Information in tkutils.set_exception(): Set excption handler sys.excepthook #======================================================== # optimize_mu_pi #======================================================== Check infile parameter: Input file [D:/git/tkProg/tkprog_COE/electrical/Hall/HQ-aIGZO/1e17.xlsx] is given. Paramter file is set to [D:\git\tkProg\tkprog_COE\electrical\Hall\HQ-aIGZO\1e17.in]. Update paramers from command line arguments i=1 positional arg: lfit Read configration file [D:\git\tkProg\tkprog_COE\electrical\Hall\HQ-aIGZO\1e17.in] Read parameters from [D:\git\tkProg\tkprog_COE\electrical\Hall\HQ-aIGZO\1e17.in] a1: 1 a2: 0 a3: 0 a4: 0 a5: 0 alpha: 0 colors: ['black', 'red', 'blue', 'green', 'orange', 'darkgreen', 'darkorange', 'navy', 'blue', 'darkgreen', 'darkorange', 'navy', 'slategray', 'hotpink', 'olive', 'chocolate', 'magenta', 'green', 'yellow', 'cyan'] converted_file: cv: daemon: 0 datafile: mu.xlsx debug: 0 dx: 1e-05 Eop: 0.0446 ffitfiles: 0 fhistory: 1 figsize: [12, 8] file1: lfit file2: file3: file4: file5: fitfile: mu-fit.xlsx fitfile_template: {filebody}_parameters.csv fix_N: False fmax_plot: 10 fmax_record: 2000 fmin_max_mlr: 60 fontsize: 14 fplot: 1 ga_nmaxiter: 3 graphupdateinterval: 10 h_diff: 1e-05 help: 0 hidden_layer_sizes: 3,3,3 historyfile: D:\git\tkProg\tkprog_COE\electrical\Hall\HQ-aIGZO\1e17-history.xlsx infile: D:/git/tkProg/tkprog_COE/electrical/Hall/HQ-aIGZO/1e17.xlsx initialize_method: simplex input_template: interval: 50 ix_plot: ix_sort: jac: 3-points kvariance: 6 l1_ratio: 0.5 legend_fontsize: 12 linewidth: 2 logfile: D:\git\tkProg\tkprog_COE\electrical\Hall\HQ-aIGZO\1e17-out.txt max_iter_mlp: 1000 max_num_probes: 1 maxiter: 1000 method: nelder-mead mode: fit mu_label: 0 mutation_rate: 0.01 n_label: 1 n_restarts_optimizer: 50 ncal_error: 11 Nmax: 1e+23 nmax_surrogate_trial: 5 nmaxcall: 10000 nmaxdata: 20 nmaxiter: 1000 Nmin: 0 nohup: 0 nparents: 5 nsample: 100 nT: 96 num_rand_basis: 400 nx: 5 outcomponentxlsfile: D:\git\tkProg\tkprog_COE\electrical\Hall\HQ-aIGZO\1e17-mu-components.xlsx outfile: input-repeat.xlsx outxlsfile: D:\git\tkProg\tkprog_COE\electrical\Hall\HQ-aIGZO\1e17-muT-fit.xlsx p1: 0 p2: -0.5 p3: 0.5 p4: -1 p5: 1 parameterfile: D:\git\tkProg\tkprog_COE\electrical\Hall\HQ-aIGZO\1e17.in plot_interval: 1 print_interval: 1 print_level: 0 run: 1 sample: score_mode: EI seed: sigma0: 0 sigma_label: 3 surrogate_method: cg surrogate_model: gpr surrogate_scores: max T_label: 0 target_var: 0 Tcalmax: 1000 Tcalmin: 50 test_data_ratio: 0.2 Tmax: 1e+100 Tmin: -1e+100 tol: 1e-05 trainfile: fit_db.xlsx Tstep: 10 use_cv: 0 use_simple: 0 use_tkplt: 0 VB: 0 x0: x1: x_scale: xatol: 0.0001 xlabel: T(K) y_scale: log ylabel: ue(cm2/Vs) infile: D:/git/tkProg/tkprog_COE/electrical/Hall/HQ-aIGZO/1e17.xlsx parameterfile: D:\git\tkProg\tkprog_COE\electrical\Hall\HQ-aIGZO\1e17.in stop file: D:\git\tkProg\tkprog_COE\electrical\Hall\HQ-aIGZO\stop Linear LSQ: In tkminimize_func.read_parameters(): Read parameters from [D:\git\tkProg\tkprog_COE\electrical\Hall\HQ-aIGZO\1e17.in] mode : lfit fplot : 1 fhistory : 1 ffitfiles : 0 stop path : D:\git\tkProg\tkprog_COE\electrical\Hall\HQ-aIGZO\stop infile : D:/git/tkProg/tkprog_COE/electrical/Hall/HQ-aIGZO/1e17.xlsx outfile : input-repeat.xlsx config parameter file : D:\git\tkProg\tkprog_COE\electrical\Hall\HQ-aIGZO\1e17.in fitting parameter file: D:\git\tkProg\tkprog_COE\electrical\Hall\HQ-aIGZO\1e17_parameters.csv history file : D:\git\tkProg\tkprog_COE\electrical\Hall\HQ-aIGZO\1e17-history.xlsx Fitting configuration method : nelder-mead jac : 3-points tol : 1e-05 nmaxiter: 1000 nmaxcall: 10000 y_scale : log Fitting parameters red from [D:\git\tkProg\tkprog_COE\electrical\Hall\HQ-aIGZO\1e17_parameters.csv]: 00: VB= 1.09361e-10 eV (id=1) (linear=0) penality: 1 * (0 - 0.5) 01: sigma0= 0 eV (id=0) (linear=0) penality: 1 * (0 - 0.5) 02: Eop= 0.0446 eV (id=0) (linear=0) penality: 1 * (0 - 0.1) 03: aop= 0.01 (id=1) (linear=1) penality: 1 * (0 - 0.01) 04: p1= 0 (id=0) (linear=0) penality: 1 * (-1.5 - 2) 05: a1= 0.00997381 (id=1) (linear=1) penality: 1 * (0 - 0.01) 06: p2= 0.5 (id=0) (linear=0) penality: 1 * (-1.5 - 2) 07: a2= 0.000187323 (id=1) (linear=1) penality: 1 * (0 - 100) 08: p3= -1.5 (id=0) (linear=0) penality: 1 * (-1.5 - 2) 09: a3= 100 (id=1) (linear=1) penality: 1 * (0 - 100) 10: p4= 1.5 (id=0) (linear=0) penality: 1 * (-1.5 - 2) 11: a4= 5.7829 (id=1) (linear=1) penality: 1 * (0 - 100) 12: p5= 1 (id=0) (linear=0) penality: 1 * (-1.5 - 2) 13: a5= 100 (id=1) (linear=1) penality: 1 * (0 - 100) Build initial simplex 0: 1.094e-10 0.01 0.009974 0.0001873 100 5.783 100 1: 0.01 0.01 0.009974 0.0001873 100 5.783 100 2: 1.094e-10 0.0101 0.009974 0.0001873 100 5.783 100 3: 1.094e-10 0.01 0.01007 0.0001873 100 5.783 100 4: 1.094e-10 0.01 0.009974 0.0002873 100 5.783 100 5: 1.094e-10 0.01 0.009974 0.0001873 100 5.783 100 6: 1.094e-10 0.01 0.009974 0.0001873 100 5.783 100 7: 1.094e-10 0.01 0.009974 0.0001873 100 5.783 100 Linear least-squares fitting: minimize_func.read_input_data(): Read input data from [D:/git/tkProg/tkprog_COE/electrical/Hall/HQ-aIGZO/1e17.xlsx] target samples: T range: -1e+100 - 1e+100 K Read [D:/git/tkProg/tkprog_COE/electrical/Hall/HQ-aIGZO/1e17.xlsx] T, mu 83.06 0.5562 104.3 1.223 150.6 3.081 199.2 5.102 274.7 7.857 297.6 8.588 Linear variables: varname :['aop', 'a1', 'a2', 'a3', 'a4', 'a5'] optid :[1, 1, 1, 1, 1, 1] fitting parameters:[0.01, 0.009973814304698111, 0.00018732339730689716, 100, 5.782896869722078, 100] constants: pi: None 0 0.5 -1.5 1.5 1 Eb: 1.094e-10 s_phi: 0 Eop: 0.0446 mlsq_general_optid: nvars =6 nmatrix =6 ai = 0.01 0.00997 0.000187 100 5.78 100 linid = 1 1 1 1 1 1 matrix index= 0 1 2 3 4 5 nData =6 optimiz_mup.mslq_general3:: Vector and Matrix: Si = 0.08347, 3.38, 0.3321, 4560, 0.003439, 0.03348 Sij= 0.08518, 0.515, 0.03248, 2207, 0.0001367, 0.002084 0.515, 6, 0.4783, 1.617e+04, 0.003571, 0.04029 0.03248, 0.4783, 0.04029, 1109, 0.0003308, 0.003571 2207, 1.617e+04, 1109, 6.012e+07, 6, 79.54 0.0001367, 0.003571, 0.0003308, 6, 3.133e-06, 3.174e-05 0.002084, 0.04029, 0.003571, 79.54, 3.174e-05, 0.0003308 ai(new)= 49.7, -42.42, 1537, -0.002237, 6.544e+04, -1.738e+04 ai(org)= 0.01, 0.009974, 0.0001873, 100, 5.783, 100 ai_all= 49.7, -42.42, 1537, -0.002237, 6.544e+04, -1.738e+04 pk=[1.0936131413375939e-10, 0, 0.0446, 0.01, 0, 0.009973814304698111, 0.5, 0.00018732339730689716, -1.5, 100, 1.5, 5.782896869722078, 1, 100] ai_all=[49.700148552656174, -42.415155582129955, 1536.7787724733353, -0.0022370924471033504, 65438.64362335205, -17381.16922569275] linear fit results: varname : aop a1 a2 a3 a4 a5 ai_new : 49.7 -42.42 1537 -0.002237 6.544e+04 -1.738e+04 ai_all : 49.7 -42.42 1537 -0.002237 6.544e+04 -1.738e+04 optid_lin : 1 1 1 1 1 1 all parameters: varname : VB sigma0 Eop aop p1 a1 p2 a2 p3 a3 p4 a4 p5 a5 fit.pk(org) : 1.094e-10 0 0.0446 0.01 0 0.009974 0.5 0.0001873 -1.5 100 1.5 5.783 1 100 pk_all(new) : 1.094e-10 0 0.0446 -0.002237 0 49.7 0.5 -42.42 -1.5 1537 1.5 -0.002237 1 6.544e+04 Save parameters to [D:\git\tkProg\tkprog_COE\electrical\Hall\HQ-aIGZO\1e17.in] Save configuration parameters to [D:\git\tkProg\tkprog_COE\electrical\Hall\HQ-aIGZO\1e17.in] Save fitting parameters to [D:\git\tkProg\tkprog_COE\electrical\Hall\HQ-aIGZO\1e17_parameters.csv] (save_parameters) **Warning: [a1=0.0446] is larger than [0.01]. Add penalty [ 2.24] to fmin **Warning: [a2=-0.0022370924471033504] is smaller than [0]. Add penalty [ 133] to fmin tkFit_mxy_flex.save_history(): Add fmin and parameters to [D:\git\tkProg\tkprog_COE\electrical\Hall\HQ-aIGZO\1e17-history.xlsx] tkFit_mxy_flex.add_history() 267 **append to [D:\git\tkProg\tkprog_COE\electrical\Hall\HQ-aIGZO\1e17-history.xlsx] DB #1006: fmin= 137 xdiff= 0 1.09361e-10 0 0.0446 0 0 0.01 0.5 0 -1.5 0 1.5 49.7001 1 0.5 Simulated data: T (K) mu(obs) (cm2/Vs) ini 83.06 0.5562 12.24 104.3 1.223 16.27 150.6 3.081 24.87 199.2 5.102 33.13 274.7 7.857 43.99 297.6 8.588 46.82 plot time at startup:24/11/21 12:20:17 time at end:24/11/21 12:20:18 Elapsed time from startup to end: 1.00503 sec