text: import numpy as np
  
  coef_20_1_bre = np.array([-0.9185611])
  
  se_20_1_bre = np.array([0.4706831])
  
  time_20_1_bre = np.array([0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,1.1,1.2,1.3,1.4,1.5])
  
  hazard_20_1_bre = np.array([0,0,0.04139181,0.1755379,0.3121216,0.3121216,0.4263121,0.6196358,0.6196358,0.6196358,0.909556,1.31083,1.31083])
  
  coef_20_1_et_bre = np.array([-0.8907007])
  
  se_20_1_et_bre = np.array([0.4683384])
  
  time_20_1_et_bre = np.array([0])
  
  hazard_20_1_et_bre = np.array([0])
  
  coef_20_1_st_bre = np.array([-0.5766809])
  
  se_20_1_st_bre = np.array([0.4418918])
  
  time_20_1_st_bre = np.array([0])
  
  hazard_20_1_st_bre = np.array([0])
  
  coef_20_1_et_st_bre = np.array([-0.5785683])
  
  se_20_1_et_st_bre = np.array([0.4388437])
  
  time_20_1_et_st_bre = np.array([0])
  
  hazard_20_1_et_st_bre = np.array([0])
  
  coef_20_1_efr = np.array([-0.9975319])
  
  se_20_1_efr = np.array([0.4792421])
  
  time_20_1_efr = np.array([0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,1.1,1.2,1.3,1.4,1.5])
  
  hazard_20_1_efr = np.array([0,0,0.03934634,0.1663316,0.2986427,0.2986427,0.4119189,0.6077373,0.6077373,0.6077373,0.8933041,1.285732,1.285732])
  
  coef_20_1_et_efr = np.array([-0.9679541])
  
  se_20_1_et_efr = np.array([0.4766406])
  
  time_20_1_et_efr = np.array([0])
  
  hazard_20_1_et_efr = np.array([0])
  
  coef_20_1_st_efr = np.array([-0.6345294])
  
  se_20_1_st_efr = np.array([0.4455952])
  
  time_20_1_st_efr = np.array([0])
  
  hazard_20_1_st_efr = np.array([0])
  
  coef_20_1_et_st_efr = np.array([-0.6355622])
  
  se_20_1_et_st_efr = np.array([0.4423104])
  
  time_20_1_et_st_efr = np.array([0])
  
  hazard_20_1_et_st_efr = np.array([0])
  
  coef_50_1_bre = np.array([-0.6761247])
  
  se_50_1_bre = np.array([0.25133])
  
  time_50_1_bre = np.array([0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1,1.1,1.2,1.5,1.6,1.7,1.8,1.9,2.4,2.8])
  
  hazard_50_1_bre = np.array([0,0.04895521,0.08457461,0.2073863,0.2382473,0.2793018,0.3271622,0.3842953,0.3842953,0.5310807,0.6360276,0.7648251,0.7648251,0.9294298,0.9294298,0.9294298,1.206438,1.555569,1.555569])
  
  coef_50_1_et_bre = np.array([-0.6492871])
  
  se_50_1_et_bre = np.array([0.2542493])
  
  time_50_1_et_bre = np.array([0])
  
  hazard_50_1_et_bre = np.array([0])
  
  coef_50_1_st_bre = np.array([-0.7051135])
  
  se_50_1_st_bre = np.array([0.2852093])
  
  time_50_1_st_bre = np.array([0])
  
  hazard_50_1_st_bre = np.array([0])
  
  coef_50_1_et_st_bre = np.array([-0.8672546])
  
  se_50_1_et_st_bre = np.array([0.3443235])
  
  time_50_1_et_st_bre = np.array([0])
  
  hazard_50_1_et_st_bre = np.array([0])
  
  coef_50_1_efr = np.array([-0.7119322])
  
  se_50_1_efr = np.array([0.2533563])
  
  time_50_1_efr = np.array([0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1,1.1,1.2,1.5,1.6,1.7,1.8,1.9,2.4,2.8])
  
  hazard_50_1_efr = np.array([0,0.04773902,0.08238731,0.2022993,0.2327053,0.2736316,0.3215519,0.3787123,0.3787123,0.526184,0.6323073,0.7627338,0.7627338,0.9288858,0.9288858,0.9288858,1.206835,1.556054,1.556054])
  
  coef_50_1_et_efr = np.array([-0.7103063])
  
  se_50_1_et_efr = np.array([0.2598129])
  
  time_50_1_et_efr = np.array([0])
  
  hazard_50_1_et_efr = np.array([0])
  
  coef_50_1_st_efr = np.array([-0.7417904])
  
  se_50_1_st_efr = np.array([0.2846437])
  
  time_50_1_st_efr = np.array([0])
  
  hazard_50_1_st_efr = np.array([0])
  
  coef_50_1_et_st_efr = np.array([-0.9276112])
  
  se_50_1_et_st_efr = np.array([0.3462638])
  
  time_50_1_et_st_efr = np.array([0])
  
  hazard_50_1_et_st_efr = np.array([0])
  
  coef_50_2_bre = np.array([-0.5935189,0.5035724])
  
  se_50_2_bre = np.array([0.2172841,0.2399933])
  
  time_50_2_bre = np.array([0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1,1.1,1.2,1.3,1.4,1.5,1.9,2.7,2.9])
  
  hazard_50_2_bre = np.array([0.02695812,0.09162381,0.1309537,0.1768423,0.2033353,0.2033353,0.3083449,0.3547287,0.4076453,0.4761318,0.5579718,0.7610905,0.918962,0.918962,1.136173,1.605757,2.457676,2.457676])
  
  coef_50_2_et_bre = np.array([-0.4001465,0.4415933])
  
  se_50_2_et_bre = np.array([0.1992302,0.2525949])
  
  time_50_2_et_bre = np.array([0])
  
  hazard_50_2_et_bre = np.array([0])
  
  coef_50_2_st_bre = np.array([-0.6574891,0.4416079])
  
  se_50_2_st_bre = np.array([0.2753398,0.269458])
  
  time_50_2_st_bre = np.array([0])
  
  hazard_50_2_st_bre = np.array([0])
  
  coef_50_2_et_st_bre = np.array([-0.3607069,0.2731982])
  
  se_50_2_et_st_bre = np.array([0.255415,0.306942])
  
  time_50_2_et_st_bre = np.array([0])
  
  hazard_50_2_et_st_bre = np.array([0])
  
  coef_50_2_efr = np.array([-0.6107485,0.5309737])
  
  se_50_2_efr = np.array([0.2177713,0.2440535])
  
  time_50_2_efr = np.array([0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1,1.1,1.2,1.3,1.4,1.5,1.9,2.7,2.9])
  
  hazard_50_2_efr = np.array([0.02610571,0.08933637,0.1279094,0.1731699,0.19933,0.19933,0.303598,0.3497025,0.4023939,0.4706978,0.5519237,0.7545023,0.9129989,0.9129989,1.13186,1.60574,2.472615,2.472615])
  
  coef_50_2_et_efr = np.array([-0.4092002,0.4871344])
  
  se_50_2_et_efr = np.array([0.1968905,0.2608527])
  
  time_50_2_et_efr = np.array([0])
  
  hazard_50_2_et_efr = np.array([0])
  
  coef_50_2_st_efr = np.array([-0.6631286,0.4663285])
  
  se_50_2_st_efr = np.array([0.2748224,0.273603])
  
  time_50_2_st_efr = np.array([0])
  
  hazard_50_2_st_efr = np.array([0])
  
  coef_50_2_et_st_efr = np.array([-0.3656059,0.2943912])
  
  se_50_2_et_st_efr = np.array([0.2540752,0.3124632])
  
  time_50_2_et_st_efr = np.array([0])
  
  hazard_50_2_et_st_efr = np.array([0])
  
  coef_100_5_bre = np.array([-0.529776,-0.2916374,-0.1205425,0.3493476,0.6034305])
  
  se_100_5_bre = np.array([0.1789305,0.1482505,0.1347422,0.1528205,0.1647927])
  
  time_100_5_bre = np.array([0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1,1.1,1.2,1.3,1.4,1.5,1.6,1.7,1.8,1.9,2,2.1,2.5,2.8,3.2,3.3])
  
  hazard_100_5_bre = np.array([0.02558588,0.05608812,0.1087773,0.1451098,0.1896703,0.2235791,0.3127521,0.3355107,0.439452,0.504983,0.5431706,0.5841462,0.5841462,0.5841462,0.6916466,0.7540191,0.8298704,1.027876,1.170335,1.379306,1.648758,1.943177,1.943177,1.943177,4.727101])
  
  coef_100_5_et_bre = np.array([-0.4000784,-0.1790941,-0.1378969,0.3288529,0.533246])
  
  se_100_5_et_bre = np.array([0.1745655,0.1513545,0.1393968,0.1487803,0.1686992])
  
  time_100_5_et_bre = np.array([0])
  
  hazard_100_5_et_bre = np.array([0])
  
  coef_100_5_st_bre = np.array([-0.53019,-0.3225739,-0.1241568,0.3246598,0.6196859])
  
  se_100_5_st_bre = np.array([0.1954581,0.1602811,0.1470644,0.17121,0.1784115])
  
  time_100_5_st_bre = np.array([0])
  
  hazard_100_5_st_bre = np.array([0])
  
  coef_100_5_et_st_bre = np.array([-0.3977171,-0.2166136,-0.1387623,0.3251726,0.5664705])
  
  se_100_5_et_st_bre = np.array([0.1951054,0.1707925,0.1501968,0.1699932,0.1843428])
  
  time_100_5_et_st_bre = np.array([0])
  
  hazard_100_5_et_st_bre = np.array([0])
  
  coef_100_5_efr = np.array([-0.5641909,-0.3233021,-0.1234858,0.3712328,0.6421963])
  
  se_100_5_efr = np.array([0.1804027,0.1496253,0.1338531,0.1529832,0.1670848])
  
  time_100_5_efr = np.array([0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1,1.1,1.2,1.3,1.4,1.5,1.6,1.7,1.8,1.9,2,2.1,2.5,2.8,3.2,3.3])
  
  hazard_100_5_efr = np.array([0.02393412,0.05276399,0.1028432,0.1383859,0.1823461,0.2158107,0.3037825,0.3264864,0.4306648,0.4964367,0.5348595,0.5760305,0.5760305,0.5760305,0.6842238,0.7468135,0.8228841,1.023195,1.166635,1.379361,1.652898,1.950119,1.950119,1.950119,4.910635])
  
  coef_100_5_et_efr = np.array([-0.4338666,-0.2140139,-0.1397387,0.3535993,0.5768645])
  
  se_100_5_et_efr = np.array([0.1756485,0.1527244,0.138298,0.1488427,0.1716654])
  
  time_100_5_et_efr = np.array([0])
  
  hazard_100_5_et_efr = np.array([0])
  
  coef_100_5_st_efr = np.array([-0.5530876,-0.3331652,-0.128381,0.3503472,0.6397813])
  
  se_100_5_st_efr = np.array([0.1969338,0.1614976,0.1464088,0.171299,0.1800787])
  
  time_100_5_st_efr = np.array([0])
  
  hazard_100_5_st_efr = np.array([0])
  
  coef_100_5_et_st_efr = np.array([-0.421153,-0.2350069,-0.1433638,0.3538863,0.5934568])
  
  se_100_5_et_st_efr = np.array([0.1961729,0.1724719,0.1492979,0.170464,0.1861849])
  
  time_100_5_et_st_efr = np.array([0])
  
  hazard_100_5_et_st_efr = np.array([0])
  
  coef_1000_10_bre = np.array([-0.4699279,-0.464557,-0.308411,-0.2158298,-0.09048563,0.09359662,0.112588,0.3343705,0.3480601,0.5634985])
  
  se_1000_10_bre = np.array([0.04722914,0.04785291,0.04503528,0.04586872,0.04429793,0.0446141,0.04139944,0.04464292,0.04559903,0.04864393])
  
  time_1000_10_bre = np.array([0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1,1.1,1.2,1.3,1.4,1.5,1.6,1.7,1.8,1.9,2,2.1,2.2,2.3,2.4,2.5,2.6,2.7,2.8,2.9,3,3.1,3.2,3.3,3.4,3.5,3.6,3.7,3.8,3.9,4,4.1,4.2,4.3,4.4,4.6,4.8,4.9,5,5.1,5.2,5.7,5.8,5.9,6.9])
  
  hazard_1000_10_bre = np.array([0.01610374,0.04853538,0.08984849,0.1311329,0.168397,0.2230488,0.2755388,0.3312606,0.3668702,0.4146558,0.477935,0.5290705,0.5831775,0.6503129,0.7113068,0.7830385,0.8361717,0.8910061,0.9615944,1.024011,1.113399,1.165349,1.239827,1.352902,1.409548,1.53197,1.601843,1.682158,1.714907,1.751564,1.790898,1.790898,1.83393,1.83393,1.936055,1.992303,2.050778,2.118776,2.263056,2.504999,2.739343,2.895514,3.090349,3.090349,3.391772,3.728142,4.152769,4.152769,4.152769,4.725957,4.725957,5.69653,5.69653,5.69653])
  
  coef_1000_10_et_bre = np.array([-0.410889,-0.3929442,-0.2975845,-0.1851533,-0.0918359,0.1011997,0.106735,0.2899179,0.3220672,0.5069589])
  
  se_1000_10_et_bre = np.array([0.04696754,0.04732169,0.04537707,0.04605371,0.04365232,0.04450021,0.04252475,0.04482007,0.04562374,0.04859727])
  
  time_1000_10_et_bre = np.array([0])
  
  hazard_1000_10_et_bre = np.array([0])
  
  coef_1000_10_st_bre = np.array([-0.471015,-0.4766859,-0.3070839,-0.2091938,-0.09190845,0.0964942,0.1138269,0.3307131,0.3543551,0.562492])
  
  se_1000_10_st_bre = np.array([0.04814778,0.04841938,0.04572291,0.04641227,0.04502525,0.04517603,0.04203737,0.04524356,0.04635037,0.04920866])
  
  time_1000_10_st_bre = np.array([0])
  
  hazard_1000_10_st_bre = np.array([0])
  
  coef_1000_10_et_st_bre = np.array([-0.4165849,-0.4073504,-0.2980959,-0.1765194,-0.09152798,0.1013213,0.1009838,0.2859668,0.3247608,0.5044448])
  
  se_1000_10_et_st_bre = np.array([0.04809818,0.04809499,0.0460829,0.04679922,0.0445294,0.04514045,0.04339298,0.04580591,0.04652447,0.04920744])
  
  time_1000_10_et_st_bre = np.array([0])
  
  hazard_1000_10_et_st_bre = np.array([0])
  
  coef_1000_10_efr = np.array([-0.4894399,-0.4839746,-0.3227769,-0.2261293,-0.09318482,0.09767154,0.1173205,0.3493732,0.3640146,0.5879749])
  
  se_1000_10_efr = np.array([0.0474181,0.04811855,0.04507655,0.04603044,0.04440409,0.04478202,0.04136728,0.04473343,0.045768,0.04891375])
  
  time_1000_10_efr = np.array([0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1,1.1,1.2,1.3,1.4,1.5,1.6,1.7,1.8,1.9,2,2.1,2.2,2.3,2.4,2.5,2.6,2.7,2.8,2.9,3,3.1,3.2,3.3,3.4,3.5,3.6,3.7,3.8,3.9,4,4.1,4.2,4.3,4.4,4.6,4.8,4.9,5,5.1,5.2,5.7,5.8,5.9,6.9])
  
  hazard_1000_10_efr = np.array([0.01549698,0.04680035,0.08682564,0.1269429,0.1632388,0.2167291,0.2682311,0.3231316,0.3582936,0.4054892,0.4681098,0.5188697,0.5727059,0.639571,0.7003012,0.7718979,0.825053,0.880063,0.950935,1.013828,1.103903,1.156314,1.231707,1.346235,1.40359,1.527475,1.598231,1.6795,1.712779,1.750227,1.790455,1.790455,1.834455,1.834455,1.938997,1.996804,2.056859,2.126816,2.275217,2.524027,2.76669,2.929268,3.13247,3.13247,3.448515,3.80143,4.249649,4.249649,4.249649,4.851365,4.851365,5.877307,5.877307,5.877307])
  
  coef_1000_10_et_efr = np.array([-0.4373066,-0.4131901,-0.3177637,-0.1978493,-0.09679451,0.1092037,0.1136069,0.3088907,0.3442007,0.5394121])
  
  se_1000_10_et_efr = np.array([0.04716041,0.04755342,0.04546713,0.04627802,0.04376583,0.04474868,0.04259991,0.04491564,0.04589027,0.04890847])
  
  time_1000_10_et_efr = np.array([0])
  
  hazard_1000_10_et_efr = np.array([0])
  
  coef_1000_10_st_efr = np.array([-0.4911117,-0.4960756,-0.3226152,-0.220949,-0.09478141,0.1015735,0.1195524,0.3446977,0.3695904,0.5878576])
  
  se_1000_10_st_efr = np.array([0.04833676,0.04868554,0.04578407,0.04661755,0.04518267,0.04537135,0.04202183,0.04531266,0.0464931,0.04949831])
  
  time_1000_10_st_efr = np.array([0])
  
  hazard_1000_10_st_efr = np.array([0])
  
  coef_1000_10_et_st_efr = np.array([-0.444355,-0.4283278,-0.3198815,-0.1901781,-0.09727039,0.1106191,0.1092104,0.3034778,0.3451699,0.5382381])
  
  se_1000_10_et_st_efr = np.array([0.04830664,0.04833619,0.04617371,0.04706401,0.04472699,0.0454208,0.04350539,0.04588588,0.04675675,0.04950987])
  
  time_1000_10_et_st_efr = np.array([0])
  
  hazard_1000_10_et_st_efr = np.array([0])
  