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Updated iterative_fitter.py to calculate error for all species. Added new test with mean squared error.

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pull/1/head
titusquah 5 years ago
parent 4fa69a9b44
commit 6a4aad4266
11 changed files with 743 additions and 81 deletions
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54
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216
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label,Feed H+[M],Feed H+[M].1,PC88A[M],PC88A[M].1,Feed Nd[M],Xaq Nd,D Nd,Feed Pr[M],Xaq Pr,D Pr,Feed Ce[M],Xaq Ce,D Ce,Feed La[M],Xaq La,D La,Feed Dy[M],Xaq Dy,D Dy,Feed Sm[M],Xaq Sm,D Sm,Feed Y[M],Xaq Y,D Y
Formiga (2016),0.316227766,0.316227766,1,1,0.038824182,0.036106489,0.075268817,0.011354951,0.010673654,0.063829787,0.104199378,0.102323789,0.018329939,0.046794403,0.046466843,0.007049345,0,0,0,0,0,0,0,0,0
Formiga (2016),0.1,0.1,1,1,0.038824182,0.023294509,0.666666667,0.011354951,0.007153619,0.587301587,0.104199378,0.087527477,0.19047619,0.046794403,0.044454683,0.052631579,0,0,0,0,0,0,0,0,0
Formiga (2016),0.01,0.01,1,1,0.038824182,0.011647255,2.333333333,0.011354951,0.003860683,1.941176471,0.104199378,0.065645608,0.587301587,0.046794403,0.040711131,0.149425287,0,0,0,0,0,0,0,0,0
Lyon (2016),0.02,0.02,1,1,0.048530227,0.029263727,0.658374793,0.04967791,0.035072604,0.416430595,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
Lyon (2016),0.05,0.05,1,1,0.048530227,0.032127011,0.510574018,0.04967791,0.037506822,0.324503311,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
Lyon (2016),0.1,0.1,1,1,0.048530227,0.039018303,0.243781095,0.04967791,0.043070748,0.153402537,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
Lyon (2016),0.3,0.3,1,1,0.048530227,0.046443428,0.044932079,0.04967791,0.048535318,0.023541453,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
Lyon (2016),0.01,0.01,1,1,0.259983361,0.197587354,0.315789474,0.088710553,0.071855548,0.234567901,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
Lyon (2016),0.05,0.05,1,1,0.259983361,0.207986689,0.25,0.088710553,0.07540397,0.176470588,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
Lyon (2016),0.1,0.1,1,1,0.259983361,0.21578619,0.204819277,0.088710553,0.077178181,0.149425287,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
Lyon (2016),0.3,0.3,1,1,0.259983361,0.244384359,0.063829787,0.088710553,0.08338792,0.063829787,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
Liu (2014),1.00E-05,1.00E-05,0.1,0.1,0.022739878,0.017097653,0.33,0.008303308,0.006642646,0.25,0,0,0,0.054425491,0.050393973,0.08,0,0,0,0,0,0,0,0,0
Liu (2014),1.00E-05,1.00E-05,0.3,0.3,0.022739878,0.013298174,0.71,0.008303308,0.005462702,0.52,0,0,0,0.054425491,0.048594188,0.12,0,0,0,0,0,0,0,0,0
Liu (2014),1.00E-05,1.00E-05,0.5,0.5,0.022739878,0.010932634,1.08,0.008303308,0.004537327,0.83,0,0,0,0.054425491,0.048164151,0.13,0,0,0,0,0,0,0,0,0
Liu (2014),1.00E-05,1.00E-05,0.7,0.7,0.022739878,0.009396644,1.42,0.008303308,0.003972875,1.09,0,0,0,0.054425491,0.047326514,0.15,0,0,0,0,0,0,0,0,0
Liu (2014),1.00E-05,1.00E-05,0.9,0.9,0.022739878,0.008209342,1.77,0.008303308,0.002997584,1.77,0,0,0,0.054425491,0.045735706,0.19,0,0,0,0,0,0,0,0,0
Banda (2014),1.00E-05,1.00E-05,0.1,0.1,0.022739878,0.018928042,0.201385664,0.008303308,0.007354823,0.128960943,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
Banda (2014),1.00E-05,1.00E-05,0.3,0.3,0.022739878,0.014333115,0.586527247,0.008303308,0.005999476,0.384005473,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
Banda (2014),1.00E-05,1.00E-05,0.5,0.5,0.022739878,0.010952454,1.076235839,0.008303308,0.004805359,0.727926764,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
Banda (2014),1.00E-05,1.00E-05,0.7,0.7,0.022739878,0.008786058,1.588177405,0.008303308,0.003974007,1.089404412,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
Banda (2014),1.00E-05,1.00E-05,0.9,0.9,0.022739878,0.00700602,2.245762712,0.008303308,0.00322327,1.576050934,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
Kim (2012),0.01023293,0.01023293,0.1,0.1,0.009359401,0.007999488,0.17,0.003371001,0.003064546,0.1,0.038539496,0.037783819,0.02,0.022461314,0.022020896,0.02,0,0,0,0,0,0,0,0,0
Kim (2012),0.01023293,0.01023293,0.3,0.3,0.009359401,0.006881912,0.36,0.003371001,0.002763116,0.22,0.038539496,0.035357336,0.09,0.022461314,0.021597417,0.04,0,0,0,0,0,0,0,0,0
Kim (2012),0.01023293,0.01023293,0.5,0.5,0.009359401,0.005813293,0.61,0.003371001,0.002340973,0.44,0.038539496,0.032386131,0.19,0.022461314,0.02060671,0.09,0,0,0,0,0,0,0,0,0
Kim (2012),0.01023293,0.01023293,0.7,0.7,0.009359401,0.005638193,0.66,0.003371001,0.002147134,0.57,0.038539496,0.03034606,0.27,0.022461314,0.019877269,0.13,0,0,0,0,0,0,0,0,0
Kim (2012),0.01023293,0.01023293,1,1,0.009359401,0.005005027,0.87,0.003371001,0.001862432,0.81,0.038539496,0.028547775,0.35,0.022461314,0.019702907,0.14,0,0,0,0,0,0,0,0,0
Kim (2012),0.01023293,0.01023293,2,2,0.009359401,0.003109436,2.01,0.003371001,0.001404584,1.4,0.038539496,0.021059834,0.83,0.022461314,0.018261231,0.23,0,0,0,0,0,0,0,0,0
Li (1987),0.01,0.01,1,1,0,0,0,0,0,0,0.0508008,0.0305,0.6656,0,0,0,0,0,0,0,0,0,0,0,0
Li (1987),0.01,0.01,1,1,0,0,0,0,0,0,0.09999878,0.0763,0.3106,0,0,0,0,0,0,0,0,0,0,0,0
Li (1987),0.01,0.01,1,1,0,0,0,0,0,0,0.15009456,0.1232,0.2183,0,0,0,0,0,0,0,0,0,0,0,0
Li (1987),0.01,0.01,1,1,0,0,0,0,0,0,0.2000068,0.1708,0.171,0,0,0,0,0,0,0,0,0,0,0,0
Li (1987),0.01,0.01,1,1,0,0,0,0,0,0,0.30001216,0.2672,0.1228,0,0,0,0,0,0,0,0,0,0,0,0
Li (1987),0.03,0.03,1,1,0,0,0,0,0,0,0.0499975,0.035,0.4285,0,0,0,0,0,0,0,0,0,0,0,0
Li (1987),0.03,0.03,1,1,0,0,0,0,0,0,0.10000115,0.0785,0.2739,0,0,0,0,0,0,0,0,0,0,0,0
Li (1987),0.03,0.03,1,1,0,0,0,0,0,0,0.1500032,0.128,0.1719,0,0,0,0,0,0,0,0,0,0,0,0
Li (1987),0.03,0.03,1,1,0,0,0,0,0,0,0.20000832,0.1752,0.1416,0,0,0,0,0,0,0,0,0,0,0,0
Li (1987),0.03,0.03,1,1,0,0,0,0,0,0,0.29999816,0.2728,0.0997,0,0,0,0,0,0,0,0,0,0,0,0
Li (1987),0.05,0.05,1,1,0,0,0,0,0,0,0.05010115,0.0355,0.4113,0,0,0,0,0,0,0,0,0,0,0,0
Li (1987),0.05,0.05,1,1,0,0,0,0,0,0,0.10000382,0.0826,0.2107,0,0,0,0,0,0,0,0,0,0,0,0
Li (1987),0.05,0.05,1,1,0,0,0,0,0,0,0.1499967,0.1305,0.1494,0,0,0,0,0,0,0,0,0,0,0,0
Li (1987),0.05,0.05,1,1,0,0,0,0,0,0,0.2000008,0.178,0.1236,0,0,0,0,0,0,0,0,0,0,0,0
Li (1987),0.07,0.07,1,1,0,0,0,0,0,0,0.05,0.04,0.25,0,0,0,0,0,0,0,0,0,0,0,0
Li (1987),0.07,0.07,1,1,0,0,0,0,0,0,0.10029675,0.0865,0.1595,0,0,0,0,0,0,0,0,0,0,0,0
Li (1987),0.07,0.07,1,1,0,0,0,0,0,0,0.15000153,0.1351,0.1103,0,0,0,0,0,0,0,0,0,0,0,0
Li (1987),0.07,0.07,1,1,0,0,0,0,0,0,0.20054418,0.1838,0.0911,0,0,0,0,0,0,0,0,0,0,0,0
Li (1987),0.07,0.07,1,1,0,0,0,0,0,0,0.29999441,0.2753,0.0897,0,0,0,0,0,0,0,0,0,0,0,0
Li (1987),0.07,0.07,1,1,0,0,0,0,0,0,0.29998992,0.2811,0.0672,0,0,0,0,0,0,0,0,0,0,0,0
Li (1987),0.1,0.1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0.10000029,0.0221,3.5249,0,0,0,0,0,0
Li (1987),0.1,0.1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0.15000025,0.0575,1.6087,0,0,0,0,0,0
Li (1987),0.1,0.1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0.19999582,0.1037,0.9286,0,0,0,0,0,0
Li (1987),0.1,0.1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0.25000596,0.1494,0.6734,0,0,0,0,0,0
Li (1987),0.1,0.1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0.30000931,0.1997,0.5023,0,0,0,0,0,0
Li (1987),0.3,0.3,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0.0500002,0.0106,3.717,0,0,0,0,0,0
Li (1987),0.3,0.3,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0.09999641,0.0409,1.4449,0,0,0,0,0,0
Li (1987),0.3,0.3,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0.14999806,0.0802,0.8703,0,0,0,0,0,0
Li (1987),0.3,0.3,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0.19999448,0.1252,0.5974,0,0,0,0,0,0
Li (1987),0.3,0.3,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0.29999394,0.2206,0.3599,0,0,0,0,0,0
Li (1987),0.5,0.5,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0.04999904,0.0221,1.2624,0,0,0,0,0,0
Li (1987),0.5,0.5,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0.09999996,0.0573,0.7452,0,0,0,0,0,0
Li (1987),0.5,0.5,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0.15000096,0.0984,0.5244,0,0,0,0,0,0
Li (1987),0.5,0.5,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0.19999616,0.1414,0.4144,0,0,0,0,0,0
Li (1987),0.5,0.5,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0.30001016,0.2369,0.2664,0,0,0,0,0,0
Li (1987),0.7,0.7,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0.04799855,0.0313,0.5335,0,0,0,0,0,0
Li (1987),0.7,0.7,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0.1000035,0.071,0.4085,0,0,0,0,0,0
Li (1987),0.7,0.7,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0.15000453,0.1131,0.3263,0,0,0,0,0,0
Li (1987),0.7,0.7,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0.19980428,0.1586,0.2598,0,0,0,0,0,0
Li (1987),0.7,0.7,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0.30000123,0.2517,0.1919,0,0,0,0,0,0
Li (1987),0.0001,0.0001,1,1,0,0,0,0,0,0,0,0,0,0.04999962,0.0363,0.3774,0,0,0,0,0,0,0,0,0
Li (1987),0.0001,0.0001,1,1,0,0,0,0,0,0,0,0,0,0.09999712,0.0844,0.1848,0,0,0,0,0,0,0,0,0
Li (1987),0.0001,0.0001,1,1,0,0,0,0,0,0,0,0,0,0.1499944,0.1345,0.1152,0,0,0,0,0,0,0,0,0
Li (1987),0.0001,0.0001,1,1,0,0,0,0,0,0,0,0,0,0.19999665,0.1835,0.0899,0,0,0,0,0,0,0,0,0
Li (1987),0.0001,0.0001,1,1,0,0,0,0,0,0,0,0,0,0.24999486,0.2329,0.0734,0,0,0,0,0,0,0,0,0
Li (1987),0.0001,0.0001,1,1,0,0,0,0,0,0,0,0,0,0.29998637,0.2833,0.0589,0,0,0,0,0,0,0,0,0
Li (1987),0.01,0.01,1,1,0,0,0,0,0,0,0,0,0,0.0499991,0.0395,0.2658,0,0,0,0,0,0,0,0,0
Li (1987),0.01,0.01,1,1,0,0,0,0,0,0,0,0,0,0.10000231,0.0881,0.1351,0,0,0,0,0,0,0,0,0
Li (1987),0.01,0.01,1,1,0,0,0,0,0,0,0,0,0,0.15000091,0.1367,0.0973,0,0,0,0,0,0,0,0,0
Li (1987),0.01,0.01,1,1,0,0,0,0,0,0,0,0,0,0.20009536,0.1856,0.0781,0,0,0,0,0,0,0,0,0
Li (1987),0.01,0.01,1,1,0,0,0,0,0,0,0,0,0,0.40540686,0.3846,0.0541,0,0,0,0,0,0,0,0,0
Li (1987),0.03,0.03,1,1,0,0,0,0,0,0,0,0,0,0.09999756,0.0897,0.1148,0,0,0,0,0,0,0,0,0
Li (1987),0.03,0.03,1,1,0,0,0,0,0,0,0,0,0,0.15000544,0.1391,0.0784,0,0,0,0,0,0,0,0,0
Li (1987),0.03,0.03,1,1,0,0,0,0,0,0,0,0,0,0.19999343,0.1883,0.0621,0,0,0,0,0,0,0,0,0
Li (1987),0.03,0.03,1,1,0,0,0,0,0,0,0,0,0,0.29999853,0.2881,0.0413,0,0,0,0,0,0,0,0,0
Li (1987),0.04,0.04,1,1,0,0,0,0,0,0,0,0,0,0.05030076,0.0444,0.1329,0,0,0,0,0,0,0,0,0
Li (1987),0.04,0.04,1,1,0,0,0,0,0,0,0,0,0,0.09999639,0.0919,0.0881,0,0,0,0,0,0,0,0,0
Li (1987),0.04,0.04,1,1,0,0,0,0,0,0,0,0,0,0.15000408,0.1413,0.0616,0,0,0,0,0,0,0,0,0
Li (1987),0.04,0.04,1,1,0,0,0,0,0,0,0,0,0,0.20049903,0.1917,0.0459,0,0,0,0,0,0,0,0,0
Li (1987),0.04,0.04,1,1,0,0,0,0,0,0,0,0,0,0.29948604,0.2883,0.0388,0,0,0,0,0,0,0,0,0
Lyon (2016),0.01,0.01,1,1,0.08666112,0.05352756,0.619,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
Lyon (2016),0.05,0.05,1,1,0.08666112,0.060900295,0.423,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
Lyon (2016),0.1,0.1,1,1,0.08666112,0.067916239,0.276,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
Lyon (2016),0.3,0.3,1,1,0.08666112,0.084878668,0.021,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
Lyon (2016),0.5,0.5,1,1,0.08666112,0.08666112,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
Lyon (2016),0.01,0.01,1,1,0.042983916,0.015124531,1.842,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
Lyon (2016),0.05,0.05,1,1,0.042983916,0.020410216,1.106,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
Lyon (2016),0.1,0.1,1,1,0.042983916,0.026354332,0.631,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
Lyon (2016),0.3,0.3,1,1,0.042983916,0.039290599,0.094,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
Lyon (2016),0.5,0.5,1,1,0.042983916,0.042983916,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
Lyon (2016),0.01,0.01,1,1,0.005546312,3.16E-05,174.668,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
Lyon (2016),0.05,0.05,1,1,0.005546312,0.000451912,11.273,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
Lyon (2016),0.1,0.1,1,1,0.005546312,0.001558828,2.558,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
Lyon (2016),0.3,0.3,1,1,0.005546312,0.005400498,0.027,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
Lyon (2016),0.5,0.5,1,1,0.005546312,0.005546312,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
Lee (2005),0.251,0.25,1,1,0.1,0.087622932,0.141253754,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
Lee (2005),0.251,0.25,1.5,1.5,0.1,0.07955199,0.257039578,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
Lee (2005),0.251,0.25,2,2,0.1,0.074253556,0.34673685,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
Lee (2005),0.251,0.25,2.5,2.5,0.1,0.071525275,0.398107171,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
Lee (2005),0.251,0.25,3,3,0.1,0.066613942,0.501187234,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
Lee (2005),0.134,0.13,1,1,0.1,0.075124079,0.331131121,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
Lee (2005),0.0835,0.08,1,1,0.1,0.065582051,0.52480746,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
Lee (2005),0.0285,0.03,1,1,0.1,0.059662917,0.676082975,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
Lee (2005),0.262,0.26,2,2,0.1,0.079923999,0.251188643,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
Lee (2005),0.134,0.13,2,2,0.1,0.061313682,0.630957344,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
Lee (2005),0.0835,0.08,2,2,0.1,0.051151089,0.954992586,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
Lee (2005),0.0285,0.03,2,2,0.1,0.044837645,1.230268771,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
Lee (2005),0.0114,0.01,2,2,0.1,0.041450132,1.412537545,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
Lee (2005),0.262,0.26,3,3,0.1,0.074253556,0.34673685,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
Lee (2005),0.134,0.13,3,3,0.1,0.048273748,1.071519305,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
Lee (2005),0.0835,0.08,3,3,0.1,0.040337083,1.479108388,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
Lee (2005),0.0285,0.03,3,3,0.1,0.033900091,1.9498446,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
Lee (2005),0.0114,0.01,3,3,0.1,0.030387123,2.290867653,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
Li (1987),0.01,0.01,1,1,0.05000119,0.0239,1.0921,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
Li (1987),0.01,0.01,1,1,0.09999803,0.0683,0.4641,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
Li (1987),0.01,0.01,1,1,0.1500057,0.117,0.2821,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
Li (1987),0.01,0.01,1,1,0.200004,0.168,0.1905,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
Li (1987),0.01,0.01,1,1,0.30001149,0.2637,0.1377,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
Li (1987),0.05,0.05,1,1,0.03453582,0.0201,0.7182,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
Li (1987),0.05,0.05,1,1,0.09996185,0.0757,0.3205,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
Li (1987),0.05,0.05,1,1,0.19999308,0.1722,0.1614,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
Li (1987),0.1,0.1,1,1,0.04999817,0.0389,0.2853,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
Li (1987),0.1,0.1,1,1,0.1499974,0.133,0.1278,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
Li (1987),0.1,0.1,1,1,0.19999876,0.1807,0.1068,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
Li (1987),0.1,0.1,1,1,0.2500032,0.232,0.0776,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
Li (1987),0.1,0.1,1,1,0.30001216,0.2807,0.0688,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
Li (1987),0.3,0.3,1,1,0.01996974,0.0189,0.0566,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
Li (1987),0.3,0.3,1,1,0.04997968,0.0478,0.0456,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
Li (1987),0.3,0.3,1,1,0.15000144,0.1464,0.0246,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
Li (1987),0.3,0.3,1,1,0.29998765,0.2957,0.0145,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
Li (1987),0.5,0.5,1,1,0.04999786,0.0482,0.0373,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
Li (1987),0.5,0.5,1,1,0.10000096,0.0976,0.0246,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
Li (1987),0.5,0.5,1,1,0.20000784,0.1968,0.0163,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
Lyon (2016),0.03,0.03,1,1,0,0,0,0.09296866,0.06556323,0.418,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
Lyon (2016),0.05,0.05,1,1,0,0,0,0.09296866,0.066835844,0.391,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
Lyon (2016),0.1,0.1,1,1,0,0,0,0.09296866,0.080076365,0.161,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
Lyon (2016),0.3,0.3,1,1,0,0,0,0.09296866,0.091957131,0.011,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
Lyon (2016),0.5,0.5,1,1,0,0,0,0.09296866,0.09296866,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
Lyon (2016),0.03,0.03,1,1,0,0,0,0.047548856,0.02373882,1.003,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
Lyon (2016),0.05,0.05,1,1,0,0,0,0.047548856,0.025646632,0.854,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
Lyon (2016),0.1,0.1,1,1,0,0,0,0.047548856,0.034988121,0.359,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
Lyon (2016),0.3,0.3,1,1,0,0,0,0.047548856,0.045371046,0.048,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
Lyon (2016),0.5,0.5,1,1,0,0,0,0.047548856,0.047548856,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
Lyon (2016),0.01,0.01,1,1,0,0,0,0.00638716,5.54E-05,114.332,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
Lyon (2016),0.05,0.05,1,1,0,0,0,0.00638716,0.000669233,8.544,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
Lyon (2016),0.1,0.1,1,1,0,0,0,0.00638716,0.002264147,1.821,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
Lyon (2016),0.3,0.3,1,1,0,0,0,0.00638716,0.006323921,0.01,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
Lyon (2016),0.5,0.5,1,1,0,0,0,0.00638716,0.00638716,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
Li (1987),0.01,0.01,1,1,0,0,0,0.04999995,0.0259,0.9305,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
Li (1987),0.01,0.01,1,1,0,0,0,0.09999925,0.0725,0.3793,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
Li (1987),0.01,0.01,1,1,0,0,0,0.20000715,0.1673,0.1955,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
Li (1987),0.01,0.01,1,1,0,0,0,0.29999192,0.2648,0.1329,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
Li (1987),0.03,0.03,1,1,0,0,0,0.05000109,0.0307,0.6287,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
Li (1987),0.03,0.03,1,1,0,0,0,0.10009624,0.0776,0.2899,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
Li (1987),0.03,0.03,1,1,0,0,0,0.20000456,0.1736,0.1521,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
Li (1987),0.03,0.03,1,1,0,0,0,0.2999997,0.273,0.0989,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
Li (1987),0.05,0.05,1,1,0,0,0,0.05002915,0.0349,0.4335,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
Li (1987),0.05,0.05,1,1,0,0,0,0.09997622,0.0809,0.2358,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
Li (1987),0.05,0.05,1,1,0,0,0,0.14999553,0.1299,0.1547,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
Li (1987),0.05,0.05,1,1,0,0,0,0.25001088,0.2272,0.1004,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
Li (1987),0.07,0.07,1,1,0,0,0,0.04999995,0.0385,0.2987,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
Li (1987),0.07,0.07,1,1,0,0,0,0.09999936,0.0864,0.1574,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
Li (1987),0.07,0.07,1,1,0,0,0,0.14998624,0.1343,0.1168,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
Li (1987),0.07,0.07,1,1,0,0,0,0.24999474,0.2334,0.0711,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
Li (1987),0.1,0.1,1,1,0,0,0,0.0499992,0.0415,0.2048,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
Li (1987),0.1,0.1,1,1,0,0,0,0.10000199,0.0901,0.1099,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
Li (1987),0.1,0.1,1,1,0,0,0,0.1999998,0.189,0.0582,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
Li (1987),0.1,0.1,1,1,0,0,0,0.30000544,0.2888,0.0388,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
Li (1987),0.01,0.01,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0500503,0.0115,3.3522,0,0,0
Li (1987),0.01,0.01,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0999792,0.048,1.0829,0,0,0
Li (1987),0.01,0.01,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.1500016,0.0944,0.589,0,0,0
Li (1987),0.01,0.01,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.24999968,0.1864,0.3412,0,0,0
Li (1987),0.01,0.01,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.30000263,0.2371,0.2653,0,0,0
Li (1987),0.05,0.05,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.05032248,0.0156,2.2258,0,0,0
Li (1987),0.05,0.05,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.10000216,0.0556,0.7986,0,0,0
Li (1987),0.05,0.05,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.15000255,0.1035,0.4493,0,0,0
Li (1987),0.05,0.05,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.30392343,0.1997,0.5219,0,0,0
Li (1987),0.05,0.05,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.30000411,0.2471,0.2141,0,0,0
Li (1987),0.1,0.1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.04999989,0.0219,1.2831,0,0,0
Li (1987),0.1,0.1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.09999773,0.0623,0.6051,0,0,0
Li (1987),0.1,0.1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.15000375,0.1105,0.3575,0,0,0
Li (1987),0.1,0.1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.2500038,0.2073,0.206,0,0,0
Li (1987),0.1,0.1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.30061536,0.2572,0.1688,0,0,0
Li (1987),0.3,0.3,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0502452,0.0408,0.2315,0,0,0
Li (1987),0.3,0.3,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.09999592,0.0842,0.1876,0,0,0
Li (1987),0.3,0.3,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.14999496,0.1334,0.1244,0,0,0
Li (1987),0.3,0.3,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.1999935,0.1814,0.1025,0,0,0
Li (1987),0.3,0.3,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.30001088,0.2782,0.0784,0,0,0
Li (1987),0.1,0.1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.1500005,0.047,2.1915
Li (1987),0.1,0.1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.19999809,0.0857,1.3337
Li (1987),0.1,0.1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.2999996,0.1772,0.693
Li (1987),0.5,0.5,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.05,0.016,2.125
Li (1987),0.5,0.5,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.1000004,0.0445,1.2472
Li (1987),0.5,0.5,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.15000048,0.0828,0.8116
Li (1987),0.5,0.5,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.19999436,0.1214,0.6474
Li (1987),0.5,0.5,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.29999366,0.2083,0.4402
Li (1987),1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.04999995,0.0333,0.5015
Li (1987),1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.09999846,0.0694,0.4409
Li (1987),1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.15000104,0.1108,0.3538
Li (1987),1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.19999756,0.1526,0.3106
Li (1987),1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.29999472,0.2416,0.2417
Li (1987),1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.25000384,0.1984,0.2601
Li (1987),1.5,1.5,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0500013,0.0405,0.2346
Li (1987),1.5,1.5,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.09999854,0.0838,0.1933
Li (1987),1.5,1.5,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.19999977,0.1753,0.1409
Li (1987),2,2,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.09999804,0.0908,0.1013
Li (1987),2,2,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.19992651,0.1851,0.0801
Li (1987),2,2,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.30001335,0.2821,0.0635
1 label Feed H+[M] Feed H+[M].1 PC88A[M] PC88A[M].1 Feed Nd[M] Xaq Nd D Nd Feed Pr[M] Xaq Pr D Pr Feed Ce[M] Xaq Ce D Ce Feed La[M] Xaq La D La Feed Dy[M] Xaq Dy D Dy Feed Sm[M] Xaq Sm D Sm Feed Y[M] Xaq Y D Y
2 Formiga (2016) 0.316227766 0.316227766 1 1 0.038824182 0.036106489 0.075268817 0.011354951 0.010673654 0.063829787 0.104199378 0.102323789 0.018329939 0.046794403 0.046466843 0.007049345 0 0 0 0 0 0 0 0 0
3 Formiga (2016) 0.1 0.1 1 1 0.038824182 0.023294509 0.666666667 0.011354951 0.007153619 0.587301587 0.104199378 0.087527477 0.19047619 0.046794403 0.044454683 0.052631579 0 0 0 0 0 0 0 0 0
4 Formiga (2016) 0.01 0.01 1 1 0.038824182 0.011647255 2.333333333 0.011354951 0.003860683 1.941176471 0.104199378 0.065645608 0.587301587 0.046794403 0.040711131 0.149425287 0 0 0 0 0 0 0 0 0
5 Lyon (2016) 0.02 0.02 1 1 0.048530227 0.029263727 0.658374793 0.04967791 0.035072604 0.416430595 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
6 Lyon (2016) 0.05 0.05 1 1 0.048530227 0.032127011 0.510574018 0.04967791 0.037506822 0.324503311 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
7 Lyon (2016) 0.1 0.1 1 1 0.048530227 0.039018303 0.243781095 0.04967791 0.043070748 0.153402537 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
8 Lyon (2016) 0.3 0.3 1 1 0.048530227 0.046443428 0.044932079 0.04967791 0.048535318 0.023541453 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
9 Lyon (2016) 0.01 0.01 1 1 0.259983361 0.197587354 0.315789474 0.088710553 0.071855548 0.234567901 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
10 Lyon (2016) 0.05 0.05 1 1 0.259983361 0.207986689 0.25 0.088710553 0.07540397 0.176470588 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
11 Lyon (2016) 0.1 0.1 1 1 0.259983361 0.21578619 0.204819277 0.088710553 0.077178181 0.149425287 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
12 Lyon (2016) 0.3 0.3 1 1 0.259983361 0.244384359 0.063829787 0.088710553 0.08338792 0.063829787 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
13 Liu (2014) 1.00E-05 1.00E-05 0.1 0.1 0.022739878 0.017097653 0.33 0.008303308 0.006642646 0.25 0 0 0 0.054425491 0.050393973 0.08 0 0 0 0 0 0 0 0 0
14 Liu (2014) 1.00E-05 1.00E-05 0.3 0.3 0.022739878 0.013298174 0.71 0.008303308 0.005462702 0.52 0 0 0 0.054425491 0.048594188 0.12 0 0 0 0 0 0 0 0 0
15 Liu (2014) 1.00E-05 1.00E-05 0.5 0.5 0.022739878 0.010932634 1.08 0.008303308 0.004537327 0.83 0 0 0 0.054425491 0.048164151 0.13 0 0 0 0 0 0 0 0 0
16 Liu (2014) 1.00E-05 1.00E-05 0.7 0.7 0.022739878 0.009396644 1.42 0.008303308 0.003972875 1.09 0 0 0 0.054425491 0.047326514 0.15 0 0 0 0 0 0 0 0 0
17 Liu (2014) 1.00E-05 1.00E-05 0.9 0.9 0.022739878 0.008209342 1.77 0.008303308 0.002997584 1.77 0 0 0 0.054425491 0.045735706 0.19 0 0 0 0 0 0 0 0 0
18 Banda (2014) 1.00E-05 1.00E-05 0.1 0.1 0.022739878 0.018928042 0.201385664 0.008303308 0.007354823 0.128960943 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
19 Banda (2014) 1.00E-05 1.00E-05 0.3 0.3 0.022739878 0.014333115 0.586527247 0.008303308 0.005999476 0.384005473 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
20 Banda (2014) 1.00E-05 1.00E-05 0.5 0.5 0.022739878 0.010952454 1.076235839 0.008303308 0.004805359 0.727926764 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
21 Banda (2014) 1.00E-05 1.00E-05 0.7 0.7 0.022739878 0.008786058 1.588177405 0.008303308 0.003974007 1.089404412 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
22 Banda (2014) 1.00E-05 1.00E-05 0.9 0.9 0.022739878 0.00700602 2.245762712 0.008303308 0.00322327 1.576050934 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
23 Kim (2012) 0.01023293 0.01023293 0.1 0.1 0.009359401 0.007999488 0.17 0.003371001 0.003064546 0.1 0.038539496 0.037783819 0.02 0.022461314 0.022020896 0.02 0 0 0 0 0 0 0 0 0
24 Kim (2012) 0.01023293 0.01023293 0.3 0.3 0.009359401 0.006881912 0.36 0.003371001 0.002763116 0.22 0.038539496 0.035357336 0.09 0.022461314 0.021597417 0.04 0 0 0 0 0 0 0 0 0
25 Kim (2012) 0.01023293 0.01023293 0.5 0.5 0.009359401 0.005813293 0.61 0.003371001 0.002340973 0.44 0.038539496 0.032386131 0.19 0.022461314 0.02060671 0.09 0 0 0 0 0 0 0 0 0
26 Kim (2012) 0.01023293 0.01023293 0.7 0.7 0.009359401 0.005638193 0.66 0.003371001 0.002147134 0.57 0.038539496 0.03034606 0.27 0.022461314 0.019877269 0.13 0 0 0 0 0 0 0 0 0
27 Kim (2012) 0.01023293 0.01023293 1 1 0.009359401 0.005005027 0.87 0.003371001 0.001862432 0.81 0.038539496 0.028547775 0.35 0.022461314 0.019702907 0.14 0 0 0 0 0 0 0 0 0
28 Kim (2012) 0.01023293 0.01023293 2 2 0.009359401 0.003109436 2.01 0.003371001 0.001404584 1.4 0.038539496 0.021059834 0.83 0.022461314 0.018261231 0.23 0 0 0 0 0 0 0 0 0
29 Li (1987) 0.01 0.01 1 1 0 0 0 0 0 0 0.0508008 0.0305 0.6656 0 0 0 0 0 0 0 0 0 0 0 0
30 Li (1987) 0.01 0.01 1 1 0 0 0 0 0 0 0.09999878 0.0763 0.3106 0 0 0 0 0 0 0 0 0 0 0 0
31 Li (1987) 0.01 0.01 1 1 0 0 0 0 0 0 0.15009456 0.1232 0.2183 0 0 0 0 0 0 0 0 0 0 0 0
32 Li (1987) 0.01 0.01 1 1 0 0 0 0 0 0 0.2000068 0.1708 0.171 0 0 0 0 0 0 0 0 0 0 0 0
33 Li (1987) 0.01 0.01 1 1 0 0 0 0 0 0 0.30001216 0.2672 0.1228 0 0 0 0 0 0 0 0 0 0 0 0
34 Li (1987) 0.03 0.03 1 1 0 0 0 0 0 0 0.0499975 0.035 0.4285 0 0 0 0 0 0 0 0 0 0 0 0
35 Li (1987) 0.03 0.03 1 1 0 0 0 0 0 0 0.10000115 0.0785 0.2739 0 0 0 0 0 0 0 0 0 0 0 0
36 Li (1987) 0.03 0.03 1 1 0 0 0 0 0 0 0.1500032 0.128 0.1719 0 0 0 0 0 0 0 0 0 0 0 0
37 Li (1987) 0.03 0.03 1 1 0 0 0 0 0 0 0.20000832 0.1752 0.1416 0 0 0 0 0 0 0 0 0 0 0 0
38 Li (1987) 0.03 0.03 1 1 0 0 0 0 0 0 0.29999816 0.2728 0.0997 0 0 0 0 0 0 0 0 0 0 0 0
39 Li (1987) 0.05 0.05 1 1 0 0 0 0 0 0 0.05010115 0.0355 0.4113 0 0 0 0 0 0 0 0 0 0 0 0
40 Li (1987) 0.05 0.05 1 1 0 0 0 0 0 0 0.10000382 0.0826 0.2107 0 0 0 0 0 0 0 0 0 0 0 0
41 Li (1987) 0.05 0.05 1 1 0 0 0 0 0 0 0.1499967 0.1305 0.1494 0 0 0 0 0 0 0 0 0 0 0 0
42 Li (1987) 0.05 0.05 1 1 0 0 0 0 0 0 0.2000008 0.178 0.1236 0 0 0 0 0 0 0 0 0 0 0 0
43 Li (1987) 0.07 0.07 1 1 0 0 0 0 0 0 0.05 0.04 0.25 0 0 0 0 0 0 0 0 0 0 0 0
44 Li (1987) 0.07 0.07 1 1 0 0 0 0 0 0 0.10029675 0.0865 0.1595 0 0 0 0 0 0 0 0 0 0 0 0
45 Li (1987) 0.07 0.07 1 1 0 0 0 0 0 0 0.15000153 0.1351 0.1103 0 0 0 0 0 0 0 0 0 0 0 0
46 Li (1987) 0.07 0.07 1 1 0 0 0 0 0 0 0.20054418 0.1838 0.0911 0 0 0 0 0 0 0 0 0 0 0 0
47 Li (1987) 0.07 0.07 1 1 0 0 0 0 0 0 0.29999441 0.2753 0.0897 0 0 0 0 0 0 0 0 0 0 0 0
48 Li (1987) 0.07 0.07 1 1 0 0 0 0 0 0 0.29998992 0.2811 0.0672 0 0 0 0 0 0 0 0 0 0 0 0
49 Li (1987) 0.1 0.1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0.10000029 0.0221 3.5249 0 0 0 0 0 0
50 Li (1987) 0.1 0.1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0.15000025 0.0575 1.6087 0 0 0 0 0 0
51 Li (1987) 0.1 0.1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0.19999582 0.1037 0.9286 0 0 0 0 0 0
52 Li (1987) 0.1 0.1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0.25000596 0.1494 0.6734 0 0 0 0 0 0
53 Li (1987) 0.1 0.1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0.30000931 0.1997 0.5023 0 0 0 0 0 0
54 Li (1987) 0.3 0.3 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0.0500002 0.0106 3.717 0 0 0 0 0 0
55 Li (1987) 0.3 0.3 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0.09999641 0.0409 1.4449 0 0 0 0 0 0
56 Li (1987) 0.3 0.3 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0.14999806 0.0802 0.8703 0 0 0 0 0 0
57 Li (1987) 0.3 0.3 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0.19999448 0.1252 0.5974 0 0 0 0 0 0
58 Li (1987) 0.3 0.3 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0.29999394 0.2206 0.3599 0 0 0 0 0 0
59 Li (1987) 0.5 0.5 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0.04999904 0.0221 1.2624 0 0 0 0 0 0
60 Li (1987) 0.5 0.5 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0.09999996 0.0573 0.7452 0 0 0 0 0 0
61 Li (1987) 0.5 0.5 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0.15000096 0.0984 0.5244 0 0 0 0 0 0
62 Li (1987) 0.5 0.5 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0.19999616 0.1414 0.4144 0 0 0 0 0 0
63 Li (1987) 0.5 0.5 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0.30001016 0.2369 0.2664 0 0 0 0 0 0
64 Li (1987) 0.7 0.7 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0.04799855 0.0313 0.5335 0 0 0 0 0 0
65 Li (1987) 0.7 0.7 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0.1000035 0.071 0.4085 0 0 0 0 0 0
66 Li (1987) 0.7 0.7 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0.15000453 0.1131 0.3263 0 0 0 0 0 0
67 Li (1987) 0.7 0.7 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0.19980428 0.1586 0.2598 0 0 0 0 0 0
68 Li (1987) 0.7 0.7 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0.30000123 0.2517 0.1919 0 0 0 0 0 0
69 Li (1987) 0.0001 0.0001 1 1 0 0 0 0 0 0 0 0 0 0.04999962 0.0363 0.3774 0 0 0 0 0 0 0 0 0
70 Li (1987) 0.0001 0.0001 1 1 0 0 0 0 0 0 0 0 0 0.09999712 0.0844 0.1848 0 0 0 0 0 0 0 0 0
71 Li (1987) 0.0001 0.0001 1 1 0 0 0 0 0 0 0 0 0 0.1499944 0.1345 0.1152 0 0 0 0 0 0 0 0 0
72 Li (1987) 0.0001 0.0001 1 1 0 0 0 0 0 0 0 0 0 0.19999665 0.1835 0.0899 0 0 0 0 0 0 0 0 0
73 Li (1987) 0.0001 0.0001 1 1 0 0 0 0 0 0 0 0 0 0.24999486 0.2329 0.0734 0 0 0 0 0 0 0 0 0
74 Li (1987) 0.0001 0.0001 1 1 0 0 0 0 0 0 0 0 0 0.29998637 0.2833 0.0589 0 0 0 0 0 0 0 0 0
75 Li (1987) 0.01 0.01 1 1 0 0 0 0 0 0 0 0 0 0.0499991 0.0395 0.2658 0 0 0 0 0 0 0 0 0
76 Li (1987) 0.01 0.01 1 1 0 0 0 0 0 0 0 0 0 0.10000231 0.0881 0.1351 0 0 0 0 0 0 0 0 0
77 Li (1987) 0.01 0.01 1 1 0 0 0 0 0 0 0 0 0 0.15000091 0.1367 0.0973 0 0 0 0 0 0 0 0 0
78 Li (1987) 0.01 0.01 1 1 0 0 0 0 0 0 0 0 0 0.20009536 0.1856 0.0781 0 0 0 0 0 0 0 0 0
79 Li (1987) 0.01 0.01 1 1 0 0 0 0 0 0 0 0 0 0.40540686 0.3846 0.0541 0 0 0 0 0 0 0 0 0
80 Li (1987) 0.03 0.03 1 1 0 0 0 0 0 0 0 0 0 0.09999756 0.0897 0.1148 0 0 0 0 0 0 0 0 0
81 Li (1987) 0.03 0.03 1 1 0 0 0 0 0 0 0 0 0 0.15000544 0.1391 0.0784 0 0 0 0 0 0 0 0 0
82 Li (1987) 0.03 0.03 1 1 0 0 0 0 0 0 0 0 0 0.19999343 0.1883 0.0621 0 0 0 0 0 0 0 0 0
83 Li (1987) 0.03 0.03 1 1 0 0 0 0 0 0 0 0 0 0.29999853 0.2881 0.0413 0 0 0 0 0 0 0 0 0
84 Li (1987) 0.04 0.04 1 1 0 0 0 0 0 0 0 0 0 0.05030076 0.0444 0.1329 0 0 0 0 0 0 0 0 0
85 Li (1987) 0.04 0.04 1 1 0 0 0 0 0 0 0 0 0 0.09999639 0.0919 0.0881 0 0 0 0 0 0 0 0 0
86 Li (1987) 0.04 0.04 1 1 0 0 0 0 0 0 0 0 0 0.15000408 0.1413 0.0616 0 0 0 0 0 0 0 0 0
87 Li (1987) 0.04 0.04 1 1 0 0 0 0 0 0 0 0 0 0.20049903 0.1917 0.0459 0 0 0 0 0 0 0 0 0
88 Li (1987) 0.04 0.04 1 1 0 0 0 0 0 0 0 0 0 0.29948604 0.2883 0.0388 0 0 0 0 0 0 0 0 0
89 Lyon (2016) 0.01 0.01 1 1 0.08666112 0.05352756 0.619 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
90 Lyon (2016) 0.05 0.05 1 1 0.08666112 0.060900295 0.423 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
91 Lyon (2016) 0.1 0.1 1 1 0.08666112 0.067916239 0.276 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
92 Lyon (2016) 0.3 0.3 1 1 0.08666112 0.084878668 0.021 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
93 Lyon (2016) 0.5 0.5 1 1 0.08666112 0.08666112 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
94 Lyon (2016) 0.01 0.01 1 1 0.042983916 0.015124531 1.842 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
95 Lyon (2016) 0.05 0.05 1 1 0.042983916 0.020410216 1.106 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
96 Lyon (2016) 0.1 0.1 1 1 0.042983916 0.026354332 0.631 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
97 Lyon (2016) 0.3 0.3 1 1 0.042983916 0.039290599 0.094 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
98 Lyon (2016) 0.5 0.5 1 1 0.042983916 0.042983916 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
99 Lyon (2016) 0.01 0.01 1 1 0.005546312 3.16E-05 174.668 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
100 Lyon (2016) 0.05 0.05 1 1 0.005546312 0.000451912 11.273 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
101 Lyon (2016) 0.1 0.1 1 1 0.005546312 0.001558828 2.558 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
102 Lyon (2016) 0.3 0.3 1 1 0.005546312 0.005400498 0.027 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
103 Lyon (2016) 0.5 0.5 1 1 0.005546312 0.005546312 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
104 Lee (2005) 0.251 0.25 1 1 0.1 0.087622932 0.141253754 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
105 Lee (2005) 0.251 0.25 1.5 1.5 0.1 0.07955199 0.257039578 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
106 Lee (2005) 0.251 0.25 2 2 0.1 0.074253556 0.34673685 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
107 Lee (2005) 0.251 0.25 2.5 2.5 0.1 0.071525275 0.398107171 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
108 Lee (2005) 0.251 0.25 3 3 0.1 0.066613942 0.501187234 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
109 Lee (2005) 0.134 0.13 1 1 0.1 0.075124079 0.331131121 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
110 Lee (2005) 0.0835 0.08 1 1 0.1 0.065582051 0.52480746 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
111 Lee (2005) 0.0285 0.03 1 1 0.1 0.059662917 0.676082975 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
112 Lee (2005) 0.262 0.26 2 2 0.1 0.079923999 0.251188643 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
113 Lee (2005) 0.134 0.13 2 2 0.1 0.061313682 0.630957344 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
114 Lee (2005) 0.0835 0.08 2 2 0.1 0.051151089 0.954992586 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
115 Lee (2005) 0.0285 0.03 2 2 0.1 0.044837645 1.230268771 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
116 Lee (2005) 0.0114 0.01 2 2 0.1 0.041450132 1.412537545 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
117 Lee (2005) 0.262 0.26 3 3 0.1 0.074253556 0.34673685 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
118 Lee (2005) 0.134 0.13 3 3 0.1 0.048273748 1.071519305 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
119 Lee (2005) 0.0835 0.08 3 3 0.1 0.040337083 1.479108388 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
120 Lee (2005) 0.0285 0.03 3 3 0.1 0.033900091 1.9498446 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
121 Lee (2005) 0.0114 0.01 3 3 0.1 0.030387123 2.290867653 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
122 Li (1987) 0.01 0.01 1 1 0.05000119 0.0239 1.0921 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
123 Li (1987) 0.01 0.01 1 1 0.09999803 0.0683 0.4641 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
124 Li (1987) 0.01 0.01 1 1 0.1500057 0.117 0.2821 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
125 Li (1987) 0.01 0.01 1 1 0.200004 0.168 0.1905 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
126 Li (1987) 0.01 0.01 1 1 0.30001149 0.2637 0.1377 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
127 Li (1987) 0.05 0.05 1 1 0.03453582 0.0201 0.7182 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
128 Li (1987) 0.05 0.05 1 1 0.09996185 0.0757 0.3205 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
129 Li (1987) 0.05 0.05 1 1 0.19999308 0.1722 0.1614 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
130 Li (1987) 0.1 0.1 1 1 0.04999817 0.0389 0.2853 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
131 Li (1987) 0.1 0.1 1 1 0.1499974 0.133 0.1278 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
132 Li (1987) 0.1 0.1 1 1 0.19999876 0.1807 0.1068 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
133 Li (1987) 0.1 0.1 1 1 0.2500032 0.232 0.0776 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
134 Li (1987) 0.1 0.1 1 1 0.30001216 0.2807 0.0688 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
135 Li (1987) 0.3 0.3 1 1 0.01996974 0.0189 0.0566 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
136 Li (1987) 0.3 0.3 1 1 0.04997968 0.0478 0.0456 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
137 Li (1987) 0.3 0.3 1 1 0.15000144 0.1464 0.0246 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
138 Li (1987) 0.3 0.3 1 1 0.29998765 0.2957 0.0145 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
139 Li (1987) 0.5 0.5 1 1 0.04999786 0.0482 0.0373 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
140 Li (1987) 0.5 0.5 1 1 0.10000096 0.0976 0.0246 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
141 Li (1987) 0.5 0.5 1 1 0.20000784 0.1968 0.0163 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
142 Lyon (2016) 0.03 0.03 1 1 0 0 0 0.09296866 0.06556323 0.418 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
143 Lyon (2016) 0.05 0.05 1 1 0 0 0 0.09296866 0.066835844 0.391 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
144 Lyon (2016) 0.1 0.1 1 1 0 0 0 0.09296866 0.080076365 0.161 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
145 Lyon (2016) 0.3 0.3 1 1 0 0 0 0.09296866 0.091957131 0.011 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
146 Lyon (2016) 0.5 0.5 1 1 0 0 0 0.09296866 0.09296866 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
147 Lyon (2016) 0.03 0.03 1 1 0 0 0 0.047548856 0.02373882 1.003 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
148 Lyon (2016) 0.05 0.05 1 1 0 0 0 0.047548856 0.025646632 0.854 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
149 Lyon (2016) 0.1 0.1 1 1 0 0 0 0.047548856 0.034988121 0.359 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
150 Lyon (2016) 0.3 0.3 1 1 0 0 0 0.047548856 0.045371046 0.048 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
151 Lyon (2016) 0.5 0.5 1 1 0 0 0 0.047548856 0.047548856 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
152 Lyon (2016) 0.01 0.01 1 1 0 0 0 0.00638716 5.54E-05 114.332 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
153 Lyon (2016) 0.05 0.05 1 1 0 0 0 0.00638716 0.000669233 8.544 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
154 Lyon (2016) 0.1 0.1 1 1 0 0 0 0.00638716 0.002264147 1.821 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
155 Lyon (2016) 0.3 0.3 1 1 0 0 0 0.00638716 0.006323921 0.01 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
156 Lyon (2016) 0.5 0.5 1 1 0 0 0 0.00638716 0.00638716 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
157 Li (1987) 0.01 0.01 1 1 0 0 0 0.04999995 0.0259 0.9305 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
158 Li (1987) 0.01 0.01 1 1 0 0 0 0.09999925 0.0725 0.3793 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
159 Li (1987) 0.01 0.01 1 1 0 0 0 0.20000715 0.1673 0.1955 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
160 Li (1987) 0.01 0.01 1 1 0 0 0 0.29999192 0.2648 0.1329 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
161 Li (1987) 0.03 0.03 1 1 0 0 0 0.05000109 0.0307 0.6287 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
162 Li (1987) 0.03 0.03 1 1 0 0 0 0.10009624 0.0776 0.2899 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
163 Li (1987) 0.03 0.03 1 1 0 0 0 0.20000456 0.1736 0.1521 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
164 Li (1987) 0.03 0.03 1 1 0 0 0 0.2999997 0.273 0.0989 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
165 Li (1987) 0.05 0.05 1 1 0 0 0 0.05002915 0.0349 0.4335 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
166 Li (1987) 0.05 0.05 1 1 0 0 0 0.09997622 0.0809 0.2358 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
167 Li (1987) 0.05 0.05 1 1 0 0 0 0.14999553 0.1299 0.1547 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
168 Li (1987) 0.05 0.05 1 1 0 0 0 0.25001088 0.2272 0.1004 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
169 Li (1987) 0.07 0.07 1 1 0 0 0 0.04999995 0.0385 0.2987 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
170 Li (1987) 0.07 0.07 1 1 0 0 0 0.09999936 0.0864 0.1574 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
171 Li (1987) 0.07 0.07 1 1 0 0 0 0.14998624 0.1343 0.1168 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
172 Li (1987) 0.07 0.07 1 1 0 0 0 0.24999474 0.2334 0.0711 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
173 Li (1987) 0.1 0.1 1 1 0 0 0 0.0499992 0.0415 0.2048 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
174 Li (1987) 0.1 0.1 1 1 0 0 0 0.10000199 0.0901 0.1099 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
175 Li (1987) 0.1 0.1 1 1 0 0 0 0.1999998 0.189 0.0582 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
176 Li (1987) 0.1 0.1 1 1 0 0 0 0.30000544 0.2888 0.0388 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
177 Li (1987) 0.01 0.01 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0500503 0.0115 3.3522 0 0 0
178 Li (1987) 0.01 0.01 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0999792 0.048 1.0829 0 0 0
179 Li (1987) 0.01 0.01 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.1500016 0.0944 0.589 0 0 0
180 Li (1987) 0.01 0.01 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.24999968 0.1864 0.3412 0 0 0
181 Li (1987) 0.01 0.01 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.30000263 0.2371 0.2653 0 0 0
182 Li (1987) 0.05 0.05 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.05032248 0.0156 2.2258 0 0 0
183 Li (1987) 0.05 0.05 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.10000216 0.0556 0.7986 0 0 0
184 Li (1987) 0.05 0.05 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.15000255 0.1035 0.4493 0 0 0
185 Li (1987) 0.05 0.05 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.30392343 0.1997 0.5219 0 0 0
186 Li (1987) 0.05 0.05 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.30000411 0.2471 0.2141 0 0 0
187 Li (1987) 0.1 0.1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.04999989 0.0219 1.2831 0 0 0
188 Li (1987) 0.1 0.1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.09999773 0.0623 0.6051 0 0 0
189 Li (1987) 0.1 0.1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.15000375 0.1105 0.3575 0 0 0
190 Li (1987) 0.1 0.1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.2500038 0.2073 0.206 0 0 0
191 Li (1987) 0.1 0.1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.30061536 0.2572 0.1688 0 0 0
192 Li (1987) 0.3 0.3 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0502452 0.0408 0.2315 0 0 0
193 Li (1987) 0.3 0.3 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.09999592 0.0842 0.1876 0 0 0
194 Li (1987) 0.3 0.3 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.14999496 0.1334 0.1244 0 0 0
195 Li (1987) 0.3 0.3 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.1999935 0.1814 0.1025 0 0 0
196 Li (1987) 0.3 0.3 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.30001088 0.2782 0.0784 0 0 0
197 Li (1987) 0.1 0.1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.1500005 0.047 2.1915
198 Li (1987) 0.1 0.1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.19999809 0.0857 1.3337
199 Li (1987) 0.1 0.1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.2999996 0.1772 0.693
200 Li (1987) 0.5 0.5 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.05 0.016 2.125
201 Li (1987) 0.5 0.5 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.1000004 0.0445 1.2472
202 Li (1987) 0.5 0.5 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.15000048 0.0828 0.8116
203 Li (1987) 0.5 0.5 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.19999436 0.1214 0.6474
204 Li (1987) 0.5 0.5 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.29999366 0.2083 0.4402
205 Li (1987) 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.04999995 0.0333 0.5015
206 Li (1987) 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.09999846 0.0694 0.4409
207 Li (1987) 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.15000104 0.1108 0.3538
208 Li (1987) 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.19999756 0.1526 0.3106
209 Li (1987) 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.29999472 0.2416 0.2417
210 Li (1987) 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.25000384 0.1984 0.2601
211 Li (1987) 1.5 1.5 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0500013 0.0405 0.2346
212 Li (1987) 1.5 1.5 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.09999854 0.0838 0.1933
213 Li (1987) 1.5 1.5 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.19999977 0.1753 0.1409
214 Li (1987) 2 2 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.09999804 0.0908 0.1013
215 Li (1987) 2 2 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.19992651 0.1851 0.0801
216 Li (1987) 2 2 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.30001335 0.2821 0.0635

58
data/xmls/PC88A_HCL_NdPrCeLaDySmY_w_pitzer.xml
Unescape View File

@ -33,7 +33,7 @@
<thermo> <thermo>
<const_cp Tmax="300.0" Tmin="298.0"> <const_cp Tmax="300.0" Tmin="298.0">
<t0 units="K">298.14999999999998</t0> <t0 units="K">298.14999999999998</t0>
<h0 units="J/mol" updated="Updated at 7:6 7-16-2020">-1376877.154483614</h0> <h0 units="J/mol" updated="Updated at 11:58 7-17-2020">-1376882.3191117246</h0>
<s0 units="J/mol/K"> 558.9824 </s0> <s0 units="J/mol/K"> 558.9824 </s0>
<cp0 units="J/mol/K"> 0.0</cp0> <cp0 units="J/mol/K"> 0.0</cp0>
</const_cp> </const_cp>
@ -50,7 +50,7 @@
<const_cp Tmax="300.0" Tmin="298.0"> <const_cp Tmax="300.0" Tmin="298.0">
<t0 units="K">298.14999999999998</t0> <t0 units="K">298.14999999999998</t0>
<h0 units="J/mol" updated="Updated at 23:0 7-15-2020">-4926549.797810851</h0> <h0 units="J/mol" updated="Updated at 11:58 7-17-2020">-4925566.309854757</h0>
<s0 units="J/mol/K"> 1117.965 </s0> <s0 units="J/mol/K"> 1117.965 </s0>
<cp0 units="J/mol/K">0.0</cp0> <cp0 units="J/mol/K">0.0</cp0>
</const_cp> </const_cp>
@ -67,7 +67,7 @@
<const_cp Tmax="300.0" Tmin="298.0"> <const_cp Tmax="300.0" Tmin="298.0">
<t0 units="K">298.14999999999998</t0> <t0 units="K">298.14999999999998</t0>
<h0 units="J/mol" updated="Updated at 23:0 7-15-2020">-4935519.640701385</h0> <h0 units="J/mol" updated="Updated at 11:58 7-17-2020">-4938249.845712334</h0>
<s0 units="J/mol/K"> 1117.965 </s0> <s0 units="J/mol/K"> 1117.965 </s0>
<cp0 units="J/mol/K">0.0</cp0> <cp0 units="J/mol/K">0.0</cp0>
</const_cp> </const_cp>
@ -85,7 +85,7 @@
<const_cp Tmax="300.0" Tmin="298.0"> <const_cp Tmax="300.0" Tmin="298.0">
<t0 units="K">298.14999999999998</t0> <t0 units="K">298.14999999999998</t0>
<h0 units="J/mol" updated="Updated at 23:0 7-15-2020">-4928317.781440989</h0> <h0 units="J/mol" updated="Updated at 11:58 7-17-2020">-4920387.823199008</h0>
<s0 units="J/mol/K"> 1117.965 </s0> <s0 units="J/mol/K"> 1117.965 </s0>
<cp0 units="J/mol/K">0.0</cp0> <cp0 units="J/mol/K">0.0</cp0>
</const_cp> </const_cp>
@ -103,7 +103,7 @@
<const_cp Tmax="300.0" Tmin="298.0"> <const_cp Tmax="300.0" Tmin="298.0">
<t0 units="K">298.14999999999998</t0> <t0 units="K">298.14999999999998</t0>
<h0 units="J/mol" updated="Updated at 23:0 7-15-2020">-4927428.65973482</h0> <h0 units="J/mol" updated="Updated at 11:58 7-17-2020">-4933548.865580005</h0>
<s0 units="J/mol/K"> 1117.965 </s0> <s0 units="J/mol/K"> 1117.965 </s0>
<cp0 units="J/mol/K">0.0</cp0> <cp0 units="J/mol/K">0.0</cp0>
</const_cp> </const_cp>
@ -120,7 +120,7 @@
<const_cp Tmax="300.0" Tmin="298.0"> <const_cp Tmax="300.0" Tmin="298.0">
<t0 units="K">298.14999999999998</t0> <t0 units="K">298.14999999999998</t0>
<h0 units="J/mol" updated="Updated at 23:0 7-15-2020">-4935155.356789877</h0> <h0 units="J/mol" updated="Updated at 11:58 7-17-2020">-4932560.171447597</h0>
<s0 units="J/mol/K"> 1117.965 </s0> <s0 units="J/mol/K"> 1117.965 </s0>
<cp0 units="J/mol/K">0.0</cp0> <cp0 units="J/mol/K">0.0</cp0>
</const_cp> </const_cp>
@ -138,7 +138,7 @@
<const_cp Tmax="300.0" Tmin="298.0"> <const_cp Tmax="300.0" Tmin="298.0">
<t0 units="K">298.14999999999998</t0> <t0 units="K">298.14999999999998</t0>
<h0 units="J/mol" updated="Updated at 23:0 7-15-2020">-4944228.17930387</h0> <h0 units="J/mol" updated="Updated at 11:58 7-17-2020">-4944840.781582316</h0>
<s0 units="J/mol/K"> 1117.965 </s0> <s0 units="J/mol/K"> 1117.965 </s0>
<cp0 units="J/mol/K">0.0</cp0> <cp0 units="J/mol/K">0.0</cp0>
</const_cp> </const_cp>
@ -157,7 +157,7 @@
<const_cp Tmax="300.0" Tmin="298.0"> <const_cp Tmax="300.0" Tmin="298.0">
<t0 units="K">298.14999999999998</t0> <t0 units="K">298.14999999999998</t0>
<h0 units="J/mol" updated="Updated at 23:0 7-15-2020">-4925606.187988869</h0> <h0 units="J/mol" updated="Updated at 11:58 7-17-2020">-4924696.189921901</h0>
<s0 units="J/mol/K"> 1117.965 </s0> <s0 units="J/mol/K"> 1117.965 </s0>
<cp0 units="J/mol/K">0.0</cp0> <cp0 units="J/mol/K">0.0</cp0>
</const_cp> </const_cp>
@ -245,10 +245,10 @@
</binarySaltParameters> </binarySaltParameters>
<binarySaltParameters anion="Cl-" cation="Pr+++"> <binarySaltParameters anion="Cl-" cation="Pr+++">
<beta0 updated="Updated at 1:53 7-16-2020"> 0.9335616000781043, 0.0, 0.0, 0.0, 0.0 </beta0> <beta0 updated="Updated at 11:58 7-17-2020"> 0.05879108748614492, 0.0, 0.0, 0.0, 0.0 </beta0>
<beta1 updated="Updated at 1:53 7-16-2020"> 10.555396864028587, 0.0, 0.0, 0.0, 0.0 </beta1> <beta1 updated="Updated at 11:58 7-17-2020"> 0.5448324180244323, 0.0, 0.0, 0.0, 0.0 </beta1>
<beta2> 0.0, 0.0, 0.0, 0.0, 0.0 </beta2> <beta2> 0.0, 0.0, 0.0, 0.0, 0.0 </beta2>
<Cphi updated="Updated at 23:0 7-15-2020"> -0.02066999867229882, 0.0, 0.0, 0.0, 0.0 </Cphi> <Cphi updated="Updated at 10:47 7-17-2020"> -0.02066999867229882, 0.0, 0.0, 0.0, 0.0 </Cphi>
<Alpha1> 2 </Alpha1> <Alpha1> 2 </Alpha1>
<Alpha2> 0 </Alpha2> <Alpha2> 0 </Alpha2>
<source> <source>
@ -261,10 +261,10 @@
</binarySaltParameters> </binarySaltParameters>
<binarySaltParameters anion="Cl-" cation="Nd+++"> <binarySaltParameters anion="Cl-" cation="Nd+++">
<beta0 updated="Updated at 1:36 7-16-2020"> 0.951206693355039, 0.0, 0.0, 0.0, 0.0 </beta0> <beta0 updated="Updated at 11:58 7-17-2020"> 1.2137222016802447, 0.0, 0.0, 0.0, 0.0 </beta0>
<beta1 updated="Updated at 1:36 7-16-2020"> 9.262520235612426, 0.0, 0.0, 0.0, 0.0 </beta1> <beta1 updated="Updated at 11:58 7-17-2020"> 7.748226963005033, 0.0, 0.0, 0.0, 0.0 </beta1>
<beta2> 0.0, 0.0, 0.0, 0.0, 0.0 </beta2> <beta2> 0.0, 0.0, 0.0, 0.0, 0.0 </beta2>
<Cphi updated="Updated at 23:0 7-15-2020"> -0.01963615126026457, 0.0, 0.0, 0.0, 0.0 </Cphi> <Cphi updated="Updated at 10:47 7-17-2020"> -0.01963615126026457, 0.0, 0.0, 0.0, 0.0 </Cphi>
<Alpha1> 2 </Alpha1> <Alpha1> 2 </Alpha1>
<Alpha2> 0 </Alpha2> <Alpha2> 0 </Alpha2>
<source> <source>
@ -277,10 +277,10 @@
</binarySaltParameters> </binarySaltParameters>
<binarySaltParameters anion="Cl-" cation="La+++"> <binarySaltParameters anion="Cl-" cation="La+++">
<beta0 updated="Updated at 2:48 7-16-2020"> 0.5362669407654791, 0.0, 0.0, 0.0, 0.0 </beta0> <beta0 updated="Updated at 11:58 7-17-2020"> 1.022316535866388, 0.0, 0.0, 0.0, 0.0 </beta0>
<beta1 updated="Updated at 2:48 7-16-2020"> 19.878544145607385, 0.0, 0.0, 0.0, 0.0 </beta1> <beta1 updated="Updated at 11:58 7-17-2020"> 0.5296311209773129, 0.0, 0.0, 0.0, 0.0 </beta1>
<beta2> 0.0, 0.0, 0.0, 0.0, 0.0 </beta2> <beta2> 0.0, 0.0, 0.0, 0.0, 0.0 </beta2>
<Cphi updated="Updated at 23:0 7-15-2020"> -0.024339999997603376, 0.0, 0.0, 0.0, 0.0 </Cphi> <Cphi updated="Updated at 10:47 7-17-2020"> -0.024339999997603376, 0.0, 0.0, 0.0, 0.0 </Cphi>
<Alpha1> 2 </Alpha1> <Alpha1> 2 </Alpha1>
<Alpha2> 0 </Alpha2> <Alpha2> 0 </Alpha2>
<source> <source>
@ -293,10 +293,10 @@
</binarySaltParameters> </binarySaltParameters>
<binarySaltParameters anion="Cl-" cation="Dy+++"> <binarySaltParameters anion="Cl-" cation="Dy+++">
<beta0 updated="Updated at 2:48 7-16-2020"> 0.6129504699810289, 0.0, 0.0, 0.0, 0.0 </beta0> <beta0 updated="Updated at 11:58 7-17-2020"> 0.7646397332938777, 0.0, 0.0, 0.0, 0.0 </beta0>
<beta1 updated="Updated at 2:48 7-16-2020"> 5.359936728131316, 0.0, 0.0, 0.0, 0.0 </beta1> <beta1 updated="Updated at 11:58 7-17-2020"> 7.849320590516409, 0.0, 0.0, 0.0, 0.0 </beta1>
<beta2> 0.0, 0.0, 0.0, 0.0, 0.0 </beta2> <beta2> 0.0, 0.0, 0.0, 0.0, 0.0 </beta2>
<Cphi updated="Updated at 23:0 7-15-2020"> -0.019699989216349984, 0.0, 0.0, 0.0, 0.0 </Cphi> <Cphi updated="Updated at 10:47 7-17-2020"> -0.019699989216349984, 0.0, 0.0, 0.0, 0.0 </Cphi>
<Alpha1> 2 </Alpha1> <Alpha1> 2 </Alpha1>
<Alpha2> 0 </Alpha2> <Alpha2> 0 </Alpha2>
<source> <source>
@ -309,10 +309,10 @@
</binarySaltParameters> </binarySaltParameters>
<binarySaltParameters anion="Cl-" cation="Ce+++"> <binarySaltParameters anion="Cl-" cation="Ce+++">
<beta0 updated="Updated at 2:2 7-16-2020"> 0.3803360903827252, 0.0, 0.0, 0.0, 0.0 </beta0> <beta0 updated="Updated at 11:58 7-17-2020"> 0.2035235053232368, 0.0, 0.0, 0.0, 0.0 </beta0>
<beta1 updated="Updated at 2:2 7-16-2020"> 0.4715334506120701, 0.0, 0.0, 0.0, 0.0 </beta1> <beta1 updated="Updated at 11:58 7-17-2020"> 21.120426174002823, 0.0, 0.0, 0.0, 0.0 </beta1>
<beta2> 0.0, 0.0, 0.0, 0.0, 0.0 </beta2> <beta2> 0.0, 0.0, 0.0, 0.0, 0.0 </beta2>
<Cphi updated="Updated at 23:0 7-15-2020"> -0.02618999999473301, 0.0, 0.0, 0.0, 0.0 </Cphi> <Cphi updated="Updated at 10:47 7-17-2020"> -0.02618999999473301, 0.0, 0.0, 0.0, 0.0 </Cphi>
<Alpha1> 2 </Alpha1> <Alpha1> 2 </Alpha1>
<Alpha2> 0 </Alpha2> <Alpha2> 0 </Alpha2>
<source> <source>
@ -325,10 +325,10 @@
</binarySaltParameters> </binarySaltParameters>
<binarySaltParameters anion="Cl-" cation="Y+++"> <binarySaltParameters anion="Cl-" cation="Y+++">
<beta0 updated="Updated at 1:26 7-16-2020"> 0.8864576562457522, 0.0, 0.0, 0.0, 0.0 </beta0> <beta0 updated="Updated at 11:58 7-17-2020"> 0.8852802073165494, 0.0, 0.0, 0.0, 0.0 </beta0>
<beta1 updated="Updated at 1:26 7-16-2020"> 9.87156845120858, 0.0, 0.0, 0.0, 0.0 </beta1> <beta1 updated="Updated at 11:58 7-17-2020"> 9.334653075341238, 0.0, 0.0, 0.0, 0.0 </beta1>
<beta2> 0.0, 0.0, 0.0, 0.0, 0.0 </beta2> <beta2> 0.0, 0.0, 0.0, 0.0, 0.0 </beta2>
<Cphi updated="Updated at 23:0 7-15-2020"> -0.015467323909969704, 0.0, 0.0, 0.0, 0.0 </Cphi> <Cphi updated="Updated at 10:47 7-17-2020"> -0.015467323909969704, 0.0, 0.0, 0.0, 0.0 </Cphi>
<Alpha1> 2 </Alpha1> <Alpha1> 2 </Alpha1>
<Alpha2> 0 </Alpha2> <Alpha2> 0 </Alpha2>
<source> <source>
@ -341,10 +341,10 @@
</binarySaltParameters> </binarySaltParameters>
<binarySaltParameters anion="Cl-" cation="Sm+++"> <binarySaltParameters anion="Cl-" cation="Sm+++">
<beta0 updated="Updated at 7:6 7-16-2020"> 0.6989806463779159, 0.0, 0.0, 0.0, 0.0 </beta0> <beta0 updated="Updated at 11:58 7-17-2020"> 0.6989081585234721, 0.0, 0.0, 0.0, 0.0 </beta0>
<beta1 updated="Updated at 1:25 7-16-2020"> 6.8744371885601625, 0.0, 0.0, 0.0, 0.0 </beta1> <beta1 updated="Updated at 11:58 7-17-2020"> 6.877882600430561, 0.0, 0.0, 0.0, 0.0 </beta1>
<beta2> 0.0, 0.0, 0.0, 0.0, 0.0 </beta2> <beta2> 0.0, 0.0, 0.0, 0.0, 0.0 </beta2>
<Cphi updated="Updated at 23:0 7-15-2020"> -0.019920000110321332, 0.0, 0.0, 0.0, 0.0 </Cphi> <Cphi updated="Updated at 10:47 7-17-2020"> -0.019920000110321332, 0.0, 0.0, 0.0, 0.0 </Cphi>
<Alpha1> 2 </Alpha1> <Alpha1> 2 </Alpha1>
<Alpha2> 0 </Alpha2> <Alpha2> 0 </Alpha2>
<source> <source>

8
docs/Examples/iterative_fitter.py
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@ -75,7 +75,7 @@ estimator.set_custom_objects_dict({'lin_param_df': new_lin_param_df})
estimator.set_dependant_params_dict(dependant_params_dict) estimator.set_dependant_params_dict(dependant_params_dict)
estimator.update_xml(ext_h0_dict, estimator.update_xml(ext_h0_dict,
dependant_params_dict=dependant_params_dict) dependant_params_dict=dependant_params_dict)
eps = 1e-8 eps = 1e-20
mini_eps = 1e-4 mini_eps = 1e-4
pitzer_guess_dict = {'species': [], pitzer_guess_dict = {'species': [],
'beta0': [], 'beta0': [],
@ -105,7 +105,7 @@ i = 0
rel_diff = 1000 rel_diff = 1000
obj_diff1 = 1000 obj_diff1 = 1000
obj_diff2 = 1000 obj_diff2 = 1000
while obj_diff1 > eps and obj_diff2 > eps: while obj_diff1 > eps or obj_diff2 > eps:
i += 1 i += 1
print(i) print(i)
best_obj = 1e20 best_obj = 1e20
@ -154,7 +154,7 @@ while obj_diff1 > eps and obj_diff2 > eps:
estimator.set_opt_dict(info_dict) estimator.set_opt_dict(info_dict)
estimator.update_custom_objects_dict(info_dict) estimator.update_custom_objects_dict(info_dict)
estimator.update_xml(info_dict) estimator.update_xml(info_dict)
obj_kwargs = {'species_list': [species], 'epsilon': 1e-100} obj_kwargs = {'species_list': species_list, 'epsilon': 1e-100}
bounds = [(1e-1, 1e1)] * len(info_dict) bounds = [(1e-1, 1e1)] * len(info_dict)
optimizer_kwargs = {"method": 'l-bfgs-b', optimizer_kwargs = {"method": 'l-bfgs-b',
"bounds": bounds} "bounds": bounds}
@ -237,7 +237,7 @@ while obj_diff1 > eps and obj_diff2 > eps:
del(output_dict['rel_diff'][-1]) del(output_dict['rel_diff'][-1])
output_dict['rel_diff'].append(rel_diff) output_dict['rel_diff'].append(rel_diff)
output_df = pd.DataFrame(output_dict) output_df = pd.DataFrame(output_dict)
output_df.to_csv('outputs/iterative_fitter_output1.csv') output_df.to_csv('outputs/iterative_fitter_output4.csv')
obj_diff1 = np.abs(output_dict['best_obj'][-1]-output_dict['best_obj'][-2]) obj_diff1 = np.abs(output_dict['best_obj'][-1]-output_dict['best_obj'][-2])
if i > 2: if i > 2:
obj_diff2 = np.abs( obj_diff2 = np.abs(

128
docs/Examples/iterative_fitter_eval_grapher.py
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@ -0,0 +1,128 @@
import llepe
import pandas as pd
import numpy as np
import json
import matplotlib as plt
import matplotlib
font = {'family': 'sans serif',
'size': 24}
matplotlib.rc('font', **font)
plt.rc('xtick', labelsize=18)
plt.rc('ytick', labelsize=18)
def ext_to_complex(h0, custom_obj_dict, mini_species):
linear_params = custom_obj_dict['lin_param_df']
row = linear_params[linear_params['species'] == mini_species]
return row['slope'].values[0] * h0[0] + row['intercept'].values[0]
def mod_lin_param_df(lp_df, input_val, mini_species, mini_lin_param):
new_lp_df = lp_df.copy()
index = new_lp_df.index[new_lp_df['species'] == mini_species].tolist()[0]
new_lp_df.at[index, mini_lin_param] = input_val
return new_lp_df
info_df = pd.read_csv('outputs/iterative_fitter_output2.csv')
pitzer_params_filename = "../../data/jsons/min_h0_pitzer_params.txt"
with open(pitzer_params_filename) as file:
pitzer_params_dict = json.load(file)
pitzer_params_df = pd.DataFrame(pitzer_params_dict)
species_list = 'Nd,Pr,Ce,La,Dy,Sm,Y'.split(',')
pitzer_param_list = ['beta0', 'beta1']
labeled_data = pd.read_csv("../../data/csvs/"
"PC88A_HCL_NdPrCeLaDySmY.csv")
exp_data = labeled_data.drop(labeled_data.columns[0], axis=1)
xml_file = "PC88A_HCL_NdPrCeLaDySmY_w_pitzer.xml"
lin_param_df = pd.read_csv("../../data/csvs"
"/zeroes_removed_min_h0_pitzer_lin_params.csv")
estimator_params = {'exp_data': exp_data,
'phases_xml_filename': xml_file,
'phase_names': ['HCl_electrolyte', 'PC88A_liquid'],
'aq_solvent_name': 'H2O(L)',
'extractant_name': '(HA)2(org)',
'diluant_name': 'dodecane',
'complex_names': ['{0}(H(A)2)3(org)'.format(species)
for species in species_list],
'extracted_species_ion_names': ['{0}+++'.format(species)
for species in
species_list],
'aq_solvent_rho': 1000.0,
'extractant_rho': 960.0,
'diluant_rho': 750.0,
'temp_xml_file_path': 'outputs/temp.xml',
'objective_function': llepe.lmse_perturbed_obj
}
dependant_params_dict = {}
for species, complex_name in zip(species_list,
estimator_params['complex_names']):
inner_dict = {'upper_element_name': 'species',
'upper_attrib_name': 'name',
'upper_attrib_value': complex_name,
'lower_element_name': 'h0',
'lower_attrib_name': None,
'lower_attrib_value': None,
'input_format': '{0}',
'function': ext_to_complex,
'kwargs': {"mini_species": species},
'independent_params': '(HA)2(org)_h0'}
dependant_params_dict['{0}_h0'.format(complex_name)] = inner_dict
info_dict = {'(HA)2(org)_h0': {'upper_element_name': 'species',
'upper_attrib_name': 'name',
'upper_attrib_value': '(HA)2(org)',
'lower_element_name': 'h0',
'lower_attrib_name': None,
'lower_attrib_value': None,
'input_format': '{0}',
'input_value':
info_df.iloc[-1, :]['best_ext_h0']}}
for species in species_list:
for pitzer_param in pitzer_param_list:
pitzer_str = "{0}_{1}".format(species, pitzer_param)
value = info_df.iloc[-1, :][pitzer_str]
pitzer_params_dict[pitzer_str]['input_value'] = value
lin_str = "{0}_slope".format(species)
inner_dict = {'custom_object_name': 'lin_param_df',
'function': mod_lin_param_df,
'kwargs': {'mini_species': species,
'mini_lin_param': 'slope'},
'input_value': 3
}
info_dict[lin_str] = inner_dict
lin_str = "{0}_intercept".format(species)
value = info_df.iloc[-1, :][lin_str]
inner_dict = {'custom_object_name': 'lin_param_df',
'function': mod_lin_param_df,
'kwargs': {'mini_species': species,
'mini_lin_param': 'intercept'},
'input_value': value
}
info_dict[lin_str] = inner_dict
info_dict.update(pitzer_params_dict)
estimator = llepe.LLEPE(**estimator_params)
estimator.set_custom_objects_dict({'lin_param_df': lin_param_df})
estimator.update_custom_objects_dict(info_dict)
estimator.update_xml(info_dict,
dependant_params_dict=dependant_params_dict)
exp_data = estimator.get_exp_df()
feed_cols = []
for col in exp_data.columns:
if 'aq_i' in col:
feed_cols.append(col)
exp_data['total_re'] = exp_data[feed_cols].sum(axis=1)
for species in species_list:
save_name = 'outputs/parity_iterative_fitter_{0}_org_eq'.format(species)
fig, ax = estimator.parity_plot('{0}_org_eq'.format(species),
c_data=exp_data[
'total_re'].values,
c_label='Feed total RE '
'molarity (mol/L)',
print_r_squared=True,
save_path=save_name)
# short_info_dict = {}
# for key, value in info_dict.items():
# short_info_dict[key] = value['input_value']
# with open("outputs/iterative_fitter_short_info_dict.txt", 'w') as file:
# json.dump(short_info_dict, file)

1
docs/Examples/iterative_fitter_info_dict
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@ -0,0 +1 @@
{"(HA)2(org)_h0": {"upper_element_name": "species", "upper_attrib_name": "name", "upper_attrib_value": "(HA)2(org)", "lower_element_name": "h0", "lower_attrib_name": null, "lower_attrib_value": null, "input_format": "{0}", "input_value": -1376882.3191117246}, "Nd_slope": {"custom_object_name": "lin_param_df", "function":

0
docs/Examples/iterative_fitter_info_dict.txt
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274
docs/Examples/iterative_fitter_w_mse.py
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@ -0,0 +1,274 @@
import llepe
import pandas as pd
import numpy as np
import json
def mod_lin_param_df(lp_df, input_val, mini_species, mini_lin_param):
new_lp_df = lp_df.copy()
index = new_lp_df.index[new_lp_df['species'] == mini_species].tolist()[0]
new_lp_df.at[index, mini_lin_param] = input_val
return new_lp_df
def ext_to_complex(h0, custom_obj_dict, mini_species):
linear_params = custom_obj_dict['lin_param_df']
mini_row = linear_params[linear_params['species'] == mini_species]
val = mini_row['slope'].values[0] * h0[0] + mini_row['intercept'].values[0]
return val
species_list = 'Nd,Pr,Ce,La,Dy,Sm,Y'.split(',')
pitzer_param_list = ['beta0', 'beta1']
lin_param_list = ['intercept']
short_info_filename = 'outputs/iterative_fitter_short_info_dict.txt'
with open(short_info_filename) as file:
short_info_dict = json.load(file)
labeled_data = pd.read_csv("../../data/csvs/"
"zeroes_removed_PC88A_HCL_NdPrCeLaDySmY.csv")
exp_data = labeled_data.drop(labeled_data.columns[0], axis=1)
xml_file = "PC88A_HCL_NdPrCeLaDySmY_w_pitzer.xml"
lin_param_df = pd.read_csv("../../data/csvs"
"/zeroes_removed_min_h0_pitzer_lin_params.csv")
new_lin_param_df = lin_param_df.copy()
for ind, row in lin_param_df.iterrows():
new_lin_param_df.at[ind, 'slope'] = 3
species = row['species']
val = short_info_dict['{0}_intercept'.format(species)]
new_lin_param_df.at[ind, 'intercept'] = val
estimator_params = {'exp_data': exp_data,
'phases_xml_filename': xml_file,
'phase_names': ['HCl_electrolyte', 'PC88A_liquid'],
'aq_solvent_name': 'H2O(L)',
'extractant_name': '(HA)2(org)',
'diluant_name': 'dodecane',
'complex_names': ['{0}(H(A)2)3(org)'.format(species)
for species in species_list],
'extracted_species_ion_names': ['{0}+++'.format(species)
for species in
species_list],
'aq_solvent_rho': 1000.0,
'extractant_rho': 960.0,
'diluant_rho': 750.0,
'temp_xml_file_path': 'outputs/temp1.xml',
'objective_function': llepe.mean_squared_error,
'custom_objects_dict': {'lin_param_df': new_lin_param_df}
}
dependant_params_dict = {}
for species, complex_name in zip(species_list,
estimator_params['complex_names']):
inner_dict = {'upper_element_name': 'species',
'upper_attrib_name': 'name',
'upper_attrib_value': complex_name,
'lower_element_name': 'h0',
'lower_attrib_name': None,
'lower_attrib_value': None,
'input_format': '{0}',
'function': ext_to_complex,
'kwargs': {"mini_species": species},
'independent_params': '(HA)2(org)_h0',
}
dependant_params_dict['{0}_h0'.format(complex_name)] = inner_dict
info_dict = {'(HA)2(org)_h0': {'upper_element_name': 'species',
'upper_attrib_name': 'name',
'upper_attrib_value': '(HA)2(org)',
'lower_element_name': 'h0',
'lower_attrib_name': None,
'lower_attrib_value': None,
'input_format': '{0}',
'input_value':
short_info_dict['(HA)2(org)_h0']}}
for species in species_list:
for param in pitzer_param_list:
name = "{0}_{1}".format(species, param)
inner_dict = {'upper_element_name': 'binarySaltParameters',
'upper_attrib_name': 'cation',
'upper_attrib_value': '{0}+++'.format(species),
'lower_element_name': param,
'lower_attrib_name': None,
'lower_attrib_value': None,
'input_format': ' {0}, 0.0, 0.0, 0.0, 0.0 ',
'input_value':
short_info_dict[name]}
info_dict[name] = inner_dict
for param in lin_param_list:
name = "{0}_{1}".format(species, param)
inner_dict = {'custom_object_name': 'lin_param_df',
'function': mod_lin_param_df,
'kwargs': {'mini_species': species,
'mini_lin_param': param},
'input_value': short_info_dict[name]
}
estimator = llepe.LLEPE(**estimator_params)
estimator.update_xml(info_dict,
dependant_params_dict=dependant_params_dict)
estimator.set_dependant_params_dict(dependant_params_dict)
eps = 1e-20
mini_eps = 1e-4
pitzer_guess_dict = {'species': [],
'beta0': [],
'beta1': []}
for species in species_list:
pitzer_guess_dict['species'].append(species)
for param in pitzer_param_list:
mini_dict = info_dict['{0}_{1}'.format(species, param)]
value = mini_dict['input_value']
pitzer_guess_dict[param].append(value)
pitzer_guess_df = pd.DataFrame(pitzer_guess_dict)
ext_h0_guess = info_dict['(HA)2(org)_h0']['input_value']
lin_guess_df = new_lin_param_df.copy()
ignore_list = []
optimizer = 'scipy_minimize'
output_dict = {'iter': [0],
'best_obj': [1e20],
'rel_diff': [1e20],
'best_ext_h0': [1e20]}
for species in species_list:
for lin_param in lin_param_list:
output_dict['{0}_{1}'.format(species, lin_param)] = [1e20]
for pitzer_param in pitzer_param_list:
output_dict['{0}_{1}'.format(species, pitzer_param)] = [1e20]
i = 0
rel_diff = 1000
obj_diff1 = 1000
obj_diff2 = 1000
while obj_diff1 > eps or obj_diff2 > eps:
i += 1
print(i)
best_obj = 1e20
best_ext_h0 = 0
output_dict['iter'].append(i)
for species in species_list:
print(species)
lower_species = species.lower()
info_dict = {'(HA)2(org)_h0': {'upper_element_name': 'species',
'upper_attrib_name': 'name',
'upper_attrib_value': '(HA)2(org)',
'lower_element_name': 'h0',
'lower_attrib_name': None,
'lower_attrib_value': None,
'input_format': '{0}',
'input_value': ext_h0_guess}}
for pitzer_param in pitzer_param_list:
if '{0}_{1}'.format(species, pitzer_param) not in ignore_list:
pitzer_row = pitzer_guess_df[
pitzer_guess_df['species'] == species]
inner_dict = {'upper_element_name': 'binarySaltParameters',
'upper_attrib_name': 'cation',
'upper_attrib_value':
'{0}+++'.format(species),
'lower_element_name': pitzer_param,
'lower_attrib_name': None,
'lower_attrib_value': None,
'input_format': ' {0}, 0.0, 0.0, 0.0, 0.0 ',
'input_value':
pitzer_row[pitzer_param].values[0]
}
info_dict['{0}_{1}'.format(
species, pitzer_param)] = inner_dict
for lin_param in lin_param_list:
if '{0}_{1}'.format(species, lin_param) not in ignore_list:
lin_row = lin_guess_df[lin_guess_df['species'] == species]
inner_dict = {'custom_object_name': 'lin_param_df',
'function': mod_lin_param_df,
'kwargs': {'mini_species': species,
'mini_lin_param': lin_param},
'input_value': lin_row[lin_param].values[0]
}
info_dict['{0}_{1}'.format(
species, lin_param)] = inner_dict
estimator.set_opt_dict(info_dict)
estimator.update_custom_objects_dict(info_dict)
estimator.update_xml(info_dict)
obj_kwargs = {'species_list': species_list}
bounds = [(1e-1, 1e1)] * len(info_dict)
optimizer_kwargs = {"method": 'l-bfgs-b',
"bounds": bounds}
opt_dict, obj_value = estimator.fit(
objective_kwargs=obj_kwargs,
optimizer_kwargs=optimizer_kwargs)
if obj_value < best_obj:
best_obj = obj_value
best_ext_h0 = opt_dict['(HA)2(org)_h0']['input_value']
for lin_param in lin_param_list:
if '{0}_{1}'.format(species, lin_param) not in ignore_list:
mini_dict = opt_dict['{0}_{1}'.format(species, lin_param)]
value = mini_dict['input_value']
output_dict['{0}_{1}'.format(species, lin_param)].append(value)
else:
value = output_dict['{0}_{1}'.format(species, lin_param)][-1]
output_dict['{0}_{1}'.format(species, lin_param)].append(value)
for pitzer_param in pitzer_param_list:
if '{0}_{1}'.format(species, pitzer_param) not in ignore_list:
mini_dict = opt_dict['{0}_{1}'.format(species, pitzer_param)]
value = mini_dict['input_value']
output_dict['{0}_{1}'.format(
species, pitzer_param)].append(value)
else:
value = output_dict['{0}_{1}'.format(
species, pitzer_param)][-1]
output_dict['{0}_{1}'.format(
species, pitzer_param)].append(value)
estimator.update_custom_objects_dict(info_dict)
estimator.update_xml(opt_dict)
pitzer_guess_dict = {'species': []}
for pitzer_param in pitzer_param_list:
pitzer_guess_dict[pitzer_param] = []
lin_guess_dict = {'species': []}
for lin_param in lin_param_list:
lin_guess_dict[lin_param] = []
for species in species_list:
pitzer_guess_dict['species'].append(species)
lin_guess_dict['species'].append(species)
for pitzer_param in pitzer_param_list:
pitzer_str = '{0}_{1}'.format(species, pitzer_param)
value_list = output_dict['{0}_{1}'.format(species, pitzer_param)]
value = value_list[-1]
pitzer_guess_dict[pitzer_param].append(value)
if i > 2:
mini_rel_diff1 = np.abs(value_list[-1]
- value_list[-2]) / (
np.abs(value_list[-2]))
mini_rel_diff2 = np.abs(value_list[-2] - value_list[-3]) / (
np.abs(value_list[-3]))
if mini_rel_diff1 < mini_eps and mini_rel_diff2 < mini_eps:
if pitzer_str not in ignore_list:
ignore_list.append(pitzer_str)
for lin_param in lin_param_list:
lin_str = '{0}_{1}'.format(species, lin_param)
value_list = output_dict['{0}_{1}'.format(species, lin_param)]
value = value_list[-1]
lin_guess_dict[lin_param].append(value)
if i > 2:
mini_rel_diff1 = np.abs(value_list[-1]
- value_list[-2]) / (
np.abs(value_list[-2]))
mini_rel_diff2 = np.abs(value_list[-2] - value_list[-3]) / (
np.abs(value_list[-3]))
if mini_rel_diff1 < mini_eps and mini_rel_diff2 < mini_eps:
if lin_str not in ignore_list:
ignore_list.append(lin_str)
pitzer_guess_df = pd.DataFrame(pitzer_guess_dict)
lin_guess_df = pd.DataFrame(lin_guess_dict)
ext_h0_guess = best_ext_h0
output_dict['best_ext_h0'].append(best_ext_h0)
output_dict['best_obj'].append(best_obj)
output_dict['rel_diff'].append(100)
output_df = pd.DataFrame(output_dict)
old_row = output_df.iloc[-2, :].values[4:]
new_row = output_df.iloc[-1, :].values[4:]
rel_diff = np.sum(np.abs(new_row - old_row) / np.abs(old_row))
del (output_dict['rel_diff'][-1])
output_dict['rel_diff'].append(rel_diff)
output_df = pd.DataFrame(output_dict)
output_df.to_csv('outputs/iterative_fitter_w_mse_output.csv')
obj_diff1 = np.abs(
output_dict['best_obj'][-1] - output_dict['best_obj'][-2])
if i > 2:
obj_diff2 = np.abs(
output_dict['best_obj'][-1] - output_dict['best_obj'][-3])

1
docs/Examples/outputs/iterative_fitter_info_dict.txt
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@ -0,0 +1 @@
{"(HA)2(org)_h0": {"upper_element_name": "species", "upper_attrib_name": "name", "upper_attrib_value": "(HA)2(org)", "lower_element_name": "h0", "lower_attrib_name": null, "lower_attrib_value": null, "input_format": "{0}", "input_value": -1376882.3191117246}, "Nd_slope": {"custom_object_name": "lin_param_df", "function":

58
docs/Examples/outputs/temp.xml
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@ -33,7 +33,7 @@
<thermo> <thermo>
<const_cp Tmax="300.0" Tmin="298.0"> <const_cp Tmax="300.0" Tmin="298.0">
<t0 units="K">298.14999999999998</t0> <t0 units="K">298.14999999999998</t0>
<h0 units="J/mol" updated="Updated at 7:6 7-16-2020">-1376877.154483614</h0> <h0 units="J/mol" updated="Updated at 10:47 7-17-2020">-1376882.3191117246</h0>
<s0 units="J/mol/K"> 558.9824 </s0> <s0 units="J/mol/K"> 558.9824 </s0>
<cp0 units="J/mol/K"> 0.0</cp0> <cp0 units="J/mol/K"> 0.0</cp0>
</const_cp> </const_cp>
@ -50,7 +50,7 @@
<const_cp Tmax="300.0" Tmin="298.0"> <const_cp Tmax="300.0" Tmin="298.0">
<t0 units="K">298.14999999999998</t0> <t0 units="K">298.14999999999998</t0>
<h0 units="J/mol" updated="Updated at 23:0 7-15-2020">-4926549.797810851</h0> <h0 units="J/mol" updated="Updated at 10:47 7-17-2020">-4925566.309854757</h0>
<s0 units="J/mol/K"> 1117.965 </s0> <s0 units="J/mol/K"> 1117.965 </s0>
<cp0 units="J/mol/K">0.0</cp0> <cp0 units="J/mol/K">0.0</cp0>
</const_cp> </const_cp>
@ -67,7 +67,7 @@
<const_cp Tmax="300.0" Tmin="298.0"> <const_cp Tmax="300.0" Tmin="298.0">
<t0 units="K">298.14999999999998</t0> <t0 units="K">298.14999999999998</t0>
<h0 units="J/mol" updated="Updated at 23:0 7-15-2020">-4935519.640701385</h0> <h0 units="J/mol" updated="Updated at 10:47 7-17-2020">-4938249.845712334</h0>
<s0 units="J/mol/K"> 1117.965 </s0> <s0 units="J/mol/K"> 1117.965 </s0>
<cp0 units="J/mol/K">0.0</cp0> <cp0 units="J/mol/K">0.0</cp0>
</const_cp> </const_cp>
@ -85,7 +85,7 @@
<const_cp Tmax="300.0" Tmin="298.0"> <const_cp Tmax="300.0" Tmin="298.0">
<t0 units="K">298.14999999999998</t0> <t0 units="K">298.14999999999998</t0>
<h0 units="J/mol" updated="Updated at 23:0 7-15-2020">-4928317.781440989</h0> <h0 units="J/mol" updated="Updated at 10:47 7-17-2020">-4920387.823199008</h0>
<s0 units="J/mol/K"> 1117.965 </s0> <s0 units="J/mol/K"> 1117.965 </s0>
<cp0 units="J/mol/K">0.0</cp0> <cp0 units="J/mol/K">0.0</cp0>
</const_cp> </const_cp>
@ -103,7 +103,7 @@
<const_cp Tmax="300.0" Tmin="298.0"> <const_cp Tmax="300.0" Tmin="298.0">
<t0 units="K">298.14999999999998</t0> <t0 units="K">298.14999999999998</t0>
<h0 units="J/mol" updated="Updated at 23:0 7-15-2020">-4927428.65973482</h0> <h0 units="J/mol" updated="Updated at 10:47 7-17-2020">-4933548.865580005</h0>
<s0 units="J/mol/K"> 1117.965 </s0> <s0 units="J/mol/K"> 1117.965 </s0>
<cp0 units="J/mol/K">0.0</cp0> <cp0 units="J/mol/K">0.0</cp0>
</const_cp> </const_cp>
@ -120,7 +120,7 @@
<const_cp Tmax="300.0" Tmin="298.0"> <const_cp Tmax="300.0" Tmin="298.0">
<t0 units="K">298.14999999999998</t0> <t0 units="K">298.14999999999998</t0>
<h0 units="J/mol" updated="Updated at 23:0 7-15-2020">-4935155.356789877</h0> <h0 units="J/mol" updated="Updated at 10:47 7-17-2020">-4932560.171447597</h0>
<s0 units="J/mol/K"> 1117.965 </s0> <s0 units="J/mol/K"> 1117.965 </s0>
<cp0 units="J/mol/K">0.0</cp0> <cp0 units="J/mol/K">0.0</cp0>
</const_cp> </const_cp>
@ -138,7 +138,7 @@
<const_cp Tmax="300.0" Tmin="298.0"> <const_cp Tmax="300.0" Tmin="298.0">
<t0 units="K">298.14999999999998</t0> <t0 units="K">298.14999999999998</t0>
<h0 units="J/mol" updated="Updated at 23:0 7-15-2020">-4944228.17930387</h0> <h0 units="J/mol" updated="Updated at 10:47 7-17-2020">-4944840.781582316</h0>
<s0 units="J/mol/K"> 1117.965 </s0> <s0 units="J/mol/K"> 1117.965 </s0>
<cp0 units="J/mol/K">0.0</cp0> <cp0 units="J/mol/K">0.0</cp0>
</const_cp> </const_cp>
@ -157,7 +157,7 @@
<const_cp Tmax="300.0" Tmin="298.0"> <const_cp Tmax="300.0" Tmin="298.0">
<t0 units="K">298.14999999999998</t0> <t0 units="K">298.14999999999998</t0>
<h0 units="J/mol" updated="Updated at 23:0 7-15-2020">-4925606.187988869</h0> <h0 units="J/mol" updated="Updated at 10:47 7-17-2020">-4924696.189921901</h0>
<s0 units="J/mol/K"> 1117.965 </s0> <s0 units="J/mol/K"> 1117.965 </s0>
<cp0 units="J/mol/K">0.0</cp0> <cp0 units="J/mol/K">0.0</cp0>
</const_cp> </const_cp>
@ -245,10 +245,10 @@
</binarySaltParameters> </binarySaltParameters>
<binarySaltParameters anion="Cl-" cation="Pr+++"> <binarySaltParameters anion="Cl-" cation="Pr+++">
<beta0 updated="Updated at 1:53 7-16-2020"> 0.9335616000781043, 0.0, 0.0, 0.0, 0.0 </beta0> <beta0 updated="Updated at 10:47 7-17-2020"> 0.05879108748614492, 0.0, 0.0, 0.0, 0.0 </beta0>
<beta1 updated="Updated at 1:53 7-16-2020"> 10.555396864028587, 0.0, 0.0, 0.0, 0.0 </beta1> <beta1 updated="Updated at 10:47 7-17-2020"> 0.5448324180244323, 0.0, 0.0, 0.0, 0.0 </beta1>
<beta2> 0.0, 0.0, 0.0, 0.0, 0.0 </beta2> <beta2> 0.0, 0.0, 0.0, 0.0, 0.0 </beta2>
<Cphi updated="Updated at 23:0 7-15-2020"> -0.02066999867229882, 0.0, 0.0, 0.0, 0.0 </Cphi> <Cphi updated="Updated at 10:47 7-17-2020"> -0.02066999867229882, 0.0, 0.0, 0.0, 0.0 </Cphi>
<Alpha1> 2 </Alpha1> <Alpha1> 2 </Alpha1>
<Alpha2> 0 </Alpha2> <Alpha2> 0 </Alpha2>
<source> <source>
@ -261,10 +261,10 @@
</binarySaltParameters> </binarySaltParameters>
<binarySaltParameters anion="Cl-" cation="Nd+++"> <binarySaltParameters anion="Cl-" cation="Nd+++">
<beta0 updated="Updated at 1:36 7-16-2020"> 0.951206693355039, 0.0, 0.0, 0.0, 0.0 </beta0> <beta0 updated="Updated at 10:47 7-17-2020"> 1.2137222016802447, 0.0, 0.0, 0.0, 0.0 </beta0>
<beta1 updated="Updated at 1:36 7-16-2020"> 9.262520235612426, 0.0, 0.0, 0.0, 0.0 </beta1> <beta1 updated="Updated at 10:47 7-17-2020"> 7.748226963005033, 0.0, 0.0, 0.0, 0.0 </beta1>
<beta2> 0.0, 0.0, 0.0, 0.0, 0.0 </beta2> <beta2> 0.0, 0.0, 0.0, 0.0, 0.0 </beta2>
<Cphi updated="Updated at 23:0 7-15-2020"> -0.01963615126026457, 0.0, 0.0, 0.0, 0.0 </Cphi> <Cphi updated="Updated at 10:47 7-17-2020"> -0.01963615126026457, 0.0, 0.0, 0.0, 0.0 </Cphi>
<Alpha1> 2 </Alpha1> <Alpha1> 2 </Alpha1>
<Alpha2> 0 </Alpha2> <Alpha2> 0 </Alpha2>
<source> <source>
@ -277,10 +277,10 @@
</binarySaltParameters> </binarySaltParameters>
<binarySaltParameters anion="Cl-" cation="La+++"> <binarySaltParameters anion="Cl-" cation="La+++">
<beta0 updated="Updated at 2:48 7-16-2020"> 0.5362669407654791, 0.0, 0.0, 0.0, 0.0 </beta0> <beta0 updated="Updated at 10:47 7-17-2020"> 1.022316535866388, 0.0, 0.0, 0.0, 0.0 </beta0>
<beta1 updated="Updated at 2:48 7-16-2020"> 19.878544145607385, 0.0, 0.0, 0.0, 0.0 </beta1> <beta1 updated="Updated at 10:47 7-17-2020"> 0.5296311209773129, 0.0, 0.0, 0.0, 0.0 </beta1>
<beta2> 0.0, 0.0, 0.0, 0.0, 0.0 </beta2> <beta2> 0.0, 0.0, 0.0, 0.0, 0.0 </beta2>
<Cphi updated="Updated at 23:0 7-15-2020"> -0.024339999997603376, 0.0, 0.0, 0.0, 0.0 </Cphi> <Cphi updated="Updated at 10:47 7-17-2020"> -0.024339999997603376, 0.0, 0.0, 0.0, 0.0 </Cphi>
<Alpha1> 2 </Alpha1> <Alpha1> 2 </Alpha1>
<Alpha2> 0 </Alpha2> <Alpha2> 0 </Alpha2>
<source> <source>
@ -293,10 +293,10 @@
</binarySaltParameters> </binarySaltParameters>
<binarySaltParameters anion="Cl-" cation="Dy+++"> <binarySaltParameters anion="Cl-" cation="Dy+++">
<beta0 updated="Updated at 2:48 7-16-2020"> 0.6129504699810289, 0.0, 0.0, 0.0, 0.0 </beta0> <beta0 updated="Updated at 10:47 7-17-2020"> 0.7646397332938777, 0.0, 0.0, 0.0, 0.0 </beta0>
<beta1 updated="Updated at 2:48 7-16-2020"> 5.359936728131316, 0.0, 0.0, 0.0, 0.0 </beta1> <beta1 updated="Updated at 10:47 7-17-2020"> 7.849320590516409, 0.0, 0.0, 0.0, 0.0 </beta1>
<beta2> 0.0, 0.0, 0.0, 0.0, 0.0 </beta2> <beta2> 0.0, 0.0, 0.0, 0.0, 0.0 </beta2>
<Cphi updated="Updated at 23:0 7-15-2020"> -0.019699989216349984, 0.0, 0.0, 0.0, 0.0 </Cphi> <Cphi updated="Updated at 10:47 7-17-2020"> -0.019699989216349984, 0.0, 0.0, 0.0, 0.0 </Cphi>
<Alpha1> 2 </Alpha1> <Alpha1> 2 </Alpha1>
<Alpha2> 0 </Alpha2> <Alpha2> 0 </Alpha2>
<source> <source>
@ -309,10 +309,10 @@
</binarySaltParameters> </binarySaltParameters>
<binarySaltParameters anion="Cl-" cation="Ce+++"> <binarySaltParameters anion="Cl-" cation="Ce+++">
<beta0 updated="Updated at 2:2 7-16-2020"> 0.3803360903827252, 0.0, 0.0, 0.0, 0.0 </beta0> <beta0 updated="Updated at 10:47 7-17-2020"> 0.2035235053232368, 0.0, 0.0, 0.0, 0.0 </beta0>
<beta1 updated="Updated at 2:2 7-16-2020"> 0.4715334506120701, 0.0, 0.0, 0.0, 0.0 </beta1> <beta1 updated="Updated at 10:47 7-17-2020"> 21.120426174002823, 0.0, 0.0, 0.0, 0.0 </beta1>
<beta2> 0.0, 0.0, 0.0, 0.0, 0.0 </beta2> <beta2> 0.0, 0.0, 0.0, 0.0, 0.0 </beta2>
<Cphi updated="Updated at 23:0 7-15-2020"> -0.02618999999473301, 0.0, 0.0, 0.0, 0.0 </Cphi> <Cphi updated="Updated at 10:47 7-17-2020"> -0.02618999999473301, 0.0, 0.0, 0.0, 0.0 </Cphi>
<Alpha1> 2 </Alpha1> <Alpha1> 2 </Alpha1>
<Alpha2> 0 </Alpha2> <Alpha2> 0 </Alpha2>
<source> <source>
@ -325,10 +325,10 @@
</binarySaltParameters> </binarySaltParameters>
<binarySaltParameters anion="Cl-" cation="Y+++"> <binarySaltParameters anion="Cl-" cation="Y+++">
<beta0 updated="Updated at 1:26 7-16-2020"> 0.8864576562457522, 0.0, 0.0, 0.0, 0.0 </beta0> <beta0 updated="Updated at 10:47 7-17-2020"> 0.8852802073165494, 0.0, 0.0, 0.0, 0.0 </beta0>
<beta1 updated="Updated at 1:26 7-16-2020"> 9.87156845120858, 0.0, 0.0, 0.0, 0.0 </beta1> <beta1 updated="Updated at 10:47 7-17-2020"> 9.334653075341238, 0.0, 0.0, 0.0, 0.0 </beta1>
<beta2> 0.0, 0.0, 0.0, 0.0, 0.0 </beta2> <beta2> 0.0, 0.0, 0.0, 0.0, 0.0 </beta2>
<Cphi updated="Updated at 23:0 7-15-2020"> -0.015467323909969704, 0.0, 0.0, 0.0, 0.0 </Cphi> <Cphi updated="Updated at 10:47 7-17-2020"> -0.015467323909969704, 0.0, 0.0, 0.0, 0.0 </Cphi>
<Alpha1> 2 </Alpha1> <Alpha1> 2 </Alpha1>
<Alpha2> 0 </Alpha2> <Alpha2> 0 </Alpha2>
<source> <source>
@ -341,10 +341,10 @@
</binarySaltParameters> </binarySaltParameters>
<binarySaltParameters anion="Cl-" cation="Sm+++"> <binarySaltParameters anion="Cl-" cation="Sm+++">
<beta0 updated="Updated at 7:6 7-16-2020"> 0.6989806463779159, 0.0, 0.0, 0.0, 0.0 </beta0> <beta0 updated="Updated at 10:47 7-17-2020"> 0.6989081585234721, 0.0, 0.0, 0.0, 0.0 </beta0>
<beta1 updated="Updated at 1:25 7-16-2020"> 6.8744371885601625, 0.0, 0.0, 0.0, 0.0 </beta1> <beta1 updated="Updated at 10:47 7-17-2020"> 6.877882600430561, 0.0, 0.0, 0.0, 0.0 </beta1>
<beta2> 0.0, 0.0, 0.0, 0.0, 0.0 </beta2> <beta2> 0.0, 0.0, 0.0, 0.0, 0.0 </beta2>
<Cphi updated="Updated at 23:0 7-15-2020"> -0.019920000110321332, 0.0, 0.0, 0.0, 0.0 </Cphi> <Cphi updated="Updated at 10:47 7-17-2020"> -0.019920000110321332, 0.0, 0.0, 0.0, 0.0 </Cphi>
<Alpha1> 2 </Alpha1> <Alpha1> 2 </Alpha1>
<Alpha2> 0 </Alpha2> <Alpha2> 0 </Alpha2>
<source> <source>

26
llepe/objectives.py
Unescape View File

@ -57,3 +57,29 @@ def ind_lmse_perturbed_obj(predicted_dict,
objectives.append(np.mean(fun1)) objectives.append(np.mean(fun1))
objectives = np.array(objectives) objectives = np.array(objectives)
return objectives return objectives
def mean_squared_error(predicted_dict,
measured_df,
species_list):
meas_aq = np.concatenate([measured_df['{0}_aq_eq'.format(species)].values
for species in species_list])
pred_aq = np.concatenate([
predicted_dict['{0}_aq_eq'.format(species)]
for species in species_list])
meas_d = np.concatenate([measured_df['{0}_d_eq'.format(species)].values
for species in species_list])
pred_d = np.concatenate([
predicted_dict['{0}_d_eq'.format(species)]
for species in species_list])
meas_org = meas_aq * meas_d
pred_org = np.concatenate([
predicted_dict['{0}_org_eq'.format(species)]
for species in species_list])
aq_obj = (meas_aq - pred_aq)**2
org_obj = (meas_org - pred_org)**2
objs = np.concatenate([aq_obj, org_obj])
obj = np.mean(objs)
return obj

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