mirror of
https://github.com/ANL-CEEESA/LLEPE.git
synced 2025-12-06 01:48:53 -06:00
Updated iterative_fitter.py to calculate error for all species. Added new test with mean squared error.
This commit is contained in:
titusquah
54
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216
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216
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Normal file
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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
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||||
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
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||||
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
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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
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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
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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
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||||
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
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||||
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
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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
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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
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||||
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
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||||
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
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||||
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
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||||
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
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||||
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
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||||
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
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||||
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
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||||
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
|
||||
|
@@ -33,7 +33,7 @@
|
||||
<thermo>
|
||||
<const_cp Tmax="300.0" Tmin="298.0">
|
||||
<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>
|
||||
<cp0 units="J/mol/K"> 0.0</cp0>
|
||||
</const_cp>
|
||||
@@ -50,7 +50,7 @@
|
||||
<const_cp Tmax="300.0" Tmin="298.0">
|
||||
<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>
|
||||
<cp0 units="J/mol/K">0.0</cp0>
|
||||
</const_cp>
|
||||
@@ -67,7 +67,7 @@
|
||||
<const_cp Tmax="300.0" Tmin="298.0">
|
||||
<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>
|
||||
<cp0 units="J/mol/K">0.0</cp0>
|
||||
</const_cp>
|
||||
@@ -85,7 +85,7 @@
|
||||
<const_cp Tmax="300.0" Tmin="298.0">
|
||||
<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>
|
||||
<cp0 units="J/mol/K">0.0</cp0>
|
||||
</const_cp>
|
||||
@@ -103,7 +103,7 @@
|
||||
<const_cp Tmax="300.0" Tmin="298.0">
|
||||
<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>
|
||||
<cp0 units="J/mol/K">0.0</cp0>
|
||||
</const_cp>
|
||||
@@ -120,7 +120,7 @@
|
||||
<const_cp Tmax="300.0" Tmin="298.0">
|
||||
<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>
|
||||
<cp0 units="J/mol/K">0.0</cp0>
|
||||
</const_cp>
|
||||
@@ -138,7 +138,7 @@
|
||||
<const_cp Tmax="300.0" Tmin="298.0">
|
||||
<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>
|
||||
<cp0 units="J/mol/K">0.0</cp0>
|
||||
</const_cp>
|
||||
@@ -157,7 +157,7 @@
|
||||
<const_cp Tmax="300.0" Tmin="298.0">
|
||||
<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>
|
||||
<cp0 units="J/mol/K">0.0</cp0>
|
||||
</const_cp>
|
||||
@@ -245,10 +245,10 @@
|
||||
</binarySaltParameters>
|
||||
|
||||
<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>
|
||||
<beta1 updated="Updated at 1:53 7-16-2020"> 10.555396864028587, 0.0, 0.0, 0.0, 0.0 </beta1>
|
||||
<beta0 updated="Updated at 11:58 7-17-2020"> 0.05879108748614492, 0.0, 0.0, 0.0, 0.0 </beta0>
|
||||
<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>
|
||||
<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>
|
||||
<Alpha2> 0 </Alpha2>
|
||||
<source>
|
||||
@@ -261,10 +261,10 @@
|
||||
</binarySaltParameters>
|
||||
|
||||
<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>
|
||||
<beta1 updated="Updated at 1:36 7-16-2020"> 9.262520235612426, 0.0, 0.0, 0.0, 0.0 </beta1>
|
||||
<beta0 updated="Updated at 11:58 7-17-2020"> 1.2137222016802447, 0.0, 0.0, 0.0, 0.0 </beta0>
|
||||
<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>
|
||||
<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>
|
||||
<Alpha2> 0 </Alpha2>
|
||||
<source>
|
||||
@@ -277,10 +277,10 @@
|
||||
</binarySaltParameters>
|
||||
|
||||
<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>
|
||||
<beta1 updated="Updated at 2:48 7-16-2020"> 19.878544145607385, 0.0, 0.0, 0.0, 0.0 </beta1>
|
||||
<beta0 updated="Updated at 11:58 7-17-2020"> 1.022316535866388, 0.0, 0.0, 0.0, 0.0 </beta0>
|
||||
<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>
|
||||
<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>
|
||||
<Alpha2> 0 </Alpha2>
|
||||
<source>
|
||||
@@ -293,10 +293,10 @@
|
||||
</binarySaltParameters>
|
||||
|
||||
<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>
|
||||
<beta1 updated="Updated at 2:48 7-16-2020"> 5.359936728131316, 0.0, 0.0, 0.0, 0.0 </beta1>
|
||||
<beta0 updated="Updated at 11:58 7-17-2020"> 0.7646397332938777, 0.0, 0.0, 0.0, 0.0 </beta0>
|
||||
<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>
|
||||
<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>
|
||||
<Alpha2> 0 </Alpha2>
|
||||
<source>
|
||||
@@ -309,10 +309,10 @@
|
||||
</binarySaltParameters>
|
||||
|
||||
<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>
|
||||
<beta1 updated="Updated at 2:2 7-16-2020"> 0.4715334506120701, 0.0, 0.0, 0.0, 0.0 </beta1>
|
||||
<beta0 updated="Updated at 11:58 7-17-2020"> 0.2035235053232368, 0.0, 0.0, 0.0, 0.0 </beta0>
|
||||
<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>
|
||||
<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>
|
||||
<Alpha2> 0 </Alpha2>
|
||||
<source>
|
||||
@@ -325,10 +325,10 @@
|
||||
</binarySaltParameters>
|
||||
|
||||
<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>
|
||||
<beta1 updated="Updated at 1:26 7-16-2020"> 9.87156845120858, 0.0, 0.0, 0.0, 0.0 </beta1>
|
||||
<beta0 updated="Updated at 11:58 7-17-2020"> 0.8852802073165494, 0.0, 0.0, 0.0, 0.0 </beta0>
|
||||
<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>
|
||||
<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>
|
||||
<Alpha2> 0 </Alpha2>
|
||||
<source>
|
||||
@@ -341,10 +341,10 @@
|
||||
</binarySaltParameters>
|
||||
|
||||
<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>
|
||||
<beta1 updated="Updated at 1:25 7-16-2020"> 6.8744371885601625, 0.0, 0.0, 0.0, 0.0 </beta1>
|
||||
<beta0 updated="Updated at 11:58 7-17-2020"> 0.6989081585234721, 0.0, 0.0, 0.0, 0.0 </beta0>
|
||||
<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>
|
||||
<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>
|
||||
<Alpha2> 0 </Alpha2>
|
||||
<source>
|
||||
|
||||
@@ -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.update_xml(ext_h0_dict,
|
||||
dependant_params_dict=dependant_params_dict)
|
||||
eps = 1e-8
|
||||
eps = 1e-20
|
||||
mini_eps = 1e-4
|
||||
pitzer_guess_dict = {'species': [],
|
||||
'beta0': [],
|
||||
@@ -105,7 +105,7 @@ i = 0
|
||||
rel_diff = 1000
|
||||
obj_diff1 = 1000
|
||||
obj_diff2 = 1000
|
||||
while obj_diff1 > eps and obj_diff2 > eps:
|
||||
while obj_diff1 > eps or obj_diff2 > eps:
|
||||
i += 1
|
||||
print(i)
|
||||
best_obj = 1e20
|
||||
@@ -154,7 +154,7 @@ while obj_diff1 > eps and obj_diff2 > eps:
|
||||
estimator.set_opt_dict(info_dict)
|
||||
estimator.update_custom_objects_dict(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)
|
||||
optimizer_kwargs = {"method": 'l-bfgs-b',
|
||||
"bounds": bounds}
|
||||
@@ -237,7 +237,7 @@ while obj_diff1 > eps and obj_diff2 > eps:
|
||||
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_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])
|
||||
if i > 2:
|
||||
obj_diff2 = np.abs(
|
||||
|
||||
128
docs/Examples/iterative_fitter_eval_grapher.py
Normal file
128
docs/Examples/iterative_fitter_eval_grapher.py
Normal file
@@ -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
Normal file
1
docs/Examples/iterative_fitter_info_dict
Normal file
@@ -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
Normal file
0
docs/Examples/iterative_fitter_info_dict.txt
Normal file
274
docs/Examples/iterative_fitter_w_mse.py
Normal file
274
docs/Examples/iterative_fitter_w_mse.py
Normal file
@@ -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
Normal file
1
docs/Examples/outputs/iterative_fitter_info_dict.txt
Normal file
@@ -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":
|
||||
@@ -33,7 +33,7 @@
|
||||
<thermo>
|
||||
<const_cp Tmax="300.0" Tmin="298.0">
|
||||
<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>
|
||||
<cp0 units="J/mol/K"> 0.0</cp0>
|
||||
</const_cp>
|
||||
@@ -50,7 +50,7 @@
|
||||
<const_cp Tmax="300.0" Tmin="298.0">
|
||||
<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>
|
||||
<cp0 units="J/mol/K">0.0</cp0>
|
||||
</const_cp>
|
||||
@@ -67,7 +67,7 @@
|
||||
<const_cp Tmax="300.0" Tmin="298.0">
|
||||
<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>
|
||||
<cp0 units="J/mol/K">0.0</cp0>
|
||||
</const_cp>
|
||||
@@ -85,7 +85,7 @@
|
||||
<const_cp Tmax="300.0" Tmin="298.0">
|
||||
<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>
|
||||
<cp0 units="J/mol/K">0.0</cp0>
|
||||
</const_cp>
|
||||
@@ -103,7 +103,7 @@
|
||||
<const_cp Tmax="300.0" Tmin="298.0">
|
||||
<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>
|
||||
<cp0 units="J/mol/K">0.0</cp0>
|
||||
</const_cp>
|
||||
@@ -120,7 +120,7 @@
|
||||
<const_cp Tmax="300.0" Tmin="298.0">
|
||||
<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>
|
||||
<cp0 units="J/mol/K">0.0</cp0>
|
||||
</const_cp>
|
||||
@@ -138,7 +138,7 @@
|
||||
<const_cp Tmax="300.0" Tmin="298.0">
|
||||
<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>
|
||||
<cp0 units="J/mol/K">0.0</cp0>
|
||||
</const_cp>
|
||||
@@ -157,7 +157,7 @@
|
||||
<const_cp Tmax="300.0" Tmin="298.0">
|
||||
<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>
|
||||
<cp0 units="J/mol/K">0.0</cp0>
|
||||
</const_cp>
|
||||
@@ -245,10 +245,10 @@
|
||||
</binarySaltParameters>
|
||||
|
||||
<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>
|
||||
<beta1 updated="Updated at 1:53 7-16-2020"> 10.555396864028587, 0.0, 0.0, 0.0, 0.0 </beta1>
|
||||
<beta0 updated="Updated at 10:47 7-17-2020"> 0.05879108748614492, 0.0, 0.0, 0.0, 0.0 </beta0>
|
||||
<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>
|
||||
<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>
|
||||
<Alpha2> 0 </Alpha2>
|
||||
<source>
|
||||
@@ -261,10 +261,10 @@
|
||||
</binarySaltParameters>
|
||||
|
||||
<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>
|
||||
<beta1 updated="Updated at 1:36 7-16-2020"> 9.262520235612426, 0.0, 0.0, 0.0, 0.0 </beta1>
|
||||
<beta0 updated="Updated at 10:47 7-17-2020"> 1.2137222016802447, 0.0, 0.0, 0.0, 0.0 </beta0>
|
||||
<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>
|
||||
<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>
|
||||
<Alpha2> 0 </Alpha2>
|
||||
<source>
|
||||
@@ -277,10 +277,10 @@
|
||||
</binarySaltParameters>
|
||||
|
||||
<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>
|
||||
<beta1 updated="Updated at 2:48 7-16-2020"> 19.878544145607385, 0.0, 0.0, 0.0, 0.0 </beta1>
|
||||
<beta0 updated="Updated at 10:47 7-17-2020"> 1.022316535866388, 0.0, 0.0, 0.0, 0.0 </beta0>
|
||||
<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>
|
||||
<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>
|
||||
<Alpha2> 0 </Alpha2>
|
||||
<source>
|
||||
@@ -293,10 +293,10 @@
|
||||
</binarySaltParameters>
|
||||
|
||||
<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>
|
||||
<beta1 updated="Updated at 2:48 7-16-2020"> 5.359936728131316, 0.0, 0.0, 0.0, 0.0 </beta1>
|
||||
<beta0 updated="Updated at 10:47 7-17-2020"> 0.7646397332938777, 0.0, 0.0, 0.0, 0.0 </beta0>
|
||||
<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>
|
||||
<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>
|
||||
<Alpha2> 0 </Alpha2>
|
||||
<source>
|
||||
@@ -309,10 +309,10 @@
|
||||
</binarySaltParameters>
|
||||
|
||||
<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>
|
||||
<beta1 updated="Updated at 2:2 7-16-2020"> 0.4715334506120701, 0.0, 0.0, 0.0, 0.0 </beta1>
|
||||
<beta0 updated="Updated at 10:47 7-17-2020"> 0.2035235053232368, 0.0, 0.0, 0.0, 0.0 </beta0>
|
||||
<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>
|
||||
<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>
|
||||
<Alpha2> 0 </Alpha2>
|
||||
<source>
|
||||
@@ -325,10 +325,10 @@
|
||||
</binarySaltParameters>
|
||||
|
||||
<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>
|
||||
<beta1 updated="Updated at 1:26 7-16-2020"> 9.87156845120858, 0.0, 0.0, 0.0, 0.0 </beta1>
|
||||
<beta0 updated="Updated at 10:47 7-17-2020"> 0.8852802073165494, 0.0, 0.0, 0.0, 0.0 </beta0>
|
||||
<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>
|
||||
<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>
|
||||
<Alpha2> 0 </Alpha2>
|
||||
<source>
|
||||
@@ -341,10 +341,10 @@
|
||||
</binarySaltParameters>
|
||||
|
||||
<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>
|
||||
<beta1 updated="Updated at 1:25 7-16-2020"> 6.8744371885601625, 0.0, 0.0, 0.0, 0.0 </beta1>
|
||||
<beta0 updated="Updated at 10:47 7-17-2020"> 0.6989081585234721, 0.0, 0.0, 0.0, 0.0 </beta0>
|
||||
<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>
|
||||
<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>
|
||||
<Alpha2> 0 </Alpha2>
|
||||
<source>
|
||||
|
||||
@@ -57,3 +57,29 @@ def ind_lmse_perturbed_obj(predicted_dict,
|
||||
objectives.append(np.mean(fun1))
|
||||
objectives = np.array(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
|
||||
|
||||
Reference in New Issue
Block a user