Delete unused test data files.
diff --git a/commons-math-legacy/src/test/resources/org/apache/commons/math4/legacy/optimization/general/Hahn1.dat b/commons-math-legacy/src/test/resources/org/apache/commons/math4/legacy/optimization/general/Hahn1.dat
deleted file mode 100644
index 0e493a4..0000000
--- a/commons-math-legacy/src/test/resources/org/apache/commons/math4/legacy/optimization/general/Hahn1.dat
+++ /dev/null
@@ -1,296 +0,0 @@
-NIST/ITL StRD
-Dataset Name: Hahn1 (Hahn1.dat)
-
-File Format: ASCII
- Starting Values (lines 41 to 47)
- Certified Values (lines 41 to 52)
- Data (lines 61 to 296)
-
-Procedure: Nonlinear Least Squares Regression
-
-Description: These data are the result of a NIST study involving
- the thermal expansion of copper. The response
- variable is the coefficient of thermal expansion, and
- the predictor variable is temperature in degrees
- kelvin.
-
-
-Reference: Hahn, T., NIST (197?).
- Copper Thermal Expansion Study.
-
-
-
-
-
-Data: 1 Response (y = coefficient of thermal expansion)
- 1 Predictor (x = temperature, degrees kelvin)
- 236 Observations
- Average Level of Difficulty
- Observed Data
-
-Model: Rational Class (cubic/cubic)
- 7 Parameters (b1 to b7)
-
- y = (b1+b2*x+b3*x**2+b4*x**3) /
- (1+b5*x+b6*x**2+b7*x**3) + e
-
-
- Starting values Certified Values
-
- Start 1 Start 2 Parameter Standard Deviation
- b1 = 10 1 1.0776351733E+00 1.7070154742E-01
- b2 = -1 -0.1 -1.2269296921E-01 1.2000289189E-02
- b3 = 0.05 0.005 4.0863750610E-03 2.2508314937E-04
- b4 = -0.00001 -0.000001 -1.4262662514E-06 2.7578037666E-07
- b5 = -0.05 -0.005 -5.7609940901E-03 2.4712888219E-04
- b6 = 0.001 0.0001 2.4053735503E-04 1.0449373768E-05
- b7 = -0.000001 -0.0000001 -1.2314450199E-07 1.3027335327E-08
-
-Residual Sum of Squares: 1.5324382854E+00
-Residual Standard Deviation: 8.1803852243E-02
-Degrees of Freedom: 229
-Number of Observations: 236
-
-
-
-
-
-
-
-Data: y x
- .591E0 24.41E0
- 1.547E0 34.82E0
- 2.902E0 44.09E0
- 2.894E0 45.07E0
- 4.703E0 54.98E0
- 6.307E0 65.51E0
- 7.03E0 70.53E0
- 7.898E0 75.70E0
- 9.470E0 89.57E0
- 9.484E0 91.14E0
- 10.072E0 96.40E0
- 10.163E0 97.19E0
- 11.615E0 114.26E0
- 12.005E0 120.25E0
- 12.478E0 127.08E0
- 12.982E0 133.55E0
- 12.970E0 133.61E0
- 13.926E0 158.67E0
- 14.452E0 172.74E0
- 14.404E0 171.31E0
- 15.190E0 202.14E0
- 15.550E0 220.55E0
- 15.528E0 221.05E0
- 15.499E0 221.39E0
- 16.131E0 250.99E0
- 16.438E0 268.99E0
- 16.387E0 271.80E0
- 16.549E0 271.97E0
- 16.872E0 321.31E0
- 16.830E0 321.69E0
- 16.926E0 330.14E0
- 16.907E0 333.03E0
- 16.966E0 333.47E0
- 17.060E0 340.77E0
- 17.122E0 345.65E0
- 17.311E0 373.11E0
- 17.355E0 373.79E0
- 17.668E0 411.82E0
- 17.767E0 419.51E0
- 17.803E0 421.59E0
- 17.765E0 422.02E0
- 17.768E0 422.47E0
- 17.736E0 422.61E0
- 17.858E0 441.75E0
- 17.877E0 447.41E0
- 17.912E0 448.7E0
- 18.046E0 472.89E0
- 18.085E0 476.69E0
- 18.291E0 522.47E0
- 18.357E0 522.62E0
- 18.426E0 524.43E0
- 18.584E0 546.75E0
- 18.610E0 549.53E0
- 18.870E0 575.29E0
- 18.795E0 576.00E0
- 19.111E0 625.55E0
- .367E0 20.15E0
- .796E0 28.78E0
- 0.892E0 29.57E0
- 1.903E0 37.41E0
- 2.150E0 39.12E0
- 3.697E0 50.24E0
- 5.870E0 61.38E0
- 6.421E0 66.25E0
- 7.422E0 73.42E0
- 9.944E0 95.52E0
- 11.023E0 107.32E0
- 11.87E0 122.04E0
- 12.786E0 134.03E0
- 14.067E0 163.19E0
- 13.974E0 163.48E0
- 14.462E0 175.70E0
- 14.464E0 179.86E0
- 15.381E0 211.27E0
- 15.483E0 217.78E0
- 15.59E0 219.14E0
- 16.075E0 262.52E0
- 16.347E0 268.01E0
- 16.181E0 268.62E0
- 16.915E0 336.25E0
- 17.003E0 337.23E0
- 16.978E0 339.33E0
- 17.756E0 427.38E0
- 17.808E0 428.58E0
- 17.868E0 432.68E0
- 18.481E0 528.99E0
- 18.486E0 531.08E0
- 19.090E0 628.34E0
- 16.062E0 253.24E0
- 16.337E0 273.13E0
- 16.345E0 273.66E0
- 16.388E0 282.10E0
- 17.159E0 346.62E0
- 17.116E0 347.19E0
- 17.164E0 348.78E0
- 17.123E0 351.18E0
- 17.979E0 450.10E0
- 17.974E0 450.35E0
- 18.007E0 451.92E0
- 17.993E0 455.56E0
- 18.523E0 552.22E0
- 18.669E0 553.56E0
- 18.617E0 555.74E0
- 19.371E0 652.59E0
- 19.330E0 656.20E0
- 0.080E0 14.13E0
- 0.248E0 20.41E0
- 1.089E0 31.30E0
- 1.418E0 33.84E0
- 2.278E0 39.70E0
- 3.624E0 48.83E0
- 4.574E0 54.50E0
- 5.556E0 60.41E0
- 7.267E0 72.77E0
- 7.695E0 75.25E0
- 9.136E0 86.84E0
- 9.959E0 94.88E0
- 9.957E0 96.40E0
- 11.600E0 117.37E0
- 13.138E0 139.08E0
- 13.564E0 147.73E0
- 13.871E0 158.63E0
- 13.994E0 161.84E0
- 14.947E0 192.11E0
- 15.473E0 206.76E0
- 15.379E0 209.07E0
- 15.455E0 213.32E0
- 15.908E0 226.44E0
- 16.114E0 237.12E0
- 17.071E0 330.90E0
- 17.135E0 358.72E0
- 17.282E0 370.77E0
- 17.368E0 372.72E0
- 17.483E0 396.24E0
- 17.764E0 416.59E0
- 18.185E0 484.02E0
- 18.271E0 495.47E0
- 18.236E0 514.78E0
- 18.237E0 515.65E0
- 18.523E0 519.47E0
- 18.627E0 544.47E0
- 18.665E0 560.11E0
- 19.086E0 620.77E0
- 0.214E0 18.97E0
- 0.943E0 28.93E0
- 1.429E0 33.91E0
- 2.241E0 40.03E0
- 2.951E0 44.66E0
- 3.782E0 49.87E0
- 4.757E0 55.16E0
- 5.602E0 60.90E0
- 7.169E0 72.08E0
- 8.920E0 85.15E0
- 10.055E0 97.06E0
- 12.035E0 119.63E0
- 12.861E0 133.27E0
- 13.436E0 143.84E0
- 14.167E0 161.91E0
- 14.755E0 180.67E0
- 15.168E0 198.44E0
- 15.651E0 226.86E0
- 15.746E0 229.65E0
- 16.216E0 258.27E0
- 16.445E0 273.77E0
- 16.965E0 339.15E0
- 17.121E0 350.13E0
- 17.206E0 362.75E0
- 17.250E0 371.03E0
- 17.339E0 393.32E0
- 17.793E0 448.53E0
- 18.123E0 473.78E0
- 18.49E0 511.12E0
- 18.566E0 524.70E0
- 18.645E0 548.75E0
- 18.706E0 551.64E0
- 18.924E0 574.02E0
- 19.1E0 623.86E0
- 0.375E0 21.46E0
- 0.471E0 24.33E0
- 1.504E0 33.43E0
- 2.204E0 39.22E0
- 2.813E0 44.18E0
- 4.765E0 55.02E0
- 9.835E0 94.33E0
- 10.040E0 96.44E0
- 11.946E0 118.82E0
- 12.596E0 128.48E0
- 13.303E0 141.94E0
- 13.922E0 156.92E0
- 14.440E0 171.65E0
- 14.951E0 190.00E0
- 15.627E0 223.26E0
- 15.639E0 223.88E0
- 15.814E0 231.50E0
- 16.315E0 265.05E0
- 16.334E0 269.44E0
- 16.430E0 271.78E0
- 16.423E0 273.46E0
- 17.024E0 334.61E0
- 17.009E0 339.79E0
- 17.165E0 349.52E0
- 17.134E0 358.18E0
- 17.349E0 377.98E0
- 17.576E0 394.77E0
- 17.848E0 429.66E0
- 18.090E0 468.22E0
- 18.276E0 487.27E0
- 18.404E0 519.54E0
- 18.519E0 523.03E0
- 19.133E0 612.99E0
- 19.074E0 638.59E0
- 19.239E0 641.36E0
- 19.280E0 622.05E0
- 19.101E0 631.50E0
- 19.398E0 663.97E0
- 19.252E0 646.9E0
- 19.89E0 748.29E0
- 20.007E0 749.21E0
- 19.929E0 750.14E0
- 19.268E0 647.04E0
- 19.324E0 646.89E0
- 20.049E0 746.9E0
- 20.107E0 748.43E0
- 20.062E0 747.35E0
- 20.065E0 749.27E0
- 19.286E0 647.61E0
- 19.972E0 747.78E0
- 20.088E0 750.51E0
- 20.743E0 851.37E0
- 20.83E0 845.97E0
- 20.935E0 847.54E0
- 21.035E0 849.93E0
- 20.93E0 851.61E0
- 21.074E0 849.75E0
- 21.085E0 850.98E0
- 20.935E0 848.23E0
diff --git a/commons-math-legacy/src/test/resources/org/apache/commons/math4/legacy/optimization/general/Kirby2.dat b/commons-math-legacy/src/test/resources/org/apache/commons/math4/legacy/optimization/general/Kirby2.dat
deleted file mode 100644
index 75cd80f..0000000
--- a/commons-math-legacy/src/test/resources/org/apache/commons/math4/legacy/optimization/general/Kirby2.dat
+++ /dev/null
@@ -1,211 +0,0 @@
-NIST/ITL StRD
-Dataset Name: Kirby2 (Kirby2.dat)
-
-File Format: ASCII
- Starting Values (lines 41 to 45)
- Certified Values (lines 41 to 50)
- Data (lines 61 to 211)
-
-Procedure: Nonlinear Least Squares Regression
-
-Description: These data are the result of a NIST study involving
- scanning electron microscope line with standards.
-
-
-Reference: Kirby, R., NIST (197?).
- Scanning electron microscope line width standards.
-
-
-
-
-
-
-
-
-Data: 1 Response (y)
- 1 Predictor (x)
- 151 Observations
- Average Level of Difficulty
- Observed Data
-
-Model: Rational Class (quadratic/quadratic)
- 5 Parameters (b1 to b5)
-
- y = (b1 + b2*x + b3*x**2) /
- (1 + b4*x + b5*x**2) + e
-
-
- Starting values Certified Values
-
- Start 1 Start 2 Parameter Standard Deviation
- b1 = 2 1.5 1.6745063063E+00 8.7989634338E-02
- b2 = -0.1 -0.15 -1.3927397867E-01 4.1182041386E-03
- b3 = 0.003 0.0025 2.5961181191E-03 4.1856520458E-05
- b4 = -0.001 -0.0015 -1.7241811870E-03 5.8931897355E-05
- b5 = 0.00001 0.00002 2.1664802578E-05 2.0129761919E-07
-
-Residual Sum of Squares: 3.9050739624E+00
-Residual Standard Deviation: 1.6354535131E-01
-Degrees of Freedom: 146
-Number of Observations: 151
-
-
-
-
-
-
-
-
-
-Data: y x
- 0.0082E0 9.65E0
- 0.0112E0 10.74E0
- 0.0149E0 11.81E0
- 0.0198E0 12.88E0
- 0.0248E0 14.06E0
- 0.0324E0 15.28E0
- 0.0420E0 16.63E0
- 0.0549E0 18.19E0
- 0.0719E0 19.88E0
- 0.0963E0 21.84E0
- 0.1291E0 24.00E0
- 0.1710E0 26.25E0
- 0.2314E0 28.86E0
- 0.3227E0 31.85E0
- 0.4809E0 35.79E0
- 0.7084E0 40.18E0
- 1.0220E0 44.74E0
- 1.4580E0 49.53E0
- 1.9520E0 53.94E0
- 2.5410E0 58.29E0
- 3.2230E0 62.63E0
- 3.9990E0 67.03E0
- 4.8520E0 71.25E0
- 5.7320E0 75.22E0
- 6.7270E0 79.33E0
- 7.8350E0 83.56E0
- 9.0250E0 87.75E0
- 10.2670E0 91.93E0
- 11.5780E0 96.10E0
- 12.9440E0 100.28E0
- 14.3770E0 104.46E0
- 15.8560E0 108.66E0
- 17.3310E0 112.71E0
- 18.8850E0 116.88E0
- 20.5750E0 121.33E0
- 22.3200E0 125.79E0
- 22.3030E0 125.79E0
- 23.4600E0 128.74E0
- 24.0600E0 130.27E0
- 25.2720E0 133.33E0
- 25.8530E0 134.79E0
- 27.1100E0 137.93E0
- 27.6580E0 139.33E0
- 28.9240E0 142.46E0
- 29.5110E0 143.90E0
- 30.7100E0 146.91E0
- 31.3500E0 148.51E0
- 32.5200E0 151.41E0
- 33.2300E0 153.17E0
- 34.3300E0 155.97E0
- 35.0600E0 157.76E0
- 36.1700E0 160.56E0
- 36.8400E0 162.30E0
- 38.0100E0 165.21E0
- 38.6700E0 166.90E0
- 39.8700E0 169.92E0
- 40.0300E0 170.32E0
- 40.5000E0 171.54E0
- 41.3700E0 173.79E0
- 41.6700E0 174.57E0
- 42.3100E0 176.25E0
- 42.7300E0 177.34E0
- 43.4600E0 179.19E0
- 44.1400E0 181.02E0
- 44.5500E0 182.08E0
- 45.2200E0 183.88E0
- 45.9200E0 185.75E0
- 46.3000E0 186.80E0
- 47.0000E0 188.63E0
- 47.6800E0 190.45E0
- 48.0600E0 191.48E0
- 48.7400E0 193.35E0
- 49.4100E0 195.22E0
- 49.7600E0 196.23E0
- 50.4300E0 198.05E0
- 51.1100E0 199.97E0
- 51.5000E0 201.06E0
- 52.1200E0 202.83E0
- 52.7600E0 204.69E0
- 53.1800E0 205.86E0
- 53.7800E0 207.58E0
- 54.4600E0 209.50E0
- 54.8300E0 210.65E0
- 55.4000E0 212.33E0
- 56.4300E0 215.43E0
- 57.0300E0 217.16E0
- 58.0000E0 220.21E0
- 58.6100E0 221.98E0
- 59.5800E0 225.06E0
- 60.1100E0 226.79E0
- 61.1000E0 229.92E0
- 61.6500E0 231.69E0
- 62.5900E0 234.77E0
- 63.1200E0 236.60E0
- 64.0300E0 239.63E0
- 64.6200E0 241.50E0
- 65.4900E0 244.48E0
- 66.0300E0 246.40E0
- 66.8900E0 249.35E0
- 67.4200E0 251.32E0
- 68.2300E0 254.22E0
- 68.7700E0 256.24E0
- 69.5900E0 259.11E0
- 70.1100E0 261.18E0
- 70.8600E0 264.02E0
- 71.4300E0 266.13E0
- 72.1600E0 268.94E0
- 72.7000E0 271.09E0
- 73.4000E0 273.87E0
- 73.9300E0 276.08E0
- 74.6000E0 278.83E0
- 75.1600E0 281.08E0
- 75.8200E0 283.81E0
- 76.3400E0 286.11E0
- 76.9800E0 288.81E0
- 77.4800E0 291.08E0
- 78.0800E0 293.75E0
- 78.6000E0 295.99E0
- 79.1700E0 298.64E0
- 79.6200E0 300.84E0
- 79.8800E0 302.02E0
- 80.1900E0 303.48E0
- 80.6600E0 305.65E0
- 81.2200E0 308.27E0
- 81.6600E0 310.41E0
- 82.1600E0 313.01E0
- 82.5900E0 315.12E0
- 83.1400E0 317.71E0
- 83.5000E0 319.79E0
- 84.0000E0 322.36E0
- 84.4000E0 324.42E0
- 84.8900E0 326.98E0
- 85.2600E0 329.01E0
- 85.7400E0 331.56E0
- 86.0700E0 333.56E0
- 86.5400E0 336.10E0
- 86.8900E0 338.08E0
- 87.3200E0 340.60E0
- 87.6500E0 342.57E0
- 88.1000E0 345.08E0
- 88.4300E0 347.02E0
- 88.8300E0 349.52E0
- 89.1200E0 351.44E0
- 89.5400E0 353.93E0
- 89.8500E0 355.83E0
- 90.2500E0 358.32E0
- 90.5500E0 360.20E0
- 90.9300E0 362.67E0
- 91.2000E0 364.53E0
- 91.5500E0 367.00E0
- 92.2000E0 371.30E0
diff --git a/commons-math-legacy/src/test/resources/org/apache/commons/math4/legacy/optimization/general/Lanczos1.dat b/commons-math-legacy/src/test/resources/org/apache/commons/math4/legacy/optimization/general/Lanczos1.dat
deleted file mode 100644
index d23d5e4..0000000
--- a/commons-math-legacy/src/test/resources/org/apache/commons/math4/legacy/optimization/general/Lanczos1.dat
+++ /dev/null
@@ -1,84 +0,0 @@
-NIST/ITL StRD
-Dataset Name: Lanczos1 (Lanczos1.dat)
-
-File Format: ASCII
- Starting Values (lines 41 to 46)
- Certified Values (lines 41 to 51)
- Data (lines 61 to 84)
-
-Procedure: Nonlinear Least Squares Regression
-
-Description: These data are taken from an example discussed in
- Lanczos (1956). The data were generated to 14-digits
- of accuracy using
- f(x) = 0.0951*exp(-x) + 0.8607*exp(-3*x)
- + 1.5576*exp(-5*x).
-
-
-Reference: Lanczos, C. (1956).
- Applied Analysis.
- Englewood Cliffs, NJ: Prentice Hall, pp. 272-280.
-
-
-
-
-Data: 1 Response (y)
- 1 Predictor (x)
- 24 Observations
- Average Level of Difficulty
- Generated Data
-
-Model: Exponential Class
- 6 Parameters (b1 to b6)
-
- y = b1*exp(-b2*x) + b3*exp(-b4*x) + b5*exp(-b6*x) + e
-
-
-
- Starting values Certified Values
-
- Start 1 Start 2 Parameter Standard Deviation
- b1 = 1.2 0.5 9.5100000027E-02 5.3347304234E-11
- b2 = 0.3 0.7 1.0000000001E+00 2.7473038179E-10
- b3 = 5.6 3.6 8.6070000013E-01 1.3576062225E-10
- b4 = 5.5 4.2 3.0000000002E+00 3.3308253069E-10
- b5 = 6.5 4 1.5575999998E+00 1.8815731448E-10
- b6 = 7.6 6.3 5.0000000001E+00 1.1057500538E-10
-
-Residual Sum of Squares: 1.4307867721E-25
-Residual Standard Deviation: 8.9156129349E-14
-Degrees of Freedom: 18
-Number of Observations: 24
-
-
-
-
-
-
-
-
-Data: y x
- 2.513400000000E+00 0.000000000000E+00
- 2.044333373291E+00 5.000000000000E-02
- 1.668404436564E+00 1.000000000000E-01
- 1.366418021208E+00 1.500000000000E-01
- 1.123232487372E+00 2.000000000000E-01
- 9.268897180037E-01 2.500000000000E-01
- 7.679338563728E-01 3.000000000000E-01
- 6.388775523106E-01 3.500000000000E-01
- 5.337835317402E-01 4.000000000000E-01
- 4.479363617347E-01 4.500000000000E-01
- 3.775847884350E-01 5.000000000000E-01
- 3.197393199326E-01 5.500000000000E-01
- 2.720130773746E-01 6.000000000000E-01
- 2.324965529032E-01 6.500000000000E-01
- 1.996589546065E-01 7.000000000000E-01
- 1.722704126914E-01 7.500000000000E-01
- 1.493405660168E-01 8.000000000000E-01
- 1.300700206922E-01 8.500000000000E-01
- 1.138119324644E-01 9.000000000000E-01
- 1.000415587559E-01 9.500000000000E-01
- 8.833209084540E-02 1.000000000000E+00
- 7.833544019350E-02 1.050000000000E+00
- 6.976693743449E-02 1.100000000000E+00
- 6.239312536719E-02 1.150000000000E+00
diff --git a/commons-math-legacy/src/test/resources/org/apache/commons/math4/legacy/optimization/general/MGH17.dat b/commons-math-legacy/src/test/resources/org/apache/commons/math4/legacy/optimization/general/MGH17.dat
deleted file mode 100644
index 584f73c..0000000
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-NIST/ITL StRD
-Dataset Name: MGH17 (MGH17.dat)
-
-File Format: ASCII
- Starting Values (lines 41 to 45)
- Certified Values (lines 41 to 50)
- Data (lines 61 to 93)
-
-Procedure: Nonlinear Least Squares Regression
-
-Description: This problem was found to be difficult for some very
- good algorithms.
-
- See More, J. J., Garbow, B. S., and Hillstrom, K. E.
- (1981). Testing unconstrained optimization software.
- ACM Transactions on Mathematical Software. 7(1):
- pp. 17-41.
-
-Reference: Osborne, M. R. (1972).
- Some aspects of nonlinear least squares
- calculations. In Numerical Methods for Nonlinear
- Optimization, Lootsma (Ed).
- New York, NY: Academic Press, pp. 171-189.
-
-Data: 1 Response (y)
- 1 Predictor (x)
- 33 Observations
- Average Level of Difficulty
- Generated Data
-
-Model: Exponential Class
- 5 Parameters (b1 to b5)
-
- y = b1 + b2*exp[-x*b4] + b3*exp[-x*b5] + e
-
-
-
- Starting values Certified Values
-
- Start 1 Start 2 Parameter Standard Deviation
- b1 = 50 0.5 3.7541005211E-01 2.0723153551E-03
- b2 = 150 1.5 1.9358469127E+00 2.2031669222E-01
- b3 = -100 -1 -1.4646871366E+00 2.2175707739E-01
- b4 = 1 0.01 1.2867534640E-02 4.4861358114E-04
- b5 = 2 0.02 2.2122699662E-02 8.9471996575E-04
-
-Residual Sum of Squares: 5.4648946975E-05
-Residual Standard Deviation: 1.3970497866E-03
-Degrees of Freedom: 28
-Number of Observations: 33
-
-
-
-
-
-
-
-
-
-Data: y x
- 8.440000E-01 0.000000E+00
- 9.080000E-01 1.000000E+01
- 9.320000E-01 2.000000E+01
- 9.360000E-01 3.000000E+01
- 9.250000E-01 4.000000E+01
- 9.080000E-01 5.000000E+01
- 8.810000E-01 6.000000E+01
- 8.500000E-01 7.000000E+01
- 8.180000E-01 8.000000E+01
- 7.840000E-01 9.000000E+01
- 7.510000E-01 1.000000E+02
- 7.180000E-01 1.100000E+02
- 6.850000E-01 1.200000E+02
- 6.580000E-01 1.300000E+02
- 6.280000E-01 1.400000E+02
- 6.030000E-01 1.500000E+02
- 5.800000E-01 1.600000E+02
- 5.580000E-01 1.700000E+02
- 5.380000E-01 1.800000E+02
- 5.220000E-01 1.900000E+02
- 5.060000E-01 2.000000E+02
- 4.900000E-01 2.100000E+02
- 4.780000E-01 2.200000E+02
- 4.670000E-01 2.300000E+02
- 4.570000E-01 2.400000E+02
- 4.480000E-01 2.500000E+02
- 4.380000E-01 2.600000E+02
- 4.310000E-01 2.700000E+02
- 4.240000E-01 2.800000E+02
- 4.200000E-01 2.900000E+02
- 4.140000E-01 3.000000E+02
- 4.110000E-01 3.100000E+02
- 4.060000E-01 3.200000E+02