F# Function Statistics.EmpiricalInvCDF() equivalent in Python - python

I am converting a legacy piece of code from F# to python where the Statistics.EmpiricalInvCDF() method is being used. I am having trouble replicating this in python. Here is the documentation for the F# method (https://numerics.mathdotnet.com/DescriptiveStatistics.html#Empirical-Distribution-Functions)
The usecase is as follows:
given list:
[0.03230313262534688, 0.002645726276523705, 0.01159854312582566, 0.0, 0.13521878503682389, 0.04221995336868746, 0.08088206039629423, 0.057687700498838576, -0.008598786901065152, 0.042606344490953324, 0.011082772426956695, 0.07893807796386074, 0.04300878546372856, 0.05772152131808002, 0.6630361642770063, -0.6198730776408921, 0.0481094085159463, 0.1848513533230482, -0.05577620426994041, 0.1029537374891358,
0.10357978356369431, 0.12254238149057754, 0.0911269446223479, 0.01932811172408364, 0.06932530865454888, -0.004110301433067377, 0.05802985444298547, 0.014621095574236822, -0.4615159515258704, -0.0012407645052369416, 0.03286149662750687, 0.012825027001045838, 0.051867696499045116, 0.020623116252775197, 0.02446469695619617, -0.015974966074344284, 0.03939323769642985, 0.5652557818438808, 0.02508486624629729,
0.026311401768164447, 0.03827223792998286, 0.03867025726520796, 0.04285045108159155, 0.018009295529766094, -0.18204238698737224, 0.03890486510159634, 0.03852683399121745, 0.17287557452039864, 0.022755267696871983, -0.008620053014231119, -0.21249945562739217, 0.009220137475520283, 0.030352843001682523, 0.0, -0.05843788432673845, 0.04484955168754013, 0.006260357642714611, 0.026573389912263958, 0.040894080492335595, 0.031156642520786194, -0.0035593767458410395, 0.056502452728941756, -0.011567687131086034, 0.019824618387320487, 0.01763256480698061, 0.03770396875280739, -0.2169605977183593, -0.04877029469675406, 0.03168933055587481, 0.032420547672104144, 0.03079075024741443, 0.1653012750667534, -0.006804319891639613, 0.008984055440418937, 0.0199408085772636, 0.020428069981471644, 0.04359405356382086, 0.015103293654879058, -0.023375468526050273, -0.00933222120934053, 0.02321095571832252, 0.049559948387087396, -0.0004151086139435974, -0.15655953473681927, 0.004304419664092396, 0.006671355318000576, -0.02449338530147379, 0.04478202409774672, -0.2629400383137885, 0.1954843319620695, 0.03686504887911304, -0.2684481840299527, -0.019138442894774923, 0.00621584280949767, 0.06460718118510203, -0.20020564142388894, -0.035691664770196155, 0.2577033446666525, 0.032908740476793354, 0.06685955822175448, 0.04128016090027627, 0.03690468165039895, -0.02095094349421612, 0.018320834968663716, 0.07261850788704553, 0.0005996187229608711, -0.005294975699828886, 0.07868580727177386, -0.14683416493836995, 0.06367912640840617, 0.08829914546925684, 0.01925506982025991, -0.01845931029495414, 0.016455918131923757, -0.10730938773869152, 0.06959721883549888, -0.24295927354223984, 0.038191715674122884, 0.2489265941534765, 0.25367195299667944, -0.24074284601722426, 0.04790264523942938, -0.2898539131116597, 0.46076135068477636, 0.16892117107930602, 0.032233962259842104, 0.10991351750112961, 0.0947295624169787, -0.31877746856009903, 0.08839333002165493, 0.05967730549716063, -0.012206209535519416, 0.06258238056246512, 0.11311371649813944, -0.18662709898356242, 0.042107091229495866, 0.025462668074737474, 0.07075281119631686, -0.026548329376822558, 0.051226289479552996, -0.23413933396499512, 0.1898393060388359, 0.028314404612467214, 0.02164091611437012, 0.055312288886916146, -0.30925915849671126, 0.1197828763807938, 0.06086095889478898, 0.03277084074489599, 0.034771448102962066, -0.006071302642726877, 0.015918735667438268, 0.10892314394119058, 0.10288895121802186, -0.24608601414818557, 0.04226840237554879, 0.1279809775740312, -0.019354369564949375, 0.0]
emp_inv = Statistics.EmpiricalInvCDF(list, 0.01)
expected output:
emp_inv = -0.52626813800271377

Related

Applying a non aggregating function to a groupby pandas object

I have a dataframe (called cep) with two indexes (Cloud and Mode) and data columns. It looks like this :
The data columns are fitted to a linear function and I'm extracting the residuals to the fit in this way :
import pandas as pd
from scipy.optimize import least_squares
args = [-1, 15] # initial guess for the fit
def residuals(args, x, y):
"""
Residual with respect to a linear function
args : list with 2 arguments
x : array
y : array
"""
return args[0] * x + args[1] - y
def residual_function(df):
"""
Returns the array of the residuals
"""
return least_squares(residuals, args, loss='soft_l1', f_scale=0.5, args=(df.logP1, df.W)).fun
cep.groupby(['Cloud', 'Mode']).apply(lambda grp : residual_function(grp))
This gives the expected result :
Now is my issue : I'd like to insert those residual values each in their respective row in the original dataframe to compare them with other columns.
I checked that the returned arrays are of the right length to be inserted but so far I have no idea how to proceed.
I tried to follow tutorials, but the difference with the textbook problem here is that the function I applied does not aggregate the data. Do you have some hints?
Small sample data here :
Mode;Cloud;W;logP1
F;LMC;14,525;0,4939
F;LMC;13,4954;0,7491
F;LMC;14,5421;0,4249
F;LMC;12,033;1,0215
F;LMC;14,3422;0,5655
F;LMC;13,937;0,6072
F;LMC;13,53;0,737
F;LMC;15,2106;0,2309
F;LMC;14,0813;0,5721
F;LMC;14,5128;0,41
F;LMC;14,1059;0,5469
F;LMC;15,6032;0,1014
F;LMC;13,1088;0,8562
F;LMC;12,3528;1,0513
F;LMC;13,1629;0,8416
F;LMC;14,3114;0,4867
F;LMC;14,4013;0,498
F;LMC;13,5057;0,7131
F;LMC;14,3626;0,464
F;LMC;14,5973;0,4111
F;LMC;13,9286;0,6059
F;LMC;15,066;0,2711
F;LMC;12,7364;0,9466
F;LMC;13,3753;0,7442
F;LMC;13,9748;0,5854
F;LMC;12,8836;0,8946
F;LMC;14,4912;0,4206
F;LMC;14,4131;0,4567
F;LMC;12,183;1,1382
F;LMC;14,5492;0,3686
F;LMC;14,1482;0,5339
F;LMC;13,7062;0,7116
F;LMC;13,0731;0,8682
F;LMC;11,5609;1,353
F;LMC;13,9453;0,5551
F;LMC;14,0072;0,6715
F;LMC;13,9838;0,6021
F;LMC;13,9974;0,5562
F;LMC;14,3898;0,5069
F;LMC;14,4497;0,4433
F;LMC;14,3524;0,5064
F;LMC;12,9604;0,9134
F;LMC;12,9757;0,8548
F;LMC;14,2783;0,4927
F;LMC;13,7148;0,6758
F;LMC;14,2348;0,5142
F;LMC;12,6793;0,9415
F;LMC;14,2241;0,5738
F;LMC;14,472;0,4554
F;LMC;15,1508;0,2076
F;LMC;12,5414;1,0159
F;LMC;14,2102;0,5334
F;LMC;15,6086;0,1116
F;LMC;13,2986;0,8381
F;LMC;13,0136;0,8864
F;LMC;13,9774;0,585
F;LMC;14,4256;0,533
F;LMC;14,3582;0,4578
F;LMC;14,3258;0,4859
F;LMC;14,6646;0,3757
F;LMC;12,733;0,9901
F;LMC;14,6296;0,3839
F;LMC;14,054;0,5766
F;LMC;14,3194;0,4884
F;LMC;12,6602;0,9715
F;LMC;13,5909;0,5675
F;LMC;13,9268;0,6196
F;LMC;12,5813;0,9935
F;LMC;13,0824;0,8591
F;LMC;13,5097;0,7375
F;LMC;13,1938;0,5053
F;LMC;14,7357;0,3253
F;LMC;14,0624;0,6009
F;LMC;14,1528;0,533
F;LMC;14,6709;0,4007
F;LMC;14,2378;0,4875
F;LMC;11,951;1,2004
F;LMC;14,4555;0,4777
F;LMC;14,4001;0,4404
F;LMC;13,7707;0,6311
F;LMC;14,578;0,4175
F;LMC;15,8662;0,0159
F;LMC;14,055;0,5687
F;LMC;13,6238;0,7307
F;LMC;15,2572;0,2171
F;LMC;13,4022;0,7723
F;LMC;14,2392;0,5256
F;LMC;14,2505;0,4977
F;LMC;14,7174;0,3614
F;LMC;14,487;0,418
F;LMC;14,9309;0,3086
F;LMC;13,8352;0,6334
F;LMC;14,5598;0,41
F;LMC;14,5614;0,422
F;LMC;14,1486;0,5149
F;LMC;14,0304;0,4945
F;LMC;13,5781;0,6801
F;LMC;14,79;0,3218
F;LMC;12,376;1,0908
F;LMC;15,3215;0,2176
F;LMC;14,7264;0,3845
F;LMC;14,6276;0,4057
F;LMC;14,1712;0,5313
F;LMC;14,4153;0,483
F;LMC;12,905;0,9356
F;LMC;14,442;0,4309
F;LMC;12,8702;0,9159
F;LMC;12,8963;0,5775
F;LMC;13,8304;0,6467
F;LMC;14,4665;0,4165
F;LMC;13,0756;0,5794
F;LMC;13,841;0,6593
F;LMC;14,0924;0,5671
F;LMC;13,7546;0,6778
F;LMC;14,2828;0,5181
F;LMC;14,2424;0,5082
F;LMC;14,659;0,3989
F;LMC;13,7528;0,6768
F;LMC;13,7743;0,6368
F;LMC;13,2894;0,791
F;LMC;14,7512;0,3187
F;LMC;14,5241;0,4452
F;LMC;14,301;0,5121
F;LMC;13,334;0,7945
F;LMC;13,5052;0,7012
F;LMC;14,3664;0,4549
F;LMC;14,8614;0,3278
F;LMC;13,8612;0,582
F;LMC;14,2668;0,5158
F;LMC;14,3937;0,4457
F;LMC;14,0226;0,582
F;LMC;14,387;0,5565
F;LMC;14,3198;0,4362
F;LMC;14,4404;0,4701
F;LMC;14,2774;0,4939
F;LMC;13,7678;0,6557
F;LMC;14,3212;0,4882
F;LMC;14,6453;0,3696
F;LMC;13,9064;0,6084
F;LMC;13,5167;0,7581
F;LMC;14,1692;0,5134
F;LMC;14,6714;0,4136
F;LMC;14,4332;0,4507
F;LMC;14,705;0,3631
F;LMC;13,6728;0,496
F;LMC;15,358;0,1651
F;LMC;13,7592;0,6278
F;LMC;14,0626;0,5754
F;LMC;13,1127;0,8692
F;LMC;14,2108;0,498
F;LMC;14,4519;0,4449
F;LMC;14,0041;0,5666
F;LMC;14,157;0,5392
F;LMC;14,254;0,5245
F;LMC;15,4844;0,1838
F;LMC;14,0845;0,5626
F;LMC;13,0861;0,838
F;LMC;13,3144;0,831
F;LMC;14,2535;0,4911
F;LMC;14,0256;0,5723
F;LMC;14,3246;0,4938
F;LMC;14,4412;0,4136
F;LMC;14,1043;0,518
F;LMC;14,7512;0,3772
F;LMC;14,3982;0,5039
F;LMC;14,2701;0,5042
F;LMC;13,9166;0,5941
F;LMC;13,0324;0,837
F;LMC;13,4839;0,6331
F;LMC;13,4491;0,7443
F;LMC;14,4702;0,458
F;LMC;14,4814;0,4595
F;LMC;14,3008;0,4575
F;LMC;14,922;0,3313
F;LMC;14,6542;0,4263
F;LMC;14,5007;0,4838
F;LMC;14,4335;0,4829
F;LMC;14,4737;0,4586
F;LMC;14,2537;0,5442
F;LMC;14,038;0,5473
F;LMC;14,1413;0,5523
F;LMC;14,669;0,3505
F;LMC;12,3572;1,1033
F;LMC;13,868;0,6416
F;LMC;13,4292;0,816
F;LMC;11,6771;1,3442
F;LMC;14,5086;0,4654
F;LMC;14,3588;0,4807
F;LMC;14,6915;0,3674
F;LMC;15,6488;0,0647
F;LMC;12,4187;0,9791
F;LMC;14,1555;0,5235
F;LMC;14,5765;0,4281
F;LMC;14,3579;0,4596
F;LMC;13,0932;0,7957
F;LMC;14,4552;0,4216
F;LMC;13,2221;0,8505
F;LMC;14,4465;0,4466
F;LMC;14,2439;0,5032
F;LMC;14,9606;0,6308
F;LMC;14,4774;0,4424
F;LMC;14,1875;0,5361
F;LMC;13,3982;0,7644
F;LMC;13,0973;0,8595
F;LMC;13,8264;0,6334
F;LMC;13,9296;0,6164
F;LMC;14,5778;0,4033
F;LMC;13,579;0,726
F;LMC;14,0054;0,5779
F;LMC;14,1219;0,5451
F;LMC;14,3512;0,4808
F;LMC;14,5058;0,4199
F;LMC;14,598;0,4201
F;LMC;14,9516;0,2498
F;LMC;13,9944;0,6075
F;LMC;13,9462;0,557
F;LMC;14,2576;0,5148
F;LMC;14,9814;0,2929
F;LMC;14,3851;0,4573
F;LMC;14,3474;0,4606
F;LMC;14,4929;0,3882
F;LMC;14,5201;0,4234
F;LMC;13,7677;0,6548
F;LMC;14,3146;0,4695
F;LMC;14,2846;0,507
F;LMC;14,0967;0,5525
F;LMC;14,7976;0,3546
F;LMC;13,7497;0,6362
F;LMC;14,4647;0,4363
F;LMC;14,1924;0,5293
F;LMC;14,588;0,4089
F;LMC;13,4896;0,7329
F;LMC;14,695;0,3737
F;LMC;14,2672;0,4857
F;LMC;14,0784;0,5848
F;LMC;13,879;0,5743
F;LMC;14,2214;0,4988
F;LMC;12,922;0,8487
F;LMC;14,189;0,5238
F;LMC;13,9938;0,5713
F;LMC;14,379;0,4771
F;LMC;11,2308;1,3564
F;LMC;14,4472;0,4205
F;LMC;14,3739;0,4699
F;LMC;14,393;0,4416
F;LMC;13,9108;0,5927
F;LMC;14,0298;0,6058
F;LMC;15,1538;0,1961
F;LMC;13,0393;0,8731
F;LMC;13,7144;0,645
F;LMC;14,2682;0,487
F;LMC;14,3506;0,4927
F;LMC;14,0472;0,5619
F;LMC;15,1418;0,2506
F;LMC;13,1227;0,5998
F;LMC;13,5646;0,7193
F;LMC;14,5872;0,4357
F;LMC;14,2636;0,5007
F;LMC;13,9564;0,5599
F;LMC;12,8576;0,946
F;LMC;12,3042;1,1454
F;LMC;11,8416;1,3675
F;LMC;13,5498;0,7219
F;LMC;12,1976;1,1581
F;LMC;13,8632;0,6202
F;LMC;14,2952;0,4807
F;LMC;14,4349;0,4437
F;LMC;14,2392;0,5445
F;LMC;13,7248;0,7213
F;LMC;14,3395;0,5117
F;LMC;15,3588;0,2253
F;LMC;12,8509;0,9229
F;LMC;15,5192;0,1453
F;LMC;14,2072;0,4975
F;LMC;14,3524;0,4945
F;LMC;14,5152;0,4488
F;LMC;14,5106;0,4558
F;LMC;14,5759;0,3786
F;LMC;11,196;1,2374
F;LMC;14,3736;0,4788
F;LMC;14,1726;0,528
F;LMC;11,7899;1,1995
F;LMC;12,1062;1,1823
F;LMC;13,7113;0,6714
F;LMC;14,3512;0,4815
F;LMC;13,1016;0,8181
F;LMC;14,4968;0,562
F;LMC;12,4557;1,0671
F;LMC;14,0573;0,551
F;LMC;14,5916;0,4066
F;LMC;14,3214;0,488
F;LMC;13,5498;0,4885
F;LMC;14,4679;0,4273
F;LMC;14,2426;0,4816
F;LMC;13,5759;0,7052
F;LMC;14,0081;0,5769
F;LMC;14,0828;0,5379
F;LMC;12,4168;0,7578
F;LMC;14,1624;0,5052
F;LMC;13,8029;0,6621
F;LMC;14,1944;0,5145
F;LMC;13,7944;0,6184
F;LMC;15,0234;0,3158
F;LMC;13,0961;0,8282
F;LMC;13,976;0,5889
F;LMC;14,3236;0,4847
F;LMC;14,2618;0,4691
F;LMC;13,4528;0,7349
F;LMC;14,2846;0,507
F;LMC;14,4115;0,446
F;LMC;14,2199;0,5336
F;LMC;14,456;0,4423
F;LMC;14,2938;0,488
F;LMC;14,4109;0,4606
F;LMC;14,2599;0,497
F;LMC;13,9034;0,6384
F;LMC;13,6126;0,7075
F;LMC;14,5036;0,4218
F;LMC;14,0065;0,5741
F;LMC;14,8622;0,3404
F;LMC;14,635;0,3683
F;LMC;14,222;0,5454
F;LMC;14,1501;0,5548
F;LMC;14,0822;0,5705
F;LMC;13,5036;0,7267
F;LMC;14,5528;0,4161
F;LMC;14,3332;0,4614
F;LMC;14,1511;0,5471
F;LMC;14,6113;0,3934
F;LMC;14,2998;0,5031
F;LMC;14,1807;0,5352
F;LMC;13,5114;0,7013
F;LMC;12,2096;1,1344
F;LMC;14,3799;0,4304
F;LMC;12,4526;1,1135
F;LMC;14,5042;0,447
F;LMC;13,4594;0,7336
F;LMC;13,2066;0,8423
F;LMC;14,3734;0,4711
F;LMC;13,945;0,5953
F;LMC;12,9938;0,8969
F;LMC;13,4993;0,7034
F;LMC;13,9466;0,5678
F;LMC;14,1772;0,5077
F;LMC;13,5566;0,6949
F;LMC;14,021;0,5811
F;LMC;14,0264;0,646
F;LMC;12,0242;1,1666
F;LMC;14,3106;0,5027
F;LMC;14,9838;0,3164
F;LMC;14,1718;0,5266
F;LMC;14,2606;0,489
F;LMC;12,6479;1,0206
F;LMC;12,9768;0,8684
F;LMC;14,0837;0,5785
F;LMC;13,7944;0,6609
F;LMC;13,532;0,6911
F;LMC;14,835;0,3375
F;LMC;13,7378;0,6941
F;LMC;14,3618;0,4658
F;LMC;12,4782;1,0176
F;LMC;14,2216;0,4981
F;LMC;14,3958;0,4917
F;LMC;11,3796;1,3161
F;LMC;13,8073;0,6301
F;LMC;14,414;0,4601
F;LMC;12,4266;1,086
F;LMC;14,7974;0,3547
F;LMC;14,3369;0,5189
F;LMC;14,3202;0,4874
F;LMC;14,4614;0,4664
F;LMC;13,8344;0,6339
F;LMC;14,0452;0,5896
F;LMC;11,9134;1,161
F;LMC;14,2492;0,4891
F;LMC;14,1338;0,5139
F;LMC;14,439;0,4476
F;LMC;14,1446;0,5322
F;LMC;14,102;0,549
F;LMC;14,5043;0,4421
F;LMC;14,388;0,4511
F;LMC;12,3812;1,0331
F;LMC;14,5086;0,4294
F;LMC;13,6822;0,671
F;LMC;12,3012;1,0862
F;LMC;14,0848;0,534
F;LMC;14,3381;0,4886
F;LMC;14,5544;0,3908
F;LMC;14,216;0,5226
F;LMC;14,5028;0,4323
F;LMC;12,7769;0,9244
F;LMC;13,6262;0,6984
F;LMC;14,5276;0,4107
F;LMC;13,921;0,5835
F;LMC;14,6279;0,396
F;LMC;14,6304;0,3796
F;LMC;14,2079;0,4722
F;LMC;12,4538;1,0356
F;LMC;14,2662;0,4876
F;LMC;13,8493;0,6217
F;LMC;12,9806;0,8385
F;LMC;14,3148;0,4768
F;LMC;14,2225;0,49
F;LMC;14,3932;0,4084
F;LMC;13,6934;0,5829
F;LMC;14,1702;0,5297
F;LMC;11,7812;1,2435
F;LMC;14,2866;0,4778
F;LMC;15,2824;0,1739
F;LMC;14,451;0,4485
F;LMC;14,4842;0,4222
F;LMC;14,3422;0,449
F;LMC;14,4408;0,4435
F;LMC;12,527;1,0298
F;LMC;12,3746;1,1016
F;LMC;11,4802;1,3276
F;LMC;14,47;0,4643
F;LMC;14,1469;0,5183
F;SMC;14,423;0,4796
F;SMC;15,5626;0,2344
F;SMC;15,6889;0,236
F;SMC;15,3574;0,2926
F;SMC;15,8049;0,1015
F;SMC;12,9034;0,9993
F;SMC;14,0039;0,6867
F;SMC;15,9834;0,1812
F;SMC;15,7707;0,2028
F;SMC;15,777;0,1735
F;SMC;14,7121;0,4973
F;SMC;13,8691;0,7188
F;SMC;14,889;0,4123
F;SMC;14,5322;0,6233
F;SMC;15,6791;0,331
F;SMC;13,9406;0,7262
F;SMC;13,728;0,8514
F;SMC;15,1952;0,3583
F;SMC;16,0921;0,1397
F;SMC;15,6162;0,1532
F;SMC;15,786;0,2563
F;SMC;16,0774;0,1197
F;SMC;14,4397;0,599
F;SMC;15,8693;0,2072
F;SMC;15,6668;0,2452
F;SMC;15,1954;0,3509
F;SMC;14,1387;0,669
F;SMC;15,6928;0,2125
F;SMC;14,6266;0,5017
F;SMC;15,9557;0,1772
F;SMC;15,607;0,2501
F;SMC;15,9632;0,1629
F;SMC;15,7932;0,2325
F;SMC;15,7108;0,1534
F;SMC;13,037;0,9898
F;SMC;15,3998;0,2915
F;SMC;15,1724;0,3675
F;SMC;13,7222;0,7848
F;SMC;14,8296;0,5222
F;SMC;15,704;0,2407
F;SMC;13,5231;0,8378
F;SMC;14,4338;0,5303
F;SMC;14,6202;0,4843
F;SMC;16,2836;0,0473
F;SMC;15,6011;0,1758
F;SMC;16,0037;0,1571
F;SMC;13,9062;0,6286
F;SMC;16,0606;0,0557
F;SMC;13,2924;0,8905
F;SMC;15,9942;0,1997
F;SMC;15,7766;0,2395
F;SMC;10,8462;1,6309
F;SMC;15,956;0,1425
F;SMC;13,857;0,7079
F;SMC;15,3619;0,2696
F;SMC;14,0064;0,6903
F;SMC;15,6531;0,2602
F;SMC;14,9001;0,5001
F;SMC;14,3957;0,6156
F;SMC;15,4414;0,3174
F;SMC;15,8321;0,1822
F;SMC;16,3562;0,1385
F;SMC;15,8812;0,1651
F;SMC;15,1404;0,408
F;SMC;13,7978;0,8055
F;SMC;15,9291;0,132
F;SMC;15,0555;0,507
F;SMC;15,5766;0,2596
F;SMC;13,6006;0,8469
F;SMC;16,455;0,0629
F;SMC;15,8762;0,1072
F;SMC;16,2856;0,0768
F;SMC;15,8521;0,2129
F;SMC;15,7685;0,2374
F;SMC;16,1197;0,1043
F;SMC;16,0851;0,2333
F;SMC;15,8126;0,1777
F;SMC;14,3891;0,6065
F;SMC;14,6419;0,5446
F;SMC;15,3942;0,3101
F;SMC;15,5785;0,2494
F;SMC;15,661;0,2227
F;SMC;15,9648;0,1405
F;SMC;12,7911;1,0845
F;SMC;15,9351;0,1575
F;SMC;14,1764;0,6864
F;SMC;15,153;0,3624
F;SMC;15,9336;0,1232
F;SMC;15,0124;0,3796
F;SMC;16,1231;0,106
F;SMC;14,4362;0,5306
F;SMC;13,1883;0,8354
F;SMC;15,8972;0,1757
F;SMC;14,1612;0,7287
F;SMC;15,3792;0,2869
F;SMC;16,421;0,0329
F;SMC;14,833;0,4543
F;SMC;14,3997;0,5912
F;SMC;15,8797;0,1747
F;SMC;16,0337;0,1565
F;SMC;15,7371;0,2251
F;SMC;13,954;0,7293
F;SMC;14,1691;0,6695
F;SMC;15,6208;0,2211
F;SMC;14,3416;0,6492
F;SMC;14,6636;0,5423
F;SMC;16,0386;0,1506
F;SMC;14,6578;0,5604
F;SMC;15,6368;0,24
F;SMC;14,843;0,4738
F;SMC;14,9818;0,4869
F;SMC;12,4251;1,1641
F;SMC;15,0727;0,4671
F;SMC;14,1448;0,5949
F;SMC;15,2148;0,3644
F;SMC;15,9372;0,117
F;SMC;15,4336;0,3018
F;SMC;14,5416;0,557
F;SMC;16,4654;0,0436
F;SMC;14,934;0,5498
F;SMC;14,3896;0,695
F;SMC;15,3896;0,3492
F;SMC;15,8122;0,1602
F;SMC;13,7822;0,704
F;SMC;15,7938;0,1679
F;SMC;15,4049;0,3059
F;SMC;16,0742;0,1187
F;SMC;15,704;0,2036
F;SMC;14,9947;0,3748
F;SMC;15,1374;0,4001
F;SMC;13,2254;0,7136
F;SMC;14,3267;0,577
F;SMC;12,7772;1,0317
F;SMC;15,5302;0,3074
F;SMC;16,12;0,1395
F;SMC;15,9826;0,1873
F;SMC;15,9196;0,2025
F;SMC;15,5396;0,2888
F;SMC;14,0063;0,7543
F;SMC;14,6752;0,542
F;SMC;14,3782;0,6365
F;SMC;15,8015;0,2321
F;SMC;15,4898;0,0235
F;SMC;15,6376;0,2499
F;SMC;15,527;0,2697
F;SMC;15,2883;0,3324
F;SMC;15,1014;0,3996
F;SMC;14,435;0,5827
F;SMC;16,1522;0,0832
F;SMC;13,3787;0,8974
F;SMC;16,6258;0,0226
F;SMC;14,0421;0,8043
F;SMC;15,4764;0,2719
F;SMC;14,1377;0,6069
F;SMC;15,3654;0,3461
F;SMC;16,3063;0,0677
F;SMC;15,5912;0,2227
F;SMC;14,555;0,5143
F;SMC;16,2947;0,0824
F;SMC;15,2208;0,3488
F;SMC;16,8052;-0,0287
F;SMC;15,8592;0,1835
F;SMC;15,6349;0,2632
F;SMC;16,522;0,0581
F;SMC;15,7794;0,3351
F;SMC;16,095;0,1574
F;SMC;16,0564;0,1818
F;SMC;16,4614;0,0897
F;SMC;16,1351;0,1332
F;SMC;14,4711;0,5808
F;SMC;13,8768;0,6795
F;SMC;16,2458;0,1273
F;SMC;16,1994;0,0372
F;SMC;15,3434;0,3072
F;SMC;15,5384;0,2442
F;SMC;14,5322;0,5703
F;SMC;15,7762;0,3507
F;SMC;14,3793;0,5628
F;SMC;15,4777;0,3139
F;SMC;15,9216;0,1764
F;SMC;14,3758;0,5278
F;SMC;15,2363;0,3313
F;SMC;14,3224;0,3258
F;SMC;15,2266;0,3656
F;SMC;15,6305;0,174
F;SMC;14,046;0,7832
F;SMC;14,8704;0,507
F;SMC;16,0267;0,2357
F;SMC;16,0671;0,154
F;SMC;13,8434;0,6901
F;SMC;14,4167;0,5992
F;SMC;15,9808;0,125
F;SMC;16,0696;0,1131
F;SMC;15,166;0,166
F;SMC;14,1023;0,6447
F;SMC;13,9666;0,6979
F;SMC;15,64;0,2577
F;SMC;15,6974;0,2429
F;SMC;15,1257;0,3877
F;SMC;15,186;0,3295
F;SMC;14,87;0,4651
F;SMC;16,0943;0,1807
F;SMC;15,7421;0,1809
F;SMC;14,6085;0,5253
F;SMC;14,6912;0,4777
F;SMC;14,1322;0,71
F;SMC;15,3319;0,2937
F;SMC;14,9283;0,4639
F;SMC;15,3753;0,2732
F;SMC;15,0886;0,3989
F;SMC;15,3778;0,3028
F;SMC;16,4933;0,0274
F;SMC;14,7944;0,4336
F;SMC;13,7806;0,7397
F;SMC;14,1895;0,6325
F;SMC;15,947;0,1084
F;SMC;15,9606;0,1665
F;SMC;15,417;0,0976
F;SMC;15,2905;0,3652
F;SMC;14,7712;0,4453
F;SMC;14,6692;0,5412
F;SMC;16,1936;0,0286
F;SMC;15,6136;0,2097
F;SMC;15,8061;0,078
F;SMC;15,3243;0,3385
F;SMC;15,2366;0,3669
F;SMC;16,1653;0,0573
F;SMC;15,916;0,1591
F;SMC;15,2422;0,3216
F;SMC;12,2583;1,2107
F;SMC;15,6361;0,1766
F;SMC;16,0818;0,1771
F;SMC;15,6966;0,2147
F;SMC;16,193;0,0657
F;SMC;14,8256;0,4574
F;SMC;15,7214;0,2185
F;SMC;15,5803;0,2725
F;SMC;14,7322;0,4754
F;SMC;15,8964;0,1898
F;SMC;14,5428;0,4732
F;SMC;16,1362;0,1396
F;SMC;16,2832;0,0473
F;SMC;15,6508;0,2232
F;SMC;14,725;0,4998
F;SMC;16,1585;0,1106
F;SMC;15,2284;0,3727
F;SMC;15,1728;0,3718
F;SMC;14,5354;0,5431
F;SMC;15,8224;0,1256
F;SMC;15,5462;0,2633
F;SMC;14,942;0,455
F;SMC;16,02;0,1426
F;SMC;15,2292;0,2965
F;SMC;14,6639;0,4402
F;SMC;14,887;0,4365
F;SMC;15,8288;0,1924
F;SMC;14,4903;0,5274
F;SMC;15,9464;0,1638
F;SMC;15,8069;0,1999
F;SMC;14,9924;0,3985
F;SMC;15,6917;0,1355
F;SMC;15,5414;0,1628
F;SMC;15,6168;0,2157
F;SMC;15,8006;0,177
F;SMC;14,9294;0,4732
F;SMC;14,5272;0,599
F;SMC;15,7318;0,2691
F;SMC;14,5181;0,5782
F;SMC;15,8524;0,2074
F;SMC;13,773;0,747
F;SMC;15,7608;0,1586
F;SMC;13,947;0,688
F;SMC;14,9774;0,4224
F;SMC;14,5288;0,4912
F;SMC;12,4944;1,2355
F;SMC;13,8683;0,6944
F;SMC;15,7118;0,186
F;SMC;15,7392;0,2081
F;SMC;12,292;1,1395
F;SMC;14,7918;0,4632
F;SMC;15,4428;0,3367
F;SMC;14,7542;0,4279
F;SMC;15,2914;0,3575
F;SMC;14,7332;0,4836
F;SMC;14,566;0,5553
F;SMC;15,9406;0,1167
F;SMC;15,6304;0,2296
F;SMC;14,0478;0,7063
F;SMC;15,5402;0,2821
F;SMC;15,6019;0,2443
F;SMC;15,6554;0,1979
F;SMC;14,7736;0,1631
F;SMC;16,1684;0,119
F;SMC;14,5113;0,5073
F;SMC;15,5466;0,134
F;SMC;15,1128;0,3919
F;SMC;13,4782;0,8109
F;SMC;15,8534;0,2208
F;SMC;13,1824;0,9072
F;SMC;15,8466;0,1901
1;LMC;13,9452;0,4076
1;LMC;14,3302;0,3149
1;LMC;12,9682;0,6984
1;LMC;15,0586;0,1023
1;LMC;14,328;0,304
1;LMC;15,024;0,0882
1;LMC;14,0594;0,3924
1;LMC;17,2026;-0,5304
1;LMC;14,327;0,3192
1;LMC;13,8748;0,4361
1;LMC;17,155;-0,4783
1;LMC;14,3154;0,3197
1;LMC;14,3376;0,2943
1;LMC;14,462;0,3461
1;LMC;14,139;0,3647
1;LMC;16,764;-0,4451
1;LMC;15,1618;0,1008
1;LMC;14,2229;0,3328
1;LMC;13,8046;0,4946
1;LMC;14,4268;0,2703
1;LMC;15,5032;-0,0368
1;LMC;15,9052;-0,1647
1;LMC;13,908;0,4434
1;LMC;14,3352;0,2986
1;LMC;13,6286;0,5326
1;LMC;13,7934;0,4842
1;LMC;14,3979;0,2817
1;LMC;14,0496;0,4238
1;LMC;14,4368;0,2939
1;LMC;14,3242;0,3164
1;LMC;12,6825;0,7719
1;LMC;13,846;0,4483
1;LMC;14,5746;0,2727
1;LMC;14,5171;0,2641
1;LMC;14,9218;0,1209
1;LMC;14,2248;0,3411
1;LMC;14,3478;0,3109
1;LMC;14,0999;0,357
1;LMC;14,5558;0,2632
1;LMC;13,7602;0,4936
1;LMC;14,5354;0,2775
1;LMC;13,5663;0,5364
1;LMC;17,0694;-0,4754
1;LMC;14,2915;0,3346
1;LMC;14,7311;0,218
1;LMC;13,6888;0,5417
1;LMC;14,627;0,2133
1;LMC;13,4404;0,597
1;LMC;14,7168;0,2212
1;LMC;15,0594;0,3161
1;LMC;15,0425;0,1061
1;LMC;16,815;-0,4438
1;LMC;16,001;-0,1914
1;LMC;14,4216;0,2488
1;LMC;14,4748;0,286
1;LMC;13,8631;0,466
1;LMC;14,676;0,2098
1;LMC;14,4089;0,3046
1;LMC;14,2384;0,3559
1;LMC;14,2154;0,3397
1;LMC;14,059;0,3829
1;LMC;14,7006;0,2089
1;LMC;13,2151;0,6923
1;LMC;14,5228;0,2442
1;LMC;14,1972;0,3233
1;LMC;14,7161;0,2052
1;LMC;14,4328;0,2944
1;LMC;14,4018;0,2906
1;LMC;14,7142;0,2083
1;LMC;14,5522;0,2311
1;LMC;13,6784;0,5121
1;LMC;14,396;0,31
1;LMC;14,5408;0,2582
1;LMC;13,9204;0,4699
1;LMC;14,3842;0,308
1;LMC;13,9161;0,4451
1;LMC;14,5161;0,2751
1;LMC;16,6794;-0,4003
1;LMC;14,2213;0,3356
1;LMC;14,0804;0,3867
1;LMC;14,3438;0,2957
1;LMC;16,7434;-0,4476
1;LMC;14,4333;0,2808
1;LMC;14,3312;0,2889
1;LMC;14,504;0,247
1;LMC;13,2101;0,6412
1;LMC;13,8247;0,4442
1;LMC;13,962;0,4153
1;LMC;14,0806;0,3598
1;LMC;14,4793;0,2675
1;LMC;14,8813;0,1499
1;LMC;14,5757;0,2212
1;LMC;14,409;0,2996
1;LMC;13,8864;0,4335
1;LMC;14,1462;0,3252
1;LMC;13,4634;0,5562
1;LMC;14,034;0,4077
1;LMC;17,5882;-0,6029
1;LMC;13,7698;0,4653
1;LMC;14,3287;0,3083
1;LMC;13,2086;0,6234
1;LMC;13,5732;0,546
1;LMC;15,48;-0,014
1;LMC;13,1248;0,6751
1;LMC;17,1166;-0,528
1;LMC;13,9133;0,4573
1;LMC;15,0072;0,1038
1;LMC;14,1087;0,3766
1;LMC;17,1206;-0,5551
1;LMC;14,6866;0,2054
1;LMC;13,4114;0,5868
1;LMC;15,8548;-0,1511
1;LMC;12,2802;0,6877
1;LMC;17,1984;-0,5196
1;LMC;13,2713;0,6421
1;LMC;14,537;0,2466
1;LMC;15,4264;0,0006
1;LMC;15,5466;-0,0351
1;LMC;14,5549;0,3135
1;LMC;14,8506;0,1502
1;LMC;15,1214;0,0971
1;LMC;14,0284;0,3934
1;LMC;13,0608;0,6455
1;LMC;14,4624;0,2676
1;LMC;15,2442;0,0527
1;LMC;13,9045;0,4276
1;LMC;14,0536;0,3947
1;LMC;14,0503;0,3833
1;LMC;14,2145;0,3506
1;LMC;14,3653;0,2799
1;LMC;12,2534;0,6564
1;LMC;13,4538;0,5395
1;LMC;16,7458;-0,3898
1;LMC;13,799;0,4515
1;LMC;14,3382;0,2787
1;LMC;13,6368;0,5072
1;LMC;13,4912;0,5308
1;LMC;14,8163;0,1739
1;LMC;13,8256;0,4412
1;LMC;14,3908;0,2858
1;LMC;14,9267;0,0972
1;LMC;14,5064;0,2072
1;LMC;13,899;0,4303
1;LMC;14,0764;0,3825
1;LMC;14,871;0,1848
1;LMC;14,8902;0,1544
1;LMC;14,1546;0,3697
1;LMC;14,7806;0,1531
1;LMC;15,3816;0,0162
1;LMC;14,1212;0,3378
1;LMC;14,6768;0,1847
1;LMC;14,229;0,3145
1;LMC;14,3439;0,2859
1;LMC;14,5225;0,183
1;LMC;14,222;0,3029
1;LMC;14,6786;0,2644
1;LMC;14,2882;0,3067
1;LMC;17,304;-0,4965
1;LMC;13,2234;0,6359
1;LMC;14,1998;0,341
1;LMC;16,9782;-0,4488
1;SMC;14,2801;0,5215
1;SMC;16,7184;-0,1413
1;SMC;15,6902;0,0745
1;SMC;16,1686;-0,057
1;SMC;14,6436;0,3746
1;SMC;16,573;-0,1489
1;SMC;15,4925;0,1575
1;SMC;15,0159;0,3255
1;SMC;15,5657;0,1226
1;SMC;14,3219;0,4484
1;SMC;16,5712;-0,1446
1;SMC;16,1988;-0,0829
1;SMC;15,4376;0,1613
1;SMC;13,6344;0,5874
1;SMC;14,3778;0,4716
1;SMC;14,2394;0,5057
1;SMC;15,8777;0,0206
1;SMC;16,7138;-0,1735
1;SMC;15,7367;0,0683
1;SMC;14,7922;0,3067
1;SMC;17,9934;-0,5486
1;SMC;14,1358;0,5249
1;SMC;14,8562;0,3176
1;SMC;15,5588;0,1312
1;SMC;14,3;0,5272
1;SMC;15,6038;0,0537
1;SMC;14,5812;0,4347
1;SMC;14,8804;0,3115
1;SMC;14,3614;0,4934
1;SMC;16,4298;-0,0449
1;SMC;15,8712;0,0365
1;SMC;14,3527;0,5141
1;SMC;15,639;0,0993
1;SMC;14,0709;0,4997
1;SMC;16,0837;0,0029
1;SMC;14,7445;0,4165
1;SMC;16,23;-0,0246
1;SMC;15,1252;0,2608
1;SMC;16,255;-0,043
1;SMC;15,4152;0,2079
1;SMC;15,6954;0,0998
1;SMC;14,8665;0,3692
1;SMC;15,7832;0,0378
1;SMC;14,8404;-0,2293
1;SMC;15,9228;0,0104
1;SMC;16,1484;0,0015
1;SMC;15,8728;0,0054
1;SMC;14,8986;0,2908
1;SMC;16,731;-0,2169
1;SMC;15,2766;0,1077
1;SMC;15,5933;0,0706
1;SMC;14,6399;0,3879
1;SMC;16,4613;-0,0989
1;SMC;15,1788;0,1832
1;SMC;16,2002;-0,0848
1;SMC;15,0008;0,2784
1;SMC;14,7586;0,2794
1;SMC;16,3034;-0,118
1;SMC;16,4006;-0,1251
1;SMC;15,849;-0,0155
1;SMC;16,3728;-0,0437
1;SMC;13,959;0,5954
1;SMC;15,9233;0,0135
1;SMC;15,1752;0,2438
1;SMC;14,8222;0,3179
1;SMC;16,0276;0,0558
1;SMC;15,2084;0,1235
1;SMC;16,3546;-0,1292
1;SMC;14,5508;0,4422
1;SMC;15,656;0,1128
1;SMC;15,2515;0,2473
1;SMC;15,8121;0,0231
1;SMC;15,6758;0,0838
1;SMC;16,729;-0,1389
1;SMC;16,2468;-0,126
1;SMC;13,9121;0,5834
1;SMC;14,368;0,4634
1;SMC;15,7206;0,0583
1;SMC;15,6693;0,0931
1;SMC;16,2687;-0,0599
1;SMC;15,0676;0,227
1;SMC;15,5143;0,1668
1;SMC;15,7076;0,0811
1;SMC;15,566;0,0386
1;SMC;16,1032;-0,0477
1;SMC;16,2852;-0,0936
1;SMC;13,9415;0,5344
1;SMC;13,7318;0,6038
1;SMC;14,6932;0,2731
1;SMC;17,5597;-0,4531
1;SMC;15,6816;0,0183
1;SMC;16,6984;-0,0744
1;SMC;15,0062;0,2869
1;SMC;15,8423;0,0837
1;SMC;15,6786;0,1166
1;SMC;14,6876;0,3651
1;SMC;15,5642;0,1374
1;SMC;16,8114;-0,1078
1;SMC;14,795;0,2782
1;SMC;14,2601;0,4012
1;SMC;16,4018;-0,1529
1;SMC;14,9727;0,2929
1;SMC;15,5267;0,1388
1;SMC;15,0455;0,2939
1;SMC;16,1594;-0,0279
1;SMC;15,6552;0,0574
1;SMC;14,4008;0,4278
1;SMC;16,1806;-0,0993
1;SMC;15,8383;0,0532
1;SMC;15,4704;0,1488
1;SMC;16,3872;-0,0714
1;SMC;14,7915;0,3349
1;SMC;13,9011;0,5528
1;SMC;16,5788;-0,1133
1;SMC;13,9728;0,5471
1;SMC;15,8312;0,048
1;SMC;15,696;0,0947
1;SMC;16,378;-0,0909
1;SMC;15,3721;0,1404
1;SMC;14,9808;0,2511
1;SMC;15,7881;0,0277
1;SMC;15,7657;0,0796
1;SMC;15,9406;0,0803
1;SMC;15,5712;0,1499
1;SMC;15,4664;0,1231
1;SMC;16,3175;-0,0522
1;SMC;15,4929;0,1124
1;SMC;13,5586;0,3835
1;SMC;16,205;-0,0705
1;SMC;15,55;0,08
1;SMC;17,5096;-0,2768
1;SMC;15,8832;0,0417
1;SMC;17,738;-0,542
1;SMC;14,5475;0,4257
1;SMC;15,4079;0,0751
1;SMC;16,2626;0,0103
1;SMC;14,5742;0,3754
1;SMC;16,521;-0,1554
1;SMC;16,791;-0,1832
1;SMC;15,4673;0,1727
1;SMC;14,2996;0,4629
1;SMC;13,6418;0,6525
1;SMC;15,7457;0,0729
1;SMC;15,4886;0,1447
1;SMC;14,7568;0,3357
1;SMC;15,482;0,1373
1;SMC;16,1634;-0,0447
1;SMC;15,7054;0,1234
1;SMC;14,5147;0,4154
1;SMC;15,0815;0,2683
1;SMC;15,992;-0,0153
1;SMC;14,3333;0,4373
1;SMC;15,3798;0,1507
1;SMC;15,957;-0,0025
1;SMC;15,889;0,0482
1;SMC;16,3458;-0,0707
1;SMC;15,565;0,17
1;SMC;15,0304;0,273
1;SMC;14,0869;0,4998
1;SMC;14,986;0,2767
1;SMC;16,144;-0,0551
1;SMC;15,5166;0,1347
1;SMC;14,3772;0,4966
1;SMC;15,8712;0,0196
1;SMC;14,6147;0,3938
1;SMC;16,7266;-0,1534
1;SMC;15,6266;0,1039
1;SMC;14,3126;0,4288
1;SMC;15,9238;-0,016
1;SMC;16,1556;-0,0916
1;SMC;14,6832;0,3555
1;SMC;14,9996;0,3125
1;SMC;14,8072;0,313
1;SMC;17,2238;-0,2249
1;SMC;14,2168;0,4893
1;SMC;16,0782;-0,0494
1;SMC;15,9124;0,0302
1;SMC;14,6897;0,3772
1;SMC;14,8998;0,317
1;SMC;14,3068;0,4708
1;SMC;14,9732;0,2529
1;SMC;16,1034;-0,0252
1;SMC;15,2416;0,2186
1;SMC;15,9578;-0,0056
1;SMC;14,605;0,3675
1;SMC;15,3892;0,1909
1;SMC;14,1306;0,5392
1;SMC;14,2198;0,4472
1;SMC;15,9806;0,1076
1;SMC;17,3222;-0,3888
1;SMC;14,8756;0,3077
1;SMC;16,4862;-0,1431
1;SMC;15,453;0,1643
1;SMC;15,719;0,105
1;SMC;15,0462;0,2544
1;SMC;14,3558;0,4541
1;SMC;13,7118;0,6472
1;SMC;14,9858;0,3054
1;SMC;14,7582;0,3293
1;SMC;15,8872;0,0343
1;SMC;14,2318;0,4783
1;SMC;15,7902;0,1023
1;SMC;15,7548;0,0084
1;SMC;16,3536;-0,1291
1;SMC;15,7356;0,0787
1;SMC;15,0988;0,2505
1;SMC;15,007;0,1926
1;SMC;15,0572;0,2629
1;SMC;15,4202;0,1177
1;SMC;14,5873;0,4062
1;SMC;14,274;0,472
1;SMC;15,953;0,032
1;SMC;15,1688;0,1666
1;SMC;15,4486;0,1694
1;SMC;16,2714;-0,084
1;SMC;14,1066;0,444
1;SMC;14,1883;0,4876
1;SMC;14,6876;0,3783
1;SMC;16,2804;-0,0307
1;SMC;16,004;0,0296
1;SMC;15,5427;0,0665
1;SMC;15,2691;0,1932
1;SMC;15,0723;0,2626
1;SMC;16,4086;-0,135
1;SMC;16,1279;-0,0629
1;SMC;14,6822;0,3247
1;SMC;16,1232;-0,1099
1;SMC;14,3967;0,4784
1;SMC;16,1678;-0,019
1;SMC;14,3868;0,4022
1;SMC;14,738;0,3264
1;SMC;15,8982;0,0036
1;SMC;16,0884;-0,0763
1;SMC;14,7889;0,3277
1;SMC;15,5037;0,1452
1;SMC;14,9974;0,3175
1;SMC;16,1114;-0,0793
1;SMC;15,5855;0,0736
1;SMC;15,1194;0,2507
1;SMC;15,1229;0,2498
1;SMC;15,5506;0,0998
1;SMC;15,8262;0,0085
1;SMC;17,6762;-0,4719
1;SMC;15,512;0,1091
1;SMC;15,1242;0,2304
1;SMC;14,8618;0,2606
1;SMC;15,8314;-0,0355
1;SMC;13,9661;0,5273
1;SMC;15,7528;0,0473
1;SMC;15,4834;0,1461
1;SMC;16,1654;0,0084
1;SMC;17,02;-0,0819
1;SMC;15,7764;0,0479
1;SMC;15,1877;0,2523
1;SMC;15,2879;0,1914
1;SMC;16,2964;-0,0454
1;SMC;15,5908;0,1223
1;SMC;15,6662;0,0394
1;SMC;15,5124;0,1418
1;SMC;14,876;0,2962
1;SMC;16,015;-0,0057
1;SMC;14,6491;0,4071
1;SMC;16,5376;-0,1862
1;SMC;16,4474;-0,1131
1;SMC;16,0558;0,0361
1;SMC;16,6338;-0,2435
1;SMC;18,2798;-0,5471
1;SMC;15,7256;0,0648
1;SMC;16,963;-0,2991
1;SMC;15,5069;0,1115
1;SMC;15,0298;0,1803
1;SMC;16,3346;-0,1174
1;SMC;14,794;0,3238
1;SMC;14,271;0,4877
1;SMC;15,9154;0,0438
1;SMC;16,5047;-0,1339
1;SMC;16,65;-0,1978
1;SMC;14,8017;0,3421
1;SMC;15,397;0,1778
1;SMC;16,8134;-0,2104
1;SMC;14,3519;0,421
1;SMC;14,6731;0,3168
1;SMC;15,2232;0,2349
1;SMC;14,6852;0,3608
1;SMC;14,9719;0,1979
1;SMC;15,1469;0,2306
1;SMC;15,2132;0,1439
1;SMC;14,788;0,3559
1;SMC;15,638;0,131
1;SMC;15,1227;0,1846
1;SMC;15,7846;-0,0333
1;SMC;16,1864;-0,0533
1;SMC;16,4067;-0,0201
1;SMC;16,7493;-0,236
1;SMC;16,5681;-0,2147
1;SMC;15,6974;0,0783
1;SMC;16,1395;-0,074
1;SMC;14,7655;0,3273
1;SMC;14,5638;0,3947
1;SMC;16,6594;-0,1952
1;SMC;16,1283;-0,0393
1;SMC;15,9034;0,0257
1;SMC;15,8515;0,0495
1;SMC;15,0717;0,3022
1;SMC;15,3598;0,1681
1;SMC;14,4274;0,4869
1;SMC;16,2396;-0,0553
1;SMC;16,082;-0,0294
1;SMC;14,8533;0,2512
1;SMC;14,6503;0,3586
1;SMC;16,1;-0,0353
1;SMC;15,6848;0,1708
1;SMC;15,9834;0,0201
1;SMC;14,3646;0,4274
1;SMC;15,285;0,1942
1;SMC;15,1247;0,2598
1;SMC;15,7448;0,0919
1;SMC;15,6758;0,1366
1;SMC;15,0902;0,226
1;SMC;14,0126;0,5439
1;SMC;15,9319;-0,082
1;SMC;15,0558;0,2398
1;SMC;14,5532;0,4375
1;SMC;14,8176;0,3557
1;SMC;15,1869;0,2378
1;SMC;14,5042;0,3989
1;SMC;14,7118;0,2721
1;SMC;14,5803;0,3939
1;SMC;15,4836;0,1186
1;SMC;15,2548;0,2071
1;SMC;15,5388;0,1499
1;SMC;15,507;0,1285
1;SMC;13,958;0,5414
1;SMC;16,4458;-0,0405
1;SMC;15,6919;0,0892
1;SMC;14,4196;0,4557
1;SMC;15,7577;0,03
1;SMC;16,382;-0,1317
1;SMC;14,456;0,4701
1;SMC;15,5165;0,0565
1;SMC;16,198;-0,0138
1;SMC;16,1511;-0,0355
1;SMC;14,3661;0,4568
1;SMC;15,088;0,2109
1;SMC;14,3802;0,4206
1;SMC;14,7786;0,2707
1;SMC;15,2855;0,3013
1;SMC;15,3114;0,1119
1;SMC;15,43;0,1134
1;SMC;16,1082;-0,0503
1;SMC;16,2348;-0,022
1;SMC;15,9953;-0,0417
1;SMC;15,2678;0,1952
1;SMC;15,1298;0,2325
1;SMC;15,1712;0,2456
1;SMC;15,5435;0,1342
1;SMC;15,8772;0,0307

A simple solution:
arrays=df.groupby(['Mode','Cloud']).apply(lambda grp : residual_function(grp))
residuals_value=[]
[residuals_value.extend(elem.tolist()) for elem in arrays]
df["residuals"]=residuals_value

Smart Algorithm to identify numbers with wrong sign

I have a list of 1070 numbers (account balances) extracted from a source system, but due to technical issues, some of these values have the wrong sign (so I get for example 100 instead of -100)
I only have one information, once all the values have the correct sign, I will sum up to zero (but of course I have no idea how many figures have a wrong sign).
I tried to brute force a solution with the algorithm below, but it already takes forever when I am just combining 3 numbers (so reversing the sign of 3 numbers out of the 1070).
I believe that even if I optimize the my code, the number of combinations if I for example reverse 565 out of the 1070 elements is so large (5,740547E+319) that any brute force attemps will fail
I am basically trying trying to reverse one value in the list and then check if the sum is 0, then 2 values, 3 values and so on
It of course runs fine with only 6 numbers like below.
Does anybody has a suggestion of to approach that in a smarter way ?
import itertools
import numpy as np
test_list = np.array([1,2,3,4,5,9])
index_list = np.arange(0, len(test_list), dtype=np.intp)
sum_list = test_list.sum()
for L in range(1, len(test_list)+1):
print('current list lenght: ' + str(L))
for subset in itertools.combinations(index_list, L):
cur_list = np.array(test_list)
check = sum_list - (2 * cur_list[[subset]].sum())
if check == 0:
print('found:', subset)
This simple example has multiple solutions, the real values are more unique (and positive as well as negative), so I would not worry too much about having multiple solutions.
My list is the following (removed zero values):
[511720.02, 39409.62, 14680.43, 731.29, 10387.38, 2256.56, 262681.13, 5897.66, 11060.76, 144271.31, 25914.87, 13015.5, 19316.85, 40208606.69, -5846350.44, -33538929.18, -540682.74, 68089.94, 561696.88, 58050.78, 469706.4, 524.67, 472701.44, 2158456.56, 1158292.03, 100024.37, 18134.69, 337825.89, 10180.76, 2228315.58, 772.53, 8549.91, 43307.27, 39642.75, 72176.45, 323822.22, 962998.36, 135.5, 134388.73, 1473523.1, 275221.49, 34267.25, 549705.04, 247.87, 12958.72, 847714.28, 1781791.26, 3010.0, 1895276.98, 14434.11, 104587.1, 38056.23, 24962.62, 92497.96, 1227702.25, 110346.36, 391.85, 208.41, 8756.85, 782736.47, 3147519.19, 194767.75, 295568.31, 100694.2, 1893.93, 1133698.81, 523.2, 280280.91, 3078.82, 289841.77, 515.2, 175112.67, 24084.99, 35048.85, 49398.15, 41109.64, 1023.53, 782319.87, 28.14, 20.75, 13227.61, 14765.65, 41182.6, 4297.29, 1340.99, 826014.08, 6111.16, 965389.36, 673482.76, 109298.96, 39807.35, 311.79, 437149.28, 1950968.88, 325.21, 2764917.36, 8838.14, 202885.02, 25.49, 999.66, 743027.37, 759.22, 2950.0, 96146.21, 165.2, 21956.69, 1811289.92, 58.0, 6982282.03, 169464.71, 537268.28, 32655.3, 2474912.35, 1066214.61, 87962.0, 81526.67, 49053309.4, 45032405.02, 49548828.59, 48930853.47, 3533339.79, 3343489.4, 61758.14, 52772.61, 1095708.2, 819624.62, 141.17, 8611.75, 88752.23, 14669477.14, 22308370.28, 1215208.59, -183222.0, 99579.07, 621585.75, -26967.75, -787397.0, 18509117.59, 1212170.47, 60214.78, -749.37, 77455.94, 787350.1, 32760.21, 8117.1, 18493.03, 8267483.6, -15627.16, 58625.11, 38622.21, 57.11, 47565.71, 3523.6, 3205.0, 113242.59, 73.76, 782949.24, 5368.09, -4300.0, 2160047.12, -3533.08, 2460435.1, 50990.31, 93463.6, 1487.07, 22470.0, 22470.0, 999344.79, 135237.18, 9281.62, 3430281.72, 7126642.53, -53338.0, 9412.32, 3344946.88, 83075155.43, 17856133.2, 13449.34, 3845.0, 689998.56, 337645.3, 5937356.47, -2491.16, 2199069.77, 167652.42, 3331515.65, 1124710.59, 34075.85, 494639.09, 385540.84, 5891.39, 14378783.44, 7111253.62, 1247695.94, 1.0, 1.0, 138841.22, 0.5, 5979482.63, 1263134.4, 1.0, 5700.4, 12000000.0, 6100000.0, 1850000.0, 1000000.0, 500000.0, 51000.0, 77081.0, 19288853.74, 11600000.0, 4094000.0, 100000.0, 32400.0, 80000.0, 100000.0, 40810000.0, 33500000.0, 2336976.0, 812290.0, 177655.1, 1187432.01, 5224021.89, 4733986.5, 6174765.0, 1646604.82, 535146.3, 695690.19, 2858833.86, 863125.08, 859612.55, 631772.99, 100000.0, -200000.0, -177654.1, 10950.0, 25040.2, 1659670.0, 1143444.46, 3861357.87, 505930.63, 573072.0, 105000.0, 57500.0, -505929.63, -57499.0, 3402777.8, 54270.0, 105000.0, 44717.27, 1725619.5, 928972.0, 382924.78, 110015.09, 500.0, 100000.0, -1779887.33, -105000.0, -44717.27, -1311895.78, 19257544.9, 786109.73, 3885330.13, 9200000.0, 4347805.0, 464486.0, 13300000.0, 500000.0, 900000.0, 4000000.0, 550000.0, 46633144.87, 3657123.02, 2400000.0, 775000.0, 600000.0, 4980000.0, 715092.4, 250000.0, -715092.4, 5407573.99, 6516939.56, 71570143.67, -59186479.4, 1058557.22, -772031.22, 210000.0, 1753591.07, -1630557.07, 37550879.01, 2346460.63, -25169822.77, -1890843.63, 727919.84, -528559.84, 1073986.29, -556999.29, 5547811.48, -3130409.48, 8207549.38, 81942.17, 4489158.99, -7039209.16, -81942.17, -2143400.99, 89544215.17, -37091376.17, 1572011.0, -355928.0, 7478446.23, -595640.23, 32986206.95, -6325980.34, 13565577.0, -2178294.0, 32642339.14, -1005400.0, 3047129.66, 4586021.0, 67050.27, 60878226.61, -2654606.01, -2904000.0, 8393687.95, -18019508.06, -24442013.89, 1215208.59, 177570.85, -18047334.03, -1345725.77, 60214.78, -18212.52, -1823768.35, -354430.28, -8385048.47, 74505.96, -113659.2, 663309.32, -7694.18, -14335.0, 2160047.12, 4953.83, -228189.6, -100945.64, -8846.8, -2000000.0, -1102232.0, 205.2, 700000.0, 1550000.0, 500000.0, -29170503.05, -9741127.06, 2489697.93, -13599529.61, -8942775.78, -32726828.34, -14763682.06, -627846.99, -2340642.03, -4760527.23, -23148108.83, -33891335.24, -24840602.25, -39314900.39, -22877589.68, -2846969.98, -581650.3, -1269764.39, -665159.0, -2846969.98, -418.13, -4545969.04, -2281584.61, -15697827.3, -14778873.52, -18421816.45, -6420171.98, -781896.2, -1038820.41, -80650.97, -142900.19, -614263.27, -15871.81, -3386450.0, 3301075.71, -4058.89, -10407668.35, -8988470.62, -2236.0, -60178.92, -6202.25, -200000.0, -91700.3, -303039.14, -18543.12, -25149.83, -51468.44, -10100.0, -11755.05, -254974.16, -121982.45, -434.1, -853763.93, -100.08, 233961.15, 3521.99, -3776.15, -46650.33, -11412.02, -7970.43, -9798.65, -3942496.09, -1196495.36, -246462.91, -178049.1, -0.01, -18351592.93, -6551763.1, -5598.84, -264535.23, -425063.65, -48942.22, -6434834.64, -684003.27, -7617.96, -543416.91, -596612.84, -2896846.65, -203847.0, 5389.94, -37406548.94, -2609.35, -3543154.17, -83500.0, -1685476.4, -261289.34, -3342018.62, -4850000.0, 1500000.0, 4125000.0, -0.04, -89352.19, -178114.13, -54520.6, -15193100.0, -30960000.0, -3900000.0, -1000000.0, -10000000.0, -8361980.61, -8516941.31, -25000000.0, -5000000.0, -5000000.0, -8170843.24, -472127.77, -25000000.0, -1615441.58, -5697557.72, -18000.0, -810000.0, -362623.0, -526089.98, -8300000.0, -220000.0, 7.62, 15542.39, -25800625.31, -33795061.06, -20780610.38, -4665030.75, -39482892.35, -6840474.74, -18446631.31, -10152810.85, 2216320.89, -83747.0, -403572.0, 21763733.0, -3865209.08, -42267747.62, -263520555.75, 195087.06, -542668069.06, -54499963.22, -46824.57, -80703953.44, -34388289.6, -1120605.64, -1029611.15, -542066.51, -154157.05, -7932466.7, -13257498.78, -11093844.91, -2844716.35, -1361609.65, -5349483.43, -1439399.68, -2694.5, -1181.62, -418050.47, -400674.41, -246043195.73, -44799841.1, -326603330.98, -63937178.0, -37506337.11, -15076181.32, -488966.6, -230800995.11, -10509.4, -7575.0, -106584.18, -36375883.12, -45115231.08, -4305941.08, -9238.75, -516324.15, -10065.15, -2316745.72, -643656.45, -53267.0, -298191.95, -3537748.57, -399554.25, -13437.34, -2112.0, -93177.32, -306454.74, 24222.0, -12000.0, 14364.81, 19851.3, 39903.56, 12000.0, 175747.84, -353732.71, 926.66, -1311895.78, 1365487.35, 623561637.34, 1103900.52, 74.0, 36243106.75, 400408.55, 31379746.27, 1152340.15, 277020.97, 351518.86, 24432.73, 370113.15, 57822.7, 14334602.55, 5085111.06, 141482.62, 2960.2, 17357.8, 933574.99, 116678.06, 7191271.0, 1686605.85, 867515.1, -113006.87, 29156.65, 143305.39, 38947.38, 2992693.68, -504.46, -40954.64, 114478.4, 194370206.88, 24548647.12, 745995.52, 603106175.89, 15577814.31, 38526313.97, 43750465.62, 3.1, 35806829.4, 26656590.6, 1957311.8, 8649.7, 7575.15, 5230.3, 10083.33, 1076.43, 1047611.42, 172125.45, 3686.0, 134997.45, 8605.48, 677961.04, 82822.75, 133569.18, 1961196.0, 91776.21, 245448.12, 821.0, 1341.74, 137930.46, -8990.0, -26766176.74, 424769.32, 142428.86, -18789.13, 268399.38, 50263.7, -85550.06, 125538.91, 6263.29, 19500.75, 245829.52, -1365487.35, 40307915.86, 295222.37, -96036.81, 3166665.78, 1932755.66, 40400.0, -525840.25, 266808.43, 199000.0, -3737.2, 492669.16, -1969978.35, 1708500.83, 2748188.97, 643513.78, 2964400.04, 642333.18, 341338.4, 15289.73, 380235.94, 310.13, 108315.14, 638.16, 11507.44, 48799.64, -382413.8, 406563.18, 11080.63, 129207.66, 32419.8, 465.23, 53502.74, 263531.85, 370042.5, 123341.62, -9786.44, -124800.25, 71673.89, 3318959.25, 597466.94, 182928.82, 28019.14, 319467.85, 1563.75, 1660609.86, -1651940.0, 2806793.29, 84600.0, 27733.67, 714.71, 31198.43, 224970.45, 10020.87, 68734.49, 103156.32, 246821.22, 19097.3, 256539.34, 144102.4, 12122.3, 2604333.41, 191148.48, 94008.23, 6807.85, 2723.89, 5438.84, 122772.35, 156237.16, 24024.39, 705426.92, 187992.86, 132421.04, 241566.49, 102682.38, 41216.24, 4088.52, 1070878.31, 409464.57, 170804.67, 187236.66, 1298978.37, 42352.12, 1483572.01, 2083796.08, 9745.32, 15594.62, 58735.3, 83374.68, 20845.17, 739655.98, 45957.1, 24405.0, 111086.39, 150567.3, 23445.92, 2990.48, 8795.7, 26405.51, 459362.72, 51837.97, 144697.12, 4458.01, 300.0, 29541.52, 1954234.69, 66521.56, 40637.54, 29210.19, 11358.95, 38928.39, 95187.44, 113840.33, 66843.61, 34180.65, 19445.79, 52893.81, 138057.3, 205782.91, 98563.65, 111475.3, 197494.77, 6158.15, 1601.86, 116963.83, -1182305.05, 233113.73, -401948.82, 129.03, 497791.24, 6686.47, 131146.21, 26251.23, 368095.15, 25589.06, 39885.15, 882466.81, 193038.84, 293224.67, 123307.79, -35000.0, 3231.0, -5653952.45, 5469195.26, 243.15, 32610.33, 11495.93, 689585.9, 745.95, 221338.04, 83678.41, 52225.97, 14265.34, 4517.46, 2348.95, 98884.76, 1929.14, 8676.78, 126999.52, 48450.0, 23018.67, 642.63, 11311.66, 86076.58, 59632.21, 1200.0, 244068.03, 738908.91, 1111.06, 53129.02, 18323.65, 254206.26, 16409.53, 22472.94, 49361.21, 796671.0, 2618127.73, 158343.65, 5502.42, -21168.21, -33261.68, -3124673.81, -52827.56, -75.82, -71499.0, -185842.54, 776135.12, -56041.39, -151492.94, 146416.64, -363807.65, -1041536159.36, 1042111293.86, -343423458.56, 343105397.4, -8215190.59, 8189738.0, 10790777.98, 88377.0, 3968265.59, 1089147.0, 177964.0, 203973.0, 249376.0, 1107136.21, -1302612.04, -20665748.07, -268427.98, -1032648.0, -255228.63, -627322.81, -2680023.09, -2759982.21, -1260600.0, 121498.6, 64347.8, 76153.8, -146127.65, -317598.55, -147069.85, 245057.06, -245058.0, -85600.0, -728220.16, -121788.4, 41998.97, -236262.33, 330582.33, 225235.91, -86822.99, 3280734.73, 1734714.87, 14327.0, 6795.0, 49137.0, 334939.46, -375322.2, 2108892.54, 205427.44]

I can think of many ways I might try to solve this problem, but the bottom line is you'll never know what side of the equation is supposed to be negative.
+9-6-3 = 0
But this also applies to
-9+6+3 = 0
In other words: Even if you manage to sort out both sides of the equation, you'll always be left with two equally possible outcomes.

Unsure of why the StopIteration isn't handled by "yield from"

Looking at the following sample code from the "Fluent Python" book that explains the "bidirectional tunnel" functionality of yield from, I have the following question.
from collections import namedtuple
Result = namedtuple('Result', 'count average')
# the subgenerator
def averager(): # <1>
total = 0.0
count = 0
average = None
while True:
term = yield # <2>
if term is None: # <3>
break
total += term
count += 1
average = total/count
return Result(count, average) # <4>
# the delegating generator
def grouper(results, key): # <5>
while True: # <6>
results[key] = yield from averager() # <7>
# the client code, a.k.a. the caller
def main(data): # <8>
results = {}
for key, values in data.items():
group = grouper(results, key) # <9>
next(group) # <10>
for value in values:
group.send(value) # <11>
group.send(None) # important! <12>
print("wrapped up grouper")
print(results)
data = {
'girls;kg':
[40.9, 38.5, 44.3, 42.2, 45.2, 41.7, 44.5, 38.0, 40.6, 44.5],
'girls;m':
[1.6, 1.51, 1.4, 1.3, 1.41, 1.39, 1.33, 1.46, 1.45, 1.43],
'boys;kg':
[39.0, 40.8, 43.2, 40.8, 43.1, 38.6, 41.4, 40.6, 36.3],
'boys;m':
[1.38, 1.5, 1.32, 1.25, 1.37, 1.48, 1.25, 1.49, 1.46],
}
Why do I get a StopIteration exception when I replace the delegating generator above with the following delegating generator?
def grouper(results, key):
results[key] = yield from averager()
From what I have learned so far, it seems in theory that removing the while True should be fine. group.send(None) would cause the averager() coroutine to break and return the Result(...), which would be passed up to the delegating generator. And then the delegating generator would finish by assigning that Result(...) to results[key].
But what's happening instead is the following.
Traceback (mostrecent call last):
File "coroaverager3.py", line 111, in <module>
main(data)
File "coroaverager3.py", line 83, in main
group.send(None) # important! <12>
StopIteration
Any insight?

You’re right that the delegating generator assigns to results[key], but it doesn’t finish by doing that. Its execution continues, since there’s nowhere for it to suspend. Of course, it immediately falls off the end, causing the send(None) to raise StopIteration (since there’s no value (from a yield) for it to return).
The while True: is a sort of silly way of adding another place to suspend; a single extra yield after the yield from would be a bit more obvious. (It could provide a value distinct from those supplied by the inner generator if the client didn’t always know when the execution was finished.)

Problem to calculate the transformation matrix with superimposition_matrix of transformation.py

When trying to calculate a transformation matrix between two systems with the library transformations and the function superimposition_matrix, I get the following error :
File "D:\Travail\Dev\projet Jarvis\collisions\Test dans
Jarvis\JarvisRobotics_20180717_1937\JarvisRobotics\outils_matrices.py",
line 839, in set_frame_passageV000
M = transformations.superimposition_matrix(list_base, list_ext, scale=True, usesvd=True) ValueError: eigenvector_of_symmetric_44()
failed
The data in entry are two list of ten vector(x,y,z):
<class 'list'>: [[2443.9128, -501.7427, -630.8925], [2126.8356, -703.2691, -678.1219], [1534.3236, -757.3283, -678.0219], [1532.8918, -339.4951, -682.6528], [2103.9974, -226.6539, -472.8035], [2297.246, 47.3245, -475.6743], [2429.3814, 88.5478, -476.0224], [2353.675, 614.8282, -447.7145], [1931.7996, 651.5278, -448.4745], [1604.6555, 664.6893, -448.8943]]
<class 'list'>: [[406.93, -373.8, 2559.99], [612.81, -65.74, 2566.76], [679.68, 520.63, 2542.36], [271.24, 532.19, 2612.08], [114.43, -31.73, 2439.24], [-157.93, -220.7, 2490.9], [-200.13, -350.38, 2501.29], [-722.9, -260.64, 2556.52], [-750.43, 160.15, 2551.35], [-759.14, 488.8, 2545.55]]
is anybody here to help me or to guide me to go further?

def set_frame_passage_(list_pt_ref_base, list_pt_ref_ext):
str_erreur = ''
if len(list_pt_ref_base) == len(list_pt_ref_ext):
nb_pts = len(list_pt_ref_base)
array_base = np.zeros((nb_pts, 3))
array_ext = np.zeros((nb_pts, 3))
lg_array = 0
for index in range(0, nb_pts):
array_base[lg_array] = list_pt_ref_base[index][:3]
array_ext[lg_array] = list_pt_ref_ext[index][:3]
lg_array += 1
M = superimposition_matrix(array_base, array_ext, scale=True)
# valeurs de sortie
xyzrpw = set_mat4x4_2_xyzrpw(M)
mat_rdk = mat4x4_numpy_2_rdk(M)
if not (not np.all(M) and xyzrpw and mat_rdk):
return False
return RetVal([True, dt.now, 'matrice de passage calculée avec succès !', M, xyzrpw, mat_rdk])
else:
str_erreur = 'Les tailles du tableau du référentiel base('
str_erreur += str(len(list_pt_ref_base))
str_erreur += ') et du référentiel extérieur('
str_erreur += str(len(list_pt_ref_ext))
str_erreur += ') ne sont pas de la même taille !'
print(str_erreur)
RetVal([False, dt.now, str_erreur])
list_ext_brute = [[406.93, -373.8, 2559.99, 0.0, -0.0, 0.0],
[612.81, -65.74, 2566.76, 0.0, -0.0, 0.0],
[679.68, 520.63, 2542.36, 0.0, -0.0, 0.0],
[271.24, 532.19, 2612.08, 0.0, -0.0, 0.0],
[114.43, -31.73, 2439.24, 0.0, -0.0, 0.0],
[-157.93, -220.7, 2490.9, 0.0, -0.0, 0.0],
[-200.13, -350.38, 2501.29, 0.0, -0.0, 0.0],
[-722.9, -260.64, 2556.52, 0.0, -0.0, 0.0],
[-750.43, 160.15, 2551.35, 0.0, -0.0, 0.0],
[-759.14, 488.8, 2545.55, 0.0, -0.0, 0.0],
[-358.39, 151.63, 2416.5, 0.0, -0.0, 0.0],
[-289.58, -60.31, 2410.38, 0.0, -0.0, 0.0],
[-86.04, 153.06, 2369.19, 0.0, -0.0, 0.0],
[-9.05, -92.79, 2361.71, 0.0, -0.0, 0.0]]
list_base_brute = [[2443.9128, -501.7427, -630.8925, -179.0604, 0.4017, 145.2487],
[2126.8356, -703.2691, -678.1219, -179.6825, 0.552, 124.2606],
[1534.3236, -757.3283, -678.0219, 179.9398, 0.6337, 88.9192],
[1532.8918, -339.4951, -682.6528, 179.7809, 0.5975, 74.2169],
[2103.9974, -226.6539, -472.8035, -179.4523, 0.3239, 153.7627],
[2297.246, 47.3245, -475.6743, -179.4523, 0.3237, 153.7627],
[2429.3814, 88.5478, -476.0224, -179.7454, -0.5832, -109.2463],
[2353.675, 614.8282, -447.7145, -179.7454, -0.5833, -109.2462],
[1931.7996, 651.5278, -448.4745, -179.7454, -0.5835, -109.2461],
[1604.6555, 664.6893, -448.8943, -179.7455, -0.5835, -109.2461],
[1925.4354, 246.4732, -379.1885, -179.7455, -0.5836, -109.2458],
[2137.8614, 172.319, -378.1896, -179.7455, -0.5836, -109.2459],
[1919.7934, -31.8006, -376.11, -179.7455, -0.5837, -109.2459],
[2164.5074, -117.227, -374.9585, -179.7455, -0.5837, -109.2459]]
mat_ = set_frame_passage_(list_base_brute, list_ext_brute)
if mat_.b_done:
print('-------------------------------------------------------------------------------------------------------')
print('xyzrpw = ' + str(mat_.res2))
print('-------------------------------------------------------------------------------------------------------')
print('vecteur translation : x=' + str(mat_.res2[0]) + ', y=' + str(mat_.res2[1]) + ', z=' + str(mat_.res2[2]))
print('angles de rotation : r=' + str(mat_.res2[3]) + ', p=' + str(mat_.res2[4]) + ', w=' + str(mat_.res2[5]))
print('----------------------------------------------------------------------------')
print('Matrice de rotation numpy 4x4:')
print(mat_.res1)
print('----------------------------------------------------------------------------')
print('Matrice de rotation roboDK 4x4:')
print(mat_.res3)
else:
print(mat_.str_msg)

For information, I have found why I got this error:
The module transformation.py that i get is not complete and some functions are missing (in particular the one that raise an error) and due to the instruction "from transformation import *", there was apparently a default... I just use import the function that I need (superimposition_matrix) and I just test.
So now, the calculation passed but Now the result is not OK.
By an another software that we use for the robots ABB (Robotstudio), we have a function dedicated to the calculation of the Transformation Frames which is "DefAccFrame - Definition of an Accurate Frame between two systems". When we use it, we obtains the correct value (tested in real) and for now, this code does not work.
The issue is that this instruction is accessible only on ABB robots and I would to find a way to use it in any controller.
That's where I am today.
Really Thanks to everybody for all the help.

Matplotlib single array. How to place Marker?

I need help with placing a marker on a plot where I have a single array to plot it.
I am receiving the data in ASCII format from one Instrument and I need to show the maximum from the data.
data1 = ['+3.613512E+003', '+3.254965E+003', '+2.938281E+003', '+2.678804E+003', '+2.461425E+003', '+2.282802E+003', '+2.128352E+003', '+2.001264E+003', '+1.883186E+003', '+1.774222E+003', '+1.681822E+003', '+1.601852E+003', '+1.524412E+003', '+1.458368E+003', '+1.394319E+003', '+1.338477E+003', '+1.282567E+003', '+1.206847E+003', '+1.305292E+003', '+1.175656E+003', '+1.082040E+003', '+1.050308E+003', '+1.033320E+003', '+9.772695E+002', '+9.437070E+002', '+1.039622E+003', '+8.937933E+002', '+8.677546E+002', '+8.302201E+002', '+8.098771E+002', '+7.839208E+002', '+7.641392E+002', '+7.450735E+002', '+7.270004E+002', '+7.100223E+002', '+6.938470E+002', '+6.777757E+002', '+6.627379E+002', '+6.484218E+002', '+6.346696E+002', '+6.215515E+002', '+6.091346E+002', '+5.969252E+002', '+5.858983E+002', '+5.738594E+002', '+5.632513E+002', '+5.531273E+002', '+5.434016E+002', '+5.338579E+002', '+5.245516E+002', '+5.156342E+002', '+5.069659E+002', '+4.985686E+002', '+4.904806E+002', '+4.827541E+002', 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self.mplwidgetUp.axes.plot(data1,'#3FF235');
So now my question is how I can add a marker on some value.
To be honest I've tried different stuffs but I delete them and they were a lot of stuffs f.e.
self.mplwidgetUp.axes.plot(data1, marker="o", '#3FF235');
Its giving me an error:
self.mplwidgetUp.axes.plot(data1, marker="o", '#3FF235');
SyntaxError: non-keyword arg after keyword arg
I also've tried with:
self.mplwidgetUp.axes.plot(data1,'#3FF235');
self.mplwidgetUp.axes.plot(data1[270], "ro");
But it is giving me the point somewhere entirely different on the plot(in the lower left corner). Where 270 is the index of the array where is the max value (its an example i dont know exactly where the maximum is till now)
Can someone please help me I am struggling with this problem whole day.
Thank you in advance

Using this syntax:
plot(..., marker="o")
is using a "keyword" argument, as opposed to simply a "positional" argument which has no keyword= at the beginning, as in:
plot(x, y, '#3FF235')
You can mix them both, as long as the "positional" arguments come before the "keyword" arguments. This should work:
plot(x, y, '#3FF235', marker='o')
But this won't:
plot(x, y, marker='o', '#3FF235')
Note that x, y must be two separate arguments. If you want to mark only point 270, you have to do:
plot(270, data1[270], '#3FF235', marker='o')
To show the actual max, use
data1 = np.asarray(data1, float)
maxi = data1.argmax()
plot(maxi, data1[maxi], 'ro') # can combine color and marker in one positional argument

Doesn't the phrase "non-keyword arg after keyword arg" make you want to investigate what that actually means, rather than just try stuff randomly? In general, the Python syntax func(a, b) is fine (no keyword args), and func(a, option=b) is also fine (there's a keyword arg named "option" at the end; but func(option=b, a) is not allowed (it doesn't know how to interpret the non-keyword arg a after the keyword arg option=b). Your missing keyword is, I believe, color.

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