.000000 0.000000 0.428571 27 0.500000 0.500000 -0.142857 [C:Z] (scaled by GB1 & G2) = Column#: 1 2 3 4 5 6 7 8 9 10 G2 : 5.196152 3.917908 3.882100 2.433105 1.649364 1.565441 3.912480 3.006801 3.005653 1.592733 1 0.192450 0.255238 0.257593 0.082199 0.121259 0.127760 0.038339 0.049887 0.049906 0.018836 2 0.192450 0.076571 0.023183 0.410997 0.181888 0.057492 0.012780 0.004989 0.001497 0.031393 3 0.192450 0.025524 0.002576 0.082199 0.012126 0.001278 0.127796 0.016629 0.001664 0.062785 4 0.192450 -0.255238 0.257593 0.123299 -0.181888 0.191639 0.025559 -0.033258 0.033271 0.018836 5 0.192450 0.127619 0.064398 -0.410997 -0.303147 -0.159699 -0.038339 -0.024943 -0.012476 0.094178 6 0.192450 -0.255238 0.257593 0.000000 0.000000 0.000000 0.255592 -0.332579 0.332706 0.000000 7 0.192450 -0.255238 0.257593 0.205499 -0.303147 0.319399 -0.255592 0.332579 -0.332706 -0.313926 8 0.192450 -0.255238 0.257593 0.205499 -0.303147 0.319399 0.000000 0.000000 0.000000 0.000000 9 0.192450 -0.255238 0.257593 0.205499 -0.303147 0.319399 0.255592 -0.332579 0.332706 0.313926 11 0.192450 0.000000 0.000000 -0.205499 0.000000 0.000000 -0.255592 0.000000 0.000000 0.313926 10 0.192450 0.000000 0.000000 -0.205499 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 12 0.192450 0.000000 0.000000 -0.205499 0.000000 0.000000 0.255592 0.000000 0.000000 -0.313926 13 0.192450 0.000000 0.000000 0.000000 0.000000 0.000000 -0.255592 0.000000 0.000000 0.000000 14 0.192450 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 15 0.192450 0.000000 0.000000 0.000000 0.000000 0.000000 0.255592 0.000000 0.000000 0.000000 16 0.192450 0.000000 0.000000 0.205499 0.000000 0.000000 -0.255592 0.000000 0.000000 -0.313926 17 0.192450 0.000000 0.000000 0.205499 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 18 0.192450 0.000000 0.000000 0.205499 0.000000 0.000000 0.255592 0.000000 0.000000 0.313926 19 0.192450 0.255238 0.257593 -0.205499 -0.303147 -0.319399 -0.255592 -0.332579 -0.332706 0.313926 20 0.192450 0.255238 0.257593 -0.205499 -0.303147 -0.319399 0.000000 0.000000 0.000000 0.000000 21 0.192450 0.255238 0.257593 -0.205499 -0.303147 -0.319399 0.255592 0.332579 0.332706 -0.313926 22 0.192450 0.255238 0.257593 0.000000 0.000000 0.000000 -0.255592 -0.332579 -0.332706 0.000000 23 0.192450 0.255238 0.257593 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 24 0.192450 0.255238 0.257593 0.000000 0.000000 0.000000 0.255592 0.332579 0.332706 0.000000 25 0.192450 0.255238 0.257593 0.205499 0.303147 0.319399 -0.255592 -0.332579 -0.332706 -0.313926 26 0.192450 0.255238 0.257593 0.205499 0.303147 0.319399 0.000000 0.000000 0.000000 0.000000 27 0.192450 0.255238 0.257593 0.205499 0.303147 0.319399 0.255592 0.332579 0.332706 0.313926 Column#: 11 12 13 G2 : 1.227905 1.226062 2.767892 1 0.024432 0.024469 0.061935 2 0.012216 0.003670 0.175482 3 0.008144 0.000816 -0.103225 4 -0.024432 0.024469 -0.113547 5 0.061080 0.030586 -0.175482 6 0.000000 0.000000 -0.361286 7 0.407198 -0.407810 0.154837 8 0.000000 0.000000 -0.051612 9 -0.407198 0.407810 -0.258061 11 0.000000 0.000000 0.051612 10 0.000000 0.000000 -0.154837 12 0.000000 0.000000 -0.361286 13 0.000000 0.000000 0.154837 14 0.000000 0.000000 -0.051612 15 0.000000 0.000000 -0.258061 16 0.000000 0.000000 0.258061 17 0.000000 0.000000 0.051612 18 0.000000 0.000000 -0.154837 19 0.407198 0.407810 0.154837 20 0.000000 0.000000 -0.051612 21 -0.407198 -0.407810 -0.258061 22 0.000000 0.000000 0.258061 23 0.000000 0.000000 0.051612 24 0.000000 0.000000 -0.154837 25 -0.407198 -0.407810 0.361286 26 0.000000 0.000000 0.154837 27 0.407198 0.407810 -0.051612 [A:Y] (scaled by GB1 & G2) = Column#: 1 2 3 4 5 6 7 8 9 10 1 1.000000 0.289812 0.760956 0.174012 -0.207693 0.226449 0.081161 -0.061445 0.078244 0.043499 2 0.289812 1.000000 0.338797 -0.186726 0.285049 -0.276819 -0.062627 0.103732 -0.082109 0.016025 3 0.760956 0.338797 1.000000 0.195012 -0.265157 0.320151 0.080455 -0.082834 0.106364 0.016658 4 0.174012 -0.186726 0.195012 1.000000 0.374775 0.714225 0.037817 0.013669 0.014084 -0.016773 5 -0.207693 0.285049 -0.265157 0.374775 1.000000 0.329995 0.015496 0.020769 0.004075 -0.023220 6 0.226449 -0.276819 0.320151 0.714225 0.329995 1.000000 0.016817 0.004292 0.014833 -0.007139 7 0.081161 -0.062627 0.080455 0.037817 0.015496 0.016817 1.000000 0.259222 0.768809 0.166492 8 -0.061445 0.103732 -0.082834 0.013669 0.020769 0.004292 0.259222 1.000000 0.333683 -0.209646 9 0.078244 -0.082109 0.106364 0.014084 0.004075 0.014833 0.768809 0.333683 1.000000 0.209433 10 0.043499 0.016025 0.016658 -0.016773 -0.023220 -0.007139 0.166492 -0.209646 0.209433 1.000000 11 0.015673 0.021410 0.004238 -0.020418 -0.008789 -0.010602 -0.208986 0.271555 -0.271278 0.262307 12 0.016168 0.004205 0.014663 -0.005967 -0.010078 0.003143 0.209008 -0.271582 0.273022 0.772101 Column#: 11 12 13 1 0.015673 0.016168 -0.129126 2 0.021410 0.004205 0.283492 3 0.004238 0.014663 -0.034088 4 -0.020418 -0.005967 0.349582 5 -0.008789 -0.010078 0.346718 6 -0.010602 0.003143 0.172498 7 -0.208986 0.209008 -0.835826 8 0.271555 -0.271582 -0.144084 9 -0.271278 0.273022 -0.668104 10 0.262307 0.772101 -0.148090 11 1.000000 0.334038 0.163003 12 0.334038 1.000000 -0.174454 [A:Y] Angles (in degrees) = Column#: 1 2 3 4 5 6 7 8 9 10 1 0.000000 73.153316 40.451414 79.978815 101.987187 76.911877 85.344670 93.522743 85.512368 87.506914 2 73.153316 0.000000 70.196417 100.761783 73.438241 106.070439 93.590640 84.045888 94.709777 89.081786 3 40.451414 70.196417 0.000000 78.754556 105.376294 71.327948 85.385288 94.751500 83.894249 89.045513 4 79.978815 100.761783 78.754556 0.000000 67.989594 44.420239 87.832717 89.216804 89.192998 90.961064 5 101.987187 73.438241 105.376294 67.989594 0.000000 70.731543 89.112085 88.809937 89.766536 91.330552 6 76.911877 106.070439 71.327948 44.420239 70.731543 0.000000 89.036411 89.754114 89.150123 90.409041 7 85.344670 93.590640 85.385288 87.832717 89.112085 89.036411 0.000000 74.976096 39.752974 80.416093 8 93.522743 84.045888 94.751500 89.216804 88.809937 89.754114 74.976096 0.000000 70.507507 102.101590 9 85.512368 94.709777 83.894249 89.192998 89.766536 89.150123 39.752974 70.507507 0.000000 77.910857 10 87.506914 89.081786 89.045513 90.961064 91.330552 90.409041 80.416093 102.101590 77.910857 0.000000 11 89.101964 88.773199 89.757203 91.169923 90.503578 90.607482 102.062928 74.243158 105.740350 74.793015 12 89.073630 89.759060 89.159859 90.341879 90.577438 89.819932 77.935767 105.758453 74.155809 39.457106 Column#: 11 12 13 1 89.101964 89.073630 97.419101 2 88.773199 89.759060 73.531265 3 89.757203 89.159859 91.953489 4 91.169923 90.341879 69.538242 5 90.503578 90.577438 69.713303 6 90.607482 89.819932 80.066911 7 102.062928 77.935767 146.701950 8 74.243158 105.758453 98.284267 9 105.740350 74.155809 131.920886 10 74.793015 39.457106 98.516260 11 0.000000 70.485945 80.618747 12 70.485945 0.000000 100.046893 OverWrite [A:Y] : Noise Floor= 0.1000000D-11 using A( 5, 5) Determinant (i.e. the signed hypervolume): Row: Column: Fractional contrib: 5 5 1.000000000000 9 9 0.999983396784 10 10 0.955558140299 1 1 0.950096960741 11 11 0.819076953471 6 6 0.799783117546 8 8 0.544194470370 4 4 0.463868970282 7 7 0.407156481554 2 2 0.405694266581 12 12 0.207186023014 3 3 0.061769378211 Determinant= 0.000317364340 0.31736434007180259115D-03 The determinant of the inverse will be the reciprocal value. [Ai] (rank= 12) :[X] = Column#: 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10 1 2.488578 -1.102431 -0.833987 -0.379762 0.951554 -0.645723 -0.127176 0.843609 -0.745618 -0.128930 2 -1.102431 15.864580 -14.522693 2.705553 -13.316970 11.753002 0.132817 -11.972027 11.913840 -0.438719 3 -0.833987 -14.522693 16.189252 -2.417591 12.792647 -11.508509 -0.034584 11.521795 -11.565438 0.513485 4 -0.379762 2.705553 -2.417591 2.624688 -2.641511 0.607364 -0.080361 -2.125380 2.178222 -0.042104 5 0.951554 -13.316970 12.792647 -2.641511 12.614460 -10.273280 -0.125955 10.249976 -10.214261 0.393757 6 -0.645723 11.753002 -11.508509 0.607364 -10.273280 11.040150 0.162026 -9.066981 8.989257 -0.332664 7 -0.127176 0.132817 -0.034584 -0.080361 -0.125955 0.162026 2.459792 -0.113399 -1.780864 -0.035536 8 0.843609 -11.972027 11.521795 -2.125380 10.249976 -9.066981 -0.113399 11.878979 -11.086093 0.337980 9 -0.745618 11.913840 -11.565438 2.178222 -10.214261 8.989257 -1.780864 -11.086093 13.252778 -0.310358 10 -0.128930 -0.438719 0.513485 -0.042104 0.393757 -0.332664 -0.035536 0.337980 -0.310358 2.501404 11 -0.702596 9.560319 -9.204729 1.718333 -8.202761 7.246375 0.089891 -9.236410 9.202954 -0.279057 12 0.779485 -9.377622 8.960080 -1.679261 8.031470 -7.119126 -0.061542 9.087810 -9.078044 -1.653006 Column#: 11 12 13 11 12 0 1 -0.702596 0.779485 -0.268185 2 9.560319 -9.377622 0.404424 3 -9.204729 8.960080 0.000000 4 1.718333 -1.679261 0.502312 5 -8.202761 8.031470 0.000000 6 7.246375 -7.119126 0.000000 7 0.089891 -0.061542 -0.807728 8 -9.236410 9.087810 0.000000 9 9.202954 -9.078044 0.000000 10 -0.279057 -1.653006 0.000000 11 8.543010 -7.667142 0.000000 12 -7.667142 9.785613 0.000000 [X] (scaled by GB1 & G2) = Answer#: 1 1 -0.268185 2 0.404424 3 0.000000 4 0.502312 5 0.000000 6 0.000000 7 -0.807728 8 0.000000 9 0.000000 10 0.000000 11 0.000000 12 0.000000 [X] (scaled by GB1) = Answer#: 1 1 -0.142857 2 0.285714 3 0.000000 4 0.571429 5 0.000000 6 0.000000 7 -0.571429 8 0.000000 9 0.000000 10 0.000000 11 0.000000 12 0.000000 [X] (The de-scaled polynomial coefficients) = Answer# 1 Coeff# = to X()^ order(power) of: X(1) X(2) X(3) 1 0.000000 -0.32355623065705562331D-31 0 0 0 2 1.000000 0.10000000000000000000D+01 1 0 0 3 0.000000 0.39735299435465998596D-30 2 0 0 4 2.000000 0.20000000000000000000D+01 0 1 0 5 0.000000 0.68815178179872855269D-30 1 1 0 6 0.000000 -0.85184139702389406217D-30 2 1 0 7 -1.000000 -0.10000000000000000000D+01 0 0 1 8 0.000000 0.22171905381029908536D-30 1 0 1 9 0.000000 -0.25953007687385210443D-30 2 0 1 10 0.000000 0.13166395807667716659D-31 0 1 1 11 0.000000 -0.41749949206354122389D-30 1 1 1 12 0.000000 0.42161449252387408004D-30 2 1 1 Exercising the polynomial [Z]poly =[C]*[X] : Index [Z]poly - [Z]actual = [Z]error = 1 1.100000 1.100000 0.000000 -0.24651903288156618919D-31 2 2.200000 2.200000 0.000000 0.00000000000000000000D+00 3 -0.500000 -0.500000 0.000000 0.23771712735235500037D-32 4 -0.600000 -0.600000 0.000000 0.19721522630525295135D-30 5 -1.200000 -1.200000 0.000000 -0.28349688781380111757D-30 6 -3.000000 -3.000000 0.000000 -0.16609258253660714288D-30 7 2.000000 2.000000 0.000000 -0.46721560312061501543D-31 8 0.000000 0.000000 0.000000 -0.70657779776732732102D-31 9 -2.000000 -2.000000 0.000000 -0.94593999241403962660D-31 11 1.000000 1.000000 0.000000 -0.94685681078017491006D-31 10 -1.000000 -1.000000 0.000000 -0.21570415377137041554D-31 12 -3.000000 -3.000000 0.000000 0.51544850323743407897D-31 13 2.000000 2.000000 0.000000 -0.11863728457425372855D-30 14 0.000000 0.000000 0.000000 -0.32355623065705562331D-31 15 -2.000000 -2.000000 0.000000 0.53926038442842603886D-31 16 3.000000 3.000000 0.000000 -0.14258888807048997704D-30 17 1.000000 1.000000 0.000000 -0.43140830754274083108D-31 18 -1.000000 -1.000000 0.000000 0.56307226561941810822D-31 19 2.000000 2.000000 0.000000 0.11912252144247148321D-30 20 0.000000 0.000000 0.000000 0.11250074055591303635D-30 21 -2.000000 -2.000000 0.000000 0.10587895966935458949D-30 22 3.000000 3.000000 0.000000 0.92111098733196211008D-32 23 1.000000 1.000000 0.000000 0.19870725254761746823D-31 24 -1.000000 -1.000000 0.000000 0.30530340636203872545D-31 25 4.000000 4.000000 0.000000 -0.10070030169583224100D-30 26 2.000000 2.000000 0.000000 -0.72759290046389531753D-31 27 0.000000 0.000000 0.000000 -0.44818278396946833450D-31 RMS Error= 0.000000 0.10110138727188523621D-30 5 MAXabs() Error= 0.000000 -0.28349688781380111757D-30 Closing this file: HatReport.txt !--- Hat.exe processing completed. @ 2011.08.29.1142.33 L